categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.17407
| null | null |
http://arxiv.org/pdf/2403.17407v3
|
2024-04-02T04:15:36Z
|
2024-03-26T05:55:21Z
|
Transcribing Bengali Text with Regional Dialects to IPA using District
Guided Tokens
|
Accurate transcription of Bengali text to the International Phonetic Alphabet (IPA) is a challenging task due to the complex phonology of the language and context-dependent sound changes. This challenge is even more for regional Bengali dialects due to unavailability of standardized spelling conventions for these dialects, presence of local and foreign words popular in those regions and phonological diversity across different regions. This paper presents an approach to this sequence-to-sequence problem by introducing the District Guided Tokens (DGT) technique on a new dataset spanning six districts of Bangladesh. The key idea is to provide the model with explicit information about the regional dialect or "district" of the input text before generating the IPA transcription. This is achieved by prepending a district token to the input sequence, effectively guiding the model to understand the unique phonetic patterns associated with each district. The DGT technique is applied to fine-tune several transformer-based models, on this new dataset. Experimental results demonstrate the effectiveness of DGT, with the ByT5 model achieving superior performance over word-based models like mT5, BanglaT5, and umT5. This is attributed to ByT5's ability to handle a high percentage of out-of-vocabulary words in the test set. The proposed approach highlights the importance of incorporating regional dialect information into ubiquitous natural language processing systems for languages with diverse phonological variations. The following work was a result of the "Bhashamul" challenge, which is dedicated to solving the problem of Bengali text with regional dialects to IPA transcription https://www.kaggle.com/competitions/regipa/. The training and inference notebooks are available through the competition link.
|
[
"['S M Jishanul Islam' 'Sadia Ahmmed' 'Sahid Hossain Mustakim']"
] |
null | null |
2403.17410
| null | null |
http://arxiv.org/pdf/2403.17410v2
|
2024-03-28T22:28:02Z
|
2024-03-26T06:06:01Z
|
On permutation-invariant neural networks
|
Conventional machine learning algorithms have traditionally been designed under the assumption that input data follows a vector-based format, with an emphasis on vector-centric paradigms. However, as the demand for tasks involving set-based inputs has grown, there has been a paradigm shift in the research community towards addressing these challenges. In recent years, the emergence of neural network architectures such as Deep Sets and Transformers has presented a significant advancement in the treatment of set-based data. These architectures are specifically engineered to naturally accommodate sets as input, enabling more effective representation and processing of set structures. Consequently, there has been a surge of research endeavors dedicated to exploring and harnessing the capabilities of these architectures for various tasks involving the approximation of set functions. This comprehensive survey aims to provide an overview of the diverse problem settings and ongoing research efforts pertaining to neural networks that approximate set functions. By delving into the intricacies of these approaches and elucidating the associated challenges, the survey aims to equip readers with a comprehensive understanding of the field. Through this comprehensive perspective, we hope that researchers can gain valuable insights into the potential applications, inherent limitations, and future directions of set-based neural networks. Indeed, from this survey we gain two insights: i) Deep Sets and its variants can be generalized by differences in the aggregation function, and ii) the behavior of Deep Sets is sensitive to the choice of the aggregation function. From these observations, we show that Deep Sets, one of the well-known permutation-invariant neural networks, can be generalized in the sense of a quasi-arithmetic mean.
|
[
"['Masanari Kimura' 'Ryotaro Shimizu' 'Yuki Hirakawa' 'Ryosuke Goto'\n 'Yuki Saito']"
] |
null | null |
2403.17425
| null | null |
http://arxiv.org/abs/2403.17425v1
|
2024-03-26T06:42:23Z
|
2024-03-26T06:42:23Z
|
Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion
Rate Prediction with a Single Model
|
In real-world advertising systems, conversions have different types in nature and ads can be shown in different display scenarios, both of which highly impact the actual conversion rate (CVR). This results in the multi-type and multi-scenario CVR prediction problem. A desired model for this problem should satisfy the following requirements: 1) Accuracy: the model should achieve fine-grained accuracy with respect to any conversion type in any display scenario. 2) Scalability: the model parameter size should be affordable. 3) Convenience: the model should not require a large amount of effort in data partitioning, subset processing and separate storage. Existing approaches cannot simultaneously satisfy these requirements. For example, building a separate model for each (conversion type, display scenario) pair is neither scalable nor convenient. Building a unified model trained on all the data with conversion type and display scenario included as two features is not accurate enough. In this paper, we propose the Masked Multi-domain Network (MMN) to solve this problem. To achieve the accuracy requirement, we model domain-specific parameters and propose a dynamically weighted loss to account for the loss scale imbalance issue within each mini-batch. To achieve the scalability requirement, we propose a parameter sharing and composition strategy to reduce model parameters from a product space to a sum space. To achieve the convenience requirement, we propose an auto-masking strategy which can take mixed data from all the domains as input. It avoids the overhead caused by data partitioning, individual processing and separate storage. Both offline and online experimental results validate the superiority of MMN for multi-type and multi-scenario CVR prediction. MMN is now the serving model for real-time CVR prediction in UC Toutiao.
|
[
"['Wentao Ouyang' 'Xiuwu Zhang' 'Chaofeng Guo' 'Shukui Ren' 'Yupei Sui'\n 'Kun Zhang' 'Jinmei Luo' 'Yunfeng Chen' 'Dongbo Xu' 'Xiangzheng Liu'\n 'Yanlong Du']"
] |
null | null |
2403.17431
| null | null |
http://arxiv.org/pdf/2403.17431v1
|
2024-03-26T06:57:23Z
|
2024-03-26T06:57:23Z
|
Robust and Scalable Model Editing for Large Language Models
|
Large language models (LLMs) can make predictions using parametric knowledge--knowledge encoded in the model weights--or contextual knowledge--knowledge presented in the context. In many scenarios, a desirable behavior is that LLMs give precedence to contextual knowledge when it conflicts with the parametric knowledge, and fall back to using their parametric knowledge when the context is irrelevant. This enables updating and correcting the model's knowledge by in-context editing instead of retraining. Previous works have shown that LLMs are inclined to ignore contextual knowledge and fail to reliably fall back to parametric knowledge when presented with irrelevant context. In this work, we discover that, with proper prompting methods, instruction-finetuned LLMs can be highly controllable by contextual knowledge and robust to irrelevant context. Utilizing this feature, we propose EREN (Edit models by REading Notes) to improve the scalability and robustness of LLM editing. To better evaluate the robustness of model editors, we collect a new dataset, that contains irrelevant questions that are more challenging than the ones in existing datasets. Empirical results show that our method outperforms current state-of-the-art methods by a large margin. Unlike existing techniques, it can integrate knowledge from multiple edits, and correctly respond to syntactically similar but semantically unrelated inputs (and vice versa). The source code can be found at https://github.com/thunlp/EREN.
|
[
"['Yingfa Chen' 'Zhengyan Zhang' 'Xu Han' 'Chaojun Xiao' 'Zhiyuan Liu'\n 'Chen Chen' 'Kuai Li' 'Tao Yang' 'Maosong Sun']"
] |
null | null |
2403.17436
| null | null |
http://arxiv.org/pdf/2403.17436v2
|
2024-06-03T12:12:25Z
|
2024-03-26T07:05:06Z
|
Particle identification with machine learning from incomplete data in
the ALICE experiment
|
The ALICE experiment at the LHC measures properties of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. Such studies require accurate particle identification (PID). ALICE provides PID information via several detectors for particles with momentum from about 100 MeV/c up to 20 GeV/c. Traditionally, particles are selected with rectangular cuts. A much better performance can be achieved with machine learning (ML) methods. Our solution uses multiple neural networks (NN) serving as binary classifiers. Moreover, we extended our particle classifier with Feature Set Embedding and attention in order to train on data with incomplete samples. We also present the integration of the ML project with the ALICE analysis software, and we discuss domain adaptation, the ML technique needed to transfer the knowledge between simulated and real experimental data.
|
[
"['Maja Karwowska' 'Łukasz Graczykowski' 'Kamil Deja' 'Miłosz Kasak'\n 'Małgorzata Janik']"
] |
null | null |
2403.17445
| null | null |
http://arxiv.org/pdf/2403.17445v1
|
2024-03-26T07:23:46Z
|
2024-03-26T07:23:46Z
|
Incorporating Exponential Smoothing into MLP: A Simple but Effective
Sequence Model
|
Modeling long-range dependencies in sequential data is a crucial step in sequence learning. A recently developed model, the Structured State Space (S4), demonstrated significant effectiveness in modeling long-range sequences. However, It is unclear whether the success of S4 can be attributed to its intricate parameterization and HiPPO initialization or simply due to State Space Models (SSMs). To further investigate the potential of the deep SSMs, we start with exponential smoothing (ETS), a simple SSM, and propose a stacked architecture by directly incorporating it into an element-wise MLP. We augment simple ETS with additional parameters and complex field to reduce the inductive bias. Despite increasing less than 1% of parameters of element-wise MLP, our models achieve comparable results to S4 on the LRA benchmark.
|
[
"['Jiqun Chu' 'Zuoquan Lin']"
] |
null | null |
2403.17447
| null | null |
http://arxiv.org/pdf/2403.17447v1
|
2024-03-26T07:26:00Z
|
2024-03-26T07:26:00Z
|
Chain of Compression: A Systematic Approach to Combinationally Compress
Convolutional Neural Networks
|
Convolutional neural networks (CNNs) have achieved significant popularity, but their computational and memory intensity poses challenges for resource-constrained computing systems, particularly with the prerequisite of real-time performance. To release this burden, model compression has become an important research focus. Many approaches like quantization, pruning, early exit, and knowledge distillation have demonstrated the effect of reducing redundancy in neural networks. Upon closer examination, it becomes apparent that each approach capitalizes on its unique features to compress the neural network, and they can also exhibit complementary behavior when combined. To explore the interactions and reap the benefits from the complementary features, we propose the Chain of Compression, which works on the combinational sequence to apply these common techniques to compress the neural network. Validated on the image-based regression and classification networks across different data sets, our proposed Chain of Compression can significantly compress the computation cost by 100-1000 times with ignorable accuracy loss compared with the baseline model.
|
[
"['Yingtao Shen' 'Minqing Sun' 'Jie Zhao' 'An Zou']"
] |
null | null |
2403.17456
| null | null |
http://arxiv.org/pdf/2403.17456v3
|
2024-05-23T08:57:17Z
|
2024-03-26T07:41:54Z
|
Imitating Cost-Constrained Behaviors in Reinforcement Learning
|
Complex planning and scheduling problems have long been solved using various optimization or heuristic approaches. In recent years, imitation learning that aims to learn from expert demonstrations has been proposed as a viable alternative to solving these problems. Generally speaking, imitation learning is designed to learn either the reward (or preference) model or directly the behavioral policy by observing the behavior of an expert. Existing work in imitation learning and inverse reinforcement learning has focused on imitation primarily in unconstrained settings (e.g., no limit on fuel consumed by the vehicle). However, in many real-world domains, the behavior of an expert is governed not only by reward (or preference) but also by constraints. For instance, decisions on self-driving delivery vehicles are dependent not only on the route preferences/rewards (depending on past demand data) but also on the fuel in the vehicle and the time available. In such problems, imitation learning is challenging as decisions are not only dictated by the reward model but are also dependent on a cost-constrained model. In this paper, we provide multiple methods that match expert distributions in the presence of trajectory cost constraints through (a) Lagrangian-based method; (b) Meta-gradients to find a good trade-off between expected return and minimizing constraint violation; and (c) Cost-violation-based alternating gradient. We empirically show that leading imitation learning approaches imitate cost-constrained behaviors poorly and our meta-gradient-based approach achieves the best performance.
|
[
"['Qian Shao' 'Pradeep Varakantham' 'Shih-Fen Cheng']"
] |
null | null |
2403.17458
| null | null |
http://arxiv.org/pdf/2403.17458v3
|
2024-03-28T09:02:35Z
|
2024-03-26T07:46:27Z
|
Expectations Versus Reality: Evaluating Intrusion Detection Systems in
Practice
|
Our paper provides empirical comparisons between recent IDSs to provide an objective comparison between them to help users choose the most appropriate solution based on their requirements. Our results show that no one solution is the best, but is dependent on external variables such as the types of attacks, complexity, and network environment in the dataset. For example, BoT_IoT and Stratosphere IoT datasets both capture IoT-related attacks, but the deep neural network performed the best when tested using the BoT_IoT dataset while HELAD performed the best when tested using the Stratosphere IoT dataset. So although we found that a deep neural network solution had the highest average F1 scores on tested datasets, it is not always the best-performing one. We further discuss difficulties in using IDS from literature and project repositories, which complicated drawing definitive conclusions regarding IDS selection.
|
[
"['Jake Hesford' 'Daniel Cheng' 'Alan Wan' 'Larry Huynh' 'Seungho Kim'\n 'Hyoungshick Kim' 'Jin B. Hong']"
] |
null | null |
2403.17467
| null | null |
http://arxiv.org/pdf/2403.17467v1
|
2024-03-26T07:55:45Z
|
2024-03-26T07:55:45Z
|
A Unified Kernel for Neural Network Learning
|
Past decades have witnessed a great interest in the distinction and connection between neural network learning and kernel learning. Recent advancements have made theoretical progress in connecting infinite-wide neural networks and Gaussian processes. Two predominant approaches have emerged: the Neural Network Gaussian Process (NNGP) and the Neural Tangent Kernel (NTK). The former, rooted in Bayesian inference, represents a zero-order kernel, while the latter, grounded in the tangent space of gradient descents, is a first-order kernel. In this paper, we present the Unified Neural Kernel (UNK), which characterizes the learning dynamics of neural networks with gradient descents and parameter initialization. The proposed UNK kernel maintains the limiting properties of both NNGP and NTK, exhibiting behaviors akin to NTK with a finite learning step and converging to NNGP as the learning step approaches infinity. Besides, we also theoretically characterize the uniform tightness and learning convergence of the UNK kernel, providing comprehensive insights into this unified kernel. Experimental results underscore the effectiveness of our proposed method.
|
[
"['Shao-Qun Zhang' 'Zong-Yi Chen' 'Yong-Ming Tian' 'Xun Lu']"
] |
null | null |
2403.17479
| null | null |
http://arxiv.org/pdf/2403.17479v1
|
2024-03-26T08:19:29Z
|
2024-03-26T08:19:29Z
|
Natural Language Requirements Testability Measurement Based on
Requirement Smells
|
Requirements form the basis for defining software systems' obligations and tasks. Testable requirements help prevent failures, reduce maintenance costs, and make it easier to perform acceptance tests. However, despite the importance of measuring and quantifying requirements testability, no automatic approach for measuring requirements testability has been proposed based on the requirements smells, which are at odds with the requirements testability. This paper presents a mathematical model to evaluate and rank the natural language requirements testability based on an extensive set of nine requirements smells, detected automatically, and acceptance test efforts determined by requirement length and its application domain. Most of the smells stem from uncountable adjectives, context-sensitive, and ambiguous words. A comprehensive dictionary is required to detect such words. We offer a neural word-embedding technique to generate such a dictionary automatically. Using the dictionary, we could automatically detect Polysemy smell (domain-specific ambiguity) for the first time in 10 application domains. Our empirical study on nearly 1000 software requirements from six well-known industrial and academic projects demonstrates that the proposed smell detection approach outperforms Smella, a state-of-the-art tool, in detecting requirements smells. The precision and recall of smell detection are improved with an average of 0.03 and 0.33, respectively, compared to the state-of-the-art. The proposed requirement testability model measures the testability of 985 requirements with a mean absolute error of 0.12 and a mean squared error of 0.03, demonstrating the model's potential for practical use.
|
[
"['Morteza Zakeri-Nasrabadi' 'Saeed Parsa']"
] |
null | null |
2403.17480
| null | null |
http://arxiv.org/pdf/2403.17480v2
|
2024-07-01T04:35:14Z
|
2024-03-26T08:22:09Z
|
Capacity Provisioning Motivated Online Non-Convex Optimization Problem
with Memory and Switching Cost
|
An online non-convex optimization problem is considered where the goal is to minimize the flow time (total delay) of a set of jobs by modulating the number of active servers, but with a switching cost associated with changing the number of active servers over time. Each job can be processed by at most one fixed speed server at any time. Compared to the usual online convex optimization (OCO) problem with switching cost, the objective function considered is non-convex and more importantly, at each time, it depends on all past decisions and not just the present one. Both worst-case and stochastic inputs are considered; for both cases, competitive algorithms are derived.
|
[
"['Rahul Vaze' 'Jayakrishnan Nair']"
] |
null | null |
2403.17500
| null | null |
http://arxiv.org/pdf/2403.17500v1
|
2024-03-26T08:59:37Z
|
2024-03-26T08:59:37Z
|
Variational Graph Auto-Encoder Based Inductive Learning Method for
Semi-Supervised Classification
|
Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires models to generalize to unseen graph structures during inference. In recent years, graph neural networks (GNNs) have emerged as powerful graph models for inductive learning tasks such as node classification, whereas they typically heavily rely on the annotated nodes under a fully supervised training setting. Compared with the GNN-based methods, variational graph auto-encoders (VGAEs) are known to be more generalizable to capture the internal structural information of graphs independent of node labels and have achieved prominent performance on multiple unsupervised learning tasks. However, so far there is still a lack of work focusing on leveraging the VGAE framework for inductive learning, due to the difficulties in training the model in a supervised manner and avoiding over-fitting the proximity information of graphs. To solve these problems and improve the model performance of VGAEs for inductive graph representation learning, in this work, we propose the Self-Label Augmented VGAE model. To leverage the label information for training, our model takes node labels as one-hot encoded inputs and then performs label reconstruction in model training. To overcome the scarcity problem of node labels for semi-supervised settings, we further propose the Self-Label Augmentation Method (SLAM), which uses pseudo labels generated by our model with a node-wise masking approach to enhance the label information. Experiments on benchmark inductive learning graph datasets verify that our proposed model archives promising results on node classification with particular superiority under semi-supervised learning settings.
|
[
"['Hanxuan Yang' 'Zhaoxin Yu' 'Qingchao Kong' 'Wei Liu' 'Wenji Mao']"
] |
null | null |
2403.17503
| null | null |
http://arxiv.org/pdf/2403.17503v1
|
2024-03-26T09:04:18Z
|
2024-03-26T09:04:18Z
|
DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free
Class-Incremental Learning
|
Class-incremental learning (CIL) under an exemplar-free constraint has presented a significant challenge. Existing methods adhering to this constraint are prone to catastrophic forgetting, far more so than replay-based techniques that retain access to past samples. In this paper, to solve the exemplar-free CIL problem, we propose a Dual-Stream Analytic Learning (DS-AL) approach. The DS-AL contains a main stream offering an analytical (i.e., closed-form) linear solution, and a compensation stream improving the inherent under-fitting limitation due to adopting linear mapping. The main stream redefines the CIL problem into a Concatenated Recursive Least Squares (C-RLS) task, allowing an equivalence between the CIL and its joint-learning counterpart. The compensation stream is governed by a Dual-Activation Compensation (DAC) module. This module re-activates the embedding with a different activation function from the main stream one, and seeks fitting compensation by projecting the embedding to the null space of the main stream's linear mapping. Empirical results demonstrate that the DS-AL, despite being an exemplar-free technique, delivers performance comparable with or better than that of replay-based methods across various datasets, including CIFAR-100, ImageNet-100 and ImageNet-Full. Additionally, the C-RLS' equivalent property allows the DS-AL to execute CIL in a phase-invariant manner. This is evidenced by a never-before-seen 500-phase CIL ImageNet task, which performs on a level identical to a 5-phase one. Our codes are available at https://github.com/ZHUANGHP/Analytic-continual-learning.
|
[
"['Huiping Zhuang' 'Run He' 'Kai Tong' 'Ziqian Zeng' 'Cen Chen'\n 'Zhiping Lin']"
] |
null | null |
2403.17507
| null | null |
http://arxiv.org/pdf/2403.17507v1
|
2024-03-26T09:09:40Z
|
2024-03-26T09:09:40Z
|
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields
|
Machine learning force fields (MLFFs) have emerged as a promising approach to bridge the accuracy of quantum mechanical methods and the efficiency of classical force fields. However, the abundance of MLFF models and the challenge of accurately predicting atomic forces pose significant obstacles in their practical application. In this paper, we propose a novel ensemble learning framework, EL-MLFFs, which leverages the stacking method to integrate predictions from diverse MLFFs and enhance force prediction accuracy. By constructing a graph representation of molecular structures and employing a graph neural network (GNN) as the meta-model, EL-MLFFs effectively captures atomic interactions and refines force predictions. We evaluate our approach on two distinct datasets: methane molecules and methanol adsorbed on a Cu(100) surface. The results demonstrate that EL-MLFFs significantly improves force prediction accuracy compared to individual MLFFs, with the ensemble of all eight models yielding the best performance. Moreover, our ablation study highlights the crucial roles of the residual network and graph attention layers in the model's architecture. The EL-MLFFs framework offers a promising solution to the challenges of model selection and force prediction accuracy in MLFFs, paving the way for more reliable and efficient molecular simulations.
|
[
"['Bangchen Yin' 'Yue Yin' 'Yuda W. Tang' 'Hai Xiao']"
] |
null | null |
2403.17515
| null | null |
http://arxiv.org/pdf/2403.17515v1
|
2024-03-26T09:18:50Z
|
2024-03-26T09:18:50Z
|
Prediction-sharing During Training and Inference
|
Two firms are engaged in a competitive prediction task. Each firm has two sources of data -- labeled historical data and unlabeled inference-time data -- and uses the former to derive a prediction model, and the latter to make predictions on new instances. We study data-sharing contracts between the firms. The novelty of our study is to introduce and highlight the differences between contracts that share prediction models only, contracts to share inference-time predictions only, and contracts to share both. Our analysis proceeds on three levels. First, we develop a general Bayesian framework that facilitates our study. Second, we narrow our focus to two natural settings within this framework: (i) a setting in which the accuracy of each firm's prediction model is common knowledge, but the correlation between the respective models is unknown; and (ii) a setting in which two hypotheses exist regarding the optimal predictor, and one of the firms has a structural advantage in deducing it. Within these two settings we study optimal contract choice. More specifically, we find the individually rational and Pareto-optimal contracts for some notable cases, and describe specific settings where each of the different sharing contracts emerge as optimal. Finally, in the third level of our analysis we demonstrate the applicability of our concepts in a synthetic simulation using real loan data.
|
[
"['Yotam Gafni' 'Ronen Gradwohl' 'Moshe Tennenholtz']"
] |
null | null |
2403.17520
| null | null |
http://arxiv.org/pdf/2403.17520v1
|
2024-03-26T09:22:37Z
|
2024-03-26T09:22:37Z
|
Boosting Adversarial Training via Fisher-Rao Norm-based Regularization
|
Adversarial training is extensively utilized to improve the adversarial robustness of deep neural networks. Yet, mitigating the degradation of standard generalization performance in adversarial-trained models remains an open problem. This paper attempts to resolve this issue through the lens of model complexity. First, We leverage the Fisher-Rao norm, a geometrically invariant metric for model complexity, to establish the non-trivial bounds of the Cross-Entropy Loss-based Rademacher complexity for a ReLU-activated Multi-Layer Perceptron. Then we generalize a complexity-related variable, which is sensitive to the changes in model width and the trade-off factors in adversarial training. Moreover, intensive empirical evidence validates that this variable highly correlates with the generalization gap of Cross-Entropy loss between adversarial-trained and standard-trained models, especially during the initial and final phases of the training process. Building upon this observation, we propose a novel regularization framework, called Logit-Oriented Adversarial Training (LOAT), which can mitigate the trade-off between robustness and accuracy while imposing only a negligible increase in computational overhead. Our extensive experiments demonstrate that the proposed regularization strategy can boost the performance of the prevalent adversarial training algorithms, including PGD-AT, TRADES, TRADES (LSE), MART, and DM-AT, across various network architectures. Our code will be available at https://github.com/TrustAI/LOAT.
|
[
"['Xiangyu Yin' 'Wenjie Ruan']"
] |
null | null |
2403.17533
| null | null |
http://arxiv.org/pdf/2403.17533v1
|
2024-03-26T09:39:21Z
|
2024-03-26T09:39:21Z
|
BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range
Air Combat
|
Creating new air combat tactics and discovering novel maneuvers can require numerous hours of expert pilots' time. Additionally, for each different combat scenario, the same strategies may not work since small changes in equipment performance may drastically change the air combat outcome. For this reason, we created a reinforcement learning environment to help investigate potential air combat tactics in the field of beyond-visual-range (BVR) air combat: the BVR Gym. This type of air combat is important since long-range missiles are often the first weapon to be used in aerial combat. Some existing environments provide high-fidelity simulations but are either not open source or are not adapted to the BVR air combat domain. Other environments are open source but use less accurate simulation models. Our work provides a high-fidelity environment based on the open-source flight dynamics simulator JSBSim and is adapted to the BVR air combat domain. This article describes the building blocks of the environment and some use cases.
|
[
"['Edvards Scukins' 'Markus Klein' 'Lars Kroon' 'Petter Ögren']"
] |
null | null |
2403.17542
| null | null |
http://arxiv.org/pdf/2403.17542v1
|
2024-03-26T09:44:57Z
|
2024-03-26T09:44:57Z
|
VDSC: Enhancing Exploration Timing with Value Discrepancy and State
Counts
|
Despite the considerable attention given to the questions of textit{how much} and textit{how to} explore in deep reinforcement learning, the investigation into textit{when} to explore remains relatively less researched. While more sophisticated exploration strategies can excel in specific, often sparse reward environments, existing simpler approaches, such as $epsilon$-greedy, persist in outperforming them across a broader spectrum of domains. The appeal of these simpler strategies lies in their ease of implementation and generality across a wide range of domains. The downside is that these methods are essentially a blind switching mechanism, which completely disregards the agent's internal state. In this paper, we propose to leverage the agent's internal state to decide textit{when} to explore, addressing the shortcomings of blind switching mechanisms. We present Value Discrepancy and State Counts through homeostasis (VDSC), a novel approach for efficient exploration timing. Experimental results on the Atari suite demonstrate the superiority of our strategy over traditional methods such as $epsilon$-greedy and Boltzmann, as well as more sophisticated techniques like Noisy Nets.
|
[
"['Marius Captari' 'Remo Sasso' 'Matthia Sabatelli']"
] |
null | null |
2403.17550
| null | null |
http://arxiv.org/pdf/2403.17550v1
|
2024-03-26T09:58:06Z
|
2024-03-26T09:58:06Z
|
DeepMIF: Deep Monotonic Implicit Fields for Large-Scale LiDAR 3D Mapping
|
Recently, significant progress has been achieved in sensing real large-scale outdoor 3D environments, particularly by using modern acquisition equipment such as LiDAR sensors. Unfortunately, they are fundamentally limited in their ability to produce dense, complete 3D scenes. To address this issue, recent learning-based methods integrate neural implicit representations and optimizable feature grids to approximate surfaces of 3D scenes. However, naively fitting samples along raw LiDAR rays leads to noisy 3D mapping results due to the nature of sparse, conflicting LiDAR measurements. Instead, in this work we depart from fitting LiDAR data exactly, instead letting the network optimize a non-metric monotonic implicit field defined in 3D space. To fit our field, we design a learning system integrating a monotonicity loss that enables optimizing neural monotonic fields and leverages recent progress in large-scale 3D mapping. Our algorithm achieves high-quality dense 3D mapping performance as captured by multiple quantitative and perceptual measures and visual results obtained for Mai City, Newer College, and KITTI benchmarks. The code of our approach will be made publicly available.
|
[
"['Kutay Yılmaz' 'Matthias Nießner' 'Anastasiia Kornilova' 'Alexey Artemov']"
] |
null | null |
2403.17561
| null | null |
http://arxiv.org/pdf/2403.17561v3
|
2024-05-16T12:00:29Z
|
2024-03-26T10:10:53Z
|
A Survey on Deep Learning and State-of-the-art Applications
|
Deep learning, a branch of artificial intelligence, is a computational model that uses multiple layers of interconnected units (neurons) to learn intricate patterns and representations directly from raw input data. Empowered by this learning capability, it has become a powerful tool for solving complex problems and is the core driver of many groundbreaking technologies and innovations. Building a deep learning model is a challenging task due to the algorithm`s complexity and the dynamic nature of real-world problems. Several studies have reviewed deep learning concepts and applications. However, the studies mostly focused on the types of deep learning models and convolutional neural network architectures, offering limited coverage of the state-of-the-art of deep learning models and their applications in solving complex problems across different domains. Therefore, motivated by the limitations, this study aims to comprehensively review the state-of-the-art deep learning models in computer vision, natural language processing, time series analysis and pervasive computing. We highlight the key features of the models and their effectiveness in solving the problems within each domain. Furthermore, this study presents the fundamentals of deep learning, various deep learning model types and prominent convolutional neural network architectures. Finally, challenges and future directions in deep learning research are discussed to offer a broader perspective for future researchers.
|
[
"['Mohd Halim Mohd Noor' 'Ayokunle Olalekan Ige']"
] |
null | null |
2403.17572
| null | null |
http://arxiv.org/pdf/2403.17572v1
|
2024-03-26T10:25:21Z
|
2024-03-26T10:25:21Z
|
Enhancing Privacy in Federated Learning through Local Training
|
In this paper we propose the federated private local training algorithm (Fed-PLT) for federated learning, to overcome the challenges of (i) expensive communications and (ii) privacy preservation. We address (i) by allowing for both partial participation and local training, which significantly reduce the number of communication rounds between the central coordinator and computing agents. The algorithm matches the state of the art in the sense that the use of local training demonstrably does not impact accuracy. Additionally, agents have the flexibility to choose from various local training solvers, such as (stochastic) gradient descent and accelerated gradient descent. Further, we investigate how employing local training can enhance privacy, addressing point (ii). In particular, we derive differential privacy bounds and highlight their dependence on the number of local training epochs. We assess the effectiveness of the proposed algorithm by comparing it to alternative techniques, considering both theoretical analysis and numerical results from a classification task.
|
[
"['Nicola Bastianello' 'Changxin Liu' 'Karl H. Johansson']"
] |
null | null |
2403.17582
| null | null |
http://arxiv.org/pdf/2403.17582v1
|
2024-03-26T10:45:11Z
|
2024-03-26T10:45:11Z
|
Towards a Zero-Data, Controllable, Adaptive Dialog System
|
Conversational Tree Search (V"ath et al., 2023) is a recent approach to controllable dialog systems, where domain experts shape the behavior of a Reinforcement Learning agent through a dialog tree. The agent learns to efficiently navigate this tree, while adapting to information needs, e.g., domain familiarity, of different users. However, the need for additional training data hinders deployment in new domains. To address this, we explore approaches to generate this data directly from dialog trees. We improve the original approach, and show that agents trained on synthetic data can achieve comparable dialog success to models trained on human data, both when using a commercial Large Language Model for generation, or when using a smaller open-source model, running on a single GPU. We further demonstrate the scalability of our approach by collecting and testing on two new datasets: ONBOARD, a new domain helping foreign residents moving to a new city, and the medical domain DIAGNOSE, a subset of Wikipedia articles related to scalp and head symptoms. Finally, we perform human testing, where no statistically significant differences were found in either objective or subjective measures between models trained on human and generated data.
|
[
"['Dirk Väth' 'Lindsey Vanderlyn' 'Ngoc Thang Vu']"
] |
null | null |
2403.17588
| null | null |
http://arxiv.org/pdf/2403.17588v1
|
2024-03-26T10:54:07Z
|
2024-03-26T10:54:07Z
|
Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest
models
|
Random Forest (RF) is well-known as an efficient ensemble learning method in terms of predictive performance. It is also considered a Black Box because of its hundreds of deep decision trees. This lack of interpretability can be a real drawback for acceptance of RF models in several real-world applications, especially those affecting one's lives, such as in healthcare, security, and law. In this work, we present Forest-ORE, a method that makes RF interpretable via an optimized rule ensemble (ORE) for local and global interpretation. Unlike other rule-based approaches aiming at interpreting the RF model, this method simultaneously considers several parameters that influence the choice of an interpretable rule ensemble. Existing methods often prioritize predictive performance over interpretability coverage and do not provide information about existing overlaps or interactions between rules. Forest-ORE uses a mixed-integer optimization program to build an ORE that considers the trade-off between predictive performance, interpretability coverage, and model size (size of the rule ensemble, rule lengths, and rule overlaps). In addition to providing an ORE competitive in predictive performance with RF, this method enriches the ORE through other rules that afford complementary information. It also enables monitoring of the rule selection process and delivers various metrics that can be used to generate a graphical representation of the final model. This framework is illustrated through an example, and its robustness is assessed through 36 benchmark datasets. A comparative analysis of well-known methods shows that Forest-ORE provides an excellent trade-off between predictive performance, interpretability coverage, and model size.
|
[
"['Haddouchi Maissae' 'Berrado Abdelaziz']"
] |
null | null |
2403.17589
| null | null |
http://arxiv.org/pdf/2403.17589v1
|
2024-03-26T10:54:07Z
|
2024-03-26T10:54:07Z
|
Dual Memory Networks: A Versatile Adaptation Approach for
Vision-Language Models
|
With the emergence of pre-trained vision-language models like CLIP, how to adapt them to various downstream classification tasks has garnered significant attention in recent research. The adaptation strategies can be typically categorized into three paradigms: zero-shot adaptation, few-shot adaptation, and the recently-proposed training-free few-shot adaptation. Most existing approaches are tailored for a specific setting and can only cater to one or two of these paradigms. In this paper, we introduce a versatile adaptation approach that can effectively work under all three settings. Specifically, we propose the dual memory networks that comprise dynamic and static memory components. The static memory caches training data knowledge, enabling training-free few-shot adaptation, while the dynamic memory preserves historical test features online during the testing process, allowing for the exploration of additional data insights beyond the training set. This novel capability enhances model performance in the few-shot setting and enables model usability in the absence of training data. The two memory networks employ the same flexible memory interactive strategy, which can operate in a training-free mode and can be further enhanced by incorporating learnable projection layers. Our approach is tested across 11 datasets under the three task settings. Remarkably, in the zero-shot scenario, it outperforms existing methods by over 3% and even shows superior results against methods utilizing external training data. Additionally, our method exhibits robust performance against natural distribution shifts. Codes are available at url{https://github.com/YBZh/DMN}.
|
[
"['Yabin Zhang' 'Wenjie Zhu' 'Hui Tang' 'Zhiyuan Ma' 'Kaiyang Zhou'\n 'Lei Zhang']"
] |
null | null |
2403.17592
| null | null |
http://arxiv.org/pdf/2403.17592v1
|
2024-03-26T11:01:53Z
|
2024-03-26T11:01:53Z
|
On the Benefits of Over-parameterization for Out-of-Distribution
Generalization
|
In recent years, machine learning models have achieved success based on the independently and identically distributed assumption. However, this assumption can be easily violated in real-world applications, leading to the Out-of-Distribution (OOD) problem. Understanding how modern over-parameterized DNNs behave under non-trivial natural distributional shifts is essential, as current theoretical understanding is insufficient. Existing theoretical works often provide meaningless results for over-parameterized models in OOD scenarios or even contradict empirical findings. To this end, we are investigating the performance of the over-parameterized model in terms of OOD generalization under the general benign overfitting conditions. Our analysis focuses on a random feature model and examines non-trivial natural distributional shifts, where the benign overfitting estimators demonstrate a constant excess OOD loss, despite achieving zero excess in-distribution (ID) loss. We demonstrate that in this scenario, further increasing the model's parameterization can significantly reduce the OOD loss. Intuitively, the variance term of ID loss remains low due to orthogonality of long-tail features, meaning overfitting noise during training generally doesn't raise testing loss. However, in OOD cases, distributional shift increases the variance term. Thankfully, the inherent shift is unrelated to individual x, maintaining the orthogonality of long-tail features. Expanding the hidden dimension can additionally improve this orthogonality by mapping the features into higher-dimensional spaces, thereby reducing the variance term. We further show that model ensembles also improve OOD loss, akin to increasing model capacity. These insights explain the empirical phenomenon of enhanced OOD generalization through model ensembles, supported by consistent simulations with theoretical results.
|
[
"['Yifan Hao' 'Yong Lin' 'Difan Zou' 'Tong Zhang']"
] |
null | null |
2403.17601
| null | null |
http://arxiv.org/pdf/2403.17601v3
|
2024-05-23T07:41:01Z
|
2024-03-26T11:13:35Z
|
LASIL: Learner-Aware Supervised Imitation Learning For Long-term
Microscopic Traffic Simulation
|
Microscopic traffic simulation plays a crucial role in transportation engineering by providing insights into individual vehicle behavior and overall traffic flow. However, creating a realistic simulator that accurately replicates human driving behaviors in various traffic conditions presents significant challenges. Traditional simulators relying on heuristic models often fail to deliver accurate simulations due to the complexity of real-world traffic environments. Due to the covariate shift issue, existing imitation learning-based simulators often fail to generate stable long-term simulations. In this paper, we propose a novel approach called learner-aware supervised imitation learning to address the covariate shift problem in multi-agent imitation learning. By leveraging a variational autoencoder simultaneously modeling the expert and learner state distribution, our approach augments expert states such that the augmented state is aware of learner state distribution. Our method, applied to urban traffic simulation, demonstrates significant improvements over existing state-of-the-art baselines in both short-term microscopic and long-term macroscopic realism when evaluated on the real-world dataset pNEUMA.
|
[
"['Ke Guo' 'Zhenwei Miao' 'Wei Jing' 'Weiwei Liu' 'Weizi Li' 'Dayang Hao'\n 'Jia Pan']"
] |
null | null |
2403.17608
| null | null |
http://arxiv.org/pdf/2403.17608v2
|
2024-03-28T15:24:16Z
|
2024-03-26T11:39:00Z
|
Fake or JPEG? Revealing Common Biases in Generated Image Detection
Datasets
|
The widespread adoption of generative image models has highlighted the urgent need to detect artificial content, which is a crucial step in combating widespread manipulation and misinformation. Consequently, numerous detectors and associated datasets have emerged. However, many of these datasets inadvertently introduce undesirable biases, thereby impacting the effectiveness and evaluation of detectors. In this paper, we emphasize that many datasets for AI-generated image detection contain biases related to JPEG compression and image size. Using the GenImage dataset, we demonstrate that detectors indeed learn from these undesired factors. Furthermore, we show that removing the named biases substantially increases robustness to JPEG compression and significantly alters the cross-generator performance of evaluated detectors. Specifically, it leads to more than 11 percentage points increase in cross-generator performance for ResNet50 and Swin-T detectors on the GenImage dataset, achieving state-of-the-art results. We provide the dataset and source codes of this paper on the anonymous website: https://www.unbiased-genimage.org
|
[
"['Patrick Grommelt' 'Louis Weiss' 'Franz-Josef Pfreundt' 'Janis Keuper']"
] |
null | null |
2403.17632
| null | null |
http://arxiv.org/pdf/2403.17632v1
|
2024-03-26T12:08:05Z
|
2024-03-26T12:08:05Z
|
Data-driven Energy Consumption Modelling for Electric Micromobility
using an Open Dataset
|
The escalating challenges of traffic congestion and environmental degradation underscore the critical importance of embracing E-Mobility solutions in urban spaces. In particular, micro E-Mobility tools such as E-scooters and E-bikes, play a pivotal role in this transition, offering sustainable alternatives for urban commuters. However, the energy consumption patterns for these tools are a critical aspect that impacts their effectiveness in real-world scenarios and is essential for trip planning and boosting user confidence in using these. To this effect, recent studies have utilised physical models customised for specific mobility tools and conditions, but these models struggle with generalization and effectiveness in real-world scenarios due to a notable absence of open datasets for thorough model evaluation and verification. To fill this gap, our work presents an open dataset, collected in Dublin, Ireland, specifically designed for energy modelling research related to E-Scooters and E-Bikes. Furthermore, we provide a comprehensive analysis of energy consumption modelling based on the dataset using a set of representative machine learning algorithms and compare their performance against the contemporary mathematical models as a baseline. Our results demonstrate a notable advantage for data-driven models in comparison to the corresponding mathematical models for estimating energy consumption. Specifically, data-driven models outperform physical models in accuracy by up to 83.83% for E-Bikes and 82.16% for E-Scooters based on an in-depth analysis of the dataset under certain assumptions.
|
[
"['Yue Ding' 'Sen Yan' 'Maqsood Hussain Shah' 'Hongyuan Fang' 'Ji Li'\n 'Mingming Liu']"
] |
null | null |
2403.17634
| null | null |
http://arxiv.org/pdf/2403.17634v1
|
2024-03-26T12:08:58Z
|
2024-03-26T12:08:58Z
|
Retentive Decision Transformer with Adaptive Masking for Reinforcement
Learning based Recommendation Systems
|
Reinforcement Learning-based Recommender Systems (RLRS) have shown promise across a spectrum of applications, from e-commerce platforms to streaming services. Yet, they grapple with challenges, notably in crafting reward functions and harnessing large pre-existing datasets within the RL framework. Recent advancements in offline RLRS provide a solution for how to address these two challenges. However, existing methods mainly rely on the transformer architecture, which, as sequence lengths increase, can introduce challenges associated with computational resources and training costs. Additionally, the prevalent methods employ fixed-length input trajectories, restricting their capacity to capture evolving user preferences. In this study, we introduce a new offline RLRS method to deal with the above problems. We reinterpret the RLRS challenge by modeling sequential decision-making as an inference task, leveraging adaptive masking configurations. This adaptive approach selectively masks input tokens, transforming the recommendation task into an inference challenge based on varying token subsets, thereby enhancing the agent's ability to infer across diverse trajectory lengths. Furthermore, we incorporate a multi-scale segmented retention mechanism that facilitates efficient modeling of long sequences, significantly enhancing computational efficiency. Our experimental analysis, conducted on both online simulator and offline datasets, clearly demonstrates the advantages of our proposed method.
|
[
"['Siyu Wang' 'Xiaocong Chen' 'Lina Yao']"
] |
null | null |
2403.17637
| null | null |
http://arxiv.org/pdf/2403.17637v2
|
2024-04-02T12:17:30Z
|
2024-03-26T12:12:44Z
|
PeersimGym: An Environment for Solving the Task Offloading Problem with
Reinforcement Learning
|
Task offloading, crucial for balancing computational loads across devices in networks such as the Internet of Things, poses significant optimization challenges, including minimizing latency and energy usage under strict communication and storage constraints. While traditional optimization falls short in scalability; and heuristic approaches lack in achieving optimal outcomes, Reinforcement Learning (RL) offers a promising avenue by enabling the learning of optimal offloading strategies through iterative interactions. However, the efficacy of RL hinges on access to rich datasets and custom-tailored, realistic training environments. To address this, we introduce PeersimGym, an open-source, customizable simulation environment tailored for developing and optimizing task offloading strategies within computational networks. PeersimGym supports a wide range of network topologies and computational constraints and integrates a textit{PettingZoo}-based interface for RL agent deployment in both solo and multi-agent setups. Furthermore, we demonstrate the utility of the environment through experiments with Deep Reinforcement Learning agents, showcasing the potential of RL-based approaches to significantly enhance offloading strategies in distributed computing settings. PeersimGym thus bridges the gap between theoretical RL models and their practical applications, paving the way for advancements in efficient task offloading methodologies.
|
[
"['Frederico Metelo' 'Stevo Racković' 'Pedro Ákos Costa' 'Cláudia Soares']"
] |
null | null |
2403.17646
| null | null |
http://arxiv.org/pdf/2403.17646v1
|
2024-03-26T12:28:04Z
|
2024-03-26T12:28:04Z
|
Uncertainty-aware Distributional Offline Reinforcement Learning
|
Offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data. A central concern in this context is ensuring the safety of the learned policy by quantifying uncertainties associated with various actions and environmental stochasticity. Traditional approaches primarily emphasize mitigating epistemic uncertainty by learning risk-averse policies, often overlooking environmental stochasticity. In this study, we propose an uncertainty-aware distributional offline RL method to simultaneously address both epistemic uncertainty and environmental stochasticity. We propose a model-free offline RL algorithm capable of learning risk-averse policies and characterizing the entire distribution of discounted cumulative rewards, as opposed to merely maximizing the expected value of accumulated discounted returns. Our method is rigorously evaluated through comprehensive experiments in both risk-sensitive and risk-neutral benchmarks, demonstrating its superior performance.
|
[
"['Xiaocong Chen' 'Siyu Wang' 'Tong Yu' 'Lina Yao']"
] |
null | null |
2403.17656
| null | null |
http://arxiv.org/pdf/2403.17656v1
|
2024-03-26T12:39:02Z
|
2024-03-26T12:39:02Z
|
SGHormer: An Energy-Saving Graph Transformer Driven by Spikes
|
Graph Transformers (GTs) with powerful representation learning ability make a huge success in wide range of graph tasks. However, the costs behind outstanding performances of GTs are higher energy consumption and computational overhead. The complex structure and quadratic complexity during attention calculation in vanilla transformer seriously hinder its scalability on the large-scale graph data. Though existing methods have made strides in simplifying combinations among blocks or attention-learning paradigm to improve GTs' efficiency, a series of energy-saving solutions originated from biologically plausible structures are rarely taken into consideration when constructing GT framework. To this end, we propose a new spiking-based graph transformer (SGHormer). It turns full-precision embeddings into sparse and binarized spikes to reduce memory and computational costs. The spiking graph self-attention and spiking rectify blocks in SGHormer explicitly capture global structure information and recover the expressive power of spiking embeddings, respectively. In experiments, SGHormer achieves comparable performances to other full-precision GTs with extremely low computational energy consumption. The results show that SGHomer makes a remarkable progress in the field of low-energy GTs.
|
[
"['Huizhe Zhang' 'Jintang Li' 'Liang Chen' 'Zibin Zheng']"
] |
null | null |
2403.17660
| null | null |
http://arxiv.org/pdf/2403.17660v1
|
2024-03-26T12:47:04Z
|
2024-03-26T12:47:04Z
|
CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1
Perturbations
|
Optimal Power Flow (OPF) refers to a wide range of related optimization problems with the goal of operating power systems efficiently and securely. In the simplest setting, OPF determines how much power to generate in order to minimize costs while meeting demand for power and satisfying physical and operational constraints. In even the simplest case, power grid operators use approximations of the AC-OPF problem because solving the exact problem is prohibitively slow with state-of-the-art solvers. These approximations sacrifice accuracy and operational feasibility in favor of speed. This trade-off leads to costly "uplift payments" and increased carbon emissions, especially for large power grids. In the present work, we train a deep learning system (CANOS) to predict near-optimal solutions (within 1% of the true AC-OPF cost) without compromising speed (running in as little as 33--65 ms). Importantly, CANOS scales to realistic grid sizes with promising empirical results on grids containing as many as 10,000 buses. Finally, because CANOS is a Graph Neural Network, it is robust to changes in topology. We show that CANOS is accurate across N-1 topological perturbations of a base grid typically used in security-constrained analysis. This paves the way for more efficient optimization of more complex OPF problems which alter grid connectivity such as unit commitment, topology optimization and security-constrained OPF.
|
[
"['Luis Piloto' 'Sofia Liguori' 'Sephora Madjiheurem' 'Miha Zgubic'\n 'Sean Lovett' 'Hamish Tomlinson' 'Sophie Elster' 'Chris Apps'\n 'Sims Witherspoon']"
] |
null | null |
2403.17673
| null | null |
http://arxiv.org/pdf/2403.17673v2
|
2024-06-06T16:35:51Z
|
2024-03-26T13:02:43Z
|
How Private are DP-SGD Implementations?
|
We demonstrate a substantial gap between the privacy guarantees of the Adaptive Batch Linear Queries (ABLQ) mechanism under different types of batch sampling: (i) Shuffling, and (ii) Poisson subsampling; the typical analysis of Differentially Private Stochastic Gradient Descent (DP-SGD) follows by interpreting it as a post-processing of ABLQ. While shuffling-based DP-SGD is more commonly used in practical implementations, it has not been amenable to easy privacy analysis, either analytically or even numerically. On the other hand, Poisson subsampling-based DP-SGD is challenging to scalably implement, but has a well-understood privacy analysis, with multiple open-source numerically tight privacy accountants available. This has led to a common practice of using shuffling-based DP-SGD in practice, but using the privacy analysis for the corresponding Poisson subsampling version. Our result shows that there can be a substantial gap between the privacy analysis when using the two types of batch sampling, and thus advises caution in reporting privacy parameters for DP-SGD.
|
[
"['Lynn Chua' 'Badih Ghazi' 'Pritish Kamath' 'Ravi Kumar'\n 'Pasin Manurangsi' 'Amer Sinha' 'Chiyuan Zhang']"
] |
null | null |
2403.17692
| null | null |
http://arxiv.org/pdf/2403.17692v1
|
2024-03-26T13:33:16Z
|
2024-03-26T13:33:16Z
|
Manifold-Guided Lyapunov Control with Diffusion Models
|
This paper presents a novel approach to generating stabilizing controllers for a large class of dynamical systems using diffusion models. The core objective is to develop stabilizing control functions by identifying the closest asymptotically stable vector field relative to a predetermined manifold and adjusting the control function based on this finding. To achieve this, we employ a diffusion model trained on pairs consisting of asymptotically stable vector fields and their corresponding Lyapunov functions. Our numerical results demonstrate that this pre-trained model can achieve stabilization over previously unseen systems efficiently and rapidly, showcasing the potential of our approach in fast zero-shot control and generalizability.
|
[
"['Amartya Mukherjee' 'Thanin Quartz' 'Jun Liu']"
] |
null | null |
2403.17695
| null | null |
http://arxiv.org/pdf/2403.17695v1
|
2024-03-26T13:35:10Z
|
2024-03-26T13:35:10Z
|
PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition
|
We present PlainMamba: a simple non-hierarchical state space model (SSM) designed for general visual recognition. The recent Mamba model has shown how SSMs can be highly competitive with other architectures on sequential data and initial attempts have been made to apply it to images. In this paper, we further adapt the selective scanning process of Mamba to the visual domain, enhancing its ability to learn features from two-dimensional images by (i) a continuous 2D scanning process that improves spatial continuity by ensuring adjacency of tokens in the scanning sequence, and (ii) direction-aware updating which enables the model to discern the spatial relations of tokens by encoding directional information. Our architecture is designed to be easy to use and easy to scale, formed by stacking identical PlainMamba blocks, resulting in a model with constant width throughout all layers. The architecture is further simplified by removing the need for special tokens. We evaluate PlainMamba on a variety of visual recognition tasks including image classification, semantic segmentation, object detection, and instance segmentation. Our method achieves performance gains over previous non-hierarchical models and is competitive with hierarchical alternatives. For tasks requiring high-resolution inputs, in particular, PlainMamba requires much less computing while maintaining high performance. Code and models are available at https://github.com/ChenhongyiYang/PlainMamba
|
[
"['Chenhongyi Yang' 'Zehui Chen' 'Miguel Espinosa' 'Linus Ericsson'\n 'Zhenyu Wang' 'Jiaming Liu' 'Elliot J. Crowley']"
] |
null | null |
2403.17698
| null | null |
http://arxiv.org/pdf/2403.17698v1
|
2024-03-26T13:38:06Z
|
2024-03-26T13:38:06Z
|
MEP: Multiple Kernel Learning Enhancing Relative Positional Encoding
Length Extrapolation
|
When the predicted sequence length exceeds the length seen during training, the transformer's inference accuracy diminishes. Existing relative position encoding methods, such as those based on the ALiBi technique, address the length extrapolation challenge exclusively through the implementation of a single kernel function, which introduces a constant bias to every post-softmax attention scores according to their distance. These approaches do not investigate or employ multiple kernel functions to address the extrapolation challenge. Drawing on the ALiBi approach, this study proposes a novel relative positional encoding method, called MEP, which employs a weighted average to combine distinct kernel functions(such as the exponential kernel and the Gaussian kernel) to generate a bias that is applied to post-softmax attention scores. Initially, the framework utilizes various kernel functions to construct multiple kernel functions. Each kernel function adheres to a consistent mean weight coefficient, harnessing the synergistic advantages of different kernels to formulate an innovative bias function. Subsequently, specific slopes are tailored for each kernel function, applying penalties at varying rates, to enhance the model's extrapolation capabilities. Finally, this bias is seamlessly incorporated as a penalty to the post-softmax scores. We present two distinct versions of our method: a parameter-free variant that requires no new learnable parameters, which enhances length extrapolation capabilities without compromising training efficiency, and a parameterized variant capable of integrating state-of-the-art techniques. Empirical evaluations across diverse datasets have demonstrated that both variants of our method achieve state-of-the-art performance, outperforming traditional parameter-free and parameterized approaches.
|
[
"['Weiguo Gao']"
] |
null | null |
2403.17701
| null | null |
http://arxiv.org/pdf/2403.17701v4
|
2024-05-03T10:12:09Z
|
2024-03-26T13:40:18Z
|
Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical
Image Segmentation
|
Image segmentation holds a vital position in the realms of diagnosis and treatment within the medical domain. Traditional convolutional neural networks (CNNs) and Transformer models have made significant advancements in this realm, but they still encounter challenges because of limited receptive field or high computing complexity. Recently, State Space Models (SSMs), particularly Mamba and its variants, have demonstrated notable performance in the field of vision. However, their feature extraction methods may not be sufficiently effective and retain some redundant structures, leaving room for parameter reduction. Motivated by previous spatial and channel attention methods, we propose Triplet Mamba-UNet. The method leverages residual VSS Blocks to extract intensive contextual features, while Triplet SSM is employed to fuse features across spatial and channel dimensions. We conducted experiments on ISIC17, ISIC18, CVC-300, CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB, and Kvasir-Instrument datasets, demonstrating the superior segmentation performance of our proposed TM-UNet. Additionally, compared to the previous VM-UNet, our model achieves a one-third reduction in parameters.
|
[
"['Hao Tang' 'Lianglun Cheng' 'Guoheng Huang' 'Zhengguang Tan' 'Junhao Lu'\n 'Kaihong Wu']"
] |
null | null |
2403.17728
| null | null |
http://arxiv.org/pdf/2403.17728v2
|
2024-05-29T16:14:23Z
|
2024-03-26T14:17:01Z
|
Masked Autoencoders are PDE Learners
|
Neural solvers for partial differential equations (PDEs) have great potential to generate fast and accurate physics solutions, yet their practicality is currently limited by their generalizability. PDEs evolve over broad scales and exhibit diverse behaviors; predicting these phenomena will require learning representations across a wide variety of inputs which may encompass different coefficients, boundary conditions, resolutions, or even equations. As a step towards generalizable PDE modeling, we adapt masked pretraining for physics problems. Through self-supervised learning across PDEs, masked autoencoders can consolidate heterogeneous physics to learn meaningful latent representations and perform latent PDE arithmetic in this space. Furthermore, we demonstrate that masked pretraining can improve PDE coefficient regression and the classification of PDE features. Lastly, conditioning neural solvers on learned latent representations can improve time-stepping and super-resolution performance across a variety of coefficients, discretizations, or boundary conditions, as well as on unseen PDEs. We hope that masked pretraining can emerge as a unifying method across large, unlabeled, and heterogeneous datasets to learn latent physics at scale.
|
[
"['Anthony Zhou' 'Amir Barati Farimani']"
] |
null | null |
2403.17729
| null | null |
http://arxiv.org/pdf/2403.17729v2
|
2024-04-04T14:29:34Z
|
2024-03-26T14:18:43Z
|
EulerFormer: Sequential User Behavior Modeling with Complex Vector
Attention
|
To capture user preference, transformer models have been widely applied to model sequential user behavior data. The core of transformer architecture lies in the self-attention mechanism, which computes the pairwise attention scores in a sequence. Due to the permutation-equivariant nature, positional encoding is used to enhance the attention between token representations. In this setting, the pairwise attention scores can be derived by both semantic difference and positional difference. However, prior studies often model the two kinds of difference measurements in different ways, which potentially limits the expressive capacity of sequence modeling. To address this issue, this paper proposes a novel transformer variant with complex vector attention, named EulerFormer, which provides a unified theoretical framework to formulate both semantic difference and positional difference. The EulerFormer involves two key technical improvements. First, it employs a new transformation function for efficiently transforming the sequence tokens into polar-form complex vectors using Euler's formula, enabling the unified modeling of both semantic and positional information in a complex rotation form.Secondly, it develops a differential rotation mechanism, where the semantic rotation angles can be controlled by an adaptation function, enabling the adaptive integration of the semantic and positional information according to the semantic contexts.Furthermore, a phase contrastive learning task is proposed to improve the isotropy of contextual representations in EulerFormer. Our theoretical framework possesses a high degree of completeness and generality. It is more robust to semantic variations and possesses moresuperior theoretical properties in principle. Extensive experiments conducted on four public datasets demonstrate the effectiveness and efficiency of our approach.
|
[
"['Zhen Tian' 'Wayne Xin Zhao' 'Changwang Zhang' 'Xin Zhao' 'Zhongrui Ma'\n 'Ji-Rong Wen']"
] |
null | null |
2403.17745
| null | null |
http://arxiv.org/pdf/2403.17745v1
|
2024-03-26T14:36:22Z
|
2024-03-26T14:36:22Z
|
Leave No Patient Behind: Enhancing Medication Recommendation for Rare
Disease Patients
|
Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information. However, existing approaches often suffer from fairness issues, as recommendations tend to be more accurate for patients with common diseases compared to those with rare conditions. In this paper, we propose a novel model called Robust and Accurate REcommendations for Medication (RAREMed), which leverages the pretrain-finetune learning paradigm to enhance accuracy for rare diseases. RAREMed employs a transformer encoder with a unified input sequence approach to capture complex relationships among disease and procedure codes. Additionally, it introduces two self-supervised pre-training tasks, namely Sequence Matching Prediction (SMP) and Self Reconstruction (SR), to learn specialized medication needs and interrelations among clinical codes. Experimental results on two real-world datasets demonstrate that RAREMed provides accurate drug sets for both rare and common disease patients, thereby mitigating unfairness in medication recommendation systems.
|
[
"['Zihao Zhao' 'Yi Jing' 'Fuli Feng' 'Jiancan Wu' 'Chongming Gao'\n 'Xiangnan He']"
] |
null | null |
2403.17753
| null | null |
http://arxiv.org/pdf/2403.17753v2
|
2024-03-29T06:48:37Z
|
2024-03-26T14:43:57Z
|
CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream
Enhanced Rectified Transformer Model
|
Accurate, and effective traffic forecasting is vital for smart traffic systems, crucial in urban traffic planning and management. Current Spatio-Temporal Transformer models, despite their prediction capabilities, struggle with balancing computational efficiency and accuracy, favoring global over local information, and handling spatial and temporal data separately, limiting insight into complex interactions. We introduce the Criss-Crossed Dual-Stream Enhanced Rectified Transformer model (CCDSReFormer), which includes three innovative modules: Enhanced Rectified Spatial Self-attention (ReSSA), Enhanced Rectified Delay Aware Self-attention (ReDASA), and Enhanced Rectified Temporal Self-attention (ReTSA). These modules aim to lower computational needs via sparse attention, focus on local information for better traffic dynamics understanding, and merge spatial and temporal insights through a unique learning method. Extensive tests on six real-world datasets highlight CCDSReFormer's superior performance. An ablation study also confirms the significant impact of each component on the model's predictive accuracy, showcasing our model's ability to forecast traffic flow effectively.
|
[
"['Zhiqi Shao' 'Michael G. H. Bell' 'Ze Wang' 'D. Glenn Geers'\n 'Xusheng Yao' 'Junbin Gao']"
] |
null | null |
2403.17757
| null | null |
http://arxiv.org/pdf/2403.17757v1
|
2024-03-26T14:49:22Z
|
2024-03-26T14:49:22Z
|
Noise2Noise Denoising of CRISM Hyperspectral Data
|
Hyperspectral data acquired by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) have allowed for unparalleled mapping of the surface mineralogy of Mars. Due to sensor degradation over time, a significant portion of the recently acquired data is considered unusable. Here a new data-driven model architecture, Noise2Noise4Mars (N2N4M), is introduced to remove noise from CRISM images. Our model is self-supervised and does not require zero-noise target data, making it well suited for use in Planetary Science applications where high quality labelled data is scarce. We demonstrate its strong performance on synthetic-noise data and CRISM images, and its impact on downstream classification performance, outperforming benchmark methods on most metrics. This allows for detailed analysis for critical sites of interest on the Martian surface, including proposed lander sites.
|
[
"['Robert Platt' 'Rossella Arcucci' 'Cédric M. John']"
] |
null | null |
2403.17767
| null | null |
http://arxiv.org/pdf/2403.17767v2
|
2024-03-27T08:49:19Z
|
2024-03-26T14:54:35Z
|
Asymptotic Bayes risk of semi-supervised learning with uncertain
labeling
|
This article considers a semi-supervised classification setting on a Gaussian mixture model, where the data is not labeled strictly as usual, but instead with uncertain labels. Our main aim is to compute the Bayes risk for this model. We compare the behavior of the Bayes risk and the best known algorithm for this model. This comparison eventually gives new insights over the algorithm.
|
[
"['Victor Leger' 'Romain Couillet']"
] |
null | null |
2403.17768
| null | null |
http://arxiv.org/pdf/2403.17768v1
|
2024-03-26T14:54:48Z
|
2024-03-26T14:54:48Z
|
SciNews: From Scholarly Complexities to Public Narratives -- A Dataset
for Scientific News Report Generation
|
Scientific news reports serve as a bridge, adeptly translating complex research articles into reports that resonate with the broader public. The automated generation of such narratives enhances the accessibility of scholarly insights. In this paper, we present a new corpus to facilitate this paradigm development. Our corpus comprises a parallel compilation of academic publications and their corresponding scientific news reports across nine disciplines. To demonstrate the utility and reliability of our dataset, we conduct an extensive analysis, highlighting the divergences in readability and brevity between scientific news narratives and academic manuscripts. We benchmark our dataset employing state-of-the-art text generation models. The evaluation process involves both automatic and human evaluation, which lays the groundwork for future explorations into the automated generation of scientific news reports. The dataset and code related to this work are available at https://dongqi.me/projects/SciNews.
|
[
"['Dongqi Pu' 'Yifan Wang' 'Jia Loy' 'Vera Demberg']"
] |
null | null |
2403.17775
| null | null |
http://arxiv.org/pdf/2403.17775v3
|
2024-07-15T14:29:33Z
|
2024-03-26T15:07:58Z
|
Secure Aggregation is Not Private Against Membership Inference Attacks
|
Secure aggregation (SecAgg) is a commonly-used privacy-enhancing mechanism in federated learning, affording the server access only to the aggregate of model updates while safeguarding the confidentiality of individual updates. Despite widespread claims regarding SecAgg's privacy-preserving capabilities, a formal analysis of its privacy is lacking, making such presumptions unjustified. In this paper, we delve into the privacy implications of SecAgg by treating it as a local differential privacy (LDP) mechanism for each local update. We design a simple attack wherein an adversarial server seeks to discern which update vector a client submitted, out of two possible ones, in a single training round of federated learning under SecAgg. By conducting privacy auditing, we assess the success probability of this attack and quantify the LDP guarantees provided by SecAgg. Our numerical results unveil that, contrary to prevailing claims, SecAgg offers weak privacy against membership inference attacks even in a single training round. Indeed, it is difficult to hide a local update by adding other independent local updates when the updates are of high dimension. Our findings underscore the imperative for additional privacy-enhancing mechanisms, such as noise injection, in federated learning.
|
[
"['Khac-Hoang Ngo' 'Johan Östman' 'Giuseppe Durisi'\n 'Alexandre Graell i Amat']"
] |
null | null |
2403.17805
| null | null |
http://arxiv.org/pdf/2403.17805v1
|
2024-03-26T15:42:04Z
|
2024-03-26T15:42:04Z
|
Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving
|
The automated generation of diverse and complex training scenarios has been an important ingredient in many complex learning tasks. Especially in real-world application domains, such as autonomous driving, auto-curriculum generation is considered vital for obtaining robust and general policies. However, crafting traffic scenarios with multiple, heterogeneous agents is typically considered as a tedious and time-consuming task, especially in more complex simulation environments. In our work, we introduce MATS-Gym, a Multi-Agent Traffic Scenario framework to train agents in CARLA, a high-fidelity driving simulator. MATS-Gym is a multi-agent training framework for autonomous driving that uses partial scenario specifications to generate traffic scenarios with variable numbers of agents. This paper unifies various existing approaches to traffic scenario description into a single training framework and demonstrates how it can be integrated with techniques from unsupervised environment design to automate the generation of adaptive auto-curricula. The code is available at https://github.com/AutonomousDrivingExaminer/mats-gym.
|
[
"['Axel Brunnbauer' 'Luigi Berducci' 'Peter Priller' 'Dejan Nickovic'\n 'Radu Grosu']"
] |
null | null |
2403.17806
| null | null |
http://arxiv.org/pdf/2403.17806v2
|
2024-07-15T12:07:09Z
|
2024-03-26T15:44:58Z
|
Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding
Model Mechanisms
|
Many recent language model (LM) interpretability studies have adopted the circuits framework, which aims to find the minimal computational subgraph, or circuit, that explains LM behavior on a given task. Most studies determine which edges belong in a LM's circuit by performing causal interventions on each edge independently, but this scales poorly with model size. Edge attribution patching (EAP), gradient-based approximation to interventions, has emerged as a scalable but imperfect solution to this problem. In this paper, we introduce a new method - EAP with integrated gradients (EAP-IG) - that aims to better maintain a core property of circuits: faithfulness. A circuit is faithful if all model edges outside the circuit can be ablated without changing the model's performance on the task; faithfulness is what justifies studying circuits, rather than the full model. Our experiments demonstrate that circuits found using EAP are less faithful than those found using EAP-IG, even though both have high node overlap with circuits found previously using causal interventions. We conclude more generally that when using circuits to compare the mechanisms models use to solve tasks, faithfulness, not overlap, is what should be measured.
|
[
"['Michael Hanna' 'Sandro Pezzelle' 'Yonatan Belinkov']"
] |
null | null |
2403.17808
| null | null |
http://arxiv.org/pdf/2403.17808v1
|
2024-03-26T15:45:29Z
|
2024-03-26T15:45:29Z
|
Annotated Biomedical Video Generation using Denoising Diffusion
Probabilistic Models and Flow Fields
|
The segmentation and tracking of living cells play a vital role within the biomedical domain, particularly in cancer research, drug development, and developmental biology. These are usually tedious and time-consuming tasks that are traditionally done by biomedical experts. Recently, to automatize these processes, deep learning based segmentation and tracking methods have been proposed. These methods require large-scale datasets and their full potential is constrained by the scarcity of annotated data in the biomedical imaging domain. To address this limitation, we propose Biomedical Video Diffusion Model (BVDM), capable of generating realistic-looking synthetic microscopy videos. Trained only on a single real video, BVDM can generate videos of arbitrary length with pixel-level annotations that can be used for training data-hungry models. It is composed of a denoising diffusion probabilistic model (DDPM) generating high-fidelity synthetic cell microscopy images and a flow prediction model (FPM) predicting the non-rigid transformation between consecutive video frames. During inference, initially, the DDPM imposes realistic cell textures on synthetic cell masks which are generated based on real data statistics. The flow prediction model predicts the flow field between consecutive masks and applies that to the DDPM output from the previous time frame to create the next one while keeping temporal consistency. BVDM outperforms state-of-the-art synthetic live cell microscopy video generation models. Furthermore, we demonstrate that a sufficiently large synthetic dataset enhances the performance of cell segmentation and tracking models compared to using a limited amount of available real data.
|
[
"['Rüveyda Yilmaz' 'Dennis Eschweiler' 'Johannes Stegmaier']"
] |
null | null |
2403.17811
| null | null |
http://arxiv.org/abs/2403.17811v1
|
2024-03-26T15:50:37Z
|
2024-03-26T15:50:37Z
|
Are Compressed Language Models Less Subgroup Robust?
|
To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. However, little is known about their robustness to minority subgroups defined by the labels and attributes of a dataset. In this paper, we investigate the effects of 18 different compression methods and settings on the subgroup robustness of BERT language models. We show that worst-group performance does not depend on model size alone, but also on the compression method used. Additionally, we find that model compression does not always worsen the performance on minority subgroups. Altogether, our analysis serves to further research into the subgroup robustness of model compression.
|
[
"['Leonidas Gee' 'Andrea Zugarini' 'Novi Quadrianto']"
] |
null | null |
2403.17827
| null | null |
http://arxiv.org/pdf/2403.17827v1
|
2024-03-26T16:06:42Z
|
2024-03-26T16:06:42Z
|
DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from
Textual Descriptions
|
Generating natural hand-object interactions in 3D is challenging as the resulting hand and object motions are expected to be physically plausible and semantically meaningful. Furthermore, generalization to unseen objects is hindered by the limited scale of available hand-object interaction datasets. We propose DiffH2O, a novel method to synthesize realistic, one or two-handed object interactions from provided text prompts and geometry of the object. The method introduces three techniques that enable effective learning from limited data. First, we decompose the task into a grasping stage and a text-based interaction stage and use separate diffusion models for each. In the grasping stage, the model only generates hand motions, whereas in the interaction phase both hand and object poses are synthesized. Second, we propose a compact representation that tightly couples hand and object poses. Third, we propose two different guidance schemes to allow more control of the generated motions: grasp guidance and detailed textual guidance. Grasp guidance takes a single target grasping pose and guides the diffusion model to reach this grasp at the end of the grasping stage, which provides control over the grasping pose. Given a grasping motion from this stage, multiple different actions can be prompted in the interaction phase. For textual guidance, we contribute comprehensive text descriptions to the GRAB dataset and show that they enable our method to have more fine-grained control over hand-object interactions. Our quantitative and qualitative evaluation demonstrates that the proposed method outperforms baseline methods and leads to natural hand-object motions. Moreover, we demonstrate the practicality of our framework by utilizing a hand pose estimate from an off-the-shelf pose estimator for guidance, and then sampling multiple different actions in the interaction stage.
|
[
"['Sammy Christen' 'Shreyas Hampali' 'Fadime Sener' 'Edoardo Remelli'\n 'Tomas Hodan' 'Eric Sauser' 'Shugao Ma' 'Bugra Tekin']"
] |
null | null |
2403.17831
| null | null |
http://arxiv.org/pdf/2403.17831v1
|
2024-03-26T16:13:55Z
|
2024-03-26T16:13:55Z
|
Learning the Optimal Power Flow: Environment Design Matters
|
To solve the optimal power flow (OPF) problem, reinforcement learning (RL) emerges as a promising new approach. However, the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment. In this work, we collect and implement diverse environment design decisions from the literature regarding training data, observation space, episode definition, and reward function choice. In an experimental analysis, we show the significant impact of these environment design options on RL-OPF training performance. Further, we derive some first recommendations regarding the choice of these design decisions. The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.
|
[
"['Thomas Wolgast' 'Astrid Nieße']"
] |
null | null |
2403.17833
| null | null |
http://arxiv.org/pdf/2403.17833v2
|
2024-05-26T06:34:29Z
|
2024-03-26T16:14:43Z
|
GPFL: A Gradient Projection-Based Client Selection Framework for
Efficient Federated Learning
|
Federated learning client selection is crucial for determining participant clients while balancing model accuracy and communication efficiency. Existing methods have limitations in handling data heterogeneity, computational burdens, and independent client treatment. To address these challenges, we propose GPFL, which measures client value by comparing local and global descent directions. We also employ an Exploit-Explore mechanism to enhance performance. Experimental results on FEMINST and CIFAR-10 datasets demonstrate that GPFL outperforms baselines in Non-IID scenarios, achieving over 9% improvement in FEMINST test accuracy. Moreover, GPFL exhibits shorter computation times through pre-selection and parameter reuse in federated learning.
|
[
"['Shijie Na' 'Yuzhi Liang' 'Siu-Ming Yiu']"
] |
null | null |
2403.17837
| null | null |
http://arxiv.org/pdf/2403.17837v1
|
2024-03-26T16:24:42Z
|
2024-03-26T16:24:42Z
|
GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction
|
High Dynamic Range (HDR) content (i.e., images and videos) has a broad range of applications. However, capturing HDR content from real-world scenes is expensive and time-consuming. Therefore, the challenging task of reconstructing visually accurate HDR images from their Low Dynamic Range (LDR) counterparts is gaining attention in the vision research community. A major challenge in this research problem is the lack of datasets, which capture diverse scene conditions (e.g., lighting, shadows, weather, locations, landscapes, objects, humans, buildings) and various image features (e.g., color, contrast, saturation, hue, luminance, brightness, radiance). To address this gap, in this paper, we introduce GTA-HDR, a large-scale synthetic dataset of photo-realistic HDR images sampled from the GTA-V video game. We perform thorough evaluation of the proposed dataset, which demonstrates significant qualitative and quantitative improvements of the state-of-the-art HDR image reconstruction methods. Furthermore, we demonstrate the effectiveness of the proposed dataset and its impact on additional computer vision tasks including 3D human pose estimation, human body part segmentation, and holistic scene segmentation. The dataset, data collection pipeline, and evaluation code are available at: https://github.com/HrishavBakulBarua/GTA-HDR.
|
[
"['Hrishav Bakul Barua' 'Kalin Stefanov' 'KokSheik Wong' 'Abhinav Dhall'\n 'Ganesh Krishnasamy']"
] |
null | null |
2403.17844
| null | null |
http://arxiv.org/pdf/2403.17844v1
|
2024-03-26T16:33:12Z
|
2024-03-26T16:33:12Z
|
Mechanistic Design and Scaling of Hybrid Architectures
|
The development of deep learning architectures is a resource-demanding process, due to a vast design space, long prototyping times, and high compute costs associated with at-scale model training and evaluation. We set out to simplify this process by grounding it in an end-to-end mechanistic architecture design (MAD) pipeline, encompassing small-scale capability unit tests predictive of scaling laws. Through a suite of synthetic token manipulation tasks such as compression and recall, designed to probe capabilities, we identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis, training over 500 language models between 70M to 7B parameters. Surprisingly, we find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures via isolated proxy tasks. The new architectures found via MAD, based on simple ideas such as hybridization and sparsity, outperform state-of-the-art Transformer, convolutional, and recurrent architectures (Transformer++, Hyena, Mamba) in scaling, both at compute-optimal budgets and in overtrained regimes. Overall, these results provide evidence that performance on curated synthetic tasks can be predictive of scaling laws, and that an optimal architecture should leverage specialized layers via a hybrid topology.
|
[
"['Michael Poli' 'Armin W Thomas' 'Eric Nguyen' 'Pragaash Ponnusamy'\n 'Björn Deiseroth' 'Kristian Kersting' 'Taiji Suzuki' 'Brian Hie'\n 'Stefano Ermon' 'Christopher Ré' 'Ce Zhang' 'Stefano Massaroli']"
] |
null | null |
2403.17845
| null | null |
http://arxiv.org/pdf/2403.17845v1
|
2024-03-26T16:34:05Z
|
2024-03-26T16:34:05Z
|
TractOracle: towards an anatomically-informed reward function for
RL-based tractography
|
Reinforcement learning (RL)-based tractography is a competitive alternative to machine learning and classical tractography algorithms due to its high anatomical accuracy obtained without the need for any annotated data. However, the reward functions so far used to train RL agents do not encapsulate anatomical knowledge which causes agents to generate spurious false positives tracts. In this paper, we propose a new RL tractography system, TractOracle, which relies on a reward network trained for streamline classification. This network is used both as a reward function during training as well as a mean for stopping the tracking process early and thus reduce the number of false positive streamlines. This makes our system a unique method that evaluates and reconstructs WM streamlines at the same time. We report an improvement of true positive ratios by almost 20% and a reduction of 3x of false positive ratios on one dataset and an increase between 2x and 7x in the number true positive streamlines on another dataset.
|
[
"['Antoine Théberge' 'Maxime Descoteaux' 'Pierre-Marc Jodoin']"
] |
null | null |
2403.17846
| null | null |
http://arxiv.org/pdf/2403.17846v2
|
2024-06-03T17:12:25Z
|
2024-03-26T16:36:43Z
|
Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot
Navigation
|
Recent open-vocabulary robot mapping methods enrich dense geometric maps with pre-trained visual-language features. While these maps allow for the prediction of point-wise saliency maps when queried for a certain language concept, large-scale environments and abstract queries beyond the object level still pose a considerable hurdle, ultimately limiting language-grounded robotic navigation. In this work, we present HOV-SG, a hierarchical open-vocabulary 3D scene graph mapping approach for language-grounded robot navigation. Leveraging open-vocabulary vision foundation models, we first obtain state-of-the-art open-vocabulary segment-level maps in 3D and subsequently construct a 3D scene graph hierarchy consisting of floor, room, and object concepts, each enriched with open-vocabulary features. Our approach is able to represent multi-story buildings and allows robotic traversal of those using a cross-floor Voronoi graph. HOV-SG is evaluated on three distinct datasets and surpasses previous baselines in open-vocabulary semantic accuracy on the object, room, and floor level while producing a 75% reduction in representation size compared to dense open-vocabulary maps. In order to prove the efficacy and generalization capabilities of HOV-SG, we showcase successful long-horizon language-conditioned robot navigation within real-world multi-storage environments. We provide code and trial video data at http://hovsg.github.io/.
|
[
"['Abdelrhman Werby' 'Chenguang Huang' 'Martin Büchner' 'Abhinav Valada'\n 'Wolfram Burgard']"
] |
null | null |
2403.17847
| null | null |
http://arxiv.org/pdf/2403.17847v1
|
2024-03-26T16:36:50Z
|
2024-03-26T16:36:50Z
|
Climate Downscaling: A Deep-Learning Based Super-resolution Model of
Precipitation Data with Attention Block and Skip Connections
|
Human activities accelerate consumption of fossil fuels and produce greenhouse gases, resulting in urgent issues today: global warming and the climate change. These indirectly cause severe natural disasters, plenty of lives suffering and huge losses of agricultural properties. To mitigate impacts on our lands, scientists are developing renewable, reusable, and clean energies and climatologists are trying to predict the extremes. Meanwhile, governments are publicizing resource-saving policies for a more eco-friendly society and arousing environment awareness. One of the most influencing factors is the precipitation, bringing condensed water vapor onto lands. Water resources are the most significant but basic needs in society, not only supporting our livings, but also economics. In Taiwan, although the average annual precipitation is up to 2,500 millimeter (mm), the water allocation for each person is lower than the global average due to drastically geographical elevation changes and uneven distribution through the year. Thus, it is crucial to track and predict the rainfall to make the most use of it and to prevent the floods. However, climate models have limited resolution and require intensive computational power for local-scale use. Therefore, we proposed a deep convolutional neural network with skip connections, attention blocks, and auxiliary data concatenation, in order to downscale the low-resolution precipitation data into high-resolution one. Eventually, we compare with other climate downscaling methods and show better performance in metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson Correlation, structural similarity index (SSIM), and forecast indicators.
|
[
"['Chia-Hao Chiang' 'Zheng-Han Huang' 'Liwen Liu' 'Hsin-Chien Liang'\n 'Yi-Chi Wang' 'Wan-Ling Tseng' 'Chao Wang' 'Che-Ta Chen' 'Ko-Chih Wang']"
] |
null | null |
2403.17852
| null | null |
http://arxiv.org/pdf/2403.17852v2
|
2024-06-30T01:51:00Z
|
2024-03-26T16:40:08Z
|
Counterfactual Fairness through Transforming Data Orthogonal to Bias
|
Machine learning models have shown exceptional prowess in solving complex issues across various domains. However, these models can sometimes exhibit biased decision-making, resulting in unequal treatment of different groups. Despite substantial research on counterfactual fairness, methods to reduce the impact of multivariate and continuous sensitive variables on decision-making outcomes are still underdeveloped. We propose a novel data pre-processing algorithm, Orthogonal to Bias (OB), which is designed to eliminate the influence of a group of continuous sensitive variables, thus promoting counterfactual fairness in machine learning applications. Our approach, based on the assumption of a jointly normal distribution within a structural causal model (SCM), demonstrates that counterfactual fairness can be achieved by ensuring the data is orthogonal to the observed sensitive variables. The OB algorithm is model-agnostic, making it applicable to a wide range of machine learning models and tasks. Additionally, it includes a sparse variant to improve numerical stability through regularization. Empirical evaluations on both simulated and real-world datasets, encompassing settings with both discrete and continuous sensitive variables, show that our methodology effectively promotes fairer outcomes without compromising accuracy.
|
[
"['Shuyi Chen' 'Shixiang Zhu']"
] |
null | null |
2403.17853
| null | null |
http://arxiv.org/pdf/2403.17853v1
|
2024-03-26T16:42:30Z
|
2024-03-26T16:42:30Z
|
Using Domain Knowledge to Guide Dialog Structure Induction via Neural
Probabilistic Soft Logic
|
Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i.e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog. It is a critical component for modern dialog system design and discourse analysis. Existing DSI approaches are often purely data-driven, deploy models that infer latent states without access to domain knowledge, underperform when the training corpus is limited/noisy, or have difficulty when test dialogs exhibit distributional shifts from the training domain. This work explores a neural-symbolic approach as a potential solution to these problems. We introduce Neural Probabilistic Soft Logic Dialogue Structure Induction (NEUPSL DSI), a principled approach that injects symbolic knowledge into the latent space of a generative neural model. We conduct a thorough empirical investigation on the effect of NEUPSL DSI learning on hidden representation quality, few-shot learning, and out-of-domain generalization performance. Over three dialog structure induction datasets and across unsupervised and semi-supervised settings for standard and cross-domain generalization, the injection of symbolic knowledge using NEUPSL DSI provides a consistent boost in performance over the canonical baselines.
|
[
"['Connor Pryor' 'Quan Yuan' 'Jeremiah Liu' 'Mehran Kazemi'\n 'Deepak Ramachandran' 'Tania Bedrax-Weiss' 'Lise Getoor']"
] |
null | null |
2403.17868
| null | null |
http://arxiv.org/pdf/2403.17868v3
|
2024-05-16T17:20:50Z
|
2024-03-26T16:57:01Z
|
An invitation to the sample complexity of quantum hypothesis testing
|
Quantum hypothesis testing (QHT) has been traditionally studied from the information-theoretic perspective, wherein one is interested in the optimal decay rate of error probabilities as a function of the number of samples of an unknown state. In this paper, we study the sample complexity of QHT, wherein the goal is to determine the minimum number of samples needed to reach a desired error probability. By making use of the wealth of knowledge that already exists in the literature on QHT, we characterize the sample complexity of binary QHT in the symmetric and asymmetric settings, and we provide bounds on the sample complexity of multiple QHT. In more detail, we prove that the sample complexity of symmetric binary QHT depends logarithmically on the inverse error probability and inversely on the negative logarithm of the fidelity. As a counterpart of the quantum Stein's lemma, we also find that the sample complexity of asymmetric binary QHT depends logarithmically on the inverse type II error probability and inversely on the quantum relative entropy, provided that the type II error probability is sufficiently small. We then provide lower and upper bounds on the sample complexity of multiple QHT, with it remaining an intriguing open question to improve these bounds. The final part of our paper outlines and reviews how sample complexity of QHT is relevant to a broad swathe of research areas and can enhance understanding of many fundamental concepts, including quantum algorithms for simulation and search, quantum learning and classification, and foundations of quantum mechanics. As such, we view our paper as an invitation to researchers coming from different communities to study and contribute to the problem of sample complexity of QHT, and we outline a number of open directions for future research.
|
[
"['Hao-Chung Cheng' 'Nilanjana Datta' 'Nana Liu' 'Theshani Nuradha'\n 'Robert Salzmann' 'Mark M. Wilde']"
] |
null | null |
2403.17878
| null | null |
http://arxiv.org/pdf/2403.17878v2
|
2024-03-27T16:01:00Z
|
2024-03-26T17:10:15Z
|
Empowering Data Mesh with Federated Learning
|
The evolution of data architecture has seen the rise of data lakes, aiming to solve the bottlenecks of data management and promote intelligent decision-making. However, this centralized architecture is limited by the proliferation of data sources and the growing demand for timely analysis and processing. A new data paradigm, Data Mesh, is proposed to overcome these challenges. Data Mesh treats domains as a first-class concern by distributing the data ownership from the central team to each data domain, while keeping the federated governance to monitor domains and their data products. Many multi-million dollar organizations like Paypal, Netflix, and Zalando have already transformed their data analysis pipelines based on this new architecture. In this decentralized architecture where data is locally preserved by each domain team, traditional centralized machine learning is incapable of conducting effective analysis across multiple domains, especially for security-sensitive organizations. To this end, we introduce a pioneering approach that incorporates Federated Learning into Data Mesh. To the best of our knowledge, this is the first open-source applied work that represents a critical advancement toward the integration of federated learning methods into the Data Mesh paradigm, underscoring the promising prospects for privacy-preserving and decentralized data analysis strategies within Data Mesh architecture.
|
[
"['Haoyuan Li' 'Salman Toor']"
] |
null | null |
2403.17886
| null | null |
http://arxiv.org/pdf/2403.17886v5
|
2024-07-09T22:15:31Z
|
2024-03-26T17:19:23Z
|
Neural Embedding Compression For Efficient Multi-Task Earth Observation
Modelling
|
As repositories of large scale data in earth observation (EO) have grown, so have transfer and storage costs for model training and inference, expending significant resources. We introduce Neural Embedding Compression (NEC), based on the transfer of compressed embeddings to data consumers instead of raw data. We adapt foundation models (FM) through learned neural compression to generate multi-task embeddings while navigating the tradeoff between compression rate and embedding utility. We update only a small fraction of the FM parameters (10%) for a short training period (1% of the iterations of pre-training). We evaluate NEC on two EO tasks: scene classification and semantic segmentation. Compared with applying traditional compression to the raw data, NEC achieves similar accuracy with a 75% to 90% reduction in data. Even at 99.7% compression, performance drops by only 5% on the scene classification task. Overall, NEC is a data-efficient yet performant approach for multi-task EO modelling.
|
[
"['Carlos Gomes' 'Thomas Brunschwiler']"
] |
null | null |
2403.17887
| null | null |
http://arxiv.org/pdf/2403.17887v1
|
2024-03-26T17:20:04Z
|
2024-03-26T17:20:04Z
|
The Unreasonable Ineffectiveness of the Deeper Layers
|
We empirically study a simple layer-pruning strategy for popular families of open-weight pretrained LLMs, finding minimal degradation of performance on different question-answering benchmarks until after a large fraction (up to half) of the layers are removed. To prune these models, we identify the optimal block of layers to prune by considering similarity across layers; then, to "heal" the damage, we perform a small amount of finetuning. In particular, we use parameter-efficient finetuning (PEFT) methods, specifically quantization and Low Rank Adapters (QLoRA), such that each of our experiments can be performed on a single A100 GPU. From a practical perspective, these results suggest that layer pruning methods can complement other PEFT strategies to further reduce computational resources of finetuning on the one hand, and can improve the memory and latency of inference on the other hand. From a scientific perspective, the robustness of these LLMs to the deletion of layers implies either that current pretraining methods are not properly leveraging the parameters in the deeper layers of the network or that the shallow layers play a critical role in storing knowledge.
|
[
"['Andrey Gromov' 'Kushal Tirumala' 'Hassan Shapourian' 'Paolo Glorioso'\n 'Daniel A. Roberts']"
] |
null | null |
2403.17889
| null | null |
http://arxiv.org/pdf/2403.17889v1
|
2024-03-26T17:21:54Z
|
2024-03-26T17:21:54Z
|
Large scale paired antibody language models
|
Antibodies are proteins produced by the immune system that can identify and neutralise a wide variety of antigens with high specificity and affinity, and constitute the most successful class of biotherapeutics. With the advent of next-generation sequencing, billions of antibody sequences have been collected in recent years, though their application in the design of better therapeutics has been constrained by the sheer volume and complexity of the data. To address this challenge, we present IgBert and IgT5, the best performing antibody-specific language models developed to date which can consistently handle both paired and unpaired variable region sequences as input. These models are trained comprehensively using the more than two billion unpaired sequences and two million paired sequences of light and heavy chains present in the Observed Antibody Space dataset. We show that our models outperform existing antibody and protein language models on a diverse range of design and regression tasks relevant to antibody engineering. This advancement marks a significant leap forward in leveraging machine learning, large scale data sets and high-performance computing for enhancing antibody design for therapeutic development.
|
[
"['Henry Kenlay' 'Frédéric A. Dreyer' 'Aleksandr Kovaltsuk' 'Dom Miketa'\n 'Douglas Pires' 'Charlotte M. Deane']"
] |
null | null |
2403.17891
| null | null |
http://arxiv.org/pdf/2403.17891v1
|
2024-03-26T17:22:29Z
|
2024-03-26T17:22:29Z
|
Image-based Novel Fault Detection with Deep Learning Classifiers using
Hierarchical Labels
|
One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can be used to increase unknown fault detection performance without sacrificing model performance. To achieve this, we propose to utilize soft label techniques to improve the state-of-the-art deep novel fault detection techniques during the training process and novel hierarchically consistent detection statistics for online novel fault detection. Finally, we demonstrated increased detection performance on novel fault detection in inspection images from the hot steel rolling process, with results well replicated across multiple scenarios and baseline detection methods.
|
[
"['Nurettin Sergin' 'Jiayu Huang' 'Tzyy-Shuh Chang' 'Hao Yan']"
] |
null | null |
2403.17902
| null | null |
http://arxiv.org/pdf/2403.17902v2
|
2024-05-29T20:43:07Z
|
2024-03-26T17:43:15Z
|
Serpent: Scalable and Efficient Image Restoration via Multi-scale
Structured State Space Models
|
The landscape of computational building blocks of efficient image restoration architectures is dominated by a combination of convolutional processing and various attention mechanisms. However, convolutional filters, while efficient, are inherently local and therefore struggle with modeling long-range dependencies in images. In contrast, attention excels at capturing global interactions between arbitrary image regions, but suffers from a quadratic cost in image dimension. In this work, we propose Serpent, an efficient architecture for high-resolution image restoration that combines recent advances in state space models (SSMs) with multi-scale signal processing in its core computational block. SSMs, originally introduced for sequence modeling, can maintain a global receptive field with a favorable linear scaling in input size. We propose a novel hierarchical architecture inspired by traditional signal processing principles, that converts the input image into a collection of sequences and processes them in a multi-scale fashion. Our experimental results demonstrate that Serpent can achieve reconstruction quality on par with state-of-the-art techniques, while requiring orders of magnitude less compute (up to $150$ fold reduction in FLOPS) and a factor of up to $5times$ less GPU memory while maintaining a compact model size. The efficiency gains achieved by Serpent are especially notable at high image resolutions.
|
[
"['Mohammad Shahab Sepehri' 'Zalan Fabian' 'Mahdi Soltanolkotabi']"
] |
null | null |
2403.17905
| null | null |
http://arxiv.org/pdf/2403.17905v3
|
2024-05-28T10:02:12Z
|
2024-03-26T17:45:06Z
|
Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2
|
We propose a new approach for non-Cartesian magnetic resonance image reconstruction. While unrolled architectures provide robustness via data-consistency layers, embedding measurement operators in Deep Neural Network (DNN) can become impractical at large scale. Alternative Plug-and-Play (PnP) approaches, where the denoising DNNs are blind to the measurement setting, are not affected by this limitation and have also proven effective, but their highly iterative nature also affects scalability. To address this scalability challenge, we leverage the "Residual-to-Residual DNN series for high-Dynamic range imaging (R2D2)" approach recently introduced in astronomical imaging. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of DNNs taking the previous iteration's image estimate and associated data residual as inputs. The method can be interpreted as a learned version of the Matching Pursuit algorithm. We demonstrate R2D2 in simulation, considering radial k-space sampling acquisition sequences. Our preliminary results suggest that R2D2 achieves: (i) suboptimal performance compared to its unrolled incarnation R2D2-Net, which is however non-scalable due to the necessary embedding of NUFFT-based data-consistency layers; (ii) superior reconstruction quality to a scalable version of R2D2-Net embedding an FFT-based approximation for data consistency; (iii) superior reconstruction quality to PnP, while only requiring few iterations.
|
[
"['Yiwei Chen' 'Chao Tang' 'Amir Aghabiglou' 'Chung San Chu' 'Yves Wiaux']"
] |
null | null |
2403.17916
| null | null |
http://arxiv.org/pdf/2403.17916v1
|
2024-03-26T17:53:27Z
|
2024-03-26T17:53:27Z
|
CMP: Cooperative Motion Prediction with Multi-Agent Communication
|
The confluence of the advancement of Autonomous Vehicles (AVs) and the maturity of Vehicle-to-Everything (V2X) communication has enabled the capability of cooperative connected and automated vehicles (CAVs). Building on top of cooperative perception, this paper explores the feasibility and effectiveness of cooperative motion prediction. Our method, CMP, takes LiDAR signals as input to enhance tracking and prediction capabilities. Unlike previous work that focuses separately on either cooperative perception or motion prediction, our framework, to the best of our knowledge, is the first to address the unified problem where CAVs share information in both perception and prediction modules. Incorporated into our design is the unique capability to tolerate realistic V2X bandwidth limitations and transmission delays, while dealing with bulky perception representations. We also propose a prediction aggregation module, which unifies the predictions obtained by different CAVs and generates the final prediction. Through extensive experiments and ablation studies, we demonstrate the effectiveness of our method in cooperative perception, tracking, and motion prediction tasks. In particular, CMP reduces the average prediction error by 17.2% with fewer missing detections compared with the no cooperation setting. Our work marks a significant step forward in the cooperative capabilities of CAVs, showcasing enhanced performance in complex scenarios.
|
[
"['Zhuoyuan Wu' 'Yuping Wang' 'Hengbo Ma' 'Zhaowei Li' 'Hang Qiu'\n 'Jiachen Li']"
] |
null | null |
2403.17919
| null | null |
http://arxiv.org/pdf/2403.17919v3
|
2024-05-25T10:20:27Z
|
2024-03-26T17:55:02Z
|
LISA: Layerwise Importance Sampling for Memory-Efficient Large Language
Model Fine-Tuning
|
The machine learning community has witnessed impressive advancements since large language models (LLMs) first appeared. Yet, their massive memory consumption has become a significant roadblock to large-scale training. For instance, a 7B model typically requires at least 60 GB of GPU memory with full parameter training, which presents challenges for researchers without access to high-resource environments. Parameter Efficient Fine-Tuning techniques such as Low-Rank Adaptation (LoRA) have been proposed to alleviate this problem. However, in most large-scale fine-tuning settings, their performance does not reach the level of full parameter training because they confine the parameter search to a low-rank subspace. Attempting to complement this deficiency, we investigate the layerwise properties of LoRA on fine-tuning tasks and observe an unexpected but consistent skewness of weight norms across different layers. Utilizing this key observation, a surprisingly simple training strategy is discovered, which outperforms both LoRA and full parameter training in a wide range of settings with memory costs as low as LoRA. We name it Layerwise Importance Sampled AdamW (LISA), a promising alternative for LoRA, which applies the idea of importance sampling to different layers in LLMs and randomly freezes most middle layers during optimization. Experimental results show that with similar or less GPU memory consumption, LISA surpasses LoRA or even full parameter tuning in downstream fine-tuning tasks, where LISA consistently outperforms LoRA by over 10%-35% in terms of MT-Bench score while achieving on-par or better performance in MMLU, AGIEval and WinoGrande. On large models, specifically LLaMA-2-70B, LISA surpasses LoRA on MT-Bench, GSM8K, and PubMedQA, demonstrating its effectiveness across different domains.
|
[
"['Rui Pan' 'Xiang Liu' 'Shizhe Diao' 'Renjie Pi' 'Jipeng Zhang' 'Chi Han'\n 'Tong Zhang']"
] |
null | null |
2403.17921
| null | null |
http://arxiv.org/pdf/2403.17921v1
|
2024-03-26T17:55:58Z
|
2024-03-26T17:55:58Z
|
The Need for Speed: Pruning Transformers with One Recipe
|
We introduce the $textbf{O}$ne-shot $textbf{P}$runing $textbf{T}$echnique for $textbf{I}$nterchangeable $textbf{N}$etworks ($textbf{OPTIN}$) framework as a tool to increase the efficiency of pre-trained transformer architectures $textit{without requiring re-training}$. Recent works have explored improving transformer efficiency, however often incur computationally expensive re-training procedures or depend on architecture-specific characteristics, thus impeding practical wide-scale adoption. To address these shortcomings, the OPTIN framework leverages intermediate feature distillation, capturing the long-range dependencies of model parameters (coined $textit{trajectory}$), to produce state-of-the-art results on natural language, image classification, transfer learning, and semantic segmentation tasks $textit{without re-training}$. Given a FLOP constraint, the OPTIN framework will compress the network while maintaining competitive accuracy performance and improved throughput. Particularly, we show a $leq 2$% accuracy degradation from NLP baselines and a $0.5$% improvement from state-of-the-art methods on image classification at competitive FLOPs reductions. We further demonstrate the generalization of tasks and architecture with comparative performance using Mask2Former for semantic segmentation and cnn-style networks. OPTIN presents one of the first one-shot efficient frameworks for compressing transformer architectures that generalizes well across different class domains, in particular: natural language and image-related tasks, without $textit{re-training}$.
|
[
"['Samir Khaki' 'Konstantinos N. Plataniotis']"
] |
null | null |
2403.17933
| null | null |
http://arxiv.org/pdf/2403.17933v2
|
2024-07-11T17:27:49Z
|
2024-03-26T17:58:29Z
|
SLEDGE: Synthesizing Driving Environments with Generative Models and
Rule-Based Traffic
|
SLEDGE is the first generative simulator for vehicle motion planning trained on real-world driving logs. Its core component is a learned model that is able to generate agent bounding boxes and lane graphs. The model's outputs serve as an initial state for rule-based traffic simulation. The unique properties of the entities to be generated for SLEDGE, such as their connectivity and variable count per scene, render the naive application of most modern generative models to this task non-trivial. Therefore, together with a systematic study of existing lane graph representations, we introduce a novel raster-to-vector autoencoder. It encodes agents and the lane graph into distinct channels in a rasterized latent map. This facilitates both lane-conditioned agent generation and combined generation of lanes and agents with a Diffusion Transformer. Using generated entities in SLEDGE enables greater control over the simulation, e.g. upsampling turns or increasing traffic density. Further, SLEDGE can support 500m long routes, a capability not found in existing data-driven simulators like nuPlan. It presents new challenges for planning algorithms, evidenced by failure rates of over 40% for PDM, the winner of the 2023 nuPlan challenge, when tested on hard routes and dense traffic generated by our model. Compared to nuPlan, SLEDGE requires 500$times$ less storage to set up (<4 GB), making it a more accessible option and helping with democratizing future research in this field.
|
[
"['Kashyap Chitta' 'Daniel Dauner' 'Andreas Geiger']"
] |
null | null |
2403.17942
| null | null |
http://arxiv.org/pdf/2403.17942v1
|
2024-02-02T06:17:49Z
|
2024-02-02T06:17:49Z
|
A Note On Lookahead In Real Life And Computing
|
Past, Present and Future are considered to be three temporal and logical concepts which are well defined by human beings for their existence and growth. We, as human beings, have the privilege of using our intelligence to mentally execute an activity before physical occurrence of the same in the real world. Knowledge of the past, aplomb of present and visualisation for the future correspond to three concepts such as look-back, look-at and look-ahead respectively in real life as well as in diversified domains of computing. Look-Ahead(LA) deals with the future prediction of information and processing of input to produce the output in advance. In this article, our main objective is to learn, understand and explore the concept of LA and design novel models as solution for real world problems. We present three well known algorithmic frameworks used in practice based on availability of input information such as offline, online and semi-online. We introduce interesting real life applications and well known computing problems where LA plays a significant role for making a process, system or algorithm efficient. We define new types of LA and propose a taxonomy for LA based on literature review for designing novel LA models in future. Using the concept of LA, We identify and present many interesting and non-trivial research challenges as future potential research directions. Intuitively, we observe that LA can be used as a powerful tool and framework for future researchers in design of efficient computational models and algorithms for solving non-trivial and challenging optimization problems.
|
[
"['Burle Sharma' 'Rakesh Mohanty' 'Sucheta Panda']"
] |
null | null |
2403.17954
| null | null |
http://arxiv.org/pdf/2403.17954v1
|
2024-03-10T16:49:04Z
|
2024-03-10T16:49:04Z
|
Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding
for Extended-Connectivity Fingerprints
|
Extended-connectivity fingerprints (ECFPs) are a ubiquitous tool in current cheminformatics and molecular machine learning, and one of the most prevalent molecular feature extraction techniques used for chemical prediction. Atom features learned by graph neural networks can be aggregated to compound-level representations using a large spectrum of graph pooling methods; in contrast, sets of detected ECFP substructures are by default transformed into bit vectors using only a simple hash-based folding procedure. We introduce a general mathematical framework for the vectorisation of structural fingerprints via a formal operation called substructure pooling that encompasses hash-based folding, algorithmic substructure-selection, and a wide variety of other potential techniques. We go on to describe Sort & Slice, an easy-to-implement and bit-collision-free alternative to hash-based folding for the pooling of ECFP substructures. Sort & Slice first sorts ECFP substructures according to their relative prevalence in a given set of training compounds and then slices away all but the $L$ most frequent substructures which are subsequently used to generate a binary fingerprint of desired length, $L$. We computationally compare the performance of hash-based folding, Sort & Slice, and two advanced supervised substructure-selection schemes (filtering and mutual-information maximisation) for ECFP-based molecular property prediction. Our results indicate that, despite its technical simplicity, Sort & Slice robustly (and at times substantially) outperforms traditional hash-based folding as well as the other investigated methods across prediction tasks, data splitting techniques, machine-learning models and ECFP hyperparameters. We thus recommend that Sort & Slice canonically replace hash-based folding as the default substructure-pooling technique to vectorise ECFPs for supervised molecular machine learning.
|
[
"['Markus Dablander' 'Thierry Hanser' 'Renaud Lambiotte'\n 'Garrett M. Morris']"
] |
null | null |
2403.17958
| null | null |
http://arxiv.org/pdf/2403.17958v1
|
2024-03-12T22:45:05Z
|
2024-03-12T22:45:05Z
|
Deep Generative Domain Adaptation with Temporal Attention for Cross-User
Activity Recognition
|
In Human Activity Recognition (HAR), a predominant assumption is that the data utilized for training and evaluation purposes are drawn from the same distribution. It is also assumed that all data samples are independent and identically distributed ($displaystyle i.i.d.$). Contrarily, practical implementations often challenge this notion, manifesting data distribution discrepancies, especially in scenarios such as cross-user HAR. Domain adaptation is the promising approach to address these challenges inherent in cross-user HAR tasks. However, a clear gap in domain adaptation techniques is the neglect of the temporal relation embedded within time series data during the phase of aligning data distributions. Addressing this oversight, our research presents the Deep Generative Domain Adaptation with Temporal Attention (DGDATA) method. This novel method uniquely recognises and integrates temporal relations during the domain adaptation process. By synergizing the capabilities of generative models with the Temporal Relation Attention mechanism, our method improves the classification performance in cross-user HAR. A comprehensive evaluation has been conducted on three public sensor-based HAR datasets targeting different scenarios and applications to demonstrate the efficacy of the proposed DGDATA method.
|
[
"['Xiaozhou Ye' 'Kevin I-Kai Wang']"
] |
null | null |
2403.17978
| null | null |
http://arxiv.org/pdf/2403.17978v1
|
2024-03-23T15:49:13Z
|
2024-03-23T15:49:13Z
|
Holographic Global Convolutional Networks for Long-Range Prediction
Tasks in Malware Detection
|
Malware detection is an interesting and valuable domain to work in because it has significant real-world impact and unique machine-learning challenges. We investigate existing long-range techniques and benchmarks and find that they're not very suitable in this problem area. In this paper, we introduce Holographic Global Convolutional Networks (HGConv) that utilize the properties of Holographic Reduced Representations (HRR) to encode and decode features from sequence elements. Unlike other global convolutional methods, our method does not require any intricate kernel computation or crafted kernel design. HGConv kernels are defined as simple parameters learned through backpropagation. The proposed method has achieved new SOTA results on Microsoft Malware Classification Challenge, Drebin, and EMBER malware benchmarks. With log-linear complexity in sequence length, the empirical results demonstrate substantially faster run-time by HGConv compared to other methods achieving far more efficient scaling even with sequence length $geq 100,000$.
|
[
"['Mohammad Mahmudul Alam' 'Edward Raff' 'Stella Biderman' 'Tim Oates'\n 'James Holt']"
] |
null | null |
2403.17980
| null | null |
http://arxiv.org/pdf/2403.17980v1
|
2024-03-24T04:09:48Z
|
2024-03-24T04:09:48Z
|
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive
Learning
|
As the number of IoT devices increases, security concerns become more prominent. The impact of threats can be minimized by deploying Network Intrusion Detection System (NIDS) by monitoring network traffic, detecting and discovering intrusions, and issuing security alerts promptly. Most intrusion detection research in recent years has been directed towards the pair of traffic itself without considering the interrelationships among them, thus limiting the monitoring of complex IoT network attack events. Besides, anomalous traffic in real networks accounts for only a small fraction, which leads to a severe imbalance problem in the dataset that makes algorithmic learning and prediction extremely difficult. In this paper, we propose an EG-ConMix method based on E-GraphSAGE, incorporating a data augmentation module to fix the problem of data imbalance. In addition, we incorporate contrastive learning to discern the difference between normal and malicious traffic samples, facilitating the extraction of key features. Extensive experiments on two publicly available datasets demonstrate the superior intrusion detection performance of EG-ConMix compared to state-of-the-art methods. Remarkably, it exhibits significant advantages in terms of training speed and accuracy for large-scale graphs.
|
[
"['Lijin Wu' 'Shanshan Lei' 'Feilong Liao' 'Yuanjun Zheng' 'Yuxin Liu'\n 'Wentao Fu' 'Hao Song' 'Jiajun Zhou']"
] |
null | null |
2403.17982
| null | null |
http://arxiv.org/pdf/2403.17982v1
|
2024-03-24T17:52:36Z
|
2024-03-24T17:52:36Z
|
Markov chain models for inspecting response dynamics in psychological
testing
|
The importance of considering contextual probabilities in shaping response patterns within psychological testing is underscored, despite the ubiquitous nature of order effects discussed extensively in methodological literature. Drawing from concepts such as path-dependency, first-order autocorrelation, state-dependency, and hysteresis, the present study is an attempt to address how earlier responses serve as an anchor for subsequent answers in tests, surveys, and questionnaires. Introducing the notion of non-commuting observables derived from quantum physics, I highlight their role in characterizing psychological processes and the impact of measurement instruments on participants' responses. We advocate for the utilization of first-order Markov chain modeling to capture and forecast sequential dependencies in survey and test responses. The employment of the first-order Markov chain model lies in individuals' propensity to exhibit partial focus to preceding responses, with recent items most likely exerting a substantial influence on subsequent response selection. This study contributes to advancing our understanding of the dynamics inherent in sequential data within psychological research and provides a methodological framework for conducting longitudinal analyses of response patterns of test and questionnaire.
|
[
"['Andrea Bosco']"
] |
null | null |
2403.17983
| null | null |
http://arxiv.org/pdf/2403.17983v2
|
2024-06-28T18:35:22Z
|
2024-03-24T21:41:29Z
|
Is Watermarking LLM-Generated Code Robust?
|
We present the first study of the robustness of existing watermarking techniques on Python code generated by large language models. Although existing works showed that watermarking can be robust for natural language, we show that it is easy to remove these watermarks on code by semantic-preserving transformations.
|
[
"['Tarun Suresh' 'Shubham Ugare' 'Gagandeep Singh' 'Sasa Misailovic']"
] |
null | null |
2403.17992
| null | null |
http://arxiv.org/pdf/2403.17992v1
|
2024-03-26T12:20:10Z
|
2024-03-26T12:20:10Z
|
Interpretable cancer cell detection with phonon microscopy using
multi-task conditional neural networks for inter-batch calibration
|
Advances in artificial intelligence (AI) show great potential in revealing underlying information from phonon microscopy (high-frequency ultrasound) data to identify cancerous cells. However, this technology suffers from the 'batch effect' that comes from unavoidable technical variations between each experiment, creating confounding variables that the AI model may inadvertently learn. We therefore present a multi-task conditional neural network framework to simultaneously achieve inter-batch calibration, by removing confounding variables, and accurate cell classification of time-resolved phonon-derived signals. We validate our approach by training and validating on different experimental batches, achieving a balanced precision of 89.22% and an average cross-validated precision of 89.07% for classifying background, healthy and cancerous regions. Classification can be performed in 0.5 seconds with only simple prior batch information required for multiple batch corrections. Further, we extend our model to reconstruct denoised signals, enabling physical interpretation of salient features indicating disease state including sound velocity, sound attenuation and cell-adhesion to substrate.
|
[
"['Yijie Zheng' 'Rafael Fuentes-Dominguez' 'Matt Clark'\n 'George S. D. Gordon' 'Fernando Perez-Cota']"
] |
null | null |
2403.17993
| null | null |
http://arxiv.org/pdf/2403.17993v3
|
2024-07-12T20:25:55Z
|
2024-03-26T12:45:52Z
|
Mixing Artificial and Natural Intelligence: From Statistical Mechanics
to AI and Back to Turbulence
|
The paper reflects on the future role of AI in scientific research, with a special focus on turbulence studies, and examines the evolution of AI, particularly through Diffusion Models rooted in non-equilibrium statistical mechanics. It underscores the significant impact of AI on advancing reduced, Lagrangian models of turbulence through innovative use of deep neural networks. Additionally, the paper reviews various other AI applications in turbulence research and outlines potential challenges and opportunities in the concurrent advancement of AI and statistical hydrodynamics. This discussion sets the stage for a future where AI and turbulence research are intricately intertwined, leading to more profound insights and advancements in both fields.
|
[
"['Michael Chertkov']"
] |
null | null |
2403.17994
| null | null |
http://arxiv.org/pdf/2403.17994v1
|
2024-03-26T13:50:39Z
|
2024-03-26T13:50:39Z
|
Solution for Point Tracking Task of ICCV 1st Perception Test Challenge
2023
|
This report proposes an improved method for the Tracking Any Point (TAP) task, which tracks any physical surface through a video. Several existing approaches have explored the TAP by considering the temporal relationships to obtain smooth point motion trajectories, however, they still suffer from the cumulative error caused by temporal prediction. To address this issue, we propose a simple yet effective approach called TAP with confident static points (TAPIR+), which focuses on rectifying the tracking of the static point in the videos shot by a static camera. To clarify, our approach contains two key components: (1) Multi-granularity Camera Motion Detection, which could identify the video sequence by the static camera shot. (2) CMR-based point trajectory prediction with one moving object segmentation approach to isolate the static point from the moving object. Our approach ranked first in the final test with a score of 0.46.
|
[
"['Hongpeng Pan' 'Yang Yang' 'Zhongtian Fu' 'Yuxuan Zhang' 'Shian Du'\n 'Yi Xu' 'Xiangyang Ji']"
] |
null | null |
2403.17995
| null | null |
http://arxiv.org/pdf/2403.17995v1
|
2024-03-26T14:47:05Z
|
2024-03-26T14:47:05Z
|
Semi-Supervised Image Captioning Considering Wasserstein Graph Matching
|
Image captioning can automatically generate captions for the given images, and the key challenge is to learn a mapping function from visual features to natural language features. Existing approaches are mostly supervised ones, i.e., each image has a corresponding sentence in the training set. However, considering that describing images always requires a huge of manpower, we usually have limited amount of described images (i.e., image-text pairs) and a large number of undescribed images in real-world applications. Thereby, a dilemma is the "Semi-Supervised Image Captioning". To solve this problem, we propose a novel Semi-Supervised Image Captioning method considering Wasserstein Graph Matching (SSIC-WGM), which turns to adopt the raw image inputs to supervise the generated sentences. Different from traditional single modal semi-supervised methods, the difficulty of semi-supervised cross-modal learning lies in constructing intermediately comparable information among heterogeneous modalities. In this paper, SSIC-WGM adopts the successful scene graphs as intermediate information, and constrains the generated sentences from two aspects: 1) inter-modal consistency. SSIC-WGM constructs the scene graphs of the raw image and generated sentence respectively, then employs the wasserstein distance to better measure the similarity between region embeddings of different graphs. 2) intra-modal consistency. SSIC-WGM takes the data augmentation techniques for the raw images, then constrains the consistency among augmented images and generated sentences. Consequently, SSIC-WGM combines the cross-modal pseudo supervision and structure invariant measure for efficiently using the undescribed images, and learns more reasonable mapping function.
|
[
"['Yang Yang']"
] |
null | null |
2403.18018
| null | null |
http://arxiv.org/pdf/2403.18018v2
|
2024-03-28T10:19:46Z
|
2024-03-26T18:07:10Z
|
DORE: A Dataset For Portuguese Definition Generation
|
Definition modelling (DM) is the task of automatically generating a dictionary definition for a specific word. Computational systems that are capable of DM can have numerous applications benefiting a wide range of audiences. As DM is considered a supervised natural language generation problem, these systems require large annotated datasets to train the machine learning (ML) models. Several DM datasets have been released for English and other high-resource languages. While Portuguese is considered a mid/high-resource language in most natural language processing tasks and is spoken by more than 200 million native speakers, there is no DM dataset available for Portuguese. In this research, we fill this gap by introducing DORE; the first dataset for Definition MOdelling for PoRtuguEse containing more than 100,000 definitions. We also evaluate several deep learning based DM models on DORE and report the results. The dataset and the findings of this paper will facilitate research and study of Portuguese in wider contexts.
|
[
"['Anna Beatriz Dimas Furtado' 'Tharindu Ranasinghe' 'Frédéric Blain'\n 'Ruslan Mitkov']"
] |
null | null |
2403.18025
| null | null |
http://arxiv.org/pdf/2403.18025v2
|
2024-03-28T11:01:21Z
|
2024-03-26T18:23:16Z
|
Improving Pre-trained Language Model Sensitivity via Mask Specific
losses: A case study on Biomedical NER
|
Adapting language models (LMs) to novel domains is often achieved through fine-tuning a pre-trained LM (PLM) on domain-specific data. Fine-tuning introduces new knowledge into an LM, enabling it to comprehend and efficiently perform a target domain task. Fine-tuning can however be inadvertently insensitive if it ignores the wide array of disparities (e.g in word meaning) between source and target domains. For instance, words such as chronic and pressure may be treated lightly in social conversations, however, clinically, these words are usually an expression of concern. To address insensitive fine-tuning, we propose Mask Specific Language Modeling (MSLM), an approach that efficiently acquires target domain knowledge by appropriately weighting the importance of domain-specific terms (DS-terms) during fine-tuning. MSLM jointly masks DS-terms and generic words, then learns mask-specific losses by ensuring LMs incur larger penalties for inaccurately predicting DS-terms compared to generic words. Results of our analysis show that MSLM improves LMs sensitivity and detection of DS-terms. We empirically show that an optimal masking rate not only depends on the LM, but also on the dataset and the length of sequences. Our proposed masking strategy outperforms advanced masking strategies such as span- and PMI-based masking.
|
[
"['Micheal Abaho' 'Danushka Bollegala' 'Gary Leeming' 'Dan Joyce'\n 'Iain E Buchan']"
] |
null | null |
2403.18026
| null | null |
http://arxiv.org/pdf/2403.18026v1
|
2024-03-26T18:23:31Z
|
2024-03-26T18:23:31Z
|
Cross-system biological image quality enhancement based on the
generative adversarial network as a foundation for establishing a
multi-institute microscopy cooperative network
|
High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low temporal resolution can lead to a motion blur effect in living systems. Our work presents a deep learning (DL) generative-adversarial approach to the problem of obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents. We propose a generative-adversarial network (GAN) for contrast transfer between two different separate microscopy systems: a confocal microscope (producing HQ images) and a wide-field fluorescence microscope (producing LQ images). Our model proves that such transfer is possible, allowing us to receive HQ-generated images characterized by low mean squared error (MSE) values, high structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR) values. For our best model in the case of comparing HQ-generated images and HQ-ground truth images, the median values of the metrics are 6x10-4, 0.9413, and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of comparison between LQ and HQ ground truth median values of the metrics are equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively. Therefore, we observe a significant increase ranging from 14% to 49% for SSIM and PSNR respectively. These results, together with other single-system cross-modality studies, provide proof of concept for further implementation of a cross-system biological image quality enhancement.
|
[
"['Dominik Panek' 'Carina Rząca' 'Maksymilian Szczypior' 'Joanna Sorysz'\n 'Krzysztof Misztal' 'Zbigniew Baster' 'Zenon Rajfur']"
] |
null | null |
2403.18028
| null | null |
http://arxiv.org/pdf/2403.18028v2
|
2024-03-28T17:06:15Z
|
2024-03-26T18:29:39Z
|
Predicting Species Occurrence Patterns from Partial Observations
|
To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are changing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur.
|
[
"['Hager Radi Abdelwahed' 'Mélisande Teng' 'David Rolnick']"
] |
null | null |
2403.18035
| null | null |
http://arxiv.org/pdf/2403.18035v2
|
2024-03-30T13:28:54Z
|
2024-03-26T18:40:36Z
|
Bidirectional Consistency Models
|
Diffusion models (DMs) are capable of generating remarkably high-quality samples by iteratively denoising a random vector, a process that corresponds to moving along the probability flow ordinary differential equation (PF ODE). Interestingly, DMs can also invert an input image to noise by moving backward along the PF ODE, a key operation for downstream tasks such as interpolation and image editing. However, the iterative nature of this process restricts its speed, hindering its broader application. Recently, Consistency Models (CMs) have emerged to address this challenge by approximating the integral of the PF ODE, largely reducing the number of iterations. Yet, the absence of an explicit ODE solver complicates the inversion process. To resolve this, we introduce the Bidirectional Consistency Model (BCM), which learns a single neural network that enables both forward and backward traversal along the PF ODE, efficiently unifying generation and inversion tasks within one framework. Notably, our proposed method enables one-step generation and inversion while also allowing the use of additional steps to enhance generation quality or reduce reconstruction error. Furthermore, by leveraging our model's bidirectional consistency, we introduce a sampling strategy that can enhance FID while preserving the generated image content. We further showcase our model's capabilities in several downstream tasks, such as interpolation and inpainting, and present demonstrations of potential applications, including blind restoration of compressed images and defending black-box adversarial attacks.
|
[
"['Liangchen Li' 'Jiajun He']"
] |
null | null |
2403.18044
| null | null |
http://arxiv.org/pdf/2403.18044v1
|
2024-03-26T18:57:56Z
|
2024-03-26T18:57:56Z
|
Deep polytopic autoencoders for low-dimensional linear parameter-varying
approximations and nonlinear feedback design
|
Polytopic autoencoders provide low-dimensional parametrizations of states in a polytope. For nonlinear PDEs, this is readily applied to low-dimensional linear parameter-varying (LPV) approximations as they have been exploited for efficient nonlinear controller design via series expansions of the solution to the state-dependent Riccati equation. In this work, we develop a polytopic autoencoder for control applications and show how it outperforms standard linear approaches in view of LPV approximations of nonlinear systems and how the particular architecture enables higher order series expansions at little extra computational effort. We illustrate the properties and potentials of this approach to computational nonlinear controller design for large-scale systems with a thorough numerical study.
|
[
"['Jan Heiland' 'Yongho Kim' 'Steffen W. R. Werner']"
] |
null | null |
2403.18052
| null | null |
http://arxiv.org/pdf/2403.18052v2
|
2024-05-27T14:14:55Z
|
2024-03-26T19:10:08Z
|
R2D2 image reconstruction with model uncertainty quantification in radio
astronomy
|
The ``Residual-to-Residual DNN series for high-Dynamic range imaging'' (R2D2) approach was recently introduced for Radio-Interferometric (RI) imaging in astronomy. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of Deep Neural Networks (DNNs) taking the previous iteration's image estimate and associated data residual as inputs. In this work, we investigate the robustness of the R2D2 image estimation process, by studying the uncertainty associated with its series of learned models. Adopting an ensemble averaging approach, multiple series can be trained, arising from different random DNN initializations of the training process at each iteration. The resulting multiple R2D2 instances can also be leveraged to generate ``R2D2 samples'', from which empirical mean and standard deviation endow the algorithm with a joint estimation and uncertainty quantification functionality. Focusing on RI imaging, and adopting a telescope-specific approach, multiple R2D2 instances were trained to encompass the most general observation setting of the Very Large Array (VLA). Simulations and real-data experiments confirm that: (i) R2D2's image estimation capability is superior to that of the state-of-the-art algorithms; (ii) its ultra-fast reconstruction capability (arising from series with only few DNNs) makes the computation of multiple reconstruction samples and of uncertainty maps practical even at large image dimension; (iii) it is characterized by a very low model uncertainty.
|
[
"['Amir Aghabiglou' 'Chung San Chu' 'Arwa Dabbech' 'Yves Wiaux']"
] |
null | null |
2403.18063
| null | null |
http://arxiv.org/pdf/2403.18063v2
|
2024-06-03T18:22:30Z
|
2024-03-26T19:29:21Z
|
Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and
Time-Series Analysis
|
Transformers have revolutionized image modeling tasks with adaptations like DeIT, Swin, SVT, Biformer, STVit, and FDVIT. However, these models often face challenges with inductive bias and high quadratic complexity, making them less efficient for high-resolution images. State space models (SSMs) such as Mamba, V-Mamba, ViM, and SiMBA offer an alternative to handle high resolution images in computer vision tasks. These SSMs encounter two major issues. First, they become unstable when scaled to large network sizes. Second, although they efficiently capture global information in images, they inherently struggle with handling local information. To address these challenges, we introduce Heracles, a novel SSM that integrates a local SSM, a global SSM, and an attention-based token interaction module. Heracles leverages a Hartely kernel-based state space model for global image information, a localized convolutional network for local details, and attention mechanisms in deeper layers for token interactions. Our extensive experiments demonstrate that Heracles-C-small achieves state-of-the-art performance on the ImageNet dataset with 84.5% top-1 accuracy. Heracles-C-Large and Heracles-C-Huge further improve accuracy to 85.9% and 86.4%, respectively. Additionally, Heracles excels in transfer learning tasks on datasets such as CIFAR-10, CIFAR-100, Oxford Flowers, and Stanford Cars, and in instance segmentation on the MSCOCO dataset. Heracles also proves its versatility by achieving state-of-the-art results on seven time-series datasets, showcasing its ability to generalize across domains with spectral data, capturing both local and global information. The project page is available at this link.url{https://github.com/badripatro/heracles}
|
[
"['Badri N. Patro' 'Suhas Ranganath' 'Vinay P. Namboodiri'\n 'Vijay S. Agneeswaran']"
] |
null | null |
2403.18067
| null | null |
http://arxiv.org/pdf/2403.18067v1
|
2024-03-26T19:36:50Z
|
2024-03-26T19:36:50Z
|
State of the art applications of deep learning within tracking and
detecting marine debris: A survey
|
Deep learning techniques have been explored within the marine litter problem for approximately 20 years but the majority of the research has developed rapidly in the last five years. We provide an in-depth, up to date, summary and analysis of 28 of the most recent and significant contributions of deep learning in marine debris. From cross referencing the research paper results, the YOLO family significantly outperforms all other methods of object detection but there are many respected contributions to this field that have categorically agreed that a comprehensive database of underwater debris is not currently available for machine learning. Using a small dataset curated and labelled by us, we tested YOLOv5 on a binary classification task and found the accuracy was low and the rate of false positives was high; highlighting the importance of a comprehensive database. We conclude this survey with over 40 future research recommendations and open challenges.
|
[
"['Zoe Moorton' 'Dr. Zeyneb Kurt' 'Dr. Wai Lok Woo']"
] |
null | null |
2403.18072
| null | null |
http://arxiv.org/pdf/2403.18072v1
|
2024-03-26T19:49:58Z
|
2024-03-26T19:49:58Z
|
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models
using Markov Chain Monte Carlo
|
Optimal experimental design (OED) provides a systematic approach to quantify and maximize the value of experimental data. Under a Bayesian approach, conventional OED maximizes the expected information gain (EIG) on model parameters. However, we are often interested in not the parameters themselves, but predictive quantities of interest (QoIs) that depend on the parameters in a nonlinear manner. We present a computational framework of predictive goal-oriented OED (GO-OED) suitable for nonlinear observation and prediction models, which seeks the experimental design providing the greatest EIG on the QoIs. In particular, we propose a nested Monte Carlo estimator for the QoI EIG, featuring Markov chain Monte Carlo for posterior sampling and kernel density estimation for evaluating the posterior-predictive density and its Kullback-Leibler divergence from the prior-predictive. The GO-OED design is then found by maximizing the EIG over the design space using Bayesian optimization. We demonstrate the effectiveness of the overall nonlinear GO-OED method, and illustrate its differences versus conventional non-GO-OED, through various test problems and an application of sensor placement for source inversion in a convection-diffusion field.
|
[
"['Shijie Zhong' 'Wanggang Shen' 'Tommie Catanach' 'Xun Huan']"
] |
null | null |
2403.18079
| null | null |
http://arxiv.org/pdf/2403.18079v1
|
2024-03-26T19:58:39Z
|
2024-03-26T19:58:39Z
|
Paths to Equilibrium in Normal-Form Games
|
In multi-agent reinforcement learning (MARL), agents repeatedly interact across time and revise their strategies as new data arrives, producing a sequence of strategy profiles. This paper studies sequences of strategies satisfying a pairwise constraint inspired by policy updating in reinforcement learning, where an agent who is best responding in period $t$ does not switch its strategy in the next period $t+1$. This constraint merely requires that optimizing agents do not switch strategies, but does not constrain the other non-optimizing agents in any way, and thus allows for exploration. Sequences with this property are called satisficing paths, and arise naturally in many MARL algorithms. A fundamental question about strategic dynamics is such: for a given game and initial strategy profile, is it always possible to construct a satisficing path that terminates at an equilibrium strategy? The resolution of this question has implications about the capabilities or limitations of a class of MARL algorithms. We answer this question in the affirmative for mixed extensions of finite normal-form games.%
|
[
"['Bora Yongacoglu' 'Gürdal Arslan' 'Lacra Pavel' 'Serdar Yüksel']"
] |
null | null |
2403.18094
| null | null |
http://arxiv.org/pdf/2403.18094v1
|
2024-03-26T20:30:55Z
|
2024-03-26T20:30:55Z
|
A Personalized Video-Based Hand Taxonomy: Application for Individuals
with Spinal Cord Injury
|
Hand function is critical for our interactions and quality of life. Spinal cord injuries (SCI) can impair hand function, reducing independence. A comprehensive evaluation of function in home and community settings requires a hand grasp taxonomy for individuals with impaired hand function. Developing such a taxonomy is challenging due to unrepresented grasp types in standard taxonomies, uneven data distribution across injury levels, and limited data. This study aims to automatically identify the dominant distinct hand grasps in egocentric video using semantic clustering. Egocentric video recordings collected in the homes of 19 individual with cervical SCI were used to cluster grasping actions with semantic significance. A deep learning model integrating posture and appearance data was employed to create a personalized hand taxonomy. Quantitative analysis reveals a cluster purity of 67.6% +- 24.2% with with 18.0% +- 21.8% redundancy. Qualitative assessment revealed meaningful clusters in video content. This methodology provides a flexible and effective strategy to analyze hand function in the wild. It offers researchers and clinicians an efficient tool for evaluating hand function, aiding sensitive assessments and tailored intervention plans.
|
[
"['Mehdy Dousty' 'David J. Fleet' 'José Zariffa']"
] |
null | null |
2403.18100
| null | null |
http://arxiv.org/pdf/2403.18100v1
|
2024-03-26T20:59:48Z
|
2024-03-26T20:59:48Z
|
Driving Intelligent IoT Monitoring and Control through Cloud Computing
and Machine Learning
|
This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the collected data is sent to the cloud for statistical analysis, prediction, and data analysis to achieve business objectives. However, because the cloud computing model is limited by distance, it can be problematic in environments where the quality of the Internet connection is not ideal for critical operations. Therefore, edge computing, as a distributed computing architecture, moves the location of processing applications, data and services from the central node of the network to the logical edge node of the network to reduce the dependence on cloud processing and analysis of data, and achieve near-end data processing and analysis. The combination of iot and edge computing can reduce latency, improve efficiency, and enhance security, thereby driving the development of intelligent systems. The paper also introduces the development of iot monitoring and control technology, the application of edge computing in iot monitoring and control, and the role of machine learning in data analysis and fault detection. Finally, the application and effect of intelligent Internet of Things monitoring and control system in industry, agriculture, medical and other fields are demonstrated through practical cases and experimental studies.
|
[
"['Hanzhe Li' 'Xiangxiang Wang' 'Yuan Feng' 'Yaqian Qi' 'Jingxiao Tian']"
] |
null | null |
2403.18101
| null | null |
http://arxiv.org/pdf/2403.18101v1
|
2024-03-26T21:00:06Z
|
2024-03-26T21:00:06Z
|
Towards Explainable Clustering: A Constrained Declarative based Approach
|
The domain of explainable AI is of interest in all Machine Learning fields, and it is all the more important in clustering, an unsupervised task whose result must be validated by a domain expert. We aim at finding a clustering that has high quality in terms of classic clustering criteria and that is explainable, and we argue that these two dimensions must be considered when building the clustering. We consider that a good global explanation of a clustering should give the characteristics of each cluster taking into account their abilities to describe its objects (coverage) while distinguishing it from the other clusters (discrimination). Furthermore, we aim at leveraging expert knowledge, at different levels, on the structure of the expected clustering or on its explanations. In our framework an explanation of a cluster is a set of patterns, and we propose a novel interpretable constrained clustering method called ECS for declarative clustering with Explainabilty-driven Cluster Selection that integrates structural or domain expert knowledge expressed by means of constraints. It is based on the notion of coverage and discrimination that are formalized at different levels (cluster / clustering), each allowing for exceptions through parameterized thresholds. Our method relies on four steps: generation of a set of partitions, computation of frequent patterns for each cluster, pruning clusters that violates some constraints, and selection of clusters and associated patterns to build an interpretable clustering. This last step is combinatorial and we have developed a Constraint-Programming (CP) model to solve it. The method can integrate prior knowledge in the form of user constraints, both before or in the CP model.
|
[
"['Mathieu Guilbert' 'Christel Vrain' 'Thi-Bich-Hanh Dao']"
] |
null | null |
2403.18103
| null | null |
http://arxiv.org/pdf/2403.18103v1
|
2024-03-26T21:01:41Z
|
2024-03-26T21:01:41Z
|
Tutorial on Diffusion Models for Imaging and Vision
|
The astonishing growth of generative tools in recent years has empowered many exciting applications in text-to-image generation and text-to-video generation. The underlying principle behind these generative tools is the concept of diffusion, a particular sampling mechanism that has overcome some shortcomings that were deemed difficult in the previous approaches. The goal of this tutorial is to discuss the essential ideas underlying the diffusion models. The target audience of this tutorial includes undergraduate and graduate students who are interested in doing research on diffusion models or applying these models to solve other problems.
|
[
"['Stanley H. Chan']"
] |
null | null |
2403.18104
| null | null |
http://arxiv.org/pdf/2403.18104v2
|
2024-05-03T22:50:41Z
|
2024-03-26T21:04:18Z
|
Mathematical Foundation and Corrections for Full Range Head Pose
Estimation
|
Numerous works concerning head pose estimation (HPE) offer algorithms or proposed neural network-based approaches for extracting Euler angles from either facial key points or directly from images of the head region. However, many works failed to provide clear definitions of the coordinate systems and Euler or Tait-Bryan angles orders in use. It is a well-known fact that rotation matrices depend on coordinate systems, and yaw, roll, and pitch angles are sensitive to their application order. Without precise definitions, it becomes challenging to validate the correctness of the output head pose and drawing routines employed in prior works. In this paper, we thoroughly examined the Euler angles defined in the 300W-LP dataset, head pose estimation such as 3DDFA-v2, 6D-RepNet, WHENet, etc, and the validity of their drawing routines of the Euler angles. When necessary, we infer their coordinate system and sequence of yaw, roll, pitch from provided code. This paper presents (1) code and algorithms for inferring coordinate system from provided source code, code for Euler angle application order and extracting precise rotation matrices and the Euler angles, (2) code and algorithms for converting poses from one rotation system to another, (3) novel formulae for 2D augmentations of the rotation matrices, and (4) derivations and code for the correct drawing routines for rotation matrices and poses. This paper also addresses the feasibility of defining rotations with right-handed coordinate system in Wikipedia and SciPy, which makes the Euler angle extraction much easier for full-range head pose research.
|
[
"['Huei-Chung Hu' 'Xuyang Wu' 'Yuan Wang' 'Yi Fang' 'Hsin-Tai Wu']"
] |
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