categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.06458
| null | null |
http://arxiv.org/pdf/2403.06458v1
|
2024-03-11T06:36:33Z
|
2024-03-11T06:36:33Z
|
Prediction of Wort Density with LSTM Network
|
Many physical target values in technical processes are error-prone, cumbersome, or expensive to measure automatically. One example of a physical target value is the wort density, which is an important value needed for beer production. This article introduces a system that helps the brewer measure wort density through sensors in order to reduce errors in manual data collection. Instead of a direct measurement of wort density, a method is developed that calculates the density from measured values acquired by inexpensive standard sensors such as pressure or temperature. The model behind the calculation is a neural network, known as LSTM.
|
[
"['Derk Rembold' 'Bernd Stauss' 'Stefan Schwarzkopf']"
] |
null | null |
2403.06466
| null | null |
http://arxiv.org/pdf/2403.06466v1
|
2024-03-11T07:07:05Z
|
2024-03-11T07:07:05Z
|
RL-MSA: a Reinforcement Learning-based Multi-line bus Scheduling
Approach
|
Multiple Line Bus Scheduling Problem (MLBSP) is vital to save operational cost of bus company and guarantee service quality for passengers. Existing approaches typically generate a bus scheduling scheme in an offline manner and then schedule buses according to the scheme. In practice, uncertain events such as traffic congestion occur frequently, which may make the pre-determined bus scheduling scheme infeasible. In this paper, MLBSP is modeled as a Markov Decision Process (MDP). A Reinforcement Learning-based Multi-line bus Scheduling Approach (RL-MSA) is proposed for bus scheduling at both the offline and online phases. At the offline phase, deadhead decision is integrated into bus selection decision for the first time to simplify the learning problem. At the online phase, deadhead decision is made through a time window mechanism based on the policy learned at the offline phase. We develop several new and useful state features including the features for control points, bus lines and buses. A bus priority screening mechanism is invented to construct bus-related features. Considering the interests of both the bus company and passengers, a reward function combining the final reward and the step-wise reward is devised. Experiments at the offline phase demonstrate that the number of buses used of RL-MSA is decreased compared with offline optimization approaches. At the online phase, RL-MSA can cover all departure times in a timetable (i.e., service quality) without increasing the number of buses used (i.e., operational cost).
|
[
"['Yingzhuo Liu']"
] |
null | null |
2403.06482
| null | null |
http://arxiv.org/pdf/2403.06482v1
|
2024-03-11T07:44:56Z
|
2024-03-11T07:44:56Z
|
Financial Default Prediction via Motif-preserving Graph Neural Network
with Curriculum Learning
|
User financial default prediction plays a critical role in credit risk forecasting and management. It aims at predicting the probability that the user will fail to make the repayments in the future. Previous methods mainly extract a set of user individual features regarding his own profiles and behaviors and build a binary-classification model to make default predictions. However, these methods cannot get satisfied results, especially for users with limited information. Although recent efforts suggest that default prediction can be improved by social relations, they fail to capture the higher-order topology structure at the level of small subgraph patterns. In this paper, we fill in this gap by proposing a motif-preserving Graph Neural Network with curriculum learning (MotifGNN) to jointly learn the lower-order structures from the original graph and higherorder structures from multi-view motif-based graphs for financial default prediction. Specifically, to solve the problem of weak connectivity in motif-based graphs, we design the motif-based gating mechanism. It utilizes the information learned from the original graph with good connectivity to strengthen the learning of the higher-order structure. And considering that the motif patterns of different samples are highly unbalanced, we propose a curriculum learning mechanism on the whole learning process to more focus on the samples with uncommon motif distributions. Extensive experiments on one public dataset and two industrial datasets all demonstrate the effectiveness of our proposed method.
|
[
"['Daixin Wang' 'Zhiqiang Zhang' 'Yeyu Zhao' 'Kai Huang' 'Yulin Kang'\n 'Jun Zhou']"
] |
null | null |
2403.06485
| null | null |
http://arxiv.org/abs/2403.06485v1
|
2024-03-11T07:48:35Z
|
2024-03-11T07:48:35Z
|
Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid
Approach
|
Due to the scale and complexity of cloud systems, a system failure would trigger an "alert storm", i.e., massive correlated alerts. Although these alerts can be traced back to a few root causes, the overwhelming number makes it infeasible for manual handling. Alert aggregation is thus critical to help engineers concentrate on the root cause and facilitate failure resolution. Existing methods typically utilize semantic similarity-based methods or statistical methods to aggregate alerts. However, semantic similarity-based methods overlook the causal rationale of alerts, while statistical methods can hardly handle infrequent alerts. To tackle these limitations, we introduce leveraging external knowledge, i.e., Standard Operation Procedure (SOP) of alerts as a supplement. We propose COLA, a novel hybrid approach based on correlation mining and LLM (Large Language Model) reasoning for online alert aggregation. The correlation mining module effectively captures the temporal and spatial relations between alerts, measuring their correlations in an efficient manner. Subsequently, only uncertain pairs with low confidence are forwarded to the LLM reasoning module for detailed analysis. This hybrid design harnesses both statistical evidence for frequent alerts and the reasoning capabilities of computationally intensive LLMs, ensuring the overall efficiency of COLA in handling large volumes of alerts in practical scenarios. We evaluate COLA on three datasets collected from the production environment of a large-scale cloud platform. The experimental results show COLA achieves F1-scores from 0.901 to 0.930, outperforming state-of-the-art methods and achieving comparable efficiency. We also share our experience in deploying COLA in our real-world cloud system, Cloud X.
|
[
"['Jinxi Kuang' 'Jinyang Liu' 'Junjie Huang' 'Renyi Zhong' 'Jiazhen Gu'\n 'Lan Yu' 'Rui Tan' 'Zengyin Yang' 'Michael R. Lyu']"
] |
null | null |
2403.06489
| null | null |
http://arxiv.org/pdf/2403.06489v1
|
2024-03-11T07:51:27Z
|
2024-03-11T07:51:27Z
|
Graph Neural Network with Two Uplift Estimators for Label-Scarcity
Individual Uplift Modeling
|
Uplift modeling aims to measure the incremental effect, which we call uplift, of a strategy or action on the users from randomized experiments or observational data. Most existing uplift methods only use individual data, which are usually not informative enough to capture the unobserved and complex hidden factors regarding the uplift. Furthermore, uplift modeling scenario usually has scarce labeled data, especially for the treatment group, which also poses a great challenge for model training. Considering that the neighbors' features and the social relationships are very informative to characterize a user's uplift, we propose a graph neural network-based framework with two uplift estimators, called GNUM, to learn from the social graph for uplift estimation. Specifically, we design the first estimator based on a class-transformed target. The estimator is general for all types of outcomes, and is able to comprehensively model the treatment and control group data together to approach the uplift. When the outcome is discrete, we further design the other uplift estimator based on our defined partial labels, which is able to utilize more labeled data from both the treatment and control groups, to further alleviate the label scarcity problem. Comprehensive experiments on a public dataset and two industrial datasets show a superior performance of our proposed framework over state-of-the-art methods under various evaluation metrics. The proposed algorithms have been deployed online to serve real-world uplift estimation scenarios.
|
[
"['Dingyuan Zhu' 'Daixin Wang' 'Zhiqiang Zhang' 'Kun Kuang' 'Yan Zhang'\n 'Yulin Kang' 'Jun Zhou']"
] |
null | null |
2403.06499
| null | null |
http://arxiv.org/pdf/2403.06499v1
|
2024-03-11T08:11:52Z
|
2024-03-11T08:11:52Z
|
Detection of Unobserved Common Causes based on NML Code in Discrete,
Mixed, and Continuous Variables
|
Causal discovery in the presence of unobserved common causes from observational data only is a crucial but challenging problem. We categorize all possible causal relationships between two random variables into the following four categories and aim to identify one from observed data: two cases in which either of the direct causality exists, a case that variables are independent, and a case that variables are confounded by latent confounders. Although existing methods have been proposed to tackle this problem, they require unobserved variables to satisfy assumptions on the form of their equation models. In our previous study (Kobayashi et al., 2022), the first causal discovery method without such assumptions is proposed for discrete data and named CLOUD. Using Normalized Maximum Likelihood (NML) Code, CLOUD selects a model that yields the minimum codelength of the observed data from a set of model candidates. This paper extends CLOUD to apply for various data types across discrete, mixed, and continuous. We not only performed theoretical analysis to show the consistency of CLOUD in terms of the model selection, but also demonstrated that CLOUD is more effective than existing methods in inferring causal relationships by extensive experiments on both synthetic and real-world data.
|
[
"['Masatoshi Kobayashi' 'Kohei Miyagichi' 'Shin Matsushima']"
] |
null | null |
2403.06503
| null | null |
http://arxiv.org/pdf/2403.06503v1
|
2024-03-11T08:25:52Z
|
2024-03-11T08:25:52Z
|
Automatic Generation of Python Programs Using Context-Free Grammars
|
In recent years, data has emerged as the new gold, serving as a powerful tool for creating intelligent systems. However, procuring high-quality data remains challenging, especially for code. To address this, we developed TinyPy Generator, a tool that generates random Python programs using a context-free grammar. The generated programs are guaranteed to be correct by construction. Our system uses custom production rules (in the Backus-Naur Form (BNF) format) to recursively generate code. This allows us to generate code with different levels of complexity, ranging from code containing only assignments to more complex code containing conditionals and loops. Our proposed tool enables effortless large-scale Python code generation, beneficial for a wide range of applications. TinyPy Generator is particularly useful in the field of machine learning, where it can generate substantial amounts of Python code for training Python language models. Additionally, researchers who are studying programming languages can utilize this tool to create datasets for their experiments, which can help validate the robustness of code interpreters or compilers. Unlike existing research, we have open-sourced our implementation. This allows customization according to user needs and extends potential usage to other languages.
|
[
"['Kamel Yamani' 'Marwa Naïr' 'Riyadh Baghdadi']"
] |
null | null |
2403.06524
| null | null |
http://arxiv.org/pdf/2403.06524v1
|
2024-03-11T08:58:42Z
|
2024-03-11T08:58:42Z
|
Tactical Decision Making for Autonomous Trucks by Deep Reinforcement
Learning with Total Cost of Operation Based Reward
|
We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario. Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the reinforcement learning agent and the low-level controllers based on physical models. In the following, we study optimizing the performance with a realistic and multi-objective reward function based on Total Cost of Operation (TCOP) of the truck using different approaches; by adding weights to reward components, by normalizing the reward components and by using curriculum learning techniques.
|
[
"['Deepthi Pathare' 'Leo Laine' 'Morteza Haghir Chehreghani']"
] |
null | null |
2403.06528
| null | null |
http://arxiv.org/pdf/2403.06528v1
|
2024-03-11T09:10:37Z
|
2024-03-11T09:10:37Z
|
Adaptive Federated Learning Over the Air
|
We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. This approach capitalizes on the inherent superposition property of wireless channels, facilitating fast and scalable parameter aggregation. Meanwhile, it enhances the robustness of the model training process by dynamically adjusting the stepsize in accordance with the global gradient update. We derive the convergence rate of the training algorithms, encompassing the effects of channel fading and interference, for a broad spectrum of nonconvex loss functions. Our analysis shows that the AdaGrad-based algorithm converges to a stationary point at the rate of $mathcal{O}( ln{(T)} /{ T^{ 1 - frac{1}{alpha} } } )$, where $alpha$ represents the tail index of the electromagnetic interference. This result indicates that the level of heavy-tailedness in interference distribution plays a crucial role in the training efficiency: the heavier the tail, the slower the algorithm converges. In contrast, an Adam-like algorithm converges at the $mathcal{O}( 1/T )$ rate, demonstrating its advantage in expediting the model training process. We conduct extensive experiments that corroborate our theoretical findings and affirm the practical efficacy of our proposed federated adaptive gradient methods.
|
[
"['Chenhao Wang' 'Zihan Chen' 'Nikolaos Pappas' 'Howard H. Yang'\n 'Tony Q. S. Quek' 'H. Vincent Poor']"
] |
null | null |
2403.06534
| null | null |
http://arxiv.org/pdf/2403.06534v1
|
2024-03-11T09:20:40Z
|
2024-03-11T09:20:40Z
|
SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale
SAR Object Detection
|
Synthetic Aperture Radar (SAR) object detection has gained significant attention recently due to its irreplaceable all-weather imaging capabilities. However, this research field suffers from both limited public datasets (mostly comprising <2K images with only mono-category objects) and inaccessible source code. To tackle these challenges, we establish a new benchmark dataset and an open-source method for large-scale SAR object detection. Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets, providing a large-scale and diverse dataset for research purposes. To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created. With this high-quality dataset, we conducted comprehensive experiments and uncovered a crucial challenge in SAR object detection: the substantial disparities between the pretraining on RGB datasets and finetuning on SAR datasets in terms of both data domain and model structure. To bridge these gaps, we propose a novel Multi-Stage with Filter Augmentation (MSFA) pretraining framework that tackles the problems from the perspective of data input, domain transition, and model migration. The proposed MSFA method significantly enhances the performance of SAR object detection models while demonstrating exceptional generalizability and flexibility across diverse models. This work aims to pave the way for further advancements in SAR object detection. The dataset and code is available at https://github.com/zcablii/SARDet_100K.
|
[
"['Yuxuan Li' 'Xiang Li' 'Weijie Li' 'Qibin Hou' 'Li Liu' 'Ming-Ming Cheng'\n 'Jian Yang']"
] |
null | null |
2403.06535
| null | null |
http://arxiv.org/pdf/2403.06535v1
|
2024-03-11T09:21:11Z
|
2024-03-11T09:21:11Z
|
Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
|
Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server, with each agent solving varied tasks over time. To achieve efficient collaboration, agents should: i) autonomously identify beneficial collaborative relationships in a decentralized manner; and ii) adapt to dynamically changing task observations. In this paper, we propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs. To promote autonomous collaboration relationship learning, we propose a decentralized graph structure learning algorithm, eliminating the need for external priors. To facilitate adaptation to dynamic tasks, we design a memory unit to capture the agents' accumulated learning history and knowledge, while preserving finite storage consumption. To further augment the system's expressive capabilities and computational efficiency, we apply algorithm unrolling, leveraging the advantages of both mathematical optimization and neural networks. This allows the agents to `learn to collaborate' through the supervision of training tasks. Our theoretical analysis verifies that inter-agent collaboration is communication efficient under a small number of communication rounds. The experimental results verify its ability to facilitate the discovery of collaboration strategies and adaptation to dynamic learning scenarios, achieving a 98.80% reduction in MSE and a 188.87% improvement in classification accuracy. We expect our work can serve as a foundational technique to facilitate future works towards an intelligent, decentralized, and dynamic multi-agent system. Code is available at https://github.com/ShuoTang123/DeLAMA.
|
[
"['Shuo Tang' 'Rui Ye' 'Chenxin Xu' 'Xiaowen Dong' 'Siheng Chen'\n 'Yanfeng Wang']"
] |
null | null |
2403.06545
| null | null |
http://arxiv.org/pdf/2403.06545v1
|
2024-03-11T09:45:34Z
|
2024-03-11T09:45:34Z
|
ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico
Data Generation
|
The creation of in-silico datasets can expand the utility of existing annotations to new domains with different staining patterns in computational pathology. As such, it has the potential to significantly lower the cost associated with building large and pixel precise datasets needed to train supervised deep learning models. We propose a novel approach for the generation of in-silico immunohistochemistry (IHC) images by disentangling morphology specific IHC stains into separate image channels in immunofluorescence (IF) images. The proposed approach qualitatively and quantitatively outperforms baseline methods as proven by training nucleus segmentation models on the created in-silico datasets.
|
[
"['Dominik Winter' 'Nicolas Triltsch' 'Philipp Plewa' 'Marco Rosati'\n 'Thomas Padel' 'Ross Hill' 'Markus Schick' 'Nicolas Brieu']"
] |
null | null |
2403.06546
| null | null |
http://arxiv.org/pdf/2403.06546v2
|
2024-04-05T12:35:06Z
|
2024-03-11T09:46:41Z
|
OMH: Structured Sparsity via Optimally Matched Hierarchy for
Unsupervised Semantic Segmentation
|
Unsupervised Semantic Segmentation (USS) involves segmenting images without relying on predefined labels, aiming to alleviate the burden of extensive human labeling. Existing methods utilize features generated by self-supervised models and specific priors for clustering. However, their clustering objectives are not involved in the optimization of the features during training. Additionally, due to the lack of clear class definitions in USS, the resulting segments may not align well with the clustering objective. In this paper, we introduce a novel approach called Optimally Matched Hierarchy (OMH) to simultaneously address the above issues. The core of our method lies in imposing structured sparsity on the feature space, which allows the features to encode information with different levels of granularity. The structure of this sparsity stems from our hierarchy (OMH). To achieve this, we learn a soft but sparse hierarchy among parallel clusters through Optimal Transport. Our OMH yields better unsupervised segmentation performance compared to existing USS methods. Our extensive experiments demonstrate the benefits of OMH when utilizing our differentiable paradigm. We will make our code publicly available.
|
[
"['Baran Ozaydin' 'Tong Zhang' 'Deblina Bhattacharjee' 'Sabine Süsstrunk'\n 'Mathieu Salzmann']"
] |
null | null |
2403.06557
| null | null |
http://arxiv.org/pdf/2403.06557v1
|
2024-03-11T10:00:26Z
|
2024-03-11T10:00:26Z
|
Data-driven architecture to encode information in the kinematics of
robots and artificial avatars
|
We present a data-driven control architecture for modifying the kinematics of robots and artificial avatars to encode specific information such as the presence or not of an emotion in the movements of an avatar or robot driven by a human operator. We validate our approach on an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place task.
|
[
"['Francesco De Lellis' 'Marco Coraggio' 'Nathan C. Foster'\n 'Riccardo Villa' 'Cristina Becchio' 'Mario di Bernardo']"
] |
null | null |
2403.06560
| null | null |
http://arxiv.org/pdf/2403.06560v1
|
2024-03-11T10:01:21Z
|
2024-03-11T10:01:21Z
|
Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds
|
While many Machine Learning methods were developed or transposed on Riemannian manifolds to tackle data with known non Euclidean geometry, Optimal Transport (OT) methods on such spaces have not received much attention. The main OT tool on these spaces is the Wasserstein distance which suffers from a heavy computational burden. On Euclidean spaces, a popular alternative is the Sliced-Wasserstein distance, which leverages a closed-form solution of the Wasserstein distance in one dimension, but which is not readily available on manifolds. In this work, we derive general constructions of Sliced-Wasserstein distances on Cartan-Hadamard manifolds, Riemannian manifolds with non-positive curvature, which include among others Hyperbolic spaces or the space of Symmetric Positive Definite matrices. Then, we propose different applications. Additionally, we derive non-parametric schemes to minimize these new distances by approximating their Wasserstein gradient flows.
|
[
"['Clément Bonet' 'Lucas Drumetz' 'Nicolas Courty']"
] |
null | null |
2403.06563
| null | null |
http://arxiv.org/pdf/2403.06563v3
|
2024-04-05T06:39:34Z
|
2024-03-11T10:05:29Z
|
Unraveling the Mystery of Scaling Laws: Part I
|
Scaling law principles indicate a power-law correlation between loss and variables such as model size, dataset size, and computational resources utilized during training. These principles play a vital role in optimizing various aspects of model pre-training, ultimately contributing to the success of large language models such as GPT-4, Llama and Gemini. However, the original scaling law paper by OpenAI did not disclose the complete details necessary to derive the precise scaling law formulas, and their conclusions are only based on models containing up to 1.5 billion parameters. Though some subsequent works attempt to unveil these details and scale to larger models, they often neglect the training dependency of important factors such as the learning rate, context length and batch size, leading to their failure to establish a reliable formula for predicting the test loss trajectory. In this technical report, we confirm that the scaling law formulations proposed in the original OpenAI paper remain valid when scaling the model size up to 33 billion, but the constant coefficients in these formulas vary significantly with the experiment setup. We meticulously identify influential factors and provide transparent, step-by-step instructions to estimate all constant terms in scaling-law formulas by training on models with only 1M~60M parameters. Using these estimated formulas, we showcase the capability to accurately predict various attributes for models with up to 33B parameters before their training, including (1) the minimum possible test loss; (2) the minimum required training steps and processed tokens to achieve a specific loss; (3) the critical batch size with an optimal time/computation trade-off at any loss value; and (4) the complete test loss trajectory with arbitrary batch size.
|
[
"['Hui Su' 'Zhi Tian' 'Xiaoyu Shen' 'Xunliang Cai']"
] |
null | null |
2403.06569
| null | null |
http://arxiv.org/pdf/2403.06569v2
|
2024-03-12T11:40:33Z
|
2024-03-11T10:10:45Z
|
Enhancing Joint Motion Prediction for Individuals with Limb Loss Through
Model Reprogramming
|
Mobility impairment caused by limb loss is a significant challenge faced by millions of individuals worldwide. The development of advanced assistive technologies, such as prosthetic devices, has the potential to greatly improve the quality of life for amputee patients. A critical component in the design of such technologies is the accurate prediction of reference joint motion for the missing limb. However, this task is hindered by the scarcity of joint motion data available for amputee patients, in contrast to the substantial quantity of data from able-bodied subjects. To overcome this, we leverage deep learning's reprogramming property to repurpose well-trained models for a new goal without altering the model parameters. With only data-level manipulation, we adapt models originally designed for able-bodied people to forecast joint motion in amputees. The findings in this study have significant implications for advancing assistive tech and amputee mobility.
|
[
"['Sharmita Dey' 'Sarath R. Nair']"
] |
null | null |
2403.06571
| null | null |
http://arxiv.org/pdf/2403.06571v2
|
2024-06-04T20:12:47Z
|
2024-03-11T10:14:06Z
|
Scalable Online Exploration via Coverability
|
Exploration is a major challenge in reinforcement learning, especially for high-dimensional domains that require function approximation. We propose exploration objectives -- policy optimization objectives that enable downstream maximization of any reward function -- as a conceptual framework to systematize the study of exploration. Within this framework, we introduce a new objective, $L_1$-Coverage, which generalizes previous exploration schemes and supports three fundamental desiderata: 1. Intrinsic complexity control. $L_1$-Coverage is associated with a structural parameter, $L_1$-Coverability, which reflects the intrinsic statistical difficulty of the underlying MDP, subsuming Block and Low-Rank MDPs. 2. Efficient planning. For a known MDP, optimizing $L_1$-Coverage efficiently reduces to standard policy optimization, allowing flexible integration with off-the-shelf methods such as policy gradient and Q-learning approaches. 3. Efficient exploration. $L_1$-Coverage enables the first computationally efficient model-based and model-free algorithms for online (reward-free or reward-driven) reinforcement learning in MDPs with low coverability. Empirically, we find that $L_1$-Coverage effectively drives off-the-shelf policy optimization algorithms to explore the state space.
|
[
"['Philip Amortila' 'Dylan J. Foster' 'Akshay Krishnamurthy']"
] |
null | null |
2403.06576
| null | null |
http://arxiv.org/pdf/2403.06576v1
|
2024-03-11T10:26:04Z
|
2024-03-11T10:26:04Z
|
FFAD: A Novel Metric for Assessing Generated Time Series Data Utilizing
Fourier Transform and Auto-encoder
|
The success of deep learning-based generative models in producing realistic images, videos, and audios has led to a crucial consideration: how to effectively assess the quality of synthetic samples. While the Fr'{e}chet Inception Distance (FID) serves as the standard metric for evaluating generative models in image synthesis, a comparable metric for time series data is notably absent. This gap in assessment capabilities stems from the absence of a widely accepted feature vector extractor pre-trained on benchmark time series datasets. In addressing these challenges related to assessing the quality of time series, particularly in the context of Fr'echet Distance, this work proposes a novel solution leveraging the Fourier transform and Auto-encoder, termed the Fr'{e}chet Fourier-transform Auto-encoder Distance (FFAD). Through our experimental results, we showcase the potential of FFAD for effectively distinguishing samples from different classes. This novel metric emerges as a fundamental tool for the evaluation of generative time series data, contributing to the ongoing efforts of enhancing assessment methodologies in the realm of deep learning-based generative models.
|
[
"['Yang Chen' 'Dustin J. Kempton' 'Rafal A. Angryk']"
] |
null | null |
2403.06586
| null | null |
http://arxiv.org/pdf/2403.06586v1
|
2024-03-11T10:32:23Z
|
2024-03-11T10:32:23Z
|
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity
Recognition Models
|
Context-aware Human Activity Recognition (HAR) is a hot research area in mobile computing, and the most effective solutions in the literature are based on supervised deep learning models. However, the actual deployment of these systems is limited by the scarcity of labeled data that is required for training. Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate this issue, by infusing common-sense knowledge about human activities and the contexts in which they can be performed into HAR deep learning classifiers. Existing NeSy methods for context-aware HAR rely on knowledge encoded in logic-based models (e.g., ontologies) whose design, implementation, and maintenance to capture new activities and contexts require significant human engineering efforts, technical knowledge, and domain expertise. Recent works show that pre-trained Large Language Models (LLMs) effectively encode common-sense knowledge about human activities. In this work, we propose ContextGPT: a novel prompt engineering approach to retrieve from LLMs common-sense knowledge about the relationship between human activities and the context in which they are performed. Unlike ontologies, ContextGPT requires limited human effort and expertise. An extensive evaluation carried out on two public datasets shows how a NeSy model obtained by infusing common-sense knowledge from ContextGPT is effective in data scarcity scenarios, leading to similar (and sometimes better) recognition rates than logic-based approaches with a fraction of the effort.
|
[
"['Luca Arrotta' 'Claudio Bettini' 'Gabriele Civitarese' 'Michele Fiori']"
] |
null | null |
2403.06606
| null | null |
http://arxiv.org/pdf/2403.06606v2
|
2024-03-25T06:57:57Z
|
2024-03-11T10:50:53Z
|
Distributionally Generative Augmentation for Fair Facial Attribute
Classification
|
Facial Attribute Classification (FAC) holds substantial promise in widespread applications. However, FAC models trained by traditional methodologies can be unfair by exhibiting accuracy inconsistencies across varied data subpopulations. This unfairness is largely attributed to bias in data, where some spurious attributes (e.g., Male) statistically correlate with the target attribute (e.g., Smiling). Most of existing fairness-aware methods rely on the labels of spurious attributes, which may be unavailable in practice. This work proposes a novel, generation-based two-stage framework to train a fair FAC model on biased data without additional annotation. Initially, we identify the potential spurious attributes based on generative models. Notably, it enhances interpretability by explicitly showing the spurious attributes in image space. Following this, for each image, we first edit the spurious attributes with a random degree sampled from a uniform distribution, while keeping target attribute unchanged. Then we train a fair FAC model by fostering model invariance to these augmentation. Extensive experiments on three common datasets demonstrate the effectiveness of our method in promoting fairness in FAC without compromising accuracy. Codes are in https://github.com/heqianpei/DiGA.
|
[
"['Fengda Zhang' 'Qianpei He' 'Kun Kuang' 'Jiashuo Liu' 'Long Chen'\n 'Chao Wu' 'Jun Xiao' 'Hanwang Zhang']"
] |
null | null |
2403.06612
| null | null |
http://arxiv.org/pdf/2403.06612v1
|
2024-03-11T10:59:55Z
|
2024-03-11T10:59:55Z
|
Pulling back symmetric Riemannian geometry for data analysis
|
Data sets tend to live in low-dimensional non-linear subspaces. Ideal data analysis tools for such data sets should therefore account for such non-linear geometry. The symmetric Riemannian geometry setting can be suitable for a variety of reasons. First, it comes with a rich mathematical structure to account for a wide range of non-linear geometries that has been shown to be able to capture the data geometry through empirical evidence from classical non-linear embedding. Second, many standard data analysis tools initially developed for data in Euclidean space can also be generalised efficiently to data on a symmetric Riemannian manifold. A conceptual challenge comes from the lack of guidelines for constructing a symmetric Riemannian structure on the data space itself and the lack of guidelines for modifying successful algorithms on symmetric Riemannian manifolds for data analysis to this setting. This work considers these challenges in the setting of pullback Riemannian geometry through a diffeomorphism. The first part of the paper characterises diffeomorphisms that result in proper, stable and efficient data analysis. The second part then uses these best practices to guide construction of such diffeomorphisms through deep learning. As a proof of concept, different types of pullback geometries -- among which the proposed construction -- are tested on several data analysis tasks and on several toy data sets. The numerical experiments confirm the predictions from theory, i.e., that the diffeomorphisms generating the pullback geometry need to map the data manifold into a geodesic subspace of the pulled back Riemannian manifold while preserving local isometry around the data manifold for proper, stable and efficient data analysis, and that pulling back positive curvature can be problematic in terms of stability.
|
[
"['Willem Diepeveen']"
] |
null | null |
2403.06631
| null | null |
http://arxiv.org/pdf/2403.06631v1
|
2024-03-11T11:41:30Z
|
2024-03-11T11:41:30Z
|
Evaluating the Energy Efficiency of Few-Shot Learning for Object
Detection in Industrial Settings
|
In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and complexity. However, sustainability and energy efficiency have been critical requirements during deployment in contemporary industrial settings, necessitating the use of data-efficient approaches such as few-shot learning. In this paper, to alleviate the burden of lengthy model training and minimize energy consumption, a finetuning approach to adapt standard object detection models to downstream tasks is examined. Subsequently, a thorough case study and evaluation of the energy demands of the developed models, applied in object detection benchmark datasets from volatile industrial environments is presented. Specifically, different finetuning strategies as well as utilization of ancillary evaluation data during training are examined, and the trade-off between performance and efficiency is highlighted in this low-data regime. Finally, this paper introduces a novel way to quantify this trade-off through a customized Efficiency Factor metric.
|
[
"['Georgios Tsoumplekas' 'Vladislav Li' 'Ilias Siniosoglou'\n 'Vasileios Argyriou' 'Sotirios K. Goudos' 'Ioannis D. Moscholios'\n 'Panagiotis Radoglou-Grammatikis' 'Panagiotis Sarigiannidis']"
] |
null | null |
2403.06643
| null | null |
http://arxiv.org/abs/2403.06643v2
|
2024-06-28T15:51:14Z
|
2024-03-11T12:04:28Z
|
Spatial features of CO2 for occupancy detection in a naturally
ventilated school building
|
Accurate occupancy information helps to improve building energy efficiency and occupant comfort. Occupancy detection methods based on CO2 sensors have received attention due to their low cost and low intrusiveness. In naturally ventilated buildings, the accuracy of CO2-based occupancy detection is generally low in related studies due to the complex ventilation behavior and the difficulty in measuring the actual air exchange through windows. In this study, we present two novel features for occupancy detection based on the spatial distribution of the CO2 concentration. After a quantitative analysis with Support Vector Machine (SVM) as classifier, it was found that the accuracy of occupancy state detection in naturally ventilated rooms could be improved by up to 14.8 percentage points compared to the baseline, reaching 83.2 % (F1 score 0.84) without any ventilation information. With ventilation information, the accuracy reached 87.6 % (F1 score 0.89). The performance of occupancy quantity detection was significantly improved by up to 25.3 percentage points versus baseline, reaching 56 %, with root mean square error (RMSE) of 11.44 occupants, using only CO2-related features. Additional ventilation information further enhanced the performance to 61.8 % (RMSE 9.02 occupants). By incorporating spatial features, the model using only CO2-related features revealed similar performance as the model containing additional ventilation information, resulting in a better low-cost occupancy detection method for naturally ventilated buildings.
|
[
"['Qirui Huang' 'Marc Syndicus' 'Jérôme Frisch' 'Christoph van Treeck']"
] |
null | null |
2403.06644
| null | null |
http://arxiv.org/pdf/2403.06644v1
|
2024-03-11T12:07:13Z
|
2024-03-11T12:07:13Z
|
Elephants Never Forget: Testing Language Models for Memorization of
Tabular Data
|
While many have shown how Large Language Models (LLMs) can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over. In this work, we address this concern for tabular data. Starting with simple qualitative tests for whether an LLM knows the names and values of features, we introduce a variety of different techniques to assess the degrees of contamination, including statistical tests for conditional distribution modeling and four tests that identify memorization. Our investigation reveals that LLMs are pre-trained on many popular tabular datasets. This exposure can lead to invalid performance evaluation on downstream tasks because the LLMs have, in effect, been fit to the test set. Interestingly, we also identify a regime where the language model reproduces important statistics of the data, but fails to reproduce the dataset verbatim. On these datasets, although seen during training, good performance on downstream tasks might not be due to overfitting. Our findings underscore the need for ensuring data integrity in machine learning tasks with LLMs. To facilitate future research, we release an open-source tool that can perform various tests for memorization url{https://github.com/interpretml/LLM-Tabular-Memorization-Checker}.
|
[
"['Sebastian Bordt' 'Harsha Nori' 'Rich Caruana']"
] |
null | null |
2403.06645
| null | null |
http://arxiv.org/pdf/2403.06645v2
|
2024-03-18T09:22:01Z
|
2024-03-11T12:07:33Z
|
Ricci flow-based brain surface covariance descriptors for diagnosing
Alzheimer's disease
|
Automated feature extraction from MRI brain scans and diagnosis of Alzheimer's disease are ongoing challenges. With advances in 3D imaging technology, 3D data acquisition is becoming more viable and efficient than its 2D counterpart. Rather than using feature-based vectors, in this paper, for the first time, we suggest a pipeline to extract novel covariance-based descriptors from the cortical surface using the Ricci energy optimization. The covariance descriptors are components of the nonlinear manifold of symmetric positive-definite matrices, thus we focus on using the Gaussian radial basis function to apply manifold-based classification to the 3D shape problem. Applying this novel signature to the analysis of abnormal cortical brain morphometry allows for diagnosing Alzheimer's disease. Experimental studies performed on about two hundred 3D MRI brain models, gathered from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate the effectiveness of our descriptors in achieving remarkable classification accuracy.
|
[
"['Fatemeh Ahmadi' 'Mohamad Ebrahim Shiri' 'Behroz Bidabad'\n 'Maral Sedaghat' 'Pooran Memari']"
] |
null | null |
2403.06659
| null | null |
http://arxiv.org/pdf/2403.06659v3
|
2024-07-02T16:51:11Z
|
2024-03-11T12:28:55Z
|
Zero-Shot ECG Classification with Multimodal Learning and Test-time
Clinical Knowledge Enhancement
|
Electrocardiograms (ECGs) are non-invasive diagnostic tools crucial for detecting cardiac arrhythmic diseases in clinical practice. While ECG Self-supervised Learning (eSSL) methods show promise in representation learning from unannotated ECG data, they often overlook the clinical knowledge that can be found in reports. This oversight and the requirement for annotated samples for downstream tasks limit eSSL's versatility. In this work, we address these issues with the Multimodal ECG Representation Learning (MERL}) framework. Through multimodal learning on ECG records and associated reports, MERL is capable of performing zero-shot ECG classification with text prompts, eliminating the need for training data in downstream tasks. At test time, we propose the Clinical Knowledge Enhanced Prompt Engineering (CKEPE) approach, which uses Large Language Models (LLMs) to exploit external expert-verified clinical knowledge databases, generating more descriptive prompts and reducing hallucinations in LLM-generated content to boost zero-shot classification. Based on MERL, we perform the first benchmark across six public ECG datasets, showing the superior performance of MERL compared against eSSL methods. Notably, MERL achieves an average AUC score of 75.2% in zero-shot classification (without training data), 3.2% higher than linear probed eSSL methods with 10% annotated training data, averaged across all six datasets. Code and models are available at https://github.com/cheliu-computation/MERL
|
[
"['Che Liu' 'Zhongwei Wan' 'Cheng Ouyang' 'Anand Shah' 'Wenjia Bai'\n 'Rossella Arcucci']"
] |
null | null |
2403.06664
| null | null |
http://arxiv.org/pdf/2403.06664v1
|
2024-03-11T12:32:14Z
|
2024-03-11T12:32:14Z
|
Smart-Infinity: Fast Large Language Model Training using Near-Storage
Processing on a Real System
|
The recent huge advance of Large Language Models (LLMs) is mainly driven by the increase in the number of parameters. This has led to substantial memory capacity requirements, necessitating the use of dozens of GPUs just to meet the capacity. One popular solution to this is storage-offloaded training, which uses host memory and storage as an extended memory hierarchy. However, this obviously comes at the cost of storage bandwidth bottleneck because storage devices have orders of magnitude lower bandwidth compared to that of GPU device memories. Our work, Smart-Infinity, addresses the storage bandwidth bottleneck of storage-offloaded LLM training using near-storage processing devices on a real system. The main component of Smart-Infinity is SmartUpdate, which performs parameter updates on custom near-storage accelerators. We identify that moving parameter updates to the storage side removes most of the storage traffic. In addition, we propose an efficient data transfer handler structure to address the system integration issues for Smart-Infinity. The handler allows overlapping data transfers with fixed memory consumption by reusing the device buffer. Lastly, we propose accelerator-assisted gradient compression/decompression to enhance the scalability of Smart-Infinity. When scaling to multiple near-storage processing devices, the write traffic on the shared channel becomes the bottleneck. To alleviate this, we compress the gradients on the GPU and decompress them on the accelerators. It provides further acceleration from reduced traffic. As a result, Smart-Infinity achieves a significant speedup compared to the baseline. Notably, Smart-Infinity is a ready-to-use approach that is fully integrated into PyTorch on a real system. We will open-source Smart-Infinity to facilitate its use.
|
[
"['Hongsun Jang' 'Jaeyong Song' 'Jaewon Jung' 'Jaeyoung Park'\n 'Youngsok Kim' 'Jinho Lee']"
] |
null | null |
2403.06668
| null | null |
http://arxiv.org/pdf/2403.06668v3
|
2024-05-17T05:29:05Z
|
2024-03-11T12:36:14Z
|
PeerAiD: Improving Adversarial Distillation from a Specialized Peer
Tutor
|
Adversarial robustness of the neural network is a significant concern when it is applied to security-critical domains. In this situation, adversarial distillation is a promising option which aims to distill the robustness of the teacher network to improve the robustness of a small student network. Previous works pretrain the teacher network to make it robust against the adversarial examples aimed at itself. However, the adversarial examples are dependent on the parameters of the target network. The fixed teacher network inevitably degrades its robustness against the unseen transferred adversarial examples which target the parameters of the student network in the adversarial distillation process. We propose PeerAiD to make a peer network learn the adversarial examples of the student network instead of adversarial examples aimed at itself. PeerAiD is an adversarial distillation that trains the peer network and the student network simultaneously in order to specialize the peer network for defending the student network. We observe that such peer networks surpass the robustness of the pretrained robust teacher model against adversarial examples aimed at the student network. With this peer network and adversarial distillation, PeerAiD achieves significantly higher robustness of the student network with AutoAttack (AA) accuracy by up to 1.66%p and improves the natural accuracy of the student network by up to 4.72%p with ResNet-18 on TinyImageNet dataset. Code is available at https://github.com/jaewonalive/PeerAiD.
|
[
"['Jaewon Jung' 'Hongsun Jang' 'Jaeyong Song' 'Jinho Lee']"
] |
null | null |
2403.06671
| null | null |
http://arxiv.org/pdf/2403.06671v1
|
2024-03-11T12:42:31Z
|
2024-03-11T12:42:31Z
|
Untangling Gaussian Mixtures
|
Tangles were originally introduced as a concept to formalize regions of high connectivity in graphs. In recent years, they have also been discovered as a link between structural graph theory and data science: when interpreting similarity in data sets as connectivity between points, finding clusters in the data essentially amounts to finding tangles in the underlying graphs. This paper further explores the potential of tangles in data sets as a means for a formal study of clusters. Real-world data often follow a normal distribution. Accounting for this, we develop a quantitative theory of tangles in data sets drawn from Gaussian mixtures. To this end, we equip the data with a graph structure that models similarity between the points and allows us to apply tangle theory to the data. We provide explicit conditions under which tangles associated with the marginal Gaussian distributions exist asymptotically almost surely. This can be considered as a sufficient formal criterion for the separabability of clusters in the data.
|
[
"['Eva Fluck' 'Sandra Kiefer' 'Christoph Standke']"
] |
null | null |
2403.06672
| null | null |
http://arxiv.org/pdf/2403.06672v1
|
2024-03-11T12:43:44Z
|
2024-03-11T12:43:44Z
|
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive
Domains
|
Cross-silo federated learning (FL) allows data owners to train accurate machine learning models by benefiting from each others private datasets. Unfortunately, the model accuracy benefits of collaboration are often undermined by privacy defenses. Therefore, to incentivize client participation in privacy-sensitive domains, a FL protocol should strike a delicate balance between privacy guarantees and end-model accuracy. In this paper, we study the question of when and how a server could design a FL protocol provably beneficial for all participants. First, we provide necessary and sufficient conditions for the existence of mutually beneficial protocols in the context of mean estimation and convex stochastic optimization. We also derive protocols that maximize the total clients' utility, given symmetric privacy preferences. Finally, we design protocols maximizing end-model accuracy and demonstrate their benefits in synthetic experiments.
|
[
"['Nikita Tsoy' 'Anna Mihalkova' 'Teodora Todorova' 'Nikola Konstantinov']"
] |
null | null |
2403.06677
| null | null |
http://arxiv.org/pdf/2403.06677v1
|
2024-03-11T12:49:37Z
|
2024-03-11T12:49:37Z
|
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization
with Loopless Variance Reduction
|
In this study, we investigate stochastic optimization on Riemannian manifolds, focusing on the crucial variance reduction mechanism used in both Euclidean and Riemannian settings. Riemannian variance-reduced methods usually involve a double-loop structure, computing a full gradient at the start of each loop. Determining the optimal inner loop length is challenging in practice, as it depends on strong convexity or smoothness constants, which are often unknown or hard to estimate. Motivated by Euclidean methods, we introduce the Riemannian Loopless SVRG (R-LSVRG) and PAGE (R-PAGE) methods. These methods replace the outer loop with probabilistic gradient computation triggered by a coin flip in each iteration, ensuring simpler proofs, efficient hyperparameter selection, and sharp convergence guarantees. Using R-PAGE as a framework for non-convex Riemannian optimization, we demonstrate its applicability to various important settings. For example, we derive Riemannian MARINA (R-MARINA) for distributed settings with communication compression, providing the best theoretical communication complexity guarantees for non-convex distributed optimization over Riemannian manifolds. Experimental results support our theoretical findings.
|
[
"['Yury Demidovich' 'Grigory Malinovsky' 'Peter Richtárik']"
] |
null | null |
2403.06687
| null | null |
http://arxiv.org/pdf/2403.06687v2
|
2024-04-22T16:18:53Z
|
2024-03-11T13:04:21Z
|
Advancing Graph Neural Networks with HL-HGAT: A Hodge-Laplacian and
Attention Mechanism Approach for Heterogeneous Graph-Structured Data
|
Graph neural networks (GNNs) have proven effective in capturing relationships among nodes in a graph. This study introduces a novel perspective by considering a graph as a simplicial complex, encompassing nodes, edges, triangles, and $k$-simplices, enabling the definition of graph-structured data on any $k$-simplices. Our contribution is the Hodge-Laplacian heterogeneous graph attention network (HL-HGAT), designed to learn heterogeneous signal representations across $k$-simplices. The HL-HGAT incorporates three key components: HL convolutional filters (HL-filters), simplicial projection (SP), and simplicial attention pooling (SAP) operators, applied to $k$-simplices. HL-filters leverage the unique topology of $k$-simplices encoded by the Hodge-Laplacian (HL) operator, operating within the spectral domain of the $k$-th HL operator. To address computation challenges, we introduce a polynomial approximation for HL-filters, exhibiting spatial localization properties. Additionally, we propose a pooling operator to coarsen $k$-simplices, combining features through simplicial attention mechanisms of self-attention and cross-attention via transformers and SP operators, capturing topological interconnections across multiple dimensions of simplices. The HL-HGAT is comprehensively evaluated across diverse graph applications, including NP-hard problems, graph multi-label and classification challenges, and graph regression tasks in logistics, computer vision, biology, chemistry, and neuroscience. The results demonstrate the model's efficacy and versatility in handling a wide range of graph-based scenarios.
|
[
"['Jinghan Huang' 'Qiufeng Chen' 'Yijun Bian' 'Pengli Zhu' 'Nanguang Chen'\n 'Moo K. Chung' 'Anqi Qiu']"
] |
null | null |
2403.06725
| null | null |
http://arxiv.org/pdf/2403.06725v3
|
2024-07-05T16:24:29Z
|
2024-03-11T13:44:43Z
|
Improving Low-Resource Knowledge Tracing Tasks by Supervised
Pre-training and Importance Mechanism Fine-tuning
|
Knowledge tracing (KT) aims to estimate student's knowledge mastery based on their historical interactions. Recently, the deep learning based KT (DLKT) approaches have achieved impressive performance in the KT task. These DLKT models heavily rely on the large number of available student interactions. However, due to various reasons such as budget constraints and privacy concerns, observed interactions are very limited in many real-world scenarios, a.k.a, low-resource KT datasets. Directly training a DLKT model on a low-resource KT dataset may lead to overfitting and it is difficult to choose the appropriate deep neural architecture. Therefore, in this paper, we propose a low-resource KT framework called LoReKT to address above challenges. Inspired by the prevalent "pre-training and fine-tuning" paradigm, we aim to learn transferable parameters and representations from rich-resource KT datasets during the pre-training stage and subsequently facilitate effective adaptation to low-resource KT datasets. Specifically, we simplify existing sophisticated DLKT model architectures with purely a stack of transformer decoders. We design an encoding mechanism to incorporate student interactions from multiple KT data sources and develop an importance mechanism to prioritize updating parameters with high importance while constraining less important ones during the fine-tuning stage. We evaluate LoReKT on six public KT datasets and experimental results demonstrate the superiority of our approach in terms of AUC and Accuracy. To encourage reproducible research, we make our data and code publicly available at https://anonymous.4open.science/r/LoReKT-C619.
|
[
"['Hengyuan Zhang' 'Zitao Liu' 'Shuyan Huang' 'Chenming Shang' 'Bojun Zhan'\n 'Yong Jiang']"
] |
null | null |
2403.06726
| null | null |
http://arxiv.org/pdf/2403.06726v2
|
2024-03-14T16:35:41Z
|
2024-03-11T13:44:49Z
|
Probabilistic Contrastive Learning for Long-Tailed Visual Recognition
|
Long-tailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Such imbalance issue considerably impairs the performance of standard supervised learning algorithms, which are mainly designed for balanced training sets. Recent investigations have revealed that supervised contrastive learning exhibits promising potential in alleviating the data imbalance. However, the performance of supervised contrastive learning is plagued by an inherent challenge: it necessitates sufficiently large batches of training data to construct contrastive pairs that cover all categories, yet this requirement is difficult to meet in the context of class-imbalanced data. To overcome this obstacle, we propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space, and samples contrastive pairs accordingly. In fact, estimating the distributions of all classes using features in a small batch, particularly for imbalanced data, is not feasible. Our key idea is to introduce a reasonable and simple assumption that the normalized features in contrastive learning follow a mixture of von Mises-Fisher (vMF) distributions on unit space, which brings two-fold benefits. First, the distribution parameters can be estimated using only the first sample moment, which can be efficiently computed in an online manner across different batches. Second, based on the estimated distribution, the vMF distribution allows us to sample an infinite number of contrastive pairs and derive a closed form of the expected contrastive loss for efficient optimization. Our code is available at https://github.com/LeapLabTHU/ProCo.
|
[
"['Chaoqun Du' 'Yulin Wang' 'Shiji Song' 'Gao Huang']"
] |
null | null |
2403.06731
| null | null |
http://arxiv.org/pdf/2403.06731v1
|
2024-03-11T13:50:07Z
|
2024-03-11T13:50:07Z
|
On the Approximation of Kernel functions
|
Various methods in statistical learning build on kernels considered in reproducing kernel Hilbert spaces. In applications, the kernel is often selected based on characteristics of the problem and the data. This kernel is then employed to infer response variables at points, where no explanatory data were observed. The data considered here are located in compact sets in higher dimensions and the paper addresses approximations of the kernel itself. The new approach considers Taylor series approximations of radial kernel functions. For the Gauss kernel on the unit cube, the paper establishes an upper bound of the associated eigenfunctions, which grows only polynomially with respect to the index. The novel approach substantiates smaller regularization parameters than considered in the literature, overall leading to better approximations. This improvement confirms low rank approximation methods such as the Nystr"om method.
|
[
"['Paul Dommel' 'Alois Pichler']"
] |
null | null |
2403.06748
| null | null |
http://arxiv.org/pdf/2403.06748v2
|
2024-06-27T15:24:23Z
|
2024-03-11T14:14:52Z
|
Shortcut Learning in Medical Image Segmentation
|
Shortcut learning is a phenomenon where machine learning models prioritize learning simple, potentially misleading cues from data that do not generalize well beyond the training set. While existing research primarily investigates this in the realm of image classification, this study extends the exploration of shortcut learning into medical image segmentation. We demonstrate that clinical annotations such as calipers, and the combination of zero-padded convolutions and center-cropped training sets in the dataset can inadvertently serve as shortcuts, impacting segmentation accuracy. We identify and evaluate the shortcut learning on two different but common medical image segmentation tasks. In addition, we suggest strategies to mitigate the influence of shortcut learning and improve the generalizability of the segmentation models. By uncovering the presence and implications of shortcuts in medical image segmentation, we provide insights and methodologies for evaluating and overcoming this pervasive challenge and call for attention in the community for shortcuts in segmentation. Our code is public at https://github.com/nina-weng/shortcut_skinseg .
|
[
"['Manxi Lin' 'Nina Weng' 'Kamil Mikolaj' 'Zahra Bashir'\n 'Morten Bo Søndergaard Svendsen' 'Martin Tolsgaard'\n 'Anders Nymark Christensen' 'Aasa Feragen']"
] |
null | null |
2403.06750
| null | null |
http://arxiv.org/pdf/2403.06750v1
|
2024-03-11T14:20:13Z
|
2024-03-11T14:20:13Z
|
Generalising Multi-Agent Cooperation through Task-Agnostic Communication
|
Existing communication methods for multi-agent reinforcement learning (MARL) in cooperative multi-robot problems are almost exclusively task-specific, training new communication strategies for each unique task. We address this inefficiency by introducing a communication strategy applicable to any task within a given environment. We pre-train the communication strategy without task-specific reward guidance in a self-supervised manner using a set autoencoder. Our objective is to learn a fixed-size latent Markov state from a variable number of agent observations. Under mild assumptions, we prove that policies using our latent representations are guaranteed to converge, and upper bound the value error introduced by our Markov state approximation. Our method enables seamless adaptation to novel tasks without fine-tuning the communication strategy, gracefully supports scaling to more agents than present during training, and detects out-of-distribution events in an environment. Empirical results on diverse MARL scenarios validate the effectiveness of our approach, surpassing task-specific communication strategies in unseen tasks. Our implementation of this work is available at https://github.com/proroklab/task-agnostic-comms.
|
[
"['Dulhan Jayalath' 'Steven Morad' 'Amanda Prorok']"
] |
null | null |
2403.06754
| null | null |
http://arxiv.org/pdf/2403.06754v2
|
2024-03-16T12:43:33Z
|
2024-03-11T14:28:40Z
|
ALaRM: Align Language Models via Hierarchical Rewards Modeling
|
We introduce ALaRM, the first framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF), which is designed to enhance the alignment of large language models (LLMs) with human preferences. The framework addresses the limitations of current alignment approaches, which often struggle with the inconsistency and sparsity of human supervision signals, by integrating holistic rewards with aspect-specific rewards. This integration enables more precise and consistent guidance of language models towards desired outcomes, particularly in complex and open text generation tasks. By employing a methodology that filters and combines multiple rewards based on their consistency, the framework provides a reliable mechanism for improving model alignment. We validate our approach through applications in long-form question answering and machine translation tasks, employing gpt-3.5-turbo for pairwise comparisons, and demonstrate improvements over existing baselines. Our work underscores the effectiveness of hierarchical rewards modeling in refining LLM training processes for better human preference alignment. We release our code at https://ALaRM-fdu.github.io.
|
[
"['Yuhang Lai' 'Siyuan Wang' 'Shujun Liu' 'Xuanjing Huang' 'Zhongyu Wei']"
] |
null | null |
2403.06757
| null | null |
http://arxiv.org/pdf/2403.06757v2
|
2024-03-13T13:57:42Z
|
2024-03-11T14:29:56Z
|
Koopman Ensembles for Probabilistic Time Series Forecasting
|
In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed. However, the vast majority of those works are limited to deterministic predictions, while the knowledge of uncertainty is critical in fields like meteorology and climatology. In this work, we investigate the training of ensembles of models to produce stochastic outputs. We show through experiments on real remote sensing image time series that ensembles of independently trained models are highly overconfident and that using a training criterion that explicitly encourages the members to produce predictions with high inter-model variances greatly improves the uncertainty quantification of the ensembles.
|
[
"['Anthony Frion' 'Lucas Drumetz' 'Guillaume Tochon' 'Mauro Dalla Mura'\n 'Albdeldjalil Aïssa El Bey']"
] |
null | null |
2403.06759
| null | null |
http://arxiv.org/pdf/2403.06759v1
|
2024-03-11T14:31:03Z
|
2024-03-11T14:31:03Z
|
Average Calibration Error: A Differentiable Loss for Improved
Reliability in Image Segmentation
|
Deep neural networks for medical image segmentation often produce overconfident results misaligned with empirical observations. Such miscalibration, challenges their clinical translation. We propose to use marginal L1 average calibration error (mL1-ACE) as a novel auxiliary loss function to improve pixel-wise calibration without compromising segmentation quality. We show that this loss, despite using hard binning, is directly differentiable, bypassing the need for approximate but differentiable surrogate or soft binning approaches. Our work also introduces the concept of dataset reliability histograms which generalises standard reliability diagrams for refined visual assessment of calibration in semantic segmentation aggregated at the dataset level. Using mL1-ACE, we reduce average and maximum calibration error by 45% and 55% respectively, maintaining a Dice score of 87% on the BraTS 2021 dataset. We share our code here: https://github.com/cai4cai/ACE-DLIRIS
|
[
"['Theodore Barfoot' 'Luis Garcia-Peraza-Herrera' 'Ben Glocker'\n 'Tom Vercauteren']"
] |
null | null |
2403.06768
| null | null |
http://arxiv.org/pdf/2403.06768v1
|
2024-03-11T14:37:57Z
|
2024-03-11T14:37:57Z
|
XB-MAML: Learning Expandable Basis Parameters for Effective
Meta-Learning with Wide Task Coverage
|
Meta-learning, which pursues an effective initialization model, has emerged as a promising approach to handling unseen tasks. However, a limitation remains to be evident when a meta-learner tries to encompass a wide range of task distribution, e.g., learning across distinctive datasets or domains. Recently, a group of works has attempted to employ multiple model initializations to cover widely-ranging tasks, but they are limited in adaptively expanding initializations. We introduce XB-MAML, which learns expandable basis parameters, where they are linearly combined to form an effective initialization to a given task. XB-MAML observes the discrepancy between the vector space spanned by the basis and fine-tuned parameters to decide whether to expand the basis. Our method surpasses the existing works in the multi-domain meta-learning benchmarks and opens up new chances of meta-learning for obtaining the diverse inductive bias that can be combined to stretch toward the effective initialization for diverse unseen tasks.
|
[
"['Jae-Jun Lee' 'Sung Whan Yoon']"
] |
null | null |
2403.06771
| null | null |
http://arxiv.org/pdf/2403.06771v1
|
2024-03-11T14:39:24Z
|
2024-03-11T14:39:24Z
|
Redefining Event Types and Group Evolution in Temporal Data
|
Groups -- such as clusters of points or communities of nodes -- are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of ``events". However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as ``archetypes" characterized by a unique combination of quantitative dimensions that we call ``facets". Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.
|
[
"['Andrea Failla' 'Rémy Cazabet' 'Giulio Rossetti' 'Salvatore Citraro']"
] |
null | null |
2403.06797
| null | null |
http://arxiv.org/pdf/2403.06797v1
|
2024-03-11T15:15:50Z
|
2024-03-11T15:15:50Z
|
Leveraging Internal Representations of Model for Magnetic Image
Classification
|
Data generated by edge devices has the potential to train intelligent autonomous systems across various domains. Despite the emergence of diverse machine learning approaches addressing privacy concerns and utilizing distributed data, security issues persist due to the sensitive storage of data shards in disparate locations. This paper introduces a potentially groundbreaking paradigm for machine learning model training, specifically designed for scenarios with only a single magnetic image and its corresponding label image available. We harness the capabilities of Deep Learning to generate concise yet informative samples, aiming to overcome data scarcity. Through the utilization of deep learning's internal representations, our objective is to efficiently address data scarcity issues and produce meaningful results. This methodology presents a promising avenue for training machine learning models with minimal data.
|
[
"['Adarsh N L' 'Arun P V' 'Alok Porwal' 'Malcolm Aranha']"
] |
null | null |
2403.06798
| null | null |
http://arxiv.org/pdf/2403.06798v1
|
2024-03-11T15:16:20Z
|
2024-03-11T15:16:20Z
|
Dynamic Perturbation-Adaptive Adversarial Training on Medical Image
Classification
|
Remarkable successes were made in Medical Image Classification (MIC) recently, mainly due to wide applications of convolutional neural networks (CNNs). However, adversarial examples (AEs) exhibited imperceptible similarity with raw data, raising serious concerns on network robustness. Although adversarial training (AT), in responding to malevolent AEs, was recognized as an effective approach to improve robustness, it was challenging to overcome generalization decline of networks caused by the AT. In this paper, in order to reserve high generalization while improving robustness, we proposed a dynamic perturbation-adaptive adversarial training (DPAAT) method, which placed AT in a dynamic learning environment to generate adaptive data-level perturbations and provided a dynamically updated criterion by loss information collections to handle the disadvantage of fixed perturbation sizes in conventional AT methods and the dependence on external transference. Comprehensive testing on dermatology HAM10000 dataset showed that the DPAAT not only achieved better robustness improvement and generalization preservation but also significantly enhanced mean average precision and interpretability on various CNNs, indicating its great potential as a generic adversarial training method on the MIC.
|
[
"['Shuai Li' 'Xiaoguang Ma' 'Shancheng Jiang' 'Lu Meng']"
] |
null | null |
2403.06806
| null | null |
http://arxiv.org/pdf/2403.06806v1
|
2024-03-11T15:25:03Z
|
2024-03-11T15:25:03Z
|
On the Global Convergence of Policy Gradient in Average Reward Markov
Decision Processes
|
We present the first finite time global convergence analysis of policy gradient in the context of infinite horizon average reward Markov decision processes (MDPs). Specifically, we focus on ergodic tabular MDPs with finite state and action spaces. Our analysis shows that the policy gradient iterates converge to the optimal policy at a sublinear rate of $Oleft({frac{1}{T}}right),$ which translates to $Oleft({log(T)}right)$ regret, where $T$ represents the number of iterations. Prior work on performance bounds for discounted reward MDPs cannot be extended to average reward MDPs because the bounds grow proportional to the fifth power of the effective horizon. Thus, our primary contribution is in proving that the policy gradient algorithm converges for average-reward MDPs and in obtaining finite-time performance guarantees. In contrast to the existing discounted reward performance bounds, our performance bounds have an explicit dependence on constants that capture the complexity of the underlying MDP. Motivated by this observation, we reexamine and improve the existing performance bounds for discounted reward MDPs. We also present simulations to empirically evaluate the performance of average reward policy gradient algorithm.
|
[
"['Navdeep Kumar' 'Yashaswini Murthy' 'Itai Shufaro' 'Kfir Y. Levy'\n 'R. Srikant' 'Shie Mannor']"
] |
null | null |
2403.06807
| null | null |
http://arxiv.org/pdf/2403.06807v2
|
2024-06-03T11:33:51Z
|
2024-03-11T15:26:34Z
|
Multistep Consistency Models
|
Diffusion models are relatively easy to train but require many steps to generate samples. Consistency models are far more difficult to train, but generate samples in a single step. In this paper we propose Multistep Consistency Models: A unification between Consistency Models (Song et al., 2023) and TRACT (Berthelot et al., 2023) that can interpolate between a consistency model and a diffusion model: a trade-off between sampling speed and sampling quality. Specifically, a 1-step consistency model is a conventional consistency model whereas a $infty$-step consistency model is a diffusion model. Multistep Consistency Models work really well in practice. By increasing the sample budget from a single step to 2-8 steps, we can train models more easily that generate higher quality samples, while retaining much of the sampling speed benefits. Notable results are 1.4 FID on Imagenet 64 in 8 step and 2.1 FID on Imagenet128 in 8 steps with consistency distillation, using simple losses without adversarial training. We also show that our method scales to a text-to-image diffusion model, generating samples that are close to the quality of the original model.
|
[
"['Jonathan Heek' 'Emiel Hoogeboom' 'Tim Salimans']"
] |
null | null |
2403.06812
| null | null |
http://arxiv.org/pdf/2403.06812v1
|
2024-03-11T15:32:56Z
|
2024-03-11T15:32:56Z
|
Monotone Individual Fairness
|
We revisit the problem of online learning with individual fairness, where an online learner strives to maximize predictive accuracy while ensuring that similar individuals are treated similarly. We first extend the frameworks of Gillen et al. (2018); Bechavod et al. (2020), which rely on feedback from human auditors regarding fairness violations, as we consider auditing schemes that are capable of aggregating feedback from any number of auditors, using a rich class we term monotone aggregation functions. We then prove a characterization for such auditing schemes, practically reducing the analysis of auditing for individual fairness by multiple auditors to that of auditing by (instance-specific) single auditors. Using our generalized framework, we present an oracle-efficient algorithm achieving an upper bound frontier of $(mathcal{O}(T^{1/2+2b}),mathcal{O}(T^{3/4-b}))$ respectively for regret, number of fairness violations, for $0leq b leq 1/4$. We then study an online classification setting where label feedback is available for positively-predicted individuals only, and present an oracle-efficient algorithm achieving an upper bound frontier of $(mathcal{O}(T^{2/3+2b}),mathcal{O}(T^{5/6-b}))$ for regret, number of fairness violations, for $0leq b leq 1/6$. In both settings, our algorithms improve on the best known bounds for oracle-efficient algorithms. Furthermore, our algorithms offer significant improvements in computational efficiency, greatly reducing the number of required calls to an (offline) optimization oracle per round, to $tilde{mathcal{O}}(alpha^{-2})$ in the full information setting, and $tilde{mathcal{O}}(alpha^{-2} + k^2T^{1/3})$ in the partial information setting, where $alpha$ is the sensitivity for reporting fairness violations, and $k$ is the number of individuals in a round.
|
[
"['Yahav Bechavod']"
] |
null | null |
2403.06814
| null | null |
http://arxiv.org/pdf/2403.06814v1
|
2024-03-11T15:33:40Z
|
2024-03-11T15:33:40Z
|
ε-Neural Thompson Sampling of Deep Brain Stimulation for
Parkinson Disease Treatment
|
Deep Brain Stimulation (DBS) stands as an effective intervention for alleviating the motor symptoms of Parkinson's disease (PD). Traditional commercial DBS devices are only able to deliver fixed-frequency periodic pulses to the basal ganglia (BG) regions of the brain, i.e., continuous DBS (cDBS). However, they in general suffer from energy inefficiency and side effects, such as speech impairment. Recent research has focused on adaptive DBS (aDBS) to resolve the limitations of cDBS. Specifically, reinforcement learning (RL) based approaches have been developed to adapt the frequencies of the stimuli in order to achieve both energy efficiency and treatment efficacy. However, RL approaches in general require significant amount of training data and computational resources, making it intractable to integrate RL policies into real-time embedded systems as needed in aDBS. In contrast, contextual multi-armed bandits (CMAB) in general lead to better sample efficiency compared to RL. In this study, we propose a CMAB solution for aDBS. Specifically, we define the context as the signals capturing irregular neuronal firing activities in the BG regions (i.e., beta-band power spectral density), while each arm signifies the (discretized) pulse frequency of the stimulation. Moreover, an {epsilon}-exploring strategy is introduced on top of the classic Thompson sampling method, leading to an algorithm called {epsilon}-Neural Thompson sampling ({epsilon}-NeuralTS), such that the learned CMAB policy can better balance exploration and exploitation of the BG environment. The {epsilon}-NeuralTS algorithm is evaluated using a computation BG model that captures the neuronal activities in PD patients' brains. The results show that our method outperforms both existing cDBS methods and CMAB baselines.
|
[
"['Hao-Lun Hsu' 'Qitong Gao' 'Miroslav Pajic']"
] |
null | null |
2403.06816
| null | null |
http://arxiv.org/pdf/2403.06816v1
|
2024-03-11T15:33:55Z
|
2024-03-11T15:33:55Z
|
Efficient first-order algorithms for large-scale, non-smooth maximum
entropy models with application to wildfire science
|
Maximum entropy (Maxent) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, Maxent models need efficient optimization algorithms to scale well for big data applications. State-of-the-art algorithms for Maxent models, however, were not originally designed to handle big data sets; these algorithms either rely on technical devices that may yield unreliable numerical results, scale poorly, or require smoothness assumptions that many practical Maxent models lack. In this paper, we present novel optimization algorithms that overcome the shortcomings of state-of-the-art algorithms for training large-scale, non-smooth Maxent models. Our proposed first-order algorithms leverage the Kullback-Leibler divergence to train large-scale and non-smooth Maxent models efficiently. For Maxent models with discrete probability distribution of $n$ elements built from samples, each containing $m$ features, the stepsize parameters estimation and iterations in our algorithms scale on the order of $O(mn)$ operations and can be trivially parallelized. Moreover, the strong $ell_{1}$ convexity of the Kullback--Leibler divergence allows for larger stepsize parameters, thereby speeding up the convergence rate of our algorithms. To illustrate the efficiency of our novel algorithms, we consider the problem of estimating probabilities of fire occurrences as a function of ecological features in the Western US MTBS-Interagency wildfire data set. Our numerical results show that our algorithms outperform the state of the arts by one order of magnitude and yield results that agree with physical models of wildfire occurrence and previous statistical analyses of wildfire drivers.
|
[
"['Gabriel P. Langlois' 'Jatan Buch' 'Jérôme Darbon']"
] |
null | null |
2403.06817
| null | null |
http://arxiv.org/pdf/2403.06817v2
|
2024-05-19T11:43:18Z
|
2024-03-11T15:34:57Z
|
Are Targeted Messages More Effective?
|
Graph neural networks (GNN) are deep learning architectures for graphs. Essentially, a GNN is a distributed message passing algorithm, which is controlled by parameters learned from data. It operates on the vertices of a graph: in each iteration, vertices receive a message on each incoming edge, aggregate these messages, and then update their state based on their current state and the aggregated messages. The expressivity of GNNs can be characterised in terms of certain fragments of first-order logic with counting and the Weisfeiler-Lehman algorithm. The core GNN architecture comes in two different versions. In the first version, a message only depends on the state of the source vertex, whereas in the second version it depends on the states of the source and target vertices. In practice, both of these versions are used, but the theory of GNNs so far mostly focused on the first one. On the logical side, the two versions correspond to two fragments of first-order logic with counting that we call modal and guarded. The question whether the two versions differ in their expressivity has been mostly overlooked in the GNN literature and has only been asked recently (Grohe, LICS'23). We answer this question here. It turns out that the answer is not as straightforward as one might expect. By proving that the modal and guarded fragment of first-order logic with counting have the same expressivity over labelled undirected graphs, we show that in a non-uniform setting the two GNN versions have the same expressivity. However, we also prove that in a uniform setting the second version is strictly more expressive.
|
[
"['Martin Grohe' 'Eran Rosenbluth']"
] |
null | null |
2403.06826
| null | null |
http://arxiv.org/pdf/2403.06826v1
|
2024-03-11T15:43:14Z
|
2024-03-11T15:43:14Z
|
In-context Exploration-Exploitation for Reinforcement Learning
|
In-context learning is a promising approach for online policy learning of offline reinforcement learning (RL) methods, which can be achieved at inference time without gradient optimization. However, this method is hindered by significant computational costs resulting from the gathering of large training trajectory sets and the need to train large Transformer models. We address this challenge by introducing an In-context Exploration-Exploitation (ICEE) algorithm, designed to optimize the efficiency of in-context policy learning. Unlike existing models, ICEE performs an exploration-exploitation trade-off at inference time within a Transformer model, without the need for explicit Bayesian inference. Consequently, ICEE can solve Bayesian optimization problems as efficiently as Gaussian process biased methods do, but in significantly less time. Through experiments in grid world environments, we demonstrate that ICEE can learn to solve new RL tasks using only tens of episodes, marking a substantial improvement over the hundreds of episodes needed by the previous in-context learning method.
|
[
"['Zhenwen Dai' 'Federico Tomasi' 'Sina Ghiassian']"
] |
null | null |
2403.06829
| null | null |
http://arxiv.org/pdf/2403.06829v2
|
2024-03-13T17:01:57Z
|
2024-03-11T15:44:40Z
|
Constructing Variables Using Classifiers as an Aid to Regression: An
Empirical Assessment
|
This paper proposes a method for the automatic creation of variables (in the case of regression) that complement the information contained in the initial input vector. The method works as a pre-processing step in which the continuous values of the variable to be regressed are discretized into a set of intervals which are then used to define value thresholds. Then classifiers are trained to predict whether the value to be regressed is less than or equal to each of these thresholds. The different outputs of the classifiers are then concatenated in the form of an additional vector of variables that enriches the initial vector of the regression problem. The implemented system can thus be considered as a generic pre-processing tool. We tested the proposed enrichment method with 5 types of regressors and evaluated it in 33 regression datasets. Our experimental results confirm the interest of the approach.
|
[
"['Colin Troisemaine' 'Vincent Lemaire']"
] |
null | null |
2403.06833
| null | null |
http://arxiv.org/pdf/2403.06833v2
|
2024-06-03T12:04:50Z
|
2024-03-11T15:48:56Z
|
Can LLMs Separate Instructions From Data? And What Do We Even Mean By
That?
|
Instruction-tuned Large Language Models (LLMs) show impressive results in numerous practical applications, but they lack essential safety features that are common in other areas of computer science, particularly an explicit separation of instructions and data. This makes them vulnerable to manipulations such as indirect prompt injections and generally unsuitable for safety-critical tasks. Surprisingly, there is currently no established definition or benchmark to quantify this phenomenon. In this work, we close this gap by introducing a formal measure for instruction-data separation and an empirical variant that is calculable from a model's outputs. We also present a new dataset, SEP, that allows estimating the measure for real-world models. Our results on various LLMs show that the problem of instruction-data separation is real: all models fail to achieve high separation, and canonical mitigation techniques, such as prompt engineering and fine-tuning, either fail to substantially improve separation or reduce model utility. The source code and SEP dataset are openly accessible at https://github.com/egozverev/Shold-It-Be-Executed-Or-Processed.
|
[
"['Egor Zverev' 'Sahar Abdelnabi' 'Soroush Tabesh' 'Mario Fritz'\n 'Christoph H. Lampert']"
] |
null | null |
2403.06843
| null | null |
http://arxiv.org/pdf/2403.06843v1
|
2024-03-11T16:03:21Z
|
2024-03-11T16:03:21Z
|
Towards an educational tool for supporting neonatologists in the
delivery room
|
Nowadays, there is evidence that several factors may increase the risk, for an infant, to require stabilisation or resuscitation manoeuvres at birth. However, this risk factors are not completely known, and a universally applicable model for predicting high-risk situations is not available yet. Considering both these limitations and the fact that the need for resuscitation at birth is a rare event, periodic training of the healthcare personnel responsible for newborn caring in the delivery room is mandatory. In this paper, we propose a machine learning approach for identifying risk factors and their impact on the birth event from real data, which can be used by personnel to progressively increase and update their knowledge. Our final goal will be the one of designing a user-friendly mobile application, able to improve the recognition rate and the planning of the appropriate interventions on high-risk patients.
|
[
"['Giorgio Leonardi' 'Clara Maldarizzi' 'Stefania Montani' 'Manuel Striani'\n 'Mariachiara Martina Strozzi']"
] |
null | null |
2403.06854
| null | null |
http://arxiv.org/pdf/2403.06854v1
|
2024-03-11T16:09:39Z
|
2024-03-11T16:09:39Z
|
Quantifying the Sensitivity of Inverse Reinforcement Learning to
Misspecification
|
Inverse reinforcement learning (IRL) aims to infer an agent's preferences (represented as a reward function $R$) from their behaviour (represented as a policy $pi$). To do this, we need a behavioural model of how $pi$ relates to $R$. In the current literature, the most common behavioural models are optimality, Boltzmann-rationality, and causal entropy maximisation. However, the true relationship between a human's preferences and their behaviour is much more complex than any of these behavioural models. This means that the behavioural models are misspecified, which raises the concern that they may lead to systematic errors if applied to real data. In this paper, we analyse how sensitive the IRL problem is to misspecification of the behavioural model. Specifically, we provide necessary and sufficient conditions that completely characterise how the observed data may differ from the assumed behavioural model without incurring an error above a given threshold. In addition to this, we also characterise the conditions under which a behavioural model is robust to small perturbations of the observed policy, and we analyse how robust many behavioural models are to misspecification of their parameter values (such as e.g. the discount rate). Our analysis suggests that the IRL problem is highly sensitive to misspecification, in the sense that very mild misspecification can lead to very large errors in the inferred reward function.
|
[
"['Joar Skalse' 'Alessandro Abate']"
] |
null | null |
2403.06860
| null | null |
http://arxiv.org/pdf/2403.06860v2
|
2024-03-21T17:06:49Z
|
2024-03-11T16:13:58Z
|
A Geospatial Approach to Predicting Desert Locust Breeding Grounds in
Africa
|
Desert locust swarms present a major threat to agriculture and food security. Addressing this challenge, our study develops an operationally-ready model for predicting locust breeding grounds, which has the potential to enhance early warning systems and targeted control measures. We curated a dataset from the United Nations Food and Agriculture Organization's (UN-FAO) locust observation records and analyzed it using two types of spatio-temporal input features: remotely-sensed environmental and climate data as well as multi-spectral earth observation images. Our approach employed custom deep learning models (three-dimensional and LSTM-based recurrent convolutional networks), along with the geospatial foundational model Prithvi recently released by Jakubik et al., 2023. These models notably outperformed existing baselines, with the Prithvi-based model, fine-tuned on multi-spectral images from NASA's Harmonized Landsat and Sentinel-2 (HLS) dataset, achieving the highest accuracy, F1 and ROC-AUC scores (83.03%, 81.53% and 87.69%, respectively). A significant finding from our research is that multi-spectral earth observation images alone are sufficient for effective locust breeding ground prediction without the need to explicitly incorporate climatic or environmental features.
|
[
"['Ibrahim Salihu Yusuf' 'Mukhtar Opeyemi Yusuf' 'Kobby Panford-Quainoo'\n 'Arnu Pretorius']"
] |
null | null |
2403.06869
| null | null |
http://arxiv.org/pdf/2403.06869v1
|
2024-03-11T16:22:41Z
|
2024-03-11T16:22:41Z
|
Learning with Noisy Foundation Models
|
Foundation models are usually pre-trained on large-scale datasets and then adapted to downstream tasks through tuning. However, the large-scale pre-training datasets, often inaccessible or too expensive to handle, can contain label noise that may adversely affect the generalization of the model and pose unexpected risks. This paper stands out as the first work to comprehensively understand and analyze the nature of noise in pre-training datasets and then effectively mitigate its impacts on downstream tasks. Specifically, through extensive experiments of fully-supervised and image-text contrastive pre-training on synthetic noisy ImageNet-1K, YFCC15M, and CC12M datasets, we demonstrate that, while slight noise in pre-training can benefit in-domain (ID) performance, where the training and testing data share a similar distribution, it always deteriorates out-of-domain (OOD) performance, where training and testing distributions are significantly different. These observations are agnostic to scales of pre-training datasets, pre-training noise types, model architectures, pre-training objectives, downstream tuning methods, and downstream applications. We empirically ascertain that the reason behind this is that the pre-training noise shapes the feature space differently. We then propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization, which is applicable in both parameter-efficient and black-box tuning manners. We additionally conduct extensive experiments on popular vision and language models, including APIs, which are supervised and self-supervised pre-trained on realistic noisy data for evaluation. Our analysis and results demonstrate the importance of this novel and fundamental research direction, which we term as Noisy Model Learning.
|
[
"['Hao Chen' 'Jindong Wang' 'Zihan Wang' 'Ran Tao' 'Hongxin Wei' 'Xing Xie'\n 'Masashi Sugiyama' 'Bhiksha Raj']"
] |
null | null |
2403.06870
| null | null |
http://arxiv.org/pdf/2403.06870v2
|
2024-03-14T12:27:04Z
|
2024-03-11T16:23:38Z
|
Semantic Residual Prompts for Continual Learning
|
Prompt-tuning methods for Continual Learning (CL) freeze a large pre-trained model and focus training on a few parameter vectors termed prompts. Most of these methods organize these vectors in a pool of key-value pairs, and use the input image as query to retrieve the prompts (values). However, as keys are learned while tasks progress, the prompting selection strategy is itself subject to catastrophic forgetting, an issue often overlooked by existing approaches. For instance, prompts introduced to accommodate new tasks might end up interfering with previously learned prompts. To make the selection strategy more stable, we ask a foundational model (CLIP) to select our prompt within a two-level adaptation mechanism. Specifically, the first level leverages standard textual prompts for the CLIP textual encoder, leading to stable class prototypes. The second level, instead, uses these prototypes along with the query image as keys to index a second pool. The retrieved prompts serve to adapt a pre-trained ViT, granting plasticity. In doing so, we also propose a novel residual mechanism to transfer CLIP semantics to the ViT layers. Through extensive analysis on established CL benchmarks, we show that our method significantly outperforms both state-of-the-art CL approaches and the zero-shot CLIP test. Notably, our findings hold true even for datasets with a substantial domain gap w.r.t. the pre-training knowledge of the backbone model, as showcased by experiments on satellite imagery and medical datasets.
|
[
"['Martin Menabue' 'Emanuele Frascaroli' 'Matteo Boschini'\n 'Enver Sangineto' 'Lorenzo Bonicelli' 'Angelo Porrello'\n 'Simone Calderara']"
] |
null | null |
2403.06871
| null | null |
http://arxiv.org/pdf/2403.06871v1
|
2024-03-11T16:23:42Z
|
2024-03-11T16:23:42Z
|
On the Generalization Ability of Unsupervised Pretraining
|
Recent advances in unsupervised learning have shown that unsupervised pre-training, followed by fine-tuning, can improve model generalization. However, a rigorous understanding of how the representation function learned on an unlabeled dataset affects the generalization of the fine-tuned model is lacking. Existing theoretical research does not adequately account for the heterogeneity of the distribution and tasks in pre-training and fine-tuning stage. To bridge this gap, this paper introduces a novel theoretical framework that illuminates the critical factor influencing the transferability of knowledge acquired during unsupervised pre-training to the subsequent fine-tuning phase, ultimately affecting the generalization capabilities of the fine-tuned model on downstream tasks. We apply our theoretical framework to analyze generalization bound of two distinct scenarios: Context Encoder pre-training with deep neural networks and Masked Autoencoder pre-training with deep transformers, followed by fine-tuning on a binary classification task. Finally, inspired by our findings, we propose a novel regularization method during pre-training to further enhances the generalization of fine-tuned model. Overall, our results contribute to a better understanding of unsupervised pre-training and fine-tuning paradigm, and can shed light on the design of more effective pre-training algorithms.
|
[
"['Yuyang Deng' 'Junyuan Hong' 'Jiayu Zhou' 'Mehrdad Mahdavi']"
] |
null | null |
2403.06873
| null | null |
http://arxiv.org/pdf/2403.06873v2
|
2024-06-28T02:25:20Z
|
2024-03-11T16:24:26Z
|
Last Iterate Convergence of Incremental Methods and Applications in
Continual Learning
|
Incremental gradient and incremental proximal methods are a fundamental class of optimization algorithms used for solving finite sum problems, broadly studied in the literature. Yet, without strong convexity, their convergence guarantees have primarily been established for the ergodic (average) iterate. Motivated by applications in continual learning, we obtain the first convergence guarantees for the last iterate of both incremental gradient and incremental proximal methods, in general convex smooth (for both) and convex Lipschitz (for the proximal variants) settings. Our oracle complexity bounds for the last iterate nearly match (i.e., match up to a square-root-log or a log factor) the best known oracle complexity bounds for the average iterate, for both classes of methods. We further obtain generalizations of our results to weighted averaging of the iterates with increasing weights and for randomly permuted ordering of updates. We study incremental proximal methods as a model of continual learning with generalization and argue that large amount of regularization is crucial to preventing catastrophic forgetting. Our results generalize last iterate guarantees for incremental methods compared to state of the art, as such results were previously known only for overparameterized linear models, which correspond to convex quadratic problems with infinitely many solutions.
|
[
"['Xufeng Cai' 'Jelena Diakonikolas']"
] |
null | null |
2403.06874
| null | null |
http://arxiv.org/pdf/2403.06874v1
|
2024-03-11T16:26:35Z
|
2024-03-11T16:26:35Z
|
COOD: Combined out-of-distribution detection using multiple measures for
anomaly & novel class detection in large-scale hierarchical classification
|
High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper, we focus on the species recognition task in images concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1% FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 85.4% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities.
|
[
"['L. E. Hogeweg' 'R. Gangireddy' 'D. Brunink' 'V. J. Kalkman'\n 'L. Cornelissen' 'J. W. Kamminga']"
] |
null | null |
2403.06880
| null | null |
http://arxiv.org/pdf/2403.06880v2
|
2024-03-18T09:43:20Z
|
2024-03-11T16:34:23Z
|
Unveiling the Significance of Toddler-Inspired Reward Transition in
Goal-Oriented Reinforcement Learning
|
Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards. Drawing inspiration from this Toddler-Inspired Reward Transition, we set out to explore the implications of varying reward transitions when incorporated into Reinforcement Learning (RL) tasks. Central to our inquiry is the transition from sparse to potential-based dense rewards, which share optimal strategies regardless of reward changes. Through various experiments, including those in egocentric navigation and robotic arm manipulation tasks, we found that proper reward transitions significantly influence sample efficiency and success rates. Of particular note is the efficacy of the toddler-inspired Sparse-to-Dense (S2D) transition. Beyond these performance metrics, using Cross-Density Visualizer technique, we observed that transitions, especially the S2D, smooth the policy loss landscape, promoting wide minima that enhance generalization in RL models.
|
[
"['Junseok Park' 'Yoonsung Kim' 'Hee Bin Yoo' 'Min Whoo Lee' 'Kibeom Kim'\n 'Won-Seok Choi' 'Minsu Lee' 'Byoung-Tak Zhang']"
] |
null | null |
2403.06888
| null | null |
http://arxiv.org/pdf/2403.06888v2
|
2024-03-13T02:07:06Z
|
2024-03-11T16:45:19Z
|
Process signature-driven high spatio-temporal resolution alignment of
multimodal data
|
We present HiRA-Pro, a novel procedure to align, at high spatio-temporal resolutions, multimodal signals from real-world processes and systems that exhibit diverse transient, nonlinear stochastic dynamics, such as manufacturing machines. It is based on discerning and synchronizing the process signatures of salient kinematic and dynamic events in these disparate signals. HiRA-Pro addresses the challenge of aligning data with sub-millisecond phenomena, where traditional timestamp, external trigger, or clock-based alignment methods fall short. The effectiveness of HiRA-Pro is demonstrated in a smart manufacturing context, where it aligns data from 13+ channels acquired during 3D-printing and milling operations on an Optomec-LENS MTS 500 hybrid machine. The aligned data is then voxelized to generate 0.25 second aligned data chunks that correspond to physical voxels on the produced part. The superiority of HiRA-Pro is further showcased through case studies in additive manufacturing, demonstrating improved machine learning-based predictive performance due to precise multimodal data alignment. Specifically, testing classification accuracies improved by almost 35% with the application of HiRA-Pro, even with limited data, allowing for precise localization of artifacts. The paper also provides a comprehensive discussion on the proposed method, its applications, and comparative qualitative analysis with a few other alignment methods. HiRA-Pro achieves temporal-spatial resolutions of 10-1000 us and 100 um in order to generate datasets that register with physical voxels on the 3D-printed and milled part. These resolutions are at least an order of magnitude finer than the existing alignment methods that employ individual timestamps, statistical correlations, or common clocks, which achieve precision of hundreds of milliseconds.
|
[
"['Abhishek Hanchate' 'Himanshu Balhara' 'Vishal S. Chindepalli'\n 'Satish T. S. Bukkapatnam']"
] |
null | null |
2403.06890
| null | null |
http://arxiv.org/pdf/2403.06890v1
|
2024-03-11T16:47:09Z
|
2024-03-11T16:47:09Z
|
Application of Quantum Tensor Networks for Protein Classification
|
We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubits, which can be trained to solve various protein-related machine-learning problems. We classify proteins based on their subcellular locations, a pivotal task in bioinformatics that is key to understanding biological processes and disease mechanisms. Leveraging the quantum-enhanced processing capabilities, we demonstrate that Quantum Tensor Networks (QTN) can effectively handle the complexity and diversity of protein sequences. We present a detailed methodology that adapts QTN architectures to the nuanced requirements of protein data, supported by comprehensive experimental results. We demonstrate two distinct QTNs, inspired by classical recurrent neural networks (RNN) and convolutional neural networks (CNN), to solve the binary classification task mentioned above. Our top-performing quantum model has achieved a 94% accuracy rate, which is comparable to the performance of a classical model that uses the ESM2 protein language model embeddings. It's noteworthy that the ESM2 model is extremely large, containing 8 million parameters in its smallest configuration, whereas our best quantum model requires only around 800 parameters. We demonstrate that these hybrid models exhibit promising performance, showcasing their potential to compete with classical models of similar complexity.
|
[
"['Debarshi Kundu' 'Archisman Ghosh' 'Srinivasan Ekambaram' 'Jian Wang'\n 'Nikolay Dokholyan' 'Swaroop Ghosh']"
] |
null | null |
2403.06901
| null | null |
http://arxiv.org/pdf/2403.06901v1
|
2024-03-11T16:54:44Z
|
2024-03-11T16:54:44Z
|
LIBR+: Improving Intraoperative Liver Registration by Learning the
Residual of Biomechanics-Based Deformable Registration
|
The surgical environment imposes unique challenges to the intraoperative registration of organ shapes to their preoperatively-imaged geometry. Biomechanical model-based registration remains popular, while deep learning solutions remain limited due to the sparsity and variability of intraoperative measurements and the limited ground-truth deformation of an organ that can be obtained during the surgery. In this paper, we propose a novel textit{hybrid} registration approach that leverage a linearized iterative boundary reconstruction (LIBR) method based on linear elastic biomechanics, and use deep neural networks to learn its residual to the ground-truth deformation (LIBR+). We further formulate a dual-branch spline-residual graph convolutional neural network (SR-GCN) to assimilate information from sparse and variable intraoperative measurements and effectively propagate it through the geometry of the 3D organ. Experiments on a large intraoperative liver registration dataset demonstrated the consistent improvements achieved by LIBR+ in comparison to existing rigid, biomechnical model-based non-rigid, and deep-learning based non-rigid approaches to intraoperative liver registration.
|
[
"['Dingrong Wang' 'Soheil Azadvar' 'Jon Heiselman' 'Xiajun Jiang'\n 'Michael Miga' 'Linwei Wang']"
] |
null | null |
2403.06903
| null | null |
http://arxiv.org/pdf/2403.06903v2
|
2024-07-09T23:20:12Z
|
2024-03-11T16:56:01Z
|
Benign overfitting in leaky ReLU networks with moderate input dimension
|
The problem of benign overfitting asks whether it is possible for a model to perfectly fit noisy training data and still generalize well. We study benign overfitting in two-layer leaky ReLU networks trained with the hinge loss on a binary classification task. We consider input data that can be decomposed into the sum of a common signal and a random noise component, that lie on subspaces orthogonal to one another. We characterize conditions on the signal to noise ratio (SNR) of the model parameters giving rise to benign versus non-benign (or harmful) overfitting: in particular, if the SNR is high then benign overfitting occurs, conversely if the SNR is low then harmful overfitting occurs. We attribute both benign and non-benign overfitting to an approximate margin maximization property and show that leaky ReLU networks trained on hinge loss with gradient descent (GD) satisfy this property. In contrast to prior work we do not require the training data to be nearly orthogonal. Notably, for input dimension $d$ and training sample size $n$, while results in prior work require $d = Omega(n^2 log n)$, here we require only $d = Omegaleft(nright)$.
|
[
"['Kedar Karhadkar' 'Erin George' 'Michael Murray' 'Guido Montúfar'\n 'Deanna Needell']"
] |
null | null |
2403.06906
| null | null |
http://arxiv.org/pdf/2403.06906v2
|
2024-03-21T12:30:16Z
|
2024-03-11T16:57:20Z
|
Cost-Sensitive Learning to Defer to Multiple Experts with Workload
Constraints
|
Learning to defer (L2D) aims to improve human-AI collaboration systems by learning how to defer decisions to humans when they are more likely to be correct than an ML classifier. Existing research in L2D overlooks key aspects of real-world systems that impede its practical adoption, namely: i) neglecting cost-sensitive scenarios, where type 1 and type 2 errors have different costs; ii) requiring concurrent human predictions for every instance of the training dataset and iii) not dealing with human work capacity constraints. To address these issues, we propose the deferral under cost and capacity constraints framework (DeCCaF). DeCCaF is a novel L2D approach, employing supervised learning to model the probability of human error under less restrictive data requirements (only one expert prediction per instance) and using constraint programming to globally minimize the error cost subject to workload limitations. We test DeCCaF in a series of cost-sensitive fraud detection scenarios with different teams of 9 synthetic fraud analysts, with individual work capacity constraints. The results demonstrate that our approach performs significantly better than the baselines in a wide array of scenarios, achieving an average 8.4% reduction in the misclassification cost.
|
[
"['Jean V. Alves' 'Diogo Leitão' 'Sérgio Jesus' 'Marco O. P. Sampaio'\n 'Javier Liébana' 'Pedro Saleiro' 'Mário A. T. Figueiredo' 'Pedro Bizarro']"
] |
null | null |
2403.06910
| null | null |
http://arxiv.org/pdf/2403.06910v1
|
2024-03-11T17:01:13Z
|
2024-03-11T17:01:13Z
|
Responsible Artificial Intelligence: A Structured Literature Review
|
Our research endeavors to advance the concept of responsible artificial intelligence (AI), a topic of increasing importance within EU policy discussions. The EU has recently issued several publications emphasizing the necessity of trust in AI, underscoring the dual nature of AI as both a beneficial tool and a potential weapon. This dichotomy highlights the urgent need for international regulation. Concurrently, there is a need for frameworks that guide companies in AI development, ensuring compliance with such regulations. Our research aims to assist lawmakers and machine learning practitioners in navigating the evolving landscape of AI regulation, identifying focal areas for future attention. This paper introduces a comprehensive and, to our knowledge, the first unified definition of responsible AI. Through a structured literature review, we elucidate the current understanding of responsible AI. Drawing from this analysis, we propose an approach for developing a future framework centered around this concept. Our findings advocate for a human-centric approach to Responsible AI. This approach encompasses the implementation of AI methods with a strong emphasis on ethics, model explainability, and the pillars of privacy, security, and trust.
|
[
"['Sabrina Goellner' 'Marina Tropmann-Frick' 'Bostjan Brumen']"
] |
null | null |
2403.06925
| null | null |
http://arxiv.org/pdf/2403.06925v1
|
2024-03-11T17:12:09Z
|
2024-03-11T17:12:09Z
|
Simplicity Bias of Transformers to Learn Low Sensitivity Functions
|
Transformers achieve state-of-the-art accuracy and robustness across many tasks, but an understanding of the inductive biases that they have and how those biases are different from other neural network architectures remains elusive. Various neural network architectures such as fully connected networks have been found to have a simplicity bias towards simple functions of the data; one version of this simplicity bias is a spectral bias to learn simple functions in the Fourier space. In this work, we identify the notion of sensitivity of the model to random changes in the input as a notion of simplicity bias which provides a unified metric to explain the simplicity and spectral bias of transformers across different data modalities. We show that transformers have lower sensitivity than alternative architectures, such as LSTMs, MLPs and CNNs, across both vision and language tasks. We also show that low-sensitivity bias correlates with improved robustness; furthermore, it can also be used as an efficient intervention to further improve the robustness of transformers.
|
[
"['Bhavya Vasudeva' 'Deqing Fu' 'Tianyi Zhou' 'Elliott Kau' 'Youqi Huang'\n 'Vatsal Sharan']"
] |
null | null |
2403.06936
| null | null |
http://arxiv.org/pdf/2403.06936v1
|
2024-03-11T17:21:39Z
|
2024-03-11T17:21:39Z
|
Counterfactual Reasoning with Knowledge Graph Embeddings
|
Knowledge graph embeddings (KGEs) were originally developed to infer true but missing facts in incomplete knowledge repositories. In this paper, we link knowledge graph completion and counterfactual reasoning via our new task CFKGR. We model the original world state as a knowledge graph, hypothetical scenarios as edges added to the graph, and plausible changes to the graph as inferences from logical rules. We create corresponding benchmark datasets, which contain diverse hypothetical scenarios with plausible changes to the original knowledge graph and facts that should be retained. We develop COULDD, a general method for adapting existing knowledge graph embeddings given a hypothetical premise, and evaluate it on our benchmark. Our results indicate that KGEs learn patterns in the graph without explicit training. We further observe that KGEs adapted with COULDD solidly detect plausible counterfactual changes to the graph that follow these patterns. An evaluation on human-annotated data reveals that KGEs adapted with COULDD are mostly unable to recognize changes to the graph that do not follow learned inference rules. In contrast, ChatGPT mostly outperforms KGEs in detecting plausible changes to the graph but has poor knowledge retention. In summary, CFKGR connects two previously distinct areas, namely KG completion and counterfactual reasoning.
|
[
"['Lena Zellinger' 'Andreas Stephan' 'Benjamin Roth']"
] |
null | null |
2403.06940
| null | null |
http://arxiv.org/pdf/2403.06940v1
|
2024-03-11T17:26:18Z
|
2024-03-11T17:26:18Z
|
Conditional Score-Based Diffusion Model for Cortical Thickness
Trajectory Prediction
|
Alzheimer's Disease (AD) is a neurodegenerative condition characterized by diverse progression rates among individuals, with changes in cortical thickness (CTh) closely linked to its progression. Accurately forecasting CTh trajectories can significantly enhance early diagnosis and intervention strategies, providing timely care. However, the longitudinal data essential for these studies often suffer from temporal sparsity and incompleteness, presenting substantial challenges in modeling the disease's progression accurately. Existing methods are limited, focusing primarily on datasets without missing entries or requiring predefined assumptions about CTh progression. To overcome these obstacles, we propose a conditional score-based diffusion model specifically designed to generate CTh trajectories with the given baseline information, such as age, sex, and initial diagnosis. Our conditional diffusion model utilizes all available data during the training phase to make predictions based solely on baseline information during inference without needing prior history about CTh progression. The prediction accuracy of the proposed CTh prediction pipeline using a conditional score-based model was compared for sub-groups consisting of cognitively normal, mild cognitive impairment, and AD subjects. The Bland-Altman analysis shows our diffusion-based prediction model has a near-zero bias with narrow 95% confidential interval compared to the ground-truth CTh in 6-36 months. In addition, our conditional diffusion model has a stochastic generative nature, therefore, we demonstrated an uncertainty analysis of patient-specific CTh prediction through multiple realizations.
|
[
"['Qing Xiao' 'Siyeop Yoon' 'Hui Ren' 'Matthew Tivnan' 'Lichao Sun'\n 'Quanzheng Li' 'Tianming Liu' 'Yu Zhang' 'Xiang Li']"
] |
null | null |
2403.06942
| null | null |
http://arxiv.org/pdf/2403.06942v1
|
2024-03-11T17:28:46Z
|
2024-03-11T17:28:46Z
|
Grid Monitoring and Protection with Continuous Point-on-Wave
Measurements and Generative AI
|
Purpose This article presents a case for a next-generation grid monitoring and control system, leveraging recent advances in generative artificial intelligence (AI), machine learning, and statistical inference. Advancing beyond earlier generations of wide-area monitoring systems built upon supervisory control and data acquisition (SCADA) and synchrophasor technologies, we argue for a monitoring and control framework based on the streaming of continuous point-on-wave (CPOW) measurements with AI-powered data compression and fault detection. Methods and Results: The architecture of the proposed design originates from the Wiener-Kallianpur innovation representation of a random process that transforms causally a stationary random process into an innovation sequence with independent and identically distributed random variables. This work presents a generative AI approach that (i) learns an innovation autoencoder that extracts innovation sequence from CPOW time series, (ii) compresses the CPOW streaming data with innovation autoencoder and subband coding, and (iii) detects unknown faults and novel trends via nonparametric sequential hypothesis testing. Conclusion: This work argues that conventional monitoring using SCADA and phasor measurement unit (PMU) technologies is ill-suited for a future grid with deep penetration of inverter-based renewable generations and distributed energy resources. A monitoring system based on CPOW data streaming and AI data analytics should be the basic building blocks for situational awareness of a highly dynamic future grid.
|
[
"['Lang Tong' 'Xinyi Wang' 'Qing Zhao']"
] |
null | null |
2403.06952
| null | null |
http://arxiv.org/pdf/2403.06952v1
|
2024-03-11T17:35:33Z
|
2024-03-11T17:35:33Z
|
SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with
Auto-Generated Data
|
Recent text-to-image (T2I) generation models have demonstrated impressive capabilities in creating images from text descriptions. However, these T2I generation models often fall short of generating images that precisely match the details of the text inputs, such as incorrect spatial relationship or missing objects. In this paper, we introduce SELMA: Skill-Specific Expert Learning and Merging with Auto-Generated Data, a novel paradigm to improve the faithfulness of T2I models by fine-tuning models on automatically generated, multi-skill image-text datasets, with skill-specific expert learning and merging. First, SELMA leverages an LLM's in-context learning capability to generate multiple datasets of text prompts that can teach different skills, and then generates the images with a T2I model based on the prompts. Next, SELMA adapts the T2I model to the new skills by learning multiple single-skill LoRA (low-rank adaptation) experts followed by expert merging. Our independent expert fine-tuning specializes multiple models for different skills, and expert merging helps build a joint multi-skill T2I model that can generate faithful images given diverse text prompts, while mitigating the knowledge conflict from different datasets. We empirically demonstrate that SELMA significantly improves the semantic alignment and text faithfulness of state-of-the-art T2I diffusion models on multiple benchmarks (+2.1% on TIFA and +6.9% on DSG), human preference metrics (PickScore, ImageReward, and HPS), as well as human evaluation. Moreover, fine-tuning with image-text pairs auto-collected via SELMA shows comparable performance to fine-tuning with ground truth data. Lastly, we show that fine-tuning with images from a weaker T2I model can help improve the generation quality of a stronger T2I model, suggesting promising weak-to-strong generalization in T2I models.
|
[
"['Jialu Li' 'Jaemin Cho' 'Yi-Lin Sung' 'Jaehong Yoon' 'Mohit Bansal']"
] |
null | null |
2403.06955
| null | null |
http://arxiv.org/pdf/2403.06955v1
|
2024-03-11T17:39:08Z
|
2024-03-11T17:39:08Z
|
Accurate Crystal Structure Prediction of New 2D Hybrid Organic Inorganic
Perovskites
|
Low dimensional hybrid organic-inorganic perovskites (HOIPs) represent a promising class of electronically active materials for both light absorption and emission. The design space of HOIPs is extremely large, since a diverse space of organic cations can be combined with different inorganic frameworks. This immense design space allows for tunable electronic and mechanical properties, but also necessitates the development of new tools for in silico high throughput analysis of candidate structures. In this work, we present an accurate, efficient, transferable and widely applicable machine learning interatomic potential (MLIP) for predicting the structure of new 2D HOIPs. Using the MACE architecture, an MLIP is trained on 86 diverse experimentally reported HOIP structures. The model is tested on 73 unseen perovskite compositions, and achieves chemical accuracy with respect to the reference electronic structure method. Our model is then combined with a simple random structure search algorithm to predict the structure of hypothetical HOIPs given only the proposed composition. Success is demonstrated by correctly and reliably recovering the crystal structure of a set of experimentally known 2D perovskites. Such a random structure search is impossible with ab initio methods due to the associated computational cost, but is relatively inexpensive with the MACE potential. Finally, the procedure is used to predict the structure formed by a new organic cation with no previously known corresponding perovskite. Laboratory synthesis of the new hybrid perovskite confirms the accuracy of our prediction. This capability, applied at scale, enables efficient screening of thousands of combinations of organic cations and inorganic layers.
|
[
"['Nima Karimitari' 'William J. Baldwin' 'Evan W. Muller'\n 'Zachary J. L. Bare' 'W. Joshua Kennedy' 'Gábor Csányi'\n 'Christopher Sutton']"
] |
null | null |
2403.06963
| null | null |
http://arxiv.org/pdf/2403.06963v2
|
2024-07-05T20:48:04Z
|
2024-03-11T17:47:30Z
|
The pitfalls of next-token prediction
|
Can a mere next-token predictor faithfully model human intelligence? We crystallize this emerging concern and correct popular misconceptions surrounding it, and advocate a simple multi-token objective. As a starting point, we argue that the two often-conflated phases of next-token prediction -- autoregressive inference and teacher-forced training -- must be treated distinctly. The popular criticism that errors can compound during autoregressive inference, crucially assumes that teacher-forcing has learned an accurate next-token predictor. This assumption sidesteps a more deep-rooted problem we expose: in certain classes of tasks, teacher-forcing can simply fail to learn an accurate next-token predictor in the first place. We describe a general mechanism of how teacher-forcing can fail, and design a minimal planning task where both the Transformer and the Mamba architecture empirically fail in that manner -- remarkably, despite the task being straightforward to learn. Finally, we provide preliminary evidence that this failure can be resolved using a simple modification that predicts multiple tokens in advance. We hope this finding can ground future debates and inspire explorations beyond the next-token prediction paradigm. We make our code available under https://github.com/gregorbachmann/Next-Token-Failures
|
[
"['Gregor Bachmann' 'Vaishnavh Nagarajan']"
] |
null | null |
2403.06966
| null | null |
http://arxiv.org/pdf/2403.06966v2
|
2024-06-10T14:56:21Z
|
2024-03-11T17:49:18Z
|
Acquiring Diverse Skills using Curriculum Reinforcement Learning with
Mixture of Experts
|
Reinforcement learning (RL) is a powerful approach for acquiring a good-performing policy. However, learning diverse skills is challenging in RL due to the commonly used Gaussian policy parameterization. We propose textbf{Di}verse textbf{Skil}l textbf{L}earning (Di-SkilLfootnote{Videos and code are available on the project webpage: url{https://alrhub.github.io/di-skill-website/}}), an RL method for learning diverse skills using Mixture of Experts, where each expert formalizes a skill as a contextual motion primitive. Di-SkilL optimizes each expert and its associate context distribution to a maximum entropy objective that incentivizes learning diverse skills in similar contexts. The per-expert context distribution enables automatic curricula learning, allowing each expert to focus on its best-performing sub-region of the context space. To overcome hard discontinuities and multi-modalities without any prior knowledge of the environment's unknown context probability space, we leverage energy-based models to represent the per-expert context distributions and demonstrate how we can efficiently train them using the standard policy gradient objective. We show on challenging robot simulation tasks that Di-SkilL can learn diverse and performant skills.
|
[
"['Onur Celik' 'Aleksandar Taranovic' 'Gerhard Neumann']"
] |
null | null |
2403.06971
| null | null |
http://arxiv.org/pdf/2403.06971v1
|
2024-03-11T17:54:42Z
|
2024-03-11T17:54:42Z
|
A representation-learning game for classes of prediction tasks
|
We propose a game-based formulation for learning dimensionality-reducing representations of feature vectors, when only a prior knowledge on future prediction tasks is available. In this game, the first player chooses a representation, and then the second player adversarially chooses a prediction task from a given class, representing the prior knowledge. The first player aims is to minimize, and the second player to maximize, the regret: The minimal prediction loss using the representation, compared to the same loss using the original features. For the canonical setting in which the representation, the response to predict and the predictors are all linear functions, and under the mean squared error loss function, we derive the theoretically optimal representation in pure strategies, which shows the effectiveness of the prior knowledge, and the optimal regret in mixed strategies, which shows the usefulness of randomizing the representation. For general representations and loss functions, we propose an efficient algorithm to optimize a randomized representation. The algorithm only requires the gradients of the loss function, and is based on incrementally adding a representation rule to a mixture of such rules.
|
[
"['Neria Uzan' 'Nir Weinberger']"
] |
null | null |
2403.06973
| null | null |
http://arxiv.org/pdf/2403.06973v2
|
2024-04-22T02:13:32Z
|
2024-03-11T17:55:53Z
|
Bayesian Diffusion Models for 3D Shape Reconstruction
|
We present Bayesian Diffusion Models (BDM), a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-up (data-driven) procedure via joint diffusion processes. We show the effectiveness of BDM on the 3D shape reconstruction task. Compared to prototypical deep learning data-driven approaches trained on paired (supervised) data-labels (e.g. image-point clouds) datasets, our BDM brings in rich prior information from standalone labels (e.g. point clouds) to improve the bottom-up 3D reconstruction. As opposed to the standard Bayesian frameworks where explicit prior and likelihood are required for the inference, BDM performs seamless information fusion via coupled diffusion processes with learned gradient computation networks. The specialty of our BDM lies in its capability to engage the active and effective information exchange and fusion of the top-down and bottom-up processes where each itself is a diffusion process. We demonstrate state-of-the-art results on both synthetic and real-world benchmarks for 3D shape reconstruction.
|
[
"['Haiyang Xu' 'Yu Lei' 'Zeyuan Chen' 'Xiang Zhang' 'Yue Zhao' 'Yilin Wang'\n 'Zhuowen Tu']"
] |
null | null |
2403.06988
| null | null |
http://arxiv.org/pdf/2403.06988v1
|
2024-02-07T13:36:02Z
|
2024-02-07T13:36:02Z
|
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation
|
To ensure that text generated by large language models (LLMs) is in an expected format, constrained decoding proposes to enforce strict formal language constraints during generation. However, as we show in this work, not only do such methods incur performance overhead during generation, but many of them also significantly impair task accuracy, if they do not correctly align the underlying LLM sub-word vocabularies with external constraints. To address this, we present a novel decoding algorithm, DOMINO, that can enforce constraints in a fully subword-aligned fashion, while leveraging pre-computation and speculative decoding to achieve virtually no overhead and in some cases even almost 2$times$ speedup over unconstrained decoding -- thereby outperforming existing approaches by a wide margin.
|
[
"['Luca Beurer-Kellner' 'Marc Fischer' 'Martin Vechev']"
] |
null | null |
2403.06992
| null | null |
http://arxiv.org/pdf/2403.06992v1
|
2024-02-28T06:03:55Z
|
2024-02-28T06:03:55Z
|
Phase autoencoder for limit-cycle oscillators
|
We present a phase autoencoder that encodes the asymptotic phase of a limit-cycle oscillator, a fundamental quantity characterizing its synchronization dynamics. This autoencoder is trained in such a way that its latent variables directly represent the asymptotic phase of the oscillator. The trained autoencoder can perform two functions without relying on the mathematical model of the oscillator: first, it can evaluate the asymptotic phase and phase sensitivity function of the oscillator; second, it can reconstruct the oscillator state on the limit cycle in the original space from the phase value as an input. Using several examples of limit-cycle oscillators, we demonstrate that the asymptotic phase and phase sensitivity function can be estimated only from time-series data by the trained autoencoder. We also present a simple method for globally synchronizing two oscillators as an application of the trained autoencoder.
|
[
"['Koichiro Yawata' 'Kai Fukami' 'Kunihiko Taira' 'Hiroya Nakao']"
] |
null | null |
2403.06993
| null | null |
http://arxiv.org/pdf/2403.06993v1
|
2024-02-28T12:34:04Z
|
2024-02-28T12:34:04Z
|
Automatic driving lane change safety prediction model based on LSTM
|
Autonomous driving technology can improve traffic safety and reduce traffic accidents. In addition, it improves traffic flow, reduces congestion, saves energy and increases travel efficiency. In the relatively mature automatic driving technology, the automatic driving function is divided into several modules: perception, decision-making, planning and control, and a reasonable division of labor can improve the stability of the system. Therefore, autonomous vehicles need to have the ability to predict the trajectory of surrounding vehicles in order to make reasonable decision planning and safety measures to improve driving safety. By using deep learning method, a safety-sensitive deep learning model based on short term memory (LSTM) network is proposed. This model can alleviate the shortcomings of current automatic driving trajectory planning, and the output trajectory not only ensures high accuracy but also improves safety. The cell state simulation algorithm simulates the trackability of the trajectory generated by this model. The research results show that compared with the traditional model-based method, the trajectory prediction method based on LSTM network has obvious advantages in predicting the trajectory in the long time domain. The intention recognition module considering interactive information has higher prediction and accuracy, and the algorithm results show that the trajectory is very smooth based on the premise of safe prediction and efficient lane change. And autonomous vehicles can efficiently and safely complete lane changes.
|
[
"['Wenjian Sun' 'Linying Pan' 'Jingyu Xu' 'Weixiang Wan' 'Yong Wang']"
] |
null | null |
2403.06994
| null | null |
http://arxiv.org/pdf/2403.06994v1
|
2024-02-29T07:50:06Z
|
2024-02-29T07:50:06Z
|
Physics Sensor Based Deep Learning Fall Detection System
|
Fall detection based on embedded sensor is a practical and popular research direction in recent years. In terms of a specific application: fall detection methods based upon physics sensors such as [gyroscope and accelerator] have been exploited using traditional hand crafted features and feed them in machine learning models like Markov chain or just threshold based classification methods. In this paper, we build a complete system named TSFallDetect including data receiving device based on embedded sensor, mobile deep-learning model deploying platform, and a simple server, which will be used to gather models and data for future expansion. On the other hand, we exploit the sequential deep-learning methods to address this falling motion prediction problem based on data collected by inertial and film pressure sensors. We make a empirical study based on existing datasets and our datasets collected from our system separately, which shows that the deep-learning model has more potential advantage than other traditional methods, and we proposed a new deep-learning model based on the time series data to predict the fall, and it may be superior to other sequential models in this particular field.
|
[
"['Zeyuan Qu' 'Tiange Huang' 'Yuxin Ji' 'Yongjun Li']"
] |
null | null |
2403.06999
| null | null |
http://arxiv.org/pdf/2403.06999v1
|
2024-03-04T10:46:02Z
|
2024-03-04T10:46:02Z
|
Survival modeling using deep learning, machine learning and statistical
methods: A comparative analysis for predicting mortality after hospital
admission
|
Survival analysis is essential for studying time-to-event outcomes and providing a dynamic understanding of the probability of an event occurring over time. Various survival analysis techniques, from traditional statistical models to state-of-the-art machine learning algorithms, support healthcare intervention and policy decisions. However, there remains ongoing discussion about their comparative performance. We conducted a comparative study of several survival analysis methods, including Cox proportional hazards (CoxPH), stepwise CoxPH, elastic net penalized Cox model, Random Survival Forests (RSF), Gradient Boosting machine (GBM) learning, AutoScore-Survival, DeepSurv, time-dependent Cox model based on neural network (CoxTime), and DeepHit survival neural network. We applied the concordance index (C-index) for model goodness-of-fit, and integral Brier scores (IBS) for calibration, and considered the model interpretability. As a case study, we performed a retrospective analysis of patients admitted through the emergency department of a tertiary hospital from 2017 to 2019, predicting 90-day all-cause mortality based on patient demographics, clinicopathological features, and historical data. The results of the C-index indicate that deep learning achieved comparable performance, with DeepSurv producing the best discrimination (DeepSurv: 0.893; CoxTime: 0.892; DeepHit: 0.891). The calibration of DeepSurv (IBS: 0.041) performed the best, followed by RSF (IBS: 0.042) and GBM (IBS: 0.0421), all using the full variables. Moreover, AutoScore-Survival, using a minimal variable subset, is easy to interpret, and can achieve good discrimination and calibration (C-index: 0.867; IBS: 0.044). While all models were satisfactory, DeepSurv exhibited the best discrimination and calibration. In addition, AutoScore-Survival offers a more parsimonious model and excellent interpretability.
|
[
"['Ziwen Wang' 'Jin Wee Lee' 'Tanujit Chakraborty' 'Yilin Ning'\n 'Mingxuan Liu' 'Feng Xie' 'Marcus Eng Hock Ong' 'Nan Liu']"
] |
null | null |
2403.07003
| null | null |
http://arxiv.org/pdf/2403.07003v1
|
2024-03-07T12:10:19Z
|
2024-03-07T12:10:19Z
|
Evacuation Management Framework towards Smart City-wide Intelligent
Emergency Interactive Response System
|
A smart city solution toward future 6G network deployment allows small and medium sized enterprises (SMEs), industry, and government entities to connect with the infrastructures and play a crucial role in enhancing emergency preparedness with advanced sensors. The objective of this work is to propose a set of coordinated technological solutions to transform an existing emergency response system into an intelligent interactive system, thereby improving the public services and the quality of life for residents at home, on road, in hospitals, transport hubs, etc. In this context, we consider a city wide view from three different application scenes that are closely related to peoples daily life, to optimize the actions taken at relevant departments. Therefore, using artificial intelligence (AI) and machine learning (ML) techniques to enable the next generation connected vehicle experiences, we specifically focus on accidents happening in indoor households, urban roads, and at large public facilities. This smart interactive response system will benefit from advanced sensor fusion and AI by formulating a real time dynamic model.
|
[
"['Anuj Abraham' 'Yi Zhang' 'Shitala Prasad']"
] |
null | null |
2403.07004
| null | null |
http://arxiv.org/pdf/2403.07004v2
|
2024-06-05T12:20:29Z
|
2024-03-07T13:14:21Z
|
Convergence of Some Convex Message Passing Algorithms to a Fixed Point
|
A popular approach to the MAP inference problem in graphical models is to minimize an upper bound obtained from a dual linear programming or Lagrangian relaxation by (block-)coordinate descent. This is also known as convex/convergent message passing; examples are max-sum diffusion and sequential tree-reweighted message passing (TRW-S). Convergence properties of these methods are currently not fully understood. They have been proved to converge to the set characterized by local consistency of active constraints, with unknown convergence rate; however, it was not clear if the iterates converge at all (to any point). We prove a stronger result (conjectured before but never proved): the iterates converge to a fixed point of the method. Moreover, we show that the algorithm terminates within $mathcal{O}(1/varepsilon)$ iterations. We first prove this for a version of coordinate descent applied to a general piecewise-affine convex objective. Then we show that several convex message passing methods are special cases of this method. Finally, we show that a slightly different version of coordinate descent can cycle.
|
[
"['Vaclav Voracek' 'Tomas Werner']"
] |
null | null |
2403.07005
| null | null |
http://arxiv.org/pdf/2403.07005v1
|
2024-03-08T06:38:22Z
|
2024-03-08T06:38:22Z
|
Multi-Agent Reinforcement Learning with a Hierarchy of Reward Machines
|
In this paper, we study the cooperative Multi-Agent Reinforcement Learning (MARL) problems using Reward Machines (RMs) to specify the reward functions such that the prior knowledge of high-level events in a task can be leveraged to facilitate the learning efficiency. Unlike the existing work that RMs have been incorporated into MARL for task decomposition and policy learning in relatively simple domains or with an assumption of independencies among the agents, we present Multi-Agent Reinforcement Learning with a Hierarchy of RMs (MAHRM) that is capable of dealing with more complex scenarios when the events among agents can occur concurrently and the agents are highly interdependent. MAHRM exploits the relationship of high-level events to decompose a task into a hierarchy of simpler subtasks that are assigned to a small group of agents, so as to reduce the overall computational complexity. Experimental results in three cooperative MARL domains show that MAHRM outperforms other MARL methods using the same prior knowledge of high-level events.
|
[
"['Xuejing Zheng' 'Chao Yu']"
] |
null | null |
2403.07008
| null | null |
http://arxiv.org/pdf/2403.07008v2
|
2024-05-28T04:38:41Z
|
2024-03-09T02:47:11Z
|
AutoEval Done Right: Using Synthetic Data for Model Evaluation
|
The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming. AI-labeled synthetic data can be used to decrease the number of human annotations required for this purpose in a process called autoevaluation. We suggest efficient and statistically principled algorithms for this purpose that improve sample efficiency while remaining unbiased. These algorithms increase the effective human-labeled sample size by up to 50% on experiments with GPT-4.
|
[
"['Pierre Boyeau' 'Anastasios N. Angelopoulos' 'Nir Yosef' 'Jitendra Malik'\n 'Michael I. Jordan']"
] |
null | null |
2403.07012
| null | null |
http://arxiv.org/pdf/2403.07012v1
|
2024-03-09T10:01:49Z
|
2024-03-09T10:01:49Z
|
Non-Intrusive Load Monitoring with Missing Data Imputation Based on
Tensor Decomposition
|
With the widespread adoption of Non-Intrusive Load Monitoring (NILM) in building energy management, ensuring the high quality of NILM data has become imperative. However, practical applications of NILM face challenges associated with data loss, significantly impacting accuracy and reliability in energy management. This paper addresses the issue of NILM data loss by introducing an innovative tensor completion(TC) model- Proportional-Integral-Derivative (PID)-incorporated Non-negative Latent Factorization of Tensors (PNLFT) with twofold ideas: 1) To tackle the issue of slow convergence in Latent Factorization of Tensors (LFT) using Stochastic Gradient Descent (SGD), a Proportional-Integral-Derivative controller is introduced during the learning process. The PID controller utilizes historical and current information to control learning residuals. 2) Considering the characteristics of NILM data, non-negative update rules are proposed in the model's learning scheme. Experimental results on three datasets demonstrate that, compared to state-of-the-art models, the proposed model exhibits noteworthy enhancements in both convergence speed and accuracy.
|
[
"['DengYu Shi']"
] |
null | null |
2403.07013
| null | null |
http://arxiv.org/pdf/2403.07013v2
|
2024-03-15T05:46:37Z
|
2024-03-09T11:54:58Z
|
AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional
Mutual Information
|
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the analysis of protein composition in biological samples. Despite the development of various deep learning methods for identifying amino acid sequences (peptides) responsible for observed spectra, challenges persist in emph{de novo} peptide sequencing. Firstly, prior methods struggle to identify amino acids with post-translational modifications (PTMs) due to their lower frequency in training data compared to canonical amino acids, further resulting in decreased peptide-level identification precision. Secondly, diverse types of noise and missing peaks in mass spectra reduce the reliability of training data (peptide-spectrum matches, PSMs). To address these challenges, we propose AdaNovo, a novel framework that calculates conditional mutual information (CMI) between the spectrum and each amino acid/peptide, using CMI for adaptive model training. Extensive experiments demonstrate AdaNovo's state-of-the-art performance on a 9-species benchmark, where the peptides in the training set are almost completely disjoint from the peptides of the test sets. Moreover, AdaNovo excels in identifying amino acids with PTMs and exhibits robustness against data noise. The supplementary materials contain the official code.
|
[
"['Jun Xia' 'Shaorong Chen' 'Jingbo Zhou' 'Tianze Ling' 'Wenjie Du'\n 'Sizhe Liu' 'Stan Z. Li']"
] |
null | null |
2403.07015
| null | null |
http://arxiv.org/pdf/2403.07015v2
|
2024-06-19T15:17:51Z
|
2024-03-09T16:47:42Z
|
Adaptive Hyperparameter Optimization for Continual Learning Scenarios
|
Hyperparameter selection in continual learning scenarios is a challenging and underexplored aspect, especially in practical non-stationary environments. Traditional approaches, such as grid searches with held-out validation data from all tasks, are unrealistic for building accurate lifelong learning systems. This paper aims to explore the role of hyperparameter selection in continual learning and the necessity of continually and automatically tuning them according to the complexity of the task at hand. Hence, we propose leveraging the nature of sequence task learning to improve Hyperparameter Optimization efficiency. By using the functional analysis of variance-based techniques, we identify the most crucial hyperparameters that have an impact on performance. We demonstrate empirically that this approach, agnostic to continual scenarios and strategies, allows us to speed up hyperparameters optimization continually across tasks and exhibit robustness even in the face of varying sequential task orders. We believe that our findings can contribute to the advancement of continual learning methodologies towards more efficient, robust and adaptable models for real-world applications.
|
[
"['Rudy Semola' 'Julio Hurtado' 'Vincenzo Lomonaco' 'Davide Bacciu']"
] |
null | null |
2403.07022
| null | null |
http://arxiv.org/pdf/2403.07022v1
|
2024-03-10T02:34:44Z
|
2024-03-10T02:34:44Z
|
A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary
Modifiable Areal Units
|
Spatio-Temporal (ST) prediction is crucial for making informed decisions in urban location-based applications like ride-sharing. However, existing ST models often require region partition as a prerequisite, resulting in two main pitfalls. Firstly, location-based services necessitate ad-hoc regions for various purposes, requiring multiple ST models with varying scales and zones, which can be costly to support. Secondly, different ST models may produce conflicting outputs, resulting in confusing predictions. In this paper, we propose One4All-ST, a framework that can conduct ST prediction for arbitrary modifiable areal units using only one model. To reduce the cost of getting multi-scale predictions, we design an ST network with hierarchical spatial modeling and scale normalization modules to efficiently and equally learn multi-scale representations. To address prediction inconsistencies across scales, we propose a dynamic programming scheme to solve the formulated optimal combination problem, minimizing predicted error through theoretical analysis. Besides, we suggest using an extended quad-tree to index the optimal combinations for quick response to arbitrary modifiable areal units in practical online scenarios. Extensive experiments on two real-world datasets verify the efficiency and effectiveness of One4All-ST in ST prediction for arbitrary modifiable areal units. The source codes and data of this work are available at https://github.com/uctb/One4All-ST.
|
[
"['Liyue Chen' 'Jiangyi Fang' 'Tengfei Liu' 'Shaosheng Cao' 'Leye Wang']"
] |
null | null |
2403.07025
| null | null |
http://arxiv.org/pdf/2403.07025v1
|
2024-03-10T15:35:41Z
|
2024-03-10T15:35:41Z
|
Enhancing Quantum Variational Algorithms with Zero Noise Extrapolation
via Neural Networks
|
In the emergent realm of quantum computing, the Variational Quantum Eigensolver (VQE) stands out as a promising algorithm for solving complex quantum problems, especially in the noisy intermediate-scale quantum (NISQ) era. However, the ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes. This research introduces a novel approach to ameliorate this challenge by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations. By employing the Qiskit framework, we crafted parameterized quantum circuits using the RY-RZ ansatz and examined their behavior under varying levels of depolarizing noise. Our investigations spanned from determining the expectation values of a Hamiltonian, defined as a tensor product of Z operators, under different noise intensities to extracting the ground state energy. To bridge the observed outcomes under noise with the ideal noise-free scenario, we trained a Feed Forward Neural Network on the error probabilities and their associated expectation values. Remarkably, our model proficiently predicted the VQE outcome under hypothetical noise-free conditions. By juxtaposing the simulation results with real quantum device executions, we unveiled the discrepancies induced by noise and showcased the efficacy of our neural network-based ZNE technique in rectifying them. This integrative approach not only paves the way for enhanced accuracy in VQE computations on NISQ devices but also underlines the immense potential of hybrid quantum-classical paradigms in circumventing the challenges posed by quantum noise. Through this research, we envision a future where quantum algorithms can be reliably executed on noisy devices, bringing us one step closer to realizing the full potential of quantum computing.
|
[
"['Subhasree Bhattacharjee' 'Soumyadip Sarkar' 'Kunal Das'\n 'Bikramjit Sarkar']"
] |
null | null |
2403.07026
| null | null |
http://arxiv.org/pdf/2403.07026v1
|
2024-03-10T15:45:39Z
|
2024-03-10T15:45:39Z
|
Whiteness-based bilevel learning of regularization parameters in imaging
|
We consider an unsupervised bilevel optimization strategy for learning regularization parameters in the context of imaging inverse problems in the presence of additive white Gaussian noise. Compared to supervised and semi-supervised metrics relying either on the prior knowledge of reference data and/or on some (partial) knowledge on the noise statistics, the proposed approach optimizes the whiteness of the residual between the observed data and the observation model with no need of ground-truth data.We validate the approach on standard Total Variation-regularized image deconvolution problems which show that the proposed quality metric provides estimates close to the mean-square error oracle and to discrepancy-based principles.
|
[
"['Carlo Santambrogio' 'Monica Pragliola' 'Alessandro Lanza'\n 'Marco Donatelli' 'Luca Calatroni']"
] |
null | null |
2403.07027
| null | null |
http://arxiv.org/pdf/2403.07027v1
|
2024-03-10T19:20:55Z
|
2024-03-10T19:20:55Z
|
FWin transformer for dengue prediction under climate and ocean influence
|
Dengue fever is one of the most deadly mosquito-born tropical infectious diseases. Detailed long range forecast model is vital in controlling the spread of disease and making mitigation efforts. In this study, we examine methods used to forecast dengue cases for long range predictions. The dataset consists of local climate/weather in addition to global climate indicators of Singapore from 2000 to 2019. We utilize newly developed deep neural networks to learn the intricate relationship between the features. The baseline models in this study are in the class of recent transformers for long sequence forecasting tasks. We found that a Fourier mixed window attention (FWin) based transformer performed the best in terms of both the mean square error and the maximum absolute error on the long range dengue forecast up to 60 weeks.
|
[
"['Nhat Thanh Tran' 'Jack Xin' 'Guofa Zhou']"
] |
null | null |
2403.07028
| null | null |
http://arxiv.org/pdf/2403.07028v1
|
2024-03-11T02:17:42Z
|
2024-03-11T02:17:42Z
|
An Efficient Learning-based Solver Comparable to Metaheuristics for the
Capacitated Arc Routing Problem
|
Recently, neural networks (NN) have made great strides in combinatorial optimization. However, they face challenges when solving the capacitated arc routing problem (CARP) which is to find the minimum-cost tour covering all required edges on a graph, while within capacity constraints. In tackling CARP, NN-based approaches tend to lag behind advanced metaheuristics, since they lack directed arc modeling and efficient learning methods tailored for complex CARP. In this paper, we introduce an NN-based solver to significantly narrow the gap with advanced metaheuristics while exhibiting superior efficiency. First, we propose the direction-aware attention model (DaAM) to incorporate directionality into the embedding process, facilitating more effective one-stage decision-making. Second, we design a supervised reinforcement learning scheme that involves supervised pre-training to establish a robust initial policy for subsequent reinforcement fine-tuning. It proves particularly valuable for solving CARP that has a higher complexity than the node routing problems (NRPs). Finally, a path optimization method is proposed to adjust the depot return positions within the path generated by DaAM. Experiments illustrate that our approach surpasses heuristics and achieves decision quality comparable to state-of-the-art metaheuristics for the first time while maintaining superior efficiency.
|
[
"['Runze Guo' 'Feng Xue' 'Anlong Ming' 'Nicu Sebe']"
] |
null | null |
2403.07030
| null | null |
http://arxiv.org/pdf/2403.07030v2
|
2024-03-18T02:45:04Z
|
2024-03-11T03:34:14Z
|
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge
Distillation
|
Due to privacy or patent concerns, a growing number of large models are released without granting access to their training data, making transferring their knowledge inefficient and problematic. In response, Data-Free Knowledge Distillation (DFKD) methods have emerged as direct solutions. However, simply adopting models derived from DFKD for real-world applications suffers significant performance degradation, due to the discrepancy between teachers' training data and real-world scenarios (student domain). The degradation stems from the portions of teachers' knowledge that are not applicable to the student domain. They are specific to the teacher domain and would undermine students' performance. Hence, selectively transferring teachers' appropriate knowledge becomes the primary challenge in DFKD. In this work, we propose a simple but effective method AuG-KD. It utilizes an uncertainty-guided and sample-specific anchor to align student-domain data with the teacher domain and leverages a generative method to progressively trade off the learning process between OOD knowledge distillation and domain-specific information learning via mixup learning. Extensive experiments in 3 datasets and 8 settings demonstrate the stability and superiority of our approach. Code available at https://github.com/IshiKura-a/AuG-KD .
|
[
"['Zihao Tang' 'Zheqi Lv' 'Shengyu Zhang' 'Yifan Zhou' 'Xinyu Duan'\n 'Fei Wu' 'Kun Kuang']"
] |
null | null |
2403.07031
| null | null |
http://arxiv.org/pdf/2403.07031v1
|
2024-03-11T04:19:05Z
|
2024-03-11T04:19:05Z
|
The Cram Method for Efficient Simultaneous Learning and Evaluation
|
We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and tests its empirical performance. Because it utilizes the entire sample for both learning and evaluation, cramming is significantly more data-efficient than sample-splitting. The cram method also naturally accommodates online learning algorithms, making its implementation computationally efficient. To demonstrate the power of the cram method, we consider the standard policy learning setting where cramming is applied to the same data to both develop an individualized treatment rule (ITR) and estimate the average outcome that would result if the learned ITR were to be deployed. We show that under a minimal set of assumptions, the resulting crammed evaluation estimator is consistent and asymptotically normal. While our asymptotic results require a relatively weak stabilization condition of ML algorithm, we develop a simple, generic method that can be used with any policy learning algorithm to satisfy this condition. Our extensive simulation studies show that, when compared to sample-splitting, cramming reduces the evaluation standard error by more than 40% while improving the performance of learned policy. We also apply the cram method to a randomized clinical trial to demonstrate its applicability to real-world problems. Finally, we briefly discuss future extensions of the cram method to other learning and evaluation settings.
|
[
"['Zeyang Jia' 'Kosuke Imai' 'Michael Lingzhi Li']"
] |
null | null |
2403.07033
| null | null |
http://arxiv.org/pdf/2403.07033v1
|
2024-03-11T05:47:07Z
|
2024-03-11T05:47:07Z
|
Interpreting What Typical Fault Signals Look Like via Prototype-matching
|
Neural networks, with powerful nonlinear mapping and classification capabilities, are widely applied in mechanical fault diagnosis to ensure safety. However, being typical black-box models, their application is limited in high-reliability-required scenarios. To understand the classification logic and explain what typical fault signals look like, the prototype matching network (PMN) is proposed by combining the human-inherent prototype-matching with autoencoder (AE). The PMN matches AE-extracted feature with each prototype and selects the most similar prototype as the prediction result. It has three interpreting paths on classification logic, fault prototypes, and matching contributions. Conventional diagnosis and domain generalization experiments demonstrate its competitive diagnostic performance and distinguished advantages in representation learning. Besides, the learned typical fault signals (i.e., sample-level prototypes) showcase the ability for denoising and extracting subtle key features that experts find challenging to capture. This ability broadens human understanding and provides a promising solution from interpretability research to AI-for-Science.
|
[
"['Qian Chen' 'Xingjian Dong' 'Zhike Peng']"
] |
null | null |
2403.07035
| null | null |
http://arxiv.org/pdf/2403.07035v1
|
2024-03-11T08:05:01Z
|
2024-03-11T08:05:01Z
|
Multiple Population Alternate Evolution Neural Architecture Search
|
The effectiveness of Evolutionary Neural Architecture Search (ENAS) is influenced by the design of the search space. Nevertheless, common methods including the global search space, scalable search space and hierarchical search space have certain limitations. Specifically, the global search space requires a significant amount of computational resources and time, the scalable search space sacrifices the diversity of network structures and the hierarchical search space increases the search cost in exchange for network diversity. To address above limitation, we propose a novel paradigm of searching neural network architectures and design the Multiple Population Alternate Evolution Neural Architecture Search (MPAE), which can achieve module diversity with a smaller search cost. MPAE converts the search space into L interconnected units and sequentially searches the units, then the above search of the entire network be cycled several times to reduce the impact of previous units on subsequent units. To accelerate the population evolution process, we also propose the the population migration mechanism establishes an excellent migration archive and transfers the excellent knowledge and experience in the migration archive to new populations. The proposed method requires only 0.3 GPU days to search a neural network on the CIFAR dataset and achieves the state-of-the-art results.
|
[
"['Juan Zou' 'Han Chu' 'Yizhang Xia' 'Junwen Xu' 'Yuan Liu' 'Zhanglu Hou']"
] |
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