categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.12959
| null | null |
http://arxiv.org/pdf/2403.12959v1
|
2024-03-19T17:58:02Z
|
2024-03-19T17:58:02Z
|
WHAC: World-grounded Humans and Cameras
|
Estimating human and camera trajectories with accurate scale in the world coordinate system from a monocular video is a highly desirable yet challenging and ill-posed problem. In this study, we aim to recover expressive parametric human models (i.e., SMPL-X) and corresponding camera poses jointly, by leveraging the synergy between three critical players: the world, the human, and the camera. Our approach is founded on two key observations. Firstly, camera-frame SMPL-X estimation methods readily recover absolute human depth. Secondly, human motions inherently provide absolute spatial cues. By integrating these insights, we introduce a novel framework, referred to as WHAC, to facilitate world-grounded expressive human pose and shape estimation (EHPS) alongside camera pose estimation, without relying on traditional optimization techniques. Additionally, we present a new synthetic dataset, WHAC-A-Mole, which includes accurately annotated humans and cameras, and features diverse interactive human motions as well as realistic camera trajectories. Extensive experiments on both standard and newly established benchmarks highlight the superiority and efficacy of our framework. We will make the code and dataset publicly available.
|
[
"['Wanqi Yin' 'Zhongang Cai' 'Ruisi Wang' 'Fanzhou Wang' 'Chen Wei'\n 'Haiyi Mei' 'Weiye Xiao' 'Zhitao Yang' 'Qingping Sun' 'Atsushi Yamashita'\n 'Ziwei Liu' 'Lei Yang']"
] |
null | null |
2403.12961
| null | null |
http://arxiv.org/pdf/2403.12961v1
|
2024-03-19T17:59:09Z
|
2024-03-19T17:59:09Z
|
TexTile: A Differentiable Metric for Texture Tileability
|
We introduce TexTile, a novel differentiable metric to quantify the degree upon which a texture image can be concatenated with itself without introducing repeating artifacts (i.e., the tileability). Existing methods for tileable texture synthesis focus on general texture quality, but lack explicit analysis of the intrinsic repeatability properties of a texture. In contrast, our TexTile metric effectively evaluates the tileable properties of a texture, opening the door to more informed synthesis and analysis of tileable textures. Under the hood, TexTile is formulated as a binary classifier carefully built from a large dataset of textures of different styles, semantics, regularities, and human annotations.Key to our method is a set of architectural modifications to baseline pre-train image classifiers to overcome their shortcomings at measuring tileability, along with a custom data augmentation and training regime aimed at increasing robustness and accuracy. We demonstrate that TexTile can be plugged into different state-of-the-art texture synthesis methods, including diffusion-based strategies, and generate tileable textures while keeping or even improving the overall texture quality. Furthermore, we show that TexTile can objectively evaluate any tileable texture synthesis method, whereas the current mix of existing metrics produces uncorrelated scores which heavily hinders progress in the field.
|
[
"['Carlos Rodriguez-Pardo' 'Dan Casas' 'Elena Garces' 'Jorge Lopez-Moreno']"
] |
null | null |
2403.12968
| null | null |
http://arxiv.org/pdf/2403.12968v1
|
2024-03-19T17:59:56Z
|
2024-03-19T17:59:56Z
|
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic
Prompt Compression
|
This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. Considering the redundancy in natural language, existing approaches compress prompts by removing tokens or lexical units according to their information entropy obtained from a causal language model such as LLaMa-7B. The challenge is that information entropy may be a suboptimal compression metric: (i) it only leverages unidirectional context and may fail to capture all essential information needed for prompt compression; (ii) it is not aligned with the prompt compression objective. To address these issues, we propose a data distillation procedure to derive knowledge from an LLM to compress prompts without losing crucial information, and meantime, introduce an extractive text compression dataset. We formulate prompt compression as a token classification problem to guarantee the faithfulness of the compressed prompt to the original one, and use a Transformer encoder as the base architecture to capture all essential information for prompt compression from the full bidirectional context. Our approach leads to lower latency by explicitly learning the compression objective with smaller models such as XLM-RoBERTa-large and mBERT. We evaluate our method on both in-domain and out-of-domain datasets, including MeetingBank, LongBench, ZeroScrolls, GSM8K, and BBH. Despite its small size, our model shows significant performance gains over strong baselines and demonstrates robust generalization ability across different LLMs. Additionally, our model is 3x-6x faster than existing prompt compression methods, while accelerating the end-to-end latency by 1.6x-2.9x with compression ratios of 2x-5x.
|
[
"['Zhuoshi Pan' 'Qianhui Wu' 'Huiqiang Jiang' 'Menglin Xia' 'Xufang Luo'\n 'Jue Zhang' 'Qingwei Lin' 'Victor Rühle' 'Yuqing Yang' 'Chin-Yew Lin'\n 'H. Vicky Zhao' 'Lili Qiu' 'Dongmei Zhang']"
] |
null | null |
2403.12969
| null | null |
http://arxiv.org/pdf/2403.12969v1
|
2024-01-09T00:07:36Z
|
2024-01-09T00:07:36Z
|
Entangling Machine Learning with Quantum Tensor Networks
|
This paper examines the use of tensor networks, which can efficiently represent high-dimensional quantum states, in language modeling. It is a distillation and continuation of the work done in (van der Poel, 2023). To do so, we will abstract the problem down to modeling Motzkin spin chains, which exhibit long-range correlations reminiscent of those found in language. The Matrix Product State (MPS), also known as the tensor train, has a bond dimension which scales as the length of the sequence it models. To combat this, we use the factored core MPS, whose bond dimension scales sub-linearly. We find that the tensor models reach near perfect classifying ability, and maintain a stable level of performance as the number of valid training examples is decreased.
|
[
"['Constantijn van der Poel' 'Dan Zhao']"
] |
null | null |
2403.12975
| null | null |
http://arxiv.org/pdf/2403.12975v2
|
2024-07-01T07:40:03Z
|
2024-02-05T12:11:15Z
|
Training morphological neural networks with gradient descent: some
theoretical insights
|
Morphological neural networks, or layers, can be a powerful tool to boost the progress in mathematical morphology, either on theoretical aspects such as the representation of complete lattice operators, or in the development of image processing pipelines. However, these architectures turn out to be difficult to train when they count more than a few morphological layers, at least within popular machine learning frameworks which use gradient descent based optimization algorithms. In this paper we investigate the potential and limitations of differentiation based approaches and back-propagation applied to morphological networks, in light of the non-smooth optimization concept of Bouligand derivative. We provide insights and first theoretical guidelines, in particular regarding initialization and learning rates.
|
[
"['Samy Blusseau']"
] |
null | null |
2403.12977
| null | null |
http://arxiv.org/pdf/2403.12977v1
|
2024-02-10T01:16:21Z
|
2024-02-10T01:16:21Z
|
SportsNGEN: Sustained Generation of Multi-player Sports Gameplay
|
We present a transformer decoder based model, SportsNGEN, that is trained on sports player and ball tracking sequences that is capable of generating realistic and sustained gameplay. We train and evaluate SportsNGEN on a large database of professional tennis tracking data and demonstrate that by combining the generated simulations with a shot classifier and logic to start and end rallies, the system is capable of simulating an entire tennis match. In addition, a generic version of SportsNGEN can be customized to a specific player by fine-tuning on match data that includes that player. We show that our model is well calibrated and can be used to derive insights for coaches and broadcasters by evaluating counterfactual or what if options. Finally, we show qualitative results indicating the same approach works for football.
|
[
"['Lachlan Thorpe' 'Lewis Bawden' 'Karanjot Vendal' 'John Bronskill'\n 'Richard E. Turner']"
] |
null | null |
2403.12979
| null | null |
http://arxiv.org/pdf/2403.12979v2
|
2024-03-21T18:52:20Z
|
2024-02-23T19:01:47Z
|
AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for
Efficient Optimization
|
Quantum circuit transformation aims to produce equivalent circuits while optimizing for various aspects such as circuit depth, gate count, and compatibility with modern Noisy Intermediate Scale Quantum (NISQ) devices. There are two techniques for circuit transformation. The first is a rule-based approach that greedily cancels out pairs of gates that equate to the identity unitary operation. Rule-based approaches are used in quantum compilers such as Qiskit, tket, and Quilc. The second is a search-based approach that tries to find an equivalent quantum circuit by exploring the quantum circuits search space. Search-based approaches typically rely on machine learning techniques such as generative models and Reinforcement Learning (RL). In this work, we propose AltGraph, a novel search-based circuit transformation approach that generates equivalent quantum circuits using existing generative graph models. We use three main graph models: DAG Variational Autoencoder (D-VAE) with two variants: Gated Recurrent Unit (GRU) and Graph Convolutional Network (GCN), and Deep Generative Model for Graphs (DeepGMG) that take a Direct Acyclic Graph (DAG) of the quantum circuit as input and output a new DAG from which we reconstruct the equivalent quantum circuit. Next, we perturb the latent space to generate equivalent quantum circuits some of which may be more compatible with the hardware coupling map and/or enable better optimization leading to reduced gate count and circuit depth. AltGraph achieves on average a 37.55% reduction in the number of gates and a 37.75% reduction in the circuit depth post-transpiling compared to the original transpiled circuit with only 0.0074 Mean Squared Error (MSE) in the density matrix.
|
[
"['Collin Beaudoin' 'Koustubh Phalak' 'Swaroop Ghosh']"
] |
null | null |
2403.12981
| null | null |
http://arxiv.org/abs/2403.12981v1
|
2024-03-02T02:35:08Z
|
2024-03-02T02:35:08Z
|
Beyond Inference: Performance Analysis of DNN Server Overheads for
Computer Vision
|
Deep neural network (DNN) inference has become an important part of many data-center workloads. This has prompted focused efforts to design ever-faster deep learning accelerators such as GPUs and TPUs. However, an end-to-end DNN-based vision application contains more than just DNN inference, including input decompression, resizing, sampling, normalization, and data transfer. In this paper, we perform a thorough evaluation of computer vision inference requests performed on a throughput-optimized serving system. We quantify the performance impact of server overheads such as data movement, preprocessing, and message brokers between two DNNs producing outputs at different rates. Our empirical analysis encompasses many computer vision tasks including image classification, segmentation, detection, depth-estimation, and more complex processing pipelines with multiple DNNs. Our results consistently demonstrate that end-to-end application performance can easily be dominated by data processing and data movement functions (up to 56% of end-to-end latency in a medium-sized image, and $sim$ 80% impact on system throughput in a large image), even though these functions have been conventionally overlooked in deep learning system design. Our work identifies important performance bottlenecks in different application scenarios, achieves 2.25$times$ better throughput compared to prior work, and paves the way for more holistic deep learning system design.
|
[
"['Ahmed F. AbouElhamayed' 'Susanne Balle' 'Deshanand Singh'\n 'Mohamed S. Abdelfattah']"
] |
null | null |
2403.12982
| null | null |
http://arxiv.org/pdf/2403.12982v1
|
2024-03-02T12:41:25Z
|
2024-03-02T12:41:25Z
|
Knowledge-Reuse Transfer Learning Methods in Molecular and Material
Science
|
Molecules and materials are the foundation for the development of modern advanced industries such as energy storage systems and semiconductor devices. However, traditional trial-and-error methods or theoretical calculations are highly resource-intensive, and extremely long R&D (Research and Development) periods cannot meet the urgent need for molecules/materials in industrial development. Machine learning (ML) methods based on big data are expected to break this dilemma. However, the difficulty in constructing large-scale datasets of new molecules/materials due to the high cost of data acquisition and annotation limits the development of machine learning. The application of transfer learning lowers the data requirements for model training, which makes transfer learning stand out in researches addressing data quality issues. In this review, we summarize recent advances in transfer learning related to molecular and materials science. We focus on the application of transfer learning methods for the discovery of advanced molecules/materials, particularly, the construction of transfer learning frameworks for different systems, and how transfer learning can enhance the performance of models. In addition, the challenges of transfer learning are also discussed.
|
[
"['An Chen' 'Zhilong Wang' 'Karl Luigi Loza Vidaurre' 'Yanqiang Han'\n 'Simin Ye' 'Kehao Tao' 'Shiwei Wang' 'Jing Gao' 'Jinjin Li']"
] |
null | null |
2403.12983
| null | null |
http://arxiv.org/pdf/2403.12983v1
|
2024-03-02T19:38:10Z
|
2024-03-02T19:38:10Z
|
OSSCAR: One-Shot Structured Pruning in Vision and Language Models with
Combinatorial Optimization
|
Structured pruning is a promising approach for reducing the inference costs of large vision and language models. By removing carefully chosen structures, e.g., neurons or attention heads, the improvements from this approach can be realized on standard deep learning hardware. In this work, we focus on structured pruning in the one-shot (post-training) setting, which does not require model retraining after pruning. We propose a novel combinatorial optimization framework for this problem, based on a layer-wise reconstruction objective and a careful reformulation that allows for scalable optimization. Moreover, we design a new local combinatorial optimization algorithm, which exploits low-rank updates for efficient local search. Our framework is time and memory-efficient and considerably improves upon state-of-the-art one-shot methods on vision models (e.g., ResNet50, MobileNet) and language models (e.g., OPT-1.3B -- OPT-30B). For language models, e.g., OPT-2.7B, OSSCAR can lead to $125times$ lower test perplexity on WikiText with $2times$ inference time speedup in comparison to the state-of-the-art ZipLM approach. Our framework is also $6times$ -- $8times$ faster. Notably, our work considers models with tens of billions of parameters, which is up to $100times$ larger than what has been previously considered in the structured pruning literature.
|
[
"['Xiang Meng' 'Shibal Ibrahim' 'Kayhan Behdin' 'Hussein Hazimeh'\n 'Natalia Ponomareva' 'Rahul Mazumder']"
] |
null | null |
2403.12984
| null | null |
http://arxiv.org/pdf/2403.12984v2
|
2024-03-27T21:51:03Z
|
2024-03-03T11:09:32Z
|
When SMILES have Language: Drug Classification using Text Classification
Methods on Drug SMILES Strings
|
Complex chemical structures, like drugs, are usually defined by SMILES strings as a sequence of molecules and bonds. These SMILES strings are used in different complex machine learning-based drug-related research and representation works. Escaping from complex representation, in this work, we pose a single question: What if we treat drug SMILES as conventional sentences and engage in text classification for drug classification? Our experiments affirm the possibility with very competitive scores. The study explores the notion of viewing each atom and bond as sentence components, employing basic NLP methods to categorize drug types, proving that complex problems can also be solved with simpler perspectives. The data and code are available here: https://github.com/azminewasi/Drug-Classification-NLP.
|
[
"['Azmine Toushik Wasi' 'Šerbetar Karlo' 'Raima Islam' 'Taki Hasan Rafi'\n 'Dong-Kyu Chae']"
] |
null | null |
2403.12986
| null | null |
http://arxiv.org/pdf/2403.12986v2
|
2024-07-12T04:43:48Z
|
2024-03-04T06:43:16Z
|
BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced
Feature-Level Contrastive Learning
|
Semi-supervised Learning (SSL) reduces the need for extensive annotations in deep learning, but the more realistic challenge of imbalanced data distribution in SSL remains largely unexplored. In Class Imbalanced Semi-supervised Learning (CISSL), the bias introduced by unreliable pseudo-labels can be exacerbated by imbalanced data distributions. Most existing methods address this issue at instance-level through reweighting or resampling, but the performance is heavily limited by their reliance on biased backbone representation. Some other methods do perform feature-level adjustments like feature blending but might introduce unfavorable noise. In this paper, we discuss the bonus of a more balanced feature distribution for the CISSL problem, and further propose a Balanced Feature-Level Contrastive Learning method (BaCon). Our method directly regularizes the distribution of instances' representations in a well-designed contrastive manner. Specifically, class-wise feature centers are computed as the positive anchors, while negative anchors are selected by a straightforward yet effective mechanism. A distribution-related temperature adjustment is leveraged to control the class-wise contrastive degrees dynamically. Our method demonstrates its effectiveness through comprehensive experiments on the CIFAR10-LT, CIFAR100-LT, STL10-LT, and SVHN-LT datasets across various settings. For example, BaCon surpasses instance-level method FixMatch-based ABC on CIFAR10-LT with a 1.21% accuracy improvement, and outperforms state-of-the-art feature-level method CoSSL on CIFAR100-LT with a 0.63% accuracy improvement. When encountering more extreme imbalance degree, BaCon also shows better robustness than other methods.
|
[
"['Qianhan Feng' 'Lujing Xie' 'Shijie Fang' 'Tong Lin']"
] |
null | null |
2403.12987
| null | null |
http://arxiv.org/pdf/2403.12987v1
|
2024-03-04T07:40:25Z
|
2024-03-04T07:40:25Z
|
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided
Diffusion
|
In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study shows that the existing molecular generative methods and docking scores both have lacked consideration in terms of specificity, which means that generated molecules bind to almost every protein pocket with high affinity. To address this, we introduce the Delta Score, a new metric for evaluating the specificity of molecular binding. To further incorporate this insight for generation, we develop an innovative energy-guided approach using contrastive learning, with active compounds as decoys, to direct generative models toward creating molecules with high specificity. Our empirical results show that this method not only enhances the delta score but also maintains or improves traditional docking scores, successfully bridging the gap between SBDD and real-world needs.
|
[
"['Bowen Gao' 'Minsi Ren' 'Yuyan Ni' 'Yanwen Huang' 'Bo Qiang'\n 'Zhi-Ming Ma' 'Wei-Ying Ma' 'Yanyan Lan']"
] |
null | null |
2403.12991
| null | null |
http://arxiv.org/pdf/2403.12991v1
|
2024-03-05T06:37:14Z
|
2024-03-05T06:37:14Z
|
Tel2Veh: Fusion of Telecom Data and Vehicle Flow to Predict Camera-Free
Traffic via a Spatio-Temporal Framework
|
Vehicle flow, a crucial indicator for transportation, is often limited by detector coverage. With the advent of extensive mobile network coverage, we can leverage mobile user activities, or cellular traffic, on roadways as a proxy for vehicle flow. However, as counts of cellular traffic may not directly align with vehicle flow due to data from various user types, we present a new task: predicting vehicle flow in camera-free areas using cellular traffic. To uncover correlations within multi-source data, we deployed cameras on selected roadways to establish the Tel2Veh dataset, consisting of extensive cellular traffic and sparse vehicle flows. Addressing this challenge, we propose a framework that independently extracts features and integrates them with a graph neural network (GNN)-based fusion to discern disparities, thereby enabling the prediction of unseen vehicle flows using cellular traffic. This work advances the use of telecom data in transportation and pioneers the fusion of telecom and vision-based data, offering solutions for traffic management.
|
[
"['ChungYi Lin' 'Shen-Lung Tung' 'Hung-Ting Su' 'Winston H. Hsu']"
] |
null | null |
2403.12993
| null | null |
http://arxiv.org/pdf/2403.12993v1
|
2024-03-05T08:04:01Z
|
2024-03-05T08:04:01Z
|
Simple Full-Spectrum Correlated k-Distribution Model based on Multilayer
Perceptron
|
While neural networks have been successfully applied to the full-spectrum k-distribution (FSCK) method at a large range of thermodynamics with k-values predicted by a trained multilayer perceptron (MLP) model, the required a-values still need to be calculated on-the-fly, which theoretically degrades the FSCK method and may lead to errors. On the other hand, too complicated structure of the current MLP model inevitably slows down the calculation efficiency. Therefore, to compensate among accuracy, efficiency and storage, the simple MLP designed based on the nature of FSCK method are developed, i.e., the simple FSCK MLP (SFM) model, from which those correlated k-values and corresponding ka-values can be efficiently obtained. Several test cases have been carried out to compare the developed SFM model and other FSCK tools including look-up tables and traditional FSCK MLP (TFM) model. Results show that the SFM model can achieve excellent accuracy that is even better than look-up tables at a tiny computational cost that is far less than that of TFM model. Considering accuracy, efficiency and portability, the SFM model is not only an excellent tool for the prediction of spectral properties, but also provides a method to reduce the errors due to nonlinear effects.
|
[
"['Xin Wang' 'Yucheng Kuang' 'Chaojun Wang' 'Hongyuan Di' 'Boshu He']"
] |
null | null |
2403.12995
| null | null |
http://arxiv.org/pdf/2403.12995v4
|
2024-06-13T02:29:34Z
|
2024-03-05T13:35:41Z
|
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular
Modeling
|
Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling. ESM-AA achieves this by pre-training on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ESM-AA surpasses previous methods in protein-molecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins. The source codes of ESM-AA are publicly released at https://github.com/zhengkangjie/ESM-AA.
|
[
"['Kangjie Zheng' 'Siyu Long' 'Tianyu Lu' 'Junwei Yang' 'Xinyu Dai'\n 'Ming Zhang' 'Zaiqing Nie' 'Wei-Ying Ma' 'Hao Zhou']"
] |
null | null |
2403.12997
| null | null |
http://arxiv.org/pdf/2403.12997v1
|
2024-03-06T12:04:24Z
|
2024-03-06T12:04:24Z
|
A Multi-Task Oriented Semantic Communication Framework for Autonomous
Vehicles
|
Task-oriented semantic communication is an emerging technology that transmits only the relevant semantics of a message instead of the whole message to achieve a specific task. It reduces latency, compresses the data, and is more robust in low SNR scenarios. This work presents a multi-task-oriented semantic communication framework for connected and autonomous vehicles (CAVs). We propose a convolutional autoencoder (CAE) that performs the semantic encoding of the road traffic signs. These encoded images are then transmitted from one CAV to another CAV through satellite in challenging weather conditions where visibility is impaired. In addition, we propose task-oriented semantic decoders for image reconstruction and classification tasks. Simulation results show that the proposed framework outperforms the conventional schemes, such as QAM-16, regarding the reconstructed image's similarity and the classification's accuracy. In addition, it can save up to 89 % of the bandwidth by sending fewer bits.
|
[
"['Eslam Eldeeb' 'Mohammad Shehab' 'Hirley Alves']"
] |
null | null |
2403.12999
| null | null |
http://arxiv.org/pdf/2403.12999v1
|
2024-03-11T04:13:29Z
|
2024-03-11T04:13:29Z
|
Prompt Selection and Augmentation for Few Examples Code Generation in
Large Language Model and its Application in Robotics Control
|
Few-shot prompting and step-by-step reasoning have enhanced the capabilities of Large Language Models (LLMs) in tackling complex tasks including code generation. In this paper, we introduce a prompt selection and augmentation algorithm aimed at improving mathematical reasoning and robot arm operations. Our approach incorporates a multi-stage example augmentation scheme combined with an example selection scheme. This algorithm improves LLM performance by selecting a set of examples that increase diversity, minimize redundancy, and increase relevance to the question. When combined with the Program-of-Thought prompting, our algorithm demonstrates an improvement in performance on the GSM8K and SVAMP benchmarks, with increases of 0.3% and 1.1% respectively. Furthermore, in simulated tabletop environments, our algorithm surpasses the Code-as-Policies approach by achieving a 3.4% increase in successful task completions and a decrease of over 70% in the number of examples used. Its ability to discard examples that contribute little to solving the problem reduces the inferencing time of an LLM-powered robotics system. This algorithm also offers important benefits for industrial process automation by streamlining the development and deployment process, reducing manual programming effort, and enhancing code reusability.
|
[
"['On Tai Wu' 'Frodo Kin Sun Chan' 'Zunhao Zhang' 'Yan Nei Law'\n 'Benny Drescher' 'Edmond Shiao Bun Lai']"
] |
null | null |
2403.13000
| null | null |
http://arxiv.org/pdf/2403.13000v1
|
2024-03-12T16:25:38Z
|
2024-03-12T16:25:38Z
|
Duwak: Dual Watermarks in Large Language Models
|
As large language models (LLM) are increasingly used for text generation tasks, it is critical to audit their usages, govern their applications, and mitigate their potential harms. Existing watermark techniques are shown effective in embedding single human-imperceptible and machine-detectable patterns without significantly affecting generated text quality and semantics. However, the efficiency in detecting watermarks, i.e., the minimum number of tokens required to assert detection with significance and robustness against post-editing, is still debatable. In this paper, we propose, Duwak, to fundamentally enhance the efficiency and quality of watermarking by embedding dual secret patterns in both token probability distribution and sampling schemes. To mitigate expression degradation caused by biasing toward certain tokens, we design a contrastive search to watermark the sampling scheme, which minimizes the token repetition and enhances the diversity. We theoretically explain the interdependency of the two watermarks within Duwak. We evaluate Duwak extensively on Llama2 under various post-editing attacks, against four state-of-the-art watermarking techniques and combinations of them. Our results show that Duwak marked text achieves the highest watermarked text quality at the lowest required token count for detection, up to 70% tokens less than existing approaches, especially under post paraphrasing.
|
[
"['Chaoyi Zhu' 'Jeroen Galjaard' 'Pin-Yu Chen' 'Lydia Y. Chen']"
] |
null | null |
2403.13001
| null | null |
http://arxiv.org/pdf/2403.13001v1
|
2024-03-13T01:29:40Z
|
2024-03-13T01:29:40Z
|
Fundamental Components of Deep Learning: A category-theoretic approach
|
Deep learning, despite its remarkable achievements, is still a young field. Like the early stages of many scientific disciplines, it is marked by the discovery of new phenomena, ad-hoc design decisions, and the lack of a uniform and compositional mathematical foundation. From the intricacies of the implementation of backpropagation, through a growing zoo of neural network architectures, to the new and poorly understood phenomena such as double descent, scaling laws or in-context learning, there are few unifying principles in deep learning. This thesis develops a novel mathematical foundation for deep learning based on the language of category theory. We develop a new framework that is a) end-to-end, b) unform, and c) not merely descriptive, but prescriptive, meaning it is amenable to direct implementation in programming languages with sufficient features. We also systematise many existing approaches, placing many existing constructions and concepts from the literature under the same umbrella. In Part I we identify and model two main properties of deep learning systems parametricity and bidirectionality by we expand on the previously defined construction of actegories and Para to study the former, and define weighted optics to study the latter. Combining them yields parametric weighted optics, a categorical model of artificial neural networks, and more. Part II justifies the abstractions from Part I, applying them to model backpropagation, architectures, and supervised learning. We provide a lens-theoretic axiomatisation of differentiation, covering not just smooth spaces, but discrete settings of boolean circuits as well. We survey existing, and develop new categorical models of neural network architectures. We formalise the notion of optimisers and lastly, combine all the existing concepts together, providing a uniform and compositional framework for supervised learning.
|
[
"['Bruno Gavranović']"
] |
null | null |
2403.13005
| null | null |
http://arxiv.org/pdf/2403.13005v2
|
2024-04-12T16:26:04Z
|
2024-03-14T11:53:35Z
|
Leap: molecular synthesisability scoring with intermediates
|
Assessing whether a molecule can be synthesised is a primary task in drug discovery. It enables computational chemists to filter for viable compounds or bias molecular generative models. The notion of synthesisability is dynamic as it evolves depending on the availability of key compounds. A common approach in drug discovery involves exploring the chemical space surrounding synthetically-accessible intermediates. This strategy improves the synthesisability of the derived molecules due to the availability of key intermediates. Existing synthesisability scoring methods such as SAScore, SCScore and RAScore, cannot condition on intermediates dynamically. Our approach, Leap, is a GPT-2 model trained on the depth, or longest linear path, of predicted synthesis routes that allows information on the availability of key intermediates to be included at inference time. We show that Leap surpasses all other scoring methods by at least 5% on AUC score when identifying synthesisable molecules, and can successfully adapt predicted scores when presented with a relevant intermediate compound.
|
[
"['Antonia Calvi' 'Théophile Gaudin' 'Dominik Miketa' 'Dominique Sydow'\n 'Liam Wilbraham']"
] |
null | null |
2403.13008
| null | null |
http://arxiv.org/pdf/2403.13008v1
|
2024-03-16T19:04:11Z
|
2024-03-16T19:04:11Z
|
Speedrunning and path integrals
|
In this article we will explore the concept of speedrunning as a representation of a simplified version of quantum mechanics within a classical simulation. This analogy can be seen as a simplified approach to understanding the broader idea that quantum mechanics may emerge from classical mechanics simulations due to the limitations of the simulation. The concept of speedrunning will be explored from the perspective inside the simulation, where the player is seen as a "force of nature" that can be interpreted through Newton's first law. Starting from this general assumption, the aim is to build a bridge between these two fields by using the mathematical representation of path integrals. The use of such an approach as an intermediate layer between machine learning techniques aimed at finding an optimal strategy and a game simulation is also analysed. This article will focus primarily on the relationship between classical and quantum physics within the simulation, leaving aside more technical issues in field theory such as invariance with respect to Lorentz transformations and virtual particles.
|
[
"['Gabriele Lami']"
] |
null | null |
2403.13010
| null | null |
http://arxiv.org/pdf/2403.13010v1
|
2024-03-17T12:26:30Z
|
2024-03-17T12:26:30Z
|
A Dual-Tier Adaptive One-Class Classification IDS for Emerging
Cyberthreats
|
In today's digital age, our dependence on IoT (Internet of Things) and IIoT (Industrial IoT) systems has grown immensely, which facilitates sensitive activities such as banking transactions and personal, enterprise data, and legal document exchanges. Cyberattackers consistently exploit weak security measures and tools. The Network Intrusion Detection System (IDS) acts as a primary tool against such cyber threats. However, machine learning-based IDSs, when trained on specific attack patterns, often misclassify new emerging cyberattacks. Further, the limited availability of attack instances for training a supervised learner and the ever-evolving nature of cyber threats further complicate the matter. This emphasizes the need for an adaptable IDS framework capable of recognizing and learning from unfamiliar/unseen attacks over time. In this research, we propose a one-class classification-driven IDS system structured on two tiers. The first tier distinguishes between normal activities and attacks/threats, while the second tier determines if the detected attack is known or unknown. Within this second tier, we also embed a multi-classification mechanism coupled with a clustering algorithm. This model not only identifies unseen attacks but also uses them for retraining them by clustering unseen attacks. This enables our model to be future-proofed, capable of evolving with emerging threat patterns. Leveraging one-class classifiers (OCC) at the first level, our approach bypasses the need for attack samples, addressing data imbalance and zero-day attack concerns and OCC at the second level can effectively separate unknown attacks from the known attacks. Our methodology and evaluations indicate that the presented framework exhibits promising potential for real-world deployments.
|
[
"['Md. Ashraf Uddin' 'Sunil Aryal' 'Mohamed Reda Bouadjenek'\n 'Muna Al-Hawawreh' 'Md. Alamin Talukder']"
] |
null | null |
2403.13013
| null | null |
http://arxiv.org/pdf/2403.13013v1
|
2024-03-17T17:16:55Z
|
2024-03-17T17:16:55Z
|
Hierarchical Classification for Intrusion Detection System: Effective
Design and Empirical Analysis
|
With the increased use of network technologies like Internet of Things (IoT) in many real-world applications, new types of cyberattacks have been emerging. To safeguard critical infrastructures from these emerging threats, it is crucial to deploy an Intrusion Detection System (IDS) that can detect different types of attacks accurately while minimizing false alarms. Machine learning approaches have been used extensively in IDS and they are mainly using flat multi-class classification to differentiate normal traffic and different types of attacks. Though cyberattack types exhibit a hierarchical structure where similar granular attack subtypes can be grouped into more high-level attack types, hierarchical classification approach has not been explored well. In this paper, we investigate the effectiveness of hierarchical classification approach in IDS. We use a three-level hierarchical classification model to classify various network attacks, where the first level classifies benign or attack, the second level classifies coarse high-level attack types, and the third level classifies a granular level attack types. Our empirical results of using 10 different classification algorithms in 10 different datasets show that there is no significant difference in terms of overall classification performance (i.e., detecting normal and different types of attack correctly) of hierarchical and flat classification approaches. However, flat classification approach misclassify attacks as normal whereas hierarchical approach misclassify one type of attack as another attack type. In other words, the hierarchical classification approach significantly minimises attacks from misclassified as normal traffic, which is more important in critical systems.
|
[
"['Md. Ashraf Uddin' 'Sunil Aryal' 'Mohamed Reda Bouadjenek'\n 'Muna Al-Hawawreh' 'Md. Alamin Talukder']"
] |
null | null |
2403.13014
| null | null |
http://arxiv.org/pdf/2403.13014v1
|
2024-03-17T17:42:20Z
|
2024-03-17T17:42:20Z
|
General Line Coordinates in 3D
|
Interpretable interactive visual pattern discovery in lossless 3D visualization is a promising way to advance machine learning. It enables end users who are not data scientists to take control of the model development process as a self-service. It is conducted in 3D General Line Coordinates (GLC) visualization space, which preserves all n-D information in 3D. This paper presents a system which combines three types of GLC: Shifted Paired Coordinates (SPC), Shifted Tripled Coordinates (STC), and General Line Coordinates-Linear (GLC-L) for interactive visual pattern discovery. A transition from 2-D visualization to 3-D visualization allows for a more distinct visual pattern than in 2-D and it also allows for finding the best data viewing positions, which are not available in 2-D. It enables in-depth visual analysis of various class-specific data subsets comprehensible for end users in the original interpretable attributes. Controlling model overgeneralization by end users is an additional benefit of this approach.
|
[
"['Joshua Martinez' 'Boris Kovalerchuk']"
] |
null | null |
2403.13015
| null | null |
http://arxiv.org/pdf/2403.13015v1
|
2024-03-18T03:17:08Z
|
2024-03-18T03:17:08Z
|
HyperVQ: MLR-based Vector Quantization in Hyperbolic Space
|
The success of models operating on tokenized data has led to an increased demand for effective tokenization methods, particularly when applied to vision or auditory tasks, which inherently involve non-discrete data. One of the most popular tokenization methods is Vector Quantization (VQ), a key component of several recent state-of-the-art methods across various domains. Typically, a VQ Variational Autoencoder (VQVAE) is trained to transform data to and from its tokenized representation. However, since the VQVAE is trained with a reconstruction objective, there is no constraint for the embeddings to be well disentangled, a crucial aspect for using them in discriminative tasks. Recently, several works have demonstrated the benefits of utilizing hyperbolic spaces for representation learning. Hyperbolic spaces induce compact latent representations due to their exponential volume growth and inherent ability to model hierarchical and structured data. In this work, we explore the use of hyperbolic spaces for vector quantization (HyperVQ), formulating the VQ operation as a hyperbolic Multinomial Logistic Regression (MLR) problem, in contrast to the Euclidean K-Means clustering used in VQVAE. Through extensive experiments, we demonstrate that hyperVQ performs comparably in reconstruction and generative tasks while outperforming VQ in discriminative tasks and learning a highly disentangled latent space.
|
[
"['Nabarun Goswami' 'Yusuke Mukuta' 'Tatsuya Harada']"
] |
null | null |
2403.13018
| null | null |
http://arxiv.org/pdf/2403.13018v1
|
2024-03-18T13:25:12Z
|
2024-03-18T13:25:12Z
|
Invisible Backdoor Attack Through Singular Value Decomposition
|
With the widespread application of deep learning across various domains, concerns about its security have grown significantly. Among these, backdoor attacks pose a serious security threat to deep neural networks (DNNs). In recent years, backdoor attacks on neural networks have become increasingly sophisticated, aiming to compromise the security and trustworthiness of models by implanting hidden, unauthorized functionalities or triggers, leading to misleading predictions or behaviors. To make triggers less perceptible and imperceptible, various invisible backdoor attacks have been proposed. However, most of them only consider invisibility in the spatial domain, making it easy for recent defense methods to detect the generated toxic images.To address these challenges, this paper proposes an invisible backdoor attack called DEBA. DEBA leverages the mathematical properties of Singular Value Decomposition (SVD) to embed imperceptible backdoors into models during the training phase, thereby causing them to exhibit predefined malicious behavior under specific trigger conditions. Specifically, we first perform SVD on images, and then replace the minor features of trigger images with those of clean images, using them as triggers to ensure the effectiveness of the attack. As minor features are scattered throughout the entire image, the major features of clean images are preserved, making poisoned images visually indistinguishable from clean ones. Extensive experimental evaluations demonstrate that DEBA is highly effective, maintaining high perceptual quality and a high attack success rate for poisoned images. Furthermore, we assess the performance of DEBA under existing defense measures, showing that it is robust and capable of significantly evading and resisting the effects of these defense measures.
|
[
"['Wenmin Chen' 'Xiaowei Xu']"
] |
null | null |
2403.13023
| null | null |
http://arxiv.org/pdf/2403.13023v1
|
2024-03-18T20:20:00Z
|
2024-03-18T20:20:00Z
|
Thwarting Cybersecurity Attacks with Explainable Concept Drift
|
Cyber-security attacks pose a significant threat to the operation of autonomous systems. Particularly impacted are the Heating, Ventilation, and Air Conditioning (HVAC) systems in smart buildings, which depend on data gathered by sensors and Machine Learning (ML) models using the captured data. As such, attacks that alter the readings of these sensors can severely affect the HVAC system operations impacting residents' comfort and energy reduction goals. Such attacks may induce changes in the online data distribution being fed to the ML models, violating the fundamental assumption of similarity in training and testing data distribution. This leads to a degradation in model prediction accuracy due to a phenomenon known as Concept Drift (CD) - the alteration in the relationship between input features and the target variable. Addressing CD requires identifying the source of drift to apply targeted mitigation strategies, a process termed drift explanation. This paper proposes a Feature Drift Explanation (FDE) module to identify the drifting features. FDE utilizes an Auto-encoder (AE) that reconstructs the activation of the first layer of the regression Deep Learning (DL) model and finds their latent representations. When a drift is detected, each feature of the drifting data is replaced by its representative counterpart from the training data. The Minkowski distance is then used to measure the divergence between the altered drifting data and the original training data. The results show that FDE successfully identifies 85.77 % of drifting features and showcases its utility in the DL adaptation method under the CD phenomenon. As a result, the FDE method is an effective strategy for identifying drifting features towards thwarting cyber-security attacks.
|
[
"['Ibrahim Shaer' 'Abdallah Shami']"
] |
null | null |
2403.13027
| null | null |
http://arxiv.org/pdf/2403.13027v1
|
2024-03-19T01:57:09Z
|
2024-03-19T01:57:09Z
|
Towards Better Statistical Understanding of Watermarking LLMs
|
In this paper, we study the problem of watermarking large language models (LLMs). We consider the trade-off between model distortion and detection ability and formulate it as a constrained optimization problem based on the green-red algorithm of Kirchenbauer et al. (2023a). We show that the optimal solution to the optimization problem enjoys a nice analytical property which provides a better understanding and inspires the algorithm design for the watermarking process. We develop an online dual gradient ascent watermarking algorithm in light of this optimization formulation and prove its asymptotic Pareto optimality between model distortion and detection ability. Such a result guarantees an averaged increased green list probability and henceforth detection ability explicitly (in contrast to previous results). Moreover, we provide a systematic discussion on the choice of the model distortion metrics for the watermarking problem. We justify our choice of KL divergence and present issues with the existing criteria of ``distortion-free'' and perplexity. Finally, we empirically evaluate our algorithms on extensive datasets against benchmark algorithms.
|
[
"['Zhongze Cai' 'Shang Liu' 'Hanzhao Wang' 'Huaiyang Zhong' 'Xiaocheng Li']"
] |
null | null |
2403.13031
| null | null |
http://arxiv.org/pdf/2403.13031v1
|
2024-03-19T07:25:02Z
|
2024-03-19T07:25:02Z
|
RigorLLM: Resilient Guardrails for Large Language Models against
Undesired Content
|
Recent advancements in Large Language Models (LLMs) have showcased remarkable capabilities across various tasks in different domains. However, the emergence of biases and the potential for generating harmful content in LLMs, particularly under malicious inputs, pose significant challenges. Current mitigation strategies, while effective, are not resilient under adversarial attacks. This paper introduces Resilient Guardrails for Large Language Models (RigorLLM), a novel framework designed to efficiently and effectively moderate harmful and unsafe inputs and outputs for LLMs. By employing a multi-faceted approach that includes energy-based training data augmentation through Langevin dynamics, optimizing a safe suffix for inputs via minimax optimization, and integrating a fusion-based model combining robust KNN with LLMs based on our data augmentation, RigorLLM offers a robust solution to harmful content moderation. Our experimental evaluations demonstrate that RigorLLM not only outperforms existing baselines like OpenAI API and Perspective API in detecting harmful content but also exhibits unparalleled resilience to jailbreaking attacks. The innovative use of constrained optimization and a fusion-based guardrail approach represents a significant step forward in developing more secure and reliable LLMs, setting a new standard for content moderation frameworks in the face of evolving digital threats.
|
[
"['Zhuowen Yuan' 'Zidi Xiong' 'Yi Zeng' 'Ning Yu' 'Ruoxi Jia' 'Dawn Song'\n 'Bo Li']"
] |
null | null |
2403.13032
| null | null |
http://arxiv.org/pdf/2403.13032v2
|
2024-04-04T11:26:58Z
|
2024-03-19T09:33:07Z
|
Hybrid Unsupervised Learning Strategy for Monitoring Industrial Batch
Processes
|
Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced data sets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. To evaluate the performance of the HULS concept, comparative experiments are performed based on a laboratory batch
|
[
"['Christian W. Frey']"
] |
null | null |
2403.13037
| null | null |
http://arxiv.org/pdf/2403.13037v1
|
2024-03-19T14:11:20Z
|
2024-03-19T14:11:20Z
|
BiLoRA: A Bi-level Optimization Framework for Overfitting-Resilient
Low-Rank Adaptation of Large Pre-trained Models
|
Low-rank adaptation (LoRA) is a popular method for fine-tuning large-scale pre-trained models in downstream tasks by learning low-rank incremental matrices. Though LoRA and its variants effectively reduce the number of trainable parameters compared to full fine-tuning methods, they often overfit training data, resulting in sub-optimal generalization on test data. To address this problem, we introduce BiLoRA, an overfitting-alleviating fine-tuning approach based on bi-level optimization (BLO). BiLoRA employs pseudo singular value decomposition to parameterize low-rank incremental matrices and splits the training of pseudo singular vectors and values across two different subsets of training data. This division, embedded within separate levels of the BLO framework, mitigates the risk of overfitting to a single dataset. Tested on ten datasets covering natural language understanding and generation tasks and applied to various well-known large pre-trained models, BiLoRA significantly outperforms LoRA methods and other fine-tuning approaches, with similar amounts of trainable parameters.
|
[
"['Rushi Qiang' 'Ruiyi Zhang' 'Pengtao Xie']"
] |
null | null |
2403.13040
| null | null |
http://arxiv.org/abs/2403.13040v2
|
2024-06-27T17:27:13Z
|
2024-03-19T17:35:17Z
|
Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping
|
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme by utilizing physics-informed neural networks (PINNs) and a physics-guided nnU-Net-based supervised approach. When evaluated on simulated color Doppler images derived from a patient-specific computational fluid dynamics model and in vivo Doppler acquisitions, both approaches demonstrate comparable reconstruction performance to the original iVFM algorithm. The efficiency of PINNs is boosted through dual-stage optimization and pre-optimized weights. On the other hand, the nnU-Net method excels in generalizability and real-time capabilities. Notably, nnU-Net shows superior robustness on sparse and truncated Doppler data while maintaining independence from explicit boundary conditions. Overall, our results highlight the effectiveness of these methods in reconstructing intraventricular vector blood flow. The study also suggests potential applications of PINNs in ultrafast color Doppler imaging and the incorporation of fluid dynamics equations to derive biomarkers for cardiovascular diseases based on blood flow.
|
[
"['Hang Jung Ling' 'Salomé Bru' 'Julia Puig' 'Florian Vixège'\n 'Simon Mendez' 'Franck Nicoud' 'Pierre-Yves Courand' 'Olivier Bernard'\n 'Damien Garcia']"
] |
null | null |
2403.13041
| null | null |
http://arxiv.org/pdf/2403.13041v4
|
2024-06-21T08:51:29Z
|
2024-03-19T17:54:49Z
|
Provable Privacy with Non-Private Pre-Processing
|
When analysing Differentially Private (DP) machine learning pipelines, the potential privacy cost of data-dependent pre-processing is frequently overlooked in privacy accounting. In this work, we propose a general framework to evaluate the additional privacy cost incurred by non-private data-dependent pre-processing algorithms. Our framework establishes upper bounds on the overall privacy guarantees by utilising two new technical notions: a variant of DP termed Smooth DP and the bounded sensitivity of the pre-processing algorithms. In addition to the generic framework, we provide explicit overall privacy guarantees for multiple data-dependent pre-processing algorithms, such as data imputation, quantization, deduplication and PCA, when used in combination with several DP algorithms. Notably, this framework is also simple to implement, allowing direct integration into existing DP pipelines.
|
[
"['Yaxi Hu' 'Amartya Sanyal' 'Bernhard Schölkopf']"
] |
null | null |
2403.13082
| null | null |
http://arxiv.org/pdf/2403.13082v1
|
2024-03-19T18:26:45Z
|
2024-03-19T18:26:45Z
|
Pruning for Improved ADC Efficiency in Crossbar-based Analog In-memory
Accelerators
|
Deep learning has proved successful in many applications but suffers from high computational demands and requires custom accelerators for deployment. Crossbar-based analog in-memory architectures are attractive for acceleration of deep neural networks (DNN), due to their high data reuse and high efficiency enabled by combining storage and computation in memory. However, they require analog-to-digital converters (ADCs) to communicate crossbar outputs. ADCs consume a significant portion of energy and area of every crossbar processing unit, thus diminishing the potential efficiency benefits. Pruning is a well-studied technique to improve the efficiency of DNNs but requires modifications to be effective for crossbars. In this paper, we motivate crossbar-attuned pruning to target ADC-specific inefficiencies. This is achieved by identifying three key properties (dubbed D.U.B.) that induce sparsity that can be utilized to reduce ADC energy without sacrificing accuracy. The first property ensures that sparsity translates effectively to hardware efficiency by restricting sparsity levels to Discrete powers of 2. The other 2 properties encourage columns in the same crossbar to achieve both Unstructured and Balanced sparsity in order to amortize the accuracy drop. The desired D.U.B. sparsity is then achieved by regularizing the variance of $L_{0}$ norms of neighboring columns within the same crossbar. Our proposed implementation allows it to be directly used in end-to-end gradient-based training. We apply the proposed algorithm to convolutional layers of VGG11 and ResNet18 models, trained on CIFAR-10 and ImageNet datasets, and achieve up to 7.13x and 1.27x improvement, respectively, in ADC energy with less than 1% drop in accuracy.
|
[
"['Timur Ibrayev' 'Isha Garg' 'Indranil Chakraborty' 'Kaushik Roy']"
] |
null | null |
2403.13086
| null | null |
http://arxiv.org/pdf/2403.13086v3
|
2024-06-19T16:49:14Z
|
2024-03-19T18:32:48Z
|
Listenable Maps for Audio Classifiers
|
Despite the impressive performance of deep learning models across diverse tasks, their complexity poses challenges for interpretation. This challenge is particularly evident for audio signals, where conveying interpretations becomes inherently difficult. To address this issue, we introduce Listenable Maps for Audio Classifiers (L-MAC), a posthoc interpretation method that generates faithful and listenable interpretations. L-MAC utilizes a decoder on top of a pretrained classifier to generate binary masks that highlight relevant portions of the input audio. We train the decoder with a loss function that maximizes the confidence of the classifier decision on the masked-in portion of the audio while minimizing the probability of model output for the masked-out portion. Quantitative evaluations on both in-domain and out-of-domain data demonstrate that L-MAC consistently produces more faithful interpretations than several gradient and masking-based methodologies. Furthermore, a user study confirms that, on average, users prefer the interpretations generated by the proposed technique.
|
[
"['Francesco Paissan' 'Mirco Ravanelli' 'Cem Subakan']"
] |
null | null |
2403.13091
| null | null |
http://arxiv.org/pdf/2403.13091v1
|
2024-03-19T18:40:50Z
|
2024-03-19T18:40:50Z
|
JaxUED: A simple and useable UED library in Jax
|
We present JaxUED, an open-source library providing minimal dependency implementations of modern Unsupervised Environment Design (UED) algorithms in Jax. JaxUED leverages hardware acceleration to obtain on the order of 100x speedups compared to prior, CPU-based implementations. Inspired by CleanRL, we provide fast, clear, understandable, and easily modifiable implementations, with the aim of accelerating research into UED. This paper describes our library and contains baseline results. Code can be found at https://github.com/DramaCow/jaxued.
|
[
"['Samuel Coward' 'Michael Beukman' 'Jakob Foerster']"
] |
null | null |
2403.13097
| null | null |
http://arxiv.org/pdf/2403.13097v1
|
2024-03-19T18:57:53Z
|
2024-03-19T18:57:53Z
|
Simple Ingredients for Offline Reinforcement Learning
|
Offline reinforcement learning algorithms have proven effective on datasets highly connected to the target downstream task. Yet, leveraging a novel testbed (MOOD) in which trajectories come from heterogeneous sources, we show that existing methods struggle with diverse data: their performance considerably deteriorates as data collected for related but different tasks is simply added to the offline buffer. In light of this finding, we conduct a large empirical study where we formulate and test several hypotheses to explain this failure. Surprisingly, we find that scale, more than algorithmic considerations, is the key factor influencing performance. We show that simple methods like AWAC and IQL with increased network size overcome the paradoxical failure modes from the inclusion of additional data in MOOD, and notably outperform prior state-of-the-art algorithms on the canonical D4RL benchmark.
|
[
"['Edoardo Cetin' 'Andrea Tirinzoni' 'Matteo Pirotta' 'Alessandro Lazaric'\n 'Yann Ollivier' 'Ahmed Touati']"
] |
null | null |
2403.13101
| null | null |
http://arxiv.org/pdf/2403.13101v3
|
2024-05-22T07:10:12Z
|
2024-03-19T19:05:24Z
|
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge
Networks
|
The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated learning (SFL) has emerged as a promising solution by of floading the primary training workload to a server via model partitioning while enabling parallel training among edge devices. However, although system optimization substantially influences the performance of SFL under resource-constrained systems, the problem remains largely uncharted. In this paper, we provide a convergence analysis of SFL which quantifies the impact of model splitting (MS) and client-side model aggregation (MA) on the learning performance, serving as a theoretical foundation. Then, we propose AdaptSFL, a novel resource-adaptive SFL framework, to expedite SFL under resource-constrained edge computing systems. Specifically, AdaptSFL adaptively controls client-side MA and MS to balance communication-computing latency and training convergence. Extensive simulations across various datasets validate that our proposed AdaptSFL framework takes considerably less time to achieve a target accuracy than benchmarks, demonstrating the effectiveness of the proposed strategies.
|
[
"['Zheng Lin' 'Guanqiao Qu' 'Wei Wei' 'Xianhao Chen' 'Kin K. Leung']"
] |
null | null |
2403.13106
| null | null |
http://arxiv.org/pdf/2403.13106v1
|
2024-03-19T19:13:22Z
|
2024-03-19T19:13:22Z
|
Knowing Your Nonlinearities: Shapley Interactions Reveal the Underlying
Structure of Data
|
Measuring nonlinear feature interaction is an established approach to understanding complex patterns of attribution in many models. In this paper, we use Shapley Taylor interaction indices (STII) to analyze the impact of underlying data structure on model representations in a variety of modalities, tasks, and architectures. Considering linguistic structure in masked and auto-regressive language models (MLMs and ALMs), we find that STII increases within idiomatic expressions and that MLMs scale STII with syntactic distance, relying more on syntax in their nonlinear structure than ALMs do. Our speech model findings reflect the phonetic principal that the openness of the oral cavity determines how much a phoneme varies based on its context. Finally, we study image classifiers and illustrate that feature interactions intuitively reflect object boundaries. Our wide range of results illustrates the benefits of interdisciplinary work and domain expertise in interpretability research.
|
[
"['Divyansh Singhvi' 'Andrej Erkelens' 'Raghav Jain' 'Diganta Misra'\n 'Naomi Saphra']"
] |
null | null |
2403.13107
| null | null |
http://arxiv.org/pdf/2403.13107v2
|
2024-07-02T03:35:53Z
|
2024-03-19T19:15:13Z
|
Towards Unsupervised Question Answering System with Multi-level
Summarization for Legal Text
|
This paper summarizes Team SCaLAR's work on SemEval-2024 Task 5: Legal Argument Reasoning in Civil Procedure. To address this Binary Classification task, which was daunting due to the complexity of the Legal Texts involved, we propose a simple yet novel similarity and distance-based unsupervised approach to generate labels. Further, we explore the Multi-level fusion of Legal-Bert embeddings using ensemble features, including CNN, GRU, and LSTM. To address the lengthy nature of Legal explanation in the dataset, we introduce T5-based segment-wise summarization, which successfully retained crucial information, enhancing the model's performance. Our unsupervised system witnessed a 20-point increase in macro F1-score on the development set and a 10-point increase on the test set, which is promising given its uncomplicated architecture.
|
[
"['M Manvith Prabhu' 'Haricharana Srinivasa' 'Anand Kumar M']"
] |
null | null |
2403.13108
| null | null |
http://arxiv.org/pdf/2403.13108v1
|
2024-03-19T19:15:38Z
|
2024-03-19T19:15:38Z
|
Analyzing the Impact of Partial Sharing on the Resilience of Online
Federated Learning Against Model Poisoning Attacks
|
We scrutinize the resilience of the partial-sharing online federated learning (PSO-Fed) algorithm against model-poisoning attacks. PSO-Fed reduces the communication load by enabling clients to exchange only a fraction of their model estimates with the server at each update round. Partial sharing of model estimates also enhances the robustness of the algorithm against model-poisoning attacks. To gain better insights into this phenomenon, we analyze the performance of the PSO-Fed algorithm in the presence of Byzantine clients, malicious actors who may subtly tamper with their local models by adding noise before sharing them with the server. Through our analysis, we demonstrate that PSO-Fed maintains convergence in both mean and mean-square senses, even under the strain of model-poisoning attacks. We further derive the theoretical mean square error (MSE) of PSO-Fed, linking it to various parameters such as stepsize, attack probability, number of Byzantine clients, client participation rate, partial-sharing ratio, and noise variance. We also show that there is a non-trivial optimal stepsize for PSO-Fed when faced with model-poisoning attacks. The results of our extensive numerical experiments affirm our theoretical assertions and highlight the superior ability of PSO-Fed to counteract Byzantine attacks, outperforming other related leading algorithms.
|
[
"['Ehsan Lari' 'Vinay Chakravarthi Gogineni' 'Reza Arablouei'\n 'Stefan Werner']"
] |
null | null |
2403.13111
| null | null |
http://arxiv.org/pdf/2403.13111v1
|
2024-03-19T19:24:00Z
|
2024-03-19T19:24:00Z
|
Deep learning with noisy labels in medical prediction problems: a
scoping review
|
Objectives: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included. Methods: Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include "noisy label AND medical / healthcare / clinical", "un-certainty AND medical / healthcare / clinical", and "noise AND medical / healthcare / clinical". Results: A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided. Discussion: From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.
|
[
"['Yishu Wei' 'Yu Deng' 'Cong Sun' 'Mingquan Lin' 'Hongmei Jiang'\n 'Yifan Peng']"
] |
null | null |
2403.13117
| null | null |
http://arxiv.org/pdf/2403.13117v2
|
2024-05-25T09:42:20Z
|
2024-03-19T19:44:54Z
|
Optimal Flow Matching: Learning Straight Trajectories in Just One Step
|
Over the several recent years, there has been a boom in development of Flow Matching (FM) methods for generative modeling. One intriguing property pursued by the community is the ability to learn flows with straight trajectories which realize the Optimal Transport (OT) displacements. Straightness is crucial for the fast integration (inference) of the learned flow's paths. Unfortunately, most existing flow straightening methods are based on non-trivial iterative FM procedures which accumulate the error during training or exploit heuristics based on minibatch OT. To address these issues, we develop and theoretically justify the novel Optimal Flow Matching approach which allows recovering the straight OT displacement for the quadratic transport in just one FM step. The main idea of our approach is the employment of vector field for FM which are parameterized by convex functions.
|
[
"['Nikita Kornilov' 'Petr Mokrov' 'Alexander Gasnikov' 'Alexander Korotin']"
] |
null | null |
2403.13118
| null | null |
http://arxiv.org/pdf/2403.13118v1
|
2024-03-19T19:47:02Z
|
2024-03-19T19:47:02Z
|
Modal Analysis of Spatiotemporal Data via Multivariate Gaussian Process
Regression
|
Modal analysis has become an essential tool to understand the coherent structure of complex flows. The classical modal analysis methods, such as dynamic mode decomposition (DMD) and spectral proper orthogonal decomposition (SPOD), rely on a sufficient amount of data that is regularly sampled in time. However, often one needs to deal with sparse temporally irregular data, e.g., due to experimental measurements and simulation algorithm. To overcome the limitations of data scarcity and irregular sampling, we propose a novel modal analysis technique using multi-variate Gaussian process regression (MVGPR). We first establish the connection between MVGPR and the existing modal analysis techniques, DMD and SPOD, from a linear system identification perspective. Next, leveraging this connection, we develop a MVGPR-based modal analysis technique that addresses the aforementioned limitations. The capability of MVGPR is endowed by its judiciously designed kernel structure for correlation function, that is derived from the assumed linear dynamics. Subsequently, the proposed MVGPR method is benchmarked against DMD and SPOD on a range of examples, from academic and synthesized data to unsteady airfoil aerodynamics. The results demonstrate MVGPR as a promising alternative to classical modal analysis methods, especially in the scenario of scarce and temporally irregular data.
|
[
"['Jiwoo Song' 'Daning Huang']"
] |
null | null |
2403.13125
| null | null |
http://arxiv.org/pdf/2403.13125v1
|
2024-03-19T19:55:38Z
|
2024-03-19T19:55:38Z
|
Probabilistic Circuits with Constraints via Convex Optimization
|
This work addresses integrating probabilistic propositional logic constraints into the distribution encoded by a probabilistic circuit (PC). PCs are a class of tractable models that allow efficient computations (such as conditional and marginal probabilities) while achieving state-of-the-art performance in some domains. The proposed approach takes both a PC and constraints as inputs, and outputs a new PC that satisfies the constraints. This is done efficiently via convex optimization without the need to retrain the entire model. Empirical evaluations indicate that the combination of constraints and PCs can have multiple use cases, including the improvement of model performance under scarce or incomplete data, as well as the enforcement of machine learning fairness measures into the model without compromising model fitness. We believe that these ideas will open possibilities for multiple other applications involving the combination of logics and deep probabilistic models.
|
[
"['Soroush Ghandi' 'Benjamin Quost' 'Cassio de Campos']"
] |
null | null |
2403.13128
| null | null |
http://arxiv.org/pdf/2403.13128v1
|
2024-03-19T19:57:37Z
|
2024-03-19T19:57:37Z
|
AdaFish: Fast low-rank parameter-efficient fine-tuning by using
second-order information
|
Recent advancements in large-scale pretrained models have significantly improved performance across a variety of tasks in natural language processing and computer vision. However, the extensive number of parameters in these models necessitates substantial memory and computational resources for full training. To adapt these models for downstream tasks or specific application-oriented datasets, parameter-efficient fine-tuning methods leveraging pretrained parameters have gained considerable attention. However, it can still be time-consuming due to lots of parameters and epochs. In this work, we introduce AdaFish, an efficient algorithm of the second-order type designed to expedite the training process within low-rank decomposition-based fine-tuning frameworks. Our key observation is that the associated generalized Fisher information matrix is either low-rank or extremely small-scaled. Such a generalized Fisher information matrix is shown to be equivalent to the Hessian matrix. Moreover, we prove the global convergence of AdaFish, along with its iteration/oracle complexity. Numerical experiments show that our algorithm is quite competitive with the state-of-the-art AdamW method.
|
[
"['Jiang Hu' 'Quanzheng Li']"
] |
null | null |
2403.13130
| null | null |
http://arxiv.org/pdf/2403.13130v1
|
2024-03-19T19:59:54Z
|
2024-03-19T19:59:54Z
|
Self-generated Replay Memories for Continual Neural Machine Translation
|
Modern Neural Machine Translation systems exhibit strong performance in several different languages and are constantly improving. Their ability to learn continuously is, however, still severely limited by the catastrophic forgetting issue. In this work, we leverage a key property of encoder-decoder Transformers, i.e. their generative ability, to propose a novel approach to continually learning Neural Machine Translation systems. We show how this can effectively learn on a stream of experiences comprising different languages, by leveraging a replay memory populated by using the model itself as a generator of parallel sentences. We empirically demonstrate that our approach can counteract catastrophic forgetting without requiring explicit memorization of training data. Code will be publicly available upon publication. Code: https://github.com/m-resta/sg-rep
|
[
"['Michele Resta' 'Davide Bacciu']"
] |
null | null |
2403.13134
| null | null |
http://arxiv.org/pdf/2403.13134v1
|
2024-03-19T20:10:23Z
|
2024-03-19T20:10:23Z
|
Robust NAS under adversarial training: benchmark, theory, and beyond
|
Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robust architectures, especially when adversarial training is considered. In this work, we aim to address these two challenges, making twofold contributions. First, we release a comprehensive data set that encompasses both clean accuracy and robust accuracy for a vast array of adversarially trained networks from the NAS-Bench-201 search space on image datasets. Then, leveraging the neural tangent kernel (NTK) tool from deep learning theory, we establish a generalization theory for searching architecture in terms of clean accuracy and robust accuracy under multi-objective adversarial training. We firmly believe that our benchmark and theoretical insights will significantly benefit the NAS community through reliable reproducibility, efficient assessment, and theoretical foundation, particularly in the pursuit of robust architectures.
|
[
"['Yongtao Wu' 'Fanghui Liu' 'Carl-Johann Simon-Gabriel'\n 'Grigorios G Chrysos' 'Volkan Cevher']"
] |
null | null |
2403.13135
| null | null |
http://arxiv.org/pdf/2403.13135v1
|
2024-03-19T20:10:50Z
|
2024-03-19T20:10:50Z
|
A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling
of Sentinel-2 Imagery
|
The observation of the advancing and retreating pattern of polar sea ice cover stands as a vital indicator of global warming. This research aims to develop a robust, effective, and scalable system for classifying polar sea ice as thick/snow-covered, young/thin, or open water using Sentinel-2 (S2) images. Since the S2 satellite is actively capturing high-resolution imagery over the earth's surface, there are lots of images that need to be classified. One major obstacle is the absence of labeled S2 training data (images) to act as the ground truth. We demonstrate a scalable and accurate method for segmenting and automatically labeling S2 images using carefully determined color thresholds. We employ a parallel workflow using PySpark to scale and achieve 9-fold data loading and 16-fold map-reduce speedup on auto-labeling S2 images based on thin cloud and shadow-filtered color-based segmentation to generate label data. The auto-labeled data generated from this process are then employed to train a U-Net machine learning model, resulting in good classification accuracy. As training the U-Net classification model is computationally heavy and time-consuming, we distribute the U-Net model training to scale it over 8 GPUs using the Horovod framework over a DGX cluster with a 7.21x speedup without affecting the accuracy of the model. Using the Antarctic's Ross Sea region as an example, the U-Net model trained on auto-labeled data achieves a classification accuracy of 98.97% for auto-labeled training datasets when the thin clouds and shadows from the S2 images are filtered out.
|
[
"['Jurdana Masuma Iqrah' 'Wei Wang' 'Hongjie Xie' 'Sushil Prasad']"
] |
null | null |
2403.13136
| null | null |
http://arxiv.org/pdf/2403.13136v1
|
2024-03-19T20:12:46Z
|
2024-03-19T20:12:46Z
|
Multi-fidelity surrogate with heterogeneous input spaces for modeling
melt pools in laser-directed energy deposition
|
Multi-fidelity (MF) modeling is a powerful statistical approach that can intelligently blend data from varied fidelity sources. This approach finds a compelling application in predicting melt pool geometry for laser-directed energy deposition (L-DED). One major challenge in using MF surrogates to merge a hierarchy of melt pool models is the variability in input spaces. To address this challenge, this paper introduces a novel approach for constructing an MF surrogate for predicting melt pool geometry by integrating models of varying complexity, that operate on heterogeneous input spaces. The first thermal model incorporates five input parameters i.e., laser power, scan velocity, powder flow rate, carrier gas flow rate, and nozzle height. In contrast, the second thermal model can only handle laser power and scan velocity. A mapping is established between the heterogeneous input spaces so that the five-dimensional space can be morphed into a pseudo two-dimensional space. Predictions are then blended using a Gaussian process-based co-kriging method. The resulting heterogeneous multi-fidelity Gaussian process (Het-MFGP) surrogate not only improves predictive accuracy but also offers computational efficiency by reducing evaluations required from the high-dimensional, high-fidelity thermal model. The results underscore the benefits of employing Het-MFGP for modeling melt pool behavior in L-DED. The framework successfully demonstrates how to leverage multimodal data and handle scenarios where certain input parameters may be difficult to model or measure.
|
[
"['Nandana Menon' 'Amrita Basak']"
] |
null | null |
2403.13141
| null | null |
http://arxiv.org/pdf/2403.13141v1
|
2024-03-19T20:23:31Z
|
2024-03-19T20:23:31Z
|
Function Trees: Transparent Machine Learning
|
The output of a machine learning algorithm can usually be represented by one or more multivariate functions of its input variables. Knowing the global properties of such functions can help in understanding the system that produced the data as well as interpreting and explaining corresponding model predictions. A method is presented for representing a general multivariate function as a tree of simpler functions. This tree exposes the global internal structure of the function by uncovering and describing the combined joint influences of subsets of its input variables. Given the inputs and corresponding function values, a function tree is constructed that can be used to rapidly identify and compute all of the function's main and interaction effects up to high order. Interaction effects involving up to four variables are graphically visualized.
|
[
"['Jerome H. Friedman']"
] |
null | null |
2403.13148
| null | null |
http://arxiv.org/pdf/2403.13148v1
|
2024-03-19T20:52:31Z
|
2024-03-19T20:52:31Z
|
SIFT-DBT: Self-supervised Initialization and Fine-Tuning for Imbalanced
Digital Breast Tomosynthesis Image Classification
|
Digital Breast Tomosynthesis (DBT) is a widely used medical imaging modality for breast cancer screening and diagnosis, offering higher spatial resolution and greater detail through its 3D-like breast volume imaging capability. However, the increased data volume also introduces pronounced data imbalance challenges, where only a small fraction of the volume contains suspicious tissue. This further exacerbates the data imbalance due to the case-level distribution in real-world data and leads to learning a trivial classification model that only predicts the majority class. To address this, we propose a novel method using view-level contrastive Self-supervised Initialization and Fine-Tuning for identifying abnormal DBT images, namely SIFT-DBT. We further introduce a patch-level multi-instance learning method to preserve spatial resolution. The proposed method achieves 92.69% volume-wise AUC on an evaluation of 970 unique studies.
|
[
"['Yuexi Du' 'Regina J. Hooley' 'John Lewin' 'Nicha C. Dvornek']"
] |
null | null |
2403.13150
| null | null |
http://arxiv.org/pdf/2403.13150v1
|
2024-03-19T20:58:38Z
|
2024-03-19T20:58:38Z
|
Training Survival Models using Scoring Rules
|
Survival Analysis provides critical insights for partially incomplete time-to-event data in various domains. It is also an important example of probabilistic machine learning. The probabilistic nature of the predictions can be exploited by using (proper) scoring rules in the model fitting process instead of likelihood-based optimization. Our proposal does so in a generic manner and can be used for a variety of model classes. We establish different parametric and non-parametric sub-frameworks that allow different degrees of flexibility. Incorporated into neural networks, it leads to a computationally efficient and scalable optimization routine, yielding state-of-the-art predictive performance. Finally, we show that using our framework, we can recover various parametric models and demonstrate that optimization works equally well when compared to likelihood-based methods.
|
[
"['Philipp Kopper' 'David Rügamer' 'Raphael Sonabend' 'Bernd Bischl'\n 'Andreas Bender']"
] |
null | null |
2403.13164
| null | null |
http://arxiv.org/pdf/2403.13164v1
|
2024-03-19T21:31:56Z
|
2024-03-19T21:31:56Z
|
VL-ICL Bench: The Devil in the Details of Benchmarking Multimodal
In-Context Learning
|
Large language models (LLMs) famously exhibit emergent in-context learning (ICL) -- the ability to rapidly adapt to new tasks using few-shot examples provided as a prompt, without updating the model's weights. Built on top of LLMs, vision large language models (VLLMs) have advanced significantly in areas such as recognition, reasoning, and grounding. However, investigations into emph{multimodal ICL} have predominantly focused on few-shot visual question answering (VQA), and image captioning, which we will show neither exploit the strengths of ICL, nor test its limitations. The broader capabilities and limitations of multimodal ICL remain under-explored. In this study, we introduce a comprehensive benchmark VL-ICL Bench for multimodal in-context learning, encompassing a broad spectrum of tasks that involve both images and text as inputs and outputs, and different types of challenges, from {perception to reasoning and long context length}. We evaluate the abilities of state-of-the-art VLLMs against this benchmark suite, revealing their diverse strengths and weaknesses, and showing that even the most advanced models, such as GPT-4, find the tasks challenging. By highlighting a range of new ICL tasks, and the associated strengths and limitations of existing models, we hope that our dataset will inspire future work on enhancing the in-context learning capabilities of VLLMs, as well as inspire new applications that leverage VLLM ICL. The code and dataset are available at https://github.com/ys-zong/VL-ICL.
|
[
"['Yongshuo Zong' 'Ondrej Bohdal' 'Timothy Hospedales']"
] |
null | null |
2403.13176
| null | null |
http://arxiv.org/pdf/2403.13176v1
|
2024-03-19T22:05:32Z
|
2024-03-19T22:05:32Z
|
Castor: Competing shapelets for fast and accurate time series
classification
|
Shapelets are discriminative subsequences, originally embedded in shapelet-based decision trees but have since been extended to shapelet-based transformations. We propose Castor, a simple, efficient, and accurate time series classification algorithm that utilizes shapelets to transform time series. The transformation organizes shapelets into groups with varying dilation and allows the shapelets to compete over the time context to construct a diverse feature representation. By organizing the shapelets into groups, we enable the transformation to transition between levels of competition, resulting in methods that more closely resemble distance-based transformations or dictionary-based transformations. We demonstrate, through an extensive empirical investigation, that Castor yields transformations that result in classifiers that are significantly more accurate than several state-of-the-art classifiers. In an extensive ablation study, we examine the effect of choosing hyperparameters and suggest accurate and efficient default values.
|
[
"['Isak Samsten' 'Zed Lee']"
] |
null | null |
2403.13178
| null | null |
http://arxiv.org/pdf/2403.13178v1
|
2024-03-19T22:18:19Z
|
2024-03-19T22:18:19Z
|
Fast Value Tracking for Deep Reinforcement Learning
|
Reinforcement learning (RL) tackles sequential decision-making problems by creating agents that interacts with their environment. However, existing algorithms often view these problem as static, focusing on point estimates for model parameters to maximize expected rewards, neglecting the stochastic dynamics of agent-environment interactions and the critical role of uncertainty quantification. Our research leverages the Kalman filtering paradigm to introduce a novel and scalable sampling algorithm called Langevinized Kalman Temporal-Difference (LKTD) for deep reinforcement learning. This algorithm, grounded in Stochastic Gradient Markov Chain Monte Carlo (SGMCMC), efficiently draws samples from the posterior distribution of deep neural network parameters. Under mild conditions, we prove that the posterior samples generated by the LKTD algorithm converge to a stationary distribution. This convergence not only enables us to quantify uncertainties associated with the value function and model parameters but also allows us to monitor these uncertainties during policy updates throughout the training phase. The LKTD algorithm paves the way for more robust and adaptable reinforcement learning approaches.
|
[
"['Frank Shih' 'Faming Liang']"
] |
null | null |
2403.13179
| null | null |
http://arxiv.org/pdf/2403.13179v1
|
2024-03-19T22:19:29Z
|
2024-03-19T22:19:29Z
|
Predictive, scalable and interpretable knowledge tracing on structured
domains
|
Intelligent tutoring systems optimize the selection and timing of learning materials to enhance understanding and long-term retention. This requires estimates of both the learner's progress (''knowledge tracing''; KT), and the prerequisite structure of the learning domain (''knowledge mapping''). While recent deep learning models achieve high KT accuracy, they do so at the expense of the interpretability of psychologically-inspired models. In this work, we present a solution to this trade-off. PSI-KT is a hierarchical generative approach that explicitly models how both individual cognitive traits and the prerequisite structure of knowledge influence learning dynamics, thus achieving interpretability by design. Moreover, by using scalable Bayesian inference, PSI-KT targets the real-world need for efficient personalization even with a growing body of learners and learning histories. Evaluated on three datasets from online learning platforms, PSI-KT achieves superior multi-step predictive accuracy and scalable inference in continual-learning settings, all while providing interpretable representations of learner-specific traits and the prerequisite structure of knowledge that causally supports learning. In sum, predictive, scalable and interpretable knowledge tracing with solid knowledge mapping lays a key foundation for effective personalized learning to make education accessible to a broad, global audience.
|
[
"['Hanqi Zhou' 'Robert Bamler' 'Charley M. Wu' 'Álvaro Tejero-Cantero']"
] |
null | null |
2403.13196
| null | null |
http://arxiv.org/pdf/2403.13196v1
|
2024-03-19T23:13:40Z
|
2024-03-19T23:13:40Z
|
ADAPT to Robustify Prompt Tuning Vision Transformers
|
The performance of deep models, including Vision Transformers, is known to be vulnerable to adversarial attacks. Many existing defenses against these attacks, such as adversarial training, rely on full-model fine-tuning to induce robustness in the models. These defenses require storing a copy of the entire model, that can have billions of parameters, for each task. At the same time, parameter-efficient prompt tuning is used to adapt large transformer-based models to downstream tasks without the need to save large copies. In this paper, we examine parameter-efficient prompt tuning of Vision Transformers for downstream tasks under the lens of robustness. We show that previous adversarial defense methods, when applied to the prompt tuning paradigm, suffer from gradient obfuscation and are vulnerable to adaptive attacks. We introduce ADAPT, a novel framework for performing adaptive adversarial training in the prompt tuning paradigm. Our method achieves competitive robust accuracy of ~40% w.r.t. SOTA robustness methods using full-model fine-tuning, by tuning only ~1% of the number of parameters.
|
[
"['Masih Eskandar' 'Tooba Imtiaz' 'Zifeng Wang' 'Jennifer Dy']"
] |
null | null |
2403.13204
| null | null |
http://arxiv.org/pdf/2403.13204v1
|
2024-03-19T23:50:11Z
|
2024-03-19T23:50:11Z
|
Diversity-Aware Agnostic Ensemble of Sharpness Minimizers
|
There has long been plenty of theoretical and empirical evidence supporting the success of ensemble learning. Deep ensembles in particular take advantage of training randomness and expressivity of individual neural networks to gain prediction diversity, ultimately leading to better generalization, robustness and uncertainty estimation. In respect of generalization, it is found that pursuing wider local minima result in models being more robust to shifts between training and testing sets. A natural research question arises out of these two approaches as to whether a boost in generalization ability can be achieved if ensemble learning and loss sharpness minimization are integrated. Our work investigates this connection and proposes DASH - a learning algorithm that promotes diversity and flatness within deep ensembles. More concretely, DASH encourages base learners to move divergently towards low-loss regions of minimal sharpness. We provide a theoretical backbone for our method along with extensive empirical evidence demonstrating an improvement in ensemble generalizability.
|
[
"['Anh Bui' 'Vy Vo' 'Tung Pham' 'Dinh Phung' 'Trung Le']"
] |
null | null |
2403.13213
| null | null |
http://arxiv.org/pdf/2403.13213v4
|
2024-07-05T15:40:13Z
|
2024-03-20T00:22:38Z
|
From Representational Harms to Quality-of-Service Harms: A Case Study on
Llama 2 Safety Safeguards
|
Recent progress in large language models (LLMs) has led to their widespread adoption in various domains. However, these advancements have also introduced additional safety risks and raised concerns regarding their detrimental impact on already marginalized populations. Despite growing mitigation efforts to develop safety safeguards, such as supervised safety-oriented fine-tuning and leveraging safe reinforcement learning from human feedback, multiple concerns regarding the safety and ingrained biases in these models remain. Furthermore, previous work has demonstrated that models optimized for safety often display exaggerated safety behaviors, such as a tendency to refrain from responding to certain requests as a precautionary measure. As such, a clear trade-off between the helpfulness and safety of these models has been documented in the literature. In this paper, we further investigate the effectiveness of safety measures by evaluating models on already mitigated biases. Using the case of Llama 2 as an example, we illustrate how LLMs' safety responses can still encode harmful assumptions. To do so, we create a set of non-toxic prompts, which we then use to evaluate Llama models. Through our new taxonomy of LLMs responses to users, we observe that the safety/helpfulness trade-offs are more pronounced for certain demographic groups which can lead to quality-of-service harms for marginalized populations.
|
[
"['Khaoula Chehbouni' 'Megha Roshan' 'Emmanuel Ma' 'Futian Andrew Wei'\n 'Afaf Taik' 'Jackie CK Cheung' 'Golnoosh Farnadi']"
] |
null | null |
2403.13214
| null | null |
http://arxiv.org/pdf/2403.13214v1
|
2024-03-20T00:23:42Z
|
2024-03-20T00:23:42Z
|
Nellie: Automated organelle segmentation, tracking, and hierarchical
feature extraction in 2D/3D live-cell microscopy
|
The analysis of dynamic organelles remains a formidable challenge, though key to understanding biological processes. We introduce Nellie, an automated and unbiased pipeline for segmentation, tracking, and feature extraction of diverse intracellular structures. Nellie adapts to image metadata, eliminating user input. Nellie's preprocessing pipeline enhances structural contrast on multiple intracellular scales allowing for robust hierarchical segmentation of sub-organellar regions. Internal motion capture markers are generated and tracked via a radius-adaptive pattern matching scheme, and used as guides for sub-voxel flow interpolation. Nellie extracts a plethora of features at multiple hierarchical levels for deep and customizable analysis. Nellie features a Napari-based GUI that allows for code-free operation and visualization, while its modular open-source codebase invites customization by experienced users. We demonstrate Nellie's wide variety of use cases with two examples: unmixing multiple organelles from a single channel using feature-based classification and training an unsupervised graph autoencoder on mitochondrial multi-mesh graphs to quantify latent space embedding changes following ionomycin treatment.
|
[
"['Austin E. Y. T. Lefebvre' 'Gabriel Sturm' 'Ting-Yu Lin' 'Emily Stoops'\n 'Magdalena Preciado Lopez' 'Benjamin Kaufmann-Malaga' 'Kayley Hake']"
] |
null | null |
2403.13219
| null | null |
http://arxiv.org/pdf/2403.13219v1
|
2024-03-20T00:41:12Z
|
2024-03-20T00:41:12Z
|
Diffusion Model for Data-Driven Black-Box Optimization
|
Generative AI has redefined artificial intelligence, enabling the creation of innovative content and customized solutions that drive business practices into a new era of efficiency and creativity. In this paper, we focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization over complex structured variables. Consider the practical scenario where one wants to optimize some structured design in a high-dimensional space, based on massive unlabeled data (representing design variables) and a small labeled dataset. We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons. The goal is to generate new designs that are near-optimal and preserve the designed latent structures. Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models for modeling complex distributions. In particular, we propose a reward-directed conditional diffusion model, to be trained on the mixed data, for sampling a near-optimal solution conditioned on high predicted rewards. Theoretically, we establish sub-optimality error bounds for the generated designs. The sub-optimality gap nearly matches the optimal guarantee in off-policy bandits, demonstrating the efficiency of reward-directed diffusion models for black-box optimization. Moreover, when the data admits a low-dimensional latent subspace structure, our model efficiently generates high-fidelity designs that closely respect the latent structure. We provide empirical experiments validating our model in decision-making and content-creation tasks.
|
[
"['Zihao Li' 'Hui Yuan' 'Kaixuan Huang' 'Chengzhuo Ni' 'Yinyu Ye'\n 'Minshuo Chen' 'Mengdi Wang']"
] |
null | null |
2403.13241
| null | null |
http://arxiv.org/pdf/2403.13241v2
|
2024-04-28T08:29:21Z
|
2024-03-20T02:11:28Z
|
Tackling Noisy Labels with Network Parameter Additive Decomposition
|
Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows that although the networks have the ability to memorize all noisy data, they would first memorize clean training data, and then gradually memorize mislabeled training data. A simple and effective method that exploits the memorization effect to combat noisy labels is early stopping. However, early stopping cannot distinguish the memorization of clean data and mislabeled data, resulting in the network still inevitably overfitting mislabeled data in the early training stage.In this paper, to decouple the memorization of clean data and mislabeled data, and further reduce the side effect of mislabeled data, we perform additive decomposition on network parameters. Namely, all parameters are additively decomposed into two groups, i.e., parameters $mathbf{w}$ are decomposed as $mathbf{w}=bm{sigma}+bm{gamma}$. Afterward, the parameters $bm{sigma}$ are considered to memorize clean data, while the parameters $bm{gamma}$ are considered to memorize mislabeled data. Benefiting from the memorization effect, the updates of the parameters $bm{sigma}$ are encouraged to fully memorize clean data in early training, and then discouraged with the increase of training epochs to reduce interference of mislabeled data. The updates of the parameters $bm{gamma}$ are the opposite. In testing, only the parameters $bm{sigma}$ are employed to enhance generalization. Extensive experiments on both simulated and real-world benchmarks confirm the superior performance of our method.
|
[
"['Jingyi Wang' 'Xiaobo Xia' 'Long Lan' 'Xinghao Wu' 'Jun Yu'\n 'Wenjing Yang' 'Bo Han' 'Tongliang Liu']"
] |
null | null |
2403.13243
| null | null |
http://arxiv.org/pdf/2403.13243v1
|
2024-03-20T02:15:48Z
|
2024-03-20T02:15:48Z
|
A Comparative Study of Machine Learning Models Predicting Energetics of
Interacting Defects
|
Interacting defect systems are ubiquitous in materials under realistic scenarios, yet gaining an atomic-level understanding of these systems from a computational perspective is challenging - it often demands substantial resources due to the necessity of employing supercell calculations. While machine learning techniques have shown potential in accelerating materials simulations, their application to systems involving interacting defects remains relatively rare. In this work, we present a comparative study of three different methods to predict the free energy change of systems with interacting defects. We leveraging a limited dataset from Density Functional Theory(DFT) calculations to assess the performance models using materials descriptors, graph neural networks and cluster expansion. Our findings indicate that the cluster expansion model can achieve precise energetics predictions even with this limited dataset. Furthermore, with synthetic data generate from cluster expansion model at near-DFT levels, we obtained enlarged dataset to assess the demands on data for training accurate prediction models using graph neural networks for systems featuring interacting defects. A brief discussion of the computational cost for each method is provided at the end. This research provide a preliminary evaluation of applying machine learning techniques in imperfect surface systems.
|
[
"['Hao Yu']"
] |
null | null |
2403.13245
| null | null |
http://arxiv.org/pdf/2403.13245v2
|
2024-04-07T19:25:47Z
|
2024-03-20T02:16:54Z
|
Federated reinforcement learning for robot motion planning with
zero-shot generalization
|
This paper considers the problem of learning a control policy for robot motion planning with zero-shot generalization, i.e., no data collection and policy adaptation is needed when the learned policy is deployed in new environments. We develop a federated reinforcement learning framework that enables collaborative learning of multiple learners and a central server, i.e., the Cloud, without sharing their raw data. In each iteration, each learner uploads its local control policy and the corresponding estimated normalized arrival time to the Cloud, which then computes the global optimum among the learners and broadcasts the optimal policy to the learners. Each learner then selects between its local control policy and that from the Cloud for next iteration. The proposed framework leverages on the derived zero-shot generalization guarantees on arrival time and safety. Theoretical guarantees on almost-sure convergence, almost consensus, Pareto improvement and optimality gap are also provided. Monte Carlo simulation is conducted to evaluate the proposed framework.
|
[
"['Zhenyuan Yuan' 'Siyuan Xu' 'Minghui Zhu']"
] |
null | null |
2403.13246
| null | null |
http://arxiv.org/pdf/2403.13246v1
|
2024-03-20T02:17:16Z
|
2024-03-20T02:17:16Z
|
Divide-Conquer Transformer Learning for Predicting Electric Vehicle
Charging Events Using Smart Meter Data
|
Predicting electric vehicle (EV) charging events is crucial for load scheduling and energy management, promoting seamless transportation electrification and decarbonization. While prior studies have focused on EV charging demand prediction, primarily for public charging stations using historical charging data, home charging prediction is equally essential. However, existing prediction methods may not be suitable due to the unavailability of or limited access to home charging data. To address this research gap, inspired by the concept of non-intrusive load monitoring (NILM), we develop a home charging prediction method using historical smart meter data. Different from NILM detecting EV charging that has already occurred, our method provides predictive information of future EV charging occurrences, thus enhancing its utility for charging management. Specifically, our method, leverages a self-attention mechanism-based transformer model, employing a ``divide-conquer'' strategy, to process historical meter data to effectively and learn EV charging representation for charging occurrence prediction. Our method enables prediction at one-minute interval hour-ahead. Experimental results demonstrate the effectiveness of our method, achieving consistently high accuracy of over 96.81% across different prediction time spans. Notably, our method achieves high prediction performance solely using smart meter data, making it a practical and suitable solution for grid operators.
|
[
"['Fucai Ke' 'Hao Wang']"
] |
null | null |
2403.13247
| null | null |
http://arxiv.org/pdf/2403.13247v2
|
2024-03-24T20:58:50Z
|
2024-03-20T02:17:47Z
|
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis
|
A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm (FedNMUT) is proposed that is tailored to function efficiently in the presence of noisy communication channels that reflect imperfect information exchange. This algorithm uses gradient tracking to minimize the impact of data heterogeneity while minimizing communication overhead. The proposed algorithm incorporates noise into its parameters to mimic the conditions of noisy communication channels, thereby enabling consensus among clients through a communication graph topology in such challenging environments. FedNMUT prioritizes parameter sharing and noise incorporation to increase the resilience of decentralized learning systems against noisy communications. Theoretical results for the smooth non-convex objective function are provided by us, and it is shown that the $epsilon-$stationary solution is achieved by our algorithm at the rate of $mathcal{O}left(frac{1}{sqrt{T}}right)$, where $T$ is the total number of communication rounds. Additionally, via empirical validation, we demonstrated that the performance of FedNMUT is superior to the existing state-of-the-art methods and conventional parameter-mixing approaches in dealing with imperfect information sharing. This proves the capability of the proposed algorithm to counteract the negative effects of communication noise in a decentralized learning framework.
|
[
"['Vishnu Pandi Chellapandi' 'Antesh Upadhyay' 'Abolfazl Hashemi'\n 'Stanislaw H. Żak']"
] |
null | null |
2403.13249
| null | null |
http://arxiv.org/pdf/2403.13249v1
|
2024-03-20T02:21:44Z
|
2024-03-20T02:21:44Z
|
A Unified and General Framework for Continual Learning
|
Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques. However, these methods lack a unified framework and common terminology for describing their approaches. This research aims to bridge this gap by introducing a comprehensive and overarching framework that encompasses and reconciles these existing methodologies. Notably, this new framework is capable of encompassing established CL approaches as special instances within a unified and general optimization objective. An intriguing finding is that despite their diverse origins, these methods share common mathematical structures. This observation highlights the compatibility of these seemingly distinct techniques, revealing their interconnectedness through a shared underlying optimization objective. Moreover, the proposed general framework introduces an innovative concept called refresh learning, specifically designed to enhance the CL performance. This novel approach draws inspiration from neuroscience, where the human brain often sheds outdated information to improve the retention of crucial knowledge and facilitate the acquisition of new information. In essence, refresh learning operates by initially unlearning current data and subsequently relearning it. It serves as a versatile plug-in that seamlessly integrates with existing CL methods, offering an adaptable and effective enhancement to the learning process. Extensive experiments on CL benchmarks and theoretical analysis demonstrate the effectiveness of the proposed refresh learning. Code is available at url{https://github.com/joey-wang123/CL-refresh-learning}.
|
[
"['Zhenyi Wang' 'Yan Li' 'Li Shen' 'Heng Huang']"
] |
null | null |
2403.13257
| null | null |
http://arxiv.org/pdf/2403.13257v2
|
2024-03-21T03:13:30Z
|
2024-03-20T02:38:01Z
|
Arcee's MergeKit: A Toolkit for Merging Large Language Models
|
The rapid expansion of the open-source language model landscape presents an opportunity to merge the competencies of these model checkpoints by combining their parameters. Advances in transfer learning, the process of fine-tuning pretrained models for specific tasks, has resulted in the development of vast amounts of task-specific models, typically specialized in individual tasks and unable to utilize each other's strengths. Model merging facilitates the creation of multitask models without the need for additional training, offering a promising avenue for enhancing model performance and versatility. By preserving the intrinsic capabilities of the original models, model merging addresses complex challenges in AI - including the difficulties of catastrophic forgetting and multitask learning. To support this expanding area of research, we introduce MergeKit, a comprehensive, open-source library designed to facilitate the application of model merging strategies. MergeKit offers an extensible framework to efficiently merge models on any hardware, providing utility to researchers and practitioners. To date, thousands of models have been merged by the open-source community, leading to the creation of some of the worlds most powerful open-source model checkpoints, as assessed by the Open LLM Leaderboard. The library is accessible at https://github.com/arcee-ai/MergeKit.
|
[
"['Charles Goddard' 'Shamane Siriwardhana' 'Malikeh Ehghaghi' 'Luke Meyers'\n 'Vlad Karpukhin' 'Brian Benedict' 'Mark McQuade' 'Jacob Solawetz']"
] |
null | null |
2403.13268
| null | null |
http://arxiv.org/pdf/2403.13268v1
|
2024-03-20T03:07:30Z
|
2024-03-20T03:07:30Z
|
Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural
Network
|
Graph Neural Networks (GNNs) have shown promising performance in various graph learning tasks, but at the cost of resource-intensive computations. The primary overhead of GNN update stems from graph propagation and weight transformation, both involving operations on graph-scale matrices. Previous studies attempt to reduce the computational budget by leveraging graph-level or network-level sparsification techniques, resulting in downsized graph or weights. In this work, we propose Unifews, which unifies the two operations in an entry-wise manner considering individual matrix elements, and conducts joint edge-weight sparsification to enhance learning efficiency. The entry-wise design of Unifews enables adaptive compression across GNN layers with progressively increased sparsity, and is applicable to a variety of architectural designs with on-the-fly operation simplification. Theoretically, we establish a novel framework to characterize sparsified GNN learning in view of a graph optimization process, and prove that Unifews effectively approximates the learning objective with bounded error and reduced computational load. We conduct extensive experiments to evaluate the performance of our method in diverse settings. Unifews is advantageous in jointly removing more than 90% of edges and weight entries with comparable or better accuracy than baseline models. The sparsification offers remarkable efficiency improvements including 10-20x matrix operation reduction and up to 100x acceleration in graph propagation time for the largest graph at the billion-edge scale.
|
[
"['Ningyi Liao' 'Zihao Yu' 'Siqiang Luo']"
] |
null | null |
2403.13269
| null | null |
http://arxiv.org/pdf/2403.13269v3
|
2024-04-16T17:37:12Z
|
2024-03-20T03:07:50Z
|
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient
Fine-Tuning of Large Models
|
We present a novel Parameter-Efficient Fine-Tuning (PEFT) method, dubbed as Adaptive Freezing of Low Rank Adaptation (AFLoRA). Specifically, for each pre-trained frozen weight tensor, we add a parallel path of trainable low-rank matrices, namely a down-projection and an up-projection matrix, each of which is followed by a feature transformation vector. Based on a novel freezing score, we the incrementally freeze these projection matrices during fine-tuning to reduce the computation and alleviate over-fitting. Our experimental results demonstrate that we can achieve state-of-the-art performance with an average improvement of up to $0.85%$ as evaluated on GLUE benchmark while yeilding up to $9.5times$ fewer average trainable parameters. While compared in terms of runtime, AFLoRA can yield up to $1.86times$ improvement as opposed to similar PEFT alternatives. Besides the practical utility of our approach, we provide insights on the trainability requirements of LoRA paths at different modules and the freezing schedule for the different projection matrices. Code will be released.
|
[
"['Zeyu Liu' 'Souvik Kundu' 'Anni Li' 'Junrui Wan' 'Lianghao Jiang'\n 'Peter Anthony Beerel']"
] |
null | null |
2403.13286
| null | null |
http://arxiv.org/pdf/2403.13286v1
|
2024-03-20T03:56:22Z
|
2024-03-20T03:56:22Z
|
A Sampling-based Framework for Hypothesis Testing on Large Attributed
Graphs
|
Hypothesis testing is a statistical method used to draw conclusions about populations from sample data, typically represented in tables. With the prevalence of graph representations in real-life applications, hypothesis testing in graphs is gaining importance. In this work, we formalize node, edge, and path hypotheses in attributed graphs. We develop a sampling-based hypothesis testing framework, which can accommodate existing hypothesis-agnostic graph sampling methods. To achieve accurate and efficient sampling, we then propose a Path-Hypothesis-Aware SamplEr, PHASE, an m- dimensional random walk that accounts for the paths specified in a hypothesis. We further optimize its time efficiency and propose PHASEopt. Experiments on real datasets demonstrate the ability of our framework to leverage common graph sampling methods for hypothesis testing, and the superiority of hypothesis-aware sampling in terms of accuracy and time efficiency.
|
[
"['Yun Wang' 'Chrysanthi Kosyfaki' 'Sihem Amer-Yahia' 'Reynold Cheng']"
] |
null | null |
2403.13293
| null | null |
http://arxiv.org/pdf/2403.13293v1
|
2024-03-20T04:18:38Z
|
2024-03-20T04:18:38Z
|
Building Optimal Neural Architectures using Interpretable Knowledge
|
Neural Architecture Search is a costly practice. The fact that a search space can span a vast number of design choices with each architecture evaluation taking nontrivial overhead makes it hard for an algorithm to sufficiently explore candidate networks. In this paper, we propose AutoBuild, a scheme which learns to align the latent embeddings of operations and architecture modules with the ground-truth performance of the architectures they appear in. By doing so, AutoBuild is capable of assigning interpretable importance scores to architecture modules, such as individual operation features and larger macro operation sequences such that high-performance neural networks can be constructed without any need for search. Through experiments performed on state-of-the-art image classification, segmentation, and Stable Diffusion models, we show that by mining a relatively small set of evaluated architectures, AutoBuild can learn to build high-quality architectures directly or help to reduce search space to focus on relevant areas, finding better architectures that outperform both the original labeled ones and ones found by search baselines. Code available at https://github.com/Ascend-Research/AutoBuild
|
[
"['Keith G. Mills' 'Fred X. Han' 'Mohammad Salameh' 'Shengyao Lu'\n 'Chunhua Zhou' 'Jiao He' 'Fengyu Sun' 'Di Niu']"
] |
null | null |
2403.13298
| null | null |
http://arxiv.org/pdf/2403.13298v1
|
2024-03-20T04:47:13Z
|
2024-03-20T04:47:13Z
|
Rotary Position Embedding for Vision Transformer
|
Rotary Position Embedding (RoPE) performs remarkably on language models, especially for length extrapolation of Transformers. However, the impacts of RoPE on computer vision domains have been underexplored, even though RoPE appears capable of enhancing Vision Transformer (ViT) performance in a way similar to the language domain. This study provides a comprehensive analysis of RoPE when applied to ViTs, utilizing practical implementations of RoPE for 2D vision data. The analysis reveals that RoPE demonstrates impressive extrapolation performance, i.e., maintaining precision while increasing image resolution at inference. It eventually leads to performance improvement for ImageNet-1k, COCO detection, and ADE-20k segmentation. We believe this study provides thorough guidelines to apply RoPE into ViT, promising improved backbone performance with minimal extra computational overhead. Our code and pre-trained models are available at https://github.com/naver-ai/rope-vit
|
[
"['Byeongho Heo' 'Song Park' 'Dongyoon Han' 'Sangdoo Yun']"
] |
null | null |
2403.13299
| null | null |
http://arxiv.org/pdf/2403.13299v1
|
2024-03-20T04:56:02Z
|
2024-03-20T04:56:02Z
|
Bridging scales in multiscale bubble growth dynamics with correlated
fluctuations using neural operator learning
|
The intricate process of bubble growth dynamics involves a broad spectrum of physical phenomena from microscale mechanics of bubble formation to macroscale interplay between bubbles and surrounding thermo-hydrodynamics. Traditional bubble dynamics models including atomistic approaches and continuum-based methods segment the bubble dynamics into distinct scale-specific models. In order to bridge the gap between microscale stochastic fluid models and continuum-based fluid models for bubble dynamics, we develop a composite neural operator model to unify the analysis of nonlinear bubble dynamics across microscale and macroscale regimes by integrating a many-body dissipative particle dynamics (mDPD) model with a continuum-based Rayleigh-Plesset (RP) model through a novel neural network architecture, which consists of a deep operator network for learning the mean behavior of bubble growth subject to pressure variations and a long short-term memory network for learning the statistical features of correlated fluctuations in microscale bubble dynamics. Training and testing data are generated by conducting mDPD and RP simulations for nonlinear bubble dynamics with initial bubble radii ranging from 0.1 to 1.5 micrometers. Results show that the trained composite neural operator model can accurately predict bubble dynamics across scales, with a 99% accuracy for the time evaluation of the bubble radius under varying external pressure while containing correct size-dependent stochastic fluctuations in microscale bubble growth dynamics. The composite neural operator is the first deep learning surrogate for multiscale bubble growth dynamics that can capture correct stochastic fluctuations in microscopic fluid phenomena, which sets a new direction for future research in multiscale fluid dynamics modeling.
|
[
"['Minglei Lu' 'Chensen Lin' 'Martian Maxey' 'George Karniadakis' 'Zhen Li']"
] |
null | null |
2403.13300
| null | null |
http://arxiv.org/pdf/2403.13300v2
|
2024-03-30T16:58:59Z
|
2024-03-20T04:57:27Z
|
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process
Regression
|
Additive Gaussian Processes (GPs) are popular approaches for nonparametric feature selection. The common training method for these models is Bayesian Back-fitting. However, the convergence rate of Back-fitting in training additive GPs is still an open problem. By utilizing a technique called Kernel Packets (KP), we prove that the convergence rate of Back-fitting is no faster than $(1-mathcal{O}(frac{1}{n}))^t$, where $n$ and $t$ denote the data size and the iteration number, respectively. Consequently, Back-fitting requires a minimum of $mathcal{O}(nlog n)$ iterations to achieve convergence. Based on KPs, we further propose an algorithm called Kernel Multigrid (KMG). This algorithm enhances Back-fitting by incorporating a sparse Gaussian Process Regression (GPR) to process the residuals after each Back-fitting iteration. It is applicable to additive GPs with both structured and scattered data. Theoretically, we prove that KMG reduces the required iterations to $mathcal{O}(log n)$ while preserving the time and space complexities at $mathcal{O}(nlog n)$ and $mathcal{O}(n)$ per iteration, respectively. Numerically, by employing a sparse GPR with merely 10 inducing points, KMG can produce accurate approximations of high-dimensional targets within 5 iterations.
|
[
"['Lu Zou' 'Liang Ding']"
] |
null | null |
2403.13310
| null | null |
http://arxiv.org/pdf/2403.13310v1
|
2024-03-20T05:23:09Z
|
2024-03-20T05:23:09Z
|
A Semantic Search Engine for Mathlib4
|
The interactive theorem prover, Lean, enables the verification of formal mathematical proofs and is backed by an expanding community. Central to this ecosystem is its mathematical library, mathlib4, which lays the groundwork for the formalization of an expanding range of mathematical theories. However, searching for theorems in mathlib4 can be challenging. To successfully search in mathlib4, users often need to be familiar with its naming conventions or documentation strings. Therefore, creating a semantic search engine that can be used easily by individuals with varying familiarity with mathlib4 is very important. In this paper, we present a semantic search engine for mathlib4 that accepts informal queries and finds the relevant theorems. We also establish a benchmark for assessing the performance of various search engines for mathlib4.
|
[
"['Guoxiong Gao' 'Haocheng Ju' 'Jiedong Jiang' 'Zihan Qin' 'Bin Dong']"
] |
null | null |
2403.13319
| null | null |
http://arxiv.org/pdf/2403.13319v1
|
2024-03-20T05:50:04Z
|
2024-03-20T05:50:04Z
|
HyperFusion: A Hypernetwork Approach to Multimodal Integration of
Tabular and Medical Imaging Data for Predictive Modeling
|
The integration of diverse clinical modalities such as medical imaging and the tabular data obtained by the patients' Electronic Health Records (EHRs) is a crucial aspect of modern healthcare. The integrative analysis of multiple sources can provide a comprehensive understanding of a patient's condition and can enhance diagnoses and treatment decisions. Deep Neural Networks (DNNs) consistently showcase outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit. We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. We demonstrate the strength and the generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex, and multiclass Alzheimer's Disease (AD) classification conditioned by tabular data. We show that our framework outperforms both single-modality models and state-of-the-art MRI-tabular data fusion methods. The code, enclosed to this manuscript will be made publicly available.
|
[
"['Daniel Duenias' 'Brennan Nichyporuk' 'Tal Arbel' 'Tammy Riklin Raviv']"
] |
null | null |
2403.13335
| null | null |
http://arxiv.org/pdf/2403.13335v1
|
2024-03-20T06:38:13Z
|
2024-03-20T06:38:13Z
|
Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text
Detection
|
Large language models (LLMs) have reached human-like proficiency in generating diverse textual content, underscoring the necessity for effective fake text detection to avoid potential risks such as fake news in social media. Previous research has mostly tested single models on in-distribution datasets, limiting our understanding of how these models perform on different types of data for LLM-generated text detection task. We researched this by testing five specialized transformer-based models on both in-distribution and out-of-distribution datasets to better assess their performance and generalizability. Our results revealed that single transformer-based classifiers achieved decent performance on in-distribution dataset but limited generalization ability on out-of-distribution dataset. To improve it, we combined the individual classifiers models using adaptive ensemble algorithms, which improved the average accuracy significantly from 91.8% to 99.2% on an in-distribution test set and from 62.9% to 72.5% on an out-of-distribution test set. The results indicate the effectiveness, good generalization ability, and great potential of adaptive ensemble algorithms in LLM-generated text detection.
|
[
"['Zhixin Lai' 'Xuesheng Zhang' 'Suiyao Chen']"
] |
null | null |
2403.13344
| null | null |
http://arxiv.org/pdf/2403.13344v1
|
2024-03-20T07:05:19Z
|
2024-03-20T07:05:19Z
|
USE: Dynamic User Modeling with Stateful Sequence Models
|
User embeddings play a crucial role in user engagement forecasting and personalized services. Recent advances in sequence modeling have sparked interest in learning user embeddings from behavioral data. Yet behavior-based user embedding learning faces the unique challenge of dynamic user modeling. As users continuously interact with the apps, user embeddings should be periodically updated to account for users' recent and long-term behavior patterns. Existing methods highly rely on stateless sequence models that lack memory of historical behavior. They have to either discard historical data and use only the most recent data or reprocess the old and new data jointly. Both cases incur substantial computational overhead. To address this limitation, we introduce User Stateful Embedding (USE). USE generates user embeddings and reflects users' evolving behaviors without the need for exhaustive reprocessing by storing previous model states and revisiting them in the future. Furthermore, we introduce a novel training objective named future W-behavior prediction to transcend the limitations of next-token prediction by forecasting a broader horizon of upcoming user behaviors. By combining it with the Same User Prediction, a contrastive learning-based objective that predicts whether different segments of behavior sequences belong to the same user, we further improve the embeddings' distinctiveness and representativeness. We conducted experiments on 8 downstream tasks using Snapchat users' behavioral logs in both static (i.e., fixed user behavior sequences) and dynamic (i.e., periodically updated user behavior sequences) settings. We demonstrate USE's superior performance over established baselines. The results underscore USE's effectiveness and efficiency in integrating historical and recent user behavior sequences into user embeddings in dynamic user modeling.
|
[
"['Zhihan Zhou' 'Qixiang Fang' 'Leonardo Neves' 'Francesco Barbieri'\n 'Yozen Liu' 'Han Liu' 'Maarten W. Bos' 'Ron Dotsch']"
] |
null | null |
2403.13349
| null | null |
http://arxiv.org/pdf/2403.13349v2
|
2024-07-04T14:07:12Z
|
2024-03-20T07:21:37Z
|
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified
Anomaly Detection
|
Unified anomaly detection (AD) is one of the most challenges for anomaly detection, where one unified model is trained with normal samples from multiple classes with the objective to detect anomalies in these classes. For such a challenging task, popular normalizing flow (NF) based AD methods may fall into a "homogeneous mapping" issue,where the NF-based AD models are biased to generate similar latent representations for both normal and abnormal features, and thereby lead to a high missing rate of anomalies. In this paper, we propose a novel Hierarchical Gaussian mixture normalizing flow modeling method for accomplishing unified Anomaly Detection, which we call HGAD. Our HGAD consists of two key components: inter-class Gaussian mixture modeling and intra-class mixed class centers learning. Compared to the previous NF-based AD methods, the hierarchical Gaussian mixture modeling approach can bring stronger representation capability to the latent space of normalizing flows, so that even complex multi-class distribution can be well represented and learned in the latent space. In this way, we can avoid mapping different class distributions into the same single Gaussian prior, thus effectively avoiding or mitigating the "homogeneous mapping" issue. We further indicate that the more distinguishable different class centers, the more conducive to avoiding the bias issue. Thus, we further propose a mutual information maximization loss for better structuring the latent feature space. We evaluate our method on four real-world AD benchmarks, where we can significantly improve the previous NF-based AD methods and also outperform the SOTA unified AD methods.
|
[
"['Xincheng Yao' 'Ruoqi Li' 'Zefeng Qian' 'Lu Wang' 'Chongyang Zhang']"
] |
null | null |
2403.13358
| null | null |
http://arxiv.org/pdf/2403.13358v2
|
2024-04-09T07:55:41Z
|
2024-03-20T07:36:43Z
|
GeRM: A Generalist Robotic Model with Mixture-of-experts for Quadruped
Robot
|
Multi-task robot learning holds significant importance in tackling diverse and complex scenarios. However, current approaches are hindered by performance issues and difficulties in collecting training datasets. In this paper, we propose GeRM (Generalist Robotic Model). We utilize offline reinforcement learning to optimize data utilization strategies to learn from both demonstrations and sub-optimal data, thus surpassing the limitations of human demonstrations. Thereafter, we employ a transformer-based VLA network to process multi-modal inputs and output actions. By introducing the Mixture-of-Experts structure, GeRM allows faster inference speed with higher whole model capacity, and thus resolves the issue of limited RL parameters, enhancing model performance in multi-task learning while controlling computational costs. Through a series of experiments, we demonstrate that GeRM outperforms other methods across all tasks, while also validating its efficiency in both training and inference processes. Additionally, we uncover its potential to acquire emergent skills. Additionally, we contribute the QUARD-Auto dataset, collected automatically to support our training approach and foster advancements in multi-task quadruped robot learning. This work presents a new paradigm for reducing the cost of collecting robot data and driving progress in the multi-task learning community. You can reach our project and video through the link: https://songwxuan.github.io/GeRM/ .
|
[
"['Wenxuan Song' 'Han Zhao' 'Pengxiang Ding' 'Can Cui' 'Shangke Lyu'\n 'Yaning Fan' 'Donglin Wang']"
] |
null | null |
2403.13369
| null | null |
http://arxiv.org/pdf/2403.13369v1
|
2024-03-20T08:01:33Z
|
2024-03-20T08:01:33Z
|
Clinical information extraction for Low-resource languages with Few-shot
learning using Pre-trained language models and Prompting
|
Automatic extraction of medical information from clinical documents poses several challenges: high costs of required clinical expertise, limited interpretability of model predictions, restricted computational resources and privacy regulations. Recent advances in domain-adaptation and prompting methods showed promising results with minimal training data using lightweight masked language models, which are suited for well-established interpretability methods. We are first to present a systematic evaluation of these methods in a low-resource setting, by performing multi-class section classification on German doctor's letters. We conduct extensive class-wise evaluations supported by Shapley values, to validate the quality of our small training data set and to ensure the interpretability of model predictions. We demonstrate that a lightweight, domain-adapted pretrained model, prompted with just 20 shots, outperforms a traditional classification model by 30.5% accuracy. Our results serve as a process-oriented guideline for clinical information extraction projects working with low-resource.
|
[
"['Phillip Richter-Pechanski' 'Philipp Wiesenbach' 'Dominic M. Schwab'\n 'Christina Kiriakou' 'Nicolas Geis' 'Christoph Dieterich' 'Anette Frank']"
] |
null | null |
2403.13370
| null | null |
http://arxiv.org/pdf/2403.13370v1
|
2024-03-20T08:04:00Z
|
2024-03-20T08:04:00Z
|
Counting Network for Learning from Majority Label
|
The paper proposes a novel problem in multi-class Multiple-Instance Learning (MIL) called Learning from the Majority Label (LML). In LML, the majority class of instances in a bag is assigned as the bag's label. LML aims to classify instances using bag-level majority classes. This problem is valuable in various applications. Existing MIL methods are unsuitable for LML due to aggregating confidences, which may lead to inconsistency between the bag-level label and the label obtained by counting the number of instances for each class. This may lead to incorrect instance-level classification. We propose a counting network trained to produce the bag-level majority labels estimated by counting the number of instances for each class. This led to the consistency of the majority class between the network outputs and one obtained by counting the number of instances. Experimental results show that our counting network outperforms conventional MIL methods on four datasets The code is publicly available at https://github.com/Shiku-Kaito/Counting-Network-for-Learning-from-Majority-Label.
|
[
"['Kaito Shiku' 'Shinnosuke Matsuo' 'Daiki Suehiro' 'Ryoma Bise']"
] |
null | null |
2403.13374
| null | null |
http://arxiv.org/pdf/2403.13374v3
|
2024-03-27T14:57:54Z
|
2024-03-20T08:15:08Z
|
Byzantine-resilient Federated Learning With Adaptivity to Data
Heterogeneity
|
This paper deals with federated learning (FL) in the presence of malicious Byzantine attacks and data heterogeneity. A novel Robust Average Gradient Algorithm (RAGA) is proposed, which leverages the geometric median for aggregation and can freely select the round number for local updating. Different from most existing resilient approaches, which perform convergence analysis based on strongly-convex loss function or homogeneously distributed dataset, we conduct convergence analysis for not only strongly-convex but also non-convex loss function over heterogeneous dataset. According to our theoretical analysis, as long as the fraction of dataset from malicious users is less than half, RAGA can achieve convergence at rate $mathcal{O}({1}/{T^{2/3- delta}})$ where $T$ is the iteration number and $delta in (0, 2/3)$ for non-convex loss function, and at linear rate for strongly-convex loss function. Moreover, stationary point or global optimal solution is proved to obtainable as data heterogeneity vanishes. Experimental results corroborate the robustness of RAGA to Byzantine attacks and verifies the advantage of RAGA over baselines on convergence performance under various intensity of Byzantine attacks, for heterogeneous dataset.
|
[
"['Shiyuan Zuo' 'Xingrun Yan' 'Rongfei Fan' 'Han Hu' 'Hangguan Shan'\n 'Tony Q. S. Quek']"
] |
null | null |
2403.13429
| null | null |
http://arxiv.org/pdf/2403.13429v1
|
2024-03-20T09:17:12Z
|
2024-03-20T09:17:12Z
|
Detecting and Triaging Spoofing using Temporal Convolutional Networks
|
As algorithmic trading and electronic markets continue to transform the landscape of financial markets, detecting and deterring rogue agents to maintain a fair and efficient marketplace is crucial. The explosion of large datasets and the continually changing tricks of the trade make it difficult to adapt to new market conditions and detect bad actors. To that end, we propose a framework that can be adapted easily to various problems in the space of detecting market manipulation. Our approach entails initially employing a labelling algorithm which we use to create a training set to learn a weakly supervised model to identify potentially suspicious sequences of order book states. The main goal here is to learn a representation of the order book that can be used to easily compare future events. Subsequently, we posit the incorporation of expert assessment to scrutinize specific flagged order book states. In the event of an expert's unavailability, recourse is taken to the application of a more complex algorithm on the identified suspicious order book states. We then conduct a similarity search between any new representation of the order book against the expert labelled representations to rank the results of the weak learner. We show some preliminary results that are promising to explore further in this direction
|
[
"['Kaushalya Kularatnam' 'Tania Stathaki']"
] |
null | null |
2403.13441
| null | null |
http://arxiv.org/pdf/2403.13441v1
|
2024-03-20T09:34:38Z
|
2024-03-20T09:34:38Z
|
Robustness Verifcation in Neural Networks
|
In this paper we investigate formal verification problems for Neural Network computations. Of central importance will be various robustness and minimization problems such as: Given symbolic specifications of allowed inputs and outputs in form of Linear Programming instances, one question is whether there do exist valid inputs such that the network computes a valid output? And does this property hold for all valid inputs? Do two given networks compute the same function? Is there a smaller network computing the same function? The complexity of these questions have been investigated recently from a practical point of view and approximated by heuristic algorithms. We complement these achievements by giving a theoretical framework that enables us to interchange security and efficiency questions in neural networks and analyze their computational complexities. We show that the problems are conquerable in a semi-linear setting, meaning that for piecewise linear activation functions and when the sum- or maximum metric is used, most of them are in P or in NP at most.
|
[
"['Adrian Wurm']"
] |
null | null |
2403.13501
| null | null |
http://arxiv.org/pdf/2403.13501v1
|
2024-03-20T10:58:58Z
|
2024-03-20T10:58:58Z
|
VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis
|
Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content. They tend to synthesize quasi-static videos, ignoring the necessary visual change-over-time implied in the text prompt. At the same time, scaling these models to enable longer, more dynamic video synthesis often remains computationally intractable. To address this challenge, we introduce the concept of Generative Temporal Nursing (GTN), where we aim to alter the generative process on the fly during inference to improve control over the temporal dynamics and enable generation of longer videos. We propose a method for GTN, dubbed VSTAR, which consists of two key ingredients: 1) Video Synopsis Prompting (VSP) - automatic generation of a video synopsis based on the original single prompt leveraging LLMs, which gives accurate textual guidance to different visual states of longer videos, and 2) Temporal Attention Regularization (TAR) - a regularization technique to refine the temporal attention units of the pre-trained T2V diffusion models, which enables control over the video dynamics. We experimentally showcase the superiority of the proposed approach in generating longer, visually appealing videos over existing open-sourced T2V models. We additionally analyze the temporal attention maps realized with and without VSTAR, demonstrating the importance of applying our method to mitigate neglect of the desired visual change over time.
|
[
"['Yumeng Li' 'William Beluch' 'Margret Keuper' 'Dan Zhang' 'Anna Khoreva']"
] |
null | null |
2403.13502
| null | null |
http://arxiv.org/abs/2403.13502v4
|
2024-06-07T13:36:17Z
|
2024-03-20T10:59:06Z
|
Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A
Comprehensive Benchmark on the Tennessee Eastman Process
|
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to adversarial attacks. This study explores the threats in deploying deep learning models for fault diagnosis in ACS using the Tennessee Eastman Process dataset. By evaluating three neural networks with different architectures, we subject them to six types of adversarial attacks and explore five different defense methods. Our results highlight the strong vulnerability of models to adversarial samples and the varying effectiveness of defense strategies. We also propose a novel protection approach by combining multiple defense methods and demonstrate it's efficacy. This research contributes several insights into securing machine learning within ACS, ensuring robust fault diagnosis in industrial processes.
|
[
"['Vitaliy Pozdnyakov' 'Aleksandr Kovalenko' 'Ilya Makarov'\n 'Mikhail Drobyshevskiy' 'Kirill Lukyanov']"
] |
null | null |
2403.13522
| null | null |
http://arxiv.org/pdf/2403.13522v1
|
2024-03-20T11:48:10Z
|
2024-03-20T11:48:10Z
|
REAL: Representation Enhanced Analytic Learning for Exemplar-free
Class-incremental Learning
|
Exemplar-free class-incremental learning (EFCIL) aims to mitigate catastrophic forgetting in class-incremental learning without available historical data. Compared with its counterpart (replay-based CIL) that stores historical samples, the EFCIL suffers more from forgetting issues under the exemplar-free constraint. In this paper, inspired by the recently developed analytic learning (AL) based CIL, we propose a representation enhanced analytic learning (REAL) for EFCIL. The REAL constructs a dual-stream base pretraining (DS-BPT) and a representation enhancing distillation (RED) process to enhance the representation of the extractor. The DS-BPT pretrains model in streams of both supervised learning and self-supervised contrastive learning (SSCL) for base knowledge extraction. The RED process distills the supervised knowledge to the SSCL pretrained backbone and facilitates a subsequent AL-basd CIL that converts the CIL to a recursive least-square problem. Our method addresses the issue of insufficient discriminability in representations of unseen data caused by a frozen backbone in the existing AL-based CIL. Empirical results on various datasets including CIFAR-100, ImageNet-100 and ImageNet-1k, demonstrate that our REAL outperforms the state-of-the-arts in EFCIL, and achieves comparable or even more superior performance compared with the replay-based methods.
|
[
"['Run He' 'Huiping Zhuang' 'Di Fang' 'Yizhu Chen' 'Kai Tong' 'Cen Chen']"
] |
null | null |
2403.13523
| null | null |
http://arxiv.org/pdf/2403.13523v1
|
2024-03-20T11:50:16Z
|
2024-03-20T11:50:16Z
|
Have You Poisoned My Data? Defending Neural Networks against Data
Poisoning
|
The unprecedented availability of training data fueled the rapid development of powerful neural networks in recent years. However, the need for such large amounts of data leads to potential threats such as poisoning attacks: adversarial manipulations of the training data aimed at compromising the learned model to achieve a given adversarial goal. This paper investigates defenses against clean-label poisoning attacks and proposes a novel approach to detect and filter poisoned datapoints in the transfer learning setting. We define a new characteristic vector representation of datapoints and show that it effectively captures the intrinsic properties of the data distribution. Through experimental analysis, we demonstrate that effective poisons can be successfully differentiated from clean points in the characteristic vector space. We thoroughly evaluate our proposed approach and compare it to existing state-of-the-art defenses using multiple architectures, datasets, and poison budgets. Our evaluation shows that our proposal outperforms existing approaches in defense rate and final trained model performance across all experimental settings.
|
[
"['Fabio De Gaspari' 'Dorjan Hitaj' 'Luigi V. Mancini']"
] |
null | null |
2403.13537
| null | null |
http://arxiv.org/pdf/2403.13537v1
|
2024-03-20T12:14:54Z
|
2024-03-20T12:14:54Z
|
What explains the success of cross-modal fine-tuning with ORCA?
|
ORCA (Shen et al., 2023) is a recent technique for cross-modal fine-tuning, i.e., applying pre-trained transformer models to modalities beyond their training data. The technique consists primarily of training an embedder and fine-tuning the embedder and model. Despite its high performance on a variety of downstream tasks, we do not understand precisely how each of these components contribute to ORCA's success. Therefore, we run a series of ablations and find that embedder training does not help 2D tasks at all, contrary to what the original paper posits. In 1D tasks, some amount of embedder training is necessary but more is not better. In 4 out of 6 datasets we experiment with, it is model fine-tuning that makes the biggest difference. Through our ablations and baselines, we contribute a better understanding of the individual components of ORCA.
|
[
"['Paloma García-de-Herreros' 'Vagrant Gautam' 'Philipp Slusallek'\n 'Dietrich Klakow' 'Marius Mosbach']"
] |
null | null |
2403.13545
| null | null |
http://arxiv.org/pdf/2403.13545v1
|
2024-03-20T12:31:13Z
|
2024-03-20T12:31:13Z
|
Next day fire prediction via semantic segmentation
|
In this paper we present a deep learning pipeline for next day fire prediction. The next day fire prediction task consists in learning models that receive as input the available information for an area up until a certain day, in order to predict the occurrence of fire for the next day. Starting from our previous problem formulation as a binary classification task on instances (daily snapshots of each area) represented by tabular feature vectors, we reformulate the problem as a semantic segmentation task on images; there, each pixel corresponds to a daily snapshot of an area, while its channels represent the formerly tabular training features. We demonstrate that this problem formulation, built within a thorough pipeline achieves state of the art results.
|
[
"['Konstantinos Alexis' 'Stella Girtsou' 'Alexis Apostolakis'\n 'Giorgos Giannopoulos' 'Charalampos Kontoes']"
] |
null | null |
2403.13547
| null | null |
http://arxiv.org/pdf/2403.13547v2
|
2024-04-29T04:13:37Z
|
2024-03-20T12:33:51Z
|
Enhancing Traffic Incident Management with Large Language Models: A
Hybrid Machine Learning Approach for Severity Classification
|
This research showcases the innovative integration of Large Language Models into machine learning workflows for traffic incident management, focusing on the classification of incident severity using accident reports. By leveraging features generated by modern language models alongside conventional data extracted from incident reports, our research demonstrates improvements in the accuracy of severity classification across several machine learning algorithms. Our contributions are threefold. First, we present an extensive comparison of various machine learning models paired with multiple large language models for feature extraction, aiming to identify the optimal combinations for accurate incident severity classification. Second, we contrast traditional feature engineering pipelines with those enhanced by language models, showcasing the superiority of language-based feature engineering in processing unstructured text. Third, our study illustrates how merging baseline features from accident reports with language-based features can improve the severity classification accuracy. This comprehensive approach not only advances the field of incident management but also highlights the cross-domain application potential of our methodology, particularly in contexts requiring the prediction of event outcomes from unstructured textual data or features translated into textual representation. Specifically, our novel methodology was applied to three distinct datasets originating from the United States, the United Kingdom, and Queensland, Australia. This cross-continental application underlines the robustness of our approach, suggesting its potential for widespread adoption in improving incident management processes globally.
|
[
"['Artur Grigorev' 'Khaled Saleh' 'Yuming Ou' 'Adriana-Simona Mihaita']"
] |
null | null |
2403.13551
| null | null |
http://arxiv.org/pdf/2403.13551v1
|
2024-03-20T12:40:32Z
|
2024-03-20T12:40:32Z
|
Ground-A-Score: Scaling Up the Score Distillation for Multi-Attribute
Editing
|
Despite recent advancements in text-to-image diffusion models facilitating various image editing techniques, complex text prompts often lead to an oversight of some requests due to a bottleneck in processing text information. To tackle this challenge, we present Ground-A-Score, a simple yet powerful model-agnostic image editing method by incorporating grounding during score distillation. This approach ensures a precise reflection of intricate prompt requirements in the editing outcomes, taking into account the prior knowledge of the object locations within the image. Moreover, the selective application with a new penalty coefficient and contrastive loss helps to precisely target editing areas while preserving the integrity of the objects in the source image. Both qualitative assessments and quantitative analyses confirm that Ground-A-Score successfully adheres to the intricate details of extended and multifaceted prompts, ensuring high-quality outcomes that respect the original image attributes.
|
[
"['Hangeol Chang' 'Jinho Chang' 'Jong Chul Ye']"
] |
null | null |
2403.13563
| null | null |
http://arxiv.org/pdf/2403.13563v2
|
2024-05-23T11:34:45Z
|
2024-03-20T12:56:40Z
|
DL2Fence: Integrating Deep Learning and Frame Fusion for Enhanced
Detection and Localization of Refined Denial-of-Service in Large-Scale NoCs
|
This study introduces a refined Flooding Injection Rate-adjustable Denial-of-Service (DoS) model for Network-on-Chips (NoCs) and more importantly presents DL2Fence, a novel framework utilizing Deep Learning (DL) and Frame Fusion (2F) for DoS detection and localization. Two Convolutional Neural Networks models for classification and segmentation were developed to detect and localize DoS respectively. It achieves detection and localization accuracies of 95.8% and 91.7%, and precision rates of 98.5% and 99.3% in a 16x16 mesh NoC. The framework's hardware overhead notably decreases by 76.3% when scaling from 8x8 to 16x16 NoCs, and it requires 42.4% less hardware compared to state-of-the-arts. This advancement demonstrates DL2Fence's effectiveness in balancing outstanding detection performance in large-scale NoCs with extremely low hardware overhead.
|
[
"['Haoyu Wang' 'Basel Halak' 'Jianjie Ren' 'Ahmad Atamli']"
] |
null | null |
2403.13565
| null | null |
http://arxiv.org/pdf/2403.13565v1
|
2024-03-20T12:58:46Z
|
2024-03-20T12:58:46Z
|
AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for
High-dimensional Regression
|
We consider the transfer learning problem in the high dimensional setting, where the feature dimension is larger than the sample size. To learn transferable information, which may vary across features or the source samples, we propose an adaptive transfer learning method that can detect and aggregate the feature-wise (F-AdaTrans) or sample-wise (S-AdaTrans) transferable structures. We achieve this by employing a novel fused-penalty, coupled with weights that can adapt according to the transferable structure. To choose the weight, we propose a theoretically informed, data-driven procedure, enabling F-AdaTrans to selectively fuse the transferable signals with the target while filtering out non-transferable signals, and S-AdaTrans to obtain the optimal combination of information transferred from each source sample. The non-asymptotic rates are established, which recover existing near-minimax optimal rates in special cases. The effectiveness of the proposed method is validated using both synthetic and real data.
|
[
"['Zelin He' 'Ying Sun' 'Jingyuan Liu' 'Runze Li']"
] |
null | null |
2403.13578
| null | null |
http://arxiv.org/pdf/2403.13578v1
|
2024-03-20T13:24:41Z
|
2024-03-20T13:24:41Z
|
Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for
Counselor Reflection Generation
|
In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text qualities for natural language generation. We focus on the task of counselor reflection generation, where we optimize the generators to simultaneously improve the fluency, coherence, and reflection quality of generated counselor responses. We introduce two novel bandit methods, DynaOpt and C-DynaOpt, which rely on the broad strategy of combining rewards into a single value and optimizing them simultaneously. Specifically, we employ non-contextual and contextual multi-arm bandits to dynamically adjust multiple reward weights during training. Through automatic and manual evaluations, we show that our proposed techniques, DynaOpt and C-DynaOpt, outperform existing naive and bandit baselines, showcasing their potential for enhancing language models.
|
[
"['Do June Min' 'Veronica Perez-Rosas' 'Kenneth Resnicow' 'Rada Mihalcea']"
] |
null | null |
2403.13583
| null | null |
http://arxiv.org/pdf/2403.13583v2
|
2024-07-01T09:59:47Z
|
2024-03-20T13:33:55Z
|
CoCoST: Automatic Complex Code Generation with Online Searching and
Correctness Testing
|
Large Language Models have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we introduce the CoCoST framework, which enhances complex code generation by online searching for more information with planned queries and correctness testing for code refinement. Moreover, CoCoST serializes the complex inputs and outputs to improve comprehension and generates test cases to ensure the adaptability for real-world applications. CoCoST is validated through rigorous experiments on the DS-1000 and ClassEval datasets. Experimental results show that CoCoST substantially improves the quality of complex code generation, highlighting its potential to enhance the practicality of LLMs in generating complex code.
|
[
"['Xinyi He' 'Jiaru Zou' 'Yun Lin' 'Mengyu Zhou' 'Shi Han' 'Zejian Yuan'\n 'Dongmei Zhang']"
] |
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