categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.00738 | null | null | http://arxiv.org/pdf/2405.00738v1 | 2024-04-29T21:26:06Z | 2024-04-29T21:26:06Z | HLSTransform: Energy-Efficient Llama 2 Inference on FPGAs Via High Level
Synthesis | Graphics Processing Units (GPUs) have become the leading hardware accelerator for deep learning applications and are used widely in training and inference of transformers; transformers have achieved state-of-the-art performance in many areas of machine learning and are especially used in most modern Large Language Models (LLMs). However, GPUs require large amounts of energy, which poses environmental concerns, demands high operational costs, and causes GPUs to be unsuitable for edge computing. We develop an accelerator for transformers, namely, Llama 2, an open-source state-of-the-art LLM, using high level synthesis (HLS) on Field Programmable Gate Arrays (FPGAs). HLS allows us to rapidly prototype FPGA designs without writing code at the register-transfer level (RTL). We name our method HLSTransform, and the FPGA designs we synthesize with HLS achieve up to a 12.75x reduction and 8.25x reduction in energy used per token on the Xilinx Virtex UltraScale+ VU9P FPGA compared to an Intel Xeon Broadwell E5-2686 v4 CPU and NVIDIA RTX 3090 GPU respectively, while increasing inference speeds by up to 2.46x compared to CPU and maintaining 0.53x the speed of an RTX 3090 GPU despite the GPU's 4 times higher base clock rate. With the lack of existing open-source FPGA accelerators for transformers, we open-source our code and document our steps for synthesis. We hope this work will serve as a step in democratizing the use of FPGAs in transformer inference and inspire research into energy-efficient inference methods as a whole. The code can be found on https://github.com/HLSTransform/submission. | [
"['Andy He' 'Darren Key' 'Mason Bulling' 'Andrew Chang' 'Skyler Shapiro'\n 'Everett Lee']"
]
|
null | null | 2405.00739 | null | null | http://arxiv.org/pdf/2405.00739v1 | 2024-04-30T01:12:32Z | 2024-04-30T01:12:32Z | Why does Knowledge Distillation Work? Rethink its Attention and Fidelity
Mechanism | Does Knowledge Distillation (KD) really work? Conventional wisdom viewed it as a knowledge transfer procedure where a perfect mimicry of the student to its teacher is desired. However, paradoxical studies indicate that closely replicating the teacher's behavior does not consistently improve student generalization, posing questions on its possible causes. Confronted with this gap, we hypothesize that diverse attentions in teachers contribute to better student generalization at the expense of reduced fidelity in ensemble KD setups. By increasing data augmentation strengths, our key findings reveal a decrease in the Intersection over Union (IoU) of attentions between teacher models, leading to reduced student overfitting and decreased fidelity. We propose this low-fidelity phenomenon as an underlying characteristic rather than a pathology when training KD. This suggests that stronger data augmentation fosters a broader perspective provided by the divergent teacher ensemble and lower student-teacher mutual information, benefiting generalization performance. These insights clarify the mechanism on low-fidelity phenomenon in KD. Thus, we offer new perspectives on optimizing student model performance, by emphasizing increased diversity in teacher attentions and reduced mimicry behavior between teachers and student. | [
"['Chenqi Guo' 'Shiwei Zhong' 'Xiaofeng Liu' 'Qianli Feng' 'Yinglong Ma']"
]
|
null | null | 2405.00740 | null | null | http://arxiv.org/pdf/2405.00740v3 | 2024-05-14T12:48:45Z | 2024-04-30T01:19:18Z | Modeling Caption Diversity in Contrastive Vision-Language Pretraining | There are a thousand ways to caption an image. Contrastive Language Pretraining (CLIP) on the other hand, works by mapping an image and its caption to a single vector -- limiting how well CLIP-like models can represent the diverse ways to describe an image. In this work, we introduce Llip, Latent Language Image Pretraining, which models the diversity of captions that could match an image. Llip's vision encoder outputs a set of visual features that are mixed into a final representation by conditioning on information derived from the text. We show that Llip outperforms non-contextualized baselines like CLIP and SigLIP on a variety of tasks even with large-scale encoders. Llip improves zero-shot classification by an average of 2.9% zero-shot classification benchmarks with a ViT-G/14 encoder. Specifically, Llip attains a zero-shot top-1 accuracy of 83.5% on ImageNet outperforming a similarly sized CLIP by 1.4%. We also demonstrate improvement on zero-shot retrieval on MS-COCO by 6.0%. We provide a comprehensive analysis of the components introduced by the method and demonstrate that Llip leads to richer visual representations. | [
"['Samuel Lavoie' 'Polina Kirichenko' 'Mark Ibrahim' 'Mahmoud Assran'\n 'Andrew Gordon Wilson' 'Aaron Courville' 'Nicolas Ballas']"
]
|
null | null | 2405.00742 | null | null | http://arxiv.org/pdf/2405.00742v1 | 2024-04-30T05:17:46Z | 2024-04-30T05:17:46Z | Federated Graph Learning for EV Charging Demand Forecasting with
Personalization Against Cyberattacks | Mitigating cybersecurity risk in electric vehicle (EV) charging demand forecasting plays a crucial role in the safe operation of collective EV chargings, the stability of the power grid, and the cost-effective infrastructure expansion. However, existing methods either suffer from the data privacy issue and the susceptibility to cyberattacks or fail to consider the spatial correlation among different stations. To address these challenges, a federated graph learning approach involving multiple charging stations is proposed to collaboratively train a more generalized deep learning model for demand forecasting while capturing spatial correlations among various stations and enhancing robustness against potential attacks. Firstly, for better model performance, a Graph Neural Network (GNN) model is leveraged to characterize the geographic correlation among different charging stations in a federated manner. Secondly, to ensure robustness and deal with the data heterogeneity in a federated setting, a message passing that utilizes a global attention mechanism to aggregate personalized models for each client is proposed. Thirdly, by concerning cyberattacks, a special credit-based function is designed to mitigate potential threats from malicious clients or unwanted attacks. Extensive experiments on a public EV charging dataset are conducted using various deep learning techniques and federated learning methods to demonstrate the prediction accuracy and robustness of the proposed approach. | [
"['Yi Li' 'Renyou Xie' 'Chaojie Li' 'Yi Wang' 'Zhaoyang Dong']"
]
|
null | null | 2405.00743 | null | null | http://arxiv.org/pdf/2405.00743v1 | 2024-04-30T06:12:21Z | 2024-04-30T06:12:21Z | On the weight dynamics of learning networks | Neural networks have become a widely adopted tool for tackling a variety of problems in machine learning and artificial intelligence. In this contribution we use the mathematical framework of local stability analysis to gain a deeper understanding of the learning dynamics of feed forward neural networks. Therefore, we derive equations for the tangent operator of the learning dynamics of three-layer networks learning regression tasks. The results are valid for an arbitrary numbers of nodes and arbitrary choices of activation functions. Applying the results to a network learning a regression task, we investigate numerically, how stability indicators relate to the final training-loss. Although the specific results vary with different choices of initial conditions and activation functions, we demonstrate that it is possible to predict the final training loss, by monitoring finite-time Lyapunov exponents or covariant Lyapunov vectors during the training process. | [
"['Nahal Sharafi' 'Christoph Martin' 'Sarah Hallerberg']"
]
|
null | null | 2405.00746 | null | null | http://arxiv.org/pdf/2405.00746v1 | 2024-04-30T18:58:33Z | 2024-04-30T18:58:33Z | Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning | To create useful reinforcement learning (RL) agents, step zero is to design a suitable reward function that captures the nuances of the task. However, reward engineering can be a difficult and time-consuming process. Instead, human-in-the-loop (HitL) RL allows agents to learn reward functions from human feedback. Despite recent successes, many of the HitL RL methods still require numerous human interactions to learn successful reward functions. To improve the feedback efficiency of HitL RL methods (i.e., require less feedback), this paper introduces Sub-optimal Data Pre-training, SDP, an approach that leverages reward-free, sub-optimal data to improve scalar- and preference-based HitL RL algorithms. In SDP, we start by pseudo-labeling all low-quality data with rewards of zero. Through this process, we obtain free reward labels to pre-train our reward model. This pre-training phase provides the reward model a head start in learning, whereby it can identify that low-quality transitions should have a low reward, all without any actual feedback. Through extensive experiments with a simulated teacher, we demonstrate that SDP can significantly improve or achieve competitive performance with state-of-the-art (SOTA) HitL RL algorithms across nine robotic manipulation and locomotion tasks. | [
"['Calarina Muslimani' 'Matthew E. Taylor']"
]
|
null | null | 2405.00747 | null | null | http://arxiv.org/pdf/2405.00747v3 | 2024-05-27T19:59:00Z | 2024-04-30T19:48:55Z | Soft Preference Optimization: Aligning Language Models to Expert
Distributions | We propose Soft Preference Optimization (SPO), a method for aligning generative models, such as Large Language Models (LLMs), with human preferences, without the need for a reward model. SPO optimizes model outputs directly over a preference dataset through a natural loss function that integrates preference loss with a regularization term across the model's entire output distribution rather than limiting it to the preference dataset. Although SPO does not require the assumption of an existing underlying reward model, we demonstrate that, under the Bradley-Terry (BT) model assumption, it converges to a softmax of scaled rewards, with the distribution's "softness" adjustable via the softmax exponent, an algorithm parameter. We showcase SPO's methodology, its theoretical foundation, and its comparative advantages in simplicity, computational efficiency, and alignment precision. | [
"['Arsalan Sharifnassab' 'Sina Ghiassian' 'Saber Salehkaleybar'\n 'Surya Kanoria' 'Dale Schuurmans']"
]
|
null | null | 2405.00749 | null | null | http://arxiv.org/pdf/2405.00749v1 | 2024-05-01T03:37:12Z | 2024-05-01T03:37:12Z | More is Better: Deep Domain Adaptation with Multiple Sources | In many practical applications, it is often difficult and expensive to obtain large-scale labeled data to train state-of-the-art deep neural networks. Therefore, transferring the learned knowledge from a separate, labeled source domain to an unlabeled or sparsely labeled target domain becomes an appealing alternative. However, direct transfer often results in significant performance decay due to domain shift. Domain adaptation (DA) aims to address this problem by aligning the distributions between the source and target domains. Multi-source domain adaptation (MDA) is a powerful and practical extension in which the labeled data may be collected from multiple sources with different distributions. In this survey, we first define various MDA strategies. Then we systematically summarize and compare modern MDA methods in the deep learning era from different perspectives, followed by commonly used datasets and a brief benchmark. Finally, we discuss future research directions for MDA that are worth investigating. | [
"['Sicheng Zhao' 'Hui Chen' 'Hu Huang' 'Pengfei Xu' 'Guiguang Ding']"
]
|
null | null | 2405.00751 | null | null | http://arxiv.org/pdf/2405.00751v1 | 2024-05-01T04:53:14Z | 2024-05-01T04:53:14Z | F$^3$low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3)
Guided Flow Matching | Molecular dynamics (MD) is a crucial technique for simulating biological systems, enabling the exploration of their dynamic nature and fostering an understanding of their functions and properties. To address exploration inefficiency, emerging enhanced sampling approaches like coarse-graining (CG) and generative models have been employed. In this work, we propose a underline{Frame-to-Frame} generative model with guided underline{Flow}-matching (F$3$low) for enhanced sampling, which (a) extends the domain of CG modeling to the SE(3) Riemannian manifold; (b) retreating CGMD simulations as autoregressively sampling guided by the former frame via flow-matching models; (c) targets the protein backbone, offering improved insights into secondary structure formation and intricate folding pathways. Compared to previous methods, F$3$low allows for broader exploration of conformational space. The ability to rapidly generate diverse conformations via force-free generative paradigm on SE(3) paves the way toward efficient enhanced sampling methods. | [
"['Shaoning Li' 'Yusong Wang' 'Mingyu Li' 'Jian Zhang' 'Bin Shao'\n 'Nanning Zheng' 'Jian Tang']"
]
|
null | null | 2405.00754 | null | null | http://arxiv.org/pdf/2405.00754v1 | 2024-05-01T07:24:30Z | 2024-05-01T07:24:30Z | CLIPArTT: Light-weight Adaptation of CLIP to New Domains at Test Time | Pre-trained vision-language models (VLMs), exemplified by CLIP, demonstrate remarkable adaptability across zero-shot classification tasks without additional training. However, their performance diminishes in the presence of domain shifts. In this study, we introduce CLIP Adaptation duRing Test-Time (CLIPArTT), a fully test-time adaptation (TTA) approach for CLIP, which involves automatic text prompts construction during inference for their use as text supervision. Our method employs a unique, minimally invasive text prompt tuning process, wherein multiple predicted classes are aggregated into a single new text prompt, used as pseudo label to re-classify inputs in a transductive manner. Additionally, we pioneer the standardization of TTA benchmarks (e.g., TENT) in the realm of VLMs. Our findings demonstrate that, without requiring additional transformations nor new trainable modules, CLIPArTT enhances performance dynamically across non-corrupted datasets such as CIFAR-10, corrupted datasets like CIFAR-10-C and CIFAR-10.1, alongside synthetic datasets such as VisDA-C. This research underscores the potential for improving VLMs' adaptability through novel test-time strategies, offering insights for robust performance across varied datasets and environments. The code can be found at: https://github.com/dosowiechi/CLIPArTT.git | [
"['Gustavo Adolfo Vargas Hakim' 'David Osowiechi' 'Mehrdad Noori'\n 'Milad Cheraghalikhani' 'Ali Bahri' 'Moslem Yazdanpanah'\n 'Ismail Ben Ayed' 'Christian Desrosiers']"
]
|
null | null | 2405.00755 | null | null | http://arxiv.org/pdf/2405.00755v1 | 2024-05-01T07:55:08Z | 2024-05-01T07:55:08Z | Quantum AI for Alzheimer's disease early screening | Quantum machine learning is a new research field combining quantum information science and machine learning. Quantum computing technologies seem to be particularly well suited to solving problems in the health sector in an efficient way, because they may deal with large datasets more efficiently than classical AI. Alzheimer's disease is a neurodegenerative brain disorder that mostly affects elderly people, causing important cognitive impairments. It is the most common cause of dementia and it has an effect on memory, thought, learning abilities and movement control. This type of disease has no cure, consequently an early diagnosis is fundamental for reducing its impact. The analysis of handwriting can be effective for diagnosing, as many researches have conjectured. The DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset contains handwriting samples from people affected by Alzheimer's disease and a group of healthy people. Here we apply quantum AI to this use-case. In particular, we use this dataset to test kernel methods for classification task and compare their performances with the ones obtained via quantum machine learning methods. We find that quantum and classical algorithms achieve similar performances and in some cases quantum methods perform even better. Our results pave the way for future new quantum machine learning applications in early-screening diagnostics in the healthcare domain. | [
"['Giacomo Cappiello' 'Filippo Caruso']"
]
|
null | null | 2405.00770 | null | null | http://arxiv.org/pdf/2405.00770v1 | 2024-05-01T18:00:01Z | 2024-05-01T18:00:01Z | Quantum-Classical Separations in Shallow-Circuit-Based Learning with and
without Noises | We study quantum-classical separations between classical and quantum supervised learning models based on constant depth (i.e., shallow) circuits, in scenarios with and without noises. We construct a classification problem defined by a noiseless shallow quantum circuit and rigorously prove that any classical neural network with bounded connectivity requires logarithmic depth to output correctly with a larger-than-exponentially-small probability. This unconditional near-optimal quantum-classical separation originates from the quantum nonlocality property that distinguishes quantum circuits from their classical counterparts. We further derive the noise thresholds for demonstrating such a separation on near-term quantum devices under the depolarization noise model. We prove that this separation will persist if the noise strength is upper bounded by an inverse polynomial with respect to the system size, and vanish if the noise strength is greater than an inverse polylogarithmic function. In addition, for quantum devices with constant noise strength, we prove that no super-polynomial classical-quantum separation exists for any classification task defined by shallow Clifford circuits, independent of the structures of the circuits that specify the learning models. | [
"['Zhihan Zhang' 'Weiyuan Gong' 'Weikang Li' 'Dong-Ling Deng']"
]
|
null | null | 2405.00781 | null | null | http://arxiv.org/pdf/2405.00781v1 | 2024-05-01T18:00:10Z | 2024-05-01T18:00:10Z | A Review of Barren Plateaus in Variational Quantum Computing | Variational quantum computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) phenomenon. When a model exhibits a BP, its parameter optimization landscape becomes exponentially flat and featureless as the problem size increases. Importantly, all the moving pieces of an algorithm -- choices of ansatz, initial state, observable, loss function and hardware noise -- can lead to BPs when ill-suited. Due to the significant impact of BPs on trainability, researchers have dedicated considerable effort to develop theoretical and heuristic methods to understand and mitigate their effects. As a result, the study of BPs has become a thriving area of research, influencing and cross-fertilizing other fields such as quantum optimal control, tensor networks, and learning theory. This article provides a comprehensive review of the current understanding of the BP phenomenon. | [
"['Martin Larocca' 'Supanut Thanasilp' 'Samson Wang' 'Kunal Sharma'\n 'Jacob Biamonte' 'Patrick J. Coles' 'Lukasz Cincio' 'Jarrod R. McClean'\n 'Zoë Holmes' 'M. Cerezo']"
]
|
null | null | 2405.00782 | null | null | http://arxiv.org/pdf/2405.00782v1 | 2024-05-01T18:00:18Z | 2024-05-01T18:00:18Z | Rigged Dynamic Mode Decomposition: Data-Driven Generalized Eigenfunction
Decompositions for Koopman Operators | We introduce the Rigged Dynamic Mode Decomposition (Rigged DMD) algorithm, which computes generalized eigenfunction decompositions of Koopman operators. By considering the evolution of observables, Koopman operators transform complex nonlinear dynamics into a linear framework suitable for spectral analysis. While powerful, traditional Dynamic Mode Decomposition (DMD) techniques often struggle with continuous spectra. Rigged DMD addresses these challenges with a data-driven methodology that approximates the Koopman operator's resolvent and its generalized eigenfunctions using snapshot data from the system's evolution. At its core, Rigged DMD builds wave-packet approximations for generalized Koopman eigenfunctions and modes by integrating Measure-Preserving Extended Dynamic Mode Decomposition with high-order kernels for smoothing. This provides a robust decomposition encompassing both discrete and continuous spectral elements. We derive explicit high-order convergence theorems for generalized eigenfunctions and spectral measures. Additionally, we propose a novel framework for constructing rigged Hilbert spaces using time-delay embedding, significantly extending the algorithm's applicability. We provide examples, including systems with a Lebesgue spectrum, integrable Hamiltonian systems, the Lorenz system, and a high-Reynolds number lid-driven flow in a two-dimensional square cavity, demonstrating Rigged DMD's convergence, efficiency, and versatility. This work paves the way for future research and applications of decompositions with continuous spectra. | [
"['Matthew J. Colbrook' 'Catherine Drysdale' 'Andrew Horning']"
]
|
null | null | 2405.00790 | null | null | http://arxiv.org/pdf/2405.00790v1 | 2024-05-01T18:02:25Z | 2024-05-01T18:02:25Z | SCAR: Scheduling Multi-Model AI Workloads on Heterogeneous Multi-Chiplet
Module Accelerators | Emerging multi-model workloads with heavy models like recent large language models significantly increased the compute and memory demands on hardware. To address such increasing demands, designing a scalable hardware architecture became a key problem. Among recent solutions, the 2.5D silicon interposer multi-chip module (MCM)-based AI accelerator has been actively explored as a promising scalable solution due to their significant benefits in the low engineering cost and composability. However, previous MCM accelerators are based on homogeneous architectures with fixed dataflow, which encounter major challenges from highly heterogeneous multi-model workloads due to their limited workload adaptivity. Therefore, in this work, we explore the opportunity in the heterogeneous dataflow MCM AI accelerators. We identify the scheduling of multi-model workload on heterogeneous dataflow MCM AI accelerator is an important and challenging problem due to its significance and scale, which reaches O(10^18) scale even for a single model case on 6x6 chiplets. We develop a set of heuristics to navigate the huge scheduling space and codify them into a scheduler with advanced techniques such as inter-chiplet pipelining. Our evaluation on ten multi-model workload scenarios for datacenter multitenancy and AR/VR use-cases has shown the efficacy of our approach, achieving on average 35.3% and 31.4% less energy-delay product (EDP) for the respective applications settings compared to homogeneous baselines. | [
"['Mohanad Odema' 'Luke Chen' 'Hyoukjun Kwon'\n 'Mohammad Abdullah Al Faruque']"
]
|
null | null | 2405.00792 | null | null | http://arxiv.org/pdf/2405.00792v1 | 2024-05-01T18:08:03Z | 2024-05-01T18:08:03Z | Error Exponent in Agnostic PAC Learning | Statistical learning theory and the Probably Approximately Correct (PAC) criterion are the common approach to mathematical learning theory. PAC is widely used to analyze learning problems and algorithms, and have been studied thoroughly. Uniform worst case bounds on the convergence rate have been well established using, e.g., VC theory or Radamacher complexity. However, in a typical scenario the performance could be much better. In this paper, we consider PAC learning using a somewhat different tradeoff, the error exponent - a well established analysis method in Information Theory - which describes the exponential behavior of the probability that the risk will exceed a certain threshold as function of the sample size. We focus on binary classification and find, under some stability assumptions, an improved distribution dependent error exponent for a wide range of problems, establishing the exponential behavior of the PAC error probability in agnostic learning. Interestingly, under these assumptions, agnostic learning may have the same error exponent as realizable learning. The error exponent criterion can be applied to analyze knowledge distillation, a problem that so far lacks a theoretical analysis. | [
"['Adi Hendel' 'Meir Feder']"
]
|
null | null | 2405.00816 | null | null | http://arxiv.org/pdf/2405.00816v1 | 2024-05-01T18:57:41Z | 2024-05-01T18:57:41Z | Sifting out communities in large sparse networks | Research data sets are growing to unprecedented sizes and network modeling is commonly used to extract complex relationships in diverse domains, such as genetic interactions involved in disease, logistics, and social communities. As the number of nodes increases in a network, an increasing sparsity of edges is a practical limitation due to memory restrictions. Moreover, many of these sparse networks exhibit very large numbers of nodes with no adjacent edges, as well as disjoint components of nodes with no edges connecting them. A prevalent aim in network modeling is the identification of clusters, or communities, of nodes that are highly interrelated. Several definitions of strong community structure have been introduced to facilitate this task, each with inherent assumptions and biases. We introduce an intuitive objective function for quantifying the quality of clustering results in large sparse networks. We utilize a two-step method for identifying communities which is especially well-suited for this domain as the first step efficiently divides the network into the disjoint components, while the second step optimizes clustering of the produced components based on the new objective. Using simulated networks, optimization based on the new objective function consistently yields significantly higher accuracy than those based on the modularity function, with the widest gaps appearing for the noisiest networks. Additionally, applications to benchmark problems illustrate the intuitive correctness of our approach. Finally, the practicality of our approach is demonstrated in real-world data in which we identify complex genetic interactions in large-scale networks comprised of tens of thousands of nodes. Based on these three different types of trials, our results clearly demonstrate the usefulness of our two-step procedure and the accuracy of our simple objective. | [
"['Sharlee Climer' 'Kenneth Smith Jr' 'Wei Yang' 'Lisa de las Fuentes'\n 'Victor G. Dávila-Román' 'C. Charles Gu']"
]
|
null | null | 2405.00819 | null | null | http://arxiv.org/pdf/2405.00819v1 | 2024-05-01T19:00:30Z | 2024-05-01T19:00:30Z | ICU Bloodstream Infection Prediction: A Transformer-Based Approach for
EHR Analysis | We introduce RatchetEHR, a novel transformer-based framework designed for the predictive analysis of electronic health records (EHR) data in intensive care unit (ICU) settings, with a specific focus on bloodstream infection (BSI) prediction. Leveraging the MIMIC-IV dataset, RatchetEHR demonstrates superior predictive performance compared to other methods, including RNN, LSTM, and XGBoost, particularly due to its advanced handling of sequential and temporal EHR data. A key innovation in RatchetEHR is the integration of the Graph Convolutional Transformer (GCT) component, which significantly enhances the ability to identify hidden structural relationships within EHR data, resulting in more accurate clinical predictions. Through SHAP value analysis, we provide insights into influential features for BSI prediction. RatchetEHR integrates multiple advancements in deep learning which together provide accurate predictions even with a relatively small sample size and highly imbalanced dataset. This study contributes to medical informatics by showcasing the application of advanced AI techniques in healthcare and sets a foundation for further research to optimize these capabilities in EHR data analysis. | [
"['Ortal Hirszowicz' 'Dvir Aran']"
]
|
null | null | 2405.00820 | null | null | http://arxiv.org/pdf/2405.00820v2 | 2024-05-17T17:57:33Z | 2024-05-01T19:02:18Z | HLSFactory: A Framework Empowering High-Level Synthesis Datasets for
Machine Learning and Beyond | Machine learning (ML) techniques have been applied to high-level synthesis (HLS) flows for quality-of-result (QoR) prediction and design space exploration (DSE). Nevertheless, the scarcity of accessible high-quality HLS datasets and the complexity of building such datasets present challenges. Existing datasets have limitations in terms of benchmark coverage, design space enumeration, vendor extensibility, or lack of reproducible and extensible software for dataset construction. Many works also lack user-friendly ways to add more designs, limiting wider adoption of such datasets. In response to these challenges, we introduce HLSFactory, a comprehensive framework designed to facilitate the curation and generation of high-quality HLS design datasets. HLSFactory has three main stages: 1) a design space expansion stage to elaborate single HLS designs into large design spaces using various optimization directives across multiple vendor tools, 2) a design synthesis stage to execute HLS and FPGA tool flows concurrently across designs, and 3) a data aggregation stage for extracting standardized data into packaged datasets for ML usage. This tripartite architecture ensures broad design space coverage via design space expansion and supports multiple vendor tools. Users can contribute to each stage with their own HLS designs and synthesis results and extend the framework itself with custom frontends and tool flows. We also include an initial set of built-in designs from common HLS benchmarks curated open-source HLS designs. We showcase the versatility and multi-functionality of our framework through six case studies: I) Design space sampling; II) Fine-grained parallelism backend speedup; III) Targeting Intel's HLS flow; IV) Adding new auxiliary designs; V) Integrating published HLS data; VI) HLS tool version regression benchmarking. Code at https://github.com/sharc-lab/HLSFactory. | [
"['Stefan Abi-Karam' 'Rishov Sarkar' 'Allison Seigler' 'Sean Lowe'\n 'Zhigang Wei' 'Hanqiu Chen' 'Nanditha Rao' 'Lizy John' 'Aman Arora'\n 'Cong Hao']"
]
|
null | null | 2405.00837 | null | null | http://arxiv.org/pdf/2405.00837v1 | 2024-05-01T19:56:52Z | 2024-05-01T19:56:52Z | Locality Regularized Reconstruction: Structured Sparsity and Delaunay
Triangulations | Linear representation learning is widely studied due to its conceptual simplicity and empirical utility in tasks such as compression, classification, and feature extraction. Given a set of points $[mathbf{x}_1, mathbf{x}_2, ldots, mathbf{x}_n] = mathbf{X} in mathbb{R}^{d times n}$ and a vector $mathbf{y} in mathbb{R}^d$, the goal is to find coefficients $mathbf{w} in mathbb{R}^n$ so that $mathbf{X} mathbf{w} approx mathbf{y}$, subject to some desired structure on $mathbf{w}$. In this work we seek $mathbf{w}$ that forms a local reconstruction of $mathbf{y}$ by solving a regularized least squares regression problem. We obtain local solutions through a locality function that promotes the use of columns of $mathbf{X}$ that are close to $mathbf{y}$ when used as a regularization term. We prove that, for all levels of regularization and under a mild condition that the columns of $mathbf{X}$ have a unique Delaunay triangulation, the optimal coefficients' number of non-zero entries is upper bounded by $d+1$, thereby providing local sparse solutions when $d ll n$. Under the same condition we also show that for any $mathbf{y}$ contained in the convex hull of $mathbf{X}$ there exists a regime of regularization parameter such that the optimal coefficients are supported on the vertices of the Delaunay simplex containing $mathbf{y}$. This provides an interpretation of the sparsity as having structure obtained implicitly from the Delaunay triangulation of $mathbf{X}$. We demonstrate that our locality regularized problem can be solved in comparable time to other methods that identify the containing Delaunay simplex. | [
"['Marshall Mueller' 'James M. Murphy' 'Abiy Tasissa']"
]
|
null | null | 2405.00839 | null | null | http://arxiv.org/pdf/2405.00839v1 | 2024-05-01T20:03:37Z | 2024-05-01T20:03:37Z | Communication-Efficient Training Workload Balancing for Decentralized
Multi-Agent Learning | Decentralized Multi-agent Learning (DML) enables collaborative model training while preserving data privacy. However, inherent heterogeneity in agents' resources (computation, communication, and task size) may lead to substantial variations in training time. This heterogeneity creates a bottleneck, lengthening the overall training time due to straggler effects and potentially wasting spare resources of faster agents. To minimize training time in heterogeneous environments, we present a Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning (ComDML), which balances the workload among agents through a decentralized approach. Leveraging local-loss split training, ComDML enables parallel updates, where slower agents offload part of their workload to faster agents. To minimize the overall training time, ComDML optimizes the workload balancing by jointly considering the communication and computation capacities of agents, which hinges upon integer programming. A dynamic decentralized pairing scheduler is developed to efficiently pair agents and determine optimal offloading amounts. We prove that in ComDML, both slower and faster agents' models converge, for convex and non-convex functions. Furthermore, extensive experimental results on popular datasets (CIFAR-10, CIFAR-100, and CINIC-10) and their non-I.I.D. variants, with large models such as ResNet-56 and ResNet-110, demonstrate that ComDML can significantly reduce the overall training time while maintaining model accuracy, compared to state-of-the-art methods. ComDML demonstrates robustness in heterogeneous environments, and privacy measures can be seamlessly integrated for enhanced data protection. | [
"['Seyed Mahmoud Sajjadi Mohammadabadi' 'Lei Yang' 'Feng Yan'\n 'Junshan Zhang']"
]
|
null | null | 2405.00842 | null | null | http://arxiv.org/pdf/2405.00842v1 | 2024-05-01T20:10:06Z | 2024-05-01T20:10:06Z | Quickest Change Detection with Confusing Change | In the problem of quickest change detection (QCD), a change occurs at some unknown time in the distribution of a sequence of independent observations. This work studies a QCD problem where the change is either a bad change, which we aim to detect, or a confusing change, which is not of our interest. Our objective is to detect a bad change as quickly as possible while avoiding raising a false alarm for pre-change or a confusing change. We identify a specific set of pre-change, bad change, and confusing change distributions that pose challenges beyond the capabilities of standard Cumulative Sum (CuSum) procedures. Proposing novel CuSum-based detection procedures, S-CuSum and J-CuSum, leveraging two CuSum statistics, we offer solutions applicable across all kinds of pre-change, bad change, and confusing change distributions. For both S-CuSum and J-CuSum, we provide analytical performance guarantees and validate them by numerical results. Furthermore, both procedures are computationally efficient as they only require simple recursive updates. | [
"['Yu-Zhen Janice Chen' 'Jinhang Zuo' 'Venugopal V. Veeravalli'\n 'Don Towsley']"
]
|
null | null | 2405.00846 | null | null | http://arxiv.org/pdf/2405.00846v2 | 2024-05-31T14:26:47Z | 2024-05-01T20:21:44Z | Gameplay Filters: Safe Robot Walking through Adversarial Imagination | Ensuring the safe operation of legged robots in uncertain, novel environments is crucial to their widespread adoption. Despite recent advances in safety filters that can keep arbitrary task-driven policies from incurring safety failures, existing solutions for legged robot locomotion still rely on simplified dynamics and may fail when the robot is perturbed away from predefined stable gaits. This paper presents a general approach that leverages offline game-theoretic reinforcement learning to synthesize a highly robust safety filter for high-order nonlinear dynamics. This gameplay filter then maintains runtime safety by continually simulating adversarial futures and precluding task-driven actions that would cause it to lose future games (and thereby violate safety). Validated on a 36-dimensional quadruped robot locomotion task, the gameplay safety filter exhibits inherent robustness to the sim-to-real gap without manual tuning or heuristic designs. Physical experiments demonstrate the effectiveness of the gameplay safety filter under perturbations, such as tugging and unmodeled irregular terrains, while simulation studies shed light on how to trade off computation and conservativeness without compromising safety. | [
"['Duy P. Nguyen' 'Kai-Chieh Hsu' 'Wenhao Yu' 'Jie Tan' 'Jaime F. Fisac']"
]
|
null | null | 2405.00853 | null | null | http://arxiv.org/pdf/2405.00853v2 | 2024-06-17T23:04:58Z | 2024-05-01T20:34:12Z | Efficient Algorithms for Learning Monophonic Halfspaces in Graphs | We study the problem of learning a binary classifier on the vertices of a graph. In particular, we consider classifiers given by monophonic halfspaces, partitions of the vertices that are convex in a certain abstract sense. Monophonic halfspaces, and related notions such as geodesic halfspaces,have recently attracted interest, and several connections have been drawn between their properties(e.g., their VC dimension) and the structure of the underlying graph $G$. We prove several novel results for learning monophonic halfspaces in the supervised, online, and active settings. Our main result is that a monophonic halfspace can be learned with near-optimal passive sample complexity in time polynomial in $n = |V(G)|$. This requires us to devise a polynomial-time algorithm for consistent hypothesis checking, based on several structural insights on monophonic halfspaces and on a reduction to $2$-satisfiability. We prove similar results for the online and active settings. We also show that the concept class can be enumerated with delay $operatorname{poly}(n)$, and that empirical risk minimization can be performed in time $2^{omega(G)}operatorname{poly}(n)$ where $omega(G)$ is the clique number of $G$. These results answer open questions from the literature (Gonz'alez et al., 2020), and show a contrast with geodesic halfspaces, for which some of the said problems are NP-hard (Seiffarth et al., 2023). | [
"['Marco Bressan' 'Emmanuel Esposito' 'Maximilian Thiessen']"
]
|
null | null | 2405.00871 | null | null | http://arxiv.org/pdf/2405.00871v1 | 2024-05-01T21:11:29Z | 2024-05-01T21:11:29Z | Learning to Boost the Performance of Stable Nonlinear Systems | The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms. However, maintaining closed-loop stability while boosting the performance of nonlinear control systems using data-driven and deep-learning approaches stands as an important unsolved challenge. In this paper, we tackle the performance-boosting problem with closed-loop stability guarantees. Specifically, we establish a synergy between the Internal Model Control (IMC) principle for nonlinear systems and state-of-the-art unconstrained optimization approaches for learning stable dynamics. Our methods enable learning over arbitrarily deep neural network classes of performance-boosting controllers for stable nonlinear systems; crucially, we guarantee Lp closed-loop stability even if optimization is halted prematurely, and even when the ground-truth dynamics are unknown, with vanishing conservatism in the class of stabilizing policies as the model uncertainty is reduced to zero. We discuss the implementation details of the proposed control schemes, including distributed ones, along with the corresponding optimization procedures, demonstrating the potential of freely shaping the cost functions through several numerical experiments. | [
"['Luca Furieri' 'Clara Lucía Galimberti' 'Giancarlo Ferrari-Trecate']"
]
|
null | null | 2405.00876 | null | null | http://arxiv.org/pdf/2405.00876v1 | 2024-05-01T21:35:04Z | 2024-05-01T21:35:04Z | Beyond Human Vision: The Role of Large Vision Language Models in
Microscope Image Analysis | Vision language models (VLMs) have recently emerged and gained the spotlight for their ability to comprehend the dual modality of image and textual data. VLMs such as LLaVA, ChatGPT-4, and Gemini have recently shown impressive performance on tasks such as natural image captioning, visual question answering (VQA), and spatial reasoning. Additionally, a universal segmentation model by Meta AI, Segment Anything Model (SAM) shows unprecedented performance at isolating objects from unforeseen images. Since medical experts, biologists, and materials scientists routinely examine microscopy or medical images in conjunction with textual information in the form of captions, literature, or reports, and draw conclusions of great importance and merit, it is indubitably essential to test the performance of VLMs and foundation models such as SAM, on these images. In this study, we charge ChatGPT, LLaVA, Gemini, and SAM with classification, segmentation, counting, and VQA tasks on a variety of microscopy images. We observe that ChatGPT and Gemini are impressively able to comprehend the visual features in microscopy images, while SAM is quite capable at isolating artefacts in a general sense. However, the performance is not close to that of a domain expert - the models are readily encumbered by the introduction of impurities, defects, artefact overlaps and diversity present in the images. | [
"['Prateek Verma' 'Minh-Hao Van' 'Xintao Wu']"
]
|
null | null | 2405.00877 | null | null | http://arxiv.org/pdf/2405.00877v2 | 2024-06-02T19:41:04Z | 2024-05-01T21:42:38Z | Markov flow policy -- deep MC | Discounted algorithms often encounter evaluation errors due to their reliance on short-term estimations, which can impede their efficacy in addressing simple, short-term tasks and impose undesired temporal discounts ((gamma)). Interestingly, these algorithms are often tested without applying a discount, a phenomenon we refer as the textit{train-test bias}. In response to these challenges, we propose the Markov Flow Policy, which utilizes a non-negative neural network flow to enable comprehensive forward-view predictions. Through integration into the TD7 codebase and evaluation using the MuJoCo benchmark, we observe significant performance improvements, positioning MFP as a straightforward, practical, and easily implementable solution within the domain of average rewards algorithms. | [
"['Nitsan Soffair' 'Gilad Katz']"
]
|
null | null | 2405.00879 | null | null | http://arxiv.org/pdf/2405.00879v1 | 2024-05-01T21:44:47Z | 2024-05-01T21:44:47Z | Machine Learning Techniques for Data Reduction of Climate Applications | Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to be present. Then, we employ a Guaranteed Autoencoder (GAE) to compress data with differential error bounds. GAE uses QoI information to apply low-error compression to only these regions. This results in overall high compression ratios while still achieving downstream goals of simulation or data collections. Experimental results are presented for climate data generated from the E3SM Simulation model for downstream quantities such as tropical cyclone and atmospheric river detection and tracking. These results show that our approach is superior to comparable methods in the literature. | [
"['Xiao Li' 'Qian Gong' 'Jaemoon Lee' 'Scott Klasky' 'Anand Rangarajan'\n 'Sanjay Ranka']"
]
|
null | null | 2405.00885 | null | null | http://arxiv.org/pdf/2405.00885v1 | 2024-05-01T22:01:40Z | 2024-05-01T22:01:40Z | WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated
Learning over Mobile Devices via Adaptive Subnetwork Scheduling | As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices' computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing and communications capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices' communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy. | [
"['Huai-an Su' 'Jiaxiang Geng' 'Liang Li' 'Xiaoqi Qin' 'Yanzhao Hou'\n 'Xin Fu' 'Miao Pan']"
]
|
null | null | 2405.00902 | null | null | http://arxiv.org/pdf/2405.00902v1 | 2024-05-01T23:19:48Z | 2024-05-01T23:19:48Z | MESA: Cooperative Meta-Exploration in Multi-Agent Learning through
Exploiting State-Action Space Structure | Multi-agent reinforcement learning (MARL) algorithms often struggle to find strategies close to Pareto optimal Nash Equilibrium, owing largely to the lack of efficient exploration. The problem is exacerbated in sparse-reward settings, caused by the larger variance exhibited in policy learning. This paper introduces MESA, a novel meta-exploration method for cooperative multi-agent learning. It learns to explore by first identifying the agents' high-rewarding joint state-action subspace from training tasks and then learning a set of diverse exploration policies to "cover" the subspace. These trained exploration policies can be integrated with any off-policy MARL algorithm for test-time tasks. We first showcase MESA's advantage in a multi-step matrix game. Furthermore, experiments show that with learned exploration policies, MESA achieves significantly better performance in sparse-reward tasks in several multi-agent particle environments and multi-agent MuJoCo environments, and exhibits the ability to generalize to more challenging tasks at test time. | [
"['Zhicheng Zhang' 'Yancheng Liang' 'Yi Wu' 'Fei Fang']"
]
|
null | null | 2405.00906 | null | null | http://arxiv.org/pdf/2405.00906v1 | 2024-05-01T23:30:12Z | 2024-05-01T23:30:12Z | LOTUS: Improving Transformer Efficiency with Sparsity Pruning and Data
Lottery Tickets | Vision transformers have revolutionized computer vision, but their computational demands present challenges for training and deployment. This paper introduces LOTUS (LOttery Transformers with Ultra Sparsity), a novel method that leverages data lottery ticket selection and sparsity pruning to accelerate vision transformer training while maintaining accuracy. Our approach focuses on identifying and utilizing the most informative data subsets and eliminating redundant model parameters to optimize the training process. Through extensive experiments, we demonstrate the effectiveness of LOTUS in achieving rapid convergence and high accuracy with significantly reduced computational requirements. This work highlights the potential of combining data selection and sparsity techniques for efficient vision transformer training, opening doors for further research and development in this area. | [
"['Ojasw Upadhyay']"
]
|
null | null | 2405.00908 | null | null | http://arxiv.org/pdf/2405.00908v1 | 2024-05-01T23:40:12Z | 2024-05-01T23:40:12Z | Transformer-Based Self-Supervised Learning for Histopathological
Classification of Ischemic Stroke Clot Origin | Background and Purpose: Identifying the thromboembolism source in ischemic stroke is crucial for treatment and secondary prevention yet is often undetermined. This study describes a self-supervised deep learning approach in digital pathology of emboli for classifying ischemic stroke clot origin from histopathological images. Methods: The dataset included whole slide images (WSI) from the STRIP AI Kaggle challenge, consisting of retrieved clots from ischemic stroke patients following mechanical thrombectomy. Transformer-based deep learning models were developed using transfer learning and self-supervised pretraining for classifying WSI. Customizations included an attention pooling layer, weighted loss function, and threshold optimization. Various model architectures were tested and compared, and model performances were primarily evaluated using weighted logarithmic loss. Results: The model achieved a logloss score of 0.662 in cross-validation and 0.659 on the test set. Different model backbones were compared, with the swin_large_patch4_window12_384 showed higher performance. Thresholding techniques for clot origin classification were employed to balance false positives and negatives. Conclusion: The study demonstrates the extent of efficacy of transformer-based deep learning models in identifying ischemic stroke clot origins from histopathological images and emphasizes the need for refined modeling techniques specifically adapted to thrombi WSI. Further research is needed to improve model performance, interpretability, validate its effectiveness. Future enhancement could include integrating larger patient cohorts, advanced preprocessing strategies, and exploring ensemble multimodal methods for enhanced diagnostic accuracy. | [
"['K. Yeh' 'M. S. Jabal' 'V. Gupta' 'D. F. Kallmes' 'W. Brinjikji'\n 'B. S. Erdal']"
]
|
null | null | 2405.00909 | null | null | http://arxiv.org/pdf/2405.00909v1 | 2024-05-01T23:41:14Z | 2024-05-01T23:41:14Z | Quantum Federated Learning Experiments in the Cloud with Data Encoding | Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy. We explore the challenges of deploying QFL on cloud platforms, emphasizing quantum intricacies and platform limitations. The proposed data-encoding-driven QFL, with a proof of concept (GitHub Open Source) using genomic data sets on quantum simulators, shows promising results. | [
"['Shiva Raj Pokhrel' 'Naman Yash' 'Jonathan Kua' 'Gang Li' 'Lei Pan']"
]
|
null | null | 2405.00910 | null | null | http://arxiv.org/pdf/2405.00910v1 | 2024-05-01T23:46:44Z | 2024-05-01T23:46:44Z | De-Biasing Models of Biased Decisions: A Comparison of Methods Using
Mortgage Application Data | Prediction models can improve efficiency by automating decisions such as the approval of loan applications. However, they may inherit bias against protected groups from the data they are trained on. This paper adds counterfactual (simulated) ethnic bias to real data on mortgage application decisions, and shows that this bias is replicated by a machine learning model (XGBoost) even when ethnicity is not used as a predictive variable. Next, several other de-biasing methods are compared: averaging over prohibited variables, taking the most favorable prediction over prohibited variables (a novel method), and jointly minimizing errors as well as the association between predictions and prohibited variables. De-biasing can recover some of the original decisions, but the results are sensitive to whether the bias is effected through a proxy. | [
"['Nicholas Tenev']"
]
|
null | null | 2405.00914 | null | null | http://arxiv.org/pdf/2405.00914v3 | 2024-07-09T15:48:20Z | 2024-05-01T23:59:36Z | Accelerated Fully First-Order Methods for Bilevel and Minimax
Optimization | We present in this paper novel accelerated fully first-order methods in emph{Bilevel Optimization} (BLO). Firstly, for BLO under the assumption that the lower-level functions admit the typical strong convexity assumption, the emph{(Perturbed) Restarted Accelerated Fully First-order methods for Bilevel Approximation} (texttt{PRAF${}^2$BA}) algorithm leveraging emph{fully} first-order oracles is proposed, whereas the algorithm for finding approximate first-order and second-order stationary points with state-of-the-art oracle query complexities in solving complex optimization tasks. Secondly, applying as a special case of BLO the emph{nonconvex-strongly-convex} (NCSC) minimax optimization, texttt{PRAF${}^2$BA} rediscovers emph{perturbed restarted accelerated gradient descent ascent} (texttt{PRAGDA}) that achieves the state-of-the-art complexity for finding approximate second-order stationary points. Additionally, we investigate the challenge of finding stationary points of the hyper-objective function in BLO when lower-level functions lack the typical strong convexity assumption, where we identify several regularity conditions of the lower-level problems that ensure tractability and present hardness results indicating the intractability of BLO for general convex lower-level functions. Under these regularity conditions we propose the emph{Inexact Gradient-Free Method} (texttt{IGFM}), utilizing the emph{Switching Gradient Method} (texttt{SGM}) as an efficient sub-routine to find an approximate stationary point of the hyper-objective in polynomial time. Empirical studies for real-world problems are provided to further validate the outperformance of our proposed algorithms. | [
"['Chris Junchi Li']"
]
|
null | null | 2405.00915 | null | null | http://arxiv.org/pdf/2405.00915v1 | 2024-05-02T00:04:02Z | 2024-05-02T00:04:02Z | EchoScene: Indoor Scene Generation via Information Echo over Scene Graph
Diffusion | We present EchoScene, an interactive and controllable generative model that generates 3D indoor scenes on scene graphs. EchoScene leverages a dual-branch diffusion model that dynamically adapts to scene graphs. Existing methods struggle to handle scene graphs due to varying numbers of nodes, multiple edge combinations, and manipulator-induced node-edge operations. EchoScene overcomes this by associating each node with a denoising process and enables collaborative information exchange, enhancing controllable and consistent generation aware of global constraints. This is achieved through an information echo scheme in both shape and layout branches. At every denoising step, all processes share their denoising data with an information exchange unit that combines these updates using graph convolution. The scheme ensures that the denoising processes are influenced by a holistic understanding of the scene graph, facilitating the generation of globally coherent scenes. The resulting scenes can be manipulated during inference by editing the input scene graph and sampling the noise in the diffusion model. Extensive experiments validate our approach, which maintains scene controllability and surpasses previous methods in generation fidelity. Moreover, the generated scenes are of high quality and thus directly compatible with off-the-shelf texture generation. Code and trained models are open-sourced. | [
"['Guangyao Zhai' 'Evin Pınar Örnek' 'Dave Zhenyu Chen' 'Ruotong Liao'\n 'Yan Di' 'Nassir Navab' 'Federico Tombari' 'Benjamin Busam']"
]
|
null | null | 2405.00922 | null | null | http://arxiv.org/pdf/2405.00922v1 | 2024-05-02T00:34:10Z | 2024-05-02T00:34:10Z | MTDT: A Multi-Task Deep Learning Digital Twin | Traffic congestion has significant impacts on both the economy and the environment. Measures of Effectiveness (MOEs) have long been the standard for evaluating the level of service and operational efficiency of traffic intersections. However, the scarcity of traditional high-resolution loop detector data (ATSPM) presents challenges in accurately measuring MOEs or capturing the intricate temporospatial characteristics inherent in urban intersection traffic. In response to this challenge, we have introduced the Multi-Task Deep Learning Digital Twin (MTDT) as a solution for multifaceted and precise intersection traffic flow simulation. MTDT enables accurate, fine-grained estimation of loop detector waveform time series for each lane of movement, alongside successful estimation of several MOEs for each lane group associated with a traffic phase concurrently and for all approaches of an arbitrary urban intersection. Unlike existing deep learning methodologies, MTDT distinguishes itself through its adaptability to local temporal and spatial features, such as signal timing plans, intersection topology, driving behaviors, and turning movement counts. While maintaining a straightforward design, our model emphasizes the advantages of multi-task learning in traffic modeling. By consolidating the learning process across multiple tasks, MTDT demonstrates reduced overfitting, increased efficiency, and enhanced effectiveness by sharing representations learned by different tasks. Furthermore, our approach facilitates sequential computation and lends itself to complete parallelization through GPU implementation. This not only streamlines the computational process but also enhances scalability and performance. | [
"['Nooshin Yousefzadeh' 'Rahul Sengupta' 'Yashaswi Karnati'\n 'Anand Rangarajan' 'Sanjay Ranka']"
]
|
null | null | 2405.00934 | null | null | http://arxiv.org/pdf/2405.00934v1 | 2024-05-02T01:24:53Z | 2024-05-02T01:24:53Z | Benchmarking Representations for Speech, Music, and Acoustic Events | Limited diversity in standardized benchmarks for evaluating audio representation learning (ARL) methods may hinder systematic comparison of current methods' capabilities. We present ARCH, a comprehensive benchmark for evaluating ARL methods on diverse audio classification domains, covering acoustic events, music, and speech. ARCH comprises 12 datasets, that allow us to thoroughly assess pre-trained SSL models of different sizes. ARCH streamlines benchmarking of ARL techniques through its unified access to a wide range of domains and its ability to readily incorporate new datasets and models. To address the current lack of open-source, pre-trained models for non-speech audio, we also release new pre-trained models that demonstrate strong performance on non-speech datasets. We argue that the presented wide-ranging evaluation provides valuable insights into state-of-the-art ARL methods, and is useful to pinpoint promising research directions. | [
"['Moreno La Quatra' 'Alkis Koudounas' 'Lorenzo Vaiani' 'Elena Baralis'\n 'Luca Cagliero' 'Paolo Garza' 'Sabato Marco Siniscalchi']"
]
|
null | null | 2405.00937 | null | null | http://arxiv.org/pdf/2405.00937v1 | 2024-05-02T01:39:30Z | 2024-05-02T01:39:30Z | New bounds on the cohesion of complete-link and other linkage methods
for agglomeration clustering | Linkage methods are among the most popular algorithms for hierarchical clustering. Despite their relevance the current knowledge regarding the quality of the clustering produced by these methods is limited. Here, we improve the currently available bounds on the maximum diameter of the clustering obtained by complete-link for metric spaces. One of our new bounds, in contrast to the existing ones, allows us to separate complete-link from single-link in terms of approximation for the diameter, which corroborates the common perception that the former is more suitable than the latter when the goal is producing compact clusters. We also show that our techniques can be employed to derive upper bounds on the cohesion of a class of linkage methods that includes the quite popular average-link. | [
"['Sanjoy Dasgupta' 'Eduardo Laber']"
]
|
null | null | 2405.00946 | null | null | http://arxiv.org/pdf/2405.00946v2 | 2024-06-03T07:13:37Z | 2024-05-02T02:15:23Z | SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters | This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources. At the heart of SparseTSF lies the Cross-Period Sparse Forecasting technique, which simplifies the forecasting task by decoupling the periodicity and trend in time series data. This technique involves downsampling the original sequences to focus on cross-period trend prediction, effectively extracting periodic features while minimizing the model's complexity and parameter count. Based on this technique, the SparseTSF model uses fewer than *1k* parameters to achieve competitive or superior performance compared to state-of-the-art models. Furthermore, SparseTSF showcases remarkable generalization capabilities, making it well-suited for scenarios with limited computational resources, small samples, or low-quality data. The code is publicly available at this repository: https://github.com/lss-1138/SparseTSF. | [
"['Shengsheng Lin' 'Weiwei Lin' 'Wentai Wu' 'Haojun Chen' 'Junjie Yang']"
]
|
null | null | 2405.00949 | null | null | http://arxiv.org/pdf/2405.00949v1 | 2024-05-02T02:20:12Z | 2024-05-02T02:20:12Z | The Role of Model Architecture and Scale in Predicting Molecular
Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA | This study introduces a systematic framework to compare the efficacy of Large Language Models (LLMs) for fine-tuning across various cheminformatics tasks. Employing a uniform training methodology, we assessed three well-known models-RoBERTa, BART, and LLaMA-on their ability to predict molecular properties using the Simplified Molecular Input Line Entry System (SMILES) as a universal molecular representation format. Our comparative analysis involved pre-training 18 configurations of these models, with varying parameter sizes and dataset scales, followed by fine-tuning them on six benchmarking tasks from DeepChem. We maintained consistent training environments across models to ensure reliable comparisons. This approach allowed us to assess the influence of model type, size, and training dataset size on model performance. Specifically, we found that LLaMA-based models generally offered the lowest validation loss, suggesting their superior adaptability across tasks and scales. However, we observed that absolute validation loss is not a definitive indicator of model performance - contradicts previous research - at least for fine-tuning tasks: instead, model size plays a crucial role. Through rigorous replication and validation, involving multiple training and fine-tuning cycles, our study not only delineates the strengths and limitations of each model type but also provides a robust methodology for selecting the most suitable LLM for specific cheminformatics applications. This research underscores the importance of considering model architecture and dataset characteristics in deploying AI for molecular property prediction, paving the way for more informed and effective utilization of AI in drug discovery and related fields. | [
"['Lee Youngmin' 'Lang S. I. D. Andrew' 'Cai Duoduo' 'Wheat R. Stephen']"
]
|
null | null | 2405.00950 | null | null | http://arxiv.org/pdf/2405.00950v1 | 2024-05-02T02:20:19Z | 2024-05-02T02:20:19Z | Provably Efficient Reinforcement Learning for Adversarial Restless
Multi-Armed Bandits with Unknown Transitions and Bandit Feedback | Restless multi-armed bandits (RMAB) play a central role in modeling sequential decision making problems under an instantaneous activation constraint that at most B arms can be activated at any decision epoch. Each restless arm is endowed with a state that evolves independently according to a Markov decision process regardless of being activated or not. In this paper, we consider the task of learning in episodic RMAB with unknown transition functions and adversarial rewards, which can change arbitrarily across episodes. Further, we consider a challenging but natural bandit feedback setting that only adversarial rewards of activated arms are revealed to the decision maker (DM). The goal of the DM is to maximize its total adversarial rewards during the learning process while the instantaneous activation constraint must be satisfied in each decision epoch. We develop a novel reinforcement learning algorithm with two key contributors: a novel biased adversarial reward estimator to deal with bandit feedback and unknown transitions, and a low-complexity index policy to satisfy the instantaneous activation constraint. We show $tilde{mathcal{O}}(Hsqrt{T})$ regret bound for our algorithm, where $T$ is the number of episodes and $H$ is the episode length. To our best knowledge, this is the first algorithm to ensure $tilde{mathcal{O}}(sqrt{T})$ regret for adversarial RMAB in our considered challenging settings. | [
"['Guojun Xiong' 'Jian Li']"
]
|
null | null | 2405.00955 | null | null | http://arxiv.org/pdf/2405.00955v1 | 2024-05-02T02:33:15Z | 2024-05-02T02:33:15Z | Recovering Labels from Local Updates in Federated Learning | Gradient inversion (GI) attacks present a threat to the privacy of clients in federated learning (FL) by aiming to enable reconstruction of the clients' data from communicated model updates. A number of such techniques attempts to accelerate data recovery by first reconstructing labels of the samples used in local training. However, existing label extraction methods make strong assumptions that typically do not hold in realistic FL settings. In this paper we present a novel label recovery scheme, Recovering Labels from Local Updates (RLU), which provides near-perfect accuracy when attacking untrained (most vulnerable) models. More significantly, RLU achieves high performance even in realistic real-world settings where the clients in an FL system run multiple local epochs, train on heterogeneous data, and deploy various optimizers to minimize different objective functions. Specifically, RLU estimates labels by solving a least-square problem that emerges from the analysis of the correlation between labels of the data points used in a training round and the resulting update of the output layer. The experimental results on several datasets, architectures, and data heterogeneity scenarios demonstrate that the proposed method consistently outperforms existing baselines, and helps improve quality of the reconstructed images in GI attacks in terms of both PSNR and LPIPS. | [
"['Huancheng Chen' 'Haris Vikalo']"
]
|
null | null | 2405.00957 | null | null | http://arxiv.org/pdf/2405.00957v1 | 2024-05-02T02:38:32Z | 2024-05-02T02:38:32Z | IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors | Graph Neural Networks (GNNs) demonstrate excellent performance on graphs, with their core idea about aggregating neighborhood information and learning from labels. However, the prevailing challenges in most graph datasets are twofold of Insufficient High-Quality Labels and Lack of Neighborhoods, resulting in weak GNNs. Existing data augmentation methods designed to address these two issues often tackle only one. They may either require extensive training of generators, rely on overly simplistic strategies, or demand substantial prior knowledge, leading to suboptimal generalization abilities. To simultaneously address both of these two challenges, we propose an elegant method called IntraMix. IntraMix innovatively employs Mixup among low-quality labeled data of the same class, generating high-quality labeled data at minimal cost. Additionally, it establishes neighborhoods for the generated data by connecting them with data from the same class with high confidence, thereby enriching the neighborhoods of graphs. IntraMix efficiently tackles both challenges faced by graphs and challenges the prior notion of the limited effectiveness of Mixup in node classification. IntraMix serves as a universal framework that can be readily applied to all GNNs. Extensive experiments demonstrate the effectiveness of IntraMix across various GNNs and datasets. | [
"['Shenghe Zheng' 'Hongzhi Wang' 'Xianglong Liu']"
]
|
null | null | 2405.00958 | null | null | http://arxiv.org/pdf/2405.00958v1 | 2024-05-02T02:50:58Z | 2024-05-02T02:50:58Z | Generative manufacturing systems using diffusion models and ChatGPT | In this study, we introduce Generative Manufacturing Systems (GMS) as a novel approach to effectively manage and coordinate autonomous manufacturing assets, thereby enhancing their responsiveness and flexibility to address a wide array of production objectives and human preferences. Deviating from traditional explicit modeling, GMS employs generative AI, including diffusion models and ChatGPT, for implicit learning from envisioned futures, marking a shift from a model-optimum to a training-sampling decision-making. Through the integration of generative AI, GMS enables complex decision-making through interactive dialogue with humans, allowing manufacturing assets to generate multiple high-quality global decisions that can be iteratively refined based on human feedback. Empirical findings showcase GMS's substantial improvement in system resilience and responsiveness to uncertainties, with decision times reduced from seconds to milliseconds. The study underscores the inherent creativity and diversity in the generated solutions, facilitating human-centric decision-making through seamless and continuous human-machine interactions. | [
"['Xingyu Li' 'Fei Tao' 'Wei Ye' 'Aydin Nassehi' 'John W. Sutherland']"
]
|
null | null | 2405.00964 | null | null | http://arxiv.org/pdf/2405.00964v1 | 2024-05-02T03:02:25Z | 2024-05-02T03:02:25Z | Deriving Lehmer and Hölder means as maximum weighted likelihood
estimates for the multivariate exponential family | The links between the mean families of Lehmer and H"older and the weighted maximum likelihood estimator have recently been established in the case of a regular univariate exponential family. In this article, we will extend the outcomes obtained to the multivariate case. This extension provides a probabilistic interpretation of these families of means and could therefore broaden their uses in various applications. | [
"['Djemel Ziou' 'Issam Fakir']"
]
|
null | null | 2405.00965 | null | null | http://arxiv.org/pdf/2405.00965v1 | 2024-05-02T03:03:34Z | 2024-05-02T03:03:34Z | Robust Decentralized Learning with Local Updates and Gradient Tracking | As distributed learning applications such as Federated Learning, the Internet of Things (IoT), and Edge Computing grow, it is critical to address the shortcomings of such technologies from a theoretical perspective. As an abstraction, we consider decentralized learning over a network of communicating clients or nodes and tackle two major challenges: data heterogeneity and adversarial robustness. We propose a decentralized minimax optimization method that employs two important modules: local updates and gradient tracking. Minimax optimization is the key tool to enable adversarial training for ensuring robustness. Having local updates is essential in Federated Learning (FL) applications to mitigate the communication bottleneck, and utilizing gradient tracking is essential to proving convergence in the case of data heterogeneity. We analyze the performance of the proposed algorithm, Dec-FedTrack, in the case of nonconvex-strongly concave minimax optimization, and prove that it converges a stationary point. We also conduct numerical experiments to support our theoretical findings. | [
"['Sajjad Ghiasvand' 'Amirhossein Reisizadeh' 'Mahnoosh Alizadeh'\n 'Ramtin Pedarsani']"
]
|
null | null | 2405.00972 | null | null | http://arxiv.org/pdf/2405.00972v1 | 2024-05-02T03:20:08Z | 2024-05-02T03:20:08Z | CACTUS: Chemistry Agent Connecting Tool-Usage to Science | Large language models (LLMs) have shown remarkable potential in various domains, but they often lack the ability to access and reason over domain-specific knowledge and tools. In this paper, we introduced CACTUS (Chemistry Agent Connecting Tool-Usage to Science), an LLM-based agent that integrates cheminformatics tools to enable advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama2-7b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b and Mistral-7b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with domain-specific tools, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment. Furthermore, CACTUS represents a significant milestone in the field of cheminformatics, offering an adaptable tool for researchers engaged in chemistry and molecular discovery. By integrating the strengths of open-source LLMs with domain-specific tools, CACTUS has the potential to accelerate scientific advancement and unlock new frontiers in the exploration of novel, effective, and safe therapeutic candidates, catalysts, and materials. Moreover, CACTUS's ability to integrate with automated experimentation platforms and make data-driven decisions in real time opens up new possibilities for autonomous discovery. | [
"['Andrew D. McNaughton' 'Gautham Ramalaxmi' 'Agustin Kruel'\n 'Carter R. Knutson' 'Rohith A. Varikoti' 'Neeraj Kumar']"
]
|
null | null | 2405.00984 | null | null | http://arxiv.org/pdf/2405.00984v1 | 2024-05-02T03:43:19Z | 2024-05-02T03:43:19Z | FREE: Faster and Better Data-Free Meta-Learning | Data-Free Meta-Learning (DFML) aims to extract knowledge from a collection of pre-trained models without requiring the original data, presenting practical benefits in contexts constrained by data privacy concerns. Current DFML methods primarily focus on the data recovery from these pre-trained models. However, they suffer from slow recovery speed and overlook gaps inherent in heterogeneous pre-trained models. In response to these challenges, we introduce the Faster and Better Data-Free Meta-Learning (FREE) framework, which contains: (i) a meta-generator for rapidly recovering training tasks from pre-trained models; and (ii) a meta-learner for generalizing to new unseen tasks. Specifically, within the module Faster Inversion via Meta-Generator, each pre-trained model is perceived as a distinct task. The meta-generator can rapidly adapt to a specific task in just five steps, significantly accelerating the data recovery. Furthermore, we propose Better Generalization via Meta-Learner and introduce an implicit gradient alignment algorithm to optimize the meta-learner. This is achieved as aligned gradient directions alleviate potential conflicts among tasks from heterogeneous pre-trained models. Empirical experiments on multiple benchmarks affirm the superiority of our approach, marking a notable speed-up (20$times$) and performance enhancement (1.42% $sim$ 4.78%) in comparison to the state-of-the-art. | [
"['Yongxian Wei' 'Zixuan Hu' 'Zhenyi Wang' 'Li Shen' 'Chun Yuan'\n 'Dacheng Tao']"
]
|
null | null | 2405.00985 | null | null | http://arxiv.org/pdf/2405.00985v1 | 2024-05-02T03:48:08Z | 2024-05-02T03:48:08Z | Progressive Feedforward Collapse of ResNet Training | Neural collapse (NC) is a simple and symmetric phenomenon for deep neural networks (DNNs) at the terminal phase of training, where the last-layer features collapse to their class means and form a simplex equiangular tight frame aligning with the classifier vectors. However, the relationship of the last-layer features to the data and intermediate layers during training remains unexplored. To this end, we characterize the geometry of intermediate layers of ResNet and propose a novel conjecture, progressive feedforward collapse (PFC), claiming the degree of collapse increases during the forward propagation of DNNs. We derive a transparent model for the well-trained ResNet according to that ResNet with weight decay approximates the geodesic curve in Wasserstein space at the terminal phase. The metrics of PFC indeed monotonically decrease across depth on various datasets. We propose a new surrogate model, multilayer unconstrained feature model (MUFM), connecting intermediate layers by an optimal transport regularizer. The optimal solution of MUFM is inconsistent with NC but is more concentrated relative to the input data. Overall, this study extends NC to PFC to model the collapse phenomenon of intermediate layers and its dependence on the input data, shedding light on the theoretical understanding of ResNet in classification problems. | [
"['Sicong Wang' 'Kuo Gai' 'Shihua Zhang']"
]
|
null | null | 2405.00987 | null | null | http://arxiv.org/pdf/2405.00987v1 | 2024-05-02T03:49:47Z | 2024-05-02T03:49:47Z | S$^2$AC: Energy-Based Reinforcement Learning with Stein Soft Actor
Critic | Learning expressive stochastic policies instead of deterministic ones has been proposed to achieve better stability, sample complexity, and robustness. Notably, in Maximum Entropy Reinforcement Learning (MaxEnt RL), the policy is modeled as an expressive Energy-Based Model (EBM) over the Q-values. However, this formulation requires the estimation of the entropy of such EBMs, which is an open problem. To address this, previous MaxEnt RL methods either implicitly estimate the entropy, resulting in high computational complexity and variance (SQL), or follow a variational inference procedure that fits simplified actor distributions (e.g., Gaussian) for tractability (SAC). We propose Stein Soft Actor-Critic (S$^2$AC), a MaxEnt RL algorithm that learns expressive policies without compromising efficiency. Specifically, S$^2$AC uses parameterized Stein Variational Gradient Descent (SVGD) as the underlying policy. We derive a closed-form expression of the entropy of such policies. Our formula is computationally efficient and only depends on first-order derivatives and vector products. Empirical results show that S$^2$AC yields more optimal solutions to the MaxEnt objective than SQL and SAC in the multi-goal environment, and outperforms SAC and SQL on the MuJoCo benchmark. Our code is available at: https://github.com/SafaMessaoud/S2AC-Energy-Based-RL-with-Stein-Soft-Actor-Critic | [
"['Safa Messaoud' 'Billel Mokeddem' 'Zhenghai Xue' 'Linsey Pang' 'Bo An'\n 'Haipeng Chen' 'Sanjay Chawla']"
]
|
null | null | 2405.00988 | null | null | http://arxiv.org/pdf/2405.00988v1 | 2024-05-02T03:50:31Z | 2024-05-02T03:50:31Z | Context-Aware Clustering using Large Language Models | Despite the remarkable success of Large Language Models (LLMs) in text understanding and generation, their potential for text clustering tasks remains underexplored. We observed that powerful closed-source LLMs provide good quality clusterings of entity sets but are not scalable due to the massive compute power required and the associated costs. Thus, we propose CACTUS (Context-Aware ClusTering with aUgmented triplet losS), a systematic approach that leverages open-source LLMs for efficient and effective supervised clustering of entity subsets, particularly focusing on text-based entities. Existing text clustering methods fail to effectively capture the context provided by the entity subset. Moreover, though there are several language modeling based approaches for clustering, very few are designed for the task of supervised clustering. This paper introduces a novel approach towards clustering entity subsets using LLMs by capturing context via a scalable inter-entity attention mechanism. We propose a novel augmented triplet loss function tailored for supervised clustering, which addresses the inherent challenges of directly applying the triplet loss to this problem. Furthermore, we introduce a self-supervised clustering task based on text augmentation techniques to improve the generalization of our model. For evaluation, we collect ground truth clusterings from a closed-source LLM and transfer this knowledge to an open-source LLM under the supervised clustering framework, allowing a faster and cheaper open-source model to perform the same task. Experiments on various e-commerce query and product clustering datasets demonstrate that our proposed approach significantly outperforms existing unsupervised and supervised baselines under various external clustering evaluation metrics. | [
"['Sindhu Tipirneni' 'Ravinarayana Adkathimar' 'Nurendra Choudhary'\n 'Gaurush Hiranandani' 'Rana Ali Amjad' 'Vassilis N. Ioannidis'\n 'Changhe Yuan' 'Chandan K. Reddy']"
]
|
null | null | 2405.00989 | null | null | http://arxiv.org/pdf/2405.00989v1 | 2024-05-02T03:53:59Z | 2024-05-02T03:53:59Z | Estimate the building height at a 10-meter resolution based on Sentinel
data | Building height is an important indicator for scientific research and practical application. However, building height products with a high spatial resolution (10m) are still very scarce. To meet the needs of high-resolution building height estimation models, this study established a set of spatial-spectral-temporal feature databases, combining SAR data provided by Sentinel-1, optical data provided by Sentinel-2, and shape data provided by building footprints. The statistical indicators on the time scale are extracted to form a rich database of 160 features. This study combined with permutation feature importance, Shapley Additive Explanations, and Random Forest variable importance, and the final stable features are obtained through an expert scoring system. This study took 12 large, medium, and small cities in the United States as the training data. It used moving windows to aggregate the pixels to solve the impact of SAR image displacement and building shadows. This study built a building height model based on a random forest model and compared three model ensemble methods of bagging, boosting, and stacking. To evaluate the accuracy of the prediction results, this study collected Lidar data in the test area, and the evaluation results showed that its R-Square reached 0.78, which can prove that the building height can be obtained effectively. The fast production of high-resolution building height data can support large-scale scientific research and application in many fields. | [
"['Xin Yan']"
]
|
null | null | 2405.01002 | null | null | http://arxiv.org/pdf/2405.01002v2 | 2024-05-28T04:09:30Z | 2024-05-02T04:58:29Z | Spider: A Unified Framework for Context-dependent Concept Segmentation | Different from the context-independent (CI) concepts such as human, car, and airplane, context-dependent (CD) concepts require higher visual understanding ability, such as camouflaged object and medical lesion. Despite the rapid advance of many CD understanding tasks in respective branches, the isolated evolution leads to their limited cross-domain generalisation and repetitive technique innovation. Since there is a strong coupling relationship between foreground and background context in CD tasks, existing methods require to train separate models in their focused domains. This restricts their real-world CD concept understanding towards artificial general intelligence (AGI). We propose a unified model with a single set of parameters, Spider, which only needs to be trained once. With the help of the proposed concept filter driven by the image-mask group prompt, Spider is able to understand and distinguish diverse strong context-dependent concepts to accurately capture the Prompter's intention. Without bells and whistles, Spider significantly outperforms the state-of-the-art specialized models in 8 different context-dependent segmentation tasks, including 4 natural scenes (salient, camouflaged, and transparent objects and shadow) and 4 medical lesions (COVID-19, polyp, breast, and skin lesion with color colonoscopy, CT, ultrasound, and dermoscopy modalities). Besides, Spider shows obvious advantages in continuous learning. It can easily complete the training of new tasks by fine-tuning parameters less than 1% and bring a tolerable performance degradation of less than 5% for all old tasks. The source code will be publicly available at href{https://github.com/Xiaoqi-Zhao-DLUT/Spider-UniCDSeg}{Spider-UniCDSeg}. | [
"['Xiaoqi Zhao' 'Youwei Pang' 'Wei Ji' 'Baicheng Sheng' 'Jiaming Zuo'\n 'Lihe Zhang' 'Huchuan Lu']"
]
|
null | null | 2405.01004 | null | null | http://arxiv.org/pdf/2405.01004v1 | 2024-05-02T05:09:07Z | 2024-05-02T05:09:07Z | Deep Learning Models in Speech Recognition: Measuring GPU Energy
Consumption, Impact of Noise and Model Quantization for Edge Deployment | Recent transformer-based ASR models have achieved word-error rates (WER) below 4%, surpassing human annotator accuracy, yet they demand extensive server resources, contributing to significant carbon footprints. The traditional server-based architecture of ASR also presents privacy concerns, alongside reliability and latency issues due to network dependencies. In contrast, on-device (edge) ASR enhances privacy, boosts performance, and promotes sustainability by effectively balancing energy use and accuracy for specific applications. This study examines the effects of quantization, memory demands, and energy consumption on the performance of various ASR model inference on the NVIDIA Jetson Orin Nano. By analyzing WER and transcription speed across models using FP32, FP16, and INT8 quantization on clean and noisy datasets, we highlight the crucial trade-offs between accuracy, speeds, quantization, energy efficiency, and memory needs. We found that changing precision from fp32 to fp16 halves the energy consumption for audio transcription across different models, with minimal performance degradation. A larger model size and number of parameters neither guarantees better resilience to noise, nor predicts the energy consumption for a given transcription load. These, along with several other findings offer novel insights for optimizing ASR systems within energy- and memory-limited environments, crucial for the development of efficient on-device ASR solutions. The code and input data needed to reproduce the results in this article are open sourced are available on [https://github.com/zzadiues3338/ASR-energy-jetson]. | [
"['Aditya Chakravarty']"
]
|
null | null | 2405.01009 | null | null | http://arxiv.org/pdf/2405.01009v1 | 2024-05-02T05:23:58Z | 2024-05-02T05:23:58Z | Tackling Graph Oversquashing by Global and Local Non-Dissipativity | A common problem in Message-Passing Neural Networks is oversquashing -- the limited ability to facilitate effective information flow between distant nodes. Oversquashing is attributed to the exponential decay in information transmission as node distances increase. This paper introduces a novel perspective to address oversquashing, leveraging properties of global and local non-dissipativity, that enable the maintenance of a constant information flow rate. Namely, we present SWAN, a uniquely parameterized model GNN with antisymmetry both in space and weight domains, as a means to obtain non-dissipativity. Our theoretical analysis asserts that by achieving these properties, SWAN offers an enhanced ability to transmit information over extended distances. Empirical evaluations on synthetic and real-world benchmarks that emphasize long-range interactions validate the theoretical understanding of SWAN, and its ability to mitigate oversquashing. | [
"['Alessio Gravina' 'Moshe Eliasof' 'Claudio Gallicchio' 'Davide Bacciu'\n 'Carola-Bibiane Schönlieb']"
]
|
null | null | 2405.01010 | null | null | http://arxiv.org/pdf/2405.01010v1 | 2024-05-02T05:24:28Z | 2024-05-02T05:24:28Z | Efficient and Adaptive Posterior Sampling Algorithms for Bandits | We study Thompson Sampling-based algorithms for stochastic bandits with bounded rewards. As the existing problem-dependent regret bound for Thompson Sampling with Gaussian priors [Agrawal and Goyal, 2017] is vacuous when $T le 288 e^{64}$, we derive a more practical bound that tightens the coefficient of the leading term %from $288 e^{64}$ to $1270$. Additionally, motivated by large-scale real-world applications that require scalability, adaptive computational resource allocation, and a balance in utility and computation, we propose two parameterized Thompson Sampling-based algorithms: Thompson Sampling with Model Aggregation (TS-MA-$alpha$) and Thompson Sampling with Timestamp Duelling (TS-TD-$alpha$), where $alpha in [0,1]$ controls the trade-off between utility and computation. Both algorithms achieve $O left(Kln^{alpha+1}(T)/Delta right)$ regret bound, where $K$ is the number of arms, $T$ is the finite learning horizon, and $Delta$ denotes the single round performance loss when pulling a sub-optimal arm. | [
"['Bingshan Hu' 'Zhiming Huang' 'Tianyue H. Zhang' 'Mathias Lécuyer'\n 'Nidhi Hegde']"
]
|
null | null | 2405.01013 | null | null | http://arxiv.org/pdf/2405.01013v1 | 2024-05-02T05:29:22Z | 2024-05-02T05:29:22Z | Non-clairvoyant Scheduling with Partial Predictions | The non-clairvoyant scheduling problem has gained new interest within learning-augmented algorithms, where the decision-maker is equipped with predictions without any quality guarantees. In practical settings, access to predictions may be reduced to specific instances, due to cost or data limitations. Our investigation focuses on scenarios where predictions for only $B$ job sizes out of $n$ are available to the algorithm. We first establish near-optimal lower bounds and algorithms in the case of perfect predictions. Subsequently, we present a learning-augmented algorithm satisfying the robustness, consistency, and smoothness criteria, and revealing a novel tradeoff between consistency and smoothness inherent in the scenario with a restricted number of predictions. | [
"['Ziyad Benomar' 'Vianney Perchet']"
]
|
null | null | 2405.01015 | null | null | http://arxiv.org/pdf/2405.01015v2 | 2024-05-07T16:54:52Z | 2024-05-02T05:35:09Z | Network reconstruction via the minimum description length principle | A fundamental problem associated with the task of network reconstruction from dynamical or behavioral data consists in determining the most appropriate model complexity in a manner that prevents overfitting, and produces an inferred network with a statistically justifiable number of edges. The status quo in this context is based on $L_{1}$ regularization combined with cross-validation. However, besides its high computational cost, this commonplace approach unnecessarily ties the promotion of sparsity with weight "shrinkage". This combination forces a trade-off between the bias introduced by shrinkage and the network sparsity, which often results in substantial overfitting even after cross-validation. In this work, we propose an alternative nonparametric regularization scheme based on hierarchical Bayesian inference and weight quantization, which does not rely on weight shrinkage to promote sparsity. Our approach follows the minimum description length (MDL) principle, and uncovers the weight distribution that allows for the most compression of the data, thus avoiding overfitting without requiring cross-validation. The latter property renders our approach substantially faster to employ, as it requires a single fit to the complete data. As a result, we have a principled and efficient inference scheme that can be used with a large variety of generative models, without requiring the number of edges to be known in advance. We also demonstrate that our scheme yields systematically increased accuracy in the reconstruction of both artificial and empirical networks. We highlight the use of our method with the reconstruction of interaction networks between microbial communities from large-scale abundance samples involving in the order of $10^{4}$ to $10^{5}$ species, and demonstrate how the inferred model can be used to predict the outcome of interventions in the system. | [
"['Tiago P. Peixoto']"
]
|
null | null | 2405.01029 | null | null | http://arxiv.org/pdf/2405.01029v2 | 2024-05-06T11:35:57Z | 2024-05-02T06:02:07Z | MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts | Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark instances. We further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution data. The source code is available at: https://github.com/RoyalSkye/Routing-MVMoE. | [
"['Jianan Zhou' 'Zhiguang Cao' 'Yaoxin Wu' 'Wen Song' 'Yining Ma'\n 'Jie Zhang' 'Chi Xu']"
]
|
null | null | 2405.01031 | null | null | http://arxiv.org/pdf/2405.01031v2 | 2024-05-03T08:14:22Z | 2024-05-02T06:14:56Z | The Privacy Power of Correlated Noise in Decentralized Learning | Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, without additional precautions, curious users can still leverage models obtained from their peers to violate privacy. In this paper, we propose Decor, a variant of decentralized SGD with differential privacy (DP) guarantees. Essentially, in Decor, users securely exchange randomness seeds in one communication round to generate pairwise-canceling correlated Gaussian noises, which are injected to protect local models at every communication round. We theoretically and empirically show that, for arbitrary connected graphs, Decor matches the central DP optimal privacy-utility trade-off. We do so under SecLDP, our new relaxation of local DP, which protects all user communications against an external eavesdropper and curious users, assuming that every pair of connected users shares a secret, i.e., an information hidden to all others. The main theoretical challenge is to control the accumulation of non-canceling correlated noise due to network sparsity. We also propose a companion SecLDP privacy accountant for public use. | [
"['Youssef Allouah' 'Anastasia Koloskova' 'Aymane El Firdoussi'\n 'Martin Jaggi' 'Rachid Guerraoui']"
]
|
null | null | 2405.01033 | null | null | http://arxiv.org/pdf/2405.01033v1 | 2024-05-02T06:30:52Z | 2024-05-02T06:30:52Z | CrossMPT: Cross-attention Message-Passing Transformer for Error
Correcting Codes | Error correcting codes~(ECCs) are indispensable for reliable transmission in communication systems. The recent advancements in deep learning have catalyzed the exploration of ECC decoders based on neural networks. Among these, transformer-based neural decoders have achieved state-of-the-art decoding performance. In this paper, we propose a novel Cross-attention Message-Passing Transformer~(CrossMPT). CrossMPT iteratively updates two types of input vectors (i.e., magnitude and syndrome vectors) using two masked cross-attention blocks. The mask matrices in these cross-attention blocks are determined by the code's parity-check matrix that delineates the relationship between magnitude and syndrome vectors. Our experimental results show that CrossMPT significantly outperforms existing neural network-based decoders, particularly in decoding low-density parity-check codes. Notably, CrossMPT also achieves a significant reduction in computational complexity, achieving over a 50% decrease in its attention layers compared to the original transformer-based decoder, while retaining the computational complexity of the remaining layers. | [
"['Seong-Joon Park' 'Hee-Youl Kwak' 'Sang-Hyo Kim' 'Yongjune Kim'\n 'Jong-Seon No']"
]
|
null | null | 2405.01035 | null | null | http://arxiv.org/pdf/2405.01035v1 | 2024-05-02T06:33:01Z | 2024-05-02T06:33:01Z | LOQA: Learning with Opponent Q-Learning Awareness | In various real-world scenarios, interactions among agents often resemble the dynamics of general-sum games, where each agent strives to optimize its own utility. Despite the ubiquitous relevance of such settings, decentralized machine learning algorithms have struggled to find equilibria that maximize individual utility while preserving social welfare. In this paper we introduce Learning with Opponent Q-Learning Awareness (LOQA), a novel, decentralized reinforcement learning algorithm tailored to optimizing an agent's individual utility while fostering cooperation among adversaries in partially competitive environments. LOQA assumes the opponent samples actions proportionally to their action-value function Q. Experimental results demonstrate the effectiveness of LOQA at achieving state-of-the-art performance in benchmark scenarios such as the Iterated Prisoner's Dilemma and the Coin Game. LOQA achieves these outcomes with a significantly reduced computational footprint, making it a promising approach for practical multi-agent applications. | [
"['Milad Aghajohari' 'Juan Agustin Duque' 'Tim Cooijmans' 'Aaron Courville']"
]
|
null | null | 2405.01041 | null | null | http://arxiv.org/pdf/2405.01041v2 | 2024-07-01T05:06:32Z | 2024-05-02T06:53:40Z | Efficient and Flexible Method for Reducing Moderate-size Deep Neural
Networks with Condensation | Neural networks have been extensively applied to a variety of tasks, achieving astounding results. Applying neural networks in the scientific field is an important research direction that is gaining increasing attention. In scientific applications, the scale of neural networks is generally moderate-size, mainly to ensure the speed of inference during application. Additionally, comparing neural networks to traditional algorithms in scientific applications is inevitable. These applications often require rapid computations, making the reduction of neural network sizes increasingly important. Existing work has found that the powerful capabilities of neural networks are primarily due to their non-linearity. Theoretical work has discovered that under strong non-linearity, neurons in the same layer tend to behave similarly, a phenomenon known as condensation. Condensation offers an opportunity to reduce the scale of neural networks to a smaller subnetwork with similar performance. In this article, we propose a condensation reduction algorithm to verify the feasibility of this idea in practical problems. Our reduction method can currently be applied to both fully connected networks and convolutional networks, achieving positive results. In complex combustion acceleration tasks, we reduced the size of the neural network to 41.7% of its original scale while maintaining prediction accuracy. In the CIFAR10 image classification task, we reduced the network size to 11.5% of the original scale, still maintaining a satisfactory validation accuracy. Our method can be applied to most trained neural networks, reducing computational pressure and improving inference speed. | [
"['Tianyi Chen' 'Zhi-Qin John Xu']"
]
|
null | null | 2405.01052 | null | null | http://arxiv.org/pdf/2405.01052v1 | 2024-05-02T07:11:05Z | 2024-05-02T07:11:05Z | Polynomial Chaos Expanded Gaussian Process | In complex and unknown processes, global models are initially generated over the entire experimental space, but they often fail to provide accurate predictions in local areas. Recognizing this limitation, this study addresses the need for models that effectively represent both global and local experimental spaces. It introduces a novel machine learning (ML) approach: Polynomial Chaos Expanded Gaussian Process (PCEGP), leveraging polynomial chaos expansion (PCE) to calculate input-dependent hyperparameters of the Gaussian process (GP). This approach provides a mathematically interpretable method that incorporates non-stationary covariance functions and heteroscedastic noise estimation to generate locally adapted models. The model performance is compared to different algorithms in benchmark tests for regression tasks. The results demonstrate low prediction errors of the PCEGP in these benchmark applications, highlighting model performance that is often competitive with or superior to previous methods. A key advantage of the presented model is the transparency and traceability in the calculation of hyperparameters and model predictions. | [
"['Dominik Polke' 'Tim Kösters' 'Elmar Ahle' 'Dirk Söffker']"
]
|
null | null | 2405.01053 | null | null | http://arxiv.org/pdf/2405.01053v3 | 2024-05-23T07:06:24Z | 2024-05-02T07:15:23Z | Explicitly Modeling Universality into Self-Supervised Learning | The goal of universality in self-supervised learning (SSL) is to learn universal representations from unlabeled data and achieve excellent performance on all samples and tasks. However, these methods lack explicit modeling of the universality in the learning objective, and the related theoretical understanding remains limited. This may cause models to overfit in data-scarce situations and generalize poorly in real life. To address these issues, we provide a theoretical definition of universality in SSL, which constrains both the learning and evaluation universality of the SSL models from the perspective of discriminability, transferability, and generalization. Then, we propose a $sigma$-measurement to help quantify the score of one SSL model's universality. Based on the definition and measurement, we propose a general SSL framework, called GeSSL, to explicitly model universality into SSL. It introduces a self-motivated target based on $sigma$-measurement, which enables the model to find the optimal update direction towards universality. Extensive theoretical and empirical evaluations demonstrate the superior performance of GeSSL. | [
"['Jingyao Wang' 'Wenwen Qiang' 'Zeen Song' 'Lingyu Si' 'Jiangmeng Li'\n 'Changwen Zheng' 'Bing Su']"
]
|
null | null | 2405.01054 | null | null | http://arxiv.org/pdf/2405.01054v1 | 2024-05-02T07:21:12Z | 2024-05-02T07:21:12Z | Continual Learning for Robust Gate Detection under Dynamic Lighting in
Autonomous Drone Racing | In autonomous and mobile robotics, a principal challenge is resilient real-time environmental perception, particularly in situations characterized by unknown and dynamic elements, as exemplified in the context of autonomous drone racing. This study introduces a perception technique for detecting drone racing gates under illumination variations, which is common during high-speed drone flights. The proposed technique relies upon a lightweight neural network backbone augmented with capabilities for continual learning. The envisaged approach amalgamates predictions of the gates' positional coordinates, distance, and orientation, encapsulating them into a cohesive pose tuple. A comprehensive number of tests serve to underscore the efficacy of this approach in confronting diverse and challenging scenarios, specifically those involving variable lighting conditions. The proposed methodology exhibits notable robustness in the face of illumination variations, thereby substantiating its effectiveness. | [
"['Zhongzheng Qiao' 'Xuan Huy Pham' 'Savitha Ramasamy' 'Xudong Jiang'\n 'Erdal Kayacan' 'Andriy Sarabakha']"
]
|
null | null | 2405.01055 | null | null | http://arxiv.org/pdf/2405.01055v1 | 2024-05-02T07:28:27Z | 2024-05-02T07:28:27Z | Leverage Multi-source Traffic Demand Data Fusion with Transformer Model
for Urban Parking Prediction | The escalation in urban private car ownership has worsened the urban parking predicament, necessitating effective parking availability prediction for urban planning and management. However, the existing prediction methods suffer from low prediction accuracy with the lack of spatial-temporal correlation features related to parking volume, and neglect of flow patterns and correlations between similar parking lots within certain areas. To address these challenges, this study proposes a parking availability prediction framework integrating spatial-temporal deep learning with multi-source data fusion, encompassing traffic demand data from multiple sources (e.g., metro, bus, taxi services), and parking lot data. The framework is based on the Transformer as the spatial-temporal deep learning model and leverages K-means clustering to establish parking cluster zones, extracting and integrating traffic demand characteristics from various transportation modes (i.e., metro, bus, online ride-hailing, and taxi) connected to parking lots. Real-world empirical data was used to verify the effectiveness of the proposed method compared with different machine learning, deep learning, and traditional statistical models for predicting parking availability. Experimental results reveal that, with the proposed pipeline, the developed Transformer model outperforms other models in terms of various metrics, e.g., Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). By fusing multi-source demanding data with spatial-temporal deep learning techniques, this approach offers the potential to develop parking availability prediction systems that furnish more accurate and timely information to both drivers and urban planners, thereby fostering more efficient and sustainable urban mobility. | [
"['Yin Huang' 'Yongqi Dong' 'Youhua Tang' 'Li Li']"
]
|
null | null | 2405.01060 | null | null | http://arxiv.org/pdf/2405.01060v1 | 2024-05-02T07:34:12Z | 2024-05-02T07:34:12Z | A text-based, generative deep learning model for soil reflectance
spectrum simulation in the VIS-NIR (400-2499 nm) bands | Simulating soil reflectance spectra is invaluable for soil-plant radiative modeling and training machine learning models, yet it is difficult as the intricate relationships between soil structure and its constituents. To address this, a fully data-driven soil optics generative model (SOGM) for simulation of soil reflectance spectra based on soil property inputs was developed. The model is trained on an extensive dataset comprising nearly 180,000 soil spectra-property pairs from 17 datasets. It generates soil reflectance spectra from text-based inputs describing soil properties and their values rather than only numerical values and labels in binary vector format. The generative model can simulate output spectra based on an incomplete set of input properties. SOGM is based on the denoising diffusion probabilistic model (DDPM). Two additional sub-models were also built to complement the SOGM: a spectral padding model that can fill in the gaps for spectra shorter than the full visible-near-infrared range (VIS-NIR; 400 to 2499 nm), and a wet soil spectra model that can estimate the effects of water content on soil reflectance spectra given the dry spectrum predicted by the SOGM. The SOGM was up-scaled by coupling with the Helios 3D plant modeling software, which allowed for generation of synthetic aerial images of simulated soil and plant scenes. It can also be easily integrated with soil-plant radiation model used for remote sensin research like PROSAIL. The testing results of the SOGM on new datasets that not included in model training proved that the model can generate reasonable soil reflectance spectra based on available property inputs. The presented models are openly accessible on: https://github.com/GEMINI-Breeding/SOGM_soil_spectra_simulation. | [
"['Tong Lei' 'Brian N. Bailey']"
]
|
null | null | 2405.01063 | null | null | http://arxiv.org/pdf/2405.01063v2 | 2024-05-27T07:33:45Z | 2024-05-02T07:40:51Z | Fair Recommendations with Limited Sensitive Attributes: A
Distributionally Robust Optimization Approach | As recommender systems are indispensable in various domains such as job searching and e-commerce, providing equitable recommendations to users with different sensitive attributes becomes an imperative requirement. Prior approaches for enhancing fairness in recommender systems presume the availability of all sensitive attributes, which can be difficult to obtain due to privacy concerns or inadequate means of capturing these attributes. In practice, the efficacy of these approaches is limited, pushing us to investigate ways of promoting fairness with limited sensitive attribute information. Toward this goal, it is important to reconstruct missing sensitive attributes. Nevertheless, reconstruction errors are inevitable due to the complexity of real-world sensitive attribute reconstruction problems and legal regulations. Thus, we pursue fair learning methods that are robust to reconstruction errors. To this end, we propose Distributionally Robust Fair Optimization (DRFO), which minimizes the worst-case unfairness over all potential probability distributions of missing sensitive attributes instead of the reconstructed one to account for the impact of the reconstruction errors. We provide theoretical and empirical evidence to demonstrate that our method can effectively ensure fairness in recommender systems when only limited sensitive attributes are accessible. | [
"['Tianhao Shi' 'Yang Zhang' 'Jizhi Zhang' 'Fuli Feng' 'Xiangnan He']"
]
|
null | null | 2405.01067 | null | null | http://arxiv.org/pdf/2405.01067v2 | 2024-06-30T08:06:33Z | 2024-05-02T07:49:28Z | AB-Training: A Communication-Efficient Approach for Distributed Low-Rank
Learning | Communication bottlenecks severely hinder the scalability of distributed neural network training, particularly in high-performance computing (HPC) environments. We introduce AB-training, a novel data-parallel method that leverages low-rank representations and independent training groups to significantly reduce communication overhead. Our experiments demonstrate an average reduction in network traffic of approximately 70.31% across various scaling scenarios, increasing the training potential of communication-constrained systems and accelerating convergence at scale. AB-training also exhibits a pronounced regularization effect at smaller scales, leading to improved generalization while maintaining or even reducing training time. We achieve a remarkable 44.14 : 1 compression ratio on VGG16 trained on CIFAR-10 with minimal accuracy loss, and outperform traditional data parallel training by 1.55% on ResNet-50 trained on ImageNet-2012. While AB-training is promising, our findings also reveal that large batch effects persist even in low-rank regimes, underscoring the need for further research into optimized update mechanisms for massively distributed training. | [
"['Daniel Coquelin' 'Katherina Flügel' 'Marie Weiel' 'Nicholas Kiefer'\n 'Muhammed Öz' 'Charlotte Debus' 'Achim Streit' 'Markus Götz']"
]
|
null | null | 2405.01073 | null | null | http://arxiv.org/pdf/2405.01073v1 | 2024-05-02T08:06:10Z | 2024-05-02T08:06:10Z | Poisoning Attacks on Federated Learning for Autonomous Driving | Federated Learning (FL) is a decentralized learning paradigm, enabling parties to collaboratively train models while keeping their data confidential. Within autonomous driving, it brings the potential of reducing data storage costs, reducing bandwidth requirements, and to accelerate the learning. FL is, however, susceptible to poisoning attacks. In this paper, we introduce two novel poisoning attacks on FL tailored to regression tasks within autonomous driving: FLStealth and Off-Track Attack (OTA). FLStealth, an untargeted attack, aims at providing model updates that deteriorate the global model performance while appearing benign. OTA, on the other hand, is a targeted attack with the objective to change the global model's behavior when exposed to a certain trigger. We demonstrate the effectiveness of our attacks by conducting comprehensive experiments pertaining to the task of vehicle trajectory prediction. In particular, we show that, among five different untargeted attacks, FLStealth is the most successful at bypassing the considered defenses employed by the server. For OTA, we demonstrate the inability of common defense strategies to mitigate the attack, highlighting the critical need for new defensive mechanisms against targeted attacks within FL for autonomous driving. | [
"['Sonakshi Garg' 'Hugo Jönsson' 'Gustav Kalander' 'Axel Nilsson'\n 'Bhhaanu Pirange' 'Viktor Valadi' 'Johan Östman']"
]
|
null | null | 2405.01098 | null | null | http://arxiv.org/pdf/2405.01098v1 | 2024-05-02T08:54:28Z | 2024-05-02T08:54:28Z | Multivariate trace estimation using quantum state space linear algebra | In this paper, we present a quantum algorithm for approximating multivariate traces, i.e. the traces of matrix products. Our research is motivated by the extensive utility of multivariate traces in elucidating spectral characteristics of matrices, as well as by recent advancements in leveraging quantum computing for faster numerical linear algebra. Central to our approach is a direct translation of a multivariate trace formula into a quantum circuit, achieved through a sequence of low-level circuit construction operations. To facilitate this translation, we introduce emph{quantum Matrix States Linear Algebra} (qMSLA), a framework tailored for the efficient generation of state preparation circuits via primitive matrix algebra operations. Our algorithm relies on sets of state preparation circuits for input matrices as its primary inputs and yields two state preparation circuits encoding the multivariate trace as output. These circuits are constructed utilizing qMSLA operations, which enact the aforementioned multivariate trace formula. We emphasize that our algorithm's inputs consist solely of state preparation circuits, eschewing harder to synthesize constructs such as Block Encodings. Furthermore, our approach operates independently of the availability of specialized hardware like QRAM, underscoring its versatility and practicality. | [
"['Liron Mor Yosef' 'Shashanka Ubaru' 'Lior Horesh' 'Haim Avron']"
]
|
null | null | 2405.01102 | null | null | http://arxiv.org/pdf/2405.01102v2 | 2024-05-24T08:53:13Z | 2024-05-02T09:12:22Z | Less is More: on the Over-Globalizing Problem in Graph Transformers | Graph Transformer, due to its global attention mechanism, has emerged as a new tool in dealing with graph-structured data. It is well recognized that the global attention mechanism considers a wider receptive field in a fully connected graph, leading many to believe that useful information can be extracted from all the nodes. In this paper, we challenge this belief: does the globalizing property always benefit Graph Transformers? We reveal the over-globalizing problem in Graph Transformer by presenting both empirical evidence and theoretical analysis, i.e., the current attention mechanism overly focuses on those distant nodes, while the near nodes, which actually contain most of the useful information, are relatively weakened. Then we propose a novel Bi-Level Global Graph Transformer with Collaborative Training (CoBFormer), including the inter-cluster and intra-cluster Transformers, to prevent the over-globalizing problem while keeping the ability to extract valuable information from distant nodes. Moreover, the collaborative training is proposed to improve the model's generalization ability with a theoretical guarantee. Extensive experiments on various graphs well validate the effectiveness of our proposed CoBFormer. | [
"['Yujie Xing' 'Xiao Wang' 'Yibo Li' 'Hai Huang' 'Chuan Shi']"
]
|
null | null | 2405.01108 | null | null | http://arxiv.org/pdf/2405.01108v1 | 2024-05-02T09:14:59Z | 2024-05-02T09:14:59Z | Federated Learning with Heterogeneous Data Handling for Robust Vehicular
Object Detection | In the pursuit of refining precise perception models for fully autonomous driving, continual online model training becomes essential. Federated Learning (FL) within vehicular networks offers an efficient mechanism for model training while preserving raw sensory data integrity. Yet, FL struggles with non-identically distributed data (e.g., quantity skew), leading to suboptimal convergence rates during model training. In previous work, we introduced FedLA, an innovative Label-Aware aggregation method addressing data heterogeneity in FL for generic scenarios. In this paper, we introduce FedProx+LA, a novel FL method building upon the state-of-the-art FedProx and FedLA to tackle data heterogeneity, which is specifically tailored for vehicular networks. We evaluate the efficacy of FedProx+LA in continuous online object detection model training. Through a comparative analysis against conventional and state-of-the-art methods, our findings reveal the superior convergence rate of FedProx+LA. Notably, if the label distribution is very heterogeneous, our FedProx+LA approach shows substantial improvements in detection performance compared to baseline methods, also outperforming our previous FedLA approach. Moreover, both FedLA and FedProx+LA increase convergence speed by 30% compared to baseline methods. | [
"['Ahmad Khalil' 'Tizian Dege' 'Pegah Golchin' 'Rostyslav Olshevskyi'\n 'Antonio Fernandez Anta' 'Tobias Meuser']"
]
|
null | null | 2405.01109 | null | null | http://arxiv.org/pdf/2405.01109v1 | 2024-05-02T09:17:32Z | 2024-05-02T09:17:32Z | Hypergraph $p$-Laplacian regularization on point clouds for data
interpolation | As a generalization of graphs, hypergraphs are widely used to model higher-order relations in data. This paper explores the benefit of the hypergraph structure for the interpolation of point cloud data that contain no explicit structural information. We define the $varepsilon_n$-ball hypergraph and the $k_n$-nearest neighbor hypergraph on a point cloud and study the $p$-Laplacian regularization on the hypergraphs. We prove the variational consistency between the hypergraph $p$-Laplacian regularization and the continuum $p$-Laplacian regularization in a semisupervised setting when the number of points $n$ goes to infinity while the number of labeled points remains fixed. A key improvement compared to the graph case is that the results rely on weaker assumptions on the upper bound of $varepsilon_n$ and $k_n$. To solve the convex but non-differentiable large-scale optimization problem, we utilize the stochastic primal-dual hybrid gradient algorithm. Numerical experiments on data interpolation verify that the hypergraph $p$-Laplacian regularization outperforms the graph $p$-Laplacian regularization in preventing the development of spikes at the labeled points. | [
"['Kehan Shi' 'Martin Burger']"
]
|
null | null | 2405.01114 | null | null | http://arxiv.org/pdf/2405.01114v1 | 2024-05-02T09:22:54Z | 2024-05-02T09:22:54Z | Continual Imitation Learning for Prosthetic Limbs | Lower limb amputations and neuromuscular impairments severely restrict mobility, necessitating advancements beyond conventional prosthetics. Motorized bionic limbs offer promise, but their utility depends on mimicking the evolving synergy of human movement in various settings. In this context, we present a novel model for bionic prostheses' application that leverages camera-based motion capture and wearable sensor data, to learn the synergistic coupling of the lower limbs during human locomotion, empowering it to infer the kinematic behavior of a missing lower limb across varied tasks, such as climbing inclines and stairs. We propose a model that can multitask, adapt continually, anticipate movements, and refine. The core of our method lies in an approach which we call -- multitask prospective rehearsal -- that anticipates and synthesizes future movements based on the previous prediction and employs a corrective mechanism for subsequent predictions. We design an evolving architecture that merges lightweight, task-specific modules on a shared backbone, ensuring both specificity and scalability. We empirically validate our model against various baselines using real-world human gait datasets, including experiments with transtibial amputees, which encompass a broad spectrum of locomotion tasks. The results show that our approach consistently outperforms baseline models, particularly under scenarios affected by distributional shifts, adversarial perturbations, and noise. | [
"['Sharmita Dey' 'Benjamin Paassen' 'Sarath Ravindran Nair'\n 'Sabri Boughorbel' 'Arndt F. Schilling']"
]
|
null | null | 2405.01124 | null | null | http://arxiv.org/pdf/2405.01124v2 | 2024-05-08T11:29:35Z | 2024-05-02T09:38:07Z | Investigating Self-Supervised Image Denoising with Denaturation | Self-supervised learning for image denoising problems in the presence of denaturation for noisy data is a crucial approach in machine learning. However, theoretical understanding of the performance of the approach that uses denatured data is lacking. To provide better understanding of the approach, in this paper, we analyze a self-supervised denoising algorithm that uses denatured data in depth through theoretical analysis and numerical experiments. Through the theoretical analysis, we discuss that the algorithm finds desired solutions to the optimization problem with the population risk, while the guarantee for the empirical risk depends on the hardness of the denoising task in terms of denaturation levels. We also conduct several experiments to investigate the performance of an extended algorithm in practice. The results indicate that the algorithm training with denatured images works, and the empirical performance aligns with the theoretical results. These results suggest several insights for further improvement of self-supervised image denoising that uses denatured data in future directions. | [
"['Hiroki Waida' 'Kimihiro Yamazaki' 'Atsushi Tokuhisa' 'Mutsuyo Wada'\n 'Yuichiro Wada']"
]
|
null | null | 2405.01125 | null | null | http://arxiv.org/pdf/2405.01125v1 | 2024-05-02T09:38:16Z | 2024-05-02T09:38:16Z | Lipschitz constant estimation for general neural network architectures
using control tools | This paper is devoted to the estimation of the Lipschitz constant of neural networks using semidefinite programming. For this purpose, we interpret neural networks as time-varying dynamical systems, where the $k$-th layer corresponds to the dynamics at time $k$. A key novelty with respect to prior work is that we use this interpretation to exploit the series interconnection structure of neural networks with a dynamic programming recursion. Nonlinearities, such as activation functions and nonlinear pooling layers, are handled with integral quadratic constraints. If the neural network contains signal processing layers (convolutional or state space model layers), we realize them as 1-D/2-D/N-D systems and exploit this structure as well. We distinguish ourselves from related work on Lipschitz constant estimation by more extensive structure exploitation (scalability) and a generalization to a large class of common neural network architectures. To show the versatility and computational advantages of our method, we apply it to different neural network architectures trained on MNIST and CIFAR-10. | [
"['Patricia Pauli' 'Dennis Gramlich' 'Frank Allgöwer']"
]
|
null | null | 2405.01134 | null | null | http://arxiv.org/pdf/2405.01134v1 | 2024-05-02T09:50:01Z | 2024-05-02T09:50:01Z | Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole
Assembly in Space | The ability to autonomously assemble structures is crucial for the development of future space infrastructure. However, the unpredictable conditions of space pose significant challenges for robotic systems, necessitating the development of advanced learning techniques to enable autonomous assembly. In this study, we present a novel approach for learning autonomous peg-in-hole assembly in the context of space robotics. Our focus is on enhancing the generalization and adaptability of autonomous systems through deep reinforcement learning. By integrating procedural generation and domain randomization, we train agents in a highly parallelized simulation environment across a spectrum of diverse scenarios with the aim of acquiring a robust policy. The proposed approach is evaluated using three distinct reinforcement learning algorithms to investigate the trade-offs among various paradigms. We demonstrate the adaptability of our agents to novel scenarios and assembly sequences while emphasizing the potential of leveraging advanced simulation techniques for robot learning in space. Our findings set the stage for future advancements in intelligent robotic systems capable of supporting ambitious space missions and infrastructure development beyond Earth. | [
"['Andrej Orsula' 'Matthieu Geist' 'Miguel Olivares-Mendez'\n 'Carol Martinez']"
]
|
null | null | 2405.01142 | null | null | http://arxiv.org/pdf/2405.01142v1 | 2024-05-02T09:58:49Z | 2024-05-02T09:58:49Z | Sharp Bounds for Sequential Federated Learning on Heterogeneous Data | There are two paradigms in Federated Learning (FL): parallel FL (PFL), where models are trained in a parallel manner across clients; and sequential FL (SFL), where models are trained in a sequential manner across clients. In contrast to that of PFL, the convergence theory of SFL on heterogeneous data is still lacking. To resolve the theoretical dilemma of SFL, we establish sharp convergence guarantees for SFL on heterogeneous data with both upper and lower bounds. Specifically, we derive the upper bounds for strongly convex, general convex and non-convex objective functions, and construct the matching lower bounds for the strongly convex and general convex objective functions. Then, we compare the upper bounds of SFL with those of PFL, showing that SFL outperforms PFL (at least, when the level of heterogeneity is relatively high). Experimental results on quadratic functions and real data sets validate the counterintuitive comparison result. | [
"['Yipeng Li' 'Xinchen Lyu']"
]
|
null | null | 2405.01147 | null | null | http://arxiv.org/pdf/2405.01147v2 | 2024-06-02T14:50:49Z | 2024-05-02T10:05:16Z | Why Tabular Foundation Models Should Be a Research Priority | Recent text and image foundation models are incredibly impressive, and these models are attracting an ever-increasing portion of research resources. In this position piece we aim to shift the ML research community's priorities ever so slightly to a different modality: tabular data. Tabular data is the dominant modality in many fields, yet it is given hardly any research attention and significantly lags behind in terms of scale and power. We believe the time is now to start developing tabular foundation models, or what we coin a Large Tabular Model (LTM). LTMs could revolutionise the way science and ML use tabular data: not as single datasets that are analyzed in a vacuum, but contextualized with respect to related datasets. The potential impact is far-reaching: from few-shot tabular models to automating data science; from out-of-distribution synthetic data to empowering multidisciplinary scientific discovery. We intend to excite reflections on the modalities we study, and convince some researchers to study large tabular models. | [
"['Boris van Breugel' 'Mihaela van der Schaar']"
]
|
null | null | 2405.01155 | null | null | http://arxiv.org/pdf/2405.01155v1 | 2024-05-02T10:15:59Z | 2024-05-02T10:15:59Z | SynFlowNet: Towards Molecule Design with Guaranteed Synthesis Pathways | Recent breakthroughs in generative modelling have led to a number of works proposing molecular generation models for drug discovery. While these models perform well at capturing drug-like motifs, they are known to often produce synthetically inaccessible molecules. This is because they are trained to compose atoms or fragments in a way that approximates the training distribution, but they are not explicitly aware of the synthesis constraints that come with making molecules in the lab. To address this issue, we introduce SynFlowNet, a GFlowNet model whose action space uses chemically validated reactions and reactants to sequentially build new molecules. We evaluate our approach using synthetic accessibility scores and an independent retrosynthesis tool. SynFlowNet consistently samples synthetically feasible molecules, while still being able to find diverse and high-utility candidates. Furthermore, we compare molecules designed with SynFlowNet to experimentally validated actives, and find that they show comparable properties of interest, such as molecular weight, SA score and predicted protein binding affinity. | [
"['Miruna Cretu' 'Charles Harris' 'Julien Roy' 'Emmanuel Bengio'\n 'Pietro Liò']"
]
|
null | null | 2405.01157 | null | null | http://arxiv.org/pdf/2405.01157v1 | 2024-05-02T10:19:32Z | 2024-05-02T10:19:32Z | Tabular and Deep Reinforcement Learning for Gittins Index | In the realm of multi-arm bandit problems, the Gittins index policy is known to be optimal in maximizing the expected total discounted reward obtained from pulling the Markovian arms. In most realistic scenarios however, the Markovian state transition probabilities are unknown and therefore the Gittins indices cannot be computed. One can then resort to reinforcement learning (RL) algorithms that explore the state space to learn these indices while exploiting to maximize the reward collected. In this work, we propose tabular (QGI) and Deep RL (DGN) algorithms for learning the Gittins index that are based on the retirement formulation for the multi-arm bandit problem. When compared with existing RL algorithms that learn the Gittins index, our algorithms have a lower run time, require less storage space (small Q-table size in QGI and smaller replay buffer in DGN), and illustrate better empirical convergence to the Gittins index. This makes our algorithm well suited for problems with large state spaces and is a viable alternative to existing methods. As a key application, we demonstrate the use of our algorithms in minimizing the mean flowtime in a job scheduling problem when jobs are available in batches and have an unknown service time distribution. | [
"['Harshit Dhankar' 'Kshitij Mishra' 'Tejas Bodas']"
]
|
null | null | 2405.01158 | null | null | http://arxiv.org/pdf/2405.01158v1 | 2024-05-02T10:23:17Z | 2024-05-02T10:23:17Z | Interpretable Data-driven Anomaly Detection in Industrial Processes with
ExIFFI | Anomaly detection (AD) is a crucial process often required in industrial settings. Anomalies can signal underlying issues within a system, prompting further investigation. Industrial processes aim to streamline operations as much as possible, encompassing the production of the final product, making AD an essential mean to reach this goal.Conventional anomaly detection methodologies typically classify observations as either normal or anomalous without providing insight into the reasons behind these classifications.Consequently, in light of the emergence of Industry 5.0, a more desirable approach involves providing interpretable outcomes, enabling users to understand the rationale behind the results.This paper presents the first industrial application of ExIFFI, a recently developed approach focused on the production of fast and efficient explanations for the Extended Isolation Forest (EIF) Anomaly detection method. ExIFFI is tested on two publicly available industrial datasets demonstrating superior effectiveness in explanations and computational efficiency with the respect to other state-of-the-art explainable AD models. | [
"['Davide Frizzo' 'Francesco Borsatti' 'Alessio Arcudi'\n 'Antonio De Moliner' 'Roberto Oboe' 'Gian Antonio Susto']"
]
|
null | null | 2405.01186 | null | null | http://arxiv.org/pdf/2405.01186v1 | 2024-05-02T11:19:57Z | 2024-05-02T11:19:57Z | Potential Energy based Mixture Model for Noisy Label Learning | Training deep neural networks (DNNs) from noisy labels is an important and challenging task. However, most existing approaches focus on the corrupted labels and ignore the importance of inherent data structure. To bridge the gap between noisy labels and data, inspired by the concept of potential energy in physics, we propose a novel Potential Energy based Mixture Model (PEMM) for noise-labels learning. We innovate a distance-based classifier with the potential energy regularization on its class centers. Embedding our proposed classifier with existing deep learning backbones, we can have robust networks with better feature representations. They can preserve intrinsic structures from the data, resulting in a superior noisy tolerance. We conducted extensive experiments to analyze the efficiency of our proposed model on several real-world datasets. Quantitative results show that it can achieve state-of-the-art performance. | [
"['Zijia Wang' 'Wenbin Yang' 'Zhisong Liu' 'Zhen Jia']"
]
|
null | null | 2405.01189 | null | null | http://arxiv.org/pdf/2405.01189v1 | 2024-05-02T11:29:48Z | 2024-05-02T11:29:48Z | Gradient-Congruity Guided Federated Sparse Training | Edge computing allows artificial intelligence and machine learning models to be deployed on edge devices, where they can learn from local data and collaborate to form a global model. Federated learning (FL) is a distributed machine learning technique that facilitates this process while preserving data privacy. However, FL also faces challenges such as high computational and communication costs regarding resource-constrained devices, and poor generalization performance due to the heterogeneity of data across edge clients and the presence of out-of-distribution data. In this paper, we propose the Gradient-Congruity Guided Federated Sparse Training (FedSGC), a novel method that integrates dynamic sparse training and gradient congruity inspection into federated learning framework to address these issues. Our method leverages the idea that the neurons, in which the associated gradients with conflicting directions with respect to the global model contain irrelevant or less generalized information for other clients, and could be pruned during the sparse training process. Conversely, the neurons where the associated gradients with consistent directions could be grown in a higher priority. In this way, FedSGC can greatly reduce the local computation and communication overheads while, at the same time, enhancing the generalization abilities of FL. We evaluate our method on challenging non-i.i.d settings and show that it achieves competitive accuracy with state-of-the-art FL methods across various scenarios while minimizing computation and communication costs. | [
"['Chris Xing Tian' 'Yibing Liu' 'Haoliang Li' 'Ray C. C. Cheung'\n 'Shiqi Wang']"
]
|
null | null | 2405.01196 | null | null | http://arxiv.org/pdf/2405.01196v3 | 2024-05-06T08:19:20Z | 2024-05-02T11:36:17Z | Decoupling Feature Extraction and Classification Layers for Calibrated
Neural Networks | Deep Neural Networks (DNN) have shown great promise in many classification applications, yet are widely known to have poorly calibrated predictions when they are over-parametrized. Improving DNN calibration without comprising on model accuracy is of extreme importance and interest in safety critical applications such as in the health-care sector. In this work, we show that decoupling the training of feature extraction layers and classification layers in over-parametrized DNN architectures such as Wide Residual Networks (WRN) and Visual Transformers (ViT) significantly improves model calibration whilst retaining accuracy, and at a low training cost. In addition, we show that placing a Gaussian prior on the last hidden layer outputs of a DNN, and training the model variationally in the classification training stage, even further improves calibration. We illustrate these methods improve calibration across ViT and WRN architectures for several image classification benchmark datasets. | [
"['Mikkel Jordahn' 'Pablo M. Olmos']"
]
|
null | null | 2405.01198 | null | null | http://arxiv.org/pdf/2405.01198v1 | 2024-05-02T11:40:15Z | 2024-05-02T11:40:15Z | Towards Interpretable Reinforcement Learning with Constrained
Normalizing Flow Policies | Reinforcement learning policies are typically represented by black-box neural networks, which are non-interpretable and not well-suited for safety-critical domains. To address both of these issues, we propose constrained normalizing flow policies as interpretable and safe-by-construction policy models. We achieve safety for reinforcement learning problems with instantaneous safety constraints, for which we can exploit domain knowledge by analytically constructing a normalizing flow that ensures constraint satisfaction. The normalizing flow corresponds to an interpretable sequence of transformations on action samples, each ensuring alignment with respect to a particular constraint. Our experiments reveal benefits beyond interpretability in an easier learning objective and maintained constraint satisfaction throughout the entire learning process. Our approach leverages constraints over reward engineering while offering enhanced interpretability, safety, and direct means of providing domain knowledge to the agent without relying on complex reward functions. | [
"['Finn Rietz' 'Erik Schaffernicht' 'Stefan Heinrich' 'Johannes A. Stork']"
]
|
null | null | 2405.01200 | null | null | http://arxiv.org/pdf/2405.01200v1 | 2024-05-02T11:43:04Z | 2024-05-02T11:43:04Z | Learning-to-solve unit commitment based on few-shot physics-guided
spatial-temporal graph convolution network | This letter proposes a few-shot physics-guided spatial temporal graph convolutional network (FPG-STGCN) to fast solve unit commitment (UC). Firstly, STGCN is tailored to parameterize UC. Then, few-shot physics-guided learning scheme is proposed. It exploits few typical UC solutions yielded via commercial optimizer to escape from local minimum, and leverages the augmented Lagrangian method for constraint satisfaction. To further enable both feasibility and continuous relaxation for integers in learning process, straight-through estimator for Tanh-Sign composition is proposed to fully differentiate the mixed integer solution space. Case study on the IEEE benchmark justifies that, our method bests mainstream learning ways on UC feasibility, and surpasses traditional solver on efficiency. | [
"['Mei Yang' 'Gao Qiu andJunyong Liu' 'Kai Liu']"
]
|
null | null | 2405.01205 | null | null | http://arxiv.org/pdf/2405.01205v1 | 2024-05-02T11:48:14Z | 2024-05-02T11:48:14Z | Error-Driven Uncertainty Aware Training | Neural networks are often overconfident about their predictions, which undermines their reliability and trustworthiness. In this work, we present a novel technique, named Error-Driven Uncertainty Aware Training (EUAT), which aims to enhance the ability of neural models to estimate their uncertainty correctly, namely to be highly uncertain when they output inaccurate predictions and low uncertain when their output is accurate. The EUAT approach operates during the model's training phase by selectively employing two loss functions depending on whether the training examples are correctly or incorrectly predicted by the model. This allows for pursuing the twofold goal of i) minimizing model uncertainty for correctly predicted inputs and ii) maximizing uncertainty for mispredicted inputs, while preserving the model's misprediction rate. We evaluate EUAT using diverse neural models and datasets in the image recognition domains considering both non-adversarial and adversarial settings. The results show that EUAT outperforms existing approaches for uncertainty estimation (including other uncertainty-aware training techniques, calibration, ensembles, and DEUP) by providing uncertainty estimates that not only have higher quality when evaluated via statistical metrics (e.g., correlation with residuals) but also when employed to build binary classifiers that decide whether the model's output can be trusted or not and under distributional data shifts. | [
"['Pedro Mendes' 'Paolo Romano' 'David Garlan']"
]
|
null | null | 2405.01207 | null | null | http://arxiv.org/pdf/2405.01207v1 | 2024-05-02T11:48:30Z | 2024-05-02T11:48:30Z | Improving Membership Inference in ASR Model Auditing with Perturbed Loss
Features | Membership Inference (MI) poses a substantial privacy threat to the training data of Automatic Speech Recognition (ASR) systems, while also offering an opportunity to audit these models with regard to user data. This paper explores the effectiveness of loss-based features in combination with Gaussian and adversarial perturbations to perform MI in ASR models. To the best of our knowledge, this approach has not yet been investigated. We compare our proposed features with commonly used error-based features and find that the proposed features greatly enhance performance for sample-level MI. For speaker-level MI, these features improve results, though by a smaller margin, as error-based features already obtained a high performance for this task. Our findings emphasise the importance of considering different feature sets and levels of access to target models for effective MI in ASR systems, providing valuable insights for auditing such models. | [
"['Francisco Teixeira' 'Karla Pizzi' 'Raphael Olivier' 'Alberto Abad'\n 'Bhiksha Raj' 'Isabel Trancoso']"
]
|
null | null | 2405.01229 | null | null | http://arxiv.org/pdf/2405.01229v1 | 2024-05-02T12:18:14Z | 2024-05-02T12:18:14Z | Boosting Jailbreak Attack with Momentum | Large Language Models (LLMs) have achieved remarkable success across diverse tasks, yet they remain vulnerable to adversarial attacks, notably the well-documented textit{jailbreak} attack. Recently, the Greedy Coordinate Gradient (GCG) attack has demonstrated efficacy in exploiting this vulnerability by optimizing adversarial prompts through a combination of gradient heuristics and greedy search. However, the efficiency of this attack has become a bottleneck in the attacking process. To mitigate this limitation, in this paper we rethink the generation of adversarial prompts through an optimization lens, aiming to stabilize the optimization process and harness more heuristic insights from previous iterations. Specifically, we introduce the textbf{M}omentum textbf{A}ccelerated Gtextbf{C}G (textbf{MAC}) attack, which incorporates a momentum term into the gradient heuristic. Experimental results showcase the notable enhancement achieved by MAP in gradient-based attacks on aligned language models. Our code is available at https://github.com/weizeming/momentum-attack-llm. | [
"['Yihao Zhang' 'Zeming Wei']"
]
|
null | null | 2405.01233 | null | null | http://arxiv.org/pdf/2405.01233v1 | 2024-05-02T12:25:41Z | 2024-05-02T12:25:41Z | Mathematics of Differential Machine Learning in Derivative Pricing and
Hedging | This article introduces the groundbreaking concept of the financial differential machine learning algorithm through a rigorous mathematical framework. Diverging from existing literature on financial machine learning, the work highlights the profound implications of theoretical assumptions within financial models on the construction of machine learning algorithms. This endeavour is particularly timely as the finance landscape witnesses a surge in interest towards data-driven models for the valuation and hedging of derivative products. Notably, the predictive capabilities of neural networks have garnered substantial attention in both academic research and practical financial applications. The approach offers a unified theoretical foundation that facilitates comprehensive comparisons, both at a theoretical level and in experimental outcomes. Importantly, this theoretical grounding lends substantial weight to the experimental results, affirming the differential machine learning method's optimality within the prevailing context. By anchoring the insights in rigorous mathematics, the article bridges the gap between abstract financial concepts and practical algorithmic implementations. | [
"['Pedro Duarte Gomes']"
]
|
null | null | 2405.01242 | null | null | http://arxiv.org/pdf/2405.01242v3 | 2024-05-29T15:46:57Z | 2024-05-02T12:45:48Z | TRAMBA: A Hybrid Transformer and Mamba Architecture for Practical Audio
and Bone Conduction Speech Super Resolution and Enhancement on Mobile and
Wearable Platforms | We propose TRAMBA, a hybrid transformer and Mamba architecture for acoustic and bone conduction speech enhancement, suitable for mobile and wearable platforms. Bone conduction speech enhancement has been impractical to adopt in mobile and wearable platforms for several reasons: (i) data collection is labor-intensive, resulting in scarcity; (ii) there exists a performance gap between state of-art models with memory footprints of hundreds of MBs and methods better suited for resource-constrained systems. To adapt TRAMBA to vibration-based sensing modalities, we pre-train TRAMBA with audio speech datasets that are widely available. Then, users fine-tune with a small amount of bone conduction data. TRAMBA outperforms state-of-art GANs by up to 7.3% in PESQ and 1.8% in STOI, with an order of magnitude smaller memory footprint and an inference speed up of up to 465 times. We integrate TRAMBA into real systems and show that TRAMBA (i) improves battery life of wearables by up to 160% by requiring less data sampling and transmission; (ii) generates higher quality voice in noisy environments than over-the-air speech; (iii) requires a memory footprint of less than 20.0 MB. | [
"['Yueyuan Sui' 'Minghui Zhao' 'Junxi Xia' 'Xiaofan Jiang' 'Stephen Xia']"
]
|
null | null | 2405.01247 | null | null | http://arxiv.org/pdf/2405.01247v1 | 2024-05-02T12:51:01Z | 2024-05-02T12:51:01Z | Lying Graph Convolution: Learning to Lie for Node Classification Tasks | In the context of machine learning for graphs, many researchers have empirically observed that Deep Graph Networks (DGNs) perform favourably on node classification tasks when the graph structure is homophilic (ie adjacent nodes are similar). In this paper, we introduce Lying-GCN, a new DGN inspired by opinion dynamics that can adaptively work in both the heterophilic and the homophilic setting. At each layer, each agent (node) shares its own opinions (node embeddings) with its neighbours. Instead of sharing its opinion directly as in GCN, we introduce a mechanism which allows agents to lie. Such a mechanism is adaptive, thus the agents learn how and when to lie according to the task that should be solved. We provide a characterisation of our proposal in terms of dynamical systems, by studying the spectral property of the coefficient matrix of the system. While the steady state of the system collapses to zero, we believe the lying mechanism is still usable to solve node classification tasks. We empirically prove our belief on both synthetic and real-world datasets, by showing that the lying mechanism allows to increase the performances in the heterophilic setting without harming the results in the homophilic one. | [
"['Daniele Castellana']"
]
|
null | null | 2405.01249 | null | null | http://arxiv.org/pdf/2405.01249v1 | 2024-05-02T12:52:23Z | 2024-05-02T12:52:23Z | Prompt engineering paradigms for medical applications: scoping review
and recommendations for better practices | Prompt engineering is crucial for harnessing the potential of large language models (LLMs), especially in the medical domain where specialized terminology and phrasing is used. However, the efficacy of prompt engineering in the medical domain remains to be explored. In this work, 114 recent studies (2022-2024) applying prompt engineering in medicine, covering prompt learning (PL), prompt tuning (PT), and prompt design (PD) are reviewed. PD is the most prevalent (78 articles). In 12 papers, PD, PL, and PT terms were used interchangeably. ChatGPT is the most commonly used LLM, with seven papers using it for processing sensitive clinical data. Chain-of-Thought emerges as the most common prompt engineering technique. While PL and PT articles typically provide a baseline for evaluating prompt-based approaches, 64% of PD studies lack non-prompt-related baselines. We provide tables and figures summarizing existing work, and reporting recommendations to guide future research contributions. | [
"['Jamil Zaghir' 'Marco Naguib' 'Mina Bjelogrlic' 'Aurélie Névéol'\n 'Xavier Tannier' 'Christian Lovis']"
]
|
null | null | 2405.01251 | null | null | http://arxiv.org/pdf/2405.01251v1 | 2024-05-02T12:54:04Z | 2024-05-02T12:54:04Z | Revisiting semi-supervised training objectives for differentiable
particle filters | Differentiable particle filters combine the flexibility of neural networks with the probabilistic nature of sequential Monte Carlo methods. However, traditional approaches rely on the availability of labelled data, i.e., the ground truth latent state information, which is often difficult to obtain in real-world applications. This paper compares the effectiveness of two semi-supervised training objectives for differentiable particle filters. We present results in two simulated environments where labelled data are scarce. | [
"['Jiaxi Li' 'John-Joseph Brady' 'Xiongjie Chen' 'Yunpeng Li']"
]
|
null | null | 2405.01260 | null | null | http://arxiv.org/pdf/2405.01260v1 | 2024-05-02T13:06:50Z | 2024-05-02T13:06:50Z | Causal Influence in Federated Edge Inference | In this paper, we consider a setting where heterogeneous agents with connectivity are performing inference using unlabeled streaming data. Observed data are only partially informative about the target variable of interest. In order to overcome the uncertainty, agents cooperate with each other by exchanging their local inferences with and through a fusion center. To evaluate how each agent influences the overall decision, we adopt a causal framework in order to distinguish the actual influence of agents from mere correlations within the decision-making process. Various scenarios reflecting different agent participation patterns and fusion center policies are investigated. We derive expressions to quantify the causal impact of each agent on the joint decision, which could be beneficial for anticipating and addressing atypical scenarios, such as adversarial attacks or system malfunctions. We validate our theoretical results with numerical simulations and a real-world application of multi-camera crowd counting. | [
"['Mert Kayaalp' 'Yunus Inan' 'Visa Koivunen' 'Ali H. Sayed']"
]
|
null | null | 2405.01261 | null | null | http://arxiv.org/pdf/2405.01261v1 | 2024-05-02T13:07:56Z | 2024-05-02T13:07:56Z | Continuously evolving rewards in an open-ended environment | Unambiguous identification of the rewards driving behaviours of entities operating in complex open-ended real-world environments is difficult, partly because goals and associated behaviours emerge endogenously and are dynamically updated as environments change. Reproducing such dynamics in models would be useful in many domains, particularly where fixed reward functions limit the adaptive capabilities of agents. Simulation experiments described assess a candidate algorithm for the dynamic updating of rewards, RULE: Reward Updating through Learning and Expectation. The approach is tested in a simplified ecosystem-like setting where experiments challenge entities' survival, calling for significant behavioural change. The population of entities successfully demonstrate the abandonment of an initially rewarded but ultimately detrimental behaviour, amplification of beneficial behaviour, and appropriate responses to novel items added to their environment. These adjustment happen through endogenous modification of the entities' underlying reward function, during continuous learning, without external intervention. | [
"['Richard M. Bailey']"
]
|
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