categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.09621
| null | null |
http://arxiv.org/pdf/2403.09621v1
|
2024-03-14T17:55:10Z
|
2024-03-14T17:55:10Z
|
Minimax Optimal and Computationally Efficient Algorithms for
Distributionally Robust Offline Reinforcement Learning
|
Distributionally robust offline reinforcement learning (RL), which seeks robust policy training against environment perturbation by modeling dynamics uncertainty, calls for function approximations when facing large state-action spaces. However, the consideration of dynamics uncertainty introduces essential nonlinearity and computational burden, posing unique challenges for analyzing and practically employing function approximation. Focusing on a basic setting where the nominal model and perturbed models are linearly parameterized, we propose minimax optimal and computationally efficient algorithms realizing function approximation and initiate the study on instance-dependent suboptimality analysis in the context of robust offline RL. Our results uncover that function approximation in robust offline RL is essentially distinct from and probably harder than that in standard offline RL. Our algorithms and theoretical results crucially depend on a variety of new techniques, involving a novel function approximation mechanism incorporating variance information, a new procedure of suboptimality and estimation uncertainty decomposition, a quantification of the robust value function shrinkage, and a meticulously designed family of hard instances, which might be of independent interest.
|
[
"['Zhishuai Liu' 'Pan Xu']"
] |
null | null |
2403.09625
| null | null |
http://arxiv.org/pdf/2403.09625v1
|
2024-03-14T17:57:04Z
|
2024-03-14T17:57:04Z
|
Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation
|
Recent years have witnessed the strong power of 3D generation models, which offer a new level of creative flexibility by allowing users to guide the 3D content generation process through a single image or natural language. However, it remains challenging for existing 3D generation methods to create subject-driven 3D content across diverse prompts. In this paper, we introduce a novel 3D customization method, dubbed Make-Your-3D that can personalize high-fidelity and consistent 3D content from only a single image of a subject with text description within 5 minutes. Our key insight is to harmonize the distributions of a multi-view diffusion model and an identity-specific 2D generative model, aligning them with the distribution of the desired 3D subject. Specifically, we design a co-evolution framework to reduce the variance of distributions, where each model undergoes a process of learning from the other through identity-aware optimization and subject-prior optimization, respectively. Extensive experiments demonstrate that our method can produce high-quality, consistent, and subject-specific 3D content with text-driven modifications that are unseen in subject image.
|
[
"['Fangfu Liu' 'Hanyang Wang' 'Weiliang Chen' 'Haowen Sun' 'Yueqi Duan']"
] |
null | null |
2403.09629
| null | null |
http://arxiv.org/pdf/2403.09629v2
|
2024-03-18T07:56:48Z
|
2024-03-14T17:58:16Z
|
Quiet-STaR: Language Models Can Teach Themselves to Think Before
Speaking
|
When writing and talking, people sometimes pause to think. Although reasoning-focused works have often framed reasoning as a method of answering questions or completing agentic tasks, reasoning is implicit in almost all written text. For example, this applies to the steps not stated between the lines of a proof or to the theory of mind underlying a conversation. In the Self-Taught Reasoner (STaR, Zelikman et al. 2022), useful thinking is learned by inferring rationales from few-shot examples in question-answering and learning from those that lead to a correct answer. This is a highly constrained setting -- ideally, a language model could instead learn to infer unstated rationales in arbitrary text. We present Quiet-STaR, a generalization of STaR in which LMs learn to generate rationales at each token to explain future text, improving their predictions. We address key challenges, including 1) the computational cost of generating continuations, 2) the fact that the LM does not initially know how to generate or use internal thoughts, and 3) the need to predict beyond individual next tokens. To resolve these, we propose a tokenwise parallel sampling algorithm, using learnable tokens indicating a thought's start and end, and an extended teacher-forcing technique. Encouragingly, generated rationales disproportionately help model difficult-to-predict tokens and improve the LM's ability to directly answer difficult questions. In particular, after continued pretraining of an LM on a corpus of internet text with Quiet-STaR, we find zero-shot improvements on GSM8K (5.9%$rightarrow$10.9%) and CommonsenseQA (36.3%$rightarrow$47.2%) and observe a perplexity improvement of difficult tokens in natural text. Crucially, these improvements require no fine-tuning on these tasks. Quiet-STaR marks a step towards LMs that can learn to reason in a more general and scalable way.
|
[
"['Eric Zelikman' 'Georges Harik' 'Yijia Shao' 'Varuna Jayasiri'\n 'Nick Haber' 'Noah D. Goodman']"
] |
null | null |
2403.09635
| null | null |
http://arxiv.org/pdf/2403.09635v1
|
2024-03-14T17:59:14Z
|
2024-03-14T17:59:14Z
|
Transformers Get Stable: An End-to-End Signal Propagation Theory for
Language Models
|
In spite of their huge success, transformer models remain difficult to scale in depth. In this work, we develop a unified signal propagation theory and provide formulae that govern the moments of the forward and backward signal through the transformer model. Our framework can be used to understand and mitigate vanishing/exploding gradients, rank collapse, and instability associated with high attention scores. We also propose DeepScaleLM, an initialization and scaling scheme that conserves unit output/gradient moments throughout the model, enabling the training of very deep models with 100s of layers. We find that transformer models could be much deeper - our deep models with fewer parameters outperform shallow models in Language Modeling, Speech Translation, and Image Classification, across Encoder-only, Decoder-only and Encoder-Decoder variants, for both Pre-LN and Post-LN transformers, for multiple datasets and model sizes. These improvements also translate into improved performance on downstream Question Answering tasks and improved robustness for image classification.
|
[
"['Akhil Kedia' 'Mohd Abbas Zaidi' 'Sushil Khyalia' 'Jungho Jung'\n 'Harshith Goka' 'Haejun Lee']"
] |
null | null |
2403.09646
| null | null |
http://arxiv.org/pdf/2403.09646v1
|
2023-10-18T04:00:43Z
|
2023-10-18T04:00:43Z
|
On Unsupervised Image-to-image translation and GAN stability
|
The problem of image-to-image translation is one that is intruiging and challenging at the same time, for the impact potential it can have on a wide variety of other computer vision applications like colorization, inpainting, segmentation and others. Given the high-level of sophistication needed to extract patterns from one domain and successfully applying them to another, especially, in a completely unsupervised (unpaired) manner, this problem has gained much attention as of the last few years. It is one of the first problems where successful applications to deep generative models, and especially Generative Adversarial Networks achieved astounding results that are actually of realworld impact, rather than just a show of theoretical prowess; the such that has been dominating the GAN world. In this work, we study some of the failure cases of a seminal work in the field, CycleGAN [1] and hypothesize that they are GAN-stability related, and propose two general models to try to alleviate these problems. We also reach the same conclusion of the problem being ill-posed that has been also circulating in the literature lately.
|
[
"['BahaaEddin AlAila' 'Zahra Jandaghi' 'Abolfazl Farahani'\n 'Mohammad Ziad Al-Saad']"
] |
null | null |
2403.09672
| null | null |
http://arxiv.org/pdf/2403.09672v1
|
2024-02-04T08:05:58Z
|
2024-02-04T08:05:58Z
|
COMPRER: A Multimodal Multi-Objective Pretraining Framework for Enhanced
Medical Image Representation
|
Substantial advances in multi-modal Artificial Intelligence (AI) facilitate the combination of diverse medical modalities to achieve holistic health assessments. We present COMPRER , a novel multi-modal, multi-objective pretraining framework which enhances medical-image representation, diagnostic inferences, and prognosis of diseases. COMPRER employs a multi-objective training framework, where each objective introduces distinct knowledge to the model. This includes a multimodal loss that consolidates information across different imaging modalities; A temporal loss that imparts the ability to discern patterns over time; Medical-measure prediction adds appropriate medical insights; Lastly, reconstruction loss ensures the integrity of image structure within the latent space. Despite the concern that multiple objectives could weaken task performance, our findings show that this combination actually boosts outcomes on certain tasks. Here, we apply this framework to both fundus images and carotid ultrasound, and validate our downstream tasks capabilities by predicting both current and future cardiovascular conditions. COMPRER achieved higher Area Under the Curve (AUC) scores in evaluating medical conditions compared to existing models on held-out data. On the Out-of-distribution (OOD) UK-Biobank dataset COMPRER maintains favorable performance over well-established models with more parameters, even though these models were trained on $75times$ more data than COMPRER. In addition, to better assess our model's performance in contrastive learning, we introduce a novel evaluation metric, providing deeper understanding of the effectiveness of the latent space pairing.
|
[
"['Guy Lutsker' 'Hagai Rossman' 'Nastya Godiva' 'Eran Segal']"
] |
null | null |
2403.09673
| null | null |
http://arxiv.org/pdf/2403.09673v2
|
2024-03-19T05:29:23Z
|
2024-02-04T12:18:51Z
|
FoldToken: Learning Protein Language via Vector Quantization and Beyond
|
Is there a foreign language describing protein sequences and structures simultaneously? Protein structures, represented by continuous 3D points, have long posed a challenge due to the contrasting modeling paradigms of discrete sequences. We introduce textbf{FoldTokenizer} to represent protein sequence-structure as discrete symbols. This innovative approach involves projecting residue types and structures into a discrete space, guided by a reconstruction loss for information preservation. We refer to the learned discrete symbols as textbf{FoldToken}, and the sequence of FoldTokens serves as a new protein language, transforming the protein sequence-structure into a unified modality. We apply the created protein language on general backbone inpainting and antibody design tasks, building the first GPT-style model (textbf{FoldGPT}) for sequence-structure co-generation with promising results. Key to our success is the substantial enhancement of the vector quantization module, Soft Conditional Vector Quantization (textbf{SoftCVQ}).
|
[
"['Zhangyang Gao' 'Cheng Tan' 'Jue Wang' 'Yufei Huang' 'Lirong Wu'\n 'Stan Z. Li']"
] |
null | null |
2403.09674
| null | null |
http://arxiv.org/pdf/2403.09674v1
|
2024-02-04T13:21:19Z
|
2024-02-04T13:21:19Z
|
Navigating the Peril of Generated Alternative Facts: A ChatGPT-4
Fabricated Omega Variant Case as a Cautionary Tale in Medical Misinformation
|
In an era where artificial intelligence (AI) intertwines with medical research, the delineation of truth becomes increasingly complex. This study ostensibly examines a purported novel SARS-CoV-2 variant, dubbed the Omega variant, showcasing 31 unique mutations in the S gene region. However, the real undercurrent of this narrative is a demonstration of the ease with which AI, specifically ChatGPT-4, can fabricate convincing yet entirely fictional scientific data. The so-called Omega variant was identified in a fully vaccinated, previously infected 35-year-old male presenting with severe COVID-19 symptoms. Through a detailed, albeit artificial, genomic analysis and contact tracing, this study mirrors the rigorous methodology of genuine case reports, thereby setting the stage for a compelling but entirely constructed narrative. The entire case study was generated by ChatGPT-4, a large language model by OpenAI. The fabricated Omega variant features an ensemble of mutations, including N501Y and E484K, known for enhancing ACE2 receptor affinity, alongside L452R and P681H, ostensibly indicative of immune evasion. This variant's contrived interaction dynamics - severe symptoms in a vaccinated individual versus mild ones in unvaccinated contacts - were designed to mimic real-world complexities, including suggestions of antibody-dependent enhancement (ADE). While the Omega variant is a product of AI-generated fiction, the implications of this exercise are real and profound. The ease with which AI can generate believable but false scientific information, as illustrated in this case, raises significant concerns about the potential for misinformation in medicine. This study, therefore, serves as a cautionary tale, emphasizing the necessity for critical evaluation of sources, especially in an age where AI tools like ChatGPT are becoming increasingly sophisticated and widespread in their use.
|
[
"['Malik Sallam' 'Jan Egger' 'Rainer Roehrig' 'Behrus Puladi']"
] |
null | null |
2403.09680
| null | null |
http://arxiv.org/pdf/2403.09680v2
|
2024-04-08T17:51:31Z
|
2024-02-07T15:30:23Z
|
Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method)
|
This paper proposes a machine learning pre-sort stage to traditional supervised learning using Tsetlin Machines. Initially, K data-points are identified from the dataset using an expedited genetic algorithm to solve the maximum dispersion problem. These are then used as the initial placement to run the K-Medoid clustering algorithm. Finally, an expedited genetic algorithm is used to align K independent Tsetlin Machines by maximising hamming distance. For MNIST level classification problems, results demonstrate up to 10% improvement in accuracy, approx. 383X reduction in training time and approx. 86X reduction in inference time.
|
[
"['Jordan Morris']"
] |
null | null |
2403.09681
| null | null |
http://arxiv.org/pdf/2403.09681v1
|
2024-02-07T17:06:32Z
|
2024-02-07T17:06:32Z
|
ViT-MUL: A Baseline Study on Recent Machine Unlearning Methods Applied
to Vision Transformers
|
Machine unlearning (MUL) is an arising field in machine learning that seeks to erase the learned information of specific training data points from a trained model. Despite the recent active research in MUL within computer vision, the majority of work has focused on ResNet-based models. Given that Vision Transformers (ViT) have become the predominant model architecture, a detailed study of MUL specifically tailored to ViT is essential. In this paper, we present comprehensive experiments on ViTs using recent MUL algorithms and datasets. We anticipate that our experiments, ablation studies, and findings could provide valuable insights and inspire further research in this field.
|
[
"['Ikhyun Cho' 'Changyeon Park' 'Julia Hockenmaier']"
] |
null | null |
2403.09683
| null | null |
http://arxiv.org/pdf/2403.09683v1
|
2024-02-07T20:55:39Z
|
2024-02-07T20:55:39Z
|
Counterfactual Image Editing
|
Counterfactual image editing is an important task in generative AI, which asks how an image would look if certain features were different. The current literature on the topic focuses primarily on changing individual features while remaining silent about the causal relationships between these features, as present in the real world. In this paper, we formalize the counterfactual image editing task using formal language, modeling the causal relationships between latent generative factors and images through a special type of model called augmented structural causal models (ASCMs). Second, we show two fundamental impossibility results: (1) counterfactual editing is impossible from i.i.d. image samples and their corresponding labels alone; (2) even when the causal relationships between the latent generative factors and images are available, no guarantees regarding the output of the model can be provided. Third, we propose a relaxation for this challenging problem by approximating non-identifiable counterfactual distributions with a new family of counterfactual-consistent estimators. This family exhibits the desirable property of preserving features that the user cares about across both factual and counterfactual worlds. Finally, we develop an efficient algorithm to generate counterfactual images by leveraging neural causal models.
|
[
"['Yushu Pan' 'Elias Bareinboim']"
] |
null | null |
2403.09701
| null | null |
http://arxiv.org/pdf/2403.09701v2
|
2024-03-18T02:18:16Z
|
2024-03-07T19:39:47Z
|
A Natural Extension To Online Algorithms For Hybrid RL With Limited
Coverage
|
Hybrid Reinforcement Learning (RL), leveraging both online and offline data, has garnered recent interest, yet research on its provable benefits remains sparse. Additionally, many existing hybrid RL algorithms (Song et al., 2023; Nakamoto et al., 2023; Amortila et al., 2024) impose coverage assumptions on the offline dataset, but we show that this is unnecessary. A well-designed online algorithm should "fill in the gaps" in the offline dataset, exploring states and actions that the behavior policy did not explore. Unlike previous approaches that focus on estimating the offline data distribution to guide online exploration (Li et al., 2023b), we show that a natural extension to standard optimistic online algorithms -- warm-starting them by including the offline dataset in the experience replay buffer -- achieves similar provable gains from hybrid data even when the offline dataset does not have single-policy concentrability. We accomplish this by partitioning the state-action space into two, bounding the regret on each partition through an offline and an online complexity measure, and showing that the regret of this hybrid RL algorithm can be characterized by the best partition -- despite the algorithm not knowing the partition itself. As an example, we propose DISC-GOLF, a modification of an existing optimistic online algorithm with general function approximation called GOLF used in Jin et al. (2021); Xie et al. (2022a), and show that it demonstrates provable gains over both online-only and offline-only reinforcement learning, with competitive bounds when specialized to the tabular, linear and block MDP cases. Numerical simulations further validate our theory that hybrid data facilitates more efficient exploration, supporting the potential of hybrid RL in various scenarios.
|
[
"['Kevin Tan' 'Ziping Xu']"
] |
null | null |
2403.09704
| null | null |
http://arxiv.org/pdf/2403.09704v1
|
2024-03-08T21:26:49Z
|
2024-03-08T21:26:49Z
|
Alignment Studio: Aligning Large Language Models to Particular
Contextual Regulations
|
The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In contrast, in this article, we present an approach and architecture that empowers application developers to tune a model to their particular values, social norms, laws and other regulations, and orchestrate between potentially conflicting requirements in context. We lay out three main components of such an Alignment Studio architecture: Framers, Instructors, and Auditors that work in concert to control the behavior of a language model. We illustrate this approach with a running example of aligning a company's internal-facing enterprise chatbot to its business conduct guidelines.
|
[
"['Swapnaja Achintalwar' 'Ioana Baldini' 'Djallel Bouneffouf'\n 'Joan Byamugisha' 'Maria Chang' 'Pierre Dognin' 'Eitan Farchi'\n 'Ndivhuwo Makondo' 'Aleksandra Mojsilovic' 'Manish Nagireddy'\n 'Karthikeyan Natesan Ramamurthy' 'Inkit Padhi' 'Orna Raz' 'Jesus Rios'\n 'Prasanna Sattigeri' 'Moninder Singh' 'Siphiwe Thwala'\n 'Rosario A. Uceda-Sosa' 'Kush R. Varshney']"
] |
null | null |
2403.09708
| null | null |
http://arxiv.org/pdf/2403.09708v1
|
2024-03-09T19:18:27Z
|
2024-03-09T19:18:27Z
|
Institutional-Level Monitoring of Immune Checkpoint Inhibitor IrAEs
Using a Novel Natural Language Processing Algorithmic Pipeline
|
Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment but can result in severe immune-related adverse events (IrAEs). Monitoring IrAEs on a large scale is essential for personalized risk profiling and assisting in treatment decisions. Methods: In this study, we conducted an analysis of clinical notes from patients who received ICIs at the Tel Aviv Sourasky Medical Center. By employing a Natural Language Processing algorithmic pipeline, we systematically identified seven common or severe IrAEs. We examined the utilization of corticosteroids, treatment discontinuation rates following IrAEs, and constructed survival curves to visualize the occurrence of adverse events during treatment. Results: Our analysis encompassed 108,280 clinical notes associated with 1,635 patients who had undergone ICI therapy. The detected incidence of IrAEs was consistent with previous reports, exhibiting substantial variation across different ICIs. Treatment with corticosteroids varied depending on the specific IrAE, ranging from 17.3% for thyroiditis to 57.4% for myocarditis. Our algorithm demonstrated high accuracy in identifying IrAEs, as indicated by an area under the curve (AUC) of 0.89 for each suspected note and F1 scores of 0.87 or higher for five out of the seven IrAEs examined at the patient level. Conclusions: This study presents a novel, large-scale monitoring approach utilizing deep neural networks for IrAEs. Our method provides accurate results, enhancing understanding of detrimental consequences experienced by ICI-treated patients. Moreover, it holds potential for monitoring other medications, enabling comprehensive post-marketing surveillance to identify susceptible populations and establish personalized drug safety profiles.
|
[
"['Michael Shapiro' 'Herut Dor' 'Anna Gurevich-Shapiro' 'Tal Etan'\n 'Ido Wolf']"
] |
null | null |
2403.09715
| null | null |
http://arxiv.org/abs/2403.09715v1
|
2024-03-11T20:45:27Z
|
2024-03-11T20:45:27Z
|
Textual analysis of End User License Agreement for red-flagging
potentially malicious software
|
New software and updates are downloaded by end users every day. Each dowloaded software has associated with it an End Users License Agreements (EULA), but this is rarely read. An EULA includes information to avoid legal repercussions. However,this proposes a host of potential problems such as spyware or producing an unwanted affect in the target system. End users do not read these EULA's because of length of the document and users find it extremely difficult to understand. Text summarization is one of the relevant solution to these kind of problems. This require a solution which can summarize the EULA and classify the EULA as "Benign" or "Malicious". We propose a solution in which we have summarize the EULA and classify the EULA as "Benign" or "Malicious". We extract EULA text of different sofware's then we classify the text using eight different supervised classifiers. we use ensemble learning to classify the EULA as benign or malicious using five different text summarization methods. An accuracy of $95.8$% shows the effectiveness of the presented approach.
|
[
"['Behraj Khan' 'Tahir Syed' 'Zeshan Khan' 'Muhammad Rafi']"
] |
null | null |
2403.09724
| null | null |
http://arxiv.org/pdf/2403.09724v2
|
2024-06-24T01:08:24Z
|
2024-03-12T17:07:53Z
|
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution
of Text Through Knowledge Graphs
|
In the midst of widespread misinformation and disinformation through social media and the proliferation of AI-generated texts, it has become increasingly difficult for people to validate and trust information they encounter. Many fact-checking approaches and tools have been developed, but they often lack appropriate explainability or granularity to be useful in various contexts. A text validation method that is easy to use, accessible, and can perform fine-grained evidence attribution has become crucial. More importantly, building user trust in such a method requires presenting the rationale behind each prediction, as research shows this significantly influences people's belief in automated systems. Localizing and bringing users' attention to the specific problematic content is also paramount, instead of providing simple blanket labels. In this paper, we present ClaimVer, a human-centric framework tailored to meet users' informational and verification needs by generating rich annotations and thereby reducing cognitive load. Designed to deliver comprehensive evaluations of texts, it highlights each claim, verifies it against a trusted knowledge graph (KG), presents the evidence, and provides succinct, clear explanations for each claim prediction. Finally, our framework introduces an attribution score, enhancing applicability across a wide range of downstream tasks.
|
[
"['Preetam Prabhu Srikar Dammu' 'Himanshu Naidu' 'Mouly Dewan'\n 'YoungMin Kim' 'Tanya Roosta' 'Aman Chadha' 'Chirag Shah']"
] |
null | null |
2403.09727
| null | null |
http://arxiv.org/pdf/2403.09727v1
|
2024-03-12T21:06:31Z
|
2024-03-12T21:06:31Z
|
Investigating the performance of Retrieval-Augmented Generation and
fine-tuning for the development of AI-driven knowledge-based systems
|
The development of generative large language models (G-LLM) opened up new opportunities for the development of new types of knowledge-based systems similar to ChatGPT, Bing, or Gemini. Fine-tuning (FN) and Retrieval-Augmented Generation (RAG) are the techniques that can be used to implement domain adaptation for the development of G-LLM-based knowledge systems. In our study, using ROUGE, BLEU, METEOR scores, and cosine similarity, we compare and examine the performance of RAG and FN for the GPT-J-6B, OPT-6.7B, LlaMA, LlaMA-2 language models. Based on measurements shown on different datasets, we demonstrate that RAG-based constructions are more efficient than models produced with FN. We point out that connecting RAG and FN is not trivial, because connecting FN models with RAG can cause a decrease in performance. Furthermore, we outline a simple RAG-based architecture which, on average, outperforms the FN models by 16% in terms of the ROGUE score, 15% in the case of the BLEU score, and 53% based on the cosine similarity. This shows the significant advantage of RAG over FN in terms of hallucination, which is not offset by the fact that the average 8% better METEOR score of FN models indicates greater creativity compared to RAG.
|
[
"['Robert Lakatos' 'Peter Pollner' 'Andras Hajdu' 'Tamas Joo']"
] |
null | null |
2403.09742
| null | null |
http://arxiv.org/pdf/2403.09742v1
|
2024-03-13T20:12:05Z
|
2024-03-13T20:12:05Z
|
A Short Review on Novel Approaches for Maximum Clique Problem: from
Classical algorithms to Graph Neural Networks and Quantum algorithms
|
This manuscript provides a comprehensive review of the Maximum Clique Problem, a computational problem that involves finding subsets of vertices in a graph that are all pairwise adjacent to each other. The manuscript covers in a simple way classical algorithms for solving the problem and includes a review of recent developments in graph neural networks and quantum algorithms. The review concludes with benchmarks for testing classical as well as new learning, and quantum algorithms.
|
[
"['Raffaele Marino' 'Lorenzo Buffoni' 'Bogdan Zavalnij']"
] |
null | null |
2403.09743
| null | null |
http://arxiv.org/pdf/2403.09743v1
|
2024-03-13T21:39:39Z
|
2024-03-13T21:39:39Z
|
The Human Factor in Detecting Errors of Large Language Models: A
Systematic Literature Review and Future Research Directions
|
The launch of ChatGPT by OpenAI in November 2022 marked a pivotal moment for Artificial Intelligence, introducing Large Language Models (LLMs) to the mainstream and setting new records in user adoption. LLMs, particularly ChatGPT, trained on extensive internet data, demonstrate remarkable conversational capabilities across various domains, suggesting a significant impact on the workforce. However, these models are susceptible to errors - "hallucinations" and omissions, generating incorrect or incomplete information. This poses risks especially in contexts where accuracy is crucial, such as legal compliance, medicine or fine-grained process frameworks. There are both technical and human solutions to cope with this isse. This paper explores the human factors that enable users to detect errors in LLM outputs, a critical component in mitigating risks associated with their use in professional settings. Understanding these factors is essential for organizations aiming to leverage LLM technology efficiently, guiding targeted training and deployment strategies to enhance error detection by users. This approach not only aims to optimize the use of LLMs but also to prevent potential downstream issues stemming from reliance on inaccurate model responses. The research emphasizes the balance between technological advancement and human insight in maximizing the benefits of LLMs while minimizing the risks, particularly in areas where precision is paramount. This paper performs a systematic literature research on this research topic, analyses and synthesizes the findings, and outlines future research directions. Literature selection cut-off date is January 11th 2024.
|
[
"['Christian A. Schiller']"
] |
null | null |
2403.09749
| null | null |
http://arxiv.org/abs/2403.09749v1
|
2024-03-14T05:02:00Z
|
2024-03-14T05:02:00Z
|
Towards Diverse Perspective Learning with Selection over Multiple
Temporal Poolings
|
In Time Series Classification (TSC), temporal pooling methods that consider sequential information have been proposed. However, we found that each temporal pooling has a distinct mechanism, and can perform better or worse depending on time series data. We term this fixed pooling mechanism a single perspective of temporal poolings. In this paper, we propose a novel temporal pooling method with diverse perspective learning: Selection over Multiple Temporal Poolings (SoM-TP). SoM-TP dynamically selects the optimal temporal pooling among multiple methods for each data by attention. The dynamic pooling selection is motivated by the ensemble concept of Multiple Choice Learning (MCL), which selects the best among multiple outputs. The pooling selection by SoM-TP's attention enables a non-iterative pooling ensemble within a single classifier. Additionally, we define a perspective loss and Diverse Perspective Learning Network (DPLN). The loss works as a regularizer to reflect all the pooling perspectives from DPLN. Our perspective analysis using Layer-wise Relevance Propagation (LRP) reveals the limitation of a single perspective and ultimately demonstrates diverse perspective learning of SoM-TP. We also show that SoM-TP outperforms CNN models based on other temporal poolings and state-of-the-art models in TSC with extensive UCR/UEA repositories.
|
[
"['Jihyeon Seong' 'Jungmin Kim' 'Jaesik Choi']"
] |
null | null |
2403.09755
| null | null |
http://arxiv.org/pdf/2403.09755v1
|
2024-03-14T14:02:00Z
|
2024-03-14T14:02:00Z
|
Estimating the history of a random recursive tree
|
This paper studies the problem of estimating the order of arrival of the vertices in a random recursive tree. Specifically, we study two fundamental models: the uniform attachment model and the linear preferential attachment model. We propose an order estimator based on the Jordan centrality measure and define a family of risk measures to quantify the quality of the ordering procedure. Moreover, we establish a minimax lower bound for this problem, and prove that the proposed estimator is nearly optimal. Finally, we numerically demonstrate that the proposed estimator outperforms degree-based and spectral ordering procedures.
|
[
"['Simon Briend' 'Christophe Giraud' 'Gábor Lugosi' 'Déborah Sulem']"
] |
null | null |
2403.09758
| null | null |
http://arxiv.org/pdf/2403.09758v1
|
2024-03-14T15:41:15Z
|
2024-03-14T15:41:15Z
|
Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network
Kernel for Gaussian Process Regression
|
Blood flow reconstruction in the vasculature is important for many clinical applications. However, in clinical settings, the available data are often quite limited. For instance, Transcranial Doppler ultrasound (TCD) is a noninvasive clinical tool that is commonly used in the clinical settings to measure blood velocity waveform at several locations on brain's vasculature. This amount of data is grossly insufficient for training machine learning surrogate models, such as deep neural networks or Gaussian process regression. In this work, we propose a Gaussian process regression approach based on physics-informed kernels, enabling near-real-time reconstruction of blood flow in data-poor regimes. We introduce a novel methodology to reconstruct the kernel within the vascular network, which is a non-Euclidean space. The proposed kernel encodes both spatiotemporal and vessel-to-vessel correlations, thus enabling blood flow reconstruction in vessels that lack direct measurements. We demonstrate that any prediction made with the proposed kernel satisfies the conservation of mass principle. The kernel is constructed by running stochastic one-dimensional blood flow simulations, where the stochasticity captures the epistemic uncertainties, such as lack of knowledge about boundary conditions and uncertainties in vasculature geometries. We demonstrate the performance of the model on three test cases, namely, a simple Y-shaped bifurcation, abdominal aorta, and the Circle of Willis in the brain.
|
[
"['Shaghayegh Z. Ashtiani' 'Mohammad Sarabian' 'Kaveh Laksari'\n 'Hessam Babaee']"
] |
null | null |
2403.09762
| null | null |
http://arxiv.org/abs/2403.09762v1
|
2024-03-14T15:58:13Z
|
2024-03-14T15:58:13Z
|
Emotional Intelligence Through Artificial Intelligence : NLP and Deep
Learning in the Analysis of Healthcare Texts
|
This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare, with a particular focus on the incorporation of Natural Language Processing and deep learning technologies. We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes based on textual information derived from clinical narratives, patient feedback on medications, and online health discussions. The review demonstrates noteworthy progress in the precision of algorithms used for sentiment classification, the prognostic capabilities of AI models for neurodegenerative diseases, and the creation of AI-powered systems that offer support in clinical decision-making. Remarkably, the utilization of AI applications has exhibited an enhancement in personalized therapy plans by integrating patient sentiment and contributing to the early identification of mental health disorders. There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures. Nevertheless, the potential of AI to revolutionize healthcare practices is unmistakable, offering a future where healthcare is not only more knowledgeable and efficient but also more empathetic and centered around the needs of patients. This investigation underscores the transformative influence of AI on healthcare, delivering a comprehensive comprehension of its role in examining emotional content in healthcare texts and highlighting the trajectory towards a more compassionate approach to patient care. The findings advocate for a harmonious synergy between AI's analytical capabilities and the human aspects of healthcare.
|
[
"['Prashant Kumar Nag' 'Amit Bhagat' 'R. Vishnu Priya' 'Deepak kumar Khare']"
] |
null | null |
2403.09793
| null | null |
http://arxiv.org/pdf/2403.09793v1
|
2024-03-14T18:25:40Z
|
2024-03-14T18:25:40Z
|
Socially Integrated Navigation: A Social Acting Robot with Deep
Reinforcement Learning
|
Mobile robots are being used on a large scale in various crowded situations and become part of our society. The socially acceptable navigation behavior of a mobile robot with individual human consideration is an essential requirement for scalable applications and human acceptance. Deep Reinforcement Learning (DRL) approaches are recently used to learn a robot's navigation policy and to model the complex interactions between robots and humans. We propose to divide existing DRL-based navigation approaches based on the robot's exhibited social behavior and distinguish between social collision avoidance with a lack of social behavior and socially aware approaches with explicit predefined social behavior. In addition, we propose a novel socially integrated navigation approach where the robot's social behavior is adaptive and emerges from the interaction with humans. The formulation of our approach is derived from a sociological definition, which states that social acting is oriented toward the acting of others. The DRL policy is trained in an environment where other agents interact socially integrated and reward the robot's behavior individually. The simulation results indicate that the proposed socially integrated navigation approach outperforms a socially aware approach in terms of distance traveled, time to completion, and negative impact on all agents within the environment.
|
[
"['Daniel Flögel' 'Lars Fischer' 'Thomas Rudolf' 'Tobias Schürmann'\n 'Sören Hohmann']"
] |
null | null |
2403.09805
| null | null |
http://arxiv.org/pdf/2403.09805v1
|
2024-03-14T18:52:34Z
|
2024-03-14T18:52:34Z
|
On the Utility of 3D Hand Poses for Action Recognition
|
3D hand poses are an under-explored modality for action recognition. Poses are compact yet informative and can greatly benefit applications with limited compute budgets. However, poses alone offer an incomplete understanding of actions, as they cannot fully capture objects and environments with which humans interact. To efficiently model hand-object interactions, we propose HandFormer, a novel multimodal transformer. HandFormer combines 3D hand poses at a high temporal resolution for fine-grained motion modeling with sparsely sampled RGB frames for encoding scene semantics. Observing the unique characteristics of hand poses, we temporally factorize hand modeling and represent each joint by its short-term trajectories. This factorized pose representation combined with sparse RGB samples is remarkably efficient and achieves high accuracy. Unimodal HandFormer with only hand poses outperforms existing skeleton-based methods at 5x fewer FLOPs. With RGB, we achieve new state-of-the-art performance on Assembly101 and H2O with significant improvements in egocentric action recognition.
|
[
"['Md Salman Shamil' 'Dibyadip Chatterjee' 'Fadime Sener' 'Shugao Ma'\n 'Angela Yao']"
] |
null | null |
2403.09809
| null | null |
http://arxiv.org/pdf/2403.09809v1
|
2024-03-14T18:58:06Z
|
2024-03-14T18:58:06Z
|
Self-Supervised Learning for Time Series: Contrastive or Generative?
|
Self-supervised learning (SSL) has recently emerged as a powerful approach to learning representations from large-scale unlabeled data, showing promising results in time series analysis. The self-supervised representation learning can be categorized into two mainstream: contrastive and generative. In this paper, we will present a comprehensive comparative study between contrastive and generative methods in time series. We first introduce the basic frameworks for contrastive and generative SSL, respectively, and discuss how to obtain the supervision signal that guides the model optimization. We then implement classical algorithms (SimCLR vs. MAE) for each type and conduct a comparative analysis in fair settings. Our results provide insights into the strengths and weaknesses of each approach and offer practical recommendations for choosing suitable SSL methods. We also discuss the implications of our findings for the broader field of representation learning and propose future research directions. All the code and data are released at url{https://github.com/DL4mHealth/SSL_Comparison}.
|
[
"['Ziyu Liu' 'Azadeh Alavi' 'Minyi Li' 'Xiang Zhang']"
] |
null | null |
2403.09810
| null | null |
http://arxiv.org/abs/2403.09810v1
|
2024-03-14T18:59:10Z
|
2024-03-14T18:59:10Z
|
LabelAId: Just-in-time AI Interventions for Improving Human Labeling
Quality and Domain Knowledge in Crowdsourcing Systems
|
Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge. Traditional quality control measures, such as prescreening workers and refining instructions, often focus solely on optimizing economic output. This paper explores just-in-time AI interventions to enhance both labeling quality and domain-specific knowledge among crowdworkers. We introduce LabelAId, an advanced inference model combining Programmatic Weak Supervision (PWS) with FT-Transformers to infer label correctness based on user behavior and domain knowledge. Our technical evaluation shows that our LabelAId pipeline consistently outperforms state-of-the-art ML baselines, improving mistake inference accuracy by 36.7% with 50 downstream samples. We then implemented LabelAId into Project Sidewalk, an open-source crowdsourcing platform for urban accessibility. A between-subjects study with 34 participants demonstrates that LabelAId significantly enhances label precision without compromising efficiency while also increasing labeler confidence. We discuss LabelAId's success factors, limitations, and its generalizability to other crowdsourced science domains.
|
[
"['Chu Li' 'Zhihan Zhang' 'Michael Saugstad' 'Esteban Safranchik'\n 'Minchu Kulkarni' 'Xiaoyu Huang' 'Shwetak Patel' 'Vikram Iyer'\n 'Tim Althoff' 'Jon E. Froehlich']"
] |
null | null |
2403.09811
| null | null |
http://arxiv.org/pdf/2403.09811v1
|
2024-03-14T18:59:54Z
|
2024-03-14T18:59:54Z
|
Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials
|
Computational high-throughput studies, especially in research on high-entropy materials and catalysts, are hampered by high-dimensional composition spaces and myriad structural microstates. They present bottlenecks to the conventional use of density functional theory calculations, and consequently, the use of machine-learned potentials is becoming increasingly prevalent in atomic structure simulations. In this communication, we show the results of adjusting and fine-tuning the pretrained EquiformerV2 model from the Open Catalyst Project to infer adsorption energies of *OH and *O on the out-of-domain high-entropy alloy Ag-Ir-Pd-Pt-Ru. By applying an energy filter based on the local environment of the binding site the zero-shot inference is markedly improved and through few-shot fine-tuning the model yields state-of-the-art accuracy. It is also found that EquiformerV2, assuming the role of general machine learning potential, is able to inform a smaller, more focused direct inference model. This knowledge distillation setup boosts performance on complex binding sites. Collectively, this shows that foundational knowledge learned from ordered intermetallic structures, can be extrapolated to the highly disordered structures of solid-solutions. With the vastly accelerated computational throughput of these models, hitherto infeasible research in the high-entropy material space is now readily accessible.
|
[
"['Christian M. Clausen' 'Jan Rossmeisl' 'Zachary W. Ulissi']"
] |
null | null |
2403.09830
| null | null |
http://arxiv.org/pdf/2403.09830v1
|
2024-03-14T19:36:07Z
|
2024-03-14T19:36:07Z
|
Towards the Reusability and Compositionality of Causal Representations
|
Causal Representation Learning (CRL) aims at identifying high-level causal factors and their relationships from high-dimensional observations, e.g., images. While most CRL works focus on learning causal representations in a single environment, in this work we instead propose a first step towards learning causal representations from temporal sequences of images that can be adapted in a new environment, or composed across multiple related environments. In particular, we introduce DECAF, a framework that detects which causal factors can be reused and which need to be adapted from previously learned causal representations. Our approach is based on the availability of intervention targets, that indicate which variables are perturbed at each time step. Experiments on three benchmark datasets show that integrating our framework with four state-of-the-art CRL approaches leads to accurate representations in a new environment with only a few samples.
|
[
"['Davide Talon' 'Phillip Lippe' 'Stuart James' 'Alessio Del Bue'\n 'Sara Magliacane']"
] |
null | null |
2403.09857
| null | null |
http://arxiv.org/pdf/2403.09857v2
|
2024-03-25T20:08:07Z
|
2024-03-14T20:34:53Z
|
Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive
Prompt
|
Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn new classes with scarce samples while preserving knowledge of old ones. Existing FSCIL methods usually fine-tune the entire backbone, leading to overfitting and hindering the potential to learn new classes. On the other hand, recent prompt-based CIL approaches alleviate forgetting by training prompts with sufficient data in each task. In this work, we propose a novel framework named Attention-aware Self-adaptive Prompt (ASP). ASP encourages task-invariant prompts to capture shared knowledge by reducing specific information from the attention aspect. Additionally, self-adaptive task-specific prompts in ASP provide specific information and transfer knowledge from old classes to new classes with an Information Bottleneck learning objective. In summary, ASP prevents overfitting on base task and does not require enormous data in few-shot incremental tasks. Extensive experiments on three benchmark datasets validate that ASP consistently outperforms state-of-the-art FSCIL and prompt-based CIL methods in terms of both learning new classes and mitigating forgetting.
|
[
"['Chenxi Liu' 'Zhenyi Wang' 'Tianyi Xiong' 'Ruibo Chen' 'Yihan Wu'\n 'Junfeng Guo' 'Heng Huang']"
] |
null | null |
2403.09859
| null | null |
http://arxiv.org/pdf/2403.09859v1
|
2024-03-14T20:40:36Z
|
2024-03-14T20:40:36Z
|
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
|
Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by low sample efficiency, and mostly focus on low-dimensional task distributions. In parallel, model-based RL methods have been successful in solving partially observable MDPs, of which meta-RL is a special case. In this work, we leverage this success and propose a new model-based approach to meta-RL, based on elements from existing state-of-the-art model-based and meta-RL methods. We demonstrate the effectiveness of our approach on common meta-RL benchmark domains, attaining greater return with better sample efficiency (up to $15times$) while requiring very little hyperparameter tuning. In addition, we validate our approach on a slate of more challenging, higher-dimensional domains, taking a step towards real-world generalizing agents.
|
[
"['Zohar Rimon' 'Tom Jurgenson' 'Orr Krupnik' 'Gilad Adler' 'Aviv Tamar']"
] |
null | null |
2403.09863
| null | null |
http://arxiv.org/pdf/2403.09863v5
|
2024-04-17T17:58:52Z
|
2024-03-14T20:50:03Z
|
Towards White Box Deep Learning
|
Deep neural networks learn fragile "shortcut" features, rendering them difficult to interpret (black box) and vulnerable to adversarial attacks. This paper proposes semantic features as a general architectural solution to this problem. The main idea is to make features locality-sensitive in the adequate semantic topology of the domain, thus introducing a strong regularization. The proof of concept network is lightweight, inherently interpretable and achieves almost human-level adversarial test metrics - with no adversarial training! These results and the general nature of the approach warrant further research on semantic features. The code is available at https://github.com/314-Foundation/white-box-nn
|
[
"['Maciej Satkiewicz']"
] |
null | null |
2403.09867
| null | null |
http://arxiv.org/pdf/2403.09867v1
|
2024-03-14T20:59:36Z
|
2024-03-14T20:59:36Z
|
iBRF: Improved Balanced Random Forest Classifier
|
Class imbalance poses a major challenge in different classification tasks, which is a frequently occurring scenario in many real-world applications. Data resampling is considered to be the standard approach to address this issue. The goal of the technique is to balance the class distribution by generating new samples or eliminating samples from the data. A wide variety of sampling techniques have been proposed over the years to tackle this challenging problem. Sampling techniques can also be incorporated into the ensemble learning framework to obtain more generalized prediction performance. Balanced Random Forest (BRF) and SMOTE-Bagging are some of the popular ensemble approaches. In this study, we propose a modification to the BRF classifier to enhance the prediction performance. In the original algorithm, the Random Undersampling (RUS) technique was utilized to balance the bootstrap samples. However, randomly eliminating too many samples from the data leads to significant data loss, resulting in a major decline in performance. We propose to alleviate the scenario by incorporating a novel hybrid sampling approach to balance the uneven class distribution in each bootstrap sub-sample. Our proposed hybrid sampling technique, when incorporated into the framework of the Random Forest classifier, termed as iBRF: improved Balanced Random Forest classifier, achieves better prediction performance than other sampling techniques used in imbalanced classification tasks. Experiments were carried out on 44 imbalanced datasets on which the original BRF classifier produced an average MCC score of 47.03% and an F1 score of 49.09%. Our proposed algorithm outperformed the approach by producing a far better MCC score of 53.04% and an F1 score of 55%. The results obtained signify the superiority of the iBRF algorithm and its potential to be an effective sampling technique in imbalanced learning.
|
[
"['Asif Newaz' 'Md. Salman Mohosheu' 'MD. Abdullah al Noman'\n 'Dr. Taskeed Jabid']"
] |
null | null |
2403.09869
| null | null |
http://arxiv.org/pdf/2403.09869v1
|
2024-03-14T21:00:26Z
|
2024-03-14T21:00:26Z
|
Mind the GAP: Improving Robustness to Subpopulation Shifts with
Group-Aware Priors
|
Machine learning models often perform poorly under subpopulation shifts in the data distribution. Developing methods that allow machine learning models to better generalize to such shifts is crucial for safe deployment in real-world settings. In this paper, we develop a family of group-aware prior (GAP) distributions over neural network parameters that explicitly favor models that generalize well under subpopulation shifts. We design a simple group-aware prior that only requires access to a small set of data with group information and demonstrate that training with this prior yields state-of-the-art performance -- even when only retraining the final layer of a previously trained non-robust model. Group aware-priors are conceptually simple, complementary to existing approaches, such as attribute pseudo labeling and data reweighting, and open up promising new avenues for harnessing Bayesian inference to enable robustness to subpopulation shifts.
|
[
"['Tim G. J. Rudner' 'Ya Shi Zhang' 'Andrew Gordon Wilson' 'Julia Kempe']"
] |
null | null |
2403.09871
| null | null |
http://arxiv.org/pdf/2403.09871v3
|
2024-06-13T16:51:26Z
|
2024-03-14T21:01:06Z
|
ThermoHands: A Benchmark for 3D Hand Pose Estimation from Egocentric
Thermal Images
|
In this work, we present ThermoHands, a new benchmark for thermal image-based egocentric 3D hand pose estimation, aimed at overcoming challenges like varying lighting conditions and obstructions (e.g., handwear). The benchmark includes a multi-view and multi-spectral dataset collected from 28 subjects performing hand-object and hand-virtual interactions under diverse scenarios, accurately annotated with 3D hand poses through an automated process. We introduce a new baseline method, TherFormer, utilizing dual transformer modules for effective egocentric 3D hand pose estimation in thermal imagery. Our experimental results highlight TherFormer's leading performance and affirm thermal imaging's effectiveness in enabling robust 3D hand pose estimation in adverse conditions.
|
[
"['Fangqiang Ding' 'Lawrence Zhu' 'Xiangyu Wen' 'Gaowen Liu'\n 'Chris Xiaoxuan Lu']"
] |
null | null |
2403.09889
| null | null |
http://arxiv.org/pdf/2403.09889v1
|
2024-03-14T21:48:00Z
|
2024-03-14T21:48:00Z
|
Generalization of Scaled Deep ResNets in the Mean-Field Regime
|
Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate emph{scaled} ResNet in the limit of infinitely deep and wide neural networks, of which the gradient flow is described by a partial differential equation in the large-neural network limit, i.e., the emph{mean-field} regime. To derive the generalization bounds under this setting, our analysis necessitates a shift from the conventional time-invariant Gram matrix employed in the lazy training regime to a time-variant, distribution-dependent version. To this end, we provide a global lower bound on the minimum eigenvalue of the Gram matrix under the mean-field regime. Besides, for the traceability of the dynamic of Kullback-Leibler (KL) divergence, we establish the linear convergence of the empirical error and estimate the upper bound of the KL divergence over parameters distribution. Finally, we build the uniform convergence for generalization bound via Rademacher complexity. Our results offer new insights into the generalization ability of deep ResNet beyond the lazy training regime and contribute to advancing the understanding of the fundamental properties of deep neural networks.
|
[
"['Yihang Chen' 'Fanghui Liu' 'Yiping Lu' 'Grigorios G. Chrysos'\n 'Volkan Cevher']"
] |
null | null |
2403.09891
| null | null |
http://arxiv.org/pdf/2403.09891v3
|
2024-05-03T13:12:40Z
|
2024-03-14T21:52:26Z
|
Fisher Mask Nodes for Language Model Merging
|
Fine-tuning pre-trained models provides significant advantages in downstream performance. The ubiquitous nature of pre-trained models such as BERT and its derivatives in natural language processing has also led to a proliferation of task-specific fine-tuned models. As these models typically only perform one task well, additional training or ensembling is required in multi-task scenarios. The growing field of model merging provides a solution, dealing with the challenge of combining multiple task-specific models into a single multi-task model. In this study, we introduce a novel model merging method for Transformers, combining insights from previous work in Fisher-weighted averaging and the use of Fisher information in model pruning. Utilizing the Fisher information of mask nodes within the Transformer architecture, we devise a computationally efficient weighted-averaging scheme. Our method exhibits a regular and significant performance increase across various models in the BERT family, outperforming full-scale Fisher-weighted averaging in a fraction of the computational cost, with baseline performance improvements of up to +6.5 and a speedup between 57.4x and 321.7x across models. Our results prove the potential of our method in current multi-task learning environments and suggest its scalability and adaptability to new model architectures and learning scenarios.
|
[
"['Thennal D K' 'Ganesh Nathan' 'Suchithra M S']"
] |
null | null |
2403.09898
| null | null |
http://arxiv.org/pdf/2403.09898v1
|
2024-03-14T22:19:37Z
|
2024-03-14T22:19:37Z
|
TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
|
Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency. We introduce TimeMachine, an innovative model that leverages Mamba, a state-space model, to capture long-term dependencies in multivariate time series data while maintaining linear scalability and small memory footprints. TimeMachine exploits the unique properties of time series data to produce salient contextual cues at multi-scales and leverage an innovative integrated quadruple-Mamba architecture to unify the handling of channel-mixing and channel-independence situations, thus enabling effective selection of contents for prediction against global and local contexts at different scales. Experimentally, TimeMachine achieves superior performance in prediction accuracy, scalability, and memory efficiency, as extensively validated using benchmark datasets. Code availability: https://github.com/Atik-Ahamed/TimeMachine
|
[
"['Md Atik Ahamed' 'Qiang Cheng']"
] |
null | null |
2403.09901
| null | null |
http://arxiv.org/pdf/2403.09901v1
|
2024-03-14T22:25:37Z
|
2024-03-14T22:25:37Z
|
Robust Subgraph Learning by Monitoring Early Training Representations
|
Graph neural networks (GNNs) have attracted significant attention for their outstanding performance in graph learning and node classification tasks. However, their vulnerability to adversarial attacks, particularly through susceptible nodes, poses a challenge in decision-making. The need for robust graph summarization is evident in adversarial challenges resulting from the propagation of attacks throughout the entire graph. In this paper, we address both performance and adversarial robustness in graph input by introducing the novel technique SHERD (Subgraph Learning Hale through Early Training Representation Distances). SHERD leverages information from layers of a partially trained graph convolutional network (GCN) to detect susceptible nodes during adversarial attacks using standard distance metrics. The method identifies "vulnerable (bad)" nodes and removes such nodes to form a robust subgraph while maintaining node classification performance. Through our experiments, we demonstrate the increased performance of SHERD in enhancing robustness by comparing the network's performance on original and subgraph inputs against various baselines alongside existing adversarial attacks. Our experiments across multiple datasets, including citation datasets such as Cora, Citeseer, and Pubmed, as well as microanatomical tissue structures of cell graphs in the placenta, highlight that SHERD not only achieves substantial improvement in robust performance but also outperforms several baselines in terms of node classification accuracy and computational complexity.
|
[
"['Sepideh Neshatfar' 'Salimeh Yasaei Sekeh']"
] |
null | null |
2403.09904
| null | null |
http://arxiv.org/pdf/2403.09904v1
|
2024-03-14T22:29:59Z
|
2024-03-14T22:29:59Z
|
FedComLoc: Communication-Efficient Distributed Training of Sparse and
Quantized Models
|
Federated Learning (FL) has garnered increasing attention due to its unique characteristic of allowing heterogeneous clients to process their private data locally and interact with a central server, while being respectful of privacy. A critical bottleneck in FL is the communication cost. A pivotal strategy to mitigate this burden is emph{Local Training}, which involves running multiple local stochastic gradient descent iterations between communication phases. Our work is inspired by the innovative emph{Scaffnew} algorithm, which has considerably advanced the reduction of communication complexity in FL. We introduce FedComLoc (Federated Compressed and Local Training), integrating practical and effective compression into emph{Scaffnew} to further enhance communication efficiency. Extensive experiments, using the popular TopK compressor and quantization, demonstrate its prowess in substantially reducing communication overheads in heterogeneous settings.
|
[
"['Kai Yi' 'Georg Meinhardt' 'Laurent Condat' 'Peter Richtárik']"
] |
null | null |
2403.09918
| null | null |
http://arxiv.org/pdf/2403.09918v1
|
2024-03-14T23:31:41Z
|
2024-03-14T23:31:41Z
|
Attention-based Class-Conditioned Alignment for Multi-Source Domain
Adaptive Object Detection
|
Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains. Multi-source domain adaptation (MSDA) allows leveraging multiple annotated source datasets, and unlabeled target data to improve the accuracy and robustness of the detection model. Most state-of-the-art MSDA methods for OD perform feature alignment in a class-agnostic manner. This is challenging since the objects have unique modal information due to variations in object appearance across domains. A recent prototype-based approach proposed a class-wise alignment, yet it suffers from error accumulation due to noisy pseudo-labels which can negatively affect adaptation with imbalanced data. To overcome these limitations, we propose an attention-based class-conditioned alignment scheme for MSDA that aligns instances of each object category across domains. In particular, an attention module coupled with an adversarial domain classifier allows learning domain-invariant and class-specific instance representations. Experimental results on multiple benchmarking MSDA datasets indicate that our method outperforms the state-of-the-art methods and is robust to class imbalance. Our code is available at https://github.com/imatif17/ACIA.
|
[
"['Atif Belal' 'Akhil Meethal' 'Francisco Perdigon Romero'\n 'Marco Pedersoli' 'Eric Granger']"
] |
null | null |
2403.09919
| null | null |
http://arxiv.org/pdf/2403.09919v3
|
2024-05-30T17:55:19Z
|
2024-03-14T23:40:56Z
|
Recurrent Drafter for Fast Speculative Decoding in Large Language Models
|
In this paper, we introduce an improved approach of speculative decoding aimed at enhancing the efficiency of serving large language models. Our method capitalizes on the strengths of two established techniques: the classic two-model speculative decoding approach, and the more recent single-model approach, Medusa. Drawing inspiration from Medusa, our approach adopts a single-model strategy for speculative decoding. However, our method distinguishes itself by employing a single, lightweight draft head with a recurrent dependency design, akin in essence to the small, draft model uses in classic speculative decoding, but without the complexities of the full transformer architecture. And because of the recurrent dependency, we can use beam search to swiftly filter out undesired candidates with the draft head. The outcome is a method that combines the simplicity of single-model design and avoids the need to create a data-dependent tree attention structure only for inference in Medusa. We empirically demonstrate the effectiveness of the proposed method on several popular open source language models, along with a comprehensive analysis of the trade-offs involved in adopting this approach.
|
[
"['Aonan Zhang' 'Chong Wang' 'Yi Wang' 'Xuanyu Zhang' 'Yunfei Cheng']"
] |
null | null |
2403.09930
| null | null |
http://arxiv.org/pdf/2403.09930v3
|
2024-06-03T09:46:32Z
|
2024-03-15T00:09:47Z
|
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse
Behaviors via Value and Successor Features Critics
|
A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations. Over the past decade, advancements in deep reinforcement learning have led to groundbreaking achievements to solve complex continuous control tasks. However, most approaches return only one solution specialized for a specific problem. We introduce Quality-Diversity Actor-Critic (QDAC), an off-policy actor-critic deep reinforcement learning algorithm that leverages a value function critic and a successor features critic to learn high-performing and diverse behaviors. In this framework, the actor optimizes an objective that seamlessly unifies both critics using constrained optimization to (1) maximize return, while (2) executing diverse skills. Compared with other Quality-Diversity methods, QDAC achieves significantly higher performance and more diverse behaviors on six challenging continuous control locomotion tasks. We also demonstrate that we can harness the learned skills to adapt better than other baselines to five perturbed environments. Finally, qualitative analyses showcase a range of remarkable behaviors: adaptive-intelligent-robotics.github.io/QDAC.
|
[
"['Luca Grillotti' 'Maxence Faldor' 'Borja G. León' 'Antoine Cully']"
] |
null | null |
2403.09940
| null | null |
http://arxiv.org/pdf/2403.09940v1
|
2024-03-15T00:45:36Z
|
2024-03-15T00:45:36Z
|
Global Convergence Guarantees for Federated Policy Gradient Methods with
Adversaries
|
Federated Reinforcement Learning (FRL) allows multiple agents to collaboratively build a decision making policy without sharing raw trajectories. However, if a small fraction of these agents are adversarial, it can lead to catastrophic results. We propose a policy gradient based approach that is robust to adversarial agents which can send arbitrary values to the server. Under this setting, our results form the first global convergence guarantees with general parametrization. These results demonstrate resilience with adversaries, while achieving sample complexity of order $tilde{mathcal{O}}left( frac{1}{epsilon^2} left( frac{1}{N-f} + frac{f^2}{(N-f)^2}right)right)$, where $N$ is the total number of agents and $f$ is the number of adversarial agents.
|
[
"['Swetha Ganesh' 'Jiayu Chen' 'Gugan Thoppe' 'Vaneet Aggarwal']"
] |
null | null |
2403.09942
| null | null |
http://arxiv.org/pdf/2403.09942v1
|
2024-03-15T00:52:17Z
|
2024-03-15T00:52:17Z
|
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor
Segmentation
|
Glioblastoma is a highly aggressive and malignant brain tumor type that requires early diagnosis and prompt intervention. Due to its heterogeneity in appearance, developing automated detection approaches is challenging. To address this challenge, Artificial Intelligence (AI)-driven approaches in healthcare have generated interest in efficiently diagnosing and evaluating brain tumors. The Brain Tumor Segmentation Challenge (BraTS) is a platform for developing and assessing automated techniques for tumor analysis using high-quality, clinically acquired MRI data. In our approach, we utilized a multi-scale, attention-guided and hybrid U-Net-shaped model -- GLIMS -- to perform 3D brain tumor segmentation in three regions: Enhancing Tumor (ET), Tumor Core (TC), and Whole Tumor (WT). The multi-scale feature extraction provides better contextual feature aggregation in high resolutions and the Swin Transformer blocks improve the global feature extraction at deeper levels of the model. The segmentation mask generation in the decoder branch is guided by the attention-refined features gathered from the encoder branch to enhance the important attributes. Moreover, hierarchical supervision is used to train the model efficiently. Our model's performance on the validation set resulted in 92.19, 87.75, and 83.18 Dice Scores and 89.09, 84.67, and 82.15 Lesion-wise Dice Scores in WT, TC, and ET, respectively. The code is publicly available at https://github.com/yaziciz/GLIMS.
|
[
"['Ziya Ata Yazıcı' 'İlkay Öksüz' 'Hazım Kemal Ekenel']"
] |
null | null |
2403.09953
| null | null |
http://arxiv.org/pdf/2403.09953v1
|
2024-03-15T01:28:08Z
|
2024-03-15T01:28:08Z
|
Online GNN Evaluation Under Test-time Graph Distribution Shifts
|
Evaluating the performance of a well-trained GNN model on real-world graphs is a pivotal step for reliable GNN online deployment and serving. Due to a lack of test node labels and unknown potential training-test graph data distribution shifts, conventional model evaluation encounters limitations in calculating performance metrics (e.g., test error) and measuring graph data-level discrepancies, particularly when the training graph used for developing GNNs remains unobserved during test time. In this paper, we study a new research problem, online GNN evaluation, which aims to provide valuable insights into the well-trained GNNs's ability to effectively generalize to real-world unlabeled graphs under the test-time graph distribution shifts. Concretely, we develop an effective learning behavior discrepancy score, dubbed LeBeD, to estimate the test-time generalization errors of well-trained GNN models. Through a novel GNN re-training strategy with a parameter-free optimality criterion, the proposed LeBeD comprehensively integrates learning behavior discrepancies from both node prediction and structure reconstruction perspectives. This enables the effective evaluation of the well-trained GNNs' ability to capture test node semantics and structural representations, making it an expressive metric for estimating the generalization error in online GNN evaluation. Extensive experiments on real-world test graphs under diverse graph distribution shifts could verify the effectiveness of the proposed method, revealing its strong correlation with ground-truth test errors on various well-trained GNN models.
|
[
"['Xin Zheng' 'Dongjin Song' 'Qingsong Wen' 'Bo Du' 'Shirui Pan']"
] |
null | null |
2403.09961
| null | null |
http://arxiv.org/pdf/2403.09961v1
|
2024-03-15T01:55:07Z
|
2024-03-15T01:55:07Z
|
Thermal Earth Model for the Conterminous United States Using an
Interpolative Physics-Informed Graph Neural Network (InterPIGNN)
|
This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop national temperature-at-depth maps for the conterminous United States. The model was trained to approximately satisfy the three-dimensional heat conduction law by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity. We constructed surface heat flow, and temperature and thermal conductivity predictions for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km$^2$ per grid cell. Our model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8{deg} C, 5.817 mW/m$^2$ and 0.022 W/(C-m)$, respectively. The predictions were visualized in two-dimensional spatial maps across the modeled depths. This thorough modeling of the Earth's thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources.
|
[
"['Mohammad J. Aljubran' 'Roland N. Horne']"
] |
null | null |
2403.09969
| null | null |
http://arxiv.org/pdf/2403.09969v1
|
2024-03-15T02:25:04Z
|
2024-03-15T02:25:04Z
|
Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data
Fusion and Deep Learning
|
This paper investigates the prediction of vessels' arrival time to the pilotage area using multi-data fusion and deep learning approaches. Firstly, the vessel arrival contour is extracted based on Multivariate Kernel Density Estimation (MKDE) and clustering. Secondly, multiple data sources, including Automatic Identification System (AIS), pilotage booking information, and meteorological data, are fused before latent feature extraction. Thirdly, a Temporal Convolutional Network (TCN) framework that incorporates a residual mechanism is constructed to learn the hidden arrival patterns of the vessels. Extensive tests on two real-world data sets from Singapore have been conducted and the following promising results have been obtained: 1) fusion of pilotage booking information and meteorological data improves the prediction accuracy, with pilotage booking information having a more significant impact; 2) using discrete embedding for the meteorological data performs better than using continuous embedding; 3) the TCN outperforms the state-of-the-art baseline methods in regression tasks, exhibiting Mean Absolute Error (MAE) ranging from 4.58 min to 4.86 min; and 4) approximately 89.41% to 90.61% of the absolute prediction residuals fall within a time frame of 10 min.
|
[
"['Xiaocai Zhang' 'Xiuju Fu' 'Zhe Xiao' 'Haiyan Xu' 'Xiaoyang Wei'\n 'Jimmy Koh' 'Daichi Ogawa' 'Zheng Qin']"
] |
null | null |
2403.09974
| null | null |
http://arxiv.org/pdf/2403.09974v2
|
2024-07-10T08:20:56Z
|
2024-03-15T02:40:13Z
|
Unlocking the Multi-modal Potential of CLIP for Generalized Category
Discovery
|
Given unlabelled datasets containing both old and new categories, generalized category discovery (GCD) aims to accurately discover new classes while correctly classifying old classes, leveraging the class concepts learned from labeled samples. Current GCD methods only use a single visual modality of information, resulting in poor classification of visually similar classes. As a different modality, text information can provide complementary discriminative information, which motivates us to introduce it into the GCD task. However, the lack of class names for unlabelled data makes it impractical to utilize text information. To tackle this challenging problem, in this paper, we propose a Text Embedding Synthesizer (TES) to generate pseudo text embeddings for unlabelled samples. Specifically, our TES leverages the property that CLIP can generate aligned vision-language features, converting visual embeddings into tokens of the CLIP's text encoder to generate pseudo text embeddings. Besides, we employ a dual-branch framework, through the joint learning and instance consistency of different modality branches, visual and semantic information mutually enhance each other, promoting the interaction and fusion of visual and text knowledge. Our method unlocks the multi-modal potentials of CLIP and outperforms the baseline methods by a large margin on all GCD benchmarks, achieving new state-of-the-art. The code will be released at https://github.com/enguangW/GET .
|
[
"['Enguang Wang' 'Zhimao Peng' 'Zhengyuan Xie' 'Fei Yang' 'Xialei Liu'\n 'Ming-Ming Cheng']"
] |
null | null |
2403.09976
| null | null |
http://arxiv.org/pdf/2403.09976v2
|
2024-06-05T12:25:53Z
|
2024-03-15T02:46:19Z
|
AD3: Implicit Action is the Key for World Models to Distinguish the
Diverse Visual Distractors
|
Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control. However, prior research has primarily focused on heterogeneous distractors like noisy background videos, leaving homogeneous distractors that closely resemble controllable agents largely unexplored, which poses significant challenges to existing methods. To tackle this problem, we propose Implicit Action Generator (IAG) to learn the implicit actions of visual distractors, and present a new algorithm named implicit Action-informed Diverse visual Distractors Distinguisher (AD3), that leverages the action inferred by IAG to train separated world models. Implicit actions effectively capture the behavior of background distractors, aiding in distinguishing the task-irrelevant components, and the agent can optimize the policy within the task-relevant state space. Our method achieves superior performance on various visual control tasks featuring both heterogeneous and homogeneous distractors. The indispensable role of implicit actions learned by IAG is also empirically validated.
|
[
"['Yucen Wang' 'Shenghua Wan' 'Le Gan' 'Shuai Feng' 'De-Chuan Zhan']"
] |
null | null |
2403.10006
| null | null |
http://arxiv.org/pdf/2403.10006v1
|
2024-03-15T04:04:40Z
|
2024-03-15T04:04:40Z
|
Graph Enhanced Reinforcement Learning for Effective Group Formation in
Collaborative Problem Solving
|
This study addresses the challenge of forming effective groups in collaborative problem-solving environments. Recognizing the complexity of human interactions and the necessity for efficient collaboration, we propose a novel approach leveraging graph theory and reinforcement learning. Our methodology involves constructing a graph from a dataset where nodes represent participants, and edges signify the interactions between them. We conceptualize each participant as an agent within a reinforcement learning framework, aiming to learn an optimal graph structure that reflects effective group dynamics. Clustering techniques are employed to delineate clear group structures based on the learned graph. Our approach provides theoretical solutions based on evaluation metrics and graph measurements, offering insights into potential improvements in group effectiveness and reductions in conflict incidences. This research contributes to the fields of collaborative work and educational psychology by presenting a data-driven, analytical approach to group formation. It has practical implications for organizational team building, classroom settings, and any collaborative scenario where group dynamics are crucial. The study opens new avenues for exploring the application of graph theory and reinforcement learning in social and behavioral sciences, highlighting the potential for empirical validation in future work.
|
[
"['Zheng Fang' 'Fucai Ke' 'Jae Young Han' 'Zhijie Feng' 'Toby Cai']"
] |
null | null |
2403.10013
| null | null |
http://arxiv.org/pdf/2403.10013v1
|
2024-03-15T04:35:56Z
|
2024-03-15T04:35:56Z
|
LyZNet: A Lightweight Python Tool for Learning and Verifying Neural
Lyapunov Functions and Regions of Attraction
|
In this paper, we describe a lightweight Python framework that provides integrated learning and verification of neural Lyapunov functions for stability analysis. The proposed tool, named LyZNet, learns neural Lyapunov functions using physics-informed neural networks (PINNs) to solve Zubov's equation and verifies them using satisfiability modulo theories (SMT) solvers. What distinguishes this tool from others in the literature is its ability to provide verified regions of attraction close to the domain of attraction. This is achieved by encoding Zubov's partial differential equation (PDE) into the PINN approach. By embracing the non-convex nature of the underlying optimization problems, we demonstrate that in cases where convex optimization, such as semidefinite programming, fails to capture the domain of attraction, our neural network framework proves more successful. The tool also offers automatic decomposition of coupled nonlinear systems into a network of low-dimensional subsystems for compositional verification. We illustrate the tool's usage and effectiveness with several numerical examples, including both non-trivial low-dimensional nonlinear systems and high-dimensional systems. The repository of the tool can be found at https://git.uwaterloo.ca/hybrid-systems-lab/lyznet.
|
[
"['Jun Liu' 'Yiming Meng' 'Maxwell Fitzsimmons' 'Ruikun Zhou']"
] |
null | null |
2403.10015
| null | null |
http://arxiv.org/pdf/2403.10015v1
|
2024-03-15T04:39:27Z
|
2024-03-15T04:39:27Z
|
Linear optimal transport subspaces for point set classification
|
Learning from point sets is an essential component in many computer vision and machine learning applications. Native, unordered, and permutation invariant set structure space is challenging to model, particularly for point set classification under spatial deformations. Here we propose a framework for classifying point sets experiencing certain types of spatial deformations, with a particular emphasis on datasets featuring affine deformations. Our approach employs the Linear Optimal Transport (LOT) transform to obtain a linear embedding of set-structured data. Utilizing the mathematical properties of the LOT transform, we demonstrate its capacity to accommodate variations in point sets by constructing a convex data space, effectively simplifying point set classification problems. Our method, which employs a nearest-subspace algorithm in the LOT space, demonstrates label efficiency, non-iterative behavior, and requires no hyper-parameter tuning. It achieves competitive accuracies compared to state-of-the-art methods across various point set classification tasks. Furthermore, our approach exhibits robustness in out-of-distribution scenarios where training and test distributions vary in terms of deformation magnitudes.
|
[
"['Mohammad Shifat E Rabbi' 'Naqib Sad Pathan' 'Shiying Li' 'Yan Zhuang'\n 'Abu Hasnat Mohammad Rubaiyat' 'Gustavo K Rohde']"
] |
null | null |
2403.10024
| null | null |
http://arxiv.org/pdf/2403.10024v1
|
2024-03-15T05:13:38Z
|
2024-03-15T05:13:38Z
|
MR-MT3: Memory Retaining Multi-Track Music Transcription to Mitigate
Instrument Leakage
|
This paper presents enhancements to the MT3 model, a state-of-the-art (SOTA) token-based multi-instrument automatic music transcription (AMT) model. Despite SOTA performance, MT3 has the issue of instrument leakage, where transcriptions are fragmented across different instruments. To mitigate this, we propose MR-MT3, with enhancements including a memory retention mechanism, prior token sampling, and token shuffling are proposed. These methods are evaluated on the Slakh2100 dataset, demonstrating improved onset F1 scores and reduced instrument leakage. In addition to the conventional multi-instrument transcription F1 score, new metrics such as the instrument leakage ratio and the instrument detection F1 score are introduced for a more comprehensive assessment of transcription quality. The study also explores the issue of domain overfitting by evaluating MT3 on single-instrument monophonic datasets such as ComMU and NSynth. The findings, along with the source code, are shared to facilitate future work aimed at refining token-based multi-instrument AMT models.
|
[
"['Hao Hao Tan' 'Kin Wai Cheuk' 'Taemin Cho' 'Wei-Hsiang Liao'\n 'Yuki Mitsufuji']"
] |
null | null |
2403.10042
| null | null |
http://arxiv.org/pdf/2403.10042v1
|
2024-03-15T06:23:30Z
|
2024-03-15T06:23:30Z
|
Accurate and Data-Efficient Micro-XRD Phase Identification Using
Multi-Task Learning: Application to Hydrothermal Fluids
|
Traditional analysis of highly distorted micro-X-ray diffraction ({mu}-XRD) patterns from hydrothermal fluid environments is a time-consuming process, often requiring substantial data preprocessing and labeled experimental data. This study demonstrates the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations. We trained MTL models to identify phase information in {mu}-XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models showed superior accuracy compared to binary classification CNNs. Additionally, introducing a tailored cross-entropy loss function improved MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieved close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor-intensive experimental datasets.
|
[
"['Yanfei Li' 'Juejing Liu' 'Xiaodong Zhao' 'Wenjun Liu' 'Tong Geng'\n 'Ang Li' 'Xin Zhang']"
] |
null | null |
2403.10045
| null | null |
http://arxiv.org/pdf/2403.10045v1
|
2024-03-15T06:31:03Z
|
2024-03-15T06:31:03Z
|
Towards Adversarially Robust Dataset Distillation by Curvature
Regularization
|
Dataset distillation (DD) allows datasets to be distilled to fractions of their original size while preserving the rich distributional information so that models trained on the distilled datasets can achieve a comparable accuracy while saving significant computational loads. Recent research in this area has been focusing on improving the accuracy of models trained on distilled datasets. In this paper, we aim to explore a new perspective of DD. We study how to embed adversarial robustness in distilled datasets, so that models trained on these datasets maintain the high accuracy and meanwhile acquire better adversarial robustness. We propose a new method that achieves this goal by incorporating curvature regularization into the distillation process with much less computational overhead than standard adversarial training. Extensive empirical experiments suggest that our method not only outperforms standard adversarial training on both accuracy and robustness with less computation overhead but is also capable of generating robust distilled datasets that can withstand various adversarial attacks.
|
[
"['Eric Xue' 'Yijiang Li' 'Haoyang Liu' 'Yifan Shen' 'Haohan Wang']"
] |
null | null |
2403.10063
| null | null |
http://arxiv.org/pdf/2403.10063v2
|
2024-04-26T21:05:00Z
|
2024-03-15T07:05:44Z
|
Unified Projection-Free Algorithms for Adversarial DR-Submodular
Optimization
|
This paper introduces unified projection-free Frank-Wolfe type algorithms for adversarial continuous DR-submodular optimization, spanning scenarios such as full information and (semi-)bandit feedback, monotone and non-monotone functions, different constraints, and types of stochastic queries. For every problem considered in the non-monotone setting, the proposed algorithms are either the first with proven sub-linear $alpha$-regret bounds or have better $alpha$-regret bounds than the state of the art, where $alpha$ is a corresponding approximation bound in the offline setting. In the monotone setting, the proposed approach gives state-of-the-art sub-linear $alpha$-regret bounds among projection-free algorithms in 7 of the 8 considered cases while matching the result of the remaining case. Additionally, this paper addresses semi-bandit and bandit feedback for adversarial DR-submodular optimization, advancing the understanding of this optimization area.
|
[
"['Mohammad Pedramfar' 'Yididiya Y. Nadew' 'Christopher J. Quinn'\n 'Vaneet Aggarwal']"
] |
null | null |
2403.10070
| null | null |
http://arxiv.org/pdf/2403.10070v1
|
2024-03-15T07:20:21Z
|
2024-03-15T07:20:21Z
|
A Structure-Preserving Kernel Method for Learning Hamiltonian Systems
|
A structure-preserving kernel ridge regression method is presented that allows the recovery of potentially high-dimensional and nonlinear Hamiltonian functions out of datasets made of noisy observations of Hamiltonian vector fields. The method proposes a closed-form solution that yields excellent numerical performances that surpass other techniques proposed in the literature in this setup. From the methodological point of view, the paper extends kernel regression methods to problems in which loss functions involving linear functions of gradients are required and, in particular, a differential reproducing property and a Representer Theorem are proved in this context. The relation between the structure-preserving kernel estimator and the Gaussian posterior mean estimator is analyzed. A full error analysis is conducted that provides convergence rates using fixed and adaptive regularization parameters. The good performance of the proposed estimator is illustrated with various numerical experiments.
|
[
"['Jianyu Hu' 'Juan-Pablo Ortega' 'Daiying Yin']"
] |
null | null |
2403.10075
| null | null |
http://arxiv.org/abs/2403.10075v2
|
2024-03-18T01:16:04Z
|
2024-03-15T07:34:08Z
|
A survey of synthetic data augmentation methods in computer vision
|
The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image datasets which are representative of the target task. However, in many scenarios, it is often challenging to obtain sufficient image data for the target task. Data augmentation is a way to mitigate this challenge. A common practice is to explicitly transform existing images in desired ways so as to create the required volume and variability of training data necessary to achieve good generalization performance. In situations where data for the target domain is not accessible, a viable workaround is to synthesize training data from scratch--i.e., synthetic data augmentation. This paper presents an extensive review of synthetic data augmentation techniques. It covers data synthesis approaches based on realistic 3D graphics modeling, neural style transfer (NST), differential neural rendering, and generative artificial intelligence (AI) techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs). For each of these classes of methods, we focus on the important data generation and augmentation techniques, general scope of application and specific use-cases, as well as existing limitations and possible workarounds. Additionally, we provide a summary of common synthetic datasets for training computer vision models, highlighting the main features, application domains and supported tasks. Finally, we discuss the effectiveness of synthetic data augmentation methods. Since this is the first paper to explore synthetic data augmentation methods in great detail, we are hoping to equip readers with the necessary background information and in-depth knowledge of existing methods and their attendant issues.
|
[
"['Alhassan Mumuni' 'Fuseini Mumuni' 'Nana Kobina Gerrar']"
] |
null | null |
2403.10089
| null | null |
http://arxiv.org/pdf/2403.10089v3
|
2024-05-22T00:45:47Z
|
2024-03-15T08:05:16Z
|
Approximation and bounding techniques for the Fisher-Rao distances
between parametric statistical models
|
The Fisher-Rao distance between two probability distributions of a statistical model is defined as the Riemannian geodesic distance induced by the Fisher information metric. In order to calculate the Fisher-Rao distance in closed-form, we need (1) to elicit a formula for the Fisher-Rao geodesics, and (2) to integrate the Fisher length element along those geodesics. We consider several numerically robust approximation and bounding techniques for the Fisher-Rao distances: First, we report generic upper bounds on Fisher-Rao distances based on closed-form 1D Fisher-Rao distances of submodels. Second, we describe several generic approximation schemes depending on whether the Fisher-Rao geodesics or pregeodesics are available in closed-form or not. In particular, we obtain a generic method to guarantee an arbitrarily small additive error on the approximation provided that Fisher-Rao pregeodesics and tight lower and upper bounds are available. Third, we consider the case of Fisher metrics being Hessian metrics, and report generic tight upper bounds on the Fisher-Rao distances using techniques of information geometry. Uniparametric and biparametric statistical models always have Fisher Hessian metrics, and in general a simple test allows to check whether the Fisher information matrix yields a Hessian metric or not. Fourth, we consider elliptical distribution families and show how to apply the above techniques to these models. We also propose two new distances based either on the Fisher-Rao lengths of curves serving as proxies of Fisher-Rao geodesics, or based on the Birkhoff/Hilbert projective cone distance. Last, we consider an alternative group-theoretic approach for statistical transformation models based on the notion of maximal invariant which yields insights on the structures of the Fisher-Rao distance formula which may be used fruitfully in applications.
|
[
"['Frank Nielsen']"
] |
null | null |
2403.10097
| null | null |
http://arxiv.org/pdf/2403.10097v1
|
2024-03-15T08:26:59Z
|
2024-03-15T08:26:59Z
|
Adaptive Random Feature Regularization on Fine-tuning Deep Neural
Networks
|
While fine-tuning is a de facto standard method for training deep neural networks, it still suffers from overfitting when using small target datasets. Previous methods improve fine-tuning performance by maintaining knowledge of the source datasets or introducing regularization terms such as contrastive loss. However, these methods require auxiliary source information (e.g., source labels or datasets) or heavy additional computations. In this paper, we propose a simple method called adaptive random feature regularization (AdaRand). AdaRand helps the feature extractors of training models to adaptively change the distribution of feature vectors for downstream classification tasks without auxiliary source information and with reasonable computation costs. To this end, AdaRand minimizes the gap between feature vectors and random reference vectors that are sampled from class conditional Gaussian distributions. Furthermore, AdaRand dynamically updates the conditional distribution to follow the currently updated feature extractors and balance the distance between classes in feature spaces. Our experiments show that AdaRand outperforms the other fine-tuning regularization, which requires auxiliary source information and heavy computation costs.
|
[
"[\"Shin'ya Yamaguchi\" 'Sekitoshi Kanai' 'Kazuki Adachi' 'Daiki Chijiwa']"
] |
null | null |
2403.10105
| null | null |
http://arxiv.org/pdf/2403.10105v1
|
2024-03-15T08:50:39Z
|
2024-03-15T08:50:39Z
|
Belief Aided Navigation using Bayesian Reinforcement Learning for
Avoiding Humans in Blind Spots
|
Recent research on mobile robot navigation has focused on socially aware navigation in crowded environments. However, existing methods do not adequately account for human robot interactions and demand accurate location information from omnidirectional sensors, rendering them unsuitable for practical applications. In response to this need, this study introduces a novel algorithm, BNBRL+, predicated on the partially observable Markov decision process framework to assess risks in unobservable areas and formulate movement strategies under uncertainty. BNBRL+ consolidates belief algorithms with Bayesian neural networks to probabilistically infer beliefs based on the positional data of humans. It further integrates the dynamics between the robot, humans, and inferred beliefs to determine the navigation paths and embeds social norms within the reward function, thereby facilitating socially aware navigation. Through experiments in various risk laden scenarios, this study validates the effectiveness of BNBRL+ in navigating crowded environments with blind spots. The model's ability to navigate effectively in spaces with limited visibility and avoid obstacles dynamically can significantly improve the safety and reliability of autonomous vehicles.
|
[
"['Jinyeob Kim' 'Daewon Kwak' 'Hyunwoo Rim' 'Donghan Kim']"
] |
null | null |
2403.10110
| null | null |
http://arxiv.org/pdf/2403.10110v1
|
2024-03-15T08:54:25Z
|
2024-03-15T08:54:25Z
|
Meta Operator for Complex Query Answering on Knowledge Graphs
|
Knowledge graphs contain informative factual knowledge but are considered incomplete. To answer complex queries under incomplete knowledge, learning-based Complex Query Answering (CQA) models are proposed to directly learn from the query-answer samples to avoid the direct traversal of incomplete graph data. Existing works formulate the training of complex query answering models as multi-task learning and require a large number of training samples. In this work, we explore the compositional structure of complex queries and argue that the different logical operator types, rather than the different complex query types, are the key to improving generalizability. Accordingly, we propose a meta-learning algorithm to learn the meta-operators with limited data and adapt them to different instances of operators under various complex queries. Empirical results show that learning meta-operators is more effective than learning original CQA or meta-CQA models.
|
[
"['Hang Yin' 'Zihao Wang' 'Yangqiu Song']"
] |
null | null |
2403.10123
| null | null |
http://arxiv.org/pdf/2403.10123v2
|
2024-06-29T23:01:10Z
|
2024-03-15T09:14:18Z
|
Regularization-Based Efficient Continual Learning in Deep State-Space
Models
|
Deep state-space models (DSSMs) have gained popularity in recent years due to their potent modeling capacity for dynamic systems. However, existing DSSM works are limited to single-task modeling, which requires retraining with historical task data upon revisiting a forepassed task. To address this limitation, we propose continual learning DSSMs (CLDSSMs), which are capable of adapting to evolving tasks without catastrophic forgetting. Our proposed CLDSSMs integrate mainstream regularization-based continual learning (CL) methods, ensuring efficient updates with constant computational and memory costs for modeling multiple dynamic systems. We also conduct a comprehensive cost analysis of each CL method applied to the respective CLDSSMs, and demonstrate the efficacy of CLDSSMs through experiments on real-world datasets. The results corroborate that while various competing CL methods exhibit different merits, the proposed CLDSSMs consistently outperform traditional DSSMs in terms of effectively addressing catastrophic forgetting, enabling swift and accurate parameter transfer to new tasks.
|
[
"['Yuanhang Zhang' 'Zhidi Lin' 'Yiyong Sun' 'Feng Yin' 'Carsten Fritsche']"
] |
null | null |
2403.10144
| null | null |
http://arxiv.org/pdf/2403.10144v2
|
2024-05-31T13:11:15Z
|
2024-03-15T09:43:52Z
|
NLP Verification: Towards a General Methodology for Certifying
Robustness
|
Deep neural networks have exhibited substantial success in the field of Natural Language Processing and ensuring their safety and reliability is crucial: there are safety critical contexts where such models must be robust to variability or attack, and give guarantees over their output. Unlike Computer Vision, NLP lacks a unified verification methodology and, despite recent advancements in literature, they are often light on the pragmatical issues of NLP verification. In this paper, we attempt to distil and evaluate general components of an NLP verification pipeline, that emerges from the progress in the field to date. Our contributions are two-fold. Firstly, we give a general (i.e. algorithm-independent) characterisation of verifiable subspaces that result from embedding sentences into continuous spaces. We identify, and give an effective method to deal with, the technical challenge of semantic generalisability of verified subspaces; and propose it as a standard metric in the NLP verification pipelines (alongside with the standard metrics of model accuracy and model verifiability). Secondly, we propose a general methodology to analyse the effect of the embedding gap -- a problem that refers to the discrepancy between verification of geometric subspaces, and the semantic meaning of sentences which the geometric subspaces are supposed to represent. In extreme cases, poor choices in embedding of sentences may invalidate verification results. We propose a number of practical NLP methods that can help to quantify the effects of the embedding gap; and in particular we propose the metric of falsifiability of semantic subspaces as another fundamental metric to be reported as part of the NLP verification pipeline. We believe that together these general principles pave the way towards a more consolidated and effective development of this new domain.
|
[
"['Marco Casadio' 'Tanvi Dinkar' 'Ekaterina Komendantskaya'\n 'Luca Arnaboldi' 'Matthew L. Daggitt' 'Omri Isac' 'Guy Katz'\n 'Verena Rieser' 'Oliver Lemon']"
] |
null | null |
2403.10153
| null | null |
http://arxiv.org/pdf/2403.10153v3
|
2024-07-15T14:35:13Z
|
2024-03-15T09:54:04Z
|
Improving Medical Multi-modal Contrastive Learning with Expert
Annotations
|
We introduce eCLIP, an enhanced version of the CLIP model that integrates expert annotations in the form of radiologist eye-gaze heatmaps. It tackles key challenges in contrastive multi-modal medical imaging analysis, notably data scarcity and the "modality gap" -- a significant disparity between image and text embeddings that diminishes the quality of representations and hampers cross-modal interoperability. eCLIP integrates a heatmap processor and leverages mixup augmentation to efficiently utilize the scarce expert annotations, thus boosting the model's learning effectiveness. eCLIP is designed to be generally applicable to any variant of CLIP without requiring any modifications of the core architecture. Through detailed evaluations across several tasks, including zero-shot inference, linear probing, cross-modal retrieval, and Retrieval Augmented Generation (RAG) of radiology reports using a frozen Large Language Model, eCLIP showcases consistent improvements in embedding quality. The outcomes reveal enhanced alignment and uniformity, affirming eCLIP's capability to harness high-quality annotations for enriched multi-modal analysis in the medical imaging domain.
|
[
"['Yogesh Kumar' 'Pekka Marttinen']"
] |
null | null |
2403.10158
| null | null |
http://arxiv.org/pdf/2403.10158v2
|
2024-03-27T08:57:20Z
|
2024-03-15T10:01:19Z
|
Functional Graph Convolutional Networks: A unified multi-task and
multi-modal learning framework to facilitate health and social-care insights
|
This paper introduces a novel Functional Graph Convolutional Network (funGCN) framework that combines Functional Data Analysis and Graph Convolutional Networks to address the complexities of multi-task and multi-modal learning in digital health and longitudinal studies. With the growing importance of health solutions to improve health care and social support, ensure healthy lives, and promote well-being at all ages, funGCN offers a unified approach to handle multivariate longitudinal data for multiple entities and ensures interpretability even with small sample sizes. Key innovations include task-specific embedding components that manage different data types, the ability to perform classification, regression, and forecasting, and the creation of a knowledge graph for insightful data interpretation. The efficacy of funGCN is validated through simulation experiments and a real-data application.
|
[
"['Tobia Boschi' 'Francesca Bonin' 'Rodrigo Ordonez-Hurtado'\n 'Cécile Rousseau' 'Alessandra Pascale' 'John Dinsmore']"
] |
null | null |
2403.10160
| null | null |
http://arxiv.org/pdf/2403.10160v1
|
2024-03-15T10:11:26Z
|
2024-03-15T10:11:26Z
|
Online Policy Learning from Offline Preferences
|
In preference-based reinforcement learning (PbRL), a reward function is learned from a type of human feedback called preference. To expedite preference collection, recent works have leveraged emph{offline preferences}, which are preferences collected for some offline data. In this scenario, the learned reward function is fitted on the offline data. If a learning agent exhibits behaviors that do not overlap with the offline data, the learned reward function may encounter generalizability issues. To address this problem, the present study introduces a framework that consolidates offline preferences and emph{virtual preferences} for PbRL, which are comparisons between the agent's behaviors and the offline data. Critically, the reward function can track the agent's behaviors using the virtual preferences, thereby offering well-aligned guidance to the agent. Through experiments on continuous control tasks, this study demonstrates the effectiveness of incorporating the virtual preferences in PbRL.
|
[
"['Guoxi Zhang' 'Han Bao' 'Hisashi Kashima']"
] |
null | null |
2403.10164
| null | null |
http://arxiv.org/pdf/2403.10164v1
|
2024-03-15T10:18:06Z
|
2024-03-15T10:18:06Z
|
CoReEcho: Continuous Representation Learning for 2D+time
Echocardiography Analysis
|
Deep learning (DL) models have been advancing automatic medical image analysis on various modalities, including echocardiography, by offering a comprehensive end-to-end training pipeline. This approach enables DL models to regress ejection fraction (EF) directly from 2D+time echocardiograms, resulting in superior performance. However, the end-to-end training pipeline makes the learned representations less explainable. The representations may also fail to capture the continuous relation among echocardiogram clips, indicating the existence of spurious correlations, which can negatively affect the generalization. To mitigate this issue, we propose CoReEcho, a novel training framework emphasizing continuous representations tailored for direct EF regression. Our extensive experiments demonstrate that CoReEcho: 1) outperforms the current state-of-the-art (SOTA) on the largest echocardiography dataset (EchoNet-Dynamic) with MAE of 3.90 & R2 of 82.44, and 2) provides robust and generalizable features that transfer more effectively in related downstream tasks. The code is publicly available at https://github.com/fadamsyah/CoReEcho.
|
[
"['Fadillah Adamsyah Maani' 'Numan Saeed' 'Aleksandr Matsun'\n 'Mohammad Yaqub']"
] |
null | null |
2403.10168
| null | null |
http://arxiv.org/abs/2403.10168v1
|
2024-03-15T10:22:48Z
|
2024-03-15T10:22:48Z
|
Explainability through uncertainty: Trustworthy decision-making with
neural networks
|
Uncertainty is a key feature of any machine learning model and is particularly important in neural networks, which tend to be overconfident. This overconfidence is worrying under distribution shifts, where the model performance silently degrades as the data distribution diverges from the training data distribution. Uncertainty estimation offers a solution to overconfident models, communicating when the output should (not) be trusted. Although methods for uncertainty estimation have been developed, they have not been explicitly linked to the field of explainable artificial intelligence (XAI). Furthermore, literature in operations research ignores the actionability component of uncertainty estimation and does not consider distribution shifts. This work proposes a general uncertainty framework, with contributions being threefold: (i) uncertainty estimation in ML models is positioned as an XAI technique, giving local and model-specific explanations; (ii) classification with rejection is used to reduce misclassifications by bringing a human expert in the loop for uncertain observations; (iii) the framework is applied to a case study on neural networks in educational data mining subject to distribution shifts. Uncertainty as XAI improves the model's trustworthiness in downstream decision-making tasks, giving rise to more actionable and robust machine learning systems in operations research.
|
[
"['Arthur Thuy' 'Dries F. Benoit']"
] |
null | null |
2403.10175
| null | null |
http://arxiv.org/pdf/2403.10175v2
|
2024-05-14T05:58:19Z
|
2024-03-15T10:31:46Z
|
A Short Survey on Importance Weighting for Machine Learning
|
Importance weighting is a fundamental procedure in statistics and machine learning that weights the objective function or probability distribution based on the importance of the instance in some sense. The simplicity and usefulness of the idea has led to many applications of importance weighting. For example, it is known that supervised learning under an assumption about the difference between the training and test distributions, called distribution shift, can guarantee statistically desirable properties through importance weighting by their density ratio. This survey summarizes the broad applications of importance weighting in machine learning and related research.
|
[
"['Masanari Kimura' 'Hideitsu Hino']"
] |
null | null |
2403.10182
| null | null |
http://arxiv.org/pdf/2403.10182v1
|
2024-03-15T10:38:48Z
|
2024-03-15T10:38:48Z
|
Reliable uncertainty with cheaper neural network ensembles: a case study
in industrial parts classification
|
In operations research (OR), predictive models often encounter out-of-distribution (OOD) scenarios where the data distribution differs from the training data distribution. In recent years, neural networks (NNs) are gaining traction in OR for their exceptional performance in fields such as image classification. However, NNs tend to make confident yet incorrect predictions when confronted with OOD data. Uncertainty estimation offers a solution to overconfident models, communicating when the output should (not) be trusted. Hence, reliable uncertainty quantification in NNs is crucial in the OR domain. Deep ensembles, composed of multiple independent NNs, have emerged as a promising approach, offering not only strong predictive accuracy but also reliable uncertainty estimation. However, their deployment is challenging due to substantial computational demands. Recent fundamental research has proposed more efficient NN ensembles, namely the snapshot, batch, and multi-input multi-output ensemble. This study is the first to provide a comprehensive comparison of a single NN, a deep ensemble, and the three efficient NN ensembles. In addition, we propose a Diversity Quality metric to quantify the ensembles' performance on the in-distribution and OOD sets in one single metric. The OR case study discusses industrial parts classification to identify and manage spare parts, important for timely maintenance of industrial plants. The results highlight the batch ensemble as a cost-effective and competitive alternative to the deep ensemble. It outperforms the deep ensemble in both uncertainty and accuracy while exhibiting a training time speedup of 7x, a test time speedup of 8x, and 9x memory savings.
|
[
"['Arthur Thuy' 'Dries F. Benoit']"
] |
null | null |
2403.10187
| null | null |
http://arxiv.org/pdf/2403.10187v1
|
2024-03-15T10:48:16Z
|
2024-03-15T10:48:16Z
|
Grasp Anything: Combining Teacher-Augmented Policy Gradient Learning
with Instance Segmentation to Grasp Arbitrary Objects
|
Interactive grasping from clutter, akin to human dexterity, is one of the longest-standing problems in robot learning. Challenges stem from the intricacies of visual perception, the demand for precise motor skills, and the complex interplay between the two. In this work, we present Teacher-Augmented Policy Gradient (TAPG), a novel two-stage learning framework that synergizes reinforcement learning and policy distillation. After training a teacher policy to master the motor control based on object pose information, TAPG facilitates guided, yet adaptive, learning of a sensorimotor policy, based on object segmentation. We zero-shot transfer from simulation to a real robot by using Segment Anything Model for promptable object segmentation. Our trained policies adeptly grasp a wide variety of objects from cluttered scenarios in simulation and the real world based on human-understandable prompts. Furthermore, we show robust zero-shot transfer to novel objects. Videos of our experiments are available at url{https://maltemosbach.github.io/grasp_anything}.
|
[
"['Malte Mosbach' 'Sven Behnke']"
] |
null | null |
2403.10190
| null | null |
http://arxiv.org/pdf/2403.10190v1
|
2024-03-15T10:52:18Z
|
2024-03-15T10:52:18Z
|
Perceptual Quality-based Model Training under Annotator Label
Uncertainty
|
Annotators exhibit disagreement during data labeling, which can be termed as annotator label uncertainty. Annotator label uncertainty manifests in variations of labeling quality. Training with a single low-quality annotation per sample induces model reliability degradations. In this work, we first examine the effects of annotator label uncertainty in terms of the model's generalizability and prediction uncertainty. We observe that the model's generalizability and prediction uncertainty degrade with the presence of low-quality noisy labels. Meanwhile, our evaluation of existing uncertainty estimation algorithms indicates their incapability in response to annotator label uncertainty. To mitigate performance degradation, prior methods show that training models with labels collected from multiple independent annotators can enhance generalizability. However, they require massive annotations. Hence, we introduce a novel perceptual quality-based model training framework to objectively generate multiple labels for model training to enhance reliability, while avoiding massive annotations. Specifically, we first select a subset of samples with low perceptual quality scores ranked by statistical regularities of visual signals. We then assign de-aggregated labels to each sample in this subset to obtain a training set with multiple labels. Our experiments and analysis demonstrate that training with the proposed framework alleviates the degradation of generalizability and prediction uncertainty caused by annotator label uncertainty.
|
[
"['Chen Zhou' 'Mohit Prabhushankar' 'Ghassan AlRegib']"
] |
null | null |
2403.10202
| null | null |
http://arxiv.org/pdf/2403.10202v1
|
2024-03-15T11:07:38Z
|
2024-03-15T11:07:38Z
|
Learning on JPEG-LDPC Compressed Images: Classifying with Syndromes
|
In goal-oriented communications, the objective of the receiver is often to apply a Deep-Learning model, rather than reconstructing the original data. In this context, direct learning over compressed data, without any prior decoding, holds promise for enhancing the time-efficient execution of inference models at the receiver. However, conventional entropic-coding methods like Huffman and Arithmetic break data structure, rendering them unsuitable for learning without decoding. In this paper, we propose an alternative approach in which entropic coding is realized with Low-Density Parity Check (LDPC) codes. We hypothesize that Deep Learning models can more effectively exploit the internal code structure of LDPC codes. At the receiver, we leverage a specific class of Recurrent Neural Networks (RNNs), specifically Gated Recurrent Unit (GRU), trained for image classification. Our numerical results indicate that classification based on LDPC-coded bit-planes surpasses Huffman and Arithmetic coding, while necessitating a significantly smaller learning model. This demonstrates the efficiency of classification directly from LDPC-coded data, eliminating the need for any form of decompression, even partial, prior to applying the learning model.
|
[
"['Ahcen Aliouat' 'Elsa Dupraz']"
] |
null | null |
2403.10220
| null | null |
http://arxiv.org/pdf/2403.10220v1
|
2024-03-15T11:39:12Z
|
2024-03-15T11:39:12Z
|
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical
Observations
|
With the development of astronomical facilities, large-scale time series data observed by these facilities is being collected. Analyzing anomalies in these astronomical observations is crucial for uncovering potential celestial events and physical phenomena, thus advancing the scientific research process. However, existing time series anomaly detection methods fall short in tackling the unique characteristics of astronomical observations where each star is inherently independent but interfered by random concurrent noise, resulting in a high rate of false alarms. To overcome the challenges, we propose AERO, a novel two-stage framework tailored for unsupervised anomaly detection in astronomical observations. In the first stage, we employ a Transformer-based encoder-decoder architecture to learn the normal temporal patterns on each variate (i.e., star) in alignment with the characteristic of variate independence. In the second stage, we enhance the graph neural network with a window-wise graph structure learning to tackle the occurrence of concurrent noise characterized by spatial and temporal randomness. In this way, AERO is not only capable of distinguishing normal temporal patterns from potential anomalies but also effectively differentiating concurrent noise, thus decreasing the number of false alarms. We conducted extensive experiments on three synthetic datasets and three real-world datasets. The results demonstrate that AERO outperforms the compared baselines. Notably, compared to the state-of-the-art model, AERO improves the F1-score by up to 8.76% and 2.63% on synthetic and real-world datasets respectively.
|
[
"['Xinli Hao' 'Yile Chen' 'Chen Yang' 'Zhihui Du' 'Chaohong Ma' 'Chao Wu'\n 'Xiaofeng Meng']"
] |
null | null |
2403.10231
| null | null |
http://arxiv.org/pdf/2403.10231v1
|
2024-03-15T12:00:12Z
|
2024-03-15T12:00:12Z
|
Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge
Graphs
|
To deduce new facts on a knowledge graph (KG), a link predictor learns from the graph structure and collects local evidence to find the answer to a given query. However, existing methods suffer from a severe scalability problem due to the utilization of the whole KG for prediction, which hinders their promise on large scale KGs and cannot be directly addressed by vanilla sampling methods. In this work, we propose the one-shot-subgraph link prediction to achieve efficient and adaptive prediction. The design principle is that, instead of directly acting on the whole KG, the prediction procedure is decoupled into two steps, i.e., (i) extracting only one subgraph according to the query and (ii) predicting on this single, query dependent subgraph. We reveal that the non-parametric and computation-efficient heuristics Personalized PageRank (PPR) can effectively identify the potential answers and supporting evidence. With efficient subgraph-based prediction, we further introduce the automated searching of the optimal configurations in both data and model spaces. Empirically, we achieve promoted efficiency and leading performances on five large-scale benchmarks. The code is publicly available at: https://github.com/tmlr-group/one-shot-subgraph.
|
[
"['Zhanke Zhou' 'Yongqi Zhang' 'Jiangchao Yao' 'Quanming Yao' 'Bo Han']"
] |
null | null |
2403.10232
| null | null |
http://arxiv.org/pdf/2403.10232v1
|
2024-03-15T12:00:37Z
|
2024-03-15T12:00:37Z
|
Matrix Completion via Nonsmooth Regularization of Fully Connected Neural
Networks
|
Conventional matrix completion methods approximate the missing values by assuming the matrix to be low-rank, which leads to a linear approximation of missing values. It has been shown that enhanced performance could be attained by using nonlinear estimators such as deep neural networks. Deep fully connected neural networks (FCNNs), one of the most suitable architectures for matrix completion, suffer from over-fitting due to their high capacity, which leads to low generalizability. In this paper, we control over-fitting by regularizing the FCNN model in terms of the $ell_{1}$ norm of intermediate representations and nuclear norm of weight matrices. As such, the resulting regularized objective function becomes nonsmooth and nonconvex, i.e., existing gradient-based methods cannot be applied to our model. We propose a variant of the proximal gradient method and investigate its convergence to a critical point. In the initial epochs of FCNN training, the regularization terms are ignored, and through epochs, the effect of that increases. The gradual addition of nonsmooth regularization terms is the main reason for the better performance of the deep neural network with nonsmooth regularization terms (DNN-NSR) algorithm. Our simulations indicate the superiority of the proposed algorithm in comparison with existing linear and nonlinear algorithms.
|
[
"['Sajad Faramarzi' 'Farzan Haddadi' 'Sajjad Amini' 'Masoud Ahookhosh']"
] |
null | null |
2403.10250
| null | null |
http://arxiv.org/pdf/2403.10250v1
|
2024-03-15T12:38:00Z
|
2024-03-15T12:38:00Z
|
Interpretable Machine Learning for Survival Analysis
|
With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade. This is particularly relevant for survival analysis, where the adoption of IML techniques promotes transparency, accountability and fairness in sensitive areas, such as clinical decision making processes, the development of targeted therapies, interventions or in other medical or healthcare related contexts. More specifically, explainability can uncover a survival model's potential biases and limitations and provide more mathematically sound ways to understand how and which features are influential for prediction or constitute risk factors. However, the lack of readily available IML methods may have deterred medical practitioners and policy makers in public health from leveraging the full potential of machine learning for predicting time-to-event data. We present a comprehensive review of the limited existing amount of work on IML methods for survival analysis within the context of the general IML taxonomy. In addition, we formally detail how commonly used IML methods, such as such as individual conditional expectation (ICE), partial dependence plots (PDP), accumulated local effects (ALE), different feature importance measures or Friedman's H-interaction statistics can be adapted to survival outcomes. An application of several IML methods to real data on data on under-5 year mortality of Ghanaian children from the Demographic and Health Surveys (DHS) Program serves as a tutorial or guide for researchers, on how to utilize the techniques in practice to facilitate understanding of model decisions or predictions.
|
[
"['Sophie Hanna Langbein' 'Mateusz Krzyziński' 'Mikołaj Spytek'\n 'Hubert Baniecki' 'Przemysław Biecek' 'Marvin N. Wright']"
] |
null | null |
2403.10253
| null | null |
http://arxiv.org/pdf/2403.10253v1
|
2024-03-15T12:43:03Z
|
2024-03-15T12:43:03Z
|
Open Continual Feature Selection via Granular-Ball Knowledge Transfer
|
This paper presents a novel framework for continual feature selection (CFS) in data preprocessing, particularly in the context of an open and dynamic environment where unknown classes may emerge. CFS encounters two primary challenges: the discovery of unknown knowledge and the transfer of known knowledge. To this end, the proposed CFS method combines the strengths of continual learning (CL) with granular-ball computing (GBC), which focuses on constructing a granular-ball knowledge base to detect unknown classes and facilitate the transfer of previously learned knowledge for further feature selection. CFS consists of two stages: initial learning and open learning. The former aims to establish an initial knowledge base through multi-granularity representation using granular-balls. The latter utilizes prior granular-ball knowledge to identify unknowns, updates the knowledge base for granular-ball knowledge transfer, reinforces old knowledge, and integrates new knowledge. Subsequently, we devise an optimal feature subset mechanism that incorporates minimal new features into the existing optimal subset, often yielding superior results during each period. Extensive experimental results on public benchmark datasets demonstrate our method's superiority in terms of both effectiveness and efficiency compared to state-of-the-art feature selection methods.
|
[
"['Xuemei Cao' 'Xin Yang' 'Shuyin Xia' 'Guoyin Wang' 'Tianrui Li']"
] |
null | null |
2403.10259
| null | null |
http://arxiv.org/pdf/2403.10259v1
|
2024-03-15T12:47:45Z
|
2024-03-15T12:47:45Z
|
Comprehensive Study Of Predictive Maintenance In Industries Using
Classification Models And LSTM Model
|
In today's technology-driven era, the imperative for predictive maintenance and advanced diagnostics extends beyond aviation to encompass the identification of damages, failures, and operational defects in rotating and moving machines. Implementing such services not only curtails maintenance costs but also extends machine lifespan, ensuring heightened operational efficiency. Moreover, it serves as a preventive measure against potential accidents or catastrophic events. The advent of Artificial Intelligence (AI) has revolutionized maintenance across industries, enabling more accurate and efficient prediction and analysis of machine failures, thereby conserving time and resources. Our proposed study aims to delve into various machine learning classification techniques, including Support Vector Machine (SVM), Random Forest, Logistic Regression, and Convolutional Neural Network LSTM-Based, for predicting and analyzing machine performance. SVM classifies data into different categories based on their positions in a multidimensional space, while Random Forest employs ensemble learning to create multiple decision trees for classification. Logistic Regression predicts the probability of binary outcomes using input data. The primary objective of the study is to assess these algorithms' performance in predicting and analyzing machine performance, considering factors such as accuracy, precision, recall, and F1 score. The findings will aid maintenance experts in selecting the most suitable machine learning algorithm for effective prediction and analysis of machine performance.
|
[
"['Saket Maheshwari' 'Sambhav Tiwari' 'Shyam Rai'\n 'Satyam Vinayak Daman Pratap Singh']"
] |
null | null |
2403.10266
| null | null |
http://arxiv.org/pdf/2403.10266v2
|
2024-05-27T18:51:52Z
|
2024-03-15T12:53:50Z
|
DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers
|
Scaling multi-dimensional transformers to long sequences is indispensable across various domains. However, the challenges of large memory requirements and slow speeds of such sequences necessitate sequence parallelism. All existing approaches fall under the category of embedded sequence parallelism, which are limited to shard along a single sequence dimension, thereby introducing significant communication overhead. However, the nature of multi-dimensional transformers involves independent calculations across multiple sequence dimensions. To this end, we propose Dynamic Sequence Parallelism (DSP) as a novel abstraction of sequence parallelism. DSP dynamically switches the parallel dimension among all sequences according to the computation stage with efficient resharding strategy. DSP offers significant reductions in communication costs, adaptability across modules, and ease of implementation with minimal constraints. Experimental evaluations demonstrate DSP's superiority over state-of-the-art embedded sequence parallelism methods by remarkable throughput improvements ranging from 32.2% to 10x, with less than 25% communication volume.
|
[
"['Xuanlei Zhao' 'Shenggan Cheng' 'Chang Chen' 'Zangwei Zheng' 'Ziming Liu'\n 'Zheming Yang' 'Yang You']"
] |
null | null |
2403.10281
| null | null |
http://arxiv.org/pdf/2403.10281v1
|
2024-03-15T13:24:28Z
|
2024-03-15T13:24:28Z
|
Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification
with Fine-Tuning
|
In this paper, we present Pre-CoFactv3, a comprehensive framework comprised of Question Answering and Text Classification components for fact verification. Leveraging In-Context Learning, Fine-tuned Large Language Models (LLMs), and the FakeNet model, we address the challenges of fact verification. Our experiments explore diverse approaches, comparing different Pre-trained LLMs, introducing FakeNet, and implementing various ensemble methods. Notably, our team, Trifecta, secured first place in the AAAI-24 Factify 3.0 Workshop, surpassing the baseline accuracy by 103% and maintaining a 70% lead over the second competitor. This success underscores the efficacy of our approach and its potential contributions to advancing fact verification research.
|
[
"['Shang-Hsuan Chiang' 'Ming-Chih Lo' 'Lin-Wei Chao' 'Wen-Chih Peng']"
] |
null | null |
2403.10288
| null | null |
http://arxiv.org/pdf/2403.10288v1
|
2024-03-15T13:29:45Z
|
2024-03-15T13:29:45Z
|
Rough Transformers for Continuous and Efficient Time-Series Modelling
|
Time-series data in real-world medical settings typically exhibit long-range dependencies and are observed at non-uniform intervals. In such contexts, traditional sequence-based recurrent models struggle. To overcome this, researchers replace recurrent architectures with Neural ODE-based models to model irregularly sampled data and use Transformer-based architectures to account for long-range dependencies. Despite the success of these two approaches, both incur very high computational costs for input sequences of moderate lengths and greater. To mitigate this, we introduce the Rough Transformer, a variation of the Transformer model which operates on continuous-time representations of input sequences and incurs significantly reduced computational costs, critical for addressing long-range dependencies common in medical contexts. In particular, we propose multi-view signature attention, which uses path signatures to augment vanilla attention and to capture both local and global dependencies in input data, while remaining robust to changes in the sequence length and sampling frequency. We find that Rough Transformers consistently outperform their vanilla attention counterparts while obtaining the benefits of Neural ODE-based models using a fraction of the computational time and memory resources on synthetic and real-world time-series tasks.
|
[
"['Fernando Moreno-Pino' 'Álvaro Arroyo' 'Harrison Waldon' 'Xiaowen Dong'\n 'Álvaro Cartea']"
] |
null | null |
2403.10318
| null | null |
http://arxiv.org/pdf/2403.10318v2
|
2024-05-06T10:02:44Z
|
2024-03-15T14:09:46Z
|
Anytime Neural Architecture Search on Tabular Data
|
The increasing demand for tabular data analysis calls for transitioning from manual architecture design to Neural Architecture Search (NAS). This transition demands an efficient and responsive anytime NAS approach that is capable of returning current optimal architectures within any given time budget while progressively enhancing architecture quality with increased budget allocation. However, the area of research on Anytime NAS for tabular data remains unexplored. To this end, we introduce ATLAS, the first anytime NAS approach tailored for tabular data. ATLAS introduces a novel two-phase filtering-and-refinement optimization scheme with joint optimization, combining the strengths of both paradigms of training-free and training-based architecture evaluation. Specifically, in the filtering phase, ATLAS employs a new zero-cost proxy specifically designed for tabular data to efficiently estimate the performance of candidate architectures, thereby obtaining a set of promising architectures. Subsequently, in the refinement phase, ATLAS leverages a fixed-budget search algorithm to schedule the training of the promising candidates, so as to accurately identify the optimal architecture. To jointly optimize the two phases for anytime NAS, we also devise a budget-aware coordinator that delivers high NAS performance within constraints. Experimental evaluations demonstrate that our ATLAS can obtain a good-performing architecture within any predefined time budget and return better architectures as and when a new time budget is made available. Overall, it reduces the search time on tabular data by up to 82.75x compared to existing NAS approaches.
|
[
"['Naili Xing' 'Shaofeng Cai' 'Zhaojing Luo' 'Beng Chin Ooi' 'Jian Pei']"
] |
null | null |
2403.10326
| null | null |
http://arxiv.org/abs/2403.10326v1
|
2024-03-15T14:14:26Z
|
2024-03-15T14:14:26Z
|
CDGP: Automatic Cloze Distractor Generation based on Pre-trained
Language Model
|
Manually designing cloze test consumes enormous time and efforts. The major challenge lies in wrong option (distractor) selection. Having carefully-design distractors improves the effectiveness of learner ability assessment. As a result, the idea of automatically generating cloze distractor is motivated. In this paper, we investigate cloze distractor generation by exploring the employment of pre-trained language models (PLMs) as an alternative for candidate distractor generation. Experiments show that the PLM-enhanced model brings a substantial performance improvement. Our best performing model advances the state-of-the-art result from 14.94 to 34.17 (NDCG@10 score). Our code and dataset is available at https://github.com/AndyChiangSH/CDGP.
|
[
"['Shang-Hsuan Chiang' 'Ssu-Cheng Wang' 'Yao-Chung Fan']"
] |
null | null |
2403.10330
| null | null |
http://arxiv.org/pdf/2403.10330v1
|
2024-03-15T14:18:21Z
|
2024-03-15T14:18:21Z
|
Towards Non-Adversarial Algorithmic Recourse
|
The streams of research on adversarial examples and counterfactual explanations have largely been growing independently. This has led to several recent works trying to elucidate their similarities and differences. Most prominently, it has been argued that adversarial examples, as opposed to counterfactual explanations, have a unique characteristic in that they lead to a misclassification compared to the ground truth. However, the computational goals and methodologies employed in existing counterfactual explanation and adversarial example generation methods often lack alignment with this requirement. Using formal definitions of adversarial examples and counterfactual explanations, we introduce non-adversarial algorithmic recourse and outline why in high-stakes situations, it is imperative to obtain counterfactual explanations that do not exhibit adversarial characteristics. We subsequently investigate how different components in the objective functions, e.g., the machine learning model or cost function used to measure distance, determine whether the outcome can be considered an adversarial example or not. Our experiments on common datasets highlight that these design choices are often more critical in deciding whether recourse is non-adversarial than whether recourse or attack algorithms are used. Furthermore, we show that choosing a robust and accurate machine learning model results in less adversarial recourse desired in practice.
|
[
"['Tobias Leemann' 'Martin Pawelczyk' 'Bardh Prenkaj' 'Gjergji Kasneci']"
] |
null | null |
2403.10332
| null | null |
http://arxiv.org/pdf/2403.10332v1
|
2024-03-15T14:19:09Z
|
2024-03-15T14:19:09Z
|
GreedyML: A Parallel Algorithm for Maximizing Submodular Functions
|
We describe a parallel approximation algorithm for maximizing monotone submodular functions subject to hereditary constraints on distributed memory multiprocessors. Our work is motivated by the need to solve submodular optimization problems on massive data sets, for practical applications in areas such as data summarization, machine learning, and graph sparsification. Our work builds on the randomized distributed RandGreedI algorithm, proposed by Barbosa, Ene, Nguyen, and Ward (2015). This algorithm computes a distributed solution by randomly partitioning the data among all the processors and then employing a single accumulation step in which all processors send their partial solutions to one processor. However, for large problems, the accumulation step could exceed the memory available on a processor, and the processor which performs the accumulation could become a computational bottleneck. Here, we propose a generalization of the RandGreedI algorithm that employs multiple accumulation steps to reduce the memory required. We analyze the approximation ratio and the time complexity of the algorithm (in the BSP model). We also evaluate the new GreedyML algorithm on three classes of problems, and report results from massive data sets with millions of elements. The results show that the GreedyML algorithm can solve problems where the sequential Greedy and distributed RandGreedI algorithms fail due to memory constraints. For certain computationally intensive problems, the GreedyML algorithm can be faster than the RandGreedI algorithm. The observed approximation quality of the solutions computed by the GreedyML algorithm closely matches those obtained by the RandGreedI algorithm on these problems.
|
[
"['Shivaram Gopal' 'S M Ferdous' 'Hemanta K. Maji' 'Alex Pothen']"
] |
null | null |
2403.10339
| null | null |
http://arxiv.org/pdf/2403.10339v1
|
2024-03-15T14:26:53Z
|
2024-03-15T14:26:53Z
|
Generation is better than Modification: Combating High Class Homophily
Variance in Graph Anomaly Detection
|
Graph-based anomaly detection is currently an important research topic in the field of graph neural networks (GNNs). We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than those in homophilic and heterophilic graphs. For the first time, we introduce a new metric called Class Homophily Variance, which quantitatively describes this phenomenon. To mitigate its impact, we propose a novel GNN model named Homophily Edge Generation Graph Neural Network (HedGe). Previous works typically focused on pruning, selecting or connecting on original relationships, and we refer to these methods as modifications. Different from these works, our method emphasizes generating new relationships with low class homophily variance, using the original relationships as an auxiliary. HedGe samples homophily adjacency matrices from scratch using a self-attention mechanism, and leverages nodes that are relevant in the feature space but not directly connected in the original graph. Additionally, we modify the loss function to punish the generation of unnecessary heterophilic edges by the model. Extensive comparison experiments demonstrate that HedGe achieved the best performance across multiple benchmark datasets, including anomaly detection and edgeless node classification. The proposed model also improves the robustness under the novel Heterophily Attack with increased class homophily variance on other graph classification tasks.
|
[
"['Rui Zhang' 'Dawei Cheng' 'Xin Liu' 'Jie Yang' 'Yi Ouyang' 'Xian Wu'\n 'Yefeng Zheng']"
] |
null | null |
2403.10348
| null | null |
http://arxiv.org/pdf/2403.10348v2
|
2024-07-15T13:46:29Z
|
2024-03-15T14:34:34Z
|
Denoising Task Difficulty-based Curriculum for Training Diffusion Models
|
Diffusion-based generative models have emerged as powerful tools in the realm of generative modeling. Despite extensive research on denoising across various timesteps and noise levels, a conflict persists regarding the relative difficulties of the denoising tasks. While various studies argue that lower timesteps present more challenging tasks, others contend that higher timesteps are more difficult. To address this conflict, our study undertakes a comprehensive examination of task difficulties, focusing on convergence behavior and changes in relative entropy between consecutive probability distributions across timesteps. Our observational study reveals that denoising at earlier timesteps poses challenges characterized by slower convergence and higher relative entropy, indicating increased task difficulty at these lower timesteps. Building on these observations, we introduce an easy-to-hard learning scheme, drawing from curriculum learning, to enhance the training process of diffusion models. By organizing timesteps or noise levels into clusters and training models with ascending orders of difficulty, we facilitate an order-aware training regime, progressing from easier to harder denoising tasks, thereby deviating from the conventional approach of training diffusion models simultaneously across all timesteps. Our approach leads to improved performance and faster convergence by leveraging benefits of curriculum learning, while maintaining orthogonality with existing improvements in diffusion training techniques. We validate these advantages through comprehensive experiments in image generation tasks, including unconditional, class-conditional, and text-to-image generation.
|
[
"['Jin-Young Kim' 'Hyojun Go' 'Soonwoo Kwon' 'Hyun-Gyoon Kim']"
] |
null | null |
2403.10365
| null | null |
http://arxiv.org/pdf/2403.10365v1
|
2024-03-15T14:58:27Z
|
2024-03-15T14:58:27Z
|
Scalable Algorithms for Individual Preference Stable Clustering
|
In this paper, we study the individual preference (IP) stability, which is an notion capturing individual fairness and stability in clustering. Within this setting, a clustering is $alpha$-IP stable when each data point's average distance to its cluster is no more than $alpha$ times its average distance to any other cluster. In this paper, we study the natural local search algorithm for IP stable clustering. Our analysis confirms a $O(log n)$-IP stability guarantee for this algorithm, where $n$ denotes the number of points in the input. Furthermore, by refining the local search approach, we show it runs in an almost linear time, $tilde{O}(nk)$.
|
[
"['Ron Mosenzon' 'Ali Vakilian']"
] |
null | null |
2403.10368
| null | null |
http://arxiv.org/pdf/2403.10368v1
|
2024-03-15T14:59:24Z
|
2024-03-15T14:59:24Z
|
Conformal Predictions for Probabilistically Robust Scalable Machine
Learning Classification
|
Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the concept of scalable classifier was introduced to generalize the concept of classical classifier by linking it to statistical order theory and probabilistic learning theory. In this paper, we analyze the similarities between scalable classifiers and conformal predictions by introducing a new definition of a score function and defining a special set of input variables, the conformal safety set, which can identify patterns in the input space that satisfy the error coverage guarantee, i.e., that the probability of observing the wrong (possibly unsafe) label for points belonging to this set is bounded by a predefined $varepsilon$ error level. We demonstrate the practical implications of this framework through an application in cybersecurity for identifying DNS tunneling attacks. Our work contributes to the development of probabilistically robust and reliable machine learning models.
|
[
"['Alberto Carlevaro' 'Teodoro Alamo Cantarero' 'Fabrizio Dabbene'\n 'Maurizio Mongelli']"
] |
null | null |
2403.10371
| null | null |
http://arxiv.org/pdf/2403.10371v1
|
2024-03-15T15:01:48Z
|
2024-03-15T15:01:48Z
|
An Energy-Efficient Ensemble Approach for Mitigating Data Incompleteness
in IoT Applications
|
Machine Learning (ML) is becoming increasingly important for IoT-based applications. However, the dynamic and ad-hoc nature of many IoT ecosystems poses unique challenges to the efficacy of ML algorithms. One such challenge is data incompleteness, which is manifested as missing sensor readings. Many factors, including sensor failures and/or network disruption, can cause data incompleteness. Furthermore, most IoT systems are severely power-constrained. It is important that we build IoT-based ML systems that are robust against data incompleteness while simultaneously being energy efficient. This paper presents an empirical study of SECOE - a recent technique for alleviating data incompleteness in IoT - with respect to its energy bottlenecks. Towards addressing the energy bottlenecks of SECOE, we propose ENAMLE - a proactive, energy-aware technique for mitigating the impact of concurrent missing data. ENAMLE is unique in the sense that it builds an energy-aware ensemble of sub-models, each trained with a subset of sensors chosen carefully based on their correlations. Furthermore, at inference time, ENAMLE adaptively alters the number of the ensemble of models based on the amount of missing data rate and the energy-accuracy trade-off. ENAMLE's design includes several novel mechanisms for minimizing energy consumption while maintaining accuracy. We present extensive experimental studies on two distinct datasets that demonstrate the energy efficiency of ENAMLE and its ability to alleviate sensor failures.
|
[
"['Yousef AlShehri' 'Lakshmish Ramaswamy']"
] |
null | null |
2403.10373
| null | null |
http://arxiv.org/pdf/2403.10373v1
|
2024-03-15T15:04:20Z
|
2024-03-15T15:04:20Z
|
Towards a general framework for improving the performance of classifiers
using XAI methods
|
Modern Artificial Intelligence (AI) systems, especially Deep Learning (DL) models, poses challenges in understanding their inner workings by AI researchers. eXplainable Artificial Intelligence (XAI) inspects internal mechanisms of AI models providing explanations about their decisions. While current XAI research predominantly concentrates on explaining AI systems, there is a growing interest in using XAI techniques to automatically improve the performance of AI systems themselves. This paper proposes a general framework for automatically improving the performance of pre-trained DL classifiers using XAI methods, avoiding the computational overhead associated with retraining complex models from scratch. In particular, we outline the possibility of two different learning strategies for implementing this architecture, which we will call auto-encoder-based and encoder-decoder-based, and discuss their key aspects.
|
[
"['Andrea Apicella' 'Salvatore Giugliano' 'Francesco Isgrò'\n 'Roberto Prevete']"
] |
null | null |
2403.10379
| null | null |
http://arxiv.org/pdf/2403.10379v1
|
2024-03-15T15:09:13Z
|
2024-03-15T15:09:13Z
|
Regret Minimization via Saddle Point Optimization
|
A long line of works characterizes the sample complexity of regret minimization in sequential decision-making by min-max programs. In the corresponding saddle-point game, the min-player optimizes the sampling distribution against an adversarial max-player that chooses confusing models leading to large regret. The most recent instantiation of this idea is the decision-estimation coefficient (DEC), which was shown to provide nearly tight lower and upper bounds on the worst-case expected regret in structured bandits and reinforcement learning. By re-parametrizing the offset DEC with the confidence radius and solving the corresponding min-max program, we derive an anytime variant of the Estimation-To-Decisions (E2D) algorithm. Importantly, the algorithm optimizes the exploration-exploitation trade-off online instead of via the analysis. Our formulation leads to a practical algorithm for finite model classes and linear feedback models. We further point out connections to the information ratio, decoupling coefficient and PAC-DEC, and numerically evaluate the performance of E2D on simple examples.
|
[
"['Johannes Kirschner' 'Seyed Alireza Bakhtiari' 'Kushagra Chandak'\n 'Volodymyr Tkachuk' 'Csaba Szepesvári']"
] |
null | null |
2403.10395
| null | null |
http://arxiv.org/pdf/2403.10395v1
|
2024-03-15T15:27:58Z
|
2024-03-15T15:27:58Z
|
Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding
|
Encouraged by the growing availability of pre-trained 2D diffusion models, image-to-3D generation by leveraging Score Distillation Sampling (SDS) is making remarkable progress. Most existing methods combine novel-view lifting from 2D diffusion models which usually take the reference image as a condition while applying hard L2 image supervision at the reference view. Yet heavily adhering to the image is prone to corrupting the inductive knowledge of the 2D diffusion model leading to flat or distorted 3D generation frequently. In this work, we reexamine image-to-3D in a novel perspective and present Isotropic3D, an image-to-3D generation pipeline that takes only an image CLIP embedding as input. Isotropic3D allows the optimization to be isotropic w.r.t. the azimuth angle by solely resting on the SDS loss. The core of our framework lies in a two-stage diffusion model fine-tuning. Firstly, we fine-tune a text-to-3D diffusion model by substituting its text encoder with an image encoder, by which the model preliminarily acquires image-to-image capabilities. Secondly, we perform fine-tuning using our Explicit Multi-view Attention (EMA) which combines noisy multi-view images with the noise-free reference image as an explicit condition. CLIP embedding is sent to the diffusion model throughout the whole process while reference images are discarded once after fine-tuning. As a result, with a single image CLIP embedding, Isotropic3D is capable of generating multi-view mutually consistent images and also a 3D model with more symmetrical and neat content, well-proportioned geometry, rich colored texture, and less distortion compared with existing image-to-3D methods while still preserving the similarity to the reference image to a large extent. The project page is available at https://isotropic3d.github.io/. The code and models are available at https://github.com/pkunliu/Isotropic3D.
|
[
"['Pengkun Liu' 'Yikai Wang' 'Fuchun Sun' 'Jiafang Li' 'Hang Xiao'\n 'Hongxiang Xue' 'Xinzhou Wang']"
] |
null | null |
2403.10403
| null | null |
http://arxiv.org/pdf/2403.10403v1
|
2024-03-15T15:37:04Z
|
2024-03-15T15:37:04Z
|
Energy Correction Model in the Feature Space for Out-of-Distribution
Detection
|
In this work, we study the out-of-distribution (OOD) detection problem through the use of the feature space of a pre-trained deep classifier. We show that learning the density of in-distribution (ID) features with an energy-based models (EBM) leads to competitive detection results. However, we found that the non-mixing of MCMC sampling during the EBM's training undermines its detection performance. To overcome this an energy-based correction of a mixture of class-conditional Gaussian distributions. We obtains favorable results when compared to a strong baseline like the KNN detector on the CIFAR-10/CIFAR-100 OOD detection benchmarks.
|
[
"['Marc Lafon' 'Clément Rambour' 'Nicolas Thome']"
] |
null | null |
2403.10404
| null | null |
http://arxiv.org/pdf/2403.10404v1
|
2024-03-15T15:37:19Z
|
2024-03-15T15:37:19Z
|
A comparative study on machine learning approaches for rock mass
classification using drilling data
|
Current rock engineering design in drill and blast tunnelling primarily relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected during tunnel excavation, is underutilised, mainly serving for geological visualisation. This study aims to automate the translation of MWD data into actionable metrics for rock engineering. It seeks to link data to specific engineering actions, thus providing critical decision support for geological challenges ahead of the tunnel face. Leveraging a large and geologically diverse dataset of 500,000 drillholes from 15 tunnels, the research introduces models for accurate rock mass quality classification in a real-world tunnelling context. Both conventional machine learning and image-based deep learning are explored to classify MWD data into Q-classes and Q-values, examples of metrics describing the stability of the rock mass, using both tabular and image data. The results indicate that the K-nearest neighbours algorithm in an ensemble with tree-based models using tabular data, effectively classifies rock mass quality. It achieves a cross-validated balanced accuracy of 0.86 in classifying rock mass into the Q-classes A, B, C, D, E1, E2, and 0.95 for a binary classification with E versus the rest. Classification using a CNN with MWD-images for each blasting round resulted in a balanced accuracy of 0.82 for binary classification. Regressing the Q-value from tabular MWD-data achieved cross-validated R2 and MSE scores of 0.80 and 0.18 for a similar ensemble model as in classification. High performance in regression and classification boosts confidence in automated rock mass assessment. Applying advanced modelling on a unique dataset demonstrates MWD data's value in improving rock mass classification accuracy and advancing data-driven rock engineering design, reducing manual intervention.
|
[
"['Tom F. Hansen' 'Georg H. Erharter' 'Zhongqiang Liu' 'Jim Torresen']"
] |
null | null |
2403.10408
| null | null |
http://arxiv.org/abs/2403.10408v1
|
2024-03-15T15:43:02Z
|
2024-03-15T15:43:02Z
|
SocialGenPod: Privacy-Friendly Generative AI Social Web Applications
with Decentralised Personal Data Stores
|
We present SocialGenPod, a decentralised and privacy-friendly way of deploying generative AI Web applications. Unlike centralised Web and data architectures that keep user data tied to application and service providers, we show how one can use Solid -- a decentralised Web specification -- to decouple user data from generative AI applications. We demonstrate SocialGenPod using a prototype that allows users to converse with different Large Language Models, optionally leveraging Retrieval Augmented Generation to generate answers grounded in private documents stored in any Solid Pod that the user is allowed to access, directly or indirectly. SocialGenPod makes use of Solid access control mechanisms to give users full control of determining who has access to data stored in their Pods. SocialGenPod keeps all user data (chat history, app configuration, personal documents, etc) securely in the user's personal Pod; separate from specific model or application providers. Besides better privacy controls, this approach also enables portability across different services and applications. Finally, we discuss challenges, posed by the large compute requirements of state-of-the-art models, that future research in this area should address. Our prototype is open-source and available at: https://github.com/Vidminas/socialgenpod/.
|
[
"['Vidminas Vizgirda' 'Rui Zhao' 'Naman Goel']"
] |
null | null |
2403.10416
| null | null |
http://arxiv.org/pdf/2403.10416v1
|
2024-03-15T15:51:27Z
|
2024-03-15T15:51:27Z
|
Robust Sparse Estimation for Gaussians with Optimal Error under Huber
Contamination
|
We study Gaussian sparse estimation tasks in Huber's contamination model with a focus on mean estimation, PCA, and linear regression. For each of these tasks, we give the first sample and computationally efficient robust estimators with optimal error guarantees, within constant factors. All prior efficient algorithms for these tasks incur quantitatively suboptimal error. Concretely, for Gaussian robust $k$-sparse mean estimation on $mathbb{R}^d$ with corruption rate $epsilon>0$, our algorithm has sample complexity $(k^2/epsilon^2)mathrm{polylog}(d/epsilon)$, runs in sample polynomial time, and approximates the target mean within $ell_2$-error $O(epsilon)$. Previous efficient algorithms inherently incur error $Omega(epsilon sqrt{log(1/epsilon)})$. At the technical level, we develop a novel multidimensional filtering method in the sparse regime that may find other applications.
|
[
"['Ilias Diakonikolas' 'Daniel M. Kane' 'Sushrut Karmalkar' 'Ankit Pensia'\n 'Thanasis Pittas']"
] |
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