id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.10467 | YNote: A Novel Music Notation for Fine-Tuning LLMs in Music Generation | [
"cs.SD",
"cs.AI",
"eess.AS"
] | The field of music generation using Large Language Models (LLMs) is evolving rapidly, yet existing music notation systems, such as MIDI, ABC Notation, and MusicXML, remain too complex for effective fine-tuning of LLMs. These formats are difficult for both machines and humans to interpret due to their variability and in... | {
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2502.10470 | MetaDE: Evolving Differential Evolution by Differential Evolution | [
"cs.NE",
"cs.AI"
] | As a cornerstone in the Evolutionary Computation (EC) domain, Differential Evolution (DE) is known for its simplicity and effectiveness in handling challenging black-box optimization problems. While the advantages of DE are well-recognized, achieving peak performance heavily depends on its hyperparameters such as the m... | {
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2502.10473 | Diverse Transformer Decoding for Offline Reinforcement Learning Using
Financial Algorithmic Approaches | [
"cs.AI",
"cs.LG"
] | Offline Reinforcement Learning (RL) algorithms learn a policy using a fixed training dataset, which is then deployed online to interact with the environment and make decisions. Transformers, a standard choice for modeling time-series data, are gaining popularity in offline RL. In this context, Beam Search (BS), an appr... | {
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2502.10475 | X-SG$^2$S: Safe and Generalizable Gaussian Splatting with X-dimensional
Watermarks | [
"cs.CR",
"cs.AI",
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has been widely used in 3D reconstruction and 3D generation. Training to get a 3DGS scene often takes a lot of time and resources and even valuable inspiration. The increasing amount of 3DGS digital asset have brought great challenges to the copyright protection. However, it still lacks pro... | {
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2502.10476 | Multi-Objective Planning with Contextual Lexicographic Reward
Preferences | [
"cs.AI",
"cs.RO",
"cs.SY"
] | Autonomous agents are often required to plan under multiple objectives whose preference ordering varies based on context. The agent may encounter multiple contexts during its course of operation, each imposing a distinct lexicographic ordering over the objectives, with potentially different reward functions associated ... | {
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2502.10477 | Knowledge Integration Strategies in Autonomous Vehicle Prediction and
Planning: A Comprehensive Survey | [
"cs.AI",
"cs.LG"
] | This comprehensive survey examines the integration of knowledge-based approaches into autonomous driving systems, with a focus on trajectory prediction and planning. We systematically review methodologies for incorporating domain knowledge, traffic rules, and commonsense reasoning into these systems, spanning purely sy... | {
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2502.10478 | SinSim: Sinkhorn-Regularized SimCLR | [
"cs.LG",
"cs.CV",
"stat.ML"
] | Self-supervised learning has revolutionized representation learning by eliminating the need for labeled data. Contrastive learning methods, such as SimCLR, maximize the agreement between augmented views of an image but lack explicit regularization to enforce a globally structured latent space. This limitation often lea... | {
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2502.10479 | Lifetime Analysis of Circular $k$-out-of-$n$: G Balanced Systems in a
Shock Environment | [
"eess.SY",
"cs.PF",
"cs.SY",
"math.PR"
] | This paper examines the lifetime distributions of circular $k$-out-of-$n$: G balanced systems operating in a shock environment, providing a unified framework for both discrete- and continuous-time perspectives. The system remains functioning only if at least $k$ operating units satisfy a predefined balance condition (B... | {
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2502.10480 | Safe Multi-agent Satellite Servicing with Control Barrier Functions | [
"eess.SY",
"cs.RO",
"cs.SY"
] | The use of control barrier functions under uncertain pose information of multiple small servicing agents is analyzed for a satellite servicing application. The application consists of modular servicing agents deployed towards a tumbling space object from a mothership. Relative position and orientation of each agent is ... | {
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2502.10481 | Chronic Diseases Prediction Using ML | [
"cs.LG"
] | The recent increase in morbidity is primarily due to chronic diseases including Diabetes, Heart disease, Lung cancer, and brain tumours. The results for patients can be improved, and the financial burden on the healthcare system can be lessened, through the early detection and prevention of certain disorders. In this s... | {
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2502.10482 | A Self-Supervised Reinforcement Learning Approach for Fine-Tuning Large
Language Models Using Cross-Attention Signals | [
"cs.AI"
] | We propose a novel reinforcement learning framework for post training large language models that does not rely on human in the loop feedback. Instead, our approach uses cross attention signals within the model itself to derive a self supervised reward, thereby guiding iterative fine tuning of the model policy. By analy... | {
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2502.10485 | Forecasting time series with constraints | [
"stat.ML",
"cs.AI",
"cs.LG",
"math.ST",
"stat.AP",
"stat.ME",
"stat.TH"
] | Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have incorporated linear constraints into learning algorithms, such as generalized additive models and hierarchical forecasting. In this paper, we ... | {
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2502.10486 | VLM-Guard: Safeguarding Vision-Language Models via Fulfilling Safety
Alignment Gap | [
"cs.CR",
"cs.AI",
"cs.CV"
] | The emergence of vision language models (VLMs) comes with increased safety concerns, as the incorporation of multiple modalities heightens vulnerability to attacks. Although VLMs can be built upon LLMs that have textual safety alignment, it is easily undermined when the vision modality is integrated. We attribute this ... | {
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2502.10487 | Fast Proxies for LLM Robustness Evaluation | [
"cs.CR",
"cs.AI"
] | Evaluating the robustness of LLMs to adversarial attacks is crucial for safe deployment, yet current red-teaming methods are often prohibitively expensive. We compare the ability of fast proxy metrics to predict the real-world robustness of an LLM against a simulated attacker ensemble. This allows us to estimate a mode... | {
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2502.10489 | LiveVal: Time-aware Data Valuation via Adaptive Reference Points | [
"cs.LG",
"cs.AI"
] | Time-aware data valuation enhances training efficiency and model robustness, as early detection of harmful samples could prevent months of wasted computation. However, existing methods rely on model retraining or convergence assumptions or fail to capture long-term training dynamics. We propose LiveVal, an efficient ... | {
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2502.10490 | A Robust Attack: Displacement Backdoor Attack | [
"cs.CR",
"cs.AI",
"cs.CV"
] | As artificial intelligence becomes more prevalent in our lives, people are enjoying the convenience it brings, but they are also facing hidden threats, such as data poisoning and adversarial attacks. These threats can have disastrous consequences for the application of artificial intelligence, especially for some appli... | {
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2502.10491 | F-StrIPE: Fast Structure-Informed Positional Encoding for Symbolic Music
Generation | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | While music remains a challenging domain for generative models like Transformers, recent progress has been made by exploiting suitable musically-informed priors. One technique to leverage information about musical structure in Transformers is inserting such knowledge into the positional encoding (PE) module. However, T... | {
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2502.10492 | Multi-view 3D surface reconstruction from SAR images by inverse
rendering | [
"cs.CV",
"eess.SP"
] | 3D reconstruction of a scene from Synthetic Aperture Radar (SAR) images mainly relies on interferometric measurements, which involve strict constraints on the acquisition process. These last years, progress in deep learning has significantly advanced 3D reconstruction from multiple views in optical imaging, mainly thro... | {
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2502.10495 | SWA-LDM: Toward Stealthy Watermarks for Latent Diffusion Models | [
"cs.CR",
"cs.AI",
"cs.CV",
"cs.LG"
] | In the rapidly evolving landscape of image generation, Latent Diffusion Models (LDMs) have emerged as powerful tools, enabling the creation of highly realistic images. However, this advancement raises significant concerns regarding copyright infringement and the potential misuse of generated content. Current watermarki... | {
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2502.10497 | Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG,
LoRA and DoRA | [
"cs.CL",
"cs.AI"
] | Recent advancements in Generative AI have significantly improved the efficiency and adaptability of natural language processing (NLP) systems, particularly through Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA), and Weight-Decomposed Low-Rank Adaptation (DoRA). RAG integrates external knowledge to enh... | {
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2502.10498 | The Role of World Models in Shaping Autonomous Driving: A Comprehensive
Survey | [
"cs.CV"
] | Driving World Model (DWM), which focuses on predicting scene evolution during the driving process, has emerged as a promising paradigm in pursuing autonomous driving. These methods enable autonomous driving systems to better perceive, understand, and interact with dynamic driving environments. In this survey, we provid... | {
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2502.10505 | Preference learning made easy: Everything should be understood through
win rate | [
"cs.LG",
"cs.CL",
"stat.ML"
] | Preference learning, or the task of aligning generative models to preference comparison data, has yet to reach the conceptual maturity of classification, density estimation, etc. To close this gap, this work presents a framework to understand preference learning starting from the sampling distribution of pairwise prefe... | {
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2502.10510 | MixMin: Finding Data Mixtures via Convex Minimization | [
"cs.LG",
"stat.ML"
] | Modern machine learning pipelines are increasingly combining and mixing data from diverse and disparate sources, e.g., pre-training large language models. Yet, finding the optimal data mixture is a challenging and open problem. We formalize this data mixing problem as a bi-level objective: the best mixture is the one t... | {
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2502.10514 | Applying Deep Learning to Ads Conversion Prediction in Last Mile
Delivery Marketplace | [
"cs.LG"
] | Deep neural networks (DNNs) have revolutionized web-scale ranking systems, enabling breakthroughs in capturing complex user behaviors and driving performance gains. At DoorDash, we first harnessed this transformative power by transitioning our homepage Ads ranking system from traditional tree based models to cutting ed... | {
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2502.10517 | KernelBench: Can LLMs Write Efficient GPU Kernels? | [
"cs.LG",
"cs.AI",
"cs.PF",
"cs.SE"
] | Efficient GPU kernels are crucial for building performant machine learning architectures, but writing them is a time-consuming challenge that requires significant expertise; therefore, we explore using language models (LMs) to automate kernel generation. We introduce KernelBench, an open-source framework for evaluating... | {
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2502.10522 | GraphiT: Efficient Node Classification on Text-Attributed Graphs with
Prompt Optimized LLMs | [
"cs.AI",
"cs.LG"
] | The application of large language models (LLMs) to graph data has attracted a lot of attention recently. LLMs allow us to use deep contextual embeddings from pretrained models in text-attributed graphs, where shallow embeddings are often used for the text attributes of nodes. However, it is still challenging to efficie... | {
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2502.10525 | Towards Watermarking of Open-Source LLMs | [
"cs.CR",
"cs.LG"
] | While watermarks for closed LLMs have matured and have been included in large-scale deployments, these methods are not applicable to open-source models, which allow users full control over the decoding process. This setting is understudied yet critical, given the rising performance of open-source models. In this work, ... | {
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2502.10526 | Tempo: Helping Data Scientists and Domain Experts Collaboratively
Specify Predictive Modeling Tasks | [
"cs.HC",
"cs.AI"
] | Temporal predictive models have the potential to improve decisions in health care, public services, and other domains, yet they often fail to effectively support decision-makers. Prior literature shows that many misalignments between model behavior and decision-makers' expectations stem from issues of model specificati... | {
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2502.10533 | Expert-Agnostic Learning to Defer | [
"cs.LG",
"cs.HC"
] | Learning to Defer (L2D) learns autonomous systems to independently manage straightforward cases, while deferring uncertain cases to human experts. Recent advancements in this field have introduced features enabling flexibility to unseen experts at test-time, but we find these approaches have significant limitations. To... | {
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2502.10536 | PolyPath: Adapting a Large Multimodal Model for Multi-slide Pathology
Report Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The interpretation of histopathology cases underlies many important diagnostic and treatment decisions in medicine. Notably, this process typically requires pathologists to integrate and summarize findings across multiple slides per case. Existing vision-language capabilities in computational pathology have so far been... | {
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2502.10538 | Amortized Locally Decodable Codes | [
"cs.IT",
"cs.CR",
"math.IT"
] | Locally Decodable Codes (LDCs) are error correcting codes that admit efficient decoding of individual message symbols without decoding the entire message. Unfortunately, known LDC constructions offer a sub-optimal trade-off between rate, error tolerance and locality, the number of queries that the decoder must make to ... | {
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2502.10540 | From Deep Additive Kernel Learning to Last-Layer Bayesian Neural
Networks via Induced Prior Approximation | [
"cs.LG",
"stat.ML"
] | With the strengths of both deep learning and kernel methods like Gaussian Processes (GPs), Deep Kernel Learning (DKL) has gained considerable attention in recent years. From the computational perspective, however, DKL becomes challenging when the input dimension of the GP layer is high. To address this challenge, we pr... | {
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2502.10546 | Learning to be Smooth: An End-to-End Differentiable Particle Smoother | [
"cs.LG",
"cs.AI",
"cs.RO"
] | For challenging state estimation problems arising in domains like vision and robotics, particle-based representations attractively enable temporal reasoning about multiple posterior modes. Particle smoothers offer the potential for more accurate offline data analysis by propagating information both forward and backward... | {
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2502.10547 | A standardised platform for translational advances in fluidic soft
systems | [
"cs.RO"
] | Soft machines are poised to deliver significant real-world impact, with soft robotics emerging as a key sub-discipline. This field integrates biological inspiration, materials science, and embodied intelligence to create bio-robotic hybrids, blurring the boundary between engineered systems and biology. Over the past 15... | {
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2502.10550 | Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with
Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Memory is crucial for enabling agents to tackle complex tasks with temporal and spatial dependencies. While many reinforcement learning (RL) algorithms incorporate memory, the field lacks a universal benchmark to assess an agent's memory capabilities across diverse scenarios. This gap is particularly evident in tableto... | {
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2502.10552 | Synthesis of Dynamic Masks for Information-Theoretic Opacity in
Stochastic Systems | [
"eess.SY",
"cs.AI",
"cs.RO",
"cs.SY"
] | In this work, we investigate the synthesis of dynamic information releasing mechanisms, referred to as ''masks'', to minimize information leakage from a stochastic system to an external observer. Specifically, for a stochastic system, an observer aims to infer whether the final state of the system trajectory belongs to... | {
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2502.10554 | Benchmarking the rationality of AI decision making using the
transitivity axiom | [
"cs.AI"
] | Fundamental choice axioms, such as transitivity of preference, provide testable conditions for determining whether human decision making is rational, i.e., consistent with a utility representation. Recent work has demonstrated that AI systems trained on human data can exhibit similar reasoning biases as humans and that... | {
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2502.10556 | Recent Advances in Malware Detection: Graph Learning and Explainability | [
"cs.CR",
"cs.LG"
] | The rapid evolution of malware has necessitated the development of sophisticated detection methods that go beyond traditional signature-based approaches. Graph learning techniques have emerged as powerful tools for modeling and analyzing the complex relationships inherent in malware behavior, leveraging advancements in... | {
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2502.10557 | Can Large Language Model Agents Balance Energy Systems? | [
"eess.SY",
"cs.SY"
] | This paper presents a hybrid approach that integrates Large Language Models (LLMs) with a multi-scenario Stochastic Unit Commitment (SUC) framework, focusing on both efficiency and reliability under high wind generation uncertainties. Numerical experiments on small-to-medium-sized test systems show that while the tradi... | {
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2502.10559 | SAMRI-2: A Memory-based Model for Cartilage and Meniscus Segmentation in
3D MRIs of the Knee Joint | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Accurate morphometric assessment of cartilage-such as thickness/volume-via MRI is essential for monitoring knee osteoarthritis. Segmenting cartilage remains challenging and dependent on extensive expert-annotated datasets, which are heavily subjected to inter-reader variability. Recent advancements in Visual Foundation... | {
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2502.10562 | Detecting and Monitoring Bias for Subgroups in Breast Cancer Detection
AI | [
"cs.CV",
"cs.LG"
] | Automated mammography screening plays an important role in early breast cancer detection. However, current machine learning models, developed on some training datasets, may exhibit performance degradation and bias when deployed in real-world settings. In this paper, we analyze the performance of high-performing AI mode... | {
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2502.10563 | Accelerating Unbiased LLM Evaluation via Synthetic Feedback | [
"cs.LG",
"cs.CL"
] | When developing new large language models (LLMs), a key step is evaluating their final performance, often by computing the win-rate against a reference model based on external feedback. Human feedback is the gold standard, particularly for capturing nuanced qualities like coherence, readability, and alignment with huma... | {
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2502.10564 | Efficient Stabilization of Hybrid Coulomb Spacecraft Formations using
Control Lyapunov Functions | [
"math.OC",
"cs.SY",
"eess.SY"
] | A control allocation algorithm using control Lyapunov functions to determine stabilizing charges and thrusts of hybrid Coulomb spacecraft formations (HCSFs) is presented. The goal is to stabilize a desired configuration while minimizing the thruster actuation and maximizing Coulomb actuation to minimize propellant usag... | {
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2502.10567 | Efficient Hierarchical Contrastive Self-supervising Learning for Time
Series Classification via Importance-aware Resolution Selection | [
"cs.LG",
"cs.AI"
] | Recently, there has been a significant advancement in designing Self-Supervised Learning (SSL) frameworks for time series data to reduce the dependency on data labels. Among these works, hierarchical contrastive learning-based SSL frameworks, which learn representations by contrasting data embeddings at multiple resolu... | {
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2502.10568 | Observer-Aware Probabilistic Planning Under Partial Observability | [
"cs.AI"
] | In this article, we are interested in planning problems where the agent is aware of the presence of an observer, and where this observer is in a partial observability situation. The agent has to choose its strategy so as to optimize the information transmitted by observations. Building on observer-aware Markov decision... | {
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2502.10569 | HADL Framework for Noise Resilient Long-Term Time Series Forecasting | [
"cs.LG",
"cs.AI"
] | Long-term time series forecasting is critical in domains such as finance, economics, and energy, where accurate and reliable predictions over extended horizons drive strategic decision-making. Despite the progress in machine learning-based models, the impact of temporal noise in extended lookback windows remains undere... | {
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2502.10570 | Quantifying the Impact of Motion on 2D Gaze Estimation in Real-World
Mobile Interactions | [
"cs.HC",
"cs.CV"
] | Mobile gaze tracking involves inferring a user's gaze point or direction on a mobile device's screen from facial images captured by the device's front camera. While this technology inspires an increasing number of gaze-interaction applications, achieving consistent accuracy remains challenging due to dynamic user-devic... | {
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2502.10573 | An Innovative Next Activity Prediction Approach Using Process Entropy
and DAW-Transformer | [
"cs.LG",
"cs.AI"
] | Purpose - In Business Process Management (BPM), accurate prediction of the next activities is vital for operational efficiency and decision-making. Current Artificial Intelligence (AI)/Machine Learning (ML) models struggle with the complexity and evolving nature of business process event logs, balancing accuracy and in... | {
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2502.10574 | Classifier-free Guidance with Adaptive Scaling | [
"cs.CV"
] | Classifier-free guidance (CFG) is an essential mechanism in contemporary text-driven diffusion models. In practice, in controlling the impact of guidance we can see the trade-off between the quality of the generated images and correspondence to the prompt. When we use strong guidance, generated images fit the condition... | {
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2502.10577 | Man Made Language Models? Evaluating LLMs' Perpetuation of Masculine
Generics Bias | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have been shown to propagate and even amplify gender bias, in English and other languages, in specific or constrained contexts. However, no studies so far have focused on gender biases conveyed by LLMs' responses to generic instructions, especially with regard to masculine generics (MG). MG... | {
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2502.10581 | Do We Need to Verify Step by Step? Rethinking Process Supervision from a
Theoretical Perspective | [
"cs.LG",
"cs.AI",
"stat.ML"
] | As large language models have evolved, it has become crucial to distinguish between process supervision and outcome supervision -- two key reinforcement learning approaches to complex reasoning tasks. While process supervision offers intuitive advantages for long-term credit assignment, the precise relationship between... | {
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2502.10582 | Named entity recognition for Serbian legal documents: Design,
methodology and dataset development | [
"cs.CL"
] | Recent advancements in the field of natural language processing (NLP) and especially large language models (LLMs) and their numerous applications have brought research attention to design of different document processing tools and enhancements in the process of document archiving, search and retrieval. Domain of offici... | {
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2502.10585 | Prediction uncertainty-aware planning using deep ensembles and
trajectory optimisation | [
"cs.RO"
] | Human motion is stochastic and ensuring safe robot navigation in a pedestrian-rich environment requires proactive decision-making. Past research relied on incorporating deterministic future states of surrounding pedestrians which can be overconfident leading to unsafe robot behaviour. The current paper proposes a predi... | {
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2502.10587 | Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic
Regression | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Deep heteroscedastic regression models the mean and covariance of the target distribution through neural networks. The challenge arises from heteroscedasticity, which implies that the covariance is sample dependent and is often unknown. Consequently, recent methods learn the covariance through unsupervised frameworks, ... | {
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2502.10596 | Post-training an LLM for RAG? Train on Self-Generated Demonstrations | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) often struggle with knowledge intensive NLP tasks, such as answering "Who won the latest World Cup?" because the knowledge they learn during training may be insufficient or outdated. Conditioning generation on retrieved documents -- a technique known as retrieval augmented generation (RAG) ... | {
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2502.10597 | BLI: A High-performance Bucket-based Learned Index with Concurrency
Support | [
"cs.DB"
] | Learned indexes are promising to replace traditional tree-based indexes. They typically employ machine learning models to efficiently predict target positions in strictly sorted linear arrays. However, the strict sorted order 1) significantly increases insertion overhead, 2) makes it challenging to support lock-free co... | {
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2502.10599 | Federated Learning-Driven Cybersecurity Framework for IoT Networks with
Privacy-Preserving and Real-Time Threat Detection Capabilities | [
"cs.CR",
"cs.LG",
"cs.NI"
] | The rapid expansion of the Internet of Things (IoT) ecosystem has transformed various sectors but has also introduced significant cybersecurity challenges. Traditional centralized security methods often struggle to balance privacy preservation and real-time threat detection in IoT networks. To address these issues, thi... | {
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2502.10600 | Weighted quantization using MMD: From mean field to mean shift via
gradient flows | [
"stat.ML",
"cs.LG",
"cs.NA",
"math.NA"
] | Approximating a probability distribution using a set of particles is a fundamental problem in machine learning and statistics, with applications including clustering and quantization. Formally, we seek a finite weighted mixture of Dirac measures that best approximates the target distribution. While much existing work r... | {
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} |
2502.10601 | Data-driven Super-Resolution of Flood Inundation Maps using Synthetic
Simulations | [
"cs.CV",
"cs.LG"
] | The frequency of extreme flood events is increasing throughout the world. Daily, high-resolution (30m) Flood Inundation Maps (FIM) observed from space play a key role in informing mitigation and preparedness efforts to counter these extreme events. However, the temporal frequency of publicly available high-resolution F... | {
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2502.10603 | Adaptive Neural Networks for Intelligent Data-Driven Development | [
"cs.CV"
] | Advances in machine learning methods for computer vision tasks have led to their consideration for safety-critical applications like autonomous driving. However, effectively integrating these methods into the automotive development lifecycle remains challenging. Since the performance of machine learning algorithms reli... | {
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2502.10605 | Batch-Adaptive Annotations for Causal Inference with Complex-Embedded
Outcomes | [
"stat.ML",
"cs.LG"
] | Estimating the causal effects of an intervention on outcomes is crucial. But often in domains such as healthcare and social services, this critical information about outcomes is documented by unstructured text, e.g. clinical notes in healthcare or case notes in social services. For example, street outreach to homeless ... | {
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2502.10606 | HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose
Estimation | [
"cs.CV",
"cs.RO"
] | This work focuses on model-free zero-shot 6D object pose estimation for robotics applications. While existing methods can estimate the precise 6D pose of objects, they heavily rely on curated CAD models or reference images, the preparation of which is a time-consuming and labor-intensive process. Moreover, in real-worl... | {
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2502.10608 | Universal Lesion Segmentation Challenge 2023: A Comparative Research of
Different Algorithms | [
"cs.CV",
"cs.LG"
] | In recent years, machine learning algorithms have achieved much success in segmenting lesions across various tissues. There is, however, not one satisfying model that works well on all tissue types universally. In response to this need, we attempt to train a model that 1) works well on all tissue types, and 2) is capab... | {
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2502.10610 | Reachability-Aware Reinforcement Learning for Collision Avoidance in
Human-Machine Shared Control | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Human-machine shared control in critical collision scenarios aims to aid drivers' accident avoidance through intervening only when necessary. Existing methods count on replanning collision-free trajectories and imposing human-machine tracking, which usually interrupts the driver's intent and increases the risk of confl... | {
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2502.10611 | Demonstration of a planar multimodal periodic filter at THz frequencies | [
"physics.app-ph",
"cs.SY",
"eess.SY"
] | This paper presents a planar multimodal periodic filter that is constructed from alternating sections of coplanar stripline and the odd-mode of a finite-ground plane coplanar waveguide constructed on a 1 um silicon nitride substrate to facilitate operation at THz frequencies. The multimode configuration differs from st... | {
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2502.10614 | Optimizing CNN Architectures for Advanced Thoracic Disease
Classification | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Machine learning, particularly convolutional neural networks (CNNs), has shown promise in medical image analysis, especially for thoracic disease detection using chest X-ray images. In this study, we evaluate various CNN architectures, including binary classification, multi-label classification, and ResNet50 models, to... | {
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2502.10615 | Retrieval-augmented Encoders for Extreme Multi-label Text Classification | [
"cs.CL"
] | Extreme multi-label classification (XMC) seeks to find relevant labels from an extremely large label collection for a given text input. To tackle such a vast label space, current state-of-the-art methods fall into two categories. The one-versus-all (OVA) method uses learnable label embeddings for each label, excelling ... | {
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2502.10616 | Learning semantical dynamics and spatiotemporal collaboration for human
pose estimation in video | [
"cs.CV"
] | Temporal modeling and spatio-temporal collaboration are pivotal techniques for video-based human pose estimation. Most state-of-the-art methods adopt optical flow or temporal difference, learning local visual content correspondence across frames at the pixel level, to capture motion dynamics. However, such a paradigm e... | {
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2502.10620 | ProMRVL-CAD: Proactive Dialogue System with Multi-Round Vision-Language
Interactions for Computer-Aided Diagnosis | [
"cs.AI"
] | Recent advancements in large language models (LLMs) have demonstrated extraordinary comprehension capabilities with remarkable breakthroughs on various vision-language tasks. However, the application of LLMs in generating reliable medical diagnostic reports remains in the early stages. Currently, medical LLMs typically... | {
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2502.10624 | Network evasion detection with Bi-LSTM model | [
"cs.CR",
"cs.AI"
] | Network evasion detection aims to distinguish whether the network flow comes from link layer exists network evasion threat, which is a means to disguise the data traffic on detection system by confusing the signature. Since the previous research works has all sorts of frauds, we propose a architecture with deep learnin... | {
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2502.10626 | K-Edit: Language Model Editing with Contextual Knowledge Awareness | [
"cs.LG",
"cs.AI"
] | As the world changes, we need to be able to update our models and correct false information without costly retraining. Knowledge-based model editing enables precise modifications to the weights of large language models in order to modify the information encoded within. Recent approaches have seen success in enabling re... | {
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2502.10628 | On Self-Adaptive Perception Loss Function for Sequential Lossy
Compression | [
"cs.LG",
"cs.IT",
"math.IT"
] | We consider causal, low-latency, sequential lossy compression, with mean squared-error (MSE) as the distortion loss, and a perception loss function (PLF) to enhance the realism of reconstructions. As the main contribution, we propose and analyze a new PLF that considers the joint distribution between the current source... | {
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2502.10631 | ControllableGPT: A Ground-Up Designed Controllable GPT for Molecule
Optimization | [
"cs.LG",
"cs.AI",
"q-bio.BM"
] | Large Language Models (LLMs) employ three popular training approaches: Masked Language Models (MLM), Causal Language Models (CLM), and Sequence-to-Sequence Models (seq2seq). However, each approach has its strengths and limitations, and faces challenges in addressing specific tasks that require controllable and bidirect... | {
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2502.10632 | Code-Mixed Telugu-English Hate Speech Detection | [
"cs.CL"
] | Hate speech detection in low-resource languages like Telugu is a growing challenge in NLP. This study investigates transformer-based models, including TeluguHateBERT, HateBERT, DeBERTa, Muril, IndicBERT, Roberta, and Hindi-Abusive-MuRIL, for classifying hate speech in Telugu. We fine-tune these models using Low-Rank Ad... | {
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2502.10634 | Lost in the Passage: Passage-level In-context Learning Does Not
Necessarily Need a "Passage" | [
"cs.CL"
] | By simply incorporating demonstrations into the context, in-context learning (ICL) enables large language models (LLMs) to yield awesome performance on many tasks. In this paper, we focus on passage-level long-context ICL for generation tasks and find that LLMs cannot learn the intrinsic relationships between the demon... | {
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2502.10635 | Privacy Preservation through Practical Machine Unlearning | [
"cs.LG",
"cs.CR"
] | Machine Learning models thrive on vast datasets, continuously adapting to provide accurate predictions and recommendations. However, in an era dominated by privacy concerns, Machine Unlearning emerges as a transformative approach, enabling the selective removal of data from trained models. This paper examines methods s... | {
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2502.10636 | USER-VLM 360: Personalized Vision Language Models with User-aware Tuning
for Social Human-Robot Interactions | [
"cs.AI",
"cs.HC",
"cs.RO"
] | The integration of vision-language models into robotic systems constitutes a significant advancement in enabling machines to interact with their surroundings in a more intuitive manner. While VLMs offer rich multimodal reasoning, existing approaches lack user-specific adaptability, often relying on generic interaction ... | {
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2502.10637 | Proof of Response | [
"cs.DC",
"cs.AI",
"cs.CR"
] | We present a mechanism that for a network of participants allows one participant of the network (Alice) to request some data from another participant (Bob) and either receive a response from Bob within a known-in-advance, bounded time b, or receive a proof that at least one edge on the way to Bob was broken within b, o... | {
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2502.10639 | LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical
Retrieval | [
"cs.IR"
] | This paper studies fast fusion of dense retrieval and sparse lexical retrieval, and proposes a cluster-based selective dense retrieval method called CluSD guided by sparse lexical retrieval. CluSD takes a lightweight cluster-based approach and exploits the overlap of sparse retrieval results and embedding clusters in a... | {
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2502.10641 | Toward Equitable Access: Leveraging Crowdsourced Reviews to Investigate
Public Perceptions of Health Resource Accessibility | [
"cs.CL"
] | Access to health resources is a critical determinant of public well-being and societal resilience, particularly during public health crises when demand for medical services and preventive care surges. However, disparities in accessibility persist across demographic and geographic groups, raising concerns about equity. ... | {
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2502.10642 | Demographic User Modeling for Social Robotics with Multimodal
Pre-trained Models | [
"cs.AI",
"cs.CV"
] | This paper investigates the performance of multimodal pre-trained models in user profiling tasks based on visual-linguistic demographic data. These models are critical for adapting to the needs and preferences of human users in social robotics, thereby providing personalized responses and enhancing interaction quality.... | {
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2502.10645 | BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop | [
"cs.CL"
] | BabyLM aims to dissolve the boundaries between cognitive modeling and language modeling. We call for both workshop papers and for researchers to join the 3rd BabyLM competition. As in previous years, we call for participants in the data-efficient pretraining challenge in the general track. This year, we also offer a ne... | {
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2502.10646 | Dark Deceptions in DHCP: Dismantling Network Defenses | [
"cs.CR",
"cs.LG"
] | This paper explores vulnerabilities in the Dynamic Host Configuration Protocol (DHCP) and their implications on the Confidentiality, Integrity, and Availability (CIA) triad. Through an analysis of various attacks, including DHCP Starvation, Rogue DHCP Servers, Replay Attacks, and TunnelVision exploits, the paper provid... | {
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2502.10647 | A Power Transform | [
"cs.LG",
"stat.ML",
"stat.TH"
] | Power transforms, such as the Box-Cox transform and Tukey's ladder of powers, are a fundamental tool in mathematics and statistics. These transforms are primarily used for normalizing and standardizing datasets, effectively by raising values to a power. In this work I present a novel power transform, and I show that it... | {
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2502.10648 | LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and
Regularization | [
"cs.LG",
"stat.ML"
] | We introduce LLM-Lasso, a novel framework that leverages large language models (LLMs) to guide feature selection in Lasso $\ell_1$ regression. Unlike traditional methods that rely solely on numerical data, LLM-Lasso incorporates domain-specific knowledge extracted from natural language, enhanced through a retrieval-aug... | {
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2502.10650 | Generative Adversarial Networks for High-Dimensional Item Factor
Analysis: A Deep Adversarial Learning Algorithm | [
"stat.ML",
"cs.LG",
"stat.AP",
"stat.CO",
"stat.ME"
] | Advances in deep learning and representation learning have transformed item factor analysis (IFA) in the item response theory (IRT) literature by enabling more efficient and accurate parameter estimation. Variational Autoencoders (VAEs) have been one of the most impactful techniques in modeling high-dimensional latent ... | {
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2502.10652 | Deep Learning for Wound Tissue Segmentation: A Comprehensive Evaluation
using A Novel Dataset | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Deep learning (DL) techniques have emerged as promising solutions for medical wound tissue segmentation. However, a notable limitation in this field is the lack of publicly available labelled datasets and a standardised performance evaluation of state-of-the-art DL models on such datasets. This study addresses this gap... | {
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2502.10660 | User Profile with Large Language Models: Construction, Updating, and
Benchmarking | [
"cs.CL"
] | User profile modeling plays a key role in personalized systems, as it requires building accurate profiles and updating them with new information. In this paper, we present two high-quality open-source user profile datasets: one for profile construction and another for profile updating. These datasets offer a strong bas... | {
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2502.10662 | Towards Zero-Shot Task-Generalizable Learning on fMRI | [
"eess.IV",
"cs.LG"
] | Functional MRI measuring BOLD signal is an increasingly important imaging modality in studying brain functions and neurological disorders. It can be acquired in either a resting-state or a task-based paradigm. Compared to resting-state fMRI, task-based fMRI is acquired while the subject is performing a specific task de... | {
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2502.10667 | Automated Data Quality Validation in an End-to-End GNN Framework | [
"cs.DB"
] | Ensuring data quality is crucial in modern data ecosystems, especially for training or testing datasets in machine learning. Existing validation approaches rely on computing data quality metrics and/or using expert-defined constraints. Although there are automated constraint generation methods, they are often incomplet... | {
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"cs.SY": 0
} |
2502.10669 | Is Self-Supervised Pre-training on Satellite Imagery Better than
ImageNet? A Systematic Study with Sentinel-2 | [
"cs.CV"
] | Self-supervised learning (SSL) has demonstrated significant potential in pre-training robust models with limited labeled data, making it particularly valuable for remote sensing (RS) tasks. A common assumption is that pre-training on domain-aligned data provides maximal benefits on downstream tasks, particularly when c... | {
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} |
2502.10671 | Evaluating Beam Sweeping for AoA Estimation with an RIS Prototype:
Indoor/Outdoor Field Trials | [
"cs.IT",
"cs.ET",
"math.IT"
] | Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology to enhance wireless communication systems by enabling dynamic control over the propagation environment. However, practical experiments are crucial towards the validation of the theoretical potential of RISs while establishing their real-w... | {
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} |
2502.10673 | Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs | [
"cs.CR",
"cs.CL"
] | Retrieval-Augmented Generation (RAG) has become an effective method for enhancing large language models (LLMs) with up-to-date knowledge. However, it poses a significant risk of IP infringement, as IP datasets may be incorporated into the knowledge database by malicious Retrieval-Augmented LLMs (RA-LLMs) without author... | {
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} |
2502.10674 | Occlusion-aware Text-Image-Point Cloud Pretraining for Open-World 3D
Object Recognition | [
"cs.CV"
] | Recent open-world representation learning approaches have leveraged CLIP to enable zero-shot 3D object recognition. However, performance on real point clouds with occlusions still falls short due to the unrealistic pretraining settings. Additionally, these methods incur high inference costs because they rely on Transfo... | {
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} |
2502.10675 | Hierarchically-Structured Open-Vocabulary Indoor Scene Synthesis with
Pre-trained Large Language Model | [
"cs.CV"
] | Indoor scene synthesis aims to automatically produce plausible, realistic and diverse 3D indoor scenes, especially given arbitrary user requirements. Recently, the promising generalization ability of pre-trained large language models (LLM) assist in open-vocabulary indoor scene synthesis. However, the challenge lies in... | {
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} |
2502.10677 | FocalCount: Towards Class-Count Imbalance in Class-Agnostic Counting | [
"cs.CV"
] | In class-agnostic object counting, the goal is to estimate the total number of object instances in an image without distinguishing between specific categories. Existing methods often predict this count without considering class-specific outputs, leading to inaccuracies when such outputs are required. These inaccuracies... | {
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} |
2502.10678 | GenComUI: Exploring Generative Visual Aids as Medium to Support
Task-Oriented Human-Robot Communication | [
"cs.HC",
"cs.AI",
"cs.RO"
] | This work investigates the integration of generative visual aids in human-robot task communication. We developed GenComUI, a system powered by large language models that dynamically generates contextual visual aids (such as map annotations, path indicators, and animations) to support verbal task communication and facil... | {
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} |
2502.10682 | Hybrid Deepfake Image Detection: A Comprehensive Dataset-Driven Approach
Integrating Convolutional and Attention Mechanisms with Frequency Domain
Features | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Effective deepfake detection tools are becoming increasingly essential over the last few years due to the growing usage of deepfakes in unethical practices. There exists a diverse range of deepfake generation techniques, which makes it challenging to develop an accurate universal detection mechanism. The 2025 Signal Pr... | {
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} |
2502.10683 | CLoCKDistill: Consistent Location-and-Context-aware Knowledge
Distillation for DETRs | [
"cs.CV"
] | Object detection has advanced significantly with Detection Transformers (DETRs). However, these models are computationally demanding, posing challenges for deployment in resource-constrained environments (e.g., self-driving cars). Knowledge distillation (KD) is an effective compression method widely applied to CNN dete... | {
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} |
2502.10684 | A Fast Quantum Image Compression Algorithm based on Taylor Expansion | [
"quant-ph",
"cs.CV"
] | With the increasing demand for storing images, traditional image compression methods face challenges in balancing the compressed size and image quality. However, the hybrid quantum-classical model can recover this weakness by using the advantage of qubits. In this study, we upgrade a quantum image compression algorithm... | {
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} |
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