id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.13968 | Betsu-Betsu: Multi-View Separable 3D Reconstruction of Two Interacting
Objects | [
"cs.CV"
] | Separable 3D reconstruction of multiple objects from multi-view RGB images -- resulting in two different 3D shapes for the two objects with a clear separation between them -- remains a sparsely researched problem. It is challenging due to severe mutual occlusions and ambiguities along the objects' interaction boundarie... | {
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2502.13969 | Bridging Simulation and Reality: A 3D Clustering-Based Deep Learning
Model for UAV-Based RF Source Localization | [
"eess.SP",
"cs.AI"
] | Localization of radio frequency (RF) sources has critical applications, including search and rescue, jammer detection, and monitoring of hostile activities. Unmanned aerial vehicles (UAVs) offer significant advantages for RF source localization (RFSL) over terrestrial methods, leveraging autonomous 3D navigation and im... | {
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2502.13972 | IncepFormerNet: A multi-scale multi-head attention network for SSVEP
classification | [
"eess.SP",
"cs.AI",
"cs.LG"
] | In recent years, deep learning (DL) models have shown outstanding performance in EEG classification tasks, particularly in Steady-State Visually Evoked Potential(SSVEP)-based Brain-Computer-Interfaces(BCI)systems. DL methods have been successfully applied to SSVEP-BCI. This study proposes a new model called IncepFormer... | {
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2502.13974 | Segmentation-free integration of nuclei morphology and spatial
transcriptomics for retinal images | [
"eess.IV",
"cs.CV"
] | This study introduces SEFI (SEgmentation-Free Integration), a novel method for integrating morphological features of cell nuclei with spatial transcriptomics data. Cell segmentation poses a significant challenge in the analysis of spatial transcriptomics data, as tissue-specific structural complexities and densely pack... | {
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2502.13976 | Regulariza\c{c}\~ao, aprendizagem profunda e interdisciplinaridade em
problemas inversos mal-postos | [
"eess.IV",
"cs.LG"
] | In this book, written in Portuguese, we discuss what ill-posed problems are and how the regularization method is used to solve them. In the form of questions and answers, we reflect on the origins and future of regularization, relating the similarities and differences of its meaning in different areas, including invers... | {
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2502.13979 | Utilizing Effective Dynamic Graph Learning to Shield Financial Stability
from Risk Propagation | [
"q-fin.RM",
"cs.AI",
"cs.LG"
] | Financial risks can propagate across both tightly coupled temporal and spatial dimensions, posing significant threats to financial stability. Moreover, risks embedded in unlabeled data are often difficult to detect. To address these challenges, we introduce GraphShield, a novel approach with three key innovations: Enha... | {
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2502.13982 | Benchmarking Automatic Speech Recognition coupled LLM Modules for
Medical Diagnostics | [
"eess.AS",
"cs.LG"
] | Natural Language Processing (NLP) and Voice Recognition agents are rapidly evolving healthcare by enabling efficient, accessible, and professional patient support while automating grunt work. This report serves as my self project wherein models finetuned on medical call recordings are analysed through a two-stage syste... | {
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2502.13983 | Gesture-Aware Zero-Shot Speech Recognition for Patients with Language
Disorders | [
"eess.AS",
"cs.AI"
] | Individuals with language disorders often face significant communication challenges due to their limited language processing and comprehension abilities, which also affect their interactions with voice-assisted systems that mostly rely on Automatic Speech Recognition (ASR). Despite advancements in ASR that address disf... | {
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2502.13990 | Remote Sensing Semantic Segmentation Quality Assessment based on Vision
Language Model | [
"eess.IV",
"cs.LG"
] | The complexity of scenes and variations in image quality result in significant variability in the performance of semantic segmentation methods of remote sensing imagery (RSI) in supervised real-world scenarios. This makes the evaluation of semantic segmentation quality in such scenarios an issue to be resolved. However... | {
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2502.13991 | Learning to Discover Regulatory Elements for Gene Expression Prediction | [
"q-bio.GN",
"cs.AI"
] | We consider the problem of predicting gene expressions from DNA sequences. A key challenge of this task is to find the regulatory elements that control gene expressions. Here, we introduce Seq2Exp, a Sequence to Expression network explicitly designed to discover and extract regulatory elements that drive target gene ex... | {
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2502.13994 | Generative Detail Enhancement for Physically Based Materials | [
"cs.GR",
"cs.AI"
] | We present a tool for enhancing the detail of physically based materials using an off-the-shelf diffusion model and inverse rendering. Our goal is to enhance the visual fidelity of materials with detail that is often tedious to author, by adding signs of wear, aging, weathering, etc. As these appearance details are oft... | {
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2502.13996 | Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning
with Cognitive Diagnosis | [
"cs.LG"
] | Due to the widespread use of LLMs and the rising critical ethical and safety concerns, LLM unlearning methods have been developed to remove harmful knowledge and undesirable capabilities. In this context, evaluations are mostly based on single-value metrics such as QA accuracy. However, these metrics often fail to capt... | {
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2502.13998 | A Baseline Method for Removing Invisible Image Watermarks using Deep
Image Prior | [
"eess.IV",
"cs.AI"
] | Image watermarks have been considered a promising technique to help detect AI-generated content, which can be used to protect copyright or prevent fake image abuse. In this work, we present a black-box method for removing invisible image watermarks, without the need of any dataset of watermarked images or any knowledge... | {
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2502.14000 | Human-Artificial Interaction in the Age of Agentic AI: A
System-Theoretical Approach | [
"cs.MA",
"cs.AI",
"cs.HC"
] | This paper presents a novel perspective on human-computer interaction (HCI), framing it as a dynamic interplay between human and computational agents within a networked system. Going beyond traditional interface-based approaches, we emphasize the importance of coordination and communication among heterogeneous agents w... | {
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2502.14001 | Towards a perturbation-based explanation for medical AI as
differentiable programs | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Recent advancement in machine learning algorithms reaches a point where medical devices can be equipped with artificial intelligence (AI) models for diagnostic support and routine automation in clinical settings. In medicine and healthcare, there is a particular demand for sufficient and objective explainability of the... | {
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2502.14003 | Rectified Lagrangian for Out-of-Distribution Detection in Modern
Hopfield Networks | [
"cs.LG",
"cs.AI"
] | Modern Hopfield networks (MHNs) have recently gained significant attention in the field of artificial intelligence because they can store and retrieve a large set of patterns with an exponentially large memory capacity. A MHN is generally a dynamical system defined with Lagrangians of memory and feature neurons, where ... | {
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2502.14004 | Inter3D: A Benchmark and Strong Baseline for Human-Interactive 3D Object
Reconstruction | [
"cs.GR",
"cs.LG"
] | Recent advancements in implicit 3D reconstruction methods, e.g., neural rendering fields and Gaussian splatting, have primarily focused on novel view synthesis of static or dynamic objects with continuous motion states. However, these approaches struggle to efficiently model a human-interactive object with n movable pa... | {
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2502.14005 | Smaller But Better: Unifying Layout Generation with Smaller Large
Language Models | [
"cs.LG"
] | We propose LGGPT, an LLM-based model tailored for unified layout generation. First, we propose Arbitrary Layout Instruction (ALI) and Universal Layout Response (ULR) as the uniform I/O template. ALI accommodates arbitrary layout generation task inputs across multiple layout domains, enabling LGGPT to unify both task-ge... | {
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2502.14008 | MaskPrune: Mask-based LLM Pruning for Layer-wise Uniform Structures | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The remarkable performance of large language models (LLMs) in various language tasks has attracted considerable attention. However, the ever-increasing size of these models presents growing challenges for deployment and inference. Structured pruning, an effective model compression technique, is gaining increasing atten... | {
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2502.14009 | Benchmarking Self-Supervised Methods for Accelerated MRI Reconstruction | [
"eess.IV",
"cs.LG"
] | Reconstructing MRI from highly undersampled measurements is crucial for accelerating medical imaging, but is challenging due to the ill-posedness of the inverse problem. While supervised deep learning approaches have shown remarkable success, they rely on fully-sampled ground truth data, which is often impractical or i... | {
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2502.14010 | Which Attention Heads Matter for In-Context Learning? | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large language models (LLMs) exhibit impressive in-context learning (ICL) capability, enabling them to perform new tasks using only a few demonstrations in the prompt. Two different mechanisms have been proposed to explain ICL: induction heads that find and copy relevant tokens, and function vector (FV) heads whose act... | {
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2502.14011 | DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge
Devices | [
"cs.LG",
"cs.AI",
"cs.NI"
] | The Internet of Things generates massive data streams, with edge computing emerging as a key enabler for online IoT applications and 5G networks. Edge solutions facilitate real-time machine learning inference, but also require continuous adaptation to concept drifts. Ensemble-based solutions improve predictive performa... | {
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2502.14013 | Appeal prediction for AI up-scaled Images | [
"cs.GR",
"cs.AI",
"eess.IV"
] | DNN- or AI-based up-scaling algorithms are gaining in popularity due to the improvements in machine learning. Various up-scaling models using CNNs, GANs or mixed approaches have been published. The majority of models are evaluated using PSRN and SSIM or only a few example images. However, a performance evaluation with ... | {
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2502.14018 | I Want 'Em All (At Once) -- Ultrametric Cluster Hierarchies | [
"cs.LG"
] | Hierarchical clustering is a powerful tool for exploratory data analysis, organizing data into a tree of clusterings from which a partition can be chosen. This paper generalizes these ideas by proving that, for any reasonable hierarchy, one can optimally solve any center-based clustering objective over it (such as $k$-... | {
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2502.14019 | Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text
Generation Systems | [
"cs.CL",
"cs.AI",
"cs.HC"
] | As text generation systems' outputs are increasingly anthropomorphic -- perceived as human-like -- scholars have also raised increasing concerns about how such outputs can lead to harmful outcomes, such as users over-relying or developing emotional dependence on these systems. How to intervene on such system outputs to... | {
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2502.14022 | A General Framework for Augmenting Lossy Compressors with Topological
Guarantees | [
"cs.DC",
"cs.IT",
"math.IT"
] | Topological descriptors such as contour trees are widely utilized in scientific data analysis and visualization, with applications from materials science to climate simulations. It is desirable to preserve topological descriptors when data compression is part of the scientific workflow for these applications. However, ... | {
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2502.14023 | Dynamic Activation with Knowledge Distillation for Energy-Efficient
Spiking NN Ensembles | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.NE"
] | While foundation AI models excel at tasks like classification and decision-making, their high energy consumption makes them unsuitable for energy-constrained applications. Inspired by the brain's efficiency, spiking neural networks (SNNs) have emerged as a viable alternative due to their event-driven nature and compati... | {
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2502.14037 | DiffSampling: Enhancing Diversity and Accuracy in Neural Text Generation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Despite their increasing performance, large language models still tend to reproduce training data, generate several repetitions, and focus on the most common grammatical structures and words. A possible cause is the decoding strategy adopted: the most common ones either consider only the most probable tokens, reducing ... | {
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2502.14043 | Asking for Help Enables Safety Guarantees Without Sacrificing
Effectiveness | [
"cs.LG",
"cs.AI"
] | Most reinforcement learning algorithms with regret guarantees rely on a critical assumption: that all errors are recoverable. Recent work by Plaut et al. discarded this assumption and presented algorithms that avoid "catastrophe" (i.e., irreparable errors) by asking for help. However, they provided only safety guarante... | {
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2502.14044 | Enhancing Cognition and Explainability of Multimodal Foundation Models
with Self-Synthesized Data | [
"cs.CV",
"cs.LG"
] | Large multimodal models (LMMs) have shown impressive capabilities in a wide range of visual tasks. However, they often struggle with fine-grained visual reasoning, failing to identify domain-specific objectives and provide justifiable explanations for their predictions. To address this, we propose a novel visual reject... | {
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2502.14045 | Position: There are no Champions in Long-Term Time Series Forecasting | [
"cs.LG",
"cs.AI"
] | Recent advances in long-term time series forecasting have introduced numerous complex prediction models that consistently outperform previously published architectures. However, this rapid progression raises concerns regarding inconsistent benchmarking and reporting practices, which may undermine the reliability of the... | {
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2502.14047 | Towards a Learning Theory of Representation Alignment | [
"cs.LG",
"cs.AI",
"stat.ML"
] | It has recently been argued that AI models' representations are becoming aligned as their scale and performance increase. Empirical analyses have been designed to support this idea and conjecture the possible alignment of different representations toward a shared statistical model of reality. In this paper, we propose ... | {
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2502.14048 | Semantic Decomposition and Selective Context Filtering -- Text
Processing Techniques for Context-Aware NLP-Based Systems | [
"cs.CL",
"cs.AI",
"cs.HC"
] | In this paper, we present two techniques for use in context-aware systems: Semantic Decomposition, which sequentially decomposes input prompts into a structured and hierarchal information schema in which systems can parse and process easily, and Selective Context Filtering, which enables systems to systematically filte... | {
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2502.14050 | Diversity-driven Data Selection for Language Model Tuning through Sparse
Autoencoder | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Current pre-trained large language models typically need instruction tuning to align with human preferences. However, instruction tuning data is often quantity-saturated due to the large volume of data collection and fast model iteration, leaving coreset data selection important but underexplored. On the other hand, ex... | {
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2502.14051 | RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache
Compression | [
"cs.CL",
"cs.LG"
] | Transformer-based Large Language Models rely critically on KV cache to efficiently handle extended contexts during the decode phase. Yet, the size of the KV cache grows proportionally with the input length, burdening both memory bandwidth and capacity as decoding progresses. To address this challenge, we present Rocket... | {
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2502.14053 | Goggin's corrected Kalman Filter: Guarantees and Filtering Regimes | [
"cs.IT",
"math.IT"
] | In this paper we revisit a non-linear filter for {\em non-Gaussian} noises that was introduced in [1]. Goggin proved that transforming the observations by the score function and then applying the Kalman Filter (KF) to the transformed observations results in an asymptotically optimal filter. In the current paper, we stu... | {
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2502.14054 | A Low-Complexity Scheme for Multi-Message Private Information Retrieval | [
"cs.IT",
"math.IT"
] | Private Information Retrieval (PIR) is a fundamental problem in the broader fields of security and privacy. In recent years, the problem has garnered significant attention from the research community, leading to achievability schemes and converse results for many important PIR settings. This paper focuses on the Mult... | {
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2502.14060 | New Lower Bounds for Stochastic Non-Convex Optimization through
Divergence Composition | [
"stat.ML",
"cs.LG",
"math.OC"
] | We study fundamental limits of first-order stochastic optimization in a range of nonconvex settings, including L-smooth functions satisfying Quasar-Convexity (QC), Quadratic Growth (QG), and Restricted Secant Inequalities (RSI). While the convergence properties of standard algorithms are well-understood in deterministi... | {
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2502.14061 | EfficientPose 6D: Scalable and Efficient 6D Object Pose Estimation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | In industrial applications requiring real-time feedback, such as quality control and robotic manipulation, the demand for high-speed and accurate pose estimation remains critical. Despite advances improving speed and accuracy in pose estimation, finding a balance between computational efficiency and accuracy poses sign... | {
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2502.14063 | PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian
Detection | [
"cs.CV"
] | Pedestrian detection in intelligent transportation systems has made significant progress but faces two critical challenges: (1) insufficient fusion of complementary information between visible and infrared spectra, particularly in complex scenarios, and (2) sensitivity to illumination changes, such as low-light or over... | {
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2502.14064 | Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging | [
"cs.CV",
"cs.AI"
] | Vision foundation models (VFMs) are pre-trained on extensive image datasets to learn general representations for diverse types of data. These models can subsequently be fine-tuned for specific downstream tasks, significantly boosting performance across a broad range of applications. However, existing vision foundation ... | {
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2502.14066 | Experiment Design with Gaussian Process Regression with Applications to
Chance-Constrained Control | [
"eess.SY",
"cs.SY"
] | Learning for control in repeated tasks allows for well-designed experiments to gather the most useful data. We consider the setting in which we use a data-driven controller that does not have access to the true system dynamics. Rather, the controller uses inferred dynamics based on the available information. In order t... | {
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2502.14068 | A Racing Dataset and Baseline Model for Track Detection in Autonomous
Racing | [
"cs.CV",
"cs.AI",
"eess.IV"
] | A significant challenge in racing-related research is the lack of publicly available datasets containing raw images with corresponding annotations for the downstream task. In this paper, we introduce RoRaTrack, a novel dataset that contains annotated multi-camera image data from racing scenarios for track detection. Th... | {
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2502.14070 | DiffExp: Efficient Exploration in Reward Fine-tuning for Text-to-Image
Diffusion Models | [
"cs.CV",
"cs.AI"
] | Fine-tuning text-to-image diffusion models to maximize rewards has proven effective for enhancing model performance. However, reward fine-tuning methods often suffer from slow convergence due to online sample generation. Therefore, obtaining diverse samples with strong reward signals is crucial for improving sample eff... | {
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2502.14074 | Investigating Non-Transitivity in LLM-as-a-Judge | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Automatic evaluation methods based on large language models (LLMs) are emerging as the standard tool for assessing the instruction-following abilities of LLM-based agents. The most common method in this paradigm, pairwise comparisons with a baseline model, critically depends on the assumption of transitive preferences.... | {
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2502.14075 | Towards Vector Optimization on Low-Dimensional Vector Symbolic
Architecture | [
"cs.LG"
] | Vector Symbolic Architecture (VSA) is emerging in machine learning due to its efficiency, but they are hindered by issues of hyperdimensionality and accuracy. As a promising mitigation, the Low-Dimensional Computing (LDC) method significantly reduces the vector dimension by ~100 times while maintaining accuracy, by emp... | {
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2502.14079 | Population Dynamics Control with Partial Observations | [
"math.OC",
"cs.LG"
] | We study the problem of controlling population dynamics, a class of linear dynamical systems evolving on the probability simplex, from the perspective of online non-stochastic control. While Golowich et.al. 2024 analyzed the fully observable setting, we focus on the more realistic, partially observable case, where only... | {
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2502.14080 | Personalized Education with Generative AI and Digital Twins: VR, RAG,
and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development | [
"cs.CY",
"cs.AI"
] | The Fourth Industrial Revolution (4IR) technologies, such as cloud computing, machine learning, and AI, have improved productivity but introduced challenges in workforce training and reskilling. This is critical given existing workforce shortages, especially in marginalized communities like Underrepresented Minorities ... | {
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2502.14083 | Are Rules Meant to be Broken? Understanding Multilingual Moral Reasoning
as a Computational Pipeline with UniMoral | [
"cs.CL"
] | Moral reasoning is a complex cognitive process shaped by individual experiences and cultural contexts and presents unique challenges for computational analysis. While natural language processing (NLP) offers promising tools for studying this phenomenon, current research lacks cohesion, employing discordant datasets and... | {
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2502.14086 | Navigating Semantic Relations: Challenges for Language Models in
Abstract Common-Sense Reasoning | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have achieved remarkable performance in generating human-like text and solving reasoning tasks of moderate complexity, such as question-answering and mathematical problem-solving. However, their capabilities in tasks requiring deeper cognitive skills, such as common-sense understanding and ... | {
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2502.14087 | Learning from End User Data with Shuffled Differential Privacy over
Kernel Densities | [
"cs.LG",
"cs.CR",
"cs.DS"
] | We study a setting of collecting and learning from private data distributed across end users. In the shuffled model of differential privacy, the end users partially protect their data locally before sharing it, and their data is also anonymized during its collection to enhance privacy. This model has recently become a ... | {
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2502.14088 | Regression in EO: Are VLMs Up to the Challenge? | [
"cs.CV"
] | Earth Observation (EO) data encompass a vast range of remotely sensed information, featuring multi-sensor and multi-temporal, playing an indispensable role in understanding our planet's dynamics. Recently, Vision Language Models (VLMs) have achieved remarkable success in perception and reasoning tasks, bringing new ins... | {
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2502.14090 | MambaLiteSR: Image Super-Resolution with Low-Rank Mamba using Knowledge
Distillation | [
"eess.IV",
"cs.CV"
] | Generative Artificial Intelligence (AI) has gained significant attention in recent years, revolutionizing various applications across industries. Among these, advanced vision models for image super-resolution are in high demand, particularly for deployment on edge devices where real-time processing is crucial. However,... | {
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2502.14092 | Hybrid Visual Servoing of Tendon-driven Continuum Robots | [
"cs.RO",
"cs.CV",
"cs.SY",
"eess.SY"
] | This paper introduces a novel Hybrid Visual Servoing (HVS) approach for controlling tendon-driven continuum robots (TDCRs). The HVS system combines Image-Based Visual Servoing (IBVS) with Deep Learning-Based Visual Servoing (DLBVS) to overcome the limitations of each method and improve overall performance. IBVS offers ... | {
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2502.14094 | CND-IDS: Continual Novelty Detection for Intrusion Detection Systems | [
"cs.CR",
"cs.LG"
] | Intrusion detection systems (IDS) play a crucial role in IoT and network security by monitoring system data and alerting to suspicious activities. Machine learning (ML) has emerged as a promising solution for IDS, offering highly accurate intrusion detection. However, ML-IDS solutions often overlook two critical aspect... | {
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2502.14095 | Retrieving Versus Understanding Extractive Evidence in Few-Shot Learning | [
"cs.CL"
] | A key aspect of alignment is the proper use of within-document evidence to construct document-level decisions. We analyze the relationship between the retrieval and interpretation of within-document evidence for large language model in a few-shot setting. Specifically, we measure the extent to which model prediction er... | {
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2502.14096 | Aligned Multi Objective Optimization | [
"cs.LG",
"math.OC"
] | To date, the multi-objective optimization literature has mainly focused on conflicting objectives, studying the Pareto front, or requiring users to balance tradeoffs. Yet, in machine learning practice, there are many scenarios where such conflict does not take place. Recent findings from multi-task learning, reinforcem... | {
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2502.14099 | Point Cloud Geometry Scalable Coding Using a Resolution and
Quality-conditioned Latents Probability Estimator | [
"cs.CV"
] | In the current age, users consume multimedia content in very heterogeneous scenarios in terms of network, hardware, and display capabilities. A naive solution to this problem is to encode multiple independent streams, each covering a different possible requirement for the clients, with an obvious negative impact in bot... | {
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2502.14100 | Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach | [
"cs.CL",
"cs.IR"
] | Large Language Models (LLMs) enhanced with external contexts, such as through retrieval-augmented generation (RAG), often face challenges in handling imperfect evidence. They tend to over-rely on external knowledge, making them vulnerable to misleading and unhelpful contexts. To address this, we propose the concept of ... | {
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2502.14102 | Explainable Distributed Constraint Optimization Problems | [
"cs.AI"
] | The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool to model cooperative multi-agent problems that need to be solved distributively. A core assumption of existing approaches is that DCOP solutions can be easily understood, accepted, and adopted, which may not hold, as evidenced by the ... | {
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2502.14105 | Conformal Prediction under L\'evy-Prokhorov Distribution Shifts:
Robustness to Local and Global Perturbations | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.ME",
"stat.TH"
] | Conformal prediction provides a powerful framework for constructing prediction intervals with finite-sample guarantees, yet its robustness under distribution shifts remains a significant challenge. This paper addresses this limitation by modeling distribution shifts using L\'evy-Prokhorov (LP) ambiguity sets, which cap... | {
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2502.14111 | Comprehensive Review on the Control of Heat Pumps for Energy Flexibility
in Distribution Networks | [
"eess.SY",
"cs.SY"
] | Decarbonization plans promote the transition to heat pumps (HPs), creating new opportunities for their energy flexibility in demand response programs, solar photovoltaic integration and optimization of distribution networks. This paper reviews scheduling-based and real-time optimization methods for controlling HPs with... | {
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2502.14112 | To Stand on the Shoulders of Giants: Should We Protect Initial
Discoveries in Multi-Agent Exploration? | [
"cs.MA"
] | Exploring new ideas is a fundamental aspect of research and development (R\&D), which often occurs in competitive environments. Most ideas are subsequent, i.e. one idea today leads to more ideas tomorrow. According to one approach, the best way to encourage exploration is by granting protection on discoveries to the fi... | {
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2502.14113 | Object-centric Binding in Contrastive Language-Image Pretraining | [
"cs.CV",
"cs.AI"
] | Recent advances in vision language models (VLM) have been driven by contrastive models such as CLIP, which learn to associate visual information with their corresponding text descriptions. However, these models have limitations in understanding complex compositional scenes involving multiple objects and their spatial r... | {
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2502.14114 | Zero loss guarantees and explicit minimizers for generic
overparametrized Deep Learning networks | [
"cs.LG",
"cs.AI",
"math.AP",
"math.OC",
"stat.ML"
] | We determine sufficient conditions for overparametrized deep learning (DL) networks to guarantee the attainability of zero loss in the context of supervised learning, for the $\mathcal{L}^2$ cost and {\em generic} training data. We present an explicit construction of the zero loss minimizers without invoking gradient d... | {
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2502.14115 | Chasing the Timber Trail: Machine Learning to Reveal Harvest Location
Misrepresentation | [
"cs.LG",
"cs.CE",
"cs.CY"
] | Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products trade, affecting livelihoods and communities across the globe. Stable isotope ratio analysis (SIRA) is rapidly becoming an important tool for... | {
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2502.14119 | Meaning Beyond Truth Conditions: Evaluating Discourse Level
Understanding via Anaphora Accessibility | [
"cs.CL"
] | We present a hierarchy of natural language understanding abilities and argue for the importance of moving beyond assessments of understanding at the lexical and sentence levels to the discourse level. We propose the task of anaphora accessibility as a diagnostic for assessing discourse understanding, and to this end, p... | {
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2502.14120 | A Supervised Machine-Learning Approach For Turboshaft Engine Dynamic
Modeling Under Real Flight Conditions | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Rotorcraft engines are highly complex, nonlinear thermodynamic systems that operate under varying environmental and flight conditions. Simulating their dynamics is crucial for design, fault diagnostics, and deterioration control phases, and requires robust and reliable control systems to estimate engine performance thr... | {
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2502.14121 | Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A
Path to Greener Profits and Smarter Designs | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Designing modern industrial systems requires balancing several competing objectives, such as profitability, resilience, and sustainability, while accounting for complex interactions between technological, economic, and environmental factors. Multi-objective optimization (MOO) methods are commonly used to navigate these... | {
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2502.14122 | Benchmarking LLMs for Political Science: A United Nations Perspective | [
"cs.CL",
"cs.CY",
"cs.ET"
] | Large Language Models (LLMs) have achieved significant advances in natural language processing, yet their potential for high-stake political decision-making remains largely unexplored. This paper addresses the gap by focusing on the application of LLMs to the United Nations (UN) decision-making process, where the stake... | {
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2502.14123 | Understanding SGD with Exponential Moving Average: A Case Study in
Linear Regression | [
"cs.LG",
"math.OC",
"stat.ML"
] | Exponential moving average (EMA) has recently gained significant popularity in training modern deep learning models, especially diffusion-based generative models. However, there have been few theoretical results explaining the effectiveness of EMA. In this paper, to better understand EMA, we establish the risk bound of... | {
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2502.14125 | Modular Prompt Learning Improves Vision-Language Models | [
"cs.CV"
] | Pre-trained vision-language models are able to interpret visual concepts and language semantics. Prompt learning, a method of constructing prompts for text encoders or image encoders, elicits the potentials of pre-trained models and readily adapts them to new scenarios. Compared to fine-tuning, prompt learning enables ... | {
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2502.14127 | Which of These Best Describes Multiple Choice Evaluation with LLMs? A)
Forced B) Flawed C) Fixable D) All of the Above | [
"cs.CL"
] | Multiple choice question answering (MCQA) is popular for LLM evaluation due to its simplicity and human-like testing, but we argue for its reform. We first reveal flaws in MCQA's format, as it struggles to: 1) test generation/subjectivity; 2) match LLM use cases; and 3) fully test knowledge. We instead advocate for gen... | {
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2502.14129 | GlossGau: Efficient Inverse Rendering for Glossy Surface with
Anisotropic Spherical Gaussian | [
"cs.CV"
] | The reconstruction of 3D objects from calibrated photographs represents a fundamental yet intricate challenge in the domains of computer graphics and vision. Although neural reconstruction approaches based on Neural Radiance Fields (NeRF) have shown remarkable capabilities, their processing costs remain substantial. Re... | {
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2502.14131 | Gradients can train reward models: An Empirical Risk Minimization
Approach for Offline Inverse RL and Dynamic Discrete Choice Model | [
"cs.LG",
"cs.AI",
"econ.EM"
] | We study the problem of estimating Dynamic Discrete Choice (DDC) models, also known as offline Maximum Entropy-Regularized Inverse Reinforcement Learning (offline MaxEnt-IRL) in machine learning. The objective is to recover reward or $Q^*$ functions that govern agent behavior from offline behavior data. In this paper, ... | {
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2502.14132 | Can Community Notes Replace Professional Fact-Checkers? | [
"cs.CL",
"cs.AI"
] | Two commonly-employed strategies to combat the rise of misinformation on social media are (i) fact-checking by professional organisations and (ii) community moderation by platform users. Policy changes by Twitter/X and, more recently, Meta, signal a shift away from partnerships with fact-checking organisations and towa... | {
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2502.14133 | Self-Regularization with Latent Space Explanations for Controllable
LLM-based Classification | [
"cs.CL"
] | Modern text classification methods heavily rely on contextual embeddings from large language models (LLMs). Compared to human-engineered features, these embeddings provide automatic and effective representations for classification model training. However, they also introduce a challenge: we lose the ability to manually... | {
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2502.14135 | Cluster Analysis and Concept Drift Detection in Malware | [
"cs.LG",
"cs.CR"
] | Concept drift refers to gradual or sudden changes in the properties of data that affect the accuracy of machine learning models. In this paper, we address the problem of concept drift detection in the malware domain. Specifically, we propose and analyze a clustering-based approach to detecting concept drift. Using a su... | {
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2502.14137 | Collaborative Retrieval for Large Language Model-based Conversational
Recommender Systems | [
"cs.IR"
] | Conversational recommender systems (CRS) aim to provide personalized recommendations via interactive dialogues with users. While large language models (LLMs) enhance CRS with their superior understanding of context-aware user preferences, they typically struggle to leverage behavioral data, which have proven to be impo... | {
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2502.14140 | ModSkill: Physical Character Skill Modularization | [
"cs.CV",
"cs.GR",
"cs.RO"
] | Human motion is highly diverse and dynamic, posing challenges for imitation learning algorithms that aim to generalize motor skills for controlling simulated characters. Previous methods typically rely on a universal full-body controller for tracking reference motion (tracking-based model) or a unified full-body skill ... | {
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2502.14142 | Token Adaptation via Side Graph Convolution for Temporally and Spatially
Efficient Fine-tuning of 3D Point Cloud Transformers | [
"cs.CV"
] | Parameter-efficient fine-tuning (PEFT) of pre-trained 3D point cloud Transformers has emerged as a promising technique for 3D point cloud analysis. While existing PEFT methods attempt to minimize the number of tunable parameters, they still suffer from high temporal and spatial computational costs during fine-tuning. T... | {
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2502.14143 | Multi-Agent Risks from Advanced AI | [
"cs.MA",
"cs.AI",
"cs.CY",
"cs.ET",
"cs.LG"
] | The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity. These systems pose novel and under-explored risks. In this report, we provide a structured taxonomy of these risks by identifying three key failure... | {
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2502.14144 | UM_FHS at TREC 2024 PLABA: Exploration of Fine-tuning and AI agent
approach for plain language adaptations of biomedical text | [
"cs.CL"
] | This paper describes our submissions to the TREC 2024 PLABA track with the aim to simplify biomedical abstracts for a K8-level audience (13-14 years old students). We tested three approaches using OpenAI's gpt-4o and gpt-4o-mini models: baseline prompt engineering, a two-AI agent approach, and fine-tuning. Adaptations ... | {
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2502.14145 | LLM-Enhanced Dialogue Management for Full-Duplex Spoken Dialogue Systems | [
"cs.CL",
"eess.AS"
] | Achieving full-duplex communication in spoken dialogue systems (SDS) requires real-time coordination between listening, speaking, and thinking. This paper proposes a semantic voice activity detection (VAD) module as a dialogue manager (DM) to efficiently manage turn-taking in full-duplex SDS. Implemented as a lightweig... | {
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2502.14146 | Efficient and Optimal Policy Gradient Algorithm for Corrupted
Multi-armed Bandits | [
"cs.LG"
] | In this paper, we consider the stochastic multi-armed bandits problem with adversarial corruptions, where the random rewards of the arms are partially modified by an adversary to fool the algorithm. We apply the policy gradient algorithm SAMBA to this setting, and show that it is computationally efficient, and achieves... | {
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2502.14147 | Learning the P2D Model for Lithium-Ion Batteries with SOH Detection | [
"cs.LG",
"physics.chem-ph"
] | Lithium ion batteries are widely used in many applications. Battery management systems control their optimal use and charging and predict when the battery will cease to deliver the required output on a planned duty or driving cycle. Such systems use a simulation of a mathematical model of battery performance. These mod... | {
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2502.14149 | PitVQA++: Vector Matrix-Low-Rank Adaptation for Open-Ended Visual
Question Answering in Pituitary Surgery | [
"cs.CV",
"cs.AI"
] | Vision-Language Models (VLMs) in visual question answering (VQA) offer a unique opportunity to enhance intra-operative decision-making, promote intuitive interactions, and significantly advancing surgical education. However, the development of VLMs for surgical VQA is challenging due to limited datasets and the risk of... | {
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2502.14150 | Risk-Sensitive Security-Constrained Economic Dispatch: Pricing and
Algorithm Design | [
"eess.SY",
"cs.SY",
"econ.TH"
] | We propose a risk-sensitive security-constrained economic dispatch (R-SCED) formulation capturing the tradeoff between dispatch cost and resilience against potential line failures, where risk is modeled via the conditional value at risk (CVaR). In the context of our formulation, we analyze revenue adequacy and side pay... | {
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2502.14155 | Giving AI Personalities Leads to More Human-Like Reasoning | [
"cs.AI",
"cs.CL",
"cs.CY"
] | In computational cognitive modeling, capturing the full spectrum of human judgment and decision-making processes, beyond just optimal behaviors, is a significant challenge. This study explores whether Large Language Models (LLMs) can emulate the breadth of human reasoning by predicting both intuitive, fast System 1 and... | {
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2502.14156 | Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X
Collaboration | [
"cs.CV"
] | Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X datasets are limited in scope, diversity, and quality. To address these gaps, we present Mixed Signals, a comprehensive V2X dataset featuring 45... | {
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} |
2502.14158 | Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks | [
"cs.LG",
"cs.SI"
] | Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it.Current leading graph models require a large number of labeled samples for training, which unavoidably leads to overfitting in few-shot scenarios. Recent research has ... | {
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} |
2502.14160 | Efficient Inverse Multiagent Learning | [
"cs.GT",
"cs.AI",
"cs.LG",
"econ.TH"
] | In this paper, we study inverse game theory (resp. inverse multiagent learning) in which the goal is to find parameters of a game's payoff functions for which the expected (resp. sampled) behavior is an equilibrium. We formulate these problems as generative-adversarial (i.e., min-max) optimization problems, for which w... | {
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"cs.MA": 0,
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"cs.SY": 0
} |
2502.14166 | Prediction-Powered Adaptive Shrinkage Estimation | [
"stat.ML",
"cs.LG",
"stat.ME"
] | Prediction-Powered Inference (PPI) is a powerful framework for enhancing statistical estimates by combining limited gold-standard data with machine learning (ML) predictions. While prior work has demonstrated PPI's benefits for individual statistical tasks, modern applications require answering numerous parallel statis... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2502.14168 | Deep learning based infrared small object segmentation: Challenges and
future directions | [
"cs.CV"
] | Infrared sensing is a core method for supporting unmanned systems, such as autonomous vehicles and drones. Recently, infrared sensors have been widely deployed on mobile and stationary platforms for detection and classification of objects from long distances and in wide field of views. Given its success in the vision i... | {
"Other": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.14170 | Blockchain-based Framework for Scalable and Incentivized Federated
Learning | [
"cs.LG",
"cs.DC"
] | Federated Learning (FL) enables collaborative model training without sharing raw data, preserving privacy while harnessing distributed datasets. However, traditional FL systems often rely on centralized aggregating mechanisms, introducing trust issues, single points of failure, and limited mechanisms for incentivizing ... | {
"Other": 1,
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"cs.SY": 0
} |
2502.14171 | Enhancing Conversational Agents with Theory of Mind: Aligning Beliefs,
Desires, and Intentions for Human-Like Interaction | [
"cs.CL"
] | Natural language interaction with agentic Artificial Intelligence (AI), driven by Large Language Models (LLMs), is expected to remain a dominant paradigm in the near future. While humans instinctively align their communication with mental states -- an ability known as Theory of Mind (ToM), current LLM powered systems e... | {
"Other": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.14172 | Finite Sample Analysis of Distributional TD Learning with Linear
Function Approximation | [
"stat.ML",
"cs.LG"
] | In this paper, we investigate the finite-sample statistical rates of distributional temporal difference (TD) learning with linear function approximation. The aim of distributional TD learning is to estimate the return distribution of a discounted Markov decision process for a given policy {\pi}. Prior works on statisti... | {
"Other": 0,
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"cs.SD": 0,
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} |
2502.14174 | Weighted Low-rank Approximation via Stochastic Gradient Descent on
Manifolds | [
"math.OC",
"cs.AI",
"cs.LG",
"stat.ML"
] | We solve a regularized weighted low-rank approximation problem by a stochastic gradient descent on a manifold. To guarantee the convergence of our stochastic gradient descent, we establish a convergence theorem on manifolds for retraction-based stochastic gradient descents admitting confinements. On sample data from th... | {
"Other": 0,
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"cs.NE": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2502.14176 | A modal logic translation of the AGM axioms for belief revision | [
"cs.LO",
"cs.AI"
] | Building on the analysis of Bonanno (Artificial Intelligence, 2025) we introduce a simple modal logic containing three modal operators: a unimodal belief operator, a bimodal conditional operator and the unimodal global operator. For each AGM axiom for belief revision, we provide a corresponding modal axiom. The corresp... | {
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} |
2502.14177 | InstaSHAP: Interpretable Additive Models Explain Shapley Values
Instantly | [
"cs.LG",
"stat.ML"
] | In recent years, the Shapley value and SHAP explanations have emerged as one of the most dominant paradigms for providing post-hoc explanations of black-box models. Despite their well-founded theoretical properties, many recent works have focused on the limitations in both their computational efficiency and their repre... | {
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"cs.SY": 0
} |
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