id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.08378 | Learning Humanoid Standing-up Control across Diverse Postures | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Standing-up control is crucial for humanoid robots, with the potential for integration into current locomotion and loco-manipulation systems, such as fall recovery. Existing approaches are either limited to simulations that overlook hardware constraints or rely on predefined ground-specific motion trajectories, failing... | {
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2502.08391 | ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for
Whole Slide Image Classification | [
"cs.CV"
] | Multiple instance learning (MIL)-based framework has become the mainstream for processing the whole slide image (WSI) with giga-pixel size and hierarchical image context in digital pathology. However, these methods heavily depend on a substantial number of bag-level labels and solely learn from the original slides, whi... | {
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2502.08395 | IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in
LLM Writing Assistance | [
"cs.CL"
] | Large language models (LLMs) are helping millions of users write texts about diverse issues, and in doing so expose users to different ideas and perspectives. This creates concerns about issue bias, where an LLM tends to present just one perspective on a given issue, which in turn may influence how users think about th... | {
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2502.08397 | Strong bounds for large-scale Minimum Sum-of-Squares Clustering | [
"math.OC",
"cs.LG"
] | Clustering is a fundamental technique in data analysis and machine learning, used to group similar data points together. Among various clustering methods, the Minimum Sum-of-Squares Clustering (MSSC) is one of the most widely used. MSSC aims to minimize the total squared Euclidean distance between data points and their... | {
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2502.08404 | Quantifying Collective Emotions: Japan's Societal Trends Through
Enhanced Sentiment Index Using POMS2 and SNS | [
"cs.SI",
"cs.CY"
] | In this study, we constructed an emotion index that quantitatively represents the collective emotions present in the Japanese web space by utilizing Social Networking Service (SNS) post data. Building upon previous research that used blog data and the Profile of Mood States (POMS), we restructured the methodology using... | {
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2502.08414 | Sparse Estimation of Inverse Covariance and Partial Correlation Matrices
via Joint Partial Regression | [
"stat.ML",
"cs.LG"
] | We present a new method for estimating high-dimensional sparse partial correlation and inverse covariance matrices, which exploits the connection between the inverse covariance matrix and linear regression. The method is a two-stage estimation method wherein each individual feature is regressed on all other features wh... | {
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2502.08415 | A Semantic Parsing Algorithm to Solve Linear Ordering Problems | [
"cs.CL",
"cs.LO"
] | We develop an algorithm to semantically parse linear ordering problems, which require a model to arrange entities using deductive reasoning. Our method takes as input a number of premises and candidate statements, parsing them to a first-order logic of an ordering domain, and then utilizes constraint logic programming ... | {
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2502.08416 | Multifidelity Simulation-based Inference for Computationally Expensive
Simulators | [
"stat.ML",
"cs.LG"
] | Across many domains of science, stochastic models are an essential tool to understand the mechanisms underlying empirically observed data. Models can be of different levels of detail and accuracy, with models of high-fidelity (i.e., high accuracy) to the phenomena under study being often preferable. However, inferring ... | {
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2502.08417 | Handwritten Text Recognition: A Survey | [
"cs.CV",
"cs.AI"
] | Handwritten Text Recognition (HTR) has become an essential field within pattern recognition and machine learning, with applications spanning historical document preservation to modern data entry and accessibility solutions. The complexity of HTR lies in the high variability of handwriting, which makes it challenging to... | {
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2502.08426 | Semantic Learning for Molecular Communication in Internet of Bio-Nano
Things | [
"eess.SP",
"cs.ET",
"cs.LG",
"eess.IV"
] | Molecular communication (MC) provides a foundational framework for information transmission in the Internet of Bio-Nano Things (IoBNT), where efficiency and reliability are crucial. However, the inherent limitations of molecular channels, such as low transmission rates, noise, and inter-symbol interference (ISI), limit... | {
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2502.08428 | Robot-Initiated Social Control of Sedentary Behavior: Comparing the
Impact of Relationship- and Target-Focused Strategies | [
"cs.HC",
"cs.RO"
] | To design social robots to effectively promote health behavior change, it is essential to understand how people respond to various health communication strategies employed by these robots. This study examines the effectiveness of two types of social control strategies from a social robot, relationship-focused strategie... | {
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2502.08432 | Closer through commonality: Enhancing hypergraph contrastive learning
with shared groups | [
"cs.LG"
] | Hypergraphs provide a superior modeling framework for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional homogeneous graphs. However, there have been few studies on hypergraphbased contrastive learning, and ... | {
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2502.08436 | From Haystack to Needle: Label Space Reduction for Zero-shot
Classification | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We present Label Space Reduction (LSR), a novel method for improving zero-shot classification performance of Large Language Models (LLMs). LSR iteratively refines the classification label space by systematically ranking and reducing candidate classes, enabling the model to concentrate on the most relevant options. By l... | {
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2502.08438 | Composite Sketch+Text Queries for Retrieving Objects with Elusive Names
and Complex Interactions | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.IR",
"cs.MM"
] | Non-native speakers with limited vocabulary often struggle to name specific objects despite being able to visualize them, e.g., people outside Australia searching for numbats. Further, users may want to search for such elusive objects with difficult-to-sketch interactions, e.g., numbat digging in the ground. In such co... | {
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2502.08441 | Better Embeddings with Coupled Adam | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Despite their remarkable capabilities, LLMs learn word representations that exhibit the undesirable yet poorly understood feature of anisotropy. In this paper, we argue that the second moment in Adam is a cause of anisotropic embeddings, and suggest a modified optimizer called Coupled Adam to mitigate the problem. Our ... | {
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2502.08445 | LucidAtlas$: Learning Uncertainty-Aware, Covariate-Disentangled,
Individualized Atlas Representations | [
"cs.LG"
] | The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provide insight into individual as well as population level variation. We develop $\texttt{LucidAtlas}$, an approach that can represent spatially ... | {
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2502.08448 | Monge SAM: Robust Reparameterization-Invariant Sharpness-Aware
Minimization Based on Loss Geometry | [
"cs.LG",
"stat.ML"
] | Recent studies on deep neural networks show that flat minima of the loss landscape correlate with improved generalization. Sharpness-aware minimization (SAM) efficiently finds flat regions by updating the parameters according to the gradient at an adversarial perturbation. The perturbation depends on the Euclidean metr... | {
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2502.08449 | CordViP: Correspondence-based Visuomotor Policy for Dexterous
Manipulation in Real-World | [
"cs.RO",
"cs.AI"
] | Achieving human-level dexterity in robots is a key objective in the field of robotic manipulation. Recent advancements in 3D-based imitation learning have shown promising results, providing an effective pathway to achieve this goal. However, obtaining high-quality 3D representations presents two key problems: (1) the q... | {
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2502.08450 | Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated
Essay Scoring | [
"cs.CL",
"cs.AI"
] | In automated essay scoring (AES), recent efforts have shifted toward cross-prompt settings that score essays on unseen prompts for practical applicability. However, prior methods trained with essay-score pairs of specific prompts pose challenges in obtaining prompt-generalized essay representation. In this work, we pro... | {
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2502.08452 | Learning to Group and Grasp Multiple Objects | [
"cs.RO"
] | Simultaneously grasping and transporting multiple objects can significantly enhance robotic work efficiency and has been a key research focus for decades. The primary challenge lies in determining how to push objects, group them, and execute simultaneous grasping for respective groups while considering object distribut... | {
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2502.08453 | Proceedings 40th International Conference on Logic Programming | [
"cs.LO",
"cs.AI"
] | Since the first conference In Marseille in 1982, the International Conference on Logic Programming (ICLP) has been the premier international event for presenting research in logic programming. These proceedings include technical communications about, and abstracts for presentations given at the 40th ICLP held October 1... | {
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2502.08455 | Resilient Quantized Consensus in Multi-Hop Relay Networks | [
"cs.MA",
"cs.SY",
"eess.SY"
] | We study resilient quantized consensus in multi-agent systems, where some agents may malfunction. The network consists of agents taking integer-valued states, and the agents' communication is subject to asynchronous updates and time delays. We utilize the quantized weighted mean subsequence reduced algorithm where agen... | {
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2502.08457 | Learning Theory for Kernel Bilevel Optimization | [
"cs.LG"
] | Bilevel optimization has emerged as a technique for addressing a wide range of machine learning problems that involve an outer objective implicitly determined by the minimizer of an inner problem. In this paper, we investigate the generalization properties for kernel bilevel optimization problems where the inner object... | {
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2502.08458 | Examining Spanish Counseling with MIDAS: a Motivational Interviewing
Dataset in Spanish | [
"cs.CL"
] | Cultural and language factors significantly influence counseling, but Natural Language Processing research has not yet examined whether the findings of conversational analysis for counseling conducted in English apply to other languages. This paper presents a first step towards this direction. We introduce MIDAS (Motiv... | {
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2502.08468 | mmE5: Improving Multimodal Multilingual Embeddings via High-quality
Synthetic Data | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Multimodal embedding models have gained significant attention for their ability to map data from different modalities, such as text and images, into a unified representation space. However, the limited labeled multimodal data often hinders embedding performance. Recent approaches have leveraged data synthesis to addres... | {
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2502.08470 | Numerical Schemes for Signature Kernels | [
"math.NA",
"cs.LG",
"cs.NA",
"math.AP"
] | Signature kernels have emerged as a powerful tool within kernel methods for sequential data. In the paper "The Signature Kernel is the solution of a Goursat PDE", the authors identify a kernel trick that demonstrates that, for continuously differentiable paths, the signature kernel satisfies a Goursat problem for a hyp... | {
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2502.08474 | Training-Free Restoration of Pruned Neural Networks | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Although network pruning has been highly popularized to compress deep neural networks, its resulting accuracy heavily depends on a fine-tuning process that is often computationally expensive and requires the original data. However, this may not be the case in real-world scenarios, and hence a few recent works attempt t... | {
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2502.08482 | Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned
Reasoning | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Chain-of-Thought (CoT) prompting has emerged as a powerful technique for enhancing language model's reasoning capabilities. However, generating long and correct CoT trajectories is challenging. Recent studies have demonstrated that Looped Transformers possess remarkable length generalization capabilities, but their lim... | {
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2502.08486 | Referring Remote Sensing Image Segmentation via Bidirectional Alignment
Guided Joint Prediction | [
"cs.CV"
] | Referring Remote Sensing Image Segmentation (RRSIS) is critical for ecological monitoring, urban planning, and disaster management, requiring precise segmentation of objects in remote sensing imagery guided by textual descriptions. This task is uniquely challenging due to the considerable vision-language gap, the high ... | {
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2502.08488 | One-Shot Federated Learning with Classifier-Free Diffusion Models | [
"cs.LG"
] | Federated learning (FL) enables collaborative learning without data centralization but introduces significant communication costs due to multiple communication rounds between clients and the server. One-shot federated learning (OSFL) addresses this by forming a global model with a single communication round, often rely... | {
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2502.08489 | Salamandra Technical Report | [
"cs.CL"
] | This work introduces Salamandra, a suite of open-source decoder-only large language models available in three different sizes: 2, 7, and 40 billion parameters. The models were trained from scratch on highly multilingual data that comprises text in 35 European languages and code. Our carefully curated corpus is made exc... | {
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2502.08490 | Flat-Top Beamforming with Efficient Array-Fed RIS | [
"eess.SP",
"cs.SY",
"eess.SY"
] | Flat-top beam designs are essential for uniform power distribution over a wide angular sector for applications such as 5G/6G networks, satellite communications, radar systems, etc. Low sidelobe levels with steep transitions allow negligible cross sector illumination. Active array designs requiring amplitude taper suffe... | {
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2502.08496 | Fine-Tuning Topics through Weighting Aspect Keywords | [
"cs.IR",
"cs.LG"
] | Topic modeling often requires examining topics from multiple perspectives to uncover hidden patterns, especially in less explored areas. This paper presents an approach to address this need, utilizing weighted keywords from various aspects derived from a domain knowledge. The research method starts with standard topic ... | {
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2502.08502 | On the Fundamental Limits of Integrated Sensing and Communications Under
Logarithmic Loss | [
"cs.IT",
"math.IT"
] | We study a unified information-theoretic framework for integrated sensing and communications (ISAC), applicable to both monostatic and bistatic sensing scenarios. Special attention is given to the case where the sensing receiver (Rx) is required to produce a "soft" estimate of the state sequence, with logarithmic loss ... | {
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2502.08503 | Revisiting 3D LLM Benchmarks: Are We Really Testing 3D Capabilities? | [
"cs.AI"
] | In this work, we identify the "2D-Cheating" problem in 3D LLM evaluation, where these tasks might be easily solved by VLMs with rendered images of point clouds, exposing ineffective evaluation of 3D LLMs' unique 3D capabilities. We test VLM performance across multiple 3D LLM benchmarks and, using this as a reference, p... | {
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2502.08505 | Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based
Framework for Effective Label Propagation | [
"cs.LG"
] | Graph Neural Networks (GNNs) have recently become the predominant tools for studying graph data. Despite state-of-the-art performance on graph classification tasks, GNNs are overwhelmingly trained in a single domain under supervision, thus necessitating a prohibitively high demand for labels and resulting in poorly tra... | {
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2502.08507 | Explanation based In-Context Demonstrations Retrieval for Multilingual
Grammatical Error Correction | [
"cs.CL"
] | Grammatical error correction (GEC) aims to correct grammatical, spelling, and semantic errors in natural language text. With the growing of large language models (LLMs), direct text generation has gradually become the focus of the GEC methods, and few-shot in-context learning presents a cost-effective solution. However... | {
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2502.08512 | Measuring Diversity in Synthetic Datasets | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) are widely adopted to generate synthetic datasets for various natural language processing (NLP) tasks, such as text classification and summarization. However, accurately measuring the diversity of these synthetic datasets-an aspect crucial for robust model performance-remains a significant ... | {
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2502.08514 | Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial
Stance for Summary Evaluation | [
"cs.CL"
] | Faithfulness evaluators based on large language models (LLMs) are often fooled by the fluency of the text and struggle with identifying errors in the summaries. We propose an approach to summary faithfulness evaluation in which multiple LLM-based agents are assigned initial stances (regardless of what their belief migh... | {
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2502.08515 | The Paradox of Stochasticity: Limited Creativity and Computational
Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data | [
"cs.LG"
] | This study examines how temperature settings and model architectures affect the generation of structured fictional data (names, birthdates) across three large language models (LLMs): llama3.1:8b, deepseek-r1:8b, and mistral:latest. By systematically testing temperature values from 0.0 to 1.0 in increments of 0.1, we co... | {
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2502.08518 | FedMHO: Heterogeneous One-Shot Federated Learning Towards
Resource-Constrained Edge Devices | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. The iterative training process in conventional FL introduces significant computation and communication overhead, which is unfriendly for resource-... | {
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2502.08522 | Abstract questionnaires and FS-decision digraphs | [
"math.CO",
"cs.IT",
"math.IT",
"math.ST",
"stat.TH"
] | A questionnaire is a sequence of multiple choice questions aiming to collect data on a population. We define an abstract questionnaire as an ordered pair $(N,{\cal M})$, where $N$ is a positive integer and ${\cal M}=(m_0,m_1,\ldots,m_{N-1})$ is an $N$-tuple of positive integers, with $m_i$, for $i \in \{0, 1, \ldots, N... | {
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2502.08524 | LLM Pretraining with Continuous Concepts | [
"cs.LG",
"cs.CL"
] | Next token prediction has been the standard training objective used in large language model pretraining. Representations are learned as a result of optimizing for token-level perplexity. We propose Continuous Concept Mixing (CoCoMix), a novel pretraining framework that combines discrete next token prediction with conti... | {
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2502.08525 | Checkerboard Target Measurement in Unordered Point Clouds with Coloured
ICP | [
"cs.CE"
] | In this work, we investigate the problem of measuring a the centre checkerboard target in an 3D point cloud. This is an important problem which has applications in registration, long term monitoring and linking to other sensor systems. We use a 3D template matching approach based on the coloured ICP algorithm to solve ... | {
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2502.08528 | BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image
Generation | [
"astro-ph.GA",
"cs.CV"
] | The properties of black holes and accretion flows can be inferred by fitting Event Horizon Telescope (EHT) data to simulated images generated through general relativistic ray tracing (GRRT). However, due to the computationally intensive nature of GRRT, the efficiency of generating specific radiation flux images needs t... | {
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2502.08529 | Testbed Development: An Intelligent O-RAN based Cell-Free MIMO Network | [
"cs.NI",
"cs.SY",
"eess.SY"
] | Cell-free multiple input multiple output (CF-MIMO) systems improve spectral and energy efficiencies using distributed access points (APs) to provide reliable service across an area equivalent to multiple conventional cells. This paper presents a novel design and implementation of a CF-MIMO network leveraging the open r... | {
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2502.08531 | On Different Notions of Redundancy in Conditional-Independence-Based
Discovery of Graphical Models | [
"cs.LG",
"stat.ML"
] | The goal of conditional-independence-based discovery of graphical models is to find a graph that represents the independence structure of variables in a given dataset. To learn such a representation, conditional-independence-based approaches conduct a set of statistical tests that suffices to identify the graphical rep... | {
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2502.08534 | Input convex neural networks: universal approximation theorem and
implementation for isotropic polyconvex hyperelastic energies | [
"cs.CE",
"cs.AI"
] | This paper presents a novel framework of neural networks for isotropic hyperelasticity that enforces necessary physical and mathematical constraints while simultaneously satisfying the universal approximation theorem. The two key ingredients are an input convex network architecture and a formulation in the elementary p... | {
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2502.08536 | Matrix Completion with Graph Information: A Provable Nonconvex
Optimization Approach | [
"cs.LG",
"math.OC"
] | We consider the problem of matrix completion with graphs as side information depicting the interrelations between variables. The key challenge lies in leveraging the similarity structure of the graph to enhance matrix recovery. Existing approaches, primarily based on graph Laplacian regularization, suffer from several ... | {
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2502.08540 | A Survey on Image Quality Assessment: Insights, Analysis, and Future
Outlook | [
"cs.CV"
] | Image quality assessment (IQA) represents a pivotal challenge in image-focused technologies, significantly influencing the advancement trajectory of image processing and computer vision. Recently, IQA has witnessed a notable surge in innovative research efforts, driven by the emergence of novel architectural paradigms ... | {
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2502.08542 | Beyond Predictions: A Participatory Framework for Multi-Stakeholder
Decision-Making | [
"cs.LG",
"cs.MA"
] | Conventional decision-support systems, primarily based on supervised learning, focus on outcome prediction models to recommend actions. However, they often fail to account for the complexities of multi-actor environments, where diverse and potentially conflicting stakeholder preferences must be balanced. In this paper,... | {
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2502.08544 | Moment of Untruth: Dealing with Negative Queries in Video Moment
Retrieval | [
"cs.CV"
] | Video Moment Retrieval is a common task to evaluate the performance of visual-language models - it involves localising start and end times of moments in videos from query sentences. The current task formulation assumes that the queried moment is present in the video, resulting in false positive moment predictions when ... | {
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2502.08547 | Representation Learning to Advance Multi-institutional Studies with
Electronic Health Record Data | [
"cs.AI"
] | The adoption of EHRs has expanded opportunities to leverage data-driven algorithms in clinical care and research. A major bottleneck in effectively conducting multi-institutional EHR studies is the data heterogeneity across systems with numerous codes that either do not exist or represent different clinical concepts ac... | {
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2502.08549 | Copula-based mixture model identification for subgroup clustering with
imaging applications | [
"cs.CV",
"cs.LG"
] | Model-based clustering techniques have been widely applied to various application areas, while most studies focus on canonical mixtures with unique component distribution form. However, this strict assumption is often hard to satisfy. In this paper, we consider the more flexible Copula-Based Mixture Models (CBMMs) for ... | {
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2502.08550 | LLMs can implicitly learn from mistakes in-context | [
"cs.CL",
"cs.AI"
] | Learning from mistakes is a fundamental feature of human intelligence. Previous work has shown that Large Language Models (LLMs) can also learn from incorrect answers when provided with a comprehensive rationale detailing why an answer is wrong or how to correct it. In this work, we examine whether LLMs can learn from ... | {
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2502.08554 | Fostering Appropriate Reliance on Large Language Models: The Role of
Explanations, Sources, and Inconsistencies | [
"cs.HC",
"cs.AI"
] | Large language models (LLMs) can produce erroneous responses that sound fluent and convincing, raising the risk that users will rely on these responses as if they were correct. Mitigating such overreliance is a key challenge. Through a think-aloud study in which participants use an LLM-infused application to answer obj... | {
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2502.08555 | A Machine Learning-Ready Data Processing Tool for Near Real-Time
Forecasting | [
"astro-ph.SR",
"astro-ph.IM",
"cs.LG"
] | Space weather forecasting is critical for mitigating radiation risks in space exploration and protecting Earth-based technologies from geomagnetic disturbances. This paper presents the development of a Machine Learning (ML)- ready data processing tool for Near Real-Time (NRT) space weather forecasting. By merging data ... | {
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2502.08556 | Human-Centric Foundation Models: Perception, Generation and Agentic
Modeling | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM"
] | Human understanding and generation are critical for modeling digital humans and humanoid embodiments. Recently, Human-centric Foundation Models (HcFMs) inspired by the success of generalist models, such as large language and vision models, have emerged to unify diverse human-centric tasks into a single framework, surpa... | {
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2502.08557 | QA-Expand: Multi-Question Answer Generation for Enhanced Query Expansion
in Information Retrieval | [
"cs.IR",
"cs.CL",
"cs.LG",
"cs.MA"
] | Query expansion is widely used in Information Retrieval (IR) to improve search outcomes by enriching queries with additional contextual information. Although recent Large Language Model (LLM) based methods generate pseudo-relevant content and expanded terms via multiple prompts, they often yield repetitive, narrow expa... | {
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2502.08560 | Brain Latent Progression: Individual-based Spatiotemporal Disease
Progression on 3D Brain MRIs via Latent Diffusion | [
"cs.CV",
"cs.AI"
] | The growing availability of longitudinal Magnetic Resonance Imaging (MRI) datasets has facilitated Artificial Intelligence (AI)-driven modeling of disease progression, making it possible to predict future medical scans for individual patients. However, despite significant advancements in AI, current methods continue to... | {
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2502.08561 | Quality-Aware Decoding: Unifying Quality Estimation and Decoding | [
"cs.CL"
] | Quality Estimation (QE) models for Neural Machine Translation (NMT) predict the quality of the hypothesis without having access to the reference. An emerging research direction in NMT involves the use of QE models, which have demonstrated high correlations with human judgment and can enhance translations through Qualit... | {
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2502.08566 | AR Glulam: Accurate Augmented Reality Using Multiple Fiducial Markers
for Glulam Fabrication | [
"cs.ET",
"cs.CV",
"cs.HC"
] | Recent advancements in Augmented Reality (AR) have demonstrated applications in architecture, design, and fabrication. Compared to conventional 2D construction drawings, AR can be used to superimpose contextual instructions, display 3D spatial information and enable on-site engagement. Despite the potential of AR, the ... | {
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2502.08573 | A Novel Approach to for Multimodal Emotion Recognition : Multimodal
semantic information fusion | [
"cs.CV",
"cs.AI"
] | With the advancement of artificial intelligence and computer vision technologies, multimodal emotion recognition has become a prominent research topic. However, existing methods face challenges such as heterogeneous data fusion and the effective utilization of modality correlations. This paper proposes a novel multimod... | {
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2502.08574 | COAST: Intelligent Time-Adaptive Neural Operators | [
"cs.LG",
"cs.AI"
] | We introduce Causal Operator with Adaptive Solver Transformer (COAST), a novel neural operator learning method that leverages a causal language model (CLM) framework to dynamically adapt time steps. Our method predicts both the evolution of a system and its optimal time step, intelligently balancing computational effic... | {
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2502.08576 | Mapping the Landscape of Generative AI in Network Monitoring and
Management | [
"cs.NI",
"cs.AI",
"cs.LG"
] | Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges ... | {
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2502.08577 | FBFL: A Field-Based Coordination Approach for Data Heterogeneity in
Federated Learning | [
"cs.LG",
"cs.AI"
] | In the last years, Federated learning (FL) has become a popular solution to train machine learning models in domains with high privacy concerns. However, FL scalability and performance face significant challenges in real-world deployments where data across devices are non-independently and identically distributed (non-... | {
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2502.08580 | Ultrasound Image Generation using Latent Diffusion Models | [
"cs.CV"
] | Diffusion models for image generation have been a subject of increasing interest due to their ability to generate diverse, high-quality images. Image generation has immense potential in medical imaging because open-source medical images are difficult to obtain compared to natural images, especially for rare conditions.... | {
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2502.08582 | A method for classification of data with uncertainty using hypothesis
testing | [
"cs.LG"
] | Binary classification is a task that involves the classification of data into one of two distinct classes. It is widely utilized in various fields. However, conventional classifiers tend to make overconfident predictions for data that belong to overlapping regions of the two class distributions or for data outside the ... | {
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2502.08585 | Scalable Bilevel Loss Balancing for Multi-Task Learning | [
"cs.LG"
] | Multi-task learning (MTL) has been widely adopted for its ability to simultaneously learn multiple tasks. While existing gradient manipulation methods often yield more balanced solutions than simple scalarization-based approaches, they typically incur a significant computational overhead of $\mathcal{O}(K)$ in both tim... | {
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2502.08586 | Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous
Attacks | [
"cs.LG",
"cs.AI"
] | A high volume of recent ML security literature focuses on attacks against aligned large language models (LLMs). These attacks may extract private information or coerce the model into producing harmful outputs. In real-world deployments, LLMs are often part of a larger agentic pipeline including memory systems, retrieva... | {
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2502.08590 | Light-A-Video: Training-free Video Relighting via Progressive Light
Fusion | [
"cs.CV"
] | Recent advancements in image relighting models, driven by large-scale datasets and pre-trained diffusion models, have enabled the imposition of consistent lighting. However, video relighting still lags, primarily due to the excessive training costs and the scarcity of diverse, high-quality video relighting datasets. A ... | {
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2502.08593 | Toward Universal Laws of Outlier Propagation | [
"cs.LG"
] | We argue that Algorithmic Information Theory (AIT) admits a principled way to quantify outliers in terms of so-called randomness deficiency. For the probability distribution generated by a causal Bayesian network, we show that the randomness deficiency of the joint state decomposes into randomness deficiencies of each ... | {
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2502.08597 | Learning in Markets with Heterogeneous Agents: Dynamics and Survival of
Bayesian vs. No-Regret Learners | [
"cs.GT",
"cs.AI",
"cs.MA",
"econ.TH"
] | We analyze the performance of heterogeneous learning agents in asset markets with stochastic payoffs. Our agents aim to maximize the expected growth rate of their wealth but have different theories on how to learn this best. We focus on comparing Bayesian and no-regret learners in market dynamics. Bayesian learners wit... | {
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2502.08598 | Enhancing Diffusion Models Efficiency by Disentangling Total-Variance
and Signal-to-Noise Ratio | [
"cs.LG",
"stat.ML"
] | The long sampling time of diffusion models remains a significant bottleneck, which can be mitigated by reducing the number of diffusion time steps. However, the quality of samples with fewer steps is highly dependent on the noise schedule, i.e., the specific manner in which noise is introduced and the signal is reduced... | {
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2502.08599 | SPeCtrum: A Grounded Framework for Multidimensional Identity
Representation in LLM-Based Agent | [
"cs.CL"
] | Existing methods for simulating individual identities often oversimplify human complexity, which may lead to incomplete or flattened representations. To address this, we introduce SPeCtrum, a grounded framework for constructing authentic LLM agent personas by incorporating an individual's multidimensional self-concept.... | {
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2502.08600 | Two-stage hybrid models for enhancing forecasting accuracy on
heterogeneous time series | [
"cs.LG"
] | Compared to local models built in a series-by-series manner, global models leverage relevant information across time series, resulting in improved forecasting performance and generalization capacity. Constructing global models on a set of time series is becoming mainstream in the field of time series forecasting. Howev... | {
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2502.08603 | Scalable Thermodynamic Second-order Optimization | [
"cs.ET",
"cs.LG"
] | Many hardware proposals have aimed to accelerate inference in AI workloads. Less attention has been paid to hardware acceleration of training, despite the enormous societal impact of rapid training of AI models. Physics-based computers, such as thermodynamic computers, offer an efficient means to solve key primitives i... | {
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2502.08605 | CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection | [
"cs.LG",
"cs.AI"
] | Does the intrinsic curvature of complex networks hold the key to unveiling graph anomalies that conventional approaches overlook? Reconstruction-based graph anomaly detection (GAD) methods overlook such geometric outliers, focusing only on structural and attribute-level anomalies. To this end, we propose CurvGAD - a mi... | {
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2502.08606 | Distillation Scaling Laws | [
"cs.LG",
"cs.AI",
"cs.CL",
"stat.ML"
] | We provide a distillation scaling law that estimates distilled model performance based on a compute budget and its allocation between the student and teacher. Our findings reduce the risks associated with using distillation at scale; compute allocation for both the teacher and student models can now be done to maximize... | {
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2502.08610 | Quantifying Security Vulnerabilities: A Metric-Driven Security Analysis
of Gaps in Current AI Standards | [
"cs.CR",
"cs.AI"
] | As AI systems integrate into critical infrastructure, security gaps in AI compliance frameworks demand urgent attention. This paper audits and quantifies security risks in three major AI governance standards: NIST AI RMF 1.0, UK's AI and Data Protection Risk Toolkit, and the EU's ALTAI. Using a novel risk assessment me... | {
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2502.08611 | Robustly Learning Monotone Generalized Linear Models via Data
Augmentation | [
"cs.LG",
"math.OC",
"math.ST",
"stat.TH"
] | We study the task of learning Generalized Linear models (GLMs) in the agnostic model under the Gaussian distribution. We give the first polynomial-time algorithm that achieves a constant-factor approximation for \textit{any} monotone Lipschitz activation. Prior constant-factor GLM learners succeed for a substantially s... | {
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2502.08612 | Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model | [
"cs.LG"
] | Non-invasive patient monitoring for tracking and predicting adverse acute health events is an emerging area of research. We pursue in-hospital cardiac arrest (IHCA) prediction using only single-channel finger photoplethysmography (PPG) signals. Our proposed two-stage model Feature Extractor-Aggregator Network (FEAN) le... | {
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2502.08620 | Mathematical Data Science | [
"math.HO",
"cs.LG",
"math.CO",
"math.NT",
"math.RT"
] | Can machine learning help discover new mathematical structures? In this article we discuss an approach to doing this which one can call "mathematical data science". In this paradigm, one studies mathematical objects collectively rather than individually, by creating datasets and doing machine learning experiments and i... | {
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2502.08622 | Forecasting Drought Using Machine Learning in California | [
"cs.LG"
] | Drought is a frequent and costly natural disaster in California, with major negative impacts on agricultural production and water resource availability, particularly groundwater. This study investigated the performance of applying different machine learning approaches to predicting the U.S. Drought Monitor classificati... | {
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2502.08623 | Robot Data Curation with Mutual Information Estimators | [
"cs.RO"
] | The performance of imitation learning policies often hinges on the datasets with which they are trained. Consequently, investment in data collection for robotics has grown across both industrial and academic labs. However, despite the marked increase in the quantity of demonstrations collected, little work has sought t... | {
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2502.08625 | Randomness of Low-Layer Parameters Determines Confusing Samples in Terms
of Interaction Representations of a DNN | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | In this paper, we find that the complexity of interactions encoded by a deep neural network (DNN) can explain its generalization power. We also discover that the confusing samples of a DNN, which are represented by non-generalizable interactions, are determined by its low-layer parameters. In comparison, other factors,... | {
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2502.08628 | Concentration Inequalities for the Stochastic Optimization of Unbounded
Objectives with Application to Denoising Score Matching | [
"stat.ML",
"cs.LG"
] | We derive novel concentration inequalities that bound the statistical error for a large class of stochastic optimization problems, focusing on the case of unbounded objective functions. Our derivations utilize the following tools: 1) A new form of McDiarmid's inequality that is based on sample dependent one component d... | {
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2502.08631 | Ensemble based approach to quantifying uncertainty of LLM based
classifications | [
"cs.AI"
] | The output of Large Language Models (LLMs) are a function of the internal model's parameters and the input provided into the context window. The hypothesis presented here is that under a greedy sampling strategy the variance in the LLM's output is a function of the conceptual certainty embedded in the model's parametri... | {
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2502.08632 | Necessary and Sufficient Oracles: Toward a Computational Taxonomy For
Reinforcement Learning | [
"cs.LG",
"cs.CC"
] | Algorithms for reinforcement learning (RL) in large state spaces crucially rely on supervised learning subroutines to estimate objects such as value functions or transition probabilities. Since only the simplest supervised learning problems can be solved provably and efficiently, practical performance of an RL algorith... | {
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2502.08634 | Rapid Whole Brain Mesoscale In-vivo MR Imaging using Multi-scale
Implicit Neural Representation | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Purpose: To develop and validate a novel image reconstruction technique using implicit neural representations (INR) for multi-view thick-slice acquisitions while reducing the scan time but maintaining high signal-to-noise ratio (SNR). Methods: We propose Rotating-view super-resolution (ROVER)-MRI, an unsupervised neura... | {
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2502.08636 | PulseCheck457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large
Multimodal Models | [
"cs.CV"
] | Although large multimodal models (LMMs) have demonstrated remarkable capabilities in visual scene interpretation and reasoning, their capacity for complex and precise 3-dimensional spatial reasoning remains uncertain. Existing benchmarks focus predominantly on 2D spatial understanding and lack a framework to comprehens... | {
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} |
2502.08637 | Joint Transmit and Pinching Beamforming for PASS: Optimization-Based or
Learning-Based? | [
"eess.SP",
"cs.IT",
"cs.LG",
"math.IT"
] | A novel pinching antenna system (PASS)-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed. PASS consists of multiple waveguides spanning over thousands of wavelength, which equip numerous low-cost dielectric particles, named pinching antennas (PAs), to radiate signals into free space.... | {
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} |
2502.08638 | Examining Multilingual Embedding Models Cross-Lingually Through
LLM-Generated Adversarial Examples | [
"cs.CL"
] | The evaluation of cross-lingual semantic search capabilities of models is often limited to existing datasets from tasks such as information retrieval and semantic textual similarity. To allow for domain-specific evaluation, we introduce Cross Lingual Semantic Discrimination (CLSD), a novel cross-lingual semantic search... | {
"Other": 0,
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"cs.SY": 0
} |
2502.08639 | CineMaster: A 3D-Aware and Controllable Framework for Cinematic
Text-to-Video Generation | [
"cs.CV"
] | In this work, we present CineMaster, a novel framework for 3D-aware and controllable text-to-video generation. Our goal is to empower users with comparable controllability as professional film directors: precise placement of objects within the scene, flexible manipulation of both objects and camera in 3D space, and int... | {
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"cs.SY": 0
} |
2502.08640 | Utility Engineering: Analyzing and Controlling Emergent Value Systems in
AIs | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.CY"
] | As AIs rapidly advance and become more agentic, the risk they pose is governed not only by their capabilities but increasingly by their propensities, including goals and values. Tracking the emergence of goals and values has proven a longstanding problem, and despite much interest over the years it remains unclear whet... | {
"Other": 0,
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} |
2502.08642 | SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation | [
"cs.CV"
] | Recent advancements in large vision-language models have enabled highly expressive and diverse vector sketch generation. However, state-of-the-art methods rely on a time-consuming optimization process involving repeated feedback from a pretrained model to determine stroke placement. Consequently, despite producing impr... | {
"Other": 0,
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"cs.SY": 0
} |
2502.08643 | A Real-to-Sim-to-Real Approach to Robotic Manipulation with
VLM-Generated Iterative Keypoint Rewards | [
"cs.RO",
"cs.AI",
"cs.CV"
] | Task specification for robotic manipulation in open-world environments is challenging, requiring flexible and adaptive objectives that align with human intentions and can evolve through iterative feedback. We introduce Iterative Keypoint Reward (IKER), a visually grounded, Python-based reward function that serves as a ... | {
"Other": 0,
"cs.AI": 1,
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"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.08644 | Rhythmic sharing: A bio-inspired paradigm for zero-shot adaptation and
learning in neural networks | [
"cs.LG",
"cs.AI",
"math.DS",
"nlin.AO",
"physics.bio-ph"
] | The brain can rapidly adapt to new contexts and learn from limited data, a coveted characteristic that artificial intelligence algorithms have struggled to mimic. Inspired by oscillatory rhythms of the mechanical structures of neural cells, we developed a learning paradigm that is based on oscillations in link strength... | {
"Other": 0,
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"cs.SY": 0
} |
2502.08645 | Re$^3$Sim: Generating High-Fidelity Simulation Data via
3D-Photorealistic Real-to-Sim for Robotic Manipulation | [
"cs.RO"
] | Real-world data collection for robotics is costly and resource-intensive, requiring skilled operators and expensive hardware. Simulations offer a scalable alternative but often fail to achieve sim-to-real generalization due to geometric and visual gaps. To address these challenges, we propose a 3D-photorealistic real-t... | {
"Other": 0,
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"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.08646 | Poly-Autoregressive Prediction for Modeling Interactions | [
"cs.CV"
] | We introduce a simple framework for predicting the behavior of an agent in multi-agent settings. In contrast to autoregressive (AR) tasks, such as language processing, our focus is on scenarios with multiple agents whose interactions are shaped by physical constraints and internal motivations. To this end, we propose P... | {
"Other": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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