id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.07424 | RomanLens: The Role Of Latent Romanization In Multilinguality In LLMs | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) exhibit remarkable multilingual generalization despite being predominantly trained on English-centric corpora. A fundamental question arises: how do LLMs achieve such robust multilingual capabilities? We take the case of non-Roman script languages, we investigate the role of Romanization - ... | {
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2502.07425 | Towards a Foundation Model for Physics-Informed Neural Networks:
Multi-PDE Learning with Active Sampling | [
"cs.LG"
] | Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs) by embedding physical laws into neural network training. However, traditional PINN models are typically designed for single PDEs, limiting their generalizability across different physical syst... | {
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2502.07431 | ArthroPhase: A Novel Dataset and Method for Phase Recognition in
Arthroscopic Video | [
"cs.CV"
] | This study aims to advance surgical phase recognition in arthroscopic procedures, specifically Anterior Cruciate Ligament (ACL) reconstruction, by introducing the first arthroscopy dataset and developing a novel transformer-based model. We aim to establish a benchmark for arthroscopic surgical phase recognition by leve... | {
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2502.07432 | CapyMOA: Efficient Machine Learning for Data Streams in Python | [
"cs.LG"
] | CapyMOA is an open-source library designed for efficient machine learning on streaming data. It provides a structured framework for real-time learning and evaluation, featuring a flexible data representation. CapyMOA includes an extensible architecture that allows integration with external frameworks such as MOA and Py... | {
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2502.07436 | Optimizing Knowledge Distillation in Transformers: Enabling Multi-Head
Attention without Alignment Barriers | [
"cs.CV"
] | Knowledge distillation (KD) in transformers often faces challenges due to misalignment in the number of attention heads between teacher and student models. Existing methods either require identical head counts or introduce projectors to bridge dimensional gaps, limiting flexibility and efficiency. We propose Squeezing-... | {
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2502.07441 | SensPS: Sensing Personal Space Comfortable Distance between Human-Human
Using Multimodal Sensors | [
"cs.HC",
"cs.AI"
] | Personal space, also known as peripersonal space, is crucial in human social interaction, influencing comfort, communication, and social stress. Estimating and respecting personal space is essential for enhancing human-computer interaction (HCI) and smart environments. Personal space preferences vary due to individual ... | {
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2502.07442 | Hierarchical Document Parsing via Large Margin Feature Matching and
Heuristics | [
"cs.CL",
"cs.CV"
] | We present our solution to the AAAI-25 VRD-IU challenge, achieving first place in the competition. Our approach integrates large margin loss for improved feature discrimination and employs heuristic rules to refine hierarchical relationships. By combining a deep learning-based matching strategy with greedy algorithms, ... | {
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2502.07443 | Approximating Human Strategic Reasoning with LLM-Enhanced Recursive
Reasoners Leveraging Multi-agent Hypergames | [
"cs.AI",
"cs.GT"
] | LLM-driven multi-agent-based simulations have been gaining traction with applications in game-theoretic and social simulations. While most implementations seek to exploit or evaluate LLM-agentic reasoning, they often do so with a weak notion of agency and simplified architectures. We implement a role-based multi-agent ... | {
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2502.07445 | Forget What You Know about LLMs Evaluations -- LLMs are Like a Chameleon | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) often appear to excel on public benchmarks, but these high scores may mask an overreliance on dataset-specific surface cues rather than true language understanding. We introduce the Chameleon Benchmark Overfit Detector (C-BOD), a meta-evaluation framework that systematically distorts benchm... | {
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2502.07452 | Eliciting Rational Initial Weights in Gradual Argumentation | [
"cs.AI"
] | Many semantics for weighted argumentation frameworks assume that each argument is associated with an initial weight. However, eliciting these initial weights poses challenges: (1) accurately providing a specific numerical value is often difficult, and (2) individuals frequently confuse initial weights with acceptabilit... | {
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2502.07455 | RusCode: Russian Cultural Code Benchmark for Text-to-Image Generation | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Text-to-image generation models have gained popularity among users around the world. However, many of these models exhibit a strong bias toward English-speaking cultures, ignoring or misrepresenting the unique characteristics of other language groups, countries, and nationalities. The lack of cultural awareness can red... | {
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2502.07456 | FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for
Federated Learning on Heterogeneous Data | [
"cs.LG",
"cs.CV"
] | Personalized federated learning (PFL) tailors models to clients' unique data distributions while preserving privacy. However, existing aggregation-weight-based PFL methods often struggle with heterogeneous data, facing challenges in accuracy, computational efficiency, and communication overhead. We propose FedAPA, a no... | {
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2502.07457 | Bidirectional Uncertainty-Aware Region Learning for Semi-Supervised
Medical Image Segmentation | [
"cs.CV"
] | In semi-supervised medical image segmentation, the poor quality of unlabeled data and the uncertainty in the model's predictions lead to models that inevitably produce erroneous pseudo-labels. These errors accumulate throughout model training, thereby weakening the model's performance. We found that these erroneous pse... | {
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2502.07459 | PerCul: A Story-Driven Cultural Evaluation of LLMs in Persian | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Large language models predominantly reflect Western cultures, largely due to the dominance of English-centric training data. This imbalance presents a significant challenge, as LLMs are increasingly used across diverse contexts without adequate evaluation of their cultural competence in non-English languages, including... | {
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2502.07460 | Logarithmic Regret for Online KL-Regularized Reinforcement Learning | [
"cs.LG",
"stat.ML"
] | Recent advances in Reinforcement Learning from Human Feedback (RLHF) have shown that KL-regularization plays a pivotal role in improving the efficiency of RL fine-tuning for large language models (LLMs). Despite its empirical advantage, the theoretical difference between KL-regularized RL and standard RL remains largel... | {
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2502.07461 | JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed
Metadata | [
"cs.SD",
"cs.AI"
] | We introduce JamendoMaxCaps, a large-scale music-caption dataset featuring over 200,000 freely licensed instrumental tracks from the renowned Jamendo platform. The dataset includes captions generated by a state-of-the-art captioning model, enhanced with imputed metadata. We also introduce a retrieval system that levera... | {
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2502.07465 | Crime Forecasting: A Spatio-temporal Analysis with Deep Learning Models | [
"cs.LG",
"cs.AI"
] | This study uses deep-learning models to predict city partition crime counts on specific days. It helps police enhance surveillance, gather intelligence, and proactively prevent crimes. We formulate crime count prediction as a spatiotemporal sequence challenge, where both input data and prediction targets are spatiotemp... | {
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2502.07466 | Less is More: Masking Elements in Image Condition Features Avoids
Content Leakages in Style Transfer Diffusion Models | [
"cs.CV"
] | Given a style-reference image as the additional image condition, text-to-image diffusion models have demonstrated impressive capabilities in generating images that possess the content of text prompts while adopting the visual style of the reference image. However, current state-of-the-art methods often struggle to dise... | {
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2502.07467 | Integrated Sensing, Communication, and Over-The-Air Control of UAV Swarm
Dynamics | [
"eess.SP",
"cs.SY",
"eess.SY"
] | Coordinated controlling a large UAV swarm requires significant spectrum resources due to the need for bandwidth allocation per UAV, posing a challenge in resource-limited environments. Over-the-air (OTA) control has emerged as a spectrum-efficient approach, leveraging electromagnetic superposition to form control signa... | {
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2502.07469 | 5D Neural Surrogates for Nonlinear Gyrokinetic Simulations of Plasma
Turbulence | [
"physics.plasm-ph",
"cs.AI",
"cs.LG",
"stat.ML"
] | Nuclear fusion plays a pivotal role in the quest for reliable and sustainable energy production. A major roadblock to achieving commercially viable fusion power is understanding plasma turbulence, which can significantly degrade plasma confinement. Modelling turbulence is crucial to design performing plasma scenarios f... | {
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2502.07470 | On Event-Triggered Resilient Consensus Using Auxiliary Layer | [
"eess.SY",
"cs.SY"
] | Due to its design simplicity, auxiliary layer-based resilient control is widely discussed in the literature to mitigate the effects of False Data Injection (FDI) attacks. However, the increased communication burden due to additional communication links for connecting an extra layer is often overlooked in the literature... | {
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2502.07472 | Robotic In-Hand Manipulation for Large-Range Precise Object Movement:
The RGMC Champion Solution | [
"cs.RO"
] | In-hand manipulation using multiple dexterous fingers is a critical robotic skill that can reduce the reliance on large arm motions, thereby saving space and energy. This letter focuses on in-grasp object movement, which refers to manipulating an object to a desired pose through only finger motions within a stable gras... | {
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2502.07474 | ETimeline: An Extensive Timeline Generation Dataset based on Large
Language Model | [
"cs.IR"
] | Timeline generation is of great significance for a comprehensive understanding of the development of events over time. Its goal is to organize news chronologically, which helps to identify patterns and trends that may be obscured when viewing news in isolation, making it easier to track the development of stories and u... | {
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2502.07479 | WebChecker: A Versatile EVL Plugin for Validating HTML Pages with
Bootstrap Frameworks | [
"cs.SE",
"cs.AI"
] | WebChecker is a plugin for Epsilon Validation Language (EVL), designed to validate both static and dynamic HTML pages utilizing frameworks like Bootstrap. By employing configurable EVL constraints, WebChecker enforces implicit rules governing HTML and CSS frameworks. The effectiveness of the plugin is demonstrated thro... | {
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2502.07480 | Overfitting Regimes of Nadaraya-Watson Interpolators | [
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | In recent years, there has been much interest in understanding the generalization behavior of interpolating predictors, which overfit on noisy training data. Whereas standard analyses are concerned with whether a method is consistent or not, recent observations have shown that even inconsistent predictors can generaliz... | {
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2502.07486 | Automated Road Extraction and Centreline Fitting in LiDAR Point Clouds | [
"cs.CV"
] | Road information extraction from 3D point clouds is useful for urban planning and traffic management. Existing methods often rely on local features and the refraction angle of lasers from kerbs, which makes them sensitive to variable kerb designs and issues in high-density areas due to data homogeneity. We propose an a... | {
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2502.07487 | Multi-Agent Collaboration for Multilingual Code Instruction Tuning | [
"cs.CL"
] | Recent advancement in code understanding and generation demonstrates that code LLMs fine-tuned on a high-quality instruction dataset can gain powerful capabilities to address wide-ranging code-related tasks. However, most previous existing methods mainly view each programming language in isolation and ignore the knowle... | {
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2502.07488 | Improving Adaptive Moment Optimization via Preconditioner
Diagonalization | [
"cs.LG"
] | Modern adaptive optimization methods, such as Adam and its variants, have emerged as the most widely used tools in deep learning over recent years. These algorithms offer automatic mechanisms for dynamically adjusting the update step based on estimates of gradient statistics. Compared to traditional algorithms like Sto... | {
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2502.07489 | Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time
Series Forecasting Based on Biological ODEs | [
"cs.LG"
] | State-of-the-art methods for forecasting irregularly sampled time series with missing values predominantly rely on just four datasets and a few small toy examples for evaluation. While ordinary differential equations (ODE) are the prevalent models in science and engineering, a baseline model that forecasts a constant v... | {
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2502.07490 | Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn
More | [
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) are discovered to suffer from accurately retrieving key information. To address this, we propose Mask-Enhanced Autoregressive Prediction (MEAP), a simple yet effective training paradigm that seamlessly integrates Masked Language Modeling (MLM) into Next-Token Prediction (NTP) to enhance the... | {
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2502.07491 | Exploring Patterns Behind Sports | [
"cs.LG",
"cs.IR"
] | This paper presents a comprehensive framework for time series prediction using a hybrid model that combines ARIMA and LSTM. The model incorporates feature engineering techniques, including embedding and PCA, to transform raw data into a lower-dimensional representation while retaining key information. The embedding tec... | {
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2502.07492 | RoMA: Robust Malware Attribution via Byte-level Adversarial Training
with Global Perturbations and Adversarial Consistency Regularization | [
"cs.CR",
"cs.CV"
] | Attributing APT (Advanced Persistent Threat) malware to their respective groups is crucial for threat intelligence and cybersecurity. However, APT adversaries often conceal their identities, rendering attribution inherently adversarial. Existing machine learning-based attribution models, while effective, remain highly ... | {
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2502.07494 | URECA: The Chain of Two Minimum Set Cover Problems exists behind
Adaptation to Shifts in Semantic Code Search | [
"cs.AI"
] | Adaptation is to make model learn the patterns shifted from the training distribution. In general, this adaptation is formulated as the minimum entropy problem. However, the minimum entropy problem has inherent limitation -- shifted initialization cascade phenomenon. We extend the relationship between the minimum entro... | {
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2502.07495 | LLM-Sketch: Enhancing Network Sketches with LLM | [
"cs.NI",
"cs.LG"
] | Network stream mining is fundamental to many network operations. Sketches, as compact data structures that offer low memory overhead with bounded accuracy, have emerged as a promising solution for network stream mining. Recent studies attempt to optimize sketches using machine learning; however, these approaches face t... | {
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2502.07497 | On Training-Conditional Conformal Prediction and Binomial Proportion
Confidence Intervals | [
"cs.LG"
] | Estimating the expectation of a Bernoulli random variable based on N independent trials is a classical problem in statistics, typically addressed using Binomial Proportion Confidence Intervals (BPCI). In the control systems community, many critical tasks-such as certifying the statistical safety of dynamical systems-ca... | {
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2502.07500 | Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph
Problems | [
"cs.LG"
] | Deep neural networks have enabled researchers to create powerful generalized frameworks, such as transformers, that can be used to solve well-studied problems in various application domains, such as text and image. However, such generalized frameworks are not available for solving graph problems. Graph structures are u... | {
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2502.07503 | Recursive Inference Scaling: A Winning Path to Scalable Inference in
Language and Multimodal Systems | [
"cs.AI",
"cs.LG"
] | Recent research in language modeling reveals two scaling effects: the well-known improvement from increased training compute, and a lesser-known boost from applying more sophisticated or computationally intensive inference methods. Inspired by recent findings on the fractal geometry of language, we introduce Recursive ... | {
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2502.07505 | Efficient Continuous Group Convolutions for Local SE(3) Equivariance in
3D Point Clouds | [
"cs.CV"
] | Extending the translation equivariance property of convolutional neural networks to larger symmetry groups has been shown to reduce sample complexity and enable more discriminative feature learning. Further, exploiting additional symmetries facilitates greater weight sharing than standard convolutions, leading to an en... | {
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2502.07508 | Enhance-A-Video: Better Generated Video for Free | [
"cs.CV"
] | DiT-based video generation has achieved remarkable results, but research into enhancing existing models remains relatively unexplored. In this work, we introduce a training-free approach to enhance the coherence and quality of DiT-based generated videos, named Enhance-A-Video. The core idea is enhancing the cross-frame... | {
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2502.07509 | Dual Arm Steering of Deformable Linear Objects in 2-D and 3-D
Environments Using Euler's Elastica Solutions | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper describes a method for steering deformable linear objects using two robot hands in environments populated by sparsely spaced obstacles. The approach involves manipulating an elastic inextensible rod by varying the gripping endpoint positions and tangents. Closed form solutions that describe the flexible line... | {
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2502.07510 | Joint Metric Space Embedding by Unbalanced OT with Gromov-Wasserstein
Marginal Penalization | [
"cs.LG"
] | We propose a new approach for unsupervised alignment of heterogeneous datasets, which maps data from two different domains without any known correspondences to a common metric space. Our method is based on an unbalanced optimal transport problem with Gromov-Wasserstein marginal penalization. It can be seen as a counter... | {
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2502.07511 | Quantitative evaluation of unsupervised clustering algorithms for
dynamic total-body PET image analysis | [
"stat.AP",
"cs.CV"
] | Background. Recently, dynamic total-body positron emission tomography (PET) imaging has become possible due to new scanner devices. While clustering algorithms have been proposed for PET analysis already earlier, there is still little research systematically evaluating these algorithms for processing of dynamic total-b... | {
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2502.07514 | A Near-optimal, Scalable and Corruption-tolerant Framework for
Stochastic Bandits: From Single-Agent to Multi-Agent and Beyond | [
"cs.LG"
] | We investigate various stochastic bandit problems in the presence of adversarial corruption. A seminal contribution to this area is the BARBAR~\citep{gupta2019better} algorithm, which is both simple and efficient, tolerating significant levels of corruption with nearly no degradation in performance. However, its regret... | {
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2502.07516 | The Devil is in the Prompts: De-Identification Traces Enhance
Memorization Risks in Synthetic Chest X-Ray Generation | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Generative models, particularly text-to-image (T2I) diffusion models, play a crucial role in medical image analysis. However, these models are prone to training data memorization, posing significant risks to patient privacy. Synthetic chest X-ray generation is one of the most common applications in medical image analys... | {
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2502.07523 | Scaling Off-Policy Reinforcement Learning with Batch and Weight
Normalization | [
"cs.LG",
"cs.AI"
] | Reinforcement learning has achieved significant milestones, but sample efficiency remains a bottleneck for real-world applications. Recently, CrossQ has demonstrated state-of-the-art sample efficiency with a low update-to-data (UTD) ratio of 1. In this work, we explore CrossQ's scaling behavior with higher UTD ratios. ... | {
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2502.07526 | CodePhys: Robust Video-based Remote Physiological Measurement through
Latent Codebook Querying | [
"cs.CV"
] | Remote photoplethysmography (rPPG) aims to measure non-contact physiological signals from facial videos, which has shown great potential in many applications. Most existing methods directly extract video-based rPPG features by designing neural networks for heart rate estimation. Although they can achieve acceptable res... | {
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2502.07527 | NatureLM: Deciphering the Language of Nature for Scientific Discovery | [
"cs.AI",
"cs.LG"
] | Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages. Inspired by the success of these foundation models, researchers have developed foundation models for individual scientific domains, including small... | {
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2502.07528 | Forecasting the future development in quality and value of professional
football players for applications in team management | [
"stat.AP",
"cs.LG"
] | Transfers in professional football (soccer) are risky investments because of the large transfer fees and high risks involved. Although data-driven models can be used to improve transfer decisions, existing models focus on describing players' historical progress, leaving their future performance unknown. Moreover, recen... | {
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2502.07529 | Training Deep Learning Models with Norm-Constrained LMOs | [
"cs.LG",
"math.OC"
] | In this work, we study optimization methods that leverage the linear minimization oracle (LMO) over a norm-ball. We propose a new stochastic family of algorithms that uses the LMO to adapt to the geometry of the problem and, perhaps surprisingly, show that they can be applied to unconstrained problems. The resulting up... | {
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2502.07531 | VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video
Generation | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM"
] | Recent image-to-video generation methods have demonstrated success in enabling control over one or two visual elements, such as camera trajectory or object motion. However, these methods are unable to offer control over multiple visual elements due to limitations in data and network efficacy. In this paper, we introduc... | {
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2502.07532 | Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with
Diffusion | [
"cs.LG",
"physics.ao-ph"
] | Machine learning methods have been shown to be effective for weather forecasting, based on the speed and accuracy compared to traditional numerical models. While early efforts primarily concentrated on deterministic predictions, the field has increasingly shifted toward probabilistic forecasting to better capture the f... | {
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2502.07541 | Corporate Greenwashing Detection in Text -- a Survey | [
"cs.CL"
] | Greenwashing is an effort to mislead the public about the environmental impact of an entity, such as a state or company. We provide a comprehensive survey of the scientific literature addressing natural language processing methods to identify potentially misleading climate-related corporate communications, indicative o... | {
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2502.07542 | Exoplanet Transit Candidate Identification in TESS Full-Frame Images via
a Transformer-Based Algorithm | [
"astro-ph.EP",
"astro-ph.GA",
"astro-ph.IM",
"cs.AI"
] | The Transiting Exoplanet Survey Satellite (TESS) is surveying a large fraction of the sky, generating a vast database of photometric time series data that requires thorough analysis to identify exoplanetary transit signals. Automated learning approaches have been successfully applied to identify transit signals. Howeve... | {
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2502.07544 | Grammar Control in Dialogue Response Generation for Language Learning
Chatbots | [
"cs.CL"
] | Chatbots based on large language models offer cheap conversation practice opportunities for language learners. However, they are hard to control for linguistic forms that correspond to learners' current needs, such as grammar. We control grammar in chatbot conversation practice by grounding a dialogue response generati... | {
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2502.07547 | Instance-dependent Early Stopping | [
"cs.LG"
] | In machine learning practice, early stopping has been widely used to regularize models and can save computational costs by halting the training process when the model's performance on a validation set stops improving. However, conventional early stopping applies the same stopping criterion to all instances without cons... | {
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2502.07549 | HGTUL: A Hypergraph-based Model For Trajectory User Linking | [
"cs.LG",
"cs.AI"
] | Trajectory User Linking (TUL), which links anonymous trajectories with users who generate them, plays a crucial role in modeling human mobility. Despite significant advancements in this field, existing studies primarily neglect the high-order inter-trajectory relationships, which represent complex associations among mu... | {
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2502.07551 | Early Stopping Against Label Noise Without Validation Data | [
"cs.LG"
] | Early stopping methods in deep learning face the challenge of balancing the volume of training and validation data, especially in the presence of label noise. Concretely, sparing more data for validation from training data would limit the performance of the learned model, yet insufficient validation data could result i... | {
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2502.07552 | Unsupervised Translation of Emergent Communication | [
"cs.CL",
"cs.AI"
] | Emergent Communication (EC) provides a unique window into the language systems that emerge autonomously when agents are trained to jointly achieve shared goals. However, it is difficult to interpret EC and evaluate its relationship with natural languages (NL). This study employs unsupervised neural machine translation ... | {
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2502.07553 | Attention Learning is Needed to Efficiently Learn Parity Function | [
"cs.LG"
] | Transformers, with their attention mechanisms, have emerged as the state-of-the-art architectures of sequential modeling and empirically outperform feed-forward neural networks (FFNNs) across many fields, such as natural language processing and computer vision. However, their generalization ability, particularly for lo... | {
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2502.07555 | O1 Embedder: Let Retrievers Think Before Action | [
"cs.CL"
] | The growing power of large language models (LLMs) has revolutionized how people access and utilize information. Notably, the LLMs excel at performing fine-grained data representation, which facilitates precise retrieval of information. They also generate high-quality answers based on external references, enabling the p... | {
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2502.07556 | SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image
Generation with Region-Based Sketches | [
"cs.HC",
"cs.CV"
] | Text-to-image models can generate visually appealing images from text descriptions. Efforts have been devoted to improving model controls with prompt tuning and spatial conditioning. However, our formative study highlights the challenges for non-expert users in crafting appropriate prompts and specifying fine-grained s... | {
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2502.07558 | Efficient Sparsification of Simplicial Complexes via Local Densities of
States | [
"stat.ML",
"cs.CG",
"cs.DM",
"cs.NA",
"cs.SI",
"math.NA"
] | Simplicial complexes (SCs), a generalization of graph models for relational data that account for higher-order relations between data items, have become a popular abstraction for analyzing complex data using tools from topological data analysis or topological signal processing. However, the analysis of many real-world ... | {
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2502.07560 | Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning | [
"cs.CV"
] | Class-incremental learning (CIL) seeks to enable a model to sequentially learn new classes while retaining knowledge of previously learned ones. Balancing flexibility and stability remains a significant challenge, particularly when the task ID is unknown. To address this, our study reveals that the gap in feature distr... | {
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2502.07562 | LoRP-TTS: Low-Rank Personalized Text-To-Speech | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Speech synthesis models convert written text into natural-sounding audio. While earlier models were limited to a single speaker, recent advancements have led to the development of zero-shot systems that generate realistic speech from a wide range of speakers using their voices as additional prompts. However, they still... | {
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2502.07563 | LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its
Hybrid | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Linear sequence modeling approaches, such as linear attention, provide advantages like linear-time training and constant-memory inference over sequence lengths. However, existing sequence parallelism (SP) methods are either not optimized for the right-product-first feature of linear attention or use a ring-style commun... | {
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2502.07564 | An Elliptic Curve Based Solution to the Perspective-Three-Point Problem | [
"cs.CV",
"math.AG"
] | The Perspective-Three-Point Problem (P3P) is solved by first focusing on determining the directions of the lines through pairs of control points, relative to the camera, rather than the distances from the camera to the control points. The analysis of this produces an efficient, accurate and reasonably simple P3P solver... | {
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2502.07566 | Capacity of the Binary Energy Harvesting Channel | [
"cs.IT",
"math.IT"
] | The capacity of a channel with an energy-harvesting (EH) encoder and a finite battery remains an open problem, even in the noiseless case. A key instance of this scenario is the binary EH channel (BEHC), where the encoder has a unit-sized battery and binary inputs. Existing capacity expressions for the BEHC are not com... | {
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2502.07575 | Towards Efficient and Multifaceted Computer-assisted Pronunciation
Training Leveraging Hierarchical Selective State Space Model and Decoupled
Cross-entropy Loss | [
"eess.AS",
"cs.CL"
] | Prior efforts in building computer-assisted pronunciation training (CAPT) systems often treat automatic pronunciation assessment (APA) and mispronunciation detection and diagnosis (MDD) as separate fronts: the former aims to provide multiple pronunciation aspect scores across diverse linguistic levels, while the latter... | {
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2502.07577 | Automated Capability Discovery via Model Self-Exploration | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Foundation models have become general-purpose assistants, exhibiting diverse capabilities across numerous domains through training on web-scale data. It remains challenging to precisely characterize even a fraction of the full spectrum of capabilities and potential risks in any new model. Existing evaluation approaches... | {
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2502.07579 | Single-Step Consistent Diffusion Samplers | [
"cs.LG",
"stat.ML"
] | Sampling from unnormalized target distributions is a fundamental yet challenging task in machine learning and statistics. Existing sampling algorithms typically require many iterative steps to produce high-quality samples, leading to high computational costs that limit their practicality in time-sensitive or resource-c... | {
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2502.07580 | Generative Modeling with Bayesian Sample Inference | [
"cs.LG",
"stat.ML"
] | We derive a novel generative model from the simple act of Gaussian posterior inference. Treating the generated sample as an unknown variable to infer lets us formulate the sampling process in the language of Bayesian probability. Our model uses a sequence of prediction and posterior update steps to narrow down the unkn... | {
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2502.07584 | Understanding the Generalization Error of Markov algorithms through
Poissonization | [
"stat.ML",
"cs.LG"
] | Using continuous-time stochastic differential equation (SDE) proxies to stochastic optimization algorithms has proven fruitful for understanding their generalization abilities. A significant part of these approaches are based on the so-called ``entropy flows'', which greatly simplify the generalization analysis. Unfort... | {
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2502.07586 | We Can't Understand AI Using our Existing Vocabulary | [
"cs.CL",
"cs.AI"
] | This position paper argues that, in order to understand AI, we cannot rely on our existing vocabulary of human words. Instead, we should strive to develop neologisms: new words that represent precise human concepts that we want to teach machines, or machine concepts that we need to learn. We start from the premise that... | {
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2502.07587 | SEMU: Singular Value Decomposition for Efficient Machine Unlearning | [
"cs.LG"
] | While the capabilities of generative foundational models have advanced rapidly in recent years, methods to prevent harmful and unsafe behaviors remain underdeveloped. Among the pressing challenges in AI safety, machine unlearning (MU) has become increasingly critical to meet upcoming safety regulations. Most existing M... | {
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2502.07590 | DSV: Exploiting Dynamic Sparsity to Accelerate Large-Scale Video DiT
Training | [
"cs.DC",
"cs.CV"
] | Diffusion Transformers (DiTs) have shown remarkable performance in modeling and generating high-quality videos. However, the quadratic computational complexity of 3D full attention mechanism presents significant challenges in scaling video DiT training, especially for high-definition and lengthy videos, where attention... | {
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2502.07591 | DMWM: Dual-Mind World Model with Long-Term Imagination | [
"cs.LG",
"cs.AI"
] | Imagination in world models is crucial for enabling agents to learn long-horizon policy in a sample-efficient manner. Existing recurrent state-space model (RSSM)-based world models depend on single-step statistical inference to capture the environment dynamics, and, hence, they are unable to perform long-term imaginati... | {
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2502.07592 | YOLO Network For Defect Detection In Optical lenses | [
"cs.CV"
] | Mass-produced optical lenses often exhibit defects that alter their scattering properties and compromise quality standards. Manual inspection is usually adopted to detect defects, but it is not recommended due to low accuracy, high error rate and limited scalability. To address these challenges, this study presents an ... | {
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2502.07595 | Distributed Coverage Control for Time-Varying Spatial Processes | [
"cs.RO"
] | Multi-robot systems are essential for environmental monitoring, particularly for tracking spatial phenomena like pollution, soil minerals, and water salinity, and more. This study addresses the challenge of deploying a multi-robot team for optimal coverage in environments where the density distribution, describing area... | {
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2502.07596 | Enviro-IoT: Calibrating Low-Cost Environmental Sensors in Urban Settings | [
"eess.SY",
"cs.SY"
] | Low-cost miniaturised sensors offer significant advantage to monitor the environment in real-time and accurately. The area of air quality monitoring has attracted much attention in recent years because of the increasing impacts on the environment and more personally to human health and mental wellbeing. Rapid growth in... | {
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2502.07599 | DPO-Shift: Shifting the Distribution of Direct Preference Optimization | [
"cs.CL"
] | Direct Preference Optimization (DPO) and its variants have become increasingly popular for aligning language models with human preferences. These methods aim to teach models to better distinguish between chosen (or preferred) and rejected (or dispreferred) responses. However, prior research has identified that the prob... | {
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2502.07600 | PlaySlot: Learning Inverse Latent Dynamics for Controllable
Object-Centric Video Prediction and Planning | [
"cs.CV",
"cs.RO"
] | Predicting future scene representations is a crucial task for enabling robots to understand and interact with the environment. However, most existing methods rely on video sequences and simulations with precise action annotations, limiting their ability to leverage the large amount of available unlabeled video data. To... | {
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2502.07601 | Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large
Language Models | [
"cs.CV",
"cs.CL"
] | Zero-Shot Anomaly Detection (ZSAD) is an emerging AD paradigm. Unlike the traditional unsupervised AD setting that requires a large number of normal samples to train a model, ZSAD is more practical for handling data-restricted real-world scenarios. Recently, Multimodal Large Language Models (MLLMs) have shown revolutio... | {
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2502.07602 | An Improved Optimal Proximal Gradient Algorithm for Non-Blind Image
Deblurring | [
"cs.CV",
"math.OC"
] | Image deblurring remains a central research area within image processing, critical for its role in enhancing image quality and facilitating clearer visual representations across diverse applications. This paper tackles the optimization problem of image deblurring, assuming a known blurring kernel. We introduce an impro... | {
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2502.07606 | Algorithmic Aspects of Strategic Trading | [
"cs.GT",
"cs.CE",
"cs.LG"
] | Algorithmic trading in modern financial markets is widely acknowledged to exhibit strategic, game-theoretic behaviors whose complexity can be difficult to model. A recent series of papers (Chriss, 2024b,c,a, 2025) has made progress in the setting of trading for position building. Here parties wish to buy or sell a fixe... | {
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2502.07608 | Beyond Prompting: Time2Lang -- Bridging Time-Series Foundation Models
and Large Language Models for Health Sensing | [
"cs.LG",
"cs.HC"
] | Large language models (LLMs) show promise for health applications when combined with behavioral sensing data. Traditional approaches convert sensor data into text prompts, but this process is prone to errors, computationally expensive, and requires domain expertise. These challenges are particularly acute when processi... | {
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2502.07615 | Flow Distillation Sampling: Regularizing 3D Gaussians with Pre-trained
Matching Priors | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has achieved excellent rendering quality with fast training and rendering speed. However, its optimization process lacks explicit geometric constraints, leading to suboptimal geometric reconstruction in regions with sparse or no observational input views. In this work, we try to mitigate th... | {
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2502.07616 | Tractable Transformers for Flexible Conditional Generation | [
"cs.CL",
"cs.LG"
] | Non-autoregressive (NAR) generative models are valuable because they can handle diverse conditional generation tasks in a more principled way than their autoregressive (AR) counterparts, which are constrained by sequential dependency requirements. Recent advancements in NAR models, such as diffusion language models, ha... | {
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2502.07617 | Scaling Pre-training to One Hundred Billion Data for Vision Language
Models | [
"cs.CV"
] | We provide an empirical investigation of the potential of pre-training vision-language models on an unprecedented scale: 100 billion examples. We find that model performance tends to saturate at this scale on many common Western-centric classification and retrieval benchmarks, such as COCO Captions. Nevertheless, tasks... | {
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2502.07620 | Causal-Informed Contrastive Learning: Towards Bias-Resilient
Pre-training under Concept Drift | [
"cs.LG",
"cs.CV"
] | The evolution of large-scale contrastive pre-training propelled by top-tier datasets has reached a transition point in the scaling law. Consequently, sustaining and enhancing a model's pre-training capabilities in drift environments have surfaced as a notable challenge. In this paper, we initially uncover that contrast... | {
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2502.07623 | Lexical categories of stem-forming roots in Mapud\"ungun verb forms | [
"cs.CL"
] | After developing a computational system for morphological analysis of the Mapuche language, and evaluating it with texts from various authors and styles, it became necessary to verify the linguistic assumptions of the source used as the basis for implementing this tool. In the present work, the primary focus is on th... | {
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2502.07629 | Exploring Mobile Touch Interaction with Large Language Models | [
"cs.HC",
"cs.CL"
] | Interacting with Large Language Models (LLMs) for text editing on mobile devices currently requires users to break out of their writing environment and switch to a conversational AI interface. In this paper, we propose to control the LLM via touch gestures performed directly on the text. We first chart a design space t... | {
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} |
2502.07630 | Rethinking Timing Residuals: Advancing PET Detectors with Explicit TOF
Corrections | [
"physics.ins-det",
"cs.LG"
] | PET is a functional imaging method that visualizes metabolic processes. TOF information can be derived from coincident detector signals and incorporated into image reconstruction to enhance the SNR. PET detectors are typically assessed by their CTR, but timing performance is degraded by various factors. Research on tim... | {
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} |
2502.07631 | Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous
Driving | [
"cs.CV"
] | Perceiving the environment and its changes over time corresponds to two fundamental yet heterogeneous types of information: semantics and motion. Previous end-to-end autonomous driving works represent both types of information in a single feature vector. However, including motion tasks, such as prediction and planning,... | {
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} |
2502.07634 | Efficient Distributed Training through Gradient Compression with
Sparsification and Quantization Techniques | [
"cs.LG",
"cs.MM"
] | This study investigates the impact of gradient compression on distributed training performance, focusing on sparsification and quantization techniques, including top-k, DGC, and QSGD. In baseline experiments, random-k compression results in severe performance degradation, highlighting its inefficacy. In contrast, using... | {
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} |
2502.07635 | Distributed Value Decomposition Networks with Networked Agents | [
"cs.LG",
"cs.AI",
"cs.MA"
] | We investigate the problem of distributed training under partial observability, whereby cooperative multi-agent reinforcement learning agents (MARL) maximize the expected cumulative joint reward. We propose distributed value decomposition networks (DVDN) that generate a joint Q-function that factorizes into agent-wise ... | {
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} |
2502.07636 | Consistency Training with Physical Constraints | [
"cs.LG"
] | We propose a physics-aware Consistency Training (CT) method that accelerates sampling in Diffusion Models with physical constraints. Our approach leverages a two-stage strategy: (1) learning the noise-to-data mapping via CT, and (2) incorporating physics constraints as a regularizer. Experiments on toy examples show th... | {
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} |
2502.07637 | BiaSWE: An Expert Annotated Dataset for Misogyny Detection in Swedish | [
"cs.CL"
] | In this study, we introduce the process for creating BiaSWE, an expert-annotated dataset tailored for misogyny detection in the Swedish language. To address the cultural and linguistic specificity of misogyny in Swedish, we collaborated with experts from the social sciences and humanities. Our interdisciplinary team de... | {
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} |
2502.07640 | Goedel-Prover: A Frontier Model for Open-Source Automated Theorem
Proving | [
"cs.LG",
"cs.AI"
] | We introduce Goedel-Prover, an open-source large language model (LLM) that achieves the state-of-the-art (SOTA) performance in automated formal proof generation for mathematical problems. The key challenge in this field is the scarcity of formalized math statements and proofs, which we tackle in the following ways. We ... | {
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} |
2502.07642 | FoQA: A Faroese Question-Answering Dataset | [
"cs.CL",
"cs.LG"
] | We present FoQA, a Faroese extractive question-answering (QA) dataset with 2,000 samples, created using a semi-automated approach combining Large Language Models (LLMs) and human validation. The dataset was generated from Faroese Wikipedia articles using GPT-4-turbo for initial QA generation, followed by question rephr... | {
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} |
2502.07644 | SymGPT: Auditing Smart Contracts via Combining Symbolic Execution with
Large Language Models | [
"cs.AI"
] | To govern smart contracts running on Ethereum, multiple Ethereum Request for Comment (ERC) standards have been developed, each having a set of rules to guide the behaviors of smart contracts. Violating the ERC rules could cause serious security issues and financial loss, signifying the importance of verifying smart con... | {
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} |
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