id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.08127 | Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance | [
"cs.CL"
] | Recent advancements in large language models (LLMs) have shown strong general reasoning abilities, yet their effectiveness in financial reasoning remains underexplored. In this study, we comprehensively evaluate 16 powerful reasoning and general LLMs on three complex financial tasks involving financial text, tabular da... | {
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2502.08129 | Control Barrier Function-Based Quadratic Programming for SafeOperation
of Tethered UAVs | [
"eess.SY",
"cs.SY"
] | Consider an unmanned aerial vehicle (UAV) physically connected to the ground station with a tether operating in a space, tasked with performing precise maneuvers while constrained by the physical limitation of its tether, which prevents it from flying beyond a maximum allowable length. Violating this tether constraint ... | {
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2502.08130 | Selective Self-to-Supervised Fine-Tuning for Generalization in Large
Language Models | [
"cs.CL"
] | Fine-tuning Large Language Models (LLMs) on specific datasets is a common practice to improve performance on target tasks. However, this performance gain often leads to overfitting, where the model becomes too specialized in either the task or the characteristics of the training data, resulting in a loss of generalizat... | {
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2502.08132 | SS4Rec: Continuous-Time Sequential Recommendation with State Space
Models | [
"cs.IR",
"cs.LG"
] | Sequential recommendation is a key area in the field of recommendation systems aiming to model user interest based on historical interaction sequences with irregular intervals. While previous recurrent neural network-based and attention-based approaches have achieved significant results, they have limitations in captur... | {
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2502.08134 | A Survey on Data Curation for Visual Contrastive Learning: Why Crafting
Effective Positive and Negative Pairs Matters | [
"cs.CV"
] | Visual contrastive learning aims to learn representations by contrasting similar (positive) and dissimilar (negative) pairs of data samples. The design of these pairs significantly impacts representation quality, training efficiency, and computational cost. A well-curated set of pairs leads to stronger representations ... | {
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2502.08136 | In-Context Learning of Linear Dynamical Systems with Transformers: Error
Bounds and Depth-Separation | [
"cs.LG",
"stat.ML"
] | This paper investigates approximation-theoretic aspects of the in-context learning capability of the transformers in representing a family of noisy linear dynamical systems. Our first theoretical result establishes an upper bound on the approximation error of multi-layer transformers with respect to an $L^2$-testing lo... | {
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2502.08137 | Riemannian Complex Hermit Positive Definite Convolution Network for
Polarimetric SAR Image Classification | [
"cs.CV"
] | Deep learning can learn high-level semantic features in Euclidean space effectively for PolSAR images, while they need to covert the complex covariance matrix into a feature vector or complex-valued vector as the network input. However, the complex covariance matrices are essentially a complex Hermit positive definite ... | {
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2502.08141 | LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits | [
"cs.LG",
"cs.AR",
"cs.CL",
"cs.PF"
] | Fine-tuning large language models (LLMs) is increasingly costly as models scale to hundreds of billions of parameters, and even parameter-efficient fine-tuning (PEFT) methods like LoRA remain resource-intensive. We introduce LowRA, the first framework to enable LoRA fine-tuning below 2 bits per parameter with minimal p... | {
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2502.08142 | Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM
Inferences | [
"cs.AI"
] | We present Wildflare GuardRail, a guardrail pipeline designed to enhance the safety and reliability of Large Language Model (LLM) inferences by systematically addressing risks across the entire processing workflow. Wildflare GuardRail integrates several core functional modules, including Safety Detector that identifies... | {
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2502.08143 | Data-dependent Bounds with $T$-Optimal Best-of-Both-Worlds Guarantees in
Multi-Armed Bandits using Stability-Penalty Matching | [
"cs.LG"
] | Existing data-dependent and best-of-both-worlds regret bounds for multi-armed bandits problems have limited adaptivity as they are either data-dependent but not best-of-both-worlds (BOBW), BOBW but not data-dependent or have sub-optimal $O(\sqrt{T\ln{T}})$ worst-case guarantee in the adversarial regime. To overcome the... | {
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2502.08145 | Democratizing AI: Open-source Scalable LLM Training on GPU-based
Supercomputers | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Training and fine-tuning large language models (LLMs) with hundreds of billions to trillions of parameters requires tens of thousands of GPUs, and a highly scalable software stack. In this work, we present a novel four-dimensional hybrid parallel algorithm implemented in a highly scalable, portable, open-source framewo... | {
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2502.08146 | Knowledge-Guided Wasserstein Distributionally Robust Optimization | [
"cs.LG",
"stat.ME",
"stat.ML"
] | Transfer learning is a popular strategy to leverage external knowledge and improve statistical efficiency, particularly with a limited target sample. We propose a novel knowledge-guided Wasserstein Distributionally Robust Optimization (KG-WDRO) framework that adaptively incorporates multiple sources of external knowled... | {
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2502.08148 | ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning | [
"cs.AI"
] | Identifying cause-and-effect relationships is critical to understanding real-world dynamics and ultimately causal reasoning. Existing methods for identifying event causality in NLP, including those based on Large Language Models (LLMs), exhibit difficulties in out-of-distribution settings due to the limited scale and h... | {
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2502.08149 | Generalized Class Discovery in Instance Segmentation | [
"cs.CV",
"cs.AI"
] | This work addresses the task of generalized class discovery (GCD) in instance segmentation. The goal is to discover novel classes and obtain a model capable of segmenting instances of both known and novel categories, given labeled and unlabeled data. Since the real world contains numerous objects with long-tailed distr... | {
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2502.08150 | Force Matching with Relativistic Constraints: A Physics-Inspired
Approach to Stable and Efficient Generative Modeling | [
"cs.LG",
"cs.AI",
"cs.CV"
] | This paper introduces Force Matching (ForM), a novel framework for generative modeling that represents an initial exploration into leveraging special relativistic mechanics to enhance the stability of the sampling process. By incorporating the Lorentz factor, ForM imposes a velocity constraint, ensuring that sample vel... | {
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2502.08151 | Local Differential Privacy is Not Enough: A Sample Reconstruction Attack
against Federated Learning with Local Differential Privacy | [
"cs.CR",
"cs.LG"
] | Reconstruction attacks against federated learning (FL) aim to reconstruct users' samples through users' uploaded gradients. Local differential privacy (LDP) is regarded as an effective defense against various attacks, including sample reconstruction in FL, where gradients are clipped and perturbed. Existing attacks are... | {
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2502.08155 | DGSense: A Domain Generalization Framework for Wireless Sensing | [
"cs.LG",
"cs.AI"
] | Wireless sensing is of great benefits to our daily lives. However, wireless signals are sensitive to the surroundings. Various factors, e.g. environments, locations, and individuals, may induce extra impact on wireless propagation. Such a change can be regarded as a domain, in which the data distribution shifts. A vast... | {
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2502.08158 | Open-Source Factor Graph Optimization Package for GNSS: Examples and
Applications | [
"cs.RO"
] | State estimation methods using factor graph optimization (FGO) have garnered significant attention in global navigation satellite system (GNSS) research. FGO exhibits superior estimation accuracy compared with traditional state estimation methods that rely on least-squares or Kalman filters. However, only a few FGO lib... | {
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2502.08160 | Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly | [
"cs.LG",
"cs.AI"
] | Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm that enables multiple parties with distinct feature sets to jointly train machine learning models without sharing their raw data. Despite its potential to facilitate cross-organizational collaborations, the deployment of VFL syste... | {
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2502.08161 | MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural
Network for Recommendation | [
"cs.IR",
"cs.AI"
] | Graph neural networks have been widely used in recent recommender systems, where negative sampling plays an important role. Existing negative sampling methods restrict the relationship between nodes as either hard positive pairs or hard negative pairs. This leads to the loss of structural information, and lacks the mec... | {
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2502.08166 | From Individual Experience to Collective Evidence: A Reporting-Based
Framework for Identifying Systemic Harms | [
"cs.CY",
"cs.LG"
] | When an individual reports a negative interaction with some system, how can their personal experience be contextualized within broader patterns of system behavior? We study the incident database problem, where individual reports of adverse events arrive sequentially, and are aggregated over time. In this work, our goal... | {
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2502.08167 | DNNs May Determine Major Properties of Their Outputs Early, with Timing
Possibly Driven by Bias | [
"cs.LG",
"cs.CV"
] | This paper argues that deep neural networks (DNNs) mostly determine their outputs during the early stages of inference, where biases inherent in the model play a crucial role in shaping this process. We draw a parallel between this phenomenon and human decision-making, which often relies on fast, intuitive heuristics. ... | {
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2502.08168 | SARChat-Bench-2M: A Multi-Task Vision-Language Benchmark for SAR Image
Interpretation | [
"cs.CL"
] | As a powerful all-weather Earth observation tool, synthetic aperture radar (SAR) remote sensing enables critical military reconnaissance, maritime surveillance, and infrastructure monitoring. Although Vision language models (VLMs) have made remarkable progress in natural language processing and image understanding, the... | {
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2502.08169 | CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic
Feature Trust Modulus | [
"cs.CV"
] | Collaborative perception, fusing information from multiple agents, can extend perception range so as to improve perception performance. However, temporal asynchrony in real-world environments, caused by communication delays, clock misalignment, or sampling configuration differences, can lead to information mismatches. ... | {
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2502.08170 | Learning-Based Design of LQG Controllers in Quantum Coherent Feedback | [
"quant-ph",
"cs.SY",
"eess.SY"
] | In this paper, we propose a differential evolution (DE) algorithm specifically tailored for the design of Linear-Quadratic-Gaussian (LQG) controllers in quantum systems. Building upon the foundational DE framework, the algorithm incorporates specialized modules, including relaxed feasibility rules, a scheduled penalty ... | {
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2502.08177 | SycEval: Evaluating LLM Sycophancy | [
"cs.AI"
] | Large language models (LLMs) are increasingly applied in educational, clinical, and professional settings, but their tendency for sycophancy -- prioritizing user agreement over independent reasoning -- poses risks to reliability. This study introduces a framework to evaluate sycophantic behavior in ChatGPT-4o, Claude-S... | {
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2502.08178 | ParetoRAG: Leveraging Sentence-Context Attention for Robust and
Efficient Retrieval-Augmented Generation | [
"cs.CL"
] | While Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by incorporating external knowledge, they still face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information. We present ParetoRAG, an unsupervised framework that optimizes RAG ... | {
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2502.08180 | Enhancing LLM Character-Level Manipulation via Divide and Conquer | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have demonstrated strong generalization capabilities across a wide range of natural language processing (NLP) tasks. However, they exhibit notable weaknesses in character-level string manipulation, struggling with fundamental operations such as character deletion, insertion, and substitutio... | {
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2502.08181 | Latest Advancements Towards Catastrophic Forgetting under Data Scarcity:
A Comprehensive Survey on Few-Shot Class Incremental Learning | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Data scarcity significantly complicates the continual learning problem, i.e., how a deep neural network learns in dynamic environments with very few samples. However, the latest progress of few-shot class incremental learning (FSCIL) methods and related studies show insightful knowledge on how to tackle the problem. Th... | {
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2502.08189 | AnyCharV: Bootstrap Controllable Character Video Generation with
Fine-to-Coarse Guidance | [
"cs.CV"
] | Character video generation is a significant real-world application focused on producing high-quality videos featuring specific characters. Recent advancements have introduced various control signals to animate static characters, successfully enhancing control over the generation process. However, these methods often la... | {
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2502.08200 | ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for
Long-Tailed Megakaryocyte Classification | [
"cs.CV"
] | Precise classification of megakaryocytes is crucial for diagnosing myelodysplastic syndromes. Although self-supervised learning has shown promise in medical image analysis, its application to classifying megakaryocytes in stained slides faces three main challenges: (1) pervasive background noise that obscures cellular ... | {
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2502.08202 | Privacy amplification by random allocation | [
"cs.LG"
] | We consider the privacy guarantees of an algorithm in which a user's data is used in $k$ steps randomly and uniformly chosen from a sequence (or set) of $t$ differentially private steps. We demonstrate that the privacy guarantees of this sampling scheme can be upper bound by the privacy guarantees of the well-studied i... | {
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2502.08205 | Wisdom of the Crowds in Forecasting: Forecast Summarization for
Supporting Future Event Prediction | [
"cs.LG",
"cs.CL",
"cs.IR"
] | Future Event Prediction (FEP) is an essential activity whose demand and application range across multiple domains. While traditional methods like simulations, predictive and time-series forecasting have demonstrated promising outcomes, their application in forecasting complex events is not entirely reliable due to the ... | {
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2502.08206 | Optimizing Asynchronous Federated Learning: A Delicate Trade-Off Between
Model-Parameter Staleness and Update Frequency | [
"cs.LG",
"cs.PF",
"math.OC",
"math.PR"
] | Synchronous federated learning (FL) scales poorly with the number of clients due to the straggler effect. Algorithms like FedAsync and GeneralizedFedAsync address this limitation by enabling asynchronous communication between clients and the central server. In this work, we rely on stochastic modeling to better underst... | {
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2502.08208 | Exploring Exploration in Bayesian Optimization | [
"cs.LG"
] | A well-balanced exploration-exploitation trade-off is crucial for successful acquisition functions in Bayesian optimization. However, there is a lack of quantitative measures for exploration, making it difficult to analyze and compare different acquisition functions. This work introduces two novel approaches - observat... | {
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2502.08209 | Equivariant Masked Position Prediction for Efficient Molecular
Representation | [
"cs.LG",
"cs.AI"
] | Graph neural networks (GNNs) have shown considerable promise in computational chemistry. However, the limited availability of molecular data raises concerns regarding GNNs' ability to effectively capture the fundamental principles of physics and chemistry, which constrains their generalization capabilities. To address ... | {
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2502.08211 | Quality over Quantity: Boosting Data Efficiency Through Ensembled
Multimodal Data Curation | [
"cs.LG",
"cs.AI"
] | In an era overwhelmed by vast amounts of data, the effective curation of web-crawl datasets is essential for optimizing model performance. This paper tackles the challenges associated with the unstructured and heterogeneous nature of such datasets. Traditional heuristic curation methods often inadequately capture compl... | {
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2502.08213 | LLM Modules: Knowledge Transfer from a Large to a Small Model using
Enhanced Cross-Attention | [
"cs.CL",
"cs.LG"
] | In this work, we propose an architecture of LLM Modules that enables the transfer of knowledge from a large pre-trained model to a smaller model using an Enhanced Cross-Attention mechanism. In the proposed scheme, the Qwen2-1.5B model is frozen and its representations are passed through specially designed attention lay... | {
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2502.08214 | Unbiased and Error-Detecting Combinatorial Pooling Experiments with
Balanced Constant-Weight Gray Codes for Consecutive Positives Detection | [
"cs.IT",
"math.IT",
"q-bio.QM"
] | Combinatorial pooling schemes have enabled the measurement of thousands of experiments in a small number of reactions. This efficiency is achieved by distributing the items to be measured across multiple reaction units called pools. However, current methods for the design of pooling schemes do not adequately address th... | {
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2502.08216 | Deepfake Detection with Spatio-Temporal Consistency and Attention | [
"cs.CV"
] | Deepfake videos are causing growing concerns among communities due to their ever-increasing realism. Naturally, automated detection of forged Deepfake videos is attracting a proportional amount of interest of researchers. Current methods for detecting forged videos mainly rely on global frame features and under-utilize... | {
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2502.08221 | Take What You Need: Flexible Multi-Task Semantic Communications with
Channel Adaptation | [
"cs.CV",
"cs.IT",
"cs.NI",
"math.IT"
] | The growing demand for efficient semantic communication systems capable of managing diverse tasks and adapting to fluctuating channel conditions has driven the development of robust, resource-efficient frameworks. This article introduces a novel channel-adaptive and multi-task-aware semantic communication framework bas... | {
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2502.08226 | TRISHUL: Towards Region Identification and Screen Hierarchy
Understanding for Large VLM based GUI Agents | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recent advancements in Large Vision Language Models (LVLMs) have enabled the development of LVLM-based Graphical User Interface (GUI) agents under various paradigms. Training-based approaches, such as CogAgent and SeeClick, struggle with cross-dataset and cross-platform generalization due to their reliance on dataset-s... | {
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2502.08227 | Enhancing Sample Selection by Cutting Mislabeled Easy Examples | [
"cs.LG"
] | Sample selection is a prevalent approach in learning with noisy labels, aiming to identify confident samples for training. Although existing sample selection methods have achieved decent results by reducing the noise rate of the selected subset, they often overlook that not all mislabeled examples harm the model's perf... | {
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2502.08231 | Keep your distance: learning dispersed embeddings on $\mathbb{S}_d$ | [
"cs.LG"
] | Learning well-separated features in high-dimensional spaces, such as text or image embeddings, is crucial for many machine learning applications. Achieving such separation can be effectively accomplished through the dispersion of embeddings, where unrelated vectors are pushed apart as much as possible. By constraining ... | {
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2502.08232 | ChemZIP: Accelerated Modeling of Complex Aerothermochemical Interactions
in Novel Turbomachines for Sustainable High-Temperature Chemical Processes | [
"cs.CE"
] | This paper introduces a new platform to accelerate the modeling of complex aerothermochemical interactions in new turbomachines, turbo-reactors, to decarbonise chemical processes. While previous work has aerothermally demonstrated the potential to decarbonize the heat input to the reaction, optimizing the reaction effi... | {
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2502.08233 | Plantation Monitoring Using Drone Images: A Dataset and Performance
Review | [
"cs.CV"
] | Automatic monitoring of tree plantations plays a crucial role in agriculture. Flawless monitoring of tree health helps farmers make informed decisions regarding their management by taking appropriate action. Use of drone images for automatic plantation monitoring can enhance the accuracy of the monitoring process, whil... | {
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2502.08234 | Learning Human Skill Generators at Key-Step Levels | [
"cs.CV"
] | We are committed to learning human skill generators at key-step levels. The generation of skills is a challenging endeavor, but its successful implementation could greatly facilitate human skill learning and provide more experience for embodied intelligence. Although current video generation models can synthesis simple... | {
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2502.08235 | The Danger of Overthinking: Examining the Reasoning-Action Dilemma in
Agentic Tasks | [
"cs.AI"
] | Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving capabilities, but their effectiveness in interactive environments can be limited. This paper introduces and analyzes overthinking in LRMs. A phenomenon where models favor extended internal reasoning chains over environmental interaction. Throu... | {
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2502.08244 | FloVD: Optical Flow Meets Video Diffusion Model for Enhanced
Camera-Controlled Video Synthesis | [
"cs.CV"
] | This paper presents FloVD, a novel optical-flow-based video diffusion model for camera-controllable video generation. FloVD leverages optical flow maps to represent motions of the camera and moving objects. This approach offers two key benefits. Since optical flow can be directly estimated from videos, our approach all... | {
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2502.08246 | Inference-time sparse attention with asymmetric indexing | [
"cs.CL"
] | Self-attention in transformer models is an incremental associative memory that maps key vectors to value vectors. One way to speed up self-attention is to employ GPU-compliant vector search algorithms, yet the standard partitioning methods yield poor results in this context, because (1) keys and queries follow differen... | {
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2502.08253 | Multi-View Oriented GPLVM: Expressiveness and Efficiency | [
"stat.ML",
"cs.LG"
] | The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified representation from multi-view data but is hindered by challenges such as limited kernel expressiveness and low computational efficiency. To overcome these issues, we first introduce a new duality between the spectral density and t... | {
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2502.08254 | UniCoRN: Unified Commented Retrieval Network with LMMs | [
"cs.CV"
] | Multimodal retrieval methods have limitations in handling complex, compositional queries that require reasoning about the visual content of both the query and the retrieved entities. On the other hand, Large Multimodal Models (LMMs) can answer with language to more complex visual questions, but without the inherent abi... | {
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2502.08255 | Principles and Framework for the Operationalisation of Meaningful Human
Control over Autonomous Systems | [
"eess.SY",
"cs.SY"
] | This paper proposes an alignment for the operationalisation of Meaningful Human Control (MHC) for autonomous systems by proposing operational principles for MHC and introducing a generic framework for its application. With a plethora of different seemingly diverging expansions for use of MHC in practice, this work aims... | {
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2502.08259 | Balancing optimism and pessimism in offline-to-online learning | [
"cs.LG",
"cs.AI"
] | We consider what we call the offline-to-online learning setting, focusing on stochastic finite-armed bandit problems. In offline-to-online learning, a learner starts with offline data collected from interactions with an unknown environment in a way that is not under the learner's control. Given this data, the learner b... | {
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2502.08262 | GenIAS: Generator for Instantiating Anomalies in time Series | [
"cs.LG"
] | A recent and promising approach for building time series anomaly detection (TSAD) models is to inject synthetic samples of anomalies within real data sets. The existing injection mechanisms have significant limitations - most of them rely on ad hoc, hand-crafted strategies which fail to capture the natural diversity of... | {
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2502.08265 | Exploring the Potential of Large Language Models to Simulate Personality | [
"cs.CL",
"cs.AI"
] | With the advancement of large language models (LLMs), the focus in Conversational AI has shifted from merely generating coherent and relevant responses to tackling more complex challenges, such as personalizing dialogue systems. In an effort to enhance user engagement, chatbots are often designed to mimic human behavio... | {
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2502.08266 | Dealing with Annotator Disagreement in Hate Speech Classification | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Hate speech detection is a crucial task, especially on social media, where harmful content can spread quickly. Implementing machine learning models to automatically identify and address hate speech is essential for mitigating its impact and preventing its proliferation. The first step in developing an effective hate sp... | {
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2502.08271 | MoLoRec: A Generalizable and Efficient Framework for LLM-Based
Recommendation | [
"cs.IR"
] | Large Language Models (LLMs) have achieved remarkable success in recent years, owing to their impressive generalization capabilities and rich world knowledge. To capitalize on the potential of using LLMs as recommender systems, mainstream approaches typically focus on two paradigms. The first paradigm designs multi-dom... | {
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2502.08276 | Higher-order Laplacian dynamics on hypergraphs with cooperative and
antagonistic interactions | [
"eess.SY",
"cs.SY"
] | Laplacian dynamics on a signless graph characterize a class of linear interactions, where pairwise cooperative interactions between all agents lead to the convergence to a common state. On a structurally balanced signed graph, the agents converge to values of the same magnitude but opposite signs (bipartite consensus),... | {
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2502.08277 | ChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion
Rate Modeling | [
"cs.IR",
"cs.SI"
] | Post-click conversion rate (CVR) estimation is a vital task in many recommender systems of revenue businesses, e.g., e-commerce and advertising. In a perspective of sample, a typical CVR positive sample usually goes through a funnel of exposure to click to conversion. For lack of post-event labels for un-clicked sample... | {
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2502.08279 | What Is That Talk About? A Video-to-Text Summarization Dataset for
Scientific Presentations | [
"cs.CL",
"cs.AI",
"cs.CV"
] | Transforming recorded videos into concise and accurate textual summaries is a growing challenge in multimodal learning. This paper introduces VISTA, a dataset specifically designed for video-to-text summarization in scientific domains. VISTA contains 18,599 recorded AI conference presentations paired with their corresp... | {
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2502.08281 | Redefining Simplicity: Benchmarking Large Language Models from Lexical
to Document Simplification | [
"cs.CL"
] | Text simplification (TS) refers to the process of reducing the complexity of a text while retaining its original meaning and key information. Existing work only shows that large language models (LLMs) have outperformed supervised non-LLM-based methods on sentence simplification. This study offers the first comprehensiv... | {
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2502.08282 | Individualised Treatment Effects Estimation with Composite Treatments
and Composite Outcomes | [
"cs.LG",
"cs.AI"
] | Estimating individualised treatment effect (ITE) -- that is the causal effect of a set of variables (also called exposures, treatments, actions, policies, or interventions), referred to as \textit{composite treatments}, on a set of outcome variables of interest, referred to as \textit{composite outcomes}, for a unit fr... | {
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2502.08284 | Data Pricing for Graph Neural Networks without Pre-purchased Inspection | [
"cs.GT",
"cs.LG"
] | Machine learning (ML) models have become essential tools in various scenarios. Their effectiveness, however, hinges on a substantial volume of data for satisfactory performance. Model marketplaces have thus emerged as crucial platforms bridging model consumers seeking ML solutions and data owners possessing valuable da... | {
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2502.08285 | Fully-Geometric Cross-Attention for Point Cloud Registration | [
"cs.CV"
] | Point cloud registration approaches often fail when the overlap between point clouds is low due to noisy point correspondences. This work introduces a novel cross-attention mechanism tailored for Transformer-based architectures that tackles this problem, by fusing information from coordinates and features at the super-... | {
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2502.08287 | CRISP: A Framework for Cryo-EM Image Segmentation and Processing with
Conditional Random Field | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Differentiating signals from the background in micrographs is a critical initial step for cryogenic electron microscopy (cryo-EM), yet it remains laborious due to low signal-to-noise ratio (SNR), the presence of contaminants and densely packed particles of varying sizes. Although image segmentation has recently been in... | {
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2502.08297 | BEAM: Bridging Physically-based Rendering and Gaussian Modeling for
Relightable Volumetric Video | [
"cs.GR",
"cs.CV"
] | Volumetric video enables immersive experiences by capturing dynamic 3D scenes, enabling diverse applications for virtual reality, education, and telepresence. However, traditional methods struggle with fixed lighting conditions, while neural approaches face trade-offs in efficiency, quality, or adaptability for relight... | {
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2502.08298 | Improving Existing Optimization Algorithms with LLMs | [
"cs.AI",
"cs.CL",
"cs.LG",
"cs.SE"
] | The integration of Large Language Models (LLMs) into optimization has created a powerful synergy, opening exciting research opportunities. This paper investigates how LLMs can enhance existing optimization algorithms. Using their pre-trained knowledge, we demonstrate their ability to propose innovative heuristic variat... | {
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2502.08299 | When do they StOP?: A First Step Towards Automatically Identifying Team
Communication in the Operating Room | [
"cs.CV"
] | Purpose: Surgical performance depends not only on surgeons' technical skills but also on team communication within and across the different professional groups present during the operation. Therefore, automatically identifying team communication in the OR is crucial for patient safety and advances in the development of... | {
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2502.08301 | Compromising Honesty and Harmlessness in Language Models via Deception
Attacks | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Recent research on large language models (LLMs) has demonstrated their ability to understand and employ deceptive behavior, even without explicit prompting. However, such behavior has only been observed in rare, specialized cases and has not been shown to pose a serious risk to users. Additionally, research on AI align... | {
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2502.08302 | HDT: Hierarchical Discrete Transformer for Multivariate Time Series
Forecasting | [
"cs.LG",
"cs.AI"
] | Generative models have gained significant attention in multivariate time series forecasting (MTS), particularly due to their ability to generate high-fidelity samples. Forecasting the probability distribution of multivariate time series is a challenging yet practical task. Although some recent attempts have been made t... | {
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2502.08309 | Unlocking Scaling Law in Industrial Recommendation Systems with a
Three-step Paradigm based Large User Model | [
"cs.IR"
] | Recent advancements in autoregressive Large Language Models (LLMs) have achieved significant milestones, largely attributed to their scalability, often referred to as the "scaling law". Inspired by these achievements, there has been a growing interest in adapting LLMs for Recommendation Systems (RecSys) by reformulatin... | {
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2502.08312 | Word Synchronization Challenge: A Benchmark for Word Association
Responses for LLMs | [
"cs.HC",
"cs.CL"
] | This paper introduces the Word Synchronization Challenge, a novel benchmark to evaluate large language models (LLMs) in Human-Computer Interaction (HCI). This benchmark uses a dynamic game-like framework to test LLMs ability to mimic human cognitive processes through word associations. By simulating complex human inter... | {
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2502.08317 | Mitigating Hallucinations in Multimodal Spatial Relations through
Constraint-Aware Prompting | [
"cs.CL",
"cs.AI",
"cs.CV"
] | Spatial relation hallucinations pose a persistent challenge in large vision-language models (LVLMs), leading to generate incorrect predictions about object positions and spatial configurations within an image. To address this issue, we propose a constraint-aware prompting framework designed to reduce spatial relation h... | {
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2502.08319 | MultiProSE: A Multi-label Arabic Dataset for Propaganda, Sentiment, and
Emotion Detection | [
"cs.CL"
] | Propaganda is a form of persuasion that has been used throughout history with the intention goal of influencing people's opinions through rhetorical and psychological persuasion techniques for determined ends. Although Arabic ranked as the fourth most-used language on the internet, resources for propaganda detection in... | {
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2502.08321 | Screener: Self-supervised Pathology Segmentation Model for 3D Medical
Images | [
"cs.CV"
] | Accurate segmentation of all pathological findings in 3D medical images remains a significant challenge, as supervised models are limited to detecting only the few pathology classes annotated in existing datasets. To address this, we frame pathology segmentation as an unsupervised visual anomaly segmentation (UVAS) pro... | {
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2502.08323 | Contextual Compression Encoding for Large Language Models: A Novel
Framework for Multi-Layered Parameter Space Pruning | [
"cs.CL"
] | Context-aware compression techniques have gained increasing attention as model sizes continue to grow, introducing computational bottlenecks that hinder efficient deployment. A structured encoding approach was proposed to selectively eliminate redundant parameter groups while ensuring that representational fidelity was... | {
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2502.08324 | Decentralised multi-agent coordination for real-time railway traffic
management | [
"cs.MA"
] | The real-time Railway Traffic Management Problem (rtRTMP) is a challenging optimisation problem in railway transportation. It involves the efficient management of train movements while minimising delay propagation caused by unforeseen perturbations due to, e.g, temporary speed limitations or signal failures. This paper... | {
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2502.08326 | Model-Free Counterfactual Subset Selection at Scale | [
"cs.LG",
"cs.DB",
"cs.DS",
"cs.IR"
] | Ensuring transparency in AI decision-making requires interpretable explanations, particularly at the instance level. Counterfactual explanations are a powerful tool for this purpose, but existing techniques frequently depend on synthetic examples, introducing biases from unrealistic assumptions, flawed models, or skewe... | {
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2502.08331 | Brame: Hierarchical Data Management Framework for Cloud-Edge-Device
Collaboration | [
"cs.DB"
] | In the realm of big data, cloud-edge-device collaboration is prevalent in industrial scenarios. However, a systematic exploration of the theory and methodologies related to data management in this field is lacking. This paper delves into the sub-problem of data storage and scheduling within cloud-edge-device collaborat... | {
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2502.08332 | Modification and Generated-Text Detection: Achieving Dual Detection
Capabilities for the Outputs of LLM by Watermark | [
"cs.CR",
"cs.AI"
] | The development of large language models (LLMs) has raised concerns about potential misuse. One practical solution is to embed a watermark in the text, allowing ownership verification through watermark extraction. Existing methods primarily focus on defending against modification attacks, often neglecting other spoofin... | {
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2502.08333 | Foundation Models in Computational Pathology: A Review of Challenges,
Opportunities, and Impact | [
"cs.CV"
] | From self-supervised, vision-only models to contrastive visual-language frameworks, computational pathology has rapidly evolved in recent years. Generative AI "co-pilots" now demonstrate the ability to mine subtle, sub-visual tissue cues across the cellular-to-pathology spectrum, generate comprehensive reports, and res... | {
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2502.08336 | Salience-Invariant Consistent Policy Learning for Generalization in
Visual Reinforcement Learning | [
"cs.AI"
] | Generalizing policies to unseen scenarios remains a critical challenge in visual reinforcement learning, where agents often overfit to the specific visual observations of the training environment. In unseen environments, distracting pixels may lead agents to extract representations containing task-irrelevant informatio... | {
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2502.08337 | Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled
Data Center Clusters | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SY"
] | Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs) and simultaneously optimizing liquid and air (HVAC) cooling with time shift of workloads within individual data centers (DC). This paper introduces Green-DCC, which p... | {
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2502.08340 | Hierarchical Learning-based Graph Partition for Large-scale Vehicle
Routing Problems | [
"cs.LG",
"cs.AI"
] | Neural solvers based on the divide-and-conquer approach for Vehicle Routing Problems (VRPs) in general, and capacitated VRP (CVRP) in particular, integrates the global partition of an instance with local constructions for each subproblem to enhance generalization. However, during the global partition phase, misclusteri... | {
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2502.08344 | Energy and Age-Aware MAC for Low-Power Massive IoT | [
"cs.IT",
"math.IT"
] | Efficient multiple access remains a key challenge for emerging Internet of Things (IoT) networks comprising a large set of devices with sporadic activation, thus motivating significant research in the last few years. In this paper, we consider a network wherein IoT sensors capable of energy harvesting (EH) send updates... | {
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2502.08346 | Graph Foundation Models for Recommendation: A Comprehensive Survey | [
"cs.IR",
"cs.AI",
"cs.LG"
] | Recommender systems (RS) serve as a fundamental tool for navigating the vast expanse of online information, with deep learning advancements playing an increasingly important role in improving ranking accuracy. Among these, graph neural networks (GNNs) excel at extracting higher-order structural information, while large... | {
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2502.08347 | Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger
vision learners for medical image segmentation | [
"cs.CV"
] | Medical image segmentation remains a formidable challenge due to the label scarcity. Pre-training Vision Transformer (ViT) through masked image modeling (MIM) on large-scale unlabeled medical datasets presents a promising solution, providing both computational efficiency and model generalization for various downstream ... | {
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2502.08352 | Sat-DN: Implicit Surface Reconstruction from Multi-View Satellite Images
with Depth and Normal Supervision | [
"cs.CV"
] | With advancements in satellite imaging technology, acquiring high-resolution multi-view satellite imagery has become increasingly accessible, enabling rapid and location-independent ground model reconstruction. However, traditional stereo matching methods struggle to capture fine details, and while neural radiance fiel... | {
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2502.08353 | Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy | [
"cs.LG",
"cs.AI"
] | With the extensive application of Graph Neural Networks (GNNs) across various domains, their trustworthiness has emerged as a focal point of research. Some existing studies have shown that the integration of large language models (LLMs) can improve the semantic understanding and generation capabilities of GNNs, which i... | {
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2502.08355 | Loss Landscape Analysis for Reliable Quantized ML Models for Scientific
Sensing | [
"cs.LG"
] | In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensing, which necessitates quantization to be deployed and are subject to noise and perturbations due to experimental conditions. Our method allo... | {
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} |
2502.08356 | Systematic Knowledge Injection into Large Language Models via Diverse
Augmentation for Domain-Specific RAG | [
"cs.CL"
] | Retrieval-Augmented Generation (RAG) has emerged as a prominent method for incorporating domain knowledge into Large Language Models (LLMs). While RAG enhances response relevance by incorporating retrieved domain knowledge in the context, retrieval errors can still lead to hallucinations and incorrect answers. To recov... | {
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} |
2502.08363 | Top-Theta Attention: Sparsifying Transformers by Compensated
Thresholding | [
"cs.CL",
"cs.AI"
] | The attention mechanism is essential for the impressive capabilities of transformer-based Large Language Models (LLMs). However, calculating attention is computationally intensive due to its quadratic dependency on the sequence length. We introduce a novel approach called Top-Theta Attention, or simply Top-$\theta$, wh... | {
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} |
2502.08364 | A Survey on Pre-Trained Diffusion Model Distillations | [
"cs.LG"
] | Diffusion Models~(DMs) have emerged as the dominant approach in Generative Artificial Intelligence (GenAI), owing to their remarkable performance in tasks such as text-to-image synthesis. However, practical DMs, such as stable diffusion, are typically trained on massive datasets and thus usually require large storage. ... | {
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} |
2502.08365 | Towards Principled Multi-Agent Task Agnostic Exploration | [
"cs.LG",
"cs.AI"
] | In reinforcement learning, we typically refer to task-agnostic exploration when we aim to explore the environment without access to the task specification a priori. In a single-agent setting the problem has been extensively studied and mostly understood. A popular approach cast the task-agnostic objective as maximizing... | {
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} |
2502.08371 | Unveiling Global Discourse Structures: Theoretical Analysis and NLP
Applications in Argument Mining | [
"cs.CL"
] | Particularly in the structure of global discourse, coherence plays a pivotal role in human text comprehension and is a hallmark of high-quality text. This is especially true for persuasive texts, where coherent argument structures support claims effectively. This paper discusses and proposes methods for detecting, extr... | {
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} |
2502.08373 | Uncertainty Aware Human-machine Collaboration in Camouflaged Object
Detection | [
"cs.CV",
"cs.AI"
] | Camouflaged Object Detection (COD), the task of identifying objects concealed within their environments, has seen rapid growth due to its wide range of practical applications. A key step toward developing trustworthy COD systems is the estimation and effective utilization of uncertainty. In this work, we propose a huma... | {
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} |
2502.08374 | AdvSwap: Covert Adversarial Perturbation with High Frequency
Info-swapping for Autonomous Driving Perception | [
"cs.CV"
] | Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable a... | {
"Other": 0,
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} |
2502.08376 | Enhanced Load Forecasting with GAT-LSTM: Leveraging Grid and Temporal
Features | [
"cs.LG",
"eess.SP"
] | Accurate power load forecasting is essential for the efficient operation and planning of electrical grids, particularly given the increased variability and complexity introduced by renewable energy sources. This paper introduces GAT-LSTM, a hybrid model that combines Graph Attention Networks (GAT) and Long Short-Term M... | {
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} |
2502.08377 | Not All Frame Features Are Equal: Video-to-4D Generation via Decoupling
Dynamic-Static Features | [
"cs.CV"
] | Recently, the generation of dynamic 3D objects from a video has shown impressive results. Existing methods directly optimize Gaussians using whole information in frames. However, when dynamic regions are interwoven with static regions within frames, particularly if the static regions account for a large proportion, exi... | {
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} |
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