id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.05428 | Aero-engines Anomaly Detection using an Unsupervised Fisher Autoencoder | [
"eess.SP",
"cs.SY",
"eess.SY"
] | Reliable aero-engine anomaly detection is crucial for ensuring aircraft safety and operational efficiency. This research explores the application of the Fisher autoencoder as an unsupervised deep learning method for detecting anomalies in aero-engine multivariate sensor data, using a Gaussian mixture as the prior distr... | {
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2502.05431 | APE: Faster and Longer Context-Augmented Generation via Adaptive
Parallel Encoding | [
"cs.LG",
"cs.AI"
] | Context-augmented generation (CAG) techniques, including RAG and ICL, require the efficient combination of multiple contexts to generate responses to user queries. Directly inputting these contexts as a sequence introduces a considerable computational burden by re-encoding the combined selection of contexts for every r... | {
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2502.05432 | MoFM: A Large-Scale Human Motion Foundation Model | [
"cs.CV",
"cs.LG"
] | AFoundation Models (FM) have increasingly drawn the attention of researchers due to their scalability and generalization across diverse tasks. Inspired by the success of FMs and the principles that have driven advancements in Large Language Models (LLMs), we introduce MoFM as a novel Motion Foundation Model. MoFM is de... | {
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2502.05433 | AdaFlow: Efficient Long Video Editing via Adaptive Attention Slimming
And Keyframe Selection | [
"cs.CV"
] | Despite great progress, text-driven long video editing is still notoriously challenging mainly due to excessive memory overhead. Although recent efforts have simplified this task into a two-step process of keyframe translation and interpolation generation, the token-wise keyframe translation still plagues the upper lim... | {
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2502.05434 | Sample-Efficient Reinforcement Learning from Human Feedback via
Information-Directed Sampling | [
"cs.LG"
] | We study the problem of reinforcement learning from human feedback (RLHF), a critical problem in training large language models, from a theoretical perspective. Our main contribution is the design of novel sample-efficient RLHF algorithms based on information-directed sampling (IDS), an online decision-making principle... | {
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2502.05435 | Unbiased Sliced Wasserstein Kernels for High-Quality Audio Captioning | [
"eess.AS",
"cs.AI",
"cs.LG"
] | Teacher-forcing training for audio captioning usually leads to exposure bias due to training and inference mismatch. Prior works propose the contrastive method to deal with caption degeneration. However, the contrastive method ignores the temporal information when measuring similarity across acoustic and linguistic mod... | {
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2502.05437 | Approximating the total variation distance between spin systems | [
"cs.DS",
"cs.LG",
"math.PR"
] | Spin systems form an important class of undirected graphical models. For two Gibbs distributions $\mu$ and $\nu$ induced by two spin systems on the same graph $G = (V, E)$, we study the problem of approximating the total variation distance $d_{TV}(\mu,\nu)$ with an $\epsilon$-relative error. We propose a new reduction ... | {
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2502.05439 | Agentic AI Systems Applied to tasks in Financial Services: Modeling and
model risk management crews | [
"cs.AI",
"cs.CE",
"cs.CL",
"cs.LG"
] | The advent of large language models has ushered in a new era of agentic systems, where artificial intelligence programs exhibit remarkable autonomous decision-making capabilities across diverse domains. This paper explores agentic system workflows in the financial services industry. In particular, we build agentic crew... | {
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2502.05440 | Non-cooperative Stochastic Target Encirclement by Anti-synchronization
Control via Range-only Measurement | [
"cs.RO"
] | This paper investigates the stochastic moving target encirclement problem in a realistic setting. In contrast to typical assumptions in related works, the target in our work is non-cooperative and capable of escaping the circle containment by boosting its speed to maximum for a short duration. Considering the extreme e... | {
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2502.05442 | The Odyssey of the Fittest: Can Agents Survive and Still Be Good? | [
"cs.AI",
"cs.CY",
"cs.HC",
"cs.LG"
] | As AI models grow in power and generality, understanding how agents learn and make decisions in complex environments is critical to promoting ethical behavior. This paper examines the ethical implications of implementing biological drives, specifically, self preservation, into three different agents. A Bayesian agent o... | {
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2502.05444 | Diverse Image Generation with Diffusion Models and Cross Class Label
Learning for Polyp Classification | [
"eess.IV",
"cs.CV"
] | Pathologic diagnosis is a critical phase in deciding the optimal treatment procedure for dealing with colorectal cancer (CRC). Colonic polyps, precursors to CRC, can pathologically be classified into two major types: adenomatous and hyperplastic. For precise classification and early diagnosis of such polyps, the medica... | {
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2502.05445 | Unsupervised Self-Prior Embedding Neural Representation for Iterative
Sparse-View CT Reconstruction | [
"eess.IV",
"cs.CV"
] | Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view computed tomography (SVCT) inverse problems. Although these INR-based methods perform well in relatively dense SVCT reconstructions, they struggle to achieve comparable pe... | {
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2502.05446 | Stochastic Forward-Backward Deconvolution: Training Diffusion Models
with Finite Noisy Datasets | [
"cs.LG"
] | Recent diffusion-based generative models achieve remarkable results by training on massive datasets, yet this practice raises concerns about memorization and copyright infringement. A proposed remedy is to train exclusively on noisy data with potential copyright issues, ensuring the model never observes original conten... | {
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2502.05448 | Distributionally Robust Model Predictive Control with Mixture of
Gaussian Processes | [
"eess.SY",
"cs.SY",
"math.OC"
] | Despite the success of Gaussian process based Model Predictive Control (MPC) in robotic control, its applicability scope is greatly hindered by multimodal disturbances that are prevalent in real-world settings. Here we propose a novel Mixture of Gaussian Processes based Distributionally Robust MPC (MoGP-DR-MPC) framewo... | {
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2502.05449 | Iterative Deepening Sampling for Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The recent release of OpenAI's o1 models and other similar frameworks showcasing test-time scaling laws has demonstrated their exceptional capability to tackle complex reasoning tasks. Inspired by this, subsequent research has revealed that such test-time scaling laws hinge on the model's ability to search both within ... | {
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2502.05450 | ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency
Policy | [
"cs.RO",
"cs.AI"
] | Vision-Language-Action (VLA) models have shown substantial potential in real-world robotic manipulation. However, fine-tuning these models through supervised learning struggles to achieve robust performance due to limited, inconsistent demonstrations, especially in contact-rich environments. In this paper, we propose a... | {
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2502.05451 | Inversion of Magnetic Data using Learned Dictionaries and Scale Space | [
"physics.geo-ph",
"cs.CV",
"cs.LG"
] | Magnetic data inversion is an important tool in geophysics, used to infer subsurface magnetic susceptibility distributions from surface magnetic field measurements. This inverse problem is inherently ill-posed, characterized by non-unique solutions, depth ambiguity, and sensitivity to noise. Traditional inversion appro... | {
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2502.05453 | LLM-Powered Decentralized Generative Agents with Adaptive Hierarchical
Knowledge Graph for Cooperative Planning | [
"cs.AI",
"cs.MA"
] | Developing intelligent agents for long-term cooperation in dynamic open-world scenarios is a major challenge in multi-agent systems. Traditional Multi-agent Reinforcement Learning (MARL) frameworks like centralized training decentralized execution (CTDE) struggle with scalability and flexibility. They require centraliz... | {
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2502.05454 | Temporal Representation Alignment: Successor Features Enable Emergent
Compositionality in Robot Instruction Following | [
"cs.RO",
"cs.LG"
] | Effective task representations should facilitate compositionality, such that after learning a variety of basic tasks, an agent can perform compound tasks consisting of multiple steps simply by composing the representations of the constituent steps together. While this is conceptually simple and appealing, it is not cle... | {
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2502.05457 | Content-based Video Retrieval in Traffic Videos using Latent Dirichlet
Allocation Topic Model | [
"cs.CV"
] | Content-based video retrieval is one of the most challenging tasks in surveillance systems. In this study, Latent Dirichlet Allocation (LDA) topic model is used to annotate surveillance videos in an unsupervised manner. In scene understanding methods, some of the learned patterns are ambiguous and represents a mixture ... | {
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2502.05458 | Block Graph Neural Networks for tumor heterogeneity prediction | [
"cs.CV",
"cs.LG",
"stat.ML"
] | Accurate tumor classification is essential for selecting effective treatments, but current methods have limitations. Standard tumor grading, which categorizes tumors based on cell differentiation, is not recommended as a stand-alone procedure, as some well-differentiated tumors can be malignant. Tumor heterogeneity ass... | {
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2502.05459 | DCENWCNet: A Deep CNN Ensemble Network for White Blood Cell
Classification with LIME-Based Explainability | [
"cs.CV",
"cs.AI",
"q-bio.CB",
"stat.ML"
] | White blood cells (WBC) are important parts of our immune system, and they protect our body against infections by eliminating viruses, bacteria, parasites and fungi. The number of WBC types and the total number of WBCs provide important information about our health status. A traditional method, convolutional neural net... | {
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2502.05462 | Motion Planning of Nonholonomic Cooperative Mobile Manipulators | [
"cs.RO",
"cs.MA",
"cs.SY",
"eess.SY",
"math.OC"
] | We propose a real-time implementable motion planning technique for cooperative object transportation by nonholonomic mobile manipulator robots (MMRs) in an environment with static and dynamic obstacles. The proposed motion planning technique works in two steps. A novel visibility vertices-based path planning algorithm ... | {
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2502.05463 | Learning Memory and Material Dependent Constitutive Laws | [
"math.NA",
"cs.LG",
"cs.NA"
] | The theory of homogenization provides a systematic approach to the derivation of macroscale constitutive laws, obviating the need to repeatedly resolve complex microstructure. However, the unit cell problem that defines the constitutive model is typically not amenable to explicit evaluation. It is therefore of interest... | {
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2502.05464 | Prescribed-Time Newton Extremum Seeking using Delays and Time-Periodic
Gains | [
"eess.SY",
"cs.SY",
"math.OC"
] | We study prescribed-time extremum seeking (ES) for scalar maps in the presence of time delay. The problem has been solved by Yilmaz and Krstic using chirpy probing and time-varying singular gains. To alleviate the gain singularity, we present an alternative approach, employing delays with bounded time-periodic gains, f... | {
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2502.05467 | Position: LLMs Can be Good Tutors in Foreign Language Education | [
"cs.CL",
"cs.AI"
] | While recent efforts have begun integrating large language models (LLMs) into foreign language education (FLE), they often rely on traditional approaches to learning tasks without fully embracing educational methodologies, thus lacking adaptability to language learning. To address this gap, we argue that LLMs have the ... | {
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2502.05468 | Gen-DFL: Decision-Focused Generative Learning for Robust Decision Making | [
"cs.LG"
] | Decision-focused learning (DFL) integrates predictive models with downstream optimization, directly training machine learning models to minimize decision errors. While DFL has been shown to provide substantial advantages when compared to a counterpart that treats the predictive and prescriptive models separately, it ha... | {
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2502.05469 | Data-Driven Distributionally Robust Mixed-Integer Control through Lifted
Control Policy | [
"math.OC",
"cs.SY",
"eess.SY"
] | This paper investigates the finite-horizon distributionally robust mixed-integer control (DRMIC) of uncertain linear systems. However, deriving an optimal causal feedback control policy to this DRMIC problem is computationally formidable for most ambiguity sets. To address the computational challenge, we propose a nove... | {
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2502.05472 | Robust Deep Signed Graph Clustering via Weak Balance Theory | [
"cs.SI"
] | Signed graph clustering is a critical technique for discovering community structures in graphs that exhibit both positive and negative relationships. We have identified two significant challenges in this domain: i) existing signed spectral methods are highly vulnerable to noise, which is prevalent in real-world scenari... | {
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2502.05473 | LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image
Segmentation | [
"cs.CV"
] | Few-shot semantic segmentation (FSS) methods have shown great promise in handling data-scarce scenarios, particularly in medical image segmentation tasks. However, most existing FSS architectures lack sufficient interpretability and fail to fully incorporate the underlying physical structures of semantic regions. To ad... | {
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2502.05475 | You Are What You Eat -- AI Alignment Requires Understanding How Data
Shapes Structure and Generalisation | [
"cs.LG"
] | In this position paper, we argue that understanding the relation between structure in the data distribution and structure in trained models is central to AI alignment. First, we discuss how two neural networks can have equivalent performance on the training set but compute their outputs in essentially different ways an... | {
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2502.05476 | Convolutional Neural Network Segmentation for Satellite Imagery Data to
Identify Landforms Using U-Net Architecture | [
"cs.CV"
] | This study demonstrates a novel use of the U-Net architecture in the field of semantic segmentation to detect landforms using preprocessed satellite imagery. The study applies the U-Net model for effective feature extraction by using Convolutional Neural Network (CNN) segmentation techniques. Dropout is strategically u... | {
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2502.05478 | OntoTune: Ontology-Driven Self-training for Aligning Large Language
Models | [
"cs.CL"
] | Existing domain-specific Large Language Models (LLMs) are typically developed by fine-tuning general-purposed LLMs with large-scale domain-specific corpora. However, training on large-scale corpora often fails to effectively organize domain knowledge of LLMs, leading to fragmented understanding. Inspired by how humans ... | {
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2502.05479 | Model Validity in Observers: When to Increase the Complexity of Your
Model? | [
"cs.RO"
] | Model validity is key to the accurate and safe behavior of autonomous vehicles. Using invalid vehicle models in the different plan and control vehicle frameworks puts the stability of the vehicle, and thus its safety at stake. In this work, we analyze the validity of several popular vehicle models used in the literatur... | {
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2502.05482 | Robustifying Fourier Features Embeddings for Implicit Neural
Representations | [
"cs.CV"
] | Implicit Neural Representations (INRs) employ neural networks to represent continuous functions by mapping coordinates to the corresponding values of the target function, with applications e.g., inverse graphics. However, INRs face a challenge known as spectral bias when dealing with scenes containing varying frequenci... | {
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2502.05485 | HAMSTER: Hierarchical Action Models For Open-World Robot Manipulation | [
"cs.RO",
"cs.AI",
"cs.CV"
] | Large foundation models have shown strong open-world generalization to complex problems in vision and language, but similar levels of generalization have yet to be achieved in robotics. One fundamental challenge is the lack of robotic data, which are typically obtained through expensive on-robot operation. A promising ... | {
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2502.05487 | Modeling of Core Loss Based on Machine Learning and Deep Learning | [
"cs.LG",
"eess.SP"
] | This article proposes a Mix Neural Network (MNN) based on CNN-FCNN for predicting magnetic loss of different materials. In traditional magnetic core loss models, empirical equations usually need to be regressed under the same external conditions. When the magnetic core material is different, it needs to be classified a... | {
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2502.05489 | Mechanistic Interpretability of Emotion Inference in Large Language
Models | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) show promising capabilities in predicting human emotions from text. However, the mechanisms through which these models process emotional stimuli remain largely unexplored. Our study addresses this gap by investigating how autoregressive LLMs infer emotions, showing that emotion representati... | {
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2502.05491 | Lie-algebra Adaptive Tracking Control for Rigid Body Dynamics | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Adaptive tracking control for rigid body dynamics is of critical importance in control and robotics, particularly for addressing uncertainties or variations in system model parameters. However, most existing adaptive control methods are designed for systems with states in vector spaces, often neglecting the manifold co... | {
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2502.05494 | Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection | [
"cs.LG",
"cs.AI",
"stat.AP"
] | Electrocardiogram (ECG) analysis is a fundamental tool for diagnosing cardiovascular conditions, yet anomaly detection in ECG signals remains challenging due to their inherent complexity and variability. We propose Multi-scale Masked Autoencoder for ECG anomaly detection (MMAE-ECG), a novel end-to-end framework that ef... | {
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2502.05496 | Feature Explosion: a generic optimization strategy for outlier detection
algorithms | [
"cs.LG"
] | Outlier detection tasks aim at discovering potential issues or opportunities and are widely used in cybersecurity, financial security, industrial inspection, etc. To date, thousands of outlier detection algorithms have been proposed. Clearly, in real-world scenarios, such a large number of algorithms is unnecessary. In... | {
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2502.05497 | DeepThink: Aligning Language Models with Domain-Specific User Intents | [
"cs.CL"
] | Supervised fine-tuning with synthesized instructions has been a common practice for adapting LLMs to domain-specific QA tasks. However, the synthesized instructions deviate from real user questions and expected answers. This study proposes a novel framework called DeepThink to generate high-quality instructions. DeepTh... | {
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2502.05498 | Riemannian Manifold Learning for Stackelberg Games with Neural Flow
Representations | [
"cs.LG",
"cs.AI",
"cs.GT",
"cs.MA"
] | We present a novel framework for online learning in Stackelberg general-sum games, where two agents, the leader and follower, engage in sequential turn-based interactions. At the core of this approach is a learned diffeomorphism that maps the joint action space to a smooth Riemannian manifold, referred to as the Stacke... | {
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2502.05500 | Vision-Ultrasound Robotic System based on Deep Learning for Gas and Arc
Hazard Detection in Manufacturing | [
"cs.RO",
"cs.AI"
] | Gas leaks and arc discharges present significant risks in industrial environments, requiring robust detection systems to ensure safety and operational efficiency. Inspired by human protocols that combine visual identification with acoustic verification, this study proposes a deep learning-based robotic system for auton... | {
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2502.05503 | A Physical Coherence Benchmark for Evaluating Video Generation Models
via Optical Flow-guided Frame Prediction | [
"cs.CV",
"cs.AI"
] | Recent advances in video generation models demonstrate their potential as world simulators, but they often struggle with videos deviating from physical laws, a key concern overlooked by most text-to-video benchmarks. We introduce a benchmark designed specifically to assess the Physical Coherence of generated videos, Ph... | {
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2502.05504 | Physics-Conditioned Diffusion Models for Lattice Gauge Theory | [
"hep-lat",
"cs.LG"
] | We develop diffusion models for simulating lattice gauge theories, where stochastic quantization is explicitly incorporated as a physical condition for sampling. We demonstrate the applicability of this novel sampler to U(1) gauge theory in two spacetime dimensions and find that a model trained at a small inverse coupl... | {
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2502.05505 | Differentially Private Synthetic Data via APIs 3: Using Simulators
Instead of Foundation Model | [
"cs.LG",
"cs.CR",
"cs.CV",
"stat.ML"
] | Differentially private (DP) synthetic data, which closely resembles the original private data while maintaining strong privacy guarantees, has become a key tool for unlocking the value of private data without compromising privacy. Recently, Private Evolution (PE) has emerged as a promising method for generating DP synt... | {
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2502.05509 | Do Spikes Protect Privacy? Investigating Black-Box Model Inversion
Attacks in Spiking Neural Networks | [
"cs.LG",
"cs.CR",
"cs.NE"
] | As machine learning models become integral to security-sensitive applications, concerns over data leakage from adversarial attacks continue to rise. Model Inversion (MI) attacks pose a significant privacy threat by enabling adversaries to reconstruct training data from model outputs. While MI attacks on Artificial Neur... | {
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2502.05510 | Data-Driven Neural Certificate Synthesis | [
"eess.SY",
"cs.SY"
] | We investigate the problem of verifying different properties of discrete time dynamical systems, namely, reachability, safety and reach-while-avoid. To achieve this, we adopt a data driven perspective and using past systems' trajectories as data, we aim at learning a specific function termed \emph{certificate} for each... | {
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2502.05512 | IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot
Text-To-Speech System | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Recently, large language model (LLM) based text-to-speech (TTS) systems have gradually become the mainstream in the industry due to their high naturalness and powerful zero-shot voice cloning capabilities.Here, we introduce the IndexTTS system, which is mainly based on the XTTS and Tortoise model. We add some novel imp... | {
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2502.05516 | Evaluating Differential Privacy on Correlated Datasets Using Pointwise
Maximal Leakage | [
"cs.CR",
"cs.IT",
"math.IT"
] | Data-driven advancements significantly contribute to societal progress, yet they also pose substantial risks to privacy. In this landscape, differential privacy (DP) has become a cornerstone in privacy preservation efforts. However, the adequacy of DP in scenarios involving correlated datasets has sometimes been questi... | {
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2502.05517 | Evaluation of Vision Transformers for Multimodal Image Classification: A
Case Study on Brain, Lung, and Kidney Tumors | [
"cs.CV"
] | Neural networks have become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates the performance of Vision Transformers architectures, including Swin Transformer and MaxViT, in several datasets of magnetic resonance imaging (MRI) and computed tomography ... | {
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2502.05523 | Adaptive Domain Scaling for Personalized Sequential Modeling in
Recommenders | [
"cs.IR"
] | Users generally exhibit complex behavioral patterns and diverse intentions in multiple business scenarios of super applications like Douyin, presenting great challenges to current industrial multi-domain recommenders. To mitigate the discrepancies across diverse domains, researches and industrial practices generally em... | {
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2502.05526 | Towards Learning Scalable Agile Dynamic Motion Planning for Robosoccer
Teams with Policy Optimization | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.MA"
] | In fast-paced, ever-changing environments, dynamic Motion Planning for Multi-Agent Systems in the presence of obstacles is a universal and unsolved problem. Be it from path planning around obstacles to the movement of robotic arms, or in planning navigation of robot teams in settings such as Robosoccer, dynamic motion ... | {
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2502.05534 | Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion
Generation | [
"cs.CV"
] | We address the challenging problem of fine-grained text-driven human motion generation. Existing works generate imprecise motions that fail to accurately capture relationships specified in text due to: (1) lack of effective text parsing for detailed semantic cues regarding body parts, (2) not fully modeling linguistic ... | {
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2502.05535 | Rate-Matching Framework for RSMA-Enabled Multibeam LEO Satellite
Communications | [
"cs.IT",
"cs.NI",
"math.IT"
] | With the goal of ubiquitous global connectivity, multibeam low Earth orbit (LEO) satellite communication (SATCOM) has attracted significant attention in recent years. The traffic demands of users are heterogeneous within the broad coverage of SATCOM due to different geological conditions and user distributions. Motivat... | {
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2502.05537 | Sequential Stochastic Combinatorial Optimization Using Hierarchal
Reinforcement Learning | [
"cs.AI",
"cs.LG"
] | Reinforcement learning (RL) has emerged as a promising tool for combinatorial optimization (CO) problems due to its ability to learn fast, effective, and generalizable solutions. Nonetheless, existing works mostly focus on one-shot deterministic CO, while sequential stochastic CO (SSCO) has rarely been studied despite ... | {
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2502.05538 | Coalition Formation for Heterogeneous Federated Learning Enabled Channel
Estimation in RIS-assisted Cell-free MIMO | [
"cs.IT",
"math.IT"
] | Downlink channel estimation remains a significant bottleneck in reconfigurable intelligent surface-assisted cell-free multiple-input multiple-output communication systems. Conventional approaches primarily rely on centralized deep learning methods to estimate the high-dimensional and complex cascaded channels. These me... | {
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2502.05539 | SSH: Sparse Spectrum Adaptation via Discrete Hartley Transformation | [
"cs.CV",
"cs.LG"
] | Low-rank adaptation (LoRA) has been demonstrated effective in reducing the trainable parameter number when fine-tuning a large foundation model (LLM). However, it still encounters computational and memory challenges when scaling to larger models or addressing more complex task adaptation. In this work, we introduce S... | {
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2502.05540 | Demystifying Catastrophic Forgetting in Two-Stage Incremental Object
Detector | [
"cs.CV"
] | Catastrophic forgetting is a critical chanllenge for incremental object detection (IOD). Most existing methods treat the detector monolithically, relying on instance replay or knowledge distillation without analyzing component-specific forgetting. Through dissection of Faster R-CNN, we reveal a key insight: Catastrophi... | {
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2502.05542 | Democratic Training Against Universal Adversarial Perturbations | [
"cs.LG"
] | Despite their advances and success, real-world deep neural networks are known to be vulnerable to adversarial attacks. Universal adversarial perturbation, an input-agnostic attack, poses a serious threat for them to be deployed in security-sensitive systems. In this case, a single universal adversarial perturbation dec... | {
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2502.05547 | Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in
Federated Learning | [
"cs.CR",
"cs.AI"
] | Federated learning (FL) is inherently susceptible to privacy breaches and poisoning attacks. To tackle these challenges, researchers have separately devised secure aggregation mechanisms to protect data privacy and robust aggregation methods that withstand poisoning attacks. However, simultaneously addressing both conc... | {
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2502.05550 | 4DR P2T: 4D Radar Tensor Synthesis with Point Clouds | [
"cs.CV"
] | In four-dimensional (4D) Radar-based point cloud generation, clutter removal is commonly performed using the constant false alarm rate (CFAR) algorithm. However, CFAR may not fully capture the spatial characteristics of objects. To address limitation, this paper proposes the 4D Radar Point-to-Tensor (4DR P2T) model, wh... | {
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2502.05551 | FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining
Strategy | [
"cs.CL"
] | Large language models (LLMs) have significantly advanced human language understanding and generation, with pretraining data quality and organization being crucial to their performance. Multi-stage pretraining is a promising approach, but existing methods often lack quantitative criteria for data partitioning and instea... | {
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2502.05553 | Latent Structure Modulation in Large Language Models Through Stochastic
Concept Embedding Transitions | [
"cs.CL"
] | Stochastic embedding transitions introduce a probabilistic mechanism for adjusting token representations dynamically during inference, mitigating the constraints imposed through static or deterministic embeddings. A transition framework was proposed in which each token embedding evolved through probabilistic updates, e... | {
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2502.05555 | Efficient Reinforcement Learning Through Adaptively Pretrained Visual
Encoder | [
"cs.CV"
] | While Reinforcement Learning (RL) agents can successfully learn to handle complex tasks, effectively generalizing acquired skills to unfamiliar settings remains a challenge. One of the reasons behind this is the visual encoders used are task-dependent, preventing effective feature extraction in different settings. To a... | {
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2502.05556 | Knowledge is Power: Harnessing Large Language Models for Enhanced
Cognitive Diagnosis | [
"cs.AI"
] | Cognitive Diagnosis Models (CDMs) are designed to assess students' cognitive states by analyzing their performance across a series of exercises. However, existing CDMs often struggle with diagnosing infrequent students and exercises due to a lack of rich prior knowledge. With the advancement in large language models (L... | {
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2502.05557 | MMHMER:Multi-viewer and Multi-task for Handwritten Mathematical
Expression Recognition | [
"cs.CV"
] | Handwritten Mathematical Expression Recognition (HMER) methods have made remarkable progress, with most existing HMER approaches based on either a hybrid CNN/RNN-based with GRU architecture or Transformer architectures. Each of these has its strengths and weaknesses. Leveraging different model structures as viewers and... | {
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2502.05558 | Large Memory Network for Recommendation | [
"cs.IR"
] | Modeling user behavior sequences in recommender systems is essential for understanding user preferences over time, enabling personalized and accurate recommendations for improving user retention and enhancing business values. Despite its significance, there are two challenges for current sequential modeling approaches.... | {
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2502.05561 | Diffusion Model for Interest Refinement in Multi-Interest Recommendation | [
"cs.IR"
] | Multi-interest candidate matching plays a pivotal role in personalized recommender systems, as it captures diverse user interests from their historical behaviors. Most existing methods utilize attention mechanisms to generate interest representations by aggregating historical item embeddings. However, these methods onl... | {
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2502.05562 | Can Large Language Models Be Query Optimizer for Relational Databases? | [
"cs.DB"
] | Query optimization, which finds the optimized execution plan for a given query, is a complex planning and decision-making problem within the exponentially growing plan space in database management systems (DBMS). Traditional optimizers heavily rely on a certain cost model constructed by various heuristics and empirical... | {
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2502.05564 | TabICL: A Tabular Foundation Model for In-Context Learning on Large Data | [
"cs.LG",
"cs.AI"
] | The long-standing dominance of gradient-boosted decision trees on tabular data is currently challenged by tabular foundation models using In-Context Learning (ICL): setting the training data as context for the test data and predicting in a single forward pass without parameter updates. While the very recent TabPFNv2 fo... | {
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2502.05565 | Multi-Scale Conformal Prediction: A Theoretical Framework with Coverage
Guarantees | [
"math.ST",
"cs.SY",
"eess.SY",
"stat.TH"
] | We propose a multi-scale extension of conformal prediction, an approach that constructs prediction sets with finite-sample coverage guarantees under minimal statistical assumptions. Classic conformal prediction relies on a single notion of conformity, overlooking the multi-level structures that arise in applications su... | {
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2502.05567 | ATLAS: Autoformalizing Theorems through Lifting, Augmentation, and
Synthesis of Data | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Autoformalization, the process of automatically translating natural language mathematics into machine-verifiable formal language, has demonstrated advancements with the progress of large language models (LLMs). However, a key obstacle to further advancements is the scarcity of paired datasets that align natural languag... | {
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2502.05568 | Large Multimodal Models for Low-Resource Languages: A Survey | [
"cs.CL",
"cs.AI",
"cs.LG"
] | In this survey, we systematically analyze techniques used to adapt large multimodal models (LMMs) for low-resource (LR) languages, examining approaches ranging from visual enhancement and data creation to cross-modal transfer and fusion strategies. Through a comprehensive analysis of 106 studies across 75 LR languages,... | {
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2502.05573 | Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy
Learning | [
"cs.MA",
"cs.AI",
"cs.LG",
"cs.RO"
] | Multi-agent reinforcement learning (MARL) often relies on \emph{parameter sharing (PS)} to scale efficiently. However, purely shared policies can stifle each agent's unique specialization, reducing overall performance in heterogeneous environments. We propose \textbf{Low-Rank Agent-Specific Adaptation (LoRASA)}, a nove... | {
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2502.05574 | Event Stream-based Visual Object Tracking: HDETrack V2 and A
High-Definition Benchmark | [
"cs.CV",
"cs.AI"
] | We then introduce a novel hierarchical knowledge distillation strategy that incorporates the similarity matrix, feature representation, and response map-based distillation to guide the learning of the student Transformer network. We also enhance the model's ability to capture temporal dependencies by applying the tempo... | {
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2502.05575 | Graph-Based Vector Search: An Experimental Evaluation of the
State-of-the-Art | [
"cs.IR",
"cs.PF"
] | Vector data is prevalent across business and scientific applications, and its popularity is growing with the proliferation of learned embeddings. Vector data collections often reach billions of vectors with thousands of dimensions, thus, increasing the complexity of their analysis. Vector search is the backbone of many... | {
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2502.05586 | A Cost-Benefit Analysis of Additive Manufacturing as a Service | [
"cs.ET",
"cs.CE",
"cs.CY"
] | The global manufacturing landscape is undergoing a fundamental shift from resource-intensive mass production to sustainable, localised manufacturing. This paper presents a comprehensive analysis of a Cloud Crafting Platform that enables Manufacturing as a Service (MaaS) through additive manufacturing technologies. The ... | {
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2502.05588 | Optimizing Information Freshness of IEEE 802.11ax Uplink OFDMA-Based
Random Access | [
"cs.IT",
"math.IT"
] | The latest WiFi standard, IEEE 802.11ax (WiFi 6), introduces a novel uplink random access mechanism called uplink orthogonal frequency division multiple access-based random access (UORA). While existing work has evaluated the performance of UORA using conventional performance metrics, such as throughput and delay, its ... | {
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2502.05589 | On Memory Construction and Retrieval for Personalized Conversational
Agents | [
"cs.CL",
"cs.AI"
] | To deliver coherent and personalized experiences in long-term conversations, existing approaches typically perform retrieval augmented response generation by constructing memory banks from conversation history at either the turn-level, session-level, or through summarization techniques. In this paper, we present two ke... | {
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2502.05593 | Semantic Data Augmentation Enhanced Invariant Risk Minimization for
Medical Image Domain Generalization | [
"cs.CV"
] | Deep learning has achieved remarkable success in medical image classification. However, its clinical application is often hindered by data heterogeneity caused by variations in scanner vendors, imaging protocols, and operators. Approaches such as invariant risk minimization (IRM) aim to address this challenge of out-of... | {
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2502.05594 | A Hybrid Tabu Scatter Search Algorithm for Simulation-Based Optimization
of Multi-Objective Runway Operations Scheduling | [
"cs.NE"
] | This dissertation addresses the growing challenge of air traffic flow management by proposing a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling. The goal is to optimize airport capacity utilization while minimizing delays, fuel consumption, and environmental impacts. Given ... | {
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2502.05595 | Data efficient Robotic Object Throwing with Model-Based Reinforcement
Learning | [
"cs.RO"
] | Pick-and-place (PnP) operations, featuring object grasping and trajectory planning, are fundamental in industrial robotics applications. Despite many advancements in the field, PnP is limited by workspace constraints, reducing flexibility. Pick-and-throw (PnT) is a promising alternative where the robot throws objects t... | {
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2502.05599 | Online Bidding Algorithms with Strict Return on Spend (ROS) Constraint | [
"cs.GT",
"cs.DS",
"cs.LG"
] | Auto-bidding problem under a strict return-on-spend constraint (ROSC) is considered, where an algorithm has to make decisions about how much to bid for an ad slot depending on the revealed value, and the hidden allocation and payment function that describes the probability of winning the ad-slot depending on its bid. T... | {
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2502.05605 | ARIES: Stimulating Self-Refinement of Large Language Models by Iterative
Preference Optimization | [
"cs.CL",
"cs.LG"
] | A truly intelligent Large Language Model (LLM) should be capable of correcting errors in its responses through external interactions. However, even the most advanced models often face challenges in improving their outputs. In this paper, we explore how to cultivate LLMs with the self-refinement capability through itera... | {
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2502.05606 | FreeBlend: Advancing Concept Blending with Staged Feedback-Driven
Interpolation Diffusion | [
"cs.CV"
] | Concept blending is a promising yet underexplored area in generative models. While recent approaches, such as embedding mixing and latent modification based on structural sketches, have been proposed, they often suffer from incompatible semantic information and discrepancies in shape and appearance. In this work, we in... | {
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2502.05608 | Closing the Responsibility Gap in AI-based Network Management: An
Intelligent Audit System Approach | [
"cs.AI",
"cs.NI"
] | Existing network paradigms have achieved lower downtime as well as a higher Quality of Experience (QoE) through the use of Artificial Intelligence (AI)-based network management tools. These AI management systems, allow for automatic responses to changes in network conditions, lowering operation costs for operators, and... | {
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2502.05609 | Lossless Acceleration of Large Language Models with Hierarchical
Drafting based on Temporal Locality in Speculative Decoding | [
"cs.CL"
] | Accelerating inference in Large Language Models (LLMs) is critical for real-time interactions, as they have been widely incorporated into real-world services. Speculative decoding, a fully algorithmic solution, has gained attention for improving inference speed by drafting and verifying tokens, thereby generating multi... | {
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2502.05610 | Towards Sustainable NLP: Insights from Benchmarking Inference Energy in
Large Language Models | [
"cs.CL"
] | Large language models (LLMs) are increasingly recognized for their exceptional generative capabilities and versatility across various tasks. However, the high inference costs associated with these models have not received adequate attention, particularly when compared to the focus on training costs in existing research... | {
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2502.05615 | XiHeFusion: Harnessing Large Language Models for Science Communication
in Nuclear Fusion | [
"cs.CV",
"cs.AI"
] | Nuclear fusion is one of the most promising ways for humans to obtain infinite energy. Currently, with the rapid development of artificial intelligence, the mission of nuclear fusion has also entered a critical period of its development. How to let more people to understand nuclear fusion and join in its research is on... | {
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} |
2502.05620 | dynoGP: Deep Gaussian Processes for dynamic system identification | [
"stat.ML",
"cs.LG"
] | In this work, we present a novel approach to system identification for dynamical systems, based on a specific class of Deep Gaussian Processes (Deep GPs). These models are constructed by interconnecting linear dynamic GPs (equivalent to stochastic linear time-invariant dynamical systems) and static GPs (to model static... | {
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} |
2502.05622 | Social inequality and cultural factors impact the awareness and reaction
during the cryptic transmission period of pandemic | [
"cs.SI"
] | The World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern (PHEIC) on January 31, 2020. However, rumors of a "mysterious virus" had already been circulating in China in December 2019, possibly preceding the first confirmed COVID-19 case. Understanding how aware... | {
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} |
2502.05623 | Mixing Time of the Proximal Sampler in Relative Fisher Information via
Strong Data Processing Inequality | [
"cs.IT",
"cs.LG",
"math.IT",
"math.OC",
"math.ST",
"stat.TH"
] | We study the mixing time guarantee for sampling in relative Fisher information via the Proximal Sampler algorithm, which is an approximate proximal discretization of the Langevin dynamics. We show that when the target probability distribution is strongly log-concave, the relative Fisher information converges exponentia... | {
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} |
2502.05625 | Training-Free Constrained Generation With Stable Diffusion Models | [
"cs.LG"
] | Stable diffusion models represent the state-of-the-art in data synthesis across diverse domains and hold transformative potential for applications in science and engineering, e.g., by facilitating the discovery of novel solutions and simulating systems that are computationally intractable to model explicitly. However, ... | {
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} |
2502.05628 | AnyEdit: Edit Any Knowledge Encoded in Language Models | [
"cs.CL"
] | Large language models (LLMs) often produce incorrect or outdated information, necessitating efficient and precise knowledge updates. Current model editing methods, however, struggle with long-form knowledge in diverse formats, such as poetry, code snippets, and mathematical derivations. These limitations arise from the... | {
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} |
2502.05629 | TrackDiffuser: Nearly Model-Free Bayesian Filtering with Diffusion Model | [
"cs.LG",
"eess.SP"
] | State estimation remains a fundamental challenge across numerous domains, from autonomous driving, aircraft tracking to quantum system control. Although Bayesian filtering has been the cornerstone solution, its classical model-based paradigm faces two major limitations: it struggles with inaccurate state space model (S... | {
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} |
2502.05632 | Amorphous Fortress Online: Collaboratively Designing Open-Ended
Multi-Agent AI and Game Environments | [
"cs.AI"
] | This work introduces Amorphous Fortress Online -- a web-based platform where users can design petri-dish-like environments and games consisting of multi-agent AI characters. Users can play, create, and share artificial life and game environments made up of microscopic but transparent finite-state machine agents that in... | {
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} |
2502.05633 | Mol-MoE: Training Preference-Guided Routers for Molecule Generation | [
"cs.LG"
] | Recent advances in language models have enabled framing molecule generation as sequence modeling. However, existing approaches often rely on single-objective reinforcement learning, limiting their applicability to real-world drug design, where multiple competing properties must be optimized. Traditional multi-objective... | {
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} |
2502.05637 | Adversarial Machine Learning: Attacks, Defenses, and Open Challenges | [
"cs.CR",
"cs.AI"
] | Adversarial Machine Learning (AML) addresses vulnerabilities in AI systems where adversaries manipulate inputs or training data to degrade performance. This article provides a comprehensive analysis of evasion and poisoning attacks, formalizes defense mechanisms with mathematical rigor, and discusses the challenges of ... | {
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} |
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