id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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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 dis... |
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... |
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 ... |
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 l... |
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 princip... |
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 m... |
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
reductio... |
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 cr... |
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... |
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... |
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 medi... |
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 ... |
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 cont... |
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)
frame... |
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 withi... |
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... |
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 app... |
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 central... |
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 c... |
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 mixtur... |
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 a... |
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 n... |
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 algorith... |
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 intere... |
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,... |
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
th... |
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 ... |
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 no... |
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 scena... |
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 ... |
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 ... |
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... |
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
human... |
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 literat... |
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
frequen... |
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 promisin... |
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... |
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
representa... |
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 ... |
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 ... |
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. ... |
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. Deep... |
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 Stac... |
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 aut... |
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, ... |
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 cou... |
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 sy... |
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 Ne... |
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 ea... |
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 i... |
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 ques... |
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 tomograph... |
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 ... |
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 motio... |
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
linguisti... |
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. Motiv... |
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
despit... |
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 ... |
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... |
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: Catastrop... |
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 d... |
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 co... |
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, ... |
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 inst... |
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,... |
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... |
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 ... |
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 a... |
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 approache... |
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 o... |
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 empiric... |
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 ... |
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 ... |
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 langu... |
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
language... |
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 no... |
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 tem... |
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
ma... |
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. Th... |
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, it... |
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 ... |
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-... |
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. Give... |
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... |
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.... |
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 ite... |
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 ... |
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, a... |
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 mul... |
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 resear... |
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 ... |
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 stat... |
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 awa... |
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 exponent... |
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... |
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 t... |
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 ... |
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 ... |
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-objecti... |
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 o... |
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