id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.10689 | Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction | cs.LG cs.AI | The burgeoning volume of electronic health records (EHRs) has enabled deep
learning models to excel in predictive healthcare. However, for high-stakes
applications such as diagnosis prediction, model interpretability remains
paramount. Existing deep learning diagnosis prediction models with intrinsic
interpretability... |
2502.10691 | Controlling Neural Collapse Enhances Out-of-Distribution Detection and
Transfer Learning | cs.LG | Out-of-distribution (OOD) detection and OOD generalization are widely studied
in Deep Neural Networks (DNNs), yet their relationship remains poorly
understood. We empirically show that the degree of Neural Collapse (NC) in a
network layer is inversely related with these objectives: stronger NC improves
OOD detection ... |
2502.10693 | Extremely Large Full Duplex MIMO for Simultaneous Downlink
Communications and Monostatic Sensing at Sub-THz Frequencies | cs.IT cs.ET math.IT | The in-band Full Duplex (FD) technology is lately gaining attention as an
enabler for the emerging paradigm of Integrated Sensing and Communications
(ISAC), which envisions seamless integration of sensing mechanisms for
unconnected entities into next generation wireless networks. In this paper, we
present an FD Multi... |
2502.10694 | Simulations of Common Unsupervised Domain Adaptation Algorithms for
Image Classification | cs.LG cs.AI | Traditional machine learning assumes that training and test sets are derived
from the same distribution; however, this assumption does not always hold in
practical applications. This distribution disparity can lead to severe
performance drops when the trained model is used in new data sets. Domain
adaptation (DA) is ... |
2502.10697 | The Lee weight distributions of several classes of linear codes over
$\mathbb{Z}_4$ | cs.IT math.IT | Let $\mathbb{Z}_4$ denote the ring of integers modulo $4$. The Galois ring
GR$(4,m)$, which consists of $4^m$ elements, represents the Galois extension of
degree $m$ over $\mathbb{Z}_4$. The constructions of codes over $\mathbb{Z}_4$
have garnered significant interest in recent years. In this paper, building
upon pre... |
2502.10698 | Superpose Singular Features for Model Merging | cs.LG cs.AI | Model merging is a critical technique for combining the capabilities of
multiple fine-tuned models without requiring additional training. While
existing methods treat parameters as vectors, they overlook the intrinsic
structure of linear transformation matrices - the core components that comprise
the majority of mode... |
2502.10699 | Exploring Synaptic Resonance in Large Language Models: A Novel Approach
to Contextual Memory Integration | cs.CL cs.AI cs.NE | Contextual memory integration remains a high challenge in the development of
language models, particularly in tasks that require maintaining coherence over
extended sequences. Traditional approaches, such as self-attention mechanisms
and memory-augmented architectures, often prioritize short-term dependencies,
leadin... |
2502.10701 | Unpacking the Layers: Exploring Self-Disclosure Norms, Engagement
Dynamics, and Privacy Implications | cs.SI cs.HC | This paper characterizes the self-disclosure behavior of Reddit users across
11 different types of self-disclosure. We find that at least half of the users
share some type of disclosure in at least 10% of their posts, with half of
these posts having more than one type of disclosure. We show that different
types of se... |
2502.10703 | Artificial intelligence-enabled detection and assessment of Parkinson's
disease using multimodal data: A survey | cs.LG cs.SD | The rapid emergence of highly adaptable and reusable artificial intelligence
(AI) models is set to revolutionize the medical field, particularly in the
diagnosis and management of Parkinson's disease (PD). Currently, there are no
effective biomarkers for diagnosing PD, assessing its severity, or tracking its
progress... |
2502.10704 | Occlusion-aware Non-Rigid Point Cloud Registration via Unsupervised
Neural Deformation Correntropy | cs.CV cs.AI | Non-rigid alignment of point clouds is crucial for scene understanding,
reconstruction, and various computer vision and robotics tasks. Recent
advancements in implicit deformation networks for non-rigid registration have
significantly reduced the reliance on large amounts of annotated training data.
However, existing... |
2502.10705 | CoPEFT: Fast Adaptation Framework for Multi-Agent Collaborative
Perception with Parameter-Efficient Fine-Tuning | cs.AI | Multi-agent collaborative perception is expected to significantly improve
perception performance by overcoming the limitations of single-agent perception
through exchanging complementary information. However, training a robust
collaborative perception model requires collecting sufficient training data
that covers all... |
2502.10706 | Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond
Explicit Environment Modeling | cs.LG cs.AI | Out-of-distribution (OOD) generalization has emerged as a critical challenge
in graph learning, as real-world graph data often exhibit diverse and shifting
environments that traditional models fail to generalize across. A promising
solution to address this issue is graph invariant learning (GIL), which aims to
learn ... |
2502.10707 | Reading Your Heart: Learning ECG Words and Sentences via Pre-training
ECG Language Model | cs.LG cs.AI | Electrocardiogram (ECG) is essential for the clinical diagnosis of
arrhythmias and other heart diseases, but deep learning methods based on ECG
often face limitations due to the need for high-quality annotations. Although
previous ECG self-supervised learning (eSSL) methods have made significant
progress in represent... |
2502.10708 | Injecting Domain-Specific Knowledge into Large Language Models: A
Comprehensive Survey | cs.CL | Large Language Models (LLMs) have demonstrated remarkable success in various
tasks such as natural language understanding, text summarization, and machine
translation. However, their general-purpose nature often limits their
effectiveness in domain-specific applications that require specialized
knowledge, such as hea... |
2502.10709 | An Empirical Analysis of Uncertainty in Large Language Model Evaluations | cs.CL cs.AI | As LLM-as-a-Judge emerges as a new paradigm for assessing large language
models (LLMs), concerns have been raised regarding the alignment, bias, and
stability of LLM evaluators. While substantial work has focused on alignment
and bias, little research has concentrated on the stability of LLM evaluators.
In this paper... |
2502.10712 | FuncGenFoil: Airfoil Generation and Editing Model in Function Space | cs.LG cs.AI | Aircraft manufacturing is the jewel in the crown of industry, among which
generating high-fidelity airfoil geometries with controllable and editable
representations remains a fundamental challenge. While existing
deep-learning-based methods rely on predefined parametric function families,
e.g., B\'ezier curves and di... |
2502.10713 | Improving action segmentation via explicit similarity measurement | cs.CV | Existing supervised action segmentation methods depend on the quality of
frame-wise classification using attention mechanisms or temporal convolutions
to capture temporal dependencies. Even boundary detection-based methods
primarily depend on the accuracy of an initial frame-wise classification, which
can overlook pr... |
2502.10714 | Disentangle Nighttime Lens Flares: Self-supervised Generation-based Lens
Flare Removal | cs.CV | Lens flares arise from light reflection and refraction within sensor arrays,
whose diverse types include glow, veiling glare, reflective flare and so on.
Existing methods are specialized for one specific type only, and overlook the
simultaneous occurrence of multiple typed lens flares, which is common in the
real-wor... |
2502.10716 | Why Domain Generalization Fail? A View of Necessity and Sufficiency | cs.LG stat.ML | Despite a strong theoretical foundation, empirical experiments reveal that
existing domain generalization (DG) algorithms often fail to consistently
outperform the ERM baseline. We argue that this issue arises because most DG
studies focus on establishing theoretical guarantees for generalization under
unrealistic as... |
2502.10718 | Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio
Processing on Extreme Edge | cs.SD cs.AI eess.AS | The escalating challenges of managing vast sensor-generated data,
particularly in audio applications, necessitate innovative solutions. Current
systems face significant computational and storage demands, especially in
real-time applications like gunshot detection systems (GSDS), and the
proliferation of edge sensors ... |
2502.10720 | NPSim: Nighttime Photorealistic Simulation From Daytime Images With
Monocular Inverse Rendering and Ray Tracing | cs.CV cs.GR | Semantic segmentation is an important task for autonomous driving. A powerful
autonomous driving system should be capable of handling images under all
conditions, including nighttime. Generating accurate and diverse nighttime
semantic segmentation datasets is crucial for enhancing the performance of
computer vision a... |
2502.10721 | A Comprehensive Survey of Deep Learning for Multivariate Time Series
Forecasting: A Channel Strategy Perspective | cs.LG | Multivariate Time Series Forecasting (MTSF) plays a crucial role across
diverse fields, ranging from economic, energy, to traffic. In recent years,
deep learning has demonstrated outstanding performance in MTSF tasks. In MTSF,
modeling the correlations among different channels is critical, as leveraging
information f... |
2502.10723 | A Mathematics Framework of Artificial Shifted Population Risk and Its
Further Understanding Related to Consistency Regularization | cs.LG cs.AI | Data augmentation is an important technique in training deep neural networks
as it enhances their ability to generalize and remain robust. While data
augmentation is commonly used to expand the sample size and act as a
consistency regularization term, there is a lack of research on the
relationship between them. To a... |
2502.10724 | Semantics-aware Test-time Adaptation for 3D Human Pose Estimation | cs.CV | This work highlights a semantics misalignment in 3D human pose estimation.
For the task of test-time adaptation, the misalignment manifests as overly
smoothed and unguided predictions. The smoothing settles predictions towards
some average pose. Furthermore, when there are occlusions or truncations, the
adaptation be... |
2502.10725 | PropNet: a White-Box and Human-Like Network for Sentence Representation | cs.CL cs.AI | Transformer-based embedding methods have dominated the field of sentence
representation in recent years. Although they have achieved remarkable
performance on NLP missions, such as semantic textual similarity (STS) tasks,
their black-box nature and large-data-driven training style have raised
concerns, including issu... |
2502.10728 | Construction A Lattice Design Based on the Truncated Union Bound | cs.IT math.IT | This paper considers $n= 128$ dimensional construction A lattice design,
using binary codes with known minimum Hamming distance and codeword
multiplicity, the number of minimum weight codeword. A truncated theta series
of the lattice is explicitly given to obtain the truncated union bound to
estimate the word error r... |
2502.10729 | VarGes: Improving Variation in Co-Speech 3D Gesture Generation via
StyleCLIPS | cs.CV | Generating expressive and diverse human gestures from audio is crucial in
fields like human-computer interaction, virtual reality, and animation. Though
existing methods have achieved remarkable performance, they often exhibit
limitations due to constrained dataset diversity and the restricted amount of
information d... |
2502.10732 | Rule-Bottleneck Reinforcement Learning: Joint Explanation and Decision
Optimization for Resource Allocation with Language Agents | cs.LG cs.AI | Deep Reinforcement Learning (RL) is remarkably effective in addressing
sequential resource allocation problems in domains such as healthcare, public
policy, and resource management. However, deep RL policies often lack
transparency and adaptability, challenging their deployment alongside human
decision-makers. In con... |
2502.10734 | Motion planning for highly-dynamic unconditioned reflexes based on
chained Signed Distance Functions | cs.RO | The unconditioned reflex (e.g., protective reflex), which is the innate
reaction of the organism and usually performed through the spinal cord rather
than the brain, can enable organisms to escape harms from environments. In this
paper, we propose an online, highly-dynamic motion planning algorithm to endow
manipulat... |
2502.10735 | OPTISHEAR: Towards Efficient and Adaptive Pruning of Large Language
Models via Evolutionary Optimization | cs.CL | Post-training pruning has emerged as a crucial optimization technique as
large language models (LLMs) continue to grow rapidly. However, the significant
variations in weight distributions across different LLMs make fixed pruning
strategies inadequate for multiple models. In this paper, we introduce
\textbf{\textsc{Op... |
2502.10739 | BASE-SQL: A powerful open source Text-To-SQL baseline approach | cs.CL | The conversion of natural language into SQL language for querying databases
(Text-to-SQL) has broad application prospects and has attracted widespread
attention. At present, the mainstream Text-to-SQL methods are mainly divided
into in-context learning (ICL) based methods and supervised fine-tuning (SFT)
based method... |
2502.10742 | The Philosophical Foundations of Growing AI Like A Child | cs.AI | Despite excelling in high-level reasoning, current language models lack
robustness in real-world scenarios and perform poorly on fundamental
problem-solving tasks that are intuitive to humans. This paper argues that both
challenges stem from a core discrepancy between human and machine cognitive
development. While bo... |
2502.10743 | 1bit-Merging: Dynamic Quantized Merging for Large Language Models | cs.CL | Recent advances in large language models have led to specialized models
excelling in specific domains, creating a need for efficient model merging
techniques. While traditional merging approaches combine parameters into a
single static model, they often compromise task-specific performance. However,
task-specific rou... |
2502.10749 | LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model
Merging | cs.CL cs.AI | While most current approaches rely on further training techniques, such as
fine-tuning or reinforcement learning, to enhance model capacities, model
merging stands out for its ability of improving models without requiring any
additional training. In this paper, we propose a unified framework for model
merging based o... |
2502.10750 | Human-Centric Community Detection in Hybrid Metaverse Networks with
Integrated AI Entities | cs.SI cs.AI | Community detection is a cornerstone problem in social network analysis
(SNA), aimed at identifying cohesive communities with minimal external links.
However, the rise of generative AI and Metaverse introduce complexities by
creating hybrid human-AI social networks (denoted by HASNs), where traditional
methods fall s... |
2502.10760 | Why is prompting hard? Understanding prompts on binary sequence
predictors | cs.CL cs.LG stat.ML | Large language models (LLMs) can be prompted to do many tasks, but finding
good prompts is not always easy, nor is understanding some performant prompts.
We explore these issues by viewing prompting as conditioning a near-optimal
sequence predictor (LLM) pretrained on diverse data sources. Through numerous
prompt sea... |
2502.10761 | A Whole-Body Disturbance Rejection Control Framework for Dynamic Motions
in Legged Robots | cs.RO | This letter presents a control framework for legged robots that enables
self-perception and resistance to external disturbances and model
uncertainties. First, a novel disturbance estimator is proposed, integrating
adaptive control and extended state observers (ESO) to estimate external
disturbances and model uncerta... |
2502.10762 | Bone Soups: A Seek-and-Soup Model Merging Approach for Controllable
Multi-Objective Generation | cs.LG cs.AI cs.CL | User information needs are often highly diverse and varied. A key challenge
in current research is how to achieve controllable multi-objective generation
while enabling rapid adaptation to accommodate diverse user demands during test
time. Existing solutions, such as Rewarded Soup, focus on merging language
models in... |
2502.10764 | Learning to Explain Air Traffic Situation | cs.LG | Understanding how air traffic controllers construct a mental 'picture' of
complex air traffic situations is crucial but remains a challenge due to the
inherently intricate, high-dimensional interactions between aircraft, pilots,
and controllers. Previous work on modeling the strategies of air traffic
controllers and ... |
2502.10768 | Evaluating improvements on using Large Language Models (LLMs) for
property extraction in the Open Research Knowledge Graph (ORKG) | cs.IR cs.AI cs.CL | Current research highlights the great potential of Large Language Models
(LLMs) for constructing Scholarly Knowledge Graphs (SKGs). One particularly
complex step in this process is relation extraction, aimed at identifying
suitable properties to describe the content of research. This study builds
directly on previous... |
2502.10776 | A Distillation-based Future-aware Graph Neural Network for Stock Trend
Prediction | cs.LG cs.AI q-fin.PM | Stock trend prediction involves forecasting the future price movements by
analyzing historical data and various market indicators. With the advancement
of machine learning, graph neural networks (GNNs) have been extensively
employed in stock prediction due to their powerful capability to capture
spatiotemporal depend... |
2502.10777 | Fast Transmission Control Adaptation for URLLC via Channel Knowledge Map
and Meta-Learning | cs.IT math.IT | This paper considers methods for delivering ultra reliable low latency
communication (URLLC) to enable mission-critical Internet of Things (IoT)
services in wireless environments with unknown channel distribution. The
methods rely upon the historical channel gain samples of a few locations in a
target area. We formul... |
2502.10784 | Preconditioned Inexact Stochastic ADMM for Deep Model | cs.LG | The recent advancement of foundation models (FMs) has brought about a
paradigm shift, revolutionizing various sectors worldwide. The popular
optimizers used to train these models are stochastic gradient descent-based
algorithms, which face inherent limitations, such as slow convergence and
stringent assumptions for c... |
2502.10785 | REGNav: Room Expert Guided Image-Goal Navigation | cs.CV | Image-goal navigation aims to steer an agent towards the goal location
specified by an image. Most prior methods tackle this task by learning a
navigation policy, which extracts visual features of goal and observation
images, compares their similarity and predicts actions. However, if the agent
is in a different room... |
2502.10786 | Epidemic-guided deep learning for spatiotemporal forecasting of
Tuberculosis outbreak | cs.LG q-bio.QM stat.ML | Tuberculosis (TB) remains a formidable global health challenge, driven by
complex spatiotemporal transmission dynamics and influenced by factors such as
population mobility and behavioral changes. We propose an Epidemic-Guided Deep
Learning (EGDL) approach that fuses mechanistic epidemiological principles with
advanc... |
2502.10789 | ReReLRP -- Remembering and Recognizing Tasks with LRP | cs.LG | Deep neural networks have revolutionized numerous research fields and
applications. Despite their widespread success, a fundamental limitation known
as catastrophic forgetting remains, where models fail to retain their ability
to perform previously learned tasks after being trained on new ones. This
limitation is par... |
2502.10790 | Which Features are Best for Successor Features? | cs.LG math.OC stat.ML | In reinforcement learning, universal successor features (SFs) are a way to
provide zero-shot adaptation to new tasks at test time: they provide optimal
policies for all downstream reward functions lying in the linear span of a set
of base features. But it is unclear what constitutes a good set of base
features, that ... |
2502.10792 | Tackling the Zero-Shot Reinforcement Learning Loss Directly | cs.LG | Zero-shot reinforcement learning (RL) methods aim at instantly producing a
behavior for an RL task in a given environment, from a description of the
reward function. These methods are usually tested by evaluating their average
performance on a series of downstream tasks. Yet they cannot be trained
directly for that o... |
2502.10793 | Dynamic Influence Tracker: Measuring Time-Varying Sample Influence
During Training | stat.ML cs.AI cs.LG | Existing methods for measuring training sample influence on models only
provide static, overall measurements, overlooking how sample influence changes
during training. We propose Dynamic Influence Tracker (DIT), which captures the
time-varying sample influence across arbitrary time windows during training.
DIT offe... |
2502.10794 | Distraction is All You Need for Multimodal Large Language Model
Jailbreaking | cs.CV | Multimodal Large Language Models (MLLMs) bridge the gap between visual and
textual data, enabling a range of advanced applications. However, complex
internal interactions among visual elements and their alignment with text can
introduce vulnerabilities, which may be exploited to bypass safety mechanisms.
To address t... |
2502.10801 | FaceSwapGuard: Safeguarding Facial Privacy from DeepFake Threats through
Identity Obfuscation | cs.CR cs.AI cs.CV | DeepFakes pose a significant threat to our society. One representative
DeepFake application is face-swapping, which replaces the identity in a facial
image with that of a victim. Although existing methods partially mitigate these
risks by degrading the quality of swapped images, they often fail to disrupt
the identit... |
2502.10802 | CoCoEvo: Co-Evolution of Programs and Test Cases to Enhance Code
Generation | cs.SE cs.AI | Large Language Models (LLMs) have shown remarkable performance in automated
code generation. However, existing approaches often rely heavily on pre-defined
test cases, which become impractical in scenarios where such cases are
unavailable. While prior works explore filtering techniques between programs
and test cases... |
2502.10803 | PDA: Generalizable Detection of AI-Generated Images via Post-hoc
Distribution Alignment | cs.CR cs.AI cs.CV | The rapid advancement of generative models has led to the proliferation of
highly realistic AI-generated images, posing significant challenges for
detection methods to generalize across diverse and evolving generative
techniques. Existing approaches often fail to adapt to unknown models without
costly retraining, lim... |
2502.10807 | HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model | cs.LG cs.AI q-bio.GN | Advances in natural language processing and large language models have
sparked growing interest in modeling DNA, often referred to as the "language of
life". However, DNA modeling poses unique challenges. First, it requires the
ability to process ultra-long DNA sequences while preserving single-nucleotide
resolution,... |
2502.10810 | SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming
Video Understanding | cs.CV | Despite the significant advancements of Large Vision-Language Models (LVLMs)
on established benchmarks, there remains a notable gap in suitable evaluation
regarding their applicability in the emerging domain of long-context streaming
video understanding. Current benchmarks for video understanding typically
emphasize ... |
2502.10812 | ResiComp: Loss-Resilient Image Compression via Dual-Functional Masked
Visual Token Modeling | eess.IV cs.IT math.IT | Recent advancements in neural image codecs (NICs) are of significant
compression performance, but limited attention has been paid to their error
resilience.
These resulting NICs tend to be sensitive to packet losses, which are
prevalent in real-time communications.
In this paper, we investigate how to elevate the... |
2502.10813 | Transformer-Driven Modeling of Variable Frequency Features for
Classifying Student Engagement in Online Learning | cs.CV | The COVID-19 pandemic and the internet's availability have recently boosted
online learning. However, monitoring engagement in online learning is a
difficult task for teachers. In this context, timely automatic student
engagement classification can help teachers in making adaptive adjustments to
meet students' needs.... |
2502.10816 | BalanceBenchmark: A Survey for Imbalanced Learning | cs.LG cs.AI | Multimodal learning has gained attention for its capacity to integrate
information from different modalities. However, it is often hindered by the
multimodal imbalance problem, where certain modality dominates while others
remain underutilized. Although recent studies have proposed various methods to
alleviate this p... |
2502.10818 | On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs:
Bridging Recurrent and Graph Learning | cs.LG cs.AI | Graph Neural Networks (GNNs) are models that leverage the graph structure to
transmit information between nodes, typically through the message-passing
operation. While widely successful, this approach is well known to suffer from
the over-smoothing and over-squashing phenomena, which result in
representational collap... |
2502.10819 | Sensing With Communication Signals: From Information Theory to Signal
Processing | cs.IT math.IT | The Integrated Sensing and Communications (ISAC) paradigm is anticipated to
be a cornerstone of the upcoming 6G networks. In order to optimize the use of
wireless resources, 6G ISAC systems need to harness the communication data
payload signals, which are inherently random, for both sensing and
communication (S&C) pu... |
2502.10822 | NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for
Personalized Hearing Aids | eess.AS cs.AI cs.SD | The prevalence of hearing aids is increasing. However, optimizing the
amplification processes of hearing aids remains challenging due to the
complexity of integrating multiple modular components in traditional methods.
To address this challenge, we present NeuroAMP, a novel deep neural network
designed for end-to-end... |
2502.10825 | MITRE ATT&CK Applications in Cybersecurity and The Way Forward | cs.CR cs.AI | The MITRE ATT&CK framework is a widely adopted tool for enhancing
cybersecurity, supporting threat intelligence, incident response, attack
modeling, and vulnerability prioritization. This paper synthesizes research on
its application across these domains by analyzing 417 peer-reviewed
publications. We identify common... |
2502.10826 | Improved Offline Contextual Bandits with Second-Order Bounds: Betting
and Freezing | cs.LG cs.IT math.IT stat.ML | We consider the off-policy selection and learning in contextual bandits where
the learner aims to select or train a reward-maximizing policy using data
collected by a fixed behavior policy. Our contribution is two-fold. First, we
propose a novel off-policy selection method that leverages a new betting-based
confidenc... |
2502.10827 | E-3DGS: Event-Based Novel View Rendering of Large-Scale Scenes Using 3D
Gaussian Splatting | cs.CV cs.GR | Novel view synthesis techniques predominantly utilize RGB cameras, inheriting
their limitations such as the need for sufficient lighting, susceptibility to
motion blur, and restricted dynamic range. In contrast, event cameras are
significantly more resilient to these limitations but have been less explored
in this do... |
2502.10828 | The Vendiscope: An Algorithmic Microscope For Data Collections | cs.LG cond-mat.mtrl-sci cs.AI q-bio.QM | The evolution of microscopy, beginning with its invention in the late 16th
century, has continuously enhanced our ability to explore and understand the
microscopic world, enabling increasingly detailed observations of structures
and phenomena. In parallel, the rise of data-driven science has underscored the
need for ... |
2502.10833 | Order-agnostic Identifier for Large Language Model-based Generative
Recommendation | cs.IR | Leveraging Large Language Models (LLMs) for generative recommendation has
attracted significant research interest, where item tokenization is a critical
step. It involves assigning item identifiers for LLMs to encode user history
and generate the next item. Existing approaches leverage either token-sequence
identifie... |
2502.10834 | Prosocial Media | cs.CY cs.SI | Social media empower distributed content creation by algorithmically
harnessing "the social fabric" (explicit and implicit signals of association)
to serve this content. While this overcomes the bottlenecks and biases of
traditional gatekeepers, many believe it has unsustainably eroded the very
social fabric it depen... |
2502.10835 | Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large
Language Models | cs.CL | We investigate how large language models perform latent multi-hop reasoning
in prompts like "Wolfgang Amadeus Mozart's mother's spouse is". To analyze this
process, we introduce logit flow, an interpretability method that traces how
logits propagate across layers and positions toward the final prediction. Using
logit... |
2502.10838 | Generalizable speech deepfake detection via meta-learned LoRA | eess.AS cs.LG cs.SD | Generalizable deepfake detection can be formulated as a detection problem
where labels (bonafide and fake) are fixed but distributional drift affects the
deepfake set. We can always train our detector with one-selected attacks and
bonafide data, but an attacker can generate new attacks by just retraining his
generato... |
2502.10841 | SkyReels-A1: Expressive Portrait Animation in Video Diffusion
Transformers | cs.CV | We present SkyReels-A1, a simple yet effective framework built upon video
diffusion Transformer to facilitate portrait image animation. Existing
methodologies still encounter issues, including identity distortion, background
instability, and unrealistic facial dynamics, particularly in head-only
animation scenarios. ... |
2502.10842 | Mobile Robotic Multi-View Photometric Stereo | cs.CV cs.RO | Multi-View Photometric Stereo (MVPS) is a popular method for fine-detailed 3D
acquisition of an object from images. Despite its outstanding results on
diverse material objects, a typical MVPS experimental setup requires a
well-calibrated light source and a monocular camera installed on an immovable
base. This restric... |
2502.10843 | LEAPS: A discrete neural sampler via locally equivariant networks | cs.LG stat.CO stat.ML | We propose LEAPS, an algorithm to sample from discrete distributions known up
to normalization by learning a rate matrix of a continuous-time Markov chain
(CTMC). LEAPS can be seen as a continuous-time formulation of annealed
importance sampling and sequential Monte Carlo methods, extended so that the
variance of the... |
2502.10848 | Implicit Neural Representations of Molecular Vector-Valued Functions | cs.LG q-bio.QM | Molecules have various computational representations, including numerical
descriptors, strings, graphs, point clouds, and surfaces. Each representation
method enables the application of various machine learning methodologies from
linear regression to graph neural networks paired with large language models.
To complem... |
2502.10851 | To Bin or not to Bin: Alternative Representations of Mass Spectra | cs.LG physics.chem-ph q-bio.QM | Mass spectrometry, especially so-called tandem mass spectrometry, is commonly
used to assess the chemical diversity of samples. The resulting mass
fragmentation spectra are representations of molecules of which the structure
may have not been determined. This poses the challenge of experimentally
determining or compu... |
2502.10852 | Multilingual Encoder Knows more than You Realize: Shared Weights
Pretraining for Extremely Low-Resource Languages | cs.CL cs.AI | While multilingual language models like XLM-R have advanced multilingualism
in NLP, they still perform poorly in extremely low-resource languages. This
situation is exacerbated by the fact that modern LLMs such as LLaMA and Qwen
support far fewer languages than XLM-R, making text generation models
non-existent for ma... |
2502.10853 | Sparse learning with concave regularization: relaxation of the
irrepresentable condition | math.OC cs.SY eess.SY | Learning sparse models from data is an important task in all those frameworks
where relevant information should be identified within a large dataset. This
can be achieved by formulating and solving suitable sparsity promoting
optimization problems. As to linear regression models, Lasso is the most
popular convex appr... |
2502.10855 | Towards Effective Extraction and Evaluation of Factual Claims | cs.CL | A common strategy for fact-checking long-form content generated by Large
Language Models (LLMs) is extracting simple claims that can be verified
independently. Since inaccurate or incomplete claims compromise fact-checking
results, ensuring claim quality is critical. However, the lack of a
standardized evaluation fra... |
2502.10857 | Divergent Thoughts toward One Goal: LLM-based Multi-Agent Collaboration
System for Electronic Design Automation | cs.CL | Recently, with the development of tool-calling capabilities in large language
models (LLMs), these models have demonstrated significant potential for
automating electronic design automation (EDA) flows by interacting with EDA
tool APIs via EDA scripts. However, considering the limited understanding of
EDA tools, LLMs... |
2502.10858 | Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs | cs.AI cs.CL | Deep iterative chain-of-thought (CoT) reasoning enables LLMs to tackle
complex tasks by progressively activating relevant pre-trained knowledge.
However, it faces challenges in ensuring continual improvement and determining
a stopping criterion. In this paper, we investigate whether the relevant
knowledge that contri... |
2502.10862 | Accelerated co-design of robots through morphological pretraining | cs.RO | The co-design of robot morphology and neural control typically requires using
reinforcement learning to approximate a unique control policy gradient for each
body plan, demanding massive amounts of training data to measure the
performance of each design. Here we show that a universal, morphology-agnostic
controller c... |
2502.10864 | Recursions for quadratic rotation symmetric functions weights | cs.IT math.CO math.IT | A Boolean function in $n$ variables is rotation symmetric (RS) if it is
invariant under powers of $\rho(x_1, \ldots, x_n) = (x_2, \ldots, x_n, x_1)$.
An RS function is called monomial rotation symmetric (MRS) if it is generated
by applying powers of $\rho$ to a single monomial. The author showed in $2017$
that for an... |
2502.10867 | A Tutorial on LLM Reasoning: Relevant Methods behind ChatGPT o1 | cs.AI cs.CL | OpenAI o1 has shown that applying reinforcement learning to integrate
reasoning steps directly during inference can significantly improve a model's
reasoning capabilities. This result is exciting as the field transitions from
the conventional autoregressive method of generating answers to a more
deliberate approach t... |
2502.10868 | NitiBench: A Comprehensive Studies of LLM Frameworks Capabilities for
Thai Legal Question Answering | cs.CL | The application of large language models (LLMs) in the legal domain holds
significant potential for information retrieval and question answering, yet
Thai legal QA systems face challenges due to a lack of standardized evaluation
benchmarks and the complexity of Thai legal structures. This paper introduces
NitiBench, ... |
2502.10870 | Hybrid high-order methods for elasto-acoustic wave propagation in the
time domain | math.NA cs.CE cs.NA | We devise a Hybrid High-Order (HHO) method for the coupling between the
acoustic and elastic wave equations in the time domain. A first-order
formulation in time is considered. The HHO method can use equal-order and
mixed-order settings, as well as O(1)- and O(1/h)-stabilizations. An
energy-error estimate is establis... |
2502.10871 | The Representation and Recall of Interwoven Structured Knowledge in
LLMs: A Geometric and Layered Analysis | cs.CL cs.AI cs.LG | This study investigates how large language models (LLMs) represent and recall
multi-associated attributes across transformer layers. We show that
intermediate layers encode factual knowledge by superimposing related
attributes in overlapping spaces, along with effective recall even when
attributes are not explicitly ... |
2502.10874 | Indexing Join Inputs for Fast Queries and Maintenance | cs.DB | In database systems, joins are often expensive despite many years of research
producing numerous join algorithms. Precomputed and materialized join views
deliver the best query performance, whereas traditional indexes, used as
pre-sorted inputs for merge joins, permit very efficient maintenance. Neither
traditional i... |
2502.10875 | A Geometric Approach to Personalized Recommendation with Set-Theoretic
Constraints Using Box Embeddings | cs.IR cs.AI cs.LG | Personalized item recommendation typically suffers from data sparsity, which
is most often addressed by learning vector representations of users and items
via low-rank matrix factorization. While this effectively densifies the matrix
by assuming users and movies can be represented by linearly dependent latent
feature... |
2502.10876 | Super Resolution image reconstructs via total variation-based image
deconvolution: a majorization-minimization approach | cs.CV | This work aims to reconstruct image sequences with Total Variation regularity
in super-resolution. We consider, in particular, images of scenes for which the
point-to-point image transformation is a plane projective transformation. We
first describe the super-resolution image's imaging observation model, an
interpola... |
2502.10878 | Broadcast Channel Cooperative Gain: An Operational Interpretation of
Partial Information Decomposition | cs.IT cs.AI cs.LG math.IT | Partial information decomposition has recently found applications in
biological signal processing and machine learning. Despite its impacts, the
decomposition was introduced through an informal and heuristic route, and its
exact operational meaning is unclear. In this work, we fill this gap by
connecting partial info... |
2502.10881 | CiteCheck: Towards Accurate Citation Faithfulness Detection | cs.CL | Citation faithfulness detection is critical for enhancing retrieval-augmented
generation (RAG) systems, yet large-scale Chinese datasets for this task are
scarce. Existing methods face prohibitive costs due to the need for manually
annotated negative samples. To address this, we introduce the first large-scale
Chines... |
2502.10883 | Learning Identifiable Structures Helps Avoid Bias in DNN-based
Supervised Causal Learning | cs.LG cs.AI stat.ME | Causal discovery is a structured prediction task that aims to predict causal
relations among variables based on their data samples. Supervised Causal
Learning (SCL) is an emerging paradigm in this field. Existing Deep Neural
Network (DNN)-based methods commonly adopt the "Node-Edge approach", in which
the model first... |
2502.10886 | MET-Bench: Multimodal Entity Tracking for Evaluating the Limitations of
Vision-Language and Reasoning Models | cs.CL | Entity tracking is a fundamental challenge in natural language understanding,
requiring models to maintain coherent representations of entities. Previous
work has benchmarked entity tracking performance in purely text-based tasks. We
introduce MET-Bench, a multimodal entity tracking benchmark designed to
evaluate the... |
2502.10887 | RemInD: Remembering Anatomical Variations for Interpretable Domain
Adaptive Medical Image Segmentation | cs.CV | This work presents a novel Bayesian framework for unsupervised domain
adaptation (UDA) in medical image segmentation. While prior works have explored
this clinically significant task using various strategies of domain alignment,
they often lack an explicit and explainable mechanism to ensure that target
image feature... |
2502.10889 | Nonlinear Feedback Linearization and LQG/LTR Control: A Comparative
Study for a Single-Machine Infinite-Bus System | eess.SY cs.SY math.OC | This paper presents a comparative study of three advanced control strategies
for a single-machine infinite-bus (SMIB) system: the nonlinear feedback
linearizing controller (NFLC), the integral-NFLC (INFLC), and the
linear-quadratic-Gaussian/loop transfer recovery (LQG/LTR) control. The NFLC
and INFLC techniques use e... |
2502.10894 | Bridging the Sim-to-Real Gap for Athletic Loco-Manipulation | cs.RO cs.AI cs.LG | Achieving athletic loco-manipulation on robots requires moving beyond
traditional tracking rewards - which simply guide the robot along a reference
trajectory - to task rewards that drive truly dynamic, goal-oriented behaviors.
Commands such as "throw the ball as far as you can" or "lift the weight as
quickly as poss... |
2502.10896 | Developing Conversational Speech Systems for Robots to Detect Speech
Biomarkers of Cognition in People Living with Dementia | cs.CL | This study presents the development and testing of a conversational speech
system designed for robots to detect speech biomarkers indicative of cognitive
impairments in people living with dementia (PLwD). The system integrates a
backend Python WebSocket server and a central core module with a large language
model (LL... |
2502.10899 | Breaking Down the Hierarchy: A New Approach to Leukemia Classification | cs.CV cs.AI cs.LG | The complexities inherent to leukemia, multifaceted cancer affecting white
blood cells, pose considerable diagnostic and treatment challenges, primarily
due to reliance on laborious morphological analyses and expert judgment that
are susceptible to errors. Addressing these challenges, this study presents a
refined, c... |
2502.10906 | PCGRLLM: Large Language Model-Driven Reward Design for Procedural
Content Generation Reinforcement Learning | cs.AI | Reward design plays a pivotal role in the training of game AIs, requiring
substantial domain-specific knowledge and human effort. In recent years,
several studies have explored reward generation for training game agents and
controlling robots using large language models (LLMs). In the content
generation literature, t... |
2502.10907 | Local Multiple Traces Formulation for Heterogeneous Electromagnetic
Scattering: Implementation and Preconditioning | cs.CE | We consider the three-dimensional time-harmonic electromagnetic (EM) wave
scattering transmission problem involving heterogeneous scatterers. The fields
are approximated using the local multiple traces formulation (MTF), originally
introduced for acoustic scattering. This scheme assigns independent boundary
unknowns ... |
2502.10908 | Automatic Quality Assessment of First Trimester Crown-Rump-Length
Ultrasound Images | cs.CV cs.AI cs.LG | Fetal gestational age (GA) is vital clinical information that is estimated
during pregnancy in order to assess fetal growth. This is usually performed by
measuring the crown-rump-length (CRL) on an ultrasound image in the Dating scan
which is then correlated with fetal age and growth trajectory. A major issue
when pe... |
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