id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.07645 | Beyond Behavior Cloning: Robustness through Interactive Imitation and
Contrastive Learning | cs.RO | Behavior cloning (BC) traditionally relies on demonstration data, assuming
the demonstrated actions are optimal. This can lead to overfitting under noisy
data, particularly when expressive models are used (e.g., the energy-based
model in Implicit BC). To address this, we extend behavior cloning into an
iterative proc... |
2502.07646 | Causal Additive Models with Unobserved Causal Paths and Backdoor Paths | cs.LG stat.ME stat.ML | Causal additive models have been employed as tractable yet expressive
frameworks for causal discovery involving hidden variables. State-of-the-art
methodologies suggest that determining the causal relationship between a pair
of variables is infeasible in the presence of an unobserved backdoor or an
unobserved causal ... |
2502.07650 | Guiding Time-Varying Generative Models with Natural Gradients on
Exponential Family Manifold | stat.ML cs.LG | Optimising probabilistic models is a well-studied field in statistics.
However, its connection with the training of generative models remains largely
under-explored. In this paper, we show that the evolution of time-varying
generative models can be projected onto an exponential family manifold,
naturally creating a l... |
2502.07656 | A Unifying Framework for Causal Imitation Learning with Hidden
Confounders | cs.LG cs.AI | We propose a general and unifying framework for causal Imitation Learning
(IL) with hidden confounders that subsumes several existing confounded IL
settings from the literature. Our framework accounts for two types of hidden
confounders: (a) those observed by the expert, which thus influence the
expert's policy, and ... |
2502.07657 | Private Low-Rank Approximation for Covariance Matrices, Dyson Brownian
Motion, and Eigenvalue-Gap Bounds for Gaussian Perturbations | cs.DS cs.CR cs.LG cs.NA math.NA math.PR | We consider the problem of approximating a $d \times d$ covariance matrix $M$
with a rank-$k$ matrix under $(\varepsilon,\delta)$-differential privacy. We
present and analyze a complex variant of the Gaussian mechanism and obtain
upper bounds on the Frobenius norm of the difference between the matrix output
by this m... |
2502.07658 | IU4Rec: Interest Unit-Based Product Organization and Recommendation for
E-Commerce Platform | cs.IR | Most recommendation systems typically follow a product-based paradigm
utilizing user-product interactions to identify the most engaging items for
users. However, this product-based paradigm has notable drawbacks for
Xianyu~\footnote{Xianyu is China's largest online C2C e-commerce platform where
a large portion of the... |
2502.07661 | Partial-Label Learning with Conformal Candidate Cleaning | cs.LG stat.ML | Real-world data is often ambiguous; for example, human annotation produces
instances with multiple conflicting class labels. Partial-label learning (PLL)
aims at training a classifier in this challenging setting, where each instance
is associated with a set of candidate labels and one correct, but unknown,
class labe... |
2502.07663 | Human Decision-making is Susceptible to AI-driven Manipulation | cs.AI cs.CL cs.CY cs.HC | Artificial Intelligence (AI) systems are increasingly intertwined with daily
life, assisting users in executing various tasks and providing guidance on
decision-making. This integration introduces risks of AI-driven manipulation,
where such systems may exploit users' cognitive biases and emotional
vulnerabilities to ... |
2502.07677 | Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered
LLM Approach | cs.CL | Achieving a delicate balance between fostering trust in law enforcement and
protecting the rights of both officers and civilians continues to emerge as a
pressing research and product challenge in the world today. In the pursuit of
fairness and transparency, this study presents an innovative AI-driven system
designed... |
2502.07680 | Multiview Point Cloud Registration Based on Minimum Potential Energy for
Free-Form Blade Measurement | cs.CV cs.CG | Point cloud registration is an essential step for free-form blade
reconstruction in industrial measurement. Nonetheless, measuring defects of the
3D acquisition system unavoidably result in noisy and incomplete point cloud
data, which renders efficient and accurate registration challenging. In this
paper, we propose ... |
2502.07683 | exHarmony: Authorship and Citations for Benchmarking the Reviewer
Assignment Problem | cs.IR cs.CL | The peer review process is crucial for ensuring the quality and reliability
of scholarly work, yet assigning suitable reviewers remains a significant
challenge. Traditional manual methods are labor-intensive and often
ineffective, leading to nonconstructive or biased reviews. This paper
introduces the exHarmony (eHar... |
2502.07685 | Matrix3D: Large Photogrammetry Model All-in-One | cs.CV | We present Matrix3D, a unified model that performs several photogrammetry
subtasks, including pose estimation, depth prediction, and novel view synthesis
using just the same model. Matrix3D utilizes a multi-modal diffusion
transformer (DiT) to integrate transformations across several modalities, such
as images, camer... |
2502.07687 | Large Language Models as Proxies for Theories of Human Linguistic
Cognition | cs.CL | We consider the possible role of current large language models (LLMs) in the
study of human linguistic cognition. We focus on the use of such models as
proxies for theories of cognition that are relatively linguistically-neutral in
their representations and learning but differ from current LLMs in key ways. We
illust... |
2502.07693 | SoK: A Classification for AI-driven Personalized Privacy Assistants | cs.CY cs.AI | To help users make privacy-related decisions, personalized privacy assistants
based on AI technology have been developed in recent years. These AI-driven
Personalized Privacy Assistants (AI-driven PPAs) can reap significant benefits
for users, who may otherwise struggle to make decisions regarding their
personal data... |
2502.07694 | Methodology for Identifying Social Groups within a Transactional Graph | cs.SI | Social network analysis is pivotal for organizations aiming to leverage the
vast amounts of data generated from user interactions on social media and other
digital platforms. These interactions often reveal complex social structures,
such as tightly-knit groups based on common interests, which are crucial for
enhanci... |
2502.07701 | Magic 1-For-1: Generating One Minute Video Clips within One Minute | cs.CV | In this technical report, we present Magic 1-For-1 (Magic141), an efficient
video generation model with optimized memory consumption and inference latency.
The key idea is simple: factorize the text-to-video generation task into two
separate easier tasks for diffusion step distillation, namely text-to-image
generatio... |
2502.07703 | GaRLIO: Gravity enhanced Radar-LiDAR-Inertial Odometry | cs.RO | Recently, gravity has been highlighted as a crucial constraint for state
estimation to alleviate potential vertical drift. Existing online gravity
estimation methods rely on pose estimation combined with IMU measurements,
which is considered best practice when direct velocity measurements are
unavailable. However, wi... |
2502.07707 | PRVQL: Progressive Knowledge-guided Refinement for Robust Egocentric
Visual Query Localization | cs.CV | Egocentric visual query localization (EgoVQL) focuses on localizing the
target of interest in space and time from first-person videos, given a visual
query. Despite recent progressive, existing methods often struggle to handle
severe object appearance changes and cluttering background in the video due to
lacking suff... |
2502.07708 | Global linearization without hyperbolicity | math.DS cs.SY eess.SY | We give a proof of an extension of the Hartman-Grobman theorem to
nonhyperbolic but asymptotically stable equilibria of vector fields. Moreover,
the linearizing topological conjugacy is (i) defined on the entire basin of
attraction if the vector field is complete, and (ii) a $C^{k\geq 1}$
diffeomorphism on the comple... |
2502.07709 | MAGELLAN: Metacognitive predictions of learning progress guide autotelic
LLM agents in large goal spaces | cs.AI | Open-ended learning agents must efficiently prioritize goals in vast
possibility spaces, focusing on those that maximize learning progress (LP).
When such autotelic exploration is achieved by LLM agents trained with online
RL in high-dimensional and evolving goal spaces, a key challenge for LP
prediction is modeling ... |
2502.07715 | Near-Optimal Sample Complexity in Reward-Free Kernel-Based Reinforcement
Learning | cs.LG | Reinforcement Learning (RL) problems are being considered under increasingly
more complex structures. While tabular and linear models have been thoroughly
explored, the analytical study of RL under nonlinear function approximation,
especially kernel-based models, has recently gained traction for their strong
represen... |
2502.07717 | Making Language Models Robust Against Negation | cs.CL | Negation has been a long-standing challenge for language models. Previous
studies have shown that they struggle with negation in many natural language
understanding tasks. In this work, we propose a self-supervised method to make
language models more robust against negation. We introduce a novel task, Next
Sentence P... |
2502.07718 | Next-to-minimal weight of toric codes defined over hypersimplices | cs.IT math.AC math.AG math.IT | Toric codes are a type of evaluation codes introduced by J.P. Hansen in 2000.
They are produced by evaluating (a vector space composed by) polynomials at the
points of $(\mathbb{F}_q^*)^s$, the monomials of these polynomials being
related to a certain polytope. Toric codes related to hypersimplices are the
result of ... |
2502.07721 | TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning | cs.LG cs.AI | The prevalence of noisy labels in real-world datasets poses a significant
impediment to the effective deployment of deep learning models. While
meta-learning strategies have emerged as a promising approach for addressing
this challenge, existing methods often suffer from limited transferability and
task-specific desi... |
2502.07726 | DeepVL: Dynamics and Inertial Measurements-based Deep Velocity Learning
for Underwater Odometry | cs.RO | This paper presents a learned model to predict the robot-centric velocity of
an underwater robot through dynamics-aware proprioception. The method exploits
a recurrent neural network using as inputs inertial cues, motor commands, and
battery voltage readings alongside the hidden state of the previous time-step
to out... |
2502.07728 | Verifying LLM-Generated Code in the Context of Software Verification
with Ada/SPARK | cs.SE cs.AI | Large language models (LLMs) have demonstrated remarkable code generation
capabilities, but the correctness of the generated code cannot be inherently
trusted. This paper explores the feasibility of using formal software
verification, specifically the SPARK framework for Ada, to ensure the
reliability of LLM-generate... |
2502.07730 | DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force
Feedback Glove | cs.RO | Dexterous hand teleoperation plays a pivotal role in enabling robots to
achieve human-level manipulation dexterity. However, current teleoperation
systems often rely on expensive equipment and lack multi-modal sensory
feedback, restricting human operators' ability to perceive object properties
and perform complex man... |
2502.07732 | Economics of Sourcing Human Data | cs.CY cs.AI cs.CL cs.CV cs.HC cs.LG | Progress in AI has relied on human-generated data, from annotator
marketplaces to the wider Internet. However, the widespread use of large
language models now threatens the quality and integrity of human-generated data
on these very platforms. We argue that this issue goes beyond the immediate
challenge of filtering ... |
2502.07734 | EdgeEar: Efficient and Accurate Ear Recognition for Edge Devices | cs.CV cs.AI | Ear recognition is a contactless and unobtrusive biometric technique with
applications across various domains. However, deploying high-performing ear
recognition models on resource-constrained devices is challenging, limiting
their applicability and widespread adoption. This paper introduces EdgeEar, a
lightweight mo... |
2502.07735 | Revisiting Non-Acyclic GFlowNets in Discrete Environments | cs.LG stat.ML | Generative Flow Networks (GFlowNets) are a family of generative models that
learn to sample objects from a given probability distribution, potentially
known up to a normalizing constant. Instead of working in the object space,
GFlowNets proceed by sampling trajectories in an appropriately constructed
directed acyclic... |
2502.07737 | Next Block Prediction: Video Generation via Semi-Autoregressive Modeling | cs.CV cs.AI | Next-Token Prediction (NTP) is a de facto approach for autoregressive (AR)
video generation, but it suffers from suboptimal unidirectional dependencies
and slow inference speed. In this work, we propose a semi-autoregressive
(semi-AR) framework, called Next-Block Prediction (NBP), for video generation.
By uniformly d... |
2502.07738 | EIQP: Execution-time-certified and Infeasibility-detecting QP Solver | eess.SY cs.SY math.OC | Solving real-time quadratic programming (QP) is a ubiquitous task in control
engineering, such as in model predictive control and control barrier
function-based QP. In such real-time scenarios, certifying that the employed QP
algorithm can either return a solution within a predefined level of optimality
or detect QP ... |
2502.07739 | HRP: High-Rank Preheating for Superior LoRA Initialization | cs.LG | This paper studies the crucial impact of initialization on the convergence
properties of Low-Rank Adaptation (LoRA). We theoretically demonstrate that
random initialization, a widely used schema, will likely lead LoRA to random
low-rank results, rather than the best low-rank result. While this issue can be
mitigated ... |
2502.07741 | Advancing climate model interpretability: Feature attribution for Arctic
melt anomalies | cs.LG | The focus of our work is improving the interpretability of anomalies in
climate models and advancing our understanding of Arctic melt dynamics. The
Arctic and Antarctic ice sheets are experiencing rapid surface melting and
increased freshwater runoff, contributing significantly to global sea level
rise. Understanding... |
2502.07746 | HiPoNet: A Topology-Preserving Multi-View Neural Network For High
Dimensional Point Cloud and Single-Cell Data | cs.LG math.AT | In this paper, we propose HiPoNet, an end-to-end differentiable neural
network for regression, classification, and representation learning on
high-dimensional point clouds. Single-cell data can have high dimensionality
exceeding the capabilities of existing methods point cloud tailored for 3D
data. Moreover, modern s... |
2502.07747 | WHODUNIT: Evaluation benchmark for culprit detection in mystery stories | cs.CL cs.AI | We present a novel data set, WhoDunIt, to assess the deductive reasoning
capabilities of large language models (LLM) within narrative contexts.
Constructed from open domain mystery novels and short stories, the dataset
challenges LLMs to identify the perpetrator after reading and comprehending the
story. To evaluate ... |
2502.07749 | Whole-Genome Phenotype Prediction with Machine Learning: Open Problems
in Bacterial Genomics | q-bio.GN cs.LG | How can we identify causal genetic mechanisms that govern bacterial traits?
Initial efforts entrusting machine learning models to handle the task of
predicting phenotype from genotype return high accuracy scores. However,
attempts to extract any meaning from the predictive models are found to be
corrupted by falsely ... |
2502.07750 | PFedDST: Personalized Federated Learning with Decentralized Selection
Training | cs.LG cs.AI | Distributed Learning (DL) enables the training of machine learning models
across multiple devices, yet it faces challenges like non-IID data
distributions and device capability disparities, which can impede training
efficiency. Communication bottlenecks further complicate traditional Federated
Learning (FL) setups. T... |
2502.07751 | CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression
Generation | cs.CV q-bio.GN | The integration of single-cell RNA sequencing (scRNA-seq) and spatial
transcriptomics (ST) data is crucial for understanding gene expression in
spatial context. Existing methods for such integration have limited
performance, with structural similarity often below 60\%, We attribute this
limitation to the failure to c... |
2502.07752 | Towards Efficient Optimizer Design for LLM via Structured Fisher
Approximation with a Low-Rank Extension | cs.LG cs.AI stat.ML | Designing efficient optimizers for large language models (LLMs) with
low-memory requirements and fast convergence is an important and challenging
problem. This paper makes a step towards the systematic design of such
optimizers through the lens of structured Fisher information matrix (FIM)
approximation. We show that... |
2502.07753 | Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in
Discriminative Models | cs.CV | We demonstrate that discriminative models inherently contain powerful
generative capabilities, challenging the fundamental distinction between
discriminative and generative architectures. Our method, Direct Ascent
Synthesis (DAS), reveals these latent capabilities through multi-resolution
optimization of CLIP model r... |
2502.07754 | MeshSplats: Mesh-Based Rendering with Gaussian Splatting Initialization | cs.GR cs.CV | Gaussian Splatting (GS) is a recent and pivotal technique in 3D computer
graphics. GS-based algorithms almost always bypass classical methods such as
ray tracing, which offers numerous inherent advantages for rendering. For
example, ray tracing is able to handle incoherent rays for advanced lighting
effects, includin... |
2502.07755 | An Advanced NLP Framework for Automated Medical Diagnosis with DeBERTa
and Dynamic Contextual Positional Gating | cs.CL cs.AI | This paper presents a novel Natural Language Processing (NLP) framework for
enhancing medical diagnosis through the integration of advanced techniques in
data augmentation, feature extraction, and classification. The proposed
approach employs back-translation to generate diverse paraphrased datasets,
improving robust... |
2502.07758 | Novel computational workflows for natural and biomedical image
processing based on hypercomplex algebras | cs.CV cs.LG | Hypercomplex image processing extends conventional techniques in a unified
paradigm encompassing algebraic and geometric principles. This work leverages
quaternions and the two-dimensional orthogonal planes split framework
(splitting of a quaternion - representing a pixel - into pairs of orthogonal 2D
planes) for nat... |
2502.07760 | Scalable Fingerprinting of Large Language Models | cs.CR cs.LG | Model fingerprinting has emerged as a powerful tool for model owners to
identify their shared model given API access. However, to lower false discovery
rate, fight fingerprint leakage, and defend against coalitions of model users
attempting to bypass detection, we argue that {\em scalability} is critical,
i.e., scali... |
2502.07764 | Polynomial-Time Approximability of Constrained Reinforcement Learning | cs.DS cs.AI cs.LG | We study the computational complexity of approximating general constrained
Markov decision processes. Our primary contribution is the design of a
polynomial time $(0,\epsilon)$-additive bicriteria approximation algorithm for
finding optimal constrained policies across a broad class of recursively
computable constrain... |
2502.07771 | Breaking Down Bias: On The Limits of Generalizable Pruning Strategies | cs.CL cs.AI cs.CY cs.LG | We employ model pruning to examine how LLMs conceptualize racial biases, and
whether a generalizable mitigation strategy for such biases appears feasible.
Our analysis yields several novel insights. We find that pruning can be an
effective method to reduce bias without significantly increasing anomalous
model behavio... |
2502.07772 | Automatic Robot Task Planning by Integrating Large Language Model with
Genetic Programming | cs.RO cs.SY eess.SY | Accurate task planning is critical for controlling autonomous systems, such
as robots, drones, and self-driving vehicles. Behavior Trees (BTs) are
considered one of the most prominent control-policy-defining frameworks in task
planning, due to their modularity, flexibility, and reusability. Generating
reliable and ac... |
2502.07774 | Optimistic Interior Point Methods for Sequential Hypothesis Testing by
Betting | cs.LG | The technique of "testing by betting" frames nonparametric sequential
hypothesis testing as a multiple-round game, where a player bets on future
observations that arrive in a streaming fashion, accumulates wealth that
quantifies evidence against the null hypothesis, and rejects the null once the
wealth exceeds a spec... |
2502.07776 | Auditing Prompt Caching in Language Model APIs | cs.CL cs.CR cs.LG | Prompt caching in large language models (LLMs) results in data-dependent
timing variations: cached prompts are processed faster than non-cached prompts.
These timing differences introduce the risk of side-channel timing attacks. For
example, if the cache is shared across users, an attacker could identify cached
promp... |
2502.07778 | Stay-Positive: A Case for Ignoring Real Image Features in Fake Image
Detection | cs.CV | Detecting AI generated images is a challenging yet essential task. A primary
difficulty arises from the detectors tendency to rely on spurious patterns,
such as compression artifacts, which can influence its decisions. These issues
often stem from specific patterns that the detector associates with the real
data dist... |
2502.07780 | DarwinLM: Evolutionary Structured Pruning of Large Language Models | cs.LG cs.CL | Large Language Models (LLMs) have achieved significant success across various
NLP tasks. However, their massive computational costs limit their widespread
use, particularly in real-time applications. Structured pruning offers an
effective solution by compressing models and directly providing end-to-end
speed improvem... |
2502.07782 | A Flag Decomposition for Hierarchical Datasets | cs.CV | Flag manifolds encode hierarchical nested sequences of subspaces and serve as
powerful structures for various computer vision and machine learning
applications. Despite their utility in tasks such as dimensionality reduction,
motion averaging, and subspace clustering, current applications are often
restricted to extr... |
2502.07783 | Curvature Tuning: Provable Training-free Model Steering From a Single
Parameter | cs.LG | The scaling of model size and data size has reshaped the paradigm of AI. As a
result, the common protocol to leverage the latest models is to steer them
towards a specific downstream task of interest through {\em fine-tuning}.
Despite its importance, the main methods for fine-tuning remain limited to full
or low-rank... |
2502.07784 | MatSwap: Light-aware material transfers in images | cs.CV cs.GR | We present MatSwap, a method to transfer materials to designated surfaces in
an image photorealistically. Such a task is non-trivial due to the large
entanglement of material appearance, geometry, and lighting in a photograph. In
the literature, material editing methods typically rely on either cumbersome
text engine... |
2502.07785 | Pippo: High-Resolution Multi-View Humans from a Single Image | cs.CV cs.GR | We present Pippo, a generative model capable of producing 1K resolution dense
turnaround videos of a person from a single casually clicked photo. Pippo is a
multi-view diffusion transformer and does not require any additional inputs -
e.g., a fitted parametric model or camera parameters of the input image. We
pre-tra... |
2502.07786 | Counterexample Guided Program Repair Using Zero-Shot Learning and
MaxSAT-based Fault Localization | cs.SE cs.AI | Automated Program Repair (APR) for introductory programming assignments
(IPAs) is motivated by the large number of student enrollments in programming
courses each year. Since providing feedback on IPAs requires substantial time
and effort from faculty, personalized feedback often involves suggesting fixes
to students... |
2502.07789 | Do AI assistants help students write formal specifications? A study with
ChatGPT and the B-Method | cs.CY cs.AI | This paper investigates the role of AI assistants, specifically OpenAI's
ChatGPT, in teaching formal methods (FM) to undergraduate students, using the
B-method as a formal specification technique. While existing studies
demonstrate the effectiveness of AI in coding tasks, no study reports on its
impact on formal spec... |
2502.07790 | Can Generative AI be Egalitarian? | cs.CY cs.AI | The recent explosion of "foundation" generative AI models has been built upon
the extensive extraction of value from online sources, often without
corresponding reciprocation. This pattern mirrors and intensifies the
extractive practices of surveillance capitalism, while the potential for
enormous profit has challeng... |
2502.07791 | Simple demonstration of different types of coupling in multiphysics
numerical problems | cs.CE physics.comp-ph | Numerical modelling of coupled multiphysics phenomena is becoming an
increasingly important subject in applied mathematics. The main challenge in
teaching this subject is the complexity of both the mathematical models and
their numerical implementation. In this note, a simple demonstrator is proposed
that enables dem... |
2502.07794 | Regulatory Science Innovation for Generative AI and Large Language
Models in Health and Medicine: A Global Call for Action | cs.CY cs.AI | The integration of generative AI (GenAI) and large language models (LLMs) in
healthcare presents both unprecedented opportunities and challenges,
necessitating innovative regulatory approaches. GenAI and LLMs offer broad
applications, from automating clinical workflows to personalizing diagnostics.
However, the non-d... |
2502.07800 | neuro2voc: Decoding Vocalizations from Neural Activity | q-bio.NC cs.LG eess.AS | Accurate decoding of neural spike trains and relating them to motor output is
a challenging task due to the inherent sparsity and length in neural spikes and
the complexity of brain circuits. This master project investigates experimental
methods for decoding zebra finch motor outputs (in both discrete syllables and
c... |
2502.07802 | Movie Weaver: Tuning-Free Multi-Concept Video Personalization with
Anchored Prompts | cs.CV cs.GR cs.LG | Video personalization, which generates customized videos using reference
images, has gained significant attention. However, prior methods typically
focus on single-concept personalization, limiting broader applications that
require multi-concept integration. Attempts to extend these models to multiple
concepts often ... |
2502.07803 | Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language
Models Through Logic Unit Alignment | cs.AI cs.LG | Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning
capabilities of large language models (LLMs) by generating natural language
(NL) rationales that lead to the final answer. However, it struggles with
numerical computation, which has somehow led to the development of
program-aided technique... |
2502.07806 | Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid
Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive
Analysis | q-fin.CP cs.AI cs.LG | The integration of Quantum Deep Learning (QDL) techniques into the landscape
of financial risk analysis presents a promising avenue for innovation. This
study introduces a framework for credit risk assessment in the banking sector,
combining quantum deep learning techniques with adaptive modeling for Row-Type
Depende... |
2502.07807 | CP-Guard+: A New Paradigm for Malicious Agent Detection and Defense in
Collaborative Perception | cs.CR cs.AI cs.CV cs.LG | Collaborative perception (CP) is a promising method for safe connected and
autonomous driving, which enables multiple vehicles to share sensing
information to enhance perception performance. However, compared with
single-vehicle perception, the openness of a CP system makes it more vulnerable
to malicious attacks tha... |
2502.07809 | Analyzing the Resource Utilization of Lambda Functions on Mobile
Devices: Case Studies on Kotlin and Swift | cs.SE cs.CL cs.PF | With billions of smartphones in use globally, the daily time spent on these
devices contributes significantly to overall electricity consumption. Given
this scale, even minor reductions in smartphone power use could result in
substantial energy savings. This study explores the impact of Lambda functions
on resource c... |
2502.07811 | CrossVideoMAE: Self-Supervised Image-Video Representation Learning with
Masked Autoencoders | cs.CV | Current video-based Masked Autoencoders (MAEs) primarily focus on learning
effective spatiotemporal representations from a visual perspective, which may
lead the model to prioritize general spatial-temporal patterns but often
overlook nuanced semantic attributes like specific interactions or sequences
that define act... |
2502.07812 | Unpaired Image Dehazing via Kolmogorov-Arnold Transformation of Latent
Features | cs.CV | This paper proposes an innovative framework for Unsupervised Image Dehazing
via Kolmogorov-Arnold Transformation, termed UID-KAT. Image dehazing is
recognized as a challenging and ill-posed vision task that requires complex
transformations and interpretations in the feature space. Recent advancements
have introduced ... |
2502.07813 | CryptoX : Compositional Reasoning Evaluation of Large Language Models | cs.CR cs.AI | The compositional reasoning capacity has long been regarded as critical to
the generalization and intelligence emergence of large language models LLMs.
However, despite numerous reasoning-related benchmarks, the compositional
reasoning capacity of LLMs is rarely studied or quantified in the existing
benchmarks. In th... |
2502.07814 | Satellite Observations Guided Diffusion Model for Accurate
Meteorological States at Arbitrary Resolution | cs.LG cs.AI physics.ao-ph | Accurate acquisition of surface meteorological conditions at arbitrary
locations holds significant importance for weather forecasting and climate
simulation. Due to the fact that meteorological states derived from satellite
observations are often provided in the form of low-resolution grid fields, the
direct applicat... |
2502.07815 | Decoding Complexity: Intelligent Pattern Exploration with CHPDA (Context
Aware Hybrid Pattern Detection Algorithm) | cs.CR cs.AI | Detecting sensitive data such as Personally Identifiable Information (PII)
and Protected Health Information (PHI) is critical for data security platforms.
This study evaluates regex-based pattern matching algorithms and exact-match
search techniques to optimize detection speed, accuracy, and scalability. Our
benchmar... |
2502.07817 | Temporal Model On Quantum Logic | cs.AI math.LO quant-ph | This paper introduces a unified theoretical framework for modeling temporal
memory dynamics, combining concepts from temporal logic, memory decay models,
and hierarchical contexts. The framework formalizes the evolution of
propositions over time using linear and branching temporal models,
incorporating exponential de... |
2502.07819 | Enhancing kidney transplantation through multi-agent kidney exchange
programs: A comprehensive review and optimization models | cs.AI math.OC | This paper presents a comprehensive review of the last two decades of
research on Kidney Exchange Programs (KEPs), systematically categorizing and
classifying key contributions to provide readers with a structured
understanding of advancements in the field. The review highlights the evolution
of KEP methodologies and... |
2502.07820 | Low-Rank Compression for IMC Arrays | cs.AR cs.AI | In this study, we address the challenge of low-rank model compression in the
context of in-memory computing (IMC) architectures. Traditional pruning
approaches, while effective in model size reduction, necessitate additional
peripheral circuitry to manage complex dataflows and mitigate dislocation
issues, leading to ... |
2502.07821 | Amnesia as a Catalyst for Enhancing Black Box Pixel Attacks in Image
Classification and Object Detection | cs.CV cs.AI | It is well known that query-based attacks tend to have relatively higher
success rates in adversarial black-box attacks. While research on black-box
attacks is actively being conducted, relatively few studies have focused on
pixel attacks that target only a limited number of pixels. In image
classification, query-bas... |
2502.07822 | PDM-SSD: Single-Stage Three-Dimensional Object Detector With Point
Dilation | cs.CV cs.AI | Current Point-based detectors can only learn from the provided points, with
limited receptive fields and insufficient global learning capabilities for such
targets. In this paper, we present a novel Point Dilation Mechanism for
single-stage 3D detection (PDM-SSD) that takes advantage of these two
representations. Spe... |
2502.07823 | Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs | cs.AR cs.AI cs.LG | Embedded Field-Programmable Gate Arrays (eFPGAs) allow for the design of
hardware accelerators of edge Machine Learning (ML) applications at a lower
power budget compared with traditional FPGA platforms. However, the limited
eFPGA logic and memory significantly constrain compute capabilities and model
size. As such, ... |
2502.07825 | Pre-Trained Video Generative Models as World Simulators | cs.CV cs.AI | Video generative models pre-trained on large-scale internet datasets have
achieved remarkable success, excelling at producing realistic synthetic videos.
However, they often generate clips based on static prompts (e.g., text or
images), limiting their ability to model interactive and dynamic scenarios. In
this paper,... |
2502.07826 | Deep Learning in Automated Power Line Inspection: A Review | cs.CV eess.IV | In recent years, power line maintenance has seen a paradigm shift by moving
towards computer vision-powered automated inspection. The utilization of an
extensive collection of videos and images has become essential for maintaining
the reliability, safety, and sustainability of electricity transmission. A
significant ... |
2502.07827 | Implicit Language Models are RNNs: Balancing Parallelization and
Expressivity | cs.LG cs.AI | State-space models (SSMs) and transformers dominate the language modeling
landscape. However, they are constrained to a lower computational complexity
than classical recurrent neural networks (RNNs), limiting their expressivity.
In contrast, RNNs lack parallelization during training, raising fundamental
questions abo... |
2502.07828 | Some things to know about achieving artificial general intelligence | q-bio.NC cs.AI | Current and foreseeable GenAI models are not capable of achieving artificial
general intelligence because they are burdened with anthropogenic debt. They
depend heavily on human input to provide well-structured problems,
architecture, and training data. They cast every problem as a language pattern
learning problem a... |
2502.07829 | Preference Alignment on Diffusion Model: A Comprehensive Survey for
Image Generation and Editing | cs.CV cs.LG | The integration of preference alignment with diffusion models (DMs) has
emerged as a transformative approach to enhance image generation and editing
capabilities. Although integrating diffusion models with preference alignment
strategies poses significant challenges for novices at this intersection,
comprehensive and... |
2502.07830 | Captured by Captions: On Memorization and its Mitigation in CLIP Models | cs.CV cs.AI cs.LG | Multi-modal models, such as CLIP, have demonstrated strong performance in
aligning visual and textual representations, excelling in tasks like image
retrieval and zero-shot classification. Despite this success, the mechanisms by
which these models utilize training data, particularly the role of
memorization, remain u... |
2502.07832 | SHARP: Accelerating Language Model Inference by SHaring Adjacent layers
with Recovery Parameters | cs.LG cs.AI | While Large language models (LLMs) have advanced natural language processing
tasks, their growing computational and memory demands make deployment on
resource-constrained devices like mobile phones increasingly challenging. In
this paper, we propose SHARP (SHaring Adjacent Layers with Recovery
Parameters), a novel ap... |
2502.07834 | MEMHD: Memory-Efficient Multi-Centroid Hyperdimensional Computing for
Fully-Utilized In-Memory Computing Architectures | cs.AR cs.AI cs.LG | The implementation of Hyperdimensional Computing (HDC) on In-Memory Computing
(IMC) architectures faces significant challenges due to the mismatch between
highdimensional vectors and IMC array sizes, leading to inefficient memory
utilization and increased computation cycles. This paper presents MEMHD, a
Memory-Effici... |
2502.07835 | Bridging LLM-Generated Code and Requirements: Reverse Generation
technique and SBC Metric for Developer Insights | cs.SE cs.AI | The rise of Large Language Models (LLMs) in software engineering,
particularly in code generation, has garnered significant attention. However,
assessing the quality of AI-generated code remains a challenge due to the
inherent complexity of programming tasks and the lack of robust evaluation
metrics that align well w... |
2502.07836 | Advancing Precision Oncology Through Modeling of Longitudinal and
Multimodal Data | q-bio.QM cs.LG | Cancer evolves continuously over time through a complex interplay of genetic,
epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior
drives uncontrolled cell growth, metastasis, immune evasion, and therapy
resistance, posing challenges for effective monitoring and treatment. However,
today's da... |
2502.07837 | RoboBERT: An End-to-end Multimodal Robotic Manipulation Model | cs.RO cs.LG | Embodied intelligence integrates multiple modalities, enabling agents to
understand images, language, and actions simultaneously. However, existing
models always depend on additional datasets or extensive pre-training to
maximize performance improvements, consuming abundant training time and
expensive hardware cost. ... |
2502.07838 | NanoVLMs: How small can we go and still make coherent Vision Language
Models? | cs.CV cs.AI | Vision-Language Models (VLMs), such as GPT-4V and Llama 3.2 vision, have
garnered significant research attention for their ability to leverage Large
Language Models (LLMs) in multimodal tasks. However, their potential is
constrained by inherent challenges, including proprietary restrictions,
substantial computational... |
2502.07839 | Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement
Learning | cs.RO cs.LG | With the increasing prevalence of autonomous vehicles (AVs), their
vulnerability to various types of attacks has grown, presenting significant
security challenges. In this paper, we propose a reinforcement learning
(RL)-based approach for designing optimal stealthy integrity attacks on AV
actuators. We also analyze t... |
2502.07840 | TranSplat: Surface Embedding-guided 3D Gaussian Splatting for
Transparent Object Manipulation | cs.CV cs.RO | Transparent object manipulation remains a significant challenge in robotics
due to the difficulty of acquiring accurate and dense depth measurements.
Conventional depth sensors often fail with transparent objects, resulting in
incomplete or erroneous depth data. Existing depth completion methods struggle
with interfr... |
2502.07842 | Column-wise Quantization of Weights and Partial Sums for Accurate and
Efficient Compute-In-Memory Accelerators | cs.AR cs.AI cs.LG | Compute-in-memory (CIM) is an efficient method for implementing deep neural
networks (DNNs) but suffers from substantial overhead from analog-to-digital
converters (ADCs), especially as ADC precision increases. Low-precision ADCs
can reduce this overhead but introduce partial-sum quantization errors
degrading accurac... |
2502.07843 | Emotional EEG Classification using Upscaled Connectivity Matrices | cs.LG | In recent studies of emotional EEG classification, connectivity matrices have
been successfully employed as input to convolutional neural networks (CNNs),
which can effectively consider inter-regional interaction patterns in EEG.
However, we find that such an approach has a limitation that important patterns
in conne... |
2502.07844 | The establishment of static digital humans and the integration with
spinal models | eess.IV cs.CV | Adolescent idiopathic scoliosis (AIS), a prevalent spinal deformity,
significantly affects individuals' health and quality of life. Conventional
imaging techniques, such as X - rays, computed tomography (CT), and magnetic
resonance imaging (MRI), offer static views of the spine. However, they are
restricted in captur... |
2502.07845 | Spread them Apart: Towards Robust Watermarking of Generated Content | cs.CV cs.AI | Generative models that can produce realistic images have improved
significantly in recent years. The quality of the generated content has
increased drastically, so sometimes it is very difficult to distinguish between
the real images and the generated ones. Such an improvement comes at a price of
ethical concerns abo... |
2502.07846 | Memory Analysis on the Training Course of DeepSeek Models | cs.PF cs.LG | We present a theoretical analysis of GPU memory consumption during the
training of DeepSeek models such as DeepSeek-v2 and DeepSeek-v3. Our primary
objective is to clarify the device-level memory requirements associated with
various distributed training configurations. Specifically, we examine critical
factors influe... |
2502.07847 | Technical note on calibrating vision-language models under covariate
shift | cs.CV cs.LG | Despite being a successful example of emerging capability, vision-language
foundation models for low-shot vision classification have a limited ability to
sufficiently generalize to the target data distribution due to sample poverty,
leading to sensitivity to variations in the data. A popular mitigation strategy
is fi... |
2502.07849 | Understanding Classifier-Free Guidance: High-Dimensional Theory and
Non-Linear Generalizations | cs.LG cs.AI stat.ML | Recent studies have raised concerns about the effectiveness of
Classifier-Free Guidance (CFG), indicating that in low-dimensional settings, it
can lead to overshooting the target distribution and reducing sample diversity.
In this work, we demonstrate that in infinite and sufficiently high-dimensional
contexts CFG ef... |
2502.07850 | Mathematical reasoning and the computer | cs.AI | Computers have already changed the way that humans do mathematics: they
enable us to compute efficiently. But will they soon be helping us to reason?
And will they one day start reasoning themselves? We give an overview of recent
developments in neural networks, computer theorem provers and large language
models.
|
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