id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.05008 | A Transformation-based Consistent Estimation Framework: Analysis, Design
and Applications | cs.RO | In this paper, we investigate the inconsistency problem arising from
observability mismatch that frequently occurs in nonlinear systems such as
multi-robot cooperative localization and simultaneous localization and mapping.
For a general nonlinear system, we discover and theoretically prove that the
unobservable subs... |
2502.05011 | Learning the Language of NVMe Streams for Ransomware Detection | cs.LG cs.CR | We apply language modeling techniques to detect ransomware activity in NVMe
command sequences. We design and train two types of transformer-based models:
the Command-Level Transformer (CLT) performs in-context token classification to
determine whether individual commands are initiated by ransomware, and the
Patch-Lev... |
2502.05013 | Output-Feedback Full-State Targeting Model Predictive Control for
Station-Keeping on Near-Rectilinear Halo Orbits | eess.SY cs.SY math.OC | We develop a model predictive control (MPC) policy for station-keeping (SK)
on a Near-Rectilinear Halo Orbit (NRHO). The proposed policy achieves
full-state tracking of a reference NRHO via a two-maneuver control horizon
placed one revolution apart. Our method abides by the typical mission
requirement that at most on... |
2502.05014 | Seasonal Station-Keeping of Short Duration High Altitude Balloons using
Deep Reinforcement Learning | cs.LG cs.RO physics.ao-ph | Station-Keeping short-duration high-altitude balloons (HABs) in a region of
interest is a challenging path-planning problem due to partially observable,
complex, and dynamic wind flows. Deep reinforcement learning is a popular
strategy for solving the station-keeping problem. A custom simulation
environment was devel... |
2502.05017 | Bridging Voting and Deliberation with Algorithms: Field Insights from
vTaiwan and Kultur Komitee | cs.HC cs.AI econ.GN q-fin.EC | Democratic processes increasingly aim to integrate large-scale voting with
face-to-face deliberation, addressing the challenge of reconciling individual
preferences with collective decision-making. This work introduces new methods
that use algorithms and computational tools to bridge online voting with
face-to-face d... |
2502.05019 | $O(\sqrt{T})$ Static Regret and Instance Dependent Constraint Violation
for Constrained Online Convex Optimization | cs.LG cs.DS | The constrained version of the standard online convex optimization (OCO)
framework, called COCO is considered, where on every round, a convex cost
function and a convex constraint function are revealed to the learner after it
chooses the action for that round. The objective is to simultaneously minimize
the static re... |
2502.05020 | Analog and Multi-modal Manufacturing Datasets Acquired on the Future
Factories Platform V2 | cs.LG | This paper presents two industry-grade datasets captured during an 8-hour
continuous operation of the manufacturing assembly line at the Future Factories
Lab, University of South Carolina, on 08/13/2024. The datasets adhere to
industry standards, covering communication protocols, actuators, control
mechanisms, transd... |
2502.05026 | The Role of Science in the Climate Change Discussions on Reddit | cs.CY cs.SI | Collective and individual action necessary to address climate change hinges
on the public's understanding of the relevant scientific findings. In this
study, we examine the use of scientific sources in the course of 14 years of
public deliberation around climate change on one of the largest social media
platforms, Re... |
2502.05027 | Trust-Aware Diversion for Data-Effective Distillation | cs.CV | Dataset distillation compresses a large dataset into a small synthetic subset
that retains essential information. Existing methods assume that all samples
are perfectly labeled, limiting their real-world applications where incorrect
labels are ubiquitous. These mislabeled samples introduce untrustworthy
information i... |
2502.05028 | Near-Optimal Online Learning for Multi-Agent Submodular Coordination:
Tight Approximation and Communication Efficiency | cs.MA cs.LG math.OC | Coordinating multiple agents to collaboratively maximize submodular functions
in unpredictable environments is a critical task with numerous applications in
machine learning, robot planning and control. The existing approaches, such as
the OSG algorithm, are often hindered by their poor approximation guarantees
and t... |
2502.05032 | News about Global North considered Truthful! The Geo-political Veracity
Gradient in Global South News | cs.LG | While there has been much research into developing AI techniques for fake
news detection aided by various benchmark datasets, it has often been pointed
out that fake news in different geo-political regions traces different
contours. In this work we uncover, through analytical arguments and empirical
evidence, the exi... |
2502.05034 | MindAligner: Explicit Brain Functional Alignment for Cross-Subject
Visual Decoding from Limited fMRI Data | cs.CV | Brain decoding aims to reconstruct visual perception of human subject from
fMRI signals, which is crucial for understanding brain's perception mechanisms.
Existing methods are confined to the single-subject paradigm due to substantial
brain variability, which leads to weak generalization across individuals and
incurs... |
2502.05036 | nvAgent: Automated Data Visualization from Natural Language via
Collaborative Agent Workflow | cs.CL | Natural Language to Visualization (NL2Vis) seeks to convert natural-language
descriptions into visual representations of given tables, empowering users to
derive insights from large-scale data. Recent advancements in Large Language
Models (LLMs) show promise in automating code generation to transform tabular
data int... |
2502.05037 | Leveraging a Simulator for Learning Causal Representations from
Post-Treatment Covariates for CATE | cs.LG | Treatment effect estimation involves assessing the impact of different
treatments on individual outcomes. Current methods estimate Conditional Average
Treatment Effect (CATE) using observational datasets where covariates are
collected before treatment assignment and outcomes are observed afterward,
under assumptions ... |
2502.05038 | FlightForge: Advancing UAV Research with Procedural Generation of
High-Fidelity Simulation and Integrated Autonomy | cs.RO cs.CV | Robotic simulators play a crucial role in the development and testing of
autonomous systems, particularly in the realm of Uncrewed Aerial Vehicles
(UAV). However, existing simulators often lack high-level autonomy, hindering
their immediate applicability to complex tasks such as autonomous navigation in
unknown envir... |
2502.05040 | GaussRender: Learning 3D Occupancy with Gaussian Rendering | cs.CV | Understanding the 3D geometry and semantics of driving scenes is critical for
developing of safe autonomous vehicles. While 3D occupancy models are typically
trained using voxel-based supervision with standard losses (e.g.,
cross-entropy, Lovasz, dice), these approaches treat voxel predictions
independently, neglecti... |
2502.05041 | Federated Learning for Anomaly Detection in Energy Consumption Data:
Assessing the Vulnerability to Adversarial Attacks | cs.LG cs.AI cs.DC | Anomaly detection is crucial in the energy sector to identify irregular
patterns indicating equipment failures, energy theft, or other issues. Machine
learning techniques for anomaly detection have achieved great success, but are
typically centralized, involving sharing local data with a central server which
raises p... |
2502.05044 | Hybrid machine learning based scale bridging framework for permeability
prediction of fibrous structures | cs.LG | This study introduces a hybrid machine learning-based scale-bridging
framework for predicting the permeability of fibrous textile structures. By
addressing the computational challenges inherent to multiscale modeling, the
proposed approach evaluates the efficiency and accuracy of different
scale-bridging methodologie... |
2502.05049 | On the Inference of Sociodemographics on Reddit | cs.SI cs.CY | Inference of sociodemographic attributes of social media users is an
essential step for computational social science (CSS) research to link online
and offline behavior. However, there is a lack of a systematic evaluation and
clear guidelines for optimal methodologies for this task on Reddit, one of
today's largest so... |
2502.05053 | Gaze-Guided Robotic Vascular Ultrasound Leveraging Human Intention
Estimation | cs.RO | Medical ultrasound has been widely used to examine vascular structure in
modern clinical practice. However, traditional ultrasound examination often
faces challenges related to inter- and intra-operator variation. The robotic
ultrasound system (RUSS) appears as a potential solution for such challenges
because of its ... |
2502.05055 | Differentiable Mobile Display Photometric Stereo | cs.CV cs.AI cs.GR cs.LG | Display photometric stereo uses a display as a programmable light source to
illuminate a scene with diverse illumination conditions. Recently,
differentiable display photometric stereo (DDPS) demonstrated improved normal
reconstruction accuracy by using learned display patterns. However, DDPS faced
limitations in pra... |
2502.05060 | Preference-aware compensation policies for crowdsourced on-demand
services | cs.LG cs.AI math.OC | Crowdsourced on-demand services offer benefits such as reduced costs, faster
service fulfillment times, greater adaptability, and contributions to
sustainable urban transportation in on-demand delivery contexts. However, the
success of an on-demand platform that utilizes crowdsourcing relies on finding
a compensation... |
2502.05063 | Computing and Learning on Combinatorial Data | cs.AI cs.DM cs.DS | The twenty-first century is a data-driven era where human activities and
behavior, physical phenomena, scientific discoveries, technology advancements,
and almost everything that happens in the world resulting in massive
generation, collection, and utilization of data.
Connectivity in data is a crucial property. A ... |
2502.05066 | Beautiful Images, Toxic Words: Understanding and Addressing Offensive
Text in Generated Images | cs.CV | State-of-the-art visual generation models, such as Diffusion Models (DMs) and
Vision Auto-Regressive Models (VARs), produce highly realistic images. While
prior work has successfully mitigated Not Safe For Work (NSFW) content in the
visual domain, we identify a novel threat: the generation of NSFW text embedded
withi... |
2502.05069 | Exploring the Generalizability of Geomagnetic Navigation: A Deep
Reinforcement Learning approach with Policy Distillation | cs.RO | The advancement in autonomous vehicles has empowered navigation and
exploration in unknown environments. Geomagnetic navigation for autonomous
vehicles has drawn increasing attention with its independence from GPS or
inertial navigation devices. While geomagnetic navigation approaches have been
extensively investigat... |
2502.05073 | Noise Sensitivity of Hierarchical Functions and Deep Learning Lower
Bounds in General Product Measures | math.PR cs.CC cs.LG math.CO | Recent works explore deep learning's success by examining functions or data
with hierarchical structure. Complementarily, research on gradient descent
performance for deep nets has shown that noise sensitivity of functions under
independent and identically distributed (i.i.d.) Bernoulli inputs establishes
learning co... |
2502.05074 | Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in
Linear Models | cond-mat.dis-nn cs.LG stat.ML | We derive a novel deterministic equivalence for the two-point function of a
random matrix resolvent. Using this result, we give a unified derivation of the
performance of a wide variety of high-dimensional linear models trained with
stochastic gradient descent. This includes high-dimensional linear regression,
kernel... |
2502.05075 | Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of
Intrinsic Dimension | cs.LG cs.NA math.NA stat.ML | Weak-to-strong (W2S) generalization is a type of finetuning (FT) where a
strong (large) student model is trained on pseudo-labels generated by a weak
teacher. Surprisingly, W2S FT often outperforms the weak teacher. We seek to
understand this phenomenon through the observation that FT often occurs in
intrinsically lo... |
2502.05076 | Paying Attention to Facts: Quantifying the Knowledge Capacity of
Attention Layers | cs.LG cs.CL | In this paper, we investigate the ability of single-layer attention-only
transformers (i.e. attention layers) to memorize facts contained in databases
from a linear-algebraic perspective. We associate with each database a
3-tensor, propose the rank of this tensor as a measure of the size of the
database, and provide ... |
2502.05078 | Adaptive Graph of Thoughts: Test-Time Adaptive Reasoning Unifying Chain,
Tree, and Graph Structures | cs.AI cs.CL | Large Language Models (LLMs) have demonstrated impressive reasoning
capabilities, yet their performance is highly dependent on the prompting
strategy and model scale. While reinforcement learning and fine-tuning have
been deployed to boost reasoning, these approaches incur substantial
computational and data overhead.... |
2502.05084 | ChallengeMe: An Adversarial Learning-enabled Text Summarization
Framework | cs.CL cs.AI | The astonishing performance of large language models (LLMs) and their
remarkable achievements in production and daily life have led to their
widespread application in collaborative tasks. However, current large models
face challenges such as hallucination and lack of specificity in content
generation in vertical doma... |
2502.05085 | Causality can systematically address the monsters under the bench(marks) | cs.LG cs.AI | Effective and reliable evaluation is essential for advancing empirical
machine learning. However, the increasing accessibility of generalist models
and the progress towards ever more complex, high-level tasks make systematic
evaluation more challenging. Benchmarks are plagued by various biases,
artifacts, or leakage,... |
2502.05086 | REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and
Disassembly | cs.RO | Robotic manipulation remains a core challenge in robotics, particularly for
contact-rich tasks such as industrial assembly and disassembly. Existing
datasets have significantly advanced learning in manipulation but are primarily
focused on simpler tasks like object rearrangement, falling short of capturing
the comple... |
2502.05087 | Mitigating Unintended Memorization with LoRA in Federated Learning for
LLMs | cs.LG cs.AI cs.CL | Federated learning (FL) is a popular paradigm for collaborative training
which avoids direct data exposure between clients. However, data privacy issues
still remain: FL-trained large language models are capable of memorizing and
completing phrases and sentences contained in training data when given with
their prefix... |
2502.05091 | DCFormer: Efficient 3D Vision-Language Modeling with Decomposed
Convolutions | cs.CV | Vision-language models (VLMs) align visual and textual representations,
enabling high-performance zero-shot classification and image-text retrieval in
2D medical imaging. However, extending VLMs to 3D medical imaging remains
computationally challenging. Existing 3D VLMs rely on Vision Transformers
(ViTs), which are c... |
2502.05092 | Lost in Time: Clock and Calendar Understanding Challenges in Multimodal
LLMs | cs.CV cs.AI cs.CL | Understanding time from visual representations is a fundamental cognitive
skill, yet it remains a challenge for multimodal large language models (MLLMs).
In this work, we investigate the capabilities of MLLMs in interpreting time and
date through analogue clocks and yearly calendars. To facilitate this, we
curated a ... |
2502.05094 | Non-linear Quantum Monte Carlo | quant-ph cs.LG cs.NA math.NA stat.CO stat.ML | The mean of a random variable can be understood as a $\textit{linear}$
functional on the space of probability distributions. Quantum computing is
known to provide a quadratic speedup over classical Monte Carlo methods for
mean estimation. In this paper, we investigate whether a similar quadratic
speedup is achievable... |
2502.05098 | Learning Temporal Invariance in Android Malware Detectors | cs.CR cs.AI | Learning-based Android malware detectors degrade over time due to natural
distribution drift caused by malware variants and new families. This paper
systematically investigates the challenges classifiers trained with empirical
risk minimization (ERM) face against such distribution shifts and attributes
their shortcom... |
2502.05104 | Leveraging Hypernetworks and Learnable Kernels for Consumer Energy
Forecasting Across Diverse Consumer Types | cs.LG cs.AI | Consumer energy forecasting is essential for managing energy consumption and
planning, directly influencing operational efficiency, cost reduction,
personalized energy management, and sustainability efforts. In recent years,
deep learning techniques, especially LSTMs and transformers, have been greatly
successful in ... |
2502.05107 | 3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery | cs.CE cs.LG | Structure-based drug discovery, encompassing the tasks of protein-ligand
docking and pocket-aware 3D drug design, represents a core challenge in drug
discovery. However, no existing work can deal with both tasks to effectively
leverage the duality between them, and current methods for each task are
hindered by challe... |
2502.05109 | Graph Contrastive Learning for Connectome Classification | cs.LG | With recent advancements in non-invasive techniques for measuring brain
activity, such as magnetic resonance imaging (MRI), the study of structural and
functional brain networks through graph signal processing (GSP) has gained
notable prominence. GSP stands as a key tool in unraveling the interplay
between the brain'... |
2502.05110 | ApplE: An Applied Ethics Ontology with Event Context | cs.CY cs.AI | Applied ethics is ubiquitous in most domains, requiring much deliberation due
to its philosophical nature. Varying views often lead to conflicting courses of
action where ethical dilemmas become challenging to resolve. Although many
factors contribute to such a decision, the major driving forces can be
discretized an... |
2502.05111 | Flexible and Efficient Grammar-Constrained Decoding | cs.CL cs.AI | Large Language Models (LLMs) are often asked to generate structured outputs
that obey precise syntactic rules, such as code snippets or formatted data.
Grammar-constrained decoding (GCD) can guarantee that LLM outputs matches such
rules by masking out tokens that will provably lead to outputs that do not
belong to a ... |
2502.05113 | GiesKaNe: Bridging Past and Present in Grammatical Theory and Practical
Application | cs.CL | This article explores the requirements for corpus compilation within the
GiesKaNe project (University of Giessen and Kassel, Syntactic Basic Structures
of New High German). The project is defined by three central characteristics:
it is a reference corpus, a historical corpus, and a syntactically deeply
annotated tree... |
2502.05114 | SpecTUS: Spectral Translator for Unknown Structures annotation from
EI-MS spectra | cs.LG physics.data-an | Compound identification and structure annotation from mass spectra is a
well-established task widely applied in drug detection, criminal forensics,
small molecule biomarker discovery and chemical engineering.
We propose SpecTUS: Spectral Translator for Unknown Structures, a deep neural
model that addresses the task... |
2502.05115 | "It Felt Like I Was Left in the Dark": Exploring Information Needs and
Design Opportunities for Family Caregivers of Older Adult Patients in
Critical Care Settings | cs.HC cs.AI | Older adult patients constitute a rapidly growing subgroup of Intensive Care
Unit (ICU) patients. In these situations, their family caregivers are expected
to represent the unconscious patients to access and interpret patients' medical
information. However, caregivers currently have to rely on overloaded
clinicians f... |
2502.05116 | Optimizing Wireless Resource Management and Synchronization in Digital
Twin Networks | cs.NI cs.LG cs.SY eess.SY | In this paper, we investigate an accurate synchronization between a physical
network and its digital network twin (DNT), which serves as a virtual
representation of the physical network. The considered network includes a set
of base stations (BSs) that must allocate its limited spectrum resources to
serve a set of us... |
2502.05118 | Use of Winsome Robots for Understanding Human Feedback (UWU) | cs.RO cs.HC | As social robots become more common, many have adopted cute aesthetics aiming
to enhance user comfort and acceptance. However, the effect of this aesthetic
choice on human feedback in reinforcement learning scenarios remains unclear.
Previous research has shown that humans tend to give more positive than
negative fee... |
2502.05119 | Investigating the impact of kernel harmonization and deformable
registration on inspiratory and expiratory chest CT images for people with
COPD | eess.IV cs.CV | Paired inspiratory-expiratory CT scans enable the quantification of gas
trapping due to small airway disease and emphysema by analyzing lung tissue
motion in COPD patients. Deformable image registration of these scans assesses
regional lung volumetric changes. However, variations in reconstruction kernels
between pai... |
2502.05121 | Refining Integration-by-Parts Reduction of Feynman Integrals with
Machine Learning | hep-th cs.LG hep-ph | Integration-by-parts reductions of Feynman integrals pose a frequent
bottle-neck in state-of-the-art calculations in theoretical particle and
gravitational-wave physics, and rely on heuristic approaches for selecting
integration-by-parts identities, whose quality heavily influences the
performance. In this paper, we ... |
2502.05122 | Distinguishing Cause from Effect with Causal Velocity Models | stat.ML cs.LG stat.ME | Bivariate structural causal models (SCM) are often used to infer causal
direction by examining their goodness-of-fit under restricted model classes. In
this paper, we describe a parametrization of bivariate SCMs in terms of a
causal velocity by viewing the cause variable as time in a dynamical system.
The velocity im... |
2502.05127 | Self-supervised Conformal Prediction for Uncertainty Quantification in
Imaging Problems | cs.CV stat.ME | Most image restoration problems are ill-conditioned or ill-posed and hence
involve significant uncertainty. Quantifying this uncertainty is crucial for
reliably interpreting experimental results, particularly when reconstructed
images inform critical decisions and science. However, most existing image
restoration met... |
2502.05129 | Counting Fish with Temporal Representations of Sonar Video | cs.CV | Accurate estimates of salmon escapement - the number of fish migrating
upstream to spawn - are key data for conservation and fishery management.
Existing methods for salmon counting using high-resolution imaging sonar
hardware are non-invasive and compatible with computer vision processing. Prior
work in this area ha... |
2502.05130 | Latent Swap Joint Diffusion for Long-Form Audio Generation | cs.SD cs.AI cs.CV cs.MM eess.AS | Previous work on long-form audio generation using global-view diffusion or
iterative generation demands significant training or inference costs. While
recent advancements in multi-view joint diffusion for panoramic generation
provide an efficient option, they struggle with spectrum generation with severe
overlap dist... |
2502.05133 | Data-Parallel Neural Network Training via Nonlinearly Preconditioned
Trust-Region Method | cs.LG cs.NA math.NA | Parallel training methods are increasingly relevant in machine learning (ML)
due to the continuing growth in model and dataset sizes. We propose a variant
of the Additively Preconditioned Trust-Region Strategy (APTS) for training deep
neural networks (DNNs). The proposed APTS method utilizes a data-parallel
approach ... |
2502.05134 | Information-Theoretic Guarantees for Recovering Low-Rank Tensors from
Symmetric Rank-One Measurements | math.ST cs.IT math.IT math.PR stat.ML stat.TH | In this paper, we investigate the sample complexity of recovering tensors
with low symmetric rank from symmetric rank-one measurements. This setting is
particularly motivated by the study of higher-order interactions and the
analysis of two-layer neural networks with polynomial activations (polynomial
networks). Usin... |
2502.05139 | Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for
Speech, Music, and Sound | cs.SD cs.LG eess.AS | The quantification of audio aesthetics remains a complex challenge in audio
processing, primarily due to its subjective nature, which is influenced by
human perception and cultural context. Traditional methods often depend on
human listeners for evaluation, leading to inconsistencies and high resource
demands. This p... |
2502.05142 | Chest X-ray Foundation Model with Global and Local Representations
Integration | eess.IV cs.CV | Chest X-ray (CXR) is the most frequently ordered imaging test, supporting
diverse clinical tasks from thoracic disease detection to postoperative
monitoring. However, task-specific classification models are limited in scope,
require costly labeled data, and lack generalizability to out-of-distribution
datasets. To ad... |
2502.05145 | From Restless to Contextual: A Thresholding Bandit Approach to Improve
Finite-horizon Performance | cs.LG | Online restless bandits extend classic contextual bandits by incorporating
state transitions and budget constraints, representing each agent as a Markov
Decision Process (MDP). This framework is crucial for finite-horizon strategic
resource allocation, optimizing limited costly interventions for long-term
benefits. H... |
2502.05147 | LP-DETR: Layer-wise Progressive Relations for Object Detection | cs.CV cs.AI | This paper presents LP-DETR (Layer-wise Progressive DETR), a novel approach
that enhances DETR-based object detection through multi-scale relation
modeling. Our method introduces learnable spatial relationships between object
queries through a relation-aware self-attention mechanism, which adaptively
learns to balanc... |
2502.05148 | An Annotated Reading of 'The Singer of Tales' in the LLM Era | cs.CY cs.CL | The Parry-Lord oral-formulaic theory was a breakthrough in understanding how
oral narrative poetry is learned, composed, and transmitted by illiterate
bards. In this paper, we provide an annotated reading of the mechanism
underlying this theory from the lens of large language models (LLMs) and
generative artificial i... |
2502.05150 | CodeSCM: Causal Analysis for Multi-Modal Code Generation | cs.CL | In this paper, we propose CodeSCM, a Structural Causal Model (SCM) for
analyzing multi-modal code generation using large language models (LLMs). By
applying interventions to CodeSCM, we measure the causal effects of different
prompt modalities, such as natural language, code, and input-output examples,
on the model. ... |
2502.05151 | Transforming Science with Large Language Models: A Survey on AI-assisted
Scientific Discovery, Experimentation, Content Generation, and Evaluation | cs.CL cs.AI cs.CV cs.LG | With the advent of large multimodal language models, science is now at a
threshold of an AI-based technological transformation. Recently, a plethora of
new AI models and tools has been proposed, promising to empower researchers and
academics worldwide to conduct their research more effectively and efficiently.
This i... |
2502.05153 | Hummingbird: High Fidelity Image Generation via Multimodal Context
Alignment | cs.CV | While diffusion models are powerful in generating high-quality, diverse
synthetic data for object-centric tasks, existing methods struggle with
scene-aware tasks such as Visual Question Answering (VQA) and Human-Object
Interaction (HOI) Reasoning, where it is critical to preserve scene attributes
in generated images ... |
2502.05155 | Deep Dynamic Probabilistic Canonical Correlation Analysis | cs.LG stat.ML | This paper presents Deep Dynamic Probabilistic Canonical Correlation Analysis
(D2PCCA), a model that integrates deep learning with probabilistic modeling to
analyze nonlinear dynamical systems. Building on the probabilistic extensions
of Canonical Correlation Analysis (CCA), D2PCCA captures nonlinear latent
dynamics ... |
2502.05157 | Efficient distributional regression trees learning algorithms for
calibrated non-parametric probabilistic forecasts | cs.LG cs.DS | The perspective of developing trustworthy AI for critical applications in
science and engineering requires machine learning techniques that are capable
of estimating their own uncertainty. In the context of regression, instead of
estimating a conditional mean, this can be achieved by producing a predictive
interval f... |
2502.05159 | A Lightweight Method to Disrupt Memorized Sequences in LLM | cs.LG cs.CL | Large language models (LLMs) demonstrate impressive capabilities across many
tasks yet risk reproducing copyrighted content verbatim, raising legal and
ethical concerns. Although methods like differential privacy or neuron editing
can reduce memorization, they typically require costly retraining or direct
access to m... |
2502.05163 | DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM
Guardrails | cs.CL cs.LG | The rapid advancement of large language models (LLMs) has increased the need
for guardrail models to ensure responsible use, particularly in detecting
unsafe and illegal content. While substantial safety data exist in English,
multilingual guardrail modeling remains underexplored due to the scarcity of
open-source sa... |
2502.05164 | In-context denoising with one-layer transformers: connections between
attention and associative memory retrieval | cs.LG cond-mat.dis-nn | We introduce in-context denoising, a task that refines the connection between
attention-based architectures and dense associative memory (DAM) networks, also
known as modern Hopfield networks. Using a Bayesian framework, we show
theoretically and empirically that certain restricted denoising problems can be
solved op... |
2502.05165 | Multitwine: Multi-Object Compositing with Text and Layout Control | cs.CV | We introduce the first generative model capable of simultaneous multi-object
compositing, guided by both text and layout. Our model allows for the addition
of multiple objects within a scene, capturing a range of interactions from
simple positional relations (e.g., next to, in front of) to complex actions
requiring r... |
2502.05167 | NoLiMa: Long-Context Evaluation Beyond Literal Matching | cs.CL | Recent large language models (LLMs) support long contexts ranging from 128K
to 1M tokens. A popular method for evaluating these capabilities is the
needle-in-a-haystack (NIAH) test, which involves retrieving a "needle"
(relevant information) from a "haystack" (long irrelevant context). Extensions
of this approach inc... |
2502.05169 | Flopping for FLOPs: Leveraging equivariance for computational efficiency | cs.CV cs.LG | Incorporating geometric invariance into neural networks enhances parameter
efficiency but typically increases computational costs. This paper introduces
new equivariant neural networks that preserve symmetry while maintaining a
comparable number of floating-point operations (FLOPs) per parameter to
standard non-equiv... |
2502.05171 | Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth
Approach | cs.LG cs.CL | We study a novel language model architecture that is capable of scaling
test-time computation by implicitly reasoning in latent space. Our model works
by iterating a recurrent block, thereby unrolling to arbitrary depth at
test-time. This stands in contrast to mainstream reasoning models that scale up
compute by prod... |
2502.05172 | Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient | cs.LG cs.AI cs.CL | Mixture of Experts (MoE) architectures have significantly increased
computational efficiency in both research and real-world applications of
large-scale machine learning models. However, their scalability and efficiency
under memory constraints remain relatively underexplored. In this work, we
present joint scaling l... |
2502.05173 | VideoRoPE: What Makes for Good Video Rotary Position Embedding? | cs.CV | While Rotary Position Embedding (RoPE) and its variants are widely adopted
for their long-context capabilities, the extension of the 1D RoPE to video,
with its complex spatio-temporal structure, remains an open challenge. This
work first introduces a comprehensive analysis that identifies four key
characteristics ess... |
2502.05174 | MELON: Indirect Prompt Injection Defense via Masked Re-execution and
Tool Comparison | cs.CR cs.AI | Recent research has explored that LLM agents are vulnerable to indirect
prompt injection (IPI) attacks, where malicious tasks embedded in
tool-retrieved information can redirect the agent to take unauthorized actions.
Existing defenses against IPI have significant limitations: either require
essential model training ... |
2502.05175 | Fillerbuster: Multi-View Scene Completion for Casual Captures | cs.CV cs.GR | We present Fillerbuster, a method that completes unknown regions of a 3D
scene by utilizing a novel large-scale multi-view latent diffusion transformer.
Casual captures are often sparse and miss surrounding content behind objects or
above the scene. Existing methods are not suitable for handling this challenge
as the... |
2502.05176 | AuraFusion360: Augmented Unseen Region Alignment for Reference-based
360{\deg} Unbounded Scene Inpainting | cs.CV | Three-dimensional scene inpainting is crucial for applications from virtual
reality to architectural visualization, yet existing methods struggle with view
consistency and geometric accuracy in 360{\deg} unbounded scenes. We present
AuraFusion360, a novel reference-based method that enables high-quality object
remova... |
2502.05177 | Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with
Leading Short-Context Accuracy | cs.CV | We introduce Long-VITA, a simple yet effective large multi-modal model for
long-context visual-language understanding tasks. It is adept at concurrently
processing and analyzing modalities of image, video, and text over 4K frames or
1M tokens while delivering advanced performances on short-context multi-modal
tasks. ... |
2502.05178 | QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive
Multimodal Understanding and Generation | cs.CV | We introduce Quantized Language-Image Pretraining (QLIP), a visual
tokenization method that combines state-of-the-art reconstruction quality with
state-of-the-art zero-shot image understanding. QLIP trains a
binary-spherical-quantization-based autoencoder with reconstruction and
language-image alignment objectives. W... |
2502.05179 | FlashVideo:Flowing Fidelity to Detail for Efficient High-Resolution
Video Generation | cs.CV | DiT diffusion models have achieved great success in text-to-video generation,
leveraging their scalability in model capacity and data scale. High content and
motion fidelity aligned with text prompts, however, often require large model
parameters and a substantial number of function evaluations (NFEs). Realistic
and ... |
2502.05181 | Enhancing Team Diversity with Generative AI: A Novel Project Management
Framework | cs.CY cs.AI cs.LG | This research-in-progress paper presents a new project management framework
that utilises GenAI technology. The framework is designed to address the common
challenge of uniform team compositions in academic and research project teams,
particularly in universities and research institutions. It does so by
integrating s... |
2502.05183 | Modelling hydrogen integration in energy system models: Best practices
for policy insights | physics.soc-ph cs.CE math.OC | The rapid emergence of hydrogen in long-term energy strategies requires a
broad understanding on how hydrogen is currently modelled in national energy
system models. This study provides a review on hydrogen representation within
selected energy system models that are tailored towards providing policy
insights. The pa... |
2502.05186 | Multimodal Stock Price Prediction | q-fin.ST cs.AI cs.LG | In an era where financial markets are heavily influenced by many static and
dynamic factors, it has become increasingly critical to carefully integrate
diverse data sources with machine learning for accurate stock price prediction.
This paper explores a multimodal machine learning approach for stock price
prediction ... |
2502.05187 | An Adaptable Budget Planner for Enhancing Budget-Constrained
Auto-Bidding in Online Advertising | cs.GT cs.LG | In online advertising, advertisers commonly utilize auto-bidding services to
bid for impression opportunities. A typical objective of the auto-bidder is to
optimize the advertiser's cumulative value of winning impressions within
specified budget constraints. However, such a problem is challenging due to the
complex b... |
2502.05189 | Physics-Driven Self-Supervised Deep Learning for Free-Surface Multiple
Elimination | physics.geo-ph cs.LG | In recent years, deep learning (DL) has emerged as a promising alternative
approach for various seismic processing tasks, including primary estimation (or
multiple elimination), a crucial step for accurate subsurface imaging. In
geophysics, DL methods are commonly based on supervised learning from large
amounts of hi... |
2502.05190 | Physics-Trained Neural Network as Inverse Problem Solver for Potential
Fields: An Example of Downward Continuation between Arbitrary Surfaces | physics.geo-ph cs.LG | Downward continuation is a critical task in potential field processing,
including gravity and magnetic fields, which aims to transfer data from one
observation surface to another that is closer to the source of the field. Its
effectiveness directly impacts the success of detecting and highlighting
subsurface anomalou... |
2502.05195 | Using Large Language Models for Solving Thermodynamic Problems | cs.CE | Large Language Models (LLMs) have made significant progress in reasoning,
demonstrating their capability to generate human-like responses. This study
analyzes the problem-solving capabilities of LLMs in the domain of
thermodynamics. A benchmark of 22 thermodynamic problems to evaluate LLMs is
presented that contains ... |
2502.05196 | LLMs Provide Unstable Answers to Legal Questions | cs.CL cs.CY | An LLM is stable if it reaches the same conclusion when asked the identical
question multiple times. We find leading LLMs like gpt-4o, claude-3.5, and
gemini-1.5 are unstable when providing answers to hard legal questions, even
when made as deterministic as possible by setting temperature to 0. We curate
and release ... |
2502.05198 | A finite element-based machine learning model for hydro-mechanical
analysis of swelling behavior in clay-sulfate rocks | physics.geo-ph cs.LG physics.comp-ph | The hydro-mechanical behavior of clay-sulfate rocks, especially their
swelling properties, poses significant challenges in geotechnical engineering.
This study presents a hybrid constrained machine learning (ML) model developed
using the categorical boosting algorithm (CatBoost) tuned with a Bayesian
optimization alg... |
2502.05202 | Accelerating LLM Inference with Lossless Speculative Decoding Algorithms
for Heterogeneous Vocabularies | cs.CL cs.AI cs.LG | Accelerating the inference of large language models (LLMs) is a critical
challenge in generative AI. Speculative decoding (SD) methods offer substantial
efficiency gains by generating multiple tokens using a single target forward
pass. However, existing SD approaches require the drafter and target models to
share the... |
2502.05203 | Adversarial Machine Learning: Attacking and Safeguarding Image Datasets | cs.LG cs.CV | This paper examines the vulnerabilities of convolutional neural networks
(CNNs) to adversarial attacks and explores a method for their safeguarding. In
this study, CNNs were implemented on four of the most common image datasets,
namely CIFAR-10, ImageNet, MNIST, and Fashion-MNIST, and achieved high baseline
accuracy.... |
2502.05204 | Invariant Measures for Data-Driven Dynamical System Identification:
Analysis and Application | math.DS cs.LG nlin.CD physics.data-an | We propose a novel approach for performing dynamical system identification,
based upon the comparison of simulated and observed physical invariant
measures. While standard methods adopt a Lagrangian perspective by directly
treating time-trajectories as inference data, we take on an Eulerian
perspective and instead se... |
2502.05206 | Safety at Scale: A Comprehensive Survey of Large Model Safety | cs.CR cs.AI cs.CL cs.CV | The rapid advancement of large models, driven by their exceptional abilities
in learning and generalization through large-scale pre-training, has reshaped
the landscape of Artificial Intelligence (AI). These models are now
foundational to a wide range of applications, including conversational AI,
recommendation syste... |
2502.05208 | Mitigation of Camouflaged Adversarial Attacks in Autonomous Vehicles--A
Case Study Using CARLA Simulator | cs.CR cs.AI cs.LG | Autonomous vehicles (AVs) rely heavily on cameras and artificial intelligence
(AI) to make safe and accurate driving decisions. However, since AI is the core
enabling technology, this raises serious cyber threats that hinder the
large-scale adoption of AVs. Therefore, it becomes crucial to analyze the
resilience of A... |
2502.05209 | Model Tampering Attacks Enable More Rigorous Evaluations of LLM
Capabilities | cs.CR cs.AI | Evaluations of large language model (LLM) risks and capabilities are
increasingly being incorporated into AI risk management and governance
frameworks. Currently, most risk evaluations are conducted by designing inputs
that elicit harmful behaviors from the system. However, a fundamental
limitation of this approach i... |
2502.05210 | Regression and Forecasting of U.S. Stock Returns Based on LSTM | q-fin.ST cs.LG | This paper analyses the investment returns of three stock sectors, Manuf,
Hitec, and Other, in the U.S. stock market, based on the Fama-French
three-factor model, the Carhart four-factor model, and the Fama-French
five-factor model, in order to test the validity of the Fama-French
three-factor model, the Carhart four... |
2502.05211 | Decoding FL Defenses: Systemization, Pitfalls, and Remedies | cs.CR cs.AI | While the community has designed various defenses to counter the threat of
poisoning attacks in Federated Learning (FL), there are no guidelines for
evaluating these defenses. These defenses are prone to subtle pitfalls in their
experimental setups that lead to a false sense of security, rendering them
unsuitable for... |
2502.05213 | DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large
Language Models | cs.CR cs.AI | Well-trained large language models (LLMs) present significant risks,
including potential malicious use and copyright infringement. Current studies
aim to trace the distribution of LLM-generated texts by implicitly embedding
watermarks. Among these, the single-bit watermarking method can only determine
whether a given... |
2502.05214 | CoRPA: Adversarial Image Generation for Chest X-rays Using Concept
Vector Perturbations and Generative Models | eess.IV cs.AI cs.CV | Deep learning models for medical image classification tasks are becoming
widely implemented in AI-assisted diagnostic tools, aiming to enhance
diagnostic accuracy, reduce clinician workloads, and improve patient outcomes.
However, their vulnerability to adversarial attacks poses significant risks to
patient safety. C... |
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