id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.06485 | WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry | cond-mat.mtrl-sci cs.AI cs.LG | Crystalline materials often exhibit a high level of symmetry. However, most
generative models do not account for symmetry, but rather model each atom
without any constraints on its position or element. We propose a generative
model, Wyckoff Diffusion (WyckoffDiff), which generates symmetry-based
descriptions of cryst... |
2502.06486 | Biomechanical Reconstruction with Confidence Intervals from Multiview
Markerless Motion Capture | cs.CV | Advances in multiview markerless motion capture (MMMC) promise high-quality
movement analysis for clinical practice and research. While prior validation
studies show MMMC performs well on average, they do not provide what is needed
in clinical practice or for large-scale utilization of MMMC -- confidence
intervals ov... |
2502.06487 | Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection | cs.CL | Recent advances on instruction fine-tuning have led to the development of
various prompting techniques for large language models, such as explicit
reasoning steps. However, the success of techniques depends on various
parameters, such as the task, language model, and context provided. Finding an
effective prompt is, ... |
2502.06490 | Recent Advances in Discrete Speech Tokens: A Review | eess.AS cs.AI cs.MM cs.SD eess.SP | The rapid advancement of speech generation technologies in the era of large
language models (LLMs) has established discrete speech tokens as a foundational
paradigm for speech representation. These tokens, characterized by their
discrete, compact, and concise nature, are not only advantageous for efficient
transmissi... |
2502.06491 | Model-Based Offline Reinforcement Learning with Reliability-Guaranteed
Sequence Modeling | cs.LG cs.AI | Model-based offline reinforcement learning (MORL) aims to learn a policy by
exploiting a dynamics model derived from an existing dataset. Applying
conservative quantification to the dynamics model, most existing works on MORL
generate trajectories that approximate the real data distribution to facilitate
policy learn... |
2502.06494 | GuideLLM: Exploring LLM-Guided Conversation with Applications in
Autobiography Interviewing | cs.CL cs.AI | Although Large Language Models (LLMs) succeed in human-guided conversations
such as instruction following and question answering, the potential of
LLM-guided conversations-where LLMs direct the discourse and steer the
conversation's objectives-remains under-explored. In this study, we first
characterize LLM-guided co... |
2502.06498 | Decision Boundary Optimization-Informed Domain Adaptation | cs.CV | Maximum Mean Discrepancy (MMD) is widely used in a number of domain
adaptation (DA) methods and shows its effectiveness in aligning data
distributions across domains. However, in previous DA research, MMD-based DA
methods focus mostly on distribution alignment, and ignore to optimize the
decision boundary for classif... |
2502.06501 | Learning Clustering-based Prototypes for Compositional Zero-shot
Learning | cs.CV | Learning primitive (i.e., attribute and object) concepts from seen
compositions is the primary challenge of Compositional Zero-Shot Learning
(CZSL). Existing CZSL solutions typically rely on oversimplified data
assumptions, e.g., modeling each primitive with a single centroid primitive
representation, ignoring the na... |
2502.06516 | Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation | cs.LG cs.AI cs.CV stat.ML | Minority samples are underrepresented instances located in low-density
regions of a data manifold, and are valuable in many generative AI
applications, such as data augmentation, creative content generation, etc.
Unfortunately, existing diffusion-based minority generators often rely on
computationally expensive guida... |
2502.06519 | SIREN: Semantic, Initialization-Free Registration of Multi-Robot
Gaussian Splatting Maps | cs.RO cs.CV | We present SIREN for registration of multi-robot Gaussian Splatting (GSplat)
maps, with zero access to camera poses, images, and inter-map transforms for
initialization or fusion of local submaps. To realize these capabilities, SIREN
harnesses the versatility and robustness of semantics in three critical ways to
deri... |
2502.06523 | Tighter Value-Function Approximations for POMDPs | cs.AI | Solving partially observable Markov decision processes (POMDPs) typically
requires reasoning about the values of exponentially many state beliefs.
Towards practical performance, state-of-the-art solvers use value bounds to
guide this reasoning. However, sound upper value bounds are often
computationally expensive to ... |
2502.06525 | Properties of Wasserstein Gradient Flows for the Sliced-Wasserstein
Distance | stat.ML cs.LG | In this paper, we investigate the properties of the Sliced Wasserstein
Distance (SW) when employed as an objective functional. The SW metric has
gained significant interest in the optimal transport and machine learning
literature, due to its ability to capture intricate geometric properties of
probability distributio... |
2502.06526 | Convex Split Lemma without Inequalities | quant-ph cs.IT math-ph math.IT math.MP | We introduce a refinement to the convex split lemma by replacing the max
mutual information with the collision mutual information, transforming the
inequality into an equality. This refinement yields tighter achievability
bounds for quantum source coding tasks, including state merging and state
splitting. Furthermore... |
2502.06527 | CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for
Zero-Shot Customized Video Diffusion Transformers | cs.CV | Customized generation has achieved significant progress in image synthesis,
yet personalized video generation remains challenging due to temporal
inconsistencies and quality degradation. In this paper, we introduce
CustomVideoX, an innovative framework leveraging the video diffusion
transformer for personalized video... |
2502.06533 | Ignore the KL Penalty! Boosting Exploration on Critical Tokens to
Enhance RL Fine-Tuning | cs.CL cs.LG | The ability to achieve long-term goals is a key challenge in the current
development of large language models (LLMs). To address this, pre-trained LLMs
can be fine-tuned with reinforcement learning (RL) to explore solutions that
optimize a given goal. However, exploration with LLMs is difficult, as a
balance has to b... |
2502.06536 | Sample-efficient Learning of Concepts with Theoretical Guarantees: from
Data to Concepts without Interventions | stat.ML cs.LG | Machine learning is a vital part of many real-world systems, but several
concerns remain about the lack of interpretability, explainability and
robustness of black-box AI systems. Concept-based models (CBM) address some of
these challenges by learning interpretable concepts from high-dimensional data,
e.g. images, wh... |
2502.06543 | Unsupervised Learning for Feature Extraction and Temporal Alignment of
3D+t Point Clouds of Zebrafish Embryos | cs.CV | Zebrafish are widely used in biomedical research and developmental stages of
their embryos often need to be synchronized for further analysis. We present an
unsupervised approach to extract descriptive features from 3D+t point clouds of
zebrafish embryos and subsequently use those features to temporally align
corresp... |
2502.06544 | Sequence Transferability and Task Order Selection in Continual Learning | cs.LG cs.CV | In continual learning, understanding the properties of task sequences and
their relationships to model performance is important for developing advanced
algorithms with better accuracy. However, efforts in this direction remain
underdeveloped despite encouraging progress in methodology development. In this
work, we in... |
2502.06545 | Dimension-free Regret for Learning Asymmetric Linear Dynamical Systems | cs.LG stat.ML | Previously, methods for learning marginally stable linear dynamical systems
either required the transition matrix to be symmetric or incurred regret bounds
that scale polynomially with the system's hidden dimension. In this work, we
introduce a novel method that overcomes this trade-off, achieving
dimension-free regr... |
2502.06547 | Data Augmentation and Regularization for Learning Group Equivariance | stat.ML cs.LG math.OC | In many machine learning tasks, known symmetries can be used as an inductive
bias to improve model performance. In this paper, we consider learning group
equivariance through training with data augmentation. We summarize results from
a previous paper of our own, and extend the results to show that equivariance
of the... |
2502.06551 | Efficient Scientific Full Text Classification: The Case of EICAT Impact
Assessments | cs.CL | This study explores strategies for efficiently classifying scientific full
texts using both small, BERT-based models and local large language models like
Llama-3.1 8B. We focus on developing methods for selecting subsets of input
sentences to reduce input size while simultaneously enhancing classification
performance... |
2502.06552 | Diffusion Models for Computational Neuroimaging: A Survey | cs.CV | Computational neuroimaging involves analyzing brain images or signals to
provide mechanistic insights and predictive tools for human cognition and
behavior. While diffusion models have shown stability and high-quality
generation in natural images, there is increasing interest in adapting them to
analyze brain data fo... |
2502.06555 | Is API Access to LLMs Useful for Generating Private Synthetic Tabular
Data? | cs.LG cs.CR | Differentially private (DP) synthetic data is a versatile tool for enabling
the analysis of private data. Recent advancements in large language models
(LLMs) have inspired a number of algorithm techniques for improving DP
synthetic data generation. One family of approaches uses DP finetuning on the
foundation model w... |
2502.06556 | ProjectTest: A Project-level LLM Unit Test Generation Benchmark and
Impact of Error Fixing Mechanisms | cs.SE cs.CL | Unit test generation has become a promising and important use case of LLMs.
However, existing evaluation benchmarks for assessing LLM unit test generation
capabilities focus on function- or class-level code rather than more practical
and challenging project-level codebases. To address such limitation, we propose
Proj... |
2502.06557 | LiveForesighter: Generating Future Information for Live-Streaming
Recommendations at Kuaishou | cs.IR | Live-streaming, as a new-generation media to connect users and authors, has
attracted a lot of attention and experienced rapid growth in recent years.
Compared with the content-static short-video recommendation, the live-streaming
recommendation faces more challenges in giving our users a satisfactory
experience: (1)... |
2502.06559 | Can We Trust AI Benchmarks? An Interdisciplinary Review of Current
Issues in AI Evaluation | cs.AI | Quantitative Artificial Intelligence (AI) Benchmarks have emerged as
fundamental tools for evaluating the performance, capability, and safety of AI
models and systems. Currently, they shape the direction of AI development and
are playing an increasingly prominent role in regulatory frameworks. As their
influence grow... |
2502.06560 | Position: It's Time to Act on the Risk of Efficient Personalized Text
Generation | cs.CL cs.CY | The recent surge in high-quality open-sourced Generative AI text models
(colloquially: LLMs), as well as efficient finetuning techniques, has opened
the possibility of creating high-quality personalized models, i.e., models
generating text attuned to a specific individual's needs and capable of
credibly imitating the... |
2502.06563 | Large Language Models Meet Symbolic Provers for Logical Reasoning
Evaluation | cs.CL | First-order logic (FOL) reasoning, which involves sequential deduction, is
pivotal for intelligent systems and serves as a valuable task for evaluating
reasoning capabilities, particularly in chain-of-thought (CoT) contexts.
Existing benchmarks often rely on extensive human annotation or handcrafted
templates, making... |
2502.06564 | Robust Scatter Matrix Estimation for Elliptical Distributions in
Polynomial Time | cs.DS cs.LG math.ST stat.ML stat.TH | We study the problem of computationally efficient robust estimation of
scatter matrices of elliptical distributions under the strong contamination
model. We design polynomial time algorithms that achieve dimension-independent
error in Frobenius norm.
Our first result is a sequence of efficient algorithms that appro... |
2502.06567 | Membership Inference Risks in Quantized Models: A Theoretical and
Empirical Study | stat.ML cs.LG | Quantizing machine learning models has demonstrated its effectiveness in
lowering memory and inference costs while maintaining performance levels
comparable to the original models. In this work, we investigate the impact of
quantization procedures on the privacy of data-driven models, specifically
focusing on their v... |
2502.06572 | LawGPT: Knowledge-Guided Data Generation and Its Application to Legal
LLM | cs.CL cs.AI | Large language models (LLMs), both proprietary and open-source, have
demonstrated remarkable capabilities across various natural language processing
tasks. However, they face significant limitations in legal reasoning tasks.
Proprietary models introduce data privacy risks and high inference costs, while
open-source m... |
2502.06574 | On the Impact of the Utility in Semivalue-based Data Valuation | cs.AI cs.GT cs.LG | Semivalue-based data valuation in machine learning (ML) quantifies the
contribution of individual data points to a downstream ML task by leveraging
principles from cooperative game theory and the notion of utility. While this
framework has been used in practice for assessing data quality, our experiments
reveal incon... |
2502.06575 | Predictive Red Teaming: Breaking Policies Without Breaking Robots | cs.RO cs.AI cs.LG cs.SY eess.SY | Visuomotor policies trained via imitation learning are capable of performing
challenging manipulation tasks, but are often extremely brittle to lighting,
visual distractors, and object locations. These vulnerabilities can depend
unpredictably on the specifics of training, and are challenging to expose
without time-co... |
2502.06577 | The Minimal Search Space for Conditional Causal Bandits | cs.LG cs.AI stat.ML | Causal knowledge can be used to support decision-making problems. This has
been recognized in the causal bandits literature, where a causal (multi-armed)
bandit is characterized by a causal graphical model and a target variable. The
arms are then interventions on the causal model, and rewards are samples of the
targe... |
2502.06580 | Inventory Consensus Control in Supply Chain Networks using
Dissipativity-Based Control and Topology Co-Design | eess.SY cs.SY | Recent global and local phenomena have exposed vulnerabilities in critical
supply chain networks (SCNs), drawing significant attention from researchers
across various fields. Typically, SCNs are viewed as static entities regularly
optimized to maintain their optimal operation. However, the dynamic nature of
SCNs and ... |
2502.06581 | A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems | cs.NI cs.CV cs.LG | The explosive growth of video data has driven the development of distributed
video analytics in cloud-edge-terminal collaborative (CETC) systems, enabling
efficient video processing, real-time inference, and privacy-preserving
analysis. Among multiple advantages, CETC systems can distribute video
processing tasks and... |
2502.06583 | Adaptive Perception for Unified Visual Multi-modal Object Tracking | cs.CV | Recently, many multi-modal trackers prioritize RGB as the dominant modality,
treating other modalities as auxiliary, and fine-tuning separately various
multi-modal tasks. This imbalance in modality dependence limits the ability of
methods to dynamically utilize complementary information from each modality in
complex ... |
2502.06584 | Deep Reinforcement Learning based Triggering Function for Early
Classifiers of Time Series | cs.LG | Early Classification of Time Series (ECTS) has been recognized as an
important problem in many areas where decisions have to be taken as soon as
possible, before the full data availability, while time pressure increases.
Numerous ECTS approaches have been proposed, based on different triggering
functions, each taking... |
2502.06585 | Extract-QD Framework: A Generic Approach for Quality-Diversity in Noisy,
Stochastic or Uncertain Domains | cs.NE | Quality-Diversity (QD) has demonstrated potential in discovering collections
of diverse solutions to optimisation problems. Originally designed for
deterministic environments, QD has been extended to noisy, stochastic, or
uncertain domains through various Uncertain-QD (UQD) methods. However, the
large number of UQD m... |
2502.06587 | evclust: Python library for evidential clustering | cs.SE cs.CV cs.LG | A recent developing trend in clustering is the advancement of algorithms that
not only identify clusters within data, but also express and capture the
uncertainty of cluster membership. Evidential clustering addresses this by
using the Dempster-Shafer theory of belief functions, a framework designed to
manage and rep... |
2502.06589 | Hephaestus: Improving Fundamental Agent Capabilities of Large Language
Models through Continual Pre-Training | cs.CL cs.AI cs.LG | Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous
agents typically rely on complex prompting or extensive fine-tuning, which
often fails to introduce new capabilities while preserving strong
generalizability. We introduce Hephaestus-Forge, the first large-scale
pre-training corpus designed... |
2502.06591 | Diffeomorphic Temporal Alignment Nets for Time-series Joint Alignment
and Averaging | cs.LG | In time-series analysis, nonlinear temporal misalignment remains a pivotal
challenge that forestalls even simple averaging. Since its introduction, the
Diffeomorphic Temporal Alignment Net (DTAN), which we first introduced (Weber
et al., 2019) and further developed in (Weber & Freifeld, 2023), has proven
itself as an... |
2502.06593 | A Large-scale AI-generated Image Inpainting Benchmark | cs.CV | Recent advances in generative models enable highly realistic image
manipulations, creating an urgent need for robust forgery detection methods.
Current datasets for training and evaluating these methods are limited in scale
and diversity. To address this, we propose a methodology for creating
high-quality inpainting ... |
2502.06594 | A Review of Conceptualizations of Safety and Risk in Current Automated
Driving Regulation | eess.SY cs.SY | "Safety" and "Risk" are key concepts for the design and development of
automated vehicles. For the market introduction or large-scale field tests,
both concepts are not only relevant for engineers developing the vehicles, but
for all stakeholders (e.g., regulators, lawyers, or the general public) who
have stakes in t... |
2502.06595 | Surrogate models for diffusion on graphs via sparse polynomials | math.NA cs.LG cs.NA | Diffusion kernels over graphs have been widely utilized as effective tools in
various applications due to their ability to accurately model the flow of
information through nodes and edges. However, there is a notable gap in the
literature regarding the development of surrogate models for diffusion
processes on graphs... |
2502.06597 | Continual Release Moment Estimation with Differential Privacy | cs.LG stat.ML | We propose Joint Moment Estimation (JME), a method for continually and
privately estimating both the first and second moments of data with reduced
noise compared to naive approaches. JME uses the matrix mechanism and a joint
sensitivity analysis to allow the second moment estimation with no additional
privacy cost, t... |
2502.06599 | Joint parameter and state estimation for regularized time-discrete
multibody dynamics | math.OC cs.SY eess.SY | We develop a method for offline parameter estimation of discrete multibody
dynamics with regularized and frictional kinematic constraints. This setting
leads to unobserved degrees of freedom, which we handle using joint state and
parameter estimation. Our method finds the states and parameters as the
solution to a no... |
2502.06600 | Evaluation of Multilingual Image Captioning: How far can we get with
CLIP models? | cs.CL cs.AI | The evaluation of image captions, looking at both linguistic fluency and
semantic correspondence to visual contents, has witnessed a significant effort.
Still, despite advancements such as the CLIPScore metric, multilingual
captioning evaluation has remained relatively unexplored. This work presents
several strategie... |
2502.06601 | Amortized In-Context Bayesian Posterior Estimation | cs.LG cs.AI stat.ML | Bayesian inference provides a natural way of incorporating prior beliefs and
assigning a probability measure to the space of hypotheses. Current solutions
rely on iterative routines like Markov Chain Monte Carlo (MCMC) sampling and
Variational Inference (VI), which need to be re-run whenever new observations
are avai... |
2502.06604 | Do we really have to filter out random noise in pre-training data for
language models? | cs.CL | Web-scale pre-training datasets are the cornerstone of LLMs' success.
However, text data curated from the internet inevitably contains random noise
caused by decoding errors or unregulated web content. In contrast to previous
works that focus on low quality or synthetic data, our study \textbf{provides
the first syst... |
2502.06606 | MaterialFusion: High-Quality, Zero-Shot, and Controllable Material
Transfer with Diffusion Models | cs.CV | Manipulating the material appearance of objects in images is critical for
applications like augmented reality, virtual prototyping, and digital content
creation. We present MaterialFusion, a novel framework for high-quality
material transfer that allows users to adjust the degree of material
application, achieving an... |
2502.06607 | Illegal Waste Detection in Remote Sensing Images: A Case Study | cs.CV cs.AI | Environmental crime currently represents the third largest criminal activity
worldwide while threatening ecosystems as well as human health. Among the
crimes related to this activity, improper waste management can nowadays be
countered more easily thanks to the increasing availability and decreasing cost
of Very-High... |
2502.06608 | TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified
Flow Models | cs.CV cs.AI | Recent advancements in diffusion techniques have propelled image and video
generation to unprecedented levels of quality, significantly accelerating the
deployment and application of generative AI. However, 3D shape generation
technology has so far lagged behind, constrained by limitations in 3D data
scale, complexit... |
2502.06615 | Multi-Scale Feature Fusion with Image-Driven Spatial Integration for
Left Atrium Segmentation from Cardiac MRI Images | cs.CV eess.IV | Accurate segmentation of the left atrium (LA) from late gadolinium-enhanced
magnetic resonance imaging plays a vital role in visualizing diseased atrial
structures, enabling the diagnosis and management of cardiovascular diseases.
It is particularly essential for planning treatment with ablation therapy, a
key interv... |
2502.06617 | Scaling Multi-Document Event Summarization: Evaluating Compression vs.
Full-Text Approaches | cs.CL | Automatically summarizing large text collections is a valuable tool for
document research, with applications in journalism, academic research, legal
work, and many other fields. In this work, we contrast two classes of systems
for large-scale multi-document summarization (MDS): compression and full-text.
Compression-... |
2502.06618 | On the Reliability of Information Retrieval From MDS Coded Data in DNA
Storage | cs.IT cs.ET math.IT | This work presents a theoretical analysis of the probability of successfully
retrieving data encoded with MDS codes (e.g., Reed-Solomon codes) in DNA
storage systems. We study this probability under independent and identically
distributed (i.i.d.) substitution errors, focusing on a common code design
strategy that co... |
2502.06619 | Unleashing the Potential of Pre-Trained Diffusion Models for
Generalizable Person Re-Identification | cs.CV | Domain-generalizable re-identification (DG Re-ID) aims to train a model on
one or more source domains and evaluate its performance on unseen target
domains, a task that has attracted growing attention due to its practical
relevance. While numerous methods have been proposed, most rely on
discriminative or contrastive... |
2502.06627 | Towards Closing the Gap between Model-Based Systems Engineering and
Automated Vehicle Assurance: Tailoring Generic Methods by Integrating Domain
Knowledge | eess.SY cs.SY | Designing, assuring and releasing safe automated vehicles is a highly
interdisciplinary process. As complex systems, automated driving systems will
inevitably be subject to emergent properties, i. e., the properties of the
overall system will be more than just a sum of the properties of its integrated
elements. Safet... |
2502.06631 | Conformal Predictions for Human Action Recognition with Vision-Language
Models | cs.CV cs.AI | Human-In-The-Loop (HITL) frameworks are integral to many real-world computer
vision systems, enabling human operators to make informed decisions with AI
assistance. Conformal Predictions (CP), which provide label sets with rigorous
guarantees on ground truth inclusion probabilities, have recently gained
traction as a... |
2502.06632 | Few-Shot Classification and Anatomical Localization of Tissues in SPECT
Imaging | cs.CV cs.AI cs.LG | Accurate classification and anatomical localization are essential for
effective medical diagnostics and research, which may be efficiently performed
using deep learning techniques. However, availability of limited labeled data
poses a significant challenge. To address this, we adapted Prototypical
Networks and the Pr... |
2502.06633 | Combining Large Language Models with Static Analyzers for Code Review
Generation | cs.SE cs.AI | Code review is a crucial but often complex, subjective, and time-consuming
activity in software development. Over the past decades, significant efforts
have been made to automate this process. Early approaches focused on
knowledge-based systems (KBS) that apply rule-based mechanisms to detect code
issues, providing p... |
2502.06634 | Automatic Annotation Augmentation Boosts Translation between Molecules
and Natural Language | cs.LG cs.AI q-bio.BM | Recent advancements in AI for biological research focus on integrating
molecular data with natural language to accelerate drug discovery. However, the
scarcity of high-quality annotations limits progress in this area. This paper
introduces LA$^3$, a Language-based Automatic Annotation Augmentation framework
that leve... |
2502.06635 | Steel-LLM:From Scratch to Open Source -- A Personal Journey in Building
a Chinese-Centric LLM | cs.CL cs.AI | Steel-LLM is a Chinese-centric language model developed from scratch with the
goal of creating a high-quality, open-source model despite limited
computational resources. Launched in March 2024, the project aimed to train a
1-billion-parameter model on a large-scale dataset, prioritizing transparency
and the sharing o... |
2502.06636 | Enhancing healthcare infrastructure resilience through agent-based
simulation methods | cs.MA cs.SY eess.SY | Critical infrastructures face demanding challenges due to natural and
human-generated threats, such as pandemics, workforce shortages or
cyber-attacks, which might severely compromise service quality. To improve
system resilience, decision-makers would need intelligent tools for quick and
efficient resource allocatio... |
2502.06643 | MoETuner: Optimized Mixture of Expert Serving with Balanced Expert
Placement and Token Routing | cs.LG cs.DC | Mixture-of-Experts (MoE) model architecture has emerged as a promising
solution for scaling transformer models efficiently, offering sparse activation
that reduces computational costs while increasing model capacity. However, as
MoE models scale, they need to be distributed across GPU devices, thus face
critical perf... |
2502.06645 | Koopman-Equivariant Gaussian Processes | cs.LG cs.SY eess.SY stat.ML | Credible forecasting and representation learning of dynamical systems are of
ever-increasing importance for reliable decision-making. To that end, we
propose a family of Gaussian processes (GP) for dynamical systems with linear
time-invariant responses, which are nonlinear only in initial conditions. This
linearity a... |
2502.06648 | The 2021 Tokyo Olympics Multilingual News Article Dataset | cs.IR cs.AI cs.CL | In this paper, we introduce a dataset of multilingual news articles covering
the 2021 Tokyo Olympics. A total of 10,940 news articles were gathered from
1,918 different publishers, covering 1,350 sub-events of the 2021 Olympics, and
published between July 1, 2021, and August 14, 2021. These articles are written
in ni... |
2502.06649 | Estimation of Food Intake Quantity Using Inertial Signals from
Smartwatches | eess.SP cs.LG | Accurate monitoring of eating behavior is crucial for managing obesity and
eating disorders such as bulimia nervosa. At the same time, existing methods
rely on multiple and/or specialized sensors, greatly harming adherence and
ultimately, the quality and continuity of data. This paper introduces a novel
approach for ... |
2502.06650 | Prototype Contrastive Consistency Learning for Semi-Supervised Medical
Image Segmentation | cs.CV | Medical image segmentation is a crucial task in medical image analysis, but
it can be very challenging especially when there are less labeled data but with
large unlabeled data. Contrastive learning has proven to be effective for
medical image segmentation in semi-supervised learning by constructing
contrastive sampl... |
2502.06652 | Transparent NLP: Using RAG and LLM Alignment for Privacy Q&A | cs.CL | The transparency principle of the General Data Protection Regulation (GDPR)
requires data processing information to be clear, precise, and accessible.
While language models show promise in this context, their probabilistic nature
complicates truthfulness and comprehensibility.
This paper examines state-of-the-art R... |
2502.06653 | In-Context Learning (and Unlearning) of Length Biases | cs.CL | Large language models have demonstrated strong capabilities to learn
in-context, where exemplar input-output pairings are appended to the prompt for
demonstration. However, existing work has demonstrated the ability of models to
learn lexical and label biases in-context, which negatively impacts both
performance and ... |
2502.06655 | Unbiased Evaluation of Large Language Models from a Causal Perspective | cs.AI | Benchmark contamination has become a significant concern in the LLM
evaluation community. Previous Agents-as-an-Evaluator address this issue by
involving agents in the generation of questions. Despite their success, the
biases in Agents-as-an-Evaluator methods remain largely unexplored. In this
paper, we present a th... |
2502.06656 | A Frontier AI Risk Management Framework: Bridging the Gap Between
Current AI Practices and Established Risk Management | cs.AI | The recent development of powerful AI systems has highlighted the need for
robust risk management frameworks in the AI industry. Although companies have
begun to implement safety frameworks, current approaches often lack the
systematic rigor found in other high-risk industries. This paper presents a
comprehensive ris... |
2502.06658 | Generating Samples to Question Trained Models | cs.LG | There is a growing need for investigating how machine learning models
operate. With this work, we aim to understand trained machine learning models
by questioning their data preferences. We propose a mathematical framework that
allows us to probe trained models and identify their preferred samples in
various scenario... |
2502.06659 | Who Taught You That? Tracing Teachers in Model Distillation | cs.CL | Model distillation -- using outputs from a large teacher model to teach a
small student model -- is a practical means of creating efficient models for a
particular task. We ask: Can we identify a students' teacher based on its
outputs? Such "footprints" left by teacher LLMs would be interesting artifacts.
Beyond this... |
2502.06661 | iLOCO: Distribution-Free Inference for Feature Interactions | stat.ML cs.LG | Feature importance measures are widely studied and are essential for
understanding model behavior, guiding feature selection, and enhancing
interpretability. However, many machine learning fitted models involve complex,
higher-order interactions between features. Existing feature importance metrics
fail to capture th... |
2502.06663 | EfficientLLM: Scalable Pruning-Aware Pretraining for
Architecture-Agnostic Edge Language Models | cs.LG | Modern large language models (LLMs) driven by scaling laws, achieve
intelligence emergency in large model sizes. Recently, the increasing concerns
about cloud costs, latency, and privacy make it an urgent requirement to
develop compact edge language models. Distinguished from direct pretraining
that bounded by the sc... |
2502.06664 | Evaluation of Deep Audio Representations for Hearables | cs.SD cs.AI cs.LG | Effectively steering hearable devices requires understanding the acoustic
environment around the user. In the computational analysis of sound scenes,
foundation models have emerged as the state of the art to produce
high-performance, robust, multi-purpose audio representations. We introduce and
release Deep Evaluatio... |
2502.06666 | Automatic Evaluation of Healthcare LLMs Beyond Question-Answering | cs.CL cs.AI | Current Large Language Models (LLMs) benchmarks are often based on open-ended
or close-ended QA evaluations, avoiding the requirement of human labor.
Close-ended measurements evaluate the factuality of responses but lack
expressiveness. Open-ended capture the model's capacity to produce discourse
responses but are ha... |
2502.06669 | Boosting Self-Efficacy and Performance of Large Language Models via
Verbal Efficacy Stimulations | cs.CL cs.AI | Significant improvements have been observed in the zero-shot capabilities of
the Large Language Models (LLMs). Due to their high sensitivity to input,
research has increasingly focused on enhancing LLMs' performance via direct and
simple prompt engineering rather than intricate domain adaptation. Studies
suggest that... |
2502.06673 | Selecting Optimal Sampling Rate for Stable Super-Resolution | math.NA cs.IT cs.NA math.IT | We investigate the recovery of nodes and amplitudes from noisy frequency
samples in spike train signals, also known as the super-resolution (SR)
problem. When the node separation falls below the Rayleigh limit, the problem
becomes ill-conditioned. Admissible sampling rates, or decimation parameters,
improve the condi... |
2502.06674 | RAILS: Risk-Aware Iterated Local Search for Joint SLA Decomposition and
Service Provider Management in Multi-Domain Networks | cs.NI cs.LG | The emergence of the fifth generation (5G) technology has transformed mobile
networks into multi-service environments, necessitating efficient network
slicing to meet diverse Service Level Agreements (SLAs). SLA decomposition
across multiple network domains, each potentially managed by different service
providers, po... |
2502.06676 | Discovery of skill switching criteria for learning agile quadruped
locomotion | cs.RO | This paper develops a hierarchical learning and optimization framework that
can learn and achieve well-coordinated multi-skill locomotion. The learned
multi-skill policy can switch between skills automatically and naturally in
tracking arbitrarily positioned goals and recover from failures promptly. The
proposed fram... |
2502.06678 | Quantile Multi-Armed Bandits with 1-bit Feedback | stat.ML cs.IT cs.LG math.IT | In this paper, we study a variant of best-arm identification involving
elements of risk sensitivity and communication constraints. Specifically, the
goal of the learner is to identify the arm with the highest quantile reward,
while the communication from an agent (who observes rewards) and the learner
(who chooses ac... |
2502.06681 | CHIRLA: Comprehensive High-resolution Identification and
Re-identification for Large-scale Analysis | cs.CV cs.AI cs.LG | Person re-identification (Re-ID) is a key challenge in computer vision,
requiring the matching of individuals across different cameras, locations, and
time periods. While most research focuses on short-term scenarios with minimal
appearance changes, real-world applications demand robust Re-ID systems capable
of handl... |
2502.06682 | Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint
in a Driving Scene | cs.CV | Self-driving cars relying solely on ego-centric perception face limitations
in sensing, often failing to detect occluded, faraway objects. Collaborative
autonomous driving (CAV) seems like a promising direction, but collecting data
for development is non-trivial. It requires placing multiple sensor-equipped
agents in... |
2502.06683 | Solving Optimal Power Flow on a Data-Budget: Feature Selection on Smart
Meter Data | eess.SY cs.SY | How much data is needed to optimally schedule distributed energy resources
(DERs)? Does the distribution system operator (DSO) have to precisely know load
demands and solar injections at each bus of the feeder to solve an optimal
power flow (OPF)? This work exploits redundancies in OPF's structure and data
to avoid c... |
2502.06684 | EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks | cs.LG cs.AI | Recent foundational models for tabular data, such as TabPFN, have
demonstrated remarkable effectiveness in adapting to new tasks through
in-context learning. However, these models overlook a crucial equivariance
property: the arbitrary ordering of target dimensions should not influence
model predictions. In this stud... |
2502.06685 | No Trick, No Treat: Pursuits and Challenges Towards Simulation-free
Training of Neural Samplers | cs.LG stat.ML | We consider the sampling problem, where the aim is to draw samples from a
distribution whose density is known only up to a normalization constant. Recent
breakthroughs in generative modeling to approximate a high-dimensional data
distribution have sparked significant interest in developing neural
network-based method... |
2502.06689 | Neumann eigenmaps for landmark embedding | math.ST cs.LG cs.NA math.NA stat.ML stat.TH | We present Neumann eigenmaps (NeuMaps), a novel approach for enhancing the
standard diffusion map embedding using landmarks, i.e distinguished samples
within the dataset. By interpreting these landmarks as a subgraph of the larger
data graph, NeuMaps are obtained via the eigendecomposition of a renormalized
Neumann L... |
2502.06692 | Multi-label Scandinavian Language Identification (SLIDE) | cs.CL cs.AI | Identifying closely related languages at sentence level is difficult, in
particular because it is often impossible to assign a sentence to a single
language. In this paper, we focus on multi-label sentence-level Scandinavian
language identification (LID) for Danish, Norwegian Bokm\r{a}l, Norwegian
Nynorsk, and Swedis... |
2502.06693 | Recent Advances, Applications and Open Challenges in Machine Learning
for Health: Reflections from Research Roundtables at ML4H 2024 Symposium | cs.LG cs.AI cs.CY | The fourth Machine Learning for Health (ML4H) symposium was held in person on
December 15th and 16th, 2024, in the traditional, ancestral, and unceded
territories of the Musqueam, Squamish, and Tsleil-Waututh Nations in Vancouver,
British Columbia, Canada. The symposium included research roundtable sessions
to foster... |
2502.06695 | FairDropout: Using Example-Tied Dropout to Enhance Generalization of
Minority Groups | cs.LG | Deep learning models frequently exploit spurious features in training data to
achieve low training error, often resulting in poor generalization when faced
with shifted testing distributions. To address this issue, various methods from
imbalanced learning, representation learning, and classifier recalibration have
be... |
2502.06698 | Heisenberg-limited calibration of entangling gates with robust phase
estimation | quant-ph cs.SY eess.SY | The calibration of high-quality two-qubit entangling gates is an essential
component in engineering large-scale, fault-tolerant quantum computers.
However, many standard calibration techniques are based on randomized circuits
that are only quadratically sensitive to calibration errors. As a result, these
approaches a... |
2502.06703 | Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time
Scaling | cs.CL | Test-Time Scaling (TTS) is an important method for improving the performance
of Large Language Models (LLMs) by using additional computation during the
inference phase. However, current studies do not systematically analyze how
policy models, Process Reward Models (PRMs), and problem difficulty influence
TTS. This la... |
2502.06705 | RSAttAE: An Information-Aware Attention-based Autoencoder Recommender
System | cs.LG cs.IR | Recommender systems play a crucial role in modern life, including information
retrieval, the pharmaceutical industry, retail, and entertainment. The
entertainment sector, in particular, attracts significant attention and
generates substantial profits. This work proposes a new method for predicting
unknown user-movie ... |
2502.06707 | FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock
Movement Prediction | cs.CE | Recently, combining stock features with inter-stock correlations has become a
common and effective approach for stock movement prediction. However, financial
data presents significant challenges due to its low signal-to-noise ratio and
the dynamic complexity of the market, which give rise to two key limitations in
ex... |
2502.06708 | TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic
Videos using Surgical Timeline Segmentation | cs.CV | Indexing endoscopic surgical videos is vital in surgical data science,
forming the basis for systematic retrospective analysis and clinical
performance evaluation. Despite its significance, current video analytics rely
on manual indexing, a time-consuming process. Advances in computer vision,
particularly deep learni... |
2502.06710 | Learning Musical Representations for Music Performance Question
Answering | cs.CV cs.MM cs.SD eess.AS | Music performances are representative scenarios for audio-visual modeling.
Unlike common scenarios with sparse audio, music performances continuously
involve dense audio signals throughout. While existing multimodal learning
methods on the audio-video QA demonstrate impressive capabilities in general
scenarios, they ... |
2502.06715 | HoneyComb: A Parallel Worst-Case Optimal Join on Multicores | cs.DB | To achieve true scalability on massive datasets, a modern query engine needs
to be able to take advantage of large, shared-memory, multicore systems. Binary
joins are conceptually easy to parallelize on a multicore system; however,
several applications require a different approach to query evaluation, using a
Worst-C... |
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