id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
|---|---|---|---|
2502.04143 | A data-driven two-microphone method for in-situ sound absorption
measurements | cs.SD cs.LG eess.AS | This work presents a data-driven approach to estimating the sound absorption
coefficient of an infinite porous slab using a neural network and a
two-microphone measurement on a finite porous sample. A 1D-convolutional
network predicts the sound absorption coefficient from the complex-valued
transfer function between ... |
2502.04144 | HD-EPIC: A Highly-Detailed Egocentric Video Dataset | cs.CV | We present a validation dataset of newly-collected kitchen-based egocentric
videos, manually annotated with highly detailed and interconnected ground-truth
labels covering: recipe steps, fine-grained actions, ingredients with
nutritional values, moving objects, and audio annotations. Importantly, all
annotations are ... |
2502.04153 | UltraIF: Advancing Instruction Following from the Wild | cs.CL cs.AI | Instruction-following made modern large language models (LLMs) helpful
assistants. However, the key to taming LLMs on complex instructions remains
mysterious, for that there are huge gaps between models trained by open-source
community and those trained by leading companies. To bridge the gap, we propose
a simple and... |
2502.04161 | YOLOv4: A Breakthrough in Real-Time Object Detection | cs.CV | YOLOv4 achieved the best performance on the COCO dataset by combining
advanced techniques for regression (bounding box positioning) and
classification (object class identification) using the Darknet framework. To
enhance accuracy and adaptability, it employs Cross mini-Batch Normalization,
Cross-Stage-Partial-connect... |
2502.04162 | A Pseudo Markov-Chain Model and Time-Elapsed Measures of Mobility from
Collective Data | stat.AP cs.LG cs.SI stat.ML | In this paper we develop a pseudo Markov-chain model to understand
time-elapsed flows, over multiple intervals, from time and space aggregated
collective inter-location trip data, given as a time-series. Building on the
model, we develop measures of mobility that parallel those known for individual
mobility data, suc... |
2502.04163 | Multi-task Online Learning for Probabilistic Load Forecasting | stat.ML cs.LG | Load forecasting is essential for the efficient, reliable, and cost-effective
management of power systems. Load forecasting performance can be improved by
learning the similarities among multiple entities (e.g., regions, buildings).
Techniques based on multi-task learning obtain predictions by leveraging
consumption ... |
2502.04164 | Efficient Distributed Optimization under Heavy-Tailed Noise | cs.LG | Distributed optimization has become the default training paradigm in modern
machine learning due to the growing scale of models and datasets. To mitigate
communication overhead, local updates are often applied before global
aggregation, resulting in a nested optimization approach with inner and outer
steps. However, ... |
2502.04167 | Making Sense of Touch: Unsupervised Shapelet Learning in Bag-of-words
Sense | cs.LG cs.RO | This paper introduces NN-STNE, a neural network using t-distributed
stochastic neighbor embedding (t-SNE) as a hidden layer to reduce input
dimensions by mapping long time-series data into shapelet membership
probabilities. A Gaussian kernel-based mean square error preserves local data
structure, while K-means initia... |
2502.04170 | From Configuration-Space Clearance to Feature-Space Margin: Sample
Complexity in Learning-Based Collision Detection | cs.RO | Motion planning is a central challenge in robotics, with learning-based
approaches gaining significant attention in recent years. Our work focuses on a
specific aspect of these approaches: using machine-learning techniques,
particularly Support Vector Machines (SVM), to evaluate whether robot
configurations are colli... |
2502.04172 | Archetypal Analysis for Binary Data | cs.LG cs.AI stat.ML | Archetypal analysis (AA) is a matrix decomposition method that identifies
distinct patterns using convex combinations of the data points denoted
archetypes with each data point in turn reconstructed as convex combinations of
the archetypes. AA thereby forms a polytope representing trade-offs of the
distinct aspects i... |
2502.04173 | Lexical Substitution is not Synonym Substitution: On the Importance of
Producing Contextually Relevant Word Substitutes | cs.CL | Lexical Substitution is the task of replacing a single word in a sentence
with a similar one. This should ideally be one that is not necessarily only
synonymous, but also fits well into the surrounding context of the target word,
while preserving the sentence's grammatical structure. Recent advances in
Lexical Substi... |
2502.04174 | Dense Fixed-Wing Swarming using Receding-Horizon NMPC | cs.RO cs.SY eess.SY | In this paper, we present an approach for controlling a team of agile
fixed-wing aerial vehicles in close proximity to one another. Our approach
relies on receding-horizon nonlinear model predictive control (NMPC) to plan
maneuvers across an expanded flight envelope to enable inter-agent collision
avoidance. To facil... |
2502.04176 | MRAMG-Bench: A BeyondText Benchmark for Multimodal Retrieval-Augmented
Multimodal Generation | cs.LG cs.IR | Recent advancements in Retrieval-Augmented Generation (RAG) have shown
remarkable performance in enhancing response accuracy and relevance by
integrating external knowledge into generative models. However, existing RAG
methods primarily focus on providing text-only answers, even in multimodal
retrieval-augmented gene... |
2502.04180 | Multi-agent Architecture Search via Agentic Supernet | cs.LG cs.CL cs.MA | Large Language Model (LLM)-empowered multi-agent systems extend the cognitive
boundaries of individual agents through disciplined collaboration and
interaction, while constructing these systems often requires labor-intensive
manual designs. Despite the availability of methods to automate the design of
agentic workflo... |
2502.04190 | Compliant Beaded-String Jamming For Variable Stiffness Anthropomorphic
Fingers | cs.RO | Achieving human-like dexterity in robotic grippers remains an open challenge,
particularly in ensuring robust manipulation in uncertain environments. Soft
robotic hands try to address this by leveraging passive compliance, a
characteristic that is crucial to the adaptability of the human hand, to
achieve more robust ... |
2502.04192 | PixFoundation: Are We Heading in the Right Direction with Pixel-level
Vision Foundation Models? | cs.CV | Multiple works have emerged to push the boundaries on multi-modal large
language models (MLLMs) towards pixel-level understanding. Such approaches have
shown strong performance on benchmarks for referring expression segmentation
and grounded conversation generation. The current trend in pixel-level MLLMs is
to train ... |
2502.04194 | The Best Instruction-Tuning Data are Those That Fit | cs.CL cs.AI cs.LG | High-quality supervised fine-tuning (SFT) data are crucial for eliciting
strong capabilities from pretrained large language models (LLMs). Typically,
instructions are paired with multiple responses sampled from other LLMs, which
are often out of the distribution of the target model to be fine-tuned. This,
at scale, c... |
2502.04195 | Integration of Prior Knowledge into Direct Learning for Safe Control of
Linear Systems | eess.SY cs.SY | This paper integrates prior knowledge into direct learning of safe
controllers for linear uncertain systems under disturbances. To this end, we
characterize the set of all closed-loop systems that can be explained by
available prior knowledge of the system model and the disturbances. We leverage
matrix zonotopes for ... |
2502.04199 | Expanding Training Data for Endoscopic Phenotyping of Eosinophilic
Esophagitis | eess.IV cs.CV | Eosinophilic esophagitis (EoE) is a chronic esophageal disorder marked by
eosinophil-dominated inflammation. Diagnosing EoE usually involves endoscopic
inspection of the esophageal mucosa and obtaining esophageal biopsies for
histologic confirmation. Recent advances have seen AI-assisted endoscopic
imaging, guided by... |
2502.04201 | Safeguarding connected autonomous vehicle communication: Protocols,
intra- and inter-vehicular attacks and defenses | cs.CR cs.CV cs.NI | The advancements in autonomous driving technology, coupled with the growing
interest from automotive manufacturers and tech companies, suggest a rising
adoption of Connected Autonomous Vehicles (CAVs) in the near future. Despite
some evidence of higher accident rates in AVs, these incidents tend to result
in less sev... |
2502.04204 | "Short-length" Adversarial Training Helps LLMs Defend "Long-length"
Jailbreak Attacks: Theoretical and Empirical Evidence | cs.LG cs.CR stat.ML | Jailbreak attacks against large language models (LLMs) aim to induce harmful
behaviors in LLMs through carefully crafted adversarial prompts. To mitigate
attacks, one way is to perform adversarial training (AT)-based alignment, i.e.,
training LLMs on some of the most adversarial prompts to help them learn how to
beha... |
2502.04206 | Ensuring Reliability via Hyperparameter Selection: Review and Advances | cs.LG cs.IT math.IT | Hyperparameter selection is a critical step in the deployment of artificial
intelligence (AI) models, particularly in the current era of foundational,
pre-trained, models. By framing hyperparameter selection as a multiple
hypothesis testing problem, recent research has shown that it is possible to
provide statistical... |
2502.04207 | Enhanced Feature-based Image Stitching for Endoscopic Videos in
Pediatric Eosinophilic Esophagitis | cs.CV | Video endoscopy represents a major advance in the investigation of
gastrointestinal diseases. Reviewing endoscopy videos often involves frequent
adjustments and reorientations to piece together a complete view, which can be
both time-consuming and prone to errors. Image stitching techniques address
this issue by prov... |
2502.04210 | Algorithmic causal structure emerging through compression | cs.LG cs.AI cs.CC cs.IT math.IT | We explore the relationship between causality, symmetry, and compression. We
build on and generalize the known connection between learning and compression
to a setting where causal models are not identifiable. We propose a framework
where causality emerges as a consequence of compressing data across multiple
environm... |
2502.04218 | Sports and Women's Sports: Gender Bias in Text Generation with Olympic
Data | cs.CL | Large Language Models (LLMs) have been shown to be biased in prior work, as
they generate text that is in line with stereotypical views of the world or
that is not representative of the viewpoints and values of historically
marginalized demographic groups. In this work, we propose using data from
parallel men's and w... |
2502.04219 | NLP-Based .NET CLR Event Logs Analyzer | cs.SE cs.AI | In this paper, we present a tool for analyzing .NET CLR event logs based on a
novel method inspired by Natural Language Processing (NLP) approach. Our
research addresses the growing need for effective monitoring and optimization
of software systems through detailed event log analysis. We utilize a
BERT-based architec... |
2502.04223 | \'Eclair -- Extracting Content and Layout with Integrated Reading Order
for Documents | cs.CV | Optical Character Recognition (OCR) technology is widely used to extract text
from images of documents, facilitating efficient digitization and data
retrieval. However, merely extracting text is insufficient when dealing with
complex documents. Fully comprehending such documents requires an understanding
of their str... |
2502.04226 | Keep It Light! Simplifying Image Clustering Via Text-Free Adapters | cs.CV cs.LG cs.NE stat.CO stat.ML | Many competitive clustering pipelines have a multi-modal design, leveraging
large language models (LLMs) or other text encoders, and text-image pairs,
which are often unavailable in real-world downstream applications.
Additionally, such frameworks are generally complicated to train and require
substantial computation... |
2502.04229 | Dark Distillation: Backdooring Distilled Datasets without Accessing Raw
Data | cs.CR cs.AI | Dataset distillation (DD) enhances training efficiency and reduces bandwidth
by condensing large datasets into smaller synthetic ones. It enables models to
achieve performance comparable to those trained on the raw full dataset and has
become a widely adopted method for data sharing. However, security concerns in
DD ... |
2502.04230 | XAttnMark: Learning Robust Audio Watermarking with Cross-Attention | cs.SD cs.AI cs.CR cs.LG eess.AS | The rapid proliferation of generative audio synthesis and editing
technologies has raised significant concerns about copyright infringement, data
provenance, and the spread of misinformation through deepfake audio.
Watermarking offers a proactive solution by embedding imperceptible,
identifiable, and traceable marks ... |
2502.04233 | Graph machine learning for flight delay prediction due to holding
manouver | cs.LG cs.SI | Flight delays due to holding maneuvers are a critical and costly phenomenon
in aviation, driven by the need to manage air traffic congestion and ensure
safety. Holding maneuvers occur when aircraft are instructed to circle in
designated airspace, often due to factors such as airport congestion, adverse
weather, or ai... |
2502.04234 | A Classification System Approach in Predicting Chinese Censorship | cs.CL cs.LG cs.SI | This paper is dedicated to using a classifier to predict whether a Weibo post
would be censored under the Chinese internet. Through randomized sampling from
\citeauthor{Fu2021} and Chinese tokenizing strategies, we constructed a cleaned
Chinese phrase dataset with binary censorship markings. Utilizing various
probabi... |
2502.04235 | MAGA: MAssive Genre-Audience Reformulation to Pretraining Corpus
Expansion | cs.CL | Despite the remarkable capabilities of large language models across various
tasks, their continued scaling faces a critical challenge: the scarcity of
high-quality pretraining data. While model architectures continue to evolve,
the natural language data struggles to scale up. To tackle this bottleneck, we
propose \te... |
2502.04240 | Memory-dependent abstractions of stochastic systems through the lens of
transfer operators | eess.SY cs.SY | With the increasing ubiquity of safety-critical autonomous systems operating
in uncertain environments, there is a need for mathematical methods for formal
verification of stochastic models. Towards formally verifying properties of
stochastic systems, methods based on discrete, finite Markov approximations --
abstrac... |
2502.04242 | A Theoretical Framework for Data Efficient Multi-Source Transfer
Learning Based on Cram\'er-Rao Bound | cs.LG cs.AI | Multi-source transfer learning provides an effective solution to data
scarcity in real-world supervised learning scenarios by leveraging multiple
source tasks. In this field, existing works typically use all available samples
from sources in training, which constrains their training efficiency and may
lead to subopti... |
2502.04244 | An object detection approach for lane change and overtake detection from
motion profiles | cs.CV | In the application domain of fleet management and driver monitoring, it is
very challenging to obtain relevant driving events and activities from dashcam
footage while minimizing the amount of information stored and analyzed. In this
paper, we address the identification of overtake and lane change maneuvers with
a no... |
2502.04245 | TriNER: A Series of Named Entity Recognition Models For Hindi, Bengali &
Marathi | cs.CL cs.AI cs.LG | India's rich cultural and linguistic diversity poses various challenges in
the domain of Natural Language Processing (NLP), particularly in Named Entity
Recognition (NER). NER is a NLP task that aims to identify and classify tokens
into different entity groups like Person, Location, Organization, Number, etc.
This ma... |
2502.04247 | Student-t processes as infinite-width limits of posterior Bayesian
neural networks | stat.ML cs.LG math.PR | The asymptotic properties of Bayesian Neural Networks (BNNs) have been
extensively studied, particularly regarding their approximations by Gaussian
processes in the infinite-width limit. We extend these results by showing that
posterior BNNs can be approximated by Student-t processes, which offer greater
flexibility ... |
2502.04248 | Adapting to Evolving Adversaries with Regularized Continual Robust
Training | cs.LG | Robust training methods typically defend against specific attack types, such
as Lp attacks with fixed budgets, and rarely account for the fact that
defenders may encounter new attacks over time. A natural solution is to adapt
the defended model to new adversaries as they arise via fine-tuning, a method
which we call ... |
2502.04249 | Free Energy Risk Metrics for Systemically Safe AI: Gatekeeping
Multi-Agent Study | cs.AI cs.LG cs.MA physics.data-an stat.ML | We investigate the Free Energy Principle as a foundation for measuring risk
in agentic and multi-agent systems. From these principles we introduce a
Cumulative Risk Exposure metric that is flexible to differing contexts and
needs. We contrast this to other popular theories for safe AI that hinge on
massive amounts of... |
2502.04251 | Combining Language and App UI Analysis for the Automated Assessment of
Bug Reproduction Steps | cs.SE cs.LG | Bug reports are essential for developers to confirm software problems,
investigate their causes, and validate fixes. Unfortunately, reports often miss
important information or are written unclearly, which can cause delays,
increased issue resolution effort, or even the inability to solve issues. One
of the most commo... |
2502.04256 | Work in Progress: AI-Powered Engineering-Bridging Theory and Practice | eess.SY cs.SE cs.SY | This paper explores how generative AI can help automate and improve key steps
in systems engineering. It examines AI's ability to analyze system requirements
based on INCOSE's "good requirement" criteria, identifying well-formed and
poorly written requirements. The AI does not just classify requirements but
also expl... |
2502.04260 | Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge
Retention | cs.LG | Machine Unlearning allows participants to remove their data from a trained
machine learning model in order to preserve their privacy, and security.
However, the machine unlearning literature for generative models is rather
limited. The literature for image-to-image generative model (I2I model)
considers minimizing th... |
2502.04262 | Efficient Randomized Experiments Using Foundation Models | cs.LG stat.ME stat.ML | Randomized experiments are the preferred approach for evaluating the effects
of interventions, but they are costly and often yield estimates with
substantial uncertainty. On the other hand, in silico experiments leveraging
foundation models offer a cost-effective alternative that can potentially
attain higher statist... |
2502.04263 | Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via
Modality Inversion | cs.CV cs.AI cs.LG | Pre-trained multi-modal Vision-Language Models like CLIP are widely used
off-the-shelf for a variety of applications. In this paper, we show that the
common practice of individually exploiting the text or image encoders of these
powerful multi-modal models is highly suboptimal for intra-modal tasks like
image-to-imag... |
2502.04266 | Digital Gatekeeping: An Audit of Search Engine Results shows tailoring
of queries on the Israel-Palestine Conflict | cs.CY cs.IR | Search engines, often viewed as reliable gateways to information, tailor
search results using customization algorithms based on user preferences,
location, and more. While this can be useful for routine queries, it raises
concerns when the topics are sensitive or contentious, possibly limiting
exposure to diverse vie... |
2502.04268 | Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection
with Spatial Layout Among Instances | cs.CV cs.AI | With the rapidly increasing demand for oriented object detection (OOD),
recent research involving weakly-supervised detectors for learning OOD from
point annotations has gained great attention. In this paper, we rethink this
challenging task setting with the layout among instances and present
Point2RBox-v2. At the co... |
2502.04269 | How does a Multilingual LM Handle Multiple Languages? | cs.CL cs.AI | Multilingual language models have significantly advanced due to rapid
progress in natural language processing. Models like BLOOM 1.7B, trained on
diverse multilingual datasets, aim to bridge linguistic gaps. However, their
effectiveness in capturing linguistic knowledge, particularly for low-resource
languages, remai... |
2502.04270 | PILAF: Optimal Human Preference Sampling for Reward Modeling | cs.LG stat.ML | As large language models increasingly drive real-world applications, aligning
them with human values becomes paramount. Reinforcement Learning from Human
Feedback (RLHF) has emerged as a key technique, translating preference data
into reward models when oracle human values remain inaccessible. In practice,
RLHF mostl... |
2502.04271 | Variational decision diagrams for quantum-inspired machine learning
applications | quant-ph cs.LG | Decision diagrams (DDs) have emerged as an efficient tool for simulating
quantum circuits due to their capacity to exploit data redundancies in quantum
states and quantum operations, enabling the efficient computation of
probability amplitudes. However, their application in quantum machine learning
(QML) has remained... |
2502.04273 | Electrical Impedance Tomography for Anisotropic Media: a Machine
Learning Approach to Classify Inclusions | math.NA cs.LG cs.NA | We consider the problem in Electrical Impedance Tomography (EIT) of
identifying one or multiple inclusions in a background-conducting body
$\Omega\subset\mathbb{R}^2$, from the knowledge of a finite number of
electrostatic measurements taken on its boundary $\partial\Omega$ and modelled
by the Dirichlet-to-Neumann (D... |
2502.04274 | Orthogonal Representation Learning for Estimating Causal Quantities | cs.LG | Representation learning is widely used for estimating causal quantities
(e.g., the conditional average treatment effect) from observational data. While
existing representation learning methods have the benefit of allowing for
end-to-end learning, they do not have favorable theoretical properties of
Neyman-orthogonal ... |
2502.04276 | Gaussian Process Regression for Inverse Problems in Linear PDEs | stat.ML cs.LG math.AC | This paper introduces a computationally efficient algorithm in system theory
for solving inverse problems governed by linear partial differential equations
(PDEs). We model solutions of linear PDEs using Gaussian processes with priors
defined based on advanced commutative algebra and algebraic analysis. The
implement... |
2502.04281 | DECAF: Learning to be Fair in Multi-agent Resource Allocation | cs.LG cs.CY cs.MA | A wide variety of resource allocation problems operate under resource
constraints that are managed by a central arbitrator, with agents who evaluate
and communicate preferences over these resources. We formulate this broad class
of problems as Distributed Evaluation, Centralized Allocation (DECA) problems
and propose... |
2502.04286 | A Methodology for Studying Linguistic and Cultural Change in China,
1900-1950 | cs.CL | This paper presents a quantitative approach to studying linguistic and
cultural change in China during the first half of the twentieth century, a
period that remains understudied in computational humanities research. The
dramatic changes in Chinese language and culture during this time call for
greater reflection on ... |
2502.04288 | Leveraging Geolocation in Clinical Records to Improve Alzheimer's
Disease Diagnosis Using DMV Framework | cs.LG | Alzheimer's Disease (AD) early detection is critical for enabling timely
intervention and improving patient outcomes. This paper presents a DMV
framework using Llama3-70B and GPT-4o as embedding models to analyze clinical
notes and predict a continuous risk score associated with early AD onset.
Framing the task as a ... |
2502.04289 | Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials
Synthesis Planning | physics.chem-ph cs.LG | Retrosynthesis strategically plans the synthesis of a chemical target
compound from simpler, readily available precursor compounds. This process is
critical for synthesizing novel inorganic materials, yet traditional methods in
inorganic chemistry continue to rely on trial-and-error experimentation.
Emerging machine-... |
2502.04290 | Every Call is Precious: Global Optimization of Black-Box Functions with
Unknown Lipschitz Constants | cs.LG cs.AI cs.SY eess.SY math.OC stat.ML | Optimizing expensive, non-convex, black-box Lipschitz continuous functions
presents significant challenges, particularly when the Lipschitz constant of
the underlying function is unknown. Such problems often demand numerous
function evaluations to approximate the global optimum, which can be
prohibitive in terms of t... |
2502.04293 | GCE-Pose: Global Context Enhancement for Category-level Object Pose
Estimation | cs.CV | A key challenge in model-free category-level pose estimation is the
extraction of contextual object features that generalize across varying
instances within a specific category. Recent approaches leverage foundational
features to capture semantic and geometry cues from data. However, these
approaches fail under parti... |
2502.04294 | Prediction-Powered E-Values | stat.ML cs.LG stat.ME | Quality statistical inference requires a sufficient amount of data, which can
be missing or hard to obtain. To this end, prediction-powered inference has
risen as a promising methodology, but existing approaches are largely limited
to Z-estimation problems such as inference of means and quantiles. In this
paper, we a... |
2502.04295 | Beyond Prompt Content: Enhancing LLM Performance via Content-Format
Integrated Prompt Optimization | cs.CL | Large Language Models (LLMs) have shown significant capability across various
tasks, with their real-world effectiveness often driven by prompt design. While
recent research has focused on optimizing prompt content, the role of prompt
formatting, a critical but often overlooked dimension, has received limited
systema... |
2502.04296 | Learning Real-World Action-Video Dynamics with Heterogeneous Masked
Autoregression | cs.RO cs.CV cs.LG | We propose Heterogeneous Masked Autoregression (HMA) for modeling
action-video dynamics to generate high-quality data and evaluation in scaling
robot learning. Building interactive video world models and policies for
robotics is difficult due to the challenge of handling diverse settings while
maintaining computation... |
2502.04297 | Statistical guarantees for continuous-time policy evaluation: blessing
of ellipticity and new tradeoffs | cs.LG math.OC math.PR math.ST stat.TH | We study the estimation of the value function for continuous-time Markov
diffusion processes using a single, discretely observed ergodic trajectory. Our
work provides non-asymptotic statistical guarantees for the least-squares
temporal-difference (LSTD) method, with performance measured in the first-order
Sobolev nor... |
2502.04299 | MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video
Generation | cs.CV | This paper presents a method that allows users to design cinematic video
shots in the context of image-to-video generation. Shot design, a critical
aspect of filmmaking, involves meticulously planning both camera movements and
object motions in a scene. However, enabling intuitive shot design in modern
image-to-video... |
2502.04302 | Strong Equivalence in Answer Set Programming with Constraints | cs.AI cs.LO | We investigate the concept of strong equivalence within the extended
framework of Answer Set Programming with constraints. Two groups of rules are
considered strongly equivalent if, informally speaking, they have the same
meaning in any context. We demonstrate that, under certain assumptions, strong
equivalence betwe... |
2502.04306 | ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference
Optimization | cs.CL | Recent research has leveraged large language model multi-agent systems for
complex problem-solving while trying to reduce the manual effort required to
build them, driving the development of automated agent workflow optimization
methods. However, existing methods remain inflexible due to representational
limitations,... |
2502.04307 | DexterityGen: Foundation Controller for Unprecedented Dexterity | cs.RO cs.AI cs.LG cs.SY eess.SY | Teaching robots dexterous manipulation skills, such as tool use, presents a
significant challenge. Current approaches can be broadly categorized into two
strategies: human teleoperation (for imitation learning) and sim-to-real
reinforcement learning. The first approach is difficult as it is hard for
humans to produce... |
2502.04308 | HOG-Diff: Higher-Order Guided Diffusion for Graph Generation | cs.LG cs.AI cs.SI physics.soc-ph | Graph generation is a critical yet challenging task as empirical analyses
require a deep understanding of complex, non-Euclidean structures. Although
diffusion models have recently made significant achievements in graph
generation, these models typically adapt from the frameworks designed for image
generation, making... |
2502.04309 | Targeted Learning for Data Fairness | cs.LG stat.ML | Data and algorithms have the potential to produce and perpetuate
discrimination and disparate treatment. As such, significant effort has been
invested in developing approaches to defining, detecting, and eliminating
unfair outcomes in algorithms. In this paper, we focus on performing
statistical inference for fairnes... |
2502.04310 | Finding Pegasus: Enhancing Unsupervised Anomaly Detection in
High-Dimensional Data using a Manifold-Based Approach | cs.LG astro-ph.CO | Unsupervised machine learning methods are well suited to searching for
anomalies at scale but can struggle with the high-dimensional representation of
many modern datasets, hence dimensionality reduction (DR) is often performed
first. In this paper we analyse unsupervised anomaly detection (AD) from the
perspective o... |
2502.04312 | Consistency of augmentation graph and network approximability in
contrastive learning | cs.LG math.AP math.SP | Contrastive learning leverages data augmentation to develop feature
representation without relying on large labeled datasets. However, despite its
empirical success, the theoretical foundations of contrastive learning remain
incomplete, with many essential guarantees left unaddressed, particularly the
realizability a... |
2502.04313 | Great Models Think Alike and this Undermines AI Oversight | cs.LG cs.AI cs.CL | As Language Model (LM) capabilities advance, evaluating and supervising them
at scale is getting harder for humans. There is hope that other language models
can automate both these tasks, which we refer to as "AI Oversight". We study
how model similarity affects both aspects of AI oversight by proposing a
probabilist... |
2502.04314 | BOUQuET: dataset, Benchmark and Open initiative for Universal Quality
Evaluation in Translation | cs.CL | This paper presents BOUQuET, a multicentric and multi-register/domain dataset
and benchmark, and its broader collaborative extension initiative. This dataset
is handcrafted in non-English languages first, each of these source languages
being represented among the 23 languages commonly used by half of the world's
popu... |
2502.04315 | ChameleonLLM: Batch-Aware Dynamic Low-Rank Adaptation via Inference-Time
Clusters | cs.CL cs.AI cs.LG | Recent advances in large language models (LLMs) have shown remarkable
performance across diverse tasks. However, these models are typically deployed
with fixed weights, which limits their ability to adapt dynamically to the
variability inherent in real-world data during inference. This paper introduces
ChameleonLLM, ... |
2502.04317 | Factorized Implicit Global Convolution for Automotive Computational
Fluid Dynamics Prediction | cs.CV | Computational Fluid Dynamics (CFD) is crucial for automotive design,
requiring the analysis of large 3D point clouds to study how vehicle geometry
affects pressure fields and drag forces. However, existing deep learning
approaches for CFD struggle with the computational complexity of processing
high-resolution 3D dat... |
2502.04318 | sshELF: Single-Shot Hierarchical Extrapolation of Latent Features for 3D
Reconstruction from Sparse-Views | cs.CV | Reconstructing unbounded outdoor scenes from sparse outward-facing views
poses significant challenges due to minimal view overlap. Previous methods
often lack cross-scene understanding and their primitive-centric formulations
overload local features to compensate for missing global context, resulting in
blurriness in... |
2502.04320 | ConceptAttention: Diffusion Transformers Learn Highly Interpretable
Features | cs.CV cs.LG | Do the rich representations of multi-modal diffusion transformers (DiTs)
exhibit unique properties that enhance their interpretability? We introduce
ConceptAttention, a novel method that leverages the expressive power of DiT
attention layers to generate high-quality saliency maps that precisely locate
textual concept... |
2502.04321 | Variation of sentence length across time and genre | cs.CL | The goal of this paper is threefold: i) to present some practical aspects of
using full-text version of Corpus of Historical American English (COHA), the
largest diachronic multi-genre corpus of the English language, in the
investigation of a linguistic trend of change; ii) to test a widely held
assumption that sente... |
2502.04322 | Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple
Interactions | cs.LG cs.AI cs.CL cs.CY | Despite extensive safety alignment efforts, large language models (LLMs)
remain vulnerable to jailbreak attacks that elicit harmful behavior. While
existing studies predominantly focus on attack methods that require technical
expertise, two critical questions remain underexplored: (1) Are jailbroken
responses truly u... |
2502.04323 | The Uniformly Rotated Mondrian Kernel | cs.LG math.PR | First proposed by Rahimi and Recht, random features are used to decrease the
computational cost of kernel machines in large-scale problems. The Mondrian
kernel is one such example of a fast random feature approximation of the
Laplace kernel, generated by a computationally efficient hierarchical random
partition of th... |
2502.04324 | Can Grammarly and ChatGPT accelerate language change? AI-powered
technologies and their impact on the English language: wordiness vs.
conciseness | cs.CL cs.CY | The proliferation of NLP-powered language technologies, AI-based natural
language generation models, and English as a mainstream means of communication
among both native and non-native speakers make the output of AI-powered tools
especially intriguing to linguists. This paper investigates how Grammarly and
ChatGPT af... |
2502.04326 | WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal
LLMs | cs.CV cs.AI | In this paper, we introduce WorldSense, the first benchmark to assess the
multi-modal video understanding, that simultaneously encompasses visual, audio,
and text inputs. In contrast to existing benchmarks, our WorldSense has several
features: (i) collaboration of omni-modality, we design the evaluation tasks to
feat... |
2502.04327 | Value-Based Deep RL Scales Predictably | cs.LG | Scaling data and compute is critical to the success of machine learning.
However, scaling demands predictability: we want methods to not only perform
well with more compute or data, but also have their performance be predictable
from small-scale runs, without running the large-scale experiment. In this
paper, we show... |
2502.04328 | Ola: Pushing the Frontiers of Omni-Modal Language Model with Progressive
Modality Alignment | cs.CV cs.CL cs.MM cs.SD eess.AS eess.IV | Recent advances in large language models, particularly following GPT-4o, have
sparked increasing interest in developing omni-modal models capable of
understanding more modalities. While some open-source alternatives have
emerged, there is still a notable lag behind specialized single-modality models
in performance. I... |
2502.04329 | SMART: Advancing Scalable Map Priors for Driving Topology Reasoning | cs.CV cs.RO | Topology reasoning is crucial for autonomous driving as it enables
comprehensive understanding of connectivity and relationships between lanes and
traffic elements. While recent approaches have shown success in perceiving
driving topology using vehicle-mounted sensors, their scalability is hindered
by the reliance on... |
2502.04339 | Analysis of Diffusion Models for Manifold Data | math.ST cond-mat.dis-nn cs.IT cs.LG math.IT math.PR stat.TH | We analyze the time reversed dynamics of generative diffusion models. If the
exact empirical score function is used in a regime of large dimension and
exponentially large number of samples, these models are known to undergo
transitions between distinct dynamical regimes. We extend this analysis and
compute the transi... |
2502.04341 | Comparative Analysis of Community Detection Algorithms on the SNAP
Social Circles Dataset | cs.SI cs.AI | In network research, Community Detection has always been a topic of
significant interest in network science, with numerous papers and algorithms
proposing to uncover the underlying structures within networks. In this paper,
we conduct a comparative analysis of several prominent community detection
algorithms applied ... |
2502.04342 | Tutorial on Using Machine Learning and Deep Learning Models for Mental
Illness Detection | cs.CL cs.AI cs.LG | Social media has become an important source for understanding mental health,
providing researchers with a way to detect conditions like depression from
user-generated posts. This tutorial provides practical guidance to address
common challenges in applying machine learning and deep learning methods for
mental health ... |
2502.04343 | Synergistic Traffic Assignment | cs.GT cs.MA math.OC | Traffic assignment analyzes traffic flows in road networks that emerge due to
traveler interaction. Traditionally, travelers are assumed to use private cars,
so road costs grow with the number of users due to congestion. However, in
sustainable transit systems, travelers share vehicles s.t. more users on a road
lead ... |
2502.04345 | JingFang: A Traditional Chinese Medicine Large Language Model of
Expert-Level Medical Diagnosis and Syndrome Differentiation-Based Treatment | cs.CL cs.AI cs.LG | Traditional Chinese medicine (TCM) plays a vital role in health protection
and disease treatment, but its practical application requires extensive medical
knowledge and clinical experience. Existing TCM Large Language Models (LLMs)
exhibit critical limitations of uncomprehensive medical consultation and
diagnoses, an... |
2502.04346 | Multi-Lingual Cyber Threat Detection in Tweets/X Using ML, DL, and LLM:
A Comparative Analysis | cs.CL cs.AI | Cyber threat detection has become an important area of focus in today's
digital age due to the growing spread of fake information and harmful content
on social media platforms such as Twitter (now 'X'). These cyber threats, often
disguised within tweets, pose significant risks to individuals, communities,
and even na... |
2502.04347 | SCALM: Detecting Bad Practices in Smart Contracts Through LLMs | cs.CL cs.AI | As the Ethereum platform continues to mature and gain widespread usage, it is
crucial to maintain high standards of smart contract writing practices. While
bad practices in smart contracts may not directly lead to security issues, they
do elevate the risk of encountering problems. Therefore, to understand and
avoid t... |
2502.04348 | Prompt-based Depth Pruning of Large Language Models | cs.CL cs.AI | Depth pruning aims to reduce the inference cost of a large language model
without any hardware-specific complications, by simply removing several less
important transformer blocks. However, our empirical findings suggest that the
importance of a transformer block may be highly task-dependent -- a block that
is crucia... |
2502.04349 | Dynamic benchmarking framework for LLM-based conversational data capture | cs.CL cs.AI | The rapid evolution of large language models (LLMs) has transformed
conversational agents, enabling complex human-machine interactions. However,
evaluation frameworks often focus on single tasks, failing to capture the
dynamic nature of multi-turn dialogues. This paper introduces a dynamic
benchmarking framework to a... |
2502.04350 | CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance | cs.CL cs.AI cs.LG cs.SC cs.SE | Existing methods fail to effectively steer Large Language Models (LLMs)
between textual reasoning and code generation, leaving symbolic computing
capabilities underutilized. We introduce CodeSteer, an effective method for
guiding LLM code/text generation. We construct a comprehensive benchmark
SymBench comprising 37 ... |
2502.04351 | NER4all or Context is All You Need: Using LLMs for low-effort,
high-performance NER on historical texts. A humanities informed approach | cs.CL cs.AI | Named entity recognition (NER) is a core task for historical research in
automatically establishing all references to people, places, events and the
like. Yet, do to the high linguistic and genre diversity of sources, only
limited canonisation of spellings, the level of required historical domain
knowledge, and the s... |
2502.04352 | Investigating the Robustness of Deductive Reasoning with Large Language
Models | cs.CL cs.AI | Large Language Models (LLMs) have been shown to achieve impressive results
for many reasoning-based Natural Language Processing (NLP) tasks, suggesting a
degree of deductive reasoning capability. However, it remains unclear to which
extent LLMs, in both informal and autoformalisation methods, are robust on
logical de... |
2502.04353 | CognArtive: Large Language Models for Automating Art Analysis and
Decoding Aesthetic Elements | cs.CL cs.AI cs.CV | Art, as a universal language, can be interpreted in diverse ways, with
artworks embodying profound meanings and nuances. The advent of Large Language
Models (LLMs) and the availability of Multimodal Large Language Models (MLLMs)
raise the question of how these transformative models can be used to assess and
interpret... |
2502.04354 | Reviving The Classics: Active Reward Modeling in Large Language Model
Alignment | cs.CL cs.AI cs.LG | Building neural reward models from human preferences is a pivotal component
in reinforcement learning from human feedback (RLHF) and large language model
alignment research. Given the scarcity and high cost of human annotation, how
to select the most informative pairs to annotate is an essential yet
challenging open ... |
2502.04355 | LLM-ProS: Analyzing Large Language Models' Performance in Competitive
Problem Solving | cs.CL cs.AI | The rapid advancement of large language models has opened new avenues for
automating complex problem-solving tasks such as algorithmic coding and
competitive programming. This paper introduces a novel evaluation technique,
LLM-ProS, to assess the performance of state-of-the-art LLMs on International
Collegiate Progra... |
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