id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.09303 | Towards Seamless Hierarchical Federated Learning under Intermittent
Client Participation: A Stagewise Decision-Making Methodology | cs.LG cs.DC | Federated Learning (FL) offers a pioneering distributed learning paradigm
that enables devices/clients to build a shared global model. This global model
is obtained through frequent model transmissions between clients and a central
server, which may cause high latency, energy consumption, and congestion over
backhaul... |
2502.09304 | KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for
Graph-RAG | cs.IR | Graph-RAG constructs a knowledge graph from text chunks to improve retrieval
in Large Language Model (LLM)-based question answering. It is particularly
useful in domains such as biomedicine, law, and political science, where
retrieval often requires multi-hop reasoning over proprietary documents. Some
existing Graph-... |
2502.09305 | Predicting Drive Test Results in Mobile Networks Using Optimization
Techniques | cs.NI cs.AI cs.SE | Mobile network operators constantly optimize their networks to ensure
superior service quality and coverage. This optimization is crucial for
maintaining an optimal user experience and requires extensive data collection
and analysis. One of the primary methods for gathering this data is through
drive tests, where tec... |
2502.09306 | Non-asymptotic Analysis of Diffusion Annealed Langevin Monte Carlo for
Generative Modelling | stat.ML cs.LG math.PR stat.CO | We investigate the theoretical properties of general diffusion
(interpolation) paths and their Langevin Monte Carlo implementation, referred
to as diffusion annealed Langevin Monte Carlo (DALMC), under weak conditions on
the data distribution. Specifically, we analyse and provide non-asymptotic
error bounds for the a... |
2502.09307 | When the LM misunderstood the human chuckled: Analyzing garden path
effects in humans and language models | cs.CL cs.AI | Modern Large Language Models (LLMs) have shown human-like abilities in many
language tasks, sparking interest in comparing LLMs' and humans' language
processing. In this paper, we conduct a detailed comparison of the two on a
sentence comprehension task using garden-path constructions, which are
notoriously challengi... |
2502.09309 | Frequency Domain Stability and Convergence Analysis for General Reset
Control Systems Architecture | eess.SY cs.SY | A key factor that generates significant interest in reset control systems,
especially within industrial contexts, is their potential to be designed using
a frequency-domain loop-shaping procedure. On the other hand, formulating and
assessing stability analysis for these nonlinear elements often depends on
access to p... |
2502.09310 | Global Stabilization of Chemostats with Nonzero Mortality and Substrate
Dynamics | math.OC cs.SY eess.SY q-bio.PE | In "chemostat"-type population models that incorporate substrate (nutrient)
dynamics, the dependence of the birth (or growth) rate on the substrate
concentration introduces nonlinear coupling that creates a challenge for
stabilization that is global, namely, for all positive concentrations of the
biomass and nutrient... |
2502.09311 | Mitigating the Impact of Prominent Position Shift in Drone-based RGBT
Object Detection | cs.CV | Drone-based RGBT object detection plays a crucial role in many
around-the-clock applications. However, real-world drone-viewed RGBT data
suffers from the prominent position shift problem, i.e., the position of a tiny
object differs greatly in different modalities. For instance, a slight
deviation of a tiny object in ... |
2502.09316 | A Judge-free LLM Open-ended Generation Benchmark Based on the
Distributional Hypothesis | cs.CL | Evaluating the open-ended text generation of large language models (LLMs) is
challenging because of the lack of a clear ground truth and the high cost of
human or LLM-based assessments. We propose a novel benchmark that evaluates
LLMs using n-gram statistics and rules, without relying on human judgement or
LLM-as-a-j... |
2502.09318 | SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating
Mechanisms | cs.LG | In this paper, we propose a novel approach that enhances recurrent neural
networks (RNNs) by incorporating path signatures into their gating mechanisms.
Our method modifies both Long Short-Term Memory (LSTM) and Gated Recurrent Unit
(GRU) architectures by replacing their forget and reset gates, respectively,
with lea... |
2502.09319 | Bridging Jensen Gap for Max-Min Group Fairness Optimization in
Recommendation | cs.IR cs.LG | Group max-min fairness (MMF) is commonly used in fairness-aware recommender
systems (RS) as an optimization objective, as it aims to protect marginalized
item groups and ensures a fair competition platform. However, our theoretical
analysis indicates that integrating MMF constraint violates the assumption of
sample i... |
2502.09324 | Depth-Bounds for Neural Networks via the Braid Arrangement | cs.LG cs.DM cs.NE math.CO | We contribute towards resolving the open question of how many hidden layers
are required in ReLU networks for exactly representing all continuous and
piecewise linear functions on $\mathbb{R}^d$. While the question has been
resolved in special cases, the best known lower bound in general is still 2. We
focus on neura... |
2502.09325 | A Benchmark for Crime Surveillance Video Analysis with Large Models | cs.CV | Anomaly analysis in surveillance videos is a crucial topic in computer
vision. In recent years, multimodal large language models (MLLMs) have
outperformed task-specific models in various domains. Although MLLMs are
particularly versatile, their abilities to understand anomalous concepts and
details are insufficiently... |
2502.09329 | Bayesian Optimization for Simultaneous Selection of Machine Learning
Algorithms and Hyperparameters on Shared Latent Space | cs.LG | Selecting the optimal combination of a machine learning (ML) algorithm and
its hyper-parameters is crucial for the development of high-performance ML
systems. However, since the combination of ML algorithms and hyper-parameters
is enormous, the exhaustive validation requires a significant amount of time.
Many existin... |
2502.09331 | Beyond English: The Impact of Prompt Translation Strategies across
Languages and Tasks in Multilingual LLMs | cs.CL | Despite advances in the multilingual capabilities of Large Language Models
(LLMs) across diverse tasks, English remains the dominant language for LLM
research and development. So, when working with a different language, this has
led to the widespread practice of pre-translation, i.e., translating the task
prompt into... |
2502.09332 | Full Swap Regret and Discretized Calibration | cs.LG cs.GT | We study the problem of minimizing swap regret in structured normal-form
games. Players have a very large (potentially infinite) number of pure actions,
but each action has an embedding into $d$-dimensional space and payoffs are
given by bilinear functions of these embeddings. We provide an efficient
learning algorit... |
2502.09335 | Graph Diffusion Network for Drug-Gene Prediction | cs.LG cs.AI | Predicting drug-gene associations is crucial for drug development and disease
treatment. While graph neural networks (GNN) have shown effectiveness in this
task, they face challenges with data sparsity and efficient contrastive
learning implementation. We introduce a graph diffusion network for drug-gene
prediction (... |
2502.09340 | This looks like what? Challenges and Future Research Directions for
Part-Prototype Models | cs.LG | The growing interest in eXplainable Artificial Intelligence (XAI) has
prompted research into models with built-in interpretability, the most
prominent of which are part-prototype models. Part-Prototype Models (PPMs) make
decisions by comparing an input image to a set of learned prototypes, providing
human-understanda... |
2502.09341 | Neural Spatiotemporal Point Processes: Trends and Challenges | cs.LG cs.AI | Spatiotemporal point processes (STPPs) are probabilistic models for events
occurring in continuous space and time. Real-world event data often exhibit
intricate dependencies and heterogeneous dynamics. By incorporating modern deep
learning techniques, STPPs can model these complexities more effectively than
tradition... |
2502.09344 | Revisiting Topological Interference Management: A Learning-to-Code on
Graphs Perspective | cs.IT math.IT | The advance of topological interference management (TIM) has been one of the
driving forces of recent developments in network information theory. However,
state-of-the-art coding schemes for TIM are usually handcrafted for specific
families of network topologies, relying critically on experts' domain knowledge
and so... |
2502.09346 | Machine learning for modelling unstructured grid data in computational
physics: a review | cs.LG cs.CE physics.data-an physics.flu-dyn | Unstructured grid data are essential for modelling complex geometries and
dynamics in computational physics. Yet, their inherent irregularity presents
significant challenges for conventional machine learning (ML) techniques. This
paper provides a comprehensive review of advanced ML methodologies designed to
handle un... |
2502.09352 | Wasserstein distributional adversarial training for deep neural networks | cs.LG cs.CV math.OC | Design of adversarial attacks for deep neural networks, as well as methods of
adversarial training against them, are subject of intense research. In this
paper, we propose methods to train against distributional attack threats,
extending the TRADES method used for pointwise attacks. Our approach leverages
recent cont... |
2502.09355 | Simultaneous solution of incompressible Navier-Stokes flows on multiple
surfaces | cs.CE | A mechanical model and finite element method for the simultaneous solution of
Stokes and incompressible Navier-Stokes flows on multiple curved surfaces over
a bulk domain are proposed. The two-dimensional surfaces are defined implicitly
by all level sets of a scalar function, bounded by the three-dimensional bulk
dom... |
2502.09356 | Galileo: Learning Global and Local Features in Pretrained Remote Sensing
Models | cs.CV | From crop mapping to flood detection, machine learning in remote sensing has
a wide range of societally beneficial applications. The commonalities between
remote sensing data in these applications present an opportunity for pretrained
machine learning models tailored to remote sensing to reduce the labeled data
and e... |
2502.09363 | The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment
Weak Labeling for Events in Time | cs.LG | Accurate labels are critical for deriving robust machine learning models.
Labels are used to train supervised learning models and to evaluate most
machine learning paradigms. In this paper, we model the accuracy and cost of a
common weak labeling process where annotators assign presence or absence labels
to fixed-len... |
2502.09365 | Simple Path Structural Encoding for Graph Transformers | cs.LG cs.AI | Graph transformers extend global self-attention to graph-structured data,
achieving notable success in graph learning. Recently, random walk structural
encoding (RWSE) has been found to further enhance their predictive power by
encoding both structural and positional information into the edge
representation. However,... |
2502.09368 | Optimal Microcontroller Usage in Reconfigurable Intelligent Surface:
Batteryless IoT Systems Case Study | cs.IT math.IT | To enhance wireless communication in IoT systems using reconfigurable
intelligent surfaces (RISs), efficient control of programmable passive and
active elements is essential. However, increasing RIS elements requires more
microcontrollers, raising complexity and cost. This paper proposes a modular
approach ("Module")... |
2502.09369 | Language Agents as Digital Representatives in Collective Decision-Making | cs.LG cs.AI cs.CL cs.CY | Consider the process of collective decision-making, in which a group of
individuals interactively select a preferred outcome from among a universe of
alternatives. In this context, "representation" is the activity of making an
individual's preferences present in the process via participation by a proxy
agent -- i.e. ... |
2502.09374 | Mitigating multiple single-event upsets during deep neural network
inference using fault-aware training | cs.LG | Deep neural networks (DNNs) are increasingly used in safety-critical
applications. Reliable fault analysis and mitigation are essential to ensure
their functionality in harsh environments that contain high radiation levels.
This study analyses the impact of multiple single-bit single-event upsets in
DNNs by performin... |
2502.09375 | FARM: Frequency-Aware Model for Cross-Domain Live-Streaming
Recommendation | cs.IR | Live-streaming services have attracted widespread popularity due to their
real-time interactivity and entertainment value. Users can engage with
live-streaming authors by participating in live chats, posting likes, or
sending virtual gifts to convey their preferences and support. However, the
live-streaming services ... |
2502.09376 | LoRA Training Provably Converges to a Low-Rank Global Minimum or It
Fails Loudly (But it Probably Won't Fail) | cs.LG | Low-rank adaptation (LoRA) has become a standard approach for fine-tuning
large foundation models. However, our theoretical understanding of LoRA remains
limited as prior analyses of LoRA's training dynamics either rely on
linearization arguments or consider highly simplified setups. In this work, we
analyze the LoRA... |
2502.09378 | A Deep Inverse-Mapping Model for a Flapping Robotic Wing | cs.AI cs.RO | In systems control, the dynamics of a system are governed by modulating its
inputs to achieve a desired outcome. For example, to control the thrust of a
quad-copter propeller the controller modulates its rotation rate, relying on a
straightforward mapping between the input rotation rate and the resulting
thrust. This... |
2502.09379 | TRIFFID: Autonomous Robotic Aid For Increasing First Responders
Efficiency | cs.RO cs.AI | The increasing complexity of natural disaster incidents demands innovative
technological solutions to support first responders in their efforts. This
paper introduces the TRIFFID system, a comprehensive technical framework that
integrates unmanned ground and aerial vehicles with advanced artificial
intelligence funct... |
2502.09387 | Truth Knows No Language: Evaluating Truthfulness Beyond English | cs.CL cs.AI cs.CY | We introduce a professionally translated extension of the TruthfulQA
benchmark designed to evaluate truthfulness in Basque, Catalan, Galician, and
Spanish. Truthfulness evaluations of large language models (LLMs) have
primarily been conducted in English. However, the ability of LLMs to maintain
truthfulness across la... |
2502.09389 | S$^2$-Diffusion: Generalizing from Instance-level to Category-level
Skills in Robot Manipulation | cs.RO cs.AI | Recent advances in skill learning has propelled robot manipulation to new
heights by enabling it to learn complex manipulation tasks from a practical
number of demonstrations. However, these skills are often limited to the
particular action, object, and environment \textit{instances} that are shown in
the training da... |
2502.09390 | SQuARE: Sequential Question Answering Reasoning Engine for Enhanced
Chain-of-Thought in Large Language Models | cs.CL cs.AI cs.LG | In the rapidly evolving field of Natural Language Processing, Large Language
Models (LLMs) are tasked with increasingly complex reasoning challenges.
Traditional methods like chain-of-thought prompting have shown promise but
often fall short in fully leveraging a model's reasoning capabilities. This
paper introduces ... |
2502.09393 | Generalizable Reinforcement Learning with Biologically Inspired
Hyperdimensional Occupancy Grid Maps for Exploration and Goal-Directed Path
Planning | cs.RO cs.NE | Real-time autonomous systems utilize multi-layer computational frameworks to
perform critical tasks such as perception, goal finding, and path planning.
Traditional methods implement perception using occupancy grid mapping (OGM),
segmenting the environment into discretized cells with probabilistic
information. This c... |
2502.09395 | Robot Pouring: Identifying Causes of Spillage and Selecting Alternative
Action Parameters Using Probabilistic Actual Causation | cs.RO cs.LG | In everyday life, we perform tasks (e.g., cooking or cleaning) that involve a
large variety of objects and goals. When confronted with an unexpected or
unwanted outcome, we take corrective actions and try again until achieving the
desired result. The reasoning performed to identify a cause of the observed
outcome and... |
2502.09396 | A hierarchical approach for assessing the vulnerability of tree-based
classification models to membership inference attack | cs.LG cs.CR | Machine learning models can inadvertently expose confidential properties of
their training data, making them vulnerable to membership inference attacks
(MIA). While numerous evaluation methods exist, many require computationally
expensive processes, such as training multiple shadow models. This article
presents two n... |
2502.09411 | ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation | cs.CV cs.GR | Diffusion models enable high-quality and diverse visual content synthesis.
However, they struggle to generate rare or unseen concepts. To address this
challenge, we explore the usage of Retrieval-Augmented Generation (RAG) with
image generation models. We propose ImageRAG, a method that dynamically
retrieves relevant... |
2502.09416 | Rethinking Evaluation Metrics for Grammatical Error Correction: Why Use
a Different Evaluation Process than Human? | cs.CL | One of the goals of automatic evaluation metrics in grammatical error
correction (GEC) is to rank GEC systems such that it matches human preferences.
However, current automatic evaluations are based on procedures that diverge
from human evaluation. Specifically, human evaluation derives rankings by
aggregating senten... |
2502.09417 | A Survey of Reinforcement Learning for Optimization in Automation | cs.LG cs.AI cs.NE cs.RO cs.SY eess.SY | Reinforcement Learning (RL) has become a critical tool for optimization
challenges within automation, leading to significant advancements in several
areas. This review article examines the current landscape of RL within
automation, with a particular focus on its roles in manufacturing, energy
systems, and robotics. I... |
2502.09419 | On multi-token prediction for efficient LLM inference | cs.CL cs.LG | We systematically investigate multi-token prediction (MTP) capabilities
within LLMs pre-trained for next-token prediction (NTP). We first show that
such models inherently possess MTP capabilities via numerical marginalization
over intermediate token probabilities, though performance is data-dependent and
improves wit... |
2502.09423 | Transformer-Enhanced Variational Autoencoder for Crystal Structure
Prediction | cond-mat.mtrl-sci cs.AI | Crystal structure forms the foundation for understanding the physical and
chemical properties of materials. Generative models have emerged as a new
paradigm in crystal structure prediction(CSP), however, accurately capturing
key characteristics of crystal structures, such as periodicity and symmetry,
remains a signif... |
2502.09425 | A 3D Facial Reconstruction Evaluation Methodology: Comparing Smartphone
Scans with Deep Learning Based Methods Using Geometry and Morphometry
Criteria | cs.CV | Three-dimensional (3D) facial shape analysis has gained interest due to its
potential clinical applications. However, the high cost of advanced 3D facial
acquisition systems limits their widespread use, driving the development of
low-cost acquisition and reconstruction methods. This study introduces a novel
evaluatio... |
2502.09431 | On Usage of Non-Volatile Memory as Primary Storage for Database
Management Systems | cs.DB | This paper explores the implications of employing non-volatile memory (NVM)
as primary storage for a data base management system (DBMS). We investigate the
modifications necessary to be applied on top of a traditional relational DBMS
to take advantage of NVM features. As a case study, we modify the storage
engine (SE... |
2502.09432 | Dual Formulation for Non-Rectangular Lp Robust Markov Decision Processes | cs.AI cs.LG | We study robust Markov decision processes (RMDPs) with non-rectangular
uncertainty sets, which capture interdependencies across states unlike
traditional rectangular models. While non-rectangular robust policy evaluation
is generally NP-hard, even in approximation, we identify a powerful class of
$L_p$-bounded uncert... |
2502.09434 | Redistribute Ensemble Training for Mitigating Memorization in Diffusion
Models | cs.CV | Diffusion models, known for their tremendous ability to generate high-quality
samples, have recently raised concerns due to their data memorization behavior,
which poses privacy risks. Recent methods for memory mitigation have primarily
addressed the issue within the context of the text modality in cross-modal
genera... |
2502.09436 | Variable Stiffness for Robust Locomotion through Reinforcement Learning | cs.RO cs.AI | Reinforcement-learned locomotion enables legged robots to perform highly
dynamic motions but often accompanies time-consuming manual tuning of joint
stiffness. This paper introduces a novel control paradigm that integrates
variable stiffness into the action space alongside joint positions, enabling
grouped stiffness ... |
2502.09443 | Relational Conformal Prediction for Correlated Time Series | cs.LG cs.AI | We address the problem of uncertainty quantification in time series
forecasting by exploiting observations at correlated sequences. Relational deep
learning methods leveraging graph representations are among the most effective
tools for obtaining point estimates from spatiotemporal data and correlated
time series. Ho... |
2502.09445 | A Differentiable Rank-Based Objective For Better Feature Learning | stat.ML cs.LG | In this paper, we leverage existing statistical methods to better understand
feature learning from data. We tackle this by modifying the model-free variable
selection method, Feature Ordering by Conditional Independence (FOCI), which is
introduced in \cite{azadkia2021simple}. While FOCI is based on a non-parametric
c... |
2502.09446 | Drivers of cooperation in social dilemmas on higher-order networks | physics.soc-ph cs.GT cs.SI q-bio.PE | Understanding cooperation in social dilemmas requires models that capture the
complexity of real-world interactions. While network frameworks have provided
valuable insights to model the evolution of cooperation, they are unable to
encode group interactions properly. Here, we introduce a general higher-order
network ... |
2502.09447 | Pixel-Level Reasoning Segmentation via Multi-turn Conversations | cs.CV cs.CL | Existing visual perception systems focus on region-level segmentation in
single-turn dialogues, relying on complex and explicit query instructions. Such
systems cannot reason at the pixel level and comprehend dynamic user intent
that changes over interaction. Our work tackles this issue by introducing a
novel task, P... |
2502.09449 | Spiking Neural Networks for Temporal Processing: Status Quo and Future
Prospects | cs.NE | Temporal processing is fundamental for both biological and artificial
intelligence systems, as it enables the comprehension of dynamic environments
and facilitates timely responses. Spiking Neural Networks (SNNs) excel in
handling such data with high efficiency, owing to their rich neuronal dynamics
and sparse activi... |
2502.09457 | The Multilingual Mind : A Survey of Multilingual Reasoning in Language
Models | cs.CL | While reasoning and multilingual capabilities in Language Models (LMs) have
achieved remarkable progress in recent years, their integration into a unified
paradigm, multilingual reasoning, is at a nascent stage. Multilingual reasoning
requires language models to handle logical reasoning across languages while
address... |
2502.09460 | Metamorphic Testing for Pose Estimation Systems | cs.SE cs.AI cs.CV | Pose estimation systems are used in a variety of fields, from sports
analytics to livestock care. Given their potential impact, it is paramount to
systematically test their behaviour and potential for failure. This is a
complex task due to the oracle problem and the high cost of manual labelling
necessary to build gr... |
2502.09471 | Wholly-WOOD: Wholly Leveraging Diversified-quality Labels for
Weakly-supervised Oriented Object Detection | cs.CV cs.AI | Accurately estimating the orientation of visual objects with compact rotated
bounding boxes (RBoxes) has become a prominent demand, which challenges
existing object detection paradigms that only use horizontal bounding boxes
(HBoxes). To equip the detectors with orientation awareness, supervised
regression/classifica... |
2502.09473 | Learning to Predict Global Atrial Fibrillation Dynamics from Sparse
Measurements | cs.LG eess.SP | Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all
treatment with limited success in persistent AF. This may be due to our
inability to map the dynamics of AF with the limited resolution and coverage
provided by sequential contact mapping catheters, preventing effective patient
phenotyping ... |
2502.09477 | DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation
Networks for Quantitative Nanomaterial Analysis through Differentiable
Rendering and Generative Modelling | cond-mat.mtrl-sci cs.CV cs.LG | Nanomaterials exhibit distinctive properties governed by parameters such as
size, shape, and surface characteristics, which critically influence their
applications and interactions across technological, biological, and
environmental contexts. Accurate quantification and understanding of these
materials are essential ... |
2502.09479 | Assessing Generative AI value in a public sector context: evidence from
a field experiment | q-fin.GN cs.LG econ.GN q-fin.EC | The emergence of Generative AI (Gen AI) has motivated an interest in
understanding how it could be used to enhance productivity across various
tasks. We add to research results for the performance impact of Gen AI on
complex knowledge-based tasks in a public sector setting. In a pre-registered
experiment, after estab... |
2502.09482 | Standardisation of Convex Ultrasound Data Through Geometric Analysis and
Augmentation | cs.CV | The application of ultrasound in healthcare has seen increased diversity and
importance. Unlike other medical imaging modalities, ultrasound research and
development has historically lagged, particularly in the case of applications
with data-driven algorithms. A significant issue with ultrasound is the extreme
variab... |
2502.09484 | PenTest++: Elevating Ethical Hacking with AI and Automation | cs.CR cs.AI | Traditional ethical hacking relies on skilled professionals and
time-intensive command management, which limits its scalability and efficiency.
To address these challenges, we introduce PenTest++, an AI-augmented system
that integrates automation with generative AI (GenAI) to optimise ethical
hacking workflows. Devel... |
2502.09487 | Objective quantification of mood states using large language models | cs.CL cs.AI cs.LG | Emotional states influence human behaviour and cognition, leading to diverse
thought trajectories. Similarly, Large Language Models (LLMs) showcase an
excellent level of response consistency across wide-ranging contexts (prompts).
We leverage these parallels to establish a framework for quantifying mental
states. Our... |
2502.09490 | Inverse Design with Dynamic Mode Decomposition | cs.LG cs.SY eess.SY math.DS math.OC physics.flu-dyn | We introduce a computationally efficient method for the automation of inverse
design in science and engineering. Based on simple least-square regression, the
underlying dynamic mode decomposition algorithm can be used to construct a
low-rank subspace spanning multiple experiments in parameter space. The
proposed inve... |
2502.09494 | Communicating Likelihoods with Normalising Flows | hep-ph cs.LG hep-ex physics.data-an | We present a machine-learning-based workflow to model an unbinned likelihood
from its samples. A key advancement over existing approaches is the validation
of the learned likelihood using rigorous statistical tests of the joint
distribution, such as the Kolmogorov-Smirnov test of the joint distribution.
Our method en... |
2502.09495 | Cracking the Code: Enhancing Development finance understanding with
artificial intelligence | econ.GN cs.AI cs.LG q-fin.EC | Analyzing development projects is crucial for understanding donors aid
strategies, recipients priorities, and to assess development finance capacity
to adress development issues by on-the-ground actions. In this area, the
Organisation for Economic Co-operation and Developments (OECD) Creditor
Reporting System (CRS) d... |
2502.09496 | On Agnostic PAC Learning in the Small Error Regime | cs.LG stat.ML | Binary classification in the classic PAC model exhibits a curious phenomenon:
Empirical Risk Minimization (ERM) learners are suboptimal in the realizable
case yet optimal in the agnostic case. Roughly speaking, this owes itself to
the fact that non-realizable distributions $\mathcal{D}$ are simply more
difficult to l... |
2502.09497 | Improve LLM-based Automatic Essay Scoring with Linguistic Features | cs.CL cs.AI | Automatic Essay Scoring (AES) assigns scores to student essays, reducing the
grading workload for instructors. Developing a scoring system capable of
handling essays across diverse prompts is challenging due to the flexibility
and diverse nature of the writing task. Existing methods typically fall into
two categories... |
2502.09500 | Eidetic Learning: an Efficient and Provable Solution to Catastrophic
Forgetting | cs.LG | Catastrophic forgetting -- the phenomenon of a neural network learning a task
t1 and losing the ability to perform it after being trained on some other task
t2 -- is a long-standing problem for neural networks [McCloskey and Cohen,
1989]. We present a method, Eidetic Learning, that provably solves catastrophic
forget... |
2502.09501 | Prior-Constrained Association Learning for Fine-Grained Generalized
Category Discovery | cs.CV | This paper addresses generalized category discovery (GCD), the task of
clustering unlabeled data from potentially known or unknown categories with the
help of labeled instances from each known category. Compared to traditional
semi-supervised learning, GCD is more challenging because unlabeled data could
be from nove... |
2502.09502 | Scalable First-order Method for Certifying Optimal k-Sparse GLMs | cs.LG math.OC | This paper investigates the problem of certifying optimality for sparse
generalized linear models (GLMs), where sparsity is enforced through an
$\ell_0$ cardinality constraint. While branch-and-bound (BnB) frameworks can
certify optimality by pruning nodes using dual bounds, existing methods for
computing these bound... |
2502.09503 | AttentionSmithy: A Modular Framework for Rapid Transformer Development
and Customization | cs.LG cs.AI | Transformer architectures have transformed AI applications but remain complex
to customize for domain experts lacking low-level implementation expertise. We
introduce AttentionSmithy, a modular software package that simplifies
transformer innovation by breaking down key components into reusable building
blocks: atten... |
2502.09507 | When and How Does CLIP Enable Domain and Compositional Generalization? | cs.LG cs.CV | The remarkable generalization performance of contrastive vision-language
models like CLIP is often attributed to the diversity of their training
distributions. However, key questions remain unanswered: Can CLIP generalize to
an entirely unseen domain when trained on a diverse mixture of domains (domain
generalization... |
2502.09509 | EQ-VAE: Equivariance Regularized Latent Space for Improved Generative
Image Modeling | cs.LG | Latent generative models have emerged as a leading approach for high-quality
image synthesis. These models rely on an autoencoder to compress images into a
latent space, followed by a generative model to learn the latent distribution.
We identify that existing autoencoders lack equivariance to semantic-preserving
tra... |
2502.09511 | Diffusion Models for Molecules: A Survey of Methods and Tasks | cs.LG cs.AI cs.CE | Generative tasks about molecules, including but not limited to molecule
generation, are crucial for drug discovery and material design, and have
consistently attracted significant attention. In recent years, diffusion models
have emerged as an impressive class of deep generative models, sparking
extensive research an... |
2502.09517 | Coupled Rendezvous and Docking Maneuver control of satellite using
Reinforcement learning-based Adaptive Fixed-Time Sliding Mode Controller | eess.SY cs.SY | Satellite dynamics in unknown environments are inherently uncertain due to
factors such as varying gravitational fields, atmospheric drag, and
unpredictable interactions with space debris or other celestial bodies.
Traditional sliding mode controllers with fixed parameters often struggle to
maintain optimal performan... |
2502.09520 | SQ-GAN: Semantic Image Communications Using Masked Vector Quantization | cs.CV eess.IV | This work introduces Semantically Masked VQ-GAN (SQ-GAN), a novel approach
integrating generative models to optimize image compression for
semantic/task-oriented communications. SQ-GAN employs off-the-shelf semantic
semantic segmentation and a new specifically developed semantic-conditioned
adaptive mask module (SAMM... |
2502.09525 | Robust Learning of Multi-index Models via Iterative Subspace
Approximation | cs.LG cs.DS math.ST stat.ML stat.TH | We study the task of learning Multi-Index Models (MIMs) with label noise
under the Gaussian distribution. A $K$-MIM is any function $f$ that only
depends on a $K$-dimensional subspace. We focus on well-behaved MIMs with
finite ranges that satisfy certain regularity properties. Our main contribution
is a general robus... |
2502.09528 | SteROI-D: System Design and Mapping for Stereo Depth Inference on
Regions of Interest | cs.CV cs.AR | Machine learning algorithms have enabled high quality stereo depth estimation
to run on Augmented and Virtual Reality (AR/VR) devices. However, high energy
consumption across the full image processing stack prevents stereo depth
algorithms from running effectively on battery-limited devices. This paper
introduces Ste... |
2502.09529 | Exact Leader Estimation: A New Approach for Distributed Differentiation | eess.SY cs.MA cs.SY math.OC | A novel strategy aimed at cooperatively differentiating a signal among
multiple interacting agents is introduced, where none of the agents needs to
know which agent is the leader, i.e. the one producing the signal to be
differentiated. Every agent communicates only a scalar variable to its
neighbors; except for the l... |
2502.09531 | Data-Enabled Predictive Control for Flexible Spacecraft | eess.SY cs.SY | Spacecraft are vital to space exploration and are often equipped with
lightweight, flexible appendages to meet strict weight constraints. These
appendages pose significant challenges for modeling and control due to their
inherent nonlinearity. Data-driven control methods have gained traction to
address such challenge... |
2502.09532 | Mind the Gap! Choice Independence in Using Multilingual LLMs for
Persuasive Co-Writing Tasks in Different Languages | cs.CL cs.AI cs.HC | Recent advances in generative AI have precipitated a proliferation of novel
writing assistants. These systems typically rely on multilingual large language
models (LLMs), providing globalized workers the ability to revise or create
diverse forms of content in different languages. However, there is substantial
evidenc... |
2502.09533 | Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion
Model | cs.CV | Recent advances in conditional diffusion models have shown promise for
generating realistic TalkingFace videos, yet challenges persist in achieving
consistent head movement, synchronized facial expressions, and accurate lip
synchronization over extended generations. To address these, we introduce the
\textbf{M}otion-... |
2502.09534 | Fast Tensor Completion via Approximate Richardson Iteration | cs.DS cs.LG math.ST stat.TH | We study tensor completion (TC) through the lens of low-rank tensor
decomposition (TD). Many TD algorithms use fast alternating minimization
methods, which solve highly structured linear regression problems at each step
(e.g., for CP, Tucker, and tensor-train decompositions). However, such
algebraic structure is lost... |
2502.09541 | Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated
Large-Scale Data Analytics | cs.DB cs.DC | Despite the high computational throughput of GPUs, limited memory capacity
and bandwidth-limited CPU-GPU communication via PCIe links remain significant
bottlenecks for accelerating large-scale data analytics workloads. This paper
introduces Vortex, a GPU-accelerated framework designed for data analytics
workloads th... |
2502.09553 | SyntheticPop: Attacking Speaker Verification Systems With Synthetic
VoicePops | cs.CR cs.LG | Voice Authentication (VA), also known as Automatic Speaker Verification
(ASV), is a widely adopted authentication method, particularly in automated
systems like banking services, where it serves as a secondary layer of user
authentication. Despite its popularity, VA systems are vulnerable to various
attacks, includin... |
2502.09556 | Real-Time Fast Marching Tree for Mobile Robot Motion Planning in Dynamic
Environments | cs.RO cs.SY eess.SY | This paper proposes the Real-Time Fast Marching Tree (RT-FMT), a real-time
planning algorithm that features local and global path generation,
multiple-query planning, and dynamic obstacle avoidance. During the search,
RT-FMT quickly looks for the global solution and, in the meantime, generates
local paths that can be... |
2502.09560 | EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language
Models for Vision-Driven Embodied Agents | cs.AI cs.CL cs.CV | Leveraging Multi-modal Large Language Models (MLLMs) to create embodied
agents offers a promising avenue for tackling real-world tasks. While
language-centric embodied agents have garnered substantial attention,
MLLM-based embodied agents remain underexplored due to the lack of
comprehensive evaluation frameworks. To... |
2502.09561 | Enhancing Traffic Safety Analysis with Digital Twin Technology:
Integrating Vehicle Dynamics and Environmental Factors into Microscopic
Traffic Simulation | eess.SY cs.SY math.OC | Traffic safety is a critical concern in transportation engineering and urban
planning. Traditional traffic safety analysis requires trained observers to
collect data in the field, which is time-consuming, labor-intensive, and
sometimes inaccurate. In recent years, microscopic traffic simulation, which
simulates indiv... |
2502.09563 | Self-Calibrating Gaussian Splatting for Large Field of View
Reconstruction | cs.CV cs.GR | In this paper, we present a self-calibrating framework that jointly optimizes
camera parameters, lens distortion and 3D Gaussian representations, enabling
accurate and efficient scene reconstruction. In particular, our technique
enables high-quality scene reconstruction from Large field-of-view (FOV)
imagery taken wi... |
2502.09564 | Diffusing DeBias: a Recipe for Turning a Bug into a Feature | cs.LG cs.CV | Deep learning model effectiveness in classification tasks is often challenged
by the quality and quantity of training data which, whenever containing strong
spurious correlations between specific attributes and target labels, can result
in unrecoverable biases in model predictions. Tackling these biases is crucial
in... |
2502.09565 | MDCrow: Automating Molecular Dynamics Workflows with Large Language
Models | cs.AI physics.chem-ph | Molecular dynamics (MD) simulations are essential for understanding
biomolecular systems but remain challenging to automate. Recent advances in
large language models (LLM) have demonstrated success in automating complex
scientific tasks using LLM-based agents. In this paper, we introduce MDCrow, an
agentic LLM assist... |
2502.09566 | Zero-shot generation of synthetic neurosurgical data with large language
models | cs.CL cs.LG | Clinical data is fundamental to advance neurosurgical research, but access is
often constrained by data availability, small sample sizes, privacy
regulations, and resource-intensive preprocessing and de-identification
procedures. Synthetic data offers a potential solution to challenges associated
with accessing and u... |
2502.09567 | MorphNLI: A Stepwise Approach to Natural Language Inference Using Text
Morphing | cs.CL cs.AI | We introduce MorphNLI, a modular step-by-step approach to natural language
inference (NLI). When classifying the premise-hypothesis pairs into
{entailment, contradiction, neutral}, we use a language model to generate the
necessary edits to incrementally transform (i.e., morph) the premise into the
hypothesis. Then, u... |
2502.09570 | Enhancing the Utility of Higher-Order Information in Relational Learning | cs.LG stat.ML | Higher-order information is crucial for relational learning in many domains
where relationships extend beyond pairwise interactions. Hypergraphs provide a
natural framework for modeling such relationships, which has motivated recent
extensions of graph neural network architectures to hypergraphs. However,
comparisons... |
2502.09571 | DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra | cs.LG q-bio.QM | Mass spectrometry plays a fundamental role in elucidating the structures of
unknown molecules and subsequent scientific discoveries. One formulation of the
structure elucidation task is the conditional $\textit{de novo}$ generation of
molecular structure given a mass spectrum. Toward a more accurate and efficient
sci... |
2502.09573 | Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt
Engineering | cs.CV cs.CL cs.LG | In this study, we tackle industry challenges in video content classification
by exploring and optimizing GPT-based models for zero-shot classification
across seven critical categories of video quality. We contribute a novel
approach to improving GPT's performance through prompt optimization and policy
refinement, dem... |
2502.09583 | Learning to Coordinate with Experts | cs.LG stat.ML | When deployed in dynamic environments, AI agents will inevitably encounter
challenges that exceed their individual capabilities. Leveraging assistance
from expert agents-whether human or AI-can significantly enhance safety and
performance in such situations. However, querying experts is often costly,
necessitating th... |
2502.09587 | Rolling Ahead Diffusion for Traffic Scene Simulation | cs.LG cs.RO | Realistic driving simulation requires that NPCs not only mimic natural
driving behaviors but also react to the behavior of other simulated agents.
Recent developments in diffusion-based scenario generation focus on creating
diverse and realistic traffic scenarios by jointly modelling the motion of all
the agents in t... |
2502.09589 | Logical forms complement probability in understanding language model
(and human) performance | cs.CL cs.LO | With the increasing interest in using large language models (LLMs) for
planning in natural language, understanding their behaviors becomes an
important research question. This work conducts a systematic investigation of
LLMs' ability to perform logical reasoning in natural language. We introduce a
controlled dataset ... |
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