id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.03119 | Comparison of the Cox proportional hazards model and Random Survival
Forest algorithm for predicting patient-specific survival probabilities in
clinical trial data | stat.ML cs.LG | The Cox proportional hazards model is often used for model development in
data from randomized controlled trials (RCT) with time-to-event outcomes.
Random survival forests (RSF) is a machine-learning algorithm known for its
high predictive performance. We conduct a comprehensive neutral comparison
study to compare th... |
2502.03120 | At the Mahakumbh, Faith Met Tragedy: Computational Analysis of Stampede
Patterns Using Machine Learning and NLP | cs.LG cs.AI cs.CY cs.SI | This study employs machine learning, historical analysis, and natural
language processing (NLP) to examine recurring lethal stampedes at Indias mass
religious gatherings, focusing on the 2025 Mahakumbh tragedy in Prayagraj (48+
deaths) and its 1954 predecessor (700+ casualties). Through computational
modeling of crow... |
2502.03122 | HiLo: Learning Whole-Body Human-like Locomotion with Motion Tracking
Controller | cs.RO | Deep Reinforcement Learning (RL) has emerged as a promising method to develop
humanoid robot locomotion controllers. Despite the robust and stable locomotion
demonstrated by previous RL controllers, their behavior often lacks the natural
and agile motion patterns necessary for human-centric scenarios. In this work,
w... |
2502.03123 | Disentanglement in Difference: Directly Learning Semantically
Disentangled Representations by Maximizing Inter-Factor Differences | cs.LG cs.AI | In this study, Disentanglement in Difference(DiD) is proposed to address the
inherent inconsistency between the statistical independence of latent variables
and the goal of semantic disentanglement in disentanglement representation
learning. Conventional disentanglement methods achieve disentanglement
representation ... |
2502.03124 | Levelised Cost of Demand Response: Estimating the Cost-Competitiveness
of Flexible Demand | eess.SY cs.SY | To make well-informed investment decisions, energy system stakeholders
require reliable cost frameworks for demand response (DR) and storage
technologies. While the levelised cost of storage (LCOS) permits comprehensive
cost comparisons between different storage technologies, no generic cost
measure for the compariso... |
2502.03125 | Double Distillation Network for Multi-Agent Reinforcement Learning | cs.MA cs.LG | Multi-agent reinforcement learning typically employs a centralized
training-decentralized execution (CTDE) framework to alleviate the
non-stationarity in environment. However, the partial observability during
execution may lead to cumulative gap errors gathered by agents, impairing the
training of effective collabora... |
2502.03128 | Metis: A Foundation Speech Generation Model with Masked Generative
Pre-training | cs.SD cs.AI cs.LG eess.AS eess.SP | We introduce Metis, a foundation model for unified speech generation. Unlike
previous task-specific or multi-task models, Metis follows a pre-training and
fine-tuning paradigm. It is pre-trained on large-scale unlabeled speech data
using masked generative modeling and then fine-tuned to adapt to diverse speech
genera... |
2502.03129 | Teaching Large Language Models Number-Focused Headline Generation With
Key Element Rationales | cs.CL cs.LG | Number-focused headline generation is a summarization task requiring both
high textual quality and precise numerical accuracy, which poses a unique
challenge for Large Language Models (LLMs). Existing studies in the literature
focus only on either textual quality or numerical reasoning and thus are
inadequate to addr... |
2502.03132 | SPARK: A Modular Benchmark for Humanoid Robot Safety | cs.RO cs.SY eess.SY | This paper introduces the Safe Protective and Assistive Robot Kit (SPARK), a
comprehensive benchmark designed to ensure safety in humanoid autonomy and
teleoperation. Humanoid robots pose significant safety risks due to their
physical capabilities of interacting with complex environments. The physical
structures of h... |
2502.03134 | Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for
Intrusion Detection and Security Research | cs.CR cs.AI | In this paper, a dataset of IoT network traffic is presented. Our dataset was
generated by utilising the Gotham testbed, an emulated large-scale Internet of
Things (IoT) network designed to provide a realistic and heterogeneous
environment for network security research. The testbed includes 78 emulated IoT
devices op... |
2502.03135 | Underwater Soft Fin Flapping Motion with Deep Neural Network Based
Surrogate Model | cs.RO cs.LG | This study presents a novel framework for precise force control of
fin-actuated underwater robots by integrating a deep neural network (DNN)-based
surrogate model with reinforcement learning (RL). To address the complex
interactions with the underwater environment and the high experimental costs, a
DNN surrogate mode... |
2502.03139 | Fast Sampling of Cosmological Initial Conditions with Gaussian Neural
Posterior Estimation | astro-ph.CO astro-ph.IM cs.LG | Knowledge of the primordial matter density field from which the large-scale
structure of the Universe emerged over cosmic time is of fundamental importance
for cosmology. However, reconstructing these cosmological initial conditions
from late-time observations is a notoriously difficult task, which requires
advanced ... |
2502.03143 | Machine Learning-Driven Student Performance Prediction for Enhancing
Tiered Instruction | cs.LG cs.CY | Student performance prediction is one of the most important subjects in
educational data mining. As a modern technology, machine learning offers
powerful capabilities in feature extraction and data modeling, providing
essential support for diverse application scenarios, as evidenced by recent
studies confirming its e... |
2502.03144 | Group Trip Planning Query Problem with Multimodal Journey | cs.MA cs.DB cs.DS | In Group Trip Planning (GTP) Query Problem, we are given a city road network
where a number of Points of Interest (PoI) have been marked with their
respective categories (e.g., Cafeteria, Park, Movie Theater, etc.). A group of
agents want to visit one PoI from every category from their respective starting
location an... |
2502.03146 | Symmetry-Aware Bayesian Flow Networks for Crystal Generation | cs.LG cond-mat.mtrl-sci | The discovery of new crystalline materials is essential to scientific and
technological progress. However, traditional trial-and-error approaches are
inefficient due to the vast search space. Recent advancements in machine
learning have enabled generative models to predict new stable materials by
incorporating struct... |
2502.03147 | Scalable In-Context Learning on Tabular Data via Retrieval-Augmented
Large Language Models | cs.CL cs.AI | Recent studies have shown that large language models (LLMs), when customized
with post-training on tabular data, can acquire general tabular in-context
learning (TabICL) capabilities. These models are able to transfer effectively
across diverse data schemas and different task domains. However, existing
LLM-based TabI... |
2502.03148 | Abnormal Mutations: Evolution Strategies Don't Require Gaussianity | cs.NE | The mutation process in evolution strategies has been interlinked with the
normal distribution since its inception. Many lines of reasoning have been
given for this strong dependency, ranging from maximum entropy arguments to the
need for isotropy. However, some theoretical results suggest that other
distributions mi... |
2502.03159 | PICBench: Benchmarking LLMs for Photonic Integrated Circuits Design | cs.LG cs.AR | While large language models (LLMs) have shown remarkable potential in
automating various tasks in digital chip design, the field of Photonic
Integrated Circuits (PICs)-a promising solution to advanced chip
designs-remains relatively unexplored in this context. The design of PICs is
time-consuming and prone to errors ... |
2502.03162 | Low-Complexity Cram\'er-Rao Lower Bound and Sum Rate Optimization in
ISAC Systems | cs.IT eess.SP math.IT | While Cram\'er-Rao lower bound is an important metric in sensing functions in
integrated sensing and communications (ISAC) designs, its optimization usually
involves a computationally expensive solution such as semidefinite relaxation.
In this paper, we aim to develop a low-complexity yet efficient algorithm for
CRLB... |
2502.03163 | Signature Reconstruction from Randomized Signatures | math.CA cs.LG math.PR stat.ML | Controlled ordinary differential equations driven by continuous bounded
variation curves can be considered a continuous time analogue of recurrent
neural networks for the construction of expressive features of the input
curves. We ask up to which extent well known signature features of such curves
can be reconstructe... |
2502.03183 | MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume
for Enhanced Video Understanding | cs.CV cs.LG | Modern Video Large Language Models (VLLMs) often rely on uniform frame
sampling for video understanding, but this approach frequently fails to capture
critical information due to frame redundancy and variations in video content.
We propose MaxInfo, a training-free method based on the maximum volume
principle, which s... |
2502.03188 | Euska\~nolDS: A Naturally Sourced Corpus for Basque-Spanish
Code-Switching | cs.CL cs.AI | Code-switching (CS) remains a significant challenge in Natural Language
Processing (NLP), mainly due a lack of relevant data. In the context of the
contact between the Basque and Spanish languages in the north of the Iberian
Peninsula, CS frequently occurs in both formal and informal spontaneous
interactions. However... |
2502.03198 | SimSort: A Powerful Framework for Spike Sorting by Large-Scale
Electrophysiology Simulation | q-bio.NC cs.LG | Spike sorting is an essential process in neural recording, which identifies
and separates electrical signals from individual neurons recorded by electrodes
in the brain, enabling researchers to study how specific neurons communicate
and process information. Although there exist a number of spike sorting methods
which... |
2502.03199 | Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large
Language Models | cs.CL cs.AI | Despite their impressive capacities, Large language models (LLMs) often
struggle with the hallucination issue of generating inaccurate or fabricated
content even when they possess correct knowledge. In this paper, we extend the
exploration of the correlation between hidden-state prediction changes and
output factuali... |
2502.03200 | CORTEX: A Cost-Sensitive Rule and Tree Extraction Method | cs.AI cs.LG | Tree-based and rule-based machine learning models play pivotal roles in
explainable artificial intelligence (XAI) due to their unique ability to
provide explanations in the form of tree or rule sets that are easily
understandable and interpretable, making them essential for applications in
which trust in model decisi... |
2502.03201 | SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection
with Extremely Limited Labels | cs.LG | Node Anomaly Detection (NAD) has gained significant attention in the deep
learning community due to its diverse applications in real-world scenarios.
Existing NAD methods primarily embed graphs within a single Euclidean space,
while overlooking the potential of non-Euclidean spaces. Besides, to address
the prevalent ... |
2502.03202 | Low-cost analog signal chain for transmit-receive circuits of passive
induction-based resonators | eess.SP cs.SY eess.SY | Passive wireless sensors are crucial in modern medical and industrial
settings to monitor procedures and conditions. We demonstrate a circuit to
inductively excite passive resonators and to conduct their decaying signal
response to a low noise amplifier. Two design variations of a generic
transmit-receive signal chai... |
2502.03206 | A Unified and General Humanoid Whole-Body Controller for Fine-Grained
Locomotion | cs.RO cs.AI | Locomotion is a fundamental skill for humanoid robots. However, most existing
works made locomotion a single, tedious, unextendable, and passive movement.
This limits the kinematic capabilities of humanoid robots. In contrast, humans
possess versatile athletic abilities-running, jumping, hopping, and finely
adjusting... |
2502.03207 | MotionAgent: Fine-grained Controllable Video Generation via Motion Field
Agent | cs.CV cs.GR | We propose MotionAgent, enabling fine-grained motion control for text-guided
image-to-video generation. The key technique is the motion field agent that
converts motion information in text prompts into explicit motion fields,
providing flexible and precise motion guidance. Specifically, the agent
extracts the object ... |
2502.03210 | From Kernels to Features: A Multi-Scale Adaptive Theory of Feature
Learning | cond-mat.dis-nn cs.LG stat.ML | Theoretically describing feature learning in neural networks is crucial for
understanding their expressive power and inductive biases, motivating various
approaches. Some approaches describe network behavior after training through a
simple change in kernel scale from initialization, resulting in a
generalization powe... |
2502.03214 | iVISPAR -- An Interactive Visual-Spatial Reasoning Benchmark for VLMs | cs.CL cs.AI cs.CV | Vision-Language Models (VLMs) are known to struggle with spatial reasoning
and visual alignment. To help overcome these limitations, we introduce iVISPAR,
an interactive multi-modal benchmark designed to evaluate the spatial reasoning
capabilities of VLMs acting as agents. iVISPAR is based on a variant of the
sliding... |
2502.03218 | Data Dams: A Novel Framework for Regulating and Managing Data Flow in
Large-Scale Systems | cs.IR cs.DB cs.DC | In the era of big data, managing dynamic data flows efficiently is crucial as
traditional storage models struggle with real-time regulation and risk
overflow. This paper introduces Data Dams, a novel framework designed to
optimize data inflow, storage, and outflow by dynamically adjusting flow rates
to prevent conges... |
2502.03220 | Mitigating Language Bias in Cross-Lingual Job Retrieval: A Recruitment
Platform Perspective | cs.CL | Understanding the textual components of resumes and job postings is critical
for improving job-matching accuracy and optimizing job search systems in online
recruitment platforms. However, existing works primarily focus on analyzing
individual components within this information, requiring multiple specialized
tools t... |
2502.03221 | Information Theoretic Analysis of PUF-Based Tamper Protection | cs.IT cs.CR math.IT | Physical Unclonable Functions (PUFs) enable physical tamper protection for
high-assurance devices without needing a continuous power supply that is active
over the entire lifetime of the device. Several methods for PUF-based tamper
protection have been proposed together with practical quantization and error
correctio... |
2502.03227 | Adversarial Dependence Minimization | cs.LG | Many machine learning techniques rely on minimizing the covariance between
output feature dimensions to extract minimally redundant representations from
data. However, these methods do not eliminate all dependencies/redundancies, as
linearly uncorrelated variables can still exhibit nonlinear relationships. This
work ... |
2502.03228 | GARAD-SLAM: 3D GAussian splatting for Real-time Anti Dynamic SLAM | cs.RO cs.CV | The 3D Gaussian Splatting (3DGS)-based SLAM system has garnered widespread
attention due to its excellent performance in real-time high-fidelity
rendering. However, in real-world environments with dynamic objects, existing
3DGS-based SLAM systems often face mapping errors and tracking drift issues. To
address these p... |
2502.03229 | A Unified Framework for Semi-Supervised Image Segmentation and
Registration | cs.CV | Semi-supervised learning, which leverages both annotated and unannotated
data, is an efficient approach for medical image segmentation, where obtaining
annotations for the whole dataset is time-consuming and costly. Traditional
semi-supervised methods primarily focus on extracting features and learning
data distribut... |
2502.03230 | Efficient Vision Language Model Fine-tuning for Text-based Person
Anomaly Search | cs.CV cs.MM | This paper presents the HFUT-LMC team's solution to the WWW 2025 challenge on
Text-based Person Anomaly Search (TPAS). The primary objective of this
challenge is to accurately identify pedestrians exhibiting either normal or
abnormal behavior within a large library of pedestrian images. Unlike
traditional video analy... |
2502.03231 | The Other Side of the Coin: Unveiling the Downsides of Model Aggregation
in Federated Learning from a Layer-peeled Perspective | cs.LG cs.AI | In federated learning (FL), model aggregation is a critical step by which
multiple clients share their knowledge with one another. However, it is also
widely recognized that the aggregated model, when sent back to each client,
performs poorly on local data until after several rounds of local training.
This temporary ... |
2502.03232 | JAMMit! Monolithic 3D-Printing of a Bead Jamming Soft Pneumatic Arm | cs.RO | 3D-printed bellow soft pneumatic arms are widely adopted for their flexible
design, ease of fabrication, and large deformation capabilities. However, their
low stiffness limits their real-world applications. Although several methods
exist to enhance the stiffness of soft actuators, many involve complex
manufacturing ... |
2502.03236 | Pioneer: Physics-informed Riemannian Graph ODE for Entropy-increasing
Dynamics | cs.LG | Dynamic interacting system modeling is important for understanding and
simulating real world systems. The system is typically described as a graph,
where multiple objects dynamically interact with each other and evolve over
time. In recent years, graph Ordinary Differential Equations (ODE) receive
increasing research... |
2502.03238 | Long-tailed Medical Diagnosis with Relation-aware Representation
Learning and Iterative Classifier Calibration | cs.CV cs.AI cs.LG cs.MM | Recently computer-aided diagnosis has demonstrated promising performance,
effectively alleviating the workload of clinicians. However, the inherent
sample imbalance among different diseases leads algorithms biased to the
majority categories, leading to poor performance for rare categories. Existing
works formulated t... |
2502.03244 | Analysis of Value Iteration Through Absolute Probability Sequences | cs.LG | Value Iteration is a widely used algorithm for solving Markov Decision
Processes (MDPs). While previous studies have extensively analyzed its
convergence properties, they primarily focus on convergence with respect to the
infinity norm. In this work, we use absolute probability sequences to develop a
new line of anal... |
2502.03245 | Calibrated Unsupervised Anomaly Detection in Multivariate Time-series
using Reinforcement Learning | cs.LG cs.SY eess.SP eess.SY | This paper investigates unsupervised anomaly detection in multivariate
time-series data using reinforcement learning (RL) in the latent space of an
autoencoder. A significant challenge is the limited availability of anomalous
data, often leading to misclassifying anomalies as normal events, thus raising
false negativ... |
2502.03251 | RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry | cs.LG | The foundation model has heralded a new era in artificial intelligence,
pretraining a single model to offer cross-domain transferability on different
datasets. Graph neural networks excel at learning graph data, the omnipresent
non-Euclidean structure, but often lack the generalization capacity. Hence,
graph foundati... |
2502.03252 | A scale of conceptual orality and literacy: Automatic text
categorization in the tradition of "N\"ahe und Distanz" | cs.CL | Koch and Oesterreicher's model of "N\"ahe und Distanz" (N\"ahe = immediacy,
conceptual orality; Distanz = distance, conceptual literacy) is constantly used
in German linguistics. However, there is no statistical foundation for use in
corpus linguistic analyzes, while it is increasingly moving into empirical
corpus li... |
2502.03253 | How do Humans and Language Models Reason About Creativity? A Comparative
Analysis | cs.CL | Creativity assessment in science and engineering is increasingly based on
both human and AI judgment, but the cognitive processes and biases behind these
evaluations remain poorly understood. We conducted two experiments examining
how including example solutions with ratings impact creativity evaluation,
using a fine... |
2502.03257 | Efficient extraction of medication information from clinical notes: an
evaluation in two languages | cs.CL cs.IR | Objective: To evaluate the accuracy, computational cost and portability of a
new Natural Language Processing (NLP) method for extracting medication
information from clinical narratives. Materials and Methods: We propose an
original transformer-based architecture for the extraction of entities and
their relations pert... |
2502.03261 | CARROT: A Cost Aware Rate Optimal Router | stat.ML cs.LG cs.NI math.ST stat.TH | With the rapid growth in the number of Large Language Models (LLMs), there
has been a recent interest in LLM routing, or directing queries to the cheapest
LLM that can deliver a suitable response. Following this line of work, we
introduce CARROT, a Cost AwaRe Rate Optimal rouTer that can select models based
on any de... |
2502.03263 | Model Reference-Based Control with Guaranteed Predefined Performance for
Uncertain Strict-Feedback Systems | eess.SY cs.SY | To address the complexities posed by time- and state-varying uncertainties
and the computation of analytic derivatives in strict-feedback form (SFF)
systems, this study introduces a novel model reference-based control (MRBC)
framework which applies locally to each subsystem (SS), to ensure output
tracking performance... |
2502.03264 | General Time-series Model for Universal Knowledge Representation of
Multivariate Time-Series data | cs.LG | Universal knowledge representation is a central problem for multivariate time
series(MTS) foundation models and yet remains open. This paper investigates
this problem from the first principle and it makes four folds of contributions.
First, a new empirical finding is revealed: time series with different time
granular... |
2502.03266 | ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic
Environments with Vision Foundation Models | cs.CV cs.RO | Service robots operating in unstructured environments must effectively
recognize and segment unknown objects to enhance their functionality.
Traditional supervised learningbased segmentation techniques require extensive
annotated datasets, which are impractical for the diversity of objects
encountered in real-world s... |
2502.03270 | When Pre-trained Visual Representations Fall Short: Limitations in
Visuo-Motor Robot Learning | cs.RO cs.AI cs.CV cs.LG | The integration of pre-trained visual representations (PVRs) into visuo-motor
robot learning has emerged as a promising alternative to training visual
encoders from scratch. However, PVRs face critical challenges in the context of
policy learning, including temporal entanglement and an inability to generalise
even in... |
2502.03272 | Deep Learning Pipeline for Fully Automated Myocardial Infarct
Segmentation from Clinical Cardiac MR Scans | eess.IV cs.AI cs.CV | Purpose: To develop and evaluate a deep learning-based method that allows to
perform myocardial infarct segmentation in a fully-automated way.
Materials and Methods: For this retrospective study, a cascaded framework of
two and three-dimensional convolutional neural networks (CNNs), specialized on
identifying ische... |
2502.03274 | A Scalable Approach to Probabilistic Neuro-Symbolic Verification | cs.AI | Neuro-Symbolic Artificial Intelligence (NeSy AI) has emerged as a promising
direction for integrating neural learning with symbolic reasoning. In the
probabilistic variant of such systems, a neural network first extracts a set of
symbols from sub-symbolic input, which are then used by a symbolic component to
reason i... |
2502.03275 | Token Assorted: Mixing Latent and Text Tokens for Improved Language
Model Reasoning | cs.CL cs.AI cs.LG cs.LO | Large Language Models (LLMs) excel at reasoning and planning when trained on
chainof-thought (CoT) data, where the step-by-step thought process is
explicitly outlined by text tokens. However, this results in lengthy inputs
where many words support textual coherence rather than core reasoning
information, and processi... |
2502.03278 | Fault-Tolerant Control for System Availability and Continuous Operation
in Heavy-Duty Wheeled Mobile Robots | eess.SY cs.SY | When the control system in a heavy-duty wheeled mobile robot (HD-WMR)
malfunctions, deviations from ideal motion occur, significantly heightening the
risks of off-road instability and costly damage. To meet the demands for
safety, reliability, and controllability in HD-WMRs, the control system must
tolerate faults to... |
2502.03283 | SymAgent: A Neural-Symbolic Self-Learning Agent Framework for Complex
Reasoning over Knowledge Graphs | cs.AI cs.CL cs.LG | Recent advancements have highlighted that Large Language Models (LLMs) are
prone to hallucinations when solving complex reasoning problems, leading to
erroneous results. To tackle this issue, researchers incorporate Knowledge
Graphs (KGs) to improve the reasoning ability of LLMs. However, existing
methods face two li... |
2502.03285 | Deep Learning-based Event Data Coding: A Joint Spatiotemporal and
Polarity Solution | cs.CV eess.IV | Neuromorphic vision sensors, commonly referred to as event cameras, have
recently gained relevance for applications requiring high-speed, high dynamic
range and low-latency data acquisition. Unlike traditional frame-based cameras
that capture 2D images, event cameras generate a massive number of pixel-level
events, c... |
2502.03286 | Conditional Prediction by Simulation for Automated Driving | cs.RO cs.CV | Modular automated driving systems commonly handle prediction and planning as
sequential, separate tasks, thereby prohibiting cooperative maneuvers. To
enable cooperative planning, this work introduces a prediction model that
models the conditional dependencies between trajectories. For this, predictions
are generated... |
2502.03287 | STEMS: Spatial-Temporal Mapping Tool For Spiking Neural Networks | cs.NE cs.AI cs.AR cs.DC | Spiking Neural Networks (SNNs) are promising bio-inspired third-generation
neural networks. Recent research has trained deep SNN models with accuracy on
par with Artificial Neural Networks (ANNs). Although the event-driven and
sparse nature of SNNs show potential for more energy efficient computation than
ANNs, SNN n... |
2502.03292 | ALPET: Active Few-shot Learning for Citation Worthiness Detection in
Low-Resource Wikipedia Languages | cs.CL cs.AI cs.LG | Citation Worthiness Detection (CWD) consists in determining which sentences,
within an article or collection, should be backed up with a citation to
validate the information it provides. This study, introduces ALPET, a framework
combining Active Learning (AL) and Pattern-Exploiting Training (PET), to
enhance CWD for ... |
2502.03297 | IRIS: An Immersive Robot Interaction System | cs.RO cs.LG | This paper introduces IRIS, an immersive Robot Interaction System leveraging
Extended Reality (XR), designed for robot data collection and interaction
across multiple simulators, benchmarks, and real-world scenarios. While
existing XR-based data collection systems provide efficient and intuitive
solutions for large-s... |
2502.03298 | MeDiSumQA: Patient-Oriented Question-Answer Generation from Discharge
Letters | cs.CL cs.AI cs.LG | While increasing patients' access to medical documents improves medical care,
this benefit is limited by varying health literacy levels and complex medical
terminology. Large language models (LLMs) offer solutions by simplifying
medical information. However, evaluating LLMs for safe and patient-friendly
text generati... |
2502.03302 | MAP Image Recovery with Guarantees using Locally Convex Multi-Scale
Energy (LC-MUSE) Model | cs.LG cs.CV eess.IV | We propose a multi-scale deep energy model that is strongly convex in the
local neighbourhood around the data manifold to represent its probability
density, with application in inverse problems. In particular, we represent the
negative log-prior as a multi-scale energy model parameterized by a
Convolutional Neural Ne... |
2502.03304 | Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient
Zeroth-order LLM Fine-tuning | cs.LG cs.AI cs.CL | Large language models (LLMs) excel across various tasks, but standard
first-order (FO) fine-tuning demands considerable memory, significantly
limiting real-world deployment. Recently, zeroth-order (ZO) optimization stood
out as a promising memory-efficient training paradigm, avoiding backward passes
and relying solel... |
2502.03307 | Intent Alignment between Interaction and Language Spaces for
Recommendation | cs.IR | Intent-based recommender systems have garnered significant attention for
uncovering latent fine-grained preferences. Intents, as underlying factors of
interactions, are crucial for improving recommendation interpretability. Most
methods define intents as learnable parameters updated alongside interactions.
However, e... |
2502.03317 | Contact-Aware Motion Planning Among Movable Objects | cs.RO | Most existing methods for motion planning of mobile robots involve generating
collision-free trajectories. However, these methods focusing solely on contact
avoidance may limit the robots' locomotion and can not be applied to tasks
where contact is inevitable or intentional. To address these issues, we propose
a nove... |
2502.03321 | Simplifying Formal Proof-Generating Models with ChatGPT and Basic
Searching Techniques | cs.LO cs.AI | The challenge of formal proof generation has a rich history, but with modern
techniques, we may finally be at the stage of making actual progress in
real-life mathematical problems. This paper explores the integration of ChatGPT
and basic searching techniques to simplify generating formal proofs, with a
particular fo... |
2502.03322 | An efficient end-to-end computational framework for the generation of
ECG calibrated volumetric models of human atrial electrophysiology | math.NA cs.CE cs.NA q-bio.TO | Computational models of atrial electrophysiology (EP) are increasingly
utilized for applications such as the development of advanced mapping systems,
personalized clinical therapy planning, and the generation of virtual cohorts
and digital twins. These models have the potential to establish robust causal
links betwee... |
2502.03323 | Out-of-Distribution Detection using Synthetic Data Generation | cs.CL cs.AI cs.LG | Distinguishing in- and out-of-distribution (OOD) inputs is crucial for
reliable deployment of classification systems. However, OOD data is typically
unavailable or difficult to collect, posing a significant challenge for
accurate OOD detection. In this work, we present a method that harnesses the
generative capabilit... |
2502.03325 | ECM: A Unified Electronic Circuit Model for Explaining the Emergence of
In-Context Learning and Chain-of-Thought in Large Language Model | cs.CL cs.AI | Recent advancements in large language models (LLMs) have led to significant
successes across various applications, where the most noticeable is to a series
of emerging capabilities, particularly in the areas of In-Context Learning
(ICL) and Chain-of-Thought (CoT). To better understand and control model
performance, m... |
2502.03327 | Is In-Context Universality Enough? MLPs are Also Universal In-Context | stat.ML cs.LG cs.NA cs.NE math.NA math.PR | The success of transformers is often linked to their ability to perform
in-context learning. Recent work shows that transformers are universal in
context, capable of approximating any real-valued continuous function of a
context (a probability measure over $\mathcal{X}\subseteq \mathbb{R}^d$) and a
query $x\in \mathc... |
2502.03330 | Controllable GUI Exploration | cs.HC cs.AI cs.CV cs.GR | During the early stages of interface design, designers need to produce
multiple sketches to explore a design space. Design tools often fail to support
this critical stage, because they insist on specifying more details than
necessary. Although recent advances in generative AI have raised hopes of
solving this issue, ... |
2502.03332 | A Mixture-Based Framework for Guiding Diffusion Models | stat.ML cs.LG | Denoising diffusion models have driven significant progress in the field of
Bayesian inverse problems. Recent approaches use pre-trained diffusion models
as priors to solve a wide range of such problems, only leveraging
inference-time compute and thereby eliminating the need to retrain
task-specific models on the sam... |
2502.03333 | RadVLM: A Multitask Conversational Vision-Language Model for Radiology | cs.CV cs.AI | The widespread use of chest X-rays (CXRs), coupled with a shortage of
radiologists, has driven growing interest in automated CXR analysis and
AI-assisted reporting. While existing vision-language models (VLMs) show
promise in specific tasks such as report generation or abnormality detection,
they often lack support f... |
2502.03335 | Actions Speak Louder Than Words: Rate-Reward Trade-off in Markov
Decision Processes | cs.IT math.IT | The impact of communication on decision-making systems has been extensively
studied under the assumption of dedicated communication channels. We instead
consider communicating through actions, where the message is embedded into the
actions of an agent which interacts with the environment in a Markov decision
process ... |
2502.03338 | Optimal PMU Placement for Kalman Filtering of DAE Power System Models | eess.SY cs.SY | Optimal sensor placement is essential for minimizing costs and ensuring
accurate state estimation in power systems. This paper introduces a novel
method for optimal sensor placement for dynamic state estimation of power
systems modeled by differential-algebraic equations. The method identifies
optimal sensor location... |
2502.03340 | Interaction-Aware Gaussian Weighting for Clustered Federated Learning | cs.LG | Federated Learning (FL) emerged as a decentralized paradigm to train models
while preserving privacy. However, conventional FL struggles with data
heterogeneity and class imbalance, which degrade model performance. Clustered
FL balances personalization and decentralized training by grouping clients with
analogous dat... |
2502.03341 | Adaptive Variational Inference in Probabilistic Graphical Models: Beyond
Bethe, Tree-Reweighted, and Convex Free Energies | stat.ML cs.AI cs.LG | Variational inference in probabilistic graphical models aims to approximate
fundamental quantities such as marginal distributions and the partition
function. Popular approaches are the Bethe approximation, tree-reweighted, and
other types of convex free energies. These approximations are efficient but can
fail if the... |
2502.03346 | Implicit Communication in Human-Robot Collaborative Transport | cs.RO | We focus on human-robot collaborative transport, in which a robot and a user
collaboratively move an object to a goal pose. In the absence of explicit
communication, this problem is challenging because it demands tight implicit
coordination between two heterogeneous agents, who have very different sensing,
actuation,... |
2502.03347 | DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday
Life Behavior Modeling | cs.CY cs.SI | Understanding everyday life behavior of young adults through personal
devices, e.g., smartphones and smartwatches, is key for various applications,
from enhancing the user experience in mobile apps to enabling appropriate
interventions in digital health apps. Towards this goal, previous studies have
relied on dataset... |
2502.03349 | Robust Autonomy Emerges from Self-Play | cs.LG cs.AI cs.RO | Self-play has powered breakthroughs in two-player and multi-player games.
Here we show that self-play is a surprisingly effective strategy in another
domain. We show that robust and naturalistic driving emerges entirely from
self-play in simulation at unprecedented scale -- 1.6~billion~km of driving.
This is enabled ... |
2502.03350 | Optimal Task Order for Continual Learning of Multiple Tasks | stat.ML cs.LG | Continual learning of multiple tasks remains a major challenge for neural
networks. Here, we investigate how task order influences continual learning and
propose a strategy for optimizing it. Leveraging a linear teacher-student model
with latent factors, we derive an analytical expression relating task
similarity and... |
2502.03356 | Inverse Mixed Strategy Games with Generative Trajectory Models | cs.RO | Game-theoretic models are effective tools for modeling multi-agent
interactions, especially when robots need to coordinate with humans. However,
applying these models requires inferring their specifications from observed
behaviors -- a challenging task known as the inverse game problem. Existing
inverse game approach... |
2502.03358 | Minerva: A Programmable Memory Test Benchmark for Language Models | cs.CL | How effectively can LLM-based AI assistants utilize their memory (context) to
perform various tasks? Traditional data benchmarks, which are often manually
crafted, suffer from several limitations: they are static, susceptible to
overfitting, difficult to interpret, and lack actionable insights--failing to
pinpoint th... |
2502.03359 | GHOST: Gaussian Hypothesis Open-Set Technique | cs.CV cs.AI cs.LG | Evaluations of large-scale recognition methods typically focus on overall
performance. While this approach is common, it often fails to provide insights
into performance across individual classes, which can lead to fairness issues
and misrepresentation. Addressing these gaps is crucial for accurately
assessing how we... |
2502.03360 | A Beam's Eye View to Fluence Maps 3D Network for Ultra Fast VMAT
Radiotherapy Planning | eess.IV cs.AI physics.med-ph | Volumetric Modulated Arc Therapy (VMAT) revolutionizes cancer treatment by
precisely delivering radiation while sparing healthy tissues. Fluence maps
generation, crucial in VMAT planning, traditionally involves complex and
iterative, and thus time consuming processes. These fluence maps are
subsequently leveraged for... |
2502.03364 | Scaling laws in wearable human activity recognition | cs.LG | Many deep architectures and self-supervised pre-training techniques have been
proposed for human activity recognition (HAR) from wearable multimodal sensors.
Scaling laws have the potential to help move towards more principled design by
linking model capacity with pre-training data volume. Yet, scaling laws have
not ... |
2502.03365 | A Match Made in Heaven? Matching Test Cases and Vulnerabilities With the
VUTECO Approach | cs.SE cs.CR cs.LG | Software vulnerabilities are commonly detected via static analysis,
penetration testing, and fuzzing. They can also be found by running unit tests
- so-called vulnerability-witnessing tests - that stimulate the
security-sensitive behavior with crafted inputs. Developing such tests is
difficult and time-consuming; thu... |
2502.03366 | Rethinking Approximate Gaussian Inference in Classification | cs.LG stat.ML | In classification tasks, softmax functions are ubiquitously used as output
activations to produce predictive probabilities. Such outputs only capture
aleatoric uncertainty. To capture epistemic uncertainty, approximate Gaussian
inference methods have been proposed, which output Gaussian distributions over
the logit s... |
2502.03367 | SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and
Parsimonious Model Discovery in Science and Beyond | cs.LG | Symbolic regression (SR) is an emerging branch of machine learning focused on
discovering simple and interpretable mathematical expressions from data.
Although a wide-variety of SR methods have been developed, they often face
challenges such as high computational cost, poor scalability with respect to
the number of i... |
2502.03368 | PalimpChat: Declarative and Interactive AI analytics | cs.AI cs.DB cs.IR | Thanks to the advances in generative architectures and large language models,
data scientists can now code pipelines of machine-learning operations to
process large collections of unstructured data. Recent progress has seen the
rise of declarative AI frameworks (e.g., Palimpzest, Lotus, and DocETL) to
build optimized... |
2502.03369 | Learning from Active Human Involvement through Proxy Value Propagation | cs.AI cs.RO | Learning from active human involvement enables the human subject to actively
intervene and demonstrate to the AI agent during training. The interaction and
corrective feedback from human brings safety and AI alignment to the learning
process. In this work, we propose a new reward-free active human involvement
method ... |
2502.03370 | Deep Learning-Based Approach for Identification of Potato Leaf Diseases
Using Wrapper Feature Selection and Feature Concatenation | cs.CV cs.LG | The potato is a widely grown crop in many regions of the world. In recent
decades, potato farming has gained incredible traction in the world. Potatoes
are susceptible to several illnesses that stunt their development. This plant
seems to have significant leaf disease. Early Blight and Late Blight are two
prevalent l... |
2502.03373 | Demystifying Long Chain-of-Thought Reasoning in LLMs | cs.CL cs.LG | Scaling inference compute enhances reasoning in large language models (LLMs),
with long chains-of-thought (CoTs) enabling strategies like backtracking and
error correction. Reinforcement learning (RL) has emerged as a crucial method
for developing these capabilities, yet the conditions under which long CoTs
emerge re... |
2502.03375 | Interactive Visualization Recommendation with Hier-SUCB | cs.IR | Visualization recommendation aims to enable rapid visual analysis of massive
datasets. In real-world scenarios, it is essential to quickly gather and
comprehend user preferences to cover users from diverse backgrounds, including
varying skill levels and analytical tasks. Previous approaches to personalized
visualizat... |
2502.03376 | Ethical Considerations for the Military Use of Artificial Intelligence
in Visual Reconnaissance | cs.CY cs.CV | This white paper underscores the critical importance of responsibly deploying
Artificial Intelligence (AI) in military contexts, emphasizing a commitment to
ethical and legal standards. The evolving role of AI in the military goes
beyond mere technical applications, necessitating a framework grounded in
ethical princ... |
2502.03377 | Energy-Efficient Flying LoRa Gateways: A Multi-Agent Reinforcement
Learning Approach | cs.NI cs.LG | With the rapid development of next-generation Internet of Things (NG-IoT)
networks, the increasing number of connected devices has led to a surge in
power consumption. This rise in energy demand poses significant challenges to
resource availability and raises sustainability concerns for large-scale IoT
deployments. E... |
2502.03381 | Integrating automatic speech recognition into remote healthcare
interpreting: A pilot study of its impact on interpreting quality | cs.CL | This paper reports on the results from a pilot study investigating the impact
of automatic speech recognition (ASR) technology on interpreting quality in
remote healthcare interpreting settings. Employing a within-subjects experiment
design with four randomised conditions, this study utilises scripted medical
consult... |
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