id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.07056 | Autonomous Deep Agent | cs.AI cs.LG | This technical brief introduces Deep Agent, an advanced autonomous AI system
designed to manage complex multi-phase tasks through a novel hierarchical task
management architecture. The system's foundation is built on our Hierarchical
Task DAG (HTDAG) framework, which dynamically decomposes high-level objectives
into ... |
2502.07057 | Tokenization Standards for Linguistic Integrity: Turkish as a Benchmark | cs.CL | Tokenization is a fundamental preprocessing step in NLP, directly impacting
large language models' (LLMs) ability to capture syntactic, morphosyntactic,
and semantic structures. This paper introduces a novel framework for
systematically evaluating tokenization strategies, addressing challenges in
morphologically rich... |
2502.07058 | Using Contextually Aligned Online Reviews to Measure LLMs' Performance
Disparities Across Language Varieties | cs.CL cs.HC | A language can have different varieties. These varieties can affect the
performance of natural language processing (NLP) models, including large
language models (LLMs), which are often trained on data from widely spoken
varieties. This paper introduces a novel and cost-effective approach to
benchmark model performanc... |
2502.07059 | Federated Continual Learning: Concepts, Challenges, and Solutions | cs.LG cs.AI | Federated Continual Learning (FCL) has emerged as a robust solution for
collaborative model training in dynamic environments, where data samples are
continuously generated and distributed across multiple devices. This survey
provides a comprehensive review of FCL, focusing on key challenges such as
heterogeneity, mod... |
2502.07064 | Contextual Thompson Sampling via Generation of Missing Data | cs.LG cs.AI stat.ML | We introduce a framework for Thompson sampling contextual bandit algorithms,
in which the algorithm's ability to quantify uncertainty and make decisions
depends on the quality of a generative model that is learned offline. Instead
of viewing uncertainty in the environment as arising from unobservable latent
parameter... |
2502.07065 | Active Inference through Incentive Design in Markov Decision Processes | eess.SY cs.SY | We present a method for active inference with partial observations in
stochastic systems through incentive design, also known as the leader-follower
game. Consider a leader agent who aims to infer a follower agent's type given a
finite set of possible types. Different types of followers differ in either the
dynamical... |
2502.07067 | Repository-level Code Search with Neural Retrieval Methods | cs.IR | This paper presents a multi-stage reranking system for repository-level code
search, which leverages the vastly available commit histories of large
open-source repositories to aid in bug fixing. We define the task of
repository-level code search as retrieving the set of files from the current
state of a code reposito... |
2502.07068 | Specializing Large Language Models to Simulate Survey Response
Distributions for Global Populations | cs.CL | Large-scale surveys are essential tools for informing social science research
and policy, but running surveys is costly and time-intensive. If we could
accurately simulate group-level survey results, this would therefore be very
valuable to social science research. Prior work has explored the use of large
language mo... |
2502.07069 | Semantics-Aware Updates from Remote IoT Devices to Interconnected LEO
Satellites | cs.NI cs.IT math.IT | Providing timely and informative data in Integrated Terrestrial and
Non-Terrestrial Networks (T-NTNs) is critical as data volume continues to grow
while the resources available on devices remain limited. To address this, we
adopt a semantics-aware approach to optimize the Version Age of Information
(VAoI) in a status... |
2502.07070 | Comprehensive Analysis of Thermal Dissipation in Lithium-Ion Battery
Packs | eess.SY cs.SY hep-ph | Effective thermal management is critical for lithium-ion battery packs' safe
and efficient operations, particularly in applications such as drones, where
compact designs and varying airflow conditions present unique challenges. This
study investigates the thermal performance of a 16-cell lithium-ion battery
pack by o... |
2502.07071 | TRADES: Generating Realistic Market Simulations with Diffusion Models | q-fin.TR cs.AI cs.LG q-fin.CP | Financial markets are complex systems characterized by high statistical
noise, nonlinearity, and constant evolution. Thus, modeling them is extremely
hard. We address the task of generating realistic and responsive Limit Order
Book (LOB) market simulations, which are fundamental for calibrating and
testing trading st... |
2502.07072 | IRepair: An Intent-Aware Approach to Repair Data-Driven Errors in Large
Language Models | cs.CL cs.AI cs.SE | Not a day goes by without hearing about the impressive feats of large
language models (LLMs), and equally, not a day passes without hearing about
their challenges. LLMs are notoriously vulnerable to biases in their dataset,
leading to issues such as toxicity. While domain-adaptive training has been
employed to mitiga... |
2502.07076 | On the use of neural networks for the structural characterization of
polymeric porous materials | cond-mat.soft cond-mat.mtrl-sci cs.CV eess.IV | The structural characterization is an essential task in the study of porous
materials. To achieve reliable results, it requires to evaluate images with
hundreds of pores. Current methods require large time amounts and are subjected
to human errors and subjectivity. A completely automatic tool would not only
speed up ... |
2502.07077 | Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language
Models | cs.CL cs.CY cs.HC | The tendency of users to anthropomorphise large language models (LLMs) is of
growing interest to AI developers, researchers, and policy-makers. Here, we
present a novel method for empirically evaluating anthropomorphic LLM
behaviours in realistic and varied settings. Going beyond single-turn static
benchmarks, we con... |
2502.07081 | Fast Clustering of Categorical Big Data | cs.LG cs.DB | The K-Modes algorithm, developed for clustering categorical data, is of high
algorithmic simplicity but suffers from unreliable performances in clustering
quality and clustering efficiency, both heavily influenced by the choice of
initial cluster centers. In this paper, we investigate Bisecting K-Modes
(BK-Modes), a ... |
2502.07082 | "Once Upon a Time..." Literary Narrative Connectedness Progresses with
Grade Level: Potential Impact on Reading Fluency and Literacy Skills | cs.CL | Selecting an appropriate book is crucial for fostering reading habits in
children. While children exhibit varying levels of complexity when generating
oral narratives, the question arises: do children's books also differ in
narrative complexity? This study explores the narrative dynamics of literary
texts used in sch... |
2502.07085 | Real-time Optimization for Wind-to-H2 Driven Critical Infrastructures
Based on Active Constraints Identification and Integer Variables Prediction | eess.SY cs.SY | This paper proposes a concept of wind-to-hydrogen-driven critical
infrastructure (W2H-CI) as an engineering solution for decarbonizing the power
generation sector where it utilizes wind power to produce hydrogen through
electrolysis and combines it with the carbon captured from fossil fuel power
plants. First, a conv... |
2502.07087 | Evaluating the Systematic Reasoning Abilities of Large Language Models
through Graph Coloring | cs.LG | Contemporary large language models are powerful problem-solving tools, but
they exhibit weaknesses in their reasoning abilities which ongoing research
seeks to mitigate. We investigate graph coloring as a means of evaluating an
LLM's capacities for systematic step-by-step reasoning and possibility space
exploration, ... |
2502.07088 | Kernels of Selfhood: GPT-4o shows humanlike patterns of cognitive
consistency moderated by free choice | cs.CY cs.AI cs.CL cs.HC cs.LG | Large Language Models (LLMs) show emergent patterns that mimic human
cognition. We explore whether they also mirror other, less deliberative human
psychological processes. Drawing upon classical theories of cognitive
consistency, two preregistered studies tested whether GPT-4o changed its
attitudes toward Vladimir Pu... |
2502.07090 | Generative Distribution Prediction: A Unified Approach to Multimodal
Learning | stat.ML cs.AI cs.LG | Accurate prediction with multimodal data-encompassing tabular, textual, and
visual inputs or outputs-is fundamental to advancing analytics in diverse
application domains. Traditional approaches often struggle to integrate
heterogeneous data types while maintaining high predictive accuracy. We
introduce Generative Dis... |
2502.07096 | Lotus: Creating Short Videos From Long Videos With Abstractive and
Extractive Summarization | cs.HC cs.CV | Short-form videos are popular on platforms like TikTok and Instagram as they
quickly capture viewers' attention. Many creators repurpose their long-form
videos to produce short-form videos, but creators report that planning,
extracting, and arranging clips from long-form videos is challenging.
Currently, creators mak... |
2502.07101 | SMAB: MAB based word Sensitivity Estimation Framework and its
Applications in Adversarial Text Generation | cs.CL | To understand the complexity of sequence classification tasks, Hahn et al.
(2021) proposed sensitivity as the number of disjoint subsets of the input
sequence that can each be individually changed to change the output. Though
effective, calculating sensitivity at scale using this framework is costly
because of expone... |
2502.07102 | Optimal Steady-State Secondary Control of MT-HVdc Grids with Reduced
Communications | eess.SY cs.SY math.OC | In this paper, we propose a centralized secondary control for the real-time
steady-state optimization of multi-terminal HVdc grids under voltage and
current limits. First, we present the dynamic models of the grid components,
including the modular multilevel converter (MMC) stations and their different
control layers... |
2502.07107 | A Framework for Supervised and Unsupervised Segmentation and
Classification of Materials Microstructure Images | stat.AP cs.CV stat.ML | Microstructure of materials is often characterized through image analysis to
understand processing-structure-properties linkages. We propose a largely
automated framework that integrates unsupervised and supervised learning
methods to classify micrographs according to microstructure phase/class and,
for multiphase mi... |
2502.07109 | Game of Coding With an Unknown Adversary | cs.IT cs.LG math.IT | Motivated by emerging decentralized applications, the \emph{game of coding}
framework has been recently introduced to address scenarios where the
adversary's control over coded symbols surpasses the fundamental limits of
traditional coding theory. Still, the reward mechanism available in
decentralized systems, motiva... |
2502.07111 | Likelihood-Free Estimation for Spatiotemporal Hawkes processes with
missing data and application to predictive policing | cs.LG stat.AP stat.ME | With the growing use of AI technology, many police departments use
forecasting software to predict probable crime hotspots and allocate patrolling
resources effectively for crime prevention. The clustered nature of crime data
makes self-exciting Hawkes processes a popular modeling choice. However, one
significant cha... |
2502.07114 | Online Covariance Matrix Estimation in Sketched Newton Methods | stat.ML cs.LG cs.NA math.NA math.OC stat.CO | Given the ubiquity of streaming data, online algorithms have been widely used
for parameter estimation, with second-order methods particularly standing out
for their efficiency and robustness. In this paper, we study an online sketched
Newton method that leverages a randomized sketching technique to perform an
approx... |
2502.07115 | Online Scheduling for LLM Inference with KV Cache Constraints | cs.LG cs.AI math.OC | Large Language Model (LLM) inference, where a trained model generates text
one word at a time in response to user prompts, is a computationally intensive
process requiring efficient scheduling to optimize latency and resource
utilization. A key challenge in LLM inference is the management of the
Key-Value (KV) cache,... |
2502.07117 | Choroidal image analysis for OCT image sequences with applications in
systemic health | eess.IV cs.CV cs.LG cs.MS | The choroid, a highly vascular layer behind the retina, is an extension of
the central nervous system and has parallels with the renal cortex, with blood
flow far exceeding that of the brain and kidney. Thus, there has been growing
interest of choroidal blood flow reflecting physiological status of systemic
disease. ... |
2502.07119 | SAFE: Self-Supervised Anomaly Detection Framework for Intrusion
Detection | cs.CR cs.LG | The proliferation of IoT devices has significantly increased network
vulnerabilities, creating an urgent need for effective Intrusion Detection
Systems (IDS). Machine Learning-based IDS (ML-IDS) offer advanced detection
capabilities but rely on labeled attack data, which limits their ability to
identify unknown threa... |
2502.07120 | Is Long Range Sequential Modeling Necessary For Colorectal Tumor
Segmentation? | cs.CV | Segmentation of colorectal cancer (CRC) tumors in 3D medical imaging is both
complex and clinically critical, providing vital support for effective
radiation therapy planning and survival outcome assessment. Recently, 3D
volumetric segmentation architectures incorporating long-range sequence
modeling mechanisms, such... |
2502.07124 | Structural Reformation of Large Language Model Neuron Encapsulation for
Divergent Information Aggregation | cs.CL | Structured neuron encapsulation introduces a modular framework that enables
more effective aggregation and specialization of information within deep
learning architectures. A model modified through this framework demonstrated
improved perplexity scores, greater lexical variability, and enhanced
consistency in logical... |
2502.07128 | Cardiverse: Harnessing LLMs for Novel Card Game Prototyping | cs.CL cs.AI cs.MM | The prototyping of computer games, particularly card games, requires
extensive human effort in creative ideation and gameplay evaluation. Recent
advances in Large Language Models (LLMs) offer opportunities to automate and
streamline these processes. However, it remains challenging for LLMs to design
novel game mechan... |
2502.07129 | Fourier-enhanced Neural Networks For Systems Biology Applications | cs.LG q-bio.QM | In the field of systems biology, differential equations are commonly used to
model biological systems, but solving them for large-scale and complex systems
can be computationally expensive. Recently, the integration of machine learning
and mathematical modeling has offered new opportunities for scientific
discoveries... |
2502.07130 | Unconstrained Body Recognition at Altitude and Range: Comparing Four
Approaches | cs.CV cs.AI cs.LG | This study presents an investigation of four distinct approaches to long-term
person identification using body shape. Unlike short-term re-identification
systems that rely on temporary features (e.g., clothing), we focus on learning
persistent body shape characteristics that remain stable over time. We
introduce a bo... |
2502.07131 | TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model
Bring? -- A Case Study on Korea Financial Texts | cs.CL q-fin.CP | Domain specificity of embedding models is critical for effective performance.
However, existing benchmarks, such as FinMTEB, are primarily designed for
high-resource languages, leaving low-resource settings, such as Korean,
under-explored. Directly translating established English benchmarks often fails
to capture the... |
2502.07132 | Interactive Data Harmonization with LLM Agents | cs.AI cs.DB | Data harmonization is an essential task that entails integrating datasets
from diverse sources. Despite years of research in this area, it remains a
time-consuming and challenging task due to schema mismatches, varying
terminologies, and differences in data collection methodologies. This paper
presents the case for a... |
2502.07133 | Cross-platform Learning-based Fault Tolerant Surfacing Controller for
Underwater Robots | cs.RO | In this paper, we propose a novel cross-platform fault-tolerant surfacing
controller for underwater robots, based on reinforcement learning (RL). Unlike
conventional approaches, which require explicit identification of
malfunctioning actuators, our method allows the robot to surface using only the
remaining operation... |
2502.07135 | One-Shot Learning for k-SAT | cs.DS cs.LG math.ST stat.ML stat.TH | Consider a $k$-SAT formula $\Phi$ where every variable appears at most $d$
times, and let $\sigma$ be a satisfying assignment of $\Phi$ sampled
proportionally to $e^{\beta m(\sigma)}$ where $m(\sigma)$ is the number of
variables set to true and $\beta$ is a real parameter. Given $\Phi$ and
$\sigma$, can we learn the ... |
2502.07136 | A Safe Hybrid Control Framework for Car-like Robot with Guaranteed
Global Path-Invariance using a Control Barrier Function | cs.RO cs.SY eess.SY | This work proposes a hybrid framework for car-like robots with obstacle
avoidance, global convergence, and safety, where safety is interpreted as path
invariance, namely, once the robot converges to the path, it never leaves the
path. Given a priori obstacle-free feasible path where obstacles can be around
the path, ... |
2502.07138 | Towards a Robust Framework for Multimodal Hate Detection: A Study on
Video vs. Image-based Content | cs.CV cs.CL cs.LG | Social media platforms enable the propagation of hateful content across
different modalities such as textual, auditory, and visual, necessitating
effective detection methods. While recent approaches have shown promise in
handling individual modalities, their effectiveness across different modality
combinations remain... |
2502.07139 | Language-TPP: Integrating Temporal Point Processes with Language Models
for Event Analysis | cs.CL cs.LG | Temporal Point Processes (TPPs) have been widely used for event sequence
modeling, but they often struggle to incorporate rich textual event
descriptions effectively. Conversely, while Large Language Models (LLMs) have
been shown remarkable capabilities in processing textual data, they lack
mechanisms for handling te... |
2502.07140 | Few-Shot Multi-Human Neural Rendering Using Geometry Constraints | cs.CV cs.AI cs.GR | We present a method for recovering the shape and radiance of a scene
consisting of multiple people given solely a few images. Multi-human scenes are
complex due to additional occlusion and clutter. For single-human settings,
existing approaches using implicit neural representations have achieved
impressive results th... |
2502.07141 | Small steps no more: Global convergence of stochastic gradient bandits
for arbitrary learning rates | cs.LG | We provide a new understanding of the stochastic gradient bandit algorithm by
showing that it converges to a globally optimal policy almost surely using
\emph{any} constant learning rate. This result demonstrates that the stochastic
gradient algorithm continues to balance exploration and exploitation
appropriately ev... |
2502.07143 | Ask Patients with Patience: Enabling LLMs for Human-Centric Medical
Dialogue with Grounded Reasoning | cs.CL | Accurate and efficient diagnosis in online medical consultations remains a
challenge for current large language models. These models often rely on
single-turn interactions and lack the ability to refine their predictions
through follow-up questions. Additionally, their responses frequently contain
complex medical ter... |
2502.07145 | Mesh2SSM++: A Probabilistic Framework for Unsupervised Learning of
Statistical Shape Model of Anatomies from Surface Meshes | cs.CV | Anatomy evaluation is crucial for understanding the physiological state,
diagnosing abnormalities, and guiding medical interventions. Statistical shape
modeling (SSM) is vital in this process. By enabling the extraction of
quantitative morphological shape descriptors from MRI and CT scans, SSM
provides comprehensive ... |
2502.07148 | Expressing entropy and cross-entropy in expansions of common meadows | cs.IT math.IT | A common meadow is an enrichment of a field with a partial division operation
that is made total by assuming that division by zero takes the a default value,
a special element $\bot$ adjoined to the field. To a common meadow of real
numbers we add a binary logarithm $\log_2(-)$, which we also assume to be total
with ... |
2502.07151 | Conditional Distribution Quantization in Machine Learning | cs.LG | Conditional expectation \mathbb{E}(Y \mid X) often fails to capture the
complexity of multimodal conditional distributions \mathcal{L}(Y \mid X). To
address this, we propose using n-point conditional quantizations--functional
mappings of X that are learnable via gradient descent--to approximate
\mathcal{L}(Y \mid X).... |
2502.07153 | Feature Importance Depends on Properties of the Data: Towards Choosing
the Correct Explanations for Your Data and Decision Trees based Models | cs.LG cs.AI | In order to ensure the reliability of the explanations of machine learning
models, it is crucial to establish their advantages and limits and in which
case each of these methods outperform. However, the current understanding of
when and how each method of explanation can be used is insufficient. To fill
this gap, we ... |
2502.07154 | Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting
Confidence Improves Mathematical Reasoning | cs.LG cs.AI | Recent progress in large language models (LLMs) highlights the power of
scaling test-time compute to achieve strong performance on complex tasks, such
as mathematical reasoning and code generation. This raises a critical question:
how should model training be modified to optimize performance under a
subsequent test-t... |
2502.07156 | Explaining 3D Computed Tomography Classifiers with Counterfactuals | cs.CV cs.AI | Counterfactual explanations in medical imaging are critical for understanding
the predictions made by deep learning models. We extend the Latent Shift
counterfactual generation method from 2D applications to 3D computed tomography
(CT) scans. We address the challenges associated with 3D data, such as limited
training... |
2502.07158 | Early Risk Prediction of Pediatric Cardiac Arrest from Electronic Health
Records via Multimodal Fused Transformer | cs.LG cs.AI | Early prediction of pediatric cardiac arrest (CA) is critical for timely
intervention in high-risk intensive care settings. We introduce PedCA-FT, a
novel transformer-based framework that fuses tabular view of EHR with the
derived textual view of EHR to fully unleash the interactions of
high-dimensional risk factors ... |
2502.07160 | HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates | cs.CV cs.MM | Image compression under ultra-low bitrates remains challenging for both
conventional learned image compression (LIC) and generative vector-quantized
(VQ) modeling. Conventional LIC suffers from severe artifacts due to heavy
quantization, while generative VQ modeling gives poor fidelity due to the
mismatch between lea... |
2502.07161 | A Survey on Mamba Architecture for Vision Applications | cs.CV cs.AI | Transformers have become foundational for visual tasks such as object
detection, semantic segmentation, and video understanding, but their quadratic
complexity in attention mechanisms presents scalability challenges. To address
these limitations, the Mamba architecture utilizes state-space models (SSMs)
for linear sc... |
2502.07164 | Does Training on Synthetic Data Make Models Less Robust? | cs.CL cs.AI cs.LG | An increasingly common practice is to train large language models (LLMs)
using synthetic data. Often this synthetic data is produced by the same or
similar LLMs as those it is being used to train. This raises the question of
whether the synthetic data might in fact exacerbate certain "blindspots" by
reinforcing heuri... |
2502.07165 | Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent
Prompting Strategy for Text Classification | cs.CL cs.AI | We present PRINCIPLE-BASED PROMPTING, a simple but effective multi-agent
prompting strategy for text classification. It first asks multiple LLM agents
to independently generate candidate principles based on analysis of
demonstration samples with or without labels, consolidates them into final
principles via a finaliz... |
2502.07166 | Bayesian Optimization for Building Social-Influence-Free Consensus | cs.MA cs.GT cs.LG stat.ML | We introduce Social Bayesian Optimization (SBO), a vote-efficient algorithm
for consensus-building in collective decision-making. In contrast to
single-agent scenarios, collective decision-making encompasses group dynamics
that may distort agents' preference feedback, thereby impeding their capacity
to achieve a soci... |
2502.07169 | Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow
Technology | physics.comp-ph cs.LG physics.geo-ph | Geological Carbon Storage (GCS) is a key technology for achieving global
climate goals by capturing and storing CO2 in deep geological formations. Its
effectiveness and safety rely on accurate monitoring of subsurface CO2
migration using advanced time-lapse seismic imaging. A Digital Shadow framework
integrates field... |
2502.07170 | Effcient classical error correction for parity encoded spin systems | quant-ph cs.IT math.IT | Fast solvers for combinatorial optimization problems (COPs) have attracted
engineering interest in various industrial and social applications. Quantum
annealing (QA) has emerged as a promising candidate and significant efforts
have been dedicated to its development. Since COP is encoded in the Ising
interaction betwe... |
2502.07171 | Enhancing Robustness Of Digital Shadow For CO2 Storage Monitoring With
Augmented Rock Physics Modeling | physics.comp-ph cs.LG physics.geo-ph | To meet climate targets, the IPCC underscores the necessity of technologies
capable of removing gigatonnes of CO2 annually, with Geological Carbon Storage
(GCS) playing a central role. GCS involves capturing CO2 and injecting it into
deep geological formations for long-term storage, requiring precise monitoring
to en... |
2502.07172 | SemiHMER: Semi-supervised Handwritten Mathematical Expression
Recognition using pseudo-labels | cs.CV cs.AI | In this paper, we study semi-supervised Handwritten Mathematical Expression
Recognition (HMER) via exploring both labeled data and extra unlabeled data. We
propose a novel consistency regularization framework, termed SemiHMER, which
introduces dual-branch semi-supervised learning. Specifically, we enforce
consistency... |
2502.07175 | Foreign-Object Detection in High-Voltage Transmission Line Based on
Improved YOLOv8m | cs.CV cs.AI | The safe operation of high-voltage transmission lines ensures the power
grid's security. Various foreign objects attached to the transmission lines,
such as balloons, kites and nesting birds, can significantly affect the safe
and stable operation of high-voltage transmission lines. With the advancement
of computer vi... |
2502.07176 | MatrixKAN: Parallelized Kolmogorov-Arnold Network | cs.LG | Kolmogorov-Arnold Networks (KAN) are a new class of neural network
architecture representing a promising alternative to the Multilayer Perceptron
(MLP), demonstrating improved expressiveness and interpretability. However,
KANs suffer from slow training and inference speeds relative to MLPs due in
part to the recursiv... |
2502.07178 | Online Aggregation of Trajectory Predictors | cs.RO | Trajectory prediction, the task of forecasting future agent behavior from
past data, is central to safe and efficient autonomous driving. A diverse set
of methods (e.g., rule-based or learned with different architectures and
datasets) have been proposed, yet it is often the case that the performance of
these methods ... |
2502.07179 | Improved YOLOv7 model for insulator defect detection | cs.CV cs.AI | Insulators are crucial insulation components and structural supports in power
grids, playing a vital role in the transmission lines. Due to temperature
fluctuations, internal stress, or damage from hail, insulators are prone to
injury. Automatic detection of damaged insulators faces challenges such as
diverse types, ... |
2502.07181 | Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using
Deep Learning with Visual Representations | cs.LG cs.CV | This research addresses the challenge of limited data in tabular data
classification, particularly prevalent in domains with constraints like
healthcare. We propose Tab2Visual, a novel approach that transforms
heterogeneous tabular data into visual representations, enabling the
application of powerful deep learning m... |
2502.07182 | Morphing Wing Designs in Commercial Aviation | eess.SY cs.SY | With increasing demands for fuel efficiency and operational adaptability in
commercial aviation}, this paper provides a systematic review and
classification of morphing wing technologies, analyzing their aerodynamic
performance characteristics and atmospheric condition adaptability. We first
develop a comprehensive c... |
2502.07183 | Space-Aware Instruction Tuning: Dataset and Benchmark for Guide Dog
Robots Assisting the Visually Impaired | cs.RO cs.CV | Guide dog robots offer promising solutions to enhance mobility and safety for
visually impaired individuals, addressing the limitations of traditional guide
dogs, particularly in perceptual intelligence and communication. With the
emergence of Vision-Language Models (VLMs), robots are now capable of
generating natura... |
2502.07184 | Refine Knowledge of Large Language Models via Adaptive Contrastive
Learning | cs.CL cs.AI | How to alleviate the hallucinations of Large Language Models (LLMs) has
always been the fundamental goal pursued by the LLMs research community.
Looking through numerous hallucination-related studies, a mainstream category
of methods is to reduce hallucinations by optimizing the knowledge
representation of LLMs to ch... |
2502.07186 | Perceived Confidence Scoring for Data Annotation with Zero-Shot LLMs | cs.CL cs.LG | Zero-shot LLMs are now also used for textual classification tasks, e.g.,
sentiment/emotion detection of a given input as a sentence/article. However,
their performance can be suboptimal in such data annotation tasks. We introduce
a novel technique Perceived Confidence Scoring (PCS) that evaluates LLM's
confidence for... |
2502.07187 | Local Regularizers Are Not Transductive Learners | cs.LG stat.ML | We partly resolve an open question raised by Asilis et al. (COLT 2024):
whether the algorithmic template of local regularization -- an intriguing
generalization of explicit regularization, a.k.a. structural risk minimization
-- suffices to learn all learnable multiclass problems. Specifically, we
provide a negative a... |
2502.07188 | A Large-Scale Benchmark for Vietnamese Sentence Paraphrases | cs.CL | This paper presents ViSP, a high-quality Vietnamese dataset for sentence
paraphrasing, consisting of 1.2M original-paraphrase pairs collected from
various domains. The dataset was constructed using a hybrid approach that
combines automatic paraphrase generation with manual evaluation to ensure high
quality. We conduc... |
2502.07189 | Exploring Neural Network Pruning with Screening Methods | cs.LG stat.ML | Deep neural networks (DNNs) such as convolutional neural networks (CNNs) for
visual tasks, recurrent neural networks (RNNs) for sequence data, and
transformer models for rich linguistic or multimodal tasks, achieved
unprecedented performance on a wide range of tasks. The impressive performance
of modern DNNs is parti... |
2502.07190 | Understanding LLMs' Fluid Intelligence Deficiency: An Analysis of the
ARC Task | cs.AI | While LLMs have exhibited strong performance on various NLP tasks, it is
noteworthy that most of these tasks rely on utilizing the vast amount of
knowledge encoded in LLMs' parameters, rather than solving new problems without
prior knowledge. In cognitive research, the latter ability is referred to as
fluid intellige... |
2502.07191 | Bag of Tricks for Inference-time Computation of LLM Reasoning | cs.AI | With the advancement of large language models (LLMs), solving complex
reasoning tasks has gained increasing attention. Inference-time computation
methods (e.g., Best-of-N, beam search, et al.) are particularly valuable as
they can enhance reasoning performance without modifying model parameters or
requiring additiona... |
2502.07192 | OscNet: Machine Learning on CMOS Oscillator Networks | cs.CV | Machine learning and AI have achieved remarkable advancements but at the cost
of significant computational resources and energy consumption. This has created
an urgent need for a novel, energy-efficient computational fabric to replace
the current computing pipeline. Recently, a promising approach has emerged by
mimic... |
2502.07193 | Provably Efficient RLHF Pipeline: A Unified View from Contextual Bandits | cs.LG stat.ML | Reinforcement Learning from Human Feedback (RLHF) is a widely used approach
for aligning Large Language Models (LLMs) with human preferences. While recent
advancements have provided valuable insights into various stages and settings
of RLHF, a comprehensive theoretical understanding of the entire RLHF pipeline
remain... |
2502.07194 | Dense Object Detection Based on De-homogenized Queries | cs.CV cs.AI | Dense object detection is widely used in automatic driving, video
surveillance, and other fields. This paper focuses on the challenging task of
dense object detection. Currently, detection methods based on greedy
algorithms, such as non-maximum suppression (NMS), often produce many
repetitive predictions or missed de... |
2502.07196 | Parameter Optimization of Optical Six-Axis Force/Torque Sensor for
Legged Robots | cs.RO | This paper introduces a novel six-axis force/torque sensor tailored for
compact and lightweight legged robots. Unlike traditional strain gauge-based
sensors, the proposed non-contact design employs photocouplers, enhancing
resistance to physical impacts and reducing damage risk. This approach
simplifies manufacturing... |
2502.07199 | Fixed-Confidence Best Arm Identification with Decreasing Variance | cs.LG cs.IT math.IT math.ST stat.ML stat.TH | We focus on the problem of best-arm identification in a stochastic multi-arm
bandit with temporally decreasing variances for the arms' rewards. We model arm
rewards as Gaussian random variables with fixed means and variances that
decrease with time. The cost incurred by the learner is modeled as a weighted
sum of the... |
2502.07200 | Color-Quality Invariance for Robust Medical Image Segmentation | eess.IV cs.CV | Single-source domain generalization (SDG) in medical image segmentation
remains a significant challenge, particularly for images with varying color
distributions and qualities. Previous approaches often struggle when models
trained on high-quality images fail to generalize to low-quality test images
due to these colo... |
2502.07202 | Monte Carlo Tree Diffusion for System 2 Planning | cs.AI cs.LG | Diffusion models have recently emerged as a powerful tool for planning.
However, unlike Monte Carlo Tree Search (MCTS)-whose performance naturally
improves with additional test-time computation (TTC), standard diffusion-based
planners offer only limited avenues for TTC scalability. In this paper, we
introduce Monte C... |
2502.07203 | Playmate: Flexible Control of Portrait Animation via 3D-Implicit Space
Guided Diffusion | cs.CV | Recent diffusion-based talking face generation models have demonstrated
impressive potential in synthesizing videos that accurately match a speech
audio clip with a given reference identity. However, existing approaches still
encounter significant challenges due to uncontrollable factors, such as
inaccurate lip-sync,... |
2502.07205 | VINP: Variational Bayesian Inference with Neural Speech Prior for Joint
ASR-Effective Speech Dereverberation and Blind RIR Identification | eess.AS cs.AI cs.LG | Reverberant speech, denoting the speech signal degraded by the process of
reverberation, contains crucial knowledge of both anechoic source speech and
room impulse response (RIR). This work proposes a variational Bayesian
inference (VBI) framework with neural speech prior (VINP) for joint speech
dereverberation and b... |
2502.07207 | A Study on the Importance of Features in Detecting Advanced Persistent
Threats Using Machine Learning | cs.CR cs.AI cs.LG | Advanced Persistent Threats (APTs) pose a significant security risk to
organizations and industries. These attacks often lead to severe data breaches
and compromise the system for a long time. Mitigating these sophisticated
attacks is highly challenging due to the stealthy and persistent nature of
APTs. Machine learn... |
2502.07209 | Enhancing Physics-Informed Neural Networks Through Feature Engineering | cs.LG | Physics-Informed Neural Networks (PINNs) seek to solve partial differential
equations (PDEs) with deep learning. Mainstream approaches that deploy
fully-connected multi-layer deep learning architectures require prolonged
training to achieve even moderate accuracy, while recent work on feature
engineering allows highe... |
2502.07211 | Improve the Training Efficiency of DRL for Wireless Communication
Resource Allocation: The Role of Generative Diffusion Models | cs.LG | Dynamic resource allocation in mobile wireless networks involves complex,
time-varying optimization problems, motivating the adoption of deep
reinforcement learning (DRL). However, most existing works rely on pre-trained
policies, overlooking dynamic environmental changes that rapidly invalidate the
policies. Periodi... |
2502.07213 | Evaluation for Regression Analyses on Evolving Data Streams | cs.LG cs.AI | The paper explores the challenges of regression analysis in evolving data
streams, an area that remains relatively underexplored compared to
classification. We propose a standardized evaluation process for regression and
prediction interval tasks in streaming contexts. Additionally, we introduce an
innovative drift s... |
2502.07214 | Pareto Optimal Algorithmic Recourse in Multi-cost Function | cs.LG cs.AI cs.DS | In decision-making systems, algorithmic recourse aims to identify
minimal-cost actions to alter an individual features, thereby obtaining a
desired outcome. This empowers individuals to understand, question, or alter
decisions that negatively affect them. However, due to the variety and
sensitivity of system environm... |
2502.07215 | PDV: Prompt Directional Vectors for Zero-shot Composed Image Retrieval | cs.CV | Zero-shot composed image retrieval (ZS-CIR) enables image search using a
reference image and text prompt without requiring specialized text-image
composition networks trained on large-scale paired data. However, current
ZS-CIR approaches face three critical limitations in their reliance on composed
text embeddings: s... |
2502.07216 | SparseFormer: Detecting Objects in HRW Shots via Sparse Vision
Transformer | cs.CV cs.AI | Recent years have seen an increase in the use of gigapixel-level image and
video capture systems and benchmarks with high-resolution wide (HRW) shots.
However, unlike close-up shots in the MS COCO dataset, the higher resolution
and wider field of view raise unique challenges, such as extreme sparsity and
huge scale c... |
2502.07218 | LUNAR: LLM Unlearning via Neural Activation Redirection | cs.LG cs.AI | Large Language Models (LLMs) benefit from training on ever larger amounts of
textual data, but as a result, they increasingly incur the risk of leaking
private information. The ability to selectively remove knowledge from LLMs is,
therefore, a highly desirable capability. In this paper, we propose LUNAR, a
novel unle... |
2502.07219 | DOGR: Leveraging Document-Oriented Contrastive Learning in Generative
Retrieval | cs.IR | Generative retrieval constitutes an innovative approach in information
retrieval, leveraging generative language models (LM) to generate a ranked list
of document identifiers (docid) for a given query. It simplifies the retrieval
pipeline by replacing the large external index with model parameters. However,
existing ... |
2502.07221 | MLLM4PUE: Toward Universal Embeddings in Computational Pathology through
Multimodal LLMs | cs.CV | Pathology plays a critical role in diagnosing a wide range of diseases, yet
existing approaches often rely heavily on task-specific models trained on
extensive, well-labeled datasets. These methods face sustainability challenges
due to the diversity of pathologies and the labor-intensive nature of data
collection. To... |
2502.07222 | A Memory Efficient Randomized Subspace Optimization Method for Training
Large Language Models | cs.LG | The memory challenges associated with training Large Language Models (LLMs)
have become a critical concern, particularly when using the Adam optimizer. To
address this issue, numerous memory-efficient techniques have been proposed,
with GaLore standing out as a notable example designed to reduce the memory
footprint ... |
2502.07223 | Graph RAG-Tool Fusion | cs.CL | Recent developments in retrieval-augmented generation (RAG) for selecting
relevant tools from a tool knowledge base enable LLM agents to scale their
complex tool calling capabilities to hundreds or thousands of external tools,
APIs, or agents-as-tools. However, traditional RAG-based tool retrieval fails
to capture st... |
2502.07225 | CAT: Contrastive Adversarial Training for Evaluating the Robustness of
Protective Perturbations in Latent Diffusion Models | cs.CV | Latent diffusion models have recently demonstrated superior capabilities in
many downstream image synthesis tasks. However, customization of latent
diffusion models using unauthorized data can severely compromise the privacy
and intellectual property rights of data owners. Adversarial examples as
protective perturbat... |
2502.07226 | Non-Iterative Coordination of Interconnected Power Grids via
Dimension-Decomposition-Based Flexibility Aggregation | eess.SY cs.SY | The bulk power grid is divided into regional grids interconnected with
multiple tie-lines for efficient operation. Since interconnected power grids
are operated by different control centers, it is a challenging task to realize
coordinated dispatch of multiple regional grids. A viable solution is to
compute a flexibil... |
2502.07230 | Physics-Informed Recurrent Network for Gas Pipeline Network Parameters
Identification | eess.SY cs.SY | As a part of the integrated energy system (IES), gas pipeline networks can
provide additional flexibility to power systems through coordinated optimal
dispatch. An accurate pipeline network model is critical for the optimal
operation and control of IESs. However, inaccuracies or unavailability of
accurate pipeline pa... |
2502.07232 | Simplifying Adversarially Robust PAC Learning with Tolerance | cs.LG | Adversarially robust PAC learning has proved to be challenging, with the
currently best known learners [Montasser et al., 2021a] relying on improper
methods based on intricate compression schemes, resulting in sample complexity
exponential in the VC-dimension. A series of follow up work considered a
slightly relaxed ... |
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