id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.06860 | AutoSketch: VLM-assisted Style-Aware Vector Sketch Completion | cs.CV cs.GR | The ability to automatically complete a partial sketch that depicts a complex
scene, e.g., "a woman chatting with a man in the park", is very useful.
However, existing sketch generation methods create sketches from scratch; they
do not complete a partial sketch in the style of the original. To address this
challenge,... |
2502.06861 | Design Considerations in Offline Preference-based RL | cs.LG cs.AI | Offline algorithms for Reinforcement Learning from Human Preferences (RLHF),
which use only a fixed dataset of sampled responses given an input, and
preference feedback among these responses, have gained increasing prominence in
the literature on aligning language models. In this paper, we study how the
different des... |
2502.06862 | Poincar\'e Inequality for Local Log-Polyak-Lojasiewicz Measures :
Non-asymptotic Analysis in Low-temperature Regime | cs.LG math.CA math.FA math.PR stat.ML | Potential functions in highly pertinent applications, such as deep learning
in over-parameterized regime, are empirically observed to admit non-isolated
minima. To understand the convergence behavior of stochastic dynamics in such
landscapes, we propose to study the class of \logPLmeasure\ measures
$\mu_\epsilon \pro... |
2502.06863 | BF-GAN: Development of an AI-driven Bubbly Flow Image Generation Model
Using Generative Adversarial Networks | cs.CV cs.AI | A generative AI architecture called bubbly flow generative adversarial
networks (BF-GAN) is developed, designed to generate realistic and high-quality
bubbly flow images through physically conditioned inputs, jg and jf. Initially,
52 sets of bubbly flow experiments under varying conditions are conducted to
collect 14... |
2502.06864 | Knowledge Graph-Guided Retrieval Augmented Generation | cs.CL cs.AI | Retrieval-augmented generation (RAG) has emerged as a promising technology
for addressing hallucination issues in the responses generated by large
language models (LLMs). Existing studies on RAG primarily focus on applying
semantic-based approaches to retrieve isolated relevant chunks, which ignore
their intrinsic re... |
2502.06865 | Deep Ritz method with Fourier feature mapping: A deep learning approach
for solving variational models of microstructure | cs.LG | This paper presents a novel approach that combines the Deep Ritz Method (DRM)
with Fourier feature mapping to solve minimization problems comprised of
multi-well, non-convex energy potentials. These problems present computational
challenges as they lack a global minimum. Through an investigation of three
benchmark pr... |
2502.06866 | Global Ease of Living Index: a machine learning framework for
longitudinal analysis of major economies | cs.LG cs.AI econ.EM stat.AP stat.ML | The drastic changes in the global economy, geopolitical conditions, and
disruptions such as the COVID-19 pandemic have impacted the cost of living and
quality of life. It is important to understand the long-term nature of the cost
of living and quality of life in major economies. A transparent and
comprehensive livin... |
2502.06867 | Forbidden Science: Dual-Use AI Challenge Benchmark and Scientific
Refusal Tests | cs.CL cs.AI | The development of robust safety benchmarks for large language models
requires open, reproducible datasets that can measure both appropriate refusal
of harmful content and potential over-restriction of legitimate scientific
discourse. We present an open-source dataset and testing framework for
evaluating LLM safety m... |
2502.06868 | Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of
Knowledge Editing in Same-Subject | cs.CL cs.AI | Knowledge editing has become a promising approach for efficiently and
precisely updating knowledge embedded in large language models (LLMs). In this
work, we focus on Same-Subject Editing, which involves modifying multiple
attributes of a single entity to ensure comprehensive and consistent updates to
entity-centric ... |
2502.06869 | A Survey on Explainable Deep Reinforcement Learning | cs.LG cs.AI | Deep Reinforcement Learning (DRL) has achieved remarkable success in
sequential decision-making tasks across diverse domains, yet its reliance on
black-box neural architectures hinders interpretability, trust, and deployment
in high-stakes applications. Explainable Deep Reinforcement Learning (XRL)
addresses these ch... |
2502.06870 | Bridging Traffic State and Trajectory for Dynamic Road Network and
Trajectory Representation Learning | cs.LG cs.AI | Effective urban traffic management is vital for sustainable city development,
relying on intelligent systems with machine learning tasks such as traffic flow
prediction and travel time estimation. Traditional approaches usually focus on
static road network and trajectory representation learning, and overlook the
dyna... |
2502.06871 | FlavorDiffusion: Predicting Food Pairings and Chemical Interactions
Using Diffusion Models | cs.LG cs.AI | The study of food pairing has evolved beyond subjective expertise with the
advent of machine learning. This paper presents FlavorDiffusion, a novel
framework leveraging diffusion models to predict food-chemical interactions and
ingredient pairings without relying on chromatography. By integrating
graph-based embeddin... |
2502.06872 | Towards Trustworthy Retrieval Augmented Generation for Large Language
Models: A Survey | cs.CL cs.AI | Retrieval-Augmented Generation (RAG) is an advanced technique designed to
address the challenges of Artificial Intelligence-Generated Content (AIGC). By
integrating context retrieval into content generation, RAG provides reliable
and up-to-date external knowledge, reduces hallucinations, and ensures relevant
context ... |
2502.06873 | Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic
Reasoning | cs.CL cs.AI | Previous research has revealed the potential of large language models (LLMs)
to support cognitive reframing therapy; however, their focus was primarily on
text-based methods, often overlooking the importance of non-verbal evidence
crucial in real-life therapy. To alleviate this gap, we extend the textual
cognitive re... |
2502.06874 | Group Reasoning Emission Estimation Networks | cs.CL cs.AI cs.LG | Accurate greenhouse gas (GHG) emission reporting is critical for governments,
businesses, and investors. However, adoption remains limited particularly among
small and medium enterprises due to high implementation costs, fragmented
emission factor databases, and a lack of robust sector classification methods.
To addr... |
2502.06875 | Beyond Vision: How Large Language Models Interpret Facial Expressions
from Valence-Arousal Values | cs.CV cs.AI cs.CL | Large Language Models primarily operate through text-based inputs and
outputs, yet human emotion is communicated through both verbal and non-verbal
cues, including facial expressions. While Vision-Language Models analyze facial
expressions from images, they are resource-intensive and may depend more on
linguistic pri... |
2502.06876 | Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and
Harmlessness of Large Language Model via Model Merging | cs.CL cs.AI cs.LG | Achieving balanced alignment of large language models (LLMs) in terms of
Helpfulness, Honesty, and Harmlessness (3H optimization) constitutes a
cornerstone of responsible AI, with existing methods like data mixture
strategies facing limitations including reliance on expert knowledge and
conflicting optimization signa... |
2502.06877 | WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for
Wireless Communication | cs.LG | This paper introduces WirelessGPT, a pioneering foundation model specifically
designed for multi-task learning in wireless communication and sensing.
Specifically, WirelessGPT leverages large-scale wireless channel datasets for
unsupervised pretraining and extracting universal channel representations,
which captures ... |
2502.06878 | Deep Learning Meets Oversampling: A Learning Framework to Handle
Imbalanced Classification | cs.LG | Despite extensive research spanning several decades, class imbalance is still
considered a profound difficulty for both machine learning and deep learning
models. While data oversampling is the foremost technique to address this
issue, traditional sampling techniques are often decoupled from the training
phase of the... |
2502.06879 | CluStRE: Streaming Graph Clustering with Multi-Stage Refinement | cs.LG cs.DB | We present CluStRE, a novel streaming graph clustering algorithm that
balances computational efficiency with high-quality clustering using
multi-stage refinement. Unlike traditional in-memory clustering approaches,
CluStRE processes graphs in a streaming setting, significantly reducing memory
overhead while leveragin... |
2502.06882 | Multi-Agent Simulator Drives Language Models for Legal Intensive
Interaction | cs.CL cs.AI | Large Language Models (LLMs) have significantly advanced legal intelligence,
but the scarcity of scenario data impedes the progress toward interactive legal
scenarios. This paper introduces a Multi-agent Legal Simulation Driver (MASER)
to scalably generate synthetic data by simulating interactive legal scenarios.
Lev... |
2502.06884 | Learning Conformal Abstention Policies for Adaptive Risk Management in
Large Language and Vision-Language Models | cs.LG cs.AI | Large Language and Vision-Language Models (LLMs/VLMs) are increasingly used
in safety-critical applications, yet their opaque decision-making complicates
risk assessment and reliability. Uncertainty quantification (UQ) helps assess
prediction confidence and enables abstention when uncertainty is high.
Conformal predi... |
2502.06885 | Topological derivative approach for deep neural network architecture
adaptation | cs.LG cs.AI | This work presents a novel algorithm for progressively adapting neural
network architecture along the depth. In particular, we attempt to address the
following questions in a mathematically principled way: i) Where to add a new
capacity (layer) during the training process? ii) How to initialize the new
capacity? At t... |
2502.06887 | Gradient Based Method for the Fusion of Lattice Quantizers | cs.LG cs.AI | In practical applications, lattice quantizers leverage discrete lattice
points to approximate arbitrary points in the lattice. An effective lattice
quantizer significantly enhances both the accuracy and efficiency of these
approximations. In the context of high-dimensional lattice quantization,
previous work proposed... |
2502.06888 | Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware
Multi-Batch Pipeline | cs.LG cs.AI | Mixture of Experts (MoE), with its distinctive sparse structure, enables the
scaling of language models up to trillions of parameters without significantly
increasing computational costs. However, the substantial parameter size
presents a challenge for inference, as the expansion in GPU memory cannot keep
pace with t... |
2502.06889 | Secure Visual Data Processing via Federated Learning | cs.CV | As the demand for privacy in visual data management grows, safeguarding
sensitive information has become a critical challenge. This paper addresses the
need for privacy-preserving solutions in large-scale visual data processing by
leveraging federated learning. Although there have been developments in this
field, pre... |
2502.06890 | LLMs for Drug-Drug Interaction Prediction: A Comprehensive Comparison | cs.LG cs.AI q-bio.QM | The increasing volume of drug combinations in modern therapeutic regimens
needs reliable methods for predicting drug-drug interactions (DDIs). While
Large Language Models (LLMs) have revolutionized various domains, their
potential in pharmaceutical research, particularly in DDI prediction, remains
largely unexplored.... |
2502.06891 | ScaffoldGPT: A Scaffold-based Large Language Model for Drug Improvement | q-bio.BM cs.CL cs.LG | Drug optimization has become increasingly crucial in light of fast-mutating
virus strains and drug-resistant cancer cells. Nevertheless, it remains
challenging as it necessitates retaining the beneficial properties of the
original drug while simultaneously enhancing desired attributes beyond its
scope. In this work, ... |
2502.06892 | Certifying Language Model Robustness with Fuzzed Randomized Smoothing:
An Efficient Defense Against Backdoor Attacks | cs.LG cs.AI | The widespread deployment of pre-trained language models (PLMs) has exposed
them to textual backdoor attacks, particularly those planted during the
pre-training stage. These attacks pose significant risks to high-reliability
applications, as they can stealthily affect multiple downstream tasks. While
certifying robus... |
2502.06893 | A New Hybrid Intelligent Approach for Multimodal Detection of Suspected
Disinformation on TikTok | cs.CV cs.CL cs.MM cs.SC | In the context of the rapid dissemination of multimedia content, identifying
disinformation on social media platforms such as TikTok represents a
significant challenge. This study introduces a hybrid framework that combines
the computational power of deep learning with the interpretability of fuzzy
logic to detect su... |
2502.06894 | AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning
Revolution | cs.CV cs.AI | Hyperspectral imaging (HSI) captures spatial and spectral data, enabling
analysis of features invisible to conventional systems. The technology is vital
in fields such as weather monitoring, food quality control, counterfeit
detection, healthcare diagnostics, and extending into defense, agriculture, and
industrial au... |
2502.06895 | A Comprehensive Review of U-Net and Its Variants: Advances and
Applications in Medical Image Segmentation | eess.IV cs.CV | Medical images often exhibit low and blurred contrast between lesions and
surrounding tissues, with considerable variation in lesion edges and shapes
even within the same disease, leading to significant challenges in
segmentation. Therefore, precise segmentation of lesions has become an
essential prerequisite for pat... |
2502.06897 | PyPotteryInk: One-Step Diffusion Model for Sketch to Publication-ready
Archaeological Drawings | cs.GR cs.AI cs.CV | Archaeological pottery documentation traditionally requires a time-consuming
manual process of converting pencil sketches into publication-ready inked
drawings. I present PyPotteryInk, an open-source automated pipeline that
transforms archaeological pottery sketches into standardised publication-ready
drawings using ... |
2502.06898 | Large Language Models for In-File Vulnerability Localization Can Be
"Lost in the End" | cs.SE cs.AI | Recent advancements in artificial intelligence have enabled processing of
larger inputs, leading everyday software developers to increasingly rely on
chat-based large language models (LLMs) like GPT-3.5 and GPT-4 to detect
vulnerabilities across entire files, not just within functions. This new
development practice r... |
2502.06899 | A Sociotechnical Approach for Knowledge Management (KM) | cs.DB cs.AI | This article presents a sociotechnical framework for KM. This sociotechnical
vision of KM allows: (1) to remove KM from a commercial concern; (2) to divide
the different KM technologies; and (3) to question the paradigms associated
with the social and technical components of KM. It is precisely this last point
that t... |
2502.06900 | Polynomial Regret Concentration of UCB for Non-Deterministic State
Transitions | cs.LG cs.DM | Monte Carlo Tree Search (MCTS) has proven effective in solving
decision-making problems in perfect information settings. However, its
application to stochastic and imperfect information domains remains limited.
This paper extends the theoretical framework of MCTS to stochastic domains by
addressing non-deterministic ... |
2502.06901 | Enabling Autoregressive Models to Fill In Masked Tokens | cs.LG cs.AI cs.CL | Historically, LLMs have been trained using either autoregressive (AR) or
masked language modeling (MLM) objectives, with AR models gaining dominance in
recent years. However, AR models are inherently incapable of masked infilling,
which is the ability to predict masked tokens between past and future context.
In contr... |
2502.06902 | Emergence of Episodic Memory in Transformers: Characterizing Changes in
Temporal Structure of Attention Scores During Training | cs.LG cs.AI cs.CL | We investigate in-context temporal biases in attention heads and transformer
outputs. Using cognitive science methodologies, we analyze attention scores and
outputs of the GPT-2 models of varying sizes. Across attention heads, we
observe effects characteristic of human episodic memory, including temporal
contiguity, ... |
2502.06905 | Lightweight Dataset Pruning without Full Training via Example Difficulty
and Prediction Uncertainty | cs.LG cs.AI | Recent advances in deep learning rely heavily on massive datasets, leading to
substantial storage and training costs. Dataset pruning aims to alleviate this
demand by discarding redundant examples. However, many existing methods require
training a model with a full dataset over a large number of epochs before being
a... |
2502.06906 | Learning-based estimation of cattle weight gain and its influencing
factors | cs.LG cs.AI | Many cattle farmers still depend on manual methods to measure the live weight
gain of cattle at set intervals, which is time consuming, labour intensive, and
stressful for both the animals and handlers. A remote and autonomous monitoring
system using machine learning (ML) or deep learning (DL) can provide a more
effi... |
2502.06907 | Can ChatGPT Diagnose Alzheimer's Disease? | cs.LG cs.AI | Can ChatGPT diagnose Alzheimer's Disease (AD)? AD is a devastating
neurodegenerative condition that affects approximately 1 in 9 individuals aged
65 and older, profoundly impairing memory and cognitive function. This paper
utilises 9300 electronic health records (EHRs) with data from Magnetic
Resonance Imaging (MRI) ... |
2502.06909 | Satisfaction-Aware Incentive Scheme for Federated Learning in Industrial
Metaverse: DRL-Based Stackbelberg Game Approach | cs.LG cs.AI cs.GT | Industrial Metaverse leverages the Industrial Internet of Things (IIoT) to
integrate data from diverse devices, employing federated learning and
meta-computing to train models in a distributed manner while ensuring data
privacy. Achieving an immersive experience for industrial Metaverse
necessitates maintaining a bal... |
2502.06910 | TimeKAN: KAN-based Frequency Decomposition Learning Architecture for
Long-term Time Series Forecasting | cs.LG cs.AI | Real-world time series often have multiple frequency components that are
intertwined with each other, making accurate time series forecasting
challenging. Decomposing the mixed frequency components into multiple single
frequency components is a natural choice. However, the information density of
patterns varies acros... |
2502.06911 | Foundation Models for Anomaly Detection: Vision and Challenges | cs.LG cs.AI | As data continues to grow in volume and complexity across domains such as
finance, manufacturing, and healthcare, effective anomaly detection is
essential for identifying irregular patterns that may signal critical issues.
Recently, foundation models (FMs) have emerged as a powerful tool for advancing
anomaly detecti... |
2502.06913 | A Simple yet Effective DDG Predictor is An Unsupervised Antibody
Optimizer and Explainer | q-bio.QM cs.AI cs.LG | The proteins that exist today have been optimized over billions of years of
natural evolution, during which nature creates random mutations and selects
them. The discovery of functionally promising mutations is challenged by the
limited evolutionary accessible regions, i.e., only a small region on the
fitness landsca... |
2502.06914 | UniZyme: A Unified Protein Cleavage Site Predictor Enhanced with Enzyme
Active-Site Knowledge | q-bio.QM cs.AI cs.LG | Enzyme-catalyzed protein cleavage is essential for many biological functions.
Accurate prediction of cleavage sites can facilitate various applications such
as drug development, enzyme design, and a deeper understanding of biological
mechanisms. However, most existing models are restricted to an individual
enzyme, wh... |
2502.06915 | Analytic Personalized Federated Meta-Learning | cs.DC cs.LG | Analytic federated learning (AFL) which updates model weights only once by
using closed-form least-square (LS) solutions can reduce abundant training time
in gradient-free federated learning (FL). The current AFL framework cannot
support deep neural network (DNN) training, which hinders its implementation on
complex ... |
2502.06916 | Hyper Compressed Fine-Tuning of Large Foundation Models with Quantum
Inspired Adapters | cs.LG cs.AI eess.SP quant-ph | Fine-tuning pre-trained large foundation models for specific tasks has become
increasingly challenging due to the computational and storage demands
associated with full parameter updates. Parameter-Efficient Fine-Tuning (PEFT)
methods address this issue by updating only a small subset of model parameters
using adapte... |
2502.06917 | Krum Federated Chain (KFC): Using blockchain to defend against
adversarial attacks in Federated Learning | cs.LG cs.AI | Federated Learning presents a nascent approach to machine learning, enabling
collaborative model training across decentralized devices while safeguarding
data privacy. However, its distributed nature renders it susceptible to
adversarial attacks. Integrating blockchain technology with Federated Learning
offers a prom... |
2502.06918 | Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business
Processes | cs.LG cs.AI | This paper investigates the effectiveness of GPT-4o-2024-08-06, one of the
Large Language Models (LLM) from OpenAI, in detecting business process
anomalies, with a focus on rework anomalies. In our study, we developed a
GPT-4o-based tool capable of transforming event logs into a structured format
and identifying rewo... |
2502.06919 | Select before Act: Spatially Decoupled Action Repetition for Continuous
Control | cs.LG cs.AI cs.RO | Reinforcement Learning (RL) has achieved remarkable success in various
continuous control tasks, such as robot manipulation and locomotion. Different
to mainstream RL which makes decisions at individual steps, recent studies have
incorporated action repetition into RL, achieving enhanced action persistence
with impro... |
2502.06920 | Direct Estimation of Pediatric Heart Rate Variability from BOLD-fMRI: A
Machine Learning Approach Using Dynamic Connectivity | eess.IV cs.AI cs.LG | In many pediatric fMRI studies, cardiac signals are often missing or of poor
quality. A tool to extract Heart Rate Variation (HRV) waveforms directly from
fMRI data, without the need for peripheral recording devices, would be highly
beneficial. We developed a machine learning framework to accurately reconstruct
HRV f... |
2502.06921 | GraNNite: Enabling High-Performance Execution of Graph Neural Networks
on Resource-Constrained Neural Processing Units | cs.LG cs.AI cs.AR | Graph Neural Networks (GNNs) are vital for learning from graph-structured
data, enabling applications in network analysis, recommendation systems, and
speech analytics. Deploying them on edge devices like client PCs and laptops
enhances real-time processing, privacy, and cloud independence. GNNs aid
Retrieval-Augment... |
2502.06922 | Synthetic Audio Helps for Cognitive State Tasks | cs.SD cs.AI cs.CL cs.LG | The NLP community has broadly focused on text-only approaches of cognitive
state tasks, but audio can provide vital missing cues through prosody. We posit
that text-to-speech models learn to track aspects of cognitive state in order
to produce naturalistic audio, and that the signal audio models implicitly
identify i... |
2502.06923 | Do Attention Heads Compete or Cooperate during Counting? | cs.LG cs.AI | We present an in-depth mechanistic interpretability analysis of training
small transformers on an elementary task, counting, which is a crucial
deductive step in many algorithms. In particular, we investigate the
collaboration/competition among the attention heads: we ask whether the
attention heads behave as a pseud... |
2502.06924 | XAMBA: Enabling Efficient State Space Models on Resource-Constrained
Neural Processing Units | cs.LG cs.AI | State-Space Models (SSMs) have emerged as efficient alternatives to
transformers for sequential data tasks, offering linear or near-linear
scalability with sequence length, making them ideal for long-sequence
applications in NLP, vision, and edge AI, including real-time transcription,
translation, and contextual sear... |
2502.06925 | Occam's model: Selecting simpler representations for better
transferability estimation | cs.LG cs.AI | Fine-tuning models that have been pre-trained on large datasets has become a
cornerstone of modern machine learning workflows. With the widespread
availability of online model repositories, such as Hugging Face, it is now
easier than ever to fine-tune pre-trained models for specific tasks. This
raises a critical ques... |
2502.06927 | Neighborhood-Order Learning Graph Attention Network for Fake News
Detection | cs.LG cs.AI cs.CL | Fake news detection is a significant challenge in the digital age, which has
become increasingly important with the proliferation of social media and online
communication networks. Graph Neural Networks (GNN)-based methods have shown
high potential in analyzing graph-structured data for this problem. However, a
major... |
2502.06939 | Generalizable automated ischaemic stroke lesion segmentation with vision
transformers | eess.IV cs.CV cs.LG | Ischaemic stroke, a leading cause of death and disability, critically relies
on neuroimaging for characterising the anatomical pattern of injury.
Diffusion-weighted imaging (DWI) provides the highest expressivity in ischemic
stroke but poses substantial challenges for automated lesion segmentation:
susceptibility art... |
2502.06957 | GAS: Generative Avatar Synthesis from a Single Image | cs.CV | We introduce a generalizable and unified framework to synthesize
view-consistent and temporally coherent avatars from a single image, addressing
the challenging problem of single-image avatar generation. While recent methods
employ diffusion models conditioned on human templates like depth or normal
maps, they often ... |
2502.06963 | Task Offloading in Vehicular Edge Computing using Deep Reinforcement
Learning: A Survey | cs.LG cs.AI cs.DC cs.MA | The increasing demand for Intelligent Transportation Systems (ITS) has
introduced significant challenges in managing the complex,
computation-intensive tasks generated by modern vehicles while offloading tasks
to external computing infrastructures such as edge computing (EC), nearby
vehicular , and UAVs has become in... |
2502.06967 | Downlink and Uplink ISAC in Continuous-Aperture Array (CAPA) Systems | cs.IT eess.SP math.IT | A continuous-aperture array (CAPA)-based integrated sensing and
communications (ISAC) framework is proposed for both downlink and uplink
scenarios. Within this framework, continuous operator-based signal models are
employed to describe the sensing and communication processes. The performance
of communication and sens... |
2502.06970 | Model Diffusion for Certifiable Few-shot Transfer Learning | cs.LG stat.ML | In modern large-scale deep learning, a prevalent and effective workflow for
solving low-data problems is adapting powerful pre-trained foundation models
(FMs) to new tasks via parameter-efficient fine-tuning (PEFT). However, while
empirically effective, the resulting solutions lack generalisation guarantees
to certif... |
2502.06971 | User-Preference Meets Pareto-Optimality: Multi-Objective Bayesian
Optimization with Local Gradient Search | cs.LG | Incorporating user preferences into multi-objective Bayesian optimization
(MOBO) allows for personalization of the optimization procedure. Preferences
are often abstracted in the form of an unknown utility function, estimated
through pairwise comparisons of potential outcomes. However, utility-driven
MOBO methods can... |
2502.06973 | Indoor Light and Heat Estimation from a Single Panorama | cs.CV | This paper presents a novel application for directly estimating indoor light
and heat maps from captured indoor-outdoor High Dynamic Range (HDR) panoramas.
In our image-based rendering method, the indoor panorama is used to estimate
the 3D room layout, while the corresponding outdoor panorama serves as an
environment... |
2502.06975 | Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents | cs.AI | As Large Language Models (LLMs) evolve from text-completion tools into fully
fledged agents operating in dynamic environments, they must address the
challenge of continually learning and retaining long-term knowledge. Many
biological systems solve these challenges with episodic memory, which supports
single-shot lear... |
2502.06976 | Who is Helping Whom? Analyzing Inter-dependencies to Evaluate
Cooperation in Human-AI Teaming | cs.MA cs.AI | The long-standing research challenges of Human-AI Teaming(HAT) and Zero-shot
Cooperation(ZSC) have been tackled by applying multi-agent reinforcement
learning(MARL) to train an agent by optimizing the environment reward function
and evaluating their performance through task performance metrics such as task
reward. Ho... |
2502.06978 | Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flow | math.OC cs.LG | The nonlinear, non-convex AC Optimal Power Flow (AC-OPF) problem is
fundamental for power systems operations. The intrinsic complexity of AC-OPF
has fueled a growing interest in the development of optimization proxies for
the problem, i.e., machine learning models that predict high-quality,
close-to-optimal solutions... |
2502.06982 | Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale
Google TPU Systems with ML Productivity Goodput | cs.LG | Recent years have seen the emergence of machine learning (ML) workloads
deployed in warehouse-scale computing (WSC) settings, also known as ML fleets.
As the computational demands placed on ML fleets have increased due to the rise
of large models and growing demand for ML applications, it has become
increasingly crit... |
2502.06987 | Universal Vessel Segmentation for Multi-Modality Retinal Images | eess.IV cs.CV | We identify two major limitations in the existing studies on retinal vessel
segmentation: (1) Most existing works are restricted to one modality, i.e, the
Color Fundus (CF). However, multi-modality retinal images are used every day in
the study of retina and retinal diseases, and the study of vessel segmentation
on t... |
2502.06988 | A Compiler for Operations on Relations with Bag Semantics | cs.PL cs.DB | We describe an abstract loop-based intermediate representation that can
express fused implementations of relational algebra expressions on sets and
bags (multisets). The loops are abstracted away from physical data structures
thus making it easier to generate, reason about, and perform optimization like
fusion on. Th... |
2502.06990 | Investigating the Zone of Proximal Development of Language Models for
In-Context Learning | cs.CL | In this paper, we introduce a learning analytics framework to analyze the
in-context learning (ICL) behavior of large language models (LLMs) through the
lens of the Zone of Proximal Development (ZPD), an established theory in
educational psychology. ZPD delineates the space between what a learner is
capable of doing ... |
2502.06994 | SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software
Engineering | cs.SE cs.AI cs.CL | Software engineering (SE) is increasingly collaborative, with developers
working together on shared complex codebases. Effective collaboration in shared
environments requires participants -- whether humans or AI agents -- to stay on
the same page as their environment evolves. When a collaborator's understanding
diver... |
2502.06995 | Epistemic Uncertainty in Conformal Scores: A Unified Approach | stat.ML cs.LG | Conformal prediction methods create prediction bands with distribution-free
guarantees but do not explicitly capture epistemic uncertainty, which can lead
to overconfident predictions in data-sparse regions. Although recent conformal
scores have been developed to address this limitation, they are typically
designed f... |
2502.06996 | A view on learning robust goal-conditioned value functions: Interplay
between RL and MPC | eess.SY cs.SY | Reinforcement learning (RL) and model predictive control (MPC) offer a wealth
of distinct approaches for automatic decision-making. Given the impact both
fields have had independently across numerous domains, there is growing
interest in combining the general-purpose learning capability of RL with the
safety and robu... |
2502.06997 | Conditional diffusion model with spatial attention and latent embedding
for medical image segmentation | eess.IV cs.CV | Diffusion models have been used extensively for high quality image and video
generation tasks. In this paper, we propose a novel conditional diffusion model
with spatial attention and latent embedding (cDAL) for medical image
segmentation. In cDAL, a convolutional neural network (CNN) based discriminator
is used at e... |
2502.06999 | Outsourced diffusion sampling: Efficient posterior inference in latent
spaces of generative models | cs.LG | Any well-behaved generative model over a variable $\mathbf{x}$ can be
expressed as a deterministic transformation of an exogenous ('outsourced')
Gaussian noise variable $\mathbf{z}$: $\mathbf{x}=f_\theta(\mathbf{z})$. In
such a model (e.g., a VAE, GAN, or continuous-time flow-based model), sampling
of the target vari... |
2502.07001 | From Image to Video: An Empirical Study of Diffusion Representations | cs.CV cs.AI cs.LG | Diffusion models have revolutionized generative modeling, enabling
unprecedented realism in image and video synthesis. This success has sparked
interest in leveraging their representations for visual understanding tasks.
While recent works have explored this potential for image generation, the
visual understanding ca... |
2502.07003 | AstroLoc: Robust Space to Ground Image Localizer | cs.CV | Astronauts take thousands of photos of Earth per day from the International
Space Station, which, once localized on Earth's surface, are used for a
multitude of tasks, ranging from climate change research to disaster
management. The localization process, which has been performed manually for
decades, has recently bee... |
2502.07004 | Demystifying Singular Defects in Large Language Models | cs.CL | Large transformer models are known to produce high-norm tokens. In vision
transformers (ViTs), such tokens have been mathematically modeled through the
singular vectors of the linear approximations of layers. However, in large
language models (LLMs), the underlying causes of high-norm tokens remain
largely unexplored... |
2502.07005 | Geometry-aware RL for Manipulation of Varying Shapes and Deformable
Objects | cs.LG cs.RO | Manipulating objects with varying geometries and deformable objects is a
major challenge in robotics. Tasks such as insertion with different objects or
cloth hanging require precise control and effective modelling of complex
dynamics. In this work, we frame this problem through the lens of a
heterogeneous graph that ... |
2502.07007 | Grounding Creativity in Physics: A Brief Survey of Physical Priors in
AIGC | cs.CV | Recent advancements in AI-generated content have significantly improved the
realism of 3D and 4D generation. However, most existing methods prioritize
appearance consistency while neglecting underlying physical principles, leading
to artifacts such as unrealistic deformations, unstable dynamics, and
implausible objec... |
2502.07008 | Early Operative Difficulty Assessment in Laparoscopic Cholecystectomy
via Snapshot-Centric Video Analysis | cs.CV | Purpose: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is
highly variable and influences outcomes. Despite extensive LC studies in
surgical workflow analysis, limited efforts explore LCOD using intraoperative
video data. Early recognition of LCOD could allow prompt review by expert
surgeons, enhance o... |
2502.07011 | DROP: Poison Dilution via Knowledge Distillation for Federated Learning | cs.LG cs.CR cs.DC | Federated Learning is vulnerable to adversarial manipulation, where malicious
clients can inject poisoned updates to influence the global model's behavior.
While existing defense mechanisms have made notable progress, they fail to
protect against adversaries that aim to induce targeted backdoors under
different learn... |
2502.07015 | Data Warehouse Design for Multiple Source Forest Inventory Management
and Image Processing | cs.DB | This research developed a prototype data warehouse to integrate multi-source
forestry data for long-term monitoring, management, and sustainability. The
data warehouse is intended to accommodate all types of imagery from various
platforms, LiDAR point clouds, survey records, and paper documents, with the
capability t... |
2502.07016 | Confidence Intervals for Evaluation of Data Mining | stat.ML cs.LG | In data mining, when binary prediction rules are used to predict a binary
outcome, many performance measures are used in a vast array of literature for
the purposes of evaluation and comparison. Some examples include classification
accuracy, precision, recall, F measures, and Jaccard index. Typically, these
performan... |
2502.07017 | Finding Words Associated with DIF: Predicting Differential Item
Functioning using LLMs and Explainable AI | cs.CL cs.AI | We fine-tuned and compared several encoder-based Transformer large language
models (LLM) to predict differential item functioning (DIF) from the item text.
We then applied explainable artificial intelligence (XAI) methods to these
models to identify specific words associated with DIF. The data included 42,180
items d... |
2502.07021 | Federated Sinkhorn | cs.DC cs.LG | In this work we investigate the potential of solving the discrete Optimal
Transport (OT) problem with entropy regularization in a federated learning
setting. Recall that the celebrated Sinkhorn algorithm transforms the classical
OT linear program into strongly convex constrained optimization, facilitating
first order... |
2502.07022 | AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in
Corporate Statements | cs.CL cs.AI cs.LG | Despite over a decade of legislative efforts to address modern slavery in the
supply chains of large corporations, the effectiveness of government oversight
remains hampered by the challenge of scrutinizing thousands of statements
annually. While Large Language Models (LLMs) can be considered a well
established solut... |
2502.07025 | Detecting Neurodegenerative Diseases using Frame-Level Handwriting
Embeddings | cs.LG cs.CV | In this study, we explored the use of spectrograms to represent handwriting
signals for assessing neurodegenerative diseases, including 42 healthy controls
(CTL), 35 subjects with Parkinson's Disease (PD), 21 with Alzheimer's Disease
(AD), and 15 with Parkinson's Disease Mimics (PDM). We applied CNN and
CNN-BLSTM mod... |
2502.07026 | Machine Learning for Everyone: Simplifying Healthcare Analytics with
BigQuery ML | cs.LG cs.AI | Machine learning (ML) is transforming healthcare by enabling predictive
analytics, personalized treatments, and improved patient outcomes. However,
traditional ML workflows require specialized skills, infrastructure, and
resources, limiting accessibility for many healthcare professionals. This paper
explores how Goog... |
2502.07027 | Representational Alignment with Chemical Induced Fit for Molecular
Relational Learning | cs.LG cs.AI | Molecular Relational Learning (MRL) is widely applied in natural sciences to
predict relationships between molecular pairs by extracting structural
features. The representational similarity between substructure pairs determines
the functional compatibility of molecular binding sites. Nevertheless, aligning
substructu... |
2502.07029 | Leveraging Allophony in Self-Supervised Speech Models for Atypical
Pronunciation Assessment | cs.CL cs.AI cs.LG eess.AS | Allophony refers to the variation in the phonetic realization of a phoneme
based on its phonetic environment. Modeling allophones is crucial for atypical
pronunciation assessment, which involves distinguishing atypical from typical
pronunciations. However, recent phoneme classifier-based approaches often
simplify thi... |
2502.07030 | PrismAvatar: Real-time animated 3D neural head avatars on edge devices | cs.CV cs.GR cs.LG | We present PrismAvatar: a 3D head avatar model which is designed specifically
to enable real-time animation and rendering on resource-constrained edge
devices, while still enjoying the benefits of neural volumetric rendering at
training time. By integrating a rigged prism lattice with a 3D morphable head
model, we us... |
2502.07036 | Automated Consistency Analysis of LLMs | cs.CR cs.AI cs.LG | Generative AI (Gen AI) with large language models (LLMs) are being widely
adopted across the industry, academia and government. Cybersecurity is one of
the key sectors where LLMs can be and/or are already being used. There are a
number of problems that inhibit the adoption of trustworthy Gen AI and LLMs in
cybersecur... |
2502.07039 | Boosting of Classification Models with Human-in-the-Loop Computational
Visual Knowledge Discovery | cs.LG cs.HC | High-risk artificial intelligence and machine learning classification tasks,
such as healthcare diagnosis, require accurate and interpretable prediction
models. However, classifier algorithms typically sacrifice individual
case-accuracy for overall model accuracy, limiting analysis of class overlap
areas regardless o... |
2502.07042 | Building networks of shared research interests by embedding words into a
representation space | cs.SI | Departments within a university are not only administrative units, but also
an effort to gather investigators around common fields of academic study. A
pervasive challenge is connecting members with shared research interests both
within and between departments. Here I describe a workflow that adapts methods
from natu... |
2502.07045 | Scalable and Ethical Insider Threat Detection through Data Synthesis and
Analysis by LLMs | cs.CR cs.AI cs.CL cs.CY | Insider threats wield an outsized influence on organizations,
disproportionate to their small numbers. This is due to the internal access
insiders have to systems, information, and infrastructure. %One example of this
influence is where anonymous respondents submit web-based job search site
reviews, an insider threat... |
2502.07046 | SnipGen: A Mining Repository Framework for Evaluating LLMs for Code | cs.SE cs.AI cs.LG | Language Models (LLMs), such as transformer-based neural networks trained on
billions of parameters, have become increasingly prevalent in software
engineering (SE). These models, trained on extensive datasets that include code
repositories, exhibit remarkable capabilities for SE tasks. However, evaluating
their effe... |
2502.07049 | LLMs in Software Security: A Survey of Vulnerability Detection
Techniques and Insights | cs.CR cs.AI | Large Language Models (LLMs) are emerging as transformative tools for
software vulnerability detection, addressing critical challenges in the
security domain. Traditional methods, such as static and dynamic analysis,
often falter due to inefficiencies, high false positive rates, and the growing
complexity of modern s... |
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