id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
|---|---|---|---|
2502.01534 | Preference Leakage: A Contamination Problem in LLM-as-a-judge | cs.LG cs.AI cs.CL | Large Language Models (LLMs) as judges and LLM-based data synthesis have
emerged as two fundamental LLM-driven data annotation methods in model
development. While their combination significantly enhances the efficiency of
model training and evaluation, little attention has been given to the potential
contamination br... |
2502.01535 | VisTA: Vision-Text Alignment Model with Contrastive Learning using
Multimodal Data for Evidence-Driven, Reliable, and Explainable Alzheimer's
Disease Diagnosis | cs.CV cs.CL q-bio.QM | Objective: Assessing Alzheimer's disease (AD) using high-dimensional
radiology images is clinically important but challenging. Although Artificial
Intelligence (AI) has advanced AD diagnosis, it remains unclear how to design
AI models embracing predictability and explainability. Here, we propose VisTA,
a multimodal l... |
2502.01536 | VR-Robo: A Real-to-Sim-to-Real Framework for Visual Robot Navigation and
Locomotion | cs.RO cs.CV | Recent success in legged robot locomotion is attributed to the integration of
reinforcement learning and physical simulators. However, these policies often
encounter challenges when deployed in real-world environments due to
sim-to-real gaps, as simulators typically fail to replicate visual realism and
complex real-w... |
2502.01538 | FedGES: A Federated Learning Approach for BN Structure Learning | cs.LG | Bayesian Network (BN) structure learning traditionally centralizes data,
raising privacy concerns when data is distributed across multiple entities.
This research introduces Federated GES (FedGES), a novel Federated Learning
approach tailored for BN structure learning in decentralized settings using the
Greedy Equiva... |
2502.01540 | What is a Number, That a Large Language Model May Know It? | cs.CL cs.AI | Numbers are a basic part of how humans represent and describe the world
around them. As a consequence, learning effective representations of numbers is
critical for the success of large language models as they become more
integrated into everyday decisions. However, these models face a challenge:
depending on context... |
2502.01543 | Unsupervised anomaly detection in large-scale estuarine acoustic
telemetry data | cs.LG | Acoustic telemetry data plays a vital role in understanding the behaviour and
movement of aquatic animals. However, these datasets, which often consist of
millions of individual data points, frequently contain anomalous movements that
pose significant challenges. Traditionally, anomalous movements are identified
eith... |
2502.01545 | Towards Data-Driven Multi-Stage OPF | eess.SY cs.SY | The operation of large-scale power systems is usually scheduled ahead via
numerical optimization. However, this requires models of grid topology, line
parameters, and bus specifications. Classic approaches first identify the
network topology, i.e., the graph of interconnections and the associated
impedances. The powe... |
2502.01546 | Dynamic object goal pushing with mobile manipulators through model-free
constrained reinforcement learning | cs.RO cs.LG cs.SY eess.SY | Non-prehensile pushing to move and reorient objects to a goal is a versatile
loco-manipulation skill. In the real world, the object's physical properties
and friction with the floor contain significant uncertainties, which makes the
task challenging for a mobile manipulator. In this paper, we develop a
learning-based... |
2502.01547 | mWhisper-Flamingo for Multilingual Audio-Visual Noise-Robust Speech
Recognition | eess.AS cs.CV cs.SD | Audio-Visual Speech Recognition (AVSR) combines lip-based video with audio
and can improve performance in noise, but most methods are trained only on
English data. One limitation is the lack of large-scale multilingual video
data, which makes it hard hard to train models from scratch. In this work, we
propose mWhispe... |
2502.01549 | VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context
Videos | cs.IR cs.AI cs.CV | Retrieval-Augmented Generation (RAG) has demonstrated remarkable success in
enhancing Large Language Models (LLMs) through external knowledge integration,
yet its application has primarily focused on textual content, leaving the rich
domain of multi-modal video knowledge predominantly unexplored. This paper
introduce... |
2502.01550 | FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction | cs.CV cs.AI | With climate change expected to exacerbate fire weather conditions, the
accurate and timely anticipation of wildfires becomes increasingly crucial for
disaster mitigation. In this study, we utilize SeasFire, a comprehensive global
wildfire dataset with climate, vegetation, oceanic indices, and human-related
variables... |
2502.01553 | Virtual Stars, Real Fans: Understanding the VTuber Ecosystem | cs.SI cs.CY cs.HC | Livestreaming by VTubers -- animated 2D/3D avatars controlled by real
individuals -- have recently garnered substantial global followings and
achieved significant monetary success. Despite prior research highlighting the
importance of realism in audience engagement, VTubers deliberately conceal
their identities, cult... |
2502.01555 | Query Brand Entity Linking in E-Commerce Search | cs.IR cs.AI cs.LG | In this work, we address the brand entity linking problem for e-commerce
search queries. The entity linking task is done by either i)a two-stage process
consisting of entity mention detection followed by entity disambiguation or ii)
an end-to-end linking approaches that directly fetch the target entity given
the inpu... |
2502.01556 | Observation Noise and Initialization in Wide Neural Networks | cs.LG stat.ML | Performing gradient descent in a wide neural network is equivalent to
computing the posterior mean of a Gaussian Process with the Neural Tangent
Kernel (NTK-GP), for a specific choice of prior mean and with zero observation
noise. However, existing formulations of this result have two limitations: i)
the resultant NT... |
2502.01557 | Training in reverse: How iteration order influences convergence and
stability in deep learning | cs.LG math.DS stat.ML | Despite exceptional achievements, training neural networks remains
computationally expensive and is often plagued by instabilities that can
degrade convergence. While learning rate schedules can help mitigate these
issues, finding optimal schedules is time-consuming and resource-intensive.
This work explores theoreti... |
2502.01558 | Search-Based Adversarial Estimates for Improving Sample Efficiency in
Off-Policy Reinforcement Learning | cs.LG cs.AI | Sample inefficiency is a long-lasting challenge in deep reinforcement
learning (DRL). Despite dramatic improvements have been made, the problem is
far from being solved and is especially challenging in environments with sparse
or delayed rewards. In our work, we propose to use Adversarial Estimates as a
new, simple a... |
2502.01562 | Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints
Internalization | cs.LG | As the general capabilities of artificial intelligence (AI) agents continue
to evolve, their ability to learn to master multiple complex tasks through
experience remains a key challenge. Current LLM agents, particularly those
based on proprietary language models, typically rely on prompts to incorporate
knowledge abo... |
2502.01563 | Massive Values in Self-Attention Modules are the Key to Contextual
Knowledge Understanding | cs.CL | Large language models (LLMs) have achieved remarkable success in contextual
knowledge understanding. In this paper, we show that these concentrated massive
values consistently emerge in specific regions of attention queries (Q) and
keys (K) while not having such patterns in values (V) in various modern
transformer-ba... |
2502.01564 | MeetMap: Real-Time Collaborative Dialogue Mapping with LLMs in Online
Meetings | cs.HC cs.AI | Video meeting platforms display conversations linearly through transcripts or
summaries. However, ideas during a meeting do not emerge linearly. We leverage
LLMs to create dialogue maps in real time to help people visually structure and
connect ideas. Balancing the need to reduce the cognitive load on users during
th... |
2502.01565 | GauCho: Gaussian Distributions with Cholesky Decomposition for Oriented
Object Detection | cs.CV | Oriented Object Detection (OOD) has received increased attention in the past
years, being a suitable solution for detecting elongated objects in remote
sensing analysis. In particular, using regression loss functions based on
Gaussian distributions has become attractive since they yield simple and
differentiable term... |
2502.01567 | Scalable Language Models with Posterior Inference of Latent Thought
Vectors | cs.CL cs.LG stat.ML | We propose a novel family of language models, Latent-Thought Language Models
(LTMs), which incorporate explicit latent thought vectors that follow an
explicit prior model in latent space. These latent thought vectors guide the
autoregressive generation of ground tokens through a Transformer decoder.
Training employs ... |
2502.01568 | Visual Theory of Mind Enables the Invention of Writing Systems | cs.CL cs.AI | Abstract symbolic writing systems are semiotic codes that are ubiquitous in
modern society but are otherwise absent in the animal kingdom. Anthropological
evidence suggests that the earliest forms of some writing systems originally
consisted of iconic pictographs, which signify their referent via visual
resemblance. ... |
2502.01572 | MakeAnything: Harnessing Diffusion Transformers for Multi-Domain
Procedural Sequence Generation | cs.CV | A hallmark of human intelligence is the ability to create complex artifacts
through structured multi-step processes. Generating procedural tutorials with
AI is a longstanding but challenging goal, facing three key obstacles: (1)
scarcity of multi-task procedural datasets, (2) maintaining logical continuity
and visual... |
2502.01573 | Next Steps in LLM-Supported Java Verification | cs.SE cs.AI cs.LG cs.LO | Recent work has shown that Large Language Models (LLMs) are not only a
suitable tool for code generation but also capable of generating
annotation-based code specifications. Scaling these methodologies may allow us
to deduce provable correctness guarantees for large-scale software systems. In
comparison to other LLM ... |
2502.01575 | Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing
Censored Data with Recursively Imputed Trees | stat.ML cs.LG | Tailoring treatments to individual needs is a central goal in fields such as
medicine. A key step toward this goal is estimating Heterogeneous Treatment
Effects (HTE) - the way treatments impact different subgroups. While crucial,
HTE estimation is challenging with survival data, where time until an event
(e.g., deat... |
2502.01576 | Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders
for Multi-modal Large Language Models | cs.CV | Multi-modal Large Language Models (MLLMs) excel in vision-language tasks but
remain vulnerable to visual adversarial perturbations that can induce
hallucinations, manipulate responses, or bypass safety mechanisms. Existing
methods seek to mitigate these risks by applying constrained adversarial
fine-tuning to CLIP vi... |
2502.01578 | ReGLA: Refining Gated Linear Attention | cs.CL | Recent advancements in Large Language Models (LLMs) have set themselves apart
with their exceptional performance in complex language modelling tasks.
However, these models are also known for their significant computational and
storage requirements, primarily due to the quadratic computation complexity of
softmax atte... |
2502.01583 | Spectral Estimators for Multi-Index Models: Precise Asymptotics and
Optimal Weak Recovery | stat.ML cs.IT cs.LG math.IT math.PR math.ST stat.TH | Multi-index models provide a popular framework to investigate the
learnability of functions with low-dimensional structure and, also due to their
connections with neural networks, they have been object of recent intensive
study. In this paper, we focus on recovering the subspace spanned by the
signals via spectral es... |
2502.01584 | PhD Knowledge Not Required: A Reasoning Challenge for Large Language
Models | cs.AI cs.LG | Existing benchmarks for frontier models often test specialized, ``PhD-level''
knowledge that is difficult for non-experts to grasp. In contrast, we present a
benchmark based on the NPR Sunday Puzzle Challenge that requires only general
knowledge. Our benchmark is challenging for both humans and models, however
correc... |
2502.01585 | Re-examining Double Descent and Scaling Laws under Norm-based Capacity
via Deterministic Equivalence | stat.ML cs.LG math.ST stat.TH | We investigate double descent and scaling laws in terms of weights rather
than the number of parameters. Specifically, we analyze linear and random
features models using the deterministic equivalence approach from random matrix
theory. We precisely characterize how the weights norm concentrate around
deterministic qu... |
2502.01586 | SubTrack your Grad: Gradient Subspace Tracking for Memory and Time
Efficient Full-Parameter LLM Training | cs.LG | Training Large Language Models (LLMs) demand significant time and
computational resources due to their large model sizes and optimizer states. To
overcome these challenges, recent methods, such as BAdam, employ partial weight
updates to enhance time and memory efficiency, though sometimes at the cost of
performance. ... |
2502.01587 | Verbalized Bayesian Persuasion | cs.GT cs.AI cs.LG | Information design (ID) explores how a sender influence the optimal behavior
of receivers to achieve specific objectives. While ID originates from everyday
human communication, existing game-theoretic and machine learning methods often
model information structures as numbers, which limits many applications to toy
gam... |
2502.01588 | A Differentiable Alignment Framework for Sequence-to-Sequence Modeling
via Optimal Transport | cs.LG cs.SD eess.AS stat.ML | Accurate sequence-to-sequence (seq2seq) alignment is critical for
applications like medical speech analysis and language learning tools relying
on automatic speech recognition (ASR). State-of-the-art end-to-end (E2E) ASR
systems, such as the Connectionist Temporal Classification (CTC) and
transducer-based models, suf... |
2502.01590 | Downlink Beamforming with Pinching-Antenna Assisted MIMO Systems | cs.IT eess.SP math.IT | Pinching antennas have been recently proposed as a promising flexible-antenna
technology, which can be implemented by attaching low-cost pinching elements to
dielectric waveguides. This work explores the potential of employing pinching
antenna systems (PASs) for downlink transmission in a multiuser MIMO setting.
We c... |
2502.01591 | Improving Transformer World Models for Data-Efficient RL | cs.LG cs.AI | We present an approach to model-based RL that achieves a new state of the art
performance on the challenging Craftax-classic benchmark, an open-world 2D
survival game that requires agents to exhibit a wide range of general abilities
-- such as strong generalization, deep exploration, and long-term reasoning.
With a s... |
2502.01594 | Faster Adaptive Optimization via Expected Gradient Outer Product
Reparameterization | cs.LG math.OC | Adaptive optimization algorithms -- such as Adagrad, Adam, and their variants
-- have found widespread use in machine learning, signal processing and many
other settings. Several methods in this family are not rotationally
equivariant, meaning that simple reparameterizations (i.e. change of basis) can
drastically aff... |
2502.01597 | FutureVision: A methodology for the investigation of future cognition | cs.CL | This paper presents a methodology combining multimodal semantic analysis with
an eye-tracking experimental protocol to investigate the cognitive effort
involved in understanding the communication of future scenarios. To demonstrate
the methodology, we conduct a pilot study examining how visual fixation
patterns vary ... |
2502.01600 | Reinforcement Learning for Long-Horizon Interactive LLM Agents | cs.LG cs.AI | Interactive digital agents (IDAs) leverage APIs of stateful digital
environments to perform tasks in response to user requests. While IDAs powered
by instruction-tuned large language models (LLMs) can react to feedback from
interface invocations in multi-step exchanges, they have not been trained in
their respective ... |
2502.01609 | Breaking Focus: Contextual Distraction Curse in Large Language Models | cs.CL | Recent advances in Large Language Models (LLMs) have revolutionized
generative systems, achieving excellent performance across diverse domains.
Although these models perform well in controlled environments, their real-world
applications frequently encounter inputs containing both essential and
irrelevant details. Our... |
2502.01612 | Self-Improving Transformers Overcome Easy-to-Hard and Length
Generalization Challenges | cs.LG cs.AI | Large language models often struggle with length generalization and solving
complex problem instances beyond their training distribution. We present a
self-improvement approach where models iteratively generate and learn from
their own solutions, progressively tackling harder problems while maintaining a
standard tra... |
2502.01615 | Large Language Models Are Human-Like Internally | cs.CL | Recent cognitive modeling studies have reported that larger language models
(LMs) exhibit a poorer fit to human reading behavior, leading to claims of
their cognitive implausibility. In this paper, we revisit this argument through
the lens of mechanistic interpretability and argue that prior conclusions were
skewed b... |
2502.01616 | Preference VLM: Leveraging VLMs for Scalable Preference-Based
Reinforcement Learning | cs.LG | Preference-based reinforcement learning (RL) offers a promising approach for
aligning policies with human intent but is often constrained by the high cost
of human feedback. In this work, we introduce PrefVLM, a framework that
integrates Vision-Language Models (VLMs) with selective human feedback to
significantly red... |
2502.01618 | A Probabilistic Inference Approach to Inference-Time Scaling of LLMs
using Particle-Based Monte Carlo Methods | cs.LG cs.AI | Large language models (LLMs) have achieved significant performance gains via
scaling up model sizes and/or data. However, recent evidence suggests
diminishing returns from such approaches, motivating scaling the computation
spent at inference time. Existing inference-time scaling methods, usually with
reward models, ... |
2502.01619 | Learning to Generate Unit Tests for Automated Debugging | cs.SE cs.AI cs.CL cs.LG | Unit tests (UTs) play an instrumental role in assessing code correctness as
well as providing feedback to a large language model (LLM) as it iteratively
debugs faulty code, motivating automated test generation. However, we uncover a
trade-off between generating unit test inputs that reveal errors when given a
faulty ... |
2502.01620 | LLM-TA: An LLM-Enhanced Thematic Analysis Pipeline for Transcripts from
Parents of Children with Congenital Heart Disease | cs.CL cs.HC | Thematic Analysis (TA) is a fundamental method in healthcare research for
analyzing transcript data, but it is resource-intensive and difficult to scale
for large, complex datasets. This study investigates the potential of large
language models (LLMs) to augment the inductive TA process in high-stakes
healthcare sett... |
2502.01626 | MFP-VTON: Enhancing Mask-Free Person-to-Person Virtual Try-On via
Diffusion Transformer | cs.CV | The garment-to-person virtual try-on (VTON) task, which aims to generate
fitting images of a person wearing a reference garment, has made significant
strides. However, obtaining a standard garment is often more challenging than
using the garment already worn by the person. To improve ease of use, we
propose MFP-VTON,... |
2502.01627 | A Poisson Process AutoDecoder for X-ray Sources | astro-ph.IM astro-ph.HE cs.LG stat.AP | X-ray observing facilities, such as the Chandra X-ray Observatory and the
eROSITA, have detected millions of astronomical sources associated with
high-energy phenomena. The arrival of photons as a function of time follows a
Poisson process and can vary by orders-of-magnitude, presenting obstacles for
common tasks suc... |
2502.01628 | Harmonic Loss Trains Interpretable AI Models | cs.LG | In this paper, we introduce **harmonic loss** as an alternative to the
standard cross-entropy loss for training neural networks and large language
models (LLMs). Harmonic loss enables improved interpretability and faster
convergence, owing to its scale invariance and finite convergence point by
design, which can be i... |
2502.01630 | TReMu: Towards Neuro-Symbolic Temporal Reasoning for LLM-Agents with
Memory in Multi-Session Dialogues | cs.AI | Temporal reasoning in multi-session dialogues presents a significant
challenge which has been under-studied in previous temporal reasoning
benchmarks. To bridge this gap, we propose a new evaluation task for temporal
reasoning in multi-session dialogues and introduce an approach to construct a
new benchmark by augmen... |
2502.01633 | Adversarial Reasoning at Jailbreaking Time | cs.LG cs.AI | As large language models (LLMs) are becoming more capable and widespread, the
study of their failure cases is becoming increasingly important. Recent
advances in standardizing, measuring, and scaling test-time compute suggest new
methodologies for optimizing models to achieve high performance on hard tasks.
In this p... |
2502.01634 | Online Gradient Boosting Decision Tree: In-Place Updates for Efficient
Adding/Deleting Data | cs.LG cs.AI cs.CR stat.ML | Gradient Boosting Decision Tree (GBDT) is one of the most popular machine
learning models in various applications. However, in the traditional settings,
all data should be simultaneously accessed in the training procedure: it does
not allow to add or delete any data instances after training. In this paper, we
propose... |
2502.01635 | The AI Agent Index | cs.SE cs.AI | Leading AI developers and startups are increasingly deploying agentic AI
systems that can plan and execute complex tasks with limited human involvement.
However, there is currently no structured framework for documenting the
technical components, intended uses, and safety features of agentic systems. To
fill this gap... |
2502.01636 | Lifelong Sequential Knowledge Editing without Model Degradation | cs.CL cs.AI cs.LG | Prior work in parameter-modifying knowledge editing has shown that
large-scale sequential editing leads to significant model degradation. In this
paper, we study the reasons behind this and scale sequential knowledge editing
to 10,000 sequential edits, while maintaining the downstream performance of the
original mode... |
2502.01637 | Scaling Embedding Layers in Language Models | cs.CL cs.LG | We propose SCONE ($\textbf{S}$calable, $\textbf{C}$ontextualized,
$\textbf{O}$ffloaded, $\textbf{N}$-gram $\textbf{E}$mbedding), a method for
extending input embedding layers to enhance language model performance as layer
size scales. To avoid increased decoding costs, SCONE retains the original
vocabulary while intr... |
2502.01639 | SliderSpace: Decomposing the Visual Capabilities of Diffusion Models | cs.CV cs.GR cs.LG | We present SliderSpace, a framework for automatically decomposing the visual
capabilities of diffusion models into controllable and human-understandable
directions. Unlike existing control methods that require a user to specify
attributes for each edit direction individually, SliderSpace discovers multiple
interpreta... |
2502.01643 | FruitPAL: An IoT-Enabled Framework for Automatic Monitoring of Fruit
Consumption in Smart Healthcare | cs.CV | Fruits are rich sources of essential vitamins and nutrients that are vital
for human health. This study introduces two fully automated devices, FruitPAL
and its updated version, FruitPAL 2.0, which aim to promote safe fruit
consumption while reducing health risks. Both devices leverage a high-quality
dataset of fifte... |
2502.01648 | UA-1 PH2 DECISIVE Testing Handbook: Test Methods and Benchmarking
Performance Results for sUAS in Dense Urban Environments | cs.RO cs.SY eess.SY | This report outlines all test methods and reviews all results derived from
performance benchmarking of small unmanned aerial systems (sUAS) in dense urban
environments conducted during Phase 2 of the Development and Execution of
Comprehensive and Integrated Systematic Intelligent Vehicle Evaluations
(DECISIVE) projec... |
2502.01649 | Privacy-Preserving Edge Speech Understanding with Tiny Foundation Models | eess.AS cs.LG cs.SD | Robust speech recognition systems rely on cloud service providers for
inference. It needs to ensure that an untrustworthy provider cannot deduce the
sensitive content in speech. Sanitization can be done on speech content keeping
in mind that it has to avoid compromising transcription accuracy. Realizing the
under uti... |
2502.01651 | Fine-tuning LLaMA 2 interference: a comparative study of language
implementations for optimal efficiency | cs.LG cs.AI | This paper presents a comparative study aimed at optimizing Llama2 inference,
a critical aspect of machine learning and natural language processing (NLP). We
evaluate various programming languages and frameworks, including TensorFlow,
PyTorch, Python, Mojo, C++, and Java, analyzing their performance in terms of
speed... |
2502.01652 | Hybrid Group Relative Policy Optimization: A Multi-Sample Approach to
Enhancing Policy Optimization | cs.LG cs.AI | Hybrid Group Relative Policy Optimization (Hybrid GRPO) is a reinforcement
learning framework that extends Proximal Policy Optimization (PPO) and Group
Relative Policy Optimization (GRPO) by incorporating empirical multi-sample
action evaluation while preserving the stability of value function-based
learning. Unlike ... |
2502.01654 | Predicting concentration levels of air pollutants by transfer learning
and recurrent neural network | cs.LG cs.NE physics.ao-ph | Air pollution (AP) poses a great threat to human health, and people are
paying more attention than ever to its prediction. Accurate prediction of AP
helps people to plan for their outdoor activities and aids protecting human
health. In this paper, long-short term memory (LSTM) recurrent neural networks
(RNNs) have be... |
2502.01655 | A binary PSO based ensemble under-sampling model for rebalancing
imbalanced training data | cs.LG cs.AI cs.NE | Ensemble technique and under-sampling technique are both effective tools used
for imbalanced dataset classification problems. In this paper, a novel ensemble
method combining the advantages of both ensemble learning for biasing
classifiers and a new under-sampling method is proposed. The under-sampling
method is name... |
2502.01656 | Imperfect Knowledge Management -- A Case Study in a Chilean
Manufacturing Company | cs.DB cs.CY | To conceptualize living systems based on the processes that create them,
rather than their interactions with the environment, as in systems theory.
Maturana and Varela (1969) at the University of Chile introduced the term
autopoiesis (from Greek self and production). This concept emphasizes autonomy
as the defining f... |
2502.01657 | Improving Rule-based Reasoning in LLMs via Neurosymbolic Representations | cs.LG cs.AI | Large language models (LLMs) continue to face challenges in reliably solving
reasoning tasks, particularly tasks that involve precise rule following, as
often found in mathematical reasoning tasks. This paper introduces a novel
neurosymbolic method that improves LLM reasoning by encoding hidden states into
neurosymbo... |
2502.01658 | Large Language Models' Accuracy in Emulating Human Experts' Evaluation
of Public Sentiments about Heated Tobacco Products on Social Media | cs.CL cs.CY cs.SI | Sentiment analysis of alternative tobacco products on social media is
important for tobacco control research. Large Language Models (LLMs) can help
streamline the labor-intensive human sentiment analysis process. This study
examined the accuracy of LLMs in replicating human sentiment evaluation of
social media messag... |
2502.01659 | Longer Attention Span: Increasing Transformer Context Length with Sparse
Graph Processing Techniques | cs.LG cs.AI cs.DC cs.PF | Transformers have demonstrated great success in numerous domains including
natural language processing and bioinformatics. This success stems from the use
of the attention mechanism by these models in order to represent and propagate
pairwise interactions between individual tokens of sequential data. However,
the pri... |
2502.01660 | Employee Turnover Prediction: A Cross-component Attention Transformer
with Consideration of Competitor Influence and Contagious Effect | cs.LG cs.AI | Employee turnover refers to an individual's termination of employment from
the current organization. It is one of the most persistent challenges for
firms, especially those ones in Information Technology (IT) industry that
confront high turnover rates. Effective prediction of potential employee
turnovers benefits mul... |
2502.01662 | Speculative Ensemble: Fast Large Language Model Ensemble via Speculation | cs.CL cs.AI cs.LG | Ensemble methods enhance Large Language Models (LLMs) by combining multiple
models but suffer from high computational costs. In this paper, we introduce
Speculative Ensemble, a novel framework that accelerates LLM ensembles without
sacrificing performance, inspired by Speculative Decoding-where a small
proposal model... |
2502.01663 | Explainable AI for Sentiment Analysis of Human Metapneumovirus (HMPV)
Using XLNet | cs.CL | In 2024, the outbreak of Human Metapneumovirus (HMPV) in China, which later
spread to the UK and other countries, raised significant public concern. While
HMPV typically causes mild symptoms, its effects on vulnerable individuals
prompted health authorities to emphasize preventive measures. This paper
explores how se... |
2502.01665 | Entropy-based measure of rock sample heterogeneity derived from micro-CT
images | eess.IV cs.CV | This study presents an automated method for objectively measuring rock
heterogeneity via raw X-ray micro-computed tomography (micro-CT) images,
thereby addressing the limitations of traditional methods, which are
time-consuming, costly, and subjective. Unlike approaches that rely on image
segmentation, the proposed m... |
2502.01666 | Leveraging Stable Diffusion for Monocular Depth Estimation via Image
Semantic Encoding | cs.CV cs.LG | Monocular depth estimation involves predicting depth from a single RGB image
and plays a crucial role in applications such as autonomous driving, robotic
navigation, 3D reconstruction, etc. Recent advancements in learning-based
methods have significantly improved depth estimation performance. Generative
models, parti... |
2502.01667 | Refining Alignment Framework for Diffusion Models with Intermediate-Step
Preference Ranking | cs.LG cs.AI | Direct preference optimization (DPO) has shown success in aligning diffusion
models with human preference. Previous approaches typically assume a consistent
preference label between final generations and noisy samples at intermediate
steps, and directly apply DPO to these noisy samples for fine-tuning. However,
we th... |
2502.01669 | Addressing Delayed Feedback in Conversion Rate Prediction via Influence
Functions | cs.LG cs.AI cs.IR | In the realm of online digital advertising, conversion rate (CVR) prediction
plays a pivotal role in maximizing revenue under cost-per-conversion (CPA)
models, where advertisers are charged only when users complete specific
actions, such as making a purchase. A major challenge in CVR prediction lies in
the delayed fe... |
2502.01670 | A Hardware-Efficient Photonic Tensor Core: Accelerating Deep Neural
Networks with Structured Compression | cs.AR cs.ET cs.LG | Recent advancements in artificial intelligence (AI) and deep neural networks
(DNNs) have revolutionized numerous fields, enabling complex tasks by
extracting intricate features from large datasets. However, the exponential
growth in computational demands has outstripped the capabilities of traditional
electrical hard... |
2502.01671 | Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and
Generational Trends | cs.AR cs.AI | Specialized hardware accelerators aid the rapid advancement of artificial
intelligence (AI), and their efficiency impacts AI's environmental
sustainability. This study presents the first publication of a comprehensive AI
accelerator life-cycle assessment (LCA) of greenhouse gas emissions, including
the first publicat... |
2502.01672 | Doubly Robust Monte Carlo Tree Search | stat.ML cs.AI cs.LG | We present Doubly Robust Monte Carlo Tree Search (DR-MCTS), a novel algorithm
that integrates Doubly Robust (DR) off-policy estimation into Monte Carlo Tree
Search (MCTS) to enhance sample efficiency and decision quality in complex
environments. Our approach introduces a hybrid estimator that combines MCTS
rollouts w... |
2502.01673 | Multilingual State Space Models for Structured Question Answering in
Indic Languages | cs.CL cs.AI | The diversity and complexity of Indic languages present unique challenges for
natural language processing (NLP) tasks, particularly in the domain of question
answering (QA).To address these challenges, this paper explores the application
of State Space Models (SSMs),to build efficient and contextually aware QA
system... |
2502.01674 | Efficient Brain Tumor Classification with Lightweight CNN Architecture:
A Novel Approach | eess.IV cs.CV | Brain tumor classification using MRI images is critical in medical
diagnostics, where early and accurate detection significantly impacts patient
outcomes. While recent advancements in deep learning (DL), particularly CNNs,
have shown promise, many models struggle with balancing accuracy and
computational efficiency a... |
2502.01675 | Semantic Communication based on Generative AI: A New Approach to Image
Compression and Edge Optimization | cs.CV cs.AI cs.LG | As digital technologies advance, communication networks face challenges in
handling the vast data generated by intelligent devices. Autonomous vehicles,
smart sensors, and IoT systems necessitate new paradigms. This thesis addresses
these challenges by integrating semantic communication and generative models
for opti... |
2502.01676 | Benchmark on Peer Review Toxic Detection: A Challenging Task with a New
Dataset | cs.CL cs.CY | Peer review is crucial for advancing and improving science through
constructive criticism. However, toxic feedback can discourage authors and
hinder scientific progress. This work explores an important but underexplored
area: detecting toxicity in peer reviews. We first define toxicity in peer
reviews across four dis... |
2502.01677 | AI Scaling: From Up to Down and Out | cs.LG cs.AI | AI Scaling has traditionally been synonymous with Scaling Up, which builds
larger and more powerful models. However, the growing demand for efficiency,
adaptability, and collaboration across diverse applications necessitates a
broader perspective. This position paper presents a holistic framework for AI
scaling, enco... |
2502.01678 | LEAD: Large Foundation Model for EEG-Based Alzheimer's Disease Detection | cs.LG cs.AI cs.CE eess.SP | Electroencephalogram (EEG) provides a non-invasive, highly accessible, and
cost-effective solution for Alzheimer's Disease (AD) detection. However,
existing methods, whether based on manual feature extraction or deep learning,
face two major challenges: the lack of large-scale datasets for robust feature
learning and... |
2502.01679 | LIBRA: Measuring Bias of Large Language Model from a Local Context | cs.CY cs.CL cs.LG | Large Language Models (LLMs) have significantly advanced natural language
processing applications, yet their widespread use raises concerns regarding
inherent biases that may reduce utility or harm for particular social groups.
Despite the advancement in addressing LLM bias, existing research has two major
limitation... |
2502.01680 | Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree
Rules into Neural Networks | cs.LG cs.AI | Travel demand prediction is crucial for optimizing transportation planning,
resource allocation, and infrastructure development, ensuring efficient
mobility and economic sustainability. This study introduces a Neurosymbolic
Artificial Intelligence (Neurosymbolic AI) framework that integrates decision
tree (DT)-based ... |
2502.01681 | DeepGate4: Efficient and Effective Representation Learning for Circuit
Design at Scale | cs.LG cs.AR | Circuit representation learning has become pivotal in electronic design
automation, enabling critical tasks such as testability analysis, logic
reasoning, power estimation, and SAT solving. However, existing models face
significant challenges in scaling to large circuits due to limitations like
over-squashing in grap... |
2502.01682 | The exception of humour: Iconicity, Phonemic Surprisal, Memory Recall,
and Emotional Associations | cs.CL | This meta-study explores the relationships between humor, phonemic bigram
surprisal, emotional valence, and memory recall. Prior research indicates that
words with higher phonemic surprisal are more readily remembered, suggesting
that unpredictable phoneme sequences promote long-term memory recall. Emotional
valence ... |
2502.01683 | LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient | cs.CL cs.AI | The rapid advancement of large language models (LLMs) has led to a surge in
both model supply and application demands. To facilitate effective matching
between them, reliable, generic and efficient benchmark generators are widely
needed. However, human annotators are constrained by inefficiency, and current
LLM bench... |
2502.01684 | Leveraging Joint Predictive Embedding and Bayesian Inference in Graph
Self Supervised Learning | cs.LG cs.AI cs.SI | Graph representation learning has emerged as a cornerstone for tasks like
node classification and link prediction, yet prevailing self-supervised
learning (SSL) methods face challenges such as computational inefficiency,
reliance on contrastive objectives, and representation collapse. Existing
approaches often depend... |
2502.01685 | Automated Extraction of Spatio-Semantic Graphs for Identifying Cognitive
Impairment | cs.AI cs.CL cs.CV cs.SD eess.AS | Existing methods for analyzing linguistic content from picture descriptions
for assessment of cognitive-linguistic impairment often overlook the
participant's visual narrative path, which typically requires eye tracking to
assess. Spatio-semantic graphs are a useful tool for analyzing this narrative
path from transcr... |
2502.01688 | BrainOOD: Out-of-distribution Generalizable Brain Network Analysis | cs.LG q-bio.NC | In neuroscience, identifying distinct patterns linked to neurological
disorders, such as Alzheimer's and Autism, is critical for early diagnosis and
effective intervention. Graph Neural Networks (GNNs) have shown promising in
analyzing brain networks, but there are two major challenges in using GNNs: (1)
distribution... |
2502.01689 | scGSDR: Harnessing Gene Semantics for Single-Cell Pharmacological
Profiling | q-bio.GN cs.AI | The rise of single-cell sequencing technologies has revolutionized the
exploration of drug resistance, revealing the crucial role of cellular
heterogeneity in advancing precision medicine. By building computational models
from existing single-cell drug response data, we can rapidly annotate cellular
responses to drug... |
2502.01690 | HuViDPO:Enhancing Video Generation through Direct Preference
Optimization for Human-Centric Alignment | cs.CV | With the rapid development of AIGC technology, significant progress has been
made in diffusion model-based technologies for text-to-image (T2I) and
text-to-video (T2V). In recent years, a few studies have introduced the
strategy of Direct Preference Optimization (DPO) into T2I tasks, significantly
enhancing human pre... |
2502.01691 | Agent-Based Uncertainty Awareness Improves Automated Radiology Report
Labeling with an Open-Source Large Language Model | cs.CL cs.AI | Reliable extraction of structured data from radiology reports using Large
Language Models (LLMs) remains challenging, especially for complex, non-English
texts like Hebrew. This study introduces an agent-based uncertainty-aware
approach to improve the trustworthiness of LLM predictions in medical
applications. We ana... |
2502.01692 | Fast Direct: Query-Efficient Online Black-box Guidance for
Diffusion-model Target Generation | cs.LG cs.AI | Guided diffusion-model generation is a promising direction for customizing
the generation process of a pre-trained diffusion-model to address the specific
downstream tasks. Existing guided diffusion models either rely on training of
the guidance model with pre-collected datasets or require the objective
functions to ... |
2502.01693 | Predicting Steady-State Behavior in Complex Networks with Graph Neural
Networks | cs.LG cs.AI nlin.AO | In complex systems, information propagation can be defined as diffused or
delocalized, weakly localized, and strongly localized. This study investigates
the application of graph neural network models to learn the behavior of a
linear dynamical system on networks. A graph convolution and attention-based
neural network... |
2502.01694 | Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of
Search, RL and Distillation | cs.AI cs.LG stat.ML | A key paradigm to improve the reasoning capabilities of large language models
(LLMs) is to allocate more inference-time compute to search against a verifier
or reward model. This process can then be utilized to refine the pretrained
model or distill its reasoning patterns into more efficient models. In this
paper, we... |
2502.01695 | A Novel Real-Time Full-Color 3D Holographic (Diffractive) Video Capture,
Processing, and Transmission Pipeline Using Off-The-Shelf Hardware | eess.IV cs.CV | This paper details the world's first live 3D holographic (diffractive) video
call using off-the-shelf hardware. We introduce a novel pipeline that
facilitates the capture, processing, and transmission of RGBZ data, using an
iPhone for image and depth capture with VividQ's SDK for hologram generation
and hardware for ... |
2502.01697 | BARE: Combining Base and Instruction-Tuned Language Models for Better
Synthetic Data Generation | cs.CL cs.AI cs.LG | As the demand for high-quality data in model training grows, researchers and
developers are increasingly generating synthetic data to tune and train LLMs. A
common assumption about synthetic data is that sampling from instruct-tuned
models is sufficient; however, these models struggle to produce diverse
outputs-a key... |
2502.01699 | Multimodal Inverse Attention Network with Intrinsic Discriminant Feature
Exploitation for Fake News Detection | cs.LG cs.CL cs.CV cs.IR cs.MM | Multimodal fake news detection has garnered significant attention due to its
profound implications for social security. While existing approaches have
contributed to understanding cross-modal consistency, they often fail to
leverage modal-specific representations and explicit discrepant features. To
address these lim... |
2502.01700 | EdgeMark: An Automation and Benchmarking System for Embedded Artificial
Intelligence Tools | cs.LG | The integration of artificial intelligence (AI) into embedded devices, a
paradigm known as embedded artificial intelligence (eAI) or tiny machine
learning (TinyML), is transforming industries by enabling intelligent data
processing at the edge. However, the many tools available in this domain leave
researchers and de... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.