id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
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 brou... | {
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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 lan... | {
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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-wor... | {
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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 Equivale... | {
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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, ... | {
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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 either... | {
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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 power ... | {
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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 c... | {
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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 mWhisper-... | {
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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 introduces ... | {
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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, ... | {
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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, cultiv... | {
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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 input ... | {
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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 NTK-... | {
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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 theoretica... | {
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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 and... | {
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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 about... | {
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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-base... | {
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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 the ... | {
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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 terms.... | {
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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 a ... | {
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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. Wh... | {
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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 c... | {
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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 ta... | {
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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., death)... | {
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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 visi... | {
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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 attent... | {
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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 esti... | {
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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 correct ... | {
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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 quan... | {
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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. Ot... | {
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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 games... | {
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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, suffe... | {
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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 con... | {
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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 ser... | {
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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 affec... | {
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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 du... | {
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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 di... | {
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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 i... | {
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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 trans... | {
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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 by ... | {
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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 reduc... | {
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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, ca... | {
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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 co... | {
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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 settin... | {
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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, a... | {
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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 such ... | {
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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 int... | {
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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 augmenti... | {
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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 pap... | {
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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 a... | {
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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, ... | {
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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 model.... | {
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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 introd... | {
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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 interpretabl... | {
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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 fifteen... | {
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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) project ... | {
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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 utili... | {
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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, ... | {
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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 De... | {
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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 been... | {
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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 named ... | {
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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 fea... | {
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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 neurosymboli... | {
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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 messages... | {
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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 prima... | {
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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 multi... | {
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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 g... | {
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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 sent... | {
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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 met... | {
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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, particu... | {
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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 theo... | {
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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 feed... | {
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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 hardwa... | {
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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 publicatio... | {
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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 wit... | {
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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 systems ... | {
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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 and... | {
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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 optimi... | {
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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 disti... | {
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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, encomp... | {
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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 e... | {
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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 limitations.... | {
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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 sy... | {
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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 graph ... | {
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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 is... | {
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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 benchma... | {
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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 o... | {
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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 transcrip... | {
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} |
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 s... | {
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} |
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 drugs ... | {
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} |
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 prefe... | {
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} |
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 analy... | {
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} |
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 be... | {
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} |
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 f... | {
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} |
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 s... | {
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} |
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 di... | {
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} |
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 r... | {
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} |
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 limit... | {
"Other": 1,
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} |
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 deve... | {
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} |
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