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Nlp

Has anyone here switched to TeraBox recently? Is it actually worth it?

I’ve been seeing more people talk about TeraBox lately, especially around storage for AI-related workflows. Curious if anyone here has us...

Reddit - Artificial Intelligence · 1 min ·
UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·
Google quietly launched an AI dictation app that works offline
Machine Learning

Google quietly launched an AI dictation app that works offline

Google's new offline-first dictation app uses Gemma AI models to take on the apps like Wispr Flow.

TechCrunch - AI · 4 min ·

All Content

[2602.17493] Learning with Boolean threshold functions
Machine Learning

[2602.17493] Learning with Boolean threshold functions

This article presents a novel method for training neural networks on Boolean data using Boolean threshold functions (BTF), demonstrating ...

arXiv - AI · 4 min ·
[2602.17483] What Do LLMs Associate with Your Name? A Human-Centered Black-Box Audit of Personal Data
Llms

[2602.17483] What Do LLMs Associate with Your Name? A Human-Centered Black-Box Audit of Personal Data

This article presents a human-centered audit of how large language models (LLMs) associate personal data with individual names, highlight...

arXiv - AI · 3 min ·
[2602.17423] Convergence Analysis of Two-Layer Neural Networks under Gaussian Input Masking
Machine Learning

[2602.17423] Convergence Analysis of Two-Layer Neural Networks under Gaussian Input Masking

This paper explores the convergence of two-layer neural networks trained with Gaussian masked inputs, demonstrating linear convergence th...

arXiv - AI · 3 min ·
[2602.17397] A High-Level Survey of Optical Remote Sensing
Computer Vision

[2602.17397] A High-Level Survey of Optical Remote Sensing

This article provides a comprehensive overview of optical remote sensing, highlighting advancements in computer vision and drone technolo...

arXiv - AI · 3 min ·
[2602.17364] A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data
Machine Learning

[2602.17364] A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data

This article presents CACTUS, a machine learning framework designed to enhance decision-making in clinical settings by ensuring feature s...

arXiv - AI · 4 min ·
[2602.17342] From Subtle to Significant: Prompt-Driven Self-Improving Optimization in Test-Time Graph OOD Detection
Machine Learning

[2602.17342] From Subtle to Significant: Prompt-Driven Self-Improving Optimization in Test-Time Graph OOD Detection

The paper presents SIGOOD, a novel framework for improving graph out-of-distribution detection through prompt-driven self-improvement, en...

arXiv - AI · 4 min ·
[2602.17330] SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework
Machine Learning

[2602.17330] SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework

The paper presents SubQuad, an innovative pipeline for analyzing adaptive immune repertoires, addressing challenges of high computational...

arXiv - Machine Learning · 3 min ·
[2602.17327] WebFAQ 2.0: A Multilingual QA Dataset with Mined Hard Negatives for Dense Retrieval
Nlp

[2602.17327] WebFAQ 2.0: A Multilingual QA Dataset with Mined Hard Negatives for Dense Retrieval

WebFAQ 2.0 introduces a multilingual QA dataset with 198 million FAQ-based question-answer pairs across 108 languages, enhancing multilin...

arXiv - AI · 4 min ·
[2602.17315] Flickering Multi-Armed Bandits
Machine Learning

[2602.17315] Flickering Multi-Armed Bandits

The paper introduces Flickering Multi-Armed Bandits (FMAB), a new framework that adapts the set of available actions based on previous ch...

arXiv - AI · 3 min ·
[2602.17205] Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
Machine Learning

[2602.17205] Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising

The paper presents ASTERIS, a self-supervised spatiotemporal denoising algorithm that enhances detection limits in astronomical imaging, ...

arXiv - AI · 4 min ·
[2602.17183] Robustness and Reasoning Fidelity of Large Language Models in Long-Context Code Question Answering
Llms

[2602.17183] Robustness and Reasoning Fidelity of Large Language Models in Long-Context Code Question Answering

This article examines the robustness and reasoning fidelity of large language models (LLMs) in long-context code question answering, reve...

arXiv - AI · 3 min ·
[2602.17176] Universal Fine-Grained Symmetry Inference and Enforcement for Rigorous Crystal Structure Prediction
Machine Learning

[2602.17176] Universal Fine-Grained Symmetry Inference and Enforcement for Rigorous Crystal Structure Prediction

This paper presents a novel approach to crystal structure prediction by utilizing large language models for fine-grained symmetry inferen...

arXiv - AI · 4 min ·
[2602.17149] TimeOmni-VL: Unified Models for Time Series Understanding and Generation
Machine Learning

[2602.17149] TimeOmni-VL: Unified Models for Time Series Understanding and Generation

TimeOmni-VL introduces a unified framework for time series understanding and generation, overcoming limitations of existing models by int...

arXiv - AI · 3 min ·
[2602.17122] TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
Machine Learning

[2602.17122] TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series

The paper presents TIFO, a Time-Invariant Frequency Operator designed to improve representation learning in nonstationary time series by ...

arXiv - AI · 4 min ·
[2602.17098] Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization
Machine Learning

[2602.17098] Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization

This article presents a comparative study of Deep Reinforcement Learning (DRL) and Mean-Variance Optimization (MVO) for optimal portfolio...

arXiv - Machine Learning · 4 min ·
[2602.17070] General sample size analysis for probabilities of causation: a delta method approach
Data Science

[2602.17070] General sample size analysis for probabilities of causation: a delta method approach

This paper presents a delta method approach for sample size analysis in estimating probabilities of causation (PoCs), addressing the need...

arXiv - AI · 3 min ·
[2602.17063] Sign Lock-In: Randomly Initialized Weight Signs Persist and Bottleneck Sub-Bit Model Compression
Machine Learning

[2602.17063] Sign Lock-In: Randomly Initialized Weight Signs Persist and Bottleneck Sub-Bit Model Compression

The paper discusses 'Sign Lock-In,' a phenomenon in machine learning where randomly initialized weight signs persist during model trainin...

arXiv - AI · 3 min ·
[2602.17051] Evaluating Cross-Lingual Classification Approaches Enabling Topic Discovery for Multilingual Social Media Data
Nlp

[2602.17051] Evaluating Cross-Lingual Classification Approaches Enabling Topic Discovery for Multilingual Social Media Data

This article evaluates various cross-lingual classification methods for analyzing multilingual social media data, focusing on topic disco...

arXiv - Machine Learning · 4 min ·
[2602.17028] Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
Ai Startups

[2602.17028] Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles

The paper presents FATE, an innovative framework for forecasting anomaly precursors in time-series data using uncertainty-aware ensembles...

arXiv - AI · 4 min ·
[2602.17027] Transforming Behavioral Neuroscience Discovery with In-Context Learning and AI-Enhanced Tensor Methods
Data Science

[2602.17027] Transforming Behavioral Neuroscience Discovery with In-Context Learning and AI-Enhanced Tensor Methods

This article discusses the integration of AI and In-Context Learning to enhance behavioral neuroscience research, particularly in underst...

arXiv - AI · 4 min ·
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