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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

[2506.20555] DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States
Machine Learning

[2506.20555] DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States

The paper presents DeepQuark, a novel deep-neural-network approach for analyzing multiquark bound states, demonstrating superior performa...

arXiv - AI · 4 min ·
[2505.20650] FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information
Llms

[2505.20650] FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information

The paper introduces FinTagging, a benchmark for evaluating LLMs in extracting and structuring financial information, addressing limitati...

arXiv - AI · 4 min ·
[2505.15547] Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning
Machine Learning

[2505.15547] Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning

This article examines common misconceptions in graph machine learning, focusing on oversmoothing, oversquashing, and the homophily-hetero...

arXiv - AI · 4 min ·
[2505.12298] Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans
Machine Learning

[2505.12298] Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans

This article presents a novel approach using an Attention-Enhanced U-Net for the automatic segmentation of COVID-19 infected lung regions...

arXiv - AI · 3 min ·
[2505.02819] ReplaceMe: Network Simplification via Depth Pruning and Transformer Block Linearization
Machine Learning

[2505.02819] ReplaceMe: Network Simplification via Depth Pruning and Transformer Block Linearization

The paper presents ReplaceMe, a novel method for network simplification that utilizes depth pruning and transformer block linearization, ...

arXiv - Machine Learning · 4 min ·
[2503.04121] Simple Self Organizing Map with Vision Transformers
Machine Learning

[2503.04121] Simple Self Organizing Map with Vision Transformers

This paper explores the integration of Self-Organizing Maps (SOMs) with Vision Transformers (ViTs) to enhance performance on small datase...

arXiv - Machine Learning · 4 min ·
[2502.08834] Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers
Machine Learning

[2502.08834] Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers

The paper introduces Rex, a family of reversible exponential (stochastic) Runge-Kutta solvers designed to enhance the inversion accuracy ...

arXiv - AI · 4 min ·
[2602.06838] An Adaptive Differentially Private Federated Learning Framework with Bi-level Optimization
Machine Learning

[2602.06838] An Adaptive Differentially Private Federated Learning Framework with Bi-level Optimization

This paper presents an adaptive differentially private federated learning framework that addresses challenges in model efficiency and sta...

arXiv - AI · 4 min ·
[2602.00307] Autonomous Data Processing using Meta-Agents
Robotics

[2602.00307] Autonomous Data Processing using Meta-Agents

The paper presents a novel framework, Autonomous Data Processing using Meta-Agents (ADP-MA), which enhances data processing pipelines thr...

arXiv - AI · 3 min ·
[2601.07463] Puzzle it Out: Local-to-Global World Model for Offline Multi-Agent Reinforcement Learning
Machine Learning

[2601.07463] Puzzle it Out: Local-to-Global World Model for Offline Multi-Agent Reinforcement Learning

This paper presents a novel Local-to-Global (LOGO) world model for offline multi-agent reinforcement learning (MARL), improving policy ge...

arXiv - Machine Learning · 4 min ·
[2508.12026] Bongard-RWR+: Real-World Representations of Fine-Grained Concepts in Bongard Problems
Machine Learning

[2508.12026] Bongard-RWR+: Real-World Representations of Fine-Grained Concepts in Bongard Problems

The paper presents Bongard-RWR+, a dataset designed to enhance fine-grained visual reasoning in Bongard Problems using real-world images ...

arXiv - Machine Learning · 4 min ·
[2505.08021] The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic
Machine Learning

[2505.08021] The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic

This paper explores the relationship between Bounded Graph Neural Networks (GNNs) and fragments of first-order logic, providing insights ...

arXiv - AI · 3 min ·
[2503.23339] A Scalable Framework for Evaluating Health Language Models
Llms

[2503.23339] A Scalable Framework for Evaluating Health Language Models

This paper presents a scalable framework for evaluating health language models, introducing Adaptive Precise Boolean rubrics to enhance e...

arXiv - AI · 4 min ·
[2602.17641] FAMOSE: A ReAct Approach to Automated Feature Discovery
Machine Learning

[2602.17641] FAMOSE: A ReAct Approach to Automated Feature Discovery

The paper presents FAMOSE, a novel framework that utilizes the ReAct paradigm for automated feature discovery in machine learning, enhanc...

arXiv - AI · 4 min ·
[2602.17634] Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting
Llms

[2602.17634] Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting

The paper presents Reverso, an efficient time series foundation model for zero-shot forecasting, demonstrating that smaller hybrid models...

arXiv - AI · 4 min ·
[2602.17605] Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery
Ai Safety

[2602.17605] Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery

This paper presents a novel framework for geospatial discovery that integrates active learning and online meta-learning, focusing on rele...

arXiv - Machine Learning · 4 min ·
[2602.17568] Be Wary of Your Time Series Preprocessing
Machine Learning

[2602.17568] Be Wary of Your Time Series Preprocessing

This paper analyzes the impact of normalization strategies on Transformer-based models for time series representation learning, revealing...

arXiv - AI · 4 min ·
[2602.17557] Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis
Machine Learning

[2602.17557] Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis

This article presents a novel framework for diagnosing Alzheimer's and Lewy body dementia using probability-invariant random walk learnin...

arXiv - AI · 4 min ·
[2602.17532] Systematic Evaluation of Single-Cell Foundation Model Interpretability Reveals Attention Captures Co-Expression Rather Than Unique Regulatory Signal
Llms

[2602.17532] Systematic Evaluation of Single-Cell Foundation Model Interpretability Reveals Attention Captures Co-Expression Rather Than Unique Regulatory Signal

This article evaluates the interpretability of single-cell foundation models, revealing that attention mechanisms capture co-expression r...

arXiv - AI · 3 min ·
[2602.17531] Position: Evaluation of ECG Representations Must Be Fixed
Ai Startups

[2602.17531] Position: Evaluation of ECG Representations Must Be Fixed

This paper critiques current benchmarking practices in 12-lead ECG representation learning, advocating for broader evaluation criteria to...

arXiv - AI · 4 min ·
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