ML applications

Texas A&M Researchers Introduce a Two-Phase Machine Learning Method Named ‘ShockCast’ for High-Speed Flow Simulation with Neural Temporal Re-Meshing

ML applications

4 Min Read

Texas A&M Researchers Introduce a Two-Phase Machine Learning Method Named ‘ShockCast’ for High-Speed Flow Simulation with Neural Temporal Re-Meshing

By Editorial Team

June 22, 2025

Read Article

Why Apple’s Critique of AI Reasoning Is Premature

ML applications

4 Min Read

Why Apple’s Critique of AI Reasoning Is Premature

By Editorial Team

June 22, 2025

Read Article

This AI Paper Introduces WINGS: A Dual-Learner Architecture to Prevent Text-Only Forgetting in Multimodal Large Language Models

ML applications

4 Min Read

This AI Paper Introduces WINGS: A Dual-Learner Architecture to Prevent Text-Only Forgetting in Multimodal Large Language Models

By Editorial Team

June 22, 2025

Read Article

Why Generalization in Flow Matching Models Comes from Approximation, Not Stochasticity

ML applications

5 Min Read

Why Generalization in Flow Matching Models Comes from Approximation, Not Stochasticity

By Editorial Team

June 21, 2025

Read Article

Meta AI Researchers Introduced a Scalable Byte-Level Autoregressive U-Net Model That Outperforms Token-Based Transformers Across Language Modeling Benchmarks

ML applications

6 Min Read

Meta AI Researchers Introduced a Scalable Byte-Level Autoregressive U-Net Model That Outperforms Token-Based Transformers Across Language Modeling Benchmarks

By Editorial Team

June 21, 2025

Read Article

This AI Paper from Google Introduces a Causal Framework to Interpret Subgroup Fairness in Machine Learning Evaluations More Reliably

ML applications

5 Min Read

This AI Paper from Google Introduces a Causal Framework to Interpret Subgroup Fairness in Machine Learning Evaluations More Reliably

By Editorial Team

June 20, 2025

Read Article

MiniMax AI Releases MiniMax-M1: A 456B Parameter Hybrid Model for Long-Context and Reinforcement Learning RL Tasks

ML applications

4 Min Read

MiniMax AI Releases MiniMax-M1: A 456B Parameter Hybrid Model for Long-Context and Reinforcement Learning RL Tasks

By Editorial Team

June 19, 2025

Read Article

ReVisual-R1: An Open-Source 7B Multimodal Large Language Model (MLLMs) that Achieves Long, Accurate and Thoughtful Reasoning

ML applications

4 Min Read

ReVisual-R1: An Open-Source 7B Multimodal Large Language Model (MLLMs) that Achieves Long, Accurate and Thoughtful Reasoning

By Editorial Team

June 19, 2025

Read Article

HtFLlib: A Unified Benchmarking Library for Evaluating Heterogeneous Federated Learning Methods Across Modalities

ML applications

4 Min Read

HtFLlib: A Unified Benchmarking Library for Evaluating Heterogeneous Federated Learning Methods Across Modalities

By Editorial Team

June 19, 2025

Read Article

AREAL: Accelerating Large Reasoning Model Training with Fully Asynchronous Reinforcement Learning

ML applications

4 Min Read

AREAL: Accelerating Large Reasoning Model Training with Fully Asynchronous Reinforcement Learning

By Editorial Team

June 18, 2025

Read Article

Popular Posts

Patients, providers duped in records-and-payment scam • The Register

Microloan Apps May Be Poised to Destroy the Economy

OpenAI Loses 4 Key Researchers to Meta

Authors call on publishers to limit their use of AI

ML applications

Texas A&M Researchers Introduce a Two-Phase Machine Learning Method Named ‘ShockCast’ for High-Speed Flow Simulation with Neural Temporal Re-Meshing

Why Apple’s Critique of AI Reasoning Is Premature

This AI Paper Introduces WINGS: A Dual-Learner Architecture to Prevent Text-Only Forgetting in Multimodal Large Language Models

Why Generalization in Flow Matching Models Comes from Approximation, Not Stochasticity

Meta AI Researchers Introduced a Scalable Byte-Level Autoregressive U-Net Model That Outperforms Token-Based Transformers Across Language Modeling Benchmarks

This AI Paper from Google Introduces a Causal Framework to Interpret Subgroup Fairness in Machine Learning Evaluations More Reliably

MiniMax AI Releases MiniMax-M1: A 456B Parameter Hybrid Model for Long-Context and Reinforcement Learning RL Tasks

ReVisual-R1: An Open-Source 7B Multimodal Large Language Model (MLLMs) that Achieves Long, Accurate and Thoughtful Reasoning

HtFLlib: A Unified Benchmarking Library for Evaluating Heterogeneous Federated Learning Methods Across Modalities

AREAL: Accelerating Large Reasoning Model Training with Fully Asynchronous Reinforcement Learning

Recent Posts

Archives

Categories