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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 ·
Machine Learning

[D] Physicist-turned-ML-engineer looking to get into ML research. What's worth working on and where can I contribute most?

After years of focus on building products, I'm carving out time to do independent research again and trying to find the right direction. ...

Reddit - Machine Learning · 1 min ·
PSA: Anyone with a link can view your Granola notes by default | The Verge
Machine Learning

PSA: Anyone with a link can view your Granola notes by default | The Verge

Granola, the AI-powered note-taking app, makes your notes viewable by anyone with a link by default. It also turns on AI training for any...

The Verge - AI · 5 min ·

All Content

[2603.24601] FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Human Context Recognition
Llms

[2603.24601] FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Human Context Recognition

Abstract page for arXiv paper 2603.24601: FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Hum...

arXiv - Machine Learning · 3 min ·
[2603.24602] MuViS: Multimodal Virtual Sensing Benchmark
Machine Learning

[2603.24602] MuViS: Multimodal Virtual Sensing Benchmark

Abstract page for arXiv paper 2603.24602: MuViS: Multimodal Virtual Sensing Benchmark

arXiv - AI · 3 min ·
[2603.25033] Epistemic Compression: The Case for Deliberate Ignorance in High-Stakes AI
Llms

[2603.25033] Epistemic Compression: The Case for Deliberate Ignorance in High-Stakes AI

Abstract page for arXiv paper 2603.25033: Epistemic Compression: The Case for Deliberate Ignorance in High-Stakes AI

arXiv - Machine Learning · 3 min ·
[2603.24599] A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications
Machine Learning

[2603.24599] A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications

Abstract page for arXiv paper 2603.24599: A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications

arXiv - AI · 3 min ·
[2603.24596] X-OPD: Cross-Modal On-Policy Distillation for Capability Alignment in Speech LLMs
Llms

[2603.24596] X-OPD: Cross-Modal On-Policy Distillation for Capability Alignment in Speech LLMs

Abstract page for arXiv paper 2603.24596: X-OPD: Cross-Modal On-Policy Distillation for Capability Alignment in Speech LLMs

arXiv - AI · 3 min ·
[2603.25009] A Systematic Empirical Study of Grokking: Depth, Architecture, Activation, and Regularization
Machine Learning

[2603.25009] A Systematic Empirical Study of Grokking: Depth, Architecture, Activation, and Regularization

Abstract page for arXiv paper 2603.25009: A Systematic Empirical Study of Grokking: Depth, Architecture, Activation, and Regularization

arXiv - Machine Learning · 4 min ·
[2603.24595] Model2Kernel: Model-Aware Symbolic Execution For Safe CUDA Kernels
Llms

[2603.24595] Model2Kernel: Model-Aware Symbolic Execution For Safe CUDA Kernels

Abstract page for arXiv paper 2603.24595: Model2Kernel: Model-Aware Symbolic Execution For Safe CUDA Kernels

arXiv - AI · 4 min ·
[2402.05122] History of generative Artificial Intelligence (AI) chatbots: past, present, and future development
Machine Learning

[2402.05122] History of generative Artificial Intelligence (AI) chatbots: past, present, and future development

Abstract page for arXiv paper 2402.05122: History of generative Artificial Intelligence (AI) chatbots: past, present, and future development

arXiv - AI · 4 min ·
[2603.25737] Training the Knowledge Base through Evidence Distillation and Write-Back Enrichment
Machine Learning

[2603.25737] Training the Knowledge Base through Evidence Distillation and Write-Back Enrichment

Abstract page for arXiv paper 2603.25737: Training the Knowledge Base through Evidence Distillation and Write-Back Enrichment

arXiv - AI · 3 min ·
[2603.24916] Once-for-All Channel Mixers (HYPERTINYPW): Generative Compression for TinyML
Machine Learning

[2603.24916] Once-for-All Channel Mixers (HYPERTINYPW): Generative Compression for TinyML

Abstract page for arXiv paper 2603.24916: Once-for-All Channel Mixers (HYPERTINYPW): Generative Compression for TinyML

arXiv - Machine Learning · 4 min ·
[2603.24883] Learning to Staff: Offline Reinforcement Learning and Fine-Tuned LLMs for Warehouse Staffing Optimization
Llms

[2603.24883] Learning to Staff: Offline Reinforcement Learning and Fine-Tuned LLMs for Warehouse Staffing Optimization

Abstract page for arXiv paper 2603.24883: Learning to Staff: Offline Reinforcement Learning and Fine-Tuned LLMs for Warehouse Staffing Op...

arXiv - Machine Learning · 4 min ·
[2603.25720] R-C2: Cycle-Consistent Reinforcement Learning Improves Multimodal Reasoning
Machine Learning

[2603.25720] R-C2: Cycle-Consistent Reinforcement Learning Improves Multimodal Reasoning

Abstract page for arXiv paper 2603.25720: R-C2: Cycle-Consistent Reinforcement Learning Improves Multimodal Reasoning

arXiv - AI · 3 min ·
[2603.24844] Reaching Beyond the Mode: RL for Distributional Reasoning in Language Models
Llms

[2603.24844] Reaching Beyond the Mode: RL for Distributional Reasoning in Language Models

Abstract page for arXiv paper 2603.24844: Reaching Beyond the Mode: RL for Distributional Reasoning in Language Models

arXiv - AI · 4 min ·
[2603.25719] Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization?
Machine Learning

[2603.25719] Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization?

Abstract page for arXiv paper 2603.25719: Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardw...

arXiv - Machine Learning · 4 min ·
[2603.25551] Voxtral TTS
Machine Learning

[2603.25551] Voxtral TTS

Abstract page for arXiv paper 2603.25551: Voxtral TTS

arXiv - AI · 5 min ·
[2603.25633] Is Mathematical Problem-Solving Expertise in Large Language Models Associated with Assessment Performance?
Llms

[2603.25633] Is Mathematical Problem-Solving Expertise in Large Language Models Associated with Assessment Performance?

Abstract page for arXiv paper 2603.25633: Is Mathematical Problem-Solving Expertise in Large Language Models Associated with Assessment P...

arXiv - AI · 4 min ·
[2603.24828] A Practical Guide Towards Interpreting Time-Series Deep Clinical Predictive Models: A Reproducibility Study
Machine Learning

[2603.24828] A Practical Guide Towards Interpreting Time-Series Deep Clinical Predictive Models: A Reproducibility Study

Abstract page for arXiv paper 2603.24828: A Practical Guide Towards Interpreting Time-Series Deep Clinical Predictive Models: A Reproduci...

arXiv - AI · 4 min ·
[2603.25415] Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation
Machine Learning

[2603.25415] Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation

Abstract page for arXiv paper 2603.25415: Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation

arXiv - AI · 4 min ·
[2603.24790] Local learning for stable backpropagation-free neural network training towards physical learning
Machine Learning

[2603.24790] Local learning for stable backpropagation-free neural network training towards physical learning

Abstract page for arXiv paper 2603.24790: Local learning for stable backpropagation-free neural network training towards physical learning

arXiv - Machine Learning · 3 min ·
[2603.25498] EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents
Llms

[2603.25498] EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents

Abstract page for arXiv paper 2603.25498: EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents

arXiv - AI · 3 min ·
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