Machine Learning

ML algorithms, training, and inference

This Week's Best | Monthly Best | Guide | Trending

Top This Week

Machine Learning

Tube strikes make people healthier. The maths proves it [D]

https://towardsdatascience.com/using-causal-inference-to-estimate-the-impact-of-tube-strikes-on-cycling-usage-in-london/ submitted by /u/...

Reddit - Machine Learning · 1 min ·
Meta buys robotics startup to bolster its humanoid AI ambitions | TechCrunch
Machine Learning

Meta buys robotics startup to bolster its humanoid AI ambitions | TechCrunch

Meta bought humanoid startup Assured Robot Intelligence to beef up its AI models for robots, the company said.

TechCrunch - AI · 4 min ·
Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI’s models | MIT Technology Review
Machine Learning

Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI’s models | MIT Technology Review

Musk kept his cool, and OpenAI’s lawyer bulldozed him with piercing questions about his motivations for suing the company.

MIT Technology Review · 8 min ·

All Content

[2604.04743] Hallucination Basins: A Dynamic Framework for Understanding and Controlling LLM Hallucinations
Llms

[2604.04743] Hallucination Basins: A Dynamic Framework for Understanding and Controlling LLM Hallucinations

Abstract page for arXiv paper 2604.04743: Hallucination Basins: A Dynamic Framework for Understanding and Controlling LLM Hallucinations

arXiv - AI · 3 min ·
[2604.04741] Artificial Intelligence and Cost Reduction in Public Higher Education: A Scoping Review of Emerging Evidence
Llms

[2604.04741] Artificial Intelligence and Cost Reduction in Public Higher Education: A Scoping Review of Emerging Evidence

Abstract page for arXiv paper 2604.04741: Artificial Intelligence and Cost Reduction in Public Higher Education: A Scoping Review of Emer...

arXiv - AI · 4 min ·
[2604.04733] Discovering Failure Modes in Vision-Language Models using RL
Llms

[2604.04733] Discovering Failure Modes in Vision-Language Models using RL

Abstract page for arXiv paper 2604.04733: Discovering Failure Modes in Vision-Language Models using RL

arXiv - AI · 3 min ·
[2604.04732] Metaphors We Compute By: A Computational Audit of Cultural Translation vs. Thinking in LLMs
Llms

[2604.04732] Metaphors We Compute By: A Computational Audit of Cultural Translation vs. Thinking in LLMs

Abstract page for arXiv paper 2604.04732: Metaphors We Compute By: A Computational Audit of Cultural Translation vs. Thinking in LLMs

arXiv - AI · 3 min ·
[2604.04723] Individual and Combined Effects of English as a Second Language and Typos on LLM Performance
Llms

[2604.04723] Individual and Combined Effects of English as a Second Language and Typos on LLM Performance

Abstract page for arXiv paper 2604.04723: Individual and Combined Effects of English as a Second Language and Typos on LLM Performance

arXiv - AI · 4 min ·
[2604.04720] What Makes Good Multilingual Reasoning? Disentangling Reasoning Traces with Measurable Features
Machine Learning

[2604.04720] What Makes Good Multilingual Reasoning? Disentangling Reasoning Traces with Measurable Features

Abstract page for arXiv paper 2604.04720: What Makes Good Multilingual Reasoning? Disentangling Reasoning Traces with Measurable Features

arXiv - AI · 4 min ·
[2604.04664] ROSClaw: A Hierarchical Semantic-Physical Framework for Heterogeneous Multi-Agent Collaboration
Llms

[2604.04664] ROSClaw: A Hierarchical Semantic-Physical Framework for Heterogeneous Multi-Agent Collaboration

Abstract page for arXiv paper 2604.04664: ROSClaw: A Hierarchical Semantic-Physical Framework for Heterogeneous Multi-Agent Collaboration

arXiv - AI · 4 min ·
[2604.04646] Training-Free Refinement of Flow Matching with Divergence-based Sampling
Machine Learning

[2604.04646] Training-Free Refinement of Flow Matching with Divergence-based Sampling

Abstract page for arXiv paper 2604.04646: Training-Free Refinement of Flow Matching with Divergence-based Sampling

arXiv - AI · 3 min ·
[2604.04634] Preserving Forgery Artifacts: AI-Generated Video Detection at Native Scale
Machine Learning

[2604.04634] Preserving Forgery Artifacts: AI-Generated Video Detection at Native Scale

Abstract page for arXiv paper 2604.04634: Preserving Forgery Artifacts: AI-Generated Video Detection at Native Scale

arXiv - AI · 4 min ·
[2604.04593] Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering
Llms

[2604.04593] Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering

Abstract page for arXiv paper 2604.04593: Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering

arXiv - AI · 4 min ·
[2604.04565] PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning
Llms

[2604.04565] PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning

Abstract page for arXiv paper 2604.04565: PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervi...

arXiv - AI · 3 min ·
[2604.04563] Temporal Inversion for Learning Interval Change in Chest X-Rays
Llms

[2604.04563] Temporal Inversion for Learning Interval Change in Chest X-Rays

Abstract page for arXiv paper 2604.04563: Temporal Inversion for Learning Interval Change in Chest X-Rays

arXiv - AI · 3 min ·
[2604.04562] Paper Espresso: From Paper Overload to Research Insight
Llms

[2604.04562] Paper Espresso: From Paper Overload to Research Insight

Abstract page for arXiv paper 2604.04562: Paper Espresso: From Paper Overload to Research Insight

arXiv - AI · 3 min ·
[2604.04561] Mapping the Exploitation Surface: A 10,000-Trial Taxonomy of What Makes LLM Agents Exploit Vulnerabilities
Llms

[2604.04561] Mapping the Exploitation Surface: A 10,000-Trial Taxonomy of What Makes LLM Agents Exploit Vulnerabilities

Abstract page for arXiv paper 2604.04561: Mapping the Exploitation Surface: A 10,000-Trial Taxonomy of What Makes LLM Agents Exploit Vuln...

arXiv - AI · 4 min ·
[2604.04552] StableTTA: Training-Free Test-Time Adaptation that Improves Model Accuracy on ImageNet1K to 96%
Machine Learning

[2604.04552] StableTTA: Training-Free Test-Time Adaptation that Improves Model Accuracy on ImageNet1K to 96%

Abstract page for arXiv paper 2604.04552: StableTTA: Training-Free Test-Time Adaptation that Improves Model Accuracy on ImageNet1K to 96%

arXiv - AI · 3 min ·
[2604.04490] RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation
Machine Learning

[2604.04490] RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation

Abstract page for arXiv paper 2604.04490: RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation

arXiv - AI · 3 min ·
[2604.04450] Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for Constrained Generation
Llms

[2604.04450] Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for Constrained Generation

Abstract page for arXiv paper 2604.04450: Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for C...

arXiv - AI · 3 min ·
[2604.04440] Training Transformers in Cosine Coefficient Space
Llms

[2604.04440] Training Transformers in Cosine Coefficient Space

Abstract page for arXiv paper 2604.04440: Training Transformers in Cosine Coefficient Space

arXiv - AI · 3 min ·
[2604.04418] Justified or Just Convincing? Error Verifiability as a Dimension of LLM Quality
Llms

[2604.04418] Justified or Just Convincing? Error Verifiability as a Dimension of LLM Quality

Abstract page for arXiv paper 2604.04418: Justified or Just Convincing? Error Verifiability as a Dimension of LLM Quality

arXiv - AI · 3 min ·
[2604.04411] Responses Fall Short of Understanding: Revealing the Gap between Internal Representations and Responses in Visual Document Understanding
Llms

[2604.04411] Responses Fall Short of Understanding: Revealing the Gap between Internal Representations and Responses in Visual Document Understanding

Abstract page for arXiv paper 2604.04411: Responses Fall Short of Understanding: Revealing the Gap between Internal Representations and R...

arXiv - AI · 3 min ·
Previous Page 312 Next

Related Topics

Large Language Models Generative AI Natural Language Processing Computer Vision Robotics & Embodied AI AI Safety & Ethics

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Subscribe to Newsletter

Daily or weekly digest • Unsubscribe anytime