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Machine Learning

Public photos are not consent to biometric search infrastructure

The Clearview AI story still feels like one of the cleanest examples of the consent gap in applied AI. The issue is not simply that photo...

Reddit - Artificial Intelligence · 1 min ·
Llms

I built a router that automatically sends your AI tasks to the most appropriate model to handle them at low cost - 9,200 tasks in, $21 saved at $0.14 actual cost

The observation that started this: most of what people use AI for every day - summarising, drafting, classifying, extracting etc doesn't ...

Reddit - Artificial Intelligence · 1 min ·
Microsoft wants lawyers to trust its new AI agent in Word documents | The Verge
Machine Learning

Microsoft wants lawyers to trust its new AI agent in Word documents | The Verge

Microsoft’s Legal Agent comes from the work of former Robin AI engineers.

The Verge - AI · 3 min ·

All Content

[2604.05306] LLMs Should Express Uncertainty Explicitly
Llms

[2604.05306] LLMs Should Express Uncertainty Explicitly

Abstract page for arXiv paper 2604.05306: LLMs Should Express Uncertainty Explicitly

arXiv - AI · 4 min ·
[2604.05257] Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation
Machine Learning

[2604.05257] Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation

Abstract page for arXiv paper 2604.05257: Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation

arXiv - AI · 4 min ·
[2604.05250] DualDiffusion: A Speculative Decoding Strategy for Masked Diffusion Models
Llms

[2604.05250] DualDiffusion: A Speculative Decoding Strategy for Masked Diffusion Models

Abstract page for arXiv paper 2604.05250: DualDiffusion: A Speculative Decoding Strategy for Masked Diffusion Models

arXiv - Machine Learning · 3 min ·
[2604.05248] Improving Sparse Memory Finetuning
Llms

[2604.05248] Improving Sparse Memory Finetuning

Abstract page for arXiv paper 2604.05248: Improving Sparse Memory Finetuning

arXiv - Machine Learning · 3 min ·
[2604.05230] Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
Machine Learning

[2604.05230] Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks

Abstract page for arXiv paper 2604.05230: Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks

arXiv - AI · 3 min ·
[2604.05217] On the Geometry of Positional Encodings in Transformers
Llms

[2604.05217] On the Geometry of Positional Encodings in Transformers

Abstract page for arXiv paper 2604.05217: On the Geometry of Positional Encodings in Transformers

arXiv - Machine Learning · 4 min ·
[2604.05185] Cross-fitted Proximal Learning for Model-Based Reinforcement Learning
Machine Learning

[2604.05185] Cross-fitted Proximal Learning for Model-Based Reinforcement Learning

Abstract page for arXiv paper 2604.05185: Cross-fitted Proximal Learning for Model-Based Reinforcement Learning

arXiv - Machine Learning · 4 min ·
[2604.05181] General Multimodal Protein Design Enables DNA-Encoding of Chemistry
Machine Learning

[2604.05181] General Multimodal Protein Design Enables DNA-Encoding of Chemistry

Abstract page for arXiv paper 2604.05181: General Multimodal Protein Design Enables DNA-Encoding of Chemistry

arXiv - Machine Learning · 3 min ·
[2604.05164] Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning
Llms

[2604.05164] Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning

Abstract page for arXiv paper 2604.05164: Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning

arXiv - Machine Learning · 4 min ·
[2604.05134] Reasoning Through Chess: How Reasoning Evolves from Data Through Fine-Tuning and Reinforcement Learning
Llms

[2604.05134] Reasoning Through Chess: How Reasoning Evolves from Data Through Fine-Tuning and Reinforcement Learning

Abstract page for arXiv paper 2604.05134: Reasoning Through Chess: How Reasoning Evolves from Data Through Fine-Tuning and Reinforcement ...

arXiv - AI · 4 min ·
[2604.05112] Vintix II: Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner
Machine Learning

[2604.05112] Vintix II: Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner

Abstract page for arXiv paper 2604.05112: Vintix II: Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner

arXiv - AI · 3 min ·
[2604.05077] Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing
Machine Learning

[2604.05077] Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing

Abstract page for arXiv paper 2604.05077: Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representatio...

arXiv - AI · 4 min ·
[2604.05072] Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling
Llms

[2604.05072] Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling

Abstract page for arXiv paper 2604.05072: Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Mo...

arXiv - Machine Learning · 4 min ·
[2604.05064] Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
Llms

[2604.05064] Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series

Abstract page for arXiv paper 2604.05064: Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series

arXiv - AI · 3 min ·
[2604.05057] Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems
Machine Learning

[2604.05057] Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems

Abstract page for arXiv paper 2604.05057: Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Le...

arXiv - Machine Learning · 4 min ·
[2604.05045] PCA-Driven Adaptive Sensor Triage for Edge AI Inference
Machine Learning

[2604.05045] PCA-Driven Adaptive Sensor Triage for Edge AI Inference

Abstract page for arXiv paper 2604.05045: PCA-Driven Adaptive Sensor Triage for Edge AI Inference

arXiv - AI · 3 min ·
[2604.05042] Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
Machine Learning

[2604.05042] Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization

Abstract page for arXiv paper 2604.05042: Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization

arXiv - Machine Learning · 3 min ·
[2604.04999] PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities
Machine Learning

[2604.04999] PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities

Abstract page for arXiv paper 2604.04999: PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities

arXiv - AI · 4 min ·
[2604.04998] El Nino Prediction Based on Weather Forecast and Geographical Time-series Data
Machine Learning

[2604.04998] El Nino Prediction Based on Weather Forecast and Geographical Time-series Data

Abstract page for arXiv paper 2604.04998: El Nino Prediction Based on Weather Forecast and Geographical Time-series Data

arXiv - Machine Learning · 3 min ·
[2604.04996] Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems
Machine Learning

[2604.04996] Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems

Abstract page for arXiv paper 2604.04996: Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems

arXiv - Machine Learning · 3 min ·
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