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Google employees ask Sundar Pichai to say no to classified military AI use | The Verge
Machine Learning

Google employees ask Sundar Pichai to say no to classified military AI use | The Verge

Over 600 Google employees signed a letter asking CEO Sundar Pichai to refuse classified AI work with the Pentagon.

The Verge - AI · 4 min ·
Llms

Associative memory system for LLMs that learns during inference [P]

I've been working on MDA (Modular Dynamic Architecture), an online associative memory system for LLMs. Here's what I learned building it....

Reddit - Machine Learning · 1 min ·
Machine Learning

A comedian’s strategy for poisoning AI training data

Apparently the best defense against AI copying your voice is strawberry mango forklift supersize fries. submitted by /u/bekircagricelik [...

Reddit - Artificial Intelligence · 1 min ·

All Content

[2510.18814] A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning
Llms

[2510.18814] A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning

Abstract page for arXiv paper 2510.18814: A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning

arXiv - AI · 4 min ·
[2510.07985] Fewer Weights, More Problems: A Practical Attack on LLM Pruning
Llms

[2510.07985] Fewer Weights, More Problems: A Practical Attack on LLM Pruning

Abstract page for arXiv paper 2510.07985: Fewer Weights, More Problems: A Practical Attack on LLM Pruning

arXiv - AI · 4 min ·
[2509.26433] ACT: Agentic Classification Tree
Llms

[2509.26433] ACT: Agentic Classification Tree

Abstract page for arXiv paper 2509.26433: ACT: Agentic Classification Tree

arXiv - AI · 3 min ·
[2509.20349] Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing
Machine Learning

[2509.20349] Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing

Abstract page for arXiv paper 2509.20349: Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing

arXiv - Machine Learning · 3 min ·
[2509.12981] Causal Discovery via Quantile Partial Effect
Machine Learning

[2509.12981] Causal Discovery via Quantile Partial Effect

Abstract page for arXiv paper 2509.12981: Causal Discovery via Quantile Partial Effect

arXiv - Machine Learning · 4 min ·
[2509.09926] LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Scenarios
Llms

[2509.09926] LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Scenarios

Abstract page for arXiv paper 2509.09926: LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Sc...

arXiv - Machine Learning · 4 min ·
[2509.03758] A Data-Driven Interpolation Method on Smooth Manifolds via Diffusion Processes and Voronoi Tessellations
Machine Learning

[2509.03758] A Data-Driven Interpolation Method on Smooth Manifolds via Diffusion Processes and Voronoi Tessellations

Abstract page for arXiv paper 2509.03758: A Data-Driven Interpolation Method on Smooth Manifolds via Diffusion Processes and Voronoi Tess...

arXiv - Machine Learning · 4 min ·
[2509.02892] Improving Generative Methods for Causal Evaluation via Simulation-Based Inference
Machine Learning

[2509.02892] Improving Generative Methods for Causal Evaluation via Simulation-Based Inference

Abstract page for arXiv paper 2509.02892: Improving Generative Methods for Causal Evaluation via Simulation-Based Inference

arXiv - Machine Learning · 4 min ·
[2509.00203] Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements
Machine Learning

[2509.00203] Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements

Abstract page for arXiv paper 2509.00203: Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements

arXiv - Machine Learning · 4 min ·
[2508.10053] xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
Machine Learning

[2508.10053] xRFM: Accurate, scalable, and interpretable feature learning models for tabular data

Abstract page for arXiv paper 2508.10053: xRFM: Accurate, scalable, and interpretable feature learning models for tabular data

arXiv - Machine Learning · 4 min ·
[2508.08935] LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks
Machine Learning

[2508.08935] LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks

Abstract page for arXiv paper 2508.08935: LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks

arXiv - Machine Learning · 3 min ·
[2508.04503] PRISM: Lightweight Multivariate Time-Series Classification through Symmetric Multi-Resolution Convolutional Layers
Machine Learning

[2508.04503] PRISM: Lightweight Multivariate Time-Series Classification through Symmetric Multi-Resolution Convolutional Layers

Abstract page for arXiv paper 2508.04503: PRISM: Lightweight Multivariate Time-Series Classification through Symmetric Multi-Resolution C...

arXiv - AI · 4 min ·
[2508.02812] Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms
Machine Learning

[2508.02812] Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms

Abstract page for arXiv paper 2508.02812: Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms

arXiv - Machine Learning · 3 min ·
[2507.13920] Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem
Machine Learning

[2507.13920] Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem

Abstract page for arXiv paper 2507.13920: Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Pro...

arXiv - Machine Learning · 3 min ·
[2507.12165] Multi-Component VAE with Gaussian Markov Random Field
Machine Learning

[2507.12165] Multi-Component VAE with Gaussian Markov Random Field

Abstract page for arXiv paper 2507.12165: Multi-Component VAE with Gaussian Markov Random Field

arXiv - Machine Learning · 3 min ·
[2506.21744] Federated Item Response Models: A Gradient-driven Privacy-preserving Framework for Distributed Psychometric Estimation
Machine Learning

[2506.21744] Federated Item Response Models: A Gradient-driven Privacy-preserving Framework for Distributed Psychometric Estimation

Abstract page for arXiv paper 2506.21744: Federated Item Response Models: A Gradient-driven Privacy-preserving Framework for Distributed ...

arXiv - Machine Learning · 4 min ·
[2506.08125] Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning
Llms

[2506.08125] Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning

Abstract page for arXiv paper 2506.08125: Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement ...

arXiv - Machine Learning · 4 min ·
[2506.02371] SFBD Flow: A Continuous-Optimization Framework for Training Diffusion Models with Noisy Samples
Machine Learning

[2506.02371] SFBD Flow: A Continuous-Optimization Framework for Training Diffusion Models with Noisy Samples

Abstract page for arXiv paper 2506.02371: SFBD Flow: A Continuous-Optimization Framework for Training Diffusion Models with Noisy Samples

arXiv - Machine Learning · 3 min ·
[2506.01897] MLorc: Momentum Low-rank Compression for Memory Efficient Large Language Model Adaptation
Llms

[2506.01897] MLorc: Momentum Low-rank Compression for Memory Efficient Large Language Model Adaptation

Abstract page for arXiv paper 2506.01897: MLorc: Momentum Low-rank Compression for Memory Efficient Large Language Model Adaptation

arXiv - Machine Learning · 4 min ·
[2505.24535] Beyond Linear Steering: Unified Multi-Attribute Control for Language Models
Llms

[2505.24535] Beyond Linear Steering: Unified Multi-Attribute Control for Language Models

Abstract page for arXiv paper 2505.24535: Beyond Linear Steering: Unified Multi-Attribute Control for Language Models

arXiv - AI · 3 min ·
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