Machine Learning

ML algorithms, training, and inference

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Llms

Training-time intervention yields 63.4% blind-pair human preference at matched val-loss (1.2B params, 320 judgments, p = 1.98 × 10⁻⁵) [R]

TL;DR. I ran a blind A/B preference evaluation between two 1.2B-parameter LMs trained on identical data (same order, same seed, 30K steps...

Reddit - Machine Learning · 1 min ·
Machine Learning

I can't believe text normalization is so underdiscussed in streaming text-to-speech [D]

Kinda suprises me how little discussion there is around about mistakes in streaming TTS models People look for natural readers, high voic...

Reddit - Machine Learning · 1 min ·
Anthropic’s most dangerous AI model just fell into the wrong hands | The Verge
Machine Learning

Anthropic’s most dangerous AI model just fell into the wrong hands | The Verge

Anthropic’s powerful Mythos cybersecurity AI model has been accessed by a “small group of unauthorised users.”

The Verge - AI · 4 min ·

All Content

[2510.09908] Learning with Incomplete Context: Linear Contextual Bandits with Pretrained Imputation
Machine Learning

[2510.09908] Learning with Incomplete Context: Linear Contextual Bandits with Pretrained Imputation

Abstract page for arXiv paper 2510.09908: Learning with Incomplete Context: Linear Contextual Bandits with Pretrained Imputation

arXiv - Machine Learning · 3 min ·
[2509.21629] Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis
Llms

[2509.21629] Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis

Abstract page for arXiv paper 2509.21629: Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis

arXiv - Machine Learning · 3 min ·
[2508.02441] Computationally efficient Gauss-Newton reinforcement learning for model predictive control
Machine Learning

[2508.02441] Computationally efficient Gauss-Newton reinforcement learning for model predictive control

Abstract page for arXiv paper 2508.02441: Computationally efficient Gauss-Newton reinforcement learning for model predictive control

arXiv - Machine Learning · 4 min ·
[2509.07274] LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade
Llms

[2509.07274] LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade

Abstract page for arXiv paper 2509.07274: LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post...

arXiv - Machine Learning · 4 min ·
[2507.14221] Fair Representation in Parliamentary Summaries: Measuring and Mitigating Inclusion Bias
Llms

[2507.14221] Fair Representation in Parliamentary Summaries: Measuring and Mitigating Inclusion Bias

Abstract page for arXiv paper 2507.14221: Fair Representation in Parliamentary Summaries: Measuring and Mitigating Inclusion Bias

arXiv - Machine Learning · 4 min ·
[2507.14194] Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy for Complex System Prognostics
Machine Learning

[2507.14194] Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy for Complex System Prognostics

Abstract page for arXiv paper 2507.14194: Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy fo...

arXiv - Machine Learning · 3 min ·
[2507.04754] Intervening to Learn and Compose Causally Disentangled Representations
Machine Learning

[2507.04754] Intervening to Learn and Compose Causally Disentangled Representations

Abstract page for arXiv paper 2507.04754: Intervening to Learn and Compose Causally Disentangled Representations

arXiv - Machine Learning · 3 min ·
[2506.23396] AICO: Feature Significance Tests for Supervised Learning
Machine Learning

[2506.23396] AICO: Feature Significance Tests for Supervised Learning

Abstract page for arXiv paper 2506.23396: AICO: Feature Significance Tests for Supervised Learning

arXiv - Machine Learning · 4 min ·
[2506.20370] InvZW: Invariant Feature Learning via Noise-Adversarial Training for Robust Image Zero-Watermarking
Machine Learning

[2506.20370] InvZW: Invariant Feature Learning via Noise-Adversarial Training for Robust Image Zero-Watermarking

Abstract page for arXiv paper 2506.20370: InvZW: Invariant Feature Learning via Noise-Adversarial Training for Robust Image Zero-Watermar...

arXiv - Machine Learning · 4 min ·
[2503.10666] Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference
Llms

[2503.10666] Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference

Abstract page for arXiv paper 2503.10666: Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference

arXiv - Machine Learning · 4 min ·
[2502.10600] Weighted quantization using MMD: From mean field to mean shift via gradient flows
Machine Learning

[2502.10600] Weighted quantization using MMD: From mean field to mean shift via gradient flows

Abstract page for arXiv paper 2502.10600: Weighted quantization using MMD: From mean field to mean shift via gradient flows

arXiv - Machine Learning · 4 min ·
[2407.17491] Robust Adaptation of Foundation Models with Black-Box Visual Prompting
Llms

[2407.17491] Robust Adaptation of Foundation Models with Black-Box Visual Prompting

Abstract page for arXiv paper 2407.17491: Robust Adaptation of Foundation Models with Black-Box Visual Prompting

arXiv - Machine Learning · 4 min ·
[2603.19136] Adaptive Regime-Aware Stock Price Prediction Using Autoencoder-Gated Dual Node Transformers with Reinforcement Learning Control
Machine Learning

[2603.19136] Adaptive Regime-Aware Stock Price Prediction Using Autoencoder-Gated Dual Node Transformers with Reinforcement Learning Control

Abstract page for arXiv paper 2603.19136: Adaptive Regime-Aware Stock Price Prediction Using Autoencoder-Gated Dual Node Transformers wit...

arXiv - Machine Learning · 4 min ·
[2603.14218] Interleaved Resampling and Refitting: Data and Compute-Efficient Evaluation of Black-Box Predictors
Machine Learning

[2603.14218] Interleaved Resampling and Refitting: Data and Compute-Efficient Evaluation of Black-Box Predictors

Abstract page for arXiv paper 2603.14218: Interleaved Resampling and Refitting: Data and Compute-Efficient Evaluation of Black-Box Predic...

arXiv - Machine Learning · 4 min ·
[2602.00388] Safer by Diffusion, Broken by Context: Diffusion LLM's Safety Blessing and Its Failure Mode
Llms

[2602.00388] Safer by Diffusion, Broken by Context: Diffusion LLM's Safety Blessing and Its Failure Mode

Abstract page for arXiv paper 2602.00388: Safer by Diffusion, Broken by Context: Diffusion LLM's Safety Blessing and Its Failure Mode

arXiv - Machine Learning · 4 min ·
[2601.22002] Rate-Distortion Optimization for Transformer Inference
Machine Learning

[2601.22002] Rate-Distortion Optimization for Transformer Inference

Abstract page for arXiv paper 2601.22002: Rate-Distortion Optimization for Transformer Inference

arXiv - Machine Learning · 3 min ·
[2601.03111] One Sample to Rule Them All: Extreme Data Efficiency in Multidiscipline Reasoning with Reinforcement Learning
Llms

[2601.03111] One Sample to Rule Them All: Extreme Data Efficiency in Multidiscipline Reasoning with Reinforcement Learning

Abstract page for arXiv paper 2601.03111: One Sample to Rule Them All: Extreme Data Efficiency in Multidiscipline Reasoning with Reinforc...

arXiv - Machine Learning · 4 min ·
[2601.17641] RPNT: Robust Pre-trained Neural Transformer -- A Pathway for Generalized Motor Decoding
Machine Learning

[2601.17641] RPNT: Robust Pre-trained Neural Transformer -- A Pathway for Generalized Motor Decoding

Abstract page for arXiv paper 2601.17641: RPNT: Robust Pre-trained Neural Transformer -- A Pathway for Generalized Motor Decoding

arXiv - Machine Learning · 4 min ·
[2601.16884] Multigrade Neural Network Approximation
Machine Learning

[2601.16884] Multigrade Neural Network Approximation

Abstract page for arXiv paper 2601.16884: Multigrade Neural Network Approximation

arXiv - Machine Learning · 3 min ·
[2601.16514] Finite-Time Analysis of Gradient Descent for Shallow Transformers
Machine Learning

[2601.16514] Finite-Time Analysis of Gradient Descent for Shallow Transformers

Abstract page for arXiv paper 2601.16514: Finite-Time Analysis of Gradient Descent for Shallow Transformers

arXiv - Machine Learning · 3 min ·
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