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Machine Learning

How do you anonymize code for a conference submission? [D]

Hi everyone, I have a question about anonymizing code for conference submissions. I’m submitting an AI/ML paper to a conference and would...

Reddit - Machine Learning · 1 min ·
Now Meta will track what employees do on their computers to train its AI agents | The Verge
Machine Learning

Now Meta will track what employees do on their computers to train its AI agents | The Verge

Meta is reportedly using tracking software to record its employees’ mouse and keyboard activity for training data for its AI agents.

The Verge - AI · 4 min ·
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 ·

All Content

[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 ·
[2601.06810] WFR-FM: Simulation-Free Dynamic Unbalanced Optimal Transport
Machine Learning

[2601.06810] WFR-FM: Simulation-Free Dynamic Unbalanced Optimal Transport

Abstract page for arXiv paper 2601.06810: WFR-FM: Simulation-Free Dynamic Unbalanced Optimal Transport

arXiv - Machine Learning · 4 min ·
[2601.05352] When the Server Steps In: Calibrated Updates for Fair Federated Learning
Machine Learning

[2601.05352] When the Server Steps In: Calibrated Updates for Fair Federated Learning

Abstract page for arXiv paper 2601.05352: When the Server Steps In: Calibrated Updates for Fair Federated Learning

arXiv - Machine Learning · 4 min ·
[2601.02031] Output Embedding Centering for Stable LLM Pretraining
Llms

[2601.02031] Output Embedding Centering for Stable LLM Pretraining

Abstract page for arXiv paper 2601.02031: Output Embedding Centering for Stable LLM Pretraining

arXiv - Machine Learning · 3 min ·
[2512.10720] Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal Minimality
Machine Learning

[2512.10720] Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal Minimality

Abstract page for arXiv paper 2512.10720: Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal M...

arXiv - Machine Learning · 4 min ·
[2512.05069] Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection
Machine Learning

[2512.05069] Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection

Abstract page for arXiv paper 2512.05069: Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection

arXiv - Machine Learning · 4 min ·
[2511.20456] Towards Trustworthy Wi-Fi CSI-based Sensing: Systematic Evaluation of Adversarial Robustness
Machine Learning

[2511.20456] Towards Trustworthy Wi-Fi CSI-based Sensing: Systematic Evaluation of Adversarial Robustness

Abstract page for arXiv paper 2511.20456: Towards Trustworthy Wi-Fi CSI-based Sensing: Systematic Evaluation of Adversarial Robustness

arXiv - Machine Learning · 4 min ·
[2511.16145] Labels Matter More Than Models: Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection
Machine Learning

[2511.16145] Labels Matter More Than Models: Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection

Abstract page for arXiv paper 2511.16145: Labels Matter More Than Models: Rethinking the Unsupervised Paradigm in Time Series Anomaly Det...

arXiv - Machine Learning · 4 min ·
[2511.10841] FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification
Machine Learning

[2511.10841] FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification

Abstract page for arXiv paper 2511.10841: FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled T...

arXiv - Machine Learning · 4 min ·
[2511.09219] Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization
Machine Learning

[2511.09219] Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization

Abstract page for arXiv paper 2511.09219: Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimi...

arXiv - Machine Learning · 3 min ·
[2510.25147] Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk
Machine Learning

[2510.25147] Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk

Abstract page for arXiv paper 2510.25147: Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk

arXiv - Machine Learning · 3 min ·
[2510.22855] A Review of Neural Networks in Precipitation Prediction
Machine Learning

[2510.22855] A Review of Neural Networks in Precipitation Prediction

Abstract page for arXiv paper 2510.22855: A Review of Neural Networks in Precipitation Prediction

arXiv - Machine Learning · 4 min ·
[2510.25126] Bridging the Divide: End-to-End Sequence-Graph Learning
Machine Learning

[2510.25126] Bridging the Divide: End-to-End Sequence-Graph Learning

Abstract page for arXiv paper 2510.25126: Bridging the Divide: End-to-End Sequence-Graph Learning

arXiv - Machine Learning · 3 min ·
[2510.21852] Interpretable Diagnostics and Adaptive Data Assimilation for Neural ODEs via Discrete Empirical Interpolation
Machine Learning

[2510.21852] Interpretable Diagnostics and Adaptive Data Assimilation for Neural ODEs via Discrete Empirical Interpolation

Abstract page for arXiv paper 2510.21852: Interpretable Diagnostics and Adaptive Data Assimilation for Neural ODEs via Discrete Empirical...

arXiv - Machine Learning · 4 min ·
[2510.17991] Demystifying Transition Matching: When and Why It Can Beat Flow Matching
Machine Learning

[2510.17991] Demystifying Transition Matching: When and Why It Can Beat Flow Matching

Abstract page for arXiv paper 2510.17991: Demystifying Transition Matching: When and Why It Can Beat Flow Matching

arXiv - Machine Learning · 4 min ·
[2510.16609] Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods
Llms

[2510.16609] Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods

Abstract page for arXiv paper 2510.16609: Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods

arXiv - Machine Learning · 4 min ·
[2510.15555] Doubly Robust Estimation of Causal Effects in Strategic Equilibrium Systems
Machine Learning

[2510.15555] Doubly Robust Estimation of Causal Effects in Strategic Equilibrium Systems

Abstract page for arXiv paper 2510.15555: Doubly Robust Estimation of Causal Effects in Strategic Equilibrium Systems

arXiv - Machine Learning · 3 min ·
[2510.14523] On the Identifiability of Tensor Ranks via Prior Predictive Matching
Machine Learning

[2510.14523] On the Identifiability of Tensor Ranks via Prior Predictive Matching

Abstract page for arXiv paper 2510.14523: On the Identifiability of Tensor Ranks via Prior Predictive Matching

arXiv - Machine Learning · 4 min ·
[2510.01938] StelLA: Subspace Learning in Low-rank Adaptation using Stiefel Manifold
Machine Learning

[2510.01938] StelLA: Subspace Learning in Low-rank Adaptation using Stiefel Manifold

Abstract page for arXiv paper 2510.01938: StelLA: Subspace Learning in Low-rank Adaptation using Stiefel Manifold

arXiv - Machine Learning · 4 min ·
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