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Anthropic’s Mythos rollout has missed America’s cybersecurity agency | The Verge
Machine Learning

Anthropic’s Mythos rollout has missed America’s cybersecurity agency | The Verge

The Cybersecurity and Infrastructure Security Agency (CISA) doesn’t have access to Anthropic’s Mythos Preview, Axios reported.

The Verge - AI · 5 min ·
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 ·

All Content

[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 ·
[2509.18001] Unveiling m-Sharpness Through the Structure of Stochastic Gradient Noise
Machine Learning

[2509.18001] Unveiling m-Sharpness Through the Structure of Stochastic Gradient Noise

Abstract page for arXiv paper 2509.18001: Unveiling m-Sharpness Through the Structure of Stochastic Gradient Noise

arXiv - Machine Learning · 3 min ·
[2509.07252] GCond: Gradient Conflict Resolution via Accumulation-based Stabilization for Large-Scale Multi-Task Learning
Machine Learning

[2509.07252] GCond: Gradient Conflict Resolution via Accumulation-based Stabilization for Large-Scale Multi-Task Learning

Abstract page for arXiv paper 2509.07252: GCond: Gradient Conflict Resolution via Accumulation-based Stabilization for Large-Scale Multi-...

arXiv - Machine Learning · 4 min ·
[2508.17521] Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations
Machine Learning

[2508.17521] Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations

Abstract page for arXiv paper 2508.17521: Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations

arXiv - Machine Learning · 3 min ·
[2508.14285] Meta-Learning at Scale for Large Language Models via Low-Rank Amortized Bayesian Meta-Learning
Llms

[2508.14285] Meta-Learning at Scale for Large Language Models via Low-Rank Amortized Bayesian Meta-Learning

Abstract page for arXiv paper 2508.14285: Meta-Learning at Scale for Large Language Models via Low-Rank Amortized Bayesian Meta-Learning

arXiv - Machine Learning · 4 min ·
[2508.00855] A Residual Guided strategy with Generative Adversarial Networks in training Physics-Informed Transformer Networks
Machine Learning

[2508.00855] A Residual Guided strategy with Generative Adversarial Networks in training Physics-Informed Transformer Networks

Abstract page for arXiv paper 2508.00855: A Residual Guided strategy with Generative Adversarial Networks in training Physics-Informed Tr...

arXiv - Machine Learning · 3 min ·
[2506.12553] Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning
Machine Learning

[2506.12553] Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning

Abstract page for arXiv paper 2506.12553: Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine L...

arXiv - Machine Learning · 4 min ·
[2506.08514] DiffGradCAM: A Universal Class Activation Map Resistant to Adversarial Training
Machine Learning

[2506.08514] DiffGradCAM: A Universal Class Activation Map Resistant to Adversarial Training

Abstract page for arXiv paper 2506.08514: DiffGradCAM: A Universal Class Activation Map Resistant to Adversarial Training

arXiv - Machine Learning · 4 min ·
[2412.06154] Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions
Machine Learning

[2412.06154] Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions

Abstract page for arXiv paper 2412.06154: Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions

arXiv - Machine Learning · 4 min ·
[2505.24275] GradPower: Powering Gradients for Faster Language Model Pre-Training
Llms

[2505.24275] GradPower: Powering Gradients for Faster Language Model Pre-Training

Abstract page for arXiv paper 2505.24275: GradPower: Powering Gradients for Faster Language Model Pre-Training

arXiv - Machine Learning · 3 min ·
[2505.11312] Where You Place the Norm Matters: From Prejudiced to Neutral Initializations
Machine Learning

[2505.11312] Where You Place the Norm Matters: From Prejudiced to Neutral Initializations

Abstract page for arXiv paper 2505.11312: Where You Place the Norm Matters: From Prejudiced to Neutral Initializations

arXiv - Machine Learning · 4 min ·
[2504.04665] A Simultaneous Approach for Training Neural Differential-Algebraic Systems of Equations
Machine Learning

[2504.04665] A Simultaneous Approach for Training Neural Differential-Algebraic Systems of Equations

Abstract page for arXiv paper 2504.04665: A Simultaneous Approach for Training Neural Differential-Algebraic Systems of Equations

arXiv - Machine Learning · 4 min ·
[2503.03485] TEDDY: A Family Of Foundation Models For Understanding Single Cell Biology
Llms

[2503.03485] TEDDY: A Family Of Foundation Models For Understanding Single Cell Biology

Abstract page for arXiv paper 2503.03485: TEDDY: A Family Of Foundation Models For Understanding Single Cell Biology

arXiv - Machine Learning · 4 min ·
[2502.13024] Fragility-aware Classification for Understanding Risk and Improving Generalization
Machine Learning

[2502.13024] Fragility-aware Classification for Understanding Risk and Improving Generalization

Abstract page for arXiv paper 2502.13024: Fragility-aware Classification for Understanding Risk and Improving Generalization

arXiv - Machine Learning · 4 min ·
[2501.12215] Automatic selection of the best neural architecture for time series forecasting
Machine Learning

[2501.12215] Automatic selection of the best neural architecture for time series forecasting

Abstract page for arXiv paper 2501.12215: Automatic selection of the best neural architecture for time series forecasting

arXiv - Machine Learning · 4 min ·
[2410.03140] In-context Learning in Presence of Spurious Correlations
Llms

[2410.03140] In-context Learning in Presence of Spurious Correlations

Abstract page for arXiv paper 2410.03140: In-context Learning in Presence of Spurious Correlations

arXiv - Machine Learning · 3 min ·
[2408.11918] Interpretable Classification via a Rule Network with Selective Logical Operators
Machine Learning

[2408.11918] Interpretable Classification via a Rule Network with Selective Logical Operators

Abstract page for arXiv paper 2408.11918: Interpretable Classification via a Rule Network with Selective Logical Operators

arXiv - Machine Learning · 3 min ·
[2405.01158] Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI
Machine Learning

[2405.01158] Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI

Abstract page for arXiv paper 2405.01158: Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI

arXiv - Machine Learning · 3 min ·
[2310.19603] Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For Conditionally Gaussian Signals
Machine Learning

[2310.19603] Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For Conditionally Gaussian Signals

Abstract page for arXiv paper 2310.19603: Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For C...

arXiv - Machine Learning · 4 min ·
[2210.13277] CompressedScaffnew: The First Theoretical Double Acceleration of Communication from Local Training and Compression in Distributed Optimization
Machine Learning

[2210.13277] CompressedScaffnew: The First Theoretical Double Acceleration of Communication from Local Training and Compression in Distributed Optimization

Abstract page for arXiv paper 2210.13277: CompressedScaffnew: The First Theoretical Double Acceleration of Communication from Local Train...

arXiv - Machine Learning · 4 min ·
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