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Machine Learning

[P] PCA before truncation makes non-Matryoshka embeddings compressible: results on BGE-M3 [P]

Most embedding models are not Matryoshka-trained, so naive dimension truncation tends to destroy them. I tested a simple alternative: fit...

Reddit - Machine Learning · 1 min ·
Machine Learning

Looking for Feedback & Improvement Ideas[P]

Hey everyone, I recently built a machine learning project and would really appreciate some honest feedback from this community. LINK- htt...

Reddit - Machine Learning · 1 min ·
Machine Learning

Why Anthropic’s new model has cybersecurity experts rattled

submitted by /u/ThereWas [link] [comments]

Reddit - Artificial Intelligence · 1 min ·

All Content

[2603.25015] Imperative Interference: Social Register Shapes Instruction Topology in Large Language Models
Llms

[2603.25015] Imperative Interference: Social Register Shapes Instruction Topology in Large Language Models

Abstract page for arXiv paper 2603.25015: Imperative Interference: Social Register Shapes Instruction Topology in Large Language Models

arXiv - AI · 3 min ·
[2603.25126] MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation
Machine Learning

[2603.25126] MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation

Abstract page for arXiv paper 2603.25126: MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation

arXiv - AI · 4 min ·
[2603.25024] Improving Infinitely Deep Bayesian Neural Networks with Nesterov's Accelerated Gradient Method
Machine Learning

[2603.25024] Improving Infinitely Deep Bayesian Neural Networks with Nesterov's Accelerated Gradient Method

Abstract page for arXiv paper 2603.25024: Improving Infinitely Deep Bayesian Neural Networks with Nesterov's Accelerated Gradient Method

arXiv - Machine Learning · 3 min ·
[2603.25112] Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory
Llms

[2603.25112] Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory

Abstract page for arXiv paper 2603.25112: Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory

arXiv - AI · 4 min ·
[2603.24946] MobileDev-Bench: A Comprehensive Benchmark for Evaluating Language Models on Mobile Application Development
Llms

[2603.24946] MobileDev-Bench: A Comprehensive Benchmark for Evaluating Language Models on Mobile Application Development

Abstract page for arXiv paper 2603.24946: MobileDev-Bench: A Comprehensive Benchmark for Evaluating Language Models on Mobile Application...

arXiv - Machine Learning · 4 min ·
[2603.25109] MoireMix: A Formula-Based Data Augmentation for Improving Image Classification Robustness
Machine Learning

[2603.25109] MoireMix: A Formula-Based Data Augmentation for Improving Image Classification Robustness

Abstract page for arXiv paper 2603.25109: MoireMix: A Formula-Based Data Augmentation for Improving Image Classification Robustness

arXiv - AI · 4 min ·
[2603.24917] Estimating near-verbatim extraction risk in language models with decoding-constrained beam search
Llms

[2603.24917] Estimating near-verbatim extraction risk in language models with decoding-constrained beam search

Abstract page for arXiv paper 2603.24917: Estimating near-verbatim extraction risk in language models with decoding-constrained beam search

arXiv - Machine Learning · 4 min ·
[2603.25099] Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimization
Llms

[2603.25099] Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimization

Abstract page for arXiv paper 2603.25099: Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Opti...

arXiv - AI · 4 min ·
[2603.25083] Learning domain-invariant features through channel-level sparsification for Out-Of Distribution Generalization
Machine Learning

[2603.25083] Learning domain-invariant features through channel-level sparsification for Out-Of Distribution Generalization

Abstract page for arXiv paper 2603.25083: Learning domain-invariant features through channel-level sparsification for Out-Of Distribution...

arXiv - AI · 4 min ·
[2603.25063] TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualization
Llms

[2603.25063] TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualization

Abstract page for arXiv paper 2603.25063: TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visual...

arXiv - Machine Learning · 4 min ·
[2603.25056] The System Prompt Is the Attack Surface: How LLM Agent Configuration Shapes Security and Creates Exploitable Vulnerabilities
Llms

[2603.25056] The System Prompt Is the Attack Surface: How LLM Agent Configuration Shapes Security and Creates Exploitable Vulnerabilities

Abstract page for arXiv paper 2603.25056: The System Prompt Is the Attack Surface: How LLM Agent Configuration Shapes Security and Create...

arXiv - AI · 4 min ·
[2603.24764] Synthetic Cardiac MRI Image Generation using Deep Generative Models
Machine Learning

[2603.24764] Synthetic Cardiac MRI Image Generation using Deep Generative Models

Abstract page for arXiv paper 2603.24764: Synthetic Cardiac MRI Image Generation using Deep Generative Models

arXiv - Machine Learning · 3 min ·
[2603.25052] Closing the Confidence-Faithfulness Gap in Large Language Models
Llms

[2603.25052] Closing the Confidence-Faithfulness Gap in Large Language Models

Abstract page for arXiv paper 2603.25052: Closing the Confidence-Faithfulness Gap in Large Language Models

arXiv - AI · 3 min ·
[2603.24752] Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning
Machine Learning

[2603.24752] Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning

Abstract page for arXiv paper 2603.24752: Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical...

arXiv - Machine Learning · 4 min ·
[2603.25006] Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning
Machine Learning

[2603.25006] Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning

Abstract page for arXiv paper 2603.25006: Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning

arXiv - AI · 4 min ·
[2603.24989] Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model
Llms

[2603.24989] Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model

Abstract page for arXiv paper 2603.24989: Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model

arXiv - AI · 4 min ·
[2603.24986] Rethinking Health Agents: From Siloed AI to Collaborative Decision Mediators
Llms

[2603.24986] Rethinking Health Agents: From Siloed AI to Collaborative Decision Mediators

Abstract page for arXiv paper 2603.24986: Rethinking Health Agents: From Siloed AI to Collaborative Decision Mediators

arXiv - AI · 3 min ·
[2603.24705] Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
Machine Learning

[2603.24705] Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks

Abstract page for arXiv paper 2603.24705: Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks

arXiv - Machine Learning · 4 min ·
[2603.24704] Conformal Selective Prediction with General Risk Control
Machine Learning

[2603.24704] Conformal Selective Prediction with General Risk Control

Abstract page for arXiv paper 2603.24704: Conformal Selective Prediction with General Risk Control

arXiv - Machine Learning · 4 min ·
[2603.24654] Spectral methods: crucial for machine learning, natural for quantum computers?
Machine Learning

[2603.24654] Spectral methods: crucial for machine learning, natural for quantum computers?

Abstract page for arXiv paper 2603.24654: Spectral methods: crucial for machine learning, natural for quantum computers?

arXiv - Machine Learning · 4 min ·
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