arXiv Machine Learning August 6, 2026 By Dmytro Knopov

Canonical Joint Energy-Based Model on CIFAR-10: failure modes and practical indistinguishability of Predictor-Corrector and SGLD samplers

Read the original on arXiv Machine Learning →

arXiv:2608. 05025v1 Announce Type: new Abstract: Joint Energy-Based Models (JEM) unify classification and generation within a single network and support out-of-distribution (OOD) detection.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 12

Comparing SGLD and a fixed-noise Predictor-Corrector adaptation in canonical Joint Energy-Based Models on CIFAR-10

arXiv:2608. 05025v2 Announce Type: replace Abstract: Joint Energy-Based Models (JEM) unify classification and generation within a single network and support out-of-distribution (OOD) detection.

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arXiv Machine Learning

2d ago

The Integer Alibi: Localizing Cross-Kernel Divergence in INT8-Quantized LLM Inference

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AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis

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Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software

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By Arastoo Zibaeirad, Marco Vieira

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 24

Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics

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Related stories

Comparing SGLD and a fixed-noise Predictor-Corrector adaptation in canonical Joint Energy-Based Models on CIFAR-10

The Integer Alibi: Localizing Cross-Kernel Divergence in INT8-Quantized LLM Inference

Decodable but Not Detectable: A Leakage Fingerprint for Near-OOD Benchmarks

AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis

Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software

Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics