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Research#Optimization Algorithms🔬 ResearchAnalyzed: Jan 4, 2026 06:49

Clipped Gradient Methods for Nonsmooth Convex Optimization under Heavy-Tailed Noise: A Refined Analysis

Published:Dec 29, 2025 03:35
1 min read
ArXiv

Analysis

The article presents a refined analysis of clipped gradient methods for nonsmooth convex optimization in the presence of heavy-tailed noise. This suggests a focus on theoretical advancements in optimization algorithms, particularly those dealing with noisy data and non-differentiable functions. The use of "refined analysis" implies an improvement or extension of existing understanding.
Reference

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Research#Deep Learning👥 CommunityAnalyzed: Jan 10, 2026 16:46

Navigating Non-Differentiable Loss in Deep Learning: Practical Approaches

Published:Nov 4, 2019 13:11
1 min read
Hacker News

Analysis

The article likely explores challenges and solutions when using deep learning models with loss functions that are not differentiable. It's crucial for researchers and practitioners, as non-differentiable losses are prevalent in various real-world scenarios.
Reference

“The article's main focus is likely on addressing the difficulties arising from the use of non-differentiable loss functions in deep learning.”

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