Hugging Face Trending Papers July 9, 2026

Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures

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Choosing a learning rate scheduling strategy is critical to neural network training, but manual selection is costly and rarely exhaustive. While classical AutoML approaches often treat the scheduler as a secondary hyperparameter, we systematically investigate its impact on classification accuracy across a diverse pool of architectures.

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llms

arXiv Machine Learning

Jul 10

Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures

arXiv:2607. 08511v1 Announce Type: new Abstract: Choosing a learning rate scheduling strategy is critical to neural network training, but manual selection is costly and rarely exhaustive.

By Hafsa Mateen, Radu Timofte, Dmitry Ignatov

llms

arXiv Machine Learning

Aug 10

Optimization as a Dynamical System: Generative Schedules from Latent ODEs

arXiv:2509. 23052v2 Announce Type: replace Abstract: We present a new meta-learning method to determine the optimal learning rate schedule for gradient descent.

By Matt L. Sampson, Peter Melchior

llms computer-vision

arXiv Machine Learning

Jun 24

Systematic Exploration of 4-Expert Heterogeneous Mixture-of-Experts via Automated Pipeline Search

arXiv:2606. 23739v1 Announce Type: new Abstract: We present an automated large-scale search pipeline for heterogeneous 4-Expert Mixture-of-Experts (MoE4) architectures within the LEMUR neural network dataset ecosystem.

By Yashkumar R Lukhi, Harsh Rameshbhai Moradiya, Radu Timofte, Dmitry Ignatov

llms safety

arXiv Machine Learning

Jul 20

Learning Faster without Deeper Networks: A*-Inspired Batch Selection for Efficient CNN Training

arXiv:2607. 15745v1 Announce Type: new Abstract: Common practice when training Convolutional Neural Networks (CNNs) is to use randomly shuffled mini-batches.

By Anxhelo Shehu, Enes Stastoli, Arben Cela

benchmarks

Hugging Face Trending Papers

Jul 13

Similarity-Guided Curriculum Fine-Tuning of LLMs for Neural Architecture Synthesis

Introduce a MinHash-based similarity scheduling framework that constructs a progressive curriculum over neural architecture code for LLM-based neural architecture search (NAS). Using 128-permutation MinHash signatures over normalised 7-gram source code shingles, we partition the reference pool into similarity bands and present them in increasing architectural heterogeneity, with the best LoRA adapter from each stage merged cumulatively into the backbone.

llms computer-vision fine-tuning benchmarks

arXiv AI

Jul 9

Enhancing deep learning models for time series classification via knowledge distillation

arXiv:2607. 06796v1 Announce Type: cross Abstract: Deep learning has achieved remarkable success in various domains including time series analysis, computer vision and natural language processing.

By Javidan Abdullayev, Maxime Devanne, Jonathan Weber, Germain Forestier

llms computer-vision nlp efficiency benchmarks

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Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures

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