arXiv Machine Learning June 24, 2026 By Anirudh Thatipelli, Jeffrey Sam, Mathias Louboutin, Ali Siahkoohi, Rongrong Wang, Felix J. Herrmann

XConv: Low-memory stochastic backpropagation for convolutional layers

Read the original on arXiv Machine Learning →

arXiv:2106. 06998v5 Announce Type: replace Abstract: Training convolutional neural networks at scale demands substantial memory, largely because intermediate activations must be stored for backpropagation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 26

AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods

arXiv:2402.11215v4 Announce Type: replace Abstract: The choice of batch size in minibatch stochastic gradient optimization is critical for both optimization and generalization performance in large-sc...

By Tim Tsz-Kit Lau, Han Liu, Mladen Kolar

arXiv Computer Vision

Sep 22

C$^{2}$-INR: Customized Convolutional Implicit Neural Representation

arXiv:2609.22807v1 Announce Type: new Abstract: Implicit Neural Representation (INR) leverages neural networks to represent discrete signals such as images as continuous ones, where the network weigh...

By Jinglei Shi, Xinran Chang, Jiaqi Cui, Yingjie Xia, Zhaolin Xiao, Chongyi Li

benchmarks

arXiv Machine Learning

Aug 11

Gradient Under Microscope: Benchmarking Resource Utilization of Memory-Efficient Gradient Computation Methods

arXiv:2608. 08961v1 Announce Type: new Abstract: AI training's rising resource intensity is straining electricity supplies and carbon budgets, motivating systematic study of memory-efficient training on constrained hardware.

By Sarthak Mahapatra, Zihan Zhou, Khatoon Khedri, Mehdi Hosseinzadeh, Reza Rawassizadeh

llms computer-vision multimodal benchmarks

arXiv AI

Jul 1

Gradient Smoothing: Coupling Layer-wise Updates for Improved Optimization

arXiv:2606. 30813v1 Announce Type: cross Abstract: Deep neural networks with repeated architectural blocks, such as transformers, often exhibit structured relationships across layers that emerge during training.

By Haoming Meng, Anton Sugolov, Vardan Papyan

llms diffusion computer-vision reinforcement-learning

arXiv AI

Jun 15

Gefen: Optimized Stochastic Optimizer

arXiv:2606. 13894v1 Announce Type: cross Abstract: AdamW is a default optimizer for modern deep learning, but its first and second moment states add roughly two parameter-sized buffers to training memory.

By Nadav Benedek, Tomer Koren, Ohad Fried

efficiency

arXiv Machine Learning

Sep 17

FAME: An FPGA-Based Platform for Approximate Multipliers Evaluation with Pattern-Guided DNN Retraining

FAME is an FPGA-based platform that evaluates approximate multipliers directly in hardware, eliminating slow CPU/GPU LUT emulation and reducing evaluation time for DNN inference. It also introduces a pattern-guided retraining method that uses multiplier-specific patterns to recover accuracy losses. Experiments on ResNet‑18 and MobileNetV2 over ImageNet show up to 3.47× faster multiplier evaluation and a 65.5% accuracy improvement over prior retraining approaches.

By Rappy Saha, Nima Amirafshar, Jude Haris, Nima Taherinejad, Jos\'e Cano

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Related stories

AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods

C$^{2}$-INR: Customized Convolutional Implicit Neural Representation

Gradient Under Microscope: Benchmarking Resource Utilization of Memory-Efficient Gradient Computation Methods

Gradient Smoothing: Coupling Layer-wise Updates for Improved Optimization

Gefen: Optimized Stochastic Optimizer

FAME: An FPGA-Based Platform for Approximate Multipliers Evaluation with Pattern-Guided DNN Retraining