1 citations · 2 across the 7 of their papers we have counts for
8 papers
Measuring the local shape of learning-rate transfer curves
Satoki Ishikawa, Rio Yokota, Ryo Karakida
Understanding the learning-rate (LR) dependence of trained model performance is essential for better optimization of neural networks, but the appropriate size of the LR depends on…
Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
Taishi Nakamura, Satoki Ishikawa, Masaki Kawamura +4
Empirical scaling laws have driven the evolution of large language models (LLMs), yet their coefficients shift whenever the model architecture or data pipeline changes. Mixture-of-…
Brain-Inspired Stochastic Joint Embedding Representation Learning
Makoto Yamada, Kian Ming A. Chai, Ayoub Rhim +3
Representation learning is one of the key research topics in machine learning, and the framework of self-supervised learning (SSL) has revolutionized computer vision. However, thes…
Lion Cub: Minimizing Communication Overhead in Distributed Lion
Satoki Ishikawa, Tal Ben-Nun, Brian Van Essen +2
Communication overhead is a key challenge in distributed deep learning, especially on slower Ethernet interconnects, and given current hardware trends, communication is likely to b…
Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation
Satoki Ishikawa, Rio Yokota, Ryo Karakida
Local learning, which trains a network through layer-wise local targets and losses, has been studied as an alternative to backpropagation (BP) in neural computation. However, its a…
PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis
Satoki Ishikawa, Makoto Yamada, Han Bao +1
Predictive coding is a theory which hypothesises that cortex predicts sensory inputs at various levels of abstraction to minimise prediction errors. Inspired by predictive coding,…
On the Parameterization of Second-Order Optimization Effective Towards the Infinite Width
Satoki Ishikawa, Ryo Karakida
Second-order optimization has been developed to accelerate the training of deep neural networks and it is being applied to increasingly larger-scale models. In this study, towards…
ASDL: A Unified Interface for Gradient Preconditioning in PyTorch
Kazuki Osawa, Satoki Ishikawa, Rio Yokota +2
Gradient preconditioning is a key technique to integrate the second-order information into gradients for improving and extending gradient-based learning algorithms. In deep learnin…