(Phys.org)—A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published

田中専務 拓海先生、最近部下から「スリマブルって論文が良い」と言われまして。要は1つのAIモデルで速さと精度を…

NeurIPS Proceedings Search Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights Theofanis Karaletsos, Thang D Bui Advances in Neural Information Processing Systems 33 (NeurIPS 2020) Abstract Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space. A desirable class of priors would represent weights compactly, capture correla

Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural NetworksSanjeev Arora, Simon Du, Wei Hu, Zhiyu

How to Implement Transfer Learning Effectively Transfer learning allows models to leverage pre-trained networks for new tasks

Neural operators for PDE solving show different generalization behaviors under distribution shifts, with Fourier Neural Operators and Deep Operator Networks exhibiting

Neural Networks with low bit weights on low end 32 bit microcontrollers such as the CH32V003 RISC-V Microcontroller and others - cpldcpu/BitNetMCU

Long short-term memory (LSTM) is one of the robust recurrent neural network architectures for learning sequential data. However, it requires considerable computational power to learn and implement both software and hardware aspects. This paper proposed a novel LiteLSTM architecture based on reducing the LSTM computation components via the weights sharing concept to reduce the overall architecture computation cost and maintain the architecture performance. The proposed LiteLSTM can be significant for process

After having completed the <a href="http://deeplearning.ai/">deeplearning.ai</a> Deep Learning specialization taught by Andrew Ng, I have decided to work through some of the assignments of the specialization and try to figure out the code myself without only filling in certain parts of it. Doing so, I want to deepen my understanding of neural networks and help others gain intuition by documenting my progress in articles. The complete notebook is available <a href="https://github.com/lksfr/TowardsDataScience

Neural Network における "Attention" の概念をうみだした機械翻訳の論文 "Neura