54 citations · 79 across the 11 of their papers we have counts for
12 papers
Phases in a class of associative memories via hidden neurons
Toshihiro Ota, Masato Taki
Associative memory in the Hopfield network is attractor dynamics in a disordered many-body system, and higher-order and exponential extensions turn its retrieval update into softma…
On the Role of Hidden States of Modern Hopfield Network in Transformer
Tsubasa Masumura, Masato Taki
Associative memory models based on Hopfield networks and self-attention based on key-value mechanisms have been popular approaches in the study of memory mechanisms in deep learnin…
Hierarchical Associative Memory, Parallelized MLP-Mixer, and Symmetry Breaking
Ryo Karakida, Toshihiro Ota, Masato Taki
Transformers have established themselves as the leading neural network model in natural language processing and are increasingly foundational in various domains. In vision, the MLP…
Hypernuclear event detection in the nuclear emulsion with Monte Carlo simulation and machine learning
A. Kasagi, W. Dou, V. Drozd +18
This study developed a novel method for detecting hypernuclear events recorded in nuclear emulsion sheets using machine learning techniques. The artificial neural network-based obj…
iMixer: hierarchical Hopfield network implies an invertible, implicit and iterative MLP-Mixer
Toshihiro Ota, Masato Taki
In the last few years, the success of Transformers in computer vision has stimulated the discovery of many alternative models that compete with Transformers, such as the MLP-Mixer.…
FFT-based Dynamic Token Mixer for Vision
Yuki Tatsunami, Masato Taki
Multi-head-self-attention (MHSA)-equipped models have achieved notable performance in computer vision. Their computational complexity is proportional to quadratic numbers of pixels…
Example-Based Explainable AI and its Application for Remote Sensing Image Classification
Shin-nosuke Ishikawa, Masato Todo, Masato Taki +5
We present a method of explainable artificial intelligence (XAI), "What I Know (WIK)", to provide additional information to verify the reliability of a deep learning model by showi…
Sequencer: Deep LSTM for Image Classification
Yuki Tatsunami, Masato Taki
In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image…
Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Network
Teppei Matsui, Masato Taki, Trung Quang Pham +2
Deep neural networks (DNNs) can accurately decode task-related information from brain activations. However, because of the nonlinearity of the DNN, the decisions made by DNNs are h…
RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?
Yuki Tatsunami, Masato Taki
For the past ten years, CNN has reigned supreme in the world of computer vision, but recently, Transformer has been on the rise. However, the quadratic computational cost of self-a…
Machine learning application to Fermi-LAT data: sharpening all-sky map and emphasizing variable sources
Shogo Sato, Jun Kataoka, Soichiro Ito +6
A novel application of machine-learning (ML) based image processing algorithms is proposed to analyze an all-sky map (ASM) obtained using the Fermi Gamma-ray Space Telescope. An at…
Deep Residual Networks and Weight Initialization
Masato Taki
Residual Network (ResNet) is the state-of-the-art architecture that realizes successful training of really deep neural network. It is also known that good weight initialization of…