1. Materials Informatics uses Python tools like RDKit for analyzing molecular structures and properties.
2. ORGAN and MolGAN are two generative models that use GANs to generate novel molecular structures based on SMILES strings, with ORGAN incorporating reinforcement learning to optimize for desired properties.
3. Tools like RDKit enable analyzing molecular fingerprints and descriptors that can be used for machine learning applications in materials informatics.
プレゼン・ポスターで自分の研究を「伝える」 (How to do technical oral/poster presentation)Toshihiko Yamasaki
MIRU2020若手プログラム招待講演のスライドを一般公開用にアレンジしたものです。日本語で書かれています。下記の点にご注意ください
・セリフが伴ってないので内容は限定的です
・著作権等に配慮しているので中身は結構無味乾燥です。
This is an arranged version of my invited talk at MIRU 2020 young researchers' forum. This is written in Japanese.
This document discusses NNgen, a tool for generating hardware implementations of neural networks from high-level models. It can generate optimized RTL and IP-XACT from models defined using frameworks like TensorFlow or ONNX. NNgen uses the Veriloggen library for hardware synthesis from Python, generating FSMs and scheduled pipelines to implement DNN layers as hardware accelerators. It aims to bridge the gap between deep learning and hardware for deploying neural networks in embedded systems.
The document discusses Confluence and how it compares to SharePoint. It notes that Confluence is used by 83% of Fortune 500 companies, has over 190 employees, and a marketplace of 164,000 apps. In contrast to SharePoint, Confluence provides cloud-based documentation collaboration that integrates with tools like Jira and has options for on-premise or cloud-hosted deployments.
1. Materials Informatics uses Python tools like RDKit for analyzing molecular structures and properties.
2. ORGAN and MolGAN are two generative models that use GANs to generate novel molecular structures based on SMILES strings, with ORGAN incorporating reinforcement learning to optimize for desired properties.
3. Tools like RDKit enable analyzing molecular fingerprints and descriptors that can be used for machine learning applications in materials informatics.
プレゼン・ポスターで自分の研究を「伝える」 (How to do technical oral/poster presentation)Toshihiko Yamasaki
MIRU2020若手プログラム招待講演のスライドを一般公開用にアレンジしたものです。日本語で書かれています。下記の点にご注意ください
・セリフが伴ってないので内容は限定的です
・著作権等に配慮しているので中身は結構無味乾燥です。
This is an arranged version of my invited talk at MIRU 2020 young researchers' forum. This is written in Japanese.
This document discusses NNgen, a tool for generating hardware implementations of neural networks from high-level models. It can generate optimized RTL and IP-XACT from models defined using frameworks like TensorFlow or ONNX. NNgen uses the Veriloggen library for hardware synthesis from Python, generating FSMs and scheduled pipelines to implement DNN layers as hardware accelerators. It aims to bridge the gap between deep learning and hardware for deploying neural networks in embedded systems.
The document discusses Confluence and how it compares to SharePoint. It notes that Confluence is used by 83% of Fortune 500 companies, has over 190 employees, and a marketplace of 164,000 apps. In contrast to SharePoint, Confluence provides cloud-based documentation collaboration that integrates with tools like Jira and has options for on-premise or cloud-hosted deployments.