Several recent papers have explored self-supervised learning methods for vision transformers (ViT). Key approaches include:
1. Masked prediction tasks that predict masked patches of the input image.
2. Contrastive learning using techniques like MoCo to learn representations by contrasting augmented views of the same image.
3. Self-distillation methods like DINO that distill a teacher ViT into a student ViT using different views of the same image.
4. Hybrid approaches that combine masked prediction with self-distillation, such as iBOT.
Several recent papers have explored self-supervised learning methods for vision transformers (ViT). Key approaches include:
1. Masked prediction tasks that predict masked patches of the input image.
2. Contrastive learning using techniques like MoCo to learn representations by contrasting augmented views of the same image.
3. Self-distillation methods like DINO that distill a teacher ViT into a student ViT using different views of the same image.
4. Hybrid approaches that combine masked prediction with self-distillation, such as iBOT.
The document contains contact information for Ichigaku Takigawa including their email address ichigaku.takigawa@riken.jp, personal website URL https://itakigawa.github.io/, and mentions they are working with IBISML and ATR on materials informatics and bioinformatics. It also includes a link to their page https://itakigawa.page.link/IBISML for a PDF document.
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
You Only Look One-level Featureの解説と見せかけた物体検出のよもやま話Yusuke Uchida
第7回全日本コンピュータビジョン勉強会「CVPR2021読み会」(前編)の発表資料です
https://kantocv.connpass.com/event/216701/
You Only Look One-level Featureの解説と、YOLO系の雑談や、物体検出における関連する手法等を広く説明しています
The document summarizes recent research related to "theory of mind" in multi-agent reinforcement learning. It discusses three papers that propose methods for agents to infer the intentions of other agents by applying concepts from theory of mind:
1. The papers propose that in multi-agent reinforcement learning, being able to understand the intentions of other agents could help with cooperation and increase success rates.
2. The methods aim to estimate the intentions of other agents by modeling their beliefs and private information, using ideas from theory of mind in cognitive science. This involves inferring information about other agents that is not directly observable.
3. Bayesian inference is often used to reason about the beliefs, goals and private information of other agents based
The document contains contact information for Ichigaku Takigawa including their email address ichigaku.takigawa@riken.jp, personal website URL https://itakigawa.github.io/, and mentions they are working with IBISML and ATR on materials informatics and bioinformatics. It also includes a link to their page https://itakigawa.page.link/IBISML for a PDF document.
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
You Only Look One-level Featureの解説と見せかけた物体検出のよもやま話Yusuke Uchida
第7回全日本コンピュータビジョン勉強会「CVPR2021読み会」(前編)の発表資料です
https://kantocv.connpass.com/event/216701/
You Only Look One-level Featureの解説と、YOLO系の雑談や、物体検出における関連する手法等を広く説明しています
The document summarizes recent research related to "theory of mind" in multi-agent reinforcement learning. It discusses three papers that propose methods for agents to infer the intentions of other agents by applying concepts from theory of mind:
1. The papers propose that in multi-agent reinforcement learning, being able to understand the intentions of other agents could help with cooperation and increase success rates.
2. The methods aim to estimate the intentions of other agents by modeling their beliefs and private information, using ideas from theory of mind in cognitive science. This involves inferring information about other agents that is not directly observable.
3. Bayesian inference is often used to reason about the beliefs, goals and private information of other agents based
Noriyuki Aibe, "High Efficiency Connection Method on Electric Signal Lines be...直久 住川
This document presents a novel connection method called "Bit-shift connection" for electric signal lines between modular circuit boards. The method allows for independent connection of interfaces like FPGA and CPLD boards using a limited board area and number of connector pins. It works by reassigning connection pins on each board in a shifted pattern, so any module can connect to another without needing to designate pin assignments in advance. This flexible approach allows boards to be stacked and reordered freely while maintaining independent connections between the CPU board and all interfaces. The document provides examples to illustrate how the Bit-shift connection method works and compares it to conventional bus-based and independent connection topologies.
21. Design Solution Forum のトラック構成の変遷
Design, Verification, Soft&FPGA, Solution
Design, Verification, Soft&FPGA, Solution, Special Discussion
Design Tech, FPGA, Auto&IoT, Image&Vision, Special Discussion
2017年、トラック名設定せず(事実上のRISC-Vトラックを用意)
Soft&Test, SystemC&System Verilog, Automotive, Deep Learning, RISC-V/IoT
Machine Learning, Essence of System Construction, Internet of Everything, Open Source CPU,
Arm, Rambus
FPGA Solution, Internet of AtoZ, ハードもソフトもオープンソース, Hot-Tech, Rambus
Hot, Cool, Special
22. Design Solution Forum の特別企画の変遷
System Verilogハッカソン
高位合成をディスカッションする会、DSF検証研究会、フォーマル検証トーク、プロトタイプ開発トーク
高位合成をディスカッションする会、DSF検証研究会、ソースコード管理について語ろう、Linuxを語ろう、
ハードウェアエンジニアvsソフトウェアエンジニア討論会
Linuxシステム解析について語ろう、ハード屋&ソフト屋ディスカッション、フォーマル検証トーク,
ビブリオバトル、先輩エンジニアに聞いてみよう
エンジニアにとって究極の働き方改革とは?、スタートアップx成熟企業とのエンジニア・ネットワークで
新ビジネスを創ろう!!
DSF選書、働き方改革にもの申す!?エクストリームエンジニアたちの雑談会
DSF選書、DSFモノづくり企画「Retrievable Space Balloon」
DSF選書、リモート環境自慢大会、Myガジェット自慢大会、
みんなに教えたい!ウチの会社の面白いトコ・すごいトコ・変なトコ