ã„ã¨ã† on Twitter: "「関数解æžã¨æœ€é©åŒ–ã€ã¨ã„ã†ã‚¿ã‚¤ãƒˆãƒ«ã®233ページã«ã‚‚渡る神PDFを発見ã—ã¦ä¹…ã—ã¶ã‚Šã«ãƒ†ãƒ³ã‚·ãƒ§ãƒ³ä¸ŠãŒã‚‹ã€‚関数解æžã®åˆæ©çš„ãªã¨ã“ã‚ã‹ã‚‰å‡¸è§£æžã‚„éžå‡¸è§£æžã‚„å微分方程å¼ã¾ã§ã„ã‚ã„ã‚書ã„ã¦ã‚ã£ã¦ã“ã‚Œã¯ä»Šã™ãã«ã§ã‚‚èªã¿ãŸã„（ãŒè«–文。。。）… https://t.co/Yhdb9NgG3c"
「関数解æžã¨æœ€é©åŒ–ã€ã¨ã„ã†ã‚¿ã‚¤ãƒˆãƒ«ã®233ページã«ã‚‚渡る神PDFを発見ã—ã¦ä¹…ã—ã¶ã‚Šã«ãƒ†ãƒ³ã‚·ãƒ§ãƒ³ä¸ŠãŒã‚‹ã€‚関数解æžã®åˆæ©çš„ãªã¨ã“ã‚ã‹ã‚‰å‡¸è§£æžã‚„éžå‡¸è§£æžã‚„å微分方程å¼ã¾ã§ã„ã‚ã„ã‚書ã„ã¦ã‚ã£ã¦ã“ã‚Œã¯ä»Šã™ãã«ã§ã‚‚èªã¿ãŸã„（ãŒè«–文。。。）… https://t.co/Yhdb9NgG3c
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Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognitionã‚’èªã‚“ã - Qiita
Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognitionã‚’èªã‚“ã Python機械å¦ç¿’ Hara, Kensho, Hirokatsu Kataoka, and Yutaka Satoh. "Learning spatio-temporal features with 3D residual networks for action recognition." Proceedings of the ICCV Workshop on Action, Gesture, and Emotion Recognition. Vol. 2. No. 3. 2017. arXive 1.ã©ã‚“ãªã‚‚ã®ï¼Ÿ ResNetsã‚’æ‹¡å¼µã—ã¦3DCNNã«é©ç”¨ ActivityNetãŠã‚ˆã³Kineticsデータセットを3
Masaaki Imaizumi - ç ”ç©¶è§£èª¬ï¼ˆæ—¥æœ¬èªžï¼‰
A.Sannai, M.Imaizumi, "Improved Generalization Bound for Permutation Invariant Neural Networks" (arXiv 2019) ç½®æ›ä¸å¤‰ãªæ€§è³ªã‚’æŒã¤æ·±å±¤å¦ç¿’ã«ã¤ã„ã¦ã€ãã®é«˜ã„性能を数å¦çš„ã«è§£æžã—ãŸè«–æ–‡ã§ã™ã€‚ ç½®æ›ä¸å¤‰ãªæ·±å±¤å¦ç¿’ã¯ã€ç‚¹ç¾¤ã‚„ç”»åƒã‚»ãƒƒãƒˆãªã©ã®ãƒ‡ãƒ¼ã‚¿ã‚’交æ›ã—ã¦ã‚‚性質ãŒå¤‰åŒ–ã—ãªã„データã«ã¤ã„ã¦ã€é«˜ã„性能を出ã™ã“ã¨ãŒçµŒé¨“çš„ã«çŸ¥ã‚‰ã‚Œã¦ã„ã¾ã™ã€‚ æœ¬ç ”ç©¶ã¯ã€ãã®ã‚ˆã†ãªæ·±å±¤å¦ç¿’ã®æ€§è³ªã‚’æ•°å¦çš„ã«è§£æžã—ã€ãã®èª¤å·®çŽ‡ï¼ˆæ±ŽåŒ–誤差）ãŒç½®æ›ä¸å¤‰ã§ãªã„å ´åˆã«æ¯”ã¹ã¦ã€ç½®æ›è¦ç´ ã®æ•°ã®éšŽä¹—分ã ã‘改善ã™ã‚‹ã“ã¨ã‚’示ã—ã¾ã—ãŸã€‚ ç½®æ›è¦ç´ ã®æ•°ã¯ã€ä¸€èˆ¬çš„ãªç‚¹ç¾¤ã®ãƒ‡ãƒ¼ã‚¿ã«ãŠã„ã¦ã¯1,000程度ã‚ã‚Šã€ãã®éšŽä¹—分ã®èª¤å·®ã®æ”¹å–„ã¯ã€éžå¸¸ã«å¤§ããªç†è«–的誤差ã®æ”¹å–„ã«ãªã‚‹ã“ã¨ãŒæœŸå¾…ã•ã‚Œã¾ã™ã€‚ 原稿 / å…±è‘—è€…ï¼šä¸‰å†…ç ”ç©¶å“¡ï¼ˆç†ç ”）
KDD'16 論文採択：çœãƒ¡ãƒ¢ãƒªãªã‚°ãƒ©ãƒ•ã‚¹ã‚±ãƒƒãƒã®ãƒ‡ãƒ¼ã‚¿æ§‹é€ - iwiwiã®æ—¥è¨˜
国際å¦ä¼š KDD 2016 ã«è«–æ–‡ãŒæŽ¡æŠžã•ã‚Œã¾ã—ãŸï¼ŽKDD ã¯ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°åˆ†é‡Žã®æœ€ã‚‚有åãªä¼šè°ã§ã™ï¼Žç™ºè¡¨ã¯ 8 月ã«ã‚µãƒ³ãƒ•ãƒ©ãƒ³ã‚·ã‚¹ã‚³ã§ã™ï¼Žã‚ªãƒ¼ãƒ©ãƒ«ç™ºè¡¨æœ‰ã‚Šã®æŽ¡æŠžã§ã™ï¼Ž 今回ã®è«–文㯠"Compact and Scalable Graph Neighborhood Sketching" ã¨ã„ã†ã‚¿ã‚¤ãƒˆãƒ«ã§ï¼Œç§ãŒä¸»è‘—ã§ã‚ã‚Šï¼Œç ”ç©¶å®¤ã§ç‰¹ä»»æŠ€è¡“専門員ã¨ã—ã¦ãŠæ‰‹ä¼ã„ã—ã¦ã‚‚らã£ã¦ã„ãŸçŸ¢é‡Žã•ã‚“ã¨ã®å…±è‘—ã§ã™ï¼Žå†…容ã¯ã‚°ãƒ©ãƒ•å‘ã‘ãƒ‡ãƒ¼ã‚¿æ§‹é€ All-Distances Sketches ã®å®Ÿç”¨ä¸Šã®å•é¡Œç‚¹ã§ã‚る空間使用é‡ã‚’大幅ã«å‰Šæ¸›ã™ã‚‹ãŸã‚ã®æ–°ã—ã„ãƒ‡ãƒ¼ã‚¿æ§‹é€ ã®æ案ã§ã™ï¼Žä»¥å‰ã«ã€Œå¤§è¦æ¨¡ã‚°ãƒ©ãƒ•ã®ã‚³ãƒ³ãƒ‘クトã§ã‚¹ã‚±ãƒ¼ãƒ©ãƒ–ルãªå…¨è·é›¢ã‚¹ã‚±ãƒƒãƒã€ã¨ã„ã†ã‚¿ã‚¤ãƒˆãƒ«ã§äººå·¥çŸ¥èƒ½å¦ä¼šã®äººå·¥çŸ¥èƒ½åŸºæœ¬å•é¡Œç ”究会ã«ã¦è°è«–ã•ã›ã¦é ‚ã„ã¦ã„ãŸã‚‚ã®ã§ã™ï¼Ž 背景：All-Distances Sketches ã¨ã¯ï¼Ÿ All-Distances Ske
プãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã®ãƒ†ã‚¹ãƒˆåŠ©æ‰‹ - Google 検索
2024/07/29 · テストエンジニアã¯ã€ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢è£½å“やサービスãªã©ãŒä»•æ§˜ã©ãŠã‚Šã«å‹•ä½œã—ã€ãƒã‚°ã‚„エラーã¨ã„ã£ãŸå•é¡Œç‚¹ãŒãªã„ã‹ãƒ†ã‚¹ãƒˆã‚„検証を行ã†è·ç¨®ã§ã™ã€‚
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※ユーザ登録ã¯ç„¡æ–™ã§ã™ï¼Ž 本電å図書館ã®ã”利用ã«ã‚ãŸã£ã¦ã¯ã€Œæƒ…å ±å‡¦ç†å¦ä¼šé›»å図書館利用è¦ç´„ã€ã‚’ã”éµå®ˆä¸‹ã•ã„。 掲載ã•ã‚Œã¦ã„るコンテンツã«ã¯ç„¡æ–™ã®ã‚‚ã®ã¨æœ‰æ–™ã®ã‚‚ã®ãŒã‚ã‚Šã¾ã™ã€‚有料コンテンツã®ã”購入ã¯ã‚¯ãƒ¬ã‚¸ãƒƒãƒˆã‚«ãƒ¼ãƒ‰æ±ºæ¸ˆã®ã¿ã¨ãªã‚Šã¾ã™ã€‚ é ˜åŽæ›¸ã‚’ã”希望ã®æ–¹ã¯ã€Œé›»åå›³æ›¸é¤¨å°‚ç”¨é ˜åŽæ›¸ç™ºè¡Œä¾é ¼ã€ï¼ˆé€ä¿¡ãƒ•ã‚©ãƒ¼ãƒ ）ã«ã¦ã”連絡ãã ã•ã„。 複写ãŠã‚ˆã³è»¢è¼‰ã‚’ã•ã‚Œã‚‹æ–¹ã¸ä¸€èˆ¬ç¤¾å›£æ³•äººæƒ…å ±å‡¦ç†å¦ä¼šã§ã¯è¤‡å†™è¤‡è£½ãŠã‚ˆã³è»¢è¼‰è¤‡è£½ã«ä¿‚る著作権をå¦è¡“著作権å”会ã«å§”託ã—ã¦ã„ã¾ã™ã€‚当該利用をã”希望ã®æ–¹ã¯ã€å¦è¡“著作権å”会ãŒæä¾›ã—ã¦ã„る複製利用許諾システムもã—ãã¯è»¢è¼‰è¨±è«¾ã‚·ã‚¹ãƒ†ãƒ を通ã˜ã¦ç”³è«‹ãã ã•ã„。 å°šã€æœ¬ä¼šä¼šå“¡ï¼ˆè³›åŠ©ä¼šå“¡å«ã‚€ï¼‰ãŠã‚ˆã³è‘—者ãŒè»¢è¼‰åˆ©ç”¨ã®ç”³è«‹ã‚’ã•ã‚Œã‚‹å ´åˆã«ã¤ã„ã¦ã¯ã€å¦è¡“目的利用ã«é™ã‚Šã€ç„¡å„Ÿã§è»¢è¼‰åˆ©ç”¨ã„ãŸã ãã“ã¨ãŒå¯èƒ½ã§ã™ã€‚ãŸã ã—ã€åˆ©ç”¨ã®éš›ã«ã¯äºˆã‚申請ã„ãŸã ãよã†ãŠé¡˜ã„致ã—ã¾ã™ã€‚
DopeLearning: A Computational Approach to Rap Lyrics Generation
Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based
Weight Uncertainty in Neural Networks
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields co
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夕方ã«å—大沢ã§å§”員会ãŒã‚ã‚‹ã®ã§è»Šã§é€šå‹¤ã€‚2å¹´å‰ã«è»Šã§æ¯Žæ—¥é€šå‹¤ã—ã¦ã„ãŸã®ãŒä¿¡ã˜ã‚‰ã‚Œãªã„ãらã„ã€æœ€è¿‘ã¯è»Šé€šå‹¤ãŒã“ãŸãˆã‚‹ã€‚ãã‚‚ãも車ã§é€šå‹¤ã—ã¦é¦–ã‚’ç—›ã‚ãŸã‚Šè…°ã‚’ç—›ã‚ãŸã‚Šã€ã•ã¾ã–ã¾ãªå•é¡ŒãŒç”Ÿã˜ã¦é›»è»Šé€šå‹¤ã«æˆ»ã—ãŸã®ã§ã‚ã‚‹ãŒâ€¦â€¦ã€‚ åˆå‰ä¸ã¯æƒ…å ±ç†è«–ã®æŽˆæ¥ã€‚ã„ã‚ã„ã‚雑談をã—よã†ã¨æ€ã£ã¦ã„ãŸã®ã ãŒã€å…ˆé€±ã®å¾©ç¿’ã¨å°ãƒ†ã‚¹ãƒˆã‚’ã—ã¦ã„ã‚‹ã¨æ™‚é–“ãŒè¶³ã‚Šãªã„（毎年å°ãƒ†ã‚¹ãƒˆã®å›žæ•°ã‚’増やã—ã¦ã„る）。演習å•é¡Œä¸å¿ƒã®æŽˆæ¥ã«ã—ãŸã»ã†ãŒã„ã„ã®ã ã‚ã†ã‹ï¼Ÿã›ã£ã‹ã3å¹´ç›®ã®æŽˆæ¥ã§ã“ãªã‚Œã¦ãã¦ã„ã‚‹ã¨ã“ã‚を変ãˆã‚‹ã®ã¯å¤§å¤‰ãªã®ã§ã€ã‚„ã‚‹ã«ã—ã¦ã‚‚æ¥å¹´åº¦ä»¥é™ã ã‚ã†ã‹ã€‚ ãŠæ˜¼ã‹ã‚‰é€²æ—å ±å‘Šã€‚ã‚µãƒ¼ãƒ™ã‚¤ä¸ã®å¦ç”ŸãŒå¤šã„ã®ã ãŒã€ã¡ã‚ƒã‚“ã¨ç†è§£ã™ã‚‹ãŸã‚ã«æ™‚é–“ã‚’ã‹ã‘ã‚‹ã€ã¨ã„ã†ã®ã¯ã‚ˆã„ã“ã¨ã ã¨æ€ã†ãŒã€ç†è§£ã—よã†ã¨æ€ã†ã¨é–¢é€£ã™ã‚‹ä»–ã®è«–文もèªã‚“ã ã‚Šã™ã‚‹å¿…è¦ãŒã‚ˆãã‚ã‚‹ã®ã«ã€ãã®è«–æ–‡ã ã‘ã‚’èªã‚“ã§ã„ã¦ç†è§£ã§ããªã„ã€ã¨ã„ã†ã®ã¯ã€å˜ã«ï¼ˆè«–文をç†è§£ã™ã‚‹ãŸã‚ã«ã€é©åˆ‡ãªï¼‰æ™‚間を使ã£ã¦ã„ãªã„
The Curse of the Excluded Middle - ACM Queue
April 26, 2014 Volume 12, issue 4 PDF The Curse of the Excluded Middle "Mostly functional" programming does not work. Erik Meijer There is a trend in the software industry to sell "mostly functional" programming as the silver bullet for solving problems developers face with concurrency, parallelism (manycore), and, of course, Big Data. Contemporary imperative languages could continue the ongoing t
Papers We Love ã¨ã„ã†ã‚³ãƒŸãƒ¥ãƒ‹ãƒ†ã‚£ - kuenishi's blog
第4回 システム系論文輪èªä¼š - connpassãŒé–‹å‚¬ã•ã‚Œã‚‹ã¨èžã„ã¦ã€æ€ã„出ã—ãŸè©±ã€‚日本ã ã¨ã€â—‹â—‹èªã¿ä¼šã¨ã„ã†å½¢ã§ã€ç‰¹å®šã®å›½éš›ä¼šè°ã®è«–文をã†ã‚ãーã£ã¨é›†ä¸çš„ã«è¼ªèªã™ã‚‹ã‚³ãƒŸãƒ¥ãƒ‹ãƒ†ã‚£ã¯ã„ãã¤ã‹ã‚ã‚‹ã‘ã©ã€ã‚る分野ãªã‚Šã‚’ã¾ã¨ã‚ã¦å›žã—èªã¿ã—よã†ã¨ã„ã†è©¦ã¿ã¯ãªã„よã†ã«æ€ã†ã®ã§ã€ã“ã“㧠Papers We Love ã¨ã„ã†ã‚³ãƒŸãƒ¥ãƒ‹ãƒ†ã‚£ã‚’紹介ã—よã†ã¨æ€ã†ã€‚ Papers We Love Too - October 2014 - YouTube 予ã‚æ–ã£ã¦ãŠããŒã€ã¼ãã¯ã¾ã å‚åŠ ã—ãŸã“ã¨ã¯ãªã„ã—ã€åŒåƒšãŒãŸã¾ãŸã¾ãƒ•ã‚¡ã‚¦ãƒ³ãƒ€ãƒ¼ã®ä¸€äººãªã®ã§çŸ¥ã£ãŸã ã‘ã 。ã“ã‚ŒãŒãªã‹ãªã‹é¢ç™½ã„試ã¿ã 。 Papers We Love is a repository of academic computer science papers and a community who loves reading them. ã¨ã„ã†ã“ã¨ã§ã€è¨ˆç®—æ©Ÿ
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Daisuke Okanohara on Twitter: "物体検出ã«ãŠã‘る最é©ãªç‰¹å¾´ãƒ”ãƒ©ãƒŸãƒƒãƒ‰æ§‹é€ ã‚’å¼·åŒ–å¦ç¿’を使ã£ã¦æŽ¢ç´¢ã€‚入力ã¨åŒã˜ã‚µã‚¤ã‚ºã®ãƒ”ラミッドを出力ã™ã‚‹ã“ã¨ã§ã€åŒã˜æ§‹é€ ã‚’ç¹°ã‚Šè¿”ã—é©ç”¨ã§ãã€ç²¾åº¦ã®é€Ÿåº¦ã®ãƒˆãƒ¬ãƒ¼ãƒ‰ã‚ªãƒ•ã‚’ã¨ã‚Œã‚‹ã€‚発見ã•ã‚ŒãŸæ§‹é€ NAS-FPNã¯SSDLiteã‚„Mask R… https://t.co/iiyEC16Fod"
https://dl.acm.org/doi/10.1145/3139958.3139959
GitHub - zssloth/Embedded-Neural-Network: collection of works aiming at reducing model sizes or the ASIC/FPGA accelerator for machine learning
Just say NO to Paxos overhead: replacing consensus with network ordering | the morning paper
Steinberg's Conjecture is false
HemannMuKanren2013.pdf [μKanren: A Minimal Functional Core for Relational Programming]
Papers We Love
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