AutoML | Literature on Neural Architecture Search
Maintained by Difan Deng and Marius Lindauer. The following list considers papers related to neural architecture search. It is by no means complete. If you miss a paper on the list, please let us know. Please note that although NAS methods steadily improve, the quality of empirical evaluations in this field are still lagging behind compared to other areas in machine learning, AI and optimization.
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The document discusses hyperparameter optimization in machine learning models. It introduces various hyperparameters that can affect model performance, and notes that as models become more complex, the number of hyperparameters increases, making manual tuning difficult. It formulates hyperparameter optimization as a black-box optimization problem to minimize validation loss and discusses challenge
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Home » リソース » ç§ã®ãƒ–ックマーク » ã€è¨˜äº‹æ›´æ–°ã€‘ç§ã®ãƒ–ックマーク「機械å¦ç¿’ã«ãŠã‘る解釈性（Interpretability in Machine Learning）〠機械å¦ç¿’ã«ãŠã‘る解釈性 (Interpretability in Machine Learning)原 è¡ï¼ˆå¤§é˜ªå¤§å¦ç”£æ¥ç§‘å¦ç ”究所） ã¯ã˜ã‚ã«è¿‘å¹´ã®äººå·¥çŸ¥èƒ½æŠ€è¡“ã€ç‰¹ã«æ©Ÿæ¢°å¦ç¿’ã®ç™ºå±•ã«ä¼´ã„ã€ã“れらã®æŠ€è¡“ã¸ã®ç¤¾ä¼šçš„ãªæœŸå¾…ãŒé«˜ã¾ã£ã¦ã„る。 ã—ã‹ã—ã€ã“ã®ã‚ˆã†ãªæœŸå¾…ã®é«˜ã¾ã‚Šã¨åŒæ™‚ã«ã“れら技術ã¸ã®ä¸å®‰ã‚‚高ã¾ã£ã¦ã„る。 特ã«ã€æ·±å±¤å¦ç¿’モデルをåˆã‚ã¨ã™ã‚‹æ©Ÿæ¢°å¦ç¿’モデルãŒè¤‡é›‘ãªãƒ–ラックボックスã§ã‚ã‚‹ãŒã‚†ãˆã«å®‰æ˜“ã«ä¿¡é ¼ã§ããªã„ã¨ã™ã‚‹æ‡¸å¿µã®å£°ãŒä¸ŠãŒã‚Šåˆã‚ã¦ã„る。 ã“ã‚Œã«å¯¾ã—ã€ç·å‹™çœã¯AIã®åˆ©ç”¨ã®ä¸€å±¤ã®å¢—進ã¨ãã‚Œã«ä¼´ã†ãƒªã‚¹ã‚¯ã®æŠ‘制ã®ãŸã‚ã«ã€ŒAI開発ガイドライン案ã€[1]ã‚’2017å¹´ã«ç–定ã—ãŸã€‚ ã“ã®ã‚¬ã‚¤ãƒ‰ãƒ©ã‚¤ãƒ³æ¡ˆã§ã¯ã€ä¸Šè¨˜ã®ã‚ˆã†ãªæ‡¸å¿µ
AI for Drug Discovery in Two Stories
Post by Pande Lab member Evan N. Feinberg TL;DR: Today, we are excited to share two papers that tell distinct stories about AI drug discovery:1. “Spatial Graph Convolutions for Drug Discovery†describes new deep neural network architectures for modeling drug-receptor interactions. We argue that the future of predicting the interactions between a drug and its prospective target demands more than si
[1709.09018] AutoEncoder by Forest
Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (abbrv. eForest), the first tree ensemble based auto-encoder. We present a procedure for enabling forests to do backward reconstruction by utilizing the equivalent classes defined by decision paths of the trees, and demon
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