cvpaper.challenge is a collaborative initiative aimed at enhancing research efficiency in the computer vision field in Japan, involving over 50 members from various universities. It offers a comprehensive collection of over 4,000 summarized papers, promotes knowledge exchange, and implements various tips and strategies for efficient research practices. Notable contributions include curated meetings, resource sharing, and automated processes to facilitate research activities.
The document outlines strategies for enhancing research efficiency, emphasizing the importance of effective literature review, management skills, and collaborative efforts among researchers. It discusses two main methods for skill enhancement: learning from peers and leveraging online resources, while highlighting the challenges and advantages of each approach. Additionally, it provides insights into the dynamics of various research labs, communication practices, and the value of sharing knowledge across institutions.
This document discusses methods for automated machine learning (AutoML) and optimization of hyperparameters. It focuses on accelerating the Nelder-Mead method for hyperparameter optimization using predictive parallel evaluation. Specifically, it proposes using a Gaussian process to model the objective function and perform predictive evaluations in parallel to reduce the number of actual function evaluations needed by the Nelder-Mead method. The results show this approach reduces evaluations by 49-63% compared to baseline methods.
1. The document discusses probabilistic modeling and variational inference. It introduces concepts like Bayes' rule, marginalization, and conditioning.
2. An equation for the evidence lower bound is derived, which decomposes the log likelihood of data into the Kullback-Leibler divergence between an approximate and true posterior plus an expected log likelihood term.
3. Variational autoencoders are discussed, where the approximate posterior is parameterized by a neural network and optimized to maximize the evidence lower bound. Latent variables are modeled as Gaussian distributions.
The document outlines strategies for enhancing research efficiency, emphasizing the importance of effective literature review, management skills, and collaborative efforts among researchers. It discusses two main methods for skill enhancement: learning from peers and leveraging online resources, while highlighting the challenges and advantages of each approach. Additionally, it provides insights into the dynamics of various research labs, communication practices, and the value of sharing knowledge across institutions.
This document discusses methods for automated machine learning (AutoML) and optimization of hyperparameters. It focuses on accelerating the Nelder-Mead method for hyperparameter optimization using predictive parallel evaluation. Specifically, it proposes using a Gaussian process to model the objective function and perform predictive evaluations in parallel to reduce the number of actual function evaluations needed by the Nelder-Mead method. The results show this approach reduces evaluations by 49-63% compared to baseline methods.
1. The document discusses probabilistic modeling and variational inference. It introduces concepts like Bayes' rule, marginalization, and conditioning.
2. An equation for the evidence lower bound is derived, which decomposes the log likelihood of data into the Kullback-Leibler divergence between an approximate and true posterior plus an expected log likelihood term.
3. Variational autoencoders are discussed, where the approximate posterior is parameterized by a neural network and optimized to maximize the evidence lower bound. Latent variables are modeled as Gaussian distributions.
Math in Machine Learning / PCA and SVD with ApplicationsKenji Hiranabe
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Math in Machine Learning / PCA and SVD with Applications
機会学習の数学とPCA/SVD
Colab での練習コードつきです.コードはこちら.
https://colab.research.google.com/drive/1YZgZWX5a7_MGA__HV2bybSuJsqkd4XxD?usp=sharing