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An automated practice system for mastering complex skills
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.
This is a repository for paper "Towards Data-driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning".
Hierarchical Actor-Critic in Pytorch
This is the repository for the paper Hierarchical Quality-Diversity for Online Damage Recovery which was accepted at GECCO 2022.
Official implementation of paper "Deep Hierarchical Learning for 3D Segmentation"
Modularity-based graph population integration
Companion code repository to Marcou et al. 2024, Creating a computer assisted ICD coding system: Performance metric choice and use of the ICD hierarchy
Reverse Curriculum Vicinity Learning: Reinforcement Hierarchical algorithm for solving complex RL tasks.
Introduction to part-of-speech tagging and shallow parsing with keras