The scientist’s investigation covers issues in Artificial intelligence, Speech recognition, Recurrent neural network, Artificial neural network and Machine learning. In general Artificial intelligence, his work in Reinforcement learning and Deep learning is often linked to Reinforcement linking many areas of study. His study in Speech recognition is interdisciplinary in nature, drawing from both Connectionism, Handwriting and Benchmark.
His Recurrent neural network study combines topics in areas such as Time delay neural network, Language model, Hidden Markov model and Sequence learning. His work on Gradient descent as part of general Artificial neural network research is frequently linked to Auxiliary memory, Fidelity and Generative model, thereby connecting diverse disciplines of science. His research investigates the connection between Machine learning and topics such as Context that intersect with problems in Test set.
Artificial intelligence, Recurrent neural network, Speech recognition, Artificial neural network and Pattern recognition are his primary areas of study. His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Computer vision. The study incorporates disciplines such as Time delay neural network, Algorithm, Connectionism and Hidden Markov model in addition to Recurrent neural network.
Alex Graves interconnects Recurrent neural nets and Discriminative model in the investigation of issues within Speech recognition. His Artificial neural network research is multidisciplinary, relying on both Range and Keyword spotting. His biological study spans a wide range of topics, including Image, Image generation and Facial expression.
His primary scientific interests are in Artificial intelligence, Artificial neural network, Recurrent neural network, State and Prior probability. His specific area of interest is Artificial intelligence, where he studies Reinforcement learning. In his study, which falls under the umbrella issue of Reinforcement learning, Range is strongly linked to Mathematical optimization.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Feature learning and Pattern recognition. His Recurrent neural network research includes themes of Algorithm and Computation. He has included themes like Speech recognition and Speech synthesis in his State study.
Alex Graves mostly deals with Massively parallel, High fidelity, Speech recognition, State and Speech synthesis. Massively parallel is connected with Sample, Quality, Significant difference and Architecture in his research.
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Human-level control through deep reinforcement learning
Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu.
Speech recognition with deep recurrent neural networks
Alex Graves;Abdel-rahman Mohamed;Geoffrey Hinton.
international conference on acoustics, speech, and signal processing (2013)
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Alex Graves.
arXiv: Learning (2013)
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves.
international conference on machine learning (2016)
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Alex Graves;Santiago Fernández;Faustino Gomez;Jürgen Schmidhuber.
international conference on machine learning (2006)
Generating Sequences With Recurrent Neural Networks
arXiv: Neural and Evolutionary Computing (2013)
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Alex Graves;Jürgen Schmidhuber.
international joint conference on neural network (2005)
WaveNet: A Generative Model for Raw Audio
Aäron van den Oord;Sander Dieleman;Heiga Zen;Karen Simonyan.
Supervised Sequence Labelling
2005 Special Issue: Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Alex Graves;Jürgen Schmidhuber.
Neural Networks (2005)
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