2013 - Fellow of Alfred P. Sloan Foundation
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Deep learning, Pattern recognition and Natural language processing. His study in Deep belief network, Generative model, Inference, Restricted Boltzmann machine and Boltzmann machine falls within the category of Artificial intelligence. Ruslan Salakhutdinov has researched Machine learning in several fields, including Topic model, Structure and Cognitive neuroscience of visual object recognition.
His biological study spans a wide range of topics, including Artificial neural network, Concept learning, Kernel and One-shot learning. His Pattern recognition study combines topics in areas such as Object detection, Image and Autoencoder. His Supervised learning research is multidisciplinary, incorporating perspectives in Discrete mathematics, Document classification, Net and Convolutional neural network.
His primary areas of investigation include Artificial intelligence, Machine learning, Artificial neural network, Natural language processing and Reinforcement learning. His research on Artificial intelligence frequently connects to adjacent areas such as Pattern recognition. His work deals with themes such as Generative grammar and Key, which intersect with Machine learning.
His Artificial neural network research is multidisciplinary, relying on both Algorithm and Representation. The Reinforcement learning study combines topics in areas such as Function and Variety. Ruslan Salakhutdinov studies Deep learning, namely Deep belief network.
His primary scientific interests are in Artificial intelligence, Machine learning, Reinforcement learning, Natural language processing and Feature learning. Ruslan Salakhutdinov has included themes like Sample and Control in his Artificial intelligence study. His study on Machine learning also encompasses disciplines like
His Reinforcement learning study combines topics from a wide range of disciplines, such as Inference, Bayes' theorem, Function, Supervised learning and Conditional probability distribution. His work on Sentence and Natural language as part of general Natural language processing research is often related to tf–idf and Queries per second, thus linking different fields of science. His Classifier research includes themes of Artificial neural network and Training set.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava;Geoffrey Hinton;Alex Krizhevsky;Ilya Sutskever.
Journal of Machine Learning Research (2014)
Reducing the Dimensionality of Data with Neural Networks
G. E. Hinton;R. R. Salakhutdinov.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton;Nitish Srivastava;Alex Krizhevsky;Ilya Sutskever.
arXiv: Neural and Evolutionary Computing (2012)
Probabilistic Matrix Factorization
Andriy Mnih;Ruslan R Salakhutdinov.
neural information processing systems (2007)
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Zhilin Yang;Zihang Dai;Yiming Yang;Jaime G. Carbonell.
neural information processing systems (2019)
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho.
arXiv: Learning (2015)
Siamese Neural Networks for One-shot Image Recognition
Gregory Koch;Richard Zemel;Ruslan Salakhutdinov.
Human-level concept learning through probabilistic program induction.
Brenden M. Lake;Ruslan Salakhutdinov;Joshua B. Tenenbaum.
Restricted Boltzmann machines for collaborative filtering
Ruslan Salakhutdinov;Andriy Mnih;Geoffrey Hinton.
international conference on machine learning (2007)
Ryan Kiros;Yukun Zhu;Ruslan Salakhutdinov;Richard S. Zemel.
neural information processing systems (2015)
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