D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 96 Citations 122,782 252 World Ranking 180 National Ranking 106

Research.com Recognitions

Awards & Achievements

2013 - Fellow of Alfred P. Sloan Foundation

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

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 most cited work include:

  • Dropout: a simple way to prevent neural networks from overfitting (19400 citations)
  • Reducing the Dimensionality of Data with Neural Networks (11353 citations)
  • Improving neural networks by preventing co-adaptation of feature detectors (4443 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (64.10%)
  • Machine learning (32.18%)
  • Artificial neural network (15.16%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (64.10%)
  • Machine learning (32.18%)
  • Reinforcement learning (11.97%)

In recent papers he was focusing on the following fields of study:

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

  • Robustness which connect with Lipschitz continuity,
  • Key, which have a strong connection to Stability.

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.

Between 2019 and 2021, his most popular works were:

  • Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks (42 citations)
  • Learning to Explore using Active Neural SLAM (41 citations)
  • Think Locally, Act Globally: Federated Learning with Local and Global Representations (35 citations)

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.

Best Publications

Dropout: a simple way to prevent neural networks from overfitting

Nitish Srivastava;Geoffrey Hinton;Alex Krizhevsky;Ilya Sutskever.
Journal of Machine Learning Research (2014)

22730 Citations

Reducing the Dimensionality of Data with Neural Networks

G. E. Hinton;R. R. Salakhutdinov.
Science (2006)

13175 Citations

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)

7271 Citations

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Kelvin Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho.
arXiv: Learning (2015)

4026 Citations

Probabilistic Matrix Factorization

Andriy Mnih;Ruslan R Salakhutdinov.
neural information processing systems (2007)

3671 Citations

Restricted Boltzmann machines for collaborative filtering

Ruslan Salakhutdinov;Andriy Mnih;Geoffrey Hinton.
international conference on machine learning (2007)

1867 Citations

Neighbourhood Components Analysis

Jacob Goldberger;Geoffrey E. Hinton;Sam T. Roweis;Ruslan R Salakhutdinov.
neural information processing systems (2004)

1758 Citations

Human-level concept learning through probabilistic program induction.

Brenden M. Lake;Ruslan Salakhutdinov;Joshua B. Tenenbaum.
Science (2015)

1473 Citations

Multimodal Learning with Deep Boltzmann Machines

Nitish Srivastava;Ruslan Salakhutdinov.
neural information processing systems (2012)

1439 Citations

Bayesian probabilistic matrix factorization using Markov chain Monte Carlo

Ruslan Salakhutdinov;Andriy Mnih.
international conference on machine learning (2008)

1414 Citations

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