R
Ruslan Salakhutdinov
Researcher at Carnegie Mellon University
Publications - 457
Citations - 142495
Ruslan Salakhutdinov is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 107, co-authored 410 publications receiving 115921 citations. Previous affiliations of Ruslan Salakhutdinov include Carnegie Learning & University of Toronto.
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Geometric Capsule Autoencoders for 3D Point Clouds
TL;DR: The novel Multi-View Agreement voting mechanism is used to discover an object's canonical pose and its pose-invariant feature vector, and the benefits of having multiple votes agree are shown.
Proceedings Article
Neural Methods for Point-wise Dependency Estimation
Yao-Hung Hubert Tsai,Han Zhao,Han Zhao,Makoto Yamada,Louis-Philippe Morency,Ruslan Salakhutdinov +5 more
TL;DR: This work focuses on estimating point-wise dependency (PD), which quantitatively measures how likely two outcomes co-occur, and develops two methods (free of optimizing MI variational bounds): Probabilistic Classifier and Density-Ratio Fitting.
Posted Content
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations
TL;DR: On several datasets that require capturing long-term dependency structure, it is shown that path-SGD can significantly improve trainability of ReLU RNNs compared to RNN's trained with SGD, even with various recently suggested initialization schemes.
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Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-Alignment
TL;DR: An effective solution based on meta-alignment, a novel method to align representation spaces using strongly and weakly paired cross-modal data while ensuring quick generalization to new tasks across different modalities is proposed.
Proceedings Article
Words or Characters? Fine-grained Gating for Reading Comprehension
TL;DR: The authors proposed a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words for reading comprehension, achieving state-of-the-art results on the Children's Book Test dataset.