Search Results for author: Nitish Srivastava

Found 16 papers, 5 papers with code

An Attention Free Transformer

6 code implementations28 May 2021 Shuangfei Zhai, Walter Talbott, Nitish Srivastava, Chen Huang, Hanlin Goh, Ruixiang Zhang, Josh Susskind

We introduce Attention Free Transformer (AFT), an efficient variant of Transformers that eliminates the need for dot product self attention.

Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

no code implementations17 May 2021 Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua Susskind, Jian Zhang, Ruslan Salakhutdinov, Hanlin Goh

Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration.

Offline RL Q-Learning

Unconstrained Scene Generation with Locally Conditioned Radiance Fields

1 code implementation1 Apr 2021 Terrance DeVries, Miguel Angel Bautista, Nitish Srivastava, Graham W. Taylor, Joshua M. Susskind

In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose scenes into a collection of many local radiance fields that can be rendered from a free moving camera.

Scene Generation

Uncertainty Weighted Offline Reinforcement Learning

no code implementations1 Jan 2021 Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua M. Susskind, Jian Zhang, Ruslan Salakhutdinov, Hanlin Goh

Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration.

Offline RL Q-Learning

On the generalization of learning-based 3D reconstruction

no code implementations27 Jun 2020 Miguel Angel Bautista, Walter Talbott, Shuangfei Zhai, Nitish Srivastava, Joshua M. Susskind

State-of-the-art learning-based monocular 3D reconstruction methods learn priors over object categories on the training set, and as a result struggle to achieve reasonable generalization to object categories unseen during training.

3D Reconstruction

Capsules with Inverted Dot-Product Attention Routing

3 code implementations ICLR 2020 Yao-Hung Hubert Tsai, Nitish Srivastava, Hanlin Goh, Ruslan Salakhutdinov

We introduce a new routing algorithm for capsule networks, in which a child capsule is routed to a parent based only on agreement between the parent's state and the child's vote.

Image Classification

Geometric Capsule Autoencoders for 3D Point Clouds

no code implementations6 Dec 2019 Nitish Srivastava, Hanlin Goh, Ruslan Salakhutdinov

The pose encodes where the entity is, while the feature encodes what it is.

Initialization Strategies of Spatio-Temporal Convolutional Neural Networks

no code implementations25 Mar 2015 Elman Mansimov, Nitish Srivastava, Ruslan Salakhutdinov

We propose a new way of incorporating temporal information present in videos into Spatial Convolutional Neural Networks (ConvNets) trained on images, that avoids training Spatio-Temporal ConvNets from scratch.

Exploiting Image-trained CNN Architectures for Unconstrained Video Classification

no code implementations13 Mar 2015 Shengxin Zha, Florian Luisier, Walter Andrews, Nitish Srivastava, Ruslan Salakhutdinov

Our proposed late fusion of CNN- and motion-based features can further increase the mean average precision (mAP) on MED'14 from 34. 95% to 38. 74%.

Classification Event Detection +3

Unsupervised Learning of Video Representations using LSTMs

9 code implementations16 Feb 2015 Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov

We further evaluate the representations by finetuning them for a supervised learning problem - human action recognition on the UCF-101 and HMDB-51 datasets.

Action Recognition

Learning Generative Models with Visual Attention

no code implementations NeurIPS 2014 Yichuan Tang, Nitish Srivastava, Ruslan Salakhutdinov

Our model is a proper graphical model where the 2D Similarity transformation is a part of the top-down process.

Modeling Documents with Deep Boltzmann Machines

no code implementations26 Sep 2013 Nitish Srivastava, Ruslan R. Salakhutdinov, Geoffrey E. Hinton

We introduce a Deep Boltzmann Machine model suitable for modeling and extracting latent semantic representations from a large unstructured collection of documents.

Document Classification General Classification

Multimodal Learning with Deep Boltzmann Machines

no code implementations NeurIPS 2012 Nitish Srivastava, Ruslan R. Salakhutdinov

Our experimental results on bi-modal data consisting of images and text show that the Multimodal DBM can learn a good generative model of the joint space of image and text inputs that is useful for information retrieval from both unimodal and multimodal queries.

Information Retrieval Semantic Similarity +1

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