Search Results for author: Rupesh Kumar Srivastava

Found 11 papers, 9 papers with code

Reward-Weighted Regression Converges to a Global Optimum

1 code implementation19 Jul 2021 Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber

Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework.

ClipUp: A Simple and Powerful Optimizer for Distribution-based Policy Evolution

1 code implementation5 Aug 2020 Nihat Engin Toklu, Paweł Liskowski, Rupesh Kumar Srivastava

In these algorithms, gradients of the total reward with respect to the policy parameters are estimated using a population of solutions drawn from a search distribution, and then used for policy optimization with stochastic gradient ascent.

Continuous Control

Training Agents using Upside-Down Reinforcement Learning

7 code implementations5 Dec 2019 Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, Jürgen Schmidhuber

Many of its general principles are outlined in a companion report; the goal of this paper is to develop a practical learning algorithm and show that this conceptually simple perspective on agent training can produce a range of rewarding behaviors for multiple episodic environments.

ContextVP: Fully Context-Aware Video Prediction

no code implementations ECCV 2018 Wonmin Byeon, Qin Wang, Rupesh Kumar Srivastava, Petros Koumoutsakos

Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions.

Video Prediction

Recurrent Highway Networks

5 code implementations ICML 2017 Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber

We introduce a novel theoretical analysis of recurrent networks based on Gersgorin's circle theorem that illuminates several modeling and optimization issues and improves our understanding of the LSTM cell.

Language Modelling

Binding via Reconstruction Clustering

1 code implementation19 Nov 2015 Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber

Disentangled distributed representations of data are desirable for machine learning, since they are more expressive and can generalize from fewer examples.

Denoising Representation Learning

Training Very Deep Networks

3 code implementations NeurIPS 2015 Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber

Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success.

Image Classification

Highway Networks

3 code implementations3 May 2015 Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber

There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success.

Language Modelling

LSTM: A Search Space Odyssey

12 code implementations13 Mar 2015 Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber

Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995.

Handwriting Recognition Music Modeling +1

Understanding Locally Competitive Networks

no code implementations5 Oct 2014 Rupesh Kumar Srivastava, Jonathan Masci, Faustino Gomez, Jürgen Schmidhuber

Recently proposed neural network activation functions such as rectified linear, maxout, and local winner-take-all have allowed for faster and more effective training of deep neural architectures on large and complex datasets.

Cannot find the paper you are looking for? You can Submit a new open access paper.