7.1k citations · 9.2k across the 24 of their papers we have counts for
5 papers · 1 filter
EvoTorch: Scalable Evolutionary Computation in Python
Nihat Engin Toklu, Timothy Atkinson, Vojtěch Micka +2
Evolutionary computation is an important component within various fields such as artificial intelligence research, reinforcement learning, robotics, industrial automation and/or op…
ClipUp: A Simple and Powerful Optimizer for Distribution-based Policy Evolution
Nihat Engin Toklu, Paweł Liskowski, Rupesh Kumar Srivastava
Distribution-based search algorithms are an effective approach for evolutionary reinforcement learning of neural network controllers. In these algorithms, gradients of the total re…
Highway and Residual Networks learn Unrolled Iterative Estimation
Klaus Greff, Rupesh K. Srivastava, Jürgen Schmidhuber
The past year saw the introduction of new architectures such as Highway networks and Residual networks which, for the first time, enabled the training of feedforward networks with…
LSTM: A Search Space Odyssey
Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník +2
Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks hav…
Understanding Locally Competitive Networks
Rupesh Kumar Srivastava, Jonathan Masci, Faustino Gomez +1
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…