544 citations · 919 across the 7 of their papers we have counts for
7 papers
AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark
Wenhao Chai, Enxin Song, Yilun Du +6
Video detailed captioning is a key task which aims to generate comprehensive and coherent textual descriptions of video content, benefiting both video understanding and generation.…
DECORE: Deep Compression with Reinforcement Learning
Manoj Alwani, Yang Wang, Vashisht Madhavan
Deep learning has become an increasingly popular and powerful methodology for modern pattern recognition systems. However, many deep neural networks have millions or billions of pa…
Scaling MAP-Elites to Deep Neuroevolution
Cédric Colas, Joost Huizinga, Vashisht Madhavan +1
Quality-Diversity (QD) algorithms, and MAP-Elites (ME) in particular, have proven very useful for a broad range of applications including enabling real robots to recover quickly fr…
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents
Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu +8
Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less…
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Fisher Yu, Haofeng Chen, Xin Wang +5
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Re…
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti +3
Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algo…
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such +3
Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challeng…