2318 results for "earn" across all locations
Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients
J. Lehman, J. Chen, J. Clune, K. Stanley
While neuroevolution (evolving neural networks) has a successful track record across a variety of domains from reinforcement learning to artificial life, it is rarely applied to large, deep neural networks. A central reason is that while random mutation generally works in low dimensions, a random perturbation of thousands or millions of weights is likely to break existing functionality, providing no learning signal even if some individual weight changes were beneficial. […] [PDF]
The Genetic and Evolutionary Computation Conference (GECCO), 2018
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
P. Molino, H. Zheng, Y.-C. Wang
For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues. This paper proposes COTA, a system to improve speed and reliability of customer support for end users through automated ticket classification and answers selection for support representatives. […] [PDF]
ACM SIGKDD International Conference on Knowledge Discovery and Data Science (KDD), 2018
TorontoCity: Seeing the World With a Million Eyes
S. Wang; M. Bai; G. Mattyus; H. Chu; W. Luo; B. Yang; J. Liang; J. Cheverie; R. Urtasun; D. Lin.
Despite the substantial progress in recent years, the image captioning techniques are still far from being perfect. Sentences produced by existing methods, e.g. those based on RNNs, are often overly rigid and lacking in variability. […] [PDF]
International Conference on Computer Vision (ICCV), 2017
5 Audiobooks Everyone Should Listen To
5 inspiring audiobooks to help you grow and succeed
A good audiobook can turn your downtime between trips into an empowering motivational session. Here are 5 to get you started.
Go-Explore: a New Approach for Hard-Exploration Problems
A. Ecoffet, J. Huizinga, J. Lehman, K. Stanley, J. Clune
A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma’s Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, which is the dominant method to improve performance on hard-exploration domains. […] [PDF]
2019
Chinese New Year Travel Deal – Up to 30% off Rides in Hong Kong, Macau, and Taiwan
100 Days with Uber Design
The design team here at Uber has grown exponentially, and they are always looking to challenge themselves to stay connected as a team, push their creative boundaries, and be inspired. This year, they took on the 100 Day’s Project and learned a lot about themselves and each other. Check it out.
Embrace your voice this Sexual Assault Awareness Month
Uber has an ongoing commitment to drive change and help prevent sexual assault and domestic violence in our global communities.
VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
R. Wang, J. Clune, K. Stanley
Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural networks to solve difficult reinforcement learning (RL) problems. However, it remains a challenge to analyze and interpret the underlying process of neuroevolution in such high dimensions. To begin to address this challenge, this paper presents an interactive data visualization tool called VINE (Visual Inspector for NeuroEvolution) aimed at helping neuroevolution researchers and end-users better understand and explore this family of algorithms. […] [PDF]
Visualization Workshop at The Genetic and Evolutionary Computation Conference (GECCO), 2018