Uber's research projects. Projects in this organization are not built for production usage. Maintainance and supports are limited.
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Plug and Play Language Model implementation. Allows to steer topic and attributes of GPT-2 models.
Code for Go-Explore: a New Approach for Hard-Exploration Problems
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch (ICCV 2019)
[ECCV2020 Oral] Learning Lane Graph Representations for Motion Forecasting
Tool for interactive embeddings visualization
Paired Open-Ended Trailblazer (POET) and Enhanced POET
A binary release of trained deep reinforcement learning models trained in the Atari machine learning benchmark, and a software release that enables easy visualization and analysis of models, and comparison across training algorithms.
The Go programming language. This fork was created for pprof++, a Go profiler with hardware performance monitoring. Read through the link for more detail: https://eng.uber.com/pprof-go-profiler/
Using ideas from product quantization for state-of-the-art neural network compression.
Uber's Multi-Agent Routing Value Iteration Network
Map-Elites based on Evolution Strategies
Variational Auto-Regressive Gaussian Processes for Continual Learning
Constrainted based optimization