NNAISENSE

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.

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Code for reproducing experiments in Model-Based Active Exploration, ICML 2019

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A mini library for Policy Gradients with Parameter-based Exploration, with reference implementation of the ClipUp optimizer (https://arxiv.org/abs/2008.02387) from NNAISENSE.

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The Winning Solution for the Learning To Run Challenge 2017

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Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer

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Code repository for our paper: Deep Iterative Surface Normal Estimation

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Learning Action-Value Gradients in Model-based Policy Optimization

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Pytorch extension: Batch-wise eigencomputation for symmetric 3x3 matrices

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