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Name: Trevor Woods Richardson
Type: User
Company: OAM Quant
Bio: Managing Director, Quantitative Strategies at OAM Quant - previously Data Scientist at FLXAI & Research Assistant at ASU's Interactive Robotics Lab
Name: Trevor Woods Richardson
Type: User
Company: OAM Quant
Bio: Managing Director, Quantitative Strategies at OAM Quant - previously Data Scientist at FLXAI & Research Assistant at ASU's Interactive Robotics Lab
This repository implements a convolutional recurrent neural network that learns the functional mapping between video data and the probability of future collisions. The labels used to train this deep learning algorithm are generated by a self-supervised collision-detection deep learning method.
This repository implements a deep reinforcement learning algorithm designed to utilize uncertainty in robotic model dynamics to learn to avoid future collisions.
Custom built ConvLSTM cell in Tensorflow and PyTorch
This repository implements a general data-driven framework for robotic-collision detection. Train a neural network to regress future states given the current state and action. Stochatsic forward passes are used at inference time in order to produce a belief distribution over the future next state of the robot. An exponentially smoothed norm is used between the set of regressed future states and the ground truth collected in the next time-step.
Deep learning tutorials and education material for robotic scenarios -- Intel Sponsored
Monte Carlo Flow Models for Data Imputation
MisGAN: Learning from Incomplete Data with GANs
This repository tests various recurrent neural network architectures on baseline datasets SeqMNIST and pMNIST.
Disentangle cell state in recurrent models to be a matrix of shape MxN and utilize SVD to extract features and their strengths
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