Code accompanying the paper "Globally Optimal Learning for Structured Elliptical Losses", published at NeurIPS 2019
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Updated
Jan 4, 2020 - Python
Code accompanying the paper "Globally Optimal Learning for Structured Elliptical Losses", published at NeurIPS 2019
Implementation of nested theory of mind belief estimation & implicit communication intrinsic rewards.
Private learning in NeurIPS 2019
Review paper of neuropathic pain diagnosis(NeurIPS 2019) Implemented certain exercises more details in the pdf
Python implementation of the Max-value Entropy Search for Multi-Objective Bayesian Optimization method
My solution to the NeurIPS challenge Learn to Move: Walk Around
Tool for searching NeurIPS2019 proceedings
Implementation and supporting materials for the paper Multi-Criteria Dimensionality Reduction with Applications to Fairness, NeurIPS2019 and extend the prior work "The Price of Fair PCA: One Extra Dimension"
Python implementation of the Structured Graph Learning (SGL) algorithm by Kumar et. al (2019, https://papers.nips.cc/paper/9339-structured-graph-learning-via-laplacian-spectral-constraints)
Source code for "Mo′ States Mo′ Problems: Emergency Stop Mechanisms from Observation"
Submission name: QualcommAI-EfficientNet. MicroNet Challenge (NeurIPS 2019) submission - Qualcomm AI Research
Code for the paper: Foveated Convolutions: Improving Spatial Transformer Networks by Modelling the Retina
CellSignal (rxrx.ai) - NEURIPS 2019 COMPETITION
Code for 'Chasing Ghosts: Instruction Following as Bayesian State Tracking' published at NeurIPS 2019
3rd placed submission to the NeurIPS MineRL competition 2019
Pytorch based implementation of Upside Down Reinforcement Learning (UDRL) by J. Schmidhuber et al.
Code for the paper: Spatial and Colour Opponency in Anatomically Constrained Deep Networks
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