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
Code for 'Chasing Ghosts: Instruction Following as Bayesian State Tracking' published at NeurIPS 2019
CellSignal (rxrx.ai) - NEURIPS 2019 COMPETITION
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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