Papers covering a few of my Deep Learning Projects.
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Updated
Dec 23, 2020
Papers covering a few of my Deep Learning Projects.
The proceedings of top conference in 2019 on the topic of Reinforcement Learning (RL), including: AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more.
Official Website for the Workshop on Advancing Neural Networks Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ICML 2024, WANT@NeurIPS 2023)
Resources for the paper titled "Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts". Accepted at NeurIPS 2022.
Implementation of Selected Published Papers from AI, RL, NLP Conferences and reputed Journals
(One of) the official code for our NeurIPS'22 paper Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
Unsupervised Topic Modelling project using Latent Dirichlet Allocation (LDA) on the NeurIPS papers. Built as part of the final project for McGill AI Society's Accelerated Introduction to Machine Learning Course (MAIS 202).
A robust classifier for few-training-data problem based on a distributionally robust optimization framework
Code for the paper "An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context".
End-to-end demonstration of an autoencoder compression algorithm for plasma ion data from the MMS/FPI space instrument. Accompanies the publications da Silva et al., Frontiers in Astronomy and Space Sciences (2023) and da Silva et al., NeurIPS (2022)
This is the code of NeurIPS'21 paper "Towards Enabling Meta-Learning from Target Models".
Solution for the Trojan Detection Challenge (TDC2022 - https://trojandetection.ai) as part of NeurIPS 2022
[NeurIPS2023] PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas(or 360-degree image)
Crawl & Visualize NeurIPS 2022 Data from OpenReview
My submission for NeurIPS 2023 LLM Efficiency Challenge.
Fork of Official Implementation of Meta-Learning to Improve Pre-Training, NeurIPS'21 Poster. (https://arxiv.org/abs/2111.01754)
Proof-of-principle application of Gaussian process modeling to gamma-ray analyses. Code repository associated with the paper https://arxiv.org/abs/2010.10450.
Official implementation of "Relational Proxies: Emergent Relationships as Fine-Grained Discriminators", NeurIPS 2022.
[NeurIPS 2023 Spotlight] The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning
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