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  • šŸ‘‹ Hi, Iā€™m Deep Shankar Pandey, (@pandeydeep9), a 5th year Ph.D. Student in Computer Science working at Machine Learning and Data Intensive Computing Lab, RIT
  • šŸ‘€ I am interested in developing Uncertainty Aware, Robust, and trustworthy machine learning models. My research deals with developing deep learning models that can learn from limited data, with a special focus on meta-learning algorithms.
  • šŸ“« How to reach me: Best way to reach me is via email: [email protected]. I check my mail most of the days.

Deep Shankar Pandey's Projects

autograd icon autograd

Efficiently computes derivatives of numpy code.

compressai_local icon compressai_local

A PyTorch library and evaluation platform for end-to-end compression research

convcnp icon convcnp

Implementation of the Convolutional Conditional Neural Process

csrankings icon csrankings

A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide range of areas.

demo icon demo

Demo repo for tutotial articles on Opensource.com

ecnp icon ecnp

Code for the Evidential Conditional Neural Processes

few_shot_meta_learning icon few_shot_meta_learning

Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch

howtotrainyourmamlpytorch icon howtotrainyourmamlpytorch

The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) paper in Pytorch.

marveldataset2016 icon marveldataset2016

This repository is to download the MARVEL dataset 2016 for the publication "Gundogdu E., Solmaz B, Yucesoy V., Koc A., Marvel: A Large-Scale Image Dataset for Maritime Vessels, Asian Conference on Computer Vision (ACCV), 2016".

mazes icon mazes

Dynamic Mazes using different Algorithms

meta-dataset icon meta-dataset

A dataset of datasets for learning to learn from few examples

neural-processes icon neural-processes

This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).

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