Avinash (koriavinash1)

koriavinash1

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biomedia-mira
MiRL-IITM
ShaastraWebops

Avinash 's repositories

DeepBrainSeg

Fully automatic brain tumour segmentation using Deep 3-D convolutional neural networks

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BioExp

Explainability of Deep Learning Models

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BraTs2018

Brats segmentation pytorch

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pgm

Graph: Representation, Learning, and Inference Methods

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BioExp_Experiments

To check causality framework end2end

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PyTorch-GAN

PyTorch implementations of Generative Adversarial Networks.

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bayes_gmm

Bayesian Gaussian mixture models in Python.

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ar-cutpaste

Cut and paste your surroundings using AR

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carbon-registry

National Carbon Credit Registry Digital Public Good (DPG) by Digital For Climate (D4C) collaboration. Code coordinated by ExO/CDO & BPPS/Climate.

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deepscm

Repository for Deep Structural Causal Models for Tractable Counterfactual Inference

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deepstream_python_apps

DeepStream SDK Python bindings and sample applications

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DiffAN

Diffusion Models for Causal Discovery

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dino

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

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ENCO

Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"

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keract

Layers Outputs and Gradients in Keras. Made easy.

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mercure

mercure DICOM Orchestrator

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MetadataNorm

Repository for the paper "Metadata Normalization"

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mml-autumn-2022

Materials for Autumn 2022 Mathematics for Machine Learning course.

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pytorch-YOLOv4

PyTorch ,ONNX and TensorRT implementation of YOLOv4

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segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

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Symbolic-Pursuit

Github for the NIPS 2020 paper "Learning outside the black-box: at the pursuit of interpretable models"

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yolov3-keras-tf2

yolo(v3/v4) implementation in keras and tensorflow 2.2

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YOLOX

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

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