Dr. M. Umut DEMİREZEN's repositories

cryosphere-links

This is a link list with awesome data, models, and tools around the cryosphere.

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modulus-sym

Repo of abstracted workflow comparable to existing numerical solvers for training data-free and data-driven AI surrogates

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spectral_bias

Investigation of spectral bias through Fourier transform.

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slayerPytorch

PyTorch implementation of SLAYER for training Spiking Neural Networks

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glaciers

Authors' implementation of the article "Simulation, Modeling, and Authoring of Glaciers", SIGGRAPH Asia 2020

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mat_interp

mat_interp

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wire

wavelet implicit neural representations

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EvidentialDL

A package implementing evidential deep learning layers for classification and regression

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snntorch_tutorial

Tutorial for the Python package snnTorch, to be used for Senior Capstone project in creating neural networks

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graph-pooling-papers

Papers on Graph Pooling (GNN-Pooling)

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SNN-training-using-bayesian-optimization

training SNN using voxel grid based preprocessing by snntorch and tonic

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neupy

NeuPy is a Tensorflow based python library for prototyping and building neural networks

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awesome-neuroscience

A curated list of awesome neuroscience libraries, software and any content related to the domain.

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snntorch-implement4spike-deeplab

snn implementation for spike-deeplab and spike-fcn

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DF-2022

Code for DF 2022

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fourier_neural_operator

Use Fourier transform to learn operators in differential equations.

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pytorch_forward_forward

Implementation of Hinton's forward-forward (FF) algorithm - an alternative to back-propagation

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unreasonable_effective_der

Supplementary material to reproduce "The Unreasonable Effectiveness of Deep Evidential Regression"

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ECNP

Code for the Evidential Conditional Neural Processes

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Training-NNs-without-Backpropagation

Implementing an ADMM based optimization approach as an alternative to backpropagation for training neural networks.

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dlkoopman

Koopman theory implemented using deep learning

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ModelingNeuralCircuits

PDF and Code to accompany the textbook "Modeling Neural Circuits Made Simple"

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