Dmitriy Serdyuk (dmitriy-serdyuk)

dmitriy-serdyuk

Geek Repo

Company:@mila-iqia

Location:Montreal

Home Page:https://serdyuk.cc

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Dmitriy Serdyuk's repositories

arxiv2kindle

A simple script to recompile arxiv papers into kindle-like format

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carat

A library for numerical and scientific computation.

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test-shub

Test singularity hub

abstreet

A traffic simulation game exploring how small changes to roads affect cyclists, transit users, pedestrians, and drivers.

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arg

Argparser generator for declarative style configurations

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bokeh

Interactive Web Plotting for Python

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cortex

A machine learning library for PyTorch

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ddst

My zsh theme

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dmitriy-serdyuk.github.io

Github io page (outdated, now moved to gitlab)

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einops

Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

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hugo-academic

The website designer for Hugo. Build and deploy a beautiful website in minutes :rocket:

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Large-Scale-VRD.pytorch

Implementation for the AAAI2019 paper "Large-scale Visual Relationship Understanding"

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MONet-pytorch

Burgess et al. "MONet: Unsupervised Scene Decomposition and Representation"

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neuraltalk2.pytorch

image captioning model in pytorch(finetunable cnn in branch "with_finetune")

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orion

Asynchronous Distributed Hyperparameter Optimization for Deep Neural Networks. Previously known as hyperopt or metaopt.

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pase

Problem Agnostic Speech Encoder

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prototypical-network-pytorch

A re-implementation of "Prototypical Networks for Few-shot Learning"

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pyenv

Simple Python version management

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pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

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pytorch-kaldi

pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.

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pytorch_MLP_for_ASR

This code implements a basic MLP for speech recognition. The MLP is trained with pytorch, while feature extraction, alignments, and decoding are performed with Kaldi. The current implementation supports dropout and batch normalization. An example for phoneme recognition using the standard TIMIT dataset is provided.

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travis-ci

an example builder to build a container with Travis CI, and push to a Singularity Registry Server (or other endpoint)

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