Tag: pytorch

Pytorch implementation of CoordConv https://t.co/hRo7b2VdRg #deeplearning #machinelearning #ml #ai #neuralnetworks #pytorch

— PyTorch Best Practices (@PyTorchPractice) July 14, 2018

"remarkable architecture search efficiency (with 4 GPUs: 2.83% error on CIFAR10 in 1 day; 56.1 perplexity on PTB in 6 hours)"
Try it now from: https://t.co/8khIix99mahttps://t.co/HFuW0II5Hl

— PyTorch (@PyTorch) June 26, 2018

Check out Distiller, our @PyTorch based package for neural network compression research at https://t.co/px4g8yCrjS https://t.co/Fews6mK3X6

— Gal Novik (@gal_novik) June 22, 2018

Here's a sneak peek at our new Federated Learning interfaces using @PyTorch and PySyft.

Try Federated Learning Using OpenMined: https://t.co/bSMgUZjgL1

Come Join our Slack: https://t.co/e1avrEWtIo pic.twitter.com/sHeQ2568gs

— OpenMined (@openminedorg) June 21, 2018

I'm happy to announce the first release of ignite - a high level library for @pytorch helping you write compact but full-featured training loops in a few lines of code! Check it out at https://t.co/d9BBOEXMXF

— Alykhan Tejani (@alykhantejani) June 20, 2018

Apex is a PyTorch extension from @nvidia that makes it easy to use mixed-precision training and use Volta Tensor Cores to full potential. Read more at: https://t.co/9xuOLAyGJT

— PyTorch (@PyTorch) June 19, 2018

Congrats @jnhwkim and team on winning the VQA challenge at #CVPR2018.
Read their paper "Bilinear Attention Networks" at https://t.co/sqXNysYxcv
PyTorch based code at: https://t.co/XIOwVHfLtO https://t.co/FGOR3Pe9Rl

— PyTorch (@PyTorch) June 19, 2018

@PyTorch geometric by @rusty1s et al.https://t.co/rhyvOyjppS pic.twitter.com/mOWWuFClUD

— Brandon Amos (@brandondamos) June 18, 2018

Tools for PyTorch https://t.co/swXLBCQQuv #pytorch #deeplearning #machinelearning #ml #ai #neuralnetworks

— PyTorch Best Practices (@PyTorchPractice) June 16, 2018

NCRF++ : A neural CRF++ toolkit for sequence labeling tasks. Works pretty much similar to Taku's CRF++ package, but built with #PyTorch! #nlproc https://t.co/05ZL6bEcoO pic.twitter.com/ME8k7ruMdA

— Delip Rao (@deliprao) June 16, 2018

code and pre-trained models to reproduce the recent paper "Scaling Neural Machine Translation" (https://t.co/mrRDmlwax1) where we train on up to 128 GPUs with half precision floating point operations as well as delayed batching.

— PyTorch (@PyTorch) June 16, 2018

FairSeq Toolkit - Major Update
- Distributed Training
- Transformer models (big Transformer on WMT Eng-German in < 5 hours on DGX-1)
- Fast Inference: translations @ 92 sent/sec for big Transformer
- Story Generation
Read more at Michael Auli's post: https://t.co/eptKDuh0WI pic.twitter.com/d4OtJZpdFw

— PyTorch (@PyTorch) June 16, 2018