Object detection using @fastai with only 300 examples in the training set.
— Radek Osmulski (@radekosmulski) December 31, 2018
Total train time: 3 min
Please note the model definition - again very few lines of code. Annotated images myself - details in the NB. Shown in red are predictions.https://t.co/A1oYorlVOB pic.twitter.com/L8JvPzKJw8
Siamese network using @fastdotai for the @kaggle Whale Identification Challenge is here! https://t.co/0QNGcw7kr3
— Radek Osmulski (@radekosmulski) December 28, 2018
This is the unabridged, slightly messy version, but I feel okay about this. We are stepping outside what @fastdotai does out of the box and at this point I don't feel
Creating super slow motion videos by predicting missing frames using a neural network, instead of simple interpolation. With code.
— Reza Zadeh (@Reza_Zadeh) December 27, 2018
Code: https://t.co/FGm24v8bhp
Project: https://t.co/LKsMqmykNA pic.twitter.com/HrL8zM4cXa
We're releasing tutorials on our work using CCA to compare and probe representations in deep neural networks: https://t.co/DgXmb6XIIj There are Jupyter notebooks overviewing the technique, descriptions of results, and discussions of open problems. We hope this is useful resource!
— Maithra Raghu (@maithra_raghu) December 20, 2018
this repo lets you implant random things into paintings https://t.co/yT1LX4jYNL pic.twitter.com/VAiVu2Tja7
— Gene Kogan (@genekogan) December 14, 2018
I tried it on a YouTube video clip called “We followed a Waymo self-driving car for miles, here's what we saw” and here's what it generated: https://t.co/mX5AoEz248 pic.twitter.com/bR1043OSmr
— hardmaru (@hardmaru) December 12, 2018
🤝 Another great deck from @ShirinGlander…
— Mara Averick (@dataandme) December 6, 2018
📽 "Trust in ML models. Slides from TWiML & AI EMEA Meetup”https://t.co/Zpo1YOBDWs #MachineLearning
➕links to #rstats code for iml, caret & lime pic.twitter.com/a1npPMkh57
code2vec: Learning Distributed Representations of Code #POPL2019
— ML Review (@ml_review) December 6, 2018
By @urialon1@omerlevy_ @yahave
Demohttps://t.co/OvpSLOtTCp
ArXivhttps://t.co/ZQsImneOsg
Githubhttps://t.co/ylECFYOhwn pic.twitter.com/30qn1hUvTW
Advances in few-shot learning: reproducing results in PyTorch by Oscar Knagg https://t.co/U4g0oTwn6J #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #pytorch
— PyTorch Best Practices (@PyTorchPractice) December 1, 2018
Visualizing the Loss Landscape of Neural Nets #NIPS2018
— ML Review (@ml_review) November 27, 2018
By @ljk628
Explores how network architecture affects the loss landscape and its effect of generalization
PyTorchhttps://t.co/qb7F1AorsO
ArXivhttps://t.co/5BTWX9ifNS pic.twitter.com/Dscrlb44rS
Deep Frank-Wolfe For Neural Network Optimization
— Brandon Amos ✈️ #NeurIPS2018 (@brandondamos) November 26, 2018
Leonard Berrada, Andrew Zisserman, M. Pawan Kumarhttps://t.co/S0iiZ1iD5G@PyTorch code: https://t.co/1jyZLqZxJo pic.twitter.com/jwAiHbB5cq
Smooth Loss Functions for Deep Top-k Classification
— Brandon Amos ✈️ #NeurIPS2018 (@brandondamos) November 26, 2018
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
ICLR 2018https://t.co/VGXPf8HfLg@PyTorch code: https://t.co/roTTlXuS7F pic.twitter.com/1Vu0CvXiDp
