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Datasets

• Open Images v4. The biggest CV dataset EVER. I played with v2 or v3 - it was very … random and low quality at times;

Articles

• GANS + hentai genitals;
• Understanding optimization in deep learning by analyzing trajectories of gradient descent;
• Cats + RPI + mobile networks;
• Cleaning texts for OCR with UNET;
• Online speaker diarization by Google;
• A review of attention mechanisms
• Deep Latent-Variable Models for Natural Language
• Looks like PyTorch is updating their distributed docs, but little info on NCCL;

NLP:

• A move to start caring about explaining LSTMs, no real ideas though;
• Statistical MT with PyTorch;
• There is even a book about Deep encoders decoders for NLP;

Auto-Encoding Dictionary Definitions into Consistent Word Embeddings

• FAIR, link;
• Looks like a cool and novel embedding generation technique;
  
• “Coffee” is related to “cup” as coffee is a beverage often drunk in a cup, but “coffee” is not similar to “cup” in that coffee is a beverage and cup is a container;
• Key:
  • Model learns to compute word embeddings by processing dictionary definition;
  • Dictionaries are very common in almost any language;
  • Goal is to obtain better rep- resentations for more languages with less effort;
• Desirable for future natural language understanding systems;
• The model consists of a definition autoencoder: an LSTM processes the definition of a word to yield its corresponding word embedding;
• Model should be able to recover the definition from the embedding;

RL:

• Amazing RL spinning up resource;

Hardware

• NvLink ML benchmarks - 2x2080Ti - only + 20%-25% vs. 2x1080Ti;

Competitions

• New Kaggle quora toxic comment competition - kernels only - meh;

TDS picks

• Starter ideas for recommender systems;
• BERT explained;