Speech

• Acoustic event detection - quantization, distillation, separable covolutions to make models smaller and faster;
• Surprise surprise - tuning a large model for speakers with speech impairments improves quality;
• Google’s state-of-the-art speaker duarization;
• How CMU Sphinx works;
• A very cool and from the likes of it - working paper - real time voice cloning;

NLP

• NLP trends from ACL 2019;
• Yet another better BERT;
• The Illustrated GPT-2 (Visualizing Transformer Language Models);
• Facebook finally embraces FastText for misspellings;
• Key idea - use pre-trained transformers for distillation;
• Add robustness to - NMT by adding adversarial examples to train data;
• Transformer with 8B parameters;
• State of transfer learning in NLP - kind of meh article;
• OpenAI huge GPT follow-up;

ML / market

• Ben Evans about CV;
• Ben Evans newsletter (if you are still not aware );
• Fast AI about surveillance;
• CV has matured enough to solve some non-CV tasks;
• Google does NAS on TPUs;
• On-device personalization;
• Down-to-earth explanation of IT market in Russia;
• New version of Adam - RAdam;
• Real-time hand gesture tracking on mobile;

Cool libraries / papers / etc

• Inplace BatchNorm - memory usage reduction for CV;
  QRNN explanation;
• Yet another LSTM replacement - SRU. Similar to QRRN - it requires additional dependencies;
• Cycle consistency for repeating actions in video;
• Dealing with artifacts in medical CV - just train a model to filter them out;
• Useful DS sampling algorithms;
• Wavefunction collapse algorithm;

Python / coding

• Make kwargs show properly in your python documentation;
  https://blog.usejournal.com/what-is-the-difference-between-a-hard-link-and-a-symbolic-link-8c0493041b62
• A cool mixture;
  Yet another Python pattern collection;

Cool random stuff

• Automated fly brain slicing with CNNs;
• Idea - hardware-encoded limitations for embedded ML devices;
• Lottery ticket hypothesis revisited;
• Advances in conversational AI by - FAIR:
  
  • Key problem - dialogue consistency;
  • “Сreated a new NLI data set called Dialogue NLI, which is used to both improve and evaluate the consistency of dialogue models”;