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Blog posts

• NER for people from Wikipedia tags;
• Snapchat has a face swap CycleGAN-like feature?;
  Ben Evans on Google keynote;
• Blogging advice from fast.ai;
• SWA now featured in torch contrib;
• A tutorial on normalizing flows;
• Google makes voice-to-voice translation w/o intermediate text representation - spectrogram to spectrogram + vocoder;
• Chinese Face++ raises US$750m;
• Pre-training BERT on TPUs:
  • Pre-training a BERT-Base model on a TPUv2 will take about 54 hours;
• GitHub sponsors = patreon?;

Interesting papers

• New paper from Ian GoodFellow on reducing the amount of annotation required for DL:
  • Use supervised labels as well as semi-supervised predicted labels;
  • Sharpen the resulting predicted distributions;
  • Use mix-up;
  • Have 2 losses - for supervised and semi-supervised parts of the dataset;
• Parallel Neural Text-to-Speech. The paper does not recognize all the progress made by IAF vocoders in 2018, but the idea is cool - distill a recurrent spectrogram network into a non recurrent one;
• New attention blocks for CV?
  • Code:
  • Paper;
• Mobile network search = lottery ticket?;

Internet / statistics

• Huawei is one of smartphone leaders in Europe;
• Effectiveness of basic methods for account protection;

... adding a recovery phone number to your Google Account can block up to 100% of automated bots, 99% of bulk phishing attacks, and 66% of targeted attacks that occurred during our investigation.... an SMS code sent to a recovery phone number helped block 100% of automated bots, 96% of bulk phishing attacks, and 76% of targeted attacks.On-device prompts, a more secure replacement for SMS, helped prevent 100% of automated bots, 99% of bulk phishing attacks and 90% of targeted attacks.

• SF bans local gonvernment agencies from using face recognition;
  https://www.iab.com/wp-content/uploads/2019/05/Full-Year-2018-IAB-Internet-Advertising-Revenue-Report.pdf

Cool things

• A competition to map what people see into their MRI signals using CNNs. Small set (~200 images * 15 people), but very cool!;
• A paper that aims to visualize the internals of a trained BERT network?;