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Claes-Fredrik Mannby
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Transfer learning has revolutionized computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Fine-tuned Language Models (FitLaM), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a state-of-the-art language model. Our method significantly outperforms the state-of-the-art on five text classification tasks, reducing the error by 18-24% on the majority of datasets. We open-source our pretrained models and code to enable adoption by the community.
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In order to advance the state-of-the-art in image compression with #MachineLearning, we are co-sponsoring the Workshop and Challenge on Learned Image Compression (CLIC) at #CVPR2018 in June. We hope you'll enter, and may the tiniest win!
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"The security of pretty much every computer on the planet has just gotten a lot worse, and the only real solution -- which of course is not a solution -- is to throw them all away and buy new ones."
https://www.schneier.com/blog/archives/2018/01/spectre_and_mel_1.html
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This seems bad, and points to a major issue "It is clear that the Same-Origin Policy is a poor fit for trust relationships on the web today, and that other security defenses would help. But there is another dilemma for browser vendors: should they defend against this and other similar vulnerabilities, or view it as the publisher’s fault for embedding the third party at all?"

https://freedom-to-tinker.com/2017/12/27/no-boundaries-for-user-identities-web-trackers-exploit-browser-login-managers/
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