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Papers of the Month with Charlie Blake, Research Engineer at Graphcore
Manage episode 399483355 series 3533871
Helen Byrne에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Helen Byrne 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Charlie Blake from Graphcore’s research team discusses their AI Papers of the Month for January 2024.
Graphcore research has been collating and sharing a review of the most consequential AI papers internally, every month, for a number of years.
Now – for the first time – the research team is making this valuable resource public, to help the wider AI community keep up-to-date with the most exciting breakthroughs.
Papers of the Month for January 2024 (with some work from December 2023) includes:
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding
https://arxiv.org/abs/2312.05328
Authors: Talfan Evans, Shreya Pathak, Hamza Merzic, et al. (Google DeepMind, UCL)
Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
https://arxiv.org/abs/2401.00448
Authors: Nikhil Sardana and Jonathan Frankle (MosaicML)
Analyzing and Improving the Training Dynamics of Diffusion Models
https://arxiv.org/abs/2312.02696
Authors: Tero Karras et al. (Nvidia, Aalto University)
Solving olympiad geometry without human demonstrations
https://www.nature.com/articles/s41586-023-06747-5
Authors: Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, He He and Thang Luong (Google DeepMind, New York University)
To read about January’s Papers of the Month, visit the Graphcore blog.
https://www.graphcore.ai/posts/great-teachers-and-beyond-chinchilla-papers-of-the-month-jan-2024
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