Top ML Papers of the Week (Jan 16-22)
Welcome to the second edition of the?ML Papers of the Week?where we highlight top machine learning papers every week. Below are the top papers of last week (January 16-22):
1) Google AI Research Recap (2022 Edition) - an excellent summary of some notable research Google AI did in 2022. (Blog | Tweet )
2) LLMs from a cognitive science perspective - a review paper on the capabilities of LLMs from a cognitive science perspective. (Paper | Tweet )
3) AdA - an agent trained at scale that leads to a general in-content learning algorithm able to adapt to open-ended embodied 3D problems. (Paper | Tweet )
4) AtMan - an approach to help provide explanations of generative transformer models through memory-efficient attention manipulation. (Paper | Tweet )
5) Everything is Connected: Graph Neural Networks - a short overview of key concepts in graph representation learning. (Paper | Tweet )
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6) GLIGEN (Grounded-Language-to-Image Generation) - an approach that extends the functionality of existing pre-trained text-to-image diffusion models by enabling conditioning on grounding inputs. (Paper | Project | Tweet )
7) InstructPix2Pix - proposes a method with the capability of editing images from human instructions. (Paper | Tweet )
9) D-Adaptation - a new method for automatically adjusting the learning rate during training, applicable to more than a dozen diverse ML problems. (Paper | Tweet )
10) RecolorNeRF - a user-friendly color editing approach for the neural radiance field to achieve a more efficient view-consistent recoloring. (Paper | Tweet )
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