#3 동물과 소통할 미래를 위하여

dolphin handler makes the signal for "together" with her hands, followed by "create". The two trained dolphins disappear underwater, exchange sounds and then emerge, flip on to their backs and lift their tails. They have devised a new trick of their own and performed it in tandem, just as requested.

본 논문의 목표는 양질의 단어 벡터를 큰 data set 또는 vocabulary로부터 학습하는 방법을 소개하는 것입니다. 단어를 벡터로 표현하는 방식을 통해 유사한 의미의 단어가 근처에 위치할 뿐 아니라, multiple degrees of similarity(syntactic, semantic, phonetic 등의 분야의 feature를 공유)를 가질 수 있도록 만들어줍니다.

A system like AlphaFold which can accurately predict the structure of proteins is accelerating progress in many areas of research that are important for society. Our partners are already using AlphaFold to accelerate progress on important real-world problems. For instance, the Drugs for Neglected Diseases initiative (DNDi) is advancing drug discovery for neglected diseases, such as Chagas disease and leishmaniasis, which impact millions within poor and vulnerable communities.

Today, in partnership with EMBL's European Bioinformatics Institute (EMBL-EBI), we're now releasing predicted structures for nearly all catalogued proteins known to science, which will expand the AlphaFold DB by over 200x - from nearly 1 million structures to over 200 million structures - with the potential to dramatically increase our understanding of biology.

What is really needed to make an existing 2D GAN 3D-aware? To answer this question, we modify a classical GAN, i.e., StyleGANv2, as little as possible. We find that only two modifications are absolutely necessary: 1) a multiplane image style generator branch which produces a set of alpha maps conditioned on their depth; 2) a pose-conditioned discriminator.