Tomas Geffner (@tomasgeffner) 's Twitter Profile
Tomas Geffner

@tomasgeffner

(ex) tennis, (trying) ML. Research Scientist at NVIDIA, views are my own.

Previously, intern at DeepMind, VantAI, Microsoft research, and Amazon AWS.

ID: 1152591872650567680

calendar_today20-07-2019 14:51:31

20 Tweet

107 Followers

1,1K Following

Chai Discovery (@chaidiscovery) 's Twitter Profile Photo

Chai-1 has always been available for commercial use via our server. Today, we're also making Chai-1(r) code and weights available under an Apache 2.0 license, which permits broad commercial use. github.com/chaidiscovery/…

VantAI (@vant_ai) 's Twitter Profile Photo

📢 Join us next week for a talk with Karsten Kreis and Tomas Geffner on their recent paper Proteina: Scaling Flow-based Protein Structure Generative Models. As always with Michael Bronstein & Bruno Correia When: Fri, Mar 14 - 5p CET/11a ET Sign-up: genaiindrugdiscovery.com

📢 Join us next week for a talk with <a href="/karsten_kreis/">Karsten Kreis</a> and <a href="/tomasgeffner/">Tomas Geffner</a> on their recent paper Proteina: Scaling Flow-based Protein Structure Generative Models.  As always with <a href="/mmbronstein/">Michael Bronstein</a> &amp; <a href="/befcorreia/">Bruno Correia</a>

When: Fri, Mar 14 - 5p CET/11a ET   
Sign-up: genaiindrugdiscovery.com
VantAI (@vant_ai) 's Twitter Profile Photo

Announcing Neo-1: the world’s most advanced atomistic foundation model, unifying structure prediction and all-atom de novo generation for the first time - to decode and design the structure of life 🧵(1/10)

NVIDIA Healthcare (@nvidiahealth) 's Twitter Profile Photo

Hybrid explicit-latent flows are the new foundation models for protein structures. La-Proteina shows that one network can design 800-residue, all-atom structures and sequences, then perform well at motif scaffolding. Read more: nvda.ws/4nOjBSL

Hybrid explicit-latent flows are the new foundation models for protein structures.

La-Proteina shows that one network can design 800-residue, all-atom structures and sequences, then perform well at motif scaffolding. 

Read more: nvda.ws/4nOjBSL