843 citations · 1.2k across the 5 of their papers we have counts for
5 papers
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei +1132
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…
Position: Categorical Deep Learning is an Algebraic Theory of All Architectures
Bruno Gavranović, Paul Lessard, Andrew Dudzik +3
We present our position on the elusive quest for a general-purpose framework for specifying and studying deep learning architectures. Our opinion is that the key attempts made so f…
Gemini: A Family of Highly Capable Multimodal Models
Gemini Team, Rohan Anil, Sebastian Borgeaud +1340
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consist…
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning
DeepMind Interactive Agents Team, Josh Abramson, Arun Ahuja +22
A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through…
Dialectica models of type theory
Sean K. Moss, Tamara von Glehn
We present two Dialectica-like constructions for models of intensional Martin-Löf type theory based on Gödel's original Dialectica interpretation and the Diller-Nahm variant, bring…