papers

Publications (7)

cs.LG2017

Population Based Training of Neural Networks

Max Jaderberg, Valentin Dalibard, Simon Osindero +9

Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model a…

cs.CV2017

The Kinetics Human Action Video Dataset

Will Kay, Joao Carreira, Karen Simonyan +9

We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 1…

eess.SY2020

Impedance-Based Whole-System Modeling for a Composite Grid via Frame-Dynamics Embedding

Yunjie Gu, Yitong Li, Yue Zhu +1

The paper establishes a methodology to overcome the difficulty of dynamic frame alignment and system separation in impedance modeling of ac grids, and thereby enables impedance-bas…

cs.CL2024

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…

cs.CL2025

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…

physics.soc-ph2020

Early Insights into System Impacts of Smart Local Energy Systems

Marko Aunedi, Tim Green

A whole-system, investment-optimising model has been used to examine the change in total cost of meeting demand for electricity when Smart Local Energy Systems (SLES) are deployed.…

cs.LG2018

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning +15

Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based g…