activity
20152022
most citedUnderstanding the Effective Receptive Field in Deep Convolutional Neural Networks

806 citations · 2k across the 13 of their papers we have counts for

collaborators

23 papers

cs.CL2022243 cited

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77

Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.…

cs.LG2021209 cited

ETA Prediction with Graph Neural Networks in Google Maps

Austin Derrow-Pinion, Jennifer She, David Wong +14

Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time…

cs.CL20214 cited

WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset

Luyu Wang, Yujia Li, Ozlem Aslan +1

We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representat…

cs.CV2021

Computer-Aided Design as Language

Yaroslav Ganin, Sergey Bartunov, Yujia Li +2

Computer-Aided Design (CAD) applications are used in manufacturing to model everything from coffee mugs to sports cars. These programs are complex and require years of training and…

math.OC2020

Solving Mixed Integer Programs Using Neural Networks

Vinod Nair, Sergey Bartunov, Felix Gimeno +16

Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics developed with decades of research to solve large-scale MIP instances encountered in practice.…

cs.LG202019 cited

Strong Generalization and Efficiency in Neural Programs

Yujia Li, Felix Gimeno, Pushmeet Kohli +1

We study the problem of learning efficient algorithms that strongly generalize in the framework of neural program induction. By carefully designing the input / output interfaces of…