4.8k citations · 4.8k across the 6 of their papers we have counts for
6 papers
Infinite Photorealistic Worlds using Procedural Generation
Alexander Raistrick, Lahav Lipson, Zeyu Ma +12
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated…
A Unified Framework of Surrogate Loss by Refactoring and Interpolation
Lanlan Liu, Mingzhe Wang, Jia Deng
We introduce UniLoss, a unified framework to generate surrogate losses for training deep networks with gradient descent, reducing the amount of manual design of task-specific surro…
Learning to Prove Theorems by Learning to Generate Theorems
Mingzhe Wang, Jia Deng
We consider the task of automated theorem proving, a key AI task. Deep learning has shown promise for training theorem provers, but there are limited human-written theorems and pro…
Speaker Naming in Movies
Mahmoud Azab, Mingzhe Wang, Max Smith +3
We propose a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework. To evaluate the performance of our…
Premise Selection for Theorem Proving by Deep Graph Embedding
Mingzhe Wang, Yihe Tang, Jian Wang +1
We propose a deep learning-based approach to the problem of premise selection: selecting mathematical statements relevant for proving a given conjecture. We represent a higher-orde…
LINE: Large-scale Information Network Embedding
Jian Tang, Meng Qu, Mingzhe Wang +3
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classifica…