28 citations · 73 across the 5 of their papers we have counts for
5 papers
Learning to Prove Theorems via Interacting with Proof Assistants
Kaiyu Yang, Jia Deng
Humans prove theorems by relying on substantial high-level reasoning and problem-specific insights. Proof assistants offer a formalism that resembles human mathematical reasoning,…
Shape from Shading through Shape Evolution
Dawei Yang, Jia Deng
In this paper, we address the shape-from-shading problem by training deep networks with synthetic images. Unlike conventional approaches that combine deep learning and synthetic im…
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…
Scalable Annotation of Fine-Grained Categories Without Experts
Timnit Gebru, Jonathan Krause, Jia Deng +1
We present a crowdsourcing workflow to collect image annotations for visually similar synthetic categories without requiring experts. In animals, there is a direct link between tax…
Fine-Grained Car Detection for Visual Census Estimation
Timnit Gebru, Jonathan Krause, Yilun Wang +3
Targeted socioeconomic policies require an accurate understanding of a country's demographic makeup. To that end, the United States spends more than 1 billion dollars a year gather…