Yangqing Jia
E480174
Yangqing Jia is a computer scientist and software engineer known for his influential work in deep learning and computer vision, including contributions to convolutional neural network architectures and open-source frameworks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Yangqing Jia canonical | 2 |
Statements (42)
| Predicate | Object |
|---|---|
| instanceOf |
computer scientist
ⓘ
researcher ⓘ software engineer ⓘ |
| basedIn |
United States of America
ⓘ
surface form:
United States
|
| citizenship | China NERFINISHED ⓘ |
| contributedTo |
Caffe2
NERFINISHED
ⓘ
PyTorch ecosystem ⓘ |
| developed | Caffe NERFINISHED ⓘ |
| educatedAt |
Tsinghua University
NERFINISHED
ⓘ
University of California, Berkeley ⓘ |
| employer |
Alibaba
NERFINISHED
ⓘ
Alibaba Cloud NERFINISHED ⓘ Facebook NERFINISHED ⓘ Facebook AI Research NERFINISHED ⓘ |
| fieldOfWork |
artificial intelligence
ⓘ
computer vision ⓘ deep learning ⓘ machine learning ⓘ |
| gender | male ⓘ |
| hasAcademicAdvisor | Trevor Darrell NERFINISHED ⓘ |
| hasContribution |
advancing production-ready deep learning infrastructure
ⓘ
bridging research and industrial deployment of CNNs ⓘ popularizing modular deep learning model definition via prototxt in Caffe ⓘ |
| hasRole |
engineering leader
ⓘ
open-source maintainer ⓘ |
| hasWebsite | https://yjia.me/ ⓘ |
| influencedBy | convolutional neural network research community ⓘ |
| knownFor |
Caffe deep learning framework
NERFINISHED
ⓘ
computer vision ⓘ convolutional neural networks ⓘ deep learning ⓘ open-source software frameworks ⓘ |
| languageSpoken |
English
ⓘ
Mandarin Chinese NERFINISHED ⓘ |
| notableProject | ImageNet-related CNN research using Caffe ⓘ |
| notableWork | Caffe NERFINISHED ⓘ |
| positionHeld |
director of engineering at Facebook
ⓘ
head of AI at Alibaba Cloud ⓘ vice president at Alibaba ⓘ |
| researchInterest |
convolutional neural network architectures
ⓘ
distributed deep learning ⓘ large-scale visual recognition ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Yangqing Jia Description of subject: Yangqing Jia is a computer scientist and software engineer known for his influential work in deep learning and computer vision, including contributions to convolutional neural network architectures and open-source frameworks.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.