Li JING 靖礼

Meta / Facebook AI Research (FAIR)

Email: ljng[at]fb(dot)com

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Li Jing is a postdoctoral researcher at Meta / Facebook AI Research (FAIR), working with Yann LeCun. His current research interests focus on self-supervised learning, computer vision.

He obtained his PhD in physics from MIT, advised by Marin Soljacic. During his PhD, his research spans physics inspired neural networks and AI for science.

Previously, he received a BS in physics from Peking University. He was a co-founder of Lightelligence Inc.

Research Interests

  • Machine Learning
  • Self-supervised Learning
  • Computer Vision
  • AI for Science

Selected Publications

Self-supervised Learning

Masked Siamese ConvNets
Li Jing*, Jiachen Zhu*, Yann LeCun
[PDF] [Code]

Understanding Dimensional Collapse in Contrastive Self-supervised Learning
Li Jing, Pascal Vincent, Yann LeCun, Yuandong Tian
ICLR 2022
[PDF] [Code] [Talk] [Blog]

Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic
ICLR 2022
[PDF] [Code] [Talk]

Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Jure Zbontar*, Li Jing*, Ishan Misra, Yann LeCun, Stephane Deny
ICML 2021
[PDF] [Code] [Talk]

Implicit Rank-Minimizing Autoencoder
Li Jing, Jure Zbontar, Yann LeCun
NeurIPS 2020
[PDF] [Code] [Talk]

Neural Networks and Deep Learning

We Can Explain Your Research in Layman’s Terms: Towards Automating Science Journalism at Scale
Rumen Dangovski, Michelle Shen, Dawson Byrd, Li Jing, Desislava Tsvetkova, Preslav Nakov, Marin Soljacic
AAAI 2021

Vector-Vector-Matrix Architecture: A Novel Hardware-Aware Framework for Low-Latency Inference in NLP Applications
Matthew Khoury, Rumen Dangovski, Longwu Ou, Preslav Nakov, Yichen Shen, Li Jing
EMNLP 2020

Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications
Rumen Dangovski*, Li Jing*, Marin Soljacic
TACL (presented at NAACL) 2019
[PDF] [News] [Code]

Gated Orthogonal Recurrent Units: On Learning to Forget
Li Jing*, Caglar Gulcehre*, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljacic, Yoshua Bengio
Neural Computation 2019
[Paper] [Code]

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs
Li Jing*, Yichen Shen*, Tena Dubcek, John Peurifoy, Scott Skirlo, Yann LeCun, Max Tegmark, Marin Soljacic
ICML 2017
[PDF] [Code] [Talk]

AI for Science

Deep learning enabled self-adaptive invisibility cloak
Chao Qian, Bin Zheng, Yichen Shen, Li Jing, Erping Li, Lian Shen, Hongsheng Chen
Nature Photonics 2020
[PDF] [News]

Heuristic Recurrent Algorithms for Photonic Ising Machines
Charles Roques-Carmes, Yichen Shen, Cristian Zanoci, Mihika Prabhu, Fadi Atieh, Li Jing, Tena Dubcek, Vladimir Ceperic, John Joannopoulos, Dirk Englund, Marin Soljacic
Nature Communications 2020
[PDF] [News]

Migrating knowledge between physical scenarios based on artificial neural networks
Yurui Qu*, Li Jing*, Yichen Shen, Min Qiu, Marin Soljacic
ACS Photonics 2019

Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks
John Peurifoy, Yichen Shen, Li Jing*, Yi Yang, Fidel Cano-Renteria, Brendan Delacy, John Joannopoulos, Max Tegmark, Marin Soljacic
Science Advances 2018
[PDF] [News] [Code]