Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction. - Mingmin Zhao; Chengxu Zhuang; Yizhou Wang 001; Tai Sing Lee (ICLR (Workshop), 2015)

Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System. - Chengxu Zhuang; Jonas Kubilius; Mitra J. Z. Hartmann; Daniel Yamins (NIPS, 2017)

Deep Learning Predicts Correlation between a Functional Signature of Higher Visual Areas and Sparse Firing of Neurons. - Chengxu Zhuang; Yulong Wang; Daniel Yamins; Xiaolin Hu (Frontiers Comput. Neurosci., 2017)

Flexible neural representation for physics prediction. - Damian Mrowca; Chengxu Zhuang; Elias Wang; Nick Haber; Li Fei-Fei 001; Josh Tenenbaum 001; Daniel L. K. Yamins (NeurIPS, 2018)

Local Aggregation for Unsupervised Learning of Visual Embeddings. - Chengxu Zhuang; Alex Lin Zhai; Daniel Yamins (ICCV, 2019)

Local Label Propagation for Large-Scale Semi-Supervised Learning. - Chengxu Zhuang; Xuehao Ding; Divyanshu Murli; Daniel Yamins (CoRR, 2019)

Unsupervised Learning from Video with Deep Neural Embeddings. - Chengxu Zhuang; Alex Andonian; Daniel Yamins (CoRR, 2019)

Unsupervised Learning From Video With Deep Neural Embeddings. - Chengxu Zhuang; Tianwei She; Alex Andonian; Max Sobol Mark; Daniel Yamins (CVPR, 2020)

On Mutual Information in Contrastive Learning for Visual Representations. - Mike Wu; Chengxu Zhuang; Milan Mosse; Daniel Yamins; Noah D. Goodman (CoRR, 2020)

Conditional Negative Sampling for Contrastive Learning of Visual Representations. - Mike Wu; Milan Mosse; Chengxu Zhuang; Daniel Yamins; Noah D. Goodman (ICLR, 2021)

Unsupervised neural network models of the ventral visual stream. - Chengxu Zhuang; Siming Yan; Aran Nayebi; Martin Schrimpf; Michael C. Frank; James J. DiCarlo; Daniel L. K. Yamins (Proc. Natl. Acad. Sci. USA, 2021)

How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? - Chengxu Zhuang; Ziyu Xiang; Yoon Bai; Xiaoxuan Jia; Nicholas B. Turk-Browne; Kenneth A. Norman; James J. DiCarlo; Dan Yamins (NeurIPS, 2022)

Call for Papers - The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. - Alex Warstadt; Leshem Choshen; Aaron Mueller; Adina Williams; Ethan Wilcox; Chengxu Zhuang (CoRR, 2023)

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation. - Aran Nayebi; Nathan C. L. Kong; Chengxu Zhuang; Justin L. Gardner; Anthony M. Norcia; Daniel L. K. Yamins (PLoS Comput. Biol., 2023)

Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling. - Chengxu Zhuang; Evelina Fedorenko; Jacob Andreas (ACL (Findings), 2024)

Visual Grounding Helps Learn Word Meanings in Low-Data Regimes. - Chengxu Zhuang; Evelina Fedorenko; Jacob Andreas (NAACL-HLT, 2024)

[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. - Leshem Choshen; Ryan Cotterell; Michael Y. Hu; Tal Linzen; Aaron Mueller; Candace Ross; Alex Warstadt; Ethan Wilcox; Adina Williams; Chengxu Zhuang (CoRR, 2024)

The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences. - Bria Long; Violet Xiang; Stefan Stojanov; Robert Z. Sparks; Zi Yin; Grace E. Keene; Alvin W. M. Tan; Steven Y. Feng; Chengxu Zhuang; Virginia A. Marchman; Daniel L. K. Yamins; Michael C. Frank (CoRR, 2024)

Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora. - Michael Y. Hu; Aaron Mueller; Candace Ross; Adina Williams; Tal Linzen; Chengxu Zhuang; Ryan Cotterell; Leshem Choshen; Alex Warstadt; Ethan Gotlieb Wilcox (CoRR, 2024)

Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora. - Alex Warstadt; Aaron Mueller; Leshem Choshen; Ethan Wilcox; Chengxu Zhuang; Juan Ciro; Rafael Mosquera; Bhargavi Paranjape; Adina Williams; Tal Linzen; Ryan Cotterell (CoRR, 2025)