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2020 – today
- 2024
[j4]Bingbin Liu:
Large Language Models in Education Embracing Opportunities, Confronting Challenges, and Shaping the Next Chapter Together. XRDS 31(1): 7-9 (2024)- [j3]Bingbin Liu, Joy He-Yueya:
Improving Instructional Materials with Large Language Models. XRDS 31(1): 56-58 (2024) - [c11]Runtian Zhai, Bingbin Liu, Andrej Risteski, J. Zico Kolter, Pradeep Kumar Ravikumar:
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression. ICLR 2024 - [i11]Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel:
Progressive distillation induces an implicit curriculum. CoRR abs/2410.05464 (2024) - 2023
- [c10]Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Transformers Learn Shortcuts to Automata. ICLR 2023 - [c9]Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Exposing Attention Glitches with Flip-Flop Language Modeling. NeurIPS 2023 - [c8]Kaiyue Wen, Yuchen Li, Bingbin Liu, Andrej Risteski:
Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars. NeurIPS 2023 - [i10]Runtian Zhai, Bingbin Liu, Andrej Risteski, Zico Kolter, Pradeep Ravikumar:
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation. CoRR abs/2306.00788 (2023) - [i9]Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Exposing Attention Glitches with Flip-Flop Language Modeling. CoRR abs/2306.00946 (2023) - [i8]Kaiyue Wen, Yuchen Li, Bingbin Liu, Andrej Risteski:
Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars. CoRR abs/2312.01429 (2023) - [i7]Bingbin Liu, Sébastien Bubeck, Ronen Eldan, Janardhan Kulkarni, Yuanzhi Li, Anh Nguyen, Rachel Ward, Yi Zhang:
TinyGSM: achieving >80% on GSM8k with small language models. CoRR abs/2312.09241 (2023) - 2022
- [c7]Bingbin Liu, Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski:
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation. ICLR 2022 - [c6]Bingbin Liu, Daniel J. Hsu, Pradeep Ravikumar, Andrej Risteski:
Masked Prediction: A Parameter Identifiability View. NeurIPS 2022 - [i6]Bingbin Liu, Daniel Hsu, Pradeep Ravikumar, Andrej Risteski:
Masked prediction tasks: a parameter identifiability view. CoRR abs/2202.09305 (2022) - [i5]Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Transformers Learn Shortcuts to Automata. CoRR abs/2210.10749 (2022) - 2021
- [c5]Bingbin Liu, Pradeep Ravikumar, Andrej Risteski:
Contrastive learning of strong-mixing continuous-time stochastic processes. AISTATS 2021: 3151-3159 - [i4]Bingbin Liu, Pradeep Ravikumar, Andrej Risteski:
Contrastive learning of strong-mixing continuous-time stochastic processes. CoRR abs/2103.02740 (2021) - [i3]Bingbin Liu, Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski:
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation. CoRR abs/2110.11271 (2021) - 2020
- [j2]Bingbin Liu, Ehsan Adeli
, Zhangjie Cao, Kuan-Hui Lee, Abhijeet Shenoi, Adrien Gaidon, Juan Carlos Niebles:
Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction. IEEE Robotics Autom. Lett. 5(2): 3485-3492 (2020) - [c4]Arun Sai Suggala, Bingbin Liu, Pradeep Ravikumar:
Generalized Boosting. NeurIPS 2020 - [i2]Bingbin Liu, Ehsan Adeli, Zhangjie Cao, Kuan-Hui Lee, Abhijeet Shenoi, Adrien Gaidon, Juan Carlos Niebles:
Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction. CoRR abs/2002.08945 (2020)
2010 – 2019
- 2019
- [j1]Serena Yeung, Francesca Rinaldo, Jeffrey Jopling, Bingbin Liu, Rishab Mehra, N. Lance Downing, Michelle Guo, Gabriel M. Bianconi, Alexandre Alahi, Julia Lee, Brandi Campbell, Kayla Deru, William Beninati, Li Fei-Fei, Arnold Milstein:
A computer vision system for deep learning-based detection of patient mobilization activities in the ICU. npj Digit. Medicine 2 (2019) - 2018
- [c3]Bingbin Liu, Serena Yeung, Edward Chou, De-An Huang, Li Fei-Fei, Juan Carlos Niebles:
Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos. ECCV (3) 2018: 569-586 - [c2]Bingbin Liu, Michelle Guo, Edward Chou, Rishab Mehra, Serena Yeung, N. Lance Downing, Francesca Salipur, Jeffrey Jopling, Brandi Campbell, Kayla Deru, William Beninati, Arnold Milstein, Li Fei-Fei:
3D Point Cloud-Based Visual Prediction of ICU Mobility Care Activities. MLHC 2018: 17-29 - [c1]Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li Fei-Fei, Juan Carlos Niebles:
Learning to Decompose and Disentangle Representations for Video Prediction. NeurIPS 2018: 515-524 - [i1]Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li Fei-Fei, Juan Carlos Niebles:
Learning to Decompose and Disentangle Representations for Video Prediction. CoRR abs/1806.04166 (2018)
Coauthor Index
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