Gan, Chuang

Once-for-All: Train One Network and Specialize it for Efficient Deployment

Cai, H., Gan, C., Wang, T., Zhang, Z., & Han, S. (2019) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 845

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

Mao, J., Gan, C., Kohli, P., Tenenbaum, J. B., & Wu, J. (2019) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 444

ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning

Gu, Q., Kuwajerwala, A., Morin, S., Jatavallabhula, K. M., Sen, B., Agarwal, A., Rivera, C., Paul, W., Ellis, K., Chellappa, R., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA)

3D Shape Modeling and AnalysisRobot Manipulation and Learning

Cited by 406

EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction

Cai, H., Li, J., Hu, M., Gan, C., & Han, S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 356

ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning

Gu, Q., Kuwajerwala, A., Morin, S., Jatavallabhula, K. M., Sen, B., Agarwal, A., Rivera, C., Paul, W., Ellis, K., Chellappa, R., et al. (2023) · arXiv

3D Shape Modeling and AnalysisRobot Manipulation and Learning

Cited by 289

HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

Wang, H., Wu, Z., Liu, Z., Cai, H., Zhu, L., Gan, C., & Han, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Advanced Neural Network ApplicationsTopic Modeling

Cited by 227

3D-LLM: Injecting the 3D World into Large Language Models

Hong, Y., Zhen, H., Chen, P., Zheng, S., Du, Y., Chen, Z., & Gan, C. (2023) · arXiv

Multimodal Machine Learning Applications3D Shape Modeling and Analysis

Cited by 124

VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation

Gan, C., Li, Y., Li, H., Sun, C., & Gong, B. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 116

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Lin, J., Tang, J., Tang, H., Yang, S., Chen, W.-M., Wang, W.-C., Xiao, G., Dang, X., Gan, C., & Han, S. (2023) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 103

Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision

Sun, Z., Shen, Y., Zhou, Q., Zhang, H., Chen, Z., Cox, D., Yang, Y., & Gan, C. (2023) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 81

Building Cooperative Embodied Agents Modularly with Large Language Models

Zhang, H., Du, W., Shan, J., Zhou, Q., Du, Y., Tenenbaum, J. B., Shu, T., & Gan, C. (2023) · arXiv

Reinforcement Learning in RoboticsMulti-Agent Systems and Negotiation

Cited by 52

Visual Chain-of-Thought Prompting for Knowledge-Based Visual Reasoning

Chen, Z., Zhou, Q., Shen, Y., Hong, Y., Sun, Z., Gutfreund, D., & Gan, C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence

Multimodal Machine Learning ApplicationsLogic, Reasoning, and Knowledge

Cited by 48

AGENT: A Benchmark for Core Psychological Reasoning

Shu, T., Bhandwaldar, A., Gan, C., Smith, K. A., Liu, S., Gutfreund, D., Spelke, E., Tenenbaum, J. B., & Ullman, T. D. (2021) · arXiv

Child and Animal Learning DevelopmentAI-based Problem Solving and Planning

Cited by 34

3D-VLA: A 3D Vision-Language-Action Generative World Model

Zhen, H., Qiu, X., Chen, P., Yang, J., Yan, X., Du, Y., Hong, Y., & Gan, C. (2024) · arXiv

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 15

RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation

Wang, Y., Xian, Z., Chen, F., Wang, T.-H., Wang, Y., Fragkiadaki, K., Erickson, Z., Held, D., & Gan, C. (2023) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 15

The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI

Gan, C., Zhou, S., Schwartz, J., Alter, S., Bhandwaldar, A., Gutfreund, D., Yamins, D. L. K., DiCarlo, J. J., McDermott, J., Torralba, A., et al. (2021) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 13

Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties

Tung, H.-Y., Ding, M., Chen, Z., Bear, D., Gan, C., Tenenbaum, J. B., Yamins, D. L., Fan, J. E., & Smith, K. A. (2023) · arXiv

Advanced Vision and ImagingRobotics and Sensor-Based Localization

Cited by 8

Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties

Tung, H.-Y., Ding, M., Chen, Z., Bear, D., Gan, C., Tenenbaum, J., Yamins, D., Fan, J., & Smith, K. (2023) · Advances in Neural Information Processing Systems 36

Advanced Vision and ImagingRobotics and Sensor-Based Localization

Cited by 8

RoboDreamer: Learning Compositional World Models for Robot Imagination

Zhou, S., Du, Y., Chen, J., Li, Y., Yeung, D.-Y., & Gan, C. (2024) · arXiv

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 4

Generalizable Long-Horizon Manipulations with Large Language Models

Zhou, H., Ding, M., Peng, W., Tomizuka, M., Shao, L., & Gan, C. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 4

Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision

Sun, Z., Yu, L., Shen, Y., Liu, W., Yang, Y., Welleck, S., & Gan, C. (2024) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 3