Gutfreund, Dan

17 publications

ICML 2025 M+: Extending MemoryLLM with Scalable Long-Term Memory Yu Wang, Dmitry Krotov, Yuanzhe Hu, Yifan Gao, Wangchunshu Zhou, Julian Mcauley, Dan Gutfreund, Rogerio Feris, Zexue He

TMLR 2024 Constraining Generative Models for Engineering Design with Negative Data Lyle Regenwetter, Giorgio Giannone, Akash Srivastava, Dan Gutfreund, Faez Ahmed

TMLR 2024 LInK: Learning Joint Representations of Design and Performance Spaces Through Contrastive Learning for Mechanism Synthesis Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Kai Xu, Faez Ahmed

AAAI 2024 Visual Chain-of-Thought Prompting for Knowledge-Based Visual Reasoning Zhenfang Chen, Qinhong Zhou, Yikang Shen, Yining Hong, Zhiqing Sun, Dan Gutfreund, Chuang Gan

ICCV 2023 3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6d Pose Estimation Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka

NeurIPS 2023 How Hard Are Computer Vision Datasets? Calibrating Dataset Difficulty to Viewing Time David Mayo, Jesse Cummings, Xinyu Lin, Dan Gutfreund, Boris Katz, Andrei Barbu

AAAI 2023 Zero-Shot Linear Combinations of Grounded Social Interactions with Linear Social MDPs Ravi Tejwani, Yen-Ling Kuo, Tianmin Shu, Bennett Stankovits, Dan Gutfreund, Joshua B. Tenenbaum, Boris Katz, Andrei Barbu

CVPR 2022 Finding Fallen Objects via Asynchronous Audio-Visual Integration Chuang Gan, Yi Gu, Siyuan Zhou, Jeremy Schwartz, Seth Alter, James Traer, Dan Gutfreund, Joshua B. Tenenbaum, Josh H. McDermott, Antonio Torralba

NeurIPSW 2022 Workshop Version: How Hard Are Computer Vision Datasets? Calibrating Dataset Difficulty to Viewing Time David Mayo, Jesse Cummings, Xinyu Lin, Dan Gutfreund, Boris Katz, Andrei Barbu

NeurIPS 2021 3DP3: 3D Scene Perception via Probabilistic Programming Nishad Gothoskar, Marco Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Josh Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka

NeurIPS 2021 A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Josh Tenenbaum, Charles A. Sutton

ICML 2021 AGENT: A Benchmark for Core Psychological Reasoning Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin Smith, Shari Liu, Dan Gutfreund, Elizabeth Spelke, Joshua Tenenbaum, Tomer Ullman

ICLR 2020 Cz-Gem: A Framework for Disentangled Representation Learning Akash Srivastava, Yamini Bansal, Yukun Ding, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund

CVPRW 2019 Identifying Interpretable Action Concepts in Deep Networks Kandan Ramakrishnan, Mathew Monfort, Barry A. McNamara, Alex Lascelles, Dan Gutfreund, Rogério Schmidt Feris, Aude Oliva

NeurIPS 2019 ObjectNet: A Large-Scale Bias-Controlled Dataset for Pushing the Limits of Object Recognition Models Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, Boris Katz

WACV 2018 Semantically Guided Visual Question Answering Handong Zhao, Quanfu Fan, Dan Gutfreund, Yun Fu

MLJ 2013 Exploiting Label Dependencies for Improved Sample Complexity Lena Chekina, Dan Gutfreund, Aryeh Kontorovich, Lior Rokach, Bracha Shapira