[6] Shaked Brody, Uri Alon, and Eran Yahav. How attentive are graph attention networks? In Proceedings of ICLR, 2022.
[7] Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, and Pushmeet Kohli. Leveraging grammar and reinforcement learning for neural program synthesis. In Proceedings of ICLR, 2018.
[8] Rudy R Bunel, Alban Desmaison, Pawan K Mudigonda, Pushmeet Kohli, and Philip Torr. Adaptive neural compilation. In Proceedings of NeurIPS, 2016.
[9] Xinyun Chen, Chang Liu, and Dawn Song. Execution-guided neural program synthesis. In Proceedings of ICLR, 2019.
[10] Xinyun Chen, Dawn Song, and Yuandong Tian. Latent execution for neural program synthesis beyond domain-specific languages. In Proceedings of NeurIPS, 2021.
[11] Raphaël Dang-Nhu. Plans: Neuro-symbolic program learning from videos. In Proceedings of NeurIPS, 2020.
[12] Jacob Devlin, Rudy R Bunel, Rishabh Singh, Matthew Hausknecht, and Pushmeet Kohli. Neural program meta-induction. In Proceedings of NeurIPS, 2017.
[13] Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, and Pushmeet Kohli. Robustfill: Neural program learning under noisy i/o. In Proceedings of ICML, 2017.
[14] Xuguang Duan, Qi Wu, Chuang Gan, Yiwei Zhang, Wenbing Huang, Anton Van Den Hengel, and Wenwu Zhu. Watch, reason and code: Learning to represent videos using program. In Proceedings of ACM MM, 2019.
[15] Majed El Helou and Sabine Süssstrunk. Blind universal bayesian image denoising with gaussian noise level learning. IEEE Transactions on Image Processing, 2020.
[16] Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, and Armando Solar-Lezama. Write, execute, assess: Program synthesis with a repl. In Proceedings of NeurIPS, 2019.
[17] Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, and Josh Tenenbaum. Learning to infer graphics programs from hand-drawn images. In Proceedings of NeurIPS, 2018.
[18] Abhay Garg, Anand Sriraman, Kunal Pagarey, and Shirish Karande. Are transformers all that karel needs? In Proceedings of NeurIPS Workshop, 2021.
[19] Kavi Gupta, Peter Ebert Christensen, Xinyun Chen, and Dawn Song. Synthesize, execute and debug: Learning to repair for neural program synthesis. In Proceedings of NeurIPS, 2020.
[20] Jinru Hua, Mengshi Zhang, Kaiyuan Wang, and Sarfraz Khurshid. Towards practical program repair with on-demand candidate generation. In Proceedings of ICSE, 2018.
[21] Justin Huang, Dieter Fox, and Maya Cakmak. Synthesizing robot manipulation programs from a single observed human demonstration. In Proceedings of IROS, 2019.
[22] Jeevana Priya Inala, Chenglong Wang, Mei Yang, Andres Codas, Mark Encarnacion, Shuvendu Lahiri, Madanlal Musuvathi, and Jianfeng Gao. Fault-aware neural code rankers. In Proceedings of NeurIPS, 2022.
[23] Naman Jain, Skanda Vaidyanath, Arun Iyer, Nagarajan Natarajan, Suresh Parthasarathy, Sriram Rajamani, and Rahul Sharma. Jigsaw: Large language models meet program synthesis. In Proceedings of ICSE, 2022.
[24] Michal Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaskowski. Vizdoom: A doom-based ai research platform for visual reinforcement learning. In Proceedings of CIG, 2016.
[25] Adil Khan, Muhammad Naeem, Muhammad Zubair Asghar, Aziz Ud Din, and Atif Khan. Playing first-person shooter games with machine learning techniques and methods using the vizdoom game-ai research platform. Entertainment Computing, 2020.
[26] Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, and Percy S Liang. Spoc: Search-based pseudocode to code. In Proceedings of NeurIPS, 2019.
[27] Miguel Lázaro-Gredilla, Dianhuan Lin, J Swaroop Guntupalli, and Dileep George. Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs. Science Robotics, 2019.