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2020 – today
- 2023
[c63]Gabriel Orlanski, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland, Jonathan Malmaud, Jacob Austin, Rishabh Singh, Michele Catasta:
Measuring the Impact of Programming Language Distribution. ICML 2023: 26619-26645- [i51]Gabriel Orlanski, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland, Jonathan Malmaud, Jacob Austin, Rishabh Singh, Michele Catasta:
Measuring The Impact Of Programming Language Distribution. CoRR abs/2302.01973 (2023) - 2022
- [j21]Kavita Pabreja
, Anubhuti Singh, Rishabh Singh, Rishita Agnihotri, Shriam Kaushik, Tanvi Malhotra:
Prediction of Stress Level on Indian Working Professionals Using Machine Learning. Int. J. Hum. Cap. Inf. Technol. Prof. 13(1): 1-26 (2022) - [j20]Marko Vasic, Andrija Petrovic, Kaiyuan Wang, Mladen Nikolic, Rishabh Singh, Sarfraz Khurshid:
MoËT: Mixture of Expert Trees and its application to verifiable reinforcement learning. Neural Networks 151: 34-47 (2022) - [j19]Qinheping Hu, Rishabh Singh, Loris D'Antoni:
Solving Program Sketches with Large Integer Values. ACM Trans. Program. Lang. Syst. 44(2): 9:1-9:28 (2022) - [j18]Kensen Shi, David Bieber, Rishabh Singh:
TF-Coder: Program Synthesis for Tensor Manipulations. ACM Trans. Program. Lang. Syst. 44(2): 10:1-10:36 (2022) - [c62]Rishabh Singh, Shirin Goshtasbpour:
Platt-Bin: Efficient Posterior Calibrated Training for NLP Classifiers. ACL (Findings) 2022: 3673-3684 - [c61]Murtadha D. Hssayeni, Arash Andalib, Rishabh Singh, Diego Pava, Kan Li, Steven Borzak, Robert Chait, Kaustubh Kale:
ECG Fiducial Point Localization Using a Deep Learning Model. ICMLA 2022: 321-328 - [i50]Rishabh Singh, José C. Príncipe:
Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS. CoRR abs/2211.01999 (2022) - [i49]Rishabh Singh, José C. Príncipe:
Robust Dependence Measure using RKHS based Uncertainty Moments and Optimal Transport. CoRR abs/2211.02005 (2022) - 2021
- [j17]Rishabh Singh, Devansh Timbadia, Vidhi Kapoor, Rishabh Reddy, Prathamesh P. Churi, Omkar Pimple:
Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application. Educ. Inf. Technol. 26(4): 4151-4179 (2021) - [j16]Dana Fisman, Rishabh Singh, Armando Solar-Lezama:
Special Issue on Syntax-Guided Synthesis Preface. Formal Methods Syst. Des. 58(3): 469-470 (2021) - [j15]Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando Solar-Lezama, Yisong Yue:
Neurosymbolic Programming. Found. Trends Program. Lang. 7(3): 158-243 (2021) - [j14]Rishabh Singh, José C. Príncipe:
Toward a Kernel-Based Uncertainty Decomposition Framework for Data and Models. Neural Comput. 33(5): 1164-1198 (2021) - [c60]Rishabh Singh:
Bias: Bijective Input And Surjectivity In Zero Shot Learning. ICIP 2021: 1249-1253 - [c59]Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai:
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration. ICLR 2021 - [c58]Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley:
Scaling Symbolic Methods using Gradients for Neural Model Explanation. ICLR 2021 - [c57]Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou:
SpreadsheetCoder: Formula Prediction from Semi-structured Context. ICML 2021: 1661-1672 - [c56]Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer:
Latent Programmer: Discrete Latent Codes for Program Synthesis. ICML 2021: 4308-4318 - [c55]Digvijay Singh, Rishabh Singh, Rahul Ajmeria, Manik Gupta, R. N. Ponnalagu:
DAWSSM: A plug-and-play Drone Assisted Water Sampling and Sensing Module. IECON 2021: 1-6 - [c54]Shobha Vasudevan, Wenjie Jiang, David Bieber, Rishabh Singh, Hamid Shojaei, Richard Ho, Charles Sutton:
Learning Semantic Representations to Verify Hardware Designs. NeurIPS 2021: 23491-23504 - [i48]Rishabh Singh, José C. Príncipe:
A Kernel Framework to Quantify a Model's Local Predictive Uncertainty under Data Distributional Shifts. CoRR abs/2103.01374 (2021) - [i47]Vaishali V. Ingale, Rishabh Singh, Pragati Patwal:
Image to Image Translation : Generating maps from satellite images. CoRR abs/2105.09253 (2021) - [i46]Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou:
SpreadsheetCoder: Formula Prediction from Semi-structured Context. CoRR abs/2106.15339 (2021) - [i45]Sumit Kumar Varshney, Jeetu Kumar, Aditya Tiwari, Rishabh Singh, Venkata M. V. Gunturi, Narayanan C. Krishnan:
Deep Geospatial Interpolation Networks. CoRR abs/2108.06670 (2021) - [i44]Rishabh Singh, José C. Príncipe:
Quantifying Model Predictive Uncertainty with Perturbation Theory. CoRR abs/2109.10888 (2021) - 2020
- [j13]Ajay Vikram Singh, Daniel Rosenkranz, Mohammad Hasan Dad Ansari, Rishabh Singh, Anurag Kanase, Shubham Pratap Singh, Blair Johnston, Jutta Tentschert, Peter Laux, Andreas Luch:
Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction. Adv. Intell. Syst. 2(12): 2000084 (2020) - [j12]Ajay Vikram Singh, Daniel Rosenkranz, Mohammad Hasan Dad Ansari, Rishabh Singh, Anurag Kanase, Shubham Pratap Singh, Blair Johnston, Jutta Tentschert, Peter Laux, Andreas Luch:
Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction. Adv. Intell. Syst. 2(12): 2070125 (2020) - [j11]Rishabh Reddy, Rishabh Singh, Vidhi Kapoor, Prathamesh P. Churi:
Joy of Learning Through Internet Memes. Int. J. Eng. Pedagog. 10(5): 116-133 (2020) - [j10]Shengwei An, Rishabh Singh, Sasa Misailovic, Roopsha Samanta:
Augmented example-based synthesis using relational perturbation properties. Proc. ACM Program. Lang. 4(POPL): 56:1-56:24 (2020) - [c53]Rong Pan, Qinheping Hu, Rishabh Singh, Loris D'Antoni:
Solving Program Sketches with Large Integer Values. ESOP 2020: 572-598 - [c52]Rishabh Singh, Shujian Yu, José C. Príncipe:
Composite Dynamic Texture Synthesis Using Hierarchical Linear Dynamical System. ICASSP 2020: 2757-2761 - [c51]Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber:
Global Relational Models of Source Code. ICLR 2020 - [c50]Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi, Mayur Naik:
Generating Programmatic Referring Expressions via Program Synthesis. ICML 2020: 4495-4506 - [c49]Rishabh Reddy, Rishabh Singh, Vidhi Kapoor, Prathamesh P. Churi:
Cyberbullying and Indian Society: Outcomes from Social Conclave Conference. ISTAS 2020: 314-321 - [c48]Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. NeurIPS 2020 - [c47]Rishabh Singh, José C. Príncipe:
Time Series Analysis using a Kernel based Multi-Modal Uncertainty Decomposition Framework. UAI 2020: 1368-1377 - [i43]Rishabh Singh, José C. Príncipe:
Towards a Kernel based Physical Interpretation of Model Uncertainty. CoRR abs/2001.11495 (2020) - [i42]Daniel A. Abolafia, Rishabh Singh, Manzil Zaheer, Charles Sutton:
Towards Modular Algorithm Induction. CoRR abs/2003.04227 (2020) - [i41]Kensen Shi, David Bieber, Rishabh Singh:
TF-Coder: Program Synthesis for Tensor Manipulations. CoRR abs/2003.09040 (2020) - [i40]Matej Balog, Rishabh Singh, Petros Maniatis, Charles Sutton:
Neural Program Synthesis with a Differentiable Fixer. CoRR abs/2006.10924 (2020) - [i39]Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick F. Riley:
Scaling Symbolic Methods using Gradients for Neural Model Explanation. CoRR abs/2006.16322 (2020) - [i38]Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton:
BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration. CoRR abs/2007.14381 (2020) - [i37]Prem Devanbu, Matthew B. Dwyer, Sebastian G. Elbaum, Michael Lowry, Kevin Moran, Denys Poshyvanyk, Baishakhi Ray, Rishabh Singh, Xiangyu Zhang:
Deep Learning & Software Engineering: State of Research and Future Directions. CoRR abs/2009.08525 (2020) - [i36]Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. CoRR abs/2011.05363 (2020) - [i35]Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer:
Latent Programmer: Discrete Latent Codes for Program Synthesis. CoRR abs/2012.00377 (2020)
2010 – 2019
- 2019
- [c46]Abhinav Kumar, Aishwarya Gupta, Bishal Santra, K. S. Lalitha, Manasa Kolla, Mayank Gupta, Rishabh Singh:
VPDS: An AI-Based Automated Vehicle Occupancy and Violation Detection System. AAAI 2019: 9498-9503 - [c45]Richard Shin, Neel Kant, Kavi Gupta, Chris Bender, Brandon Trabucco, Rishabh Singh, Dawn Song:
Synthetic Datasets for Neural Program Synthesis. ICLR (Poster) 2019 - [c44]Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh:
Neural Program Repair by Jointly Learning to Localize and Repair. ICLR (Poster) 2019 - [c43]Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli:
Learning Transferable Graph Exploration. NeurIPS 2019: 2514-2525 - [c42]Qinheping Hu, Roopsha Samanta, Rishabh Singh, Loris D'Antoni:
Direct Manipulation for Imperative Programs. SAS 2019: 347-367 - [i34]Li Li, Minjie Fan, Rishabh Singh, Patrick Riley:
Neural-Guided Symbolic Regression with Semantic Prior. CoRR abs/1901.07714 (2019) - [i33]Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh:
Neural Program Repair by Jointly Learning to Localize and Repair. CoRR abs/1904.01720 (2019) - [i32]Rajeev Alur, Dana Fisman, Saswat Padhi, Rishabh Singh, Abhishek Udupa:
SyGuS-Comp 2018: Results and Analysis. CoRR abs/1904.07146 (2019) - [i31]Marko Vasic, Andrija Petrovic, Kaiyuan Wang, Mladen Nikolic, Rishabh Singh, Sarfraz Khurshid:
MoËT: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees. CoRR abs/1906.06717 (2019) - [i30]Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli:
Learning Transferable Graph Exploration. CoRR abs/1910.12980 (2019) - [i29]Richard Shin, Neel Kant, Kavi Gupta, Christopher Bender, Brandon Trabucco, Rishabh Singh, Dawn Song:
Synthetic Datasets for Neural Program Synthesis. CoRR abs/1912.12345 (2019) - 2018
- [j9]Rajeev Alur, Rishabh Singh, Dana Fisman, Armando Solar-Lezama:
Search-based program synthesis. Commun. ACM 61(12): 84-93 (2018) - [j8]Jeevana Priya Inala, Rishabh Singh:
WebRelate: integrating web data with spreadsheets using examples. Proc. ACM Program. Lang. 2(POPL): 2:1-2:28 (2018) - [j7]Xinyu Wang, Isil Dillig, Rishabh Singh:
Program synthesis using abstraction refinement. Proc. ACM Program. Lang. 2(POPL): 63:1-63:30 (2018) - [c41]Sitara Shah, Snigdha Petluru, Rishabh Singh, Saurabh Srivastava:
gAR-age: A Feedback-Enabled Blended Ecosystem for Vehicle Health Monitoring. AutomotiveUI 2018: 268-277 - [c40]Rishabh Singh, Kan Li, José C. Príncipe:
Nearest-Instance-Centroid-Estimation Linear Discriminant Analysis (Nice Lda). ICASSP 2018: 2846-2850 - [c39]Rudy Bunel, Matthew J. Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli:
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis. ICLR (Poster) 2018 - [c38]Roland Fernandez, Asli Celikyilmaz, Paul Smolensky, Rishabh Singh:
Learning and Analyzing Vector Encoding of Symbolic Representation. ICLR (Workshop) 2018 - [c37]Ke Wang, Rishabh Singh, Zhendong Su:
Dynamic Neural Program Embeddings for Program Repair. ICLR (Poster) 2018 - [c36]Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri:
Programmatically Interpretable Reinforcement Learning. ICML 2018: 5052-5061 - [c35]Sahil Bhatia, Pushmeet Kohli, Rishabh Singh:
Neuro-symbolic program corrector for introductory programming assignments. ICSE 2018: 60-70 - [c34]Rishabh Singh, José C. Príncipe:
Correntropy Based Hierarchical Linear Dynamical System For Speech Recognition. IJCNN 2018: 1-7 - [c33]Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, Xiaodong He:
Natural Language to Structured Query Generation via Meta-Learning. NAACL-HLT (2) 2018: 732-738 - [c32]Xin Zhang, Armando Solar-Lezama, Rishabh Singh:
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections. NeurIPS 2018: 4879-4890 - [c31]Ke Wang, Rishabh Singh, Zhendong Su:
Search, align, and repair: data-driven feedback generation for introductory programming exercises. PLDI 2018: 481-495 - [c30]Konstantin Böttinger, Patrice Godefroid, Rishabh Singh:
Deep Reinforcement Fuzzing. IEEE Symposium on Security and Privacy Workshops 2018: 116-122 - [i28]Konstantin Böttinger, Patrice Godefroid, Rishabh Singh:
Deep Reinforcement Fuzzing. CoRR abs/1801.04589 (2018) - [i27]Xin Zhang, Armando Solar-Lezama, Rishabh Singh:
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections. CoRR abs/1802.07384 (2018) - [i26]Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, Xiaodong He:
Natural Language to Structured Query Generation via Meta-Learning. CoRR abs/1803.02400 (2018) - [i25]Roland Fernandez, Asli Celikyilmaz, Rishabh Singh, Paul Smolensky:
Learning and analyzing vector encoding of symbolic representations. CoRR abs/1803.03834 (2018) - [i24]Qinheping Hu, Isaac Evavold, Roopsha Samanta, Rishabh Singh, Loris D'Antoni:
Program Repair via Direct State Manipulation. CoRR abs/1803.07522 (2018) - [i23]Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri:
Programmatically Interpretable Reinforcement Learning. CoRR abs/1804.02477 (2018) - [i22]Rudy Bunel, Matthew J. Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli:
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis. CoRR abs/1805.04276 (2018) - [i21]Surya Bhupatiraju, Kumar Krishna Agrawal, Rishabh Singh:
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis. CoRR abs/1807.00403 (2018) - [i20]Chenglong Wang, Po-Sen Huang, Alex Polozov, Marc Brockschmidt, Rishabh Singh:
Execution-Guided Neural Program Decoding. CoRR abs/1807.03100 (2018) - 2017
- [j6]Sumit Gulwani, Oleksandr Polozov, Rishabh Singh:
Program Synthesis. Found. Trends Program. Lang. 4(1-2): 1-119 (2017) - [j5]Xinyu Wang, Isil Dillig, Rishabh Singh:
Synthesis of data completion scripts using finite tree automata. Proc. ACM Program. Lang. 1(OOPSLA): 62:1-62:26 (2017) - [c29]Vivek Venugopal, Abhishek Sharma, Rishabh Singh, Abhinav Sharma, Suresh Sundaram:
A Vector Quantization Based Feature Descriptor for Online Signature Verification. ICDAR 2017: 1210-1215 - [c28]Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli:
Neuro-Symbolic Program Synthesis. ICLR (Poster) 2017 - [c27]Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli:
RobustFill: Neural Program Learning under Noisy I/O. ICML 2017: 990-998 - [c26]Patrice Godefroid, Hila Peleg, Rishabh Singh:
Learn&Fuzz: machine learning for input fuzzing. ASE 2017: 50-59 - [c25]Ke Wang, Benjamin Lin, Bjorn Rettig, Paul Pardi, Rishabh Singh:
Data-Driven Feedback Generator for Online Programing Courses. L@S 2017: 257-260 - [c24]Jacob Devlin, Rudy Bunel, Rishabh Singh, Matthew J. Hausknecht, Pushmeet Kohli:
Neural Program Meta-Induction. NIPS 2017: 2080-2088 - [c23]Loris D'Antoni, Rishabh Singh, Michael Vaughn:
NoFAQ: synthesizing command repairs from examples. ESEC/SIGSOFT FSE 2017: 582-592 - [c22]Rishabh Singh, Pushmeet Kohli:
AP: Artificial Programming. SNAPL 2017: 16:1-16:12 - [c21]Rajeev Alur, Dana Fisman, Rishabh Singh, Armando Solar-Lezama:
SyGuS-Comp 2017: Results and Analysis. SYNT@CAV 2017: 97-115 - [i19]Patrice Godefroid, Hila Peleg, Rishabh Singh:
Learn&Fuzz: Machine Learning for Input Fuzzing. CoRR abs/1701.07232 (2017) - [i18]Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli:
RobustFill: Neural Program Learning under Noisy I/O. CoRR abs/1703.07469 (2017) - [i17]Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli:
Deep API Programmer: Learning to Program with APIs. CoRR abs/1704.04327 (2017) - [i16]Xinyu Wang, Isil Dillig, Rishabh Singh:
Synthesis of Data Completion Scripts using Finite Tree Automata. CoRR abs/1707.01469 (2017) - [i15]Jacob Devlin, Rudy Bunel, Rishabh Singh, Matthew J. Hausknecht, Pushmeet Kohli:
Neural Program Meta-Induction. CoRR abs/1710.04157 (2017) - [i14]Xinyu Wang, Isil Dillig, Rishabh Singh:
Program Synthesis using Abstraction Refinement. CoRR abs/1710.07740 (2017) - [i13]Jacob Devlin, Jonathan Uesato, Rishabh Singh, Pushmeet Kohli:
Semantic Code Repair using Neuro-Symbolic Transformation Networks. CoRR abs/1710.11054 (2017) - [i12]Mohit Rajpal, William Blum, Rishabh Singh:
Not all bytes are equal: Neural byte sieve for fuzzing. CoRR abs/1711.04596 (2017) - [i11]Jeevana Priya Inala, Rishabh Singh:
WebRelate: Integrating Web Data with Spreadsheets using Examples. CoRR abs/1711.05787 (2017) - [i10]Ke Wang, Rishabh Singh, Zhendong Su:
Data-Driven Feedback Generation for Introductory Programming Exercises. CoRR abs/1711.07148 (2017) - [i9]Ke Wang, Rishabh Singh, Zhendong Su:
Dynamic Neural Program Embedding for Program Repair. CoRR abs/1711.07163 (2017) - [i8]Ute Schmid, Stephen H. Muggleton, Rishabh Singh:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382). Dagstuhl Reports 7(9): 86-108 (2017) - 2016
- [j4]Rishabh Singh:
BlinkFill: Semi-supervised Programming By Example for Syntactic String Transformations. Proc. VLDB Endow. 9(10): 816-827 (2016) - [c20]Loris D'Antoni, Roopsha Samanta, Rishabh Singh:
Qlose: Program Repair with Quantitative Objectives. CAV (2) 2016: 383-401 - [c19]Parmit K. Chilana, Rishabh Singh, Philip J. Guo:
Understanding Conversational Programmers: A Perspective from the Software Industry. CHI 2016: 1462-1472 - [c18]Xinyu Wang, Sumit Gulwani, Rishabh Singh:
FIDEX: filtering spreadsheet data using examples. OOPSLA 2016: 195-213 - [c17]Rishabh Singh, Sumit Gulwani:
Transforming spreadsheet data types using examples. POPL 2016: 343-356 - [c16]Rishabh Singh, Aman Abidi, Mohammed Abdul Qadeer:
SyncWorld: A cloud storage/synchronization service using Java and Php. WOCN 2016: 1-5 - [c15]Rajeev Alur, Dana Fisman, Rishabh Singh, Armando Solar-Lezama:
SyGuS-Comp 2016: Results and Analysis. SYNT@CAV 2016: 178-202 - [i7]Sahil Bhatia, Rishabh Singh:
Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks. CoRR abs/1603.06129 (2016) - [i6]Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow:
TerpreT: A Probabilistic Programming Language for Program Induction. CoRR abs/1608.04428 (2016) - [i5]Loris D'Antoni, Rishabh Singh, Michael Vaughn:
NoFAQ: Synthesizing Command Repairs from Examples. CoRR abs/1608.08219 (2016) - [i4]Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli:
Neuro-Symbolic Program Synthesis. CoRR abs/1611.01855 (2016) - [i3]Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow:
Summary - TerpreT: A Probabilistic Programming Language for Program Induction. CoRR abs/1612.00817 (2016) - 2015
- [j3]Elena L. Glassman, Jeremy Scott, Rishabh Singh, Philip J. Guo, Robert C. Miller:
OverCode: Visualizing Variation in Student Solutions to Programming Problems at Scale. ACM Trans. Comput. Hum. Interact. 22(2): 7:1-7:35 (2015) - [c14]Rishabh Singh, Sumit Gulwani:
Predicting a Correct Program in Programming by Example. CAV (1) 2015: 398-414 - [c13]Mikaël Mayer, Gustavo Soares, Maxim Grechkin, Vu Le, Mark Marron, Oleksandr Polozov, Rishabh Singh, Benjamin G. Zorn, Sumit Gulwani:
User Interaction Models for Disambiguation in Programming by Example. UIST 2015: 291-301 - [c12]