|N. Ho, T. Nguyen, A. B. Patel, A. Anandkumar, M. I. Jordan, R. G. Baraniuk. Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning. Accepted, DeepMath 2019.
N. Ho, T. Nguyen, A. B. Patel, A. Anandkumar, M. I. Jordan, R. G. Baraniuk. The Latent-Dependent Deep Rendering Model. Workshop on Theoretical Foundations and Applications of Deep Generative Models at ICML, 2018
Li Yang, Zhaoqi Leng, Guangyuan Yu, Ankit Patel, Wen-Jun Hu, Han Pu (2019). Deep Learning-Enhanced Variational Monte Carlo Method for Quantum Many-Body Physics. ArXiV, 2019. ArXiV
Weili Nie, Ankit B. Patel (2019). Towards A Better Understanding and Regularization of GAN Training Dynamics. UAI 2019.
Joshua J. Michalenko, Ameesh Shah, Abhinav Verma, Swarat Chaudhuri, Ankit B. Patel (2019). Finite Automata Can be Linearly Decoded from Language-Recognizing RNNs. International Conference on Learning Representations (ICLR), 2019. ICLR pdf
Weili Nie, Yang Zhang, Ankit Patel (2018). A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations. Proceedings of the International Conference of Machine Learning (ICML), PMLR 80:3809-3818, 2018. ICML ArXiV pdf
|Huaijin Chen, Wanjia Liu, Rishab Goel, Yuzhong Huang, Ashok Veeraraghavan, Ankit Patel (2018). EDR: Retinomorphic Event-Driven Representations for Motion Vision. IEEE International Conference on Computational Photography, 2018.|
|Huaijin Chen, Wanjia Liu, Rhonald Lua, Rishab Goel, Yuzhong Huang, Ashok Veeraraghavan, Ankit Patel (2019). Fast Retinomorphic Event-Driven Representations for Video Recognition and Reinforcement Learning. Accepted to IEEE Transactions in Computational Imaging. ArXiV pdf|
|Tan Nguyen, Richard Baraniuk, Ankit Patel (2016). Semi-Supervised Learning with the Deep Rendering Mixture Model. ArXiV pdf|
|Ankit Patel, Tan Nguyen, Richard Baraniuk (2016). A Probabilistic Framework for Deep Learning. NIPS 2016, Barcelona, Spain. pdf NIPS|
|Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein (2016). Training Neural Networks without Gradients: A Scalable ADMM Approach. ICML 2016, New York City USA. ICML ArXiV|
|A Probabilistic Theory of Deep Learning. Cosyne Abstracts 2016, Salt Lake City USA.(2016).|
|A Deep Learning Approach to Structured Signal Recovery. 53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA.(2015).|
|A Probabilistic Theory of Deep Learning. No. 2015-1: Rice University, Department of Electrical and Computer Engineering, Mar. 15, 2015. arXiv|
All research during this period was proprietary.
2003 – 2007
|The Emergence of Geometric Order in Proliferating Metazoan Epithelia. Matthew Gibson*, Ankit Patel*, Radhika Nagpal, Norbert Perrimon. Nature 42, pp. 1038-1041. Aug 31, 2006. *co-first authors|
|Desynchronization: A self-organizing algorithm for desynchronization and periodic resource scheduling. Ankit Patel, Julius Degesys, Radhika Nagpal. IEEE International Conference on Self-Adaptive and Self-Organizing Systems, July 2007.|
|DESYNC: Self-organizing Desynchronization and TDMA on Wireless Sensor Networks. Julius Degesys, Ian Rose, Ankit Patel, Radhika Nagpal. International Conference on Information Processing in Sensor Networks, April 2007.|
|Firefly-Inspired Sensor Network Synchronicity with Realistic Radio Effects. Geoff Werner-Allen, Geetika Tewari, Ankit Patel, Matt Welsh, Radhika Nagpal. In the ACM Conference on Embedded Networked Sensor Systems (SenSys’05), November 2005.|
|Determining the Optimal Time for Feature Aided Track Correlation Between Two Radars, [U]. Ankit Patel, Matthew Smith, Keh-Ping Dunn. In Conference on Missile Defense: Sensors, Environments, Architectures, November 2003.|