Susmit Jha

Artificial Intelligence · 2010–2018

A researcher in the Science corpus: 7 papers; trusts 18 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #6,982 of 35,345 by trust here, #8,619 by citations
• Computational Theory and Mathematics — 19 citations into it; #6,946 of 12,182 by trust here, #3,117 by citations
• Safety, Robustness & Interpretability — 17 citations into it
• Software — 12 citations into it; #138 of 1,227 by trust here, #195 by citations
• Program Analysis & Verification — 11 citations into it
• Hardware and Architecture — 10 citations into it
• Code Generation & Program Synthesis — 7 citations into it; #59 of 220 by trust here, #86 by citations
• Computer Networks and Communications — 6 citations into it

Whom Susmit Jha trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Luca Pulina
2. Armando Solar-Lezama
3. Rajeev Joshi
4. Xiaowei Huang
5. Clark W. Barrett
6. Armando Tacchella
7. Sanjit A. Seshia
8. Lydia E. Kavraki
9. Robert E. Bixby
10. Rastislav Bodík

Who trusts Susmit Jha overall

1. Sumit Gulwani
2. Rishabh Singh
3. Sergey Mechtaev
4. William R. Harris
5. Oleksandr Polozov
6. Kensen Shi
7. Zhongjun (Mark) Jin
8. Yu Feng
9. Paroma Varma
10. Andrea Arcuri

Papers from Susmit Jha's perspective

Ordered by how strongly the researchers Susmit Jha trusts endorse each paper. Same corpus, a different order for every researcher.

1. Intriguing properties of neural networks
2. Explaining and Harnessing Adversarial Examples
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Satisfiability Modulo Theories
8. Safety Verification of Deep Neural Networks ∗
9. Long Short-Term Memory
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Synthesis of loop-free programs (2011) — 367 citations
• Oracle-guided component-based program synthesis (2010) — 591 citations
• Output Range Analysis for Deep Feedforward Neural Networks (2018) — 298 citations
• Will Distributed Computing Revolutionize Peace? The Emergence of Battlefield IoT (2018) — 34 citations
• Learning and Verification of Feedback Control Systems using Feedforward Neural Networks (2018) — 74 citations
• On Learning Sparse Boolean Formulae for Explaining AI Decisions (2017) — 21 citations
• Output Range Analysis for Deep Neural Networks (2017) — 125 citations