Ashish Tiwari

Artificial Intelligence · 2010–2022

A researcher in the Science corpus: 7 papers; trusts 26 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #6,842 of 35,345 by trust here, #7,606 by citations
• Computational Theory and Mathematics — 21 citations into it; #6,945 of 12,182 by trust here, #3,117 by citations
• Software — 16 citations into it; #133 of 1,227 by trust here, #195 by citations
• Safety, Robustness & Interpretability — 15 citations into it
• Hardware and Architecture — 12 citations into it
• Information Systems — 12 citations into it; #2,102 of 8,771 by trust here, #1,895 by citations
• Program Analysis & Verification — 12 citations into it
• Code Generation & Program Synthesis — 8 citations into it; #50 of 220 by trust here, #52 by citations

Whom Ashish Tiwari trusts overall

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

1. Dimitri P. Bertsekas
2. Luca Pulina
3. Matthew Richardson
4. Shike Mei
5. Xiaojin Zhu
6. Marta Z. Kwiatkowska
7. Armando Solar-Lezama
8. Xiaowei Huang
9. Clark W. Barrett
10. Armando Tacchella

Who trusts Ashish Tiwari overall

1. Sumit Gulwani
2. William R. Harris
3. Rishabh Singh
4. Kensen Shi
5. Zhongjun (Mark) Jin
6. Sergey Mechtaev
7. Yu Feng
8. Oleksandr Polozov
9. Emina Torlak
10. Susmit Jha

Papers from Ashish Tiwari's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Intriguing properties of neural networks
3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
4. ImageNet classification with deep convolutional neural networks
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Journal of machine learning research)
8. Language Models are Few-Shot Learners (Neural Information Processing Systems)
9. 5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
10. Gradient-based learning applied to document recognition

Their papers

• Synthesis of loop-free programs (2011) — 367 citations
• Synchromesh: Reliable code generation from pre-trained language models (2022) — 213 citations
• Oracle-guided component-based program synthesis (2010) — 591 citations
• Output Range Analysis for Deep Feedforward Neural Networks (2018) — 298 citations
• Learning and Verification of Feedback Control Systems using Feedforward Neural Networks (2018) — 74 citations
• Output Range Analysis for Deep Neural Networks (2017) — 125 citations
• Trusted Machine Learning for Probabilistic Models (2016)