| FEVER: a large-scale dataset for fact extraction and VERification J Thorne, A Vlachos, C Christodoulopoulos, A Mittal arXiv preprint arXiv:1803.05355, 2018 | 1743 | 2018 |
| Hand detection using multiple proposals. A Mittal, A Zisserman, PHS Torr Bmvc 2 (3), 5, 2011 | 353 | 2011 |
| Human-body-gesture-based region and volume selection for HMD A Mittal, G Maciocci, ML Tunmer, P Mabbutt US Patent 10,133,342, 2018 | 262 | 2018 |
| The fact extraction and VERification (FEVER) shared task J Thorne, A Vlachos, O Cocarascu, C Christodoulopoulos, A Mittal arXiv preprint arXiv:1811.10971, 2018 | 250 | 2018 |
| Feverous: Fact extraction and verification over unstructured and structured information R Aly, Z Guo, M Schlichtkrull, J Thorne, A Vlachos, C Christodoulopoulos, ... arXiv preprint arXiv:2106.05707, 2021 | 179 | 2021 |
| The FEVER2. 0 shared task J Thorne, A Vlachos, O Cocarascu, C Christodoulopoulos, A Mittal Proceedings of the second workshop on Fact Extraction and VERification …, 2019 | 126 | 2019 |
| Measuring social bias in knowledge graph embeddings J Fisher, D Palfrey, C Christodoulopoulos, A Mittal arXiv preprint arXiv:1912.02761, 2019 | 91 | 2019 |
| The fact extraction and VERification over unstructured and structured information (FEVEROUS) shared task R Aly, Z Guo, MS Schlichtkrull, J Thorne, A Vlachos, ... Proceedings of the Fourth Workshop on Fact Extraction and VERification …, 2021 | 84 | 2021 |
| Generating token-level explanations for natural language inference J Thorne, A Vlachos, C Christodoulopoulos, A Mittal arXiv preprint arXiv:1904.10717, 2019 | 60 | 2019 |
| Evaluating adversarial attacks against multiple fact verification systems J Thorne, A Vlachos, C Christodoulopoulos, A Mittal Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 45 | 2019 |
| Calibration of augmented reality (ar) optical see-through display using shape-based alignment C Pedley, JD Ward, A Mittal, G Maciocci US Patent App. 14/225,042, 2015 | 32 | 2015 |
| Learning when not to answer: a ternary reward structure for reinforcement learning based question answering F Godin, A Kumar, A Mittal arXiv preprint arXiv:1902.10236, 2019 | 28 | 2019 |
| Quasi-semantic question answering A Mittal, PE Holmes, D Lin, MV Tablan US Patent 10,489,393, 2019 | 26 | 2019 |
| Labeling topics with images using a neural network N Aletras, A Mittal European conference on information retrieval, 500-505, 2017 | 25 | 2017 |
| Taxonomic Multi-class Prediction and Person Layout using Efficient Structured Ranking A Mittal, MB Blaschko, A Zisserman, PHS Torr European Conference on Computer Vision, 2012, 2012 | 20 | 2012 |
| Optimizing over a bayesian last layer N Weber, J Starc, A Mittal, R Blanco, L Màrquez NeurIPS workshop on Bayesian Deep Learning, 2018 | 18 | 2018 |
| Improvised layout of keypad entry system for mobile phones A Mittal, A Sengupta Computer Standards & Interfaces 31 (4), 693-698, 2009 | 15 | 2009 |
| Large scale question paraphrase retrieval with smoothed deep metric learning D Bonadiman, A Kumar, A Mittal arXiv preprint arXiv:1905.12786, 2019 | 14 | 2019 |
| Demand-weighted completeness prediction for a knowledge base A Hopkinson, A Gurdasani, D Palfrey, A Mittal arXiv preprint arXiv:1804.11109, 2018 | 13 | 2018 |
| Multi-stage query processing MV Tablan, S Samangooei, A Mittal, DS Palfrey, E Monti, P Ribeiro, ... US Patent 10,963,497, 2021 | 12 | 2021 |
Christos ChristodoulopoulosSenior Applied Scientist, Amazon Inc.Verified email at amazon.co.uk