| Rethinking attention with performers K Choromanski, V Likhosherstov, D Dohan, X Song, A Gane, T Sarlos, ... arXiv preprint arXiv:2009.14794, 2020 | 472 | 2020 |
| Explaining how a deep neural network trained with end-to-end learning steers a car M Bojarski, P Yeres, A Choromanska, K Choromanski, B Firner, L Jackel, ... arXiv preprint arXiv:1704.07911, 2017 | 405 | 2017 |
| Orthogonal random features FXX Yu, AT Suresh, KM Choromanski, DN Holtmann-Rice, S Kumar Advances in neural information processing systems 29, 2016 | 170 | 2016 |
| End to end learning for self-driving cars. arXiv 2016 M Bojarski, D Del Testa, D Dworakowski, B Firner, B Flepp, P Goyal, ... arXiv preprint arXiv:1604.07316 103, 2016 | 167 | 2016 |
| Scale-free graph with preferential attachment and evolving internal vertex structure K Choromański, M Matuszak, J Miȩkisz Journal of Statistical Physics 151 (6), 1175-1183, 2013 | 109 | 2013 |
| Structured evolution with compact architectures for scalable policy optimization K Choromanski, M Rowland, V Sindhwani, R Turner, A Weller International Conference on Machine Learning, 970-978, 2018 | 94 | 2018 |
| Quantization based fast inner product search R Guo, S Kumar, K Choromanski, D Simcha Artificial intelligence and statistics, 482-490, 2016 | 90 | 2016 |
| Visualbackprop: visualizing cnns for autonomous driving M Bojarski, A Choromanska, K Choromanski, B Firner, L Jackel, U Muller, ... arXiv preprint arXiv:1611.05418 2, 2016 | 74 | 2016 |
| Es-maml: Simple hessian-free meta learning X Song, W Gao, Y Yang, K Choromanski, A Pacchiano, Y Tang arXiv preprint arXiv:1910.01215, 2019 | 63 | 2019 |
| Effective diversity in population based reinforcement learning J Parker-Holder, A Pacchiano, KM Choromanski, SJ Roberts Advances in Neural Information Processing Systems 33, 18050-18062, 2020 | 60 | 2020 |
| The unreasonable effectiveness of structured random orthogonal embeddings KM Choromanski, M Rowland, A Weller Advances in neural information processing systems 30, 2017 | 60 | 2017 |
| Visualbackprop: Efficient visualization of cnns for autonomous driving M Bojarski, A Choromanska, K Choromanski, B Firner, LJ Ackel, U Muller, ... 2018 IEEE International Conference on Robotics and Automation (ICRA), 4701-4708, 2018 | 58 | 2018 |
| A theoretical and empirical comparison of gradient approximations in derivative-free optimization AS Berahas, L Cao, K Choromanski, K Scheinberg Foundations of Computational Mathematics 22 (2), 507-560, 2022 | 56 | 2022 |
| Tournaments and colouring E Berger, K Choromanski, M Chudnovsky, J Fox, M Loebl, A Scott, ... Journal of Combinatorial Theory, Series B 103 (1), 1-20, 2013 | 54 | 2013 |
| On learning from label proportions FX Yu, K Choromanski, S Kumar, T Jebara, SF Chang arXiv preprint arXiv:1402.5902, 2014 | 45 | 2014 |
| Recycling randomness with structure for sublinear time kernel expansions K Choromanski, V Sindhwani International Conference on Machine Learning, 2502-2510, 2016 | 40 | 2016 |
| Masked language modeling for proteins via linearly scalable long-context transformers K Choromanski, V Likhosherstov, D Dohan, X Song, A Gane, T Sarlos, ... arXiv preprint arXiv:2006.03555, 2020 | 39 | 2020 |
| Visualbackprop: efficient visualization of cnns M Bojarski, A Choromanska, K Choromanski, B Firner, L Jackel, U Muller, ... arXiv preprint arXiv:1611.05418, 2016 | 39 | 2016 |
| Binary embeddings with structured hashed projections A Choromanska, K Choromanski, M Bojarski, T Jebara, S Kumar, ... International Conference on Machine Learning, 344-353, 2016 | 38 | 2016 |
| Structured adaptive and random spinners for fast machine learning computations M Bojarski, A Choromanska, K Choromanski, F Fagan, C Gouy-Pailler, ... Artificial intelligence and statistics, 1020-1029, 2017 | 37 | 2017 |
