| VSE++: Improving Visual-Semantic Embeddings with Hard Negatives F Faghri, DJ Fleet, JR Kiros, S Fidler British Machine Vision Conference (BMVC), 2018 | 761 | 2018 |
| Technical report on the cleverhans v2.1.0 adversarial examples library N Papernot, F Faghri, N Carlini, I Goodfellow, R Feinman, A Kurakin, ... arXiv preprint arXiv:1610.00768, 2018 | 701* | 2018 |
| Adversarial Spheres J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ... International Conference on Learning Representations (ICLR), Workshop Track, 2018 | 313* | 2018 |
| Adversarial Manipulation of Deep Representations S Sabour, Y Cao, F Faghri, DJ Fleet International Conference on Learning Representations (ICLR), 2016 | 265 | 2016 |
| Adaptive gradient quantization for data-parallel sgd F Faghri, I Tabrizian, I Markov, D Alistarh, DM Roy, A Ramezani-Kebrya Advances in neural information processing systems 33, 3174-3185, 2020 | 26 | 2020 |
| NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization A Ramezani-Kebrya, F Faghri, I Markov, V Aksenov, D Alistarh, DM Roy Journal of Machine Learning Research 22 (114), 1-43, 2021 | 25* | 2021 |
| A Study of Gradient Variance in Deep Learning F Faghri, D Duvenaud, DJ Fleet, J Ba arXiv preprint arXiv:2007.04532, 2020 | 11* | 2020 |
| Adversarial robustness through regularization: A second-order approach A Ma, F Faghri, A Farahmand arXiv preprint arXiv:2004.01832, 2020 | 10* | 2020 |
| Graph based semi-supervised human pose estimation: When the output space comes to help N Pourdamghani, HR Rabiee, F Faghri, MH Rohban Pattern Recognition Letters 33 (12), 1529-1535, 2012 | 2 | 2012 |
| Bridging the Gap Between Adversarial Robustness and Optimization Bias F Faghri, S Gowal, C Vasconcelos, DJ Fleet, F Pedregosa, N Le Roux arXiv preprint arXiv:2102.08868, 2021 | 1 | 2021 |
| MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks A Ramezani-Kebrya, I Tabrizian, F Faghri, P Popovski arXiv preprint arXiv:2207.07941, 2022 | | 2022 |
| Training Efficiency and Robustness in Deep Learning F Faghri arXiv preprint arXiv:2112.01423, 2021 | | 2021 |
| A Non-asymptotic comparison of SVRG and SGD: tradeoffs between compute and speed Q Zhang, Y Wu, F Faghri, T Zhang, J Ba | | 2019 |
David J FleetGoogle Research, Brain Team; University of Toronto; Universiity of Toronto Scarborough; VectorVerified email at cs.toronto.edu
