| Malware Detection by Eating a Whole EXE E Raff, J Barker, J Sylvester, R Brandon, B Catanzaro, C Nicholas AAAI Workshop on Artificial Intelligence for Cyber Security, 2018 | 712 | 2018 |
| Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling S Biderman, H Schoelkopf, Q Anthony, H Bradley, K O'Brien, E Hallahan, ... International Conference on Machine Learning (ICML), 2023 | 691 | 2023 |
| Crosslingual Generalization through Multitask Finetuning N Muennighoff, T Wang, L Sutawika, A Roberts, S Biderman, TL Scao, ... Proceedings of the 61st Annual Meeting of the Association for Computational …, 2022 | 551 | 2022 |
| VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance K Crowson, S Biderman, D Kornis, D Stander, E Hallahan, L Castricato, ... European Conference on Computer Vision (ECCV), 2022 | 420* | 2022 |
| Barrage of random transforms for adversarially robust defense E Raff, J Sylvester, S Forsyth, M McLean Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 198 | 2019 |
| An investigation of byte n-gram features for malware classification E Raff, R Zak, R Cox, J Sylvester, P Yacci, R Ward, A Tracy, M McLean, ... Journal of Computer Virology and Hacking Techniques, 1-20, 2016 | 197 | 2016 |
| Learning the PE Header, Malware Detection with Minimal Domain Knowledge E Raff, J Sylvester, C Nicholas Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security …, 2017 | 167 | 2017 |
| Accounting for variance in machine learning benchmarks X Bouthillier, P Delaunay, M Bronzi, A Trofimov, B Nichyporuk, J Szeto, ... Proceedings of Machine Learning and Systems 3, 747-769, 2021 | 154 | 2021 |
| Exploratory analysis of covid-19 tweets using topic modeling, umap, and digraphs C Ordun, S Purushotham, E Raff epiDAMIK 2020: 3rd epiDAMIK ACM SIGKDD International Workshop on …, 2020 | 147* | 2020 |
| A Step Toward Quantifying Independently Reproducible Machine Learning Research E Raff Advances in Neural Information Processing Systems, 5485-5495, 2019 | 141 | 2019 |
| Emergent and predictable memorization in large language models S Biderman, US Prashanth, L Sutawika, H Schoelkopf, Q Anthony, ... NeurIPS, 2023 | 103 | 2023 |
| An Alternative to NCD for Large Sequences, Lempel-Ziv Jaccard Distance E Raff, C Nicholas Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 103 | 2017 |
| Fair Forests: Regularized Tree Induction to Minimize Model Bias E Raff, J Sylvester, S Mills AAAI/ACM Conference on AI, Ethics, and Society, 2017 | 86 | 2017 |
| JSAT: Java Statistical Analysis Tool, a Library for Machine Learning E Raff Journal of Machine Learning Research 18 (23), 1-5, 2017 | 71 | 2017 |
| Gradient Reversal Against Discrimination: A Fair Neural Network Learning Approach E Raff, J Sylvester The 5th IEEE International Conference on Data Science and Advanced Analytics, 2018 | 69* | 2018 |
| LEACE: Perfect linear concept erasure in closed form N Belrose, D Schneider-Joseph, S Ravfogel, R Cotterell, E Raff, ... NeurIPS, 2023 | 67 | 2023 |
| A Survey of Machine Learning Methods and Challenges for Windows Malware Classification E Raff, C Nicholas NeurIPS 2020 Workshop: ML Retrospectives, Surveys & Meta-Analyses (ML-RSA), 2020 | 61 | 2020 |
| Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection E Raff, W Fleshman, R Zak, HS Anderson, B Filar, M McLean The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021 | 56 | 2021 |
| Static malware detection & subterfuge: Quantifying the robustness of machine learning and current anti-virus W Fleshman, E Raff, R Zak, M McLean, C Nicholas 2018 13th International Conference on Malicious and Unwanted Software …, 2018 | 56 | 2018 |
| Fooling MOSS Detection with Pretrained Language Models S Biderman, E Raff Proceedings of the 31st ACM International Conference on Information …, 2022 | 54* | 2022 |
Charles NicholasUMBCVerified email at umbc.edu
