| Current progress and open challenges for applying deep learning across the biosciences N Sapoval, A Aghazadeh, MG Nute, DA Antunes, A Balaji, R Baraniuk, ... Nature Communications 13 (1), 1728, 2022 | 140 | 2022 |
| PipeGCN: Efficient full-graph training of graph convolutional networks with pipelined feature communication C Wan, Y Li, CR Wolfe, A Kyrillidis, NS Kim, Y Lin arXiv preprint arXiv:2203.10428, 2022 | 50 | 2022 |
| Distributed learning of fully connected neural networks using independent subnet training B Yuan, CR Wolfe, C Dun, Y Tang, A Kyrillidis, C Jermaine Proceedings of the VLDB Endowment 15 (8), 2022 | 26 | 2022 |
| Distributed learning of deep neural networks using independent subnet training B Yuan, CR Wolfe, C Dun, Y Tang, A Kyrillidis, CM Jermaine arXiv preprint arXiv:1910.02120, 2019 | 19 | 2019 |
| Demon: improved neural network training with momentum decay J Chen, C Wolfe, Z Li, A Kyrillidis ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 18 | 2022 |
| ResIST: Layer-wise decomposition of resnets for distributed training C Dun, CR Wolfe, CM Jermaine, A Kyrillidis Uncertainty in Artificial Intelligence, 610-620, 2022 | 17 | 2022 |
| GIST: Distributed training for large-scale graph convolutional networks CR Wolfe, J Yang, F Liao, A Chowdhury, C Dun, A Bayer, S Segarra, ... Journal of Applied and Computational Topology, 1-53, 2023 | 12 | 2023 |
| Rex: Revisiting budgeted training with an improved schedule J Chen, C Wolfe, T Kyrillidis Proceedings of Machine Learning and Systems 4, 64-76, 2022 | 8 | 2022 |
| E-stitchup: Data augmentation for pre-trained embeddings CR Wolfe, KT Lundgaard arXiv preprint arXiv:1912.00772, 2019 | 7 | 2019 |
| Systems and methods of data augmentation for pre-trained embeddings K Lundgaard, C Wolfe US Patent 11,461,537, 2022 | 6 | 2022 |
| Method and system utilizing ontological machine learning for labeling products in an electronic product catalog K Lundgaard, C Wolfe US Patent 11,361,362, 2022 | 6 | 2022 |
| Demon: Momentum decay for improved neural network training J Chen, C Wolfe, Z Li, A Kyrillidis | 4 | 2020 |
| Data augmentation for deep transfer learning CR Wolfe, KT Lundgaard arXiv preprint arXiv:1912.00772, 2019 | 4 | 2019 |
| Exceeding the limits of visual-linguistic multi-task learning CR Wolfe, KT Lundgaard arXiv preprint arXiv:2107.13054, 2021 | 3 | 2021 |
| Functional generative design of mechanisms with recurrent neural networks and novelty search CR Wolfe, CC Tutum, R Miikkulainen Proceedings of the Genetic and Evolutionary Computation Conference, 1373-1380, 2019 | 3 | 2019 |
| Cold start streaming learning for deep networks CR Wolfe, A Kyrillidis arXiv preprint arXiv:2211.04624, 2022 | 2 | 2022 |
| How much pre-training is enough to discover a good subnetwork? CR Wolfe, Q Wang, JL Kim, A Kyrillidis | 2 | 2021 |
| Provably efficient lottery ticket discovery CR Wolfe, Q Wang, JL Kim, A Kyrillidis arXiv preprint arXiv:2108.00259, 2021 | 2 | 2021 |
| i-SpaSP: Structured Neural Pruning via Sparse Signal Recovery CR Wolfe, A Kyrillidis Learning for Dynamics and Control Conference, 248-262, 2022 | 1 | 2022 |
| Exceeding the limits of visual-linguistic multi-task learning C Wolfe, K Lundgaard US Patent 11,915,471, 2024 | | 2024 |
