| Swarm learning for decentralized and confidential clinical machine learning S Warnat-Herresthal, H Schultze, KL Shastry, S Manamohan, ... Nature 594 (7862), 265-270, 2021 | 284 | 2021 |
| Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models GB Goh, C Siegel, A Vishnu, NO Hodas, N Baker arXiv preprint arXiv:1706.06689, 2017 | 190 | 2017 |
| Smiles2vec: An interpretable general-purpose deep neural network for predicting chemical properties GB Goh, NO Hodas, C Siegel, A Vishnu arXiv preprint arXiv:1712.02034, 2017 | 131 | 2017 |
| Gossipgrad: Scalable deep learning using gossip communication based asynchronous gradient descent J Daily, A Vishnu, C Siegel, T Warfel, V Amatya arXiv preprint arXiv:1803.05880, 2018 | 94 | 2018 |
| Desh: deep learning for system health prediction of lead times to failure in hpc A Das, F Mueller, C Siegel, A Vishnu Proceedings of the 27th International Symposium on High-Performance Parallel …, 2018 | 77 | 2018 |
| Using rule-based labels for weak supervised learning: a ChemNet for transferable chemical property prediction GB Goh, C Siegel, A Vishnu, N Hodas Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 71 | 2018 |
| Distributed tensorflow with MPI A Vishnu, C Siegel, J Daily arXiv preprint arXiv:1603.02339, 2016 | 66 | 2016 |
| How much chemistry does a deep neural network need to know to make accurate predictions? GB Goh, C Siegel, A Vishnu, N Hodas, N Baker 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 1340-1349, 2018 | 54 | 2018 |
| Scaling deep learning workloads: Nvidia dgx-1/pascal and intel knights landing NA Gawande, JA Daily, C Siegel, NR Tallent, A Vishnu Future Generation Computer Systems 108, 1162-1172, 2020 | 47 | 2020 |
| CATMoS: Collaborative acute toxicity modeling suite K Mansouri, AL Karmaus, J Fitzpatrick, G Patlewicz, P Pradeep, D Alberga, ... Environmental health perspectives 129 (4), 047013, 2021 | 37 | 2021 |
| Evaluating on-node gpu interconnects for deep learning workloads NR Tallent, NA Gawande, C Siegel, A Vishnu, A Hoisie High Performance Computing Systems. Performance Modeling, Benchmarking, and …, 2018 | 35 | 2018 |
| Effective machine learning based format selection and performance modeling for SpMV on GPUs I Nisa, C Siegel, AS Rajam, A Vishnu, P Sadayappan 2018 IEEE International Parallel and Distributed Processing Symposium …, 2018 | 27 | 2018 |
| Multimodal deep neural networks using both engineered and learned representations for biodegradability prediction GB Goh, K Sakloth, C Siegel, A Vishnu, J Pfaendtner arXiv preprint arXiv:1808.04456, 2018 | 18 | 2018 |
| Chemnet: A transferable and generalizable deep neural network for small-molecule property prediction GB Goh, CM Siegel, A Vishnu, NO Hodas Pacific Northwest National Lab.(PNNL), Richland, WA (United States), 2017 | 18 | 2017 |
| A learning framework for control-oriented modeling of buildings J Rubio-Herrero, V Chandan, C Siegel, A Vishnu, D Vrabie 2017 16th IEEE International Conference on Machine Learning and Applications …, 2017 | 16 | 2017 |
| Coldroute: effective routing of cold questions in stack exchange sites J Sun, A Vishnu, A Chakrabarti, C Siegel, S Parthasarathy Data mining and knowledge discovery 32, 1339-1367, 2018 | 15 | 2018 |
| What does fault tolerant deep learning need from mpi? V Amatya, A Vishnu, C Siegel, J Daily Proceedings of the 24th European MPI Users' Group Meeting, 1-11, 2017 | 11 | 2017 |
| Adaptive neuron apoptosis for accelerating deep learning on large scale systems C Siegel, J Daily, A Vishnu 2016 IEEE International Conference on Big Data (Big Data), 753-762, 2016 | 11 | 2016 |
| User-transparent distributed TensorFlow A Vishnu, J Manzano, C Siegel, J Daily arXiv preprint arXiv:1704.04560, 2017 | 7 | 2017 |
| Recombination of artificial neural networks A Vose, J Balma, A Heye, A Rigazzi, C Siegel, D Moise, B Robbins, ... arXiv preprint arXiv:1901.03900, 2019 | 6 | 2019 |
Abhinav VishnuFellow, AMDVerified email at amd.com
