| Improving Federated Learning Personalization via Model Agnostic Meta Learning Y Jiang, J Konečný, K Rush, S Kannan NeurIPS 2019 FL workshop, arXiv preprint arXiv:1909.12488, 2019 | 598 | 2019 |
| Communication Algorithms via Deep Learning H Kim, Y Jiang, R Rana, S Kannan, S Oh, P Viswanath Sixth International Conference on Learning Representations (ICLR), 2018 | 257 | 2018 |
| Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels Y Jiang, H Kim, H Asnani, S Kannan, S Oh, P Viswanath NeurIPS 2019 poster, 2019 | 149 | 2019 |
| Deepcode: Feedback codes via deep learning H Kim, Y Jiang, S Kannan, S Oh, P Viswanath Advances in neural information processing systems 31, 2018 | 115 | 2018 |
| LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks Y Jiang, H Kim, H Asnani, S Kannan, S Oh, P Viswanath IEEE International Conference on Communications (ICC) 2019, 2018 | 91 | 2018 |
| DeepTurbo: Deep Turbo Decoder Y Jiang, H Kim, H Asnani, S Kannan, S Oh, P Viswanath SPAWC 2019, https://arxiv.org/abs/1903.02295, 2019 | 57 | 2019 |
| Deepcode: Feedback codes via deep learning H Kim, Y Jiang, S Kannan, S Oh, P Viswanath IEEE Journal on Selected Areas in Information Theory 1 (1), 194-206, 2020 | 49 | 2020 |
| MIND: Model Independent Neural Decoder Y Jiang, H Kim, H Asnani, S Kannan SPAWC 2019, 2019 | 44 | 2019 |
| Joint channel coding and modulation via deep learning Y Jiang, H Kim, H Asnani, S Kannan, S Oh, P Viswanath 2020 IEEE 21st International Workshop on Signal Processing Advances in …, 2020 | 18 | 2020 |
| Systems and methods for utilizing dynamic codes with neural networks R Gopalan, A Chandrasekher, Y Jiang US Patent 11,088,784, 2021 | 15 | 2021 |
| Improving federated learning personalization via model agnostic meta learning. arXiv 2019 Y Jiang, J Konecný, K Rush, S Kannan arXiv preprint arXiv:1909.12488, 0 | 13 | |
| Systems and methods for artificial intelligence discovered codes R Gopalan, A Chandrasekher, Y Jiang US Patent 11,580,396, 2023 | 11 | 2023 |
| Improving federated learning personalization via model agnostic meta learning. arXiv Y Jiang, J Konečný, K Rush, S Kannan arXiv preprint arXiv:1909.12488, 2019 | 10 | 2019 |
| Convergent multi-bit feedback system A Chandrasekher, R Gopalan, Y Jiang, A Rahimzamani US Patent 11,368,251, 2022 | 9 | 2022 |
| Feedback turbo autoencoder Y Jiang, H Kim, H Asnani, S Oh, S Kannan, P Viswanath ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 9 | 2020 |
| Turbo autoencoder with a trainable interleaver K Chahine, Y Jiang, P Nuti, H Kim, J Cho ICC 2022-IEEE International Conference on Communications, 3886-3891, 2022 | 8 | 2022 |
| Targeting the pathological network: Feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders Z Cao, X Xiao, Y Zhao, Y Jiang, C Xie, ML Paillère-Martinot, E Artiges, Z Li, ... Frontiers in Neuroscience 16, 1079078, 2023 | 7 | 2023 |
| Adaptive payload extraction and retransmission in wireless data communications with error aggregations A Chandrasekher, R Gopalan, Y Jiang, A Rahimzamani US Patent 11,368,250, 2022 | 7 | 2022 |
| Improving federated learning personalization via model agnostic meta learning. CoRR abs/1909.12488 (2019) Y Jiang, J Konečný, K Rush, S Kannan | 6 | 1909 |
| Improving federated learning personalization via model agnostic meta learning, 2019 Y Jiang, J Konecný, K Rush, S Kannan arXiv preprint arXiv:1909.12488, 1909 | 6 | 1909 |
Sreeram KannanFounder, EigenLayer; Affiliate Associate Professor, University of Washington在 uw.edu 的电子邮件经过验证
