Low-dimensional hyperbolic knowledge graph embeddings I Chami, A Wolf, DC Juan, F Sala, S Ravi, C Ré arXiv preprint arXiv:2005.00545, 2020 | 309 | 2020 |
Synthesizer: Rethinking self-attention for transformer models Y Tay, D Bahri, D Metzler, DC Juan, Z Zhao, C Zheng International conference on machine learning, 10183-10192, 2021 | 262 | 2021 |
Sparse sinkhorn attention Y Tay, D Bahri, L Yang, D Metzler, DC Juan International Conference on Machine Learning, 9438-9447, 2020 | 229 | 2020 |
Dpp-net: Device-aware progressive search for pareto-optimal neural architectures JD Dong, AC Cheng, DC Juan, W Wei, M Sun Proceedings of the European Conference on Computer Vision (ECCV), 517-531, 2018 | 221* | 2018 |
Monas: Multi-objective neural architecture search using reinforcement learning CH Hsu, SH Chang, JH Liang, HP Chou, CH Liu, SC Chang, JY Pan, ... arXiv preprint arXiv:1806.10332, 2018 | 178 | 2018 |
Remix: rebalanced mixup HP Chou, SC Chang, JY Pan, W Wei, DC Juan Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 145 | 2020 |
Neuralpower: Predict and deploy energy-efficient convolutional neural networks E Cai, DC Juan, D Stamoulis, D Marculescu Asian Conference on Machine Learning, 622-637, 2017 | 144 | 2017 |
Coco-gan: Generation by parts via conditional coordinating CH Lin, CC Chang, YS Chen, DC Juan, W Wei, HT Chen Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 134 | 2019 |
A2N: Attending to neighbors for knowledge graph inference T Bansal, DC Juan, S Ravi, A McCallum Proceedings of the 57th annual meeting of the association for computational …, 2019 | 119 | 2019 |
On the robustness of self-attentive models YL Hsieh, M Cheng, DC Juan, W Wei, WL Hsu, CJ Hsieh Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 94 | 2019 |
Hyperpower: Power-and memory-constrained hyper-parameter optimization for neural networks D Stamoulis, E Cai, DC Juan, D Marculescu 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 19-24, 2018 | 74 | 2018 |
Learning the optimal operating point for many-core systems with extended range voltage/frequency scaling DC Juan, S Garg, J Park, D Marculescu 2013 International Conference on Hardware/Software Codesign and System …, 2013 | 64 | 2013 |
Svr-noc: A performance analysis tool for network-on-chips using learning-based support vector regression model Z Qian, DC Juan, P Bogdan, CY Tsui, D Marculescu, R Marculescu 2013 Design, Automation & Test in Europe Conference & Exhibition (DATE), 354-357, 2013 | 63 | 2013 |
Complement objective training HY Chen, PH Wang, CH Liu, SC Chang, JY Pan, YT Chen, W Wei, ... arXiv preprint arXiv:1903.01182, 2019 | 56 | 2019 |
Improving adversarial robustness via guided complement entropy HY Chen, JH Liang, SC Chang, JY Pan, YT Chen, W Wei, DC Juan Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 52 | 2019 |
A support vector regression (SVR)-based latency model for network-on-chip (NoC) architectures ZL Qian, DC Juan, P Bogdan, CY Tsui, D Marculescu, R Marculescu IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2015 | 50 | 2015 |
Power-aware performance increase via core/uncore reinforcement control for chip-multiprocessors DC Juan, D Marculescu Proceedings of the 2012 ACM/IEEE international symposium on Low power …, 2012 | 50 | 2012 |
Question answering with long multiple-span answers M Zhu, A Ahuja, DC Juan, W Wei, CK Reddy Findings of the Association for Computational Linguistics: EMNLP 2020, 3840-3849, 2020 | 48 | 2020 |
A learning-based autoregressive model for fast transient thermal analysis of chip-multiprocessors DC Juan, H Zhou, D Marculescu, X Li 17th Asia and South Pacific Design Automation Conference, 597-602, 2012 | 45 | 2012 |
A comprehensive and accurate latency model for network-on-chip performance analysis Z Qian, DC Juan, P Bogdan, CY Tsui, D Marculescu, R Marculescu 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC), 323-328, 2014 | 41 | 2014 |