Yue Wang
Orcid: 0000-0001-5889-0729Affiliations:
- Rice University, Department of Electrical and Computer Engineering, USA
According to our database1,
Yue Wang
authored at least 19 papers
between 2018 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
CoRR, 2024
2023
IEEE Trans. Neural Networks Learn. Syst., October, 2023
2021
CoRR, 2021
Proceedings of the 9th International Conference on Learning Representations, 2021
SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCam.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021
2020
Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference.
IEEE J. Sel. Top. Signal Process., 2020
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
A New MRAM-Based Process In-Memory Accelerator for Efficient Neural Network Training with Floating Point Precision.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2020
Proceedings of the 47th ACM/IEEE Annual International Symposium on Computer Architecture, 2020
Proceedings of the 8th International Conference on Learning Representations, 2020
DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architectures.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
Proceedings of the FPGA '20: The 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2020
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020
2019
E2-Train: Energy-Efficient Deep Network Training with Data-, Model-, and Algorithm-Level Saving.
CoRR, 2019
CoRR, 2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Live Demonstration: Bringing Powerful Deep Learning into Daily-Life Devices (Mobiles and FPGAs) Via Deep k-Means.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2019
2018
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions.
Proceedings of the 35th International Conference on Machine Learning, 2018