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cs.LG2021
Achieving Low Complexity Neural Decoders via Iterative Pruning
Vikrant Malik, Rohan Ghosh, Mehul Motani
The advancement of deep learning has led to the development of neural decoders for low latency communications. However, neural decoders can be very complex which can lead to increa…
cs.LG2021
S-Cyc: A Learning Rate Schedule for Iterative Pruning of ReLU-based Networks
Shiyu Liu, Chong Min John Tan, Mehul Motani
We explore a new perspective on adapting the learning rate (LR) schedule to improve the performance of the ReLU-based network as it is iteratively pruned. Our work and contribution…
cs.LG2021★ 5 cited
Towards Better Long-range Time Series Forecasting using Generative Adversarial Networks
Shiyu Liu, Rohan Ghosh, Mehul Motani
Long-range time series forecasting is usually based on one of two existing forecasting strategies: Direct Forecasting and Iterative Forecasting, where the former provides low bias,…