Video: MP4; File size: 522 MB; Duration: 01:11:02
Transformer network was first introduced in 2017 in the paper: Attention is all you need. They solve sequence-to-sequence tasks and are an improvement over Long Short Term Memory (LSTM) because they can handle a long range of dependencies. All of the previous architectures executed sequentially and did not use the GPU efficiently but transformers solved that problem with the multi-headed attention architecture. In this talk we will compare the, 1. Architectural differences between LSTM and Transformers. 2. Performance of LSTM vs. Transformer for a time series forecasting task based on the following criteria: a. Accuracy of prediction b. Complexity of the architecture c. Time to train
My name is Sandy Dash and I have been a PhD student in Teuscher Lab since Fall 2021. I have worked on one time-series forecasting project using Deep Neural Networks (DNN) which won the best poster award at the Winter School in Indian Institute of Technology, Jodhpur, India. I am a Principal Engineer at Ampere Computing and a former Intel employee with over a decade of experience in DRAM Memory Subsystem.
Transformer Networks, Forecasting
© 2023 Sandy Dash
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Dash, Sandy, "Modern Neural Networks for Time-Series Forecasting (Transformers vs. LSTMs)" (2023). Systems Science Friday Noon Seminar Series. 130.