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In this project we have explored the use of imaging time series to enhance forecasting results with Neural Networks. The approach has revealed itself to be extremely promising as, both in combination with an LSTM architecture and without, it has out-performed the pure LSTM architecture by a solid margin within our test datasets.
My solution to the modules of the Information Security Lab HS2021 (263-0009-00L) at ETH Zurich going from crypto operation and TLS implementations over Trusted Execution Environments to binary exploitation.
This tool evaluates the street network around a location to determine the maximum possible downhill elevation drop and compute a conservative energy buffer requirement for vehicles.
Multimodal Parquet Dataset with support for video and robot trajectories, windowed access, and checkpointing. Final project for the Large-scale AI Engineering class at ETH Zürich