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L. Seidenari
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This lecture will cover recent advances in methodologies to forecast quantities using deep
neural networks with applications to autonomous agents, video streaming and network
traffic forecasting. We first briefly introduce sequence prediction problems introducing
the main architectural choices, such as RNNs, LSTMs and Transformers. Then we will
delve into forecasting of agent motion in different settings, reporting on our recent
research in social trajectory forecasting with the use of memory augmented neural
networks. Finally, we will conclude with recent results on large models for time series
forecasting and their application to network traffic estimation.
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