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Nadhir Hassen

Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e
Sujets de recherche
Apprentissage profond
Modèles génératifs
Optimisation
Réseaux de neurones en graphes

Publications

Lag-Llama: Towards Foundation Models for Time Series Forecasting
Kashif Rasul
Andrew Robert Williams
Marin Biloš
Hena Ghonia
Anderson Schneider
Sahil Garg
Yuriy Nevmyvaka
Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-… (voir plus)Llama, a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen "out-of-distribution" time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws to fit and predict model scaling behavior. The open source code is made available at https://github.com/kashif/pytorch-transformer-ts.
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Kashif Rasul
Andrew Robert Williams
Marin Bilovs
Hena Ghonia
Anderson Schneider
Sahil Garg
Yuriy Nevmyvaka
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu
Moksh J. Jain
Bonaventure F. P. Dossou
Qianli Shen
Anirudh Goyal
Xu Ji
Kenji Kawaguchi
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu
Moksh J. Jain
Bonaventure F. P. Dossou
Qianli Shen
Anirudh Goyal
Xu Ji
Kenji Kawaguchi
Approximate Bayesian Optimisation for Neural Networks