Publications

RelationalUNet for Image Segmentation

Ivaxi Sheth

Pedro H. M. Braga

Shiva Kanth Sujit

Sahar Dastani

Samira Ebrahimi Kahou

2023-10-15

Machine Learning in Medical Imaging (publié)

doi.org

Defining Feasibility as a Criterion for Essential Surgery: A Qualitative Study with Global Children’s Surgery Experts

Alizeh Abbas

Henry E. Rice

Dan Poenaru

Lubna Samad

2023-10-14

World Journal of Surgery (publié)

doi.org

Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN

Florence Regol

Joud Chataoui

Mark Coates

2023-10-13

ArXiv (prépublication)

doi.org

arxiv.org

Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models

Ronan Legin

Matthew Ho

Pablo Lemos

Laurence Perreault-Levasseur

Shirley Ho

Yashar Hezaveh

Benjamin Wandelt

2023-10-13

Monthly Notices of the Royal Astronomical Society: Letters (publié)

doi.org

arxiv.org

Predicting Solar PV Output Based on Hybrid Deep Learning and Physical
Models: Case Study of Morocco

Samira Abousaid

Loubna Benabbou

Hanane Dagdougui

Ismail Belhaj

Abdelaziz Berrado

Hicham Bouzekri

2023-10-13

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) (publié)

doi.org

Summary of the Fourth International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2023)

Matteo Biagiola

Nicolás Cardozo

Donghwan Shin

Foutse Khomh

Andrea Stocco

Vincenzo Riccio

2023-10-13

ACM SIGSOFT Softw. Eng. Notes (publié)

doi.org

A cry for help: Early detection of brain injury in newborns

Charles Onu

Samantha Latremouille

Arsenii Gorin

Junhao Wang

Uchenna Ekwochi

P. Ubuane

O. Kehinde

Muhammad A. Salisu

Datonye Briggs

Yoshua Bengio

Doina Precup

2023-10-12

ArXiv (prépublication)

doi.org

arxiv.org

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Kashif Rasul

Arjun Ashok

Andrew Robert Williams

Arian Khorasani

George Adamopoulos

Rishika Bhagwatkar

Marin Bilovs

Hena Ghonia

N. Hassen

Anderson Schneider

Sahil Garg

Alexandre Drouin

Nicolas Chapados

Yuriy Nevmyvaka

Irina Rish

Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-sho… (voir plus)t and few-shot generalization. However, despite the success of foundation models in modalities such as natural language processing and computer vision, the development of foundation models for time series forecasting has lagged behind. We present Lag-Llama, a general-purpose foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture that uses lags as covariates. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities compared to a wide range of forecasting models on downstream datasets across domains. Moreover, when fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance, outperforming prior deep learning approaches, emerging as the best general-purpose model on average. Lag-Llama serves as a strong contender to the current state-of-art in time series forecasting and paves the way for future advancements in foundation models tailored to time series data.

2023-10-12

ArXiv (prépublication)

arxiv.org