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Publications
Defining Feasibility as a Criterion for Essential Surgery: A Qualitative Study with Global Children’s Surgery Experts
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-sho… (see more)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.
Deep Learning Benchmark for First Break Detection from Hardrock Seismic Reflection Data
Pierre-Luc St-Charles
Bruno Rousseau
Joumana Ghosn
Gilles Bellefleur
E. Schetselaar
Deep learning techniques are used to tackle a variety of tasks related to seismic data processing and interpretation. While many works have … (see more)shown the benefits of deep learning, assessing the generalization capabilities of proposed methods to data acquired in different conditions and geological environments remains challenging. This is especially true for applications in hardrock environments where seismic surveys are still relatively rare. The primary factors that impede the adoption of machine learning in geosciences include the lack of publicly available and labeled datasets, and the use of inadequate evaluation methodologies. Since machine learning models are prone to overfit and underperform when the data used to train them is site-specific, the applicability of these models on new survey data that could be considered “out-of-distribution” is rarely addressed. This is unfortunate, as evaluating predictive models in out-of-distribution settings can provide a good insight into their usefulness in real-world use cases. To tackle these issues, we propose a simple benchmarking methodology for first break picking to evaluate the transferability of deep learning models that are trained across different environments and acquisition conditions. For this, we consider a reflection seismic survey dataset acquired at five distinct hardrock mining sites combined with annotations for first break picking. We train and evaluate a baseline deep learning solution based on a U-Net for future comparisons, and discuss potential improvements to this approach.