Portrait de Mohammad Javad Darvishi Bayazi

Mohammad Javad Darvishi Bayazi

Collaborateur·rice de recherche - UdeM
Superviseur⋅e principal⋅e
Irina Rish
Co-supervisor
Irina Rish
Sujets de recherche
Apprentissage de représentations
Modèles de fondation
Neurosciences computationnelles
Séries temporelles
Site web
Google Scholar
GitHub

Publications

Introducing Brain Foundation Models
Mohammad Javad Darvishi Bayazi
Hena Ghonia
Roland Riachi
Bruno Aristimunha
Arian Khorasani
Md Rifat Arefin
Sylvain Chevallier
Amin Darabi
Guillaume Dumas
Irina Rish
Brain function represents one of the most complex systems driving our world. Decoding its signals poses significant challenges, particularly… (voir plus) due to the limited availability of data and the high cost of recordings. The existence of large hospital datasets and laboratory collections partially mitigates this issue. However, the lack of standardized recording protocols, varying numbers of channels, diverse setups, scenarios, and recording devices further complicate the task. This work addresses these challenges by introducing the Brain Foundation Model (BFM), a suite of open-source models trained on brain signals. These models serve as foundational tools for various types of time-series neuroimaging tasks. This work presents the first model of the BFM series, which is trained on electroencephalogram signal data. Our results demonstrate that BFM-EEG can generate signals more accurately than other models. Upon acceptance, we will release the model weights and pipeline.
2024-10-09
NeurIPS.cc/2024/Workshop/TSALM (publié)
openreview.net
openreview.net
When Machines Outshine Humans in Object Recognition, Benchmarking Dilemma
Mohammad Javad Darvishi Bayazi
Md Rifat Arefin
Jocelyn Faubert
Irina Rish
2024-09-14
Journal of Vision (publié)
doi.org
VFA: Vision Frequency Analysis of Foundation Models and Human
Mohammad Javad Darvishi Bayazi
Md Rifat Arefin
Jocelyn Faubert
Irina Rish
2024-07-02
ICML.cc/2024/Workshop/FM-Wild (poster)
doi.org
openreview.net
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Kashif Rasul
Arjun Ashok
Andrew Robert Williams
Hena Ghonia
Rishika Bhagwatkar
Arian Khorasani
Mohammad Javad Darvishi Bayazi
George Adamopoulos
Roland Riachi
Nadhir Hassen
Marin Biloš
Sahil Garg
Anderson Schneider
Nicolas Chapados
Alexandre Drouin
Valentina Zantedeschi
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.
2023-10-31
R0-FoMo @ Neural Information Processing Systems (poster)
doi.org
openreview.net
Neural efficiency in an aviation task with different levels of difficulty: Assessing different biometrics during a performance task
Mohammad Javad Darvishi Bayazi
Andrew Law
Sergio Mejia Romero
Sion Jennings
Irina Rish
Jocelyn Faubert
2023-07-31
Journal of Vision (publié)
doi.org
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks
Jean-christophe Gagnon-audet
Kartik Ahuja
Mohammad Javad Darvishi Bayazi
Guillaume Dumas
Irina Rish
2022-03-17
ArXiv (prépublication)
doi.org
arxiv.org
Generalizing to unseen domains via distribution matching
Isabela Albuquerque
Joao Monteiro
Mohammad Javad Darvishi Bayazi
Tiago Falk
Ioannis Mitliagkas
Supervised learning results typically rely on assumptions of i.i.d. data. Unfortunately, those assumptions are commonly violated in practice… (voir plus). In this work, we tackle this problem by focusing on domain generalization: a formalization where the data generating process at test time may yield samples from never-before-seen domains (distributions). Our work relies on a simple lemma: by minimizing a notion of discrepancy between all pairs from a set of given domains, we also minimize the discrepancy between any pairs of mixtures of domains. Using this result, we derive a generalization bound for our setting. We then show that low risk over unseen domains can be achieved by representing the data in a space where (i) the training distributions are indistinguishable, and (ii) relevant information for the task at hand is preserved. Minimizing the terms in our bound yields an adversarial formulation which estimates and minimizes pairwise discrepancies. We validate our proposed strategy on standard domain generalization benchmarks, outperforming a number of recently introduced methods. Notably, we tackle a real-world application where the underlying data corresponds to multi-channel electroencephalography time series from different subjects, each considered as a distinct domain.
2019-11-02
ArXiv (prépublication)
arxiv.org
arxiv.org