1 code implementation • 29 Sep 2023 • Leo Feng, Frederick Tung, Hossein Hajimirsadeghi, Yoshua Bengio, Mohamed Osama Ahmed
In this work, we propose Tree Cross Attention (TCA) - a module based on Cross Attention that only retrieves information from a logarithmic $\mathcal{O}(\log(N))$ number of tokens for performing inference.
no code implementations • 21 Jun 2023 • Leo Feng, Frederick Tung, Hossein Hajimirsadeghi, Yoshua Bengio, Mohamed Osama Ahmed
Modern foundation model architectures rely on attention mechanisms to effectively capture context.
no code implementations • 23 May 2023 • Leo Feng, Frederick Tung, Hossein Hajimirsadeghi, Yoshua Bengio, Mohamed Osama Ahmed
Neural Processes (NPs) are popular meta-learning methods for efficiently modelling predictive uncertainty.
1 code implementation • 15 Nov 2022 • Leo Feng, Hossein Hajimirsadeghi, Yoshua Bengio, Mohamed Osama Ahmed
We demonstrate that LBANPs can trade-off the computational cost and performance according to the number of latent vectors.
1 code implementation • 9 Nov 2022 • Saghar Irandoust, Thibaut Durand, Yunduz Rakhmangulova, Wenjie Zi, Hossein Hajimirsadeghi
We introduce some algorithmic improvements to enable training a ViT model from scratch with limited hardware (1 GPU) and time (24 hours) resources.
1 code implementation • 17 Jun 2022 • Leo Feng, Mohamed Osama Ahmed, Hossein Hajimirsadeghi, Amir Abdi
We tackle the problem of Selective Classification where the objective is to achieve the best performance on a predetermined ratio (coverage) of the dataset.
no code implementations • 17 May 2022 • Joao Monteiro, Mohamed Osama Ahmed, Hossein Hajimirsadeghi, Greg Mori
We study settings where gradient penalties are used alongside risk minimization with the goal of obtaining predictors satisfying different notions of monotonicity.
no code implementations • 29 Sep 2021 • Joao Monteiro, Mohamed Osama Ahmed, Hossein Hajimirsadeghi, Greg Mori
We study the setting where risk minimization is performed over general classes of models and consider two cases where monotonicity is treated as either a requirement to be satisfied everywhere or a useful property.
no code implementations • 20 Jun 2021 • Hamed Shirzad, Hossein Hajimirsadeghi, Amir H. Abdi, Greg Mori
We propose TD-GEN, a graph generation framework based on tree decomposition, and introduce a reduced upper bound on the maximum number of decisions needed for graph generation.
no code implementations • 25 Feb 2021 • Yu Gong, Hossein Hajimirsadeghi, JiaWei He, Thibaut Durand, Greg Mori
Learning from heterogeneous data poses challenges such as combining data from various sources and of different types.
1 code implementation • 17 Jan 2020 • Lei Chen, Jianhui Chen, Hossein Hajimirsadeghi, Greg Mori
Then, we develop an efficient weight-transfer method to explain decisions for any image without back-propagation.
no code implementations • pproximateinference AABI Symposium 2019 • Yu Gong, Hossein Hajimirsadeghi, JiaWei He, Megha Nawhal, Thibaut Durand, Greg Mori
Despite promising progress on unimodal data imputation (e. g. image inpainting), models for multimodal data imputation are far from satisfactory.
no code implementations • 2 Oct 2019 • Shih-Yang Su, Hossein Hajimirsadeghi, Greg Mori
Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes.
no code implementations • 25 Sep 2019 • Yu Gong, Hossein Hajimirsadeghi, JiaWei He, Megha Nawhal, Thibaut Durand, Greg Mori
Learning from only partially-observed data for imputation has been an active research area.
no code implementations • ICCV 2015 • Hossein Hajimirsadeghi, Greg Mori
This paper presents HCRF-Boost, a novel and general framework for learning HCRFs in functional space.
no code implementations • 30 Jun 2015 • Ali Madooei, Mark S. Drew, Hossein Hajimirsadeghi
We propose a novel approach to identify one of the most significant dermoscopic criteria in the diagnosis of Cutaneous Melanoma: the Blue-whitish structure.
no code implementations • 12 Feb 2015 • Mehran Khodabandeh, Arash Vahdat, Guang-Tong Zhou, Hossein Hajimirsadeghi, Mehrsan Javan Roshtkhari, Greg Mori, Stephen Se
We present a novel approach for discovering human interactions in videos.
no code implementations • CVPR 2015 • Hossein Hajimirsadeghi, Wang Yan, Arash Vahdat, Greg Mori
Many visual recognition problems can be approached by counting instances.
no code implementations • 26 Sep 2013 • Hossein Hajimirsadeghi, Jinling Li, Greg Mori, Mohammad Zaki, Tarek Sayed
We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks.