Sina Honari

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Machine Learning and Computer Vision


Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation  
Edoardo Remelli, Shangchen Han, Sina Honari, Pascal Fua, and Robert Wang,
Computer Vision and Pattern Recognition (CVPR), 2020 [paper][slides][video][code].




On Adversarial Mixup Resynthesis
Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, and Christopher Pal,
Neural Information Processing Systems (NeurIPS), 2019 [paper][poster][video][code].




U-Net Fixed-Point Quantization for Medical Image Segmentation
MohammadHossein AskariHemmat, Sina Honari, Lucas Rouhier, Christian S. Perone, Julien Cohen-Adad, Yvon Savaria, and Jean-Pierre David,
Medical Image Computing and Computer Assisted Intervention (MICCAI)-Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention workshop, 2019 [paper][slides][code].



Unsupervised Depth Estimation, 3D Face Rotation and Replacement
Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte,
Sina Honari, and Christopher Pal,
Neural Information Processing Systems (NIPS), 2018 [paper][slides][poster][video][code].



Distribution Matching Losses Can Hallucinate Features in Medical Image Translation
Joseph Paul Cohen, Margaux Luck, and Sina Honari,
Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018, Oral (4% of submissions) [paper][poster][slides].





How to Cure Cancer (in images) with Unpaired Image Translation
Joseph Paul Cohen, Margaux Luck, and Sina Honari,
Medical Imaging with Deep Learning (MIDL), 2018 [paper].






Improving Landmark Localization with Semi-Supervised Learning
Sina Honari, Pavlo Molchanov, Stephen Tyree, Pascal Vincent, Christopher Pal, and Jan Kautz,
Computer Vision and Pattern Recognition (CVPR), 2018 [paper][poster][slides][video].






Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Shanxin Yuan, Guillermo Garcia-Hernando, Bjorn Stenger, Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee, Pavlo Molchanov, Jan Kautz, Sina Honari, Liuhao Ge, Junsong Yuan, Xinghao Chen, Guijin Wang, Fan Yang, Kai Akiyama, Yang Wu, Qingfu Wan, Meysam Madadi, Sergio Escalera, Shile Li, Dongheui Lee, Iason Oikonomidis, Antonis Argyros, and Tae-Kyun Kim,
Computer Vision and Pattern Recognition (CVPR), 2018, spotlight presentation (6.7% of submissions) [paper][poster][slides].


Learning to generate samples from noise through infusion training
Florian Bordes, Sina Honari, and Pascal Vincent,
International Conference on Learning Representations (ICLR), 2017 [paper][code][poster].




Recombinator networks: Learning coarse-to-fine feature aggregation
Sina Honari, Jason Yosinski, Pascal Vincent, and Christopher Pal,
Computer Vision and Pattern Recognition (CVPR), 2016, spotlight presentation (9.7% of submissions) [paper][code][poster][slides].




Improving facial analysis and performance driven animation through disentangling identity and expression
David Rim, Sina Honari, Md Kamrul Hasan, and Christopher Pal,
Image and Vision Computing Journal, 2016 [paper].






Trust Estimation in Multi-Agent Systems


My master’s research was on representing trust in multi-agent systems. A multi-agent system is composed of many interacting, rational and autonomous computer systems known as agents, each with their own objectives and knowledge of each other. In general agents may cooperate with each other or pursue their own objectives greedily. Trust is the expectation or the belief that a party will act benignly and cooperatively with the trusting party. In this research I proposed a trust representation of an agent based on possibility theory which allowed merging of information received from different agents considering the uncertainties in the acquired information. This allowed representing the trust of an agent through the information acquired from other agents.



Uncertainty-Based Trust Estimation in a Multi-Valued Trust Environment,
Sina Honari, Brigitte Jaumard, and Jamal Bentahar,
International Journal on Artificial Intelligence Tools (IJAIT), 2013.

An Approach on Merging Agents’ Trust Distributions in a Possibilitic Domain,
Sina Honari, Brigitte Jaumard, and Jamal Bentahar,
Communications in Computer and Information Science (CCIS), 2012.

Merging Successive Possibility Distributions for Trust Estimation Under Uncertainty in Multi-Agent Systems,
Sina Honari, Brigitte Jaumard, and Jamal Bentahar,
4th International Conference on Agents and Artificial Intelligence (ICAART), 2012.

Under Uncertainty Trust Estimation through Unknown Agents, in a Multi-Valued Trust Environment,
Sina Honari, Brigitte Jaumard, and Jamal Bentahar,
23rd International Conference on Tools with Artificial Intelligence (ICTAI), 2011.


Automated Market Mechanism


In the final years of my undergrad I worked on market mechanism design. As part of this research I participated in the international Trading Agent Competition - Market Design Scenation (TAC-CAT). Market is a group of interacting entities through platforms like stock-exchanges, a decentralized system with interacting self-interested agents whose activities are buying and selling goods and services. In this context, a mechanism is a combination of strategies available to participant agents in the system and the outcome rules, which provides solutions to resource allocation problems. The goal here was to design market makers. The market makers can be thought of as the London Stock Exchange or NYSE which compete with each-other on the stock traders. We designed a market maker by defining its policies such as which buy and sell demands (asks and bids) can be placed in the market and how to match them, setting the price of the transactions, and establishing the market clearing policy. The goal was to design the market policies in a way to attract potential buyers and sellers, maximize the percentage of matched asks and bids, and meanwhile maximize the profit of the market maker itself through the fees charged to the traders. The market policies were evaluated while several concurrent markets were in competition.


Price Estimation of PersianCAT Market Equilibrium,
Sina Honari, Mojtaba Ebadi, Amin Fos-hati, Maziar Gomrokchi, Jamal Bentahar, and Babak Khosravifar,
International Joint Conference on Artificial Intelligence, Trading Agent Design and Analysis Workshop (IJCAI-TADA 09), 2009.

Simulating New Markets by Introducing New Accepting Policies for the Conventional Continuous Double Auction,
Sina Honari, Maziar gomrokchi, Mojtaba Ebadi, Amin Fos-hati, and Jamal Bentahar,
Agent-Directed Simulation Symposium (ADS’08), 2008.

Evaluation of PersianCAT Agent’s Accepting Policy in Continuous Double Auction, Participant in CAT 2007 Competition,
Sina Honari, Amin Fos-hati, Mojtaba Ebadi, and Maziar gomrokchi,
International Computer Society of Iran Computer Conference (CSICC), 2008.