Saeid Naderiparizi

I am a second year Ph.D. student in Computer Science at the University of British Columbia. I am a member of the PLAI lab led by Frank Wood.
My research interests are machine learning, Bayesian inference, meta-learning, and probabilistic modeling.

Email / Google Scholar / GitHub / LinkedIn

Publications

	Image Completion via Inference in Deep Generative Models , Saeid Naderiparizi, arXiv preprint, 2021 Image completion task from the perpective of amortized inference in image generative models, particularly, variational auto-encoders. It leads to diverse, realistic image completions even when the vast majority of an image is missing.
	Uncertainty in Neural Processes Saeid Naderiparizi, , , arXiv preprint, 2020 Achieving calibrated uncertainty in Neural Processes, especially in low data regime, via different architectures and training objectives.
	Planning as Inference in Epidemiological Models , , Saeid Naderiparizi, , , , , , arXiv preprint, 2020 Automating parts of infectious disease-control policy-making via performing inference in existing epidemiological dynamics models.
	Amortized rejection sampling in universal probabilistic programming Saeid Naderiparizi, , , , , , , , , , , arXiv preprint, 2019 Identifying and addressing the problems caused by rejection sampling loops in inference in universal probabilistic programming.
	Coping With Simulators That Don't Always Return , Saeid Naderiparizi, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 Model learning in models partially defined through "brittle" deterministic simulators. A "brittle" simulator is one which would fail to produce results for invalid inputs that might occur over the course of training. An ODE solver not converging to the required tolerance in the allocated time or solid bodies intersecting in robot simulators are examples of simulator failure.
	Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model , , , , Saeid Naderiparizi, , , , , , , , , , Neural Information Processing Systems (NeurIPS), 2019 A novel probabilistic programming framework that couples to the existing large-scale simulators via a cross-platform protocol, allowing automatic inference in such simulators.
	Etalumis: bringing probabilistic programming to scientific simulators at scale , , , , , , , Saeid Naderiparizi, , , , , , , , , Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, 2019

Other research projects

	LaTeq: Converting equation images to LaTeX Under the supervision of Frank Wood We trained a generative model for LaTeX equation codes, which will be used as a part of a probabilistic program to perform Bayesian inference for converting equation images to LaTeX codes.
	A bayesian approach to Visual Question Answering , Saeid Naderiparizi, Probabilistic Programming course, 2018 Using probabilistic programming and inference compilation to solve visual question answering task.
	RegNet: Regularizing Deep Networks (code) , Saeid Naderiparizi Multimodal Learning with Vision, Language and Sound course, 2018 We can define regularizers to improve model's performance in the case of not having enough data for few classes. We tried different regularizers and tested it on image classification task.
	A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Latent Structural SVMs Under the supervision of Christoph H. Lampert We designed and implemented a method for training Latent Structural SVMs with costly max-oracles using Frank-Wolfe algorithm on the dual objective. The method does not need specifying a learning rate and it gives a definite result.

Education

	University of British Columbia M.Sc. in Computer Science Supervisor: Frank Wood
	Sharif University of Technology B.Sc. in Computer Engineering Minor degree in Mathematics

Website source forked from Jon Barron.