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.

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Publications

ICIDGM

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.

NP

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.

Epidemiology

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.

LaTeq

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

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

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.

LMP-BCFW

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
UBC logo

University of British Columbia
M.Sc. in Computer Science
Supervisor: Frank Wood

Sharif logo

Sharif University of Technology
B.Sc. in Computer Engineering
Minor degree in Mathematics


Website source forked from Jon Barron.