Search Results for author: Zhizhen Zhao

Found 35 papers, 10 papers with code

A Spectral Method for Joint Community Detection and Orthogonal Group Synchronization

no code implementations25 Dec 2021 Yifeng Fan, Yuehaw Khoo, Zhizhen Zhao

Community detection and orthogonal group synchronization are both fundamental problems with a variety of important applications in science and engineering.

Community Detection

An Adversarial Learning Based Approach for Unknown View Tomographic Reconstruction

no code implementations23 Aug 2021 Mona Zehni, Zhizhen Zhao

The goal of 2D tomographic reconstruction is to recover an image given its projections from various views.

A FAIR and AI-ready Higgs boson decay dataset

no code implementations4 Aug 2021 Yifan Chen, E. A. Huerta, Javier Duarte, Philip Harris, Daniel S. Katz, Mark S. Neubauer, Daniel Diaz, Farouk Mokhtar, Raghav Kansal, Sang Eon Park, Volodymyr V. Kindratenko, Zhizhen Zhao, Roger Rusack

To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models.


Spacetime Neural Network for High Dimensional Quantum Dynamics

no code implementations4 Aug 2021 Jiangran Wang, Zhuo Chen, Di Luo, Zhizhen Zhao, Vera Mikyoung Hur, Bryan K. Clark

We develop a spacetime neural network method with second order optimization for solving quantum dynamics from the high dimensional Schr\"{o}dinger equation.

Joint Community Detection and Rotational Synchronization via Semidefinite Programming

no code implementations13 May 2021 Yifeng Fan, Yuehaw Khoo, Zhizhen Zhao

In the presence of heterogeneous data, where randomly rotated objects fall into multiple underlying categories, it is challenging to simultaneously classify them into clusters and synchronize them based on pairwise relations.

Community Detection Stochastic Block Model

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

no code implementations13 May 2021 E. A. Huerta, Zhizhen Zhao

In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal processing algorithms.

DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization

no code implementations13 May 2021 Safa Messaoud, Ismini Lourentzou, Assma Boughoula, Mona Zehni, Zhizhen Zhao, ChengXiang Zhai, Alexander G. Schwing

The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content.

reinforcement-learning Video Summarization

Spatial-temporal switching estimators for imaging locally concentrated dynamics

no code implementations19 Feb 2021 Parisa Karimi, Mark Butala, Zhizhen Zhao, Farzad Kamalabadi

The evolution of images with physics-based dynamics is often spatially localized and nonlinear.


MSR-GAN: Multi-Segment Reconstruction via Adversarial Learning

1 code implementation18 Feb 2021 Mona Zehni, Zhizhen Zhao

We formulate MSR as a distribution matching problem where the goal is to recover the signal and the probability distribution of the segments such that the distribution of the generated measurements following a known forward model is close to the real observations.

UVTomo-GAN: An adversarial learning based approach for unknown view X-ray tomographic reconstruction

1 code implementation9 Feb 2021 Mona Zehni, Zhizhen Zhao

To accommodate this, we use Gumbel-softmax approximation of samples from categorical distribution to approximate the generator's loss as a function of the unknown image and the projection distribution.

Gauge Invariant Autoregressive Neural Networks for Quantum Lattice Models

no code implementations18 Jan 2021 Di Luo, Zhuo Chen, Kaiwen Hu, Zhizhen Zhao, Vera Mikyoung Hur, Bryan K. Clark

Gauge invariance plays a crucial role in quantum mechanics from condensed matter physics to high energy physics.

Adversarial Linear Contextual Bandits with Graph-Structured Side Observations

no code implementations10 Dec 2020 Lingda Wang, Bingcong Li, Huozhi Zhou, Georgios B. Giannakis, Lav R. Varshney, Zhizhen Zhao

The second algorithm, \texttt{EXP3-LGC-IX}, is developed for a special class of problems, for which the regret is reduced to $\mathcal{O}(\sqrt{\alpha(G)dT\log{K}\log(KT)})$ for both directed as well as undirected feedback graphs.

Multi-Armed Bandits

Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem

no code implementations9 Dec 2020 Bingcong Li, Lingda Wang, Georgios B. Giannakis, Zhizhen Zhao

Relying on no problem dependent parameters in the step sizes, the convergence rate of ExtraFW for general convex problems is shown to be ${\cal O}(\frac{1}{k})$, which is optimal in the sense of matching the lower bound on the number of solved FW subproblems.

Matrix Completion

Efficient model selection in switching linear dynamic systems by graph clustering

no code implementations8 Dec 2020 Parisa Karimi, Mark Butala, Zhizhen Zhao, Farzad Kamalabadi

The computation required for a switching Kalman Filter (SKF) increases exponentially with the number of system operation modes.

Graph Clustering Model Selection

Quantification of mismatch error in randomly switching linear state-space models

1 code implementation8 Dec 2020 Parisa Karimi, Zhizhen Zhao, Mark Butala, Farzad Kamalabadi

One can use these derivations to quantify the average performance of filters beforehand and decide which filter to run in operation to have the best performance in terms of estimation error and computation complexity.

Nearly Optimal Algorithms for Piecewise-Stationary Cascading Bandits

no code implementations12 Sep 2019 Lingda Wang, Huozhi Zhou, Bingcong Li, Lav R. Varshney, Zhizhen Zhao

Cascading bandit (CB) is a popular model for web search and online advertising, where an agent aims to learn the $K$ most attractive items out of a ground set of size $L$ during the interaction with a user.

Unsupervised Co-Learning on $\mathcal{G}$-Manifolds Across Irreducible Representations

1 code implementation6 Jun 2019 Yifeng Fan, Tingran Gao, Zhizhen Zhao

We introduce a novel co-learning paradigm for manifolds naturally equipped with a group action, motivated by recent developments on learning a manifold from attached fibre bundle structures.

Community Detection

Multi-Frequency Vector Diffusion Maps

no code implementations6 Jun 2019 Yifeng Fan, Zhizhen Zhao

We introduce multi-frequency vector diffusion maps (MFVDM), a new framework for organizing and analyzing high dimensional datasets.

Dimensionality Reduction

Representation Theoretic Patterns in Multi-Frequency Class Averaging for Three-Dimensional Cryo-Electron Microscopy

no code implementations31 May 2019 Yifeng Fan, Tingran Gao, Zhizhen Zhao

We develop in this paper a novel intrinsic classification algorithm -- multi-frequency class averaging (MFCA) -- for classifying noisy projection images obtained from three-dimensional cryo-electron microscopy (cryo-EM) by the similarity among their viewing directions.

Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps

no code implementations16 Apr 2019 Yifeng Fan, Zhizhen Zhao

Cryo-electron microscopy (EM) single particle reconstruction is an entirely general technique for 3D structure determination of macromolecular complexes.

Cryogenic Electron Microscopy (cryo-EM) Denoising +2

Max-Sliced Wasserstein Distance and its use for GANs

no code implementations CVPR 2019 Ishan Deshpande, Yuan-Ting Hu, Ruoyu Sun, Ayis Pyrros, Nasir Siddiqui, Sanmi Koyejo, Zhizhen Zhao, David Forsyth, Alexander Schwing

Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning.

Image-to-Image Translation Translation

Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders

no code implementations6 Mar 2019 Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao

Denoising of time domain data is a crucial task for many applications such as communication, translation, virtual assistants etc.

Denoising Time Series +1

Statistically-informed deep learning for gravitational wave parameter estimation

no code implementations5 Mar 2019 Hongyu Shen, E. A. Huerta, Eamonn O'Shea, Prayush Kumar, Zhizhen Zhao

Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters $(m_1, m_2, a_f, \omega_R, \omega_I)$ of five binary black holes: $\texttt{GW150914}, \texttt{GW170104}, \texttt{GW170814}, \texttt{GW190521}$ and $\texttt{GW190630}$.

Contrastive Learning

Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era

no code implementations1 Feb 2019 Gabrielle Allen, Igor Andreoni, Etienne Bachelet, G. Bruce Berriman, Federica B. Bianco, Rahul Biswas, Matias Carrasco Kind, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Daniel George, Tom Gibbs, Matthew Graham, William Gropp, Anushri Gupta, Roland Haas, E. A. Huerta, Elise Jennings, Daniel S. Katz, Asad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Kenton McHenry, J. M. Miller, M. S. Neubauer, Steve Oberlin, Alexander R. Olivas Jr, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard Schutz, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Brigitta M. Sipőcz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, JinJun Xiong, Zhizhen Zhao

We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.

LanczosNet: Multi-Scale Deep Graph Convolutional Networks

1 code implementation ICLR 2019 Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel

We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution.

Node Classification

Steerable $e$PCA: Rotationally Invariant Exponential Family PCA

1 code implementation20 Dec 2018 Zhizhen Zhao, Lydia T. Liu, Amit Singer

The second is steerable PCA, a fast and accurate procedure for including all planar rotations for PCA.

Geometric Invariants for Sparse Unknown View Tomography

1 code implementation25 Nov 2018 Mona Zehni, Shuai Huang, Ivan Dokmanić, Zhizhen Zhao

For a point source model, we show that these features reveal geometric information about the model such as the radial and pairwise distances.

Real-time regression analysis with deep convolutional neural networks

1 code implementation7 May 2018 E. A. Huerta, Daniel George, Zhizhen Zhao, Gabrielle Allen

We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data.

Time Series

Multi-Segment Reconstruction Using Invariant Features

1 code implementation25 Feb 2018 Mona Zehni, Minh N. Do, Zhizhen Zhao

Instead of trying to locate the segment within the sequence through pair-wise matching, we propose a new approach that uses shift-invariant features to estimate both the underlying signal and the distribution of the positions of the segments.

Signal Processing

Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders

no code implementations27 Nov 2017 Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao

Gravitational wave signals are often extremely weak and the data from the detectors, such as LIGO, is contaminated with non-Gaussian and non-stationary noise, often containing transient disturbances which can obscure real signals.

Denoising Dictionary Learning

Mahalanobis Distance for Class Averaging of Cryo-EM Images

no code implementations10 Nov 2016 Tejal Bhamre, Zhizhen Zhao, Amit Singer

Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a technique in which the 3D structure of a molecule needs to be determined from its contrast transfer function (CTF) affected, noisy 2D projection images taken at unknown viewing directions.

General Classification

Fast Steerable Principal Component Analysis

no code implementations2 Dec 2014 Zhizhen Zhao, Yoel Shkolnisky, Amit Singer

Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2D images as large as a few hundred pixels in each direction.

Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM

no code implementations29 Sep 2013 Zhizhen Zhao, Amit Singer

Our pipeline for viewing angle classification and alignment is experimentally shown to be faster and more accurate than reference-free alignment with rotationally invariant K-means clustering, MSA/MRA 2D classification, and their modern approximations.

Classification General Classification

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