Search Results for author:
Found 42 papers, 15 papers with code
DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection
In DeepDRK, we introduce a novel formulation of the knockoff model as a learning problem under multi-source adversarial attacks.
Convolutional GRU Network for Seasonal Prediction of the El Niño-Southern Oscillation
Predicting sea surface temperature (SST) within the El Ni\~no-Southern Oscillation (ENSO) region has been extensively studied due to its significant influence on global temperature and precipitation patterns.
FAIR AI Models in High Energy Physics
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery.
FAIR for AI: An interdisciplinary and international community building perspective
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
Initialization and Alignment for Adversarial Texture Optimization
While recovery of geometry from image and video data has received a lot of attention in computer vision, methods to capture the texture for a given geometry are less mature.
Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography
Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations.
Multi-Frequency Joint Community Detection and Phase Synchronization
This problem, with a variety of real-world applications, aims to recover the cluster structure and associated phase angles simultaneously.
A Spectral Method for Joint Community Detection and Orthogonal Group Synchronization
Community detection and orthogonal group synchronization are both fundamental problems with a variety of important applications in science and engineering.
An Adversarial Learning Based Approach for Unknown View Tomographic Reconstruction
The goal of 2D tomographic reconstruction is to recover an image given its projections from various views.
Spacetime Neural Network for High Dimensional Quantum Dynamics
We develop a spacetime neural network method with second order optimization for solving quantum dynamics from the high dimensional Schr\"{o}dinger equation.
A FAIR and AI-ready Higgs boson decay dataset
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.
Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources
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
The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content.
Joint Community Detection and Rotational Synchronization via Semidefinite Programming
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.
Spatial-temporal switching estimators for imaging locally concentrated dynamics
The evolution of images with physics-based dynamics is often spatially localized and nonlinear.
MSR-GAN: Multi-Segment Reconstruction via Adversarial Learning
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
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 and Anyonic Symmetric Transformer and RNN Quantum States for Quantum Lattice Models
Symmetries such as gauge invariance and anyonic symmetry play a crucial role in quantum many-body physics.
Adversarial Linear Contextual Bandits with Graph-Structured Side Observations
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.
Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem
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.
Efficient model selection in switching linear dynamic systems by graph clustering
The computation required for a switching Kalman Filter (SKF) increases exponentially with the number of system operation modes.
Quantification of mismatch error in randomly switching linear state-space models
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.
Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms
Myocardial infarction is the leading cause of death worldwide.
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
Nearly Optimal Algorithms for Piecewise-Stationary Cascading Bandits
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
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.
Multi-Frequency Vector Diffusion Maps
We introduce multi-frequency vector diffusion maps (MFVDM), a new framework for organizing and analyzing high dimensional datasets.
Representation Theoretic Patterns in Multi-Frequency Class Averaging for Three-Dimensional Cryo-Electron Microscopy
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
Cryo-electron microscopy (EM) single particle reconstruction is an entirely general technique for 3D structure determination of macromolecular complexes.
Max-Sliced Wasserstein Distance and its use for GANs
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.
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
Denoising of time domain data is a crucial task for many applications such as communication, translation, virtual assistants etc.
Statistically-informed deep learning for gravitational wave parameter estimation
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 $\texttt{GW190630}$.
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
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
We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution.
Steerable $e$PCA: Rotationally Invariant Exponential Family PCA
The second is steerable PCA, a fast and accurate procedure for including all planar rotations for PCA.
Geometric Invariants for Sparse Unknown View Tomography
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
We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data.
Multi-Segment Reconstruction Using Invariant Features
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
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.
Mahalanobis Distance for Class Averaging of Cryo-EM Images
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.
Fast Steerable Principal Component Analysis
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
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.
