Search Results for author:
Found 26 papers, 8 papers with code
Toward Data-Driven STAP Radar
In this regard, we will generate a large, representative adaptive radar signal processing database for training and testing, analogous in spirit to the COCO dataset for natural images.
On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections
We propose a new structured pruning framework for compressing Deep Neural Networks (DNNs) with skip connections, based on measuring the statistical dependency of hidden layers and predicted outputs.
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
To this end, we propose a physics-infused deep neural network based on the Blaschke products for phase retrieval.
Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials
Recently, it has been shown that deep learning can be an alternative solution to infer the relationship between an AEM geometry and its properties using a (relatively) small pool of CEMS data.
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning
We propose an asymmetric affinity score for representing the complexity of utilizing the knowledge of one task for learning another one.
A Methodology for Exploring Deep Convolutional Features in Relation to Hand-Crafted Features with an Application to Music Audio Modeling
In this work, we instead take the perspective of relating deep features to well-studied, hand-crafted features that are meaningful for the application of interest.
Fisher Task Distance and Its Application in Neural Architecture Search
Next, we construct an online neural architecture search framework using the Fisher task distance, in which we have access to the past learned tasks.
Improved Automated Machine Learning from Transfer Learning
In this paper, we propose a neural architecture search framework based on a similarity measure between some baseline tasks and a target task.
Model-Free Energy Distance for Pruning DNNs
We measure a new model-free information between the feature maps and the output of the network.
Task-Aware Neural Architecture Search
The design of handcrafted neural networks requires a lot of time and resources.
Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the high-dimensional conditional distributions learned by a normalizing flow.
GeoStat Representations of Time Series for Fast Classification
GeoStat representations are based off of a generalization of recent methods for trajectory classification, and summarize the information of a time series in terms of comprehensive statistics of (possibly windowed) distributions of easy to compute differential geometric quantities, requiring no dynamic time warping.
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery
In this paper, we study two important problems in the automated design of neural networks -- Hyper-parameter Optimization (HPO), and Neural Architecture Search (NAS) -- through the lens of sparse recovery methods.
Perception-Distortion Trade-off with Restricted Boltzmann Machines
In this work, we introduce a new procedure for applying Restricted Boltzmann Machines (RBMs) to missing data inference tasks, based on linearization of the effective energy function governing the distribution of observations.
One-Shot Neural Architecture Search via Compressive Sensing
Neural Architecture Search remains a very challenging meta-learning problem.
LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS
Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions.
Unsupervised Demixing of Structured Signals from Their Superposition Using GANs
In this paper, we consider the observation setting in which the samples from a target distribution are given by the superposition of two structured components, and leverage GANs for learning of the structure of the components.
Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing
Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions.
Fast Low-Rank Matrix Estimation without the Condition Number
In this paper, we provide a novel algorithmic framework that achieves the best of both worlds: asymptotically as fast as factorization methods, while requiring no dependency on the condition number.
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging
We consider the problem of reconstructing signals and images from periodic nonlinearities.
Demixing Structured Superposition Signals from Periodic and Aperiodic Nonlinear Observations
We consider the demixing problem of two (or more) structured high-dimensional vectors from a limited number of nonlinear observations where this nonlinearity is due to either a periodic or an aperiodic function.
Fast Algorithms for Learning Latent Variables in Graphical Models
Existing methods for this problem assume that the precision matrix of the observed variables is the superposition of a sparse and a low-rank component.
Improved Algorithms for Matrix Recovery from Rank-One Projections
We consider the problem of estimation of a low-rank matrix from a limited number of noisy rank-one projections.
Iterative Thresholding for Demixing Structured Superpositions in High Dimensions
Specifically, we show that for certain types of structured superposition models, our method provably recovers the components given merely $n = \mathcal{O}(s)$ samples where $s$ denotes the number of nonzero entries in the underlying components.
Stable Recovery Of Sparse Vectors From Random Sinusoidal Feature Maps
Random sinusoidal features are a popular approach for speeding up kernel-based inference in large datasets.
Fast Algorithms for Demixing Sparse Signals from Nonlinear Observations
We study the problem of demixing a pair of sparse signals from noisy, nonlinear observations of their superposition.
