187 citations · 705 across the 37 of their papers we have counts for
6 papers · 1 filter
Learning to Bound: A Generative Cramér-Rao Bound
Hai Victor Habi, Hagit Messer, Yoram Bresler
The Cramér-Rao bound (CRB), a well-known lower bound on the performance of any unbiased parameter estimator, has been used to study a wide variety of problems. However, to obtain t…
Joint Dimensionality Reduction for Separable Embedding Estimation
Yanjun Li, Bihan Wen, Hao Cheng +1
Low-dimensional embeddings for data from disparate sources play critical roles in multi-modal machine learning, multimedia information retrieval, and bioinformatics. In this paper,…
Improving Robustness of Deep-Learning-Based Image Reconstruction
Ankit Raj, Yoram Bresler, Bo Li
Deep-learning-based methods for different applications have been shown vulnerable to adversarial examples. These examples make deployment of such models in safety-critical tasks qu…
GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems
Ankit Raj, Yuqi Li, Yoram Bresler
A Generative Adversarial Network (GAN) with generator trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed invers…
FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning and Applications
Bihan Wen, Saiprasad Ravishankar, Yoram Bresler
Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision. However, synthesis d…
Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI
Saiprasad Ravishankar, Yoram Bresler
Natural signals and images are well-known to be approximately sparse in transform domains such as Wavelets and DCT. This property has been heavily exploited in various applications…