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The Little Engine That Could: Regularization by Denoising (RED)
This paper provides an alternative, more powerful, and more flexible framework for achieving Regularization by Denoising (RED): using the denoising engine in defining the regulariza...
Conformalized Quantile Regression
This paper proposes a new method that is fully adaptive to heteroscedasticity, which combines conformal prediction with classical quantile regression, inheriting the advantages of both.
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
This work proposes a novel multi-layer model, ML-CSC, in which signals are assumed to emerge from a cascade of CSC layers, and presents an alternative to the forward pass, which is connected to deconvolutional, recurrent and residual networks, and has better theoretical guarantees.
RAISR: Rapid and Accurate Image Super Resolution
- Yaniv Romano, J. Isidoro, P. Milanfar
- Computer ScienceIEEE Transactions on Computational Imaging
- 3 June 2016
This work illustrates how this effective sharpening algorithm, in addition to being of independent interest, can be used as a preprocessing step to induce the learning of more effective upscaling filters with built-in sharpening and contrast enhancement effect.
Convolutional Dictionary Learning via Local Processing
- V. Papyan, Yaniv Romano, Michael Elad, Jeremias Sulam
- Computer ScienceIEEE International Conference on Computer Vision…
- 9 May 2017
This work shows how one can efficiently solve the convolutional sparse pursuit problem and train the filters involved, while operating locally on image patches, and provides an intuitive algorithm that can leverage standard techniques from the sparse representations field.
Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning
- Jeremias Sulam, V. Papyan, Yaniv Romano, Michael Elad
- Computer ScienceIEEE Transactions on Signal Processing
- 29 August 2017
This work represents a bridge between matrix factorization, sparse dictionary learning, and sparse autoencoders, and it is shown that the training of the filters is essential to allow for nontrivial signals in the model, and an online algorithm to learn the dictionaries from real data, effectively resulting in cascaded sparse convolutional layers.
Boosting of Image Denoising Algorithms
This paper introduces an interesting interpretation of the SOS algorithm as a technique for closing the gap between the local patch-modeling and the global restoration task, thereby leading to improved performance.
Classification with Valid and Adaptive Coverage
A novel conformity score is developed, which is explicitly demonstrate to be powerful and intuitive for classification problems, but whose underlying principle is potentially far more general.
Single Image Interpolation Via Adaptive Nonlocal Sparsity-Based Modeling
- Yaniv Romano, M. Protter, Michael Elad
- Computer ScienceIEEE Transactions on Image Processing
- 20 May 2014
This paper proposes a novel image interpolation method, which combines these two forces-nonlocal self-similarities and sparse representation modeling, and the proposed method is contrasted with competitive and related algorithms, and demonstrated to achieve state-of-the-art results.
- Yaniv Romano, M. Sesia, E. Candès
- Computer ScienceJournal of the American Statistical Association
- 16 November 2018
A machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models and applies this new method to a real study of mutations linked to changes in drug resistance in the human immunodeficiency virus.