Bilateral mesh denoising
- S. Fleishman, Iddo Drori, D. Cohen-Or
- Computer ScienceACM Transactions on Graphics
- 1 July 2003
It is shown that the proposed method successfully removes noise from meshes while preserving features, and excels in its simplicity both in concept and implementation.
Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit
- D. Donoho, Y. Tsaig, Iddo Drori, Jean-Luc Starck
- Computer ScienceIEEE Transactions on Information Theory
- 1 February 2012
Stagewise Orthogonal Matching Pursuit (StOMP) successively transforms the signal into a negligible residual, and numerical examples showing that StOMP rapidly and reliably finds sparse solutions in compressed sensing, decoding of error-correcting codes, and overcomplete representation are given.
Fragment-based image completion
- Iddo Drori, D. Cohen-Or, Y. Yeshurun
- MathematicsACM Transactions on Graphics
- 1 July 2003
A new method for completing missing parts caused by the removal of foreground or background elements from an image, iteratively approximating the unknown regions and composites adaptive image fragments into the image to synthesize a complete, visually plausible and coherent image.
Multiscale Representations for Manifold-Valued Data
- Inam Ur Rahman, Iddo Drori, V. Stodden, D. Donoho, P. Schröder
- MathematicsMultiscale Modeling & simulation
- 2005
Multiscale representations for data observed on equispaced grids and taking values in manifolds such as the sphere, the special orthogonal group, the positive definite matrices, and the Grassmann manifolds, using theExp and Log maps of those manifolds are described.
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning
- Qiang Ma, Suwen Ge, Danyang He, D. Thaker, Iddo Drori
- Computer ScienceArXiv
- 12 November 2019
This work introduces Graph Pointer Networks (GPNs) trained using reinforcement learning (RL) for tackling the traveling salesman problem (TSP), and demonstrates that GPNs trained on small-scale TSP50/100 problems generalize well to larger- scale TSP500/1000 problems, with shorter tour lengths and faster computational times.
Fast Minimization by Iterative Thresholding for Multidimensional NMR Spectroscopy
- Iddo Drori
- Computer ScienceEURASIP Journal on Advances in Signal Processing
- 15 November 2007
The first method which takes advantage of the sparsity of the wavelet representation of the NMR spectra and reconstructs the spectra from partial random measurements of its free induction decay by solving the following optimization problem.
AlphaD3M: Machine Learning Pipeline Synthesis
- Iddo Drori, Yamuna Krishnamurthy, J. Freire
- Computer ScienceArXiv
- 3 November 2021
This work introduces AlphaD3M, an automatic machine learning (AutoML) system based on meta reinforcement learning using sequence models with self play that achieves competitive performance while being an order of magnitude faster, reducing computation time from hours to minutes, and is explainable by design.
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
- Iddo Drori, Yamuna Krishnamurthy, J. Freire
- Computer ScienceArXiv
- 24 May 2019
This work extends AlphaD3M by using a pipeline grammar and a pre-trained model which generalizes from many different datasets and similar tasks and demonstrates improved performance compared with earlier work and existing methods on AutoML benchmark datasets for classification and regression tasks.
Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time
- Iddo Drori, Anant Kharkar, Madeleine Udell
- Computer ScienceInternational Conference on Machine Learning and…
- 6 June 2020
This work develops a new framework to solve any combinatorial optimization problem over graphs that can be formulated as a single player game defined by states, actions, and rewards, including minimum spanning tree, shortest paths, traveling salesman problem, and vehicle routing problem, without expert knowledge.
High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures
- Iddo Drori, Isht Dwivedi, I. Pe’er
- Computer ScienceArXiv
- 17 November 2018
This work uses an ensemble of strong predictors to achieve accuracy of 70.7% (on the CB513 test set using the CB6133filtered training set) and aims to set a gold standard for purity of training and testing sets.
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