289 citations · 1.7k across the 90 of their papers we have counts for
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ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
Qiang Qiu, Jose Lezama, Alex Bronstein +1
Hash codes are efficient data representations for coping with the ever growing amounts of data. In this paper, we introduce a random forest semantic hashing scheme that embeds tiny…
Towards CT-quality Ultrasound Imaging using Deep Learning
Sanketh Vedula, Ortal Senouf, Alex M. Bronstein +2
The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based i…
Subspace Least Squares Multidimensional Scaling
Amit Boyarski, Alex M. Bronstein, Michael M. Bronstein
Multidimensional Scaling (MDS) is one of the most popular methods for dimensionality reduction and visualization of high dimensional data. Apart from these tasks, it also found app…
Streaming Architecture for Large-Scale Quantized Neural Networks on an FPGA-Based Dataflow Platform
Chaim Baskin, Natan Liss, Evgenii Zheltonozhskii +2
Deep neural networks (DNNs) are used by different applications that are executed on a range of computer architectures, from IoT devices to supercomputers. The footprint of these ne…
Efficient Deformable Shape Correspondence via Kernel Matching
Zorah Lähner, Matthias Vestner, Amit Boyarski +8
We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality. We formulate the problem as matching between a set of pair-…
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence
Or Litany, Tal Remez, Emanuele Rodolà +2
We introduce a new framework for learning dense correspondence between deformable 3D shapes. Existing learning based approaches model shape correspondence as a labelling problem, w…