289 citations · 1.7k across the 90 of their papers we have counts for
8 papers · 2 filters
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…
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…
Deep Class Aware Denoising
Tal Remez, Or Litany, Raja Giryes +1
The increasing demand for high image quality in mobile devices brings forth the need for better computational enhancement techniques, and image denoising in particular. At the same…