18 citations · 32 across the 9 of their papers we have counts for
9 papers
Fast and Memory-Efficient Wavelet Convolutions via I/O-Aware Reformulation
Amit Aflalo, Shahaf E. Finder, Roy Amoyal +2
Wavelet convolution (WTConv) has emerged as an increasingly popular drop-in replacement for standard convolutions, expanding a network's receptive field exponentially with the numb…
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning
Ron Shapira Weber, Shahar Ben Ishay, Andrey Lavrinenko +2
Fast and scalable alignment of time series is a fundamental challenge in many domains. The standard solution, Dynamic Time Warping (DTW), struggles with poor scalability and sensit…
Improving the Effective Receptive Field of Message-Passing Neural Networks
Shahaf E. Finder, Ron Shapira Weber, Moshe Eliasof +2
Message-Passing Neural Networks (MPNNs) have become a cornerstone for processing and analyzing graph-structured data. However, their effectiveness is often hindered by phenomena su…
Wavelet Convolutions for Large Receptive Fields
Shahaf E. Finder, Roy Amoyal, Eran Treister +1
In recent years, there have been attempts to increase the kernel size of Convolutional Neural Nets (CNNs) to mimic the global receptive field of Vision Transformers' (ViTs) self-at…
Trainable Highly-expressive Activation Functions
Irit Chelly, Shahaf E. Finder, Shira Ifergane +1
Nonlinear activation functions are pivotal to the success of deep neural nets, and choosing the appropriate activation function can significantly affect their performance. Most net…
SpaceJAM: a Lightweight and Regularization-free Method for Fast Joint Alignment of Images
Nir Barel, Ron Shapira Weber, Nir Mualem +2
The unsupervised task of Joint Alignment (JA) of images is beset by challenges such as high complexity, geometric distortions, and convergence to poor local or even global optima.…
Wavelet Feature Maps Compression for Image-to-Image CNNs
Shahaf E. Finder, Yair Zohav, Maor Ashkenazi +1
Convolutional Neural Networks (CNNs) are known for requiring extensive computational resources, and quantization is among the best and most common methods for compressing them. Whi…
DeepDPM: Deep Clustering With an Unknown Number of Clusters
Meitar Ronen, Shahaf E. Finder, Oren Freifeld
Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric appro…
Effective Learning of a GMRF Mixture Model
Shahaf E. Finder, Eran Treister, Oren Freifeld
Learning a Gaussian Mixture Model (GMM) is hard when the number of parameters is too large given the amount of available data. As a remedy, we propose restricting the GMM to a Gaus…