18 citations · 32 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…