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20222026
most citedWavelet Convolutions for Large Receptive Fields

12 citations · 17 across the 18 of their papers we have counts for

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cs.LG2026

SoftDTW-CUDA-Torch: Memory-Efficient GPU-Accelerated Soft Dynamic Time Warping for PyTorch

Ron Shapira Weber, Oren Freifeld

We present softdtw-cuda-torch, an open-source PyTorch library for computing Soft Dynamic Time Warping (SoftDTW) on GPUs. Our implementation addresses three key limitations of exist…

cs.LG2025

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…

cs.LG2025

Consistent Amortized Clustering via Generative Flow Networks

Irit Chelly, Roy Uziel, Oren Freifeld +1

Neural models for amortized probabilistic clustering yield samples of cluster labels given a set-structured input, while avoiding lengthy Markov chain runs and the need for explici…

cs.LG2025

Diffeomorphic Temporal Alignment Nets for Time-series Joint Alignment and Averaging

Ron Shapira Weber, Oren Freifeld

In time-series analysis, nonlinear temporal misalignment remains a pivotal challenge that forestalls even simple averaging. Since its introduction, the Diffeomorphic Temporal Align…

cs.LG2022

CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures for Large-scale Analysis

Or Dinari, Raz Zamir, John W. Fisher +1

In the realm of unsupervised learning, Bayesian nonparametric mixture models, exemplified by the Dirichlet Process Mixture Model (DPMM), provide a principled approach for adapting…

cs.LG2022

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…