17 citations · 31 across the 8 of their papers we have counts for
8 papers
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts
Hyunsu Kim, Yegon Kim, Hongseok Yang +1
Group Equivariant CNNs (G-CNNs) have shown promising efficacy in various tasks, owing to their ability to capture hierarchical features in an equivariant manner. However, their equ…
Probabilistic programming interfaces for random graphs: Markov categories, graphons, and nominal sets
Nathanael L. Ackerman, Cameron E. Freer, Younesse Kaddar +5
We study semantic models of probabilistic programming languages over graphs, and establish a connection to graphons from graph theory and combinatorics. We show that every well-beh…
Learning Symmetrization for Equivariance with Orbit Distance Minimization
Tien Dat Nguyen, Jinwoo Kim, Hongseok Yang +1
We present a general framework for symmetrizing an arbitrary neural-network architecture and making it equivariant with respect to a given group. We build upon the proposals of Kim…
Regularizing Towards Soft Equivariance Under Mixed Symmetries
Hyunsu Kim, Hyungi Lee, Hongseok Yang +1
Datasets often have their intrinsic symmetries, and particular deep-learning models called equivariant or invariant models have been developed to exploit these symmetries. However,…
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
Wonyeol Lee, Xavier Rival, Hongseok Yang
We present a static analysis for discovering differentiable or more generally smooth parts of a given probabilistic program, and show how the analysis can be used to improve the pa…
Automatically generating features for learning program analysis heuristics
Kwonsoo Chae, Hakjoo Oh, Kihong Heo +1
We present a technique for automatically generating features for data-driven program analyses. Recently data-driven approaches for building a program analysis have been proposed, w…