3 citations · 5 across the 4 of their papers we have counts for
4 papers
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,…
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
Francois Caron, Fadhel Ayed, Paul Jung +3
We consider gradient-based optimisation of wide, shallow neural networks, where the output of each hidden node is scaled by a positive parameter. The scaling parameters are non-ide…
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility
Hoil Lee, Fadhel Ayed, Paul Jung +3
This article studies the infinite-width limit of deep feedforward neural networks whose weights are dependent, and modelled via a mixture of Gaussian distributions. Each hidden nod…
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation
Geon-Hyeong Kim, Jongmin Lee, Youngsoo Jang +2
We consider the problem of learning from observation (LfO), in which the agent aims to mimic the expert's behavior from the state-only demonstrations by experts. We additionally as…