56 citations · 60 across the 4 of their papers we have counts for
5 papers · 1 filter
Differentiable Forward and Backward Fixed-Point Iteration Layers
Younghan Jeon, Minsik Lee, Jin Young Choi
Recently, several studies proposed methods to utilize some classes of optimization problems in designing deep neural networks to encode constraints that conventional layers cannot…
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning
Jiwoong Park, Minsik Lee, Hyung Jin Chang +2
We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph. In contrast to the existing graph autoencoders with asym…
Neuro-Optimization: Learning Objective Functions Using Neural Networks
Younghan Jeon, Minsik Lee, Jin Young Choi
Mathematical optimization is widely used in various research fields. With a carefully-designed objective function, mathematical optimization can be quite helpful in solving many pr…
Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons
Byeongho Heo, Minsik Lee, Sangdoo Yun +1
An activation boundary for a neuron refers to a separating hyperplane that determines whether the neuron is activated or deactivated. It has been long considered in neural networks…
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary
Byeongho Heo, Minsik Lee, Sangdoo Yun +1
Many recent works on knowledge distillation have provided ways to transfer the knowledge of a trained network for improving the learning process of a new one, but finding a good te…