67 citations · 164 across the 20 of their papers we have counts for
13 papers · 1 filter
Attribution Preservation in Network Compression for Reliable Network Interpretation
Geondo Park, June Yong Yang, Sung Ju Hwang +1
Neural networks embedded in safety-sensitive applications such as self-driving cars and wearable health monitors rely on two important techniques: input attribution for hindsight a…
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks
Kyung-Su Kim, Aurélie C. Lozano, Eunho Yang
Recent theoretical works based on the neural tangent kernel (NTK) have shed light on the optimization and generalization of over-parameterized networks, and partially bridge the ga…
Bootstrapping Neural Processes
Juho Lee, Yoonho Lee, Jungtaek Kim +3
Unlike in the traditional statistical modeling for which a user typically hand-specify a prior, Neural Processes (NPs) implicitly define a broad class of stochastic processes with…
Neural Complexity Measures
Yoonho Lee, Juho Lee, Sung Ju Hwang +2
While various complexity measures for deep neural networks exist, specifying an appropriate measure capable of predicting and explaining generalization in deep networks has proven…
Few-shot Visual Reasoning with Meta-analogical Contrastive Learning
Youngsung Kim, Jinwoo Shin, Eunho Yang +1
While humans can solve a visual puzzle that requires logical reasoning by observing only few samples, it would require training over large amount of data for state-of-the-art deep…
A General Family of Stochastic Proximal Gradient Methods for Deep Learning
Jihun Yun, Aurelie C. Lozano, Eunho Yang
We study the training of regularized neural networks where the regularizer can be non-smooth and non-convex. We propose a unified framework for stochastic proximal gradient descent…