17 citations · 29 across the 5 of their papers we have counts for
5 papers · 1 filter
Provable Memorization via Deep Neural Networks using Sub-linear Parameters
Sejun Park, Jaeho Lee, Chulhee Yun +1
It is known that parameters are sufficient for neural networks to memorize arbitrary input-label pairs. By exploiting depth, we show that parameters suffice…
Minimum Width for Universal Approximation
Sejun Park, Chulhee Yun, Jaeho Lee +1
The universal approximation property of width-bounded networks has been studied as a dual of classical universal approximation results on depth-bounded networks. However, the criti…
Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning
Sejun Park, Jaeho Lee, Sangwoo Mo +1
Magnitude-based pruning is one of the simplest methods for pruning neural networks. Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable perform…
Spectral Approximate Inference
Sejun Park, Eunho Yang, Se-Young Yun +1
Given a graphical model (GM), computing its partition function is the most essential inference task, but it is computationally intractable in general. To address the issue, iterati…
Sequential Local Learning for Latent Graphical Models
Sejun Park, Eunho Yang, Jinwoo Shin
Learning parameters of latent graphical models (GM) is inherently much harder than that of no-latent ones since the latent variables make the corresponding log-likelihood non-conca…