64 citations · 251 across the 24 of their papers we have counts for
6 papers · 1 filter
Mold into a Graph: Efficient Bayesian Optimization over Mixed-Spaces
Jaeyeon Ahn, Taehyeon Kim, Seyoung Yun
Real-world optimization problems are generally not just black-box problems, but also involve mixed types of inputs in which discrete and continuous variables coexist. Such mixed-sp…
Meta-learning Amidst Heterogeneity and Ambiguity
Kyeongryeol Go, Seyoung Yun
Meta-learning aims to learn a model that can handle multiple tasks generated from an unknown but shared distribution. However, typical meta-learning algorithms have assumed the tas…
Adaptive Local Bayesian Optimization Over Multiple Discrete Variables
Taehyeon Kim, Jaeyeon Ahn, Nakyil Kim +1
In the machine learning algorithms, the choice of the hyperparameter is often an art more than a science, requiring labor-intensive search with expert experience. Therefore, automa…
BOIL: Towards Representation Change for Few-shot Learning
Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim +1
Model Agnostic Meta-Learning (MAML) is one of the most representative of gradient-based meta-learning algorithms. MAML learns new tasks with a few data samples using inner updates…
SIPA: A Simple Framework for Efficient Networks
Gihun Lee, Sangmin Bae, Jaehoon Oh +1
With the success of deep learning in various fields and the advent of numerous Internet of Things (IoT) devices, it is essential to lighten models suitable for low-power devices. I…
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…