150 citations · 221 across the 19 of their papers we have counts for
6 papers · 1 filter
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning
Beomjo Shin, Junsu Cho, Hwanjo Yu +1
Multiple Instance Learning (MIL) involves predicting a single label for a bag of instances, given positive or negative labels at bag-level, without accessing to label for each inst…
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…
MxML: Mixture of Meta-Learners for Few-Shot Classification
Minseop Park, Jungtaek Kim, Saehoon Kim +2
A meta-model is trained on a distribution of similar tasks such that it learns an algorithm that can quickly adapt to a novel task with only a handful of labeled examples. Most of…
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Juho Lee, Yoonho Lee, Jungtaek Kim +3
Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such pr…
Convex Optimization for Binary Classifier Aggregation in Multiclass Problems
Sunho Park, TaeHyun Hwang, Seungjin Choi
Multiclass problems are often decomposed into multiple binary problems that are solved by individual binary classifiers whose results are integrated into a final answer. Various me…
Learning Features with Structure-Adapting Multi-view Exponential Family Harmoniums
Yoonseop Kang, Seungjin Choi
We proposea graphical model for multi-view feature extraction that automatically adapts its structure to achieve better representation of data distribution. The proposed model, str…