scholarly journals Weakly supervised learning of information structure of scientific abstracts--is it accurate enough to benefit real-world tasks in biomedicine?

2011 ◽  
Vol 27 (22) ◽  
pp. 3179-3185 ◽  
Author(s):  
Y. Guo ◽  
A. Korhonen ◽  
I. Silins ◽  
U. Stenius
Keyword(s):  
Supervised Learning ◽  
Real World ◽  
Information Structure ◽  
Weakly Supervised Learning ◽  
Weakly Supervised

scholarly journals Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder

2021 ◽  
Author(s):  
Weijia Zhang
Keyword(s):  
Medical Imaging ◽  
Supervised Learning ◽  
Real World ◽  
State Of The Art ◽  
Experimental Results ◽  
Weakly Supervised Learning ◽  
Variational Autoencoder ◽  
Label Prediction ◽  
Weakly Supervised ◽  
Better Than

Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be identically and independently distributed, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Autoencoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.

scholarly journals Weakly Supervised Learning Creates a Fusion of Modeling Cultures

2021 ◽  
Vol 7 (1) ◽  
pp. 203-211
Author(s):  
Chengliang Tang ◽  
Gan Yuan ◽  
Tian Zheng
Keyword(s):  
Supervised Learning ◽  
Weakly Supervised Learning ◽  
Weakly Supervised

Weakly Supervised Learning through Rank-based Contextual Measures

2021 ◽  
Author(s):  
Joao Gabriel Camacho Presotto ◽  
Lucas Pascotti Valem ◽  
Nikolas Gomes de Sa ◽  
Daniel Carlos Guimaraes Pedronette ◽  
Joao Paulo Papa
Keyword(s):  
Supervised Learning ◽  
Weakly Supervised Learning ◽  
Weakly Supervised
Sign in / Sign up

Export Citation Format

Share Document