Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches
Risk stratification (characterization) of tumors from radiology images can be more accurate
and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such …
and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such …
Weldon: Weakly supervised learning of deep convolutional neural networks
In this paper, we introduce a novel framework for WEakly supervised Learning of Deep
cOnvolutional neural Networks (WELDON). Our method is dedicated to automatically …
cOnvolutional neural Networks (WELDON). Our method is dedicated to automatically …
Less is more: Learning highlight detection from video duration
Highlight detection has the potential to significantly ease video browsing, but existing
methods often suffer from expensive supervision requirements, where human viewers must …
methods often suffer from expensive supervision requirements, where human viewers must …
Ensemble learning with label proportions for bankruptcy prediction
Corporate bankruptcy prediction is an interesting and important research topic that can be
conceived in many practical applications. Recently, machine learning based methods have …
conceived in many practical applications. Recently, machine learning based methods have …
Object-based visual sentiment concept analysis and application
This paper studies the problem of modeling object-based visual concepts such as" crazy
car" and" shy dog" with a goal to extract emotion related information from social multimedia …
car" and" shy dog" with a goal to extract emotion related information from social multimedia …
Video event detection by inferring temporal instance labels
Video event detection allows intelligent indexing of video content based on events.
Traditional approaches extract features from video frames or shots, then quantize and pool …
Traditional approaches extract features from video frames or shots, then quantize and pool …
On the minimal supervision for training any binary classifier from only unlabeled data
Empirical risk minimization (ERM), with proper loss function and regularization, is the
common practice of supervised classification. In this paper, we study training arbitrary (from …
common practice of supervised classification. In this paper, we study training arbitrary (from …
On the domain adaptation and generalization of pretrained language models: A survey
Recent advances in NLP are brought by a range of large-scale pretrained language models
(PLMs). These PLMs have brought significant performance gains for a range of NLP tasks …
(PLMs). These PLMs have brought significant performance gains for a range of NLP tasks …
A survey on cost types, interaction schemes, and annotator performance models in selection algorithms for active learning in classification
Pool-based active learning (AL) aims to optimize the annotation process (ie, labeling) as the
acquisition of annotations is often time-consuming and therefore expensive. For this …
acquisition of annotations is often time-consuming and therefore expensive. For this …
Learning from label proportions: A mutual contamination framework
Learning from label proportions (LLP) is a weakly supervised setting for classification in
which unlabeled training instances are grouped into bags, and each bag is annotated with …
which unlabeled training instances are grouped into bags, and each bag is annotated with …