The emerging trends of multi-label learning
Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …
Survey on multi-output learning
The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …
It is an important learning problem for decision-making since making decisions in the real …
Deep learning for extreme multi-label text classification
Extreme multi-label text classification (XMTC) refers to the problem of assigning to each
document its most relevant subset of class labels from an extremely large label collection …
document its most relevant subset of class labels from an extremely large label collection …
Youtube-8m: A large-scale video classification benchmark
Many recent advancements in Computer Vision are attributed to large datasets. Open-
source software packages for Machine Learning and inexpensive commodity hardware …
source software packages for Machine Learning and inexpensive commodity hardware …
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
The emergence of large-scale genomic, chemical and pharmacological data provides new
opportunities for drug discovery and repositioning. In this work, we develop a computational …
opportunities for drug discovery and repositioning. In this work, we develop a computational …
[PDF][PDF] Network representation learning with rich text information.
Abstract Representation learning has shown its effectiveness in many tasks such as image
classification and text mining. Network representation learning aims at learning distributed …
classification and text mining. Network representation learning aims at learning distributed …
Temporal regularized matrix factorization for high-dimensional time series prediction
Time series prediction problems are becoming increasingly high-dimensional in modern
applications, such as climatology and demand forecasting. For example, in the latter …
applications, such as climatology and demand forecasting. For example, in the latter …
Learning a deep convnet for multi-label classification with partial labels
Deep ConvNets have shown great performance for single-label image classification (eg
ImageNet), but it is necessary to move beyond the single-label classification task because …
ImageNet), but it is necessary to move beyond the single-label classification task because …
Sparse local embeddings for extreme multi-label classification
The objective in extreme multi-label learning is to train a classifier that can automatically tag
a novel data point with the most relevant subset of labels from an extremely large label set …
a novel data point with the most relevant subset of labels from an extremely large label set …
Multi-label learning with global and local label correlation
It is well-known that exploiting label correlations is important to multi-label learning. Existing
approaches either assume that the label correlations are global and shared by all instances; …
approaches either assume that the label correlations are global and shared by all instances; …