888 research outputs found
The Emerging Trends of Multi-Label Learning
Exabytes of data are generated daily by humans, leading to the growing need
for new efforts in dealing with the grand challenges for multi-label learning
brought by big data. For example, extreme multi-label classification is an
active and rapidly growing research area that deals with classification tasks
with an extremely large number of classes or labels; utilizing massive data
with limited supervision to build a multi-label classification model becomes
valuable for practical applications, etc. Besides these, there are tremendous
efforts on how to harvest the strong learning capability of deep learning to
better capture the label dependencies in multi-label learning, which is the key
for deep learning to address real-world classification tasks. However, it is
noted that there has been a lack of systemic studies that focus explicitly on
analyzing the emerging trends and new challenges of multi-label learning in the
era of big data. It is imperative to call for a comprehensive survey to fulfill
this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202
Bringing UMAP Closer to the Speed of Light with GPU Acceleration
The Uniform Manifold Approximation and Projection (UMAP) algorithm has become
widely popular for its ease of use, quality of results, and support for
exploratory, unsupervised, supervised, and semi-supervised learning. While many
algorithms can be ported to a GPU in a simple and direct fashion, such efforts
have resulted in inefficient and inaccurate versions of UMAP. We show a number
of techniques that can be used to make a faster and more faithful GPU version
of UMAP, and obtain speedups of up to 100x in practice. Many of these design
choices/lessons are general purpose and may inform the conversion of other
graph and manifold learning algorithms to use GPUs. Our implementation has been
made publicly available as part of the open source RAPIDS cuML library
(https://github.com/rapidsai/cuml)
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