73,797 research outputs found
Deep tree-ensembles for multi-output prediction
Recently, deep neural networks have expanded the state-of-art in various
scientific fields and provided solutions to long standing problems across
multiple application domains. Nevertheless, they also suffer from weaknesses
since their optimal performance depends on massive amounts of training data and
the tuning of an extended number of parameters. As a countermeasure, some
deep-forest methods have been recently proposed, as efficient and low-scale
solutions. Despite that, these approaches simply employ label classification
probabilities as induced features and primarily focus on traditional
classification and regression tasks, leaving multi-output prediction
under-explored. Moreover, recent work has demonstrated that tree-embeddings are
highly representative, especially in structured output prediction. In this
direction, we propose a novel deep tree-ensemble (DTE) model, where every layer
enriches the original feature set with a representation learning component
based on tree-embeddings. In this paper, we specifically focus on two
structured output prediction tasks, namely multi-label classification and
multi-target regression. We conducted experiments using multiple benchmark
datasets and the obtained results confirm that our method provides superior
results to state-of-the-art methods in both tasks
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Mammogram classification is directly related to computer-aided diagnosis of
breast cancer. Traditional methods rely on regions of interest (ROIs) which
require great efforts to annotate. Inspired by the success of using deep
convolutional features for natural image analysis and multi-instance learning
(MIL) for labeling a set of instances/patches, we propose end-to-end trained
deep multi-instance networks for mass classification based on whole mammogram
without the aforementioned ROIs. We explore three different schemes to
construct deep multi-instance networks for whole mammogram classification.
Experimental results on the INbreast dataset demonstrate the robustness of
proposed networks compared to previous work using segmentation and detection
annotations.Comment: MICCAI 2017 Camera Read
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Multi-Entity Dependence Learning (MEDL) explores conditional correlations
among multiple entities. The availability of rich contextual information
requires a nimble learning scheme that tightly integrates with deep neural
networks and has the ability to capture correlation structures among
exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional
multivariate distribution as a generating process. As a result, the variational
lower bound of the joint likelihood can be optimized via a conditional
variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was
motivated by two real-world applications in computational sustainability: one
studies the spatial correlation among multiple bird species using the eBird
data and the other models multi-dimensional landscape composition and human
footprint in the Amazon rainforest with satellite images. We show that
MEDL_CVAE captures rich dependency structures, scales better than previous
methods, and further improves on the joint likelihood taking advantage of very
large datasets that are beyond the capacity of previous methods.Comment: The first two authors contribute equall
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