9,876 research outputs found
Combining similarity in time and space for training set formation under concept drift
Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a method for training set selection, particularly relevant when the expected drift is gradual. Training set selection at each time step is based on the distance to the target instance. The distance function combines similarity in space and in time. The method determines an optimal training set size online at every time step using cross validation. It is a wrapper approach, it can be used plugging in different base classifiers. The proposed method shows the best accuracy in the peer group on the real and artificial drifting data. The method complexity is reasonable for the field applications
AffinityNet: semi-supervised few-shot learning for disease type prediction
While deep learning has achieved great success in computer vision and many
other fields, currently it does not work very well on patient genomic data with
the "big p, small N" problem (i.e., a relatively small number of samples with
high-dimensional features). In order to make deep learning work with a small
amount of training data, we have to design new models that facilitate few-shot
learning. Here we present the Affinity Network Model (AffinityNet), a data
efficient deep learning model that can learn from a limited number of training
examples and generalize well. The backbone of the AffinityNet model consists of
stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention
pooling layer is a generalization of the Graph Attention Model (GAM), and can
be applied to not only graphs but also any set of objects regardless of whether
a graph is given or not. As a new deep learning module, kNN attention pooling
layers can be plugged into any neural network model just like convolutional
layers. As a simple special case of kNN attention pooling layer, feature
attention layer can directly select important features that are useful for
classification tasks. Experiments on both synthetic data and cancer genomic
data from TCGA projects show that our AffinityNet model has better
generalization power than conventional neural network models with little
training data. The code is freely available at
https://github.com/BeautyOfWeb/AffinityNet .Comment: 14 pages, 6 figure
Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences
With the rapid proliferation of smart mobile devices, users now take millions
of photos every day. These include large numbers of clothing and accessory
images. We would like to answer questions like `What outfit goes well with this
pair of shoes?' To answer these types of questions, one has to go beyond
learning visual similarity and learn a visual notion of compatibility across
categories. In this paper, we propose a novel learning framework to help answer
these types of questions. The main idea of this framework is to learn a feature
transformation from images of items into a latent space that expresses
compatibility. For the feature transformation, we use a Siamese Convolutional
Neural Network (CNN) architecture, where training examples are pairs of items
that are either compatible or incompatible. We model compatibility based on
co-occurrence in large-scale user behavior data; in particular co-purchase data
from Amazon.com. To learn cross-category fit, we introduce a strategic method
to sample training data, where pairs of items are heterogeneous dyads, i.e.,
the two elements of a pair belong to different high-level categories. While
this approach is applicable to a wide variety of settings, we focus on the
representative problem of learning compatible clothing style. Our results
indicate that the proposed framework is capable of learning semantic
information about visual style and is able to generate outfits of clothes, with
items from different categories, that go well together.Comment: ICCV 201
Improving Trust in Deep Neural Networks with Nearest Neighbors
Deep neural networks are used increasingly for perception and decision-making in UAVs. For example, they can be used to recognize objects from images and decide what actions the vehicle should take. While deep neural networks can perform very well at complex tasks, their decisions may be unintuitive to a human operator. When a human disagrees with a neural network prediction, due to the black box nature of deep neural networks, it can be unclear whether the system knows something the human does not or whether the system is malfunctioning. This uncertainty is problematic when it comes to ensuring safety. As a result, it is important to develop technologies for explaining neural network decisions for trust and safety. This paper explores a modification to the deep neural network classification layer to produce both a predicted label and an explanation to support its prediction. Specifically, at test time, we replace the final output layer of the neural network classifier by a k-nearest neighbor classifier. The nearest neighbor classifier produces 1) a predicted label through voting and 2) the nearest neighbors involved in the prediction, which represent the most similar examples from the training dataset. Because prediction and explanation are derived from the same underlying process, this approach guarantees that the explanations are always relevant to the predictions. We demonstrate the approach on a convolutional neural network for a UAV image classification task. We perform experiments using a forest trail image dataset and show empirically that the hybrid classifier can produce intuitive explanations without loss of predictive performance compared to the original neural network. We also show how the approach can be used to help identify potential issues in the network and training process
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, there
have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have
aroused great interest recently, which update the representation of each node
by aggregating information of its neighbors. However, most GNNs have shallow
layers with a limited receptive field and may not achieve satisfactory
performance especially when the number of labeled nodes is quite small. To
address this challenge, we innovatively propose a graph few-shot learning (GFL)
algorithm that incorporates prior knowledge learned from auxiliary graphs to
improve classification accuracy on the target graph. Specifically, a
transferable metric space characterized by a node embedding and a
graph-specific prototype embedding function is shared between auxiliary graphs
and the target, facilitating the transfer of structural knowledge. Extensive
experiments and ablation studies on four real-world graph datasets demonstrate
the effectiveness of our proposed model.Comment: Full paper (with Appendix) of AAAI 202
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