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Empirical study on meta-feature characterization for multi-objective optimization problems
Algorithm recommendation based on meta-learning was studied previously. The research on the meta-features extraction, which is a key for the success...
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A novel multi-task learning technique for offline handwritten short answer spotting and recognition
Off-line examination is still being used in many parts of the world as it is a more economical way of conducting exams when compared to...
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Improving generalization for geometric variations in images for efficient deep learning
Deep Learning models for tasks such as image classification have a hard time adapting to the unseen geometric variations (such as scale, perspective,...
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A program to create new geometry proof problems
In a previous paper Todd (Submitted to AMAI,
2022 ), linear systems corresponding to sets of angle bisector conditions are analyzed. In a system which... -
Active learning for data streams: a survey
Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The...
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Supervised deep learning for content-aware image retargeting with Fourier Convolutions
Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for...
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Manifold learning by a deep Gaussian process autoencoder
The paper presents a novel manifold learning algorithm, the deep Gaussian process autoencoder (DPGA), based on deep Gaussian processes. Deep Gaussian...
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Improving the Performance of the Support Vector Machine: Two Geometrical Scaling Methods
In this chapter, we discuss two possible ways of improving the performance of the SVM, using geometric methods. The first adapts the kernel by...
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Image edge preservation via low-rank residuals for robust subspace learning
In order to maintain low-rank characteristics, existing low-rank representation methods concentrate on capturing data’s low-frequency signals, which...
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Mind the gap: challenges of deep learning approaches to Theory of Mind
Theory of Mind (ToM) is an essential ability of humans to infer the mental states of others. Here we provide a coherent summary of the potential,...
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Subspace clustering via adaptive-loss regularized representation learning with latent affinities
High-dimensional data that lies on several subspaces tend to be highly correlated and contaminated by various noises, and its affinities across...
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Jointly projection and graph-regularization coupled discriminative dictionary learning for image classification
Analysis synthesis dictionary pair learning methods have achieved good performance in image classification. Due to the redundancy contained in the...
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The general framework for few-shot learning by kernel HyperNetworks
Few-shot models aim at making predictions using a minimal number of labeled examples from a given task. The main challenge in this area is the one-shot ...
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From Tsetlin’s School of Learning Automata towards Artificial Intelligence
AbstractThough there is a widely spread opinion that Artificial Intelligence had been formulated as an independent science first of all in the United...
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Representation learning via an integrated autoencoder for unsupervised domain adaptation
The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the...
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A computational geometric learning approach for person axial and slanting depth prediction using single RGB camera
The paper proposes a non triangulation method for estimating the depth of a person in slanting position relative to camera lens center. The influence...
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A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation
Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep...
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Crafting a robotic swarm pursuit–evasion capture strategy using deep reinforcement learning
In this paper we study the multi-agent pursuit–evasion problem, and present an extension of the Multi-Agent Deep Deterministic Policy Gradient...
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Domain-invariant feature learning with label information integration for cross-domain classification
Traditional methods for unsupervised cross-domain classification learn a common low-dimensional subspace using images from a well-labeled source...
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Deep learning based text detection using resnet for feature extraction
Popular deep learning models for text segmentation include CTPN, EAST, and PixelLink. However, they are not very well capable of dealing with the...
