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01/11/2023
A Distinct Unsupervised Reference Model From The Environment Helps Continual Learning
The existing continual learning methods are mainly focused on fully-supe...
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10/29/2022
BIMRL: Brain Inspired Meta Reinforcement Learning
Sample efficiency has been a key issue in reinforcement learning (RL). A...
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09/20/2022
SCGG: A Deep Structure-Conditioned Graph Generative Model
Deep learning-based graph generation approaches have remarkable capaciti...
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08/05/2022
Isoform Function Prediction Using Deep Neural Network
Isoforms are mRNAs produced from the same gene site in the phenomenon ca...
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11/08/2021
HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization
Breast cancer is the most common malignancy in women, being responsible ...
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10/07/2021
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation
Graph data structures are fundamental for studying connected entities. W...
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05/10/2021
Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning
Minimum Excess Risk (MER) in Bayesian learning is defined as the differe...
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02/27/2021
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
Disease prediction is a well-known classification problem in medical app...
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02/01/2021
Semi-Supervised Disentanglement of Class-Related and Class-Independent Factors in VAE
In recent years, extending variational autoencoder's framework to learn ...
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12/31/2020
Deep Graph Generators: A Survey
Deep generative models have achieved great success in areas such as imag...
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08/24/2019
DGSAN: Discrete Generative Self-Adversarial Network
Although GAN-based methods have received many achievements in the last f...
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04/08/2019
Jointly Measuring Diversity and Quality in Text Generation Models
Text generation is an important Natural Language Processing task with va...
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11/21/2018
Adversarial Classifier for Imbalanced Problems
Adversarial approach has been widely used for data generation in the las...
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11/21/2018
Multi-layered Graph Embedding with Graph Convolution Networks
Recently, graph embedding emerges as an effective approach for graph ana...
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11/21/2018
Multi-layered Graph Embedding with Graph Convolutional Networks
Recently, graph embedding emerges as an effective approach for graph ana...
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06/18/2016
An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels
Multi-label classification has received considerable interest in recent ...
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05/29/2016
Semi-supervised Zero-Shot Learning by a Clustering-based Approach
In some of object recognition problems, labeled data may not be availabl...
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01/20/2016
Selecting Efficient Features via a Hyper-Heuristic Approach
By Emerging huge databases and the need to efficient learning algorithms...
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12/15/2015
