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Found 18 papers, 2 papers with code
Hybrid quantum-classical convolutional neural network for phytoplankton classification
The taxonomic composition and abundance of phytoplankton, having direct impact on marine ecosystem dynamic and global environment change, are listed as essential ocean variables.
Quantum Recurrent Neural Networks for Sequential Learning
Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources.
Progressively Unfreezing Perceptual GAN
Generative adversarial networks (GANs) are widely used in image generation tasks, yet the generated images are usually lack of texture details.
SymmetricNet: A mesoscale eddy detection method based on multivariate fusion data
However, the existing detection methods mainly based on traditional detection methods typically only use Sea Surface Height (SSH) as a variable to detect, resulting in inaccurate performance.
Weak Edge Identification Nets for Ocean Front Detection
The method uses the stochastic gradient descent and the correlation loss function to obtain a good ocean front image output.
Student's t-Generative Adversarial Networks
We propose a new method referring to conditional GAN, which equipments the latent noise with mixture of Student's t-distribution with attention mechanism in addition to class information.
Structure Learning of Deep Neural Networks with Q-Learning
Based on reinforcement learning and taking advantages of the superiority of these networks, we propose a novel automatic process to design a multi-block neural network, whose architecture contains multiple types of blocks mentioned above, with the purpose to do structure learning of deep neural networks and explore the possibility whether different blocks can be composed together to form a well-behaved neural network.
Long Short-Term Attention
Attention is an important cognition process of humans, which helps humans concentrate on critical information during their perception and learning.
Recurrent Attention Unit
In this paper, we propose a RNN model, called Recurrent Attention Unit (RAU), which seamlessly integrates the attention mechanism into the interior of GRU by adding an attention gate.
Structure Learning of Deep Networks via DNA Computing Algorithm
The indicates that: 1) Using DNA computing algorithm to learn deep architectures is feasible; 2) Local minima should not be a problem of deep networks; 3) We can use early stop to kill the models with the bad performance just after several runs of training.
Generative Adversarial Networks with Decoder-Encoder Output Noise
The deep convolutional generative adversarial networks (DCGANs) were then proposed to leverage the quality of generated images.
Prediction of Sea Surface Temperature using Long Short-Term Memory
We formulate the SST prediction problem as a time series regression problem.
Perception Driven Texture Generation
In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input.
An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning
Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, including both linear ones and nonlinear ones, supervised ones and unsupervised ones.
Banzhaf Random Forests
Random forests are a type of ensemble method which makes predictions by combining the results of several independent trees.
A Deep Hashing Learning Network
For most existing hashing methods, an image is first encoded as a vector of hand-crafted visual feature, followed by a hash projection and quantization step to get the compact binary vector.
A PCA-Based Convolutional Network
Particularly, each feature extraction stage includes two layers: a convolutional layer and a feature pooling layer.
Large Margin Low Rank Tensor Analysis
Furthermore, the generalization of our model based on similarity between the learned low dimensional embeddings can be viewed as counterpart of recognition of human brain.
