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Found 15 papers, 6 papers with code
MVEB: Self-Supervised Learning with Multi-View Entropy Bottleneck
One can learn this representation by maximizing the mutual information between the representation and the supervised view while eliminating superfluous information.
Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modeling
To substantiate this claim, we introduce the Cross-variable Decorrelation Aware feature Modeling (CDAM) for Channel-mixing approaches, aiming to refine Channel-mixing by minimizing redundant information between channels while enhancing relevant mutual information.
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
Recent advancements in deep learning have led to the development of various models for long-term multivariate time-series forecasting (LMTF), many of which have shown promising results.
Generative Oversampling for Imbalanced Data via Majority-Guided VAE
Learning with imbalanced data is a challenging problem in deep learning.
Structure-Preserving Graph Representation Learning
To this end, we propose a novel Structure-Preserving Graph Representation Learning (SPGRL) method, to fully capture the structure information of graphs.
Self-Supervision Can Be a Good Few-Shot Learner
Specifically, we maximize the mutual information (MI) of instances and their representations with a low-bias MI estimator to perform self-supervised pre-training.
cross-domain few-shot learning
Unsupervised Few-Shot Image Classification
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Self-supervised Consensus Representation Learning for Attributed Graph
Self-supervised loss is designed to maximize the agreement of the embeddings of the same node in the topology graph and the feature graph.
Boosting Few-Shot Classification with View-Learnable Contrastive Learning
The goal of few-shot classification is to classify new categories with few labeled examples within each class.
Rectifying the Shortcut Learning of Background for Few-Shot Learning
The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL).
AFINet: Attentive Feature Integration Networks for Image Classification
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification.
Auto-view contrastive learning for few-shot image recognition
Few-shot learning aims to recognize new classes with few annotated instances within each category.
AFINets: Attentive Feature Integration Networks for Image Classification
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks, e. g., image classification.
Mutual Information Gradient Estimation for Representation Learning
To this end, we propose the Mutual Information Gradient Estimator (MIGE) for representation learning based on the score estimation of implicit distributions.
Structured Pruning of Recurrent Neural Networks through Neuron Selection
Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of applications.
Low-rank Kernel Learning for Graph-based Clustering
By formulating graph construction and kernel learning in a unified framework, the graph and consensus kernel can be iteratively enhanced by each other.
