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Found 11 papers, 4 papers with code
Tackling Missing Values in Probabilistic Wind Power Forecasting: A Generative Approach
Although it is natural to consider addressing this issue by imputing missing values before model estimation and forecasting, we suggest treating missing values and forecasting targets indifferently and predicting all unknown values simultaneously based on observations.
Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach
It relies on a base distribution and a set of bijective mappings.
Automated machine learning for secure key rate in discrete-modulated continuous-variable quantum key distribution
To improve this issue, a neural network model predicting key rates in nearly real time has been proposed previously.
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling
User Modeling plays an essential role in industry.
Interest-oriented Universal User Representation via Contrastive Learning
Universal user representation has received many interests recently, with which we can be free from the cumbersome work of training a specific model for each downstream application.
Exploiting Behavioral Consistence for Universal User Representation
In this work, we focus on developing universal user representation model.
Elliptic Blowup Equations for 6d SCFTs. IV: Matters
Given the recent geometrical classification of 6d $(1, 0)$ SCFTs, a major question is how to compute for this large class their elliptic genera.
High Energy Physics - Theory Mathematical Physics Mathematical Physics
Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification
A representative sequential convolutional recurrent neural network architecture with the two-layer convolutional neural network and subsequent two-layer long short-term memory neural network is developed to suggest the option for fast automatic modulation classification.
Progressive Sparse Local Attention for Video object detection
Instead of relying on optical flow, this paper proposes a novel module called Progressive Sparse Local Attention (PSLA), which establishes the spatial correspondence between features across frames in a local region with progressively sparser stride and uses the correspondence to propagate features.
Structure-Aware Convolutional Neural Networks
Convolutional neural networks (CNNs) are inherently subject to invariable filters that can only aggregate local inputs with the same topological structures.
A Random Matrix Theoretical Approach to Early Event Detection in Smart Grid
With the statistical procedure, the proposed method is universal and fast; moreover, it is robust against traditional EED challenges (such as error accumulations, spurious correlations, and even bad data in core area).
