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Found 14 papers, 3 papers with code
Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting
Electric load forecasting is an indispensable component of electric power system planning and management.
SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events
In this paper, we propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework~(SaDI), which ensembles long-term trend, short-term trend, and period modelings to capture temporal characteristics in different components.
Robust Dominant Periodicity Detection for Time Series with Missing Data
In this paper, we propose a robust and effective periodicity detection algorithm for time series with block missing data.
Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach
The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set.
NetRCA: An Effective Network Fault Cause Localization Algorithm
Localizing the root cause of network faults is crucial to network operation and maintenance.
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition
Many real-world time series exhibit multiple seasonality with different lengths.
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game
From the analysis, we identify issues of SS tasks which allow a severely mode-collapsed generator to excel the SS tasks.
Ranked #4 on
Image Generation
on ImageNet 32x32
Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph Embedding
Our idea is that graph information which captures local data structures is an excellent complement to deep GMM.
Low-Rank Phase Retrieval via Variational Bayesian Learning
We propose a hierarchical prior model for low-rank phase retrieval, in which a Gaussian-Wishart hierarchical prior is placed on the underlying low-rank matrix to promote the low-rankness of the matrix.
Simultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning
In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns.
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models
The problem of low rank matrix completion is considered in this paper.
Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems
Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems.
Information Theory Information Theory
An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors
In this paper, we focus on Tucker decomposition which represents an Nth-order tensor in terms of N factor matrices and a core tensor via multilinear operations.
Sparse Bayesian Dictionary Learning with a Gaussian Hierarchical Model
In this paper, we propose a new hierarchical Bayesian model for dictionary learning, in which a Gaussian-inverse Gamma hierarchical prior is used to promote the sparsity of the representation.
