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Found 41 papers, 12 papers with code
TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency.
CPSDBench: A Large Language Model Evaluation Benchmark and Baseline for Chinese Public Security Domain
Large Language Models (LLMs) have demonstrated significant potential and effectiveness across multiple application domains.
MambaTab: A Simple Yet Effective Approach for Handling Tabular Data
Tabular data remains ubiquitous across domains despite growing use of images and texts for machine learning.
Ranked #1 on
Classification
on Adult
Variational Nonlinear Kalman Filtering with Unknown Process Noise Covariance
Motivated by the maneuvering target tracking with sensors such as radar and sonar, this paper considers the joint and recursive estimation of the dynamic state and the time-varying process noise covariance in nonlinear state space models.
RIS-Assisted Joint Uplink Communication and Imaging: Phase Optimization and Bayesian Echo Decoupling
Achieving integrated sensing and communication (ISAC) via uplink transmission is challenging due to the unknown waveform and the coupling of communication and sensing echoes.
Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction
Interpreting critical variables involved in complex biological processes related to survival time can help understand prediction from survival models, evaluate treatment efficacy, and develop new therapies for patients.
PRIME: Uncovering Circadian Oscillation Patterns and Associations with AD in Untimed Genome-wide Gene Expression across Multiple Brain Regions
Then, we apply it to study oscillation patterns in untimed genome-wide gene expression from 19 human brain regions of controls and AD patients.
Alcohol Intake Differentiates AD and LATE: A Telltale Lifestyle from Two Large-Scale Datasets
Alzheimer's disease (AD), as a progressive brain disease, affects cognition, memory, and behavior.
Log-based Sparse Nonnegative Matrix Factorization for Data Representation
Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations.
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively.
Top-$k$ Regularization for Supervised Feature Selection
Feature selection identifies subsets of informative features and reduces dimensions in the original feature space, helping provide insights into data generation or a variety of domain problems.
Hyperspectral Image Denoising with Log-Based Robust PCA
It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs).
Deepened Graph Auto-Encoders Help Stabilize and Enhance Link Prediction
Graph neural networks have been used for a variety of learning tasks, such as link prediction, node classification, and node clustering.
On Channel Reciprocity in Reconfigurable Intelligent Surface Assisted Wireless Network
Channel reciprocity greatly facilitates downlink precoding in time-division duplexing (TDD) multiple-input multiple-output (MIMO) communications without the need for channel state information (CSI) feedback.
Information Theory Information Theory
Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band
The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size.
Adaptive Weighted Discriminator for Training Generative Adversarial Networks
Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning.
Ranked #4 on
Conditional Image Generation
on CIFAR-100
Conditional Image Generation
Generative Adversarial Network
+1
Kernel Two-Dimensional Ridge Regression for Subspace Clustering
It directly uses 2D data as inputs such that the learning of representations benefits from inherent structures and relationships of the data.
Algorithmic Stability and Generalization of an Unsupervised Feature Selection Algorithm
Feature selection, as a vital dimension reduction technique, reduces data dimension by identifying an essential subset of input features, which can facilitate interpretable insights into learning and inference processes.
Fractal Autoencoders for Feature Selection
In this paper, we propose an innovative framework for unsupervised feature selection, called fractal autoencoders (FAE).
Structured Graph Learning for Clustering and Semi-supervised Classification
Furthermore, most existing graph-based methods conduct clustering and semi-supervised classification on the graph learned from the original data matrix, which doesn't have explicit cluster structure, thus they might not achieve the optimal performance.
Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering
In particular, projection matrices are sought under the guidance of building new data representations, such that the spatial information is retained and projections are enhanced by the goal of clustering, which helps construct optimal projection directions.
MIMO Transmission through Reconfigurable Intelligent Surface: System Design, Analysis, and Implementation
Reconfigurable intelligent surface (RIS) is a new paradigm that has great potential to achieve cost-effective, energy-efficient information modulation for wireless transmission, by the ability to change the reflection coefficients of the unit cells of a programmable metasurface.
Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.
Nonnegative Matrix Factorization with Local Similarity Learning
Existing nonnegative matrix factorization methods focus on learning global structure of the data to construct basis and coefficient matrices, which ignores the local structure that commonly exists among data.
Automated Classification of Seizures against Nonseizures: A Deep Learning Approach
In order to capture essential seizure features, this paper integrates an emerging deep learning model, the independently recurrent neural network (IndRNN), with a dense structure and an attention mechanism to exploit temporal and spatial discriminating features and overcome seizure variabilities.
RES-PCA: A Scalable Approach to Recovering Low-rank Matrices
This drawback has limited the application of RPCA in solving real world problems.
Discriminative Ridge Machine: A Classifier for High-Dimensional Data or Imbalanced Data
As a special case we focus on a quadratic model that admits a closed-form analytical solution.
A Novel Independent RNN Approach to Classification of Seizures against Non-seizures
In current clinical practices, electroencephalograms (EEG) are reviewed and analyzed by trained neurologists to provide supports for therapeutic decisions.
Lie Group Auto-Encoder
Moreover, we derive an intrinsic loss for UTDAT Lie group which can be calculated as l-2 loss in the tangent space.
Exploiting Edge Features in Graph Neural Networks
The proposed framework can consolidate current graph neural network models; e. g. graph convolutional networks (GCN) and graph attention networks (GAT).
Unified Spectral Clustering with Optimal Graph
Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers.
Subspace Clustering via Variance Regularized Ridge Regression
Spectral clustering based subspace clustering methods have emerged recently.
Twin Learning for Similarity and Clustering: A Unified Kernel Approach
Thus the learned similarity matrix is often not suitable, let alone optimal, for the subsequent clustering.
Top-N Recommendation on Graphs
To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix.
Top-N Recommendation with Novel Rank Approximation
Some empirical results demonstrate that it can provide a better approximation to original problems than convex relaxation.
Top-N Recommender System via Matrix Completion
Top-N recommender systems have been investigated widely both in industry and academia.
Robust PCA via Nonconvex Rank Approximation
This approximation to the matrix rank is tighter than the nuclear norm.
Robust Subspace Clustering via Tighter Rank Approximation
For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method.
Robust Subspace Clustering via Smoothed Rank Approximation
However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum.
Variational Planning for Graph-based MDPs
The KL divergence is optimized using the belief propagation algorithm, with complexity exponential in only the cluster size of the graph.
Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework
In this paper, we propose a robust minimax framework to interpret the relationship between data and regularization terms for a large class of loss functions.
