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Found 19 papers, 1 papers with code
Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK
In recent years, the convergence of data-driven machine learning models with Data Assimilation (DA) offers a promising avenue for enhancing weather forecasting.
Slice3D: Multi-Slice, Occlusion-Revealing, Single View 3D Reconstruction
Our key observation is that object slicing is more advantageous than altering views to reveal occluded structures.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
Both Spatial and Frequency Cues Contribute to High-Fidelity Image Inpainting
Specifically, we introduce an extra Frequency Branch and Frequency Loss on the spatial-based network to impose direct supervision on the frequency information, and propose a Frequency-Spatial Cross-Attention Block (FSCAB) to fuse multi-domain features and combine the corresponding characteristics.
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework.
Learning to Recommend from Sparse Data via Generative User Feedback
To address this, we propose a learning framework that improves collaborative filtering with a synthetic feedback loop (CF-SFL) to simulate the user feedback.
Zero-Shot Recognition via Optimal Transport
{Specifically, we build a conditional generative model to generate features from seen-class attributes, and establish an optimal transport between the distribution of the generated features and that of the real features.}
Synthetic Data Generation and Adaption for Object Detection in Smart Vending Machines
This paper presents an improved scheme for the generation and adaption of synthetic images for the training of deep Convolutional Neural Networks(CNNs) to perform the object detection task in smart vending machines.
Towards a Robust Deep Neural Network in Texts: A Survey
Recently, studies have revealed adversarial examples in the text domain, which could effectively evade various DNN-based text analyzers and further bring the threats of the proliferation of disinformation.
Principal Component Analysis with Tensor Train Subspace
Tensor train is a hierarchical tensor network structure that helps alleviate the curse of dimensionality by parameterizing large-scale multidimensional data via a set of network of low-rank tensors.
Wide Compression: Tensor Ring Nets
Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications.
Topic Compositional Neural Language Model
The TCNLM learns the global semantic coherence of a document via a neural topic model, and the probability of each learned latent topic is further used to build a Mixture-of-Experts (MoE) language model, where each expert (corresponding to one topic) is a recurrent neural network (RNN) that accounts for learning the local structure of a word sequence.
Tensor Train Neighborhood Preserving Embedding
In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace.
Efficient Low Rank Tensor Ring Completion
Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS representation.
Earliness-Aware Deep Convolutional Networks for Early Time Series Classification
Unlike most existing methods for early classification of time series data, that are designed to solve this problem under the assumption of the availability of a good set of pre-defined (often hand-crafted) features, our framework can jointly perform feature learning (by learning a deep hierarchy of \emph{shapelets} capturing the salient characteristics in each time series), along with a dynamic truncation model to help our deep feature learning architecture focus on the early parts of each time series.
Unsupervised clustering under the Union of Polyhedral Cones (UOPC) model
Similar to the Union of Subspaces (UOS) model where each data from each subspace is generated from a (unknown) basis, in the UOPC model each data from each cone is assumed to be generated from a finite number of (unknown) \emph{extreme rays}. To cluster data under this model, we consider several algorithms - (a) Sparse Subspace Clustering by Non-negative constraints Lasso (NCL), (b) Least squares approximation (LSA), and (c) K-nearest neighbor (KNN) algorithm to arrive at affinity between data points.
Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model
Using the matrix product state (MPS) representation of tensor train decompositions, in this paper we propose a tensor completion algorithm which alternates over the matrices (tensors) in the MPS representation.
On Deterministic Conditions for Subspace Clustering under Missing Data
In this paper we present deterministic conditions for success of sparse subspace clustering (SSC) under missing data, when data is assumed to come from a Union of Subspaces (UoS) model.
On deterministic conditions for subspace clustering under missing data
We provide extensive set of simulation results for clustering as well as completion of data under missing entries, under the UoS model.
