Search Results for author:
Found 22 papers, 7 papers with code
Building the Directed Semantic Graph for Coherent Long Text Generation
These directed subgraphs are considered to well preserve extra but relevant content to the short input text, and then they are decoded by the employed pre-trained model to generate coherent long text.
Transformer-Patcher: One Mistake worth One Neuron
Our method outperforms previous fine-tuning and HyperNetwork-based methods and achieves state-of-the-art performance for Sequential Model Editing (SME).
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
Mixture of Attention Heads: Selecting Attention Heads Per Token
This paper proposes the Mixture of Attention Heads (MoA), a new architecture that combines multi-head attention with the MoE mechanism.
GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs
Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
VDPC: Variational Density Peak Clustering Algorithm
Furthermore, we divide all data points into different levels according to their local density and propose a unified clustering framework by combining the advantages of both DPC and DBSCAN.
Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model
With the surge of graph embedding mechanism, it has also been adopted to community detection.
DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation
Then, the demand-aware graph neural network is designed to extract session demand graph to learn the demand-aware item embedddings for the later recommendations.
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning
Graph representation learning has long been an important yet challenging task for various real-world applications.
RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating
In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation.
Developing Univariate Neurodegeneration Biomarkers with Low-Rank and Sparse Subspace Decomposition
With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25$\%$ reduction in the mean annual change with 80$\%$ power and two-tailed $P=0. 05$ are 116, 279 and 387 for the longitudinal $A\beta+$ AD, $A\beta+$ mild cognitive impairment (MCI) and $A\beta+$ CU groups, respectively.
Generating Long Financial Report using Conditional Variational Autoencoders with Knowledge Distillation
Particularly, we choose Bi-GRU as the encoder and decoder component of CVAE, and learn the latent variable distribution from input news.
SiENet: Siamese Expansion Network for Image Extrapolation
In this paper, a novel two-stage siamese adversarial model for image extrapolation, named Siamese Expansion Network (SiENet) is proposed.
Automatically Generating Macro Research Reports from a Piece of News
As we all know, it requires the macro analysts to write such reports within a short period of time after the important economic news are released.
Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models
Our results show that object detection can help improve the accuracy of some skin disease classes.
Structure Matters: Towards Generating Transferable Adversarial Images
In this paper, we propose the novel concepts of structure patterns and structure-aware perturbations that relax the small perturbation constraint while still keeping images natural.
Heterogeneous-Temporal Graph Convolutional Networks: Make the Community Detection Much Better
Community detection has long been an important yet challenging task to analyze complex networks with a focus on detecting topological structures of graph data.
Wasserstein Autoencoders for Collaborative Filtering
Two different cost functions are designed for measuring the distance between the implicit feedback data and its re-generated version of data.
Structure-Preserving Transformation: Generating Diverse and Transferable Adversarial Examples
A common requirement in all these works is that the malicious perturbations should be small enough (measured by an L_p norm for some p) so that they are imperceptible to humans.
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification
Given insufficient data, while many techniques have been developed to help combat overfitting, the challenge remains if one tries to train deep networks, especially in the ill-posed extremely low data regimes: only a small set of labeled data are available, and nothing -- including unlabeled data -- else.
Towards well-specified semi-supervised model-based classifiers via structural adaptation
This issue is known as model misspecification.
