Search Results for author:
Found 25 papers, 13 papers with code
Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment
In this work, we propose a novel technique that uses rules derived from neural network computations to infer data preconditions for a DNN model to determine the trustworthiness of its predictions.
MotifPiece: A Data-Driven Approach for Effective Motif Extraction and Molecular Representation Learning
In this paper, we develop a data-driven motif extraction technique known as MotifPiece, which employs statistical measures to define motifs.
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection
In this paper, we propose to combine such causal-based vulnerability detection algorithms with deep learning, aiming to achieve more efficient and effective vulnerability detection.
On the optimization and generalization of overparameterized implicit neural networks
We show that global convergence is guaranteed, even if only the implicit layer is trained.
Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths
Implicit deep learning has recently become popular in the machine learning community since these implicit models can achieve competitive performance with state-of-the-art deep networks while using significantly less memory and computational resources.
Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks
We propose a novel molecular graph representation learning method by constructing a heterogeneous motif graph to address this issue.
MotifExplainer: a Motif-based Graph Neural Network Explainer
Most existing GNN explanation methods identify the most important edges or nodes but fail to consider substructures, which are more important for graph data.
A global convergence theory for deep ReLU implicit networks via over-parameterization
Implicit deep learning has received increasing attention recently due to the fact that it generalizes the recursive prediction rules of many commonly used neural network architectures.
Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence
We study text representation methods using deep models.
Weighted Line Graph Convolutional Networks
Line graphs have shown to be effective in improving feature learning in graph neural networks.
Teleport Graph Convolutional Networks
To address these limitations, we propose a teleport graph convolution layer (TeleGCL) that uses teleport functions to enable each node to aggregate information from a much larger neighborhood.
Topology-Aware Graph Pooling Networks
In addition, graph topology is incorporated in global voting to compute the importance score of each node globally in the entire graph.
Towards Deeper Graph Neural Networks
Based on our theoretical and empirical analysis, we propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
Ranked #2 on
Node Classification
on AMZ Computers
Kronecker Attention Networks
Use of attention operators on high-order data requires flattening of the spatial or spatial-temporal dimensions into a vector, which is assumed to follow a multivariate normal distribution.
Topology-Aware Pooling via Graph Attention
Previous studies used global ranking methods to sample some of the important nodes, but most of them are not able to incorporate graph topology information in computing ranking scores.
Graph Representation Learning via Hard and Channel-Wise Attention Networks
To further reduce the requirements on computational resources, we propose the cGAO that performs attention operations along channels.
Ranked #8 on
Graph Classification
on D&D
(using extra training data)
Graph U-Nets
We further propose the gUnpool layer as the inverse operation of the gPool layer.
Ranked #4 on
Graph Classification
on D&D
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
Another limitation of GCN when used on graph-based text representation tasks is that, GCNs do not consider the order information of nodes in graph.
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
Compared to prior CNNs designed for mobile devices, ChannelNets achieve a significant reduction in terms of the number of parameters and computational cost without loss in accuracy.
Large-Scale Learnable Graph Convolutional Networks
However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations.
Ranked #2 on
Document Classification
on Cora
Efficient and Invariant Convolutional Neural Networks for Dense Prediction
In this paper, we propose a set of methods based on kernel rotation and flip to enable rotation and flip invariance in convolutional neural networks.
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation
In the simplest case, the proposed multi-stage VAE divides the decoder into two components in which the second component generates refined images based on the course images generated by the first component.
Pixel Deconvolutional Networks
When used in image generation tasks, our PixelDCL can largely overcome the checkerboard problem suffered by regular deconvolution operations.
