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Found 15 papers, 5 papers with code
Backdoor Attack with Mode Mixture Latent Modification
Backdoor attacks become a significant security concern for deep neural networks in recent years.
Understanding Community Bias Amplification in Graph Representation Learning
In this work, we discover a phenomenon of community bias amplification in graph representation learning, which refers to the exacerbation of performance bias between different classes by graph representation learning.
StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning
This allows the encoder not to perform any message passing during the training stage, and significantly reduces the number of sample pairs in the contrastive loss.
Defending Adversarial Examples by Negative Correlation Ensemble
The security issues in DNNs, such as adversarial examples, have attracted much attention.
Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data
In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter.
Learning nonlinear dynamics in synchronization of knowledge-based leader-following networks
This paper proposes a learning-based fully distributed observer for a class of nonlinear leader systems, which can simultaneously learn the leader's dynamics and states.
Google Landmark Retrieval 2021 Competition Third Place Solution
We present our solutions to the Google Landmark Challenges 2021, for both the retrieval and the recognition tracks.
AdaL: Adaptive Gradient Transformation Contributes to Convergences and Generalizations
Inspired by this, we propose AdaL, with a transformation on the original gradient.
ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models
However, many existing works can not be widely used because of the constraints of functional form of generative models or the sensitivity to hyperparameters.
Real-time Forecast Models for TBM Load Parameters Based on Machine Learning Methods
In this paper, based on in-situ TBM operational data, we use the machine-learning (ML) methods to build the real-time forecast models for TBM load parameters, which can instantaneously provide the future values of the TBM load parameters as long as the current data are collected.
Dynamic Anchor Learning for Arbitrary-Oriented Object Detection
With the newly introduced DAL, we achieve superior detection performance for arbitrary-oriented objects with only a few horizontal preset anchors.
Ranked #1 on
Multi-Oriented Scene Text Detection
on ICDAR2015
(using extra training data)
Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective
Based on our theoretical and empirical analysis, we establish a universal theoretical framework of GCN from an optimization perspective and derive a novel convolutional kernel named GCN+ which has lower parameter amount while relieving the over-smoothing inherently.
Stochastic Graph Recurrent Neural Network
In addition, to alleviate KL-vanishing problem in SGRNN, a simple and interpretable structure is proposed based on the lower bound of KL-divergence.
ELMV: an Ensemble-Learning Approach for Analyzing Electrical Health Records with Significant Missing Values
We propose a novel Ensemble-Learning for Missing Value (ELMV) framework, which introduces an effective approach to construct multiple subsets of the original EHR data with a much lower missing rate, as well as mobilizing a dedicated support set for the ensemble learning in the purpose of reducing the bias caused by substantial missing values.
Adaptive Cooperative Tracking and Parameter Estimation of an Uncertain Leader over General Directed Graphs
Distributed adaptive observers are designed for all follower nodes, simultaneously estimate the state and parameters of the leader node.
