
-
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have attracted more and more attenti...
read it
-
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space
Deep neural networks (DNNs) have been widely adopted in different applic...
read it
-
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions
Understanding the actions of both humans and artificial intelligence (AI...
read it
-
Unlearnable Examples: Making Personal Data Unexploitable
The volume of "free" data on the internet has been key to the current su...
read it
-
A Unified Approach to Interpreting and Boosting Adversarial Transferability
In this paper, we use the interaction inside adversarial perturbations t...
read it
-
Improving Query Efficiency of Black-box Adversarial Attack
Deep neural networks (DNNs) have demonstrated excellent performance on v...
read it
-
Temporal Calibrated Regularization for Robust Noisy Label Learning
Deep neural networks (DNNs) exhibit great success on many tasks with the...
read it
-
Normalized Loss Functions for Deep Learning with Noisy Labels
Robust loss functions are essential for training accurate deep neural ne...
read it
-
Revisiting Loss Landscape for Adversarial Robustness
The study on improving the robustness of deep neural networks against ad...
read it
-
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
Skip connections are an essential component of current state-of-the-art ...
read it
-
Symmetric Cross Entropy for Robust Learning with Noisy Labels
Training accurate deep neural networks (DNNs) in the presence of noisy l...
read it
-
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems
Deep neural networks (DNNs) have become popular for medical image analys...
read it
-
Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation
Unsupervised domain adaptation aims to transfer the classifier learned f...
read it
-
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem...
read it
-
Dimensionality-Driven Learning with Noisy Labels
Datasets with significant proportions of noisy (incorrect) class labels ...
read it
-
Decoupled Networks
Inner product-based convolution has been a central component of convolut...
read it
-
Iterative Learning with Open-set Noisy Labels
Large-scale datasets possessing clean label annotations are crucial for ...
read it
-
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Deep Neural Networks (DNNs) have recently been shown to be vulnerable ag...
read it
-
Residual Convolutional CTC Networks for Automatic Speech Recognition
Deep learning approaches have been widely used in Automatic Speech Recog...
read it
-
Unifying Decision Trees Split Criteria Using Tsallis Entropy
The construction of efficient and effective decision trees remains a key...
read it