453 citations · 1.5k across the 44 of their papers we have counts for
17 papers · 1 filter
DAM-GT: Dual Positional Encoding-Based Attention Masking Graph Transformer for Node Classification
Chenyang Li, Jinsong Chen, John E. Hopcroft +1
Neighborhood-aware tokenized graph Transformers have recently shown great potential for node classification tasks. Despite their effectiveness, our in-depth analysis of neighborhoo…
Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse
Kun He, Zijian Song, Shuoxi Zhang +1
Class-Incremental Learning (CIL) is a critical capability for real-world applications, enabling learning systems to adapt to new tasks while retaining knowledge from previous ones.…
Rethinking Tokenized Graph Transformers for Node Classification
Jinsong Chen, Chenyang Li, GaiChao Li +2
Node tokenized graph Transformers (GTs) have shown promising performance in node classification. The generation of token sequences is the key module in existing tokenized GTs which…
Tokenizing 3D Molecule Structure with Quantized Spherical Coordinates
Kaiyuan Gao, Yusong Wang, Haoxiang Guan +5
The application of language models (LMs) to molecular structure generation using line notations such as SMILES and SELFIES has been well-established in the field of cheminformatics…
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
Jinsong Chen, Hanpeng Liu, John E. Hopcroft +1
While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for co…
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning
Jinsong Chen, Gaichao Li, John E. Hopcroft +1
The emerging graph Transformers have achieved impressive performance for graph representation learning over graph neural networks (GNNs). In this work, we regard the self-attention…
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
Binghui Li, Jikai Jin, Han Zhong +2
It is well-known that modern neural networks are vulnerable to adversarial examples. To mitigate this problem, a series of robust learning algorithms have been proposed. However, a…
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability
Yifeng Xiong, Jiadong Lin, Min Zhang +2
The black-box adversarial attack has attracted impressive attention for its practical use in the field of deep learning security. Meanwhile, it is very challenging as there is no a…
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks
Jiadong Lin, Chuanbiao Song, Kun He +2
Deep learning models are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on benign inputs. However, under the black-box setting, most exist…
Adversarially Robust Generalization Just Requires More Unlabeled Data
Runtian Zhai, Tianle Cai, Di He +4
Neural network robustness has recently been highlighted by the existence of adversarial examples. Many previous works show that the learned networks do not perform well on perturbe…
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
Liwei Wang, Lunjia Hu, Jiayuan Gu +4
It is widely believed that learning good representations is one of the main reasons for the success of deep neural networks. Although highly intuitive, there is a lack of theory an…
Improving the Generalization of Adversarial Training with Domain Adaptation
Chuanbiao Song, Kun He, Liwei Wang +1
By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial tra…
Curvature-based Comparison of Two Neural Networks
Tao Yu, Huan Long, John E. Hopcroft
In this paper we show the similarities and differences of two deep neural networks by comparing the manifolds composed of activation vectors in each fully connected layer of them.…
Snapshot Ensembles: Train 1, get M for free
Gao Huang, Yixuan Li, Geoff Pleiss +3
Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally…
Convergent Learning: Do different neural networks learn the same representations?
Yixuan Li, Jason Yosinski, Jeff Clune +2
Recent success in training deep neural networks have prompted active investigation into the features learned on their intermediate layers. Such research is difficult because it req…
Deep Manifold Traversal: Changing Labels with Convolutional Features
Jacob R. Gardner, Paul Upchurch, Matt J. Kusner +4
Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in or…
Sign Stable Projections, Sign Cauchy Projections and Chi-Square Kernels
Ping Li, Gennady Samorodnitsky, John Hopcroft
The method of stable random projections is popular for efficiently computing the Lp distances in high dimension (where 0<p<=2), using small space. Because it adopts nonadaptive lin…