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Found 29 papers, 11 papers with code
Tokenizing 3D Molecule Structure with Quantized Spherical Coordinates
Two primary obstacles emerge: (1) the difficulty in designing a 3D line notation that ensures SE(3)-invariant atomic coordinates, and (2) the non-trivial task of tokenizing continuous coordinates for use in LMs, which inherently require discrete inputs.
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
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 constructing token sequences overlooks valuable information from other nodes, hindering their ability to fully harness graph information for learning optimal node representations.
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning
Graph pooling methods have been widely used on downsampling graphs, achieving impressive results on multiple graph-level tasks like graph classification and graph generation.
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning
In this way, SignGT could learn informative node representations from both long-range dependencies and local topology information.
Ranked #7 on
Node Classification
on Actor
PIAT: Parameter Interpolation based Adversarial Training for Image Classification
Besides, we suggest to use the Normalized Mean Square Error (NMSE) to further improve the robustness by aligning the clean and adversarial examples.
Class-aware Information for Logit-based Knowledge Distillation
Knowledge distillation aims to transfer knowledge to the student model by utilizing the predictions/features of the teacher model, and feature-based distillation has recently shown its superiority over logit-based distillation.
On the Complexity of Bayesian Generalization
Specifically, at the $representational \ level$, we seek to answer how the complexity varies when a visual concept is mapped to the representation space.
Local Magnification for Data and Feature Augmentation
And further combination with our feature augmentation techniques, termed LOMA_IF&FO, can continue to strengthen the model and outperform advanced intensity transformation methods for data augmentation.
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
Moreover, we establish an improved upper bound of $\exp({\mathcal{O}}(k))$ for the network size to achieve low robust generalization error when the data lies on a manifold with intrinsic dimension $k$ ($k \ll d$).
Uncovering the Local Hidden Community Structure in Social Networks
We theoretically show that our method can avoid some situations that a broken community and the local community are regarded as one community in the subgraph, leading to the inaccuracy on detection which can be caused by global hidden community detection methods.
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability
The black-box adversarial attack has attracted impressive attention for its practical use in the field of deep learning security.
Stochastic Variance Reduced Ensemble Adversarial Attack
Black-box adversarial attack has attracted much attention for its practical use in deep learning applications, and it is very challenging as there is no access to the architecture and weights of the target model.
Structure Amplification on Multi-layer Stochastic Block Models
Our previous work proposes a general structure amplification technique called HICODE that uncovers many layers of functional hidden structure in complex networks.
Integrating Large Circular Kernels into CNNs through Neural Architecture Search
We first propose a simple yet efficient implementation of the convolution using circular kernels, and empirically show the significant advantages of large circular kernels over the counterpart square kernels.
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples
A recent work targets unrestricted adversarial example using generative model but their method is based on a search in the neighborhood of input noise, so actually their output is still constrained by input.
Single Image Reflection Removal through Cascaded Refinement
IBCLN is a cascaded network that iteratively refines the estimates of transmission and reflection layers in a manner that they can boost the prediction quality to each other, and information across steps of the cascade is transferred using an LSTM.
Ranked #3 on
Reflection Removal
on SIR^2(Postcard)
Hierarchical hidden community detection for protein complex prediction
Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities.
Molecular Networks
Robust Local Features for Improving the Generalization of Adversarial Training
We continue to propose a new approach called Robust Local Features for Adversarial Training (RLFAT), which first learns the robust local features by adversarial training on the RBS-transformed adversarial examples, and then transfers the robust local features into the training of normal adversarial examples.
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks
While SIM is based on our discovery on the scale-invariant property of deep learning models, for which we leverage to optimize the adversarial perturbations over the scale copies of the input images so as to avoid "overfitting" on the white-box model being attacked and generate more transferable adversarial examples.
A New Anchor Word Selection Method for the Separable Topic Discovery
In this work, we propose a new method for the anchor word selection by associating the word co-occurrence probability with the words similarity and assuming that the most different words on semantic are potential candidates for the anchor words.
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples
In this way, AT-GAN can learn the distribution of adversarial examples that is very close to the distribution of real data.
Improving the Generalization of Adversarial Training with Domain Adaptation
Our intuition is to regard the adversarial training on FGSM adversary as a domain adaption task with limited number of target domain samples.
Curvature-based Comparison of Two Neural Networks
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.
Krylov Subspace Approximation for Local Community Detection
Community detection is an important information mining task in many fields including computer science, social sciences, biology and physics.
Social and Information Networks
The Local Dimension of Deep Manifold
Our results show that the dimensions of different categories are close to each other and decline quickly along the convolutional layers and fully connected layers.
Randomness in Deconvolutional Networks for Visual Representation
Toward a deeper understanding on the inner work of deep neural networks, we investigate CNN (convolutional neural network) using DCN (deconvolutional network) and randomization technique, and gain new insights for the intrinsic property of this network architecture.
Snapshot Ensembles: Train 1, get M for free
In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no additional training cost.
Hidden Community Detection in Social Networks
We introduce a new paradigm that is important for community detection in the realm of network analysis.
Deep Manifold Traversal: Changing Labels with Convolutional Features
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 order to alter the class membership.
