Search Results for author:
Found 23 papers, 8 papers with code
Masked Autoencoders for Generic Event Boundary Detection CVPR'2022 Kinetics-GEBD Challenge
In this paper, we apply Masked Autoencoders to improve algorithm performance on the GEBD tasks.
Why Adversarial Training of ReLU Networks Is Difficult?
We also prove that adversarial training tends to strengthen the influence of unconfident input samples with large gradient norms in an exponential manner.
GRAND+: Scalable Graph Random Neural Networks
In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning.
Ranked #1 on
Node Classification
on MAG-scholar-C
Flexible-Modal Face Anti-Spoofing: A Benchmark
Can we train a unified model, and flexibly deploy it under various modality scenarios?
Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings
Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they struggle to reason rare or emerging unseen entities.
SCR: Training Graph Neural Networks with Consistency Regularization
However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
Ranked #1 on
Node Property Prediction
on ogbn-mag
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness
This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.
A Unified Game-Theoretic Interpretation of Adversarial Robustness
This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i. e.} the view of multi-order interactions between input variables of DNNs.
A HYPOTHESIS FOR THE COGNITIVE DIFFICULTY OF IMAGES
This paper proposes a hypothesis to analyze the underlying reason for the cognitive difficulty of an image from two perspectives, i. e. a cognitive image usually makes a DNN strongly activated by cognitive concepts; discarding massive non-cognitive concepts may also help the DNN focus on cognitive concepts.
A Hypothesis for the Aesthetic Appreciation in Neural Networks
This paper proposes a hypothesis for the aesthetic appreciation that aesthetic images make a neural network strengthen salient concepts and discard inessential concepts.
A Game-Theoretic Taxonomy of Visual Concepts in DNNs
In this paper, we rethink how a DNN encodes visual concepts of different complexities from a new perspective, i. e. the game-theoretic multi-order interactions between pixels in an image.
A Unified Game-Theoretic Interpretation of Adversarial Robustness
This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.
Volume properties and rigidity on self-expanders of mean curvature flow
We also give the upper and lower bounds for the bottom of the spectrum of the $L$-stability operator and discuss the $L$-stability of some special self-expanders.
Differential Geometry
Technical Note: Game-Theoretic Interactions of Different Orders
In this study, we define interaction components of different orders between two input variables based on game theory.
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning
In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher.
Building Interpretable Interaction Trees for Deep NLP Models
This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing.
Rotation-Equivariant Neural Networks for Privacy Protection
Compared to the traditional neural network, the RENN uses d-ary vectors/tensors as features, in which each element is a d-ary number.
Explaining Knowledge Distillation by Quantifying the Knowledge
Whereas, in the scenario of learning from raw data, the DNN learns visual concepts sequentially.
Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding
The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces.
Initialization for Network Embedding: A Graph Partition Approach
Network embedding has been intensively studied in the literature and widely used in various applications, such as link prediction and node classification.
An Attention-Based Approach for Single Image Super Resolution
We propose an attention-based approach to give a discrimination between texture areas and smooth areas.
Properties on n-dimensional convolution for image deconvolution
Convolution system is linear and time invariant, and can describe the optical imaging process.
Tracking Deformable Parts via Dynamic Conditional Random Fields
Despite the success of many advanced tracking methods in this area, tracking targets with drastic variation of appearance such as deformation, view change and partial occlusion in video sequences is still a challenge in practical applications.
