Search Results for author:
Found 32 papers, 8 papers with code
FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer
For 3D point cloud transformers, existing efforts focus primarily on pushing their accuracy to the state-of-the-art level.
Zebra: Deeply Integrating System-Level Provenance Search and Tracking for Efficient Attack Investigation
System auditing has emerged as a key approach for monitoring system call events and investigating sophisticated attacks.
Aircraft Ground Taxiing Deduction and Conflict Early Warning Method Based on Control Command Information
Aircraft taxiing conflict is a threat to the safety of airport operations, mainly due to the human error in control command infor-mation.
Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations
There is an inescapable long-tailed class-imbalance issue in many real-world classification problems.
A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms
Understanding causality helps to structure interventions to achieve specific goals and enables predictions under interventions.
Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction
To learn it applicably, we propose a general clause-level encoding model named EA-GAT comprising E-GAT and Activation Sort.
Video-TransUNet: Temporally Blended Vision Transformer for CT VFSS Instance Segmentation
Our findings suggest that the proposed model can indeed enhance the TransUNet architecture via exploiting temporal information and improving segmentation performance by a significant margin.
Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour
Furthermore, we design a novel Interaction-Aware Transformer (IAT) to dynamically learn the graph-level representation of social behaviours and update the node-level representation, guided by our proposed interaction-aware self-attention mechanism.
Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction
The empirical and theoretical analysis demonstrates that the MDL loss improves the robustness and generalization of the model simultaneously for natural training.
FACM: Correct the Output of Deep Neural Network with Middle Layers Features against Adversarial Samples
In the strong adversarial attacks against deep neural network (DNN), the output of DNN will be misclassified if and only if the last feature layer of the DNN is completely destroyed by adversarial samples, while our studies found that the middle feature layers of the DNN can still extract the effective features of the original normal category in these adversarial attacks.
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
Multi-sensor fusion is essential for an accurate and reliable autonomous driving system.
Ranked #2 on
3D Multi-Object Tracking
on nuScenes
Enhancing the Transferability of Adversarial Examples via a Few Queries
Due to the vulnerability of deep neural networks, the black-box attack has drawn great attention from the community.
Self-supervised learning in non-small cell lung cancer discovers novel morphological clusters linked to patient outcome and molecular phenotypes
Histopathological images provide the definitive source of cancer diagnosis, containing information used by pathologists to identify and subclassify malignant disease, and to guide therapeutic choices.
Dynamic Curriculum Learning for Great Ape Detection in the Wild
We propose a novel end-to-end curriculum learning approach for sparsely labelled animal datasets leveraging large volumes of unlabelled data to improve supervised species detectors.
Mixed Strategies for Security Games with General Defending Requirements
In this work, we initiate the study of mixed strategies for the security games in which the targets can have different defending requirements.
ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence
Intelligent applications based on machine learning are impacting many parts of our lives.
DTWSSE: Data Augmentation with a Siamese Encoder for Time Series
In order to reasonably measure the distance of the time series, DTW, which has been verified to be an effective method forts, is employed as the distance metric.
Review of end-to-end speech synthesis technology based on deep learning
As an indispensable part of modern human-computer interaction system, speech synthesis technology helps users get the output of intelligent machine more easily and intuitively, thus has attracted more and more attention.
WakaVT: A Sequential Variational Transformer for Waka Generation
To further explore the creative potential of natural language generation systems in Japanese poetry creation, we propose a novel Waka generation model, WakaVT, which automatically produces Waka poems given user-specified keywords.
A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions
This paper attempts to provide an overview of various composition tasks under different music generation levels, covering most of the currently popular music generation tasks using deep learning.
Back to the Future: Cycle Encoding Prediction for Self-supervised Contrastive Video Representation Learning
In this paper we show that learning video feature spaces in which temporal cycles are maximally predictable benefits action classification.
Latent Dirichlet Allocation Model Training with Differential Privacy
To address the privacy issue in LDA, we systematically investigate the privacy protection of the main-stream LDA training algorithm based on Collapsed Gibbs Sampling (CGS) and propose several differentially private LDA algorithms for typical training scenarios.
Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction
To filter out irrelevant noises and alleviate the error propagation, DSAN dynamically extracts valuable information by applying self-attention over the noisy input and bridges each output directly to the purified inputs via implementing a switch-attention mechanism.
OL4EL: Online Learning for Edge-cloud Collaborative Learning on Heterogeneous Edges with Resource Constraints
However, heterogeneous and limited computation and communication resources on edge servers (or edges) pose great challenges on distributed ML and formulate a new paradigm of Edge Learning (i. e. edge-cloud collaborative machine learning).
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Although both classical Gaussian mechanisms [1, 2] assume $0 < \epsilon \leq 1$, our review finds that many studies in the literature have used the classical Gaussian mechanisms under values of $\epsilon$ and $\delta$ where the added noise amounts of [1, 2] do not achieve $(\epsilon,\delta)$-DP.
MG-VAE: Deep Chinese Folk Songs Generation with Specific Regional Style
In this paper, we propose MG-VAE, a music generative model based on VAE (Variational Auto-Encoder) that is capable of capturing specific music style and generating novel tunes for Chinese folk songs (Min Ge) in a manipulatable way.
Music Generation
Multimedia
Sound
Audio and Speech Processing
Deep Learning for Estimating Synaptic Health of Primary Neuronal Cell Culture
Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery.
Great Ape Detection in Challenging Jungle Camera Trap Footage via Attention-Based Spatial and Temporal Feature Blending
We propose the first multi-frame video object detection framework trained to detect great apes.
Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis
Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data.
On Privacy Protection of Latent Dirichlet Allocation Model Training
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications.
Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas
To investigate such ethical dilemmas, recent studies have adopted preference aggregation, in which each voter expresses her/his preferences over decisions for the possible ethical dilemma scenarios, and a centralized system aggregates these preferences to obtain the winning decision.
Authorship attribution of source code by using back propagation neural network based on particle swarm optimization
Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm.
