Search Results for author:
Found 16 papers, 7 papers with code
MDHP-Net: Detecting Injection Attacks on In-vehicle Network using Multi-Dimensional Hawkes Process and Temporal Model
We then propose an injection attack detector, MDHP-Net, which integrates optimal MDHP parameters with MDHP-LSTM blocks to enhance temporal feature extraction.
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery
To overcome this challenge, we present a novel method named contextual representation anchor Network (CRA), where an anchor refers to a cluster center of the representations of molecules and serves as a bridge to transfer enriched contextual knowledge into molecular representations and enhance their expressiveness.
Dual-Label Learning With Irregularly Present Labels
DLL features a dual-tower model architecture that explicitly captures the information exchange between labels, aimed at maximizing the utility of partially available labels in understanding label correlation.
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs
To our knowledge, this is the first work to integrate KANs into GNN architectures tailored for molecular representation learning.
Physical formula enhanced multi-task learning for pharmacokinetics prediction
Overall, our work illustrates the benefits and potential of using PEMAL in AIDD and other scenarios with data scarcity and noise.
OFVL-MS: Once for Visual Localization across Multiple Indoor Scenes
In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task.
Multiscale Positive-Unlabeled Detection of AI-Generated Texts
Recent releases of Large Language Models (LLMs), e. g. ChatGPT, are astonishing at generating human-like texts, but they may impact the authenticity of texts.
Pillar R-CNN for Point Cloud 3D Object Detection
The performance of point cloud 3D object detection hinges on effectively representing raw points, grid-based voxels or pillars.
OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching
Local feature matching is an essential component in many visual applications.
An Electromagnetic-Information-Theory Based Model for Efficient Characterization of MIMO Systems in Complex Space
In this manuscript, an Electromagnetic-Information-Theory (EMIT) based model is developed for efficient characterization of MIMO systems in complex space.
DeepMatcher: A Deep Transformer-based Network for Robust and Accurate Local Feature Matching
In this work, we propose DeepMatcher, a deep Transformer-based network built upon our investigation of local feature matching in detector-free methods.
Poly-PC: A Polyhedral Network for Multiple Point Cloud Tasks at Once
In this work, we show that it is feasible to perform multiple tasks concurrently on point cloud with a straightforward yet effective multi-task network.
CO-Net: Learning Multiple Point Cloud Tasks at Once with A Cohesive Network
Finally, we propose a sign-based gradient surgery to promote the training of CO-Net, thereby emphasizing the usage of task-shared parameters and guaranteeing that each task can be thoroughly optimized.
RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments
Behavioral and semantic relationships play a vital role on intelligent self-driving vehicles and ADAS systems.
PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detection
Real-time and high-performance 3D object detection is of critical importance for autonomous driving.
Ranked #3 on
3D Object Detection
on nuScenes LiDAR only
Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles
And this provides us a great opportunity to think about how shall these data be organized and exploited.
