• Publications
  • Influence
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification
TLDR
A graph-based evidence aggregating and reasoning (GEAR) framework which enables information to transfer on a fully-connected evidence graph and then utilizes different aggregators to collect multi-evidence information is proposed.
Deep Neural Machine Translation with Linear Associative Unit
TLDR
A novel linear associative units (LAU) is proposed to reduce the gradient propagation path inside the recurrent unit to achieve comparable results with the state-of-the-art Neural Machine Translation.
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
TLDR
The proposed BERT-style pretraining strategy improves the performance of standard point cloud Transformers and the representations learned by Point-BERT transfer well to new tasks and domains, where the models largely advance the state-of-the-art of few-shot point cloud classification task.
Optimal dimensionality reduction of sensor data in multisensor estimation fusion
TLDR
This paper will answer the above questions by using the matrix decomposition, pseudo-inverse, and eigenvalue techniques.
An efficient algorithm for optimal linear estimation fusion in distributed multisensor systems
TLDR
An efficient iterative algorithm for distributed multisensor estimation fusion without any restrictive assumption on the noise covariance is presented and reduces the computational complexity significantly since the number of iterative steps is less than thenumber of sensors.
Fully Hyperbolic Neural Networks
TLDR
It is proved that linear transformation in tangent spaces used by existing hyperbolic networks is a relaxation of the Lorentz rotation and does not include the boost, implicitly limiting the capabilities of existing hyperBolic networks.
FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds
TLDR
A novel framework called FVNet for 3D front-view proposal generation and object detection from point clouds, which achieves real-time performance with 12ms per point cloud sample and outperforms state-of-the-art techniques which take either camera images or point clouds as input, in terms of accuracy and inference time.
Minimax Robust Optimal Estimation Fusion in Distributed Multisensor Systems With Uncertainties
TLDR
This paper demonstrates that when the error covariance matrix suffers disturbance, the proposed fusion method is more robust than the nominal fusion method which ignores the uncertainties, and can improve the performance when the disturbance is considerably large.
...
...