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Found 13 papers, 2 papers with code
Writing Style Aware Document-level Event Extraction
This verifies the writing style contains valuable information that could improve the performance of the event extraction task.
Ghost Imaging Based on Recurrent Neural Network
The testing results show that the proposed method can achieve image reconstruction at a very low sampling rate (0. 38$\%$).
Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting
Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge.
History Encoding Representation Design for Human Intention Inference
In this extended abstract, we investigate the design of learning representation for human intention inference.
Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging
Ghost imaging (GI) has been paid attention gradually because of its lens-less imaging capability, turbulence-free imaging and high detection sensitivity.
Generative-Adversarial-Networks-based Ghost Recognition
The proposed method can improve the problems caused by conventional recognition methods that based on target image information, and provide a certain turbulence-free ability.
RoBERTa-wwm-ext Fine-Tuning for Chinese Text Classification
Bidirectional Encoder Representations from Transformers (BERT) have shown to be a promising way to dramatically improve the performance across various Natural Language Processing tasks [Devlin et al., 2019].
COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing
General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics.
Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks
To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods.
End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning
Current autonomous driving systems are composed of a perception system and a decision system.
Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control
Although deep reinforcement learning (deep RL) methods have lots of strengths that are favorable if applied to autonomous driving, real deep RL applications in autonomous driving have been slowed down by the modeling gap between the source (training) domain and the target (deployment) domain.
Cascade Attribute Learning Network
We propose the cascade attribute learning network (CALNet), which can learn attributes in a control task separately and assemble them together.
Imaging around corners with single-pixel detector by computational ghost imaging
We have designed a single-pixel camera with imaging around corners based on computational ghost imaging.
