Medical Dialogue Generation serves a critical role in telemedicine by
fa...
Accumulating substantial volumes of real-world driving data proves pivot...
Safe Reinforcement Learning (RL) aims to find a policy that achieves hig...
Incorporating external graph knowledge into neural chatbot models has be...
Imitation Learning (IL) is a widely used framework for learning imitativ...
Hierarchical reinforcement learning (RL) can accelerate long-horizon
dec...
Previous work in phonetically-grounded language generation has mainly fo...
Autonomous racing control is a challenging research problem as vehicles ...
One of the main challenges in modern recommendation systems is how to
ef...
Commonsense knowledge is crucial to many natural language processing tas...
Deep neural networks (DNNs) have promoted the development of computer ai...
Post-click Conversion Rate (CVR) prediction task plays an essential role...
Traffic simulation plays a crucial role in evaluating and improving
auto...
Neural Architecture Search (NAS) has shown promising performance in the
...
Mixed-precision quantization (MPQ) suffers from time-consuming policy se...
Autonomous racing has become a popular sub-topic of autonomous driving i...
Medical dialogue generation aims to generate responses according to a hi...
One of the key challenges of automatic story generation is how to genera...
Story generation aims to generate a long narrative conditioned on a give...
To improve the performance of long text generation, recent studies have
...
Offline Reinforcement learning (RL) has shown potent in many safe-critic...
Deep learning has recently achieved significant progress in trajectory
f...
Conventional model quantization methods use a fixed quantization scheme ...
Conditional behavior prediction (CBP) builds up the foundation for a coh...
Motion forecasting in highly interactive scenarios is a challenging prob...
The exponentially large discrete search space in mixed-precision quantiz...
In recent years much effort has been devoted to applying neural models t...
Modelling and forecasting homogeneous age-specific mortality rates of
mu...
Multi-agent behavior modeling and trajectory forecasting are crucial for...
Reinforcement Learning (RL) has been shown effective in domains where th...
This paper proposes a two-fold factor model for high-dimensional functio...
Explainability is essential for autonomous vehicles and other robotics
s...
Neural networks have made great progress in pixel to pixel image process...
The COVID-19 pandemic so far has caused huge negative impacts on differe...
Probabilistic vehicle trajectory prediction is essential for robust safe...
Learning from demonstrations is a popular tool for accelerating and redu...
Although deep reinforcement learning (deep RL) methods have lots of stre...