We apply reinforcement learning to video compressive sensing to adapt the compression ratio.
Auto-regressive language models with the left-to-right generation order have been a predominant paradigm for language generation.
The recent proliferation of computing technologies, e. g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e. g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Distributed, Parallel, and Cluster Computing Robotics
In this work, we show that such adversarial-based methods can only reduce the domain style gap, but cannot address the domain content distribution gap that is shown to be important for object detectors.
In this paper, we propose a hybrid inference algorithm, the Neurally-Guided Structure Inference (NG-SI), keeping the advantages of both search-based and data-driven methods.
At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services.
We introduce Texygen, a benchmarking platform to support research on open-domain text generation models.
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.
Ranked #1 on Text Generation on COCO Captions