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Found 11 papers, 5 papers with code
Controllable Text Generation with Neurally-Decomposed Oracle
We propose a general and efficient framework to control auto-regressive generation models with NeurAlly-Decomposed Oracle (NADO).
Reinforcement Learning for Adaptive Video Compressive Sensing
We apply reinforcement learning to video compressive sensing to adapt the compression ratio.
InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model
We propose InsNet, an expressive insertion-based text generator with efficient training and flexible decoding (parallel or sequential).
Computing Systems for Autonomous Driving: State-of-the-Art and Challenges
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
Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning
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.
Neurally-Guided Structure Inference
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.
OpenEI: An Open Framework for Edge Intelligence
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.
CoT: Cooperative Training for Generative Modeling of Discrete Data
In this paper, we study the generative models of sequential discrete data.
Neural Text Generation: Past, Present and Beyond
This paper presents a systematic survey on recent development of neural text generation models.
Texygen: A Benchmarking Platform for Text Generation Models
We introduce Texygen, a benchmarking platform to support research on open-domain text generation models.
Long Text Generation via Adversarial Training with Leaked Information
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.
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