As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment.
Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of interaction to learn new skills.
The adaptive adjusting term is composed of two complementary factors: 1) quantity factor, which pays more attention to tail classes, and 2) difficulty factor, which adaptively pays more attention to hard instances in the training process.
Most semi-supervised few-shot learning methods select pseudo-labeled data of unlabeled set by task-specific confidence estimation.
Drive-thru is a popular sales channel in the fast food industry where consumers can make food purchases without leaving their cars.
Recent approaches for 3D object detection have made tremendous progresses due to the development of deep learning.
In this paper, we propose graph-based hindsight goal generation (G-HGG), an extension of HGG selecting hindsight goals based on shortest distances in an obstacle-avoiding graph, which is a discrete representation of the environment.
If the uncertainty of an enterprise's revenue forecasting can be estimated, a more proper credit limit can be granted.
Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is beneficial to mobile robotic applications.
Over the last years, a great success of deep neural networks (DNNs) has been witnessed in computer vision and other fields.
Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation.
We present a video captioning approach that encodes features by progressively completing syntactic structure (LSTM-CSS).
2 code implementations • 16 Apr 2018 • Jason Dai, Yiheng Wang, Xin Qiu, Ding Ding, Yao Zhang, Yanzhang Wang, Xianyan Jia, Cherry Zhang, Yan Wan, Zhichao Li, Jiao Wang, Shengsheng Huang, Zhongyuan Wu, Yang Wang, Yuhao Yang, Bowen She, Dongjie Shi, Qi Lu, Kai Huang, Guoqiong Song
This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms.