The dearth of AI talents capable of manually designing neural architecture such as AlexNet and ResNet has spurred research in automatic architecture design. Google’s Cloud AutoML is an example of a system that enables developers with limited machine learning expertise to train high quality models. The trade-off, however, is AutoML’s high computational costs.
From Hayao Miyazaki’s Spirited Away to Satoshi Kon’s Paprika, Japanese anime has made it okay for adults everywhere to enjoy cartoons again. Now, a team of Tsinghua University and Cardiff University researchers have introduced CartoonGAN — an AI-powered technology that simulates the styles of Japanese anime maestri from snapshots of real world scenery.
At the recent IEEE International Conference on Robotics and Automation (ICRA) in Brisbane, Australia, the Best Student Paper award went to ETH Zurich Autonomous Systems Laboratory (ASL)’s Miguel de la Iglesia Valls et al. for Design of an Autonomous Racecar: Perception, State Estimation and System Integration.
IBM today announced it will release the world’s largest facial attribute dataset in order to fight bias in artificial intelligence systems used to recognize human faces. The dataset was built by IBM research scientists and contains one million images, five times the image count of the current largest facial attribute dataset. It will be publically available this fall.
The NVIDIA DeepStream Software Development Kit (SDK) was originally released in 2017 to simplify the deployment of scalable intelligent video analytics (IVA) powered by deep learning. Developers can use DeepStream to process, understand and categorize video frames in real time and within stringent throughput and latency requirements.
Earlier this week the Association for Computational Linguistics (ACL) 2018 announced its Best Two Short Papers, neither of which had yet been published. Today the AI community got its first look at one of the winners when Know What You Don’t Know: Unanswerable Questions for SQuAD was released on arXiv.
GcForest, a decision tree ensemble approach that is much easier to train than deep neural networks, has received a lot of attention from researchers since it was introduced by Prof. Zhihua Zhou and his student Ji Feng last year. Based on their previous work, Zhou, Feng and Nanjing University colleague Yang Yu have now proposed Multi-layered Gradient Boosting Decision Trees (mGBDTs).
Synced recently spoke with Delian Capital Senior Vice President Xuesong Fan about the current status of automated driving startups. Fan graduated from Harbin Engineering University and worked for years on Chinese satellite engineering. In 2015 came down to earth, applying his experience to self-driving car investments.
In his keynote at the Tencent Cloud Service Summit 2018 in Guangzhou today, Tencent Founder and CEO Pony Ma introduced his company’s new Super Brain platform and a Three Nets cloud concept comprising the “Internet of Things,” “Internet of Humans” and “Internet of Intelligence.”
To boost learning research aimed at endowing robots with better generalization capabilities, Yi Wu from UC Berkeley and Yuxin Wu, Georgia Gkioxari, and Yuandong Tian from Facebook AI research recently published the paper Building Generalizable Agents with a Realistic and Rich 3D Environment.
To combine the advantages of these two methods, the authors of this paper first adapts the multi-source NMT model, by employing different encoders to capture the semantics of the source language, then the decoder is used to generate the final output by the multiple context vector representations coming from the encoder.