Self-assembly plays an essential role in many natural processes, involving the formation and evolution of living or non-living structures, and shows potential applications in many emerging domains.
Multiagent Systems Distributed, Parallel, and Cluster Computing Robotics
To address the information of node and edge types, we bring the idea of heterogeneous graphs to learning on source code and present a new formula of building heterogeneous program graphs from ASTs with additional type information for nodes and edges.
Inspired by the IR-based and template-based approaches, in this paper, we propose a neural comment generation approach where we use the existing comments of similar code snippets as exemplars to guide comment generation.
In this paper, we take a further step and discuss the probability of directly completing a whole line of code instead of a single token.
no code implementations • 3 May 2020 • Kai Zhang, Shuhang Gu, Radu Timofte, Taizhang Shang, Qiuju Dai, Shengchen Zhu, Tong Yang, Yandong Guo, Younghyun Jo, Sejong Yang, Seon Joo Kim, Lin Zha, Jiande Jiang, Xinbo Gao, Wen Lu, Jing Liu, Kwangjin Yoon, Taegyun Jeon, Kazutoshi Akita, Takeru Ooba, Norimichi Ukita, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Dongliang He, Wenhao Wu, Yukang Ding, Chao Li, Fu Li, Shilei Wen, Jianwei Li, Fuzhi Yang, Huan Yang, Jianlong Fu, Byung-Hoon Kim, JaeHyun Baek, Jong Chul Ye, Yuchen Fan, Thomas S. Huang, Junyeop Lee, Bokyeung Lee, Jungki Min, Gwantae Kim, Kanghyu Lee, Jaihyun Park, Mykola Mykhailych, Haoyu Zhong, Yukai Shi, Xiaojun Yang, Zhijing Yang, Liang Lin, Tongtong Zhao, Jinjia Peng, Huibing Wang, Zhi Jin, Jiahao Wu, Yifu Chen, Chenming Shang, Huanrong Zhang, Jeongki Min, Hrishikesh P. S, Densen Puthussery, Jiji C. V
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
As far as we have concerned, we are the first to apply graph neural networks on the domain of code clone detection.
To enable the knowledge sharing between related tasks, we creatively propose a Multi-Task Learning (MTL) framework to learn two related tasks in code completion jointly.
And through feedback, each player is provided with personalized feedback information based on the current COG and the player's exploration result, in order to accelerate his/her puzzle-solving process.
Color demosaicking (CDM) is a critical first step for the acquisition of high-quality RGB images with single chip cameras.
It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format.
This paper addresses the question: Why do neural dialog systems generate short and meaningless replies?
Neural network-based dialog systems are attracting increasing attention in both academia and industry.
Such approaches are time- and memory-intensive because of the large numbers of parameters for word embeddings and the output layer.
Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years.
Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain.
However, existing neural networks for relation classification are usually of shallow architectures (e. g., one-layer convolutional neural networks or recurrent networks).
Ranked #2 on Relation Classification on SemEval 2010 Task 8
In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences.
Ranked #84 on Natural Language Inference on SNLI
Provided a specific word, we use RNNs to generate previous words and future words, either simultaneously or asynchronously, resulting in two model variants.
This paper envisions an end-to-end program generation scenario using recurrent neural networks (RNNs): Users can express their intention in natural language; an RNN then automatically generates corresponding code in a characterby-by-character fashion.
This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP.
Relation classification is an important research arena in the field of natural language processing (NLP).
Ranked #4 on Relation Classification on SemEval 2010 Task 8
Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems.
This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence modeling.
Ranked #5 on Text Classification on TREC-6
Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the artificial intelligence community.
In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis.