We revisit the existing excellent Transformers from the perspective of practical application.
Meanwhile, to compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher).
In this survey, a timely and systematical review of the research efforts on self-supervised recommendation (SSR) is presented.
In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles.
Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue.
Promising results on extensive simulations and hardware-in-the-loop experiments show that the proposed collaborative estimation can significantly enhance estimation and iteratively improve the performance from vehicle to vehicle, despite vehicle heterogeneity, model uncertainty, and measurement noises.
We then construct a comprehensive benchmark with a total of 16 implemented models to evaluate several information fusion strategies~(i. e. early, late, and intermediate fusion) with state-of-the-art LiDAR detection algorithms.
Ranked #2 on 3D Object Detection on OPV2V
In this paper, for informative session-based data augmentation, we combine self-supervised learning with co-training, and then develop a framework to enhance session-based recommendation.
Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples, the detection of which is crucial for the wide application of these DNN models.
Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected.
no code implementations • 17 May 2021 • Andrey Ignatov, Grigory Malivenko, Radu Timofte, Sheng Chen, Xin Xia, Zhaoyan Liu, Yuwei Zhang, Feng Zhu, Jiashi Li, Xuefeng Xiao, Yuan Tian, Xinglong Wu, Christos Kyrkou, Yixin Chen, Zexin Zhang, Yunbo Peng, Yue Lin, Saikat Dutta, Sourya Dipta Das, Nisarg A. Shah, Himanshu Kumar, Chao Ge, Pei-Lin Wu, Jin-Hua Du, Andrew Batutin, Juan Pablo Federico, Konrad Lyda, Levon Khojoyan, Abhishek Thanki, Sayak Paul, Shahid Siddiqui
To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
While Transformer-based approaches achieve promising performance, they do not explicitly incorporate the code structure information which is important for capturing code semantics.
Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution.
With the rapid increase in the amount of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language.
Given numerous research efforts in addressing the security issues of smart contracts, we wondered how software practitioners build security into smart contracts in practice.
Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task.
In recent years, deep learning has dominated progress in the field of medical image analysis.
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.
Hence, in this study, we perform an empirical study on academic AI repositories to highlight good software engineering practices of popular academic AI repositories for AI researchers.
Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT in identifying emerging app issues, improving the state-of-the-art method by 22. 3% in terms of F1-score.
We further compare the identified 16 security categories across different sources based on their popularity and impact.
Experimental results show the importance of replicability and reproducibility, where the reported performance of a DL model could not be replicated for an unstable optimization process.
CodeMatcher first collects metadata for query words to identify irrelevant/noisy ones, then iteratively performs fuzzy search with important query words on the codebase that is indexed by the Elasticsearch tool, and finally reranks a set of returned candidate code according to how the tokens in the candidate code snippet sequentially matched the important words in a query.
Stack Overflow has been heavily used by software developers as a popular way to seek programming-related information from peers via the internet.
In this way, PAD-NAS can automatically design the operations for each layer and achieve a trade-off between search space quality and model diversity.
As far as we have concerned, we are the first to apply graph neural networks on the domain of code clone detection.
Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app.
However, many bugs and vulnerabilities have been identified in many contracts which raises serious concerns about smart contract security, not to mention that the blockchain systems on which the smart contracts are built can be buggy.
The CiFs can be easily incorporated into existing deep convolutional neural networks (DCNNs), which leads to new Circulant Binary Convolutional Networks (CBCNs).
We build a dataset with over 41K PRs and evaluate our approach on this dataset through ROUGE and a human evaluation.
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.
In addition to the uses by individual developers, SmartEmbed can also be applied to studies of smart contracts in a large scale.
Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications.