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Found 36 papers, 19 papers with code
MoCoViT: Mobile Convolutional Vision Transformer
Recently, Transformer networks have achieved impressive results on a variety of vision tasks.
TRT-ViT: TensorRT-oriented Vision Transformer
We revisit the existing excellent Transformers from the perspective of practical application.
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
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).
Self-Supervised Learning for Recommender Systems: A Survey
In this survey, a timely and systematical review of the research efforts on self-supervised recommendation (SSR) is presented.
SepViT: Separable Vision Transformer
SepViT helps to carry out the information interaction within and among the windows via a depthwise separable self-attention.
V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer
In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles.
Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
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.
Cloud-Assisted Collaborative Road Information Discovery with Gaussian Process: Application to Road Profile Estimation
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.
OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication
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
Self-Supervised Graph Co-Training for Session-based Recommendation
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.
GGT: Graph-Guided Testing for Adversarial Sample Detection of Deep Neural Network
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.
Socially-Aware Self-Supervised Tri-Training for Recommendation
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.
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
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.
Code Structure Guided Transformer for Source Code Summarization
While Transformer-based approaches achieve promising performance, they do not explicitly incorporate the code structure information which is important for capturing code semantics.
Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network
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.
deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search
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.
Smart Contract Security: a Practitioners' Perspective
Given numerous research efforts in addressing the security issues of smart contracts, we wondered how software practitioners build security into smart contracts in practice.
Software Engineering
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
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.
Deformable Gabor Feature Networks for Biomedical Image Classification
In recent years, deep learning has dominated progress in the field of medical image analysis.
Retrieve and Refine: Exemplar-based Neural Comment Generation
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.
What Makes a Popular Academic AI Repository?
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.
Software Engineering
Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing
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.
Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity
We further compare the identified 16 security categories across different sources based on their popularity and impact.
On the Replicability and Reproducibility of Deep Learning in Software Engineering
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: Searching Code Based on Sequential Semantics of Important Query Words
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.
Generating Question Titles for Stack Overflow from Mined Code Snippets
Stack Overflow has been heavily used by software developers as a popular way to seek programming-related information from peers via the internet.
Software Engineering
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search
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.
Detecting Code Clones with Graph Neural Networkand Flow-Augmented Abstract Syntax Tree
As far as we have concerned, we are the first to apply graph neural networks on the domain of code clone detection.
Automating App Review Response Generation
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.
Checking Smart Contracts with Structural Code Embedding
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.
Software Engineering
Circulant Binary Convolutional Networks: Enhancing the Performance of 1-bit DCNNs with Circulant Back Propagation
The CiFs can be easily incorporated into existing deep convolutional neural networks (DCNNs), which leads to new Circulant Binary Convolutional Networks (CBCNs).
Code Generation as a Dual Task of Code Summarization
Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development.
Automatic Generation of Pull Request Descriptions
We build a dataset with over 41K PRs and evaluate our approach on this dataset through ROUGE and a human evaluation.
Software Engineering
A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning
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.
SmartEmbed: A Tool for Clone and Bug Detection in Smart Contracts through Structural Code Embedding
In addition to the uses by individual developers, SmartEmbed can also be applied to studies of smart contracts in a large scale.
Software Engineering
RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs
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.
