In this paper, we propose a new approach with multimodal contrastive learning and soft data augmentation for code search.
Code search is to search reusable code snippets from source code corpus based on natural languages queries.
The information retrieval-based methods reuse the commit messages of similar code diffs, while the neural-based methods learn the semantic connection between code diffs and commit messages.
The Temporal Link Prediction task of WSDM Cup 2022 expects a single model that can work well on two kinds of temporal graphs simultaneously, which have quite different characteristics and data properties, to predict whether a link of a given type will occur between two given nodes within a given time span.
They utilize labels from the source domain as the supervision signal and are jointly trained on both the source graph and the target graph.
Regularization can mitigate the generalization gap between training and inference by introducing inductive bias.
For node-level tasks, GNNs have strong power to model the homophily property of graphs (i. e., connected nodes are more similar) while their ability to capture the heterophily property is often doubtful.
To achieve a profound understanding of how far we are from solving this problem and provide suggestions to future research, in this paper, we conduct a systematic and in-depth analysis of 5 state-of-the-art neural code summarization models on 6 widely used BLEU variants, 4 pre-processing operations and their combinations, and 3 widely used datasets.
We find that: (1) Different variants of the BLEU metric are used in previous works, which affects the evaluation and understanding of existing methods.
On the other hand, as a specific query may focus on one or several perspectives, it is difficult for a single query representation module to represent different user intents.
To simultaneously extract spatial and relational information from tables, we propose a novel neural network architecture, TabularNet.
Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks.
Further analysis indicates that the locality and globality of the traffic networks are critical to traffic flow prediction and the proposed TSSRGCN model can adapt to the various temporal traffic patterns.
We carried out experiments on discrete and continuous time series data.
Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data.
Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks.