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Found 14 papers, 4 papers with code
Federated Graph Learning with Adaptive Importance-based Sampling
For privacy-preserving graph learning tasks involving distributed graph datasets, federated learning (FL)-based GCN (FedGCN) training is required.
UWStereo: A Large Synthetic Dataset for Underwater Stereo Matching
To enable further advance in underwater stereo matching, we introduce a large synthetic dataset called UWStereo.
BadEdit: Backdooring large language models by model editing
It boasts superiority over existing backdoor injection techniques in several areas: (1) Practicality: BadEdit necessitates only a minimal dataset for injection (15 samples).
Generative Pretraining at Scale: Transformer-Based Encoding of Transactional Behavior for Fraud Detection
In this work, we introduce an innovative autoregressive model leveraging Generative Pretrained Transformer (GPT) architectures, tailored for fraud detection in payment systems.
LMs: Understanding Code Syntax and Semantics for Code Analysis
We break down the abilities needed for artificial intelligence~(AI) models to address SE tasks related to code analysis into three categories: 1) syntax understanding, 2) static behavior understanding, and 3) dynamic behavior understanding.
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities
These structures are fundamental to understanding code.
Learning Program Representations with a Tree-Structured Transformer
Learning vector representations for programs is a critical step in applying deep learning techniques for program understanding tasks.
Integrating Tree Path in Transformer for Code Representation
Learning distributed representation of source code requires modelling its syntax and semantics.
Learning to Represent Programs with Heterogeneous Graphs
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.
Towards Full-line Code Completion with Neural Language Models
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.
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.
Learning to Anneal and Prune Proximity Graphs for Similarity Search
This paper studies similarity search, which is a crucial enabler of many feature vector--based applications.
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models
Neural language models (NLMs) have recently gained a renewed interest by achieving state-of-the-art performance across many natural language processing (NLP) tasks.
Learning Intrinsic Sparse Structures within Long Short-Term Memory
This work aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the sizes of basic structures within LSTM units, including input updates, gates, hidden states, cell states and outputs.
