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Found 15 papers, 9 papers with code
Automated Similarity Metric Generation for Recommendation
Given the critical role of similarity metrics in recommender systems, existing methods mainly employ handcrafted similarity metrics to capture the complex characteristics of user-item interactions.
CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing
Large Language Models (LLMs) have revolutionized code generation ability by converting natural language descriptions into executable code.
Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning
Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications.
Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training
Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals.
Soft-Labeled Contrastive Pre-training for Function-level Code Representation
In this paper, we present \textbf{SCodeR}, a \textbf{S}oft-labeled contrastive pre-training framework with two positive sample construction methods to learn functional-level \textbf{Code} \textbf{R}epresentation.
ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals
A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management.
Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios
Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy.
Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning
Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL).
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
For those reasons, there is a high demand for trustworthy and rigorous methods to verify the robustness of neural network models.
A Real-World Radio Frequency Signal Dataset Based on LTE System and Variable Channels
However, most of these datasets are collected from 2. 4G WiFi devices and through similar channel environments.
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Our attack technique targets the inherent vulnerabilities of NLP models, allowing us to generate samples even without interacting with the victim NLP model, as long as it is based on pre-trained language models (PLMs).
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training
Moreover, our case study shows that our visual solution can well reflect the characteristics of various training scenarios, showing good potential of DVI as a debugging tool for analyzing deep learning training processes.
Generalizing Neural Networks by Reflecting Deviating Data in Production
However, inputs may deviate from the training dataset distribution in real deployments.
sFuzz: An Efficient Adaptive Fuzzer for Solidity Smart Contracts
In this work, we present an adaptive fuzzer for smart contracts on the Ethereum platform called sFuzz.
Software Engineering
SR2CNN: Zero-Shot Learning for Signal Recognition
This paper proposes a ZSL framework, signal recognition and reconstruction convolutional neural networks (SR2CNN), to address relevant problems in this situation.
