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Found 21 papers, 3 papers with code
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts
Universal adversarial texts (UATs) refer to short pieces of text units that can largely affect the predictions of NLP models.
Monash-Summ@LongSumm 20 SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline
The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task.
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey
Wireless FL (WFL) is a distributed method of training a global deep learning model in which a large number of participants each train a local model on their training datasets and then upload the local model updates to a central server.
Learning to Learn for Few-shot Continual Active Learning
Recent advances in CL are mostly confined to a supervised learning setting, especially in NLP domain.
Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System
As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios.
From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding
To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures.
Hybrid Variational Autoencoder for Time Series Forecasting
Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables.
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation
Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models.
Attention Distraction: Watermark Removal Through Continual Learning with Selective Forgetting
Fine-tuning attacks are effective in removing the embedded watermarks in deep learning models.
Temporal Knowledge Graph Completion: A Survey
KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time.
Learning Based Task Offloading in Digital Twin Empowered Internet of Vehicles
In specific, to preserve the precious computing resource at different levels for most appropriate computing tasks, we integrate a learning scheme based on the prediction of futuristic computing tasks in DT.
Semantic Code Search for Smart Contracts
To make the model more focused on the key contextual information, we use a multi-head attention network to generate embeddings for code features.
Prototype-Guided Memory Replay for Continual Learning
Continual learning (CL) refers to a machine learning paradigm that learns continuously without forgetting previously acquired knowledge.
Federated Learning Meets Natural Language Processing: A Survey
Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models.
A Bytecode-based Approach for Smart Contract Classification
Our experimental studies on over 3, 300 real-world Ethereum smart contracts show that our model can classify smart contracts without source code and has better performance than baseline models.
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy.
Chaotic-to-Fine Clustering for Unlabeled Plant Disease Images
Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive.
SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline
The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task.
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains.
Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
Based on the ELBOs, we propose a VAE-based Bayesian MF framework.
Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for Sparse and Imbalanced Count Data
Under synthetic data evaluation, VAE-BPTF tended to recover the right number of latent factors and posterior parameter values.
