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Found 8 papers, 3 papers with code
Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency
To achieve NTE, we devise a novel Syntax&Semantic-Enhanced Negation Extraction model, namely SSENE, which is built based on a generative pretrained language model (PLM) {of Encoder-Decoder architecture} with a multi-task learning framework.
ToNER: Type-oriented Named Entity Recognition with Generative Language Model
In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task.
CARSS: Cooperative Attention-guided Reinforcement Subpath Synthesis for Solving Traveling Salesman Problem
The algorithm's primary objective is to enhance efficiency in terms of training memory consumption, testing time, and scalability, through the adoption of a multi-agent divide and conquer paradigm.
Multi-agent Reinforcement Learning
Traveling Salesman Problem
FedNC: A Secure and Efficient Federated Learning Method with Network Coding
Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency.
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning
By adopting layer-wise pruning in local training and federated updating, we formulate an explicit FL pruning framework, FedLP (Federated Layer-wise Pruning), which is model-agnostic and universal for different types of deep learning models.
NeuroPrim: An Attention-based Model for Solving NP-hard Spanning Tree Problems
We apply our framework to three difficult problems on Euclidean space: the Degree-constrained Minimum Spanning Tree (DCMST) problem, the Minimum Routing Cost Spanning Tree (MRCST) problem, and the Steiner Tree Problem in graphs (STP).
Improving Information Cascade Modeling by Social Topology and Dual Role User Dependency
However, the user dependencies in a cascade sequence captured by sequential models are generally unidirectional and inconsistent with diffusion trees.
Adversarial Joint Training with Self-Attention Mechanism for Robust End-to-End Speech Recognition
Inspired by the extensive applications of the generative adversarial networks (GANs) in speech enhancement and ASR tasks, we propose an adversarial joint training framework with the self-attention mechanism to boost the noise robustness of the ASR system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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