Search Results for author:
Found 11 papers, 2 papers with code
PA Ph&Tech at SemEval-2022 Task 11: NER Task with Ensemble Embedding from Reinforcement Learning
From pretrained contextual embedding to document-level embedding, the selection and construction of embedding have drawn more and more attention in the NER domain in recent research.
HCL: Improving Graph Representation with Hierarchical Contrastive Learning
Contrastive learning has emerged as a powerful tool for graph representation learning.
SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models
By assigning different weights to each model for different inputs, we adopted the Transformer layer to integrate the advantages of diverse models effectively.
Low Resource Named Entity Recognition
named-entity-recognition
+2
PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking
This paper describes the PASH participation in TREC 2021 Deep Learning Track.
CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer
Anti-cancer drug discoveries have been serendipitous, we sought to present the Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, a challenging and realistic benchmark dataset to facilitate scalable, robust, and reproducible graph machine learning research for anti-cancer drug discovery.
Revisiting Open World Object Detection
Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones.
Superpixel-Based Building Damage Detection from Post-earthquake Very High Resolution Imagery Using Deep Neural Networks
This paper presents a novel superpixel based approach combining DNN and a modified segmentation method, to detect damaged buildings from VHR imagery.
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
Self-supervised learning has gradually emerged as a powerful technique for graph representation learning.
Winner Team Mia at TextVQA Challenge 2021: Vision-and-Language Representation Learning with Pre-trained Sequence-to-Sequence Model
TextVQA requires models to read and reason about text in images to answer questions about them.
An effective self-supervised framework for learning expressive molecular global representations to drug discovery
In MPG, we proposed a powerful GNN for modelling molecular graph named MolGNet, and designed an effective self-supervised strategy for pre-training the model at both the node and graph-level.
Learn molecular representations from large-scale unlabeled molecules for drug discovery
Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules.
