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Found 22 papers, 6 papers with code
Training Entire-Space Models for Target-oriented Opinion Words Extraction
Moreover, the performance of these models on the first type of instance cannot reflect their performance on entire space.
EIT: Efficiently Lead Inductive Biases to ViT
In four popular small-scale datasets, compared with ViT, EIT has an accuracy improvement of 12. 6% on average with fewer parameters and FLOPs.
Aspect-Sentiment-Multiple-Opinion Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term (aspect), sentiment and opinion term (opinion) triplets from sentences and can tell a complete story, i. e., the discussed aspect, the sentiment toward the aspect, and the cause of the sentiment.
Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans
Our TS-MDL first adopts an end-to-end \emph{i}MeshSegNet method (i. e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan.
Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge
Mitotic figure count is an important marker of tumor proliferation and has been shown to be associated with patients' prognosis.
Investigation of Bare-bones Algorithms from Quantum Perspective: A Quantum Dynamical Global Optimizer
Correspondingly, the basic search behaviour is derived, which constitutes the basic iterative process of a simple optimization system.
Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors
Background: Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate.
A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task
Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences and tries to provide a complete solution for aspect-based sentiment analysis (ABSA).
Ranked #1 on
Aspect-Sentiment-Opinion Triplet Extraction
on Res14
K-XLNet: A General Method for Combining Explicit Knowledge with Language Model Pretraining
Though pre-trained language models such as Bert and XLNet, have rapidly advanced the state-of-the-art on many NLP tasks, they implicit semantics only relying on surface information between words in corpus.
LightMBERT: A Simple Yet Effective Method for Multilingual BERT Distillation
The multilingual pre-trained language models (e. g, mBERT, XLM and XLM-R) have shown impressive performance on cross-lingual natural language understanding tasks.
Improving Task-Agnostic BERT Distillation with Layer Mapping Search
Comprehensive experiments on the evaluation benchmarks demonstrate that 1) layer mapping strategy has a significant effect on task-agnostic BERT distillation and different layer mappings can result in quite different performances; 2) the optimal layer mapping strategy from the proposed search process consistently outperforms the other heuristic ones; 3) with the optimal layer mapping, our student model achieves state-of-the-art performance on the GLUE tasks.
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals
To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery.
Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition
The introduced deep feature mining approach can precisely recognize human motion intents from raw EEG signals, which paves the road to translate the EEG based MI recognition to practical BCI systems.
TinyBERT: Distilling BERT for Natural Language Understanding
To accelerate inference and reduce model size while maintaining accuracy, we first propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models.
Ranked #1 on
Natural Language Inference
on MultiNLI Dev
Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis
We present experiments for cross-domain semantic dependency analysis with a neural Maximum Subgraph parser.
Convolutional Neural Network for Universal Sentence Embeddings
This paper proposes a simple CNN model for creating general-purpose sentence embeddings that can transfer easily across domains and can also act as effective initialization for downstream tasks.
Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach
In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes.
Prediction of Manipulation Actions
In psychophysical experiments, we evaluated human observers' skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object.
Compositional Memory for Visual Question Answering
We then feed the episodes to a standard question answering module together with the contextual visual information and linguistic information.
Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
Almost always in state of the art approaches a large amount of "best views" are computed for 3D models, with the hope that the query sketch matches one of these 2D projections of 3D models using predefined features.
Beyond Physical Connections: Tree Models in Human Pose Estimation
Our method outperformed the state of the art on the LSP, both in the scenarios when the training images are from the same dataset and from the PARSE dataset.
Learning Visual Symbols for Parsing Human Poses in Images
When the structure of the compositional parts is a tree, we derive an efficient approach to estimating human poses in images.
