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Found 33 papers, 14 papers with code
Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning
Multiple defendants in a criminal fact description generally exhibit complex interactions, and cannot be well handled by existing Legal Judgment Prediction (LJP) methods which focus on predicting judgment results (e. g., law articles, charges, and terms of penalty) for single-defendant cases.
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher
Based on this idea, we design Iteratively Coupled Multiple Instance Learning (ICMIL) to couple the embedder and the bag classifier at a low cost.
A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare
It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategies for tasks such as dialogue systems, machine translation, and question-answering.
Graph Neural Prompting with Large Language Models
While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost.
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout
Here, we propose a goal-conditioned HRL framework named Guided Cooperation via Model-based Rollout (GCMR), aiming to bridge inter-layer information synchronization and cooperation by exploiting forward dynamics.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
MiChao-HuaFen 1.0: A Specialized Pre-trained Corpus Dataset for Domain-specific Large Models
With the advancement of deep learning technologies, general-purpose large models such as GPT-4 have demonstrated exceptional capabilities across various domains.
FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching
Wherein, a basic module performs target association based on IoU of detection boxes between successive frames to deal with morphological change of fish; an interaction module combines IoU of detection boxes and IoU of fish entity to handle occlusions; a refind module use spatio-temporal information uses spatio-temporal information to overcome the tracking failure resulting from the missed detection by the detector under complex environment.
LOIS: Looking Out of Instance Semantics for Visual Question Answering
Visual question answering (VQA) has been intensively studied as a multimodal task that requires effort in bridging vision and language to infer answers correctly.
Distributed Graph Embedding with Information-Oriented Random Walks
Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks.
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification
In ICMIL, we use category information in the bag-level classifier to guide the patch-level fine-tuning of the patch feature extractor.
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19, 461 whole-slide images of prostate cancer from multiple centers.
A Better Choice: Entire-space Datasets for Aspect Sentiment Triplet Extraction
Aspect sentiment triplet extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences.
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.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Deep Transformers Thirst for Comprehensive-Frequency Data
We study an NLP model called LSRA, which introduces IB with a pyramid-free structure.
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
Aspect-Based Sentiment Analysis
Aspect-Sentiment-Opinion Triplet Extraction
+2
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.
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.
Ranked #5 on
Facial Attribute Classification
on LFWA
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.
