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ردیف | عنوان | نوع |
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1 |
Gated hierarchical multi-task learning network for judicial decision prediction
شبکه یادگیری سلسله مراتبی چند وظیفه ای برای پیش بینی تصمیم قضایی-2020 Judicial Decision Prediction (JDP) aims to predict legal judgments given the fact description of a criminal
case. It consists of multiple subtasks, e.g., law article prediction, charge prediction, and term of penalty
prediction. Generally, a fact description contains in-depth semantic information. Besides, there exist
complex dependencies among subtasks. For instance, law article prediction could guide charge prediction
and term of penalty prediction. Nonetheless, the majority of previous approaches usually capture indepth
semantic information of fact description inadequately or neglect the dependencies among subtasks.
In this paper, we propose a novel gated hierarchical multi-task learning network, named GHEDAP,
to jointly model multiple subtasks in JDP. Specifically, GHE-DAP combines a Gated Hierarchical
Encoder (GHE) to extract in-depth semantic information of fact description from multiple perspectives,
and a Dependencies Auto-learning Predictor (DAP) to learn the dependencies among subtasks dynamically.
We evaluate our model on several representative subtasks, and the experimental results demonstrate
that our model outperforms state-of-art baselines consistently and significantly for JDP. Keywords: Judicial decision prediction | Multi-task learning | Gated hierarchical encoder | Dependencies auto-learning predictor |
مقاله انگلیسی |
2 |
Remote sensing image captioning via Variational Autoencoder and Reinforcement Learning
زیرنویس تصویر سنجش از دور از طریق خودکارگذار تناسبی و یادگیری تقویتی-2020 Image captioning, i.e., generating the natural semantic descriptions of given image, is an essential
task for machines to understand the content of the image. Remote sensing image captioning is a
part of the field. Most of the current remote sensing image captioning models suffered the overfitting
problem and failed to utilize the semantic information in images. To this end, we propose a Variational
Autoencoder and Reinforcement Learning based Two-stage Multi-task Learning Model (VRTMM) for
the remote sensing image captioning task. In the first stage, we finetune the CNN jointly with the
Variational Autoencoder. In the second stage, the Transformer generates the text description using
both spatial and semantic features. Reinforcement Learning is then applied to enhance the quality of
the generated sentences. Our model surpasses the previous state of the art records by a large margin
on all seven scores on Remote Sensing Image Caption Dataset. The experiment result indicates our
model is effective on remote sensing image captioning and achieves the new state-of-the-art result. Keywords: Transformer | Variational Autoencoder | Transfer learning | Remote sensing image captioning | Self-attention mechanisms | Convolutional neural network | Reinforcement learning |
مقاله انگلیسی |
3 |
Emotional editing constraint conversation content generation based on reinforcement learning
ویرایش احساسی تولید محتوای مکالمه محدود بر اساس یادگیری تقویتی-2020 In recent years, the generation of conversation content based on deep neural networks has attracted many re- searchers. However, traditional neural language models tend to generate general replies, lacking logical and emotional factors. This paper proposes a conversation content generation model that combines reinforcement learning with emotional editing constraints to generate more meaningful and customizable emotional replies. The model divides the replies into three clauses based on pre-generated keywords and uses the emotional editor to further optimize the final reply. The model combines multi-task learning with multiple indicator rewards to comprehensively optimize the quality of replies. Experiments shows that our model can not only improve the fluency of the replies, but also significantly enhance the logical relevance and emotional relevance of the replies. Keywords: Emotional conversation generation | Affective computing | Emotional editing | Reinforcement learning | Multitask learning |
مقاله انگلیسی |
4 |
Multi-task least squares twin support vector machine for classification
حداقل مربعات جزئی چند وظیفه ای ماشین بردار پشتیبانی برای طبقه بندی-2019 With the bloom of machine learning, pattern recognition plays an important role in many aspects. How- ever, traditional pattern recognition mainly focuses on single task learning (STL), and the multi-task learning (MTL) has largely been ignored. Compared to STL, MTL can improve the performance of learn- ing methods through the shared information among all tasks. Inspired by the recently proposed di- rected multi-task twin support vector machine (DMTSVM) and the least squares twin support vector ma- chine (LSTWSVM), we put forward a novel multi-task least squares twin support vector machine (MTLS- TWSVM). Instead of two dual quadratic programming problems (QPPs) solved in DMTSVM, our algorithm only needs to deal with two smaller linear equations. This leads to simple solutions, and the calculation can be effectively accelerated. Thus, our proposed model can be applied to the large scale datasets. In addition, it can deal with linear inseparable samples by using kernel trick. Experiments on three popular multi-task datasets show the effectiveness of our proposed methods. Finally, we apply it to two popular image datasets, and the experimental results also demonstrate the validity of our proposed algorithm. Keywords: Pattern recognition | Multi-task learning | Relation learning | Least square twin support vector machine |
مقاله انگلیسی |
5 |
Deep Representation Learning for Individualized Treatment Effect Estimation using Electronic Health Records
یادگیری بازنمایی عمیق برای ارزیابی اثر درمانی شخصی با استفاده از سوابق الکترونیکی بهداشت-2019 Utilizing clinical observational data to estimate individualized treatment effects (ITE)
is a challenging task, as confounding inevitably exists in clinical data. Most of the existing
models for ITE estimation tackle this problem by creating unbiased estimators of the
treatment effects. Although valuable, learning a balanced representation is sometimes
directly opposed to the objective of learning an effective and discriminative model for
ITE estimation. We propose a novel hybrid model bridging multi-task deep learning and
K-nearest neighbors (KNN) for ITE estimation. In detail, the proposed model firstly
adopts multi-task deep learning to extract both outcome-predictive and treatment-specific
latent representations from Electronic Health Records (EHR), by jointly performing the
outcome prediction and treatment category classification. Thereafter, we estimate
counterfactual outcomes by KNN based on the learned hidden representations. We
validate the proposed model on a widely used semi-simulated dataset, i.e. IHDP, and a
real-world clinical dataset consisting of 736 heart failure (HF) patients. The performance
of our model remains robust and reaches 1.7 and 0.23 in terms of Precision in the
estimation of heterogeneous effect (PEHE) and average treatment effect (ATE),
respectively, on IHDP dataset, and 0.703 and 0.796 in terms of accuracy and F1 score
respectively, on HF dataset. The results demonstrate that the proposed model achieves
competitive performance over state-of-the-art models. In addition, the results reveal
several findings which are consistent with existing medical domain knowledge, and
discover certain suggestive hypotheses that could be validated through further
investigations in the clinical domain. Keywords: Individualized Treatment Effect Estimation | Counterfactual Inference | Deep Representation Learning | Multi-task Learning | K-Nearest Neighbors |
مقاله انگلیسی |