相关论文
1987 - Behavior Research Methods, Instruments, & Computers
Experiences from a life sciences laboratory: Using a paired-associates task as a teaching heuristic for programming instruction

A method is presented for using a paired-associates learning task as a model for teaching programming skills to psychology students with limited computer experience.

2020 - EDM
Effective Forum Curation via Multi-task Learning

Despite several advantages of online education, lack of effective student-instructor interaction, especially when students need timely help, poses significant pedagogical challenges. Motivated by this, we address the problems of automatically identifying posts that express confusion or urgency from Massive Open Online Course (MOOC) forums. To this end, we first investigate the extent to which the tasks of confusion detection and urgency detection are correlated so as to explore the possibility of utilizing a multitasking set-up. We then propose two LSTM-based multitask learning frameworks to leverage shared information and transfer knowledge across these related tasks. Our experiments demonstrate that the approaches improve over single-task models. Our best-performing model is especially useful in identifying posts that express both confusion and urgency, which can be of particular relevance for forum curation.

2020 - Applied Intelligence
Usr-mtl: an unsupervised sentence representation learning framework with multi-task learning

Developing the utilized intelligent systems is increasingly important to learn effective text representations, especially extract the sentence features. Numerous previous studies have been concentrated on the task of sentence representation learning based on deep learning approaches. However, the present approaches are mostly proposed with the single task or replied on the labeled corpus when learning the embedding of the sentences. In this paper, we assess the factors in learning sentence representation and propose an efficient unsupervised learning framework with multi-task learning (USR-MTL), in which various text learning tasks are merged into the unitized framework. With the syntactic and semantic features of sentences, three different factors to some extent are reflected in the task of the sentence representation learning that is the wording, or the ordering of the neighbored sentences of a target sentence in other words. Hence, we integrate the word-order learning task, word prediction task, and the sentence-order learning task into the proposed framework to attain meaningful sentence embeddings. Here, the process of sentence embedding learning is reformulated as a multi-task learning framework of the sentence-level task and the two word-level tasks. Moreover, the proposed framework is motivated by an unsupervised learning algorithm utilizing the unlabeled corpus. Based on the experimental results, our approach achieves the state-of-the-art performances on the downstream natural language processing tasks compared to the popular unsupervised representation learning techniques. The experiments on representation visualization and task analysis demonstrate the effectiveness of the tasks in the proposed framework in creating reasonable sentence representations proving the capacity of the proposed unsupervised multi-task framework for the sentence representation learning.

2021 - WANLP
ALUE: Arabic Language Understanding Evaluation

The emergence of Multi-task learning (MTL)models in recent years has helped push thestate of the art in Natural Language Un-derstanding (NLU). We strongly believe thatmany NLU problems in Arabic are especiallypoised to reap the benefits of such models. Tothis end we propose the Arabic Language Un-derstanding Evaluation Benchmark (ALUE),based on 8 carefully selected and previouslypublished tasks. For five of these, we providenew privately held evaluation datasets to en-sure the fairness and validity of our benchmark.We also provide a diagnostic dataset to helpresearchers probe the inner workings of theirmodels.Our initial experiments show thatMTL models outperform their singly trainedcounterparts on most tasks. But in order to en-tice participation from the wider community,we stick to publishing singly trained baselinesonly. Nonetheless, our analysis reveals thatthere is plenty of room for improvement inArabic NLU. We hope that ALUE will playa part in helping our community realize someof these improvements. Interested researchersare invited to submit their results to our online,and publicly accessible leaderboard.

2014 - 2014 IEEE International Conference on Image Processing (ICIP)
Robust object tracking via multi-task dynamic sparse model

Recently, sparse representation has been widely applied to some generative tracking methods, which learn the representation of each particle independently and do not consider the correlation between the representation of each particle in the time domain. In this paper, we formulate the object tracking in a particle filter framework as a multi-task dynamic sparse learning problem, which we denote as Multi-Task Dynamic Sparse Tracking(MTDST). By exploring the popular sparsity-inducing ℓ1, 2 mixed norms, we regularize the representation problem to enforce joint sparsity and learn the particle representations together. Meanwhile, we also introduce the innovation sparse term in the tracking model. As compared to previous methods, our method mines the independencies between particles and the correlation of particle representation in the time domain, which improves the tracking performance. In addition, because the loft least square is robust to the outliers, we adopt the loft least square to replace the least square to calculate the likelihood probability. In the updating scheme, we eliminate the influences of occlusion pixels when updating the templates. The comprehensive experiments on the several challenging image sequences demonstrate that the proposed method consistently outperforms the existing state-of-the-art methods.

2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction
Improving Performance of Facial Expression Recognition using Multi-task Learning of Neural Networks

Facial expression recognition is an important topic in the field of human-agent interaction, because facial expression is simple and impressive signal which human can send to others. Though there have been numerous studies on facial image analysis, the performance of expression recognition is still not acceptable due to the diversity of human expression and enormous variations in facial images. In this paper, we try to improve the performance of facial expression recognition by using multi-task learning techniques of neural networks. Through computational experiments on a benchmark database, we show positive possibility of performance improvement using multi-task learning.

2019 - arXiv: Learning
Intermittent Learning: On-Device Machine Learning on Intermittently Powered System

This paper introduces intermittent learning - the goal of which is to enable energy harvested computing platforms capable of executing certain classes of machine learning tasks effectively and efficiently. We identify unique challenges to intermittent learning relating to the data and application semantics of machine learning tasks, and to address these challenges, we devise 1) an algorithm that determines a sequence of actions to achieve the desired learning objective under tight energy constraints, and 2) propose three heuristics that help an intermittent learner decide whether to learn or discard training examples at run-time which increases the energy efficiency of the system. We implement and evaluate three intermittent learning applications that learn the 1) air quality, 2) human presence, and 3) vibration using solar, RF, and kinetic energy harvesters, respectively. We demonstrate that the proposed framework improves the energy efficiency of a learner by up to 100% and cuts down the number of learning examples by up to 50% when compared to state-of-the-art intermittent computing systems that do not implement the proposed intermittent learning framework.

2020 - Proceedings of the 29th ACM International Conference on Information & Knowledge Management
Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space

Traditional Learning to Rank (LTR) models in E-commerce are usually trained on logged data from a single domain. However, data may come from multiple domains, such as hundreds of countries in international E-commerce platforms. Learning a single ranking function obscures domain differences, while learning multiple functions for each domain may also be inferior due to ignoring the correlations between domains. It can be formulated as a multi-task learning problem where multiple tasks share the same feature and label space. To solve the above problem, which we name Multi-Scenario Learning to Rank, we propose the Hybrid of implicit and explicit Mixture-of-Experts (HMoE) approach. Our proposed solution takes advantage of Multi-task Mixture-of-Experts to implicitly identify distinctions and commonalities between tasks in the feature space, and improves the performance with a stacked model learning task relationships in the label space explicitly. Furthermore, to enhance the flexibility, we propose an end-to-end optimization method with a task-constrained back-propagation strategy. We empirically verify that the optimization method is more effective than two-stage optimization required by the stacked approach. Experiments on real-world industrial datasets demonstrate that HMoE significantly outperforms the popular multi-task learning methods. HMoE is in-use in the search system of AliExpress and achieved 1.92% revenue gain in the period of one-week online A/B testing. We also release a sampled version of our dataset to facilitate future research.

2020 - ArXiv
Small Towers Make Big Differences

Multi-task learning aims at solving multiple machine learning tasks at the same time. A good solution to a multi-task learning problem should be generalizable in addition to being Pareto optimal. In this paper, we provide some insights on understanding the trade-off between Pareto efficiency and generalization as a result of parameterization in multi-task deep learning models. As a multi-objective optimization problem, enough parameterization is needed for handling task conflicts in a constrained solution space; however, from a multi-task generalization perspective, over-parameterization undermines the benefit of learning a shared representation which helps harder tasks or tasks with limited training examples. A delicate balance between multi-task generalization and multi-objective optimization is therefore needed for finding a better trade-off between efficiency and generalization. To this end, we propose a method of under-parameterized self-auxiliaries for multi-task models to achieve the best of both worlds. It is task-agnostic and works with other multi-task learning algorithms. Empirical results show that small towers of under-parameterized self-auxiliaries can make big differences in improving Pareto efficiency in various multi-task applications.

1990 - Neuropsychologia
Parkinson's disease patient's behaviour in a covered maze learning task

A computerized maze task was constructed that allowed only partial vision of the maze structure and produced measurements for separate analysis of cognitive processes described as impaired in Parkinson's disease. Eighteen patients suffering from mild Parkinson's disease and 18 individually matched normal controls were investigated. Baseline task response times were found to be identical for both groups. Differences between patients' and controls' performance could be related to (a) a response bias in Parkinson patients that favoured repetition of the previous action and slowed down shifting and (b) an impairment of multistep plan generation. It is speculated that the response bias reflects the disinhibition of cortico-thalamo-cortical reverberation loops which results from striatal dopamine depletion.

2018 - AAAI
Efficient and Scalable Multi-task Regression on Massive Number of Tasks

Many real-world large-scale regression problems can be formulated as Multi-task Learning (MTL) problems with a massive number of tasks, as in retail and transportation domains. However, existing MTL methods still fail to offer both the generalization performance and the scalability for such problems. Scaling up MTL methods to problems with a tremendous number of tasks is a big challenge. Here, we propose a novel algorithm, named Convex Clustering Multi-Task regression Learning (CCMTL), which integrates with convex clustering on the k-nearest neighbor graph of the prediction models. Further, CCMTL efficiently solves the underlying convex problem with a newly proposed optimization method. CCMTL is accurate, efficient to train, and empirically scales linearly in the number of tasks. On both synthetic and real-world datasets, the proposed CCMTL outperforms seven state-of-the-art (SoA) multi-task learning methods in terms of prediction accuracy as well as computational efficiency. On a real-world retail dataset with 23,812 tasks, CCMTL requires only around 30 seconds to train on a single thread, while the SoA methods need up to hours or even days.

2019 - ArXiv
Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model

The scarcity of labeled training data across many languages is a significant roadblock for multilingual neural language processing. We approach the lack of in-language training data using sentence embeddings that map text written in different languages, but with similar meanings, to nearby embedding space representations. The representations are produced using a dual-encoder based model trained to maximize the representational similarity between sentence pairs drawn from parallel data. The representations are enhanced using multitask training and unsupervised monolingual corpora. The effectiveness of our multilingual sentence embeddings are assessed on a comprehensive collection of monolingual, cross-lingual, and zero-shot/few-shot learning tasks.

2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Multiple Relational Attention Network for Multi-task Learning

Multi-task learning is a successful machine learning framework which improves the performance of prediction models by leveraging knowledge among tasks, e.g., the relationships between different tasks. Most of existing multi-task learning methods focus on guiding learning process by predefined task relationships. In fact, these methods have not fully exploited the associated relationships during the learning process. On the one hand, replacing predefined task relationships by adaptively learned ones may result in higher prediction accuracy as it can avoid the risk of misguiding caused by improperly predefined relationships. On the other hand, apart from the task relationships, feature-task dependence and feature-feature interactions could also be employed to guide the learning process. Along this line, we propose aMultiple Relational Attention Network (MRAN) framework for multi-task learning, in which three types of relationships are considered. Correspondingly, MRAN consists of three attention-based relationship learning modules: 1) a task-task relationship learning module which captures the relationships among tasks automatically and controls the positive and negative knowledge transfer adaptively; 2) a feature-feature interaction learning module that handles the complicated interactions among features; 3) a task-feature dependence learning module, which can associate the related features with target tasks separately. To evaluate the effectiveness of the proposed MARN, experiments are conducted on two public datasets and a real-world dataset crawled from a review hosting site. Experimental results demonstrate the superiority of our method over both classical and the state-of-the-art multi-task learning methods.

2017 - International Joint Conference on Artificial Intelligence
Dynamic Multi-Task Learning with Convolutional Neural Network

Multi-task learning and deep convolutional neural network (CNN) have been successfully used in various fields. This paper considers the integration of CNN and multi-task learning in a novel way to further improve the performance of multiple related tasks. Existing multi-task CNN models usually empirically combine different tasks into a group which is then trained jointly with a strong assumption of model commonality. Furthermore, traditional approaches usually only consider small number of tasks with rigid structure, which is not suitable for large-scale applications. In light of this, we propose a dynamic multi-task CNN model to handle these problems. The proposed model directly learns the task relations from data instead of subjective task grouping. Due to its flexible structure, it supports task-wise incremental training, which is useful for efficient training of massive tasks. Specifically, we add a new task transfer connection (TTC) between the layers of each task. The learned TTC is able to reflect the correlation among different tasks guiding the model dynamically adjusting the multiplexing of the information among different tasks. With the help of TTC, multiple related tasks can further boost the whole performance for each other. Experiments demonstrate that the proposed dynamic multi-task CNN model outperforms traditional approaches.

2020 - ArXiv
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

Multi-task learning is an open and challenging problem in computer vision. The typical way of conducting multi-task learning with deep neural networks is either through handcrafting schemes that share all initial layers and branch out at an adhoc point or through using separate task-specific networks with an additional feature sharing/fusion mechanism. Unlike existing methods, we propose an adaptive sharing approach, called AdaShare, that decides what to share across which tasks for achieving the best recognition accuracy, while taking resource efficiency into account. Specifically, our main idea is to learn the sharing pattern through a task-specific policy that selectively chooses which layers to execute for a given task in the multi-task network. We efficiently optimize the task-specific policy jointly with the network weights using standard back-propagation. Experiments on three challenging and diverse benchmark datasets with a variable number of tasks well demonstrate the efficacy of our approach over state-of-the-art methods.

2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
A Multi-Task Learning Formulation for Survival Analysis

Predicting the occurrence of a particular event of interest at future time points is the primary goal of survival analysis. The presence of incomplete observations due to time limitations or loss of data traces is known as censoring which brings unique challenges in this domain and differentiates survival analysis from other standard regression methods. The popularly used survival analysis methods such as Cox proportional hazard model and parametric survival regression suffer from some strict assumptions and hypotheses that are not realistic in most of the real-world applications. To overcome the weaknesses of these two types of methods, in this paper, we reformulate the survival analysis problem as a multi-task learning problem and propose a new multi-task learning based formulation to predict the survival time by estimating the survival status at each time interval during the study duration. We propose an indicator matrix to enable the multi-task learning algorithm to handle censored instances and incorporate some of the important characteristics of survival problems such as non-negative non-increasing list structure into our model through max-heap projection. We employ the L2,1-norm penalty which enables the model to learn a shared representation across related tasks and hence select important features and alleviate over-fitting in high-dimensional feature spaces; thus, reducing the prediction error of each task. To efficiently handle the two non-smooth constraints, in this paper, we propose an optimization method which employs Alternating Direction Method of Multipliers (ADMM) algorithm to solve the proposed multi-task learning problem. We demonstrate the performance of the proposed method using real-world microarray gene expression high-dimensional benchmark datasets and show that our method outperforms state-of-the-art methods.

2021 - Neural Information Processing Systems
Efficiently Identifying Task Groupings for Multi-Task Learning

Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naively training all tasks together in one model often degrades performance, and exhaustively searching through combinations of task groupings can be prohibitively expensive. As a result, efficiently identifying the tasks that would benefit from training together remains a challenging design question without a clear solution. In this paper, we suggest an approach to select which tasks should train together in multi-task learning models. Our method determines task groupings in a single run by training all tasks together and quantifying the effect to which one task's gradient would affect another task's loss. On the large-scale Taskonomy computer vision dataset, we find this method can decrease test loss by 10.0% compared to simply training all tasks together while operating 11.6 times faster than a state-of-the-art task grouping method.

2009 - arXiv: Machine Learning
Taking Advantage of Sparsity in Multi-Task Learning

We study the problem of estimating multiple linear regression equations for the purpose of both prediction and variable selection. Following recent work on multi-task learning Argyriou et al. [2008], we assume that the regression vectors share the same sparsity pattern. This means that the set of relevant predictor variables is the same across the different equations. This assumption leads us to consider the Group Lasso as a candidate estimation method. We show that this estimator enjoys nice sparsity oracle inequalities and variable selection properties. The results hold under a certain restricted eigenvalue condition and a coherence condition on the design matrix, which naturally extend recent work in Bickel et al. [2007], Lounici [2008]. In particular, in the multi-task learning scenario, in which the number of tasks can grow, we are able to remove completely the effect of the number of predictor variables in the bounds. Finally, we show how our results can be extended to more general noise distributions, of which we only require the variance to be finite.

2018 - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
End-To-End Multi-Task Learning With Attention

We propose a novel multi-task learning architecture, which allows learning of task-specific feature-level attention. Our design, the Multi-Task Attention Network (MTAN), consists of a single shared network containing a global feature pool, together with a soft-attention module for each task. These modules allow for learning of task-specific features from the global features, whilst simultaneously allowing for features to be shared across different tasks. The architecture can be trained end-to-end and can be built upon any feed-forward neural network, is simple to implement, and is parameter efficient. We evaluate our approach on a variety of datasets, across both image-to-image predictions and image classification tasks. We show that our architecture is state-of-the-art in multi-task learning compared to existing methods, and is also less sensitive to various weighting schemes in the multi-task loss function. Code is available at https://github.com/lorenmt/mtan.

2017 - IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition

This paper proposes a hierarchical clustering multi-task learning (HC-MTL) method for joint human action grouping and recognition. Specifically, we formulate the objective function into the group-wise least square loss regularized by low rank and sparsity with respect to two latent variables, model parameters and grouping information, for joint optimization. To handle this non-convex optimization, we decompose it into two sub-tasks, multi-task learning and task relatedness discovery. First, we convert this non-convex objective function into the convex formulation by fixing the latent grouping information. This new objective function focuses on multi-task learning by strengthening the shared-action relationship and action-specific feature learning. Second, we leverage the learned model parameters for the task relatedness measure and clustering. In this way, HC-MTL can attain both optimal action models and group discovery by alternating iteratively. The proposed method is validated on three kinds of challenging datasets, including six realistic action datasets (Hollywood2, YouTube, UCF Sports, UCF50, HMDB51 <inline-formula> <tex-math notation="LaTeX">$\&$</tex-math><alternatives><inline-graphic xlink:href="liu-ieq1-2537337.gif"/> </alternatives></inline-formula> UCF101), two constrained datasets (KTH <inline-formula><tex-math notation="LaTeX"> $\&$</tex-math><alternatives><inline-graphic xlink:href="liu-ieq2-2537337.gif"/></alternatives></inline-formula> TJU), and two multi-view datasets (MV-TJU <inline-formula><tex-math notation="LaTeX">$\&$</tex-math><alternatives> <inline-graphic xlink:href="liu-ieq3-2537337.gif"/></alternatives></inline-formula> IXMAS). The extensive experimental results show that: 1) HC-MTL can produce competing performances to the state of the arts for action recognition and grouping; 2) HC-MTL can overcome the difficulty in heuristic action grouping simply based on human knowledge; 3) HC-MTL can avoid the possible inconsistency between the subjective action grouping depending on human knowledge and objective action grouping based on the feature subspace distributions of multiple actions. Comparison with the popular clustered multi-task learning further reveals that the discovered latent relatedness by HC-MTL aids inducing the group-wise multi-task learning and boosts the performance. To the best of our knowledge, ours is the first work that breaks the assumption that all actions are either independent for individual learning or correlated for joint modeling and proposes HC-MTL for automated, joint action grouping and modeling.

A Convex Formulation for Learning Task Relationships in Multi-Task Learning

Dit-Yan Yeung
|
Yu Zhang
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D. Yeung
|
Yu Zhang

Abstract:Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this paper, we propose a regularization formulation for learning the relationships between tasks in multi-task learning. This formulation can be viewed as a novel generalization of the regularization framework for single-task learning. Besides modeling positive task correlation, our method, called multi-task relationship learning (MTRL), can also describe negative task correlation and identify outlier tasks based on the same underlying principle. Under this regularization framework, the objective function of MTRL is convex. For efficiency, we use an alternating method to learn the optimal model parameters for each task as well as the relationships between tasks. We study MTRL in the symmetric multi-task learning setting and then generalize it to the asymmetric setting as well. We also study the relationships between MTRL and some existing multi-task learning methods. Experiments conducted on a toy problem as well as several benchmark data sets demonstrate the effectiveness of MTRL.

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引用
Multi-task learning deep neural networks for automatic speech recognition
2015
Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers
Medical Image Anal.
2020
Online Federated Multitask Learning
2020
Online Federated Multitask Learning
2019 IEEE International Conference on Big Data (Big Data)
2019
Reliable Gender Prediction Based on Users’ Video Viewing Behavior
2016 IEEE 16th International Conference on Data Mining (ICDM)
2016
Multi-task proximal support vector machine
Pattern Recognit.
2015
Generalized Dictionary for Multitask Learning with Boosting
IJCAI
2016
Multi-Task Learning with Group-Specific Feature Space Sharing
ECML/PKDD
2015
Varying-coefficient models for geospatial transfer learning
Machine Learning
2017
Varying-coefficient models with isotropic Gaussian process priors
ArXiv
2015
Online Learning of Multiple Tasks and Their Relationships
AISTATS
2011
Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications
IJCAI
2020
Robust Task Learning Based on Nonlinear Regression With Mixtures of Student-t Distributions
IEEE Access
2020
A Unified Framework for Structured Low-rank Matrix Learning
ICML
2017
A Saddle Point Approach to Structured Low-rank Matrix Learning in Large-scale Applications
ArXiv
2017
Asymmetric multi-task learning based on task relatedness and loss
ICML 2016
2016
Multitask Learning of Deep Neural Networks for Low-Resource Speech Recognition
IEEE/ACM Transactions on Audio, Speech, and Language Processing
2015
Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs
AISTATS
2014
Going deeper with convolutions
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2014
Collaboratively Training Sentiment Classifiers for Multiple Domains
IEEE Transactions on Knowledge and Data Engineering
2017