ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection

In recent years, multitask learning has turned out to be of great success in various applications. Though single task model training has promised great results throughout these years, it ignores valuable information which might help in estimating better learning parameters. By virtue of learning-related tasks, multitask learning has been able to generalize the models even better. In this paper, we try to enhance the feature mapping of multitask deep learning models by sharing features among related tasks and inductive transfer learning. We have explored the learning of task relationship among various tasks for acquiring more benefits from multitask learning. The proposed enhanced model is compared with two state-of-the-art multitask deep learning models, namely Hyperface and FaceNet. The results show better performance of the proposed model in predicting age, gender and ethnicity on the UTK face dataset.KeywordsNeural networksMultitask deep learningDeep learningRecurrent neural networksConvolution neural networks

Deep learning has shown great promise in hydrological modeling, especially when large sample data sets are used to capture generalizable patterns across basins. However, challenges remain in addressing data scarcity and ensuring model reliability, particularly when key hydrological observations are modeled as individual tasks. In this study, we shift from traditional single‐task learning (STL) to multi‐task learning (MTL) to leverage the interconnections among hydrological variables and potentially improve modeling outcomes in data‐limited settings. Using a Long Short‐Term Memory (LSTM) neural network with the Catchment Attributes and Meteorology for Large‐Sample Studies data set, we developed an MTL model to predict streamflow and evapotranspiration across 591 basins. The MTL model exhibited comparable predictions for streamflow and evapotranspiration to STL models, with similar spatiotemporal generalization across varying data sizes. MTL's strength appeared when using LSTM cell state probes to predict the non‐target variable, surface soil moisture (SSM), showing slightly higher correlation coefficients. This highlights MTL's ability to capture intrinsic hydrological rules, enhancing model reliability. Leveraging this ability, we further explored MTL's advantages under two data‐limited scenarios: one with less‐observed SSM data and another with no available streamflow data. In both cases, MTL, supported by another well‐observed variable, outperformed STL models by a notable difference. These findings highlight MTL's potential to address the challenges of hydrological modeling in data‐limited basins. As Earth observation data continues to grow, MTL could become a valuable approach for building more reliable and generalizable hydrological models.

Multi-task learning aims to learn multiple tasks jointly by exploiting their relatedness to improve the generalization performance for each task. Traditionally, to perform multi-task learning, one needs to centralize data from all the tasks to a single machine. However, in many real-world applications, data of different tasks may be geo-distributed over different local machines. Due to heavy communication caused by transmitting the data and the issue of data privacy and security, it is impossible to send data of different task to a master machine to perform multi-task learning. Therefore, in this paper, we propose a distributed multi-task learning framework that simultaneously learns predictive models for each task as well as task relationships between tasks alternatingly in the parameter server paradigm. In our framework, we first offer a general dual form for a family of regularized multi-task relationship learning methods. Subsequently, we propose a communication-efficient primal-dual distributed optimization algorithm to solve the dual problem by carefully designing local subproblems to make the dual problem decomposable. Moreover, we provide a theoretical convergence analysis for the proposed algorithm, which is specific for distributed multi-task relationship learning. We conduct extensive experiments on both synthetic and real-world datasets to evaluate our proposed framework in terms of effectiveness and convergence.

Multi-task learning (MTL) is a machine learning paradigm that improves the performance of each task by exploiting useful information contained in multiple related tasks. However, the relatedness of tasks can be exploited by attackers to launch data poisoning attacks, which has been demonstrated a big threat to single-task learning. In this paper, we provide the first study on the vulnerability of MTL. Specifically, we focus on multi-task relationship learning (MTRL) models, a popular subclass of MTL models where task relationships are quantized and are learned directly from training data. We formulate the problem of computing optimal poisoning attacks on MTRL as a bilevel program that is adaptive to arbitrary choice of target tasks and attacking tasks. We propose an efficient algorithm called PATOM for computing optimal attack strategies. PATOM leverages the optimality conditions of the subproblem of MTRL to compute the implicit gradients of the upper level objective function. Experimental results on real-world datasets show that MTRL models are very sensitive to poisoning attacks and the attacker can significantly degrade the performance of target tasks, by either directly poisoning the target tasks or indirectly poisoning the related tasks exploiting the task relatedness. We also found that the tasks being attacked are always strongly correlated, which provides a clue for defending against such attacks.

Maize is an essential cereal for humans and animals worldwide, and it is one of the staple food in Kenya. One of the main challenges facing the maize crop in Kenya is the presence of diseases spreading quickly. Early recognition of maize pathogen and disease help at preventing the disease from spreading throughout the field. This paper proposes a regularized Multitask learning (MTL)–Convolutional Neural Networks (CNN) model for simultaneously identifying maize disease and its pathogen from diseased maize images. MTL allows training one model for multiple tasks at a time, which may improve the accuracy of each task by taking advantage of their commonalities. Our baseline is made of two CNN classification models, one of them being overfitting. We then build an MTL based on the two models, which increases the test accuracy of the overfitting model from 60.08% to 74.48%. The results show that the accuracy rises to 77.44% while combining MTL to the Early stopping method. However, the test accuracy goes up to 85.22 percent when MTL is combined with Early Stopping and Transfer Learning. The model is deployed to an android mobile application for maize farmers as end-users which is very important for costs reduction and time saving.

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

This article investigates the efficacy of a regularized multitask learning (MTL) framework based on SVM (M-SVM) to answer whether MTL always provides reliable results and how MTL outperforms independent learning. We first find that the M-SVM is Bayes risk consistent in the limit of a large sample size. This implies that despite the task dissimilarities, the M-SVM always produces a reliable decision rule for each task in terms of the misclassification error when the data size is large enough. Furthermore, we find that the task-interaction vanishes as the data size goes to infinity, and the convergence rates of the M-SVM and its single-task counterpart have the same upper bound. The former suggests that the M-SVM cannot improve the limit classifier's performance; based on the latter, we conjecture that the optimal convergence rate is not improved when the task number is fixed. As a novel insight into MTL, our theoretical and experimental results achieved an excellent agreement that the benefit of the MTL methods lies in the improvement of the preconvergence-rate (PCR) factor (to be denoted in Section III) rather than the convergence rate. Moreover, this improvement of PCR factors is more significant when the data size is small. In addition, our experimental results of five other MTL methods demonstrate the generality of this new insight.

SummaryDeep learning has made great achievements in the field of speech recognition. With the popularization of embedded devices such as intelligent speaker and the demand for dialect interaction scenes, it poses great challenges to far‐field speech recognition and dialect language recognition. In order to solve the dialect language recognition of embedded devices in far‐field speech recognition, we propose a deep learning neural network model with multitask learning. First, the audio is passed through the end‐to‐end noise reduction model to improve the effect of audio recognition. Then we define dialect recognition as the main task and dialect area as the auxiliary task, using the multitask learning method to improve the accuracy of dialect classification. The experimental results show that the end‐to‐end noise reduction model can improve the accuracy of audio recognition, and the best effect can be 7.54% higher than the baseline, and the accuracy of dialect language recognition can be improved by about 5% through multi task learning model.

Plan recognition is a key component of player modeling. Player plan recognition focuses on modeling how and when players select goals and formulate action sequences to achieve their goals during gameplay. By occasionally asking players to describe their plans, it is possible to devise robust plan recognition models that jointly reason about player goals and action sequences in coordination with player input. In this work, we present a player plan recognition framework that leverages data from player interactions with a planning support tool embedded in an educational game for middle school science education, CRYSTAL ISLAND. Players are prompted to use the planning tool to describe their goals and planned actions in CRYSTAL ISLAND. We use this data to devise data-driven player plan recognition models using multi-label multi-task learning. Specifically, we compare single-task and multi-task learning approaches for both goal prediction and action sequence prediction. Results indicate that multi-task learning yields significant benefits for action sequence prediction. Additionally, we find that incorporating automated detectors of plan completion in plan recognition models improves predictive performance in both tasks.

Multi-task feature selection with sparse regularization to extract common and task-specific features

Magnetotelluric (MT) forward modeling is a key technique in magnetotelluric sounding, and deep learning has been widely applied to MT forward modeling. In three-dimensional (3-D) problems, although existing methods can predict forward modeling results with high accuracy, they often use multiple networks to simulate multiple forward modeling parameters, resulting in low efficiency. We apply multi-task learning (MTL) to 3-D MT forward modeling to achieve simultaneous inference of apparent resistivity and impedance phase, effectively improving overall efficiency. Furthermore, through comparative analysis of feature map differences in various decoder layers of the network, we identify the optimal branching point for multi-task learning decoders. This enhances the feature extraction capabilities of the network and improves the prediction accuracy of forward modeling parameters. Additionally, we introduce an uncertainty-based loss function to dynamically balance the learning weights between tasks, addressing the shortcomings of traditional loss functions. Experiments demonstrate that compared with single-task networks and existing multi-task networks, the proposed network (MT-FeatureNet) achieves the best results in terms of Structural Similarity Index Measure (SSIM), Mean Relative Error (MRE), and Mean Absolute Error (MAE). The proposed multi-task learning model not only improves the efficiency and accuracy of 3-D MT forward modeling but also provides a novel approach to the design of multi-task learning network structures.

Density-aware and background-aware network for crowd counting via multi-task learning

It is challenging to obtain extensive annotated data for under-resourced languages, so we investigate whether it is beneficial to train models using multi-task learning. Sentiment analysis and offensive language identification share similar discourse properties. The selection of these tasks is motivated by the lack of large labelled data for user-generated code-mixed datasets. This paper works with code-mixed YouTube comments for Tamil, Malayalam, and Kannada languages. Our framework is applicable to other sequence classification problems irrespective to the size of the datasets. Experiments show that our multi-task learning model can achieve high results compared to single-task learning while reducing the time and space constraints required to train the models on individual tasks. Analysis of fine-tuned models indicates the preference of multi-task learning over single task learning resulting in a higher weighted F1 score on all three languages. We apply two multi-task learning approaches to three Dravidian languages, Kannada, Malayalam, and Tamil. Maximum scores on Kannada and Malayalam were achieved by mBERT subjected to cross entropy loss and with an approach of hard parameter sharing. Best scores on Tamil was achieved by DistilBERT subjected to cross entropy loss with soft parameter sharing as the architecture type. For the tasks of sentiment analysis and offensive language identification, the best performing model scored a weighted F1-Score of (66.8%, 90.5%), (59%, 70%) and (62.1%,75.3%) for Kannada, Malayalam and Tamil on sentiment analysis and offensive language identification respectively.

Objective. Deep learning (DL)-based automated contouring and treatment planning has been proven to improve the efficiency and accuracy of radiotherapy. However, conventional radiotherapy treatment planning process has the automated contouring and treatment planning as separate tasks. Moreover in DL, the contouring and dose prediction tasks for automated treatment planning are done independently.Approach. In this study, we applied the multi-task learning (MTL) approach in order to seamlessly integrate automated contouring and voxel-based dose prediction tasks, as MTL can leverage common information between the two tasks and be able to increase the efficiency of the automated tasks. We developed our MTL framework using the two datasets: in-house prostate cancer dataset and the publicly available head and neck cancer dataset, OpenKBP.Main results. Compared to the sequential DL contouring and treatment planning tasks, our proposed method using MTL improved the mean absolute difference of dose volume histogram metrics of prostate and head and neck sites by 19.82% and 16.33%, respectively. Our MTL model for automated contouring and dose prediction tasks demonstrated enhanced dose prediction performance while maintaining or sometimes even improving the contouring accuracy. Compared to the baseline automated contouring model with the Dice score coefficients of 0.818 for prostate and 0.674 for head and neck datasets, our MTL approach achieved average scores of 0.824 and 0.716 for these datasets, respectively.Significance. Our study highlights the potential of the proposed automated contouring and planning using MTL to support the development of efficient and accurate automated treatment planning for radiotherapy.

To develop and validate a multi-task deep learning (MTDL) model using multiphase contrast-enhanced CT (CECT) for simultaneously assessing histological subtypes, clinical stages, and anatomical complexity grades of solid malignant renal tumors. This two-center retrospective study included patients with solid malignant renal tumors and their preoperative kidney CECT images. A progressive layered extraction (PLE)-based MTDL model was trained and externally tested. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA), compared with the results of five radiologists. Among 798 patients (mean age, 54 ± 12 years; 279 females; Center A: n = 620, Center B: n = 178), 597 (74.8%) had clear cell renal cell carcinomas (ccRCC), 150 (18.8%) were clinical staging III/IV, and 187 (23.4%) had high anatomical complexity. On the external test set, the MTDL model achieved AUCs of 0.89 (95% CI: 0.82, 0.94) for distinguishing ccRCC from non-ccRCC, 0.87 (95% CI: 0.81, 0.93) for clinical staging (I/II vs. III/IV), and 0.87 (95% CI: 0.82, 0.92) for anatomical complexity grading (low-intermediate vs. high). The MTDL model outperformed single-task deep learning (STDL) in clinical staging (AUC: 0.87 vs. 0.82, p = 0.022), showed higher net benefit on DCA, and demonstrated better diagnostic performance than junior radiologists in histological subtyping and clinical staging. Additionally, it used 68% less memory and was 60% faster than STDL models. The CECT-based MTDL model demonstrated robust performance in simultaneously predicting histological subtypes, clinical stages, and anatomical complexity grades of malignant renal tumors. Question Accurate preoperative description of the histological subtyping, clinical staging, and anatomical complexity of malignant renal tumors is crucial for treatment decision-making. Findings By sharing features, the multi-task deep learning algorithm model enhances clinical staging performance and significantly improves computational efficiency in predicting all three tasks simultaneously. Clinical relevance The multi-task deep learning algorithm model enables rapid and accurate comprehensive preoperative evaluation of renal tumors, which assists surgeons in optimizing surgical plans and promotes the advancement of renal tumor management toward precision and efficiency.