Biomedical knowledge graph verification with multitask learning architectures.

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

We present a unified multi-task learning architecture for fast and accurate pedestrian detection. Different from existing methods which often focus on either a new loss function or architecture, we propose an improved multi-task convolutional neural network learning architecture to effectively and efficiently interfuse the task of pedestrian detection and semantic segmentation. To achieve this, we integrate a lightweight semantic segmentation branch to Faster R-CNN detection framework that enables end-to-end hard parameter sharing in order to boost the detection performance and maintain computational efficiency as follows. Firstly, a Semantic Segmentation to Feature Module (SS2FM) refines the convolutional features in RPN stage by integrating the features generated from the semantic segmentation branch. Secondly, a Semantic Segmentation to Confidence Module (SS2CM) refines the classification confidence in RPN stage by fusing it with the semantic segmentation confidence. We also introduce an effective anchor matching point transform to alleviate the problem of feature misalignment for heavily occluded pedestrians. The proposed unified multi-task learning architecture lends itself well to more robust pedestrian detection in diverse scenarios with negligible computation overhead. In addition, the proposed architecture can achieve high detection performance with low resolution input images, which significantly reduces the computational complexity. Experiment results on CityPersons and Caltech datasets show that our method is the fastest among all state-of-the-art pedestrian detection methods while exhibiting competitive detection performance.

Historical text normalization often relies on small training datasets. Recent work has shown that multi-task learning can lead to significant improvements by exploiting synergies with related datasets, but there has been no systematic study of different multi-task learning architectures. This paper evaluates 63 multi-task learning configurations for sequence-to-sequence-based historical text normalization across ten datasets from eight languages, using autoencoding, grapheme-to-phoneme mapping, and lemmatization as auxiliary tasks. We observe consistent, significant improvements across languages when training data for the target task is limited, but minimal or no improvements when training data is abundant. We also show that zero-shot learning outperforms the simple, but relatively strong, identity baseline.

In order to improve the accuracy of the multiple load forecasting of a regional integrated energy system, a short-term multiple load forecasting model based on the quantum weighted GRU and multi-task learning framework is proposed in this paper. Firstly, correlation analysis is carried out using a maximum information coefficient to select the input of the model. Then, a multi-task learning architecture is constructed based on the quantum weighted GRU neural network, and the coupling information among multiple loads is learned through the sharing layer in order to improve the prediction accuracy of multiple loads. Finally, the PSO algorithm is used to optimize the parameters of the quantum weighted GRU. The simulation data of a regional integrated energy system in northern China are used to predict the power and cooling loads on summer weekdays and rest days, and the results show that, compared with the LSTM, GRU and single task learning QWGRU models, the proposed model is more effective in the multiple load forecasting of a regional integrated energy system.

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

The automatic semantic structuring of scientific text allows for more efficient reading of research articles and is an important indexing step for academic search engines. Sequential sentence classification is an essential structuring task and targets the categorisation of sentences based on their content and context. However, the potential of transfer learning for sentence classification across different scientific domains and text types, such as full papers and abstracts, has not yet been explored in prior work. In this paper, we present a systematic analysis of transfer learning for scientific sequential sentence classification. For this purpose, we derive seven research questions and present several contributions to address them: (1) We suggest a novel uniform deep learning architecture and multi-task learning for cross-domain sequential sentence classification in scientific text. (2) We tailor two transfer learning methods to deal with the given task, namely sequential transfer learning and multi-task learning. (3) We compare the results of the two best models using qualitative examples in a case study. (4) We provide an approach for the semi-automatic identification of semantically related classes across annotation schemes and analyse the results for four annotation schemes. The clusters and underlying semantic vectors are validated using k-means clustering. (5) Our comprehensive experimental results indicate that when using the proposed multi-task learning architecture, models trained on datasets from different scientific domains benefit from one another. Our approach significantly outperforms state of the art on full paper datasets while being on par for datasets consisting of abstracts.

Multiple-choice (MC) Visual Question Answering (VQA) is a similar but essentially different task to open-ended VQA because the answer options are provided. Most of existing works tackle them in a unified pipeline by solving a multi-class problem to infer the best answer from a predefined answer set. The option that matches the best answer is selected for MC VQA. Nevertheless, this violates human thinking logics. Normally, people examine the questions, answer options and the reference image before inferring a MC VQA. For MC VQA, human either rely on the question and answer options to directly deduce a correct answer if the question is not image-related, or read the question and answer options and then purposefully search for answers in a reference image. Therefore, we propose a novel approach, namely Multi-task Learning with Adaptive-attention (MTA), to simulate human logics for MC VQA. Specifically, we first fuse the answer options and question features, and then adaptively attend to the visual features for inferring a MC VQA. Furthermore, we design our model as a multi-task learning architecture by integrating the open-ended VQA task to further boost the performance of MC VQA. We evaluate our approach on two standard benchmark datasets: VQA and Visual7W and our approach sets new records on both datasets for MC VQA task, reaching 73.5% and 65.9% average accuracy respectively.

Multi-task learning aims to improve generalization performance of multiple prediction tasks by appropriately sharing relevant information across them. In the context of deep neural networks, this idea is often realized by hand-designed network architectures with layers that are shared across tasks and branches that encode task-specific features. However, the space of possible multi-task deep architectures is combinatorially large and often the final architecture is arrived at by manual exploration of this space, which can be both error-prone and tedious. We propose an automatic approach for designing compact multi-task deep learning architectures. Our approach starts with a thin multi-layer network and dynamically widens it in a greedy manner during training. By doing so iteratively, it creates a tree-like deep architecture, on which similar tasks reside in the same branch until at the top layers. Evaluation on person attributes classification tasks involving facial and clothing attributes suggests that the models produced by the proposed method are fast, compact and can closely match or exceed the state-of-the-art accuracy from strong baselines by much more expensive models.

A deep convolutional neural network to simultaneously localize and recognize waste types in images

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

We propose a lightweight multi-task deep learning architecture tailored for simultaneous retinal disease classification and blood vessel segmentation. Leveraging a shared Mo-bileNetV2 encoder, the model branches into task-specific heads for multi-class diabetic retinopathy (DR) grading and binary vessel segmentation. It is trained on the APTOS 2019 dataset for classification and the DRIVE dataset for segmentation using a label-masked, unified training strategy. The proposed model achieves 76.67% classification accuracy and a 0.5868 Dice score on segmentation, demonstrating competitive performance with significantly reduced computational complexity. Designed for real-time deployment on mobile devices, this model addresses the critical need for scalable, low-resource ophthalmic screening tools. Future work explores the integration of uncertainty estimation to enhance clinical interpretability and decision support.

Semantic parsers map natural language utterances to meaning representations. The lack of a single standard for meaning representations led to the creation of a plethora of semantic parsing datasets. To unify different datasets and train a single model for them, we investigate the use of Multi-Task Learning (MTL) architectures. We experiment with five datasets (Geoquery, NLMaps, TOP, Overnight, AMR). We find that an MTL architecture that shares the entire network across datasets yields competitive or better parsing accuracies than the single-task baselines, while reducing the total number of parameters by 68%. We further provide evidence that MTL has also better compositional generalization than single-task models. We also present a comparison of task sampling methods and propose a competitive alternative to widespread proportional sampling strategies.

This is an accepted manuscript of an article published by Association for Computational Linguistics in Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019), available online: https://www.aclweb.org/anthology/W19-5119
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As the need for efficient warehouse logistics has increased in manufacturing systems, the use of automated guided vehicles (AGVs) has also increased to reduce travel time. The AGVs are controlled by a system using laser sensors or floor-embedded wires to transport pallets and their loads. Because such control systems have only predefined palletizing strategies, AGVs may fail to engage incorrectly positioned pallets. In this study, we consider a vision sensor-based method to address this shortcoming by recognizing a pallet’s position. We propose a multi-task deep learning architecture that simultaneously predicts distances and rotation based on images obtained from a visionary sensor. These predictions complement each other in learning, allowing a multi-task model to learn and execute tasks impossible with single-task models. The proposed model can accurately predict the rotation and displacement of the pallets to derive information necessary for the control system. This information can be used to optimize a palletizing strategy. The superiority of the proposed model was verified by an experiment on images of stored pallets that were collected from a visionary sensor attached to an AGV.

Multitask learning has shown promising performance in learning multiple related tasks simultaneously, and variants of model architectures have been proposed, especially for supervised classification problems. One goal of multitask learning is to extract a good representation that sufficiently captures the relevant part of the input about the output for each learning task. To achieve this objective, in this paper we design a multitask learning architecture based on the observation that correlations exist between outputs of some related tasks (e.g. entity recognition and relation extraction tasks), and they reflect the relevant features that need to be extracted from the input. As outputs are unobserved, our proposed model exploits task predictions in lower layers of the neural model, also referred to as early predictions in this work. But we control the injection of early predictions to ensure that we extract good task-specific representations for classification. We refer to this model as a Progressive Multitask learning model with Explicit Interactions (PMEI). Extensive experiments on multiple benchmark datasets produce state-of-the-art results on the joint entity and relation extraction task.