Reviewerly/exHarmony huggingface dataset:
Balancing Selection and Diversity in Ensemble Learning with Exponential Mixture Model

In practical machine learning scenarios, there may be multiple predictors available for the same task. Ensemble learning combines these predictors to obtain a predictor with higher generalization performance. Weighted averaging is one of the most basic methods, which can be generalized by a formulation of an exponential mixture model. In this formulation, the weight optimization in ensemble learning is represented as selecting the predictors to be used by concentrating the weights and maintaining the diversity of the predictors to be used by distributing the weights. It has been theoretically shown that if the balance between these two factors is adjusted to be equal, the generalization performance improves. However, having an equal balance may not always be optimal, as there could be better alternatives. In this paper, we propose a method to obtain a predictor with higher generalization performance by adjusting the balance between selecting predictors and maintaining their diversity. Numerical experiments showed that when there is a large amount of training data and an unbiased label distribution, adjusting the balance can result in improved generalization performance.
K-Fold Cross-Valuation for Machine Learning Using Shapley Value

Research on data valuation using Shapley value has recently garnered significant attention. Existing approaches typically estimate the value of the training set by using the model’s performance on a validation set as a utility function. However, since the validation set is often a small subset of the complete dataset, a dataset shift between the training and validation sets may lead to biased data valuation. To address this issue, this paper proposes a k-fold cross-validation method based on the Shapley value. Specifically, the dataset is divided into k subsets, and each subset is employed in turn as a validation set to evaluate the valuation of the training set composed of the remaining $$k-1$$ subsets by using the Shapley value. The average of $$k-1$$ valuations of each data instance is taken as the valuation result. Given the exponential correlation between the Shapley value’s computation overhead and the volume of data, we propose the Monte Carlo permutation, incremental learning, and batch data valuation methodologies. This approach aids in approximating the true Shapley value as precisely as possible while simultaneously reducing computation time. Extensive experiments have demonstrated the effectiveness of our method, especially in the presence of noise and outliers in the validation set.
TransVQ-VAE: Generating Diverse Images Using Hierarchical Representation Learning

Understanding how to learn feature representations for images and generate high-quality images under unsupervised learning was challenging. One of the main difficulties in feature learning has been the problem of posterior collapse in variational inference. This paper proposes a hierarchical aggregated vector-quantized variational autoencoder, called TransVQ-VAE. Firstly, the multi-scale feature information based on the hierarchical Transformer is complementarily encoded to represent the global and structural dependencies of the input features. Then, it is compared to the latent encoding space with a linear difference to reduce the feature dimensionality. Finally, the decoder generates synthetic samples with higher diversity and fidelity compared to previous ones. In addition, we propose a dual self-attention module in the encoding process that uses spatial and channel information to capture distant texture correlations, contributing to the consistency and realism of the generated images. Experimental results on MNIST, CIFAR-10, CelebA-HQ, and ImageNet datasets show that our approach significantly improves the diversity and visual quality of the generated images.
UG-Net: Unsupervised-Guided Network for Biomedical Image Segmentation and Classification

Biomedical image segmentation and classification are two critical components in computer-aided diagnosis systems. However, various deep convolutional neural networks are trained by a single task, ignoring the potential contribution of mutually performing multiple tasks. In this paper, we propose an unsupervised-guided network for automated white blood cell (WBC) and skin lesion segmentation and classification called UG-Net. UG-Net consists of an unsupervised-based strategy (US) module, an enhanced segmentation network, and a mask-guided classification network. On the one hand, the proposed US module produces coarse masks that provide a prior localization map for the proposed segmentation network to enhance it in locating and segmenting a target object accurately. On the other hand, the enhanced coarse masks predicted by the proposed segmentation network are then fed into the proposed classification network for accurate classification. Moreover, a novel contextual encoding module is presented to capture high-level information and preserve spatial information. Meanwhile, a hybrid loss is defined to alleviate the imbalance training problem. Experimental results show that our approach achieves state-of-the-art segmentation performance on two public biomedical image datasets.
Single Image Super-Resolution with Sequential Multi-axis Blocked Attention

Single image super-resolution is an ill-posed inverse problem which has no unique solution because the low resolution image can be mapped to many different undegraded high resolution images. Previous methods based on deep neural networks try to utilize non-local attention mechanisms to leverage self-similarity prior in natural images in order to tackle the ill-posedness of SISR and improve the performance for SISR. However, because non-local attention has a quadratic order computation complexity with respect to the number of attention locations and the very big spatial sizes of feature maps of SISR networks, the non-local attention mechanisms utilized in current methods can not achieve a good trade-off between global modelling capability of self-similarity to improve performance and lower computation complexity to be efficient and scalable. In this paper, we propose to utilize a sequential multi-axis blocked attention (S-MXBA) mechanism in a deep neural network (MXBASRN) to achieve a good trade-off between performance and efficiency for SISR. S-MXBA splits the input feature map into blocks of appropriate size to balance the size of each block and the number of all the blocks, then does non-local attention inside each block followed by non-local attention to the same relative locations across all blocks. In this way, MXBASRN both improves global modelling capability of self-similarity to boost performance and decreases computation complexity to sub-quadratic order to be more efficient and scalable. Experiments demonstrate MXBASRN works effectively and efficiently for SISR compared to state-of-the-art methods. Especially, MXBASRN achieves comparable performance to recent non-local attention based SISR methods of NLSN and ENLCN with about one-third parameters of them. Code will be available at https://github.com/yangbincheng/MXBASRN.
Rethinking Feature Context in Learning Image-Guided Depth Completion

Depth completion (DC) is a classical computer vision task, which aims to estimate the 3D structure of the observed scene by utilizing the sparse depth from the Lidar and the RGB image from the camera. Treating DC as a regression task, most recent papers ignore the importance of feature representation. In this paper, we discuss the feature context in image-guided depth completion and propose a novel dual-arch feature extractor that includes a CNN branch and transformer branch. By combining the efficient CNN layers and effective transformer blocks, our proposed method achieves significant improvement compared with the baseline network. Experimental results and ablation study show the proposed approach can help existing DC methods perform better with limited extra computation.
Air-to-Ground Active Object Tracking via Reinforcement Learning

Over the years, active object tracking has emerged as a prominent topic in object tracking. However, most of these methods are unsuitable for tracking ground objects in high-altitude environments. Therefore, the paper proposes an air-to-ground active object tracking method based on reinforcement learning for high-altitude environments, which consists of a state recognition model and a reinforcement learning module. The state recognition model leverages the correlation between observed states and image quality (as measured by object recognition probability) as prior knowledge to guide the training of reinforcement learning. Then, the reinforcement learning module can actively control the PTZ camera to achieve stable tracking and successfully recover tracking after object loss. Additionally, the study introduces a UE-free simulator that increases the efficiency of the training process by over nine times. High-altitude experimental results with the proposed method show significantly enhanced stability and robustness compared to the PID method. Furthermore, the results also indicate that the proposed method can significantly improve the image quality of the observation.
Brain. Thought. Word

&lt;p&gt;The real test of long-term value for any scientific theory is when that theory encounters phenomena that did not exist at the time it was created. Such a test situation for cultural-historical psychology is happening today, in the era of the digital revolution. This article attempts to show the results of this test with a very important caveat: both the digital revolution itself and the studies of its consequences are currently in the process of rapid development, and a general overview can only be preliminary.&lt;/p&gt;
LIIVSR: A Unidirectional Recurrent Video Super-Resolution Framework with Gaussian Detail Enhancement and Local Information Interaction Modules

The most popular methods for video super-resolution either rely on a time-sliding window approach to handle low-resolution frames, or utilize a recurrent structure that leverages previously estimated hidden features to recover the current frame. The existing methods do not make better use of initialization and local information. In this paper, we propose a video super-resolution (LIIVSR) framework with Gaussian detail enhancement and local information interaction modules. The proposed Gaussian detail enhancement module enhances the detail part of the hidden features to retain more motion details. To effectively utilize inter-frame local information, we propose a local information interaction module as a propagation framework. The information initialization module effectively extracts relevant information for video frames as the starting information for subsequent long-distance propagation. The multi-residual module obtains local forward and backward information from the coarse extraction of features. The local refinement module further interacts with features to extract fine local forward and backward information. Finally, this local information is used to derive the final super-resolution (SR) output. Our proposed LIIVSR framework achieves state-of-the-art performance on several benchmark datasets, outperforming existing methods in both speed and performance.
A Further Exploration of Deep Multi-Agent Reinforcement Learning with Hybrid Action Space

The research of extending deep reinforcement learning (DRL) to multi-agent field have solved many complicated problems and made great achievements. However, almost all of these studies only focus on discrete or continuous action space and there are few works having ever applied multi-agent deep reinforcement learning (MADRL) to discrete-continuous hybrid action space which is common in practice problems. In this paper, two novel approaches are proposed to address multi-agent problems in discrete-continuous hybrid action space with centralized training but decentralized execution framework. The first approach, multi-agent hybrid deep deterministic policy gradients (MAHDDPG), extends the hybrid deep deterministic policy gradients (HDDPG) to multi-agent settings. However, like most other DRL algorithms, MAHDDPG still faces the problem that it may converge to the local optimal solution. To alleviate this problem, the second approach, multi-agent hybrid soft actor-critic (MAHSAC) is proposed. It extends hybrid soft actor-critic (HSAC) to multi-agent settings which introduces the idea of maximum entropy to strengthen the exploration of action space. Our experiments are running on a multi-agent particle world and a challenging task, whose results show that these methods are effective and significantly outperform independent HDDPG and HSAC methods.
Risks and intrigue of "digitalization”. Afterword to the articles by Gromyko and Smirnov

&lt;p&gt;As part of the reflection on the articles of Yu.V. Gromyko and S.A. Smirnov, the risks and opportunities of the "digitalization" of learning are considered. Risks are related to the "absorption" of subjective forms of activity in computer programs. Opportunities are in the construction of programs that initiate not only following algorithms, but also their trial and error correction.&lt;/p&gt;
Improving Generalization of Multi-agent Reinforcement Learning Through Domain-Invariant Feature Extraction

The limited generalization ability of reinforcement learning constrains its potential applications, particularly in complex scenarios such as multi-agent systems. To overcome this limitation and enhance the generalization capability of MARL algorithms, this paper proposes a three-stage method that integrates domain randomization and domain adaptation to extract effective features for policy learning. Specifically, the first stage samples environments provided for training and testing in the following stages using domain randomization. The second stage pretrains a domain-invariant feature extractor (DIFE) which employs cycle consistency to disentangle domain-invariant and domain-specific features. The third stage utilizes DIFE for policy learning. Experimental results in MPE tasks demonstrate that our approach yields better performance and generalization ability. Meanwhile, the features captured by DIFE are more interpretable for subsequent policy learning in visualization analysis.
UIT: Unifying Pre-training Objectives for Image-Text Understanding

In the recent past, pre-trained models in vision-language research have witnessed a dramatic increase. However, most of these models are typically pre-trained independently, following either a contrastive, image-to-text generative, or text-to-image generative objective. This paper presents a unique framework, UIT, which fuses these pre-training objectives using a unicoder-decoder architecture that comprises an image unicoder, a text unicoder, and a bi-modal decoder. The image/text unicoders can interchange between encoding and decoding roles for different tasks, offering versatility and shared understanding that enhances both image-to-text and text-to-image transformations. UIT outshines existing models in a variety of tasks, such as retrieval, captioning, VQA, and SNLI-VE, demonstrating particular prowess in zero-shot situations. It delivers notable results in tasks like zero-shot ImageNet classification, zero-shot text-to-image synthesis, and zero-shot captioning.
Group-Agent Reinforcement Learning

It can largely benefit the reinforcement learning (RL) process of each agent if multiple geographically distributed agents perform their separate RL tasks cooperatively. Different from multi-agent reinforcement learning (MARL) where multiple agents are in a common environment and should learn to cooperate or compete with each other, in this case each agent has its separate environment and only communicates with others to share knowledge without any cooperative or competitive behaviour as a learning outcome. In fact, this scenario exists widely in real life whose concept can be utilised in many applications, but is not well understood yet and not well formulated. As the first effort, we propose group-agent system for RL as a formulation of this scenario and the third type of RL system with respect to single-agent and multi-agent systems. We then propose a distributed RL framework called DDAL (Decentralised Distributed Asynchronous Learning) designed for group-agent reinforcement learning (GARL). We show through experiments that DDAL achieved desirable performance with very stable training and has good scalability.
SkaNet: Split Kernel Attention Network

Recently, convolutional neural networks (CNNs) and vision transformers (ViTs) have shown impressive results in the area of light-weight models for edge devices. However, the dominant CNNs and ViTs architectures rely heavily on a structured grid or sequence representation of images, which can result in inflexible handling of complex or irregular objects within them. In this paper, we propose SkaNet, an innovative, high-performance hybrid architecture that synergistically integrates the benefits of both CNNs and ViTs, and further enhances these advantages by graph representation learning. Specifically in SkaNet, we introduce a novel linear attention named split kernel attention (SKA) that exploits graph convolution to capture global semantic information and facilitate flexible recognition of irregular objects, splits input tensors into multiple channel groups adaptively, and fuses aforementioned modules into linear attention to efficiently aggregate contextual information. Extensive experiments demonstrate that SkaNet outperforms popular light-weight CNN and ViT-based models on common vision tasks and datasets. For classification on ImageNet-1k, SkaNet-S, with 5.5M parameters, achieves an impressive top-1 accuracy of 79.5%, surpassing MobileViT-S with an absolute gain of 1.1%. Furthermore, SkaNet-S exhibits superior performance in semantic segmentation on PASCAL VOC 2012 and object detection on COCO 2017. Our source code is available on GitHub at: https://github.com/charryglomc/skanet .
Label-Guided Graphormer for Hierarchy Text Classification

Hierarchical text classification (HTC), a subtask of multi-label classification, remains a challenging problem due to its complex label topology hierarchy. Although various methods have been proposed for modeling hierarchical information in recent research, less attention has been paid to techniques that utilize label information hidden in the training data, which can be potentially beneficial for hierarchical classification. In this paper, we focus on enhancing label representations from the perspective of contextual interactions and semantic complements, which refer to label co-occurrences and keywords respectively. And a Label-guided Graphormer (LGraphormer) is proposed for the purpose of integrating these promising features. The final prediction is performed by feeding its output embeddings into a text encoder in a hierarchy-aware manner. Our extensive experiments show that the proposed approach achieves state-of-the-art performance on three widely used public datasets, demonstrating the effectiveness of our methodology. Our code is available at: https://github.com/ContentTech/LGraphormerHTC.git .
Towards Better Core Elements Extraction for Customer Service Dialogue Text

Customer service conversations are usually short, but contain rich information, such as product details, customer demands and some emotions. Therefore, it is crucial to extract the core elements to fully understand the dialogue. Different from written texts, in the dialogue text, it can be challenging to differentiate between target elements and others, and the proportion of core elements is relatively low. In this paper, we propose a boundary enhanced span classification model with negative sampling strategy to address this issue. On the one hand, the enhancement of boundary representation improves the model’s ability to identify core element boundaries. On the other hand, the negative sampling strategy improves the model’s ability to recognize negative cases in different dialogue scenarios. Experimental results on the Jingdong customer service corpus demonstrate the effectiveness of our approach.
Syntax-Aware Complex-Valued Neural Machine Translation

Syntax has been proven to be remarkably effective in neural machine translation (NMT). Previous models obtained syntax information from syntactic parsing tools and integrated it into NMT models to improve translation performance. In this work, we propose a method to incorporate syntax information into a complex-valued Encoder-Decoder architecture. The proposed model jointly learns word-level and syntax-level attention scores from the source side to the target side using an attention mechanism. Importantly, it is not dependent on specific network architectures and can be directly integrated into any existing sequence-to-sequence (Seq2Seq) framework. The experimental results demonstrate that the proposed method can bring significant improvements in BLEU scores on two datasets. In particular, the proposed method achieves a greater improvement in BLEU scores in translation tasks involving language pairs with significant syntactic differences.
How to Extract and Interact? Nested Siamese Text Matching with Interaction and Extraction

Text matching is a core problem in Natural Language Processing, and one popular approach is the Siamese model. To enhance the performance of the Siamese matching model, researchers have proposed diverse network blocks, which can be divided into Information Interaction Blocks and Feature Extraction Blocks based on their functions. Traditional approaches, employing a pipeline-based arrangement for these blocks, encounter limitations when it comes to capturing focused semantics via local reasoning. To better systematize these two types of blocks, we propose a nested Siamese text matching framework, with the introduction of the Interaction Extraction Module (IEM) and the Function Amplification Module (FAM). Specifically, the IEM acts as a Feature Extraction or Information Interaction Block, nested within both the FAM and the model structure itself. IEM assumes the responsibility for global matching within the model while FAM leverages the Late Attention and nested IEMs to sharpen local matching and reasoning capabilities. Through our experiments, the performance of this nested Siamese model has proven to be superior to that of other pipeline-based Siamese matching models.
SGNN: A New Method for Learning Representations on Signed Networks

Graph Convolutional Neural Networks (GCNNs) have emerged as a powerful tool for processing graph-structured data and achieving outstanding performance in various scenarios, including node classification, link prediction, and graph visualization. However, in signed networks, which contain links with logically opposite relations, two types of associations can more accurately capture relationships in the real world. In this paper, we propose a novel optimization objective for learning representations in signed networks by capitalizing on the structural balance theory. Additionally, we introduce a new message propagation framework and develop a scalable signed graph neural network model, SGNN, which significantly outperforms existing signed graph embedding models when applied to the link classification task on four empirical networks. Moreover, our message propagation framework enables the construction of deeper GCNNs. Overall, our study presents a new approach for learning meaningful representations on signed networks, utilizing a novel message propagation framework and optimization objective that captures the complex relationships between nodes in a signed graph.