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200c03de-4f3c-4ec1-b59a-0addc3aa2d3f | scale-scaling-up-the-complexity-for-advanced | 2306.09237 | null | https://arxiv.org/abs/2306.09237v1 | https://arxiv.org/pdf/2306.09237v1.pdf | SCALE: Scaling up the Complexity for Advanced Language Model Evaluation | Recent strides in Large Language Models (LLMs) have saturated many NLP benchmarks (even professional domain-specific ones), emphasizing the need for novel, more challenging novel ones to properly assess LLM capabilities. In this paper, we introduce a novel NLP benchmark that poses challenges to current LLMs across four key dimensions: processing long documents (up to 50K tokens), utilizing domain specific knowledge (embodied in legal texts), multilingual understanding (covering five languages), and multitasking (comprising legal document to document Information Retrieval, Court View Generation, Leading Decision Summarization, Citation Extraction, and eight challenging Text Classification tasks). Our benchmark comprises diverse legal NLP datasets from the Swiss legal system, allowing for a comprehensive study of the underlying Non-English, inherently multilingual, federal legal system. Despite recent advances, efficiently processing long documents for intense review/analysis tasks remains an open challenge for language models. Also, comprehensive, domain-specific benchmarks requiring high expertise to develop are rare, as are multilingual benchmarks. This scarcity underscores our contribution's value, considering most public models are trained predominantly on English corpora, while other languages remain understudied, particularly for practical domain-specific NLP tasks. Our benchmark allows for testing and advancing the state-of-the-art LLMs. As part of our study, we evaluate several pre-trained multilingual language models on our benchmark to establish strong baselines as a point of reference. Despite the large size of our datasets (tens to hundreds of thousands of examples), existing publicly available models struggle with most tasks, even after in-domain pretraining. We publish all resources (benchmark suite, pre-trained models, code) under a fully permissive open CC BY-SA license. | ['Joel Niklaus', 'Daniel E. Ho', 'Ilias Chalkidis', 'Matthias Stürmer', 'Veton Matoshi', 'Ronja Stern', 'Vishvaksenan Rasiah'] | 2023-06-15 | null | null | null | null | ['text-classification', 'information-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.93291926e-02 3.96052003e-02 -7.43994772e-01 -2.17412710e-01
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0db61c43-9f1d-4e3c-9a0f-b37ca6fbb9e1 | decoupled-variational-embedding-for-signed | 2008.12450 | null | https://arxiv.org/abs/2008.12450v1 | https://arxiv.org/pdf/2008.12450v1.pdf | Decoupled Variational Embedding for Signed Directed Networks | Node representation learning for signed directed networks has received considerable attention in many real-world applications such as link sign prediction, node classification and node recommendation. The challenge lies in how to adequately encode the complex topological information of the networks. Recent studies mainly focus on preserving the first-order network topology which indicates the closeness relationships of nodes. However, these methods generally fail to capture the high-order topology which indicates the local structures of nodes and serves as an essential characteristic of the network topology. In addition, for the first-order topology, the additional value of non-existent links is largely ignored. In this paper, we propose to learn more representative node embeddings by simultaneously capturing the first-order and high-order topology in signed directed networks. In particular, we reformulate the representation learning problem on signed directed networks from a variational auto-encoding perspective and further develop a decoupled variational embedding (DVE) method. DVE leverages a specially designed auto-encoder structure to capture both the first-order and high-order topology of signed directed networks, and thus learns more representative node embedding. Extensive experiments are conducted on three widely used real-world datasets. Comprehensive results on both link sign prediction and node recommendation task demonstrate the effectiveness of DVE. Qualitative results and analysis are also given to provide a better understanding of DVE. | ['Yan-Feng Wang', 'Xu Chen', 'Ya zhang', 'Jiangchao Yao', 'Maosen Li'] | 2020-08-28 | null | null | null | null | ['link-sign-prediction'] | ['graphs'] | [-9.95899886e-02 2.40291297e-01 -5.07306814e-01 -5.53545713e-01
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a76544c2-a0a6-48e8-ac48-2e1b8c180282 | flocks-of-stochastic-parrots-differentially | 2305.15594 | null | https://arxiv.org/abs/2305.15594v1 | https://arxiv.org/pdf/2305.15594v1.pdf | Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models | Large language models (LLMs) are excellent in-context learners. However, the sensitivity of data contained in prompts raises privacy concerns. Our work first shows that these concerns are valid: we instantiate a simple but highly effective membership inference attack against the data used to prompt LLMs. To address this vulnerability, one could forego prompting and resort to fine-tuning LLMs with known algorithms for private gradient descent. However, this comes at the expense of the practicality and efficiency offered by prompting. Therefore, we propose to privately learn to prompt. We first show that soft prompts can be obtained privately through gradient descent on downstream data. However, this is not the case for discrete prompts. Thus, we orchestrate a noisy vote among an ensemble of LLMs presented with different prompts, i.e., a flock of stochastic parrots. The vote privately transfers the flock's knowledge into a single public prompt. We show that LLMs prompted with our private algorithms closely match the non-private baselines. For example, using GPT3 as the base model, we achieve a downstream accuracy of 92.7% on the sst2 dataset with ($\epsilon=0.147, \delta=10^{-6}$)-differential privacy vs. 95.2% for the non-private baseline. Through our experiments, we also show that our prompt-based approach is easily deployed with existing commercial APIs. | ['Franziska Boenisch', 'Nicolas Papernot', 'Adam Dziedzic', 'Haonan Duan'] | 2023-05-24 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [-7.70723075e-02 1.57380298e-01 6.94939569e-02 -7.01244473e-01
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bf793681-c474-468b-8501-59c91712089b | stock-price-prediction-using-principle | 1803.05075 | null | http://arxiv.org/abs/1803.05075v1 | http://arxiv.org/pdf/1803.05075v1.pdf | Stock Price Prediction using Principle Components | The literature provides strong evidence that stock prices can be predicted
from past price data. Principal component analysis (PCA) is a widely used
mathematical technique for dimensionality reduction and analysis of data by
identifying a small number of principal components to explain the variation
found in a data set. In this paper, we describe a general method for stock
price prediction using covariance information, in terms of a dimension
reduction operation based on principle component analysis. Projecting the noisy
observation onto a principle subspace leads to a well-conditioned problem. We
illustrate our method on daily stock price values for five companies in
different industries. We investigate the results based on mean squared error
and directional change statistic of prediction, as measures of performance, and
volatility of prediction as a measure of risk. | [] | 2018-03-13 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-4.69785780e-01 -4.41247314e-01 8.68506655e-02 -1.48152292e-01
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9baf4213-50a1-4b3d-87df-de72913e3e7f | offline-policy-optimization-in-rl-with | 2212.14405 | null | https://arxiv.org/abs/2212.14405v1 | https://arxiv.org/pdf/2212.14405v1.pdf | Offline Policy Optimization in RL with Variance Regularizaton | Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy RL algorithms suffer from distributional shift, due to mismatch between dataset and the target policy, leading to high variance and over-estimation of value functions. In this work, we propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization (OVAR) can be used to augment any existing offline policy optimization algorithms. We show that the regularizer leads to a lower bound to the offline policy optimization objective, which can help avoid over-estimation errors, and explains the benefits of our approach across a range of continuous control domains when compared to existing state-of-the-art algorithms. | ['Doina Precup', 'Lihong Li', 'Zhaoran Wang', 'Animesh Garg', 'Zhuoran Yang', 'Samin Yeasar Arnob', 'Homanga Bharadhwaj', 'Samarth Sinha', 'Riashat Islam'] | 2022-12-29 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 4.89612762e-03 1.54216588e-01 -6.66791320e-01 1.21012695e-01
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aac571f0-1b32-4765-a9a5-26c7786aa73d | cosst-multi-organ-segmentation-with-partially | 2304.14030 | null | https://arxiv.org/abs/2304.14030v2 | https://arxiv.org/pdf/2304.14030v2.pdf | COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training | Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sample size and only partially labeled, i.e., only a subset of organs are annotated. Therefore, it is crucial to investigate how to learn a unified model on the available partially labeled datasets to leverage their synergistic potential. In this paper, we empirically and systematically study the partial-label segmentation with in-depth analyses on the existing approaches and identify three distinct types of supervision signals, including two signals derived from ground truth and one from pseudo label. We propose a novel training framework termed COSST, which effectively and efficiently integrates comprehensive supervision signals with self-training. Concretely, we first train an initial unified model using two ground truth-based signals and then iteratively incorporate the pseudo label signal to the initial model using self-training. To mitigate performance degradation caused by unreliable pseudo labels, we assess the reliability of pseudo labels via outlier detection in latent space and exclude the most unreliable pseudo labels from each self-training iteration. Extensive experiments are conducted on six CT datasets for three partial-label segmentation tasks. Experimental results show that our proposed COSST achieves significant improvement over the baseline method, i.e., individual networks trained on each partially labeled dataset. Compared to the state-of-the-art partial-label segmentation methods, COSST demonstrates consistent superior performance on various segmentation tasks and with different training data size. | ['Sasa Grbic', 'Ipek Oguz', 'Guillaume Chabin', 'Jianing Wang', 'Hao Li', 'Riqiang Gao', 'Zhoubing Xu', 'Han Liu'] | 2023-04-27 | null | null | null | null | ['outlier-detection', 'pseudo-label'] | ['methodology', 'miscellaneous'] | [ 5.03810942e-01 3.36232364e-01 -5.20394564e-01 -6.00227773e-01
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ca61db9e-df98-479f-8b2b-8fcb3d15069f | context-aware-emotion-recognition-networks | 1908.05913 | null | https://arxiv.org/abs/1908.05913v1 | https://arxiv.org/pdf/1908.05913v1.pdf | Context-Aware Emotion Recognition Networks | Traditional techniques for emotion recognition have focused on the facial expression analysis only, thus providing limited ability to encode context that comprehensively represents the emotional responses. We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner. The key idea is to hide human faces in a visual scene and seek other contexts based on an attention mechanism. Our networks consist of two sub-networks, including two-stream encoding networks to seperately extract the features of face and context regions, and adaptive fusion networks to fuse such features in an adaptive fashion. We also introduce a novel benchmark for context-aware emotion recognition, called CAER, that is more appropriate than existing benchmarks both qualitatively and quantitatively. On several benchmarks, CAER-Net proves the effect of context for emotion recognition. Our dataset is available at http://caer-dataset.github.io. | ['Seungryong Kim', 'Jungin Park', 'Sunok Kim', 'Kwanghoon Sohn', 'Jiyoung Lee'] | 2019-08-16 | context-aware-emotion-recognition-networks-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Lee_Context-Aware_Emotion_Recognition_Networks_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Lee_Context-Aware_Emotion_Recognition_Networks_ICCV_2019_paper.pdf | iccv-2019-10 | ['emotion-recognition-in-context'] | ['natural-language-processing'] | [ 2.33577907e-01 -4.51921284e-01 -1.52800474e-02 -8.05559039e-01
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e1af1d48-2785-42d8-a00c-a879360bbbda | multi-modal-cross-domain-alignment-network | 2209.11572 | null | https://arxiv.org/abs/2209.11572v2 | https://arxiv.org/pdf/2209.11572v2.pdf | Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval | As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given language query. Most previous methods depend heavily on numerous manual annotations (i.e., moment boundaries), which are extremely expensive to acquire in practice. In addition, due to the domain gap between different datasets, directly applying these pre-trained models to an unseen domain leads to a significant performance drop. In this paper, we focus on a novel task: cross-domain VMR, where fully-annotated datasets are available in one domain (``source domain''), but the domain of interest (``target domain'') only contains unannotated datasets. As far as we know, we present the first study on cross-domain VMR. To address this new task, we propose a novel Multi-Modal Cross-Domain Alignment (MMCDA) network to transfer the annotation knowledge from the source domain to the target domain. However, due to the domain discrepancy between the source and target domains and the semantic gap between videos and queries, directly applying trained models to the target domain generally leads to a performance drop. To solve this problem, we develop three novel modules: (i) a domain alignment module is designed to align the feature distributions between different domains of each modality; (ii) a cross-modal alignment module aims to map both video and query features into a joint embedding space and to align the feature distributions between different modalities in the target domain; (iii) a specific alignment module tries to obtain the fine-grained similarity between a specific frame and the given query for optimal localization. By jointly training these three modules, our MMCDA can learn domain-invariant and semantic-aligned cross-modal representations. | ['Yuchong Hu', 'Pan Zhou', 'Daizong Liu', 'Xiang Fang'] | 2022-09-23 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 1.49239108e-01 -4.80958790e-01 -4.16919261e-01 -2.90946901e-01
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592f8466-240d-45e1-81b2-fe601421eccb | differential-covariance-a-new-class-of | 1706.02451 | null | http://arxiv.org/abs/1706.02451v1 | http://arxiv.org/pdf/1706.02451v1.pdf | Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings | With our ability to record more neurons simultaneously, making sense of these
data is a challenge. Functional connectivity is one popular way to study the
relationship between multiple neural signals. Correlation-based methods are a
set of currently well-used techniques for functional connectivity estimation.
However, due to explaining away and unobserved common inputs (Stevenson et al.,
2008), they produce spurious connections. The general linear model (GLM), which
models spikes trains as Poisson processes (Okatan et al., 2005; Truccolo et
al., 2005; Pillow et al., 2008), avoids these confounds. We develop here a new
class of methods by using differential signals based on simulated intracellular
voltage recordings. It is equivalent to a regularized AR(2) model. We also
expand the method to simulated local field potential (LFP) recordings and
calcium imaging. In all of our simulated data, the differential
covariance-based methods achieved better or similar performance to the GLM
method and required fewer data samples. This new class of methods provides
alternative ways to analyze neural signals. | ['Terrence J. Sejnowski', 'Maxim Bazhenov', 'Giri P. Krishnan', 'Anup Das', 'Tiger W. Lin'] | 2017-06-08 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [ 2.25357264e-01 -6.83214307e-01 3.62580627e-01 -1.04319714e-01
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3a46e82b-f039-4bc6-9fea-e6c3266f7512 | improvising-the-learning-of-neural-networks | 2109.14746 | null | https://arxiv.org/abs/2109.14746v2 | https://arxiv.org/pdf/2109.14746v2.pdf | Improvising the Learning of Neural Networks on Hyperspherical Manifold | The impact of convolution neural networks (CNNs) in the supervised settings provided tremendous increment in performance. The representations learned from CNN's operated on hyperspherical manifold led to insightful outcomes in face recognition, face identification, and other supervised tasks. A broad range of activation functions were developed with hypersphere intuition which performs superior to softmax in euclidean space. The main motive of this research is to provide insights. First, the stereographic projection is implied to transform data from Euclidean space ($\mathbb{R}^{n}$) to hyperspherical manifold ($\mathbb{S}^{n}$) to analyze the performance of angular margin losses. Secondly, proving theoretically and practically that decision boundaries constructed on hypersphere using stereographic projection obliges the learning of neural networks. Experiments have demonstrated that applying stereographic projection on existing state-of-the-art angular margin objective functions improved performance for standard image classification data sets (CIFAR-10,100). Further, we ran our experiments on malaria-thin blood smear images, resulting in effective outcomes. The code is publicly available at:https://github.com/barulalithb/stereo-angular-margin. | ['Madhu G', 'Akshay Patel Shilhora', 'Sai Vardhan Kanumolu', 'Lalith Bharadwaj Baru'] | 2021-09-29 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 9.29084271e-02 5.64072967e-01 2.43269414e-01 -7.19643652e-01
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47c71a02-db91-4d93-be36-d35a0617d063 | transformer-based-sar-image-despeckling | 2201.09355 | null | https://arxiv.org/abs/2201.09355v1 | https://arxiv.org/pdf/2201.09355v1.pdf | Transformer-based SAR Image Despeckling | Synthetic Aperture Radar (SAR) images are usually degraded by a multiplicative noise known as speckle which makes processing and interpretation of SAR images difficult. In this paper, we introduce a transformer-based network for SAR image despeckling. The proposed despeckling network comprises of a transformer-based encoder which allows the network to learn global dependencies between different image regions - aiding in better despeckling. The network is trained end-to-end with synthetically generated speckled images using a composite loss function. Experiments show that the proposed method achieves significant improvements over traditional and convolutional neural network-based despeckling methods on both synthetic and real SAR images. | ['Vishal M. Patel', 'Jeya Maria Jose Valanarasu', 'Wele Gedara Chaminda Bandara', 'Malsha V. Perera'] | 2022-01-23 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 8.14431906e-01 -2.32104018e-01 5.52432120e-01 -6.88648343e-01
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c138f03e-7f9f-4590-8523-474e5a48afe1 | exploring-cross-lingual-transfer-learning | null | null | https://aclanthology.org/2021.findings-acl.177 | https://aclanthology.org/2021.findings-acl.177.pdf | Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation | null | ['Hui Jiang', 'Thi Ngoc Quynh Do', 'Judith Gaspers', 'Chao Wang'] | null | null | null | null | findings-acl-2021-8 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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1c730ca0-002e-4ec6-a28c-a99ad49b6d22 | cir-net-cross-modality-interaction-and | 2210.02843 | null | https://arxiv.org/abs/2210.02843v1 | https://arxiv.org/pdf/2210.02843v1.pdf | CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection | Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement. For the cross-modality interaction, 1) a progressive attention guided integration unit is proposed to sufficiently integrate RGB-D feature representations in the encoder stage, and 2) a convergence aggregation structure is proposed, which flows the RGB and depth decoding features into the corresponding RGB-D decoding streams via an importance gated fusion unit in the decoder stage. For the cross-modality refinement, we insert a refinement middleware structure between the encoder and the decoder, in which the RGB, depth, and RGB-D encoder features are further refined by successively using a self-modality attention refinement unit and a cross-modality weighting refinement unit. At last, with the gradually refined features, we predict the saliency map in the decoder stage. Extensive experiments on six popular RGB-D SOD benchmarks demonstrate that our network outperforms the state-of-the-art saliency detectors both qualitatively and quantitatively. | ['Yao Zhao', 'Qingming Huang', 'Xiaochun Cao', 'Chongyi Li', 'Chen Zhang', 'Qinwei Lin', 'Runmin Cong'] | 2022-10-06 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 3.07376713e-01 2.21364498e-01 -1.03617176e-01 -2.50332326e-01
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78a25396-1f0a-4581-81fc-4263fcd47a8b | learnings-from-data-integration-for-augmented | 2304.04576 | null | https://arxiv.org/abs/2304.04576v1 | https://arxiv.org/pdf/2304.04576v1.pdf | Learnings from Data Integration for Augmented Language Models | One of the limitations of large language models is that they do not have access to up-to-date, proprietary or personal data. As a result, there are multiple efforts to extend language models with techniques for accessing external data. In that sense, LLMs share the vision of data integration systems whose goal is to provide seamless access to a large collection of heterogeneous data sources. While the details and the techniques of LLMs differ greatly from those of data integration, this paper shows that some of the lessons learned from research on data integration can elucidate the research path we are conducting today on language models. | ['Jane Dwivedi-Yu', 'Alon Halevy'] | 2023-04-10 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-6.53827429e-01 1.78271994e-01 -8.53824317e-01 -4.85452712e-01
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953426e4-dec9-4171-98f6-1a995eb40710 | continual-active-learning-using-pseudo | 2111.13069 | null | https://arxiv.org/abs/2111.13069v2 | https://arxiv.org/pdf/2111.13069v2.pdf | Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics | Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies resulting in a heterogeneous set of acquisition settings. When training a deep learning model on an initial static training set, model performance and reliability suffer from changes of acquisition characteristics as data and targets may become inconsistent. Continual learning can help to adapt models to the changing environment by training on a continuous data stream. However, continual manual expert labelling of medical imaging requires substantial effort. Thus, ways to use labelling resources efficiently on a well chosen sub-set of new examples is necessary to render this strategy feasible. Here, we propose a method for continual active learning operating on a stream of medical images in a multi-scanner setting. The approach automatically recognizes shifts in image acquisition characteristics - new domains -, selects optimal examples for labelling and adapts training accordingly. Labelling is subject to a limited budget, resembling typical real world scenarios. To demonstrate generalizability, we evaluate the effectiveness of our method on three tasks: cardiac segmentation, lung nodule detection and brain age estimation. Results show that the proposed approach outperforms other active learning methods, while effectively counteracting catastrophic forgetting. | ['Georg Langs', 'Helmut Prosch', 'Christian Herold', 'Johannes Hofmanninger', 'Matthias Perkonigg'] | 2021-11-25 | null | null | null | null | ['age-estimation', 'cardiac-segmentation', 'lung-nodule-detection', 'age-estimation'] | ['computer-vision', 'medical', 'medical', 'miscellaneous'] | [ 7.47470081e-01 3.41502100e-01 -2.42544994e-01 -6.62198126e-01
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db371a54-1d50-4b22-bbdc-2ff4c7538027 | robust-handwriting-recognition-with-limited | 2008.08148 | null | https://arxiv.org/abs/2008.08148v1 | https://arxiv.org/pdf/2008.08148v1.pdf | Robust Handwriting Recognition with Limited and Noisy Data | Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved. Most existing approaches focus on handwriting datasets that have clearly written text and carefully segmented labels. In this paper, we instead focus on learning handwritten characters from maintenance logs, a constrained setting where data is very limited and noisy. We break the problem into two consecutive stages of word segmentation and word recognition respectively and utilize data augmentation techniques to train both stages. Extensive comparisons with popular baselines for scene-text detection and word recognition show that our system achieves a lower error rate and is more suited to handle noisy and difficult documents | ['Tzu-Hsiang Lin', 'Saket Dingliwal', 'Zhuo Li', 'Collin McCormack', 'Amrith Setlur', 'Jae Lim', 'Hai Pham', 'Kang Huang', 'Tam Vu', 'Barnabas Poczos'] | 2020-08-18 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.50905716e-01 -4.82762605e-01 -5.40659010e-01 -5.54953337e-01
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8ace22fd-7f4b-4901-ab86-1abc70769ce0 | stage-spatio-temporal-attention-on-graph | 1912.04316 | null | https://arxiv.org/abs/1912.04316v3 | https://arxiv.org/pdf/1912.04316v3.pdf | Video action detection by learning graph-based spatio-temporal interactions | Action Detection is a complex task that aims to detect and classify human actions in video clips. Typically, it has been addressed by processing fine-grained features extracted from a video classification backbone. Recently, thanks to the robustness of object and people detectors, a deeper focus has been added on relationship modelling. Following this line, we propose a graph-based framework to learn high-level interactions between people and objects, in both space and time. In our formulation, spatio-temporal relationships are learned through self-attention on a multi-layer graph structure which can connect entities from consecutive clips, thus considering long-range spatial and temporal dependencies. The proposed module is backbone independent by design and does not require end-to-end training. Extensive experiments are conducted on the AVA dataset, where our model demonstrates state-of-the-art results and consistent improvements over baselines built with different backbones. Code is publicly available at https://github.com/aimagelab/STAGE_action_detection. | ['Lorenzo Baraldi', 'Simone Bronzin', 'Rita Cucchiara', 'Matteo Tomei', 'Simone Calderara'] | 2019-12-09 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [-6.36662357e-03 3.26620750e-02 -2.58087903e-01 -3.07627141e-01
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c4293ffe-103e-47f6-915e-7ee224917660 | multi-granularity-hierarchical-attention | 1811.11934 | null | http://arxiv.org/abs/1811.11934v1 | http://arxiv.org/pdf/1811.11934v1.pdf | Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering | This paper describes a novel hierarchical attention network for reading
comprehension style question answering, which aims to answer questions for a
given narrative paragraph. In the proposed method, attention and fusion are
conducted horizontally and vertically across layers at different levels of
granularity between question and paragraph. Specifically, it first encode the
question and paragraph with fine-grained language embeddings, to better capture
the respective representations at semantic level. Then it proposes a
multi-granularity fusion approach to fully fuse information from both global
and attended representations. Finally, it introduces a hierarchical attention
network to focuses on the answer span progressively with multi-level
softalignment. Extensive experiments on the large-scale SQuAD and TriviaQA
datasets validate the effectiveness of the proposed method. At the time of
writing the paper (Jan. 12th 2018), our model achieves the first position on
the SQuAD leaderboard for both single and ensemble models. We also achieves
state-of-the-art results on TriviaQA, AddSent and AddOne-Sent datasets. | ['Wei Wang', 'Ming Yan', 'Chen Wu'] | 2018-11-29 | multi-granularity-hierarchical-attention-1 | https://aclanthology.org/P18-1158 | https://aclanthology.org/P18-1158.pdf | acl-2018-7 | ['triviaqa'] | ['miscellaneous'] | [ 5.08447662e-02 2.26456523e-01 3.56976539e-02 -4.97988522e-01
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f96df501-88e4-4474-85ae-b67d546d6e54 | factored-attention-and-embedding-for | 2203.06458 | null | https://arxiv.org/abs/2203.06458v1 | https://arxiv.org/pdf/2203.06458v1.pdf | Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation | Echocardiography is widely used to clinical practice for diagnosis and treatment, e.g., on the common congenital heart defects. The traditional manual manipulation is error-prone due to the staff shortage, excess workload, and less experience, leading to the urgent requirement of an automated computer-aided reporting system to lighten the workload of ultrasonologists considerably and assist them in decision making. Despite some recent successful attempts in automatical medical report generation, they are trapped in the ultrasound report generation, which involves unstructured-view images and topic-related descriptions. To this end, we investigate the task of the unstructured-view topic-related ultrasound report generation, and propose a novel factored attention and embedding model (termed FAE-Gen). The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which 1) capture the homogeneous and heterogeneous morphological characteristic across different views, and 2) generate the descriptions with different syntactic patterns and different emphatic contents for different topics. Experimental evaluations are conducted on a to-be-released large-scale clinical cardiovascular ultrasound dataset (CardUltData). Both quantitative comparisons and qualitative analysis demonstrate the effectiveness and the superiority of FAE-Gen over seven commonly-used metrics. | ['Yue Gao', 'Xiaojing Ma', 'Shengchuang Zhang', 'Xuri Ge', 'Chengpeng Dai', 'Rongrong Ji', 'Fuhai Chen'] | 2022-03-12 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 1.03203833e-01 4.28908542e-02 1.37840092e-01 -3.27826798e-01
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c278fc1e-b1de-413a-b68b-0e032482c6dd | avoiding-negative-side-effects-and-promoting | null | null | https://openreview.net/forum?id=HJe7bxBYvr | https://openreview.net/pdf?id=HJe7bxBYvr | Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning | With the recent proliferation of the usage of reinforcement learning (RL) agents for solving real-world tasks, safety emerges as a necessary ingredient for their successful application. In this paper, we focus on ensuring the safety of the agent while making sure that the agent does not cause any unnecessary disruptions to its environment. The current approaches to this problem, such as manually constraining the agent or adding a safety penalty to the reward function, can introduce bad incentives. In complex domains, these approaches are simply intractable, as they require knowing apriori all the possible unsafe scenarios an agent could encounter. We propose a model-based approach to safety that allows the agent to look into the future and be aware of the future consequences of its actions. We learn the transition dynamics of the environment and generate a directed graph called the imaginative module. This graph encapsulates all possible trajectories that can be followed by the agent, allowing the agent to efficiently traverse through the imagined environment without ever taking any action in reality. A baseline state, which can either represent a safe or an unsafe state (based on whichever is easier to define) is taken as a human input, and the imaginative module is used to predict whether the current actions of the agent can cause it to end up in dangerous states in the future. Our imaginative module can be seen as a ``plug-and-play'' approach to ensuring safety, as it is compatible with any existing RL algorithm and any task with discrete action space. Our method induces the agent to act safely while learning to solve the task. We experimentally validate our proposal on two gridworld environments and a self-driving car simulator, demonstrating that our approach to safety visits unsafe states significantly less frequently than a baseline. | ['Benjamin Eysenbach', 'Dhruv Ramani'] | 2019-09-25 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.59117514e-01 5.97517908e-01 -5.21448180e-02 -1.88774783e-02
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6fb146c9-9803-4e6e-aaab-d8708721a204 | intensity-scan-context-coding-intensity-and | 2003.05656 | null | https://arxiv.org/abs/2003.05656v1 | https://arxiv.org/pdf/2003.05656v1.pdf | Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection | Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM). It is often tackled with light detection and ranging (LiDAR) sensor due to its view-point and illumination invariant properties. Existing works on 3D loop closure detection often leverage the matching of local or global geometrical-only descriptors, but without considering the intensity reading. In this paper we explore the intensity property from LiDAR scan and show that it can be effective for place recognition. Concretely, we propose a novel global descriptor, intensity scan context (ISC), that explores both geometry and intensity characteristics. To improve the efficiency for loop closure detection, an efficient two-stage hierarchical re-identification process is proposed, including a binary-operation based fast geometric relation retrieval and an intensity structure re-identification. Thorough experiments including both local experiment and public datasets test have been conducted to evaluate the performance of the proposed method. Our method achieves higher recall rate and recall precision than existing geometric-only methods. | ['Lihua Xie', 'Han Wang', 'Chen Wang'] | 2020-03-12 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 4.25435871e-01 -7.73288786e-01 -3.44491273e-01 -5.95769584e-01
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e88d42b6-b705-4426-8ea3-ecf43d368230 | self-distillation-with-meta-learning-for-1 | 2305.12209 | null | https://arxiv.org/abs/2305.12209v1 | https://arxiv.org/pdf/2305.12209v1.pdf | Self-Distillation with Meta Learning for Knowledge Graph Completion | In this paper, we propose a selfdistillation framework with meta learning(MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddings and tackle the longtail samples. Specifically, we first propose a dynamic pruning technique to obtain a small pruned model from a large source model, where the pruning mask of the pruned model could be updated adaptively per epoch after the model weights are updated. The pruned model is supposed to be more sensitive to difficult to memorize samples(e.g., longtail samples) than the source model. Then, we propose a onestep meta selfdistillation method for distilling comprehensive knowledge from the source model to the pruned model, where the two models coevolve in a dynamic manner during training. In particular, we exploit the performance of the pruned model, which is trained alongside the source model in one iteration, to improve the source models knowledge transfer ability for the next iteration via meta learning. Extensive experiments show that MetaSD achieves competitive performance compared to strong baselines, while being 10x smaller than baselines. | ['Min Yang', 'Chengming Li', 'Junhao Liu', 'Yunshui Li'] | 2023-05-20 | self-distillation-with-meta-learning-for | https://aclanthology.org/2022.findings-emnlp.149/ | https://aclanthology.org/2022.findings-emnlp.149.pdf | findings-of-the-association-for-computational-2 | ['knowledge-graph-completion'] | ['knowledge-base'] | [ 2.16097817e-01 3.97112995e-01 -5.38639367e-01 1.65547252e-01
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f338840b-c87d-426b-87ae-3d84cf75bc6e | knowledge-distillation-for-neural-transducer | 2305.15971 | null | https://arxiv.org/abs/2305.15971v1 | https://arxiv.org/pdf/2305.15971v1.pdf | Knowledge Distillation for Neural Transducer-based Target-Speaker ASR: Exploiting Parallel Mixture/Single-Talker Speech Data | Neural transducer (RNNT)-based target-speaker speech recognition (TS-RNNT) directly transcribes a target speaker's voice from a multi-talker mixture. It is a promising approach for streaming applications because it does not incur the extra computation costs of a target speech extraction frontend, which is a critical barrier to quick response. TS-RNNT is trained end-to-end given the input speech (i.e., mixtures and enrollment speech) and reference transcriptions. The training mixtures are generally simulated by mixing single-talker signals, but conventional TS-RNNT training does not utilize single-speaker signals. This paper proposes using knowledge distillation (KD) to exploit the parallel mixture/single-talker speech data. Our proposed KD scheme uses an RNNT system pretrained with the target single-talker speech input to generate pseudo labels for the TS-RNNT training. Experimental results show that TS-RNNT systems trained with the proposed KD scheme outperform a baseline TS-RNNT. | ['Taichi Asami', 'Atsunori Ogawa', 'Ryo Masumura', 'Tomohiro Tanaka', 'Kohei Matsuura', 'Takanori Ashihara', 'Marc Delcroix', 'Tsubasa Ochiai', 'Hiroshi Sato', 'Takafumi Moriya'] | 2023-05-25 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 3.49176377e-01 1.27380952e-01 -1.23415023e-01 -4.26060110e-01
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3fb2247a-6828-4552-a648-1458d6275f83 | analysis-of-scheduling-schemes-based-on | 2307.02368 | null | https://arxiv.org/abs/2307.02368v1 | https://arxiv.org/pdf/2307.02368v1.pdf | Analysis of Scheduling schemes based on Carrier Aggregation in LTE-Advanced and their improvement | In this paper I focused on resource scheduling in the downlink of LTE-Advanced with aggregation of multiple Component Carriers (CCs). When Carrier Aggregation (CA) is applied, a well-designed resource scheduling scheme is essential to the LTE-A system. Joint User Scheduling (JUS), Separated Random User Scheduling (SRUS), Separated Burst-Level Scheduling (SBLS) are three different resource scheduling schemes. JUS is optimal in performance but with high complexity and not considering quality of experience (QoE) parameters. Whereas SRUS and SBLS are contrary and users will acquire few resources because they do not support CA and the system fairness is disappointing. The author propose a novel Carrier Scheduling (CS) scheme, termed as "Quality of Service and Channel Scheduling" (QSCS). Connected CCs of one user can be changed in burst level and these changes are based on checking of services priority and quality of signal that user experiences. Simulation results show that the proposed algorithm can effectively enhance throughput of users like JUS and also it chooses best CCs based on QoS and channel quality parameters. The simulation results also show that achieved QoE is much better than other algorithms. | ['Sajjad Emdadi Mahdimahalleh'] | 2023-07-05 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [-6.50565187e-03 -1.86739132e-01 -3.58290613e-01 -1.13606289e-01
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f1325beb-6828-4705-81c3-fd59f4f473ee | on-the-choice-of-perception-loss-function-for | 2305.19301 | null | https://arxiv.org/abs/2305.19301v1 | https://arxiv.org/pdf/2305.19301v1.pdf | On the Choice of Perception Loss Function for Learned Video Compression | We study causal, low-latency, sequential video compression when the output is subjected to both a mean squared-error (MSE) distortion loss as well as a perception loss to target realism. Motivated by prior approaches, we consider two different perception loss functions (PLFs). The first, PLF-JD, considers the joint distribution (JD) of all the video frames up to the current one, while the second metric, PLF-FMD, considers the framewise marginal distributions (FMD) between the source and reconstruction. Using information theoretic analysis and deep-learning based experiments, we demonstrate that the choice of PLF can have a significant effect on the reconstruction, especially at low-bit rates. In particular, while the reconstruction based on PLF-JD can better preserve the temporal correlation across frames, it also imposes a significant penalty in distortion compared to PLF-FMD and further makes it more difficult to recover from errors made in the earlier output frames. Although the choice of PLF decisively affects reconstruction quality, we also demonstrate that it may not be essential to commit to a particular PLF during encoding and the choice of PLF can be delegated to the decoder. In particular, encoded representations generated by training a system to minimize the MSE (without requiring either PLF) can be {\em near universal} and can generate close to optimal reconstructions for either choice of PLF at the decoder. We validate our results using (one-shot) information-theoretic analysis, detailed study of the rate-distortion-perception tradeoff of the Gauss-Markov source model as well as deep-learning based experiments on moving MNIST and KTH datasets. | ['Ashish Khisti', 'Wei Yu', 'Jun Chen', 'Buu Phan', 'Sadaf Salehkalaibar'] | 2023-05-30 | null | null | null | null | ['video-compression'] | ['computer-vision'] | [ 5.35235107e-01 7.44856447e-02 -1.35471627e-01 -4.45211902e-02
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23aca737-e4e9-465e-bc4f-8515d213a63f | unifiedabsa-a-unified-absa-framework-based-on | 2211.10986 | null | https://arxiv.org/abs/2211.10986v1 | https://arxiv.org/pdf/2211.10986v1.pdf | UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning | Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level sentiment information. There are many ABSA tasks, and the current dominant paradigm is to train task-specific models for each task. However, application scenarios of ABSA tasks are often diverse. This solution usually requires a large amount of labeled data from each task to perform excellently. These dedicated models are separately trained and separately predicted, ignoring the relationship between tasks. To tackle these issues, we present UnifiedABSA, a general-purpose ABSA framework based on multi-task instruction tuning, which can uniformly model various tasks and capture the inter-task dependency with multi-task learning. Extensive experiments on two benchmark datasets show that UnifiedABSA can significantly outperform dedicated models on 11 ABSA tasks and show its superiority in terms of data efficiency. | ['Jianfei Yu', 'Rui Xia', 'Zengzhi Wang'] | 2022-11-20 | null | null | null | null | ['aspect-term-extraction-and-sentiment', 'aspect-extraction', 'aspect-based-sentiment-analysis', 'aspect-oriented-opinion-extraction', 'aspect-category-opinion-sentiment-quadruple', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.32773926e-02 -6.02284670e-01 -3.61800879e-01 -5.99534929e-01
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5e9c97c4-91b8-4f7f-a7ae-6e7b9ee0522e | noisy-universal-domain-adaptation-via | 2304.10333 | null | https://arxiv.org/abs/2304.10333v1 | https://arxiv.org/pdf/2304.10333v1.pdf | Noisy Universal Domain Adaptation via Divergence Optimization for Visual Recognition | To transfer the knowledge learned from a labeled source domain to an unlabeled target domain, many studies have worked on universal domain adaptation (UniDA), where there is no constraint on the label sets of the source domain and target domain. However, the existing UniDA methods rely on source samples with correct annotations. Due to the limited resources in the real world, it is difficult to obtain a large amount of perfectly clean labeled data in a source domain in some applications. As a result, we propose a novel realistic scenario named Noisy UniDA, in which classifiers are trained using noisy labeled data from the source domain as well as unlabeled domain data from the target domain that has an uncertain class distribution. A multi-head convolutional neural network framework is proposed in this paper to address all of the challenges faced in the Noisy UniDA at once. Our network comprises a single common feature generator and multiple classifiers with various decision bounds. We can detect noisy samples in the source domain, identify unknown classes in the target domain, and align the distribution of the source and target domains by optimizing the divergence between the outputs of the various classifiers. The proposed method outperformed the existing methods in most of the settings after a thorough analysis of the various domain adaption scenarios. The source code is available at \url{https://github.com/YU1ut/Divergence-Optimization}. | ['Yoshitaka Ushiku', 'Atsushi Hashimoto', 'Qing Yu'] | 2023-04-20 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [-2.52008140e-02 -1.82205766e-01 -1.06525667e-01 -5.61475217e-01
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bcdd6765-9896-4949-8560-2155457c6264 | maximum-entropy-model-based-reinforcement | 2112.01195 | null | https://arxiv.org/abs/2112.01195v1 | https://arxiv.org/pdf/2112.01195v1.pdf | Maximum Entropy Model-based Reinforcement Learning | Recent advances in reinforcement learning have demonstrated its ability to solve hard agent-environment interaction tasks on a super-human level. However, the application of reinforcement learning methods to practical and real-world tasks is currently limited due to most RL state-of-art algorithms' sample inefficiency, i.e., the need for a vast number of training episodes. For example, OpenAI Five algorithm that has beaten human players in Dota 2 has trained for thousands of years of game time. Several approaches exist that tackle the issue of sample inefficiency, that either offers a more efficient usage of already gathered experience or aim to gain a more relevant and diverse experience via a better exploration of an environment. However, to our knowledge, no such approach exists for model-based algorithms, that showed their high sample efficiency in solving hard control tasks with high-dimensional state space. This work connects exploration techniques and model-based reinforcement learning. We have designed a novel exploration method that takes into account features of the model-based approach. We also demonstrate through experiments that our method significantly improves the performance of the model-based algorithm Dreamer. | ['Aleksei Shpilman', 'Oleg Svidchenko'] | 2021-12-02 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-1.68723240e-01 1.88459858e-01 -9.39766318e-02 2.29411915e-01
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53d92600-0619-44f0-b5f7-84f40131e14e | composite-task-completion-dialogue-policy | 1704.03084 | null | http://arxiv.org/abs/1704.03084v3 | http://arxiv.org/pdf/1704.03084v3.pdf | Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning | Building a dialogue agent to fulfill complex tasks, such as travel planning,
is challenging because the agent has to learn to collectively complete multiple
subtasks. For example, the agent needs to reserve a hotel and book a flight so
that there leaves enough time for commute between arrival and hotel check-in.
This paper addresses this challenge by formulating the task in the mathematical
framework of options over Markov Decision Processes (MDPs), and proposing a
hierarchical deep reinforcement learning approach to learning a dialogue
manager that operates at different temporal scales. The dialogue manager
consists of: (1) a top-level dialogue policy that selects among subtasks or
options, (2) a low-level dialogue policy that selects primitive actions to
complete the subtask given by the top-level policy, and (3) a global state
tracker that helps ensure all cross-subtask constraints be satisfied.
Experiments on a travel planning task with simulated and real users show that
our approach leads to significant improvements over three baselines, two based
on handcrafted rules and the other based on flat deep reinforcement learning. | ['Kam-Fai Wong', 'Jianfeng Gao', 'Xiujun Li', 'Asli Celikyilmaz', 'Sungjin Lee', 'Lihong Li', 'Baolin Peng'] | 2017-04-10 | composite-task-completion-dialogue-policy-1 | https://aclanthology.org/D17-1237 | https://aclanthology.org/D17-1237.pdf | emnlp-2017-9 | ['task-completion-dialogue-policy-learning'] | ['natural-language-processing'] | [-1.44367395e-02 6.55053318e-01 -1.29961416e-01 -6.52299523e-01
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e2c4c75e-92e8-421c-9135-fd6c4eb4cd9e | human-motion-detection-using-sharpened | 2202.11667 | null | https://arxiv.org/abs/2202.11667v1 | https://arxiv.org/pdf/2202.11667v1.pdf | Human Motion Detection Using Sharpened Dimensionality Reduction and Clustering | Sharpened dimensionality reduction (SDR), which belongs to the class of multidimensional projection techniques, has recently been introduced to tackle the challenges in the exploratory and visual analysis of high-dimensional data. SDR has been applied to various real-world datasets, such as human activity sensory data and astronomical datasets. However, manually labeling the samples from the generated projection are expensive. To address this problem, we propose here to use clustering methods such as k-means, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Spectral Clustering to easily label the 2D projections of high-dimensional data. We test our pipeline of SDR and the clustering methods on a range of synthetic and real-world datasets, including two different public human activity datasets extracted from smartphone accelerometer or gyroscope recordings of various movements. We apply clustering to assess the visual cluster separation of SDR, both qualitatively and quantitatively. We conclude that clustering SDR results yields better labeling results than clustering plain DR, and that k-means is the recommended clustering method for SDR in terms of clustering accuracy, ease-of-use, and computational scalability. | ['Jos B. T. M. Roerdink', 'Youngjoo Kim', 'Jeewon Heo'] | 2022-02-23 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 4.63126525e-02 -4.91181910e-01 7.72794038e-02 -2.25772962e-01
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856c40ed-440d-47e7-ad14-2a887c4a891c | reformulating-dover-lap-label-mapping-as-a | 2104.01954 | null | https://arxiv.org/abs/2104.01954v2 | https://arxiv.org/pdf/2104.01954v2.pdf | Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem | We recently proposed DOVER-Lap, a method for combining overlap-aware speaker diarization system outputs. DOVER-Lap improved upon its predecessor DOVER by using a label mapping method based on globally-informed greedy search. In this paper, we analyze this label mapping in the framework of a maximum orthogonal graph partitioning problem, and present three inferences. First, we show that DOVER-Lap label mapping is exponential in the input size, which poses a challenge when combining a large number of hypotheses. We then revisit the DOVER label mapping algorithm and propose a modification which performs similar to DOVER-Lap while being computationally tractable. We also derive an approximation bound for the algorithm in terms of the maximum number of hypotheses speakers. Finally, we describe a randomized local search algorithm which provides a near-optimal $(1-\epsilon)$-approximate solution to the problem with high probability. We empirically demonstrate the effectiveness of our methods on the AMI meeting corpus. Our code is publicly available: https://github.com/desh2608/dover-lap. | ['Sanjeev Khudanpur', 'Desh Raj'] | 2021-04-05 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 1.61597505e-01 2.57826239e-01 -1.82984576e-01 -4.93761867e-01
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cf3e7e31-54d4-4584-a46d-a329b313c9e9 | abi-neural-ensemble-model-for-gender | 1902.08856 | null | http://arxiv.org/abs/1902.08856v1 | http://arxiv.org/pdf/1902.08856v1.pdf | ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction | We present our system for the CLIN29 shared task on cross-genre gender
detection for Dutch. We experimented with a multitude of neural models (CNN,
RNN, LSTM, etc.), more "traditional" models (SVM, RF, LogReg, etc.), different
feature sets as well as data pre-processing. The final results suggested that
using tokenized, non-lowercased data works best for most of the neural models,
while a combination of word clusters, character trigrams and word lists showed
to be most beneficial for the majority of the more "traditional" (that is,
non-neural) models, beating features used in previous tasks such as n-grams,
character n-grams, part-of-speech tags and combinations thereof. In
contradiction with the results described in previous comparable shared tasks,
our neural models performed better than our best traditional approaches with
our best feature set-up. Our final model consisted of a weighted ensemble model
combining the top 25 models. Our final model won both the in-domain gender
prediction task and the cross-genre challenge, achieving an average accuracy of
64.93% on the in-domain gender prediction task, and 56.26% on cross-genre
gender prediction. | ['Eva Vanmassenhove', 'Dimitar Shterionov', 'Andy Way', 'Amit Moryossef', 'Alberto Poncelas'] | 2019-02-23 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-2.34636948e-01 1.59262940e-01 -1.78253561e-01 -5.33818007e-01
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ff68052f-fa56-43fc-a45e-2aeb56e3a411 | enhancing-pre-trained-models-with-text | 2209.04179 | null | https://arxiv.org/abs/2209.04179v1 | https://arxiv.org/pdf/2209.04179v1.pdf | Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation | Today the pre-trained language models achieve great success for question generation (QG) task and significantly outperform traditional sequence-to-sequence approaches. However, the pre-trained models treat the input passage as a flat sequence and are thus not aware of the text structure of input passage. For QG task, we model text structure as answer position and syntactic dependency, and propose answer localness modeling and syntactic mask attention to address these limitations. Specially, we present localness modeling with a Gaussian bias to enable the model to focus on answer-surrounded context, and propose a mask attention mechanism to make the syntactic structure of input passage accessible in question generation process. Experiments on SQuAD dataset show that our proposed two modules improve performance over the strong pre-trained model ProphetNet, and combing them together achieves very competitive results with the state-of-the-art pre-trained model. | ['Yunfang Wu', 'Fanyi Qu', 'Xin Jia', 'Zichen Wu'] | 2022-09-09 | null | https://aclanthology.org/2022.coling-1.571 | https://aclanthology.org/2022.coling-1.571.pdf | coling-2022-10 | ['question-generation'] | ['natural-language-processing'] | [ 1.31399810e-01 5.54745078e-01 -6.86807698e-03 -2.40783423e-01
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3e4e7c99-3f30-4d13-b849-a474687d35be | fetmrqc-automated-quality-control-for-fetal | 2304.05879 | null | https://arxiv.org/abs/2304.05879v1 | https://arxiv.org/pdf/2304.05879v1.pdf | FetMRQC: Automated Quality Control for fetal brain MRI | Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where large and unpredictable fetal motion can lead to substantial artifacts in the acquired images. Existing methods for fetal brain quality assessment operate at the \textit{slice} level, and fail to get a comprehensive picture of the quality of an image, that can only be achieved by looking at the \textit{entire} brain volume. In this work, we propose FetMRQC, a machine learning framework for automated image quality assessment tailored to fetal brain MRI, which extracts an ensemble of quality metrics that are then used to predict experts' ratings. Based on the manual ratings of more than 1000 low-resolution stacks acquired across two different institutions, we show that, compared with existing quality metrics, FetMRQC is able to generalize out-of-domain, while being interpretable and data efficient. We also release a novel manual quality rating tool designed to facilitate and optimize quality rating of fetal brain images. Our tool, along with all the code to generate, train and evaluate the model will be released upon acceptance of the paper. | ['Meritxell Bach Cuadra', 'Elisenda Eixarch', 'Yvan Gomez', 'Oscar Esteban', 'Thomas Sanchez'] | 2023-04-12 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 1.09737411e-01 2.14722499e-01 3.56732100e-01 -7.56886780e-01
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80573398-4eb2-4181-a071-ed1bbf557db9 | stochastic-subgraph-neighborhood-pooling-for | 2304.08556 | null | https://arxiv.org/abs/2304.08556v1 | https://arxiv.org/pdf/2304.08556v1.pdf | Stochastic Subgraph Neighborhood Pooling for Subgraph Classification | Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph. Subgraph classification has applications such as predicting the cellular function of a group of proteins or identifying rare diseases given a collection of phenotypes. Graph neural networks (GNNs) are the de facto solution for node, link, and graph-level tasks but fail to perform well on subgraph classification tasks. Even GNNs tailored for graph classification are not directly transferable to subgraph classification as they ignore the external topology of the subgraph, thus failing to capture how the subgraph is located within the larger graph. The current state-of-the-art models for subgraph classification address this shortcoming through either labeling tricks or multiple message-passing channels, both of which impose a computation burden and are not scalable to large graphs. To address the scalability issue while maintaining generalization, we propose Stochastic Subgraph Neighborhood Pooling (SSNP), which jointly aggregates the subgraph and its neighborhood (i.e., external topology) information without any computationally expensive operations such as labeling tricks. To improve scalability and generalization further, we also propose a simple data augmentation pre-processing step for SSNP that creates multiple sparse views of the subgraph neighborhood. We show that our model is more expressive than GNNs without labeling tricks. Our extensive experiments demonstrate that our models outperform current state-of-the-art methods (with a margin of up to 2%) while being up to 3X faster in training. | ['Amirali Salehi-Abari', 'Paul Louis', 'Shweta Ann Jacob'] | 2023-04-17 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 3.04460168e-01 3.51006836e-01 -4.97272909e-01 -2.43728638e-01
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83cc901f-dbc5-4965-8928-6fe4f1b9aa24 | a-spatio-temporal-multilayer-perceptron-for | 2204.11511 | null | https://arxiv.org/abs/2204.11511v2 | https://arxiv.org/pdf/2204.11511v2.pdf | A Spatio-Temporal Multilayer Perceptron for Gesture Recognition | Gesture recognition is essential for the interaction of autonomous vehicles with humans. While the current approaches focus on combining several modalities like image features, keypoints and bone vectors, we present neural network architecture that delivers state-of-the-art results only with body skeleton input data. We propose the spatio-temporal multilayer perceptron for gesture recognition in the context of autonomous vehicles. Given 3D body poses over time, we define temporal and spatial mixing operations to extract features in both domains. Additionally, the importance of each time step is re-weighted with Squeeze-and-Excitation layers. An extensive evaluation of the TCG and Drive&Act datasets is provided to showcase the promising performance of our approach. Furthermore, we deploy our model to our autonomous vehicle to show its real-time capability and stable execution. | ['Vasileios Belagiannis', 'Klaus Dietmayer', 'Youssef Dawoud', 'Alexander Tsaregorodtsev', 'Adrian Holzbock'] | 2022-04-25 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 1.47121519e-01 -1.11172006e-01 -4.07767862e-01 -4.75850284e-01
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4af10b58-98de-41c1-94ce-12a07ec81997 | neural-network-surgery-with-sets | 1912.06719 | null | https://arxiv.org/abs/1912.06719v2 | https://arxiv.org/pdf/1912.06719v2.pdf | Neural Network Surgery with Sets | The cost to train machine learning models has been increasing exponentially, making exploration and research into the correct features and architecture a costly or intractable endeavor at scale. However, using a technique named "surgery" OpenAI Five was continuously trained to play the game DotA 2 over the course of 10 months through 20 major changes in features and architecture. Surgery transfers trained weights from one network to another after a selection process to determine which sections of the model are unchanged and which must be re-initialized. In the past, the selection process relied on heuristics, manual labor, or pre-existing boundaries in the structure of the model, limiting the ability to salvage experiments after modifications of the feature set or input reorderings. We propose a solution to automatically determine which components of a neural network model should be salvaged and which require retraining. We achieve this by allowing the model to operate over discrete sets of features and use set-based operations to determine the exact relationship between inputs and outputs, and how they change across tweaks in model architecture. In this paper, we introduce the methodology for enabling neural networks to operate on sets, derive two methods for detecting feature-parameter interaction maps, and show their equivalence. We empirically validate that we can surgery weights across feature and architecture changes to the OpenAI Five model. | ['Susan Zhang', 'Jonathan Raiman', 'Christy Dennison'] | 2019-12-13 | null | null | null | null | ['dota-2'] | ['playing-games'] | [ 2.59888113e-01 -6.54253885e-02 1.03829548e-01 -3.65274876e-01
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a16de9aa-6d83-419b-be9c-bd34961af785 | single-reference-image-based-scene-relighting | 1708.07066 | null | http://arxiv.org/abs/1708.07066v1 | http://arxiv.org/pdf/1708.07066v1.pdf | Single Reference Image based Scene Relighting via Material Guided Filtering | Image relighting is to change the illumination of an image to a target
illumination effect without known the original scene geometry, material
information and illumination condition. We propose a novel outdoor scene
relighting method, which needs only a single reference image and is based on
material constrained layer decomposition. Firstly, the material map is
extracted from the input image. Then, the reference image is warped to the
input image through patch match based image warping. Lastly, the input image is
relit using material constrained layer decomposition. The experimental results
reveal that our method can produce similar illumination effect as that of the
reference image on the input image using only a single reference image. | ['Xiao-Dong Li', 'Xin Jin', 'Xianggang Jiang', 'Ningning Liu', 'Yannan Li', 'Shiming Ge', 'Chaoen Xiao'] | 2017-08-23 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 9.55470085e-01 -1.73841313e-01 1.98898911e-01 -7.36937597e-02
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6ed95b03-06c7-4db2-a511-282c3fe02a6a | virtual-vs-reality-external-validation-of | 2203.03074 | null | https://arxiv.org/abs/2203.03074v1 | https://arxiv.org/pdf/2203.03074v1.pdf | Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography | Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with numerous applications of deep learning for COVID-19 diagnosis. Virtual imaging trials (VITs) could provide a solution for objective evaluation of these models. In this work utilizing the VITs, we created the CVIT-COVID dataset including 180 virtually imaged computed tomography (CT) images from simulated COVID-19 and normal phantom models under different COVID-19 morphology and imaging properties. We evaluated the performance of an open-source, deep-learning model from the University of Waterloo trained with multi-institutional data and an in-house model trained with the open clinical dataset called MosMed. We further validated the model's performance against open clinical data of 305 CT images to understand virtual vs. real clinical data performance. The open-source model was published with nearly perfect performance on the original Waterloo dataset but showed a consistent performance drop in external testing on another clinical dataset (AUC=0.77) and our simulated CVIT-COVID dataset (AUC=0.55). The in-house model achieved an AUC of 0.87 while testing on the internal test set (MosMed test set). However, performance dropped to an AUC of 0.65 and 0.69 when evaluated on clinical and our simulated CVIT-COVID dataset. The VIT framework offered control over imaging conditions, allowing us to show there was no change in performance as CT exposure was changed from 28.5 to 57 mAs. The VIT framework also provided voxel-level ground truth, revealing that performance of in-house model was much higher at AUC=0.87 for diffuse COVID-19 infection size >2.65% lung volume versus AUC=0.52 for focal disease with <2.65% volume. The virtual imaging framework enabled these uniquely rigorous analyses of model performance. | ['Joseph Y. Lo', 'Ehsan Samei', 'W. Paul Segars', 'Maciej A. Mazurowski', 'Rafael B. Fricks', 'Saman Sotoudeh-Paima', 'Ehsan Abadi', 'Fakrul Islam Tushar'] | 2022-03-07 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-1.28570542e-01 1.61358565e-01 3.76239861e-03 -2.01285928e-01
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69379f3d-ae55-49cb-9604-9e155739e261 | consistentnerf-enhancing-neural-radiance | 2305.11031 | null | https://arxiv.org/abs/2305.11031v1 | https://arxiv.org/pdf/2305.11031v1.pdf | ConsistentNeRF: Enhancing Neural Radiance Fields with 3D Consistency for Sparse View Synthesis | Neural Radiance Fields (NeRF) has demonstrated remarkable 3D reconstruction capabilities with dense view images. However, its performance significantly deteriorates under sparse view settings. We observe that learning the 3D consistency of pixels among different views is crucial for improving reconstruction quality in such cases. In this paper, we propose ConsistentNeRF, a method that leverages depth information to regularize both multi-view and single-view 3D consistency among pixels. Specifically, ConsistentNeRF employs depth-derived geometry information and a depth-invariant loss to concentrate on pixels that exhibit 3D correspondence and maintain consistent depth relationships. Extensive experiments on recent representative works reveal that our approach can considerably enhance model performance in sparse view conditions, achieving improvements of up to 94% in PSNR, 76% in SSIM, and 31% in LPIPS compared to the vanilla baselines across various benchmarks, including DTU, NeRF Synthetic, and LLFF. | ['Ziwei Liu', 'Gim Hee Lee', 'Zhenguo Li', 'Tianyang Hu', 'Lanqing Hong', 'Longhui Yu', 'Kaiyu Li', 'Kaichen Zhou', 'Shoukang Hu'] | 2023-05-18 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 8.57913122e-02 -4.78496552e-01 -1.96127340e-01 -4.54231411e-01
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5b27abd0-021b-475f-9165-13ddcb8fede9 | selective-memory-recursive-least-squares | 2211.07909 | null | https://arxiv.org/abs/2211.07909v1 | https://arxiv.org/pdf/2211.07909v1.pdf | Selective Memory Recursive Least Squares: Uniformly Allocated Approximation Capabilities of RBF Neural Networks in Real-Time Learning | When performing real-time learning tasks, the radial basis function neural network (RBFNN) is expected to make full use of the training samples such that its learning accuracy and generalization capability are guaranteed. Since the approximation capability of the RBFNN is finite, training methods with forgetting mechanisms such as the forgetting factor recursive least squares (FFRLS) and stochastic gradient descent (SGD) methods are widely used to maintain the learning ability of the RBFNN to new knowledge. However, with the forgetting mechanisms, some useful knowledge will get lost simply because they are learned a long time ago, which we refer to as the passive knowledge forgetting phenomenon. To address this problem, this paper proposes a real-time training method named selective memory recursive least squares (SMRLS) in which the feature space of the RBFNN is evenly discretized into a finite number of partitions and a synthesized objective function is developed to replace the original objective function of the ordinary recursive least squares (RLS) method. SMRLS is featured with a memorization mechanism that synthesizes the samples within each partition in real-time into representative samples uniformly distributed over the feature space, and thus overcomes the passive knowledge forgetting phenomenon and improves the generalization capability of the learned knowledge. Compared with the SGD or FFRLS methods, SMRLS achieves improved learning performance (learning speed, accuracy and generalization capability), which is demonstrated by corresponding simulation results. | ['Yanan Li', 'Jiangang Li', 'Yiming Fei'] | 2022-11-15 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-1.28568932e-01 -7.44266063e-02 -1.00544713e-01 -2.18375877e-01
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ff5bd7b3-2501-4021-9d00-3c10dd621a90 | massive-migration-from-the-steppe-is-a-source | 1502.02783 | null | http://arxiv.org/abs/1502.02783v1 | http://arxiv.org/pdf/1502.02783v1.pdf | Massive migration from the steppe is a source for Indo-European languages in Europe | We generated genome-wide data from 69 Europeans who lived between 8,000-3,000
years ago by enriching ancient DNA libraries for a target set of almost four
hundred thousand polymorphisms. Enrichment of these positions decreases the
sequencing required for genome-wide ancient DNA analysis by a median of around
250-fold, allowing us to study an order of magnitude more individuals than
previous studies and to obtain new insights about the past. We show that the
populations of western and far eastern Europe followed opposite trajectories
between 8,000-5,000 years ago. At the beginning of the Neolithic period in
Europe, ~8,000-7,000 years ago, closely related groups of early farmers
appeared in Germany, Hungary, and Spain, different from indigenous
hunter-gatherers, whereas Russia was inhabited by a distinctive population of
hunter-gatherers with high affinity to a ~24,000 year old Siberian6 . By
~6,000-5,000 years ago, a resurgence of hunter-gatherer ancestry had occurred
throughout much of Europe, but in Russia, the Yamnaya steppe herders of this
time were descended not only from the preceding eastern European
hunter-gatherers, but from a population of Near Eastern ancestry. Western and
Eastern Europe came into contact ~4,500 years ago, as the Late Neolithic Corded
Ware people from Germany traced ~3/4 of their ancestry to the Yamnaya,
documenting a massive migration into the heartland of Europe from its eastern
periphery. This steppe ancestry persisted in all sampled central Europeans
until at least ~3,000 years ago, and is ubiquitous in present-day Europeans.
These results provide support for the theory of a steppe origin of at least
some of the Indo-European languages of Europe. | [] | 2015-02-10 | null | null | null | null | ['dna-analysis'] | ['medical'] | [ 1.30133390e-01 1.24012664e-01 1.88561976e-01 -9.90467612e-03
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32e3d5fe-d0ad-4a08-b86e-5a29e921fdc2 | parallelisable-existential-rules-a-story-of | 2107.06054 | null | https://arxiv.org/abs/2107.06054v1 | https://arxiv.org/pdf/2107.06054v1.pdf | Parallelisable Existential Rules: a Story of Pieces | In this paper, we consider existential rules, an expressive formalism well suited to the representation of ontological knowledge and data-to-ontology mappings in the context of ontology-based data integration. The chase is a fundamental tool to do reasoning with existential rules as it computes all the facts entailed by the rules from a database instance. We introduce parallelisable sets of existential rules, for which the chase can be computed in a single breadth-first step from any instance. The question we investigate is the characterization of such rule sets. We show that parallelisable rule sets are exactly those rule sets both bounded for the chase and belonging to a novel class of rules, called pieceful. The pieceful class includes in particular frontier-guarded existential rules and (plain) datalog. We also give another characterization of parallelisable rule sets in terms of rule composition based on rewriting. | ['Michaël Thomazo', 'Marie-Laure Mugnier', 'Maxime Buron'] | 2021-07-13 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 2.66757697e-01 7.21483052e-01 1.23850606e-01 -2.74549901e-01
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bdb8ce9d-bd85-406c-a7ee-a86ffb95cc8a | swissalps-at-semeval-2017-task-3-attention | null | null | https://aclanthology.org/S17-2054 | https://aclanthology.org/S17-2054.pdf | SwissAlps at SemEval-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering | In this paper we propose a system for reranking answers for a given question. Our method builds on a siamese CNN architecture which is extended by two attention mechanisms. The approach was evaluated on the datasets of the SemEval-2017 competition for Community Question Answering (cQA), where it achieved 7th place obtaining a MAP score of 86:24 points on the Question-Comment Similarity subtask. | ['Mark Cieliebak', 'Jan Milan Deriu'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [-1.42369524e-01 1.42963290e-01 1.25525445e-01 -2.14645743e-01
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74ccbdc5-393f-4076-8e15-e2a7cf63a736 | coreference-aware-double-channel-attention | 2305.08348 | null | https://arxiv.org/abs/2305.08348v2 | https://arxiv.org/pdf/2305.08348v2.pdf | Coreference-aware Double-channel Attention Network for Multi-party Dialogue Reading Comprehension | We tackle Multi-party Dialogue Reading Comprehension (abbr., MDRC). MDRC stands for an extractive reading comprehension task grounded on a batch of dialogues among multiple interlocutors. It is challenging due to the requirement of understanding cross-utterance contexts and relationships in a multi-turn multi-party conversation. Previous studies have made great efforts on the utterance profiling of a single interlocutor and graph-based interaction modeling. The corresponding solutions contribute to the answer-oriented reasoning on a series of well-organized and thread-aware conversational contexts. However, the current MDRC models still suffer from two bottlenecks. On the one hand, a pronoun like "it" most probably produces multi-skip reasoning throughout the utterances of different interlocutors. On the other hand, an MDRC encoder is potentially puzzled by fuzzy features, i.e., the mixture of inner linguistic features in utterances and external interactive features among utterances. To overcome the bottlenecks, we propose a coreference-aware attention modeling method to strengthen the reasoning ability. In addition, we construct a two-channel encoding network. It separately encodes utterance profiles and interactive relationships, so as to relieve the confusion among heterogeneous features. We experiment on the benchmark corpora Molweni and FriendsQA. Experimental results demonstrate that our approach yields substantial improvements on both corpora, compared to the fine-tuned BERT and ELECTRA baselines. The maximum performance gain is about 2.5\% F1-score. Besides, our MDRC models outperform the state-of-the-art in most cases. | ['Yu Hong', 'Mengxing Dong', 'Yifan Fan', 'Bowei Zou', 'Yanling Li'] | 2023-05-15 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [ 2.95368701e-01 5.10406017e-01 1.44054800e-01 -6.28094375e-01
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14096639-abfb-4503-baec-570b27799829 | boxgraph-semantic-place-recognition-and-pose | 2206.15154 | null | https://arxiv.org/abs/2206.15154v1 | https://arxiv.org/pdf/2206.15154v1.pdf | BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR | This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching. We model 3D point clouds as fully-connected graphs of semantically identified components where each vertex corresponds to an object instance and encodes its shape. Optimal vertex association across graphs allows for full 6-Degree-of-Freedom (DoF) pose estimation and place recognition by measuring similarity. This representation is very concise, condensing the size of maps by a factor of 25 against the state-of-the-art, requiring only 3kB to represent a 1.4MB laser scan. We verify the efficacy of our system on the SemanticKITTI dataset, where we achieve a new state-of-the-art in place recognition, with an average of 88.4% recall at 100% precision where the next closest competitor follows with 64.9%. We also show accurate metric pose estimation performance - estimating 6-DoF pose with median errors of 10 cm and 0.33 deg. | ['Paul Newman', 'Matthew Gadd', 'Daniele De Martini', 'Georgi Pramatarov'] | 2022-06-30 | null | null | null | null | ['graph-matching'] | ['graphs'] | [-1.43372655e-01 3.86179149e-01 -4.04936112e-02 -2.14935899e-01
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05d639b3-7ced-4c51-832c-70261f52fdd1 | mmfn-multi-modal-fusion-net-for-end-to-end | null | null | https://github.com/Kin-Zhang/mmfn | https://github.com/Kin-Zhang/mmfn | MMFN: Multi-Modal Fusion Net for End-to-End Autonomous Driving | Under review | ['Lujia Wang', 'Ren Xin', 'Feiyi Chen', 'Ruoyu Geng', 'Mingkai Tang', 'Qingwen Zhang'] | 2022-03-01 | null | null | null | iros-in-submission-2022-3 | ['carla-map-leaderboard'] | ['robots'] | [ 6.63066685e-01 1.78641975e-01 -1.05959380e+00 -1.42309278e-01
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0799b3d4-cabb-47e8-a3fb-933e69abbfad | business-taxonomy-construction-using-concept | 1906.09694 | null | https://arxiv.org/abs/1906.09694v1 | https://arxiv.org/pdf/1906.09694v1.pdf | Business Taxonomy Construction Using Concept-Level Hierarchical Clustering | Business taxonomies are indispensable tools for investors to do equity research and make professional decisions. However, to identify the structure of industry sectors in an emerging market is challenging for two reasons. First, existing taxonomies are designed for mature markets, which may not be the appropriate classification for small companies with innovative business models. Second, emerging markets are fast-developing, thus the static business taxonomies cannot promptly reflect the new features. In this article, we propose a new method to construct business taxonomies automatically from the content of corporate annual reports. Extracted concepts are hierarchically clustered using greedy affinity propagation. Our method requires less supervision and is able to discover new terms. Experiments and evaluation on the Chinese National Equities Exchange and Quotations (NEEQ) market show several advantages of the business taxonomy we build. Our results provide an effective tool for understanding and investing in the new growth companies. | ['Win-Bin Huang', 'Frank Z. Xing', 'Haodong Bai', 'Erik Cambria'] | 2019-06-24 | business-taxonomy-construction-using-concept-1 | https://aclanthology.org/W19-5501 | https://aclanthology.org/W19-5501.pdf | ws-2019-8 | ['business-taxonomy-construction'] | ['miscellaneous'] | [-7.23970771e-01 -1.75287619e-01 -6.27980053e-01 -1.09053634e-01
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1effb89f-8672-4fc5-8891-ee60005bc7c2 | image-clustering-using-an-augmented | 2011.04094 | null | https://arxiv.org/abs/2011.04094v1 | https://arxiv.org/pdf/2011.04094v1.pdf | Image Clustering using an Augmented Generative Adversarial Network and Information Maximization | Image clustering has recently attracted significant attention due to the increased availability of unlabelled datasets. The efficiency of traditional clustering algorithms heavily depends on the distance functions used and the dimensionality of the features. Therefore, performance degradation is often observed when tackling either unprocessed images or high-dimensional features extracted from processed images. To deal with these challenges, we propose a deep clustering framework consisting of a modified generative adversarial network (GAN) and an auxiliary classifier. The modification employs Sobel operations prior to the discriminator of the GAN to enhance the separability of the learned features. The discriminator is then leveraged to generate representations as the input to an auxiliary classifier. An adaptive objective function is utilised to train the auxiliary classifier for clustering the representations, aiming to increase the robustness by minimizing the divergence of multiple representations generated by the discriminator. The auxiliary classifier is implemented with a group of multiple cluster-heads, where a tolerance hyper-parameter is used to tackle imbalanced data. Our results indicate that the proposed method significantly outperforms state-of-the-art clustering methods on CIFAR-10 and CIFAR-100, and is competitive on the STL10 and MNIST datasets. | ['Spencer A. Thomas', 'Yaochu Jin', 'Foivos Ntelemis'] | 2020-11-08 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 3.29032809e-01 -1.44190285e-02 2.34214500e-01 -3.34557384e-01
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27026765-1a12-41e2-9bfa-5b9ba9a43725 | semi-supervised-object-detection-with-1 | 2107.05031 | null | https://arxiv.org/abs/2107.05031v1 | https://arxiv.org/pdf/2107.05031v1.pdf | Semi-Supervised Object Detection with Adaptive Class-Rebalancing Self-Training | This study delves into semi-supervised object detection (SSOD) to improve detector performance with additional unlabeled data. State-of-the-art SSOD performance has been achieved recently by self-training, in which training supervision consists of ground truths and pseudo-labels. In current studies, we observe that class imbalance in SSOD severely impedes the effectiveness of self-training. To address the class imbalance, we propose adaptive class-rebalancing self-training (ACRST) with a novel memory module called CropBank. ACRST adaptively rebalances the training data with foreground instances extracted from the CropBank, thereby alleviating the class imbalance. Owing to the high complexity of detection tasks, we observe that both self-training and data-rebalancing suffer from noisy pseudo-labels in SSOD. Therefore, we propose a novel two-stage filtering algorithm to generate accurate pseudo-labels. Our method achieves satisfactory improvements on MS-COCO and VOC benchmarks. When using only 1\% labeled data in MS-COCO, our method achieves 17.02 mAP improvement over supervised baselines, and 5.32 mAP improvement compared with state-of-the-art methods. | ['Bin Wang', 'Tianxiang Pan', 'Fangyuan Zhang'] | 2021-07-11 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.86185318e-01 6.58290740e-03 -3.19304049e-01 -5.50932765e-01
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39d67976-a4ad-4474-af12-424d56f73779 | energy-minimization-in-ris-assisted-uav | 2208.08639 | null | https://arxiv.org/abs/2208.08639v1 | https://arxiv.org/pdf/2208.08639v1.pdf | Energy Minimization in RIS-Assisted UAV-Enabled Wireless Power Transfer Systems | Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) systems offer significant advantages in coverage and deployment flexibility, but suffer from endurance limitations due to the limited onboard energy. This paper proposes to improve the energy efficiency of UAV-enabled WPT systems with multiple ground sensors by utilizing reconfigurable intelligent surface (RIS). Specifically, the total energy consumption of the UAV is minimized, while meeting the energy requirement of each sensor. Firstly, we consider a fly-hover-broadcast (FHB) protocol, in which the UAV radiates radio frequency (RF) signals only at several hovering locations. The energy minimization problem is formulated to jointly optimize the UAV's trajectory, hovering time and the RIS's reflection coefficients. To solve this complex non-convex problem, we propose an efficient algorithm. Specifically, the successive convex approximation (SCA) framework is adopted to jointly optimize the UAV's trajectory and hovering time, in which a minorization-maximization (MM) algorithm that maximizes the minimum charged energy of all sensors is provided to update the reflection coefficients. Then, we investigate the general scenario in which the RF signals are radiated during the flight, aiming to minimize the total energy consumption of the UAV by jointly optimizing the UAV's trajectory, flight time and the RIS's reflection coefficients. By applying the path discretization (PD) protocol, the optimization problem is formulated with a finite number of variables. A high-quality solution for this more challenging problem is obtained. Finally, our simulation results demonstrate the effectiveness of the proposed algorithm and the benefits of RIS in energy saving. | ['Cunhua Pan', 'Li Li', 'Zhangjie Peng', 'Zhenkun Zhang', 'Hong Ren'] | 2022-08-18 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 4.26482975e-01 1.91422328e-01 -2.84848567e-02 3.36247325e-01
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9a637d3e-6bd7-4bb8-a355-870f4e109bf5 | deciphering-undersegmented-ancient-scripts | 2010.11054 | null | https://arxiv.org/abs/2010.11054v1 | https://arxiv.org/pdf/2010.11054v1.pdf | Deciphering Undersegmented Ancient Scripts Using Phonetic Prior | Most undeciphered lost languages exhibit two characteristics that pose significant decipherment challenges: (1) the scripts are not fully segmented into words; (2) the closest known language is not determined. We propose a decipherment model that handles both of these challenges by building on rich linguistic constraints reflecting consistent patterns in historical sound change. We capture the natural phonological geometry by learning character embeddings based on the International Phonetic Alphabet (IPA). The resulting generative framework jointly models word segmentation and cognate alignment, informed by phonological constraints. We evaluate the model on both deciphered languages (Gothic, Ugaritic) and an undeciphered one (Iberian). The experiments show that incorporating phonetic geometry leads to clear and consistent gains. Additionally, we propose a measure for language closeness which correctly identifies related languages for Gothic and Ugaritic. For Iberian, the method does not show strong evidence supporting Basque as a related language, concurring with the favored position by the current scholarship. | ['Regina Barzilay', 'Yuan Cao', 'Enrico Santus', 'Frederik Hartmann', 'Jiaming Luo'] | 2020-10-21 | null | null | null | null | ['decipherment'] | ['natural-language-processing'] | [-3.29921618e-02 -1.38540650e-02 -1.50368288e-01 -1.97411627e-01
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c63d52cc-7479-4a5b-adae-abfef8a44a02 | sageformer-series-aware-graph-enhanced | 2307.01616 | null | https://arxiv.org/abs/2307.01616v1 | https://arxiv.org/pdf/2307.01616v1.pdf | SageFormer: Series-Aware Graph-Enhanced Transformers for Multivariate Time Series Forecasting | Multivariate time series forecasting plays a critical role in diverse domains. While recent advancements in deep learning methods, especially Transformers, have shown promise, there remains a gap in addressing the significance of inter-series dependencies. This paper introduces SageFormer, a Series-aware Graph-enhanced Transformer model designed to effectively capture and model dependencies between series using graph structures. SageFormer tackles two key challenges: effectively representing diverse temporal patterns across series and mitigating redundant information among series. Importantly, the proposed series-aware framework seamlessly integrates with existing Transformer-based models, augmenting their ability to model inter-series dependencies. Through extensive experiments on real-world and synthetic datasets, we showcase the superior performance of SageFormer compared to previous state-of-the-art approaches. | ['Yuantao Gu', 'Xin Wang', 'Zhenwei Zhang'] | 2023-07-04 | null | null | null | null | ['time-series-forecasting', 'multivariate-time-series-forecasting'] | ['time-series', 'time-series'] | [ 1.07480317e-01 -5.88467956e-01 -1.87577456e-02 -2.16427490e-01
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6b554815-6681-4837-8d72-f89e13ad1460 | scicap-a-knowledge-augmented-dataset-to-study | 2306.03491 | null | https://arxiv.org/abs/2306.03491v1 | https://arxiv.org/pdf/2306.03491v1.pdf | SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure Captioning | In scholarly documents, figures provide a straightforward way of communicating scientific findings to readers. Automating figure caption generation helps move model understandings of scientific documents beyond text and will help authors write informative captions that facilitate communicating scientific findings. Unlike previous studies, we reframe scientific figure captioning as a knowledge-augmented image captioning task that models need to utilize knowledge embedded across modalities for caption generation. To this end, we extended the large-scale SciCap dataset~\cite{hsu-etal-2021-scicap-generating} to SciCap+ which includes mention-paragraphs (paragraphs mentioning figures) and OCR tokens. Then, we conduct experiments with the M4C-Captioner (a multimodal transformer-based model with a pointer network) as a baseline for our study. Our results indicate that mention-paragraphs serves as additional context knowledge, which significantly boosts the automatic standard image caption evaluation scores compared to the figure-only baselines. Human evaluations further reveal the challenges of generating figure captions that are informative to readers. The code and SciCap+ dataset will be publicly available at https://github.com/ZhishenYang/scientific_figure_captioning_dataset | ['Naoaki Okazaki', 'Hideki Tanaka', 'Raj Dabre', 'Zhishen Yang'] | 2023-06-06 | null | null | null | null | ['optical-character-recognition', 'image-captioning'] | ['computer-vision', 'computer-vision'] | [ 4.77080941e-01 4.41994637e-01 -4.13914137e-02 -3.09271663e-01
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b35dbd43-bf6e-4fcf-92b0-f96e5147d742 | demand-side-scheduling-based-on-deep-actor | 2005.01979 | null | https://arxiv.org/abs/2005.01979v2 | https://arxiv.org/pdf/2005.01979v2.pdf | Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids | We consider the problem of demand-side energy management, where each household is equipped with a smart meter that is able to schedule home appliances online. The goal is to minimize the overall cost under a real-time pricing scheme. While previous works have introduced centralized approaches in which the scheduling algorithm has full observability, we propose the formulation of a smart grid environment as a Markov game. Each household is a decentralized agent with partial observability, which allows scalability and privacy-preservation in a realistic setting. The grid operator produces a price signal that varies with the energy demand. We propose an extension to a multi-agent, deep actor-critic algorithm to address partial observability and the perceived non-stationarity of the environment from the agent's viewpoint. This algorithm learns a centralized critic that coordinates training of decentralized agents. Our approach thus uses centralized learning but decentralized execution. Simulation results show that our online deep reinforcement learning method can reduce both the peak-to-average ratio of total energy consumed and the cost of electricity for all households based purely on instantaneous observations and a price signal. | ['Wenbo Wang', 'Joash Lee', 'Dusit Niyato'] | 2020-05-05 | null | null | null | null | ['distributional-reinforcement-learning', 'smart-grid-prediction'] | ['methodology', 'miscellaneous'] | [-5.36031604e-01 3.77299905e-01 3.85305309e-03 -1.34532601e-01
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50d16a48-06d6-458f-8531-87e6a14d66b7 | um-cam-uncertainty-weighted-multi-resolution | 2306.11490 | null | https://arxiv.org/abs/2306.11490v1 | https://arxiv.org/pdf/2306.11490v1.pdf | UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentation | Accurate segmentation of the fetal brain from Magnetic Resonance Image (MRI) is important for prenatal assessment of fetal development. Although deep learning has shown the potential to achieve this task, it requires a large fine annotated dataset that is difficult to collect. To address this issue, weakly-supervised segmentation methods with image-level labels have gained attention, which are commonly based on class activation maps from a classification network trained with image tags. However, most of these methods suffer from incomplete activation regions, due to the low-resolution localization without detailed boundary cues. To this end, we propose a novel weakly-supervised method with image-level labels based on semantic features and context information exploration. We first propose an Uncertainty-weighted Multi-resolution Class Activation Map (UM-CAM) to generate high-quality pixel-level supervision. Then, we design a Geodesic distance-based Seed Expansion (GSE) method to provide context information for rectifying the ambiguous boundaries of UM-CAM. Extensive experiments on a fetal brain dataset show that our UM-CAM can provide more accurate activation regions with fewer false positive regions than existing CAM variants, and our proposed method outperforms state-of-the-art weakly-supervised methods with image-level labels. | ['Guotai Wang', 'Shaoting Zhang', 'Tao Lu', 'Jia Fu'] | 2023-06-20 | null | null | null | null | ['weakly-supervised-segmentation', 'brain-segmentation'] | ['computer-vision', 'medical'] | [ 4.83619094e-01 6.16680264e-01 -3.70484710e-01 -8.54131460e-01
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e606a74f-f815-4f5e-9f8b-f132717c2392 | retrospective-motion-correction-in-gradient | 2303.17239 | null | https://arxiv.org/abs/2303.17239v1 | https://arxiv.org/pdf/2303.17239v1.pdf | Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNs | Magnetic Resonance Imaging allows high resolution data acquisition with the downside of motion sensitivity due to relatively long acquisition times. Even during the acquisition of a single 2D slice, motion can severely corrupt the image. Retrospective motion correction strategies do not interfere during acquisition time but operate on the motion affected data. Known methods suited to this scenario are compressed sensing (CS), generative adversarial networks (GANs), and motion estimation. In this paper we propose a strategy to correct for motion artifacts using Deep Convolutional Neuronal Networks (Deep CNNs) in a reliable and verifiable manner by explicit motion estimation. The sensitivity encoding (SENSE) redundancy that multiple receiver coils provide, has in the past been used for acceleration, noise reduction and rigid motion compensation. We show that using Deep CNNs the concepts of rigid motion compensation can be generalized to more complex motion fields. Using a simulated synthetic data set, our proposed supervised network is evaluated on motion corrupted MRIs of abdomen and head. We compare our results with rigid motion compensation and GANs. | ['Bernadette N. Hahn', 'Mathias S. Feinler'] | 2023-03-30 | null | null | null | null | ['motion-compensation', 'motion-estimation'] | ['computer-vision', 'computer-vision'] | [ 8.55696619e-01 3.23562890e-01 1.60648167e-01 -3.10882211e-01
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66cae12e-e8dd-4415-b90a-cad4316ac57d | a-pipeline-for-creative-visual-storytelling | 1807.08077 | null | http://arxiv.org/abs/1807.08077v1 | http://arxiv.org/pdf/1807.08077v1.pdf | A Pipeline for Creative Visual Storytelling | Computational visual storytelling produces a textual description of events
and interpretations depicted in a sequence of images. These texts are made
possible by advances and cross-disciplinary approaches in natural language
processing, generation, and computer vision. We define a computational creative
visual storytelling as one with the ability to alter the telling of a story
along three aspects: to speak about different environments, to produce
variations based on narrative goals, and to adapt the narrative to the
audience. These aspects of creative storytelling and their effect on the
narrative have yet to be explored in visual storytelling. This paper presents a
pipeline of task-modules, Object Identification, Single-Image Inferencing, and
Multi-Image Narration, that serve as a preliminary design for building a
creative visual storyteller. We have piloted this design for a sequence of
images in an annotation task. We present and analyze the collected corpus and
describe plans towards automation. | ['Stephanie M. Lukin', 'Reginald Hobbs', 'Clare R. Voss'] | 2018-07-21 | a-pipeline-for-creative-visual-storytelling-1 | https://aclanthology.org/W18-1503 | https://aclanthology.org/W18-1503.pdf | ws-2018-6 | ['visual-storytelling'] | ['natural-language-processing'] | [ 5.08684039e-01 3.30133706e-01 4.02078927e-01 -3.30100209e-01
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97325127-3700-4047-ae29-fee6913e2217 | zero-knowledge-zero-shot-learning-for-novel | 2302.04427 | null | https://arxiv.org/abs/2302.04427v1 | https://arxiv.org/pdf/2302.04427v1.pdf | Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery | Generalized Zero-Shot Learning (GZSL) and Open-Set Recognition (OSR) are two mainstream settings that greatly extend conventional visual object recognition. However, the limitations of their problem settings are not negligible. The novel categories in GZSL require pre-defined semantic labels, making the problem setting less realistic; the oversimplified unknown class in OSR fails to explore the innate fine-grained and mixed structures of novel categories. In light of this, we are motivated to consider a new problem setting named Zero-Knowledge Zero-Shot Learning (ZK-ZSL) that assumes no prior knowledge of novel classes and aims to classify seen and unseen samples and recover semantic attributes of the fine-grained novel categories for further interpretation. To achieve this, we propose a novel framework that recovers the clustering structures of both seen and unseen categories where the seen class structures are guided by source labels. In addition, a structural alignment loss is designed to aid the semantic learning of unseen categories with their recovered structures. Experimental results demonstrate our method's superior performance in classification and semantic recovery on four benchmark datasets. | ['Hongfu Liu', 'Zhaonan Li'] | 2023-02-09 | null | null | null | null | ['object-recognition', 'generalized-zero-shot-learning', 'generalized-zero-shot-learning', 'open-set-learning'] | ['computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 4.95486557e-01 1.63297325e-01 -2.70149171e-01 -5.07265568e-01
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018df2bc-f46d-46ef-925e-ec5ac404a11e | rapid-retrofitting-ieee-802-11ay-access | 2109.04819 | null | https://arxiv.org/abs/2109.04819v3 | https://arxiv.org/pdf/2109.04819v3.pdf | RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing | In this work we present RAPID, the first joint communication and radar system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. Unlike existing approaches for human sensing at millimeter-wave frequencies, which rely on special-purpose radars, RAPID achieves radar-level sensing accuracy with IEEE 802.11ay access points, thus avoiding the burden of installing ad-hoc sensors. RAPID enables contactless human sensing applications, such as people tracking, Human Activity Recognition (HAR), and person identification without requiring modifications to the standard packet structure. Specifically, we leverage IEEE 802.11ay beam training to accurately localize and track multiple individuals within the same environment. Then, we propose a new way of using beam tracking to extract micro-Doppler signatures from the time-varying Channel Impulse Response (CIR) estimated from reflected packets. Such signatures are fed to a deep learning classifier to perform HAR and person identification. RAPID is implemented on a cutting-edge IEEE 802.11ay-compatible FPGA platform with phased antenna arrays, and evaluated on a large dataset of CIR measurements. It is robust across different environments and subjects, and outperforms state-of-the-art sub-6 GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks multiple subjects, reaching HAR and person identification accuracies of 94% and 90%, respectively. | ['Joerg Widmer', 'Enver Bashirov', 'Francesca Meneghello', 'Michele Rossi', 'Jesus Omar Lacruz', 'Jacopo Pegoraro'] | 2021-09-10 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 2.08872333e-01 -3.87615785e-02 2.81454786e-03 -2.81569362e-01
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beedd3d1-9eab-4871-9632-b29a38bcd720 | leurn-learning-explainable-univariate-rules | 2303.14937 | null | https://arxiv.org/abs/2303.14937v1 | https://arxiv.org/pdf/2303.14937v1.pdf | LEURN: Learning Explainable Univariate Rules with Neural Networks | In this paper, we propose LEURN: a neural network architecture that learns univariate decision rules. LEURN is a white-box algorithm that results into univariate trees and makes explainable decisions in every stage. In each layer, LEURN finds a set of univariate rules based on an embedding of the previously checked rules and their corresponding responses. Both rule finding and final decision mechanisms are weighted linear combinations of these embeddings, hence contribution of all rules are clearly formulated and explainable. LEURN can select features, extract feature importance, provide semantic similarity between a pair of samples, be used in a generative manner and can give a confidence score. Thanks to a smoothness parameter, LEURN can also controllably behave like decision trees or vanilla neural networks. Besides these advantages, LEURN achieves comparable performance to state-of-the-art methods across 30 tabular datasets for classification and regression problems. | ['Caglar Aytekin'] | 2023-03-27 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 1.43496946e-01 6.32397175e-01 -7.97780991e-01 -7.54420280e-01
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030e2877-4dd3-4213-9e1d-e04aa53661ae | continual-transformers-redundancy-free | 2201.06268 | null | https://arxiv.org/abs/2201.06268v3 | https://arxiv.org/pdf/2201.06268v3.pdf | Continual Transformers: Redundancy-Free Attention for Online Inference | Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a time. Consequently, their use during online inference on time-series data entails considerable redundancy due to the overlap in successive token sequences. In this work, we propose novel formulations of the Scaled Dot-Product Attention, which enable Transformers to perform efficient online token-by-token inference on a continual input stream. Importantly, our modifications are purely to the order of computations, while the outputs and learned weights are identical to those of the original Transformer Encoder. We validate our Continual Transformer Encoder with experiments on the THUMOS14, TVSeries and GTZAN datasets with remarkable results: Our Continual one- and two-block architectures reduce the floating point operations per prediction by up to 63x and 2.6x, respectively, while retaining predictive performance. | ['Alexandros Iosifidis', 'Arian Bakhtiarnia', 'Lukas Hedegaard'] | 2022-01-17 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 2.41500154e-01 -1.67035520e-01 -2.12291911e-01 -3.40302765e-01
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1bd106b5-289c-45de-9795-6e8221f87288 | structural-scaffolds-for-citation-intent | 1904.01608 | null | https://arxiv.org/abs/1904.01608v2 | https://arxiv.org/pdf/1904.01608v2.pdf | Structural Scaffolds for Citation Intent Classification in Scientific Publications | Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose structural scaffolds, a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents. Our model achieves a new state-of-the-art on an existing ACL anthology dataset (ACL-ARC) with a 13.3% absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods. In addition, we introduce a new dataset of citation intents (SciCite) which is more than five times larger and covers multiple scientific domains compared with existing datasets. Our code and data are available at: https://github.com/allenai/scicite. | ['Waleed Ammar', 'Madeleine van Zuylen', 'Field Cady', 'Arman Cohan'] | 2019-04-02 | structural-scaffolds-for-citation-intent-1 | https://aclanthology.org/N19-1361 | https://aclanthology.org/N19-1361.pdf | naacl-2019-6 | ['citation-intent-classification'] | ['natural-language-processing'] | [-2.27503836e-01 -1.57556891e-01 -7.00270712e-01 -8.29402953e-02
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9b9eea30-e7d2-4112-89d6-8924b371160e | patch-craft-video-denoising-by-deep-modeling | 2103.13767 | null | https://arxiv.org/abs/2103.13767v2 | https://arxiv.org/pdf/2103.13767v2.pdf | Patch Craft: Video Denoising by Deep Modeling and Patch Matching | The non-local self-similarity property of natural images has been exploited extensively for solving various image processing problems. When it comes to video sequences, harnessing this force is even more beneficial due to the temporal redundancy. In the context of image and video denoising, many classically-oriented algorithms employ self-similarity, splitting the data into overlapping patches, gathering groups of similar ones and processing these together somehow. With the emergence of convolutional neural networks (CNN), the patch-based framework has been abandoned. Most CNN denoisers operate on the whole image, leveraging non-local relations only implicitly by using a large receptive field. This work proposes a novel approach for leveraging self-similarity in the context of video denoising, while still relying on a regular convolutional architecture. We introduce a concept of patch-craft frames - artificial frames that are similar to the real ones, built by tiling matched patches. Our algorithm augments video sequences with patch-craft frames and feeds them to a CNN. We demonstrate the substantial boost in denoising performance obtained with the proposed approach. | ['Peyman Milanfar', 'Michael Elad', 'Gregory Vaksman'] | 2021-03-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Vaksman_Patch_Craft_Video_Denoising_by_Deep_Modeling_and_Patch_Matching_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Vaksman_Patch_Craft_Video_Denoising_by_Deep_Modeling_and_Patch_Matching_ICCV_2021_paper.pdf | iccv-2021-1 | ['color-image-denoising', 'video-denoising', 'patch-matching'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.26090479e-01 -7.00908303e-02 2.12125346e-01 -3.32875967e-01
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bc9d55e3-40d8-4af1-9f4a-b93c0b2fa4c2 | synthetic-data-generation-and-multi-task | null | null | https://aclanthology.org/2021.wnut-1.29 | https://aclanthology.org/2021.wnut-1.29.pdf | Synthetic Data Generation and Multi-Task Learning for Extracting Temporal Information from Health-Related Narrative Text | Extracting temporal information is critical to process health-related text. Temporal information extraction is a challenging task for language models because it requires processing both texts and numbers. Moreover, the fundamental challenge is how to obtain a large-scale training dataset. To address this, we propose a synthetic data generation algorithm. Also, we propose a novel multi-task temporal information extraction model and investigate whether multi-task learning can contribute to performance improvement by exploiting additional training signals with the existing training data. For experiments, we collected a custom dataset containing unstructured texts with temporal information of sleep-related activities. Experimental results show that utilising synthetic data can improve the performance when the augmentation factor is 3. The results also show that when multi-task learning is used with an appropriate amount of synthetic data, the performance can significantly improve from 82. to 88.6 and from 83.9 to 91.9 regarding micro-and macro-average exact match scores of normalised time prediction, respectively. | ['Bart Vanrumste', 'Stijn Luca', 'Dietwig Lowet', 'Heereen Shim'] | null | null | null | null | wnut-acl-2021-11 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 5.73366404e-01 -9.07365829e-02 -1.99442953e-01 -3.75165969e-01
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1d0dddb5-afd3-4a42-8e64-5e7c9f489f0c | capacitance-resistance-model-and-recurrent | 2109.08779 | null | https://arxiv.org/abs/2109.08779v1 | https://arxiv.org/pdf/2109.08779v1.pdf | Capacitance Resistance Model and Recurrent Neural Network for Well Connectivity Estimation : A Comparison Study | In this report, two commonly used data-driven models for predicting well production under a waterflood setting: the capacitance resistance model (CRM) and recurrent neural networks (RNN) are compared. Both models are completely data-driven and are intended to learn the reservoir behavior during a water flood from historical data. This report serves as a technical guide to the python-based implementation of the CRM model available from the associated GitHub repository. | ['Deepthi Sen'] | 2021-09-17 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [-1.79627553e-01 -2.03559309e-01 -1.84535190e-01 -2.38280356e-01
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ce11d37d-e2c8-45d8-9734-5732b16eed49 | peer-a-comprehensive-and-multi-task-benchmark | 2206.02096 | null | https://arxiv.org/abs/2206.02096v2 | https://arxiv.org/pdf/2206.02096v2.pdf | PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding | We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field. In this paper, we propose such a benchmark called PEER, a comprehensive and multi-task benchmark for Protein sEquence undERstanding. PEER provides a set of diverse protein understanding tasks including protein function prediction, protein localization prediction, protein structure prediction, protein-protein interaction prediction, and protein-ligand interaction prediction. We evaluate different types of sequence-based methods for each task including traditional feature engineering approaches, different sequence encoding methods as well as large-scale pre-trained protein language models. In addition, we also investigate the performance of these methods under the multi-task learning setting. Experimental results show that large-scale pre-trained protein language models achieve the best performance for most individual tasks, and jointly training multiple tasks further boosts the performance. The datasets and source codes of this benchmark are all available at https://github.com/DeepGraphLearning/PEER_Benchmark | ['Jian Tang', 'Runcheng Liu', 'Chang Ma', 'Yangtian Zhang', 'Zhaocheng Zhu', 'Jiarui Lu', 'Zuobai Zhang', 'Minghao Xu'] | 2022-06-05 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 2.84120739e-01 -3.77762467e-01 -2.65744269e-01 -4.97232676e-01
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da4ce8fe-ac37-4761-a100-d9d4655b2056 | bridging-unpaired-facial-photos-and-sketches | 2102.00635 | null | https://arxiv.org/abs/2102.00635v3 | https://arxiv.org/pdf/2102.00635v3.pdf | Bridging Unpaired Facial Photos And Sketches By Line-drawings | In this paper, we propose a novel method to learn face sketch synthesis models by using unpaired data. Our main idea is bridging the photo domain $\mathcal{X}$ and the sketch domain $Y$ by using the line-drawing domain $\mathcal{Z}$. Specially, we map both photos and sketches to line-drawings by using a neural style transfer method, i.e. $F: \mathcal{X}/\mathcal{Y} \mapsto \mathcal{Z}$. Consequently, we obtain \textit{pseudo paired data} $(\mathcal{Z}, \mathcal{Y})$, and can learn the mapping $G:\mathcal{Z} \mapsto \mathcal{Y}$ in a supervised learning manner. In the inference stage, given a facial photo, we can first transfer it to a line-drawing and then to a sketch by $G \circ F$. Additionally, we propose a novel stroke loss for generating different types of strokes. Our method, termed sRender, accords well with human artists' rendering process. Experimental results demonstrate that sRender can generate multi-style sketches, and significantly outperforms existing unpaired image-to-image translation methods. | ['Lingna Dai', 'Jingjie Zhu', 'Xiang Li', 'Meimei Shang', 'Fei Gao'] | 2021-02-01 | null | null | null | null | ['face-sketch-synthesis'] | ['computer-vision'] | [ 5.87379217e-01 -9.82585456e-03 2.13221572e-02 -5.38929343e-01
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994bc695-57ef-44c5-a9c3-18ecd1059695 | mitigating-dataset-harms-requires-stewardship | 2108.02922 | null | https://arxiv.org/abs/2108.02922v2 | https://arxiv.org/pdf/2108.02922v2.pdf | Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers | Machine learning datasets have elicited concerns about privacy, bias, and unethical applications, leading to the retraction of prominent datasets such as DukeMTMC, MS-Celeb-1M, and Tiny Images. In response, the machine learning community has called for higher ethical standards in dataset creation. To help inform these efforts, we studied three influential but ethically problematic face and person recognition datasets -- Labeled Faces in the Wild (LFW), MS-Celeb-1M, and DukeMTM -- by analyzing nearly 1000 papers that cite them. We found that the creation of derivative datasets and models, broader technological and social change, the lack of clarity of licenses, and dataset management practices can introduce a wide range of ethical concerns. We conclude by suggesting a distributed approach to harm mitigation that considers the entire life cycle of a dataset. | ['Arvind Narayanan', 'Arunesh Mathur', 'Kenny Peng'] | 2021-08-06 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 2.20689207e-01 4.53444451e-01 -2.11143255e-01 -7.77170897e-01
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1535ebfd-7ec5-4a7d-b749-16eb963227f8 | ethically-aligned-deep-learning-unbiased | 2111.05149 | null | https://arxiv.org/abs/2111.05149v1 | https://arxiv.org/pdf/2111.05149v1.pdf | Ethically aligned Deep Learning: Unbiased Facial Aesthetic Prediction | Facial beauty prediction (FBP) aims to develop a machine that automatically makes facial attractiveness assessment. In the past those results were highly correlated with human ratings, therefore also with their bias in annotating. As artificial intelligence can have racist and discriminatory tendencies, the cause of skews in the data must be identified. Development of training data and AI algorithms that are robust against biased information is a new challenge for scientists. As aesthetic judgement usually is biased, we want to take it one step further and propose an Unbiased Convolutional Neural Network for FBP. While it is possible to create network models that can rate attractiveness of faces on a high level, from an ethical point of view, it is equally important to make sure the model is unbiased. In this work, we introduce AestheticNet, a state-of-the-art attractiveness prediction network, which significantly outperforms competitors with a Pearson Correlation of 0.9601. Additionally, we propose a new approach for generating a bias-free CNN to improve fairness in machine learning. | ['Matthias Rätsch', 'Xueping Su', 'Tobias Gerlach', 'Leping Peng', 'Thomas Weber', 'Michael Danner'] | 2021-11-09 | null | null | null | null | ['facial-beauty-prediction'] | ['computer-vision'] | [ 8.08958523e-03 6.69318080e-01 -5.61769456e-02 -9.40126479e-01
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d4a24b13-2b42-4e1d-ae80-f468b450d52b | towards-emotion-aided-multi-modal-dialogue | null | null | https://aclanthology.org/2020.acl-main.402 | https://aclanthology.org/2020.acl-main.402.pdf | Towards Emotion-aided Multi-modal Dialogue Act Classification | The task of Dialogue Act Classification (DAC) that purports to capture communicative intent has been studied extensively. But these studies limit themselves to text. Non-verbal features (change of tone, facial expressions etc.) can provide cues to identify DAs, thus stressing the benefit of incorporating multi-modal inputs in the task. Also, the emotional state of the speaker has a substantial effect on the choice of the dialogue act, since conversations are often influenced by emotions. Hence, the effect of emotion too on automatic identification of DAs needs to be studied. In this work, we address the role of \textit{both} multi-modality and emotion recognition (ER) in DAC. DAC and ER help each other by way of multi-task learning. One of the major contributions of this work is a new dataset- multimodal Emotion aware Dialogue Act dataset called EMOTyDA, collected from open-sourced dialogue datasets. To demonstrate the utility of EMOTyDA, we build an attention based (self, inter-modal, inter-task) multi-modal, multi-task Deep Neural Network (DNN) for joint learning of DAs and emotions. We show empirically that multi-modality and multi-tasking achieve better performance of DAC compared to uni-modal and single task DAC variants. | ['Sriparna Saha', 'Aditya Patra', 'Tulika Saha', 'Pushpak Bhattacharyya'] | 2020-07-01 | null | null | null | acl-2020-6 | ['dialogue-act-classification'] | ['natural-language-processing'] | [-1.32588357e-01 -6.10599108e-02 1.58735782e-01 -5.58984876e-01
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1654cd7b-a7db-48fb-99eb-bc48698c4f99 | adversarial-audio-super-resolution-with | null | null | https://openreview.net/forum?id=H1eH4n09KX | https://openreview.net/pdf?id=H1eH4n09KX | Adversarial Audio Super-Resolution with Unsupervised Feature Losses | Neural network-based methods have recently demonstrated state-of-the-art results on image synthesis and super-resolution tasks, in particular by using variants of generative adversarial networks (GANs) with supervised feature losses. Nevertheless, previous feature loss formulations rely on the availability of large auxiliary classifier networks, and labeled datasets that enable such classifiers to be trained. Furthermore, there has been comparatively little work to explore the applicability of GAN-based methods to domains other than images and video. In this work we explore a GAN-based method for audio processing, and develop a convolutional neural network architecture to perform audio super-resolution. In addition to several new architectural building blocks for audio processing, a key component of our approach is the use of an autoencoder-based loss that enables training in the GAN framework, with feature losses derived from unlabeled data. We explore the impact of our architectural choices, and demonstrate significant improvements over previous works in terms of both objective and perceptual quality. | ['Visvesh Sathe', 'Sung Kim'] | 2018-09-27 | null | null | null | null | ['audio-super-resolution', 'audio-super-resolution'] | ['audio', 'music'] | [ 6.83056295e-01 2.50573188e-01 1.09208770e-01 -1.46871299e-01
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b419d3f7-c834-446a-9261-063a4d216fe0 | harmonic-quantum-neural-networks | 2212.07462 | null | https://arxiv.org/abs/2212.07462v1 | https://arxiv.org/pdf/2212.07462v1.pdf | Harmonic (Quantum) Neural Networks | Harmonic functions are abundant in nature, appearing in limiting cases of Maxwell's, Navier-Stokes equations, the heat and the wave equation. Consequently, there are many applications of harmonic functions, spanning applications from industrial process optimisation to robotic path planning and the calculation of first exit times of random walks. Despite their ubiquity and relevance, there have been few attempts to develop effective means of representing harmonic functions in the context of machine learning architectures, either in machine learning on classical computers, or in the nascent field of quantum machine learning. Architectures which impose or encourage an inductive bias towards harmonic functions would facilitate data-driven modelling and the solution of inverse problems in a range of applications. For classical neural networks, it has already been established how leveraging inductive biases can in general lead to improved performance of learning algorithms. The introduction of such inductive biases within a quantum machine learning setting is instead still in its nascent stages. In this work, we derive exactly-harmonic (conventional- and quantum-) neural networks in two dimensions for simply-connected domains by leveraging the characteristics of holomorphic complex functions. We then demonstrate how these can be approximately extended to multiply-connected two-dimensional domains using techniques inspired by domain decomposition in physics-informed neural networks. We further provide architectures and training protocols to effectively impose approximately harmonic constraints in three dimensions and higher, and as a corollary we report divergence-free network architectures in arbitrary dimensions. Our approaches are demonstrated with applications to heat transfer, electrostatics and robot navigation, with comparisons to physics-informed neural networks included. | ['Vincent E. Elfving', 'Jeong-il Kye', 'Brad Kim', 'Yunjun Choi', 'Hyukgeun Cha', 'Seong-hyok Kim', 'Chul Lee', 'Mario Dagrada', 'Antonio A. Gentile', 'Atiyo Ghosh'] | 2022-12-14 | null | null | null | null | ['robot-navigation'] | ['robots'] | [ 5.81273019e-01 4.16535318e-01 3.00464816e-02 -2.35877529e-01
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bdc81b91-6edb-45e6-8c76-55374e57b022 | meta-learning-enabled-score-based-generative | 2305.02509 | null | https://arxiv.org/abs/2305.02509v1 | https://arxiv.org/pdf/2305.02509v1.pdf | Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI | Magnetic resonance imaging (MRI) is known to have reduced signal-to-noise ratios (SNR) at lower field strengths, leading to signal degradation when producing a low-field MRI image from a high-field one. Therefore, reconstructing a high-field-like image from a low-field MRI is a complex problem due to the ill-posed nature of the task. Additionally, obtaining paired low-field and high-field MR images is often not practical. We theoretically uncovered that the combination of these challenges renders conventional deep learning methods that directly learn the mapping from a low-field MR image to a high-field MR image unsuitable. To overcome these challenges, we introduce a novel meta-learning approach that employs a teacher-student mechanism. Firstly, an optimal-transport-driven teacher learns the degradation process from high-field to low-field MR images and generates pseudo-paired high-field and low-field MRI images. Then, a score-based student solves the inverse problem of reconstructing a high-field-like MR image from a low-field MRI within the framework of iterative regularization, by learning the joint distribution of pseudo-paired images to act as a regularizer. Experimental results on real low-field MRI data demonstrate that our proposed method outperforms state-of-the-art unpaired learning methods. | ['Dong Liang', 'Haifeng Wang', 'Yanjie Zhu', 'Qingyong Zhu', 'Jing Cheng', 'Yuanyuan Liu', 'Chentao Cao', 'Congcong Liu', 'Zhuo-Xu Cui'] | 2023-05-04 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 7.45159149e-01 -9.40207615e-02 3.12278420e-03 -4.96411443e-01
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8a6ab022-ef84-4ec3-be87-16aeeb845928 | domain-adaptation-through-synthesis-for | 1804.10094 | null | http://arxiv.org/abs/1804.10094v1 | http://arxiv.org/pdf/1804.10094v1.pdf | Domain Adaptation through Synthesis for Unsupervised Person Re-identification | Drastic variations in illumination across surveillance cameras make the
person re-identification problem extremely challenging. Current large scale
re-identification datasets have a significant number of training subjects, but
lack diversity in lighting conditions. As a result, a trained model requires
fine-tuning to become effective under an unseen illumination condition. To
alleviate this problem, we introduce a new synthetic dataset that contains
hundreds of illumination conditions. Specifically, we use 100 virtual humans
illuminated with multiple HDR environment maps which accurately model realistic
indoor and outdoor lighting. To achieve better accuracy in unseen illumination
conditions we propose a novel domain adaptation technique that takes advantage
of our synthetic data and performs fine-tuning in a completely unsupervised
way. Our approach yields significantly higher accuracy than semi-supervised and
unsupervised state-of-the-art methods, and is very competitive with supervised
techniques. | ['Jean-Francois Lalonde', 'Peter Carr', 'Slawomir Bak'] | 2018-04-26 | domain-adaptation-through-synthesis-for-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Slawomir_Bak_Domain_Adaptation_through_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Slawomir_Bak_Domain_Adaptation_through_ECCV_2018_paper.pdf | eccv-2018-9 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 2.36713797e-01 -5.12544930e-01 2.86233932e-01 -6.30779803e-01
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91b31a04-8c94-4b14-8b59-7e40b394b95e | interactive-evaluation-of-dialog-track-at | 2207.14403 | null | https://arxiv.org/abs/2207.14403v1 | https://arxiv.org/pdf/2207.14403v1.pdf | Interactive Evaluation of Dialog Track at DSTC9 | The ultimate goal of dialog research is to develop systems that can be effectively used in interactive settings by real users. To this end, we introduced the Interactive Evaluation of Dialog Track at the 9th Dialog System Technology Challenge. This track consisted of two sub-tasks. The first sub-task involved building knowledge-grounded response generation models. The second sub-task aimed to extend dialog models beyond static datasets by assessing them in an interactive setting with real users. Our track challenges participants to develop strong response generation models and explore strategies that extend them to back-and-forth interactions with real users. The progression from static corpora to interactive evaluation introduces unique challenges and facilitates a more thorough assessment of open-domain dialog systems. This paper provides an overview of the track, including the methodology and results. Furthermore, it provides insights into how to best evaluate open-domain dialog models | ['Maxine Eskenazi', 'David Traum', 'Seyed Hossein Alavi', 'Carla Gordon', 'Yulan Feng', 'Shikib Mehri'] | 2022-07-28 | null | https://aclanthology.org/2022.lrec-1.616 | https://aclanthology.org/2022.lrec-1.616.pdf | lrec-2022-6 | ['interactive-evaluation-of-dialog', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-1.76201820e-01 6.07609272e-01 1.90709025e-01 -6.87232554e-01
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14806eb7-3aae-4585-aaac-a5bf2238b19d | a-novel-local-binary-pattern-based-blind | 2101.06383 | null | https://arxiv.org/abs/2101.06383v1 | https://arxiv.org/pdf/2101.06383v1.pdf | A Novel Local Binary Pattern Based Blind Feature Image Steganography | Steganography methods in general terms tend to embed more and more secret bits in the cover images. Most of these methods are designed to embed secret information in such a way that the change in the visual quality of the resulting stego image is not detectable. There exists some methods which preserve the global structure of the cover after embedding. However, the embedding capacity of these methods is very less. In this paper a novel feature based blind image steganography technique is proposed, which preserves the LBP (Local binary pattern) feature of the cover with comparable embedding rates. Local binary pattern is a well known image descriptor used for image representation. The proposed scheme computes the local binary pattern to hide the bits of the secret image in such a way that the local relationship that exists in the cover are preserved in the resulting stego image. The performance of the proposed steganography method has been tested on several images of different types to show the robustness. State of the art LSB based steganography methods are compared with the proposed method to show the effectiveness of feature based image steganography | ['Anand Singh Jalal', 'Soumendu Chakraborty'] | 2021-01-16 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 6.67401671e-01 1.04373433e-02 8.89257491e-02 -1.20965587e-02
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d6eb50df-7b2f-4cc7-abee-f4a0b6db3e1c | lgpsolver-solving-logic-grid-puzzles | null | null | https://aclanthology.org/2020.findings-emnlp.100 | https://aclanthology.org/2020.findings-emnlp.100.pdf | LGPSolver - Solving Logic Grid Puzzles Automatically | Logic grid puzzle (LGP) is a type of word problem where the task is to solve a problem in logic. Constraints for the problem are given in the form of textual clues. Once these clues are transformed into formal logic, a deductive reasoning process provides the solution. Solving logic grid puzzles in a fully automatic manner has been a challenge since a precise understanding of clues is necessary to develop the corresponding formal logic representation. To meet this challenge, we propose a solution that uses a DistilBERT-based classifier to classify a clue into one of the predefined predicate types for logic grid puzzles. Another novelty of the proposed solution is the recognition of comparison structures in clues. By collecting comparative adjectives from existing dictionaries and utilizing a semantic framework to catch comparative quantifiers, the semantics of clues concerning comparison structures are better understood, ensuring conversion to correct logic representation. Our approach solves logic grid puzzles in a fully automated manner with 100{\%} accuracy on the given puzzle datasets and outperforms state-of-the-art solutions by a large margin. | ['Selma Tekir', 'Elgun Jabrayilzade'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['logic-grid-puzzle', 'formal-logic'] | ['miscellaneous', 'reasoning'] | [ 1.48577899e-01 1.96849167e-01 -2.81009942e-01 -2.07829490e-01
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363e5c78-a6e7-4c58-9215-44bfe5d41373 | encoding-carbon-emission-flow-in-energy | 2305.13538 | null | https://arxiv.org/abs/2305.13538v1 | https://arxiv.org/pdf/2305.13538v1.pdf | Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach | Decarbonizing the energy supply is essential and urgent to mitigate the increasingly visible climate change. Its basis is identifying emission responsibility during power allocation by the carbon emission flow (CEF) model. However, the main challenge of CEF application is the intractable nonlinear relationship between carbon emission and power allocation. So this paper leverages the high approximation capability and the mixed-integer linear programming (MILP) representability of the deep neural networks to tackle the complex CEF model in carbon-electricity coordinated optimization. The compact constraint learning approach is proposed to learn the mapping from power injection to bus emission with sparse neural networks (SNNs). Then the trained SNNs are transformed equivalently as MILP constraints in the downstream optimization. In light of the ``high emission with high price'' principle, the blocked carbon price mechanism is designed to price emissions from the demand side. Based on the constraint learning and mechanism design, this paper proposes the carbon-aware energy management model in the tractable MILP form to unlock the carbon reduction potential from the demand side. The case study verifies the approximation accuracy and sparsity of SNN with fewer parameters for accelerating optimization solution and reduction effectiveness of demand-side capability for mitigating emission. | ['Hongbin Sun', 'Yinliang Xu', 'Linwei Sang'] | 2023-05-22 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 8.77784193e-02 -8.10067728e-02 -7.41989970e-01 -7.27543458e-02
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217ba661-66ea-4f6e-9b11-a5267ab4f1e4 | psa-det3d-pillar-set-abstraction-for-3d | 2210.10983 | null | https://arxiv.org/abs/2210.10983v2 | https://arxiv.org/pdf/2210.10983v2.pdf | PSA-Det3D: Pillar Set Abstraction for 3D object Detection | Small object detection for 3D point cloud is a challenging problem because of two limitations: (1) Perceiving small objects is much more diffcult than normal objects due to the lack of valid points. (2) Small objects are easily blocked which breaks the shape of their meshes in 3D point cloud. In this paper, we propose a pillar set abstraction (PSA) and foreground point compensation (FPC) and design a point-based detection network, PSA-Det3D, to improve the detection performance for small object. The PSA embeds a pillar query operation on the basis of set abstraction (SA) to expand its receptive field of the network, which can aggregate point-wise features effectively. To locate more occluded objects, we persent a proposal generation layer consisting of a foreground point segmentation and a FPC module. Both the foreground points and the estimated centers are finally fused together to generate the detection result. The experiments on the KITTI 3D detection benchmark show that our proposed PSA-Det3D outperforms other algorithms with high accuracy for small object detection. | ['Haifeng Hu', 'Dihu Chena', 'Zhijie Zheng', 'Jingwen Zhao', 'Zhicong Huang'] | 2022-10-20 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 1.12904459e-01 -2.32532844e-01 3.51091951e-01 -1.68435648e-01
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64c9de21-7007-44b9-9d7f-a167d5efca8d | data-augmentation-with-adversarial-training | null | null | https://aclanthology.org/2021.acl-long.401 | https://aclanthology.org/2021.acl-long.401.pdf | Data Augmentation with Adversarial Training for Cross-Lingual NLI | Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a cross-lingual model that can then be applied to multiple new languages. In practice, however, we still face the problem of scarce labeled data, leading to subpar results. In this paper, we propose a novel data augmentation strategy for better cross-lingual natural language inference by enriching the data to reflect more diversity in a semantically faithful way. To this end, we propose two methods of training a generative model to induce synthesized examples, and then leverage the resulting data using an adversarial training regimen for more robustness. In a series of detailed experiments, we show that this fruitful combination leads to substantial gains in cross-lingual inference. | ['Gerard de Melo', 'Dongkuan Xu', 'Zuohui Fu', 'Yaxin Zhu', 'Xin Dong'] | 2021-08-01 | null | null | null | acl-2021-5 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 2.75019467e-01 1.96854487e-01 -3.55137616e-01 -4.92804199e-01
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5c172387-1f7e-4ce4-bb73-981650c4470a | template-free-prompt-tuning-for-few-shot-ner-1 | null | null | https://openreview.net/forum?id=ocsgIiRIxxO | https://openreview.net/pdf?id=ocsgIiRIxxO | Template-free Prompt Tuning for Few-shot NER | Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words. However, when applied to token-level labeling tasks such as NER, it would be time-consuming to enumerate the template queries over all potential entity spans. In this work, we propose a more elegant method to reformulate NER tasks as LM problems without any templates. Specifically, we discard the template construction process while maintaining the word prediction paradigm of pre-training models to predict a class-related pivot word (or label word) at the entity position. Meanwhile, we also explore principled ways to automatically search for appropriate label words that the pre-trained models can easily adapt to. While avoiding the complicated template-based process, the proposed LM objective also reduces the gap between different objectives used in pre-training and fine-tuning, thus it can better benefit the few-shot performance. Experimental results demonstrate the effectiveness of the proposed method over bert-tagger and template-based method under few-shot settings. Moreover, the decoding speed of the proposed method is up to 1930.12 times faster than the template-based method. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['few-shot-ner'] | ['natural-language-processing'] | [ 2.68267810e-01 7.26415738e-02 4.68473695e-03 -3.38352978e-01
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a1e059c1-b2cb-452f-a356-5750eb7f4928 | a-dataset-of-reverberant-spatial-sound-scenes | 2006.01919 | null | https://arxiv.org/abs/2006.01919v1 | https://arxiv.org/pdf/2006.01919v1.pdf | A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection | This report presents the dataset and the evaluation setup of the Sound Event Localization & Detection (SELD) task for the DCASE 2020 Challenge. The SELD task refers to the problem of trying to simultaneously classify a known set of sound event classes, detect their temporal activations, and estimate their spatial directions or locations while they are active. To train and test SELD systems, datasets of diverse sound events occurring under realistic acoustic conditions are needed. Compared to the previous challenge, a significantly more complex dataset was created for DCASE 2020. The two key differences are a more diverse range of acoustical conditions, and dynamic conditions, i.e. moving sources. The spatial sound scenes are created using real room impulse responses captured in a continuous manner with a slowly moving excitation source. Both static and moving sound events are synthesized from them. Ambient noise recorded on location is added to complete the generation of scene recordings. A baseline SELD method accompanies the dataset, based on a convolutional recurrent neural network, to provide benchmark scores for the task. The baseline is an updated version of the one used in the previous challenge, with input features and training modifications to improve its performance. | ['Archontis Politis', 'Sharath Adavanne', 'Tuomas Virtanen'] | 2020-06-02 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 2.85553843e-01 -6.36252284e-01 6.60198331e-01 -1.84819072e-01
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3d987284-68ad-4493-97f0-2c0b6ab4fb3f | on-the-state-of-german-abstractive-text | 2301.07095 | null | https://arxiv.org/abs/2301.07095v1 | https://arxiv.org/pdf/2301.07095v1.pdf | On the State of German (Abstractive) Text Summarization | With recent advancements in the area of Natural Language Processing, the focus is slowly shifting from a purely English-centric view towards more language-specific solutions, including German. Especially practical for businesses to analyze their growing amount of textual data are text summarization systems, which transform long input documents into compressed and more digestible summary texts. In this work, we assess the particular landscape of German abstractive text summarization and investigate the reasons why practically useful solutions for abstractive text summarization are still absent in industry. Our focus is two-fold, analyzing a) training resources, and b) publicly available summarization systems. We are able to show that popular existing datasets exhibit crucial flaws in their assumptions about the original sources, which frequently leads to detrimental effects on system generalization and evaluation biases. We confirm that for the most popular training dataset, MLSUM, over 50% of the training set is unsuitable for abstractive summarization purposes. Furthermore, available systems frequently fail to compare to simple baselines, and ignore more effective and efficient extractive summarization approaches. We attribute poor evaluation quality to a variety of different factors, which are investigated in more detail in this work: A lack of qualitative (and diverse) gold data considered for training, understudied (and untreated) positional biases in some of the existing datasets, and the lack of easily accessible and streamlined pre-processing strategies or analysis tools. We provide a comprehensive assessment of available models on the cleaned datasets, and find that this can lead to a reduction of more than 20 ROUGE-1 points during evaluation. The code for dataset filtering and reproducing results can be found online at https://github.com/dennlinger/summaries | ['Michael Gertz', 'Jing Fan', 'Dennis Aumiller'] | 2023-01-17 | null | null | null | null | ['abstractive-text-summarization', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.36313003e-01 1.74023449e-01 -2.07894832e-01 -2.24515930e-01
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e49f7209-e9c5-4070-8545-0f7d3218b060 | tax-free-3dmm-conditional-face-generation | 2305.13460 | null | https://arxiv.org/abs/2305.13460v2 | https://arxiv.org/pdf/2305.13460v2.pdf | 'Tax-free' 3DMM Conditional Face Generation | 3DMM conditioned face generation has gained traction due to its well-defined controllability; however, the trade-off is lower sample quality: Previous works such as DiscoFaceGAN and 3D-FM GAN show a significant FID gap compared to the unconditional StyleGAN, suggesting that there is a quality tax to pay for controllability. In this paper, we challenge the assumption that quality and controllability cannot coexist. To pinpoint the previous issues, we mathematically formalize the problem of 3DMM conditioned face generation. Then, we devise simple solutions to the problem under our proposed framework. This results in a new model that effectively removes the quality tax between 3DMM conditioned face GANs and the unconditional StyleGAN. | ['James Tompkin', 'Yue Wang', 'Xinjie Yi', 'Zhiqiu Yu', 'Yiwen Huang'] | 2023-05-22 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 4.85490859e-01 6.07059896e-01 -3.19183081e-01 -8.93340334e-02
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817510cc-4b57-454b-a16b-27653ada0333 | clvos23-a-long-video-object-segmentation | 2304.04259 | null | https://arxiv.org/abs/2304.04259v1 | https://arxiv.org/pdf/2304.04259v1.pdf | CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning | Continual learning in real-world scenarios is a major challenge. A general continual learning model should have a constant memory size and no predefined task boundaries, as is the case in semi-supervised Video Object Segmentation (VOS), where continual learning challenges particularly present themselves in working on long video sequences. In this article, we first formulate the problem of semi-supervised VOS, specifically online VOS, as a continual learning problem, and then secondly provide a public VOS dataset, CLVOS23, focusing on continual learning. Finally, we propose and implement a regularization-based continual learning approach on LWL, an existing online VOS baseline, to demonstrate the efficacy of continual learning when applied to online VOS and to establish a CLVOS23 baseline. We apply the proposed baseline to the Long Videos dataset as well as to two short video VOS datasets, DAVIS16 and DAVIS17. To the best of our knowledge, this is the first time that VOS has been defined and addressed as a continual learning problem. | ['Paul Fieguth', 'Zeyad Moustafa', 'Amir Nazemi'] | 2023-04-09 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.31348118e-01 -1.92237958e-01 -5.11011839e-01 -2.68739164e-01
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9021ed4d-86cc-4156-b076-00595659237e | wavelet-domain-residual-network-wavresnet-for | 1703.01383 | null | http://arxiv.org/abs/1703.01383v1 | http://arxiv.org/pdf/1703.01383v1.pdf | Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction | Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT
are computationally complex because of the repeated use of the forward and
backward projection. Inspired by this success of deep learning in computer
vision applications, we recently proposed a deep convolutional neural network
(CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT
Grand Challenge. However, some of the texture are not fully recovered, which
was unfamiliar to some radiologists. To cope with this problem, here we propose
a direct residual learning approach on directional wavelet domain to solve this
problem and to improve the performance against previous work. In particular,
the new network estimates the noise of each input wavelet transform, and then
the de-noised wavelet coefficients are obtained by subtracting the noise from
the input wavelet transform bands. The experimental results confirm that the
proposed network has significantly improved performance, preserving the detail
texture of the original images. | ['Jong Chul Ye', 'Junhong Min', 'Eunhee Kang'] | 2017-03-04 | null | null | null | null | ['low-dose-x-ray-ct-reconstruction'] | ['medical'] | [ 2.57651240e-01 -6.02177206e-05 1.97688699e-01 -2.96863586e-01
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65dce070-9171-4990-9729-4bd020023806 | regression-trees-and-random-forest-based | 1606.07578 | null | http://arxiv.org/abs/1606.07578v1 | http://arxiv.org/pdf/1606.07578v1.pdf | Regression Trees and Random forest based feature selection for malaria risk exposure prediction | This paper deals with prediction of anopheles number, the main vector of
malaria risk, using environmental and climate variables. The variables
selection is based on an automatic machine learning method using regression
trees, and random forests combined with stratified two levels cross validation.
The minimum threshold of variables importance is accessed using the quadratic
distance of variables importance while the optimal subset of selected variables
is used to perform predictions. Finally the results revealed to be
qualitatively better, at the selection, the prediction , and the CPU time point
of view than those obtained by GLM-Lasso method. | ['Bienvenue Kouwayè'] | 2016-06-24 | null | null | null | null | ['malaria-risk-exposure-prediction'] | ['medical'] | [ 1.99868113e-01 -1.07580952e-01 -2.50401914e-01 -6.08209014e-01
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7e9a7163-72b7-4800-b897-d0a98ef7f948 | action-classification-with-locality | 1408.3810 | null | http://arxiv.org/abs/1408.3810v2 | http://arxiv.org/pdf/1408.3810v2.pdf | Action Classification with Locality-constrained Linear Coding | We propose an action classification algorithm which uses Locality-constrained
Linear Coding (LLC) to capture discriminative information of human body
variations in each spatiotemporal subsequence of a video sequence. Our proposed
method divides the input video into equally spaced overlapping spatiotemporal
subsequences, each of which is decomposed into blocks and then cells. We use
the Histogram of Oriented Gradient (HOG3D) feature to encode the information in
each cell. We justify the use of LLC for encoding the block descriptor by
demonstrating its superiority over Sparse Coding (SC). Our sequence descriptor
is obtained via a logistic regression classifier with L2 regularization. We
evaluate and compare our algorithm with ten state-of-the-art algorithms on five
benchmark datasets. Experimental results show that, on average, our algorithm
gives better accuracy than these ten algorithms. | ['Ajmal Mian', 'Arif Mahmood', 'Hossein Rahmani', 'Du Huynh'] | 2014-08-17 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 3.91239762e-01 -5.95556736e-01 -6.83439374e-01 -1.12553872e-01
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a523db1d-8d23-4c82-b698-4cadef8504eb | sequential-randomized-smoothing-for-1 | 2112.03000 | null | https://arxiv.org/abs/2112.03000v2 | https://arxiv.org/pdf/2112.03000v2.pdf | Sequential Randomized Smoothing for Adversarially Robust Speech Recognition | While Automatic Speech Recognition has been shown to be vulnerable to adversarial attacks, defenses against these attacks are still lagging. Existing, naive defenses can be partially broken with an adaptive attack. In classification tasks, the Randomized Smoothing paradigm has been shown to be effective at defending models. However, it is difficult to apply this paradigm to ASR tasks, due to their complexity and the sequential nature of their outputs. Our paper overcomes some of these challenges by leveraging speech-specific tools like enhancement and ROVER voting to design an ASR model that is robust to perturbations. We apply adaptive versions of state-of-the-art attacks, such as the Imperceptible ASR attack, to our model, and show that our strongest defense is robust to all attacks that use inaudible noise, and can only be broken with very high distortion. | ['Bhiksha Raj', 'Raphael Olivier'] | 2021-11-05 | sequential-randomized-smoothing-for | https://aclanthology.org/2021.emnlp-main.514 | https://aclanthology.org/2021.emnlp-main.514.pdf | emnlp-2021-11 | ['robust-speech-recognition'] | ['speech'] | [ 4.72042561e-01 1.17955416e-01 2.26334363e-01 -1.32533893e-01
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65fb116c-ec31-47eb-90ab-703415529e7a | ms-gwnn-multi-scale-graph-wavelet-neural | 2012.14619 | null | https://arxiv.org/abs/2012.14619v1 | https://arxiv.org/pdf/2012.14619v1.pdf | MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis | Breast cancer is one of the most common cancers in women worldwide, and early detection can significantly reduce the mortality rate of breast cancer. It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer. And thus, it is the key to design an accurate computer-aided detection (CAD) system to capture multi-scale contextual features in a cancerous tissue. In this work, we present a novel graph convolutional neural network for histopathological image classification of breast cancer. The new method, named multi-scale graph wavelet neural network (MS-GWNN), leverages the localization property of spectral graph wavelet to perform multi-scale analysis. By aggregating features at different scales, MS-GWNN can encode the multi-scale contextual interactions in the whole pathological slide. Experimental results on two public datasets demonstrate the superiority of the proposed method. Moreover, through ablation studies, we find that multi-scale analysis has a significant impact on the accuracy of cancer diagnosis. | ['Quanzheng Li', 'Mo Zhang'] | 2020-12-29 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 3.40058625e-01 -5.08174598e-02 -2.48123422e-01 -2.12286845e-01
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22ea2a4d-5b8c-4423-89b8-b51d8c02cc24 | a-general-framework-for-multi-step-ahead | 2207.14219 | null | https://arxiv.org/abs/2207.14219v6 | https://arxiv.org/pdf/2207.14219v6.pdf | A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting | The exponential growth of machine learning (ML) has prompted a great deal of interest in quantifying the uncertainty of each prediction for a user-defined level of confidence since nowadays ML is increasingly being used in high-stakes settings. Reliable ML via prediction intervals (PIs) that take into account jointly the epistemic and aleatory uncertainty is therefore imperative and is a step towards increased trust in model forecasts. Conformal prediction (CP) is a lightweight distribution-free uncertainty quantification framework that works for any black-box model, yielding PIs that are valid under the mild assumption of exchangeability. CP-type methods are gaining popularity due to being easy to implement and computationally cheap; however, the exchangeability assumption immediately excludes time series forecasting from the stage. Although recent papers tackle distribution shift and asymptotic versions of CP, this is not enough for the general time series forecasting problem of producing H-step ahead valid PIs. To attain such a goal, we propose a new method called AEnbMIMOCQR (Adaptive ensemble batch multi-input multi-output conformalized quantile regression), which produces valid PIs asymptotically and is appropriate for heteroscedastic time series. We compare the proposed method against state-of-the-art competitive methods in the NN5 forecasting competition dataset. All the code and data to reproduce the experiments are made available. | ['José Moreira', 'Ana Maria Tomé', 'Martim Sousa'] | 2022-07-28 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-1.40644327e-01 1.25733256e-01 -8.08422565e-02 -6.30656660e-01
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322ca9a9-75ba-44a6-8004-8f00e8c0eee3 | transfer-reinforcement-learning-under | 2003.04427 | null | https://arxiv.org/abs/2003.04427v1 | https://arxiv.org/pdf/2003.04427v1.pdf | Transfer Reinforcement Learning under Unobserved Contextual Information | In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a demonstrator agent that has access to a context-aware policy and can generate transition and reward data based on that policy. These data constitute the experience of the demonstrator. Then, the goal is to transfer this experience, excluding the underlying contextual information, to a learner agent that does not have access to the environmental context, so that they can learn a control policy using fewer samples. It is well known that, disregarding the causal effect of the contextual information, can introduce bias in the transition and reward models estimated by the learner, resulting in a learned suboptimal policy. To address this challenge, in this paper, we develop a method to obtain causal bounds on the transition and reward functions using the demonstrator's data, which we then use to obtain causal bounds on the value functions. Using these value function bounds, we propose new Q learning and UCB-Q learning algorithms that converge to the true value function without bias. We provide numerical experiments for robot motion planning problems that validate the proposed value function bounds and demonstrate that the proposed algorithms can effectively make use of the data from the demonstrator to accelerate the learning process of the learner. | ['Yan Zhang', 'Michael M. Zavlanos'] | 2020-03-09 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 1.86815649e-01 3.85910094e-01 -3.85939568e-01 -1.33038551e-01
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aa00a4a4-2dd2-4d9c-aff1-76447066fe45 | stargraph-a-coarse-to-fine-representation | 2205.14209 | null | https://arxiv.org/abs/2205.14209v2 | https://arxiv.org/pdf/2205.14209v2.pdf | StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph | Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood. We propose a method named StarGraph, which gives a novel way to utilize the neighborhood information for large-scale knowledge graphs to obtain entity representations. An incomplete two-hop neighborhood subgraph for each target node is at first generated, then processed by a modified self-attention network to obtain the entity representation, which is used to replace the entity embedding in conventional methods. We achieved SOTA performance on ogbl-wikikg2 and got competitive results on fb15k-237. The experimental results proves that StarGraph is efficient in parameters, and the improvement made on ogbl-wikikg2 demonstrates its great effectiveness of representation learning on large-scale knowledge graphs. The code is now available at \url{https://github.com/hzli-ucas/StarGraph}. | ['Yuhui Yin', 'Yafeng Deng', 'Linhui Feng', 'Xiangrui Gao', 'Hongzhu Li'] | 2022-05-27 | null | null | null | null | ['entity-embeddings'] | ['methodology'] | [-5.64670742e-01 5.35496116e-01 -7.19297409e-01 -6.09397404e-02
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9e79fac0-9298-4848-9ba2-1f92de1f7466 | generalised-agent-for-solving-higher-board | 2212.12252 | null | https://arxiv.org/abs/2212.12252v1 | https://arxiv.org/pdf/2212.12252v1.pdf | Generalised agent for solving higher board states of tic tac toe using Reinforcement Learning | Tic Tac Toe is amongst the most well-known games. It has already been shown that it is a biased game, giving more chances to win for the first player leaving only a draw or a loss as possibilities for the opponent, assuming both the players play optimally. Thus on average majority of the games played result in a draw. The majority of the latest research on how to solve a tic tac toe board state employs strategies such as Genetic Algorithms, Neural Networks, Co-Evolution, and Evolutionary Programming. But these approaches deal with a trivial board state of 3X3 and very little research has been done for a generalized algorithm to solve 4X4,5X5,6X6 and many higher states. Even though an algorithm exists which is Min-Max but it takes a lot of time in coming up with an ideal move due to its recursive nature of implementation. A Sample has been created on this link \url{https://bk-tic-tac-toe.herokuapp.com/} to prove this fact. This is the main problem that this study is aimed at solving i.e providing a generalized algorithm(Approximate method, Learning-Based) for higher board states of tic tac toe to make precise moves in a short period. Also, the code changes needed to accommodate higher board states will be nominal. The idea is to pose the tic tac toe game as a well-posed learning problem. The study and its results are promising, giving a high win to draw ratio with each epoch of training. This study could also be encouraging for other researchers to apply the same algorithm to other similar board games like Minesweeper, Chess, and GO for finding efficient strategies and comparing the results. | ['Bhavuk Kalra'] | 2022-12-23 | null | null | null | null | ['board-games'] | ['playing-games'] | [-1.75643861e-01 2.07104087e-01 -8.95608887e-02 1.91668242e-01
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250d284f-6cec-4a9f-a497-c0beb36b9330 | rebuild-and-ensemble-exploring-defense-1 | 2203.14207 | null | https://arxiv.org/abs/2203.14207v2 | https://arxiv.org/pdf/2203.14207v2.pdf | Text Adversarial Purification as Defense against Adversarial Attacks | Adversarial purification is a successful defense mechanism against adversarial attacks without requiring knowledge of the form of the incoming attack. Generally, adversarial purification aims to remove the adversarial perturbations therefore can make correct predictions based on the recovered clean samples. Despite the success of adversarial purification in the computer vision field that incorporates generative models such as energy-based models and diffusion models, using purification as a defense strategy against textual adversarial attacks is rarely explored. In this work, we introduce a novel adversarial purification method that focuses on defending against textual adversarial attacks. With the help of language models, we can inject noise by masking input texts and reconstructing the masked texts based on the masked language models. In this way, we construct an adversarial purification process for textual models against the most widely used word-substitution adversarial attacks. We test our proposed adversarial purification method on several strong adversarial attack methods including Textfooler and BERT-Attack and experimental results indicate that the purification algorithm can successfully defend against strong word-substitution attacks. | ['Xipeng Qiu', 'Demin Song', 'Linyang Li'] | 2022-03-27 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 4.72316921e-01 -4.91269492e-03 5.24380267e-01 1.70726001e-01
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b5e407c5-ee34-492b-9088-5da1a7cb03cc | biomedical-multi-hop-question-answering-using | 2211.05351 | null | https://arxiv.org/abs/2211.05351v1 | https://arxiv.org/pdf/2211.05351v1.pdf | Biomedical Multi-hop Question Answering Using Knowledge Graph Embeddings and Language Models | Biomedical knowledge graphs (KG) are heterogenous networks consisting of biological entities as nodes and relations between them as edges. These entities and relations are extracted from millions of research papers and unified in a single resource. The goal of biomedical multi-hop question-answering over knowledge graph (KGQA) is to help biologist and scientist to get valuable insights by asking questions in natural language. Relevant answers can be found by first understanding the question and then querying the KG for right set of nodes and relationships to arrive at an answer. To model the question, language models such as RoBERTa and BioBERT are used to understand context from natural language question. One of the challenges in KGQA is missing links in the KG. Knowledge graph embeddings (KGE) help to overcome this problem by encoding nodes and edges in a dense and more efficient way. In this paper, we use a publicly available KG called Hetionet which is an integrative network of biomedical knowledge assembled from 29 different databases of genes, compounds, diseases, and more. We have enriched this KG dataset by creating a multi-hop biomedical question-answering dataset in natural language for testing the biomedical multi-hop question-answering system and this dataset will be made available to the research community. The major contribution of this research is an integrated system that combines language models with KG embeddings to give highly relevant answers to free-form questions asked by biologists in an intuitive interface. Biomedical multi-hop question-answering system is tested on this data and results are highly encouraging. | ['Mukta A. Paliwal', 'Shraddha S. Mane', 'Dattaraj J. Rao'] | 2022-11-10 | null | null | null | null | ['knowledge-graph-embeddings', 'multi-hop-question-answering', 'knowledge-graph-embeddings'] | ['graphs', 'knowledge-base', 'methodology'] | [-1.41000703e-01 5.97416401e-01 -9.28580910e-02 -2.74446338e-01
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d57cb622-45f8-465b-9a2e-44d41613ebb9 | cross-version-defect-prediction-with-class | 2212.14404 | null | https://arxiv.org/abs/2212.14404v1 | https://arxiv.org/pdf/2212.14404v1.pdf | Cross Version Defect Prediction with Class Dependency Embeddings | Software Defect Prediction aims at predicting which software modules are the most probable to contain defects. The idea behind this approach is to save time during the development process by helping find bugs early. Defect Prediction models are based on historical data. Specifically, one can use data collected from past software distributions, or Versions, of the same target application under analysis. Defect Prediction based on past versions is called Cross Version Defect Prediction (CVDP). Traditionally, Static Code Metrics are used to predict defects. In this work, we use the Class Dependency Network (CDN) as another predictor for defects, combined with static code metrics. CDN data contains structural information about the target application being analyzed. Usually, CDN data is analyzed using different handcrafted network measures, like Social Network metrics. Our approach uses network embedding techniques to leverage CDN information without having to build the metrics manually. In order to use the embeddings between versions, we incorporate different embedding alignment techniques. To evaluate our approach, we performed experiments on 24 software release pairs and compared it against several benchmark methods. In these experiments, we analyzed the performance of two different graph embedding techniques, three anchor selection approaches, and two alignment techniques. We also built a meta-model based on two different embeddings and achieved a statistically significant improvement in AUC of 4.7% (p < 0.002) over the baseline method. | ['Rami Puzis', 'Lior Rokach', 'Moti Cohen'] | 2022-12-29 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-1.73075035e-01 1.30182758e-01 -1.67266279e-01 -2.52276868e-01
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19b1cc54-73f1-4c63-9cdf-4d8684fc7a8e | evaluation-of-self-taught-learning-based | 2204.12624 | null | https://arxiv.org/abs/2204.12624v1 | https://arxiv.org/pdf/2204.12624v1.pdf | Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition | This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER). The idea is to create complementary representations promoting diversity by varying the autoencoders' initialization, architecture, and training data. SVM, Bagging, Random Forest, and a dynamic ensemble selection method are evaluated as final classification methods. Experimental results on Jaffe and Cohn-Kanade datasets using a leave-one-subject-out protocol show that FER methods based on the proposed diverse representations compare favorably against state-of-the-art approaches that also explore unsupervised feature learning. | ['Alessandro L. Koerich', 'Jean Paul Barddal', 'Alceu de S. Britto Jr.', 'Leonardo L. Veras', 'Bruna Delazeri'] | 2022-04-26 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 1.01990178e-01 1.70333445e-01 -8.53088871e-02 -8.75563025e-01
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82af3470-e34c-48c7-969f-5b6da1222aad | drvertgraduate-uncertainty-aware-deep | 1910.11777 | null | https://arxiv.org/abs/1910.11777v2 | https://arxiv.org/pdf/1910.11777v2.pdf | DR$\vert$GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images | Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and follow up of patients, but the screening process can be tiresome and prone to errors. Deep learning approaches have shown promising performance as computer-aided diagnosis(CAD) systems, but their black-box behaviour hinders the clinical application. We propose DR$\vert$GRADUATE, a novel deep learning-based DR grading CAD system that supports its decision by providing a medically interpretable explanation and an estimation of how uncertain that prediction is, allowing the ophthalmologist to measure how much that decision should be trusted. We designed DR$\vert$GRADUATE taking into account the ordinal nature of the DR grading problem. A novel Gaussian-sampling approach built upon a Multiple Instance Learning framework allow DR$\vert$GRADUATE to infer an image grade associated with an explanation map and a prediction uncertainty while being trained only with image-wise labels. DR$\vert$GRADUATE was trained on the Kaggle training set and evaluated across multiple datasets. In DR grading, a quadratic-weighted Cohen's kappa (QWK) between 0.71 and 0.84 was achieved in five different datasets. We show that high QWK values occur for images with low prediction uncertainty, thus indicating that this uncertainty is a valid measure of the predictions' quality. Further, bad quality images are generally associated with higher uncertainties, showing that images not suitable for diagnosis indeed lead to less trustworthy predictions. Additionally, tests on unfamiliar medical image data types suggest that DR$\vert$GRADUATE allows outlier detection. The attention maps generally highlight regions of interest for diagnosis. These results show the great potential of DR$\vert$GRADUATE as a second-opinion system in DR severity grading. | ['Aurélio Campilho', 'Ana Maria Mendonça', 'Ângela Carneiro', 'Luís Mendonça', 'Teresa Araújo', 'Carolina Maia', 'Susana Penas', 'Guilherme Aresta'] | 2019-10-25 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-1.51410070e-03 4.98105288e-01 -1.13546878e-01 -7.75285602e-01
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8.93753827e-01 -7.88888216e-01 -2.87652284e-01 -4.00538296e-01
-1.21946716e+00 2.86912978e-01 5.07275701e-01 -4.02149335e-02
8.30837011e-01 -8.75413954e-01 -9.15476382e-01 2.12558344e-01
6.27607584e-01 1.85395882e-01 5.15343070e-01 1.20492923e+00
-4.98332947e-01 2.28059158e-01 -8.72948244e-02 -1.10545182e+00
-1.22180533e+00 1.70153111e-01 7.59429216e-01 -8.54555592e-02
-6.17853343e-01 8.71261537e-01 2.17611473e-02 -8.79789442e-02
2.55467266e-01 -8.42535853e-01 -5.34292638e-01 1.60652012e-01
6.84433579e-01 5.24252057e-01 3.11455846e-01 -2.27649450e-01
-4.49821353e-02 8.70934963e-01 -2.30168194e-01 2.37645894e-01
1.15804100e+00 -1.65980071e-01 -4.35607024e-02 1.95267498e-01
6.36520922e-01 -2.80572921e-01 -1.29622221e+00 -5.22619579e-03
-1.40140519e-01 -7.15776086e-01 3.55741739e-01 -1.47931170e+00
-1.14765668e+00 1.01834738e+00 1.20636725e+00 2.54226416e-01
1.03500307e+00 1.05251390e-02 2.01562375e-01 1.08446650e-01
2.84717232e-01 -8.53548050e-01 -1.70825750e-01 -2.31330860e-02
8.86830568e-01 -1.78479207e+00 8.76890030e-03 -1.05764583e-01
-1.02218461e+00 1.21128201e+00 5.74143708e-01 -5.97364120e-02
4.97959346e-01 -1.49093136e-01 4.12242740e-01 -3.79792392e-01
-6.20956957e-01 -1.34211153e-01 7.15861320e-01 8.59164655e-01
3.63334298e-01 2.10060552e-01 -3.15547049e-01 6.39694273e-01
-9.33938101e-02 3.40619504e-01 7.84320056e-01 1.76706254e-01
-5.21866977e-01 -8.58598948e-01 -1.24071941e-01 1.13957953e+00
-3.28453571e-01 2.71092693e-04 -9.84427035e-02 4.58594292e-01
4.82093126e-01 7.95558929e-01 1.60508618e-01 -2.10031375e-01
3.68974507e-01 -1.98203161e-01 3.33401769e-01 -7.02667773e-01
-4.18046564e-01 -1.96046695e-01 1.39882118e-01 -6.45835996e-01
-2.48512059e-01 -4.93277133e-01 -1.15425825e+00 -2.18144149e-01
-2.60082275e-01 -6.80766106e-02 4.45710689e-01 8.54355097e-01
4.51433271e-01 5.29139519e-01 4.81294930e-01 -3.63838404e-01
-6.45489633e-01 -7.89985597e-01 -7.00826645e-01 2.26354569e-01
5.26979029e-01 -5.97752571e-01 -6.44743860e-01 -7.74082169e-02] | [15.674602508544922, -3.7103593349456787] |
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