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04e43d5e-b9ef-4924-aabe-cfa84dda29a4 | consistency-guided-scene-flow-estimation | 2006.11242 | null | https://arxiv.org/abs/2006.11242v2 | https://arxiv.org/pdf/2006.11242v2.pdf | Consistency Guided Scene Flow Estimation | Consistency Guided Scene Flow Estimation (CGSF) is a self-supervised framework for the joint reconstruction of 3D scene structure and motion from stereo video. The model takes two temporal stereo pairs as input, and predicts disparity and scene flow. The model self-adapts at test time by iteratively refining its predictions. The refinement process is guided by a consistency loss, which combines stereo and temporal photo-consistency with a geometric term that couples disparity and 3D motion. To handle inherent modeling error in the consistency loss (e.g. Lambertian assumptions) and for better generalization, we further introduce a learned, output refinement network, which takes the initial predictions, the loss, and the gradient as input, and efficiently predicts a correlated output update. In multiple experiments, including ablation studies, we show that the proposed model can reliably predict disparity and scene flow in challenging imagery, achieves better generalization than the state-of-the-art, and adapts quickly and robustly to unseen domains. | ['Luc van Gool', 'Yuhua Chen', 'Cordelia Schmid', 'Cristian Sminchisescu'] | 2020-06-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/157_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520120.pdf | eccv-2020-8 | ['scene-flow-estimation'] | ['computer-vision'] | [ 2.10199803e-01 -2.39346370e-01 -3.13787088e-02 -5.26918530e-01
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ac1ff35b-5c0d-4b6c-9665-4fba48d40985 | beyond-information-exchange-an-approach-to | 2212.10805 | null | https://arxiv.org/abs/2212.10805v1 | https://arxiv.org/pdf/2212.10805v1.pdf | Beyond Information Exchange: An Approach to Deploy Network Properties for Information Diffusion | Information diffusion in Online Social Networks is a new and crucial problem in social network analysis field and requires significant research attention. Efficient diffusion of information are of critical importance in diverse situations such as; pandemic prevention, advertising, marketing etc. Although several mathematical models have been developed till date, but previous works lacked systematic analysis and exploration of the influence of neighborhood for information diffusion. In this paper, we have proposed Common Neighborhood Strategy (CNS) algorithm for information diffusion that demonstrates the role of common neighborhood in information propagation throughout the network. The performance of CNS algorithm is evaluated on several real-world datasets in terms of diffusion speed and diffusion outspread and compared with several widely used information diffusion models. Empirical results show CNS algorithm enables better information diffusion both in terms of diffusion speed and diffusion outspread. | ['Ravi Kishore Devarapalli', 'Anupam Biswas', 'Soumita Das'] | 2022-12-21 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-1.54246330e-01 -1.86052784e-01 -4.50363398e-01 1.11026838e-01
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fa1519c6-4654-45a9-a987-8d24c4736b45 | slendergnn-accurate-robust-and-interpretable | 2210.04081 | null | https://arxiv.org/abs/2210.04081v4 | https://arxiv.org/pdf/2210.04081v4.pdf | Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining | How can we solve semi-supervised node classification in various graphs possibly with noisy features and structures? Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limited due to the difficulty of training, hyperparameter tuning, and the selection of a model itself. Einstein said that we should "make everything as simple as possible, but not simpler." We rephrase it into the careful simplicity principle: a carefully-designed simple model can surpass sophisticated ones in real-world graphs. Based on the principle, we propose SlimG for semi-supervised node classification, which exhibits four desirable properties: It is (a) accurate, winning or tying on 10 out of 13 real-world datasets; (b) robust, being the only one that handles all scenarios of graph data (homophily, heterophily, random structure, noisy features, etc.); (c) fast and scalable, showing up to 18 times faster training in million-scale graphs; and (d) interpretable, thanks to the linearity and sparsity. We explain the success of SlimG through a systematic study of the designs of existing GNNs, sanity checks, and comprehensive ablation studies. | ['Christos Faloutsos', 'Shubhranshu Shekhar', 'Meng-Chieh Lee', 'Jaemin Yoo'] | 2022-10-08 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 6.29991293e-02 3.40009928e-01 -2.92482972e-01 -2.11307779e-01
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2263778f-3cc9-45f2-b7b3-cb3000b406c0 | evaluation-of-non-negative-matrix | 2110.00418 | null | https://arxiv.org/abs/2110.00418v1 | https://arxiv.org/pdf/2110.00418v1.pdf | Evaluation of Non-Negative Matrix Factorization and n-stage Latent Dirichlet Allocation for Emotion Analysis in Turkish Tweets | With the development of technology, the use of social media has become quite common. Analyzing comments on social media in areas such as media and advertising plays an important role today. For this reason, new and traditional natural language processing methods are used to detect the emotion of these shares. In this paper, the Latent Dirichlet Allocation, namely LDA, and Non-Negative Matrix Factorization methods in topic modeling were used to determine which emotion the Turkish tweets posted via Twitter. In addition, the accuracy of a proposed n-level method based on LDA was analyzed. Dataset consists of 5 emotions, namely angry, fear, happy, sad and confused. NMF was the most successful method among all topic modeling methods in this study. Then, the F1-measure of Random Forest, Naive Bayes and Support Vector Machine methods was analyzed by obtaining a file suitable for Weka by using the word weights and class labels of the topics. Among the Weka results, the most successful method was n-stage LDA, and the most successful algorithm was Random Forest. | ['Tolgahan Cakaloglu', 'Banu Diri', 'Zekeriya Anil Guven'] | 2021-09-27 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-6.58571184e-01 -2.56186008e-01 -2.52022713e-01 -3.20725143e-01
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e28bb039-ec20-4cf9-beb2-33dbc7112bd6 | accelerating-the-training-of-video-super | 2205.05069 | null | https://arxiv.org/abs/2205.05069v2 | https://arxiv.org/pdf/2205.05069v2.pdf | Accelerating the Training of Video Super-Resolution Models | Despite that convolution neural networks (CNN) have recently demonstrated high-quality reconstruction for video super-resolution (VSR), efficiently training competitive VSR models remains a challenging problem. It usually takes an order of magnitude more time than training their counterpart image models, leading to long research cycles. Existing VSR methods typically train models with fixed spatial and temporal sizes from beginning to end. The fixed sizes are usually set to large values for good performance, resulting to slow training. However, is such a rigid training strategy necessary for VSR? In this work, we show that it is possible to gradually train video models from small to large spatial/temporal sizes, i.e., in an easy-to-hard manner. In particular, the whole training is divided into several stages and the earlier stage has smaller training spatial shape. Inside each stage, the temporal size also varies from short to long while the spatial size remains unchanged. Training is accelerated by such a multigrid training strategy, as most of computation is performed on smaller spatial and shorter temporal shapes. For further acceleration with GPU parallelization, we also investigate the large minibatch training without the loss in accuracy. Extensive experiments demonstrate that our method is capable of largely speeding up training (up to $6.2\times$ speedup in wall-clock training time) without performance drop for various VSR models. The code is available at https://github.com/TencentARC/Efficient-VSR-Training. | ['Ying Shan', 'Zhongang Qi', 'Xintao Wang', 'Lijian Lin'] | 2022-05-10 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [-1.64141413e-02 -2.97685564e-01 -1.57763716e-02 -1.85119018e-01
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9aadc115-980e-494a-8c77-d0e7fba7c4ca | exploiting-prompt-learning-with-pre-trained | 2210.16539 | null | https://arxiv.org/abs/2210.16539v2 | https://arxiv.org/pdf/2210.16539v2.pdf | Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection | Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression. Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Textual embedding features produced by pre-trained language models (PLMs) such as BERT are widely used in such systems. However, PLM domain fine-tuning is commonly based on the masked word or sentence prediction costs that are inconsistent with the back-end AD detection task. To this end, this paper investigates the use of prompt-based fine-tuning of PLMs that consistently uses AD classification errors as the training objective function. Disfluency features based on hesitation or pause filler token frequencies are further incorporated into prompt phrases during PLM fine-tuning. The decision voting based combination among systems using different PLMs (BERT and RoBERTa) or systems with different fine-tuning paradigms (conventional masked-language modelling fine-tuning and prompt-based fine-tuning) is further applied. Mean, standard deviation and the maximum among accuracy scores over 15 experiment runs are adopted as performance measurements for the AD detection system. Mean detection accuracy of 84.20% (with std 2.09%, best 87.5%) and 82.64% (with std 4.0%, best 89.58%) were obtained using manual and ASR speech transcripts respectively on the ADReSS20 test set consisting of 48 elderly speakers. | ['Helen Meng', 'Xunying Liu', 'Shoukang Hu', 'Bo Zheng', 'Tianzi Wang', 'Jiajun Deng', 'Yi Wang'] | 2022-10-29 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 1.39448047e-01 3.06501895e-01 5.20629659e-02 -5.54449439e-01
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e282f3c4-1555-4510-9592-e41dc9a67e5c | improving-semantic-segmentation-via-video | 1812.01593 | null | https://arxiv.org/abs/1812.01593v3 | https://arxiv.org/pdf/1812.01593v3.pdf | Improving Semantic Segmentation via Video Propagation and Label Relaxation | Semantic segmentation requires large amounts of pixel-wise annotations to learn accurate models. In this paper, we present a video prediction-based methodology to scale up training sets by synthesizing new training samples in order to improve the accuracy of semantic segmentation networks. We exploit video prediction models' ability to predict future frames in order to also predict future labels. A joint propagation strategy is also proposed to alleviate mis-alignments in synthesized samples. We demonstrate that training segmentation models on datasets augmented by the synthesized samples leads to significant improvements in accuracy. Furthermore, we introduce a novel boundary label relaxation technique that makes training robust to annotation noise and propagation artifacts along object boundaries. Our proposed methods achieve state-of-the-art mIoUs of 83.5% on Cityscapes and 82.9% on CamVid. Our single model, without model ensembles, achieves 72.8% mIoU on the KITTI semantic segmentation test set, which surpasses the winning entry of the ROB challenge 2018. Our code and videos can be found at https://nv-adlr.github.io/publication/2018-Segmentation. | ['Bryan Catanzaro', 'Andrew Tao', 'Shawn Newsam', 'Kevin J. Shih', 'Karan Sapra', 'Yi Zhu', 'Fitsum A. Reda'] | 2018-12-04 | improving-semantic-segmentation-via-video-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhu_Improving_Semantic_Segmentation_via_Video_Propagation_and_Label_Relaxation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Improving_Semantic_Segmentation_via_Video_Propagation_and_Label_Relaxation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-propagation'] | ['computer-vision'] | [ 5.85167885e-01 4.80706453e-01 -5.56467474e-01 -6.37925804e-01
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8695ac1f-b85f-47f8-8658-d4c8d2bf383b | contactdb-analyzing-and-predicting-grasp | 1904.06830 | null | http://arxiv.org/abs/1904.06830v1 | http://arxiv.org/pdf/1904.06830v1.pdf | ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging | Grasping and manipulating objects is an important human skill. Since
hand-object contact is fundamental to grasping, capturing it can lead to
important insights. However, observing contact through external sensors is
challenging because of occlusion and the complexity of the human hand. We
present ContactDB, a novel dataset of contact maps for household objects that
captures the rich hand-object contact that occurs during grasping, enabled by
use of a thermal camera. Participants in our study grasped 3D printed objects
with a post-grasp functional intent. ContactDB includes 3750 3D meshes of 50
household objects textured with contact maps and 375K frames of synchronized
RGB-D+thermal images. To the best of our knowledge, this is the first
large-scale dataset that records detailed contact maps for human grasps.
Analysis of this data shows the influence of functional intent and object size
on grasping, the tendency to touch/avoid 'active areas', and the high frequency
of palm and proximal finger contact. Finally, we train state-of-the-art image
translation and 3D convolution algorithms to predict diverse contact patterns
from object shape. Data, code and models are available at
https://contactdb.cc.gatech.edu. | ['Samarth Brahmbhatt', 'James Hays', 'Cusuh Ham', 'Charles C. Kemp'] | 2019-04-15 | contactdb-analyzing-and-predicting-grasp-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Brahmbhatt_ContactDB_Analyzing_and_Predicting_Grasp_Contact_via_Thermal_Imaging_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Brahmbhatt_ContactDB_Analyzing_and_Predicting_Grasp_Contact_via_Thermal_Imaging_CVPR_2019_paper.pdf | cvpr-2019-6 | ['human-grasp-contact-prediction'] | ['miscellaneous'] | [ 1.10091127e-01 -3.62595171e-01 8.92637447e-02 -2.75447547e-01
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cb7b8a3a-f652-488f-ac1c-055458d1ed24 | webal-1-workshop-on-artificial-life-and-the | 1406.2507 | null | http://arxiv.org/abs/1406.2507v4 | http://arxiv.org/pdf/1406.2507v4.pdf | WebAL-1: Workshop on Artificial Life and the Web 2014 Proceedings | Proceedings of WebAL-1: Workshop on Artificial Life and the Web 2014, held at
the 14th International Conference on the Synthesis and Simulation of Living
Systems (ALIFE 14), New York, NY, 31 July 2014. | ['Tim Taylor'] | 2014-06-10 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-1.57508492e-01 3.31702918e-01 3.67939383e-01 4.56862241e-01
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1981ef0e-9b35-4cd3-b119-74fbff20448b | what-are-you-anxious-about-examining-subjects | 2209.13595 | null | https://arxiv.org/abs/2209.13595v1 | https://arxiv.org/pdf/2209.13595v1.pdf | What Are You Anxious About? Examining Subjects of Anxiety during the COVID-19 Pandemic | COVID-19 poses disproportionate mental health consequences to the public during different phases of the pandemic. We use a computational approach to capture the specific aspects that trigger an online community's anxiety about the pandemic and investigate how these aspects change over time. First, we identified nine subjects of anxiety (SOAs) in a sample of Reddit posts ($N$=86) from r/COVID19\_support using thematic analysis. Then, we quantified Reddit users' anxiety by training algorithms on a manually annotated sample ($N$=793) to automatically label the SOAs in a larger chronological sample ($N$=6,535). The nine SOAs align with items in various recently developed pandemic anxiety measurement scales. We observed that Reddit users' concerns about health risks remained high in the first eight months of the pandemic. These concerns diminished dramatically despite the surge of cases occurring later. In general, users' language disclosing the SOAs became less intense as the pandemic progressed. However, worries about mental health and the future increased steadily throughout the period covered in this study. People also tended to use more intense language to describe mental health concerns than health risks or death concerns. Our results suggest that this online group's mental health condition does not necessarily improve despite COVID-19 gradually weakening as a health threat due to appropriate countermeasures. Our system lays the groundwork for population health and epidemiology scholars to examine aspects that provoke pandemic anxiety in a timely fashion. | ['Daniela V. Negraia', 'Sophie Lohmann', 'Steven R. Wilson', 'Lucia L. Chen'] | 2022-09-27 | null | null | null | null | ['epidemiology'] | ['medical'] | [-4.28617224e-02 4.02089506e-01 -2.24790514e-01 -1.21435158e-01
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93407aa6-b44f-4bd1-86f7-612c6f4cff13 | metaphorical-expressions-in-automatic-arabic | null | null | https://aclanthology.org/2020.lrec-1.604 | https://aclanthology.org/2020.lrec-1.604.pdf | Metaphorical Expressions in Automatic Arabic Sentiment Analysis | Over the recent years, Arabic language resources and NLP tools have been under rapid development. One of the important tasks for Arabic natural language processing is the sentiment analysis. While a significant improvement has been achieved in this research area, the existing computational models and tools still suffer from the lack of capability of dealing with Arabic metaphorical expressions. Metaphor has an important role in Arabic language due to its unique history and culture. Metaphors provide a linguistic mechanism for expressing ideas and notions that can be different from their surface form. Therefore, in order to efficiently identify true sentiment of Arabic language data, a computational model needs to be able to {``}read between lines{''}. In this paper, we examine the issue of metaphors in automatic Arabic sentiment analysis by carrying out an experiment, in which we observe the performance of a state-of-art Arabic sentiment tool on metaphors and analyse the result to gain a deeper insight into the issue. Our experiment evidently shows that metaphors have a significant impact on the performance of current Arabic sentiment tools, and it is an important task to develop Arabic language resources and computational models for Arabic metaphors. | ['Scott Piao', 'Israa Alsiyat'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.04230925e-01 -2.53897130e-01 1.21048845e-01 -4.64118659e-01
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fb41bc8f-4f95-4952-b548-1c2926829e2b | a-comparative-study-on-deep-learning-methods | 2210.14031 | null | https://arxiv.org/abs/2210.14031v1 | https://arxiv.org/pdf/2210.14031v1.pdf | A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images | Deep learning (DL) stereo matching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo images lacking a systematic evaluation on how robust DL methods are on satellite stereo images with varying radiometric and geometric configurations. This paper provides an evaluation of four DL stereo matching methods through hundreds of multi-date multi-site satellite stereo pairs with varying geometric configurations, against the traditional well-practiced Census-SGM (Semi-global matching), to comprehensively understand their accuracy, robustness, generalization capabilities, and their practical potential. The DL methods include a learning-based cost metric through convolutional neural networks (MC-CNN) followed by SGM, and three end-to-end (E2E) learning models using Geometry and Context Network (GCNet), Pyramid Stereo Matching Network (PSMNet), and LEAStereo. Our experiments show that E2E algorithms can achieve upper limits of geometric accuracies, while may not generalize well for unseen data. The learning-based cost metric and Census-SGM are rather robust and can consistently achieve acceptable results. All DL algorithms are robust to geometric configurations of stereo pairs and are less sensitive in comparison to the Census-SGM, while learning-based cost metrics can generalize on satellite images when trained on different datasets (airborne or ground-view). | ['Rongjun Qin', 'Hessah Albanwan'] | 2022-10-25 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 1.04408428e-01 -4.90608305e-01 2.12626770e-01 -6.05727017e-01
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26f9c82b-f252-4dde-9c83-43f3d7e09673 | supervised-dimensionality-reduction-by-a | 2006.12127 | null | https://arxiv.org/abs/2006.12127v1 | https://arxiv.org/pdf/2006.12127v1.pdf | Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN features | We explore the application of linear discriminant analysis (LDA) to the features obtained in different layers of pretrained deep convolutional neural networks (CNNs). The advantage of LDA compared to other techniques in dimensionality reduction is that it reduces dimensions while preserving the global structure of data, so distances in the low-dimensional structure found are meaningful. The LDA applied to the CNN features finds that the centroids of classes corresponding to the similar data lay closer than classes corresponding to different data. We applied the method to a modification of the MNIST dataset with ten additional classes, each new class with half of the images from one of the standard ten classes. The method finds the new classes close to the corresponding standard classes we took the data form. We also applied the method to a dataset of images of butterflies to find that related subspecies are found to be close. For both datasets, we find a performance similar to state-of-the-art methods. | ['Gonzalo G. de Polavieja', 'Francisco J. H. Heras'] | 2020-06-22 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-4.43775266e-01 -1.79365993e-01 1.29209682e-01 -6.36130512e-01
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6a97694a-4dff-4abe-9390-6145b8776ef8 | investigating-efficiently-extending | 2208.04347 | null | https://arxiv.org/abs/2208.04347v1 | https://arxiv.org/pdf/2208.04347v1.pdf | Investigating Efficiently Extending Transformers for Long Input Summarization | While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs continues to be a significant challenge. One such task is long input summarization, where inputs are longer than the maximum input context of most pretrained models. Through an extensive set of experiments, we investigate what model architectural changes and pretraining paradigms can most efficiently adapt a pretrained Transformer for long input summarization. We find that a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance and efficiency, and that an additional pretraining phase on long sequences meaningfully improves downstream summarization performance. Based on our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens. PEGASUS-X achieves strong performance on long input summarization tasks comparable with much larger models while adding few additional parameters and not requiring model parallelism to train. | ['Peter J. Liu', 'Yao Zhao', 'Jason Phang'] | 2022-08-08 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [ 5.71451902e-01 3.29969257e-01 -4.61289227e-01 -3.36110324e-01
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8dc84ad7-1c63-47d8-81be-de4529d85074 | network-aided-intelligent-traffic-steering-in | 2302.02711 | null | https://arxiv.org/abs/2302.02711v2 | https://arxiv.org/pdf/2302.02711v2.pdf | Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework | To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction. | ['Symeon Chatzinotas', 'Diep N. Nguyen', 'Dinh Thai Hoang', 'Nguyen Cong Luong', 'Markku Juntti', 'Dinh C. Nguyen', 'Nhan Thanh Nguyen', 'Thang X. Vu', 'Van-Dinh Nguyen'] | 2023-02-06 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.52269834e-01 -2.55213659e-02 -6.79091990e-01 -2.32222915e-01
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f81361dd-b900-4a9c-9fd0-035487993fef | weed-density-and-distribution-estimation-for | 2011.02193 | null | https://arxiv.org/abs/2011.02193v2 | https://arxiv.org/pdf/2011.02193v2.pdf | Weed Density and Distribution Estimation for Precision Agriculture using Semi-Supervised Learning | Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective chemical treatment of these regions. Advances in analyzing farm images have resulted in solutions to identify weed plants. However, a majority of these approaches are based on supervised learning methods which requires huge amount of manually annotated images. As a result, these supervised approaches are economically infeasible for the individual farmer because of the wide variety of plant species being cultivated. In this paper, we propose a deep learning-based semi-supervised approach for robust estimation of weed density and distribution across farmlands using only limited color images acquired from autonomous robots. This weed density and distribution can be useful in a site-specific weed management system for selective treatment of infected areas using autonomous robots. In this work, the foreground vegetation pixels containing crops and weeds are first identified using a Convolutional Neural Network (CNN) based unsupervised segmentation. Subsequently, the weed infected regions are identified using a fine-tuned CNN, eliminating the need for designing hand-crafted features. The approach is validated on two datasets of different crop/weed species (1) Crop Weed Field Image Dataset (CWFID), which consists of carrot plant images and the (2) Sugar Beets dataset. The proposed method is able to localize weed-infested regions a maximum recall of 0.99 and estimate weed density with a maximum accuracy of 82.13%. Hence, the proposed approach is shown to generalize to different plant species without the need for extensive labeled data. | ['Ujjwal Verma', 'Sidharth R', 'Armaan Ashfaque', 'Shantam Shorewala'] | 2020-11-04 | null | null | null | null | ['unet-segmentation'] | ['computer-vision'] | [ 6.03611469e-01 4.05479819e-02 -2.91830778e-01 5.89566305e-02
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1f4ed1fb-89df-4711-b261-3a5ff2cb94e0 | rmultinet-an-r-package-for-multilayer | 2302.04437 | null | https://arxiv.org/abs/2302.04437v1 | https://arxiv.org/pdf/2302.04437v1.pdf | rMultiNet: An R Package For Multilayer Networks Analysis | This paper develops an R package rMultiNet to analyze multilayer network data. We provide two general frameworks from recent literature, e.g. mixture multilayer stochastic block model(MMSBM) and mixture multilayer latent space model(MMLSM) to generate the multilayer network. We also provide several methods to reveal the embedding of both nodes and layers followed by further data analysis methods, such as clustering. Three real data examples are processed in the package. The source code of rMultiNet is available at https://github.com/ChenyuzZZ73/rMultiNet. | ['Dong Xia', 'Chenyu Ren', 'Zhongyuan Lyu', 'Ting Li'] | 2023-02-09 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-3.68166447e-01 7.41288066e-02 -1.22551739e-01 -3.34934860e-01
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f27ec2d7-3f2f-48a9-814b-5a4c5030e7ef | gpt3mix-leveraging-large-scale-language | 2104.08826 | null | https://arxiv.org/abs/2104.08826v2 | https://arxiv.org/pdf/2104.08826v2.pdf | GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation | Large-scale language models such as GPT-3 are excellent few-shot learners, allowing them to be controlled via natural text prompts. Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. This paper proposes a novel data augmentation technique that leverages large-scale language models to generate realistic text samples from a mixture of real samples. We also propose utilizing soft-labels predicted by the language models, effectively distilling knowledge from the large-scale language models and creating textual perturbations simultaneously. We perform data augmentation experiments on diverse classification tasks and show that our method hugely outperforms existing text augmentation methods. Ablation studies and a qualitative analysis provide more insights into our approach. | ['Woomyeong Park', 'Sang-Woo Lee', 'Jaewook Kang', 'Dongju Park', 'Kang Min Yoo'] | 2021-04-18 | null | https://aclanthology.org/2021.findings-emnlp.192 | https://aclanthology.org/2021.findings-emnlp.192.pdf | findings-emnlp-2021-11 | ['text-augmentation'] | ['natural-language-processing'] | [ 2.07002923e-01 5.50359905e-01 -6.13432825e-01 -2.23330393e-01
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45d396f6-fed1-4b94-af24-b877817d4b67 | extending-the-use-of-mdl-for-high-dimensional | 2201.11171 | null | https://arxiv.org/abs/2201.11171v1 | https://arxiv.org/pdf/2201.11171v1.pdf | Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling | In the signal processing and statistics literature, the minimum description length (MDL) principle is a popular tool for choosing model complexity. Successful examples include signal denoising and variable selection in linear regression, for which the corresponding MDL solutions often enjoy consistent properties and produce very promising empirical results. This paper demonstrates that MDL can be extended naturally to the high-dimensional setting, where the number of predictors $p$ is larger than the number of observations $n$. It first considers the case of linear regression, then allows for outliers in the data, and lastly extends to the robust fitting of nonparametric additive models. Results from numerical experiments are presented to demonstrate the efficiency and effectiveness of the MDL approach. | ['Thomas C. M. Lee', 'Raymond K. W. Wong', 'Zhenyu Wei'] | 2022-01-26 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.81044453e-01 -2.20880300e-01 -3.70383374e-02 -2.78613687e-01
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8c04ad26-7ffd-4113-85a6-957320a4e9e7 | all-for-one-and-one-for-all-improving-music | 2010.04228 | null | https://arxiv.org/abs/2010.04228v4 | https://arxiv.org/pdf/2010.04228v4.pdf | All for One and One for All: Improving Music Separation by Bridging Networks | This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes. First, by using MDL we take advantage of the frequency and time domain representation of audio signals. Next, we utilize the relationship among instruments by jointly considering them. We do this on the one hand by modifying the network architecture and introducing a CrossNet structure. On the other hand, we consider combinations of instrument estimates by using a new combination loss (CL). MDL and CL can easily be applied to many existing DNN-based separation methods as they are merely loss functions which are only used during training and which do not affect the inference step. Experimental results show that the performance of Open-Unmix (UMX), a well-known and state-of-the-art open source library for music separation, can be improved by utilizing our above schemes. Our modifications of UMX are open-sourced together with this paper. | ['Yuki Mitsufuji', 'Shusuke Takahashi', 'Stefan Uhlich', 'Ryosuke Sawata'] | 2020-10-08 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.09151039e-02 -4.53391463e-01 3.16473208e-02 -7.78904781e-02
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2a9bbe29-d0b5-4a4a-a447-e008e9486007 | mongoose-path-wise-smooth-bayesian | 2302.11533 | null | https://arxiv.org/abs/2302.11533v1 | https://arxiv.org/pdf/2302.11533v1.pdf | MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning | In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems. A primary contributor to the cost of evaluating such black-box objective functions is often the effort required to prepare the system for measurement. We consider a common scenario where preparation costs grow as the distance between successive evaluations increases. In this setting, smooth optimisation trajectories are preferred and the jumpy paths produced by the standard myopic (i.e.\ one-step-optimal) Bayesian optimisation methods are sub-optimal. Our algorithm, MONGOOSE, uses a meta-learnt parametric policy to generate smooth optimisation trajectories, achieving performance gains over existing methods when optimising functions with large movement costs. | ['Henry B. Moss', 'Laurence Aitchison', 'Adam X. Yang'] | 2023-02-22 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.81375965e-01 3.49801034e-01 1.03412934e-01 -1.49602979e-01
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79107e54-55bb-4a5b-9db0-47194b611ab4 | metric-learning-with-adaptive-density | 1511.05939 | null | http://arxiv.org/abs/1511.05939v2 | http://arxiv.org/pdf/1511.05939v2.pdf | Metric Learning with Adaptive Density Discrimination | Distance metric learning (DML) approaches learn a transformation to a
representation space where distance is in correspondence with a predefined
notion of similarity. While such models offer a number of compelling benefits,
it has been difficult for these to compete with modern classification
algorithms in performance and even in feature extraction.
In this work, we propose a novel approach explicitly designed to address a
number of subtle yet important issues which have stymied earlier DML
algorithms. It maintains an explicit model of the distributions of the
different classes in representation space. It then employs this knowledge to
adaptively assess similarity, and achieve local discrimination by penalizing
class distribution overlap.
We demonstrate the effectiveness of this idea on several tasks. Our approach
achieves state-of-the-art classification results on a number of fine-grained
visual recognition datasets, surpassing the standard softmax classifier and
outperforming triplet loss by a relative margin of 30-40%. In terms of
computational performance, it alleviates training inefficiencies in the
traditional triplet loss, reaching the same error in 5-30 times fewer
iterations. Beyond classification, we further validate the saliency of the
learnt representations via their attribute concentration and hierarchy recovery
properties, achieving 10-25% relative gains on the softmax classifier and
25-50% on triplet loss in these tasks. | ['Piotr Dollar', 'Manohar Paluri', 'Oren Rippel', 'Lubomir Bourdev'] | 2015-11-18 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 4.69913661e-01 1.61934756e-02 -3.45408350e-01 -7.21714437e-01
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be63e3a9-8e18-4029-8ca1-3ee09e586e11 | safe-reinforcement-learning-for-multi-energy | 2207.03830 | null | https://arxiv.org/abs/2207.03830v4 | https://arxiv.org/pdf/2207.03830v4.pdf | Safe reinforcement learning for multi-energy management systems with known constraint functions | Reinforcement learning (RL) is a promising optimal control technique for multi-energy management systems. It does not require a model a priori - reducing the upfront and ongoing project-specific engineering effort and is capable of learning better representations of the underlying system dynamics. However, vanilla RL does not provide constraint satisfaction guarantees - resulting in various potentially unsafe interactions within its safety-critical environment. In this paper, we present two novel safe RL methods, namely SafeFallback and GiveSafe, where the safety constraint formulation is decoupled from the RL formulation. These provide hard-constraint, rather than soft- and chance-constraint, satisfaction guarantees both during training a (near) optimal policy (which involves exploratory and exploitative, i.e. greedy, steps) as well as during deployment of any policy (e.g. random agents or offline trained RL agents). This without the need of solving a mathematical program, resulting in less computational power requirements and a more flexible constraint function formulation (no derivative information is required). In a simulated multi-energy systems case study we have shown that both methods start with a significantly higher utility (i.e. useful policy) compared to a vanilla RL benchmark and Optlayer benchmark (94,6% and 82,8% compared to 35,5% and 77,8%) and that the proposed SafeFallback method even can outperform the vanilla RL benchmark (102,9% to 100%). We conclude that both methods are viably safety constraint handling techniques applicable beyond RL, as demonstrated with random policies while still providing hard-constraint guarantees. | ['Maarten Messagie', 'Ann Nowé', 'Rüdiger Franke', 'Luis Ramirez Camargo', 'Glenn Ceusters'] | 2022-07-08 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.99993956e-02 4.29738969e-01 -3.55455786e-01 5.63861616e-02
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1a102df1-e58f-457d-95d6-2f9e920a3e1f | revisiting-activation-regularization-for | 1708.01009 | null | http://arxiv.org/abs/1708.01009v1 | http://arxiv.org/pdf/1708.01009v1.pdf | Revisiting Activation Regularization for Language RNNs | Recurrent neural networks (RNNs) serve as a fundamental building block for
many sequence tasks across natural language processing. Recent research has
focused on recurrent dropout techniques or custom RNN cells in order to improve
performance. Both of these can require substantial modifications to the machine
learning model or to the underlying RNN configurations. We revisit traditional
regularization techniques, specifically L2 regularization on RNN activations
and slowness regularization over successive hidden states, to improve the
performance of RNNs on the task of language modeling. Both of these techniques
require minimal modification to existing RNN architectures and result in
performance improvements comparable or superior to more complicated
regularization techniques or custom cell architectures. These regularization
techniques can be used without any modification on optimized LSTM
implementations such as the NVIDIA cuDNN LSTM. | ['Richard Socher', 'Stephen Merity', 'Bryan McCann'] | 2017-08-03 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.81649253e-01 -1.50829628e-01 -7.57830665e-02 -2.46237636e-01
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714363ec-7f00-44fc-b5e8-7b7a0cff8d07 | neural-diffusion-processes | 2206.03992 | null | https://arxiv.org/abs/2206.03992v2 | https://arxiv.org/pdf/2206.03992v2.pdf | Neural Diffusion Processes | Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and a reduced complexity of inference. Building on the successes of denoising diffusion models for generative modelling, we propose Neural Diffusion Processes (NDPs), a novel approach that learns to sample from a rich distribution over functions through its finite marginals. By introducing a custom attention block we are able to incorporate properties of stochastic processes, such as exchangeability, directly into the NDP's architecture. We empirically show that NDPs can capture functional distributions close to the true Bayesian posterior, demonstrating that they can successfully emulate the behaviour of Gaussian processes and surpass the performance of neural processes. NDPs enable a variety of downstream tasks, including regression, implicit hyperparameter marginalisation, non-Gaussian posterior prediction and global optimisation. | ['Fergus Simpson', 'Zoubin Ghahramani', 'Alan Saul', 'Vincent Dutordoir'] | 2022-06-08 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.44990027e-01 4.83266383e-01 2.29377657e-01 -5.76728769e-02
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4c5dd3f2-6980-4317-81b1-0d1c347c2b21 | performance-analysis-of-semi-supervised | 2002.12164 | null | https://arxiv.org/abs/2002.12164v1 | https://arxiv.org/pdf/2002.12164v1.pdf | Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs | Extracting large amounts of data from biological samples is not feasible due to radiation issues, and image processing in the small-data regime is one of the critical challenges when working with a limited amount of data. In this work, we applied an existing algorithm named Variational Auto Encoder (VAE) that pre-trains a latent space representation of the data to capture the features in a lower-dimension for the small-data regime input. The fine-tuned latent space provides constant weights that are useful for classification. Here we will present the performance analysis of the VAE algorithm with different latent space sizes in the semi-supervised learning using the CIFAR-10 dataset. | ['Varun Mannam', 'Arman Kazemi'] | 2020-02-26 | performance-analysis-of-semi-supervised-1 | https://arxiv.org/pdf/2002.12164.pdf | https://arxiv.org/pdf/2002.12164.pdf | null | ['small-data'] | ['computer-vision'] | [ 3.22602510e-01 -5.32618864e-03 -3.31034213e-02 -4.07675833e-01
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7013a4f4-aefd-4313-b3ea-605c1f80f5e7 | contrastive-learning-with-logic-driven-data | 2305.12599 | null | https://arxiv.org/abs/2305.12599v1 | https://arxiv.org/pdf/2305.12599v1.pdf | Contrastive Learning with Logic-driven Data Augmentation for Logical Reasoning over Text | Pre-trained large language model (LLM) is under exploration to perform NLP tasks that may require logical reasoning. Logic-driven data augmentation for representation learning has been shown to improve the performance of tasks requiring logical reasoning, but most of these data rely on designed templates and therefore lack generalization. In this regard, we propose an AMR-based logical equivalence-driven data augmentation method (AMR-LE) for generating logically equivalent data. Specifically, we first parse a text into the form of an AMR graph, next apply four logical equivalence laws (contraposition, double negation, commutative and implication laws) on the AMR graph to construct a logically equivalent/inequivalent AMR graph, and then convert it into a logically equivalent/inequivalent sentence. To help the model to better learn these logical equivalence laws, we propose a logical equivalence-driven contrastive learning training paradigm, which aims to distinguish the difference between logical equivalence and inequivalence. Our AMR-LE (Ensemble) achieves #2 on the ReClor leaderboard https://eval.ai/web/challenges/challenge-page/503/leaderboard/1347 . Our model shows better performance on seven downstream tasks, including ReClor, LogiQA, MNLI, MRPC, RTE, QNLI, and QQP. The source code and dataset are public at https://github.com/Strong-AI-Lab/Logical-Equivalence-driven-AMR-Data-Augmentation-for-Representation-Learning . | ['Jiamou Liu', 'Michael Witbrock', 'Yonghua Zhu', 'Yang Chen', 'Nathan Young', 'Neset Tan', 'Wanjun Zhong', 'Zhenyun Deng', 'Alex Yuxuan Peng', 'Qiming Bao'] | 2023-05-21 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.87113285e-01 3.20430040e-01 -2.41178185e-01 -6.26728415e-01
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04418d00-4482-4671-88a5-ff3a79855f42 | ensemble-based-fine-tuning-strategy-for | null | null | https://aclanthology.org/2022.clinicalnlp-1.11 | https://aclanthology.org/2022.clinicalnlp-1.11.pdf | Ensemble-based Fine-Tuning Strategy for Temporal Relation Extraction from the Clinical Narrative | In this paper, we investigate ensemble methods for fine-tuning transformer-based pretrained models for clinical natural language processing tasks, specifically temporal relation extraction from the clinical narrative. Our experimental results on the THYME data show that ensembling as a fine-tuning strategy can further boost model performance over single learners optimized for hyperparameters. Dynamic snapshot ensembling is particularly beneficial as it fine-tunes a wide array of parameters and results in a 2.8% absolute improvement in F1 over the base single learner. | ['Guergana Savova', 'Steven Bethard', 'Timothy Miller', 'Lijing Wang'] | null | null | null | null | naacl-clinicalnlp-2022-7 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 1.79270968e-01 3.75662357e-01 -4.93201554e-01 -4.93271619e-01
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412d8a83-f783-41ec-8514-4448afbc5533 | right-to-be-forgotten-in-the-era-of-large | 2307.03941 | null | https://arxiv.org/abs/2307.03941v1 | https://arxiv.org/pdf/2307.03941v1.pdf | Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions | The Right to be Forgotten (RTBF) was first established as the result of the ruling of Google Spain SL, Google Inc. v AEPD, Mario Costeja Gonz\'alez, and was later included as the Right to Erasure under the General Data Protection Regulation (GDPR) of European Union to allow individuals the right to request personal data be deleted by organizations. Specifically for search engines, individuals can send requests to organizations to exclude their information from the query results. With the recent development of Large Language Models (LLMs) and their use in chatbots, LLM-enabled software systems have become popular. But they are not excluded from the RTBF. Compared with the indexing approach used by search engines, LLMs store, and process information in a completely different way. This poses new challenges for compliance with the RTBF. In this paper, we explore these challenges and provide our insights on how to implement technical solutions for the RTBF, including the use of machine unlearning, model editing, and prompting engineering. | ['Xiwei Xu', 'Mark Staples', 'Zhenchang Xing', 'Shidong Pan', 'Thong Hoang', 'Pamela Finckenberg-Broman', 'Dawen Zhang'] | 2023-07-08 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 2.70747729e-02 3.73489439e-01 -4.06848878e-01 4.01161611e-02
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23c11f2d-4d9f-4a6c-b5a5-accc6c4da047 | revisiting-conversation-discourse-for | 2306.03975 | null | https://arxiv.org/abs/2306.03975v2 | https://arxiv.org/pdf/2306.03975v2.pdf | Revisiting Conversation Discourse for Dialogue Disentanglement | Dialogue disentanglement aims to detach the chronologically ordered utterances into several independent sessions. Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attribute. In this paper, we propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. First of all, in feature encoding stage, we construct the heterogeneous graph representations to model the various dialogue-specific discourse structural features, including the static speaker-role structures (i.e., speaker-utterance and speaker-mentioning structure) and the dynamic contextual structures (i.e., the utterance-distance and partial-replying structure). We then develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Second, in model learning stage, we perform optimization with a hierarchical ranking loss mechanism, which groups dialogue utterances into different discourse levels and carries training covering pair-wise and session-wise levels hierarchically. Third, in inference stage, we devise an easy-first decoding algorithm, which performs utterance pairing under the easy-to-hard manner with a global context, breaking the constraint of traditional sequential decoding order. On two benchmark datasets, our overall system achieves new state-of-the-art performances on all evaluations. In-depth analyses further demonstrate the efficacy of each proposed idea and also reveal how our methods help advance the task. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications. | ['Donghong Ji', 'Tat-Seng Chua', 'Yinwei Wei', 'Lizi Liao', 'Shengqiong Wu', 'Fei Li', 'Hao Fei', 'Bobo Li'] | 2023-06-06 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 4.06540185e-01 6.15726531e-01 -3.88721973e-01 -6.88844919e-01
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e583c591-d45e-4d92-9754-74c981a36f96 | nearly-optimal-vc-dimension-and-pseudo | 2305.08466 | null | https://arxiv.org/abs/2305.08466v1 | https://arxiv.org/pdf/2305.08466v1.pdf | Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives | This paper addresses the problem of nearly optimal Vapnik--Chervonenkis dimension (VC-dimension) and pseudo-dimension estimations of the derivative functions of deep neural networks (DNNs). Two important applications of these estimations include: 1) Establishing a nearly tight approximation result of DNNs in the Sobolev space; 2) Characterizing the generalization error of machine learning methods with loss functions involving function derivatives. This theoretical investigation fills the gap of learning error estimations for a wide range of physics-informed machine learning models and applications including generative models, solving partial differential equations, operator learning, network compression, distillation, regularization, etc. | ['Yang Xiang', 'Haizhao Yang', 'Yahong Yang'] | 2023-05-15 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 2.07087189e-01 3.18416327e-01 1.52733311e-01 -2.16461003e-01
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908f38b0-b5ce-48e6-9f5e-897b282ef5bf | a-multiple-kernel-testing-procedure-for-non | 2206.07239 | null | https://arxiv.org/abs/2206.07239v1 | https://arxiv.org/pdf/2206.07239v1.pdf | A Multiple kernel testing procedure for non-proportional hazards in factorial designs | In this paper we propose a Multiple kernel testing procedure to infer survival data when several factors (e.g. different treatment groups, gender, medical history) and their interaction are of interest simultaneously. Our method is able to deal with complex data and can be seen as an alternative to the omnipresent Cox model when assumptions such as proportionality cannot be justified. Our methodology combines well-known concepts from Survival Analysis, Machine Learning and Multiple Testing: differently weighted log-rank tests, kernel methods and multiple contrast tests. By that, complex hazard alternatives beyond the classical proportional hazard set-up can be detected. Moreover, multiple comparisons are performed by fully exploiting the dependence structure of the single testing procedures to avoid a loss of power. In all, this leads to a flexible and powerful procedure for factorial survival designs whose theoretical validity is proven by martingale arguments and the theory for $V$-statistics. We evaluate the performance of our method in an extensive simulation study and illustrate it by a real data analysis. | ['Nicolás Rivera', 'Tamara Fernández', 'Marc Ditzhaus'] | 2022-06-15 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 9.58878919e-02 -2.06762120e-01 -4.03218150e-01 -3.11213762e-01
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d00cf849-ad75-42bd-958a-084de1f59607 | anoonly-semi-supervised-anomaly-detection | 2305.18798 | null | https://arxiv.org/abs/2305.18798v1 | https://arxiv.org/pdf/2305.18798v1.pdf | AnoOnly: Semi-Supervised Anomaly Detection without Loss on Normal Data | Semi-supervised anomaly detection (SSAD) methods have demonstrated their effectiveness in enhancing unsupervised anomaly detection (UAD) by leveraging few-shot but instructive abnormal instances. However, the dominance of homogeneous normal data over anomalies biases the SSAD models against effectively perceiving anomalies. To address this issue and achieve balanced supervision between heavily imbalanced normal and abnormal data, we develop a novel framework called AnoOnly (Anomaly Only). Unlike existing SSAD methods that resort to strict loss supervision, AnoOnly suspends it and introduces a form of weak supervision for normal data. This weak supervision is instantiated through the utilization of batch normalization, which implicitly performs cluster learning on normal data. When integrated into existing SSAD methods, the proposed AnoOnly demonstrates remarkable performance enhancements across various models and datasets, achieving new state-of-the-art performance. Additionally, our AnoOnly is natively robust to label noise when suffering from data contamination. Our code is publicly available at https://github.com/cool-xuan/AnoOnly. | ['Heng Tao Shen', 'Fumin Shen', 'Xing Xu', 'Yi Qu', 'Peiyu Yang', 'Yixuan Zhou'] | 2023-05-30 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 1.83551595e-01 1.35663738e-02 -2.19478041e-01 -5.95065773e-01
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79694382-8dea-4267-bef2-76ce1cfa6145 | seget-deep-neural-network-with-rich | 1811.11729 | null | http://arxiv.org/abs/1811.11729v1 | http://arxiv.org/pdf/1811.11729v1.pdf | SegET: Deep Neural Network with Rich Contextual Features for Cellular Structures Segmentation in Electron Tomography Image | Electron tomography (ET) allows high-resolution reconstructions of
macromolecular complexes at nearnative state. Cellular structures segmentation
in the reconstruction data from electron tomographic images is often required
for analyzing and visualizing biological structures, making it a powerful tool
for quantitative descriptions of whole cell structures and understanding
biological functions. However, these cellular structures are rather difficult
to automatically separate or quantify from view owing to complex molecular
environment and the limitations of reconstruction data of ET. In this paper, we
propose a single end-to-end deep fully-convolutional semantic segmentation
network dubbed SegET with rich contextual features which fully exploitsthe
multi-scale and multi-level contextual information and reduces the loss of
details of cellular structures in ET images. We trained and evaluated our
network on the electron tomogram of the CTL Immunological Synapse from Cell
Image library. Our results demonstrate that SegET can automatically segment
accurately and outperform all other baseline methods on each individual
structure in our ET dataset. | ['Zhi-Yong Liu', 'Xiaohua Wan', 'Lifa Zhu', 'Fa Zhang', 'Enze Zhang'] | 2018-11-28 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 1.21047541e-01 -3.80596578e-01 1.06556304e-01 -3.38136345e-01
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e266e013-6d34-4a3d-9a28-5935387b425e | control-a-video-controllable-text-to-video | 2305.13840 | null | https://arxiv.org/abs/2305.13840v1 | https://arxiv.org/pdf/2305.13840v1.pdf | Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models | This paper presents a controllable text-to-video (T2V) diffusion model, named Video-ControlNet, that generates videos conditioned on a sequence of control signals, such as edge or depth maps. Video-ControlNet is built on a pre-trained conditional text-to-image (T2I) diffusion model by incorporating a spatial-temporal self-attention mechanism and trainable temporal layers for efficient cross-frame modeling. A first-frame conditioning strategy is proposed to facilitate the model to generate videos transferred from the image domain as well as arbitrary-length videos in an auto-regressive manner. Moreover, Video-ControlNet employs a novel residual-based noise initialization strategy to introduce motion prior from an input video, producing more coherent videos. With the proposed architecture and strategies, Video-ControlNet can achieve resource-efficient convergence and generate superior quality and consistent videos with fine-grained control. Extensive experiments demonstrate its success in various video generative tasks such as video editing and video style transfer, outperforming previous methods in terms of consistency and quality. Project Page: https://controlavideo.github.io/ | ['Liang Lin', 'Xuefeng Xiao', 'Xin Xia', 'Jiashi Li', 'Hefeng Wu', 'Pan Xie', 'Jie Wu', 'Weifeng Chen'] | 2023-05-23 | null | null | null | null | ['style-transfer', 'video-style-transfer', 'video-generation', 'text-to-video-generation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 2.98682421e-01 -1.81260929e-01 -8.21818337e-02 -1.67619258e-01
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d3b90019-a222-420e-9530-f20084a1f55a | modelling-customer-lifetime-value-in-the | 2304.03038 | null | https://arxiv.org/abs/2304.03038v1 | https://arxiv.org/pdf/2304.03038v1.pdf | Modelling customer lifetime-value in the retail banking industry | Understanding customer lifetime value is key to nurturing long-term customer relationships, however, estimating it is far from straightforward. In the retail banking industry, commonly used approaches rely on simple heuristics and do not take advantage of the high predictive ability of modern machine learning techniques. We present a general framework for modelling customer lifetime value which may be applied to industries with long-lasting contractual and product-centric customer relationships, of which retail banking is an example. This framework is novel in facilitating CLV predictions over arbitrary time horizons and product-based propensity models. We also detail an implementation of this model which is currently in production at a large UK lender. In testing, we estimate an 43% improvement in out-of-time CLV prediction error relative to a popular baseline approach. Propensity models derived from our CLV model have been used to support customer contact marketing campaigns. In testing, we saw that the top 10% of customers ranked by their propensity to take up investment products were 3.2 times more likely to take up an investment product in the next year than a customer chosen at random. | ['Raad Khraishi', 'Salvatore Mercuri', 'Greig Cowan'] | 2023-04-06 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-2.43399099e-01 2.36152232e-01 -7.78157711e-01 -7.86153972e-01
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ddec5eef-5bab-45af-968e-5c594ed543f4 | spatiotemporal-modeling-of-multivariate | 2211.11176 | null | https://arxiv.org/abs/2211.11176v3 | https://arxiv.org/pdf/2211.11176v3.pdf | Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models | Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in multivariate biosignals is challenging due to (1) long-range temporal dependencies and (2) complex spatial correlations between the electrodes. To address these challenges, we propose representing multivariate biosignals as time-dependent graphs and introduce GraphS4mer, a general graph neural network (GNN) architecture that improves performance on biosignal classification tasks by modeling spatiotemporal dependencies in biosignals. Specifically, (1) we leverage the Structured State Space architecture, a state-of-the-art deep sequence model, to capture long-range temporal dependencies in biosignals and (2) we propose a graph structure learning layer in GraphS4mer to learn dynamically evolving graph structures in the data. We evaluate our proposed model on three distinct biosignal classification tasks and show that GraphS4mer consistently improves over existing models, including (1) seizure detection from electroencephalographic signals, outperforming a previous GNN with self-supervised pre-training by 3.1 points in AUROC; (2) sleep staging from polysomnographic signals, a 4.1 points improvement in macro-F1 score compared to existing sleep staging models; and (3) 12-lead electrocardiogram classification, outperforming previous state-of-the-art models by 2.7 points in macro-F1 score. | ['Tina Baykaner', 'Daniel L. Rubin', 'Christopher Lee-Messer', 'Khaled K. Saab', 'Liangqiong Qu', 'Jared A. Dunnmon', 'Siyi Tang'] | 2022-11-21 | null | null | null | null | ['graph-structure-learning', 'seizure-detection', 'sleep-staging'] | ['graphs', 'medical', 'medical'] | [ 2.97320992e-01 -1.23641290e-01 1.26732826e-01 -7.69678950e-02
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efce321a-10fc-40a4-bc4d-b9ae5a0d58a5 | editvae-unsupervised-part-aware-controllable | 2110.06679 | null | https://arxiv.org/abs/2110.06679v2 | https://arxiv.org/pdf/2110.06679v2.pdf | EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation | This paper tackles the problem of parts-aware point cloud generation. Unlike existing works which require the point cloud to be segmented into parts a priori, our parts-aware editing and generation are performed in an unsupervised manner. We achieve this with a simple modification of the Variational Auto-Encoder which yields a joint model of the point cloud itself along with a schematic representation of it as a combination of shape primitives. In particular, we introduce a latent representation of the point cloud which can be decomposed into a disentangled representation for each part of the shape. These parts are in turn disentangled into both a shape primitive and a point cloud representation, along with a standardising transformation to a canonical coordinate system. The dependencies between our standardising transformations preserve the spatial dependencies between the parts in a manner that allows meaningful parts-aware point cloud generation and shape editing. In addition to the flexibility afforded by our disentangled representation, the inductive bias introduced by our joint modeling approach yields state-of-the-art experimental results on the ShapeNet dataset. | ['Christian Walder', 'Miaomiao Liu', 'Shidi Li'] | 2021-10-13 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 3.55814427e-01 5.68391263e-01 1.74372613e-01 -2.67460167e-01
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ce84e8cd-a5d2-4a25-a0b6-a5e82958cffc | use-of-speaker-recognition-approaches-for | 2107.11506 | null | https://arxiv.org/abs/2107.11506v2 | https://arxiv.org/pdf/2107.11506v2.pdf | Use of speaker recognition approaches for learning and evaluating embedding representations of musical instrument sounds | Constructing an embedding space for musical instrument sounds that can meaningfully represent new and unseen instruments is important for downstream music generation tasks such as multi-instrument synthesis and timbre transfer. The framework of Automatic Speaker Verification (ASV) provides us with architectures and evaluation methodologies for verifying the identities of unseen speakers, and these can be repurposed for the task of learning and evaluating a musical instrument sound embedding space that can support unseen instruments. Borrowing from state-of-the-art ASV techniques, we construct a musical instrument recognition model that uses a SincNet front-end, a ResNet architecture, and an angular softmax objective function. Experiments on the NSynth and RWC datasets show our model's effectiveness in terms of equal error rate (EER) for unseen instruments, and ablation studies show the importance of data augmentation and the angular softmax objective. Experiments also show the benefit of using a CQT-based filterbank for initializing SincNet over a Mel filterbank initialization. Further complementary analysis of the learned embedding space is conducted with t-SNE visualizations and probing classification tasks, which show that including instrument family labels as a multi-task learning target can help to regularize the embedding space and incorporate useful structure, and that meaningful information such as playing style, which was not included during training, is contained in the embeddings of unseen instruments. | ['Junichi Yamagishi', 'Erica Cooper', 'Xuan Shi'] | 2021-07-24 | null | null | null | null | ['instrument-recognition', 'music-generation', 'music-generation'] | ['audio', 'audio', 'music'] | [ 2.36532629e-01 2.54094243e-01 1.08838819e-01 -2.41929233e-01
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74478ca9-8d40-4234-b85c-98dfebfbf18e | compressing-neural-networks-towards | 2107.11442 | null | https://arxiv.org/abs/2107.11442v2 | https://arxiv.org/pdf/2107.11442v2.pdf | Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition | We present a novel global compression framework for deep neural networks that automatically analyzes each layer to identify the optimal per-layer compression ratio, while simultaneously achieving the desired overall compression. Our algorithm hinges on the idea of compressing each convolutional (or fully-connected) layer by slicing its channels into multiple groups and decomposing each group via low-rank decomposition. At the core of our algorithm is the derivation of layer-wise error bounds from the Eckart Young Mirsky theorem. We then leverage these bounds to frame the compression problem as an optimization problem where we wish to minimize the maximum compression error across layers and propose an efficient algorithm towards a solution. Our experiments indicate that our method outperforms existing low-rank compression approaches across a wide range of networks and data sets. We believe that our results open up new avenues for future research into the global performance-size trade-offs of modern neural networks. Our code is available at https://github.com/lucaslie/torchprune. | ['Daniela Rus', 'Dan Feldman', 'Oren Gal', 'Alaa Maalouf', 'Lucas Liebenwein'] | 2021-07-23 | null | http://proceedings.neurips.cc/paper/2021/hash/2adcfc3929e7c03fac3100d3ad51da26-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/2adcfc3929e7c03fac3100d3ad51da26-Paper.pdf | neurips-2021-12 | ['low-rank-compression'] | ['computer-code'] | [ 4.35879171e-01 2.24827752e-01 -4.97498363e-01 -4.61251557e-01
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5b507dc3-c9c2-4b95-9c63-236959dcc854 | random-forest-with-learned-representations | 1901.07828 | null | http://arxiv.org/abs/1901.07828v1 | http://arxiv.org/pdf/1901.07828v1.pdf | Random Forest with Learned Representations for Semantic Segmentation | In this work, we present a random forest framework that learns the weights,
shapes, and sparsities of feature representations for real-time semantic
segmentation. Typical filters (kernels) have predetermined shapes and
sparsities and learn only weights. A few feature extraction methods fix weights
and learn only shapes and sparsities. These predetermined constraints restrict
learning and extracting optimal features. To overcome this limitation, we
propose an unconstrained representation that is able to extract optimal
features by learning weights, shapes, and sparsities. We, then, present the
random forest framework that learns the flexible filters using an iterative
optimization algorithm and segments input images using the learned
representations. We demonstrate the effectiveness of the proposed method using
a hand segmentation dataset for hand-object interaction and using two semantic
segmentation datasets. The results show that the proposed method achieves
real-time semantic segmentation using limited computational and memory
resources. | ['Truong Q. Nguyen', 'Byeongkeun Kang'] | 2019-01-23 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 4.82671082e-01 1.48036927e-01 -5.13350487e-01 -3.92491698e-01
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efedc614-db13-482a-a251-c3e65cefcee5 | squares-a-sql-synthesizer-using-query-reverse | null | null | https://dl.acm.org/doi/10.14778/3415478.3415492 | http://www.vldb.org/pvldb/vol13/p2853-orvalho.pdf | SQUARES: A SQL Synthesizer Using Query Reverse Engineering | Nowadays, many data analysts are domain experts, but they lack programming skills. As a result, many of them can provide examples of data transformations but are unable to produce the desired query. Hence, there is an increasing need for systems capable of solving the problem of Query Reverse Engineering (QRE). Given a database and output table, these systems have to find the query that generated this table. We present SQUARES, a program synthesis tool based on input-output examples that can help data analysts to extract and transform data by synthesizing SQL queries, and table manipulation programs using the R language. | ['Vasco Manquinho', 'Ruben Martins', 'Miguel Terra-Neves; Miguel Ventura', 'Pedro Orvalho'] | 2020-08-31 | null | null | null | null | ['enumerative-search', 'sql-synthesis'] | ['computer-code', 'computer-code'] | [ 4.71902974e-02 -1.33331423e-03 -7.58687630e-02 -7.39600897e-01
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b8f6b1b3-0d6e-4ce9-97e9-ca59b3df711a | unsupervised-person-re-identification | 1705.10444 | null | http://arxiv.org/abs/1705.10444v2 | http://arxiv.org/pdf/1705.10444v2.pdf | Unsupervised Person Re-identification: Clustering and Fine-tuning | The superiority of deeply learned pedestrian representations has been
reported in very recent literature of person re-identification (re-ID). In this
paper, we consider the more pragmatic issue of learning a deep feature with no
or only a few labels. We propose a progressive unsupervised learning (PUL)
method to transfer pretrained deep representations to unseen domains. Our
method is easy to implement and can be viewed as an effective baseline for
unsupervised re-ID feature learning. Specifically, PUL iterates between 1)
pedestrian clustering and 2) fine-tuning of the convolutional neural network
(CNN) to improve the original model trained on the irrelevant labeled dataset.
Since the clustering results can be very noisy, we add a selection operation
between the clustering and fine-tuning. At the beginning when the model is
weak, CNN is fine-tuned on a small amount of reliable examples which locate
near to cluster centroids in the feature space. As the model becomes stronger
in subsequent iterations, more images are being adaptively selected as CNN
training samples. Progressively, pedestrian clustering and the CNN model are
improved simultaneously until algorithm convergence. This process is naturally
formulated as self-paced learning. We then point out promising directions that
may lead to further improvement. Extensive experiments on three large-scale
re-ID datasets demonstrate that PUL outputs discriminative features that
improve the re-ID accuracy. | ['Yi Yang', 'Hehe Fan', 'Liang Zheng'] | 2017-05-30 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-3.21365707e-02 -1.29108995e-01 -1.41210556e-01 -5.89473724e-01
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36053604-52bd-4453-899a-ed971b9fd2ff | neural-combinatory-constituency-parsing | 2106.06689 | null | https://arxiv.org/abs/2106.06689v1 | https://arxiv.org/pdf/2106.06689v1.pdf | Neural Combinatory Constituency Parsing | We propose two fast neural combinatory models for constituency parsing: binary and multi-branching. Our models decompose the bottom-up parsing process into 1) classification of tags, labels, and binary orientations or chunks and 2) vector composition based on the computed orientations or chunks. These models have theoretical sub-quadratic complexity and empirical linear complexity. The binary model achieves an F1 score of 92.54 on Penn Treebank, speeding at 1327.2 sents/sec. Both the models with XLNet provide near state-of-the-art accuracies for English. Syntactic branching tendency and headedness of a language are observed during the training and inference processes for Penn Treebank, Chinese Treebank, and Keyaki Treebank (Japanese). | ['Mamoru Komachi', 'Aizhan Imankulova', 'Longtu Zhang', 'Zhousi Chen'] | 2021-06-12 | null | https://aclanthology.org/2021.findings-acl.194 | https://aclanthology.org/2021.findings-acl.194.pdf | findings-acl-2021-8 | ['constituency-parsing'] | ['natural-language-processing'] | [-1.82500109e-01 5.90804636e-01 -4.15965527e-01 -9.11070168e-01
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d64c42d2-cfec-40e7-a0ee-8cccb9df5259 | memory-augmented-sequential-paragraph | 2102.03741 | null | https://arxiv.org/abs/2102.03741v1 | https://arxiv.org/pdf/2102.03741v1.pdf | Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering | Retrieving information from correlative paragraphs or documents to answer open-domain multi-hop questions is very challenging. To deal with this challenge, most of the existing works consider paragraphs as nodes in a graph and propose graph-based methods to retrieve them. However, in this paper, we point out the intrinsic defect of such methods. Instead, we propose a new architecture that models paragraphs as sequential data and considers multi-hop information retrieval as a kind of sequence labeling task. Specifically, we design a rewritable external memory to model the dependency among paragraphs. Moreover, a threshold gate mechanism is proposed to eliminate the distraction of noise paragraphs. We evaluate our method on both full wiki and distractor subtask of HotpotQA, a public textual multi-hop QA dataset requiring multi-hop information retrieval. Experiments show that our method achieves significant improvement over the published state-of-the-art method in retrieval and downstream QA task performance. | ['Guoping Hu', 'Shijin Wang', 'Ting Liu', 'Yiming Cui', 'Nan Shao'] | 2021-02-07 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.12462327e-01 3.82982254e-01 -2.78485596e-01 -1.85037404e-01
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9f3e5b7a-1076-4a12-82e0-4b4aa02da6a2 | a-technique-to-create-weaker-abstract-board | 2209.00711 | null | https://arxiv.org/abs/2209.00711v1 | https://arxiv.org/pdf/2209.00711v1.pdf | A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning | Board games, with the exception of solo games, need at least one other player to play. Because of this, we created Artificial Intelligent (AI) agents to play against us when an opponent is missing. These AI agents are created in a number of ways, but one challenge with these agents is that an agent can have superior ability compared to us. In this work, we describe how to create weaker AI agents that play board games. We use Tic-Tac-Toe, Nine-Men's Morris, and Mancala, and our technique uses a Reinforcement Learning model where an agent uses the Q-learning algorithm to learn these games. We show how these agents can learn to play the board game perfectly, and we then describe our approach to making weaker versions of these agents. Finally, we provide a methodology to compare AI agents. | ['Indrima Upadhyay', 'Peter Jamieson'] | 2022-09-01 | null | null | null | null | ['board-games'] | ['playing-games'] | [-1.22789375e-01 4.58952218e-01 1.75779581e-01 2.23886460e-01
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08c4847d-ce2b-491e-a888-087130592776 | orthogonal-features-based-eeg-signals | 2104.08120 | null | https://arxiv.org/abs/2104.08120v1 | https://arxiv.org/pdf/2104.08120v1.pdf | Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN AutoEncoder | This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts (MA), introduced by the movement of muscles. The existing EEG denoising methods make use of decomposition, thresholding and filtering techniques. In the proposed approach, EEG signals are first transformed to orthogonal domain using Tchebichef moments before feeding to the proposed architecture. A new hyper-parameter ($\alpha$) is introduced which refers to the fractional order with respect to which gradients are calculated during back-propagation. It is observed that by tuning $\alpha$, the quality of the restored signal improves significantly. Motivated by the high usage of portable low energy devices which make use of compressed deep learning architectures, the trainable parameters of the proposed architecture are compressed using randomized singular value decomposition (RSVD) algorithm. The experiments are performed on the standard EEG datasets, namely, Mendeley and Bonn. The study shows that the proposed fractional and compressed architecture performs better than existing state-of-the-art signal denoising methods. | ['Ahlad Kumar', 'Subham Nagar'] | 2021-04-16 | null | null | null | null | ['eeg-denoising'] | ['methodology'] | [ 2.10820526e-01 -1.86169460e-01 5.79852521e-01 -2.87254602e-01
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d8d99680-30d7-4f6e-8a9d-c0fc7ab3e902 | emergent-and-predictable-memorization-in | 2304.11158 | null | https://arxiv.org/abs/2304.11158v2 | https://arxiv.org/pdf/2304.11158v2.pdf | Emergent and Predictable Memorization in Large Language Models | Memorization, or the tendency of large language models (LLMs) to output entire sequences from their training data verbatim, is a key concern for safely deploying language models. In particular, it is vital to minimize a model's memorization of sensitive datapoints such as those containing personal identifiable information (PII). The prevalence of such undesirable memorization can pose issues for model trainers, and may even require discarding an otherwise functional model. We therefore seek to predict which sequences will be memorized before a large model's full train-time by extrapolating the memorization behavior of lower-compute trial runs. We measure memorization of the Pythia model suite and plot scaling laws for forecasting memorization, allowing us to provide equi-compute recommendations to maximize the reliability (recall) of such predictions. We additionally provide further novel discoveries on the distribution of memorization scores across models and data. We release all code and data necessary to reproduce the results in this paper at https://github.com/EleutherAI/pythia | ['Edward Raff', 'Shivanshu Purohit', 'Quentin Anthony', 'Hailey Schoelkopf', 'Lintang Sutawika', 'USVSN Sai Prashanth', 'Stella Biderman'] | 2023-04-21 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 8.04287493e-02 -4.34012599e-02 -1.36894450e-01 -3.91526818e-01
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d327332c-62a3-4ad3-8847-c5c5910c0587 | improving-model-s-focus-improves-performance | 2303.00818 | null | https://arxiv.org/abs/2303.00818v1 | https://arxiv.org/pdf/2303.00818v1.pdf | Improving Model's Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors | Deep learning-based models generalize better to unknown data samples after being guided "where to look" by incorporating human perception into training strategies. We made an observation that the entropy of the model's salience trained in that way is lower when compared to salience entropy computed for models training without human perceptual intelligence. Thus the question: does further increase of model's focus, by lowering the entropy of model's class activation map, help in further increasing the performance? In this paper we propose and evaluate several entropy-based new loss function components controlling the model's focus, covering the full range of the level of such control, from none to its "aggressive" minimization. We show, using a problem of synthetic face detection, that improving the model's focus, through lowering entropy, leads to models that perform better in an open-set scenario, in which the test samples are synthesized by unknown generative models. We also show that optimal performance is obtained when the model's loss function blends three aspects: regular classification, low-entropy of the model's focus, and human-guided saliency. | ['Christopher Sweet', 'Adam Czajka', 'Jacob Piland'] | 2023-03-01 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 5.06795347e-01 7.34920084e-01 -3.22619677e-02 -4.95198309e-01
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f3352e7c-8eb2-4722-9aae-1fe6d454e44a | prediction-interval-for-neural-network-models | 2210.04318 | null | https://arxiv.org/abs/2210.04318v4 | https://arxiv.org/pdf/2210.04318v4.pdf | Prediction intervals for neural network models using weighted asymmetric loss functions | We propose a simple and efficient approach to generate a prediction intervals (PI) for approximated and forecasted trends. Our method leverages a weighted asymmetric loss function to estimate the lower and upper bounds of the PI, with the weights determined by its coverage probability. We provide a concise mathematical proof of the method, show how it can be extended to derive PIs for parametrised functions and argue why the method works for predicting PIs of dependent variables. The presented tests of the method on a real-world forecasting task using a neural network-based model show that it can produce reliable PIs in complex machine learning scenarios. | ['Agnieszka Werpachowska', 'Yunpeng Han', 'Milo Grillo'] | 2022-10-09 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-4.49157767e-02 3.27856660e-01 -6.52690709e-01 -6.61756217e-01
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050d9fda-42cc-4f90-a1cc-53c8c59e2df1 | the-effects-of-super-resolution-on-object | 1812.04098 | null | http://arxiv.org/abs/1812.04098v3 | http://arxiv.org/pdf/1812.04098v3.pdf | The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery | We explore the application of super-resolution techniques to satellite
imagery, and the effects of these techniques on object detection algorithm
performance. Specifically, we enhance satellite imagery beyond its native
resolution, and test if we can identify various types of vehicles, planes, and
boats with greater accuracy than native resolution. Using the Very Deep
Super-Resolution (VDSR) framework and a custom Random Forest Super-Resolution
(RFSR) framework we generate enhancement levels of 2x, 4x, and 8x over five
distinct resolutions ranging from 30 cm to 4.8 meters. Using both native and
super-resolved data, we then train several custom detection models using the
SIMRDWN object detection framework. SIMRDWN combines a number of popular object
detection algorithms (e.g. SSD, YOLO) into a unified framework that is designed
to rapidly detect objects in large satellite images. This approach allows us to
quantify the effects of super-resolution techniques on object detection
performance across multiple classes and resolutions. We also quantify the
performance of object detection as a function of native resolution and object
pixel size. For our test set we note that performance degrades from mean
average precision (mAP) = 0.53 at 30 cm resolution, down to mAP = 0.11 at 4.8 m
resolution. Super-resolving native 30 cm imagery to 15 cm yields the greatest
benefit; a 13-36% improvement in mAP. Super-resolution is less beneficial at
coarser resolutions, though still provides a small improvement in performance. | ['Adam Van Etten', 'Jacob Shermeyer'] | 2018-12-10 | null | null | null | null | ['satellite-image-super-resolution'] | ['computer-vision'] | [ 4.53346521e-01 -3.51545990e-01 1.69906303e-01 -1.14825822e-01
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c1911afb-8e63-4626-9e77-27b06f9c59c8 | automated-classification-of-stroke-blood-clot | 2304.13775 | null | https://arxiv.org/abs/2304.13775v1 | https://arxiv.org/pdf/2304.13775v1.pdf | Automated Classification of Stroke Blood Clot Origin using Whole-Slide Digital Pathology Images | The classification of the origin of blood clots is a crucial step in diagnosing and treating ischemic stroke. Various imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound have been employed to detect and locate blood clots within the body. However, identifying the origin of a blood clot remains challenging due to the complexity of the blood flow dynamics and the limitations of the imaging techniques. The study suggests a novel methodology for classifying the source of a blood clot through the integration of data from whole-slide digital pathology images, which are utilized to fine-tune several cutting-edge computer vision models. Upon comparison, the SwinTransformerV2 model outperforms all the other models and achieves an accuracy score of 94.24%, precision score of 94.41%, recall score of 94.09%, and, f1-score of 94.06%. Our approach shows promising results in detecting the origin of blood clots in different vascular regions and can potentially improve the diagnosis and management of ischemic stroke. | ['D. Elangovan', 'G. Senthilkumar', 'M. Logeshwaran', 'P. J. Joe Nikesh', 'Koushik Sivarama Krishnan'] | 2023-04-26 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [-2.01080754e-01 -3.54296535e-01 -4.73264843e-01 -5.54763479e-03
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8f2c4fb4-a802-46f8-b394-8aa21aff2f23 | decentralized-machine-learning-for | 2207.14584 | null | https://arxiv.org/abs/2207.14584v2 | https://arxiv.org/pdf/2207.14584v2.pdf | Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum | The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research or can utilize the omnipresent data produced by the personal medical devices. Besides, the EHR systems are centrally orchestrated, which could potentially lead to a single point of failure. Therefore, in this article, we explore novel approaches for decentralizing machine learning over distributed ledgers to create intelligent EHR systems that can utilize information from personal medical devices for improved knowledge extraction. Consequently, we proposed and evaluated a conceptual EHR to enable anonymous predictive analysis across multiple medical institutions. The evaluation results indicate that the decentralized EHR can be deployed over the computing continuum with reduced machine learning time of up to 60% and consensus latency of below 8 seconds. | ['Radu Prodan', 'Sasko Ristov', 'Dragi Kimovski'] | 2022-07-29 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [-3.47672433e-01 5.89646041e-01 -1.54012278e-01 -3.04182023e-01
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9ee230ee-8a1f-4b80-a2b7-c2e231a3485b | a-framework-for-refining-text-classification | 2305.17401 | null | https://arxiv.org/abs/2305.17401v2 | https://arxiv.org/pdf/2305.17401v2.pdf | A Framework For Refining Text Classification and Object Recognition from Academic Articles | With the widespread use of the internet, it has become increasingly crucial to extract specific information from vast amounts of academic articles efficiently. Data mining techniques are generally employed to solve this issue. However, data mining for academic articles is challenging since it requires automatically extracting specific patterns in complex and unstructured layout documents. Current data mining methods for academic articles employ rule-based(RB) or machine learning(ML) approaches. However, using rule-based methods incurs a high coding cost for complex typesetting articles. On the other hand, simply using machine learning methods requires annotation work for complex content types within the paper, which can be costly. Furthermore, only using machine learning can lead to cases where patterns easily recognized by rule-based methods are mistakenly extracted. To overcome these issues, from the perspective of analyzing the standard layout and typesetting used in the specified publication, we emphasize implementing specific methods for specific characteristics in academic articles. We have developed a novel Text Block Refinement Framework (TBRF), a machine learning and rule-based scheme hybrid. We used the well-known ACL proceeding articles as experimental data for the validation experiment. The experiment shows that our approach achieved over 95% classification accuracy and 90% detection accuracy for tables and figures. | ['Shinobu Hasegawa', 'Wen Gu', 'Koichi Ota', 'Jinghong Li'] | 2023-05-27 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-2.95773540e-02 -1.10058226e-01 -3.96201164e-01 -5.56795895e-02
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8ddd4af0-e77d-490d-a7c4-8435db408402 | eli5-long-form-question-answering | 1907.09190 | null | https://arxiv.org/abs/1907.09190v1 | https://arxiv.org/pdf/1907.09190v1.pdf | ELI5: Long Form Question Answering | We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K threads from the Reddit forum ``Explain Like I'm Five'' (ELI5) where an online community provides answers to questions which are comprehensible by five year olds. Compared to existing datasets, ELI5 comprises diverse questions requiring multi-sentence answers. We provide a large set of web documents to help answer the question. Automatic and human evaluations show that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline. However, our best model is still far from human performance since raters prefer gold responses in over 86% of cases, leaving ample opportunity for future improvement. | ['Jason Weston', 'Yacine Jernite', 'Michael Auli', 'David Grangier', 'Angela Fan', 'Ethan Perez'] | 2019-07-22 | eli5-long-form-question-answering-1 | https://aclanthology.org/P19-1346 | https://aclanthology.org/P19-1346.pdf | acl-2019-7 | ['long-form-question-answering'] | ['natural-language-processing'] | [-1.32923067e-01 4.75631803e-01 1.26405358e-01 -5.55325449e-01
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0473dffa-9b32-47ae-a481-8449fef54f87 | a-comparative-study-of-neural-network | 1910.11144 | null | https://arxiv.org/abs/1910.11144v1 | https://arxiv.org/pdf/1910.11144v1.pdf | A Comparative Study of Neural Network Compression | There has recently been an increasing desire to evaluate neural networks locally on computationally-limited devices in order to exploit their recent effectiveness for several applications; such effectiveness has nevertheless come together with a considerable increase in the size of modern neural networks, which constitute a major downside in several of the aforementioned computationally-limited settings. There has thus been a demand of compression techniques for neural networks. Several proposal in this direction have been made, which famously include hashing-based methods and pruning-based ones. However, the evaluation of the efficacy of these techniques has so far been heterogeneous, with no clear evidence in favor of any of them over the others. The goal of this work is to address this latter issue by providing a comparative study. While most previous studies test the capability of a technique in reducing the number of parameters of state-of-the-art networks , we follow [CWT + 15] in evaluating their performance on basic ar-chitectures on the MNIST dataset and variants of it, which allows for a clearer analysis of some aspects of their behavior. To the best of our knowledge, we are the first to directly compare famous approaches such as HashedNet, Optimal Brain Damage (OBD), and magnitude-based pruning with L1 and L2 regularization among them and against equivalent-size feed-forward neural networks with simple (fully-connected) and structural (convolutional) neural networks. Rather surprisingly, our experiments show that (iterative) pruning-based methods are substantially better than the HashedNet architecture, whose compression doesn't appear advantageous to a carefully chosen convolutional network. We also show that, when the compression level is high, the famous OBD pruning heuristics deteriorates to the point of being less efficient than simple magnitude-based techniques. | ['Hossein Baktash', 'Emanuele Natale', 'Laurent Viennot'] | 2019-10-24 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 2.11648881e-01 2.33004630e-01 1.72469784e-02 -3.67781132e-01
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0bdca38e-925a-4e94-99ea-14ddd176f52d | object-centric-image-generation-from-layouts | 2003.07449 | null | https://arxiv.org/abs/2003.07449v2 | https://arxiv.org/pdf/2003.07449v2.pdf | Object-Centric Image Generation from Layouts | Despite recent impressive results on single-object and single-domain image generation, the generation of complex scenes with multiple objects remains challenging. In this paper, we start with the idea that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes well. Our layout-to-image-generation method, which we call Object-Centric Generative Adversarial Network (or OC-GAN), relies on a novel Scene-Graph Similarity Module (SGSM). The SGSM learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We also propose changes to the conditioning mechanism of the generator that enhance its object instance-awareness. Apart from improving image quality, our contributions mitigate two failure modes in previous approaches: (1) spurious objects being generated without corresponding bounding boxes in the layout, and (2) overlapping bounding boxes in the layout leading to merged objects in images. Extensive quantitative evaluation and ablation studies demonstrate the impact of our contributions, with our model outperforming previous state-of-the-art approaches on both the COCO-Stuff and Visual Genome datasets. Finally, we address an important limitation of evaluation metrics used in previous works by introducing SceneFID -- an object-centric adaptation of the popular Fr{\'e}chet Inception Distance metric, that is better suited for multi-object images. | ['R. Devon Hjelm', 'Tristan Sylvain', 'Shikhar Sharma', 'Yoshua Bengio', 'Pengchuan Zhang'] | 2020-03-16 | null | null | null | null | ['layout-to-image-generation'] | ['computer-vision'] | [ 7.59984970e-01 2.02731863e-01 3.87951821e-01 -1.33654714e-01
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c2fb1824-26f0-4a65-90c7-6b0778d7fd29 | test-time-batch-statistics-calibration-for | 2110.04065 | null | https://arxiv.org/abs/2110.04065v1 | https://arxiv.org/pdf/2110.04065v1.pdf | Test-time Batch Statistics Calibration for Covariate Shift | Deep neural networks have a clear degradation when applying to the unseen environment due to the covariate shift. Conventional approaches like domain adaptation requires the pre-collected target data for iterative training, which is impractical in real-world applications. In this paper, we propose to adapt the deep models to the novel environment during inference. An previous solution is test time normalization, which substitutes the source statistics in BN layers with the target batch statistics. However, we show that test time normalization may potentially deteriorate the discriminative structures due to the mismatch between target batch statistics and source parameters. To this end, we present a general formulation $\alpha$-BN to calibrate the batch statistics by mixing up the source and target statistics for both alleviating the domain shift and preserving the discriminative structures. Based on $\alpha$-BN, we further present a novel loss function to form a unified test time adaptation framework Core, which performs the pairwise class correlation online optimization. Extensive experiments show that our approaches achieve the state-of-the-art performance on total twelve datasets from three topics, including model robustness to corruptions, domain generalization on image classification and semantic segmentation. Particularly, our $\alpha$-BN improves 28.4\% to 43.9\% on GTA5 $\rightarrow$ Cityscapes without any training, even outperforms the latest source-free domain adaptation method. | ['Zhou Zhao', 'Jingjing Li', 'Fuming You'] | 2021-10-06 | test-time-batch-statistics-calibration-for-1 | https://openreview.net/forum?id=9gz8qakpyhG | https://openreview.net/pdf?id=9gz8qakpyhG | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 1.55242562e-01 -5.07418990e-01 -1.42658487e-01 -8.77466619e-01
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c4578303-4564-429e-b03a-2565f824e57d | prime-probe-1-javascript-0-overcoming-browser | 2103.04952 | null | https://arxiv.org/abs/2103.04952v1 | https://arxiv.org/pdf/2103.04952v1.pdf | Prime+Probe 1, JavaScript 0: Overcoming Browser-based Side-Channel Defenses | The "eternal war in cache" has reached browsers, with multiple cache-based side-channel attacks and countermeasures being suggested. A common approach for countermeasures is to disable or restrict JavaScript features deemed essential for carrying out attacks. To assess the effectiveness of this approach, in this work we seek to identify those JavaScript features which are essential for carrying out a cache-based attack. We develop a sequence of attacks with progressively decreasing dependency on JavaScript features, culminating in the first browser-based side-channel attack which is constructed entirely from Cascading Style Sheets (CSS) and HTML, and works even when script execution is completely blocked. We then show that avoiding JavaScript features makes our techniques architecturally agnostic, resulting in microarchitectural website fingerprinting attacks that work across hardware platforms including Intel Core, AMD Ryzen, Samsung Exynos, and Apple M1 architectures. As a final contribution, we evaluate our techniques in hardened browser environments including the Tor browser, Deter-Fox (Cao el al., CCS 2017), and Chrome Zero (Schwartz et al., NDSS 2018). We confirm that none of these approaches completely defend against our attacks. We further argue that the protections of Chrome Zero need to be more comprehensively applied, and that the performance and user experience of Chrome Zero will be severely degraded if this approach is taken. | ['Yuval Yarom', 'Yossi Oren', 'Daniel Genkin', "Sioli O'Connell", 'Ayush Agarwal', 'Anatoly Shusterman'] | 2021-03-08 | null | null | null | null | ['website-fingerprinting-attacks'] | ['adversarial'] | [ 4.69099358e-03 -4.58874613e-01 -2.68848270e-01 9.80682820e-02
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850d7a3d-b489-4324-8fe1-466bdd0f74b1 | a-unified-framework-for-fast-large-scale | 2303.12751 | null | https://arxiv.org/abs/2303.12751v1 | https://arxiv.org/pdf/2303.12751v1.pdf | A Unified Framework for Fast Large-Scale Portfolio Optimization | We develop a unified framework for fast large-scale portfolio optimization with shrinkage and regularization for different objectives such as minimum variance, mean-variance, and maximum Sharpe ratio with various constraints on the portfolio weights. For all of the optimization problems, we derive the corresponding quadratic programming problems and implement them in an open-source Python library. We use the proposed framework to evaluate the out-of-sample portfolio performance of popular covariance matrix estimators such as sample covariance matrix, linear and nonlinear shrinkage estimators, and the covariance matrix from the instrumented principal component analysis (IPCA). We use 65 years of monthly returns from (on average) 585 largest companies in the US market, and 94 monthly firm-specific characteristics for the IPCA model. We show that the regularization of the portfolio norms greatly benefits the performance of the IPCA model in portfolio optimization, resulting in outperformance linear and nonlinear shrinkage estimators even under realistic constraints on the portfolio weights. | ['Abolfazl Safikhani', 'Pawel Polak', 'Ronakdilip Shah', 'Weichuan Deng'] | 2023-03-22 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-3.42829168e-01 -2.23953024e-01 -4.23295610e-02 -4.96990353e-01
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49628913-dfbb-4b6e-bddc-13781ab5cea2 | prolificdreamer-high-fidelity-and-diverse | 2305.16213 | null | https://arxiv.org/abs/2305.16213v1 | https://arxiv.org/pdf/2305.16213v1.pdf | ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation | Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., $7.5$). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., $512\times512$) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic. Project page: https://ml.cs.tsinghua.edu.cn/prolificdreamer/ | ['Jun Zhu', 'Hang Su', 'Chongxuan Li', 'Fan Bao', 'Yikai Wang', 'Cheng Lu', 'Zhengyi Wang'] | 2023-05-25 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 1.93409659e-02 -1.26641497e-01 1.75020799e-01 1.74050391e-01
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1124290c-3d66-437b-8fb8-32a554e3b6c6 | neural-wavelet-domain-diffusion-for-3d-shape-1 | 2302.00190 | null | https://arxiv.org/abs/2302.00190v1 | https://arxiv.org/pdf/2302.00190v1.pdf | Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation | This paper presents a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain. Specifically, we propose a compact wavelet representation with a pair of coarse and detail coefficient volumes to implicitly represent 3D shapes via truncated signed distance functions and multi-scale biorthogonal wavelets. Then, we design a pair of neural networks: a diffusion-based generator to produce diverse shapes in the form of the coarse coefficient volumes and a detail predictor to produce compatible detail coefficient volumes for introducing fine structures and details. Further, we may jointly train an encoder network to learn a latent space for inverting shapes, allowing us to enable a rich variety of whole-shape and region-aware shape manipulations. Both quantitative and qualitative experimental results manifest the compelling shape generation, inversion, and manipulation capabilities of our approach over the state-of-the-art methods. | ['Chi-Wing Fu', 'Ruihui Li', 'Zhengzhe Liu', 'Ka-Hei Hui', 'Jingyu Hu'] | 2023-02-01 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 2.11708799e-01 3.41236353e-01 9.64751020e-02 -1.40787870e-01
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8a11fc95-6d77-49d4-8255-fdf70fd5177e | document-level-multi-event-extraction-with | 2305.18926 | null | https://arxiv.org/abs/2305.18926v1 | https://arxiv.org/pdf/2305.18926v1.pdf | Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization | Document-level multi-event extraction aims to extract the structural information from a given document automatically. Most recent approaches usually involve two steps: (1) modeling entity interactions; (2) decoding entity interactions into events. However, such approaches ignore a global view of inter-dependency of multiple events. Moreover, an event is decoded by iteratively merging its related entities as arguments, which might suffer from error propagation and is computationally inefficient. In this paper, we propose an alternative approach for document-level multi-event extraction with event proxy nodes and Hausdorff distance minimization. The event proxy nodes, representing pseudo-events, are able to build connections with other event proxy nodes, essentially capturing global information. The Hausdorff distance makes it possible to compare the similarity between the set of predicted events and the set of ground-truth events. By directly minimizing Hausdorff distance, the model is trained towards the global optimum directly, which improves performance and reduces training time. Experimental results show that our model outperforms previous state-of-the-art method in F1-score on two datasets with only a fraction of training time. | ['Yulan He', 'Lin Gui', 'Xinyu Wang'] | 2023-05-30 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 2.75028944e-01 3.87021065e-01 -1.79483488e-01 -3.15242410e-01
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56df1845-bbec-494a-bd13-ee86f1dbfe8b | q-malizing-flow-and-infinitesimal-density | 2305.11857 | null | https://arxiv.org/abs/2305.11857v2 | https://arxiv.org/pdf/2305.11857v2.pdf | Optimal transport flow and infinitesimal density ratio estimation | Continuous normalizing flows are widely used in generative tasks, where a flow network transports from a data distribution $P$ to a normal distribution. A flow model that transports from $P$ to an arbitrary $Q$, where both $P$ and $Q$ are accessible via finite samples, is of various application interests, particularly in the recently developed telescoping density ratio estimation (DRE) which calls for the construction of intermediate densities to bridge between the two densities. In this work, we propose such a flow by a neural-ODE model which is trained from empirical samples to transport invertibly from $P$ to $Q$ (and vice versa) and optimally by minimizing the transport cost. The trained flow model allows us to perform infinitesimal DRE along the time-parametrized $\log$-density by training an additional continuous-time network using classification loss, whose time integration provides a telescopic DRE. The effectiveness of the proposed model is empirically demonstrated on high-dimensional mutual information estimation and energy-based generative models of image data. | ['Yao Xie', 'Xiuyuan Cheng', 'Chen Xu'] | 2023-05-19 | null | null | null | null | ['density-ratio-estimation', 'mutual-information-estimation'] | ['methodology', 'methodology'] | [-1.33554880e-02 3.94100957e-02 -7.88371935e-02 -3.76470864e-01
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783d9358-1b98-43d9-a5eb-e8d45e98cfee | sill-net-feature-augmentation-with-separated | 2102.03539 | null | https://arxiv.org/abs/2102.03539v3 | https://arxiv.org/pdf/2102.03539v3.pdf | Sill-Net: Feature Augmentation with Separated Illumination Representation | For visual object recognition tasks, the illumination variations can cause distinct changes in object appearance and thus confuse the deep neural network based recognition models. Especially for some rare illumination conditions, collecting sufficient training samples could be time-consuming and expensive. To solve this problem, in this paper we propose a novel neural network architecture called Separating-Illumination Network (Sill-Net). Sill-Net learns to separate illumination features from images, and then during training we augment training samples with these separated illumination features in the feature space. Experimental results demonstrate that our approach outperforms current state-of-the-art methods in several object classification benchmarks. | ['ChangShui Zhang', 'Ziang Yan', 'Zhong Cao', 'Haipeng Zhang'] | 2021-02-06 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 2.53122240e-01 -6.51682973e-01 -1.61922321e-01 -7.00673580e-01
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3584ba76-cea0-41f2-9d8f-cf230d5e7462 | coursera-corpus-mining-and-multistage-fine | 1912.11739 | null | https://arxiv.org/abs/1912.11739v2 | https://arxiv.org/pdf/1912.11739v2.pdf | Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation | Lectures translation is a case of spoken language translation and there is a lack of publicly available parallel corpora for this purpose. To address this, we examine a language independent framework for parallel corpus mining which is a quick and effective way to mine a parallel corpus from publicly available lectures at Coursera. Our approach determines sentence alignments, relying on machine translation and cosine similarity over continuous-space sentence representations. We also show how to use the resulting corpora in a multistage fine-tuning based domain adaptation for high-quality lectures translation. For Japanese--English lectures translation, we extracted parallel data of approximately 40,000 lines and created development and test sets through manual filtering for benchmarking translation performance. We demonstrate that the mined corpus greatly enhances the quality of translation when used in conjunction with out-of-domain parallel corpora via multistage training. This paper also suggests some guidelines to gather and clean corpora, mine parallel sentences, address noise in the mined data, and create high-quality evaluation splits. For the sake of reproducibility, we will release our code for parallel data creation. | ['Sadao Kurohashi', 'Raj Dabre', 'Haiyue Song', 'Atsushi Fujita'] | 2019-12-26 | coursera-corpus-mining-and-multistage-fine-1 | https://aclanthology.org/2020.lrec-1.449 | https://aclanthology.org/2020.lrec-1.449.pdf | lrec-2020-5 | ['parallel-corpus-mining'] | ['natural-language-processing'] | [ 2.72027880e-01 -2.83613116e-01 -1.30258620e-01 -5.92861176e-01
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fed69f8c-80d8-420c-bb81-642fce73755c | d2s-document-to-slide-generation-via-query | 2105.03664 | null | https://arxiv.org/abs/2105.03664v1 | https://arxiv.org/pdf/2105.03664v1.pdf | D2S: Document-to-Slide Generation Via Query-Based Text Summarization | Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benchmarking. In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years' NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach: 1) Use slide titles to retrieve relevant and engaging text, figures, and tables; 2) Summarize the retrieved context into bullet points with long-form question answering. Our evaluation suggests that long-form QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation. | ['Nancy X. R. Wang', 'Yunfeng Zhang', 'Dakuo Wang', 'Yufang Hou', 'Edward Sun'] | 2021-05-08 | null | https://aclanthology.org/2021.naacl-main.111 | https://aclanthology.org/2021.naacl-main.111.pdf | naacl-2021-4 | ['long-form-question-answering'] | ['natural-language-processing'] | [ 3.23764652e-01 1.85421005e-01 -3.36504132e-02 -2.25109115e-01
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1d6db917-09cb-40df-959d-32f751143abc | deep-nfa-a-deep-textit-a-contrario-framework | 2303.01363 | null | https://arxiv.org/abs/2303.01363v1 | https://arxiv.org/pdf/2303.01363v1.pdf | Deep-NFA: a Deep $\textit{a contrario}$ Framework for Small Object Detection | The detection of small objects is a challenging task in computer vision. Conventional object detection methods have difficulty in finding the balance between high detection and low false alarm rates. In the literature, some methods have addressed this issue by enhancing the feature map responses, but without guaranteeing robustness with respect to the number of false alarms induced by background elements. To tackle this problem, we introduce an $\textit{a contrario}$ decision criterion into the learning process to take into account the unexpectedness of small objects. This statistic criterion enhances the feature map responses while controlling the number of false alarms (NFA) and can be integrated into any semantic segmentation neural network. Our add-on NFA module not only allows us to obtain competitive results for small target and crack detection tasks respectively, but also leads to more robust and interpretable results. | ['Arnaud Woiselle', 'Sidonie Lefebvre', 'Sylvie Le Hegarat-Mascle', 'Alina Ciocarlan'] | 2023-03-02 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 4.28217322e-01 -1.46271318e-01 2.34006077e-01 -4.21515286e-01
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c8592cde-1718-41c4-9863-9b5e07e660d6 | adversarial-style-augmentation-for-domain-1 | 2207.04892 | null | https://arxiv.org/abs/2207.04892v2 | https://arxiv.org/pdf/2207.04892v2.pdf | Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation | In this paper, we consider the problem of domain generalization in semantic segmentation, which aims to learn a robust model using only labeled synthetic (source) data. The model is expected to perform well on unseen real (target) domains. Our study finds that the image style variation can largely influence the model's performance and the style features can be well represented by the channel-wise mean and standard deviation of images. Inspired by this, we propose a novel adversarial style augmentation (AdvStyle) approach, which can dynamically generate hard stylized images during training and thus can effectively prevent the model from overfitting on the source domain. Specifically, AdvStyle regards the style feature as a learnable parameter and updates it by adversarial training. The learned adversarial style feature is used to construct an adversarial image for robust model training. AdvStyle is easy to implement and can be readily applied to different models. Experiments on two synthetic-to-real semantic segmentation benchmarks demonstrate that AdvStyle can significantly improve the model performance on unseen real domains and show that we can achieve the state of the art. Moreover, AdvStyle can be employed to domain generalized image classification and produces a clear improvement on the considered datasets. | ['Nicu Sebe', 'Gim Hee Lee', 'Yuyang Zhao', 'Zhun Zhong'] | 2022-07-11 | adversarial-style-augmentation-for-domain | https://openreview.net/forum?id=L_sHGieq1D | https://openreview.net/pdf?id=L_sHGieq1D | null | ['scene-segmentation'] | ['computer-vision'] | [ 6.62619531e-01 3.06627065e-01 -6.21842267e-03 -4.56333011e-01
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d516a695-481b-4582-b652-aa2d85282286 | adaptive-conformal-regression-with-jackknife | 2305.19901 | null | https://arxiv.org/abs/2305.19901v1 | https://arxiv.org/pdf/2305.19901v1.pdf | Adaptive Conformal Regression with Jackknife+ Rescaled Scores | Conformal regression provides prediction intervals with global coverage guarantees, but often fails to capture local error distributions, leading to non-homogeneous coverage. We address this with a new adaptive method based on rescaling conformal scores with an estimate of local score distribution, inspired by the Jackknife+ method, which enables the use of calibration data in conformal scores without breaking calibration-test exchangeability. Our approach ensures formal global coverage guarantees and is supported by new theoretical results on local coverage, including an a posteriori bound on any calibration score. The strength of our approach lies in achieving local coverage without sacrificing calibration set size, improving the applicability of conformal prediction intervals in various settings. As a result, our method provides prediction intervals that outperform previous methods, particularly in the low-data regime, making it especially relevant for real-world applications such as healthcare and biomedical domains where uncertainty needs to be quantified accurately despite low sample data. | ['Maria Rodriguez Martinez', 'Mattia Rigotti', 'Nicolas Deutschmann'] | 2023-05-31 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 5.77049315e-01 6.33741498e-01 -5.46248496e-01 -4.87137526e-01
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11e00a18-f6ea-4302-b7b6-88010cbe7c8a | foresee-what-you-will-learn-data-augmentation | 2301.07845 | null | https://arxiv.org/abs/2301.07845v2 | https://arxiv.org/pdf/2301.07845v2.pdf | Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment | Existing domain generalization aims to learn a generalizable model to perform well even on unseen domains. For many real-world machine learning applications, the data distribution often shifts gradually along domain indices. For example, a self-driving car with a vision system drives from dawn to dusk, with the sky darkening gradually. Therefore, the system must be able to adapt to changes in ambient illumination and continue to drive safely on the road. In this paper, we formulate such problems as Evolving Domain Generalization, where a model aims to generalize well on a target domain by discovering and leveraging the evolving pattern of the environment. We then propose Directional Domain Augmentation (DDA), which simulates the unseen target features by mapping source data as augmentations through a domain transformer. Specifically, we formulate DDA as a bi-level optimization problem and solve it through a novel meta-learning approach in the representation space. We evaluate the proposed method on both synthetic datasets and realworld datasets, and empirical results show that our approach can outperform other existing methods. | ['Boyu Wang', 'Charles Ling', 'Fan Zhou', 'Wei Wang', 'Qiuhao Zeng'] | 2023-01-19 | null | null | null | null | ['evolving-domain-generalization'] | ['computer-vision'] | [ 4.64726239e-01 -3.00654262e-01 -1.38368621e-01 -6.12720668e-01
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51be1746-fd1b-46a1-b2e6-ee5bfd144fac | a-new-image-codec-paradigm-for-human-and | 2112.10071 | null | https://arxiv.org/abs/2112.10071v1 | https://arxiv.org/pdf/2112.10071v1.pdf | A New Image Codec Paradigm for Human and Machine Uses | With the AI of Things (AIoT) development, a huge amount of visual data, e.g., images and videos, are produced in our daily work and life. These visual data are not only used for human viewing or understanding but also for machine analysis or decision-making, e.g., intelligent surveillance, automated vehicles, and many other smart city applications. To this end, a new image codec paradigm for both human and machine uses is proposed in this work. Firstly, the high-level instance segmentation map and the low-level signal features are extracted with neural networks. Then, the instance segmentation map is further represented as a profile with the proposed 16-bit gray-scale representation. After that, both 16-bit gray-scale profile and signal features are encoded with a lossless codec. Meanwhile, an image predictor is designed and trained to achieve the general-quality image reconstruction with the 16-bit gray-scale profile and signal features. Finally, the residual map between the original image and the predicted one is compressed with a lossy codec, used for high-quality image reconstruction. With such designs, on the one hand, we can achieve scalable image compression to meet the requirements of different human consumption; on the other hand, we can directly achieve several machine vision tasks at the decoder side with the decoded 16-bit gray-scale profile, e.g., object classification, detection, and segmentation. Experimental results show that the proposed codec achieves comparable results as most learning-based codecs and outperforms the traditional codecs (e.g., BPG and JPEG2000) in terms of PSNR and MS-SSIM for image reconstruction. At the same time, it outperforms the existing codecs in terms of the mAP for object detection and segmentation. | ['Huaxiang Zhang', 'Zhengguang Li', 'Tsui-Shan Chang', 'Zhuo Chen', 'Weisi Lin', 'Lili Meng', 'Jian Jin', 'Sien Chen'] | 2021-12-19 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 4.85403031e-01 -3.89367312e-01 -3.23246449e-01 -1.73233688e-01
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f42ec4f6-a770-4002-bf51-1718c183222b | thinking-about-causation-a-causal-language | 2010.16217 | null | https://arxiv.org/abs/2010.16217v1 | https://arxiv.org/pdf/2010.16217v1.pdf | Thinking About Causation: A Causal Language with Epistemic Operators | This paper proposes a formal framework for modeling the interaction of causal and (qualitative) epistemic reasoning. To this purpose, we extend the notion of a causal model with a representation of the epistemic state of an agent. On the side of the object language, we add operators to express knowledge and the act of observing new information. We provide a sound and complete axiomatization of the logic, and discuss the relation of this framework to causal team semantics. | ['Kaibo Xie', 'Fernando R. Velázquez-Quesada', 'Sonja Smets', 'Katrin Schulz', 'Fausto Barbero'] | 2020-10-30 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [-1.31843552e-01 9.74568486e-01 -2.16866046e-01 -3.99463981e-01
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c30a8d09-2b92-45c6-af58-5dbb539c9df0 | rgb-infrared-cross-modality-person-re | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Wu_RGB-Infrared_Cross-Modality_Person_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Wu_RGB-Infrared_Cross-Modality_Person_ICCV_2017_paper.pdf | RGB-Infrared Cross-Modality Person Re-Identification | Person re-identification (Re-ID) is an important problem in video surveillance, aiming to match pedestrian images across camera views. Currently, most works focus on RGB-based Re-ID. However, in some applications, RGB images are not suitable, e.g. in a dark environment or at night. Infrared (IR) imaging becomes necessary in many visual systems. To that end, matching RGB images with infrared images is required, which are heterogeneous with very different visual characteristics. For person Re-ID, this is a very challenging cross-modality problem that has not been studied so far. In this work, we address the RGB-IR cross-modality Re-ID problem and contribute a new multiple modality Re-ID dataset named SYSU-MM01, including RGB and IR images of 491 identities from 6 cameras, giving in total 287,628 RGB images and 15,792 IR images. To explore the RGB-IR Re-ID problem, we evaluate existing popular cross-domain models, including three commonly used neural network structures (one-stream, two-stream and asymmetric FC layer) and analyse the relation between them. We further propose deep zero-padding for training one-stream network towards automatically evolving domain-specific nodes in the network for cross-modality matching. Our experiments show that RGB-IR cross-modality matching is very challenging but still feasible using the proposed model with deep zero-padding, giving the best performance. Our dataset is available at http://isee.sysu.edu.cn/project/RGBIRReID.htm.
| ['Jian-Huang Lai', 'Shaogang Gong', 'Hong-Xing Yu', 'Wei-Shi Zheng', 'Ancong Wu'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['cross-view-person-re-identification'] | ['computer-vision'] | [ 2.35945582e-01 -6.76860154e-01 1.65265761e-02 -1.93839192e-01
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f8eb728e-5b3e-4faa-b9b2-8e543981e505 | k-means-on-a-log-cholesky-manifold-with | 2008.03454 | null | https://arxiv.org/abs/2008.03454v2 | https://arxiv.org/pdf/2008.03454v2.pdf | $k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences | We state theoretical properties for $k$-means clustering of Symmetric Positive Definite (SPD) matrices, in a non-Euclidean space, that provides a natural and favourable representation of these data. We then provide a novel application for this method, to time-series clustering of pixels in a sequence of Synthetic Aperture Radar images, via their finite-lag autocovariance matrices. | ['Hien Nguyen', 'Daniel Fryer', 'Pascal Castellazzi'] | 2020-08-08 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 2.54388034e-01 -5.93817413e-01 4.40279752e-01 -5.84522426e-01
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de8e77c6-7d32-4135-8b09-5c8cbaf27050 | a-multimodal-translation-based-approach-for | null | null | https://aclanthology.org/S18-2027 | https://aclanthology.org/S18-2027.pdf | A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning | Current methods for knowledge graph (KG) representation learning focus solely on the structure of the KG and do not exploit any kind of external information, such as visual and linguistic information corresponding to the KG entities. In this paper, we propose a multimodal translation-based approach that defines the energy of a KG triple as the sum of sub-energy functions that leverage both multimodal (visual and linguistic) and structural KG representations. Next, a ranking-based loss is minimized using a simple neural network architecture. Moreover, we introduce a new large-scale dataset for multimodal KG representation learning. We compared the performance of our approach to other baselines on two standard tasks, namely knowledge graph completion and triple classification, using our as well as the WN9-IMG dataset. The results demonstrate that our approach outperforms all baselines on both tasks and datasets. | ['Hatem Mousselly-Sergieh', 'Iryna Gurevych', 'Stefan Roth', 'Teresa Botschen'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['triple-classification'] | ['graphs'] | [-1.95785705e-02 2.50623465e-01 -5.87075055e-01 -3.10494959e-01
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67338371-6968-4d7a-8a6a-7d30265539af | analysis-of-a-deep-learning-model-for-12-lead | 2211.01738 | null | https://arxiv.org/abs/2211.01738v2 | https://arxiv.org/pdf/2211.01738v2.pdf | Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria | Despite their remarkable performance, deep neural networks remain unadopted in clinical practice, which is considered to be partially due to their lack in explainability. In this work, we apply attribution methods to a pre-trained deep neural network (DNN) for 12-lead electrocardiography classification to open this "black box" and understand the relationship between model prediction and learned features. We classify data from a public data set and the attribution methods assign a "relevance score" to each sample of the classified signals. This allows analyzing what the network learned during training, for which we propose quantitative methods: average relevance scores over a) classes, b) leads, and c) average beats. The analyses of relevance scores for atrial fibrillation (AF) and left bundle branch block (LBBB) compared to healthy controls show that their mean values a) increase with higher classification probability and correspond to false classifications when around zero, and b) correspond to clinical recommendations regarding which lead to consider. Furthermore, c) visible P-waves and concordant T-waves result in clearly negative relevance scores in AF and LBBB classification, respectively. In summary, our analysis suggests that the DNN learned features similar to cardiology textbook knowledge. | ['Anne-Christin Hauschild', 'Nicolai Spicher', 'Tim Seidler', 'Henning Dathe', 'Carolin Müller', 'Dagmar Krefting', 'Jacqueline Michelle Beinecke', 'Theresa Bender'] | 2022-11-03 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.25940749e-01 2.98991084e-01 -3.79484385e-01 -5.93188286e-01
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fd03ee20-1892-42b6-9534-0ad2d2d7ff77 | sagess-sampling-graph-denoising-diffusion | 2306.16827 | null | https://arxiv.org/abs/2306.16827v1 | https://arxiv.org/pdf/2306.16827v1.pdf | SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation | Over recent years, denoising diffusion generative models have come to be considered as state-of-the-art methods for synthetic data generation, especially in the case of generating images. These approaches have also proved successful in other applications such as tabular and graph data generation. However, due to computational complexity, to this date, the application of these techniques to graph data has been restricted to small graphs, such as those used in molecular modeling. In this paper, we propose SaGess, a discrete denoising diffusion approach, which is able to generate large real-world networks by augmenting a diffusion model (DiGress) with a generalized divide-and-conquer framework. The algorithm is capable of generating larger graphs by sampling a covering of subgraphs of the initial graph in order to train DiGress. SaGess then constructs a synthetic graph using the subgraphs that have been generated by DiGress. We evaluate the quality of the synthetic data sets against several competitor methods by comparing graph statistics between the original and synthetic samples, as well as evaluating the utility of the synthetic data set produced by using it to train a task-driven model, namely link prediction. In our experiments, SaGess, outperforms most of the one-shot state-of-the-art graph generating methods by a significant factor, both on the graph metrics and on the link prediction task. | ['Andrew Elliott', 'Gesine Reinert', 'Carsten Maple', 'Mihai Cucuringu', 'Praveen Selvaraj', 'Stratis Limnios'] | 2023-06-29 | null | null | null | null | ['link-prediction', 'graph-generation', 'synthetic-data-generation', 'synthetic-data-generation'] | ['graphs', 'graphs', 'medical', 'miscellaneous'] | [ 3.18605959e-01 5.61279058e-01 2.96949089e-01 1.47389978e-01
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57d25616-25bd-42b4-bdac-895cb01f43d9 | coarse-to-fine-point-cloud-registration-with | 2210.02045 | null | https://arxiv.org/abs/2210.02045v2 | https://arxiv.org/pdf/2210.02045v2.pdf | Coarse-to-Fine Point Cloud Registration with SE(3)-Equivariant Representations | Point cloud registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose differences, or leverage global shapes, which leads to inconsistency when facing distribution variances such as partial overlapping. Combining the advantages of both types of methods, we adopt a coarse-to-fine pipeline that concurrently handles both issues. We first reduce the pose differences between input point clouds by aligning global features; then we match the local features to further refine the inaccurate alignments resulting from distribution variances. As global feature alignment requires the features to preserve the poses of input point clouds and local feature matching expects the features to be invariant to these poses, we propose an SE(3)-equivariant feature extractor to simultaneously generate two types of features. In this feature extractor, representations that preserve the poses are first encoded by our novel SE(3)-equivariant network and then converted into pose-invariant ones by a pose-detaching module. Experiments demonstrate that our proposed method increases the recall rate by 20% compared to state-of-the-art methods when facing both pose differences and distribution variances. | ['Winston H. Hsu', 'Wen-Chin Chen', 'Hsin-Ying Lee', 'Tung-I Chen', 'Cheng-Wei Lin'] | 2022-10-05 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-2.98071885e-03 -2.30416402e-01 1.05382726e-01 -4.27924246e-01
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677e363c-a5e2-4ee6-962a-7a258792547f | agnn-alternating-graph-regularized-neural | 2304.07014 | null | https://arxiv.org/abs/2304.07014v1 | https://arxiv.org/pdf/2304.07014v1.pdf | AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing | Graph Convolutional Network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which shallow networks are extensively adopted. This may be problematic for complex graph datasets because a deeper GCN should be beneficial to propagating information across remote neighbors. Recent works have devoted effort to addressing over-smoothing problems, including establishing residual connection structure or fusing predictions from multi-layer models. Because of the indistinguishable embeddings from deep layers, it is reasonable to generate more reliable predictions before conducting the combination of outputs from various layers. In light of this, we propose an Alternating Graph-regularized Neural Network (AGNN) composed of Graph Convolutional Layer (GCL) and Graph Embedding Layer (GEL). GEL is derived from the graph-regularized optimization containing Laplacian embedding term, which can alleviate the over-smoothing problem by periodic projection from the low-order feature space onto the high-order space. With more distinguishable features of distinct layers, an improved Adaboost strategy is utilized to aggregate outputs from each layer, which explores integrated embeddings of multi-hop neighbors. The proposed model is evaluated via a large number of experiments including performance comparison with some multi-layer or multi-order graph neural networks, which reveals the superior performance improvement of AGNN compared with state-of-the-art models. | ['Wenzhong Guo', 'Claudia Plant', 'Shiping Wang', 'Zhenghong Lin', 'Zhihao Wu', 'Zhaoliang Chen'] | 2023-04-14 | null | null | null | null | ['graph-embedding'] | ['graphs'] | [ 1.35953382e-01 2.82679617e-01 -6.67852014e-02 -1.67820349e-01
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4184e088-199c-4faf-a9ad-9c9954bb1249 | donet-dual-objective-networks-for-skin-lesion | 2008.08278 | null | https://arxiv.org/abs/2008.08278v1 | https://arxiv.org/pdf/2008.08278v1.pdf | DONet: Dual Objective Networks for Skin Lesion Segmentation | Skin lesion segmentation is a crucial step in the computer-aided diagnosis of dermoscopic images. In the last few years, deep learning based semantic segmentation methods have significantly advanced the skin lesion segmentation results. However, the current performance is still unsatisfactory due to some challenging factors such as large variety of lesion scale and ambiguous difference between lesion region and background. In this paper, we propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. Concretely, the two objectives are actually defined by different loss functions. In this way, the two decoders are encouraged to produce differentiated probability maps to match different optimization targets, resulting in complementary predictions accordingly. The complementary information learned by these two objectives are further aggregated together to make the final prediction, by which the uncertainty existing in segmentation maps can be significantly alleviated. Besides, to address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM) to model the complex correlation among skin lesions, where the features with different scale contexts are efficiently integrated to form a more robust representation. Extensive experiments on two popular benchmarks well demonstrate the effectiveness of the proposed DONet. In particular, our DONet achieves 0.881 and 0.931 dice score on ISIC 2018 and $\text{PH}^2$, respectively. Code will be made public available. | ['Xueming Qian', 'Yunchao Wei', 'Yi Yang', 'Yaxiong Wang', 'Li Zhu'] | 2020-08-19 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.55753589e-01 6.75144643e-02 -1.87836617e-01 -3.85460287e-01
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c2315cfe-3f82-4b1f-a8c2-d5925d11eaf8 | causal-language-model-for-zero-shot | null | null | https://openreview.net/forum?id=wFEl0shQ9F1 | https://openreview.net/pdf?id=wFEl0shQ9F1 | Causal Language Model for Zero-shot Constrained Keyphrase Generation | Recently, most of the state-of-the-art keyphrase prediction models are based on a supervised generative model.Although it shows noticeable improvement over statistical methods, it still struggles with low performance on out of the domain and low-resource data. To overcome these limitations, unsupervised methods have also been critical and studied. But the unsupervised method also has a defect in the necessary process which extracting candidates before selecting keyphrases. As not including various forms of phrases, we note that the unsupervised method can't ensure oracle keyphrase.In this paper, we present zero-shot constrained keyphrase generation by leveraging a large-scale language model. To generate diverse keyphrases, we explore controlling a phrase during the generation. Finally, we evaluate benchmark datasets of the scholar domain. It results in better performances than unsupervised methods on several datasets without going through the candidate extraction stage. For domain robustness, we evaluate out-of-domain DUC compare with NUS. Since our method doesn't fine-tune to a corpus of a specific domain, it's better than supervised methods based on Sequence-to-Sequence. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 1.54839441e-01 -5.47666363e-02 -4.71215457e-01 1.05705135e-01
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c73f7f13-157c-442f-ba67-59a1a3134af4 | faithfulness-aware-decoding-strategies-for | 2303.03278 | null | https://arxiv.org/abs/2303.03278v1 | https://arxiv.org/pdf/2303.03278v1.pdf | Faithfulness-Aware Decoding Strategies for Abstractive Summarization | Despite significant progress in understanding and improving faithfulness in abstractive summarization, the question of how decoding strategies affect faithfulness is less studied. We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive summarization. We find a consistent trend where beam search with large beam sizes produces the most faithful summaries while nucleus sampling generates the least faithful ones. We propose two faithfulness-aware generation methods to further improve faithfulness over current generation techniques: (1) ranking candidates generated by beam search using automatic faithfulness metrics and (2) incorporating lookahead heuristics that produce a faithfulness score on the future summary. We show that both generation methods significantly improve faithfulness across two datasets as evaluated by four automatic faithfulness metrics and human evaluation. To reduce computational cost, we demonstrate a simple distillation approach that allows the model to generate faithful summaries with just greedy decoding. Our code is publicly available at https://github.com/amazon-science/faithful-summarization-generation | ['Mohit Bansal', 'Markus Dreyer', 'Kathleen McKeown', 'Mengwen Liu', 'David Wan'] | 2023-03-06 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 1.79846838e-01 2.76455462e-01 -6.21362805e-01 -1.63510993e-01
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102e1a67-c596-4895-ad6c-e5c87430407c | gnn-sl-sequence-labeling-based-on-nearest | 2212.02017 | null | https://arxiv.org/abs/2212.02017v2 | https://arxiv.org/pdf/2212.02017v2.pdf | GNN-SL: Sequence Labeling Based on Nearest Examples via GNN | To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL model output with similar tagging examples retrieved from the whole training set. Since not all the retrieved tagging examples benefit the model prediction, we construct a heterogeneous graph, and leverage graph neural networks (GNNs) to transfer information between the retrieved tagging examples and the input word sequence. The augmented node which aggregates information from neighbors is used to do prediction. This strategy enables the model to directly acquire similar tagging examples and improves the general quality of predictions. We conduct a variety of experiments on three typical sequence labeling tasks: Named Entity Recognition (NER), Part of Speech Tagging (POS), and Chinese Word Segmentation (CWS) to show the significant performance of our GNN-SL. Notably, GNN-SL achieves SOTA results of 96.9 (+0.2) on PKU, 98.3 (+0.4) on CITYU, 98.5 (+0.2) on MSR, and 96.9 (+0.2) on AS for the CWS task, and results comparable to SOTA performances on NER datasets, and POS datasets. | ['Guoyin Wang', 'Lingjuan Lyu', 'Tianwei Zhang', 'Jiwei Li', 'Rongbin Ouyang', 'Yuxian Meng', 'Shuhe Wang'] | 2022-12-05 | null | null | null | null | ['part-of-speech-tagging', 'chinese-word-segmentation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.05916250e-01 4.01279837e-01 -4.12057281e-01 -2.75122702e-01
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1cad684a-76b2-43e0-a38a-91d091adcce9 | discovering-topics-with-neural-topic-models-1 | 1911.10924 | null | https://arxiv.org/abs/1911.10924v1 | https://arxiv.org/pdf/1911.10924v1.pdf | Discovering topics with neural topic models built from PLSA assumptions | In this paper we present a model for unsupervised topic discovery in texts corpora. The proposed model uses documents, words, and topics lookup table embedding as neural network model parameters to build probabilities of words given topics, and probabilities of topics given documents. These probabilities are used to recover by marginalization probabilities of words given documents. For very large corpora where the number of documents can be in the order of billions, using a neural auto-encoder based document embedding is more scalable then using a lookup table embedding as classically done. We thus extended the lookup based document embedding model to continuous auto-encoder based model. Our models are trained using probabilistic latent semantic analysis (PLSA) assumptions. We evaluated our models on six datasets with a rich variety of contents. Conducted experiments demonstrate that the proposed neural topic models are very effective in capturing relevant topics. Furthermore, considering perplexity metric, conducted evaluation benchmarks show that our topic models outperform latent Dirichlet allocation (LDA) model which is classically used to address topic discovery tasks. | ['Sileye 0. Ba'] | 2019-11-25 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-1.49874881e-01 5.40895343e-01 -4.94569957e-01 -5.45913696e-01
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1198d83f-4d45-4a3a-9dfd-79bbda480b27 | mining-non-redundant-local-process-models | 1712.04159 | null | http://arxiv.org/abs/1712.04159v2 | http://arxiv.org/pdf/1712.04159v2.pdf | Mining Non-Redundant Local Process Models From Sequence Databases | Sequential pattern mining techniques extract patterns corresponding to
frequent subsequences from a sequence database. A practical limitation of these
techniques is that they overload the user with too many patterns. Local Process
Model (LPM) mining is an alternative approach coming from the field of process
mining. While in traditional sequential pattern mining, a pattern describes one
subsequence, an LPM captures a set of subsequences. Also, while traditional
sequential patterns only match subsequences that are observed in the sequence
database, an LPM may capture subsequences that are not explicitly observed, but
that are related to observed subsequences. In other words, LPMs generalize the
behavior observed in the sequence database. These properties make it possible
for a set of LPMs to cover the behavior of a much larger set of sequential
patterns. Yet, existing LPM mining techniques still suffer from the pattern
explosion problem because they produce sets of redundant LPMs. In this paper,
we propose several heuristics to mine a set of non-redundant LPMs either from a
set of redundant LPMs or from a set of sequential patterns. We empirically
compare the proposed heuristics between them and against existing (local)
process mining techniques in terms of coverage, redundancy, and complexity of
the produced sets of LPMs. | ['Niek Tax', 'Marlon Dumas'] | 2017-12-12 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 9.08298314e-01 -1.12080730e-01 -3.30217987e-01 -1.15023762e-01
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f09ebe32-a95c-42bd-9238-b011793cac39 | lifting-uniform-learners-via-distributional | 2303.16208 | null | https://arxiv.org/abs/2303.16208v2 | https://arxiv.org/pdf/2303.16208v2.pdf | Lifting uniform learners via distributional decomposition | We show how any PAC learning algorithm that works under the uniform distribution can be transformed, in a blackbox fashion, into one that works under an arbitrary and unknown distribution $\mathcal{D}$. The efficiency of our transformation scales with the inherent complexity of $\mathcal{D}$, running in $\mathrm{poly}(n, (md)^d)$ time for distributions over $\{\pm 1\}^n$ whose pmfs are computed by depth-$d$ decision trees, where $m$ is the sample complexity of the original algorithm. For monotone distributions our transformation uses only samples from $\mathcal{D}$, and for general ones it uses subcube conditioning samples. A key technical ingredient is an algorithm which, given the aforementioned access to $\mathcal{D}$, produces an optimal decision tree decomposition of $\mathcal{D}$: an approximation of $\mathcal{D}$ as a mixture of uniform distributions over disjoint subcubes. With this decomposition in hand, we run the uniform-distribution learner on each subcube and combine the hypotheses using the decision tree. This algorithmic decomposition lemma also yields new algorithms for learning decision tree distributions with runtimes that exponentially improve on the prior state of the art -- results of independent interest in distribution learning. | ['Li-Yang Tan', 'Ali Malik', 'Jane Lange', 'Guy Blanc'] | 2023-03-27 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [-9.52274427e-02 3.61701220e-01 -3.79553199e-01 -2.98052907e-01
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e49557f7-1953-4516-9a8a-5ee279413786 | multiplicative-tree-structured-long-short | null | null | https://aclanthology.org/S18-2032 | https://aclanthology.org/S18-2032.pdf | Multiplicative Tree-Structured Long Short-Term Memory Networks for Semantic Representations | Tree-structured LSTMs have shown advantages in learning semantic representations by exploiting syntactic information. Most existing methods model tree structures by bottom-up combinations of constituent nodes using the same shared compositional function and often making use of input word information only. The inability to capture the richness of compositionality makes these models lack expressive power. In this paper, we propose multiplicative tree-structured LSTMs to tackle this problem. Our model makes use of not only word information but also relation information between words. It is more expressive, as different combination functions can be used for each child node. In addition to syntactic trees, we also investigate the use of Abstract Meaning Representation in tree-structured models, in order to incorporate both syntactic and semantic information from the sentence. Experimental results on common NLP tasks show the proposed models lead to better sentence representation and AMR brings benefits in complex tasks. | ['Nam Khanh Tran', 'Weiwei Cheng'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['learning-semantic-representations'] | ['methodology'] | [ 2.78756112e-01 4.66934681e-01 -3.55443120e-01 -5.18500268e-01
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427f9949-19a9-4c5e-b3c5-fd02b1536fa1 | a-learning-based-trajectory-planning-of | 2209.09206 | null | https://arxiv.org/abs/2209.09206v1 | https://arxiv.org/pdf/2209.09206v1.pdf | A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks | Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion. \textit{Age of Information} (AoI) is a metric that quantifies information timeliness, i.e., the freshness of the received information or status update. This work considers a setup of deployed sensors in an IoT network, where multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages. This ensures that the received information at the base station is as fresh as possible. The complex optimization problem is efficiently solved using a deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower AoI than the random walk scheme. Our proposed algorithm reduces the average age by approximately $25\%$ and requires down to $50\%$ less energy when compared to the baseline scheme. | ['Matti Latva-aho', 'Hirley Alves', 'Nurul Huda Mahmood', 'Mohammad Shehab', "Jean Michel de Souza Sant'Ana", 'Dian Echevarría Pérez', 'Eslam Eldeeb'] | 2022-09-13 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-1.07612357e-01 3.88881564e-01 -2.71982223e-01 -1.59068003e-01
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869dd2a6-8cae-4301-ae6d-13e0dd670c43 | a-topological-classifier-to-characterize | 2303.04231 | null | https://arxiv.org/abs/2303.04231v1 | https://arxiv.org/pdf/2303.04231v1.pdf | A topological classifier to characterize brain states: When shape matters more than variance | Despite the remarkable accuracies attained by machine learning classifiers to separate complex datasets in a supervised fashion, most of their operation falls short to provide an informed intuition about the structure of data, and, what is more important, about the phenomena being characterized by the given datasets. By contrast, topological data analysis (TDA) is devoted to study the shape of data clouds by means of persistence descriptors and provides a quantitative characterization of specific topological features of the dataset under scrutiny. In this article we introduce a novel TDA-based classifier that works on the principle of assessing quantifiable changes on topological metrics caused by the addition of new input to a subset of data. We used this classifier with a high-dimensional electro-encephalographic (EEG) dataset recorded from eleven participants during a decision-making experiment in which three motivational states were induced through a manipulation of social pressure. After processing a band-pass filtered version of EEG signals, we calculated silhouettes from persistence diagrams associated with each motivated state, and classified unlabeled signals according to their impact on each reference silhouette. Our results show that in addition to providing accuracies within the range of those of a nearest neighbour classifier, the TDA classifier provides formal intuition of the structure of the dataset as well as an estimate of its intrinsic dimension. Towards this end, we incorporated dimensionality reduction methods to our procedure and found that the accuracy of our TDA classifier is generally not sensitive to explained variance but rather to shape, contrary to what happens with most machine learning classifiers. | ['Ignasi Cos', 'Carles Casacuberta', 'Fritz-Pere Nobbe Fisas', 'Gloria Cecchini', 'Aina Ferrà'] | 2023-03-07 | null | null | null | null | ['topological-data-analysis', 'eeg', 'eeg'] | ['graphs', 'methodology', 'time-series'] | [ 2.78814137e-01 -1.53336162e-02 2.77495205e-01 -3.52993220e-01
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084de56d-3a43-48cb-b406-fb1063a44971 | can-question-rewriting-help-conversational | 2204.06239 | null | https://arxiv.org/abs/2204.06239v1 | https://arxiv.org/pdf/2204.06239v1.pdf | Can Question Rewriting Help Conversational Question Answering? | Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form. Despite seeming plausible, little evidence is available to justify QR as a mitigation method for CQA. To verify the effectiveness of QR in CQA, we investigate a reinforcement learning approach that integrates QR and CQA tasks and does not require corresponding QR datasets for targeted CQA. We find, however, that the RL method is on par with the end-to-end baseline. We provide an analysis of the failure and describe the difficulty of exploiting QR for CQA. | ['Bryan Wilie', 'Samuel Cahyawijaya', 'Yan Xu', 'Etsuko Ishii'] | 2022-04-13 | null | https://aclanthology.org/2022.insights-1.13 | https://aclanthology.org/2022.insights-1.13.pdf | insights-acl-2022-5 | ['question-rewriting'] | ['natural-language-processing'] | [ 8.11460614e-02 7.26219952e-01 3.66094768e-01 -2.69451231e-01
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6aab6b3c-36ec-46ff-b8bd-0dbcafdb48ca | monash-university-uea-ucr-time-series | 2006.10996 | null | https://arxiv.org/abs/2006.10996v3 | https://arxiv.org/pdf/2006.10996v3.pdf | Monash University, UEA, UCR Time Series Extrinsic Regression Archive | Time series research has gathered lots of interests in the last decade, especially for Time Series Classification (TSC) and Time Series Forecasting (TSF). Research in TSC has greatly benefited from the University of California Riverside and University of East Anglia (UCR/UEA) Time Series Archives. On the other hand, the advancement in Time Series Forecasting relies on time series forecasting competitions such as the Makridakis competitions, NN3 and NN5 Neural Network competitions, and a few Kaggle competitions. Each year, thousands of papers proposing new algorithms for TSC and TSF have utilized these benchmarking archives. These algorithms are designed for these specific problems, but may not be useful for tasks such as predicting the heart rate of a person using photoplethysmogram (PPG) and accelerometer data. We refer to this problem as Time Series Extrinsic Regression (TSER), where we are interested in a more general methodology of predicting a single continuous value, from univariate or multivariate time series. This prediction can be from the same time series or not directly related to the predictor time series and does not necessarily need to be a future value or depend heavily on recent values. To the best of our knowledge, research into TSER has received much less attention in the time series research community and there are no models developed for general time series extrinsic regression problems. Most models are developed for a specific problem. Therefore, we aim to motivate and support the research into TSER by introducing the first TSER benchmarking archive. This archive contains 19 datasets from different domains, with varying number of dimensions, unequal length dimensions, and missing values. In this paper, we introduce the datasets in this archive and did an initial benchmark on existing models. | ['Geoffrey I. Webb', 'Francois Petitjean', 'Christoph Bergmeir', 'Chang Wei Tan'] | 2020-06-19 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-7.07184225e-02 -6.17184043e-01 -1.89718351e-01 -3.98510873e-01
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b6e30ea1-9ba5-4ad1-9617-0e724c88497d | ctrgan-cycle-transformers-gan-for-gait | 2206.15248 | null | https://arxiv.org/abs/2206.15248v4 | https://arxiv.org/pdf/2206.15248v4.pdf | CTrGAN: Cycle Transformers GAN for Gait Transfer | We introduce a novel approach for gait transfer from unconstrained videos in-the-wild. In contrast to motion transfer, the objective here is not to imitate the source's motions by the target, but rather to replace the walking source with the target, while transferring the target's typical gait. Our approach can be trained only once with multiple sources and is able to transfer the gait of the target from unseen sources, eliminating the need for retraining for each new source independently. Furthermore, we propose a novel metrics for gait transfer based on gait recognition models that enable to quantify the quality of the transferred gait, and show that existing techniques yield a discrepancy that can be easily detected. We introduce Cycle Transformers GAN (CTrGAN), that consist of a decoder and encoder, both Transformers, where the attention is on the temporal domain between complete images rather than the spatial domain between patches. Using a widely-used gait recognition dataset, we demonstrate that our approach is capable of producing over an order of magnitude more realistic personalized gaits than existing methods, even when used with sources that were not available during training. As part of our solution, we present a detector that determines whether a video is real or generated by our model. | ['Gil Ben-Artzi', 'Hay Hoffman', 'Noam Gaash', 'Shahar Mahpod'] | 2022-06-30 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [ 5.17318785e-01 1.39289081e-01 1.58121213e-01 1.59187749e-01
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ac6b420e-e3f2-4db2-9878-28174d7ec6e3 | codi-co-evolving-contrastive-diffusion-models | 2304.12654 | null | https://arxiv.org/abs/2304.12654v1 | https://arxiv.org/pdf/2304.12654v1.pdf | CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis | With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios. Owing to the recent advances in generative modeling, fake data generated by tabular data synthesis models become sophisticated and realistic. However, there still exists a difficulty in modeling discrete variables (columns) of tabular data. In this work, we propose to process continuous and discrete variables separately (but being conditioned on each other) by two diffusion models. The two diffusion models are co-evolved during training by reading conditions from each other. In order to further bind the diffusion models, moreover, we introduce a contrastive learning method with a negative sampling method. In our experiments with 11 real-world tabular datasets and 8 baseline methods, we prove the efficacy of the proposed method, called CoDi. | ['Noseong Park', 'Jayoung Kim', 'Chaejeong Lee'] | 2023-04-25 | null | null | null | null | ['type'] | ['speech'] | [ 1.56202167e-01 1.92888170e-01 -2.02928424e-01 -1.57027751e-01
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50167a5c-6f9a-40de-8267-ed02e0e2f257 | what-is-where-by-looking-weakly-supervised | 2206.09358 | null | https://arxiv.org/abs/2206.09358v2 | https://arxiv.org/pdf/2206.09358v2.pdf | What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs | Given an input image, and nothing else, our method returns the bounding boxes of objects in the image and phrases that describe the objects. This is achieved within an open world paradigm, in which the objects in the input image may not have been encountered during the training of the localization mechanism. Moreover, training takes place in a weakly supervised setting, where no bounding boxes are provided. To achieve this, our method combines two pre-trained networks: the CLIP image-to-text matching score and the BLIP image captioning tool. Training takes place on COCO images and their captions and is based on CLIP. Then, during inference, BLIP is used to generate a hypothesis regarding various regions of the current image. Our work generalizes weakly supervised segmentation and phrase grounding and is shown empirically to outperform the state of the art in both domains. It also shows very convincing results in the novel task of weakly-supervised open-world purely visual phrase-grounding presented in our work. For example, on the datasets used for benchmarking phrase-grounding, our method results in a very modest degradation in comparison to methods that employ human captions as an additional input. Our code is available at https://github.com/talshaharabany/what-is-where-by-looking and a live demo can be found at https://replicate.com/talshaharabany/what-is-where-by-looking. | ['Lior Wolf', 'Yoad Tewel', 'Tal Shaharabany'] | 2022-06-19 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 3.23942214e-01 3.18656415e-01 -1.50469124e-01 -2.35307395e-01
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ddfb90ca-ac13-4df1-bb37-22c8c7411813 | simplified-continuous-high-dimensional-belief | 2302.06697 | null | https://arxiv.org/abs/2302.06697v1 | https://arxiv.org/pdf/2302.06697v1.pdf | Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints | Online decision making under uncertainty in partially observable domains, also known as Belief Space Planning, is a fundamental problem in robotics and Artificial Intelligence. Due to an abundance of plausible future unravelings, calculating an optimal course of action inflicts an enormous computational burden on the agent. Moreover, in many scenarios, e.g., information gathering, it is required to introduce a belief-dependent constraint. Prompted by this demand, in this paper, we consider a recently introduced probabilistic belief-dependent constrained POMDP. We present a technique to adaptively accept or discard a candidate action sequence with respect to a probabilistic belief-dependent constraint, before expanding a complete set of future observations samples and without any loss in accuracy. Moreover, using our proposed framework, we contribute an adaptive method to find a maximal feasible return (e.g., information gain) in terms of Value at Risk for the candidate action sequence with substantial acceleration. On top of that, we introduce an adaptive simplification technique for a probabilistically constrained setting. Such an approach provably returns an identical-quality solution while dramatically accelerating online decision making. Our universal framework applies to any belief-dependent constrained continuous POMDP with parametric beliefs, as well as nonparametric beliefs represented by particles. In the context of an information-theoretic constraint, our presented framework stochastically quantifies if a cumulative information gain along the planning horizon is sufficiently significant (e.g. for, information gathering, active SLAM). We apply our method to active SLAM, a highly challenging problem of high dimensional Belief Space Planning. Extensive realistic simulations corroborate the superiority of our proposed ideas. | ['Vadim Indelman', 'Andrey Zhitnikov'] | 2023-02-13 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 3.37717652e-01 4.60472256e-01 -1.57853186e-01 -2.22333744e-01
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92cec554-0893-4699-9ebb-55a30d5e97b9 | a-semi-supervised-model-for-persian-rumor | null | null | https://doi.org/10.1007/s11042-020-10077-3 | https://link.springer.com/article/10.1007/s11042-020-10077-3 | A semi-supervised model for Persian rumor verification based on content information | Rumor is a collective attempt to interpret a vague but attractive situation by using the power of words. In social networks, false-rumors may have significantly different contextual characteristics from true-rumors at lexical, syntactic, semantic levels. Therefore, this study presents the BERT-SAWS semi-supervised learning model for early verification of Persian rumor by investigating content-based and context features at three views: Contextual Word Embeddings (CWE), speech act, and Writing Style (WS). This model is built by loading pre-trained Bidirectional Encoder Representations from Transformers (BERT) as an unsupervised language representation, fine-tuning it using a small Persian rumor dataset, and combining with a supervised learning model to provide an enriched text representation of the content of the rumor. This text representation enables the model to have a better comprehending of the rumor language to verify rumors better than baseline models for two reasons: (i) early rumor verification by focusing on content-based and context-based features of the source rumor. (ii) overcoming the problem of the shortcoming of the dataset in deep neural networks by loading pre-trained BERT, fine-tuning it using the Persian rumor dataset, and combining with speech act and WS-based features. The empirical results of applying the model on Twitter and Telegram datasets demonstrated that BERT-SAWS can enhance the performance of the classifier from 2% to 18%. It indicates that speech act and WS alongside semantic contextual vectors are helpful features in the rumor verification task. | ['Arash Sharifi', 'Mohammad-Reza Feizi-Derakhshi', 'Zoleikha Jahanbakhsh-Nagadeh'] | 2020-11-20 | null | null | null | multimedia-tools-and-applications-2020-11 | ['rumour-detection'] | ['natural-language-processing'] | [-3.65036041e-01 2.34091327e-01 -3.36454481e-01 -5.64717352e-01
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c51902d6-5f8f-4f28-92fa-1657962a8cf7 | uncertainty-aware-self-training-for-low | 2302.08659 | null | https://arxiv.org/abs/2302.08659v1 | https://arxiv.org/pdf/2302.08659v1.pdf | Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling | Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filling, etc. However, the satisfying results achieved by traditional supervised-based approaches heavily depend on the large amounts of human annotation data, which may not be feasible in real-world scenarios due to data privacy and computation efficiency issues. This paper presents SeqUST, a novel uncertain-aware self-training framework for NSL to address the labeled data scarcity issue and to effectively utilize unlabeled data. Specifically, we incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to perform uncertainty estimation at the token level and then select reliable language tokens from unlabeled data based on the model confidence and certainty. A well-designed masked sequence labeling task with a noise-robust loss supports robust training, which aims to suppress the problem of noisy pseudo labels. In addition, we develop a Gaussian-based consistency regularization technique to further improve the model robustness on Gaussian-distributed perturbed representations. This effectively alleviates the over-fitting dilemma originating from pseudo-labeled augmented data. Extensive experiments over six benchmarks demonstrate that our SeqUST framework effectively improves the performance of self-training, and consistently outperforms strong baselines by a large margin in low-resource scenarios | ['Aoying Zhou', 'Ming Gao', 'Jun Huang', 'Chengyu Wang', 'Jianing Wang'] | 2023-02-17 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 2.68347681e-01 1.50797352e-01 -5.16712427e-01 -7.58457661e-01
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9cbaa560-cb12-44f1-80ff-e86d8dc679da | contrastive-clustering-to-mine-pseudo | null | null | https://openreview.net/forum?id=pN1JOdrSY9 | https://openreview.net/pdf?id=pN1JOdrSY9 | Contrastive Clustering to Mine Pseudo Parallel Data for Unsupervised Translation | Modern unsupervised machine translation systems mostly train their models by generating synthetic parallel training data from large unlabeled monolingual corpora of different languages through various means, such as iterative back-translation. However, there may exist small amount of actual parallel data hidden in the sea of unlabeled data, which has not been exploited. We develop a new fine-tuning objective, called Language-Agnostic Constraint for SwAV loss, or LAgSwAV, which enables a pre-trained model to extract such pseudo-parallel data from the monolingual corpora in a fully unsupervised manner. We then propose an effective strategy to utilize the obtained synthetic data to augment unsupervised machine translation. Our method achieves the state of the art in the WMT'14 English-French, WMT'16 German-English and English-Romanian bilingual unsupervised translation tasks, with 40.2, 36.8, 37.0 BLEU, respectively. We also achieve substantial improvements in the FLoRes low-resource English-Nepali and English-Sinhala unsupervised tasks with 5.3 and 5.4 BLEU, respectively.
| ['Shafiq Joty', 'Philipp Koehn', 'Changhan Wang', 'Yun Tang', 'Hongyu Gong', 'Xuan-Phi Nguyen'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 2.01882854e-01 -1.23676471e-01 -5.03597677e-01 -5.39857745e-01
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924a9544-37f6-4083-83c3-515323e12a9d | wman-weakly-supervised-moment-alignment-1 | 1909.13784 | null | https://arxiv.org/abs/1909.13784v2 | https://arxiv.org/pdf/1909.13784v2.pdf | LoGAN: Latent Graph Co-Attention Network for Weakly-Supervised Video Moment Retrieval | The goal of weakly-supervised video moment retrieval is to localize the video segment most relevant to the given natural language query without access to temporal annotations during training. Prior strongly- and weakly-supervised approaches often leverage co-attention mechanisms to learn visual-semantic representations for localization. However, while such approaches tend to focus on identifying relationships between elements of the video and language modalities, there is less emphasis on modeling relational context between video frames given the semantic context of the query. Consequently, the above-mentioned visual-semantic representations, built upon local frame features, do not contain much contextual information. To address this limitation, we propose a Latent Graph Co-Attention Network (LoGAN) that exploits fine-grained frame-by-word interactions to reason about correspondences between all possible pairs of frames, given the semantic context of the query. Comprehensive experiments across two datasets, DiDeMo and Charades-Sta, demonstrate the effectiveness of our proposed latent co-attention model where it outperforms current state-of-the-art (SOTA) weakly-supervised approaches by a significant margin. Notably, it even achieves a 11% improvement to Recall@1 accuracy over strongly-supervised SOTA methods on DiDeMo. | ['Reuben Tan', 'Kate Saenko', 'Bryan A. Plummer', 'Huijuan Xu'] | 2019-09-27 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-1.28544077e-01 -2.42624179e-01 -8.10864627e-01 -4.17810738e-01
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-5.13415694e-01 5.97222090e-01 -2.00104583e-02 7.72025585e-02
1.14362025e+00 -4.51319158e-01 -1.10039534e-03 6.38177097e-01
1.52125335e+00 -1.43699616e-01 -1.33756125e+00 -8.56478095e-01
1.77046135e-01 -6.38439357e-01 1.98713347e-01 -3.08587849e-01
-1.12399244e+00 9.83029306e-01 3.68283004e-01 -2.23831415e-01
8.45306337e-01 4.63893384e-01 5.84662616e-01 6.21817827e-01
3.37824792e-01 -7.28962839e-01 6.21822059e-01 4.06345516e-01
6.95588529e-01 -1.45593917e+00 8.38586316e-02 -1.94023043e-01
-6.88709915e-01 9.98738050e-01 7.11893857e-01 -7.03304261e-02
6.28408134e-01 -3.45379025e-01 -3.27586569e-02 -5.03797054e-01
-1.00546598e+00 -4.78077292e-01 5.43522835e-01 4.76953477e-01
5.57134390e-01 -3.35825622e-01 9.18742269e-02 1.84638008e-01
6.69425905e-01 4.98113856e-02 4.69365343e-02 1.02993643e+00
-1.91766635e-01 -6.70756757e-01 -5.05675487e-02 -1.12684041e-01
-4.32492584e-01 -5.61353713e-02 -1.32079139e-01 6.99488878e-01
-1.96136087e-01 9.09577847e-01 3.53688776e-01 -7.29299486e-02
-7.80744702e-02 -1.51122939e-02 2.86883205e-01 -7.24234879e-01
-1.20577604e-01 3.23897928e-01 -4.23724800e-02 -9.09424841e-01
-1.02906609e+00 -5.77158630e-01 -1.23037601e+00 -6.64549619e-02
-2.50754431e-02 2.03316152e-01 1.96165517e-01 1.21118426e+00
4.23838317e-01 3.02008390e-01 3.52166533e-01 -8.33207011e-01
1.41105369e-01 -6.68647110e-01 -2.75077343e-01 7.43614376e-01
3.99602830e-01 -7.97735393e-01 -3.11963439e-01 5.19581676e-01] | [10.06683349609375, 0.7848902344703674] |
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