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8d8bc1ed-0ef0-4277-8cf3-3cab20611843 | nodeformer-a-scalable-graph-structure | 2306.08385 | null | https://arxiv.org/abs/2306.08385v1 | https://arxiv.org/pdf/2306.08385v1.pdf | NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification | Graph neural networks have been extensively studied for learning with inter-connected data. Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing, heterophily, handling long-range dependencies, edge incompleteness and particularly, the absence of graphs altogether. While a plausible solution is to learn new adaptive topology for message passing, issues concerning quadratic complexity hinder simultaneous guarantees for scalability and precision in large networks. In this paper, we introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes, as an important building block for a pioneering Transformer-style network for node classification on large graphs, dubbed as \textsc{NodeFormer}. Specifically, the efficient computation is enabled by a kernerlized Gumbel-Softmax operator that reduces the algorithmic complexity to linearity w.r.t. node numbers for learning latent graph structures from large, potentially fully-connected graphs in a differentiable manner. We also provide accompanying theory as justification for our design. Extensive experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs (with up to 2M nodes) and graph-enhanced applications (e.g., image classification) where input graphs are missing. | ['Junchi Yan', 'David Wipf', 'Zenan Li', 'Wentao Zhao', 'Qitian Wu'] | 2023-06-14 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 2.89125532e-01 5.32926917e-01 -4.08760726e-01 -4.08873528e-01
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d5a00604-93bf-4b40-bf24-c9e93b62b14d | safe-exploration-for-efficient-policy | 2202.13234 | null | https://arxiv.org/abs/2202.13234v2 | https://arxiv.org/pdf/2202.13234v2.pdf | Safe Exploration for Efficient Policy Evaluation and Comparison | High-quality data plays a central role in ensuring the accuracy of policy evaluation. This paper initiates the study of efficient and safe data collection for bandit policy evaluation. We formulate the problem and investigate its several representative variants. For each variant, we analyze its statistical properties, derive the corresponding exploration policy, and design an efficient algorithm for computing it. Both theoretical analysis and experiments support the usefulness of the proposed methods. | ['Rui Song', 'Branislav Kveton', 'Runzhe Wan'] | 2022-02-26 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.65433764e-01 -3.35382819e-01 -1.31958067e+00 -2.19503954e-01
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e3666279-0884-42f1-b10d-739e64a951ff | machine-unlearning-its-nature-scope-and | 2305.15242 | null | https://arxiv.org/abs/2305.15242v1 | https://arxiv.org/pdf/2305.15242v1.pdf | Machine Unlearning: its nature, scope, and importance for a "delete culture" | The article explores the cultural shift from recording to deleting information in the digital age and its implications on privacy, intellectual property (IP), and Large Language Models like ChatGPT. It begins by defining a delete culture where information, in principle legal, is made unavailable or inaccessible because unacceptable or undesirable, especially but not only due to its potential to infringe on privacy or IP. Then it focuses on two strategies in this context: deleting, to make information unavailable; and blocking, to make it inaccessible. The article argues that both strategies have significant implications, particularly for machine learning (ML) models where information is not easily made unavailable. However, the emerging research area of Machine Unlearning (MU) is highlighted as a potential solution. MU, still in its infancy, seeks to remove specific data points from ML models, effectively making them 'forget' completely specific information. If successful, MU could provide a feasible means to manage the overabundance of information and ensure a better protection of privacy and IP. However, potential ethical risks, such as misuse, overuse, and underuse of MU, should be systematically studied to devise appropriate policies. | ['Luciano Floridi'] | 2023-05-24 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 3.60053837e-01 4.90800828e-01 -5.29350579e-01 3.60278748e-02
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7297111d-c1b2-4954-be0c-ad5a6eeb7690 | adversarial-connective-exploiting-networks | 1704.00217 | null | http://arxiv.org/abs/1704.00217v1 | http://arxiv.org/pdf/1704.00217v1.pdf | Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification | Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark. | ['Hai Zhao', 'Zhiting Hu', 'Lianhui Qin', 'Zhisong Zhang', 'Eric P. Xing'] | 2017-04-01 | adversarial-connective-exploiting-networks-1 | https://aclanthology.org/P17-1093 | https://aclanthology.org/P17-1093.pdf | acl-2017-7 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 4.70629692e-01 1.01139402e+00 -4.24189419e-01 -5.60714483e-01
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8a2bb0ac-bff8-4c38-9668-92b25de26715 | object-centric-stereo-matching-for-3d-object | 1909.07566 | null | https://arxiv.org/abs/1909.07566v2 | https://arxiv.org/pdf/1909.07566v2.pdf | Object-Centric Stereo Matching for 3D Object Detection | Safe autonomous driving requires reliable 3D object detection-determining the 6 DoF pose and dimensions of objects of interest. Using stereo cameras to solve this task is a cost-effective alternative to the widely used LiDAR sensor. The current state-of-the-art for stereo 3D object detection takes the existing PSMNet stereo matching network, with no modifications, and converts the estimated disparities into a 3D point cloud, and feeds this point cloud into a LiDAR-based 3D object detector. The issue with existing stereo matching networks is that they are designed for disparity estimation, not 3D object detection; the shape and accuracy of object point clouds are not the focus. Stereo matching networks commonly suffer from inaccurate depth estimates at object boundaries, which we define as streaking, because background and foreground points are jointly estimated. Existing networks also penalize disparity instead of the estimated position of object point clouds in their loss functions. We propose a novel 2D box association and object-centric stereo matching method that only estimates the disparities of the objects of interest to address these two issues. Our method achieves state-of-the-art results on the KITTI 3D and BEV benchmarks. | ['Jason Ku', 'Steven L. Waslander', 'Chengyao Li', 'Alex D. Pon'] | 2019-09-17 | null | null | null | null | ['stereo-matching', '3d-object-detection-from-stereo-images'] | ['computer-vision', 'computer-vision'] | [-6.90795481e-02 -3.40685487e-01 -1.66398212e-01 -3.83150667e-01
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61cde2b8-4ce9-49ae-b32a-8a72ae747bb7 | cp-net-contour-perturbed-reconstruction | 2201.08215 | null | https://arxiv.org/abs/2201.08215v2 | https://arxiv.org/pdf/2201.08215v2.pdf | CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning | Self-supervised learning has not been fully explored for point cloud analysis. Current frameworks are mainly based on point cloud reconstruction. Given only 3D coordinates, such approaches tend to learn local geometric structures and contours, while failing in understanding high level semantic content. Consequently, they achieve unsatisfactory performance in downstream tasks such as classification, segmentation, etc. To fill this gap, we propose a generic Contour-Perturbed Reconstruction Network (CP-Net), which can effectively guide self-supervised reconstruction to learn semantic content in the point cloud, and thus promote discriminative power of point cloud representation. First, we introduce a concise contour-perturbed augmentation module for point cloud reconstruction. With guidance of geometry disentangling, we divide point cloud into contour and content components. Subsequently, we perturb the contour components and preserve the content components on the point cloud. As a result, self supervisor can effectively focus on semantic content, by reconstructing the original point cloud from such perturbed one. Second, we use this perturbed reconstruction as an assistant branch, to guide the learning of basic reconstruction branch via a distinct dual-branch consistency loss. In this case, our CP-Net not only captures structural contour but also learn semantic content for discriminative downstream tasks. Finally, we perform extensive experiments on a number of point cloud benchmarks. Part segmentation results demonstrate that our CP-Net (81.5% of mIoU) outperforms the previous self-supervised models, and narrows the gap with the fully-supervised methods. For classification, we get a competitive result with the fully-supervised methods on ModelNet40 (92.5% accuracy) and ScanObjectNN (87.9% accuracy). The codes and models will be released afterwards. | ['Zhipeng Zhou', 'Yali Wang', 'Yu Qiao', 'Hongbin Xu', 'Mingye Xu'] | 2022-01-20 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [-1.05708741e-01 1.98702872e-01 -5.65135002e-01 -3.35154742e-01
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e0344d68-608e-4417-8d6f-7f69d4198b04 | dif-fusion-towards-high-color-fidelity-in | 2301.08072 | null | https://arxiv.org/abs/2301.08072v1 | https://arxiv.org/pdf/2301.08072v1.pdf | Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models | Color plays an important role in human visual perception, reflecting the spectrum of objects. However, the existing infrared and visible image fusion methods rarely explore how to handle multi-spectral/channel data directly and achieve high color fidelity. This paper addresses the above issue by proposing a novel method with diffusion models, termed as Dif-Fusion, to generate the distribution of the multi-channel input data, which increases the ability of multi-source information aggregation and the fidelity of colors. In specific, instead of converting multi-channel images into single-channel data in existing fusion methods, we create the multi-channel data distribution with a denoising network in a latent space with forward and reverse diffusion process. Then, we use the the denoising network to extract the multi-channel diffusion features with both visible and infrared information. Finally, we feed the multi-channel diffusion features to the multi-channel fusion module to directly generate the three-channel fused image. To retain the texture and intensity information, we propose multi-channel gradient loss and intensity loss. Along with the current evaluation metrics for measuring texture and intensity fidelity, we introduce a new evaluation metric to quantify color fidelity. Extensive experiments indicate that our method is more effective than other state-of-the-art image fusion methods, especially in color fidelity. | ['Jiayi Ma', 'Yue Deng', 'Shaobo Xia', 'Leyuan Fang', 'Jun Yue'] | 2023-01-19 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 3.06887478e-01 -1.07356441e+00 2.94858545e-01 -2.29076356e-01
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ef87b544-11c9-401a-87a3-515d880ba6cb | arrhythmia-classifier-based-on-ultra | 2304.01568 | null | https://arxiv.org/abs/2304.01568v1 | https://arxiv.org/pdf/2304.01568v1.pdf | Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network | Reasonably and effectively monitoring arrhythmias through ECG signals has significant implications for human health. With the development of deep learning, numerous ECG classification algorithms based on deep learning have emerged. However, most existing algorithms trade off high accuracy for complex models, resulting in high storage usage and power consumption. This also inevitably increases the difficulty of implementation on wearable Artificial Intelligence-of-Things (AIoT) devices with limited resources. In this study, we proposed a universally applicable ultra-lightweight binary neural network(BNN) that is capable of 5-class and 17-class arrhythmia classification based on ECG signals. Our BNN achieves 96.90% (full precision 97.09%) and 97.50% (full precision 98.00%) accuracy for 5-class and 17-class classification, respectively, with state-of-the-art storage usage (3.76 KB and 4.45 KB). Compared to other binarization works, our approach excels in supporting two multi-classification modes while achieving the smallest known storage space. Moreover, our model achieves optimal accuracy in 17-class classification and boasts an elegantly simple network architecture. The algorithm we use is optimized specifically for hardware implementation. Our research showcases the potential of lightweight deep learning models in the healthcare industry, specifically in wearable medical devices, which hold great promise for improving patient outcomes and quality of life. Code is available on: https://github.com/xpww/ECG_BNN_Net | ['Hao liu', 'Zijin Liu', 'Hanshi Sun', 'Ao Wang', 'Zhongxing Wu', 'Ninghao Pu'] | 2023-04-04 | null | null | null | null | ['ecg-classification'] | ['medical'] | [-7.49423206e-02 -3.25299263e-01 -2.87414432e-01 -4.09756899e-01
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21108133-32a2-4a41-be0f-3ce0c802bc6a | improving-localization-for-semi-supervised | 2206.10186 | null | https://arxiv.org/abs/2206.10186v1 | https://arxiv.org/pdf/2206.10186v1.pdf | Improving Localization for Semi-Supervised Object Detection | Nowadays, Semi-Supervised Object Detection (SSOD) is a hot topic, since, while it is rather easy to collect images for creating a new dataset, labeling them is still an expensive and time-consuming task. One of the successful methods to take advantage of raw images on a Semi-Supervised Learning (SSL) setting is the Mean Teacher technique, where the operations of pseudo-labeling by the Teacher and the Knowledge Transfer from the Student to the Teacher take place simultaneously. However, the pseudo-labeling by thresholding is not the best solution since the confidence value is not strictly related to the prediction uncertainty, not permitting to safely filter predictions. In this paper, we introduce an additional classification task for bounding box localization to improve the filtering of the predicted bounding boxes and obtain higher quality on Student training. Furthermore, we empirically prove that bounding box regression on the unsupervised part can equally contribute to the training as much as category classification. Our experiments show that our IL-net (Improving Localization net) increases SSOD performance by 1.14% AP on COCO dataset in limited-annotation regime. The code is available at https://github.com/IMPLabUniPr/unbiased-teacher/tree/ilnet | ['Andrea Prati', 'Akbar Karimi', 'Leonardo Rossi'] | 2022-06-21 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 9.05760676e-02 4.41832870e-01 -2.78856993e-01 -5.72004437e-01
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75190630-a4d7-4242-843c-033b0be3da75 | on-evolving-attention-towards-domain | 2103.13561 | null | https://arxiv.org/abs/2103.13561v1 | https://arxiv.org/pdf/2103.13561v1.pdf | On Evolving Attention Towards Domain Adaptation | Towards better unsupervised domain adaptation (UDA). Recently, researchers propose various domain-conditioned attention modules and make promising progresses. However, considering that the configuration of attention, i.e., the type and the position of attention module, affects the performance significantly, it is more generalized to optimize the attention configuration automatically to be specialized for arbitrary UDA scenario. For the first time, this paper proposes EvoADA: a novel framework to evolve the attention configuration for a given UDA task without human intervention. In particular, we propose a novel search space containing diverse attention configurations. Then, to evaluate the attention configurations and make search procedure UDA-oriented (transferability + discrimination), we apply a simple and effective evaluation strategy: 1) training the network weights on two domains with off-the-shelf domain adaptation methods; 2) evolving the attention configurations under the guide of the discriminative ability on the target domain. Experiments on various kinds of cross-domain benchmarks, i.e., Office-31, Office-Home, CUB-Paintings, and Duke-Market-1510, reveal that the proposed EvoADA consistently boosts multiple state-of-the-art domain adaptation approaches, and the optimal attention configurations help them achieve better performance. | ['Xing Sun', 'Rongrong Ji', 'Feiyue Huang', 'WeiMing Dong', 'Jian Liang', 'Xiawu Zheng', 'Ke Li', 'Kekai Sheng'] | 2021-03-25 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 7.36781433e-02 -4.72178608e-01 -2.23140374e-01 -2.58973598e-01
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7b6a19b0-4b1a-447f-a258-7e05d4116e91 | adaptive-base-class-suppression-and-prior | 2303.14240 | null | https://arxiv.org/abs/2303.14240v1 | https://arxiv.org/pdf/2303.14240v1.pdf | Adaptive Base-class Suppression and Prior Guidance Network for One-Shot Object Detection | One-shot object detection (OSOD) aims to detect all object instances towards the given category specified by a query image. Most existing studies in OSOD endeavor to explore effective cross-image correlation and alleviate the semantic feature misalignment, however, ignoring the phenomenon of the model bias towards the base classes and the generalization degradation on the novel classes. Observing this, we propose a novel framework, namely Base-class Suppression and Prior Guidance (BSPG) network to overcome the problem. Specifically, the objects of base categories can be explicitly detected by a base-class predictor and adaptively eliminated by our base-class suppression module. Moreover, a prior guidance module is designed to calculate the correlation of high-level features in a non-parametric manner, producing a class-agnostic prior map to provide the target features with rich semantic cues and guide the subsequent detection process. Equipped with the proposed two modules, we endow the model with a strong discriminative ability to distinguish the target objects from distractors belonging to the base classes. Extensive experiments show that our method outperforms the previous techniques by a large margin and achieves new state-of-the-art performance under various evaluation settings. | ['Eryun Liu', 'Hangguan Shan', 'Xinyu Xiao', 'Wenwen Zhang'] | 2023-03-24 | null | null | null | null | ['one-shot-object-detection'] | ['computer-vision'] | [ 1.72261626e-01 -6.73500150e-02 -2.60993391e-01 -3.51815820e-01
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1aaf99d6-5b65-4536-8992-e91959f3ec5e | deep-convolutional-neural-networks-for-breast | 1802.00752 | null | http://arxiv.org/abs/1802.00752v2 | http://arxiv.org/pdf/1802.00752v2.pdf | Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis | Breast cancer is one of the main causes of cancer death worldwide. Early
diagnostics significantly increases the chances of correct treatment and
survival, but this process is tedious and often leads to a disagreement between
pathologists. Computer-aided diagnosis systems showed potential for improving
the diagnostic accuracy. In this work, we develop the computational approach
based on deep convolution neural networks for breast cancer histology image
classification. Hematoxylin and eosin stained breast histology microscopy image
dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast
Cancer Histology Images. Our approach utilizes several deep neural network
architectures and gradient boosted trees classifier. For 4-class classification
task, we report 87.2% accuracy. For 2-class classification task to detect
carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity
96.5/88.0% at the high-sensitivity operating point. To our knowledge, this
approach outperforms other common methods in automated histopathological image
classification. The source code for our approach is made publicly available at
https://github.com/alexander-rakhlin/ICIAR2018 | ['Vladimir Iglovikov', 'Alexey Shvets', 'Alexander Rakhlin', 'Alexandr A. Kalinin'] | 2018-02-02 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection', 'breast-cancer-histology-image-classification', 'histopathological-image-classification'] | ['knowledge-base', 'medical', 'medical', 'medical'] | [ 7.22676069e-02 5.20143844e-03 -1.66015357e-01 -3.76253545e-01
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fb293b76-c373-47b5-bb5f-3f7efea05480 | spectrogram-feature-losses-for-music-source | 1901.05061 | null | https://arxiv.org/abs/1901.05061v3 | https://arxiv.org/pdf/1901.05061v3.pdf | Spectrogram Feature Losses for Music Source Separation | In this paper we study deep learning-based music source separation, and explore using an alternative loss to the standard spectrogram pixel-level L2 loss for model training. Our main contribution is in demonstrating that adding a high-level feature loss term, extracted from the spectrograms using a VGG net, can improve separation quality vis-a-vis a pure pixel-level loss. We show this improvement in the context of the MMDenseNet, a State-of-the-Art deep learning model for this task, for the extraction of drums and vocal sounds from songs in the musdb18 database, covering a broad range of western music genres. We believe that this finding can be generalized and applied to broader machine learning-based systems in the audio domain. | ['Abhimanyu Sahai', 'Romann Weber', 'Brian McWilliams'] | 2019-01-15 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.03532958e-01 -1.64483368e-01 7.99993202e-02 1.41325772e-01
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323e7610-d471-46e6-a626-fd2243a027e6 | parity-calibration | 2305.18655 | null | https://arxiv.org/abs/2305.18655v2 | https://arxiv.org/pdf/2305.18655v2.pdf | Parity Calibration | In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather than the actual value of the future observation. In this context, we introduce the notion of parity calibration, which captures the goal of calibrated forecasting for the increase-decrease (or "parity") event in a timeseries. Parity probabilities can be extracted from a forecasted distribution for the output, but we show that such a strategy leads to theoretical unpredictability and poor practical performance. We then observe that although the original task was regression, parity calibration can be expressed as binary calibration. Drawing on this connection, we use an online binary calibration method to achieve parity calibration. We demonstrate the effectiveness of our approach on real-world case studies in epidemiology, weather forecasting, and model-based control in nuclear fusion. | ['Chirag Gupta', 'Aaron Rumack', 'Youngseog Chung'] | 2023-05-29 | null | null | null | null | ['epidemiology', 'weather-forecasting'] | ['medical', 'miscellaneous'] | [ 6.02739155e-01 3.86299133e-01 -2.67678976e-01 -6.08209074e-01
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0855db8c-d74b-448e-a1c8-900ff8cd9a04 | towards-robust-3d-object-recognition-with | 2205.03654 | null | https://arxiv.org/abs/2205.03654v1 | https://arxiv.org/pdf/2205.03654v1.pdf | Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation | Three-dimensional (3D) object recognition is crucial for intelligent autonomous agents such as autonomous vehicles and robots alike to operate effectively in unstructured environments. Most state-of-art approaches rely on relatively dense point clouds and performance drops significantly for sparse point clouds. Unsupervised domain adaption allows to minimise the discrepancy between dense and sparse point clouds with minimal unlabelled sparse point clouds, thereby saving additional sparse data collection, annotation and retraining costs. In this work, we propose a novel method for point cloud based object recognition with competitive performance with state-of-art methods on dense and sparse point clouds while being trained only with dense point clouds. | ['Mohsen Kaboli', 'Ravinder Dahiya', 'Cong Wang', 'Prajval Kumar Murali'] | 2022-05-07 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-7.15485513e-02 -6.14670068e-02 -9.10199154e-03 -3.83634120e-01
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3ccceffe-94b1-4fba-8220-1fba22f70c95 | enriching-abusive-language-detection-with | 2206.08445 | null | https://arxiv.org/abs/2206.08445v1 | https://arxiv.org/pdf/2206.08445v1.pdf | Enriching Abusive Language Detection with Community Context | Uses of pejorative expressions can be benign or actively empowering. When models for abuse detection misclassify these expressions as derogatory, they inadvertently censor productive conversations held by marginalized groups. One way to engage with non-dominant perspectives is to add context around conversations. Previous research has leveraged user- and thread-level features, but it often neglects the spaces within which productive conversations take place. Our paper highlights how community context can improve classification outcomes in abusive language detection. We make two main contributions to this end. First, we demonstrate that online communities cluster by the nature of their support towards victims of abuse. Second, we establish how community context improves accuracy and reduces the false positive rates of state-of-the-art abusive language classifiers. These findings suggest a promising direction for context-aware models in abusive language research. | ['Derek Ruths', 'Haji Mohammad Saleem', 'Jana Kurrek'] | 2022-06-16 | null | https://aclanthology.org/2022.woah-1.13 | https://aclanthology.org/2022.woah-1.13.pdf | naacl-woah-2022-7 | ['abusive-language', 'abuse-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.65954726e-04 -1.17797181e-01 -9.63793218e-01 -3.53607625e-01
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539d301a-e868-45e0-b37c-b649fa9e4def | a-cnn-rnn-framework-for-crop-yield-prediction | 1911.09045 | null | https://arxiv.org/abs/1911.09045v2 | https://arxiv.org/pdf/1911.09045v2.pdf | A CNN-RNN Framework for Crop Yield Prediction | Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNN) and recurrent neural networks (RNN) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully-connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN have three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. (2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields. | ['Sotirios V. Archontoulis', 'Lizhi Wang', 'Saeed Khaki'] | 2019-11-20 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [ 1.27709927e-02 -2.82608330e-01 -4.38361228e-01 -1.71660855e-01
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4d93c89c-5e66-4b28-9a4f-c3be23438f23 | creating-and-using-large-monolingual-parallel | null | null | https://aclanthology.org/L14-1094 | https://aclanthology.org/L14-1094.pdf | Creating and using large monolingual parallel corpora for sentential paraphrase generation | null | ['Emiel Krahmer', 'Antal Van den Bosch', 'er', 'S Wubben'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['sentence-compression'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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8e793f84-e914-42c7-b3a9-519968e4cacb | the-best-of-both-worlds-dual-channel-language | null | null | https://aclanthology.org/2022.ltedi-1.14 | https://aclanthology.org/2022.ltedi-1.14.pdf | The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada | In recent years, various methods have been developed to control the spread of negativity by removing profane, aggressive, and offensive comments from social media platforms. There is, however, a scarcity of research focusing on embracing positivity and reinforcing supportive and reassuring content in online forums. As a result, we concentrate our research on developing systems to detect hope speech in code-mixed Kannada. As a result, we present DC-LM, a dual-channel language model that sees hope speech by using the English translations of the code-mixed dataset for additional training. The approach is jointly modelled on both English and code-mixed Kannada to enable effective cross-lingual transfer between the languages. With a weighted F1-score of 0.756, the method outperforms other models. We aim to initiate research in Kannada while encouraging researchers to take a pragmatic approach to inspire positive and supportive online content. | ['Bharathi Raja Chakravarthi', 'Ruba Priyadharshini', 'Sangeetha S', 'Siddhanth U Hegde', 'Adeep Hande'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-4.48556662e-01 3.70862752e-01 -3.72046977e-01 -3.84442747e-01
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09d404ee-9984-47c4-b9c8-bba4c090afca | fewer-features-perform-well-at-native | null | null | https://aclanthology.org/W17-5028 | https://aclanthology.org/W17-5028.pdf | Fewer features perform well at Native Language Identification task | This paper describes our results at the NLI shared task 2017. We participated in essays, speech, and fusion task that uses text, speech, and i-vectors for the task of identifying the native language of the given input. In the essay track, a linear SVM system using word bigrams and character 7-grams performed the best. In the speech track, an LDA classifier based only on i-vectors performed better than a combination system using text features from speech transcriptions and i-vectors. In the fusion task, we experimented with systems that used combination of i-vectors with higher order n-grams features, combination of i-vectors with word unigrams, a mean probability ensemble, and a stacked ensemble system. Our finding is that word unigrams in combination with i-vectors achieve higher score than systems trained with larger number of $n$-gram features. Our best-performing systems achieved F1-scores of 87.16{\%}, 83.33{\%} and 91.75{\%} on the essay track, the speech track and the fusion track respectively. | ['{\\c{C}}a{\\u{g}}r{\\i} {\\c{C}}{\\"o}ltekin', 'Taraka Rama'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [-1.11858465e-01 -2.19731390e-01 -1.46912277e-01 -4.13530409e-01
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2ab58188-a866-4bc0-b7ec-54e9582bebf3 | efficient-training-of-multi-task-neural | 2305.06361 | null | https://arxiv.org/abs/2305.06361v1 | https://arxiv.org/pdf/2305.06361v1.pdf | Efficient Training of Multi-task Neural Solver with Multi-armed Bandits | Efficiently training a multi-task neural solver for various combinatorial optimization problems (COPs) has been less studied so far. In this paper, we propose a general and efficient training paradigm based on multi-armed bandits to deliver a unified multi-task neural solver. To this end, we resort to the theoretical loss decomposition for multiple tasks under an encoder-decoder framework, which enables more efficient training via proper bandit task-sampling algorithms through an intra-task influence matrix. Our method achieves much higher overall performance with either limited training budgets or the same training epochs, compared to standard training schedules, which can be promising for advising efficient training of other multi-task large models. Additionally, the influence matrix can provide empirical evidence of some common practices in the area of learning to optimize, which in turn supports the validity of our approach. | ['Tianshu Yu', 'Chenguang Wang'] | 2023-05-10 | null | null | null | null | ['combinatorial-optimization', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 1.63436353e-01 6.63017258e-02 -7.53265858e-01 -3.08490783e-01
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7d8bc13f-170e-46a1-b452-ebda5b5572ef | machine-learning-prediction-errors-better | null | null | https://arxiv.org/abs/1702.05532 | https://arxiv.org/pdf/1702.05532.pdf | Machine learning prediction errors better than DFT accuracy | We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules. The performance of each regressor/representation/property combination is assessed using learning curves which report out-of-sample errors as a function of training set size with up to ∼117k distinct molecules. Molecular structures and properties at hybrid density functional theory (DFT) level of theory used for training and testing come from the QM9 database [Ramakrishnan et al, {\em Scientific Data} {\bf 1} 140022 (2014)] and include dipole moment, polarizability, HOMO/LUMO energies and gap, electronic spatial extent, zero point vibrational energy, enthalpies and free energies of atomization, heat capacity and the highest fundamental vibrational frequency. Various representations from the literature have been studied (Coulomb matrix, bag of bonds, BAML and ECFP4, molecular graphs (MG)), as well as newly developed distribution based variants including histograms of distances (HD), and angles (HDA/MARAD), and dihedrals (HDAD). Regressors include linear models (Bayesian ridge regression (BR) and linear regression with elastic net regularization (EN)), random forest (RF), kernel ridge regression (KRR) and two types of neural net works, graph convolutions (GC) and gated graph networks (GG). We present numerical evidence that ML model predictions deviate from DFT less than DFT deviates from experiment for all properties. Furthermore, our out-of-sample prediction errors with respect to hybrid DFT reference are on par with, or close to, chemical accuracy. Our findings suggest that ML models could be more accurate than hybrid DFT if explicitly electron correlated quantum (or experimental) data was available. | ['O. Anatole von Lilienfeld', 'George E. Dahl', 'Samuel S. Schoenholz', 'Bing Huang', 'Steven Kearnes', 'Patrick F. Riley', 'Luke Hutchison', 'Justin Gilmer', 'Felix A. Faber', 'Oriol Vinyals'] | 2017-02-17 | null | null | null | j-chem-theory-comput-2017-2 | ['formation-energy'] | ['miscellaneous'] | [ 2.44900346e-01 5.18629067e-02 -4.31139112e-01 -2.53116578e-01
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68e20746-447f-4d75-b1f8-e892d26d942c | the-graph-feature-fusion-technique-for | 2303.10556 | null | https://arxiv.org/abs/2303.10556v1 | https://arxiv.org/pdf/2303.10556v1.pdf | The Graph feature fusion technique for speaker recognition based on wav2vec2.0 framework | Pre-trained wav2vec2.0 model has been proved its effectiveness for speaker recognition. However, current feature processing methods are focusing on classical pooling on the output features of the pre-trained wav2vec2.0 model, such as mean pooling, max pooling etc. That methods take the features as the independent and irrelevant units, ignoring the inter-relationship among all the features, and do not take the features as an overall representation of a speaker. Gated Recurrent Unit (GRU), as a feature fusion method, can also be considered as a complicated pooling technique, mainly focuses on the temporal information, which may show poor performance in some situations that the main information is not on the temporal dimension. In this paper, we investigate the graph neural network (GNN) as a backend processing module based on wav2vec2.0 framework to provide a solution for the mentioned matters. The GNN takes all the output features as the graph signal data and extracts the related graph structure information of features for speaker recognition. Specifically, we first give a simple proof that the GNN feature fusion method can outperform than the mean, max, random pooling methods and so on theoretically. Then, we model the output features of wav2vec2.0 as the vertices of a graph, and construct the graph adjacency matrix by graph attention network (GAT). Finally, we follow the message passing neural network (MPNN) to design our message function, vertex update function and readout function to transform the speaker features into the graph features. The experiments show our performance can provide a relative improvement compared to the baseline methods. Code is available at xxx. | ['Zhen Yang', 'Haiyan Guo', 'Zirui Ge'] | 2023-03-19 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [-1.80373207e-01 7.42208958e-02 3.18889827e-01 -2.45100901e-01
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75237661-438b-4ebf-a581-43ad7251be30 | poet-pose-estimation-transformer-for-single | 2211.14125 | null | https://arxiv.org/abs/2211.14125v1 | https://arxiv.org/pdf/2211.14125v1.pdf | PoET: Pose Estimation Transformer for Single-View, Multi-Object 6D Pose Estimation | Accurate 6D object pose estimation is an important task for a variety of robotic applications such as grasping or localization. It is a challenging task due to object symmetries, clutter and occlusion, but it becomes more challenging when additional information, such as depth and 3D models, is not provided. We present a transformer-based approach that takes an RGB image as input and predicts a 6D pose for each object in the image. Besides the image, our network does not require any additional information such as depth maps or 3D object models. First, the image is passed through an object detector to generate feature maps and to detect objects. Then, the feature maps are fed into a transformer with the detected bounding boxes as additional information. Afterwards, the output object queries are processed by a separate translation and rotation head. We achieve state-of-the-art results for RGB-only approaches on the challenging YCB-V dataset. We illustrate the suitability of the resulting model as pose sensor for a 6-DoF state estimation task. Code is available at https://github.com/aau-cns/poet. | ['Jan Steinbrener', 'Stephan Weiss', 'Wolfgang Granig', 'Mohamed Amin Hamdad', 'Thomas Jantos'] | 2022-11-25 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.71081677e-01 -9.56241712e-02 -5.26736006e-02 -4.79378611e-01
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a944cba7-f3fb-4833-be91-a6ba79b2060c | rlip-relational-language-image-pre-training | 2209.01814 | null | https://arxiv.org/abs/2209.01814v3 | https://arxiv.org/pdf/2209.01814v3.pdf | RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection | The task of Human-Object Interaction (HOI) detection targets fine-grained visual parsing of humans interacting with their environment, enabling a broad range of applications. Prior work has demonstrated the benefits of effective architecture design and integration of relevant cues for more accurate HOI detection. However, the design of an appropriate pre-training strategy for this task remains underexplored by existing approaches. To address this gap, we propose Relational Language-Image Pre-training (RLIP), a strategy for contrastive pre-training that leverages both entity and relation descriptions. To make effective use of such pre-training, we make three technical contributions: (1) a new Parallel entity detection and Sequential relation inference (ParSe) architecture that enables the use of both entity and relation descriptions during holistically optimized pre-training; (2) a synthetic data generation framework, Label Sequence Extension, that expands the scale of language data available within each minibatch; (3) mechanisms to account for ambiguity, Relation Quality Labels and Relation Pseudo-Labels, to mitigate the influence of ambiguous/noisy samples in the pre-training data. Through extensive experiments, we demonstrate the benefits of these contributions, collectively termed RLIP-ParSe, for improved zero-shot, few-shot and fine-tuning HOI detection performance as well as increased robustness to learning from noisy annotations. Code will be available at https://github.com/JacobYuan7/RLIP. | ['Mingqian Tang', 'Dong Ni', 'Ziyuan Huang', 'Tao Feng', 'Samuel Albanie', 'Jianwen Jiang', 'Hangjie Yuan'] | 2022-09-05 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 5.07872820e-01 2.04247043e-01 -1.58947408e-02 -5.68115413e-01
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a0cfbc3b-fedc-4c03-bf72-ec77f886905c | unisar-a-unified-structure-aware-1 | 2203.07781 | null | https://arxiv.org/abs/2203.07781v2 | https://arxiv.org/pdf/2203.07781v2.pdf | UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL | Existing text-to-SQL semantic parsers are typically designed for particular settings such as handling queries that span multiple tables, domains or turns which makes them ineffective when applied to different settings. We present UniSAr (Unified Structure-Aware Autoregressive Language Model), which benefits from directly using an off-the-shelf language model architecture and demonstrates consistently high performance under different settings. Specifically, UniSAr extends existing autoregressive language models to incorporate three non-invasive extensions to make them structure-aware: (1) adding structure mark to encode database schema, conversation context, and their relationships; (2) constrained decoding to decode well structured SQL for a given database schema; and (3) SQL completion to complete potential missing JOIN relationships in SQL based on database schema. On seven well-known text-to-SQL datasets covering multi-domain, multi-table and multi-turn, UniSAr demonstrates highly comparable or better performance to the most advanced specifically-designed text-to-SQL models. Importantly, our UniSAr is non-invasive, such that other core model advances in text-to-SQL can also adopt our extensions to further enhance performance. | ['Dechen Zhan', 'Wanxiang Che', 'Jian-Guang Lou', 'Dingzirui Wang', 'Mingyang Pan', 'Yan Gao', 'Longxu Dou'] | 2022-03-15 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-3.58567536e-02 3.14232081e-01 -3.74818027e-01 -9.16507661e-01
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e465734c-3fc0-4274-b281-6ecc9cf35ce3 | the-nos-project-opening-routes-for-the | null | null | https://aclanthology.org/2022.tdle-1.6 | https://aclanthology.org/2022.tdle-1.6.pdf | The Nós Project: Opening routes for the Galician language in the field of language technologies | The development of language technologies (LTs) such as machine translation, text analytics, and dialogue systems is essential in the current digital society, culture and economy. These LTs, widely supported in languages in high demand worldwide, such as English, are also necessary for smaller and less economically powerful languages, as they are a driving force in the democratization of the communities that use them due to their great social and cultural impact. As an example, dialogue systems allow us to communicate with machines in our own language; machine translation increases access to contents in different languages, thus facilitating intercultural relations; and text-to-speech and speech-to-text systems broaden different categories of users’ access to technology. In the case of Galician (co-official language, together with Spanish, in the autonomous region of Galicia, located in northwestern Spain), incorporating the language into state-of-the-art AI applications can not only significantly favor its prestige (a decisive factor in language normalization), but also guarantee citizens’ language rights, reduce social inequality, and narrow the digital divide. This is the main motivation behind the Nós Project (Proxecto Nós), which aims to have a significant contribution to the development of LTs in Galician (currently considered a low-resource language) by providing openly licensed resources, tools, and demonstrators in the area of intelligent technologies. | ['Xosé Luis Regueira', 'Senén Barro', 'Manuel González González', 'Alberto Bugarín-Diz', 'Elisa Fernández Rei', 'Pablo Gamallo', 'Marcos García', 'José Ramom Pichel', 'John E. Ortega', 'Adina Ioana Vladu', 'Carmen Magariños', 'Iria de-Dios-Flores'] | null | null | null | null | tdle-lrec-2022-6 | ['culture'] | ['speech'] | [-3.50831121e-01 3.51128012e-01 -3.40455472e-01 2.74105836e-02
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6f3ca63d-8b7a-4371-9a24-326702d5af08 | burst-image-restoration-and-enhancement | 2110.03680 | null | https://arxiv.org/abs/2110.03680v2 | https://arxiv.org/pdf/2110.03680v2.pdf | Burst Image Restoration and Enhancement | Modern handheld devices can acquire burst image sequence in a quick succession. However, the individual acquired frames suffer from multiple degradations and are misaligned due to camera shake and object motions. The goal of Burst Image Restoration is to effectively combine complimentary cues across multiple burst frames to generate high-quality outputs. Towards this goal, we develop a novel approach by solely focusing on the effective information exchange between burst frames, such that the degradations get filtered out while the actual scene details are preserved and enhanced. Our central idea is to create a set of pseudo-burst features that combine complementary information from all the input burst frames to seamlessly exchange information. However, the pseudo-burst cannot be successfully created unless the individual burst frames are properly aligned to discount inter-frame movements. Therefore, our approach initially extracts pre-processed features from each burst frame and matches them using an edge-boosting burst alignment module. The pseudo-burst features are then created and enriched using multi-scale contextual information. Our final step is to adaptively aggregate information from the pseudo-burst features to progressively increase resolution in multiple stages while merging the pseudo-burst features. In comparison to existing works that usually follow a late fusion scheme with single-stage upsampling, our approach performs favorably, delivering state-of-the-art performance on burst superresolution, burst low-light image enhancement, and burst denoising tasks. The source code and pre-trained models are available at \url{https://github.com/akshaydudhane16/BIPNet}. | ['Fahad Shahbaz Khan', 'Ming-Hsuan Yang', 'Salman Khan', 'Syed Waqas Zamir', 'Akshay Dudhane'] | 2021-10-07 | burst-image-restoration-and-enhancement-1 | http://openaccess.thecvf.com//content/CVPR2022/html/Dudhane_Burst_Image_Restoration_and_Enhancement_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Dudhane_Burst_Image_Restoration_and_Enhancement_CVPR_2022_paper.pdf | cvpr-2022-1 | ['burst-image-super-resolution'] | ['computer-vision'] | [ 5.51334023e-01 -5.47912896e-01 -3.38524915e-02 -1.43188134e-01
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80b17758-52cf-4c6f-bc4c-1db3ea74e6c8 | sgpn-similarity-group-proposal-network-for-3d | 1711.08588 | null | https://arxiv.org/abs/1711.08588v2 | https://arxiv.org/pdf/1711.08588v2.pdf | SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation | We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a single network to predict point grouping proposals and a corresponding semantic class for each proposal, from which we can directly extract instance segmentation results. Important to the effectiveness of SGPN is its novel representation of 3D instance segmentation results in the form of a similarity matrix that indicates the similarity between each pair of points in embedded feature space, thus producing an accurate grouping proposal for each point. To the best of our knowledge, SGPN is the first framework to learn 3D instance-aware semantic segmentation on point clouds. Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results. We also demonstrate its flexibility by seamlessly incorporating 2D CNN features into the framework to boost performance. | ['Ronald Yu', 'Weiyue Wang', 'Ulrich Neumann', 'Qiangui Huang'] | 2017-11-23 | sgpn-similarity-group-proposal-network-for-3d-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_SGPN_Similarity_Group_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_SGPN_Similarity_Group_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-instance-segmentation-1', '3d-semantic-instance-segmentation', '3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.00630768e-01 2.69193143e-01 -1.52201997e-02 -5.68786502e-01
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9614b560-90fa-4827-864e-85dfe5846772 | using-the-verifiability-of-details-as-a-test | null | null | https://aclanthology.org/W16-0803 | https://aclanthology.org/W16-0803.pdf | Using the verifiability of details as a test of deception: A conceptual framework for the automation of the verifiability approach | null | ['Bruno Verschuere', 'Bennett Kleinberg', 'Galit Nahari'] | 2016-06-01 | null | null | null | ws-2016-6 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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66e30a96-acec-4158-9b89-0597d36f9af1 | sparse-coding-of-shape-trajectories-for | 1908.03231 | null | https://arxiv.org/abs/1908.03231v1 | https://arxiv.org/pdf/1908.03231v1.pdf | Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition | The detection and tracking of human landmarks in video streams has gained in reliability partly due to the availability of affordable RGB-D sensors. The analysis of such time-varying geometric data is playing an important role in the automatic human behavior understanding. However, suitable shape representations as well as their temporal evolution, termed trajectories, often lie to nonlinear manifolds. This puts an additional constraint (i.e., nonlinearity) in using conventional Machine Learning techniques. As a solution, this paper accommodates the well-known Sparse Coding and Dictionary Learning approach to study time-varying shapes on the Kendall shape spaces of 2D and 3D landmarks. We illustrate effective coding of 3D skeletal sequences for action recognition and 2D facial landmark sequences for macro- and micro-expression recognition. To overcome the inherent nonlinearity of the shape spaces, intrinsic and extrinsic solutions were explored. As main results, shape trajectories give rise to more discriminative time-series with suitable computational properties, including sparsity and vector space structure. Extensive experiments conducted on commonly-used datasets demonstrate the competitiveness of the proposed approaches with respect to state-of-the-art. | ['Hassen Drira', 'Boulbaba Ben Amor', 'Amor Ben Tanfous'] | 2019-08-08 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 6.24689795e-02 -3.43583584e-01 -1.91213980e-01 -3.01897854e-01
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c96586b0-13e9-4929-9711-497ddacce531 | dynamic-prompt-learning-via-policy-gradient | 2209.14610 | null | https://arxiv.org/abs/2209.14610v3 | https://arxiv.org/pdf/2209.14610v3.pdf | Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning | Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks written in text form, such as math word problems (MWP). However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data. To fill the gap, we present Tabular Math Word Problems (TabMWP), a new dataset containing 38,431 open-domain grade-level problems that require mathematical reasoning on both textual and tabular data. Each question in TabMWP is aligned with a tabular context, which is presented as an image, semi-structured text, and a structured table. There are two types of questions: free-text and multi-choice, and each problem is annotated with gold solutions to reveal the multi-step reasoning process. We evaluate different pre-trained models on TabMWP, including the GPT-3 model in a few-shot setting. As earlier studies suggest, since few-shot GPT-3 relies on the selection of in-context examples, its performance is unstable and can degrade to near chance. The unstable issue is more severe when handling complex problems like TabMWP. To mitigate this, we further propose a novel approach, PromptPG, which utilizes policy gradient to learn to select in-context examples from a small amount of training data and then constructs the corresponding prompt for the test example. Experimental results show that our method outperforms the best baseline by 5.31% on the accuracy metric and reduces the prediction variance significantly compared to random selection, which verifies its effectiveness in selecting in-context examples. | ['Ashwin Kalyan', 'Peter Clark', 'Tanmay Rajpurohit', 'Song-Chun Zhu', 'Ying Nian Wu', 'Kai-Wei Chang', 'Liang Qiu', 'Pan Lu'] | 2022-09-29 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 3.99912074e-02 2.69241244e-01 -1.48561314e-01 -3.21927816e-01
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19828986-a02f-41a7-8e62-d307b4d0e430 | data-driven-spectrum-cartography-via-deep | 1911.12810 | null | http://arxiv.org/abs/1911.12810v1 | http://arxiv.org/pdf/1911.12810v1.pdf | Data-Driven Spectrum Cartography via Deep Completion Autoencoders | Spectrum maps, which provide RF spectrum metrics such as power spectral
density for every location in a geographic area, find numerous applications in
wireless communications such as interference control, spectrum management,
resource allocation, and network planning to name a few. Spectrum cartography
techniques construct these maps from a collection of measurements collected by
spatially distributed sensors. Due to the nature of the propagation of
electromagnetic waves, spectrum maps are complicated functions of the spatial
coordinates. For this reason, model-free approaches have been preferred.
However, all existing schemes rely on some interpolation algorithm unable to
learn from data. This work proposes a novel approach to spectrum cartography
where propagation phenomena are learned from data. The resulting algorithms can
therefore construct a spectrum map from a significantly smaller number of
measurements than existing schemes since the spatial structure of shadowing and
other phenomena is previously learned from maps in other environments. Besides
the aforementioned new paradigm, this is also the first work to perform
spectrum cartography with deep neural networks. To exploit the manifold
structure of spectrum maps, a deep network architecture is proposed based on
completion autoencoders. | [] | 2019-11-28 | null | null | null | null | ['spectrum-cartography'] | ['computer-vision'] | [ 2.98299253e-01 -2.00236797e-01 2.35197265e-02 -1.87103018e-01
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e95209c6-a99c-4e05-8761-06a2fbc1a0c9 | intrinsic-image-decomposition-using-paradigms | 2011.10512 | null | https://arxiv.org/abs/2011.10512v1 | https://arxiv.org/pdf/2011.10512v1.pdf | Intrinsic Image Decomposition using Paradigms | Intrinsic image decomposition is the classical task of mapping image to albedo. The WHDR dataset allows methods to be evaluated by comparing predictions to human judgements ("lighter", "same as", "darker"). The best modern intrinsic image methods learn a map from image to albedo using rendered models and human judgements. This is convenient for practical methods, but cannot explain how a visual agent without geometric, surface and illumination models and a renderer could learn to recover intrinsic images. This paper describes a method that learns intrinsic image decomposition without seeing WHDR annotations, rendered data, or ground truth data. The method relies on paradigms - fake albedos and fake shading fields - together with a novel smoothing procedure that ensures good behavior at short scales on real images. Long scale error is controlled by averaging. Our method achieves WHDR scores competitive with those of strong recent methods allowed to see training WHDR annotations, rendered data, and ground truth data. Because our method is unsupervised, we can compute estimates of the test/train variance of WHDR scores; these are quite large, and it is unsafe to rely small differences in reported WHDR. | ['Jason J. Rock', 'D. A. Forsyth'] | 2020-11-20 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 4.85118955e-01 4.92239356e-01 4.08186108e-01 -5.48570812e-01
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91cb59b2-e76c-4c3d-9d0d-27571cb28059 | towards-perspective-free-object-counting-with | null | null | http://agamenon.tsc.uah.es/Investigacion/gram/publications/eccv2016-onoro.pdf | http://agamenon.tsc.uah.es/Investigacion/gram/publications/eccv2016-onoro.pdf | Towards perspective-free object counting with deep learning | In this paper we address the problem of counting objects
instances in images. Our models are able to precisely estimate the number of vehicles in a traffic congestion, or to count the humans in a very
crowded scene. Our first contribution is the proposal of a novel convolutional neural network solution, named Counting CNN (CCNN). Essentially, the CCNN is formulated as a regression model where the network
learns how to map the appearance of the image patches to their corresponding object density maps. Our second contribution consists in
a scale-aware counting model, the Hydra CNN, able to estimate object
densities in different very crowded scenarios where no geometric information of the scene can be provided. Hydra CNN learns a multiscale
non-linear regression model which uses a pyramid of image patches extracted at multiple scales to perform the final density prediction. We
report an extensive experimental evaluation, using up to three different
object counting benchmarks, where we show how our solutions achieve
a state-of-the-art performance. | ['Roberto J. L´opez-Sastre', 'Daniel O˜noro-Rubio'] | 2016-01-01 | null | null | null | journal-2016-1 | ['object-counting'] | ['computer-vision'] | [-3.75450760e-01 -2.58387119e-01 3.98945324e-02 -5.07450521e-01
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62c3febe-aa0c-4a35-9031-5445c5f6ae0e | hyt-nas-hybrid-transformers-neural | 2303.04440 | null | https://arxiv.org/abs/2303.04440v2 | https://arxiv.org/pdf/2303.04440v2.pdf | HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices | Vision Transformers have enabled recent attention-based Deep Learning (DL) architectures to achieve remarkable results in Computer Vision (CV) tasks. However, due to the extensive computational resources required, these architectures are rarely implemented on resource-constrained platforms. Current research investigates hybrid handcrafted convolution-based and attention-based models for CV tasks such as image classification and object detection. In this paper, we propose HyT-NAS, an efficient Hardware-aware Neural Architecture Search (HW-NAS) including hybrid architectures targeting vision tasks on tiny devices. HyT-NAS improves state-of-the-art HW-NAS by enriching the search space and enhancing the search strategy as well as the performance predictors. Our experiments show that HyT-NAS achieves a similar hypervolume with less than ~5x training evaluations. Our resulting architecture outperforms MLPerf MobileNetV1 by 6.3% accuracy improvement with 3.5x less number of parameters on Visual Wake Words. | ['Hamza Ouarnoughi', 'Smail Niar', 'Hadjer Benmeziane', 'Lotfi Abdelkrim Mecharbat'] | 2023-03-08 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.77662134e-01 -3.74287665e-01 -2.60926992e-01 -3.12419266e-01
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c51976c8-173d-4838-9a5d-c8c01cd79285 | directionally-convolutional-networks-for-3d | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Xu_Directionally_Convolutional_Networks_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Xu_Directionally_Convolutional_Networks_ICCV_2017_paper.pdf | Directionally Convolutional Networks for 3D Shape Segmentation | Previous approaches on 3D shape segmentation mostly rely on heuristic processing and hand-tuned geometric descriptors. In this paper, we propose a novel 3D shape representation learning approach, Directionally Convolutional Network (DCN), to solve the shape segmentation problem. DCN extends convolution operations from images to the surface mesh of 3D shapes. With DCN, we learn effective shape representations from raw geometric features, i.e., face normals and distances, to achieve robust segmentation. More specifically, a two-stream segmentation framework is proposed: one stream is made up by the proposed DCN with the face normals as the input, and the other stream is implemented by a neural network with the face distance histogram as the input. The learned shape representations from the two streams are fused by an element-wise product. Finally, Conditional Random Field (CRF) is applied to optimize the segmentation. Through extensive experiments conducted on benchmark datasets, we demonstrate that our approach outperforms the current state-of-the-arts (both classic and deep learning-based) on a large variety of 3D shapes.
| ['Zichun Zhong', 'Haotian Xu', 'Ming Dong'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['3d-shape-representation'] | ['computer-vision'] | [ 1.82489589e-01 -4.51669618e-02 -3.96929011e-02 -7.56082475e-01
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ad8c1bd8-29ab-48b7-9987-9791c08f16d8 | inductive-matrix-completion-and-root-music | 2209.07642 | null | https://arxiv.org/abs/2209.07642v2 | https://arxiv.org/pdf/2209.07642v2.pdf | Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems | This paper studies the estimation of cascaded channels in passive intelligent reflective surface (IRS)- aided multiple-input multiple-output (MIMO) systems employing hybrid precoders and combiners. We propose a low-complexity solution that estimates the channel parameters progressively. The angles of departure (AoDs) and angles of arrival (AoAs) at the transmitter and receiver, respectively, are first estimated using inductive matrix completion (IMC) followed by root-MUSIC based super-resolution spectrum estimation. Forward-backward spatial smoothing (FBSS) is applied to address the coherence issue. Using the estimated AoAs and AoDs, the training precoders and combiners are then optimized and the angle differences between the AoAs and AoDs at the IRS are estimated using the least squares (LS) method followed by FBSS and the root-MUSIC algorithm. Finally, the composite path gains of the cascaded channel are estimated using on-grid sparse recovery with a small-size dictionary. The simulation results suggest that the proposed estimator can achieve improved channel parameter estimation performance with lower complexity as compared to several recently reported alternatives, thanks to the exploitation of the knowledge of the array responses and low-rankness of the channel using low-complexity algorithms at all the stages. | ['Y. Yu', 'J. Yuan', 'J. Xi', 'J. Tong', 'K. F. Masood'] | 2022-09-15 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 4.16741312e-01 -1.70130640e-01 4.24700081e-01 2.30851308e-01
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ee8e505a-4ffa-49e1-99ec-6ef4c0664f92 | enhancing-deep-learning-based-3-lead-ecg | 2208.07088 | null | https://arxiv.org/abs/2208.07088v1 | https://arxiv.org/pdf/2208.07088v1.pdf | Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration | Nowadays, an increasing number of people are being diagnosed with cardiovascular diseases (CVDs), the leading cause of death globally. The gold standard for identifying these heart problems is via electrocardiogram (ECG). The standard 12-lead ECG is widely used in clinical practice and the majority of current research. However, using a lower number of leads can make ECG more pervasive as it can be integrated with portable or wearable devices. This article introduces two novel techniques to improve the performance of the current deep learning system for 3-lead ECG classification, making it comparable with models that are trained using standard 12-lead ECG. Specifically, we propose a multi-task learning scheme in the form of the number of heartbeats regression and an effective mechanism to integrate patient demographic data into the system. With these two advancements, we got classification performance in terms of F1 scores of 0.9796 and 0.8140 on two large-scale ECG datasets, i.e., Chapman and CPSC-2018, respectively, which surpassed current state-of-the-art ECG classification methods, even those trained on 12-lead data. To encourage further development, our source code is publicly available at https://github.com/lhkhiem28/LightX3ECG. | ['Cuong D. Do', 'Tu A. Nguyen', 'Thao B. T. Nguyen', 'Hieu H. Pham', 'Khiem H. Le'] | 2022-08-15 | null | null | null | null | ['ecg-classification'] | ['medical'] | [-6.32061958e-02 -4.04589951e-01 -5.29333539e-02 -4.07817632e-01
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5fdf597c-ce44-4eeb-a901-09226b9a0f54 | leveraging-pretrained-representations-with | 2303.08019 | null | https://arxiv.org/abs/2303.08019v1 | https://arxiv.org/pdf/2303.08019v1.pdf | Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection | With the global population aging rapidly, Alzheimer's disease (AD) is particularly prominent in older adults, which has an insidious onset and leads to a gradual, irreversible deterioration in cognitive domains (memory, communication, etc.). Speech-based AD detection opens up the possibility of widespread screening and timely disease intervention. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features. Based on these features, the paper also proposes a novel task-oriented approach by modeling the relationship between the participants' description and the cognitive task. Experiments are carried out on the ADReSS dataset in a binary classification setup, and models are evaluated on the unseen test set. Results and comparison with recent literature demonstrate the efficiency and superior performance of proposed acoustic, linguistic and task-oriented methods. The findings also show the importance of semantic and syntactic information, and feasibility of automation and generalization with the promising audio-only and task-oriented methods for the AD detection task. | ['Helen Meng', 'Xunying Liu', 'Xixin Wu', 'Dongsheng Li', 'Bo Zheng', 'Junan Li', 'Kaitao Song', 'Jinchao Li'] | 2023-03-14 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 2.81434059e-01 -7.70912096e-02 1.66294530e-01 -5.77835619e-01
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caf44b70-3759-40aa-a61d-803cfd6880dc | an-embarrassingly-simple-approach-to-zero | null | null | https://dl.acm.org/doi/10.5555/3045118.3045347 | http://jmlr.org/proceedings/papers/v37/romera-paredes15.pdf | An embarrassingly simple approach to zero-shot learning | Zero-shot learning consists in learning how to recognise new concepts by just having a description of them. Many sophisticated approaches have been proposed to address the challenges this problem comprises. In this paper we describe a zero-shot learning approach that can be implemented in just one line of code, yet it is able to outperform state of the art approaches on standard datasets. The approach is based on a more general framework which models the relationships between features, attributes, and classes as a two linear layers network, where the weights of the top layer are not learned but are given by the environment. We further provide a learning bound on the generalisation error of this kind of approaches, by casting them as domain adaptation methods. In experiments carried out on three standard real datasets, we found that our approach is able to perform significantly better than the state of art on all of them, obtaining a ratio of improvement up to 17%. | ['Philip H. S. Torr', 'Bernardino Romera-Paredes'] | 2015-07-06 | null | null | null | proceedings-of-the-international-conference-1 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 2.90150881e-01 3.23478669e-01 -5.85968345e-02 -5.96324503e-01
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2753bb40-27d5-4631-9818-49bc05b3cda7 | towards-writing-style-adaptation-in | 2302.06318 | null | https://arxiv.org/abs/2302.06318v1 | https://arxiv.org/pdf/2302.06318v1.pdf | Towards Writing Style Adaptation in Handwriting Recognition | One of the challenges of handwriting recognition is to transcribe a large number of vastly different writing styles. State-of-the-art approaches do not explicitly use information about the writer's style, which may be limiting overall accuracy due to various ambiguities. We explore models with writer-dependent parameters which take the writer's identity as an additional input. The proposed models can be trained on datasets with partitions likely written by a single author (e.g. single letter, diary, or chronicle). We propose a Writer Style Block (WSB), an adaptive instance normalization layer conditioned on learned embeddings of the partitions. We experimented with various placements and settings of WSB and contrastively pre-trained embeddings. We show that our approach outperforms a baseline with no WSB in a writer-dependent scenario and that it is possible to estimate embeddings for new writers. However, domain adaptation using simple finetuning in a writer-independent setting provides superior accuracy at a similar computational cost. The proposed approach should be further investigated in terms of training stability and embedding regularization to overcome such a baseline. | ['Martin Kišš', 'Michal Hradiš', 'Jan Kohút'] | 2023-02-13 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 1.85574144e-01 -2.44077131e-01 -1.49102911e-01 -5.94265163e-01
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daa591a6-9cb3-4348-83b9-f02b4fef68ca | don-t-eclipse-your-arts-due-to-small | null | null | https://aclanthology.org/2020.acl-main.339 | https://aclanthology.org/2020.acl-main.339.pdf | Don't Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction | The current aspect extraction methods suffer from boundary errors. In general, these errors lead to a relatively minor difference between the extracted aspects and the ground-truth. However, they hurt the performance severely. In this paper, we propose to utilize a pointer network for repositioning the boundaries. Recycling mechanism is used, which enables the training data to be collected without manual intervention. We conduct the experiments on the benchmark datasets SE14 of laptop and SE14-16 of restaurant. Experimental results show that our method achieves substantial improvements over the baseline, and outperforms state-of-the-art methods. | ['Zhenkai Wei', 'Yu Hong', 'Meng Cheng', 'Bowei Zou', 'Jianmin Yao'] | 2020-07-01 | null | null | null | acl-2020-6 | ['aspect-extraction'] | ['natural-language-processing'] | [-9.56013054e-02 7.04627559e-02 -5.07428050e-01 -2.92966634e-01
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b066a2a8-cd8d-4eb6-afdd-a165c3c8dc67 | deepastrouda-semi-supervised-universal-domain | 2302.02005 | null | https://arxiv.org/abs/2302.02005v2 | https://arxiv.org/pdf/2302.02005v2.pdf | DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection | Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, \textit{DeepAstroUDA}, as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities ($3$-class and $10$-class problems), with anomaly detection: 1) datasets created after different numbers of observing years from a single survey (LSST mock data of $1$ and $10$ years of observations); 2) data from different surveys (SDSS and DECaLS); and 3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of domain adaptation between very discrepant observational datasets. \textit{DeepAstroUDA} is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to $40\%$ on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset. | ['S. M. Wild', 'G. N. Perdue', 'B. Nord', 'S. Madireddy', 'K. Pedro', 'A. Lewis', 'A. Ćiprijanović'] | 2023-02-03 | null | null | null | null | ['morphology-classification', 'universal-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [-3.22908759e-02 -3.34545404e-01 1.08078867e-01 -5.59136510e-01
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3c2e9190-29cb-4867-883f-3a98805afd17 | boun-at-semeval-2021-task-9-text-augmentation | null | null | https://aclanthology.org/2021.semeval-1.52 | https://aclanthology.org/2021.semeval-1.52.pdf | BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data | In this paper, we present our text augmentation based approach for the Table Statement Support Subtask (Phase A) of SemEval-2021 Task 9. We experiment with different text augmentation techniques such as back translation and synonym swapping using Word2Vec and WordNet. We show that text augmentation techniques lead to 2.5{\%} improvement in F1 on the test set. Further, we investigate the impact of domain adaptation and joint learning on fact verification in tabular data by utilizing the SemTabFacts and TabFact datasets. We observe that joint learning improves the F1 scores on the SemTabFacts and TabFact test sets by 3.31{\%} and 0.77{\%}, respectively. | ['Arzucan {\\"O}zg{\\"u}r', 'Bekir Y{\\i}ld{\\i}r{\\i}m', 'Yusuf Y{\\"u}ksel', 'Abdullatif K{\\"o}ksal'] | 2021-08-01 | null | null | null | semeval-2021 | ['text-augmentation'] | ['natural-language-processing'] | [ 2.70769119e-01 4.04650718e-01 -6.06762111e-01 -3.04124177e-01
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1f7a654d-a323-4395-9cc9-c966bb695612 | attention-enhanced-deep-learning-for-device | 2304.13105 | null | https://arxiv.org/abs/2304.13105v1 | https://arxiv.org/pdf/2304.13105v1.pdf | Attention-Enhanced Deep Learning for Device-Free Through-the-Wall Presence Detection Using Indoor WiFi System | Accurate detection of human presence in indoor environments is important for various applications, such as energy management and security. In this paper, we propose a novel system for human presence detection using the channel state information (CSI) of WiFi signals. Our system named attention-enhanced deep learning for presence detection (ALPD) employs an attention mechanism to automatically select informative subcarriers from the CSI data and a bidirectional long short-term memory (LSTM) network to capture temporal dependencies in CSI. Additionally, we utilize a static feature to improve the accuracy of human presence detection in static states. We evaluate the proposed ALPD system by deploying a pair of WiFi access points (APs) for collecting CSI dataset, which is further compared with several benchmarks. The results demonstrate that our ALPD system outperforms the benchmarks in terms of accuracy, especially in the presence of interference. Moreover, bidirectional transmission data is beneficial to training improving stability and accuracy, as well as reducing the costs of data collection for training. Overall, our proposed ALPD system shows promising results for human presence detection using WiFi CSI signals. | ['Kai-Ten Feng', 'An-Hung Hsiao', 'Kuan-I Lu', 'Li-Hsiang Shen'] | 2023-04-25 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 7.64254555e-02 -7.13767827e-01 5.35686910e-02 -2.62613833e-01
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2396cba3-9146-4e79-8e04-33cad90dffe9 | distributed-learning-of-deep-neural-networks | 1910.02120 | null | https://arxiv.org/abs/1910.02120v7 | https://arxiv.org/pdf/1910.02120v7.pdf | Distributed Learning of Deep Neural Networks using Independent Subnet Training | Distributed machine learning (ML) can bring more computational resources to bear than single-machine learning, thus enabling reductions in training time. Distributed learning partitions models and data over many machines, allowing model and dataset sizes beyond the available compute power and memory of a single machine. In practice though, distributed ML is challenging when distribution is mandatory, rather than chosen by the practitioner. In such scenarios, data could unavoidably be separated among workers due to limited memory capacity per worker or even because of data privacy issues. There, existing distributed methods will utterly fail due to dominant transfer costs across workers, or do not even apply. We propose a new approach to distributed fully connected neural network learning, called independent subnet training (IST), to handle these cases. In IST, the original network is decomposed into a set of narrow subnetworks with the same depth. These subnetworks are then trained locally before parameters are exchanged to produce new subnets and the training cycle repeats. Such a naturally "model parallel" approach limits memory usage by storing only a portion of network parameters on each device. Additionally, no requirements exist for sharing data between workers (i.e., subnet training is local and independent) and communication volume and frequency are reduced by decomposing the original network into independent subnets. These properties of IST can cope with issues due to distributed data, slow interconnects, or limited device memory, making IST a suitable approach for cases of mandatory distribution. We show experimentally that IST results in training times that are much lower than common distributed learning approaches. | ['Yuxin Tang', 'Chen Dun', 'Cameron R. Wolfe', 'Christopher M. Jermaine', 'Binhang Yuan', 'Anastasios Kyrillidis'] | 2019-10-04 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-1.90671265e-01 1.47821933e-01 -3.40357155e-01 -3.73096585e-01
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6eb66f85-98a9-4ca6-9777-6364425a5213 | improving-passage-retrieval-with-zero-shot | 2204.07496 | null | https://arxiv.org/abs/2204.07496v4 | https://arxiv.org/pdf/2204.07496v4.pdf | Improving Passage Retrieval with Zero-Shot Question Generation | We propose a simple and effective re-ranking method for improving passage retrieval in open question answering. The re-ranker re-scores retrieved passages with a zero-shot question generation model, which uses a pre-trained language model to compute the probability of the input question conditioned on a retrieved passage. This approach can be applied on top of any retrieval method (e.g. neural or keyword-based), does not require any domain- or task-specific training (and therefore is expected to generalize better to data distribution shifts), and provides rich cross-attention between query and passage (i.e. it must explain every token in the question). When evaluated on a number of open-domain retrieval datasets, our re-ranker improves strong unsupervised retrieval models by 6%-18% absolute and strong supervised models by up to 12% in terms of top-20 passage retrieval accuracy. We also obtain new state-of-the-art results on full open-domain question answering by simply adding the new re-ranker to existing models with no further changes. | ['Luke Zettlemoyer', 'Joelle Pineau', 'Wen-tau Yih', 'Armen Aghajanyan', 'Mandar Joshi', 'Mike Lewis', 'Devendra Singh Sachan'] | 2022-04-15 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-2.84086596e-02 1.03399314e-01 -1.47307545e-01 3.38409729e-02
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fdff8702-1770-4cdc-980c-1f7affcc56ca | monocular-arbitrary-moving-object-discovery | null | null | https://www.bmvc2021-virtualconference.com/conference/papers/paper_1500.html | https://www.bmvc2021-virtualconference.com/assets/papers/1500.pdf | Monocular Arbitrary Moving Object Discovery and Segmentation | We propose a method for discovery and segmentation of objects that are, or their parts are, independently moving in the scene. Given three monocular video frames, the method outputs semantically meaningful regions, i.e. regions corresponding to the whole object, even when only a part of it moves.
The architecture of the CNN-based end-to-end method, called Raptor, combines semantic and motion backbones, which pass their outputs to a final region segmentation network. The semantic backbone is trained in a class-agnostic manner in order to generalise to object classes beyond the training data. The core of the motion branch is a geometrical cost volume computed from optical flow, optical expansion, mono-depth and the estimated camera motion.
Evaluation of the proposed architecture on the instance motion segmentation and binary moving-static segmentation problems on KITTI, DAVIS-Moving and YTVOS-Moving datasets shows that the proposed method achieves state-of-the-art results on all the datasets and is able to generalise well to various environments. For the KITTI dataset, we provide an upgraded instance motion segmentation annotation which covers all moving objects. Dataset, code and models are available on the github project page github.com/michalneoral/Raptor. | ['Jiří Matas', 'Jan Šochman', 'Michal Neoral'] | 2021-11-22 | null | null | null | the-32nd-british-machine-vision-conference | ['motion-segmentation'] | ['computer-vision'] | [ 4.91273329e-02 -8.42832327e-02 -3.03171873e-01 -3.14309746e-01
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682b1b58-9761-400b-a04a-4ba0f678b404 | speechmatrix-a-large-scale-mined-corpus-of | null | null | https://research.facebook.com/publications/speechmatrix/ | https://scontent-lhr8-2.xx.fbcdn.net/v/t39.8562-6/310002966_605149234737289_5204270723809834290_n.pdf?_nc_cat=102&ccb=1-7&_nc_sid=ad8a9d&_nc_ohc=FN2KnupyKI0AX90B5UO&_nc_ht=scontent-lhr8-2.xx&oh=00_AT9iFWHchGOnkzVTmwiYIDElIXSnwilSGhDwRQdFh99rlA&oe=63560915https://scontent-lhr8-2.xx.fbcdn.net/v/t39.8562-6/310002966_605149234737289_5204270723809834290_n.pdf?_nc_cat=102&ccb=1-7&_nc_sid=ad8a9d&_nc_ohc=FN2KnupyKI0AX90B5UO&_nc_ht=scontent-lhr8-2.xx&oh=00_AT9iFWHchGOnkzVTmwiYIDElIXSnwilSGhDwRQdFh99rlA&oe=63560915 | SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations | We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. It contains speech alignments in 136 language pairs with a total of 418 thousand hours of speech. To evaluate the quality of this parallel speech, we train bilingual speech-to-speech translation models on mined data only and establish extensive baseline results on EuroParl-ST, VoxPopuli and FLEURS test sets. Enabled by the multilinguality of SpeechMatrix, we also explore multilingual speech-to-speech translation, a topic which was addressed by few other works. We also demonstrate that model pre-training and sparse scaling using Mixture-of-Experts bring large gains to translation performance. The mined data and models are freely available. | ['Holger Schwenk', 'Benoît Sagot', 'Juan Pino', 'Changhan Wang', 'Vedanuj Goswani', 'Ann Lee', 'Jingfei Du', 'Ning Dong', 'Hongyu Gong', 'Paul-Ambroise Duquenne'] | 2022-10-19 | null | null | null | arxiv-2022-10 | ['speech-to-speech-translation'] | ['speech'] | [-4.68200222e-02 3.57836694e-01 -3.19270819e-01 -4.81695235e-01
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4187a73f-ce9a-48c2-b514-1e9f12bb420a | utfpr-at-semeval-2021-task-1-complexity | null | null | https://aclanthology.org/2021.semeval-1.78 | https://aclanthology.org/2021.semeval-1.78.pdf | UTFPR at SemEval-2021 Task 1: Complexity Prediction by Combining BERT Vectors and Classic Features | We describe the UTFPR systems submitted to the Lexical Complexity Prediction shared task of SemEval 2021. They perform complexity prediction by combining classic features, such as word frequency, n-gram frequency, word length, and number of senses, with BERT vectors. We test numerous feature combinations and machine learning models in our experiments and find that BERT vectors, even if not optimized for the task at hand, are a great complement to classic features. We also find that employing the principle of compositionality can potentially help in phrase complexity prediction. Our systems place 45th out of 55 for single words and 29th out of 38 for phrases. | ['Gustavo Henrique Paetzold'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-2.08693504e-01 -2.29701206e-01 -3.35004568e-01 -1.16655879e-01
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34ff5de6-aa21-4380-ae19-2d4a438545f5 | game-state-learning-via-game-scene | 2207.01289 | null | https://arxiv.org/abs/2207.01289v2 | https://arxiv.org/pdf/2207.01289v2.pdf | Game State Learning via Game Scene Augmentation | Having access to accurate game state information is of utmost importance for any artificial intelligence task including game-playing, testing, player modeling, and procedural content generation. Self-Supervised Learning (SSL) techniques have shown to be capable of inferring accurate game state information from the high-dimensional pixel input of game footage into compressed latent representations. Contrastive Learning is a popular SSL paradigm where the visual understanding of the game's images comes from contrasting dissimilar and similar game states defined by simple image augmentation methods. In this study, we introduce a new game scene augmentation technique -- named GameCLR -- that takes advantage of the game-engine to define and synthesize specific, highly-controlled renderings of different game states, thereby, boosting contrastive learning performance. We test our GameCLR technique on images of the CARLA driving simulator environment and compare it against the popular SimCLR baseline SSL method. Our results suggest that GameCLR can infer the game's state information from game footage more accurately compared to the baseline. Our proposed approach allows us to conduct game artificial intelligence research by directly utilizing screen pixels as input. | ['Georgios N. Yannakakis', 'Antonios Liapis', 'Konstantinos Makantasis', 'Chintan Trivedi'] | 2022-07-04 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.61551994e-01 6.69140443e-02 1.22146280e-02 -1.30105644e-01
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57117572-3296-4bdc-966a-08cc19f91833 | uncertainty-aware-unlikelihood-learning | 2306.00418 | null | https://arxiv.org/abs/2306.00418v2 | https://arxiv.org/pdf/2306.00418v2.pdf | Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction | Recently, aspect sentiment quad prediction has received widespread attention in the field of aspect-based sentiment analysis. Existing studies extract quadruplets via pre-trained generative language models to paraphrase the original sentence into a templated target sequence. However, previous works only focus on what to generate but ignore what not to generate. We argue that considering the negative samples also leads to potential benefits. In this work, we propose a template-agnostic method to control the token-level generation, which boosts original learning and reduces mistakes simultaneously. Specifically, we introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models, acquiring the noises and errors. We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens. Finally, we introduce minimization entropy to balance the effects of marginalized unlikelihood learning. Extensive experiments on four public datasets demonstrate the effectiveness of our approach on various generation templates. | ['Minlie Huang', 'Shiwan Zhao', 'Hang Gao', 'Liqi Zhang', 'Zhen Zhang', 'Yike Wu', 'Yinhao Bai', 'Mengting Hu'] | 2023-06-01 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 2.74532169e-01 9.53430682e-02 -3.14552277e-01 -7.33366251e-01
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39507cca-0814-41ae-9002-8c4f0e0a5798 | multi-contextual-design-of-convolutional | 2106.10430 | null | https://arxiv.org/abs/2106.10430v2 | https://arxiv.org/pdf/2106.10430v2.pdf | Multi-Contextual Design of Convolutional Neural Network for Steganalysis | In recent times, deep learning-based steganalysis classifiers became popular due to their state-of-the-art performance. Most deep steganalysis classifiers usually extract noise residuals using high-pass filters as preprocessing steps and feed them to their deep model for classification. It is observed that recent steganographic embedding does not always restrict their embedding in the high-frequency zone; instead, they distribute it as per embedding policy. Therefore, besides noise residual, learning the embedding zone is another challenging task. In this work, unlike the conventional approaches, the proposed model first extracts the noise residual using learned denoising kernels to boost the signal-to-noise ratio. After preprocessing, the sparse noise residuals are fed to a novel Multi-Contextual Convolutional Neural Network (M-CNET) that uses heterogeneous context size to learn the sparse and low-amplitude representation of noise residuals. The model performance is further improved by incorporating the Self-Attention module to focus on the areas prone to steganalytic embedding. A set of comprehensive experiments is performed to show the proposed scheme's efficacy over the prior arts. Besides, an ablation study is given to justify the contribution of various modules of the proposed architecture. | ['Pinaki Mitra', 'Arijit Sur', 'Brijesh Singh'] | 2021-06-19 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 5.81173062e-01 -4.86649983e-02 -4.88917828e-02 8.43174607e-02
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9407618e-0dbc-41c3-b255-032f07b3d5c7 | learning-personalized-decision-support | 2304.06701 | null | https://arxiv.org/abs/2304.06701v1 | https://arxiv.org/pdf/2304.06701v1.pdf | Learning Personalized Decision Support Policies | Individual human decision-makers may benefit from different forms of support to improve decision outcomes. However, a key question is which form of support will lead to accurate decisions at a low cost. In this work, we propose learning a decision support policy that, for a given input, chooses which form of support, if any, to provide. We consider decision-makers for whom we have no prior information and formalize learning their respective policies as a multi-objective optimization problem that trades off accuracy and cost. Using techniques from stochastic contextual bandits, we propose $\texttt{THREAD}$, an online algorithm to personalize a decision support policy for each decision-maker, and devise a hyper-parameter tuning strategy to identify a cost-performance trade-off using simulated human behavior. We provide computational experiments to demonstrate the benefits of $\texttt{THREAD}$ compared to offline baselines. We then introduce $\texttt{Modiste}$, an interactive tool that provides $\texttt{THREAD}$ with an interface. We conduct human subject experiments to show how $\texttt{Modiste}$ learns policies personalized to each decision-maker and discuss the nuances of learning decision support policies online for real users. | ['Ameet Talwalkar', 'Adrian Weller', 'Emma Kallina', 'Parameswaran Kamalaruban', 'Katherine M. Collins', 'Valerie Chen', 'Umang Bhatt'] | 2023-04-13 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.71707267e-01 6.65585846e-02 -7.09820867e-01 -9.34682310e-01
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29519d99-59c9-4a63-ab62-0a8417d437f5 | constrained-bilinear-factorization-multi-view | 1906.08107 | null | https://arxiv.org/abs/1906.08107v2 | https://arxiv.org/pdf/1906.08107v2.pdf | Constrained Bilinear Factorization Multi-view Subspace Clustering | Multi-view clustering is an important and fundamental problem. Many multi-view subspace clustering methods have been proposed, and most of them assume that all views share a same coefficient matrix. However, the underlying information of multi-view data are not fully exploited under this assumption, since the coefficient matrices of different views should have the same clustering properties rather than be uniform among multiple views. To this end, this paper proposes a novel Constrained Bilinear Factorization Multi-view Subspace Clustering (CBF-MSC) method. Specifically, the bilinear factorization with an orthonormality constraint and a low-rank constraint is imposed for all coefficient matrices to make them have the same trace-norm instead of being equivalent, so as to explore the consensus information of multi-view data more fully. Finally, an Augmented Lagrangian Multiplier (ALM) based algorithm is designed to optimize the objective function. Comprehensive experiments tested on nine benchmark datasets validate the effectiveness and competitiveness of the proposed approach compared with several state-of-the-arts. | ['Zhiqiang Tian', 'Xiuyi Jia', 'Shanmin Pang', 'Qinghai Zheng', 'Jihua Zhu', 'Zhongyu Li'] | 2019-06-19 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.46175611e-01 -5.84392011e-01 -1.01820670e-01 -1.71756029e-01
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41960cf6-1747-434b-836a-8492660bdbd8 | pointrnn-point-recurrent-neural-network-for | 1910.08287 | null | https://arxiv.org/abs/1910.08287v2 | https://arxiv.org/pdf/1910.08287v2.pdf | PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing | In this paper, we introduce a Point Recurrent Neural Network (PointRNN) for moving point cloud processing. At each time step, PointRNN takes point coordinates $\boldsymbol{P} \in \mathbb{R}^{n \times 3}$ and point features $\boldsymbol{X} \in \mathbb{R}^{n \times d}$ as input ($n$ and $d$ denote the number of points and the number of feature channels, respectively). The state of PointRNN is composed of point coordinates $\boldsymbol{P}$ and point states $\boldsymbol{S} \in \mathbb{R}^{n \times d'}$ ($d'$ denotes the number of state channels). Similarly, the output of PointRNN is composed of $\boldsymbol{P}$ and new point features $\boldsymbol{Y} \in \mathbb{R}^{n \times d''}$ ($d''$ denotes the number of new feature channels). Since point clouds are orderless, point features and states from two time steps can not be directly operated. Therefore, a point-based spatiotemporally-local correlation is adopted to aggregate point features and states according to point coordinates. We further propose two variants of PointRNN, i.e., Point Gated Recurrent Unit (PointGRU) and Point Long Short-Term Memory (PointLSTM). We apply PointRNN, PointGRU and PointLSTM to moving point cloud prediction, which aims to predict the future trajectories of points in a set given their history movements. Experimental results show that PointRNN, PointGRU and PointLSTM are able to produce correct predictions on both synthetic and real-world datasets, demonstrating their ability to model point cloud sequences. The code has been released at \url{https://github.com/hehefan/PointRNN}. | ['Yi Yang', 'Hehe Fan'] | 2019-10-18 | null | null | null | null | ['moving-point-cloud-processing'] | ['time-series'] | [-1.14486583e-01 -5.41647911e-01 2.61019580e-02 -1.14453159e-01
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ca423c74-78ec-4b2b-ab33-30c9de8fb43b | few-shot-speaker-identification-using | 2204.11180 | null | https://arxiv.org/abs/2204.11180v1 | https://arxiv.org/pdf/2204.11180v1.pdf | Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention | Although few-shot learning has attracted much attention from the fields of image and audio classification, few efforts have been made on few-shot speaker identification. In the task of few-shot learning, overfitting is a tough problem mainly due to the mismatch between training and testing conditions. In this paper, we propose a few-shot speaker identification method which can alleviate the overfitting problem. In the proposed method, the model of a depthwise separable convolutional network with channel attention is trained with a prototypical loss function. Experimental datasets are extracted from three public speech corpora: Aishell-2, VoxCeleb1 and TORGO. Experimental results show that the proposed method exceeds state-of-the-art methods for few-shot speaker identification in terms of accuracy and F-score. | ['Qianhua He', 'Wei Li', 'Wenchang Cao', 'Hao Chen', 'Wucheng Wang', 'Yanxiong Li'] | 2022-04-24 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 8.80132604e-04 -2.84376681e-01 -1.25422508e-01 -5.43079674e-01
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a63080bd-c6da-4ecc-9163-9160db545257 | preference-grounded-token-level-guidance-for | 2306.00398 | null | https://arxiv.org/abs/2306.00398v1 | https://arxiv.org/pdf/2306.00398v1.pdf | Preference-grounded Token-level Guidance for Language Model Fine-tuning | Aligning language models (LMs) with preferences is an important problem in natural language generation. A key challenge is that preferences are typically provided at the sequence level while LM training and generation both occur at the token level. There is, therefore, a granularity mismatch between the preference and the LM training losses, which may complicate the learning problem. In this paper, we address this issue by developing an alternate training process, where we iterate between grounding the sequence-level preference into token-level training guidance, and improving the LM with the learned guidance. For guidance learning, we design a framework that extends the pairwise-preference learning in imitation learning to both variable-length LM generation and utilizing the preference among multiple generations. For LM training, based on the amount of supervised data, we present two minimalist learning objectives that utilize the learned guidance. In experiments, our method performs competitively on two distinct representative LM tasks -- discrete-prompt generation and text summarization. | ['Mingyuan Zhou', 'Caiming Xiong', 'Yihao Feng', 'Congying Xia', 'Shujian Zhang', 'Shentao Yang'] | 2023-06-01 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 7.50837028e-01 2.56993622e-01 -5.80600321e-01 -2.87597388e-01
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9ce3038a-97f0-4a80-b67a-bcea7ebef921 | improving-adversarial-robustness-by-1 | 2210.09643 | null | https://arxiv.org/abs/2210.09643v2 | https://arxiv.org/pdf/2210.09643v2.pdf | Improving Adversarial Robustness by Contrastive Guided Diffusion Process | Synthetic data generation has become an emerging tool to help improve the adversarial robustness in classification tasks since robust learning requires a significantly larger amount of training samples compared with standard classification tasks. Among various deep generative models, the diffusion model has been shown to produce high-quality synthetic images and has achieved good performance in improving the adversarial robustness. However, diffusion-type methods are typically slow in data generation as compared with other generative models. Although different acceleration techniques have been proposed recently, it is also of great importance to study how to improve the sample efficiency of generated data for the downstream task. In this paper, we first analyze the optimality condition of synthetic distribution for achieving non-trivial robust accuracy. We show that enhancing the distinguishability among the generated data is critical for improving adversarial robustness. Thus, we propose the Contrastive-Guided Diffusion Process (Contrastive-DP), which adopts the contrastive loss to guide the diffusion model in data generation. We verify our theoretical results using simulations and demonstrate the good performance of Contrastive-DP on image datasets. | ['Guang Cheng', 'Liyan Xie', 'Yidong Ouyang'] | 2022-10-18 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.71203434e-01 -2.13079989e-01 1.58520937e-01 -2.81474572e-02
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af454f84-4208-407b-b640-025c1a5975e0 | towards-annotating-and-creating-summary | null | null | https://aclanthology.org/D19-5408 | https://aclanthology.org/D19-5408.pdf | Towards Annotating and Creating Summary Highlights at Sub-sentence Level | Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences. They are also better suited than individual words and phrases that can potentially lead to disfluent, fragmented summaries. In this paper we seek to generate summary highlights by annotating summary-worthy sub-sentences and teaching classifiers to do the same. We frame the task as jointly selecting important sentences and identifying a single most informative textual unit from each sentence. This formulation dramatically reduces the task complexity involved in sentence compression. Our study provides new benchmarks and baselines for generating highlights at the sub-sentence level. | ['Fei Liu', 'Parminder Bhatia', 'Kristjan Arumae'] | 2019-11-01 | null | null | null | ws-2019-11 | ['sentence-compression'] | ['natural-language-processing'] | [ 5.50798237e-01 2.88215131e-01 -3.71774912e-01 -2.95007050e-01
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a4da29b6-b9e3-4cf4-8f4d-84a9b35b35c9 | temporal-pointwise-convolutional-networks-for | 2007.09483 | null | https://arxiv.org/abs/2007.09483v4 | https://arxiv.org/pdf/2007.09483v4.pdf | Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit | The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff. Most critical is the efficient allocation of resource-heavy Intensive Care Unit (ICU) beds to the patients who need life support. Central to solving this problem is knowing for how long the current set of ICU patients are likely to stay in the unit. In this work, we propose a new deep learning model based on the combination of temporal convolution and pointwise (1x1) convolution, to solve the length of stay prediction task on the eICU and MIMIC-IV critical care datasets. The model - which we refer to as Temporal Pointwise Convolution (TPC) - is specifically designed to mitigate common challenges with Electronic Health Records, such as skewness, irregular sampling and missing data. In doing so, we have achieved significant performance benefits of 18-68% (metric and dataset dependent) over the commonly used Long-Short Term Memory (LSTM) network, and the multi-head self-attention network known as the Transformer. By adding mortality prediction as a side-task, we can improve performance further still, resulting in a mean absolute deviation of 1.55 days (eICU) and 2.28 days (MIMIC-IV) on predicting remaining length of stay. | ['Pietro Liò', 'Stephanie Hyland', 'Emma Rocheteau'] | 2020-07-18 | null | null | null | null | ['remaining-length-of-stay', 'length-of-stay-prediction', 'predicting-patient-outcomes'] | ['medical', 'medical', 'medical'] | [-7.63526037e-02 -2.14304686e-01 1.01398736e-01 -3.45968604e-01
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23150b9b-f570-45bf-8e5a-80a4c22baf25 | detcid-detection-of-elongated-touching-cells | 2007.06716 | null | https://arxiv.org/abs/2007.06716v1 | https://arxiv.org/pdf/2007.06716v1.pdf | DETCID: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Deep Adversarial Network | Clostridioides difficile infection (C. diff) is the most common cause of death due to secondary infection in hospital patients in the United States. Detection of C. diff cells in scanning electron microscopy (SEM) images is an important task to quantify the efficacy of the under-development treatments. However, detecting C. diff cells in SEM images is a challenging problem due to the presence of inhomogeneous illumination and occlusion. An Illumination normalization pre-processing step destroys the texture and adds noise to the image. Furthermore, cells are often clustered together resulting in touching cells and occlusion. In this paper, DETCID, a deep cell detection method using adversarial training, specifically robust to inhomogeneous illumination and occlusion, is proposed. An adversarial network is developed to provide region proposals and pass the proposals to a feature extraction network. Furthermore, a modified IoU metric is developed to allow the detection of touching cells in various orientations. The results indicate that DETCID outperforms the state-of-the-art in detection of touching cells in SEM images by at least 20 percent improvement of mean average precision. | ['Ioannis A. Kakadiaris', 'Ali Memariani'] | 2020-07-13 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 4.48638856e-01 -5.55581152e-01 6.94825888e-01 1.07239306e-01
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10752c6b-e083-40a2-a3b8-4233c6442158 | entsum-a-data-set-for-entity-centric-2 | null | null | https://aclanthology.org/2022.acl-long.237 | https://aclanthology.org/2022.acl-long.237.pdf | EntSUM: A Data Set for Entity-Centric Extractive Summarization | Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document.We introduce a human-annotated data set EntSUM for controllable summarization with a focus on named entities as the aspects to control.We conduct an extensive quantitative analysis to motivate the task of entity-centric summarization and show that existing methods for controllable summarization fail to generate entity-centric summaries. We propose extensions to state-of-the-art summarization approaches that achieve substantially better results on our data set. Our analysis and results show the challenging nature of this task and of the proposed data set. | ['Daniel Preotiuc-Pietro', 'Mayank Kulkarni', 'Mounica Maddela'] | null | null | null | null | acl-2022-5 | ['extractive-summarization'] | ['natural-language-processing'] | [ 2.25250497e-01 8.31466496e-01 -5.61563849e-01 -4.16319340e-01
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41c295cd-7ba2-4112-b67c-34cad7ccd55d | feature-affinity-based-pseudo-labeling-for | 1805.06118 | null | http://arxiv.org/abs/1805.06118v1 | http://arxiv.org/pdf/1805.06118v1.pdf | Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification | Person re-identification aims to match a person's identity across multiple
camera streams. Deep neural networks have been successfully applied to the
challenging person re-identification task. One remarkable bottleneck is that
the existing deep models are data hungry and require large amounts of labeled
training data. Acquiring manual annotations for pedestrian identity matchings
in large-scale surveillance camera installations is a highly cumbersome task.
Here, we propose the first semi-supervised approach that performs
pseudo-labeling by considering complex relationships between unlabeled and
labeled training samples in the feature space. Our approach first approximates
the actual data manifold by learning a generative model via adversarial
training. Given the trained model, data augmentation can be performed by
generating new synthetic data samples which are unlabeled. An open research
problem is how to effectively use this additional data for improved feature
learning. To this end, this work proposes a novel Feature Affinity based
Pseudo-Labeling (FAPL) approach with two possible label encodings under a
unified setting. Our approach measures the affinity of unlabeled samples with
the underlying clusters of labeled data samples using the intermediate feature
representations from deep networks. FAPL trains with the joint supervision of
cross-entropy loss together with a center regularization term, which not only
ensures discriminative feature representation learning but also simultaneously
predicts pseudo-labels for unlabeled data. Our extensive experiments on two
standard large-scale datasets, Market-1501 and DukeMTMC-reID, demonstrate
significant performance boosts over closely related competitors and outperforms
state-of-the-art person re-identification techniques in most cases. | ['Shanshan Zhang', 'Guodong Ding', 'Salman Khan', 'Fatih Porikli', 'Zhenmin Tang', 'Jian Zhang'] | 2018-05-16 | null | null | null | null | ['semi-supervised-person-re-identification'] | ['computer-vision'] | [ 6.85973316e-02 -2.67810106e-01 -3.73655558e-02 -7.72599399e-01
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ea221ed2-60e5-4cf5-aae6-cbb7f57e6603 | spiking-network-initialisation-and-firing | 2305.08879 | null | https://arxiv.org/abs/2305.08879v1 | https://arxiv.org/pdf/2305.08879v1.pdf | Spiking Network Initialisation and Firing Rate Collapse | In recent years, newly developed methods to train spiking neural networks (SNNs) have rendered them as a plausible alternative to Artificial Neural Networks (ANNs) in terms of accuracy, while at the same time being much more energy efficient at inference and potentially at training time. However, it is still unclear what constitutes a good initialisation for an SNN. We often use initialisation schemes developed for ANN training which are often inadequate and require manual tuning. In this paper, we attempt to tackle this issue by using techniques from the ANN initialisation literature as well as computational neuroscience results. We show that the problem of weight initialisation for ANNs is a more nuanced problem than it is for ANNs due to the spike-and-reset non-linearity of SNNs and the firing rate collapse problem. We firstly identify and propose several solutions to the firing rate collapse problem under different sets of assumptions which successfully solve the issue by leveraging classical random walk and Wiener processes results. Secondly, we devise a general strategy for SNN initialisation which combines variance propagation techniques from ANNs and different methods to obtain the expected firing rate and membrane potential distribution based on diffusion and shot-noise approximations. Altogether, we obtain theoretical results to solve the SNN initialisation which consider the membrane potential distribution in the presence of a threshold. Yet, to what extent can these methods be successfully applied to SNNs on real datasets remains an open question. | ['Dan F. M Goodman', 'Nicolas Perez-Nieves'] | 2023-05-13 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 6.26795530e-01 -2.37399951e-01 6.18298650e-01 2.22486556e-01
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a69b4579-0d3d-4981-b056-e718a24cbf68 | evolving-graph-gaussian-processes | 2106.15127 | null | https://arxiv.org/abs/2106.15127v2 | https://arxiv.org/pdf/2106.15127v2.pdf | Evolving-Graph Gaussian Processes | Graph Gaussian Processes (GGPs) provide a data-efficient solution on graph structured domains. Existing approaches have focused on static structures, whereas many real graph data represent a dynamic structure, limiting the applications of GGPs. To overcome this we propose evolving-Graph Gaussian Processes (e-GGPs). The proposed method is capable of learning the transition function of graph vertices over time with a neighbourhood kernel to model the connectivity and interaction changes between vertices. We assess the performance of our method on time-series regression problems where graphs evolve over time. We demonstrate the benefits of e-GGPs over static graph Gaussian Process approaches. | ['Ville Kyrki', 'Markus Heinonen', 'David Blanco-Mulero'] | 2021-06-29 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-2.07929507e-01 2.19296608e-02 2.31069744e-01 1.31390974e-01
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fa06a83d-3e67-4017-947b-02a714a3ef36 | a-combinatorial-semi-bandit-approach-to | 2301.07156 | null | https://arxiv.org/abs/2301.07156v1 | https://arxiv.org/pdf/2301.07156v1.pdf | A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles | In this work, we address the problem of long-distance navigation for battery electric vehicles (BEVs), where one or more charging sessions are required to reach the intended destination. We consider the availability and performance of the charging stations to be unknown and stochastic, and develop a combinatorial semi-bandit framework for exploring the road network to learn the parameters of the queue time and charging power distributions. Within this framework, we first outline a pre-processing for the road network graph to handle the constrained combinatorial optimization problem in an efficient way. Then, for the pre-processed graph, we use a Bayesian approach to model the stochastic edge weights, utilizing conjugate priors for the one-parameter exponential and two-parameter gamma distributions, the latter of which is novel to multi-armed bandit literature. Finally, we apply combinatorial versions of Thompson Sampling, BayesUCB and Epsilon-greedy to the problem. We demonstrate the performance of our framework on long-distance navigation problem instances in country-sized road networks, with simulation experiments in Norway, Sweden and Finland. | ['Morteza Haghir Chehreghani', 'Niklas Åkerblom'] | 2023-01-17 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 6.15292415e-02 3.28407168e-01 -4.89064604e-01 -3.46936166e-01
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8ffd0d32-0b41-4256-bc6c-909a1a6d44de | global-meets-local-effective-multi-label | 2211.12716 | null | https://arxiv.org/abs/2211.12716v1 | https://arxiv.org/pdf/2211.12716v1.pdf | Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision | Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Although region-based methods are less likely to encounter issues with model generalizability than label-dependency methods, they often generate hundreds of meaningless or noisy proposals with non-discriminative information, and the contextual dependency among the localized regions is often ignored or over-simplified. This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning. Specifically, we propose category-aware weak supervision to concentrate on non-existent categories so as to provide deterministic information for local feature learning, restricting the local branch to focus on more high-quality regions of interest. Moreover, we develop a cross-granularity attention module to explore the complementary information between global and local features, which can build the high-order feature correlation containing not only global-to-local, but also local-to-local relations. Both advantages guarantee a boost in the performance of the whole network. Extensive experiments on two large-scale datasets (MS-COCO and VOC 2007) demonstrate that our framework achieves superior performance over state-of-the-art methods. | ['Yuan Xie', 'Chengjie Wang', 'Wei zhang', 'Wenlong Wu', 'Tianliang Zhang', 'Bin-Bin Gao', 'Xi Wang', 'Guannan Jiang', 'Wei Tang', 'Jun Liu', 'Jiawei Zhan'] | 2022-11-23 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [-4.77006733e-02 -3.74464720e-01 -4.69741374e-01 -8.34411979e-01
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c99548f8-51c3-4370-84e7-cf6dea1d2171 | weighted-first-order-model-counting-with | 2302.09830 | null | https://arxiv.org/abs/2302.09830v2 | https://arxiv.org/pdf/2302.09830v2.pdf | Weighted First Order Model Counting with Directed Acyclic Graph Axioms | Statistical Relational Learning (SRL) integrates First-Order Logic (FOL) and probability theory for learning and inference over relational data. Probabilistic inference and learning in many SRL models can be reduced to Weighted First Order Model Counting (WFOMC). However, WFOMC is known to be intractable ($\mathrm{\#P_1-}$ complete). Hence, logical fragments that admit polynomial time WFOMC are of significant interest. Such fragments are called domain liftable. Recent line of works have shown the two-variable fragment of FOL, extended with counting quantifiers ($\mathrm{C^2}$) to be domain-liftable. However, many properties of real-world data can not be modelled in $\mathrm{C^2}$. In fact many ubiquitous properties of real-world data are inexressible in FOL. Acyclicity is one such property, found in citation networks, genealogy data, temporal data e.t.c. In this paper we aim to address this problem by investigating the domain liftability of directed acyclicity constraints. We show that the fragment $\mathrm{C^2}$ with a Directed Acyclic Graph (DAG) axiom, i.e., a predicate in the language is axiomatized to represent a DAG, is domain-liftable. We present a method based on principle of inclusion-exclusion for WFOMC of $\mathrm{C^2}$ formulas extended with DAG axioms. | ['Luciano Serafini', 'Sagar Malhotra'] | 2023-02-20 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 1.19329415e-01 5.36605835e-01 -3.99605989e-01 -4.07754719e-01
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2a34c5f5-56ad-4431-9e6a-292728d4e893 | broaden-the-vision-geo-diverse-visual | 2109.06860 | null | https://arxiv.org/abs/2109.06860v1 | https://arxiv.org/pdf/2109.06860v1.pdf | Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning | Commonsense is defined as the knowledge that is shared by everyone. However, certain types of commonsense knowledge are correlated with culture and geographic locations and they are only shared locally. For example, the scenarios of wedding ceremonies vary across regions due to different customs influenced by historical and religious factors. Such regional characteristics, however, are generally omitted in prior work. In this paper, we construct a Geo-Diverse Visual Commonsense Reasoning dataset (GD-VCR) to test vision-and-language models' ability to understand cultural and geo-location-specific commonsense. In particular, we study two state-of-the-art Vision-and-Language models, VisualBERT and ViLBERT trained on VCR, a standard multimodal commonsense benchmark with images primarily from Western regions. We then evaluate how well the trained models can generalize to answering the questions in GD-VCR. We find that the performance of both models for non-Western regions including East Asia, South Asia, and Africa is significantly lower than that for Western region. We analyze the reasons behind the performance disparity and find that the performance gap is larger on QA pairs that: 1) are concerned with culture-related scenarios, e.g., weddings, religious activities, and festivals; 2) require high-level geo-diverse commonsense reasoning rather than low-order perception and recognition. Dataset and code are released at https://github.com/WadeYin9712/GD-VCR. | ['Kai-Wei Chang', 'Nanyun Peng', 'Ziniu Hu', 'Liunian Harold Li', 'Da Yin'] | 2021-09-14 | null | https://aclanthology.org/2021.emnlp-main.162 | https://aclanthology.org/2021.emnlp-main.162.pdf | emnlp-2021-11 | ['visual-commonsense-reasoning'] | ['reasoning'] | [-1.06817447e-01 -5.17419338e-01 -3.08082141e-02 -3.48015904e-01
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9368ff08-02f3-4329-9ff8-d23c77a34bf0 | intra-and-inter-constraint-based-video | 1502.06080 | null | http://arxiv.org/abs/1502.06080v1 | http://arxiv.org/pdf/1502.06080v1.pdf | Intra-and-Inter-Constraint-based Video Enhancement based on Piecewise Tone Mapping | Video enhancement plays an important role in various video applications. In
this paper, we propose a new intra-and-inter-constraint-based video enhancement
approach aiming to 1) achieve high intra-frame quality of the entire picture
where multiple region-of-interests (ROIs) can be adaptively and simultaneously
enhanced, and 2) guarantee the inter-frame quality consistencies among video
frames. We first analyze features from different ROIs and create a piecewise
tone mapping curve for the entire frame such that the intra-frame quality of a
frame can be enhanced. We further introduce new inter-frame constraints to
improve the temporal quality consistency. Experimental results show that the
proposed algorithm obviously outperforms the state-of-the-art algorithms. | ['Zhenzhong Chen', 'Chongyang Zhang', 'Weiyao Lin', 'Yuanzhe Chen', 'Jun Xie', 'Ning Xu'] | 2015-02-21 | null | null | null | null | ['video-enhancement', 'tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 2.11959258e-01 -5.05458593e-01 -1.82781741e-01 -3.90538543e-01
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179c01f9-e360-4408-904e-ce05f50b4d5c | agiqa-3k-an-open-database-for-ai-generated | 2306.04717 | null | https://arxiv.org/abs/2306.04717v2 | https://arxiv.org/pdf/2306.04717v2.pdf | AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment | With the rapid advancements of the text-to-image generative model, AI-generated images (AGIs) have been widely applied to entertainment, education, social media, etc. However, considering the large quality variance among different AGIs, there is an urgent need for quality models that are consistent with human subjective ratings. To address this issue, we extensively consider various popular AGI models, generated AGI through different prompts and model parameters, and collected subjective scores at the perceptual quality and text-to-image alignment, thus building the most comprehensive AGI subjective quality database AGIQA-3K so far. Furthermore, we conduct a benchmark experiment on this database to evaluate the consistency between the current Image Quality Assessment (IQA) model and human perception, while proposing StairReward that significantly improves the assessment performance of subjective text-to-image alignment. We believe that the fine-grained subjective scores in AGIQA-3K will inspire subsequent AGI quality models to fit human subjective perception mechanisms at both perception and alignment levels and to optimize the generation result of future AGI models. The database is released on https://github.com/lcysyzxdxc/AGIQA-3k-Database. | ['Weisi Lin', 'Guangtao Zhai', 'Xiaohong Liu', 'Xiongkuo Min', 'Wei Sun', 'HaoNing Wu', 'ZiCheng Zhang', 'Chunyi Li'] | 2023-06-07 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [-2.29371842e-02 -3.59799385e-01 8.58334303e-02 -3.94610971e-01
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ef4bdffa-eb64-4ef1-b5df-7c29835f1a3c | controlled-generation-of-unseen-faults-for | 2204.14068 | null | https://arxiv.org/abs/2204.14068v2 | https://arxiv.org/pdf/2204.14068v2.pdf | Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation | New operating conditions can result in a significant performance drop of fault diagnostics models due to the domain shift between the training and the testing data distributions. While several domain adaptation approaches have been proposed to overcome such domain shifts, their application is limited if the fault classes represented in the two domains are not the same. To enable a better transferability of the trained models between two different domains, particularly in setups where only the healthy data class is shared between the two domains, we propose a new framework for Partial and Open-Partial domain adaptation based on generating distinct fault signatures with a Wasserstein GAN. The main contribution of the proposed framework is the controlled synthetic fault data generation with two main distinct characteristics. Firstly, the proposed methodology enables to generate unobserved fault types in the target domain by having only access to the healthy samples in the target domain and faulty samples in the source domain. Secondly, the fault generation can be controlled to precisely generate distinct fault types and fault severity levels. The proposed method is especially suited in extreme domain adaption settings that are particularly relevant in the context of complex and safety-critical systems, where only one class is shared between the two domains. We evaluate the proposed framework on Partial as well as Open-Partial domain adaptation tasks on two bearing fault diagnostics case studies. Our experiments conducted in different label space settings showcase the versatility of the proposed framework. The proposed methodology provided superior results compared to other methods given large domain gaps. | ['Prof. Dr. Olga Fink', 'Dr. Gabriel Michau', 'Katharina Rombach'] | 2022-04-29 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 5.69621563e-01 2.29109392e-01 2.18830436e-01 -2.39676848e-01
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d047a68c-c0bf-4c9e-8e90-9e461d58fedb | sc-mil-supervised-contrastive-multiple | 2303.13405 | null | https://arxiv.org/abs/2303.13405v1 | https://arxiv.org/pdf/2303.13405v1.pdf | SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology | Multiple Instance learning (MIL) models have been extensively used in pathology to predict biomarkers and risk-stratify patients from gigapixel-sized images. Machine learning problems in medical imaging often deal with rare diseases, making it important for these models to work in a label-imbalanced setting. Furthermore, these imbalances can occur in out-of-distribution (OOD) datasets when the models are deployed in the real-world. We leverage the idea that decoupling feature and classifier learning can lead to improved decision boundaries for label imbalanced datasets. To this end, we investigate the integration of supervised contrastive learning with multiple instance learning (SC-MIL). Specifically, we propose a joint-training MIL framework in the presence of label imbalance that progressively transitions from learning bag-level representations to optimal classifier learning. We perform experiments with different imbalance settings for two well-studied problems in cancer pathology: subtyping of non-small cell lung cancer and subtyping of renal cell carcinoma. SC-MIL provides large and consistent improvements over other techniques on both in-distribution (ID) and OOD held-out sets across multiple imbalanced settings. | ['Amaro Taylor-Weiner', 'John Abel', 'Archit Khosla', 'Anand Sampat', 'Chintan Shah', 'Harshith Padigela', 'Syed Ashar Javed', 'Siddhant Shingi', 'Dinkar Juyal'] | 2023-03-23 | null | null | null | null | ['multiple-instance-learning', 'imbalanced-classification'] | ['methodology', 'miscellaneous'] | [ 5.03548086e-01 1.56140430e-02 -9.48277414e-01 -5.50004125e-01
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3b38b823-336c-445d-b981-daef62708020 | a-hybrid-approach-for-smart-alert-generation | 2306.07983 | null | https://arxiv.org/abs/2306.07983v1 | https://arxiv.org/pdf/2306.07983v1.pdf | A Hybrid Approach for Smart Alert Generation | Anomaly detection is an important task in network management. However, deploying intelligent alert systems in real-world large-scale networking systems is challenging when we take into account (i) scalability, (ii) data heterogeneity, and (iii) generalizability and maintainability. In this paper, we propose a hybrid model for an alert system that combines statistical models with a whitelist mechanism to tackle these challenges and reduce false positive alerts. The statistical models take advantage of a large database to detect anomalies in time-series data, while the whitelist filters out persistently alerted nodes to further reduce false positives. Our model is validated using qualitative data from customer support cases. Future work includes more feature engineering and input data, as well as including human feedback in the model development process. | ['Zhiyuan Yao', 'Sophine Zhang', 'Yao Zhao'] | 2023-06-02 | null | null | null | null | ['anomaly-detection', 'feature-engineering', 'management'] | ['methodology', 'methodology', 'miscellaneous'] | [-1.10309407e-01 -3.89985070e-02 1.80511311e-01 -3.37022394e-01
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d1c0ea53-6222-4631-ba7a-3ba6eb48a893 | mast-multiscale-audio-spectrogram | 2211.01515 | null | https://arxiv.org/abs/2211.01515v2 | https://arxiv.org/pdf/2211.01515v2.pdf | MAST: Multiscale Audio Spectrogram Transformers | We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification, which brings the concept of multiscale feature hierarchies to the Audio Spectrogram Transformer (AST). Given an input audio spectrogram, we first patchify and project it into an initial temporal resolution and embedding dimension, post which the multiple stages in MAST progressively expand the embedding dimension while reducing the temporal resolution of the input. We use a pyramid structure that allows early layers of MAST operating at a high temporal resolution but low embedding space to model simple low-level acoustic information and deeper temporally coarse layers to model high-level acoustic information with high-dimensional embeddings. We also extend our approach to present a new Self-Supervised Learning (SSL) method called SS-MAST, which calculates a symmetric contrastive loss between latent representations from a student and a teacher encoder, leveraging patch-drop, a novel audio augmentation approach that we introduce. In practice, MAST significantly outperforms AST by an average accuracy of 3.4% across 8 speech and non-speech tasks from the LAPE Benchmark, achieving state-of-the-art results on keyword spotting in Speech Commands. Additionally, our proposed SS-MAST achieves an absolute average improvement of 2.6% over the previously proposed SSAST. | ['Dinesh Manocha', 'S. Umesh', 'Ashish Seth', 'Sreyan Ghosh'] | 2022-11-02 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 2.73075253e-01 -6.88289627e-02 1.19825199e-01 -1.04731485e-01
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15262d6c-c44c-4871-a47b-61bc9106d05c | cvae-based-re-anchoring-for-implicit | null | null | https://aclanthology.org/2021.findings-emnlp.110 | https://aclanthology.org/2021.findings-emnlp.110.pdf | CVAE-based Re-anchoring for Implicit Discourse Relation Classification | Training implicit discourse relation classifiers suffers from data sparsity. Variational AutoEncoder (VAE) appears to be the proper solution. It is because ideally VAE is capable of generating inexhaustible varying samples, and this facilitates selective data augmentation. However, our experiments show that coupling VAE with the RoBERTa-based classifier results in severe performance degradation. We ascribe the unusual phenomenon to erroneous sampling that would happen when VAE pursued variations. To overcome the problem, we develop a re-anchoring strategy, where Conditional VAE (CVAE) is used for estimating the risk of erroneous sampling, and meanwhile migrating the anchor to reduce the risk. The test results on PDTB v2.0 illustrate that, compared to the RoBERTa-based baseline, re-anchoring yields substantial improvements. Besides, we observe that re-anchoring can cooperate with other auxiliary strategies (transfer learning and interactive attention mechanism) to further improve the baseline, obtaining the F-scores of about 55%, 63%, 80% and 44% for the four main relation types (Comparison, Contingency, Expansion, Temporality) in the binary classification (Yes/No) scenario. | ['Guodong Zhou', 'Yu Sun', 'Yu Hong', 'Zujun Dou'] | null | null | null | null | findings-emnlp-2021-11 | ['relation-classification', 'implicit-discourse-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.54640500e-02 7.72336721e-01 -6.13016188e-01 -1.11765452e-01
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d5422543-cdb9-46b6-855c-45e7424ff322 | image-stylization-from-predefined-to | 2002.10945 | null | https://arxiv.org/abs/2002.10945v1 | https://arxiv.org/pdf/2002.10945v1.pdf | Image Stylization: From Predefined to Personalized | We present a framework for interactive design of new image stylizations using a wide range of predefined filter blocks. Both novel and off-the-shelf image filtering and rendering techniques are extended and combined to allow the user to unleash their creativity to intuitively invent, modify, and tune new styles from a given set of filters. In parallel to this manual design, we propose a novel procedural approach that automatically assembles sequences of filters, leading to unique and novel styles. An important aim of our framework is to allow for interactive exploration and design, as well as to enable videos and camera streams to be stylized on the fly. In order to achieve this real-time performance, we use the \textit{Best Linear Adaptive Enhancement} (BLADE) framework -- an interpretable shallow machine learning method that simulates complex filter blocks in real time. Our representative results include over a dozen styles designed using our interactive tool, a set of styles created procedurally, and new filters trained with our BLADE approach. | ['Bartlomiej Wronski', 'Ignacio Garcia-Dorado', 'Pascal Getreuer', 'Peyman Milanfar'] | 2020-02-22 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 3.16104531e-01 4.11794940e-03 4.83030796e-01 -1.87403426e-01
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04248523-da0a-4e6b-a5b3-f6cd03bede1c | neural-network-fragile-watermarking-with-no | 2208.07585 | null | https://arxiv.org/abs/2208.07585v1 | https://arxiv.org/pdf/2208.07585v1.pdf | Neural network fragile watermarking with no model performance degradation | Deep neural networks are vulnerable to malicious fine-tuning attacks such as data poisoning and backdoor attacks. Therefore, in recent research, it is proposed how to detect malicious fine-tuning of neural network models. However, it usually negatively affects the performance of the protected model. Thus, we propose a novel neural network fragile watermarking with no model performance degradation. In the process of watermarking, we train a generative model with the specific loss function and secret key to generate triggers that are sensitive to the fine-tuning of the target classifier. In the process of verifying, we adopt the watermarked classifier to get labels of each fragile trigger. Then, malicious fine-tuning can be detected by comparing secret keys and labels. Experiments on classic datasets and classifiers show that the proposed method can effectively detect model malicious fine-tuning with no model performance degradation. | ['Xinpeng Zhang', 'Heng Yin', 'Zhaoxia Yin'] | 2022-08-16 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 5.13919711e-01 -1.54457644e-01 -3.13266337e-01 -1.66759044e-01
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6aed6303-5e51-4a02-a98c-b283bea040fe | identifying-predictive-causal-factors-from | null | null | https://aclanthology.org/D19-1238 | https://aclanthology.org/D19-1238.pdf | Identifying Predictive Causal Factors from News Streams | We propose a new framework to uncover the relationship between news events and real world phenomena. We present the Predictive Causal Graph (PCG) which allows to detect latent relationships between events mentioned in news streams. This graph is constructed by measuring how the occurrence of a word in the news influences the occurrence of another (set of) word(s) in the future. We show that PCG can be used to extract latent features from news streams, outperforming other graph-based methods in prediction error of 10 stock price time series for 12 months. We then extended PCG to be applicable for longer time windows by allowing time-varying factors, leading to stock price prediction error rates between 1.5{\%} and 5{\%} for about 4 years. We then manually validated PCG, finding that 67{\%} of the causation semantic frame arguments present in the news corpus were directly connected in the PCG, the remaining being connected through a semantically relevant intermediate node. | ['an', 'Samuel Fraiberger', 'Sun Chakraborty', 'Ananth Balashankar', 'Lakshminarayanan Subramanian'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['stock-price-prediction'] | ['time-series'] | [-3.63658066e-03 4.40883785e-01 -5.90962172e-01 -7.79363438e-02
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c3b1114c-83b5-4586-aaf8-d8a1afa30d58 | looking-through-glass-knowledge-discovery | 2101.01508 | null | https://arxiv.org/abs/2101.01508v1 | https://arxiv.org/pdf/2101.01508v1.pdf | Looking Through Glass: Knowledge Discovery from Materials Science Literature using Natural Language Processing | Most of the knowledge in materials science literature is in the form of unstructured data such as text and images. Here, we present a framework employing natural language processing, which automates text and image comprehension and precision knowledge extraction from inorganic glasses' literature. The abstracts are automatically categorized using latent Dirichlet allocation (LDA), providing a way to classify and search semantically linked publications. Similarly, a comprehensive summary of images and plots are presented using the 'Caption Cluster Plot' (CCP), which provides direct access to the images buried in the papers. Finally, we combine the LDA and CCP with the chemical elements occurring in the manuscript to present an 'Elemental map', a topical and image-wise distribution of chemical elements in the literature. Overall, the framework presented here can be a generic and powerful tool to extract and disseminate material-specific information on composition-structure-processing-property dataspaces, allowing insights into fundamental problems relevant to the materials science community and accelerated materials discovery. | ['N. M. Anoop Krishnan', 'Nitya Nand Gosvami', 'Manish Agarwal', 'Mohd Zaki', 'Sourav Sahoo', 'Vineeth Venugopal'] | 2021-01-05 | null | null | null | null | ['image-comprehension'] | ['computer-vision'] | [ 2.43675694e-01 -3.20002474e-02 -3.27129364e-01 1.17086712e-02
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ae661ec0-5490-4211-a89a-203febde4058 | high-resolution-swin-transformer-for | 2207.11553 | null | https://arxiv.org/abs/2207.11553v1 | https://arxiv.org/pdf/2207.11553v1.pdf | High-Resolution Swin Transformer for Automatic Medical Image Segmentation | The Resolution of feature maps is critical for medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation are U-Net-like architecture that contains an encoder that utilizes a sequence of Transformer blocks to convert the input medical image from high-resolution representation into low-resolution feature maps and a decoder that gradually recovers the high-resolution representation from low-resolution feature maps. Unlike previous studies, in this paper, we utilize the network design style from the High-Resolution Network (HRNet), replace the convolutional layers with Transformer blocks, and continuously exchange information from the different resolution feature maps that are generated by Transformer blocks. The newly Transformer-based network presented in this paper is denoted as High-Resolution Swin Transformer Network (HRSTNet). Extensive experiments illustrate that HRSTNet can achieve comparable performance with the state-of-the-art Transformer-based U-Net-like architecture on Brain Tumor Segmentation(BraTS) 2021 and the liver dataset from Medical Segmentation Decathlon. The code of HRSTNet will be publicly available at https://github.com/auroua/HRSTNet. | ['Jimin Liang', 'Haihong Hu', 'Kaitai Guo', 'Shenghan Ren', 'Chen Wei'] | 2022-07-23 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 2.54723847e-01 4.87857401e-01 -1.55351654e-01 -2.68445551e-01
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3fbe638f-2e7c-433b-85b1-e91880f1832e | age-range-estimation-using-mtcnn-and-vgg-face | 2104.08585 | null | https://arxiv.org/abs/2104.08585v1 | https://arxiv.org/pdf/2104.08585v1.pdf | Age Range Estimation using MTCNN and VGG-Face Model | The Convolutional Neural Network has amazed us with its usage on several applications. Age range estimation using CNN is emerging due to its application in myriad of areas which makes it a state-of-the-art area for research and improve the estimation accuracy. A deep CNN model is used for identification of people's age range in our proposed work. At first, we extracted only face images from image dataset using MTCNN to remove unnecessary features other than face from the image. Secondly, we used random crop technique for data augmentation to improve the model performance. We have used the concept of transfer learning in our research. A pretrained face recognition model i.e VGG-Face is used to build our model for identification of age range whose performance is evaluated on Adience Benchmark for confirming the efficacy of our work. The performance in test set outperformed existing state-of-the-art by substantial margins. | ['Subodh Chandra Shakya', 'Ashutosh Chauhan', 'Prashanga Pokharel', 'Dipesh Gyawali'] | 2021-04-17 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 7.22917840e-02 -2.65142601e-02 8.73067528e-02 -7.44532764e-01
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a5d5672f-3a81-4013-ba49-b86445a6b4ef | improving-deep-pancreas-segmentation-in-ct | 1707.04912 | null | http://arxiv.org/abs/1707.04912v2 | http://arxiv.org/pdf/1707.04912v2.pdf | Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function | Deep neural networks have demonstrated very promising performance on accurate
segmentation of challenging organs (e.g., pancreas) in abdominal CT and MRI
scans. The current deep learning approaches conduct pancreas segmentation by
processing sequences of 2D image slices independently through deep, dense
per-pixel masking for each image, without explicitly enforcing spatial
consistency constraint on segmentation of successive slices. We propose a new
convolutional/recurrent neural network architecture to address the contextual
learning and segmentation consistency problem. A deep convolutional sub-network
is first designed and pre-trained from scratch. The output layer of this
network module is then connected to recurrent layers and can be fine-tuned for
contextual learning, in an end-to-end manner. Our recurrent sub-network is a
type of Long short-term memory (LSTM) network that performs segmentation on an
image by integrating its neighboring slice segmentation predictions, in the
form of a dependent sequence processing. Additionally, a novel
segmentation-direct loss function (named Jaccard Loss) is proposed and deep
networks are trained to optimize Jaccard Index (JI) directly. Extensive
experiments are conducted to validate our proposed deep models, on quantitative
pancreas segmentation using both CT and MRI scans. Our method outperforms the
state-of-the-art work on CT [11] and MRI pancreas segmentation [1],
respectively. | ['Fuyong Xing', 'Yuanpu Xie', 'Le Lu', 'Jinzheng Cai', 'Lin Yang'] | 2017-07-16 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 3.67572010e-01 3.03935379e-01 -2.99910754e-01 -7.07976162e-01
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7a0ed39a-63b7-4cdf-a9cb-55d13f13388f | factors-affecting-the-performance-of | 2306.12444 | null | https://arxiv.org/abs/2306.12444v1 | https://arxiv.org/pdf/2306.12444v1.pdf | Factors Affecting the Performance of Automated Speaker Verification in Alzheimer's Disease Clinical Trials | Detecting duplicate patient participation in clinical trials is a major challenge because repeated patients can undermine the credibility and accuracy of the trial's findings and result in significant health and financial risks. Developing accurate automated speaker verification (ASV) models is crucial to verify the identity of enrolled individuals and remove duplicates, but the size and quality of data influence ASV performance. However, there has been limited investigation into the factors that can affect ASV capabilities in clinical environments. In this paper, we bridge the gap by conducting analysis of how participant demographic characteristics, audio quality criteria, and severity level of Alzheimer's disease (AD) impact the performance of ASV utilizing a dataset of speech recordings from 659 participants with varying levels of AD, obtained through multiple speech tasks. Our results indicate that ASV performance: 1) is slightly better on male speakers than on female speakers; 2) degrades for individuals who are above 70 years old; 3) is comparatively better for non-native English speakers than for native English speakers; 4) is negatively affected by clinician interference, noisy background, and unclear participant speech; 5) tends to decrease with an increase in the severity level of AD. Our study finds that voice biometrics raise fairness concerns as certain subgroups exhibit different ASV performances owing to their inherent voice characteristics. Moreover, the performance of ASV is influenced by the quality of speech recordings, which underscores the importance of improving the data collection settings in clinical trials. | ['Jekaterina Novikova', 'Ali Akram', 'Marija Stanojevic', 'Malikeh Ehghaghi'] | 2023-06-20 | null | null | null | null | ['fairness', 'fairness', 'speaker-verification'] | ['computer-vision', 'miscellaneous', 'speech'] | [ 1.51972264e-01 -6.04948290e-02 -3.57095182e-01 -2.50266701e-01
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bb0a6354-63ad-4bd0-89e8-e2c90b936cf7 | distribution-aware-binarization-of-neural | 1804.02941 | null | http://arxiv.org/abs/1804.02941v1 | http://arxiv.org/pdf/1804.02941v1.pdf | Distribution-Aware Binarization of Neural Networks for Sketch Recognition | Deep neural networks are highly effective at a range of computational tasks.
However, they tend to be computationally expensive, especially in
vision-related problems, and also have large memory requirements. One of the
most effective methods to achieve significant improvements in
computational/spatial efficiency is to binarize the weights and activations in
a network. However, naive binarization results in accuracy drops when applied
to networks for most tasks. In this work, we present a highly generalized,
distribution-aware approach to binarizing deep networks that allows us to
retain the advantages of a binarized network, while reducing accuracy drops. We
also develop efficient implementations for our proposed approach across
different architectures. We present a theoretical analysis of the technique to
show the effective representational power of the resulting layers, and explore
the forms of data they model best. Experiments on popular datasets show that
our technique offers better accuracies than naive binarization, while retaining
the same benefits that binarization provides - with respect to run-time
compression, reduction of computational costs, and power consumption. | ['Rohit Gajawada', 'Vishal Batchu', 'Anoop Namboodiri', 'Ameya Prabhu', 'Sri Aurobindo Munagala'] | 2018-04-09 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 6.38494194e-02 -2.67508119e-01 -4.43982863e-04 -3.23764026e-01
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9019ee17-ee0d-42ed-8477-3e18a1cc9a31 | towards-enhanced-controllability-of-diffusion | 2302.14368 | null | https://arxiv.org/abs/2302.14368v2 | https://arxiv.org/pdf/2302.14368v2.pdf | Towards Enhanced Controllability of Diffusion Models | Denoising Diffusion models have shown remarkable capabilities in generating realistic, high-quality and diverse images. However, the extent of controllability during generation is underexplored. Inspired by techniques based on GAN latent space for image manipulation, we train a diffusion model conditioned on two latent codes, a spatial content mask and a flattened style embedding. We rely on the inductive bias of the progressive denoising process of diffusion models to encode pose/layout information in the spatial structure mask and semantic/style information in the style code. We propose two generic sampling techniques for improving controllability. We extend composable diffusion models to allow for some dependence between conditional inputs, to improve the quality of generations while also providing control over the amount of guidance from each latent code and their joint distribution. We also propose timestep dependent weight scheduling for content and style latents to further improve the translations. We observe better controllability compared to existing methods and show that without explicit training objectives, diffusion models can be used for effective image manipulation and image translation. | ['Ajinkya Kale', 'David I. Inouye', 'Jingwan Lu', 'Krishna Kumar Singh', 'Vinh Khuc', 'Midhun Harikumar', 'Hareesh Ravi', 'Wonwoong Cho'] | 2023-02-28 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 6.73663795e-01 2.69252598e-01 -4.16883565e-02 -2.73100853e-01
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dc4aaeb3-a495-445e-8894-4ff82a03a209 | neural-implicit-vision-language-feature | 2303.10962 | null | https://arxiv.org/abs/2303.10962v1 | https://arxiv.org/pdf/2303.10962v1.pdf | Neural Implicit Vision-Language Feature Fields | Recently, groundbreaking results have been presented on open-vocabulary semantic image segmentation. Such methods segment each pixel in an image into arbitrary categories provided at run-time in the form of text prompts, as opposed to a fixed set of classes defined at training time. In this work, we present a zero-shot volumetric open-vocabulary semantic scene segmentation method. Our method builds on the insight that we can fuse image features from a vision-language model into a neural implicit representation. We show that the resulting feature field can be segmented into different classes by assigning points to natural language text prompts. The implicit volumetric representation enables us to segment the scene both in 3D and 2D by rendering feature maps from any given viewpoint of the scene. We show that our method works on noisy real-world data and can run in real-time on live sensor data dynamically adjusting to text prompts. We also present quantitative comparisons on the ScanNet dataset. | ['Roland Siegwart', 'Lionel Ott', 'Jen Jen Chung', 'Francesco Milano', 'Kenneth Blomqvist'] | 2023-03-20 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 8.45479965e-01 4.70917255e-01 6.29628375e-02 -7.86650002e-01
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7cedc45c-48a3-430c-8934-1b8eeea240e3 | regularized-hesselm-and-inclined-entropy | 1907.05888 | null | https://arxiv.org/abs/1907.05888v1 | https://arxiv.org/pdf/1907.05888v1.pdf | Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction | Our study concerns with automated predicting of congestive heart failure (CHF) through the analysis of electrocardiography (ECG) signals. A novel machine learning approach, regularized hessenberg decomposition based extreme learning machine (R-HessELM), and feature models; squared, circled, inclined and grid entropy measurement were introduced and used for prediction of CHF. This study proved that inclined entropy measurements features well represent characteristics of ECG signals and together with R-HessELM approach overall accuracy of 98.49% was achieved. | ['Gökhan Altan', 'Yakup Kutlu', 'Apdullah Yayık'] | 2019-07-12 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [-1.87956169e-01 7.66021432e-03 5.92959821e-01 -4.02143389e-01
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f8b02c4b-5002-47d4-be45-a85ee076e430 | fine-grained-categorization-and-dataset | 1512.05227 | null | http://arxiv.org/abs/1512.05227v2 | http://arxiv.org/pdf/1512.05227v2.pdf | Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop | Existing fine-grained visual categorization methods often suffer from three
challenges: lack of training data, large number of fine-grained categories, and
high intraclass vs. low inter-class variance. In this work we propose a generic
iterative framework for fine-grained categorization and dataset bootstrapping
that handles these three challenges. Using deep metric learning with humans in
the loop, we learn a low dimensional feature embedding with anchor points on
manifolds for each category. These anchor points capture intra-class variances
and remain discriminative between classes. In each round, images with high
confidence scores from our model are sent to humans for labeling. By comparing
with exemplar images, labelers mark each candidate image as either a "true
positive" or a "false positive". True positives are added into our current
dataset and false positives are regarded as "hard negatives" for our metric
learning model. Then the model is retrained with an expanded dataset and hard
negatives for the next round. To demonstrate the effectiveness of the proposed
framework, we bootstrap a fine-grained flower dataset with 620 categories from
Instagram images. The proposed deep metric learning scheme is evaluated on both
our dataset and the CUB-200-2001 Birds dataset. Experimental evaluations show
significant performance gain using dataset bootstrapping and demonstrate
state-of-the-art results achieved by the proposed deep metric learning methods. | ['Yuanqing Lin', 'Serge Belongie', 'Feng Zhou', 'Yin Cui'] | 2015-12-16 | fine-grained-categorization-and-dataset-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Cui_Fine-Grained_Categorization_and_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Cui_Fine-Grained_Categorization_and_CVPR_2016_paper.pdf | cvpr-2016-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [-2.26067156e-01 -3.09701413e-01 -8.22496191e-02 -8.53819132e-01
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6e8e2112-fd31-495d-82d4-68f295127c9c | diabetic-retinopathy-detection-by-retinal | 2001.05835 | null | https://arxiv.org/abs/2001.05835v1 | https://arxiv.org/pdf/2001.05835v1.pdf | Diabetic Retinopathy detection by retinal image recognizing | Many people are affected by diabetes around the world. This disease may have type 1 and 2. Diabetes brings with it several complications including diabetic retinopathy, which is a disease that if not treated correctly can lead to irreversible damage in the patient's vision. The earlier it is detected, the better the chances that the patient will not lose vision. Methods of automating manual procedures are currently in evidence and the diagnostic process for retinopathy is manual with the physician analyzing the patient's retina on the monitor. The practice of image recognition can aid this detection by recognizing Diabetic Retinopathy patterns and comparing it with the patient's retina in diagnosis. This method can also assist in the act of telemedicine, in which people without access to the exam can benefit from the diagnosis provided by the application. The application development took place through convolutional neural networks, which do digital image processing analyzing each image pixel. The use of VGG-16 as a pre-trained model to the application basis was very useful and the final model accuracy was 82%. | ['Gilberto Luis De Conto Junior'] | 2020-01-14 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 2.10076526e-01 1.97296694e-01 9.35276821e-02 -4.72544342e-01
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1f583772-1462-42f8-a1a6-0887a3c95842 | mask-textspotter-an-end-to-end-trainable-2 | 1908.08207 | null | https://arxiv.org/abs/1908.08207v1 | https://arxiv.org/pdf/1908.08207v1.pdf | Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes | Unifying text detection and text recognition in an end-to-end training fashion has become a new trend for reading text in the wild, as these two tasks are highly relevant and complementary. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end-to-end trainable neural network named as Mask TextSpotter is presented. Different from the previous text spotters that follow the pipeline consisting of a proposal generation network and a sequence-to-sequence recognition network, Mask TextSpotter enjoys a simple and smooth end-to-end learning procedure, in which both detection and recognition can be achieved directly from two-dimensional space via semantic segmentation. Further, a spatial attention module is proposed to enhance the performance and universality. Benefiting from the proposed two-dimensional representation on both detection and recognition, it easily handles text instances of irregular shapes, for instance, curved text. We evaluate it on four English datasets and one multi-language dataset, achieving consistently superior performance over state-of-the-art methods in both detection and end-to-end text recognition tasks. Moreover, we further investigate the recognition module of our method separately, which significantly outperforms state-of-the-art methods on both regular and irregular text datasets for scene text recognition. | ['Wenhao Wu', 'Pengyuan Lyu', 'Minghui Liao', 'Minghang He', 'Cong Yao', 'Xiang Bai'] | 2019-08-22 | mask-textspotter-an-end-to-end-trainable-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Pengyuan_Lyu_Mask_TextSpotter_An_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Pengyuan_Lyu_Mask_TextSpotter_An_ECCV_2018_paper.pdf | eccv-2018-9 | ['text-spotting'] | ['computer-vision'] | [ 8.01241815e-01 -3.67914319e-01 2.28297293e-01 -3.44816923e-01
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2d9bf649-fd2d-4aae-be77-e80dbba39d34 | improving-gans-with-a-dynamic-discriminator | 2209.09897 | null | https://arxiv.org/abs/2209.09897v1 | https://arxiv.org/pdf/2209.09897v1.pdf | Improving GANs with A Dynamic Discriminator | Discriminator plays a vital role in training generative adversarial networks (GANs) via distinguishing real and synthesized samples. While the real data distribution remains the same, the synthesis distribution keeps varying because of the evolving generator, and thus effects a corresponding change to the bi-classification task for the discriminator. We argue that a discriminator with an on-the-fly adjustment on its capacity can better accommodate such a time-varying task. A comprehensive empirical study confirms that the proposed training strategy, termed as DynamicD, improves the synthesis performance without incurring any additional computation cost or training objectives. Two capacity adjusting schemes are developed for training GANs under different data regimes: i) given a sufficient amount of training data, the discriminator benefits from a progressively increased learning capacity, and ii) when the training data is limited, gradually decreasing the layer width mitigates the over-fitting issue of the discriminator. Experiments on both 2D and 3D-aware image synthesis tasks conducted on a range of datasets substantiate the generalizability of our DynamicD as well as its substantial improvement over the baselines. Furthermore, DynamicD is synergistic to other discriminator-improving approaches (including data augmentation, regularizers, and pre-training), and brings continuous performance gain when combined for learning GANs. | ['Bolei Zhou', 'Bo Dai', 'Deli Zhao', 'Yinghao Xu', 'Yujun Shen', 'Ceyuan Yang'] | 2022-09-20 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 4.13913310e-01 2.34675393e-01 -2.81370938e-01 -3.85939935e-03
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f7d9710d-9cd8-4e02-975b-11c657c54b36 | multi-level-anomaly-detection-on-time-varying | 1410.4355 | null | http://arxiv.org/abs/1410.4355v4 | http://arxiv.org/pdf/1410.4355v4.pdf | Multi-Level Anomaly Detection on Time-Varying Graph Data | This work presents a novel modeling and analysis framework for graph
sequences which addresses the challenge of detecting and contextualizing
anomalies in labelled, streaming graph data. We introduce a generalization of
the BTER model of Seshadhri et al. by adding flexibility to community
structure, and use this model to perform multi-scale graph anomaly detection.
Specifically, probability models describing coarse subgraphs are built by
aggregating probabilities at finer levels, and these closely related
hierarchical models simultaneously detect deviations from expectation. This
technique provides insight into a graph's structure and internal context that
may shed light on a detected event. Additionally, this multi-scale analysis
facilitates intuitive visualizations by allowing users to narrow focus from an
anomalous graph to particular subgraphs or nodes causing the anomaly.
For evaluation, two hierarchical anomaly detectors are tested against a
baseline Gaussian method on a series of sampled graphs. We demonstrate that our
graph statistics-based approach outperforms both a distribution-based detector
and the baseline in a labeled setting with community structure, and it
accurately detects anomalies in synthetic and real-world datasets at the node,
subgraph, and graph levels. To illustrate the accessibility of information made
possible via this technique, the anomaly detector and an associated interactive
visualization tool are tested on NCAA football data, where teams and
conferences that moved within the league are identified with perfect recall,
and precision greater than 0.786. | ['Robert A. Bridges', 'John Collins', 'Jason Laska', 'Erik M. Ferragut', 'Blair D. Sullivan'] | 2014-10-16 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 9.18760225e-02 1.15812808e-01 2.68701106e-01 5.74502014e-02
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0f49d523-0572-456a-9a4c-2b796bff794c | cloud-net-an-end-to-end-cloud-detection | 1901.10077 | null | http://arxiv.org/abs/1901.10077v1 | http://arxiv.org/pdf/1901.10077v1.pdf | Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery | Cloud detection in satellite images is an important first-step in many remote
sensing applications. This problem is more challenging when only a limited
number of spectral bands are available. To address this problem, a deep
learning-based algorithm is proposed in this paper. This algorithm consists of
a Fully Convolutional Network (FCN) that is trained by multiple patches of
Landsat 8 images. This network, which is called Cloud-Net, is capable of
capturing global and local cloud features in an image using its convolutional
blocks. Since the proposed method is an end-to-end solution, no complicated
pre-processing step is required. Our experimental results prove that the
proposed method outperforms the state-of-the-art method over a benchmark
dataset by 8.7\% in Jaccard Index. | ['Sorour Mohajerani', 'Parvaneh Saeedi'] | 2019-01-29 | cloud-net-an-end-to-end-cloud-detection-1 | null | null | conference-2019-ieee-international-geoscience | ['cloud-detection'] | ['computer-vision'] | [ 1.81090981e-01 -7.55558252e-01 2.61949658e-01 -3.57724190e-01
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1e3cbb3d-0c4b-4c5b-b8f5-2b2a5b78cb2a | distill-to-label-weakly-supervised-instance | 1907.12926 | null | https://arxiv.org/abs/1907.12926v1 | https://arxiv.org/pdf/1907.12926v1.pdf | Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation | Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis. In contrast to conventional instance segmentation scenarios in computer vision, the problems that we consider are characterized by a small number of training images and non-local patterns that lead to the diagnosis. In this paper, we explore the use of multiple instance learning (MIL) to design an instance label generator under this weakly supervised setting. Motivated by the observation that an MIL model can handle bags of varying sizes, we propose to repurpose an MIL model originally trained for bag-level classification to produce reliable predictions for single instances, i.e., bags of size $1$. To this end, we introduce a novel regularization strategy based on virtual adversarial training for improving MIL training, and subsequently develop a knowledge distillation technique for repurposing the trained MIL model. Using empirical studies on colon cancer and breast cancer detection from histopathological images, we show that the proposed approach produces high-quality instance-level prediction and significantly outperforms state-of-the MIL methods. | ['Satyananda Kashyap', 'Jayaraman J. Thiagarajan', 'Alexandros Karagyris'] | 2019-07-26 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 7.14119077e-01 6.33381546e-01 -5.04719794e-01 -4.66734737e-01
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3e699796-c2f4-4832-99a8-2d84bd262919 | an-energy-efficient-service-composition | 2006.16771 | null | https://arxiv.org/abs/2006.16771v1 | https://arxiv.org/pdf/2006.16771v1.pdf | An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment | By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy-saving problem in mobile nodes. To mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms. | ['Tarik A. Rashid', 'Godar J. Ibrahim', 'Mobayode O. Akinsolu'] | 2020-05-19 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 6.27454221e-02 -5.10442078e-01 -1.59826100e-01 -1.55691430e-01
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c4eafb22-c5ab-4c31-bb6e-645a9448e39a | boosting-event-extraction-with-denoised | 2305.09598 | null | https://arxiv.org/abs/2305.09598v1 | https://arxiv.org/pdf/2305.09598v1.pdf | Boosting Event Extraction with Denoised Structure-to-Text Augmentation | Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations. In most NLP applications, involving a large scale of synthetic training data is a practical and effective approach to alleviate the problem of data scarcity. However, when applying to the task of event extraction, recent data augmentation methods often neglect the problem of grammatical incorrectness, structure misalignment, and semantic drifting, leading to unsatisfactory performances. In order to solve these problems, we propose a denoised structure-to-text augmentation framework for event extraction DAEE, which generates additional training data through the knowledge-based structure-to-text generation model and selects the effective subset from the generated data iteratively with a deep reinforcement learning agent. Experimental results on several datasets demonstrate that the proposed method generates more diverse text representations for event extraction and achieves comparable results with the state-of-the-art. | ['Dawei Yin', 'Shuaiqiang Wang', 'Tong Zhou', 'Chong Feng', 'Xiao Liu', 'Ge Shi', 'Xiaochi Wei', 'Heyan Huang', 'Bo wang'] | 2023-05-16 | null | null | null | null | ['text-augmentation', 'event-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.07854605e-01 5.29113352e-01 -7.49823451e-02 -3.49199593e-01
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362eda62-392e-40ea-b001-a4722ebd5149 | loopnet-musical-loop-synthesis-conditioned-on | 2105.10371 | null | https://arxiv.org/abs/2105.10371v1 | https://arxiv.org/pdf/2105.10371v1.pdf | LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters | Loops, seamlessly repeatable musical segments, are a cornerstone of modern music production. Contemporary artists often mix and match various sampled or pre-recorded loops based on musical criteria such as rhythm, harmony and timbral texture to create compositions. Taking such criteria into account, we present LoopNet, a feed-forward generative model for creating loops conditioned on intuitive parameters. We leverage Music Information Retrieval (MIR) models as well as a large collection of public loop samples in our study and use the Wave-U-Net architecture to map control parameters to audio. We also evaluate the quality of the generated audio and propose intuitive controls for composers to map the ideas in their minds to an audio loop. | ['Emilia Gómez', 'Xavier Serra', 'António Ramires', 'Pritish Chandna'] | 2021-05-21 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [-3.02567147e-03 -2.32280970e-01 6.65177219e-03 -1.74664304e-01
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