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ce1702eb-4711-4af1-91cd-73738448975a | a-new-insight-into-the-secondary-path | 1811.03755 | null | http://arxiv.org/abs/1811.03755v2 | http://arxiv.org/pdf/1811.03755v2.pdf | A new insight into the secondary path modeling problem in active noise control | The close relationship between the feedforward ANC system and the stereo
acoustic echo cancellation system is revealed in this paper. Accordingly, the
convergence behavior of the ANC system can be analyzed by investigating the
joint auto-correlation matrix of the reference and the filtered reference
signal. It is proved that the straightforward secondary path modeling can be
carried out without the injection of any additive noise as long as the control
filter is of a sufficiently long length. Furthermore, by taking advantage of
the time-varying characteristic of the control filter, effective modeling of
the secondary path can be even achieved without any restriction on the control
filter length. | [] | 2019-01-18 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.93035895e-01 7.82194808e-02 6.52642965e-01 1.59202203e-01
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2e84b643-d486-4260-9a5a-8919d7701a17 | domain-adaptation-strategies-for-cancer | 2207.06193 | null | https://arxiv.org/abs/2207.06193v1 | https://arxiv.org/pdf/2207.06193v1.pdf | Domain adaptation strategies for cancer-independent detection of lymph node metastases | Recently, large, high-quality public datasets have led to the development of convolutional neural networks that can detect lymph node metastases of breast cancer at the level of expert pathologists. Many cancers, regardless of the site of origin, can metastasize to lymph nodes. However, collecting and annotating high-volume, high-quality datasets for every cancer type is challenging. In this paper we investigate how to leverage existing high-quality datasets most efficiently in multi-task settings for closely related tasks. Specifically, we will explore different training and domain adaptation strategies, including prevention of catastrophic forgetting, for colon and head-and-neck cancer metastasis detection in lymph nodes. Our results show state-of-the-art performance on both cancer metastasis detection tasks. Furthermore, we show the effectiveness of repeated adaptation of networks from one cancer type to another to obtain multi-task metastasis detection networks. Last, we show that leveraging existing high-quality datasets can significantly boost performance on new target tasks and that catastrophic forgetting can be effectively mitigated using regularization. | ['Geert Litjens', 'Jeroen van der Laak', 'Bram van Ginneken', 'Marcory van Dijk', 'Maschenka Balkenhol', 'Péter Bándi'] | 2022-07-13 | null | null | null | null | ['cancer-metastasis-detection'] | ['medical'] | [ 8.25092494e-02 -1.22914284e-01 -7.23942876e-01 -8.23058039e-02
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13e2eb92-eb6e-44de-a16d-d8b0694c7673 | do-transformers-really-perform-bad-for-graph | 2106.05234 | null | https://arxiv.org/abs/2106.05234v5 | https://arxiv.org/pdf/2106.05234v5.pdf | Do Transformers Really Perform Bad for Graph Representation? | The Transformer architecture has become a dominant choice in many domains, such as natural language processing and computer vision. Yet, it has not achieved competitive performance on popular leaderboards of graph-level prediction compared to mainstream GNN variants. Therefore, it remains a mystery how Transformers could perform well for graph representation learning. In this paper, we solve this mystery by presenting Graphormer, which is built upon the standard Transformer architecture, and could attain excellent results on a broad range of graph representation learning tasks, especially on the recent OGB Large-Scale Challenge. Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model. To this end, we propose several simple yet effective structural encoding methods to help Graphormer better model graph-structured data. Besides, we mathematically characterize the expressive power of Graphormer and exhibit that with our ways of encoding the structural information of graphs, many popular GNN variants could be covered as the special cases of Graphormer. | ['Tie-Yan Liu', 'Yanming Shen', 'Di He', 'Guolin Ke', 'Shuxin Zheng', 'Shengjie Luo', 'Tianle Cai', 'Chengxuan Ying'] | 2021-06-09 | null | null | null | null | ['graph-property-prediction', 'graph-regression'] | ['graphs', 'graphs'] | [ 2.01332405e-01 3.27925891e-01 -3.82574022e-01 2.16990281e-02
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f8fd9f0d-bbe0-4dc5-855e-858a51319dd9 | neural-keyphrase-generation-via-reinforcement | 1906.04106 | null | https://arxiv.org/abs/1906.04106v1 | https://arxiv.org/pdf/1906.04106v1.pdf | Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards | Generating keyphrases that summarize the main points of a document is a fundamental task in natural language processing. Although existing generative models are capable of predicting multiple keyphrases for an input document as well as determining the number of keyphrases to generate, they still suffer from the problem of generating too few keyphrases. To address this problem, we propose a reinforcement learning (RL) approach for keyphrase generation, with an adaptive reward function that encourages a model to generate both sufficient and accurate keyphrases. Furthermore, we introduce a new evaluation method that incorporates name variations of the ground-truth keyphrases using the Wikipedia knowledge base. Thus, our evaluation method can more robustly evaluate the quality of predicted keyphrases. Extensive experiments on five real-world datasets of different scales demonstrate that our RL approach consistently and significantly improves the performance of the state-of-the-art generative models with both conventional and new evaluation methods. | ['Hou Pong Chan', 'Irwin King', 'Wang Chen', 'Lu Wang'] | 2019-06-10 | neural-keyphrase-generation-via-reinforcement-1 | https://aclanthology.org/P19-1208 | https://aclanthology.org/P19-1208.pdf | acl-2019-7 | ['keyphrase-generation'] | ['natural-language-processing'] | [-6.86332062e-02 3.06802560e-02 -2.91729838e-01 4.78582904e-02
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06ae982b-8348-4d7e-8fea-b51f4be65d22 | catastrophic-interference-in-reinforcement | 2109.00525 | null | https://arxiv.org/abs/2109.00525v2 | https://arxiv.org/pdf/2109.00525v2.pdf | Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation | The powerful learning ability of deep neural networks enables reinforcement learning agents to learn competent control policies directly from continuous environments. In theory, to achieve stable performance, neural networks assume i.i.d. inputs, which unfortunately does no hold in the general reinforcement learning paradigm where the training data is temporally correlated and non-stationary. This issue may lead to the phenomenon of "catastrophic interference" and the collapse in performance. In this paper, we present IQ, i.e., interference-aware deep Q-learning, to mitigate catastrophic interference in single-task deep reinforcement learning. Specifically, we resort to online clustering to achieve on-the-fly context division, together with a multi-head network and a knowledge distillation regularization term for preserving the policy of learned contexts. Built upon deep Q networks, IQ consistently boosts the stability and performance when compared to existing methods, verified with extensive experiments on classic control and Atari tasks. The code is publicly available at: https://github.com/Sweety-dm/Interference-aware-Deep-Q-learning. | ['Bo Yuan', 'Bin Liang', 'Xueqian Wang', 'Tiantian Zhang'] | 2021-09-01 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-1.85040057e-01 -1.53671816e-01 -5.29242456e-02 7.40869045e-02
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5869291d-9d1c-4548-8965-729523ce55dc | heart-rate-estimation-in-intense-exercise | 2208.02509 | null | https://arxiv.org/abs/2208.02509v1 | https://arxiv.org/pdf/2208.02509v1.pdf | Heart rate estimation in intense exercise videos | Estimating heart rate from video allows non-contact health monitoring with applications in patient care, human interaction, and sports. Existing work can robustly measure heart rate under some degree of motion by face tracking. However, this is not always possible in unconstrained settings, as the face might be occluded or even outside the camera. Here, we present IntensePhysio: a challenging video heart rate estimation dataset with realistic face occlusions, severe subject motion, and ample heart rate variation. To ensure heart rate variation in a realistic setting we record each subject for around 1-2 hours. The subject is exercising (at a moderate to high intensity) on a cycling ergometer with an attached video camera and is given no instructions regarding positioning or movement. We have 11 subjects, and approximately 20 total hours of video. We show that the existing remote photo-plethysmography methods have difficulty in estimating heart rate in this setting. In addition, we present IBIS-CNN, a new baseline using spatio-temporal superpixels, which improves on existing models by eliminating the need for a visible face/face tracking. We will make the code and data publically available soon. | ['Jan van Gemert', 'Thijs Eijsvogels', 'Puck Alkemade', 'Nergis Tomen', 'Anwesh Marwade', 'Yeshwanth Napolean'] | 2022-08-04 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 3.22125167e-01 9.96539090e-03 -2.66183943e-01 -2.41839170e-01
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1a4684a1-22e2-4f53-bf2e-90cd86357008 | language-model-detoxification-in-dialogue | 2301.10368 | null | https://arxiv.org/abs/2301.10368v1 | https://arxiv.org/pdf/2301.10368v1.pdf | Language Model Detoxification in Dialogue with Contextualized Stance Control | To reduce the toxic degeneration in a pretrained Language Model (LM), previous work on Language Model detoxification has focused on reducing the toxicity of the generation itself (self-toxicity) without consideration of the context. As a result, a type of implicit offensive language where the generations support the offensive language in the context is ignored. Different from the LM controlling tasks in previous work, where the desired attributes are fixed for generation, the desired stance of the generation depends on the offensiveness of the context. Therefore, we propose a novel control method to do context-dependent detoxification with the stance taken into consideration. We introduce meta prefixes to learn the contextualized stance control strategy and to generate the stance control prefix according to the input context. The generated stance prefix is then combined with the toxicity control prefix to guide the response generation. Experimental results show that our proposed method can effectively learn the context-dependent stance control strategies while keeping a low self-toxicity of the underlying LM. | ['Xifeng Yan', 'Jing Qian'] | 2023-01-25 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 5.27209103e-01 8.23209658e-02 -4.08253223e-01 -1.97351173e-01
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f40bd620-baf4-4b76-9c72-08fe6cd5d043 | uncertainty-estimation-in-medical-image | 2008.08837 | null | https://arxiv.org/abs/2008.08837v1 | https://arxiv.org/pdf/2008.08837v1.pdf | Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior | Uncertainty quantification in inverse medical imaging tasks with deep learning has received little attention. However, deep models trained on large data sets tend to hallucinate and create artifacts in the reconstructed output that are not anatomically present. We use a randomly initialized convolutional network as parameterization of the reconstructed image and perform gradient descent to match the observation, which is known as deep image prior. In this case, the reconstruction does not suffer from hallucinations as no prior training is performed. We extend this to a Bayesian approach with Monte Carlo dropout to quantify both aleatoric and epistemic uncertainty. The presented method is evaluated on the task of denoising different medical imaging modalities. The experimental results show that our approach yields well-calibrated uncertainty. That is, the predictive uncertainty correlates with the predictive error. This allows for reliable uncertainty estimates and can tackle the problem of hallucinations and artifacts in inverse medical imaging tasks. | ['Malte Tölle', 'Max-Heinrich Laves', 'Tobias Ortmaier'] | 2020-08-20 | null | null | null | null | ['medical-image-denoising'] | ['computer-vision'] | [ 1.82986781e-01 5.59759200e-01 2.91004300e-01 -3.08661669e-01
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d8fd3bcb-c826-4622-8b45-b4aec7883517 | une-comparaison-des-algorithmes-d | 2303.13590 | null | https://arxiv.org/abs/2303.13590v1 | https://arxiv.org/pdf/2303.13590v1.pdf | Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes | Survival analysis is an essential tool for the study of health data. An inherent component of such data is the presence of missing values. In recent years, researchers proposed new learning algorithms for survival tasks based on neural networks. Here, we studied the predictive performance of such algorithms coupled with different methods for handling missing values on simulated data that reflect a realistic situation, i.e., when individuals belong to unobserved clusters. We investigated different patterns of missing data. The results show that, without further feature engineering, no single imputation method is better than the others in all cases. The proposed methodology can be used to compare other missing data patterns and/or survival models. The Python code is accessible via the package survivalsim. -- L'analyse de survie est un outil essentiel pour l'\'etude des donn\'ees de sant\'e. Une composante inh\'erente \`a ces donn\'ees est la pr\'esence de valeurs manquantes. Ces derni\`eres ann\'ees, de nouveaux algorithmes d'apprentissage pour la survie, bas\'es sur les r\'eseaux de neurones, ont \'et\'e con\c{c}us. L'objectif de ce travail est d'\'etudier la performance en pr\'ediction de ces algorithmes coupl\'es \`a diff\'erentes m\'ethodes pour g\'erer les valeurs manquantes, sur des donn\'ees simul\'ees qui refl\`etent une situation rencontr\'ee en pratique, c'est-\`a dire lorsque les individus peuvent \^etre group\'es selon leurs covariables. Diff\'erents sch\'emas de donn\'ees manquantes sont \'etudi\'es. Les r\'esultats montrent que, sans l'ajout de variables suppl\'ementaires, aucune m\'ethode d'imputation n'est meilleure que les autres dans tous les cas. La m\'ethodologie propos\'ee peut \^etre utilis\'ee pour comparer d'autres mod\`eles de survie. Le code en Python est accessible via le package survivalsim. | ['Sébastien Benzekry', 'Paul Dufossé'] | 2023-03-23 | null | null | null | null | ['feature-engineering', 'survival-analysis'] | ['methodology', 'miscellaneous'] | [ 4.16124538e-02 -1.92019999e-01 3.17768961e-01 -4.35844928e-01
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ae7e93c6-a2ef-4dbd-82bf-dc57b733a818 | occ-bev-multi-camera-unified-pre-training-via | 2305.18829 | null | https://arxiv.org/abs/2305.18829v2 | https://arxiv.org/pdf/2305.18829v2.pdf | Occ-BEV: Multi-Camera Unified Pre-training via 3D Scene Reconstruction | Multi-camera 3D perception has emerged as a prominent research field in autonomous driving, offering a viable and cost-effective alternative to LiDAR-based solutions. However, existing multi-camera algorithms primarily rely on monocular image pre-training, which overlooks the spatial and temporal correlations among different camera views. To address this limitation, we propose the first multi-camera unified pre-training framework called Occ-BEV, which involves initially reconstructing the 3D scene as the foundational stage and subsequently fine-tuning the model on downstream tasks. Specifically, a 3D decoder is designed for leveraging Bird's Eye View (BEV) features from multi-view images to predict the 3D geometric occupancy to enable the model to capture a more comprehensive understanding of the 3D environment. A significant benefit of Occ-BEV is its capability of utilizing a considerable volume of unlabeled image-LiDAR pairs for pre-training purposes. The proposed multi-camera unified pre-training framework demonstrates promising results in key tasks such as multi-camera 3D object detection and surrounding semantic scene completion. When compared to monocular pre-training methods on the nuScenes dataset, Occ-BEV shows a significant improvement of about 2.0% in mAP and 2.0% in NDS for multi-camera 3D object detection, as well as a 3% increase in mIoU for surrounding semantic scene completion. Codes are publicly available at https://github.com/chaytonmin/Occ-BEV. | ['Bin Dai', 'Jiaolong Xu', 'Jimei Li', 'Zhichao Zhang', 'Weizhong Jiang', 'Hanzhang Xue', 'Shubin Si', 'Fuyang Li', 'Yiming Nie', 'Liang Xiao', 'Dawei Zhao', 'Xinli Xu', 'Chen Min'] | 2023-05-30 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [-4.76834178e-02 -3.77171963e-01 -7.96524435e-02 -5.21906078e-01
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0c7d5937-8d9f-49eb-9480-cbc3b31a7905 | reducing-large-internet-topologies-for-faster | null | null | https://link.springer.com/chapter/10.1007/11422778_27 | http://www.cs.ucr.edu/~michalis/PAPERS/sampling-networking-05.pdf | Reducing Large Internet Topologies for Faster Simulations | In this paper, we develop methods to “sample” a small realistic graph from a large real network. Despite recent activity, the modeling and generation of realistic graphs is still not a resolved issue. All previous work has attempted to grow a graph from scratch. We address the complementary problem of shrinking a graph. In more detail, this work has three parts. First, we propose a number of reduction methods that can be categorized into three classes: (a) deletion methods, (b) contraction methods, and (c) exploration methods. We prove that some of them maintain key properties of the initial graph. We implement our methods and show that we can effectively reduce the nodes of a graph by as much as 70% while maintaining its important properties. In addition, we show that our reduced graphs compare favourably against construction-based generators. Apart from its use in simulations, the problem of graph sampling is of independent interest. | ['J. -H. Cui', 'M. Faloutsos', 'A. G. Percus', 'V. Krishnamurthy', 'M. Chrobak', 'L. Lao'] | 2020-05-13 | null | null | null | 2020-5 | ['graph-sampling'] | ['graphs'] | [ 5.47187209e-01 8.89538050e-01 -1.03499793e-01 1.78598478e-01
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8803e4fe-c1d0-497f-90be-eb7d24f3b482 | improved-transition-based-parsing-and-tagging | null | null | https://aclanthology.org/D15-1159 | https://aclanthology.org/D15-1159.pdf | Improved Transition-Based Parsing and Tagging with Neural Networks | null | ['Slav Petrov', 'David Weiss', 'Chris Alberti', 'Greg Coppola'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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cab90a9b-535e-4c96-a6c2-e3cbac97354d | cluster-based-point-set-saliency | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Tasse_Cluster-Based_Point_Set_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Tasse_Cluster-Based_Point_Set_ICCV_2015_paper.pdf | Cluster-Based Point Set Saliency | We propose a cluster-based approach to point set saliency detection, a challenge since point sets lack topological information. A point set is first decomposed into small clusters, using fuzzy clustering. We evaluate cluster uniqueness and spatial distribution of each cluster and combine these values into a cluster saliency function. Finally, the probabilities of points belonging to each cluster are used to assign a saliency to each point. Our approach detects fine-scale salient features and uninteresting regions consistently have lower saliency values. We evaluate the proposed saliency model by testing our saliency-based keypoint detection against a 3D interest point detection benchmark. The evaluation shows that our method achieves a good balance between false positive and false negative error rates, without using any topological information. | ['Jiri Kosinka', 'Neil Dodgson', 'Flora Ponjou Tasse'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['interest-point-detection'] | ['computer-vision'] | [-2.07520336e-01 -1.41902328e-01 -2.14568526e-01 -3.72537225e-02
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083e564d-8808-409b-a694-9ea441726f7d | rfpose-ot-rf-based-3d-human-pose-estimation | 2301.13013 | null | https://arxiv.org/abs/2301.13013v1 | https://arxiv.org/pdf/2301.13013v1.pdf | RFPose-OT: RF-Based 3D Human Pose Estimation via Optimal Transport Theory | This paper introduces a novel framework, i.e., RFPose-OT, to enable the 3D human pose estimation from Radio Frequency (RF) signals. Different from existing methods that predict human poses from RF signals on the signal level directly, we consider the structure difference between the RF signals and the human poses, propose to transform the RF signals to the pose domain on the feature level based on Optimal Transport (OT) theory, and generate human poses from the transformed features. To evaluate RFPose-OT, we build a radio system and a multi-view camera system to acquire the RF signal data and the ground-truth human poses. The experimental results in basic indoor environment, occlusion indoor environment, and outdoor environment, all demonstrate that RFPose-OT can predict 3D human poses with higher precision than the state-of-the-art methods. | ['Yan Chen', 'Yang Hu', 'Chunyang Xie', 'Zhi Lu', 'Zhi Wu', 'Dongheng Zhang', 'Cong Yu'] | 2022-12-26 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 2.24951342e-01 -2.50343144e-01 2.81198800e-01 -4.75160837e-01
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ded43742-e72c-4302-91e0-b57ce82ab689 | ol-a-bonjour-salve-xformal-a-benchmark-for | null | null | https://aclanthology.org/2021.naacl-main.256 | https://aclanthology.org/2021.naacl-main.256.pdf | Ol\'a, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer | We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian. Results on XFORMAL suggest that state-of-the-art style transfer approaches perform close to simple baselines, indicating that style transfer is even more challenging when moving multilingual. | ['Joel Tetreault', 'Ke Zhang', 'Di Lu', 'Eleftheria Briakou'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['formality-style-transfer'] | ['natural-language-processing'] | [-1.50335729e-01 5.19240424e-02 -2.11466849e-01 -5.13697326e-01
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03f1ec6f-a89d-4045-8752-fa33a4b1d8c6 | casapose-class-adaptive-and-semantic-aware | 2210.05318 | null | https://arxiv.org/abs/2210.05318v3 | https://arxiv.org/pdf/2210.05318v3.pdf | CASAPose: Class-Adaptive and Semantic-Aware Multi-Object Pose Estimation | Applications in the field of augmented reality or robotics often require joint localisation and 6D pose estimation of multiple objects. However, most algorithms need one network per object class to be trained in order to provide the best results. Analysing all visible objects demands multiple inferences, which is memory and time-consuming. We present a new single-stage architecture called CASAPose that determines 2D-3D correspondences for pose estimation of multiple different objects in RGB images in one pass. It is fast and memory efficient, and achieves high accuracy for multiple objects by exploiting the output of a semantic segmentation decoder as control input to a keypoint recognition decoder via local class-adaptive normalisation. Our new differentiable regression of keypoint locations significantly contributes to a faster closing of the domain gap between real test and synthetic training data. We apply segmentation-aware convolutions and upsampling operations to increase the focus inside the object mask and to reduce mutual interference of occluding objects. For each inserted object, the network grows by only one output segmentation map and a negligible number of parameters. We outperform state-of-the-art approaches in challenging multi-object scenes with inter-object occlusion and synthetic training. | ['Peter Eisert', 'Anna Hilsmann', 'Niklas Gard'] | 2022-10-11 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 4.39327121e-01 1.93171188e-01 2.46724501e-01 -4.91553485e-01
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3c0602c8-e8b7-4f77-a693-8171c17acf93 | sequential-experimental-design-for-spectral | 2305.07040 | null | https://arxiv.org/abs/2305.07040v1 | https://arxiv.org/pdf/2305.07040v1.pdf | Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model | In this study, we demonstrate a sequential experimental design for spectral measurements by active learning using parametric models as predictors. In spectral measurements, it is necessary to reduce the measurement time because of sample fragility and high energy costs. To improve the efficiency of experiments, sequential experimental designs are proposed, in which the subsequent measurement is designed by active learning using the data obtained before the measurement. Conventionally, parametric models are employed in data analysis; when employed for active learning, they are expected to afford a sequential experimental design that improves the accuracy of data analysis. However, due to the complexity of the formulas, a sequential experimental design using general parametric models has not been realized. Therefore, we applied Bayesian inference-based data analysis using the exchange Monte Carlo method to realize a sequential experimental design with general parametric models. In this study, we evaluated the effectiveness of the proposed method by applying it to Bayesian spectral deconvolution and Bayesian Hamiltonian selection in X-ray photoelectron spectroscopy. Using numerical experiments with artificial data, we demonstrated that the proposed method improves the accuracy of model selection and parameter estimation while reducing the measurement time compared with the results achieved without active learning or with active learning using the Gaussian process regression. | ['Masato Okada', 'Masaichiro Mizumaki', 'Shun Katakami', 'Kenji Nagata', 'Tomohiro Nabika'] | 2023-05-11 | null | null | null | null | ['experimental-design', 'bayesian-inference'] | ['methodology', 'methodology'] | [ 5.74997067e-01 -1.67392835e-01 -3.33551504e-02 -5.09051085e-01
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f38d9a18-86ce-445d-9af2-7dd02a1b30bb | curricular-subgoals-for-inverse-reinforcement | 2306.08232 | null | https://arxiv.org/abs/2306.08232v1 | https://arxiv.org/pdf/2306.08232v1.pdf | Curricular Subgoals for Inverse Reinforcement Learning | Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success in imitation learning. To promote expert-like behavior, existing IRL methods mainly focus on learning global reward functions to minimize the trajectory difference between the imitator and the expert. However, these global designs are still limited by the redundant noise and error propagation problems, leading to the unsuitable reward assignment and thus downgrading the agent capability in complex multi-stage tasks. In this paper, we propose a novel Curricular Subgoal-based Inverse Reinforcement Learning (CSIRL) framework, that explicitly disentangles one task with several local subgoals to guide agent imitation. Specifically, CSIRL firstly introduces decision uncertainty of the trained agent over expert trajectories to dynamically select subgoals, which directly determines the exploration boundary of different task stages. To further acquire local reward functions for each stage, we customize a meta-imitation objective based on these curricular subgoals to train an intrinsic reward generator. Experiments on the D4RL and autonomous driving benchmarks demonstrate that the proposed methods yields results superior to the state-of-the-art counterparts, as well as better interpretability. Our code is available at https://github.com/Plankson/CSIRL. | ['Mingli Song', 'YunFu Liu', 'Tianhao Chen', 'Jingyuan Cong', 'Jiangtao Zhang', 'Hongyan Wu', 'Shuqi Xu', 'Yunpeng Qing', 'Shunyu Liu'] | 2023-06-14 | null | null | null | null | ['imitation-learning', 'd4rl'] | ['methodology', 'robots'] | [-2.00165167e-01 1.16861850e-01 -4.59896773e-01 5.52186333e-02
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6319ce87-5388-4e57-b667-5f496b72df6f | supervised-adaptation-of-sequence-to-sequence | null | null | https://aclanthology.org/2020.lifelongnlp-1.2 | https://aclanthology.org/2020.lifelongnlp-1.2.pdf | Supervised Adaptation of Sequence-to-Sequence Speech Recognition Systems using Batch-Weighting | When training speech recognition systems, one often faces the situation that sufficient amounts of training data for the language in question are available but only small amounts of data for the domain in question. This problem is even bigger for end-to-end speech recognition systems that only accept transcribed speech as training data, which is harder and more expensive to obtain than text data. In this paper we present experiments in adapting end-to-end speech recognition systems by a method which is called batch-weighting and which we contrast against regular fine-tuning, i.e., to continue to train existing neural speech recognition models on adaptation data. We perform experiments using theses techniques in adapting to topic, accent and vocabulary, showing that batch-weighting consistently outperforms fine-tuning. In order to show the generalization capabilities of batch-weighting we perform experiments in several languages, i.e., Arabic, English and German. Due to its relatively small computational requirements batch-weighting is a suitable technique for supervised life-long learning during the life-time of a speech recognition system, e.g., from user corrections. | ['Alexander Waibel', 'Sebastian Stüker', 'Kaihang Song', 'Tuan-Nam Nguyen', 'Juan Hussain', 'Christian Huber'] | null | null | null | null | aacl-lifelongnlp-2020-12 | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 1.54251233e-01 1.13892801e-01 6.39994070e-02 -8.01762938e-01
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df857f1f-774a-4fef-bc31-5bc2eedd8a3f | cross-database-micro-expression-recognition-a | 1812.07742 | null | https://arxiv.org/abs/1812.07742v2 | https://arxiv.org/pdf/1812.07742v2.pdf | Cross-Database Micro-Expression Recognition: A Benchmark | Cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problem in micro-expression analysis. CDMER is more challenging than the conventional micro-expression recognition (MER), because the training and testing samples in CDMER come from different micro-expression databases, resulting in the inconsistency of the feature distributions between the training and testing sets. In this paper, we contribute to this topic from three aspects. First, we establish a CDMER experimental evaluation protocol aiming to allow the researchers to conveniently work on this topic and provide a standard platform for evaluating their proposed methods. Second, we conduct benchmark experiments by using NINE state-of-the-art domain adaptation (DA) methods and SIX popular spatiotemporal descriptors for respectively investigating CDMER problem from two different perspectives. Third, we propose a novel DA method called region selective transfer regression (RSTR) to deal with the CDMER task. Our RSTR takes advantage of one important cue for recognizing micro-expressions, i.e., the different contributions of the facial local regions in MER. The overall superior performance of RSTR demonstrates that taking into consideration the important cues benefiting MER, e.g., the facial local region information, contributes to develop effective DA methods for dealing with CDMER problem. | ['Chuangao Tang', 'Yuan Zong', 'Xiaopeng Hong', 'Zhen Cui', 'Wenming Zheng', 'Tong Zhang', 'Guoying Zhao'] | 2018-12-19 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [-2.34543364e-02 -7.32842147e-01 -2.24536479e-01 -3.84210110e-01
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807cc2bb-ee3c-4594-aa4b-f1b9b330d765 | second-order-winobias-sowinobias-test-set-for | 2109.14047 | null | https://arxiv.org/abs/2109.14047v1 | https://arxiv.org/pdf/2109.14047v1.pdf | Second Order WinoBias (SoWinoBias) Test Set for Latent Gender Bias Detection in Coreference Resolution | We observe an instance of gender-induced bias in a downstream application, despite the absence of explicit gender words in the test cases. We provide a test set, SoWinoBias, for the purpose of measuring such latent gender bias in coreference resolution systems. We evaluate the performance of current debiasing methods on the SoWinoBias test set, especially in reference to the method's design and altered embedding space properties. See https://github.com/hillarydawkins/SoWinoBias. | ['Hillary Dawkins'] | 2021-09-28 | null | https://aclanthology.org/2021.gebnlp-1.12 | https://aclanthology.org/2021.gebnlp-1.12.pdf | acl-gebnlp-2021-8 | ['gender-bias-detection', 'gender-bias-detection'] | ['miscellaneous', 'natural-language-processing'] | [-2.31982842e-01 5.17201900e-01 -5.55586755e-01 -5.96246421e-01
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9dbff942-c21e-41f6-b4e1-e7558d0348ab | benchmark-of-data-preprocessing-methods-for | 2303.03094 | null | https://arxiv.org/abs/2303.03094v1 | https://arxiv.org/pdf/2303.03094v1.pdf | Benchmark of Data Preprocessing Methods for Imbalanced Classification | Severe class imbalance is one of the main conditions that make machine learning in cybersecurity difficult. A variety of dataset preprocessing methods have been introduced over the years. These methods modify the training dataset by oversampling, undersampling or a combination of both to improve the predictive performance of classifiers trained on this dataset. Although these methods are used in cybersecurity occasionally, a comprehensive, unbiased benchmark comparing their performance over a variety of cybersecurity problems is missing. This paper presents a benchmark of 16 preprocessing methods on six cybersecurity datasets together with 17 public imbalanced datasets from other domains. We test the methods under multiple hyperparameter configurations and use an AutoML system to train classifiers on the preprocessed datasets, which reduces potential bias from specific hyperparameter or classifier choices. Special consideration is also given to evaluating the methods using appropriate performance measures that are good proxies for practical performance in real-world cybersecurity systems. The main findings of our study are: 1) Most of the time, a data preprocessing method that improves classification performance exists. 2) Baseline approach of doing nothing outperformed a large portion of methods in the benchmark. 3) Oversampling methods generally outperform undersampling methods. 4) The most significant performance gains are brought by the standard SMOTE algorithm and more complicated methods provide mainly incremental improvements at the cost of often worse computational performance. | ['Tomáš Komárek', 'Jan Brabec', 'Radovan Haluška'] | 2023-03-06 | null | null | null | null | ['automl', 'imbalanced-classification'] | ['methodology', 'miscellaneous'] | [ 3.43802512e-01 -1.20149627e-01 -4.88605618e-01 -1.78796917e-01
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39b81033-9176-4d7c-a064-24e4b2114bd1 | hc-search-for-structured-prediction-in | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Lam_HC-Search_for_Structured_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Lam_HC-Search_for_Structured_2015_CVPR_paper.pdf | HC-Search for Structured Prediction in Computer Vision | The mainstream approach to structured prediction problems in computer vision is to learn an energy function such that the solution minimizes that function. At prediction time, this approach must solve an often-challenging optimization problem. Search-based methods provide an alternative that has the potential to achieve higher performance. These methods learn to control a search procedure that constructs and evaluates candidate solutions. The recently-developed HC-Search method has been shown to achieve state-of-the-art results in natural language processing, but mixed success when applied to vision problems. This paper studies whether HC-Search can achieve similarly competitive performance on basic vision tasks such as object detection, scene labeling, and monocular depth estimation, where the leading paradigm is energy minimization. To this end, we introduce a search operator suited to the vision domain that improves a candidate solution by probabilistically sampling likely object configurations in the scene from the hierarchical Berkeley segmentation. We complement this search operator by applying the DAgger algorithm to robustly train the search heuristic so it learns from its previous mistakes. Our evaluation shows that these improvements reduce the branching factor and search depth, and thus give a significant performance boost. Our state-of-the-art results on scene labeling and depth estimation suggest that HC-Search provides a suitable tool for learning and inference in vision. | ['Sinisa Todorovic', 'Janardhan Rao Doppa', 'Thomas G. Dietterich', 'Michael Lam'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['scene-labeling'] | ['computer-vision'] | [ 4.04770702e-01 1.05064712e-01 -2.47135714e-01 -6.48003519e-01
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11727da7-538c-44d7-b694-68c75c28595d | multimodal-emotion-recognition-with-high | 2111.10202 | null | https://arxiv.org/abs/2111.10202v1 | https://arxiv.org/pdf/2111.10202v1.pdf | Multimodal Emotion Recognition with High-level Speech and Text Features | Automatic emotion recognition is one of the central concerns of the Human-Computer Interaction field as it can bridge the gap between humans and machines. Current works train deep learning models on low-level data representations to solve the emotion recognition task. Since emotion datasets often have a limited amount of data, these approaches may suffer from overfitting, and they may learn based on superficial cues. To address these issues, we propose a novel cross-representation speech model, inspired by disentanglement representation learning, to perform emotion recognition on wav2vec 2.0 speech features. We also train a CNN-based model to recognize emotions from text features extracted with Transformer-based models. We further combine the speech-based and text-based results with a score fusion approach. Our method is evaluated on the IEMOCAP dataset in a 4-class classification problem, and it surpasses current works on speech-only, text-only, and multimodal emotion recognition. | ['Koichi Shinoda', 'Kuniaki Uto', 'Mariana Rodrigues Makiuchi'] | 2021-09-29 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.79072872e-01 -6.67814165e-02 -2.59615481e-02 -7.16803193e-01
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c0331c71-79ad-4596-9164-b3f675fdef95 | elimination-of-central-artefacts-of-l-spect | 2008.07893 | null | http://arxiv.org/abs/2008.07893v1 | http://arxiv.org/pdf/2008.07893v1.pdf | Elimination of Central Artefacts of L-SPECT with Modular Partial Ring Detectors by Shifting Center of Scanning | The Lightfield Single Photon Emission Computed Tomography (L-SPECT) system is
developed to overcome some of the drawbacks in conventional SPECT by applying
the idea of plenoptic imaging. This system displayed improved performance in
terms of reduced information loss and scanning time when compared to the SPECT
system which has a conventional collimator. The SPECT system is transformed
into L-SPECT system by replacing the conventional collimators with micro-range
multi-pinhole arrays. The field of view (FOV) of the L-SPECT system is enhanced
by reshaping the detector head into ring-type by tiling small detector modules.
The L-SPECT system with modular partial ring detectors (MPRD L-SPECT) exhibits
cylindrical artefacts during volumetric reconstruction. Hence, here the work is
focused to remove the cylindrical artefacts in the reconstruction of the
examined objects by changing the scanning orbit. The enhancement is done such
that the center of scanning of the L-SPECT system with MPRD L-SPECT is shifted
at different values. The reconstruction quality of MPRD L-SPECT with and
without center shifting is evaluated in terms of the Full Width at Half Maximum
(FWHM) and Modulation Transfer Function (MTF). Moreover, the visual comparison
is also examined. The results indicate that center shifting of MPRD L-SPECT
overcomes the problem of the central artefact with improved FWHM. By increasing
MPRD's scanning center shift gap, the spatial resolution can be further
improved. | [] | 2020-08-18 | null | null | null | null | ['lightfield'] | ['computer-vision'] | [ 1.16112240e-01 3.98197584e-02 2.47500762e-01 -4.64251637e-02
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59f3bf7b-9e20-4928-b37a-5ac6fc997197 | high-frequency-space-diffusion-models-for | 2208.05481 | null | https://arxiv.org/abs/2208.05481v4 | https://arxiv.org/pdf/2208.05481v4.pdf | High-Frequency Space Diffusion Models for Accelerated MRI | Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation. It can be used as a deep generative prior to solve the inverse problem in MR reconstruction. However, the existing VP-SDE can be treated as maximizing the energy of the MR image to be reconstructed and may lead to SDE sequence divergence. The VE-SDE based MR reconstruction is not consistent with actual diffusion process. In addition, both VE- and VP-SDEs-based models suffer from a time-consuming sampling procedure, resulting long reconstruction time. In this study, a new SDE focusing on the diffusion process in high-frequency space is designed specifically for robust MR reconstruction based on diffusion models. Experiments on the publicly fastMRI dataset show that HFS-SDE based reconstruction method outperforms the parallel imaging, supervised deep learning, and existing VE- and VP-SDEs-based methods in terms of reconstruction accuracy. It also improves the stability of MR reconstruction and accelerates sampling procedure of reverse diffusion. | ['Yanjie Zhu', 'Dong Liang', 'Hairong Zheng', 'Shaonan Liu', 'Zhuo-Xu Cui', 'Chentao Cao'] | 2022-08-10 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 6.51838332e-02 -3.03124011e-01 3.01245391e-01 -2.41468787e-01
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746f0f45-8835-4c97-9adb-6c59c31d97ff | domain-transfer-based-data-augmentation-for | null | null | https://aclanthology.org/2020.coling-main.399 | https://aclanthology.org/2020.coling-main.399.pdf | Domain Transfer based Data Augmentation for Neural Query Translation | Query translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR). Due to the lack of parallel query samples, neural-based QT models are usually optimized with synthetic data which are derived from large-scale monolingual queries. Nevertheless, such kind of pseudo corpus is mostly produced by a general-domain translation model, making it be insufficient to guide the learning of QT model. In this paper, we extend the data augmentation with a domain transfer procedure, thus to revise synthetic candidates to search-aware examples. Specifically, the domain transfer model is built upon advanced Transformer, in which layer coordination and mixed attention are exploited to speed up the refining process and leverage parameters from a pre-trained cross-lingual language model. In order to examine the effectiveness of the proposed method, we collected French-to-English and Spanish-to-English QT test sets, each of which consists of 10,000 parallel query pairs with careful manual-checking. Qualitative and quantitative analyses reveal that our model significantly outperforms strong baselines and the related domain transfer methods on both translation quality and retrieval accuracy. | ['Weihua Luo', 'Boxing Chen', 'Haibo Zhang', 'Baosong Yang', 'Liang Yao'] | 2020-12-01 | null | null | null | coling-2020-8 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 5.60960770e-02 -2.65258342e-01 -5.20702720e-01 -2.77649432e-01
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35200f3f-3680-4fa2-bc2d-2cc5612364a5 | demystifying-how-self-supervised-features-1 | 2110.09022 | null | https://arxiv.org/abs/2110.09022v3 | https://arxiv.org/pdf/2110.09022v3.pdf | Mitigating Memorization of Noisy Labels via Regularization between Representations | Designing robust loss functions is popular in learning with noisy labels while existing designs did not explicitly consider the overfitting property of deep neural networks (DNNs). As a result, applying these losses may still suffer from overfitting/memorizing noisy labels as training proceeds. In this paper, we first theoretically analyze the memorization effect and show that a lower-capacity model may perform better on noisy datasets. However, it is non-trivial to design a neural network with the best capacity given an arbitrary task. To circumvent this dilemma, instead of changing the model architecture, we decouple DNNs into an encoder followed by a linear classifier and propose to restrict the function space of a DNN by a representation regularizer. Particularly, we require the distance between two self-supervised features to be positively related to the distance between the corresponding two supervised model outputs. Our proposed framework is easily extendable and can incorporate many other robust loss functions to further improve performance. Extensive experiments and theoretical analyses support our claims. Code is available at github.com/UCSC-REAL/SelfSup_NoisyLabel. | ['Yang Liu', 'Xing Sun', 'Zhaowei Zhu', 'Hao Cheng'] | 2021-10-18 | demystifying-how-self-supervised-features | https://openreview.net/forum?id=R5sVzzXhW8n | https://openreview.net/pdf?id=R5sVzzXhW8n | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.81858271e-01 3.08735549e-01 -7.20964372e-02 -7.39120305e-01
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55f84b50-4d35-4787-b8c7-3f30a0a6737b | localized-data-fusion-for-kernel-k-means | null | null | http://papers.nips.cc/paper/5236-localized-data-fusion-for-kernel-k-means-clustering-with-application-to-cancer-biology | http://papers.nips.cc/paper/5236-localized-data-fusion-for-kernel-k-means-clustering-with-application-to-cancer-biology.pdf | Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology | In many modern applications from, for example, bioinformatics and computer vision, samples have multiple feature representations coming from different data sources. Multiview learning algorithms try to exploit all these available information to obtain a better learner in such scenarios. In this paper, we propose a novel multiple kernel learning algorithm that extends kernel k-means clustering to the multiview setting, which combines kernels calculated on the views in a localized way to better capture sample-specific characteristics of the data. We demonstrate the better performance of our localized data fusion approach on a human colon and rectal cancer data set by clustering patients. Our method finds more relevant prognostic patient groups than global data fusion methods when we evaluate the results with respect to three commonly used clinical biomarkers. | ['Mehmet Gönen', 'Adam A. Margolin'] | 2014-12-01 | null | null | null | neurips-2014-12 | ['multiview-learning'] | ['computer-vision'] | [-1.0385326e-01 -3.8466486e-01 -5.7769048e-01 -5.5034810e-01
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b1824971-77ec-493b-8707-1184befdd46c | gpu-based-parallel-optimization-for-real-time | 1810.03988 | null | http://arxiv.org/abs/1810.03988v2 | http://arxiv.org/pdf/1810.03988v2.pdf | GPU based Parallel Optimization for Real Time Panoramic Video Stitching | Panoramic video is a sort of video recorded at the same point of view to
record the full scene. With the development of video surveillance and the
requirement for 3D converged video surveillance in smart cities, CPU and GPU
are required to possess strong processing abilities to make panoramic video.
The traditional panoramic products depend on post processing, which results in
high power consumption, low stability and unsatisfying performance in real
time. In order to solve these problems,we propose a real-time panoramic video
stitching framework.The framework we propose mainly consists of three
algorithms, LORB image feature extraction algorithm, feature point matching
algorithm based on LSH and GPU parallel video stitching algorithm based on
CUDA.The experiment results show that the algorithm mentioned can improve the
performance in the stages of feature extraction of images stitching and
matching, the running speed of which is 11 times than that of the traditional
ORB algorithm and 639 times than that of the traditional SIFT algorithm. Based
on analyzing the GPU resources occupancy rate of each resolution image
stitching, we further propose a stream parallel strategy to maximize the
utilization of GPU resources. Compared with the L-ORB algorithm, the efficiency
of this strategy is improved by 1.6-2.5 times, and it can make full use of GPU
resources. The performance of the system accomplished in the paper is 29.2
times than that of the former embedded one, while the power dissipation is
reduced to 10W. | ['Lin Li', 'Jingling Yuan', 'Chengyao Du', 'Tao Li', 'Mincheng Chen', 'Jiansheng Dong'] | 2018-10-04 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 3.36704373e-01 -9.47181523e-01 -2.85624564e-02 2.04945192e-01
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96527fd7-354d-4b13-9d3b-c2a66421bc44 | large-scale-radio-frequency-signal | 2207.09918 | null | https://arxiv.org/abs/2207.09918v1 | https://arxiv.org/pdf/2207.09918v1.pdf | Large Scale Radio Frequency Signal Classification | Existing datasets used to train deep learning models for narrowband radio frequency (RF) signal classification lack enough diversity in signal types and channel impairments to sufficiently assess model performance in the real world. We introduce the Sig53 dataset consisting of 5 million synthetically-generated samples from 53 different signal classes and expertly chosen impairments. We also introduce TorchSig, a signals processing machine learning toolkit that can be used to generate this dataset. TorchSig incorporates data handling principles that are common to the vision domain, and it is meant to serve as an open-source foundation for future signals machine learning research. Initial experiments using the Sig53 dataset are conducted using state of the art (SoTA) convolutional neural networks (ConvNets) and Transformers. These experiments reveal Transformers outperform ConvNets without the need for additional regularization or a ConvNet teacher, which is contrary to results from the vision domain. Additional experiments demonstrate that TorchSig's domain-specific data augmentations facilitate model training, which ultimately benefits model performance. Finally, TorchSig supports on-the-fly synthetic data creation at training time, thus enabling massive scale training sessions with virtually unlimited datasets. | ['Robert D. Miller', 'Craig Lennon', 'Silvija Kokalj-Filipovic', 'Bradley Comar', 'Phillip Vallance', 'Garrett Vanhoy', 'Manbir Gulati', 'Luke Boegner'] | 2022-07-20 | null | null | null | null | ['classification'] | ['methodology'] | [ 3.11752647e-01 -1.95402116e-01 6.03796169e-02 -5.29614687e-01
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6cd71865-2ef6-4fcc-a3fe-fc258264940a | multi-target-regression-via-random-linear | 1404.5065 | null | http://arxiv.org/abs/1404.5065v1 | http://arxiv.org/pdf/1404.5065v1.pdf | Multi-Target Regression via Random Linear Target Combinations | Multi-target regression is concerned with the simultaneous prediction of
multiple continuous target variables based on the same set of input variables.
It arises in several interesting industrial and environmental application
domains, such as ecological modelling and energy forecasting. This paper
presents an ensemble method for multi-target regression that constructs new
target variables via random linear combinations of existing targets. We discuss
the connection of our approach with multi-label classification algorithms, in
particular RA$k$EL, which originally inspired this work, and a family of recent
multi-label classification algorithms that involve output coding. Experimental
results on 12 multi-target datasets show that it performs significantly better
than a strong baseline that learns a single model for each target using
gradient boosting and compares favourably to multi-objective random forest
approach, which is a state-of-the-art approach. The experiments further show
that our approach improves more when stronger unconditional dependencies exist
among the targets. | ['Ioannis Vlahavas', 'Eleftherios Spyromitros-Xioufis', 'Grigorios Tsoumakas', 'Aikaterini Vrekou'] | 2014-04-20 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 7.61134982e-01 -3.84853743e-02 -5.60853243e-01 -6.57091975e-01
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2c04150d-c035-4818-9519-7407debc988a | cardiacgen-a-hierarchical-deep-generative | 2211.08385 | null | https://arxiv.org/abs/2211.08385v1 | https://arxiv.org/pdf/2211.08385v1.pdf | CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals | We present CardiacGen, a Deep Learning framework for generating synthetic but physiologically plausible cardiac signals like ECG. Based on the physiology of cardiovascular system function, we propose a modular hierarchical generative model and impose explicit regularizing constraints for training each module using multi-objective loss functions. The model comprises 2 modules, an HRV module focused on producing realistic Heart-Rate-Variability characteristics and a Morphology module focused on generating realistic signal morphologies for different modalities. We empirically show that in addition to having realistic physiological features, the synthetic data from CardiacGen can be used for data augmentation to improve the performance of Deep Learning based classifiers. CardiacGen code is available at https://github.com/SENSE-Lab-OSU/cardiac_gen_model. | ['Emre Ertin', 'Tushar Agarwal'] | 2022-11-15 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 1.13763371e-02 3.18282872e-01 1.72372222e-01 -3.73493254e-01
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ea2f95dc-0ee4-4501-830c-ac6cf7db3e2d | weakly-supervised-instance-segmentation-by | 2001.11207 | null | https://arxiv.org/abs/2001.11207v3 | https://arxiv.org/pdf/2001.11207v3.pdf | Weakly Supervised Instance Segmentation by Deep Community Learning | We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks. This task is formulated as a combination of weakly supervised object detection and semantic segmentation, where individual objects of the same class are identified and segmented separately. We address this problem by designing a unified deep neural network architecture, which has a positive feedback loop of object detection with bounding box regression, instance mask generation, instance segmentation, and feature extraction. Each component of the network makes active interactions with others to improve accuracy, and the end-to-end trainability of our model makes our results more robust and reproducible. The proposed algorithm achieves state-of-the-art performance in the weakly supervised setting without any additional training such as Fast R-CNN and Mask R-CNN on the standard benchmark dataset. The implementation of our algorithm is available on the project webpage: https://cv.snu.ac.kr/research/WSIS_CL. | ['Jeany Son', 'Jaedong Hwang', 'Bohyung Han', 'Seohyun Kim'] | 2020-01-30 | null | null | null | null | ['weakly-supervised-instance-segmentation', 'image-level-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.40460262e-01 4.12825257e-01 -3.84730220e-01 -3.91893327e-01
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f5ad4cb5-4589-4f90-b19e-4c533760f4cc | covid-19-event-extraction-from-twitter-via | 2303.10659 | null | https://arxiv.org/abs/2303.10659v2 | https://arxiv.org/pdf/2303.10659v2.pdf | COVID-19 event extraction from Twitter via extractive question answering with continuous prompts | As COVID-19 ravages the world, social media analytics could augment traditional surveys in assessing how the pandemic evolves and capturing consumer chatter that could help healthcare agencies in addressing it. This typically involves mining disclosure events that mention testing positive for the disease or discussions surrounding perceptions and beliefs in preventative or treatment options. The 2020 shared task on COVID-19 event extraction (conducted as part of the W-NUT workshop during the EMNLP conference) introduced a new Twitter dataset for benchmarking event extraction from COVID-19 tweets. In this paper, we cast the problem of event extraction as extractive question answering using recent advances in continuous prompting in language models. On the shared task test dataset, our approach leads to over 5% absolute micro-averaged F1-score improvement over prior best results, across all COVID-19 event slots. Our ablation study shows that continuous prompts have a major impact on the eventual performance. | ['Ramakanth Kavuluru', 'Yuhang Jiang'] | 2023-03-19 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.95679948e-01 4.18974787e-01 -4.76446569e-01 -2.68805534e-01
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0e0e50d8-fa2a-481d-8ad2-d7689327bb52 | two-view-geometry-scoring-without-1 | 2306.01596 | null | https://arxiv.org/abs/2306.01596v1 | https://arxiv.org/pdf/2306.01596v1.pdf | Two-View Geometry Scoring Without Correspondences | Camera pose estimation for two-view geometry traditionally relies on RANSAC. Normally, a multitude of image correspondences leads to a pool of proposed hypotheses, which are then scored to find a winning model. The inlier count is generally regarded as a reliable indicator of "consensus". We examine this scoring heuristic, and find that it favors disappointing models under certain circumstances. As a remedy, we propose the Fundamental Scoring Network (FSNet), which infers a score for a pair of overlapping images and any proposed fundamental matrix. It does not rely on sparse correspondences, but rather embodies a two-view geometry model through an epipolar attention mechanism that predicts the pose error of the two images. FSNet can be incorporated into traditional RANSAC loops. We evaluate FSNet on fundamental and essential matrix estimation on indoor and outdoor datasets, and establish that FSNet can successfully identify good poses for pairs of images with few or unreliable correspondences. Besides, we show that naively combining FSNet with MAGSAC++ scoring approach achieves state of the art results. | ['Daniyar Turmukhambetov', 'Gabriel J. Brostow', 'Victor Adrian Prisacariu', 'Eric Brachmann', 'Axel Barroso-Laguna'] | 2023-06-02 | two-view-geometry-scoring-without | http://openaccess.thecvf.com//content/CVPR2023/html/Barroso-Laguna_Two-View_Geometry_Scoring_Without_Correspondences_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Barroso-Laguna_Two-View_Geometry_Scoring_Without_Correspondences_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pose-estimation'] | ['computer-vision'] | [-1.92172974e-02 -1.09128170e-01 -2.84389913e-04 -4.31759417e-01
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32f46c1f-d757-4b9e-b851-86bdccc937c1 | negotiated-reasoning-on-provably-addressing | 2306.05353 | null | https://arxiv.org/abs/2306.05353v1 | https://arxiv.org/pdf/2306.05353v1.pdf | Negotiated Reasoning: On Provably Addressing Relative Over-Generalization | Over-generalization is a thorny issue in cognitive science, where people may become overly cautious due to past experiences. Agents in multi-agent reinforcement learning (MARL) also have been found to suffer relative over-generalization (RO) as people do and stuck to sub-optimal cooperation. Recent methods have shown that assigning reasoning ability to agents can mitigate RO algorithmically and empirically, but there has been a lack of theoretical understanding of RO, let alone designing provably RO-free methods. This paper first proves that RO can be avoided when the MARL method satisfies a consistent reasoning requirement under certain conditions. Then we introduce a novel reasoning framework, called negotiated reasoning, that first builds the connection between reasoning and RO with theoretical justifications. After that, we propose an instantiated algorithm, Stein variational negotiated reasoning (SVNR), which uses Stein variational gradient descent to derive a negotiation policy that provably avoids RO in MARL under maximum entropy policy iteration. The method is further parameterized with neural networks for amortized learning, making computation efficient. Numerical experiments on many RO-challenged environments demonstrate the superiority and efficiency of SVNR compared to state-of-the-art methods in addressing RO. | ['Xiangfeng Wang', 'Jun Wang', 'Hongyuan Zha', 'Bo Jin', 'Wenhao Li', 'Junjie Sheng'] | 2023-06-08 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-2.01816395e-01 5.00461221e-01 -2.03875840e-01 -5.45284431e-03
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cb1ceaa2-8fd4-4240-b920-8cf08d2a9e3a | 191104470 | 1911.04470 | null | https://arxiv.org/abs/1911.04470v1 | https://arxiv.org/pdf/1911.04470v1.pdf | Semi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval | Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain gap between sketches and natural images. How to align abstract sketches and natural images into a common high-level semantic space remains a key problem in SBIR. In this paper, we propose a novel semi-heterogeneous three-way joint embedding network (Semi3-Net), which integrates three branches (a sketch branch, a natural image branch, and an edgemap branch) to learn more discriminative cross-domain feature representations for the SBIR task. The key insight lies with how we cultivate the mutual and subtle relationships amongst the sketches, natural images, and edgemaps. A semi-heterogeneous feature mapping is designed to extract bottom features from each domain, where the sketch and edgemap branches are shared while the natural image branch is heterogeneous to the other branches. In addition, a joint semantic embedding is introduced to embed the features from different domains into a common high-level semantic space, where all of the three branches are shared. To further capture informative features common to both natural images and the corresponding edgemaps, a co-attention model is introduced to conduct common channel-wise feature recalibration between different domains. A hybrid-loss mechanism is designed to align the three branches, where an alignment loss and a sketch-edgemap contrastive loss are presented to encourage the network to learn invariant cross-domain representations. Experimental results on two widely used category-level datasets (Sketchy and TU-Berlin Extension) demonstrate that the proposed method outperforms state-of-the-art methods. | ['Yi-Zhe Song', 'Zhanyu Ma', 'Yuxin Song', 'Bo Peng', 'Ling Shao', 'Jianjun Lei'] | 2019-11-10 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.57281607e-01 -3.17908853e-01 -2.63537854e-01 -3.29433650e-01
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ca10e2c0-408a-42f1-b5b6-d947403ccf56 | textcraft-zero-shot-generation-of-high | 2211.01427 | null | https://arxiv.org/abs/2211.01427v4 | https://arxiv.org/pdf/2211.01427v4.pdf | CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language | Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints by producing high-fidelity and diverse 3D shapes without the need for (text, shape) pairs during training. CLIP-Sculptor achieves this in a multi-resolution approach that first generates in a low-dimensional latent space and then upscales to a higher resolution for improved shape fidelity. For improved shape diversity, we use a discrete latent space which is modeled using a transformer conditioned on CLIP's image-text embedding space. We also present a novel variant of classifier-free guidance, which improves the accuracy-diversity trade-off. Finally, we perform extensive experiments demonstrating that CLIP-Sculptor outperforms state-of-the-art baselines. The code is available at https://ivl.cs.brown.edu/#/projects/clip-sculptor. | ['Daniel Ritchie', 'Srinath Sridhar', 'Amir Hosein Khasahmadi', 'Hooman Shayani', 'Karl Willis', 'Vivian Liu', 'Rao Fu', 'Aditya Sanghi'] | 2022-11-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sanghi_CLIP-Sculptor_Zero-Shot_Generation_of_High-Fidelity_and_Diverse_Shapes_From_Natural_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sanghi_CLIP-Sculptor_Zero-Shot_Generation_of_High-Fidelity_and_Diverse_Shapes_From_Natural_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-to-shape-generation', 'text-to-3d'] | ['computer-vision', 'computer-vision'] | [ 2.85397470e-01 -1.31342322e-01 8.51399973e-02 -4.14617509e-01
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650d405c-592b-4c3e-a235-042050073725 | combining-experience-replay-with-exploration | 1905.07579 | null | https://arxiv.org/abs/1905.07579v1 | https://arxiv.org/pdf/1905.07579v1.pdf | Combining Experience Replay with Exploration by Random Network Distillation | Our work is a simple extension of the paper "Exploration by Random Network Distillation". More in detail, we show how to efficiently combine Intrinsic Rewards with Experience Replay in order to achieve more efficient and robust exploration (with respect to PPO/RND) and consequently better results in terms of agent performances and sample efficiency. We are able to do it by using a new technique named Prioritized Oversampled Experience Replay (POER), that has been built upon the definition of what is the important experience useful to replay. Finally, we evaluate our technique on the famous Atari game Montezuma's Revenge and some other hard exploration Atari games. | ['Francesco Sovrano'] | 2019-05-18 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-1.78540602e-01 5.54205954e-01 -2.72024989e-01 2.08843514e-01
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7ad3e7ce-2c2a-4394-ab2a-28aadbecbbee | deep-learning-for-cardiologist-level | 1912.07618 | null | https://arxiv.org/abs/1912.07618v3 | https://arxiv.org/pdf/1912.07618v3.pdf | Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms | Myocardial infarction is the leading cause of death worldwide. In this paper, we design domain-inspired neural network models to detect myocardial infarction. First, we study the contribution of various leads. This systematic analysis, first of its kind in the literature, indicates that out of 15 ECG leads, data from the v6, vz, and ii leads are critical to correctly identify myocardial infarction. Second, we use this finding and adapt the ConvNetQuake neural network model--originally designed to identify earthquakes--to attain state-of-the-art classification results for myocardial infarction, achieving $99.43\%$ classification accuracy on a record-wise split, and $97.83\%$ classification accuracy on a patient-wise split. These two results represent cardiologist-level performance level for myocardial infarction detection after feeding only 10 seconds of raw ECG data into our model. Third, we show that our multi-ECG-channel neural network achieves cardiologist-level performance without the need of any kind of manual feature extraction or data pre-processing. | ['Arjun Gupta', 'Issam Moussa', 'Zhizhen Zhao', 'E. A. Huerta'] | 2019-12-16 | deep-learning-for-cardiologist-level-1 | null | null | arxiv-2019-12 | ['myocardial-infarction-detection'] | ['medical'] | [ 1.03288218e-01 -1.14835359e-01 1.27095133e-01 -2.54587233e-01
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b9825f6e-18b9-4391-b4b6-37c2a460ba22 | deep-structured-prediction-for-facial-1 | 2010.09035 | null | https://arxiv.org/abs/2010.09035v1 | https://arxiv.org/pdf/2010.09035v1.pdf | Deep Structured Prediction for Facial Landmark Detection | Existing deep learning based facial landmark detection methods have achieved excellent performance. These methods, however, do not explicitly embed the structural dependencies among landmark points. They hence cannot preserve the geometric relationships between landmark points or generalize well to challenging conditions or unseen data. This paper proposes a method for deep structured facial landmark detection based on combining a deep Convolutional Network with a Conditional Random Field. We demonstrate its superior performance to existing state-of-the-art techniques in facial landmark detection, especially a better generalization ability on challenging datasets that include large pose and occlusion. | ['Qiang Ji', 'Hui Su', 'Lisha Chen'] | 2020-10-18 | deep-structured-prediction-for-facial | http://papers.nips.cc/paper/8515-deep-structured-prediction-for-facial-landmark-detection | http://papers.nips.cc/paper/8515-deep-structured-prediction-for-facial-landmark-detection.pdf | neurips-2019-12 | ['face-alignment'] | ['computer-vision'] | [-3.12534571e-01 -1.88393787e-01 -1.89373776e-01 -7.51247287e-01
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816610f2-e221-4104-921e-c033fb0f873e | pragmatically-informative-text-generation | 1904.01301 | null | http://arxiv.org/abs/1904.01301v2 | http://arxiv.org/pdf/1904.01301v2.pdf | Pragmatically Informative Text Generation | We improve the informativeness of models for conditional text generation
using techniques from computational pragmatics. These techniques formulate
language production as a game between speakers and listeners, in which a
speaker should generate output text that a listener can use to correctly
identify the original input that the text describes. While such approaches are
widely used in cognitive science and grounded language learning, they have
received less attention for more standard language generation tasks. We
consider two pragmatic modeling methods for text generation: one where
pragmatics is imposed by information preservation, and another where pragmatics
is imposed by explicit modeling of distractors. We find that these methods
improve the performance of strong existing systems for abstractive
summarization and generation from structured meaning representations. | ['Daniel Fried', 'Sheng Shen', 'Jacob Andreas', 'Dan Klein'] | 2019-04-02 | pragmatically-informative-text-generation-1 | https://aclanthology.org/N19-1410 | https://aclanthology.org/N19-1410.pdf | naacl-2019-6 | ['conditional-text-generation', 'grounded-language-learning'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.96569979e-01 1.15158784e+00 -2.14345455e-01 -3.70235652e-01
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5b075136-d0ba-4953-a0b6-d89f115c0882 | hippocluster-an-efficient-hippocampus | 2205.12338 | null | https://arxiv.org/abs/2205.12338v1 | https://arxiv.org/pdf/2205.12338v1.pdf | Hippocluster: an efficient, hippocampus-inspired algorithm for graph clustering | Random walks can reveal communities or clusters in networks, because they are more likely to stay within a cluster than leave it. Thus, one family of community detection algorithms uses random walks to measure distance between pairs of nodes in various ways, and then applies K-Means or other generic clustering methods to these distances. Interestingly, information processing in the brain may suggest a simpler method of learning clusters directly from random walks. Drawing inspiration from the hippocampus, we describe a simple two-layer neural learning framework. Neurons in one layer are associated with graph nodes and simulate random walks. These simulations cause neurons in the second layer to become tuned to graph clusters through simple associative learning. We show that if these neuronal interactions are modelled a particular way, the system is essentially a variant of K-Means clustering applied directly in the walk-space, bypassing the usual step of computing node distances/similarities. The result is an efficient graph clustering method. Biological information processing systems are known for high efficiency and adaptability. In tests on benchmark graphs, our framework demonstrates this high data-efficiency, low memory use, low complexity, and real-time adaptation to graph changes, while still achieving clustering quality comparable to other algorithms. | ['Artur Luczak', 'Eric Chalmers'] | 2022-05-19 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 6.64223209e-02 2.53125340e-01 1.92125365e-01 -1.59158587e-01
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a24476ab-35d6-4ab0-90df-4ac8489bd36a | large-vocabulary-audio-visual-speech | 2109.04894 | null | https://arxiv.org/abs/2109.04894v1 | https://arxiv.org/pdf/2109.04894v1.pdf | Large-vocabulary Audio-visual Speech Recognition in Noisy Environments | Audio-visual speech recognition (AVSR) can effectively and significantly improve the recognition rates of small-vocabulary systems, compared to their audio-only counterparts. For large-vocabulary systems, however, there are still many difficulties, such as unsatisfactory video recognition accuracies, that make it hard to improve over audio-only baselines. In this paper, we specifically consider such scenarios, focusing on the large-vocabulary task of the LRS2 database, where audio-only performance is far superior to video-only accuracies, making this an interesting and challenging setup for multi-modal integration. To address the inherent difficulties, we propose a new fusion strategy: a recurrent integration network is trained to fuse the state posteriors of multiple single-modality models, guided by a set of model-based and signal-based stream reliability measures. During decoding, this network is used for stream integration within a hybrid recognizer, where it can thus cope with the time-variant reliability and information content of its multiple feature inputs. We compare the results with end-to-end AVSR systems as well as with competitive hybrid baseline models, finding that the new fusion strategy shows superior results, on average even outperforming oracle dynamic stream weighting, which has so far marked the -- realistically unachievable -- upper bound for standard stream weighting. Even though the pure lipreading performance is low, audio-visual integration is helpful under all -- clean, noisy, and reverberant -- conditions. On average, the new system achieves a relative word error rate reduction of 42.18\% compared to the audio-only model, pointing at a high effectiveness of the proposed integration approach. | ['Dorothea Kolossa', 'Steffen Zeiler', 'Wentao Yu'] | 2021-09-10 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [ 4.12009865e-01 -1.71159565e-01 -6.50558993e-02 7.09798634e-02
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70153a96-e414-4d4f-90c4-c6e5f0428347 | using-word-embeddings-for-italian-crime-news | null | null | https://ieeexplore.ieee.org/document/9555723 | https://annals-csis.org/proceedings/2021/drp/pdf/118.pdf | Using Word Embeddings for Italian Crime News Categorization | Several studies have shown that the use of embeddings improves outcomes in many Natural Language Processing (NLP) activities, including text categorization. This paper focuses on how word embeddings can be used on newspaper articles related to crimes. The scope is the categorization of the news articles based on the type of crime they report. We compare different Word2Vec models and methods to obtain word embeddings. Then, we exploit both supervised and unsupervised Machine Learning categorization algorithms. Experiments were conducted on an Italian dataset of 15,361 crime news articles showing very promising results. | ['Laura Po', 'Giovanni Bonisoli', 'Federica Rollo'] | 2021-10-08 | null | null | null | conference-on-computer-science-and | ['text-categorization'] | ['natural-language-processing'] | [-3.95198971e-01 -7.82398358e-02 -5.60045123e-01 -2.64829725e-01
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4e2c32c8-1110-40c8-b7f0-ccdbac3863d1 | renovating-parsing-r-cnn-for-accurate-1 | 2009.09447 | null | https://arxiv.org/abs/2009.09447v1 | https://arxiv.org/pdf/2009.09447v1.pdf | Renovating Parsing R-CNN for Accurate Multiple Human Parsing | Multiple human parsing aims to segment various human parts and associate each part with the corresponding instance simultaneously. This is a very challenging task due to the diverse human appearance, semantic ambiguity of different body parts, and complex background. Through analysis of multiple human parsing task, we observe that human-centric global perception and accurate instance-level parsing scoring are crucial for obtaining high-quality results. But the most state-of-the-art methods have not paid enough attention to these issues. To reverse this phenomenon, we present Renovating Parsing R-CNN (RP R-CNN), which introduces a global semantic enhanced feature pyramid network and a parsing re-scoring network into the existing high-performance pipeline. The proposed RP R-CNN adopts global semantic representation to enhance multi-scale features for generating human parsing maps, and regresses a confidence score to represent its quality. Extensive experiments show that RP R-CNN performs favorably against state-of-the-art methods on CIHP and MHP-v2 datasets. Code and models are available at https://github.com/soeaver/RP-R-CNN. | ['Qing Song', 'Xueshi Xin', 'Wenhe Jia', 'Songcen Xu', 'Lu Yang', 'Chun Liu', 'Mengjie Hu', 'Zhihui Wang'] | 2020-09-20 | renovating-parsing-r-cnn-for-accurate | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1600_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570409.pdf | eccv-2020-8 | ['human-parsing'] | ['computer-vision'] | [ 2.02116415e-01 2.57225245e-01 7.69439489e-02 -6.17906928e-01
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dd535711-8a20-40c1-97fd-852b27a0bd25 | semantic-dependency-parsing-via-book | null | null | https://aclanthology.org/P17-1077 | https://aclanthology.org/P17-1077.pdf | Semantic Dependency Parsing via Book Embedding | We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing. The spine of the book is made up of a sequence of words, and each page contains a subset of noncrossing arcs. To build a semantic graph for a given sentence, we design new Maximum Subgraph algorithms to generate noncrossing graphs on each page, and a Lagrangian Relaxation-based algorithm tocombine pages into a book. Experiments demonstrate the effectiveness of the bookembedding framework across a wide range of conditions. Our parser obtains comparable results with a state-of-the-art transition-based parser. | ['Weiwei Sun', 'Junjie Cao', 'Xiaojun Wan'] | 2017-07-01 | null | null | null | acl-2017-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 6.87278137e-02 9.04332876e-01 -7.09841192e-01 -4.95178908e-01
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f012b781-7580-44f4-8ba1-b20d36378b36 | universal-adversarial-perturbations-and-image | 2103.05469 | null | https://arxiv.org/abs/2103.05469v1 | https://arxiv.org/pdf/2103.05469v1.pdf | Universal Adversarial Perturbations and Image Spam Classifiers | As the name suggests, image spam is spam email that has been embedded in an image. Image spam was developed in an effort to evade text-based filters. Modern deep learning-based classifiers perform well in detecting typical image spam that is seen in the wild. In this chapter, we evaluate numerous adversarial techniques for the purpose of attacking deep learning-based image spam classifiers. Of the techniques tested, we find that universal perturbation performs best. Using universal adversarial perturbations, we propose and analyze a new transformation-based adversarial attack that enables us to create tailored "natural perturbations" in image spam. The resulting spam images benefit from both the presence of concentrated natural features and a universal adversarial perturbation. We show that the proposed technique outperforms existing adversarial attacks in terms of accuracy reduction, computation time per example, and perturbation distance. We apply our technique to create a dataset of adversarial spam images, which can serve as a challenge dataset for future research in image spam detection. | ['Mark Stamp', 'Andy Phung'] | 2021-03-07 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 5.88045418e-01 -2.21456498e-01 4.97232944e-01 -2.39682361e-01
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715267f6-014f-4a4e-859a-cb34310f0aaa | learning-monocular-3d-human-pose-estimation | 1803.04775 | null | http://arxiv.org/abs/1803.04775v2 | http://arxiv.org/pdf/1803.04775v2.pdf | Learning Monocular 3D Human Pose Estimation from Multi-view Images | Accurate 3D human pose estimation from single images is possible with
sophisticated deep-net architectures that have been trained on very large
datasets. However, this still leaves open the problem of capturing motions for
which no such database exists. Manual annotation is tedious, slow, and
error-prone. In this paper, we propose to replace most of the annotations by
the use of multiple views, at training time only. Specifically, we train the
system to predict the same pose in all views. Such a consistency constraint is
necessary but not sufficient to predict accurate poses. We therefore complement
it with a supervised loss aiming to predict the correct pose in a small set of
labeled images, and with a regularization term that penalizes drift from
initial predictions. Furthermore, we propose a method to estimate camera pose
jointly with human pose, which lets us utilize multi-view footage where
calibration is difficult, e.g., for pan-tilt or moving handheld cameras. We
demonstrate the effectiveness of our approach on established benchmarks, as
well as on a new Ski dataset with rotating cameras and expert ski motion, for
which annotations are truly hard to obtain. | ['Mathieu Salzmann', 'Erich Müller', 'Frédéric Meyer', 'Jörg Spörri', 'Pascal Fua', 'Isinsu Katircioglu', 'Victor Constantin', 'Helge Rhodin'] | 2018-03-13 | learning-monocular-3d-human-pose-estimation-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Rhodin_Learning_Monocular_3D_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Rhodin_Learning_Monocular_3D_CVPR_2018_paper.pdf | cvpr-2018-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 1.70727316e-02 8.50450844e-02 4.38832399e-03 -3.31919432e-01
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53f4e5f0-0410-4e0c-977e-6bfaedefa073 | quantum-artificial-vision-for-defect | 2208.04988 | null | https://arxiv.org/abs/2208.04988v1 | https://arxiv.org/pdf/2208.04988v1.pdf | Quantum artificial vision for defect detection in manufacturing | In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their classical counterparts. Specifically, we consider two approaches: a quantum Support Vector Machine (QSVM) on a universal gate-based quantum computer, and QBoost on a quantum annealer. The quantum vision systems are benchmarked for an unbalanced dataset of images where the aim is to detect defects in manufactured car pieces. We see that the quantum algorithms outperform their classical counterparts in several ways, with QBoost allowing for larger problems to be analyzed with present-day quantum annealers. Data preprocessing, including dimensionality reduction and contrast enhancement, is also discussed, as well as hyperparameter tuning in QBoost. To the best of our knowledge, this is the first implementation of quantum computer vision systems for a problem of industrial relevance in a manufacturing production line. | ['Roman Orus', 'Josu Bilbao', 'Aizea Lojo', 'Ana Adell', 'Xabier De Carlos', 'Daniel Estepa', 'Samuel Mugel', 'Gianni Del Bimbo', 'Victor Onofre', 'Daniel Guijo'] | 2022-08-09 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 5.13444245e-01 1.93070117e-02 3.32517475e-01 -1.21207349e-01
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6b47c553-6e25-4056-81f8-0dbfe06608c7 | binding-actions-to-objects-in-world-models | 2204.13022 | null | https://arxiv.org/abs/2204.13022v1 | https://arxiv.org/pdf/2204.13022v1.pdf | Binding Actions to Objects in World Models | We study the problem of binding actions to objects in object-factored world models using action-attention mechanisms. We propose two attention mechanisms for binding actions to objects, soft attention and hard attention, which we evaluate in the context of structured world models for five environments. Our experiments show that hard attention helps contrastively-trained structured world models to learn to separate individual objects in an object-based grid-world environment. Further, we show that soft attention increases performance of factored world models trained on a robotic manipulation task. The learned action attention weights can be used to interpret the factored world model as the attention focuses on the manipulated object in the environment. | ['Thomas Kipf', 'Lawson L. S. Wong', 'Jan-Willem van de Meent', 'Robert Platt', 'Ondrej Biza'] | 2022-04-27 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 1.45880565e-01 5.01440585e-01 -1.77366421e-01 -1.38703227e-01
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b95b21f3-e8bb-4852-9f3b-9b876e74ea99 | marrnet-3d-shape-reconstruction-via-25d | 1711.03129 | null | http://arxiv.org/abs/1711.03129v1 | http://arxiv.org/pdf/1711.03129v1.pdf | MarrNet: 3D Shape Reconstruction via 2.5D Sketches | 3D object reconstruction from a single image is a highly under-determined
problem, requiring strong prior knowledge of plausible 3D shapes. This
introduces challenges for learning-based approaches, as 3D object annotations
are scarce in real images. Previous work chose to train on synthetic data with
ground truth 3D information, but suffered from domain adaptation when tested on
real data. In this work, we propose MarrNet, an end-to-end trainable model that
sequentially estimates 2.5D sketches and 3D object shape. Our disentangled,
two-step formulation has three advantages. First, compared to full 3D shape,
2.5D sketches are much easier to be recovered from a 2D image; models that
recover 2.5D sketches are also more likely to transfer from synthetic to real
data. Second, for 3D reconstruction from 2.5D sketches, systems can learn
purely from synthetic data. This is because we can easily render realistic 2.5D
sketches without modeling object appearance variations in real images,
including lighting, texture, etc. This further relieves the domain adaptation
problem. Third, we derive differentiable projective functions from 3D shape to
2.5D sketches; the framework is therefore end-to-end trainable on real images,
requiring no human annotations. Our model achieves state-of-the-art performance
on 3D shape reconstruction. | ['William T. Freeman', 'Joshua B. Tenenbaum', 'Jiajun Wu', 'Yifan Wang', 'Xingyuan Sun', 'Tianfan Xue'] | 2017-11-08 | marrnet-3d-shape-reconstruction-via-25d-1 | http://papers.nips.cc/paper/6657-marrnet-3d-shape-reconstruction-via-25d-sketches | http://papers.nips.cc/paper/6657-marrnet-3d-shape-reconstruction-via-25d-sketches.pdf | neurips-2017-12 | ['3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image'] | ['computer-vision', 'computer-vision'] | [ 3.63723710e-02 2.61601925e-01 -6.43897057e-02 -2.53566921e-01
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3d1ad776-41d9-4df4-a5f5-f7b9501a882a | a-reminder-of-its-brittleness-language-reward | 2305.16621 | null | https://arxiv.org/abs/2305.16621v1 | https://arxiv.org/pdf/2305.16621v1.pdf | A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following Agents | Teaching agents to follow complex written instructions has been an important yet elusive goal. One technique for improving learning efficiency is language reward shaping (LRS), which is used in reinforcement learning (RL) to reward actions that represent progress towards a sparse reward. We argue that the apparent success of LRS is brittle, and prior positive findings can be attributed to weak RL baselines. Specifically, we identified suboptimal LRS designs that reward partially matched trajectories, and we characterised a novel type of reward perturbation that addresses this issue based on the concept of loosening task constraints. We provided theoretical and empirical evidence that agents trained using LRS rewards converge more slowly compared to pure RL agents. | ['Trevor Cohn', 'Nir Lipovetzky', 'Sukai Huang'] | 2023-05-26 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 5.06526046e-02 2.26903707e-01 -3.39771479e-01 7.56672323e-02
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80d54261-eddc-493d-b08d-916f10f1b02b | autoextend-combining-word-embeddings-with | null | null | https://aclanthology.org/J17-3004 | https://aclanthology.org/J17-3004.pdf | AutoExtend: Combining Word Embeddings with Semantic Resources | We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The obtained embeddings live in the same vector space as the input word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet, GermaNet, and Freebase as semantic resources. AutoExtend achieves state-of-the-art performance on Word-in-Context Similarity and Word Sense Disambiguation tasks. | ['Hinrich Sch{\\"u}tze', 'Sascha Rothe'] | 2017-09-01 | null | null | null | cl-2017-9 | ['learning-word-embeddings'] | ['methodology'] | [-6.06940985e-01 -8.56212899e-02 -5.87737620e-01 -2.43791863e-01
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24d739f2-ccfc-411e-819a-aa06d986cefe | the-effects-of-varying-penetration-rates-of | 2306.01177 | null | https://arxiv.org/abs/2306.01177v1 | https://arxiv.org/pdf/2306.01177v1.pdf | The Effects of Varying Penetration Rates of L4-L5 Autonomous Vehicles on Fuel Efficiency and Mobility of Traffic Networks | Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the fuel usage and mobility effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both autonomous and non-autonomous vehicles. In this paper, L4-L5 AVs with varying penetration rates in total traffic flow were simulated using the microscopic traffic simulator Vissim on urban, mixed and freeway roadways. The roadways used in these simulations were replicas of real roadways in and around Columbus, Ohio, including an AV shuttle routes in operation. The road-specific information regarding each roadway, such as the number of traffic lights and positions, number of STOP signs and positions, and speed limits, were gathered using OpenStreetMap with SUMO. In simulating L4-L5 AVs, the All-Knowing CoEXist AV and a vehicle with Wiedemann 74 driver were taken to represent AV and non-AV driving, respectively. Then, the driving behaviors, such as headway time and car following, desired acceleration and deceleration profiles of AV, and non-AV car following and lane change models were modified. The effect of having varying penetration rates of L4-L5 AVs were then evaluated using criteria such as average fuel consumption, existence of queues and their average/maximum length, total number of vehicles in the simulation, average delay experience by all vehicles, total number of stops experienced by all vehicles, and total emission of CO, NOx and volatile organic compounds (VOC) from the vehicles in the simulation. The results show that while increasing penetration rates of L4-L5 AVs generally improve overall fuel efficiency and mobility of the traffic network, there were also cases when the opposite trend was observed. | ['Levent Guvenc', 'Bilin Aksun-Guvenc', 'Karina Meneses Cime', 'M. Ridvan Cantas', 'Ozgenur Kavas-Torris'] | 2023-06-01 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-6.75327241e-01 -1.29048869e-01 -8.94762874e-02 7.71728680e-02
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cf2b8366-39c6-4309-9bbb-f92e71861eb4 | aspect-based-sentiment-analysis-through-edu | 2202.02535 | null | https://arxiv.org/abs/2202.02535v1 | https://arxiv.org/pdf/2202.02535v1.pdf | Aspect-based Sentiment Analysis through EDU-level Attentions | A sentence may express sentiments on multiple aspects. When these aspects are associated with different sentiment polarities, a model's accuracy is often adversely affected. We observe that multiple aspects in such hard sentences are mostly expressed through multiple clauses, or formally known as elementary discourse units (EDUs), and one EDU tends to express a single aspect with unitary sentiment towards that aspect. In this paper, we propose to consider EDU boundaries in sentence modeling, with attentions at both word and EDU levels. Specifically, we highlight sentiment-bearing words in EDU through word-level sparse attention. Then at EDU level, we force the model to attend to the right EDU for the right aspect, by using EDU-level sparse attention and orthogonal regularization. Experiments on three benchmark datasets show that our simple EDU-Attention model outperforms state-of-the-art baselines. Because EDU can be automatically segmented with high accuracy, our model can be applied to sentences directly without the need of manual EDU boundary annotation. | ['Yequan Wang', 'Aixin Sun', 'Ting Lin'] | 2022-02-05 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 2.53612906e-01 5.30441046e-01 -5.58610320e-01 -6.58378303e-01
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3429d433-2538-4a83-8678-0316f2ed6ef7 | towards-end-to-end-text-spotting-with | 1707.03985 | null | http://arxiv.org/abs/1707.03985v1 | http://arxiv.org/pdf/1707.03985v1.pdf | Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks | In this work, we jointly address the problem of text detection and
recognition in natural scene images based on convolutional recurrent neural
networks. We propose a unified network that simultaneously localizes and
recognizes text with a single forward pass, avoiding intermediate processes
like image cropping and feature re-calculation, word separation, or character
grouping. In contrast to existing approaches that consider text detection and
recognition as two distinct tasks and tackle them one by one, the proposed
framework settles these two tasks concurrently. The whole framework can be
trained end-to-end, requiring only images, the ground-truth bounding boxes and
text labels. Through end-to-end training, the learned features can be more
informative, which improves the overall performance. The convolutional features
are calculated only once and shared by both detection and recognition, which
saves processing time. Our proposed method has achieved competitive performance
on several benchmark datasets. | ['Chunhua Shen', 'Peng Wang', 'Hui Li'] | 2017-07-13 | towards-end-to-end-text-spotting-with-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Li_Towards_End-To-End_Text_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Towards_End-To-End_Text_ICCV_2017_paper.pdf | iccv-2017-10 | ['text-spotting', 'image-cropping'] | ['computer-vision', 'computer-vision'] | [ 6.42599106e-01 -4.51541603e-01 -8.79501458e-03 -2.97688365e-01
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5f34576a-71f1-4149-bf06-810ffe0b334c | logic-and-commonsense-guided-temporal | 2211.16865 | null | https://arxiv.org/abs/2211.16865v2 | https://arxiv.org/pdf/2211.16865v2.pdf | Logic and Commonsense-Guided Temporal Knowledge Graph Completion | A temporal knowledge graph (TKG) stores the events derived from the data involving time. Predicting events is extremely challenging due to the time-sensitive property of events. Besides, the previous TKG completion (TKGC) approaches cannot represent both the timeliness and the causality properties of events, simultaneously. To address these challenges, we propose a Logic and Commonsense-Guided Embedding model (LCGE) to jointly learn the time-sensitive representation involving timeliness and causality of events, together with the time-independent representation of events from the perspective of commonsense. Specifically, we design a temporal rule learning algorithm to construct a rule-guided predicate embedding regularization strategy for learning the causality among events. Furthermore, we could accurately evaluate the plausibility of events via auxiliary commonsense knowledge. The experimental results of TKGC task illustrate the significant performance improvements of our model compared with the existing approaches. More interestingly, our model is able to provide the explainability of the predicted results in the view of causal inference. The source code and datasets of this paper are available at https://github.com/ngl567/LCGE. | ['Bo Li', 'Guanglin Niu'] | 2022-11-30 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-9.10369605e-02 6.85945675e-02 -4.56021219e-01 -4.56768036e-01
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bbade855-97fb-477d-b307-26da749d1230 | tracking-based-semi-automatic-annotation-for | 2103.15488 | null | https://arxiv.org/abs/2103.15488v1 | https://arxiv.org/pdf/2103.15488v1.pdf | Tracking Based Semi-Automatic Annotation for Scene Text Videos | Recently, video scene text detection has received increasing attention due to its comprehensive applications. However, the lack of annotated scene text video datasets has become one of the most important problems, which hinders the development of video scene text detection. The existing scene text video datasets are not large-scale due to the expensive cost caused by manual labeling. In addition, the text instances in these datasets are too clear to be a challenge. To address the above issues, we propose a tracking based semi-automatic labeling strategy for scene text videos in this paper. We get semi-automatic scene text annotation by labeling manually for the first frame and tracking automatically for the subsequent frames, which avoid the huge cost of manual labeling. Moreover, a paired low-quality scene text video dataset named Text-RBL is proposed, consisting of raw videos, blurry videos, and low-resolution videos, labeled by the proposed convenient semi-automatic labeling strategy. Through an averaging operation and bicubic down-sampling operation over the raw videos, we can efficiently obtain blurry videos and low-resolution videos paired with raw videos separately. To verify the effectiveness of Text-RBL, we propose a baseline model combined with the text detector and tracker for video scene text detection. Moreover, a failure detection scheme is designed to alleviate the baseline model drift issue caused by complex scenes. Extensive experiments demonstrate that Text-RBL with paired low-quality videos labeled by the semi-automatic method can significantly improve the performance of the text detector in low-quality scenes. | ['Shan Cao', 'Shugong Xu', 'Zhiwei Jia', 'Xiufeng Jiang', 'Jiajun Zhu'] | 2021-03-29 | null | null | null | null | ['scene-text-detection', 'text-annotation'] | ['computer-vision', 'natural-language-processing'] | [ 3.04299176e-01 -7.79970825e-01 1.20355271e-01 -8.83581638e-02
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1d761c2f-3085-4823-b00b-f0f5db62dba1 | a-generalized-framework-for-critical-heat | 2212.09107 | null | https://arxiv.org/abs/2212.09107v3 | https://arxiv.org/pdf/2212.09107v3.pdf | A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation | The detection of critical heat flux (CHF) is crucial in heat boiling applications as failure to do so can cause rapid temperature ramp leading to device failures. Many machine learning models exist to detect CHF, but their performance reduces significantly when tested on data from different domains. To deal with datasets from new domains a model needs to be trained from scratch. Moreover, the dataset needs to be annotated by a domain expert. To address this issue, we propose a new framework to support the generalizability and adaptability of trained CHF detection models in an unsupervised manner. This approach uses an unsupervised Image-to-Image (UI2I) translation model to transform images in the target dataset to look like they were obtained from the same domain the model previously trained on. Unlike other frameworks dealing with domain shift, our framework does not require retraining or fine-tuning of the trained classification model nor does it require synthesized datasets in the training process of either the classification model or the UI2I model. The framework was tested on three boiling datasets from different domains, and we show that the CHF detection model trained on one dataset was able to generalize to the other two previously unseen datasets with high accuracy. Overall, the framework enables CHF detection models to adapt to data generated from different domains without requiring additional annotation effort or retraining of the model. | ['Md Mahfuzur Rahman Siddiquee', 'Tejaswi Soori', 'Ying Sun', 'Teresa Wu', 'Hyunsoo Yoon', 'Han Hu', 'Firas Al-Hindawi'] | 2022-12-18 | null | null | null | null | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 6.51446521e-01 3.78359668e-02 2.48088259e-02 -5.72423518e-01
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0abb447c-eb91-4de7-858f-aa1d9504f6a6 | enlarged-large-margin-loss-for-imbalanced | 2306.09132 | null | https://arxiv.org/abs/2306.09132v1 | https://arxiv.org/pdf/2306.09132v1.pdf | Enlarged Large Margin Loss for Imbalanced Classification | We propose a novel loss function for imbalanced classification. LDAM loss, which minimizes a margin-based generalization bound, is widely utilized for class-imbalanced image classification. Although, by using LDAM loss, it is possible to obtain large margins for the minority classes and small margins for the majority classes, the relevance to a large margin, which is included in the original softmax cross entropy loss, is not be clarified yet. In this study, we reconvert the formula of LDAM loss using the concept of the large margin softmax cross entropy loss based on the softplus function and confirm that LDAM loss includes a wider large margin than softmax cross entropy loss. Furthermore, we propose a novel Enlarged Large Margin (ELM) loss, which can further widen the large margin of LDAM loss. ELM loss utilizes the large margin for the maximum logit of the incorrect class in addition to the basic margin used in LDAM loss. Through experiments conducted on imbalanced CIFAR datasets and large-scale datasets with long-tailed distribution, we confirmed that classification accuracy was much improved compared with LDAM loss and conventional losses for imbalanced classification. | ['Kazuhiro Hotta', 'Sota Kato'] | 2023-06-15 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-1.42550528e-01 1.05539240e-01 -5.39786279e-01 -7.94789314e-01
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bb0fbe56-b814-4b1d-a77b-35479cfcd507 | a-survey-on-energy-optimization-techniques-in | 2204.07967 | null | https://arxiv.org/abs/2204.07967v1 | https://arxiv.org/pdf/2204.07967v1.pdf | A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches | Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature. | ['Muhammad Ali Imran', 'Sajjad Hussain', 'Qammer H. Abbasi', 'Michael S. Mollel', 'Ali Makine Abdel-Salam', 'Cihat Ozturk', 'Metin Ozturk', 'Kenechi G. Omeke', 'Iftikhar Ahmad', 'Attai Ibrahim Abubakar'] | 2022-04-17 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.75762826e-01 -9.71529484e-02 -3.47502470e-01 1.97643027e-01
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a4df6370-3954-4a14-9806-2cf5cf431ba8 | coarse-to-fine-entity-representations-for | 2012.02507 | null | https://arxiv.org/abs/2012.02507v2 | https://arxiv.org/pdf/2012.02507v2.pdf | Coarse-to-Fine Entity Representations for Document-level Relation Extraction | Document-level Relation Extraction (RE) requires extracting relations expressed within and across sentences. Recent works show that graph-based methods, usually constructing a document-level graph that captures document-aware interactions, can obtain useful entity representations thus helping tackle document-level RE. These methods either focus more on the entire graph, or pay more attention to a part of the graph, e.g., paths between the target entity pair. However, we find that document-level RE may benefit from focusing on both of them simultaneously. Therefore, to obtain more comprehensive entity representations, we propose the Coarse-to-Fine Entity Representation model (CFER) that adopts a coarse-to-fine strategy involving two phases. First, CFER uses graph neural networks to integrate global information in the entire graph at a coarse level. Next, CFER utilizes the global information as a guidance to selectively aggregate path information between the target entity pair at a fine level. In classification, we combine the entity representations from both two levels into more comprehensive representations for relation extraction. Experimental results on two document-level RE datasets, DocRED and CDR, show that CFER outperforms existing models and is robust to the uneven label distribution. | ['Zhifang Sui', 'Baobao Chang', 'Shuang Zeng', 'Jing Ren', 'Damai Dai'] | 2020-12-04 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.02699675e-01 3.20409149e-01 -5.11728764e-01 -1.84839353e-01
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24abb3d1-8383-458a-8ec2-81a8dffa19db | last-at-semeval-2021-task-1-improving-multi | 2105.09653 | null | https://arxiv.org/abs/2105.09653v1 | https://arxiv.org/pdf/2105.09653v1.pdf | LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures | This paper describes the system developed by the Laboratoire d'analyse statistique des textes (LAST) for the Lexical Complexity Prediction shared task at SemEval-2021. The proposed system is made up of a LightGBM model fed with features obtained from many word frequency lists, published lexical norms and psychometric data. For tackling the specificity of the multi-word task, it uses bigram association measures. Despite that the only contextual feature used was sentence length, the system achieved an honorable performance in the multi-word task, but poorer in the single word task. The bigram association measures were found useful, but to a limited extent. | ['Yves Bestgen'] | 2021-05-20 | null | https://aclanthology.org/2021.semeval-1.71 | https://aclanthology.org/2021.semeval-1.71.pdf | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-1.99110627e-01 -6.37074411e-02 -2.88629174e-01 -3.10740322e-01
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f8fd7a2b-5d61-426b-926c-9b5a52778e43 | lipschitz-normalization-for-self-attention | 2103.04886 | null | https://arxiv.org/abs/2103.04886v3 | https://arxiv.org/pdf/2103.04886v3.pdf | Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks | Attention based neural networks are state of the art in a large range of applications. However, their performance tends to degrade when the number of layers increases. In this work, we show that enforcing Lipschitz continuity by normalizing the attention scores can significantly improve the performance of deep attention models. First, we show that, for deep graph attention networks (GAT), gradient explosion appears during training, leading to poor performance of gradient-based training algorithms. To address this issue, we derive a theoretical analysis of the Lipschitz continuity of attention modules and introduce LipschitzNorm, a simple and parameter-free normalization for self-attention mechanisms that enforces the model to be Lipschitz continuous. We then apply LipschitzNorm to GAT and Graph Transformers and show that their performance is substantially improved in the deep setting (10 to 30 layers). More specifically, we show that a deep GAT model with LipschitzNorm achieves state of the art results for node label prediction tasks that exhibit long-range dependencies, while showing consistent improvements over their unnormalized counterparts in benchmark node classification tasks. | ['Aladin Virmaux', 'Kevin Scaman', 'George Dasoulas'] | 2021-03-08 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-6.12744242e-02 4.56223696e-01 -3.54760557e-01 -5.43591678e-01
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47f764d7-d62a-436a-a51b-b4d63861e4dc | noisy-positive-unlabeled-learning-with-self | 2306.07512 | null | https://arxiv.org/abs/2306.07512v1 | https://arxiv.org/pdf/2306.07512v1.pdf | Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning | This paper studies speculative reasoning task on real-world knowledge graphs (KG) that contain both \textit{false negative issue} (i.e., potential true facts being excluded) and \textit{false positive issue} (i.e., unreliable or outdated facts being included). State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues. The new reasoning task is formulated as a noisy Positive-Unlabeled learning problem. We propose a variational framework, namely nPUGraph, that jointly estimates the correctness of both collected and uncollected facts (which we call \textit{label posterior}) and updates model parameters during training. The label posterior estimation facilitates speculative reasoning from two perspectives. First, it improves the robustness of a label posterior-aware graph encoder against false positive links. Second, it identifies missing facts to provide high-quality grounds of reasoning. They are unified in a simple yet effective self-training procedure. Empirically, extensive experiments on three benchmark KG and one Twitter dataset with various degrees of false negative/positive cases demonstrate the effectiveness of nPUGraph. | ['Tarek F. Abdelzaher', 'Hanghang Tong', 'Shengzhong Liu', 'Yuchen Yan', 'Jinning Li', 'Dachun Sun', 'Yichen Lu', 'Baoyu Li', 'Ruijie Wang'] | 2023-06-13 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-1.71503481e-02 9.67454612e-01 -7.41704404e-01 -3.83860111e-01
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9be4e9cc-6de9-4ae9-af42-8ef3e09d7c27 | adaptive-low-precision-training-for | 2212.05735 | null | https://arxiv.org/abs/2212.05735v1 | https://arxiv.org/pdf/2212.05735v1.pdf | Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction | Embedding tables are usually huge in click-through rate (CTR) prediction models. To train and deploy the CTR models efficiently and economically, it is necessary to compress their embedding tables at the training stage. To this end, we formulate a novel quantization training paradigm to compress the embeddings from the training stage, termed low-precision training (LPT). Also, we provide theoretical analysis on its convergence. The results show that stochastic weight quantization has a faster convergence rate and a smaller convergence error than deterministic weight quantization in LPT. Further, to reduce the accuracy degradation, we propose adaptive low-precision training (ALPT) that learns the step size (i.e., the quantization resolution) through gradient descent. Experiments on two real-world datasets confirm our analysis and show that ALPT can significantly improve the prediction accuracy, especially at extremely low bit widths. For the first time in CTR models, we successfully train 8-bit embeddings without sacrificing prediction accuracy. The code of ALPT is publicly available. | ['Ruixuan Li', 'Rui Zhang', 'Ruiming Tang', 'Xing Tang', 'Wei zhang', 'Lu Hou', 'Huifeng Guo', 'Shiwei Li'] | 2022-12-12 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-5.25480621e-02 -3.72185290e-01 -7.23599434e-01 -2.34708369e-01
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2b1f4162-1a4a-44cf-be24-9d70158e2543 | uncertainty-aware-reward-based-deep | 2302.10195 | null | https://arxiv.org/abs/2302.10195v1 | https://arxiv.org/pdf/2302.10195v1.pdf | Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information | Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessarily true based on social science studies. Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches. To this end, we propose an intent classification framework that can best identify the correct intent of fake news. We will leverage deep reinforcement learning (DRL) that can optimize the structural representation of each tweet by removing noisy words from the input sequence when appending an actor to the long short-term memory (LSTM) intent classifier. Policy gradient DRL model (e.g., REINFORCE) can lead the actor to a higher delayed reward. We also devise a new uncertainty-aware immediate reward using a subjective opinion that can explicitly deal with multidimensional uncertainty for effective decision-making. Via 600K training episodes from a fake news tweets dataset with an annotated intent class, we evaluate the performance of uncertainty-aware reward in DRL. Evaluation results demonstrate that our proposed framework efficiently reduces the number of selected words to maintain a high 95\% multi-class accuracy. | ['Jin-Hee Cho', 'Dong H. Jeong', 'Feng Chen', 'Lance M. Kaplan', 'Audun Jøsang', 'Qisheng Zhang', 'Xinwei An', 'Qi Zhang', 'Zhen Guo'] | 2023-02-19 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [-1.00632265e-01 8.00118148e-02 -5.90889037e-01 -4.94104207e-01
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9f8e0bfa-8591-45a3-9cb4-c1886764b4ff | leveraging-human-computation-for-quality | null | null | https://epub.ub.uni-muenchen.de/91046/ | https://epub.ub.uni-muenchen.de/91046/1/BA_Johannah.Sprinz.pdf | Leveraging Human Computation for Quality Assurance in Open Source Communities | Software developed under the open source development model (OSSD) has risen to significant importance over the recent decades. With more and more critical components being developed under the OSSD, the need for extensive quality assurance (QA) increases. This thesis investigates any potential for conducting formalized user testing through inexperienced volunteer community members under the OSSD. A human computation platform to aggregate such test results was designed and named open crowdsourced user-testing suite (OPEN-CUTS). A usability study of a prototype of OPEN-CUTS confirms the viability of this approach and points to potential future research questions. | ['Johannah Sprinz'] | 2022-01-29 | null | null | null | open-access-lmu-2022-1 | ['manufacturing-quality-control'] | ['computer-vision'] | [-7.18861282e-01 4.14148629e-01 3.73267263e-01 -4.00129586e-01
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f26dd1d9-5cf0-4270-b217-909bbed4c2f1 | from-shortcuts-to-triggers-backdoor-defense | 2305.14910 | null | https://arxiv.org/abs/2305.14910v1 | https://arxiv.org/pdf/2305.14910v1.pdf | From Shortcuts to Triggers: Backdoor Defense with Denoised PoE | Language models are often at risk of diverse backdoor attacks, especially data poisoning. Thus, it is important to investigate defense solutions for addressing them. Existing backdoor defense methods mainly focus on backdoor attacks with explicit triggers, leaving a universal defense against various backdoor attacks with diverse triggers largely unexplored. In this paper, we propose an end-to-end ensemble-based backdoor defense framework, DPoE (Denoised Product-of-Experts), which is inspired by the shortcut nature of backdoor attacks, to defend various backdoor attacks. DPoE consists of two models: a shallow model that captures the backdoor shortcuts and a main model that is prevented from learning the backdoor shortcuts. To address the label flip caused by backdoor attackers, DPoE incorporates a denoising design. Experiments on SST-2 dataset show that DPoE significantly improves the defense performance against various types of backdoor triggers including word-level, sentence-level, and syntactic triggers. Furthermore, DPoE is also effective under a more challenging but practical setting that mixes multiple types of trigger. | ['Muhao Chen', 'Chaowei Xiao', 'Fei Wang', 'Qin Liu'] | 2023-05-24 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-7.68796653e-02 -4.46771681e-01 -2.60979265e-01 -1.43195182e-01
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a48de1d3-5b98-412a-b82a-971d45968027 | twitter-user-geolocation-using-deep-multiview | 1805.04612 | null | http://arxiv.org/abs/1805.04612v1 | http://arxiv.org/pdf/1805.04612v1.pdf | Twitter User Geolocation using Deep Multiview Learning | Predicting the geographical location of users on social networks like Twitter
is an active research topic with plenty of methods proposed so far. Most of the
existing work follows either a content-based or a network-based approach. The
former is based on user-generated content while the latter exploits the
structure of the network of users. In this paper, we propose a more generic
approach, which incorporates not only both content-based and network-based
features, but also other available information into a unified model. Our
approach, named Multi-Entry Neural Network (MENET), leverages the latest
advances in deep learning and multiview learning. A realization of MENET with
textual, network and metadata features results in an effective method for
Twitter user geolocation, achieving the state of the art on two well-known
datasets. | ['Nikos Deligiannis', 'Bruno Cornelis', 'Tien Huu Do', 'Duc Minh Nguyen', 'Evaggelia Tsiligianni'] | 2018-05-11 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [-4.06034887e-01 -2.32419103e-01 -5.65524936e-01 -3.57746422e-01
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8de7fedf-b717-43b5-a366-d8831dab4089 | wizardlm-empowering-large-language-models-to | 2304.12244 | null | https://arxiv.org/abs/2304.12244v2 | https://arxiv.org/pdf/2304.12244v2.pdf | WizardLM: Empowering Large Language Models to Follow Complex Instructions | Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans. Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM. Human evaluations on a complexity-balanced test bed and Vicuna's testset show that instructions from Evol-Instruct are superior to human-created ones. By analyzing the human evaluation results of the high complexity part, we demonstrate that outputs from our WizardLM are preferred to outputs from OpenAI ChatGPT. In GPT-4 automatic evaluation, WizardLM achieves more than 90\% capacity of ChatGPT on 17 out of 29 skills. Even though WizardLM still lags behind ChatGPT in some aspects, our findings suggest that fine-tuning with AI-evolved instructions is a promising direction for enhancing LLMs. Our code and data are public at https://github.com/nlpxucan/WizardLM | ['Daxin Jiang', 'Chongyang Tao', 'Jiazhan Feng', 'Pu Zhao', 'Xiubo Geng', 'Kai Zheng', 'Qingfeng Sun', 'Can Xu'] | 2023-04-24 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 5.53712286e-02 1.54012769e-01 1.26054063e-01 -3.99049491e-01
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75416c51-ecad-4b40-b672-3a1ad9f12b31 | synchronous-image-label-diffusion-probability | 2307.01740 | null | https://arxiv.org/abs/2307.01740v1 | https://arxiv.org/pdf/2307.01740v1.pdf | Synchronous Image-Label Diffusion Probability Model with Application to Stroke Lesion Segmentation on Non-contrast CT | Stroke lesion volume is a key radiologic measurement for assessing the prognosis of Acute Ischemic Stroke (AIS) patients, which is challenging to be automatically measured on Non-Contrast CT (NCCT) scans. Recent diffusion probabilistic models have shown potentials of being used for image segmentation. In this paper, a novel Synchronous image-label Diffusion Probability Model (SDPM) is proposed for stroke lesion segmentation on NCCT using Markov diffusion process. The proposed SDPM is fully based on a Latent Variable Model (LVM), offering a complete probabilistic elaboration. An additional net-stream, parallel with a noise prediction stream, is introduced to obtain initial noisy label estimates for efficiently inferring the final labels. By optimizing the specified variational boundaries, the trained model can infer multiple label estimates for reference given the input images with noises. The proposed model was assessed on three stroke lesion datasets including one public and two private datasets. Compared to several U-net and transformer-based segmentation methods, our proposed SDPM model is able to achieve state-of-the-art performance. The code is publicly available. | ['Qiu Wu', 'Aravind Ganesh', 'Ethan MacDonald', 'Tonghua Wan', 'Jianhai Zhang'] | 2023-07-04 | null | null | null | null | ['lesion-segmentation'] | ['medical'] | [ 3.27507257e-01 6.22183690e-03 -3.93112659e-01 -5.68740487e-01
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5382d7db-8421-4ab9-95cd-43d56ab2279a | can-we-still-use-peaq-a-performance-analysis | 2212.01467 | null | https://arxiv.org/abs/2212.01467v1 | https://arxiv.org/pdf/2212.01467v1.pdf | Can we still use PEAQ? A Performance Analysis of the ITU Standard for the Objective Assessment of Perceived Audio Quality | The Perceptual Evaluation of Audio Quality (PEAQ) method as described in the International Telecommunication Union (ITU) recommendation ITU-R BS.1387 has been widely used for computationally estimating the quality of perceptually coded audio signals without the need for extensive subjective listening tests. However, many reports have highlighted clear limitations of the scheme after the end of its standardization, particularly involving signals coded with newer technologies such as bandwidth extension or parametric multi-channel coding. Until now, no other method for measuring the quality of both speech and audio signals has been standardized by the ITU. Therefore, a further investigation of the causes for these limitations would be beneficial to a possible update of said scheme. Our experimental results indicate that the performance of PEAQ's model of disturbance loudness is still as good as (and sometimes superior to) other state-of-the-art objective measures, albeit with varying performance depending on the type of degraded signal content (i.e. speech or music). This finding evidences the need for an improved cognitive model. In addition, results indicate that an updated mapping of Model Output Values (MOVs) to PEAQ's Distortion Index (DI) based on newer training data can greatly improve performance. Finally, some suggestions for the improvement of PEAQ are provided based on the reported results and comparison to other systems. | ['Jürgen Herre', 'Pablo M. Delgado'] | 2022-12-02 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 6.66167438e-02 -3.67691368e-01 1.99986905e-01 -1.57645375e-01
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94974dd3-58db-405b-a7c3-f2bebb06e690 | minirbt-a-two-stage-distilled-small-chinese | 2304.00717 | null | https://arxiv.org/abs/2304.00717v1 | https://arxiv.org/pdf/2304.00717v1.pdf | MiniRBT: A Two-stage Distilled Small Chinese Pre-trained Model | In natural language processing, pre-trained language models have become essential infrastructures. However, these models often suffer from issues such as large size, long inference time, and challenging deployment. Moreover, most mainstream pre-trained models focus on English, and there are insufficient studies on small Chinese pre-trained models. In this paper, we introduce MiniRBT, a small Chinese pre-trained model that aims to advance research in Chinese natural language processing. MiniRBT employs a narrow and deep student model and incorporates whole word masking and two-stage distillation during pre-training to make it well-suited for most downstream tasks. Our experiments on machine reading comprehension and text classification tasks reveal that MiniRBT achieves 94% performance relative to RoBERTa, while providing a 6.8x speedup, demonstrating its effectiveness and efficiency. | ['Shijin Wang', 'Yiming Cui', 'Ziqing Yang', 'Xin Yao'] | 2023-04-03 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.55302489e-01 -2.03938425e-01 -1.95413485e-01 -4.67675298e-01
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762e3ce4-23cb-4d2c-919b-d3712c02c3bc | 190411093 | 1904.11093 | null | http://arxiv.org/abs/1904.11093v1 | http://arxiv.org/pdf/1904.11093v1.pdf | Deep Sparse Representation-based Classification | We present a transductive deep learning-based formulation for the sparse
representation-based classification (SRC) method. The proposed network consists
of a convolutional autoencoder along with a fully-connected layer. The role of
the autoencoder network is to learn robust deep features for classification. On
the other hand, the fully-connected layer, which is placed in between the
encoder and the decoder networks, is responsible for finding the sparse
representation. The estimated sparse codes are then used for classification.
Various experiments on three different datasets show that the proposed network
leads to sparse representations that give better classification results than
state-of-the-art SRC methods. The source code is available at:
github.com/mahdiabavisani/DSRC. | ['Vishal M. Patel', 'Mahdi Abavisani'] | 2019-04-24 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [-1.85999855e-01 9.34028029e-02 -4.13605928e-01 -3.70001763e-01
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96e184f1-711e-448b-9715-827dd50d8dd2 | an-efficient-short-time-discrete-cosine | null | null | https://ieeexplore.ieee.org/document/9947051?source=authoralert | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9947051 | An Efficient Short-Time Discrete Cosine Transform and Attentive MultiResUNet Framework for Music Source Separation | The music source separation problem, where the task at hand is to estimate the audio components that are present in a mixture, has been at the centre of research activity for a long time. In more recent frameworks, the problem is tackled by creating deep learning models, which attempt to extract information from each component by using Short-Time Fourier Transform (STFT) spectrograms as input. Most approaches assume that one source is present at each time-frequency point, which allows to allocate this point from the mixture to the desired source. Since this assumption is strong and is reported not to hold in practice, there is a problem that arises from the use of the magnitude of the STFT as input to these networks, which is the absence of the Fourier phase information during the separated source reconstruction. The recovery of the Fourier phase information is neither easily tractable, nor computationally efficient to estimate. In this paper, we propose a novel Attentive MultiResUNet architecture, that uses real-valued Short-Time Discrete Cosine Transform data as inputs. This step avoids the phase recovery problem, by estimating the appropriate values within the network itself, rather than employing complex estimation or post-processing algorithms. The proposed novel network features a U-Net type structure with residual skip connections and an attention mechanism that correlates the skip connection and the decoder output at the previous level. The proposed network is used for the first time in source separation and is more computationally efficient than state-of-the-art separation networks and features favourable performance compared to the state-of-the-art with a fraction of the computational cost. | ['N. Mitianoudis', 'A. Bousis', 'T. Sgouros'] | 2022-11-14 | null | null | null | ieee-access-2022-11 | ['music-source-separation'] | ['music'] | [ 4.25422698e-01 -1.02255508e-01 -1.28302234e-03 6.22037463e-02
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7bf8e16a-6fdc-47c2-b65f-51b8c2aa2974 | reinforced-labels-multi-agent-deep | 2303.01388 | null | https://arxiv.org/abs/2303.01388v1 | https://arxiv.org/pdf/2303.01388v1.pdf | Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-feature Label Placement | Over the past few years, Reinforcement Learning combined with Deep Learning techniques has successfully proven to solve complex problems in various domains including robotics, self-driving cars, finance, and gaming. In this paper, we are introducing Reinforcement Learning (RL) to another domain - visualization. Our novel point-feature label placement method utilizes Multi-Agent Deep Reinforcement Learning (MADRL) to learn label placement strategy, which is the first machine-learning-driven labeling method in contrast to existing hand-crafted algorithms designed by human experts. To facilitate the RL learning paradigm, we developed an environment where an agent acts as a proxy for a label, a short textual annotation that augments visualizations like geographical maps, illustrations, and technical drawings. Our results demonstrate that the strategy trained by our method significantly outperforms the random strategy of an untrained agent and also performs superior to the compared methods designed by human experts in terms of completeness (i.e., the number of placed labels). The trade-off is increased computation time, making the proposed method slower than compared methods. Nevertheless, our method is ideal for situations where the labeling can be computed in advance, and completeness is essential, such as cartographic maps, technical drawings, and medical atlases. Additionally, we conducted a user study to assess the perceived performance. The outcomes revealed that the participants considered the proposed method to be significantly better than the other examined methods. This indicates that the improved completeness is not just reflected in the quantitative metrics but also in the subjective evaluation of the participants. | ['Martin Čadík', 'Ladislav Čmolík', 'Petr Bobák'] | 2023-03-02 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [-2.29670256e-01 2.80479252e-01 -6.43900558e-02 -1.31571293e-01
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f601632b-1e94-4008-98ca-0d0ef29dfa49 | hplflownet-hierarchical-permutohedral-lattice-1 | 1906.05332 | null | https://arxiv.org/abs/1906.05332v1 | https://arxiv.org/pdf/1906.05332v1.pdf | HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds | We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds. Inspired by Bilateral Convolutional Layers (BCL), we propose novel DownBCL, UpBCL, and CorrBCL operations that restore structural information from unstructured point clouds, and fuse information from two consecutive point clouds. Operating on discrete and sparse permutohedral lattice points, our architectural design is parsimonious in computational cost. Our model can efficiently process a pair of point cloud frames at once with a maximum of 86K points per frame. Our approach achieves state-of-the-art performance on the FlyingThings3D and KITTI Scene Flow 2015 datasets. Moreover, trained on synthetic data, our approach shows great generalization ability on real-world data and on different point densities without fine-tuning. | ['Yong-Jae lee', 'Yijie Wang', 'Xiuye Gu', 'Panqu Wang', 'Chongruo wu'] | 2019-06-12 | hplflownet-hierarchical-permutohedral-lattice | http://openaccess.thecvf.com/content_CVPR_2019/html/Gu_HPLFlowNet_Hierarchical_Permutohedral_Lattice_FlowNet_for_Scene_Flow_Estimation_on_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Gu_HPLFlowNet_Hierarchical_Permutohedral_Lattice_FlowNet_for_Scene_Flow_Estimation_on_CVPR_2019_paper.pdf | cvpr-2019-6 | ['scene-flow-estimation'] | ['computer-vision'] | [-2.88301319e-01 -4.52431828e-01 1.34762123e-01 -1.99230343e-01
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2142d4f6-4194-4bd3-a545-da60be3d11f8 | deep-learning-technique-for-human-parsing-a | 2301.00394 | null | https://arxiv.org/abs/2301.00394v1 | https://arxiv.org/pdf/2301.00394v1.pdf | Deep Learning Technique for Human Parsing: A Survey and Outlook | Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly increased interest in the computer vision community and has been utilized in a broad range of practical applications, from security monitoring, to social media, to visual special effects, just to name a few. Although deep learning-based human parsing solutions have made remarkable achievements, many important concepts, existing challenges, and potential research directions are still confusing. In this survey, we comprehensively review three core sub-tasks: single human parsing, multiple human parsing, and video human parsing, by introducing their respective task settings, background concepts, relevant problems and applications, representative literature, and datasets. We also present quantitative performance comparisons of the reviewed methods on benchmark datasets. Additionally, to promote sustainable development of the community, we put forward a transformer-based human parsing framework, providing a high-performance baseline for follow-up research through universal, concise, and extensible solutions. Finally, we point out a set of under-investigated open issues in this field and suggest new directions for future study. We also provide a regularly updated project page, to continuously track recent developments in this fast-advancing field: https://github.com/soeaver/awesome-human-parsing. | ['Qing Song', 'Shan Li', 'Wenhe Jia', 'Lu Yang'] | 2023-01-01 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 5.40996671e-01 1.99100539e-01 -2.80095905e-01 -4.59363401e-01
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d6b985a1-9da1-4d8a-b331-3d0c777cd8c2 | copynext-explicit-span-copying-and-alignment | 2010.15266 | null | https://arxiv.org/abs/2010.15266v1 | https://arxiv.org/pdf/2010.15266v1.pdf | CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models | Copy mechanisms are employed in sequence to sequence models (seq2seq) to generate reproductions of words from the input to the output. These frameworks, operating at the lexical type level, fail to provide an explicit alignment that records where each token was copied from. Further, they require contiguous token sequences from the input (spans) to be copied individually. We present a model with an explicit token-level copy operation and extend it to copying entire spans. Our model provides hard alignments between spans in the input and output, allowing for nontraditional applications of seq2seq, like information extraction. We demonstrate the approach on Nested Named Entity Recognition, achieving near state-of-the-art accuracy with an order of magnitude increase in decoding speed. | ['Benjamin Van Durme', 'Mahsa Yarmohammadi', 'Guanghui Qin', 'Patrick Xia', 'Abhinav Singh'] | 2020-10-28 | null | https://aclanthology.org/2020.spnlp-1.2 | https://aclanthology.org/2020.spnlp-1.2.pdf | emnlp-spnlp-2020-11 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [ 6.74942195e-01 2.49308467e-01 -2.00773895e-01 -2.11427823e-01
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df983ea7-2b73-4fa0-8ab9-6131602f7da8 | machine-learning-approaches-for-type-2 | 2104.07820 | null | https://arxiv.org/abs/2104.07820v2 | https://arxiv.org/pdf/2104.07820v2.pdf | Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management | Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature. In this document we seek to remedy this omission in literature with an encompassing overview of diabetes complication prediction as well as situating this problem in the context of real world healthcare management. We illustrate various problems encountered in real world clinical scenarios via our own experience with building and deploying such models. In this manuscript we illustrate a Machine Learning (ML) framework for addressing the problem of predicting Type 2 Diabetes Mellitus (T2DM) together with a solution for risk stratification, intervention and management. These ML models align with how physicians think about disease management and mitigation, which comprises these four steps: Identify, Stratify, Engage, Measure. | ['Ankur Teredesai', 'Muhammad Aurangzeb Ahmad', 'Vikas Kumar', 'Carly Eckert', 'Jody Chiam', 'Ashish Singh', 'Aloysius Lim'] | 2021-04-15 | null | null | null | null | ['diabetes-prediction'] | ['medical'] | [ 5.24431705e-01 1.95962533e-01 -7.29508102e-01 -8.93748581e-01
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941e2391-babd-4fe8-be06-769947495db0 | bootstrapping-weakly-supervised-segmentation | 2003.11087 | null | https://arxiv.org/abs/2003.11087v1 | https://arxiv.org/pdf/2003.11087v1.pdf | Bootstrapping Weakly Supervised Segmentation-free Word Spotting through HMM-based Alignment | Recent work in word spotting in handwritten documents has yielded impressive results. This progress has largely been made by supervised learning systems, which are dependent on manually annotated data, making deployment to new collections a significant effort. In this paper, we propose an approach that utilises transcripts without bounding box annotations to train segmentation-free query-by-string word spotting models, given a partially trained model. This is done through a training-free alignment procedure based on hidden Markov models. This procedure creates a tentative mapping between word region proposals and the transcriptions to automatically create additional weakly annotated training data, without choosing any single alignment possibility as the correct one. When only using between 1% and 7% of the fully annotated training sets for partial convergence, we automatically annotate the remaining training data and successfully train using it. On all our datasets, our final trained model then comes within a few mAP% of the performance from a model trained with the full training set used as ground truth. We believe that this will be a significant advance towards a more general use of word spotting, since digital transcription data will already exist for parts of many collections of interest. | ['Tomas Wilkinson', 'Carl Nettelblad'] | 2020-03-24 | null | null | null | null | ['word-spotting-in-handwritten-documents'] | ['computer-vision'] | [ 7.17847228e-01 3.05395395e-01 -1.34369761e-01 -4.31596845e-01
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f45e2be9-4b55-4cd5-bf4b-8ce570661706 | foveated-downsampling-techniques | null | null | https://openreview.net/forum?id=rkldVXKU8H | https://openreview.net/pdf?id=rkldVXKU8H | Foveated Downsampling Techniques | Foveation is an important part of human vision, and a number of deep networks have also used foveation. However, there have been few systematic comparisons between foveating and non-foveating deep networks, and between different variable-resolution downsampling methods. Here we define several such methods, and compare their performance on ImageNet recognition with a Densenet-121 network. The best variable-resolution method slightly outperforms uniform downsampling. Thus in our experiments, foveation does not substantially help or hinder object recognition in deep networks. | ['Anonymous'] | 2019-09-11 | null | null | null | null | ['foveation'] | ['computer-vision'] | [-1.32802501e-01 -1.21497959e-01 4.03781608e-02 -2.88386703e-01
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6238be7a-b97a-48d0-9fb9-aa50573bf85b | coherent-comment-generation-for-chinese | 1906.01231 | null | https://arxiv.org/abs/1906.01231v1 | https://arxiv.org/pdf/1906.01231v1.pdf | Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model | Automatic article commenting is helpful in encouraging user engagement and interaction on online news platforms. However, the news documents are usually too long for traditional encoder-decoder based models, which often results in general and irrelevant comments. In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. By organizing the article into graph structure, our model can better understand the internal structure of the article and the connection between topics, which makes it better able to understand the story. We collect and release a large scale news-comment corpus from a popular Chinese online news platform Tencent Kuaibao. Extensive experiment results show that our model can generate much more coherent and informative comments compared with several strong baseline models. | ['ShengLi Yan', 'Yunfang Wu', 'Yancheng He', 'Xu sun', 'Jingjing Xu', 'Wei Li'] | 2019-06-04 | null | null | null | null | ['graph-to-sequence', 'comment-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.81194898e-02 7.89821804e-01 -5.14822781e-01 -3.03500473e-01
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0eda7834-9631-498a-96c5-942a39b34995 | disasternets-embedding-machine-learning-in | 2306.09815 | null | https://arxiv.org/abs/2306.09815v1 | https://arxiv.org/pdf/2306.09815v1.pdf | DisasterNets: Embedding Machine Learning in Disaster Mapping | Disaster mapping is a critical task that often requires on-site experts and is time-consuming. To address this, a comprehensive framework is presented for fast and accurate recognition of disasters using machine learning, termed DisasterNets. It consists of two stages, space granulation and attribute granulation. The space granulation stage leverages supervised/semi-supervised learning, unsupervised change detection, and domain adaptation with/without source data techniques to handle different disaster mapping scenarios. Furthermore, the disaster database with the corresponding geographic information field properties is built by using the attribute granulation stage. The framework is applied to earthquake-triggered landslide mapping and large-scale flood mapping. The results demonstrate a competitive performance for high-precision, high-efficiency, and cross-scene recognition of disasters. To bridge the gap between disaster mapping and machine learning communities, we will provide an openly accessible tool based on DisasterNets. The framework and tool will be available at https://github.com/HydroPML/DisasterNets. | ['Xiao Xiang Zhu', 'Yilei Shi', 'Qingsong Xu'] | 2023-06-16 | null | null | null | null | ['change-detection', 'scene-recognition'] | ['computer-vision', 'computer-vision'] | [-3.72965708e-02 -2.49019250e-01 4.77008261e-02 -2.73546785e-01
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06439763-e183-4f35-8ddd-040bdc14c2ba | instructzero-efficient-instruction | 2306.03082 | null | https://arxiv.org/abs/2306.03082v1 | https://arxiv.org/pdf/2306.03082v1.pdf | InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models | Large language models~(LLMs) are instruction followers, but it can be challenging to find the best instruction for different situations, especially for black-box LLMs on which backpropagation is forbidden. Instead of directly optimizing the discrete instruction, we optimize a low-dimensional soft prompt applied to an open-source LLM to generate the instruction for the black-box LLM. On each iteration of the proposed method, which we call InstructZero, a soft prompt is converted into an instruction using the open-source LLM, which is then submitted to the black-box LLM for zero-shot evaluation, and the performance is sent to Bayesian optimization to produce new soft prompts improving the zero-shot performance. We evaluate InstructZero on different combinations of open-source LLMs and APIs including Vicuna and ChatGPT. Our results show that InstructZero outperforms SOTA auto-instruction methods across a variety of downstream tasks. Our code and data are publicly available at https://github.com/Lichang-Chen/InstructZero. | ['Tianyi Zhou', 'Heng Huang', 'Tom Goldstein', 'Jiuhai Chen', 'Lichang Chen'] | 2023-06-05 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-1.09372556e-01 -2.12303121e-02 -5.32824636e-01 -6.06679857e-01
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809ab3ca-ec07-4c08-9ea0-1dee20433507 | an-automatic-learning-of-an-algerian-dialect | null | null | https://aclanthology.org/L18-1133 | https://aclanthology.org/L18-1133.pdf | An Automatic Learning of an Algerian Dialect Lexicon by using Multilingual Word Embeddings | null | ['Kamel Sma{\\"\\i}li', 'Abidi Karima'] | 2018-05-01 | an-automatic-learning-of-an-algerian-dialect-1 | https://aclanthology.org/L18-1133 | https://aclanthology.org/L18-1133.pdf | lrec-2018-5 | ['multilingual-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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b2e8bb8c-9c69-4d80-8263-b54ca54eb595 | turning-large-language-models-into-cognitive | 2306.03917 | null | https://arxiv.org/abs/2306.03917v1 | https://arxiv.org/pdf/2306.03917v1.pdf | Turning large language models into cognitive models | Large language models are powerful systems that excel at many tasks, ranging from translation to mathematical reasoning. Yet, at the same time, these models often show unhuman-like characteristics. In the present paper, we address this gap and ask whether large language models can be turned into cognitive models. We find that -- after finetuning them on data from psychological experiments -- these models offer accurate representations of human behavior, even outperforming traditional cognitive models in two decision-making domains. In addition, we show that their representations contain the information necessary to model behavior on the level of individual subjects. Finally, we demonstrate that finetuning on multiple tasks enables large language models to predict human behavior in a previously unseen task. Taken together, these results suggest that large, pre-trained models can be adapted to become generalist cognitive models, thereby opening up new research directions that could transform cognitive psychology and the behavioral sciences as a whole. | ['Eric Schulz', 'Marcel Binz'] | 2023-06-06 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 8.23458284e-02 2.14693740e-01 -1.64291501e-01 -4.13534820e-01
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c6db8d86-46b4-41d4-8412-a0c09a78ac01 | deep-learning-with-partially-labeled-data-for | 2306.05294 | null | https://arxiv.org/abs/2306.05294v1 | https://arxiv.org/pdf/2306.05294v1.pdf | Deep Learning with Partially Labeled Data for Radio Map Reconstruction | In this paper, we address the problem of Received Signal Strength map reconstruction based on location-dependent radio measurements and utilizing side knowledge about the local region; for example, city plan, terrain height, gateway position. Depending on the quantity of such prior side information, we employ Neural Architecture Search to find an optimized Neural Network model with the best architecture for each of the supposed settings. We demonstrate that using additional side information enhances the final accuracy of the Received Signal Strength map reconstruction on three datasets that correspond to three major cities, particularly in sub-areas near the gateways where larger variations of the average received signal power are typically observed. | ['Christophe Villien', 'Benoit Denis', 'Massih-Reza Amini', 'Alkesandra Malkova'] | 2023-06-07 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 9.94280167e-03 -4.62222099e-02 -2.25137711e-01 -5.84487021e-01
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ba7b959a-bdaf-4a70-bcc8-169d0f19c6f1 | multi-lingual-and-cross-genre-discourse-unit | null | null | https://aclanthology.org/W19-2714 | https://aclanthology.org/W19-2714.pdf | Multi-lingual and Cross-genre Discourse Unit Segmentation | We describe a series of experiments applied to data sets from different languages and genres annotated for coherence relations according to different theoretical frameworks. Specifically, we investigate the feasibility of a unified (theory-neutral) approach toward discourse segmentation; a process which divides a text into minimal discourse units that are involved in s coherence relation. We apply a RandomForest and an LSTM based approach for all data sets, and we improve over a simple baseline assuming simple sentence or clause-like segmentation. Performance however varies a lot depending on language, and more importantly genre, with f-scores ranging from 73.00 to 94.47. | ['Robin Sch{\\"a}fer', 'Peter Bourgonje'] | 2019-06-01 | null | null | null | ws-2019-6 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 2.77829736e-01 6.73074782e-01 -3.93561542e-01 -3.98013800e-01
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780f46a5-c8e1-437b-9c0b-475f0aea3e22 | abode-ab-initio-antibody-design-using | 2306.01005 | null | https://arxiv.org/abs/2306.01005v1 | https://arxiv.org/pdf/2306.01005v1.pdf | AbODE: Ab Initio Antibody Design using Conjoined ODEs | Antibodies are Y-shaped proteins that neutralize pathogens and constitute the core of our adaptive immune system. De novo generation of new antibodies that target specific antigens holds the key to accelerating vaccine discovery. However, this co-design of the amino acid sequence and the 3D structure subsumes and accentuates some central challenges from multiple tasks, including protein folding (sequence to structure), inverse folding (structure to sequence), and docking (binding). We strive to surmount these challenges with a new generative model AbODE that extends graph PDEs to accommodate both contextual information and external interactions. Unlike existing approaches, AbODE uses a single round of full-shot decoding and elicits continuous differential attention that encapsulates and evolves with latent interactions within the antibody as well as those involving the antigen. We unravel fundamental connections between AbODE and temporal networks as well as graph-matching networks. The proposed model significantly outperforms existing methods on standard metrics across benchmarks. | ['Vikas Garg', 'Markus Heinonen', 'Yogesh Verma'] | 2023-05-31 | null | null | null | null | ['graph-matching', 'protein-folding'] | ['graphs', 'natural-language-processing'] | [ 3.54377538e-01 -1.72757387e-01 -1.60938829e-01 -1.21707603e-01
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3f5b7179-5a11-4cab-8548-0d1b2f6f4325 | improving-compositional-generalization-with | 2110.08467 | null | https://arxiv.org/abs/2110.08467v2 | https://arxiv.org/pdf/2110.08467v2.pdf | Improving Compositional Generalization with Self-Training for Data-to-Text Generation | Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). Such representations are compositional and it is costly to collect responses for all possible combinations of atomic meaning schemata, thereby necessitating few-shot generalization to novel MRs. In this work, we systematically study the compositional generalization of the state-of-the-art T5 models in few-shot data-to-text tasks. We show that T5 models fail to generalize to unseen MRs, and we propose a template-based input representation that considerably improves the model's generalization capability. To further improve the model's performance, we propose an approach based on self-training using fine-tuned BLEURT for pseudo response selection. On the commonly-used SGD and Weather benchmarks, the proposed self-training approach improves tree accuracy by 46%+ and reduces the slot error rates by 73%+ over the strong T5 baselines in few-shot settings. | ['Emma Strubell', 'Ankur P. Parikh', 'Mihir Kale', 'Yi Tay', 'Jinfeng Rao', 'Sanket Vaibhav Mehta'] | 2021-10-16 | null | https://aclanthology.org/2022.acl-long.289 | https://aclanthology.org/2022.acl-long.289.pdf | acl-2022-5 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 8.00661087e-01 5.11055887e-01 -2.40299538e-01 -5.96457839e-01
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1de8e5dd-c1b1-471d-9ed5-2569de316118 | engineering-a-direct-k-way-hypergraph | null | null | https://epubs.siam.org/doi/10.1137/1.9781611974768.3 | https://epubs.siam.org/doi/10.1137/1.9781611974768.3 | Engineering a direct k-way Hypergraph Partitioning Algorithm | We develop a fast and high quality multilevel algorithm that directly partitions hypergraphs into k balanced blocks – without the detour over recursive bipartitioning. In particular, our algorithm efficiently implements the powerful FM local search heuristics for the complicated k-way case. This is important for objective functions which depend on the number of blocks connected by a hyperedge. We also remove several further bottlenecks in processing large hyperedges, develop a faster contraction algorithm, and a new adaptive stopping rule for local search. To further reduce the size of hyperedges, we develop a pin-sparsifier based on the min-hashing technique that clusters vertices with similar neighborhood. Extensive experiments indicate that our KaHyPar-partitioner compares favorably with the best previous systems. KaHyPar is faster than hMetis and computes better solutions. KaHyPar's results are considerably better than the (faster) PaToH partitioner. | ['Sebastian Schlag', 'Yaroslav Akhremtsev', 'Tobias Heuer', 'Peter Sanders'] | 2017-01-18 | null | null | null | alenex-2017-2017-1 | ['hypergraph-partitioning'] | ['graphs'] | [-4.21553478e-02 7.39115551e-02 -6.03247106e-01 -8.94686282e-02
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66bdf572-bc77-4ad0-864e-375a34c215f7 | composed-image-retrieval-with-text-feedback | 2211.07394 | null | https://arxiv.org/abs/2211.07394v4 | https://arxiv.org/pdf/2211.07394v4.pdf | Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization | We investigate composed image retrieval with text feedback. Users gradually look for the target of interest by moving from coarse to fine-grained feedback. However, existing methods merely focus on the latter, i.e., fine-grained search, by harnessing positive and negative pairs during training. This pair-based paradigm only considers the one-to-one distance between a pair of specific points, which is not aligned with the one-to-many coarse-grained retrieval process and compromises the recall rate. In an attempt to fill this gap, we introduce a unified learning approach to simultaneously modeling the coarse- and fine-grained retrieval by considering the multi-grained uncertainty. The key idea underpinning the proposed method is to integrate fine- and coarse-grained retrieval as matching data points with small and large fluctuations, respectively. Specifically, our method contains two modules: uncertainty modeling and uncertainty regularization. (1) The uncertainty modeling simulates the multi-grained queries by introducing identically distributed fluctuations in the feature space. (2) Based on the uncertainty modeling, we further introduce uncertainty regularization to adapt the matching objective according to the fluctuation range. Compared with existing methods, the proposed strategy explicitly prevents the model from pushing away potential candidates in the early stage and thus improves the recall rate. On the three public datasets, \ie, FashionIQ, Fashion200k, and Shoes, the proposed method has achieved +4.03%, + 3.38%, and + 2.40% Recall@50 accuracy over a strong baseline, respectively. | ['Tat-Seng Chua', 'Leigang Qu', 'Wei Ji', 'Zhedong Zheng', 'Yiyang Chen'] | 2022-11-14 | null | null | null | null | ['composed-image-retrieval', 'multi-modal'] | ['computer-vision', 'miscellaneous'] | [-1.60955518e-01 -5.13995051e-01 -4.16357875e-01 -4.37814265e-01
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38d72e22-1dcf-45f8-b0d2-7ad968d11449 | a-knowledge-aware-sequence-to-tree-network | null | null | https://aclanthology.org/2020.emnlp-main.579 | https://aclanthology.org/2020.emnlp-main.579.pdf | A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving | With the advancements in natural language processing tasks, math word problem solving has received increasing attention. Previous methods have achieved promising results but ignore background common-sense knowledge not directly provided by the problem. In addition, during generation, they focus on local features while neglecting global information. To incorporate external knowledge and global expression information, we propose a novel knowledge-aware sequence-to-tree (KA-S2T) network in which the entities in the problem sequences and their categories are modeled as an entity graph. Based on this entity graph, a graph attention network is used to capture knowledge-aware problem representations. Further, we use a tree-structured decoder with a state aggregation mechanism to capture the long-distance dependency and global expression information. Experimental results on the Math23K dataset revealed that the KA-S2T model can achieve better performance than previously reported best results. | ['Xuanjing Huang', 'Jinlan Fu', 'Qi Zhang', 'Qinzhuo Wu'] | null | null | null | null | emnlp-2020-11 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 3.24507326e-01 1.18892990e-01 -2.18830168e-01 -5.32832026e-01
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40d8cfb9-f53e-493d-9134-c45fa1a974a0 | toward-improving-confidence-in-autonomous | null | null | https://doi.org/10.1109/MC.2021.3075054 | https://hull-repository.worktribe.com/preview/3757643/SafeML_II_Author_Version.pdf | Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems | The application of artificial intelligence (AI) and data-driven decision-making systems in autonomous vehicles is growing rapidly. As autonomous vehicles operate in dynamic environments, the risk that they can face an unknown observation is relatively high due to insufficient training data, distributional shift, or cyber-security attack. Thus, AI-based algorithms should make dependable decisions to improve their interpretation of the environment, lower the risk of autonomous driving, and avoid catastrophic accidents. This paper proposes an approach named SafeML II, which applies empirical cumulative distribution function (ECDF)-based statistical distance measures in a designed human-in-the-loop procedure to ensure the safety of machine learning-based classifiers in autonomous vehicle software. The approach is model-agnostic and it can cover various machine learning and deep learning classifiers. The German Traffic Sign Recognition Benchmark (GTSRB) is used to illustrate the capabilities of the proposed approach. | ['Yiannis Papadopoulos', 'Vinod Vasudevan Nair', 'Amr Abdullatif', 'Sohag Kabir', 'Koorosh Aslansefat'] | 2021-08-03 | null | null | null | computer-2021-8 | ['traffic-sign-recognition'] | ['computer-vision'] | [ 9.46168154e-02 1.64904311e-01 -2.03002259e-01 -5.90912163e-01
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2cb391b4-a755-4d47-9efb-029686919b30 | u-need-a-fine-grained-dataset-for-user-needs | 2305.04774 | null | https://arxiv.org/abs/2305.04774v1 | https://arxiv.org/pdf/2305.04774v1.pdf | U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation | Conversational recommender systems (CRSs) aim to understand the information needs and preferences expressed in a dialogue to recommend suitable items to the user. Most of the existing conversational recommendation datasets are synthesized or simulated with crowdsourcing, which has a large gap with real-world scenarios. To bridge the gap, previous work contributes a dataset E-ConvRec, based on pre-sales dialogues between users and customer service staff in E-commerce scenarios. However, E-ConvRec only supplies coarse-grained annotations and general tasks for making recommendations in pre-sales dialogues. Different from that, we use real user needs as a clue to explore the E-commerce conversational recommendation in complex pre-sales dialogues, namely user needs-centric E-commerce conversational recommendation (UNECR). In this paper, we construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios. U-NEED consists of 3 types of resources: (i) 7,698 fine-grained annotated pre-sales dialogues in 5 top categories (ii) 333,879 user behaviors and (iii) 332,148 product knowledge tuples. To facilitate the research of UNECR, we propose 5 critical tasks: (i) pre-sales dialogue understanding (ii) user needs elicitation (iii) user needs-based recommendation (iv) pre-sales dialogue generation and (v) pre-sales dialogue evaluation. We establish baseline methods and evaluation metrics for each task. We report experimental results of 5 tasks on U-NEED. We also report results in 3 typical categories. Experimental results indicate that the challenges of UNECR in various categories are different. | ['Wanxiang Che', 'Yongbin Li', 'Hengbin Cui', 'Ziyu Zhuang', 'Yifan Chen', 'Fan Feng', 'Hang Wang', 'Yan Fan', 'Baohua Dong', 'Weinan Zhang', 'Yuanxing Liu'] | 2023-05-05 | null | null | null | null | ['dialogue-evaluation', 'dialogue-generation', 'dialogue-understanding', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [-2.26379305e-01 1.91842943e-01 -1.88956305e-01 -7.63196051e-01
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8.89250517e-01 2.93880582e-01 -7.07214594e-01 1.87210703e+00
-3.75645608e-01 -1.42946973e-01 4.54487681e-01 5.86026132e-01
-3.04139882e-01 -1.17973948e+00 1.10997804e-01 5.87891638e-01
-1.50183305e-01 5.02668358e-02 -7.57095098e-01 4.42031890e-01
-1.90728962e-01 2.03232455e+00 -2.37756446e-01 -9.58114803e-01
8.67591500e-01 -2.29359388e-01 -3.00057769e-01 -1.00583637e+00
-1.50671816e+00 1.86024845e-01 9.16190088e-01 -5.11060297e-01
-5.06305754e-01 -1.68592796e-01 -1.10187554e+00 -4.40174580e-01
-5.47671974e-01 9.30513144e-01 6.24509633e-01 9.08198714e-01
3.84082615e-01 9.77304503e-02 9.75504458e-01 -8.82010818e-01
-5.35365462e-01 -1.33526695e+00 -7.31456757e-01 7.41855443e-01
-2.63346016e-01 -4.53286380e-01 -2.64519960e-01 -5.18142106e-03] | [12.358187675476074, 7.46424674987793] |
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