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efc2639e-f0b6-4283-9758-7cf646cd3a28 | unsupervised-extractive-summarization-via | null | null | https://aclanthology.org/P15-2138 | https://aclanthology.org/P15-2138.pdf | Unsupervised extractive summarization via coverage maximization with syntactic and semantic concepts | null | ['Anders S{\\o}gaard', 'Natalie Schluter'] | 2015-07-01 | unsupervised-extractive-summarization-via-1 | https://aclanthology.org/P15-2138 | https://aclanthology.org/P15-2138.pdf | ijcnlp-2015-7 | ['unsupervised-extractive-summarization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.371952056884766, 3.6428489685058594] |
0f07e9ad-33af-4487-b4da-6183298032cb | learning-to-abstract-for-memory-augmented | null | null | https://aclanthology.org/P19-1371 | https://aclanthology.org/P19-1371.pdf | Learning to Abstract for Memory-augmented Conversational Response Generation | Neural generative models for open-domain chit-chat conversations have become an active area of research in recent years. A critical issue with most existing generative models is that the generated responses lack informativeness and diversity. A few researchers attempt to leverage the results of retrieval models to stre... | ['Zhiliang Tian', 'Nevin L. Zhang', 'Xiaopeng Li', 'Wei Bi'] | 2019-07-01 | null | null | null | acl-2019-7 | ['conversational-response-generation'] | ['natural-language-processing'] | [-1.52085349e-02 5.89902773e-02 -1.95332989e-01 -6.08087480e-01
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3.32359463e-01 1.06149089e+00 2.43865788e-01 -4.69761878... | [12.51496410369873, 8.307197570800781] |
4f1c91ed-3e03-4fb4-a867-3858ad5b93d8 | mitosis-detection-from-partial-annotation-by | 2307.04113 | null | https://arxiv.org/abs/2307.04113v1 | https://arxiv.org/pdf/2307.04113v1.pdf | Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping | Detection of mitosis events plays an important role in biomedical research. Deep-learning-based mitosis detection methods have achieved outstanding performance with a certain amount of labeled data. However, these methods require annotations for each imaging condition. Collecting labeled data involves time-consuming hu... | ['Ryoma Bise', 'Shinichiro Chuma', 'Ami Katanaya', 'Kazuya Nishimura'] | 2023-07-09 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 5.12730420e-01 4.13478352e-02 -2.99827904e-01 -4.37634498e-01
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3.67246985e-01 6.05651200e-01 5.28021514e-01 4.34670717... | [14.651463508605957, -3.1859095096588135] |
e655da11-970c-4399-8277-beec3ea27b2f | zoo-guide-to-network-embedding | 2305.03474 | null | https://arxiv.org/abs/2305.03474v1 | https://arxiv.org/pdf/2305.03474v1.pdf | Zoo Guide to Network Embedding | Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted lots of interest in the past few decad... | ['Ginestra Bianconi', 'Anaïs Baudot', 'Rubén J. Sánchez-García', 'Anthony Baptista'] | 2023-05-05 | null | null | null | null | ['community-detection', 'network-embedding'] | ['graphs', 'methodology'] | [ 3.10788512e-01 2.90043503e-01 -4.59671050e-01 3.18100601e-02
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-8.26843500e-01 2.90055245e-01 4.93199192e-02 -1.54601455... | [7.075277805328369, 5.823516845703125] |
e666bb3f-e0c0-4971-a27f-8f25aedcf57f | nadi-2021-the-second-nuanced-arabic-dialect | 2103.08466 | null | https://arxiv.org/abs/2103.08466v2 | https://arxiv.org/pdf/2103.08466v2.pdf | NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task | We present the findings and results of the Second Nuanced Arabic Dialect Identification Shared Task (NADI 2021). This Shared Task includes four subtasks: country-level Modern Standard Arabic (MSA) identification (Subtask 1.1), country-level dialect identification (Subtask 1.2), province-level MSA identification (Subtas... | ['Nizar Habash', 'Houda Bouamor', 'AbdelRahim Elmadany', 'Chiyu Zhang', 'Muhammad Abdul-Mageed'] | 2021-03-04 | null | https://aclanthology.org/2021.wanlp-1.28 | https://aclanthology.org/2021.wanlp-1.28.pdf | eacl-wanlp-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-2.46678725e-01 -1.88171506e-01 -7.75506273e-02 -4.66641515e-01
-1.27100992e+00 -1.06415057e+00 1.27929485e+00 2.23057166e-01
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-3.16333920e-01 7.47777939e-01 4.27252054e-02 -7.86284626... | [10.17859172821045, 10.773660659790039] |
23a34946-ecc9-44ae-8b15-de63cc8eb3fc | msvd-turkish-a-comprehensive-multimodal | 2012.07098 | null | https://arxiv.org/abs/2012.07098v1 | https://arxiv.org/pdf/2012.07098v1.pdf | MSVD-Turkish: A Comprehensive Multimodal Dataset for Integrated Vision and Language Research in Turkish | Automatic generation of video descriptions in natural language, also called video captioning, aims to understand the visual content of the video and produce a natural language sentence depicting the objects and actions in the scene. This challenging integrated vision and language problem, however, has been predominantl... | ['Lucia Specia', 'Pranava Madhyastha', 'Aykut Erdem', 'Erkut Erdem', 'Menekse Kuyu', 'Ozan Caglayan', 'Begum Citamak'] | 2020-12-13 | null | null | null | null | ['video-description', 'multimodal-machine-translation'] | ['computer-vision', 'natural-language-processing'] | [ 1.83834255e-01 -9.52091888e-02 -1.12510927e-01 -3.32987964e-01
-7.63356924e-01 -8.67669642e-01 8.57484877e-01 -5.96673563e-02
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2.60718137e-01 6.81075990e-01 2.81031430e-03 -2.45792598... | [11.087691307067871, 1.2733968496322632] |
5c2afe22-b5e4-4122-a3bf-7247944285ff | joint-generative-and-contrastive-learning-for | 2012.09071 | null | https://arxiv.org/abs/2012.09071v2 | https://arxiv.org/pdf/2012.09071v2.pdf | Joint Generative and Contrastive Learning for Unsupervised Person Re-identification | Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed versions) of an input. In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one join... | ['Francois Bremond', 'Antitza Dantcheva', 'Benoit Lagadec', 'Yaohui Wang', 'Hao Chen'] | 2020-12-16 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Joint_Generative_and_Contrastive_Learning_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Joint_Generative_and_Contrastive_Learning_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.pdf | cvpr-2021-1 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 3.14215332e-01 5.75432926e-02 -1.07916474e-01 -6.87878132e-01
-7.48413384e-01 -7.05896258e-01 1.14145052e+00 -3.77115577e-01
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4.78670895e-01 7.94539392e-01 -4.05770570e-01 -1.51413620... | [14.662910461425781, 0.966994047164917] |
8a80cdd4-95cc-4c01-905c-5012b7f83898 | latent-semantic-search-and-information | 1912.00180 | null | https://arxiv.org/abs/1912.00180v1 | https://arxiv.org/pdf/1912.00180v1.pdf | Latent Semantic Search and Information Extraction Architecture | The motivation, concept, design and implementation of latent semantic search for search engines have limited semantic search, entity extraction and property attribution features, have insufficient accuracy and response time of latent search, may impose privacy concerns and the search results are unavailable in offline ... | ['Anton Kolonin'] | 2019-11-30 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [-2.22077176e-01 3.19922715e-01 -4.77968127e-01 -1.71524286e-01
1.09151542e-01 -9.20349658e-01 9.40964341e-01 -3.04115620e-02
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-8.12295359e-04 1.02655435e+00 5.41868806e-01 3.42256278... | [9.635781288146973, 7.743766784667969] |
f1b9cdc9-8f76-492d-8f7a-5bfa0caac643 | optimal-positioning-of-pmus-for-fault | 2104.07211 | null | https://arxiv.org/abs/2104.07211v2 | https://arxiv.org/pdf/2104.07211v2.pdf | Optimal Positioning of PMUs for Fault Detection and Localization in Active Distribution Networks | This paper considers the problem of fault detection and localization in active distribution networks using PMUs. The proposed algorithm consists in computing a set of weighted least squares state estimates whose results are used to detect, characterize and localize the occurrence of a fault. Moreover, a criteria to min... | ['M. Cabiati', 'C. Bossi', 'F. Silvestro', 'G. -P. Schiapparelli', 'B. Gabriele', 'F. Conte'] | 2021-04-15 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-9.69145596e-02 -3.62571515e-02 -8.73601716e-03 1.27457559e-01
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-3.98548692e-01 4.66260850e-01 1.79298595e-01 5.49636073... | [5.951407432556152, 2.5444555282592773] |
37614788-cb3d-49b2-a140-4ad025acd5e3 | ff2-a-feature-fusion-two-stream-framework-for | 2211.04699 | null | https://arxiv.org/abs/2211.04699v1 | https://arxiv.org/pdf/2211.04699v1.pdf | FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration | To accomplish punctuation restoration, most existing methods focus on introducing extra information (e.g., part-of-speech) or addressing the class imbalance problem. Recently, large-scale transformer-based pre-trained language models (PLMS) have been utilized widely and obtained remarkable success. However, the PLMS ar... | ['Mengqi Zhang', 'Lifeng Shi', 'Hao Zhang', 'Yao Zhao', 'Kebin Fang', 'Yangjun Wu'] | 2022-11-09 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 2.14781836e-01 -1.54522419e-01 -3.43811691e-01 -5.90498865e-01
-1.02276123e+00 -9.09206942e-02 4.63573724e-01 1.51267111e-01
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5.12522757e-01 -1.09833842e-02 3.43417108e-01 -2.49042958... | [11.669296264648438, 5.617599010467529] |
00cefbbd-1920-4eea-80c6-9c3809f67852 | rapid-face-mask-detection-and-person | 2112.09951 | null | https://arxiv.org/abs/2112.09951v1 | https://arxiv.org/pdf/2112.09951v1.pdf | Rapid Face Mask Detection and Person Identification Model based on Deep Neural Networks | As Covid-19 has been constantly getting mutated and in three or four months a new variant gets introduced to us and it comes with more deadly problems. The things that prevent us from getting Covid is getting vaccinated and wearing a face mask. In this paper, we have implemented a new Face Mask Detection and Person Rec... | ['GhufranUllah', 'Mohd. Belal', 'Abdullah Ahmad Khan'] | 2021-12-18 | null | null | null | null | ['person-identification', 'person-recognition'] | ['computer-vision', 'computer-vision'] | [-3.24867219e-02 5.52953184e-02 2.31076419e-01 -4.84204620e-01
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1.66093141e-01 4.90047485e-01 -4.52909619e-02 -2.43478611... | [13.367984771728516, 0.9263292551040649] |
e0c44d1d-c06b-466a-9663-ab1603c56c3f | attribute-prototype-network-for-any-shot | 2204.01208 | null | https://arxiv.org/abs/2204.01208v1 | https://arxiv.org/pdf/2204.01208v1.pdf | Attribute Prototype Network for Any-Shot Learning | Any-shot image classification allows to recognize novel classes with only a few or even zero samples. For the task of zero-shot learning, visual attributes have been shown to play an important role, while in the few-shot regime, the effect of attributes is under-explored. To better transfer attribute-based knowledge fr... | ['Zeynep Akata', 'Bernt Schiele', 'Jiuniu Wang', 'Yongqin Xian', 'Wenjia Xu'] | 2022-04-04 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 1.30107701e-01 2.30444536e-01 -5.02922952e-01 -5.78531027e-01
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-1.04675710e-01 4.40088063e-01 1.03518456e-01 -1.81040794... | [10.00623893737793, 2.3533005714416504] |
c4654d8f-1aca-4961-b309-86bd50e42d1f | rethinking-the-encoding-of-satellite-image | 2305.02086 | null | https://arxiv.org/abs/2305.02086v1 | https://arxiv.org/pdf/2305.02086v1.pdf | Rethinking the Encoding of Satellite Image Time Series | Representation learning of Satellite Image Time Series (SITS) presents its unique challenges, such as prohibitive computation burden caused by high spatiotemporal resolutions, irregular acquisition times, and complex spatiotemporal interactions, leading to highly-specialized neural network architectures for SITS analys... | ['Roy Sterritt', 'Peter Nicholl', 'Yaxin Bi', 'Xin Cai'] | 2023-05-03 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 5.89735210e-01 -3.77959907e-01 1.62704498e-01 -3.43426764e-01
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-3.91283512e-01 3.93235624e-01 2.76428163e-01 -3.84313971... | [9.556787490844727, -1.4883288145065308] |
164486fd-f774-470f-a0a3-4a254ee3cce9 | how-to-select-which-active-learning-strategy | 2306.03543 | null | https://arxiv.org/abs/2306.03543v1 | https://arxiv.org/pdf/2306.03543v1.pdf | How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget | In Active Learning (AL), a learner actively chooses which unlabeled examples to query for labels from an oracle, under some budget constraints. Different AL query strategies are more suited to different problems and budgets. Therefore, in practice, knowing in advance which AL strategy is most suited for the problem at ... | ['Daphna Weinshall', 'Guy Hacohen'] | 2023-06-06 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 9.19224247e-02 5.65957129e-02 -8.92044842e-01 -5.22186399e-01
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2.05768153e-01 8.64366770e-01 3.51755679e-01 3.05981666... | [9.312766075134277, 4.05462646484375] |
9caf3d7c-06f1-4803-8436-a6237c0597b3 | transmission-guided-bayesian-generative-model | 2303.00900 | null | https://arxiv.org/abs/2303.00900v1 | https://arxiv.org/pdf/2303.00900v1.pdf | Transmission-Guided Bayesian Generative Model for Smoke Segmentation | Smoke segmentation is essential to precisely localize wildfire so that it can be extinguished in an early phase. Although deep neural networks have achieved promising results on image segmentation tasks, they are prone to be overconfident for smoke segmentation due to its non-rigid shape and transparent appearance. Thi... | ['Nick Barnes', 'Jing Zhang', 'Siyuan Yan'] | 2023-03-02 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 5.05264640e-01 -6.49705529e-02 -7.34042153e-02 -4.01668519e-01
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1.92956835e-01 3.14882934e-01 5.32357275e-01 3.92971426... | [10.20374584197998, -2.498854637145996] |
08d7d2d4-3b2a-425b-9fb9-38b9a96d179b | semi-siamese-network-for-robust-change | 2212.08583 | null | https://arxiv.org/abs/2212.08583v1 | https://arxiv.org/pdf/2212.08583v1.pdf | Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing | Automatic defect detection for 3D printing processes, which shares many characteristics with change detection problems, is a vital step for quality control of 3D printed products. However, there are some critical challenges in the current state of practice. First, existing methods for computer vision-based process moni... | ['Qian Yang', 'Anson W. K. Ma', 'Ethan Chadwick', 'Yushuo Niu'] | 2022-12-16 | null | null | null | null | ['change-detection', 'defect-detection'] | ['computer-vision', 'computer-vision'] | [ 4.80716676e-01 -4.10146356e-01 5.87022841e-01 -1.10306829e-01
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3.29887182e-01 7.66756833e-01 4.55969959e-01 6.64145872... | [7.387163162231445, 1.802209496498108] |
c7f57a52-bad1-46aa-8c01-2a5ae19ef896 | graph-based-semantical-extractive-text | 2212.09701 | null | https://arxiv.org/abs/2212.09701v1 | https://arxiv.org/pdf/2212.09701v1.pdf | Graph-based Semantical Extractive Text Analysis | In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community... | ['Mina Samizadeh'] | 2022-12-19 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 5.91477454e-01 2.48063222e-01 -3.32132876e-01 -1.79127883e-02
-5.61814487e-01 -5.16973794e-01 6.84039295e-01 9.78812099e-01
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2.98884977e-02 5.00111520e-01 4.53882694e-01 -2.00952306... | [12.264927864074707, 9.48764419555664] |
2d7906f8-45a4-478c-ac58-d6d425e0eb9c | mazajak-an-online-arabic-sentiment-analyser | null | null | https://aclanthology.org/W19-4621 | https://aclanthology.org/W19-4621.pdf | Mazajak: An Online Arabic Sentiment Analyser | Sentiment analysis (SA) is one of the most useful natural language processing applications. Literature is flooding with many papers and systems addressing this task, but most of the work is focused on English. In this paper, we present {``}Mazajak{''}, an online system for Arabic SA. The system is based on a deep learn... | ['Walid Magdy', 'Ibrahim Abu Farha'] | 2019-08-01 | null | null | null | ws-2019-8 | ['twitter-sentiment-analysis', 'arabic-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [-6.20795906e-01 -4.23265249e-01 1.19949892e-01 -7.13119924e-01
-3.15738261e-01 -6.97965324e-01 6.38631046e-01 3.82612258e-01
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-1.04942068e-01 5.79013705e-01 -2.65650060e-02 -1.45466483... | [11.12539291381836, 7.044964790344238] |
bc4ff6cb-7e2f-4bf6-8f3c-3505e0c9dd54 | hierarchically-clustered-pca-and-cca-via-a | 2211.16553 | null | https://arxiv.org/abs/2211.16553v3 | https://arxiv.org/pdf/2211.16553v3.pdf | Simple and Scalable Algorithms for Cluster-Aware Precision Medicine | AI-enabled precision medicine promises a transformational improvement in healthcare outcomes by enabling data-driven personalized diagnosis, prognosis, and treatment. However, the well-known "curse of dimensionality" and the clustered structure of biomedical data together interact to present a joint challenge in the hi... | ['Logan Grosenick', 'Conor Liston', 'Amanda M. Buch'] | 2022-11-29 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 2.66527645e-02 -1.41621470e-01 -1.77132979e-01 -1.46999270e-01
-6.40732586e-01 -6.05557621e-01 5.06483555e-01 5.45571685e-01
-2.28072524e-01 3.51300448e-01 5.80176830e-01 -1.14765748e-01
-7.39910543e-01 -2.59744644e-01 -3.12107969e-02 -1.00319922e+00
-4.10450459e-01 7.93941498e-01 -4.27986383e-01 2.31742531... | [7.0783610343933105, 5.206086158752441] |
44b48036-90dd-4a21-bfda-333f75e89565 | simple-and-efficient-learning-using | 1604.01518 | null | http://arxiv.org/abs/1604.01518v1 | http://arxiv.org/pdf/1604.01518v1.pdf | Simple and Efficient Learning using Privileged Information | The Support Vector Machine using Privileged Information (SVM+) has been
proposed to train a classifier to utilize the additional privileged information
that is only available in the training phase but not available in the test
phase. In this work, we propose an efficient solution for SVM+ by simply
utilizing the square... | ['Yong liu', 'Xinxing Xu', 'Joey Tianyi Zhou', 'IvorW. Tsang', 'Zheng Qin', 'Rick Siow Mong Goh'] | 2016-04-06 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 2.98833370e-01 -2.85176355e-02 -5.23681283e-01 -5.25123119e-01
-4.71463233e-01 -5.94920814e-01 4.34479415e-01 1.29808500e-01
-4.57092136e-01 1.20266950e+00 -6.20948672e-01 -5.84007621e-01
-2.28594616e-01 -7.21550822e-01 -5.47709465e-01 -9.05417383e-01
1.57423884e-01 1.18953004e-01 3.84518772e-01 -2.54771203... | [8.258935928344727, 4.138667583465576] |
0e03cbf9-15cd-4d0d-ae09-3c26a8ae4638 | character-focused-video-thumbnail-retrieval | 2204.06563 | null | https://arxiv.org/abs/2204.06563v1 | https://arxiv.org/pdf/2204.06563v1.pdf | Character-focused Video Thumbnail Retrieval | We explore retrieving character-focused video frames as candidates for being video thumbnails. To evaluate each frame of the video based on the character(s) present in it, characters (faces) are evaluated in two aspects: Facial-expression: We train a CNN model to measure whether a face has an acceptable facial expressi... | ['Hossein Taghavi', 'Nagendra Kamath', 'Shervin Ardeshir'] | 2022-04-13 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 2.87413508e-01 8.78780931e-02 -2.77223229e-01 -3.12672257e-01
-3.12920690e-01 -4.87623781e-01 4.58051234e-01 1.12959497e-01
-8.25926438e-02 2.41031244e-01 5.70813954e-01 2.94723839e-01
-1.19789340e-01 -5.39326429e-01 -6.19509935e-01 -6.90735579e-01
-1.76264077e-01 -1.16620451e-01 8.01950693e-02 1.59808397... | [10.204224586486816, 0.45890605449676514] |
f129b3ba-9491-4068-95ec-c03b3b9e1c50 | alime-mkg-a-multi-modal-knowledge-graph-for | 2109.07411 | null | https://arxiv.org/abs/2109.07411v1 | https://arxiv.org/pdf/2109.07411v1.pdf | AliMe MKG: A Multi-modal Knowledge Graph for Live-streaming E-commerce | Live streaming is becoming an increasingly popular trend of sales in E-commerce. The core of live-streaming sales is to encourage customers to purchase in an online broadcasting room. To enable customers to better understand a product without jumping out, we propose AliMe MKG, a multi-modal knowledge graph that aims at... | ['Ji Zhang', 'Zhongzhou Zhao', 'Wei Zhou', 'Zhixiong Zeng', 'Yunzhou Shi', 'Fu Sun', 'Feng-Lin Li', 'Hehong Chen', 'Guohai Xu'] | 2021-09-13 | null | null | null | null | ['multi-modal-knowledge-graph'] | ['knowledge-base'] | [-2.60151446e-01 1.29861280e-01 -6.32910907e-01 -6.07434332e-01
-7.36894310e-01 -6.00572884e-01 -1.76969081e-01 5.02292633e-01
1.77971140e-01 2.62165163e-02 3.40678513e-01 -1.25449210e-01
-5.51169097e-01 -1.05451643e+00 -4.13110405e-01 -2.35087574e-01
-1.87427819e-01 7.00176597e-01 2.76851743e-01 -5.82804561... | [10.17806339263916, 5.747937202453613] |
e34eb2c9-72ed-4cc2-ba9c-1168d564975d | reproduction-study-using-public-data-of | null | null | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0217541 | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0217541&type=printable | Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs | We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets. We re-implemented the main method in the original study since the source code is... | ['Kajsa Møllersen', 'Mike Voets', 'Lars Ailo Bongo'] | 2019-06-06 | null | null | null | plos-one-2019-6 | ['diabetic-retinopathy-grading'] | ['medical'] | [-4.03899312e-01 -9.33082029e-02 -1.65610164e-01 -3.48386854e-01
-7.39919841e-01 -5.13098896e-01 -8.70000571e-02 1.13375477e-01
-6.49732828e-01 8.33172083e-01 3.76501709e-01 -8.60888302e-01
-2.61309475e-01 -7.08361685e-01 -6.35821164e-01 -5.37620306e-01
-1.35643288e-01 -6.85169222e-03 1.99635729e-01 3.69101286... | [15.823871612548828, -3.998631238937378] |
c8acad60-eb49-4528-91d3-96a42a092edd | yolopose-transformer-based-multi-object-6d | 2205.02536 | null | https://arxiv.org/abs/2205.02536v1 | https://arxiv.org/pdf/2205.02536v1.pdf | YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression | 6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications. The state-of-the-art models for pose estimation are convolutional neural network (CNN)-based. Lately, Transformers, an architecture originally proposed for natural language processing, is achieving state-of-the-art resul... | ['Sven Behnke', 'Arul Selvam Periyasamy', 'Arash Amini'] | 2022-05-05 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation', 'robot-manipulation'] | ['computer-vision', 'computer-vision', 'robots'] | [-2.03709245e-01 1.00075901e-01 -2.12996706e-01 -4.79588389e-01
-7.35560417e-01 -1.74839109e-01 5.84549963e-01 -8.03663358e-02
-5.28819621e-01 -2.21620593e-02 -2.33399123e-01 1.34807080e-02
8.38488862e-02 -4.67555135e-01 -1.24210453e+00 -4.02415693e-01
2.76760273e-02 1.00255823e+00 3.34142178e-01 -3.02013069... | [7.4562578201293945, -2.6246466636657715] |
5d310e87-9e5d-4413-9ac1-8f66b6e53c79 | monocular-object-and-plane-slam-in-structured | 1809.03415 | null | https://arxiv.org/abs/1809.03415v2 | https://arxiv.org/pdf/1809.03415v2.pdf | Monocular Object and Plane SLAM in Structured Environments | In this paper, we present a monocular Simultaneous Localization and Mapping (SLAM) algorithm using high-level object and plane landmarks. The built map is denser, more compact and semantic meaningful compared to feature point based SLAM. We first propose a high order graphical model to jointly infer the 3D object and l... | ['Shichao Yang', 'Sebastian Scherer'] | 2018-09-10 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-2.09353939e-01 -1.67412266e-01 -2.19169036e-01 -6.38135850e-01
-3.59120846e-01 -8.34025204e-01 6.88128173e-01 -9.88684222e-03
-2.77591735e-01 7.15770721e-01 -1.87141038e-02 -4.19905931e-02
-3.27913344e-01 -8.11619401e-01 -1.03172565e+00 -3.25621516e-02
6.77130744e-02 1.12990618e+00 5.65567434e-01 5.88400923... | [7.349660873413086, -2.267838954925537] |
df4b726a-6d63-4021-ab6b-1f0ae23768f1 | bsnlp2019-shared-task-submission-multisource | null | null | https://aclanthology.org/W19-3710 | https://aclanthology.org/W19-3710.pdf | BSNLP2019 Shared Task Submission: Multisource Neural NER Transfer | This paper describes the Cognitive Computation (CogComp) Group{'}s submissions to the multilingual named entity recognition shared task at the Balto-Slavic Natural Language Processing (BSNLP) Workshop. The final model submitted is a multi-source neural NER system with multilingual BERT embeddings, trained on the concat... | ['Tatiana Tsygankova', 'Dan Roth', 'Stephen Mayhew'] | 2019-08-01 | null | null | null | ws-2019-8 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-4.12126243e-01 -1.05369084e-01 7.94543102e-02 -5.95125616e-01
-1.10592067e+00 -8.14105392e-01 7.46957123e-01 5.75106025e-01
-1.38177931e+00 9.67291296e-01 8.78404021e-01 -4.32533652e-01
1.15716130e-01 -5.01627624e-01 -3.64867389e-01 8.36227462e-03
1.06317684e-01 8.25611949e-01 2.27650329e-01 -5.11550725... | [9.868691444396973, 9.795039176940918] |
8fb526f8-f197-4edd-90be-1b0c3d654770 | lifting-from-the-deep-convolutional-3d-pose | 1701.00295 | null | http://arxiv.org/abs/1701.00295v4 | http://arxiv.org/pdf/1701.00295v4.pdf | Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image | We propose a unified formulation for the problem of 3D human pose estimation
from a single raw RGB image that reasons jointly about 2D joint estimation and
3D pose reconstruction to improve both tasks. We take an integrated approach
that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN
architecture... | ['Denis Tome', 'Chris Russell', 'Lourdes Agapito'] | 2017-01-01 | lifting-from-the-deep-convolutional-3d-pose-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Tome_Lifting_From_the_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Tome_Lifting_From_the_CVPR_2017_paper.pdf | cvpr-2017-7 | ['monocular-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.78607333e-01 2.62663931e-01 -1.44217266e-02 -3.84498805e-01
-9.26949978e-01 -2.10688472e-01 2.93117732e-01 -1.49181217e-01
-9.53837216e-01 4.84871298e-01 4.14812982e-01 7.48884603e-02
1.54910803e-01 -1.66520923e-01 -8.53039443e-01 -6.92492947e-02
-5.37898578e-02 1.16086423e+00 3.07067394e-01 -3.31075221... | [6.958017349243164, -0.9192792773246765] |
38852fac-2f68-4efe-a56a-b92caf34c9c9 | who-wrote-this-code-watermarking-for-code | 2305.15060 | null | https://arxiv.org/abs/2305.15060v1 | https://arxiv.org/pdf/2305.15060v1.pdf | Who Wrote this Code? Watermarking for Code Generation | Large language models for code have recently shown remarkable performance in generating executable code. However, this rapid advancement has been accompanied by many legal and ethical concerns, such as code licensing issues, code plagiarism, and malware generation, making watermarking machine-generated code a very time... | ['Gunhee Kim', 'Jamin Shin', 'Sangdoo Yun', 'Hwaran Lee', 'Ilgee Hong', 'Jaewoo Ahn', 'Seokhee Hong', 'Taehyun Lee'] | 2023-05-24 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 5.90820491e-01 1.42924944e-02 -5.33140481e-01 3.57171148e-01
-8.97556365e-01 -6.55722618e-01 7.72096455e-01 5.01648188e-01
-7.59784579e-02 4.66943532e-01 2.90006101e-02 -7.25597203e-01
6.17716014e-01 -7.22630739e-01 -5.63312113e-01 -3.62507373e-01
-2.77875423e-01 -3.64656687e-01 6.14556611e-01 9.06162262... | [6.971961975097656, 7.824173450469971] |
53da3ebe-42f5-432e-a718-40325a686059 | augmenting-an-assisted-living-lab-with-non | 2002.05593 | null | http://arxiv.org/abs/2002.05593v1 | http://arxiv.org/pdf/2002.05593v1.pdf | Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring | The need for reducing our energy consumption footprint and the increasing
number of electric devices in today's homes is calling for new solutions that
allow users to efficiently manage their energy consumption. Real-time feedback
at device level would be of a significant benefit for this application. In
addition, the ... | [] | 2020-02-13 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-1.23625204e-01 9.15189087e-02 2.07802221e-01 -5.06833553e-01
-1.73169702e-01 -5.44599891e-01 3.45239550e-01 6.24083281e-01
-1.47242725e-01 7.07395017e-01 3.78750749e-02 -3.08007300e-01
6.40352070e-02 -1.15780365e+00 1.45710751e-01 -6.94896877e-01
-1.24521039e-01 2.22859263e-01 2.15830043e-01 -1.90317541... | [5.961048603057861, 2.5485730171203613] |
e0df412d-5945-4e19-a851-8a06a04b7d05 | head-pose-estimation-based-on-multivariate | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Geng_Head_Pose_Estimation_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Geng_Head_Pose_Estimation_2014_CVPR_paper.pdf | Head Pose Estimation Based on Multivariate Label Distribution | Accurate ground truth pose is essential to the training of most existing head pose estimation algorithms. However, in many cases, the "ground truth" pose is obtained in rather subjective ways, such as asking the human subjects to stare at different markers on the wall. In such case, it is better to use soft labels rath... | ['Yu Xia', 'Xin Geng'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['head-pose-estimation'] | ['computer-vision'] | [-1.67586580e-01 3.60871613e-01 -1.18047319e-01 -7.91286707e-01
-9.45030510e-01 -2.27745980e-01 4.97132689e-02 7.14100599e-02
-5.35617769e-01 8.96551788e-01 1.27562225e-01 1.47520006e-01
2.48741999e-01 -2.80022413e-01 -6.96214557e-01 -7.36032188e-01
1.76251993e-01 7.47148514e-01 1.96430668e-01 2.24225730... | [7.061539173126221, -1.100740671157837] |
a921f0da-a827-4624-b5d4-257ade4d2b4b | forgery-attack-detection-in-surveillance | 2201.09487 | null | https://arxiv.org/abs/2201.09487v1 | https://arxiv.org/pdf/2201.09487v1.pdf | Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information | The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities. Traditional video forensics approaches can localize forgery traces using spatial-temporal analysis on relatively long video clips, while falling short in real-tim... | ['Qian Zhang', 'Tao Jiang', 'Wei Wang', 'Xiang Li', 'Yong Huang'] | 2022-01-24 | null | null | null | null | ['video-forensics'] | ['computer-vision'] | [ 3.80979866e-01 -6.10620618e-01 -2.01307699e-01 1.57720432e-01
-7.38046885e-01 -1.10611081e+00 1.74123093e-01 -4.18521702e-01
-2.16366500e-01 2.62548089e-01 -1.68423176e-01 -5.31027555e-01
-1.11403592e-01 -6.35524929e-01 -8.01133454e-01 -6.16660357e-01
-5.74383438e-01 -6.16309464e-01 5.17414868e-01 2.30011955... | [12.594165802001953, 1.0755265951156616] |
7192bd53-ef15-4fc5-93ee-da07403340e3 | building-blocks-of-a-task-oriented-dialogue | null | null | https://aclanthology.org/2021.nlpmc-1.7 | https://aclanthology.org/2021.nlpmc-1.7.pdf | Building blocks of a task-oriented dialogue system in the healthcare domain | There has been significant progress in dialogue systems research. However, dialogue systems research in the healthcare domain is still in its infancy. In this paper, we analyse recent studies and outline three building blocks of a task-oriented dialogue system in the healthcare domain: i) privacy-preserving data collec... | ['Bart Vanrumste', 'Stijn Luca', 'Dietwig Lowet', 'Heereen Shim'] | null | null | null | null | naacl-nlpmc-2021-6 | ['dialogue-management'] | ['natural-language-processing'] | [-3.55161689e-02 1.38380003e+00 1.63169235e-01 -8.33695650e-01
-7.82672644e-01 -3.19618374e-01 9.23588097e-01 6.15360975e-01
-4.23777610e-01 1.21577442e+00 9.49080169e-01 -3.53407770e-01
-5.11333235e-02 -4.04404104e-01 2.68032819e-01 -1.74660519e-01
1.40386060e-01 1.04999053e+00 1.53131053e-01 -7.24457324... | [12.607176780700684, 8.27609634399414] |
e52d0435-ed2e-4f15-ade7-ac066fe86d54 | attack-is-good-augmentation-towards-skeleton | 2304.04023 | null | https://arxiv.org/abs/2304.04023v1 | https://arxiv.org/pdf/2304.04023v1.pdf | Attack is Good Augmentation: Towards Skeleton-Contrastive Representation Learning | Contrastive learning, relying on effective positive and negative sample pairs, is beneficial to learn informative skeleton representations in unsupervised skeleton-based action recognition. To achieve these positive and negative pairs, existing weak/strong data augmentation methods have to randomly change the appearanc... | ['Mike Zheng Shou', 'Yixiao Ge', 'Guo-Sen Xie', 'Rui Yan', 'Xiangbo Shu', 'Binqian Xu'] | 2023-04-08 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 7.99941778e-01 3.24998409e-01 -4.88566160e-01 -6.15980774e-02
-9.57616568e-01 -6.56962097e-02 7.72210717e-01 -2.57053345e-01
-3.13037634e-01 6.79591715e-01 4.03957486e-01 1.99726999e-01
6.47886842e-02 -1.02977979e+00 -7.40399778e-01 -9.32151020e-01
-2.64612306e-03 4.75776196e-01 3.88956368e-01 -4.47025090... | [8.376723289489746, 0.7557919025421143] |
f15d6307-54ff-48e0-b32d-3629cd733a54 | detecting-english-grammatical-errors-based-on | null | null | https://aclanthology.org/O13-1006 | https://aclanthology.org/O13-1006.pdf | Detecting English Grammatical Errors based on Machine Translation | null | ['Jian-Cheng Wu', 'Jim Chang', 'Jason S. Chang'] | 2013-10-01 | detecting-english-grammatical-errors-based-on-1 | https://aclanthology.org/O13-1006 | https://aclanthology.org/O13-1006.pdf | roclingijclclp-2013-10 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.235448837280273, 3.619205951690674] |
daf5bc35-ee5a-45ca-a6f2-939bd29d2a35 | variational-encoding-approach-for | null | null | https://www.sciencedirect.com/science/article/pii/S0951832022000321 | https://www.sciencedirect.com/science/article/pii/S0951832022000321 | Variational encoding approach for interpretable assessment of remaining useful life estimation | A new method for evaluating aircraft engine monitoring data is proposed. Commonly, prognostics and health management systems use knowledge of the degradation processes of certain engine components together with professional expert opinion to predict the Remaining Useful Life (RUL). New data-driven approaches have emerg... | ['Luciano Sanchez', 'Nahuel Costa'] | 2022-02-23 | null | null | null | reliability-engineering-system-safety-ress | ['remaining-useful-lifetime-estimation'] | ['time-series'] | [ 1.14477128e-01 -8.53691250e-02 -9.69837233e-02 -2.80193806e-01
-6.21244669e-01 -1.36298463e-01 4.82695103e-01 3.54822576e-01
2.99970154e-04 7.43424773e-01 -9.98190865e-02 -2.94992656e-01
-6.11908257e-01 -7.98688948e-01 -5.59415042e-01 -1.03970897e+00
2.18753424e-02 6.11476958e-01 1.23278394e-01 -7.14083686... | [6.803813934326172, 2.5328869819641113] |
507fd53c-f86e-456b-8ceb-6db11afe40fa | a-novel-long-term-iterative-mining-scheme-for | 2206.09564 | null | https://arxiv.org/abs/2206.09564v1 | https://arxiv.org/pdf/2206.09564v1.pdf | A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection | The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely considering the current consecutive limited frames. However, the short-term methodology has one c... | ['Chong Peng', 'Yuming Fang', 'Hengsen Wang', 'Chenglizhao Chen'] | 2022-06-20 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 2.06684425e-01 -1.31955624e-01 -2.37280935e-01 -1.38646392e-02
-3.74263585e-01 -1.11734182e-01 5.63982964e-01 -3.28937136e-02
-5.00511885e-01 6.39043093e-01 1.35676162e-02 -2.21715644e-02
-1.60983115e-01 -5.58176994e-01 -4.69278544e-01 -7.07546234e-01
7.44308624e-03 3.78629118e-02 1.10554016e+00 -3.49910110... | [9.707141876220703, -0.4311903119087219] |
38e2a2de-6f59-4130-bf28-31ef6ee049e1 | license-plate-recognition-with-compressive | 1902.05386 | null | http://arxiv.org/abs/1902.05386v1 | http://arxiv.org/pdf/1902.05386v1.pdf | License Plate Recognition with Compressive Sensing Based Feature Extraction | License plate recognition is the key component to many automatic traffic
control systems. It enables the automatic identification of vehicles in many
applications. Such systems must be able to identify vehicles from images taken
in various conditions including low light, rain, snow, etc. In order to reduce
the complexi... | ['Nikola Vukovic', 'Andrej Jokic'] | 2019-02-07 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 5.39052784e-01 -8.30604851e-01 -7.12298900e-02 -1.97734237e-01
-3.31279516e-01 -5.38272738e-01 5.33386588e-01 -5.27111471e-01
-3.06226909e-01 6.33823276e-01 -2.72250444e-01 -2.74878085e-01
-1.24795660e-01 -8.61320734e-01 -1.26321912e-01 -8.94977212e-01
6.64068103e-01 3.18447381e-01 4.62631464e-01 7.91469403... | [9.780550956726074, -5.013743877410889] |
43568814-b637-475f-a6b0-4e3359f53c42 | equivariant-spherical-cnn-for-data-efficient | 2307.03298 | null | https://arxiv.org/abs/2307.03298v1 | https://arxiv.org/pdf/2307.03298v1.pdf | Equivariant Spherical CNN for Data Efficient and High-Performance Medical Image Processing | This work highlights the significance of equivariant networks as efficient and high-performance approaches for tomography applications. Our study builds upon the limitations of Convolutional Neural Networks (CNNs), which have shown promise in post-processing various medical imaging systems. However, the efficiency of c... | ['Hamid Sabet', 'Yuemeng Feng', 'Amirreza Hashemi'] | 2023-07-06 | null | null | null | null | ['image-reconstruction', 'denoising'] | ['computer-vision', 'computer-vision'] | [ 2.74584889e-01 9.85060036e-02 5.78238308e-01 -3.77459735e-01
-7.58517444e-01 -2.18328193e-01 2.72026390e-01 -5.68439662e-02
-7.89775610e-01 4.86199021e-01 3.39185297e-01 -3.12440127e-01
-3.28893900e-01 -8.90748143e-01 -7.39711344e-01 -6.46691561e-01
-1.47597222e-02 1.60126165e-01 1.99962854e-01 -3.95211399... | [14.085856437683105, -2.5743587017059326] |
1487dd9a-0a0d-4dd0-be4a-c7ad0c19bdc8 | facial-landmark-points-detection-using | 2111.07047 | null | https://arxiv.org/abs/2111.07047v1 | https://arxiv.org/pdf/2111.07047v1.pdf | Facial Landmark Points Detection Using Knowledge Distillation-Based Neural Networks | Facial landmark detection is a vital step for numerous facial image analysis applications. Although some deep learning-based methods have achieved good performances in this task, they are often not suitable for running on mobile devices. Such methods rely on networks with many parameters, which makes the training and i... | ['Mohammad H. Mahoor', 'Ali Pourramezan Fard'] | 2021-11-13 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-1.03370316e-01 4.00111943e-01 -1.97074190e-01 -5.60433388e-01
-5.51905572e-01 1.22736409e-01 3.98131996e-01 -2.66601026e-01
-4.82073128e-01 3.52723122e-01 -3.24520528e-01 -4.42568436e-02
-4.22314107e-02 -8.41104448e-01 -7.23146439e-01 -8.53930116e-01
-5.77842668e-02 4.33162212e-01 3.64634007e-01 -1.61167942... | [13.487844467163086, 0.49170851707458496] |
1ce25df2-1a6b-4888-96a6-7729792fa00c | language-models-as-zero-shot-planners-1 | 2201.07207 | null | https://arxiv.org/abs/2201.07207v2 | https://arxiv.org/pdf/2201.07207v2.pdf | Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents | Can world knowledge learned by large language models (LLMs) be used to act in interactive environments? In this paper, we investigate the possibility of grounding high-level tasks, expressed in natural language (e.g. "make breakfast"), to a chosen set of actionable steps (e.g. "open fridge"). While prior work focused o... | ['Igor Mordatch', 'Deepak Pathak', 'Pieter Abbeel', 'Wenlong Huang'] | 2022-01-18 | language-models-as-zero-shot-planners | https://openreview.net/forum?id=6NT1a56mNim | https://openreview.net/pdf?id=6NT1a56mNim | null | ['robot-task-planning'] | ['robots'] | [ 2.26864234e-01 8.85616720e-01 -3.17143798e-01 -4.34143841e-01
-1.02390587e+00 -6.66865349e-01 9.34761584e-01 -1.31526351e-01
-2.74926901e-01 9.39267635e-01 7.34009326e-01 -5.31607151e-01
-5.17836064e-02 -7.80557990e-01 -1.02193439e+00 -1.54038489e-01
-2.24497080e-01 8.04059148e-01 2.68474609e-01 -4.39399660... | [4.406820774078369, 0.9415757060050964] |
7bb9eacc-3e7e-4235-841d-063c936ae676 | tackling-the-story-ending-biases-in-the-story | null | null | https://aclanthology.org/P18-2119 | https://aclanthology.org/P18-2119.pdf | Tackling the Story Ending Biases in The Story Cloze Test | The Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning. There have been a variety of models tackling the SCT so far. Although the original goal behind the SCT was to require systems to perform deep language understanding and commonsense reasoning for successful narrative... | ['Nasrin Mostafazadeh', 'James Allen', 'Bakhsh', 'Rishi Sharma', 'Omid eh'] | 2018-07-01 | null | null | null | acl-2018-7 | ['cloze-test'] | ['natural-language-processing'] | [ 1.70869187e-01 1.90003470e-01 -1.17512442e-01 -3.38287205e-01
-9.01129305e-01 -9.56304193e-01 1.08696628e+00 2.68862516e-01
-3.41877937e-01 7.67729700e-01 9.65254188e-01 -3.18351328e-01
-3.79751287e-02 -5.39436281e-01 -5.93679547e-01 -9.49324220e-02
1.94917977e-01 5.78783095e-01 4.38159645e-01 -5.15687644... | [11.187148094177246, 8.806142807006836] |
dc143d7a-f210-40f6-b06f-262a063206c3 | end-to-end-models-for-chemical-protein | 2304.01344 | null | https://arxiv.org/abs/2304.01344v1 | https://arxiv.org/pdf/2304.01344v1.pdf | End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies | End-to-end relation extraction (E2ERE) is an important task in information extraction, more so for biomedicine as scientific literature continues to grow exponentially. E2ERE typically involves identifying entities (or named entity recognition (NER)) and associated relations, while most RE tasks simply assume that the ... | ['Ramakanth Kavuluru', 'Xuguang Ai'] | 2023-04-03 | null | null | null | null | ['chemical-protein-interaction-extraction', 'relation-classification'] | ['medical', 'natural-language-processing'] | [ 2.14610994e-01 4.74143445e-01 -1.70257702e-01 -2.25152418e-01
-9.60523486e-01 -6.26004696e-01 3.14603269e-01 1.04446447e+00
-6.13848567e-01 1.16652083e+00 2.22511455e-01 -4.84027594e-01
-2.95752436e-01 -6.51198983e-01 -6.22256339e-01 -3.28158170e-01
-1.43480003e-01 5.86506307e-01 -5.16239703e-02 -7.00017512... | [8.509446144104004, 8.725177764892578] |
9ab6f356-af42-41c7-93da-e7f25cea0484 | token-event-role-structure-based-multi | 2306.17733 | null | https://arxiv.org/abs/2306.17733v1 | https://arxiv.org/pdf/2306.17733v1.pdf | Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction | Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, ex... | ['Xiping Liu', 'Dexi Liu', 'Hui Xiong', 'Keli Xiao', 'Changxuan Wan', 'Qizhi Wan'] | 2023-06-30 | null | null | null | null | ['retrieval', 'event-extraction', 'document-level-event-extraction', 'information-retrieval'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.67245883e-01 -4.54611797e-03 -5.24370134e-01 -2.09239557e-01
-1.26711762e+00 -5.42553961e-01 5.69135249e-01 1.01493025e+00
-7.19366491e-01 8.32006335e-01 4.48424429e-01 -2.41243258e-01
-2.79283762e-01 -9.09123421e-01 -5.11449516e-01 -5.14206707e-01
-3.14261287e-01 1.22139305e-02 5.09348691e-01 3.67965698... | [9.094644546508789, 9.161330223083496] |
cfe91fe9-3864-40b6-ba51-73d86209179b | structured-prediction-as-translation-between-1 | 2101.05779 | null | https://arxiv.org/abs/2101.05779v3 | https://arxiv.org/pdf/2101.05779v3.pdf | Structured Prediction as Translation between Augmented Natural Languages | We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue ... | ['Stefano Soatto', 'Bing Xiang', 'Cicero Nogueira dos santos', 'Rishita Anubhai', 'Alessandro Achille', 'Jie Ma', 'Jason Krone', 'Ben Athiwaratkun', 'Giovanni Paolini'] | 2021-01-14 | structured-prediction-as-translation-between | https://openreview.net/forum?id=US-TP-xnXI | https://openreview.net/pdf?id=US-TP-xnXI | iclr-2021-1 | ['joint-entity-and-relation-extraction', 'nested-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.13137347e-01 8.43846798e-01 -7.37275481e-01 -4.40117598e-01
-1.20202279e+00 -7.75703549e-01 1.06838071e+00 5.98084152e-01
-7.85883009e-01 1.18291616e+00 5.43650806e-01 -5.88774122e-02
3.84958684e-02 -4.77142334e-01 -4.82591093e-01 -1.79786980e-01
-1.49949148e-01 1.17248964e+00 3.92940581e-01 -3.64434838... | [9.656341552734375, 9.01121711730957] |
1b737380-4667-46d6-9fa3-2d86a78c745d | weakly-supervised-scene-text-generation-for | 2306.14269 | null | https://arxiv.org/abs/2306.14269v2 | https://arxiv.org/pdf/2306.14269v2.pdf | Weakly Supervised Scene Text Generation for Low-resource Languages | A large number of annotated training images is crucial for training successful scene text recognition models. However, collecting sufficient datasets can be a labor-intensive and costly process, particularly for low-resource languages. To address this challenge, auto-generating text data has shown promise in alleviatin... | ['Yue Lu', 'Cong Liu', 'Bing Yin', 'Palaiahankote Shivakum', 'Hongjian Zhan', 'Xinyuan Chen', 'Yangchen Xie'] | 2023-06-25 | null | null | null | null | ['scene-text-recognition', 'text-generation'] | ['computer-vision', 'natural-language-processing'] | [ 6.76530123e-01 -4.25995409e-01 1.00357518e-01 -5.59857905e-01
-7.46863365e-01 -6.96786404e-01 8.71103287e-01 -1.59327522e-01
-1.87360004e-01 3.62670541e-01 2.08136991e-01 -2.24657089e-01
6.52068019e-01 -7.31463075e-01 -8.08527827e-01 -5.55686474e-01
1.01411271e+00 3.46774876e-01 9.59157720e-02 -2.54827023... | [11.8420991897583, 1.865731120109558] |
0139bf15-0c3f-4070-a780-ae860a79b9a1 | conki-contrastive-knowledge-injection-for | 2306.15796 | null | https://arxiv.org/abs/2306.15796v1 | https://arxiv.org/pdf/2306.15796v1.pdf | ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis | Multimodal Sentiment Analysis leverages multimodal signals to detect the sentiment of a speaker. Previous approaches concentrate on performing multimodal fusion and representation learning based on general knowledge obtained from pretrained models, which neglects the effect of domain-specific knowledge. In this paper, ... | ['Di Niu', 'Lei Yang', 'Xiaoli Wang', 'Weidong Guo', 'Baoxun Wang', 'Feiran Sun', 'Shi-ang Qi', 'Mingjun Zhao', 'Yakun Yu'] | 2023-06-27 | null | null | null | null | ['contrastive-learning', 'multimodal-sentiment-analysis', 'contrastive-learning', 'general-knowledge', 'sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'computer-vision', 'methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 1.87475279e-01 -7.98953101e-02 -1.86504677e-01 -4.65254992e-01
-1.08750093e+00 -7.68347204e-01 7.59634674e-01 2.00252950e-01
-1.97123274e-01 4.15403187e-01 6.63091719e-01 2.05650285e-01
2.22056592e-03 -4.28383827e-01 -6.32551372e-01 -7.94288635e-01
4.26705688e-01 1.11458145e-01 -1.87150881e-01 -3.43156099... | [13.138045310974121, 5.108783721923828] |
2720945a-f747-423a-b219-95323a8f7db9 | exploiting-socially-aware-tasks-for-embodied | 2212.00767 | null | https://arxiv.org/abs/2212.00767v2 | https://arxiv.org/pdf/2212.00767v2.pdf | Exploiting Proximity-Aware Tasks for Embodied Social Navigation | Learning how to navigate among humans in an occluded and spatially constrained indoor environment, is a key ability required to embodied agent to be integrated into our society. In this paper, we propose an end-to-end architecture that exploits Proximity-Aware Tasks (referred as to Risk and Proximity Compass) to inject... | ['Lamberto Ballan', 'Angel X. Chang', 'Luciano Serafini', 'Tommaso Campari', 'Enrico Cancelli'] | 2022-12-01 | null | null | null | null | ['common-sense-reasoning', 'social-navigation'] | ['reasoning', 'robots'] | [-1.66897178e-01 2.78257608e-01 3.22976708e-01 -6.13207102e-01
-1.18192203e-01 -4.67105597e-01 7.89981604e-01 2.58106798e-01
-1.02840340e+00 9.84141946e-01 1.95974380e-01 -1.66363031e-01
-5.46446502e-01 -9.62647021e-01 -8.49093318e-01 -2.59109765e-01
-1.02405500e+00 4.20033693e-01 3.41899693e-01 -7.89366901... | [4.751788139343262, 0.8859140276908875] |
2c723c0d-ae64-4235-a535-0fed644b2b03 | automated-pancreas-segmentation-using-multi | 2009.13148 | null | https://arxiv.org/abs/2009.13148v1 | https://arxiv.org/pdf/2009.13148v1.pdf | Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning | The performance of deep learning-based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous... | ['Wei-Chung Wang', 'Kensaku MORI', 'Wei-Chih Liao', 'Kao-Lang Liu', 'Po-Ting Chen', 'Kazunari Misawa', 'Dong Yang', 'Chen Shen', 'Pochuan Wang', 'Holger R. Roth', 'Masahiro Oda', 'Daguang Xu'] | 2020-09-28 | null | null | null | null | ['pancreas-segmentation', 'automated-pancreas-segmentation'] | ['medical', 'medical'] | [-3.07416767e-01 8.64912346e-02 -2.76702255e-01 -6.73324943e-01
-1.02371502e+00 -3.15405786e-01 1.81247205e-01 1.16205379e-01
-7.89112329e-01 7.33618259e-01 1.15066446e-01 -5.93161225e-01
-7.14171827e-02 -9.35512960e-01 -7.75100231e-01 -5.42079568e-01
3.22116800e-02 3.97871912e-01 -1.37955606e-01 2.70327568... | [6.118232250213623, 6.4580488204956055] |
b973865b-3d0e-44ef-a15f-6b7e5d6581c8 | multi-microphone-automatic-speech | 2306.04268 | null | https://arxiv.org/abs/2306.04268v1 | https://arxiv.org/pdf/2306.04268v1.pdf | Multi-microphone Automatic Speech Segmentation in Meetings Based on Circular Harmonics Features | Speaker diarization is the task of answering Who spoke and when? in an audio stream. Pipeline systems rely on speech segmentation to extract speakers' segments and achieve robust speaker diarization. This paper proposes a common framework to solve three segmentation tasks in the distant speech scenario: Voice Activity ... | ['Jean-Hugh Thomas', 'Silvio Montrésor', 'Anthony Larcher', 'Théo Mariotte'] | 2023-06-07 | null | null | null | null | ['action-detection', 'change-detection', 'activity-detection', 'speaker-diarization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'speech'] | [ 6.84819147e-02 -2.39141136e-01 2.41444290e-01 -3.87709022e-01
-1.47765625e+00 -7.94834137e-01 5.97352505e-01 2.89570123e-01
-2.96948761e-01 1.66419998e-01 5.34491241e-01 -2.10345998e-01
-8.67807940e-02 -2.63263971e-01 -1.18071727e-01 -8.32639277e-01
-1.27347559e-01 1.02519006e-01 4.75586981e-01 -8.06618109... | [14.797289848327637, 5.873857021331787] |
1e7770f2-aa7d-41e6-9388-97133f2011e3 | towards-resilient-and-secure-smart-grids | null | null | https://www.mdpi.com/2079-9292/12/12/2554 | https://www.mdpi.com/2079-9292/12/12/2554 | Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach | In an attempt to provide reliable power distribution, smart grids integrate monitoring, communication, and control technologies for better energy consumption and management. As a result of such cyberphysical links, smart grids become vulnerable to cyberattacks, highlighting the significance of detecting and monitoring ... | ['Yassine Amirat', 'Mohamed Benbouzid', 'Tarek Berghout'] | 2023-06-06 | null | null | null | mdpi-electronics-2023-6 | ['adversarial-attack', 'color-image-denoising', 'feature-engineering'] | ['adversarial', 'computer-vision', 'methodology'] | [-1.06638238e-01 -3.10515046e-01 6.30387738e-02 1.22326396e-01
-2.15578854e-01 -8.26070309e-01 4.71445918e-01 3.94031197e-01
8.91183391e-02 8.67763162e-01 -2.63967872e-01 -2.81550556e-01
-2.31239393e-01 -1.03819919e+00 -4.97672290e-01 -1.14449692e+00
-8.71073663e-01 2.51085609e-02 -3.17891359e-01 -6.83131590... | [6.064669132232666, 2.5823442935943604] |
636130b9-771e-42eb-8f69-2d19faaa8707 | transformer-based-deep-learning-model-for | 2208.08300 | null | https://arxiv.org/abs/2208.08300v1 | https://arxiv.org/pdf/2208.08300v1.pdf | Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market | In modern capital market the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors. With calculated and thoughtful investment, stock market can ensure a handsome profit with minimal capital investment, while incorrect predi... | ['Mohammad Shafiul Alam', 'Shahidul Islam Khan', 'Muhammad Ibrahim', 'Maishameem Meherin Muhu', 'Md. Mainul Ahsan', 'Anika Bintee Aftab', 'Tashreef Muhammad'] | 2022-08-17 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-8.94174039e-01 -4.07371551e-01 -1.00918263e-02 -1.68151215e-01
-2.45755970e-01 -7.79459357e-01 6.45631731e-01 -2.33225375e-02
-2.91056424e-01 7.67610908e-01 3.52240473e-01 -5.87549627e-01
-9.75132585e-02 -1.21874619e+00 -2.59106338e-01 -4.75979626e-01
-3.78257245e-01 2.07151279e-01 9.07119550e-03 -5.80817759... | [4.463914394378662, 4.240828990936279] |
220ed4ef-3432-4a46-906e-9b287b840b1c | cia-ssd-confident-iou-aware-single-stage | 2012.03015 | null | https://arxiv.org/abs/2012.03015v1 | https://arxiv.org/pdf/2012.03015v1.pdf | CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud | Existing single-stage detectors for locating objects in point clouds often treat object localization and category classification as separate tasks, so the localization accuracy and classification confidence may not well align. To address this issue, we present a new single-stage detector named the Confident IoU-Aware S... | ['Chi-Wing Fu', 'Li Jiang', 'Sijin Chen', 'Weiliang Tang', 'Wu Zheng'] | 2020-12-05 | null | null | null | null | ['birds-eye-view-object-detection'] | ['computer-vision'] | [-3.06040883e-01 -1.83983505e-01 -1.08892582e-01 -7.13711739e-01
-1.12072170e+00 -4.37891215e-01 4.33880121e-01 1.84464410e-01
-4.28071022e-01 1.82928413e-01 -3.86068225e-01 -2.87904590e-01
1.18954793e-01 -5.68801284e-01 -8.79475415e-01 -5.28309643e-01
2.27075592e-01 3.93623263e-01 9.98194218e-01 3.08563471... | [8.460977554321289, -0.6140543818473816] |
df8462e7-ae8c-4b96-9c16-0142635f1a4f | qcnext-a-next-generation-framework-for-joint | 2306.10508 | null | https://arxiv.org/abs/2306.10508v1 | https://arxiv.org/pdf/2306.10508v1.pdf | QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction | Estimating the joint distribution of on-road agents' future trajectories is essential for autonomous driving. In this technical report, we propose a next-generation framework for joint multi-agent trajectory prediction called QCNeXt. First, we adopt the query-centric encoding paradigm for the task of joint multi-agent ... | ['Yu-Kai Huang', 'Yung-Hui Li', 'JianPing Wang', 'Zihao Wen', 'Zikang Zhou'] | 2023-06-18 | null | null | null | null | ['trajectory-prediction', 'motion-forecasting'] | ['computer-vision', 'computer-vision'] | [-2.32950911e-01 -5.80455502e-03 -4.75372642e-01 -3.64501387e-01
-9.54695582e-01 -4.13835794e-01 9.33504939e-01 9.72350240e-02
-3.82900894e-01 4.76172388e-01 6.04781210e-01 -2.42838010e-01
-1.24249637e-01 -8.34930182e-01 -1.05345047e+00 -5.76704621e-01
-3.25899392e-01 6.49453223e-01 3.69097590e-01 -3.27009559... | [5.872568130493164, 0.8277908563613892] |
3d77177a-b524-4dd9-9d93-a1c7a8999859 | revisiting-acceptability-judgements | 2305.14091 | null | https://arxiv.org/abs/2305.14091v2 | https://arxiv.org/pdf/2305.14091v2.pdf | Revisiting Acceptability Judgements | Years have passed since the NLP community has last focused on linguistic acceptability. In this work, we revisit this topic in the context of large language models. We introduce CoLAC - Corpus of Linguistic Acceptability in Chinese, the first large-scale non-English acceptability dataset that is verified by native spea... | ['Rui Wang', 'Peng Zhang', 'Jiahui Huang', 'Yina Ma', 'Aini Li', 'Jackie Yan-Ki Lai', 'Weifang Huang', 'Ziyin Zhang', 'Hai Hu'] | 2023-05-23 | null | null | null | null | ['cross-lingual-transfer', 'linguistic-acceptability'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.39361218e-02 4.07919437e-01 -1.08397044e-01 -7.51516163e-01
-1.43360329e+00 -8.30752730e-01 6.35217369e-01 3.78416359e-01
-6.80075228e-01 8.49570870e-01 2.70564318e-01 -7.36517787e-01
7.73489475e-02 -3.64281714e-01 -7.68846154e-01 -1.81854725e-01
1.04699597e-01 7.45437443e-01 7.72016728e-03 -3.76593411... | [10.841387748718262, 9.669989585876465] |
4ca7fb35-e24a-43f5-95ac-37b67a7f3dad | chupa-carving-3d-clothed-humans-from-skinned | 2305.11870 | null | https://arxiv.org/abs/2305.11870v2 | https://arxiv.org/pdf/2305.11870v2.pdf | Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D Diffusion Probabilistic Models | We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic details, the generation of 3D human meshes has been a challenging problem. To address this, we decompose the problem into 2D normal map generatio... | ['Hanbyul Joo', 'Daesik Kim', 'Sookwan Han', 'Myunggi Lee', 'Kwangho Lee', 'Patrick Kwon', 'Byungjun Kim'] | 2023-05-19 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 1.31478846e-01 2.14322045e-01 6.28364503e-01 -6.81512132e-02
-4.22407806e-01 -4.84258085e-01 6.08471990e-01 -3.86930078e-01
2.34657094e-01 5.38079858e-01 3.18845183e-01 3.02026361e-01
4.94201869e-01 -1.08013427e+00 -6.84034586e-01 -4.48457092e-01
3.18349540e-01 7.68246889e-01 2.35137835e-01 -5.84225476... | [12.08906078338623, -0.6892684698104858] |
38f9fd8a-ccc4-44ef-a343-8aa0334f8756 | character-region-attention-for-text-spotting | 2007.09629 | null | https://arxiv.org/abs/2007.09629v1 | https://arxiv.org/pdf/2007.09629v1.pdf | Character Region Attention For Text Spotting | A scene text spotter is composed of text detection and recognition modules. Many studies have been conducted to unify these modules into an end-to-end trainable model to achieve better performance. A typical architecture places detection and recognition modules into separate branches, and a RoI pooling is commonly used... | ['Seung Shin', 'Hwalsuk Lee', 'Jeonghun Baek', 'Junyeop Lee', 'Youngmin Baek', 'Sungrae Park', 'Daehyun Nam'] | 2020-07-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6775_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740494.pdf | eccv-2020-8 | ['text-spotting'] | ['computer-vision'] | [ 2.75818128e-02 -2.08309203e-01 -5.51104806e-02 -4.19171363e-01
-4.28223968e-01 -4.08323765e-01 4.11387295e-01 -7.00685754e-02
-3.71482849e-01 -7.77564868e-02 1.73195690e-01 1.42072067e-01
5.77343464e-01 -7.37639308e-01 -7.15621710e-01 -6.43697798e-01
5.01243889e-01 2.95373142e-01 7.95057118e-01 -1.55018672... | [12.014619827270508, 2.2162253856658936] |
78a0f95b-e62e-4264-bc4f-3a37ad861df1 | approximate-fisher-kernels-of-non-iid-image | 1510.00857 | null | http://arxiv.org/abs/1510.00857v1 | http://arxiv.org/pdf/1510.00857v1.pdf | Approximate Fisher Kernels of non-iid Image Models for Image Categorization | The bag-of-words (BoW) model treats images as sets of local descriptors and
represents them by visual word histograms. The Fisher vector (FV)
representation extends BoW, by considering the first and second order
statistics of local descriptors. In both representations local descriptors are
assumed to be identically and... | ['Ramazan Gokberk Cinbis', 'Cordelia Schmid', 'Jakob Verbeek'] | 2015-10-03 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 3.91686969e-02 -1.22264430e-01 -3.60044271e-01 -3.95836532e-01
-9.15257633e-01 -5.96614599e-01 1.12653208e+00 3.58185560e-01
-5.07560611e-01 2.91280955e-01 4.91181552e-01 1.84010565e-01
-3.08180749e-01 -7.07186878e-01 -6.00657225e-01 -1.08770072e+00
-2.87611663e-01 2.10734963e-01 2.76977599e-01 -6.35496825... | [9.094504356384277, 2.776909828186035] |
7f186746-fadc-4f40-8b2c-0417658d77e8 | decoding-p300-variability-using-convolutional | null | null | http://dx.doi.org/10.3389/fnhum.2019.00201 | https://www.frontiersin.org/articles/10.3389/fnhum.2019.00201/pdf | Decoding P300 Variability using Convolutional Neural Networks | Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition. By learning from large amounts of data, the representations encoded by these deep networks are often invariant to mode... | ['Stephen M. Gordon', 'Vernon J. Lawhern', 'Jonathan Touryan', 'Anthony J. Ries', 'Jonathan R. McDaniel', 'Amelia J. Solon'] | 2019-06-14 | null | null | null | frontiers-in-human-neuroscience-2019-6 | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 4.93271649e-01 -2.47729301e-01 7.08332062e-01 -4.78455901e-01
-5.64998925e-01 -4.82933819e-01 4.23193574e-01 2.68205911e-01
-5.55736899e-01 6.38330817e-01 1.49555624e-01 -1.42002702e-01
-3.52151245e-02 -3.89845908e-01 -9.27074552e-01 -7.03168511e-01
-3.21206301e-01 -2.18106955e-01 1.22732192e-01 -2.05901951... | [13.079389572143555, 3.4349663257598877] |
3ef8be0b-425a-426f-9723-b34abde85546 | geometry-aware-supertagging-with | 2203.12235 | null | https://arxiv.org/abs/2203.12235v3 | https://arxiv.org/pdf/2203.12235v3.pdf | Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions | The syntactic categories of categorial grammar formalisms are structured units made of smaller, indivisible primitives, bound together by the underlying grammar's category formation rules. In the trending approach of constructive supertagging, neural models are increasingly made aware of the internal category structure... | ['Michael Moortgat', 'Konstantinos Kogkalidis'] | 2022-03-23 | null | null | null | null | ['ccg-supertagging'] | ['natural-language-processing'] | [ 4.62270901e-02 7.50402153e-01 -4.70109247e-02 -4.11400944e-01
-2.03294486e-01 -9.63387847e-01 1.07882547e+00 2.97029078e-01
-2.25990400e-01 2.17640027e-01 4.83206481e-01 -5.84217310e-01
-1.00721933e-01 -1.12846899e+00 -3.65275174e-01 -6.38881266e-01
-3.52830231e-01 5.84215343e-01 2.21677691e-01 -5.36439896... | [6.933982849121094, 6.30259370803833] |
1a12eaf4-785c-42ea-b2ce-f7f15509aac8 | prompting-large-language-models-for-zero-shot | 2306.16007 | null | https://arxiv.org/abs/2306.16007v1 | https://arxiv.org/pdf/2306.16007v1.pdf | Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition | The integration of Language Models (LMs) has proven to be an effective way to address domain shifts in speech recognition. However, these approaches usually require a significant amount of target domain text data for the training of LMs. Different from these methods, in this work, with only a domain-specific text promp... | ['Shujie Liu', 'Jinyu Li', 'Yu Wu', 'Yuang Li'] | 2023-06-28 | null | null | null | null | ['speech-recognition'] | ['speech'] | [ 2.10700378e-01 1.11681670e-01 -7.80799463e-02 -4.77521896e-01
-1.42791343e+00 -2.45402679e-01 6.69552803e-01 -5.80454525e-03
-8.84189487e-01 5.81929743e-01 4.30909216e-01 -5.02954900e-01
1.78809538e-01 -1.95831180e-01 -6.46970630e-01 -3.18794250e-01
5.89539349e-01 8.30193400e-01 5.63162625e-01 -4.53018904... | [14.3560791015625, 6.8195624351501465] |
d332e866-0b01-4977-a80d-834ccab88f02 | s2abel-a-dataset-for-entity-linking-from | 2305.00366 | null | https://arxiv.org/abs/2305.00366v1 | https://arxiv.org/pdf/2305.00366v1.pdf | S2abEL: A Dataset for Entity Linking from Scientific Tables | Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications. When applied to tables in scientific papers, EL is a step toward large-scale scientific knowledge bases that could enable advanced scientific questi... | ['Doug Downey', 'Aakanksha Naik', 'Sergey Feldman', 'Erin Bransom', 'Bailey Kuehl', 'Yuze Lou'] | 2023-04-30 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [-4.68013942e-01 4.53382969e-01 -6.49531186e-01 1.96850430e-02
-1.28173077e+00 -1.19982600e+00 3.28177869e-01 1.22533834e+00
-2.75734514e-01 1.48878860e+00 4.25777972e-01 -5.15444756e-01
-2.46746302e-01 -1.04804730e+00 -1.19316089e+00 -2.63667256e-01
6.64226934e-02 1.14620912e+00 1.32826582e-01 1.81924284... | [9.045316696166992, 8.379312515258789] |
1be6a349-1251-4229-a575-632826394686 | tp-lsd-tri-points-based-line-segment-detector-1 | 2009.05505 | null | https://arxiv.org/abs/2009.05505v1 | https://arxiv.org/pdf/2009.05505v1.pdf | TP-LSD: Tri-Points Based Line Segment Detector | This paper proposes a novel deep convolutional model, Tri-Points Based Line Segment Detector (TP-LSD), to detect line segments in an image at real-time speed. The previous related methods typically use the two-step strategy, relying on either heuristic post-process or extra classifier. To realize one-step detection wit... | ['Fangbo Qin', 'Xiao Liu', 'Siyu Huang', 'Pengfei Xiong', 'Yijia He', 'Ning Ding'] | 2020-09-11 | tp-lsd-tri-points-based-line-segment-detector | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5931_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720766.pdf | eccv-2020-8 | ['line-segment-detection'] | ['computer-vision'] | [ 1.57863468e-01 -2.30854750e-02 -3.11640091e-02 -2.92216480e-01
-7.09477246e-01 -4.68665749e-01 4.17653531e-01 6.14844978e-01
-6.34960473e-01 3.90370309e-01 -6.29148364e-01 -5.47563434e-01
1.82199925e-01 -9.16388512e-01 -7.55689859e-01 -3.36212486e-01
-1.65386960e-01 2.42555216e-01 1.04545462e+00 -1.27427161... | [8.30942440032959, -1.5278304815292358] |
6a6e18cf-ce67-44f4-90e6-4a1e1b003be4 | deep-speech-synthesis-from-mri-based | 2307.02471 | null | https://arxiv.org/abs/2307.02471v1 | https://arxiv.org/pdf/2307.02471v1.pdf | Deep Speech Synthesis from MRI-Based Articulatory Representations | In this paper, we study articulatory synthesis, a speech synthesis method using human vocal tract information that offers a way to develop efficient, generalizable and interpretable synthesizers. While recent advances have enabled intelligible articulatory synthesis using electromagnetic articulography (EMA), these met... | ['Gopala K. Anumanchipalli', 'Shinji Watanabe', 'Louis Goldstein', 'Alan W Black', 'Jiachen Lian', 'Yubin Zhang', 'Yijing Lu', 'Tingle Li', 'Peter Wu'] | 2023-07-05 | null | null | null | null | ['denoising', 'speech-synthesis'] | ['computer-vision', 'speech'] | [ 2.16935366e-01 1.71359584e-01 -7.91595131e-02 -2.17254698e-01
-8.97660255e-01 -6.05834723e-01 7.78090477e-01 -5.19224107e-01
-1.13537483e-01 6.36463463e-01 7.77760923e-01 -1.63497224e-01
-2.35654801e-01 -3.34912062e-01 -4.11969543e-01 -7.57295609e-01
1.40777186e-01 -1.76729802e-02 -2.94125974e-01 9.47222672... | [14.988422393798828, 6.172701835632324] |
51dabe52-f7cc-462b-9261-4e5120afb26f | a-visual-domain-transfer-learning-approach | 2107.13237 | null | https://arxiv.org/abs/2107.13237v2 | https://arxiv.org/pdf/2107.13237v2.pdf | A Visual Domain Transfer Learning Approach for Heartbeat Sound Classification | Heart disease is the most common reason for human mortality that causes almost one-third of deaths throughout the world. Detecting the disease early increases the chances of survival of the patient and there are several ways a sign of heart disease can be detected early. This research proposes to convert cleansed and n... | ['Sidharth Pancholi', 'Uddipan Mukherjee'] | 2021-07-28 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 9.11222119e-03 -2.93939173e-01 4.27788496e-01 -2.82202184e-01
-2.82443017e-01 -4.75251734e-01 1.66096330e-01 5.05477965e-01
-2.84725696e-01 7.48996615e-01 9.62182358e-02 -3.08275372e-01
-2.19386712e-01 -7.01105773e-01 1.21984787e-01 -3.83946657e-01
-2.67124802e-01 1.10261932e-01 2.63589978e-01 8.28856677... | [14.352523803710938, 3.3308584690093994] |
271e5f0f-07b0-43ee-809f-a6c8ff49fdb5 | multiple-object-tracking-in-cluttered-and | 1309.6391 | null | http://arxiv.org/abs/1309.6391v1 | http://arxiv.org/pdf/1309.6391v1.pdf | Multiple-object tracking in cluttered and crowded public spaces | This paper addresses the problem of tracking moving objects of variable
appearance in challenging scenes rich with features and texture. Reliable
tracking is of pivotal importance in surveillance applications. It is made
particularly difficult by the nature of objects encountered in such scenes:
these too change in app... | ['Ognjen Arandjelović', 'Rhys Martin'] | 2013-09-25 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 1.80808127e-01 -6.31144345e-01 2.92059928e-01 -1.14740819e-01
-1.91618335e-02 -7.99862623e-01 5.51151812e-01 5.73892780e-02
-4.69901621e-01 6.42391503e-01 -4.44858223e-01 -3.19148488e-02
9.01804864e-02 -3.23986918e-01 -5.15730858e-01 -7.12127507e-01
-4.03239459e-01 4.22775030e-01 1.01011741e+00 -3.05600539... | [6.825959205627441, -1.81807541847229] |
2c2c7d0c-cba5-44ab-8f59-ebbcdb3cc7e1 | uhrnet-a-deep-learning-based-method-for | 2304.14503 | null | https://arxiv.org/abs/2304.14503v1 | https://arxiv.org/pdf/2304.14503v1.pdf | UHRNet: A Deep Learning-Based Method for Accurate 3D Reconstruction from a Single Fringe-Pattern | The quick and accurate retrieval of an object height from a single fringe pattern in Fringe Projection Profilometry has been a topic of ongoing research. While a single shot fringe to depth CNN based method can restore height map directly from a single pattern, its accuracy is currently inferior to the traditional phas... | ['Hui Li', 'Xingyang Qi', 'Canlin Zhou', 'Yixiao Wang'] | 2023-04-23 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 2.60455400e-01 1.80662908e-02 2.83714116e-01 -3.57285053e-01
-6.66792214e-01 2.46848911e-01 2.59580672e-01 -3.38709533e-01
-3.86504322e-01 7.15163887e-01 9.28670689e-02 2.46239364e-01
-3.66316915e-01 -1.20460081e+00 -1.00258970e+00 -5.93985736e-01
1.80157572e-02 3.92319262e-01 5.71865439e-01 -1.51137292... | [8.746968269348145, -2.615817070007324] |
e712b1cc-5798-4a07-883a-067d8ebad458 | budget-constrained-interactive-search-for | 2012.01945 | null | https://arxiv.org/abs/2012.01945v3 | https://arxiv.org/pdf/2012.01945v3.pdf | Budget Constrained Interactive Search for Multiple Targets | Interactive graph search leverages human intelligence to categorize target labels in a hierarchy, which are useful for image classification, product categorization, and database search. However, many existing studies of interactive graph search aim at identifying a single target optimally, and suffer from the limitatio... | ['Jianliang Xu', 'Zhaonian Zou', 'Jiaxin Jiang', 'Byron Choi', 'Xin Huang', 'Xuliang Zhu'] | 2020-12-03 | null | null | null | null | ['product-categorization'] | ['miscellaneous'] | [ 2.29124039e-01 3.16355497e-01 -7.70988643e-01 -1.98347270e-01
-8.59364450e-01 -7.05421925e-01 -4.15128022e-02 5.16428709e-01
-2.70454705e-01 3.54856580e-01 -2.86011368e-01 -4.46667880e-01
-7.19992042e-01 -9.35704768e-01 -6.37468755e-01 -5.21072149e-01
-9.54073593e-02 7.60917604e-01 7.22865403e-01 -1.75662767... | [7.141654014587402, 5.260804653167725] |
54f90ee9-b433-4242-bbf4-6b9925696fd6 | instant-3d-object-tracking-with-applications | 2006.13194 | null | https://arxiv.org/abs/2006.13194v1 | https://arxiv.org/pdf/2006.13194v1.pdf | Instant 3D Object Tracking with Applications in Augmented Reality | Tracking object poses in 3D is a crucial building block for Augmented Reality applications. We propose an instant motion tracking system that tracks an object's pose in space (represented by its 3D bounding box) in real-time on mobile devices. Our system does not require any prior sensory calibration or initialization ... | ['Matthias Grundmann', 'Tingbo Hou', 'Jianing Wei', 'Liangkai Zhang', 'Artsiom Ablavatski', 'Adel Ahmadyan'] | 2020-06-23 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [-2.21983284e-01 -4.05523479e-01 -2.25259379e-01 1.95668638e-01
-6.21934891e-01 -7.82035112e-01 2.53297657e-01 -3.00694942e-01
-5.97754478e-01 3.91564012e-01 -3.31053555e-01 -3.64525408e-01
3.82532418e-01 -5.32138646e-01 -1.07167077e+00 -3.38776469e-01
-5.05028367e-02 4.54539329e-01 6.01681292e-01 1.26038194... | [6.969762802124023, -2.119643449783325] |
1ab2efeb-6d1b-481b-9040-8d171ddd7122 | generating-personalized-recipes-from | 1909.00105 | null | https://arxiv.org/abs/1909.00105v1 | https://arxiv.org/pdf/1909.00105v1.pdf | Generating Personalized Recipes from Historical User Preferences | Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete natural-text... | ['Jianmo Ni', 'Shuyang Li', 'Julian McAuley', 'Bodhisattwa Prasad Majumder'] | 2019-08-31 | generating-personalized-recipes-from-1 | https://aclanthology.org/D19-1613 | https://aclanthology.org/D19-1613.pdf | ijcnlp-2019-11 | ['recipe-generation'] | ['miscellaneous'] | [ 3.41688156e-01 1.83620989e-01 -1.59348011e-01 -6.10338151e-01
-7.12937891e-01 -8.28502655e-01 5.07134795e-01 2.80768722e-01
8.44949409e-02 5.88042915e-01 1.39074647e+00 -6.12618700e-02
2.76883215e-01 -9.74780619e-01 -8.88399124e-01 -2.13994920e-01
2.30964184e-01 4.74119335e-01 -6.93510532e-01 -7.99076080... | [11.518588066101074, 4.550482273101807] |
991b1c90-d027-4a72-bfff-79c3c29a3946 | reveal-to-revise-an-explainable-ai-life-cycle | 2303.12641 | null | https://arxiv.org/abs/2303.12641v2 | https://arxiv.org/pdf/2303.12641v2.pdf | Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models | State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-making, such as in medical applications like skin cancer detection. To tackle this problem, we propose Reveal to Revise (R2R), a framework entail... | ['Sebastian Lapuschkin', 'Wojciech Samek', 'Maximilian Dreyer', 'Frederik Pahde'] | 2023-03-22 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 5.73499501e-01 6.46604180e-01 -7.62471706e-02 -1.71998754e-01
-7.45499492e-01 -4.55502391e-01 6.41862035e-01 1.52142614e-01
-2.50905097e-01 6.97433531e-01 1.34115756e-01 -5.82632780e-01
-4.59460586e-01 -4.43295956e-01 -8.44978273e-01 -4.79950905e-01
1.37063771e-01 2.34700173e-01 -1.45679563e-01 3.24159175... | [8.871697425842285, 5.309967994689941] |
0ae8758e-c51d-4151-81be-385c0feb8a1a | pik-fix-restoring-and-colorizing-old-photo | 2205.01902 | null | https://arxiv.org/abs/2205.01902v3 | https://arxiv.org/pdf/2205.01902v3.pdf | Pik-Fix: Restoring and Colorizing Old Photos | Restoring and inpainting the visual memories that are present, but often impaired, in old photos remains an intriguing but unsolved research topic. Decades-old photos often suffer from severe and commingled degradation such as cracks, defocus, and color-fading, which are difficult to treat individually and harder to re... | ['Hongkai Yu', 'Alan Bovik', 'Jiaqi Ma', 'Zibo Meng', 'Jinlong Li', 'Xiaoyu Dong', 'Yuanqi Du', 'Zhengzhong Tu', 'Runsheng Xu'] | 2022-05-04 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 2.35242888e-01 -2.50098974e-01 3.59316885e-01 -1.98952049e-01
-7.73999572e-01 -3.65307242e-01 4.26428705e-01 -2.77664900e-01
-2.87460625e-01 1.00026798e+00 2.77817994e-01 9.33091342e-02
2.93636739e-01 -6.31265104e-01 -1.05579555e+00 -9.44174707e-01
1.05437279e-01 -8.88437256e-02 3.17041695e-01 -1.75771937... | [11.143068313598633, -2.1214654445648193] |
ae93856b-f898-45b8-bb13-b9cdc0684f13 | predictive-experience-replay-for-continual | 2303.06572 | null | https://arxiv.org/abs/2303.06572v1 | https://arxiv.org/pdf/2303.06572v1.pdf | Predictive Experience Replay for Continual Visual Control and Forecasting | Learning physical dynamics in a series of non-stationary environments is a challenging but essential task for model-based reinforcement learning (MBRL) with visual inputs. It requires the agent to consistently adapt to novel tasks without forgetting previous knowledge. In this paper, we present a new continual learning... | ['Xiaokang Yang', 'Yunbo Wang', 'Siyu Gao', 'Xiangming Zhu', 'Geng Chen', 'Wendong Zhang'] | 2023-03-12 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [-3.74182940e-01 -1.60040036e-01 -1.67572871e-01 2.50946492e-01
-2.57309675e-01 -3.90244126e-01 7.28374839e-01 -9.84221324e-02
-4.56642121e-01 1.01280105e+00 -4.92813531e-03 -1.50913224e-01
-1.45008266e-01 -3.99621993e-01 -1.15334785e+00 -1.03911126e+00
-2.92960972e-01 6.06060743e-01 4.11988497e-01 -4.15771127... | [4.308984756469727, 1.456843376159668] |
23ea3dcd-ab40-437e-9acf-17220b882507 | singing-voice-synthesis-based-on-a-musical | 2212.13703 | null | https://arxiv.org/abs/2212.13703v2 | https://arxiv.org/pdf/2212.13703v2.pdf | Singing Voice Synthesis Based on a Musical Note Position-Aware Attention Mechanism | This paper proposes a novel sequence-to-sequence (seq2seq) model with a musical note position-aware attention mechanism for singing voice synthesis (SVS). A seq2seq modeling approach that can simultaneously perform acoustic and temporal modeling is attractive. However, due to the difficulty of the temporal modeling of ... | ['Keiichi Tokuda', 'Yoshihiko Nankaku', 'Kei Hashimoto', 'Yukiya Hono'] | 2022-12-28 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-1.87798534e-02 -2.11768895e-02 4.86755520e-02 -5.98062649e-02
-6.78016126e-01 -3.91270965e-01 3.06595325e-01 -4.27858651e-01
-1.90491840e-01 2.98618674e-01 4.06762332e-01 5.88075034e-02
-1.84825156e-02 -2.17645392e-01 -2.96384960e-01 -5.94433427e-01
1.12512156e-01 -5.42464443e-02 2.27698684e-01 -2.99584627... | [15.51501750946045, 6.172844886779785] |
ee0ed370-1b76-4a69-9a0d-739903b18dc2 | an-intelligent-algorithmic-trading-based-on-a | 2208.10707 | null | https://arxiv.org/abs/2208.10707v2 | https://arxiv.org/pdf/2208.10707v2.pdf | An intelligent algorithmic trading based on a risk-return reinforcement learning algorithm | This scientific paper propose a novel portfolio optimization model using an improved deep reinforcement learning algorithm. The objective function of the optimization model is the weighted sum of the expectation and value at risk(VaR) of portfolio cumulative return. The proposed algorithm is based on actor-critic archi... | ['Boyi Jin'] | 2022-08-23 | null | null | null | null | ['algorithmic-trading', 'portfolio-optimization'] | ['time-series', 'time-series'] | [-3.79794806e-01 -1.33172870e-01 -8.74832049e-02 -1.45108506e-01
-5.83784223e-01 -4.60104406e-01 2.59775758e-01 -1.28978640e-01
-4.37617987e-01 9.14234817e-01 3.52618918e-02 -3.93993199e-01
-4.85105753e-01 -1.26892328e+00 -7.00839818e-01 -7.69126177e-01
5.42948544e-02 3.03102344e-01 -2.02490062e-01 -2.55377412... | [4.492221355438232, 3.972017288208008] |
cbeb3858-add6-4d8b-999c-edb4dce5703d | solving-the-hp-model-with-nested-monte-carlo | 2301.09533 | null | https://arxiv.org/abs/2301.09533v2 | https://arxiv.org/pdf/2301.09533v2.pdf | Solving the HP model with Nested Monte Carlo Search | In this paper we present a new Monte Carlo Search (MCS) algorithm for finding the ground state energy of proteins in the HP-model. We also compare it briefly to other MCS algorithms not usually used on the HP-model and provide an overview of the algorithms used on HP-model. The algorithm presented in this paper does no... | ['Tristan Cazenave', 'Milo Roucairol'] | 2023-01-23 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 2.44697690e-01 -2.36969993e-01 -7.51269311e-02 -6.78290948e-02
-4.52297240e-01 -4.03291941e-01 2.69934237e-01 3.55309665e-01
-2.99354047e-01 1.39799881e+00 4.39345799e-02 -5.80268621e-01
-8.85273814e-02 -7.30775654e-01 -1.01769435e+00 -1.22042656e+00
-4.89426464e-01 5.40893793e-01 3.66845131e-01 -1.07804336... | [4.734993934631348, 5.2795634269714355] |
707d2dfb-df64-49c4-936c-d15d624d6132 | instance-aware-hashing-for-multi-label-image | 1603.03234 | null | http://arxiv.org/abs/1603.03234v1 | http://arxiv.org/pdf/1603.03234v1.pdf | Instance-Aware Hashing for Multi-Label Image Retrieval | Similarity-preserving hashing is a commonly used method for nearest neighbour
search in large-scale image retrieval. For image retrieval, deep-networks-based
hashing methods are appealing since they can simultaneously learn effective
image representations and compact hash codes. This paper focuses on
deep-networks-base... | ['Yunchao Wei', 'Xiangbo Shu', 'Shuicheng Yan', 'Pan Yan', 'Hanjiang Lai'] | 2016-03-10 | null | null | null | null | ['multi-label-image-retrieval'] | ['computer-vision'] | [-3.06339506e-02 -2.90051132e-01 -5.79333425e-01 -4.79475737e-01
-1.23527539e+00 -5.39674461e-01 3.02408785e-01 7.63320804e-01
-4.78440821e-01 3.60783756e-01 1.57232344e-01 3.21028262e-01
-1.89056650e-01 -8.84764850e-01 -6.51816607e-01 -1.13015604e+00
-5.86446561e-02 6.70671403e-01 1.26973033e-01 2.23837748... | [11.349483489990234, 0.9395710825920105] |
700c29db-f701-4eaa-a1c3-7e7f42752dc5 | why-should-i-trust-you-bellman-the-bellman | 2201.12417 | null | https://arxiv.org/abs/2201.12417v2 | https://arxiv.org/pdf/2201.12417v2.pdf | Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error | In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy. While the Bellman equation is uniquely solved by the true value function over all state-action pairs, we find that the Bellman error (the difference between both sides of the equation) is a poor proxy for the ... | ['Shixiang Shane Gu', 'Ofir Nachum', 'Doina Precup', 'David Meger', 'Scott Fujimoto'] | 2022-01-28 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-1.20712988e-01 3.55072349e-01 -4.57233816e-01 -2.09537894e-01
-8.48531425e-01 -7.94201255e-01 3.48742247e-01 3.07888508e-01
-4.84971344e-01 1.19952559e+00 -1.31232545e-01 -3.68257582e-01
-4.66278881e-01 -4.71438169e-01 -5.63548148e-01 -8.24026942e-01
-5.73159009e-02 3.93657714e-01 1.06489502e-01 -2.70705044... | [4.300815105438232, 2.4032363891601562] |
cd60a6b8-2aad-4fc3-b5f1-ed5f6d83de93 | online-unsupervised-video-object-segmentation | 2306.12048 | null | https://arxiv.org/abs/2306.12048v1 | https://arxiv.org/pdf/2306.12048v1.pdf | Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering | Online unsupervised video object segmentation (UVOS) uses the previous frames as its input to automatically separate the primary object(s) from a streaming video without using any further manual annotation. A major challenge is that the model has no access to the future and must rely solely on the history, i.e., the se... | ['Zhengguo Li', 'Zhong Liu', 'Xingming Wu', 'Weihai Chen', 'Lin Xi'] | 2023-06-21 | null | null | null | null | ['contrastive-learning', 'optical-flow-estimation', 'video-object-segmentation', 'video-semantic-segmentation', 'unsupervised-video-object-segmentation', 'contrastive-learning', 'clustering'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 2.46677846e-01 -9.25680846e-02 -1.19411871e-01 -1.86820924e-01
-4.84607905e-01 -5.37706137e-01 2.45327175e-01 2.21763290e-02
-5.64066887e-01 2.49457881e-01 -2.97496587e-01 -3.81446742e-02
-1.25980705e-01 -6.20731711e-01 -7.67596960e-01 -9.17410493e-01
3.61141525e-02 3.47466826e-01 6.28192961e-01 2.14217111... | [9.074275016784668, -0.17959581315517426] |
3238df93-5519-4a1b-8258-0f48d636bc6f | operator-valued-kernels-for-learning-from | 1510.08231 | null | http://arxiv.org/abs/1510.08231v3 | http://arxiv.org/pdf/1510.08231v3.pdf | Operator-valued Kernels for Learning from Functional Response Data | In this paper we consider the problems of supervised classification and
regression in the case where attributes and labels are functions: a data is
represented by a set of functions, and the label is also a function. We focus
on the use of reproducing kernel Hilbert space theory to learn from such
functional data. Basi... | ['Stéphane Canu', 'Philippe Preux', 'Hachem Kadri', 'Julien Audiffren', 'Emmanuel Duflos', 'Alain Rakotomamonjy'] | 2015-10-28 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 2.16418386e-01 6.54082149e-02 -1.30216852e-01 -7.04213083e-01
-5.46744704e-01 -3.10532749e-01 3.09310675e-01 4.45001610e-02
-4.42620933e-01 6.71263576e-01 -1.29383147e-01 -1.38513237e-01
-4.72685695e-01 -4.36811209e-01 -3.69007498e-01 -9.67941821e-01
-6.45466864e-01 5.02390973e-02 -2.19866693e-01 -9.59141105... | [7.589210510253906, 4.087778568267822] |
06d5bde2-c85e-42a7-8632-62fcb6ac09cb | few-shot-multimodal-multitask-multilingual | 2303.12489 | null | https://arxiv.org/abs/2303.12489v1 | https://arxiv.org/pdf/2303.12489v1.pdf | Few-shot Multimodal Multitask Multilingual Learning | While few-shot learning as a transfer learning paradigm has gained significant traction for scenarios with limited data, it has primarily been explored in the context of building unimodal and unilingual models. Furthermore, a significant part of the existing literature in the domain of few-shot multitask learning perfo... | ['Vinija Jain', 'Aman Chadha'] | 2023-02-19 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [ 2.94279784e-01 -7.86800459e-02 -1.02350675e-01 -3.18049431e-01
-1.16716528e+00 -5.26519060e-01 8.56493294e-01 5.65180853e-02
-7.81669140e-01 4.85833466e-01 -2.19391026e-02 -4.22906220e-01
-1.19965069e-01 -7.08735943e-01 -8.56668234e-01 -4.95969474e-01
3.84395778e-01 6.19982362e-01 2.70182639e-01 -4.44637060... | [10.625100135803223, 1.8218022584915161] |
3e82026c-9943-4f46-b722-7e8fa549ac5d | etat-de-lart-en-compression-multi-phrases | null | null | https://aclanthology.org/2021.jeptalnrecital-recital.6 | https://aclanthology.org/2021.jeptalnrecital-recital.6.pdf | Etat de l’art en compression multi-phrases pour la synthèse de documents (State-of-the-art of multi-sentence compression for document summarization) | La compression multi-phrases est utilisée dans différentes tâches de résumé (microblogs, opinions, réunions ou articles de presse). Leur objectif est de proposer une reformulation compressée et grammaticalement correcte des phrases sources tout en gardant les faits principaux. Dans cet article, nous présentons l’état d... | ['Kévin Espasa'] | null | null | null | null | jep-taln-recital-2021-6 | ['sentence-compression'] | ['natural-language-processing'] | [ 1.10067695e-01 -5.40107861e-02 8.85421559e-02 -2.45534733e-01
-6.22139215e-01 -7.47740805e-01 8.00063729e-01 1.17153072e+00
-5.81276596e-01 7.98546076e-01 6.43764734e-01 -6.55210391e-02
-3.02467868e-02 -1.12517691e+00 -9.69118893e-01 -3.29746842e-01
-9.15045366e-02 2.98535138e-01 8.80773962e-02 -5.09001315... | [14.097638130187988, 13.307659149169922] |
7d2d1f13-9862-4618-adc2-281afd455cca | heavy-tails-in-sgd-and-compressibility-of | 2106.03795 | null | https://arxiv.org/abs/2106.03795v1 | https://arxiv.org/pdf/2106.03795v1.pdf | Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks | Neural network compression techniques have become increasingly popular as they can drastically reduce the storage and computation requirements for very large networks. Recent empirical studies have illustrated that even simple pruning strategies can be surprisingly effective, and several theoretical studies have shown ... | ['Umut Şimşekli', 'Gaël Richard', 'Murat A. Erdogdu', 'Milad Sefidgaran', 'Melih Barsbey'] | 2021-06-07 | null | http://proceedings.neurips.cc/paper/2021/hash/f5c3dd7514bf620a1b85450d2ae374b1-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/f5c3dd7514bf620a1b85450d2ae374b1-Paper.pdf | neurips-2021-12 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 1.75044045e-01 3.60032879e-02 -1.65035367e-01 -1.87066257e-01
-6.25153929e-02 -2.62327701e-01 1.40651867e-01 8.16377029e-02
-6.65153801e-01 8.95386755e-01 -3.67440641e-01 -5.12132823e-01
-5.99519193e-01 -7.11455464e-01 -9.42467391e-01 -1.01067054e+00
-4.92437959e-01 3.78311276e-01 1.81293637e-01 -1.62033781... | [8.06533145904541, 3.4932730197906494] |
f8299c1c-6902-4415-b70b-47162e6e09a4 | a-lightweight-domain-adversarial-neural | 2305.07446 | null | https://arxiv.org/abs/2305.07446v1 | https://arxiv.org/pdf/2305.07446v1.pdf | A Lightweight Domain Adversarial Neural Network Based on Knowledge Distillation for EEG-based Cross-subject Emotion Recognition | Individual differences of Electroencephalogram (EEG) could cause the domain shift which would significantly degrade the performance of cross-subject strategy. The domain adversarial neural networks (DANN), where the classification loss and domain loss jointly update the parameters of feature extractor, are adopted to d... | ['Zhiqun Pan', 'Yiheng Tang', 'Jiapeng Zhang', 'Yongxiong Wang', 'Zhe Wang'] | 2023-05-12 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [-6.62903767e-03 -3.89853001e-01 2.24924341e-01 -3.33292991e-01
-6.66415811e-01 -5.30313253e-01 3.29437912e-01 -2.75065005e-01
-3.11587542e-01 8.59535098e-01 4.72666137e-02 2.43281171e-01
-5.09908855e-01 -3.68178755e-01 -6.20256424e-01 -1.19222355e+00
-3.56042534e-01 -3.65462214e-01 -3.97824273e-02 -1.92317113... | [13.176750183105469, 3.531377077102661] |
09bbe923-c185-45a0-8054-eebca5b73f34 | get-to-the-point-summarization-with-pointer | 1704.04368 | null | http://arxiv.org/abs/1704.04368v2 | http://arxiv.org/pdf/1704.04368v2.pdf | Get To The Point: Summarization with Pointer-Generator Networks | Neural sequence-to-sequence models have provided a viable new approach for
abstractive text summarization (meaning they are not restricted to simply
selecting and rearranging passages from the original text). However, these
models have two shortcomings: they are liable to reproduce factual details
inaccurately, and the... | ['Abigail See', 'Peter J. Liu', 'Christopher D. Manning'] | 2017-04-14 | get-to-the-point-summarization-with-pointer-1 | https://aclanthology.org/P17-1099 | https://aclanthology.org/P17-1099.pdf | acl-2017-7 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.24017274e-01 2.51783609e-01 -1.05132312e-01 2.85552628e-02
-9.11489189e-01 -5.72877049e-01 7.62539268e-01 3.17991078e-01
-4.25496161e-01 9.87034678e-01 1.00968444e+00 -2.28658199e-01
3.00908238e-01 -6.42116189e-01 -7.33912647e-01 -2.47795820e-01
3.64288509e-01 5.60255945e-01 9.26634893e-02 -6.72816932... | [12.273171424865723, 9.292778015136719] |
e87dc4f9-60b5-4f40-bb25-d2d711673d09 | pushing-paraphrase-away-from-original | 2109.01862 | null | https://arxiv.org/abs/2109.01862v1 | https://arxiv.org/pdf/2109.01862v1.pdf | Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach | In recent years, neural paraphrase generation based on Seq2Seq has achieved superior performance, however, the generated paraphrase still has the problem of lack of diversity. In this paper, we focus on improving the diversity between the generated paraphrase and the original sentence, i.e., making generated paraphrase... | ['Xiaojun Wan', 'Zhe Lin'] | 2021-09-04 | null | https://aclanthology.org/2021.findings-acl.135 | https://aclanthology.org/2021.findings-acl.135.pdf | findings-acl-2021-8 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.30006093e-01 -4.09919471e-02 -2.59270728e-01 -4.10662442e-01
-9.32395339e-01 -5.95785379e-01 3.46044004e-01 7.78450444e-02
-1.97718114e-01 1.05012512e+00 9.45742071e-01 -1.31377533e-01
2.14589626e-01 -7.76251853e-01 -9.06629324e-01 -2.35753477e-01
8.25470924e-01 7.02843666e-02 6.76027918e-03 -4.77513939... | [11.735238075256348, 9.301515579223633] |
ce7c1b70-3dc6-47f7-addc-97c6d32b5996 | cross-project-software-vulnerability-1 | 2209.10406 | null | https://arxiv.org/abs/2209.10406v1 | https://arxiv.org/pdf/2209.10406v1.pdf | Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle | Software vulnerabilities (SVs) have become a common, serious and crucial concern due to the ubiquity of computer software. Many machine learning-based approaches have been proposed to solve the software vulnerability detection (SVD) problem. However, there are still two open and significant issues for SVD in terms of i... | ['Dinh Phung', 'Hung Nguyen', 'John Grundy', 'Chakkrit Tantithamthavorn', 'Trung Le', 'Van Nguyen'] | 2022-09-19 | cross-project-software-vulnerability | https://openreview.net/forum?id=f6R69En9_tH | https://openreview.net/pdf?id=f6R69En9_tH | null | ['vulnerability-detection'] | ['miscellaneous'] | [-1.48691133e-01 -7.64973834e-02 -6.13768250e-02 -2.00312063e-01
-1.02745569e+00 -8.46172750e-01 2.25898936e-01 1.24783970e-01
-1.71665862e-01 3.07813883e-01 -5.08330837e-02 -6.47078037e-01
2.44292207e-02 -6.77494049e-01 -5.49159110e-01 -4.93913144e-01
9.40254852e-02 -2.75616258e-01 4.91227776e-01 -6.68533891... | [7.10014533996582, 7.7795891761779785] |
9bb992dc-369c-415e-b15e-8ac8b52fe4c0 | towards-adaptive-unknown-authentication-for | 2207.04494 | null | https://arxiv.org/abs/2207.04494v1 | https://arxiv.org/pdf/2207.04494v1.pdf | Towards Adaptive Unknown Authentication for Universal Domain Adaptation by Classifier Paradox | Universal domain adaptation (UniDA) is a general unsupervised domain adaptation setting, which addresses both domain and label shifts in adaptation. Its main challenge lies in how to identify target samples in unshared or unknown classes. Previous methods commonly strive to depict sample "confidence" along with a thres... | ['Songcan Chen', 'Yao Liu', 'Yunyun Wang'] | 2022-07-10 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 5.33546388e-01 -2.59893417e-01 -3.56099725e-01 -5.99501610e-01
-7.92122006e-01 -8.70286822e-01 4.18833703e-01 2.56199270e-01
-1.07358791e-01 1.09338450e+00 -2.82992631e-01 -3.09028804e-01
-7.97369182e-02 -7.20912278e-01 -5.15038729e-01 -9.24710512e-01
3.02440643e-01 6.37543380e-01 2.83094674e-01 7.08748773... | [10.32612419128418, 3.156432867050171] |
eb48409b-50a4-4d12-aee5-526bdc3b6347 | anti-unification-and-generalization-a-survey | 2302.00277 | null | https://arxiv.org/abs/2302.00277v5 | https://arxiv.org/pdf/2302.00277v5.pdf | Anti-unification and Generalization: A Survey | Anti-unification (AU) is a fundamental operation for generalization computation used for inductive inference. It is the dual operation to unification, an operation at the foundation of automated theorem proving. Interest in AU from the AI and related communities is growing, but without a systematic study of the concept... | ['Temur Kutsia', 'David M. Cerna'] | 2023-02-01 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 6.93448722e-01 4.96636271e-01 -6.54395223e-01 -9.97685343e-02
-3.55225243e-02 -7.17701256e-01 8.42741013e-01 9.56321135e-02
6.77891150e-02 1.18800092e+00 -3.71980667e-01 -1.21521187e+00
-2.63987660e-01 -1.21301138e+00 -7.32997954e-01 -3.54764193e-01
-2.15966359e-01 4.98521388e-01 9.46504623e-02 -6.05120718... | [8.855131149291992, 6.92598819732666] |
b0bde91c-681a-4601-98d9-a340e30e4997 | encoding-based-saliency-detection-for-videos | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Mauthner_Encoding_Based_Saliency_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Mauthner_Encoding_Based_Saliency_2015_CVPR_paper.pdf | Encoding Based Saliency Detection for Videos and Images | We present a novel video saliency detection method to support human activity recognition and weakly supervised training of activity detection algorithms. Recent research has emphasized the need for analyzing salient information in videos to minimize dataset bias or to supervise weakly labeled training of activity dete... | ['Horst Bischof', 'Thomas Mauthner', 'Horst Possegger', 'Georg Waltner'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['video-saliency-detection'] | ['computer-vision'] | [ 6.65734231e-01 8.94613788e-02 -5.92325866e-01 -4.01054800e-01
-4.61514324e-01 -4.72923398e-01 5.58111846e-01 1.25294477e-01
-4.89168733e-01 7.26289749e-01 4.00202572e-01 1.75684333e-01
2.90529221e-01 -5.18006012e-02 -7.78980136e-01 -5.71161389e-01
-5.39027117e-02 -2.38535821e-01 6.65147603e-01 1.91589653... | [8.623332977294922, 0.38948023319244385] |
16c71e55-e79f-40cc-a1e5-29704101a883 | from-zero-to-hero-human-in-the-loop-entity | null | null | https://aclanthology.org/2020.acl-main.624 | https://aclanthology.org/2020.acl-main.624.pdf | From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains | Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB). It is crucial in a considerable number of fields like humanities, technical writing and biomedical sciences to enrich texts with semantics and discover more knowledge. The use of EL in such domains requires hand... | ['Jan-Christoph Klie', 'Iryna Gurevych', 'Richard Eckart de Castilho'] | 2020-07-01 | null | null | null | acl-2020-6 | ['text-annotation'] | ['natural-language-processing'] | [-2.56352097e-01 3.21123064e-01 -1.16627894e-01 -1.92207694e-01
-7.72858500e-01 -9.13205504e-01 4.63572800e-01 8.00229073e-01
-9.78694975e-01 1.03315318e+00 3.03567857e-01 -4.48904693e-01
-2.70897567e-01 -5.92662394e-01 -3.95584136e-01 -1.93548366e-01
1.42495468e-01 9.17620003e-01 7.69265831e-01 -5.67877650... | [9.451077461242676, 8.963886260986328] |
1eda0306-bb97-4978-9228-fea1209e06b4 | assessing-grammatical-correctness-in-language | null | null | https://aclanthology.org/2021.bea-1.15 | https://aclanthology.org/2021.bea-1.15.pdf | Assessing Grammatical Correctness in Language Learning | We present experiments on assessing the grammatical correctness of learners’ answers in a language-learning System (references to the System, and the links to the released data and code are withheld for anonymity). In particular, we explore the problem of detecting alternative-correct answers: when more than one inflec... | ['Roman Yangarber', 'Anisia Katinskaia'] | null | null | null | null | eacl-bea-2021-4 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-3.57478261e-01 3.43827516e-01 3.81923258e-01 -3.47251892e-01
-9.89017367e-01 -9.92599249e-01 1.50532514e-01 6.85615480e-01
-6.47738278e-01 9.55596685e-01 6.72625378e-02 -9.75397825e-01
2.66288016e-02 -8.76181841e-01 -9.07340646e-01 3.56550403e-02
1.21130005e-01 3.71463835e-01 3.04097325e-01 -4.28045869... | [10.958133697509766, 10.453315734863281] |
25b542b4-6373-4bee-85a2-90894fd44c24 | tryondiffusion-a-tale-of-two-unets-1 | 2306.08276 | null | https://arxiv.org/abs/2306.08276v1 | https://arxiv.org/pdf/2306.08276v1.pdf | TryOnDiffusion: A Tale of Two UNets | Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person. A key challenge is to synthesize a photorealistic detail-preserving visualization of the garment, while warping the garment to accommodate a significant bo... | ['Ira Kemelmacher-Shlizerman', 'Mohammad Norouzi', 'Chitwan Saharia', 'William Chan', 'Fitsum Reda', 'Tyler Zhu', 'Dawei Yang', 'Luyang Zhu'] | 2023-06-14 | tryondiffusion-a-tale-of-two-unets | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_TryOnDiffusion_A_Tale_of_Two_UNets_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_TryOnDiffusion_A_Tale_of_Two_UNets_CVPR_2023_paper.pdf | cvpr-2023-1 | ['virtual-try-on'] | ['computer-vision'] | [ 1.10522516e-01 1.35148495e-01 2.50375122e-01 -1.92777321e-01
1.02875948e-01 -6.45026386e-01 7.23906398e-01 -4.81736541e-01
2.77306568e-02 3.98734808e-01 5.49880981e-01 1.40379995e-01
1.29915595e-01 -4.36728984e-01 -5.88054895e-01 -4.04675633e-01
-2.53873004e-04 3.41425955e-01 2.14731768e-02 -3.75724465... | [11.907524108886719, -0.8383541703224182] |
1ced93d2-6d30-4f9d-a603-85c2ed4af95b | 3d-geometric-salient-patterns-analysis-on-3d | 1906.07645 | null | https://arxiv.org/abs/1906.07645v1 | https://arxiv.org/pdf/1906.07645v1.pdf | 3D Geometric salient patterns analysis on 3D meshes | Pattern analysis is a wide domain that has wide applicability in many fields. In fact, texture analysis is one of those fields, since the texture is defined as a set of repetitive or quasi-repetitive patterns. Despite its importance in analyzing 3D meshes, geometric texture analysis is less studied by geometry processi... | ['Jean-Marie Favreau', 'Fakhri Torkhani', 'Alice Othmani'] | 2019-06-18 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 5.72903097e-01 -1.16446115e-01 3.00056964e-01 -2.19559088e-01
-2.23580137e-01 -4.84591872e-01 4.72012818e-01 6.52536452e-01
-2.27152817e-02 2.81846225e-01 -2.86198556e-01 7.40048662e-02
-5.23953259e-01 -1.19333458e+00 -4.15770143e-01 -4.64632064e-01
-1.12997349e-02 8.37598741e-01 6.22396231e-01 -1.92663312... | [8.454460144042969, -2.599250555038452] |
6330fbc4-7010-49a4-ad2f-25a148cdcd60 | losparse-structured-compression-of-large | 2306.11222 | null | https://arxiv.org/abs/2306.11222v2 | https://arxiv.org/pdf/2306.11222v2.pdf | LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation | Transformer models have achieved remarkable results in various natural language tasks, but they are often prohibitively large, requiring massive memories and computational resources. To reduce the size and complexity of these models, we propose LoSparse (Low-Rank and Sparse approximation), a novel model compression tec... | ['Tuo Zhao', 'Weizhu Chen', 'Pengcheng He', 'Chen Liang', 'Qingru Zhang', 'Yifan Yu', 'Yixiao Li'] | 2023-06-20 | null | null | null | null | ['model-compression', 'text-generation', 'question-answering'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 3.48873645e-01 6.81763142e-02 -1.77006021e-01 -2.30690047e-01
-5.59307158e-01 -2.65204310e-01 4.13578600e-01 5.16946949e-02
-3.46160233e-01 6.18290961e-01 4.77099597e-01 -1.62275452e-02
-2.19638050e-01 -9.00002062e-01 -7.46691108e-01 -4.80076104e-01
3.74282151e-02 7.65599310e-01 4.00599360e-01 -1.04629606... | [8.708120346069336, 3.599560260772705] |
58d9a36f-c336-4d68-9a08-16f845cfb4e0 | posenet-a-convolutional-network-for-real-time | 1505.07427 | null | http://arxiv.org/abs/1505.07427v4 | http://arxiv.org/pdf/1505.07427v4.pdf | PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization | We present a robust and real-time monocular six degree of freedom
relocalization system. Our system trains a convolutional neural network to
regress the 6-DOF camera pose from a single RGB image in an end-to-end manner
with no need of additional engineering or graph optimisation. The algorithm can
operate indoors and o... | ['Matthew Grimes', 'Alex Kendall', 'Roberto Cipolla'] | 2015-05-27 | posenet-a-convolutional-network-for-real-time-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Kendall_PoseNet_A_Convolutional_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Kendall_PoseNet_A_Convolutional_ICCV_2015_paper.pdf | iccv-2015-12 | ['camera-relocalization'] | ['computer-vision'] | [ 3.46391797e-02 -4.75485027e-02 2.92872041e-01 -4.32091981e-01
-6.22471333e-01 -7.67459095e-01 5.29953420e-01 -3.68632257e-01
-7.06976712e-01 6.76342189e-01 -2.41416067e-01 -2.72859782e-01
-1.66674271e-01 -4.03127313e-01 -1.26449060e+00 -3.62488776e-01
-1.12052657e-01 4.05071080e-01 9.65424478e-02 -3.26336116... | [7.660409927368164, -2.2010600566864014] |
564378c3-8b76-481e-9b8b-5c4c084fba3f | knowledge-reasoning-via-jointly-modeling | 2301.02781 | null | https://arxiv.org/abs/2301.02781v1 | https://arxiv.org/pdf/2301.02781v1.pdf | Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules | Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule... | ['Jun Zhao', 'Kang Liu', 'Shizhu He', 'Yinyu Lan'] | 2023-01-07 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.02507509e-02 4.58372474e-01 -5.67911983e-01 -1.79305539e-01
-2.45979041e-01 -3.96843523e-01 5.16507149e-01 4.54079956e-01
-2.56652296e-01 7.29575396e-01 2.84456879e-01 -5.16351998e-01
-6.60579920e-01 -1.25626850e+00 -7.87334859e-01 -3.67772877e-01
7.76760057e-02 5.86243808e-01 5.91077328e-01 -2.80353487... | [8.823484420776367, 7.823940277099609] |
ec3989f3-3029-4dce-aaee-8febdcfc965f | 191013296 | 1910.13296 | null | https://arxiv.org/abs/1910.13296v2 | https://arxiv.org/pdf/1910.13296v2.pdf | Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation | Sequence-to-Sequence (S2S) models recently started to show state-of-the-art performance for automatic speech recognition (ASR). With these large and deep models overfitting remains the largest problem, outweighing performance improvements that can be obtained from better architectures. One solution to the overfitting p... | ['Jan Niehues', 'Thai-Son Nguyen', 'Sebastian Stueker', 'Alex Waibel'] | 2019-10-29 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 3.92642319e-01 1.84014648e-01 4.06792201e-02 -5.06664634e-01
-9.42093074e-01 -4.51760620e-01 8.32741559e-01 -9.60082784e-02
-4.78409648e-01 4.56352651e-01 5.04248738e-01 -7.58526504e-01
3.43585730e-01 -9.41083431e-02 -5.91272652e-01 -4.55747187e-01
1.38096929e-01 3.78465980e-01 2.42538974e-01 -6.28405094... | [14.477646827697754, 6.550546169281006] |
722d2a72-b421-417c-b32d-21e7ce221735 | are-character-level-translations-worth-the | 2302.14220 | null | https://arxiv.org/abs/2302.14220v2 | https://arxiv.org/pdf/2302.14220v2.pdf | Are Character-level Translations Worth the Wait? Comparing Character- and Subword-level Models for Machine Translation | Pretrained character-level language models were recently shown to be competitive with popular subword models across a range of NLP tasks. However, there has been little research on their effectiveness for neural machine translation (NMT). This work performs an extensive comparison across multiple languages and experime... | ['Arianna Bisazza', 'Antonio Toral', 'Gabriele Sarti', 'Gertjan van Noord', 'Lukas Edman'] | 2023-02-28 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 5.66560388e-01 -3.22323032e-02 -7.32424080e-01 -1.16804816e-01
-1.08564484e+00 -6.08998477e-01 9.36543226e-01 1.57198235e-01
-5.80240428e-01 7.67730474e-01 5.68515658e-01 -9.80628669e-01
2.67700613e-01 -5.35202503e-01 -1.04272258e+00 -1.15717463e-01
2.95898944e-01 7.79516757e-01 -4.60673809e-01 -5.72239935... | [11.545110702514648, 10.161425590515137] |
b1b30e7a-5f25-4d32-b21a-9e8c6e052e17 | variational-model-perturbation-for-source | 2210.10378 | null | https://arxiv.org/abs/2210.10378v1 | https://arxiv.org/pdf/2210.10378v1.pdf | Variational Model Perturbation for Source-Free Domain Adaptation | We aim for source-free domain adaptation, where the task is to deploy a model pre-trained on source domains to target domains. The challenges stem from the distribution shift from the source to the target domain, coupled with the unavailability of any source data and labeled target data for optimization. Rather than fi... | ['Cees G. M. Snoek', 'Jingjing Li', 'XianTong Zhen', 'Mengmeng Jing'] | 2022-10-19 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 2.44075388e-01 5.53293154e-02 -3.27414155e-01 -4.77852583e-01
-1.11873674e+00 -8.64174306e-01 4.98234481e-01 -3.39835793e-01
-4.41086978e-01 8.10399115e-01 1.80089831e-01 1.13047846e-01
-1.44787222e-01 -7.30646908e-01 -9.21371698e-01 -7.86292255e-01
3.31881642e-01 7.23317325e-01 4.19669122e-01 -1.68050945... | [10.334623336791992, 3.1874454021453857] |
efc42e50-74da-4f62-81f6-99b1ebcc9136 | on-designing-machine-learning-models-for | 1907.04846 | null | https://arxiv.org/abs/1907.04846v1 | https://arxiv.org/pdf/1907.04846v1.pdf | On Designing Machine Learning Models for Malicious Network Traffic Classification | Machine learning (ML) started to become widely deployed in cyber security settings for shortening the detection cycle of cyber attacks. To date, most ML-based systems are either proprietary or make specific choices of feature representations and machine learning models. The success of these techniques is difficult to a... | ['Tina Eliassi-Rad', 'Timothy Sakharaov', 'Alina Oprea', 'Talha Ongun', 'Simona Boboila'] | 2019-07-10 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.50553569e-01 -3.22110325e-01 -3.96074444e-01 -2.05137089e-01
-3.86517256e-01 -6.99668705e-01 6.58130944e-01 4.00086641e-01
-2.56983519e-01 5.80080330e-01 -1.04843117e-01 -9.51077700e-01
-4.61974353e-01 -8.06593359e-01 -2.46958911e-01 -4.96800542e-01
-2.39231855e-01 3.11413735e-01 1.07185416e-01 -1.80491313... | [5.3680100440979, 7.27393913269043] |
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