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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5f75a59c-d14d-4dd1-8250-becff89bc293 | fsuie-a-novel-fuzzy-span-mechanism-for | 2306.14913 | null | https://arxiv.org/abs/2306.14913v1 | https://arxiv.org/pdf/2306.14913v1.pdf | FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction | Universal Information Extraction (UIE) has been introduced as a unified framework for various Information Extraction (IE) tasks and has achieved widespread success. Despite this, UIE models have limitations. For example, they rely heavily on span boundaries in the data during training, which does not reflect the realit... | ['Hai Zhao', 'Bo Du', 'Lefei Zhang', 'Zuchao Li', 'Tianshuo Peng'] | 2023-06-19 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 1.13807231e-01 -1.59819514e-01 -4.23544973e-01 -2.41517365e-01
-6.87478542e-01 -4.07981902e-01 3.10374618e-01 1.91656709e-01
-5.55289090e-01 7.18455315e-01 7.07474351e-02 -1.44023761e-01
-4.02266741e-01 -5.80666482e-01 -5.61738074e-01 -7.19667375e-02
7.54093751e-02 1.96535885e-01 3.06310117e-01 -1.81412533... | [9.451504707336426, 8.869220733642578] |
1a89ab01-ce0b-434e-a564-419ec0ca2a2d | 3d-object-detection-with-pointformer | 2012.11409 | null | https://arxiv.org/abs/2012.11409v3 | https://arxiv.org/pdf/2012.11409v3.pdf | 3D Object Detection with Pointformer | Feature learning for 3D object detection from point clouds is very challenging due to the irregularity of 3D point cloud data. In this paper, we propose Pointformer, a Transformer backbone designed for 3D point clouds to learn features effectively. Specifically, a Local Transformer module is employed to model interacti... | ['Gao Huang', 'Li Erran Li', 'Shiji Song', 'Zhuofan Xia', 'Xuran Pan'] | 2020-12-21 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Pan_3D_Object_Detection_With_Pointformer_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Pan_3D_Object_Detection_With_Pointformer_CVPR_2021_paper.pdf | cvpr-2021-1 | ['object-proposal-generation'] | ['computer-vision'] | [-2.05535904e-01 -3.67699087e-01 2.16240212e-01 -4.11658406e-01
-6.85268342e-01 -3.59182984e-01 4.99489486e-01 4.59016472e-01
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-6.18301146e-02 -1.06297064e+00 -9.71149921e-01 -4.96800244e-01
-1.81753755e-01 6.19848549e-01 7.69113600e-01 -1.69504628... | [7.777080535888672, -2.9340178966522217] |
39327e6e-dd2c-4cf6-af15-576967ee8454 | title2event-benchmarking-open-event | 2211.00869 | null | https://arxiv.org/abs/2211.00869v1 | https://arxiv.org/pdf/2211.00869v1.pdf | Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset | Event extraction (EE) is crucial to downstream tasks such as new aggregation and event knowledge graph construction. Most existing EE datasets manually define fixed event types and design specific schema for each of them, failing to cover diverse events emerging from the online text. Moreover, news titles, an important... | ['Tianhua Zhou', 'Xiang Chen', 'Jin Ma', 'Nan Yang', 'Xiaoling Bai', 'Wei Wang', 'Jun Gao', 'Changlong Yu', 'Wangyang Ying', 'Yangfan Zhang', 'Yanan Zhang', 'Haolin Deng'] | 2022-11-02 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [-1.86128750e-01 2.71124154e-01 -4.83017772e-01 -2.15104446e-01
-1.23955238e+00 -6.81388199e-01 6.08377874e-01 7.99342096e-01
-5.61840236e-01 1.11032188e+00 8.74354661e-01 -1.46574765e-01
2.91456212e-03 -1.00325871e+00 -8.49647105e-01 -7.43948892e-02
-2.40035564e-01 3.47108096e-01 6.29310966e-01 -9.86928418... | [9.03965950012207, 9.212798118591309] |
f33192d2-3737-427b-8bd6-b475b43a3616 | machine-fault-classification-using | 2301.02243 | null | https://arxiv.org/abs/2301.02243v1 | https://arxiv.org/pdf/2301.02243v1.pdf | Machine Fault Classification using Hamiltonian Neural Networks | A new approach is introduced to classify faults in rotating machinery based on the total energy signature estimated from sensor measurements. The overall goal is to go beyond using black-box models and incorporate additional physical constraints that govern the behavior of mechanical systems. Observational data is used... | ['Gabriel Terejanu', 'Sourav Banerjee', 'Jawad Chowdhury', 'Jeremy Shen'] | 2023-01-04 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-5.12322634e-02 7.67464787e-02 -1.06282108e-01 -1.45425692e-01
-4.69066910e-02 -1.43801764e-01 1.94609523e-01 1.54287994e-01
2.77444324e-03 5.45753181e-01 -6.77567661e-01 -2.96812356e-01
-6.34248435e-01 -6.49438441e-01 -4.65758979e-01 -1.05960107e+00
-2.01274723e-01 1.21902473e-01 1.31302699e-01 -3.35992903... | [6.70926570892334, 2.4325714111328125] |
d13300ca-6c25-4ae0-82c6-8808fdfb64c6 | an-internal-validity-index-based-on-density | null | null | https://ieeexplore.ieee.org/document/8672850 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8672850 | An Internal Validity Index Based on Density-Involved Distance | It is crucial to evaluate the quality of clustering results in cluster analysis. Although many cluster validity indices (CVIs) have been proposed in the literature, they have some limitations when dealing with non-spherical datasets. One reason is that the measure of cluster separation does not consider the impact of o... | ['Caiming Zhong', 'Lianyu Hu'] | 2019-03-22 | null | null | null | null | ['face-clustering', 'imagedocument-clustering', 'spectral-graph-clustering', 'clustering-ensemble', 'clustering-algorithms-evaluation'] | ['computer-vision', 'computer-vision', 'graphs', 'graphs', 'methodology'] | [-5.55228531e-01 -3.96365404e-01 -8.69618431e-02 -2.34723657e-01
-2.29046181e-01 -4.91905749e-01 3.00486654e-01 4.56130594e-01
-2.37664044e-01 4.50393617e-01 -3.53091583e-03 -1.39634117e-01
-5.25506675e-01 -7.70567715e-01 1.37084946e-02 -8.62742066e-01
-5.08856364e-02 4.52166796e-01 4.15700734e-01 1.75947040... | [7.597014904022217, 4.581263065338135] |
c0e71bb1-2a65-4d5e-b26d-b3cf96cbd522 | deception-detection-in-news-reports-in-the | null | null | https://aclanthology.org/W17-4213 | https://aclanthology.org/W17-4213.pdf | Deception Detection in News Reports in the Russian Language: Lexics and Discourse | News verification and automated fact checking tend to be very important issues in our world. The research is initial. We collected a corpus for Russian (174 news reports, truthful and fake ones). We held two experiments, for both we applied SVMs algorithm (linear/rbf kernel) and Random Forest to classify the news repor... | ['Dina Pisarevskaya'] | 2017-09-01 | null | null | null | ws-2017-9 | ['deception-detection', 'rumour-detection'] | ['miscellaneous', 'natural-language-processing'] | [-3.43056500e-01 5.19638538e-01 -5.49312353e-01 -4.48532701e-01
-2.07321659e-01 -5.11434019e-01 1.08883727e+00 4.81210530e-01
-3.61735076e-01 1.29754651e+00 5.73903859e-01 -6.17324948e-01
1.19347991e-02 -9.02080357e-01 -4.71878141e-01 -5.29540837e-01
2.53128827e-01 4.77913439e-01 4.77434397e-01 -5.79844475... | [8.235981941223145, 10.253518104553223] |
70849e14-944f-4378-95ee-89080add2cf8 | learning-implicit-probability-distribution | 2211.11394 | null | https://arxiv.org/abs/2211.11394v1 | https://arxiv.org/pdf/2211.11394v1.pdf | Learning Implicit Probability Distribution Functions for Symmetric Orientation Estimation from RGB Images Without Pose Labels | Object pose estimation is a necessary prerequisite for autonomous robotic manipulation, but the presence of symmetry increases the complexity of the pose estimation task. Existing methods for object pose estimation output a single 6D pose. Thus, they lack the ability to reason about symmetries. Lately, modeling object ... | ['Sven Behnke', 'Luis Denninger', 'Arul Selvam Periyasamy'] | 2022-11-21 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 2.52598047e-01 2.68899381e-01 -1.07261255e-01 -5.71915746e-01
-7.44170904e-01 -6.69444799e-01 6.96402311e-01 -1.92560837e-01
-3.05468053e-01 2.80050635e-01 -3.36911708e-01 -1.15878686e-01
-2.60147840e-01 -6.96657360e-01 -1.11158550e+00 -5.10344982e-01
2.10117251e-01 1.15138710e+00 2.76191622e-01 1.97682500... | [7.421225547790527, -2.6115598678588867] |
d013a1a7-e2f5-4bb0-a33c-87e68ae54b1c | automatic-diagnosis-of-short-duration-12-lead | 1811.12194 | null | http://arxiv.org/abs/1811.12194v2 | http://arxiv.org/pdf/1811.12194v2.pdf | Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network | We present a model for predicting electrocardiogram (ECG) abnormalities in
short-duration 12-lead ECG signals which outperformed medical doctors on the
4th year of their cardiology residency. Such exams can provide a full
evaluation of heart activity and have not been studied in previous end-to-end
machine learning pap... | ['Thomas B. Schön', 'Wagner Meira Jr.', 'Jéssica A. Canazart', 'Paulo R. Gomes', 'Gabriela Paixão', 'Antônio H. Ribeiro', 'Milton Pifano', 'Antonio Luiz Ribeiro', 'Derick Oliveira', 'Manoel Horta Ribeiro'] | 2018-11-28 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 4.28847790e-01 1.94040179e-01 1.36307195e-01 -6.30725741e-01
-8.50779474e-01 -6.08300984e-01 -4.44765985e-01 2.41997942e-01
-3.07907552e-01 6.17134273e-01 -2.87466813e-02 -9.41790640e-01
-2.97112197e-01 -8.08406293e-01 -5.74507058e-01 -2.08434448e-01
-6.38541341e-01 6.21713400e-01 -3.24179947e-01 1.21344298... | [14.340518951416016, 3.29978084564209] |
4f960617-5996-4d69-b6dc-e83dd142f226 | a-multi-turn-machine-reading-comprehension | 2209.07972 | null | https://arxiv.org/abs/2209.07972v1 | https://arxiv.org/pdf/2209.07972v1.pdf | A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction | Emotion-cause pair extraction (ECPE) is an emerging task in emotion cause analysis, which extracts potential emotion-cause pairs from an emotional document. Most recent studies use end-to-end methods to tackle the ECPE task. However, these methods either suffer from a label sparsity problem or fail to model complicated... | ['Zhijing Wu', 'Jing Xu', 'Dandan song', 'Changzhi Zhou'] | 2022-09-16 | null | https://aclanthology.org/2022.coling-1.584 | https://aclanthology.org/2022.coling-1.584.pdf | coling-2022-10 | ['emotion-cause-pair-extraction', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.82475138e-01 1.58887044e-01 -1.17744640e-01 -6.61034703e-01
-9.25311685e-01 -5.82178831e-01 4.94116813e-01 2.53050804e-01
-3.31018604e-02 5.94953895e-01 4.20155138e-01 -1.07919060e-01
-2.45456889e-01 -4.68333960e-01 -6.79025590e-01 -4.31791812e-01
3.70582461e-01 3.85946524e-03 -2.65575647e-01 -3.74379277... | [12.625020980834961, 6.2092156410217285] |
14aaeacb-70ea-4adf-bcac-53e9e544f4c4 | a-bert-based-one-pass-multi-task-model-for | null | null | https://aclanthology.org/2020.bionlp-1.7 | https://aclanthology.org/2020.bionlp-1.7.pdf | A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction | Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text. However, the current approach is inefficient as it requires multiple passes through each input sequence. We extend a recently-proposed one-pass model for relation classification to a ... | ['Farig Sadeque', 'Dmitriy Dligach', 'Guergana Savova', 'Chen Lin', 'Timothy Miller', 'Steven Bethard'] | 2020-07-01 | null | null | null | ws-2020-7 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 3.32331270e-01 5.09117186e-01 -6.49838209e-01 -2.38622814e-01
-1.16091156e+00 -3.12865943e-01 4.73766744e-01 1.02317142e+00
-6.64691269e-01 7.34891355e-01 2.70107448e-01 -6.46049619e-01
-4.92286086e-01 -7.37279773e-01 -3.89748931e-01 -4.67827022e-01
-5.15062153e-01 8.74705017e-01 4.20238733e-01 -1.80374414... | [8.510612487792969, 8.966499328613281] |
07c595eb-a5cb-4479-91ae-991070b7fd38 | pmi-sampler-patch-similarity-guided-frame | 2304.06866 | null | https://arxiv.org/abs/2304.06866v1 | https://arxiv.org/pdf/2304.06866v1.pdf | PMI Sampler: Patch similarity guided frame selection for Aerial Action Recognition | We present a new algorithm for selection of informative frames in video action recognition. Our approach is designed for aerial videos captured using a moving camera where human actors occupy a small spatial resolution of video frames. Our algorithm utilizes the motion bias within aerial videos, which enables the selec... | ['Dinesh Manocha', 'Divya Kothandaraman', 'Xijun Wang', 'Ruiqi Xian'] | 2023-04-14 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 5.49169660e-01 -4.23157841e-01 -3.29906225e-01 -1.28441408e-01
-6.38686597e-01 -5.33871830e-01 3.72340977e-01 -1.07288092e-01
-5.48399031e-01 5.36259234e-01 3.31662923e-01 4.15169448e-01
-4.74372841e-02 -6.59897506e-01 -6.50528073e-01 -9.71150219e-01
-4.78596956e-01 -5.30389071e-01 7.55049944e-01 3.50814685... | [8.405574798583984, 0.46320053935050964] |
4bf42f90-4bdf-4e70-bd7c-b8659afe6430 | m-2-sgd-stable-stochastic-optimization-via-a | 2304.04172 | null | https://arxiv.org/abs/2304.04172v1 | https://arxiv.org/pdf/2304.04172v1.pdf | $μ^2$-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism | We consider stochastic convex optimization problems where the objective is an expectation over smooth functions. For this setting we suggest a novel gradient estimate that combines two recent mechanism that are related to notion of momentum. Then, we design an SGD-style algorithm as well as an accelerated version that ... | ['Kfir Y. Levy'] | 2023-04-09 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.30454922e-01 -5.97335882e-02 -2.10911017e-02 -1.79595947e-01
-1.07997310e+00 -5.41684985e-01 4.92370218e-01 -1.21135570e-01
-7.63349414e-01 9.40674603e-01 1.98994815e-01 -9.34334099e-02
-3.04729640e-01 -5.62315762e-01 -7.94093251e-01 -9.41078186e-01
-1.56266972e-01 -5.15364460e-04 2.29674831e-01 -3.03966165... | [6.874557971954346, 4.258936405181885] |
2caf7854-d687-4126-9d88-44f3a301068d | representation-learning-for-aspect-category | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/9194 | https://ojs.aaai.org/index.php/AAAI/article/view/9194/9053 | Representation Learning for Aspect Category Detection in Online Reviews | User-generated reviews are valuable resources for decision making. Identifying the aspect categories discussed in a given review sentence (e.g., “food” and “service” in restaurant reviews) is an important task of sentiment analysis and opinion mining. Given a predefined aspect category set, most previous researches lev... | ['Jianguo Xiao', 'Xiaojun Wan', 'Xinjie Zhou'] | 2015-02-09 | null | null | null | aaai-2015-2 | ['feature-engineering', 'aspect-category-detection'] | ['methodology', 'natural-language-processing'] | [ 2.01620921e-01 -1.60211906e-01 -5.02366841e-01 -7.87877440e-01
-7.84359038e-01 -3.03975046e-01 5.61829388e-01 7.63499081e-01
-5.85023582e-01 3.08377087e-01 3.28135639e-01 -2.46244252e-01
1.97894722e-01 -9.43146408e-01 -3.72568071e-01 -5.73295653e-01
2.66393036e-01 1.16558820e-01 -1.41325414e-01 -3.52593184... | [11.272974967956543, 6.6876654624938965] |
6e45839a-e730-4049-8048-7ddcec7cf5e1 | an-iterative-emotion-interaction-network-for | null | null | https://aclanthology.org/2020.coling-main.360 | https://aclanthology.org/2020.coling-main.360.pdf | An Iterative Emotion Interaction Network for Emotion Recognition in Conversations | Emotion recognition in conversations (ERC) has received much attention recently in the natural language processing community. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly model the emotion interaction between utterances by modeling dialogue context,... | ['Bing Qin', 'Huipeng Chen', 'Yijian Tian', 'Yang Wu', 'Yanyan Zhao', 'Xin Lu'] | 2020-12-01 | null | null | null | coling-2020-8 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 4.44159210e-02 2.49234170e-01 9.31749493e-02 -8.66326511e-01
-3.47088754e-01 -4.91454422e-01 4.27170455e-01 -1.57129496e-01
-2.85715759e-01 6.05515540e-01 4.84160155e-01 -1.59312189e-01
5.19511104e-01 -4.52490658e-01 -6.35632649e-02 -5.12279689e-01
2.07323819e-01 2.16623396e-01 -2.59180039e-01 -3.75775933... | [13.010128021240234, 6.134117603302002] |
5cbc166a-4f17-4c8e-8454-2292ba9618e8 | openndd-open-set-recognition-for | 2306.16045 | null | https://arxiv.org/abs/2306.16045v1 | https://arxiv.org/pdf/2306.16045v1.pdf | OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection | Neurodevelopmental disorders (NDDs) are a highly prevalent group of disorders and represent strong clinical behavioral similarities, and that make it very challenging for accurate identification of different NDDs such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). Moreover, there... | ['Lifang Wei', 'Lanyan Xue', 'Riqing Chen', 'Changcai Yang', 'Zhenshan Shi', 'Xiumei Liu', 'Xinyue Chang', 'Zihao Guan', 'Jiaming Yu'] | 2023-06-28 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 1.36809930e-01 1.09660946e-01 4.51302156e-02 -2.89147913e-01
-2.11566970e-01 -3.57245475e-01 5.64694777e-02 2.20850334e-01
-1.01855800e-01 6.09834433e-01 -2.04538524e-01 1.17230058e-01
-5.66369116e-01 -3.73412251e-01 -5.08798063e-01 -5.38451016e-01
-1.97081670e-01 5.76374650e-01 2.66457647e-02 3.64383198... | [12.717962265014648, 3.0230801105499268] |
14ce419d-1709-414f-b8c4-b451e2b00ba9 | a-large-scale-varying-view-rgb-d-action | 1904.10681 | null | http://arxiv.org/abs/1904.10681v1 | http://arxiv.org/pdf/1904.10681v1.pdf | A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition | Current researches of action recognition mainly focus on single-view and
multi-view recognition, which can hardly satisfies the requirements of
human-robot interaction (HRI) applications to recognize actions from arbitrary
views. The lack of datasets also sets up barriers. To provide data for
arbitrary-view action reco... | ['Wei-Shi Zheng', 'Yang Yang', 'Yanli Ji', 'Feixiang Xu', 'Fumin Shen', 'Heng Tao Shen'] | 2019-04-24 | null | null | null | null | ['action-analysis'] | ['computer-vision'] | [ 1.48215532e-01 -4.30176049e-01 -3.64319354e-01 -4.04260814e-01
-3.95955414e-01 -1.75795913e-01 3.69480789e-01 -9.74685371e-01
-2.87262708e-01 3.77229542e-01 5.33962011e-01 2.75730312e-01
6.14706278e-02 -5.76711357e-01 -3.23089898e-01 -7.47749388e-01
4.43246514e-01 2.15073004e-01 4.16546226e-01 -2.13621423... | [7.911269664764404, 0.3645169138908386] |
edc8d3f5-c3e2-43ff-98ae-311b543c9a1c | behavioral-estimates-of-conceptual-structure | 2304.02754 | null | https://arxiv.org/abs/2304.02754v1 | https://arxiv.org/pdf/2304.02754v1.pdf | Behavioral estimates of conceptual structure are robust across tasks in humans but not large language models | Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain. For many years, such use involved extracting vector-space representations of words and using distances among these to predict or understand human behavior in various semantic ... | ['Timothy T Rogers', 'Kushin Mukherjee', 'Lisa Padua', 'Siddharth Suresh'] | 2023-04-05 | null | null | null | null | ['culture'] | ['speech'] | [ 1.18974934e-03 -9.66761261e-02 2.15084478e-02 -5.39074957e-01
2.90026635e-01 -7.23477602e-01 8.40773344e-01 3.54063690e-01
-8.14119697e-01 1.97816178e-01 4.11722153e-01 -4.16333020e-01
-4.35530186e-01 -8.00573111e-01 -1.25782177e-01 -2.12944269e-01
2.55351782e-01 7.66247869e-01 8.50724336e-03 -5.42133629... | [10.115897178649902, 8.342057228088379] |
0f91f672-509d-47f5-aa28-567e4911cb58 | iterative-frame-level-representation-learning | 2112.01402 | null | https://arxiv.org/abs/2112.01402v2 | https://arxiv.org/pdf/2112.01402v2.pdf | Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentation | Temporal action segmentation classifies the action of each frame in (long) video sequences. Due to the high cost of frame-wise labeling, we propose the first semi-supervised method for temporal action segmentation. Our method hinges on unsupervised representation learning, which, for temporal action segmentation, poses... | ['Angela Yao', 'Rahul Rahaman', 'Dipika Singhania'] | 2021-12-02 | null | null | null | null | ['semi-supervised-video-classification'] | ['computer-vision'] | [ 6.48387671e-01 -1.87300965e-02 -5.34983218e-01 -3.80954653e-01
-7.56304324e-01 -8.13409209e-01 6.26520693e-01 -2.06000641e-01
-4.18760657e-01 5.07099569e-01 4.97266233e-01 3.43794934e-02
-2.09161073e-01 -1.91989526e-01 -7.85736978e-01 -6.65109754e-01
-4.12070572e-01 1.31983876e-01 6.77877367e-01 1.32999539... | [8.458768844604492, 0.536331295967102] |
418cf33b-23f9-4aa4-8eea-25c85f3b15e2 | api-boosting-multi-agent-reinforcement | 2203.05285 | null | https://arxiv.org/abs/2203.05285v2 | https://arxiv.org/pdf/2203.05285v2.pdf | Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework | The state space in Multiagent Reinforcement Learning (MARL) grows exponentially with the agent number. Such a curse of dimensionality results in poor scalability and low sample efficiency, inhibiting MARL for decades. To break this curse, we propose a unified agent permutation framework that exploits the permutation in... | ['Weixun Wang', 'Jianye Hao', 'Zhen Wang', 'Yan Zheng', 'Dong Li', 'Yaodong Yang', 'Hangyu Mao', 'Xiaotian Hao'] | 2022-03-10 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-2.19027456e-02 1.17480800e-01 -3.74181539e-01 1.62646487e-01
-1.78544462e-01 -9.59615946e-01 7.32843876e-01 -2.19870403e-01
-6.92750752e-01 9.86725032e-01 -2.61572888e-03 -2.55268127e-01
-6.01822078e-01 -9.86755967e-01 -9.24765706e-01 -8.81188571e-01
-4.60663944e-01 5.91437459e-01 1.83705762e-01 -3.31606746... | [3.6895687580108643, 1.9995744228363037] |
2205633f-34ef-460e-8028-7167f691159b | self-supervised-pretraining-of-visual | 2103.01988 | null | https://arxiv.org/abs/2103.01988v2 | https://arxiv.org/pdf/2103.01988v2.pdf | Self-supervised Pretraining of Visual Features in the Wild | Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment, that is the highly curated ImageNet dataset. However, the premise of self-supervised learning is that it can learn from any random image an... | ['Piotr Bojanowski', 'Armand Joulin', 'Ishan Misra', 'Vitaliy Liptchinsky', 'Mannat Singh', 'Vivek Pai', 'Pengchao Wang', 'Min Xu', 'Benjamin Lefaudeux', 'Mathilde Caron', 'Priya Goyal'] | 2021-03-02 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.08175978e-01 3.46485287e-01 -3.92541319e-01 -2.71832347e-01
-5.21977723e-01 -1.91002652e-01 6.92062557e-01 6.15439527e-02
-6.89843416e-01 7.39519596e-01 9.71326455e-02 -8.82093236e-02
7.67166689e-02 -7.09115088e-01 -1.13240731e+00 -7.03404248e-01
-2.03885823e-01 5.67742586e-01 3.95811886e-01 -1.84893593... | [9.73292064666748, 2.4603285789489746] |
b0c0473b-cc8a-4828-9e25-22c21b4c7e94 | in-defense-of-online-models-for-video | 2207.10661 | null | https://arxiv.org/abs/2207.10661v1 | https://arxiv.org/pdf/2207.10661v1.pdf | In Defense of Online Models for Video Instance Segmentation | In recent years, video instance segmentation (VIS) has been largely advanced by offline models, while online models gradually attracted less attention possibly due to their inferior performance. However, online methods have their inherent advantage in handling long video sequences and ongoing videos while offline model... | ['Xiang Bai', 'Alan Yuille', 'Song Bai', 'Yi Jiang', 'Qihao Liu', 'Junfeng Wu'] | 2022-07-21 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 6.69996440e-02 -1.14186570e-01 -4.33512479e-01 2.81184763e-02
-6.53877020e-01 -6.88901901e-01 5.04128098e-01 1.51307896e-01
-5.64493418e-01 4.98758912e-01 -1.98658765e-03 -1.01142257e-01
5.71758039e-02 -3.77223372e-01 -9.90112424e-01 -4.53610092e-01
-4.61036474e-01 2.76715189e-01 7.64592230e-01 1.29028991... | [9.193657875061035, 0.04817787930369377] |
b50a09dd-e638-4e2e-86f2-5c34e927ed18 | synthetic-dataset-generation-of-driver | 2102.00252 | null | https://arxiv.org/abs/2102.00252v1 | https://arxiv.org/pdf/2102.00252v1.pdf | Synthetic Dataset Generation of Driver Telematics | This article describes techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset. The synthetic dataset generated has 100,000 policies that included observations about driver's claims experience together with associated classical risk variables and ... | ['Emiliano A. Valdez', 'Jean-Philippe Boucher', 'Banghee So'] | 2021-01-30 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 1.81449398e-01 3.80556911e-01 -2.77195990e-01 -7.68100858e-01
-6.16645694e-01 -6.91848844e-02 7.35024154e-01 4.14332747e-01
-5.23515522e-01 1.18118799e+00 2.83278555e-01 -1.04166329e+00
-5.08939922e-01 -1.08975983e+00 -6.46887541e-01 -4.61531818e-01
1.82230145e-01 8.34353089e-01 -2.28492260e-01 -3.73611629... | [7.662376880645752, 5.006927967071533] |
4979600e-c1de-4216-892f-257a3e116a6b | predictive-linguistic-cues-for-fake-news-a | 2211.14505 | null | https://arxiv.org/abs/2211.14505v1 | https://arxiv.org/pdf/2211.14505v1.pdf | Predictive linguistic cues for fake news: a societal artificial intelligence problem | Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algo... | ['Ponnurangam Kumaraguru', 'Nagender Aneja', 'Sandhya Aneja'] | 2022-11-26 | null | null | null | null | ['news-generation'] | ['natural-language-processing'] | [-2.11224079e-01 9.91143063e-02 -3.99703562e-01 -4.85204577e-01
-3.47051293e-01 -7.07011521e-01 9.90169764e-01 4.53270495e-01
-3.38254660e-01 1.04378569e+00 5.19008100e-01 -1.39320910e-01
2.76135266e-01 -1.07224143e+00 -7.22038209e-01 -2.91832566e-01
1.41928375e-01 2.48786315e-01 1.67373180e-01 -3.83032948... | [8.143823623657227, 10.2449951171875] |
258c04cf-e7f3-4881-9c44-2cd2fd18f2ef | transformers-and-cnns-both-beat-humans-on | 2209.06629 | null | https://arxiv.org/abs/2209.06629v1 | https://arxiv.org/pdf/2209.06629v1.pdf | Transformers and CNNs both Beat Humans on SBIR | Sketch-based image retrieval (SBIR) is the task of retrieving natural images (photos) that match the semantics and the spatial configuration of hand-drawn sketch queries. The universality of sketches extends the scope of possible applications and increases the demand for efficient SBIR solutions. In this paper, we stud... | ['Thierry Dutoit', 'Saïd Mahmoudi', 'Stéphane Dupont', 'Omar Seddati'] | 2022-09-14 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 8.09002519e-02 -4.01506573e-01 -1.98421761e-01 -1.14575379e-01
-8.99888933e-01 -9.90611672e-01 9.74270999e-01 -2.60671943e-01
-3.48601043e-01 3.94513518e-01 1.23721279e-01 -1.41841665e-01
-4.15095925e-01 -5.51026404e-01 -6.27707481e-01 -3.89564902e-01
1.52135998e-01 5.40938795e-01 4.59485531e-01 -4.50077653... | [11.568347930908203, 0.5038918256759644] |
4259ec41-fef7-4bdc-8cc2-42b18c976a86 | gaussian-constrained-attention-network-for | 2010.09169 | null | https://arxiv.org/abs/2010.09169v1 | https://arxiv.org/pdf/2010.09169v1.pdf | Gaussian Constrained Attention Network for Scene Text Recognition | Scene text recognition has been a hot topic in computer vision. Recent methods adopt the attention mechanism for sequence prediction which achieve convincing results. However, we argue that the existing attention mechanism faces the problem of attention diffusion, in which the model may not focus on a certain character... | ['Weiping Wang', 'Fei Yang', 'Yu Zhou', 'Xugong Qin', 'Zhi Qiao'] | 2020-10-19 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 1.44373149e-01 -1.12513743e-01 -1.99598018e-02 -1.99421197e-01
-3.08438182e-01 9.96590704e-02 3.50546122e-01 -1.53728589e-01
-4.47864532e-01 3.01631451e-01 2.30968744e-01 -2.57963657e-01
1.65297136e-01 -5.93893051e-01 -4.04747754e-01 -8.67968500e-01
6.01888657e-01 3.09707731e-01 6.44339383e-01 -7.57455900... | [11.860895156860352, 2.131948947906494] |
7ff330ba-7ecc-41de-aeb3-e7ca873d8530 | probabilistic-neural-programs | 1612.00712 | null | http://arxiv.org/abs/1612.00712v1 | http://arxiv.org/pdf/1612.00712v1.pdf | Probabilistic Neural Programs | We present probabilistic neural programs, a framework for program induction
that permits flexible specification of both a computational model and inference
algorithm while simultaneously enabling the use of deep neural networks.
Probabilistic neural programs combine a computation graph for specifying a
neural network w... | ['Kenton W. Murray', 'Jayant Krishnamurthy'] | 2016-12-02 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 3.00291747e-01 4.29209083e-01 -4.77959633e-01 -6.70065999e-01
-6.19397581e-01 -6.54842198e-01 7.19281197e-01 3.64311188e-01
-1.33721396e-01 2.80554801e-01 1.64160416e-01 -1.10799098e+00
1.29665256e-01 -1.44552493e+00 -1.12431872e+00 -2.41234109e-01
-1.02310464e-01 8.63714337e-01 4.09199923e-01 1.79885954... | [8.50281810760498, 7.272164344787598] |
a256ebc9-a5f7-47f6-954f-f4bcc1a415e7 | multi-model-ensemble-analysis-with-neural | 2202.04152 | null | https://arxiv.org/abs/2202.04152v4 | https://arxiv.org/pdf/2202.04152v4.pdf | Multi-model Ensemble Analysis with Neural Network Gaussian Processes | Multi-model ensemble analysis integrates information from multiple climate models into a unified projection. However, existing integration approaches based on model averaging can dilute fine-scale spatial information and incur bias from rescaling low-resolution climate models. We propose a statistical approach, called ... | ['Ryan Sriver', 'Bo Li', 'Trevor Harris'] | 2022-02-08 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-2.49068618e-01 -3.83682936e-01 3.09547246e-01 -2.17109442e-01
-8.64579737e-01 -5.29225111e-01 8.69890213e-01 -9.43706855e-02
-2.71607518e-01 1.24033427e+00 3.32127362e-01 -8.16253662e-01
7.94508532e-02 -1.27110744e+00 -3.61958981e-01 -9.89973962e-01
-2.65907466e-01 4.19764280e-01 -1.64250229e-02 -4.24678564... | [6.552944183349609, 2.9638559818267822] |
1fefa5a2-83d4-4ad1-92a3-6c4a28748809 | meta-gating-framework-for-fast-and-continuous | 2306.13277 | null | https://arxiv.org/abs/2306.13277v1 | https://arxiv.org/pdf/2306.13277v1.pdf | Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments | With the great success of deep learning (DL) in image classification, speech recognition, and other fields, more and more studies have applied various neural networks (NNs) to wireless resource allocation. Generally speaking, these artificial intelligent (AI) models are trained under some special learning hypotheses, e... | ['Yunlong Cai', 'Guanding Yu', 'Mengyuan Lee', 'Qiushuo Hou'] | 2023-06-23 | null | null | null | null | ['meta-learning'] | ['methodology'] | [ 1.68037802e-01 -3.54309589e-01 -3.51490229e-01 -2.10239574e-01
3.36452760e-02 1.04368150e-01 1.71978310e-01 -8.54992568e-02
-6.53317511e-01 8.45004141e-01 -1.38428032e-01 -2.18160450e-01
-4.25671220e-01 -9.88652706e-01 -4.13111061e-01 -1.06838036e+00
-1.01866826e-01 2.34825671e-01 2.55571306e-01 -3.48102972... | [6.323454856872559, 1.5984814167022705] |
57c5c71a-7334-479e-bafa-af7c8780321c | probabilistic-soft-logic-for-semantic-textual | null | null | https://aclanthology.org/P14-1114 | https://aclanthology.org/P14-1114.pdf | Probabilistic Soft Logic for Semantic Textual Similarity | null | ['Katrin Erk', 'Islam Beltagy', 'Raymond Mooney'] | 2014-06-01 | null | null | null | acl-2014-6 | ['video-description'] | ['computer-vision'] | [-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... | [-7.173243522644043, 3.5775160789489746] |
d00e8b32-bd3b-4b2b-bc78-8e5974fc9bf9 | walkingtime-dynamic-graph-embedding-using | 2111.10928 | null | https://arxiv.org/abs/2111.10928v1 | https://arxiv.org/pdf/2111.10928v1.pdf | WalkingTime: Dynamic Graph Embedding Using Temporal-Topological Flows | Increased attention has been paid over the last four years to dynamic network embedding. Existing dynamic embedding methods, however, consider the problem as limited to the evolution of a topology over a sequence of global, discrete states. We propose a novel embedding algorithm, WalkingTime, based on a fundamentally d... | ['David Bayani'] | 2021-11-22 | null | null | null | null | ['dynamic-graph-embedding', 'network-embedding'] | ['graphs', 'methodology'] | [-4.79089804e-02 1.43072098e-01 -3.46505046e-01 -1.02373064e-01
4.65902358e-01 -8.27161252e-01 1.04969883e+00 7.33406484e-01
-1.94664836e-01 6.48347855e-01 3.57436448e-01 -4.89941686e-01
-1.01803231e+00 -1.34989154e+00 -1.22390278e-01 -5.16009569e-01
-1.28568256e+00 9.32416081e-01 4.64645088e-01 -3.68988007... | [7.160407066345215, 6.074899196624756] |
9691c13e-1589-4e85-bccc-b6b5ab825452 | the-devil-is-in-the-labels-noisy-label-1 | 2206.03014 | null | https://arxiv.org/abs/2206.03014v1 | https://arxiv.org/pdf/2206.03014v1.pdf | The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation | Unbiased SGG has achieved significant progress over recent years. However, almost all existing SGG models have overlooked the ground-truth annotation qualities of prevailing SGG datasets, i.e., they always assume: 1) all the manually annotated positive samples are equally correct; 2) all the un-annotated negative sampl... | ['Jun Xiao', 'Songyang Zhang', 'Zhimeng Zhang', 'Yifeng Huang', 'Long Chen', 'Lin Li'] | 2022-06-07 | the-devil-is-in-the-labels-noisy-label | http://openaccess.thecvf.com//content/CVPR2022/html/Li_The_Devil_Is_in_the_Labels_Noisy_Label_Correction_for_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_The_Devil_Is_in_the_Labels_Noisy_Label_Correction_for_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-graph-generation'] | ['computer-vision'] | [ 4.62968320e-01 3.34479690e-01 -2.94087797e-01 -4.63906586e-01
-1.23120999e+00 -7.65427470e-01 3.50606680e-01 3.00774425e-02
-5.27755879e-02 9.33703244e-01 -8.24801475e-02 -2.12829053e-01
1.27820790e-01 -7.87758291e-01 -6.32852077e-01 -1.00712252e+00
4.90509748e-01 7.80966401e-01 4.95552719e-01 1.39127716... | [9.452346801757812, 3.8596174716949463] |
d69d4a62-c39f-4ffb-ab81-a650b0322530 | towards-unconstrained-end-to-end-text | 1908.09231 | null | https://arxiv.org/abs/1908.09231v1 | https://arxiv.org/pdf/1908.09231v1.pdf | Towards Unconstrained End-to-End Text Spotting | We propose an end-to-end trainable network that can simultaneously detect and recognize text of arbitrary shape, making substantial progress on the open problem of reading scene text of irregular shape. We formulate arbitrary shape text detection as an instance segmentation problem; an attention model is then used to d... | ['Ying Xiao', 'Michalis Raptis', 'Yasuhisa Fujii', 'Siyang Qin', 'Alessandro Bissacco'] | 2019-08-24 | towards-unconstrained-end-to-end-text-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Qin_Towards_Unconstrained_End-to-End_Text_Spotting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Qin_Towards_Unconstrained_End-to-End_Text_Spotting_ICCV_2019_paper.pdf | iccv-2019-10 | ['text-spotting'] | ['computer-vision'] | [ 8.60530555e-01 3.02084833e-01 2.47984789e-02 -3.23608994e-01
-1.41060925e+00 -9.17895079e-01 6.85223520e-01 1.03815258e-01
-3.99371386e-01 1.00379251e-01 1.12892009e-01 -3.99480551e-01
5.29596806e-01 -3.81147772e-01 -9.82796788e-01 -4.87734616e-01
6.01298094e-01 7.42338300e-01 3.98331851e-01 1.43961832... | [11.955204010009766, 2.2715554237365723] |
e51d7ee8-04f4-4158-a44e-388ca8698908 | system-iii-learning-with-domain-knowledge-for | 2304.11593 | null | https://arxiv.org/abs/2304.11593v1 | https://arxiv.org/pdf/2304.11593v1.pdf | System III: Learning with Domain Knowledge for Safety Constraints | Reinforcement learning agents naturally learn from extensive exploration. Exploration is costly and can be unsafe in $\textit{safety-critical}$ domains. This paper proposes a novel framework for incorporating domain knowledge to help guide safe exploration and boost sample efficiency. Previous approaches impose constra... | ['Alesandro Abbate', 'Hosien Hasanbieg', 'Fazl Barez'] | 2023-04-23 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.20613822e-01 4.34510589e-01 -1.61642507e-01 -2.80242950e-01
-5.23472726e-01 -6.96549714e-01 3.21789473e-01 2.75894612e-01
-1.02397573e+00 1.24261284e+00 -3.31051916e-01 -5.24959862e-01
-9.20674264e-01 -1.01852620e+00 -9.07254338e-01 -5.79300582e-01
-5.97991049e-01 2.46945351e-01 1.50467485e-01 -5.76456606... | [4.505274295806885, 2.147359848022461] |
ee8e36c1-89a0-4511-bd8d-fdcc2df33b17 | a-semantically-consistent-and-syntactically | null | null | https://aclanthology.org/2020.coling-main.102 | https://aclanthology.org/2020.coling-main.102.pdf | A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation | Paraphrase generation aims to generate semantically consistent sentences with different syntactic realizations. Most of the recent studies rely on the typical encoder-decoder framework where the generation process is deterministic. However, in practice, the ability to generate multiple syntactically different paraphras... | ['Yaohui Jin', 'Hao He', 'Liqiang Xiao', 'Jidong Tian', 'Wenqing Chen'] | 2020-12-01 | null | null | null | coling-2020-8 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.82050422e-01 2.18329336e-02 -1.14416040e-01 -6.06424749e-01
-1.03451884e+00 -7.72630036e-01 5.23239791e-01 -3.85146439e-01
1.18409529e-01 9.85977173e-01 6.89201474e-01 -5.20308129e-03
4.01027083e-01 -1.05690968e+00 -9.13050771e-01 -6.91007614e-01
8.35377991e-01 3.45546156e-01 8.26426372e-02 -3.08246851... | [11.706475257873535, 9.307408332824707] |
3d3a1deb-1da9-4efb-a46c-4945184f3b64 | fair-and-skill-diverse-student-group | 2301.09984 | null | https://arxiv.org/abs/2301.09984v1 | https://arxiv.org/pdf/2301.09984v1.pdf | Fair and skill-diverse student group formation via constrained k-way graph partitioning | Forming the right combination of students in a group promises to enable a powerful and effective environment for learning and collaboration. However, defining a group of students is a complex task which has to satisfy multiple constraints. This work introduces an unsupervised algorithm for fair and skill-diverse studen... | ['Danilo Mandic', 'Ljubisa Stankovic', 'Imad Jaimoukha', 'Alexander Jenkins'] | 2023-01-12 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 2.55754799e-01 4.49982852e-01 -2.56639898e-01 -4.06774998e-01
-3.42190951e-01 -8.01762283e-01 3.13963711e-01 5.13159633e-01
-4.56207693e-01 5.72888017e-01 9.58595723e-02 -2.71674693e-01
-1.38529551e+00 -8.35717440e-01 8.40531196e-03 -7.94470489e-01
2.74555653e-01 7.34038949e-01 -1.88700438e-01 -1.10769302... | [7.554752349853516, 4.587821960449219] |
a7466225-ae9a-4252-be49-5d97feaa5e5f | chimst-a-chinese-medical-corpus-for-word | null | null | https://aclanthology.org/2022.lrec-1.607 | https://aclanthology.org/2022.lrec-1.607.pdf | ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition | Chinese word segmentation (CWS) and named entity recognition (NER) are two important tasks in Chinese natural language processing. To achieve good model performance on these tasks, existing neural approaches normally require a large amount of labeled training data, which is often unavailable for specific domains such a... | ['Yan Song', 'Fei Xia', 'Han Qin', 'Yuanhe Tian'] | null | null | null | null | lrec-2022-6 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 2.08855063e-01 2.05909163e-01 -3.03283572e-01 -6.55000985e-01
-1.19394672e+00 -3.16574961e-01 -2.32092336e-01 4.34030145e-01
-1.14348483e+00 6.59817338e-01 3.38138044e-01 -8.15466702e-01
5.01317680e-01 -6.13308311e-01 1.08451629e-02 -3.79315495e-01
1.22354396e-01 5.96367478e-01 2.73819208e-01 -8.35221782... | [8.638930320739746, 8.888469696044922] |
89650a79-a5b4-4c5e-9141-7b1f522cc91c | multilingual-end-to-end-entity-linking | 2306.08896 | null | https://arxiv.org/abs/2306.08896v1 | https://arxiv.org/pdf/2306.08896v1.pdf | Multilingual End to End Entity Linking | Entity Linking is one of the most common Natural Language Processing tasks in practical applications, but so far efficient end-to-end solutions with multilingual coverage have been lacking, leading to complex model stacks. To fill this gap, we release and open source BELA, the first fully end-to-end multilingual entity... | ['Nicola Cancedda', 'Frédéric A. Dreyer', 'Borislav Kozlovskii', 'Simone Merello', 'Louis Martin', 'Kashyap Popat', 'Nora Kassner', 'Mikhail Plekhanov'] | 2023-06-15 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [-7.83460796e-01 3.10063571e-01 -4.97022390e-01 -1.54196739e-01
-1.18791091e+00 -9.98493075e-01 5.35681665e-01 7.63941169e-01
-8.67936015e-01 1.07318807e+00 3.95029336e-01 -2.52301276e-01
-1.16797881e-02 -5.52759469e-01 -7.48368859e-01 4.33008254e-01
-1.34252459e-01 1.15831697e+00 5.32356799e-01 -5.03924727... | [9.5497465133667, 9.02913761138916] |
1cc2da6b-4006-4b6d-8a21-4655745f1db5 | modelling-low-resource-accents-without-accent | 2301.04606 | null | https://arxiv.org/abs/2301.04606v1 | https://arxiv.org/pdf/2301.04606v1.pdf | Modelling low-resource accents without accent-specific TTS frontend | This work focuses on modelling a speaker's accent that does not have a dedicated text-to-speech (TTS) frontend, including a grapheme-to-phoneme (G2P) module. Prior work on modelling accents assumes a phonetic transcription is available for the target accent, which might not be the case for low-resource, regional accent... | ['Marius Cotescu', 'Kayoko Yanagisawa', 'Kamil Deja', 'Marta Czarnowska', 'Georgi Tinchev'] | 2023-01-11 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 1.34868801e-01 3.56771559e-01 3.72467011e-01 -4.55446541e-01
-1.08152676e+00 -8.85274410e-01 5.62512994e-01 -1.34848565e-01
-4.12885278e-01 7.94045568e-01 4.43083376e-01 -5.77033758e-01
3.99840832e-01 -3.93060625e-01 -5.66412628e-01 -5.54428995e-01
4.63780105e-01 7.95878589e-01 1.52552113e-01 -5.38884223... | [14.677705764770508, 6.743370056152344] |
40369488-61f3-421c-9db5-eb386c4ef70d | traffic-sign-classification-using-deep-and | 2209.15251 | null | https://arxiv.org/abs/2209.15251v1 | https://arxiv.org/pdf/2209.15251v1.pdf | Traffic Sign Classification Using Deep and Quantum Neural Networks | Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchm... | ['Tomasz Kryjak', 'Sylwia Kuros'] | 2022-09-30 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 2.06916049e-01 -3.61944586e-01 -2.54883617e-01 -3.26932818e-01
-9.78877246e-02 -1.35556594e-01 8.94928932e-01 -6.39743388e-01
-9.06699896e-01 7.44538307e-01 -6.32132471e-01 -6.90547645e-01
4.46816981e-02 -9.25090790e-01 -3.15254897e-01 -9.76679564e-01
2.73209333e-01 3.95004541e-01 5.16993523e-01 -6.49345934... | [5.598118782043457, 4.931694507598877] |
6d1be044-376d-4a16-b65e-056ad8fca5d3 | recovering-3d-human-mesh-from-monocular | 2203.01923 | null | https://arxiv.org/abs/2203.01923v3 | https://arxiv.org/pdf/2203.01923v3.pdf | Recovering 3D Human Mesh from Monocular Images: A Survey | Estimating human pose and shape from monocular images is a long-standing problem in computer vision. Since the release of statistical body models, 3D human mesh recovery has been drawing broader attention. With the same goal of obtaining well-aligned and physically plausible mesh results, two paradigms have been develo... | ['LiMin Wang', 'Yebin Liu', 'Hongwen Zhang', 'Yating Tian'] | 2022-03-03 | null | null | null | null | ['3d-human-pose-and-shape-estimation', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [ 4.55726646e-02 -4.74995524e-02 -3.53675455e-01 -2.77585447e-01
-5.84797800e-01 -5.37185557e-02 2.80572116e-01 -1.86260730e-01
-1.73116639e-01 6.19799018e-01 2.33966067e-01 3.15601975e-01
7.02556446e-02 -3.66840988e-01 -6.41656995e-01 -4.67774242e-01
-6.99600158e-03 5.45902610e-01 6.13531023e-02 -1.97713152... | [7.073060035705566, -1.0967241525650024] |
29e6aab6-a3f3-4d2a-a63f-303cf4c49e8f | enhancing-black-box-few-shot-text | 2305.13785 | null | https://arxiv.org/abs/2305.13785v1 | https://arxiv.org/pdf/2305.13785v1.pdf | Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation | Training or finetuning large-scale language models (LLMs) such as GPT-3 requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One practical area of research is to treat these models as black boxes and interact with them through their inferen... | ['Haizhou Li', 'Yan Zhang', 'Yiming Chen', 'Bin Wang', 'Jiahui Xu', 'Chen Zhang', 'Danqing Luo'] | 2023-05-23 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.75192112e-01 3.65303993e-01 -6.30763352e-01 -5.14250040e-01
-9.52606499e-01 -3.95338178e-01 8.78566861e-01 2.89049119e-01
-8.51325095e-01 5.44448495e-01 1.75261050e-01 -7.81838775e-01
2.62815177e-01 -5.32945216e-01 -6.20044947e-01 -4.67867911e-01
2.84830421e-01 8.03549170e-01 1.88466772e-01 -2.89417237... | [10.769956588745117, 8.109831809997559] |
13fec705-bb28-45c8-b6d1-70386d7cca8d | cmdfusion-bidirectional-fusion-network-with | 2307.04091 | null | https://arxiv.org/abs/2307.04091v1 | https://arxiv.org/pdf/2307.04091v1.pdf | CMDFusion: Bidirectional Fusion Network with Cross-modality Knowledge Distillation for LIDAR Semantic Segmentation | 2D RGB images and 3D LIDAR point clouds provide complementary knowledge for the perception system of autonomous vehicles. Several 2D and 3D fusion methods have been explored for the LIDAR semantic segmentation task, but they suffer from different problems. 2D-to-3D fusion methods require strictly paired data during inf... | ['Qifeng Chen', 'Yingya Zhang', 'Maochun Luo', 'Hang Zheng', 'Kun Li', 'Yixuan Pei', 'Shiwei Zhang', 'Jun Cen'] | 2023-07-09 | null | null | null | null | ['semantic-segmentation', 'autonomous-vehicles', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.67680174e-01 7.20654940e-03 -3.07427526e-01 -5.45897841e-01
-4.71855789e-01 -7.04801738e-01 6.74857378e-01 -1.24208473e-01
-4.19654787e-01 7.20632672e-01 -5.46567857e-01 -5.73992372e-01
-1.21342577e-01 -1.34948552e+00 -7.80850530e-01 -6.44622743e-01
5.06061137e-01 7.20908403e-01 7.03872204e-01 -2.26525426... | [8.21925163269043, -2.541196346282959] |
8532b7f0-cbb1-46f4-be0f-24440a44781a | dressing-3d-humans-using-a-conditional-mesh | 1907.13615 | null | https://arxiv.org/abs/1907.13615v3 | https://arxiv.org/pdf/1907.13615v3.pdf | Learning to Dress 3D People in Generative Clothing | Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to... | ['Gerard Pons-Moll', 'Sergi Pujades', 'Qianli Ma', 'Michael J. Black', 'Anurag Ranjan', 'Jinlong Yang', 'Siyu Tang'] | 2019-07-31 | learning-to-dress-3d-people-in-generative | http://openaccess.thecvf.com/content_CVPR_2020/html/Ma_Learning_to_Dress_3D_People_in_Generative_Clothing_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ma_Learning_to_Dress_3D_People_in_Generative_Clothing_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-shape-modeling', '3d-shape-generation', '3d-human-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.11197357e-03 -9.18707624e-02 1.24869764e-01 -3.65585089e-01
-2.26277649e-01 -7.92029262e-01 2.40244240e-01 -6.04142606e-01
2.65580565e-01 4.59646016e-01 2.44463354e-01 2.68876016e-01
2.97914535e-01 -8.74811471e-01 -9.37775731e-01 -3.84445637e-01
4.74276692e-02 6.90224230e-01 3.65310861e-03 -3.43950510... | [7.134521484375, -1.3346192836761475] |
4d7ad9fb-9205-483e-97ed-2c4cb6b199ca | counter-twit-an-italian-corpus-for-online | null | null | https://aclanthology.org/2022.woah-1.6 | https://aclanthology.org/2022.woah-1.6.pdf | Counter-TWIT: An Italian Corpus for Online Counterspeech in Ecological Contexts | This work describes the process of creating a corpus of Twitter conversations annotated for the presence of counterspeech in response to toxic speech related to axes of discrimination linked to sexism, racism and homophobia. The main novelty is an annotated dataset comprising relevant tweets in their context of occurre... | ['Viviana Patti', 'Biancamaria Cepollaro', 'Valerio Basile', 'Pierpaolo Goffredo'] | null | null | null | null | naacl-woah-2022-7 | ['counterspeech-detection'] | ['natural-language-processing'] | [ 4.58378345e-01 5.09051859e-01 -3.03781718e-01 -4.77762103e-01
-7.43541300e-01 -7.02151358e-01 1.13231611e+00 8.25818539e-01
-6.30307257e-01 4.64290857e-01 1.05226088e+00 -3.12513024e-01
-1.83128342e-01 -5.67920387e-01 -4.00808752e-02 -3.42947721e-01
6.58884943e-02 4.96357381e-01 1.99045464e-02 -5.43694377... | [8.73162841796875, 10.473784446716309] |
f70c1366-e015-4734-a566-aa0e9a190f2e | an-explainable-cnn-approach-for-medical-codes | 2101.11430 | null | https://arxiv.org/abs/2101.11430v1 | https://arxiv.org/pdf/2101.11430v1.pdf | An Explainable CNN Approach for Medical Codes Prediction from Clinical Text | Method: We develop CNN-based methods for automatic ICD coding based on clinical text from intensive care unit (ICU) stays. We come up with the Shallow and Wide Attention convolutional Mechanism (SWAM), which allows our model to learn local and low-level features for each label. The key idea behind our model design is t... | ['Fei Teng', 'Shu Yuan Hu'] | 2021-01-14 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 1.29540384e-01 2.60556996e-01 -2.16582105e-01 -6.77286088e-01
-6.93109632e-01 -3.04260314e-01 1.53332707e-02 6.57870173e-01
-6.44218996e-02 4.11374509e-01 5.81812739e-01 -6.78200185e-01
-5.20530999e-01 -6.34930968e-01 -6.71573877e-01 -4.35853124e-01
-3.52259219e-01 7.62318611e-01 -4.98191804e-01 -9.43698511... | [8.012423515319824, 6.803910255432129] |
199213bd-e536-47a0-92c1-5c644f2df036 | learning-to-substitute-spans-towards | 2306.02840 | null | https://arxiv.org/abs/2306.02840v1 | https://arxiv.org/pdf/2306.02840v1.pdf | Learning to Substitute Spans towards Improving Compositional Generalization | Despite the rising prevalence of neural sequence models, recent empirical evidences suggest their deficiency in compositional generalization. One of the current de-facto solutions to this problem is compositional data augmentation, aiming to incur additional compositional inductive bias. Nonetheless, the improvement of... | ['Defu Lian', 'Ying WEI', 'Zhaoyi Li'] | 2023-06-05 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 8.73681307e-01 -3.51399891e-02 -3.21456224e-01 -2.58538246e-01
-6.51711702e-01 -7.95524597e-01 4.79353577e-01 3.25459123e-01
-6.06219471e-01 1.03322077e+00 1.86338693e-01 -5.29745817e-01
8.00115615e-02 -7.24675179e-01 -1.13172376e+00 -7.49033630e-01
8.19118097e-02 4.71827298e-01 1.10797517e-01 -6.36690676... | [11.162951469421387, 9.017003059387207] |
2108cd23-de01-4556-b602-1bc0ec15f57d | music-data-analysis-a-state-of-the-art-survey | 1411.5014 | null | http://arxiv.org/abs/1411.5014v1 | http://arxiv.org/pdf/1411.5014v1.pdf | Music Data Analysis: A State-of-the-art Survey | Music accounts for a significant chunk of interest among various online
activities. This is reflected by wide array of alternatives offered in music
related web/mobile apps, information portals, featuring millions of artists,
songs and events attracting user activity at similar scale. Availability of
large scale struct... | ['Shubhanshu Gupta'] | 2014-11-18 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [-8.39323476e-02 4.89045307e-02 -5.91759920e-01 2.11998448e-01
-2.98947394e-01 -8.36874247e-01 4.87671494e-01 7.28794992e-01
1.80229228e-02 6.46445513e-01 8.68312538e-01 3.41457099e-01
-1.07175827e+00 -9.06084478e-01 9.43122208e-02 1.05832763e-01
-2.68317223e-01 4.04276013e-01 4.51686502e-01 -3.93918246... | [15.927717208862305, 5.204950332641602] |
c72d5b91-6d0c-4361-a429-f73fcefa59d2 | attribute-aware-pooling-for-pedestrian | 1907.11837 | null | https://arxiv.org/abs/1907.11837v1 | https://arxiv.org/pdf/1907.11837v1.pdf | Attribute Aware Pooling for Pedestrian Attribute Recognition | This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm. Existing vanilla CNNs cannot be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the at... | ['Yunhe Wang', 'Kai Han', 'Chuanjian Liu', 'Chunjing Xu', 'Han Shu', 'Chang Xu'] | 2019-07-27 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [ 1.41872615e-01 -1.28980070e-01 -2.68433660e-01 -5.67777753e-01
-3.73359561e-01 -5.45122504e-01 7.21353829e-01 4.28020000e-01
-3.43009889e-01 9.29408371e-01 1.48584962e-01 -5.48800230e-02
6.91484660e-02 -1.15469360e+00 -4.57344770e-01 -1.12467682e+00
-1.91374570e-02 2.92017847e-01 7.91315213e-02 -3.03281039... | [14.388602256774902, 0.9819066524505615] |
60845098-2e1e-4baa-8995-54217bddf825 | multiscene-a-large-scale-dataset-and | 2104.02846 | null | https://arxiv.org/abs/2104.02846v3 | https://arxiv.org/pdf/2104.02846v3.pdf | MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images | Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial imagery. Over the past few years, most studies focus on classifying an image into one scene category, while in real-world scenarios, it is more often that a single image contains multiple scenes. Therefore, in this paper, ... | ['Xiao Xiang Zhu', 'Pu Jin', 'Lichao Mou', 'Yuansheng Hua'] | 2021-04-07 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 4.59897488e-01 -3.87282670e-01 2.98590481e-01 -5.46844304e-01
-8.94520760e-01 -9.74703312e-01 2.25840196e-01 3.87225933e-02
-5.26272953e-01 6.23798370e-01 -3.64684761e-02 -1.92350253e-01
6.04661629e-02 -9.35213625e-01 -1.07510316e+00 -5.24147570e-01
2.88524389e-01 9.02477056e-02 9.22389254e-02 -1.34966835... | [9.334287643432617, -0.7401118278503418] |
23c6dc96-0007-43c2-b501-a4e0a0843692 | rfid-towards-rational-fusion-in-decoder-for | 2305.17041 | null | https://arxiv.org/abs/2305.17041v1 | https://arxiv.org/pdf/2305.17041v1.pdf | RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering | Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of generating answers by simultaneously referring to multiple passages. Although representative models like Fusion-in-Decoder (FiD) have been proposed to address this challenge, these systems can inadvertently rely on spurious features ins... | ['Yue Zhang', 'Haofei Yu', 'Cunxiang Wang'] | 2023-05-26 | null | null | null | null | ['natural-questions', 'triviaqa', 'open-domain-question-answering'] | ['miscellaneous', 'miscellaneous', 'natural-language-processing'] | [ 2.51545936e-01 5.97724438e-01 2.46626973e-01 -4.29214507e-01
-1.58210957e+00 -8.67169857e-01 1.01507354e+00 1.83428571e-01
-3.71889062e-02 7.93638885e-01 7.58845031e-01 -6.09290123e-01
-1.57125458e-01 -9.40671265e-01 -6.69887066e-01 8.41055512e-02
4.53831047e-01 5.98816097e-01 5.51539600e-01 -7.28632390... | [11.181917190551758, 7.979570388793945] |
cad5c997-ae8d-4d6d-b68e-b1868dfc50c1 | intelligent-grimm-open-ended-visual | 2306.00973 | null | https://arxiv.org/abs/2306.00973v1 | https://arxiv.org/pdf/2306.00973v1.pdf | Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion Models | Generative models have recently exhibited exceptional capabilities in various scenarios, for example, image generation based on text description. In this work, we focus on the task of generating a series of coherent image sequence based on a given storyline, denoted as open-ended visual storytelling. We make the follow... | ['Weidi Xie', 'Xiaoyun Zhang', 'Yujie Zhong', 'HaoNing Wu', 'Chang Liu'] | 2023-06-01 | null | null | null | null | ['style-transfer', 'visual-storytelling'] | ['computer-vision', 'natural-language-processing'] | [ 3.20177615e-01 -1.48958907e-01 1.77977756e-01 -2.15718746e-01
-4.88725394e-01 -4.74522531e-01 9.55493629e-01 -2.93372333e-01
-1.70447171e-01 5.27832627e-01 5.20161808e-01 6.69305795e-04
4.44699138e-01 -6.52457714e-01 -8.35595310e-01 -4.29425478e-01
4.92759913e-01 1.95144534e-01 2.32533768e-01 -2.17792928... | [11.134588241577148, 0.25394365191459656] |
314657c8-5bbb-4549-bbca-722e1c253941 | real-time-eeg-classification-via-coresets-for | 1901.00512 | null | http://arxiv.org/abs/1901.00512v1 | http://arxiv.org/pdf/1901.00512v1.pdf | Real-Time EEG Classification via Coresets for BCI Applications | A brain-computer interface (BCI) based on the motor imagery (MI) paradigm
translates one's motor intention into a control signal by classifying the
Electroencephalogram (EEG) signal of different tasks. However, most existing
systems either (i) use a high-quality algorithm to train the data off-line and
run only classif... | ['Dan Feldman', 'Alex Frid', 'Eitan Netzer'] | 2019-01-02 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 6.74973369e-01 -1.02794521e-01 4.00427461e-01 -2.86904305e-01
-4.63059664e-01 -3.53825986e-01 2.88433075e-01 6.85124565e-03
-5.93129694e-01 8.87817562e-01 -2.43600577e-01 -2.38937169e-01
-7.20349193e-01 -7.31160760e-01 -8.04091275e-01 -6.93084657e-01
-7.91064203e-01 6.10810995e-01 2.97436506e-01 5.16196005... | [13.15620231628418, 3.4453818798065186] |
0ae2bf74-a3e6-46f7-87e9-5e96079de369 | europarl-st-a-multilingual-corpus-for-speech | 1911.03167 | null | https://arxiv.org/abs/1911.03167v3 | https://arxiv.org/pdf/1911.03167v3.pdf | Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates | Current research into spoken language translation (SLT),or speech-to-text translation, is often hampered by the lack of specific data resources for this task, as currently available SLT datasets are restricted to a limited set of language pairs. In this paper we present Europarl-ST, a novel multilingual SLT corpus cont... | ['Adrià Giménez', 'Nahuel Roselló', 'Joan Albert Silvestre-Cerdà', 'Javier Iranzo-Sánchez', 'Jorge Civera', 'Alfons Juan', 'Albert Sanchis', 'Javier Jorge'] | 2019-11-08 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.40024537e-01 -1.42655671e-01 -2.85670370e-01 -2.83395469e-01
-1.53001750e+00 -8.24855387e-01 9.11236942e-01 8.34856778e-02
-3.60998333e-01 9.45522249e-01 5.94199359e-01 -6.61491275e-01
2.95664877e-01 -1.53322592e-01 -3.26375693e-01 -3.86271358e-01
4.28300142e-01 9.15732026e-01 -6.73203394e-02 -2.73670793... | [14.365409851074219, 7.188567638397217] |
eabb2dd6-74c1-408e-ab12-a89f5eeefcc8 | how-to-train-your-hippo-state-space-models | 2206.12037 | null | https://arxiv.org/abs/2206.12037v2 | https://arxiv.org/pdf/2206.12037v2.pdf | How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections | Linear time-invariant state space models (SSM) are a classical model from engineering and statistics, that have recently been shown to be very promising in machine learning through the Structured State Space sequence model (S4). A core component of S4 involves initializing the SSM state matrix to a particular matrix ca... | ['Christopher Ré', 'Atri Rudra', 'Aman Timalsina', 'Isys Johnson', 'Albert Gu'] | 2022-06-24 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [-8.40421394e-03 -3.10095549e-01 -3.39336038e-01 -4.24608625e-02
-9.29806903e-02 -8.40589404e-01 1.03377593e+00 -1.42211661e-01
-1.42610073e-01 6.16606832e-01 -8.21400061e-02 -7.08944321e-01
-6.90463841e-01 -4.57170695e-01 -7.12278962e-01 -9.63435471e-01
-7.15870738e-01 4.22709852e-01 3.67098272e-01 -8.36962044... | [7.061464309692383, 3.4678077697753906] |
36982849-3b2e-4c52-a973-cfdbfff060d1 | sheaf-neural-networks-for-graph-based | 2304.09097 | null | https://arxiv.org/abs/2304.09097v1 | https://arxiv.org/pdf/2304.09097v1.pdf | Sheaf Neural Networks for Graph-based Recommender Systems | Recent progress in Graph Neural Networks has resulted in wide adoption by many applications, including recommendation systems. The reason for Graph Neural Networks' superiority over other approaches is that many problems in recommendation systems can be naturally modeled as graphs, where nodes can be either users or it... | ['Fabrizio Silvestri', 'Pietro Liò', 'Giulia Cassarà', 'Antonio Purificato'] | 2023-04-07 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 4.92015816e-02 1.31111547e-01 -5.90418696e-01 -2.73637205e-01
1.39294118e-01 -4.65217888e-01 5.64210117e-01 7.01820791e-01
-3.33341748e-01 3.35616052e-01 2.37046033e-01 -4.67006326e-01
-6.07407928e-01 -9.90125418e-01 -7.58652091e-01 -3.97588819e-01
-6.00720704e-01 5.69691956e-01 3.03793579e-01 -2.94186771... | [10.107765197753906, 5.608708381652832] |
bb4ae463-42a7-4c0d-9002-59fea6f5a893 | neural-rgbrd-sensing-depth-and-uncertainty | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Neural_RGBrD_Sensing_Depth_and_Uncertainty_From_a_Video_Camera_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Neural_RGBrD_Sensing_Depth_and_Uncertainty_From_a_Video_Camera_CVPR_2019_paper.pdf | Neural RGB(r)D Sensing: Depth and Uncertainty From a Video Camera | Depth sensing is crucial for 3D reconstruction and scene understanding. Active depth sensors provide dense metric measurements, but often suffer from limitations such as restricted operating ranges, low spatial resolution, sensor interference, and high power consumption. In this paper, we propose a deep learning (DL) m... | [' Jan Kautz', ' Srinivasa G. Narasimhan', ' Kihwan Kim', ' Jinwei Gu', 'Chao Liu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 4.74203467e-01 -2.00251658e-02 -9.02076624e-03 -5.54722309e-01
-7.40406334e-01 -2.55401552e-01 3.07545751e-01 7.36294836e-02
-6.50275707e-01 5.29102445e-01 2.70721829e-03 -3.97430882e-02
2.18211133e-02 -1.08269143e+00 -7.54943848e-01 -6.94053710e-01
2.56959617e-01 3.83946478e-01 6.97641075e-01 7.20048845... | [8.880178451538086, -2.587003707885742] |
fa4e3307-1509-4981-9936-5ee201975f29 | bayesian-detection-of-a-sinusoidal-signal | 2211.05977 | null | https://arxiv.org/abs/2211.05977v1 | https://arxiv.org/pdf/2211.05977v1.pdf | Bayesian Detection of a Sinusoidal Signal with Randomly Varying Frequency | The problem of detecting a sinusoidal signal with randomly varying frequency has a long history. It is one of the core problems in signal processing, arising in many applications including, for example, underwater acoustic frequency line tracking, demodulation of FM radio communications, laser phase drift in optical co... | ['A. Melatos', 'B. Moran', 'R. J. Evans', 'S. Suvorova', 'Changrong Liu'] | 2022-11-11 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 2.88833857e-01 -2.72990048e-01 3.66012812e-01 -5.06738424e-02
-8.22813272e-01 -4.04750437e-01 4.64720935e-01 -4.85242270e-02
-7.87554622e-01 9.14992332e-01 -3.28529686e-01 -4.33492541e-01
-2.62297571e-01 -4.81671423e-01 -3.16139162e-01 -9.53022718e-01
-5.60392916e-01 4.74031061e-01 4.15128857e-01 1.30637527... | [6.78201150894165, 3.6482651233673096] |
056308b4-2d82-417b-a6eb-8c9528c11499 | sequence-to-sequence-natural-language-to | 1907.04198 | null | https://arxiv.org/abs/1907.04198v1 | https://arxiv.org/pdf/1907.04198v1.pdf | Sequence-to-Sequence Natural Language to Humanoid Robot Sign Language | This paper presents a study on natural language to sign language translation with human-robot interaction application purposes. By means of the presented methodology, the humanoid robot TEO is expected to represent Spanish sign language automatically by converting text into movements, thanks to the performance of neura... | ['Bartek Łukawski', 'Jennifer J. Gago', 'Valentina Vasco', 'Juan G. Victores', 'Ugo Pattacini', 'Vadim Tikhanoff', 'Carlos Balaguer'] | 2019-07-09 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 3.84317875e-01 2.13937566e-01 -5.09911031e-02 -3.66079837e-01
-3.55993360e-02 -3.18851084e-01 5.64357877e-01 -5.34460425e-01
-5.84512472e-01 7.10401893e-01 6.85088476e-03 -2.50742763e-01
-2.44763672e-01 -4.01065975e-01 -4.09226179e-01 -2.09338471e-01
1.44748956e-01 7.87396133e-01 2.27663174e-01 -2.52667308... | [9.080869674682617, -6.354935646057129] |
611e83bf-2052-40e4-b3d5-3bbed10b88cf | a-provable-splitting-approach-for-symmetric | 2301.10499 | null | https://arxiv.org/abs/2301.10499v1 | https://arxiv.org/pdf/2301.10499v1.pdf | A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization | The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks. Unfortunately, designing fast algorithms for the symmetric NMF is not as easy as for its nonsymmetric counterpart, since the latter a... | ['Kai Liu', 'Qiuwei Li', 'Zhihui Zhu', 'Xiao Li'] | 2023-01-25 | null | null | null | null | ['image-clustering', 'type'] | ['computer-vision', 'speech'] | [ 3.12478870e-01 2.42541078e-02 -1.96225986e-01 -1.89126417e-01
-5.88058829e-01 -7.70120084e-01 2.88222879e-01 -1.75307304e-01
-3.85461569e-01 6.16648793e-01 6.74994383e-03 -5.74772418e-01
-5.21304727e-01 -3.23123604e-01 -7.79584587e-01 -1.18990028e+00
-4.10781205e-02 7.68085659e-01 -2.16171145e-01 -1.56071842... | [7.2476725578308105, 4.620192050933838] |
00ea0610-a5d4-46aa-a0b1-fe9a89219171 | precise-zero-shot-dense-retrieval-without | 2212.10496 | null | https://arxiv.org/abs/2212.10496v1 | https://arxiv.org/pdf/2212.10496v1.pdf | Precise Zero-Shot Dense Retrieval without Relevance Labels | While dense retrieval has been shown effective and efficient across tasks and languages, it remains difficult to create effective fully zero-shot dense retrieval systems when no relevance label is available. In this paper, we recognize the difficulty of zero-shot learning and encoding relevance. Instead, we propose to ... | ['Jamie Callan', 'Jimmy Lin', 'Xueguang Ma', 'Luyu Gao'] | 2022-12-20 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [ 1.47772223e-01 -1.84499100e-02 -2.63219327e-01 -1.78368345e-01
-1.37464714e+00 -3.51496637e-01 7.92587459e-01 4.44984645e-01
-5.99509239e-01 5.62592506e-01 5.51892698e-01 -2.13332802e-01
-3.27646405e-01 -8.97437572e-01 -6.25127316e-01 -3.73506665e-01
7.27586225e-02 7.85266459e-01 1.87948853e-01 -4.98929799... | [11.419508934020996, 7.748104095458984] |
1711ff3a-bc9d-428b-859b-1af897fd1dd3 | variational-bayesian-framework-for-advanced | 2305.13872 | null | https://arxiv.org/abs/2305.13872v1 | https://arxiv.org/pdf/2305.13872v1.pdf | Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables | Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains challenging for existing methods to address advanced conditional generative problems without annotations, which can enable multiple applications ... | ['Yuan Shen', 'Santiago Mazuelas', 'Yuxiao Li'] | 2023-05-23 | null | null | null | null | ['unsupervised-image-to-image-translation', 'image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 4.17661458e-01 2.28205711e-01 -5.36485687e-02 -5.77638090e-01
-9.71738756e-01 -3.04614127e-01 1.01785076e+00 -8.51461709e-01
5.13034090e-02 7.07355917e-01 8.40535983e-02 -2.17865422e-01
1.41757339e-01 -6.71422720e-01 -9.74770069e-01 -8.45410466e-01
7.52669513e-01 9.09306824e-01 -2.00283024e-02 1.96297318... | [11.528578758239746, -0.2565932273864746] |
c8a81c3d-40e3-4b1e-9374-6bff73998420 | style-transfer-from-non-parallel-text-by | 1705.09655 | null | http://arxiv.org/abs/1705.09655v2 | http://arxiv.org/pdf/1705.09655v2.pdf | Style Transfer from Non-Parallel Text by Cross-Alignment | This paper focuses on style transfer on the basis of non-parallel text. This
is an instance of a broad family of problems including machine translation,
decipherment, and sentiment modification. The key challenge is to separate the
content from other aspects such as style. We assume a shared latent content
distribution... | ['Regina Barzilay', 'Tianxiao Shen', 'Tao Lei', 'Tommi Jaakkola'] | 2017-05-26 | style-transfer-from-non-parallel-text-by-1 | http://papers.nips.cc/paper/7259-style-transfer-from-non-parallel-text-by-cross-alignment | http://papers.nips.cc/paper/7259-style-transfer-from-non-parallel-text-by-cross-alignment.pdf | neurips-2017-12 | ['decipherment'] | ['natural-language-processing'] | [ 1.02647161e+00 -1.62191793e-01 -1.13554344e-01 -4.71122622e-01
-1.16573060e+00 -1.02439094e+00 8.25577676e-01 -7.60980844e-02
-3.32778960e-01 9.22601104e-01 7.81190932e-01 -4.70251054e-01
3.77045155e-01 -5.44750869e-01 -8.84329617e-01 -6.96453691e-01
5.96504629e-01 2.57147789e-01 -5.29043913e-01 -6.11613154... | [11.64294719696045, 9.913911819458008] |
7e51cab5-82c9-4cad-9ae8-e8af91660c24 | real-time-optimization-for-wind-to-h2-driven | 2306.17395 | null | https://arxiv.org/abs/2306.17395v1 | https://arxiv.org/pdf/2306.17395v1.pdf | Real-time Optimization for Wind-to-H2 Driven Critical Infrastructures: High-fidelity Active Constraints and Integer Variables Prediction Enhanced by Feature Space Expansion | This paper focuses on developing a real-time optimal operation model for a new engineering system, wind-to-hydrogen-driven low-carbon critical infrastructure (W2H-LCCI), that utilizes wind power to generate hydrogen through electrolysis and combines it with carbon capture to reduce carbon emissions from the power secto... | ['QiFeng Li', 'Mostafa Goodarzi'] | 2023-06-30 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-7.25369453e-02 1.11931033e-01 -3.56460690e-01 3.17980886e-01
-1.21150367e-01 -6.78855717e-01 3.12236309e-01 -1.02497943e-01
3.95009294e-02 1.05121732e+00 -2.31123745e-01 -5.69081128e-01
-7.98405290e-01 -8.96499872e-01 -2.85999358e-01 -9.09829557e-01
-2.18613774e-01 4.79398966e-01 -2.71336496e-01 -8.20041448... | [5.6154680252075195, 2.4917960166931152] |
3f372e91-ea28-424c-abf2-31f4f3b46a7f | boosting-image-forgery-detection-using | 1802.03154 | null | http://arxiv.org/abs/1802.03154v2 | http://arxiv.org/pdf/1802.03154v2.pdf | Boosting Image Forgery Detection using Resampling Features and Copy-move analysis | Realistic image forgeries involve a combination of splicing, resampling,
cloning, region removal and other methods. While resampling detection
algorithms are effective in detecting splicing and resampling, copy-move
detection algorithms excel in detecting cloning and region removal. In this
paper, we combine these comp... | ['Lawrence Peterson', 'Amit K. Roy-Chowdhury', 'Tajuddin Manhar Mohammed', 'Lakshmanan Nataraj', 'Jawadul H. Bappy', 'Jason Bunk', 'B. S. Manjunath', 'Shivkumar Chandrasekaran', 'Arjuna Flenner'] | 2018-02-09 | null | null | null | null | ['image-manipulation-detection'] | ['computer-vision'] | [ 8.77916098e-01 -5.55563569e-01 1.21091574e-01 -2.18354668e-02
-9.98799264e-01 -7.79777765e-01 6.97883487e-01 1.79342762e-01
-5.06260335e-01 5.10135353e-01 3.63755703e-01 -3.97342473e-01
5.61562061e-01 -7.35802591e-01 -8.59645009e-01 -5.76941550e-01
2.72787005e-01 -3.42572570e-01 2.65990615e-01 -2.03244045... | [12.380146026611328, 1.0070366859436035] |
1d6c622a-9994-4b58-ac4f-870a929d3da9 | streamlining-social-media-information | 2306.16001 | null | https://arxiv.org/abs/2306.16001v1 | https://arxiv.org/pdf/2306.16001v1.pdf | Streamlining Social Media Information Retrieval for Public Health Research with Deep Learning | The utilization of social media in epidemic surveillance has been well established. Nonetheless, bias is often introduced when pre-defined lexicons are used to retrieve relevant corpus. This study introduces a framework aimed at curating extensive dictionaries of medical colloquialisms and Unified Medical Language Syst... | ['Jie Yang', 'Li Zhou', 'Ying-Chih Lo', 'Peilin Zhou', 'Yujie Zhang', 'Minghui Li', 'Shixu Lin', 'Yining Hua'] | 2023-06-28 | null | null | null | null | ['retrieval', 'named-entity-recognition-ner', 'information-retrieval', 'cg'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.79749336e-02 1.30133629e-01 -3.52786928e-01 -1.49109796e-01
-9.44875419e-01 -4.34842676e-01 5.52215457e-01 1.22265005e+00
-1.00467622e+00 6.29942477e-01 4.72168028e-01 -2.11642742e-01
-2.62594849e-01 -1.00410485e+00 -2.38391295e-01 -2.84956515e-01
-1.74847066e-01 6.51479959e-01 -3.25981192e-02 -3.30837131... | [8.453349113464355, 8.985177993774414] |
36c77fea-4fda-4003-b782-8adc2bd30f34 | estimating-generic-3d-room-structures-from-2d | 2306.09077 | null | https://arxiv.org/abs/2306.09077v1 | https://arxiv.org/pdf/2306.09077v1.pdf | Estimating Generic 3D Room Structures from 2D Annotations | Indoor rooms are among the most common use cases in 3D scene understanding. Current state-of-the-art methods for this task are driven by large annotated datasets. Room layouts are especially important, consisting of structural elements in 3D, such as wall, floor, and ceiling. However, they are difficult to annotate, es... | ['Vittorio Ferrari', 'Matthias Nießner', 'Kevis-Kokitsi Maninis', 'Stefan Popov', 'Denys Rozumnyi'] | 2023-06-15 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [-2.40746532e-02 9.85226855e-02 4.95052159e-01 -5.29771090e-01
-2.54552633e-01 -9.28182781e-01 2.09915385e-01 1.30444095e-01
-1.63509436e-02 2.73279548e-01 3.44691157e-01 -3.90259624e-01
1.62884519e-02 -5.63756585e-01 -7.37039506e-01 -3.60613048e-01
-1.49367407e-01 5.92229426e-01 6.24961741e-02 -1.42434999... | [8.744352340698242, -2.8953888416290283] |
b7cd326e-bae3-4180-999f-c5a703c34134 | jarvis-a-neuro-symbolic-commonsense-reasoning | 2208.13266 | null | https://arxiv.org/abs/2208.13266v3 | https://arxiv.org/pdf/2208.13266v3.pdf | JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents | Building a conversational embodied agent to execute real-life tasks has been a long-standing yet quite challenging research goal, as it requires effective human-agent communication, multi-modal understanding, long-range sequential decision making, etc. Traditional symbolic methods have scaling and generalization issues... | ['Zonglin Di', 'Xin Eric Wang', 'Xuehai He', 'Jialu Wang', 'Yue Fan', 'Jing Gu', 'Kaiwen Zhou', 'Kaizhi Zheng'] | 2022-08-28 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [-4.14334051e-02 4.10560906e-01 6.70607314e-02 -3.38644385e-01
-6.43849969e-01 -4.25473541e-01 1.12469792e+00 -2.91089386e-01
-1.84659734e-01 8.26717556e-01 5.05442858e-01 -2.30345145e-01
-1.23221442e-01 -5.49866319e-01 -4.15184736e-01 -4.51067477e-01
-7.77172148e-02 1.04000938e+00 1.85899511e-01 -7.90635526... | [4.390313625335693, 0.8004306554794312] |
789581fa-d0d8-4d32-9f1a-0475d1450561 | shape-preserving-facial-landmarks-with-graph | 2210.07233 | null | https://arxiv.org/abs/2210.07233v1 | https://arxiv.org/pdf/2210.07233v1.pdf | Shape Preserving Facial Landmarks with Graph Attention Networks | Top-performing landmark estimation algorithms are based on exploiting the excellent ability of large convolutional neural networks (CNNs) to represent local appearance. However, it is well known that they can only learn weak spatial relationships. To address this problem, we propose a model based on the combination of ... | ['Luis Baumela', 'José M. Buenaposada', 'Andrés Prados-Torreblanca'] | 2022-10-13 | null | null | null | null | ['head-pose-estimation', 'face-alignment', 'facial-landmark-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.11487371e-01 3.22115481e-01 -1.37978807e-01 -7.06979215e-01
-6.40420198e-01 -2.56669194e-01 6.25594914e-01 3.03306192e-01
-3.11235845e-01 2.54826277e-01 4.79975522e-01 2.84053922e-01
-1.63964555e-02 -5.30605793e-01 -9.03756857e-01 -4.72460598e-01
-1.75026849e-01 4.93128300e-01 2.39154771e-02 -2.89939195... | [13.529370307922363, 0.39648887515068054] |
8d708d4f-ef8c-4e54-8a50-15e70b9c529b | casia-at-semeval-2022-task-11-chinese-named | null | null | https://aclanthology.org/2022.semeval-1.208 | https://aclanthology.org/2022.semeval-1.208.pdf | CASIA at SemEval-2022 Task 11: Chinese Named Entity Recognition for Complex and Ambiguous Entities | This paper describes our approach to develop a complex named entity recognition system in SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition,Track 9 - Chinese. In this task, we need to identify the entity boundaries and categorylabels for the six identified categories of CW,LOC, PER, GRP, CO... | ['Jun Zhao', 'Kang Liu', 'Yubo Chen', 'Dianbo Sui', 'Sirui Li', 'Zhucong Li', 'Zhen Gan', 'Jia Fu'] | null | null | null | null | semeval-naacl-2022-7 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-1.97192878e-01 2.49338984e-01 8.92403796e-02 -5.80513775e-01
-7.84717083e-01 -1.04589951e+00 6.48648977e-01 7.65407532e-02
-9.48086500e-01 9.68235075e-01 1.46034077e-01 -4.72462088e-01
5.21708548e-01 -4.45877075e-01 -5.55599272e-01 -1.60206601e-01
-5.45233265e-02 7.06968486e-01 3.78659129e-01 -2.69839287... | [9.731067657470703, 9.599936485290527] |
9d581abe-5b63-42d2-a5ff-e11903c7730c | self-supervised-pretraining-for-2d-medical | 2209.00314 | null | https://arxiv.org/abs/2209.00314v1 | https://arxiv.org/pdf/2209.00314v1.pdf | Self-Supervised Pretraining for 2D Medical Image Segmentation | Supervised machine learning provides state-of-the-art solutions to a wide range of computer vision problems. However, the need for copious labelled training data limits the capabilities of these algorithms in scenarios where such input is scarce or expensive. Self-supervised learning offers a way to lower the need for ... | ['Bálint Gyires-Tóth', 'András Kalapos'] | 2022-09-01 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 5.82434773e-01 5.21530926e-01 -2.46068925e-01 -7.11768448e-01
-9.11766708e-01 -4.68071550e-01 3.67491484e-01 2.03365088e-01
-9.83528256e-01 6.84338808e-01 -3.13377947e-01 -3.25257689e-01
-3.70251425e-02 -5.16098738e-01 -6.53705657e-01 -7.42703438e-01
1.64866894e-01 8.58328640e-01 3.44446838e-01 -4.34242226... | [14.712061882019043, -2.299292802810669] |
2004cdbf-47a4-4915-9c84-2e20456f6dd0 | weakly-supervised-classification-and-1 | null | null | https://arxiv.org/abs/2107.04878 | https://arxiv.org/pdf/2107.04878.pdf | Weakly-Supervised Classification and Detection of Bird Sounds in the Wild. | It is easier to hear birds than see them, however, they still play an essential role in nature and they are excellent indicators of deteriorating environmental quality and pollution. Recent advances in Machine Learning and Convolutional Neural Networks allow us to detect and classify bird sounds, by doing this, we can ... | ['Szilard Bessenyei', 'Nitin D. Movva', 'Prateek Agnihotri', 'Kumar Shubham', 'Marcos V. Conde'] | 2021-07-10 | null | null | null | clef-2021-7 | ['audio-tagging', 'bird-audio-detection'] | ['audio', 'audio'] | [-1.40906200e-01 -7.74305105e-01 4.76103306e-01 -2.89925635e-02
-2.02139206e-02 -1.01754665e+00 2.71620005e-01 3.14402044e-01
-8.20181847e-01 3.68196815e-01 3.37437153e-01 8.49617496e-02
3.03995848e-01 -1.02345502e+00 -6.96721077e-01 -5.52361727e-01
-3.88013154e-01 -2.03209057e-01 5.80329716e-01 -2.21034631... | [15.184000968933105, 5.2476911544799805] |
ccc9ce33-9c2d-4a36-9d1a-ea393bb77426 | reflection-based-word-attribute-transfer-2 | 2007.02598 | null | https://arxiv.org/abs/2007.02598v2 | https://arxiv.org/pdf/2007.02598v2.pdf | Reflection-based Word Attribute Transfer | Word embeddings, which often represent such analogic relations as king - man + woman = queen, can be used to change a word's attribute, including its gender. For transferring king into queen in this analogy-based manner, we subtract a difference vector man - woman based on the knowledge that king is male. However, deve... | ['Katsuhito Sudoh', 'Yoichi Ishibashi', 'Koichiro Yoshino', 'Satoshi Nakamura'] | 2020-07-06 | reflection-based-word-attribute-transfer-1 | https://aclanthology.org/2020.acl-srw.8 | https://aclanthology.org/2020.acl-srw.8.pdf | acl-2020-6 | ['word-attribute-transfer'] | ['natural-language-processing'] | [ 1.68890692e-02 2.26711571e-01 -2.95824166e-02 -6.52904689e-01
-1.82448193e-01 -6.75569415e-01 7.90775239e-01 1.90100357e-01
-7.67446995e-01 7.45565951e-01 4.20451820e-01 -2.97708899e-01
2.15660974e-01 -1.22144222e+00 -5.58865488e-01 -6.11290812e-01
4.44764018e-01 6.08885288e-01 -5.58368601e-02 -7.58706987... | [10.55379581451416, 8.838455200195312] |
042bacab-890e-4114-8cc7-bf22cff12844 | differentially-private-synthetic-data | 2304.11336 | null | https://arxiv.org/abs/2304.11336v1 | https://arxiv.org/pdf/2304.11336v1.pdf | Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders | Synthetic data has been hailed as the silver bullet for privacy preserving data analysis. If a record is not real, then how could it violate a person's privacy? In addition, deep-learning based generative models are employed successfully to approximate complex high-dimensional distributions from data and draw realistic... | ['Gerhard Wunder', 'Benedikt Groß'] | 2023-04-22 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 2.25505024e-01 4.98857617e-01 1.84623823e-01 -5.18470764e-01
-7.42742479e-01 -8.43415916e-01 3.66279125e-01 9.57675278e-02
-4.95944887e-01 9.92133200e-01 -3.59180593e-03 -3.01391065e-01
-1.00142911e-01 -1.03632760e+00 -8.76387060e-01 -1.08285820e+00
2.30870366e-01 4.75065857e-01 -4.35240865e-01 3.09740677... | [6.082329273223877, 6.861213684082031] |
22e002b0-01f1-474e-8c66-a83b25e278a2 | collective-named-entity-disambiguation-using | null | null | https://aclanthology.org/C14-1147 | https://aclanthology.org/C14-1147.pdf | Collective Named Entity Disambiguation using Graph Ranking and Clique Partitioning Approaches | null | ['Robert Gaizauskas', 'Ayman Alhelbawy'] | 2014-08-01 | collective-named-entity-disambiguation-using-1 | https://aclanthology.org/C14-1147 | https://aclanthology.org/C14-1147.pdf | coling-2014-8 | ['graph-ranking'] | ['graphs'] | [-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... | [-7.232183456420898, 3.8678805828094482] |
ec9568dd-4e0f-49dc-b96e-3b938d86f0d9 | a-public-ground-truth-dataset-for-handwritten | 2107.10373 | null | https://arxiv.org/abs/2107.10373v1 | https://arxiv.org/pdf/2107.10373v1.pdf | A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images | The development of digitization methods for line drawings (especially in the area of electrical engineering) relies on the availability of publicly available training and evaluation data. This paper presents such an image set along with annotations. The dataset consists of 1152 images of 144 circuits by 12 drafters and... | ['Yakun Li', 'Johannes Bayer', 'Felix Thoma'] | 2021-07-21 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 6.30361080e-01 8.59246850e-02 4.32847403e-02 -3.61465931e-01
-5.26073515e-01 -8.75526607e-01 6.49262488e-01 1.84058264e-01
5.76912798e-02 4.26492244e-01 -4.04804438e-01 -4.98135179e-01
-3.34943235e-02 -5.76691449e-01 -5.88838279e-01 -5.70190132e-01
1.02911711e-01 5.08676708e-01 1.74251869e-01 1.31873116... | [11.663287162780762, 2.6599414348602295] |
b6c2519b-a5fc-4d38-a286-22d6c22b267f | a-parameter-efficient-learning-approach-to | 2305.11244 | null | https://arxiv.org/abs/2305.11244v1 | https://arxiv.org/pdf/2305.11244v1.pdf | A Parameter-Efficient Learning Approach to Arabic Dialect Identification with Pre-Trained General-Purpose Speech Model | In this work, we explore Parameter-Efficient-Learning (PEL) techniques to repurpose a General-Purpose-Speech (GSM) model for Arabic dialect identification (ADI). Specifically, we investigate different setups to incorporate trainable features into a multi-layer encoder-decoder GSM formulation under frozen pre-trained se... | ['Jesper N. Tegner', 'David Gomez-Cabrero', 'Narsis A. Kiani', 'Sumeer Ahmad Khan', 'Chao-Han Huck Yang', 'Srijith Radhakrishnan'] | 2023-05-18 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [ 9.48506445e-02 3.62636149e-02 1.83656409e-01 -8.55317235e-01
-1.32854784e+00 -9.86239254e-01 5.39366484e-01 -2.74164468e-01
-5.68697810e-01 3.58256966e-01 1.06767520e-01 -7.27984726e-01
3.71746004e-01 -4.57918793e-01 -6.47180378e-01 -4.98280853e-01
-1.07481509e-01 8.08049560e-01 -1.69251561e-01 -6.80576682... | [14.057117462158203, 6.968779563903809] |
e96b1fa0-7133-4792-9fc2-7023adea644f | training-free-synthesized-face-sketch | 1603.07823 | null | http://arxiv.org/abs/1603.07823v1 | http://arxiv.org/pdf/1603.07823v1.pdf | Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics | Face sketch synthesis has wide applications ranging from digital
entertainments to law enforcements. Objective image quality assessment scores
and face recognition accuracy are two mainly used tools to evaluate the
synthesis performance. In this paper, we proposed a synthesized face sketch
recognition framework based o... | ['Nannan Wang', 'Leiyu Sun', 'Jie Li', 'Xinbo Gao', 'Bin Song'] | 2016-03-25 | null | null | null | null | ['sketch-recognition', 'face-sketch-synthesis'] | ['computer-vision', 'computer-vision'] | [ 2.78374523e-01 -3.18973690e-01 -2.90777296e-01 -2.95931578e-01
-5.06851017e-01 -3.20689648e-01 8.56478751e-01 -6.77285433e-01
5.45329601e-02 6.67911053e-01 1.67317092e-01 4.75335196e-02
-3.25131148e-01 -6.93261743e-01 -1.84795201e-01 -4.51310843e-01
2.76413232e-01 -1.85403153e-01 -2.13338032e-01 3.41303647... | [12.757986068725586, 0.20524750649929047] |
4701115c-849c-4b1e-b1e8-2172595dfd88 | learning-based-dimensionality-reduction-for | 2209.13586 | null | https://arxiv.org/abs/2209.13586v1 | https://arxiv.org/pdf/2209.13586v1.pdf | Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors | A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization. State-of-the-art descriptors, from hand-crafted descriptors such as SIFT to learned ones such as HardNet, are usually high di... | ['Cyrill Stachniss', 'Marc Pollefeys', 'Viktor Larsson', 'Mihai Dusmanu', 'Xieyuanli Chen', 'Hao Dong'] | 2022-09-27 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-4.43226956e-02 -5.00634134e-01 -4.53104943e-01 -3.86231482e-01
-9.42697704e-01 -4.97048438e-01 8.20407987e-01 2.13180438e-01
-5.21086872e-01 1.43646389e-01 3.77501100e-02 1.75169215e-01
-6.34894133e-01 -7.08854139e-01 -6.74989104e-01 -9.40489173e-01
-1.85291141e-01 1.64563656e-01 4.57707569e-02 4.54890355... | [10.243514060974121, 0.020847955718636513] |
e5ecb44d-70a9-4777-bb79-42243a1a87f3 | tassy-a-text-annotation-survey-system | 2112.07391 | null | https://arxiv.org/abs/2112.07391v2 | https://arxiv.org/pdf/2112.07391v2.pdf | TASSY -- A Text Annotation Survey System | We present a free and open-source tool for creating web-based surveys that include text annotation tasks. Existing tools offer either text annotation or survey functionality but not both. Combining the two input types is particularly relevant for investigating a reader's perception of a text which also depends on the r... | ['Bela Gipp', 'Norman Meuschke', 'Kanishka Sinha', 'Timo Spinde'] | 2021-12-14 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 2.23115236e-02 3.67289335e-01 -5.09818792e-01 -5.61503023e-02
-5.56468666e-01 -7.98232019e-01 7.83849835e-01 9.75426912e-01
-6.34946644e-01 3.60183835e-01 1.13389075e+00 -1.05043674e+00
1.03293315e-01 -5.83623707e-01 -3.75875056e-01 -8.52168724e-03
8.89775038e-01 4.34430502e-02 3.46537948e-01 -4.01494503... | [8.911907196044922, 9.906786918640137] |
d737559b-2c80-4ba8-8a60-7c8811d191d3 | frames-a-corpus-for-adding-memory-to-goal | 1704.00057 | null | http://arxiv.org/abs/1704.00057v2 | http://arxiv.org/pdf/1704.00057v2.pdf | Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems | This paper presents the Frames dataset (Frames is available at
http://datasets.maluuba.com/Frames), a corpus of 1369 human-human dialogues
with an average of 15 turns per dialogue. We developed this dataset to study
the role of memory in goal-oriented dialogue systems. Based on Frames, we
introduce a task called frame ... | ['Jeremie Zumer', 'Shikhar Sharma', 'Rahul Mehrotra', 'Kaheer Suleman', 'Layla El Asri', 'Justin Harris', 'Hannes Schulz', 'Emery Fine'] | 2017-03-31 | frames-a-corpus-for-adding-memory-to-goal-1 | https://aclanthology.org/W17-5526 | https://aclanthology.org/W17-5526.pdf | ws-2017-8 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-4.85119671e-02 6.40074909e-01 -1.74174979e-01 -3.40107411e-01
-6.10099018e-01 -8.25154841e-01 1.42017448e+00 1.11702822e-01
-3.16638172e-01 9.77999747e-01 7.72023082e-01 -1.59067392e-01
6.30376279e-01 -5.27020097e-01 -2.21138149e-01 -2.91135132e-01
-5.72005250e-02 4.67033207e-01 5.64040124e-01 -6.59987390... | [12.830575942993164, 7.9419989585876465] |
f04bdc6f-a5a8-4a38-a669-c39845f028c6 | cross-lingual-sentiment-classification-with | null | null | https://aclanthology.org/P16-1133 | https://aclanthology.org/P16-1133.pdf | Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning | null | ['Xinjie Zhou', 'Xiaojun Wan', 'Jianguo Xiao'] | 2016-08-01 | null | null | null | acl-2016-8 | ['stock-market-prediction'] | ['time-series'] | [-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... | [-7.381019115447998, 3.7459816932678223] |
cae3a043-d17d-4665-a091-3b2d8e1e66ea | coarse-to-fine-domain-adaptive-semantic | 2103.13041 | null | https://arxiv.org/abs/2103.13041v1 | https://arxiv.org/pdf/2103.13041v1.pdf | Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization | Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works. However, the domain shifts/discrepancies problem in this task compromise the final segmentation performance. Based on our observation, the main causes of the domain shifts... | ['Yizhou Yu', 'Zifeng Wu', 'Xiangru Lin', 'Haoyu Ma'] | 2021-03-24 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper.pdf | cvpr-2021-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.82299632e-01 1.31863328e-02 -3.98554057e-02 -7.16881156e-01
-7.52866209e-01 -4.64700401e-01 3.47103864e-01 -7.46907368e-02
-3.95310789e-01 4.41763490e-01 -2.62903780e-01 1.51027709e-01
-6.30072057e-02 -5.16799629e-01 -7.83159077e-01 -1.00139749e+00
7.22901285e-01 4.79442865e-01 6.97026491e-01 -7.90798068... | [9.664740562438965, 1.3712453842163086] |
801b73f9-91fa-4498-b6bd-0891111aae70 | uctrl-unbiased-contrastive-representation | 2305.12768 | null | https://arxiv.org/abs/2305.12768v1 | https://arxiv.org/pdf/2305.12768v1.pdf | uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering | Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield recommendation lists with popularity bias. Previous studies have utilized inverse propensity weighting (IPW) or causal inference to mitigate this problem. However, they solely employ pointw... | ['Jongwuk Lee', 'Mincheol Yoon', 'Seongmin Park', 'Jae-woong Lee'] | 2023-05-22 | null | null | null | null | ['causal-inference', 'collaborative-filtering', 'causal-inference'] | ['knowledge-base', 'miscellaneous', 'miscellaneous'] | [ 7.81302229e-02 -1.04280010e-01 -7.40911484e-01 -5.87434471e-01
-8.39370012e-01 -4.90145981e-01 5.93254030e-01 2.73286670e-01
-4.27864492e-01 7.66923368e-01 5.75714588e-01 -4.74704176e-01
-5.24908662e-01 -9.34501588e-01 -7.31417239e-01 -4.46577698e-01
-3.87132280e-02 3.51751387e-01 -4.44564484e-02 1.87586527... | [9.891554832458496, 5.530436038970947] |
9156ea15-489d-46e3-9ef4-9bc15cc1635a | piml-toolbox-for-interpretable-machine | 2305.04214 | null | https://arxiv.org/abs/2305.04214v2 | https://arxiv.org/pdf/2305.04214v2.pdf | PiML Toolbox for Interpretable Machine Learning Model Development and Validation | PiML (read $\pi$-ML, /`pai.`em.`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with machine learning workflows in both low-code and high-code modes, including data pipeline, model training, model interpretation and explanat... | ['Ningzhou Zeng', 'Yu Su', 'Zebin Yang', 'Aijun Zhang', 'Agus Sudjianto'] | 2023-05-07 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-5.68366647e-01 5.04681885e-01 -3.22781712e-01 -6.27685130e-01
-4.83646572e-01 -7.39725947e-01 2.41082639e-01 4.58438873e-01
4.32369918e-01 4.70206827e-01 -1.09389760e-01 -1.00183392e+00
-7.89230585e-01 -4.74629283e-01 -3.28595400e-01 -3.13488245e-01
5.26553988e-02 1.23510909e+00 -6.90102994e-01 1.20793320... | [8.57674503326416, 5.926393985748291] |
6f5e8014-49fb-42de-bee4-68e36a775189 | exploring-state-change-capture-of | 2211.08728 | null | https://arxiv.org/abs/2211.08728v1 | https://arxiv.org/pdf/2211.08728v1.pdf | Exploring State Change Capture of Heterogeneous Backbones @ Ego4D Hands and Objects Challenge 2022 | Capturing the state changes of interacting objects is a key technology for understanding human-object interactions. This technical report describes our method using heterogeneous backbones for the Ego4D Object State Change Classification and PNR Temporal Localization Challenge. In the challenge, we used the heterogeneo... | ['LiMin Wang', 'Tong Lu', 'Jiahao Wang', 'Guo Chen', 'Yin-Dong Zheng'] | 2022-11-16 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [-5.82666159e-01 -5.09085476e-01 -1.86009273e-01 -9.65025797e-02
-2.85431027e-01 -8.43536377e-01 6.34375572e-01 -3.64130884e-01
-3.06630522e-01 9.99240354e-02 2.84423918e-01 2.18952954e-01
-2.25917008e-02 -1.40611112e-01 -7.69711196e-01 -3.04424405e-01
-6.05899513e-01 4.53341484e-01 7.01745689e-01 -8.30026269... | [8.460501670837402, 0.5425087809562683] |
eea87304-b23b-4275-96df-523c91305f88 | from-news-to-medical-cross-domain-discourse | 1904.06682 | null | http://arxiv.org/abs/1904.06682v1 | http://arxiv.org/pdf/1904.06682v1.pdf | From News to Medical: Cross-domain Discourse Segmentation | The first step in discourse analysis involves dividing a text into segments.
We annotate the first high-quality small-scale medical corpus in English with
discourse segments and analyze how well news-trained segmenters perform on this
domain. While we expectedly find a drop in performance, the nature of the
segmentatio... | ['Katrin Erk', 'Junyi Jessy Li', 'Titan Page', 'Elisa Ferracane'] | 2019-04-14 | from-news-to-medical-cross-domain-discourse-1 | https://aclanthology.org/W19-2704 | https://aclanthology.org/W19-2704.pdf | ws-2019-6 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 5.55510998e-01 1.22487056e+00 -4.97727960e-01 -4.71189171e-01
-1.27470458e+00 -6.84860647e-01 3.70633751e-01 6.83006048e-01
-4.79343981e-01 9.05852318e-01 9.99688804e-01 -5.69471180e-01
1.39285877e-01 -2.76619673e-01 -5.60678005e-01 -3.68135124e-01
-1.26272766e-02 8.50909770e-01 5.39081335e-01 -1.44869417... | [10.774921417236328, 9.514791488647461] |
899303b3-6376-4f39-888e-7e35eddc1e06 | planning-as-theorem-proving-with-heuristics | 2303.13638 | null | https://arxiv.org/abs/2303.13638v3 | https://arxiv.org/pdf/2303.13638v3.pdf | Planning as Theorem Proving with Heuristics | Planning as theorem proving in situation calculus was abandoned 50 years ago as an impossible project. But we have developed a Theorem Proving Lifted Heuristic (TPLH) planner that searches for a plan in a tree of situations using the A* search algorithm. It is controlled by a delete relaxation-based domain independent ... | ['Ryan Young', 'Mikhail Soutchanski'] | 2023-03-23 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 5.44433713e-01 1.20998085e+00 -5.07411778e-01 -1.52934998e-01
-7.81071186e-01 -8.41633260e-01 6.18991375e-01 2.34904811e-01
-1.17382035e-01 1.18734455e+00 6.19164467e-01 -1.13980985e+00
-5.33439517e-01 -1.15286183e+00 -5.12259066e-01 -8.40375647e-02
-5.47915280e-01 7.70418406e-01 1.04734564e+00 -5.06673396... | [4.9830145835876465, 1.8829270601272583] |
06d8546b-afe3-4fb3-8a7e-b358e67856a7 | inducing-temporal-relations-from-time-anchor | null | null | https://aclanthology.org/N18-1166 | https://aclanthology.org/N18-1166.pdf | Inducing Temporal Relations from Time Anchor Annotation | Recognizing temporal relations among events and time expressions has been an essential but challenging task in natural language processing. Conventional annotation of judging temporal relations puts a heavy load on annotators. In reality, the existing annotated corpora include annotations on only {``}salient{''} event ... | ['Yusuke Miyao', 'Fei Cheng'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 3.00828218e-01 1.21269301e-01 -5.41419327e-01 -5.68835616e-01
-7.99744725e-01 -8.54973018e-01 8.71225595e-01 7.80854166e-01
-6.34117067e-01 7.44663537e-01 5.07324815e-01 -3.41874748e-01
-3.30865443e-01 -4.81296599e-01 -1.64147943e-01 -6.12426400e-01
-7.68346071e-01 4.16033745e-01 5.62743247e-01 -3.99356365... | [9.085232734680176, 9.239348411560059] |
1f0af272-9e42-4d7d-be61-e10d73f59a7f | depth-image-upsampling-based-on-guided-filter | 1811.04620 | null | http://arxiv.org/abs/1811.04620v1 | http://arxiv.org/pdf/1811.04620v1.pdf | Depth Image Upsampling based on Guided Filter with Low Gradient Minimization | In this paper, we present a novel upsampling framework to enhance the spatial
resolution of the depth image. In our framework, the upscaling of a
low-resolution depth image is guided by a corresponding intensity images, we
formulate it as a cost aggregation problem with the guided filter. However, the
guided filter doe... | ['Hang Yang', 'Zhongbo Zhang'] | 2018-11-12 | null | null | null | null | ['depth-image-upsampling'] | ['computer-vision'] | [ 3.38114053e-01 4.31813523e-02 1.05895296e-01 -4.84272391e-01
-4.83464867e-01 1.55285344e-01 1.84506014e-01 -2.09901914e-01
-5.58739781e-01 8.92046928e-01 2.51302898e-01 2.41778940e-01
-8.34643692e-02 -1.16450548e+00 -6.50974810e-01 -8.73100877e-01
3.01096261e-01 -2.32064128e-01 4.42532927e-01 -3.24863009... | [9.552664756774902, -2.4135329723358154] |
d285d449-45a0-434d-95d8-14f0ac1022e3 | you-don-t-only-look-once-constructing-spatial | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Sun_You_Dont_Only_Look_Once_Constructing_Spatial-Temporal_Memory_for_Integrated_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Sun_You_Dont_Only_Look_Once_Constructing_Spatial-Temporal_Memory_for_Integrated_ICCV_2021_paper.pdf | You Don't Only Look Once: Constructing Spatial-Temporal Memory for Integrated 3D Object Detection and Tracking | Humans are able to continuously detect and track surrounding objects by constructing a spatial-temporal memory of the objects when looking around. In contrast, 3D object detectors in existing tracking-by-detection systems often search for objects in every new video frame from scratch, without fully leveraging memor... | ['Xiaowei Zhou', 'Hujun Bao', 'Guofeng Zhang', 'Linghao Chen', 'Siyu Zhang', 'Yiming Xie', 'Jiaming Sun'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['object-proposal-generation'] | ['computer-vision'] | [-5.17366707e-01 -3.97773415e-01 -1.35098204e-01 -1.10785529e-01
-1.58917218e-01 -4.87764508e-01 4.18004215e-01 2.13338748e-01
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-3.05998117e-01 6.31705701e-01 1.18260372e+00 1.94049641... | [6.739696502685547, -2.2174954414367676] |
d9642014-e578-441c-a2c2-d77f2d3b3178 | multinews-a-web-collection-of-an-aligned | null | null | https://aclanthology.org/W17-5602 | https://aclanthology.org/W17-5602.pdf | MultiNews: A Web collection of an Aligned Multimodal and Multilingual Corpus | Integrating Natural Language Processing (NLP) and computer vision is a promising effort. However, the applicability of these methods directly depends on the availability of a specific multimodal data that includes images and texts. In this paper, we present a collection of a Multimodal corpus of comparable texts and th... | ['Pintu Lohar', 'Haithem Afli', 'Andy Way'] | 2017-11-01 | null | null | null | ws-2017-11 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-6.55972213e-02 -1.96823865e-01 -2.06799492e-01 -2.20341325e-01
-1.00834560e+00 -8.17278266e-01 1.17784417e+00 4.46353167e-01
-9.93761003e-01 7.69243598e-01 3.81806552e-01 -9.57678184e-02
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3.48542333e-01 6.26364052e-01 2.52529860e-01 -3.60882670... | [11.27396297454834, 1.4368525743484497] |
e8126213-5cb3-48bf-a053-5c8e8b057937 | predicting-grokking-long-before-it-happens-a | 2306.13253 | null | https://arxiv.org/abs/2306.13253v1 | https://arxiv.org/pdf/2306.13253v1.pdf | Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok | This paper focuses on predicting the occurrence of grokking in neural networks, a phenomenon in which perfect generalization emerges long after signs of overfitting or memorization are observed. It has been reported that grokking can only be observed with certain hyper-parameters. This makes it critical to identify the... | ['Guillaume Dumas', 'Irina Rish', 'Mohammad Pezeshki', 'Hattie Zhou', 'Pascal Jr. Tikeng Notsawo'] | 2023-06-23 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 3.45910341e-01 -7.04149008e-02 6.35494962e-02 6.35832474e-02
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-4.35922116e-01 -7.35868275e-01 -9.94074702e-01 -1.07187629e+00
-4.24523503e-01 -4.68716621e-02 3.42008501e-01 -2.71396548... | [7.8855509757995605, 3.510448932647705] |
c009b560-a277-4beb-bfe9-62536fbed194 | dynamical-graph-echo-state-networks-with | 2307.01237 | null | https://arxiv.org/abs/2307.01237v1 | https://arxiv.org/pdf/2307.01237v1.pdf | Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification | The Dissemination Process Classification (DPC) is a popular application of temporal graph classification. The aim of DPC is to classify different spreading patterns of information or pestilence within a community represented by discrete-time temporal graphs. Recently, a reservoir computing-based model named Dynamical G... | ['Gouhei Tanaka', 'Kantaro Fujiwara', 'Ziqiang Li'] | 2023-07-03 | null | null | null | null | ['graph-classification', 'classification-1'] | ['graphs', 'methodology'] | [ 2.54136720e-03 -4.56047088e-01 -1.69458359e-01 1.19515426e-01
-5.49715236e-02 -3.56865585e-01 1.18394613e+00 6.65364802e-01
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-3.59141320e-01 -1.04456663e+00 -2.54954517e-01 -1.04105937e+00
-9.46967363e-01 8.56209174e-02 5.98120511e-01 6.21401072... | [6.762235164642334, 2.8561012744903564] |
71097244-38ec-479a-899c-39a358385f97 | facial-action-unit-detection-with | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Jacob_Facial_Action_Unit_Detection_With_Transformers_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Jacob_Facial_Action_Unit_Detection_With_Transformers_CVPR_2021_paper.pdf | Facial Action Unit Detection With Transformers | The Facial Action Coding System is a taxonomy for fine-grained facial expression analysis. This paper proposes a method for detecting Facial Action Units (FAU), which define particular face muscle activity, from an input image. FAU detection is formulated as a multi-task learning problem, where image features and a... | ['Bjorn Stenger', 'Geethu Miriam Jacob'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 2.76445329e-01 -5.20766787e-02 -3.93795818e-01 -6.07134819e-01
-8.11179399e-01 -2.57706344e-01 6.84504390e-01 -5.96655726e-01
-4.56476808e-01 5.25994837e-01 1.68085545e-01 4.58811164e-01
1.05657969e-02 -2.14057118e-01 -6.68736875e-01 -9.46235716e-01
-2.87353575e-01 -8.41739587e-03 -1.67418882e-01 -1.53951511... | [13.571906089782715, 1.7125388383865356] |
37a7746d-d4c3-4cb3-9c95-6225aecd4ad9 | a-robots-sense-making-of-fallacies-and | 1906.09689 | null | https://arxiv.org/abs/1906.09689v2 | https://arxiv.org/pdf/1906.09689v2.pdf | A robot's sense-making of fallacies and rhetorical tropes. Creating ontologies of what humans try to say | In the design of user-friendly robots, human communication should be understood by the system beyond mere logics and literal meaning. Robot communication-design has long ignored the importance of communication and politeness rules that are 'forgiving' and 'suspending disbelief' and cannot handle the basically metaphori... | ['Johan F. Hoorn', 'Denice J. Tuinhof'] | 2019-06-24 | null | null | null | null | ['logical-fallacies'] | ['miscellaneous'] | [-1.45798549e-01 1.09504151e+00 1.06489472e-01 -3.20657581e-01
3.07788134e-01 -7.17276096e-01 8.39056849e-01 1.92796290e-01
-3.12607616e-01 6.24245405e-01 6.90350771e-01 -7.74057627e-01
-1.48232430e-01 -4.85672891e-01 -3.49948496e-01 -1.89196970e-02
2.38122553e-01 2.99382985e-01 -1.01347610e-01 -9.40871239... | [9.122544288635254, 6.384304523468018] |
60268e05-e10f-4bff-90a6-f7a2601776c4 | video-understanding-based-on-human-action-and | 2010.12968 | null | https://arxiv.org/abs/2010.12968v2 | https://arxiv.org/pdf/2010.12968v2.pdf | Improved Actor Relation Graph based Group Activity Recognition | Video understanding is to recognize and classify different actions or activities appearing in the video. A lot of previous work, such as video captioning, has shown promising performance in producing general video understanding. However, it is still challenging to generate a fine-grained description of human actions an... | ['Xinran Tie', 'Zijian Kuang'] | 2020-10-24 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 4.29436475e-01 -8.72932896e-02 -2.03216076e-01 -5.17354965e-01
9.41704139e-02 -3.63511324e-01 6.53380573e-01 1.12307161e-01
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2.69695502e-02 -5.50833821e-01 -7.41925538e-01 -5.50056279e-01
-3.34109157e-01 1.22119173e-01 3.65755230e-01 -5.14443554... | [8.794004440307617, 0.6912034749984741] |
1b03646e-070e-49ae-b241-cc71d6e5f3e5 | grass-contrastive-learning-with-gradient | 2306.15868 | null | https://arxiv.org/abs/2306.15868v2 | https://arxiv.org/pdf/2306.15868v2.pdf | GraSS: Contrastive Learning with Gradient Guided Sampling Strategy for Remote Sensing Image Semantic Segmentation | Self-supervised contrastive learning (SSCL) has achieved significant milestones in remote sensing image (RSI) understanding. Its essence lies in designing an unsupervised instance discrimination pretext task to extract image features from a large number of unlabeled images that are beneficial for downstream tasks. Howe... | ['Haifeng Li', 'Chengli Peng', 'Yunsheng Zhang', 'Chao Tao', 'Zhen Ren', 'Zhaoyang Zhang'] | 2023-06-28 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 7.64639676e-01 -7.16651157e-02 -2.18428627e-01 -4.93308753e-01
-8.69876802e-01 -2.26869389e-01 4.26777959e-01 1.52829826e-01
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-3.43833208e-01 -1.14227891e+00 -6.16307378e-01 -9.99448180e-01
-2.49268651e-01 3.09352487e-01 2.78666735e-01 -2.44636431... | [9.661078453063965, -1.2366619110107422] |
51939067-89a4-45a8-9abc-68970d793de1 | ntua-islab-at-semeval-2019-task-9-mining | null | null | https://aclanthology.org/S19-2215 | https://aclanthology.org/S19-2215.pdf | NTUA-ISLab at SemEval-2019 Task 9: Mining Suggestions in the wild | As online customer forums and product comparison sites increase their societal influence, users are actively expressing their opinions and posting their recommendations on their fellow customers online. However, systems capable of recognizing suggestions still lack in stability. Suggestion Mining, a novel and challengi... | ['Georgios Siolas', 'ros', 'Rol Potamias', 'Alex Neofytou', 'os Alex'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [-4.89814952e-02 4.07040626e-01 -4.25996870e-01 -4.95767206e-01
-4.54657257e-01 -4.10033464e-01 9.29599226e-01 8.75283718e-01
-5.01074314e-01 4.00871098e-01 5.71152493e-02 -5.41082561e-01
-2.93962508e-01 -6.45942152e-01 -1.30152345e-01 -1.62615448e-01
-7.10915476e-02 3.61833572e-01 7.83557817e-02 -3.73526692... | [10.893308639526367, 7.470451831817627] |
b8c70fe2-9350-4005-97cc-d5bde88a4d7c | on-manipulating-signals-of-user-item-graph-a | 2306.03624 | null | https://arxiv.org/abs/2306.03624v1 | https://arxiv.org/pdf/2306.03624v1.pdf | On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering | Collaborative filtering (CF) is an important research direction in recommender systems that aims to make recommendations given the information on user-item interactions. Graph CF has attracted more and more attention in recent years due to its effectiveness in leveraging high-order information in the user-item bipartit... | ['Yan Zhang', 'Dongmei Zhang', 'Shi Han', 'Qiang Fu', 'Xiaojun Ma', 'Xu Chen', 'Lun Du', 'Jiayan Guo'] | 2023-06-06 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-2.54503340e-01 -5.69013536e-01 -2.53541827e-01 -1.30014285e-01
-2.95455754e-02 -3.09312552e-01 -8.31263792e-03 -1.01238945e-02
1.84091270e-01 3.83411199e-01 7.21529722e-01 -3.45540613e-01
-6.41300261e-01 -9.40209508e-01 -2.89652944e-01 -6.40834928e-01
-1.84594885e-01 -1.29039526e-01 -9.88909900e-02 -5.80179274... | [10.126494407653809, 5.619290351867676] |
cbea1b4a-0201-415d-9294-d823b73bfd3c | augment-to-detect-anomalies-with-continuous | 2207.01112 | null | https://arxiv.org/abs/2207.01112v1 | https://arxiv.org/pdf/2207.01112v1.pdf | Augment to Detect Anomalies with Continuous Labelling | Anomaly detection is to recognize samples that differ in some respect from the training observations. These samples which do not conform to the distribution of normal data are called outliers or anomalies. In real-world anomaly detection problems, the outliers are absent, not well defined, or have a very limited number... | ['Yalda Mohsenzadeh', 'Anthony Wong', 'Vahid Reza Khazaie'] | 2022-07-03 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 2.73087353e-01 -2.66012639e-01 1.10376455e-01 -4.69226122e-01
-3.79055619e-01 -2.79478788e-01 4.65125889e-01 4.67783511e-01
-2.01059729e-01 4.13074017e-01 -4.74967629e-01 -3.53598416e-01
8.26522037e-02 -6.70811355e-01 -6.57071888e-01 -8.06114614e-01
-2.25930676e-01 3.79774034e-01 1.19094178e-01 -1.63561068... | [7.608783721923828, 2.3297250270843506] |
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