paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9273f84a-ada6-4b8d-9ace-d95ed37769b4 | towards-deep-symbolic-reinforcement-learning | 1609.05518 | null | http://arxiv.org/abs/1609.05518v2 | http://arxiv.org/pdf/1609.05518v2.pdf | Towards Deep Symbolic Reinforcement Learning | Deep reinforcement learning (DRL) brings the power of deep neural networks to
bear on the generic task of trial-and-error learning, and its effectiveness has
been convincingly demonstrated on tasks such as Atari video games and the game
of Go. However, contemporary DRL systems inherit a number of shortcomings from
the ... | ['Murray Shanahan', 'Kai Arulkumaran', 'Marta Garnelo'] | 2016-09-18 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-9.34233144e-03 2.06046924e-01 3.87594439e-02 -2.58885980e-01
-3.91251504e-01 -5.64924181e-01 5.89587152e-01 1.73418391e-02
-6.08587623e-01 8.88094664e-01 -3.03340405e-01 -7.28834093e-01
-4.45647806e-01 -1.11421204e+00 -9.39065933e-01 -1.79467872e-01
-2.00103730e-01 7.29155302e-01 5.09817719e-01 -8.49499106... | [3.919968605041504, 1.428778052330017] |
1fad0795-e082-44b3-9e6b-5c17fd40171e | mask-scalar-prediction-for-improving-robust | 2204.12092 | null | https://arxiv.org/abs/2204.12092v1 | https://arxiv.org/pdf/2204.12092v1.pdf | Mask scalar prediction for improving robust automatic speech recognition | Using neural network based acoustic frontends for improving robustness of streaming automatic speech recognition (ASR) systems is challenging because of the causality constraints and the resulting distortion that the frontend processing introduces in speech. Time-frequency masking based approaches have been shown to wo... | ['Yuma Koizumi', 'Nathan Howard', 'Sankaran Panchapagesan', 'James Walker', 'Arun Narayanan'] | 2022-04-26 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 5.66131651e-01 3.67841311e-02 4.93393153e-01 -5.44554591e-01
-1.32034624e+00 -5.00403404e-01 5.76518774e-01 2.11311802e-01
-8.04864407e-01 2.02170402e-01 6.16796494e-01 -5.15330613e-01
-5.96122397e-03 -2.11440912e-03 -5.24043918e-01 -6.76852465e-01
-1.79581419e-01 -2.26230800e-01 4.63364571e-01 -4.76912767... | [14.935440063476562, 6.003940582275391] |
138e424c-f399-42ac-a84a-36dc40e724fd | tag-based-attention-guided-bottom-up-approach | 2204.10765 | null | https://arxiv.org/abs/2204.10765v1 | https://arxiv.org/pdf/2204.10765v1.pdf | Tag-Based Attention Guided Bottom-Up Approach for Video Instance Segmentation | Video Instance Segmentation is a fundamental computer vision task that deals with segmenting and tracking object instances across a video sequence. Most existing methods typically accomplish this task by employing a multi-stage top-down approach that usually involves separate networks to detect and segment objects in e... | ['Mubarak Shah', 'Jyoti Kini'] | 2022-04-22 | null | null | null | null | ['video-instance-segmentation', 'temporal-tagging'] | ['computer-vision', 'natural-language-processing'] | [ 4.58983570e-01 1.43006351e-02 -3.78507197e-01 -3.76113564e-01
-9.51428771e-01 -5.57293832e-01 3.55998605e-01 1.70430973e-01
-6.44686460e-01 3.69198054e-01 -3.33655506e-01 -1.08103342e-01
1.10366113e-01 -5.12968540e-01 -9.90568459e-01 -6.59418941e-01
-1.70866400e-01 4.62597370e-01 9.42615449e-01 2.94524431... | [9.140584945678711, -0.11249562352895737] |
628a22c0-e159-4b24-9f64-9d836d067326 | data-efficient-training-of-cnns-and | 2303.02095 | null | https://arxiv.org/abs/2303.02095v2 | https://arxiv.org/pdf/2303.02095v2.pdf | Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective | Coreset selection is among the most effective ways to reduce the training time of CNNs, however, only limited is known on how the resultant models will behave under variations of the coreset size, and choice of datasets and models. Moreover, given the recent paradigm shift towards transformer-based models, it is still ... | ['Irtiza Hasan', 'Deepak K. Gupta', 'Dilip K. Prasad', 'Animesh Gupta'] | 2023-03-03 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.66167790e-01 -1.36199579e-01 -1.26940340e-01 -3.14051360e-01
-3.38797122e-01 -5.78487098e-01 4.92544264e-01 2.04006042e-02
-6.63790822e-01 5.81260800e-01 -1.26905395e-02 -3.08985919e-01
-4.66161937e-01 -9.40300882e-01 -7.80203044e-01 -8.01837564e-01
1.87511370e-01 7.45613456e-01 5.86998701e-01 -1.99399307... | [8.719452857971191, 3.2121341228485107] |
e51e298a-7d21-4de9-9f78-1c07ddfe1385 | probing-model-signal-awareness-via-prediction | 2011.14934 | null | https://arxiv.org/abs/2011.14934v2 | https://arxiv.org/pdf/2011.14934v2.pdf | Probing Model Signal-Awareness via Prediction-Preserving Input Minimization | This work explores the signal awareness of AI models for source code understanding. Using a software vulnerability detection use case, we evaluate the models' ability to capture the correct vulnerability signals to produce their predictions. Our prediction-preserving input minimization (P2IM) approach systematically re... | ['Yunhui Zheng', 'Sahil Suneja', 'Jim Laredo', 'Alessandro Morari', 'Yufan Zhuang'] | 2020-11-25 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [ 5.85608482e-01 3.13251048e-01 -1.29689306e-01 -2.17267171e-01
-1.04007411e+00 -8.43165517e-01 5.24256408e-01 1.91959396e-01
4.15680408e-02 2.31998309e-01 1.79653734e-01 -6.85104847e-01
-3.07659768e-02 -6.92415714e-01 -1.01505804e+00 -3.26597065e-01
-2.46126056e-01 2.55936179e-02 3.59873116e-01 -3.23203266... | [7.164441108703613, 7.764323711395264] |
11725bbb-4360-4f80-a141-aade11f6228a | third-party-aligner-for-neural-word | 2211.04198 | null | https://arxiv.org/abs/2211.04198v1 | https://arxiv.org/pdf/2211.04198v1.pdf | Third-Party Aligner for Neural Word Alignments | Word alignment is to find translationally equivalent words between source and target sentences. Previous work has demonstrated that self-training can achieve competitive word alignment results. In this paper, we propose to use word alignments generated by a third-party word aligner to supervise the neural word alignmen... | ['Min Zhang', 'Yuqi Zhang', 'Xiangyu Duan', 'Chuanqi Dong', 'Jinpeng Zhang'] | 2022-11-08 | null | null | null | null | ['word-alignment'] | ['natural-language-processing'] | [ 8.44058618e-02 8.45555365e-02 -4.14731115e-01 -5.40765405e-01
-1.26502430e+00 -6.28196001e-01 4.20218617e-01 2.45703459e-01
-8.56637418e-01 5.20236611e-01 5.14161468e-01 -4.62933511e-01
5.35337448e-01 -5.51507175e-01 -8.38381827e-01 -5.66245615e-01
3.84622723e-01 6.13815129e-01 -5.21735512e-02 -6.09791875... | [11.339228630065918, 10.230688095092773] |
1a67c887-e751-4683-9dc3-90c22cc90a8d | robust-detection-and-attribution-of-climate | 2212.04905 | null | https://arxiv.org/abs/2212.04905v1 | https://arxiv.org/pdf/2212.04905v1.pdf | Robust detection and attribution of climate change under interventions | Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variability (detection), and whether observed changes can be assigned to specific external drivers (attribution). We propose a direct D&A approach b... | ['Reto Knutti', 'Guillaume Obozinski', 'Nicolai Meinshausen', 'Sebastian Sippel', 'Enikő Székely'] | 2022-12-09 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 6.73585892e-01 -1.23776168e-01 -3.20127487e-01 -1.55552626e-01
-2.81574488e-01 -9.15266216e-01 1.07989347e+00 2.37009540e-01
-9.73804388e-04 9.54700708e-01 2.65405148e-01 -7.83508301e-01
-3.46837759e-01 -1.07721639e+00 -9.49730992e-01 -9.31574345e-01
-2.14062169e-01 -2.20289946e-01 1.48597568e-01 1.09028190... | [7.689138889312744, 5.03073263168335] |
401808bf-5fcf-46c0-8892-c7bc2d6cf522 | efficient-parallel-split-learning-over | 2303.15991 | null | https://arxiv.org/abs/2303.15991v3 | https://arxiv.org/pdf/2303.15991v3.pdf | Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks | The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL), allowing mu... | ['Yuguang Fang', 'Kaibin Huang', 'Yue Gao', 'Xianhao Chen', 'Yiqin Deng', 'Guangyu Zhu', 'Zheng Lin'] | 2023-03-26 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 4.71132435e-02 -1.55805618e-01 -5.19558191e-01 -6.04247451e-01
-6.13983393e-01 -4.72065240e-01 5.04682884e-02 -1.65386617e-01
-6.43186212e-01 7.62376487e-01 -1.60285056e-01 -5.87850809e-01
-2.19428048e-01 -6.47735298e-01 -7.45798647e-01 -9.00943220e-01
-1.23342872e-02 -9.87033844e-02 -5.12359999e-02 5.12229621... | [5.926358699798584, 6.076547622680664] |
c2fff7de-ab37-41de-bdda-12dbb24e32a9 | learning-policies-for-social-network | 1907.11625 | null | https://arxiv.org/abs/1907.11625v5 | https://arxiv.org/pdf/1907.11625v5.pdf | Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling | A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art methods rely on hand-crafted sampling algorithms; these methods sample nodes and their neighbours in a carefully constructed order and choose... | ['Priyesh Vijayan', 'Balaraman Ravindran', 'Harshavardhan Kamarthi', 'Bryan Wilder', 'Milind Tambe'] | 2019-07-08 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 4.42697525e-01 9.74444270e-01 -5.46149254e-01 -1.88906655e-01
-8.29876289e-02 -5.27867973e-01 8.05150211e-01 2.94457003e-02
-1.61813721e-01 1.07193613e+00 2.88430840e-01 8.23353380e-02
-4.57067132e-01 -1.12923729e+00 -7.60594308e-01 -5.65037191e-01
-7.06939816e-01 1.20465374e+00 2.20747679e-01 -4.61185098... | [6.971278667449951, 5.763023853302002] |
66fc6169-be48-4c67-a7a0-040c56809442 | an-adversarial-generative-network-designed | null | null | https://www.mdpi.com/2072-4292/14/18/4619 | https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.mdpi.com%2F2072-4292%2F14%2F18%2F4619%2Fpdf&data=05%7C01%7Cnlandro%40uninsubria.it%7C9403cb44323448a0e66908da9a123570%7C9252ed8bdffc401c86ca6237da9991fa%7C0%7C0%7C637991700433335562%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJB... | An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of Mars | In computer vision, stereoscopy allows the three-dimensional reconstruction of a scene using two 2D images taken from two slightly different points of view, to extract spatial information on the depth of the scene in the form of a map of disparities. In stereophotogrammetry, the disparity map is essential in extracting... | ['Mattia Gatti', 'Emanuele Simioni', 'Claudio Pernechele', 'Nicola Landro', 'Gabriele Cremonese', 'Cristina Re', 'Ignazio Gallo', 'Riccardo La Grassa'] | 2022-08-15 | null | null | null | remote-sensing-2022-8 | ['stereo-matching-1'] | ['computer-vision'] | [ 4.35642540e-01 4.34899002e-01 3.33498389e-01 -9.75646675e-02
-6.47004902e-01 -4.96659726e-01 8.30524921e-01 -5.96980393e-01
-4.61037576e-01 8.60832572e-01 6.42677173e-02 -6.26792610e-02
-9.20920521e-02 -1.32079363e+00 -8.90254438e-01 -5.75886965e-01
1.48191795e-01 6.05590045e-01 1.71873972e-01 -4.80692148... | [8.935978889465332, -2.6388516426086426] |
03b7e580-1603-4551-96f8-1d096815c9ea | fine-grained-age-estimation-in-the-wild-with | 1805.10445 | null | https://arxiv.org/abs/1805.10445v2 | https://arxiv.org/pdf/1805.10445v2.pdf | Fine-Grained Age Estimation in the wild with Attention LSTM Networks | Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision, which has a wide range of practical application values. Accuracy of age estimation of face images in the wild is relatively low for existing methods, because they only take into account the... | ['Xingfang Yuan', 'Zhenbing Zhao', 'Zhanyu Ma', 'Na Liu', 'Ke Zhang', 'Ce Gao', 'Xinyao Guo'] | 2018-05-26 | null | null | null | null | ['age-and-gender-classification'] | ['computer-vision'] | [-1.94393739e-01 -1.03506602e-01 1.21742971e-02 -7.34023452e-01
-7.71870464e-02 1.25329763e-01 3.46926987e-01 -3.17042232e-01
-6.18919671e-01 5.13099909e-01 9.01425183e-02 3.16060275e-01
-1.42219663e-01 -9.42544222e-01 -4.44134980e-01 -1.01197445e+00
-2.84229010e-01 4.10054058e-01 -2.80269146e-01 9.42647159... | [13.601990699768066, 0.8194431066513062] |
88208b02-9a06-46c4-94be-e88d9167f338 | 3d-human-action-recognition-with-siamese-lstm | 1807.02131 | null | http://arxiv.org/abs/1807.02131v1 | http://arxiv.org/pdf/1807.02131v1.pdf | 3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning | This paper proposes a new 3D Human Action Recognition system as a two-phase
system: (1) Deep Metric Learning Module which learns a similarity metric
between two 3D joint sequences using Siamese-LSTM networks; (2) A Multiclass
Classification Module that uses the output of the first module to produce the
final recognitio... | ['Yusuf Sinan Akgul', 'Seyma Yucer'] | 2018-07-05 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 7.82664269e-02 -2.53101319e-01 -5.57412617e-02 -6.52639627e-01
-5.68950176e-01 -2.29717106e-01 6.56973958e-01 -4.10021484e-01
-8.63211632e-01 5.44954896e-01 1.67943805e-01 -5.49975038e-02
2.27526277e-01 -6.71581089e-01 -4.59446430e-01 -3.52960765e-01
-4.02490526e-01 7.13448644e-01 8.91325772e-01 -1.93317354... | [7.893026351928711, 0.4752243757247925] |
90157feb-3601-4aef-9fb1-68561ced7454 | denoise-and-contrast-for-category-agnostic | 2103.16671 | null | https://arxiv.org/abs/2103.16671v1 | https://arxiv.org/pdf/2103.16671v1.pdf | Denoise and Contrast for Category Agnostic Shape Completion | In this paper, we present a deep learning model that exploits the power of self-supervision to perform 3D point cloud completion, estimating the missing part and a context region around it. Local and global information are encoded in a combined embedding. A denoising pretext task provides the network with the needed lo... | ['Tatiana Tommasi', 'Enrico Magli', 'Giulia Fracastoro', 'Diego Valsesia', 'Antonio Alliegro'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Alliegro_Denoise_and_Contrast_for_Category_Agnostic_Shape_Completion_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Alliegro_Denoise_and_Contrast_for_Category_Agnostic_Shape_Completion_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [-2.17565857e-02 5.84376216e-01 4.15849574e-02 -2.93851316e-01
-6.87397599e-01 -8.02437961e-01 8.33164930e-01 2.20406681e-01
-2.42711365e-01 4.07385617e-01 -3.75268795e-02 3.90706658e-01
-8.46928433e-02 -8.74002457e-01 -9.80185032e-01 -9.47332740e-01
1.56491458e-01 9.71467674e-01 2.23213732e-01 -2.77681887... | [8.359477996826172, -3.332738161087036] |
0fac0ed4-1162-443b-b075-b6d7667d5435 | on-handling-catastrophic-forgetting-for | 2302.09310 | null | https://arxiv.org/abs/2302.09310v1 | https://arxiv.org/pdf/2302.09310v1.pdf | On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge | Human activity recognition (HAR) has been a classic research problem. In particular, with recent machine learning (ML) techniques, the recognition task has been largely investigated by companies and integrated into their products for customers. However, most of them apply a predefined activity set and conduct the learn... | ['Hakim Hacid', 'George Arvanitakis', 'Jingwei Zuo'] | 2023-02-18 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 1.89665690e-01 -4.86375429e-02 -4.03065681e-01 -2.33122230e-01
-1.65854141e-01 -3.92394274e-01 1.15986869e-01 3.74649525e-01
-6.12376571e-01 5.68529904e-01 -3.85760218e-01 -3.32640201e-01
-4.17088345e-02 -7.79085815e-01 -5.59864998e-01 -7.49641597e-01
1.13228392e-02 2.47187838e-01 5.58653995e-02 4.72461730... | [5.945842742919922, 6.197783470153809] |
6653cddb-b815-465d-827a-9a2db6a3e040 | extracting-candidate-factors-affecting-long | 2103.06446 | null | https://arxiv.org/abs/2103.06446v1 | https://arxiv.org/pdf/2103.06446v1.pdf | Extracting candidate factors affecting long-term trends of student abilities across subjects | Long-term student achievement data provide useful information to formulate the research question of what types of student skills would impact future trends across subjects. However, few studies have focused on long-term data. This is because the criteria of examinations vary depending on their designers; additionally, ... | ['Atsushi Yoshikawa', 'Hiroki Kuno', 'Satoshi Takahashi'] | 2021-03-11 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-4.54257339e-01 -4.87118751e-01 -5.65843105e-01 -5.19025803e-01
-7.20017910e-01 -6.35814130e-01 3.55725080e-01 4.76504773e-01
-1.72276959e-01 6.86850190e-01 6.14716232e-01 -7.50211954e-01
-1.09204662e+00 -1.06155157e+00 -7.83738494e-01 -5.70805371e-01
5.37998714e-02 -6.07484467e-02 3.58274519e-01 -5.17534800... | [10.154556274414062, 7.194987773895264] |
5caaf88d-532b-472a-9363-97f54b316dbb | causal-dependence-plots-for-interpretable | 2303.04209 | null | https://arxiv.org/abs/2303.04209v2 | https://arxiv.org/pdf/2303.04209v2.pdf | Causal Dependence Plots | Explaining artificial intelligence or machine learning models is increasingly important. To use such data-driven systems wisely we must understand how they interact with the world, including how they depend causally on data inputs. In this work we develop Causal Dependence Plots (CDPs) to visualize how one variable--an... | ['Sakina Hansen', 'Lucius E. J. Bynum', 'Joshua R. Loftus'] | 2023-03-07 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.47358844e-01 2.22781777e-01 -6.34616315e-01 -6.27746224e-01
-9.77032855e-02 -5.43890238e-01 8.20059896e-01 4.08993453e-01
-7.86181241e-02 1.13257837e+00 5.55541873e-01 -1.06716883e+00
-5.51641166e-01 -9.99345660e-01 -1.02731490e+00 -5.66157639e-01
-4.24849242e-01 3.54198694e-01 -1.87807113e-01 -1.19309714... | [7.978455066680908, 5.394549369812012] |
8c006142-7544-4854-a6ba-ee14c0bb6ed6 | a-multi-task-approach-to-learning | null | null | https://aclanthology.org/P18-2035 | https://aclanthology.org/P18-2035.pdf | A Multi-task Approach to Learning Multilingual Representations | We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model. Our architecture can transparently use both monolingual... | ['Shrikanth Narayanan', 'Karan Singla', 'Dogan Can'] | 2018-07-01 | null | null | null | acl-2018-7 | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [-5.17200708e-01 -3.42626721e-01 -6.00065947e-01 -6.12950444e-01
-1.49080884e+00 -7.51538694e-01 7.63891041e-01 4.19262350e-01
-9.49671626e-01 7.54748166e-01 6.39907956e-01 -6.03986561e-01
4.41930175e-01 -4.27085042e-01 -7.03090250e-01 -2.53998280e-01
2.14025348e-01 6.81037843e-01 -3.00579160e-01 -5.31807780... | [11.06834888458252, 9.921958923339844] |
dd6cfc87-0a2c-45a8-8518-3f7ded2977c8 | madiff-offline-multi-agent-learning-with | 2305.17330 | null | https://arxiv.org/abs/2305.17330v1 | https://arxiv.org/pdf/2305.17330v1.pdf | MADiff: Offline Multi-agent Learning with Diffusion Models | Diffusion model (DM), as a powerful generative model, recently achieved huge success in various scenarios including offline reinforcement learning, where the policy learns to conduct planning by generating trajectory in the online evaluation. However, despite the effectiveness shown for single-agent learning, it remain... | ['Weinan Zhang', 'Stefano Ermon', 'Yong Yu', 'Minkai Xu', 'Bingyi Kang', 'Liyuan Mao', 'Minghuan Liu', 'Zhengbang Zhu'] | 2023-05-27 | null | null | null | null | ['trajectory-prediction', 'offline-rl'] | ['computer-vision', 'playing-games'] | [-6.53650701e-01 3.41533124e-01 -1.35169297e-01 2.89238364e-01
-6.89732909e-01 -5.20526171e-01 1.02859426e+00 1.80833116e-01
-4.24245358e-01 9.90630925e-01 4.59291972e-02 -2.18927607e-01
-4.02029723e-01 -8.82334769e-01 -6.14188254e-01 -8.64813387e-01
-4.13579971e-01 1.58371627e+00 1.36168480e-01 -5.66612363... | [3.7567954063415527, 1.9501842260360718] |
31bf6d77-0b2a-4f12-a893-bbcaca9b7153 | 3d-siamrpn-an-end-to-end-learning-method-for | 2108.05630 | null | https://arxiv.org/abs/2108.05630v1 | https://arxiv.org/pdf/2108.05630v1.pdf | 3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud | 3D single object tracking is a key issue for autonomous following robot, where the robot should robustly track and accurately localize the target for efficient following. In this paper, we propose a 3D tracking method called 3D-SiamRPN Network to track a single target object by using raw 3D point cloud data. The propos... | ['Sebastian Scherer', 'Yubo Cui', 'Sifan Zhou', 'Zheng Fang'] | 2021-08-12 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-4.99012142e-01 -4.33356166e-01 -3.19723263e-02 7.29066953e-02
-2.12584019e-01 -2.40476146e-01 3.75033349e-01 -7.34208450e-02
-4.94989723e-01 1.38612270e-01 -4.81585950e-01 -5.37424609e-02
-2.30947822e-01 -8.31034541e-01 -6.52713418e-01 -7.06035376e-01
-9.86820459e-02 5.71106315e-01 9.62702751e-01 -2.71714479... | [6.589848518371582, -2.348017930984497] |
d9bbbb06-a397-4dbb-9367-3f00b04111f0 | banditsum-extractive-summarization-as-a | 1809.09672 | null | https://arxiv.org/abs/1809.09672v3 | https://arxiv.org/pdf/1809.09672v3.pdf | BanditSum: Extractive Summarization as a Contextual Bandit | In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call our approach BanditSum as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to sum... | ['Herke van Hoof', 'Eric Crawford', 'Yikang Shen', 'Jackie Chi Kit Cheung', 'Yue Dong'] | 2018-09-25 | banditsum-extractive-summarization-as-a-1 | https://aclanthology.org/D18-1409 | https://aclanthology.org/D18-1409.pdf | emnlp-2018-10 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.58008075e-01 4.80307132e-01 -8.74508023e-01 -2.72193700e-01
-1.52156973e+00 -5.52749157e-01 7.44127691e-01 3.42268288e-01
-4.92643774e-01 1.40591216e+00 9.20977533e-01 -2.44110376e-01
-6.41395971e-02 -3.79486501e-01 -8.78334284e-01 -3.98150861e-01
3.85548994e-02 7.37601876e-01 -1.58399418e-01 -6.40866607... | [12.527968406677246, 9.49083423614502] |
cccbe072-0958-4969-a84d-59b11b823590 | applying-naive-bayes-classification-to-google | 1608.08574 | null | http://arxiv.org/abs/1608.08574v1 | http://arxiv.org/pdf/1608.08574v1.pdf | Applying Naive Bayes Classification to Google Play Apps Categorization | There are over one million apps on Google Play Store and over half a million
publishers. Having such a huge number of apps and developers can pose a
challenge to app users and new publishers on the store. Discovering apps can be
challenging if apps are not correctly published in the right category, and, in
turn, reduce... | ['Babatunde Olabenjo'] | 2016-08-30 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-2.25836545e-01 -1.49743855e-01 -5.01515448e-01 -2.61948049e-01
-7.24746108e-01 -7.41005063e-01 1.69779249e-02 2.04108104e-01
-4.30318452e-02 5.28773725e-01 2.33588498e-02 -6.22042954e-01
-3.23111415e-02 -7.27981389e-01 -5.62077343e-01 5.56362746e-03
2.26251706e-01 3.50252181e-01 8.11575174e-01 -1.65493682... | [14.396560668945312, 9.651606559753418] |
0d695f6a-2212-44b3-9c22-44360bd4ed7a | facing-the-hard-problems-in-fgvc | 2006.13190 | null | https://arxiv.org/abs/2006.13190v2 | https://arxiv.org/pdf/2006.13190v2.pdf | Facing the Hard Problems in FGVC | In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy. This work carefully analyzes the performance of recent SOTA methods, quantitatively, but more importantly, qualitatively. We show that these models universally struggle with certain "ha... | ['Andrew Merrill', 'Matt Gwilliam', 'Connor Anderson', 'Adam Teuscher', 'Ryan Farrell'] | 2020-06-23 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 3.97664830e-02 -4.86184776e-01 -1.32790416e-01 -3.17293435e-01
-1.04384637e+00 -7.37114251e-01 6.21450543e-01 -1.21037886e-01
-1.98917165e-01 4.59650338e-01 1.45668253e-01 -4.61704999e-01
-4.13045660e-02 -3.14370006e-01 -3.38962704e-01 -4.33749735e-01
1.15590088e-01 2.65659839e-01 1.83200762e-01 -3.54820974... | [9.658976554870605, 2.2874464988708496] |
26545c6d-e6c9-45df-9b84-527b48a21213 | drug-drug-interaction-extraction-via | 1705.03261 | null | http://arxiv.org/abs/1705.03261v2 | http://arxiv.org/pdf/1705.03261v2.pdf | Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers | Drug-drug interaction (DDI) is a vital information when physicians and
pharmacists intend to co-administer two or more drugs. Thus, several DDI
databases are constructed to avoid mistakenly combined use. In recent years,
automatically extracting DDIs from biomedical text has drawn researchers'
attention. However, the e... | ['Qingbo Wu', 'Shasha Li', 'Jie Yu', 'Zibo Yi'] | 2017-05-09 | null | null | null | null | ['drug-drug-interaction-extraction'] | ['natural-language-processing'] | [ 1.01855643e-01 -1.00779168e-01 -5.25421262e-01 -5.27552187e-01
-4.68436807e-01 -4.06418115e-01 4.33973610e-01 6.44906640e-01
-1.05302095e-01 1.04464912e+00 3.08972716e-01 -7.71695912e-01
-2.32300371e-01 -6.63481355e-01 -6.60485446e-01 -4.79488999e-01
3.48236226e-02 6.26345098e-01 -5.64496815e-01 1.63257495... | [8.278645515441895, 8.602011680603027] |
ce46139d-9ef0-4da5-864b-6dcd4cfbb44c | rmssinger-realistic-music-score-based-singing | 2305.10686 | null | https://arxiv.org/abs/2305.10686v1 | https://arxiv.org/pdf/2305.10686v1.pdf | RMSSinger: Realistic-Music-Score based Singing Voice Synthesis | We are interested in a challenging task, Realistic-Music-Score based Singing Voice Synthesis (RMS-SVS). RMS-SVS aims to generate high-quality singing voices given realistic music scores with different note types (grace, slur, rest, etc.). Though significant progress has been achieved, recent singing voice synthesis (SV... | ['Zhou Zhao', 'Huadai Liu', 'Chenye Cui', 'Rongjie Huang', 'Zhenhui Ye', 'Jinglin Liu', 'Jinzheng He'] | 2023-05-18 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 5.56251891e-02 -2.93254256e-01 5.83757795e-02 2.16723442e-01
-1.12628138e+00 -8.62320364e-01 7.48934895e-02 -2.79945165e-01
6.65131509e-02 3.24753791e-01 3.69849771e-01 -8.65826979e-02
-2.35554054e-01 -3.83378088e-01 -2.79652655e-01 -5.87867141e-01
1.74669951e-01 2.01706395e-01 1.54737309e-01 -2.21067473... | [15.726492881774902, 5.790154457092285] |
1691c742-b542-4b3e-93b8-f75ab85fe67d | sketch-to-text-generation-toward-contextual | null | null | https://aclanthology.org/W16-6607 | https://aclanthology.org/W16-6607.pdf | Sketch-to-Text Generation: Toward Contextual, Creative, and Coherent Composition | null | ['Yejin Choi'] | 2016-09-01 | null | null | null | ws-2016-9 | ['sketch-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.456973552703857, 3.557210922241211] |
726af6c2-3be9-48c0-b8da-3b052b67d44c | minimizing-fuzzy-interpretations-in-fuzzy | 2303.11438 | null | https://arxiv.org/abs/2303.11438v1 | https://arxiv.org/pdf/2303.11438v1.pdf | Minimizing Fuzzy Interpretations in Fuzzy Description Logics by Using Crisp Bisimulations | The problem of minimizing finite fuzzy interpretations in fuzzy description logics (FDLs) is worth studying. For example, the structure of a fuzzy/weighted social network can be treated as a fuzzy interpretation in FDLs, where actors are individuals and actions are roles. Minimizing the structure of a fuzzy/weighted so... | ['Linh Anh Nguyen'] | 2023-03-13 | null | null | null | null | ['abstract-algebra'] | ['reasoning'] | [ 2.28323147e-01 5.47149360e-01 1.08608894e-01 -5.88364661e-01
1.82972103e-01 -6.35590434e-01 3.65422904e-01 3.25142413e-01
-4.35920626e-01 6.82978213e-01 -3.55481058e-01 -1.15835942e-01
-8.91878366e-01 -1.69262958e+00 -6.78163290e-01 -3.82269472e-01
-3.55102539e-01 9.03105259e-01 4.24968392e-01 -7.91515172... | [8.67697525024414, 6.796308517456055] |
33660e78-ae64-4d48-bf83-24af8b1bde78 | warwick-image-forensics-dataset-for-device | 2004.10469 | null | https://arxiv.org/abs/2004.10469v2 | https://arxiv.org/pdf/2004.10469v2.pdf | Warwick Image Forensics Dataset for Device Fingerprinting In Multimedia Forensics | Device fingerprints like sensor pattern noise (SPN) are widely used for provenance analysis and image authentication. Over the past few years, the rapid advancement in digital photography has greatly reshaped the pipeline of image capturing process on consumer-level mobile devices. The flexibility of camera parameter s... | ['Chang-Tsun Li', 'Yujue Zhou', 'Yijun Quan', 'Li Li'] | 2020-04-22 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 7.14525640e-01 -7.65276551e-01 -6.41200542e-02 -2.55722612e-01
-6.76562846e-01 -9.68159795e-01 4.28159833e-01 -9.92795676e-02
-4.14687127e-01 4.07546252e-01 -1.34038657e-01 -5.25492132e-01
-1.47821829e-01 -4.01860476e-01 -4.79571372e-01 -4.25214946e-01
3.15778017e-01 -1.81934498e-02 3.77401531e-01 2.86856294... | [12.398831367492676, 0.9878551363945007] |
bab59edf-905d-4a38-af57-6781c2d2bade | language-models-are-unsupervised-multitask | null | null | https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf | https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf | Language Models are Unsupervised Multitask Learners | Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically
approached with supervised learning on taskspecific datasets. We demonstrate that language
models begin to learn these tasks without any explicit supervision when trained on a ne... | ['Jeffrey Wu', 'Rewon Child', 'Ilya Sutskever', 'David Luan', 'Alec Radford', 'Dario Amodei'] | 2019-02-14 | null | null | null | preprint-2019-2 | ['multi-task-language-understanding'] | ['methodology'] | [ 3.14117014e-01 4.85305578e-01 -3.09291512e-01 -4.30970311e-01
-1.40857041e+00 -5.54647326e-01 8.75506938e-01 2.51183093e-01
-6.59386456e-01 7.24698126e-01 6.01609290e-01 -6.06776595e-01
2.98882604e-01 -5.11231840e-01 -9.62856293e-01 -4.88411374e-02
7.86850154e-02 7.91061997e-01 2.97311276e-01 -5.29704750... | [11.396390914916992, 8.589491844177246] |
9ceaa338-8d85-4089-846e-dfe79b509233 | runne-2022-shared-task-recognizing-nested | 2205.11159 | null | https://arxiv.org/abs/2205.11159v1 | https://arxiv.org/pdf/2205.11159v1.pdf | RuNNE-2022 Shared Task: Recognizing Nested Named Entities | The RuNNE Shared Task approaches the problem of nested named entity recognition. The annotation schema is designed in such a way, that an entity may partially overlap or even be nested into another entity. This way, the named entity "The Yermolova Theatre" of type "organization" houses another entity "Yermolova" of typ... | ['Elena Tutubalina', 'Vladimir Ivanov', 'Tatiana Batura', 'Igor Rozhkov', 'Natalia Loukachevitch', 'Maxim Zmeev', 'Ekaterina Artemova'] | 2022-05-23 | null | null | null | null | ['dialogue-evaluation', 'nested-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-3.82456362e-01 3.65227878e-01 2.94214189e-02 -2.09446132e-01
-8.93058717e-01 -1.00649643e+00 7.65616596e-01 2.81118870e-01
-8.50742877e-01 1.11908305e+00 5.20802319e-01 -7.89698437e-02
-3.32329012e-02 -6.44526601e-01 -5.25163114e-01 -4.95825171e-01
9.41240937e-02 9.30516660e-01 6.39280155e-02 -6.59711599... | [9.666693687438965, 9.585479736328125] |
bb7a301c-3a6f-4a53-947d-f4a91b613a92 | attentional-pyramid-pooling-of-salient-visual | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Peng_Attentional_Pyramid_Pooling_of_Salient_Visual_Residuals_for_Place_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Peng_Attentional_Pyramid_Pooling_of_Salient_Visual_Residuals_for_Place_Recognition_ICCV_2021_paper.pdf | Attentional Pyramid Pooling of Salient Visual Residuals for Place Recognition | The core of visual place recognition (VPR) lies in how to identify task-relevant visual cues and embed them into discriminative representations. Focusing on these two points, we propose a novel encoding strategy named Attentional Pyramid Pooling of Salient Visual Residuals (APPSVR). It incorporates three types of a... | ['Danwei Wang', 'Heshan Li', 'Jun Zhang', 'Guohao Peng'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['visual-place-recognition'] | ['computer-vision'] | [ 2.60918289e-01 -2.83690453e-01 -3.75076920e-01 -2.99130172e-01
-7.08825648e-01 -2.38208354e-01 7.67710268e-01 4.20174778e-01
-3.85461926e-01 3.19052875e-01 7.63161719e-01 2.43535593e-01
-2.18716726e-01 -3.86144489e-01 -6.37075186e-01 -8.21291268e-01
-1.38284951e-01 -3.16747665e-01 6.64458930e-01 -2.51729608... | [9.880424499511719, -0.022373413667082787] |
cd2cedbe-c9b8-4dbc-b20c-5f2dacfc6c61 | end-to-end-audio-strikes-back-boosting | 2204.11479 | null | https://arxiv.org/abs/2204.11479v5 | https://arxiv.org/pdf/2204.11479v5.pdf | End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network | While efficient architectures and a plethora of augmentations for end-to-end image classification tasks have been suggested and heavily investigated, state-of-the-art techniques for audio classifications still rely on numerous representations of the audio signal together with large architectures, fine-tuned from large ... | ['Asaf Noy', 'Gilad Sharir', 'Tal Ridnik', 'Gadi Zimerman', 'Avi Gazneli'] | 2022-04-25 | null | null | null | null | ['environmental-sound-classification', 'sound-classification', 'keyword-spotting'] | ['audio', 'audio', 'speech'] | [ 1.49971351e-01 -2.25310937e-01 -1.20392509e-01 -4.01758879e-01
-1.30269921e+00 -3.43341887e-01 3.11919808e-01 -7.70280957e-02
-3.48489553e-01 4.42250609e-01 1.91913843e-01 -8.70665461e-02
-8.39979872e-02 -5.80874681e-01 -7.57784545e-01 -5.27525127e-01
-3.39472413e-01 1.16090573e-01 8.26084688e-02 -1.30943969... | [15.185273170471191, 5.207318305969238] |
9fc73a78-52fe-4f70-b34e-87dec6b129ca | cultural-and-geographical-influences-on-image | null | null | https://aclanthology.org/2021.naacl-main.19 | https://aclanthology.org/2021.naacl-main.19.pdf | Cultural and Geographical Influences on Image Translatability of Words across Languages | Neural Machine Translation (NMT) models have been observed to produce poor translations when there are few/no parallel sentences to train the models. In the absence of parallel data, several approaches have turned to the use of images to learn translations. Since images of words, e.g., horse may be unchanged across lan... | ['Derry Tanti Wijaya', 'Chris Callison-Burch', 'Mohammad Sadegh Rasooli', 'Isidora Tourni', 'Nikzad Khani'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['multimodal-machine-translation', 'multilingual-nlp'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.22814842e-01 -1.81554213e-01 -1.79220706e-01 -3.05437744e-01
-4.95603085e-01 -7.59658337e-01 1.12729895e+00 -1.59997940e-01
-4.23289955e-01 5.26009560e-01 5.75693309e-01 -3.67458254e-01
4.16074753e-01 -7.43648887e-01 -1.05313432e+00 -5.40561140e-01
5.20627141e-01 3.93365294e-01 -2.00943619e-01 -4.37182665... | [11.36248779296875, 1.2710766792297363] |
2a8024ff-6f1c-426f-9857-d7e4569424ff | l-co-net-learned-condensation-optimization | 2004.11253 | null | https://arxiv.org/abs/2004.11253v1 | https://arxiv.org/pdf/2004.11253v1.pdf | L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI | In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation. We validated our framework on the ACDC dataset featuring one healthy and four pathology groups imaged throughout the... | ['S. M. Kamrul Hasan', 'Cristian A. Linte'] | 2020-04-21 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.26897955e-01 2.64258176e-01 -1.18928395e-01 -4.47365582e-01
-5.83165705e-01 -5.41543067e-01 1.29728615e-01 4.10728216e-01
-4.14248049e-01 7.28972137e-01 -3.77277844e-02 -4.44887787e-01
2.03601763e-01 -6.46545768e-01 -1.56662002e-01 -7.33399987e-01
-5.09139895e-01 7.88443625e-01 2.69663006e-01 3.82977515... | [14.191396713256836, -2.4773669242858887] |
c3efef07-4f96-4d8d-a70e-5088e75b7459 | every-pixel-counts-joint-learning-of-geometry | 1810.06125 | null | https://arxiv.org/abs/1810.06125v2 | https://arxiv.org/pdf/1810.06125v2.pdf | Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding | Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames by watching unlabeled videos via deep convolutional network has made significant progress recently. Current state-of-the-art (SoTA) methods treat the two tasks independently. One typical assumption of the existing depth estimati... | ['Yang Wang', 'Wei Xu', 'Peng Wang', 'Ram Nevatia', 'Chenxu Luo', 'Alan Yuille', 'Zhenheng Yang'] | 2018-10-14 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-6.78896829e-02 -2.66308159e-01 -1.32058084e-01 -2.25418851e-01
-5.04431009e-01 -7.35441446e-01 5.42579174e-01 -5.84902942e-01
-4.53187466e-01 5.86275518e-01 4.98551205e-02 -2.57656544e-01
2.94811368e-01 -7.23448575e-01 -9.40980673e-01 -7.93639898e-01
-1.92343164e-02 1.96176067e-01 3.62579048e-01 2.92798400... | [8.556461334228516, -1.995349645614624] |
f0d82eef-191a-4020-98bb-ec594bc232c1 | reflective-decoding-unsupervised-paraphrasing-1 | 2010.08566 | null | https://arxiv.org/abs/2010.08566v4 | https://arxiv.org/pdf/2010.08566v4.pdf | Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models | Publicly available, large pretrained LanguageModels (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks that break the unidirectional assumption, such as paraphrasing or text-infilling, necessitating task-specific su... | ['Yejin Choi', 'Jena Hwang', 'Chandra Bhagavatula', 'Ari Holtzman', 'Ximing Lu', 'Peter West'] | 2020-10-16 | reflective-decoding-unsupervised-paraphrasing | https://aclanthology.org/2021.acl-long.114 | https://aclanthology.org/2021.acl-long.114.pdf | acl-2021-5 | ['conditional-text-generation', 'text-infilling'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.17733896e-01 3.01514596e-01 -3.46693814e-01 -3.93585861e-01
-9.57959890e-01 -8.07283998e-01 9.99934673e-01 1.80040404e-01
-5.08013606e-01 9.25019622e-01 7.45481610e-01 -7.15246439e-01
3.55611503e-01 -7.24572539e-01 -1.00862777e+00 -3.18921745e-01
7.06372321e-01 5.30947804e-01 -1.45478755e-01 -4.21011567... | [11.694026947021484, 9.109589576721191] |
53fbb2cc-61fe-41aa-b4c3-a3ae8442717e | motionmixer-mlp-based-3d-human-body-pose | 2207.00499 | null | https://arxiv.org/abs/2207.00499v1 | https://arxiv.org/pdf/2207.00499v1.pdf | MotionMixer: MLP-based 3D Human Body Pose Forecasting | In this work, we present MotionMixer, an efficient 3D human body pose forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D body pose dependencies by sequentially mixing both modalities. Given a stacked sequence of 3D body poses, a spatial-MLP extracts fine grained... | ['Vasileios Belagiannis', 'Klaus Dietmayer', 'Ulrich Kressel', 'Adrian Holzbock', 'Arij Bouazizi'] | 2022-07-01 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [-1.07950285e-01 -1.86666362e-02 -2.60209262e-01 -3.28917861e-01
-6.90044701e-01 -2.48573020e-01 6.90974712e-01 -3.09436560e-01
-6.08358204e-01 3.70760977e-01 6.67503119e-01 2.52433956e-01
1.73840031e-01 -3.20151985e-01 -9.94013488e-01 -6.53970122e-01
-4.56528366e-01 6.20660067e-01 1.51461184e-01 -5.92629351... | [7.21196174621582, -0.30095362663269043] |
dc33d5ab-96bd-4474-a649-a9fe584b33c5 | the-alzheimer-s-disease-prediction-of | 2002.03419 | null | https://arxiv.org/abs/2002.03419v2 | https://arxiv.org/pdf/2002.03419v2.pdf | The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up | We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a... | ['Alex Diaz-Papkovich', 'Sach Mukherjee', 'Steven Kiddle', 'Zhiyue Huang', 'James Howlett', 'Steven M. Hill', 'Paul Manser', 'Christina Rabe', 'Keli Liu', 'Vikram Venkatraghavan', 'Lauge Sorensen', 'Sebastien Ourselin', 'Mads Nielsen', 'Mostafa M. Ghazi', 'Denisa Rimocea', 'Raluca Pop', 'Alex Kelner', 'Ionut Buciuman',... | 2020-02-09 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [-2.00365454e-01 -6.25701100e-02 -1.03051439e-01 -5.67724943e-01
-7.70268202e-01 -3.89702380e-01 4.88922298e-01 5.41823149e-01
-6.98649526e-01 8.45423281e-01 4.99732733e-01 -5.79108655e-01
-4.81481910e-01 -6.08328640e-01 -1.05062373e-01 -3.80886316e-01
-7.48786628e-01 9.15809751e-01 9.40062404e-02 -3.16970646... | [14.148172378540039, -1.7080553770065308] |
739a67ff-0753-4199-b3ba-827162178625 | explainable-models-via-compression-of-tree | 2206.07904 | null | https://arxiv.org/abs/2206.07904v1 | https://arxiv.org/pdf/2206.07904v1.pdf | Explainable Models via Compression of Tree Ensembles | Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs). While effective, they lose one of the most important aspect of PLMs -- interpretability. In this paper we consider the problem of co... | ['Prasad Tadepalli', 'Roni Khardon', 'Saket Joshi', 'Sriraam Natarajan', 'Siwen Yan'] | 2022-06-16 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.28038990e-01 5.52157104e-01 -4.18258011e-01 -5.85616052e-01
-6.97008967e-01 -2.10739389e-01 5.14469981e-01 3.01773101e-01
1.63403377e-01 8.81891906e-01 -8.04090779e-03 -6.96231484e-01
-4.33205456e-01 -1.18770015e+00 -7.04231560e-01 -5.72304130e-01
-3.91349606e-02 1.10443366e+00 1.35379106e-01 -1.97226748... | [8.776001930236816, 6.318315029144287] |
e0eccb06-5dd2-4580-b9e6-ec8382cf2e06 | relevance-topic-model-for-unstructured-social | null | null | http://papers.nips.cc/paper/4979-relevance-topic-model-for-unstructured-social-group-activity-recognition | http://papers.nips.cc/paper/4979-relevance-topic-model-for-unstructured-social-group-activity-recognition.pdf | Relevance Topic Model for Unstructured Social Group Activity Recognition | Unstructured social group activity recognition in web videos is a challenging task due to 1) the semantic gap between class labels and low-level visual features and 2) the lack of labeled training data. To tackle this problem, we propose a relevance topic model" for jointly learning meaningful mid-level representations... | ['Liang Wang', 'Fang Zhao', 'Tieniu Tan', 'Yongzhen Huang'] | 2013-12-01 | null | null | null | neurips-2013-12 | ['group-activity-recognition'] | ['computer-vision'] | [ 2.97720253e-01 2.68030822e-01 -7.63041377e-01 -4.82349426e-01
-8.86561692e-01 2.78873090e-03 6.05855107e-01 -1.20543363e-02
1.66615117e-02 6.11770749e-01 6.44316256e-01 3.10796648e-01
-1.32495835e-02 -3.50881755e-01 -9.12838101e-01 -9.48015571e-01
-4.66216542e-02 2.51518756e-01 1.12248719e-01 5.00921190... | [9.57181167602539, 0.8908429145812988] |
97bcdd0d-8bbd-4d2c-ba9b-0b9fb7956256 | nearest-subspace-search-in-the-signed | 2110.05606 | null | https://arxiv.org/abs/2110.05606v2 | https://arxiv.org/pdf/2110.05606v2.pdf | Nearest Subspace Search in The Signed Cumulative Distribution Transform Space for 1D Signal Classification | This paper presents a new method to classify 1D signals using the signed cumulative distribution transform (SCDT). The proposed method exploits certain linearization properties of the SCDT to render the problem easier to solve in the SCDT space. The method uses the nearest subspace search technique in the SCDT domain t... | ['Gustavo K. Rohde', 'Shiying Li', 'Yan Zhuang', 'Mohammad Shifat-E-Rabbi', 'Abu Hasnat Mohammad Rubaiyat'] | 2021-10-11 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.89099038e-01 -2.88013667e-01 -1.20259803e-02 -5.00807226e-01
-6.61597788e-01 -4.29229915e-01 1.90333739e-01 6.94465339e-02
-3.15856546e-01 7.47187674e-01 -2.93839246e-01 -5.11907756e-01
-4.24374819e-01 -4.72097993e-01 -3.95575792e-01 -6.90218270e-01
-3.95853370e-01 1.08168989e-01 1.79099515e-01 7.69788325... | [14.217724800109863, 3.212298631668091] |
ffcf99b8-6277-4e07-86b1-9dab6f50c0dd | sinai-voting-system-for-twitter-sentiment | null | null | https://aclanthology.org/S14-2100 | https://aclanthology.org/S14-2100.pdf | SINAI: Voting System for Twitter Sentiment Analysis | null | ["L. Alfonso Ure{\\~n}a-L{\\'o}pez", "Salud Mar{\\'\\i}a Jim{\\'e}nez-Zafra", "Eugenio Mart{\\'\\i}nez-C{\\'a}mara", 'Maite Martin'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.34555721282959, 3.523607015609741] |
2e0087f8-767b-475f-a6cd-522b2f971f9f | collaborative-learning-for-hand-and-object | 2204.13062 | null | https://arxiv.org/abs/2204.13062v1 | https://arxiv.org/pdf/2204.13062v1.pdf | Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution | Estimating the pose and shape of hands and objects under interaction finds numerous applications including augmented and virtual reality. Existing approaches for hand and object reconstruction require explicitly defined physical constraints and known objects, which limits its application domains. Our algorithm is agnos... | ['Hyung Jin Chang', 'Ales Leonardis', 'Kwang In Kim', 'Tze Ho Elden Tse'] | 2022-04-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tse_Collaborative_Learning_for_Hand_and_Object_Reconstruction_With_Attention-Guided_Graph_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tse_Collaborative_Learning_for_Hand_and_Object_Reconstruction_With_Attention-Guided_Graph_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-pose-estimation', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 5.15657142e-02 1.17242806e-01 -6.51889145e-02 -9.20295641e-02
-2.95446664e-01 -5.83131611e-01 4.86463934e-01 -2.24852443e-01
3.54123190e-02 4.90613550e-01 6.68124780e-02 -1.56845063e-01
-4.13627326e-01 -6.69481635e-01 -9.29641008e-01 -6.17028773e-01
-1.26159817e-01 1.05260348e+00 1.61677390e-01 -4.70280647... | [6.6443963050842285, -1.0910840034484863] |
8db488e9-1065-4c59-8083-8d916bfea903 | a-fair-loss-function-for-network-pruning | 2211.10285 | null | https://arxiv.org/abs/2211.10285v1 | https://arxiv.org/pdf/2211.10285v1.pdf | A Fair Loss Function for Network Pruning | Model pruning can enable the deployment of neural networks in environments with resource constraints. While pruning may have a small effect on the overall performance of the model, it can exacerbate existing biases into the model such that subsets of samples see significantly degraded performance. In this paper, we int... | ['Alexander Wong', 'Robbie Meyer'] | 2022-11-18 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.32588845e-01 3.41495901e-01 -5.55297971e-01 -8.94664586e-01
-3.14665139e-02 3.10536604e-02 1.01821974e-01 -9.34038404e-03
-6.62282467e-01 9.99914646e-01 -3.91790032e-01 -5.14574409e-01
-1.07667193e-01 -6.77036107e-01 -1.56368375e-01 -6.19357646e-01
-3.79507281e-02 -6.80735558e-02 9.81029123e-02 9.33511779... | [8.971882820129395, 5.029055595397949] |
f834ab7b-6057-45e7-a49d-6085be58f581 | event-collapse-in-contrast-maximization | 2207.04007 | null | https://arxiv.org/abs/2207.04007v2 | https://arxiv.org/pdf/2207.04007v2.pdf | Event Collapse in Contrast Maximization Frameworks | Contrast maximization (CMax) is a framework that provides state-of-the-art results on several event-based computer vision tasks, such as ego-motion or optical flow estimation. However, it may suffer from a problem called event collapse, which is an undesired solution where events are warped into too few pixels. As prio... | ['Guillermo Gallego', 'Yoshimitsu Aoki', 'Shintaro Shiba'] | 2022-07-08 | null | null | null | null | ['event-based-vision', 'event-based-motion-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.48015812e-01 -2.17146024e-01 1.59357056e-01 -2.67791543e-02
-2.69232213e-01 -4.87945229e-01 8.76744986e-01 3.58750559e-02
-5.61847270e-01 6.82947636e-01 1.24206305e-01 -4.32777889e-02
-1.45187825e-01 -6.53343916e-01 -4.52112257e-01 -8.34356725e-01
-2.96804905e-01 3.31953093e-02 6.38943613e-01 -2.34535769... | [8.773351669311523, -1.4438514709472656] |
effbdf87-a220-42bf-9914-af0e25069858 | mixhop-higher-order-graph-convolution | 1905.00067 | null | https://arxiv.org/abs/1905.00067v3 | https://arxiv.org/pdf/1905.00067v3.pdf | MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing | Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships. To address this weakness, we propose a new model, MixHop, that can learn these relationships, including difference operat... | ['Hrayr Harutyunyan', 'Sami Abu-El-Haija', 'Bryan Perozzi', 'Amol Kapoor', 'Aram Galstyan', 'Nazanin Alipourfard', 'Greg Ver Steeg', 'Kristina Lerman'] | 2019-04-30 | null | null | null | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-8.63913000e-02 2.30827495e-01 -5.31412601e-01 -5.99724591e-01
-2.25427285e-01 -7.58443654e-01 7.15488672e-01 4.16532874e-01
-1.98460281e-01 4.73670334e-01 3.98498267e-01 -6.36618972e-01
-4.86479849e-01 -9.46302712e-01 -9.03564334e-01 -3.01828086e-01
-6.06176972e-01 4.05571789e-01 6.32923171e-02 -1.83237657... | [6.937525749206543, 6.244732856750488] |
bb69abb7-4909-4de0-bfa1-fd62deb3ef62 | multi-label-hate-speech-and-abusive-language | null | null | https://aclanthology.org/W19-3506 | https://aclanthology.org/W19-3506.pdf | Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter | Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen. Moreover, hate speech has a target, category, and level that also needs to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research disc... | ['Indra Budi', 'Muhammad Okky Ibrohim'] | 2019-08-01 | null | null | null | ws-2019-8 | ['abuse-detection'] | ['natural-language-processing'] | [ 1.70625627e-01 -3.13024521e-01 -1.20769799e-01 -3.33771139e-01
-1.12995520e-01 -8.51427376e-01 8.97373736e-01 6.42554641e-01
-3.26659560e-01 7.60016382e-01 4.21816170e-01 -5.95805228e-01
8.54374170e-02 -5.92905343e-01 2.33751521e-01 -7.77248561e-01
4.43545073e-01 2.85788059e-01 3.72069597e-01 -2.69217134... | [8.799783706665039, 10.550085067749023] |
2fade9dc-96bf-43d6-be8a-5b8e6613c816 | serc-syntactic-and-semantic-sequence-based | 2111.02265 | null | https://arxiv.org/abs/2111.02265v2 | https://arxiv.org/pdf/2111.02265v2.pdf | SERC: Syntactic and Semantic Sequence based Event Relation Classification | Temporal and causal relations play an important role in determining the dependencies between events. Classifying the temporal and causal relations between events has many applications, such as generating event timelines, event summarization, textual entailment and question answering. Temporal and causal relations are c... | ['Sumit Bhatia', 'Raghava Mutharaju', 'Kritika Venkatachalam'] | 2021-11-03 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 2.83496410e-01 -2.07737744e-01 -5.76183379e-01 -8.22030485e-01
-4.59234864e-01 -5.96783578e-01 1.30289757e+00 9.68749523e-01
-4.14635122e-01 9.44082916e-01 1.11819661e+00 -4.25975740e-01
-5.28679550e-01 -9.21934128e-01 -4.98301536e-01 -2.20958307e-01
-9.33432996e-01 1.25223771e-01 4.98368651e-01 -8.68856236... | [9.063157081604004, 9.278310775756836] |
d3875ac1-41e0-4617-b729-176195214e0e | comparative-study-on-the-effects-of-noise-in | 2306.01110 | null | https://arxiv.org/abs/2306.01110v2 | https://arxiv.org/pdf/2306.01110v2.pdf | Comparative Study on the Effects of Noise in ML-Based Anxiety Detection | Wearable health devices are ushering in a new age of continuous and noninvasive remote monitoring. One application of this technology is in anxiety detection. Many advancements in anxiety detection have happened in controlled lab settings, but noise prevents these advancements from generalizing to real-world conditions... | ['Elizabeth Hsiao-Wecksler', 'Abdul Alkurdi', 'Samuel Schapiro'] | 2023-06-01 | null | null | null | null | ['anxiety-detection'] | ['medical'] | [ 3.11835706e-01 -2.43152589e-01 8.91213194e-02 -6.07135534e-01
-5.69171131e-01 -3.59660685e-01 1.45918280e-01 5.62153280e-01
-4.44695234e-01 4.19471830e-01 2.86025137e-01 -5.40657938e-02
-2.73430045e-03 -3.86104494e-01 -2.23084420e-01 -3.32480311e-01
-2.21215501e-01 -1.77353427e-01 -3.13928932e-01 -5.85995913... | [13.644989967346191, 3.2144501209259033] |
4f8a9750-2895-46e0-953a-7e6f2dcad7c9 | terminology-localization-guidelines-for-the | null | null | https://aclanthology.org/L14-1130 | https://aclanthology.org/L14-1130.pdf | Terminology localization guidelines for the national scenario | This paper presents a set of principles and practical guidelines for terminology work in the national scenario to ensure a harmonized approach in term localization. These linguistic principles and guidelines are elaborated by the Terminology Commission in Latvia in the domain of Information and Communication Technology... | ['Andrejs Vasi{\\c{l}}jevs', 'M{\\=a}rcis Pinnis', 'Iveta Kei{\\v{s}}a', 'Ilze Ilzi{\\c{n}}a', 'Juris Borzovs'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['lexical-analysis'] | ['natural-language-processing'] | [-1.35528743e-01 -1.67338312e-01 -9.26857740e-02 1.46340340e-01
-6.34332240e-01 -1.09743500e+00 7.34878957e-01 5.45132756e-01
-8.54794919e-01 7.06990361e-01 5.74642539e-01 -7.47420430e-01
-5.95947027e-01 -4.89708513e-01 8.49730298e-02 -5.89350998e-01
5.77622116e-01 5.35218954e-01 -7.49021545e-02 -8.19661677... | [10.079548835754395, 9.585622787475586] |
6d980754-b9dc-4073-add1-a8f971a57db2 | exact-complete-and-universal-continuous-time | cond-mat/9703200 | null | https://arxiv.org/abs/cond-mat/9703200v2 | https://arxiv.org/pdf/cond-mat/9703200v2.pdf | Exact, Complete, and Universal Continuous-Time Worldline Monte Carlo Approach to the Statistics of Discrete Quantum Systems | We show how the worldline quantum Monte Carlo procedure, which usually relies on an artificial time discretization, can be formulated directly in continuous time, rendering the scheme exact. For an arbitrary system with discrete Hilbert space, none of the configuration update procedures contain small parameters. We fin... | ['I. S. Tupitsyn', 'B. V. Svistunov', "N. V. Prokof'ev"] | 1997-03-24 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-3.56650725e-02 -2.69933015e-01 3.92683715e-01 4.84250225e-02
-5.11119187e-01 -7.45187104e-01 9.70544577e-01 -2.36263469e-01
-7.75267839e-01 1.41305828e+00 -4.16641951e-01 -6.58414900e-01
9.88122374e-02 -1.33318377e+00 -2.83949703e-01 -1.33941162e+00
1.06816040e-03 6.30586326e-01 2.37532169e-01 -5.01425982... | [5.613612651824951, 4.89025354385376] |
fac4e09b-0423-4b3e-ad35-5bf953a14e41 | factored-action-spaces-in-deep-reinforcement | null | null | https://openreview.net/forum?id=naSAkn2Xo46 | https://openreview.net/pdf?id=naSAkn2Xo46 | Factored Action Spaces in Deep Reinforcement Learning | Very large action spaces constitute a critical challenge for deep Reinforcement Learning (RL) algorithms. An existing approach consists in splitting the action space into smaller components and choosing either independently or sequentially actions in each dimension. This approach led to astonishing results for the Star... | ['Olivier Sigaud', 'Karim Beguir', 'Nicolas Perrin', 'Alexandre Laterre', 'Louis Monier', 'Jean-Baptiste Sevestre', 'Valentin Macé', 'Thomas Pierrot'] | 2021-01-01 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-1.35815337e-01 9.88631770e-02 -4.81008470e-01 4.02705342e-01
-7.30310917e-01 -7.14437664e-01 7.05791473e-01 -2.75041282e-01
-6.97129786e-01 1.43698967e+00 1.62748381e-01 -4.38642561e-01
-5.52196741e-01 -4.96672422e-01 -7.86969483e-01 -1.19892037e+00
-3.53583455e-01 6.05463028e-01 1.03948787e-01 -5.66769898... | [4.152717113494873, 2.2191076278686523] |
a8e8069b-7447-4542-90e9-d84b5108d533 | enhancing-pure-pixel-identification | 1406.5286 | null | http://arxiv.org/abs/1406.5286v1 | http://arxiv.org/pdf/1406.5286v1.pdf | Enhancing Pure-Pixel Identification Performance via Preconditioning | In this paper, we analyze different preconditionings designed to enhance
robustness of pure-pixel search algorithms, which are used for blind
hyperspectral unmixing and which are equivalent to near-separable nonnegative
matrix factorization algorithms. Our analysis focuses on the successive
projection algorithm (SPA), ... | ['Wing-Kin Ma', 'Nicolas Gillis'] | 2014-06-20 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.64934611e-01 -3.53054404e-02 1.59844160e-01 3.28039676e-01
-5.96070468e-01 -7.69255698e-01 2.96069771e-01 -3.07602179e-03
-3.59746039e-01 6.39710307e-01 8.85054320e-02 -5.44003785e-01
-6.44072056e-01 -6.03985310e-01 -6.83574617e-01 -1.33371592e+00
7.48842582e-02 3.79092902e-01 -8.76551270e-02 -3.66418004... | [10.044205665588379, -1.9766162633895874] |
5881e370-468a-48d2-add6-6d888d34962a | multiple-instance-ensembling-for-paranasal | 2303.17915 | null | https://arxiv.org/abs/2303.17915v1 | https://arxiv.org/pdf/2303.17915v1.pdf | Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus | Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionall... | ['Alexander Schlaefer', 'Anna Sophie Hoffmann', 'Christian Betz', 'Dennis Eggert', 'Bastian Cheng', 'Marvin Petersen', 'Elina Petersen', 'Dirk Beyersdorff', 'Benjamin Tobias Becker', 'Finn Behrendt', 'Debayan Bhattacharya'] | 2023-03-31 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 2.55455524e-01 3.54852140e-01 6.56185597e-02 -2.04695627e-01
-6.01550460e-01 -3.00451815e-01 4.19188321e-01 4.44428265e-01
-4.78282273e-01 2.83256739e-01 -3.38442326e-02 -6.24021888e-01
-1.92862973e-01 -7.60953426e-01 -4.94489759e-01 -5.51771283e-01
-1.87478557e-01 6.45234168e-01 3.07081550e-01 -4.70195040... | [14.729256629943848, -2.352813959121704] |
fc812f14-fdca-476e-a379-f26d99731363 | mixnet-for-generalized-face-presentation | 2010.13246 | null | https://arxiv.org/abs/2010.13246v1 | https://arxiv.org/pdf/2010.13246v1.pdf | MixNet for Generalized Face Presentation Attack Detection | The non-intrusive nature and high accuracy of face recognition algorithms have led to their successful deployment across multiple applications ranging from border access to mobile unlocking and digital payments. However, their vulnerability against sophisticated and cost-effective presentation attack mediums raises ess... | ['Richa Singh', 'Mayank Vatsa', 'Akshay Agarwal', 'Sushant Kumar Singh', 'Nilay Sanghvi'] | 2020-10-25 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.54059845e-01 -3.93876523e-01 1.09223146e-02 -2.53894448e-01
-3.72119308e-01 -6.29004121e-01 6.23331726e-01 -1.97055086e-01
-2.59395223e-02 3.29616666e-01 -2.09993050e-01 -5.50363898e-01
-2.65305042e-01 -6.35439575e-01 -4.96555746e-01 -7.14898407e-01
-3.92255306e-01 6.64268434e-02 1.65760383e-01 -4.86163884... | [13.03641128540039, 1.1251246929168701] |
3892b2e8-f78e-4624-825b-cb506f2b9ae3 | the-role-of-the-vagus-nerve-during-fetal | 2106.01756 | null | https://arxiv.org/abs/2106.01756v1 | https://arxiv.org/pdf/2106.01756v1.pdf | The role of the vagus nerve during fetal development and its relationship with the environment | The autonomic nervous system (ANS) regulatory capacity begins before birth as the sympathetic and parasympathetic activity contributes significantly to the fetus' development. Several studies have shown how vagus nerve is involved in many vital processes during fetal, perinatal and postnatal life: from the regulation o... | ['Andrea Manzotti', 'Marco Chiera', 'Stefano Vecchi', 'Chiara Viglione', 'Marta C. Antonelli', 'Martin G. Frasch', 'Francesco Cerritelli'] | 2021-06-03 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 7.50140771e-02 1.48594573e-01 -4.73349899e-01 9.74181760e-03
9.03802752e-01 -6.91700339e-01 -6.89868331e-02 5.00783324e-01
-1.19186267e-01 4.60981011e-01 1.92206204e-01 -3.40649366e-01
-9.25661847e-02 -7.68435955e-01 -7.95740962e-01 -7.14659929e-01
-1.26792252e-01 -2.14456812e-01 -3.81112814e-01 7.93858990... | [14.003363609313965, 3.0131752490997314] |
bbf90c17-630b-4cc3-b0ec-86e73b568600 | sl3d-self-supervised-self-labeled-3d | 2210.16810 | null | https://arxiv.org/abs/2210.16810v3 | https://arxiv.org/pdf/2210.16810v3.pdf | SL3D: Self-supervised-Self-labeled 3D Recognition | Deep learning has attained remarkable success in many 3D visual recognition tasks, including shape classification, object detection, and semantic segmentation. However, many of these results rely on manually collecting densely annotated real-world 3D data, which is highly time-consuming and expensive to obtain, limitin... | ['Xiaojuan Qi', 'Jiajun Shen', 'Lan Ma', 'Fernando Julio Cendra'] | 2022-10-30 | null | null | null | null | ['unsupervised-3d-semantic-segmentation'] | ['computer-vision'] | [-6.46017566e-02 -1.36861518e-01 -3.56961638e-01 -5.63353717e-01
-6.81954265e-01 -6.53726339e-01 4.43323940e-01 -3.41161415e-02
-1.02651648e-01 5.23746654e-04 -3.59473974e-02 -3.18688720e-01
9.17028487e-02 -6.17933035e-01 -4.96971458e-01 -6.74375117e-01
3.57318193e-01 7.72301495e-01 6.45063072e-02 7.12369323... | [8.030029296875, -3.307176351547241] |
1dd8cb87-7fb3-4d33-84c3-66f7ae60f25e | an-improved-model-ensembled-of-different | 2305.17156 | null | https://arxiv.org/abs/2305.17156v1 | https://arxiv.org/pdf/2305.17156v1.pdf | An Improved Model Ensembled of Different Hyper-parameter Tuned Machine Learning Algorithms for Fetal Health Prediction | Fetal health is a critical concern during pregnancy as it can impact the well-being of both the mother and the baby. Regular monitoring and timely interventions are necessary to ensure the best possible outcomes. While there are various methods to monitor fetal health in the mother's womb, the use of artificial intelli... | ['Sharmin Akter', 'Md. Simul Hasan Talukder'] | 2023-05-26 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 1.54491112e-01 6.26896992e-02 -3.10763091e-01 -6.02606535e-01
-3.30113955e-02 -1.40213847e-01 9.57357883e-02 5.05982101e-01
8.59938189e-02 8.90733600e-01 5.74403964e-02 -5.89967191e-01
-6.01805270e-01 -9.51794863e-01 -3.36087197e-01 -9.04917777e-01
-9.11332369e-02 4.15575445e-01 -3.58571894e-02 1.42188683... | [8.419797897338867, 4.903549671173096] |
9dc1f82b-27cf-4546-8bd0-929290081ef4 | augmented-understanding-and-automated | 2007.08710 | null | https://arxiv.org/abs/2007.08710v1 | https://arxiv.org/pdf/2007.08710v1.pdf | Augmented Understanding and Automated Adaptation of Curation Rules | Over the past years, there has been many efforts to curate and increase the added value of the raw data. Data curation has been defined as activities and processes an analyst undertakes to transform the raw data into contextualized data and knowledge. Data curation enables decision-makers and data analyst to extract va... | ['Alireza Tabebordbar'] | 2020-07-17 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 2.04415351e-01 -6.53188850e-04 7.56111071e-02 -6.84863448e-01
-6.01405561e-01 -9.88769948e-01 5.75187057e-02 7.40107536e-01
-5.14661789e-01 5.32859147e-01 1.84356153e-01 -4.89499509e-01
-5.56852400e-01 -8.37088168e-01 -1.52982026e-01 -1.41394585e-01
1.01754121e-01 5.60055256e-01 9.24831033e-02 -6.20228164... | [9.238991737365723, 7.9433112144470215] |
1ad169b7-8025-43dc-a2c8-ab823ada8b9b | design-analysis-and-application-of-a | 1702.00158 | null | http://arxiv.org/abs/1702.00158v1 | http://arxiv.org/pdf/1702.00158v1.pdf | Design, Analysis and Application of A Volumetric Convolutional Neural Network | The design, analysis and application of a volumetric convolutional neural
network (VCNN) are studied in this work. Although many CNNs have been proposed
in the literature, their design is empirical. In the design of the VCNN, we
propose a feed-forward K-means clustering algorithm to determine the filter
number and size... | ['C. -C. Jay Kuo', 'Xiaqing Pan', 'Yueru Chen'] | 2017-02-01 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-3.08748186e-01 -2.48259380e-02 7.78005794e-02 -3.11314493e-01
3.66841257e-01 -3.60099375e-01 3.33540201e-01 7.70464242e-02
-3.22041959e-01 2.66247094e-01 3.30580329e-03 -4.51363832e-01
-2.34201908e-01 -9.54976559e-01 -5.98213136e-01 -8.45971823e-01
-1.76366419e-02 2.26541862e-01 5.60709536e-01 2.26055130... | [9.062631607055664, 1.8223704099655151] |
88ecb568-0b63-49a3-8178-4e35aed39da4 | rethinking-feature-based-knowledge | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Rethinking_Feature-Based_Knowledge_Distillation_for_Face_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Rethinking_Feature-Based_Knowledge_Distillation_for_Face_Recognition_CVPR_2023_paper.pdf | Rethinking Feature-Based Knowledge Distillation for Face Recognition | With the continual expansion of face datasets, feature-based distillation prevails for large-scale face recognition. In this work, we attempt to remove identity supervision in student training, to spare the GPU memory from saving massive class centers. However, this naive removal leads to inferior distillation resu... | ['Sungjoo Suh', 'Ran Yang', 'Min Yang', 'Ji-won Baek', 'Seungju Han', 'Hui Li', 'Zidong Guo', 'Jingzhi Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['face-recognition'] | ['computer-vision'] | [-1.81538668e-02 -1.20683961e-01 -1.35471433e-01 -4.12302464e-01
-4.33421195e-01 -4.66208100e-01 5.88618875e-01 -5.81772625e-02
-3.02874267e-01 5.47391832e-01 6.76206350e-02 -5.08252323e-01
-1.56451866e-01 -8.53198051e-01 -5.50353229e-01 -9.13035154e-01
4.00399178e-01 2.45268807e-01 1.73300609e-01 -2.30770022... | [13.214987754821777, 0.6638567447662354] |
3df909b4-ac56-4f57-a53d-a344ffb94b13 | deep-learning-based-human-pose-estimation-a | 2012.13392 | null | https://arxiv.org/abs/2012.13392v5 | https://arxiv.org/pdf/2012.13392v5.pdf | Deep Learning-Based Human Pose Estimation: A Survey | Human pose estimation aims to locate the human body parts and build human body representation (e.g., body skeleton) from input data such as images and videos. It has drawn increasing attention during the past decade and has been utilized in a wide range of applications including human-computer interaction, motion analy... | ['Sijie Zhu', 'Chen Chen', 'Mubarak Shah', 'Nasser Kehtarnavaz', 'Ju Shen', 'Taojiannan Yang', 'Wenhan Wu', 'Ce Zheng'] | 2020-12-24 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.67997569e-01 1.75671577e-02 -4.92024809e-01 -1.71621621e-01
-3.55276048e-01 -6.71801791e-02 3.54130976e-02 -2.73902148e-01
-4.10411388e-01 6.14159048e-01 2.20431522e-01 3.80761594e-01
6.37742952e-02 -4.33458298e-01 -4.51768786e-01 -4.13524330e-01
-2.17564270e-01 5.64903140e-01 5.29512465e-02 -2.63156712... | [7.04214334487915, -0.8054134845733643] |
07c1a0cf-0ef5-4927-95f1-3efa34846441 | modal-features-for-image-texture | 2005.01928 | null | https://arxiv.org/abs/2005.01928v1 | https://arxiv.org/pdf/2005.01928v1.pdf | Modal features for image texture classification | Feature extraction is a key step in image processing for pattern recognition and machine learning processes. Its purpose lies in reducing the dimensionality of the input data through the computing of features which accurately describe the original information. In this article, a new feature extraction method based on D... | ['Thomas Lacombe', 'Maurice Pillet', 'Hugues Favreliere'] | 2020-05-05 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 5.89034259e-01 -2.59254515e-01 1.37188062e-01 -1.20043650e-01
-6.27570868e-01 -1.40616462e-01 7.73599207e-01 6.39301986e-02
-4.30264413e-01 4.27731782e-01 -9.56452079e-03 1.27336606e-01
-5.01719475e-01 -9.12345946e-01 -1.88475579e-01 -1.09580517e+00
1.11029400e-02 1.67552277e-01 1.86760753e-01 -1.36676118... | [12.427581787109375, 0.563108503818512] |
ab4f28c5-af11-4b82-be0a-53e481dbe119 | textual-analogy-parsing-whats-shared-and | 1809.02700 | null | http://arxiv.org/abs/1809.02700v1 | http://arxiv.org/pdf/1809.02700v1.pdf | Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts | To understand a sentence like "whereas only 10% of White Americans live at or
below the poverty line, 28% of African Americans do" it is important not only
to identify individual facts, e.g., poverty rates of distinct demographic
groups, but also the higher-order relations between them, e.g., the disparity
between them... | ['Christopher D. Manning', 'Dan Jurafsky', 'Percy Liang', 'Matthew Lamm', 'Arun Tejasvi Chaganty'] | 2018-09-07 | textual-analogy-parsing-whats-shared-and-1 | https://aclanthology.org/D18-1008 | https://aclanthology.org/D18-1008.pdf | emnlp-2018-10 | ['textual-analogy-parsing'] | ['natural-language-processing'] | [ 2.25247949e-01 5.44309556e-01 -6.57420456e-01 -7.19097137e-01
-6.43605232e-01 -5.41469634e-01 6.14182115e-01 8.33844841e-01
-1.83654666e-01 9.92009878e-01 1.15082657e+00 -9.07557249e-01
1.45367563e-01 -1.05145812e+00 -4.37584043e-01 -2.21563235e-01
3.64896774e-01 6.25622630e-01 -3.36227119e-01 -4.44643646... | [10.013097763061523, 8.269102096557617] |
e40d4639-7af0-410f-927d-4756e75486e2 | canet-a-context-aware-network-for-shadow | 2108.09894 | null | https://arxiv.org/abs/2108.09894v1 | https://arxiv.org/pdf/2108.09894v1.pdf | CANet: A Context-Aware Network for Shadow Removal | In this paper, we propose a novel two-stage context-aware network named CANet for shadow removal, in which the contextual information from non-shadow regions is transferred to shadow regions at the embedded feature spaces. At Stage-I, we propose a contextual patch matching (CPM) module to generate a set of potential ma... | ['Chunxia Xiao', 'Ling Zhang', 'Chengjiang Long', 'Zipei Chen'] | 2021-08-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_CANet_A_Context-Aware_Network_for_Shadow_Removal_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_CANet_A_Context-Aware_Network_for_Shadow_Removal_ICCV_2021_paper.pdf | iccv-2021-1 | ['shadow-removal', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [ 9.75748479e-01 -5.11359349e-02 3.19767475e-01 -6.44777298e-01
-5.32271206e-01 -1.72958210e-01 3.53018999e-01 -3.94217938e-01
1.21739753e-01 7.94688761e-01 3.41390282e-01 -1.87054634e-01
2.72498459e-01 -7.53140330e-01 -7.42370903e-01 -8.17334831e-01
5.06520420e-02 -1.52160957e-01 9.32128847e-01 -2.67777562... | [10.841943740844727, -4.097996234893799] |
1ac78f4c-11c7-4726-a19f-679565f65268 | what-makes-an-effective-scalarising-function | 2104.04790 | null | https://arxiv.org/abs/2104.04790v1 | https://arxiv.org/pdf/2104.04790v1.pdf | What Makes an Effective Scalarising Function for Multi-Objective Bayesian Optimisation? | Performing multi-objective Bayesian optimisation by scalarising the objectives avoids the computation of expensive multi-dimensional integral-based acquisition functions, instead of allowing one-dimensional standard acquisition functions\textemdash such as Expected Improvement\textemdash to be applied. Here, two infill... | ['Wei Yu', 'Alma Rahat', 'Tinkle Chugh', 'Clym Stock-Williams'] | 2021-04-10 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.33381271e-01 1.48845986e-01 2.88598359e-01 2.57282890e-02
-6.53961480e-01 -5.23892164e-01 5.71749389e-01 1.35941163e-01
-5.98258495e-01 1.04901123e+00 -3.63367200e-02 -5.24007916e-01
-1.50489366e+00 -4.54074264e-01 -2.44787008e-01 -1.35224497e+00
-1.34663820e-01 5.73099315e-01 -2.45764554e-01 -2.50288665... | [6.045073509216309, 3.5438008308410645] |
c9184376-2598-44db-a442-e65f50de6a14 | open-retrieval-conversational-machine-reading | 2102.08633 | null | https://arxiv.org/abs/2102.08633v3 | https://arxiv.org/pdf/2102.08633v3.pdf | Open-Retrieval Conversational Machine Reading | In conversational machine reading, systems need to interpret natural language rules, answer high-level questions such as "May I qualify for VA health care benefits?", and ask follow-up clarification questions whose answer is necessary to answer the original question. However, existing works assume the rule text is prov... | ['Michael R. Lyu', 'Chien-Sheng Wu', 'Irwin King', 'Jingjing Li', 'Yifan Gao'] | 2021-02-17 | null | null | null | null | ['discourse-segmentation'] | ['natural-language-processing'] | [ 6.04903638e-01 8.41727078e-01 -5.09978890e-01 -3.67061287e-01
-1.30975866e+00 -8.46804738e-01 8.23257983e-01 5.53833663e-01
-3.53000432e-01 9.40113902e-01 9.43517923e-01 -1.09434772e+00
-3.74288380e-01 -7.49943256e-01 -5.93860328e-01 3.93440314e-02
7.15851724e-01 9.73159671e-01 4.21163976e-01 -8.34743559... | [11.877347946166992, 8.039953231811523] |
87341114-4ed7-412f-ab17-a2f5a6fb096b | a-backbone-replaceable-fine-tuning-network | 2010.09501 | null | https://arxiv.org/abs/2010.09501v2 | https://arxiv.org/pdf/2010.09501v2.pdf | A Backbone Replaceable Fine-tuning Framework for Stable Face Alignment | Heatmap regression based face alignment has achieved prominent performance on static images. However, the stability and accuracy are remarkably discounted when applying the existing methods on dynamic videos. We attribute the degradation to random noise and motion blur, which are common in videos. The temporal informat... | ['Shihong Xia', 'Zihao Zhang', 'Zhenfeng Fan', 'Yingjie Guo', 'Xu sun'] | 2020-10-19 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 3.56654674e-02 -2.70744711e-01 -6.70199543e-02 -5.44763863e-01
-5.99426627e-01 -2.00627044e-01 3.96435082e-01 -3.35666895e-01
-4.92193937e-01 4.23144907e-01 5.62958531e-02 2.81335980e-01
-1.22543285e-02 -2.99303383e-01 -7.65307605e-01 -8.63212287e-01
-4.42315862e-02 -1.11836240e-01 8.57916176e-02 -8.26121047... | [13.401424407958984, 0.42878925800323486] |
ca7582ab-28c5-4c6a-bbea-bd5f1d58a5b0 | faq-retrieval-using-query-question-similarity | 1905.02851 | null | https://arxiv.org/abs/1905.02851v2 | https://arxiv.org/pdf/1905.02851v2.pdf | FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance | Frequently Asked Question (FAQ) retrieval is an important task where the objective is to retrieve an appropriate Question-Answer (QA) pair from a database based on a user's query. We propose a FAQ retrieval system that considers the similarity between a user's query and a question as well as the relevance between the q... | ['Sadao Kurohashi', 'Ribeka Tanaka', 'Wataru Sakata', 'Tomohide Shibata'] | 2019-05-08 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [-2.05602199e-01 -2.23202199e-01 -2.90317964e-02 -5.03005743e-01
-1.57992113e+00 -6.74670279e-01 5.40513933e-01 5.56606531e-01
-6.62490129e-01 6.30568206e-01 2.50686795e-01 -1.72384918e-01
-4.47538614e-01 -8.84898961e-01 -4.62452561e-01 -2.60599881e-01
5.21388531e-01 7.36859441e-01 1.08606970e+00 -6.80386305... | [11.197094917297363, 7.9530768394470215] |
5ab7b4d3-d154-4626-be3f-3a3230289c75 | benchmark-platform-for-ultra-fine-grained | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yu_Benchmark_Platform_for_Ultra-Fine-Grained_Visual_Categorization_Beyond_Human_Performance_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yu_Benchmark_Platform_for_Ultra-Fine-Grained_Visual_Categorization_Beyond_Human_Performance_ICCV_2021_paper.pdf | Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance | Deep learning methods have achieved remarkable success in fine-grained visual categorization. Such successful categorization at sub-ordinate level, e.g., different animal or plant species, however relies heavily on the visual differences that human can observe and the ground-truths are labelled on the basis of such... | ['Shengwu Xiong', 'Xiaohui Yuan', 'Yongsheng Gao', 'Yang Zhao', 'Xiaohan Yu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 1.50744319e-01 -9.95363444e-02 -3.20846409e-01 -3.96750987e-01
-3.34779263e-01 -1.22046041e+00 6.81774914e-01 4.33727264e-01
2.12765541e-02 5.74261904e-01 -2.05807194e-01 -6.37339771e-01
-2.28404328e-01 -8.94035280e-01 -7.86758959e-01 -6.50016665e-01
1.23170719e-01 2.69548088e-01 -1.04992278e-01 3.67339328... | [9.613199234008789, 2.0974984169006348] |
dc343f7f-04f8-42ad-b83c-c363b84b8de5 | optimizing-filter-size-in-convolutional | 1707.08630 | null | http://arxiv.org/abs/1707.08630v2 | http://arxiv.org/pdf/1707.08630v2.pdf | Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition | Recognizing facial action units (AUs) during spontaneous facial displays is a
challenging problem. Most recently, Convolutional Neural Networks (CNNs) have
shown promise for facial AU recognition, where predefined and fixed convolution
filter sizes are employed. In order to achieve the best performance, the
optimal fil... | ['Xiao-Feng Wang', "James O'Reilly", 'Zibo Meng', 'Zhiyuan Li', 'Yan Tong', 'Shizhong Han', 'Jie Cai'] | 2017-07-26 | optimizing-filter-size-in-convolutional-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Han_Optimizing_Filter_Size_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Han_Optimizing_Filter_Size_CVPR_2018_paper.pdf | cvpr-2018-6 | ['facial-action-unit-detection'] | ['computer-vision'] | [ 3.36763799e-01 3.68174389e-02 8.12662989e-02 -5.06563008e-01
-2.96768665e-01 -5.84982522e-02 2.53720790e-01 -4.52993840e-01
-6.39635444e-01 5.28384149e-01 -2.03931242e-01 1.99190885e-01
7.64157772e-02 -6.60386443e-01 -6.90805137e-01 -9.00867224e-01
-9.69290733e-03 -2.82866418e-01 1.14906363e-01 -8.32280237... | [13.583348274230957, 1.7136709690093994] |
96f036e8-0fb3-4ab7-b9ac-5f445ba76544 | non-intrusive-electrical-appliances | 1911.13257 | null | https://arxiv.org/abs/1911.13257v1 | https://arxiv.org/pdf/1911.13257v1.pdf | Non-Intrusive Electrical Appliances Monitoring and Classification using K-Nearest Neighbors | Non-Intrusive Load Monitoring (NILM) is the method of detecting an individual device's energy signal from an aggregated energy consumption signature [1]. As existing energy meters provide very little to no information regarding the energy consumption of individual appliances apart from the aggregated power rating, the ... | ['Mohammad Mahmudur Rahman Khan', 'Md. Abu Bakr Siddique', 'Shadman Sakib'] | 2019-11-22 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.71036109e-01 -2.04594433e-01 -3.86982292e-01 -4.47699279e-01
-5.69745064e-01 -6.90834343e-01 3.13659489e-01 2.39964500e-01
1.21476036e-02 5.78213513e-01 1.88923746e-01 -1.23687044e-01
-1.94446295e-01 -1.11830831e+00 7.04168975e-02 -9.99820054e-01
-1.28185796e-02 8.17146376e-02 -2.01883689e-01 7.27740675... | [5.9948930740356445, 2.574981451034546] |
4b347f43-d2e3-4d6e-8c8f-6d9141e28a68 | e-ner-evidential-deep-learning-for | 2305.17854 | null | https://arxiv.org/abs/2305.17854v1 | https://arxiv.org/pdf/2305.17854v1.pdf | E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition | Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments. Evidential deep learning (EDL) has recently been proposed as a promising solution to explicitly model predictive un... | ['Bingzhe Wu', 'Zhe Liu', 'Zhirui Zhang', 'Lemao Liu', 'Haotian Wang', 'Minlie Huang', 'Shiwan Zhao', 'Mengting Hu', 'Zhen Zhang'] | 2023-05-29 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [-6.30194664e-01 3.20069522e-01 -1.38343588e-01 -4.53479409e-01
-1.15018547e+00 -5.54826915e-01 7.02769339e-01 2.53008336e-01
-6.73456669e-01 9.15725052e-01 2.82588631e-01 -5.32432981e-02
-1.78638577e-01 -7.07908630e-01 -6.98947012e-01 -3.19160670e-01
1.94197342e-01 6.27666056e-01 -8.61173645e-02 1.40762568... | [9.639742851257324, 9.419672966003418] |
09669827-04e2-4950-8586-21dfc3539490 | explainable-machine-learning-control-robust | 2001.10056 | null | https://arxiv.org/abs/2001.10056v1 | https://arxiv.org/pdf/2001.10056v1.pdf | Explainable Machine Learning Control -- robust control and stability analysis | Recently, the term explainable AI became known as an approach to produce models from artificial intelligence which allow interpretation. Since a long time, there are models of symbolic regression in use that are perfectly explainable and mathematically tractable: in this contribution we demonstrate how to use symbolic ... | ['Markus Abel', 'Thomas Isele', 'Markus Quade'] | 2020-01-23 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.68467301e-01 7.37909615e-01 -6.55351356e-02 -9.04366225e-02
8.26219246e-02 -4.10823584e-01 6.13787591e-01 -1.10930018e-01
4.58763763e-02 1.05708814e+00 -6.52867913e-01 -3.71592879e-01
-6.74671531e-01 -5.74119568e-01 -7.95850396e-01 -6.66651070e-01
-5.63754328e-02 4.84971493e-01 -2.18918577e-01 -5.15280247... | [8.617801666259766, 6.60901403427124] |
215538a5-3b4c-45ec-8add-0d15ac05159e | fantrack-3d-multi-object-tracking-with | 1905.02843 | null | https://arxiv.org/abs/1905.02843v1 | https://arxiv.org/pdf/1905.02843v1.pdf | FANTrack: 3D Multi-Object Tracking with Feature Association Network | We propose a data-driven approach to online multi-object tracking (MOT) that uses a convolutional neural network (CNN) for data association in a tracking-by-detection framework. The problem of multi-target tracking aims to assign noisy detections to a-priori unknown and time-varying number of tracked objects across a s... | ['Erkan Baser', 'Krzysztof Czarnecki', 'Prarthana Bhattacharyya', 'Venkateshwaran Balasubramanian'] | 2019-05-07 | null | null | null | null | ['online-multi-object-tracking', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.10522762e-01 -5.09369671e-01 -8.08647722e-02 -2.75780290e-01
-9.52998579e-01 -8.21464896e-01 4.41451252e-01 -1.21373244e-01
-6.07787132e-01 5.26874661e-01 -2.02559695e-01 -5.61300181e-02
-3.39974687e-02 -3.41696113e-01 -1.09854603e+00 -4.74206448e-01
-2.65418142e-01 7.89559722e-01 6.19450927e-01 1.89658388... | [6.343481063842773, -2.050182342529297] |
e764adc1-ae27-4a5d-b0c1-34a9d2f964fa | near-real-time-distributed-state-estimation | 2207.11117 | null | https://arxiv.org/abs/2207.11117v1 | https://arxiv.org/pdf/2207.11117v1.pdf | Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks | Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) service... | ['Dejan Vukobratovic', 'Mirjana Maksimovic', 'Dragisa Miskovic', 'Merim Dzaferagic', 'Darijo Raca', 'Mirsad Cosovic', 'Miodrag Forcan', 'Ognjen Kundacina'] | 2022-07-22 | null | null | null | null | ['energy-management'] | ['time-series'] | [-7.52668202e-01 3.41795027e-01 -3.61042082e-01 -1.76746666e-01
1.90962330e-01 -5.14234126e-01 6.75139785e-01 -1.47790313e-01
6.22818887e-01 1.03211856e+00 -2.68903244e-02 -7.19810009e-01
-4.19689864e-01 -1.18970263e+00 1.69521913e-01 -9.56386685e-01
-7.70371556e-01 8.87816966e-01 -1.35878980e-01 -2.16359094... | [5.861753940582275, 2.632553815841675] |
741c429f-49e4-4f41-85e6-834c9b10aed3 | live-speech-portraits-real-time | 2109.10595 | null | https://arxiv.org/abs/2109.10595v2 | https://arxiv.org/pdf/2109.10595v2.pdf | Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation | To the best of our knowledge, we first present a live system that generates personalized photorealistic talking-head animation only driven by audio signals at over 30 fps. Our system contains three stages. The first stage is a deep neural network that extracts deep audio features along with a manifold projection to pro... | ['Xun Cao', 'Jinxiang Chai', 'Yuanxun Lu'] | 2021-09-22 | null | null | null | null | ['talking-head-generation', 'talking-face-generation'] | ['computer-vision', 'computer-vision'] | [ 1.38818026e-01 5.77458918e-01 3.24839920e-01 -5.66863775e-01
-8.57640624e-01 -1.25420973e-01 5.82699895e-01 -8.17384541e-01
8.93072337e-02 4.43840921e-01 7.72224188e-01 4.86153424e-01
4.31157917e-01 -4.33360696e-01 -7.93441713e-01 -5.88852167e-01
3.92438099e-02 5.24676204e-01 4.12362143e-02 -3.50581795... | [13.112815856933594, -0.41175463795661926] |
34e764da-4f89-451a-a1dd-cb99097e78dd | from-query-tools-to-causal-architects | 2306.16902 | null | https://arxiv.org/abs/2306.16902v1 | https://arxiv.org/pdf/2306.16902v1.pdf | From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data | Large Language Models (LLMs) exhibit exceptional abilities for causal analysis between concepts in numerous societally impactful domains, including medicine, science, and law. Recent research on LLM performance in various causal discovery and inference tasks has given rise to a new ladder in the classical three-stage f... | ['Huanhuan Chen', 'Xiangyu Wang', 'Lyvzhou Chen', 'Taiyu Ban'] | 2023-06-29 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.20431048e-01 3.58585536e-01 -1.15955400e+00 -4.14724439e-01
-6.06054306e-01 -4.03201371e-01 9.68954563e-01 5.48317671e-01
2.08777577e-01 8.97399187e-01 9.41261232e-01 -1.05795038e+00
-8.94045651e-01 -9.07340348e-01 -9.64944720e-01 -2.26255640e-01
-5.67589581e-01 4.05715346e-01 -9.83320624e-02 -8.24720189... | [8.052512168884277, 5.500290870666504] |
ce6d3d5a-859c-499a-9d3a-0755ba828b7d | orthogonal-annotation-benefits-barely | 2303.13090 | null | https://arxiv.org/abs/2303.13090v1 | https://arxiv.org/pdf/2303.13090v1.pdf | Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation | Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes involve information from different directions, e.g., transverse, sagittal, and coronal planes, so as to naturally provide complementary views... | ['Yang Gao', 'Yinghuan Shi', 'Qian Yu', 'Lei Qi', 'Shumeng Li', 'Heng Cai'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cai_Orthogonal_Annotation_Benefits_Barely-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cai_Orthogonal_Annotation_Benefits_Barely-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 1.29762679e-01 3.72416168e-01 -4.44838464e-01 -6.19765222e-01
-5.59820235e-01 -2.97726959e-01 1.64896905e-01 1.13751158e-01
-2.70704597e-01 5.02025008e-01 3.57438207e-01 -3.43919657e-02
-1.53162740e-02 -4.44226772e-01 -3.01816463e-01 -6.53329372e-01
-9.23572760e-03 4.58053917e-01 3.69009167e-01 3.09857786... | [14.624194145202637, -2.1456692218780518] |
1cc58f6b-67c2-4c11-9422-1d388086ed96 | rethinking-planar-homography-estimation-using | null | null | https://link.springer.com/chapter/10.1007/978-3-030-20876-9_36 | https://eprints.qut.edu.au/126933/ | Rethinking Planar Homography Estimation Using Perspective Fields | Planar homography estimation refers to the problem of computing a bijective linear mapping of pixels between two images. While this problem has been studied with convolutional neural networks (CNNs), existing methods simply regress the location of the four corners using a dense layer preceded by a fully-connected layer... | ['Simon Denman', 'Rui Zeng', 'Clinton Fookes', 'Sridha Sridharan'] | 2019-05-26 | null | null | null | accv-2018-2019-5 | ['homography-estimation'] | ['computer-vision'] | [ 2.66629457e-01 7.33914152e-02 -9.22694430e-02 -1.14446461e-01
-1.69820026e-01 -3.54556978e-01 5.25369644e-01 -2.92329133e-01
-3.17434847e-01 4.11300838e-01 -5.49529726e-03 1.26360372e-01
6.08709119e-02 -1.05972660e+00 -1.17154610e+00 -6.02005243e-01
3.06560397e-01 9.68637019e-02 3.27564567e-01 -3.17870766... | [8.627096176147461, -2.2246315479278564] |
c9b03785-b455-48a3-a4c7-a1a4b813daf4 | towards-few-shot-inductive-link-prediction-on | 2307.01204 | null | https://arxiv.org/abs/2307.01204v1 | https://arxiv.org/pdf/2307.01204v1.pdf | Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach | Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities. Therefore, recent inductive met... | ['Chen Gong', 'Quoc Viet Hung Nguyen', 'Shirui Pan', 'Linhao Luo', 'Zicheng Zhao'] | 2023-06-26 | null | null | null | null | ['inductive-link-prediction', 'link-prediction', 'knowledge-graphs'] | ['graphs', 'graphs', 'knowledge-base'] | [-1.35946423e-01 8.30262601e-01 -8.03433418e-01 -4.75472748e-01
-3.41824949e-01 -4.08839285e-01 3.20321590e-01 4.07094687e-01
3.67288172e-01 8.37390840e-01 1.03259012e-01 -1.42213762e-01
-6.04590476e-01 -1.59654462e+00 -1.22675860e+00 -1.97502375e-01
-4.91791219e-01 9.28323865e-01 5.40637851e-01 -3.06312114... | [8.801983833312988, 7.941936492919922] |
d48f9f14-b6fa-4fbc-a96c-9a90ac466611 | rethinking-multiple-instance-learning-for | 2307.02249 | null | https://arxiv.org/abs/2307.02249v1 | https://arxiv.org/pdf/2307.02249v1.pdf | Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need | Weakly supervised whole slide image classification is usually formulated as a multiple instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut out of it are treated as instances. Existing methods either train an instance classifier through pseudo-labeling or aggregate instance feature... | ['Zhijian Song', 'Manning Wang', 'Xiaoyuan Luo', 'Yingfan Ma', 'Linhao Qu'] | 2023-07-05 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'classification-1', 'multiple-instance-learning', 'pseudo-label'] | ['computer-vision', 'methodology', 'methodology', 'methodology', 'miscellaneous'] | [ 5.52014709e-01 1.59227893e-01 -6.02945745e-01 -5.87257743e-01
-1.35703731e+00 -4.83281106e-01 5.69825649e-01 3.49528491e-01
3.57577838e-02 7.99735725e-01 -1.42108500e-01 5.80701642e-02
-6.90015778e-02 -8.83821011e-01 -9.35515106e-01 -1.07096124e+00
1.69427589e-01 4.73789006e-01 2.76754051e-02 1.81206569... | [15.077125549316406, -2.6004250049591064] |
c253adbc-1673-4add-8054-ebd06e063c27 | bernnet-learning-arbitrary-graph-spectral | 2106.10994 | null | https://arxiv.org/abs/2106.10994v3 | https://arxiv.org/pdf/2106.10994v3.pdf | BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation | Many representative graph neural networks, e.g., GPR-GNN and ChebNet, approximate graph convolutions with graph spectral filters. However, existing work either applies predefined filter weights or learns them without necessary constraints, which may lead to oversimplified or ill-posed filters. To overcome these issues,... | ['Hongteng Xu', 'Zengfeng Huang', 'Zhewei Wei', 'Mingguo He'] | 2021-06-21 | null | http://proceedings.neurips.cc/paper/2021/hash/76f1cfd7754a6e4fc3281bcccb3d0902-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/76f1cfd7754a6e4fc3281bcccb3d0902-Paper.pdf | neurips-2021-12 | ['node-classification-on-non-homophilic'] | ['graphs'] | [-1.85642973e-01 1.26073569e-01 -1.61101729e-01 -9.78371128e-02
-2.05066055e-01 -4.50653642e-01 -1.26546085e-01 -2.11432904e-01
-1.81164965e-02 5.40852487e-01 -7.16338051e-04 -3.42381269e-01
-1.68039307e-01 -8.92525613e-01 -8.14703286e-01 -5.61245382e-01
-2.79601395e-01 1.19672436e-02 1.28813162e-02 -7.67172361... | [6.85575008392334, 6.061163425445557] |
3b2db1e1-b053-45e0-a6cf-91b5de543e9a | the-s-hock-dataset-analyzing-crowds-at-the | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Conigliaro_The_S-Hock_Dataset_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Conigliaro_The_S-Hock_Dataset_2015_CVPR_paper.pdf | The S-Hock Dataset: Analyzing Crowds at the Stadium | The topic of crowd modeling in computer vision usually assumes a single generic typology of crowd, which is very simplistic. In this paper we adopt a taxonomy that is widely accepted in sociology, focusing on a particular category, the spectator crowd, which is formed by people "interested in watching something specifi... | ['Chiara Bassetti', 'Paolo Rota', 'Nicola Conci', 'Marco Cristani', 'Nicu Sebe', 'Francesco Setti', 'Davide Conigliaro'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['head-pose-estimation'] | ['computer-vision'] | [-6.51229501e-01 7.66045973e-02 3.76183838e-01 -1.45529613e-01
-2.24905629e-02 -4.41299766e-01 8.13723981e-01 5.06207883e-01
-6.38822794e-01 7.51375914e-01 4.51500684e-01 3.49378854e-01
2.22494248e-02 -5.83295584e-01 -4.54058856e-01 -7.21628666e-01
-1.66784391e-01 1.15655220e+00 7.02636063e-01 -7.72679567... | [13.660503387451172, 0.3933747410774231] |
08f6a4e5-9194-474b-aebd-49b7b4896434 | event-cameras-contrast-maximization-and | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Stoffregen_Event_Cameras_Contrast_Maximization_and_Reward_Functions_An_Analysis_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Stoffregen_Event_Cameras_Contrast_Maximization_and_Reward_Functions_An_Analysis_CVPR_2019_paper.pdf | Event Cameras, Contrast Maximization and Reward Functions: An Analysis | Event cameras asynchronously report timestamped changes in pixel intensity and offer advantages over conventional raster scan cameras in terms of low-latency, low redundancy sensing and high dynamic range. In recent years, much of research in event based vision has been focused on performing tasks such as optic flow es... | [' Lindsay Kleeman', 'Timo Stoffregen'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['event-based-vision'] | ['computer-vision'] | [ 4.03098643e-01 -4.95511830e-01 -3.80941108e-02 -3.51482809e-01
-4.02726382e-01 -5.45694470e-01 7.51574516e-01 3.04199249e-01
-9.05097961e-01 7.99471438e-01 1.29671544e-01 2.05842689e-01
-1.84662178e-01 -4.74677831e-01 -4.58207250e-01 -7.04220474e-01
-2.94551671e-01 2.49688342e-01 7.30077684e-01 4.37373549... | [8.638711929321289, -1.3324073553085327] |
4f3f6796-9c30-41c1-b1c8-2aa80f588206 | translating-a-visual-lego-manual-to-a-machine | 2207.12572 | null | https://arxiv.org/abs/2207.12572v1 | https://arxiv.org/pdf/2207.12572v1.pdf | Translating a Visual LEGO Manual to a Machine-Executable Plan | We study the problem of translating an image-based, step-by-step assembly manual created by human designers into machine-interpretable instructions. We formulate this problem as a sequential prediction task: at each step, our model reads the manual, locates the components to be added to the current shape, and infers th... | ['Jiajun Wu', 'Chin-Yi Cheng', 'Jiayuan Mao', 'Yunzhi Zhang', 'Ruocheng Wang'] | 2022-07-25 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 2.20088646e-01 1.99759737e-01 2.09210720e-02 -3.15813273e-01
-5.10330498e-01 -8.70887935e-01 3.86396259e-01 -3.29265118e-01
-2.16104358e-01 3.51520404e-02 -3.13875116e-02 -1.04913302e-01
3.11725717e-02 -4.54375416e-01 -1.06549382e+00 -2.86810815e-01
2.97632754e-01 1.42411804e+00 4.69894230e-01 -1.02961116... | [7.498264789581299, -2.5653605461120605] |
6d1c4406-ea16-4dcb-b944-b0611263c791 | a-three-way-knot-privacy-fairness-and | 2306.15567 | null | https://arxiv.org/abs/2306.15567v1 | https://arxiv.org/pdf/2306.15567v1.pdf | A Three-Way Knot: Privacy, Fairness, and Predictive Performance Dynamics | As the frontier of machine learning applications moves further into human interaction, multiple concerns arise regarding automated decision-making. Two of the most critical issues are fairness and data privacy. On the one hand, one must guarantee that automated decisions are not biased against certain groups, especiall... | ['Luís Antunes', 'Nuno Moniz', 'Tânia Carvalho'] | 2023-06-27 | null | null | null | null | ['fairness', 'fairness', 'decision-making'] | ['computer-vision', 'miscellaneous', 'reasoning'] | [ 3.08660030e-01 2.39993170e-01 -4.32179809e-01 -5.28954089e-01
-3.37344468e-01 -8.02289128e-01 3.31513435e-01 5.61253667e-01
-7.42624402e-01 6.64788902e-01 1.69423908e-01 -6.43468022e-01
-2.70388693e-01 -6.21580958e-01 -2.14169011e-01 -6.62881017e-01
9.45013911e-02 3.56938243e-02 -4.28056896e-01 4.27367277... | [6.249993324279785, 6.8419599533081055] |
49828b5f-5a5e-4451-9851-30a772473ae1 | gaanet-ghost-auto-anchor-network-for | 2305.03425 | null | https://arxiv.org/abs/2305.03425v1 | https://arxiv.org/pdf/2305.03425v1.pdf | GAANet: Ghost Auto Anchor Network for Detecting Varying Size Drones in Dark | The usage of drones has tremendously increased in different sectors spanning from military to industrial applications. Despite all the benefits they offer, their misuse can lead to mishaps, and tackling them becomes more challenging particularly at night due to their small size and low visibility conditions. To overcom... | ['Abbas Jamalipour', 'Yansha Deng', 'Zeeshan Kaleem', 'Maham Misbah', 'Misha Urooj Khan'] | 2023-05-05 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-5.16657978e-02 -3.89477253e-01 3.76659691e-01 7.12246224e-02
-4.45513010e-01 -5.38300395e-01 3.26460987e-01 -2.32354984e-01
-5.82718313e-01 3.81423175e-01 -3.97889435e-01 3.39442976e-02
9.78944544e-03 -7.14315891e-01 -5.95219851e-01 -8.90314877e-01
-2.84185022e-01 -1.69191539e-01 4.56520289e-01 -1.64559945... | [8.663582801818848, -0.8635901808738708] |
08694a17-b6ac-4f54-88dd-345a46b622cc | semi-supervised-neural-machine-translation-1 | 2304.00557 | null | https://arxiv.org/abs/2304.00557v1 | https://arxiv.org/pdf/2304.00557v1.pdf | Semi-supervised Neural Machine Translation with Consistency Regularization for Low-Resource Languages | The advent of deep learning has led to a significant gain in machine translation. However, most of the studies required a large parallel dataset which is scarce and expensive to construct and even unavailable for some languages. This paper presents a simple yet effective method to tackle this problem for low-resource l... | ['Dien Dinh', 'Long Nguyen', 'Giang Nguyen', 'Thang M. Pham', 'Viet H. Pham'] | 2023-04-02 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.96745092e-01 -3.09337483e-04 -2.39882916e-01 -6.48228467e-01
-1.67439187e+00 -7.47480631e-01 5.86824715e-01 2.60357320e-01
-7.72825062e-01 1.16588116e+00 3.74832302e-01 -3.19210738e-01
3.74983609e-01 -5.30445099e-01 -1.04925442e+00 -3.53071690e-01
2.45123252e-01 7.00979590e-01 -4.15693700e-01 -3.48512888... | [11.587265014648438, 10.282210350036621] |
348eaa7c-0883-49e6-bf8f-78a15e8be74a | kieglfn-a-unified-acne-grading-framework-on | null | null | http://dx.doi.org/10.1016/j.cmpb.2022.106911 | http://dx.doi.org/10.1016/j.cmpb.2022.106911 | KIEGLFN: A unified acne grading framework on face images | Grading the severity level is an extremely important procedure for correct diagnoses and personalized treatment schemes for acne. However, the acne grading criteria are not unified in the medical field. This work aims to develop an acne diagnosis system that can be generalized to various criteria. Methods: A unified ac... | ['Gongning Luo', 'Bingmei Liu', 'Xue Cheng', 'Haiyan You', 'Yi Guan', 'Dongxin Chen', 'Zhaoyang Ma', 'Jingchi Jiang', 'Yi Lin'] | 2022-06-01 | null | null | null | computer-methods-and-programs-in-biomedicine-4 | ['acne-severity-grading'] | ['medical'] | [ 1.95040911e-01 -2.81171203e-01 -1.08291797e-01 -3.04403126e-01
-6.89224780e-01 -5.05024970e-01 2.67270118e-01 2.28438571e-01
-2.54432708e-01 4.71034884e-01 -3.65630165e-02 1.04983158e-01
-4.75914091e-01 -9.49044943e-01 6.03746139e-02 -9.04201984e-01
3.74343187e-01 4.09765005e-01 2.54006803e-01 -2.52368599... | [15.656312942504883, -2.99452805519104] |
f8fa257a-4c2e-4092-b774-3205e140dad4 | blind-image-deconvolution-using-student-s-t | 2006.14780 | null | https://arxiv.org/abs/2006.14780v1 | https://arxiv.org/pdf/2006.14780v1.pdf | Blind Image Deconvolution using Student's-t Prior with Overlapping Group Sparsity | In this paper, we solve blind image deconvolution problem that is to remove blurs form a signal degraded image without any knowledge of the blur kernel. Since the problem is ill-posed, an image prior plays a significant role in accurate blind deconvolution. Traditional image prior assumes coefficients in filtered domai... | ['Deokyoung Kang', 'Suk I. Yoo', 'In S. Jeon'] | 2020-06-26 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 2.24675760e-01 -4.83463109e-01 4.59706903e-01 -1.67912379e-01
-2.21679598e-01 -3.72319072e-01 1.81053072e-01 -9.75731373e-01
-1.05068013e-01 9.91121709e-01 6.70376420e-01 -6.55463785e-02
-3.52942735e-01 -5.23316078e-02 -3.51321042e-01 -7.69490123e-01
2.66420305e-01 -2.03305289e-01 -5.42358197e-02 3.50641571... | [11.604630470275879, -2.736362934112549] |
812a4024-9de3-42b2-b2f8-11f3218b37c8 | medical-image-enhancement-using-histogram | 2003.06615 | null | https://arxiv.org/abs/2003.06615v1 | https://arxiv.org/pdf/2003.06615v1.pdf | Medical Image Enhancement Using Histogram Processing and Feature Extraction for Cancer Classification | MRI (Magnetic Resonance Imaging) is a technique used to analyze and diagnose the problem defined by images like cancer or tumor in a brain. Physicians require good contrast images for better treatment purpose as it contains maximum information of the disease. MRI images are low contrast images which make diagnoses diff... | ['Sakshi Patel', 'Rajesh Kumar Muthu', 'Bharath K P'] | 2020-03-14 | null | null | null | null | ['medical-image-enhancement'] | ['computer-vision'] | [ 4.32269275e-01 -1.06865875e-01 -9.16649923e-02 -3.28073710e-01
6.47261366e-02 -1.09256327e-01 3.83101553e-01 6.07830524e-01
-7.18962193e-01 8.34198892e-01 7.52484277e-02 -1.22125424e-01
-2.24681646e-01 -7.64268279e-01 1.94337908e-02 -1.08853996e+00
-2.93246776e-01 4.93434310e-01 4.69038010e-01 -1.32565528... | [14.935662269592285, -2.8001461029052734] |
ebb5a41a-f51e-4f09-825c-d7db62f158c4 | approxdet-content-and-contention-aware | 2010.10754 | null | https://arxiv.org/abs/2010.10754v1 | https://arxiv.org/pdf/2010.10754v1.pdf | ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles | Advanced video analytic systems, including scene classification and object detection, have seen widespread success in various domains such as smart cities and autonomous transportation. With an ever-growing number of powerful client devices, there is incentive to move these heavy video analytics workloads from the clou... | ['Saurabh Bagchi', 'Yin Li', 'Somali Chaterji', 'Subrata Mitra', 'Jayoung Lee', 'Pengcheng Wang', 'Chen-Lin Zhang', 'ran Xu'] | 2020-10-21 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-3.19622427e-01 -7.42066324e-01 -4.59395051e-01 -3.52164209e-01
-4.81189936e-01 -5.53223491e-01 2.38747656e-01 3.51681374e-02
-6.55891478e-01 1.13649711e-01 -1.89592883e-01 -5.50652146e-01
1.78485900e-01 -5.54230452e-01 -6.22588336e-01 -3.30525428e-01
-3.68653327e-01 5.01755595e-01 9.56756711e-01 4.02243175... | [8.391895294189453, -0.40456217527389526] |
ae977c5b-8d5a-4de9-bb69-221f617f1018 | score-level-multi-cue-fusion-for-sign | 2009.14139 | null | https://arxiv.org/abs/2009.14139v1 | https://arxiv.org/pdf/2009.14139v1.pdf | Score-level Multi Cue Fusion for Sign Language Recognition | Sign Languages are expressed through hand and upper body gestures as well as facial expressions. Therefore, Sign Language Recognition (SLR) needs to focus on all such cues. Previous work uses hand-crafted mechanisms or network aggregation to extract the different cue features, to increase SLR performance. This is slow ... | ['Ahmet Alp Kındıroğlu', 'Oğulcan Özdemir', 'Çağrı Gökçe', 'Lale Akarun'] | 2020-09-29 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 3.42568398e-01 -6.81167021e-02 -3.77937078e-01 -5.06526351e-01
-9.82711792e-01 -4.01220292e-01 7.84531057e-01 -7.73250759e-01
-4.95333612e-01 4.32062835e-01 7.52248704e-01 -1.55651525e-01
3.07937205e-01 -1.95157319e-01 -4.61592197e-01 -6.45658255e-01
2.97777236e-01 2.42477998e-01 2.28276893e-01 -2.88621485... | [9.1649808883667, -6.483510494232178] |
481d1cee-41fd-499d-96b3-986db24d6d1a | enhancing-code-classification-by-mixup-based | 2210.03003 | null | https://arxiv.org/abs/2210.03003v2 | https://arxiv.org/pdf/2210.03003v2.pdf | MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation | Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted significant attention from the software engineering community. Due to its data-driven nature, a DNN model requires massive and high-quality labeled... | ['Jianjun Zhao', 'Zhenya Zhang', 'Yves Le Traon', 'Mike Papadakis', 'Maxime Cordy', 'Yuejun Guo', 'Qiang Hu', 'Zeming Dong'] | 2022-10-06 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 1.45796373e-01 -8.90238360e-02 -3.27941060e-01 -3.02559316e-01
-5.53398252e-01 -5.98314941e-01 1.67061642e-01 2.53132582e-01
-3.81314039e-01 3.75439644e-01 -6.08115382e-02 -6.04466558e-01
3.91160905e-01 -7.88926244e-01 -8.31099272e-01 -2.37336338e-01
1.31428450e-01 2.48044506e-02 -1.70444131e-01 -2.11779460... | [7.401084899902344, 7.8787336349487305] |
f40d3e86-5d30-4af0-be6c-8ccc216f7c5e | stochastic-modeling-for-learnable-human-pose | 2110.00280 | null | https://arxiv.org/abs/2110.00280v3 | https://arxiv.org/pdf/2110.00280v3.pdf | Generalizable Human Pose Triangulation | We address the problem of generalizability for multi-view 3D human pose estimation. The standard approach is to first detect 2D keypoints in images and then apply triangulation from multiple views. Even though the existing methods achieve remarkably accurate 3D pose estimation on public benchmarks, most of them are lim... | ['Tomislav Pribanić', 'Tomislav Petković', 'David Bojanić', 'Kristijan Bartol'] | 2021-10-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bartol_Generalizable_Human_Pose_Triangulation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bartol_Generalizable_Human_Pose_Triangulation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-pose-estimation'] | ['computer-vision'] | [ 6.95364773e-02 -2.71228492e-01 5.87757155e-02 -3.90843526e-02
-9.73331451e-01 -8.08914602e-01 3.81288350e-01 -1.41404882e-01
-5.85812867e-01 3.19688559e-01 1.80261150e-01 1.58524513e-01
1.18011653e-01 -2.34607056e-01 -7.89167404e-01 -4.24685448e-01
1.06617406e-01 7.32476711e-01 3.04870993e-01 -2.89419144... | [7.039351463317871, -0.9691698551177979] |
18b12f95-8ed7-452e-9357-adb7ac44e53e | prom-a-phrase-level-copying-mechanism-with | 2305.06647 | null | https://arxiv.org/abs/2305.06647v1 | https://arxiv.org/pdf/2305.06647v1.pdf | PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization | Based on the remarkable achievements of pre-trained language models in abstractive summarization, the copying mechanism has proved helpful by improving the factuality, stability, and overall performance. This work proposes PROM, a new PhRase-level cOpying Mechanism that enhances attention on n-grams, which can be appli... | ['Nan Duan', 'Hai Zhao', 'Pengcheng He', 'Yeyun Gong', 'Xinbei Ma'] | 2023-05-11 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.60244125e-01 3.39580625e-01 -7.10642993e-01 -1.47453472e-01
-1.11105168e+00 -2.15197951e-01 7.70712614e-01 5.46658754e-01
-5.13859630e-01 7.54702687e-01 1.15048051e+00 -6.16199486e-02
3.35021138e-01 -7.26599574e-01 -6.74057782e-01 -3.60969037e-01
3.87680717e-02 3.35372031e-01 3.05916876e-01 -6.28525078... | [12.418853759765625, 9.371118545532227] |
dc29a7f9-aff0-4c48-9a3d-aeea5b25ea25 | self-supervised-learning-of-event-guided | 2306.15507 | null | https://arxiv.org/abs/2306.15507v1 | https://arxiv.org/pdf/2306.15507v1.pdf | Self-supervised Learning of Event-guided Video Frame Interpolation for Rolling Shutter Frames | This paper makes the first attempt to tackle the challenging task of recovering arbitrary frame rate latent global shutter (GS) frames from two consecutive rolling shutter (RS) frames, guided by the novel event camera data. Although events possess high temporal resolution, beneficial for video frame interpolation (VFI)... | ['Lin Wang', 'Guoqiang Liang', 'Yunfan Lu'] | 2023-06-27 | null | null | null | null | ['self-supervised-learning', 'video-frame-interpolation'] | ['computer-vision', 'computer-vision'] | [ 5.31444013e-01 -3.85992825e-01 -1.63362414e-01 -3.92063260e-01
-9.06092823e-01 -3.55539918e-01 5.83731353e-01 -4.10423100e-01
-3.33037078e-01 7.58342147e-01 1.52738214e-01 2.98967324e-02
2.21100613e-01 -6.20666921e-01 -9.96170580e-01 -6.56274080e-01
2.32630014e-01 -1.34944260e-01 4.98506725e-01 1.16081260... | [10.740681648254395, -1.6667948961257935] |
191f3d83-05d5-4f6e-a082-296b58df3dbc | linear-relaxations-for-finding-diverse | null | null | http://papers.nips.cc/paper/6500-linear-relaxations-for-finding-diverse-elements-in-metric-spaces | http://papers.nips.cc/paper/6500-linear-relaxations-for-finding-diverse-elements-in-metric-spaces.pdf | Linear Relaxations for Finding Diverse Elements in Metric Spaces | Choosing a diverse subset of a large collection of points in a metric space is a fundamental problem, with applications in feature selection, recommender systems, web search, data summarization, etc. Various notions of diversity have been proposed, tailored to different applications. The general algorithmic goal is to ... | ['Mehrdad Ghadiri', 'Vahab Mirrokni', 'Aditya Bhaskara', 'Ola Svensson'] | 2016-12-01 | null | null | null | neurips-2016-12 | ['data-summarization'] | ['miscellaneous'] | [ 1.23688228e-01 -1.85148213e-02 -3.05155128e-01 -2.34928623e-01
-5.95887303e-01 -6.73791170e-01 1.03201516e-01 5.33952773e-01
-1.77875936e-01 9.40470695e-01 1.27387121e-01 1.10916227e-01
-7.56728232e-01 -1.00712085e+00 -6.53947294e-01 -1.08537710e+00
-2.89697140e-01 5.77362835e-01 2.62448378e-02 -3.34704965... | [6.659061431884766, 4.83515739440918] |
9f531191-44d7-4de3-a708-7fd8cc58b57b | audio-content-analysis | 2101.00132 | null | https://arxiv.org/abs/2101.00132v1 | https://arxiv.org/pdf/2101.00132v1.pdf | Audio Content Analysis | Preprint for a book chapter introducing Audio Content Analysis. With a focus on Music Information Retrieval systems, this chapter defines musical audio content, introduces the general process of audio content analysis, and surveys basic approaches to audio content analysis. The various tasks in Audio Content Analysis a... | ['Alexander Lerch'] | 2021-01-01 | null | null | null | null | ['genre-classification', 'music-emotion-recognition', 'music-classification', 'music-transcription'] | ['computer-vision', 'music', 'music', 'music'] | [ 5.91609597e-01 -5.63569129e-01 -1.97871909e-01 1.64503276e-01
-1.10649085e+00 -1.05939829e+00 -1.08230084e-01 3.62741739e-01
-4.36017103e-02 1.44737467e-01 4.60544646e-01 2.86393464e-01
-7.38651395e-01 -2.12268531e-01 1.62004620e-01 -7.17287421e-01
-2.73381919e-01 1.19624697e-01 -1.02208667e-02 -9.06481519... | [15.941455841064453, 5.259307861328125] |
7b796040-508c-48df-8096-5fe8555a4fa2 | interpreting-outliers-localized-logistic | 1702.06354 | null | http://arxiv.org/abs/1702.06354v1 | http://arxiv.org/pdf/1702.06354v1.pdf | Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation | We propose an inlier-based outlier detection method capable of both
identifying the outliers and explaining why they are outliers, by identifying
the outlier-specific features. Specifically, we employ an inlier-based outlier
detection criterion, which uses the ratio of inlier and test probability
densities as a measure... | ['Makoto Yamada', 'Samuel Kaski', 'Song Liu'] | 2017-02-21 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-4.97818202e-01 -2.68368363e-01 1.33032799e-02 -2.27934033e-01
-8.09664488e-01 -2.97828764e-01 3.93671870e-01 6.90819860e-01
-1.94358211e-02 7.58972943e-01 6.36155233e-02 -1.05648682e-01
-2.04052344e-01 -4.44936156e-01 -8.54489744e-01 -6.75967991e-01
-3.73566478e-01 3.93079668e-01 6.24697357e-02 4.27781284... | [7.569726943969727, 2.674499988555908] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.