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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cb29abcf-8c05-4830-8fef-5c786d1b1633 | attend-memorize-and-generate-towards-faithful-1 | 2203.00732 | null | https://arxiv.org/abs/2203.00732v1 | https://arxiv.org/pdf/2203.00732v1.pdf | Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots | Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data. Despite many efforts having been made towards generating impressive fluent sentences by fine-tuning powerful pre-trained language models, the faithfulness of generated content still needs t... | ['Philip S. Yu', 'Yao Wan', 'Ye Liu', 'Wenting Zhao'] | 2022-03-01 | attend-memorize-and-generate-towards-faithful | https://aclanthology.org/2021.findings-emnlp.347 | https://aclanthology.org/2021.findings-emnlp.347.pdf | findings-emnlp-2021-11 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.48318046e-01 3.91110957e-01 -1.18735723e-01 -2.69467860e-01
-8.77848864e-01 -3.80086809e-01 1.07433569e+00 2.19238430e-01
-3.53820361e-02 1.14471436e+00 9.70313311e-01 1.47242369e-02
4.90506917e-01 -1.29464829e+00 -5.97611904e-01 -2.14932814e-01
4.12561297e-01 6.79615438e-01 8.78864378e-02 -9.14010823... | [11.703781127929688, 8.851287841796875] |
66d27375-2389-4888-9192-258c555567bb | weighted-anisotropic-isotropic-total | 2307.00439 | null | https://arxiv.org/abs/2307.00439v1 | https://arxiv.org/pdf/2307.00439v1.pdf | Weighted Anisotropic-Isotropic Total Variation for Poisson Denoising | Poisson noise commonly occurs in images captured by photon-limited imaging systems such as in astronomy and medicine. As the distribution of Poisson noise depends on the pixel intensity value, noise levels vary from pixels to pixels. Hence, denoising a Poisson-corrupted image while preserving important details can be c... | ['Jack Xin', 'Fredrick Park', 'Yifei Lou', 'Kevin Bui'] | 2023-07-01 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 4.72609639e-01 -5.39900661e-01 3.25475872e-01 -5.54059558e-02
-6.74447358e-01 -3.48109454e-01 1.86620414e-01 -1.57979012e-01
-8.24611247e-01 8.91159832e-01 -5.02316914e-02 3.81806903e-02
1.90205947e-01 -8.49868298e-01 -6.16052806e-01 -1.23703384e+00
5.00646174e-01 5.17346337e-02 3.21784407e-01 4.71579790... | [11.629626274108887, -2.5683696269989014] |
bf18f094-75b8-49a3-a859-79e2b51ece54 | constructing-colloquial-dataset-for-persian | 2306.12679 | null | https://arxiv.org/abs/2306.12679v1 | https://arxiv.org/pdf/2306.12679v1.pdf | Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs | Introduction: Microblogging websites have massed rich data sources for sentiment analysis and opinion mining. In this regard, sentiment classification has frequently proven inefficient because microblog posts typically lack syntactically consistent terms and representatives since users on these social networks do not l... | ['Zeinab Rajabi', 'Farzaneh Rahmani', 'Leyla Rabiei', 'Mojtaba Mazoochi'] | 2023-06-22 | null | null | null | null | ['word-embeddings', 'sentiment-analysis', 'opinion-mining', 'persian-sentiment-anlysis'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-6.57622278e-01 -1.94170043e-01 -1.49049228e-02 -5.08473754e-01
-1.67083547e-01 -5.07351160e-01 5.23753285e-01 4.15796369e-01
-9.09481347e-01 8.27754140e-01 4.94359702e-01 -4.04529244e-01
3.18236798e-01 -1.04155016e+00 -4.79589365e-02 -4.57130373e-01
1.55199900e-01 1.46449938e-01 -2.41594285e-01 -1.10949278... | [11.200174331665039, 6.942314624786377] |
ed129eb1-3528-4e9e-a385-bd189043bbb0 | self-supervised-sparse-to-dense-motion | 2008.07872 | null | https://arxiv.org/abs/2008.07872v1 | https://arxiv.org/pdf/2008.07872v1.pdf | Self-supervised Sparse to Dense Motion Segmentation | Observable motion in videos can give rise to the definition of objects moving with respect to the scene. The task of segmenting such moving objects is referred to as motion segmentation and is usually tackled either by aggregating motion information in long, sparse point trajectories, or by directly producing per frame... | ['Margret Keuper', 'Kalun Ho', 'Peter Ochs', 'Amirhossein Kardoost'] | 2020-08-18 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 4.56552863e-01 7.98271075e-02 -2.72133380e-01 -3.20848197e-01
-8.72884095e-01 -5.68032742e-01 6.07108772e-01 -8.03866163e-02
-6.42430127e-01 5.28616369e-01 1.53480306e-01 3.95579711e-02
4.93509583e-02 -6.14381790e-01 -8.97096992e-01 -7.94770062e-01
-1.41401544e-01 6.64917946e-01 6.25478029e-01 2.55602241... | [9.140424728393555, -0.25388726592063904] |
176dabcc-9aa8-44f7-b4b9-be82ed58c7c1 | visual-sentiment-prediction-with-deep | 1411.5731 | null | http://arxiv.org/abs/1411.5731v1 | http://arxiv.org/pdf/1411.5731v1.pdf | Visual Sentiment Prediction with Deep Convolutional Neural Networks | Images have become one of the most popular types of media through which users
convey their emotions within online social networks. Although vast amount of
research is devoted to sentiment analysis of textual data, there has been very
limited work that focuses on analyzing sentiment of image data. In this work,
we propo... | ['Li-Jia Li', 'Kuang-Chih Lee', 'Suleyman Cetintas', 'Can Xu'] | 2014-11-21 | null | null | null | null | ['visual-sentiment-prediction'] | ['computer-vision'] | [ 1.15398735e-01 -1.94553539e-01 -2.85752833e-01 -6.62082732e-01
-6.90789595e-02 -3.50712419e-01 5.18204868e-01 2.18310907e-01
-5.09545684e-01 4.23828155e-01 1.36488885e-01 -4.28235561e-01
6.22605681e-01 -8.86213839e-01 -7.42325425e-01 -3.34147125e-01
3.39343607e-01 -2.13886663e-01 4.00644355e-02 -3.18376184... | [10.9176664352417, 2.693315267562866] |
19b1de35-291e-4378-95cc-9fb77a431a90 | parallelizing-legendre-memory-unit-training | 2102.11417 | null | https://arxiv.org/abs/2102.11417v2 | https://arxiv.org/pdf/2102.11417v2.pdf | Parallelizing Legendre Memory Unit Training | Recently, a new recurrent neural network (RNN) named the Legendre Memory Unit (LMU) was proposed and shown to achieve state-of-the-art performance on several benchmark datasets. Here we leverage the linear time-invariant (LTI) memory component of the LMU to construct a simplified variant that can be parallelized during... | ['Chris Eliasmith', 'Narsimha Chilkuri'] | 2021-02-22 | parallelizing-legendre-memory-unit-training-1 | https://arxiv.org/abs/2102.11417 | https://arxiv.org/pdf/2102.11417.pdf | null | ['sequential-image-classification'] | ['computer-vision'] | [ 5.45695312e-02 -7.23649487e-02 -2.27879882e-01 -1.71583742e-01
-7.87501097e-01 -6.31634414e-01 7.29906797e-01 -2.53846526e-01
-6.85765088e-01 4.77833599e-01 1.11220784e-01 -8.97384524e-01
3.29277486e-01 -8.36020470e-01 -1.01316822e+00 -6.96915209e-01
1.34891793e-01 6.86463773e-01 2.12884456e-01 -3.73787671... | [10.803276062011719, 6.806011199951172] |
adecbd42-75ff-4f6c-a9b1-7f6083914c26 | a-comprehensive-evaluation-on-multi-channel | 2202.10286 | null | https://arxiv.org/abs/2202.10286v1 | https://arxiv.org/pdf/2202.10286v1.pdf | A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detection | The vulnerability against presentation attacks is a crucial problem undermining the wide-deployment of face recognition systems. Though presentation attack detection (PAD) systems try to address this problem, the lack of generalization and robustness continues to be a major concern. Several works have shown that using ... | ['Sebastien Marcel', 'David Geissbuhler', 'Anjith George'] | 2022-02-21 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.95066291e-01 -3.70497495e-01 -7.29599819e-02 -1.79519325e-01
-6.69078290e-01 -7.79925287e-01 4.77633148e-01 -2.14746725e-02
-3.77866477e-01 2.72229254e-01 -1.43926308e-01 -4.01406229e-01
-2.42403328e-01 -6.48262203e-01 -5.33195734e-01 -1.06980157e+00
-4.44071293e-01 -2.20999703e-01 2.58212060e-01 -3.79727155... | [13.073018074035645, 1.097410798072815] |
af7a4476-939d-4120-af30-9c1f6cafaf3b | disclip-open-vocabulary-referring-expression | 2305.19108 | null | https://arxiv.org/abs/2305.19108v1 | https://arxiv.org/pdf/2305.19108v1.pdf | DisCLIP: Open-Vocabulary Referring Expression Generation | Referring Expressions Generation (REG) aims to produce textual descriptions that unambiguously identifies specific objects within a visual scene. Traditionally, this has been achieved through supervised learning methods, which perform well on specific data distributions but often struggle to generalize to new images an... | ['Gal Chechik', 'Ethan Fetaya', 'Aviv Shamsian', 'Eitan Shaar', 'Lior Bracha'] | 2023-05-30 | null | null | null | null | ['referring-expression-generation', 'referring-expression'] | ['computer-vision', 'computer-vision'] | [ 4.60099041e-01 2.47159317e-01 2.82816049e-02 -5.95390439e-01
-1.13977981e+00 -7.23646760e-01 8.89009416e-01 2.08988488e-01
-1.91923589e-01 5.13793111e-01 3.29536200e-01 7.67076164e-02
3.62180978e-01 -5.76897800e-01 -9.16033566e-01 -3.37526411e-01
3.60788912e-01 5.84549069e-01 1.05524912e-01 -1.57100439... | [10.80776309967041, 1.378513216972351] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.