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
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8c394854-8713-4b2d-8632-c5b61117f8fa | sub-label-dependencies-for-neural | null | null | https://aclanthology.org/W18-3904 | https://aclanthology.org/W18-3904.pdf | Sub-label dependencies for Neural Morphological Tagging -- The Joint Submission of University of Colorado and University of Helsinki for VarDial 2018 | This paper presents the submission of the UH{\&}CU team (Joint University of Colorado and University of Helsinki team) for the VarDial 2018 shared task on morphosyntactic tagging of Croatian, Slovenian and Serbian tweets. Our system is a bidirectional LSTM tagger which emits tags as character sequences using an LSTM ge... | ['Senka Drobac', 'Miikka Silfverberg'] | 2018-08-01 | null | null | null | coling-2018-8 | ['morphological-tagging'] | ['natural-language-processing'] | [-1.82575490e-02 2.29612365e-01 -7.68639743e-02 -3.74233454e-01
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-1.11813307e-01 1.06878996e+00 2.81452626e-01 -4.21628922... | [9.97833251953125, 9.789222717285156] |
0ba73b45-266d-4987-8989-e74f0dd2cd21 | wssod-a-new-pipeline-for-weakly-and-semi | 2105.11293 | null | https://arxiv.org/abs/2105.11293v1 | https://arxiv.org/pdf/2105.11293v1.pdf | WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection | The performance of object detection, to a great extent, depends on the availability of large annotated datasets. To alleviate the annotation cost, the research community has explored a number of ways to exploit unlabeled or weakly labeled data. However, such efforts have met with limited success so far. In this work, w... | ['Wayne Zhang', 'Dahua Lin', 'Kai Chen', 'Xinjiang Wang', 'Yuhang Cao', 'Shijie Fang'] | 2021-05-21 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.67131323e-01 2.93003201e-01 -2.37717882e-01 -5.19725442e-01
-1.01810575e+00 -3.66313815e-01 7.43725300e-01 8.71475339e-02
-8.25812221e-01 6.43785179e-01 -1.73804343e-01 8.00480768e-02
3.95615399e-01 -3.09507757e-01 -6.45747125e-01 -8.98062825e-01
3.67952466e-01 3.51014048e-01 6.38636887e-01 2.77781278... | [9.237126350402832, 1.2222390174865723] |
5e62f93f-25a9-4cec-bd29-108e3eb5dbc9 | how-to-evaluate-the-next-system-automatic | 1912.04664 | null | https://arxiv.org/abs/1912.04664v1 | https://arxiv.org/pdf/1912.04664v1.pdf | How to Evaluate the Next System: Automatic Dialogue Evaluation from the Perspective of Continual Learning | Automatic dialogue evaluation plays a crucial role in open-domain dialogue research. Previous works train neural networks with limited annotation for conducting automatic dialogue evaluation, which would naturally affect the evaluation fairness as dialogue systems close to the scope of training corpus would have more p... | ['dianhai yu', 'Zhongheng He', 'Xiangyang Zhou', 'Lu Li'] | 2019-12-10 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 1.27446085e-01 5.75007796e-01 1.04317367e-01 -6.35895252e-01
-6.20406568e-01 -7.53198922e-01 5.95564306e-01 9.34540331e-02
-8.55364621e-01 1.24686849e+00 4.36118513e-01 -3.40231836e-01
2.20238611e-01 -5.75193942e-01 -9.10970196e-02 -4.22808826e-01
8.14079586e-03 1.09710872e+00 9.69861075e-02 -6.39158368... | [12.90770149230957, 8.001869201660156] |
4eddf75d-56dd-431e-908f-6bbf14671a0a | causal-discovery-from-conditionally-1 | 2110.06257 | null | https://arxiv.org/abs/2110.06257v1 | https://arxiv.org/pdf/2110.06257v1.pdf | Causal discovery from conditionally stationary time-series | Causal discovery, i.e., inferring underlying cause-effect relationships from observations of a scene or system, is an inherent mechanism in human cognition, but has been shown to be highly challenging to automate. The majority of approaches in the literature aiming for this task consider constrained scenarios with full... | ['Hedvig Kjellstrom', 'Ruibo Tu', 'Carles Balsells Rodas'] | 2021-10-12 | causal-discovery-from-conditionally | https://openreview.net/forum?id=q9zIvzRaU94 | https://openreview.net/pdf?id=q9zIvzRaU94 | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 4.82684761e-01 -7.45157152e-02 3.48813832e-02 -4.11812097e-01
-3.72322500e-01 -5.34687817e-01 1.23581302e+00 2.54701972e-01
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-5.60402691e-01 6.35285258e-01 1.57260969e-01 2.63443828... | [7.844395160675049, 5.057373046875] |
771b5c9b-473b-4300-8f8b-e461fa7a718a | uncovering-and-categorizing-social-biases-in | 2305.16253 | null | https://arxiv.org/abs/2305.16253v2 | https://arxiv.org/pdf/2305.16253v2.pdf | Uncovering and Categorizing Social Biases in Text-to-SQL | Content Warning: This work contains examples that potentially implicate stereotypes, associations, and other harms that could be offensive to individuals in certain social groups.} Large pre-trained language models are acknowledged to carry social biases towards different demographics, which can further amplify existin... | ['Jian-Guang Lou', 'Elliott Ash', 'Xiaokang Chen', 'Zhe Su', 'Yan Gao', 'Yan Liu'] | 2023-05-25 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-1.97361261e-02 5.07210732e-01 -2.53215164e-01 -6.96775436e-01
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-1.23654090e-01 3.48655730e-01 -2.15028211e-01 -8.01241636... | [9.111984252929688, 10.214293479919434] |
415d2ebd-6451-4e15-8eac-6765b86d95a0 | autobots-lt-edi-eacl2021-one-world-one-family | null | null | https://aclanthology.org/2021.ltedi-1.21 | https://aclanthology.org/2021.ltedi-1.21.pdf | Autobots@LT-EDI-EACL2021: One World, One Family: Hope Speech Detection with BERT Transformer Model | The rapid rise of online social networks like YouTube, Facebook, Twitter allows people to express their views more widely online. However, at the same time, it can lead to an increase in conflict and hatred among consumers in the form of freedom of speech. Therefore, it is essential to take a positive strengthening met... | ['Radhika Mamidi', 'Sunil Gundapu'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-5.85325897e-01 4.46673125e-01 -7.20601022e-01 -1.95825681e-01
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2.97584742e-01 -1.87977210e-01 -3.10301390e-02 -5.01502454... | [8.905678749084473, 10.622347831726074] |
5abe884a-a357-4e60-80ba-fb2cca7f9f42 | mapping-the-buried-cable-by-ground | 2201.11253 | null | https://arxiv.org/abs/2201.11253v1 | https://arxiv.org/pdf/2201.11253v1.pdf | Mapping the Buried Cable by Ground Penetrating Radar and Gaussian-Process Regression | With the rapid expansion of urban areas and the increasingly use of electricity, the need for locating buried cables is becoming urgent. In this paper, a noval method to locate underground cables based on Ground Penetrating Radar (GPR) and Gaussian-process regression is proposed. Firstly, the coordinate system of the d... | ['Huanhuan Chen', 'Shengfei Lyu', 'Qiuju Chen', 'Xiren Zhou'] | 2022-01-25 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-1.89337507e-01 -4.35014814e-01 5.98661423e-01 -1.30926207e-01
-7.48156726e-01 -4.16889250e-01 -1.96077645e-01 6.69285804e-02
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-9.13645253e-02 2.76558101e-01 3.12392443e-01 3.37869555... | [6.770392894744873, 1.468284249305725] |
8f751ca4-7252-4f94-bd5f-3dd3f215feba | rotational-projection-statistics-for-3d-local | 1304.3192 | null | http://arxiv.org/abs/1304.3192v1 | http://arxiv.org/pdf/1304.3192v1.pdf | Rotational Projection Statistics for 3D Local Surface Description and Object Recognition | Recognizing 3D objects in the presence of noise, varying mesh resolution,
occlusion and clutter is a very challenging task. This paper presents a novel
method named Rotational Projection Statistics (RoPS). It has three major
modules: Local Reference Frame (LRF) definition, RoPS feature description and
3D object recogni... | ['Mohammed Bennamoun', 'Ferdous Sohel', 'Min Lu', 'Jianwei Wan', 'Yulan Guo'] | 2013-04-11 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 7.61908293e-02 -7.34123051e-01 8.40030685e-02 -3.26100439e-01
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-4.77740675e-01 -7.56429672e-01 -4.80180293e-01 -7.97136605e-01
-2.64350325e-01 5.24020731e-01 4.33050722e-01 1.50951073... | [8.171692848205566, -2.3587357997894287] |
8451526a-a6d6-412a-8c14-ab7e24152518 | deep-multi-facial-patches-aggregation-network | 2002.09298 | null | https://arxiv.org/abs/2002.09298v1 | https://arxiv.org/pdf/2002.09298v1.pdf | Deep Multi-Facial Patches Aggregation Network For Facial Expression Recognition | In this paper, we propose an approach for Facial Expressions Recognition (FER) based on a deep multi-facial patches aggregation network. Deep features are learned from facial patches using deep sub-networks and aggregated within one deep architecture for expression classification . Several problems may affect the perfo... | ['Ahmed Rachid Hazourli', 'Alice Othmani', 'Amine Djeghri', 'Hanan Salam'] | 2020-02-20 | null | null | null | null | ['facial-expression-generation'] | ['computer-vision'] | [ 3.36557329e-01 1.27773985e-01 7.65363798e-02 -6.85970008e-01
-5.87252498e-01 6.35062307e-02 2.68108994e-01 -2.62764752e-01
-3.93813342e-01 9.49582756e-01 -3.03097934e-01 2.61620939e-01
2.15472594e-01 -8.15611243e-01 -7.70309448e-01 -9.03026640e-01
-5.76305948e-02 1.81653291e-01 -3.03723872e-01 -3.49356025... | [13.596585273742676, 1.7560434341430664] |
bb5caddc-06c6-4322-8f1b-5a1ea78324bf | examining-risks-of-racial-biases-in-nlp-tools | 2305.19409 | null | https://arxiv.org/abs/2305.19409v1 | https://arxiv.org/pdf/2305.19409v1.pdf | Examining risks of racial biases in NLP tools for child protective services | Although much literature has established the presence of demographic bias in natural language processing (NLP) models, most work relies on curated bias metrics that may not be reflective of real-world applications. At the same time, practitioners are increasingly using algorithmic tools in high-stakes settings, with pa... | ['Yulia Tsvetkov', 'David Steier', 'Emily Putnam-Hornstein', 'Alexandra Chouldechova', 'Nupoor Gandhi', 'Amanda Coston', 'Anjalie Field'] | 2023-05-30 | null | null | null | null | ['named-entity-recognition-ner', 'coreference-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.76296103e-01 5.03797174e-01 -6.73489213e-01 -7.02195227e-01
-9.21072006e-01 -7.52169371e-01 5.81919014e-01 7.56854653e-01
-8.60015452e-01 7.95657694e-01 1.10064912e+00 -8.11705172e-01
-3.96411389e-01 -8.05262268e-01 -5.37273586e-01 -3.50599885e-01
2.49726698e-01 4.63544369e-01 -6.90159023e-01 1.49132952... | [8.878366470336914, 5.675219535827637] |
f28c67d3-1967-42b8-bbb2-fe592cfc5251 | evadedroid-a-practical-evasion-attack-on | 2110.03301 | null | https://arxiv.org/abs/2110.03301v3 | https://arxiv.org/pdf/2110.03301v3.pdf | EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection | Over the last decade, researchers have extensively explored the vulnerabilities of Android malware detectors to adversarial examples through the development of evasion attacks; however, the practicality of these attacks in real-world scenarios remains arguable. The majority of studies have assumed attackers know the de... | ['Veelasha Moonsamy', 'Hamid Bostani'] | 2021-10-07 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 6.41496003e-01 -1.14205293e-01 -4.22907054e-01 1.59377053e-01
-7.58356750e-01 -1.39449096e+00 7.67545819e-01 -2.08140790e-01
-6.33470993e-03 5.51389456e-01 -5.37524402e-01 -8.22820663e-01
1.35121390e-01 -9.72182870e-01 -9.27035987e-01 -5.54100215e-01
-3.33167106e-01 2.05920801e-01 3.69613498e-01 -2.43237704... | [14.40007209777832, 9.66585636138916] |
122fe8db-f2cb-422a-96bd-160fa7e83a51 | deepskeleton-skeleton-map-for-3d-human-pose | 1711.10796 | null | http://arxiv.org/abs/1711.10796v1 | http://arxiv.org/pdf/1711.10796v1.pdf | DeepSkeleton: Skeleton Map for 3D Human Pose Regression | Despite recent success on 2D human pose estimation, 3D human pose estimation
still remains an open problem. A key challenge is the ill-posed depth ambiguity
nature. This paper presents a novel intermediate feature representation named
skeleton map for regression. It distills structural context from irrelavant
propertie... | ['xiangyang xue', 'Wei zhang', 'Qingfu Wan'] | 2017-11-29 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 2.75859952e-01 3.08309853e-01 1.26565829e-01 -3.61035913e-01
-7.12131262e-01 -3.25612605e-01 5.11397600e-01 -2.63851702e-01
-6.71107292e-01 7.34056175e-01 7.30741099e-02 1.49383768e-01
-6.08503036e-02 -2.65917480e-01 -8.15045357e-01 -3.74339521e-01
-4.44674119e-02 8.35819185e-01 1.50164887e-01 -4.08176512... | [6.956085205078125, -0.9579131603240967] |
7600da50-4b33-4635-b367-a5763a49cd96 | what-is-your-metric-telling-you-evaluating | 2205.11454 | null | https://arxiv.org/abs/2205.11454v1 | https://arxiv.org/pdf/2205.11454v1.pdf | What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability | Classifier calibration has received recent attention from the machine learning community due both to its practical utility in facilitating decision making, as well as the observation that modern neural network classifiers are poorly calibrated. Much of this focus has been towards the goal of learning classifiers such t... | ['Eric Heim', 'Jacob Oaks', 'John Kirchenbauer'] | 2022-05-23 | null | null | null | null | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [ 2.47496977e-01 -6.46270290e-02 -1.93387225e-01 -9.96157587e-01
-6.93144321e-01 -5.91432095e-01 4.70675498e-01 4.10491914e-01
-4.00790900e-01 6.75747991e-01 -1.00675762e-01 -5.99071562e-01
-4.15463507e-01 -7.01303959e-01 -4.23288345e-01 -4.88197893e-01
2.55129248e-01 2.87040681e-01 -1.65603608e-01 -1.20951757... | [8.589374542236328, 4.3698577880859375] |
2ce230cd-a85a-4cc3-870b-81d7154026a6 | stargan-v2-diverse-image-synthesis-for | 1912.01865 | null | https://arxiv.org/abs/1912.01865v2 | https://arxiv.org/pdf/1912.01865v2.pdf | StarGAN v2: Diverse Image Synthesis for Multiple Domains | A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains... | ['Jung-Woo Ha', 'Jaejun Yoo', 'Yunjey Choi', 'Youngjung Uh'] | 2019-12-04 | stargan-v2-diverse-image-synthesis-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Choi_StarGAN_v2_Diverse_Image_Synthesis_for_Multiple_Domains_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Choi_StarGAN_v2_Diverse_Image_Synthesis_for_Multiple_Domains_CVPR_2020_paper.pdf | cvpr-2020-6 | ['fundus-to-angiography-generation', 'multimodal-unsupervised-image-to-image'] | ['computer-vision', 'computer-vision'] | [ 7.39113381e-03 -2.55295068e-01 -2.60004133e-01 -5.34082770e-01
-7.61210561e-01 -7.29566395e-01 7.00714707e-01 -6.19199216e-01
-1.90157015e-02 5.88301480e-01 9.43608880e-02 -1.91667959e-01
3.62306833e-01 -6.26716554e-01 -7.59844363e-01 -3.95696104e-01
5.17231487e-02 4.39083546e-01 1.86345994e-01 -1.84962526... | [11.68975830078125, -0.3038899600505829] |
8502efce-9415-4e47-b416-437cb8cd825d | optimal-energy-system-scheduling-using-a | 2305.05484 | null | https://arxiv.org/abs/2305.05484v1 | https://arxiv.org/pdf/2305.05484v1.pdf | Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm | The massive integration of renewable-based distributed energy resources (DERs) inherently increases the energy system's complexity, especially when it comes to defining its operational schedule. Deep reinforcement learning (DRL) algorithms arise as a promising solution due to their data-driven and model-free features. ... | ['Peter Palensky', 'Edgar Mauricio Salazar Duque', 'Pedro P. Vergara', 'Hou Shengren'] | 2023-05-09 | null | null | null | null | ['energy-management'] | ['time-series'] | [-2.50367045e-01 -3.27283174e-01 -5.30458212e-01 -3.03789433e-02
-3.41904193e-01 -6.91242874e-01 2.18352437e-01 2.14376986e-01
-1.86385959e-01 1.22366941e+00 -2.97822356e-01 -4.66086805e-01
-8.35813463e-01 -1.02590799e+00 -3.76775712e-01 -1.00404751e+00
-3.88672054e-01 5.20350754e-01 -6.29763842e-01 -2.74651825... | [5.636808395385742, 2.5613152980804443] |
fec37987-8382-4e06-a4c7-109d5e9567b5 | learning-to-estimate-6dof-pose-from-limited | 2306.07598 | null | https://arxiv.org/abs/2306.07598v1 | https://arxiv.org/pdf/2306.07598v1.pdf | Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB Images | The accurate estimation of six degrees-of-freedom (6DoF) object poses is essential for many applications in robotics and augmented reality. However, existing methods for 6DoF pose estimation often depend on CAD templates or dense support views, restricting their usefulness in realworld situations. In this study, we pre... | ['Zhangyang Wang', 'Chenxin Li', 'Peihao Wang', 'Brandon Y. Feng', 'Zhiwen Fan', 'Panwang Pan'] | 2023-06-13 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [-1.24257617e-01 -7.70023763e-02 -2.16581702e-01 -2.14608237e-01
-8.76739323e-01 -3.83759290e-01 3.28114122e-01 -3.07610005e-01
-1.87151089e-01 3.97042155e-01 1.50813699e-01 4.49273765e-01
-2.22129092e-01 -5.35728395e-01 -7.81825185e-01 -5.35341740e-01
1.53685361e-01 8.57929528e-01 5.85799038e-01 -3.05596411... | [7.407684326171875, -2.551175117492676] |
3dfb0940-e8d8-4c32-99eb-ae03c040f8b8 | geometric-structure-preserving-warp-for | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Du_Geometric_Structure_Preserving_Warp_for_Natural_Image_Stitching_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Du_Geometric_Structure_Preserving_Warp_for_Natural_Image_Stitching_CVPR_2022_paper.pdf | Geometric Structure Preserving Warp for Natural Image Stitching | Preserving geometric structures in the scene plays a vital role in image stitching. However, most of the existing methods ignore the large-scale layouts reflected by straight lines or curves, decreasing overall stitching quality. To address this issue, this work presents a structure-preserving stitching approach th... | ['Jiaxin Wang', 'Xinchao Wang', 'Shaoli Huang', 'Jiguang Cui', 'Jifeng Ning', 'Peng Du'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['image-stitching', 'edge-detection'] | ['computer-vision', 'computer-vision'] | [ 3.04269165e-01 -5.07407844e-01 -2.16931343e-01 -2.81048939e-02
-2.04360083e-01 -5.09621322e-01 3.81434888e-01 -6.64171129e-02
7.23932162e-02 2.72957832e-01 2.62937814e-01 7.55111203e-02
-6.83828490e-04 -7.40175605e-01 -6.19523585e-01 -9.87905264e-01
2.25881875e-01 -1.16692930e-01 4.21193480e-01 -3.08574259... | [9.346170425415039, -2.360717535018921] |
62a9b15b-77eb-40ac-8fcd-c83b506a20e5 | weighted-training-for-cross-task-learning | 2105.14095 | null | https://arxiv.org/abs/2105.14095v2 | https://arxiv.org/pdf/2105.14095v2.pdf | Weighted Training for Cross-Task Learning | In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks. We show that TAWT is easy to implement, is computationally efficient, requires little hyperparameter tuni... | ['Weijie J. Su', 'Dan Roth', 'Hangfeng He', 'Koby Crammer', 'Shuxiao Chen'] | 2021-05-28 | weighted-training-for-cross-task-learning-1 | https://openreview.net/forum?id=ltM1RMZntpu | https://openreview.net/pdf?id=ltM1RMZntpu | iclr-2022-4 | ['predicate-detection'] | ['natural-language-processing'] | [ 4.22329366e-01 2.07023159e-01 -3.86678845e-01 -4.07228380e-01
-1.37771451e+00 -6.94262445e-01 5.49118638e-01 3.81158739e-01
-9.48844314e-01 6.65932655e-01 4.09199923e-01 -4.87751514e-01
-4.86735970e-01 -2.88298875e-01 -5.21352470e-01 -6.75080597e-01
-3.05981845e-01 3.32472056e-01 2.88072318e-01 3.80612314... | [10.572081565856934, 8.797649383544922] |
80f93d27-2c5a-474f-8c09-9820e3dbc127 | exploiting-geometric-constraints-on-dense | 1909.13258 | null | https://arxiv.org/abs/1909.13258v2 | https://arxiv.org/pdf/1909.13258v2.pdf | EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency | The existing approaches for salient motion segmentation are unable to explicitly learn geometric cues and often give false detections on prominent static objects. We exploit multiview geometric constraints to avoid such shortcomings. To handle the nonrigid background like a sea, we also propose a robust fusion mechanis... | ['Mohsen Ali', 'Richard Hartley', 'Muhammad Faisal', 'Ijaz Akhter'] | 2019-09-29 | epo-net-exploiting-geometric-constraints-on | https://arxiv.org/abs/1909.13258 | https://arxiv.org/pdf/1909.13258 | wacv-2020-3 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [-1.41362362e-02 -1.68536395e-01 -2.97549844e-01 -1.91488490e-01
-6.20762527e-01 -8.55158925e-01 6.51351511e-01 -2.32522249e-01
-5.23668885e-01 4.92002517e-01 1.17485709e-01 -1.31180137e-01
2.43305698e-01 -4.30201620e-01 -9.56678391e-01 -7.18859673e-01
-4.74425554e-02 7.60397390e-02 8.18356335e-01 6.68672621... | [8.989665985107422, -0.5231369733810425] |
4e24c826-d670-4f04-8975-8df52679ead7 | efficient-and-direct-inference-of-heart-rate | 2303.13637 | null | https://arxiv.org/abs/2303.13637v1 | https://arxiv.org/pdf/2303.13637v1.pdf | Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning | Heart Rate Variability (HRV) measures the variation of the time between consecutive heartbeats and is a major indicator of physical and mental health. Recent research has demonstrated that photoplethysmography (PPG) sensors can be used to infer HRV. However, many prior studies had high errors because they only employed... | ['Wei Wang', 'Houbing Song', 'Dakai Zhu', 'Mimi Xie', 'Jingye Xu', 'Yuntong Zhang'] | 2023-03-23 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-variability'] | ['medical', 'medical'] | [ 2.75267094e-01 -1.11909909e-02 -1.83745399e-01 -4.55872893e-01
-3.35262865e-01 -1.83515862e-01 -1.15647145e-01 1.32921278e-01
-2.43411943e-01 9.92256165e-01 1.08755872e-01 -3.59619766e-01
3.09114426e-01 -1.03880882e+00 -1.22692764e-01 -4.63484317e-01
-1.02954343e-01 -1.18764304e-01 -3.34293962e-01 1.30753025... | [13.900629043579102, 3.0652058124542236] |
f8b11423-64b4-4797-b1d3-b70dfa951ce4 | learning-continuous-grasping-function-with-a | 2207.05053 | null | https://arxiv.org/abs/2207.05053v3 | https://arxiv.org/pdf/2207.05053v3.pdf | Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations | We propose to learn to generate grasping motion for manipulation with a dexterous hand using implicit functions. With continuous time inputs, the model can generate a continuous and smooth grasping plan. We name the proposed model Continuous Grasping Function (CGF). CGF is learned via generative modeling with a Conditi... | ['Xiaolong Wang', 'Yuzhe Qin', 'Binghao Huang', 'Jiashun Wang', 'Jianglong Ye'] | 2022-07-11 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [-1.27961233e-01 3.82135838e-01 -2.64494307e-03 -2.42817387e-01
-5.07890224e-01 -5.46307921e-01 5.54192781e-01 -5.91459990e-01
-1.37096882e-01 8.94106627e-01 4.80450876e-03 -6.43512905e-02
-1.85576439e-01 -8.57779682e-01 -1.16836345e+00 -6.28193259e-01
-3.07517320e-01 1.11057949e+00 -9.00692344e-02 -8.32692608... | [4.747663497924805, 0.5648707747459412] |
76872cc2-56b6-45df-a528-f4ea1ea87ded | finstreder-simple-and-fast-spoken-language | 2206.14589 | null | https://arxiv.org/abs/2206.14589v1 | https://arxiv.org/pdf/2206.14589v1.pdf | Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text models | In Spoken Language Understanding (SLU) the task is to extract important information from audio commands, like the intent of what a user wants the system to do and special entities like locations or numbers. This paper presents a simple method for embedding intents and entities into Finite State Transducers, and, in com... | ['Wolfgang Reif', 'Alexander Poeppel', 'Daniel Bermuth'] | 2022-06-29 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 2.75385499e-01 3.89502317e-01 -6.95472807e-02 -7.07462132e-01
-1.05450046e+00 -7.32850611e-01 7.50944555e-01 4.40816820e-01
-5.76219141e-01 7.64466822e-01 5.88102818e-01 -5.57872057e-01
4.66157436e-01 -5.08883297e-01 -6.39085352e-01 -1.56972840e-01
-3.25374693e-01 6.04095101e-01 4.71528530e-01 -4.85640317... | [14.051546096801758, 6.940502643585205] |
b86e611c-bcaa-40db-a65f-dc933b179a3e | using-neural-network-for-identifying | 1806.07713 | null | http://arxiv.org/abs/1806.07713v1 | http://arxiv.org/pdf/1806.07713v1.pdf | Using Neural Network for Identifying Clickbaits in Online News Media | Online news media sometimes use misleading headlines to lure users to open
the news article. These catchy headlines that attract users but disappointed
them at the end, are called Clickbaits. Because of the importance of automatic
clickbait detection in online medias, lots of machine learning methods were
proposed and ... | ['Hui Jiang', 'Amin Omidvar', 'Aijun An'] | 2018-06-20 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-6.38222456e-01 -3.74392033e-01 -4.64619517e-01 -3.19732964e-01
-8.61340344e-01 -3.04291427e-01 7.30885029e-01 5.95411479e-01
-2.73890406e-01 8.09610784e-01 3.09242457e-01 -3.86904120e-01
-2.36320034e-01 -5.96763611e-01 -7.39321113e-01 -1.52230188e-01
-1.65243432e-01 1.03354767e-01 4.27597344e-01 -1.57179788... | [7.751511096954346, 9.784372329711914] |
937e318d-b180-4653-a417-a319de82fb75 | a-baseline-framework-for-part-level-action | 2110.03368 | null | https://arxiv.org/abs/2110.03368v2 | https://arxiv.org/pdf/2110.03368v2.pdf | A Baseline Framework for Part-level Action Parsing and Action Recognition | This technical report introduces our 2nd place solution to Kinetics-TPS Track on Part-level Action Parsing in ICCV DeeperAction Workshop 2021. Our entry is mainly based on YOLOF for instance and part detection, HRNet for human pose estimation, and CSN for video-level action recognition and frame-level part state parsin... | ['Tao Mei', 'Wu Liu', 'Kun Liu', 'Xinchen Liu', 'Xiaodong Chen'] | 2021-10-07 | null | null | null | null | ['action-parsing'] | ['natural-language-processing'] | [ 1.29449368e-01 5.01026034e-01 -3.15005660e-01 -3.35261226e-01
-1.23837554e+00 -5.44795573e-01 1.44560084e-01 -5.14227152e-01
-5.91864586e-01 5.18362045e-01 5.31619966e-01 2.86917865e-01
7.57282913e-01 1.34054702e-02 -9.59290862e-01 -4.23977435e-01
-2.46787235e-01 6.09620869e-01 9.17989433e-01 -1.94182813... | [8.069623947143555, 0.3946479856967926] |
ebce1245-bfbe-4677-9322-84aa0b18f005 | deep-analysis-of-visual-product-reviews | 2207.09499 | null | https://arxiv.org/abs/2207.09499v1 | https://arxiv.org/pdf/2207.09499v1.pdf | Deep Analysis of Visual Product Reviews | With the proliferation of the e-commerce industry, analyzing customer feedback is becoming indispensable to a service provider. In recent days, it can be noticed that customers upload the purchased product images with their review scores. In this paper, we undertake the task of analyzing such visual reviews, which is v... | ['Muhammad Saqib', 'Soumi Chattopadhyay', 'Chandranath Adak'] | 2022-07-19 | null | null | null | null | ['product-categorization'] | ['miscellaneous'] | [ 1.51281431e-01 2.59531066e-02 -3.41563970e-02 -5.96660852e-01
-5.76907814e-01 -4.91935849e-01 5.92633545e-01 7.25821316e-01
-4.42845762e-01 4.36681211e-01 2.70056054e-02 -4.94971156e-01
2.21081659e-01 -6.09806895e-01 -3.93103272e-01 -5.54643631e-01
1.01554886e-01 2.19111964e-01 4.13467810e-02 -3.39500904... | [11.207559585571289, 6.625596523284912] |
6887c18f-2531-4d10-a15c-82e64d47057d | small-data-no-problem-exploring-the-viability | null | null | https://aclanthology.org/2021.mrl-1.11 | https://aclanthology.org/2021.mrl-1.11.pdf | Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages | Pretrained multilingual language models have been shown to work well on many languages for a variety of downstream NLP tasks. However, these models are known to require a lot of training data. This consequently leaves out a huge percentage of the world’s languages as they are under-resourced. Furthermore, a major motiv... | ['Jimmy Lin', 'Yuxin Zhu', 'Kelechi Ogueji'] | null | null | null | null | emnlp-mrl-2021-11 | ['pretrained-multilingual-language-models'] | ['natural-language-processing'] | [-6.85164094e-01 -5.76098412e-02 -5.90571702e-01 -4.13081735e-01
-1.25777161e+00 -8.23069692e-01 6.50800347e-01 1.58275187e-01
-8.94757926e-01 1.05588078e+00 3.10544699e-01 -8.72607231e-01
3.89865965e-01 -6.03391051e-01 -6.89758241e-01 -1.71305716e-01
2.24862844e-01 8.58659744e-01 -3.20001990e-02 -3.13476652... | [10.652469635009766, 9.897492408752441] |
7566eff4-fbef-443b-ad40-b4615b2976da | fact-federated-adversarial-cross-training | 2306.00607 | null | https://arxiv.org/abs/2306.00607v1 | https://arxiv.org/pdf/2306.00607v1.pdf | FACT: Federated Adversarial Cross Training | Federated Learning (FL) facilitates distributed model development to aggregate multiple confidential data sources. The information transfer among clients can be compromised by distributional differences, i.e., by non-i.i.d. data. A particularly challenging scenario is the federated model adaptation to a target client w... | ['Michael Altenbuchinger', 'Andreas Schäfer', 'Jonas Lippl', 'Stefan Schrod'] | 2023-06-01 | null | null | null | null | ['source-free-domain-adaptation', 'unsupervised-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 6.66956753e-02 -1.36012316e-01 -4.63988125e-01 -4.95012522e-01
-1.28454006e+00 -1.34223127e+00 8.89299035e-01 4.00100425e-02
-3.86505604e-01 9.47245002e-01 -9.30513963e-02 -4.19718117e-01
1.77466720e-01 -7.90791571e-01 -9.57672417e-01 -6.82936132e-01
-1.53544173e-01 1.08061028e+00 1.86530337e-01 -1.41914397... | [10.355576515197754, 3.169701099395752] |
ae2e96f3-3a72-42f1-a1ba-9d25c6aeaf5d | learning-dynamic-preference-structure | 2111.11886 | null | https://arxiv.org/abs/2111.11886v1 | https://arxiv.org/pdf/2111.11886v1.pdf | Learning Dynamic Preference Structure Embedding From Temporal Networks | The dynamics of temporal networks lie in the continuous interactions between nodes, which exhibit the dynamic node preferences with time elapsing. The challenges of mining temporal networks are thus two-fold: the dynamic structure of networks and the dynamic node preferences. In this paper, we investigate the dynamic g... | ['Hao Xu', 'Chun Chen', 'Xinyu Wang', 'Xingen Wang', 'Mingli Song', 'Chengchao Shen', 'Yu Wang', 'Zunlei Feng', 'Tongya Zheng'] | 2021-11-23 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [-3.66853811e-02 3.11666071e-01 -4.09075946e-01 -2.66911149e-01
2.37082206e-02 -4.58620489e-01 7.08902299e-01 -9.25724953e-02
-1.26945019e-01 6.41576469e-01 2.84212261e-01 -3.04657280e-01
-7.54151344e-01 -7.90286005e-01 -3.47773761e-01 -9.72106040e-01
-5.61202645e-01 6.93485796e-01 4.11705494e-01 -1.49642065... | [7.286764144897461, 5.990217208862305] |
52ad832f-eb6b-4d62-aa13-a88438a8bed9 | vampnet-music-generation-via-masked-acoustic | 2307.04686 | null | https://arxiv.org/abs/2307.04686v1 | https://arxiv.org/pdf/2307.04686v1.pdf | VampNet: Music Generation via Masked Acoustic Token Modeling | We introduce VampNet, a masked acoustic token modeling approach to music synthesis, compression, inpainting, and variation. We use a variable masking schedule during training which allows us to sample coherent music from the model by applying a variety of masking approaches (called prompts) during inference. VampNet is... | ['Bryan Pardo', 'Rithesh Kumar', 'Prem Seetharaman', 'Hugo Flores Garcia'] | 2023-07-10 | null | null | null | null | ['music-compression', 'music-generation', 'music-generation'] | ['audio', 'audio', 'music'] | [ 2.15405107e-01 -1.79195121e-01 -3.44350599e-02 1.21637680e-01
-1.17945921e+00 -7.96639383e-01 4.72286135e-01 -4.12482798e-01
6.96833879e-02 3.90764862e-01 6.51835859e-01 -1.73256606e-01
-2.39113607e-02 -4.69549209e-01 -7.65547156e-01 -5.31489909e-01
-2.06062853e-01 2.41732731e-01 -2.10704476e-01 -2.04649493... | [15.626253128051758, 5.8331298828125] |
06c53016-2a45-404b-8a04-807be5a042c9 | machine-learning-for-large-scale-optimization | 2301.03377 | null | https://arxiv.org/abs/2301.03377v1 | https://arxiv.org/pdf/2301.03377v1.pdf | Machine Learning for Large-Scale Optimization in 6G Wireless Networks | The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements and machine learning capabilities, which leads to a growing need for highly ... | ['Wei zhang', 'Jun Zhang', 'Lin Bai', 'Liqun Fu', 'Yong Zhou', 'Zixin Wang', 'Yuanming Shi', 'Lixiang Lian', 'Yandong Shi'] | 2023-01-03 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-1.59352064e-01 4.92196754e-02 -7.27486968e-01 -2.62702644e-01
-3.70929122e-01 -3.67160648e-01 -1.69388726e-01 3.68354246e-02
1.45461783e-01 9.19793963e-01 -3.35159898e-02 -5.99238932e-01
-9.51233804e-01 -7.83896923e-01 -3.21833134e-01 -7.42251873e-01
-7.87845314e-01 8.18903565e-01 -5.22126555e-01 -2.68526584... | [5.974393844604492, 1.6959322690963745] |
89e8a48d-237d-4cf2-808c-99026b1c6e37 | precognition-in-task-oriented-dialogue | 2203.03244 | null | https://arxiv.org/abs/2203.03244v1 | https://arxiv.org/pdf/2203.03244v1.pdf | Precognition in Task-oriented Dialogue Understanding: Posterior Regularization by Future Context | Task-oriented dialogue systems have become overwhelmingly popular in recent researches. Dialogue understanding is widely used to comprehend users' intent, emotion and dialogue state in task-oriented dialogue systems. Most previous works on such discriminative tasks only models current query or historical conversations.... | ['Yongliang Wang', 'Bingzhu Du', 'Chao Liu', 'Yuchi Zhang', 'Nan Su'] | 2022-03-07 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [-1.02297321e-01 1.06884770e-01 -3.34614068e-01 -7.87955880e-01
-4.44725037e-01 -4.87040818e-01 9.76653218e-01 5.45337237e-02
-4.83739853e-01 9.86815155e-01 6.22877777e-01 -2.34897330e-01
4.68732089e-01 -4.37702537e-01 9.02778469e-03 -4.92721885e-01
1.27309814e-01 3.19146127e-01 1.04657836e-01 -5.10345519... | [12.695860862731934, 7.747383117675781] |
832ab623-616e-4712-bf80-93a82ebab24c | a-two-step-approach-to-sentence-compression | null | null | https://aclanthology.org/P12-2033 | https://aclanthology.org/P12-2033.pdf | A Two-step Approach to Sentence Compression of Spoken Utterances | null | ['Yang Liu', 'Xian Qian', 'Dong Wang'] | 2012-07-01 | null | null | null | acl-2012-7 | ['meeting-summarization'] | ['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... | [-7.34088134765625, 3.6750025749206543] |
67f606c4-652c-4887-91e8-666bbe2ccf50 | taen-temporal-aware-embedding-network-for-few | 2004.10141 | null | https://arxiv.org/abs/2004.10141v2 | https://arxiv.org/pdf/2004.10141v2.pdf | TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition | Classification of new class entities requires collecting and annotating hundreds or thousands of samples that is often prohibitively costly. Few-shot learning suggests learning to classify new classes using just a few examples. Only a small number of studies address the challenge of few-shot learning on spatio-temporal... | ['Udi Barzelay', 'Rami Ben-Ari', 'Mor Shpigel', 'Ophir Azulai', 'Daniel Rotman'] | 2020-04-21 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 1.70797259e-01 -8.79036412e-02 -6.42387509e-01 -4.11483198e-01
-5.76957643e-01 -4.13288653e-01 9.88989949e-01 1.91784933e-01
-6.27995372e-01 6.60762489e-01 5.90654731e-01 3.36609602e-01
-1.81793690e-01 -6.82172716e-01 -7.32128620e-01 -3.61267716e-01
-7.63846815e-01 1.82187468e-01 7.23939359e-01 1.05258584... | [8.646477699279785, 0.8722423911094666] |
f18ded8f-1460-439b-ab16-96a9a745062f | using-explainable-ai-to-cross-validate-socio | 2302.08605 | null | https://arxiv.org/abs/2302.08605v1 | https://arxiv.org/pdf/2302.08605v1.pdf | Using Explainable AI to Cross-Validate Socio-economic Disparities Among Covid-19 Patient Mortality | This paper applies eXplainable Artificial Intelligence (XAI) methods to investigate the socioeconomic disparities in COVID patient mortality. An Extreme Gradient Boosting (XGBoost) prediction model is built based on a de-identified Austin area hospital dataset to predict the mortality of COVID-19 patients. We apply two... | ['Ying Ding', 'Justin F. Rousseau', 'Jacek Gwizdka', 'Esther Melamed', 'Redoan Rahman', 'Li Shi'] | 2023-02-16 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [-3.03113639e-01 1.38194099e-01 -6.28657520e-01 -4.80654210e-01
-1.98656425e-01 6.41525015e-02 4.92864013e-01 6.79036021e-01
-1.97776467e-01 1.10013723e+00 8.88031960e-01 -1.00821221e+00
-1.01577079e+00 -6.52771294e-01 -4.35819119e-01 -3.69227320e-01
-2.69002408e-01 5.20241499e-01 -8.22953105e-01 -3.09357613... | [8.237771987915039, 5.801236629486084] |
69dab021-d675-4325-8d7c-1c946615abd7 | warped-linear-models-for-time-series | 1711.09156 | null | http://arxiv.org/abs/1711.09156v1 | http://arxiv.org/pdf/1711.09156v1.pdf | Warped-Linear Models for Time Series Classification | This article proposes and studies warped-linear models for time series
classification. The proposed models are time-warp invariant analogues of linear
models. Their construction is in line with time series averaging and extensions
of k-means and learning vector quantization to dynamic time warping (DTW)
spaces. The mai... | ['Brijnesh J. Jain'] | 2017-11-24 | null | null | null | null | ['time-series-averaging'] | ['time-series'] | [ 1.75444081e-01 -5.55685818e-01 -4.88697618e-01 -5.13513267e-01
-8.81745934e-01 -1.00455761e+00 8.48761678e-01 1.65226102e-01
-5.80914319e-01 5.84276140e-01 2.48883307e-01 -1.39595106e-01
-8.49148631e-01 -6.62205577e-01 -2.01701373e-01 -9.23458397e-01
-9.23715591e-01 3.92020643e-01 1.85720354e-01 -3.50377142... | [7.298186779022217, 3.308553457260132] |
61098973-c06f-4892-96ea-2209b151f397 | dialog-simulation-with-realistic-variations | 2011.08243 | null | https://arxiv.org/abs/2011.08243v1 | https://arxiv.org/pdf/2011.08243v1.pdf | Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems | Goal-oriented dialog systems enable users to complete specific goals like requesting information about a movie or booking a ticket. Typically the dialog system pipeline contains multiple ML models, including natural language understanding, state tracking and action prediction (policy learning). These models are trained... | ['Dilek Hakkani-Tur', 'Suranjit Adhikari', 'Charlie Shucheng Zhu', 'Tagyoung Chung', 'Angeliki Metallinou', 'Matt Zhao', 'Shubhra Chandra', 'Nehal Belgamwar', 'Maryam Fazel-Zarandi', 'Arijit Biswas', 'Daniel Elkind', 'Vincent Auvray', 'Chien-Wei Lin'] | 2020-11-16 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-4.30257954e-02 6.96593344e-01 -4.64518443e-02 -7.58053303e-01
-8.12181175e-01 -9.29973364e-01 1.04434264e+00 1.01476684e-01
-1.44227222e-01 1.17767537e+00 5.25819540e-01 -2.67963558e-01
2.96722233e-01 -6.24198735e-01 -7.31540099e-02 -1.31225258e-01
2.56753594e-01 1.29494870e+00 3.84236455e-01 -7.34320879... | [12.922344207763672, 7.994905948638916] |
d9f8cb9a-5984-4a5e-b941-6fae4a3ea227 | comparing-feature-based-classifiers-and | null | null | https://doi.org/10.22489/CinC.2017.360-239 | http://prucka.com/2017CinC/pdf/360-239.pdf | Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG | The diagnosis of cardiovascular diseases such as atrial fibrillation (AF) is a lengthy and expensive procedure that often requires visual inspection of ECG signals by experts. In order to improve patient management and reduce healthcare costs, automated detection of these pathologies is of utmost importance. In this st... | ['Fernando Andreotti', 'Marco A. F. Pimentel', 'Adam Mahdi', 'Oliver Carr', 'Maarten De Vos'] | 2017-09-24 | null | null | null | 2017-computing-in-cardiology-cinc-2017-9 | ['arrhythmia-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 2.42731586e-01 -2.47751586e-02 2.39398554e-01 -2.36282527e-01
-8.24783862e-01 -7.20449269e-01 -8.16123113e-02 4.36970264e-01
-4.41471756e-01 7.85329342e-01 -9.99814272e-02 -5.82719862e-01
-1.83781832e-01 -4.35953200e-01 -2.70069659e-01 -5.49938321e-01
-4.79340553e-01 5.01871586e-01 -2.09972724e-01 2.86501348... | [14.325573921203613, 3.298762083053589] |
e008e407-3a3a-46dd-b471-185aa0662ee1 | context-faithful-prompting-for-large-language | 2303.11315 | null | https://arxiv.org/abs/2303.11315v1 | https://arxiv.org/pdf/2303.11315v1.pdf | Context-faithful Prompting for Large Language Models | Large language models (LLMs) encode parametric knowledge about world facts and have shown remarkable performance in knowledge-driven NLP tasks. However, their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks (e.g., knowledge acq... | ['Muhao Chen', 'Hoifung Poon', 'Sheng Zhang', 'Wenxuan Zhou'] | 2023-03-20 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.39292759e-01 7.66784310e-01 -5.88704050e-01 -5.65518081e-01
-9.39149082e-01 -6.19188607e-01 8.52816761e-01 5.21385133e-01
-4.92154300e-01 1.24236262e+00 6.44179583e-01 -6.86814308e-01
-5.51936589e-03 -1.04604197e+00 -8.35293412e-01 -3.09773684e-01
2.52009988e-01 4.39893007e-01 2.36757129e-01 -3.62373143... | [10.176116943359375, 7.9428558349609375] |
84cbfc1e-22d0-4336-a2fc-a0b91a9f21e1 | deep-face-image-retrieval-a-comparative-study | 1812.05490 | null | http://arxiv.org/abs/1812.05490v1 | http://arxiv.org/pdf/1812.05490v1.pdf | Deep Face Image Retrieval: a Comparative Study with Dictionary Learning | Facial image retrieval is a challenging task since faces have many similar
features (areas), which makes it difficult for the retrieval systems to
distinguish faces of different people. With the advent of deep learning, deep
networks are often applied to extract powerful features that are used in many
areas of computer... | ['M. Sohel Rahman', 'Ahmad S. Tarawneh', 'Dmitry Chetverikov', 'Chaman Verma', 'Ahmad B. A. Hassanat', 'Ceyhun Celik'] | 2018-12-13 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [-5.38699448e-01 -5.00771999e-01 -2.82148421e-01 -4.92189348e-01
-2.06439674e-01 1.96880624e-02 6.11364961e-01 -4.89699692e-02
-3.75114530e-01 4.16861564e-01 3.73668107e-03 1.93648234e-01
-7.93053865e-01 -7.96862304e-01 -3.42355847e-01 -8.94911885e-01
-5.16369581e-01 4.59968656e-01 -5.73146045e-01 -2.46625066... | [12.940523147583008, 0.7503724098205566] |
0867497e-4603-4499-9782-e7a451235afa | down-the-rabbit-hole-detecting-online | 2301.11579 | null | https://arxiv.org/abs/2301.11579v1 | https://arxiv.org/pdf/2301.11579v1.pdf | Down the Rabbit Hole: Detecting Online Extremism, Radicalisation, and Politicised Hate Speech | Social media is a modern person's digital voice to project and engage with new ideas and mobilise communities $\unicode{x2013}$ a power shared with extremists. Given the societal risks of unvetted content-moderating algorithms for Extremism, Radicalisation, and Hate speech (ERH) detection, responsible software engineer... | ['Panos Patros', 'Aaron Dant', 'Philip Feldman', 'Jarod Govers'] | 2023-01-27 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 3.17930788e-01 2.32690737e-01 -3.20237637e-01 3.64116907e-01
-1.18324891e-01 -8.60013366e-01 9.13448334e-01 5.18802106e-01
-2.94031829e-01 5.80340810e-02 1.05744636e+00 -8.94688725e-01
-3.22307050e-01 -4.68885511e-01 -1.21530909e-02 -1.63763747e-01
1.93426788e-01 -1.97408184e-01 -2.15618312e-01 -4.49482709... | [8.729939460754395, 10.371533393859863] |
18135194-f161-4d18-a16d-d938797fb679 | deep-learning-for-medical-imaging-from | 2209.02929 | null | https://arxiv.org/abs/2209.02929v1 | https://arxiv.org/pdf/2209.02929v1.pdf | Deep Learning for Medical Imaging From Diagnosis Prediction to its Counterfactual Explanation | Deep neural networks (DNN) have achieved unprecedented performance in computer-vision tasks almost ubiquitously in business, technology, and science. While substantial efforts are made to engineer highly accurate architectures and provide usable model explanations, most state-of-the-art approaches are first designed fo... | ['Sumedha Singla'] | 2022-09-07 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 1.09870046e-01 6.36777937e-01 -4.45960402e-01 -7.36946583e-01
-6.74975738e-02 -7.03791156e-02 4.56878424e-01 -2.51254559e-01
-3.09235025e-02 4.57211435e-01 3.51545841e-01 -6.49303257e-01
-3.40070844e-01 -3.15527380e-01 -2.84409225e-01 -7.29943365e-02
3.48076165e-01 5.24487913e-01 -1.96772516e-01 -5.16738221... | [8.974401473999023, 5.571403980255127] |
2d33a40c-f94b-4c0d-8f86-a030051b40cc | chatgpt-beyond-english-towards-a | 2304.05613 | null | https://arxiv.org/abs/2304.05613v1 | https://arxiv.org/pdf/2304.05613v1.pdf | ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning | Over the last few years, large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP) that fundamentally transform research and developments in the field. ChatGPT represents one of the most exciting LLM systems developed recently to showcase impressive skills for la... | ['Thien Huu Nguyen', 'Trung Bui', 'Franck Dernoncourt', 'Hieu Man', 'Amir Pouran Ben Veyseh', 'Nghia Trung Ngo', 'Viet Dac Lai'] | 2023-04-12 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [-9.41924974e-02 5.49187921e-02 -2.47042656e-01 -7.10568130e-02
-1.09066451e+00 -6.13494217e-01 9.46959794e-01 -1.60535853e-02
-3.69047344e-01 1.05628157e+00 4.46030736e-01 -6.35597050e-01
3.28467309e-01 -7.50720799e-01 -4.61017072e-01 -3.24079692e-01
6.67004213e-02 8.21423769e-01 2.24580050e-01 -5.69890380... | [11.42872142791748, 8.988014221191406] |
e5efbcdf-554f-40b0-a973-b89238a77520 | haloc-hardware-aware-automatic-low-rank | 2301.09422 | null | https://arxiv.org/abs/2301.09422v2 | https://arxiv.org/pdf/2301.09422v2.pdf | HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks | Low-rank compression is an important model compression strategy for obtaining compact neural network models. In general, because the rank values directly determine the model complexity and model accuracy, proper selection of layer-wise rank is very critical and desired. To date, though many low-rank compression approac... | ['Bo Yuan', 'Dingwen Tao', 'Lizhi Xiang', 'Yang Sui', 'Miao Yin', 'Yu Gong', 'Chengming Zhang', 'Jinqi Xiao'] | 2023-01-20 | null | null | null | null | ['low-rank-compression'] | ['computer-code'] | [ 2.19227523e-01 -4.80670989e-01 -1.97476685e-01 -4.91346240e-01
-6.26031220e-01 -9.55774188e-02 1.65791765e-01 4.62233275e-02
-7.07993448e-01 5.31979620e-01 5.72007410e-02 -4.54896063e-01
-4.64213639e-01 -7.27522790e-01 -6.96934640e-01 -6.69551611e-01
-2.41264198e-02 3.99595588e-01 3.58760148e-01 5.03410809... | [8.575440406799316, 3.0162808895111084] |
f4997b8c-068a-4787-83c2-f7d05456efa8 | learning-to-segment-with-limited-annotations | 2205.13109 | null | https://arxiv.org/abs/2205.13109v1 | https://arxiv.org/pdf/2205.13109v1.pdf | Learning to segment with limited annotations: Self-supervised pretraining with regression and contrastive loss in MRI | Obtaining manual annotations for large datasets for supervised training of deep learning (DL) models is challenging. The availability of large unlabeled datasets compared to labeled ones motivate the use of self-supervised pretraining to initialize DL models for subsequent segmentation tasks. In this work, we consider ... | ['Ali Bilgin', 'Maria Altbach', 'Diego Martin', 'Rohit Philip', 'Zhiyang Fu', 'Lavanya Umapathy'] | 2022-05-26 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 5.67091227e-01 7.43973017e-01 -3.72131675e-01 -1.01706970e+00
-9.04577196e-01 -5.03467381e-01 5.25391340e-01 5.26412010e-01
-9.18056548e-01 7.05913186e-01 5.12741096e-02 -2.64637649e-01
4.65449840e-02 -5.06233513e-01 -8.74212384e-01 -4.93313909e-01
-3.23921561e-01 7.76596844e-01 2.68497676e-01 1.31312147... | [14.739346504211426, -2.226040840148926] |
d4857e67-53dd-476c-a042-ef04a144c85d | segsalsa-str-a-convex-formulation-to | 1504.07028 | null | http://arxiv.org/abs/1504.07028v1 | http://arxiv.org/pdf/1504.07028v1.pdf | SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization | We present a supervised hyperspectral image segmentation algorithm based on a
convex formulation of a marginal maximum a posteriori segmentation with hidden
fields and structure tensor regularization: Segmentation via the Constraint
Split Augmented Lagrangian Shrinkage by Structure Tensor Regularization
(SegSALSA-STR).... | ['Jelena Kovacevic', 'Jose Bioucas-Dias', 'Filipe Condessa'] | 2015-04-27 | null | null | null | null | ['hyperspectral-image-segmentation'] | ['computer-vision'] | [ 8.59081328e-01 2.26208881e-01 2.23308265e-01 -2.14513063e-01
-6.77421570e-01 -5.46046436e-01 2.96480477e-01 -2.24260874e-02
-3.79258692e-01 5.86560011e-01 -1.62818506e-01 -2.21272379e-01
-6.67509794e-01 -6.17192209e-01 -5.21776140e-01 -1.21140957e+00
4.15665023e-02 5.03384054e-01 -1.43228188e-01 -1.55508533... | [10.067710876464844, -2.0222268104553223] |
374ccc26-020a-4321-9889-e489fb706c8e | memobert-pre-training-model-with-prompt-based | 2111.00865 | null | https://arxiv.org/abs/2111.00865v1 | https://arxiv.org/pdf/2111.00865v1.pdf | MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition | Multimodal emotion recognition study is hindered by the lack of labelled corpora in terms of scale and diversity, due to the high annotation cost and label ambiguity. In this paper, we propose a pre-training model \textbf{MEmoBERT} for multimodal emotion recognition, which learns multimodal joint representations throug... | ['Haizhou Li', 'Xinchao Wang', 'Qin Jin', 'Ruichen Li', 'Jinming Zhao'] | 2021-10-27 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 3.28818232e-01 -2.13781908e-01 -7.23273605e-02 -7.75443375e-01
-9.23268199e-01 -6.84782088e-01 3.90717000e-01 -1.23801529e-01
-6.74375057e-01 6.19611621e-01 3.15453887e-01 4.05688188e-04
1.99903652e-01 -7.55432770e-02 -5.24895668e-01 -7.00152636e-01
1.79592356e-01 1.50017247e-01 -3.90625656e-01 -7.75896013... | [13.22551441192627, 5.182877063751221] |
d3a544a2-a050-4e5b-9116-833dc464899f | embarrassingly-simple-mixup-for-time-series | 2304.04271 | null | https://arxiv.org/abs/2304.04271v1 | https://arxiv.org/pdf/2304.04271v1.pdf | Embarrassingly Simple MixUp for Time-series | Labeling time series data is an expensive task because of domain expertise and dynamic nature of the data. Hence, we often have to deal with limited labeled data settings. Data augmentation techniques have been successfully deployed in domains like computer vision to exploit the use of existing labeled data. We adapt o... | ['Jaideep Srivastava', 'Karan Aggarwal'] | 2023-04-09 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 1.99190512e-01 -1.66785419e-01 -5.92985034e-01 -3.67782176e-01
-6.54514432e-01 -7.74622858e-01 7.62488484e-01 2.51826108e-01
-4.19704705e-01 6.13876998e-01 1.69983685e-01 -3.94387960e-01
1.52262568e-01 -5.65242112e-01 -2.81385899e-01 -6.51063800e-01
-2.45861188e-01 3.44265640e-01 -2.48902157e-01 -8.99141952... | [7.281513214111328, 2.9338676929473877] |
208e0314-e648-4aac-ac19-dcf4d1f3af40 | promptfusion-decoupling-stability-and | 2303.07223 | null | https://arxiv.org/abs/2303.07223v1 | https://arxiv.org/pdf/2303.07223v1.pdf | PromptFusion: Decoupling Stability and Plasticity for Continual Learning | Continual learning refers to the capability of continuously learning from a stream of data. Current research mainly focuses on relieving catastrophic forgetting, and most of their success is at the cost of limiting the performance of newly incoming tasks. Such a trade-off is referred to as the stabilityplasticity dilem... | ['Yu-Gang Jiang', 'Menglin Jia', 'Xintong Han', 'Zuxuan Wu', 'Haoran Chen'] | 2023-03-13 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 5.15587665e-02 -1.81720689e-01 -2.15530708e-01 -2.35670313e-01
-6.08694613e-01 -3.30186367e-01 4.55272287e-01 2.03859597e-01
-7.40232646e-01 9.10142243e-01 -2.45868087e-01 -1.11653000e-01
-2.36590028e-01 -5.65434396e-01 -8.42876792e-01 -8.94658029e-01
7.69834071e-02 3.90218079e-01 6.19232059e-01 -1.92943543... | [9.845757484436035, 3.4486210346221924] |
fd013cb7-36f0-4dd8-82c3-0144810649f5 | segmentation-of-argumentative-texts-with | null | null | https://aclanthology.org/W19-4501 | https://aclanthology.org/W19-4501.pdf | Segmentation of Argumentative Texts with Contextualised Word Representations | The segmentation of argumentative units is an important subtask of argument mining, which is frequently addressed at a coarse granularity, usually assuming argumentative units to be no smaller than sentences. Approaches focusing at the clause-level granularity, typically address the task as sequence labeling at the tok... | ['Georgios Petasis'] | 2019-08-01 | null | null | null | ws-2019-8 | ['contextualised-word-representations'] | ['natural-language-processing'] | [ 4.14071083e-01 6.73917890e-01 -5.04484594e-01 -4.26025480e-01
-7.65878141e-01 -8.88459682e-01 8.80153239e-01 1.00232208e+00
-7.67667294e-01 8.84528697e-01 4.03347343e-01 -7.45902121e-01
-4.92613800e-02 -7.57916987e-01 -5.18755257e-01 -4.14727956e-01
-1.09544548e-03 6.07328653e-01 2.66107023e-01 1.43686101... | [10.186649322509766, 9.563204765319824] |
bd3745f5-151c-4d17-81b9-2f7d7206bbd0 | prevention-of-cyberattacks-in-wsn-and-packet | 2306.09448 | null | https://arxiv.org/abs/2306.09448v1 | https://arxiv.org/pdf/2306.09448v1.pdf | Prevention of cyberattacks in WSN and packet drop by CI framework and information processing protocol using AI and Big Data | As the reliance on wireless sensor networks (WSNs) rises in numerous sectors, cyberattack prevention and data transmission integrity become essential problems. This study provides a complete framework to handle these difficulties by integrating a cognitive intelligence (CI) framework, an information processing protocol... | ['Shreyanth S'] | 2023-06-15 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 2.68879116e-01 1.18735790e-01 -5.23952860e-03 -1.15914047e-01
2.89359629e-01 -5.08472979e-01 6.04396880e-01 7.64326394e-01
-5.31193197e-01 6.14709079e-01 -3.17625940e-01 -4.15786028e-01
-4.73644555e-01 -1.52881646e+00 -5.48652187e-02 -9.57524538e-01
-4.90876257e-01 -1.37168150e-02 4.31376666e-01 -2.60777056... | [5.210951805114746, 7.12512731552124] |
e04d4631-6d93-4e38-84df-548a54ef0416 | synthetic-alone-exploring-the-dark-side-of | 2306.14377 | null | https://arxiv.org/abs/2306.14377v1 | https://arxiv.org/pdf/2306.14377v1.pdf | Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction | Data-centric AI approach aims to enhance the model performance without modifying the model and has been shown to impact model performance positively. While recent attention has been given to data-centric AI based on synthetic data, due to its potential for performance improvement, data-centric AI has long been exclusiv... | ['Heuiseok Lim', 'Hyeonseok Moon', 'Sugyeong Eo', 'Jaehyung Seo', 'Seolhwa Lee', 'Seonmin Koo', 'Chanjun Park'] | 2023-06-26 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 4.35198575e-01 2.49994203e-01 1.40346631e-01 -3.64054054e-01
-5.93235493e-01 -3.09972137e-01 8.18280995e-01 4.95627671e-01
-8.37882638e-01 6.85932934e-01 -1.66168734e-02 -1.63223878e-01
-9.23828036e-02 -8.44898999e-01 -1.14439857e+00 -3.32937062e-01
1.31280273e-01 5.53348958e-01 -4.08094227e-02 -3.78184408... | [11.23824691772461, 9.085269927978516] |
4aab54d5-4a52-47b0-b65a-4bdfafee247f | utility-assessment-of-synthetic-data | 2211.14428 | null | https://arxiv.org/abs/2211.14428v1 | https://arxiv.org/pdf/2211.14428v1.pdf | Utility Assessment of Synthetic Data Generation Methods | Big data analysis poses the dual problem of privacy preservation and utility, i.e., how accurate data analyses remain after transforming original data in order to protect the privacy of the individuals that the data is about - and whether they are accurate enough to be meaningful. In this paper, we thus investigate acr... | ['Sonja Buchegger', 'Niklas Reje', 'Md Sakib Nizam Khan'] | 2022-11-23 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 3.20385128e-01 3.75117689e-01 -2.48502456e-02 -5.70881605e-01
-8.53721201e-01 -9.78469491e-01 6.47269070e-01 5.56112409e-01
-6.28294170e-01 1.06548524e+00 2.70176858e-01 -3.27298790e-01
-4.46765661e-01 -9.24628437e-01 -9.90747452e-01 -6.36324108e-01
-4.88762259e-02 4.44547653e-01 -2.42783561e-01 1.45342469... | [6.177578449249268, 6.893073558807373] |
0aab189c-eabe-4754-a5e4-ed1006e63eb0 | automated-identication-of-atrial-fibrillation | 2306.15096 | null | https://arxiv.org/abs/2306.15096v1 | https://arxiv.org/pdf/2306.15096v1.pdf | Automated Identication of Atrial Fibrillation from Single-lead ECGs Using Multi-branching ResNet | Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. It is of critical importance to develop an advanced analyt... | ['Bing Yao', 'Stavros Stavrakis', 'Jianxin Xie'] | 2023-06-26 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 2.60652810e-01 -5.12599349e-01 2.59578049e-01 -2.62413830e-01
-4.34743583e-01 -4.38981086e-01 -1.98366940e-01 2.32940599e-01
-1.25827223e-01 7.65878737e-01 8.24200958e-02 -6.67012870e-01
-2.59108216e-01 -6.89195931e-01 -4.53051776e-02 -6.16483569e-01
-6.26699626e-01 3.15125972e-01 -4.96685296e-01 1.47997350... | [14.281596183776855, 3.264986038208008] |
6d13eaf4-0da2-43a3-8010-da21182d8640 | graph-convolutional-transformer-learning-the | 1906.04716 | null | https://arxiv.org/abs/1906.04716v3 | https://arxiv.org/pdf/1906.04716v3.pdf | Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer | Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure underlying EHR data (e.g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure... | ['Zhen Xu', 'Yujia Li', 'Yuan Xue', 'Gerardo Flores', 'Michael W. Dusenberry', 'Edward Choi', 'Andrew M. Dai'] | 2019-06-11 | null | null | null | null | ['graph-reconstruction', 'readmission-prediction'] | ['graphs', 'medical'] | [ 2.06991509e-01 6.30745351e-01 -1.89290136e-01 -5.16777694e-01
-6.07937813e-01 -1.62506312e-01 9.22640637e-02 7.82816410e-01
2.02902868e-01 6.56506240e-01 8.33219945e-01 -6.41732574e-01
-2.08814040e-01 -1.03492343e+00 -8.35178494e-01 -3.60004187e-01
-2.42648855e-01 6.64615273e-01 -4.12887901e-01 -1.63665693... | [7.758249282836914, 6.451534271240234] |
bb96c04f-2f03-4f82-b55c-bf3ea857c05f | denseran-for-offline-handwritten-chinese | 1808.04134 | null | http://arxiv.org/abs/1808.04134v1 | http://arxiv.org/pdf/1808.04134v1.pdf | DenseRAN for Offline Handwritten Chinese Character Recognition | Recently, great success has been achieved in offline handwritten Chinese
character recognition by using deep learning methods. Chinese characters are
mainly logographic and consist of basic radicals, however, previous research
mostly treated each Chinese character as a whole without explicitly considering
its internal ... | ['Zi-Rui Wang', 'Yixing Zhu', 'Wenchao Wang', 'Jun Du', 'Jianshu Zhang'] | 2018-08-13 | null | null | null | null | ['offline-handwritten-chinese-character', 'offline-handwritten-chinese-character'] | ['computer-vision', 'natural-language-processing'] | [ 2.26845562e-01 -3.22521806e-01 2.20351554e-02 2.01252148e-01
-3.17596316e-01 -4.27699715e-01 4.99375731e-01 -9.72212628e-02
-3.80787373e-01 4.28010076e-01 1.68898031e-01 -2.35238060e-01
6.92889750e-01 -1.05540776e+00 -6.53220952e-01 -8.98496091e-01
2.34196097e-01 2.03094363e-01 1.18460674e-02 -9.78260040... | [11.913246154785156, 2.191969394683838] |
f153e4b5-6f83-4c21-990c-23eb769fd0d1 | palette-image-to-image-diffusion-models-1 | 2111.05826 | null | https://arxiv.org/abs/2111.05826v2 | https://arxiv.org/pdf/2111.05826v2.pdf | Palette: Image-to-Image Diffusion Models | This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely colorization, inpainting, uncropping, and JPEG restoration. Our simple implementation of image-to-image diffusion models ... | ['Mohammad Norouzi', 'David J. Fleet', 'Tim Salimans', 'Jonathan Ho', 'Chris A. Lee', 'Huiwen Chang', 'William Chan', 'Chitwan Saharia'] | 2021-11-10 | palette-image-to-image-diffusion-models | https://openreview.net/forum?id=FPGs276lUeq | https://openreview.net/pdf?id=FPGs276lUeq | null | ['jpeg-decompression', 'uncropping'] | ['computer-vision', 'computer-vision'] | [ 4.97122526e-01 -6.57417178e-02 -2.18004078e-01 -2.21362844e-01
-1.27076197e+00 -5.66585720e-01 8.12169969e-01 -3.82634103e-01
-5.64665318e-01 6.08096898e-01 4.14831847e-01 -2.57900447e-01
3.11680615e-01 -3.27051520e-01 -8.93097878e-01 -7.35757768e-01
2.66316265e-01 2.82236010e-01 -5.22835255e-02 -1.20595090... | [11.466636657714844, -0.26934704184532166] |
4b85e02f-df5e-407a-ba64-f36868b8c08c | accurate-airway-tree-segmentation-in-ct-scans | 2306.09116 | null | https://arxiv.org/abs/2306.09116v1 | https://arxiv.org/pdf/2306.09116v1.pdf | Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning | Intrathoracic airway segmentation in computed tomography (CT) is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease (COPD), asthma and lung cancer. Unlike other organs with simpler shapes or topology, the airway's complex tree structure imposes an unbearable burden to ... | ['Dakai Jin', 'Xianghua Ye', 'Le Lu', 'Yun Gu', 'Jia Ge', 'Xin Sun', 'Haogang Yu', 'Minghui Zhang', 'Dandan Zheng', 'Dazhou Guo', 'Puyang Wang'] | 2023-06-15 | null | null | null | null | ['computed-tomography-ct', 'anatomy', 'pseudo-label', 'self-learning'] | ['methodology', 'miscellaneous', 'miscellaneous', 'natural-language-processing'] | [ 5.14485061e-01 4.09875512e-01 -2.83844024e-01 -3.14444929e-01
-1.24514699e+00 -7.55614638e-01 -8.06108303e-03 1.53687567e-01
-1.83724746e-01 6.44108891e-01 9.18329805e-02 -6.39611781e-01
-2.08449185e-01 -6.33079171e-01 -4.92486238e-01 -6.55656397e-01
1.74344227e-01 1.02629995e+00 5.55984616e-01 2.23498523... | [14.975198745727539, -2.184054374694824] |
ddfa30c7-af9e-44aa-9176-4a88785f3e46 | u-net-fixed-point-quantization-for-medical | 1908.01073 | null | https://arxiv.org/abs/1908.01073v2 | https://arxiv.org/pdf/1908.01073v2.pdf | U-Net Fixed-Point Quantization for Medical Image Segmentation | Model quantization is leveraged to reduce the memory consumption and the computation time of deep neural networks. This is achieved by representing weights and activations with a lower bit resolution when compared to their high precision floating point counterparts. The suitable level of quantization is directly relate... | ['Jean-Pierre David', 'Julien Cohen-Adad', 'Yvon Savaria', 'Lucas Rouhier', 'Sina Honari', 'MohammadHossein AskariHemmat', 'Christian S. Perone'] | 2019-08-02 | null | null | null | null | ['unet-quantization', 'pancreas-segmentation'] | ['computer-vision', 'medical'] | [ 4.83575821e-01 9.70810354e-02 -1.48137659e-01 -2.89568275e-01
-6.70868993e-01 -4.15940374e-01 1.01818211e-01 6.32771850e-01
-9.71091926e-01 7.08936751e-01 -3.57029349e-01 -4.15387630e-01
1.80016682e-02 -9.08182800e-01 -6.97770655e-01 -7.56734848e-01
-1.54235493e-02 2.48210207e-01 4.65604126e-01 6.87951893... | [8.595264434814453, 3.013394594192505] |
2b24f98d-0240-4825-89cc-3567d0b148c7 | attention-based-convolutional-neural-network-3 | 2303.02518 | null | https://arxiv.org/abs/2303.02518v1 | https://arxiv.org/pdf/2303.02518v1.pdf | Attention-based convolutional neural network for perfusion T2-weighted MR images preprocessing | Accurate skull-stripping is crucial preprocessing in dynamic susceptibility contrast-enhanced perfusion magnetic resonance data analysis. The presence of non-brain tissues impacts the perfusion parameters assessment. In this study, we propose different integration strategies for the spatial and channel squeeze and exci... | ['Oleksii Diumin', 'Svitlana Alkhimova'] | 2023-03-04 | null | null | null | null | ['skull-stripping', 'anatomy'] | ['medical', 'miscellaneous'] | [ 2.27088526e-01 -1.23170100e-01 3.11813146e-01 -3.38015586e-01
-2.61890382e-01 -1.68963715e-01 3.35739106e-01 -2.42602751e-02
-7.72510469e-01 8.93176794e-01 4.36837047e-01 -3.71075183e-01
-4.56611127e-01 -3.71599764e-01 -4.23247576e-01 -9.91902709e-01
-2.76228815e-01 1.71149522e-01 4.88054693e-01 -2.13013321... | [14.137109756469727, -2.3563241958618164] |
4499eb7a-37a7-4be9-be27-22d18eae21fe | arnet-automatic-refinement-network-for-noisy | 2211.04774 | null | https://arxiv.org/abs/2211.04774v5 | https://arxiv.org/pdf/2211.04774v5.pdf | IRNet: Iterative Refinement Network for Noisy Partial Label Learning | Partial label learning (PLL) is a typical weakly supervised learning, where each sample is associated with a set of candidate labels. The basic assumption of PLL is that the ground-truth label must reside in the candidate set. However, this assumption may not be satisfied due to the unprofessional judgment of the annot... | ['JianHua Tao', 'Bin Liu', 'Licai Sun', 'Lan Chen', 'Mingyu Xu', 'Zheng Lian'] | 2022-11-09 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 3.52075368e-01 2.43141696e-01 -3.63992006e-01 -3.61449122e-01
-9.66133952e-01 -3.32542628e-01 4.25753668e-02 2.22105950e-01
-4.69761252e-01 9.45737958e-01 -1.12669110e-01 -2.58768424e-02
-1.33982822e-01 -7.16369212e-01 -6.40884161e-01 -9.09852684e-01
4.85403776e-01 3.76151145e-01 1.72030881e-01 2.48428226... | [9.442492485046387, 3.9844112396240234] |
24bb9d5b-0ee2-4a80-81f2-4edb71a884f3 | inferring-implicit-relations-with-language | 2204.13778 | null | https://arxiv.org/abs/2204.13778v2 | https://arxiv.org/pdf/2204.13778v2.pdf | Inferring Implicit Relations in Complex Questions with Language Models | A prominent challenge for modern language understanding systems is the ability to answer implicit reasoning questions, where the required reasoning steps for answering the question are not mentioned in the text explicitly. In this work, we investigate why current models struggle with implicit reasoning question answeri... | ['Jonathan Berant', 'Mor Geva', 'Uri Katz'] | 2022-04-28 | null | null | null | null | ['implicit-relations'] | ['natural-language-processing'] | [ 4.41424578e-01 1.05871654e+00 1.78374872e-02 -3.86740983e-01
-7.53333628e-01 -7.89372504e-01 9.73710120e-01 5.96336424e-01
-6.95653036e-02 6.96947336e-01 3.89210075e-01 -9.79078472e-01
-5.39930284e-01 -1.31561053e+00 -4.24757272e-01 -2.55441461e-02
3.00575554e-01 9.89437461e-01 5.38663805e-01 -5.90698957... | [9.9564790725708, 7.592714786529541] |
4c3b7442-4079-4088-b3e3-9c80a7f416c6 | tensor-networks-meet-neural-networks-a-survey | 2302.09019 | null | https://arxiv.org/abs/2302.09019v2 | https://arxiv.org/pdf/2302.09019v2.pdf | Tensor Networks Meet Neural Networks: A Survey and Future Perspectives | Tensor networks (TNs) and neural networks (NNs) are two fundamental data modeling approaches. TNs were introduced to solve the curse of dimensionality in large-scale tensors by converting an exponential number of dimensions to polynomial complexity. As a result, they have attracted significant attention in the fields o... | ['Andrzej Cichocki', 'Zenglin Xu', 'Guangxi Li', 'Xiangli Yang', 'Yu Pan', 'Maolin Wang'] | 2023-01-22 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 3.24096292e-01 -6.89064711e-02 -3.90889376e-01 -1.27584621e-01
-3.04364026e-01 -5.71418762e-01 4.04577464e-01 -2.44871713e-02
-1.83295503e-01 5.28732479e-01 -1.51009917e-01 -5.70379436e-01
-6.78430080e-01 -8.07674348e-01 -5.95815003e-01 -8.01734328e-01
-2.14000642e-01 9.50219110e-02 -2.66763151e-01 -5.58905602... | [5.872995853424072, 5.042004585266113] |
252faeb3-a9d3-4848-af32-cf0cb36989c5 | incorporating-joint-embeddings-into-goal | 2001.10468 | null | https://arxiv.org/abs/2001.10468v1 | https://arxiv.org/pdf/2001.10468v1.pdf | Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning | Attention-based encoder-decoder neural network models have recently shown promising results in goal-oriented dialogue systems. However, these models struggle to reason over and incorporate state-full knowledge while preserving their end-to-end text generation functionality. Since such models can greatly benefit from us... | ['Firas Kassawat', 'Debanjan Chaudhuri', 'Jens Lehmann'] | 2020-01-28 | null | null | null | null | ['goal-oriented-dialog', 'goal-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.61893055e-01 1.19265473e+00 -3.09305917e-02 -4.57763791e-01
-8.81890059e-01 -4.27245170e-01 1.00829399e+00 3.81160006e-02
-3.71338725e-01 9.41537559e-01 8.23819578e-01 -2.85653621e-01
2.07038641e-01 -8.54495585e-01 -6.73548520e-01 -1.87230468e-01
2.67059624e-01 7.53418267e-01 -1.01677753e-01 -6.58172131... | [12.336528778076172, 8.533047676086426] |
ac07df4f-6e0c-49b2-b810-ec709d386ac4 | assessing-bias-in-face-image-quality | 2211.15265 | null | https://arxiv.org/abs/2211.15265v1 | https://arxiv.org/pdf/2211.15265v1.pdf | Assessing Bias in Face Image Quality Assessment | Face image quality assessment (FIQA) attempts to improve face recognition (FR) performance by providing additional information about sample quality. Because FIQA methods attempt to estimate the utility of a sample for face recognition, it is reasonable to assume that these methods are heavily influenced by the underlyi... | ['Vitomir Štruc', 'Žiga Babnik'] | 2022-11-28 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 3.18919793e-02 -1.30566105e-01 1.32373618e-02 -8.96451116e-01
-7.19520271e-01 -4.12844419e-01 7.14996457e-01 -3.99134248e-01
-4.24749434e-01 5.89160800e-01 3.52628738e-01 -1.11029018e-02
-1.16821162e-01 -9.01136160e-01 -3.54230672e-01 -7.60474980e-01
1.25582546e-01 5.16012728e-01 -6.24391079e-01 -1.73447937... | [13.053277969360352, 1.1629164218902588] |
fcbbed36-7d90-4b2f-b881-208d5863a25e | face-shape-guided-deep-feature-alignment-for | 2209.07220 | null | https://arxiv.org/abs/2209.07220v1 | https://arxiv.org/pdf/2209.07220v1.pdf | Face Shape-Guided Deep Feature Alignment for Face Recognition Robust to Face Misalignment | For the past decades, face recognition (FR) has been actively studied in computer vision and pattern recognition society. Recently, due to the advances in deep learning, the FR technology shows high performance for most of the benchmark datasets. However, when the FR algorithm is applied to a real-world scenario, the p... | ['Yong Man Ro', 'Kimin Yun', 'Hyung-Il Kim'] | 2022-09-15 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 3.49044025e-01 -1.29345939e-01 3.28970760e-01 -6.63175821e-01
-4.50093180e-01 -3.51186305e-01 4.42726284e-01 -6.06228948e-01
-1.11965612e-02 3.88771594e-01 -1.97913170e-01 1.50940254e-01
-4.25252728e-02 -6.51789486e-01 -8.67349923e-01 -8.53851140e-01
4.04879749e-01 2.23224252e-01 -2.44622752e-01 1.05914529... | [13.227621078491211, 0.43013837933540344] |
b2e32d41-79cb-4c66-a7b4-f3d88bbe5567 | data-driven-prediction-of-battery-cycle-life | 2110.09687 | null | https://arxiv.org/abs/2110.09687v1 | https://arxiv.org/pdf/2110.09687v1.pdf | Data Driven Prediction of Battery Cycle Life Before Capacity Degradation | Ubiquitous use of lithium-ion batteries across multiple industries presents an opportunity to explore cost saving initiatives as the price to performance ratio continually decreases in a competitive environment. Manufacturers using lithium-ion batteries ranging in applications from mobile phones to electric vehicles ne... | ['Kurt I. Kuhn', 'Jamie Peck', 'Caitlin Feltner', 'Anmol Singh'] | 2021-10-19 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-3.69004793e-02 -4.78896946e-01 -4.15840536e-01 -4.21971947e-01
-4.64787155e-01 -3.45802039e-01 3.77506137e-01 5.26552380e-04
-2.92532057e-01 1.01486003e+00 -1.61754951e-01 -9.97625589e-01
-4.12810177e-01 -7.50603497e-01 -4.66018915e-01 -6.45843327e-01
1.72664523e-01 9.52639639e-01 5.50908335e-05 5.30984513... | [6.354377269744873, 2.740863561630249] |
bc9a05cb-2f80-43e3-9040-9d672be4d589 | cop-factual-inconsistency-detection-by | 2212.01611 | null | https://arxiv.org/abs/2212.01611v2 | https://arxiv.org/pdf/2212.01611v2.pdf | CoP: Factual Inconsistency Detection by Controlling the Preference | Abstractive summarization is the process of generating a summary given a document as input. Although significant progress has been made, the factual inconsistency between the document and the generated summary still limits its practical applications. Previous work found that the probabilities assigned by the generation... | ['Jiajun Chen', 'ShuJian Huang', 'Xiang Geng', 'Shuaijie She'] | 2022-12-03 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 6.50651082e-02 6.29405379e-01 -4.84210521e-01 -5.95577180e-01
-9.28285420e-01 -7.19742715e-01 9.66599047e-01 6.39277816e-01
-2.56258190e-01 8.25363576e-01 5.78420341e-01 -1.91635620e-02
-1.03901483e-01 -7.08917975e-01 -5.88552058e-01 -5.53728521e-01
5.54639101e-01 6.45013809e-01 2.93179631e-01 -1.91925354... | [12.146148681640625, 9.292502403259277] |
af88d7f1-e37d-4b7b-af1a-bbe8cd943640 | the-limitations-of-limited-context-for | 2106.01580 | null | https://arxiv.org/abs/2106.01580v1 | https://arxiv.org/pdf/2106.01580v1.pdf | The Limitations of Limited Context for Constituency Parsing | Incorporating syntax into neural approaches in NLP has a multitude of practical and scientific benefits. For instance, a language model that is syntax-aware is likely to be able to produce better samples; even a discriminative model like BERT with a syntax module could be used for core NLP tasks like unsupervised synta... | ['Andrej Risteski', 'Yuchen Li'] | 2021-06-03 | null | https://aclanthology.org/2021.acl-long.208 | https://aclanthology.org/2021.acl-long.208.pdf | acl-2021-5 | ['constituency-parsing'] | ['natural-language-processing'] | [ 4.10729975e-01 5.82057476e-01 -1.43538073e-01 -5.54933429e-01
-7.82847285e-01 -8.79898846e-01 5.50931811e-01 2.65280932e-01
-4.39383984e-01 5.61352670e-01 5.91443181e-01 -1.00490046e+00
-1.69714212e-01 -9.42277133e-01 -7.57027984e-01 -5.79168200e-01
-7.28416368e-02 3.63659114e-01 -3.15619558e-02 -1.27001807... | [10.435135841369629, 9.510151863098145] |
627b51ab-8110-43c4-8d53-1ebaf5ff75bc | ds4dh-at-semeval-2022-task-11-multilingual | null | null | https://aclanthology.org/2022.semeval-1.212 | https://aclanthology.org/2022.semeval-1.212.pdf | DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models | In this paper, we describe our proposed method for the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER). The goal of this task is to locate and classify named entities in unstructured short complex texts in 11 different languages.After training a variety of contextual language models on ... | ['Douglas Teodoro', 'Hossein Rouhizadeh'] | null | null | null | null | semeval-naacl-2022-7 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-4.73223239e-01 5.28321788e-02 4.15565558e-02 -3.21601629e-01
-1.45289958e+00 -8.69744837e-01 9.52883303e-01 3.30964059e-01
-1.18848670e+00 1.10144258e+00 3.68197888e-01 -3.58779639e-01
3.20161998e-01 -3.01386267e-01 -5.21114945e-01 -1.03498027e-01
8.71824473e-03 7.09869146e-01 2.22781926e-01 -3.67549747... | [9.829479217529297, 9.750079154968262] |
f27b8599-664e-4204-ae06-f66a28a4a881 | a-generalised-deep-meta-learning-model-for | 2303.13324 | null | https://arxiv.org/abs/2303.13324v1 | https://arxiv.org/pdf/2303.13324v1.pdf | A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance Images | Background and Objectives: Cardiovascular magnetic resonance (CMR) imaging is a powerful modality in functional and anatomical assessment for various cardiovascular diseases. Sufficient image quality is essential to achieve proper diagnosis and treatment. A large number of medical images, the variety of imaging artefac... | ['Alejandro F. Frangi', 'Ahmad Ali Abin', 'Mohsen Ebrahimi Moghaddam', 'Hossein Simchi', 'Shahabedin Nabavi'] | 2023-03-23 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 3.25328410e-01 -4.98393215e-02 5.48401177e-02 -3.42847168e-01
-1.12296295e+00 -3.72462600e-01 3.57641906e-01 1.13708571e-01
-6.36497021e-01 8.46445978e-01 1.58550054e-01 -1.87809870e-01
-3.88815701e-01 -2.91365266e-01 -2.58999556e-01 -7.90392339e-01
-2.90557027e-01 6.50735915e-01 2.69080192e-01 3.18017960... | [14.15661334991455, -2.4412853717803955] |
a2d653ac-3032-430c-b8ac-d6b11350333e | unsupervised-high-impedance-fault-detection | 2301.01867 | null | https://arxiv.org/abs/2301.01867v1 | https://arxiv.org/pdf/2301.01867v1.pdf | Unsupervised High Impedance Fault Detection Using Autoencoder and Principal Component Analysis | Detection of high impedance faults (HIF) has been one of the biggest challenges in the power distribution network. The low current magnitude and diverse characteristics of HIFs make them difficult to be detected by over-current relays. Recently, data-driven methods based on machine learning models are gaining popularit... | ['James Stoupis', 'Mohammad Razeghi-Jahromi', 'Yingxiang Liu'] | 2023-01-05 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-1.80048034e-01 -4.91104603e-01 1.85366675e-01 -2.12239027e-01
-3.19694728e-01 -3.55350733e-01 2.94326156e-01 3.77482086e-01
2.69411653e-01 6.85866833e-01 -1.08100891e-01 1.16523961e-03
-6.59172595e-01 -8.57345343e-01 -1.33573279e-01 -1.20065057e+00
-5.03091514e-01 3.63510638e-01 8.16518441e-02 -3.86394322... | [6.3098673820495605, 2.500185012817383] |
72cf61ac-bb13-401f-9ee3-b0235e700f5c | ensembles-of-vision-transformers-as-a-new | 2203.01726 | null | https://arxiv.org/abs/2203.01726v3 | https://arxiv.org/pdf/2203.01726v3.pdf | Ensembles of Vision Transformers as a New Paradigm for Automated Classification in Ecology | Monitoring biodiversity is paramount to manage and protect natural resources. Collecting images of organisms over large temporal or spatial scales is a promising practice to monitor the biodiversity of natural ecosystems, providing large amounts of data with minimal interference with the environment. Deep learning mode... | ['F. Pomati', 'P. Brun', 'M. Baity-Jesi', 'T. Bulas', 'E. Merz', 'M. Reyes', 'T. Hardeman', 'S. Kyathanahally'] | 2022-03-03 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 1.60665959e-01 -5.14930785e-01 2.58004487e-01 -6.89486712e-02
-4.40627849e-03 -7.15934873e-01 6.23971641e-01 4.73552495e-01
-9.90409195e-01 9.78873372e-01 -1.75314277e-01 -1.09943636e-01
-2.84464598e-01 -1.05761302e+00 -7.44285643e-01 -9.02650654e-01
-5.22491217e-01 3.77967983e-01 3.90877634e-01 -1.68899268... | [9.142046928405762, -1.3071895837783813] |
bc878668-c6fe-4748-ac3b-afbb7f783845 | swintextspotter-scene-text-spotting-via | 2203.10209 | null | https://arxiv.org/abs/2203.10209v1 | https://arxiv.org/pdf/2203.10209v1.pdf | SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition | End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition. However, recent state-of-the-art methods usually incorporate detection and recognition simply by sharing the backbone, which does not directly ... | ['Lianwen Jin', 'Kai Ding', 'Nicholas Yuan', 'Shenggao Zhu', 'Dahua Lin', 'Chongyu Liu', 'Zhenghao Peng', 'Yuliang Liu', 'Mingxin Huang'] | 2022-03-19 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Huang_SwinTextSpotter_Scene_Text_Spotting_via_Better_Synergy_Between_Text_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Huang_SwinTextSpotter_Scene_Text_Spotting_via_Better_Synergy_Between_Text_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['text-spotting', 'scene-text-detection'] | ['computer-vision', 'computer-vision'] | [ 2.44498715e-01 -3.70878220e-01 -6.64603114e-02 -3.93830985e-01
-8.38690519e-01 -5.76756120e-01 8.01429570e-01 -2.42031693e-01
-5.42089403e-01 1.57067776e-01 5.54209411e-01 -2.66579151e-01
1.87068358e-01 -3.93962681e-01 -6.41372979e-01 -4.21361148e-01
8.07300985e-01 4.07267183e-01 2.98959136e-01 -1.56312868... | [11.920682907104492, 2.2241227626800537] |
1485b4f7-6f2a-4156-9e40-e6a2576341c4 | aspera-aspect-based-rating-prediction-based | null | null | https://aclanthology.org/W19-3605 | https://aclanthology.org/W19-3605.pdf | AspeRa: Aspect-Based Rating Prediction Based on User Reviews | We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items. It is based on aspect extraction with neural networks and combines the advantages of deep learning and topic modeling. It is mainly designed for recommendations, but an important secondary go... | ['Elena Tutubalina', 'Sergey Nikolenko', 'Ilya Shenbin', 'Valentin Malykh', 'Anton Alekseev'] | 2019-08-01 | null | null | null | ws-2019-8 | ['aspect-extraction'] | ['natural-language-processing'] | [-3.37077707e-01 5.64586341e-01 -9.52060223e-01 -5.88676572e-01
-4.58143264e-01 -2.28347868e-01 8.67885053e-01 3.31132978e-01
2.12390665e-02 4.68405962e-01 7.76583552e-01 -6.08501077e-01
-4.46295261e-01 -8.75456750e-01 -5.03063560e-01 -7.11515825e-03
1.40179008e-01 7.52025306e-01 -3.49953651e-01 -5.57911754... | [11.34514331817627, 6.624413967132568] |
a856aadc-4e3d-4e07-ab86-b64dd883e892 | normalizing-flow-based-neural-process-for-few | 2304.08183 | null | https://arxiv.org/abs/2304.08183v1 | https://arxiv.org/pdf/2304.08183v1.pdf | Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion | Knowledge graphs (KGs), as a structured form of knowledge representation, have been widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC), which aims to predict missing facts for unseen relations with few-shot associated facts, has attracted increasing attention from practitioners and r... | ['Shirui Pan', 'Gholamreza Haffari', 'Yuan-Fang Li', 'Linhao Luo'] | 2023-04-17 | null | null | null | null | ['metric-learning', 'knowledge-graph-completion', 'metric-learning'] | ['computer-vision', 'knowledge-base', 'methodology'] | [-3.97726238e-01 2.63840973e-01 -4.08279121e-01 -3.43982011e-01
-6.63680553e-01 -1.91608235e-01 3.55606586e-01 9.39428732e-02
1.03860654e-01 8.63309085e-01 3.39254081e-01 -2.18710989e-01
-4.27220285e-01 -1.09140825e+00 -7.80139208e-01 -4.26507384e-01
1.60832465e-01 5.24241328e-01 2.64405936e-01 -3.95879507... | [8.794621467590332, 7.955242156982422] |
283ca44c-4281-45d3-980a-0019b82b2208 | multi-scale-prediction-for-robust-hand | 1804.08220 | null | http://arxiv.org/abs/1804.08220v1 | http://arxiv.org/pdf/1804.08220v1.pdf | Multi-scale prediction for robust hand detection and classification | In this paper, we present a multi-scale Fully Convolutional Networks
(MSP-RFCN) to robustly detect and classify human hands under various
challenging conditions. In our approach, the input image is passed through the
proposed network to generate score maps, based on multi-scale predictions. The
network has been specifi... | ['Robert Laganiere', 'Yong Wang', 'Xinbin Luo', 'Ding Lu', 'Shan Fu'] | 2018-04-23 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [ 9.22990069e-02 -3.54341000e-01 6.31992072e-02 -6.09642938e-02
-2.40174875e-01 -5.06032467e-01 4.78000522e-01 -7.43550956e-01
-6.87724113e-01 4.31718498e-01 -1.98807344e-01 1.07941590e-01
2.41837949e-01 -5.84697425e-01 -4.87646520e-01 -5.01488924e-01
2.40767822e-02 5.36667168e-01 8.35631073e-01 -3.25931579... | [6.577028274536133, -0.6516555547714233] |
a7532e54-b0ac-4b6b-aa0a-0b128c3d75e8 | convergence-and-complexity-of-stochastic | 2201.01652 | null | https://arxiv.org/abs/2201.01652v3 | https://arxiv.org/pdf/2201.01652v3.pdf | Stochastic regularized majorization-minimization with weakly convex and multi-convex surrogates | Stochastic majorization-minimization (SMM) is a class of stochastic optimization algorithms that proceed by sampling new data points and minimizing a recursive average of surrogate functions of an objective function. The surrogates are required to be strongly convex and convergence rate analysis for the general non-con... | ['Hanbaek Lyu'] | 2022-01-05 | null | null | null | null | ['image-deep-networks'] | ['computer-vision'] | [-7.93758966e-03 9.47860107e-02 -4.41670902e-02 -2.79324949e-01
-1.22895110e+00 -4.73504990e-01 -2.40561202e-01 1.06120229e-01
-7.87964582e-01 9.32162225e-01 -1.33092269e-01 -3.51964295e-01
-5.00710726e-01 -3.95790994e-01 -1.05766332e+00 -9.65930521e-01
-4.51805085e-01 3.35087299e-01 -2.08030120e-01 6.48847222... | [6.533515453338623, 4.534720420837402] |
b6a3b307-3887-433b-9c7c-7852f53e97cb | integrating-markov-processes-with-structural | 1911.02175 | null | https://arxiv.org/abs/1911.02175v1 | https://arxiv.org/pdf/1911.02175v1.pdf | Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems | This manuscript contributes a general and practical framework for casting a Markov process model of a system at equilibrium as a structural causal model, and carrying out counterfactual inference. Markov processes mathematically describe the mechanisms in the system, and predict the system's equilibrium behavior upon i... | ['Olga Vitek', 'Kaushal Paneri', 'Robert Osazuwa Ness'] | 2019-11-06 | integrating-markov-processes-with-structural-1 | http://papers.nips.cc/paper/9569-integrating-markov-processes-with-structural-causal-modeling-enables-counterfactual-inference-in-complex-systems | http://papers.nips.cc/paper/9569-integrating-markov-processes-with-structural-causal-modeling-enables-counterfactual-inference-in-complex-systems.pdf | neurips-2019-12 | ['counterfactual-inference'] | ['miscellaneous'] | [ 4.66458261e-01 4.17249292e-01 -3.57400715e-01 3.25751156e-01
-1.51967660e-01 -6.23979211e-01 9.73530650e-01 1.66436240e-01
-1.40923962e-01 1.29542994e+00 2.72329777e-01 -9.14534390e-01
-4.43585962e-01 -7.68221915e-01 -1.04468465e+00 -7.63886750e-01
-4.09883112e-01 4.21690702e-01 -2.86348701e-01 2.51376629... | [8.014569282531738, 5.371291637420654] |
e18f3cab-d061-4d16-8c93-57d719f7ed35 | multimodal-prompt-learning-for-product-title | 2307.01969 | null | https://arxiv.org/abs/2307.01969v1 | https://arxiv.org/pdf/2307.01969v1.pdf | Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels | Generating an informative and attractive title for the product is a crucial task for e-commerce. Most existing works follow the standard multimodal natural language generation approaches, e.g., image captioning, and employ the large scale of human-labelled datasets to train desirable models. However, for novel products... | ['Yuexian Zou', 'Bing Yin', 'Chenyu You', 'Qingyu Yin', 'Zheng Li', 'Fenglin Liu', 'Bang Yang'] | 2023-07-05 | null | null | null | null | ['image-captioning', 'text-generation'] | ['computer-vision', 'natural-language-processing'] | [ 7.31258810e-01 2.17237458e-01 -4.11144644e-01 -5.51636994e-01
-9.95458722e-01 -6.98263228e-01 6.82277679e-01 -5.62375747e-02
-5.61028719e-02 5.06168783e-01 1.91485241e-01 7.62844533e-02
1.90626234e-01 -4.56768960e-01 -8.12096417e-01 -6.66963458e-01
5.81457496e-01 5.13171136e-01 -2.46578187e-01 -4.08751726... | [11.0477933883667, 0.9930055737495422] |
ccd322a7-35e0-4d40-b8f4-12b12fbdee1e | implicit-identity-driven-deepfake-face | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Implicit_Identity_Driven_Deepfake_Face_Swapping_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Implicit_Identity_Driven_Deepfake_Face_Swapping_Detection_CVPR_2023_paper.pdf | Implicit Identity Driven Deepfake Face Swapping Detection | In this paper, we consider the face swapping detection from the perspective of face identity. Face swapping aims to replace the target face with the source face and generate the fake face that the human cannot distinguish between real and fake. We argue that the fake face contains the explicit identity and implicit... | ['Dengpan Ye', 'Qian Wang', 'Qin Zou', 'Jiaxin Ai', 'Jifan Yang', 'Zhongyuan Wang', 'Baojin Huang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['face-swapping'] | ['computer-vision'] | [ 2.83471406e-01 5.14324307e-01 -1.71758588e-02 -3.60608786e-01
-2.55432576e-01 -7.08162010e-01 4.23777729e-01 -6.70963228e-01
-2.76639108e-02 5.58837235e-01 -1.54027283e-01 1.48497030e-01
2.06011340e-01 -7.08179235e-01 -6.46825969e-01 -8.90916109e-01
1.91841424e-01 1.96288183e-01 -2.57594138e-01 -5.76691441... | [12.764129638671875, 0.22504787147045135] |
c8d6efd4-862f-49ab-8eee-588165227b68 | towards-real-world-hdrtv-reconstruction-a | 2211.03058 | null | https://arxiv.org/abs/2211.03058v1 | https://arxiv.org/pdf/2211.03058v1.pdf | Towards Real World HDRTV Reconstruction: A Data Synthesis-based Approach | Existing deep learning based HDRTV reconstruction methods assume one kind of tone mapping operators (TMOs) as the degradation procedure to synthesize SDRTV-HDRTV pairs for supervised training. In this paper, we argue that, although traditional TMOs exploit efficient dynamic range compression priors, they have several d... | ['Zhiwei Xiong', 'Chang Chen', 'Fenglong Song', 'Yong Li', 'Tao Wang', 'Zhen Cheng'] | 2022-11-06 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 4.00826067e-01 -9.73611251e-02 -2.92692363e-01 -5.83557844e-01
-7.45851219e-01 -3.54555726e-01 4.82927620e-01 -6.68938220e-01
-5.24290614e-02 8.01097810e-01 2.32979611e-01 -1.60186738e-01
2.48357475e-01 -9.36813056e-01 -1.10329056e+00 -7.72132397e-01
3.48204553e-01 2.64965385e-01 2.05982938e-01 -3.06803048... | [11.014676094055176, -2.026592493057251] |
a605ff1d-58ea-4e46-a945-5cbbffce6c88 | second-order-information-in-first-order | 1912.09926 | null | https://arxiv.org/abs/1912.09926v1 | https://arxiv.org/pdf/1912.09926v1.pdf | Second-order Information in First-order Optimization Methods | In this paper, we try to uncover the second-order essence of several first-order optimization methods. For Nesterov Accelerated Gradient, we rigorously prove that the algorithm makes use of the difference between past and current gradients, thus approximates the Hessian and accelerates the training. For adaptive method... | ['Shange Tang', 'Licong Lin', 'Yuzheng Hu'] | 2019-12-20 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-2.06666812e-01 -4.65092584e-02 -3.68987955e-02 -5.13743877e-01
-4.51522082e-01 -3.66371363e-01 3.39476317e-01 2.10348666e-01
-9.10940886e-01 7.49394655e-01 -2.46255789e-02 -7.33503163e-01
-1.23943366e-01 -5.95008492e-01 -6.99666381e-01 -9.07029331e-01
-2.86510944e-01 3.60480487e-01 2.96717405e-01 -5.19885421... | [7.621766567230225, 3.8066296577453613] |
537b36d2-cabc-40da-8546-a108d91754ed | wallpaper-texture-generation-and-style | 2106.11482 | null | https://arxiv.org/abs/2106.11482v1 | https://arxiv.org/pdf/2106.11482v1.pdf | Wallpaper Texture Generation and Style Transfer Based on Multi-label Semantics | Textures contain a wealth of image information and are widely used in various fields such as computer graphics and computer vision. With the development of machine learning, the texture synthesis and generation have been greatly improved. As a very common element in everyday life, wallpapers contain a wealth of texture... | ['Junyu Dong', 'Lin Qi', 'Huiyu Zhou', 'Eric Rigall', 'Tiange Zhang', 'Xiaohan Feng', 'Ying Gao'] | 2021-06-22 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 5.32312572e-01 3.73676457e-02 1.06237076e-01 -3.14725846e-01
-1.46559924e-01 -6.64349616e-01 6.27707183e-01 -3.83722544e-01
2.66974062e-01 7.44506121e-01 4.58818004e-02 1.21156245e-01
1.05487334e-03 -1.30405223e+00 -6.22173011e-01 -7.23498464e-01
6.84839904e-01 3.26504171e-01 -1.23209238e-01 -3.14367205... | [11.698387145996094, -0.4594956934452057] |
c0946a4d-0471-4de3-8b1c-54dba6e3cf7e | neural-language-taskonomy-which-nlp-tasks-are | 2205.01404 | null | https://arxiv.org/abs/2205.01404v1 | https://arxiv.org/pdf/2205.01404v1.pdf | Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity? | Several popular Transformer based language models have been found to be successful for text-driven brain encoding. However, existing literature leverages only pretrained text Transformer models and has not explored the efficacy of task-specific learned Transformer representations. In this work, we explore transfer lear... | ['Bapi Raju Surampudi', 'Manish Gupta', 'Mounika Marreddy', 'Veeral Agarwal', 'Jashn Arora', 'Subba Reddy Oota'] | 2022-05-03 | null | https://aclanthology.org/2022.naacl-main.235 | https://aclanthology.org/2022.naacl-main.235.pdf | naacl-2022-7 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 4.13204253e-01 4.81934875e-01 -6.85245469e-02 -5.07308364e-01
-6.17176771e-01 -2.14626729e-01 1.26119649e+00 3.38223100e-01
-3.92555326e-01 5.60944676e-01 1.35965550e+00 1.09067475e-02
-3.19471389e-01 -7.95308769e-01 -4.24943358e-01 -3.83472383e-01
3.77065279e-02 3.81615520e-01 3.18722688e-02 -2.42938161... | [10.394036293029785, 8.420644760131836] |
2e6c3d81-9911-4105-8f1b-9151c0f85d87 | object-propagation-via-inter-frame-attentions | 2111.07529 | null | https://arxiv.org/abs/2111.07529v3 | https://arxiv.org/pdf/2111.07529v3.pdf | Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation | Video instance segmentation aims to detect, segment, and track objects in a video. Current approaches extend image-level segmentation algorithms to the temporal domain. However, this results in temporally inconsistent masks. In this work, we identify the mask quality due to temporal stability as a performance bottlenec... | ['Hanspeter Pfister', 'Song Bai', 'Donglai Wei', 'Zudi Lin', 'Won-Dong Jang', 'Anirudh S Chakravarthy'] | 2021-11-15 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.38089284e-01 6.54044747e-02 -3.95739496e-01 -1.35959119e-01
-6.26646042e-01 -6.38100863e-01 1.92967132e-01 -3.28089714e-01
-4.15963292e-01 6.05379045e-01 -2.41395133e-03 -1.09366290e-01
3.31118822e-01 -4.18779820e-01 -8.29695344e-01 -4.98118848e-01
1.33880787e-02 5.51729240e-02 9.07039106e-01 1.27207324... | [9.130178451538086, -0.15287771821022034] |
c4f5c712-8031-4143-a9ed-a04ea2059cce | experimental-estimation-of-number-of-clusters | 1503.03168 | null | http://arxiv.org/abs/1503.03168v1 | http://arxiv.org/pdf/1503.03168v1.pdf | Experimental Estimation of Number of Clusters Based on Cluster Quality | Text Clustering is a text mining technique which divides the given set of
text documents into significant clusters. It is used for organizing a huge
number of text documents into a well-organized form. In the majority of the
clustering algorithms, the number of clusters must be specified apriori, which
is a drawback of... | ['Desikan Kalyani', 'Grace G. Hannah'] | 2015-03-10 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-3.60471696e-01 -8.24892893e-02 -1.65657505e-01 -4.84503716e-01
-1.34098884e-02 -5.74505448e-01 5.27384222e-01 8.92757893e-01
-4.47203666e-01 3.76049846e-01 1.27457753e-02 -6.14076972e-01
-4.29375976e-01 -1.00201929e+00 2.20351234e-01 -7.00873792e-01
-3.34934354e-01 1.32169235e+00 4.11825955e-01 1.47531360... | [10.264469146728516, 7.0787129402160645] |
6e84c35f-6702-4bc4-9c8b-c9ffb08b4755 | sidenoter-scholarly-paper-browsing-system | null | null | https://aclanthology.org/C16-2029 | https://aclanthology.org/C16-2029.pdf | SideNoter: Scholarly Paper Browsing System based on PDF Restructuring and Text Annotation | In this paper, we discuss our ongoing efforts to construct a scientific paper browsing system that helps users to read and understand advanced technical content distributed in PDF. Since PDF is a format specifically designed for printing, layout and logical structures of documents are indistinguishably embedded in the ... | ['Akiko Aizawa', 'Takeshi Abekawa'] | 2016-12-01 | sidenoter-scholarly-paper-browsing-system-1 | https://aclanthology.org/C16-2029 | https://aclanthology.org/C16-2029.pdf | coling-2016-12 | ['text-annotation'] | ['natural-language-processing'] | [ 3.65451783e-01 3.67160171e-01 4.66260090e-02 -3.52239490e-01
-9.19203997e-01 -1.28629291e+00 5.46591818e-01 2.21186340e-01
-1.39703110e-01 1.09345925e+00 2.81847239e-01 -7.24705160e-01
-3.32269460e-01 -4.76848572e-01 -8.14547718e-01 2.25263730e-01
2.66254425e-01 7.27738500e-01 4.84140068e-01 1.04147427... | [9.881878852844238, 8.121674537658691] |
8f1111c8-d398-4d0f-9b72-abbb8a8c471e | hierarchical-pre-training-for-sequence | 2009.11152 | null | https://arxiv.org/abs/2009.11152v3 | https://arxiv.org/pdf/2009.11152v3.pdf | Hierarchical Pre-training for Sequence Labelling in Spoken Dialog | Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken l... | ['Matthieu Labeau', 'Pierre Colombo', 'Matteo Manica', 'Emile Chapuis', 'Chloe Clavel'] | 2020-09-23 | null | https://aclanthology.org/2020.findings-emnlp.239 | https://aclanthology.org/2020.findings-emnlp.239.pdf | findings-of-the-association-for-computational | ['emotion-recognition-in-conversation', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.53291553e-01 4.40830946e-01 1.83748871e-01 -1.02269888e+00
-9.79938984e-01 -7.68634260e-01 7.55031943e-01 -2.04783008e-01
-6.34011090e-01 7.65846014e-01 7.64945805e-01 -2.38426894e-01
6.18292332e-01 -8.25226307e-02 -3.35257620e-01 -3.16227198e-01
-5.85831888e-02 1.02320969e+00 5.40084876e-02 -7.96248674... | [12.807656288146973, 7.788235187530518] |
d4e062bb-d9d1-48d7-a1ea-ce950ff78cfc | utilizing-automated-breast-cancer-detection | 1905.10841 | null | https://arxiv.org/abs/1905.10841v3 | https://arxiv.org/pdf/1905.10841v3.pdf | Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer | Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network (CNN) analysis pipelines to generate combined maps of cancer regions and tumor infiltrating lymphocytes ... | ['Jonas S. Almeida', 'ASHISH SHARMA', 'Tahsin Kurc', 'Shahira Abousamra', 'Rebecca Batiste', 'Han Le', 'Erich Bremer', 'Alison L. Van Dyke', 'Danielle Fassler', 'Arvind Rao', 'Tianhao Zhao', 'Rajarsi Gupta', 'Joel Saltz', 'Le Hou', 'Dimitris Samaras'] | 2019-05-26 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 3.37353408e-01 6.84107393e-02 -2.76920915e-01 -3.27585250e-01
-1.14774477e+00 -3.66920739e-01 5.01176894e-01 9.37508583e-01
-6.32013381e-01 5.92682719e-01 4.04487073e-01 -1.03308320e+00
-1.48405060e-01 -8.56795847e-01 -3.38963509e-01 -9.16526616e-01
-2.59105980e-01 7.98735678e-01 2.28896201e-01 -1.56424940... | [15.134242057800293, -3.089184522628784] |
69b502ae-bd99-4e25-97c7-9d08f128c05b | knowledge-base-relation-detection-via-multi | 1803.00612 | null | http://arxiv.org/abs/1803.00612v2 | http://arxiv.org/pdf/1803.00612v2.pdf | Knowledge Base Relation Detection via Multi-View Matching | Relation detection is a core component for Knowledge Base Question Answering
(KBQA). In this paper, we propose a KB relation detection model via multi-view
matching which utilizes more useful information extracted from question and KB.
The matching inside each view is through multiple perspectives to compare two
input ... | ['Wei zhang', 'Yang Yu', 'Mo Yu', 'Zhiguo Wang', 'Kazi Saidul Hasan'] | 2018-03-01 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.91173160e-01 4.72503871e-01 -2.39417955e-01 -3.14826936e-01
-1.32717335e+00 -6.32745743e-01 2.92324811e-01 2.68423587e-01
-2.00887740e-01 7.35687077e-01 3.19259018e-01 -4.58916008e-01
-1.60531268e-01 -1.07315767e+00 -6.57292128e-01 3.81521322e-02
3.61085594e-01 8.70343208e-01 9.49591637e-01 -8.96808803... | [10.61073112487793, 7.932063102722168] |
5a9cec87-c7a5-4642-a122-bd422e677959 | cmb-ai-lab-at-semeval-2022-task-11-a-two | null | null | https://aclanthology.org/2022.semeval-1.221 | https://aclanthology.org/2022.semeval-1.221.pdf | CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification | This paper presents a solution for the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition. What is challenging in this task is detecting semantically ambiguous and complex entities in short and low-context settings. Our team (CMB AI Lab) propose a two-stage method to recognize the named entities: first,... | ['Yaohan He', 'Wenyi Lv', 'Jiangzhou Ji', 'Yixiao Yang', 'Hongyi Liu', 'Keyu Pu'] | null | null | null | null | semeval-naacl-2022-7 | ['boundary-detection'] | ['computer-vision'] | [-3.93938631e-01 3.22417796e-01 -5.14363609e-02 -6.38879359e-01
-1.13451076e+00 -7.92208135e-01 3.96555126e-01 1.13129899e-01
-9.55020905e-01 9.71446872e-01 5.11329830e-01 -2.83228427e-01
4.71786141e-01 -2.89834976e-01 -7.17202067e-01 1.48697734e-01
-1.71621636e-01 5.11038840e-01 2.26180196e-01 -5.51170334... | [9.673691749572754, 9.553919792175293] |
4a521970-61b1-452c-8117-4cd86c4677ea | the-chinese-causative-passive-homonymy | null | null | https://aclanthology.org/2022.lrec-1.460 | https://aclanthology.org/2022.lrec-1.460.pdf | The Chinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for NLI and a Probing Task | The disambiguation of causative-passive homonymy (CPH) is potentially tricky for machines, as the causative and the passive are not distinguished by the sentences’ syntactic structure. By transforming CPH disambiguation to a challenging natural language inference (NLI) task, we present the first Chinese Adversarial NLI... | ['Katja Markert', 'Shanshan Xu'] | null | null | null | null | lrec-2022-6 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.91574246e-01 6.10383272e-01 -2.77319700e-01 -2.33014032e-01
-7.87698746e-01 -9.68675911e-01 1.05593002e+00 -1.19413927e-01
-3.96363884e-01 7.31676638e-01 6.44465804e-01 -4.79818791e-01
-2.07208134e-02 -8.52057219e-01 -6.00331843e-01 -3.49246919e-01
2.53259301e-01 7.78955877e-01 -1.25278428e-01 -4.89914864... | [10.617630958557129, 9.27602481842041] |
6828bf48-5905-4340-856b-a97f6658698d | forecasting-irregularly-sampled-time-series | 2305.12932 | null | https://arxiv.org/abs/2305.12932v1 | https://arxiv.org/pdf/2305.12932v1.pdf | Forecasting Irregularly Sampled Time Series using Graphs | Forecasting irregularly sampled time series with missing values is a crucial task for numerous real-world applications such as healthcare, astronomy, and climate sciences. State-of-the-art approaches to this problem rely on Ordinary Differential Equations (ODEs) but are known to be slow and to require additional featur... | ['Lars Schmidt-Thieme', 'Stefan Born', 'Shayan Javed', 'Johannes Burchert', 'Nourhan Ahmed', 'Randolf Sholz', 'Kiran Madusudanan', 'Vijaya Krishna Yalavarthi'] | 2023-05-22 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 1.65232196e-01 5.05189896e-02 1.43702656e-01 -2.10261457e-02
6.60721809e-02 -3.02168161e-01 2.37772390e-01 1.36430010e-01
3.71691883e-01 7.41344929e-01 -1.62505601e-02 -5.69880188e-01
-3.76971364e-01 -9.85843480e-01 -6.36096895e-01 -6.86551154e-01
-8.95984530e-01 4.56671178e-01 -8.70233309e-03 -6.35402739... | [6.8328633308410645, 2.9107511043548584] |
d84c0bd3-de97-4530-90bd-e30fb39b627f | improved-cross-view-completion-pre-training | 2211.10408 | null | https://arxiv.org/abs/2211.10408v2 | https://arxiv.org/pdf/2211.10408v2.pdf | Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow | Despite impressive performance for high-level downstream tasks, self-supervised pre-training methods have not yet fully delivered on dense geometric vision tasks such as stereo matching or optical flow. The application of selfsupervised concepts, such as instance discrimination or masked image modeling, to geometric ta... | ['Romain Brégier', 'Vaibhav Arora', 'Yohann Cabon', 'Vincent Leroy', 'Thomas Lucas', 'Jérôme Revaud', 'Boris Chidlovskii', 'Leonid Antsfeld', 'Gabriela Csurka', 'Philippe Weinzaepfel'] | 2022-11-18 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 2.86599904e-01 2.58714110e-01 8.70631412e-02 -2.64526308e-01
-5.00395596e-01 -5.10936141e-01 1.09413600e+00 -5.16592758e-03
-4.39977348e-01 4.89258915e-01 3.96229714e-01 -2.70669132e-01
-1.09069809e-01 -6.40370309e-01 -7.71828234e-01 -4.29267377e-01
-5.52089773e-02 4.61162955e-01 4.10845369e-01 -3.96324396... | [8.650961875915527, -2.315253734588623] |
19f8f0e8-7cb8-4179-811b-f8dec360d023 | robust-object-detection-via-instance-level | 2104.08381 | null | https://arxiv.org/abs/2104.08381v2 | https://arxiv.org/pdf/2104.08381v2.pdf | Robust Object Detection via Instance-Level Temporal Cycle Confusion | Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications. In this work, we study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object det... | ['Trevor Darrell', 'Joseph E. Gonzalez', 'Xiaolong Wang', 'Fisher Yu', 'Benlin Liu', 'Thomas E. Huang', 'Xin Wang'] | 2021-04-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Robust_Object_Detection_via_Instance-Level_Temporal_Cycle_Confusion_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Robust_Object_Detection_via_Instance-Level_Temporal_Cycle_Confusion_ICCV_2021_paper.pdf | iccv-2021-1 | ['robust-object-detection'] | ['computer-vision'] | [-0.05404698 -0.36887953 -0.23872696 -0.32657912 -0.71755284 -0.60548437
0.5684946 -0.19181702 -0.4334521 0.42420977 -0.02835524 0.2255503
0.19997011 -0.38383794 -0.7862049 -0.79254544 -0.14558223 0.33908227
1.0683919 -0.10697916 0.0291035 0.36080799 -1.6019633 0.64201313
0.45842552 0.9652832 0.3... | [9.521862983703613, 1.6226415634155273] |
5d7ea6da-9a66-48a7-af46-b49d2b99fe2f | scibert-pretrained-contextualized-embeddings | 1903.10676 | null | https://arxiv.org/abs/1903.10676v3 | https://arxiv.org/pdf/1903.10676v3.pdf | SciBERT: A Pretrained Language Model for Scientific Text | Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised pretraining on a large mu... | ['Kyle Lo', 'Iz Beltagy', 'Arman Cohan'] | 2019-03-26 | scibert-a-pretrained-language-model-for | https://aclanthology.org/D19-1371 | https://aclanthology.org/D19-1371.pdf | ijcnlp-2019-11 | ['participant-intervention-comparison-outcome', 'medical-named-entity-recognition', 'citation-intent-classification'] | ['medical', 'natural-language-processing', 'natural-language-processing'] | [ 1.23645151e-02 -2.50663664e-02 -2.81680554e-01 -7.43026316e-01
-1.48870575e+00 -1.16233957e+00 4.71779913e-01 5.64186931e-01
-4.71217215e-01 1.17374659e+00 8.84931013e-02 -5.43532789e-01
2.48529553e-01 -3.31350684e-01 -1.00497174e+00 -3.76706898e-01
8.56809467e-02 6.16513968e-01 1.29004437e-02 4.29720640... | [9.114622116088867, 8.54814338684082] |
2a7100b2-bc14-45cd-beec-9a39521cdf60 | a-projective-geometric-view-for-6d-pose | 2302.00227 | null | https://arxiv.org/abs/2302.00227v2 | https://arxiv.org/pdf/2302.00227v2.pdf | A Projective Geometric View for 6D Pose Estimation in mmWave MIMO Systems | Millimeter-wave (mmWave) systems in the 30--300 GHz bands are among the fundamental enabling technologies of 5G and beyond 5G, providing large bandwidths, not only for high data rate communication, but also for precise positioning services, in support of high accuracy demanding applications such as vehicle positioning.... | ['Henk Wymeersch', 'Shengqiang Shen'] | 2023-02-01 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [-2.76447982e-01 2.72167940e-02 -1.62367299e-01 -9.41791162e-02
-5.40980756e-01 -7.02163041e-01 2.29746729e-01 8.95446818e-03
-1.43893272e-01 6.84494913e-01 -2.60403454e-01 -6.39774799e-01
-5.86399615e-01 -8.02964032e-01 -4.07842994e-01 -9.96219635e-01
-2.46693730e-01 6.22185886e-01 -5.42952120e-01 -5.08840233... | [6.290822505950928, 1.1947555541992188] |
909edb6d-4c41-4167-a868-ae9a275cb3a2 | practical-privacy-preserving-gaussian-process | 2306.14498 | null | https://arxiv.org/abs/2306.14498v1 | https://arxiv.org/pdf/2306.14498v1.pdf | Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing | Gaussian process regression (GPR) is a non-parametric model that has been used in many real-world applications that involve sensitive personal data (e.g., healthcare, finance, etc.) from multiple data owners. To fully and securely exploit the value of different data sources, this paper proposes a privacy-preserving GPR... | ['Zenglin Xu', 'Yue Yu', 'Hui Wang', 'Shuang Qin', 'JiaQi Zhang', 'Yehong Zhang', 'Jinglong Luo'] | 2023-06-26 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 3.77228409e-01 -3.51197660e-01 1.71879619e-01 -2.97430009e-01
-7.73135841e-01 -5.69608390e-01 2.01836422e-01 2.55985230e-01
-4.99497056e-01 6.51066720e-01 -1.08422048e-01 -5.17386138e-01
-2.46501744e-01 -9.94446874e-01 -5.29782116e-01 -1.24598420e+00
2.87839547e-02 -9.47013944e-02 -9.77339521e-02 -1.11064404... | [5.920133113861084, 6.67006778717041] |
95c4084f-4735-4d3e-a748-a66a71ce4f9a | halsie-hybrid-approach-to-learning | 2211.10754 | null | https://arxiv.org/abs/2211.10754v3 | https://arxiv.org/pdf/2211.10754v3.pdf | HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalities | We present HALSIE, a novel hybrid approach for semantic segmentation by simultaneously leveraging image and event modalities. Event cameras are vision sensors that detect changes in per-pixel intensity to generate asynchronous 'event streams'. They offer significant advantages over standard frame-based cameras due to t... | ['Kaushik Roy', 'Marco Apolinario', 'Chamika Liyanagedera', 'Adarsh Kosta', 'Shristi Das Biswas'] | 2022-11-19 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 7.03115284e-01 -3.19423527e-01 -1.45066708e-01 -3.05027992e-01
-9.18583989e-01 -5.11610687e-01 6.15044951e-01 -4.76850830e-02
-7.61002123e-01 7.51238108e-01 -3.60135995e-02 -1.13808408e-01
2.80027449e-01 -6.94100201e-01 -8.67617428e-01 -7.40898907e-01
2.00747792e-02 -1.05701335e-01 7.63358831e-01 2.83173919... | [8.647269248962402, -1.108654499053955] |
de052560-cfb0-43db-8356-d7d4d2b360d6 | friend-or-foe-exploring-the-implications-of | 2306.09928 | null | https://arxiv.org/abs/2306.09928v1 | https://arxiv.org/pdf/2306.09928v1.pdf | Friend or Foe? Exploring the Implications of Large Language Models on the Science System | The advent of ChatGPT by OpenAI has prompted extensive discourse on its potential implications for science and higher education. While the impact on education has been a primary focus, there is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific pra... | ['Fabian Sofsky', 'Jörg Pohle', 'Melissa Laufer', 'Marcel Hebing', 'Benedikt Fecher'] | 2023-06-16 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 7.66905546e-02 8.64234447e-01 -3.62069428e-01 2.34687049e-02
-4.51374292e-01 -6.47622705e-01 7.25401878e-01 3.61981481e-01
-4.56498176e-01 3.77239525e-01 7.40989208e-01 -1.06543720e+00
-1.21398218e-01 -4.44181561e-01 -8.66282225e-01 -3.81282687e-01
8.27192843e-01 5.38119301e-02 -8.14584643e-02 1.84796765... | [10.19016170501709, 7.292768478393555] |
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