paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
feb8ace8-549a-4140-915c-19d43f15f743 | graffmatch-global-matching-of-3d-lines-and | 2212.12745 | null | https://arxiv.org/abs/2212.12745v1 | https://arxiv.org/pdf/2212.12745v1.pdf | GraffMatch: Global Matching of 3D Lines and Planes for Wide Baseline LiDAR Registration | Using geometric landmarks like lines and planes can increase navigation accuracy and decrease map storage requirements compared to commonly-used LiDAR point cloud maps. However, landmark-based registration for applications like loop closure detection is challenging because a reliable initial guess is not available. Glo... | ['Jonathan P. How', 'Devarth Parikh', 'Parker C. Lusk'] | 2022-12-24 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 2.24607095e-01 -3.31376344e-01 -4.30406891e-02 -4.67000574e-01
-8.18955779e-01 -7.22159982e-01 8.30837667e-01 7.41968334e-01
-6.44107521e-01 4.72609937e-01 -3.94533545e-01 -1.94247857e-01
-3.69371444e-01 -9.81282473e-01 -7.46799409e-01 -2.76394933e-01
-2.43538007e-01 7.31075108e-01 3.55665624e-01 -2.31739968... | [7.555839538574219, -2.4481711387634277] |
aab6d14f-e0af-4b2f-b704-f3488d2e31d9 | open-high-resolution-satellite-imagery-the | 2207.06418 | null | https://arxiv.org/abs/2207.06418v1 | https://arxiv.org/pdf/2207.06418v1.pdf | Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution | Analyzing the planet at scale with satellite imagery and machine learning is a dream that has been constantly hindered by the cost of difficult-to-access highly-representative high-resolution imagery. To remediate this, we introduce here the WorldStrat dataset. The largest and most varied such publicly available datase... | ['Freddie Kalaitzis', 'Ivan Oršolić', 'Julien Cornebise'] | 2022-07-13 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 3.60293478e-01 -1.42690852e-01 -2.11668223e-01 -2.98338354e-01
-1.23749626e+00 -6.88925982e-01 8.24810565e-01 -2.35929847e-01
-6.42271578e-01 9.54418480e-01 3.24484289e-01 -2.95395344e-01
-3.14670980e-01 -1.16357148e+00 -7.17106402e-01 -8.86508048e-01
-6.88220799e-01 5.86582065e-01 -1.70018926e-01 -4.95658517... | [9.429708480834961, -1.486242651939392] |
63bd6bd1-7073-4c8c-ad9f-a33f8993f8db | syntax-and-domain-aware-model-for | 2302.03908 | null | https://arxiv.org/abs/2302.03908v2 | https://arxiv.org/pdf/2302.03908v2.pdf | Syntax and Domain Aware Model for Unsupervised Program Translation | There is growing interest in software migration as the development of software and society. Manually migrating projects between languages is error-prone and expensive. In recent years, researchers have begun to explore automatic program translation using supervised deep learning techniques by learning from large-scale ... | ['Li Zhang', 'Jia Li', 'Fang Liu'] | 2023-02-08 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-2.82330900e-01 -5.43812215e-01 -6.32288575e-01 -5.24597704e-01
-8.50063801e-01 -6.58996046e-01 9.63414162e-02 -1.34179443e-01
-3.46783519e-01 3.44277352e-01 -3.39167789e-02 -7.41435349e-01
6.37084484e-01 -7.79037058e-01 -1.01491034e+00 -7.94839114e-02
4.43440706e-01 1.84818760e-01 1.34012997e-01 -2.65941858... | [7.64813756942749, 7.947559356689453] |
fe3ebe05-9e7c-4597-b879-c977dfb9049c | music-transcription-by-deep-learning-with | 1712.03228 | null | http://arxiv.org/abs/1712.03228v1 | http://arxiv.org/pdf/1712.03228v1.pdf | Music Transcription by Deep Learning with Data and "Artificial Semantic" Augmentation | In this progress paper the previous results of the single note recognition by
deep learning are presented. The several ways for data augmentation and
"artificial semantic" augmentation are proposed to enhance efficiency of deep
learning approaches for monophonic and polyphonic note recognition by increase
of dimensions... | ['Sergii Stirenko', 'Vadym Ovcharenko', 'Yuri Gordienko', 'Vladyslav Sarnatskyi', 'Mariia Tkachenko', 'Anis Rojbi'] | 2017-12-08 | null | null | null | null | ['music-transcription'] | ['music'] | [ 3.23174208e-01 1.17291607e-01 1.65034592e-01 -1.79296836e-01
-8.59841287e-01 -4.69273806e-01 5.04628301e-01 -1.45842597e-01
-5.98599553e-01 1.00023437e+00 7.26513445e-01 3.79853368e-01
8.17488730e-02 -3.76579523e-01 -3.30739260e-01 -6.42165005e-01
8.27194080e-02 1.14026248e-01 -6.04383126e-02 4.89210598... | [15.684369087219238, 5.30488920211792] |
1c6bacb0-1984-4369-9a24-a9a6fb6fc7ab | salypath-a-deep-based-architecture-for-visual | 2107.00559 | null | https://arxiv.org/abs/2107.00559v1 | https://arxiv.org/pdf/2107.00559v1.pdf | SALYPATH: A Deep-Based Architecture for visual attention prediction | Human vision is naturally more attracted by some regions within their field of view than others. This intrinsic selectivity mechanism, so-called visual attention, is influenced by both high- and low-level factors; such as the global environment (illumination, background texture, etc.), stimulus characteristics (color, ... | ['Rachid Harba', 'Aladine Chetouani', 'Marouane Tliba', 'Mohamed Amine Kerkouri'] | 2021-06-29 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [ 6.01341128e-01 -8.55512992e-02 -2.66193300e-01 -5.56952953e-01
-1.54950231e-01 -9.65910032e-02 6.04708493e-01 2.23988578e-01
-3.17912430e-01 4.95666355e-01 4.08192426e-01 2.32829005e-01
-2.11894624e-02 -5.66004694e-01 -8.95827770e-01 -5.45652807e-01
1.16939798e-01 7.82048404e-02 7.27148712e-01 -6.93797767... | [9.804020881652832, -0.3516635000705719] |
486353ef-2d2f-4e00-9c56-5ab5e66947b2 | pruning-vs-quantization-which-is-better | 2307.02973 | null | https://arxiv.org/abs/2307.02973v1 | https://arxiv.org/pdf/2307.02973v1.pdf | Pruning vs Quantization: Which is Better? | Neural network pruning and quantization techniques are almost as old as neural networks themselves. However, to date only ad-hoc comparisons between the two have been published. In this paper, we set out to answer the question on which is better: neural network quantization or pruning? By answering this question, we ho... | ['Tijmen Blankevoort', 'Arash Behboodi', 'Mart van Baalen', 'Markus Nagel', 'Andrey Kuzmin'] | 2023-07-06 | null | null | null | null | ['network-pruning', 'quantization'] | ['methodology', 'methodology'] | [ 5.20056009e-01 1.80461496e-01 -1.77006140e-01 -5.91856718e-01
-5.37325859e-01 -8.18693116e-02 7.56485164e-02 3.07101578e-01
-9.11387980e-01 6.56023979e-01 -2.30154935e-02 -7.16288626e-01
-2.10825458e-01 -6.56804025e-01 -8.66210222e-01 -4.82980460e-01
-1.62505656e-01 2.15423957e-01 1.84485897e-01 2.23466858... | [8.532161712646484, 3.14851975440979] |
33a81e94-93f4-4801-a941-f62d7a8d4209 | auto-encoding-knowledge-graph-for | 2111.04318 | null | https://arxiv.org/abs/2111.04318v2 | https://arxiv.org/pdf/2111.04318v2.pdf | Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation | Medical report generation, which aims to automatically generate a long and coherent report of a given medical image, has been receiving growing research interests. Existing approaches mainly adopt a supervised manner and heavily rely on coupled image-report pairs. However, in the medical domain, building a large-scale ... | ['Xu sun', 'Sheng Wang', 'Shen Ge', 'Xian Wu', 'Chenyu You', 'Fenglin Liu'] | 2021-11-08 | null | http://proceedings.neurips.cc/paper/2021/hash/876e1c59023b1a0e95808168e1a8ff89-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/876e1c59023b1a0e95808168e1a8ff89-Paper.pdf | neurips-2021-12 | ['medical-report-generation'] | ['medical'] | [ 3.42265874e-01 5.42950571e-01 -3.60996425e-01 -5.32457888e-01
-1.15586090e+00 -2.42657259e-01 4.80336845e-01 1.32294983e-01
-1.11672536e-01 4.94446844e-01 2.76659638e-01 3.37869227e-02
4.31323890e-03 -9.16623294e-01 -6.19018197e-01 -5.31984866e-01
1.86122954e-01 5.93637526e-01 9.55665261e-02 2.95054197... | [15.032795906066895, -1.4013023376464844] |
f41e346b-a6d6-42b6-abe0-a3afe85e2ff5 | bi-directional-joint-neural-networks-for | 2202.13079 | null | https://arxiv.org/abs/2202.13079v1 | https://arxiv.org/pdf/2202.13079v1.pdf | Bi-directional Joint Neural Networks for Intent Classification and Slot Filling | Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks proceeded independently. However, more recently joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong rel... | ['Josiah Poon', 'Henry Weld', 'Huichun Li', 'Siqu Long', 'Soyeon Caren Han'] | 2022-02-26 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 2.73956239e-01 3.93705308e-01 -7.51557589e-01 -6.94441199e-01
-9.53869760e-01 -2.46935755e-01 6.54147267e-01 1.85922563e-01
-5.17785728e-01 7.59520590e-01 5.67641556e-01 -6.34231031e-01
2.82383859e-01 -7.85987437e-01 -2.03124747e-01 -1.34309247e-01
4.35865343e-01 7.68286467e-01 4.13404435e-01 -3.51127207... | [12.511347770690918, 7.347854137420654] |
2c7d54e4-954c-4a7d-b4d9-9996f3a9ee50 | bach-style-music-authoring-system-based-on | 2110.02640 | null | https://arxiv.org/abs/2110.02640v1 | https://arxiv.org/pdf/2110.02640v1.pdf | Bach Style Music Authoring System based on Deep Learning | With the continuous improvement in various aspects in the field of artificial intelligence, the momentum of artificial intelligence with deep learning capabilities into the field of music is coming. The research purpose of this paper is to design a Bach style music authoring system based on deep learning. We use a LSTM... | ['Lican Huang', 'Minghe Kong'] | 2021-10-06 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [-9.39051658e-02 -1.60696179e-01 1.78421691e-01 6.26230091e-02
-1.07563168e-01 -5.34346521e-01 3.13234270e-01 -7.20602870e-01
-3.19783092e-01 5.06596446e-01 2.08545756e-02 6.27799556e-02
-3.29516232e-01 -8.34676981e-01 -4.35386509e-01 -5.99794745e-01
-5.74170761e-02 5.91502488e-01 -4.60067213e-01 -3.57057512... | [16.033348083496094, 5.479020595550537] |
3aa03601-cfa4-421c-aa09-082faf133bb5 | using-objective-words-in-the-reviews-to | 1709.08521 | null | http://arxiv.org/abs/1709.08521v1 | http://arxiv.org/pdf/1709.08521v1.pdf | Using objective words in the reviews to improve the colloquial arabic sentiment analysis | One of the main difficulties in sentiment analysis of the Arabic language is
the presence of the colloquialism. In this paper, we examine the effect of
using objective words in conjunction with sentimental words on sentiment
classification for the colloquial Arabic reviews, specifically Jordanian
colloquial reviews. Th... | ['Omar Al-Harbi'] | 2017-09-25 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-2.77881980e-01 -2.35272534e-02 -8.63696709e-02 -5.97442389e-01
-1.51240364e-01 -7.10541785e-01 6.29101217e-01 4.54193443e-01
-3.91754001e-01 1.00068712e+00 3.97749752e-01 -2.03107193e-01
2.66824037e-01 -6.88525915e-01 -1.30668417e-01 -7.61350393e-01
3.83718818e-01 3.29586059e-01 -1.52615145e-01 -9.06337082... | [11.0400390625, 6.894489765167236] |
a4603d17-0f45-4bf6-9ac7-9f2ab8da1c69 | unsupervised-neural-dependency-parsing | null | null | https://aclanthology.org/D16-1073 | https://aclanthology.org/D16-1073.pdf | Unsupervised Neural Dependency Parsing | null | ['Kewei Tu', 'Yong Jiang', 'Wenjuan Han'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['dependency-grammar-induction'] | ['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... | [-7.221755027770996, 3.7373082637786865] |
9c165da6-eeb6-4016-a1cc-316335ebcf3c | a-flexible-frame-rate-vision-aided-inertial | 2210.12476 | null | https://arxiv.org/abs/2210.12476v1 | https://arxiv.org/pdf/2210.12476v1.pdf | A Flexible-Frame-Rate Vision-Aided Inertial Object Tracking System for Mobile Devices | Real-time object pose estimation and tracking is challenging but essential for emerging augmented reality (AR) applications. In general, state-of-the-art methods address this problem using deep neural networks which indeed yield satisfactory results. Nevertheless, the high computational cost of these methods makes them... | ['Yi-Ping Hung', 'Hsiao-Ching Tseng', 'I-Ju Hsieh', 'Kuan-Wei Tseng', 'Yo-Chung Lau'] | 2022-10-22 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-2.17345566e-01 -5.48048854e-01 -1.26317739e-01 -2.36005589e-01
-4.94103909e-01 -2.82832682e-01 -4.73709852e-02 -4.37518805e-01
-3.92997384e-01 5.43864131e-01 -2.73479283e-01 -3.47310781e-01
1.86340079e-01 -5.92489660e-01 -8.93943310e-01 -5.69400549e-01
1.79503679e-01 3.83615136e-01 4.53037590e-01 1.09313335... | [7.119724273681641, -1.7976369857788086] |
cb33f052-ed45-445c-8e2d-b51e605dddd5 | open-set-adversarial-defense-with-clean | 2202.05953 | null | https://arxiv.org/abs/2202.05953v1 | https://arxiv.org/pdf/2202.05953v1.pdf | Open-set Adversarial Defense with Clean-Adversarial Mutual Learning | Open-set recognition and adversarial defense study two key aspects of deep learning that are vital for real-world deployment. The objective of open-set recognition is to identify samples from open-set classes during testing, while adversarial defense aims to robustify the network against images perturbed by imperceptib... | ['Vishal M. Patel', 'Pong C. Yuen', 'Pramuditha Perera', 'Rui Shao'] | 2022-02-12 | null | null | null | null | ['adversarial-defense', 'open-set-learning'] | ['adversarial', 'miscellaneous'] | [ 4.64485049e-01 -3.05712037e-02 1.85142502e-01 -2.54450798e-01
-1.22586775e+00 -1.08428442e+00 4.13927376e-01 -5.28554499e-01
4.43414412e-02 5.00712633e-01 -7.84698725e-02 -2.62182266e-01
-1.33599669e-01 -9.48075414e-01 -1.08922529e+00 -1.06280959e+00
-2.73836702e-01 -2.24401385e-01 -2.96737641e-01 -3.87108654... | [5.549810886383057, 7.979093551635742] |
dc909f77-2ed7-459e-a121-740fa9b4d4f9 | finite-element-model-updating-using-fish | 1308.2307 | null | http://arxiv.org/abs/1308.2307v1 | http://arxiv.org/pdf/1308.2307v1.pdf | Finite Element Model Updating Using Fish School Search Optimization Method | A recent nature inspired optimization algorithm, Fish School Search (FSS) is
applied to the finite element model (FEM) updating problem. This method is
tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm
are compared with two other metaheuristic algorithms; Genetic Algorithm (GA)
and Particle... | ['T. Marwala', 'F. De Lima Neto', 'L. Mthembu', 'I. Boulkabeit'] | 2013-08-10 | null | null | null | null | ['nature-inspired-optimization-algorithm'] | ['computer-code'] | [ 1.39468715e-01 -5.44331789e-01 3.04336131e-01 3.93048048e-01
5.03213346e-01 -3.15684766e-01 2.85564572e-01 3.42723615e-02
-5.67066789e-01 1.15087366e+00 -1.18887231e-01 -1.16501085e-01
-8.94488752e-01 -1.10809624e+00 -1.52424663e-01 -1.00869083e+00
-2.52790675e-02 5.08978009e-01 5.87856054e-01 -9.16184783... | [5.635908126831055, 3.4574685096740723] |
e0591688-29b1-45fb-89aa-de481babac3a | stacked-convolutional-and-recurrent-neural-1 | 1706.02047 | null | http://arxiv.org/abs/1706.02047v1 | http://arxiv.org/pdf/1706.02047v1.pdf | Stacked Convolutional and Recurrent Neural Networks for Bird Audio Detection | This paper studies the detection of bird calls in audio segments using
stacked convolutional and recurrent neural networks. Data augmentation by
blocks mixing and domain adaptation using a novel method of test mixing are
proposed and evaluated in regard to making the method robust to unseen data.
The contributions of t... | ['Emre Çakır', 'Sharath Adavanne', 'Tuomas Virtanen', 'Konstantinos Drossos'] | 2017-06-07 | null | null | null | null | ['bird-audio-detection'] | ['audio'] | [ 3.41443568e-01 -2.84515768e-01 5.56282759e-01 -4.60766643e-01
-6.99443936e-01 -7.45044291e-01 2.66417086e-01 1.41014203e-01
-7.15712130e-01 5.10336876e-01 1.90270960e-01 -9.93227512e-02
-2.34793186e-01 -4.56385344e-01 -4.19780999e-01 -5.45679748e-01
-8.57756793e-01 -1.33878917e-01 2.36226708e-01 -2.22138360... | [15.226616859436035, 5.311604022979736] |
e63b0780-e6e0-420e-aa23-ab016b6888f8 | localized-trajectories-for-2d-and-3d-action | 1904.05244 | null | http://arxiv.org/abs/1904.05244v1 | http://arxiv.org/pdf/1904.05244v1.pdf | Localized Trajectories for 2D and 3D Action Recognition | The Dense Trajectories concept is one of the most successful approaches in
action recognition, suitable for scenarios involving a significant amount of
motion. However, due to noise and background motion, many generated
trajectories are irrelevant to the actual human activity and can potentially
lead to performance deg... | ['Björn Ottersten', 'Enjie Ghorbel', 'Konstantinos Papadopoulos', 'Girum Demisse', 'Djamila Aouada', 'Michel Antunes'] | 2019-04-10 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 4.15687114e-02 -4.03208464e-01 -3.08000356e-01 -8.79229456e-02
-3.85270596e-01 -4.88008022e-01 7.91349947e-01 -8.02949145e-02
-3.10426205e-01 6.28464222e-01 6.34450853e-01 3.51133704e-01
-2.07114115e-01 -6.45951033e-01 -4.91755515e-01 -8.65160406e-01
2.40532476e-02 1.90079466e-01 5.37233174e-01 -7.30184019... | [7.9792094230651855, 0.36264243721961975] |
dcc35a6d-615d-44d5-a096-8afd949ca32d | leveraging-hidden-positives-for-unsupervised | 2303.15014 | null | https://arxiv.org/abs/2303.15014v1 | https://arxiv.org/pdf/2303.15014v1.pdf | Leveraging Hidden Positives for Unsupervised Semantic Segmentation | Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer (ViT) backbone shows exceptional performance, there is still a lack of consideration for task-specific training guidance and local semantic ... | ['Jae-Pil Heo', 'SuBeen Lee', 'WonJun Moon', 'Hyun Seok Seong'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Seong_Leveraging_Hidden_Positives_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Seong_Leveraging_Hidden_Positives_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 3.32379520e-01 3.83034229e-01 -3.07974279e-01 -5.84763348e-01
-8.84442985e-01 -2.72025555e-01 4.67311680e-01 2.03893200e-01
-3.09097618e-01 5.01531243e-01 2.26512626e-02 2.28576630e-01
-9.22552943e-02 -6.82753861e-01 -8.26618433e-01 -7.49936819e-01
1.11455575e-01 3.91855597e-01 5.85217535e-01 -3.20226103... | [9.561334609985352, 0.591437041759491] |
940b9260-6089-4b4c-ae50-1816e675c22b | dual-reweighted-lp-norm-minimization-for-salt | 1811.09173 | null | https://arxiv.org/abs/1811.09173v3 | https://arxiv.org/pdf/1811.09173v3.pdf | Dual Reweighted Lp-Norm Minimization for Salt-and-pepper Noise Removal | The robust principal component analysis (RPCA), which aims to estimate underlying low-rank and sparse structures from the degraded observation data, has found wide applications in computer vision. It is usually replaced by the principal component pursuit (PCP) model in order to pursue the convex property, leading to th... | ['Huiwen Dong', 'Chuangbai Xiao', 'Jing Yu'] | 2018-11-22 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 2.49679431e-01 -3.68250817e-01 4.44510542e-02 6.11543581e-02
-8.84261847e-01 -2.40060002e-01 1.67989433e-01 -2.24741861e-01
-2.03800574e-01 5.63490987e-01 6.60767019e-01 3.49194445e-02
-3.40983272e-01 -4.41621304e-01 -5.49017549e-01 -1.27488792e+00
-8.03985372e-02 -2.74828523e-01 6.82947412e-02 -8.61743242... | [11.311866760253906, -2.4016928672790527] |
d3e0e706-5359-433d-b52f-db607790e5e0 | end-to-end-sensor-modeling-for-lidar-point | 1907.07748 | null | https://arxiv.org/abs/1907.07748v1 | https://arxiv.org/pdf/1907.07748v1.pdf | End-to-end sensor modeling for LiDAR Point Cloud | Advanced sensors are a key to enable self-driving cars technology. Laser scanner sensors (LiDAR, Light Detection And Ranging) became a fundamental choice due to its long-range and robustness to low light driving conditions. The problem of designing a control software for self-driving cars is a complex task to explicitl... | ['Moemen Abdel-Razek', 'Mohamed Elsobky', 'Khaled Elmadawi', 'Mohamed Zahran', 'Hesham M. Eraqi'] | 2019-07-17 | null | null | null | null | ['sensor-modeling'] | ['computer-vision'] | [-8.57052859e-03 -3.78203452e-01 -1.24545678e-01 -7.62521923e-01
-3.65198255e-01 -1.74334630e-01 5.90018928e-01 7.09642768e-02
-3.45605642e-01 7.25713193e-01 -6.09699249e-01 -4.67559963e-01
-1.63850054e-01 -1.17174029e+00 -7.52991199e-01 -6.09554470e-01
-9.37140882e-02 9.07110095e-01 7.74437249e-01 -5.31771839... | [7.9723801612854, -1.940273404121399] |
625a9acf-8bdb-4edc-aaf5-5180add06319 | alphaevolve-a-learning-framework-to-discover | 2103.16196 | null | https://arxiv.org/abs/2103.16196v2 | https://arxiv.org/pdf/2103.16196v2.pdf | AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment | Alphas are stock prediction models capturing trading signals in a stock market. A set of effective alphas can generate weakly correlated high returns to diversify the risk. Existing alphas can be categorized into two classes: Formulaic alphas are simple algebraic expressions of scalar features, and thus can generalize ... | ['Beng Chin Ooi', 'Zhaojing Luo', 'Gang Chen', 'Meihui Zhang', 'Wei Wang', 'Can Cui'] | 2021-03-30 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-5.96686602e-01 1.83337498e-02 -4.30965692e-01 -3.95407438e-01
-1.42151460e-01 -5.74421763e-01 4.11508262e-01 1.28252253e-01
-2.84011029e-02 1.01182079e+00 -1.54872477e-01 -3.93857867e-01
-3.69847924e-01 -1.65918946e+00 -5.46684027e-01 -4.28679764e-01
-7.32759118e-01 6.07413292e-01 6.59075260e-01 -7.26338267... | [4.543420791625977, 4.175474166870117] |
ac2af3aa-c0c0-48fe-bbd7-117793b6922b | recursive-neural-structural-correspondence | null | null | https://aclanthology.org/P18-1202 | https://aclanthology.org/P18-1202.pdf | Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction | Fine-grained opinion analysis aims to extract aspect and opinion terms from each sentence for opinion summarization. Supervised learning methods have proven to be effective for this task. However, in many domains, the lack of labeled data hinders the learning of a precise extraction model. In this case, unsupervised do... | ['Sinno Jialin Pan', 'Wenya Wang'] | 2018-07-01 | null | null | null | acl-2018-7 | ['extract-aspect', 'fine-grained-opinion-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.09082735e-01 2.86673278e-01 -3.70691568e-01 -7.03112006e-01
-7.82831490e-01 -8.04280996e-01 4.84325886e-01 4.92402136e-01
-2.25745127e-01 1.11163759e+00 5.17185926e-01 -3.42568427e-01
2.05727324e-01 -7.77933657e-01 -5.17726064e-01 -5.41073799e-01
3.65765303e-01 6.03935122e-01 1.80715218e-01 -5.81451237... | [11.376290321350098, 6.802160739898682] |
af07af70-e86c-43e0-87d1-81bbdf6c877f | a-data-driven-latent-semantic-analysis-for | 2207.14687 | null | https://arxiv.org/abs/2207.14687v7 | https://arxiv.org/pdf/2207.14687v7.pdf | A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling | With the advent and popularity of big data mining and huge text analysis in modern times, automated text summarization became prominent for extracting and retrieving important information from documents. This research investigates aspects of automatic text summarization from the perspectives of single and multiple docu... | ['Mahmoud El-Haj', 'Elaine L. L. Pang', 'Daniel F. O. Onah'] | 2022-07-23 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [-1.94652304e-02 5.06472826e-01 -1.77289113e-01 1.22287542e-01
-6.05162382e-01 -3.92077029e-01 8.10323954e-01 1.03749549e+00
-9.74274352e-02 7.57470191e-01 1.25948572e+00 -3.47566158e-02
-5.97342491e-01 -5.68320096e-01 1.38958484e-01 -8.28243077e-01
-1.85476840e-01 6.30475581e-01 6.40191808e-02 -1.05310082... | [12.407842636108398, 9.571134567260742] |
19099f8a-1dce-4a70-aacd-64c6fca7bff4 | evaluating-and-modelling-hanabi-playing | 1704.07069 | null | http://arxiv.org/abs/1704.07069v1 | http://arxiv.org/pdf/1704.07069v1.pdf | Evaluating and Modelling Hanabi-Playing Agents | Agent modelling involves considering how other agents will behave, in order
to influence your own actions. In this paper, we explore the use of agent
modelling in the hidden-information, collaborative card game Hanabi. We
implement a number of rule-based agents, both from the literature and of our
own devising, in addi... | ['Diego Perez-Liebana', 'Joseph Walton-Rivers', 'Piers R. Williams', 'Simon M. Lucas', 'Richard Bartle'] | 2017-04-24 | null | null | null | null | ['game-of-hanabi'] | ['playing-games'] | [-1.45912662e-01 5.46880305e-01 2.39962429e-01 5.51391095e-02
-4.20955628e-01 -4.29801971e-01 1.08914292e+00 -2.56759167e-01
-8.28571379e-01 1.09844983e+00 5.16586721e-01 -5.06023347e-01
-3.68294179e-01 -1.02443123e+00 -2.69063145e-01 -6.26900554e-01
-3.08515906e-01 1.28657186e+00 5.66650033e-01 -6.55414999... | [3.4856793880462646, 1.5815770626068115] |
8590d353-4e3b-4578-85a0-c284d9479bbf | light-sampling-field-and-brdf-representation-1 | 2304.05472 | null | https://arxiv.org/abs/2304.05472v1 | https://arxiv.org/pdf/2304.05472v1.pdf | Light Sampling Field and BRDF Representation for Physically-based Neural Rendering | Physically-based rendering (PBR) is key for immersive rendering effects used widely in the industry to showcase detailed realistic scenes from computer graphics assets. A well-known caveat is that producing the same is computationally heavy and relies on complex capture devices. Inspired by the success in quality and e... | ['Yajie Zhao', 'Yunxuan Cai', 'Wenbin Teng', 'Hanyuan Xiao', 'Jing Yang'] | 2023-04-11 | light-sampling-field-and-brdf-representation | https://openreview.net/forum?id=yYEb8v65X8 | https://openreview.net/pdf?id=yYEb8v65X8 | iclr-2023-3 | ['neural-rendering'] | ['computer-vision'] | [ 3.60383630e-01 -9.84586701e-02 6.95291400e-01 -4.33031648e-01
-3.32339883e-01 -2.25766554e-01 5.46305239e-01 -4.48770851e-01
1.31878063e-01 6.45872712e-01 -1.01899758e-01 -4.75125968e-01
2.12799147e-01 -1.22197056e+00 -7.76679993e-01 -5.29622674e-01
2.04286858e-01 6.87103346e-02 -2.54533757e-02 -3.40589195... | [9.58371353149414, -3.1157357692718506] |
90cf0b25-67e8-4614-9d2e-4e6fd9664259 | is-a-green-screen-really-necessary-for-real | 2011.11961 | null | https://arxiv.org/abs/2011.11961v4 | https://arxiv.org/pdf/2011.11961v4.pdf | MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition | Existing portrait matting methods either require auxiliary inputs that are costly to obtain or involve multiple stages that are computationally expensive, making them less suitable for real-time applications. In this work, we present a light-weight matting objective decomposition network (MODNet) for portrait matting i... | ['Qiong Yan', 'Kaican Li', 'Jiayu Sun', 'Rynson W. H. Lau', 'Zhanghan Ke'] | 2020-11-24 | null | null | null | null | ['video-matting'] | ['computer-vision'] | [ 2.06253529e-01 -4.27821785e-01 -2.93540120e-01 -2.86842853e-01
-7.35663354e-01 -2.80051947e-01 2.86900192e-01 -5.49791694e-01
-2.44905084e-01 5.15475392e-01 -3.85132432e-02 -4.63734865e-02
1.67563409e-01 -6.82424426e-01 -7.40679562e-01 -6.41816795e-01
3.96996439e-01 2.33826116e-01 1.45514637e-01 -2.26865858... | [10.632401466369629, -0.8946421146392822] |
84f2e4b9-146e-41c4-85aa-226e694297b6 | netwalk-a-flexible-deep-embedding-approach | null | null | https://www.kdd.org/kdd2018/accepted-papers/view/netwalk-a-flexible-deep-embedding-approach-for-anomaly-detection-in-dynamic | https://dl.acm.org/doi/pdf/10.1145/3219819.3220024 | NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks | Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose “behaviors’’ deviate from underlying majority of the network, in a real-time fashion. Recently... | ['Wenchao Yu; Wei Cheng; Charu Aggarwal; Kai Zhang; Haifeng Chen; Wei Wang'] | 2018-07-19 | null | null | null | acm-sigkdd-international-conference-on | ['learning-network-representations'] | ['methodology'] | [-3.67342904e-02 3.36773545e-02 4.17145677e-02 4.09239829e-02
3.49732012e-01 -5.26863694e-01 3.42650115e-01 5.54977536e-01
1.35086998e-02 3.37837130e-01 -2.30451658e-01 -1.62029326e-01
-5.30598879e-01 -1.25172675e+00 -6.14406288e-01 -7.57065058e-01
-7.90695369e-01 6.49878681e-01 4.08650577e-01 -2.22343326... | [7.1270904541015625, 6.124075889587402] |
cc5bab27-f0ee-4b71-bbac-e59642b0aa12 | frankmocap-fast-monocular-3d-hand-and-body | 2008.08324 | null | https://arxiv.org/abs/2008.08324v1 | https://arxiv.org/pdf/2008.08324v1.pdf | FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration | Although the essential nuance of human motion is often conveyed as a combination of body movements and hand gestures, the existing monocular motion capture approaches mostly focus on either body motion capture only ignoring hand parts or hand motion capture only without considering body motion. In this paper, we presen... | ['Hanbyul Joo', 'Yu Rong', 'Takaaki Shiratori'] | 2020-08-19 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [-5.66740096e-01 -7.41388738e-01 -5.63070178e-01 2.78065473e-01
-4.00975466e-01 -6.93767846e-01 4.65242773e-01 -1.26177394e+00
-2.85147697e-01 4.94904816e-01 5.62212288e-01 2.13152096e-01
4.57417786e-01 -3.88767302e-01 -4.87171888e-01 -5.83451211e-01
2.77780414e-01 5.85024238e-01 3.24031293e-01 -8.72600377... | [7.158892631530762, -0.6883317232131958] |
e3936dbf-261d-4268-827e-fdaeab4d1a87 | employing-linear-prediction-coding-in-feature | null | null | https://aclanthology.org/O13-5008 | https://aclanthology.org/O13-5008.pdf | 雜訊環境下應用線性估測編碼於特徵時序列之強健性語音辨識 (Employing Linear Prediction Coding in Feature Time Sequences for Robust Speech Recognition in Noisy Environments) [In Chinese] | null | ['Jeih-weih Hung', 'Wen-yu Tseng', 'Hao-teng Fan'] | 2013-12-01 | employing-linear-prediction-coding-in-feature-3 | https://aclanthology.org/O13-1015 | https://aclanthology.org/O13-1015.pdf | roclingijclclp-2013-10 | ['robust-speech-recognition'] | ['speech'] | [-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... | [-7.289156913757324, 3.623215675354004] |
166f6b61-83b9-4737-8ee0-7656794864ea | as-projective-as-possible-image-stitching | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Zaragoza_As-Projective-As-Possible_Image_Stitching_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Zaragoza_As-Projective-As-Possible_Image_Stitching_2013_CVPR_paper.pdf | As-Projective-As-Possible Image Stitching with Moving DLT | We investigate projective estimation under model inadequacies, i.e., when the underpinning assumptions of the projective model are not fully satisfied by the data. We focus on the task of image stitching which is customarily solved by estimating a projective warp -a model that is justified when the scene is planar or w... | ['David Suter', 'Tat-Jun Chin', 'Michael S. Brown', 'Julio Zaragoza'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['image-stitching'] | ['computer-vision'] | [ 7.11079895e-01 4.54163626e-02 2.21476942e-01 2.44535264e-02
-3.83221358e-01 -7.17847288e-01 8.29070985e-01 -3.62190038e-01
-2.99557865e-01 5.49279451e-01 -2.43720170e-02 -1.63086653e-01
-1.00397021e-01 -2.97949404e-01 -7.34724164e-01 -8.86115611e-01
9.23851728e-02 2.37866536e-01 2.06443787e-01 -1.59055263... | [9.367898941040039, -2.3596506118774414] |
89c36a5a-bc0b-4683-b868-199d76897a76 | learning-high-precision-bounding-box-for | 2106.01883 | null | https://arxiv.org/abs/2106.01883v5 | https://arxiv.org/pdf/2106.01883v5.pdf | Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence | Existing rotated object detectors are mostly inherited from the horizontal detection paradigm, as the latter has evolved into a well-developed area. However, these detectors are difficult to perform prominently in high-precision detection due to the limitation of current regression loss design, especially for objects w... | ['Junchi Yan', 'Qi Tian', 'Wentao Wang', 'Qi Ming', 'Jirui Yang', 'Xiaojiang Yang', 'Xue Yang'] | 2021-06-03 | null | http://proceedings.neurips.cc/paper/2021/hash/98f13708210194c475687be6106a3b84-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/98f13708210194c475687be6106a3b84-Paper.pdf | neurips-2021-12 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-2.51981854e-01 -2.31526002e-01 -1.65300593e-01 -1.03071921e-01
-5.66802204e-01 -4.24196571e-01 2.69664854e-01 -2.45799109e-01
-5.96281469e-01 2.55766034e-01 -1.21343926e-01 -2.44059607e-01
-1.43663213e-01 -6.12004101e-01 -5.74613690e-01 -1.01570117e+00
1.44249320e-01 1.45336539e-01 5.51955283e-01 -2.45414376... | [8.620624542236328, -0.8266986012458801] |
e34053b1-347c-4e46-a4e3-b3225b8550d5 | on-the-construction-of-distribution-free | 2203.03150 | null | https://arxiv.org/abs/2203.03150v1 | https://arxiv.org/pdf/2203.03150v1.pdf | On the Construction of Distribution-Free Prediction Intervals for an Image Regression Problem in Semiconductor Manufacturing | The high-volume manufacturing of the next generation of semiconductor devices requires advances in measurement signal analysis. Many in the semiconductor manufacturing community have reservations about the adoption of deep learning; they instead prefer other model-based approaches for some image regression problems, an... | ['Serap A. Savari', 'Inimfon I. Akpabio'] | 2022-03-07 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 2.70255595e-01 -6.81722760e-02 3.12322471e-02 -5.51645458e-01
-1.23007238e+00 -4.98488247e-02 2.91313440e-01 2.56354451e-01
-1.44459397e-01 8.10722768e-01 -4.01227832e-01 -6.75440967e-01
-7.10744262e-01 -6.93041205e-01 -4.47963655e-01 -8.28121483e-01
3.12539518e-01 7.87239015e-01 -1.77303150e-01 1.81576852... | [7.047554969787598, 2.1998209953308105] |
5ec652c4-0cd5-416c-9d10-c5ba5222c936 | applications-of-artificial-intelligence-1 | 2105.15103 | null | https://arxiv.org/abs/2105.15103v1 | https://arxiv.org/pdf/2105.15103v1.pdf | Applications of Artificial Intelligence, Machine Learning and related techniques for Computer Networking Systems | This article presents a primer/overview of applications of Artificial Intelligence and Machine Learning (AI/ML) techniques to address problems in the domain of computer networking. In particular, the techniques have been used to support efficient and accurate traffic prediction, traffic classification, anomaly detectio... | ['Krishna M. Sivalingam'] | 2021-04-21 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.55456156e-01 -3.54129463e-01 -6.31741643e-01 -4.81007665e-01
1.20210379e-01 -1.27036765e-01 1.56419277e-01 1.25932798e-01
-1.32643104e-01 9.83106792e-01 -8.53792250e-01 -9.81617332e-01
-7.83039987e-01 -7.30762303e-01 9.54748541e-02 -4.48017299e-01
-7.71107435e-01 9.95507956e-01 3.46708000e-01 2.15782430... | [5.080911636352539, 7.200492858886719] |
98107cbe-b51e-4511-8598-ed490f20f334 | on-recognizing-transparent-objects-in | 1606.01001 | null | http://arxiv.org/abs/1606.01001v1 | http://arxiv.org/pdf/1606.01001v1.pdf | On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities | Current object recognition methods fail on object sets that include both
diffuse, reflective and transparent materials, although they are very common in
domestic scenarios. We show that a combination of cues from multiple sensor
modalities, including specular reflectance and unavailable depth information,
allows us to ... | ['Paul Plöger', 'Frederik Hegger', 'Alexander Hagg'] | 2016-06-03 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 7.68185735e-01 -4.56105977e-01 7.19570294e-02 -4.80960339e-01
-3.78552616e-01 -5.03559530e-01 4.14232790e-01 -5.83630562e-01
4.77881804e-02 5.72777569e-01 1.67643860e-01 3.86333048e-01
-1.41220748e-01 -8.86665940e-01 -2.44798571e-01 -7.77971208e-01
1.46785483e-01 1.66086644e-01 3.61937046e-01 -2.93372601... | [9.696157455444336, -2.790952682495117] |
a14eee39-9744-4de3-80a6-53170534a826 | learning-illuminant-estimation-from-object | 1805.09264 | null | http://arxiv.org/abs/1805.09264v1 | http://arxiv.org/pdf/1805.09264v1.pdf | Learning Illuminant Estimation from Object Recognition | In this paper we present a deep learning method to estimate the illuminant of
an image. Our model is not trained with illuminant annotations, but with the
objective of improving performance on an auxiliary task such as object
recognition. To the best of our knowledge, this is the first example of a deep
learning archit... | ['Joost Van de Weijer', 'Raimondo Schettini', 'Marco Buzzelli'] | 2018-05-23 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 1.75243855e-01 -3.11901689e-01 2.79449880e-01 -5.69839597e-01
-6.36730433e-01 -6.53343797e-01 6.60546780e-01 -2.42917687e-01
-5.16819239e-01 7.02914596e-01 -1.36860758e-01 -1.68079048e-01
2.61765450e-01 -4.37364072e-01 -9.29874778e-01 -8.18469286e-01
1.03782669e-01 4.81610119e-01 -1.80930406e-01 1.14852466... | [10.270445823669434, -2.560140609741211] |
93268256-8a0c-42a5-98e3-4900bfbfc04c | task-adaptive-few-shot-node-classification | 2206.11972 | null | https://arxiv.org/abs/2206.11972v1 | https://arxiv.org/pdf/2206.11972v1.pdf | Task-Adaptive Few-shot Node Classification | Node classification is of great importance among various graph mining tasks. In practice, real-world graphs generally follow the long-tail distribution, where a large number of classes only consist of limited labeled nodes. Although Graph Neural Networks (GNNs) have achieved significant improvements in node classificat... | ['Jundong Li', 'Chen Chen', 'Chuxu Zhang', 'Kaize Ding', 'Song Wang'] | 2022-06-23 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 2.35375583e-01 8.71917233e-02 -5.25269568e-01 -2.94079095e-01
-2.90032327e-01 -1.36269927e-01 5.39291501e-01 3.17863792e-01
-2.89639562e-01 5.84436655e-01 -8.14812034e-02 -2.20265895e-01
-2.21841335e-01 -1.20088184e+00 -3.79998982e-01 -7.54131913e-01
7.90832713e-02 2.78959543e-01 4.29556519e-01 -2.55334824... | [7.427776336669922, 6.159577369689941] |
bf73e1c8-d168-4c79-bfaa-f3a1968ea423 | image-retargeting-by-content-aware-synthesis | 1403.6566 | null | http://arxiv.org/abs/1403.6566v2 | http://arxiv.org/pdf/1403.6566v2.pdf | Image Retargeting by Content-Aware Synthesis | Real-world images usually contain vivid contents and rich textural details,
which will complicate the manipulation on them. In this paper, we design a new
framework based on content-aware synthesis to enhance content-aware image
retargeting. By detecting the textural regions in an image, the textural image
content can ... | ['Tong-Yee Lee', 'Xiaopeng Zhang', 'Weiming Dong', 'Fuzhang Wu', 'Yan Kong', 'Xing Mei'] | 2014-03-26 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 7.67141640e-01 -8.74032974e-02 -3.61844003e-02 -5.11043929e-02
-5.62865473e-02 -5.00416398e-01 4.80538577e-01 -8.97037834e-02
-1.28318176e-01 4.38720822e-01 3.56177002e-01 6.18290976e-02
1.60383299e-01 -8.60160112e-01 -5.38076699e-01 -6.72075689e-01
3.30543160e-01 -2.56471813e-01 9.12561297e-01 -4.93960947... | [11.127891540527344, -1.1675915718078613] |
aa2f7984-f5ac-499b-b27f-59e87517c4bf | individualized-rank-aggregation-using-nuclear | 1410.0860 | null | http://arxiv.org/abs/1410.0860v1 | http://arxiv.org/pdf/1410.0860v1.pdf | Individualized Rank Aggregation using Nuclear Norm Regularization | In recent years rank aggregation has received significant attention from the
machine learning community. The goal of such a problem is to combine the
(partially revealed) preferences over objects of a large population into a
single, relatively consistent ordering of those objects. However, in many
cases, we might not w... | ['Yu Lu', 'Sahand N. Negahban'] | 2014-10-03 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [ 2.34295517e-01 1.46007817e-02 -8.06473121e-02 -8.17740977e-01
-1.05773914e+00 -7.10393190e-01 2.79429048e-01 2.47364223e-01
-7.05772042e-01 9.36504602e-01 8.05124283e-01 2.26770472e-02
-7.58071899e-01 -5.82901239e-01 -5.19819200e-01 -8.14956605e-01
-5.00820994e-01 1.02256298e+00 -3.55671644e-01 -1.61699846... | [9.523265838623047, 5.506854057312012] |
0fe8c421-1d29-4be7-bdc1-df571d22e15b | enhancement-on-model-interpretability-and | 2204.03173 | null | https://arxiv.org/abs/2204.03173v3 | https://arxiv.org/pdf/2204.03173v3.pdf | Automated Sleep Staging via Parallel Frequency-Cut Attention | This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance. The framework consists of two parts: the first part extracts informative features by partitioning the input EEG spectrogram... | ['Shigehiko Kanaya', 'Wei Chen', 'Lingwei Zhu', 'MD Altaf-Ul-Amin', 'Naoaki Ono', 'Toshiyo Tamura', 'Ming Huang', 'Ziwei Yang', 'Zheng Chen'] | 2022-04-07 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 1.02617100e-01 1.32008240e-01 1.15406156e-01 -4.09378141e-01
-5.19736230e-01 -2.80539989e-01 2.20316634e-01 2.73903191e-01
-4.98946071e-01 5.87448955e-01 3.50697637e-01 -5.41694053e-02
-5.35867572e-01 -1.56005025e-01 1.03172027e-01 -6.55836463e-01
-5.26369512e-01 1.27317056e-01 -9.26389396e-02 1.67254061... | [13.49147891998291, 3.5084829330444336] |
f3e57e61-d886-4e74-92de-44ae69d772b5 | supervised-knowledge-may-hurt-novel-class | 2306.03648 | null | https://arxiv.org/abs/2306.03648v1 | https://arxiv.org/pdf/2306.03648v1.pdf | Supervised Knowledge May Hurt Novel Class Discovery Performance | Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset by leveraging prior knowledge of a labeled set comprising disjoint but related classes. Given that most existing literature focuses primarily on utilizing supervised knowledge from a labeled set at the methodology level, this paper consi... | ['Haojin Yang', 'Christoph Meinel', 'Di Hu', 'Ben Dai', 'Jona Otholt', 'Ziyun Li'] | 2023-06-06 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery', 'semantic-textual-similarity', 'semantic-similarity'] | ['computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [ 2.28091583e-01 1.48852915e-01 -4.00151044e-01 -6.97544873e-01
-3.62858951e-01 -6.36695445e-01 6.19578123e-01 1.98679954e-01
-3.25318009e-01 7.14402318e-01 2.80080196e-02 -1.91253617e-01
-4.12304699e-01 -7.64801741e-01 -5.63413143e-01 -5.26867151e-01
1.99698746e-01 1.53347760e-01 3.28744441e-01 -3.53604904... | [9.657169342041016, 3.0069901943206787] |
db06ccb1-fe5e-4a45-afd2-aad89842ca30 | performance-comparison-of-3d-correspondence | 1909.00866 | null | https://arxiv.org/abs/1909.00866v1 | https://arxiv.org/pdf/1909.00866v1.pdf | Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds | Plant Phenomics can be used to monitor the health and the growth of plants. Computer vision applications like stereo reconstruction, image retrieval, object tracking, and object recognition play an important role in imaging based plant phenotyping. This paper offers a comparative evaluation of some popular 3D correspon... | ['Shiva Azimi', 'Tapan K. Gandhi'] | 2019-09-02 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.06379586e-01 -5.85673034e-01 -2.08166465e-01 -3.49004716e-02
-4.24966753e-01 -1.08591449e+00 5.14437735e-01 6.60463572e-01
1.99924991e-01 1.27712395e-02 -5.05882084e-01 -4.78023648e-01
-4.31160480e-01 -8.11384201e-01 -2.23509416e-01 -7.62239099e-01
1.16207730e-02 1.02301049e+00 2.95223176e-01 -2.73338184... | [8.952740669250488, -1.7834653854370117] |
aeb57cbb-436a-449e-9f5a-aec420c6a87f | enriching-relation-extraction-with-openie | 2212.09376 | null | https://arxiv.org/abs/2212.09376v1 | https://arxiv.org/pdf/2212.09376v1.pdf | Enriching Relation Extraction with OpenIE | Relation extraction (RE) is a sub-discipline of information extraction (IE) which focuses on the prediction of a relational predicate from a natural-language input unit (such as a sentence, a clause, or even a short paragraph consisting of multiple sentences and/or clauses). Together with named-entity recognition (NER)... | ['Martin Theobald', 'Maria Biryukov', 'Alessandro Temperoni'] | 2022-12-19 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 2.93233931e-01 6.75821185e-01 -4.98258203e-01 -3.00599456e-01
-7.89332092e-01 -7.26612508e-01 6.50716066e-01 6.18663609e-01
-3.71064514e-01 1.11135030e+00 5.27857363e-01 -4.68022555e-01
-8.24614763e-02 -1.12988555e+00 -8.50045025e-01 -2.61667401e-01
-2.16874480e-01 6.56206727e-01 2.25423113e-01 -3.78956169... | [9.397538185119629, 8.621182441711426] |
c1196b97-ce6f-4f3f-b97b-5e57a58d7922 | shrec-2021-classification-in-cryo-electron | 2203.10035 | null | https://arxiv.org/abs/2203.10035v1 | https://arxiv.org/pdf/2203.10035v1.pdf | SHREC 2021: Classification in cryo-electron tomograms | Cryo-electron tomography (cryo-ET) is an imaging technique that allows three-dimensional visualization of macro-molecular assemblies under near-native conditions. Cryo-ET comes with a number of challenges, mainly low signal-to-noise and inability to obtain images from all angles. Computational methods are key to analyz... | ['Fa Zhang', 'Xuefeng Cui', 'Cheng Chen', 'Yaoyu Wang', 'Min Xu', 'Sinuo Liu', 'Xiangrui Zeng', 'Konstantinos Moustakas', 'Evangelia I. Zacharaki', 'Stavros Gerolymatos', 'Giorgos Papoulias', 'Filiz Bunyak', 'Tommi White', 'Nguyen P. Nguyen', 'Emmanuel Moebel', 'Daisuke Kihara', 'Xiao Wang', 'Friedrich Förster', 'Remco... | 2022-03-18 | null | null | null | null | ['template-matching', 'electron-tomography', 'tomographic-reconstructions'] | ['computer-vision', 'medical', 'medical'] | [ 2.08657250e-01 -6.93538785e-01 2.79471487e-01 -2.63172150e-01
-9.77107882e-01 -6.57881737e-01 4.82033461e-01 2.21385345e-01
-5.22681415e-01 9.64095771e-01 -3.52952629e-01 -3.01498890e-01
8.07783678e-02 -4.08844709e-01 -7.39009917e-01 -1.09143710e+00
-2.34928846e-01 1.06079221e+00 3.87577981e-01 8.00118819... | [13.401782989501953, -3.0903818607330322] |
a429e41c-ddd6-4442-b164-ece3e47cf53b | an-overview-of-healthcare-data-analytics-with | 2111.14623 | null | https://arxiv.org/abs/2111.14623v1 | https://arxiv.org/pdf/2111.14623v1.pdf | An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic | In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of challenges in big data problems and describe how innovative analyti... | ['Weng Kee Wong', 'Chee Wei Tan', 'Oleksandr Sverdlov', 'Yevgen Ryeznik', 'Zhe Fei'] | 2021-11-25 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 1.32727534e-01 -2.96355098e-01 -6.35410696e-02 5.48389144e-02
-2.94968218e-01 -1.05241120e-01 1.48266479e-01 8.07973683e-01
-3.91206741e-01 9.32643712e-01 2.17714339e-01 -3.94017547e-01
-7.99002528e-01 -9.68871057e-01 -1.89209610e-01 -8.41144264e-01
-3.36492836e-01 1.27174783e+00 -2.70209312e-01 -4.37451452... | [6.03144645690918, 4.973541259765625] |
46c7e92b-b0f6-4025-b74a-090ec52737ac | ame-cam-attentive-multiple-exit-cam-for | 2306.14505 | null | https://arxiv.org/abs/2306.14505v1 | https://arxiv.org/pdf/2306.14505v1.pdf | AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor | Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning. To reduce the labor and expertise required for labeling, weakly-supervised semantic segmentation (WSSS) methods with class activation mapping (CAM) have been proposed. However... | ['Tsung-Yi Ho', 'Yiyu Shi', 'Xinrong Hu', 'Yu-Jen Chen'] | 2023-06-26 | null | null | null | null | ['weakly-supervised-segmentation', 'weakly-supervised-semantic-segmentation', 'tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'medical'] | [ 5.10715365e-01 3.86031151e-01 -2.47144982e-01 -5.37169695e-01
-1.12185442e+00 -1.48848221e-01 3.98484468e-01 2.33692780e-01
-7.41383553e-01 7.54249990e-01 1.97401658e-01 -1.73655644e-01
1.55843467e-01 -5.98657906e-01 -1.44394830e-01 -6.52149379e-01
3.21749181e-01 5.11976779e-01 7.13408768e-01 9.18316096... | [14.726114273071289, -2.1976664066314697] |
6a960d3b-6452-4954-9ba6-274c46d3ca32 | joint-symmetry-detection-and-shape-matching | 2112.02713 | null | https://arxiv.org/abs/2112.02713v2 | https://arxiv.org/pdf/2112.02713v2.pdf | Joint Symmetry Detection and Shape Matching for Non-Rigid Point Cloud | Despite the success of deep functional maps in non-rigid 3D shape matching, there exists no learning framework that models both self-symmetry and shape matching simultaneously. This is despite the fact that errors due to symmetry mismatch are a major challenge in non-rigid shape matching. In this paper, we propose a no... | ['Maks Ovsjanikov', 'Abhishek Sharma'] | 2021-12-05 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [-6.76626712e-03 -1.40477687e-01 -1.50371029e-03 -5.96444130e-01
-8.41137886e-01 -8.72839928e-01 7.22728431e-01 1.40541136e-01
-2.53487825e-01 1.55202717e-01 4.27001685e-01 -7.30719343e-02
-1.26768366e-01 -8.06055903e-01 -8.86259377e-01 -4.77302372e-01
4.01851803e-01 6.99837208e-01 2.49784097e-01 -3.11523229... | [8.444252967834473, -2.6582179069519043] |
e9e08a0a-1a0c-440f-a95b-3afc37a6302c | hdr-video-reconstruction-with-a-large-dynamic | 2304.04773 | null | https://arxiv.org/abs/2304.04773v2 | https://arxiv.org/pdf/2304.04773v2.pdf | HDR Video Reconstruction with a Large Dynamic Dataset in Raw and sRGB Domains | High dynamic range (HDR) video reconstruction is attracting more and more attention due to the superior visual quality compared with those of low dynamic range (LDR) videos. The availability of LDR-HDR training pairs is essential for the HDR reconstruction quality. However, there are still no real LDR-HDR pairs for dyn... | ['Jingyu Yang', 'Zhenyu Zhou', 'Xuanwu Yin', 'Biting Yu', 'Yubo Peng', 'Huanjing Yue'] | 2023-04-10 | null | null | null | null | ['video-reconstruction', 'hdr-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.49107152e-01 -3.97926062e-01 1.73615664e-02 -2.88073361e-01
-4.40889776e-01 -1.86375871e-01 3.47142369e-01 -7.46068358e-01
-3.31706971e-01 6.42682731e-01 4.18369114e-01 -1.01376869e-01
1.02389172e-01 -8.08504164e-01 -8.90698075e-01 -7.43897796e-01
2.56208241e-01 -1.18847407e-01 5.41075468e-01 -3.58572513... | [10.887472152709961, -2.1245927810668945] |
5b2e7c64-b76b-4a5e-8381-17c7f7421a0e | indiscernible-object-counting-in-underwater | 2304.11677 | null | https://arxiv.org/abs/2304.11677v1 | https://arxiv.org/pdf/2304.11677v1.pdf | Indiscernible Object Counting in Underwater Scenes | Recently, indiscernible scene understanding has attracted a lot of attention in the vision community. We further advance the frontier of this field by systematically studying a new challenge named indiscernible object counting (IOC), the goal of which is to count objects that are blended with respect to their surroundi... | ['Luc van Gool', 'Deng-Ping Fan', 'Christos Sakaridis', 'Ce Liu', 'Yun Liu', 'Zhaochong An', 'Guolei Sun'] | 2023-04-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_Indiscernible_Object_Counting_in_Underwater_Scenes_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_Indiscernible_Object_Counting_in_Underwater_Scenes_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-counting'] | ['computer-vision'] | [-6.37184829e-02 -5.75259984e-01 1.99733824e-01 -1.54173076e-01
-5.21490932e-01 -6.57764852e-01 6.98242903e-01 2.87578255e-02
-1.12202334e+00 4.32455063e-01 2.48064891e-01 1.19286805e-01
9.33961272e-02 -7.83378005e-01 -8.01811874e-01 -6.01524174e-01
-2.07267538e-01 5.95723629e-01 5.25201976e-01 9.06522851... | [8.779523849487305, 0.015572444535791874] |
8559bc13-1bb5-45af-a0b4-73a281d72cb2 | a-1d-cnn-based-deep-learning-technique-for | 2105.00528 | null | https://arxiv.org/abs/2105.00528v1 | https://arxiv.org/pdf/2105.00528v1.pdf | A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors | Internet of Things (IoT) enabled wearable sensors for health monitoring are widely used to reduce the cost of personal healthcare and improve quality of life. The sleep apnea-hypopnea syndrome, characterized by the abnormal reduction or pause in breathing, greatly affects the quality of sleep of an individual. This pap... | ['Deepu John', 'Barry Cardiff', 'Arlene John'] | 2021-05-02 | null | null | null | null | ['sleep-apnea-detection'] | ['medical'] | [ 2.93384522e-01 -8.51347074e-02 -6.70749918e-02 -3.50638419e-01
-2.97705799e-01 -5.29478453e-02 -6.01589680e-01 4.60867375e-01
-5.46459138e-01 7.15724647e-01 3.42761613e-02 -1.57727778e-01
-3.45100433e-01 -7.81514764e-01 -1.01792552e-01 -6.39660120e-01
-3.91799271e-01 -4.22904640e-02 -1.73655733e-01 2.01603904... | [13.77737045288086, 3.3968377113342285] |
165125ae-994c-4ef9-b906-902279abe1ed | unsupervised-cross-lingual-information | 1805.00879 | null | http://arxiv.org/abs/1805.00879v1 | http://arxiv.org/pdf/1805.00879v1.pdf | Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only | We propose a fully unsupervised framework for ad-hoc cross-lingual
information retrieval (CLIR) which requires no bilingual data at all. The
framework leverages shared cross-lingual word embedding spaces in which terms,
queries, and documents can be represented, irrespective of their actual
language. The shared embeddi... | ['Ivan Vulić', 'Goran Glavaš', 'Simone Paolo Ponzetto', 'Robert Litschko'] | 2018-05-02 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-1.69056103e-01 -2.66764313e-01 -4.72408712e-01 -2.40016907e-01
-1.45125747e+00 -1.02717412e+00 1.09658837e+00 2.10130155e-01
-8.49799871e-01 4.13918644e-01 4.82019246e-01 -5.41513324e-01
-1.38564005e-01 -4.46636230e-01 -4.77843165e-01 -3.03466529e-01
1.01325303e-01 7.75369406e-01 -2.84954697e-01 -6.25163496... | [11.242423057556152, 9.839028358459473] |
9dfc7324-89b3-4c2d-ae96-336c71f6239a | math-agents-computational-infrastructure | 2307.02502 | null | https://arxiv.org/abs/2307.02502v1 | https://arxiv.org/pdf/2307.02502v1.pdf | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving, yet their genomics application is limited. This project introduces Math Agents and mathematical embedding as fresh... | ['Renato P. dos Santos', 'Eric Roland', 'Takashi Kido', 'Melanie Swan'] | 2023-07-04 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [-1.94522738e-01 5.10910094e-01 4.30166610e-02 -2.08520323e-01
-6.00812137e-01 -5.78023076e-01 5.39379060e-01 6.51816070e-01
-2.30738297e-01 6.09992981e-01 3.97596151e-01 -8.50302577e-01
-5.76850951e-01 -1.15566623e+00 -8.94183695e-01 -1.37638971e-01
-2.18195379e-01 1.17793489e+00 -2.20048398e-01 -4.78898704... | [9.381101608276367, 7.22774076461792] |
5912659a-85d3-488b-8ef6-68719befb435 | reconsider-improved-re-ranking-using-span | null | null | https://aclanthology.org/2021.naacl-main.100 | https://aclanthology.org/2021.naacl-main.100.pdf | RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering | State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples. This training scheme possibly explains empirical observations that these models achieve... | ['Wen-tau Yih', 'Yashar Mehdad', 'Sewon Min', 'Srinivasan Iyer'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['triviaqa'] | ['miscellaneous'] | [ 4.15606111e-01 6.59770191e-01 -2.87049613e-03 -4.31194901e-01
-1.99143350e+00 -8.32646132e-01 2.53061235e-01 5.97522020e-01
-5.74594080e-01 9.23234344e-01 5.63289225e-01 -5.36159992e-01
-2.96039402e-01 -6.66037858e-01 -7.45254695e-01 1.30861029e-01
9.75092724e-02 9.60530400e-01 7.40497410e-01 -7.36647666... | [11.301006317138672, 8.008299827575684] |
d39956bd-54ef-4dd9-8481-83c8627050e0 | one-shot-hyperspectral-imaging-using-faced | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Takatani_One-Shot_Hyperspectral_Imaging_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Takatani_One-Shot_Hyperspectral_Imaging_CVPR_2017_paper.pdf | One-Shot Hyperspectral Imaging Using Faced Reflectors | Hyperspectral imaging is a useful technique for various computer vision tasks such as material recognition. However, such technique usually requires an expensive and professional setup and is time-consuming because a conventional hyperspectral image consists of a large number of observations. In this paper, we propose ... | ['Tsuyoshi Takatani', 'Takahito Aoto', 'Yasuhiro Mukaigawa'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['material-recognition'] | ['computer-vision'] | [ 1.00764287e+00 -6.95994198e-01 4.81209606e-01 -1.12797907e-02
-2.03104019e-01 -6.64434910e-01 2.80802965e-01 -3.55201632e-01
-3.30016881e-01 5.39399326e-01 -5.57087958e-01 -2.30087474e-01
-1.41420767e-01 -1.06737208e+00 -4.12965566e-01 -1.11514461e+00
6.26721919e-01 1.52323470e-01 2.23780885e-01 -2.19517708... | [10.278006553649902, -2.407461643218994] |
7c3d8463-0419-4b87-9d91-dd0f3faa2939 | multi-view-interactive-collaborative | 2305.18306 | null | https://arxiv.org/abs/2305.18306v1 | https://arxiv.org/pdf/2305.18306v1.pdf | Multi-View Interactive Collaborative Filtering | In many scenarios, recommender system user interaction data such as clicks or ratings is sparse, and item turnover rates (e.g., new articles, job postings) high. Given this, the integration of contextual "side" information in addition to user-item ratings is highly desirable. Whilst there are algorithms that can handle... | ['Umashanger Thayasivam', 'Maria Lentini'] | 2023-05-14 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-8.57795700e-02 -3.16015869e-01 -9.55154657e-01 -5.68523824e-01
-7.76813030e-01 -4.48415846e-01 3.05296421e-01 -1.90263703e-01
-4.03702229e-01 7.20047295e-01 6.39280260e-01 -5.57071209e-01
-6.96935117e-01 -5.02545595e-01 -2.37878248e-01 -4.25526410e-01
7.23952353e-02 8.22671711e-01 -2.49513939e-01 -3.02079409... | [9.968794822692871, 5.588112831115723] |
dfddf069-8625-4703-b72e-35384debd85a | on-the-prime-number-divisibility-by-deep | 2304.01333 | null | https://arxiv.org/abs/2304.01333v2 | https://arxiv.org/pdf/2304.01333v2.pdf | Classification of integers based on residue classes via modern deep learning algorithms | Computing the residue when dividing a given integer by prime numbers like 2, 3, or others may appear trivial to human beings, but it can be less straightforward for computers in the absence of pre-defined algorithms. In this paper, we tested multiple deep learning architectures and feature engineering approaches on cla... | ['Kai Wang', 'Mian Umair Ahsan', 'Jingye Yang', 'Da Wu'] | 2023-04-03 | null | null | null | null | ['feature-engineering', 'automl'] | ['methodology', 'methodology'] | [-8.68237913e-02 -2.00049013e-01 1.66180402e-01 -2.65885502e-01
-7.52582729e-01 -8.01020563e-01 5.23161232e-01 4.10419405e-01
-4.98450637e-01 6.20845258e-01 -4.34812993e-01 -9.39427137e-01
-1.18290074e-01 -1.05602610e+00 -7.59896338e-01 -2.72799611e-01
-4.05123740e-01 2.85966814e-01 -1.06339782e-01 -4.10308033... | [8.738572120666504, 7.099165439605713] |
33e0eaf5-f306-4805-8e6c-619dfe59d0a3 | neural-user-simulation-for-corpus-based-1 | 1805.06966 | null | http://arxiv.org/abs/1805.06966v1 | http://arxiv.org/pdf/1805.06966v1.pdf | Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems | User Simulators are one of the major tools that enable offline training of
task-oriented dialogue systems. For this task the Agenda-Based User Simulator
(ABUS) is often used. The ABUS is based on hand-crafted rules and its output is
in semantic form. Issues arise from both properties such as limited diversity
and the i... | ['Milica Gasic', 'Florian Kreyssig', 'Inigo Casanueva', 'Pawel Budzianowski'] | 2018-05-17 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [ 2.57411867e-01 7.06141710e-01 1.91203237e-01 -4.57350850e-01
-5.40338218e-01 -5.61143160e-01 9.51532841e-01 -4.16317210e-03
-7.83686996e-01 1.19130623e+00 2.07486212e-01 -5.25910497e-01
3.05522114e-01 -4.85322446e-01 -5.33426642e-01 -3.87716085e-01
9.94253010e-02 1.09118462e+00 4.04363722e-01 -7.39436388... | [13.0616455078125, 8.032208442687988] |
b0fa1189-f969-4efd-bbcc-bd4a022003bc | are-alphazero-like-agents-robust-to | 2211.03769 | null | https://arxiv.org/abs/2211.03769v1 | https://arxiv.org/pdf/2211.03769v1.pdf | Are AlphaZero-like Agents Robust to Adversarial Perturbations? | The success of AlphaZero (AZ) has demonstrated that neural-network-based Go AIs can surpass human performance by a large margin. Given that the state space of Go is extremely large and a human player can play the game from any legal state, we ask whether adversarial states exist for Go AIs that may lead them to play su... | ['Cho-Jui Hsieh', 'I-Chen Wu', 'Meng-Yu Tsai', 'Ti-Rong Wu', 'huan zhang', 'Li-Cheng Lan'] | 2022-11-07 | null | null | null | null | ['game-of-go', 'board-games'] | ['playing-games', 'playing-games'] | [ 2.60455072e-01 6.23734593e-01 1.81765780e-01 4.40085709e-01
-1.02440417e+00 -1.18989909e+00 4.17381853e-01 -6.46209180e-01
-6.28065526e-01 1.06006789e+00 -2.55035996e-01 -7.80342400e-01
-2.62760162e-01 -1.09756231e+00 -9.93494749e-01 -8.44231486e-01
-2.93064237e-01 6.75820291e-01 4.16648984e-01 -8.21649671... | [3.581098794937134, 1.568150281906128] |
12c31e17-8c8c-4c07-9a20-a1b474555dc7 | fine-tuning-of-convolutional-neural-networks | null | null | https://aclanthology.org/2022.sltat-1.5 | https://aclanthology.org/2022.sltat-1.5.pdf | Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples | In this paper, we investigate the capability of convolutional neural networks to recognize in sign language video frames the six basic Ekman facial expressions for ‘fear’, ‘disgust’, ‘surprise’, ‘sadness’, ‘happiness’, ‘anger’ along with the ‘neutral’ class. Given the limited amount of annotated facial expression data ... | ['Eleftherios Avramidis', 'Fabrizio Nunnari', 'Neha Deshpande'] | null | null | null | null | sltat-lrec-2022-6 | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.19810158e-01 -9.52645242e-02 -5.93718477e-02 -8.71645629e-01
-1.89166620e-01 -1.73267588e-01 7.37343848e-01 -3.58578503e-01
-8.78164589e-01 6.07326090e-01 1.09992802e-01 3.37165147e-02
-2.63833776e-02 -3.87433022e-01 -3.02422464e-01 -7.95665026e-01
-3.10686707e-01 8.09156597e-02 -1.20838158e-01 -4.37792480... | [13.530777931213379, 1.8690687417984009] |
5f1dc885-9738-410d-847d-0622fee60759 | adversary-aware-partial-label-learning-with | 2304.00498 | null | https://arxiv.org/abs/2304.00498v1 | https://arxiv.org/pdf/2304.00498v1.pdf | Adversary-Aware Partial label learning with Label distillation | To ensure that the data collected from human subjects is entrusted with a secret, rival labels are introduced to conceal the information provided by the participants on purpose. The corresponding learning task can be formulated as a noisy partial-label learning problem. However, conventional partial-label learning (PLL... | ['Ivor W. Tsang', 'Yueming Lyu', 'Cheng Chen'] | 2023-04-02 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 2.15032786e-01 2.43786946e-01 -2.28512540e-01 -2.91870028e-01
-1.33589721e+00 -6.65729046e-01 4.53829467e-01 1.19076356e-01
-5.14900684e-01 9.23252523e-01 -4.14948046e-01 -6.94299564e-02
-2.14086071e-01 -4.27649230e-01 -7.13288903e-01 -1.18277061e+00
5.01490235e-02 4.27522898e-01 -2.90310532e-01 1.78693175... | [9.377683639526367, 3.9858951568603516] |
6d188968-a8f1-476c-8592-c81b404475c4 | pain-evaluation-in-video-using-extended | null | null | http://proceedings.mlr.press/v116/xu20a.html | http://proceedings.mlr.press/v116/xu20a/xu20a.pdf | Pain Evaluation in Video using Extended Multitask Learning from Multidimensional Measurements | Previous work on automated pain detection from facial expressions has primarily focused on frame-level pain metrics based on specific facial muscle activations, such as Prkachin and Solomon Pain Intensity (PSPI). However, the current gold standard pain metric is the patient's self-reported visual analog scale (VAS) lev... | ['Virginia R. de Sa', 'Jeannie S. Huang', 'Xiaojing Xu'] | 2019-12-13 | null | null | null | null | ['pain-intensity-regression'] | ['medical'] | [ 2.37732932e-01 -4.18922342e-02 -6.73547924e-01 -3.38893592e-01
-1.45578337e+00 -2.43503079e-01 -4.05877046e-02 5.28032556e-02
-7.70139933e-01 5.93149722e-01 2.14945197e-01 5.57446182e-02
-6.17006980e-02 -3.80600899e-01 -5.63246429e-01 -5.26248157e-01
-3.14853400e-01 9.74913910e-02 -1.44463912e-01 1.03083238... | [13.599246978759766, 1.998848795890808] |
ad000ef1-f6a0-4121-b775-a07e08be7dd0 | an-entire-renal-anatomy-extraction-network | 2305.13616 | null | https://arxiv.org/abs/2305.13616v1 | https://arxiv.org/pdf/2305.13616v1.pdf | An Entire Renal Anatomy Extraction Network for Advanced CAD During Partial Nephrectomy | Partial nephrectomy (PN) is common surgery in urology. Digitization of renal anatomies brings much help to many computer-aided diagnosis (CAD) techniques during PN. However, the manual delineation of kidney vascular system and tumor on each slice is time consuming, error-prone, and inconsistent. Therefore, we proposed ... | ['Dongkai Zhou', 'Ying Yang', 'Nan Ma'] | 2023-05-23 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-1.90484628e-01 3.62119615e-01 -1.23747177e-01 -4.94820863e-01
-3.40094894e-01 -4.87183899e-01 4.74907234e-02 1.06720515e-01
-6.21492147e-01 8.38418722e-01 -1.26312494e-01 -3.81161153e-01
-1.34844720e-01 -9.59721565e-01 -4.58288938e-01 -6.57864034e-01
-1.40542328e-01 5.60282588e-01 1.89140383e-02 2.35746756... | [14.57791519165039, -2.6438276767730713] |
2b4879d7-d5a5-4273-8e3d-be31e430f00e | improving-compositional-generalization-in-2 | 2209.01352 | null | https://arxiv.org/abs/2209.01352v1 | https://arxiv.org/pdf/2209.01352v1.pdf | Improving Compositional Generalization in Math Word Problem Solving | Compositional generalization refers to a model's capability to generalize to newly composed input data based on the data components observed during training. It has triggered a series of compositional generalization analysis on different tasks as generalization is an important aspect of language and problem solving ski... | ['Ee-Peng Lim', 'Jing Jiang', 'Lei Wang', 'Yunshi Lan'] | 2022-09-03 | null | null | null | null | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 3.15945834e-01 -4.14740741e-02 -3.45590591e-01 -4.52732056e-01
-4.04733509e-01 -7.56904364e-01 3.77942979e-01 1.04969226e-01
-1.62192822e-01 6.37959421e-01 2.26720616e-01 -4.98243153e-01
-2.59698629e-01 -8.80833030e-01 -6.66254818e-01 -4.48798299e-01
2.19188884e-01 4.18383658e-01 6.75483420e-02 -4.85057801... | [9.988358497619629, 7.670563697814941] |
15191457-cab7-4799-ac49-5e4671be48ea | learning-ontologies-with-epistemic-reasoning | 1902.03273 | null | http://arxiv.org/abs/1902.03273v1 | http://arxiv.org/pdf/1902.03273v1.pdf | Learning Ontologies with Epistemic Reasoning: The EL Case | We investigate the problem of learning description logic ontologies from
entailments via queries, using epistemic reasoning. We introduce a new learning
model consisting of epistemic membership and example queries and show that
polynomial learnability in this model coincides with polynomial learnability in
Angluin's ex... | ['Nicolas Troquard', 'Ana Ozaki'] | 2019-02-08 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [-2.60531269e-02 1.17392480e+00 4.96311449e-02 -5.56017280e-01
-8.45977008e-01 -7.89955020e-01 5.84428906e-01 2.03910634e-01
-1.66575655e-01 1.05151892e+00 4.75439616e-02 -4.23945814e-01
-9.00435328e-01 -1.36741781e+00 -1.21880698e+00 -3.33143562e-01
-6.78804874e-01 8.38311851e-01 7.57782638e-01 -3.86509806... | [8.626334190368652, 6.686056613922119] |
3cef6dd2-ad2a-404e-908d-91032c9adfce | natural-questions-in-icelandic | null | null | https://aclanthology.org/2022.lrec-1.477 | https://aclanthology.org/2022.lrec-1.477.pdf | Natural Questions in Icelandic | We present the first extractive question answering (QA) dataset for Icelandic, Natural Questions in Icelandic (NQiI). Developing such datasets is important for the development and evaluation of Icelandic QA systems. It also aids in the development of QA methods that need to work for a wide range of morphologically and ... | ['Hafsteinn Einarsson', 'Vésteinn Snæbjarnarson'] | null | null | null | null | lrec-2022-6 | ['natural-questions'] | ['miscellaneous'] | [-7.13083670e-02 3.98264021e-01 6.79241419e-01 -5.20548940e-01
-1.52240312e+00 -1.31028092e+00 5.90854466e-01 4.04179752e-01
-7.07076848e-01 9.99836266e-01 6.10178351e-01 -7.33983874e-01
-1.86204031e-01 -9.13375020e-01 -5.38943648e-01 -1.42666042e-01
3.06971490e-01 1.38282430e+00 -1.85717810e-02 -1.05531597... | [11.357394218444824, 8.145805358886719] |
0b665820-a4dc-48f8-a4eb-7fa2aa73acd5 | lifelong-unsupervised-domain-adaptive-person | 2112.06632 | null | https://arxiv.org/abs/2112.06632v2 | https://arxiv.org/pdf/2112.06632v2.pdf | Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation | Unsupervised domain adaptive person re-identification (ReID) has been extensively investigated to mitigate the adverse effects of domain gaps. Those works assume the target domain data can be accessible all at once. However, for the real-world streaming data, this hinders the timely adaptation to changing data statisti... | ['Zheng-Jun Zha', 'Zicheng Liu', 'Jiang Wang', 'Quanzeng You', 'Peng Chu', 'Wenjun Zeng', 'Cuiling Lan', 'Zhizheng Zhang', 'Zhipeng Huang'] | 2021-12-13 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Huang_Lifelong_Unsupervised_Domain_Adaptive_Person_Re-Identification_With_Coordinated_Anti-Forgetting_and_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Huang_Lifelong_Unsupervised_Domain_Adaptive_Person_Re-Identification_With_Coordinated_Anti-Forgetting_and_CVPR_2022_paper.pdf | cvpr-2022-1 | ['person-retrieval'] | ['computer-vision'] | [-5.32058403e-02 -4.26251769e-01 6.09498192e-03 -3.92681032e-01
-3.34794343e-01 -1.34823546e-01 4.70269203e-01 2.66531557e-01
-7.74960101e-01 9.10560668e-01 2.14425042e-01 3.80153090e-01
-4.71445352e-01 -7.35244334e-01 -4.88801718e-01 -7.73994446e-01
3.70849855e-02 8.02759528e-01 3.21108341e-01 -2.27750406... | [14.772913932800293, 1.2338731288909912] |
2f7583eb-08b0-4018-8c0d-6ea5e3748a6d | styletrf-stylizing-tensorial-radiance-fields | 2212.09330 | null | https://arxiv.org/abs/2212.09330v1 | https://arxiv.org/pdf/2212.09330v1.pdf | StyleTRF: Stylizing Tensorial Radiance Fields | Stylized view generation of scenes captured casually using a camera has received much attention recently. The geometry and appearance of the scene are typically captured as neural point sets or neural radiance fields in the previous work. An image stylization method is used to stylize the captured appearance by trainin... | ['P. J. Narayanan', 'Saurabh Saini', 'Sirikonda Dhawal', 'Rahul Goel'] | 2022-12-19 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 2.95605510e-01 4.36566845e-02 1.97662205e-01 -4.23021942e-01
-3.97049785e-01 -7.11784959e-01 7.32241273e-01 -8.47549796e-01
-6.21698871e-02 6.02033317e-01 1.53221473e-01 2.31780428e-02
3.35849166e-01 -6.71885252e-01 -1.04375911e+00 -5.72589219e-01
6.48573697e-01 4.75856692e-01 -3.58606130e-02 -1.15081236... | [9.32887077331543, -3.203643560409546] |
fa8152fa-5e5e-4dba-ba91-538fe1be36c7 | shadfa-0-1-the-iranian-movie-knowledge-graph | 2210.07822 | null | https://arxiv.org/abs/2210.07822v1 | https://arxiv.org/pdf/2210.07822v1.pdf | Shadfa 0.1: The Iranian Movie Knowledge Graph and Graph-Embedding-Based Recommender System | Movies are a great source of entertainment. However, the problem arises when one is trying to find the desired content within this vast amount of data which is significantly increasing every year. Recommender systems can provide appropriate algorithms to solve this problem. The content_based technique has found popular... | ['Mohammad-R. Akbarzadeh-T', 'Mohammad Karrabi', 'Hannane Ebrahimian', 'Hadi Kalamati', 'Rayhane Pouyan'] | 2022-10-14 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-3.41826260e-01 -4.21832055e-01 -3.13519150e-01 -2.48958230e-01
-7.07786605e-02 -4.99890745e-01 3.49042356e-01 5.51110387e-01
-5.58452368e-01 5.06656408e-01 7.02770174e-01 4.33365665e-02
-7.66948640e-01 -1.08899641e+00 -1.40103519e-01 -4.50919271e-01
6.21544942e-02 2.81492174e-02 2.56520212e-01 -5.19158840... | [10.118931770324707, 5.911219120025635] |
f4b902db-26ea-47d3-b129-76a330cb37e8 | fdn-finite-difference-network-with | 2108.07974 | null | https://arxiv.org/abs/2108.07974v2 | https://arxiv.org/pdf/2108.07974v2.pdf | FDN: Finite Difference Network with Hierarchical Convolutional Features for Text-independent Speaker Verification | In recent years, using raw waveforms as input for deep networks has been widely explored for the speaker verification system. For example, RawNet and RawNet2 extracted speaker's feature embeddings from waveforms automatically for recognizing their voice, which can vastly reduce the front-end computation and obtain stat... | ['Lan Wang', 'Nan Yan', 'Jin Li'] | 2021-08-18 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-5.55849075e-01 -3.81443918e-01 -1.15074642e-01 -7.76611090e-01
-6.32600307e-01 -3.74846965e-01 2.54234493e-01 -3.02093059e-01
-2.74060786e-01 7.34933689e-02 5.61979592e-01 -2.95305878e-01
2.68935055e-01 -4.49512422e-01 -1.73269898e-01 -6.62225306e-01
-2.30891742e-02 -1.76804438e-01 -1.19135432e-01 -4.07006860... | [14.36009693145752, 5.987492561340332] |
3f34be35-d24f-4c8d-a17c-dd8179162383 | cardiologist-level-arrhythmia-detection-and | null | null | https://doi.org/10.1038/s41591-018-0268-3 | https://arxiv.org/pdf/1707.01836.pdf | Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network | Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluat... | ['Andrew Y. Ng', 'Mintu P. Turakhia', 'Masoumeh Haghpanahi', 'Codie Bourn', 'Pranav Rajpurkar', 'Geoffrey H. Tison', 'Awni Y. Hannun'] | 2019-01-07 | null | null | null | nature-medicine-2019-1 | ['arrhythmia-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 2.28202343e-01 -2.11423174e-01 2.92080175e-02 -4.36331004e-01
-1.08497679e+00 -9.49824691e-01 -4.55879092e-01 4.30719882e-01
-4.34937924e-01 6.62765801e-01 -9.38656926e-02 -9.28412139e-01
-5.67393005e-01 -5.49476266e-01 -2.77390629e-01 -4.63865697e-01
-4.09651458e-01 8.15545619e-01 -5.41005909e-01 4.12258446... | [14.38037395477295, 3.3278591632843018] |
258b9308-b062-443a-8595-235027fd5d07 | em-based-bounding-of-unidentifiable-queries | 2011.02912 | null | https://arxiv.org/abs/2011.02912v3 | https://arxiv.org/pdf/2011.02912v3.pdf | Causal Expectation-Maximisation | Structural causal models are the basic modelling unit in Pearl's causal theory; in principle they allow us to solve counterfactuals, which are at the top rung of the ladder of causation. But they often contain latent variables that limit their application to special settings. This appears to be a consequence of the fac... | ['Rafael Cabañas', 'Alessandro Antonucci', 'Marco Zaffalon'] | 2020-11-04 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 2.96297699e-01 8.60018015e-01 -2.24285409e-01 -9.78541970e-02
-5.50621986e-01 -6.15855694e-01 9.67204452e-01 3.79108898e-02
-2.95184493e-01 1.18702590e+00 5.13812065e-01 -8.69695842e-01
-7.60066271e-01 -9.75006640e-01 -7.71425724e-01 -7.56631374e-01
-4.42991883e-01 6.46538973e-01 1.11620501e-01 1.90160051... | [8.1520357131958, 5.676864147186279] |
c14c6c71-bc48-4aff-b518-f7985119a4b8 | gumdrop-at-the-disrpt2019-shared-task-a-model | 1904.10419 | null | https://arxiv.org/abs/1904.10419v2 | https://arxiv.org/pdf/1904.10419v2.pdf | GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection | In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompa... | ['Amir Zeldes', 'YIlun Zhu', 'Yue Yu', 'Siyao Peng', 'Mackenzie Gong', 'Yang Liu', 'Yan Liu'] | 2019-04-23 | gumdrop-at-the-disrpt2019-shared-task-a-model-1 | https://aclanthology.org/W19-2717 | https://aclanthology.org/W19-2717.pdf | ws-2019-6 | ['discourse-segmentation', 'connective-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.76760238e-01 6.44223809e-01 -2.14188561e-01 -3.71690303e-01
-9.65667784e-01 -8.22550058e-01 7.99768388e-01 3.12577486e-01
-2.05807507e-01 8.39469433e-01 6.10163987e-01 -7.66030014e-01
3.71721685e-01 -4.13187593e-01 -4.00409311e-01 -2.85857707e-01
-3.54153998e-02 7.11327791e-01 5.85025489e-01 -3.31851840... | [10.894290924072266, 9.471261978149414] |
2b5d7ecf-a508-476d-8256-015865d71e4f | feed-forward-source-free-latent-domain | 2207.07624 | null | https://arxiv.org/abs/2207.07624v1 | https://arxiv.org/pdf/2207.07624v1.pdf | Feed-Forward Source-Free Latent Domain Adaptation via Cross-Attention | We study the highly practical but comparatively under-studied problem of latent-domain adaptation, where a source model should be adapted to a target dataset that contains a mixture of unlabelled domain-relevant and domain-irrelevant examples. Furthermore, motivated by the requirements for data privacy and the need for... | ['Timothy Hospedales', 'Shell Xu Hu', 'Da Li', 'Ondrej Bohdal'] | 2022-07-15 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 5.90650320e-01 6.14262223e-01 -5.20594120e-01 -7.46343911e-01
-1.15665066e+00 -7.00974882e-01 5.96897423e-01 4.13585752e-02
-6.38232887e-01 1.14329767e+00 3.57382223e-02 -6.87746182e-02
-1.70965761e-01 -5.72993755e-01 -8.87811124e-01 -6.76902592e-01
1.43967569e-01 9.39517975e-01 5.56352176e-02 9.65566188... | [10.350144386291504, 3.2447855472564697] |
fc59d399-c6df-488c-a5af-fc72bee14e2a | unexpected-sawtooth-artifact-in-beat-to-beat | 1809.01722 | null | https://arxiv.org/abs/1809.01722v2 | https://arxiv.org/pdf/1809.01722v2.pdf | Unexpected sawtooth artifact in beat-to-beat pulse transit time measured from patient monitor data | Object: It is increasingly popular to collect as much data as possible in the hospital setting from clinical monitors for research purposes. However, in this setup the data calibration issue is often not discussed and, rather, implicitly assumed, while the clinical monitors might not be designed for the data analysis p... | ['Martin G. Frasch', 'Hau-Tieng Wu', 'Chen-Yun Lin', 'Yu-Lun Lo', 'Yu-Ting Lin'] | 2018-08-27 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 4.72370356e-01 6.94729984e-02 3.36376876e-01 -3.25081982e-02
-4.00330365e-01 -5.53708911e-01 -8.82215425e-02 4.81476605e-01
-3.93714309e-01 7.16137767e-01 -1.19716436e-01 -6.03553057e-01
-1.37426630e-01 -3.30529422e-01 -4.22384351e-01 -8.26281846e-01
-4.73232716e-01 2.56611139e-01 2.06720717e-02 3.33422959... | [14.05119514465332, 2.9988596439361572] |
4b7ab365-85ae-4b0d-b336-2f1631e69435 | leave-one-out-kernel-optimization-for-shadow | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Vicente_Leave-One-Out_Kernel_Optimization_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Vicente_Leave-One-Out_Kernel_Optimization_ICCV_2015_paper.pdf | Leave-One-Out Kernel Optimization for Shadow Detection | The objective of this work is to detect shadows in images. We pose this as the problem of labeling image regions, where each region corresponds to a group of superpixels. To predict the label of each region, we train a kernel Least-Squares SVM for separating shadow and non-shadow regions. The parameters of the kernel a... | ['Tomas F. Yago Vicente', 'Minh Hoai', 'Dimitris Samaras'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['shadow-detection'] | ['computer-vision'] | [ 5.62812567e-01 1.33489013e-01 -4.25003529e-01 -6.94442511e-01
-8.64185512e-01 -6.80572808e-01 4.60631520e-01 -1.23446226e-01
-3.44394535e-01 6.56098008e-01 -2.21629679e-01 -3.66125524e-01
3.93242180e-01 -4.44925874e-01 -8.32948208e-01 -8.82086635e-01
-1.35356337e-02 2.83628255e-01 8.95488381e-01 3.85436475... | [9.408207893371582, 0.8174193501472473] |
ab7235ca-68ec-48cd-9884-5ed45ce49332 | taguchi-based-design-of-sequential | 2207.10992 | null | https://arxiv.org/abs/2207.10992v1 | https://arxiv.org/pdf/2207.10992v1.pdf | Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners | Fasteners play a critical role in securing various parts of machinery. Deformations such as dents, cracks, and scratches on the surface of fasteners are caused by material properties and incorrect handling of equipment during production processes. As a result, quality control is required to ensure safe and reliable ope... | ['Kayode Owa', 'Garima Joshi', 'Isibor Kennedy Ihianle', 'Renu Vig', 'Pushkar Bharadwaj', 'Tanya Aggarwal', 'Krishan Kumar Chauhan', 'Manjeet Kaur'] | 2022-07-22 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.57658309e-01 4.24056463e-02 2.47394100e-01 -2.10004643e-01
-1.34774387e-01 -2.69683450e-01 -1.01950698e-01 4.87348229e-01
-2.34675765e-01 4.19090718e-01 -5.83535373e-01 -1.70920312e-01
-3.59005839e-01 -9.87463117e-01 -4.73262101e-01 -7.59779096e-01
2.76585609e-01 1.05850741e-01 3.77219141e-01 -3.18715096... | [7.339228630065918, 1.8817611932754517] |
fc3b7743-fba0-4fba-a00b-135258b4b828 | document-expansion-by-query-prediction | 1904.08375 | null | https://arxiv.org/abs/1904.08375v2 | https://arxiv.org/pdf/1904.08375v2.pdf | Document Expansion by Query Prediction | One technique to improve the retrieval effectiveness of a search engine is to expand documents with terms that are related or representative of the documents' content.From the perspective of a question answering system, this might comprise questions the document can potentially answer. Following this observation, we pr... | ['Rodrigo Nogueira', 'Kyunghyun Cho', 'Wei Yang', 'Jimmy Lin'] | 2019-04-17 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [ 4.11623597e-01 -2.72949070e-01 -1.49982333e-01 -8.91826004e-02
-1.41099012e+00 -7.70045817e-01 9.99133170e-01 3.82941127e-01
-7.33156443e-01 4.61819917e-01 4.17108029e-01 -4.04913813e-01
-6.01721585e-01 -7.87752867e-01 -7.13805616e-01 -2.26827711e-02
8.96103233e-02 9.31414187e-01 6.46399856e-01 -6.80813253... | [11.510384559631348, 7.558253288269043] |
7277b6a0-19d4-46c5-8aab-56ad9d3a2a46 | q-deckrec-a-fast-deck-recommendation-system | 1806.09771 | null | http://arxiv.org/abs/1806.09771v1 | http://arxiv.org/pdf/1806.09771v1.pdf | Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games | Deck building is a crucial component in playing Collectible Card Games
(CCGs). The goal of deck building is to choose a fixed-sized subset of cards
from a large card pool, so that they work well together in-game against
specific opponents. Existing methods either lack flexibility to adapt to
different opponents or requ... | ['Magy Seif El-Nasr', 'Truong-Huy Nguyen', 'Seth Cooper', 'Chris Amato', 'Zhengxing Chen', 'Yizhou Sun'] | 2018-06-26 | null | null | null | null | ['card-games'] | ['playing-games'] | [-2.33574629e-01 -5.55123866e-01 -3.47435743e-01 -9.50201675e-02
-8.23397934e-01 -1.09768653e+00 2.49088287e-01 -3.01089913e-01
-5.32364368e-01 7.91687012e-01 -1.40690356e-01 -5.97722471e-01
-5.85018516e-01 -1.11263680e+00 -4.97516721e-01 -4.41708893e-01
-8.87634382e-02 1.17708349e+00 6.20790243e-01 -6.64856911... | [3.493525505065918, 1.4773659706115723] |
17a0217f-db28-4394-87bb-97a5f6b4abcd | deep-learning-based-counting-methods-datasets | 2303.02632 | null | https://arxiv.org/abs/2303.02632v2 | https://arxiv.org/pdf/2303.02632v2.pdf | Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review | The number of objects is considered an important factor in a variety of tasks in the agricultural domain. Automated counting can improve farmers decisions regarding yield estimation, stress detection, disease prevention, and more. In recent years, deep learning has been increasingly applied to many agriculture-related ... | ['Yael Edan', 'Liu Huijun', 'Guy Farjon'] | 2023-03-05 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 7.51801804e-02 -7.79390335e-01 -2.85576791e-01 -2.51937717e-01
1.36259273e-01 -7.98723459e-01 2.33553946e-01 8.79721999e-01
-6.91472769e-01 3.44836801e-01 -2.08466113e-01 -5.24026334e-01
2.20277771e-01 -1.36799026e+00 -4.81595039e-01 -7.50896871e-01
-1.76864028e-01 3.30936581e-01 -8.52774009e-02 -5.67232929... | [9.13414478302002, -1.4677510261535645] |
c479461d-b231-4783-ac32-d5e03e03dc54 | harp-personalized-hand-reconstruction-from-a | 2212.09530 | null | https://arxiv.org/abs/2212.09530v3 | https://arxiv.org/pdf/2212.09530v3.pdf | HARP: Personalized Hand Reconstruction from a Monocular RGB Video | We present HARP (HAnd Reconstruction and Personalization), a personalized hand avatar creation approach that takes a short monocular RGB video of a human hand as input and reconstructs a faithful hand avatar exhibiting a high-fidelity appearance and geometry. In contrast to the major trend of neural implicit representa... | ['Siyu Tang', 'Otmar Hilliges', 'Sergey Prokudin', 'Korrawe Karunratanakul'] | 2022-12-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Karunratanakul_HARP_Personalized_Hand_Reconstruction_From_a_Monocular_RGB_Video_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Karunratanakul_HARP_Personalized_Hand_Reconstruction_From_a_Monocular_RGB_Video_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [ 3.52924801e-02 -3.93646099e-02 8.00893754e-02 7.03112334e-02
-4.79934454e-01 -6.61334932e-01 5.03826201e-01 -7.30745614e-01
9.59939416e-03 5.98163426e-01 3.86184782e-01 1.12364024e-01
1.01068869e-01 -5.49900472e-01 -7.16255963e-01 -6.50947869e-01
7.47259855e-02 8.41992259e-01 8.80414769e-02 -3.61370593... | [7.169700622558594, -1.282175064086914] |
775da4f1-bbd3-4554-9f76-d8e1391086b2 | a-hybrid-data-association-framework-for | 1703.10764 | null | http://arxiv.org/abs/1703.10764v1 | http://arxiv.org/pdf/1703.10764v1.pdf | A Hybrid Data Association Framework for Robust Online Multi-Object Tracking | Global optimization algorithms have shown impressive performance in
data-association based multi-object tracking, but handling online data remains
a difficult hurdle to overcome. In this paper, we present a hybrid data
association framework with a min-cost multi-commodity network flow for robust
online multi-object tra... | ['Yuwei Wu', 'Yunde Jia', 'Min Yang'] | 2017-03-31 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-4.68305200e-01 -4.42261219e-01 -5.93760192e-01 -1.71350449e-01
-6.46792471e-01 -4.50525910e-01 -1.49341613e-01 -1.85595065e-01
-4.02199805e-01 6.97879255e-01 -2.55971611e-01 -7.72636011e-02
-6.84550345e-01 -2.28977188e-01 -7.87398577e-01 -7.83506513e-01
-3.89444500e-01 5.10997176e-01 5.05715072e-01 2.63835579... | [6.419556140899658, -2.077073574066162] |
359437f3-b66c-40a2-a41e-85a120af2503 | gcfsr-a-generative-and-controllable-face | 2203.07319 | null | https://arxiv.org/abs/2203.07319v1 | https://arxiv.org/pdf/2203.07319v1.pdf | GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors | Face image super resolution (face hallucination) usually relies on facial priors to restore realistic details and preserve identity information. Recent advances can achieve impressive results with the help of GAN prior. They either design complicated modules to modify the fixed GAN prior or adopt complex training strat... | ['Chao Dong', 'Lean Fu', 'Kai Chen', 'Wu Shi', 'Jingwen He'] | 2022-03-14 | null | http://openaccess.thecvf.com//content/CVPR2022/html/He_GCFSR_A_Generative_and_Controllable_Face_Super_Resolution_Method_Without_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/He_GCFSR_A_Generative_and_Controllable_Face_Super_Resolution_Method_Without_CVPR_2022_paper.pdf | cvpr-2022-1 | ['face-hallucination'] | ['computer-vision'] | [ 4.81148720e-01 1.88258678e-01 1.30200803e-01 -4.32838291e-01
-7.75954783e-01 -2.92462558e-01 6.69799864e-01 -8.43693793e-01
2.54320968e-02 7.83453524e-01 2.74233997e-01 1.42764002e-01
2.16401249e-01 -9.32138145e-01 -6.57493532e-01 -8.18982184e-01
2.81458110e-01 1.95230708e-01 -1.07423075e-01 -4.97709990... | [12.552632331848145, -0.2348136156797409] |
5eccdd01-1e0c-4baf-b31f-24a15d79da5c | exploiting-topic-information-for-joint-intent | null | null | https://openreview.net/forum?id=YXvbGWz1AGP | https://openreview.net/pdf?id=YXvbGWz1AGP | Exploiting Topic Information for Joint Intent Detection and Slot Filling | Intent detection and slot filling are two important basic tasks in natural language understanding. Actually, there are multiple intents in an utterance. How to map different intents to corresponding slot becomes a new challenge for recent research. Existing models solve this problem by using neural layers to adaptively... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['slot-filling'] | ['natural-language-processing'] | [ 3.73931915e-01 3.38019788e-01 -6.09791577e-01 -8.78827512e-01
-6.07201993e-01 -2.03043923e-01 5.62913895e-01 4.56719339e-01
-5.49643755e-01 7.16437340e-01 7.05554962e-01 -2.11524114e-01
1.23858944e-01 -7.49770701e-01 -3.13745201e-01 -2.78066069e-01
1.44789740e-01 4.65364397e-01 3.07557911e-01 -5.60928106... | [12.569488525390625, 7.349836826324463] |
17bee82c-ac31-4a5a-bdcf-fd506d250187 | multiple-instance-neuroimage-transformer | 2208.09567 | null | https://arxiv.org/abs/2208.09567v1 | https://arxiv.org/pdf/2208.09567v1.pdf | Multiple Instance Neuroimage Transformer | For the first time, we propose using a multiple instance learning based convolution-free transformer model, called Multiple Instance Neuroimage Transformer (MINiT), for the classification of T1weighted (T1w) MRIs. We first present several variants of transformer models adopted for neuroimages. These models extract non-... | ['Ehsan Adeli', 'Kilian M. Pohl', 'Yuyin Zhou', 'Daniel K. Do', 'Qingyu Zhao', 'Ayush Singla'] | 2022-08-19 | null | null | null | null | ['brain-morphometry'] | ['medical'] | [ 1.95874542e-01 5.05100787e-01 -6.60137683e-02 -7.46229291e-01
-7.01463521e-01 -3.20322245e-01 6.37171209e-01 1.93584785e-01
-4.12228286e-01 3.41149181e-01 2.11537004e-01 -2.77070403e-01
-3.58765900e-01 -6.95938349e-01 -1.15468347e+00 -4.13687527e-01
-5.23866832e-01 8.77532601e-01 3.28397937e-02 1.50542825... | [14.191361427307129, -1.9273695945739746] |
a458ea21-37b9-461e-b5c6-3d8d7f80884a | generating-varied-training-corpora-in | null | null | https://aclanthology.org/2020.inlg-1.34 | https://aclanthology.org/2020.inlg-1.34.pdf | Generating Varied Training Corpora in Runyankore Using a Combined Semantic and Syntactic, Pattern-Grammar-based Approach | Machine learning algorithms have been applied to achieve high levels of accuracy in tasks associated with the processing of natural language. However, these algorithms require large amounts of training data in order to perform efficiently. Since most Bantu languages lack the required training corpora because they are c... | ['Joan Byamugisha'] | null | null | null | null | inlg-acl-2020-12 | ['word-similarity'] | ['natural-language-processing'] | [ 5.71162224e-01 1.91509947e-01 2.53429145e-01 -4.86737132e-01
-7.26828814e-01 -7.59594202e-01 6.27842069e-01 5.74069440e-01
-8.19982529e-01 7.29853034e-01 4.20712471e-01 -7.52302706e-01
-2.82460731e-02 -8.13935876e-01 -1.37523100e-01 -5.74401200e-01
1.04505919e-01 5.54339945e-01 -1.28834724e-01 -4.01064217... | [10.370850563049316, 9.727494239807129] |
ee54b984-2cb8-4bef-b296-b2ca742ecc1c | thraws-a-novel-dataset-for-thermal-hotspots | 2305.11891 | null | https://arxiv.org/abs/2305.11891v1 | https://arxiv.org/pdf/2305.11891v1.pdf | THRawS: A Novel Dataset for Thermal Hotspots Detection in Raw Sentinel-2 Data | Nowadays, most of the datasets leveraging space-borne Earth Observation (EO) data are based on high-end levels products, which are ortho-rectified, coregistered, calibrated, and further processed to mitigate the impact of noise and distortions. Nevertheless, given the growing interest to apply Artificial Intelligence (... | ['Nicolas Longépé', 'Olivier Colin', 'Alix De Beussche', 'Federico Serva', 'Roberto Del Prete', 'Gabriele Meoni'] | 2023-05-12 | null | null | null | null | ['classification'] | ['methodology'] | [ 4.52838659e-01 3.96200605e-02 4.23650175e-01 -2.69680440e-01
-6.56724036e-01 -4.05081272e-01 9.94057417e-01 3.18132699e-01
-5.86708367e-01 5.73782563e-01 8.70732665e-02 -3.02582026e-01
-3.66206050e-01 -1.26421654e+00 -5.11074722e-01 -1.02501774e+00
-6.78918481e-01 2.80901402e-01 -5.79666980e-02 -6.45749390... | [9.744267463684082, -1.6957331895828247] |
fda7adb8-f4b0-4bf9-adfd-b087c6caf859 | guiding-visual-attention-in-deep | 2206.10587 | null | https://arxiv.org/abs/2206.10587v2 | https://arxiv.org/pdf/2206.10587v2.pdf | Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements | Deep Convolutional Neural Networks (DCNNs) were originally inspired by principles of biological vision, have evolved into best current computational models of object recognition, and consequently indicate strong architectural and functional parallelism with the ventral visual pathway throughout comparisons with neuroim... | ['Walter R. Gruber', 'Sebastian J. Denzler', 'Leonard E. van Dyck'] | 2022-06-21 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 4.25308049e-01 1.78797930e-01 2.90993661e-01 -2.92807877e-01
5.57787776e-01 -4.05473888e-01 9.89459574e-01 -9.25687999e-02
-7.58380175e-01 3.99966240e-01 1.00534439e-01 -1.85819924e-01
-3.50611299e-01 -4.13400322e-01 -6.44203544e-01 -7.72503972e-01
1.25234842e-01 -1.54508531e-01 2.82512069e-01 -4.71855313... | [10.176288604736328, 2.2160637378692627] |
fc423369-1a6c-4abe-b783-0e8e6c280336 | covlr-coordinating-cross-modal-consistency | 2304.07567 | null | https://arxiv.org/abs/2304.07567v1 | https://arxiv.org/pdf/2304.07567v1.pdf | CoVLR: Coordinating Cross-Modal Consistency and Intra-Modal Structure for Vision-Language Retrieval | Current vision-language retrieval aims to perform cross-modal instance search, in which the core idea is to learn the consistent visionlanguage representations. Although the performance of cross-modal retrieval has greatly improved with the development of deep models, we unfortunately find that traditional hard consist... | ['Wenjie Li', 'Xiangyu Wu', 'Zhongtian Fu', 'Yang Yang'] | 2023-04-15 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [-1.26929745e-01 -3.80550563e-01 -3.66427302e-01 -1.30894050e-01
-1.30596685e+00 -5.38741112e-01 9.27429855e-01 -2.48998955e-01
-2.49706447e-01 4.28219020e-01 1.01315089e-01 1.78813383e-01
-4.59175378e-01 -3.17163438e-01 -7.09181786e-01 -1.02867794e+00
4.98715192e-01 4.98950332e-01 1.00055598e-02 -1.11985050... | [10.87809944152832, 1.3771897554397583] |
9581bd1d-d666-434c-aeee-88f8d1133ab1 | segment-anything-is-a-good-pseudo-label | 2305.01275 | null | https://arxiv.org/abs/2305.01275v1 | https://arxiv.org/pdf/2305.01275v1.pdf | Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation | Weakly supervised semantic segmentation with weak labels is a long-lived ill-posed problem. Mainstream methods mainly focus on improving the quality of pseudo labels. In this report, we attempt to explore the potential of 'prompt to masks' from the powerful class-agnostic large segmentation model, segment-anything. Spe... | ['YuQi Yang', 'Peng-Tao Jiang'] | 2023-05-02 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 5.49587250e-01 5.52589178e-01 -3.96305442e-01 -9.20136273e-01
-1.18156600e+00 -9.56509709e-01 4.80568945e-01 -2.45430946e-01
-4.94404882e-01 8.42055738e-01 -6.27481844e-03 -2.20024362e-01
5.98003507e-01 -5.47822773e-01 -8.85330200e-01 -5.18106639e-01
5.26071250e-01 5.82724333e-01 4.24679607e-01 1.70862302... | [9.581050872802734, 0.6445053815841675] |
229b078c-591a-49dd-918c-7cfc69b72fce | material-recognition-via-heat-transfer-given | 2012.02176 | null | https://arxiv.org/abs/2012.02176v1 | https://arxiv.org/pdf/2012.02176v1.pdf | Material Recognition via Heat Transfer Given Ambiguous Initial Conditions | Humans and robots can recognize materials with distinct thermal effusivities by making physical contact and observing temperatures during heat transfer. This works well with room temperature materials and humans and robots at human body temperatures. Past research has shown that cooling or heating a material can result... | ['Charles C. Kemp', 'Joshua Wade', 'Henry M. Clever', 'Tapomayukh Bhattacharjee'] | 2020-12-03 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 5.67030013e-01 2.71107882e-01 4.15494978e-01 -3.92100781e-01
-2.49886736e-01 -6.10423744e-01 3.94410670e-01 3.85494493e-02
-5.28779984e-01 4.44588989e-01 -4.46037620e-01 8.81155133e-02
3.47656220e-01 -2.92672813e-01 -5.50593138e-01 -7.20565915e-01
-1.24616034e-01 6.32862568e-01 -1.30457699e-01 -1.05651416... | [5.753418922424316, -0.6731293797492981] |
5c6e8219-cdf9-47d5-a6be-677489440ae3 | membership-inference-attacks-against-1 | 2302.03262 | null | https://arxiv.org/abs/2302.03262v2 | https://arxiv.org/pdf/2302.03262v2.pdf | Membership Inference Attacks against Diffusion Models | Diffusion models have attracted attention in recent years as innovative generative models. In this paper, we investigate whether a diffusion model is resistant to a membership inference attack, which evaluates the privacy leakage of a machine learning model. We primarily discuss the diffusion model from the standpoints... | ['Naoto Yanai', 'Takayuki Miura', 'Tomoya Matsumoto'] | 2023-02-07 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 1.65925190e-01 2.08092541e-01 -6.99698180e-02 -6.29438534e-02
-6.69471860e-01 -1.17190969e+00 1.00251913e+00 -2.33966112e-01
-2.40314558e-01 6.59897804e-01 2.01774433e-01 -6.78627551e-01
-5.06047271e-02 -1.02203953e+00 -8.39135230e-01 -7.92369723e-01
-1.96850121e-01 1.71472192e-01 -1.95695445e-01 1.13626979... | [5.923239231109619, 7.231140613555908] |
e3554d5e-5544-4e6a-8a9f-491e21ae89eb | improved-topic-representations-of-medical | null | null | https://aclanthology.org/2020.nlpcovid19-2.12 | https://aclanthology.org/2020.nlpcovid19-2.12.pdf | Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration | Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models bas... | ['Simon Šuster', 'Timothy Baldwin', 'Karin Verspoor', 'Yulia Otmakhova'] | null | null | null | null | emnlp-nlp-covid19-2020-12 | ['topic-models'] | ['natural-language-processing'] | [-4.08887178e-01 8.40159133e-03 -4.76282060e-01 -2.43458122e-01
-4.61632937e-01 -1.82749312e-02 7.17202365e-01 8.72245967e-01
-5.60903728e-01 9.40031350e-01 7.59446800e-01 -2.69042611e-01
-2.79405892e-01 -8.79179716e-01 2.04837993e-01 -8.66795003e-01
-3.86285365e-01 9.83507276e-01 2.21783087e-01 1.17956184... | [10.112195014953613, 7.285647392272949] |
c77b4966-e17a-4852-a9db-b4811bbdbbf8 | led-a-dataset-for-life-event-extraction-from | 2304.08327 | null | https://arxiv.org/abs/2304.08327v1 | https://arxiv.org/pdf/2304.08327v1.pdf | LED: A Dataset for Life Event Extraction from Dialogs | Lifelogging has gained more attention due to its wide applications, such as personalized recommendations or memory assistance. The issues of collecting and extracting personal life events have emerged. People often share their life experiences with others through conversations. However, extracting life events from conv... | ['Hsin-Hsi Chen', 'Hideki Nakayama', 'Hen-Hsen Huang', 'An-Zi Yen', 'Yi-Pei Chen'] | 2023-04-17 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 6.19507208e-02 4.07188296e-01 -4.05647367e-01 -6.14349067e-01
-6.20024979e-01 -6.19225919e-01 1.26949465e+00 3.06447208e-01
-4.42241818e-01 1.11330962e+00 1.24792016e+00 -1.11823604e-02
2.64709204e-01 -7.23437786e-01 5.64180352e-02 -2.03938946e-01
1.41454756e-01 3.44535142e-01 1.84019178e-01 -4.09408122... | [9.188844680786133, 9.263701438903809] |
4c9523e0-66bd-4419-a835-5c08127dcf6e | measurement-of-individual-alteration-in | 2302.05177 | null | https://arxiv.org/abs/2302.05177v1 | https://arxiv.org/pdf/2302.05177v1.pdf | Measurement of Individual Alteration in Perioperative ECGs During Elective Percutaneous Coronary Intervention | The increasing availability of wearable electrocardiography (ECG) devices enables the continuous monitoring of individual ECG alterations. This could be beneficial for patients suffering from acute ischemia but with non-standard ECG findings that do not fit to the subject-independent and absolute thresholds defined in ... | ['Nicolai Spicher', 'Tim Kacprowski', 'Dagmar Krefting', 'Henning Dathe', 'Ennio Idrobo-Avila', 'Theresa Bender', 'Philip Gemke'] | 2023-02-10 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 5.16197979e-01 -5.20801544e-01 -1.12615280e-01 -1.29207909e-01
-7.78430223e-01 -1.30415344e+00 -3.48286539e-01 6.93561137e-01
-4.49033707e-01 7.11716294e-01 -8.19519013e-02 -1.03620493e+00
-5.83209157e-01 -3.33621055e-01 -8.05640966e-02 -4.17701960e-01
-8.08450937e-01 4.64529842e-01 -7.75029976e-03 3.29991847... | [14.244291305541992, 3.224187135696411] |
9a5b1d3b-a4a4-42c0-80cd-ca46b91dc62d | neaf-learning-neural-angle-fields-for-point | 2211.16869 | null | https://arxiv.org/abs/2211.16869v1 | https://arxiv.org/pdf/2211.16869v1.pdf | NeAF: Learning Neural Angle Fields for Point Normal Estimation | Normal estimation for unstructured point clouds is an important task in 3D computer vision. Current methods achieve encouraging results by mapping local patches to normal vectors or learning local surface fitting using neural networks. However, these methods are not generalized well to unseen scenarios and are sensitiv... | ['Zhizhong Han', 'Yu-Shen Liu', 'Baorui Ma', 'Junsheng Zhou', 'Shujuan Li'] | 2022-11-30 | null | null | null | null | ['surface-normals-estimation'] | ['computer-vision'] | [ 3.37281376e-01 3.17794271e-02 -1.54440418e-01 -6.72640264e-01
-7.23694623e-01 -2.93694288e-01 4.65541601e-01 -5.16631529e-02
-2.17571646e-01 1.26545921e-01 -1.43892318e-01 1.80575863e-01
-8.86563584e-03 -9.33192253e-01 -1.07768750e+00 -6.39291525e-01
2.06184626e-01 8.01160157e-01 3.66166592e-01 -1.15192896... | [8.152814865112305, -3.4314024448394775] |
cb4483d5-8066-4920-b751-244c84135824 | stc-ids-spatial-temporal-correlation-feature | 2204.10990 | null | https://arxiv.org/abs/2204.10990v2 | https://arxiv.org/pdf/2204.10990v2.pdf | STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles | Intrusion detection is an important defensive measure for automotive communications security. Accurate frame detection models assist vehicles to avoid malicious attacks. Uncertainty and diversity regarding attack methods make this task challenging. However, the existing works have the limitation of only considering loc... | ['Aoxue Li', 'Mu Han', 'Pengzhou Cheng', 'Fengwei Zhang'] | 2022-04-23 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 5.04419841e-02 -5.40847182e-01 -4.08236295e-01 -3.14432472e-01
-5.63259244e-01 -1.19266853e-01 5.95283687e-01 4.77375686e-02
-4.26871121e-01 4.62377727e-01 -1.39328599e-01 -4.63435113e-01
-3.04456595e-02 -7.88583875e-01 -5.63393772e-01 -7.12117374e-01
-3.19034368e-01 -2.41518050e-01 6.74832404e-01 -2.90314108... | [7.770087718963623, 1.7503784894943237] |
c564306e-2cc5-4413-8ac3-9aeb88e73dfa | transforming-worlds-automated-involutive-mcmc | null | null | https://openreview.net/forum?id=8Itm8dQnJRc | https://openreview.net/pdf?id=8Itm8dQnJRc | Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models | Open-universe probabilistic models enable Bayesian inference about how many objects underlie data, and how they are related. Effective inference in OUPMs remains a challenge, however, often requiring the use of custom, trans-dimensional MCMC kernels, based on heuristics, deep learning, or domain knowledge, that can be ... | ['Vikash Mansinghka', 'Marco Cusumano-Towner', 'Stuart Russell', 'Matin Ghavamizadeh', 'Alexander K. Lew', 'George Matheos'] | 2020-11-23 | null | null | null | pproximateinference-aabi-symposium-2021-1 | ['probabilistic-programming'] | ['methodology'] | [ 1.13631219e-01 6.90100417e-02 -7.79774413e-02 -6.39329433e-01
-6.73979461e-01 -8.74085188e-01 9.65812147e-01 3.44391584e-01
-4.33746606e-01 7.16467142e-01 -3.48435231e-02 -7.80195296e-01
-4.66218829e-01 -1.30712366e+00 -8.81027997e-01 -4.27311510e-01
-2.04512358e-01 1.12939727e+00 5.42335987e-01 2.60845721... | [8.432159423828125, 6.4592814445495605] |
d1a90fc3-4413-41e6-af07-fed9a3bbbad3 | object-proposal-generation-using-two-stage | 1407.5242 | null | http://arxiv.org/abs/1407.5242v1 | http://arxiv.org/pdf/1407.5242v1.pdf | Object Proposal Generation using Two-Stage Cascade SVMs | Object proposal algorithms have shown great promise as a first step for
object recognition and detection. Good object proposal generation algorithms
require high object recall rate as well as low computational cost, because
generating object proposals is usually utilized as a preprocessing step. The
problem of how to a... | ['Ziming Zhang', 'Philip H. S. Torr'] | 2014-07-20 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 9.01865661e-02 -4.27344352e-01 -4.59574610e-01 -5.67944944e-01
-7.53475130e-01 -1.48396239e-01 3.61425966e-01 3.00747901e-01
-7.03839421e-01 5.14258504e-01 -4.36231762e-01 -3.07242908e-02
3.98289599e-02 -9.42831516e-01 -6.15230441e-01 -8.28169525e-01
3.15808952e-01 4.69335377e-01 8.81348491e-01 6.91627152... | [9.228340148925781, 0.86128169298172] |
595112b8-bd95-4b32-be1d-fda38a5feb7d | on-the-relationship-between-rnn-hidden-state | 2306.16854 | null | https://arxiv.org/abs/2306.16854v1 | https://arxiv.org/pdf/2306.16854v1.pdf | On the Relationship Between RNN Hidden State Vectors and Semantic Ground Truth | We examine the assumption that the hidden-state vectors of recurrent neural networks (RNNs) tend to form clusters of semantically similar vectors, which we dub the clustering hypothesis. While this hypothesis has been assumed in the analysis of RNNs in recent years, its validity has not been studied thoroughly on moder... | ['Thomas Pock', 'Bernhard K. Aichernig', 'Ingo Pill', 'Martin Tappler', 'Edi Muškardin'] | 2023-06-29 | null | null | null | null | ['clustering'] | ['methodology'] | [ 1.23931989e-01 1.37420148e-01 -1.71766013e-01 -3.41662526e-01
-2.65442491e-01 -7.42258847e-01 8.18279088e-01 8.39400291e-03
-3.26469690e-01 2.46329576e-01 2.66717285e-01 -6.72333241e-01
-1.05315901e-01 -5.08732259e-01 -3.92200142e-01 -9.24014926e-01
-1.00963131e-01 6.87463284e-01 3.15180987e-01 -1.56016588... | [14.384725570678711, 6.549631595611572] |
698d8ef5-95cf-4559-a5b6-7ae7f2a854a6 | icdar2019-robust-reading-challenge-on | 1909.07145 | null | https://arxiv.org/abs/1909.07145v1 | https://arxiv.org/pdf/1909.07145v1.pdf | ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) | This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top ... | ['ChuanMing Fang', 'Chee-Kheng Chng', 'Junyu Han', 'Errui Ding', 'Yuliang Liu', 'Lianwen Jin', 'Jingtuo Liu', 'Chun Chet Ng', 'Chee Seng Chan', 'Zihan Ni', 'Shuaitao Zhang', 'Yipeng Sun', 'Dimosthenis Karatzas', 'Canjie Luo'] | 2019-09-16 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 2.74333835e-01 -2.69347042e-01 3.55290353e-01 -1.25556767e-01
-1.10356700e+00 -6.36573255e-01 1.10218823e+00 3.03699195e-01
-5.92800379e-01 2.69066304e-01 4.17077869e-01 -1.08456850e-01
3.89801025e-01 -4.20983374e-01 -8.24527860e-01 -3.70434493e-01
2.92744100e-01 5.84032476e-01 2.67467767e-01 -7.46128634... | [11.94224739074707, 2.3065192699432373] |
8037b51c-7c09-4246-ae94-ff7763ba96f9 | semantic-image-cropping | 2107.07153 | null | https://arxiv.org/abs/2107.07153v1 | https://arxiv.org/pdf/2107.07153v1.pdf | Semantic Image Cropping | Automatic image cropping techniques are commonly used to enhance the aesthetic quality of an image; they do it by detecting the most beautiful or the most salient parts of the image and removing the unwanted content to have a smaller image that is more visually pleasing. In this thesis, I introduce an additional dimens... | ['Oriol Corcoll'] | 2021-07-15 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 4.74859685e-01 3.90710592e-01 1.53256685e-01 -2.05651909e-01
-2.15714455e-01 -6.61292076e-01 3.38398278e-01 9.79356691e-02
-1.98246449e-01 3.81197274e-01 1.61640495e-01 -1.26745373e-01
2.10212708e-01 -1.16247201e+00 -9.66773808e-01 -3.88844103e-01
5.96478224e-01 -6.56535253e-02 1.63245760e-02 -5.46130240... | [11.474088668823242, -0.9405665397644043] |
ba08b636-c8f9-49b7-a641-2f00aca844ae | learning-to-count-grave-sites-for-cemetery | null | null | https://ieeexplore.ieee.org/abstract/document/9203976 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9203976 | Learning to Count Grave Sites for Cemetery Observation Models With Satellite Imagery | Understanding how people occupy open spaces is important for research in support of population modeling, policy, national security, emergency response, and sustainability. For the past decade, there has been an increase in research toward capturing and reporting population dynamics and patterns of life at the building ... | ['Marie Urban', 'Nikhil Makkar', 'Lauryn Bragg', 'Sarah Walters', 'Rohan Dhamdhere', 'Dalton Lunga'] | 2020-09-22 | null | null | null | ieee-geoscience-and-remote-sensing-letters-4 | ['object-counting'] | ['computer-vision'] | [ 3.18640471e-02 -4.07620996e-01 5.13575040e-02 -2.64862031e-01
-2.63992429e-01 -5.60471177e-01 8.30260932e-01 6.28264427e-01
-8.26476038e-01 1.06209302e+00 7.37338185e-01 -4.19049799e-01
-1.12075031e-01 -1.54339623e+00 -4.71851647e-01 -6.20839059e-01
-3.45522255e-01 4.24861759e-01 -2.01162100e-01 -5.60874343... | [9.34260368347168, -1.2613507509231567] |
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