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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
07f5f450-632e-47df-a432-ca0545608ab0 | lafin-generative-landmark-guided-face | 1911.11394 | null | https://arxiv.org/abs/1911.11394v1 | https://arxiv.org/pdf/1911.11394v1.pdf | LaFIn: Generative Landmark Guided Face Inpainting | It is challenging to inpaint face images in the wild, due to the large variation of appearance, such as different poses, expressions and occlusions. A good inpainting algorithm should guarantee the realism of output, including the topological structure among eyes, nose and mouth, as well as the attribute consistency on... | ['Xiaojie Guo', 'Lin Ma', 'Haibin Ling', 'Jiayi Ma', 'Yang Yang'] | 2019-11-26 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [-1.57376602e-01 2.44490966e-01 2.10598975e-01 -5.93830585e-01
-3.94543201e-01 -2.23565146e-01 3.80116761e-01 -4.47685868e-01
1.40560195e-01 7.16802120e-01 2.07126513e-01 4.61379290e-01
1.07245803e-01 -5.39500952e-01 -8.61127615e-01 -7.38565445e-01
3.70894112e-02 2.52058685e-01 -3.55669141e-01 -3.43566358... | [12.831965446472168, -0.062403514981269836] |
67ec7117-efc2-49f2-8d8b-9f82cf22428c | structured-set-matching-networks-for-one-shot | 1712.01867 | null | http://arxiv.org/abs/1712.01867v2 | http://arxiv.org/pdf/1712.01867v2.pdf | Structured Set Matching Networks for One-Shot Part Labeling | Diagrams often depict complex phenomena and serve as a good test bed for
visual and textual reasoning. However, understanding diagrams using natural
image understanding approaches requires large training datasets of diagrams,
which are very hard to obtain. Instead, this can be addressed as a matching
problem either bet... | ['Aniruddha Kembhavi', 'Ali Farhadi', 'Jayant Krishnamurthy', 'Jonghyun Choi'] | 2017-12-05 | structured-set-matching-networks-for-one-shot-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Choi_Structured_Set_Matching_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Choi_Structured_Set_Matching_CVPR_2018_paper.pdf | cvpr-2018-6 | ['set-matching'] | ['computer-vision'] | [ 6.68644249e-01 2.37753317e-01 -1.79259181e-01 -7.71082938e-01
-8.27845752e-01 -6.19004071e-01 8.84005785e-01 3.34714651e-01
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1.56766862e-01 -8.04214239e-01 -1.20716918e+00 -2.64089257e-01
3.95512015e-01 8.05564582e-01 5.92306435e-01 -4.06552367... | [10.43994140625, 1.604631781578064] |
47c63ebf-464e-4ea1-a5c9-d95738d2b9b6 | g2l-a-global-to-local-alignment-method-for | null | null | https://www.sciencedirect.com/science/article/pii/S1877050922012170 | https://www.sciencedirect.com/science/article/pii/S1877050922012170 | G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation | Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer knowledge from a source dataset with dense pixel-level annotations to an unlabeled target dataset. However, the performance of UDA methods often suffers from the domain shift, which is the discrepancy between the feature distributions of th... | ['Thi-Oanh Nguyen', 'Dinh Viet Sang', 'Kieu Dang Nam', 'Nguyen Viet Manh'] | 2022-09-07 | null | null | null | kes-2022-9 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 5.71680605e-01 3.18748131e-02 -2.33127326e-01 -5.43534577e-01
-1.19000840e+00 -7.92065322e-01 6.24521852e-01 -4.71218266e-02
-2.95023203e-01 5.27808368e-01 -6.49918243e-02 1.04444558e-02
5.62985577e-02 -7.82412469e-01 -8.19562614e-01 -1.05456436e+00
4.97271657e-01 3.64617497e-01 5.01094580e-01 -1.23736210... | [9.725829124450684, 1.3441134691238403] |
529bb926-da95-4b9d-98d4-8987023bdbd9 | cross-functional-analysis-of-generalisation | 2305.12951 | null | https://arxiv.org/abs/2305.12951v1 | https://arxiv.org/pdf/2305.12951v1.pdf | Cross-functional Analysis of Generalisation in Behavioural Learning | In behavioural testing, system functionalities underrepresented in the standard evaluation setting (with a held-out test set) are validated through controlled input-output pairs. Optimising performance on the behavioural tests during training (behavioural learning) would improve coverage of phenomena not sufficiently r... | ['Benjamin Roth', 'Pedro Henrique Luz de Araujo'] | 2023-05-22 | null | null | null | null | ['paraphrase-identification', 'reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.81021726e-01 2.48282954e-01 1.06416769e-01 -5.89420438e-01
-9.28064406e-01 -9.60426807e-01 7.59523213e-01 5.55050194e-01
-5.88765979e-01 6.60739005e-01 -2.27142125e-02 -4.77175087e-01
-5.75168729e-01 -7.13067770e-01 -8.07452023e-01 -2.98975319e-01
2.49018237e-01 4.09485817e-01 3.49949330e-01 -3.12775463... | [9.101856231689453, 4.648229598999023] |
62ad8d6f-e40c-4f86-ba85-bbff74bc0d05 | coda-prompt-continual-decomposed-attention | 2211.13218 | null | https://arxiv.org/abs/2211.13218v2 | https://arxiv.org/pdf/2211.13218v2.pdf | CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning | Computer vision models suffer from a phenomenon known as catastrophic forgetting when learning novel concepts from continuously shifting training data. Typical solutions for this continual learning problem require extensive rehearsal of previously seen data, which increases memory costs and may violate data privacy. Re... | ['Zsolt Kira', 'Rogerio Feris', 'Rameswar Panda', 'Assaf Arbelle', 'Donghyun Kim', 'Paola Cascante-Bonilla', 'Vyshnavi Gutta', 'Leonid Karlinsky', 'James Seale Smith'] | 2022-11-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Smith_CODA-Prompt_COntinual_Decomposed_Attention-Based_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Smith_CODA-Prompt_COntinual_Decomposed_Attention-Based_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['novel-concepts'] | ['reasoning'] | [ 4.70167249e-01 3.61912930e-03 5.17049953e-02 -3.77867669e-01
-8.89428973e-01 -6.03937805e-01 9.15223777e-01 1.07147202e-01
-9.80436921e-01 7.28684783e-01 2.29526684e-02 -2.95576870e-01
1.81402996e-01 -2.85015762e-01 -1.03881800e+00 -6.28040075e-01
2.88595021e-01 3.63535970e-01 4.64752942e-01 -1.09571703... | [9.819397926330566, 3.338397264480591] |
65909c96-46b5-4b35-8543-4191ebf7717a | towards-better-characterization-of-1 | null | null | https://aclanthology.org/2022.acl-long.588 | https://aclanthology.org/2022.acl-long.588.pdf | Towards Better Characterization of Paraphrases | To effectively characterize the nature of paraphrase pairs without expert human annotation, we proposes two new metrics: word position deviation (WPD) and lexical deviation (LD). WPD measures the degree of structural alteration, while LD measures the difference in vocabulary used. We apply these metrics to better under... | ['De Wen Soh', 'Timothy Liu'] | null | null | null | null | acl-2022-5 | ['paraphrase-generation', 'paraphrase-identification', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 5.21799862e-01 -3.62944640e-02 -2.86716968e-01 -2.30377346e-01
-8.74551177e-01 -1.16411090e+00 6.72434032e-01 5.63707471e-01
-3.94551039e-01 5.58185637e-01 7.11941242e-01 -5.62044442e-01
-8.82438645e-02 -6.12799227e-01 -7.01704264e-01 -6.01618588e-02
6.20232284e-01 3.70892674e-01 2.08988309e-01 -3.11985999... | [11.362866401672363, 9.214247703552246] |
7d8a6d8e-339d-4711-925a-b80a6ade5a8f | 190501965 | 1905.01965 | null | http://arxiv.org/abs/1905.01965v1 | http://arxiv.org/pdf/1905.01965v1.pdf | Arabic Text Diacritization Using Deep Neural Networks | Diacritization of Arabic text is both an interesting and a challenging
problem at the same time with various applications ranging from speech
synthesis to helping students learning the Arabic language. Like many other
tasks or problems in Arabic language processing, the weak efforts invested into
this problem and the l... | ['Mahmoud Al-Ayyoub', "Bara' Al-Jawarneh", 'Ibraheem Tuffaha', 'Ali Fadel'] | 2019-04-25 | null | null | null | null | ['arabic-text-diacritization'] | ['natural-language-processing'] | [-7.05435723e-02 8.25574547e-02 7.11420998e-02 -3.26045454e-01
-6.47005141e-01 -7.02007234e-01 5.91973901e-01 3.23973566e-01
-5.66169143e-01 7.87359834e-01 -1.61472941e-03 -6.05364501e-01
-1.35380328e-01 -8.01085770e-01 -1.82395905e-01 -8.14795673e-01
1.20595627e-01 7.45567203e-01 3.50982964e-01 -1.10447013... | [10.403687477111816, 10.366752624511719] |
85e0656d-bb90-4bff-b1f7-904d2e7d3e38 | deep-hough-transform-line-priors | 2007.09493 | null | https://arxiv.org/abs/2007.09493v1 | https://arxiv.org/pdf/2007.09493v1.pdf | Deep Hough-Transform Line Priors | Classical work on line segment detection is knowledge-based; it uses carefully designed geometric priors using either image gradients, pixel groupings, or Hough transform variants. Instead, current deep learning methods do away with all prior knowledge and replace priors by training deep networks on large manually anno... | ['Silvia L. Pintea', 'Yancong Lin', 'Jan C. van Gemert'] | 2020-07-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4061_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670324.pdf | eccv-2020-8 | ['line-segment-detection'] | ['computer-vision'] | [-1.27046540e-01 1.45316407e-01 -2.89950550e-01 -5.94594419e-01
-7.08062232e-01 -7.99881041e-01 6.18376315e-01 2.07498297e-01
-5.99518359e-01 6.04234636e-01 -2.08174586e-02 -3.65392983e-01
1.11699410e-01 -1.03052700e+00 -1.25702822e+00 -1.65835366e-01
-1.31351009e-01 4.66403693e-01 6.54934108e-01 -3.86573136... | [8.28592300415039, -1.7132563591003418] |
968c10ad-7445-46d2-9363-56c4eda96f07 | revisiting-im2gps-in-the-deep-learning-era | 1705.04838 | null | http://arxiv.org/abs/1705.04838v1 | http://arxiv.org/pdf/1705.04838v1.pdf | Revisiting IM2GPS in the Deep Learning Era | Image geolocalization, inferring the geographic location of an image, is a
challenging computer vision problem with many potential applications. The
recent state-of-the-art approach to this problem is a deep image classification
approach in which the world is spatially divided into cells and a deep network
is trained t... | ['Nam Vo', 'James Hays', 'Nathan Jacobs'] | 2017-05-13 | revisiting-im2gps-in-the-deep-learning-era-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Vo_Revisiting_IM2GPS_in_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Vo_Revisiting_IM2GPS_in_ICCV_2017_paper.pdf | iccv-2017-10 | ['photo-geolocation-estimation'] | ['computer-vision'] | [-3.29509974e-01 -2.18985572e-01 -2.15587497e-01 -3.84185165e-01
-1.26111639e+00 -5.90189040e-01 8.71281028e-01 2.54073203e-01
-9.79210854e-01 7.19680786e-01 4.85027060e-02 7.75975883e-02
-7.64129609e-02 -1.03135097e+00 -1.18689263e+00 -9.04719293e-01
1.97392106e-02 8.70972812e-01 4.47388478e-02 2.56495476... | [7.724888324737549, -1.8601247072219849] |
9a478c3b-7d4c-4397-a8b3-2d897680784a | automated-imbalanced-classification-via | 2205.02553 | null | https://arxiv.org/abs/2205.02553v2 | https://arxiv.org/pdf/2205.02553v2.pdf | Automated Imbalanced Classification via Layered Learning | In this paper we address imbalanced binary classification (IBC) tasks. Applying resampling strategies to balance the class distribution of training instances is a common approach to tackle these problems. Many state-of-the-art methods find instances of interest close to the decision boundary to drive the resampling pro... | ['Paula Branco', 'Colin Bellinger', 'Luis Torgo', 'Vitor Cerqueira'] | 2022-05-05 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.84260088e-01 1.90615907e-01 -4.45552051e-01 -3.42896968e-01
-5.70464432e-01 7.36316815e-02 5.46061397e-01 6.83041275e-01
-3.67730707e-01 9.86398339e-01 -2.42030025e-01 -1.70030624e-01
-4.30197090e-01 -9.77028608e-01 -6.24449015e-01 -9.03168797e-01
-1.07901329e-02 8.85830402e-01 4.59006518e-01 8.16824436... | [8.751688003540039, 4.155538082122803] |
5232b07b-0f36-40a8-a033-c4046891d295 | on-the-trade-off-between-redundancy-and-local | 2205.10192 | null | https://arxiv.org/abs/2205.10192v1 | https://arxiv.org/pdf/2205.10192v1.pdf | On the Trade-off between Redundancy and Local Coherence in Summarization | Extractive summarization systems are known to produce poorly coherent and, if not accounted for, highly redundant text. In this work, we tackle the problem of summary redundancy in unsupervised extractive summarization of long, highly-redundant documents. For this, we leverage a psycholinguistic theory of human reading... | ['Shay B. Cohen', 'Matthias Galle', 'Ronald Cardenas'] | 2022-05-20 | null | null | null | null | ['unsupervised-extractive-summarization', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.16051888e-01 6.27469718e-01 -1.01299666e-01 3.36096175e-02
-9.55403388e-01 -6.23765886e-01 8.11322272e-01 1.01642287e+00
-5.87176681e-01 8.66691053e-01 1.01946867e+00 -1.30169287e-01
-1.89681396e-01 -6.00023746e-01 -5.30553222e-01 -2.67204612e-01
1.17047489e-01 2.76342273e-01 2.50725374e-02 -5.29536977... | [12.497393608093262, 9.503808975219727] |
45d031d2-41b7-4a9e-b7c2-d3586acd9f9b | rnn-with-particle-flow-for-probabilistic | 2106.06064 | null | https://arxiv.org/abs/2106.06064v1 | https://arxiv.org/pdf/2106.06064v1.pdf | RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting | Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical models often fall short in handling the complexity and high non-linearity present in time-series data. Recent advances in deep learning allow for better modelling of spatial and tempo... | ['Mark Coates', 'Yingxue Zhang', 'Liheng Ma', 'Soumyasundar Pal'] | 2021-06-10 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-3.66519898e-01 -4.15273994e-01 -3.79579425e-01 -4.04359579e-01
-6.93337679e-01 -2.92541087e-01 9.52655494e-01 1.39782712e-01
3.09454575e-02 8.98347497e-01 2.01355115e-01 -7.95377910e-01
-5.04830301e-01 -9.40596938e-01 -7.52930701e-01 -8.17850947e-01
-7.36102581e-01 7.77803123e-01 3.62285942e-01 -2.33082799... | [6.928041458129883, 3.178102970123291] |
4f7ab4a3-427f-4f03-a216-49d8ee7ac1ee | the-learnable-typewriter-a-generative | 2302.01660 | null | https://arxiv.org/abs/2302.01660v3 | https://arxiv.org/pdf/2302.01660v3.pdf | The Learnable Typewriter: A Generative Approach to Text Analysis | We present a generative document-specific approach to character analysis and recognition in text lines. Our main idea is to build on unsupervised multi-object segmentation methods and in particular those that reconstruct images based on a limited amount of visual elements, called sprites. Taking as input a set of text ... | ['Mathieu Aubry', 'Tom Monnier', 'Julien Gaubil', 'Nicolas Gonthier', 'Ioannis Siglidis'] | 2023-02-03 | null | null | null | null | ['unsupervised-text-recognition'] | ['computer-vision'] | [ 4.74599212e-01 -1.18819259e-01 9.73693952e-02 -9.95548293e-02
-5.23174524e-01 -1.01495934e+00 8.75459969e-01 -5.99129908e-02
-2.53914297e-01 4.43447918e-01 -4.74053137e-02 -1.93279818e-01
2.47799736e-02 -5.77472508e-01 -1.02152586e+00 -4.02821213e-01
2.60975718e-01 8.92480850e-01 4.06505853e-01 -4.18128729... | [11.762754440307617, 2.472449779510498] |
e92ca7d3-a151-4340-8a3a-408ceb57c3e2 | babynet-reconstructing-3d-faces-of-babies | 2203.05908 | null | https://arxiv.org/abs/2203.05908v1 | https://arxiv.org/pdf/2203.05908v1.pdf | BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs | We present a 3D face reconstruction system that aims at recovering the 3D facial geometry of babies from uncalibrated photographs, BabyNet. Since the 3D facial geometry of babies differs substantially from that of adults, baby-specific facial reconstruction systems are needed. BabyNet consists of two stages: 1) a 3D gr... | ['Federico M. Sukno', 'Gemma Piella', 'Marius George Linguraru', 'Antonio R. Porras', 'Araceli Morales'] | 2022-03-11 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.03052959e-01 7.64080107e-01 2.89019883e-01 -6.77711248e-01
-2.48268276e-01 -1.90196574e-01 4.19060677e-01 -4.18111950e-01
1.27982236e-02 6.04030043e-02 2.72535264e-01 8.73510167e-02
3.47796857e-01 -8.94617140e-01 -1.09208024e+00 -5.51858902e-01
1.14918230e-02 7.71703660e-01 -1.87006339e-01 5.08265458... | [13.045133590698242, -0.06396350264549255] |
fa513282-f86b-452c-bf5b-aebc06957571 | commonsense-knowledge-graph-completion-via | 2305.17019 | null | https://arxiv.org/abs/2305.17019v1 | https://arxiv.org/pdf/2305.17019v1.pdf | Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering | The nodes in the commonsense knowledge graph (CSKG) are normally represented by free-form short text (e.g., word or phrase). Different nodes may represent the same concept. This leads to the problems of edge sparsity and node redundancy, which challenges CSKG representation and completion. On the one hand, edge sparsit... | ['Rui Xia', 'Xiangqing Shen', 'Siwei Wu'] | 2023-05-26 | null | null | null | null | ['knowledge-graph-completion', 'graph-representation-learning'] | ['knowledge-base', 'methodology'] | [ 6.52916655e-02 3.91524822e-01 -4.87571299e-01 -1.93771183e-01
-1.18655778e-01 -2.61080265e-01 2.49415368e-01 4.35407788e-01
-1.17586821e-01 5.03378332e-01 2.57306695e-01 -1.78993210e-01
-1.88146710e-01 -9.67166126e-01 -5.21015406e-01 -6.49239302e-01
-4.29376736e-02 3.36167306e-01 8.05009995e-03 -2.42393956... | [8.70995044708252, 7.847475051879883] |
39439767-f07d-4dc2-ac86-baccca322d1c | comic-an-unsupervised-change-detection-method | 2304.00721 | null | https://arxiv.org/abs/2304.00721v1 | https://arxiv.org/pdf/2304.00721v1.pdf | COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks | In this paper, we consider the problem of change detection (CD) with two heterogeneous remote sensing (RS) images. For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is transl... | ['Pramod K. Varshney', 'Xueqian Wang', 'Zhuoyue Wang', 'Gang Li', 'Chengxi Li'] | 2023-04-03 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 6.19665861e-01 -3.79020721e-01 2.87168473e-01 2.42378954e-02
-4.48852032e-01 -4.90439862e-01 5.88615537e-01 -3.60583603e-01
-3.59739810e-01 7.08586097e-01 -3.89766544e-01 -9.47810039e-02
-5.18756248e-02 -1.17056847e+00 -7.80812681e-01 -1.36075974e+00
2.71214724e-01 8.32176581e-02 1.58914387e-01 -1.04393698... | [10.012829780578613, -1.8464372158050537] |
f9febb84-434c-4668-8881-960ae701624e | beam-search-for-learning-a-deep-convolutional | 1612.04774 | null | http://arxiv.org/abs/1612.04774v1 | http://arxiv.org/pdf/1612.04774v1.pdf | Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes | This paper addresses 3D shape recognition. Recent work typically represents a
3D shape as a set of binary variables corresponding to 3D voxels of a uniform
3D grid centered on the shape, and resorts to deep convolutional neural
networks(CNNs) for modeling these binary variables. Robust learning of such
CNNs is currentl... | ['Xu Xu', 'Sinisa Todorovic'] | 2016-12-14 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-recognition'] | ['computer-vision', 'computer-vision'] | [-4.45293747e-02 1.12683877e-01 -2.94578671e-01 -2.39162028e-01
-3.24687302e-01 -6.16509438e-01 5.82271457e-01 7.81488046e-02
-2.10363939e-01 3.04048300e-01 -2.38416255e-01 -5.68650961e-01
-1.05879813e-01 -9.90797639e-01 -8.07904184e-01 -6.62886679e-01
-2.37165794e-01 8.97463262e-01 2.74125606e-01 2.48869896... | [8.109986305236816, -3.6530933380126953] |
03ac9c5c-9642-49bf-a4a5-81c720c71bb4 | knowledge-based-recurrent-attentive-neural | 1803.05263 | null | http://arxiv.org/abs/1803.05263v4 | http://arxiv.org/pdf/1803.05263v4.pdf | Feature Selective Small Object Detection via Knowledge-based Recurrent Attentive Neural Network | At present, the performance of deep neural network in general object
detection is comparable to or even surpasses that of human beings. However, due
to the limitations of deep learning itself, the small proportion of feature
pixels, and the occurence of blur and occlusion, the detection of small objects
in complex scen... | ['Shitao Chen', 'Zhiqiang Jian', 'Nanning Zheng', 'Kai Yi'] | 2018-03-13 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.10757738e-02 -2.64804393e-01 7.01503158e-02 -4.05712336e-01
9.58074033e-02 -2.15560511e-01 4.63884652e-01 -4.35080118e-02
-8.04619133e-01 5.27947485e-01 -3.40983957e-01 -2.63175368e-01
-2.14296415e-01 -9.41633046e-01 -5.35751402e-01 -7.19929993e-01
-1.35268122e-01 1.55050859e-01 8.46944869e-01 -5.72676063... | [8.534674644470215, -0.7052953839302063] |
c0b9c29f-7e28-4f5a-89c6-6470c9b33767 | st-2-small-data-text-style-transfer-via-multi | 2004.11742 | null | https://arxiv.org/abs/2004.11742v1 | https://arxiv.org/pdf/2004.11742v1.pdf | ST$^2$: Small-data Text Style Transfer via Multi-task Meta-Learning | Text style transfer aims to paraphrase a sentence in one style into another style while preserving content. Due to lack of parallel training data, state-of-art methods are unsupervised and rely on large datasets that share content. Furthermore, existing methods have been applied on very limited categories of styles suc... | ['Xiwen Chen', 'Kenny Q. Zhu'] | 2020-04-24 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 5.82199812e-01 -1.56434268e-01 -3.78296673e-01 -7.19785094e-01
-3.88135791e-01 -6.05249822e-01 6.49756849e-01 -2.89519243e-02
-4.06695783e-01 1.01731074e+00 5.72749257e-01 -6.94603920e-02
2.30862007e-01 -9.29213226e-01 -3.27144027e-01 -8.03563967e-02
9.18137968e-01 6.38262570e-01 1.63480297e-01 -7.18227983... | [11.597508430480957, 9.569331169128418] |
f5236f06-f5d9-4b03-8558-3e0263fccc93 | buffer-balancing-accuracy-efficiency-and | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ao_BUFFER_Balancing_Accuracy_Efficiency_and_Generalizability_in_Point_Cloud_Registration_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ao_BUFFER_Balancing_Accuracy_Efficiency_and_Generalizability_in_Point_Cloud_Registration_CVPR_2023_paper.pdf | BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud Registration | An ideal point cloud registration framework should have superior accuracy, acceptable efficiency, and strong generalizability. However, this is highly challenging since existing registration techniques are either not accurate enough, far from efficient, or generalized poorly. It remains an open question that how to... | ['Yulan Guo', 'Kai Xu', 'Hanyun Wang', 'Qingyong Hu', 'Sheng Ao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-registration', 'open-question'] | ['computer-vision', 'natural-language-processing'] | [-2.33803928e-01 -1.89021096e-01 -1.51294857e-01 -2.58226633e-01
-1.20093656e+00 -3.61412525e-01 5.59177458e-01 1.12452082e-01
-1.91746742e-01 3.60450685e-01 1.61400679e-02 -7.00749680e-02
-2.45318368e-01 -7.36988068e-01 -7.93401480e-01 -6.23871446e-01
-1.03704967e-01 3.97055924e-01 1.97417825e-01 -2.91366428... | [7.712629318237305, -3.1196982860565186] |
89ae5744-dbed-43f9-bef7-4ff38b3380b3 | codereviewer-pre-training-for-automating-code | 2203.09095 | null | https://arxiv.org/abs/2203.09095v2 | https://arxiv.org/pdf/2203.09095v2.pdf | Automating Code Review Activities by Large-Scale Pre-training | Code review is an essential part to software development lifecycle since it aims at guaranteeing the quality of codes. Modern code review activities necessitate developers viewing, understanding and even running the programs to assess logic, functionality, latency, style and other factors. It turns out that developers ... | ['Neel Sundaresan', 'Shengyu Fu', 'Alexey Svyatkovskiy', 'Jared Green', 'Deep Majumder', 'Grant Jenks', 'Shailesh Jannu', 'Nan Duan', 'Daya Guo', 'Shuai Lu', 'Zhiyu Li'] | 2022-03-17 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 1.03735521e-01 -2.61900663e-01 -3.86780471e-01 -5.49138784e-01
-8.76341045e-01 -5.58257759e-01 3.14551920e-01 3.30960870e-01
-4.40762043e-02 4.62372489e-02 -9.20472369e-02 -7.69306242e-01
3.64929318e-01 -4.40792352e-01 -7.87204444e-01 3.16703677e-01
2.21714348e-01 -2.30230853e-01 1.08561225e-01 -2.46183276... | [7.616643905639648, 7.935215473175049] |
4f5f2946-2af1-49bf-96d0-8bd83547ebff | som-semantic-obviousness-metric-for-image | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_SOM_Semantic_Obviousness_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_SOM_Semantic_Obviousness_2015_CVPR_paper.pdf | SOM: Semantic Obviousness Metric for Image Quality Assessment | Image quality assessment (IQA) tries to estimate human perception based image visual quality in an objective manner. Existing approaches target this problem with or without reference images. For no-reference image quality assessment, there is no given reference image or any knowledge of the distortion type of the ima... | ['Houqiang Li', 'Wengang Zhou', 'Lei Wu', 'Peng Zhang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['image-quality-estimation', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 3.51178259e-01 -4.52055424e-01 2.32749097e-02 -5.23902416e-01
-8.47930431e-01 -2.96002269e-01 4.19424057e-01 1.19135395e-01
-3.03386897e-01 4.32599276e-01 1.78187445e-01 5.98007739e-02
-4.26617116e-01 -8.93047273e-01 -4.46496546e-01 -6.39520347e-01
2.04878837e-01 -1.71020627e-01 4.92783219e-01 -3.32157284... | [11.771757125854492, -1.9146490097045898] |
55e72a2c-0679-4ef2-9946-98620f77199e | decoding-the-underlying-meaning-of-multimodal | 2305.17678 | null | https://arxiv.org/abs/2305.17678v2 | https://arxiv.org/pdf/2305.17678v2.pdf | Decoding the Underlying Meaning of Multimodal Hateful Memes | Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable explanations that uncover the underlying meaning and support the classification output. A major reason for the lack of explainable hateful me... | ['Roy Ka-Wei Lee', 'Wen-Haw Chong', 'Ming Shan Hee'] | 2023-05-28 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [ 6.70780092e-02 5.83179116e-01 -2.17552707e-01 -1.93599403e-01
-3.64051044e-01 -3.98923576e-01 1.01131642e+00 2.27518842e-01
3.63677591e-01 1.05773723e+00 8.82049441e-01 -2.38972470e-01
2.19729304e-01 -4.74435091e-01 -7.40076602e-01 -2.76654959e-01
4.94556099e-01 4.59399641e-01 -2.51807332e-01 -4.28715438... | [8.596968650817871, 10.669794082641602] |
46554a10-9aa3-4001-922c-1b0cb78fa948 | adapting-end-to-end-neural-speaker | 1811.03055 | null | http://arxiv.org/abs/1811.03055v1 | http://arxiv.org/pdf/1811.03055v1.pdf | Adapting End-to-End Neural Speaker Verification to New Languages and Recording Conditions with Adversarial Training | In this article we propose a novel approach for adapting speaker embeddings
to new domains based on adversarial training of neural networks. We apply our
embeddings to the task of text-independent speaker verification, a challenging,
real-world problem in biometric security. We further the development of
end-to-end spe... | ['Patrick Kenny', 'Gautam Bhattacharya', 'Jahangir Alam'] | 2018-11-07 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 3.99483442e-01 1.48363158e-01 1.65179372e-01 -6.10366464e-01
-1.22208822e+00 -8.96186650e-01 9.27078187e-01 -1.36101320e-01
-6.39010727e-01 3.40175152e-01 4.50082272e-01 -4.06305969e-01
2.12227240e-01 -1.07242875e-01 -5.46878397e-01 -7.14955091e-01
-9.49630961e-02 4.24005628e-01 -1.33375525e-01 -4.17094320... | [14.302343368530273, 6.083889007568359] |
95658bb7-e618-49e1-9d46-31a838334125 | ta-da-topic-aware-domain-adaptation-for | 2301.06902 | null | https://arxiv.org/abs/2301.06902v1 | https://arxiv.org/pdf/2301.06902v1.pdf | TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract) | Keyphrase identification and classification is a Natural Language Processing and Information Retrieval task that involves extracting relevant groups of words from a given text related to the main topic. In this work, we focus on extracting keyphrases from scientific documents. We introduce TA-DA, a Topic-Aware Domain A... | ['Florin Pop', 'Mihai Dascalu', 'Dumitru-Clementin Cercel', 'Andrei-Marius Avram', 'George-Eduard Zaharia', 'Răzvan-Alexandru Smădu'] | 2022-12-30 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 3.20465595e-01 -1.42363757e-01 -4.97302532e-01 2.32120976e-01
-1.46741676e+00 -1.08168054e+00 1.02686059e+00 1.04542756e+00
-7.66699076e-01 8.92060637e-01 5.02554059e-01 -3.97044390e-01
-1.16337590e-01 -6.46260798e-01 -6.95995212e-01 -6.53912544e-01
2.27584913e-01 4.71572131e-01 2.27563590e-01 -1.08814284... | [12.3106689453125, 8.890589714050293] |
56a44dbf-7a65-49f3-8c74-7ce5ef6f61ca | pansharpening-prisma-data-for-marine-plastic | null | null | https://ieeexplore.ieee.org/abstract/document/9406795 | https://ieeexplore.ieee.org/abstract/document/9406795 | Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes | Hyperspectral PRISMA images are new and have not yet been evaluated for their ability to detect marine plastic litter. The hyperspectral PRISMA images have a fine spectral resolution, however, their spatial resolution is not high enough to enable the discrimination of small plastic objects in the ocean. Pansharpening w... | ['Paolo Corradi', 'Enrico Barbone', 'Giulio Ceriola', 'Antonello Aiello', 'Nicolò Taggio', 'Pol Kolokoussis', 'Konstantinos Topouzelis', 'Vassilia Karathanassi', 'Viktoria Kristollari', 'Maria Kremezi'] | 2021-04-19 | null | null | null | ieee-access-2021-4 | ['pansharpening'] | ['computer-vision'] | [ 8.29016447e-01 -3.77953947e-01 3.03484470e-01 3.23788971e-01
-3.88422191e-01 -8.18652809e-01 1.62375420e-01 -6.62957206e-02
-1.94119573e-01 6.95621848e-01 -3.69254231e-01 -1.00358218e-01
-4.92474794e-01 -9.66866136e-01 -1.71801955e-01 -1.39263797e+00
-1.57167995e-03 1.35965258e-01 4.25106645e-01 1.49018606... | [10.025980949401855, -2.0701217651367188] |
4c70ef07-0b85-414a-ab3b-ad2f364c9f43 | grasping-field-learning-implicit | 2008.04451 | null | https://arxiv.org/abs/2008.04451v3 | https://arxiv.org/pdf/2008.04451v3.pdf | Grasping Field: Learning Implicit Representations for Human Grasps | Robotic grasping of house-hold objects has made remarkable progress in recent years. Yet, human grasps are still difficult to synthesize realistically. There are several key reasons: (1) the human hand has many degrees of freedom (more than robotic manipulators); (2) the synthesized hand should conform to the surface o... | ['Korrawe Karunratanakul', 'Michael Black', 'Yan Zhang', 'Jinlong Yang', 'Siyu Tang', 'Krikamol Muandet'] | 2020-08-10 | null | null | null | null | ['3d-object-reconstruction', 'grasp-generation'] | ['computer-vision', 'computer-vision'] | [-5.08795083e-02 2.30714560e-01 -2.31711958e-02 -1.36251062e-01
-3.26254487e-01 -5.90656459e-01 4.38708484e-01 -3.36558938e-01
1.91514149e-01 4.32603776e-01 1.09956078e-01 1.30703330e-01
-3.52119952e-01 -9.68168795e-01 -1.22699142e+00 -6.25579059e-01
-6.26617372e-02 1.04562283e+00 1.53046086e-01 -2.49026060... | [5.819517612457275, -0.8511770367622375] |
cbbfdebf-bf23-4cfa-bb1d-6ca9e64beaa7 | simple-question-answering-by-attentive | 1606.03391 | null | http://arxiv.org/abs/1606.03391v2 | http://arxiv.org/pdf/1606.03391v2.pdf | Simple Question Answering by Attentive Convolutional Neural Network | This work focuses on answering single-relation factoid questions over
Freebase. Each question can acquire the answer from a single fact of form
(subject, predicate, object) in Freebase. This task, simple question answering
(SimpleQA), can be addressed via a two-step pipeline: entity linking and fact
selection. In fact ... | ['Bo-Wen Zhou', 'Hinrich Schütze', 'Bing Xiang', 'Mo Yu', 'Wenpeng Yin'] | 2016-06-10 | simple-question-answering-by-attentive-2 | https://aclanthology.org/C16-1164 | https://aclanthology.org/C16-1164.pdf | coling-2016-12 | ['fact-selection'] | ['natural-language-processing'] | [-2.18841836e-01 9.07842100e-01 -4.25342888e-01 -5.26003003e-01
-1.46939707e+00 -7.14748144e-01 4.47122872e-01 6.66257739e-01
-4.92838889e-01 1.15058482e+00 4.78843480e-01 -3.50449234e-01
1.44248784e-01 -1.38150895e+00 -1.29351020e+00 3.14914659e-02
-4.57255468e-02 7.53028572e-01 8.87246072e-01 -7.25747645... | [10.55048656463623, 7.99493408203125] |
fdd79637-58cb-4c79-b6e1-082f09f74f15 | when-high-performing-models-behave-poorly-in | null | null | https://openreview.net/forum?id=9kBDWEmA6i | https://openreview.net/pdf?id=9kBDWEmA6i | When high-performing models behave poorly in practice: periodic sampling can help | Training a deep neural network (DNN) for breast cancer detection from medical images suffers from the (hopefully) low prevalence of the pathology.
For a sensible amount of positive cases, images must be collected from numerous places resulting in large heterogeneous datasets with different acquisition devices, populati... | ['Pierre Fillard', 'Paul Wambergue', 'Yaroslav Nikulin', 'Luis Montero', 'Julien GUILLAUMIN', 'Stanislas Chambon'] | 2021-09-29 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 3.71789366e-01 3.43309164e-01 -5.43909490e-01 -6.40969574e-01
-8.31420422e-01 -2.81153768e-01 2.98402965e-01 3.14639807e-01
-5.19704640e-01 6.39709055e-01 -4.35666069e-02 -4.76669729e-01
-2.62864202e-01 -9.36661959e-01 -7.17542768e-01 -1.18366265e+00
-3.47557701e-02 7.35714436e-01 2.54460126e-01 3.65567267... | [14.92452621459961, -2.5518369674682617] |
b7efefad-b88f-4c0b-a75e-c80df65b551e | low-complexity-approximate-convolutional | 2208.00087 | null | https://arxiv.org/abs/2208.00087v1 | https://arxiv.org/pdf/2208.00087v1.pdf | Low-complexity Approximate Convolutional Neural Networks | In this paper, we present an approach for minimizing the computational complexity of trained Convolutional Neural Networks (ConvNet). The idea is to approximate all elements of a given ConvNet and replace the original convolutional filters and parameters (pooling and bias coefficients; and activation function) with eff... | ['A. Leite', 'C. Garcia', 'S. Duffner', 'R. J. Cintra'] | 2022-07-29 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 2.01893285e-01 1.91975862e-01 3.46195102e-01 -4.95052546e-01
5.31174801e-02 -3.03574890e-01 6.35352790e-01 2.01947004e-01
-1.00561237e+00 6.29843771e-01 -4.14346904e-01 -4.47516590e-01
-3.69621925e-02 -8.09571147e-01 -8.11109722e-01 -5.57509780e-01
-3.24806035e-01 7.00138286e-02 2.06521526e-01 -2.50786036... | [8.511841773986816, 2.9668972492218018] |
265074d6-3754-4d6e-ace3-2ced1fd6cb81 | biomedicalclinical-nlp | null | null | https://aclanthology.org/C14-3001 | https://aclanthology.org/C14-3001.pdf | Biomedical/Clinical NLP | null | ['Meliha Yeti{\\c{s}}gen', 'Ozlem Uzuner', 'Amber Stubbs'] | 2014-08-01 | biomedicalclinical-nlp-1 | https://aclanthology.org/C14-3001 | https://aclanthology.org/C14-3001.pdf | coling-2014-8 | ['temporal-information-extraction'] | ['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... | [-7.420321941375732, 3.659557819366455] |
605212a0-42b7-4ecd-94d3-18e17b056521 | spatial-aware-token-for-weakly-supervised | 2303.10438 | null | https://arxiv.org/abs/2303.10438v1 | https://arxiv.org/pdf/2303.10438v1.pdf | Spatial-Aware Token for Weakly Supervised Object Localization | Weakly supervised object localization (WSOL) is a challenging task aiming to localize objects with only image-level supervision. Recent works apply visual transformer to WSOL and achieve significant success by exploiting the long-range feature dependency in self-attention mechanism. However, existing transformer-based ... | ['Zheng-Jun Zha', 'Jiebo Luo', 'Yang Cao', 'Wei Zhai', 'Pingyu Wu'] | 2023-03-18 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [ 2.74610817e-01 3.49818431e-02 -3.63203138e-01 -5.21417856e-01
-1.10339212e+00 -3.58511239e-01 3.95513505e-01 -3.06006148e-02
-4.89651501e-01 6.92803144e-01 -1.12644173e-01 4.84922249e-03
1.56291202e-01 -5.91034830e-01 -1.25938523e+00 -9.13034260e-01
2.30833888e-01 1.64943859e-01 5.82995713e-01 2.72305995... | [9.581969261169434, 0.7826972603797913] |
0aec0683-8f27-4036-88b3-dbb0268fb88a | dictionary-attacks-on-speaker-verification | 2204.11304 | null | https://arxiv.org/abs/2204.11304v2 | https://arxiv.org/pdf/2204.11304v2.pdf | Dictionary Attacks on Speaker Verification | In this paper, we propose dictionary attacks against speaker verification - a novel attack vector that aims to match a large fraction of speaker population by chance. We introduce a generic formulation of the attack that can be used with various speech representations and threat models. The attacker uses adversarial op... | ['Nasir Memon', 'Anubhav Jain', 'Pawel Korus', 'Mirko Marras'] | 2022-04-24 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 3.80273491e-01 5.88065565e-01 1.86895490e-01 -9.51855481e-02
-1.16702378e+00 -1.11078799e+00 6.17685735e-01 -2.36734450e-01
-2.48463720e-01 5.67414522e-01 1.01919360e-02 -3.47980589e-01
3.24303925e-01 -6.35594845e-01 -6.26567066e-01 -6.28939331e-01
-3.40844244e-01 3.41395885e-01 -7.50106424e-02 -3.58230442... | [13.9780855178833, 5.840764999389648] |
30ed7788-7de9-44ef-8e2b-4c3f008622cd | adaptive-cross-batch-normalization-for-metric | 2303.17127 | null | https://arxiv.org/abs/2303.17127v1 | https://arxiv.org/pdf/2303.17127v1.pdf | Adaptive Cross Batch Normalization for Metric Learning | Metric learning is a fundamental problem in computer vision whereby a model is trained to learn a semantically useful embedding space via ranking losses. Traditionally, the effectiveness of a ranking loss depends on the minibatch size, and is, therefore, inherently limited by the memory constraints of the underlying ha... | ['Stephen Gould', 'Anton Van Den Hengel', 'Matt Ma', 'Thalaiyasingam Ajanthan'] | 2023-03-30 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-9.95453894e-02 -2.99157590e-01 -1.37355030e-01 -3.69315535e-01
-1.01215947e+00 -4.63523060e-01 4.43955988e-01 3.31433386e-01
-6.97477341e-01 5.45622647e-01 -1.55168682e-01 -7.00582191e-02
-2.43592590e-01 -4.25949514e-01 -7.60864675e-01 -8.64276707e-01
-6.02761954e-02 2.91120857e-01 3.84691715e-01 7.23324865... | [9.39780330657959, 3.0406529903411865] |
cde30961-3d39-4067-a67b-108f5d144448 | sitaka-at-semeval-2017-task-4-sentiment | null | null | https://aclanthology.org/S17-2115 | https://aclanthology.org/S17-2115.pdf | SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features | This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by some lexicons. The polar... | ['Mohammed Jabreel', 'Antonio Moreno'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.77152157e-01 8.80104899e-02 -2.09663883e-01 -5.57782590e-01
-1.05823763e-01 -8.75521600e-01 1.13712752e+00 6.27342284e-01
-7.41599798e-01 6.61584795e-01 5.10470092e-01 -2.12858930e-01
8.73548314e-02 -9.87905085e-01 1.95936277e-03 -4.17491376e-01
-1.62622586e-01 4.79841173e-01 9.16216522e-02 -1.32466984... | [11.091548919677734, 6.925734519958496] |
137743e4-501d-45a7-ac27-65270bc51748 | inducing-document-plans-for-concept-to-text | null | null | https://aclanthology.org/D13-1157 | https://aclanthology.org/D13-1157.pdf | Inducing Document Plans for Concept-to-Text Generation | null | ['Ioannis Konstas', 'Mirella Lapata'] | 2013-10-01 | null | null | null | emnlp-2013-10 | ['concept-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.327576637268066, 3.7176694869995117] |
2e9a8eb1-ad1f-419f-bcf9-a462912f069c | scalable-graph-convolutional-network-training | 2212.05009 | null | https://arxiv.org/abs/2212.05009v2 | https://arxiv.org/pdf/2212.05009v2.pdf | Scalable Graph Convolutional Network Training on Distributed-Memory Systems | Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary. Since the convolution operation on graphs induces irregular memory access patterns, designing a m... | ['Hakan Ferhatosmanoglu', 'Aparajita Haldar', 'Gunduz Vehbi Demirci'] | 2022-12-09 | null | null | null | null | ['hypergraph-partitioning', 'graph-partitioning', 'blocking'] | ['graphs', 'graphs', 'natural-language-processing'] | [-2.47870088e-01 9.51712281e-02 -1.62108347e-01 -2.88924873e-01
-2.29336277e-01 -3.56790960e-01 1.38928056e-01 4.33067143e-01
-4.17869031e-01 4.26494747e-01 -3.18471014e-01 -6.88339949e-01
-4.01760012e-01 -1.42593169e+00 -9.78902638e-01 -7.56920755e-01
-7.36497223e-01 6.02212191e-01 1.90388411e-01 1.07159831... | [6.993686676025391, 5.75738525390625] |
3f25d302-0675-4ee4-93cb-fc1895632cb7 | nebula-i-a-general-framework-for | 2205.09470 | null | https://arxiv.org/abs/2205.09470v1 | https://arxiv.org/pdf/2205.09470v1.pdf | Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters | The ever-growing model size and scale of compute have attracted increasing interests in training deep learning models over multiple nodes. However, when it comes to training on cloud clusters, especially across remote clusters, huge challenges are faced. In this work, we introduce a general framework, Nebula-I, for col... | ['dianhai yu', 'Yanjun Ma', 'Yu Sun', 'Ge Li', 'Yue Yu', 'Yaqian Han', 'Shaohuai Shi', 'Bin Wang', 'Long Li', 'Yongshuai Hou', 'Peng Liu', 'Shuohuan Wang', 'Yuang Liu', 'Xianjie Mo', 'Siyu Ding', 'Weibao Gong', 'Zhihua Wu', 'Yang Xiang'] | 2022-05-19 | null | null | null | null | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [-4.17202652e-01 -4.07391459e-01 -2.54341274e-01 -4.68579739e-01
-5.94344854e-01 -4.40443277e-01 3.30240726e-01 -1.13293186e-01
-8.26830089e-01 7.20988393e-01 -5.88697195e-01 -6.71911061e-01
3.84895056e-02 -1.18983018e+00 -9.58054781e-01 -9.22923565e-01
-1.50367722e-01 6.12842858e-01 3.68732214e-02 1.14231296... | [8.537464141845703, 3.145561695098877] |
67a2daa7-e077-4b8f-9c93-79783c150937 | from-shapley-values-to-generalized-additive | 2209.04012 | null | https://arxiv.org/abs/2209.04012v3 | https://arxiv.org/pdf/2209.04012v3.pdf | From Shapley Values to Generalized Additive Models and back | In explainable machine learning, local post-hoc explanation algorithms and inherently interpretable models are often seen as competing approaches. This work offers a partial reconciliation between the two by establishing a correspondence between Shapley Values and Generalized Additive Models (GAMs). We introduce $n$-Sh... | ['Ulrike Von Luxburg', 'Sebastian Bordt'] | 2022-09-08 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 6.43174648e-02 8.56830060e-01 -4.57301468e-01 -5.68998158e-01
-5.15390992e-01 -6.66168749e-01 3.54537874e-01 2.95834597e-02
1.84116900e-01 7.65071809e-01 7.70211071e-02 -4.84618008e-01
-8.07716191e-01 -7.98681319e-01 -6.90602422e-01 -7.04397142e-01
-9.81801152e-02 8.55763018e-01 -4.81089562e-01 -2.98664510... | [8.687579154968262, 5.534951210021973] |
0dce2d27-4e36-478c-abdd-4638f4db0d3b | learning-an-unreferenced-metric-for-online | 2005.00583 | null | https://arxiv.org/abs/2005.00583v1 | https://arxiv.org/pdf/2005.00583v1.pdf | Learning an Unreferenced Metric for Online Dialogue Evaluation | Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue. There have been recent efforts to develop automatic dialogue evaluation metrics, but most of them do not generalize to unseen datasets and/or need a human-generated reference resp... | ['William L. Hamilton', 'Ryan Lowe', 'Prasanna Parthasarathi', 'Koustuv Sinha', 'Jasmine Wang', 'Joelle Pineau'] | 2020-05-01 | learning-an-unreferenced-metric-for-online-1 | https://aclanthology.org/2020.acl-main.220 | https://aclanthology.org/2020.acl-main.220.pdf | acl-2020-6 | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 2.39607036e-01 3.87557030e-01 -1.73324049e-02 -7.81782329e-01
-1.05246174e+00 -1.00206316e+00 9.73468244e-01 2.94861108e-01
-4.74430561e-01 8.81118774e-01 6.10262096e-01 -3.49669963e-01
6.02841713e-02 -4.18232441e-01 -1.02296136e-01 -2.02249616e-01
-8.88137668e-02 8.83128166e-01 1.68547630e-01 -3.77522886... | [12.77562427520752, 8.0027494430542] |
cb4140f2-d560-4a8c-8960-3c59fa3d0db5 | federated-minimax-optimization-improved | 2203.04850 | null | https://arxiv.org/abs/2203.04850v1 | https://arxiv.org/pdf/2203.04850v1.pdf | Federated Minimax Optimization: Improved Convergence Analyses and Algorithms | In this paper, we consider nonconvex minimax optimization, which is gaining prominence in many modern machine learning applications such as GANs. Large-scale edge-based collection of training data in these applications calls for communication-efficient distributed optimization algorithms, such as those used in federate... | ['Pramod K. Varshney', 'Gauri Joshi', 'Rohan Panda', 'Pranay Sharma'] | 2022-03-09 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-2.18149513e-01 -1.54171839e-01 -4.34318244e-01 -3.97263259e-01
-1.47672927e+00 -5.94863594e-01 -5.91951832e-02 2.88539320e-01
-3.41053575e-01 1.00449777e+00 1.78040743e-01 -5.39778531e-01
-4.11678106e-01 -7.78475761e-01 -1.09994423e+00 -1.01471543e+00
-2.61247337e-01 6.13037586e-01 -3.49849463e-01 -1.79032236... | [6.287607669830322, 4.994508743286133] |
b308acc4-61f5-4b5b-a735-06251a6e0f7f | low-rank-prune-and-factorize-for-language | 2306.14152 | null | https://arxiv.org/abs/2306.14152v1 | https://arxiv.org/pdf/2306.14152v1.pdf | Low-Rank Prune-And-Factorize for Language Model Compression | The components underpinning PLMs -- large weight matrices -- were shown to bear considerable redundancy. Matrix factorization, a well-established technique from matrix theory, has been utilized to reduce the number of parameters in PLM. However, it fails to retain satisfactory performance under moderate to high compres... | ['Kenny Q. Zhu', 'Siyu Ren'] | 2023-06-25 | null | null | null | null | ['network-pruning', 'model-compression', 'question-answering'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 3.19103956e-01 9.03866068e-02 -4.28081661e-01 -1.11074060e-01
-5.44813752e-01 -3.73561054e-01 2.18016684e-01 2.76988912e-02
-2.32549444e-01 3.59064907e-01 3.53559107e-01 -6.74040139e-01
-5.42567730e-01 -4.91647214e-01 -7.32387602e-01 -4.73386288e-01
-5.75564764e-02 1.97299808e-01 7.92796686e-02 -9.38858315... | [8.716279983520508, 3.595669746398926] |
04e36eb9-7a33-4a57-b5f3-6f47b5d2d7a3 | bridging-the-gap-between-human-action | 2101.08851 | null | https://arxiv.org/abs/2101.08851v1 | https://arxiv.org/pdf/2101.08851v1.pdf | Bridging the gap between Human Action Recognition and Online Action Detection | Action recognition, early prediction, and online action detection are complementary disciplines that are often studied independently. Most online action detection networks use a pre-trained feature extractor, which might not be optimal for its new task. We address the task-specific feature extraction with a teacher-stu... | ['Rita Noumeir', 'Alban Main de Boissiere'] | 2021-01-21 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 4.58274633e-01 2.08981223e-02 -7.10728347e-01 -1.13096841e-01
-5.41315854e-01 -5.01645029e-01 6.46547437e-01 -1.82104945e-01
-6.18059158e-01 2.82568216e-01 -2.15833411e-02 -1.25292018e-01
-3.39496762e-01 -5.20039141e-01 -5.50801337e-01 -7.38080919e-01
-1.91941574e-01 4.30506244e-02 7.77980626e-01 7.05798119... | [8.426241874694824, 0.6518515348434448] |
c352e563-09e2-4d2c-9826-2fa2e817c4a2 | fgahoi-fine-grained-anchors-for-human-object | 2301.04019 | null | https://arxiv.org/abs/2301.04019v1 | https://arxiv.org/pdf/2301.04019v1.pdf | FGAHOI: Fine-Grained Anchors for Human-Object Interaction Detection | Human-Object Interaction (HOI), as an important problem in computer vision, requires locating the human-object pair and identifying the interactive relationships between them. The HOI instance has a greater span in spatial, scale, and task than the individual object instance, making its detection more susceptible to no... | ['Ying WEI', 'Shanze Wang', 'Yuefeng Wang', 'Shuailei Ma'] | 2023-01-08 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 1.75577641e-01 -4.16147470e-01 3.96441907e-01 -2.20523685e-01
-7.60315120e-01 -2.40223750e-01 4.01654840e-01 1.11754043e-02
-3.74544293e-01 3.46858799e-01 1.36331171e-01 1.24072999e-01
-2.56501973e-01 -5.98536193e-01 -5.73449016e-01 -7.39449084e-01
1.59485504e-01 3.83795917e-01 7.25865424e-01 -1.15633443... | [9.48404598236084, 1.3236284255981445] |
eb96c376-3035-4881-97d1-4181ac1b6da9 | asl-video-corpora-sign-bank-resources | 2201.07899 | null | https://arxiv.org/abs/2201.07899v1 | https://arxiv.org/pdf/2201.07899v1.pdf | ASL Video Corpora & Sign Bank: Resources Available through the American Sign Language Linguistic Research Project (ASLLRP) | The American Sign Language Linguistic Research Project (ASLLRP) provides Internet access to high-quality ASL video data, generally including front and side views and a close-up of the face. The manual and non-manual components of the signing have been linguistically annotated using SignStream(R). The recently expanded ... | ['Dimitris Metaxas', 'Augustine Opoku', 'Carol Neidle'] | 2022-01-19 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 1.31718919e-01 -2.60961890e-01 -4.74558890e-01 -4.55941767e-01
-9.70553339e-01 -7.75364757e-01 3.90541464e-01 -3.51133883e-01
-7.62607217e-01 3.59173417e-01 6.21464849e-01 -3.60286772e-01
-5.42063154e-02 -1.10027976e-01 -2.02435628e-01 -3.09304535e-01
-4.35996950e-02 3.69286329e-01 6.42499149e-01 -2.29386285... | [9.132732391357422, -6.4366888999938965] |
16e69a31-f044-4006-b4c9-fa0790710c8b | quick-starting-dialog-systems-with-paraphrase | 2204.02546 | null | https://arxiv.org/abs/2204.02546v2 | https://arxiv.org/pdf/2204.02546v2.pdf | Quick Starting Dialog Systems with Paraphrase Generation | Acquiring training data to improve the robustness of dialog systems can be a painstakingly long process. In this work, we propose a method to reduce the cost and effort of creating new conversational agents by artificially generating more data from existing examples, using paraphrase generation. Our proposed approach c... | ['Marie-Jean Meurs', 'Nada Naji', 'Eric Charton', 'Marc Queudot', 'Raouf Belbahar', 'Louis Marceau'] | 2022-04-06 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.66622758e-01 4.54928964e-01 2.07047820e-01 -7.60485172e-01
-7.12568879e-01 -8.17710698e-01 7.25255072e-01 -2.92999744e-01
-5.38149536e-01 1.00760770e+00 2.72964448e-01 -5.48210919e-01
3.34433258e-01 -6.60641849e-01 -2.30381861e-01 -1.19191498e-01
4.75992054e-01 9.55418229e-01 9.18057859e-02 -8.09300303... | [12.858478546142578, 7.95370626449585] |
48b72a5f-5e0d-434b-9ced-74f3824d2a4e | rethinking-kernel-methods-for-node | 1910.02548 | null | https://arxiv.org/abs/1910.02548v1 | https://arxiv.org/pdf/1910.02548v1.pdf | Rethinking Kernel Methods for Node Representation Learning on Graphs | Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification. However, the use of kernel methods for node classification, which is a related problem to graph representation learning, is still ill-posed and the state-of-the-art methods are heavily based on heuristics.... | ['Dimitris N. Metaxas', 'Xi Peng', 'Yu Tian', 'Long Zhao'] | 2019-10-06 | rethinking-kernel-methods-for-node-1 | http://papers.nips.cc/paper/9342-rethinking-kernel-methods-for-node-representation-learning-on-graphs | http://papers.nips.cc/paper/9342-rethinking-kernel-methods-for-node-representation-learning-on-graphs.pdf | neurips-2019-12 | ['graph-similarity'] | ['graphs'] | [-6.48336485e-02 2.40761876e-01 -5.03319919e-01 -1.93768293e-01
-3.38557571e-01 -5.82147121e-01 4.72136259e-01 7.51529217e-01
-1.27488002e-01 1.74717769e-01 -3.38900164e-02 -6.29566908e-01
-3.87827933e-01 -1.02193129e+00 -4.45540220e-01 -6.97972953e-01
-6.23644233e-01 2.88880289e-01 3.28815043e-01 -2.86727041... | [7.110255718231201, 6.120093822479248] |
99683b38-4244-41c3-9097-d4d1f22a9810 | metric-learning-for-user-defined-keyword | 2211.00439 | null | https://arxiv.org/abs/2211.00439v1 | https://arxiv.org/pdf/2211.00439v1.pdf | Metric Learning for User-defined Keyword Spotting | The goal of this work is to detect new spoken terms defined by users. While most previous works address Keyword Spotting (KWS) as a closed-set classification problem, this limits their transferability to unseen terms. The ability to define custom keywords has advantages in terms of user experience. In this paper, we pr... | ['Joon Son Chung', 'Youngjoon Jang', 'Byeong-Yeol Kim', 'Youshin Lim', 'Jihwan Park', 'Youkyum Kim', 'Jaemin Jung'] | 2022-11-01 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.40104669e-01 -2.28509858e-01 -1.79890066e-01 -5.90893269e-01
-1.08694983e+00 -5.82214773e-01 4.79477584e-01 1.47047117e-01
-7.79956043e-01 3.71626437e-01 2.39120677e-01 -3.34732801e-01
-3.72493565e-01 -4.13937002e-01 -3.74028802e-01 -3.55892539e-01
-1.26127601e-02 2.92091638e-01 4.53869015e-01 -5.33593595... | [14.223097801208496, 6.367535591125488] |
6967b3fe-509a-4c38-8dc0-997ca4e89dea | deep-retrosynthetic-reaction-prediction-using | null | null | https://pubs.acs.org/doi/10.1021/jacsau.1c00246 | https://pubs.acs.org/doi/pdf/10.1021/jacsau.1c00246 | Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention | As a fundamental problem in chemistry, retrosynthesis aims at designing reaction pathways and intermediates for a target compound. The goal of artificial intelligence (AI)-aided retrosynthesis is to automate this process by learning from the previous chemical reactions to make new predictions. Although several models h... | ['Yousung Jung', 'Shuan Chen'] | 2021-08-05 | null | null | null | jacs-au-2021-8 | ['retrosynthesis'] | ['medical'] | [ 4.31692302e-01 1.94816515e-01 -5.85350692e-01 -3.80663462e-02
-5.37513852e-01 -1.06923521e+00 8.90890419e-01 5.54550767e-01
-1.06977329e-01 1.19083095e+00 2.76720762e-01 -4.30008978e-01
1.17386200e-01 -7.45602489e-01 -9.08412158e-01 -1.10025144e+00
1.41147420e-01 3.44908983e-01 1.01001233e-01 -3.68661255... | [4.507110118865967, 6.102417469024658] |
45c26a93-c686-47da-9495-cc08e2941aef | imaginator-pre-trained-image-text-joint | 2305.10438 | null | https://arxiv.org/abs/2305.10438v1 | https://arxiv.org/pdf/2305.10438v1.pdf | IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images | Word embeddings, i.e., semantically meaningful vector representation of words, are largely influenced by the distributional hypothesis "You shall know a word by the company it keeps" (Harris, 1954), whereas modern prediction-based neural network embeddings rely on design choices and hyperparameter optimization. Word em... | ['Amit Sheth', 'Amitava Das', 'Aman Chadha', 'Megha Chakraborty', 'Parth Patwa', 'Sathyanarayanan Ramamoorthy', 'Shreyash Mishra', 'S Suryavardan', 'Varuna Krishna'] | 2023-05-12 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [-8.89731124e-02 -2.74846137e-01 -2.43724301e-01 -3.09743732e-01
-6.32903814e-01 -6.36193752e-01 1.17865479e+00 2.38024309e-01
-8.14448893e-01 2.23014683e-01 5.12806773e-01 -3.43429327e-01
-1.37042075e-01 -6.59677505e-01 -6.22603893e-01 -6.28267527e-01
2.69705117e-01 2.38053098e-01 -2.63267279e-01 -2.78798819... | [10.610618591308594, 1.8340221643447876] |
0641b196-c42c-4cdd-862b-6818535b76e7 | classes-matter-a-fine-grained-adversarial | 2007.09222 | null | https://arxiv.org/abs/2007.09222v1 | https://arxiv.org/pdf/2007.09222v1.pdf | Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation | Despite great progress in supervised semantic segmentation,a large performance drop is usually observed when deploying the model in the wild. Domain adaptation methods tackle the issue by aligning the source domain and the target domain. However, most existing methods attempt to perform the alignment from a holistic vi... | ['Ling-Yu Duan', 'Wei zhang', 'Haoran Wang', 'Tong Shen', 'Tao Mei'] | 2020-07-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2246_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590630.pdf | eccv-2020-8 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.31930488e-01 -5.22955656e-02 -3.76074702e-01 -5.16336381e-01
-1.12086701e+00 -1.05747592e+00 6.59519732e-01 -3.21861170e-02
-3.36895883e-01 6.31533921e-01 -3.52178477e-02 -7.17901066e-02
1.82646848e-02 -8.73539150e-01 -7.45942652e-01 -8.14378619e-01
4.60773319e-01 7.70209312e-01 3.65593344e-01 -4.28209275... | [9.762271881103516, 1.5491828918457031] |
442bb40d-69a4-4d97-bd09-6024589b61f6 | coherence-modeling-improves-implicit | null | null | https://aclanthology.org/W18-5040 | https://aclanthology.org/W18-5040.pdf | Coherence Modeling Improves Implicit Discourse Relation Recognition | The research described in this paper examines how to learn linguistic knowledge associated with discourse relations from unlabeled corpora. We introduce an unsupervised learning method on text coherence that could produce numerical representations that improve implicit discourse relation recognition in a semi-supervise... | ['Noriki Nishida', 'Hideki Nakayama'] | 2018-07-01 | null | null | null | ws-2018-7 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 2.00241223e-01 1.10682988e+00 -1.07302439e+00 -6.24071181e-01
-8.74555826e-01 -3.70158404e-01 8.86151731e-01 4.03388619e-01
-3.81494731e-01 1.13594234e+00 9.01709020e-01 -4.57961321e-01
-1.15677640e-01 -9.20625329e-01 -7.91336149e-02 -4.55526233e-01
-3.40214491e-01 7.43082702e-01 -4.27575111e-02 -4.43121821... | [10.835021018981934, 9.341409683227539] |
dba62dbb-9f7f-463f-8747-dbb87282bc37 | two-stream-amtnet-for-action-detection | 2004.01494 | null | https://arxiv.org/abs/2004.01494v1 | https://arxiv.org/pdf/2004.01494v1.pdf | Two-Stream AMTnet for Action Detection | In this paper, we propose Two-Stream AMTnet, which leverages recent advances in video-based action representation[1] and incremental action tube generation[2]. Majority of the present action detectors follow a frame-based representation, a late-fusion followed by an offline action tube building steps. These are sub-opt... | ['Fabio Cuzzolin', 'Suman Saha', 'Gurkirt Singh'] | 2020-04-03 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 4.04684067e-01 -5.54398373e-02 -4.13923353e-01 4.71494459e-02
-4.61719453e-01 -2.97921419e-01 8.32986414e-01 -1.56098098e-01
-6.51231229e-01 5.02029598e-01 1.57636657e-01 -1.78276584e-01
6.14050822e-03 -6.93135738e-01 -6.48649693e-01 -6.68906569e-01
-3.19648325e-01 2.32374147e-01 9.80493903e-01 -4.26731199... | [8.307982444763184, 0.35372331738471985] |
1f828277-8a78-47e2-944e-075934163855 | provable-identifiability-of-two-layer-relu | 2305.04267 | null | https://arxiv.org/abs/2305.04267v1 | https://arxiv.org/pdf/2305.04267v1.pdf | Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization | LASSO regularization is a popular regression tool to enhance the prediction accuracy of statistical models by performing variable selection through the $\ell_1$ penalty, initially formulated for the linear model and its variants. In this paper, the territory of LASSO is extended to two-layer ReLU neural networks, a fas... | ['Jie Ding', 'Ganghua Wang', 'Gen Li'] | 2023-05-07 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 5.55645645e-01 1.20638460e-01 -4.53275532e-01 -4.38156366e-01
-4.91138816e-01 -2.04502910e-01 -1.32250160e-01 -1.81037575e-01
-4.51984048e-01 1.14631164e+00 -5.73428690e-01 -4.18629825e-01
-5.89006543e-01 -6.36340559e-01 -1.10715330e+00 -1.16812873e+00
-4.83063996e-01 2.10873842e-01 -5.39896488e-01 -1.59647197... | [7.990443229675293, 3.8977432250976562] |
af0e1118-a0fb-490b-b5b8-e749cd5cd0f2 | fastinst-a-simple-query-based-model-for-real | 2303.08594 | null | https://arxiv.org/abs/2303.08594v2 | https://arxiv.org/pdf/2303.08594v2.pdf | FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation | Recent attention in instance segmentation has focused on query-based models. Despite being non-maximum suppression (NMS)-free and end-to-end, the superiority of these models on high-accuracy real-time benchmarks has not been well demonstrated. In this paper, we show the strong potential of query-based models on efficie... | ['Xuansong Xie', 'Yifeng Geng', 'Pengyu Li', 'Junjie He'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/He_FastInst_A_Simple_Query-Based_Model_for_Real-Time_Instance_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/He_FastInst_A_Simple_Query-Based_Model_for_Real-Time_Instance_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 1.03683487e-01 4.68962751e-02 -3.68231326e-01 -3.18279058e-01
-1.34322667e+00 -4.51599181e-01 2.81856567e-01 -1.51464298e-01
-8.04496169e-01 4.71515000e-01 -2.97014683e-01 -4.70889270e-01
3.28409284e-01 -6.92745328e-01 -9.76576686e-01 -3.50981981e-01
2.34186366e-01 4.29083258e-01 7.03679800e-01 8.13925862... | [9.442736625671387, 0.09846793115139008] |
ef42628d-aaab-433f-b8ca-b209d9b7a7b8 | liveness-score-based-regression-neural | 2302.09461 | null | https://arxiv.org/abs/2302.09461v2 | https://arxiv.org/pdf/2302.09461v2.pdf | Liveness score-based regression neural networks for face anti-spoofing | Previous anti-spoofing methods have used either pseudo maps or user-defined labels, and the performance of each approach depends on the accuracy of the third party networks generating pseudo maps and the way in which the users define the labels. In this paper, we propose a liveness score-based regression network for ov... | ['Changick Kim', 'JinHo Shin', 'Hunjae Yoo', 'Minyoung Jung', 'Youngjun Kwak'] | 2023-02-19 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 5.81329584e-01 6.94194809e-02 -4.50507224e-01 -6.54247344e-01
-2.70666003e-01 -4.62519169e-01 7.76491940e-01 1.28599361e-01
-2.91188270e-01 6.72772467e-01 -1.12449333e-01 -2.19119221e-01
-5.30780852e-02 -8.27947915e-01 -4.88083363e-01 -6.33595526e-01
-1.46054268e-01 3.09706122e-01 3.72977853e-01 -1.91490844... | [13.002016067504883, 1.150745153427124] |
4f362727-9a26-4fe4-9287-71c5e38d25a6 | community-detection-using-low-dimensional | 2111.05267 | null | https://arxiv.org/abs/2111.05267v1 | https://arxiv.org/pdf/2111.05267v1.pdf | Community detection using low-dimensional network embedding algorithms | With the increasing relevance of large networks in important areas such as the study of contact networks for spread of disease, or social networks for their impact on geopolitics, it has become necessary to study machine learning tools that are scalable to very large networks, often containing millions of nodes. One ma... | ['Souvik Dhara', 'Shankar Bhamidi', 'Aman Barot'] | 2021-11-04 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 7.71934912e-02 3.25355738e-01 -2.18687341e-01 6.99104667e-02
-2.48752132e-01 -6.59692585e-01 5.23143411e-01 4.37609971e-01
-2.44747266e-01 4.85826671e-01 1.27147973e-01 -5.61437666e-01
-4.90253150e-01 -1.40044510e+00 -6.41268194e-01 -6.98333919e-01
-7.72958159e-01 9.02821481e-01 2.30296224e-01 -1.82509303... | [6.998685359954834, 5.648447036743164] |
a5351f19-eb31-42b4-8b1a-053f3dcfd08a | act3d-infinite-resolution-action-detection | 2306.17817 | null | https://arxiv.org/abs/2306.17817v1 | https://arxiv.org/pdf/2306.17817v1.pdf | Act3D: Infinite Resolution Action Detection Transformer for Robotic Manipulation | 3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, typically demanding high-resolution 3D perceptual grids that are computationally expensive to proc... | ['Katerina Fragkiadaki', 'Nikolaos Gkanatsios', 'Zhou Xian', 'Theophile Gervet'] | 2023-06-30 | null | null | null | null | ['pose-prediction', 'action-detection', 'robot-manipulation'] | ['computer-vision', 'computer-vision', 'robots'] | [-3.83417130e-01 -6.69296160e-02 -4.42399770e-01 1.16030037e-01
-7.53419995e-01 -7.71454990e-01 6.01115763e-01 -5.69578670e-02
-2.55920947e-01 4.19025183e-01 5.69466114e-01 -2.69629985e-01
-9.90257934e-02 -6.07498586e-01 -1.22301853e+00 -3.81799400e-01
-6.12696707e-02 8.86032224e-01 1.16071314e-01 -3.70543242... | [4.795214653015137, 0.4447139501571655] |
e3569946-73a2-4543-bac0-317e46a9a9ca | deceptive-review-spam-detection-via | null | null | https://aclanthology.org/D16-1187 | https://aclanthology.org/D16-1187.pdf | Deceptive Review Spam Detection via Exploiting Task Relatedness and Unlabeled Data | null | ['Xiao-Li Li', 'Peng Yang', 'Peng Cheng', 'Peilin Zhao', 'Zhen Hai', 'Guangxia Li'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['spam-detection'] | ['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... | [-7.29145622253418, 3.77490496635437] |
0159be51-65a4-43b5-8ebb-573caa876b64 | turning-the-pipeline-into-a-loop-iterated | null | null | https://aclanthology.org/W12-1913 | https://aclanthology.org/W12-1913.pdf | Turning the pipeline into a loop: Iterated unsupervised dependency parsing and PoS induction | null | ['Christos Christodoulopoulos', 'Sharon Goldwater', 'Mark Steedman'] | 2012-06-01 | null | null | null | ws-2012-6 | ['unsupervised-dependency-parsing'] | ['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... | [-7.423657417297363, 3.592952013015747] |
4af3207b-12e3-47c7-b7cb-88deaa5e0372 | parameter-efficient-image-to-video-transfer | 2206.13559 | null | https://arxiv.org/abs/2206.13559v3 | https://arxiv.org/pdf/2206.13559v3.pdf | ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning | Capitalizing on large pre-trained models for various downstream tasks of interest have recently emerged with promising performance. Due to the ever-growing model size, the standard full fine-tuning based task adaptation strategy becomes prohibitively costly in terms of model training and storage. This has led to a new ... | ['Hongsheng Li', 'Jing Shao', 'Xiatian Zhu', 'Ziyi Lin', 'Junting Pan'] | 2022-06-27 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 2.75106817e-01 -1.49089009e-01 -4.03671741e-01 -2.42154554e-01
-8.30297291e-01 -4.05141652e-01 5.00202358e-01 -2.30120927e-01
-5.26295364e-01 6.35182977e-01 6.80132955e-02 -3.41373533e-01
-2.79581789e-02 -5.66509068e-01 -1.12238657e+00 -7.08611727e-01
2.14513421e-01 2.74016976e-01 3.60766143e-01 3.44490372... | [9.501213073730469, 0.8942694067955017] |
e7d7f490-43a1-4ec0-a2e0-cc400866a7ce | asynchronous-decentralized-federated-lifelong | 2303.06783 | null | https://arxiv.org/abs/2303.06783v1 | https://arxiv.org/pdf/2303.06783v1.pdf | Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging | Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single location or device. However, this framework still requires a centralized global model to consolidate individual models into one, and the devices tr... | ['Vishwa S. Parekh', 'Vladimir Braverman', 'Michael A. Jacobs', 'Guangyao Zheng'] | 2023-03-12 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [-3.88330370e-01 3.03923547e-01 -4.07491863e-01 -3.05233628e-01
-1.11985362e+00 -5.99265158e-01 4.15596902e-01 -1.60143021e-02
-7.85759926e-01 1.00152719e+00 -3.42437178e-01 -4.06911284e-01
-2.82748997e-01 -4.51037794e-01 -7.03059614e-01 -1.02358222e+00
-4.01641786e-01 6.95939124e-01 2.56775409e-01 1.49269581... | [6.043201446533203, 6.4255828857421875] |
90df8948-5738-4b6a-9da6-10fff2c356b3 | unpaired-motion-style-transfer-from-video-to | 2005.05751 | null | https://arxiv.org/abs/2005.05751v1 | https://arxiv.org/pdf/2005.05751v1.pdf | Unpaired Motion Style Transfer from Video to Animation | Transferring the motion style from one animation clip to another, while preserving the motion content of the latter, has been a long-standing problem in character animation. Most existing data-driven approaches are supervised and rely on paired data, where motions with the same content are performed in different styles... | ['Daniel Cohen-Or', 'Yijia Weng', 'Kfir Aberman', 'Dani Lischinski', 'Baoquan Chen'] | 2020-05-12 | null | null | null | null | ['motion-style-transfer'] | ['computer-code'] | [ 3.87925088e-01 -1.47924289e-01 -1.92072049e-01 -3.07819963e-01
-2.69923002e-01 -1.05351734e+00 8.28724623e-01 -4.38773215e-01
-4.09802735e-01 5.39763927e-01 3.88733953e-01 1.45830646e-01
4.46358889e-01 -8.30583870e-01 -8.82517874e-01 -8.52845967e-01
1.04591146e-01 3.78100514e-01 2.55811423e-01 -2.96583891... | [10.81522274017334, -0.674333393573761] |
ad459cba-dc32-496d-a673-86800e882dcc | diffurec-a-diffusion-model-for-sequential | 2304.00686 | null | https://arxiv.org/abs/2304.00686v3 | https://arxiv.org/pdf/2304.00686v3.pdf | DiffuRec: A Diffusion Model for Sequential Recommendation | Mainstream solutions to Sequential Recommendation (SR) represent items with fixed vectors. These vectors have limited capability in capturing items' latent aspects and users' diverse preferences. As a new generative paradigm, Diffusion models have achieved excellent performance in areas like computer vision and natural... | ['Chenliang Li', 'Aixin Sun', 'Zihao Li'] | 2023-04-03 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [-5.95255420e-02 -3.58980864e-01 -5.27127445e-01 -3.43871146e-01
-4.63065475e-01 -6.82763100e-01 6.64380789e-01 2.03960184e-02
-5.80736957e-02 4.06408936e-01 7.32830346e-01 -2.32130915e-01
-4.08319384e-02 -8.15885305e-01 -5.48370719e-01 -6.21733665e-01
3.22179962e-03 6.43657207e-01 -1.20930240e-01 -3.59942704... | [10.207050323486328, 5.61790657043457] |
3384bd1a-2063-4c30-a1c9-974c3f16c5fc | a-survey-on-recent-deep-learning-driven | 2110.02511 | null | https://arxiv.org/abs/2110.02511v1 | https://arxiv.org/pdf/2110.02511v1.pdf | A Survey on Recent Deep Learning-driven Singing Voice Synthesis Systems | Singing voice synthesis (SVS) is a task that aims to generate audio signals according to musical scores and lyrics. With its multifaceted nature concerning music and language, producing singing voices indistinguishable from that of human singers has always remained an unfulfilled pursuit. Nonetheless, the advancements ... | ['Yi-Wen Liu', 'Xiao-Han Wang', 'Ching-Ting Cheng', 'Yung-Chuan Chang', 'Fu-Rong Yang', 'Yin-Ping Cho'] | 2021-10-06 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 7.42356703e-02 -1.15094427e-02 1.10765107e-01 6.26157373e-02
-8.43146920e-01 -6.05335236e-01 4.75218564e-01 -6.26600683e-01
1.30579084e-01 6.01508379e-01 3.58731061e-01 6.70392215e-02
-1.08122051e-01 -3.88340205e-01 -3.65055263e-01 -8.32249582e-01
1.10269018e-01 2.11156264e-01 -2.33225986e-01 -6.93822682... | [15.596126556396484, 6.10410737991333] |
ed312bf8-0087-4299-b641-a94ed9bc4d7b | amr-parsing-with-action-pointer-transformer | 2104.14674 | null | https://arxiv.org/abs/2104.14674v3 | https://arxiv.org/pdf/2104.14674v3.pdf | AMR Parsing with Action-Pointer Transformer | Abstract Meaning Representation parsing is a sentence-to-graph prediction task where target nodes are not explicitly aligned to sentence tokens. However, since graph nodes are semantically based on one or more sentence tokens, implicit alignments can be derived. Transition-based parsers operate over the sentence from l... | ['Radu Florian', 'Ramón Fernandez Astudillo', 'Tahira Naseem', 'Jiawei Zhou'] | 2021-04-29 | null | https://aclanthology.org/2021.naacl-main.443 | https://aclanthology.org/2021.naacl-main.443.pdf | naacl-2021-4 | ['hard-attention'] | ['methodology'] | [ 7.04504192e-01 9.04200435e-01 -2.58505970e-01 -5.35803080e-01
-1.20516276e+00 -6.70817256e-01 4.65238810e-01 5.64434171e-01
-1.90713167e-01 3.90881270e-01 5.60072124e-01 -9.07977939e-01
4.21169668e-01 -8.49995077e-01 -7.71309733e-01 -1.80705294e-01
7.15793967e-02 4.65812445e-01 1.93651542e-01 -5.28367639... | [10.348581314086914, 9.313835144042969] |
0066bafe-4205-4bed-a37c-cdad9e968557 | transferability-properties-of-graph-neural | 2112.04629 | null | https://arxiv.org/abs/2112.04629v3 | https://arxiv.org/pdf/2112.04629v3.pdf | Transferability Properties of Graph Neural Networks | Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provably successful at learning representations from data supported on moderate-scale graphs. However, they are difficult to learn on large-scale ... | ['Alejandro Ribeiro', 'Luiz F. O. Chamon', 'Luana Ruiz'] | 2021-12-09 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 9.37045366e-02 6.01802111e-01 3.18622366e-02 -1.53856918e-01
2.08082959e-01 -6.52408481e-01 3.55584234e-01 -2.18078196e-02
1.23157566e-02 6.16769791e-01 1.02604426e-01 -4.64417845e-01
-4.72461462e-01 -1.31049109e+00 -1.13221657e+00 -5.65484524e-01
-4.85079944e-01 3.64221871e-01 3.39248419e-01 -2.45264694... | [6.817269802093506, 6.0553812980651855] |
da88bca8-734c-44ab-9634-da79df268846 | real-time-automatic-fetal-brain-extraction-in | 1710.09338 | null | http://arxiv.org/abs/1710.09338v1 | http://arxiv.org/pdf/1710.09338v1.pdf | Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning | Brain segmentation is a fundamental first step in neuroimage analysis. In the
case of fetal MRI, it is particularly challenging and important due to the
arbitrary orientation of the fetus, organs that surround the fetal head, and
intermittent fetal motion. Several promising methods have been proposed but are
limited in... | ['Simon K. Warfield', 'Abdelhakim Ouaalam', 'Clemente Velasco-Annis', 'Seyed Raein Hashemi', 'Deniz Erdogmus', 'Ali Gholipour', 'Seyed Sadegh Mohseni Salehi', 'Judy A. Estroff'] | 2017-10-25 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 2.57114738e-01 1.21490695e-01 3.49366337e-01 -5.22125125e-01
-3.80311459e-01 -5.92965305e-01 3.56428295e-01 -4.85983007e-02
-7.41520762e-01 4.36069071e-01 -2.66066104e-01 -2.19126269e-01
-1.05423123e-01 -6.84980452e-01 -5.96681893e-01 -6.98439419e-01
-7.24378586e-01 7.89790928e-01 5.26123583e-01 2.77628511... | [14.145432472229004, -2.4218266010284424] |
4099e9ab-296b-4bff-afe0-4b0c5d4dc649 | chinese-discourse-segmentation-using | 1809.01497 | null | http://arxiv.org/abs/1809.01497v1 | http://arxiv.org/pdf/1809.01497v1.pdf | Chinese Discourse Segmentation Using Bilingual Discourse Commonality | Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and
is a fundamental task in discourse analysis. For Chinese, previous researches
identify EDUs just through discriminating the functions of punctuations. In
this paper, we argue that Chinese EDUs may not end at the punctuation positions
and shoul... | ['Jingfeng Yang', 'Sujian Li'] | 2018-08-30 | null | null | null | null | ['discourse-segmentation'] | ['natural-language-processing'] | [ 2.05086485e-01 1.74012512e-01 -4.53694284e-01 -3.39172602e-01
-8.38371575e-01 -9.96289611e-01 6.90636218e-01 9.41949524e-03
-5.44619322e-01 9.81639445e-01 7.74213195e-01 -5.75476766e-01
6.72378361e-01 -7.06803203e-01 -4.69482005e-01 -4.08936709e-01
2.89737284e-01 3.15284938e-01 5.45985401e-02 -4.82231438... | [10.797489166259766, 9.501106262207031] |
e9a54172-5447-43cb-bf03-b2b1b65d1dd0 | sentiment-analysis-using-aligned-word | 2305.15380 | null | https://arxiv.org/abs/2305.15380v1 | https://arxiv.org/pdf/2305.15380v1.pdf | Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages | In this paper, we present an approach for translating word embeddings from a majority language into 4 minority languages: Erzya, Moksha, Udmurt and Komi-Zyrian. Furthermore, we align these word embeddings and present a novel neural network model that is trained on English data to conduct sentiment analysis and then app... | ['Jack Rueter', 'Mika Hämäläinen', 'Khalid Alnajjar'] | 2023-05-24 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-2.35242963e-01 1.54169098e-01 -4.27632660e-01 -3.68196636e-01
-1.49759129e-01 -8.62942159e-01 6.17114544e-01 1.90777913e-01
-9.83616173e-01 6.59856558e-01 5.03270507e-01 -7.33679116e-01
3.51010352e-01 -9.98077571e-01 -3.16473186e-01 -2.04384178e-01
1.20577775e-01 3.89500409e-01 -3.91237855e-01 -7.42382109... | [10.822205543518066, 9.780878067016602] |
0c6b3dcd-2115-488b-a9dc-9bac8a5fad18 | logit-clipping-for-robust-learning-against | 2212.04055 | null | https://arxiv.org/abs/2212.04055v3 | https://arxiv.org/pdf/2212.04055v3.pdf | Mitigating Memorization of Noisy Labels by Clipping the Model Prediction | In the presence of noisy labels, designing robust loss functions is critical for securing the generalization performance of deep neural networks. Cross Entropy (CE) loss has been shown to be not robust to noisy labels due to its unboundedness. To alleviate this issue, existing works typically design specialized robust ... | ['Yixuan Li', 'Bo An', 'Gang Niu', 'Lei Feng', 'Renchunzi Xie', 'Huiping Zhuang', 'Hongxin Wei'] | 2022-12-08 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 6.29324466e-02 -6.97409511e-02 2.00403240e-02 -4.79227036e-01
-8.34658623e-01 -5.00544310e-01 -1.02700219e-02 1.58368975e-01
-4.91650015e-01 7.70505309e-01 -6.06796425e-03 -1.62985533e-01
-2.61517107e-01 -6.60419106e-01 -7.43813336e-01 -1.09280133e+00
1.44394875e-01 -5.40570736e-01 1.94961697e-01 4.11690176... | [9.21224308013916, 3.7150564193725586] |
6882efa2-2419-40c9-8ba6-08fc2fcb2b42 | directional-graph-networks-1 | 2010.02863 | null | https://arxiv.org/abs/2010.02863v4 | https://arxiv.org/pdf/2010.02863v4.pdf | Directional Graph Networks | The lack of anisotropic kernels in graph neural networks (GNNs) strongly limits their expressiveness, contributing to well-known issues such as over-smoothing. To overcome this limitation, we propose the first globally consistent anisotropic kernels for GNNs, allowing for graph convolutions that are defined according t... | ['Pietro Liò', 'Gabriele Corso', 'William L. Hamilton', 'Vincent Létourneau', 'Saro Passaro', 'Dominique Beaini'] | 2020-10-06 | directional-graph-networks | https://openreview.net/forum?id=FUdBF49WRV1 | https://openreview.net/pdf?id=FUdBF49WRV1 | null | ['graph-regression'] | ['graphs'] | [ 5.30352630e-02 2.09671587e-01 9.14144423e-03 -3.96154612e-01
1.43427327e-01 -5.50570488e-01 7.38387346e-01 2.38607213e-01
-6.92998528e-01 6.87875211e-01 1.59606129e-01 -2.88633376e-01
-3.19489092e-01 -1.06345010e+00 -6.47544861e-01 -9.64741170e-01
-4.61013526e-01 3.69170398e-01 4.33263958e-01 -2.81597257... | [6.870100498199463, 6.119685173034668] |
b7047f7e-a6cb-40a0-9e72-5ea426ebc63b | peach-pre-training-sequence-to-sequence | 2304.01282 | null | https://arxiv.org/abs/2304.01282v2 | https://arxiv.org/pdf/2304.01282v2.pdf | PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document Generation | Multilingual pre-training significantly improves many multilingual NLP tasks, including machine translation. Most existing methods are based on some variants of masked language modeling and text-denoising objectives on monolingual data. Multilingual pre-training on monolingual data ignores the availability of parallel ... | ['Azadeh Shakery', 'Yadollah Yaghoobzadeh', 'Sara Tavakoli', 'Amirhossein Abaskohi', 'Alireza Salemi'] | 2023-04-03 | null | null | null | null | ['word-translation', 'multilingual-nlp'] | ['natural-language-processing', 'natural-language-processing'] | [ 0.07543976 -0.38729203 -0.3408765 -0.21735054 -1.3659414 -0.74106634
0.6097439 -0.04439998 -0.64423674 1.0193474 0.36511204 -0.73070693
0.52897847 -0.66324383 -0.9007852 -0.5628237 0.43911216 0.8849761
-0.32222754 -0.7484785 -0.08220997 -0.1613217 -1.0015316 0.6001757
1.5089802 -0.03017328 0.95... | [11.622221946716309, 10.272724151611328] |
a24736af-061a-4479-9f13-7889c76fc8da | keeping-the-questions-conversational-using | 2304.07125 | null | https://arxiv.org/abs/2304.07125v1 | https://arxiv.org/pdf/2304.07125v1.pdf | Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answering | Having an intelligent dialogue agent that can engage in conversational question answering (ConvQA) is now no longer limited to Sci-Fi movies only and has, in fact, turned into a reality. These intelligent agents are required to understand and correctly interpret the sequential turns provided as the context of the given... | ['Adnan Mahmood', 'Wei Emma Zhang', 'Quan Z. Sheng', 'Munazza Zaib'] | 2023-04-14 | null | null | null | null | ['question-rewriting'] | ['natural-language-processing'] | [ 4.63706702e-01 8.77295792e-01 2.92955369e-01 -6.67904317e-01
-8.87890160e-01 -9.04972613e-01 1.06300271e+00 -1.16633080e-01
-2.98011243e-01 9.31513846e-01 7.94735014e-01 -4.39499319e-01
-1.07279554e-01 -8.40924323e-01 -3.20776552e-01 -1.67095765e-01
4.44472045e-01 8.06546986e-01 1.29096657e-01 -9.49761033... | [12.101201057434082, 8.01165771484375] |
4208656d-99c7-4d7c-be25-835f1e7608b3 | word-and-document-embedding-with-vmf-mixture | null | null | https://aclanthology.org/P19-1321 | https://aclanthology.org/P19-1321.pdf | Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors | Word embedding models typically learn two types of vectors: target word vectors and context word vectors. These vectors are normally learned such that they are predictive of some word co-occurrence statistic, but they are otherwise unconstrained. However, the words from a given language can be organized in various natu... | ['Steven Schockaert', 'Shoaib Jameel'] | 2019-07-01 | null | null | null | acl-2019-7 | ['document-embedding'] | ['methodology'] | [-1.63268164e-01 8.23724717e-02 -5.64702988e-01 -4.32518512e-01
-4.27446693e-01 -7.15662718e-01 1.02352786e+00 4.55650806e-01
-5.88262856e-01 4.34134632e-01 6.95867002e-01 -3.66917908e-01
2.40778606e-02 -8.73171329e-01 -4.54536140e-01 -8.65838349e-01
2.53902916e-02 4.09059554e-01 -1.49745643e-01 -2.20301300... | [10.387907981872559, 8.630146980285645] |
d06ffe52-d01a-40cf-b2c8-6d162d59a3aa | virtual-testbed-for-monocular-visual | 2007.00737 | null | https://arxiv.org/abs/2007.00737v1 | https://arxiv.org/pdf/2007.00737v1.pdf | Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems | Monocular visual navigation methods have seen significant advances in the last decade, recently producing several real-time solutions for autonomously navigating small unmanned aircraft systems without relying on GPS. This is critical for military operations which may involve environments where GPS signals are degraded... | ['Scott L. Nykl', 'Robert C. Leishman', 'Kyung Kim'] | 2020-07-01 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.73777753e-01 -3.08527529e-01 1.86085403e-01 -1.39958695e-01
-4.41432036e-02 -1.06695151e+00 4.25524145e-01 -1.37616202e-01
-2.86777347e-01 7.87793398e-01 -3.85283321e-01 -9.13723052e-01
-1.49432555e-01 -4.70036477e-01 -1.59567103e-01 -4.16563421e-01
-6.00501716e-01 7.55684376e-01 4.82280433e-01 -7.25431681... | [7.294764518737793, -1.9625645875930786] |
b643a5d9-ec38-41d1-8565-ab65f195d2e5 | feature-representation-learning-for-click | 2302.02241 | null | https://arxiv.org/abs/2302.02241v1 | https://arxiv.org/pdf/2302.02241v1.pdf | Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives | Representation learning has been a critical topic in machine learning. In Click-through Rate Prediction, most features are represented as embedding vectors and learned simultaneously with other parameters in the model. With the development of CTR models, feature representation learning has become a trending topic and h... | ['Xue Liu', 'Xiuqiang He', 'Chen Ma', 'Haolun Wu', 'Dugang Liu', 'Xing Tang', 'Fuyuan Lyu'] | 2023-02-04 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 2.01161072e-01 -3.31276387e-01 -7.59021878e-01 -5.20570815e-01
-5.98858178e-01 -4.19878662e-01 7.30972588e-01 2.53506482e-01
-3.25675339e-01 5.98763466e-01 1.35845810e-01 -9.48472694e-02
-3.73424888e-01 -8.19246829e-01 -1.87820762e-01 -5.71810722e-01
-3.53617311e-01 -1.39002502e-02 1.57062083e-01 -2.60479897... | [10.105230331420898, 5.611598491668701] |
58cd269e-3bcc-497c-8880-ec0d76b19d74 | a-swarm-variant-for-the-schrodinger-solver | 2104.04795 | null | https://arxiv.org/abs/2104.04795v2 | https://arxiv.org/pdf/2104.04795v2.pdf | A Swarm Variant for the Schrödinger Solver | This paper introduces application of the Exponentially Averaged Momentum Particle Swarm Optimization (EM-PSO) as a derivative-free optimizer for Neural Networks. It adopts PSO's major advantages such as search space exploration and higher robustness to local minima compared to gradient-descent optimizers such as Adam. ... | ['Snehanshu Saha', 'Anwesh Bhattacharya', 'Omatharv Bharat Vaidya', 'Urvil Nileshbhai Jivani'] | 2021-04-10 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [-2.54934698e-01 1.94106232e-02 -5.16054500e-03 2.06867028e-02
2.14939967e-01 -9.24572051e-02 2.96622843e-01 -1.61446184e-01
-1.01241446e+00 1.61570966e+00 -5.59353411e-01 -2.70216435e-01
-5.07859170e-01 -6.16252184e-01 -6.32061899e-01 -9.68465328e-01
-3.42241436e-01 5.42513430e-01 -3.23880315e-02 -5.42562902... | [6.793582916259766, 3.601534128189087] |
5e7ad0da-0234-40ab-b45a-6f6de50d0ee7 | best-bert-pre-training-for-sign-language | 2302.05075 | null | https://arxiv.org/abs/2302.05075v3 | https://arxiv.org/pdf/2302.05075v3.pdf | BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization | In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model. Considering the dominance of hand and body in sign language expression, we organize them as pose triplet units and feed them into the Transformer... | ['Houqiang Li', 'Jiaxin Shi', 'Wengang Zhou', 'Hezhen Hu', 'Weichao Zhao'] | 2023-02-10 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 4.75590944e-01 1.63534015e-01 -2.79239118e-01 -5.67764580e-01
-8.99721086e-01 -3.05771798e-01 5.19124210e-01 -7.31616557e-01
-6.42192900e-01 3.29976022e-01 6.08574569e-01 -7.68329278e-02
1.56665429e-01 -1.97662055e-01 -6.65600419e-01 -9.80447829e-01
1.56945176e-02 1.30966797e-01 2.69273035e-02 -6.87910020... | [9.216391563415527, -6.512582302093506] |
0fd278fa-5cd5-4df0-9239-85db36ae118a | salient-object-detection-in-video-using-deep | 1810.07097 | null | http://arxiv.org/abs/1810.07097v1 | http://arxiv.org/pdf/1810.07097v1.pdf | Salient Object Detection in Video using Deep Non-Local Neural Networks | Detection of salient objects in image and video is of great importance in
many computer vision applications. In spite of the fact that the state of the
art in saliency detection for still images has been changed substantially over
the last few years, there have been few improvements in video saliency
detection. This pa... | ['Mohammad Shokri', 'Kimya Taba', 'Ahad Harati'] | 2018-10-16 | null | null | null | null | ['video-salient-object-detection', 'video-saliency-detection'] | ['computer-vision', 'computer-vision'] | [ 5.60856104e-01 -2.85449207e-01 -2.30065629e-01 -1.65387839e-01
-2.80080438e-01 -1.49956197e-02 5.86710513e-01 2.70352364e-01
-5.43394625e-01 7.27883816e-01 2.58866876e-01 1.87912554e-01
1.75138842e-02 -3.62597346e-01 -7.00104713e-01 -7.02450216e-01
-2.75825679e-01 -1.53929204e-01 1.25425518e+00 -3.46187651... | [9.778133392333984, -0.3835332691669464] |
4a9c4d54-0780-41c8-a2c4-d941c379478f | hybrid-distillation-connecting-masked | 2306.15876 | null | https://arxiv.org/abs/2306.15876v1 | https://arxiv.org/pdf/2306.15876v1.pdf | Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners | Representation learning has been evolving from traditional supervised training to Contrastive Learning (CL) and Masked Image Modeling (MIM). Previous works have demonstrated their pros and cons in specific scenarios, i.e., CL and supervised pre-training excel at capturing longer-range global patterns and enabling bette... | ['Qi Tian', 'Hongkai Xiong', 'Junni Zou', 'Wenrui Dai', 'Jin Li', 'Yaoming Wang', 'Xiaopeng Zhang', 'Bowen Shi'] | 2023-06-28 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 1.08548239e-01 -2.22278297e-01 -2.65880585e-01 -3.08312744e-01
-4.29477632e-01 -2.12877676e-01 5.00868499e-01 -1.89693272e-01
-3.04589897e-01 5.54326713e-01 2.01005951e-01 -8.08269605e-02
-2.19705775e-01 -5.75295210e-01 -4.20350999e-01 -1.03413415e+00
1.42840341e-01 -1.00961775e-01 2.24621311e-01 -9.42419320... | [9.43166732788086, 2.711895704269409] |
aa39f889-ec54-4d11-bbb6-61951fcf0067 | on-search-strategies-for-document-level | 2306.05116 | null | https://arxiv.org/abs/2306.05116v1 | https://arxiv.org/pdf/2306.05116v1.pdf | On Search Strategies for Document-Level Neural Machine Translation | Compared to sentence-level systems, document-level neural machine translation (NMT) models produce a more consistent output across a document and are able to better resolve ambiguities within the input. There are many works on document-level NMT, mostly focusing on modifying the model architecture or training strategy ... | ['Hermann Ney', 'Christian Herold'] | 2023-06-08 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 5.68191886e-01 -1.22829288e-01 -3.80018026e-01 -2.73985833e-01
-1.06015027e+00 -6.90403581e-01 8.47335815e-01 2.77666867e-01
-4.20167178e-01 1.06420636e+00 4.45667207e-01 -8.11517000e-01
8.67438838e-02 -4.95836794e-01 -7.21823931e-01 -3.74314517e-01
6.01165414e-01 7.51525581e-01 -1.36463881e-01 -6.94226384... | [11.540983200073242, 10.189888000488281] |
8fc25ac8-07c8-4384-ae01-484115246528 | do-images-really-do-the-talking-analysing-the | 2108.03886 | null | https://arxiv.org/abs/2108.03886v1 | https://arxiv.org/pdf/2108.03886v1.pdf | Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification | A meme is an part of media created to share an opinion or emotion across the internet. Due to its popularity, memes have become the new forms of communication on social media. However, due to its nature, they are being used in harmful ways such as trolling and cyberbullying progressively. Various data modelling methods... | ['Bharathi Raja Chakravarthi', 'B Bharathi', 'Sathiyaraj Thangasamy', 'Ratnasingam Sakuntharaj', 'Sajeetha Thavareesan', 'Ruba Priyadharshini', 'Adeep Hande', 'Siddhanth U Hegde'] | 2021-08-09 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-1.34225816e-01 -2.10412011e-01 -3.40150669e-02 -5.87959215e-03
-2.70222127e-01 -6.25626922e-01 1.07887793e+00 5.74850142e-01
-6.65743172e-01 4.08761054e-01 5.11962414e-01 -1.07530296e-01
3.11574489e-02 -5.91572464e-01 -3.03851187e-01 -8.88756633e-01
1.86337546e-01 2.03578994e-01 3.33804399e-01 -5.87490082... | [8.511380195617676, 10.711441040039062] |
a3649590-1f6d-406b-807a-787cfbcd62ad | rethinking-bisenet-for-real-time-semantic | 2104.13188 | null | https://arxiv.org/abs/2104.13188v1 | https://arxiv.org/pdf/2104.13188v1.pdf | Rethinking BiSeNet For Real-time Semantic Segmentation | BiSeNet has been proved to be a popular two-stream network for real-time segmentation. However, its principle of adding an extra path to encode spatial information is time-consuming, and the backbones borrowed from pretrained tasks, e.g., image classification, may be inefficient for image segmentation due to the defici... | ['Xiaolin Wei', 'Junfeng Luo', 'Zhenhua Chai', 'Xiaoming Wei', 'Junshi Huang', 'Shenqi Lai', 'Mingyuan Fan'] | 2021-04-27 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Fan_Rethinking_BiSeNet_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Fan_Rethinking_BiSeNet_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['dichotomous-image-segmentation'] | ['computer-vision'] | [ 7.13090301e-02 -2.37447500e-01 -6.77968487e-02 -4.82484549e-01
-5.16260147e-01 -3.83204937e-01 1.53447226e-01 -1.18705079e-01
-7.01933026e-01 4.54743743e-01 -1.57095134e-01 -3.16861868e-01
5.89354038e-02 -1.09043014e+00 -8.49604845e-01 -7.19404399e-01
-4.88660038e-02 9.24523324e-02 6.75301671e-01 -4.69998978... | [9.26986026763916, -0.5506977438926697] |
a8774a2c-d05f-4453-8396-938ce6a84572 | vote-n-rank-revision-of-benchmarking-with | 2210.05769 | null | https://arxiv.org/abs/2210.05769v3 | https://arxiv.org/pdf/2210.05769v3.pdf | Vote'n'Rank: Revision of Benchmarking with Social Choice Theory | The development of state-of-the-art systems in different applied areas of machine learning (ML) is driven by benchmarks, which have shaped the paradigm of evaluating generalisation capabilities from multiple perspectives. Although the paradigm is shifting towards more fine-grained evaluation across diverse tasks, the d... | ['Ekaterina Artemova', 'Daniel Karabekyan', 'Tatiana Shavrina', 'Elena Tutubalina', 'Andrey Kravchenko', 'Mikhail Florinskiy', 'Vladislav Mikhailov', 'Mark Rofin'] | 2022-10-11 | null | null | null | null | ['skills-evaluation', 'result-aggregation'] | ['computer-vision', 'methodology'] | [ 2.33094946e-01 1.89179797e-02 -3.10433894e-01 -5.52678585e-01
-1.13179171e+00 -5.57064950e-01 1.06677485e+00 3.45389396e-01
-7.65784502e-01 6.73242152e-01 2.19935939e-01 -3.36964995e-01
-7.70205200e-01 -3.48341167e-01 -2.99268961e-01 -7.25467086e-01
2.14160353e-01 5.95664203e-01 3.82503611e-03 -2.51524031... | [9.05693531036377, 4.660508632659912] |
9f1f647c-cc3f-45d7-bcbd-4796a0cbfc0e | audio-visual-scene-aware-dialog | 1901.09107 | null | https://arxiv.org/abs/1901.09107v2 | https://arxiv.org/pdf/1901.09107v2.pdf | Audio-Visual Scene-Aware Dialog | We introduce the task of scene-aware dialog. Our goal is to generate a complete and natural response to a question about a scene, given video and audio of the scene and the history of previous turns in the dialog. To answer successfully, agents must ground concepts from the question in the video while leveraging contex... | ['Peter Anderson', 'Chiori Hori', 'Tim K. Marks', 'Vincent Cartillier', 'Stefan Lee', 'Huda Alamri', 'Jue Wang', 'Dhruv Batra', 'Irfan Essa', 'Devi Parikh', 'Anoop Cherian', 'Abhishek Das'] | 2019-01-25 | null | null | null | null | ['scene-aware-dialogue'] | ['computer-vision'] | [ 1.80849716e-01 1.26441732e-01 1.84210896e-01 -7.51648307e-01
-1.04822242e+00 -9.37870741e-01 9.14108515e-01 6.38363808e-02
-3.80923003e-01 5.64113140e-01 9.87186968e-01 -3.76549140e-02
5.29278874e-01 -3.95013034e-01 -5.30311406e-01 -1.33939683e-01
1.11137807e-01 5.42532265e-01 5.55902123e-01 -3.20524931... | [10.840079307556152, 1.0759210586547852] |
df2c891d-7390-4ed3-9f51-9503dd620297 | d2gclf-document-to-graph-classifier-for-legal | null | null | https://aclanthology.org/2022.findings-naacl.170 | https://aclanthology.org/2022.findings-naacl.170.pdf | D2GCLF: Document-to-Graph Classifier for Legal Document Classification | Legal document classification is an essential task in law intelligence to automate the labor-intensive law case filing process. Unlike traditional document classification problems, legal documents should be classified by reasons and facts instead of topics. We propose a Document-to-Graph Classifier (D2GCLF), which extr... | ['Ruofan Wang', 'Benjamin Liu', 'Robert Amor', 'Kaiqi Zhao', 'Qiqi Wang'] | null | null | null | null | findings-naacl-2022-7 | ['document-classification'] | ['natural-language-processing'] | [ 1.33315787e-01 4.67842162e-01 -7.97698319e-01 -3.30197573e-01
-6.72186911e-01 -7.61435151e-01 8.28100562e-01 5.18108606e-01
1.43282101e-01 6.84740245e-01 4.95960534e-01 -1.21965253e+00
-6.83085799e-01 -1.11604369e+00 -2.38089353e-01 -2.15651274e-01
1.88443467e-01 8.37300897e-01 1.92288309e-01 -1.06976107... | [9.559313774108887, 8.816235542297363] |
668b72e5-13c7-4ee6-9bd9-030642018f21 | sampling-is-matter-point-guided-3d-human-mesh-1 | 2304.09502 | null | https://arxiv.org/abs/2304.09502v1 | https://arxiv.org/pdf/2304.09502v1.pdf | Sampling is Matter: Point-guided 3D Human Mesh Reconstruction | This paper presents a simple yet powerful method for 3D human mesh reconstruction from a single RGB image. Most recently, the non-local interactions of the whole mesh vertices have been effectively estimated in the transformer while the relationship between body parts also has begun to be handled via the graph model. E... | ['Wonjun Kim', 'Gi-Mun Um', 'Hyukmin Kwon', 'Hyunwoo Park', 'Mi-Gyeong Gwon', 'Jeonghwan Kim'] | 2023-04-19 | sampling-is-matter-point-guided-3d-human-mesh | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_Sampling_Is_Matter_Point-Guided_3D_Human_Mesh_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_Sampling_Is_Matter_Point-Guided_3D_Human_Mesh_Reconstruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.95191836e-02 2.26233438e-01 1.05263423e-02 -1.97337732e-01
-4.78270620e-01 2.95424126e-02 2.17909649e-01 3.32736634e-02
-1.11632176e-01 3.27034235e-01 8.83603171e-02 3.75649512e-01
1.62479267e-01 -8.12599123e-01 -8.49899113e-01 -5.74934721e-01
1.28931940e-01 8.88722360e-01 2.26724282e-01 -1.92559347... | [7.119654655456543, -1.29257333278656] |
05b0b39c-d651-4ec0-88bd-834fe8506e54 | not-a-cute-stroke-analysis-of-rule-and-neural | null | null | https://aclanthology.org/2020.louhi-1.4 | https://aclanthology.org/2020.louhi-1.4.pdf | Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports | We present an in-depth comparison of three clinical information extraction (IE) systems designed to perform entity recognition and negation detection on brain imaging reports: EdIE-R, a bespoke rule-based system, and two neural network models, EdIE-BiLSTM and EdIE-BERT, both multi-task learning models with a BiLSTM and... | ['William Whiteley', 'Richard Tobin', 'Claire Grover', 'Beatrice Alex', 'Andreas Grivas'] | null | null | null | null | emnlp-louhi-2020-11 | ['negation-detection'] | ['natural-language-processing'] | [ 2.37731859e-01 7.59024322e-01 -2.70881027e-01 -6.40331507e-01
-9.45697665e-01 -2.03577399e-01 3.31965595e-01 6.38032258e-01
-9.75222170e-01 1.21270812e+00 4.87090081e-01 -6.00062311e-01
-7.30011940e-01 -5.63681483e-01 -6.11392677e-01 -1.71069086e-01
-1.56786889e-01 7.40123868e-01 3.91896427e-01 -6.68484494... | [8.488519668579102, 8.783380508422852] |
eb1eabd7-77ef-4902-8621-a69f6e14f8d4 | a-quantitative-metric-for-privacy-leakage-in | 2102.13472 | null | https://arxiv.org/abs/2102.13472v1 | https://arxiv.org/pdf/2102.13472v1.pdf | A Quantitative Metric for Privacy Leakage in Federated Learning | In the federated learning system, parameter gradients are shared among participants and the central modulator, while the original data never leave their protected source domain. However, the gradient itself might carry enough information for precise inference of the original data. By reporting their parameter gradients... | ['Jing Xiao', 'Jianzong Wang', 'Xinghua Zhu', 'Yong liu'] | 2021-02-24 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [-3.00640941e-01 -1.18873440e-01 -1.74345151e-01 -4.12362605e-01
-6.58034325e-01 -8.82750154e-01 4.48208898e-01 9.70532671e-02
-4.14335847e-01 8.32937479e-01 1.62539080e-01 -4.35161799e-01
-1.77483886e-01 -9.02797222e-01 -7.15682983e-01 -9.91657078e-01
-1.52959108e-01 -3.34489167e-01 -1.10253453e-01 2.96125352... | [5.855691432952881, 6.714925765991211] |
d43aa7e5-2921-4138-bcb9-3df66e8e6249 | safe-model-based-design-of-experiments-using | 2011.10009 | null | https://arxiv.org/abs/2011.10009v2 | https://arxiv.org/pdf/2011.10009v2.pdf | Safe model-based design of experiments using Gaussian processes | Construction of kinetic models has become an indispensable step in the development and scale up of processes in the industry. Model-based design of experiments (MBDoE) has been widely used for the purpose of improving parameter precision in nonlinear dynamic systems. This process needs to account for both parametric an... | ['Federico Galvanin', 'Panagiotis Petsagkourakis'] | 2020-11-19 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.08320974e-01 2.58132696e-01 3.70445102e-01 2.20362961e-01
-2.45560527e-01 -3.01025182e-01 4.05213296e-01 7.34920025e-01
-4.64925766e-01 8.16258669e-01 -5.82048297e-01 -3.21731925e-01
-7.96544373e-01 -7.36319542e-01 -4.14322823e-01 -9.23007190e-01
1.03135854e-01 6.05452895e-01 -4.97429892e-02 8.60042721... | [5.4492950439453125, 2.4981322288513184] |
d8ceaae0-450f-4b45-9f8e-cc877fdad235 | deftri-a-few-shot-label-fused-contextual | null | null | https://aclanthology.org/2022.ecnlp-1.1 | https://aclanthology.org/2022.ecnlp-1.1.pdf | DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce | Defect Triage is a time-sensitive and critical process in a large-scale agile software development lifecycle for e-commerce. Inefficiencies arising from human and process dependencies in this domain have motivated research in automated approaches using machine learning to accurately assign defects to qualified teams. T... | ['Ipsita Mohanty'] | null | null | null | null | ecnlp-acl-2022-5 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 4.46264327e-01 2.02444583e-01 1.81957081e-01 -8.93740773e-01
-7.96871901e-01 -3.67910773e-01 1.16132200e-01 6.88197494e-01
8.50659162e-02 -6.03937097e-02 -1.24768615e-02 -8.27996954e-02
-1.23338588e-01 -7.58605719e-01 -4.12368715e-01 -1.03237525e-01
5.17095514e-02 9.97182488e-01 -4.35716271e-01 -3.53872716... | [7.70637845993042, 7.725573539733887] |
c68ddb69-4a08-42fb-ba21-4be398cea727 | a-haar-wavelet-based-perceptual-similarity | 1607.06140 | null | http://arxiv.org/abs/1607.06140v4 | http://arxiv.org/pdf/1607.06140v4.pdf | A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment | In most practical situations, the compression or transmission of images and
videos creates distortions that will eventually be perceived by a human
observer. Vice versa, image and video restoration techniques, such as
inpainting or denoising, aim to enhance the quality of experience of human
viewers. Correctly assessin... | ['Thomas Wiegand', 'Gitta Kutyniok', 'Sebastian Bosse', 'Rafael Reisenhofer'] | 2016-07-20 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [ 3.96456957e-01 -4.12464410e-01 1.10500410e-01 -5.21161482e-02
-5.51178336e-01 -1.59237608e-01 4.95500803e-01 3.79876137e-01
-2.58670717e-01 4.47364420e-01 2.60329008e-01 1.79877222e-01
-2.66231120e-01 -6.15847290e-01 -3.27948809e-01 -6.41204357e-01
-1.59059018e-01 -3.59141380e-01 5.02789438e-01 -4.53185976... | [11.739140510559082, -1.9137109518051147] |
ced03bbe-086a-4b0f-a13d-2ce4b267ef40 | xnli-evaluating-cross-lingual-sentence | 1809.05053 | null | http://arxiv.org/abs/1809.05053v1 | http://arxiv.org/pdf/1809.05053v1.pdf | XNLI: Evaluating Cross-lingual Sentence Representations | State-of-the-art natural language processing systems rely on supervision in
the form of annotated data to learn competent models. These models are
generally trained on data in a single language (usually English), and cannot be
directly used beyond that language. Since collecting data in every language is
not realistic,... | ['Ruty Rinott', 'Holger Schwenk', 'Adina Williams', 'Veselin Stoyanov', 'Samuel R. Bowman', 'Guillaume Lample', 'Alexis Conneau'] | 2018-09-13 | xnli-evaluating-cross-lingual-sentence-1 | https://aclanthology.org/D18-1269 | https://aclanthology.org/D18-1269.pdf | emnlp-2018-10 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 1.94987729e-01 4.99707125e-02 -4.08437222e-01 -7.83045113e-01
-1.43258500e+00 -8.56340885e-01 5.90890706e-01 1.81359768e-01
-7.43136227e-01 1.02288747e+00 4.36713696e-01 -8.93839359e-01
4.62292492e-01 -7.07524061e-01 -1.12509847e+00 2.21322253e-02
2.58136302e-01 9.38876987e-01 -2.59729475e-01 -5.66181719... | [10.97197151184082, 9.597756385803223] |
f3851e62-da16-42ae-b5c9-3c0c5671881f | hhp-net-a-light-heteroscedastic-neural | 2111.01440 | null | https://arxiv.org/abs/2111.01440v2 | https://arxiv.org/pdf/2111.01440v2.pdf | HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty | In this paper we introduce a novel method to estimate the head pose of people in single images starting from a small set of head keypoints. To this purpose, we propose a regression model that exploits keypoints computed automatically by 2D pose estimation algorithms and outputs the head pose represented by yaw, pitch, ... | ['Francesca Odone', 'Nicoletta Noceti', 'Federico Figari Tomenotti', 'Giorgio Cantarini'] | 2021-11-02 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-3.38442296e-01 2.62087554e-01 2.28254616e-01 -6.12223566e-01
-7.24814475e-01 -2.75452226e-01 6.34147167e-01 3.95550221e-01
-7.01945245e-01 8.20648313e-01 2.44557589e-01 3.04818153e-01
-6.72843307e-02 -5.34327626e-01 -7.38789678e-01 -6.41397297e-01
-2.43822366e-01 9.39628243e-01 6.88219517e-02 -5.69852553... | [13.65499210357666, 0.3250398635864258] |
285e7dd3-aa99-47f4-be82-fe3a36785be8 | k-salsa-k-anonymous-synthetic-averaging-of | 2303.10824 | null | https://arxiv.org/abs/2303.10824v1 | https://arxiv.org/pdf/2303.10824v1.pdf | k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignment | The application of modern machine learning to retinal image analyses offers valuable insights into a broad range of human health conditions beyond ophthalmic diseases. Additionally, data sharing is key to fully realizing the potential of machine learning models by providing a rich and diverse collection of training dat... | ['Hyunghoon Cho', 'Michael Morley', 'Hyunwoo J. Kim', 'Hyeonjin Park', 'Minkyu Jeon'] | 2023-03-20 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 5.20661354e-01 2.85349935e-01 -1.41338274e-01 -5.11627614e-01
-8.97546172e-01 -7.47222185e-01 2.49608710e-01 -2.44814411e-01
-1.17525429e-01 7.45629013e-01 2.84042448e-01 -5.37381411e-01
6.04925752e-02 -6.05354607e-01 -7.28681445e-01 -7.81641304e-01
1.87351733e-01 -2.04961404e-01 -4.54290211e-01 2.88395405... | [14.119329452514648, -1.7425519227981567] |
7ab30a36-39e3-4771-b7e3-27fd70908bf8 | artificial-influence-an-analysis-of-ai-driven | 2303.08721 | null | https://arxiv.org/abs/2303.08721v1 | https://arxiv.org/pdf/2303.08721v1.pdf | Artificial Influence: An Analysis Of AI-Driven Persuasion | Persuasion is a key aspect of what it means to be human, and is central to business, politics, and other endeavors. Advancements in artificial intelligence (AI) have produced AI systems that are capable of persuading humans to buy products, watch videos, click on search results, and more. Even systems that are not expl... | ['Thomas Woodside', 'Matthew Burtell'] | 2023-03-15 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 5.72719753e-01 8.76706779e-01 -2.67074406e-01 -4.36470121e-01
-1.56742528e-01 -8.70566547e-01 1.16665924e+00 1.78541541e-01
-6.23486817e-01 7.62268066e-01 7.52040207e-01 -1.12311137e+00
-1.01615399e-01 -8.88700128e-01 -4.43159014e-01 -2.47082710e-01
6.59606159e-01 2.43729815e-01 -7.69323157e-03 -5.85073352... | [9.194931030273438, 6.3230390548706055] |
c3d34bc6-5495-4a77-9871-c3c2d99807f6 | adversarial-representation-learning-for-text | 1908.10534 | null | https://arxiv.org/abs/1908.10534v1 | https://arxiv.org/pdf/1908.10534v1.pdf | Adversarial Representation Learning for Text-to-Image Matching | For many computer vision applications such as image captioning, visual question answering, and person search, learning discriminative feature representations at both image and text level is an essential yet challenging problem. Its challenges originate from the large word variance in the text domain as well as the diff... | ['Ioannis A. Kakadiaris', 'Xiang Xu', 'Nikolaos Sarafianos'] | 2019-08-28 | adversarial-representation-learning-for-text-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Sarafianos_Adversarial_Representation_Learning_for_Text-to-Image_Matching_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Sarafianos_Adversarial_Representation_Learning_for_Text-to-Image_Matching_ICCV_2019_paper.pdf | iccv-2019-10 | ['person-search'] | ['computer-vision'] | [ 5.46094954e-01 -2.86345065e-01 -2.18238816e-01 -3.50835621e-01
-1.41250336e+00 -7.01016545e-01 9.82382476e-01 5.75375035e-02
-6.35497630e-01 2.30512276e-01 1.91153288e-01 -6.69725016e-02
-3.00541855e-02 -3.97414833e-01 -7.51743853e-01 -4.18903083e-01
3.84441286e-01 5.46672046e-01 -1.28275886e-01 -6.31357580... | [11.033486366271973, 1.278006672859192] |
57c629ed-c974-4901-be37-f0ea59a8d664 | going-deeper-with-brain-morphometry-using | 2009.03303 | null | https://arxiv.org/abs/2009.03303v1 | https://arxiv.org/pdf/2009.03303v1.pdf | Going deeper with brain morphometry using neural networks | Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases. Recent advances in this domain indicate that deep convolutional neural networks can infer morphometric measurements within a few seconds. Nevertheless, the accuracy of the devised model for insightfu... | ['Olivier Salvado', 'Clinton Fookes', 'Vincent Doré', 'Léo Lebrat', 'Jason Dowling', 'Pierrick Bourgeat', 'Rodrigo Santa Cruz', 'Jurgen Fripp'] | 2020-09-07 | null | null | null | null | ['brain-morphometry'] | ['medical'] | [ 2.11887155e-02 1.19919248e-01 1.42678574e-01 -5.23720622e-01
-8.84189367e-01 3.19560207e-02 2.41691083e-01 1.67078137e-01
-7.66070962e-01 8.91075671e-01 9.84283979e-05 -3.83686759e-02
-2.49124900e-01 -8.49955261e-01 -5.23172200e-01 -6.14193618e-01
-4.01232928e-01 6.22312784e-01 2.30702400e-01 -3.59935313... | [14.144695281982422, -2.1942598819732666] |
e2e8f063-42b0-44dc-8e08-00fb1864af67 | dyadformer-a-multi-modal-transformer-for-long | 2109.09487 | null | https://arxiv.org/abs/2109.09487v1 | https://arxiv.org/pdf/2109.09487v1.pdf | Dyadformer: A Multi-modal Transformer for Long-Range Modeling of Dyadic Interactions | Personality computing has become an emerging topic in computer vision, due to the wide range of applications it can be used for. However, most works on the topic have focused on analyzing the individual, even when applied to interaction scenarios, and for short periods of time. To address these limitations, we present ... | ['Cristina Palmero', 'Sergio Escalera', 'Thomas B. Moeslund', 'David Leiva', 'Georgina Guilera', 'David Gallardo-Pujol', 'Julio C. S. Jacques Junior', 'Sorina Smeureanu', 'Javier Selva', 'Albert Clapés', 'David Curto'] | 2021-09-20 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [-2.40646034e-01 2.90294252e-02 4.91533522e-03 -7.48938978e-01
1.71557561e-01 -1.91034555e-01 8.71533513e-01 3.10767144e-01
-2.80786455e-01 5.07511377e-01 2.38763422e-01 6.30308867e-01
-4.26801205e-01 -5.31564534e-01 -1.77108169e-01 -5.51619470e-01
-5.67676008e-01 6.97016597e-01 -1.95105001e-01 -1.78003758... | [13.322381019592285, 4.966462135314941] |
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