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d272e659-fd91-41c8-bc1a-1a45e16c80b0 | femda-une-methode-de-classification-robuste | 2307.01954 | null | https://arxiv.org/abs/2307.01954v1 | https://arxiv.org/pdf/2307.01954v1.pdf | FEMDA: Une méthode de classification robuste et flexible | Linear and Quadratic Discriminant Analysis (LDA and QDA) are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the underlying Gaussian assumption that is not robust. This paper studies the robustness to scale changes in the data of a new ... | ['Frederic Pascal', 'Matthieu Jonckheere', 'Pierre Houdouin'] | 2023-07-04 | null | null | null | null | ['classification-1'] | ['methodology'] | [-5.18764138e-01 -6.30276918e-01 3.83623205e-02 -3.92189622e-01
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-3.01353186e-01 8.69178712e-01 4.91434932e-01 1.68261126... | [7.79159688949585, 4.186161041259766] |
a4b17dbc-0ffe-4015-9ead-661633c78187 | evolutionary-multitasking-with-solution-space | 2212.05679 | null | https://arxiv.org/abs/2212.05679v2 | https://arxiv.org/pdf/2212.05679v2.pdf | Evolutionary Multitasking with Solution Space Cutting for Point Cloud Registration | Point cloud registration (PCR) is a popular research topic in computer vision. Recently, the registration method in an evolutionary way has received continuous attention because of its robustness to the initial pose and flexibility in objective function design. However, most evolving registration methods cannot tackle ... | ['Qiguang Miao', 'Wenping Ma', 'Yibo Liu', 'Zedong Tang', 'Hangqi Ding', 'Maoguo Gong', 'Peiran Gong', 'Wu Yue'] | 2022-12-12 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 1.92764059e-01 -6.22278214e-01 2.28128314e-01 -5.05557517e-03
-5.26739061e-01 8.36359262e-02 2.43486837e-01 5.93072362e-02
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3.68319191e-02 4.63674605e-01 3.26724499e-01 -5.23180723... | [5.7641377449035645, 3.4495413303375244] |
0ba621ce-17e1-4633-9a06-4ed0411494ad | decomposed-inductive-procedure-learning | 2110.13233 | null | https://arxiv.org/abs/2110.13233v1 | https://arxiv.org/pdf/2110.13233v1.pdf | Decomposed Inductive Procedure Learning | Recent advances in machine learning have made it possible to train artificially intelligent agents that perform with super-human accuracy on a great diversity of complex tasks. However, the process of training these capabilities often necessitates millions of annotated examples -- far more than humans typically need in... | ['Kenneth Koedinger', 'Erik Harpstead', 'Christopher MacLellan', 'Daniel Weitekamp'] | 2021-10-25 | null | null | null | null | ['procedure-learning'] | ['computer-vision'] | [ 2.50159442e-01 6.26443446e-01 2.17186585e-01 -3.09133738e-01
-3.36437792e-01 -5.87255001e-01 9.89940464e-01 3.25107574e-01
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-1.69925094e-01 7.63871968e-01 3.15858930e-01 -3.66211116... | [4.135249614715576, 1.4664220809936523] |
88b4e9a8-a8c2-4601-b02e-a291deeaf9a3 | deeptract-a-probabilistic-deep-learning | 1812.05129 | null | https://arxiv.org/abs/1812.05129v3 | https://arxiv.org/pdf/1812.05129v3.pdf | DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography | We present DeepTract, a deep-learning framework for estimating white matter fibers orientation and streamline tractography. We adopt a data-driven approach for fiber reconstruction from diffusion weighted images (DWI), which does not assume a specific diffusion model. We use a recurrent neural network for mapping seque... | ['Tammy Riklin-Raviv', 'Itay Benou'] | 2018-12-12 | null | null | null | null | ['probabilistic-deep-learning', 'white-matter-fiber-tractography'] | ['computer-vision', 'medical'] | [-5.06721079e-01 -4.70543265e-01 -6.20319955e-02 -2.25457072e-01
-6.31316304e-01 -7.41553664e-01 4.00284082e-01 -4.60943848e-01
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-5.72302997e-01 7.94054091e-01 1.37758106e-01 2.78389573... | [13.849977493286133, -2.3789420127868652] |
d3927fe6-fc88-44d9-ae58-ec3c26c1c876 | w2n-switching-from-weak-supervision-to-noisy | 2207.12104 | null | https://arxiv.org/abs/2207.12104v1 | https://arxiv.org/pdf/2207.12104v1.pdf | W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection | Weakly-supervised object detection (WSOD) aims to train an object detector only requiring the image-level annotations. Recently, some works have managed to select the accurate boxes generated from a well-trained WSOD network to supervise a semi-supervised detection framework for better performance. However, these appro... | ['WangMeng Zuo', 'Erjin Zhou', 'Bowen Dong', 'Yiping Bao', 'Zitong Huang'] | 2022-07-25 | null | null | null | null | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 2.07621396e-01 2.68637985e-01 -3.47546965e-01 -4.60710257e-01
-9.73570466e-01 -3.18327218e-01 4.02089477e-01 -2.47833300e-02
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3.77426982e-01 5.60233533e-01 1.01585865e+00 1.13851644... | [9.215463638305664, 1.297062635421753] |
634706f6-fec9-4d80-a261-6c0c57f244b1 | ontology-aware-network-for-zero-shot-sketch | 2302.10040 | null | https://arxiv.org/abs/2302.10040v1 | https://arxiv.org/pdf/2302.10040v1.pdf | Ontology-aware Network for Zero-shot Sketch-based Image Retrieval | Zero-Shot Sketch-Based Image Retrieval (ZSSBIR) is an emerging task. The pioneering work focused on the modal gap but ignored inter-class information. Although recent work has begun to consider the triplet-based or contrast-based loss to mine inter-class information, positive and negative samples need to be carefully s... | ['Deqiang Cheng', 'Ziqiang Wang', 'He Jiang', 'Haoxiang Zhang'] | 2023-02-20 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.71738052e-01 -4.26681459e-01 -7.03918815e-01 -4.97786313e-01
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-6.18295431e-01 -6.97219968e-01 -2.71597713e-01 -7.13032365e-01
9.45916101e-02 3.25493038e-01 3.07188720e-01 -1.76279023... | [11.576814651489258, 0.7023018002510071] |
86610d76-055a-46d7-a454-72adf8fa2e8d | image-clustering-without-ground-truth | 1610.07758 | null | http://arxiv.org/abs/1610.07758v1 | http://arxiv.org/pdf/1610.07758v1.pdf | Image Clustering without Ground Truth | Cluster analysis has become one of the most exercised research areas over the
past few decades in computer science. As a consequence, numerous clustering
algorithms have already been developed to find appropriate partitions of a set
of objects. Given multiple such clustering solutions, it is a challenging task
to obtai... | ['Tripti Prasad', 'Abhisek Dash', 'Sujoy Chatterjee', 'Malay Bhattacharyya'] | 2016-10-25 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [-1.56944469e-02 -1.94571003e-01 4.88209426e-01 -1.99452311e-01
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3.85549143e-02 9.23413217e-01 4.07701850e-01 1.90114379... | [7.644064426422119, 4.599337100982666] |
fe898684-6990-428a-b050-9fd01924c1fe | metaue-model-based-meta-learning-for | 2303.06543 | null | https://arxiv.org/abs/2303.06543v1 | https://arxiv.org/pdf/2303.06543v1.pdf | MetaUE: Model-based Meta-learning for Underwater Image Enhancement | The challenges in recovering underwater images are the presence of diverse degradation factors and the lack of ground truth images. Although synthetic underwater image pairs can be used to overcome the problem of inadequately observing data, it may result in over-fitting and enhancement degradation. This paper proposes... | ['Yuping Duan', 'Ke Tang', 'Haorui Yan', 'Zhenwei Zhang'] | 2023-03-12 | null | null | null | null | ['underwater-image-restoration', 'image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 1.27028301e-01 -3.68317902e-01 7.82389343e-01 -4.59362060e-01
-7.46928096e-01 -1.53826252e-01 -7.67039955e-02 -3.11491549e-01
-4.23946857e-01 7.26070821e-01 1.36711285e-01 -3.57294045e-02
-1.02825642e-01 -8.93326700e-01 -8.49625230e-01 -1.23500848e+00
-1.16339810e-01 -4.11172211e-01 9.37961116e-02 -5.18926561... | [10.702558517456055, -3.531609296798706] |
0f289bf9-e89c-4395-87c5-2533f41c9fd1 | network-free-unsupervised-semantic | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Feng_Network-Free_Unsupervised_Semantic_Segmentation_With_Synthetic_Images_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Feng_Network-Free_Unsupervised_Semantic_Segmentation_With_Synthetic_Images_CVPR_2023_paper.pdf | Network-Free, Unsupervised Semantic Segmentation With Synthetic Images | We derive a method that yields highly accurate semantic segmentation maps without the use of any additional neural network, layers, manually annotated training data, or supervised training. Our method is based on the observation that the correlation of a set of pixels belonging to the same semantic segment do not c... | ['Aleix Martinez', 'Eduard Ramon', 'Wentong Liao', 'Raghudeep Gadde', 'Qianli Feng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 1.01613808e+00 7.33234286e-01 2.49307364e-01 -6.10929966e-01
-8.36563528e-01 -9.50461328e-01 5.64258814e-01 -4.38579232e-01
-1.25053421e-01 8.17606330e-01 -1.66450903e-01 -1.77251920e-02
4.47458863e-01 -9.70962942e-01 -1.06172752e+00 -5.42527378e-01
6.47091210e-01 7.11822510e-01 3.08467686e-01 -1.48290768... | [11.469635963439941, -0.37535035610198975] |
7ee0199c-7b2a-4929-ba72-8ca4d828d699 | drone-data-aware-low-rank-compression-for | null | null | http://proceedings.neurips.cc/paper/2021/hash/f56de5ef149cf0aedcc8f4797031e229-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/f56de5ef149cf0aedcc8f4797031e229-Paper.pdf | DRONE: Data-aware Low-rank Compression for Large NLP Models | The representations learned by large-scale NLP models such as BERT have been widely used in various tasks. However, the increasing model size of the pre-trained models also brings efficiency challenges, including inference speed and model size when deploying models on mobile devices. Specifically, most operations in BE... | ['Cho-Jui Hsieh', 'Inderjit Dhillon', 'Hsiang-Fu Yu', 'Pei-Hung Chen'] | 2021-12-01 | null | https://openreview.net/forum?id=sthiz9zeXGG | https://openreview.net/pdf?id=sthiz9zeXGG | neurips-2021-12 | ['low-rank-compression'] | ['computer-code'] | [-3.30822542e-02 8.88232961e-02 -2.69210398e-01 -2.24366426e-01
-6.85699761e-01 -4.31222916e-01 2.76244223e-01 -6.29965812e-02
-6.10678434e-01 4.68971521e-01 3.90282162e-02 -5.44158041e-01
-3.09765637e-01 -8.62474144e-01 -1.11806738e+00 -3.35069627e-01
-6.99835569e-02 8.53950083e-01 -7.99595043e-02 -1.11627474... | [8.708028793334961, 3.601494073867798] |
08e7b326-6f4a-49d3-96f0-53a1c65ae0f6 | first-explore-then-exploit-meta-learning | 2307.02276 | null | https://arxiv.org/abs/2307.02276v1 | https://arxiv.org/pdf/2307.02276v1.pdf | First-Explore, then Exploit: Meta-Learning Intelligent Exploration | Standard reinforcement learning (RL) agents never intelligently explore like a human (i.e. by taking into account complex domain priors and previous explorations). Even the most basic intelligent exploration strategies such as exhaustive search are only inefficiently or poorly approximated by approaches such as novelty... | ['Jeff Clune', 'Ben Norman'] | 2023-07-05 | null | null | null | null | ['meta-learning', 'reinforcement-learning-1'] | ['methodology', 'methodology'] | [ 9.38775390e-02 5.30947924e-01 -6.22570455e-01 2.22339749e-01
-7.75151849e-01 -8.38616192e-01 6.14344954e-01 3.26164998e-02
-1.01844966e+00 1.31877553e+00 -5.92992157e-02 -3.95632297e-01
-2.73569763e-01 -8.72060359e-01 -8.45139802e-01 -8.74432504e-01
-4.15659636e-01 8.11981082e-01 6.65841624e-02 -2.77959973... | [3.9212708473205566, 1.7372854948043823] |
2b7abf0b-33a5-4c79-aa04-168359ce33a3 | part-based-pseudo-label-refinement-for | 2203.14675 | null | https://arxiv.org/abs/2203.14675v1 | https://arxiv.org/pdf/2203.14675v1.pdf | Part-based Pseudo Label Refinement for Unsupervised Person Re-identification | Unsupervised person re-identification (re-ID) aims at learning discriminative representations for person retrieval from unlabeled data. Recent techniques accomplish this task by using pseudo-labels, but these labels are inherently noisy and deteriorate the accuracy. To overcome this problem, several pseudo-label refine... | ['Sung-Eui Yoon', 'Seunghoon Hong', 'Woo Jae Kim', 'Yoonki Cho'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cho_Part-Based_Pseudo_Label_Refinement_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cho_Part-Based_Pseudo_Label_Refinement_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['person-retrieval', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [-1.20225407e-01 -2.41724104e-01 -2.99666356e-02 -6.86209917e-01
-8.39361191e-01 -4.31590468e-01 6.25939310e-01 1.71208426e-01
-4.69415277e-01 6.21400177e-01 5.24384916e-01 5.92575610e-01
-3.28771144e-01 -5.99582314e-01 -2.63457537e-01 -9.15230155e-01
3.11114103e-01 4.31058675e-01 -1.33613840e-01 6.80765286... | [14.814799308776855, 1.0656388998031616] |
505081bf-4558-4903-82bd-5924ce453ef0 | scanet-self-paced-semi-curricular-attention | 2304.08444 | null | https://arxiv.org/abs/2304.08444v1 | https://arxiv.org/pdf/2304.08444v1.pdf | SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing | The presence of non-homogeneous haze can cause scene blurring, color distortion, low contrast, and other degradations that obscure texture details. Existing homogeneous dehazing methods struggle to handle the non-uniform distribution of haze in a robust manner. The crucial challenge of non-homogeneous dehazing is to ef... | ['Wenqi Ren', 'Shengfeng He', 'Jingxiang Qu', 'Yuxu Lu', 'Ryan Wen Liu', 'Yuan Gao', 'Yu Guo'] | 2023-04-17 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.21027268e-01 -2.09211946e-01 2.26982147e-01 -1.53073877e-01
-5.39492071e-01 -1.31448194e-01 2.49338925e-01 -3.59060854e-01
-1.05015874e-01 7.06934988e-01 3.85712802e-01 -1.75270498e-01
-6.94611892e-02 -8.37696731e-01 -1.03112710e+00 -9.49886560e-01
4.16694611e-01 -9.07991678e-02 5.17552912e-01 -3.47367406... | [10.942460060119629, -3.109165906906128] |
afdf8f1c-6eb5-4513-b932-38cbd4589e85 | realistic-face-reenactment-via-self | 2003.12957 | null | https://arxiv.org/abs/2003.12957v1 | https://arxiv.org/pdf/2003.12957v1.pdf | Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose | Recent works have shown how realistic talking face images can be obtained under the supervision of geometry guidance, e.g., facial landmark or boundary. To alleviate the demand for manual annotations, in this paper, we propose a novel self-supervised hybrid model (DAE-GAN) that learns how to reenact face naturally give... | ['Yong liu', 'Jiangning Zhang', 'Yusu Pan', 'Xianfang Zeng', 'Mengmeng Wang'] | 2020-03-29 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [-1.80796739e-02 3.69696736e-01 1.45557463e-01 -7.05898583e-01
-6.90884233e-01 -5.24720430e-01 5.07099271e-01 -1.13392913e+00
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1.77530780e-01 -7.01535225e-01 -1.01140141e+00 -9.05813634e-01
4.74143118e-01 3.70718360e-01 -4.62404221e-01 -2.86862820... | [12.845610618591309, -0.21419517695903778] |
9325de24-25e7-4dad-8342-3e655d81b1c6 | detecting-stylistic-deception | null | null | https://aclanthology.org/W12-0414 | https://aclanthology.org/W12-0414.pdf | Detecting Stylistic Deception | null | ['Patrick Juola'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.246927738189697, 3.7058560848236084] |
364426f8-fa56-4065-87ed-3b35819ee3ce | uncertainty-based-network-for-few-shot-image | 2205.08157 | null | https://arxiv.org/abs/2205.08157v1 | https://arxiv.org/pdf/2205.08157v1.pdf | Uncertainty-based Network for Few-shot Image Classification | The transductive inference is an effective technique in the few-shot learning task, where query sets update prototypes to improve themselves. However, these methods optimize the model by considering only the classification scores of the query instances as confidence while ignoring the uncertainty of these classificatio... | ['Tong Lu', 'Tao Wang', 'Yin-Dong Zheng', 'Chunhao Cai', 'Qian Xu', 'Minglei Yuan'] | 2022-05-17 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 3.55853513e-02 1.53431334e-02 -6.04712248e-01 -7.02208102e-01
-7.97654927e-01 -1.09551013e-01 4.03787911e-01 2.13043258e-01
-3.85969102e-01 8.15743148e-01 9.06938091e-02 2.54695028e-01
-5.28040886e-01 -1.12461829e+00 -4.61525381e-01 -4.37595695e-01
1.50933906e-01 7.55988777e-01 6.00228012e-01 4.76856343... | [10.136016845703125, 3.3736631870269775] |
bda6d78e-7f84-47d1-805a-89b93c4d35e6 | graph-based-3d-multi-person-pose-estimation | 2109.05885 | null | https://arxiv.org/abs/2109.05885v1 | https://arxiv.org/pdf/2109.05885v1.pdf | Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images | This paper studies the task of estimating the 3D human poses of multiple persons from multiple calibrated camera views. Following the top-down paradigm, we decompose the task into two stages, i.e. person localization and pose estimation. Both stages are processed in coarse-to-fine manners. And we propose three task-spe... | ['Wanli Ouyang', 'Dong Liu', 'Chen Qian', 'Lei Bai', 'Wentao Liu', 'Sheng Jin', 'Size Wu'] | 2021-09-13 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wu_Graph-Based_3D_Multi-Person_Pose_Estimation_Using_Multi-View_Images_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wu_Graph-Based_3D_Multi-Person_Pose_Estimation_Using_Multi-View_Images_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.13003278e-01 -4.94015180e-02 8.67401361e-02 -3.56903195e-01
-7.75846481e-01 -3.91338736e-01 4.54505324e-01 -4.42644246e-02
-4.92121220e-01 3.38880986e-01 3.11256379e-01 3.98662627e-01
1.18133172e-01 -6.35286510e-01 -8.15390170e-01 -3.47197771e-01
-4.37485203e-02 1.09891903e+00 1.25870690e-01 -1.40792698... | [7.02625846862793, -0.9148258566856384] |
72bbbbc5-577b-4b71-b44a-ed9f69e0a8ce | a-frustratingly-easy-approach-for-joint | 2010.12812 | null | https://arxiv.org/abs/2010.12812v2 | https://arxiv.org/pdf/2010.12812v2.pdf | A Frustratingly Easy Approach for Entity and Relation Extraction | End-to-end relation extraction aims to identify named entities and extract relations between them. Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task learning through shared representations. In this work, we present a simple pipeli... | ['Danqi Chen', 'Zexuan Zhong'] | 2020-10-24 | null | https://aclanthology.org/2021.naacl-main.5 | https://aclanthology.org/2021.naacl-main.5.pdf | naacl-2021-4 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 1.67401940e-01 9.73568439e-01 -3.50344568e-01 -5.99791825e-01
-1.11454201e+00 -3.77152205e-01 7.71713674e-01 5.86259127e-01
-3.96246374e-01 9.75397408e-01 4.36207741e-01 -4.76411343e-01
-1.03423834e-01 -9.79164600e-01 -8.82171214e-01 -1.69036627e-01
-3.96256834e-01 7.79175758e-01 1.98892936e-01 -1.58619940... | [9.376158714294434, 8.782285690307617] |
61c8102a-e32d-46f2-9ec0-dc30a9cc8bb2 | augmenting-multi-turn-text-to-sql-datasets | 2210.12096 | null | https://arxiv.org/abs/2210.12096v1 | https://arxiv.org/pdf/2210.12096v1.pdf | Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play | The task of context-dependent text-to-SQL aims to convert multi-turn user utterances to formal SQL queries. This is a challenging task due to both the scarcity of training data from which to learn complex contextual dependencies and to generalize to unseen databases. In this paper we explore augmenting the training dat... | ['Linfeng Song', 'Phil Blunsom', 'Tao Yu', 'Zihuiwen Ye', 'Qi Liu'] | 2022-10-21 | null | null | null | null | ['sql-to-text', 'text-to-sql'] | ['computer-code', 'computer-code'] | [ 2.70824075e-01 5.20256996e-01 -1.12320401e-01 -1.14129972e+00
-1.30129778e+00 -8.87994945e-01 7.22264111e-01 1.24373786e-01
-2.78368592e-01 8.02780926e-01 6.04619145e-01 -4.24567312e-01
1.85923815e-01 -9.78402793e-01 -1.08286715e+00 1.74086675e-01
1.66667670e-01 1.25339627e+00 3.74308228e-01 -7.09137321... | [10.029142379760742, 7.9431023597717285] |
73add660-3db0-4810-9538-6d0e7fafcbeb | pc-rgnn-point-cloud-completion-and-graph | 2012.10412 | null | https://arxiv.org/abs/2012.10412v3 | https://arxiv.org/pdf/2012.10412v3.pdf | PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection | LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions. On the one hand, w... | ['Yunhong Wang', 'Di Huang', 'Yanan Zhang'] | 2020-12-18 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-7.09639788e-02 3.55177745e-02 -5.80696203e-02 -3.40585291e-01
-8.17266166e-01 -3.09138864e-01 6.28765166e-01 1.46272093e-01
-3.28623086e-01 3.25968891e-01 -1.00064874e-01 -2.69642770e-01
-5.06530236e-03 -8.12997401e-01 -9.16809440e-01 -5.10682642e-01
1.57580495e-01 6.32370353e-01 7.35943317e-01 -3.69774967... | [7.9801483154296875, -2.8016834259033203] |
8cf063c6-d81f-4075-bcb5-aac0624b58e3 | understanding-spatial-relations-through-1 | 2007.09551 | null | https://arxiv.org/abs/2007.09551v1 | https://arxiv.org/pdf/2007.09551v1.pdf | Understanding Spatial Relations through Multiple Modalities | Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general. Spatial relations between objects can either be explicit -- expressed as spatial prepositions, or implicit -- expressed by spatial verbs such as ... | ['Dan Roth', 'Soham Dan', 'Hangfeng He'] | 2020-07-19 | understanding-spatial-relations-through | https://aclanthology.org/2020.lrec-1.288 | https://aclanthology.org/2020.lrec-1.288.pdf | lrec-2020-5 | ['implicit-relations'] | ['natural-language-processing'] | [-4.43350784e-02 1.45634755e-01 -2.73542166e-01 -6.27955794e-01
9.13963318e-02 -8.27476978e-01 9.90408957e-01 5.34981310e-01
-5.89641571e-01 6.50180817e-01 5.58353841e-01 -5.91399252e-01
-4.50349271e-01 -1.00820720e+00 -6.88694656e-01 -3.09305459e-01
-2.91201115e-01 3.71769696e-01 5.28069139e-01 -1.29501849... | [10.448945999145508, 1.782551884651184] |
b3a0c5b5-c000-412c-b954-b2bc7e33949e | arrowgan-learning-to-generate-videos-by | 2101.03710 | null | https://arxiv.org/abs/2101.03710v1 | https://arxiv.org/pdf/2101.03710v1.pdf | ArrowGAN : Learning to Generate Videos by Learning Arrow of Time | Training GANs on videos is even more sophisticated than on images because videos have a distinguished dimension: time. While recent methods designed a dedicated architecture considering time, generated videos are still far from indistinguishable from real videos. In this paper, we introduce ArrowGAN framework, where th... | ['Hyeran Byun', 'Youngjung Uh', 'Kibeom Hong'] | 2021-01-11 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 1.17198028e-01 9.91107449e-02 -3.77478957e-01 -2.11219475e-01
-6.72784865e-01 -6.50913596e-01 1.07177019e+00 -1.05783379e+00
-3.38550024e-02 8.39217901e-01 4.32281524e-01 -3.06760669e-01
2.59891152e-01 -8.17860544e-01 -1.14027154e+00 -8.72681916e-01
-1.58312038e-01 9.72241312e-02 -2.74186373e-01 -3.95778604... | [10.8882474899292, -0.5894728899002075] |
4d2419c4-4bc5-432d-81aa-d2987210da30 | the-cultivated-practices-of-text-to-image | 2306.11393 | null | https://arxiv.org/abs/2306.11393v1 | https://arxiv.org/pdf/2306.11393v1.pdf | The Cultivated Practices of Text-to-Image Generation | Humankind is entering a novel creative era in which anybody can synthesize digital information using generative artificial intelligence (AI). Text-to-image generation, in particular, has become vastly popular and millions of practitioners produce AI-generated images and AI art online. This chapter first gives an overvi... | ['Jonas Oppenlaender'] | 2023-06-20 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 6.11333907e-01 5.18915415e-01 2.56341219e-01 2.16087982e-01
-1.29603222e-01 -6.40972733e-01 1.04342079e+00 -4.30472344e-01
9.90303233e-03 6.65559530e-01 6.56960905e-01 -8.02925527e-02
4.39455472e-02 -1.01180613e+00 -6.36525095e-01 -3.20287436e-01
4.00967062e-01 3.37448508e-01 -4.91267860e-01 -4.12233710... | [9.375419616699219, 6.3292717933654785] |
5a642ad9-36f3-42c8-984f-ce8bc9f9b2e0 | patchbatch-a-batch-augmented-loss-for-optical | 1512.01815 | null | http://arxiv.org/abs/1512.01815v2 | http://arxiv.org/pdf/1512.01815v2.pdf | PatchBatch: a Batch Augmented Loss for Optical Flow | We propose a new pipeline for optical flow computation, based on Deep
Learning techniques. We suggest using a Siamese CNN to independently, and in
parallel, compute the descriptors of both images. The learned descriptors are
then compared efficiently using the L2 norm and do not require network
processing of patch pair... | ['Lior Wolf', 'David Gadot'] | 2015-12-06 | patchbatch-a-batch-augmented-loss-for-optical-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Gadot_PatchBatch_A_Batch_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Gadot_PatchBatch_A_Batch_CVPR_2016_paper.pdf | cvpr-2016-6 | ['patch-matching'] | ['computer-vision'] | [-3.75309885e-01 -5.37244916e-01 -8.05746838e-02 -2.38434821e-01
-5.61282814e-01 -6.46229088e-01 6.93909883e-01 3.65119964e-01
-7.63374090e-01 7.37877011e-01 1.15908735e-01 1.67837381e-01
-8.48238692e-02 -7.21950412e-01 -5.89280605e-01 -4.25414443e-01
-2.01197386e-01 3.18208933e-01 4.13079083e-01 -3.08246128... | [8.77652359008789, -1.8855361938476562] |
5ffa8c44-1bfc-48e9-a50f-63e4a9fbcdcb | image-decomposition-using-a-robust-regression | 1609.03874 | null | http://arxiv.org/abs/1609.03874v2 | http://arxiv.org/pdf/1609.03874v2.pdf | Image Decomposition Using a Robust Regression Approach | This paper considers how to separate text and/or graphics from smooth
background in screen content and mixed content images and proposes an algorithm
to perform this segmentation task. The proposed methods make use of the fact
that the background in each block is usually smoothly varying and can be
modeled well by a li... | ['Yao Wang', 'Shervin Minaee'] | 2016-09-13 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 5.12794673e-01 -7.50216618e-02 1.26961410e-01 -3.16312701e-01
-5.05376220e-01 -3.75927210e-01 4.03131783e-01 -7.05453828e-02
-2.15213094e-02 5.95447183e-01 -3.18730712e-01 -2.07120582e-01
2.27754027e-01 -4.93768424e-01 -4.59766239e-01 -9.60194409e-01
3.36743206e-01 6.77034020e-01 9.25902784e-01 6.26791492... | [8.991046905517578, -0.856682538986206] |
7180709d-004b-4e6a-93c4-9a6d8afd1bef | multi-granularity-argument-mining-in-legal | 2210.09472 | null | https://arxiv.org/abs/2210.09472v2 | https://arxiv.org/pdf/2210.09472v2.pdf | Multi-granularity Argument Mining in Legal Texts | In this paper, we explore legal argument mining using multiple levels of granularity. Argument mining has usually been conceptualized as a sentence classification problem. In this work, we conceptualize argument mining as a token-level (i.e., word-level) classification problem. We use a Longformer model to classify the... | ['Kevin Ashley', 'Huihui Xu'] | 2022-10-17 | null | null | null | null | ['sentence-classification', 'argument-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.01108569e-01 4.39230800e-01 -1.06178057e+00 -4.32272315e-01
-9.76521611e-01 -5.13914883e-01 7.69226313e-01 1.17576122e+00
-3.67918670e-01 7.59991705e-01 7.21685708e-01 -1.24036849e+00
-2.17561051e-01 -1.14027774e+00 -1.48113370e-01 -6.68845847e-02
8.90290439e-02 2.51500070e-01 9.54785123e-02 -3.35374922... | [9.521957397460938, 9.590855598449707] |
5ac37bf0-fba6-4516-ab77-487d9ec774d1 | automerge-a-framework-for-map-assembling-and | 2207.06965 | null | https://arxiv.org/abs/2207.06965v4 | https://arxiv.org/pdf/2207.06965v4.pdf | AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments | We present AutoMerge, a LiDAR data processing framework for assembling a large number of map segments into a complete map. Traditional large-scale map merging methods are fragile to incorrect data associations, and are primarily limited to working only offline. AutoMerge utilizes multi-perspective fusion and adaptive l... | ['Sebastian Scherer', 'Howie Choset', 'Ji Zhang', 'Ruohai Ge', 'Shiqi Zhao', 'Haowen Lai', 'Peng Yin'] | 2022-07-14 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-3.13285947e-01 -2.06829086e-02 -1.34292468e-01 -3.93736690e-01
-1.47342205e+00 -9.44484234e-01 6.30681038e-01 7.92195857e-01
-3.57353657e-01 8.78431201e-01 -9.87216830e-02 -6.28747344e-01
-3.64275992e-01 -1.29177225e+00 -8.87476444e-01 1.42415032e-01
-4.26137686e-01 1.15951633e+00 9.84990001e-01 -3.35754991... | [7.805464744567871, -2.603060722351074] |
be5b3c62-9fe5-43a2-b230-8e3eefecf8ca | intelligent-systems-for-information-security | 1401.3592 | null | http://arxiv.org/abs/1401.3592v1 | http://arxiv.org/pdf/1401.3592v1.pdf | Intelligent Systems for Information Security | This thesis aims to use intelligent systems to extend and improve performance
and security of cryptographic techniques. Genetic algorithms framework for
cryptanalysis problem is addressed. A novel extension to the differential
cryptanalysis using genetic algorithm is proposed and a fitness measure based
on the differen... | ['Ayman M. Bahaa-Eldin'] | 2014-01-15 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 6.50106728e-01 1.37011945e-01 3.96300107e-01 -2.90184468e-01
2.44544640e-01 -8.10290456e-01 5.09064019e-01 3.97017956e-01
-6.53081834e-01 9.36043262e-01 -5.19756258e-01 -8.51403296e-01
-4.11777049e-01 -1.17408276e+00 -6.21748149e-01 -9.02662754e-01
-3.79195362e-01 4.04295325e-01 1.22082224e-02 -8.72861564... | [5.728446960449219, 4.673893928527832] |
1e00473d-a36e-4cf7-b291-e4a0612352e1 | deep-generative-views-to-mitigate-gender | 2208.08382 | null | https://arxiv.org/abs/2208.08382v1 | https://arxiv.org/pdf/2208.08382v1.pdf | Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups | Published studies have suggested the bias of automated face-based gender classification algorithms across gender-race groups. Specifically, unequal accuracy rates were obtained for women and dark-skinned people. To mitigate the bias of gender classifiers, the vision community has developed several strategies. However, ... | ['Ajita Rattani', 'Sreeraj Ramachandran'] | 2022-08-17 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 2.30206817e-01 2.19754279e-01 -3.79702449e-01 -7.36322284e-01
-4.18783545e-01 -4.31748420e-01 8.20625842e-01 -2.69585680e-02
-1.85451299e-01 6.64959669e-01 2.44660050e-01 -1.12154409e-01
2.06847176e-01 -7.72183359e-01 -1.22038431e-01 -5.63129187e-01
1.36300579e-01 8.77849981e-02 -4.57946241e-01 4.84458357... | [13.024470329284668, 1.2373592853546143] |
81865afe-17b6-42be-a521-afde4150d9da | koopman-type-inverse-operator-for-linear-non | 2305.04158 | null | https://arxiv.org/abs/2305.04158v1 | https://arxiv.org/pdf/2305.04158v1.pdf | Koopman-type inverse operator for linear non-minimum phase systems with disturbances | In this paper, a novel Koopman-type inverse operator for linear time-invariant non-minimum phase systems with stochastic disturbances is proposed. This operator employs functions of the desired output to directly calculate the input. Furthermore, it can be applied as a data-driven approach for systems with unknown para... | ['Xiaoqiang Ji', 'Yuhan Li'] | 2023-05-07 | null | null | null | null | ['type'] | ['speech'] | [ 2.45158955e-01 -1.35312006e-01 -2.02288702e-02 1.82301611e-01
-5.95527709e-01 -6.21928632e-01 4.86756653e-01 -1.00437589e-01
-1.99458510e-01 1.16042924e+00 -3.15237343e-01 -4.13368434e-01
-8.85392845e-01 -5.00763237e-01 -3.51815671e-01 -9.85795319e-01
2.80116480e-02 4.67427760e-01 1.89487651e-01 -4.14805919... | [5.418814182281494, 2.5676114559173584] |
c9045753-f3f1-4174-ad14-9b55816a3596 | a-greedy-approach-to-ell_0infty-based | 1812.10538 | null | http://arxiv.org/abs/1812.10538v1 | http://arxiv.org/pdf/1812.10538v1.pdf | A Greedy Approach to $\ell_{0,\infty}$ Based Convolutional Sparse Coding | Sparse coding techniques for image processing traditionally rely on a
processing of small overlapping patches separately followed by averaging. This
has the disadvantage that the reconstructed image no longer obeys the sparsity
prior used in the processing. For this purpose convolutional sparse coding has
been introduc... | ['Raja Giryes', 'Elad Plaut'] | 2018-12-26 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 3.72188449e-01 -1.24827467e-01 2.33072508e-02 -8.97139087e-02
-5.95797837e-01 -1.85934156e-01 4.46915068e-02 -3.85949835e-02
-3.66827250e-01 5.85997164e-01 6.70294091e-02 -2.83337720e-02
-1.85551584e-01 -7.23619401e-01 -5.33400118e-01 -1.04684854e+00
4.98334244e-02 -2.45024368e-01 -1.59643739e-01 -1.77260965... | [11.52099895477295, -2.2462193965911865] |
17fb9bd7-f458-49e2-a2dd-c474d87ad3cf | uncertainty-aware-camera-pose-estimation-from-1 | 2107.03890 | null | https://arxiv.org/abs/2107.03890v1 | https://arxiv.org/pdf/2107.03890v1.pdf | Uncertainty-Aware Camera Pose Estimation from Points and Lines | Perspective-n-Point-and-Line (P$n$PL) algorithms aim at fast, accurate, and robust camera localization with respect to a 3D model from 2D-3D feature correspondences, being a major part of modern robotic and AR/VR systems. Current point-based pose estimation methods use only 2D feature detection uncertainties, and the l... | ['Francesc Moreno-Noguer', 'Antonio Agudo', 'Luis Ferraz Colomina', 'Alexander Vakhitov'] | 2021-07-08 | uncertainty-aware-camera-pose-estimation-from | http://openaccess.thecvf.com//content/CVPR2021/html/Vakhitov_Uncertainty-Aware_Camera_Pose_Estimation_From_Points_and_Lines_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Vakhitov_Uncertainty-Aware_Camera_Pose_Estimation_From_Points_and_Lines_CVPR_2021_paper.pdf | cvpr-2021-1 | ['camera-localization'] | ['computer-vision'] | [-4.39179242e-01 -7.98207745e-02 2.51376741e-02 -2.21903324e-01
-7.70264447e-01 -8.20427537e-01 6.67099774e-01 -5.11699310e-03
-4.96290982e-01 6.89170361e-01 -2.36195326e-01 2.11060256e-01
-1.94664568e-01 -5.64848185e-01 -1.07212293e+00 -4.49931026e-01
5.49938828e-02 1.12554038e+00 3.50471735e-01 -2.95831442... | [7.3489580154418945, -2.161762237548828] |
a143122a-71f1-4423-8255-514a6169c34d | shct-a-successively-hierarchical-conditional | null | null | https://openreview.net/forum?id=ZCmUqcIjuGc | https://openreview.net/pdf?id=ZCmUqcIjuGc | SHCT: A Successively Hierarchical Conditional Transformer for Controllable Paraphrase Generation | Paraphrase generation has consistently been a challenging area in the field of NLP. Despite the considerable achievements made by previous work, existing methods lack a flexible way to include multiple controllable attributes to enhance the diversity of paraphrased sentences. To overcome this challenge, we propose a Su... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 2.64567174e-02 -1.26697332e-01 -3.28463241e-02 -3.57998163e-01
-8.11586082e-01 -3.73836011e-01 6.03887975e-01 -4.38073784e-01
-1.04874149e-01 9.05478418e-01 4.86551166e-01 -1.34876788e-01
5.91351911e-02 -7.86027908e-01 -8.81329954e-01 -7.47752488e-01
6.91296518e-01 3.89379084e-01 -8.59817676e-03 -2.51384854... | [11.743955612182617, 9.271878242492676] |
677c0560-13a3-4276-b263-dc352a969d89 | a-hybrid-approach-combining-statistical | null | null | https://aclanthology.org/W18-3728 | https://aclanthology.org/W18-3728.pdf | A Hybrid Approach Combining Statistical Knowledge with Conditional Random Fields for Chinese Grammatical Error Detection | This paper presents a method of combining Conditional Random Fields (CRFs) model with a post-processing layer using Google n-grams statistical information tailored to detect word selection and word order errors made by learners of Chinese as Foreign Language (CFL). We describe the architecture of the model and its perf... | ['Chilin Shih', 'Yiyi Wang'] | 2018-07-01 | null | null | null | ws-2018-7 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-1.06555469e-01 1.16723776e-01 7.78719559e-02 -6.32674694e-01
-8.95253718e-01 -4.87655908e-01 4.86228257e-01 7.94573963e-01
-1.03777373e+00 8.57614160e-01 2.57827342e-01 -8.04654419e-01
5.67175522e-02 -6.19761705e-01 -7.37678170e-01 2.90794726e-02
2.91958265e-02 2.57467687e-01 3.57278854e-01 -5.04959747... | [11.024953842163086, 10.728608131408691] |
6ffee3b1-9382-4dcb-80c4-fdd3552899ac | mpe4g-multimodal-pretrained-encoder-for-co | 2305.15740 | null | https://arxiv.org/abs/2305.15740v1 | https://arxiv.org/pdf/2305.15740v1.pdf | MPE4G: Multimodal Pretrained Encoder for Co-Speech Gesture Generation | When virtual agents interact with humans, gestures are crucial to delivering their intentions with speech. Previous multimodal co-speech gesture generation models required encoded features of all modalities to generate gestures. If some input modalities are removed or contain noise, the model may not generate the gestu... | ['Hanseok Ko', 'Insung Ham', 'Seonghyeok Noh', 'Gwantae Kim'] | 2023-05-25 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 4.60563570e-01 2.38617420e-01 -2.46480912e-01 -3.71087551e-01
-5.39491355e-01 -3.98316562e-01 1.02743328e+00 -8.46729279e-01
-2.61186212e-01 4.40814465e-01 8.05893123e-01 7.24698380e-02
3.97795945e-01 -4.85233754e-01 -6.53982043e-01 -6.75093830e-01
2.16708884e-01 3.68061215e-01 -1.18998982e-01 -2.73062676... | [5.6316728591918945, -0.12270453572273254] |
02b7d2f0-8468-40fd-8f8d-52b2e4c36d97 | scattering-transform-based-image-clustering | 2011.11586 | null | https://arxiv.org/abs/2011.11586v2 | https://arxiv.org/pdf/2011.11586v2.pdf | Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement | In the last few years, large improvements in image clustering have been driven by the recent advances in deep learning. However, due to the architectural complexity of deep neural networks, there is no mathematical theory that explains the success of deep clustering techniques. In this work we introduce Projected-Scatt... | ['Veniamin I. Morgenshtern', 'Angel Villar-Corrales'] | 2020-11-23 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-4.09548692e-02 -3.42418253e-01 3.06951553e-01 -2.14294776e-01
-5.17857552e-01 -5.90535820e-01 4.24913198e-01 -2.57060640e-02
-2.59844005e-01 1.46125872e-02 4.89500538e-02 -8.43003616e-02
-5.33597708e-01 -5.70707738e-01 -5.42468548e-01 -1.33993530e+00
-1.51215523e-01 5.40388525e-01 3.27865303e-01 4.17553857... | [8.81336498260498, 3.515444755554199] |
7f229dc4-e5ec-4e24-aa72-2a4b675e6e31 | investigating-post-pretraining-representation | 2109.12028 | null | https://arxiv.org/abs/2109.12028v1 | https://arxiv.org/pdf/2109.12028v1.pdf | Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering | Human knowledge is collectively encoded in the roughly 6500 languages spoken around the world, but it is not distributed equally across languages. Hence, for information-seeking question answering (QA) systems to adequately serve speakers of all languages, they need to operate cross-lingually. In this work we investiga... | ['Antonios Anastasopoulos', 'Fahim Faisal'] | 2021-09-24 | null | https://aclanthology.org/2021.mrqa-1.14 | https://aclanthology.org/2021.mrqa-1.14.pdf | emnlp-mrqa-2021-11 | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-5.43148339e-01 1.48461643e-03 -2.19804361e-01 -6.66265965e-01
-1.57824731e+00 -9.78334665e-01 6.88166440e-01 1.96515560e-01
-6.38217688e-01 6.19674981e-01 6.48780227e-01 -9.22372818e-01
1.05750591e-01 -5.76632142e-01 -6.58458710e-01 -1.11763198e-02
2.95458078e-01 8.25166881e-01 -5.69221424e-03 -6.06755257... | [11.129227638244629, 9.3582124710083] |
ac3102c6-7083-41a3-968b-2a52df1eb955 | do-neural-topic-models-really-need-dropout | 2303.15973 | null | https://arxiv.org/abs/2303.15973v1 | https://arxiv.org/pdf/2303.15973v1.pdf | Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling | Dropout is a widely used regularization trick to resolve the overfitting issue in large feedforward neural networks trained on a small dataset, which performs poorly on the held-out test subset. Although the effectiveness of this regularization trick has been extensively studied for convolutional neural networks, there... | ['Debarshi Kumar Sanyal', 'Avishek Lahiri', 'Suman Adhya'] | 2023-03-28 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-5.06040715e-02 4.51736599e-01 -1.10973090e-01 -5.86845219e-01
-6.87265575e-01 -3.26704144e-01 5.25696218e-01 -8.98306258e-03
-3.23150456e-01 6.86736763e-01 2.78716713e-01 -2.71935761e-01
-1.28647640e-01 -6.97582006e-01 -1.08963907e+00 -7.61059821e-01
2.89614499e-01 5.48485219e-01 1.58299297e-01 4.13984895... | [10.449676513671875, 6.956477165222168] |
9c5bb0de-8d91-4cd5-8bf1-6f3a69332f91 | mist-towards-improved-adversarial-examples | 2305.12683 | null | https://arxiv.org/abs/2305.12683v1 | https://arxiv.org/pdf/2305.12683v1.pdf | Mist: Towards Improved Adversarial Examples for Diffusion Models | Diffusion Models (DMs) have empowered great success in artificial-intelligence-generated content, especially in artwork creation, yet raising new concerns in intellectual properties and copyright. For example, infringers can make profits by imitating non-authorized human-created paintings with DMs. Recent researches su... | ['Xiaoyu Wu', 'Chumeng Liang'] | 2023-05-22 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 5.27519226e-01 3.33783507e-01 5.78527004e-02 4.16622870e-02
-6.32227421e-01 -1.31892085e+00 9.33932304e-01 -6.39828384e-01
-1.44397676e-01 8.59007895e-01 -2.78721172e-02 -2.31672302e-01
-3.46311539e-01 -9.99697626e-01 -7.08926141e-01 -6.32248223e-01
1.65676430e-01 2.97751218e-01 -1.48307800e-01 -2.79505581... | [5.63887882232666, 7.9793195724487305] |
7d11843f-6b13-4eaa-b7a0-dada8eedec97 | practical-and-configurable-network-traffic | 2107.06080 | null | https://arxiv.org/abs/2107.06080v1 | https://arxiv.org/pdf/2107.06080v1.pdf | Practical and Configurable Network Traffic Classification Using Probabilistic Machine Learning | Network traffic classification that is widely applicable and highly accurate is valuable for many network security and management tasks. A flexible and easily configurable classification framework is ideal, as it can be customized for use in a wide variety of networks. In this paper, we propose a highly configurable an... | ['Jacobus Van der Merwe', 'Jeff M. Phillips', 'Joe Breen', 'Jiahui Chen'] | 2021-07-10 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.43189520e-01 -7.85798371e-01 -6.49851382e-01 -6.13977313e-01
-2.55353391e-01 -6.89144671e-01 2.33960465e-01 8.74197334e-02
-1.79106817e-01 1.02696860e+00 -9.08828259e-01 -1.09298730e+00
-4.38638628e-01 -1.06424320e+00 1.44871876e-01 -5.38080752e-01
-3.01220804e-01 8.74903381e-01 8.03699911e-01 -4.77672741... | [5.070288181304932, 7.217759609222412] |
6b6c6fe6-4ddb-40d5-891b-3e803e59d439 | ice-inter-instance-contrastive-encoding-for | 2103.16364 | null | https://arxiv.org/abs/2103.16364v2 | https://arxiv.org/pdf/2103.16364v2.pdf | ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification | Unsupervised person re-identification (ReID) aims at learning discriminative identity features without annotations. Recently, self-supervised contrastive learning has gained increasing attention for its effectiveness in unsupervised representation learning. The main idea of instance contrastive learning is to match a s... | ['Francois Bremond', 'Benoit Lagadec', 'Hao Chen'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_ICE_Inter-Instance_Contrastive_Encoding_for_Unsupervised_Person_Re-Identification_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_ICE_Inter-Instance_Contrastive_Encoding_for_Unsupervised_Person_Re-Identification_ICCV_2021_paper.pdf | iccv-2021-1 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 2.33228743e-01 -1.80646572e-02 -2.40643710e-01 -6.92646444e-01
-6.34076297e-01 -5.05982459e-01 7.50341833e-01 2.21318692e-01
-4.15859818e-01 6.18155837e-01 4.84276623e-01 6.06123090e-01
-4.81994636e-02 -4.58397090e-01 -6.45712495e-01 -6.19534373e-01
1.42181292e-01 2.82010496e-01 -2.01974466e-01 4.12533395... | [14.708587646484375, 1.0096876621246338] |
e8f7c3d1-3991-499f-ab09-10121ee77034 | fadman-federated-anomaly-detection-across | 2205.14196 | null | https://arxiv.org/abs/2205.14196v1 | https://arxiv.org/pdf/2205.14196v1.pdf | FadMan: Federated Anomaly Detection across Multiple Attributed Networks | Anomaly subgraph detection has been widely used in various applications, ranging from cyber attack in computer networks to malicious activities in social networks. Despite an increasing need for federated anomaly detection across multiple attributed networks, only a limited number of approaches are available for this p... | ['Qiang Yang', 'Lixin Fan', 'Wenjun Wang', 'Ning Zhang', 'Nannan Wu'] | 2022-05-27 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 6.65812287e-03 2.36210540e-01 -1.78804010e-01 -2.35245511e-01
-2.70951629e-01 -5.16513228e-01 3.52332383e-01 5.97415090e-01
-8.53983685e-02 4.84629422e-01 -1.09514810e-01 -4.06722307e-01
-5.33295989e-01 -9.30557907e-01 -4.28759784e-01 -7.90096939e-01
-3.45294207e-01 4.61101025e-01 2.25263268e-01 -1.86142758... | [6.627865791320801, 5.75784158706665] |
28d83e22-2549-4969-9b58-a402022e7c9f | the-role-of-visual-saliency-in-the-automation | 1812.11960 | null | http://arxiv.org/abs/1812.11960v1 | http://arxiv.org/pdf/1812.11960v1.pdf | The role of visual saliency in the automation of seismic interpretation | In this paper, we propose a workflow based on SalSi for the detection and
delineation of geological structures such as salt domes. SalSi is a seismic
attribute designed based on the modeling of human visual system that detects
the salient features and captures the spatial correlation within seismic
volumes for delineat... | ['Tariq Alshawi', 'Ghassan AlRegib', 'Zhiling Long', 'Muhammad Amir Shafiq'] | 2018-12-31 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [-1.37259126e-01 -2.37167608e-02 1.01749933e+00 -1.52559966e-01
-5.25522172e-01 -7.18570709e-01 6.91888213e-01 6.06876791e-01
-5.20193160e-01 1.70865685e-01 3.87293398e-01 -3.00955415e-01
-3.14299613e-01 -7.82136202e-01 -1.63173661e-01 -8.10297608e-01
-5.46294034e-01 4.98640507e-01 8.83317709e-01 -3.79590780... | [9.418212890625, -0.7180619239807129] |
6d45da0a-4314-4f8a-aea4-b35820c14ca2 | open-world-compositional-zero-shot-learning | 2101.12609 | null | https://arxiv.org/abs/2101.12609v3 | https://arxiv.org/pdf/2101.12609v3.pdf | Open World Compositional Zero-Shot Learning | Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen compositions, we operate in the open world setting, where the search space includes a large number of unseen compositions some of which might... | ['Zeynep Akata', 'Yongqin Xian', 'Muhammad Ferjad Naeem', 'Massimiliano Mancini'] | 2021-01-29 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Mancini_Open_World_Compositional_Zero-Shot_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Mancini_Open_World_Compositional_Zero-Shot_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 2.77937561e-01 -3.29977348e-02 -2.05402330e-01 2.69226998e-01
-5.83182156e-01 -8.34169209e-01 8.70234013e-01 -5.03914654e-02
-3.83625895e-01 3.69437069e-01 2.38656759e-01 -1.36546567e-01
1.68677211e-01 -6.76318824e-01 -8.36705267e-01 -8.93436134e-01
7.72494897e-02 5.99778712e-01 5.12690127e-01 -1.36573002... | [10.282177925109863, 2.2178797721862793] |
74cfaf50-4d19-449f-bc6d-7ffab3edbe41 | variational-sequential-optimal-experimental | 2306.10430 | null | https://arxiv.org/abs/2306.10430v1 | https://arxiv.org/pdf/2306.10430v1.pdf | Variational Sequential Optimal Experimental Design using Reinforcement Learning | We introduce variational sequential Optimal Experimental Design (vsOED), a new method for optimally designing a finite sequence of experiments under a Bayesian framework and with information-gain utilities. Specifically, we adopt a lower bound estimator for the expected utility through variational approximation to the ... | ['Xun Huan', 'Jiayuan Dong', 'Wanggang Shen'] | 2023-06-17 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-2.95208339e-02 7.70239830e-02 -4.72733349e-01 -2.01236531e-01
-8.33026767e-01 -4.33494627e-01 3.08637321e-01 -3.23023498e-01
-6.36183977e-01 1.19001269e+00 1.03470773e-01 -7.47510791e-01
-6.92996502e-01 -2.14543656e-01 -6.33340657e-01 -6.18820488e-01
-2.69799978e-01 2.67990530e-01 -2.22190559e-01 3.38174939... | [6.480053901672363, 3.9418070316314697] |
848bffdd-f320-486a-96b1-b01eb6480047 | perceptual-image-enhancement-for-smartphone | 2210.13552 | null | https://arxiv.org/abs/2210.13552v1 | https://arxiv.org/pdf/2210.13552v1.pdf | Perceptual Image Enhancement for Smartphone Real-Time Applications | Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, dif... | ['Radu Timofte', 'Javier Vazquez-Corral', 'Florin Vasluianu', 'Marcos V. Conde'] | 2022-10-24 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 2.85917580e-01 -5.92524290e-01 2.79771924e-01 9.93952975e-02
-4.73788053e-01 -1.86540455e-01 1.05514325e-01 -2.17219457e-01
-4.22547370e-01 5.03030598e-01 -8.56836811e-02 -3.33359182e-01
1.35883927e-01 -5.06849289e-01 -8.02157760e-01 -5.22620261e-01
9.09230337e-02 -5.20357311e-01 3.18711609e-01 1.85695030... | [10.933300971984863, -2.168522596359253] |
13f2bb10-1ec7-40c7-84a0-86e75922a907 | don-t-stop-pretraining-adapt-language-models | 2004.10964 | null | https://arxiv.org/abs/2004.10964v3 | https://arxiv.org/pdf/2004.10964v3.pdf | Don't Stop Pretraining: Adapt Language Models to Domains and Tasks | Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and com... | ['Doug Downey', 'Ana Marasović', 'Kyle Lo', 'Noah A. Smith', 'Iz Beltagy', 'Swabha Swayamdipta', 'Suchin Gururangan'] | 2020-04-23 | don-t-stop-pretraining-adapt-language-models-1 | https://aclanthology.org/2020.acl-main.740 | https://aclanthology.org/2020.acl-main.740.pdf | acl-2020-6 | ['citation-intent-classification'] | ['natural-language-processing'] | [ 5.03371477e-01 5.69588393e-02 -5.27274132e-01 -5.62149227e-01
-1.09651625e+00 -7.93576658e-01 7.28962898e-01 2.79260099e-01
-1.02674091e+00 9.19987440e-01 4.33896035e-01 -5.77191770e-01
-9.28600281e-02 -2.97290355e-01 -4.61638898e-01 -1.52065337e-01
2.83051789e-01 9.91323471e-01 2.32105747e-01 -1.86265811... | [10.60505485534668, 8.158111572265625] |
e10560d2-87bc-47a0-83df-f5747f5bef93 | cov3d-detection-of-the-presence-and-severity | 2207.12218 | null | https://arxiv.org/abs/2207.12218v1 | https://arxiv.org/pdf/2207.12218v1.pdf | Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets | Deep learning has been used to assist in the analysis of medical imaging. One such use is the classification of Computed Tomography (CT) scans when detecting for COVID-19 in subjects. This paper presents Cov3d, a three dimensional convolutional neural network for detecting the presence and severity of COVID19 from ches... | ['Robert Turnbull'] | 2022-07-05 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 4.07091193e-02 1.32440820e-01 -1.54511541e-01 -3.07840556e-01
-1.18275785e+00 -3.39079797e-01 1.42084450e-01 3.29011619e-01
-4.44671273e-01 2.70735860e-01 2.98122495e-01 -5.36121964e-01
-2.21425325e-01 -3.61061215e-01 -4.78178591e-01 -5.73520839e-01
-4.46089447e-01 9.55314100e-01 1.91258237e-01 3.85730118... | [15.329541206359863, -1.8997148275375366] |
98e5117c-066f-4023-98d0-34d2fd4d2583 | evaluating-the-capability-of-large-scale | 2307.03972 | null | https://arxiv.org/abs/2307.03972v1 | https://arxiv.org/pdf/2307.03972v1.pdf | Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task | Large-scale language models (LLMs) has shown remarkable capability in various of Natural Language Processing (NLP) tasks and attracted lots of attention recently. However, some studies indicated that large language models fail to achieve promising result beyond the state-of-the-art models in English grammatical error c... | ['Yunfang Wu', 'Fanyi Qu'] | 2023-07-08 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [-2.86535740e-01 -1.38440698e-01 2.51422644e-01 -6.12406194e-01
-1.06195295e+00 -1.02103837e-01 3.03447753e-01 4.86375272e-01
-8.00036728e-01 8.22032213e-01 1.25187278e-01 -4.15300041e-01
1.45730615e-01 -4.53678429e-01 -6.90810740e-01 -1.72506586e-01
-4.08033021e-02 4.32598203e-01 3.73831302e-01 -2.54693389... | [11.059443473815918, 10.738810539245605] |
6fd58f30-f916-482a-af0f-466407bfeff6 | gated-ensemble-of-spatio-temporal-mixture-of | 2012.15408 | null | https://arxiv.org/abs/2012.15408v2 | https://arxiv.org/pdf/2012.15408v2.pdf | Gated Ensemble of Spatio-temporal Mixture of Experts for Multi-task Learning in Ride-hailing System | Designing spatio-temporal forecasting models separately in a task-wise and city-wise manner pose a burden for the expanding transportation network companies. Therefore, a multi-task learning architecture is proposed in this study by developing gated ensemble of spatio-temporal mixture of experts network (GESME-Net) wit... | ['D. Wang', 'M. Abrar', 'S. N. Sadeek', 'S. M. Rifaat', 'M. H. Rahman'] | 2020-12-31 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-2.94408113e-01 -5.39399385e-01 -1.05673611e-01 -5.04693687e-01
-9.32214618e-01 -3.08461726e-01 6.90119147e-01 -5.61524034e-01
-4.63993996e-02 6.46959722e-01 4.97283757e-01 -6.73174918e-01
-3.40100050e-01 -7.86506712e-01 -4.64615881e-01 -8.71419907e-01
2.80059408e-02 4.54170585e-01 -9.49851647e-02 -2.43001878... | [6.4812235832214355, 2.1677863597869873] |
5a361efe-331d-4641-9a4d-c2a72ec8d4a9 | solving-the-rubiks-cube-without-human | 1805.07470 | null | http://arxiv.org/abs/1805.07470v1 | http://arxiv.org/pdf/1805.07470v1.pdf | Solving the Rubik's Cube Without Human Knowledge | A generally intelligent agent must be able to teach itself how to solve
problems in complex domains with minimal human supervision. Recently, deep
reinforcement learning algorithms combined with self-play have achieved
superhuman proficiency in Go, Chess, and Shogi without human data or domain
knowledge. In these envir... | ['Alexander Shmakov', 'Forest Agostinelli', 'Pierre Baldi', 'Stephen McAleer'] | 2018-05-18 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [-7.63875842e-02 4.26750273e-01 1.38312951e-01 -4.29585315e-02
-3.83013308e-01 -9.24750626e-01 2.17848793e-01 4.47363630e-02
-6.19204581e-01 1.42673647e+00 -5.73759854e-01 -4.84147817e-01
-2.80157238e-01 -9.94966209e-01 -7.06847668e-01 -6.17209911e-01
-1.90757468e-01 1.03784287e+00 2.09993318e-01 -6.26194239... | [3.7382218837738037, 1.5492373704910278] |
bcc8fb90-7845-48c2-b96a-c971778b96ea | long-range-graph-benchmark | 2206.08164 | null | https://arxiv.org/abs/2206.08164v3 | https://arxiv.org/pdf/2206.08164v3.pdf | Long Range Graph Benchmark | Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer. In principle, such networks are not able to capture long-range interactions (LRI) that may be desired or necessary for learning a given tas... | ['Dominique Beaini', 'Anh Tuan Luu', 'Guy Wolf', 'Ali Parviz', 'Mikhail Galkin', 'Ladislav Rampášek', 'Vijay Prakash Dwivedi'] | 2022-06-16 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 2.92504340e-01 5.01362979e-01 -2.09941670e-01 -3.65293115e-01
-7.56931454e-02 -5.73356926e-01 9.90597129e-01 7.01650202e-01
-1.46330073e-01 7.69556582e-01 -1.46502657e-02 -7.28795946e-01
-5.28914869e-01 -1.27493656e+00 -1.25260222e+00 -5.29389679e-01
-8.18182707e-01 9.10694182e-01 4.95528251e-01 -4.87009138... | [6.888023853302002, 6.205175399780273] |
b9428949-09cf-4a78-9071-afa30e395baa | a-novel-1d-state-space-for-efficient-music | 2111.00704 | null | https://arxiv.org/abs/2111.00704v2 | https://arxiv.org/pdf/2111.00704v2.pdf | A Novel 1D State Space for Efficient Music Rhythmic Analysis | Inferring music time structures has a broad range of applications in music production, processing and analysis. Scholars have proposed various methods to analyze different aspects of time structures, such as beat, downbeat, tempo and meter. Many state-of-the-art (SOFA) methods, however, are computationally expensive. T... | ['Zhiyao Duan', 'Andreas Ehmann', 'Matthew McCallum', 'Mojtaba Heydari'] | 2021-11-01 | null | null | null | null | ['inference-optimization'] | ['audio'] | [-5.10459905e-03 -5.64480484e-01 -3.01738113e-01 2.06716999e-01
-5.88089943e-01 -8.86250198e-01 4.33200389e-01 -3.62048894e-02
8.52780975e-03 7.07764626e-01 1.39004067e-01 -2.85279602e-01
-5.42065442e-01 -5.17543554e-01 -1.84257075e-01 -6.18729353e-01
-8.13156590e-02 4.78267968e-01 3.89977127e-01 -1.82352021... | [15.900257110595703, 5.448034286499023] |
1c78f99c-f548-44ff-802d-8f36cc58cac2 | ecnu-at-semeval-2017-task-8-rumour-evaluation | null | null | https://aclanthology.org/S17-2086 | https://aclanthology.org/S17-2086.pdf | ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models | This paper describes our submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours. Given a rumoured tweet and a lot of reply tweets, the subtask A is to label whether these tweets are support, deny, query or comment, and the subtask B aims to predict the veracity (i.e., true, fal... | ['Man Lan', 'Yuanbin Wu', 'Feixiang Wang'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['rumour-detection'] | ['natural-language-processing'] | [-3.06914628e-01 3.65542322e-01 -5.17241061e-01 -4.28259730e-01
-3.55919957e-01 -3.17931086e-01 7.53429532e-01 5.68615735e-01
-9.68116298e-02 1.20827353e+00 4.54091311e-01 -4.68265951e-01
3.03529918e-01 -7.14536190e-01 -3.63361210e-01 -1.44991487e-01
1.84418503e-02 5.57190239e-01 6.87105134e-02 -2.92519033... | [8.20391845703125, 10.1229248046875] |
7a2b66b5-a8a5-4d3f-912a-e4ae636c5858 | incorporating-coincidental-water-data-into | 2101.07190 | null | https://arxiv.org/abs/2101.07190v1 | https://arxiv.org/pdf/2101.07190v1.pdf | Incorporating Coincidental Water Data into Non-intrusive Load Monitoring | Non-intrusive load monitoring (NILM) as the process of extracting the usage pattern of appliances from the aggregated power signal is among successful approaches aiding residential energy management. In recent years, high volume datasets on power profiles have become available, which has helped make classification meth... | ['Sadegh Bolouki', 'Hamidreza Momeni', 'Elnaz Azizi', 'Mohammad-Mehdi Keramati'] | 2021-01-18 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 5.40685430e-02 -3.44805300e-01 -3.84100229e-02 -2.89823472e-01
-3.40334147e-01 -3.68884891e-01 4.71929818e-01 1.65349230e-01
-9.97362584e-02 7.51131535e-01 1.71439439e-01 2.09124032e-02
-4.88910407e-01 -1.09952867e+00 -1.31323963e-01 -1.27119160e+00
-3.39940846e-01 1.30822614e-01 -3.87529343e-01 -1.67288147... | [6.028477191925049, 2.603626012802124] |
2d46ff2e-0d57-4605-849c-50ce05531bcd | bayesian-reparameterization-of-reward | 2305.11340 | null | https://arxiv.org/abs/2305.11340v1 | https://arxiv.org/pdf/2305.11340v1.pdf | Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models | Recently, reward-conditioned reinforcement learning (RCRL) has gained popularity due to its simplicity, flexibility, and off-policy nature. However, we will show that current RCRL approaches are fundamentally limited and fail to address two critical challenges of RCRL -- improving generalization on high reward-to-go (R... | ['Marco Pavone', 'Ding Zhao', 'Tong Che', 'Wenhao Ding'] | 2023-05-18 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-1.62970290e-01 -8.82731080e-02 -7.38163471e-01 -3.68657559e-01
-1.06204879e+00 -5.97286105e-01 4.34315205e-01 -1.27456859e-01
-8.89774919e-01 1.04352844e+00 5.35899475e-02 -6.90444052e-01
-3.51801068e-01 -8.26023161e-01 -9.30275142e-01 -7.78639376e-01
-3.89286846e-01 4.71310347e-01 1.78778052e-01 -4.72676784... | [4.091960906982422, 2.0774753093719482] |
835199ca-ef1f-4f9d-bc0f-45faea67b43e | exploiting-structure-for-fast-kernel-learning | 1808.03351 | null | http://arxiv.org/abs/1808.03351v1 | http://arxiv.org/pdf/1808.03351v1.pdf | Exploiting Structure for Fast Kernel Learning | We propose two methods for exact Gaussian process (GP) inference and learning
on massive image, video, spatial-temporal, or multi-output datasets with
missing values (or "gaps") in the observed responses. The first method ignores
the gaps using sparse selection matrices and a highly effective low-rank
preconditioner is... | ['Trefor W. Evans', 'Prasanth B. Nair'] | 2018-08-09 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 2.85911888e-01 -1.80959493e-01 3.14642489e-01 -1.33102283e-01
-1.06313491e+00 -5.52528858e-01 7.44271815e-01 1.23444004e-02
-5.16885996e-01 8.96099925e-01 5.78823611e-02 -6.20932460e-01
-2.54844099e-01 -8.76161695e-01 -1.11720335e+00 -9.88972068e-01
-3.04159492e-01 6.97995603e-01 5.40559413e-03 3.19944888... | [6.9478440284729, 3.8501272201538086] |
b49da377-eb91-4c3b-9df2-9e894e5091e9 | laeo-net-revisiting-people-looking-at-each | 2101.02136 | null | https://arxiv.org/abs/2101.02136v1 | https://arxiv.org/pdf/2101.02136v1.pdf | LAEO-Net++: revisiting people Looking At Each Other in videos | Capturing the 'mutual gaze' of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net++, a new deep CNN for determining LAEO in v... | ['Andrew Zisserman', 'Pablo Medina-Suarez', 'Vicky Kalogeiton', 'Manuel J. Marin-Jimenez'] | 2021-01-06 | null | null | null | null | ['mutual-gaze'] | ['computer-vision'] | [-3.65421832e-01 -3.17288011e-01 -1.02138273e-01 -3.62370998e-01
-1.30246812e-02 -5.75804174e-01 5.45443475e-01 -3.31037194e-02
-3.91183048e-01 3.02162111e-01 3.75912398e-01 2.43439861e-02
1.46791600e-02 -6.10048234e-01 -7.35525489e-01 -4.78509992e-01
-5.27615786e-01 5.24807751e-01 2.84285188e-01 -2.32505620... | [8.222574234008789, 0.5661910176277161] |
ea9c982a-6edd-4ee6-bb27-0735c2327754 | perceptual-attacks-of-no-reference-image | 2210.00933 | null | https://arxiv.org/abs/2210.00933v1 | https://arxiv.org/pdf/2210.00933v1.pdf | Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop | No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references. NR-IQA models are extensively studied in computational vision, and are widely used for performance evaluation and perceptual optimization of man-made v... | ['Kede Ma', 'Xiaokang Yang', 'Guodong Guo', 'Guangtao Zhai', 'Xiongkuo Min', 'Dingquan Li', 'Weixia Zhang'] | 2022-10-03 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 4.47570711e-01 1.76158436e-02 -2.52187485e-03 1.24272518e-01
-5.59498608e-01 -7.16306567e-01 8.43723059e-01 1.32616246e-02
-3.24172109e-01 4.25604343e-01 -5.32150120e-02 -5.02903104e-01
-4.07451779e-01 -3.85834634e-01 -7.18262196e-01 -6.97299600e-01
-3.38451833e-01 -2.41141364e-01 9.18110386e-02 -2.26776645... | [9.943950653076172, 2.0022573471069336] |
0c24dbd8-e103-456a-971d-dea51192eb48 | making-binary-classification-from-multiple | 2306.07036 | null | https://arxiv.org/abs/2306.07036v1 | https://arxiv.org/pdf/2306.07036v1.pdf | Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision | Training a classifier exploiting a huge amount of supervised data is expensive or even prohibited in a situation, where the labeling cost is high. The remarkable progress in working with weaker forms of supervision is binary classification from multiple unlabeled datasets which requires the knowledge of exact class pri... | ['Tongliang Liu', 'Masashi Sugiyama', 'Gang Niu', 'Bo Han', 'Jun Yu', 'Xiaobo Xia', 'Yuhao Wu'] | 2023-06-12 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 4.49681878e-01 2.51231521e-01 -7.24152267e-01 -7.13811815e-01
-8.15959215e-01 -5.83392143e-01 3.28472346e-01 2.07933977e-01
-3.69919956e-01 1.06125689e+00 -4.32973057e-01 -4.16926950e-01
-1.98783100e-01 -8.09610665e-01 -7.19439447e-01 -9.77971017e-01
3.51407915e-01 7.26372302e-01 1.96580097e-01 1.80760473... | [9.31001091003418, 3.98052978515625] |
5a3ef850-3396-45d0-bc03-787463075320 | openapepose-a-database-of-annotated-ape | 2212.00741 | null | https://arxiv.org/abs/2212.00741v1 | https://arxiv.org/pdf/2212.00741v1.pdf | OpenApePose: a database of annotated ape photographs for pose estimation | Because of their close relationship with humans, non-human apes (chimpanzees, bonobos, gorillas, orangutans, and gibbons, including siamangs) are of great scientific interest. The goal of understanding their complex behavior would be greatly advanced by the ability to perform video-based pose tracking. Tracking, howeve... | ['Benjamin Hayden', 'Jan Zimmermann', 'Jessica Raper', 'Rebecca Richardson', 'Praneet Bala', 'Nisarg Desai'] | 2022-11-30 | null | null | null | null | ['pose-tracking'] | ['computer-vision'] | [-3.72303903e-01 -4.32348810e-02 -1.47329971e-01 -1.45301580e-01
-3.99533749e-01 -6.19347394e-01 4.49185878e-01 -3.15272778e-01
-1.33371782e+00 8.66630018e-01 -4.22078110e-02 3.67085040e-01
1.82163641e-01 -5.35457134e-01 -8.25000584e-01 -1.65301576e-01
-9.47772145e-01 8.62603962e-01 6.39091730e-01 -1.83669433... | [7.6716156005859375, -0.8754842877388] |
bc9b3687-a944-428f-9c94-b72dcd53ced2 | localize-group-and-select-boosting-text-vqa | 2108.08965 | null | https://arxiv.org/abs/2108.08965v1 | https://arxiv.org/pdf/2108.08965v1.pdf | Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling | As an important task in multimodal context understanding, Text-VQA (Visual Question Answering) aims at question answering through reading text information in images. It differentiates from the original VQA task as Text-VQA requires large amounts of scene-text relationship understanding, in addition to the cross-modal g... | ['Carolyn P. Rose', 'Jean Oh', 'Yansen Wang', 'Zhen Fan', 'Xiaopeng Lu'] | 2021-08-20 | null | null | null | null | ['data-ablation', 'text-clustering'] | ['computer-vision', 'natural-language-processing'] | [ 5.14964223e-01 -1.68072075e-01 -5.98943383e-02 -4.88845050e-01
-1.29642582e+00 -9.44952846e-01 8.15352440e-01 4.91139174e-01
-2.78965890e-01 2.14490429e-01 4.43626165e-01 -4.96423095e-01
5.02346829e-02 -3.16833705e-01 -8.01880360e-01 -3.23981971e-01
5.46516597e-01 6.86646581e-01 4.52704102e-01 -2.98036456... | [11.122151374816895, 1.8400673866271973] |
e9c1e4d3-ae5b-444d-85fd-f0ba3bd39fd8 | respiratory-rate-estimation-from-face-videos | 1909.03503 | null | https://arxiv.org/abs/1909.03503v1 | https://arxiv.org/pdf/1909.03503v1.pdf | Respiratory Rate Estimation from Face Videos | Vital signs, such as heart rate (HR), heart rate variability (HRV), respiratory rate (RR), are important indicators for a person's health. Vital signs are traditionally measured with contact sensors, and may be inconvenient and cause discomfort during continuous monitoring. Commercial cameras are promising contact-free... | ['Min Wu', 'Mingliang Chen', 'Qiang Zhu', 'Harrison Zhang', 'Quanzeng Wang'] | 2019-09-08 | null | null | null | null | ['heart-rate-variability', 'respiratory-rate-estimation'] | ['medical', 'medical'] | [ 3.73100102e-01 -3.69585931e-01 -1.42150298e-01 -2.28698194e-01
-1.14153763e-02 -1.62354112e-01 -6.95504919e-02 -7.53313482e-01
-1.02368303e-01 7.63358831e-01 2.41829455e-01 3.72594476e-01
1.62623405e-01 -4.29794312e-01 2.94268668e-01 -8.60674798e-01
8.51710215e-02 -7.14006960e-01 -3.50521117e-01 4.06157598... | [13.898090362548828, 2.8249173164367676] |
75944290-705e-4316-acd5-31ef064b4ffb | attentionhtr-handwritten-text-recognition | 2201.09390 | null | https://arxiv.org/abs/2201.09390v3 | https://arxiv.org/pdf/2201.09390v3.pdf | AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks | This work proposes an attention-based sequence-to-sequence model for handwritten word recognition and explores transfer learning for data-efficient training of HTR systems. To overcome training data scarcity, this work leverages models pre-trained on scene text images as a starting point towards tailoring the handwriti... | ['Ekta Vats', 'Dmitrijs Kass'] | 2022-01-23 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 5.04595935e-01 -2.64193505e-01 -2.83309788e-01 -5.29465199e-01
-7.12787569e-01 -4.26507890e-01 8.33256841e-01 -5.01507461e-01
-5.35064638e-01 4.52933699e-01 4.10877496e-01 -5.34333348e-01
2.22254142e-01 -3.61343086e-01 -6.76766872e-01 -5.87793827e-01
3.89143884e-01 4.56182033e-01 -8.93461630e-02 -1.75551802... | [11.892358779907227, 2.3911352157592773] |
233ac4fb-ca27-47fd-a08d-5adaace75299 | serf-interpretable-sleep-staging-using | 2209.11174 | null | https://arxiv.org/abs/2209.11174v2 | https://arxiv.org/pdf/2209.11174v2.pdf | SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features | The accuracy of recent deep learning based clinical decision support systems is promising. However, lack of model interpretability remains an obstacle to widespread adoption of artificial intelligence in healthcare. Using sleep as a case study, we propose a generalizable method to combine clinical interpretability with... | ['Cassie S. Mitchell', 'Irfan Al-Hussaini'] | 2022-09-21 | null | null | null | null | ['sleep-quality-prediction', 'sleep-staging'] | ['medical', 'medical'] | [ 1.91452801e-02 4.75429803e-01 -3.72006625e-01 -8.35432172e-01
-6.18621171e-01 -2.27858365e-01 -1.78371504e-01 4.43606734e-01
-5.37179291e-01 8.06238592e-01 4.84021842e-01 -7.16111839e-01
-3.49452466e-01 -2.34268129e-01 1.16459532e-02 -4.42779660e-01
1.09401673e-01 6.66213810e-01 -2.74178684e-01 5.55116311... | [13.504387855529785, 3.532036542892456] |
fc99c478-b4be-4c74-92c2-b0508b6f5012 | stateless-and-rule-based-verification-for | 2204.07430 | null | https://arxiv.org/abs/2204.07430v2 | https://arxiv.org/pdf/2204.07430v2.pdf | Stateless and Rule-Based Verification For Compliance Checking Applications | Underlying computational model has an important role in any computation. The state and transition (such as in automata) and rule and value (such as in Lisp and logic programming) are two comparable and counterpart computational models. Both of deductive and model checking verification techniques are relying on a notion... | ['Ehsaneddin Asgari', 'Mohammad Izadi', 'Mohammad Reza Besharati'] | 2022-04-14 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 7.90047422e-02 2.75385708e-01 -4.59628403e-01 -4.17232811e-01
-1.54258147e-01 -4.66032624e-01 9.75181818e-01 3.23870838e-01
7.86520392e-02 5.19525051e-01 -6.69426993e-02 -9.71476555e-01
-5.43573558e-01 -1.25290704e+00 -3.31774563e-01 3.48936729e-02
-7.78181255e-02 6.60200000e-01 6.09544218e-01 -6.01894438... | [8.65793514251709, 6.772846221923828] |
363c1099-5280-4ae8-8a51-8579ba28add5 | locally-interpretable-model-agnostic | 2108.06907 | null | https://arxiv.org/abs/2108.06907v2 | https://arxiv.org/pdf/2108.06907v2.pdf | Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability | Albeit the tremendous performance improvements in designing complex artificial intelligence (AI) systems in data-intensive domains, the black-box nature of these systems leads to the lack of trustworthiness. Post-hoc interpretability methods explain the prediction of a black-box ML model for a single instance, and such... | ['Ranjitha Prasad', 'Aditya Saini'] | 2021-08-16 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 1.63270757e-01 8.94948065e-01 -3.35257828e-01 -6.16262615e-01
-1.21680415e+00 -4.01648730e-01 8.98397744e-01 1.86823174e-01
-1.22262128e-01 8.67369831e-01 1.22429296e-01 -2.11938933e-01
-6.58230901e-01 -3.87997836e-01 -1.23423898e+00 -8.58881593e-01
-7.30181038e-02 1.07577145e+00 -2.08996639e-01 3.13446581... | [8.71823787689209, 5.411235332489014] |
7da661ef-d679-4ef5-a15b-68a7abdd8a5f | scaling-native-language-identification-with | 2211.10117 | null | https://arxiv.org/abs/2211.10117v1 | https://arxiv.org/pdf/2211.10117v1.pdf | Scaling Native Language Identification with Transformer Adapters | Native language identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language. It is useful for a variety of purposes including marketing, security and educational applications. NLI is usually framed as a multi-label clas... | ['Gerold Schneider', 'Ahmet Yavuz Uluslu'] | 2022-11-18 | null | null | null | null | ['marketing', 'native-language-identification'] | ['miscellaneous', 'natural-language-processing'] | [ 5.96551597e-01 -1.27638280e-01 -4.17816937e-01 -5.09553432e-01
-1.00986576e+00 -8.53933632e-01 8.48797560e-01 -5.67977540e-02
-2.19615817e-01 9.33137357e-01 3.67787182e-02 -4.47536737e-01
-1.16901165e-02 -5.44631600e-01 -5.58549345e-01 -2.83855557e-01
3.00105631e-01 9.63946342e-01 -3.55157375e-01 2.70942539... | [10.395227432250977, 10.523723602294922] |
d9f4e37b-f60c-4622-905b-bd7335328420 | using-under-trained-deep-ensembles-to-learn | 2009.11128 | null | https://arxiv.org/abs/2009.11128v2 | https://arxiv.org/pdf/2009.11128v2.pdf | Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise | Improper or erroneous labelling can pose a hindrance to reliable generalization for supervised learning. This can have negative consequences, especially for critical fields such as healthcare. We propose an effective new approach for learning under extreme label noise, based on under-trained deep ensembles. Each ensemb... | ['Mohan Kankanhalli', 'Stein Kristiansen', 'Konstantinos Nikolaidis', 'Vera Goebel', 'Thomas Plagemann'] | 2020-09-23 | null | null | null | null | ['sleep-apnea-detection'] | ['medical'] | [ 5.53281069e-01 4.08676445e-01 9.08128265e-03 -6.75131798e-01
-1.04776180e+00 -4.43404943e-01 5.89874648e-02 4.58324283e-01
-5.33118188e-01 1.09173405e+00 -9.50153321e-02 -3.05812657e-01
-1.00923507e-02 -4.64932591e-01 -3.94587040e-01 -9.61352348e-01
1.33854985e-01 3.97710860e-01 -8.68608207e-02 2.50256419... | [9.931060791015625, 3.453364849090576] |
ee6e4a05-c31b-44b9-ba6d-eec1a3b0fe55 | column-networks-for-collective-classification | 1609.04508 | null | http://arxiv.org/abs/1609.04508v2 | http://arxiv.org/pdf/1609.04508v2.pdf | Column Networks for Collective Classification | Relational learning deals with data that are characterized by relational
structures. An important task is collective classification, which is to jointly
classify networked objects. While it holds a great promise to produce a better
accuracy than non-collective classifiers, collective classification is
computational cha... | ['Truyen Tran', 'Dinh Phung', 'Trang Pham', 'Svetha Venkatesh'] | 2016-09-15 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [-6.99475333e-02 3.92941982e-02 -7.01880991e-01 -4.27667141e-01
-5.06245732e-01 -4.71653193e-01 6.46910250e-01 9.19589579e-01
3.40397768e-02 7.63787985e-01 1.47569403e-01 -4.82631534e-01
-9.65194941e-01 -1.04763627e+00 -9.04105604e-01 -4.79235679e-01
-6.50046289e-01 5.70716023e-01 3.43798131e-01 -7.97467679... | [7.127668380737305, 6.323044776916504] |
e5c180e9-7b1d-48cd-881e-2a91d75397e1 | causal-razors | 2302.10331 | null | https://arxiv.org/abs/2302.10331v2 | https://arxiv.org/pdf/2302.10331v2.pdf | Causal Razors | When performing causal discovery, assumptions have to be made on how the true causal mechanism corresponds to the underlying joint probability distribution. These assumptions are labeled as causal razors in this work. We review numerous causal razors that appeared in the literature, and offer a comprehensive logical co... | ['Wai-Yin Lam'] | 2023-02-20 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 2.27043957e-01 2.28177175e-01 -8.58222961e-01 -3.59652817e-01
-2.44713470e-01 -6.99568391e-01 9.18313622e-01 2.02111110e-01
4.93720500e-03 1.04112720e+00 5.99893212e-01 -8.67626071e-01
-1.04066467e+00 -6.41432166e-01 -5.55108190e-01 -5.11521876e-01
-4.84658509e-01 6.04708552e-01 1.47292361e-01 4.68799099... | [8.018372535705566, 5.479950904846191] |
16b2b305-39ca-4e2a-8d72-97546226bad0 | deep-learning-for-survival-analysis-a-review | 2305.14961 | null | https://arxiv.org/abs/2305.14961v1 | https://arxiv.org/pdf/2305.14961v1.pdf | Deep Learning for Survival Analysis: A Review | The influx of deep learning (DL) techniques into the field of survival analysis in recent years, coupled with the increasing availability of high-dimensional omics data and unstructured data like images or text, has led to substantial methodological progress; for instance, learning from such high-dimensional or unstruc... | ['Andreas Bender', 'Raphael Sonabend', 'Philipp Kopper', 'Simon Wiegrebe'] | 2023-05-24 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-2.64816415e-02 -4.82021600e-01 -5.52823126e-01 -2.92423487e-01
-9.47406232e-01 -5.83375871e-01 3.27908367e-01 7.41713166e-01
-3.69504094e-01 9.28962588e-01 3.14924181e-01 -4.60283339e-01
-3.78734648e-01 -7.55253971e-01 -1.96038872e-01 -9.04594541e-01
-5.40592134e-01 5.57294130e-01 -2.18039423e-01 1.66905478... | [7.696544170379639, 5.569269180297852] |
61c5a8cf-7672-49cc-82d5-09299122945e | unbnlp-at-semeval-2021-task-1-predicting | null | null | https://aclanthology.org/2021.semeval-1.83 | https://aclanthology.org/2021.semeval-1.83.pdf | UNBNLP at SemEval-2021 Task 1: Predicting lexical complexity with masked language models and character-level encoders | In this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1. We explore the use of statistical baseline features, masked language models, and character-level encoders to predict the complexity of a target token in context. Our ... | ['Paul Cook', 'Samin Fakharian', 'Ali Hakimi Parizi', 'Milton King'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [ 1.15459539e-01 4.43405174e-02 -6.18846655e-01 -8.39168727e-01
-9.89831030e-01 -3.63207012e-01 4.90610719e-01 6.27196729e-01
-1.04373753e+00 7.80276120e-01 5.40547490e-01 -5.25274456e-01
6.09578550e-01 -5.01659036e-01 -3.35805207e-01 3.65921147e-02
-1.02665685e-01 1.35816708e-01 1.50737599e-01 -3.32342982... | [10.648832321166992, 10.449344635009766] |
8ddd88a4-ed43-46f5-beab-cdc53485194b | target-specific-de-novo-design-of-drug | 2302.07868 | null | https://arxiv.org/abs/2302.07868v5 | https://arxiv.org/pdf/2302.07868v5.pdf | Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks | Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, have been developed with the purpose of picking completely new samples from a partially known space. Generative... | ['Tunca Doğan', 'Abdurrahman Olğaç', 'Ahmet Rifaioğlu', 'Deniz Cansen Kahraman', 'Altay Koyaş', 'Heval Ataş Güvenilir', 'Hayriye Çelikbilek', 'Ahmet Sarıgün', 'Elif Çevrim', 'Atabey Ünlü'] | 2023-02-15 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 4.39638525e-01 1.44069239e-01 -3.71129394e-01 9.32304040e-02
-7.60346651e-01 -7.40176618e-01 5.32445312e-01 3.74744833e-01
3.60404365e-02 1.47012949e+00 -9.18508843e-02 -4.63547766e-01
3.30998190e-02 -1.05731177e+00 -9.77278590e-01 -1.00937057e+00
6.92701936e-02 8.54431152e-01 -1.01359211e-01 -1.88098416... | [5.011440753936768, 5.74326229095459] |
53e26e02-0044-42cc-819c-538e94f8b2f4 | analyzing-categorical-time-series-with-the-r | 2304.12332 | null | https://arxiv.org/abs/2304.12332v1 | https://arxiv.org/pdf/2304.12332v1.pdf | Analyzing categorical time series with the R package ctsfeatures | Time series data are ubiquitous nowadays. Whereas most of the literature on the topic deals with real-valued time series, categorical time series have received much less attention. However, the development of data mining techniques for this kind of data has substantially increased in recent years. The R package ctsfeat... | ['José Antonio Vilar Fernández', 'Ángel López Oriona'] | 2023-04-24 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [-4.03702892e-02 -3.35112959e-01 5.21381013e-02 -5.16955733e-01
-3.81884724e-01 -6.99491322e-01 5.56703568e-01 8.68006468e-01
-2.82802671e-01 5.22798479e-01 -2.91489542e-01 -4.29761231e-01
-5.26979268e-01 -6.57551885e-01 -1.61793604e-01 -1.00752258e+00
-8.93483102e-01 1.68910071e-01 1.77545890e-01 -1.03144974... | [7.251550674438477, 3.358781337738037] |
cf1349ea-9383-41e0-9d33-310be6fa1164 | mbse-analysis-for-energy-sustainability | 2208.01514 | null | https://arxiv.org/abs/2208.01514v1 | https://arxiv.org/pdf/2208.01514v1.pdf | MBSE analysis for energy sustainability improvement in manufacturing industry | With the ever increasing complexity of Industry 4.0 systems, plant energy management systems developed to improve energy sustainability become equally complex. Based on a Model-Based Systems Engineering analysis, this paper aims to provide a general approach to perform holistic development of an autonomous energy manag... | ['Jean-Luc Dion', 'Arkadiusz Kosecki', 'Martin Ghienne', 'Olivia Penas', 'Romain Delabeye'] | 2022-08-02 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 7.02062398e-02 -1.13137374e-02 1.50466561e-01 1.37429431e-01
5.31946421e-01 -6.11269295e-01 8.91881704e-01 3.52500856e-01
3.66989315e-01 5.52395582e-01 -5.51459551e-01 -4.44670886e-01
-5.81601202e-01 -1.15548611e+00 -5.64187355e-02 -4.05161232e-01
2.11965516e-01 4.73572314e-01 -1.67450801e-01 -2.21791536... | [5.8464741706848145, 2.4720022678375244] |
2a38e2fe-af59-4026-9e31-0d406eccdd61 | graph-ranking-for-collective-named-entity | null | null | https://aclanthology.org/P14-2013 | https://aclanthology.org/P14-2013.pdf | Graph Ranking for Collective Named Entity Disambiguation | null | ['Robert Gaizauskas', 'Ayman Alhelbawy'] | 2014-06-01 | null | null | null | acl-2014-6 | ['graph-ranking'] | ['graphs'] | [-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... | [-7.445267677307129, 3.6603121757507324] |
2bceb98b-26f8-4530-bc14-3abbdb2bd5ec | 3d-sis-3d-semantic-instance-segmentation-of | 1812.07003 | null | http://arxiv.org/abs/1812.07003v3 | http://arxiv.org/pdf/1812.07003v3.pdf | 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans | We introduce 3D-SIS, a novel neural network architecture for 3D semantic
instance segmentation in commodity RGB-D scans. The core idea of our method is
to jointly learn from both geometric and color signal, thus enabling accurate
instance predictions. Rather than operate solely on 2D frames, we observe that
most comput... | ['Matthias Nießner', 'Ji Hou', 'Angela Dai'] | 2018-12-17 | 3d-sis-3d-semantic-instance-segmentation-of-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Hou_3D-SIS_3D_Semantic_Instance_Segmentation_of_RGB-D_Scans_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Hou_3D-SIS_3D_Semantic_Instance_Segmentation_of_RGB-D_Scans_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-instance-segmentation-1', '3d-semantic-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.15946633e-01 2.58277833e-01 -3.95500511e-02 -5.95821738e-01
-1.00501454e+00 -6.92962110e-01 4.10998702e-01 1.17816128e-01
-2.91245997e-01 6.22802265e-02 -5.03525473e-02 -2.23536745e-01
2.36346424e-01 -9.68057871e-01 -1.12483335e+00 -5.04502773e-01
-8.04729238e-02 7.58330703e-01 4.31167603e-01 1.90703064... | [8.38707447052002, -2.9787211418151855] |
a09e4b32-bd7b-4b1c-8779-488077479a52 | face-recognition-in-movie-trailers-via-mean | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Ortiz_Face_Recognition_in_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Ortiz_Face_Recognition_in_2013_CVPR_paper.pdf | Face Recognition in Movie Trailers via Mean Sequence Sparse Representation-Based Classification | This paper presents an end-to-end video face recognition system, addressing the difficult problem of identifying a video face track using a large dictionary of still face images of a few hundred people, while rejecting unknown individuals. A straightforward application of the popular n-minimization for face recognition... | ['Enrique. G. Ortiz', 'Mubarak Shah', 'Alan Wright'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.16603354e-01 -4.83287692e-01 -1.96355447e-01 -7.21267760e-01
-7.46047497e-01 -7.57629812e-01 3.35665911e-01 -6.38270795e-01
-3.75382006e-01 5.49653947e-01 -2.49854073e-01 2.56755710e-01
9.49190930e-02 -1.62847117e-01 -8.28175128e-01 -8.33641231e-01
-7.79600292e-02 3.85242462e-01 -1.85003951e-01 1.15181863... | [13.299652099609375, 0.7752413153648376] |
5b176237-5a2c-4084-b7b2-31937f997b87 | transformer-training-strategies-for | 2306.10891 | null | https://arxiv.org/abs/2306.10891v1 | https://arxiv.org/pdf/2306.10891v1.pdf | Transformer Training Strategies for Forecasting Multiple Load Time Series | Recent work uses Transformers for load forecasting, which are the state of the art for sequence modeling tasks in data-rich domains. In the smart grid of the future, accurate load forecasts must be provided on the level of individual clients of an energy supplier. While the total amount of electrical load data availabl... | ['Veit Hagenmeyer', 'Ralf Mikut', 'Benjamin Schäfer', 'Oliver Neumann', 'Benedikt Heidrich', 'Maximilian Beichter', 'Matthias Hertel'] | 2023-06-19 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-5.83795235e-02 -2.21546948e-01 -2.60737091e-01 -3.78093004e-01
-5.66210926e-01 -5.24224937e-01 5.97110868e-01 5.71818799e-02
3.75202484e-02 6.61568940e-01 3.80252838e-01 -6.94839358e-01
5.84117062e-02 -1.08311903e+00 -2.39643499e-01 -8.78301799e-01
-7.39859715e-02 9.67171967e-01 -6.12947494e-02 -2.98731565... | [6.162865161895752, 2.875365734100342] |
068432ae-9f15-4c3f-8d2c-ac0d1cac6d8c | an-overview-of-structural-coverage-metrics | 2208.03407 | null | https://arxiv.org/abs/2208.03407v1 | https://arxiv.org/pdf/2208.03407v1.pdf | An Overview of Structural Coverage Metrics for Testing Neural Networks | Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios. In this article, we provide an overview of structural coverage metrics for testing DNN models, including neuron coverage (NC), k-multisect... | ['Corina S. Pasareanu', 'Luca Manolache', 'Rishi Dange', 'Divya Gopinath', 'Youcheng Sun', 'Muhammad Usman'] | 2022-08-05 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 2.02168137e-01 1.69618800e-01 -5.84877608e-03 -4.00620133e-01
-2.10778967e-01 -6.65446460e-01 3.23300391e-01 -2.49715261e-02
-3.87248904e-01 9.48731303e-01 -1.98330924e-01 -7.64907479e-01
-3.06863755e-01 -7.86131740e-01 -7.59563982e-01 -5.12467384e-01
-1.31737351e-01 5.10682344e-01 6.43452883e-01 -1.28867835... | [6.585072040557861, 7.646378040313721] |
d27cc83b-0886-4f2c-9cb1-2d0210802074 | consistent-and-complementary-graph | 2004.03106 | null | https://arxiv.org/abs/2004.03106v1 | https://arxiv.org/pdf/2004.03106v1.pdf | Consistent and Complementary Graph Regularized Multi-view Subspace Clustering | This study investigates the problem of multi-view clustering, where multiple views contain consistent information and each view also includes complementary information. Exploration of all information is crucial for good multi-view clustering. However, most traditional methods blindly or crudely combine multiple views f... | ['Jun Wang', 'Shanmin Pang', 'Qinghai Zheng', 'Lei Chen', 'Jihua Zhu', 'Zhongyu Li'] | 2020-04-07 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.55298460e-01 -2.44796515e-01 -1.19553231e-01 -2.23489434e-01
-6.59298122e-01 -6.98594153e-01 3.19751263e-01 2.43667420e-03
4.37544612e-03 1.86037630e-01 2.95910597e-01 3.11839759e-01
-4.62986887e-01 -4.48963583e-01 -4.27677870e-01 -1.13560915e+00
9.51402858e-02 4.16409254e-01 7.77347609e-02 6.68387413... | [8.238896369934082, 4.633297920227051] |
db336120-3478-46bb-ad98-5536a2394ec5 | constraint-based-inference-of-heuristics-for | 2105.14194 | null | https://arxiv.org/abs/2105.14194v1 | https://arxiv.org/pdf/2105.14194v1.pdf | Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization | The Foreign Exchange (Forex) is a large decentralized market, on which trading analysis and algorithmic trading are popular. Research efforts have been focusing on proof of efficiency of certain technical indicators. We demonstrate, however, that the values of indicator functions are not reproducible and often reduce t... | ['Qiben Yan', 'Nikolay Ivanov'] | 2021-05-11 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-6.66430056e-01 -2.05021307e-01 -4.71944124e-01 -1.50263071e-01
-9.47679400e-01 -1.19606781e+00 8.23432267e-01 9.83100533e-02
-1.36488885e-01 9.72675979e-01 -5.01630567e-02 -6.45026624e-01
-6.22326672e-01 -6.89547181e-01 -3.95121306e-01 -4.48941052e-01
-3.41087699e-01 9.68343675e-01 -1.12332679e-01 1.51616916... | [4.715696334838867, 4.0537333488464355] |
a5581e68-4e2c-4d61-8b53-f44115b372a4 | deep-speaker-feature-learning-for-text | 1705.03670 | null | http://arxiv.org/abs/1705.03670v1 | http://arxiv.org/pdf/1705.03670v1.pdf | Deep Speaker Feature Learning for Text-independent Speaker Verification | Recently deep neural networks (DNNs) have been used to learn speaker
features. However, the quality of the learned features is not sufficiently
good, so a complex back-end model, either neural or probabilistic, has to be
used to address the residual uncertainty when applied to speaker verification,
just as with raw fea... | ['Zhiyuan Tang', 'Lantian Li', 'Dong Wang', 'Yixiang Chen', 'Ying Shi'] | 2017-05-10 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-1.76804841e-01 7.12997327e-03 1.22072026e-01 -1.07705355e+00
-1.23483956e+00 -4.60049152e-01 6.40518606e-01 -2.14791238e-01
-2.88458019e-01 5.06093919e-01 9.39031988e-02 -3.05930048e-01
-7.18024597e-02 -2.73272514e-01 -6.48276746e-01 -9.49304163e-01
-4.84432250e-01 1.90388039e-01 -9.43698883e-02 1.61322430... | [14.380437850952148, 6.058828353881836] |
a31efd49-e09d-4215-9250-a02274893a31 | surrogate-neural-networks-for-efficient | 2303.17468 | null | https://arxiv.org/abs/2303.17468v1 | https://arxiv.org/pdf/2303.17468v1.pdf | Surrogate Neural Networks for Efficient Simulation-based Trajectory Planning Optimization | This paper presents a novel methodology that uses surrogate models in the form of neural networks to reduce the computation time of simulation-based optimization of a reference trajectory. Simulation-based optimization is necessary when there is no analytical form of the system accessible, only input-output data that c... | ['Jonathan P. How', 'Piero Miotto', 'Matthew Stoeckle', 'Rebecca Russell', 'Evelyn Ruff'] | 2023-03-30 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-1.93135932e-01 -3.35979134e-01 2.71953847e-02 -3.74158472e-02
-5.50411582e-01 -7.63162553e-01 4.78590041e-01 -1.12061657e-01
-4.55343992e-01 9.38384354e-01 -2.20487803e-01 -9.63447630e-01
-3.97939831e-01 -8.28489244e-01 -7.91750908e-01 -5.97902894e-01
-2.86662340e-01 4.97168779e-01 -1.34712443e-01 -5.98188102... | [5.261869430541992, 2.1408259868621826] |
a228a8e9-ab0d-4af2-9d60-0ea67c309bc9 | look-harder-a-neural-machine-translation | null | null | https://aclanthology.org/P19-1290 | https://aclanthology.org/P19-1290.pdf | Look Harder: A Neural Machine Translation Model with Hard Attention | Soft-attention based Neural Machine Translation (NMT) models have achieved promising results on several translation tasks. These models attend all the words in the source sequence for each target token, which makes them ineffective for long sequence translation. In this work, we propose a hard-attention based NMT model... | ['Sathish Reddy Indurthi', 'Sangha Kim', 'Insoo Chung'] | 2019-07-01 | null | null | null | acl-2019-7 | ['hard-attention'] | ['methodology'] | [ 3.20119470e-01 -8.06995761e-03 -4.52266216e-01 -2.45291308e-01
-1.14055014e+00 -3.47771585e-01 5.81134379e-01 -2.42474630e-01
-4.28750515e-01 1.09509766e+00 3.56995553e-01 -7.23672569e-01
4.41602498e-01 -4.18580860e-01 -8.78073931e-01 -5.10210812e-01
4.98256862e-01 7.64325440e-01 -2.56004840e-01 -4.25979316... | [11.71902847290039, 10.043811798095703] |
fcb7fdd4-6be7-4b30-b9da-25a50976cb0e | towards-large-scale-simulations-of-open-ended | 2304.05639 | null | https://arxiv.org/abs/2304.05639v1 | https://arxiv.org/pdf/2304.05639v1.pdf | Towards Large-Scale Simulations of Open-Ended Evolution in Continuous Cellular Automata | Inspired by biological and cultural evolution, there have been many attempts to explore and elucidate the necessary conditions for open-endedness in artificial intelligence and artificial life. Using a continuous cellular automata called Lenia as the base system, we built large-scale evolutionary simulations using para... | ['Bert Wang-Chak Chan'] | 2023-04-12 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-1.09354839e-01 -1.06765099e-01 3.95320803e-01 2.19790190e-01
7.42895842e-01 -4.22763258e-01 7.56113291e-01 4.38172817e-02
-2.43569225e-01 1.07976711e+00 -4.19456251e-02 -1.08552173e-01
-2.36937791e-01 -1.05334902e+00 -2.28168771e-01 -9.27203536e-01
-4.74123716e-01 4.85497892e-01 2.84828424e-01 -4.52621967... | [5.600257873535156, 4.104466915130615] |
4de0a5e9-26d3-4ad3-b42d-d459f76cba5e | max-pooling-with-vision-transformers | 2210.17400 | null | https://arxiv.org/abs/2210.17400v1 | https://arxiv.org/pdf/2210.17400v1.pdf | Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentation | Weakly Supervised Semantic Segmentation (WSSS) research has explored many directions to improve the typical pipeline CNN plus class activation maps (CAM) plus refinements, given the image-class label as the only supervision. Though the gap with the fully supervised methods is reduced, further abating the spread seems u... | ['Fiora Pirri', 'Marco Schaerf', 'Marta Sanzari', 'Damiano Zappia', 'Simone Rossetti'] | 2022-10-31 | null | null | null | null | ['weakly-supervised-object-detection', 'weakly-supervised-object-localization'] | ['computer-vision', 'computer-vision'] | [ 5.70928514e-01 6.01348877e-01 -1.19670495e-01 -6.31093919e-01
-8.38598371e-01 -5.63690484e-01 5.54273069e-01 -1.28889769e-01
-7.28688717e-01 5.62149405e-01 -2.50950634e-01 -1.30740225e-01
1.20312601e-01 -6.09636545e-01 -1.10255206e+00 -7.93965876e-01
2.81545013e-01 4.03767735e-01 7.42570698e-01 -2.22301155... | [9.482600212097168, 0.41934022307395935] |
4a6b0375-f9bf-4786-9f99-3746ea315ae9 | contrastive-model-adaptation-for-cross | 2303.05194 | null | https://arxiv.org/abs/2303.05194v1 | https://arxiv.org/pdf/2303.05194v1.pdf | Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation | Standard unsupervised domain adaptation methods adapt models from a source to a target domain using labeled source data and unlabeled target data jointly. In model adaptation, on the other hand, access to the labeled source data is prohibited, i.e., only the source-trained model and unlabeled target data are available.... | ['Luc van Gool', 'Tim Brödermann', 'Christos Sakaridis', 'David Bruggemann'] | 2023-03-09 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 5.14011264e-01 -1.00784093e-01 -5.41432083e-01 -6.99695647e-01
-1.15260422e+00 -9.16413426e-01 4.84274328e-01 -2.12730803e-02
-3.37810457e-01 4.63097662e-01 6.19231761e-02 -7.86403269e-02
6.30544052e-02 -6.59780741e-01 -8.24923217e-01 -7.53674388e-01
3.78497481e-01 7.02298343e-01 2.00539410e-01 -4.80969511... | [9.743281364440918, 1.3745609521865845] |
4c56df19-2b0d-4d2c-a550-9686d98f8b3a | learning-representations-from-product-titles | 1811.01166 | null | https://arxiv.org/abs/1811.01166v3 | https://arxiv.org/pdf/1811.01166v3.pdf | Learning Representations from Product Titles for Modeling Shopping Transactions | Shopping transaction analysis is important for understanding the shopping behaviors of customers. Existing models such as association rules are poor at modeling products that have short purchase histories and cannot be applied to new products (the cold-start problem). In this paper, we propose BASTEXT, an efficient mod... | ['Binh Nguyen', 'Atsuhiro Takasu'] | 2018-11-03 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-3.33798558e-01 -4.52728003e-01 -9.52779293e-01 -8.43707919e-01
-3.57345879e-01 -7.10475445e-01 3.55035484e-01 4.65935051e-01
-3.07556063e-01 1.50875643e-01 4.46816117e-01 -3.39647442e-01
-1.72980338e-01 -1.06199634e+00 -7.82952726e-01 -2.42938802e-01
-1.91362098e-01 1.01544654e+00 7.89932609e-02 -5.49402356... | [10.076894760131836, 5.900309085845947] |
459d869b-71bc-4833-9ba4-7f138a860485 | find-beauty-in-the-rare-contrastive | 2302.08662 | null | https://arxiv.org/abs/2302.08662v1 | https://arxiv.org/pdf/2302.08662v1.pdf | Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression | Automatic image cropping algorithms aim to recompose images like human-being photographers by generating the cropping boxes with improved composition quality. Cropping box regression approaches learn the beauty of composition from annotated cropping boxes. However, the bias of annotations leads to quasi-trivial recompo... | ['Weicai Zhong', 'Zhiguo Cao', 'Hao Lu', 'Jiale Zhang', 'Yinpeng Chen', 'Zhiyu Pan'] | 2023-02-17 | null | null | null | null | ['image-cropping', 'philosophy'] | ['computer-vision', 'miscellaneous'] | [ 5.39758146e-01 1.45152127e-02 -2.40873545e-01 -1.36781007e-01
-1.53163657e-01 -5.38529217e-01 6.55148923e-01 -1.88192755e-01
1.86230958e-01 3.74073297e-01 2.78746128e-01 1.59896523e-01
2.64849178e-02 -5.66660702e-01 -8.38925242e-01 -1.01405263e+00
1.17189877e-01 1.79871112e-01 1.62255064e-01 -2.54723310... | [11.364068984985352, -0.8224357962608337] |
3403481d-dfb7-4680-a7e6-c73b84552789 | temporally-layered-architecture-for-efficient | 2305.18701 | null | https://arxiv.org/abs/2305.18701v1 | https://arxiv.org/pdf/2305.18701v1.pdf | Temporally Layered Architecture for Efficient Continuous Control | We present a temporally layered architecture (TLA) for temporally adaptive control with minimal energy expenditure. The TLA layers a fast and a slow policy together to achieve temporal abstraction that allows each layer to focus on a different time scale. Our design draws on the energy-saving mechanism of the human bra... | ['Hava Siegelmann', 'Terrence Sejnowski', 'Devdhar Patel'] | 2023-05-30 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 6.16022479e-03 -2.06746131e-01 -5.34229755e-01 1.21386513e-01
-4.41948295e-01 -5.97487330e-01 7.04349875e-01 6.87201396e-02
-5.06552517e-01 8.01172793e-01 2.75353193e-01 -3.37283343e-01
-3.00700605e-01 -4.61501658e-01 -6.51215255e-01 -7.53005743e-01
-5.98120630e-01 9.30312555e-03 1.90154344e-01 -6.78109080... | [4.233820915222168, 1.6537929773330688] |
d6e4956e-1560-49a1-b017-473a245c4811 | how-much-and-when-do-we-need-higher-order | 2001.11181 | null | https://arxiv.org/abs/2001.11181v3 | https://arxiv.org/pdf/2001.11181v3.pdf | How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction | Hypergraphs provide a natural way of representing group relations, whose complexity motivates an extensive array of prior work to adopt some form of abstraction and simplification of higher-order interactions. However, the following question has yet to be addressed: How much abstraction of group interactions is suffici... | ['HyungSeok Song', 'Se-eun Yoon', 'Yung Yi', 'Kijung Shin'] | 2020-01-30 | null | null | null | null | ['hyperedge-prediction'] | ['graphs'] | [ 2.55963147e-01 4.88836169e-01 -2.40136191e-01 -2.65785992e-01
-3.77884209e-01 -6.86540842e-01 5.95360696e-01 4.06942099e-01
1.54447686e-02 7.92951822e-01 2.90652871e-01 -6.10687256e-01
-5.41822195e-01 -8.98070753e-01 -8.23887587e-01 -4.33117270e-01
-6.28263414e-01 6.70659006e-01 3.50799143e-01 -3.51350635... | [7.0719122886657715, 5.883218765258789] |
7081d631-de25-4d87-b09c-758013c0b082 | attention-based-graph-neural-network-for-semi | 1803.03735 | null | http://arxiv.org/abs/1803.03735v1 | http://arxiv.org/pdf/1803.03735v1.pdf | Attention-based Graph Neural Network for Semi-supervised Learning | Recently popularized graph neural networks achieve the state-of-the-art
accuracy on a number of standard benchmark datasets for graph-based
semi-supervised learning, improving significantly over existing approaches.
These architectures alternate between a propagation layer that aggregates the
hidden states of the local... | ['Li-Jia Li', 'Sewoong Oh', 'Kiran K. Thekumparampil', 'Chong Wang'] | 2018-03-10 | attention-based-graph-neural-network-for-semi-1 | https://openreview.net/forum?id=rJg4YGWRb | https://openreview.net/pdf?id=rJg4YGWRb | iclr-2018-1 | ['graph-regression'] | ['graphs'] | [ 8.31241384e-02 6.84224069e-01 -6.10158682e-01 -5.29270947e-01
-2.09015772e-01 -3.83897811e-01 7.47579992e-01 4.04466540e-01
-1.39577746e-01 6.74306035e-01 2.85789460e-01 -5.57757080e-01
-2.52747148e-01 -1.01570368e+00 -9.70708370e-01 -5.30240595e-01
-4.18018609e-01 5.30604720e-01 5.06803811e-01 -1.45336449... | [6.970187187194824, 6.216841220855713] |
bd154d7e-7856-413c-8993-100b720751c2 | a-multi-granularity-matching-attention | 2303.15870 | null | https://arxiv.org/abs/2303.15870v1 | https://arxiv.org/pdf/2303.15870v1.pdf | A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval | Query intent classification, which aims at assisting customers to find desired products, has become an essential component of the e-commerce search. Existing query intent classification models either design more exquisite models to enhance the representation learning of queries or explore label-graph and multi-task to ... | ['Sulong Xu', 'Songlin Wang', 'Haiqing Hu', 'Mingming Li', 'Yiming Qiu', 'Chunyuan Yuan'] | 2023-03-28 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [-2.19288543e-02 -2.90014178e-01 -7.83582091e-01 -7.67821312e-01
-6.16878808e-01 -5.07496059e-01 4.60292846e-01 1.62803113e-01
-1.88402295e-01 -1.87828630e-01 3.41558099e-01 -3.36443216e-01
-1.63220644e-01 -7.41767645e-01 -3.05714399e-01 -1.55249581e-01
1.44657806e-01 4.24980134e-01 4.40352224e-02 -4.46952909... | [10.228209495544434, 5.732882022857666] |
b519b8f5-80fb-4b19-882d-8fc158aa0ec5 | recognizing-people-by-body-shape-using-deep | 2305.19160 | null | https://arxiv.org/abs/2305.19160v1 | https://arxiv.org/pdf/2305.19160v1.pdf | Recognizing People by Body Shape Using Deep Networks of Images and Words | Common and important applications of person identification occur at distances and viewpoints in which the face is not visible or is not sufficiently resolved to be useful. We examine body shape as a biometric across distance and viewpoint variation. We propose an approach that combines standard object classification ne... | ["Alice J. O'Toole", 'Carlos D. Castillo', 'Veda Nandan Gandi', 'Matthew Q. Hill', 'Thomas M. Metz', 'Lucas Jaggernauth', 'Blake A. Myers'] | 2023-05-30 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [-4.36741933e-02 -9.02417526e-02 1.02054335e-01 -4.47849274e-01
-4.00707811e-01 -9.80208755e-01 7.04065740e-01 1.86327517e-01
-6.16250277e-01 4.78181571e-01 1.52574927e-01 -5.31224571e-02
-4.25597340e-01 -7.79595375e-01 -1.47784859e-01 -5.48770249e-01
-2.10470349e-01 5.71078897e-01 -1.89121455e-01 -4.66037631... | [13.68407917022705, 0.9857144951820374] |
4557f96a-0ae7-4ec1-abae-635e5da63eee | l3cube-mahasent-md-a-multi-domain-marathi | 2306.13888 | null | https://arxiv.org/abs/2306.13888v1 | https://arxiv.org/pdf/2306.13888v1.pdf | L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models | The exploration of sentiment analysis in low-resource languages, such as Marathi, has been limited due to the availability of suitable datasets. In this work, we present L3Cube-MahaSent-MD, a multi-domain Marathi sentiment analysis dataset, with four different domains - movie reviews, general tweets, TV show subtitles,... | ['Raviraj Joshi', 'Rahul Tangsali', 'Isha Joshi', 'Aditya Vyawahare', 'Aabha Pingle'] | 2023-06-24 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-4.27676558e-01 -4.70136732e-01 -4.93912935e-01 -7.85648286e-01
-1.25619566e+00 -1.05251086e+00 8.70196998e-01 4.23048049e-01
-5.65742135e-01 6.98031485e-01 5.07465184e-01 -6.09992668e-02
1.16085425e-01 -5.31694293e-01 -5.67183733e-01 -3.42476934e-01
1.28090277e-01 6.55901432e-01 9.16475579e-02 -1.18241990... | [11.189703941345215, 6.974056243896484] |
c744d46d-a9b1-4c3d-b238-850fc1a23aed | a-survey-on-incomplete-multi-view-clustering | 2208.08040 | null | https://arxiv.org/abs/2208.08040v1 | https://arxiv.org/pdf/2208.08040v1.pdf | A Survey on Incomplete Multi-view Clustering | Conventional multi-view clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available ... | ['Jinxing Li', 'Zhao Zhang', 'Yong Xu', 'Bob Zhang', 'Lunke Fei', 'Zheng Zhang', 'Jie Wen'] | 2022-08-17 | null | null | null | null | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [-1.25114232e-01 -2.59815216e-01 -3.42922747e-01 -2.58255333e-01
-6.02320492e-01 -6.81174934e-01 1.62236571e-01 7.87358359e-02
2.13612616e-01 1.50444940e-01 8.96677673e-02 1.33960634e-01
-3.09034675e-01 -4.40076351e-01 -7.12449849e-02 -1.14317501e+00
1.91258937e-01 4.48872328e-01 -9.42557864e-03 2.02205434... | [8.275586128234863, 4.59217643737793] |
a81546ab-78e1-46e0-89e3-6c4234c39aa9 | take-more-positives-a-contrastive-learning | 2101.04340 | null | https://arxiv.org/abs/2101.04340v2 | https://arxiv.org/pdf/2101.04340v2.pdf | Take More Positives: An Empirical Study of Contrastive Learing in Unsupervised Person Re-Identification | Unsupervised person re-identification (re-ID) aims at closing the performance gap to supervised methods. These methods build reliable relationship between data points while learning representations. However, we empirically show that the reason why they are successful is not only their label generation mechanisms, but a... | ['Lin Ma', 'Qian Zhang', 'Xiangyuan Lan', 'Ran Song', 'Wei zhang', 'Xuanyu He'] | 2021-01-12 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 2.43477121e-01 2.97080755e-01 -3.22403699e-01 -4.30098414e-01
-2.91986078e-01 -3.29755723e-01 8.15593123e-01 1.69048294e-01
-6.54029906e-01 8.25043440e-01 3.51334155e-01 5.40770851e-02
-9.43389609e-02 -6.86532080e-01 -4.84005272e-01 -5.37803471e-01
2.64841050e-01 8.49317133e-01 9.63211358e-02 -2.45576706... | [14.81615161895752, 1.1041351556777954] |
f93fe532-b025-44a4-865f-d524702eb66b | evrnet-efficient-video-restoration-on-edge | 2012.02228 | null | https://arxiv.org/abs/2012.02228v1 | https://arxiv.org/pdf/2012.02228v1.pdf | EVRNet: Efficient Video Restoration on Edge Devices | Video transmission applications (e.g., conferencing) are gaining momentum, especially in times of global health pandemic. Video signals are transmitted over lossy channels, resulting in low-quality received signals. To restore videos on recipient edge devices in real-time, we introduce an efficient video restoration ne... | ['Vikas Chandra', 'Rakesh Ranjan', 'Vikram Mulukutla', 'Varun Nasery', 'Fitsum Reda', 'Amit Kumar', 'Sachin Mehta'] | 2020-12-03 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [ 6.07127786e-01 -3.23145628e-01 6.76750541e-02 -1.42444998e-01
-6.67415559e-01 -3.42925876e-01 1.39850648e-02 -5.82996488e-01
-5.10990798e-01 7.29551196e-01 6.01674139e-01 -2.38303691e-01
-4.57783565e-02 -4.14864272e-01 -7.70937085e-01 -7.34943688e-01
-5.41819513e-01 -1.44849837e-01 1.62684351e-01 -1.22012340... | [11.152521133422852, -2.068045139312744] |
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