paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8d8bc1ed-0ef0-4277-8cf3-3cab20611843 | nodeformer-a-scalable-graph-structure | 2306.08385 | null | https://arxiv.org/abs/2306.08385v1 | https://arxiv.org/pdf/2306.08385v1.pdf | NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification | Graph neural networks have been extensively studied for learning with inter-connected data. Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing, heterophily, handling long-range dependencies, edge incompleteness and particularly, the absence of graphs altogether. While a plausible so... | ['Junchi Yan', 'David Wipf', 'Zenan Li', 'Wentao Zhao', 'Qitian Wu'] | 2023-06-14 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 2.89125532e-01 5.32926917e-01 -4.08760726e-01 -4.08873528e-01
-2.67721653e-01 -5.59473872e-01 3.86739492e-01 4.37249929e-01
-2.78229237e-01 7.56700575e-01 -3.57922614e-01 -6.03537023e-01
-5.21214366e-01 -1.09925103e+00 -9.96849239e-01 -9.62733150e-01
-9.26180542e-01 4.81469452e-01 -9.46355015e-02 -2.07373947... | [6.881038188934326, 6.043458938598633] |
d5a00604-93bf-4b40-bf24-c9e93b62b14d | safe-exploration-for-efficient-policy | 2202.13234 | null | https://arxiv.org/abs/2202.13234v2 | https://arxiv.org/pdf/2202.13234v2.pdf | Safe Exploration for Efficient Policy Evaluation and Comparison | High-quality data plays a central role in ensuring the accuracy of policy evaluation. This paper initiates the study of efficient and safe data collection for bandit policy evaluation. We formulate the problem and investigate its several representative variants. For each variant, we analyze its statistical properties, ... | ['Rui Song', 'Branislav Kveton', 'Runzhe Wan'] | 2022-02-26 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.65433764e-01 -3.35382819e-01 -1.31958067e+00 -2.19503954e-01
-8.86179626e-01 -6.83014274e-01 3.93180311e-01 3.54018025e-02
-6.55497670e-01 1.30721343e+00 2.65601009e-01 -9.78034496e-01
-8.48300397e-01 -4.72029686e-01 -7.18178809e-01 -9.18132603e-01
-4.95847762e-02 6.58258140e-01 3.31196049e-03 4.48436648... | [4.540794372558594, 3.2346692085266113] |
e3666279-0884-42f1-b10d-739e64a951ff | machine-unlearning-its-nature-scope-and | 2305.15242 | null | https://arxiv.org/abs/2305.15242v1 | https://arxiv.org/pdf/2305.15242v1.pdf | Machine Unlearning: its nature, scope, and importance for a "delete culture" | The article explores the cultural shift from recording to deleting information in the digital age and its implications on privacy, intellectual property (IP), and Large Language Models like ChatGPT. It begins by defining a delete culture where information, in principle legal, is made unavailable or inaccessible because... | ['Luciano Floridi'] | 2023-05-24 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 3.60053837e-01 4.90800828e-01 -5.29350579e-01 3.60278748e-02
-5.34512460e-01 -8.57690990e-01 4.43948537e-01 2.55615145e-01
-5.70185065e-01 8.85263324e-01 4.51378942e-01 -8.08010340e-01
-2.11748406e-01 -4.64457780e-01 -5.32689750e-01 -4.78005856e-01
3.30184430e-01 -1.72786370e-01 -5.86190283e-01 2.72706062... | [8.881572723388672, 6.664671421051025] |
7297111d-c1b2-4954-be0c-ad5a6eeb7690 | adversarial-connective-exploiting-networks | 1704.00217 | null | http://arxiv.org/abs/1704.00217v1 | http://arxiv.org/pdf/1704.00217v1.pdf | Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification | Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another ... | ['Hai Zhao', 'Zhiting Hu', 'Lianhui Qin', 'Zhisong Zhang', 'Eric P. Xing'] | 2017-04-01 | adversarial-connective-exploiting-networks-1 | https://aclanthology.org/P17-1093 | https://aclanthology.org/P17-1093.pdf | acl-2017-7 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 4.70629692e-01 1.01139402e+00 -4.24189419e-01 -5.60714483e-01
-6.70391798e-01 -6.21677577e-01 9.95752633e-01 -9.00881086e-03
-3.75352353e-01 7.99273074e-01 3.03434521e-01 -7.82044604e-02
1.31354049e-01 -6.11460268e-01 -6.63727880e-01 -6.91330791e-01
-2.30683118e-01 4.13371533e-01 6.11347035e-02 -4.90933657... | [10.779228210449219, 9.218978881835938] |
8a2bb0ac-bff8-4c38-9668-92b25de26715 | object-centric-stereo-matching-for-3d-object | 1909.07566 | null | https://arxiv.org/abs/1909.07566v2 | https://arxiv.org/pdf/1909.07566v2.pdf | Object-Centric Stereo Matching for 3D Object Detection | Safe autonomous driving requires reliable 3D object detection-determining the 6 DoF pose and dimensions of objects of interest. Using stereo cameras to solve this task is a cost-effective alternative to the widely used LiDAR sensor. The current state-of-the-art for stereo 3D object detection takes the existing PSMNet s... | ['Jason Ku', 'Steven L. Waslander', 'Chengyao Li', 'Alex D. Pon'] | 2019-09-17 | null | null | null | null | ['stereo-matching', '3d-object-detection-from-stereo-images'] | ['computer-vision', 'computer-vision'] | [-6.90795481e-02 -3.40685487e-01 -1.66398212e-01 -3.83150667e-01
-4.45149601e-01 -4.80739057e-01 5.05160332e-01 5.45046069e-02
-8.06857109e-01 3.08723509e-01 -4.78730261e-01 -3.44394445e-01
2.96136945e-01 -7.16769278e-01 -9.18547213e-01 -5.06486535e-01
8.90945569e-02 9.71262693e-01 1.05065405e+00 -1.02202125... | [7.751735687255859, -2.6133573055267334] |
61cde2b8-4ce9-49ae-b32a-8a72ae747bb7 | cp-net-contour-perturbed-reconstruction | 2201.08215 | null | https://arxiv.org/abs/2201.08215v2 | https://arxiv.org/pdf/2201.08215v2.pdf | CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning | Self-supervised learning has not been fully explored for point cloud analysis. Current frameworks are mainly based on point cloud reconstruction. Given only 3D coordinates, such approaches tend to learn local geometric structures and contours, while failing in understanding high level semantic content. Consequently, th... | ['Zhipeng Zhou', 'Yali Wang', 'Yu Qiao', 'Hongbin Xu', 'Mingye Xu'] | 2022-01-20 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [-1.05708741e-01 1.98702872e-01 -5.65135002e-01 -3.35154742e-01
-9.56688643e-01 -6.35562658e-01 4.11030889e-01 1.28334686e-01
3.66003774e-02 1.62973464e-01 -2.46015772e-01 -1.79536343e-01
1.28363878e-01 -9.37950313e-01 -1.06167614e+00 -6.32991374e-01
2.02237114e-01 7.22570479e-01 4.75859314e-01 -1.28825054... | [8.04240894317627, -3.3607380390167236] |
e0344d68-608e-4417-8d6f-7f69d4198b04 | dif-fusion-towards-high-color-fidelity-in | 2301.08072 | null | https://arxiv.org/abs/2301.08072v1 | https://arxiv.org/pdf/2301.08072v1.pdf | Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models | Color plays an important role in human visual perception, reflecting the spectrum of objects. However, the existing infrared and visible image fusion methods rarely explore how to handle multi-spectral/channel data directly and achieve high color fidelity. This paper addresses the above issue by proposing a novel metho... | ['Jiayi Ma', 'Yue Deng', 'Shaobo Xia', 'Leyuan Fang', 'Jun Yue'] | 2023-01-19 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 3.06887478e-01 -1.07356441e+00 2.94858545e-01 -2.29076356e-01
-8.04471076e-01 -3.05640787e-01 2.40022421e-01 -3.45139265e-01
-3.70403528e-01 6.24228060e-01 2.44531736e-01 3.77130136e-02
-1.64481938e-01 -9.46914077e-01 -2.72843719e-01 -1.15266132e+00
4.29740250e-01 -5.66015661e-01 1.61841623e-02 -2.75712222... | [10.668999671936035, -2.0432004928588867] |
ef87b544-11c9-401a-87a3-515d880ba6cb | arrhythmia-classifier-based-on-ultra | 2304.01568 | null | https://arxiv.org/abs/2304.01568v1 | https://arxiv.org/pdf/2304.01568v1.pdf | Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network | Reasonably and effectively monitoring arrhythmias through ECG signals has significant implications for human health. With the development of deep learning, numerous ECG classification algorithms based on deep learning have emerged. However, most existing algorithms trade off high accuracy for complex models, resulting ... | ['Hao liu', 'Zijin Liu', 'Hanshi Sun', 'Ao Wang', 'Zhongxing Wu', 'Ninghao Pu'] | 2023-04-04 | null | null | null | null | ['ecg-classification'] | ['medical'] | [-7.49423206e-02 -3.25299263e-01 -2.87414432e-01 -4.09756899e-01
-4.82510388e-01 -1.31025493e-01 -5.05694687e-01 5.28682351e-01
-5.10244310e-01 9.60938513e-01 -3.84205639e-01 -5.91781080e-01
-4.20467198e-01 -9.82660353e-01 -2.15159684e-01 -7.68059552e-01
-2.63511449e-01 3.86714697e-01 -1.89804614e-01 2.81133920... | [14.012109756469727, 3.2283565998077393] |
21108133-32a2-4a41-be0f-3ce0c802bc6a | improving-localization-for-semi-supervised | 2206.10186 | null | https://arxiv.org/abs/2206.10186v1 | https://arxiv.org/pdf/2206.10186v1.pdf | Improving Localization for Semi-Supervised Object Detection | Nowadays, Semi-Supervised Object Detection (SSOD) is a hot topic, since, while it is rather easy to collect images for creating a new dataset, labeling them is still an expensive and time-consuming task. One of the successful methods to take advantage of raw images on a Semi-Supervised Learning (SSL) setting is the Mea... | ['Andrea Prati', 'Akbar Karimi', 'Leonardo Rossi'] | 2022-06-21 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 9.05760676e-02 4.41832870e-01 -2.78856993e-01 -5.72004437e-01
-9.48999763e-01 -5.94603121e-01 3.75919908e-01 4.94031936e-01
-6.49429083e-01 8.38969052e-01 -4.68394548e-01 -1.86194688e-01
1.94569409e-03 -7.28572369e-01 -1.02731454e+00 -9.60082650e-01
2.82484472e-01 6.52289331e-01 8.07596862e-01 2.92571694... | [9.19118595123291, 1.3535526990890503] |
75190630-a4d7-4242-843c-033b0be3da75 | on-evolving-attention-towards-domain | 2103.13561 | null | https://arxiv.org/abs/2103.13561v1 | https://arxiv.org/pdf/2103.13561v1.pdf | On Evolving Attention Towards Domain Adaptation | Towards better unsupervised domain adaptation (UDA). Recently, researchers propose various domain-conditioned attention modules and make promising progresses. However, considering that the configuration of attention, i.e., the type and the position of attention module, affects the performance significantly, it is more ... | ['Xing Sun', 'Rongrong Ji', 'Feiyue Huang', 'WeiMing Dong', 'Jian Liang', 'Xiawu Zheng', 'Ke Li', 'Kekai Sheng'] | 2021-03-25 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 7.36781433e-02 -4.72178608e-01 -2.23140374e-01 -2.58973598e-01
-4.95159686e-01 -4.31597292e-01 4.12074059e-01 -1.66916937e-01
-6.00913584e-01 6.33090854e-01 -8.48740991e-03 -9.07685086e-02
-2.35033289e-01 -7.53391266e-01 -3.97798479e-01 -7.37062156e-01
5.76065481e-01 8.06546450e-01 2.51235306e-01 -3.51882458... | [10.32754898071289, 3.0159215927124023] |
7b6a19b0-4b1a-447f-a258-7e05d4116e91 | adaptive-base-class-suppression-and-prior | 2303.14240 | null | https://arxiv.org/abs/2303.14240v1 | https://arxiv.org/pdf/2303.14240v1.pdf | Adaptive Base-class Suppression and Prior Guidance Network for One-Shot Object Detection | One-shot object detection (OSOD) aims to detect all object instances towards the given category specified by a query image. Most existing studies in OSOD endeavor to explore effective cross-image correlation and alleviate the semantic feature misalignment, however, ignoring the phenomenon of the model bias towards the ... | ['Eryun Liu', 'Hangguan Shan', 'Xinyu Xiao', 'Wenwen Zhang'] | 2023-03-24 | null | null | null | null | ['one-shot-object-detection'] | ['computer-vision'] | [ 1.72261626e-01 -6.73500150e-02 -2.60993391e-01 -3.51815820e-01
-5.13857901e-01 -1.43815488e-01 5.81192970e-01 -2.22346168e-02
-4.36483711e-01 2.05557585e-01 -1.30307779e-01 2.97578603e-01
-2.68957257e-01 -6.20008886e-01 -4.79214877e-01 -9.66323018e-01
1.16158821e-01 1.48879260e-01 8.05871785e-01 -2.61035692... | [9.41714096069336, 1.5237895250320435] |
1aaf99d6-5b65-4536-8992-e91959f3ec5e | deep-convolutional-neural-networks-for-breast | 1802.00752 | null | http://arxiv.org/abs/1802.00752v2 | http://arxiv.org/pdf/1802.00752v2.pdf | Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis | Breast cancer is one of the main causes of cancer death worldwide. Early
diagnostics significantly increases the chances of correct treatment and
survival, but this process is tedious and often leads to a disagreement between
pathologists. Computer-aided diagnosis systems showed potential for improving
the diagnostic a... | ['Vladimir Iglovikov', 'Alexey Shvets', 'Alexander Rakhlin', 'Alexandr A. Kalinin'] | 2018-02-02 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection', 'breast-cancer-histology-image-classification', 'histopathological-image-classification'] | ['knowledge-base', 'medical', 'medical', 'medical'] | [ 7.22676069e-02 5.20143844e-03 -1.66015357e-01 -3.76253545e-01
-1.00939250e+00 -3.67530793e-01 1.69546753e-01 6.35804296e-01
-6.73482597e-01 6.97237551e-01 -2.79721439e-01 -6.91650271e-01
-4.13148180e-02 -7.88717687e-01 -2.65081525e-01 -1.19156623e+00
-1.04541741e-01 5.20167291e-01 -9.34955403e-02 4.89179492... | [15.157552719116211, -2.974637746810913] |
fb293b76-c373-47b5-bb5f-3f7efea05480 | spectrogram-feature-losses-for-music-source | 1901.05061 | null | https://arxiv.org/abs/1901.05061v3 | https://arxiv.org/pdf/1901.05061v3.pdf | Spectrogram Feature Losses for Music Source Separation | In this paper we study deep learning-based music source separation, and explore using an alternative loss to the standard spectrogram pixel-level L2 loss for model training. Our main contribution is in demonstrating that adding a high-level feature loss term, extracted from the spectrograms using a VGG net, can improve... | ['Abhimanyu Sahai', 'Romann Weber', 'Brian McWilliams'] | 2019-01-15 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.03532958e-01 -1.64483368e-01 7.99993202e-02 1.41325772e-01
-1.40930450e+00 -8.08814108e-01 2.45424241e-01 -1.39733538e-01
-3.42046231e-01 4.30394143e-01 2.92598635e-01 1.03492327e-01
-5.00954449e-01 -3.94533038e-01 -6.41982257e-01 -9.11311448e-01
-4.01020497e-01 1.24369180e-02 -2.98379101e-02 -1.02279231... | [15.590486526489258, 5.4553422927856445] |
323e7610-d471-46e6-a626-fd2243a027e6 | parity-calibration | 2305.18655 | null | https://arxiv.org/abs/2305.18655v2 | https://arxiv.org/pdf/2305.18655v2.pdf | Parity Calibration | In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather than the actual value of the future observation. In this context, we introduce the notion of parity calibration, which captures the goal of ca... | ['Chirag Gupta', 'Aaron Rumack', 'Youngseog Chung'] | 2023-05-29 | null | null | null | null | ['epidemiology', 'weather-forecasting'] | ['medical', 'miscellaneous'] | [ 6.02739155e-01 3.86299133e-01 -2.67678976e-01 -6.08209074e-01
-6.92370236e-01 -5.57829380e-01 6.12491310e-01 4.56178844e-01
-1.64203748e-01 8.03664386e-01 4.88793515e-02 -7.68016517e-01
-1.68158308e-01 -8.60316694e-01 -8.16774368e-01 -7.67112851e-01
-8.85291100e-02 5.21668196e-01 -1.82660937e-01 -1.14316404... | [7.350693702697754, 4.098961353302002] |
0855db8c-d74b-448e-a1c8-900ff8cd9a04 | towards-robust-3d-object-recognition-with | 2205.03654 | null | https://arxiv.org/abs/2205.03654v1 | https://arxiv.org/pdf/2205.03654v1.pdf | Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation | Three-dimensional (3D) object recognition is crucial for intelligent autonomous agents such as autonomous vehicles and robots alike to operate effectively in unstructured environments. Most state-of-art approaches rely on relatively dense point clouds and performance drops significantly for sparse point clouds. Unsuper... | ['Mohsen Kaboli', 'Ravinder Dahiya', 'Cong Wang', 'Prajval Kumar Murali'] | 2022-05-07 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-7.15485513e-02 -6.14670068e-02 -9.10199154e-03 -3.83634120e-01
-5.07389247e-01 -3.93778354e-01 7.87832201e-01 2.12949753e-01
-4.26877707e-01 5.88803351e-01 -6.51399374e-01 -1.12115204e-01
-5.32524544e-04 -7.75798202e-01 -7.24665463e-01 -5.84084451e-01
-1.47274081e-02 1.41451716e+00 6.51736498e-01 -2.87409183... | [7.874919414520264, -3.2202539443969727] |
3ccceffe-94b1-4fba-8220-1fba22f70c95 | enriching-abusive-language-detection-with | 2206.08445 | null | https://arxiv.org/abs/2206.08445v1 | https://arxiv.org/pdf/2206.08445v1.pdf | Enriching Abusive Language Detection with Community Context | Uses of pejorative expressions can be benign or actively empowering. When models for abuse detection misclassify these expressions as derogatory, they inadvertently censor productive conversations held by marginalized groups. One way to engage with non-dominant perspectives is to add context around conversations. Previ... | ['Derek Ruths', 'Haji Mohammad Saleem', 'Jana Kurrek'] | 2022-06-16 | null | https://aclanthology.org/2022.woah-1.13 | https://aclanthology.org/2022.woah-1.13.pdf | naacl-woah-2022-7 | ['abusive-language', 'abuse-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.65954726e-04 -1.17797181e-01 -9.63793218e-01 -3.53607625e-01
-4.56348866e-01 -9.97026861e-01 9.52995181e-01 3.97764951e-01
-3.96032810e-01 7.02429652e-01 8.81102026e-01 -7.56061971e-01
3.33871841e-01 -6.10330105e-01 1.13949746e-01 -3.17192972e-01
-1.51416108e-01 -2.03567033e-04 -3.12121421e-01 -2.86022902... | [8.647661209106445, 10.443222999572754] |
539d301a-e868-45e0-b37c-b649fa9e4def | a-cnn-rnn-framework-for-crop-yield-prediction | 1911.09045 | null | https://arxiv.org/abs/1911.09045v2 | https://arxiv.org/pdf/1911.09045v2.pdf | A CNN-RNN Framework for Crop Yield Prediction | Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNN) and recurrent neural networks (RNN) for crop yield ... | ['Sotirios V. Archontoulis', 'Lizhi Wang', 'Saeed Khaki'] | 2019-11-20 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [ 1.27709927e-02 -2.82608330e-01 -4.38361228e-01 -1.71660855e-01
2.34132007e-01 -5.26248157e-01 2.05355644e-01 3.64272833e-01
-1.32907918e-02 9.13113654e-01 2.62496948e-01 -8.39006066e-01
-1.89313620e-01 -1.37128723e+00 -7.44623065e-01 -7.81082571e-01
-4.17659849e-01 -3.24704051e-01 -3.00569713e-01 -5.59262693... | [9.346291542053223, -1.6217533349990845] |
4d93c89c-5e66-4b28-9a4f-c3be23438f23 | creating-and-using-large-monolingual-parallel | null | null | https://aclanthology.org/L14-1094 | https://aclanthology.org/L14-1094.pdf | Creating and using large monolingual parallel corpora for sentential paraphrase generation | null | ['Emiel Krahmer', 'Antal Van den Bosch', 'er', 'S Wubben'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['sentence-compression'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.371502876281738, 3.6413931846618652] |
8e793f84-e914-42c7-b3a9-519968e4cacb | the-best-of-both-worlds-dual-channel-language | null | null | https://aclanthology.org/2022.ltedi-1.14 | https://aclanthology.org/2022.ltedi-1.14.pdf | The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada | In recent years, various methods have been developed to control the spread of negativity by removing profane, aggressive, and offensive comments from social media platforms. There is, however, a scarcity of research focusing on embracing positivity and reinforcing supportive and reassuring content in online forums. As ... | ['Bharathi Raja Chakravarthi', 'Ruba Priyadharshini', 'Sangeetha S', 'Siddhanth U Hegde', 'Adeep Hande'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-4.48556662e-01 3.70862752e-01 -3.72046977e-01 -3.84442747e-01
-8.36352110e-01 -5.41671455e-01 6.42682731e-01 1.92912877e-01
-3.40913087e-01 3.68391931e-01 7.62904525e-01 -3.13312113e-01
3.67259353e-01 -3.15344512e-01 -2.29762599e-01 -2.31030822e-01
1.30449340e-01 -2.30533496e-01 -3.24536741e-01 -6.55427933... | [9.025472640991211, 10.558903694152832] |
09d404ee-9984-47c4-b9c8-bba4c090afca | fewer-features-perform-well-at-native | null | null | https://aclanthology.org/W17-5028 | https://aclanthology.org/W17-5028.pdf | Fewer features perform well at Native Language Identification task | This paper describes our results at the NLI shared task 2017. We participated in essays, speech, and fusion task that uses text, speech, and i-vectors for the task of identifying the native language of the given input. In the essay track, a linear SVM system using word bigrams and character 7-grams performed the best. ... | ['{\\c{C}}a{\\u{g}}r{\\i} {\\c{C}}{\\"o}ltekin', 'Taraka Rama'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [-1.11858465e-01 -2.19731390e-01 -1.46912277e-01 -4.13530409e-01
-1.04701769e+00 -6.32841706e-01 1.02717924e+00 3.59712929e-01
-4.49478537e-01 6.14113390e-01 5.52897871e-01 -7.75114357e-01
-1.95029948e-03 -4.14703041e-01 -1.23300150e-01 -3.96175683e-01
2.08575204e-01 3.90119523e-01 6.23889454e-02 -2.15216622... | [10.20824909210205, 10.5488862991333] |
2ab58188-a866-4bc0-b7ec-54e9582bebf3 | efficient-training-of-multi-task-neural | 2305.06361 | null | https://arxiv.org/abs/2305.06361v1 | https://arxiv.org/pdf/2305.06361v1.pdf | Efficient Training of Multi-task Neural Solver with Multi-armed Bandits | Efficiently training a multi-task neural solver for various combinatorial optimization problems (COPs) has been less studied so far. In this paper, we propose a general and efficient training paradigm based on multi-armed bandits to deliver a unified multi-task neural solver. To this end, we resort to the theoretical l... | ['Tianshu Yu', 'Chenguang Wang'] | 2023-05-10 | null | null | null | null | ['combinatorial-optimization', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 1.63436353e-01 6.63017258e-02 -7.53265858e-01 -3.08490783e-01
-1.17421615e+00 -2.46254683e-01 2.39723951e-01 -2.64608473e-01
-3.78484130e-01 1.12308407e+00 4.17715013e-02 -3.69320095e-01
-5.36390126e-01 -2.94158608e-01 -1.19028759e+00 -7.37787008e-01
8.86398479e-02 4.75229472e-01 -4.72138524e-01 1.46825416... | [8.534928321838379, 4.02037239074707] |
7d8bc13f-170e-46a1-b452-ebda5b5572ef | machine-learning-prediction-errors-better | null | null | https://arxiv.org/abs/1702.05532 | https://arxiv.org/pdf/1702.05532.pdf | Machine learning prediction errors better than DFT accuracy | We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules. The performance of each regressor/representation/property combination is assessed using learning curves which repo... | ['O. Anatole von Lilienfeld', 'George E. Dahl', 'Samuel S. Schoenholz', 'Bing Huang', 'Steven Kearnes', 'Patrick F. Riley', 'Luke Hutchison', 'Justin Gilmer', 'Felix A. Faber', 'Oriol Vinyals'] | 2017-02-17 | null | null | null | j-chem-theory-comput-2017-2 | ['formation-energy'] | ['miscellaneous'] | [ 2.44900346e-01 5.18629067e-02 -4.31139112e-01 -2.53116578e-01
-8.71686876e-01 -3.57767671e-01 6.52898669e-01 8.71647716e-01
-3.96481156e-01 1.52636206e+00 6.11427836e-02 -8.10946524e-01
-3.68321687e-01 -1.01296008e+00 -8.26198339e-01 -1.27979422e+00
-5.13599396e-01 3.59765679e-01 -1.17114916e-01 -2.41746649... | [5.152107238769531, 5.4207329750061035] |
68e20746-447f-4d75-b1f8-e892d26d942c | the-graph-feature-fusion-technique-for | 2303.10556 | null | https://arxiv.org/abs/2303.10556v1 | https://arxiv.org/pdf/2303.10556v1.pdf | The Graph feature fusion technique for speaker recognition based on wav2vec2.0 framework | Pre-trained wav2vec2.0 model has been proved its effectiveness for speaker recognition. However, current feature processing methods are focusing on classical pooling on the output features of the pre-trained wav2vec2.0 model, such as mean pooling, max pooling etc. That methods take the features as the independent and i... | ['Zhen Yang', 'Haiyan Guo', 'Zirui Ge'] | 2023-03-19 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [-1.80373207e-01 7.42208958e-02 3.18889827e-01 -2.45100901e-01
-1.92789197e-01 -1.35455206e-01 5.05302966e-01 -9.68736224e-03
-2.27262542e-01 2.78628409e-01 4.95787740e-01 -2.44676277e-01
4.02987711e-02 -9.54941273e-01 -4.86345977e-01 -8.96617353e-01
-2.19742179e-01 -2.06949383e-01 3.20249915e-01 -4.80204105... | [14.333178520202637, 6.063625812530518] |
75237661-438b-4ebf-a581-43ad7251be30 | poet-pose-estimation-transformer-for-single | 2211.14125 | null | https://arxiv.org/abs/2211.14125v1 | https://arxiv.org/pdf/2211.14125v1.pdf | PoET: Pose Estimation Transformer for Single-View, Multi-Object 6D Pose Estimation | Accurate 6D object pose estimation is an important task for a variety of robotic applications such as grasping or localization. It is a challenging task due to object symmetries, clutter and occlusion, but it becomes more challenging when additional information, such as depth and 3D models, is not provided. We present ... | ['Jan Steinbrener', 'Stephan Weiss', 'Wolfgang Granig', 'Mohamed Amin Hamdad', 'Thomas Jantos'] | 2022-11-25 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.71081677e-01 -9.56241712e-02 -5.26736006e-02 -4.79378611e-01
-6.45774245e-01 -6.99737608e-01 2.95330256e-01 1.28430203e-01
-4.41244453e-01 1.93083212e-01 -3.35482478e-01 -5.26478402e-02
4.30891551e-02 -5.56862473e-01 -1.04498005e+00 -6.01775467e-01
1.14366554e-01 9.28114235e-01 5.77000916e-01 3.85043398... | [7.342477798461914, -2.4830241203308105] |
a944cba7-f3fb-4833-be91-a6ba79b2060c | rlip-relational-language-image-pre-training | 2209.01814 | null | https://arxiv.org/abs/2209.01814v3 | https://arxiv.org/pdf/2209.01814v3.pdf | RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection | The task of Human-Object Interaction (HOI) detection targets fine-grained visual parsing of humans interacting with their environment, enabling a broad range of applications. Prior work has demonstrated the benefits of effective architecture design and integration of relevant cues for more accurate HOI detection. Howev... | ['Mingqian Tang', 'Dong Ni', 'Ziyuan Huang', 'Tao Feng', 'Samuel Albanie', 'Jianwen Jiang', 'Hangjie Yuan'] | 2022-09-05 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 5.07872820e-01 2.04247043e-01 -1.58947408e-02 -5.68115413e-01
-9.00011241e-01 -4.55050796e-01 4.32352066e-01 1.40187785e-01
-2.70182520e-01 4.32546258e-01 4.83430386e-01 -2.30021864e-01
7.05238283e-02 -3.92572314e-01 -7.77594626e-01 -1.22734986e-01
-1.11628458e-01 4.16419148e-01 2.37124577e-01 -2.74265036... | [9.888175010681152, 1.4783568382263184] |
a0cfbc3b-fedc-4c03-bf72-ec77f886905c | unisar-a-unified-structure-aware-1 | 2203.07781 | null | https://arxiv.org/abs/2203.07781v2 | https://arxiv.org/pdf/2203.07781v2.pdf | UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL | Existing text-to-SQL semantic parsers are typically designed for particular settings such as handling queries that span multiple tables, domains or turns which makes them ineffective when applied to different settings. We present UniSAr (Unified Structure-Aware Autoregressive Language Model), which benefits from direct... | ['Dechen Zhan', 'Wanxiang Che', 'Jian-Guang Lou', 'Dingzirui Wang', 'Mingyang Pan', 'Yan Gao', 'Longxu Dou'] | 2022-03-15 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-3.58567536e-02 3.14232081e-01 -3.74818027e-01 -9.16507661e-01
-1.48396587e+00 -8.67487967e-01 2.57084250e-01 1.86154261e-01
7.74199292e-02 2.37006158e-01 6.65439725e-01 -8.76107693e-01
1.13093920e-01 -1.04971135e+00 -1.06924796e+00 2.39076450e-01
1.76835909e-01 1.04702616e+00 2.78912276e-01 -4.84540820... | [9.952927589416504, 7.830907821655273] |
e465734c-3fc0-4274-b281-6ecc9cf35ce3 | the-nos-project-opening-routes-for-the | null | null | https://aclanthology.org/2022.tdle-1.6 | https://aclanthology.org/2022.tdle-1.6.pdf | The Nós Project: Opening routes for the Galician language in the field of language technologies | The development of language technologies (LTs) such as machine translation, text analytics, and dialogue systems is essential in the current digital society, culture and economy. These LTs, widely supported in languages in high demand worldwide, such as English, are also necessary for smaller and less economically powe... | ['Xosé Luis Regueira', 'Senén Barro', 'Manuel González González', 'Alberto Bugarín-Diz', 'Elisa Fernández Rei', 'Pablo Gamallo', 'Marcos García', 'José Ramom Pichel', 'John E. Ortega', 'Adina Ioana Vladu', 'Carmen Magariños', 'Iria de-Dios-Flores'] | null | null | null | null | tdle-lrec-2022-6 | ['culture'] | ['speech'] | [-3.50831121e-01 3.51128012e-01 -3.40455472e-01 2.74105836e-02
-2.11324051e-01 -6.49938881e-01 1.21046662e+00 4.93608087e-01
-7.72210240e-01 6.85132265e-01 7.82756150e-01 -5.57891548e-01
1.92142606e-01 -1.01590002e+00 1.97842047e-02 -1.71282247e-01
5.32684743e-01 7.10296810e-01 -3.58638942e-01 -1.03940380... | [10.3222017288208, 10.078441619873047] |
6f3ca63d-8b7a-4371-9a24-326702d5af08 | burst-image-restoration-and-enhancement | 2110.03680 | null | https://arxiv.org/abs/2110.03680v2 | https://arxiv.org/pdf/2110.03680v2.pdf | Burst Image Restoration and Enhancement | Modern handheld devices can acquire burst image sequence in a quick succession. However, the individual acquired frames suffer from multiple degradations and are misaligned due to camera shake and object motions. The goal of Burst Image Restoration is to effectively combine complimentary cues across multiple burst fram... | ['Fahad Shahbaz Khan', 'Ming-Hsuan Yang', 'Salman Khan', 'Syed Waqas Zamir', 'Akshay Dudhane'] | 2021-10-07 | burst-image-restoration-and-enhancement-1 | http://openaccess.thecvf.com//content/CVPR2022/html/Dudhane_Burst_Image_Restoration_and_Enhancement_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Dudhane_Burst_Image_Restoration_and_Enhancement_CVPR_2022_paper.pdf | cvpr-2022-1 | ['burst-image-super-resolution'] | ['computer-vision'] | [ 5.51334023e-01 -5.47912896e-01 -3.38524915e-02 -1.43188134e-01
-8.35666358e-01 -9.31439847e-02 3.97787035e-01 6.64104074e-02
-2.91932523e-01 7.26617515e-01 2.71275461e-01 3.22963357e-01
3.81363519e-02 -5.95535755e-01 -5.08217514e-01 -1.01761150e+00
2.15857387e-01 -3.67691785e-01 5.27240694e-01 -2.74679214... | [10.918386459350586, -2.0040431022644043] |
80b17758-52cf-4c6f-bc4c-1db3ea74e6c8 | sgpn-similarity-group-proposal-network-for-3d | 1711.08588 | null | https://arxiv.org/abs/1711.08588v2 | https://arxiv.org/pdf/1711.08588v2.pdf | SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation | We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a single network to predict point grouping proposals and a corresponding semantic class for each proposal, from which we can directly extract instance segm... | ['Ronald Yu', 'Weiyue Wang', 'Ulrich Neumann', 'Qiangui Huang'] | 2017-11-23 | sgpn-similarity-group-proposal-network-for-3d-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_SGPN_Similarity_Group_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_SGPN_Similarity_Group_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-instance-segmentation-1', '3d-semantic-instance-segmentation', '3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.00630768e-01 2.69193143e-01 -1.52201997e-02 -5.68786502e-01
-5.57059050e-01 -5.21127403e-01 5.31044424e-01 2.66962618e-01
7.66267478e-02 -3.08327645e-01 -2.46947572e-01 -1.34515896e-01
-2.04322100e-01 -1.08876050e+00 -9.45362151e-01 -1.81372970e-01
-2.84565061e-01 9.17509079e-01 5.82908273e-01 -2.71769315... | [8.009840965270996, -3.304892063140869] |
9614b560-90fa-4827-864e-85dfe5846772 | using-the-verifiability-of-details-as-a-test | null | null | https://aclanthology.org/W16-0803 | https://aclanthology.org/W16-0803.pdf | Using the verifiability of details as a test of deception: A conceptual framework for the automation of the verifiability approach | null | ['Bruno Verschuere', 'Bennett Kleinberg', 'Galit Nahari'] | 2016-06-01 | null | null | null | ws-2016-6 | ['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... | [-7.330742359161377, 3.706559419631958] |
66e30a96-acec-4158-9b89-0597d36f9af1 | sparse-coding-of-shape-trajectories-for | 1908.03231 | null | https://arxiv.org/abs/1908.03231v1 | https://arxiv.org/pdf/1908.03231v1.pdf | Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition | The detection and tracking of human landmarks in video streams has gained in reliability partly due to the availability of affordable RGB-D sensors. The analysis of such time-varying geometric data is playing an important role in the automatic human behavior understanding. However, suitable shape representations as wel... | ['Hassen Drira', 'Boulbaba Ben Amor', 'Amor Ben Tanfous'] | 2019-08-08 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 6.24689795e-02 -3.43583584e-01 -1.91213980e-01 -3.01897854e-01
-2.69415289e-01 -2.74884373e-01 4.19792861e-01 -3.43117192e-02
-3.07752877e-01 2.58712620e-01 5.52820601e-02 3.07942092e-01
-4.25833553e-01 -2.81803071e-01 -2.43962616e-01 -8.48013461e-01
-4.22268838e-01 1.33334950e-01 -8.98233429e-02 -9.56746712... | [7.912685394287109, 3.8320798873901367] |
c96586b0-13e9-4929-9711-497ddacce531 | dynamic-prompt-learning-via-policy-gradient | 2209.14610 | null | https://arxiv.org/abs/2209.14610v3 | https://arxiv.org/pdf/2209.14610v3.pdf | Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning | Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks written in text form, such as math word problems (MWP... | ['Ashwin Kalyan', 'Peter Clark', 'Tanmay Rajpurohit', 'Song-Chun Zhu', 'Ying Nian Wu', 'Kai-Wei Chang', 'Liang Qiu', 'Pan Lu'] | 2022-09-29 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 3.99912074e-02 2.69241244e-01 -1.48561314e-01 -3.21927816e-01
-9.75863874e-01 -7.02314973e-01 4.04405624e-01 2.64900088e-01
-2.97056884e-01 7.42010057e-01 1.98024347e-01 -6.08404338e-01
-4.30776775e-01 -1.16444814e+00 -8.31326604e-01 -2.41791323e-01
4.13968295e-01 8.88072848e-01 2.66993523e-01 -2.95729160... | [9.762481689453125, 7.409705638885498] |
19828986-a02f-41a7-8e62-d307b4d0e430 | data-driven-spectrum-cartography-via-deep | 1911.12810 | null | http://arxiv.org/abs/1911.12810v1 | http://arxiv.org/pdf/1911.12810v1.pdf | Data-Driven Spectrum Cartography via Deep Completion Autoencoders | Spectrum maps, which provide RF spectrum metrics such as power spectral
density for every location in a geographic area, find numerous applications in
wireless communications such as interference control, spectrum management,
resource allocation, and network planning to name a few. Spectrum cartography
techniques const... | [] | 2019-11-28 | null | null | null | null | ['spectrum-cartography'] | ['computer-vision'] | [ 2.98299253e-01 -2.00236797e-01 2.35197265e-02 -1.87103018e-01
-9.59276035e-02 -3.79853338e-01 6.27945602e-01 6.23090602e-02
-1.91332266e-01 9.12131369e-01 2.28635937e-01 -4.43290263e-01
-6.76538706e-01 -1.20169055e+00 -4.82964844e-01 -7.99991190e-01
-3.32093209e-01 2.19947338e-01 -2.76630282e-01 -4.84930336... | [6.388161659240723, 1.2128592729568481] |
e95209c6-a99c-4e05-8761-06a2fbc1a0c9 | intrinsic-image-decomposition-using-paradigms | 2011.10512 | null | https://arxiv.org/abs/2011.10512v1 | https://arxiv.org/pdf/2011.10512v1.pdf | Intrinsic Image Decomposition using Paradigms | Intrinsic image decomposition is the classical task of mapping image to albedo. The WHDR dataset allows methods to be evaluated by comparing predictions to human judgements ("lighter", "same as", "darker"). The best modern intrinsic image methods learn a map from image to albedo using rendered models and human judgemen... | ['Jason J. Rock', 'D. A. Forsyth'] | 2020-11-20 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 4.85118955e-01 4.92239356e-01 4.08186108e-01 -5.48570812e-01
-8.22436869e-01 -4.81650233e-01 8.12377572e-01 -3.15840781e-01
-3.65324616e-01 6.88085973e-01 1.93052992e-01 -7.49361739e-02
2.76719451e-01 -6.64223850e-01 -8.90698135e-01 -1.02900803e+00
2.68746167e-01 2.83632934e-01 3.87731075e-01 -3.11638445... | [9.814903259277344, -2.949580669403076] |
91cb59b2-e76c-4c3d-9d0d-27571cb28059 | towards-perspective-free-object-counting-with | null | null | http://agamenon.tsc.uah.es/Investigacion/gram/publications/eccv2016-onoro.pdf | http://agamenon.tsc.uah.es/Investigacion/gram/publications/eccv2016-onoro.pdf | Towards perspective-free object counting with deep learning | In this paper we address the problem of counting objects
instances in images. Our models are able to precisely estimate the number of vehicles in a traffic congestion, or to count the humans in a very
crowded scene. Our first contribution is the proposal of a novel convolutional neural network solution, named Countin... | ['Roberto J. L´opez-Sastre', 'Daniel O˜noro-Rubio'] | 2016-01-01 | null | null | null | journal-2016-1 | ['object-counting'] | ['computer-vision'] | [-3.75450760e-01 -2.58387119e-01 3.98945324e-02 -5.07450521e-01
-4.30085629e-01 1.44782029e-02 7.80664563e-01 2.22655088e-01
-9.50918972e-01 7.07905948e-01 -2.25944713e-01 5.73873408e-02
3.37723613e-01 -1.30822980e+00 -9.13982093e-01 -3.69008929e-01
-2.39356384e-01 1.16230261e+00 7.33882844e-01 7.16124251... | [8.467133522033691, -0.2311514914035797] |
62c3febe-aa0c-4a35-9031-5445c5f6ae0e | hyt-nas-hybrid-transformers-neural | 2303.04440 | null | https://arxiv.org/abs/2303.04440v2 | https://arxiv.org/pdf/2303.04440v2.pdf | HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices | Vision Transformers have enabled recent attention-based Deep Learning (DL) architectures to achieve remarkable results in Computer Vision (CV) tasks. However, due to the extensive computational resources required, these architectures are rarely implemented on resource-constrained platforms. Current research investigate... | ['Hamza Ouarnoughi', 'Smail Niar', 'Hadjer Benmeziane', 'Lotfi Abdelkrim Mecharbat'] | 2023-03-08 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.77662134e-01 -3.74287665e-01 -2.60926992e-01 -3.12419266e-01
-3.52775335e-01 -2.64398545e-01 4.22549814e-01 -1.78201482e-01
-9.62165058e-01 2.72684485e-01 -1.14776781e-02 -6.66320324e-01
2.50583053e-01 -4.91397560e-01 -8.41719627e-01 -4.57646906e-01
4.24746424e-01 3.60200107e-01 4.06531185e-01 1.20337062... | [8.588685989379883, 2.94102144241333] |
c51976c8-173d-4838-9a5d-c8c01cd79285 | directionally-convolutional-networks-for-3d | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Xu_Directionally_Convolutional_Networks_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Xu_Directionally_Convolutional_Networks_ICCV_2017_paper.pdf | Directionally Convolutional Networks for 3D Shape Segmentation | Previous approaches on 3D shape segmentation mostly rely on heuristic processing and hand-tuned geometric descriptors. In this paper, we propose a novel 3D shape representation learning approach, Directionally Convolutional Network (DCN), to solve the shape segmentation problem. DCN extends convolution operations from ... | ['Zichun Zhong', 'Haotian Xu', 'Ming Dong'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['3d-shape-representation'] | ['computer-vision'] | [ 1.82489589e-01 -4.51669618e-02 -3.96929011e-02 -7.56082475e-01
-6.42760932e-01 -6.70890450e-01 6.03862226e-01 7.34442696e-02
-2.35985965e-01 1.38033852e-02 -1.30056560e-01 -2.86230475e-01
1.14266843e-01 -1.03212416e+00 -7.57381320e-01 -5.71519732e-01
9.00929645e-02 7.60170579e-01 1.79449648e-01 7.16633201... | [8.016037940979004, -3.550856828689575] |
ad8c1bd8-29ab-48b7-9987-9791c08f16d8 | inductive-matrix-completion-and-root-music | 2209.07642 | null | https://arxiv.org/abs/2209.07642v2 | https://arxiv.org/pdf/2209.07642v2.pdf | Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems | This paper studies the estimation of cascaded channels in passive intelligent reflective surface (IRS)- aided multiple-input multiple-output (MIMO) systems employing hybrid precoders and combiners. We propose a low-complexity solution that estimates the channel parameters progressively. The angles of departure (AoDs) a... | ['Y. Yu', 'J. Yuan', 'J. Xi', 'J. Tong', 'K. F. Masood'] | 2022-09-15 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 4.16741312e-01 -1.70130640e-01 4.24700081e-01 2.30851308e-01
-9.00055289e-01 -2.19120324e-01 2.47233942e-01 -7.44389892e-02
-2.72828162e-01 7.37343550e-01 3.46984982e-01 -7.33751506e-02
-4.60102618e-01 -4.89779890e-01 -4.14529532e-01 -1.12258315e+00
-4.97976422e-01 -2.46229604e-01 -2.45594606e-01 -3.13270092... | [6.408625602722168, 1.322953462600708] |
ee8e505a-4ffa-49e1-99ec-6ef4c0664f92 | enhancing-deep-learning-based-3-lead-ecg | 2208.07088 | null | https://arxiv.org/abs/2208.07088v1 | https://arxiv.org/pdf/2208.07088v1.pdf | Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration | Nowadays, an increasing number of people are being diagnosed with cardiovascular diseases (CVDs), the leading cause of death globally. The gold standard for identifying these heart problems is via electrocardiogram (ECG). The standard 12-lead ECG is widely used in clinical practice and the majority of current research.... | ['Cuong D. Do', 'Tu A. Nguyen', 'Thao B. T. Nguyen', 'Hieu H. Pham', 'Khiem H. Le'] | 2022-08-15 | null | null | null | null | ['ecg-classification'] | ['medical'] | [-6.32061958e-02 -4.04589951e-01 -5.29333539e-02 -4.07817632e-01
-7.94212282e-01 -2.45837167e-01 -3.14133108e-01 5.06637156e-01
-3.99474055e-01 7.12723851e-01 -1.90161332e-01 -6.16010666e-01
-1.10880554e-01 -7.42808700e-01 -1.97206736e-01 -5.93886077e-01
-1.75686613e-01 3.48954529e-01 -2.39729971e-01 1.09116063... | [14.308778762817383, 3.2284328937530518] |
5fdf597c-ce44-4eeb-a901-09226b9a0f54 | leveraging-pretrained-representations-with | 2303.08019 | null | https://arxiv.org/abs/2303.08019v1 | https://arxiv.org/pdf/2303.08019v1.pdf | Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection | With the global population aging rapidly, Alzheimer's disease (AD) is particularly prominent in older adults, which has an insidious onset and leads to a gradual, irreversible deterioration in cognitive domains (memory, communication, etc.). Speech-based AD detection opens up the possibility of widespread screening and... | ['Helen Meng', 'Xunying Liu', 'Xixin Wu', 'Dongsheng Li', 'Bo Zheng', 'Junan Li', 'Kaitao Song', 'Jinchao Li'] | 2023-03-14 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 2.81434059e-01 -7.70912096e-02 1.66294530e-01 -5.77835619e-01
-1.23482668e+00 1.72018376e-03 7.51387417e-01 3.18788856e-01
-7.88582385e-01 6.99603677e-01 4.97708559e-01 1.57026112e-01
-2.36258179e-01 -4.81088817e-01 9.07958150e-02 -3.00841987e-01
-4.85972643e-01 5.98709226e-01 3.75833899e-01 -1.38296410... | [13.924514770507812, 5.379210948944092] |
caf44b70-3759-40aa-a61d-803cfd6880dc | an-embarrassingly-simple-approach-to-zero | null | null | https://dl.acm.org/doi/10.5555/3045118.3045347 | http://jmlr.org/proceedings/papers/v37/romera-paredes15.pdf | An embarrassingly simple approach to zero-shot learning | Zero-shot learning consists in learning how to recognise new concepts by just having a description of them. Many sophisticated approaches have been proposed to address the challenges this problem comprises. In this paper we describe a zero-shot learning approach that can be implemented in just one line of code, yet it ... | ['Philip H. S. Torr', 'Bernardino Romera-Paredes'] | 2015-07-06 | null | null | null | proceedings-of-the-international-conference-1 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 2.90150881e-01 3.23478669e-01 -5.85968345e-02 -5.96324503e-01
-5.29321492e-01 -1.57811597e-01 9.45106566e-01 3.54265153e-01
-7.00742543e-01 5.77595592e-01 -1.25520900e-01 5.05312979e-02
-2.83056051e-01 -7.70562112e-01 -6.53561592e-01 -5.52136123e-01
-1.99382052e-01 6.45897508e-01 7.64546394e-01 -3.88540000... | [9.930137634277344, 3.009136915206909] |
2753bb40-27d5-4631-9818-49bc05b3cda7 | towards-writing-style-adaptation-in | 2302.06318 | null | https://arxiv.org/abs/2302.06318v1 | https://arxiv.org/pdf/2302.06318v1.pdf | Towards Writing Style Adaptation in Handwriting Recognition | One of the challenges of handwriting recognition is to transcribe a large number of vastly different writing styles. State-of-the-art approaches do not explicitly use information about the writer's style, which may be limiting overall accuracy due to various ambiguities. We explore models with writer-dependent paramete... | ['Martin Kišš', 'Michal Hradiš', 'Jan Kohút'] | 2023-02-13 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 1.85574144e-01 -2.44077131e-01 -1.49102911e-01 -5.94265163e-01
-4.79729056e-01 -1.09163284e+00 1.06518877e+00 -9.51735023e-03
-5.35659671e-01 6.42110169e-01 3.28703552e-01 -1.39093176e-01
1.50834052e-02 -3.66987079e-01 -4.81745034e-01 -5.88236272e-01
6.48087025e-01 6.90055609e-01 1.06632024e-01 -2.32323974... | [11.742884635925293, 2.5027074813842773] |
daa591a6-9cb3-4348-83b9-f02b4fef68ca | don-t-eclipse-your-arts-due-to-small | null | null | https://aclanthology.org/2020.acl-main.339 | https://aclanthology.org/2020.acl-main.339.pdf | Don't Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction | The current aspect extraction methods suffer from boundary errors. In general, these errors lead to a relatively minor difference between the extracted aspects and the ground-truth. However, they hurt the performance severely. In this paper, we propose to utilize a pointer network for repositioning the boundaries. Recy... | ['Zhenkai Wei', 'Yu Hong', 'Meng Cheng', 'Bowei Zou', 'Jianmin Yao'] | 2020-07-01 | null | null | null | acl-2020-6 | ['aspect-extraction'] | ['natural-language-processing'] | [-9.56013054e-02 7.04627559e-02 -5.07428050e-01 -2.92966634e-01
-4.14484292e-01 -4.24178362e-01 2.42791682e-01 -7.54245967e-02
-2.42658883e-01 6.61668599e-01 3.06112707e-01 -3.17472070e-01
9.99057218e-02 -9.62269604e-01 -5.95605016e-01 -4.65408176e-01
8.10560063e-02 2.53893388e-03 4.85767037e-01 -7.29339868... | [9.960394859313965, 8.746295928955078] |
b066a2a8-cd8d-4eb6-afdd-a165c3c8dc67 | deepastrouda-semi-supervised-universal-domain | 2302.02005 | null | https://arxiv.org/abs/2302.02005v2 | https://arxiv.org/pdf/2302.02005v2.pdf | DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection | Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present ... | ['S. M. Wild', 'G. N. Perdue', 'B. Nord', 'S. Madireddy', 'K. Pedro', 'A. Lewis', 'A. Ćiprijanović'] | 2023-02-03 | null | null | null | null | ['morphology-classification', 'universal-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [-3.22908759e-02 -3.34545404e-01 1.08078867e-01 -5.59136510e-01
-5.85563898e-01 -9.85098541e-01 6.87423587e-01 2.45694723e-03
-4.62929070e-01 7.83100247e-01 -4.77491736e-01 -5.42473316e-01
-4.11881596e-01 -6.63853407e-01 -4.79424477e-01 -8.68972838e-01
9.47891101e-02 1.00486398e+00 6.93290889e-01 -8.83764774... | [7.861208915710449, 2.785400629043579] |
3c2e9190-29cb-4867-883f-3a98805afd17 | boun-at-semeval-2021-task-9-text-augmentation | null | null | https://aclanthology.org/2021.semeval-1.52 | https://aclanthology.org/2021.semeval-1.52.pdf | BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data | In this paper, we present our text augmentation based approach for the Table Statement Support Subtask (Phase A) of SemEval-2021 Task 9. We experiment with different text augmentation techniques such as back translation and synonym swapping using Word2Vec and WordNet. We show that text augmentation techniques lead to 2... | ['Arzucan {\\"O}zg{\\"u}r', 'Bekir Y{\\i}ld{\\i}r{\\i}m', 'Yusuf Y{\\"u}ksel', 'Abdullatif K{\\"o}ksal'] | 2021-08-01 | null | null | null | semeval-2021 | ['text-augmentation'] | ['natural-language-processing'] | [ 2.70769119e-01 4.04650718e-01 -6.06762111e-01 -3.04124177e-01
-1.04930627e+00 -8.13187122e-01 8.63776505e-01 7.15461791e-01
-5.92663467e-01 1.21421313e+00 3.77178103e-01 -6.46034598e-01
4.95752729e-02 -6.30104542e-01 -7.53907323e-01 -5.06501719e-02
1.66896433e-01 7.51578927e-01 1.47937164e-02 -5.04393816... | [9.927148818969727, 8.672983169555664] |
1f7a654d-a323-4395-9cc9-c966bb695612 | attention-enhanced-deep-learning-for-device | 2304.13105 | null | https://arxiv.org/abs/2304.13105v1 | https://arxiv.org/pdf/2304.13105v1.pdf | Attention-Enhanced Deep Learning for Device-Free Through-the-Wall Presence Detection Using Indoor WiFi System | Accurate detection of human presence in indoor environments is important for various applications, such as energy management and security. In this paper, we propose a novel system for human presence detection using the channel state information (CSI) of WiFi signals. Our system named attention-enhanced deep learning fo... | ['Kai-Ten Feng', 'An-Hung Hsiao', 'Kuan-I Lu', 'Li-Hsiang Shen'] | 2023-04-25 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 7.64254555e-02 -7.13767827e-01 5.35686910e-02 -2.62613833e-01
-6.19926512e-01 -1.04058973e-01 3.29831213e-01 -3.74909163e-01
-4.69896406e-01 8.17791462e-01 3.75118017e-01 -3.55734259e-01
-3.21668416e-01 -7.60661364e-01 -6.49160564e-01 -7.90680051e-01
-4.49924082e-01 -3.60839874e-01 -7.82481581e-02 -7.07449168... | [6.6312055587768555, 0.7544185519218445] |
2396cba3-9146-4e79-8e04-33cad90dffe9 | distributed-learning-of-deep-neural-networks | 1910.02120 | null | https://arxiv.org/abs/1910.02120v7 | https://arxiv.org/pdf/1910.02120v7.pdf | Distributed Learning of Deep Neural Networks using Independent Subnet Training | Distributed machine learning (ML) can bring more computational resources to bear than single-machine learning, thus enabling reductions in training time. Distributed learning partitions models and data over many machines, allowing model and dataset sizes beyond the available compute power and memory of a single machine... | ['Yuxin Tang', 'Chen Dun', 'Cameron R. Wolfe', 'Christopher M. Jermaine', 'Binhang Yuan', 'Anastasios Kyrillidis'] | 2019-10-04 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-1.90671265e-01 1.47821933e-01 -3.40357155e-01 -3.73096585e-01
-4.27047282e-01 -7.14028537e-01 2.22505406e-01 1.83164358e-01
-5.94633758e-01 9.24984872e-01 -5.50495744e-01 -4.27400351e-01
-3.55279207e-01 -8.37884247e-01 -6.81851983e-01 -8.54958713e-01
1.70317348e-02 8.93936574e-01 3.37988615e-01 6.23106062... | [6.079430103302002, 6.456934452056885] |
6eb66f85-98a9-4ca6-9777-6364425a5213 | improving-passage-retrieval-with-zero-shot | 2204.07496 | null | https://arxiv.org/abs/2204.07496v4 | https://arxiv.org/pdf/2204.07496v4.pdf | Improving Passage Retrieval with Zero-Shot Question Generation | We propose a simple and effective re-ranking method for improving passage retrieval in open question answering. The re-ranker re-scores retrieved passages with a zero-shot question generation model, which uses a pre-trained language model to compute the probability of the input question conditioned on a retrieved passa... | ['Luke Zettlemoyer', 'Joelle Pineau', 'Wen-tau Yih', 'Armen Aghajanyan', 'Mandar Joshi', 'Mike Lewis', 'Devendra Singh Sachan'] | 2022-04-15 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-2.84086596e-02 1.03399314e-01 -1.47307545e-01 3.38409729e-02
-1.79216099e+00 -7.34450519e-01 7.40400910e-01 6.37202978e-01
-6.30433381e-01 8.29201758e-01 5.90063214e-01 -2.52150416e-01
-3.49075258e-01 -8.44465494e-01 -8.82239938e-01 -9.01189819e-02
1.21002294e-01 9.12097275e-01 8.08459580e-01 -7.86053240... | [11.454547882080078, 7.757139682769775] |
fdff8702-1770-4cdc-980c-1f7affcc56ca | monocular-arbitrary-moving-object-discovery | null | null | https://www.bmvc2021-virtualconference.com/conference/papers/paper_1500.html | https://www.bmvc2021-virtualconference.com/assets/papers/1500.pdf | Monocular Arbitrary Moving Object Discovery and Segmentation | We propose a method for discovery and segmentation of objects that are, or their parts are, independently moving in the scene. Given three monocular video frames, the method outputs semantically meaningful regions, i.e. regions corresponding to the whole object, even when only a part of it moves.
The architecture of t... | ['Jiří Matas', 'Jan Šochman', 'Michal Neoral'] | 2021-11-22 | null | null | null | the-32nd-british-machine-vision-conference | ['motion-segmentation'] | ['computer-vision'] | [ 4.91273329e-02 -8.42832327e-02 -3.03171873e-01 -3.14309746e-01
-4.85365152e-01 -8.04706991e-01 4.60306853e-01 -4.15334433e-01
-4.39602882e-01 3.48815084e-01 -1.98493272e-01 -6.03169203e-02
1.07455172e-01 -4.71231192e-01 -8.09065580e-01 -7.28684783e-01
-4.54433337e-02 6.78047240e-01 1.03442049e+00 1.28106922... | [8.544044494628906, -1.3542943000793457] |
682b1b58-9761-400b-a04a-4ba0f678b404 | speechmatrix-a-large-scale-mined-corpus-of | null | null | https://research.facebook.com/publications/speechmatrix/ | https://scontent-lhr8-2.xx.fbcdn.net/v/t39.8562-6/310002966_605149234737289_5204270723809834290_n.pdf?_nc_cat=102&ccb=1-7&_nc_sid=ad8a9d&_nc_ohc=FN2KnupyKI0AX90B5UO&_nc_ht=scontent-lhr8-2.xx&oh=00_AT9iFWHchGOnkzVTmwiYIDElIXSnwilSGhDwRQdFh99rlA&oe=63560915https://scontent-lhr8-2.xx.fbcdn.net/v/t39.8562-6/310002966_60514... | SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations | We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. It contains speech alignments in 136 language pairs with a total of 418 thousand hours of speech. To evaluate the quality of this parallel speech, we train bilingual spee... | ['Holger Schwenk', 'Benoît Sagot', 'Juan Pino', 'Changhan Wang', 'Vedanuj Goswani', 'Ann Lee', 'Jingfei Du', 'Ning Dong', 'Hongyu Gong', 'Paul-Ambroise Duquenne'] | 2022-10-19 | null | null | null | arxiv-2022-10 | ['speech-to-speech-translation'] | ['speech'] | [-4.68200222e-02 3.57836694e-01 -3.19270819e-01 -4.81695235e-01
-1.77245605e+00 -7.86270142e-01 9.98509169e-01 -3.11200202e-01
-4.82862830e-01 9.33586538e-01 7.43527412e-01 -8.63727808e-01
4.52587992e-01 -1.83597818e-01 -8.49793077e-01 -3.71822178e-01
1.25601426e-01 1.04491711e+00 -1.58999547e-01 -5.85847080... | [14.423768043518066, 7.210793972015381] |
4187a73f-ce9a-48c2-b514-1e9f12bb420a | utfpr-at-semeval-2021-task-1-complexity | null | null | https://aclanthology.org/2021.semeval-1.78 | https://aclanthology.org/2021.semeval-1.78.pdf | UTFPR at SemEval-2021 Task 1: Complexity Prediction by Combining BERT Vectors and Classic Features | We describe the UTFPR systems submitted to the Lexical Complexity Prediction shared task of SemEval 2021. They perform complexity prediction by combining classic features, such as word frequency, n-gram frequency, word length, and number of senses, with BERT vectors. We test numerous feature combinations and machine le... | ['Gustavo Henrique Paetzold'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-2.08693504e-01 -2.29701206e-01 -3.35004568e-01 -1.16655879e-01
-5.71121693e-01 -8.32965851e-01 4.57637906e-01 4.73392874e-01
-7.99894691e-01 6.81315243e-01 6.23814642e-01 -8.38348985e-01
-1.21074721e-01 -5.46787143e-01 -1.69606909e-01 -7.20210895e-02
-4.76778865e-01 4.36424553e-01 2.00669333e-01 -6.08257651... | [10.673240661621094, 10.45430850982666] |
34ff5de6-aa21-4380-ae19-2d4a438545f5 | game-state-learning-via-game-scene | 2207.01289 | null | https://arxiv.org/abs/2207.01289v2 | https://arxiv.org/pdf/2207.01289v2.pdf | Game State Learning via Game Scene Augmentation | Having access to accurate game state information is of utmost importance for any artificial intelligence task including game-playing, testing, player modeling, and procedural content generation. Self-Supervised Learning (SSL) techniques have shown to be capable of inferring accurate game state information from the high... | ['Georgios N. Yannakakis', 'Antonios Liapis', 'Konstantinos Makantasis', 'Chintan Trivedi'] | 2022-07-04 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.61551994e-01 6.69140443e-02 1.22146280e-02 -1.30105644e-01
-5.29764533e-01 -5.72364151e-01 7.39882052e-01 -3.70716713e-02
-3.96426588e-01 4.88843173e-01 1.52557984e-01 -6.00633085e-01
1.87577248e-01 -1.02741110e+00 -6.52590990e-01 -3.64870995e-01
2.83351708e-02 4.12468314e-01 4.77016211e-01 -6.38761818... | [11.01691722869873, -0.24560517072677612] |
57117572-3296-4bdc-966a-08cc19f91833 | uncertainty-aware-unlikelihood-learning | 2306.00418 | null | https://arxiv.org/abs/2306.00418v2 | https://arxiv.org/pdf/2306.00418v2.pdf | Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction | Recently, aspect sentiment quad prediction has received widespread attention in the field of aspect-based sentiment analysis. Existing studies extract quadruplets via pre-trained generative language models to paraphrase the original sentence into a templated target sequence. However, previous works only focus on what t... | ['Minlie Huang', 'Shiwan Zhao', 'Hang Gao', 'Liqi Zhang', 'Zhen Zhang', 'Yike Wu', 'Yinhao Bai', 'Mengting Hu'] | 2023-06-01 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 2.74532169e-01 9.53430682e-02 -3.14552277e-01 -7.33366251e-01
-1.19792914e+00 -6.44042253e-01 6.89681947e-01 -1.22449584e-01
-3.69334400e-01 8.32789123e-01 3.45745265e-01 -2.77052104e-01
4.23028618e-01 -1.04260242e+00 -8.95190179e-01 -5.80557704e-01
6.43089592e-01 2.62111306e-01 -1.50902003e-01 -1.73918411... | [11.480672836303711, 6.7404046058654785] |
39507cca-0814-41ae-9002-8c4f0e0a5798 | multi-contextual-design-of-convolutional | 2106.10430 | null | https://arxiv.org/abs/2106.10430v2 | https://arxiv.org/pdf/2106.10430v2.pdf | Multi-Contextual Design of Convolutional Neural Network for Steganalysis | In recent times, deep learning-based steganalysis classifiers became popular due to their state-of-the-art performance. Most deep steganalysis classifiers usually extract noise residuals using high-pass filters as preprocessing steps and feed them to their deep model for classification. It is observed that recent stega... | ['Pinaki Mitra', 'Arijit Sur', 'Brijesh Singh'] | 2021-06-19 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 5.81173062e-01 -4.86649983e-02 -4.88917828e-02 8.43174607e-02
-5.88118494e-01 1.42527282e-01 5.66873193e-01 -3.42115134e-01
-2.56280214e-01 1.70523480e-01 1.59691885e-01 -2.22005084e-01
4.62971807e-01 -8.57032597e-01 -6.24359190e-01 -1.15551841e+00
-2.03038916e-01 -3.79500568e-01 1.78310558e-01 -4.91468757... | [4.296449661254883, 8.055971145629883] |
9407618e-0dbc-41c3-b255-032f07b3d5c7 | learning-personalized-decision-support | 2304.06701 | null | https://arxiv.org/abs/2304.06701v1 | https://arxiv.org/pdf/2304.06701v1.pdf | Learning Personalized Decision Support Policies | Individual human decision-makers may benefit from different forms of support to improve decision outcomes. However, a key question is which form of support will lead to accurate decisions at a low cost. In this work, we propose learning a decision support policy that, for a given input, chooses which form of support, i... | ['Ameet Talwalkar', 'Adrian Weller', 'Emma Kallina', 'Parameswaran Kamalaruban', 'Katherine M. Collins', 'Valerie Chen', 'Umang Bhatt'] | 2023-04-13 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.71707267e-01 6.65585846e-02 -7.09820867e-01 -9.34682310e-01
-9.26909328e-01 -7.08956242e-01 2.38669932e-01 2.09096536e-01
-6.49371386e-01 7.25056171e-01 2.17175540e-02 -1.00577474e+00
-5.14825642e-01 -6.37594521e-01 -5.02797067e-01 -3.06225747e-01
-1.06395647e-01 7.92279005e-01 -7.65068606e-02 7.40197375... | [4.515774250030518, 3.1542913913726807] |
29519d99-59c9-4a63-ab62-0a8417d437f5 | constrained-bilinear-factorization-multi-view | 1906.08107 | null | https://arxiv.org/abs/1906.08107v2 | https://arxiv.org/pdf/1906.08107v2.pdf | Constrained Bilinear Factorization Multi-view Subspace Clustering | Multi-view clustering is an important and fundamental problem. Many multi-view subspace clustering methods have been proposed, and most of them assume that all views share a same coefficient matrix. However, the underlying information of multi-view data are not fully exploited under this assumption, since the coefficie... | ['Zhiqiang Tian', 'Xiuyi Jia', 'Shanmin Pang', 'Qinghai Zheng', 'Jihua Zhu', 'Zhongyu Li'] | 2019-06-19 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.46175611e-01 -5.84392011e-01 -1.01820670e-01 -1.71756029e-01
-3.38696629e-01 -7.06629753e-01 3.18154752e-01 -2.82214761e-01
-1.22256294e-01 3.32374007e-01 3.85208935e-01 1.19439766e-01
-3.08539987e-01 -3.85125101e-01 -2.53518224e-01 -1.12078810e+00
3.27860624e-01 6.30026683e-02 1.60636858e-03 -1.06548471... | [8.256275177001953, 4.635499954223633] |
41960cf6-1747-434b-836a-8492660bdbd8 | pointrnn-point-recurrent-neural-network-for | 1910.08287 | null | https://arxiv.org/abs/1910.08287v2 | https://arxiv.org/pdf/1910.08287v2.pdf | PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing | In this paper, we introduce a Point Recurrent Neural Network (PointRNN) for moving point cloud processing. At each time step, PointRNN takes point coordinates $\boldsymbol{P} \in \mathbb{R}^{n \times 3}$ and point features $\boldsymbol{X} \in \mathbb{R}^{n \times d}$ as input ($n$ and $d$ denote the number of points an... | ['Yi Yang', 'Hehe Fan'] | 2019-10-18 | null | null | null | null | ['moving-point-cloud-processing'] | ['time-series'] | [-1.14486583e-01 -5.41647911e-01 2.61019580e-02 -1.14453159e-01
-5.55176437e-01 -5.43772757e-01 5.51793694e-01 7.46496068e-03
-3.28235358e-01 6.34045959e-01 -7.00037718e-01 -5.23946464e-01
-4.25736785e-01 -1.29278541e+00 -1.02216053e+00 -6.93573415e-01
-6.97943568e-01 4.20969367e-01 2.41143003e-01 -4.46880400... | [7.984322547912598, -3.3434951305389404] |
ca423c74-78ec-4b2b-ab33-30c9de8fb43b | few-shot-speaker-identification-using | 2204.11180 | null | https://arxiv.org/abs/2204.11180v1 | https://arxiv.org/pdf/2204.11180v1.pdf | Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention | Although few-shot learning has attracted much attention from the fields of image and audio classification, few efforts have been made on few-shot speaker identification. In the task of few-shot learning, overfitting is a tough problem mainly due to the mismatch between training and testing conditions. In this paper, we... | ['Qianhua He', 'Wei Li', 'Wenchang Cao', 'Hao Chen', 'Wucheng Wang', 'Yanxiong Li'] | 2022-04-24 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 8.80132604e-04 -2.84376681e-01 -1.25422508e-01 -5.43079674e-01
-1.18138409e+00 1.14414595e-01 3.45698267e-01 -3.64722043e-01
-3.74360502e-01 3.58368218e-01 8.86575058e-02 1.85591698e-01
1.30459443e-02 -2.17964575e-01 -3.14624488e-01 -8.24053586e-01
3.20731014e-01 7.00862110e-02 1.63279131e-01 -1.04584083... | [14.376789093017578, 5.98995304107666] |
a63080bd-c6da-4ecc-9163-9160db545257 | preference-grounded-token-level-guidance-for | 2306.00398 | null | https://arxiv.org/abs/2306.00398v1 | https://arxiv.org/pdf/2306.00398v1.pdf | Preference-grounded Token-level Guidance for Language Model Fine-tuning | Aligning language models (LMs) with preferences is an important problem in natural language generation. A key challenge is that preferences are typically provided at the sequence level while LM training and generation both occur at the token level. There is, therefore, a granularity mismatch between the preference and ... | ['Mingyuan Zhou', 'Caiming Xiong', 'Yihao Feng', 'Congying Xia', 'Shujian Zhang', 'Shentao Yang'] | 2023-06-01 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 7.50837028e-01 2.56993622e-01 -5.80600321e-01 -2.87597388e-01
-1.43013239e+00 -8.36383343e-01 6.13388598e-01 9.58453119e-03
-4.04174089e-01 9.62088227e-01 5.61681032e-01 -4.50295419e-01
2.15179488e-01 -6.23152256e-01 -8.05854201e-01 -6.21588707e-01
4.27321911e-01 5.59156597e-01 -1.47913530e-01 -1.57014072... | [11.70391845703125, 9.154379844665527] |
9ce3038a-97f0-4a80-b67a-bcea7ebef921 | improving-adversarial-robustness-by-1 | 2210.09643 | null | https://arxiv.org/abs/2210.09643v2 | https://arxiv.org/pdf/2210.09643v2.pdf | Improving Adversarial Robustness by Contrastive Guided Diffusion Process | Synthetic data generation has become an emerging tool to help improve the adversarial robustness in classification tasks since robust learning requires a significantly larger amount of training samples compared with standard classification tasks. Among various deep generative models, the diffusion model has been shown ... | ['Guang Cheng', 'Liyan Xie', 'Yidong Ouyang'] | 2022-10-18 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.71203434e-01 -2.13079989e-01 1.58520937e-01 -2.81474572e-02
-8.95897031e-01 -5.43649733e-01 8.28892469e-01 -2.53176838e-01
-2.26479456e-01 8.25404167e-01 4.32573296e-02 -9.09070820e-02
-1.16167724e-01 -9.45131242e-01 -6.59731507e-01 -1.12306654e+00
2.29187846e-01 1.16598360e-01 9.77789313e-02 -1.06366031... | [11.691642761230469, -0.35350921750068665] |
af454f84-4208-407b-b640-025c1a5975e0 | towards-annotating-and-creating-summary | null | null | https://aclanthology.org/D19-5408 | https://aclanthology.org/D19-5408.pdf | Towards Annotating and Creating Summary Highlights at Sub-sentence Level | Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences. They are also better suited than individual words and phrases that can potentially lead to disfluent,... | ['Fei Liu', 'Parminder Bhatia', 'Kristjan Arumae'] | 2019-11-01 | null | null | null | ws-2019-11 | ['sentence-compression'] | ['natural-language-processing'] | [ 5.50798237e-01 2.88215131e-01 -3.71774912e-01 -2.95007050e-01
-1.42855299e+00 -5.54891348e-01 4.91549104e-01 8.11740160e-01
-3.38532537e-01 1.28961766e+00 9.19796288e-01 -1.57923251e-01
2.09562391e-01 -5.92253506e-01 -4.47508603e-01 -5.67930222e-01
1.24940770e-02 7.11137205e-02 -4.70046476e-02 -1.23864807... | [12.592476844787598, 9.546032905578613] |
a4da29b6-b9e3-4cf4-8f4d-84a9b35b35c9 | temporal-pointwise-convolutional-networks-for | 2007.09483 | null | https://arxiv.org/abs/2007.09483v4 | https://arxiv.org/pdf/2007.09483v4.pdf | Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit | The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff. Most critical is the efficient allocation of resource-heavy Intensive Care Unit (ICU) beds to the patients who need life support. Central to solving this problem is knowing for how l... | ['Pietro Liò', 'Stephanie Hyland', 'Emma Rocheteau'] | 2020-07-18 | null | null | null | null | ['remaining-length-of-stay', 'length-of-stay-prediction', 'predicting-patient-outcomes'] | ['medical', 'medical', 'medical'] | [-7.63526037e-02 -2.14304686e-01 1.01398736e-01 -3.45968604e-01
-5.83933711e-01 -8.44847411e-03 -2.28952780e-01 3.61179292e-01
-7.02476084e-01 8.69103491e-01 4.33377773e-01 -7.59917915e-01
-5.77160358e-01 -6.53503001e-01 -6.39121294e-01 -5.61864674e-01
-2.07725078e-01 6.10463262e-01 -4.32439595e-01 1.95102721... | [7.935437202453613, 6.204479217529297] |
23150b9b-f570-45bf-8e5a-80a4c22baf25 | detcid-detection-of-elongated-touching-cells | 2007.06716 | null | https://arxiv.org/abs/2007.06716v1 | https://arxiv.org/pdf/2007.06716v1.pdf | DETCID: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Deep Adversarial Network | Clostridioides difficile infection (C. diff) is the most common cause of death due to secondary infection in hospital patients in the United States. Detection of C. diff cells in scanning electron microscopy (SEM) images is an important task to quantify the efficacy of the under-development treatments. However, detecti... | ['Ioannis A. Kakadiaris', 'Ali Memariani'] | 2020-07-13 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 4.48638856e-01 -5.55581152e-01 6.94825888e-01 1.07239306e-01
-6.87990546e-01 -6.65031195e-01 2.68282086e-01 5.52663028e-01
-6.68190241e-01 6.13581717e-01 -4.85104531e-01 3.25392038e-02
4.87566024e-01 -5.66032112e-01 -5.05501568e-01 -1.28235137e+00
1.26227662e-01 4.70746070e-01 -1.94450300e-02 2.35985592... | [14.8453950881958, -3.1429507732391357] |
10752c6b-e083-40a2-a3b8-4233c6442158 | entsum-a-data-set-for-entity-centric-2 | null | null | https://aclanthology.org/2022.acl-long.237 | https://aclanthology.org/2022.acl-long.237.pdf | EntSUM: A Data Set for Entity-Centric Extractive Summarization | Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document.We introduce a human-annotated data set EntSUM for contr... | ['Daniel Preotiuc-Pietro', 'Mayank Kulkarni', 'Mounica Maddela'] | null | null | null | null | acl-2022-5 | ['extractive-summarization'] | ['natural-language-processing'] | [ 2.25250497e-01 8.31466496e-01 -5.61563849e-01 -4.16319340e-01
-1.31500590e+00 -9.13918734e-01 7.36494124e-01 7.97821939e-01
-1.82744250e-01 1.06817114e+00 1.32242846e+00 1.29872724e-01
-5.03944978e-02 -5.49223721e-01 -3.37160826e-01 -6.10562451e-02
1.15979932e-01 8.01773787e-01 4.67528962e-02 -3.49249154... | [12.487316131591797, 9.414695739746094] |
41c295cd-7ba2-4112-b67c-34cad7ccd55d | feature-affinity-based-pseudo-labeling-for | 1805.06118 | null | http://arxiv.org/abs/1805.06118v1 | http://arxiv.org/pdf/1805.06118v1.pdf | Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification | Person re-identification aims to match a person's identity across multiple
camera streams. Deep neural networks have been successfully applied to the
challenging person re-identification task. One remarkable bottleneck is that
the existing deep models are data hungry and require large amounts of labeled
training data. ... | ['Shanshan Zhang', 'Guodong Ding', 'Salman Khan', 'Fatih Porikli', 'Zhenmin Tang', 'Jian Zhang'] | 2018-05-16 | null | null | null | null | ['semi-supervised-person-re-identification'] | ['computer-vision'] | [ 6.85973316e-02 -2.67810106e-01 -3.73655558e-02 -7.72599399e-01
-6.15328968e-01 -5.49288034e-01 7.60221958e-01 -9.52376723e-02
-6.88338876e-01 6.96404338e-01 1.00771368e-01 2.89424360e-01
3.43818009e-01 -6.73596859e-01 -6.76351309e-01 -5.53838730e-01
1.67036459e-01 8.37870598e-01 -3.16291511e-01 9.19833109... | [14.726776123046875, 1.0299437046051025] |
ea221ed2-60e5-4cf5-aae6-cbb7f57e6603 | spiking-network-initialisation-and-firing | 2305.08879 | null | https://arxiv.org/abs/2305.08879v1 | https://arxiv.org/pdf/2305.08879v1.pdf | Spiking Network Initialisation and Firing Rate Collapse | In recent years, newly developed methods to train spiking neural networks (SNNs) have rendered them as a plausible alternative to Artificial Neural Networks (ANNs) in terms of accuracy, while at the same time being much more energy efficient at inference and potentially at training time. However, it is still unclear wh... | ['Dan F. M Goodman', 'Nicolas Perez-Nieves'] | 2023-05-13 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 6.26795530e-01 -2.37399951e-01 6.18298650e-01 2.22486556e-01
-1.61987990e-01 -4.44261760e-01 6.26904786e-01 1.77035332e-01
-9.44748342e-01 1.04085433e+00 -4.00760651e-01 -2.82618284e-01
-4.74111229e-01 -7.76359260e-01 -5.58315694e-01 -1.29368973e+00
6.73257411e-02 2.77551651e-01 5.09214580e-01 -9.56484079... | [8.087684631347656, 2.8728740215301514] |
a69b4579-0d3d-4981-b056-e718a24cbf68 | evolving-graph-gaussian-processes | 2106.15127 | null | https://arxiv.org/abs/2106.15127v2 | https://arxiv.org/pdf/2106.15127v2.pdf | Evolving-Graph Gaussian Processes | Graph Gaussian Processes (GGPs) provide a data-efficient solution on graph structured domains. Existing approaches have focused on static structures, whereas many real graph data represent a dynamic structure, limiting the applications of GGPs. To overcome this we propose evolving-Graph Gaussian Processes (e-GGPs). The... | ['Ville Kyrki', 'Markus Heinonen', 'David Blanco-Mulero'] | 2021-06-29 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-2.07929507e-01 2.19296608e-02 2.31069744e-01 1.31390974e-01
-2.39055291e-01 -4.67931539e-01 9.22863305e-01 5.06356180e-01
1.84830790e-03 4.33246046e-01 -7.93634728e-02 -2.94304997e-01
-3.37640315e-01 -1.20098007e+00 -5.16829014e-01 -9.65388834e-01
-9.49724495e-01 7.94702947e-01 5.13875544e-01 1.45001978... | [7.123786449432373, 5.738951206207275] |
fa06a83d-3e67-4017-947b-02a714a3ef36 | a-combinatorial-semi-bandit-approach-to | 2301.07156 | null | https://arxiv.org/abs/2301.07156v1 | https://arxiv.org/pdf/2301.07156v1.pdf | A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles | In this work, we address the problem of long-distance navigation for battery electric vehicles (BEVs), where one or more charging sessions are required to reach the intended destination. We consider the availability and performance of the charging stations to be unknown and stochastic, and develop a combinatorial semi-... | ['Morteza Haghir Chehreghani', 'Niklas Åkerblom'] | 2023-01-17 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 6.15292415e-02 3.28407168e-01 -4.89064604e-01 -3.46936166e-01
-1.11376584e+00 -7.43425667e-01 2.95281708e-01 -2.36672550e-01
-3.69652450e-01 1.34024072e+00 -2.17344388e-01 -9.80775058e-01
-1.28381658e+00 -9.30956841e-01 -9.57393825e-01 -9.67428744e-01
-2.21773043e-01 1.07090831e+00 -1.36734068e-01 1.89857055... | [4.651200294494629, 3.081906795501709] |
8ffd0d32-0b41-4256-bc6c-909a1a6d44de | global-meets-local-effective-multi-label | 2211.12716 | null | https://arxiv.org/abs/2211.12716v1 | https://arxiv.org/pdf/2211.12716v1.pdf | Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision | Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Although region-based methods are less likely to encounter issues with model generalizability than label-dependency methods, they often genera... | ['Yuan Xie', 'Chengjie Wang', 'Wei zhang', 'Wenlong Wu', 'Tianliang Zhang', 'Bin-Bin Gao', 'Xi Wang', 'Guannan Jiang', 'Wei Tang', 'Jun Liu', 'Jiawei Zhan'] | 2022-11-23 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [-4.77006733e-02 -3.74464720e-01 -4.69741374e-01 -8.34411979e-01
-1.02503490e+00 -3.72411937e-01 4.82520610e-01 -2.67756172e-03
-3.40893686e-01 5.45584023e-01 1.86096326e-01 7.98191056e-02
-2.76609272e-01 -5.48603237e-01 -5.20645738e-01 -1.08388543e+00
1.69553027e-01 1.97005033e-01 3.55653286e-01 -3.11459862... | [9.80048942565918, 3.815509557723999] |
c99548f8-51c3-4370-84e7-cf6dea1d2171 | weighted-first-order-model-counting-with | 2302.09830 | null | https://arxiv.org/abs/2302.09830v2 | https://arxiv.org/pdf/2302.09830v2.pdf | Weighted First Order Model Counting with Directed Acyclic Graph Axioms | Statistical Relational Learning (SRL) integrates First-Order Logic (FOL) and probability theory for learning and inference over relational data. Probabilistic inference and learning in many SRL models can be reduced to Weighted First Order Model Counting (WFOMC). However, WFOMC is known to be intractable ($\mathrm{\#P_... | ['Luciano Serafini', 'Sagar Malhotra'] | 2023-02-20 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 1.19329415e-01 5.36605835e-01 -3.99605989e-01 -4.07754719e-01
-4.63759303e-01 -5.64422727e-01 5.97590208e-01 2.03470677e-01
-4.27587599e-01 1.30991340e+00 -4.24521953e-01 -9.20359552e-01
-9.28638875e-01 -1.33944726e+00 -1.05408716e+00 -6.13223612e-01
-8.30381155e-01 9.18615043e-01 5.26888072e-01 2.29539290... | [8.623248100280762, 6.715024471282959] |
2a34c5f5-56ad-4431-9e6a-292728d4e893 | broaden-the-vision-geo-diverse-visual | 2109.06860 | null | https://arxiv.org/abs/2109.06860v1 | https://arxiv.org/pdf/2109.06860v1.pdf | Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning | Commonsense is defined as the knowledge that is shared by everyone. However, certain types of commonsense knowledge are correlated with culture and geographic locations and they are only shared locally. For example, the scenarios of wedding ceremonies vary across regions due to different customs influenced by historica... | ['Kai-Wei Chang', 'Nanyun Peng', 'Ziniu Hu', 'Liunian Harold Li', 'Da Yin'] | 2021-09-14 | null | https://aclanthology.org/2021.emnlp-main.162 | https://aclanthology.org/2021.emnlp-main.162.pdf | emnlp-2021-11 | ['visual-commonsense-reasoning'] | ['reasoning'] | [-1.06817447e-01 -5.17419338e-01 -3.08082141e-02 -3.48015904e-01
-5.95579565e-01 -7.99556971e-01 8.80226910e-01 -7.10394233e-03
-4.35159624e-01 5.85076213e-01 7.07339764e-01 -3.45388919e-01
2.01474637e-01 -7.09801972e-01 -6.16561294e-01 -3.79068673e-01
5.15739501e-01 2.76197195e-01 -2.08205432e-01 -9.10345852... | [10.81527328491211, 1.766588568687439] |
9368ff08-02f3-4329-9ff8-d23c77a34bf0 | intra-and-inter-constraint-based-video | 1502.06080 | null | http://arxiv.org/abs/1502.06080v1 | http://arxiv.org/pdf/1502.06080v1.pdf | Intra-and-Inter-Constraint-based Video Enhancement based on Piecewise Tone Mapping | Video enhancement plays an important role in various video applications. In
this paper, we propose a new intra-and-inter-constraint-based video enhancement
approach aiming to 1) achieve high intra-frame quality of the entire picture
where multiple region-of-interests (ROIs) can be adaptively and simultaneously
enhanced... | ['Zhenzhong Chen', 'Chongyang Zhang', 'Weiyao Lin', 'Yuanzhe Chen', 'Jun Xie', 'Ning Xu'] | 2015-02-21 | null | null | null | null | ['video-enhancement', 'tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 2.11959258e-01 -5.05458593e-01 -1.82781741e-01 -3.90538543e-01
-4.00461882e-01 -2.89689619e-02 -2.42928118e-02 -1.76878840e-01
-2.80334890e-01 6.72424138e-01 1.00749433e-01 1.43856794e-01
-1.59437403e-01 -6.17819846e-01 -2.91901380e-01 -6.75413668e-01
-1.79373160e-01 -8.76793563e-01 8.97207201e-01 -1.54699504... | [11.079170227050781, -1.8530778884887695] |
179c01f9-e360-4408-904e-ce05f50b4d5c | agiqa-3k-an-open-database-for-ai-generated | 2306.04717 | null | https://arxiv.org/abs/2306.04717v2 | https://arxiv.org/pdf/2306.04717v2.pdf | AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment | With the rapid advancements of the text-to-image generative model, AI-generated images (AGIs) have been widely applied to entertainment, education, social media, etc. However, considering the large quality variance among different AGIs, there is an urgent need for quality models that are consistent with human subjectiv... | ['Weisi Lin', 'Guangtao Zhai', 'Xiaohong Liu', 'Xiongkuo Min', 'Wei Sun', 'HaoNing Wu', 'ZiCheng Zhang', 'Chunyi Li'] | 2023-06-07 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [-2.29371842e-02 -3.59799385e-01 8.58334303e-02 -3.94610971e-01
-5.96434236e-01 -3.12163740e-01 3.51756066e-01 -1.46800891e-01
3.97618413e-02 3.00162017e-01 2.14780584e-01 9.40407738e-02
-2.40622520e-01 -9.32551682e-01 -3.93302649e-01 -4.85539168e-01
2.57094830e-01 2.38837898e-01 1.98277533e-01 -2.44943604... | [11.78335189819336, -1.7496838569641113] |
ef4bdffa-eb64-4ef1-b5df-7c29835f1a3c | controlled-generation-of-unseen-faults-for | 2204.14068 | null | https://arxiv.org/abs/2204.14068v2 | https://arxiv.org/pdf/2204.14068v2.pdf | Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation | New operating conditions can result in a significant performance drop of fault diagnostics models due to the domain shift between the training and the testing data distributions. While several domain adaptation approaches have been proposed to overcome such domain shifts, their application is limited if the fault class... | ['Prof. Dr. Olga Fink', 'Dr. Gabriel Michau', 'Katharina Rombach'] | 2022-04-29 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 5.69621563e-01 2.29109392e-01 2.18830436e-01 -2.39676848e-01
-6.80175543e-01 -3.98441941e-01 5.20378590e-01 1.32732168e-01
-5.47528565e-02 1.20168495e+00 -2.88500160e-01 4.30693999e-02
-5.08473933e-01 -8.19737911e-01 -6.33282006e-01 -1.02477312e+00
1.22834496e-01 1.08501351e+00 2.26677105e-01 -2.15854347... | [10.139235496520996, 3.0375545024871826] |
d047a68c-c0bf-4c9e-8e90-9e461d58fedb | sc-mil-supervised-contrastive-multiple | 2303.13405 | null | https://arxiv.org/abs/2303.13405v1 | https://arxiv.org/pdf/2303.13405v1.pdf | SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology | Multiple Instance learning (MIL) models have been extensively used in pathology to predict biomarkers and risk-stratify patients from gigapixel-sized images. Machine learning problems in medical imaging often deal with rare diseases, making it important for these models to work in a label-imbalanced setting. Furthermor... | ['Amaro Taylor-Weiner', 'John Abel', 'Archit Khosla', 'Anand Sampat', 'Chintan Shah', 'Harshith Padigela', 'Syed Ashar Javed', 'Siddhant Shingi', 'Dinkar Juyal'] | 2023-03-23 | null | null | null | null | ['multiple-instance-learning', 'imbalanced-classification'] | ['methodology', 'miscellaneous'] | [ 5.03548086e-01 1.56140430e-02 -9.48277414e-01 -5.50004125e-01
-1.39872372e+00 -2.78723598e-01 1.41821086e-01 7.39437640e-01
-2.58568108e-01 7.84381509e-01 1.34323062e-02 -3.97689104e-01
-2.85807043e-01 -6.39773250e-01 -6.32230878e-01 -8.69827926e-01
-2.16294423e-01 8.79295528e-01 -1.70247242e-01 2.91190952... | [15.051453590393066, -2.5825040340423584] |
3b38b823-336c-445d-b981-daef62708020 | a-hybrid-approach-for-smart-alert-generation | 2306.07983 | null | https://arxiv.org/abs/2306.07983v1 | https://arxiv.org/pdf/2306.07983v1.pdf | A Hybrid Approach for Smart Alert Generation | Anomaly detection is an important task in network management. However, deploying intelligent alert systems in real-world large-scale networking systems is challenging when we take into account (i) scalability, (ii) data heterogeneity, and (iii) generalizability and maintainability. In this paper, we propose a hybrid mo... | ['Zhiyuan Yao', 'Sophine Zhang', 'Yao Zhao'] | 2023-06-02 | null | null | null | null | ['anomaly-detection', 'feature-engineering', 'management'] | ['methodology', 'methodology', 'miscellaneous'] | [-1.10309407e-01 -3.89985070e-02 1.80511311e-01 -3.37022394e-01
1.16249777e-01 -5.52346051e-01 2.40464911e-01 6.73394442e-01
-2.18818765e-02 2.66121238e-01 -6.25611693e-02 -6.23845994e-01
-6.81657493e-01 -1.07026589e+00 2.06103977e-02 -6.47831783e-02
-4.31065261e-01 3.53214771e-01 1.04393399e+00 -1.01808496... | [7.2951459884643555, 2.900512218475342] |
d1c0ea53-6222-4631-ba7a-3ba6eb48a893 | mast-multiscale-audio-spectrogram | 2211.01515 | null | https://arxiv.org/abs/2211.01515v2 | https://arxiv.org/pdf/2211.01515v2.pdf | MAST: Multiscale Audio Spectrogram Transformers | We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification, which brings the concept of multiscale feature hierarchies to the Audio Spectrogram Transformer (AST). Given an input audio spectrogram, we first patchify and project it into an initial temporal resolution and embedding dimension, post... | ['Dinesh Manocha', 'S. Umesh', 'Ashish Seth', 'Sreyan Ghosh'] | 2022-11-02 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 2.73075253e-01 -6.88289627e-02 1.19825199e-01 -1.04731485e-01
-1.29148924e+00 -3.69866818e-01 1.10558465e-01 1.74843773e-01
-2.57947713e-01 1.19571052e-01 4.61656421e-01 -2.41665646e-01
6.00503981e-02 -4.36370313e-01 -6.88029945e-01 -4.55611438e-01
-3.55395734e-01 -1.25889540e-01 4.96850282e-01 -3.77882309... | [15.233017921447754, 5.27075719833374] |
15262d6c-c44c-4871-a47b-61bc9106d05c | cvae-based-re-anchoring-for-implicit | null | null | https://aclanthology.org/2021.findings-emnlp.110 | https://aclanthology.org/2021.findings-emnlp.110.pdf | CVAE-based Re-anchoring for Implicit Discourse Relation Classification | Training implicit discourse relation classifiers suffers from data sparsity. Variational AutoEncoder (VAE) appears to be the proper solution. It is because ideally VAE is capable of generating inexhaustible varying samples, and this facilitates selective data augmentation. However, our experiments show that coupling VA... | ['Guodong Zhou', 'Yu Sun', 'Yu Hong', 'Zujun Dou'] | null | null | null | null | findings-emnlp-2021-11 | ['relation-classification', 'implicit-discourse-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.54640500e-02 7.72336721e-01 -6.13016188e-01 -1.11765452e-01
-7.63184607e-01 -1.83327332e-01 7.59683728e-01 6.71534687e-02
-3.00077587e-01 1.00213921e+00 4.64333892e-01 -4.28716004e-01
6.64709955e-02 -7.79594958e-01 -7.07862496e-01 -7.89864838e-01
1.03019342e-01 4.72379088e-01 -4.09667715e-02 -4.57292587... | [10.498878479003906, 8.581122398376465] |
d5422543-cdb9-46b6-855c-45e7424ff322 | image-stylization-from-predefined-to | 2002.10945 | null | https://arxiv.org/abs/2002.10945v1 | https://arxiv.org/pdf/2002.10945v1.pdf | Image Stylization: From Predefined to Personalized | We present a framework for interactive design of new image stylizations using a wide range of predefined filter blocks. Both novel and off-the-shelf image filtering and rendering techniques are extended and combined to allow the user to unleash their creativity to intuitively invent, modify, and tune new styles from a ... | ['Bartlomiej Wronski', 'Ignacio Garcia-Dorado', 'Pascal Getreuer', 'Peyman Milanfar'] | 2020-02-22 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 3.16104531e-01 4.11794940e-03 4.83030796e-01 -1.87403426e-01
-7.21734911e-02 -9.70225096e-01 5.91948986e-01 -1.05586059e-01
-8.31630006e-02 3.14359069e-01 8.66762251e-02 -3.87237728e-01
-1.39506320e-02 -1.00100660e+00 -4.84328985e-01 -7.88937286e-02
-6.99739009e-02 3.25206578e-01 4.85300273e-01 -5.36181509... | [11.684242248535156, -0.44084301590919495] |
04248523-da0a-4e6b-a5b3-f6cd03bede1c | neural-network-fragile-watermarking-with-no | 2208.07585 | null | https://arxiv.org/abs/2208.07585v1 | https://arxiv.org/pdf/2208.07585v1.pdf | Neural network fragile watermarking with no model performance degradation | Deep neural networks are vulnerable to malicious fine-tuning attacks such as data poisoning and backdoor attacks. Therefore, in recent research, it is proposed how to detect malicious fine-tuning of neural network models. However, it usually negatively affects the performance of the protected model. Thus, we propose a ... | ['Xinpeng Zhang', 'Heng Yin', 'Zhaoxia Yin'] | 2022-08-16 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 5.13919711e-01 -1.54457644e-01 -3.13266337e-01 -1.66759044e-01
-1.42600775e-01 -1.11565638e+00 5.13079882e-01 9.69214290e-02
-4.36201006e-01 6.19282603e-01 -3.42518717e-01 -2.03170896e-01
1.76329166e-01 -1.07581913e+00 -1.05371273e+00 -9.65839922e-01
2.35833600e-01 -2.99879670e-01 6.67426348e-01 1.75697982... | [5.593916416168213, 7.8025922775268555] |
6aed6303-5e51-4a02-a98c-b283bea040fe | identifying-predictive-causal-factors-from | null | null | https://aclanthology.org/D19-1238 | https://aclanthology.org/D19-1238.pdf | Identifying Predictive Causal Factors from News Streams | We propose a new framework to uncover the relationship between news events and real world phenomena. We present the Predictive Causal Graph (PCG) which allows to detect latent relationships between events mentioned in news streams. This graph is constructed by measuring how the occurrence of a word in the news influenc... | ['an', 'Samuel Fraiberger', 'Sun Chakraborty', 'Ananth Balashankar', 'Lakshminarayanan Subramanian'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['stock-price-prediction'] | ['time-series'] | [-3.63658066e-03 4.40883785e-01 -5.90962172e-01 -7.79363438e-02
-4.03113484e-01 -8.29271734e-01 1.26260769e+00 9.29971993e-01
1.14450917e-01 7.31960058e-01 9.84525740e-01 -4.63780165e-01
-4.18800622e-01 -1.25874305e+00 -8.35677505e-01 -3.37187082e-01
-6.63837552e-01 2.31534377e-01 5.97551763e-01 -2.53293514... | [9.02526569366455, 9.325356483459473] |
c3b1114c-83b5-4586-aaf8-d8a1afa30d58 | looking-through-glass-knowledge-discovery | 2101.01508 | null | https://arxiv.org/abs/2101.01508v1 | https://arxiv.org/pdf/2101.01508v1.pdf | Looking Through Glass: Knowledge Discovery from Materials Science Literature using Natural Language Processing | Most of the knowledge in materials science literature is in the form of unstructured data such as text and images. Here, we present a framework employing natural language processing, which automates text and image comprehension and precision knowledge extraction from inorganic glasses' literature. The abstracts are aut... | ['N. M. Anoop Krishnan', 'Nitya Nand Gosvami', 'Manish Agarwal', 'Mohd Zaki', 'Sourav Sahoo', 'Vineeth Venugopal'] | 2021-01-05 | null | null | null | null | ['image-comprehension'] | ['computer-vision'] | [ 2.43675694e-01 -3.20002474e-02 -3.27129364e-01 1.17086712e-02
-1.09832859e+00 -8.54334712e-01 6.87605500e-01 9.52198565e-01
-6.28270442e-03 5.36062062e-01 4.32568192e-01 -2.08520353e-01
-2.27906555e-01 -9.54740584e-01 -5.76318502e-01 -1.29518223e+00
1.77720159e-01 4.12180632e-01 9.39439759e-02 3.70855093... | [11.372138977050781, 1.3159958124160767] |
ae661ec0-5490-4211-a89a-203febde4058 | high-resolution-swin-transformer-for | 2207.11553 | null | https://arxiv.org/abs/2207.11553v1 | https://arxiv.org/pdf/2207.11553v1.pdf | High-Resolution Swin Transformer for Automatic Medical Image Segmentation | The Resolution of feature maps is critical for medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation are U-Net-like architecture that contains an encoder that utilizes a sequence of Transformer blocks to convert the input medical image from high-resolution representa... | ['Jimin Liang', 'Haihong Hu', 'Kaitai Guo', 'Shenghan Ren', 'Chen Wei'] | 2022-07-23 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 2.54723847e-01 4.87857401e-01 -1.55351654e-01 -2.68445551e-01
-9.07727182e-01 -6.40882701e-02 2.59785920e-01 -3.87695938e-01
-2.74198353e-01 7.05433011e-01 1.47064015e-01 -3.58701944e-01
2.47375816e-02 -1.12976050e+00 -6.15841866e-01 -4.69269454e-01
4.55682240e-02 3.29848230e-01 6.84822142e-01 -3.80776584... | [14.579889297485352, -2.584230422973633] |
3fbe638f-2e7c-433b-85b1-e91880f1832e | age-range-estimation-using-mtcnn-and-vgg-face | 2104.08585 | null | https://arxiv.org/abs/2104.08585v1 | https://arxiv.org/pdf/2104.08585v1.pdf | Age Range Estimation using MTCNN and VGG-Face Model | The Convolutional Neural Network has amazed us with its usage on several applications. Age range estimation using CNN is emerging due to its application in myriad of areas which makes it a state-of-the-art area for research and improve the estimation accuracy. A deep CNN model is used for identification of people's age... | ['Subodh Chandra Shakya', 'Ashutosh Chauhan', 'Prashanga Pokharel', 'Dipesh Gyawali'] | 2021-04-17 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 7.22917840e-02 -2.65142601e-02 8.73067528e-02 -7.44532764e-01
1.41236559e-01 -3.38504553e-01 5.12463510e-01 -3.41739476e-01
-5.91242015e-01 7.74472952e-01 1.75273538e-01 1.60205401e-02
7.84319453e-03 -1.02043569e+00 -6.53546035e-01 -5.07869244e-01
-1.41347021e-01 2.73408175e-01 -2.60904968e-01 -1.53552294... | [13.543861389160156, 1.0135960578918457] |
a5d5672f-3a81-4013-ba49-b86445a6b4ef | improving-deep-pancreas-segmentation-in-ct | 1707.04912 | null | http://arxiv.org/abs/1707.04912v2 | http://arxiv.org/pdf/1707.04912v2.pdf | Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function | Deep neural networks have demonstrated very promising performance on accurate
segmentation of challenging organs (e.g., pancreas) in abdominal CT and MRI
scans. The current deep learning approaches conduct pancreas segmentation by
processing sequences of 2D image slices independently through deep, dense
per-pixel maski... | ['Fuyong Xing', 'Yuanpu Xie', 'Le Lu', 'Jinzheng Cai', 'Lin Yang'] | 2017-07-16 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 3.67572010e-01 3.03935379e-01 -2.99910754e-01 -7.07976162e-01
-9.61193502e-01 -3.29146475e-01 1.27787083e-01 2.87323803e-01
-6.38115883e-01 3.02061975e-01 1.43820420e-01 -3.31583500e-01
1.11593366e-01 -6.33601069e-01 -9.65889752e-01 -8.57612848e-01
-5.51192284e-01 5.21214545e-01 1.89259216e-01 3.93982202... | [14.572980880737305, -2.68637752532959] |
7a0ed39a-63b7-4cdf-a9cb-55d13f13388f | factors-affecting-the-performance-of | 2306.12444 | null | https://arxiv.org/abs/2306.12444v1 | https://arxiv.org/pdf/2306.12444v1.pdf | Factors Affecting the Performance of Automated Speaker Verification in Alzheimer's Disease Clinical Trials | Detecting duplicate patient participation in clinical trials is a major challenge because repeated patients can undermine the credibility and accuracy of the trial's findings and result in significant health and financial risks. Developing accurate automated speaker verification (ASV) models is crucial to verify the id... | ['Jekaterina Novikova', 'Ali Akram', 'Marija Stanojevic', 'Malikeh Ehghaghi'] | 2023-06-20 | null | null | null | null | ['fairness', 'fairness', 'speaker-verification'] | ['computer-vision', 'miscellaneous', 'speech'] | [ 1.51972264e-01 -6.04948290e-02 -3.57095182e-01 -2.50266701e-01
-1.21808922e+00 -5.57912827e-01 1.29602924e-01 4.71679479e-01
-5.92002451e-01 5.19501388e-01 9.34359074e-01 -6.87462270e-01
-1.72259554e-01 -2.10604370e-01 -3.65508258e-01 -2.49283254e-01
-1.48880184e-02 5.79816736e-02 -4.33587581e-01 3.80780995... | [14.072285652160645, 5.893041133880615] |
bb0a6354-63ad-4bd0-89e8-e2c90b936cf7 | distribution-aware-binarization-of-neural | 1804.02941 | null | http://arxiv.org/abs/1804.02941v1 | http://arxiv.org/pdf/1804.02941v1.pdf | Distribution-Aware Binarization of Neural Networks for Sketch Recognition | Deep neural networks are highly effective at a range of computational tasks.
However, they tend to be computationally expensive, especially in
vision-related problems, and also have large memory requirements. One of the
most effective methods to achieve significant improvements in
computational/spatial efficiency is to... | ['Rohit Gajawada', 'Vishal Batchu', 'Anoop Namboodiri', 'Ameya Prabhu', 'Sri Aurobindo Munagala'] | 2018-04-09 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 6.38494194e-02 -2.67508119e-01 -4.43982863e-04 -3.23764026e-01
-2.71710694e-01 -2.51834422e-01 3.91575903e-01 2.96901643e-01
-1.02956283e+00 4.30505484e-01 -3.70753445e-02 -3.82759660e-01
-5.38195372e-01 -9.35574234e-01 -7.98524857e-01 -8.43518853e-01
-1.42296940e-01 3.29706997e-01 5.00519931e-01 7.35574812... | [8.542659759521484, 3.084635019302368] |
9019ee17-ee0d-42ed-8477-3e18a1cc9a31 | towards-enhanced-controllability-of-diffusion | 2302.14368 | null | https://arxiv.org/abs/2302.14368v2 | https://arxiv.org/pdf/2302.14368v2.pdf | Towards Enhanced Controllability of Diffusion Models | Denoising Diffusion models have shown remarkable capabilities in generating realistic, high-quality and diverse images. However, the extent of controllability during generation is underexplored. Inspired by techniques based on GAN latent space for image manipulation, we train a diffusion model conditioned on two latent... | ['Ajinkya Kale', 'David I. Inouye', 'Jingwan Lu', 'Krishna Kumar Singh', 'Vinh Khuc', 'Midhun Harikumar', 'Hareesh Ravi', 'Wonwoong Cho'] | 2023-02-28 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 6.73663795e-01 2.69252598e-01 -4.16883565e-02 -2.73100853e-01
-5.87802768e-01 -8.40333819e-01 7.92471886e-01 -2.30443388e-01
4.89021540e-02 4.78117436e-01 5.69985390e-01 6.54122382e-02
-5.25978860e-03 -1.05654812e+00 -9.43963885e-01 -7.25430667e-01
2.07564592e-01 3.63585532e-01 6.72626961e-03 -2.43611008... | [11.515474319458008, -0.291903018951416] |
dc4aaeb3-a495-445e-8894-4ff82a03a209 | neural-implicit-vision-language-feature | 2303.10962 | null | https://arxiv.org/abs/2303.10962v1 | https://arxiv.org/pdf/2303.10962v1.pdf | Neural Implicit Vision-Language Feature Fields | Recently, groundbreaking results have been presented on open-vocabulary semantic image segmentation. Such methods segment each pixel in an image into arbitrary categories provided at run-time in the form of text prompts, as opposed to a fixed set of classes defined at training time. In this work, we present a zero-shot... | ['Roland Siegwart', 'Lionel Ott', 'Jen Jen Chung', 'Francesco Milano', 'Kenneth Blomqvist'] | 2023-03-20 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 8.45479965e-01 4.70917255e-01 6.29628375e-02 -7.86650002e-01
-7.06168354e-01 -8.25664580e-01 5.86729109e-01 4.02991056e-01
-4.05232191e-01 9.18692257e-03 -1.74040779e-01 -1.17929786e-01
5.86533360e-02 -1.06644094e+00 -7.82923698e-01 -4.07796115e-01
2.35127762e-01 9.62456167e-01 7.29864299e-01 2.78520095... | [8.491547584533691, -2.9712653160095215] |
7cedc45c-48a3-430c-8934-1b8eeea240e3 | regularized-hesselm-and-inclined-entropy | 1907.05888 | null | https://arxiv.org/abs/1907.05888v1 | https://arxiv.org/pdf/1907.05888v1.pdf | Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction | Our study concerns with automated predicting of congestive heart failure (CHF) through the analysis of electrocardiography (ECG) signals. A novel machine learning approach, regularized hessenberg decomposition based extreme learning machine (R-HessELM), and feature models; squared, circled, inclined and grid entropy me... | ['Gökhan Altan', 'Yakup Kutlu', 'Apdullah Yayık'] | 2019-07-12 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [-1.87956169e-01 7.66021432e-03 5.92959821e-01 -4.02143389e-01
-2.33603448e-01 -1.77055616e-02 -1.33094162e-01 4.86178160e-01
-2.57730722e-01 1.01346135e+00 1.49736226e-01 -3.21884006e-01
-5.71336031e-01 -3.06227207e-01 3.54498327e-01 -4.48829830e-01
-8.47361326e-01 5.92895031e-01 -6.57931209e-01 -2.04519838... | [14.1475248336792, 3.1753957271575928] |
f8b02c4b-5002-47d4-be45-a85ee076e430 | fine-grained-categorization-and-dataset | 1512.05227 | null | http://arxiv.org/abs/1512.05227v2 | http://arxiv.org/pdf/1512.05227v2.pdf | Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop | Existing fine-grained visual categorization methods often suffer from three
challenges: lack of training data, large number of fine-grained categories, and
high intraclass vs. low inter-class variance. In this work we propose a generic
iterative framework for fine-grained categorization and dataset bootstrapping
that h... | ['Yuanqing Lin', 'Serge Belongie', 'Feng Zhou', 'Yin Cui'] | 2015-12-16 | fine-grained-categorization-and-dataset-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Cui_Fine-Grained_Categorization_and_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Cui_Fine-Grained_Categorization_and_CVPR_2016_paper.pdf | cvpr-2016-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [-2.26067156e-01 -3.09701413e-01 -8.22496191e-02 -8.53819132e-01
-5.70827603e-01 -7.58554161e-01 5.77986538e-01 2.12886721e-01
-4.95276898e-01 7.94443190e-01 -8.38017166e-02 1.64153337e-01
-4.33399409e-01 -9.09886479e-01 -4.90609705e-01 -5.71766734e-01
-1.63114056e-01 6.75078750e-01 2.26697132e-01 2.33458176... | [9.769067764282227, 2.1304931640625] |
6e8e2112-fd31-495d-82d4-68f295127c9c | diabetic-retinopathy-detection-by-retinal | 2001.05835 | null | https://arxiv.org/abs/2001.05835v1 | https://arxiv.org/pdf/2001.05835v1.pdf | Diabetic Retinopathy detection by retinal image recognizing | Many people are affected by diabetes around the world. This disease may have type 1 and 2. Diabetes brings with it several complications including diabetic retinopathy, which is a disease that if not treated correctly can lead to irreversible damage in the patient's vision. The earlier it is detected, the better the ch... | ['Gilberto Luis De Conto Junior'] | 2020-01-14 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 2.10076526e-01 1.97296694e-01 9.35276821e-02 -4.72544342e-01
5.48313931e-02 -2.00481817e-01 -3.15207504e-02 7.73980319e-02
-4.11469758e-01 6.18152142e-01 2.13254035e-01 -6.10488057e-01
-1.07244477e-01 -9.15960968e-01 -2.25105688e-01 -6.79605007e-01
2.60140359e-01 4.06275272e-01 1.30655676e-01 8.15289766... | [15.833284378051758, -3.999589443206787] |
1f583772-1462-42f8-a1a6-0887a3c95842 | mask-textspotter-an-end-to-end-trainable-2 | 1908.08207 | null | https://arxiv.org/abs/1908.08207v1 | https://arxiv.org/pdf/1908.08207v1.pdf | Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes | Unifying text detection and text recognition in an end-to-end training fashion has become a new trend for reading text in the wild, as these two tasks are highly relevant and complementary. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in nat... | ['Wenhao Wu', 'Pengyuan Lyu', 'Minghui Liao', 'Minghang He', 'Cong Yao', 'Xiang Bai'] | 2019-08-22 | mask-textspotter-an-end-to-end-trainable-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Pengyuan_Lyu_Mask_TextSpotter_An_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Pengyuan_Lyu_Mask_TextSpotter_An_ECCV_2018_paper.pdf | eccv-2018-9 | ['text-spotting'] | ['computer-vision'] | [ 8.01241815e-01 -3.67914319e-01 2.28297293e-01 -3.44816923e-01
-9.44634855e-01 -5.53394556e-01 9.54052329e-01 3.45463790e-02
-7.01863348e-01 -1.16976546e-02 -1.31319612e-01 -3.24462891e-01
4.15166378e-01 -4.99449909e-01 -6.98702097e-01 -6.35250092e-01
7.83771574e-01 6.78378046e-01 4.26724374e-01 -1.04219839... | [11.95304012298584, 2.2739181518554688] |
2d9bf649-fd2d-4aae-be77-e80dbba39d34 | improving-gans-with-a-dynamic-discriminator | 2209.09897 | null | https://arxiv.org/abs/2209.09897v1 | https://arxiv.org/pdf/2209.09897v1.pdf | Improving GANs with A Dynamic Discriminator | Discriminator plays a vital role in training generative adversarial networks (GANs) via distinguishing real and synthesized samples. While the real data distribution remains the same, the synthesis distribution keeps varying because of the evolving generator, and thus effects a corresponding change to the bi-classifica... | ['Bolei Zhou', 'Bo Dai', 'Deli Zhao', 'Yinghao Xu', 'Yujun Shen', 'Ceyuan Yang'] | 2022-09-20 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 4.13913310e-01 2.34675393e-01 -2.81370938e-01 -3.85939935e-03
-6.90049708e-01 -8.46038580e-01 8.35111320e-01 -4.83391583e-01
-1.20828219e-01 7.45096147e-01 1.82365656e-01 -3.28953505e-01
1.37916073e-01 -8.87899578e-01 -7.55588353e-01 -1.03165877e+00
3.49033445e-01 2.56453931e-01 1.05186626e-01 -2.32403979... | [11.616487503051758, -0.26525697112083435] |
f7d9710d-9cd8-4e02-975b-11c657c54b36 | multi-level-anomaly-detection-on-time-varying | 1410.4355 | null | http://arxiv.org/abs/1410.4355v4 | http://arxiv.org/pdf/1410.4355v4.pdf | Multi-Level Anomaly Detection on Time-Varying Graph Data | This work presents a novel modeling and analysis framework for graph
sequences which addresses the challenge of detecting and contextualizing
anomalies in labelled, streaming graph data. We introduce a generalization of
the BTER model of Seshadhri et al. by adding flexibility to community
structure, and use this model ... | ['Robert A. Bridges', 'John Collins', 'Jason Laska', 'Erik M. Ferragut', 'Blair D. Sullivan'] | 2014-10-16 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 9.18760225e-02 1.15812808e-01 2.68701106e-01 5.74502014e-02
-4.07677919e-01 -7.77290702e-01 6.59812987e-01 1.35127139e+00
6.39980882e-02 2.68537998e-01 2.44369730e-01 -4.88943726e-01
-2.75831133e-01 -9.16457295e-01 -3.52638662e-01 -4.79526281e-01
-1.02595043e+00 4.00842160e-01 7.13235319e-01 -2.86486119... | [6.678502082824707, 5.7750444412231445] |
0f49d523-0572-456a-9a4c-2b796bff794c | cloud-net-an-end-to-end-cloud-detection | 1901.10077 | null | http://arxiv.org/abs/1901.10077v1 | http://arxiv.org/pdf/1901.10077v1.pdf | Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery | Cloud detection in satellite images is an important first-step in many remote
sensing applications. This problem is more challenging when only a limited
number of spectral bands are available. To address this problem, a deep
learning-based algorithm is proposed in this paper. This algorithm consists of
a Fully Convolut... | ['Sorour Mohajerani', 'Parvaneh Saeedi'] | 2019-01-29 | cloud-net-an-end-to-end-cloud-detection-1 | null | null | conference-2019-ieee-international-geoscience | ['cloud-detection'] | ['computer-vision'] | [ 1.81090981e-01 -7.55558252e-01 2.61949658e-01 -3.57724190e-01
-5.19393623e-01 -3.68060142e-01 3.96468937e-01 -2.76030656e-02
-5.72050154e-01 5.89540124e-01 -4.96258706e-01 -3.13726693e-01
-9.82284099e-02 -1.12396753e+00 -6.12658799e-01 -8.38399768e-01
-2.80944586e-01 6.39011860e-02 1.88324019e-01 -7.07315952... | [9.775973320007324, -1.7193024158477783] |
1e3cbb3d-0c4b-4c5b-b8f5-2b2a5b78cb2a | distill-to-label-weakly-supervised-instance | 1907.12926 | null | https://arxiv.org/abs/1907.12926v1 | https://arxiv.org/pdf/1907.12926v1.pdf | Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation | Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis. In contrast to conventional instance segmentation scenarios in computer vision, the problems that we consider are characterized by ... | ['Satyananda Kashyap', 'Jayaraman J. Thiagarajan', 'Alexandros Karagyris'] | 2019-07-26 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 7.14119077e-01 6.33381546e-01 -5.04719794e-01 -4.66734737e-01
-1.40648937e+00 -5.02808154e-01 2.52460331e-01 4.77138728e-01
-3.60439867e-01 8.84637594e-01 -3.91316324e-01 -4.36937988e-01
-5.32678626e-02 -8.50538552e-01 -1.11516333e+00 -9.41088915e-01
1.05268449e-01 5.48493922e-01 1.45307286e-02 2.03856647... | [14.64723014831543, -2.2720930576324463] |
3e699796-c2f4-4832-99a8-2d84bd262919 | an-energy-efficient-service-composition | 2006.16771 | null | https://arxiv.org/abs/2006.16771v1 | https://arxiv.org/pdf/2006.16771v1.pdf | An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment | By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which... | ['Tarik A. Rashid', 'Godar J. Ibrahim', 'Mobayode O. Akinsolu'] | 2020-05-19 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 6.27454221e-02 -5.10442078e-01 -1.59826100e-01 -1.55691430e-01
-2.48516113e-01 -6.32788718e-01 2.37057179e-01 -3.91443193e-01
-1.76517904e-01 6.23929739e-01 -1.40479326e-01 -3.24472308e-01
-5.82612455e-01 -1.02132452e+00 -1.43136680e-01 -1.07134962e+00
3.90358013e-03 5.32384992e-01 3.36536914e-01 -2.94494271... | [8.592794418334961, 6.941083908081055] |
c4eafb22-c5ab-4c31-bb6e-645a9448e39a | boosting-event-extraction-with-denoised | 2305.09598 | null | https://arxiv.org/abs/2305.09598v1 | https://arxiv.org/pdf/2305.09598v1.pdf | Boosting Event Extraction with Denoised Structure-to-Text Augmentation | Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations. In most NLP applications, involving a large scale of synthetic training data is a practical and effective approach to alleviate the problem of data scarcity. However, when appl... | ['Dawei Yin', 'Shuaiqiang Wang', 'Tong Zhou', 'Chong Feng', 'Xiao Liu', 'Ge Shi', 'Xiaochi Wei', 'Heyan Huang', 'Bo wang'] | 2023-05-16 | null | null | null | null | ['text-augmentation', 'event-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.07854605e-01 5.29113352e-01 -7.49823451e-02 -3.49199593e-01
-1.20606351e+00 -3.40964079e-01 7.69059241e-01 3.35289180e-01
-4.01627600e-01 1.18946683e+00 5.68751335e-01 -1.47816196e-01
3.84571729e-04 -9.57393587e-01 -8.31590116e-01 -4.59772736e-01
3.03997606e-01 8.30069602e-01 -8.17877352e-02 -1.57470554... | [9.221752166748047, 9.07797622680664] |
362eda62-392e-40ea-b001-a4722ebd5149 | loopnet-musical-loop-synthesis-conditioned-on | 2105.10371 | null | https://arxiv.org/abs/2105.10371v1 | https://arxiv.org/pdf/2105.10371v1.pdf | LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters | Loops, seamlessly repeatable musical segments, are a cornerstone of modern music production. Contemporary artists often mix and match various sampled or pre-recorded loops based on musical criteria such as rhythm, harmony and timbral texture to create compositions. Taking such criteria into account, we present LoopNet,... | ['Emilia Gómez', 'Xavier Serra', 'António Ramires', 'Pritish Chandna'] | 2021-05-21 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [-3.02567147e-03 -2.32280970e-01 6.65177219e-03 -1.74664304e-01
-8.11104059e-01 -1.11426580e+00 6.10217690e-01 -1.16806373e-01
3.07607085e-01 3.07086438e-01 7.12088525e-01 8.57195333e-02
-4.46776509e-01 -8.77602696e-01 -8.23227167e-01 -2.92192288e-02
2.11577088e-01 6.63911939e-01 -2.56855398e-01 -5.43027699... | [16.042268753051758, 5.535011291503906] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.