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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3be3da2f-e293-4bd8-9546-77968c11260d | neural-posterior-estimation-with | 2207.05636 | null | https://arxiv.org/abs/2207.05636v1 | https://arxiv.org/pdf/2207.05636v1.pdf | Neural Posterior Estimation with Differentiable Simulators | Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a lar... | ['Eric Aubourg', 'Benjamin Remy', 'Alexandre Boucaud', 'François Lanusse', 'Justine Zeghal'] | 2022-07-12 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 1.48571972e-02 -2.22011745e-01 2.03540787e-01 -2.78109372e-01
-8.01430941e-01 -2.64419079e-01 6.57826483e-01 -1.59402072e-01
-5.98656476e-01 1.38583755e+00 -3.58255446e-01 -5.47686636e-01
-1.61325455e-01 -7.77962327e-01 -9.10484970e-01 -7.44058669e-01
-1.11440249e-01 5.03009796e-01 2.98455417e-01 2.85195291... | [6.8207244873046875, 3.9060211181640625] |
16797a4c-cc19-41aa-9124-a3622cb04594 | cross-attention-based-style-distribution-for | 2208.00712 | null | https://arxiv.org/abs/2208.00712v1 | https://arxiv.org/pdf/2208.00712v1.pdf | Cross Attention Based Style Distribution for Controllable Person Image Synthesis | Controllable person image synthesis task enables a wide range of applications through explicit control over body pose and appearance. In this paper, we propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer. The module intentionally s... | ['Qingli Li', 'Changxin Gao', 'Li Sun', 'Xinyuan Chen', 'Mingyu Yin', 'Xinyue Zhou'] | 2022-08-01 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 5.89737147e-02 -4.46443520e-02 -1.64475422e-02 -5.68343699e-01
-2.51767725e-01 -3.95311654e-01 5.05698800e-01 -3.55272740e-01
-1.67625949e-01 3.84110510e-01 3.58118117e-01 5.16175568e-01
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5.22112072e-01 4.97698069e-01 1.99285939e-01 -5.27051508... | [12.057969093322754, -0.9352614879608154] |
372761a5-6aee-4641-a267-fd0cca885eeb | a-corpus-based-study-of-corporate-image | null | null | https://aclanthology.org/2022.csrnlp-1.3 | https://aclanthology.org/2022.csrnlp-1.3.pdf | A Corpus-based Study of Corporate Image Represented in Corporate Social Responsibility Report: A Case Study of China Mobile and Vodafone | By examination of the high-frequency nouns, verbs, and keywords, the present study probes into the similarities and differences of corporate images represented in Corporate Social Responsibility (CSR) reports of China Mobile and Vodafone. The results suggest that: 1) both China Mobile and Vodafone prefer using some pos... | ['Liang Xu', 'Xing Chen'] | null | null | null | null | csrnlp-lrec-2022-6 | ['culture'] | ['speech'] | [-3.30832213e-01 2.99478918e-01 -4.96882290e-01 6.02343716e-02
1.91406891e-01 -2.87524939e-01 6.89922392e-01 4.12086815e-01
-3.59433949e-01 4.37054306e-01 9.98201072e-01 -2.51630008e-01
-2.48764202e-01 -7.71249354e-01 -1.20546492e-02 -6.85775280e-01
5.48703015e-01 -3.23552549e-01 -1.16162300e-01 -7.21996427... | [9.074763298034668, 6.255584716796875] |
918e6ff8-cb22-4fae-beba-f52a999d8cb5 | recurrent-autoregressive-networks-for-online | 1711.02741 | null | http://arxiv.org/abs/1711.02741v2 | http://arxiv.org/pdf/1711.02741v2.pdf | Recurrent Autoregressive Networks for Online Multi-Object Tracking | The main challenge of online multi-object tracking is to reliably associate
object trajectories with detections in each video frame based on their tracking
history. In this work, we propose the Recurrent Autoregressive Network (RAN), a
temporal generative modeling framework to characterize the appearance and
motion dyn... | ['Yu Xiang', 'Silvio Savarese', 'Kuan Fang', 'Xiaocheng Li'] | 2017-11-07 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-3.33035678e-01 -7.14316130e-01 -1.64254323e-01 -1.53366119e-01
-4.51513737e-01 -5.20862162e-01 5.98617256e-01 -4.02487874e-01
-3.31524938e-01 3.44980210e-01 1.19983569e-01 1.91817075e-01
6.67417720e-02 -5.58230579e-01 -9.33754623e-01 -6.23364091e-01
-4.18190747e-01 3.54610622e-01 7.46907532e-01 5.79160631... | [6.301468849182129, -2.0810887813568115] |
af45a4ac-c57f-459b-9fde-ecad2a01e570 | collaborative-multi-bs-power-management-for | 2304.07976 | null | https://arxiv.org/abs/2304.07976v1 | https://arxiv.org/pdf/2304.07976v1.pdf | Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning | Network energy efficiency is a main pillar in the design and operation of wireless communication systems. In this paper, we investigate a dense radio access network (dense-RAN) capable of radiated power management at the base station (BS). Aiming to improve the long-term network energy efficiency, an optimization probl... | ['Naofal Al-Dhahir', 'Zhendong Wang', 'Haoran Wei', 'Jianpo Liu', 'Jun Li', 'Wen Chen', 'Yuchao Chang'] | 2023-04-17 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.46282744e-01 2.07316086e-01 -3.20315391e-01 2.89145917e-01
-3.72387350e-01 -2.64616251e-01 -1.58571631e-01 -2.82858700e-01
-3.42016965e-01 1.07057607e+00 -2.10893750e-01 -5.78087807e-01
-7.12400079e-01 -1.06533897e+00 -2.35705271e-01 -1.47630668e+00
-3.34724069e-01 1.69909462e-01 -3.98116320e-01 1.13640130... | [5.91804313659668, 1.640189290046692] |
e2538118-3161-411b-a4c9-ebd7e793986c | cgdtest-a-constrained-gradient-descent | 2304.01826 | null | https://arxiv.org/abs/2304.01826v1 | https://arxiv.org/pdf/2304.01826v1.pdf | CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks | In this paper, we propose a new Deep Neural Network (DNN) testing algorithm called the Constrained Gradient Descent (CGD) method, and an implementation we call CGDTest aimed at exposing security and robustness issues such as adversarial robustness and bias in DNNs. Our CGD algorithm is a gradient-descent (GD) method, w... | ['Vijay Ganesh', 'Piyush Jha', 'Guanting Pan', 'Laura Graves', 'Vineel Nagisetty'] | 2023-04-04 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [-1.89462468e-01 -6.21430203e-02 -7.13118985e-02 -3.14832717e-01
-5.05049050e-01 -1.19482732e+00 7.92561293e-01 -4.52897280e-01
-5.79244673e-01 7.61840761e-01 -2.19041288e-01 -9.73609626e-01
-1.58521578e-01 -8.61155152e-01 -9.89125848e-01 -4.21517044e-01
-2.91101545e-01 3.38567525e-01 5.28858244e-01 -4.15227592... | [5.708015441894531, 7.850716590881348] |
6db7bf4f-ee32-45df-9670-1a9f5d27a69a | semi-supervised-segmentation-of-salt-bodies | 1904.04445 | null | https://arxiv.org/abs/1904.04445v3 | https://arxiv.org/pdf/1904.04445v3.pdf | Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks | Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention. One of the essential challenges of seismic imaging is detecting subsurface salt structure which is indispensable for identification of hydrocarbon reservoirs and drill path planning. Unfo... | ['Yauhen Babakhin', 'Hirotoshi Kitamura', 'Artsiom Sanakoyeu'] | 2019-04-09 | null | null | null | null | ['geophysics', 'seismic-imaging'] | ['miscellaneous', 'miscellaneous'] | [ 3.41741264e-01 -1.27768010e-01 2.10255384e-01 -4.41445380e-01
-8.41689646e-01 -4.88372803e-01 4.15847659e-01 2.42338419e-01
-7.75989115e-01 2.64054149e-01 -8.87543336e-02 -4.60256070e-01
1.11521527e-01 -8.51727366e-01 -6.15263760e-01 -9.52010989e-01
1.95618290e-02 6.35258853e-01 7.31743753e-01 -2.48909444... | [7.224246978759766, 2.028026819229126] |
5505fd1f-15d6-4721-99a4-4b16fc616cdc | can-gan-learn-topological-features-of-a-graph | 1707.06197 | null | http://arxiv.org/abs/1707.06197v1 | http://arxiv.org/pdf/1707.06197v1.pdf | Can GAN Learn Topological Features of a Graph? | This paper is first-line research expanding GANs into graph topology
analysis. By leveraging the hierarchical connectivity structure of a graph, we
have demonstrated that generative adversarial networks (GANs) can successfully
capture topological features of any arbitrary graph, and rank edge sets by
different stages a... | ['Pin-Yu Chen', 'Min Hwan Oh', 'Toyotaro Suzumura', 'Hal Cooper', 'Weiyi Liu', 'Sailung Yeung'] | 2017-07-19 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 2.34715715e-01 6.06424689e-01 -1.86341003e-01 9.56814438e-02
-1.90135181e-01 -1.02173841e+00 6.22930110e-01 -3.22169550e-02
2.63180137e-01 7.17491210e-01 3.21558088e-01 -2.83442676e-01
-2.65707105e-01 -1.48326254e+00 -7.01353490e-01 -3.01313400e-01
-4.39041466e-01 6.26084447e-01 4.20365557e-02 -4.54168379... | [6.991308212280273, 6.213389873504639] |
e191a2fa-af09-496c-a816-634207234d66 | longform-optimizing-instruction-tuning-for | 2304.08460 | null | https://arxiv.org/abs/2304.08460v1 | https://arxiv.org/pdf/2304.08460v1.pdf | LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction | Instruction tuning enables language models to generalize more effectively and better follow user intent. However, obtaining instruction data can be costly and challenging. Prior works employ methods such as expensive human annotation, crowd-sourced datasets with alignment issues, or generating noisy examples via LLMs. ... | ['Hinrich Schütze', 'Anna Korhonen', 'Timo Schick', 'Abdullatif Köksal'] | 2023-04-17 | null | null | null | null | ['recipe-generation', 'long-form-question-answering', 'news-generation'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 9.21450183e-02 1.01771191e-01 -2.52197951e-01 -3.94888699e-01
-1.37870169e+00 -9.08115387e-01 8.13239694e-01 -5.10729142e-02
-3.77301335e-01 9.88738894e-01 6.78610384e-01 -6.37558758e-01
3.60981047e-01 -7.79982805e-01 -1.12501836e+00 -6.71634674e-02
4.91895616e-01 7.03051805e-01 -6.99257851e-02 -7.28692234... | [11.1998291015625, 8.649285316467285] |
6312fa04-2aa0-4fab-86ba-5a59ddaa3fc4 | hierarchic-temporal-convolutional-network | null | null | https://ieeexplore.ieee.org/document/9812509 | https://ieeexplore.ieee.org/document/9812509 | Hierarchic Temporal Convolutional Network With Cross-Domain Encoder for Music Source Separation | Recently, the time-domain-based methods (i.e., the method of modeling the raw waveform directly) for audio source separation have shown tremendous potential. In this paper, we propose a model which combines the complexed spectrogram domain feature and time-domain feature by a cross-domain encoder (CDE) and adopts the h... | ['Hao Huang', 'Liang He', 'Wenzhong Yang', 'Yadong Chen', 'Ying Hu'] | 2022-06-30 | null | null | null | ieee-signal-processing-letters-2022-6 | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 1.76407874e-01 -4.09529686e-01 1.92821771e-01 -5.86587451e-02
-1.10452116e+00 -5.03881872e-01 1.07091144e-01 -4.04736310e-01
-1.52859181e-01 3.91302586e-01 1.48069665e-01 -8.90033320e-02
-3.24234456e-01 -3.72016460e-01 -4.69179958e-01 -7.01614082e-01
-3.80338192e-01 -4.34131891e-01 1.59038991e-01 -1.28683895... | [15.396627426147461, 5.473220348358154] |
fbb55fb5-5625-4069-847a-f6a7cc497872 | deep-video-matting-via-spatio-temporal | 2104.11208 | null | https://arxiv.org/abs/2104.11208v1 | https://arxiv.org/pdf/2104.11208v1.pdf | Deep Video Matting via Spatio-Temporal Alignment and Aggregation | Despite the significant progress made by deep learning in natural image matting, there has been so far no representative work on deep learning for video matting due to the inherent technical challenges in reasoning temporal domain and lack of large-scale video matting datasets. In this paper, we propose a deep learning... | ['Yu-Wing Tai', 'Chi-Keung Tang', 'Qiao Gu', 'Guanzhi Wang', 'Yanan sun'] | 2021-04-22 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Sun_Deep_Video_Matting_via_Spatio-Temporal_Alignment_and_Aggregation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Sun_Deep_Video_Matting_via_Spatio-Temporal_Alignment_and_Aggregation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-matting'] | ['computer-vision'] | [ 6.41630888e-02 -9.66454446e-02 -7.06366971e-02 -2.14758977e-01
-7.79890597e-01 -2.23435804e-01 3.65121514e-01 -5.19731045e-01
-2.14845121e-01 5.42402387e-01 3.69636178e-01 -1.89258322e-01
1.61786675e-01 -6.05210185e-01 -1.22282624e+00 -3.78638715e-01
-7.39916265e-02 1.73813522e-01 2.62861371e-01 -4.31429856... | [10.664950370788574, -0.8955824971199036] |
27f2b62a-d9db-489a-bf8c-48805c0a3709 | detecting-arguments-in-cjeu-decisions-on | null | null | https://aclanthology.org/2022.argmining-1.14 | https://aclanthology.org/2022.argmining-1.14.pdf | Detecting Arguments in CJEU Decisions on Fiscal State Aid | The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state ai... | ['Paolo Torroni', 'Giovanni Sartor', 'Federico Ruggeri', 'Elena Palmieri', 'Francesca Lagioia', 'Francesco Godano', 'Federico Galli', 'Andrea Galassi', 'Piera Santin', 'Giulia Grundler'] | null | null | null | null | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 4.30434421e-02 4.68846023e-01 -9.61469352e-01 -2.92670548e-01
-7.20656455e-01 -9.08307433e-01 9.77731049e-01 6.60866857e-01
-6.54998481e-01 1.24487746e+00 7.51802206e-01 -1.42728281e+00
-4.14622813e-01 -6.87914789e-01 -2.46569559e-01 -1.48213565e-01
1.15604050e-01 5.23269832e-01 2.45696574e-01 -5.14061093... | [9.566206932067871, 9.549662590026855] |
80e21cb9-4643-440e-afd1-58126697b6c1 | asset-pricing-and-deep-learning | 2209.12014 | null | https://arxiv.org/abs/2209.12014v1 | https://arxiv.org/pdf/2209.12014v1.pdf | Asset Pricing and Deep Learning | Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing. I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set of predictive signals ... | ['Chen Zhang'] | 2022-09-24 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-7.40906477e-01 -2.42572755e-01 -5.59942842e-01 -2.00803369e-01
-2.10893184e-01 -4.12277490e-01 5.88297963e-01 -6.37955248e-01
-1.04480304e-01 4.84052122e-01 2.79073298e-01 -1.09216702e+00
-5.29484510e-01 -1.06620419e+00 -6.73950136e-01 -6.48677349e-01
-1.81859836e-01 3.40340823e-01 -5.58913767e-01 -3.69227499... | [4.471303939819336, 4.176861763000488] |
20be2036-f68f-4f04-9235-85f624c18745 | small-language-models-for-tabular-data | 2211.02941 | null | https://arxiv.org/abs/2211.02941v3 | https://arxiv.org/pdf/2211.02941v3.pdf | Small Language Models for Tabular Data | Supervised deep learning is most commonly applied to difficult problems defined on large and often extensively curated datasets. Here we demonstrate the ability of deep representation learning to address problems of classification and regression from small and poorly formed tabular datasets by encoding input informatio... | ['Benjamin L. Badger'] | 2022-11-05 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 2.76914269e-01 3.14170420e-01 -3.35264772e-01 -5.68244874e-01
-8.24893117e-01 -7.87541330e-01 4.78641629e-01 6.97110295e-01
-3.36228877e-01 6.31630242e-01 2.74327844e-01 -5.24540961e-01
-2.93853015e-01 -1.19167876e+00 -1.28087556e+00 -4.50894922e-01
-2.17929184e-01 7.88145304e-01 -4.54346836e-01 -1.78472877... | [9.652084350585938, 7.434591770172119] |
4fe7c3f5-c0bc-4403-a825-e8855eff4ab0 | scoregrad-multivariate-probabilistic-time | 2106.10121 | null | https://arxiv.org/abs/2106.10121v1 | https://arxiv.org/pdf/2106.10121v1.pdf | ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models | Multivariate time series prediction has attracted a lot of attention because of its wide applications such as intelligence transportation, AIOps. Generative models have achieved impressive results in time series modeling because they can model data distribution and take noise into consideration. However, many existing ... | ['Yuanqing Xia', 'Yufeng Zhan', 'Tong Zhou', 'Hongwei Zhang', 'Tijin Yan'] | 2021-06-18 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-4.35802668e-01 -7.03677773e-01 1.08544737e-01 -3.63338053e-01
-8.32880855e-01 -3.06237102e-01 6.67997956e-01 -3.50204080e-01
9.84080508e-02 5.67016840e-01 -1.70388520e-02 -1.08985886e-01
-2.70814002e-01 -9.78549004e-01 -4.53529835e-01 -8.96058261e-01
-1.82272717e-01 5.22114336e-01 2.76429534e-01 -6.78660125... | [6.915626049041748, 3.091158151626587] |
33568d25-795d-41cd-97ae-86f325eeebc1 | investigating-failures-to-generalize-for | 2303.09092 | null | https://arxiv.org/abs/2303.09092v1 | https://arxiv.org/pdf/2303.09092v1.pdf | Investigating Failures to Generalize for Coreference Resolution Models | Coreference resolution models are often evaluated on multiple datasets. Datasets vary, however, in how coreference is realized -- i.e., how the theoretical concept of coreference is operationalized in the dataset -- due to factors such as the choice of corpora and annotation guidelines. We investigate the extent to whi... | ['Jackie Chi Kit Cheung', 'Adam Trischler', 'Kaheer Suleman', 'Alexandra Olteanu', 'Ian Porada'] | 2023-03-16 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 4.19973768e-02 2.13249654e-01 -5.12805283e-01 -3.98716986e-01
-8.35873544e-01 -1.10284007e+00 7.62456775e-01 4.62816805e-01
-5.43753028e-01 5.75084984e-01 1.13829052e+00 -2.34318569e-01
-5.55406690e-01 -5.32322586e-01 -5.73451042e-01 -2.09864914e-01
2.23022938e-01 1.00394177e+00 1.77237839e-01 -4.16161507... | [9.346334457397461, 9.512125968933105] |
0d8ce309-c674-44c1-ac88-a52250ea846d | osis-efficient-one-stage-network-for-3d | 2303.07011 | null | https://arxiv.org/abs/2303.07011v1 | https://arxiv.org/pdf/2303.07011v1.pdf | OSIS: Efficient One-stage Network for 3D Instance Segmentation | Current 3D instance segmentation models generally use multi-stage methods to extract instance objects, including clustering, feature extraction, and post-processing processes. However, these multi-stage approaches rely on hyperparameter settings and hand-crafted processes, which restrict the inference speed of the mode... | ['Xi Yang', 'Chuan Tang'] | 2023-03-13 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 5.07471621e-01 1.20247953e-01 -2.93302417e-01 -6.69355810e-01
-7.15496361e-01 -3.54563832e-01 2.15597421e-01 -3.75096276e-02
-6.26339674e-01 1.78040072e-01 -5.73850870e-01 -4.67209935e-01
-4.20635641e-02 -9.57282245e-01 -1.02763343e+00 -5.21046162e-01
2.75795639e-01 8.80307734e-01 5.36620259e-01 4.14044082... | [8.037376403808594, -3.1371748447418213] |
a4cdb29b-a297-4d97-8ab4-681a3d659196 | utilizing-chatgpt-generated-data-to-retrieve | 2307.02313 | null | https://arxiv.org/abs/2307.02313v2 | https://arxiv.org/pdf/2307.02313v2.pdf | Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social Media | In this work, we present the contribution of the BLUE team in the eRisk Lab task on searching for symptoms of depression. The task consists of retrieving and ranking Reddit social media sentences that convey symptoms of depression from the BDI-II questionnaire. Given that synthetic data provided by LLMs have been prove... | ['Ana-Maria Bucur'] | 2023-07-05 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-2.60929912e-01 6.38583958e-01 1.05912022e-01 -4.58345979e-01
-8.13603759e-01 -2.68020809e-01 6.28907681e-01 7.99290597e-01
-5.80765188e-01 6.34140790e-01 9.40426052e-01 1.94867566e-01
-4.98834312e-01 -8.14030945e-01 1.53200477e-01 -1.38830200e-01
1.09911062e-01 8.38271916e-01 -2.01336578e-01 -8.93237174... | [8.928421020507812, 9.624839782714844] |
893b32a1-6b25-4e4f-806a-aa0a938855e8 | syntactically-robust-training-on-partially | 2301.06841 | null | https://arxiv.org/abs/2301.06841v1 | https://arxiv.org/pdf/2301.06841v1.pdf | Syntactically Robust Training on Partially-Observed Data for Open Information Extraction | Open Information Extraction models have shown promising results with sufficient supervision. However, these models face a fundamental challenge that the syntactic distribution of training data is partially observable in comparison to the real world. In this paper, we propose a syntactically robust training framework th... | ['Bin Xu', 'Juanzi Li', 'Lei Hou', 'Yuxiang Chen', 'Ji Qi'] | 2023-01-17 | null | null | null | null | ['paraphrase-generation', 'open-information-extraction', 'semantic-textual-similarity', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.65091679e-01 3.52277160e-01 -2.97784656e-01 -5.63511848e-01
-8.85067523e-01 -6.70974612e-01 5.97776115e-01 -1.13639407e-01
-1.49100691e-01 7.88249373e-01 3.24878514e-01 -1.07887648e-02
-1.74648896e-01 -6.53814852e-01 -8.51517856e-01 -4.88676727e-01
6.31416559e-01 4.62433070e-01 1.58974588e-01 -2.51996100... | [11.461881637573242, 9.376086235046387] |
b1eb10b3-99f1-422e-b6a0-341c134a9a58 | dam-al-dilated-attention-mechanism-with | 2112.13559 | null | https://arxiv.org/abs/2112.13559v1 | https://arxiv.org/pdf/2112.13559v1.pdf | DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation | While Magnetic Resonance Imaging (MRI) has played an essential role in infant brain analysis, segmenting MRI into a number of tissues such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is crucial and complex due to the extremely low intensity contrast between tissues at around 6-9 months of age ... | ['Ngan T. H Le', 'Minh-Triet Tran', 'Gia-Han Diep', 'Dinh-Hieu Hoang'] | 2021-12-27 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 7.99462348e-02 2.16122314e-01 3.87420267e-01 -3.56405109e-01
-1.84782714e-01 1.39314219e-01 3.45782340e-01 1.68617994e-01
-5.44353306e-01 4.92676675e-01 4.15041119e-01 -6.42664805e-02
-7.84448385e-02 -5.04720747e-01 -6.99625969e-01 -6.30543768e-01
-5.15671015e-01 2.99189776e-01 5.16981602e-01 2.32537523... | [14.240853309631348, -2.3076980113983154] |
672cbd5b-a35b-4b8d-8b0d-2f32222d76ff | field-based-plot-extraction-using-uav-rgb | 2109.00632 | null | https://arxiv.org/abs/2109.00632v1 | https://arxiv.org/pdf/2109.00632v1.pdf | Field-Based Plot Extraction Using UAV RGB Images | Unmanned Aerial Vehicles (UAVs) have become popular for use in plant phenotyping of field based crops, such as maize and sorghum, due to their ability to acquire high resolution data over field trials. Field experiments, which may comprise thousands of plants, are planted according to experimental designs to evaluate v... | ['Edward J. Delp', 'Melba Crawford', 'Enyu Cai', 'Sriram Baireddy', 'Changye Yang'] | 2021-09-01 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.42756540e-01 -2.32518837e-01 -4.07001942e-01 2.84585189e-02
1.19739212e-01 -1.21098769e+00 1.23452824e-02 6.64390564e-01
2.20267817e-01 8.16134453e-01 -4.34063405e-01 -1.04107583e+00
-4.84735891e-02 -1.27375472e+00 -3.15196395e-01 -5.74681342e-01
-1.63733482e-01 -2.66866475e-01 4.40766186e-01 -3.29513371... | [9.082182884216309, -1.6110081672668457] |
51d77a6f-5496-463e-b2af-0fa7b96479cb | enhancing-sequential-recommendation-with | 2205.14837 | null | https://arxiv.org/abs/2205.14837v2 | https://arxiv.org/pdf/2205.14837v2.pdf | Enhancing Sequential Recommendation with Graph Contrastive Learning | The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss... | ['Chunyan Miao', 'Lizhen Cui', 'wei he', 'Chenyi Lei', 'Hao Xiong', 'Yonghui Xu', 'Yong liu', 'Yixin Zhang'] | 2022-05-30 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 3.41944426e-01 -2.30269805e-01 -7.02334642e-01 -3.30603689e-01
4.85387519e-02 -4.32110399e-01 3.24614853e-01 2.39110813e-01
-5.21703474e-02 3.40847999e-01 5.41622341e-01 -2.94996679e-01
-3.81024659e-01 -7.97293186e-01 -5.97563207e-01 -5.36867142e-01
-1.70082822e-01 2.15894848e-01 9.83692035e-02 -4.04100120... | [10.199026107788086, 5.605485439300537] |
09348113-0d7c-44c5-a1d0-0270ccd70066 | activity-detection-in-long-surgical-videos | 2205.02805 | null | https://arxiv.org/abs/2205.02805v3 | https://arxiv.org/pdf/2205.02805v3.pdf | An Empirical Study on Activity Recognition in Long Surgical Videos | Activity recognition in surgical videos is a key research area for developing next-generation devices and workflow monitoring systems. Since surgeries are long processes with highly-variable lengths, deep learning models used for surgical videos often consist of a two-stage setup using a backbone and temporal sequence ... | ['Muhammad Abdullah Jamal', 'Aidean Sharghi', 'Ali Mottaghi', 'Zhuohong He', 'Omid Mohareri'] | 2022-05-05 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 4.47716027e-01 -7.87323490e-02 -6.51533961e-01 -9.68018770e-02
-5.97256780e-01 -5.45249701e-01 5.86888790e-01 -4.10782499e-03
-7.36004710e-01 5.61532497e-01 6.72155261e-01 -3.24478716e-01
-3.34761679e-01 -2.28420109e-01 -6.21169984e-01 -8.11092973e-01
-4.84833479e-01 2.95624554e-01 2.11810678e-01 2.90636301... | [14.10906982421875, -3.363959789276123] |
60361f12-0062-468e-829a-d659a2352599 | point-cloud-completion-with-pretrained-text | 2306.10533 | null | https://arxiv.org/abs/2306.10533v1 | https://arxiv.org/pdf/2306.10533v1.pdf | Point-Cloud Completion with Pretrained Text-to-image Diffusion Models | Point-cloud data collected in real-world applications are often incomplete. Data is typically missing due to objects being observed from partial viewpoints, which only capture a specific perspective or angle. Additionally, data can be incomplete due to occlusion and low-resolution sampling. Existing completion approach... | ['Gal Chechik', 'Ohad Rahamim', 'Yoni Kasten'] | 2023-06-18 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 4.77981806e-01 3.80418226e-02 2.13108018e-01 -4.65790242e-01
-1.20140171e+00 -7.73388386e-01 4.73758221e-01 2.25681961e-02
1.77251741e-01 2.79140025e-01 1.66180227e-02 1.67667761e-01
1.31303249e-02 -8.12012672e-01 -1.11373842e+00 -1.74268350e-01
3.32588583e-01 1.42455709e+00 3.72597128e-01 -3.57186794... | [8.40218734741211, -3.145479679107666] |
1a810792-8b77-4278-8241-6488ccf29c10 | provable-and-practical-efficient-exploration | 2305.18246 | null | https://arxiv.org/abs/2305.18246v1 | https://arxiv.org/pdf/2305.18246v1.pdf | Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo | We present a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning (RL). One of the key shortcomings of existing Thompson sampling algorithms is the need to perform a Gaussian approximation of the posterior distribution, which is not a good surrogate in most practical setting... | ['Kamyar Azizzadenesheli', 'Anima Anandkumar', 'Doina Precup', 'A. Rupam Mahmood', 'Pan Xu', 'Qingfeng Lan', 'Haque Ishfaq'] | 2023-05-29 | null | null | null | null | ['thompson-sampling', 'efficient-exploration'] | ['methodology', 'methodology'] | [-2.38054037e-01 5.05208261e-02 -1.80071592e-01 -7.60016497e-03
-1.25118709e+00 -4.32737827e-01 3.55051130e-01 -1.91788375e-02
-9.17139471e-01 1.08966255e+00 -1.09256022e-01 -5.48233986e-01
-1.23432867e-01 -8.64731371e-01 -9.65478420e-01 -9.19274271e-01
-2.74624258e-01 8.33055735e-01 -1.15478732e-01 -5.69550619... | [4.1612348556518555, 2.255988836288452] |
dec4bf4a-90b0-48e6-805e-48c66ff384cc | bevers-a-general-simple-and-performant | 2303.16974 | null | https://arxiv.org/abs/2303.16974v1 | https://arxiv.org/pdf/2303.16974v1.pdf | BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification | Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retri... | ['Stephen Scott', 'Mitchell DeHaven'] | 2023-03-29 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [-5.82749285e-02 -7.24978969e-02 -6.11994386e-01 -4.36353870e-02
-1.72163928e+00 -8.37451935e-01 1.03802693e+00 5.79380453e-01
-3.60497743e-01 8.39078367e-01 4.26607758e-01 -2.67115265e-01
-6.13121614e-02 -5.43271959e-01 -4.86591905e-01 -5.82316285e-03
4.18372691e-01 4.41937298e-01 5.69608331e-01 6.26371289... | [8.9137544631958, 9.558188438415527] |
e0237984-416c-40ed-90a8-82288d2ca88c | on-the-complementary-nature-of-knowledge | 2010.05732 | null | https://arxiv.org/abs/2010.05732v1 | https://arxiv.org/pdf/2010.05732v1.pdf | On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling | We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling. We show that a language model-inspired knowledge graph embedding approach yields both improved knowledge graph embeddings and fine-grain entity type representations. Our work al... | ['Francis Ferraro', 'Rajat Patel'] | 2020-10-12 | null | https://aclanthology.org/2020.deelio-1.11 | https://aclanthology.org/2020.deelio-1.11.pdf | emnlp-deelio-2020-11 | ['type-prediction'] | ['computer-code'] | [-7.11832941e-01 7.95873523e-01 -1.04025948e+00 -1.33350760e-01
-1.10221356e-01 -6.87534869e-01 5.43698132e-01 7.41356432e-01
-4.19332266e-01 7.64116108e-01 6.68075025e-01 -4.70786035e-01
-3.35531503e-01 -1.55391276e+00 -8.53588164e-01 1.60072356e-01
-4.12883818e-01 6.25521362e-01 1.14072196e-01 -1.92286715... | [8.886982917785645, 7.955042362213135] |
f12eb0e8-17a6-4e5c-a58c-4db8b6584b91 | progressive-multi-view-human-mesh-recovery | 2212.05223 | null | https://arxiv.org/abs/2212.05223v1 | https://arxiv.org/pdf/2212.05223v1.pdf | Progressive Multi-view Human Mesh Recovery with Self-Supervision | To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-view ambiguities. Existing solutions typically suffer from poor generalization performance to new settings, largely due to the limited diversit... | ['Ziyan Wu', 'David Doermann', 'Junsong Yuan', 'Terrence Chen', 'Benjamin Planche', 'Meng Zheng', 'Liangchen Song', 'Xuan Gong'] | 2022-12-10 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [ 2.67198205e-01 -2.64920533e-01 -6.67471020e-03 -1.96136013e-01
-1.09475136e+00 -5.99339545e-01 4.69683290e-01 -2.63249815e-01
-9.03155953e-02 6.31575227e-01 2.04441488e-01 3.38336259e-01
-8.90000388e-02 -6.05327189e-01 -9.75205243e-01 -4.24263328e-01
2.43611559e-01 9.15388465e-01 3.57447684e-01 -2.04518259... | [7.971161842346191, -2.4196279048919678] |
1fe048c8-6cd2-4004-ac89-b15edc90f364 | in-situ-process-quality-monitoring-and-defect | 2112.01921 | null | https://arxiv.org/abs/2112.01921v1 | https://arxiv.org/pdf/2112.01921v1.pdf | In situ process quality monitoring and defect detection for direct metal laser melting | Quality control and quality assurance are challenges in Direct Metal Laser Melting (DMLM). Intermittent machine diagnostics and downstream part inspections catch problems after undue cost has been incurred processing defective parts. In this paper we demonstrate two methodologies for in-process fault detection and part... | ['Thomas Spears', 'Subhrajit Roychowdhury', 'Xiaohu Ping', 'Gabriel Lipsa', 'Michael Lexa', 'H. Kirk Mathews', 'Saikat Ray Majumder', 'Sarah Felix'] | 2021-12-03 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.31324935e-01 6.76644742e-02 -4.29442115e-02 -3.49836111e-01
-6.96300447e-01 -1.24861792e-01 1.08817600e-01 2.06023961e-01
4.40309078e-01 4.25267875e-01 -7.24971175e-01 -2.11395975e-02
-5.65009356e-01 -6.57604039e-01 -4.31771517e-01 -7.96331227e-01
1.82691708e-01 9.48146999e-01 3.16988409e-01 9.22316685... | [6.8296380043029785, 2.3790061473846436] |
38ba3c24-6406-4bfb-81c1-228f7e50d5fc | skoltechnlp-at-semeval-2021-task-5-leveraging | null | null | https://aclanthology.org/2021.semeval-1.126 | https://aclanthology.org/2021.semeval-1.126.pdf | SkoltechNLP at SemEval-2021 Task 5: Leveraging Sentence-level Pre-training for Toxic Span Detection | This work describes the participation of the Skoltech NLP group team (Sk) in the Toxic Spans Detection task at SemEval-2021. The goal of the task is to identify the most toxic fragments of a given sentence, which is a binary sequence tagging problem. We show that fine-tuning a RoBERTa model for this problem is a strong... | ['Alexander Panchenko', 'Nikita Semenov', 'Olga Kozlova', 'Varvara Logacheva', 'Igor Markov', 'David Dale'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 2.58431882e-01 1.47748902e-01 -1.35310248e-01 -7.84756094e-02
-1.50136781e+00 -1.03392303e+00 6.49349451e-01 5.00678480e-01
-7.20745146e-01 1.08097899e+00 4.03168797e-01 -2.72261381e-01
8.36450383e-02 -3.69431913e-01 -7.85997510e-01 -5.61872780e-01
2.63519045e-02 4.39137548e-01 4.13692325e-01 -1.13214418... | [8.944388389587402, 10.6254243850708] |
a5d09585-a9e0-4fdb-b40f-f701bbaae96f | psnet-parallel-symmetric-network-for-video | 2210.05912 | null | https://arxiv.org/abs/2210.05912v1 | https://arxiv.org/pdf/2210.05912v1.pdf | PSNet: Parallel Symmetric Network for Video Salient Object Detection | For the video salient object detection (VSOD) task, how to excavate the information from the appearance modality and the motion modality has always been a topic of great concern. The two-stream structure, including an RGB appearance stream and an optical flow motion stream, has been widely used as a typical pipeline fo... | ['Sam Kwong', 'Yao Zhao', 'Guanghui Yue', 'Jianjun Lei', 'Weiyu Song', 'Runmin Cong'] | 2022-10-12 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 3.65577519e-01 -3.44336092e-01 -1.01139113e-01 -2.30552673e-01
-1.89961985e-01 -2.08213050e-02 7.01065779e-01 -2.11683378e-01
-4.38833594e-01 4.43923324e-01 3.22448641e-01 1.33398905e-01
4.88753691e-02 -5.06294727e-01 -6.28622115e-01 -9.95965362e-01
3.50508660e-01 -2.29531378e-01 9.49194729e-01 -4.42922115... | [9.449562072753906, -0.38089337944984436] |
992fe2d8-4a74-448d-80bf-3f293f41c3fb | ar-diffusion-auto-regressive-diffusion-model | 2305.09515 | null | https://arxiv.org/abs/2305.09515v2 | https://arxiv.org/pdf/2305.09515v2.pdf | AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation | Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generation via generating all tokens within a sequence concurrently. However, natural language exhibits a far more pronounced sequential dependency i... | ['Weizhu Chen', 'Nan Duan', 'Jian Guo', 'Zhongyu Wei', 'Juntao Li', 'Hai-Tao Zheng', 'Jian Jiao', 'Yelong Shen', 'Yeyun Gong', 'Xiao Liu', 'Zhihao Fan', 'Tong Wu'] | 2023-05-16 | null | null | null | null | ['text-summarization', 'common-sense-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 5.15794337e-01 9.16261598e-02 1.44010028e-02 2.03325655e-02
-7.05900848e-01 -4.25199211e-01 1.12430477e+00 1.95839375e-01
-3.99153590e-01 8.17659020e-01 6.56586468e-01 -3.26048881e-01
3.14116329e-01 -9.36481714e-01 -4.94452387e-01 -8.97137642e-01
1.87707275e-01 2.08026052e-01 -2.57869195e-02 -4.23016936... | [12.06943416595459, 9.101019859313965] |
a653be3f-cea4-4d89-8c62-dc5b0e2596ca | unified-visual-relationship-detection-with | 2303.08998 | null | https://arxiv.org/abs/2303.08998v1 | https://arxiv.org/pdf/2303.08998v1.pdf | Unified Visual Relationship Detection with Vision and Language Models | This work focuses on training a single visual relationship detector predicting over the union of label spaces from multiple datasets. Merging labels spanning different datasets could be challenging due to inconsistent taxonomies. The issue is exacerbated in visual relationship detection when second-order visual semanti... | ['Ting Liu', 'Hartwig Adam', 'Ming-Hsuan Yang', 'Florian Schroff', 'Yin Cui', 'Boqing Gong', 'Liangzhe Yuan', 'Long Zhao'] | 2023-03-16 | null | null | null | null | ['human-object-interaction-detection', 'visual-relationship-detection', 'scene-graph-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.53678283e-01 -3.58243063e-02 -4.10279810e-01 -3.35462511e-01
-7.41871953e-01 -5.83564222e-01 7.30956852e-01 3.93705755e-01
-2.58269638e-01 1.33147240e-01 3.84981692e-01 -1.85246304e-01
1.29348278e-01 -4.36371624e-01 -7.10255206e-01 -1.14548825e-01
-1.01390176e-01 6.12270355e-01 3.74271244e-01 4.16010618... | [10.274496078491211, 1.6285871267318726] |
03ea1fb6-96fd-482d-921b-06d140acaf0a | a-machine-learning-framework-for-sleeping | 1910.01092 | null | https://arxiv.org/abs/1910.01092v2 | https://arxiv.org/pdf/1910.01092v2.pdf | A Machine Learning framework for Sleeping Cell Detection in a Smart-city IoT Telecommunications Infrastructure | The smooth operation of largely deployed Internet of Things (IoT) applications will depend on, among other things, effective infrastructure failure detection. Access failures in wireless network Base Stations (BSs) produce a phenomenon called "sleeping cells", which can render a cell catatonic without triggering any al... | ['Filippo Malandra', 'Orestes Manzanilla-Salazar', 'Constant Wette', 'Brunilde Sanso', 'Hakim Mellah'] | 2019-10-02 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-1.27771080e-01 5.95596135e-02 -1.42019644e-01 -6.19273931e-02
-2.35136375e-01 -3.79726201e-01 4.78501230e-01 6.13288045e-01
-1.93089366e-01 1.03921056e+00 -3.34443480e-01 -7.00803816e-01
-5.01386106e-01 -1.11580670e+00 -4.28155661e-01 -1.12582648e+00
-5.10269761e-01 6.68625891e-01 5.59403718e-01 1.81763902... | [6.144965648651123, 1.4303029775619507] |
48c2a39e-a49c-46e3-95a1-cf143f988fab | think-global-and-act-local-bayesian | 2102.07188 | null | https://arxiv.org/abs/2102.07188v2 | https://arxiv.org/pdf/2102.07188v2.pdf | Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces | High-dimensional black-box optimisation remains an important yet notoriously challenging problem. Despite the success of Bayesian optimisation methods on continuous domains, domains that are categorical, or that mix continuous and categorical variables, remain challenging. We propose a novel solution -- we combine loca... | ['Michael A. Osborne', 'Cong Lu', 'Binxin Ru', 'Huong Ha', 'Vu Nguyen', 'Xingchen Wan'] | 2021-02-14 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.97822317e-01 -1.31985396e-01 -2.50143409e-01 -4.95240241e-01
-1.26780868e+00 -5.22421181e-01 8.26299012e-01 1.33227840e-01
-8.09519112e-01 9.32995796e-01 2.08063930e-01 -3.56773376e-01
-8.20331216e-01 -2.99150914e-01 -4.20093060e-01 -9.35634136e-01
-3.29053134e-01 7.99973726e-01 -5.70116118e-02 2.20376834... | [6.445954322814941, 3.9055633544921875] |
d651951c-d9c6-46ae-856e-612ef546968f | easydgl-encode-train-and-interpret-for | 2303.12341 | null | https://arxiv.org/abs/2303.12341v1 | https://arxiv.org/pdf/2303.12341v1.pdf | EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning | Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility. This paper aims to design an easy-to-use pipeline (termed as EasyDGL which is also due to its implementation by DGL toolkit) composed of three key modules with ... | ['Junchi Yan', 'Xiaokang Yang', 'Nianzu Yang', 'Haoyu Geng', 'Chao Chen'] | 2023-03-22 | null | null | null | null | ['dynamic-link-prediction', 'fraud-detection', 'sequential-recommendation'] | ['graphs', 'miscellaneous', 'miscellaneous'] | [ 2.32134193e-01 2.60027587e-01 -1.59002453e-01 -9.35759768e-02
-3.39263469e-01 -2.02546015e-01 6.81283593e-01 9.85827819e-02
3.65055233e-01 4.89310205e-01 1.73224345e-01 -3.73567700e-01
-5.10696828e-01 -1.03601861e+00 -8.96076024e-01 -9.38480794e-01
-9.15940225e-01 6.41882241e-01 2.23426402e-01 -2.81189561... | [7.192079067230225, 5.845260143280029] |
89691f08-8702-4a46-9770-ed38ce0471e7 | a-review-and-comparative-study-of-close-range | 2306.09014 | null | https://arxiv.org/abs/2306.09014v1 | https://arxiv.org/pdf/2306.09014v1.pdf | A Review and Comparative Study of Close-Range Geometric Camera Calibration Tools | In many camera-based applications, it is necessary to find the geometric relationship between incoming rays and image pixels, i.e., the projection model, through the geometric camera calibration (GCC). Aiming to provide practical calibration guidelines, this work surveys and evaluates the existing GCC tools. The survey... | ['Alper Yilmaz', 'Yijia He', 'Junhui Liu', 'Binliang Wang', 'Grzegorz Jozkow', 'Yuxin Shao', 'Yuan Zhuang', 'Jianzhu Huai'] | 2023-06-15 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [-2.78051734e-01 -6.29518390e-01 4.54709023e-01 -3.02175611e-01
9.47858393e-02 -9.09822643e-01 4.44389492e-01 -5.33294797e-01
-1.82495683e-01 2.33796999e-01 -5.35514764e-02 -5.68620384e-01
4.60581407e-02 -6.13823235e-01 -5.52556694e-01 -5.32016158e-01
4.48425204e-01 -1.30218849e-01 5.46046078e-01 -5.84809035... | [8.190723419189453, -2.2565338611602783] |
26ea26c4-fd5a-446d-a979-265c8826e3a1 | attention-based-ingredient-phrase-parser | 2210.02535 | null | https://arxiv.org/abs/2210.02535v1 | https://arxiv.org/pdf/2210.02535v1.pdf | Attention-based Ingredient Phrase Parser | As virtual personal assistants have now penetrated the consumer market, with products such as Siri and Alexa, the research community has produced several works on task-oriented dialogue tasks such as hotel booking, restaurant booking, and movie recommendation. Assisting users to cook is one of these tasks that are expe... | ['Aldo Lipani', 'To Eun Kim', 'MeiHui Wang', 'Pin Ni', 'Zhengxiang Shi'] | 2022-10-05 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.36700468e-02 2.35932797e-01 -3.47168207e-01 -8.06830883e-01
-4.92884934e-01 -9.19073343e-01 8.79583601e-03 7.30312347e-01
-2.87461758e-01 5.35549462e-01 5.87774098e-01 -3.61717075e-01
2.01225340e-01 -8.52672458e-01 -4.93171066e-01 -3.89776945e-01
1.47790134e-01 6.25662684e-01 -4.05664779e-02 -7.00880826... | [11.517009735107422, 4.695313453674316] |
219af7eb-b938-45df-8763-4007caf5b9f3 | unbiased-monte-carlo-cluster-updates-with | 2105.05650 | null | https://arxiv.org/abs/2105.05650v3 | https://arxiv.org/pdf/2105.05650v3.pdf | Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks | Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to such distributions, but suffer from either intrinsic bias or high... | ['Giuseppe Carleo', 'Riccardo Rossi', 'Dian Wu'] | 2021-05-12 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [-1.10018052e-01 -2.25429818e-01 -1.74255863e-01 -1.69242740e-01
-6.38135910e-01 -1.25577703e-01 8.31434309e-01 -1.80181593e-01
-3.97723109e-01 1.21735418e+00 6.88056573e-02 -3.60710531e-01
-1.62839711e-01 -8.15470815e-01 -4.65224326e-01 -1.02322817e+00
-2.51543880e-01 1.08949137e+00 2.23412722e-01 -1.82879344... | [5.688281059265137, 4.82820987701416] |
a2b340ef-84ea-46f2-9dcd-663f54f077da | learning-the-right-layers-a-data-driven-layer | 2306.00152 | null | https://arxiv.org/abs/2306.00152v1 | https://arxiv.org/pdf/2306.00152v1.pdf | Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs | Clustering (or community detection) on multilayer graphs poses several additional complications with respect to standard graphs as different layers may be characterized by different structures and types of information. One of the major challenges is to establish the extent to which each layer contributes to the cluster... | ['Francesco Tudisco', 'Francesco Rinaldi', 'Andrea Cristofari', 'Sara Venturini'] | 2023-05-31 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 4.05960977e-01 1.21174991e-01 -2.06658542e-01 -2.22190544e-01
-5.22554994e-01 -5.54453909e-01 5.28202951e-01 5.21064818e-01
-4.49523926e-01 5.18095613e-01 -2.86657717e-02 -8.37083831e-02
-3.81805569e-01 -4.82889950e-01 -5.84874094e-01 -1.11035645e+00
-5.13526618e-01 7.07529306e-01 2.23040119e-01 3.21268797... | [7.2025909423828125, 5.344334602355957] |
951c30c3-96df-4c0b-9380-f7c462b1ed21 | a-simple-parametric-classification-baseline | 2211.11727 | null | https://arxiv.org/abs/2211.11727v2 | https://arxiv.org/pdf/2211.11727v2.pdf | Parametric Classification for Generalized Category Discovery: A Baseline Study | Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. Previous studies argued that parametric classifiers are prone to overfitting to seen categories, and endorsed using a non-parametric classifier formed with semi-supervised k-means... | ['Xiaojuan Qi', 'Bingchen Zhao', 'Xin Wen'] | 2022-11-21 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 2.27064833e-01 3.05103809e-01 -6.08350694e-01 -8.06548655e-01
-7.94265032e-01 -5.42353094e-01 6.28064752e-01 1.65220454e-01
-3.42341252e-02 1.04636407e+00 -8.39537457e-02 -2.81451464e-01
-3.07855099e-01 -6.14219129e-01 -6.04447842e-01 -8.01665008e-01
-1.77315921e-01 7.43636429e-01 7.84594193e-02 4.58221644... | [8.815450668334961, 4.166370868682861] |
66ffc61e-5fdd-43c9-97bf-835913119aa2 | acoustic-word-embeddings-for-untranscribed | 2306.02153 | null | https://arxiv.org/abs/2306.02153v1 | https://arxiv.org/pdf/2306.02153v1.pdf | Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling | Acoustic word embeddings are typically created by training a pooling function using pairs of word-like units. For unsupervised systems, these are mined using k-nearest neighbor (KNN) search, which is slow. Recently, mean-pooled representations from a pre-trained self-supervised English model were suggested as a promisi... | ['Sharon Goldwater', 'Hao Tang', 'Ondrej Klejch', 'Ramon Sanabria'] | 2023-06-03 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [ 1.89202845e-01 1.21349785e-02 -2.65077382e-01 -5.78869939e-01
-1.58930886e+00 -6.72724783e-01 6.21367335e-01 2.87334710e-01
-1.14336336e+00 5.20279229e-01 4.31217164e-01 -3.64460766e-01
2.80831873e-01 -7.11664557e-01 -7.21416295e-01 -5.45879781e-01
1.07438685e-02 4.59483624e-01 4.12622839e-01 -4.28597853... | [14.231948852539062, 6.7878193855285645] |
f6af1e79-e184-4a72-8b5a-344880dc881c | sea-net-squeeze-and-excitation-attention-net | 2010.15344 | null | https://arxiv.org/abs/2010.15344v1 | https://arxiv.org/pdf/2010.15344v1.pdf | Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading | Diabetes is one of the most common disease in individuals. \textit{Diabetic retinopathy} (DR) is a complication of diabetes, which could lead to blindness. Automatic DR grading based on retinal images provides a great diagnostic and prognostic value for treatment planning. However, the subtle differences among severity... | ['XiaoLi Li', 'Zeng Zeng', 'Kartik Chopra', 'Ziyuan Zhao'] | 2020-10-29 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-4.96606007e-02 -1.95540860e-01 -3.59327672e-03 -6.52972996e-01
-4.25847769e-01 6.76502436e-02 1.09778628e-01 -1.25049829e-01
-3.14467221e-01 8.35946918e-01 3.54078084e-01 -9.03791040e-02
-2.72487164e-01 -6.80235326e-01 -5.56175783e-02 -1.02210581e+00
3.89770418e-01 -8.54516178e-02 8.28522146e-02 6.45995140... | [15.812490463256836, -3.9801650047302246] |
4940b11d-40bd-46e2-a549-436d0c07b6c0 | videopipe-2022-challenge-real-world-video | 2210.11158 | null | https://arxiv.org/abs/2210.11158v1 | https://arxiv.org/pdf/2210.11158v1.pdf | VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection | Video understanding is an important problem in computer vision. Currently, the well-studied task in this research is human action recognition, where the clips are manually trimmed from the long videos, and a single class of human action is assumed for each clip. However, we may face more complicated scenarios in the in... | ['Yali Wang', 'Yu Qiao', 'Yi Dai', 'Wei Yao', 'Fei Xie', 'Haiping Tang', 'Lixia Qiu', 'Yabing Jiang', 'Guixin Liang', 'Ying Li', 'Xuan Zhang', 'Yi Liu'] | 2022-10-20 | null | null | null | null | ['video-defect-classification', 'temporal-defect-localization'] | ['computer-code', 'computer-vision'] | [ 1.54187694e-01 -2.68210173e-01 -1.59605756e-01 -1.51439086e-01
-6.00942671e-01 -4.34069991e-01 -1.53343938e-03 -1.04628816e-01
1.48367047e-01 2.63459265e-01 6.97018728e-02 -2.51027733e-01
8.88374224e-02 -5.06239116e-01 -7.81471550e-01 -8.40497375e-01
-6.50154706e-03 7.31601417e-02 5.04754722e-01 1.20266769... | [7.9050469398498535, 1.5161805152893066] |
c1950e98-0d02-4896-8b4f-2b5d104a0934 | graph-regularized-probabilistic-matrix | 2210.10784 | null | https://arxiv.org/abs/2210.10784v1 | https://arxiv.org/pdf/2210.10784v1.pdf | Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction | Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a su... | ['Angshul Majumdar', 'Kriti Kumar', 'Emilie Chouzenoux', 'Stuti Jain'] | 2022-10-19 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 1.03116617e-01 -5.52577116e-02 -6.44629359e-01 -1.74015537e-01
-6.10691369e-01 -4.41738397e-01 3.39059502e-01 4.81980145e-01
-7.26753771e-02 8.26929390e-01 1.48891702e-01 -8.16274762e-01
-4.68444824e-01 -3.06943357e-01 -5.22017896e-01 -6.77814603e-01
-2.67298847e-01 5.69280982e-01 -4.38865036e-01 1.85379729... | [5.525824546813965, 5.855812072753906] |
64f3aebb-b403-4670-8f6d-9fee614495ba | evaluation-of-online-dialogue-policy-learning | null | null | https://aclanthology.org/L12-1126 | https://aclanthology.org/L12-1126.pdf | Evaluation of Online Dialogue Policy Learning Techniques | The number of applied Dialogue Systems is ever increasing in several service providing and other applications as a way to efficiently and inexpensively serve large numbers of customers. A DS that employs some form of adaptation to the environment and its users is called an Adaptive Dialogue System (ADS). A significant ... | ['ros', 'Alex Papangelis', 'Vangelis Karkaletsis', 'Fillia Makedon'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['dialogue-management'] | ['natural-language-processing'] | [-3.17894369e-01 3.99481595e-01 3.04303132e-02 -5.95642090e-01
-2.58890241e-01 -5.46823859e-01 8.16620648e-01 3.13208640e-01
-4.88806516e-01 1.12404776e+00 2.16448635e-01 -1.13480061e-01
7.69931674e-02 -7.00603545e-01 3.12559128e-01 -3.72933567e-01
-1.85627155e-02 9.89249945e-01 5.65879226e-01 -1.19844139... | [13.065800666809082, 7.962672233581543] |
e3a1b463-02dd-4f84-8945-678a234305ce | revisiting-image-reconstruction-for-semi | 2303.09794 | null | https://arxiv.org/abs/2303.09794v1 | https://arxiv.org/pdf/2303.09794v1.pdf | Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation | Autoencoding, which aims to reconstruct the input images through a bottleneck latent representation, is one of the classic feature representation learning strategies. It has been shown effective as an auxiliary task for semi-supervised learning but has become less popular as more sophisticated methods have been propose... | ['Javen Qinfeng Shi', 'Jinan Zou', 'Lingqiao Liu', 'HaiMing Xu', 'YuHao Lin'] | 2023-03-17 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 4.94899571e-01 3.84856254e-01 -1.82296410e-01 -4.78127152e-01
-6.45668864e-01 -4.50775266e-01 6.88274622e-01 2.35596523e-02
-4.28233117e-01 4.68646914e-01 1.01473488e-01 -2.50330591e-03
3.56146693e-02 -6.51091754e-01 -9.53344584e-01 -9.40730572e-01
4.73980635e-01 5.55406213e-01 5.17008364e-01 -6.89840817... | [9.562963485717773, 0.9703558087348938] |
e1578e39-1ea1-4cd1-a459-be0193d021ed | rnnids-enhancing-network-intrusion-detection | null | null | https://doi.org/10.1016/j.cose.2020.102151 | https://arxiv.org/pdf/1807.03212.pdf | RNNIDS: Enhancing Network Intrusion Detection Systems through Deep Learning | Security of information passing through the Internet is threatened by today’s most advanced malware
ranging from orchestrated botnets to simpler polymorphic worms. These threats, as examples of zero-day
attacks, are able to change their behavior several times in the early phases of their existence to bypass the
netw... | ['Fatemeh Ganji', 'Jean-Pierre Seifertac', 'Soroush M. Sohia'] | 2020-12-21 | null | null | null | scienceredirect-2020-12 | ['network-intrusion-detection'] | ['miscellaneous'] | [ 2.05076158e-01 -4.61919129e-01 3.84039320e-02 1.11911789e-01
1.41254112e-01 -1.01592731e+00 6.92881703e-01 -3.77840549e-01
-2.58978784e-01 6.71706855e-01 -5.02075016e-01 -7.50891626e-01
1.50327489e-03 -1.01190484e+00 -3.36629331e-01 -4.20632422e-01
-3.25072438e-01 4.69630897e-01 8.37905824e-01 -6.42722487... | [5.384665012359619, 7.3871002197265625] |
9ff2e827-bed0-4649-a9bc-2c1c3af30c1c | more-interpretable-graph-similarity | 2208.04580 | null | https://arxiv.org/abs/2208.04580v3 | https://arxiv.org/pdf/2208.04580v3.pdf | More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference | Graph similarity measurement, which computes the distance/similarity between two graphs, arises in various graph-related tasks. Recent learning-based methods lack interpretability, as they directly transform interaction information between two graphs into one hidden vector and then map it to similarity. To cope with th... | ['Fei Ma', 'Ye Ma', 'Binjie Hong', 'Zixun Lan'] | 2022-08-09 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 4.61699694e-01 3.19918305e-01 -3.82948011e-01 -5.79031169e-01
-3.03599179e-01 -4.51082766e-01 7.59514391e-01 5.70919693e-01
-4.57975380e-02 2.75543511e-01 2.49311581e-01 -4.43158299e-01
-2.62464702e-01 -1.03489816e+00 -6.70659900e-01 -5.04101038e-01
-2.80374289e-01 2.99119174e-01 9.09715146e-02 -2.35500425... | [7.159734725952148, 6.281630039215088] |
cbf6231a-133d-413a-9bf8-2ca36f6a7df5 | cur-transformer-a-convolutional-unbiased | null | null | https://dl.acm.org/doi/full/10.1145/3566125 | https://dl.acm.org/doi/pdf/10.1145/3566125 | CUR Transformer: A Convolutional Unbiased Regional Transformer for Image Denoising | Image denoising is a fundamental problem in computer vision and multimedia computation. Non-local filters are effective for image denoising. But existing deep learning methods that use non-local computation structures are mostly designed for high-level tasks, and global self-attention is usually adopted. For the task o... | ['Xuan Dong', 'Xiaojie Wang', 'Ke Yan', 'Xiaoyan Hu', 'Xia Wang', 'Weixin Li', 'Kang Xu'] | 2023-02-25 | null | null | null | journal-2023-2 | ['jpeg-compression-artifact-reduction', 'image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.00977415e-01 -6.53867364e-01 5.86042143e-02 -3.26615989e-01
-7.99380183e-01 -6.42172024e-02 4.76751290e-02 3.50589156e-02
-5.51633894e-01 2.44533271e-01 1.86254784e-01 -7.08276033e-02
9.15668011e-02 -1.00368834e+00 -8.98666382e-01 -1.07151186e+00
3.06880087e-01 -6.67413652e-01 5.63874066e-01 -2.64510810... | [11.109371185302734, -2.086163282394409] |
bed872e7-d87e-467c-baaa-d4b45bdf2602 | tsgcnext-dynamic-static-multi-graph | 2304.11631 | null | https://arxiv.org/abs/2304.11631v1 | https://arxiv.org/pdf/2304.11631v1.pdf | TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential | Skeleton-based action recognition has achieved remarkable results in human action recognition with the development of graph convolutional networks (GCNs). However, the recent works tend to construct complex learning mechanisms with redundant training and exist a bottleneck for long time-series. To solve these problems,... | ['Yuxin Tian', 'Zijie Cai', 'Yijing Lu', 'Miao Yao', 'Pengpeng Chen', 'Dongjingdin Liu'] | 2023-04-23 | null | null | null | null | ['skeleton-based-action-recognition', 'action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.86022536e-02 -2.65832543e-01 -6.73475266e-02 -1.35057613e-01
-1.55905649e-01 1.70521587e-01 3.17911178e-01 -9.64433104e-02
-6.17273152e-01 3.42655450e-01 2.62517065e-01 -2.98829786e-02
-1.30169481e-01 -9.78194714e-01 -6.39045715e-01 -6.33294940e-01
-4.74151194e-01 3.08837742e-02 7.73944080e-01 -1.78274959... | [7.853048801422119, 0.35736146569252014] |
f4e7a72e-4102-4ac0-bb20-62a47d6faf3a | guided-depth-map-super-resolution-a-survey | 2302.09598 | null | https://arxiv.org/abs/2302.09598v2 | https://arxiv.org/pdf/2302.09598v2.pdf | Guided Depth Map Super-resolution: A Survey | Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image, is a longstanding and fundamental problem, it has attracted considerable attention from computer vision and image processing communities. ... | ['Xiangyang Ji', 'Debin Zhao', 'Junjun Jiang', 'Xianming Liu', 'Zhiwei Zhong'] | 2023-02-19 | null | null | null | null | ['depth-image-upsampling', 'depth-map-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 5.30139029e-01 -1.39959231e-01 -1.47586018e-01 -3.40116888e-01
-1.18953359e+00 -2.11678110e-02 1.45985857e-01 -5.52575648e-01
-1.05602778e-01 8.58512998e-01 1.01747885e-01 3.51100713e-01
-2.88490444e-01 -8.67146730e-01 -4.13823277e-01 -8.85495245e-01
-1.34952739e-01 -1.35954648e-01 3.08923215e-01 -1.32754132... | [10.151205062866211, -2.3769478797912598] |
e40b5fb7-d67c-437c-8380-5929b8e73a36 | mathprompter-mathematical-reasoning-using | 2303.05398 | null | https://arxiv.org/abs/2303.05398v1 | https://arxiv.org/pdf/2303.05398v1.pdf | MathPrompter: Mathematical Reasoning using Large Language Models | Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers. Unlike natural language understanding, math problems typically have a single correct answer, making the task of generating accurate solutions more challenging for LLMs. To the best of our k... | ['Harsh Shrivastava', 'Liang Du', 'Shima Imani'] | 2023-03-04 | null | null | null | null | ['mathematical-reasoning', 'arithmetic-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 3.57108451e-02 5.68225384e-01 2.78578997e-01 -5.22424698e-01
-8.82929504e-01 -5.74439108e-01 4.87568319e-01 4.92578268e-01
-1.01189375e-01 7.80169427e-01 1.36998996e-01 -7.95030892e-01
-2.00552925e-01 -1.18970740e+00 -8.03337693e-01 -2.43544881e-03
4.87563312e-01 7.28227437e-01 1.22953683e-01 -4.62777376... | [9.647418975830078, 7.350124835968018] |
615c7a06-943d-40c4-925b-d0f4d13cd4ad | multi-scale-fusion-methodologies-for-head-and | 2210.16704 | null | https://arxiv.org/abs/2210.16704v1 | https://arxiv.org/pdf/2210.16704v1.pdf | Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation | Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning. The varying anatomic locations and dimensions of H\&N nodal Gross Tumor Volumes (GTVn) and H\&N primary gross tumor volume (GTVp) are difficult to obtain due to lack of accurate and reliable delineat... | ['Ulas Bagci', 'Mohamed E. Abazeed', 'Bulent Aydogan', 'Debesh Jha', 'Abhishek Srivastava'] | 2022-10-29 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 7.50696361e-02 5.18780947e-01 -5.28195143e-01 -9.20959562e-02
-1.39077234e+00 -6.49550080e-01 2.68124074e-01 4.52938527e-01
-2.36731097e-01 6.32088482e-01 5.87410271e-01 -9.15568590e-01
-1.72978923e-01 -6.96334481e-01 -1.18126139e-01 -8.69421840e-01
2.57218838e-01 7.27652192e-01 -1.26410872e-01 -1.97009653... | [14.709993362426758, -2.5164854526519775] |
5d00fbb1-6bb1-4500-822a-9f7ea32a337c | ensemble-modeling-with-contrastive-knowledge | 2304.14668 | null | https://arxiv.org/abs/2304.14668v3 | https://arxiv.org/pdf/2304.14668v3.pdf | Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation | Sequential recommendation aims to capture users' dynamic interest and predicts the next item of users' preference. Most sequential recommendation methods use a deep neural network as sequence encoder to generate user and item representations. Existing works mainly center upon designing a stronger sequence encoder. Howe... | ['Victor S. Sheng', 'Lei Zhao', 'Guanfeng Liu', 'Fuzhen Zhuang', 'Pengpeng Zhao', 'Huanhuan Yuan', 'Hanwen Du'] | 2023-04-28 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 2.71439433e-01 -3.13579470e-01 -4.72202629e-01 -4.19576317e-01
-3.91058892e-01 -7.21013188e-01 4.86769706e-01 -4.36006933e-01
-3.01540166e-01 7.91891336e-01 3.53805542e-01 -5.49038589e-01
-5.34927845e-02 -8.10002327e-01 -9.72214043e-01 -6.03661120e-01
1.06034525e-01 3.15533936e-01 -7.14828521e-02 -2.41869926... | [10.143059730529785, 5.616687297821045] |
0f6893e7-fef0-4405-9159-99e2a239b6ab | smart-metro-deep-learning-approaches-to | 2304.07303 | null | https://arxiv.org/abs/2304.07303v1 | https://arxiv.org/pdf/2304.07303v1.pdf | Smart Metro: Deep Learning Approaches to Forecasting the MRT Line 3 Ridership | Since its establishment in 1999, the Metro Rail Transit Line 3 (MRT3) has served as a transportation option for numerous passengers in Metro Manila, Philippines. The Philippine government's transportation department records more than a thousand people using the MRT3 daily and forecasting the daily passenger count may b... | ['Gabriel Avelino Sampedro', 'Shekinah Lor Huyo-a', 'Mideth Abisado', 'Mary Grace Verzon', 'Jean Allyson Junsay', 'Jayrald Empino'] | 2023-04-14 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-8.12340558e-01 -5.58595181e-01 -5.36139786e-01 -3.62670600e-01
-4.04724509e-01 -4.05864209e-01 2.40275830e-01 4.09146786e-01
-4.70675796e-01 1.12243462e+00 1.70991391e-01 -9.17470217e-01
-2.17090517e-01 -1.38232410e+00 -2.63874680e-01 -5.08755624e-01
2.03827806e-02 5.92379034e-01 1.10938564e-01 -5.48744261... | [6.20851469039917, 1.8472944498062134] |
92dc3913-f659-49de-af1f-5703a1fb564b | compressing-cross-lingual-multi-task-models | 2211.15927 | null | https://arxiv.org/abs/2211.15927v1 | https://arxiv.org/pdf/2211.15927v1.pdf | Compressing Cross-Lingual Multi-Task Models at Qualtrics | Experience management is an emerging business area where organizations focus on understanding the feedback of customers and employees in order to improve their end-to-end experiences. This results in a unique set of machine learning problems to help understand how people feel, discover issues they care about, and find ... | ['Aaron Colak', 'Zhengzheng Xing', 'Wei Du', 'Yashmeet Gambhir', 'Samir Joshi', 'Daniel Perry', 'Daniel Campos'] | 2022-11-29 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [ 2.73635775e-01 -1.07165113e-01 -5.53874910e-01 -7.16133833e-01
-1.18235779e+00 -2.72515893e-01 5.50802462e-02 6.10953808e-01
-4.14968491e-01 3.62313777e-01 2.24145174e-01 -5.29554307e-01
-2.02215277e-02 -3.32733959e-01 -4.34372663e-01 -1.64141327e-01
2.00862184e-01 6.47181869e-01 -4.29256171e-01 -3.11825536... | [9.938096046447754, 6.177868366241455] |
fe10e85e-9c86-43cc-b713-24b77732440a | from-two-to-one-a-new-scene-text-recognizer | 2108.09661 | null | https://arxiv.org/abs/2108.09661v1 | https://arxiv.org/pdf/2108.09661v1.pdf | From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network | In this paper, we abandon the dominant complex language model and rethink the linguistic learning process in the scene text recognition. Different from previous methods considering the visual and linguistic information in two separate structures, we propose a Visual Language Modeling Network (VisionLAN), which views th... | ['Yongdong Zhang', 'Shenggao Zhu', 'Jing Wang', 'Shancheng Fang', 'Hongtao Xie', 'Yuxin Wang'] | 2021-08-22 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_From_Two_to_One_A_New_Scene_Text_Recognizer_With_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_From_Two_to_One_A_New_Scene_Text_Recognizer_With_ICCV_2021_paper.pdf | iccv-2021-1 | ['scene-text-recognition'] | ['computer-vision'] | [-5.51476656e-03 -5.54656863e-01 -1.77331686e-01 -4.25347477e-01
-2.77683318e-01 -3.87998521e-01 7.72984505e-01 9.23447311e-03
-6.61556959e-01 9.22106728e-02 5.35670146e-02 -3.28888506e-01
5.34974456e-01 -5.39667308e-01 -5.44657290e-01 -7.05301166e-01
6.10896051e-01 1.84267417e-01 2.57881790e-01 -3.55205685... | [11.757828712463379, 2.0848379135131836] |
5ea792b0-6aa9-4df0-937e-213262cdeba3 | tghop-an-explainable-efficient-and | 2107.04020 | null | https://arxiv.org/abs/2107.04020v1 | https://arxiv.org/pdf/2107.04020v1.pdf | TGHop: An Explainable, Efficient and Lightweight Method for Texture Generation | An explainable, efficient and lightweight method for texture generation, called TGHop (an acronym of Texture Generation PixelHop), is proposed in this work. Although synthesis of visually pleasant texture can be achieved by deep neural networks, the associated models are large in size, difficult to explain in theory, a... | ['C. -C. Jay Kuo', 'Kaitai Zhang', 'Ganning Zhao', 'Xuejing Lei'] | 2021-07-08 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 4.64159459e-01 3.80026996e-01 1.69676736e-01 2.33513694e-02
-6.64222360e-01 -7.55269751e-02 2.54038274e-01 -3.98302823e-01
5.04630327e-01 8.28023374e-01 -2.07847565e-01 1.55182824e-01
1.79639701e-02 -1.18624198e+00 -9.62840497e-01 -1.14946783e+00
2.62596011e-01 5.07968724e-01 -9.27428529e-03 -4.21205945... | [11.398276329040527, -1.0992320775985718] |
961f937d-a03f-4c85-a709-31a6f338939f | figureqa-an-annotated-figure-dataset-for | 1710.07300 | null | http://arxiv.org/abs/1710.07300v2 | http://arxiv.org/pdf/1710.07300v2.pdf | FigureQA: An Annotated Figure Dataset for Visual Reasoning | We introduce FigureQA, a visual reasoning corpus of over one million
question-answer pairs grounded in over 100,000 images. The images are
synthetic, scientific-style figures from five classes: line plots, dot-line
plots, vertical and horizontal bar graphs, and pie charts. We formulate our
reasoning task by generating ... | ['Vincent Michalski', 'Yoshua Bengio', 'Samira Ebrahimi Kahou', 'Akos Kadar', 'Adam Trischler', 'Adam Atkinson'] | 2017-10-19 | figureqa-an-annotated-figure-dataset-for-1 | https://openreview.net/forum?id=SyunbfbAb | https://openreview.net/pdf?id=SyunbfbAb | iclr-2018-1 | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [ 9.67098624e-02 4.69870716e-01 2.38222882e-01 -3.94099861e-01
-8.25382948e-01 -1.02646184e+00 7.28788257e-01 5.78233719e-01
7.52415061e-02 5.20774186e-01 1.80115059e-01 -7.46917963e-01
4.58964258e-02 -7.71839142e-01 -9.50691164e-01 -1.40400320e-01
5.72164208e-02 5.18338442e-01 3.61477762e-01 -3.18175197... | [11.163628578186035, 1.9907842874526978] |
97011aa6-2526-4a98-96b4-c35a0cc589b2 | crowd-sourced-data-analysis-mapping-of | 1903.12495 | null | http://arxiv.org/abs/1903.12495v1 | http://arxiv.org/pdf/1903.12495v1.pdf | Crowd Sourced Data Analysis: Mapping of Programming Concepts to Syntactical Patterns | Since programming concepts do not match their syntactic representations, code
search is a very tedious task. For instance in Java or C, array doesn't match
[], so using "array" as a query, one cannot find what they are looking for.
Often developers have to search code whether to understand any code, or to
reuse some pa... | ['Deepak Thukral', 'Darvesh Punia'] | 2019-03-28 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.08708106e-01 -2.63840109e-01 -2.14334324e-01 -2.43328527e-01
-2.90257245e-01 -1.23736274e+00 3.58064860e-01 8.46979260e-01
-7.90985078e-02 1.56592384e-01 -8.57710689e-02 -1.02057660e+00
-5.22144977e-03 -1.10635090e+00 -3.99344355e-01 7.94236660e-02
6.21016473e-02 -1.08196281e-01 8.43573093e-01 -2.99785584... | [7.552624702453613, 8.041665077209473] |
b5286c28-bc54-4e1e-8762-8d8bb5736cd7 | tivgan-text-to-image-to-video-generation-with | 2009.02018 | null | https://arxiv.org/abs/2009.02018v2 | https://arxiv.org/pdf/2009.02018v2.pdf | TiVGAN: Text to Image to Video Generation with Step-by-Step Evolutionary Generator | Advances in technology have led to the development of methods that can create desired visual multimedia. In particular, image generation using deep learning has been extensively studied across diverse fields. In comparison, video generation, especially on conditional inputs, remains a challenging and less explored area... | ['Do-Yeon Kim', 'Junmo Kim', 'Donggyu Joo'] | 2020-09-04 | null | null | null | null | ['image-to-video'] | ['computer-vision'] | [ 7.02393949e-01 -2.23609973e-02 1.43315107e-01 -5.83584718e-02
-8.24153006e-01 -3.71588767e-01 7.99711645e-01 -4.11934048e-01
-7.24463165e-02 1.09181738e+00 1.57294080e-01 -5.13366759e-02
4.55134898e-01 -8.70725095e-01 -1.08460200e+00 -7.90798724e-01
4.11911249e-01 6.36284724e-02 1.65828496e-01 1.87867999... | [10.920517921447754, -0.26702454686164856] |
e4264818-ee57-4fbb-9b52-fbfec76339a3 | deep-learning-for-musical-form-recognition | null | null | http://dx.doi.org/10.13140/RG.2.2.33554.12481 | https://raw.githubusercontent.com/danielathome19/Form-NN/master/PaperAndPresentation/Master%20Thesis.pdf | Deep Learning for Musical Form: Recognition and Analysis | Musical form analysis is a rigorous task that frequently challenges the expertise of human analysts and signal processing algorithms alike. While numerous systems have been proposed to perform the tasks of musical segmentation, genre classification, and single-label segment classification in popular music, none have sp... | ['Daniel Szelogowski'] | 2022-04-29 | null | null | null | master-thesis-2022-4 | ['genre-classification'] | ['computer-vision'] | [ 3.49097043e-01 -3.96957785e-01 1.24761790e-01 -1.34338945e-01
-8.06332529e-01 -1.11948562e+00 6.42545894e-02 1.52210489e-01
-3.37375760e-01 3.69899035e-01 -1.04390539e-01 -4.79205437e-02
-3.93066794e-01 -4.65790153e-01 -9.75618884e-02 -5.74358582e-01
2.10733995e-01 7.57954597e-01 -8.65993872e-02 -1.90164819... | [15.94118881225586, 5.213170051574707] |
094037e5-bc63-4859-b981-9c34b2453d30 | parallax-tolerant-image-stitching | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Zhang_Parallax-tolerant_Image_Stitching_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Zhang_Parallax-tolerant_Image_Stitching_2014_CVPR_paper.pdf | Parallax-tolerant Image Stitching | Parallax handling is a challenging task for image stitching. This paper presents a local stitching method to handle parallax based on the observation that input images do not need to be perfectly aligned over the whole overlapping region for stitching. Instead, they only need to be aligned in a way that there exists a ... | ['Feng Liu', 'Fan Zhang'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['image-stitching'] | ['computer-vision'] | [ 6.79722667e-01 -1.78980842e-01 -2.33461156e-01 8.34446177e-02
-5.83119035e-01 -9.96809602e-01 5.28755426e-01 -1.29070446e-01
-6.25055097e-03 2.11290017e-01 1.76791117e-01 -7.51048401e-02
-8.13472643e-02 -6.34179235e-01 -9.32120383e-01 -7.41914988e-01
2.38493025e-01 3.16273510e-01 4.44764197e-01 -3.20183784... | [9.410402297973633, -2.3608312606811523] |
0fa78808-ac8d-410c-93ec-c88d90922139 | latent-preserving-generative-adversarial | 2209.01555 | null | https://arxiv.org/abs/2209.01555v1 | https://arxiv.org/pdf/2209.01555v1.pdf | Latent Preserving Generative Adversarial Network for Imbalance classification | Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem. Classic classification algorithms tend to be biased towards the majority class, leaving the classifier vulnerable to misclassification of the minority class. While the literature... | ['Hussein A. Abbass', 'Senthilnath Jayavelu', 'Sreenatha G. Anavatti', 'Mahardhika Pratama', 'Md Meftahul Ferdaus', 'Tanmoy Dam'] | 2022-09-04 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 1.20603599e-01 5.30086756e-02 -2.67254204e-01 -5.66281259e-01
-8.15328002e-01 -3.75375450e-01 2.50156432e-01 -5.80766313e-02
-1.62359089e-01 8.78258646e-01 -1.69478104e-01 -2.26848498e-01
2.19746888e-01 -1.11128879e+00 -5.85831344e-01 -8.49047005e-01
3.28819275e-01 7.96691597e-01 -8.20545778e-02 -9.42597613... | [9.025907516479492, 3.989884376525879] |
e31035ab-94d7-4263-8c53-4b38cc3e64f7 | kari-kanari-qcri-s-end-to-end-systems-for-the | 2106.05885 | null | https://arxiv.org/abs/2106.05885v2 | https://arxiv.org/pdf/2106.05885v2.pdf | Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition | The success in designing Code-Switching (CS) ASR often depends on the availability of the transcribed CS resources. Such dependency harms the development of ASR in low-resourced languages such as Bengali and Hindi. In this paper, we exploit the transfer learning approach to design End-to-End (E2E) CS ASR systems for th... | ['Ahmed Ali', 'Najim Dehak', 'Shammur Chowdhury', 'Amir Hussein'] | 2021-06-10 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 5.83339157e-03 -2.19659790e-01 3.15359533e-01 -4.30089623e-01
-1.73745418e+00 -8.38419318e-01 2.61081070e-01 -2.93805122e-01
-8.03552389e-01 3.72880518e-01 2.62140393e-01 -9.42906022e-01
3.36153418e-01 -2.93108761e-01 -8.02202761e-01 -3.51577550e-01
2.61882246e-01 5.18589854e-01 -8.49830806e-02 -7.02723444... | [14.394051551818848, 7.0228986740112305] |
3aa8901a-4275-40f6-9cd0-7168140b9ab6 | affective-eeg-based-person-identification | 1807.03147 | null | http://arxiv.org/abs/1807.03147v3 | http://arxiv.org/pdf/1807.03147v3.pdf | Affective EEG-Based Person Identification Using the Deep Learning Approach | Electroencephalography (EEG) is another mode for performing Person
Identification (PI). Due to the nature of the EEG signals, EEG-based PI is
typically done while the person is performing some kind of mental task, such as
motor control. However, few works have considered EEG-based PI while the person
is in different me... | ['Ekapol Chuangsuwanich', 'Nannapas Banluesombatkul', 'Karis Matchaparn', 'Apiwat Ditthapron', 'Theerawit Wilaiprasitporn', 'Tanaboon Tongbuasirilai'] | 2018-07-05 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 8.83451942e-03 -2.01030493e-01 3.70346189e-01 -1.43048614e-01
-4.78016973e-01 -1.58308581e-01 2.71781087e-01 -2.01346382e-01
-5.48166394e-01 1.06342638e+00 1.82453915e-02 1.33673251e-01
-8.53809789e-02 -6.68949187e-01 -4.94424224e-01 -8.61440480e-01
-2.05710009e-01 -1.11875005e-01 -4.29337591e-01 -2.42172882... | [13.175280570983887, 3.452174186706543] |
d23ecee6-bcc0-4a90-a848-132dc58bc455 | human-intracranial-eeg-quantitative-analysis | 1904.03603 | null | http://arxiv.org/abs/1904.03603v1 | http://arxiv.org/pdf/1904.03603v1.pdf | Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction | Objective: The aim of this study is to develop an efficient and reliable
epileptic seizure prediction system using intracranial EEG (iEEG) data,
especially for people with drug-resistant epilepsy. The prediction procedure
should yield accurate results in a fast enough fashion to alert patients of
impending seizures. Me... | ['Ramy Hussein', 'Rabab Ward', 'Yi Guo', 'Mohamed Osama Ahmed', 'Levin Kuhlmann', 'Z. Jane Wang'] | 2019-04-07 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.84506886e-02 -3.91065150e-01 4.04110640e-01 -2.15027153e-01
-6.01818979e-01 -3.29447269e-01 9.37232971e-02 1.29581600e-01
-2.45661184e-01 6.67992055e-01 2.77232528e-01 -3.51210088e-01
-4.05108780e-01 -4.83386070e-01 -2.72852540e-01 -8.10582936e-01
-7.70107448e-01 1.36422455e-01 -2.01486781e-01 -8.78064856... | [13.222505569458008, 3.5171661376953125] |
53ec37bb-bcbb-4053-b3ef-ec47d2df70f3 | emotion-detection-from-eeg-using-transfer | 2306.05680 | null | https://arxiv.org/abs/2306.05680v1 | https://arxiv.org/pdf/2306.05680v1.pdf | Emotion Detection from EEG using Transfer Learning | The detection of emotions using an Electroencephalogram (EEG) is a crucial area in brain-computer interfaces and has valuable applications in fields such as rehabilitation and medicine. In this study, we employed transfer learning to overcome the challenge of limited data availability in EEG-based emotion detection. Th... | ['Sana Parveen K', 'Jerrin Thomas Panachakel', 'Ranjana H', 'Ashish Abraham Samuel', 'Sidharth Sidharth'] | 2023-06-09 | null | null | null | null | ['emotion-classification', 'eeg', 'emotion-classification', 'eeg'] | ['computer-vision', 'methodology', 'natural-language-processing', 'time-series'] | [ 2.01180980e-01 -1.52889282e-01 3.62133741e-01 -4.05678838e-01
-4.47063357e-01 -2.31151670e-01 2.44689614e-01 3.36792529e-01
-7.48381019e-01 1.07079256e+00 7.52599817e-03 -1.19615719e-02
-4.29107845e-01 -6.04344964e-01 -3.31407905e-01 -8.64586353e-01
-5.80807328e-01 -2.56185949e-01 -4.74048257e-02 -2.23110288... | [13.254937171936035, 3.351513147354126] |
3c1faceb-99a1-4ffa-9e43-44020c7a6114 | the-use-of-the-word-gamma-u-psion-nai-k-appa | 2210.11837 | null | https://arxiv.org/abs/2210.11837v1 | https://arxiv.org/pdf/2210.11837v1.pdf | The use of the word "\{gamma}\u{psion}ναι\k{appa}ο\k{appa}τονια" (femicide) in Greek-speaking Twitter | Between 2019 and 2022, Greek media attention has been attracted by a rather unusually high number of femicide cases which have been trending for several weeks up to months in the public debate and one of the contributing factors is the feedback loop between traditional media and social media. In this paper we are inves... | ['Konstantinos Perifanos', 'Ioanna Tsounidi', 'Athina Kontostavlaki', 'Nikoleta Gkatzoli', 'Efstathia Bambili', 'Aglaia Aggistrioti'] | 2022-10-21 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 1.22431852e-01 3.66447270e-01 4.62048091e-02 2.40845650e-01
-3.92846644e-01 -7.09833980e-01 9.90651131e-01 7.03413308e-01
-5.78303874e-01 8.86314809e-01 5.39882898e-01 -6.72524452e-01
-3.32281440e-01 -6.34698927e-01 -1.34803370e-01 -5.73206902e-01
3.18974555e-01 2.82156289e-01 4.30671219e-03 -5.16224504... | [8.769037246704102, 10.394815444946289] |
7336505b-0228-45b7-8af9-de456131e5ac | kuleuven-liir-at-semeval-2017-task-12-cross | null | null | https://aclanthology.org/S17-2181 | https://aclanthology.org/S17-2181.pdf | KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records | In this paper, we describe the system of the KULeuven-LIIR submission for Clinical TempEval 2017. We participated in all six subtasks, using a combination of Support Vector Machines (SVM) for event and temporal expression detection, and a structured perceptron for extracting temporal relations. Moreover, we present and... | ['Marie-Francine Moens', 'Artuur Leeuwenberg'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 2.67553896e-01 1.76438734e-01 -5.56973279e-01 -3.87957245e-01
-5.88419497e-01 -5.45613825e-01 6.93614006e-01 9.50215042e-01
-7.65145838e-01 9.40713108e-01 3.25009078e-01 -2.13252440e-01
-3.55827332e-01 -2.99597621e-01 -1.90807953e-01 -5.19923627e-01
-5.73377848e-01 5.75574636e-01 4.27713960e-01 -3.94509077... | [8.51677131652832, 9.021197319030762] |
9dad9782-6e2f-4f6b-8f9d-1ec11a06a1bd | sanskritshala-a-neural-sanskrit-nlp-toolkit | 2302.09527 | null | https://arxiv.org/abs/2302.09527v2 | https://arxiv.org/pdf/2302.09527v2.pdf | SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes | We present a neural Sanskrit Natural Language Processing (NLP) toolkit named SanskritShala (a school of Sanskrit) to facilitate computational linguistic analyses for several tasks such as word segmentation, morphological tagging, dependency parsing, and compound type identification. Our systems currently report state-o... | ['Pawan Goyal', 'Tushar Sandhan', 'Laxmidhar Behera', 'Anshul Agarwal', 'Jivnesh Sandhan'] | 2023-02-19 | null | null | null | null | ['word-similarity', 'dependency-parsing', 'morphological-tagging'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.63578236e-01 -2.45662361e-01 -2.46626407e-01 -3.11409980e-01
-5.86219132e-01 -1.09158564e+00 3.03260267e-01 4.98199254e-01
-7.77457356e-01 4.33715969e-01 3.11319232e-01 -9.18950915e-01
-4.51941043e-03 -5.54611087e-01 -2.91175723e-01 -4.76434857e-01
3.09085637e-01 8.02691579e-01 2.05757141e-01 -3.09976161... | [10.487292289733887, 10.012885093688965] |
d2e8639f-b73b-453e-a52a-db2fcd2b6da8 | nested-named-entity-recognition-with-span | null | null | https://aclanthology.org/2022.acl-long.63 | https://aclanthology.org/2022.acl-long.63.pdf | Nested Named Entity Recognition with Span-level Graphs | Span-based methods with the neural networks backbone have great potential for the nested named entity recognition (NER) problem. However, they face problems such as degenerating when positive instances and negative instances largely overlap. Besides, the generalization ability matters a lot in nested NER, as a large pr... | ['Yong Yu', 'Weinan Zhang', 'Dongyu Ru', 'Juncheng Wan'] | null | null | null | null | acl-2022-5 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-5.50326586e-01 -1.56622991e-01 -2.15763301e-01 -3.17841977e-01
-6.38243318e-01 -7.54393816e-01 1.12549216e-01 4.65207577e-01
-7.16351926e-01 8.17485332e-01 2.18118072e-01 -6.92690164e-02
-2.09389642e-01 -1.24548411e+00 -4.73174572e-01 -3.69062513e-01
-4.93341714e-01 3.52906704e-01 2.41398409e-01 -3.11609894... | [9.56633472442627, 9.405906677246094] |
a31a51b6-c777-4482-9005-b1fa8c7107b8 | rockafellian-relaxation-in-optimization-under | 2204.04762 | null | https://arxiv.org/abs/2204.04762v3 | https://arxiv.org/pdf/2204.04762v3.pdf | Rockafellian Relaxation in Optimization under Uncertainty: Asymptotically Exact Formulations | In practice, optimization models are often prone to unavoidable inaccuracies due to dubious assumptions and corrupted data. Traditionally, this placed special emphasis on risk-based and robust formulations, and their focus on ``conservative" decisions. We develop, in contrast, an ``optimistic" framework based on Rockaf... | ['Eric Eckstrand', 'Louis L. Chen', 'Johannes O. Royset'] | 2022-04-10 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 3.16911280e-01 1.92976400e-01 2.61960067e-02 -3.45905095e-01
-1.10372043e+00 -5.92289209e-01 2.70324260e-01 2.46186286e-01
-6.30235970e-01 9.24230933e-01 1.24337450e-02 -2.02473015e-01
-6.90547347e-01 -3.99797738e-01 -6.93862736e-01 -1.11914992e+00
9.98624340e-02 2.41156742e-01 -3.18043053e-01 -2.12676618... | [6.754144668579102, 4.3026204109191895] |
bcc541e9-843c-48e9-a425-2c13683a7a57 | learning-restoration-is-not-enough | 2305.10640 | null | https://arxiv.org/abs/2305.10640v1 | https://arxiv.org/pdf/2305.10640v1.pdf | Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal | Shadow removal is to restore shadow regions to their shadow-free counterparts while leaving non-shadow regions unchanged. State-of-the-art shadow removal methods train deep neural networks on collected shadow & shadow-free image pairs, which are desired to complete two distinct tasks via shared weights, i.e., data rest... | ['Song Wang', 'Ivor Tsang', 'Wei Feng', 'Pingping Cai', 'Qing Guo', 'Xiaoguang Li'] | 2023-05-18 | null | null | null | null | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 5.65827549e-01 1.31973267e-01 3.61784816e-01 -3.89147580e-01
-3.23526233e-01 -3.23621035e-01 3.94556105e-01 -5.28261900e-01
-3.50750387e-02 7.99275994e-01 2.94043005e-01 -5.88235795e-01
2.36732498e-01 -8.42777014e-01 -5.79237342e-01 -1.16962886e+00
3.21652532e-01 2.70061642e-01 7.06611574e-01 -2.31331334... | [10.846863746643066, -4.100343704223633] |
c4660f6d-b917-4602-ab08-83fdb7807f3d | conditions-for-open-ended-evolution-in | 2004.02720 | null | https://arxiv.org/abs/2004.02720v1 | https://arxiv.org/pdf/2004.02720v1.pdf | Conditions for Open-Ended Evolution in Immigration Games | The Immigration Game (invented by Don Woods in 1971) extends the solitaire Game of Life (invented by John Conway in 1970) to enable two-player competition. The Immigration Game can be used in a model of evolution by natural selection, where fitness is measured with competitions. The rules for the Game of Life belong to... | ['Peter D. Turney'] | 2020-04-06 | null | null | null | null | ['solitaire'] | ['playing-games'] | [ 1.48127787e-02 4.00823094e-02 2.37762481e-01 2.69956499e-01
3.67028147e-01 -7.67931819e-01 5.22923052e-01 -2.46270329e-01
-7.62438893e-01 1.07304299e+00 -9.71653163e-02 -1.02435732e+00
-3.80787164e-01 -1.05158377e+00 -4.42226708e-01 -6.79020047e-01
-2.32877776e-01 4.52421248e-01 5.30228913e-01 -9.58944380... | [5.509002685546875, 4.08230447769165] |
3502ee6b-4db1-45d9-a86c-f366428ea2a5 | mixcycle-mixup-assisted-semi-supervised-3d | 2303.09219 | null | https://arxiv.org/abs/2303.09219v1 | https://arxiv.org/pdf/2303.09219v1.pdf | MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency | 3D single object tracking (SOT) is an indispensable part of automated driving. Existing approaches rely heavily on large, densely labeled datasets. However, annotating point clouds is both costly and time-consuming. Inspired by the great success of cycle tracking in unsupervised 2D SOT, we introduce the first semi-supe... | ['Mathieu Salzmann', 'Yanning Zhang', "Chu'ai Zhang", 'Kun Sun', 'Jiaqi Yang', 'Qiao Wu'] | 2023-03-16 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-1.16268426e-01 -1.06311016e-01 -2.92368531e-01 -2.94127434e-01
-6.82062984e-01 -4.90570813e-01 4.37296212e-01 -2.55501177e-02
-4.12542224e-01 4.23663020e-01 -4.63203639e-01 -3.04527223e-01
3.79779674e-02 -5.08499265e-01 -9.97544408e-01 -6.92226112e-01
9.91631076e-02 6.03602111e-01 5.72284579e-01 -3.95531692... | [6.644373893737793, -2.280320882797241] |
435bd1d9-8fc1-4e56-88e1-0ed33efa8b90 | night-time-haze-and-glow-removal-using-deep | 1902.00855 | null | http://arxiv.org/abs/1902.00855v1 | http://arxiv.org/pdf/1902.00855v1.pdf | Night Time Haze and Glow Removal using Deep Dilated Convolutional Network | In this paper, we address the single image haze removal problem in a
nighttime scene. The night haze removal is a severely ill-posed problem
especially due to the presence of various visible light sources with varying
colors and non-uniform illumination. These light sources are of different
shapes and introduce noticea... | ['Shiba Kuanar', 'Monalisa Bilas', 'Dwarikanath Mahapatra', 'K. R. Rao'] | 2019-02-03 | null | null | null | null | ['single-image-haze-removal'] | ['computer-vision'] | [ 6.73230961e-02 -5.32543659e-01 8.28146458e-01 -2.89795280e-01
-2.52005070e-01 -2.58304745e-01 3.07869732e-01 -6.17367148e-01
-2.81656057e-01 7.51182199e-01 9.90985036e-02 -1.11591175e-01
-1.40491471e-01 -7.38807261e-01 -6.49197459e-01 -1.28697217e+00
1.91320628e-01 -1.65262043e-01 3.74109656e-01 -8.05625796... | [10.907658576965332, -3.192570209503174] |
a532cfe6-1e66-4641-8b38-1dd506a8c528 | systematic-review-for-ai-based-language | 2111.04455 | null | https://arxiv.org/abs/2111.04455v1 | https://arxiv.org/pdf/2111.04455v1.pdf | Systematic Review for AI-based Language Learning Tools | The Second Language Acquisition field has been significantly impacted by a greater emphasis on individualized learning and rapid developments in artificial intelligence (AI). Although increasingly adaptive language learning tools are being developed with the application of AI to the Computer Assisted Language Learning ... | ['Heeyoul Choi', 'Jin Ha Woo'] | 2021-10-29 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 5.08688912e-02 5.03885388e-01 -5.05553544e-01 -2.16849044e-01
-5.64137459e-01 -7.27062285e-01 4.24103230e-01 8.58964205e-01
-5.97681403e-01 4.46135521e-01 3.17562699e-01 -6.53549254e-01
-3.16061825e-01 -6.20912313e-01 -3.53810668e-01 -1.21633410e-02
2.01458633e-01 4.75288719e-01 1.14107039e-02 -2.89427519... | [11.766698837280273, 8.350015640258789] |
80ee79c0-fcc0-483c-93d2-5466083f96ff | towards-learning-through-open-domain-dialog | 2202.03040 | null | https://arxiv.org/abs/2202.03040v1 | https://arxiv.org/pdf/2202.03040v1.pdf | Towards Learning Through Open-Domain Dialog | The development of artificial agents able to learn through dialog without domain restrictions has the potential to allow machines to learn how to perform tasks in a similar manner to humans and change how we relate to them. However, research in this area is practically nonexistent. In this paper, we identify the modifi... | ['David Martins de Matos', 'Ricardo Ribeiro', 'Eugénio Ribeiro'] | 2022-02-07 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [ 7.41359368e-02 8.66603076e-01 -1.65590309e-02 -8.74066770e-01
2.48840600e-01 -9.05658126e-01 9.89975214e-01 2.68769532e-01
-4.84371126e-01 1.14912248e+00 3.46861422e-01 -2.27986246e-01
-5.92394285e-02 -9.21104670e-01 -2.70029783e-01 -1.26783431e-01
1.58379346e-01 8.94732714e-01 8.42112660e-01 -6.55834019... | [12.856478691101074, 7.970150947570801] |
e0a927a1-8313-4568-8222-10708c483f73 | inferring-networks-from-random-walk-based | null | null | http://papers.nips.cc/paper/7628-inferring-networks-from-random-walk-based-node-similarities | http://papers.nips.cc/paper/7628-inferring-networks-from-random-walk-based-node-similarities.pdf | Inferring Networks From Random Walk-Based Node Similarities | Digital presence in the world of online social media entails significant privacy risks. In this work we consider a privacy threat to a social network in which an attacker has access to a subset of random walk-based node similarities, such as effective resistances (i.e., commute times) or personalized PageRank scores. U... | ['Babis Tsourakakis', 'Cameron Musco', 'Jeremy Hoskins', 'Christopher Musco'] | 2018-12-01 | null | null | null | neurips-2018-12 | ['graph-similarity'] | ['graphs'] | [ 6.48527592e-02 5.83648503e-01 -3.44357282e-01 -1.58056527e-01
-5.25548398e-01 -9.54977393e-01 2.39323765e-01 6.79962933e-01
-3.35048676e-01 5.30515552e-01 -6.29755929e-02 -5.15222549e-01
-5.71741045e-01 -1.26789093e+00 -6.89936697e-01 -4.67464119e-01
-7.67801881e-01 4.94152188e-01 6.48495778e-02 -1.24433875... | [6.845629692077637, 5.562986850738525] |
30c00fc9-e8ba-4892-bd7a-09b76bfcc9fa | modeling-dense-cross-modal-interactions-for | null | null | https://www.ijcai.org/Proceedings/2020/558 | https://www.ijcai.org/proceedings/2020/0558.pdf | Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction | Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how to effectively model such cross-modal interactions is critical for the final performance. Previous works have used simple methods such as label-feature concatenation... | ['Fang Liu', 'Zhiping Cai', 'Minghao Hu', 'Shan Zhao'] | 2020-07-01 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.33276284e-01 2.44011447e-01 -3.74604374e-01 -6.21969640e-01
-8.55097950e-01 -4.04052794e-01 6.86231017e-01 3.09264511e-01
-4.62224722e-01 7.09136724e-01 2.94780612e-01 -1.53792962e-01
-1.79480702e-01 -8.34118783e-01 -7.64725745e-01 -5.20681918e-01
-4.76786569e-02 5.16371369e-01 -5.30109778e-02 -1.99026912... | [9.222228050231934, 8.719186782836914] |
606d423f-ca54-4bc0-ae8a-fb439fe66afc | simplifying-and-empowering-transformers-for | 2306.10759 | null | https://arxiv.org/abs/2306.10759v1 | https://arxiv.org/pdf/2306.10759v1.pdf | Simplifying and Empowering Transformers for Large-Graph Representations | Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points. Transformers, as an emerging class of foundation encoders for graph-structured data, have shown promising performance on small graphs due to its global attention capable of cap... | ['Junchi Yan', 'Yatao Bian', 'Haitian Jiang', 'Fan Nie', 'Hengrui Zhang', 'Chenxiao Yang', 'Wentao Zhao', 'Qitian Wu'] | 2023-06-19 | null | null | null | null | ['property-prediction', 'philosophy'] | ['medical', 'miscellaneous'] | [ 1.22308828e-01 4.41658765e-01 -2.84242541e-01 -2.40745351e-01
-4.72681671e-01 -4.24567670e-01 5.56245625e-01 4.92044330e-01
-2.61308253e-01 6.47712469e-01 2.35873148e-01 -7.01660514e-01
-2.86783814e-01 -1.17610407e+00 -1.03602505e+00 -6.01363361e-01
-5.74178159e-01 4.38399464e-01 2.09730104e-01 -4.61887181... | [6.982994556427002, 6.222074031829834] |
bb97dd07-cb3a-4275-8cd5-8800377b8b25 | spatiotemporal-capsule-neural-network-for | 2303.02880 | null | https://arxiv.org/abs/2303.02880v1 | https://arxiv.org/pdf/2303.02880v1.pdf | Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction | Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network. Rec... | ['Chau Yuen', 'Yong Liang Guan', 'Yan Qin'] | 2023-03-06 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-4.07479823e-01 -1.18178232e-02 -8.43813300e-01 -1.82000473e-01
-2.57122666e-01 -1.59622267e-01 5.59550047e-01 -2.56293535e-01
4.87931184e-02 8.19631338e-01 2.68194824e-01 -7.90385604e-01
-4.25954700e-01 -1.00044382e+00 -5.22199869e-01 -6.32543921e-01
-5.33245564e-01 -1.26338052e-02 2.34853134e-01 -1.21074706... | [6.207125663757324, 1.7025973796844482] |
ca18b4ce-d273-4a2c-b79a-9a460ff7584b | towards-versatile-embodied-navigation | 2210.16822 | null | https://arxiv.org/abs/2210.16822v1 | https://arxiv.org/pdf/2210.16822v1.pdf | Towards Versatile Embodied Navigation | With the emergence of varied visual navigation tasks (e.g, image-/object-/audio-goal and vision-language navigation) that specify the target in different ways, the community has made appealing advances in training specialized agents capable of handling individual navigation tasks well. Given plenty of embodied navigati... | ['Wenguan Wang', 'Luc van Gool', 'Wei Liang', 'Hanqing Wang'] | 2022-10-30 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [-1.28876820e-01 -2.51834869e-01 1.34770095e-01 -4.78859991e-02
-7.61294663e-01 -8.10823679e-01 8.51585388e-01 -5.24781011e-02
-8.35810661e-01 4.58183736e-01 3.81861031e-01 -2.75484264e-01
-2.14194655e-01 -4.49648768e-01 -5.42380333e-01 -7.22409368e-01
-2.16770127e-01 5.82715631e-01 3.32904994e-01 -5.31087220... | [4.465583801269531, 0.609258770942688] |
516ead46-5f47-48d2-b51d-6dd4c82b3815 | 3d-oocs-learning-prostate-segmentation-with | 2110.15664 | null | https://arxiv.org/abs/2110.15664v2 | https://arxiv.org/pdf/2110.15664v2.pdf | 3D-OOCS: Learning Prostate Segmentation with Inductive Bias | Despite the great success of convolutional neural networks (CNN) in 3D medical image segmentation tasks, the methods currently in use are still not robust enough to the different protocols utilized by different scanners, and to the variety of image properties or artefacts they produce. To this end, we introduce OOCS-en... | ['Anca-Ligia Grosu', 'Radu Grosu', 'Constantinos Zamboglou', 'Tobias Fechter', 'Dejan Kostyszyn', 'Zahra Babaiee', 'Shrajan Bhandary'] | 2021-10-29 | null | null | null | null | ['prostate-zones-segmentation'] | ['computer-vision'] | [ 5.5991435e-01 5.3219074e-01 5.9846360e-03 -3.1986746e-01
-1.4620817e-01 -4.3424338e-01 6.7098743e-01 2.6836365e-01
-6.4912796e-01 3.9812595e-01 2.8640414e-03 -4.5687890e-01
-8.9230254e-02 -5.3291065e-01 -8.2757342e-01 -6.1678368e-01
-4.7454694e-01 2.4336670e-01 4.8953587e-01 -2.7489272e-01
9.1936283e-02... | [14.339847564697266, -2.552886962890625] |
1c6d8f6c-5508-4259-9418-ae885e2df8bc | tunable-convolutions-with-parametric-multi | 2304.00898 | null | https://arxiv.org/abs/2304.00898v1 | https://arxiv.org/pdf/2304.00898v1.pdf | Tunable Convolutions with Parametric Multi-Loss Optimization | Behavior of neural networks is irremediably determined by the specific loss and data used during training. However it is often desirable to tune the model at inference time based on external factors such as preferences of the user or dynamic characteristics of the data. This is especially important to balance the perce... | ['Aleš Leonardis', 'Steven McDonagh', 'Francesca Babiloni', 'Thomas Tanay', 'Matteo Maggioni'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Maggioni_Tunable_Convolutions_With_Parametric_Multi-Loss_Optimization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Maggioni_Tunable_Convolutions_With_Parametric_Multi-Loss_Optimization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['deblurring'] | ['computer-vision'] | [ 1.92273825e-01 -4.02097046e-01 -1.79897174e-02 -3.80178005e-01
-4.67828363e-01 -6.44032776e-01 3.84380519e-01 -1.46737933e-01
-6.08159900e-01 5.60364902e-01 -8.84280875e-02 -8.33717808e-02
-1.38484076e-01 -6.08955145e-01 -8.08671117e-01 -8.14564884e-01
6.88759312e-02 3.40750605e-01 1.87496990e-01 -1.90897942... | [11.369669914245605, -1.814149022102356] |
ae58b59d-32d2-4993-a34e-8699317309ee | learning-decomposed-representation-for | 2006.07040 | null | https://arxiv.org/abs/2006.07040v2 | https://arxiv.org/pdf/2006.07040v2.pdf | Learning Decomposed Representation for Counterfactual Inference | The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. In general,... | ['Fei Wu', 'Yueting Zhuang', 'Qiang Zhu', 'Runze Wu', 'Kun Kuang', 'Bo Li', 'Anpeng Wu', 'Junkun Yuan'] | 2020-06-12 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 3.08846712e-01 -1.76547449e-02 -1.27431214e+00 -3.95010173e-01
-7.51478016e-01 -3.30557257e-01 4.06457394e-01 2.60396987e-01
-3.44583303e-01 1.27297592e+00 9.90741909e-01 -5.30366600e-01
-4.58805352e-01 -8.91065896e-01 -7.56148517e-01 -8.21052849e-01
-3.77038956e-01 5.23460150e-01 -5.38650930e-01 3.70971829... | [8.02977466583252, 5.3528947830200195] |
6884b080-1281-4253-98c0-b2e9e4e137ed | evaluation-of-self-supervised-pre-training | 2305.09366 | null | https://arxiv.org/abs/2305.09366v1 | https://arxiv.org/pdf/2305.09366v1.pdf | Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors | The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings. This information could be used for developmental research and to support clinical decision-making, such as detection of developmental proble... | ['Okko Räsänen', 'Sampsa Vanhatalo', 'Manu Airaksinen', 'Einari Vaaras'] | 2023-05-16 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 3.98027360e-01 -7.14561716e-02 -2.68351138e-01 -5.94489932e-01
-6.63442314e-01 -3.98908734e-01 8.46732333e-02 7.82198429e-01
-6.35955572e-01 3.47354680e-01 2.20299110e-01 -2.60689348e-01
-4.02384341e-01 -5.89219272e-01 -7.44440377e-01 -5.01775324e-01
-2.11547151e-01 3.88467252e-01 4.54878241e-01 2.21309483... | [13.09050464630127, 3.2119007110595703] |
6b1e0744-543f-400b-ab50-639050c86f59 | road-segmentation-of-remotely-sensed-images | null | null | https://www.mdpi.com/2072-4292/9/7/680 | https://www.mdpi.com/2072-4292/9/7/680/pdf | Road Segmentation of Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields | Object segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and satellite (or high-resolution, HR) images, has been applied to many application domains, especially in road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts at apply... | ['Teerapong Panboonyuen'] | 2017-07-01 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 5.70687473e-01 -6.75533935e-02 9.02259201e-02 -4.67612833e-01
-4.62808222e-01 -3.71986330e-01 5.65294087e-01 -1.02865867e-01
-6.25810146e-01 1.01255357e+00 -2.53564328e-01 -7.09538639e-01
-5.33849001e-01 -1.40912259e+00 -5.84794402e-01 -6.14682376e-01
-2.51511425e-01 1.23974063e-01 4.00130481e-01 -1.27541393... | [9.331293106079102, -1.4283013343811035] |
85451c06-61ba-4d0e-bad2-7eba46ea2c17 | translation-consistent-semi-supervised | 2203.14523 | null | https://arxiv.org/abs/2203.14523v2 | https://arxiv.org/pdf/2203.14523v2.pdf | Translation Consistent Semi-supervised Segmentation for 3D Medical Images | 3D medical image segmentation methods have been successful, but their dependence on large amounts of voxel-level annotated data is a disadvantage that needs to be addressed given the high cost to obtain such annotation. Semi-supervised learning (SSL) solve this issue by training models with a large unlabelled and a sma... | ['Gustavo Carneiro', 'Vasileios Belagiannis', 'Fengbei Liu', 'Yuanhong Chen', 'Chong Wang', 'Yu Tian', 'Yuyuan Liu'] | 2022-03-28 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 4.28065032e-01 5.13750255e-01 -1.42434388e-01 -5.32992601e-01
-1.08416188e+00 -5.86880744e-01 5.82833886e-01 1.48468941e-01
-5.01501024e-01 7.37803459e-01 -7.44636357e-02 -2.53179103e-01
1.64604783e-02 -3.06877822e-01 -7.81481326e-01 -8.96496892e-01
9.29302499e-02 6.22027814e-01 4.58333999e-01 3.09050560... | [14.54741096496582, -2.1239633560180664] |
dd4dc3dc-3142-4d1d-81d4-0e1f96643be4 | noddle-node2vec-based-deep-learning-model-for | 2305.16421 | null | https://arxiv.org/abs/2305.16421v1 | https://arxiv.org/pdf/2305.16421v1.pdf | NODDLE: Node2vec based deep learning model for link prediction | Computing the probability of an edge's existence in a graph network is known as link prediction. While traditional methods calculate the similarity between two given nodes in a static network, recent research has focused on evaluating networks that evolve dynamically. Although deep learning techniques and network repre... | ['Vijay Mago', 'Aditya Singhal', 'Kazi Zainab Khanam'] | 2023-05-25 | null | null | null | null | ['link-prediction'] | ['graphs'] | [-6.25948370e-01 1.41035870e-01 -2.16999292e-01 -2.07824603e-01
1.83284864e-01 -1.86469451e-01 5.87234855e-01 3.14299285e-01
-2.18117669e-01 6.77841842e-01 3.23213562e-02 -3.87373149e-01
-2.63177454e-01 -1.23552418e+00 -4.54047680e-01 -4.57892537e-01
-4.67233866e-01 7.28056550e-01 2.18424767e-01 -3.48655224... | [7.181975364685059, 6.110024452209473] |
258bb43d-0fda-413e-8fe3-9770b1d19881 | decentralised-semi-supervised-onboard | 2305.04059 | null | https://arxiv.org/abs/2305.04059v1 | https://arxiv.org/pdf/2305.04059v1.pdf | Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit | Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs. We showcase the training of a machine learning model on a satellite constellation for scene classification using semi-supervised learning while accounting for operational constr... | ['Gabriele Meoni', 'Vinutha Magal Shreenath', 'Pablo Gomez', 'Johan Östman'] | 2023-05-06 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 1.03184529e-01 1.65754229e-01 -4.75541949e-01 -8.49080026e-01
-7.35509634e-01 -5.95108211e-01 4.88410383e-01 -4.15371507e-02
-6.28407419e-01 8.28371584e-01 -3.60900730e-01 -5.75881362e-01
-4.42270368e-01 -6.74611032e-01 -7.00000823e-01 -9.55199838e-01
-1.03127110e+00 4.16410863e-01 -2.12660894e-01 -2.54623204... | [9.566845893859863, -1.459147572517395] |
fdc7e7a4-41e9-4c04-969e-e0dcf010e5e9 | deep-neural-networks-can-predict-mortality | 1904.07032 | null | https://arxiv.org/abs/1904.07032v3 | https://arxiv.org/pdf/1904.07032v3.pdf | Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data | The electrocardiogram (ECG) is a widely-used medical test, typically consisting of 12 voltage versus time traces collected from surface recordings over the heart. Here we hypothesize that a deep neural network can predict an important future clinical event (one-year all-cause mortality) from ECG voltage-time traces. We... | ['Aalpen A. Patel', 'David P. vanMaanen', 'Sushravya Raghunath', 'Joshua Stough', 'H. Lester Kirchner', 'Brian P. Delisle', 'Alvaro E. Ulloa Cerna', 'Joseph B. Leader', 'Dominik Beer', 'Brandon K. Fornwalt', 'Dustin N. Hartzel', 'Christopher W. Good', 'Christopher M. Haggerty', 'Linyuan Jing', 'Amro Alsaid'] | 2019-04-15 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 1.51321545e-01 7.72075653e-02 2.51039155e-02 -4.86681461e-01
-1.17138016e+00 -6.39479876e-01 -1.74747586e-01 7.55187631e-01
-4.16742802e-01 9.17685449e-01 6.41234368e-02 -9.75689054e-01
-4.82077301e-01 -7.50181317e-01 -5.10358334e-01 -4.10898358e-01
-1.05680001e+00 5.64637542e-01 -3.72285783e-01 4.28950250... | [14.319972038269043, 3.2851905822753906] |
2df7f92b-bbec-4989-83fa-f5bdd4fa2769 | fedhealth-a-federated-transfer-learning | 1907.09173 | null | https://arxiv.org/abs/1907.09173v2 | https://arxiv.org/pdf/1907.09173v2.pdf | FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare | With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activities, sleep, sports, etc. Smart healthcare achieves great success by training machine learning models on a large quantity of user data. Howev... | ['Chaohui Yu', 'Yiqiang Chen', 'Wen Gao', 'Jindong Wang', 'Xin Qin'] | 2019-07-22 | null | null | null | null | ['wearable-activity-recognition'] | ['time-series'] | [-2.93908012e-03 -2.30371603e-03 -4.60599899e-01 -4.22815174e-01
-6.48458123e-01 -2.13430941e-01 8.35130662e-02 2.34494582e-01
-1.81382194e-01 8.62987995e-01 4.50648576e-01 -2.44988531e-01
9.42603126e-03 -9.95156765e-01 -4.54088688e-01 -7.06995070e-01
-8.87290388e-02 1.13135502e-01 -2.31365517e-01 3.09351176... | [6.172980785369873, 6.283412456512451] |
2142c61c-295e-4d73-af4a-64643023678b | 190910148 | 1909.10148 | null | https://arxiv.org/abs/1909.10148v1 | https://arxiv.org/pdf/1909.10148v1.pdf | Dependency-Guided LSTM-CRF for Named Entity Recognition | Dependency tree structures capture long-distance and syntactic relationships between words in a sentence. The syntactic relations (e.g., nominal subject, object) can potentially infer the existence of certain named entities. In addition, the performance of a named entity recognizer could benefit from the long-distance ... | ['Zhanming Jie', 'Wei Lu'] | 2019-09-23 | dependency-guided-lstm-crf-for-named-entity | https://aclanthology.org/D19-1399 | https://aclanthology.org/D19-1399.pdf | ijcnlp-2019-11 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-5.68034947e-01 -3.16503868e-02 -2.39069611e-01 -9.26286519e-01
-3.66232485e-01 -4.92569238e-01 3.64181966e-01 3.34818453e-01
-6.18857861e-01 8.41194212e-01 6.94200575e-01 -4.24015075e-01
-2.23748554e-02 -8.74896586e-01 -6.05673611e-01 -5.15358984e-01
-6.54068708e-01 1.80600345e-01 -6.69745952e-02 -1.27650782... | [9.653787612915039, 9.567285537719727] |
12a82613-d162-41ba-b147-6d8efb7d4de4 | efficient-movie-scene-detection-using-state | 2212.14427 | null | https://arxiv.org/abs/2212.14427v2 | https://arxiv.org/pdf/2212.14427v2.pdf | Efficient Movie Scene Detection using State-Space Transformers | The ability to distinguish between different movie scenes is critical for understanding the storyline of a movie. However, accurately detecting movie scenes is often challenging as it requires the ability to reason over very long movie segments. This is in contrast to most existing video recognition models, which are t... | ['Gedas Bertasius', 'Tony Braskich', 'Kishan Shamsundar Athrey', 'Mahmudul Hasan', 'Md Mohaiminul Islam'] | 2022-12-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Islam_Efficient_Movie_Scene_Detection_Using_State-Space_Transformers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Islam_Efficient_Movie_Scene_Detection_Using_State-Space_Transformers_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-recognition'] | ['computer-vision'] | [ 3.57535511e-01 -7.07089603e-01 -1.04738131e-01 -3.86535734e-01
-5.97138166e-01 -7.56463468e-01 5.98445773e-01 5.77275082e-02
-2.72053748e-01 3.60545889e-02 2.95772433e-01 -1.73779458e-01
1.46076888e-01 -5.02110898e-01 -9.13672984e-01 -5.88707030e-01
-4.04569730e-02 -1.98277712e-01 6.86297178e-01 -8.20431113... | [8.901327133178711, 0.5182328224182129] |
b2fcfd76-19dd-45a8-a73b-27088b14efcd | multimodal-forgery-detection-using-ensemble | null | null | http://www.apsipa.org/proceedings/2022/APSIPA%202022/ThAM1-6/1570840386.pdf | http://www.apsipa.org/proceedings/2022/APSIPA%202022/ThAM1-6/1570840386.pdf | Multimodal Forgery Detection Using Ensemble Learning | The recent rapid revolution in Artificial Intelligence (AI) technology has enabled the creation of hyper-realistic deepfakes, and detecting deepfake videos (also known as AIsynthesized videos) has become a critical task. The existing systems generally do not fully consider the unified processing of audio and video data... | ['Hsin-Min Wang', 'Yu Tsao', 'Chia Wen Lin', 'Wasim Ahmad', 'Sahibzada Adil Shahzad', 'Ammarah Hashmi'] | 2022-11-07 | null | null | null | asia-pacific-signal-and-information-1 | ['multimodal-forgery-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 1.05774023e-01 -4.53823805e-01 -5.60793690e-02 -6.62665144e-02
-7.13307381e-01 -3.72259974e-01 5.05449533e-01 -2.41163433e-01
-2.98873752e-01 4.22977507e-01 1.47668093e-01 -2.06374064e-01
2.58864701e-01 -5.11293113e-01 -6.02543175e-01 -7.86187470e-01
1.17729120e-01 -2.01974422e-01 3.25090319e-01 -1.91929832... | [13.028109550476074, 1.3521004915237427] |
49b1e6bf-af1d-4269-98c2-4919059374a8 | multiscale-audio-spectrogram-transformer-for | 2303.10757 | null | https://arxiv.org/abs/2303.10757v1 | https://arxiv.org/pdf/2303.10757v1.pdf | Multiscale Audio Spectrogram Transformer for Efficient Audio Classification | Audio event has a hierarchical architecture in both time and frequency and can be grouped together to construct more abstract semantic audio classes. In this work, we develop a multiscale audio spectrogram Transformer (MAST) that employs hierarchical representation learning for efficient audio classification. Specifica... | ['Mohamed Omar', 'Wentao Zhu'] | 2023-03-19 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 5.05095795e-02 -4.41939056e-01 4.21920568e-01 -3.42936277e-01
-1.19856954e+00 -5.34441769e-01 -7.30480924e-02 5.21203756e-01
-4.22515303e-01 3.90414745e-01 3.13975155e-01 9.55439359e-02
-8.89794603e-02 -6.75356150e-01 -3.47489238e-01 -4.11291510e-01
-6.22373521e-01 -1.92117080e-01 4.95959342e-01 -6.18138835... | [15.208403587341309, 5.2109808921813965] |
53224710-d2c0-4ce1-9b10-54dba23acdb9 | black-box-generation-of-adversarial-text | 1801.04354 | null | http://arxiv.org/abs/1801.04354v5 | http://arxiv.org/pdf/1801.04354v5.pdf | Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers | Although various techniques have been proposed to generate adversarial
samples for white-box attacks on text, little attention has been paid to
black-box attacks, which are more realistic scenarios. In this paper, we
present a novel algorithm, DeepWordBug, to effectively generate small text
perturbations in a black-box... | ['Yanjun Qi', 'Jack Lanchantin', 'Ji Gao', 'Mary Lou Soffa'] | 2018-01-13 | null | null | null | null | ['adversarial-text', 'spam-detection'] | ['adversarial', 'natural-language-processing'] | [ 4.61444557e-01 1.81429163e-01 5.34629412e-02 -4.03742045e-01
-9.08591092e-01 -7.04795241e-01 6.79302812e-01 2.69647390e-01
-5.25065005e-01 6.97136223e-01 9.98960137e-02 -7.63480723e-01
5.72697699e-01 -8.52746367e-01 -9.26002562e-01 -6.76822603e-01
4.23323572e-01 1.63704708e-01 1.69104740e-01 -3.77562910... | [5.985134124755859, 8.118221282958984] |
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