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
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da5b2d69-c550-4d50-aceb-e18cb411b29e | deep-graph-clustering-via-mutual-information | 2205.05168 | null | https://arxiv.org/abs/2205.05168v1 | https://arxiv.org/pdf/2205.05168v1.pdf | Deep Graph Clustering via Mutual Information Maximization and Mixture Model | Attributed graph clustering or community detection which learns to cluster the nodes of a graph is a challenging task in graph analysis. In this paper, we introduce a contrastive learning framework for learning clustering-friendly node embedding. Although graph contrastive learning has shown outstanding performance in ... | ['Abdolreza Mirzaei', 'Mehran Safayani', 'Maedeh Ahmadi'] | 2022-05-10 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.65726915e-01 2.59129316e-01 -2.91238725e-01 -2.02050701e-01
-3.71921510e-01 -3.00264299e-01 4.80005205e-01 4.27605927e-01
-4.40154448e-02 1.28085807e-01 -1.13786772e-01 -1.90441892e-01
-3.10241580e-01 -7.92986989e-01 -2.46476904e-01 -9.55563128e-01
-4.65934277e-01 4.55859512e-01 -1.86653938e-02 2.60631889... | [7.314606666564941, 5.825501918792725] |
c1b75bde-33f1-4f7e-83bc-321de0b8f14b | distilling-effective-supervision-for-robust | 2106.11099 | null | https://arxiv.org/abs/2106.11099v1 | https://arxiv.org/pdf/2106.11099v1.pdf | Distilling effective supervision for robust medical image segmentation with noisy labels | Despite the success of deep learning methods in medical image segmentation tasks, the human-level performance relies on massive training data with high-quality annotations, which are expensive and time-consuming to collect. The fact is that there exist low-quality annotations with label noise, which leads to suboptimal... | ['Ji Wu', 'Jialin Shi'] | 2021-06-21 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 5.01243114e-01 2.61005074e-01 -1.34721115e-01 -7.44313240e-01
-1.55830538e+00 -1.58603072e-01 -8.98671448e-02 3.66215408e-02
-6.59882724e-01 6.18878543e-01 4.58110683e-02 -7.42488801e-02
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2.72531092e-01 2.40780115e-01 2.90262878e-01 2.03815147... | [14.589777946472168, -2.1051273345947266] |
9551a6ca-7ed4-4d33-8a21-8c9cfe5befe2 | addressing-distribution-shift-in-online | null | null | https://openreview.net/forum?id=9hgEG-k57Zj | https://openreview.net/pdf?id=9hgEG-k57Zj | Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets | Recent progress in offline reinforcement learning (RL) has made it possible to train strong RL agents from previously-collected, static datasets. However, depending on the quality of the trained agents and the application being considered, it is often desirable to improve such offline RL agents with further online inte... | ['Jinwoo Shin', 'Pieter Abbeel', 'Kimin Lee', 'Younggyo Seo', 'SeungHyun Lee'] | 2021-01-01 | null | null | null | null | ['d4rl'] | ['robots'] | [-3.37104201e-01 -2.15459913e-01 -2.26094186e-01 -8.82865787e-02
-9.78556871e-01 -8.39914620e-01 5.10307789e-01 2.07994506e-01
-7.51019657e-01 1.18280911e+00 6.13870993e-02 -2.99511015e-01
4.84286342e-03 -6.88033581e-01 -8.56445134e-01 -9.87632573e-01
4.02057320e-02 7.14697540e-01 2.76136011e-01 -3.26171398... | [4.060313701629639, 2.1995160579681396] |
da3175c4-1eab-416b-8fa2-ac89e606abf1 | an-empirical-comparison-of-deep-neural | 2005.01194 | null | https://arxiv.org/abs/2005.01194v1 | https://arxiv.org/pdf/2005.01194v1.pdf | An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs | Researchers have proposed a variety of predictive business process monitoring (PBPM) techniques aiming to predict future process behaviour during the process execution. Especially, techniques for the next activity prediction anticipate great potential in improving operational business processes. To gain more accurate p... | ['B. Eskofier', 'J. Brunk', 'A. Nguyen', 'K. Revoredo', 'J. Becker', 'S. Zilker', 'S. Weinzierl', 'M. Matzner'] | 2020-05-03 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 5.73226511e-01 1.25955820e-01 -4.52302784e-01 -3.63260061e-01
-1.20402761e-02 -1.50432229e-01 8.08031440e-01 6.85018539e-01
-4.53777499e-02 4.43140209e-01 5.24115205e-01 -5.09129345e-01
-5.69744647e-01 -1.11290109e+00 -3.02941829e-01 -2.03372180e-01
-2.72969007e-01 6.42226219e-01 8.23762640e-03 2.55512297... | [8.586065292358398, 5.955399036407471] |
2d16f461-7ce1-40af-8b9a-413e65051ce7 | regformer-an-efficient-projection-aware | 2303.12384 | null | https://arxiv.org/abs/2303.12384v1 | https://arxiv.org/pdf/2303.12384v1.pdf | RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration | Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing registration works general... | ['Hesheng Wang', 'Marc Pollefeys', 'Chaokang Jiang', 'Zhe Liu', 'Guangming Wang', 'Jiuming Liu'] | 2023-03-22 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-2.83832792e-02 -6.78398669e-01 2.11799070e-02 -5.97725868e-01
-1.04717946e+00 -4.11050290e-01 4.99016970e-01 1.19649865e-01
-4.47541177e-01 2.44839728e-01 5.31809330e-02 1.28495589e-01
-2.32973769e-01 -7.84214914e-01 -7.65652597e-01 -5.78787148e-01
2.18147919e-01 5.83185673e-01 4.21932876e-01 -1.53917775... | [7.6908769607543945, -3.015469551086426] |
c8d45f55-c253-4f1c-be87-c5352f74c51e | a-multimodal-corpus-for-mutual-gaze-and-joint | null | null | https://aclanthology.org/L18-1019 | https://aclanthology.org/L18-1019.pdf | A Multimodal Corpus for Mutual Gaze and Joint Attention in Multiparty Situated Interaction | null | ['Patrik Jonell', 'Alex', 'Vanya Avramova', 'Gabriel Skantze', 'Dimosthenis Kontogiorgos', 'Catharine Oertel', 'Simon erson', 'Jonas Beskow', 'Joakim Gustafson'] | 2018-05-01 | a-multimodal-corpus-for-mutual-gaze-and-joint-1 | https://aclanthology.org/L18-1019 | https://aclanthology.org/L18-1019.pdf | lrec-2018-5 | ['mutual-gaze'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.164504528045654, 3.819340229034424] |
86b66993-4391-41b3-a2fc-97868f97e4c1 | graphon-aided-joint-estimation-of-multiple | 2202.05686 | null | https://arxiv.org/abs/2202.05686v1 | https://arxiv.org/pdf/2202.05686v1.pdf | Graphon-aided Joint Estimation of Multiple Graphs | We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model. We adopt a graphon as our random graph model, which is a nonparametric model from which graphs of potentially different sizes can be d... | ['Santiago Segarra', 'Madeline Navarro'] | 2022-02-11 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 2.40888298e-01 5.02773285e-01 -2.84777135e-01 -9.13780183e-02
-2.87713468e-01 -9.00006831e-01 7.74582028e-01 1.25238225e-01
5.26166819e-02 1.18307126e+00 -2.98221141e-01 -3.46229553e-01
-5.23206294e-01 -1.10051394e+00 -7.85510957e-01 -5.68683624e-01
-5.20893455e-01 1.10823047e+00 4.83941853e-01 3.54872018... | [6.935458183288574, 5.330173015594482] |
dc50ab44-4f5a-4a4b-a053-24bac1ae1451 | bighand22m-benchmark-hand-pose-dataset-and | 1704.02612 | null | http://arxiv.org/abs/1704.02612v2 | http://arxiv.org/pdf/1704.02612v2.pdf | BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis | In this paper we introduce a large-scale hand pose dataset, collected using a
novel capture method. Existing datasets are either generated synthetically or
captured using depth sensors: synthetic datasets exhibit a certain level of
appearance difference from real depth images, and real datasets are limited in
quantity ... | ['Tae-Kyun Kim', 'Siddhant Jain', 'Bjorn Stenger', 'Shanxin Yuan', 'Qi Ye'] | 2017-04-09 | bighand2-2m-benchmark-hand-pose-dataset-and | http://openaccess.thecvf.com/content_cvpr_2017/html/Yuan_BigHand2.2M_Benchmark_Hand_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Yuan_BigHand2.2M_Benchmark_Hand_CVPR_2017_paper.pdf | cvpr-2017-7 | ['art-analysis'] | ['computer-vision'] | [-6.96076974e-02 -2.36217082e-01 -3.08123320e-01 -7.94893429e-02
-6.55984044e-01 -7.53569901e-01 3.49445075e-01 -6.56424522e-01
-3.93142134e-01 7.55350828e-01 6.92599356e-01 5.38208485e-01
5.54719232e-02 -3.04041743e-01 -6.66861773e-01 -3.82698417e-01
-5.89395931e-04 9.06569898e-01 1.21164866e-01 -1.97532818... | [6.571592330932617, -0.8496820330619812] |
cbd2cd9f-e6e1-45c9-a7ab-52b916143b90 | polyphonic-pitch-detection-with-convolutional | 2202.02115 | null | https://arxiv.org/abs/2202.02115v1 | https://arxiv.org/pdf/2202.02115v1.pdf | Polyphonic pitch detection with convolutional recurrent neural networks | Recent directions in automatic speech recognition (ASR) research have shown that applying deep learning models from image recognition challenges in computer vision is beneficial. As automatic music transcription (AMT) is superficially similar to ASR, in the sense that methods often rely on transforming spectrograms to ... | ['Sven Ahlbäck', 'Carl Thomé'] | 2022-02-04 | null | null | null | null | ['music-transcription'] | ['music'] | [ 2.91507691e-01 -2.10718185e-01 1.71112180e-01 7.09993392e-02
-1.06263220e+00 -6.86309695e-01 6.13286316e-01 -3.52506995e-01
-3.54277015e-01 2.02160150e-01 3.76236141e-01 -1.49484694e-01
-1.62617788e-02 -1.54411599e-01 -7.13460505e-01 -6.34870708e-01
1.17260721e-02 1.18542150e-01 -3.94600958e-01 -3.25406224... | [15.59126091003418, 5.556508541107178] |
abd25c4b-48e8-4460-be48-7dc7aa711568 | linear-disentangled-representation-learning | 1701.03102 | null | http://arxiv.org/abs/1701.03102v1 | http://arxiv.org/pdf/1701.03102v1.pdf | Linear Disentangled Representation Learning for Facial Actions | Limited annotated data available for the recognition of facial expression and
action units embarrasses the training of deep networks, which can learn
disentangled invariant features. However, a linear model with just several
parameters normally is not demanding in terms of training data. In this paper,
we propose an el... | ['Trac. D. Tran', 'Xiang Xiang'] | 2017-01-11 | null | null | null | null | ['sparse-representation-based-classification', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 2.81236202e-01 1.29659876e-01 -1.29291281e-01 -5.12949824e-01
-6.70959353e-01 -3.94483507e-01 6.26342118e-01 -7.42844462e-01
-3.33701551e-01 7.10204422e-01 3.15970391e-01 2.14245617e-01
-1.55125886e-01 -5.65819740e-02 -7.09609807e-01 -1.15780246e+00
-2.35751107e-01 2.14034006e-01 -5.16241133e-01 -2.35818356... | [13.192313194274902, 0.5150875449180603] |
cfced395-477d-4ffc-8daa-9dce5ff2323b | cosmopower-jax-high-dimensional-bayesian | 2305.06347 | null | https://arxiv.org/abs/2305.06347v2 | https://arxiv.org/pdf/2305.06347v2.pdf | CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators | We present CosmoPower-JAX, a JAX-based implementation of the CosmoPower framework, which accelerates cosmological inference by building neural emulators of cosmological power spectra. We show how, using the automatic differentiation, batch evaluation and just-in-time compilation features of JAX, and running the inferen... | ['A. Spurio Mancini', 'D. Piras'] | 2023-05-10 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-3.86592984e-01 -3.25927168e-01 4.65782493e-01 -2.30597794e-01
-7.40488648e-01 -7.49526203e-01 1.03263736e+00 -1.53486148e-01
-6.10653341e-01 7.04340577e-01 -6.16055615e-02 -7.75099158e-01
-7.85786361e-02 -1.09917533e+00 -2.84865409e-01 -8.64347816e-01
-2.83335924e-01 9.81519163e-01 6.23186707e-01 5.99599481... | [7.065707683563232, 3.5368220806121826] |
2510e0a7-404b-4727-b277-0275b79d060e | recyclable-semi-supervised-method-based-on | 2306.02894 | null | https://arxiv.org/abs/2306.02894v1 | https://arxiv.org/pdf/2306.02894v1.pdf | Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing | Pixel-level Scene Understanding is one of the fundamental problems in computer vision, which aims at recognizing object classes, masks and semantics of each pixel in the given image. Since the real-world is actually video-based rather than a static state, learning to perform video semantic segmentation is more reasonab... | ['Ning Wang', 'Xiaofeng Zhang', 'Si Gao', 'Chengjian Zheng', 'Diankai Zhang', 'Shaoli Liu', 'Biao Wu'] | 2023-06-05 | null | null | null | null | ['scene-parsing', 'video-semantic-segmentation', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.45558000e-01 1.61242321e-01 -1.87089831e-01 -6.50770962e-01
-8.11405599e-01 -4.81978416e-01 2.38837838e-01 -3.39347184e-01
-5.20415425e-01 4.77697700e-01 -2.54774272e-01 -3.15022498e-01
4.87366945e-01 -5.19833148e-01 -1.06323993e+00 -6.57098532e-01
4.59314525e-01 3.89935434e-01 9.37406957e-01 7.12578148... | [9.200881958007812, -0.020463505759835243] |
c6669ec7-b874-418f-8eee-a662e89e1938 | generating-music-with-sentiment-using | 2212.11134 | null | https://arxiv.org/abs/2212.11134v1 | https://arxiv.org/pdf/2212.11134v1.pdf | Generating music with sentiment using Transformer-GANs | The field of Automatic Music Generation has seen significant progress thanks to the advent of Deep Learning. However, most of these results have been produced by unconditional models, which lack the ability to interact with their users, not allowing them to guide the generative process in meaningful and practical ways.... | ['João Florindo', 'Jose Fornari', 'Pedro Neves'] | 2022-12-21 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 2.88427800e-01 2.05167308e-02 2.07847014e-01 -2.68976837e-01
-6.27376080e-01 -6.53202236e-01 7.23807395e-01 8.87733027e-02
-1.87313870e-01 7.37144649e-01 2.10701123e-01 2.72460103e-01
-6.83459416e-02 -7.90841699e-01 -4.76220250e-01 -8.45565319e-01
1.34528816e-01 5.65761566e-01 -9.57595259e-02 -3.09707761... | [15.913276672363281, 5.500870227813721] |
34279f95-c0ba-4bce-9a2f-cd5f8f7f62dd | video-object-segmentation-using-teacher | 1810.07733 | null | http://arxiv.org/abs/1810.07733v4 | http://arxiv.org/pdf/1810.07733v4.pdf | Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting | Video object segmentation is an essential task in robot manipulation to
facilitate grasping and learning affordances. Incremental learning is important
for robotics in unstructured environments, since the total number of objects
and their variations can be intractable. Inspired by the children learning
process, human r... | ['Mennatullah Siam', 'Chen Jiang', 'Martin Jagersand', 'Laura Petrich', 'Steven Lu', 'Mohamed Elhoseiny', 'Mahmoud Gamal'] | 2018-10-17 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 2.05935985e-01 3.02152395e-01 -3.43398869e-01 -3.00982386e-01
-1.67635873e-01 -7.22030640e-01 2.64666229e-01 -1.24602363e-01
-4.95322436e-01 5.08896828e-01 -4.69584405e-01 1.10572994e-01
-1.08797684e-01 -6.01509452e-01 -1.34640455e+00 -8.02909076e-01
-2.25779384e-01 9.14128184e-01 8.30068827e-01 -1.25604495... | [6.0224199295043945, -0.9436632990837097] |
ac5af762-5374-440a-a914-7ff6d3a2025d | facing-changes-continual-entity-alignment-for | 2207.11436 | null | https://arxiv.org/abs/2207.11436v1 | https://arxiv.org/pdf/2207.11436v1.pdf | Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs | Entity alignment is a basic and vital technique in knowledge graph (KG) integration. Over the years, research on entity alignment has resided on the assumption that KGs are static, which neglects the nature of growth of real-world KGs. As KGs grow, previous alignment results face the need to be revisited while new enti... | ['Wei Hu', 'Kexin Han', 'Yiqiao Jiang', 'Zequn Sun', 'Wenqiang Liu', 'Yuanning Cui', 'Yuxin Wang'] | 2022-07-23 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-1.78119972e-01 6.51005626e-01 -5.43891668e-01 -2.97346056e-01
-4.33328450e-01 -5.41423976e-01 4.16783899e-01 6.51235759e-01
-4.53059822e-01 9.26108956e-01 2.92865872e-01 -2.05407351e-01
4.74580787e-02 -1.21662736e+00 -1.08552432e+00 -4.00458544e-01
-1.18767016e-01 7.95410335e-01 2.73119688e-01 -4.58021790... | [8.792752265930176, 8.021483421325684] |
b663c027-fb3f-40e4-960d-1c143f9088bd | ecg-classification-with-a-convolutional | 2009.13320 | null | https://arxiv.org/abs/2009.13320v2 | https://arxiv.org/pdf/2009.13320v2.pdf | ECG Classification with a Convolutional Recurrent Neural Network | We developed a convolutional recurrent neural network to classify 12-lead ECG signals for the challenge of PhysioNet/ Computing in Cardiology 2020 as team Pink Irish Hat. The model combines convolutional and recurrent layers, takes sliding windows of ECG signals as input and yields the probability of each class as outp... | ['Ricard Delgado-Gonzalo', 'Jérôme Van Zaen', 'Mathieu Lemay', 'Halla Sigurthorsdottir'] | 2020-09-28 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.77917862e-01 1.33635864e-01 -2.12024711e-02 -3.37442696e-01
-9.87154543e-01 -2.92605042e-01 -8.11320767e-02 1.23151504e-01
-5.72915912e-01 6.13714397e-01 2.51907885e-01 -4.79594380e-01
-4.00462486e-02 -3.93417269e-01 -6.09499514e-01 -6.41725183e-01
-6.08660758e-01 1.77719980e-01 -6.33078963e-02 4.52878885... | [14.351513862609863, 3.3171379566192627] |
2b26ec3b-80cc-4fd8-b7b5-f1728ce2eb4b | cipcad-bench-continuous-industrial-process | 2208.01529 | null | https://arxiv.org/abs/2208.01529v1 | https://arxiv.org/pdf/2208.01529v1.pdf | CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods | Causal relationships are commonly examined in manufacturing processes to support faults investigations, perform interventions, and make strategic decisions. Industry 4.0 has made available an increasing amount of data that enable data-driven Causal Discovery (CD). Considering the growing number of recently proposed CD ... | ['Paolo Fiorini', "Diego Dall'Alba", 'Giovanni Menegozzo'] | 2022-08-02 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 3.39781582e-01 -4.93232943e-02 -1.61853850e-01 -1.14509895e-01
-3.67218375e-01 -4.11528289e-01 8.37788105e-01 7.49235094e-01
3.11256230e-01 8.52394760e-01 -4.63084653e-02 -3.28992397e-01
-7.83172488e-01 -1.05308557e+00 -6.05509102e-01 -6.06333375e-01
-4.09541041e-01 5.96707702e-01 7.84096494e-02 1.12970658... | [6.946234226226807, 2.4979119300842285] |
6bec4b6b-9635-4a80-8eb6-b89a7cb64a35 | gazing-at-social-interactions-between | null | null | https://www.frontiersin.org/articles/10.3389/fnbot.2021.639999/full | https://www.frontiersin.org/articles/10.3389/fnbot.2021.639999/full | Gazing at Social Interactions Between Foraging and Decision Theory | Finding the underlying principles of social attention in humans seems to be essential for the design of the interaction between natural and artificial agents. Here, we focus on the computational modeling of gaze dynamics as exhibited by humans when perceiving socially relevant multimodal information. The audio-visual l... | ['Giuseppe Boccignone', "Alessandro D'Amelio"] | 2022-03-30 | null | null | null | frontiers-in-neurorobotics-2022-3 | ['eye-tracking'] | ['computer-vision'] | [ 4.34501767e-01 1.21606579e-02 2.69045621e-01 -3.14687341e-02
1.59357116e-01 -6.66104138e-01 5.35128832e-01 1.89100862e-01
-6.76843047e-01 5.88913023e-01 3.71290594e-02 -1.48336828e-01
-4.44985867e-01 -1.24847345e-01 -2.74885803e-01 -9.80943024e-01
-4.94891584e-01 -9.00681168e-02 2.75533050e-01 -4.51035112... | [10.060013771057129, 1.6292059421539307] |
2290e887-098e-4fe4-b7de-901576e27e16 | generating-3d-faces-using-convolutional-mesh | 1807.10267 | null | http://arxiv.org/abs/1807.10267v3 | http://arxiv.org/pdf/1807.10267v3.pdf | Generating 3D faces using Convolutional Mesh Autoencoders | Learned 3D representations of human faces are useful for computer vision
problems such as 3D face tracking and reconstruction from images, as well as
graphics applications such as character generation and animation. Traditional
models learn a latent representation of a face using linear subspaces or
higher-order tensor... | ['Michael J. Black', 'Anurag Ranjan', 'Timo Bolkart', 'Soubhik Sanyal'] | 2018-07-26 | generating-3d-faces-using-convolutional-mesh-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Anurag_Ranjan_Generating_3D_Faces_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Anurag_Ranjan_Generating_3D_Faces_ECCV_2018_paper.pdf | eccv-2018-9 | ['3d-face-modeling'] | ['computer-vision'] | [-1.54862508e-01 1.34896800e-01 -3.37780118e-02 -5.92409790e-01
-4.02585596e-01 -3.43930811e-01 3.72568548e-01 -5.80595970e-01
2.86274403e-01 2.69593924e-01 2.66333848e-01 3.36061805e-01
3.40378702e-01 -7.28347898e-01 -9.94384050e-01 -5.94634414e-01
-1.69675097e-01 7.54950523e-01 -5.57761252e-01 -8.92805234... | [13.049311637878418, -0.03677217662334442] |
179cfaf7-9e27-4dfe-b10a-888ed8b9001b | a-two-stage-bayesian-optimisation-for | 2206.15115 | null | https://arxiv.org/abs/2206.15115v1 | https://arxiv.org/pdf/2206.15115v1.pdf | A Two-Stage Bayesian Optimisation for Automatic Tuning of an Unscented Kalman Filter for Vehicle Sideslip Angle Estimation | This paper presents a novel methodology to auto-tune an Unscented Kalman Filter (UKF). It involves using a Two-Stage Bayesian Optimisation (TSBO), based on a t-Student Process to optimise the process noise parameters of a UKF for vehicle sideslip angle estimation. Our method minimises performance metrics, given by the ... | ['R. Happee', 'M. Alirezaei', 'B. Shyrokau', 'A. Bertipaglia'] | 2022-06-30 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.36318022e-01 3.21403667e-02 3.06151628e-01 -2.22756028e-01
-9.01379883e-01 -3.75115097e-01 9.28798974e-01 -9.07531455e-02
-5.85830986e-01 1.01401627e+00 -5.03869019e-02 -5.65621138e-01
-5.97686052e-01 -6.15569115e-01 -6.70730114e-01 -1.10915089e+00
1.53951170e-02 5.61517954e-01 3.31739515e-01 -8.58361498... | [5.202939033508301, 2.1534390449523926] |
cf7b2e55-a328-4f25-9f2e-7c5ef3680695 | improving-heterogeneous-graph-learning-with | 2307.04514 | null | https://arxiv.org/abs/2307.04514v1 | https://arxiv.org/pdf/2307.04514v1.pdf | Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product Manifold | In graph representation learning, it is important that the complex geometric structure of the input graph, e.g. hidden relations among nodes, is well captured in embedding space. However, standard Euclidean embedding spaces have a limited capacity in representing graphs of varying structures. A promising candidate for ... | ['The-Anh Ta', 'Dung D. Le', 'Tuc Nguyen-Van'] | 2023-07-10 | null | null | null | null | ['graph-embedding', 'graph-learning', 'knowledge-graph-embedding', 'representation-learning', 'graph-representation-learning', 'word-similarity'] | ['graphs', 'graphs', 'graphs', 'methodology', 'methodology', 'natural-language-processing'] | [-1.89721137e-01 4.44537729e-01 -3.20922732e-01 -4.01739962e-02
1.23634739e-02 -7.93581128e-01 4.95203823e-01 5.02563655e-01
1.62736341e-01 -3.10092280e-03 3.81910980e-01 -4.93837535e-01
-5.41604996e-01 -1.25022662e+00 -3.59796941e-01 -6.39287770e-01
-3.65783602e-01 3.85187715e-01 1.46096453e-01 -5.17498970... | [7.191052436828613, 6.056935787200928] |
036ae0a1-d4bc-4cbb-80ab-7af7a35b5837 | constructing-the-f-graph-with-a-symmetric | 1912.07871 | null | https://arxiv.org/abs/1912.07871v1 | https://arxiv.org/pdf/1912.07871v1.pdf | Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering | Based on further studying the low-rank subspace clustering (LRSC) and L2-graph subspace clustering algorithms, we propose a F-graph subspace clustering algorithm with a symmetric constraint (FSSC), which constructs a new objective function with a symmetric constraint basing on F-norm, whose the most significant advanta... | ['Wen-Bo Hu', 'Xiao-Jun Wu', 'Kai Xu'] | 2019-12-17 | null | null | null | null | ['motion-segmentation', 'face-clustering'] | ['computer-vision', 'computer-vision'] | [ 2.85839625e-02 -4.29642856e-01 -5.46694174e-02 5.81084527e-02
-2.42111474e-01 -3.67576927e-01 -1.91393849e-02 -4.94892627e-01
-9.13322717e-02 1.81504130e-01 4.03193921e-01 -1.03101708e-01
-5.06554425e-01 -3.16495478e-01 -1.64946571e-01 -1.30651081e+00
-1.40248630e-02 1.86268330e-01 1.10201836e-01 9.77482498... | [7.9735260009765625, 4.488919734954834] |
62b18ff7-80a9-44ac-98cf-9d51e3677fd5 | subsurface-structure-analysis-using | 1812.08756 | null | http://arxiv.org/abs/1812.08756v1 | http://arxiv.org/pdf/1812.08756v1.pdf | Subsurface structure analysis using computational interpretation and learning: A visual signal processing perspective | Understanding Earth's subsurface structures has been and continues to be an
essential component of various applications such as environmental monitoring,
carbon sequestration, and oil and gas exploration. By viewing the seismic
volumes that are generated through the processing of recorded seismic traces,
researchers we... | ['M. Alfarraj', 'Z. Wang', 'M. Deriche', 'M. Shafiq', 'Z. Long', 'Y. Alaudah', 'H. Di', 'G. AlRegib'] | 2018-12-20 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [ 6.57181561e-01 1.20065406e-01 2.68133640e-01 -2.65200496e-01
-5.22196949e-01 -3.53726000e-01 6.93965077e-01 2.58411795e-01
-3.26571167e-01 2.29149014e-01 3.35706919e-01 -5.37060738e-01
-1.25578851e-01 -9.85003591e-01 -3.13446224e-01 -8.13409567e-01
-6.83783114e-01 2.63336599e-01 4.08224881e-01 -3.40101719... | [6.939342975616455, 2.4223568439483643] |
866751f8-ce23-483b-aa12-fe313ea784f9 | boosting-factor-specific-functional | 1609.06070 | null | http://arxiv.org/abs/1609.06070v2 | http://arxiv.org/pdf/1609.06070v2.pdf | Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals | The link between different psychophysiological measures during emotion
episodes is not well understood. To analyse the functional relationship between
electroencephalography (EEG) and facial electromyography (EMG), we apply
historical function-on-function regression models to EEG and EMG data that were
simultaneously r... | ['Kornelia Gentsch', 'David Rügamer', 'Sarah Brockhaus', 'Klaus Scherer', 'Sonja Greven'] | 2016-09-20 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 2.76694566e-01 -5.07391870e-01 -1.16262451e-01 -5.19698262e-01
-5.42863309e-01 -1.70036286e-01 4.71500695e-01 -1.97788298e-01
-9.03129339e-01 1.12185776e+00 1.30854055e-01 -1.36493742e-01
-6.08004510e-01 -6.90852180e-02 -7.99774110e-01 -5.20893335e-01
-9.24605131e-01 -4.05510282e-03 -3.89132023e-01 -1.34765685... | [13.0013427734375, 3.4048194885253906] |
a8de1332-336c-42a8-9c99-f072da3c23c2 | congrat-self-supervised-contrastive | 2305.14321 | null | https://arxiv.org/abs/2305.14321v1 | https://arxiv.org/pdf/2305.14321v1.pdf | ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings | We propose ConGraT(Contrastive Graph-Text pretraining), a general, self-supervised method for jointly learning separate representations of texts and nodes in a parent (or ``supervening'') graph, where each text is associated with one of the nodes. Datasets fitting this paradigm are common, from social media (users and ... | ['Deb Roy', 'Jad Kabbara', 'Brandon Roy', 'Wonjune Kang', 'Hang Jiang', 'Suyash Fulay', 'William Brannon'] | 2023-05-23 | null | null | null | null | ['link-prediction'] | ['graphs'] | [ 3.97244930e-01 4.90943789e-01 -5.47168791e-01 -5.95241189e-01
-4.28990066e-01 -6.54236317e-01 1.08723330e+00 6.80040479e-01
-1.43242538e-01 1.80352792e-01 5.24070323e-01 -3.74713302e-01
-2.70574782e-02 -8.57348800e-01 -1.01320148e+00 -3.28898907e-01
-6.29289076e-02 7.18680680e-01 3.20202410e-02 1.75035298... | [7.18619441986084, 6.244821548461914] |
080f7b46-94fb-4003-becc-7e5ad80e9a5a | nested-named-entity-recognition-as-holistic | 2204.08006 | null | https://arxiv.org/abs/2204.08006v1 | https://arxiv.org/pdf/2204.08006v1.pdf | Nested Named Entity Recognition as Holistic Structure Parsing | As a fundamental natural language processing task and one of core knowledge extraction techniques, named entity recognition (NER) is widely used to extract information from texts for downstream tasks. Nested NER is a branch of NER in which the named entities (NEs) are nested with each other. However, most of the previo... | ['Hai Zhao', 'Zuchao Li', 'Yifei Yang'] | 2022-04-17 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-3.74746043e-03 9.40935910e-02 -3.48754376e-01 -4.05753851e-01
-8.58252168e-01 -7.14089751e-01 4.54086363e-01 4.87092763e-01
-6.29156351e-01 7.61955082e-01 7.66461253e-01 -4.51611787e-01
9.10529401e-03 -9.25772667e-01 -5.98239362e-01 -2.49774575e-01
-1.61735788e-01 1.08978838e-01 3.49163204e-01 -5.27225792... | [9.665306091308594, 9.487525939941406] |
d16cd15e-b56b-4e0a-8073-b763660d501d | the-objectfolder-benchmark-multisensory-1 | 2306.00956 | null | https://arxiv.org/abs/2306.00956v1 | https://arxiv.org/pdf/2306.00956v1.pdf | The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects | We introduce the ObjectFolder Benchmark, a benchmark suite of 10 tasks for multisensory object-centric learning, centered around object recognition, reconstruction, and manipulation with sight, sound, and touch. We also introduce the ObjectFolder Real dataset, including the multisensory measurements for 100 real-world ... | ['Jiajun Wu', 'Li Fei-Fei', 'Yunzhu Li', 'Jeannette Bohg', 'Tanmay Agarwal', 'Hao Li', 'Yiming Dou', 'Ruohan Gao'] | 2023-06-01 | the-objectfolder-benchmark-multisensory | http://openaccess.thecvf.com//content/CVPR2023/html/Gao_The_ObjectFolder_Benchmark_Multisensory_Learning_With_Neural_and_Real_Objects_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gao_The_ObjectFolder_Benchmark_Multisensory_Learning_With_Neural_and_Real_Objects_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-recognition'] | ['computer-vision'] | [ 1.44057140e-01 -4.50953215e-01 1.43566042e-01 -2.80449629e-01
-9.19523537e-01 -6.66853249e-01 4.54094142e-01 1.35304302e-01
-1.30969167e-01 1.20862871e-01 2.48188272e-01 4.57029015e-01
-1.51910186e-01 -5.37137032e-01 -1.51969171e+00 -4.74979341e-01
-3.69150117e-02 3.96658480e-01 8.90935510e-02 9.36154127... | [7.0136942863464355, -2.1501410007476807] |
1d991f75-1b03-4aac-8694-8dc7a1e171c2 | variational-template-machine-for-data-to-text-1 | 2002.01127 | null | https://arxiv.org/abs/2002.01127v2 | https://arxiv.org/pdf/2002.01127v2.pdf | Variational Template Machine for Data-to-Text Generation | How to generate descriptions from structured data organized in tables? Existing approaches using neural encoder-decoder models often suffer from lacking diversity. We claim that an open set of templates is crucial for enriching the phrase constructions and realizing varied generations. Learning such templates is prohib... | ['Lei LI', 'Rong Ye', 'Hao Zhou', 'Wenxian Shi', 'Zhongyu Wei'] | 2020-02-04 | null | https://openreview.net/forum?id=HkejNgBtPB | https://openreview.net/pdf?id=HkejNgBtPB | iclr-2020-1 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.86467642e-01 5.37889719e-01 -3.34125131e-01 -5.63505948e-01
-1.29220092e+00 -7.73714960e-01 8.29119027e-01 -1.01810686e-01
1.53769013e-02 1.15458512e+00 6.76011920e-01 -1.63080599e-02
6.92156479e-02 -9.52204287e-01 -1.06824362e+00 -3.86766613e-01
3.82938802e-01 1.02020991e+00 -1.53665826e-01 -3.40933681... | [11.570198059082031, 8.847511291503906] |
f9aab7f9-62eb-40ee-a6e9-c9a0f49dbbf0 | understanding-person-identification-via-gait | 2203.04179 | null | https://arxiv.org/abs/2203.04179v4 | https://arxiv.org/pdf/2203.04179v4.pdf | Understanding Person Identification through Gait | Gait recognition is the process of identifying humans from their bipedal locomotion such as walking or running. As such, gait data is privacy sensitive information and should be anonymized where possible. With the rise of higher quality gait recording techniques, such as depth cameras or motion capture suits, an increa... | ['Admantini Hatzipanayioti', 'Thorsten Strufe', 'Shu-Chen Li', 'Evelyn Muschter', 'Simon Hanisch'] | 2022-03-08 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.73294917e-01 -2.15337258e-02 -7.15605840e-02 -4.28596348e-01
-6.12444818e-01 -6.28195226e-01 3.45241040e-01 1.40230298e-01
-6.46084547e-01 6.84787929e-01 7.81431913e-01 2.51906991e-01
1.74723089e-01 -7.41510808e-01 -4.21616942e-01 -3.58479708e-01
-2.81397581e-01 2.88955867e-01 -4.58080731e-02 -2.60145813... | [14.263510704040527, 1.395126223564148] |
2fdfc7e0-2da9-4d61-8be4-205909e3ca5c | detecting-clusters-of-anomalies-on-low | 1511.01047 | null | http://arxiv.org/abs/1511.01047v1 | http://arxiv.org/pdf/1511.01047v1.pdf | Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data | In a variety of applications, one desires to detect groups of anomalous data
samples, with a group potentially manifesting its atypicality (relative to a
reference model) on a low-dimensional subset of the full measured set of
features. Samples may only be weakly atypical individually, whereas they may be
strongly atyp... | ['George Kesidis', 'Zhicong Qiu', 'David J. Miller'] | 2015-06-10 | null | null | null | null | ['group-anomaly-detection'] | ['methodology'] | [ 2.55792260e-01 -2.17773601e-01 1.71499670e-01 -4.89778459e-01
6.17781319e-02 -5.33536792e-01 6.77039087e-01 3.84200454e-01
7.72311240e-02 5.16748905e-01 -4.03029859e-01 -3.73154551e-01
-6.09821141e-01 -6.72567546e-01 -2.16209307e-01 -9.57561970e-01
-8.30381274e-01 7.24656463e-01 5.10782957e-01 6.88198134... | [7.50612735748291, 2.667022705078125] |
62cd1a66-5dd6-449a-ad29-e941bfbadc70 | fast-multi-view-clustering-via-ensembles | 2203.11572 | null | https://arxiv.org/abs/2203.11572v4 | https://arxiv.org/pdf/2203.11572v4.pdf | Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity | Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, they typically fuse multi-view information via one-stage fusion, neglecting the poss... | ['Jian-Huang Lai', 'Chang-Dong Wang', 'Dong Huang'] | 2022-03-22 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-2.45611131e-01 -3.22699487e-01 -2.39578202e-01 -2.82023609e-01
-8.52606654e-01 -8.43518674e-01 4.84686494e-01 4.95108739e-02
1.62302136e-01 3.64404470e-01 3.41110885e-01 1.41773354e-02
-4.86544043e-01 -6.89517915e-01 -2.93050021e-01 -1.05388117e+00
1.02266818e-01 4.73312438e-01 2.14267522e-01 1.04085337... | [8.209094047546387, 4.630855560302734] |
f0cec7e9-322b-4f72-8f0d-b25d2b623fe1 | uncertainty-quantification-using-bayesian | null | null | https://openreview.net/forum?id=Sk_P2Q9sG | https://openreview.net/pdf?id=Sk_P2Q9sG | Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation | Most recent research of neural networks in the field of computer vision has focused on improving accuracy of point predictions by developing various network architectures or learning algorithms. Uncertainty quantification accompanied by point estimation can lead to a more informed decision, and the quality of predictio... | ['Joong-Ho Won', 'Beom Joon Kim', 'Yongchan Kwon', 'Myunghee Cho Paik'] | 2018-04-10 | null | null | null | midl-2018-conference-2018-4 | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [ 2.74737954e-01 6.50153458e-01 -2.80762643e-01 -6.70276105e-01
-9.76201117e-01 -2.79549271e-01 5.93004405e-01 3.69403273e-01
-2.70983636e-01 1.04351687e+00 3.09614122e-01 -4.78073716e-01
-6.12185836e-01 -8.98321748e-01 -6.50185287e-01 -7.48060346e-01
8.62622336e-02 5.29862463e-01 7.53083155e-02 4.98233616... | [14.239113807678223, -2.054959774017334] |
a48cf463-f67a-4651-89e5-4ac1e7998f1d | line-drawing-guided-progressive-inpainting-of | 2211.06649 | null | https://arxiv.org/abs/2211.06649v1 | https://arxiv.org/pdf/2211.06649v1.pdf | Line Drawing Guided Progressive Inpainting of Mural Damages | Mural image inpainting refers to repairing the damage or missing areas in a mural image to restore the visual appearance. Most existing image-inpainting methods tend to take a target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in ... | ['Xiaoguang Wang', 'Xianfeng Huang', 'Chengfang Song', 'Long Chen', 'Hongkai Yu', 'Fan Zhang', 'Qin Zou', 'Luxi Li'] | 2022-11-12 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 6.13981247e-01 -7.30902329e-02 7.91635737e-02 1.49768218e-01
-2.60799021e-01 -3.11043352e-01 2.83746451e-01 -1.02216890e-03
-8.45008194e-02 8.08053076e-01 2.40047164e-02 -2.42979098e-02
2.29953289e-01 -8.69515121e-01 -7.53225923e-01 -6.17662668e-01
4.75575238e-01 -8.40992108e-02 1.76507369e-01 -1.49915874... | [11.23507308959961, -1.4116097688674927] |
11b9746b-278a-46a8-a9f1-2c1490c5b1d7 | a-model-driven-deep-neural-network-for-single | 2005.01333 | null | https://arxiv.org/abs/2005.01333v1 | https://arxiv.org/pdf/2005.01333v1.pdf | A Model-driven Deep Neural Network for Single Image Rain Removal | Deep learning (DL) methods have achieved state-of-the-art performance in the task of single image rain removal. Most of current DL architectures, however, are still lack of sufficient interpretability and not fully integrated with physical structures inside general rain streaks. To this issue, in this paper, we propose... | ['Qian Zhao', 'Qi Xie', 'Hong Wang', 'Deyu Meng'] | 2020-05-04 | a-model-driven-deep-neural-network-for-single-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_A_Model-Driven_Deep_Neural_Network_for_Single_Image_Rain_Removal_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_A_Model-Driven_Deep_Neural_Network_for_Single_Image_Rain_Removal_CVPR_2020_paper.pdf | cvpr-2020-6 | ['single-image-deraining'] | ['computer-vision'] | [-3.20066988e-01 -2.17931375e-01 2.98728883e-01 -5.49141824e-01
-1.62437513e-01 -1.82424277e-01 1.62714839e-01 -2.35742554e-01
-1.09801963e-01 6.34352386e-01 2.80430298e-02 -4.59247559e-01
-1.44305483e-01 -6.90730691e-01 -6.46533251e-01 -9.67432857e-01
-9.74675789e-02 7.45328292e-02 -1.08241521e-01 -4.73023176... | [10.920653343200684, -3.2582292556762695] |
6f1f0e4b-cf3c-4b8c-b6e8-e1783b3659a0 | oriented-objects-as-pairs-of-middle-lines | 1912.10694 | null | https://arxiv.org/abs/1912.10694v3 | https://arxiv.org/pdf/1912.10694v3.pdf | Oriented Objects as pairs of Middle Lines | The detection of oriented objects is frequently appeared in the field of natural scene text detection as well as object detection in aerial images. Traditional detectors for oriented objects are common to rotate anchors on the basis of the RCNN frameworks, which will multiple the number of anchors with a variety of ang... | ['Hao-Ran Wei', 'Yue Zhang', 'Xian Sun', 'Hongqi Wang', 'Zhonghan Chang', 'Hao Li'] | 2019-12-23 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.47196525e-01 1.38967127e-01 -2.36343041e-01 -2.02546045e-01
-1.54554799e-01 -6.48977935e-01 4.09021109e-01 8.48545320e-03
-3.30127418e-01 -1.75549220e-02 -1.52459487e-01 -8.92437026e-02
6.55741543e-02 -6.77599847e-01 -4.53264743e-01 -5.48527539e-01
-1.08046733e-01 3.00571501e-01 9.45007205e-01 -2.70784795... | [8.695822715759277, -0.632073163986206] |
7f0d31de-6ef3-4487-a7e5-d55690b634f0 | perch-perception-via-search-for-multi-object | 1510.05613 | null | http://arxiv.org/abs/1510.05613v2 | http://arxiv.org/pdf/1510.05613v2.pdf | PERCH: Perception via Search for Multi-Object Recognition and Localization | In many robotic domains such as flexible automated manufacturing or personal
assistance, a fundamental perception task is that of identifying and localizing
objects whose 3D models are known. Canonical approaches to this problem include
discriminative methods that find correspondences between feature descriptors
comput... | ['Venkatraman Narayanan', 'Maxim Likhachev'] | 2015-10-19 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 6.45633519e-01 2.53724605e-01 -5.25972433e-02 -3.59634608e-01
-9.31791604e-01 -7.95917332e-01 5.16867220e-01 -7.01768138e-03
1.84593990e-01 3.62745464e-01 -1.54774740e-01 -1.73794135e-01
-6.21729076e-01 -5.50766528e-01 -9.88505900e-01 -6.25402689e-01
-8.77417028e-02 9.75048125e-01 2.94461250e-01 -5.97977042... | [5.90219259262085, -0.9133263826370239] |
895a07fe-7e59-4b39-9d24-294dfa99c098 | farsbase-kbp-a-knowledge-base-population | 2005.01879 | null | https://arxiv.org/abs/2005.01879v1 | https://arxiv.org/pdf/2005.01879v1.pdf | FarsBase-KBP: A Knowledge Base Population System for the Persian Knowledge Graph | While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information. Unlike English knowledge bases such as Wikidata, which have tremendous community support, the populati... | ['Behrouz Minaei-Bidgoli', 'Majid Asgari-Bidhendi', 'Behrooz Janfada'] | 2020-05-04 | null | null | null | null | ['knowledge-base-population'] | ['natural-language-processing'] | [-4.40069169e-01 7.49251187e-01 -3.66891086e-01 6.45658076e-02
-3.37533116e-01 -5.43026984e-01 6.09559953e-01 5.16703546e-01
-5.64749241e-01 1.47754955e+00 -2.62929887e-01 -1.50943488e-01
-5.74638069e-01 -1.35198545e+00 -5.62204540e-01 -1.49567574e-01
2.67448038e-01 1.02444613e+00 8.78185093e-01 -6.75857961... | [9.280203819274902, 8.266240119934082] |
b8af47aa-75ab-442e-b499-483fc2012b77 | deep-neural-heart-rate-variability-analysis | 1612.09205 | null | http://arxiv.org/abs/1612.09205v1 | http://arxiv.org/pdf/1612.09205v1.pdf | Deep neural heart rate variability analysis | Despite of the pain and limited accuracy of blood tests for early recognition
of cardiovascular disease, they dominate risk screening and triage. On the
other hand, heart rate variability is non-invasive and cheap, but not
considered accurate enough for clinical practice. Here, we tackle heart beat
interval based class... | ['Tamas Madl'] | 2016-12-29 | null | null | null | null | ['heart-rate-variability', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 3.92196402e-02 -2.31687278e-02 -7.78421536e-02 -4.70100731e-01
-3.49924952e-01 -3.05062383e-01 8.18051621e-02 3.94788831e-01
-6.45663321e-01 1.02281618e+00 -2.25707084e-01 -7.61106730e-01
-4.92535502e-01 -5.50648570e-01 -2.42364913e-01 -6.05086505e-01
-7.63612926e-01 7.00079083e-01 -1.67950526e-01 1.98451385... | [14.302852630615234, 3.255481719970703] |
9ac5be40-80a6-4b70-8ea4-1ff394dc1555 | conversational-search-with-mixed-initiative-1 | null | null | https://aclanthology.org/2022.dialdoc-1.7 | https://aclanthology.org/2022.dialdoc-1.7.pdf | Conversational Search with Mixed-Initiative - Asking Good Clarification Questions backed-up by Passage Retrieval | We deal with the scenario of conversational search, where user queries are under-specified or ambiguous. This calls for a mixed-initiative setup. User-asks (queries) and system-answers, as well as system-asks (clarification questions) and user response, in order to clarify her information needs. We focus on the task of... | ['David Konopnicki', 'Asaf Yehudai', 'Doron Cohen', 'Yosi Mass'] | null | null | null | null | dialdoc-acl-2022-5 | ['passage-retrieval', 'conversational-search'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.11109692e-01 2.95861781e-01 -9.42954198e-02 -4.24357742e-01
-1.51364827e+00 -8.08478475e-01 9.00130093e-01 4.05747920e-01
-5.09439707e-01 7.66722858e-01 8.12608719e-01 -6.31280422e-01
-2.44124740e-01 -3.88635069e-01 -6.41825795e-02 5.57710193e-02
4.17551011e-01 1.14348865e+00 2.28414074e-01 -6.18144870... | [12.1542387008667, 7.836090564727783] |
a6d146d3-18dd-4919-96f8-8b713a12c985 | kernelized-covariance-for-action-recognition | 1604.06582 | null | http://arxiv.org/abs/1604.06582v2 | http://arxiv.org/pdf/1604.06582v2.pdf | Kernelized Covariance for Action Recognition | In this paper we aim at increasing the descriptive power of the covariance
matrix, limited in capturing linear mutual dependencies between variables only.
We present a rigorous and principled mathematical pipeline to recover the
kernel trick for computing the covariance matrix, enhancing it to model more
complex, non-l... | ['Andrea Zunino', 'Jacopo Cavazza', 'Vittorio Murino', 'Marco San Biagio'] | 2016-04-22 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.74560994e-01 -5.99390268e-02 1.27900556e-01 -3.68198931e-01
-1.85804918e-01 -6.04253173e-01 7.47890413e-01 3.76297883e-03
-5.02443612e-01 4.76711243e-01 2.42350593e-01 -2.49983475e-01
-5.63654840e-01 -3.36556256e-01 -3.88756931e-01 -6.58239424e-01
-2.27306813e-01 2.89826572e-01 1.70266926e-01 -9.37032998... | [7.82554817199707, 3.891082286834717] |
027203c5-f6f6-4576-b3e6-496b2ef2152a | banglawriting-a-multi-purpose-offline-bangla | 2011.07499 | null | https://arxiv.org/abs/2011.07499v3 | https://arxiv.org/pdf/2011.07499v3.pdf | BanglaWriting: A multi-purpose offline Bangla handwriting dataset | This article presents a Bangla handwriting dataset named BanglaWriting that contains single-page handwritings of 260 individuals of different personalities and ages. Each page includes bounding-boxes that bounds each word, along with the unicode representation of the writing. This dataset contains 21,234 words and 32,7... | ['Muhammad Mohsin Kabir', 'Mazedul Islam Emon', 'M. Ameer Ali', 'Abu Quwsar Ohi', 'M. F. Mridha'] | 2020-11-15 | null | null | null | null | ['handwritten-word-segmentation'] | ['computer-vision'] | [-7.32573345e-02 -3.55758011e-01 -2.55084515e-01 -3.13147426e-01
-6.49301847e-03 -7.84230471e-01 4.94684994e-01 5.15252119e-03
-4.77267176e-01 9.13790882e-01 1.07063293e-01 -3.94109666e-01
3.14807817e-02 -9.02661681e-01 -1.77129701e-01 -7.73347914e-01
5.02196014e-01 7.59644568e-01 -9.08292308e-02 -2.95009375... | [11.859137535095215, 2.5770435333251953] |
880012af-ec92-46f5-b9de-9d740acf9c3c | smix-enhancing-centralized-value-functions | 1911.04094 | null | https://arxiv.org/abs/1911.04094v5 | https://arxiv.org/pdf/1911.04094v5.pdf | SMIX($λ$): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning | Learning a stable and generalizable centralized value function (CVF) is a crucial but challenging task in multi-agent reinforcement learning (MARL), as it has to deal with the issue that the joint action space increases exponentially with the number of agents in such scenarios. This paper proposes an approach, named SM... | ['Chao Wen', 'Yuhui Wang', 'Xiaoyang Tan', 'Xinghu Yao'] | 2019-11-11 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-1.48376673e-01 -6.06911704e-02 -7.09831864e-02 3.38167548e-02
-1.14070392e+00 -3.76926750e-01 4.98074085e-01 3.49952072e-01
-1.02136123e+00 1.26451862e+00 -3.51473540e-01 -3.23871523e-01
-6.68395221e-01 -7.09572494e-01 -9.46742058e-01 -1.06733751e+00
-1.32547498e-01 5.50616026e-01 1.98314309e-01 -4.75605100... | [4.028697967529297, 2.2886595726013184] |
a2e76229-3ed0-48ec-89da-83d56567169a | audio-classification-of-bit-representation | 1904.04364 | null | https://arxiv.org/abs/1904.04364v2 | https://arxiv.org/pdf/1904.04364v2.pdf | Audio Classification of Bit-Representation Waveform | This study investigated the waveform representation for audio signal classification. Recently, many studies on audio waveform classification such as acoustic event detection and music genre classification have been published. Most studies on audio waveform classification have proposed the use of a deep learning (neural... | ['Hiromitsu Nishizaki', 'Naoki Sawada', 'Masaki Okawa', 'Takuya Saito'] | 2019-04-08 | null | null | null | null | ['genre-classification', 'music-classification'] | ['computer-vision', 'music'] | [ 6.96820498e-01 -4.06432092e-01 4.43286926e-01 -3.15705240e-01
-7.54225552e-01 -4.28011745e-01 1.65298268e-01 2.98942536e-01
-2.43407756e-01 5.92510462e-01 6.24206327e-02 -2.62789071e-01
-2.01330855e-01 -7.44437814e-01 -2.42807657e-01 -6.69892192e-01
-3.19229424e-01 -3.07364076e-01 1.57985032e-01 9.93252248... | [15.218988418579102, 5.317022800445557] |
c9ef37c4-2c0c-44d6-9178-a4a8a0dd6bd1 | physics-informed-neural-networks-pinns-for-4 | 2110.13361 | null | https://arxiv.org/abs/2110.13361v2 | https://arxiv.org/pdf/2110.13361v2.pdf | A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs | Physics-informed neural networks (PINNs) as a means of discretizing partial differential equations (PDEs) are garnering much attention in the Computational Science and Engineering (CS&E) world. At least two challenges exist for PINNs at present: an understanding of accuracy and convergence characteristics with respect ... | ['Robert M. Kirby', 'Akil Narayan', 'Shandian Zhe', 'Michael Penwarden'] | 2021-10-26 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.57427788e-01 -1.08247317e-01 -1.80837676e-01 -9.59159806e-02
-8.32295656e-01 -5.70501924e-01 6.06440902e-01 -2.46510044e-01
-5.84113717e-01 8.73907924e-01 -2.81838596e-01 -3.21926504e-01
-8.80687118e-01 -6.18835628e-01 -1.05933869e+00 -9.68875289e-01
-3.63032222e-01 9.65119958e-01 7.41602927e-02 -3.07833791... | [6.485883712768555, 3.5526180267333984] |
f5ebaecc-1094-4a0b-9803-90d4a1b1c91d | privacy-leakage-of-sift-features-via-deep | 2009.01030 | null | https://arxiv.org/abs/2009.01030v1 | https://arxiv.org/pdf/2009.01030v1.pdf | Privacy Leakage of SIFT Features via Deep Generative Model based Image Reconstruction | Many practical applications, e.g., content based image retrieval and object recognition, heavily rely on the local features extracted from the query image. As these local features are usually exposed to untrustworthy parties, the privacy leakage problem of image local features has received increasing attention in recen... | ['Jiantao Zhou', 'Haiwei Wu'] | 2020-09-02 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 2.58937627e-01 -1.71060339e-01 -1.74529374e-01 -3.40639263e-01
-1.09664810e+00 -8.90893102e-01 4.46734339e-01 -1.79220885e-01
-3.97445589e-01 5.70585966e-01 -1.16210699e-01 -1.79293707e-01
4.04100679e-02 -1.16230392e+00 -1.16851163e+00 -1.27847481e+00
9.28391740e-02 -4.27621081e-02 -1.51085973e-01 1.29018828... | [12.621984481811523, 0.7164384722709656] |
eb675a80-6be7-4966-bade-a94d12dafc55 | masked-and-permuted-implicit-context-learning | 2305.16172 | null | https://arxiv.org/abs/2305.16172v1 | https://arxiv.org/pdf/2305.16172v1.pdf | Masked and Permuted Implicit Context Learning for Scene Text Recognition | Scene Text Recognition (STR) is a challenging task due to variations in text style, shape, and background. Incorporating linguistic information is an effective way to enhance the robustness of STR models. Existing methods rely on permuted language modeling (PLM) or masked language modeling (MLM) to learn contextual inf... | ['Weiping Wang', 'Dongbao Yang', 'Zhilong Ji', 'Ye Yuan', 'Yu Zhou', 'Jin Wei', 'Zhi Qiao', 'Xiaomeng Yang'] | 2023-05-25 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 7.08666205e-01 -2.94422776e-01 -2.03209728e-01 -4.92601395e-01
-6.59826159e-01 -3.36022645e-01 8.99050057e-01 -6.65835738e-02
-4.91614938e-01 3.41826797e-01 3.89282644e-01 -5.57758629e-01
5.03369808e-01 -5.01237869e-01 -6.59391999e-01 -6.91599488e-01
4.64871049e-01 2.04899475e-01 3.19520235e-01 -7.40479752... | [11.911980628967285, 2.178516387939453] |
667f2460-ad61-4f15-82cc-533a20e71d3f | generating-smooth-pose-sequences-for-diverse | 2108.08422 | null | https://arxiv.org/abs/2108.08422v3 | https://arxiv.org/pdf/2108.08422v3.pdf | Generating Smooth Pose Sequences for Diverse Human Motion Prediction | Recent progress in stochastic motion prediction, i.e., predicting multiple possible future human motions given a single past pose sequence, has led to producing truly diverse future motions and even providing control over the motion of some body parts. However, to achieve this, the state-of-the-art method requires lear... | ['Mathieu Salzmann', 'Miaomiao Liu', 'Wei Mao'] | 2021-08-19 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Mao_Generating_Smooth_Pose_Sequences_for_Diverse_Human_Motion_Prediction_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Mao_Generating_Smooth_Pose_Sequences_for_Diverse_Human_Motion_Prediction_ICCV_2021_paper.pdf | iccv-2021-1 | ['human-pose-forecasting'] | ['computer-vision'] | [ 1.03828507e-02 1.74003035e-01 -2.57486790e-01 -2.29833290e-01
-7.26932764e-01 -5.26685953e-01 6.96248293e-01 -6.16107941e-01
-1.03095576e-01 7.98871994e-01 6.51813626e-01 9.25632194e-02
3.84973645e-01 -5.22802591e-01 -8.28578174e-01 -5.77414930e-01
-1.22127876e-01 5.67095220e-01 1.91588491e-01 -2.79909015... | [7.28617000579834, -0.24027295410633087] |
309c4c6f-e0b2-4dee-a362-94d5cf70b2bb | graph-networks-as-a-universal-machine | 1812.05055 | null | http://arxiv.org/abs/1812.05055v1 | http://arxiv.org/pdf/1812.05055v1.pdf | Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals | Graph networks are a new machine learning (ML) paradigm that supports both
relational reasoning and combinatorial generalization. Here, we develop, for
the first time, universal MatErials Graph Network (MEGNet) models for accurate
property prediction in both molecules and crystals. We demonstrate that our
MEGNet models... | ['Yunxing Zuo', 'Chi Chen', 'Weike Ye', 'Shyue Ping Ong', 'Chen Zheng'] | 2018-12-12 | graph-networks-as-a-universal-machine-1 | null | null | chem-mater-2018-12 | ['formation-energy'] | ['miscellaneous'] | [ 1.71816394e-01 5.02626538e-01 -4.88226384e-01 -2.56933481e-01
-4.29145396e-01 -2.31637076e-01 5.57794571e-01 7.19277799e-01
-4.00867350e-02 1.09495103e+00 3.98063302e-01 -4.33421075e-01
-2.49456644e-01 -1.27755439e+00 -9.73693490e-01 -8.64754021e-01
-3.39034915e-01 7.03265190e-01 -1.72811206e-02 -3.77510548... | [5.200201034545898, 5.546816825866699] |
20d311df-53eb-43ba-ab47-251382a38ad4 | scalable-and-effective-conductance-based | 2211.12511 | null | https://arxiv.org/abs/2211.12511v1 | https://arxiv.org/pdf/2211.12511v1.pdf | Scalable and Effective Conductance-based Graph Clustering | Conductance-based graph clustering has been recognized as a fundamental operator in numerous graph analysis applications. Despite the significant success of conductance-based graph clustering, existing algorithms are either hard to obtain satisfactory clustering qualities, or have high time and space complexity to achi... | ['Tao Jia', 'Rong-Hua Li', 'Longlong Lin'] | 2022-11-22 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.68892205e-01 3.74425645e-03 -8.03539976e-02 1.07813247e-01
-8.43947172e-01 -9.08991039e-01 -1.55117989e-01 5.59679449e-01
-2.27986634e-01 3.84838074e-01 -6.34040475e-01 -5.48724651e-01
-5.73136687e-01 -1.09682143e+00 -5.79507709e-01 -9.96358454e-01
-4.94041920e-01 6.50967777e-01 6.16157293e-01 1.33575127... | [6.892030239105225, 5.1037278175354] |
97aa8592-cb6f-4487-9376-34906ec32e5b | wut-at-semeval-2019-task-9-domain-adversarial | null | null | https://aclanthology.org/S19-2221 | https://aclanthology.org/S19-2221.pdf | WUT at SemEval-2019 Task 9: Domain-Adversarial Neural Networks for Domain Adaptation in Suggestion Mining | We present a system for cross-domain suggestion mining, prepared for the SemEval-2019 Task 9: Suggestion Mining from Online Reviews and Forums (Subtask B). Our submitted solution for this text classification problem explores the idea of treating different suggestions{'} sources as one of the settings of Transfer Learni... | ['Piotr Andruszkiewicz', 'Mateusz Klimaszewski'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 2.15616688e-01 5.67079425e-01 -5.57590187e-01 -5.14211357e-01
-9.81698275e-01 -7.56189167e-01 1.13330710e+00 2.40591288e-01
-5.26585340e-01 9.75945055e-01 3.10978740e-02 -8.47178698e-01
-8.90835673e-02 -5.42761326e-01 -6.96381509e-01 -2.21166849e-01
-1.64193418e-02 9.37479913e-01 3.44701171e-01 -7.21859336... | [10.881022453308105, 7.590798377990723] |
fffecc9b-0225-4cec-958f-d0830442a852 | detecting-bot-behaviour-in-social-media-using | null | null | https://aran.library.nuigalway.ie/handle/10379/15683 | http://aics2019.datascienceinstitute.ie/papers/aics_35.pdf | Detecting Bot Behaviour in Social Media using Digital DNA Compression | A major challenge faced by online social networks such as Facebook and Twitter is the remarkable rise of fake and automated bot accounts over the last few years. Some of these accounts have been reported to engage in undesirable activities such as spamming, political campaigning and spreading falsehood on the platform.... | ['Conor Hayes', 'Nivranshu Pasricha'] | 2019-12-05 | null | null | null | 27th-irish-conference-on-artificial | ['twitter-bot-detection'] | ['miscellaneous'] | [ 5.30775249e-01 3.01398009e-01 -2.34612629e-01 -1.02126062e-01
-1.73550740e-01 -9.32959497e-01 1.11022818e+00 6.03843749e-01
-4.83892888e-01 6.13424242e-01 4.59543973e-01 -6.25672877e-01
2.50759870e-01 -9.94136453e-01 -3.57529163e-01 -5.44070303e-01
-8.70682448e-02 3.73437345e-01 3.62072319e-01 -1.22249141... | [8.171903610229492, 10.209074974060059] |
2e0b3569-6002-41a2-bbf5-cb2b1c56178d | the-first-comprehensive-dataset-with-multiple | 2303.02562 | null | https://arxiv.org/abs/2303.02562v2 | https://arxiv.org/pdf/2303.02562v2.pdf | The First Comprehensive Dataset with Multiple Distortion Types for Visual Just-Noticeable Differences | Recently, with the development of deep learning, a number of Just Noticeable Difference (JND) datasets have been built for JND modeling. However, all the existing JND datasets only label the JND points based on the level of compression distortion. Hence, JND models learned from such datasets can only be used for image/... | ['Weisi Lin', 'Yuan Xue', 'Jian Jin', 'Yaxuan Liu'] | 2023-03-05 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [-2.19500866e-02 -7.40405321e-01 -1.53415039e-01 -4.54574913e-01
-6.11296594e-01 -2.59274542e-01 5.02986491e-01 1.47677302e-01
-1.42394722e-01 4.34416085e-01 5.13678372e-01 -5.36503755e-02
-2.91006211e-02 -9.82689083e-01 -4.25435513e-01 -4.43828017e-01
3.09820682e-01 -8.97653177e-02 1.45538211e-01 -2.34869406... | [11.785974502563477, -1.8291007280349731] |
1527365e-811a-4305-bb55-a3ac570f03e2 | dtw-siamesenet-dynamic-time-warped-siamese | 2303.00171 | null | https://arxiv.org/abs/2303.00171v1 | https://arxiv.org/pdf/2303.00171v1.pdf | DTW-SiameseNet: Dynamic Time Warped Siamese Network for Mispronunciation Detection and Correction | Personal Digital Assistants (PDAs) - such as Siri, Alexa and Google Assistant, to name a few - play an increasingly important role to access information and complete tasks spanning multiple domains, and by diverse groups of users. A text-to-speech (TTS) module allows PDAs to interact in a natural, human-like manner, an... | ['Srinivas Chappidi', 'Becci Williamson', 'Prabal Vashisht', 'Daniela de la Parra Aguilar', 'Kriti Bhasin', 'Raviteja Anantha'] | 2023-03-01 | null | null | null | null | ['metric-learning', 'metric-learning', 'speech-synthesis', 'dynamic-time-warping'] | ['computer-vision', 'methodology', 'speech', 'time-series'] | [ 6.85381815e-02 -4.64124143e-01 -1.58123132e-02 -4.04627979e-01
-1.51994777e+00 -7.10698128e-01 4.43919599e-01 3.40201077e-03
-5.69784522e-01 7.91555762e-01 4.73357707e-01 -3.07841390e-01
2.48530418e-01 -4.05613631e-01 -5.26989460e-01 -3.47374558e-01
2.41419941e-01 6.15406811e-01 3.11406143e-02 -8.68057311... | [14.309563636779785, 6.001967430114746] |
dff4f6ad-667e-4210-b834-7d347b8e5261 | learning-towards-the-largest-margins-1 | 2206.11589 | null | https://arxiv.org/abs/2206.11589v1 | https://arxiv.org/pdf/2206.11589v1.pdf | Learning Towards the Largest Margins | One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power. The classical softmax loss does not explicitly encourage discriminative learning of features. A popular direction of research is to incorporat... | ['Xiangyang Ji', 'Xin Gao', 'Junjun Jiang', 'Deming Zhai', 'Xianming Liu', 'Xiong Zhou'] | 2022-06-23 | learning-towards-the-largest-margins | https://openreview.net/forum?id=hqkhcFHOeKD | https://openreview.net/pdf?id=hqkhcFHOeKD | iclr-2022-4 | ['imbalanced-classification'] | ['miscellaneous'] | [ 1.95681572e-01 9.82427225e-02 -4.64406639e-01 -6.28794551e-01
-4.44865108e-01 -1.53215945e-01 2.63643265e-01 3.19886178e-01
-4.06967640e-01 6.70745969e-01 -2.19366029e-02 -5.57712838e-02
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3.57229039e-02 -4.40211557e-02 -3.30600947e-01 4.12448198... | [9.208586692810059, 3.651777982711792] |
63b63b41-a28b-4e87-ad71-565deab098ad | grounding-language-models-to-images-for | 2301.13823 | null | https://arxiv.org/abs/2301.13823v4 | https://arxiv.org/pdf/2301.13823v4.pdf | Grounding Language Models to Images for Multimodal Inputs and Outputs | We propose an efficient method to ground pretrained text-only language models to the visual domain, enabling them to process arbitrarily interleaved image-and-text data, and generate text interleaved with retrieved images. Our method leverages the abilities of language models learnt from large scale text-only pretraini... | ['Daniel Fried', 'Ruslan Salakhutdinov', 'Jing Yu Koh'] | 2023-01-31 | null | null | null | null | ['multimodal-generation'] | ['natural-language-processing'] | [ 3.75752479e-01 3.26516509e-01 5.87884299e-02 -2.12295890e-01
-1.14327824e+00 -9.06956851e-01 1.06081891e+00 -4.91165370e-02
-5.87786615e-01 4.32604998e-01 4.58703190e-01 -5.20113170e-01
4.32762504e-01 -7.11993039e-01 -1.04588854e+00 -3.44659001e-01
8.15425627e-03 5.40131867e-01 1.37073338e-01 -2.57294238... | [10.93132495880127, 1.6032183170318604] |
5732a607-902a-4de2-807d-adb28deb877c | contextual-transformer-for-offline-meta | 2211.08016 | null | https://arxiv.org/abs/2211.08016v1 | https://arxiv.org/pdf/2211.08016v1.pdf | Contextual Transformer for Offline Meta Reinforcement Learning | The pretrain-finetuning paradigm in large-scale sequence models has made significant progress in natural language processing and computer vision tasks. However, such a paradigm is still hindered by several challenges in Reinforcement Learning (RL), including the lack of self-supervised pretraining algorithms based on o... | ['Yaodong Yang', 'Yali Du', 'Jun Wang', 'Haifeng Zhang', 'Xian Hong Wu Fung', 'Zhaowei Zhang', 'Xidong Feng', 'Ye Li', 'Runji Lin'] | 2022-11-15 | null | null | null | null | ['smac-1', 'smac', 'd4rl'] | ['playing-games', 'playing-games', 'robots'] | [ 3.79126519e-01 -2.07836658e-01 -3.70286345e-01 -3.90598625e-01
-7.43719816e-01 -8.28766346e-01 8.99160802e-01 -9.55249369e-03
-9.53990281e-01 8.39003623e-01 3.45643491e-01 -4.75528538e-01
1.83925137e-01 -4.15539742e-01 -1.00649762e+00 -6.87041521e-01
-3.18999588e-02 4.66740102e-01 1.20324932e-01 -4.73635375... | [4.088040351867676, 1.7933942079544067] |
59a3c369-3f53-4d36-9d17-d841dce6622e | risk-sensitive-policy-with-distributional | 2212.14743 | null | https://arxiv.org/abs/2212.14743v1 | https://arxiv.org/pdf/2212.14743v1.pdf | Risk-Sensitive Policy with Distributional Reinforcement Learning | Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk associated with the actions taken, which may be critical in certain app... | ['Damien Ernst', 'Thibaut Théate'] | 2022-12-30 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [ 1.47090435e-01 5.85319459e-01 -2.72728711e-01 -3.40793520e-01
-6.49307191e-01 -3.97427410e-01 6.09335721e-01 5.28468311e-01
-1.02014303e+00 9.39244807e-01 3.29654887e-02 -4.54397231e-01
-7.53828108e-01 -1.19813871e+00 -3.59905481e-01 -9.37085450e-01
-9.88628268e-02 3.42468053e-01 -2.28522271e-01 -1.77660570... | [4.214129447937012, 2.5618948936462402] |
c4404389-1058-4586-9a6e-167f6e500309 | optimizing-nlu-reranking-using-entity | null | null | https://aclanthology.org/2021.naacl-industry.3 | https://aclanthology.org/2021.naacl-industry.3.pdf | Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems | In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved. This may result in intent classification and slot tagging errors. In this work, we propose to leverage Entit... | ['Yang Liu', 'Yue Liu', 'Chengwei Su', 'Han Wang', 'Xin He', 'Shuyan Dong', 'Mohsen Malmir', 'Jiangning Chen', 'Tong Wang'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['entity-resolution'] | ['natural-language-processing'] | [ 1.63393810e-01 4.65601206e-01 -4.19967741e-01 -8.96963060e-01
-9.48960841e-01 -7.80264735e-01 7.00794101e-01 3.88532519e-01
-5.83495796e-01 8.76745880e-01 7.14056492e-01 -1.50106668e-01
1.34041429e-01 -4.37325984e-01 -3.58859301e-01 1.61372572e-01
3.55124742e-01 1.05487287e+00 3.19853842e-01 -3.36710811... | [12.612105369567871, 7.5748796463012695] |
c61f8d44-b695-46d0-bfc4-3596594962bc | transfer-of-fully-convolutional-policy-value | 2102.12375 | null | https://arxiv.org/abs/2102.12375v1 | https://arxiv.org/pdf/2102.12375v1.pdf | Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants | In this paper, we use fully convolutional architectures in AlphaZero-like self-play training setups to facilitate transfer between variants of board games as well as distinct games. We explore how to transfer trained parameters of these architectures based on shared semantics of channels in the state and action represe... | ['Olivier Teytaud', 'Cameron Browne', 'Matthew Stephenson', 'Eric Piette', 'Vegard Mella', 'Dennis J. N. J. Soemers'] | 2021-02-24 | null | null | null | null | ['board-games'] | ['playing-games'] | [-3.05742532e-01 1.95659339e-01 -5.26699005e-03 -1.00422092e-01
-6.18176281e-01 -8.03330243e-01 6.46802723e-01 -3.67468596e-01
-8.17282081e-01 8.71676564e-01 1.81985632e-01 -4.12646651e-01
9.81967598e-02 -1.11720347e+00 -6.50826454e-01 -1.15202680e-01
-3.66070330e-01 6.69407964e-01 7.67543137e-01 -1.11582661... | [3.6667985916137695, 1.4573943614959717] |
7741aedc-5141-4c3c-af28-d8283deda82d | modular-approach-to-machine-reading | 2210.01750 | null | https://arxiv.org/abs/2210.01750v1 | https://arxiv.org/pdf/2210.01750v1.pdf | Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts | In this work we present a Mixture of Task-Aware Experts Network for Machine Reading Comprehension on a relatively small dataset. We particularly focus on the issue of common-sense learning, enforcing the common ground knowledge by specifically training different expert networks to capture different kinds of relationshi... | ['Gabriel Bayomi Tinoco Kalejaiye', 'Anusha Kamath', 'Anirudha Rayasam'] | 2022-10-04 | null | null | null | null | ['machine-reading-comprehension', 'common-sense-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 4.01348561e-01 2.08936855e-01 -1.22789718e-01 -5.56997597e-01
-9.18676972e-01 -6.45281255e-01 5.19571424e-01 3.05052161e-01
-7.12452590e-01 6.43471539e-01 3.87981772e-01 -4.53383178e-01
-4.54582930e-01 -6.41207218e-01 -9.26292896e-01 -1.21582128e-01
4.31442916e-01 7.53241479e-01 5.19248903e-01 -4.95541900... | [10.87026596069336, 8.32504940032959] |
51f04347-ab1b-4447-95a4-4db8ca8666a8 | piecewise-classifier-mappings-learning-fine | 1805.04288 | null | https://arxiv.org/abs/1805.04288v2 | https://arxiv.org/pdf/1805.04288v2.pdf | Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples | Humans are capable of learning a new fine-grained concept with very little supervision, \emph{e.g.}, few exemplary images for a species of bird, yet our best deep learning systems need hundreds or thousands of labeled examples. In this paper, we try to reduce this gap by studying the fine-grained image recognition prob... | ['Jianxin Wu', 'Xiu-Shen Wei', 'Chunhua Shen', 'Peng Wang', 'Lingqiao Liu'] | 2018-05-11 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 3.52153718e-01 9.65888351e-02 -1.07818104e-01 -6.97983205e-01
-6.38971686e-01 -5.01219571e-01 7.89755642e-01 -7.47376159e-02
-3.78663987e-01 6.55091882e-01 -3.25632654e-02 1.96857035e-01
-1.92903653e-01 -1.05595219e+00 -9.49961364e-01 -6.16738021e-01
9.84408110e-02 3.71019781e-01 3.16205770e-01 -1.47308260... | [9.955596923828125, 2.464423418045044] |
e0bd7439-8ae1-418b-b5e8-048befab401f | pyramid-multi-view-stereo-net-with-self | 1912.03001 | null | https://arxiv.org/abs/1912.03001v2 | https://arxiv.org/pdf/1912.03001v2.pdf | Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation | n this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstruction. Different from using mean square variance to generate cost volume in previous deep-learning based MVS methods, our \textbf{VA-MVSNet} in... | ['Yu-Wing Tai', 'Zizhuang Wei', 'Runze Zhang', 'Mingyu Ding', 'Yisong Chen', 'Hongwei Yi', 'Guoping Wang'] | 2019-12-06 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/961_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540732.pdf | eccv-2020-8 | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.86620101e-01 -1.24077134e-01 3.09806079e-01 -3.62242758e-01
-1.05633891e+00 -4.70154107e-01 1.81328565e-01 -6.72335401e-02
-1.18876621e-01 5.72949350e-01 1.20370679e-01 -1.09019808e-01
-2.37059042e-01 -1.04014611e+00 -1.02590406e+00 -5.75385630e-01
2.25133240e-01 5.45368016e-01 6.21989787e-01 -2.54726321... | [8.835527420043945, -2.8564202785491943] |
1e6a6c29-749c-4d7f-b0d0-e43ad04589f5 | unified-question-answering-in-slovene | 2211.09159 | null | https://arxiv.org/abs/2211.09159v1 | https://arxiv.org/pdf/2211.09159v1.pdf | Unified Question Answering in Slovene | Question answering is one of the most challenging tasks in language understanding. Most approaches are developed for English, while less-resourced languages are much less researched. We adapt a successful English question-answering approach, called UnifiedQA, to the less-resourced Slovene language. Our adaptation uses ... | ['Marko Robnik-Šikonja', 'Katja Logar'] | 2022-11-16 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-2.05305498e-02 1.22456208e-01 -1.72567084e-01 -4.01380658e-01
-1.59984028e+00 -9.65815663e-01 3.47080112e-01 1.91451870e-02
-7.90582120e-01 8.69100273e-01 5.12523890e-01 -1.01088059e+00
2.50196811e-02 -8.21859896e-01 -6.68793917e-01 3.00615519e-01
5.64407289e-01 8.74229014e-01 4.39308196e-01 -9.13991630... | [11.373373031616211, 8.316915512084961] |
58560e62-7cab-4dd4-a1d9-4bfbb22d8120 | learning-practically-feasible-policies-for | 2108.13680 | null | https://arxiv.org/abs/2108.13680v3 | https://arxiv.org/pdf/2108.13680v3.pdf | Learning Practically Feasible Policies for Online 3D Bin Packing | We tackle the Online 3D Bin Packing Problem, a challenging yet practically useful variant of the classical Bin Packing Problem. In this problem, the items are delivered to the agent without informing the full sequence information. Agent must directly pack these items into the target bin stably without changing their ar... | ['Kai Xu', 'Hui Huang', 'Xin Xu', 'Chenyang Zhu', 'Hang Zhao'] | 2021-08-31 | null | null | null | null | ['3d-bin-packing'] | ['miscellaneous'] | [-3.17912757e-01 1.81083366e-01 -5.68616211e-01 7.78122768e-02
-4.64693129e-01 -5.53311706e-01 -1.05881214e-01 3.30229908e-01
-5.20934224e-01 9.85534251e-01 -4.37658161e-01 -6.36363566e-01
-4.72354978e-01 -8.62466156e-01 -1.19137537e+00 -9.82200205e-01
-7.58105278e-01 1.20438194e+00 2.78376825e-02 -3.49587858... | [4.939422130584717, 2.681424856185913] |
a43ad7dd-bb70-4f2d-801b-f70951be0166 | accurate-nuclear-segmentation-with-center | 1907.03951 | null | https://arxiv.org/abs/1907.03951v2 | https://arxiv.org/pdf/1907.03951v2.pdf | Accurate Nuclear Segmentation with Center Vector Encoding | Nuclear segmentation is important and frequently demanded for pathology image analysis, yet is also challenging due to nuclear crowdedness and possible occlusion. In this paper, we present a novel bottom-up method for nuclear segmentation. The concepts of Center Mask and Center Vector are introduced to better depict th... | ['Jiahui Li', 'Zhiqiang Hu', 'Shuang Yang'] | 2019-07-09 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 3.63665879e-01 1.54983044e-01 -2.97330737e-01 -1.75394222e-01
-9.39157605e-01 -3.81899118e-01 3.64476621e-01 4.20924395e-01
-3.37677658e-01 7.92617083e-01 1.10925265e-01 2.19378173e-02
5.69902621e-02 -4.05357808e-01 -2.92392910e-01 -1.10900462e+00
6.04214035e-02 3.72366160e-01 3.85649443e-01 6.54590502... | [14.971701622009277, -3.042515277862549] |
dcad0e9a-d38d-492f-9da2-1ed7e46e2099 | learning-an-explicit-hyperparameter | 2107.02378 | null | https://arxiv.org/abs/2107.02378v3 | https://arxiv.org/pdf/2107.02378v3.pdf | Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks | Meta learning has attracted much attention recently in machine learning community. Contrary to conventional machine learning aiming to learn inherent prediction rules to predict labels for new query data, meta learning aims to learn the learning methodology for machine learning from observed tasks, so as to generalize ... | ['Zongben Xu', 'Deyu Meng', 'Jun Shu'] | 2021-07-06 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 4.62967128e-01 3.45725447e-01 -5.72713971e-01 -4.23696756e-01
-8.69763613e-01 -1.75906643e-01 5.69701552e-01 3.50098759e-01
-3.70444268e-01 8.78333211e-01 -3.62374872e-01 1.71564579e-01
-7.48917162e-01 -9.74423468e-01 -6.80254400e-01 -7.07000256e-01
-4.97878306e-02 4.45669770e-01 1.21146932e-01 -3.93026918... | [9.99670124053955, 3.1926109790802] |
9ec10025-508e-4195-a3c4-747e6ed97bcf | using-aspect-extraction-approaches-to | 1804.08666 | null | https://arxiv.org/abs/1804.08666v2 | https://arxiv.org/pdf/1804.08666v2.pdf | Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles | Reviews of products or services on Internet marketplace websites contain a rich amount of information. Users often wish to survey reviews or review snippets from the perspective of a certain aspect, which has resulted in a large body of work on aspect identification and extraction from such corpora. In this work, we ev... | ['Avneesh Saluja', 'Skyler Wharton', 'Christopher Mitcheltree'] | 2018-04-23 | using-aspect-extraction-approaches-to-1 | https://aclanthology.org/N18-3009 | https://aclanthology.org/N18-3009.pdf | naacl-2018-6 | ['aspect-extraction'] | ['natural-language-processing'] | [ 2.47908875e-01 3.43125671e-01 -6.58345878e-01 -8.34276617e-01
-9.12252128e-01 -8.08898926e-01 7.73577750e-01 3.41370076e-01
-4.68464583e-01 3.59454453e-01 5.93687654e-01 -5.29290020e-01
1.46561205e-01 -6.42621934e-01 -2.91131169e-01 -3.01360726e-01
3.61116856e-01 5.97704828e-01 -3.40724468e-01 -4.41777319... | [11.395294189453125, 6.710931777954102] |
abdac949-e1f8-4156-bb60-b9de92bbaa0d | kafsp-knowledge-aware-fuzzy-semantic-parsing | null | null | https://aclanthology.org/2022.acl-long.35 | https://aclanthology.org/2022.acl-long.35.pdf | KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base | In this paper, we study two issues of semantic parsing approaches to conversational question answering over a large-scale knowledge base: (1) The actions defined in grammar are not sufficient to handle uncertain reasoning common in real-world scenarios. (2) Knowledge base information is not well exploited and incorpora... | ['Deyi Xiong', 'Junzhuo Li'] | null | null | null | null | acl-2022-5 | ['entity-disambiguation'] | ['natural-language-processing'] | [-7.97015801e-02 5.81048191e-01 -9.27769020e-02 -7.43613303e-01
-8.77413690e-01 -5.46530962e-01 2.85456985e-01 3.23296040e-01
-2.37756744e-01 7.62167752e-01 1.76723942e-01 -2.76895165e-01
-1.87914386e-01 -1.18488359e+00 -7.37778783e-01 -1.96196814e-03
3.71610820e-01 7.34849632e-01 7.87530482e-01 -7.79461265... | [10.655118942260742, 7.980432987213135] |
c1f02615-567d-432b-8e8a-996978376571 | energy-forecasting-in-smart-grid-systems-a | 2011.12598 | null | https://arxiv.org/abs/2011.12598v3 | https://arxiv.org/pdf/2011.12598v3.pdf | Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques | Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering ... | ['ZhaoYang Dong', 'Md. Enamul Haque', 'Md. Apel Mahmud', 'Shama Naz Islam', 'Devinder Kaur'] | 2020-11-25 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-5.50781310e-01 -5.83379984e-01 -5.80580719e-02 -4.19794083e-01
-7.88219452e-01 -3.75334680e-01 8.32565367e-01 2.75757074e-01
1.27894431e-01 1.07897997e+00 1.66730881e-01 -6.66328430e-01
-5.57959437e-01 -1.06070149e+00 -2.18755335e-01 -1.43867540e+00
-5.48940957e-01 6.30119920e-01 -4.25962687e-01 -2.08191201... | [6.157478332519531, 2.8176751136779785] |
25f37fc8-1708-413c-93d1-b03b0a86a2a7 | learning-to-answer-visual-questions-from-web | 2205.05019 | null | https://arxiv.org/abs/2205.05019v2 | https://arxiv.org/pdf/2205.05019v2.pdf | Learning to Answer Visual Questions from Web Videos | Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and prevents scalability. In this work, we propose to avoid manual annotation and generate a large-scale training dataset for video question answerin... | ['Cordelia Schmid', 'Ivan Laptev', 'Josef Sivic', 'Antoine Miech', 'Antoine Yang'] | 2022-05-10 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 1.86961234e-01 -1.27901718e-01 -2.20852485e-03 -4.13931280e-01
-1.43380368e+00 -1.00297725e+00 5.58937609e-01 -4.07901615e-01
-3.36294204e-01 4.75109547e-01 4.74443346e-01 -2.41435349e-01
2.94176459e-01 -4.79294419e-01 -8.91349733e-01 -3.69110376e-01
3.10747951e-01 3.46277088e-01 5.06891966e-01 -1.85655862... | [10.4135160446167, 1.0277085304260254] |
c9a3aac8-2584-4d18-a351-472f7869d498 | an-ensemble-deep-learning-approach-for-covid | 2305.10115 | null | https://arxiv.org/abs/2305.10115v1 | https://arxiv.org/pdf/2305.10115v1.pdf | An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans | Chest X-rays have been widely used for COVID-19 screening; however, 3D computed tomography (CT) is a more effective modality. We present our findings on COVID-19 severity prediction from chest CT scans using the STOIC dataset. We developed an ensemble deep learning based model that incorporates multiple neural networks... | ['Kevin McGuinness', "Noel O'Connor", 'Suzanne Little', 'Mayug Maniparambil', 'Sidra Aleem'] | 2023-05-17 | null | null | null | null | ['severity-prediction', 'computed-tomography-ct'] | ['computer-vision', 'methodology'] | [ 7.88670629e-02 -1.42987326e-01 -4.25335646e-01 -6.44414783e-01
-1.13223457e+00 -5.14513910e-01 2.39661410e-01 4.30321127e-01
-5.17387331e-01 6.66136980e-01 2.17512339e-01 -8.99304032e-01
-3.58400971e-01 -7.60943234e-01 -6.65003121e-01 -1.57594815e-01
-3.30892615e-02 1.08028853e+00 7.27117732e-02 1.50459379... | [15.328960418701172, -1.8935619592666626] |
5b9c0fde-3133-45da-9a5c-77413c8f116d | leveraging-auxiliary-tasks-with-affinity | 2107.11787 | null | https://arxiv.org/abs/2107.11787v2 | https://arxiv.org/pdf/2107.11787v2.pdf | Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation | Semantic segmentation is a challenging task in the absence of densely labelled data. Only relying on class activation maps (CAM) with image-level labels provides deficient segmentation supervision. Prior works thus consider pre-trained models to produce coarse saliency maps to guide the generation of pseudo segmentatio... | ['Dan Xu', 'Ferdous Sohel', 'Farid Boussaid', 'Mohammed Bennamoun', 'Wanli Ouyang', 'Lian Xu'] | 2021-07-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xu_Leveraging_Auxiliary_Tasks_With_Affinity_Learning_for_Weakly_Supervised_Semantic_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_Leveraging_Auxiliary_Tasks_With_Affinity_Learning_for_Weakly_Supervised_Semantic_ICCV_2021_paper.pdf | iccv-2021-1 | ['multi-label-image-classification', 'auxiliary-learning'] | ['computer-vision', 'methodology'] | [ 8.65529776e-01 4.06742215e-01 -5.30205429e-01 -6.54024243e-01
-1.06088924e+00 -4.30209070e-01 4.77731705e-01 2.75082052e-01
-4.79457140e-01 7.98388004e-01 -1.54951839e-02 1.40186995e-01
1.78177565e-01 -4.26793754e-01 -1.02046335e+00 -7.43139207e-01
4.42632943e-01 3.95465881e-01 7.77203441e-01 -8.87870342... | [9.671947479248047, 0.6888312101364136] |
97f5c812-d545-4cb4-ad72-6150800a76e0 | defending-substitution-based-profile | 2207.11237 | null | https://arxiv.org/abs/2207.11237v1 | https://arxiv.org/pdf/2207.11237v1.pdf | Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders | While sequential recommender systems achieve significant improvements on capturing user dynamics, we argue that sequential recommenders are vulnerable against substitution-based profile pollution attacks. To demonstrate our hypothesis, we propose a substitution-based adversarial attack algorithm, which modifies the inp... | ['Dong Wang', 'Lanyu Shang', 'Ziyi Kou', 'Huimin Zeng', 'Zhenrui Yue'] | 2022-07-19 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.70885086e-01 -1.33455291e-01 -1.99177265e-01 3.26929353e-02
-8.04830372e-01 -1.29482007e+00 6.13872528e-01 -4.02163416e-02
-2.70888031e-01 3.71143192e-01 4.85325843e-01 -3.50504160e-01
-2.93040033e-02 -9.14065361e-01 -9.35624361e-01 -7.21589565e-01
-3.38914096e-01 4.93740827e-01 3.26100998e-02 -5.25356233... | [5.812811374664307, 7.7030229568481445] |
d7eb4cdb-837d-4a28-953b-f92607ab2056 | pan-tilt-camera-and-pir-sensor-fusion-based | 1510.07390 | null | http://arxiv.org/abs/1510.07390v1 | http://arxiv.org/pdf/1510.07390v1.pdf | Pan-Tilt Camera and PIR Sensor Fusion Based Moving Object Detection for Mobile Security Robots | One of fundamental issues for security robots is to detect and track people
in the surroundings. The main problems of this task are real-time constraints,
a changing background, varying illumination conditions and a non-rigid shape of
the person to be tracked. In this paper, we propose a solution for tracking
with a pa... | ['MyongSong Choe', 'YongChol Sin', 'GyongIl Ryang'] | 2015-10-26 | null | null | null | null | ['moving-object-detection', 'mobile-security'] | ['computer-vision', 'miscellaneous'] | [ 1.41852200e-01 -6.75301373e-01 6.15817606e-01 -5.66294380e-02
3.16994905e-01 -5.55816293e-01 3.07187587e-01 -3.07922870e-01
-1.02118993e+00 7.02707648e-01 -4.41617638e-01 1.04588598e-01
4.76052351e-02 -6.55060828e-01 -2.09660500e-01 -7.69708633e-01
1.62182778e-01 3.31805378e-01 8.82889211e-01 -2.05165058... | [6.907871723175049, -1.8249270915985107] |
36758592-a23d-45a1-800a-bc598797d664 | cta-rnn-channel-and-temporal-wise-attention | 2203.17023 | null | https://arxiv.org/abs/2203.17023v1 | https://arxiv.org/pdf/2203.17023v1.pdf | CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition | Previous research has looked into ways to improve speech emotion recognition (SER) by utilizing both acoustic and linguistic cues of speech. However, the potential association between state-of-the-art ASR models and the SER task has yet to be investigated. In this paper, we propose a novel channel and temporal-wise att... | ['Pengyuan Zhang', 'Chengxin Chen'] | 2022-03-31 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [-1.14059128e-01 -4.17693667e-02 1.71333432e-01 -8.38429332e-01
-9.93347883e-01 -3.54618877e-01 2.04516441e-01 -9.56417620e-02
-5.23595035e-01 1.76236421e-01 5.83418012e-01 -2.29229540e-01
6.04828179e-01 -1.38852954e-01 -5.99525094e-01 -4.78490114e-01
-8.79150108e-02 -2.39491224e-01 -2.36623332e-01 -4.11408156... | [13.779556274414062, 5.900847434997559] |
ba4d29df-bdec-42c5-9d13-978325faffb9 | a-survey-on-large-population-systems-and | 2209.03859 | null | https://arxiv.org/abs/2209.03859v1 | https://arxiv.org/pdf/2209.03859v1.pdf | A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning | The analysis and control of large-population systems is of great interest to diverse areas of research and engineering, ranging from epidemiology over robotic swarms to economics and finance. An increasingly popular and effective approach to realizing sequential decision-making in multi-agent systems is through multi-a... | ['Heinz Koeppl', 'Mengguang Li', 'Yannick Eich', 'Ahmed Elshamanhory', 'Gizem Ekinci', 'Anam Tahir', 'Kai Cui'] | 2022-09-08 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 2.88796723e-02 1.26031311e-02 -1.80914000e-01 5.21727443e-01
-1.56073764e-01 -4.15810078e-01 4.51780587e-01 4.49557155e-01
-6.09773874e-01 1.36119580e+00 -6.23468816e-01 -3.09730828e-01
-7.39804268e-01 -9.04395223e-01 -2.77565479e-01 -1.14936888e+00
-7.60944486e-01 6.77325606e-01 6.92927688e-02 -7.14594364... | [3.920341968536377, 2.137979030609131] |
574db743-191e-42bd-86cd-ddc3cf6791f2 | exploring-large-language-models-for-classical | 2305.13698 | null | https://arxiv.org/abs/2305.13698v1 | https://arxiv.org/pdf/2305.13698v1.pdf | Exploring Large Language Models for Classical Philology | Recent advances in NLP have led to the creation of powerful language models for many languages including Ancient Greek and Latin. While prior work on Classical languages unanimously uses BERT, in this work we create four language models for Ancient Greek that vary along two dimensions to study their versatility for tas... | ['Anette Frank', 'Frederick Riemenschneider'] | 2023-05-23 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [-1.73643306e-01 3.13237280e-01 -1.84126824e-01 -3.82356286e-01
-1.05503380e+00 -7.66211271e-01 9.58994985e-01 -2.67660916e-02
-8.20149779e-01 6.51460350e-01 8.01083148e-01 -7.71203160e-01
4.61086221e-02 -7.83749521e-01 -4.43583667e-01 -2.68272489e-01
5.72841577e-02 8.47427845e-01 6.82831556e-02 -5.18163323... | [10.856406211853027, 9.913775444030762] |
073fc28c-3474-4b83-b6b9-0355cc360c9d | pvnet-a-joint-convolutional-network-of-point | 1808.07659 | null | http://arxiv.org/abs/1808.07659v1 | http://arxiv.org/pdf/1808.07659v1.pdf | PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition | 3D object recognition has attracted wide research attention in the field of
multimedia and computer vision. With the recent proliferation of deep learning,
various deep models with different representations have achieved the
state-of-the-art performance. Among them, point cloud and multi-view based 3D
shape representat... | ['Yue Gao', 'Yifan Feng', 'Rongrong Ji', 'Haoxuan You'] | 2018-08-23 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-recognition', '3d-object-recognition', '3d-shape-representation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-3.67626965e-01 -4.64122832e-01 2.61418819e-02 -4.80054528e-01
-6.66172504e-01 -4.41554040e-01 8.02077830e-01 -5.01628146e-02
6.57572150e-02 -1.62275061e-01 1.55621529e-01 9.98636708e-02
-2.10512325e-01 -7.70010769e-01 -5.74928761e-01 -7.98708200e-01
3.40428382e-01 5.53597510e-01 1.34176165e-01 -1.08179957... | [8.149139404296875, -3.850801706314087] |
999c5c79-795d-4af7-974d-7902dbabd790 | precise-aerial-image-matching-based-on-deep | 2107.08768 | null | https://arxiv.org/abs/2107.08768v1 | https://arxiv.org/pdf/2107.08768v1.pdf | Precise Aerial Image Matching based on Deep Homography Estimation | Aerial image registration or matching is a geometric process of aligning two aerial images captured in different environments. Estimating the precise transformation parameters is hindered by various environments such as time, weather, and viewpoints. The characteristics of the aerial images are mainly composed of a str... | ['Seong-Whan Lee', 'Yong-Ju Lee', 'Myeong-Seok Oh'] | 2021-07-19 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 2.89961070e-01 -3.70366991e-01 8.22170451e-02 -2.97587365e-01
-2.20856413e-01 -5.88550925e-01 4.09990907e-01 -4.48961884e-01
-2.92283654e-01 3.03913146e-01 -2.60390848e-01 -1.96927600e-02
-3.03794652e-01 -7.60286689e-01 -8.30353677e-01 -6.64003968e-01
2.45123193e-01 3.58354926e-01 8.40997621e-02 -4.24387187... | [8.642317771911621, -2.1907479763031006] |
7c32bc01-6f33-468d-a69a-ff2b6a4a4e1d | automated-classification-of-sleep-stages-and | 1809.08443 | null | http://arxiv.org/abs/1809.08443v1 | http://arxiv.org/pdf/1809.08443v1.pdf | Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning | Sleep scoring is a necessary and time-consuming task in sleep studies. In
animal models (such as mice) or in humans, automating this tedious process
promises to facilitate long-term studies and to promote sleep biology as a
data-driven field. We introduce a deep neural network model that is able to
predict different st... | ['Justus T. C. Schwabedal', 'Moritz D. Brandt', 'Stephan Bialonski', 'Daniel Sippel'] | 2018-09-22 | null | null | null | null | ['sleep-stage-detection'] | ['medical'] | [ 1.50593266e-01 -4.10878122e-01 1.47270158e-01 -5.49191117e-01
-2.73301482e-01 -4.35973078e-01 2.60915965e-01 1.18186735e-01
-8.63890588e-01 1.00681329e+00 -3.93724404e-02 -4.04102623e-01
-1.22004583e-01 -3.17879915e-01 -3.25552762e-01 -6.84066236e-01
-2.89137185e-01 3.67094636e-01 2.07767621e-01 -1.56927973... | [13.478666305541992, 3.524874687194824] |
9049e17c-b6c5-4066-908d-a34efc9f4338 | calibrated-nonparametric-scan-statistics-for | 2206.12786 | null | https://arxiv.org/abs/2206.12786v1 | https://arxiv.org/pdf/2206.12786v1.pdf | Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs | We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs. Scan statistics identify connected subgraphs that are interesting or unexpected through maximization of a likelihood ratio statistic; in particular, nonpara... | ['Feng Chen', 'Daniel B. Neill', 'Chunpai Wang'] | 2022-06-26 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 5.15046120e-01 1.98241338e-01 -1.89884901e-01 -8.88123512e-02
-4.22061831e-01 -6.39383733e-01 3.45470518e-01 2.85561740e-01
1.16426684e-01 8.56836379e-01 -2.52557278e-01 -5.88570476e-01
-5.56702733e-01 -1.07043672e+00 -8.37985754e-01 -8.03688705e-01
-7.17998683e-01 5.44255316e-01 7.59004176e-01 2.40725756... | [7.015446186065674, 5.208978652954102] |
f88eae4e-38ba-4c6a-8a6e-25714527a4a2 | boosting-deep-ctr-prediction-with-a-plug-and | null | null | https://aclanthology.org/2022.coling-1.249 | https://aclanthology.org/2022.coling-1.249.pdf | Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation | Understanding news content is critical to improving the quality of news recommendation. To achieve this goal, recent studies have attempted to apply pre-trained language models (PLMs) such as BERT for semantic-enhanced news recommendation. Despite their great success in offline evaluation, it is still a challenge to ap... | ['Xiaoming Wu', 'Quanyu Dai', 'Jieming Zhu', 'Qijiong Liu'] | null | null | null | null | coling-2022-10 | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 3.04647200e-02 -1.16441071e-01 -3.71195853e-01 -5.71150839e-01
-1.06771827e+00 -3.73960227e-01 5.91752946e-01 2.82746460e-03
-5.06789744e-01 6.27956271e-01 3.76060963e-01 -5.87089658e-01
-8.68652016e-02 -6.47817135e-01 -9.76294577e-01 -5.30898422e-02
1.39846904e-02 3.63333732e-01 1.65660173e-01 -3.38882744... | [10.258197784423828, 5.769360065460205] |
6878764f-995c-4ddd-a396-aff43c0cbd61 | wide-activation-for-efficient-and-accurate | 1808.08718 | null | http://arxiv.org/abs/1808.08718v2 | http://arxiv.org/pdf/1808.08718v2.pdf | Wide Activation for Efficient and Accurate Image Super-Resolution | Keras-based implementation of WDSR, EDSR and SRGAN for single image super-resolution | ['Zhaowen Wang', 'Jianchao Yang', 'Xinchao Wang', 'Ning Xu', 'Yuchen Fan', 'Thomas Huang', 'Jiahui Yu'] | 2018-08-27 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 9.23140109e-01 4.60193425e-01 -1.05345547e-02 1.44847766e-01
-5.14781237e-01 -5.50331652e-01 5.84267557e-01 -1.37631786e+00
-3.43372732e-01 1.07992470e+00 3.71504486e-01 -4.64029759e-01
1.52319968e-01 -1.02053404e+00 -2.44257256e-01 -8.00235689e-01
-1.86576188e-01 2.47913137e-01 1.08373642e+00 -8.26467097... | [4.337944030761719, 8.03076457977295] |
e255df31-f060-427d-b3fb-a1bbd5f3c117 | pfedsim-similarity-aware-model-aggregation | 2305.15706 | null | https://arxiv.org/abs/2305.15706v1 | https://arxiv.org/pdf/2305.15706v1.pdf | pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning | The federated learning (FL) paradigm emerges to preserve data privacy during model training by only exposing clients' model parameters rather than original data. One of the biggest challenges in FL lies in the non-IID (not identical and independently distributed) data (a.k.a., data heterogeneity) distributed on clients... | ['Shui Yu', 'Jessie Hui Wang', 'Gang Liu', 'Yipeng Zhou', 'Jiahao Tan'] | 2023-05-25 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [ 1.34755746e-02 -1.67804375e-01 -3.99304360e-01 -5.80796778e-01
-8.47530842e-01 -7.38659024e-01 5.65279603e-01 -2.50464268e-02
-3.30818236e-01 5.47911823e-01 1.43348649e-01 -1.82655215e-01
-2.13644624e-01 -6.61608338e-01 -8.65684032e-01 -1.05180502e+00
1.73894361e-01 3.32089365e-01 1.83705568e-01 2.61780143... | [5.85145902633667, 6.441000938415527] |
204b86e1-ddb6-4fdd-9b30-b76c3676db11 | holistic-image-manipulation-detection-using | 2104.05693 | null | https://arxiv.org/abs/2104.05693v1 | https://arxiv.org/pdf/2104.05693v1.pdf | Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices | Digital image forensics aims to detect images that have been digitally manipulated. Realistic image forgeries involve a combination of splicing, resampling, region removal, smoothing and other manipulation methods. While most detection methods in literature focus on detecting a particular type of manipulation, it is ch... | ['B. S. Manjunath', 'Shivkumar Chandrasekaran', 'Tajuddin Manhar Mohammed', 'Michael Goebel', 'Lakshmanan Nataraj'] | 2021-04-12 | null | null | null | null | ['image-manipulation-detection', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 5.03000379e-01 -4.33824599e-01 1.49695590e-01 -3.31231020e-02
-1.06903636e+00 -6.56803668e-01 5.96736550e-01 3.09469908e-01
-5.44134617e-01 2.33985677e-01 -1.14451669e-01 -5.59462845e-01
4.27757263e-01 -7.39670277e-01 -9.20636594e-01 -4.61050421e-01
-2.15750575e-01 -1.47390276e-01 2.74133563e-01 9.06012058... | [12.402562141418457, 1.023018479347229] |
ba07f3f2-15b6-452c-a75c-6a9ae519e2d2 | monitoring-term-drift-based-on-semantic | 1502.01753 | null | http://arxiv.org/abs/1502.01753v1 | http://arxiv.org/pdf/1502.01753v1.pdf | Monitoring Term Drift Based on Semantic Consistency in an Evolving Vector Field | Based on the Aristotelian concept of potentiality vs. actuality allowing for
the study of energy and dynamics in language, we propose a field approach to
lexical analysis. Falling back on the distributional hypothesis to
statistically model word meaning, we used evolving fields as a metaphor to
express time-dependent c... | ['Ioannis Kompatsiaris', 'Sándor Darányi', 'Efstratios Kontopoulos', 'Theodoros Moysiadis', 'Peter Wittek'] | 2015-02-05 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [-2.51153708e-01 -2.81995654e-01 -1.62804633e-01 -2.86196202e-01
2.62652218e-01 -8.43809724e-01 9.70760822e-01 6.81850076e-01
-7.76953101e-01 1.94373325e-01 4.78039771e-01 -2.32221559e-01
-4.86367524e-01 -8.29479337e-01 -2.24363506e-01 -5.64258933e-01
-3.59525353e-01 2.49029696e-01 2.21025541e-01 -4.10631657... | [10.204349517822266, 8.95897388458252] |
33ec88aa-565f-4ffb-82ab-c05c4ba1035b | optimized-participation-of-multiple-fusion | 1805.12270 | null | http://arxiv.org/abs/1805.12270v1 | http://arxiv.org/pdf/1805.12270v1.pdf | Optimized Participation of Multiple Fusion Functions in Consensus Creation: An Evolutionary Approach | Recent studies show that ensemble methods enhance the stability and
robustness of unsupervised learning. These approaches are successfully utilized
to construct multiple clustering and combine them into a one representative
consensus clustering of an improved quality. The quality of the consensus
clustering is directly... | ['Elaheh Rashedi', 'Abdolreza Mirzaei'] | 2018-05-31 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [-2.82249957e-01 -5.95336258e-01 2.93803602e-01 -2.55665869e-01
-5.76695085e-01 -5.06681740e-01 3.71766716e-01 2.57187903e-01
-2.68738002e-01 8.48094225e-01 -9.50237215e-02 4.33552891e-01
-7.25502729e-01 -6.92010820e-01 -1.31681710e-01 -1.57799947e+00
2.03100473e-01 2.78324008e-01 -7.52047598e-02 5.09680957... | [7.638338088989258, 4.5386810302734375] |
3af1d008-218a-4f3c-bcbe-4dbdde266341 | multi-view-multi-person-3d-pose-estimation | 2104.02273 | null | https://arxiv.org/abs/2104.02273v1 | https://arxiv.org/pdf/2104.02273v1.pdf | Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo | Existing approaches for multi-view multi-person 3D pose estimation explicitly establish cross-view correspondences to group 2D pose detections from multiple camera views and solve for the 3D pose estimation for each person. Establishing cross-view correspondences is challenging in multi-person scenes, and incorrect cor... | ['Gim Hee Lee', 'Jiahao Lin'] | 2021-04-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lin_Multi-View_Multi-Person_3D_Pose_Estimation_With_Plane_Sweep_Stereo_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lin_Multi-View_Multi-Person_3D_Pose_Estimation_With_Plane_Sweep_Stereo_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.53833956e-01 -2.18846172e-01 1.48707807e-01 -4.36101168e-01
-1.24834716e+00 -6.37354016e-01 5.80217183e-01 -7.57159069e-02
-4.18646783e-01 3.37811649e-01 4.73055661e-01 5.10970354e-01
2.04806149e-01 -5.57166338e-01 -6.72429919e-01 -2.86236584e-01
4.29211020e-01 8.43950033e-01 1.64162040e-01 1.05772547... | [7.043490409851074, -0.9693453311920166] |
aaf15698-45fb-49a3-a46d-e64c49b14b02 | codet5-open-code-large-language-models-for | 2305.07922 | null | https://arxiv.org/abs/2305.07922v2 | https://arxiv.org/pdf/2305.07922v2.pdf | CodeT5+: Open Code Large Language Models for Code Understanding and Generation | Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations in terms of architecture and pretraining tasks. First, they often adopt a specific architecture (encoder-only or decoder-only) or rely on a unified enc... | ['Steven C. H. Hoi', 'Junnan Li', 'Nghi D. Q. Bui', 'Akhilesh Deepak Gotmare', 'Hung Le', 'Yue Wang'] | 2023-05-13 | null | null | null | null | ['code-search', 'code-search', 'arithmetic-reasoning'] | ['computer-code', 'computer-vision', 'reasoning'] | [ 1.63689807e-01 -2.33956844e-01 -4.23692137e-01 -3.35280985e-01
-1.05645132e+00 -5.81884027e-01 4.74189311e-01 1.15425447e-02
-1.64842337e-01 2.05317020e-01 1.62387013e-01 -8.73693764e-01
2.28990823e-01 -5.72050750e-01 -9.65653300e-01 -2.83021957e-01
5.64244092e-02 3.38677853e-01 3.34065408e-02 -4.69466180... | [7.700126647949219, 7.90936279296875] |
718aa02a-6f7f-41af-84b5-e9000ae0650d | a-first-look-at-llm-powered-generative-news | 2305.06566 | null | https://arxiv.org/abs/2305.06566v2 | https://arxiv.org/pdf/2305.06566v2.pdf | A First Look at LLM-Powered Generative News Recommendation | Personalized news recommendation systems have become essential tools for users to navigate the vast amount of online news content, yet existing news recommenders face significant challenges such as the cold-start problem, user profile modeling, and news content understanding. Previous works have typically followed an i... | ['Xiao-Ming Wu', 'Tetsuya Sakai', 'Nuo Chen', 'Qijiong Liu'] | 2023-05-11 | null | null | null | null | ['news-generation'] | ['natural-language-processing'] | [-2.31850445e-02 6.27869442e-02 -5.40760815e-01 -5.69534481e-01
-8.82961631e-01 -4.99416441e-01 7.20786333e-01 -1.22586772e-01
-5.22946753e-02 6.15615487e-01 1.33151543e+00 -1.57008767e-01
-1.54905140e-01 -6.07354224e-01 -4.63351607e-01 -8.96247178e-02
2.69340098e-01 5.95590770e-01 1.39658093e-01 -6.91609502... | [10.328009605407715, 5.828691005706787] |
2b9da343-3b4c-4c38-ac09-a30ba9ebeb6a | weighted-low-rank-matrix-approximation-and | 2109.11057 | null | https://arxiv.org/abs/2109.11057v1 | https://arxiv.org/pdf/2109.11057v1.pdf | Weighted Low Rank Matrix Approximation and Acceleration | Low-rank matrix approximation is one of the central concepts in machine learning, with applications in dimension reduction, de-noising, multivariate statistical methodology, and many more. A recent extension to LRMA is called low-rank matrix completion (LRMC). It solves the LRMA problem when some observations are missi... | ['Trevor Hastie', 'Elena Tuzhilina'] | 2021-09-22 | null | null | null | null | ['low-rank-matrix-completion'] | ['methodology'] | [ 3.26756567e-01 -1.98519126e-01 -1.36653841e-01 -1.62791505e-01
-6.21551812e-01 -2.41907790e-01 4.19909716e-01 -6.95269033e-02
-4.67596650e-01 5.98382711e-01 4.12244231e-01 -4.65707958e-01
-6.57863855e-01 -4.57322955e-01 -5.56533396e-01 -9.28943276e-01
-3.63152504e-01 4.23454434e-01 -2.66397804e-01 -3.67602825... | [7.1392927169799805, 4.572071075439453] |
8a624536-f40f-40e0-ba91-cf2a867a098a | a-simple-and-effective-model-for-multi-hop | null | null | https://openreview.net/forum?id=IV5YUaQ4pzG | https://openreview.net/pdf?id=IV5YUaQ4pzG | A Simple and Effective Model for Multi-Hop Question Generation | Previous research on automated question generation has almost exclusively focused on generating factoid questions whose answers can be extracted from a single document. However, there is an increasing interest in developing systems that are capable of more complex multi-hop question generation (QG), where answering the... | ['Anonymous'] | 2021-09-17 | null | null | null | acl-arr-september-2021-9 | ['sentence-classification'] | ['natural-language-processing'] | [ 3.75479639e-01 8.72913420e-01 2.09274963e-01 -2.36189827e-01
-1.04109251e+00 -6.60390615e-01 9.95710254e-01 6.53204739e-01
-2.04288885e-01 7.89323509e-01 4.46245164e-01 -7.31156468e-01
-3.03248137e-01 -1.07473123e+00 -4.44087535e-01 8.86198953e-02
1.77565277e-01 7.16041386e-01 5.95538795e-01 -7.77335703... | [11.439347267150879, 8.19583797454834] |
43a9e5b9-b55c-4add-9348-20eb878e0154 | token-transformer-can-class-token-help-window | 2211.06083 | null | https://arxiv.org/abs/2211.06083v2 | https://arxiv.org/pdf/2211.06083v2.pdf | Token Transformer: Can class token help window-based transformer build better long-range interactions? | Compared with the vanilla transformer, the window-based transformer offers a better trade-off between accuracy and efficiency. Although the window-based transformer has made great progress, its long-range modeling capabilities are limited due to the size of the local window and the window connection scheme. To address ... | ['Xuesong Yin', 'Yuanqi Chang', 'Jiawei Mao'] | 2022-11-11 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [ 3.62746492e-02 -9.33907107e-02 -3.02904725e-01 -4.07638639e-01
-5.20392835e-01 -6.74038678e-02 4.07047182e-01 -1.79819744e-02
-2.45925769e-01 4.00967509e-01 1.34510741e-01 -4.67764348e-01
2.43809864e-01 -8.55872035e-01 -5.74410796e-01 -7.54407763e-01
-1.53984046e-02 -3.84013236e-01 8.84936094e-01 1.94090661... | [9.707976341247559, 1.0887463092803955] |
9c0071bd-d26d-4f86-9807-44b6871b1fea | riga-rotation-invariant-and-globally-aware | 2209.13252 | null | https://arxiv.org/abs/2209.13252v1 | https://arxiv.org/pdf/2209.13252v1.pdf | RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration | Successful point cloud registration relies on accurate correspondences established upon powerful descriptors. However, existing neural descriptors either leverage a rotation-variant backbone whose performance declines under large rotations, or encode local geometry that is less distinctive. To address this issue, we in... | ['Slobodan Ilic', 'Benjamin Busam', 'Kai Wang', 'Ivan Shugurov', 'Mahdi Saleh', 'Zheng Qin', 'Ji Hou', 'Hao Yu'] | 2022-09-27 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-1.92434669e-01 -3.10991734e-01 -3.85778457e-01 -4.60692644e-01
-9.89070237e-01 -7.36468732e-01 7.90878117e-01 4.47463691e-01
-1.08313657e-01 1.83209717e-01 1.13703281e-01 4.01141286e-01
-2.17929602e-01 -8.81090164e-01 -8.30549121e-01 -5.91911554e-01
-2.47289483e-02 4.39435214e-01 3.17846745e-01 -3.56453568... | [7.774737358093262, -2.9232728481292725] |
ae7c912f-cf4a-4a33-b793-141a38eba133 | towards-minimizing-efforts-for-morphing | 2305.18216 | null | https://arxiv.org/abs/2305.18216v1 | https://arxiv.org/pdf/2305.18216v1.pdf | Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection | Face Morphing Attacks pose a threat to the security of identity documents, especially with respect to a subsequent access control process, because it enables both individuals involved to exploit the same document. In this study, face embeddings serve two purposes: pre-selecting images for large-scale Morphing Attack ge... | ['Christoph Busch', 'Juan Tapia', 'Kiran Raja', 'Roman Kessler'] | 2023-05-29 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 1.23348914e-01 -1.98476270e-01 2.28053227e-01 -2.90629506e-01
-5.09383857e-01 -9.61640060e-01 7.89697230e-01 9.44703817e-02
-3.56829375e-01 1.25078544e-01 -1.69521198e-01 -4.22620624e-01
-1.24057829e-01 -9.40994203e-01 -4.12239194e-01 -4.05573517e-01
-3.14777583e-01 2.27108523e-01 -2.90931165e-01 -3.74728918... | [13.0278959274292, 1.0629799365997314] |
68d81bb5-6f21-4c49-b86b-08a99adee71b | hausanlp-at-semeval-2023-task-12-leveraging | 2304.13634 | null | https://arxiv.org/abs/2304.13634v1 | https://arxiv.org/pdf/2304.13634v1.pdf | HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis | We present the findings of SemEval-2023 Task 12, a shared task on sentiment analysis for low-resource African languages using Twitter dataset. The task featured three subtasks; subtask A is monolingual sentiment classification with 12 tracks which are all monolingual languages, subtask B is multilingual sentiment class... | ['Abdulmalik Yusuf Jamoh', 'Abdulkadir Abdullahi', 'Mahmoud Said Ahmad', 'Sanah Abdullahi Muaz', 'Murja Sani Gadanya', 'Saminu Mohammad Aliyu', 'Shamsuddeen Umaru Adamu', 'Musa Bello', 'Nur Bala Rabiu', 'Aliyu Yusuf', 'Aliyu Rabiu Shuaibu', 'Amina Abubakar Imam', 'Ahmad Mustapha Wali', 'Falalu Ibrahim Lawan', 'Saheed A... | 2023-04-26 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-5.20056605e-01 -4.57262039e-01 -2.19765425e-01 -4.94772553e-01
-9.77445483e-01 -8.77797782e-01 9.16929126e-01 4.91303176e-01
-5.85164428e-01 8.18644047e-01 5.68098605e-01 -5.76578438e-01
4.55882758e-01 -4.29869592e-01 -3.45652997e-01 -3.44062358e-01
5.70924534e-03 4.91675258e-01 -1.76137999e-01 -1.28594518... | [11.176915168762207, 6.968197345733643] |
b52e9938-a321-4824-ad5f-dd4ba771f4aa | r-tuning-regularized-prompt-tuning-in-open | 2303.05122 | null | https://arxiv.org/abs/2303.05122v1 | https://arxiv.org/pdf/2303.05122v1.pdf | R-Tuning: Regularized Prompt Tuning in Open-Set Scenarios | In realistic open-set scenarios where labels of a part of testing data are totally unknown, current prompt methods on vision-language (VL) models always predict the unknown classes as the downstream training classes. The exhibited label bias causes difficulty in the open set recognition (OSR), by which an image should ... | ['Junchi Yan', 'Qi Tian', 'Min Cao', 'Xiaopeng Zhang', 'Ning Liao'] | 2023-03-09 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.83359307e-01 1.39728621e-01 -4.53136951e-01 -6.36414707e-01
-1.04102993e+00 -7.75810719e-01 4.70199406e-01 -2.94495612e-01
-3.76537323e-01 5.78157961e-01 1.23324715e-01 -4.37751412e-01
1.66575745e-01 -5.55107832e-01 -6.79900467e-01 -5.55389822e-01
4.13844138e-01 6.31824672e-01 3.63188833e-01 -4.48055975... | [9.942891120910645, 1.9193633794784546] |
144f8a2d-e6d7-4bb9-b904-971b6c48a45e | giraffe-using-deep-reinforcement-learning-to | 1509.01549 | null | http://arxiv.org/abs/1509.01549v2 | http://arxiv.org/pdf/1509.01549v2.pdf | Giraffe: Using Deep Reinforcement Learning to Play Chess | This report presents Giraffe, a chess engine that uses self-play to discover
all its domain-specific knowledge, with minimal hand-crafted knowledge given by
the programmer. Unlike previous attempts using machine learning only to perform
parameter-tuning on hand-crafted evaluation functions, Giraffe's learning
system al... | ['Matthew Lai'] | 2015-09-04 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [-5.29190660e-01 -2.36439884e-01 -8.92157927e-02 -2.66688138e-01
-6.85709894e-01 -1.19442475e+00 3.57754737e-01 1.49827033e-01
-5.19487500e-01 7.17398822e-01 -4.33161706e-01 -9.47016120e-01
-1.49882510e-01 -9.40700054e-01 -5.32461703e-01 2.80245114e-02
-1.92051664e-01 7.08562493e-01 8.04654002e-01 -8.23249936... | [3.461700677871704, 1.4481619596481323] |
cf0c51e3-516e-4419-81cd-97b7e93c5ad0 | learning-to-exploit-the-sequence-specific | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qin_Learning_To_Exploit_the_Sequence-Specific_Prior_Knowledge_for_Image_Processing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qin_Learning_To_Exploit_the_Sequence-Specific_Prior_Knowledge_for_Image_Processing_CVPR_2023_paper.pdf | Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization | The hardware image signal processing (ISP) pipeline is the intermediate layer between the imaging sensor and the downstream application, processing the sensor signal into an RGB image. The ISP is less programmable and consists of a series of processing modules. Each processing module handles a subtask and contains ... | ['Weiming Hu', 'Bing Li', 'Wentao Ma', 'Juan Wang', 'Weihua Xiong', 'Longfei Han', 'Haina Qin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['hyperparameter-optimization'] | ['methodology'] | [ 4.85694915e-01 -3.11476678e-01 1.90371778e-02 -5.70672929e-01
-7.50650346e-01 -7.03209817e-01 6.96714297e-02 -1.06387705e-01
-4.40226972e-01 -7.57618546e-02 -2.39375040e-01 -2.02810004e-01
-1.27596289e-01 -4.95398223e-01 -5.06832600e-01 -9.22599077e-01
2.92457819e-01 2.68081933e-01 5.46500206e-01 1.18402079... | [9.488543510437012, -0.3373841941356659] |
430ded44-6ca8-4625-96a3-c39477a76893 | constructing-multilingual-code-search-dataset | 2306.15604 | null | https://arxiv.org/abs/2306.15604v1 | https://arxiv.org/pdf/2306.15604v1.pdf | Constructing Multilingual Code Search Dataset Using Neural Machine Translation | Code search is a task to find programming codes that semantically match the given natural language queries. Even though some of the existing datasets for this task are multilingual on the programming language side, their query data are only in English. In this research, we create a multilingual code search dataset in f... | ['Hitomi Yanaka', 'Shuai Lu', 'Nan Duan', 'Ryo Sekizawa'] | 2023-06-27 | null | null | null | null | ['code-search', 'code-search', 'machine-translation'] | ['computer-code', 'computer-vision', 'natural-language-processing'] | [-2.59426147e-01 -4.23479766e-01 -7.20514715e-01 -3.53520393e-01
-9.00991261e-01 -7.14966655e-01 5.09023726e-01 1.82316408e-01
-4.29024518e-01 1.12936750e-01 1.05235577e-01 -8.73480260e-01
1.87247694e-01 -7.66427815e-01 -8.06580663e-01 1.95822045e-01
1.88349530e-01 4.76851076e-01 4.34282511e-01 -4.58691925... | [7.595117092132568, 7.986221790313721] |
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