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3e323792-b289-4c3b-98c2-26b62b18d6d5 | pu-gan-a-point-cloud-upsampling-adversarial | 1907.10844 | null | https://arxiv.org/abs/1907.10844v1 | https://arxiv.org/pdf/1907.10844v1.pdf | PU-GAN: a Point Cloud Upsampling Adversarial Network | Point clouds acquired from range scans are often sparse, noisy, and non-uniform. This paper presents a new point cloud upsampling network called PU-GAN, which is formulated based on a generative adversarial network (GAN), to learn a rich variety of point distributions from the latent space and upsample points over patc... | ['Pheng-Ann Heng', 'Daniel Cohen-Or', 'Chi-Wing Fu', 'Xianzhi Li', 'Ruihui Li'] | 2019-07-25 | pu-gan-a-point-cloud-upsampling-adversarial-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_PU-GAN_A_Point_Cloud_Upsampling_Adversarial_Network_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_PU-GAN_A_Point_Cloud_Upsampling_Adversarial_Network_ICCV_2019_paper.pdf | iccv-2019-10 | ['point-cloud-super-resolution'] | ['computer-vision'] | [ 1.82355136e-01 2.25304991e-01 8.25240836e-02 -4.36762750e-01
-1.08316362e+00 -2.26397425e-01 4.38875735e-01 -5.57213426e-01
3.73858929e-01 6.95178509e-01 2.29417443e-01 3.55884284e-01
1.52914166e-01 -1.31496048e+00 -1.28175962e+00 -6.41767859e-01
3.23922396e-01 5.99611759e-01 -1.03669532e-01 -3.07809599... | [8.815909385681152, -3.625425100326538] |
c4eafb0f-576c-4eeb-9ef8-f8091de0b828 | unsupervised-video-anomaly-detection-with | 2307.01533 | null | https://arxiv.org/abs/2307.01533v1 | https://arxiv.org/pdf/2307.01533v1.pdf | Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations | This paper aims to address the unsupervised video anomaly detection (VAD) problem, which involves classifying each frame in a video as normal or abnormal, without any access to labels. To accomplish this, the proposed method employs conditional diffusion models, where the input data is the spatiotemporal features extra... | ['Elisa Ricci', 'Cigdem Beyan', "Nicola Dall'Asen", 'Anil Osman Tur'] | 2023-07-04 | null | null | null | null | ['video-anomaly-detection', 'anomaly-detection'] | ['computer-vision', 'methodology'] | [ 1.36161774e-01 -3.06359619e-01 -3.99536699e-01 -1.59935132e-01
-4.63775367e-01 -1.80602565e-01 6.78960264e-01 2.22782910e-01
-3.13923568e-01 1.77742735e-01 2.17850938e-01 -2.44614616e-01
1.56800628e-01 -5.61281562e-01 -5.54654241e-01 -8.27317894e-01
-4.15109903e-01 -6.50964528e-02 3.94383907e-01 2.50505000... | [7.815058708190918, 1.646422028541565] |
5ad34c60-5094-46a9-bc14-e88e0296608b | camel-curvature-augmented-manifold-embedding | 2303.02561 | null | https://arxiv.org/abs/2303.02561v1 | https://arxiv.org/pdf/2303.02561v1.pdf | CAMEL: Curvature-Augmented Manifold Embedding and Learning | A novel method, named Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed for high dimensional data classification, dimension reduction, and visualization. CAMEL utilizes a topology metric defined on the Riemannian manifold, and a unique Riemannian metric for both distance and curvature to enhance ... | ['Yongming Liu', 'Nan Xu'] | 2023-03-05 | null | null | null | null | ['unity'] | ['computer-vision'] | [-7.21470892e-01 -1.01483107e-01 8.76840763e-03 -2.62802273e-01
-2.63003975e-01 -4.98405397e-01 5.50243139e-01 -9.79919732e-02
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-5.71725070e-01 -3.62370253e-01 1.34006236e-02 -9.62919772e-01
-6.92671061e-01 -7.02856630e-02 -1.60620540e-01 -2.14770228... | [7.8772382736206055, 4.129769325256348] |
16aadca9-dbbc-47fa-9082-10d4b4118892 | kasam-spline-additive-models-for-function | 2205.06376 | null | https://arxiv.org/abs/2205.06376v1 | https://arxiv.org/pdf/2205.06376v1.pdf | KASAM: Spline Additive Models for Function Approximation | Neural networks have been criticised for their inability to perform continual learning due to catastrophic forgetting and rapid unlearning of a past concept when a new concept is introduced. Catastrophic forgetting can be alleviated by specifically designed models and training techniques. This paper outlines a novel Sp... | ['Anna Bosman', 'Pieter Janse van Rensburg', 'Heinrich van Deventer'] | 2022-05-12 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 3.86621319e-02 -1.39152437e-01 6.88331127e-02 -1.00336313e-01
-3.06686163e-01 -4.11946699e-02 3.17473799e-01 2.41260409e-01
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-4.13310677e-01 4.05234009e-01 2.90793031e-01 -2.28921786... | [9.656839370727539, 3.488178253173828] |
3e30488b-e5cd-4505-ad1f-f5b4716b0111 | patch-based-deep-autoencoder-for-point-cloud | 2110.09109 | null | https://arxiv.org/abs/2110.09109v1 | https://arxiv.org/pdf/2110.09109v1.pdf | Patch-Based Deep Autoencoder for Point Cloud Geometry Compression | The ever-increasing 3D application makes the point cloud compression unprecedentedly important and needed. In this paper, we propose a patch-based compression process using deep learning, focusing on the lossy point cloud geometry compression. Unlike existing point cloud compression networks, which apply feature extrac... | ['Pan Gao', 'Kang You'] | 2021-10-18 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 1.41308188e-01 -7.04346672e-02 -2.19735764e-02 -1.52009815e-01
-7.38207102e-01 -1.16296165e-01 1.52283251e-01 1.96742013e-01
-1.85636565e-01 4.36514318e-01 -7.50445276e-02 -1.21748731e-01
-9.42941830e-02 -1.28946948e+00 -1.29404998e+00 -5.18768787e-01
1.84041321e-01 5.60295045e-01 -1.09911479e-01 1.10598296... | [8.389054298400879, -3.470832109451294] |
815edc1f-d99e-4ee1-945e-22ba2f4105c1 | inductive-biased-estimation-learning | 2110.01571 | null | https://arxiv.org/abs/2110.01571v4 | https://arxiv.org/pdf/2110.01571v4.pdf | Causal Representation Learning for Context-Aware Face Transfer | Human face synthesis involves transferring knowledge about the identity and identity-dependent face shape (IDFS) of a human face to target face images where the context (e.g., facial expressions, head poses, and other background factors) may change dramatically. Human faces are non-rigid, so facial expression leads to ... | ['Ran He', 'Chaoyou Fu', 'Huaibo Huang', 'Gege Gao'] | 2021-10-04 | null | null | null | null | ['face-transfer', 'counterfactual-inference'] | ['computer-vision', 'miscellaneous'] | [ 4.71223652e-01 3.70401859e-01 -6.62460774e-02 -7.64264047e-01
-3.95783007e-01 -5.62928736e-01 6.81664348e-01 -9.11395013e-01
-1.47829369e-01 7.54832983e-01 3.10470074e-01 3.13987643e-01
2.05374971e-01 -7.26951838e-01 -1.15721095e+00 -8.86206269e-01
8.02910924e-02 2.30123326e-01 -5.07353544e-01 8.16437304... | [12.735845565795898, -0.05997651815414429] |
47b3b3b3-9200-4aca-b475-6cdabd992fac | neural-network-based-sleep-phases | 2105.11452 | null | https://arxiv.org/abs/2105.11452v1 | https://arxiv.org/pdf/2105.11452v1.pdf | Neural Network Based Sleep Phases Classification for Resource Constraint Environments | Sleep is restoration process of the body. The efficiency of this restoration process is directly correlated to the amount of time spent at each sleep phase. Hence, automatic tracking of sleep via wearable devices has attracted both the researchers and industry. Current state-of-the-art sleep tracking solutions are memo... | ['Alisher Kholmatov', 'Murat Aslan', 'Berkay Köprü'] | 2021-05-25 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 2.10186556e-01 -2.07213357e-01 -2.43803576e-01 -2.95521796e-01
1.93369761e-01 -1.17467031e-01 -3.53694558e-01 3.79641503e-02
-5.27973115e-01 7.32716084e-01 -7.34682232e-02 -1.56942740e-01
-3.08252443e-02 -5.47697067e-01 4.27270867e-03 -6.19265676e-01
1.43113911e-01 4.83963005e-02 2.21521512e-01 1.03301071... | [13.583189964294434, 3.4210731983184814] |
7d9fed27-1e1b-4007-affa-087cc6263750 | pacer-a-fully-push-forward-based | 2306.06637 | null | https://arxiv.org/abs/2306.06637v1 | https://arxiv.org/pdf/2306.06637v1.pdf | PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm | In this paper, we propose the first fully push-forward-based Distributional Reinforcement Learning algorithm, called Push-forward-based Actor-Critic EncourageR (PACER). Specifically, PACER establishes a stochastic utility value policy gradient theorem and simultaneously leverages the push-forward operator in the constr... | ['Bin Dai', 'Hui Qian', 'Lingwei Peng', 'Yichao Fu', 'Chao Zhang', 'Wensong Bai'] | 2023-06-11 | null | null | null | null | ['distributional-reinforcement-learning', 'continuous-control'] | ['methodology', 'playing-games'] | [-3.58175248e-01 2.44064182e-01 -6.32144809e-01 -1.74395010e-01
-1.10593271e+00 -2.90366739e-01 7.77956069e-01 7.51320645e-02
-5.37393093e-01 1.19718277e+00 3.77469212e-01 -5.58986902e-01
-3.61514986e-01 -7.12674975e-01 -7.64323533e-01 -8.04301441e-01
6.44804165e-02 3.29919338e-01 -1.57221124e-01 -3.74855101... | [4.091549396514893, 2.3396780490875244] |
e2c6c19b-0319-4e85-a8fe-84d40dc2b157 | a-parameterised-quantum-circuit-approach-to | 2102.06697 | null | https://arxiv.org/abs/2102.06697v2 | https://arxiv.org/pdf/2102.06697v2.pdf | Matching Point Sets with Quantum Circuit Learning | In this work, we propose a parameterised quantum circuit learning approach to point set matching problem. In contrast to previous annealing-based methods, we propose a quantum circuit-based framework whose parameters are optimised via descending the gradients w.r.t a kernel-based loss function. We formulate the shape m... | ['Hanchen Wang', 'Mohammadreza Noormandipour'] | 2021-02-12 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 1.47000611e-01 -7.40852728e-02 -1.78171560e-01 -2.53437400e-01
-1.42043090e+00 -7.86931574e-01 4.92682099e-01 2.27600738e-01
-4.36033696e-01 6.23343647e-01 2.80044228e-02 -3.52375269e-01
-4.10671115e-01 -1.07278931e+00 -9.57109988e-01 -9.51848924e-01
2.11232767e-01 8.23827147e-01 1.19487993e-01 -2.71862149... | [5.581118583679199, 4.9209465980529785] |
a5d32989-575b-4f30-a2db-348e93465b49 | skip-prediction-using-boosting-trees-based-on | 1903.11833 | null | http://arxiv.org/abs/1903.11833v1 | http://arxiv.org/pdf/1903.11833v1.pdf | Skip prediction using boosting trees based on acoustic features of tracks in sessions | The Spotify Sequential Skip Prediction Challenge focuses on predicting if a
track in a session will be skipped by the user or not. In this paper, we
describe our approach to this problem and the final system that was submitted
to the challenge by our team from the Music Technology Group (MTG) under the
name "aferraro".... | ['Dmitry Bogdanov', 'Andrés Ferraro', 'Xavier Serra'] | 2019-03-28 | null | null | null | null | ['sequential-skip-prediction'] | ['time-series'] | [-6.05148030e-03 -2.05035761e-01 -4.46360677e-01 -3.91746342e-01
-1.01112354e+00 -6.43377066e-01 4.93159920e-01 -2.38979742e-01
-1.03753686e-01 8.23081672e-01 5.41243792e-01 -2.87039906e-01
-1.85078695e-01 -1.51787013e-01 -6.89018905e-01 -3.57799917e-01
-1.19324453e-01 4.28371400e-01 3.71394873e-01 -2.14408368... | [15.629568099975586, 5.19087553024292] |
6771f9a6-0594-44ec-bfee-4f69d7653c0f | table-structure-recognition-based-on-cell | null | null | https://aclanthology.org/R19-1001 | https://aclanthology.org/R19-1001.pdf | Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach | In this paper, we present a relationship extraction based methodology for table structure recognition in PDF documents. The proposed deep learning-based method takes a bottom-up approach to table recognition in PDF documents. We outline the shortcomings of conventional approaches based on heuristics and machine learnin... | ['Viveka Vyeth', 'Muzaffar Bashir Shah', 'Shabir Ahmad Bhat', 'Darshan Adiga'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['table-recognition'] | ['computer-vision'] | [ 5.12149483e-02 1.32085666e-01 -3.11401248e-01 -5.04754364e-01
-6.50342882e-01 -6.09848917e-01 4.16234821e-01 8.32908094e-01
-8.18502605e-02 1.14933741e+00 1.67368799e-01 -4.91272092e-01
-5.17299533e-01 -1.25948155e+00 -9.00684416e-01 -3.19051683e-01
-9.16311964e-02 7.44848371e-01 -2.60481268e-01 -2.12799832... | [11.674581527709961, 3.0170154571533203] |
60720feb-9091-4025-85bc-6cebad0cc859 | aced-accelerated-computational | 2011.04426 | null | https://arxiv.org/abs/2011.04426v4 | https://arxiv.org/pdf/2011.04426v4.pdf | AutoMat: Accelerated Computational Electrochemical systems Discovery | Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on hu... | ['Christopher Rackauckas', 'Bharath Ramsundar', 'Hongyi Lin', 'Adarsh Dave', 'David Farina', 'Jiankun Pu', 'Shang Zhu', 'Valentin Sulzer', 'Vinay I. Hegde', 'Eric Muckley', 'Rachel Kurchin', 'Emil Annevelink', 'Venkatasubramanian Viswanathan', 'Viral Shah', 'Bryce Meredig', 'James Saal', 'Alan Edelman', 'Matthew Johnso... | 2020-11-03 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 2.66749382e-01 -3.25925887e-01 8.69147480e-02 8.55769739e-02
-9.22114372e-01 -9.15507078e-01 6.59061849e-01 5.64425468e-01
-2.92426467e-01 1.19935775e+00 -7.89454728e-02 -7.68746316e-01
-2.73024559e-01 -7.87840843e-01 -7.69358039e-01 -6.99491680e-01
-5.95475473e-02 7.64992416e-01 2.07248628e-02 -3.09922785... | [5.715658664703369, 4.522204875946045] |
60ee7843-62ff-454b-95c7-f81e8727d255 | semi-supervised-relational-contrastive | 2304.05047 | null | https://arxiv.org/abs/2304.05047v2 | https://arxiv.org/pdf/2304.05047v2.pdf | Semi-Supervised Relational Contrastive Learning | Disease diagnosis from medical images via supervised learning is usually dependent on tedious, error-prone, and costly image labeling by medical experts. Alternatively, semi-supervised learning and self-supervised learning offer effectiveness through the acquisition of valuable insights from readily available unlabeled... | ['Abdullah-Al-Zubaer Imran', 'Demetri Terzopoulos', 'Adam Wang', 'Attiano Purpura-Pontoniere'] | 2023-04-11 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 8.25325549e-01 6.74173295e-01 -7.67870247e-01 -6.47461057e-01
-1.31722283e+00 -5.73065460e-01 6.59201205e-01 2.91396320e-01
-3.59571934e-01 7.55989492e-01 9.72928628e-02 -3.76025736e-01
-3.22422296e-01 -3.19011956e-01 -4.47845221e-01 -7.56022573e-01
-1.17565200e-01 6.92849338e-01 1.71086378e-02 2.83685744... | [14.929798126220703, -2.325411558151245] |
f178c512-212c-49c8-acda-4d24b28b2872 | on-the-performance-of-time-pooling-strategies | null | null | https://aclanthology.org/2020.lrec-1.438 | https://aclanthology.org/2020.lrec-1.438.pdf | On The Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification | Automatic speech processing applications often have to deal with the problem of aggregating local descriptors (i.e., representations of input speech data corresponding to specific portions across the time dimension) and turning them into a single fixed-dimension representation, known as global descriptor, on top of whi... | ['Tiago Falk', 'Md Jahangir Alam', 'Joao Monteiro'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['spoken-language-identification'] | ['speech'] | [ 2.83984691e-01 -1.98312968e-01 -1.99278854e-02 -3.93782854e-01
-1.05826819e+00 -6.82223678e-01 8.96598697e-01 4.48712140e-01
-7.72339821e-01 3.34905565e-01 4.11862522e-01 -2.80544788e-01
-2.36755386e-01 -3.60971183e-01 -1.00725144e-01 -8.41731608e-01
-2.29093611e-01 5.44184625e-01 1.87940389e-01 -1.20494612... | [14.25148868560791, 6.2147321701049805] |
33e94240-5a50-45f1-a5f0-eaeb5bec16fe | floweval-a-consensus-based-dialogue | 2202.06633 | null | https://arxiv.org/abs/2202.06633v2 | https://arxiv.org/pdf/2202.06633v2.pdf | FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows | Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem. We explore the potential of dialogue evaluation featuring dialog act information, which was hardly explicitly modeled in previous methods. However, defined at the utterance level in general, dialog act ... | ['LiWei Wang', 'Michael R. Lyu', 'Dong Yu', 'Yangfeng Ji', 'Wanyu Du', 'Yanyang Li', 'Jianqiao Zhao'] | 2022-02-14 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 9.02015716e-02 4.51969028e-01 -9.55247581e-02 -7.31953323e-01
-8.55310023e-01 -1.03252661e+00 1.14528775e+00 1.18815027e-01
-3.06668520e-01 1.11836088e+00 9.72824335e-01 -3.05153340e-01
3.72843802e-01 -6.62117481e-01 -9.85251367e-03 -6.23054206e-02
2.43208617e-01 8.30007493e-01 3.41744542e-01 -9.56238270... | [12.738754272460938, 8.075746536254883] |
c9932343-3033-49fd-a510-d288c4ce7a1b | joint-self-supervised-image-volume | 2212.01893 | null | https://arxiv.org/abs/2212.01893v1 | https://arxiv.org/pdf/2212.01893v1.pdf | Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering | Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from... | ['Daniel Sonntag', 'Pengtao Xie', 'Shadi Albarqouni', 'Paul Swoboda', 'Nhat Ho', 'Binh T. Nguyen', 'Tri Cao', 'Mai T. N. Truong', 'Hoang Nguyen', 'Duy M. H. Nguyen'] | 2022-12-04 | null | null | null | null | ['lung-nodule-detection', 'brain-segmentation'] | ['medical', 'medical'] | [ 1.60443291e-01 4.47123021e-01 -4.28029984e-01 -6.40530825e-01
-9.78365123e-01 -4.84930724e-01 4.41109210e-01 2.20745876e-01
-3.72539699e-01 3.02679777e-01 4.70009863e-01 -3.18781883e-01
-1.48593485e-01 -5.83072543e-01 -4.48650360e-01 -8.13660204e-01
-1.51494354e-01 6.71667457e-01 6.92061782e-02 2.63102800... | [14.750313758850098, -2.2086448669433594] |
1e9039c5-3684-4cbf-a363-637202504b8b | disenhcn-disentangled-hypergraph | 2208.06794 | null | https://arxiv.org/abs/2208.06794v1 | https://arxiv.org/pdf/2208.06794v1.pdf | DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction | Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Existing solutions based on tensor decomposition or graph embedding suffer from the following two major limitations: 1) ignoring the fine-grained... | ['Yong Li', 'Depeng Jin', 'Tong Li', 'Quanming Yao', 'Chen Gao', 'Yinfeng Li'] | 2022-08-14 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [-1.63082987e-01 -1.58627033e-01 -7.65814841e-01 -2.24941790e-01
-6.91710636e-02 -3.30989391e-01 6.40296876e-01 1.14936866e-01
-2.61921026e-02 3.64928186e-01 9.80880618e-01 -4.35346931e-01
-6.42456889e-01 -1.07326889e+00 -2.68666506e-01 -5.20151019e-01
-4.50328678e-01 2.97304422e-01 1.97945237e-01 -4.32563424... | [10.170360565185547, 5.631577491760254] |
eaa4fe45-732f-49f0-8b24-73da0cd31f1a | exploring-non-contrastive-representation-1 | 2111.11821 | null | https://arxiv.org/abs/2111.11821v2 | https://arxiv.org/pdf/2111.11821v2.pdf | Learning Representation for Clustering via Prototype Scattering and Positive Sampling | Existing deep clustering methods rely on either contrastive or non-contrastive representation learning for downstream clustering task. Contrastive-based methods thanks to negative pairs learn uniform representations for clustering, in which negative pairs, however, may inevitably lead to the class collision issue and c... | ['Hongming Shan', 'Junping Zhang', 'Jie Chen', 'Zhizhong Huang'] | 2021-11-23 | exploring-non-contrastive-representation | https://openreview.net/forum?id=JZrETJlgyq | https://openreview.net/pdf?id=JZrETJlgyq | null | ['image-clustering'] | ['computer-vision'] | [-2.88620323e-01 1.49307288e-02 2.07110733e-01 -3.70163769e-01
-1.02299428e+00 -4.00501013e-01 5.59475183e-01 -7.96361081e-03
-7.28034601e-02 3.02240372e-01 1.61208212e-01 2.86061078e-01
-3.87989521e-01 -6.30823493e-01 -4.72016543e-01 -1.29905236e+00
-1.11681737e-01 5.53007841e-01 2.94054225e-02 7.67916963... | [9.136565208435059, 3.3244616985321045] |
4d86d041-91fd-484a-b740-ed61f84ad822 | a-survey-on-graph-neural-networks-for-time | 2307.03759 | null | https://arxiv.org/abs/2307.03759v1 | https://arxiv.org/pdf/2307.03759v1.pdf | A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection | Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in gr... | ['Shirui Pan', 'Irwin King', 'Geoffrey I. Webb', 'Cesare Alippi', 'Daniele Zambon', 'Qingsong Wen', 'Huan Yee Koh', 'Ming Jin'] | 2023-07-07 | null | null | null | null | ['imputation', 'anomaly-detection', 'imputation', 'time-series-forecasting', 'imputation', 'time-series'] | ['computer-vision', 'methodology', 'miscellaneous', 'time-series', 'time-series', 'time-series'] | [ 7.90243503e-03 -4.33725387e-01 -1.53868169e-01 2.27039605e-02
4.03369755e-01 -4.82340485e-01 2.80899495e-01 4.52810585e-01
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-6.53184533e-01 -1.02119291e+00 -3.12449366e-01 -5.22015214e-01
-9.83149767e-01 2.24820361e-01 -2.98995852e-01 -4.87410098... | [7.162200927734375, 2.835583448410034] |
53b32c84-093c-4a6f-9e3b-f7ba701e8f30 | nonlinear-constructive-observer-design-for | 2303.05900 | null | https://arxiv.org/abs/2303.05900v1 | https://arxiv.org/pdf/2303.05900v1.pdf | Nonlinear constructive observer design for direct homography estimation | Feature-based homography estimation approaches rely on extensive image processing for feature extraction and matching, and do not adequately account for the information provided by the image. Therefore, developing efficient direct techniques to extract the homography from images is essential. This paper presents a nove... | ['Tarek Hamel', 'Robert Mahony', 'Pieter van Goor', 'Tarek Bouazza'] | 2023-03-10 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.25267589e-02 -7.79214140e-04 1.96572137e-03 -2.31718067e-02
1.69188390e-03 -3.09804648e-01 3.39959472e-01 -6.10418558e-01
-1.91529363e-01 4.66701239e-01 -2.94939220e-01 -1.13327667e-01
-2.58562177e-01 -4.01513755e-01 -4.39901590e-01 -7.03808188e-01
-2.81794444e-02 3.80009855e-03 -8.05820823e-02 -9.44088325... | [7.87992525100708, -2.2381324768066406] |
047a8571-7298-4c16-b00d-abe5d13dae0c | non-adversarial-training-of-neural-sdes-with | 2305.16274 | null | https://arxiv.org/abs/2305.16274v1 | https://arxiv.org/pdf/2305.16274v1.pdf | Non-adversarial training of Neural SDEs with signature kernel scores | Neural SDEs are continuous-time generative models for sequential data. State-of-the-art performance for irregular time series generation has been previously obtained by training these models adversarially as GANs. However, as typical for GAN architectures, training is notoriously unstable, often suffers from mode colla... | ['Cristopher Salvi', 'Maud Lemercier', 'Blanka Horvath', 'Zacharia Issa'] | 2023-05-25 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 1.71281502e-01 1.38000652e-01 3.12684178e-01 1.31791145e-01
-9.04575169e-01 -6.39672577e-01 9.44530010e-01 -1.72146559e-01
-2.24524558e-01 1.17591906e+00 -2.06040248e-01 -4.47139829e-01
-4.27153707e-01 -1.10948133e+00 -8.67949724e-01 -1.09850264e+00
-4.07161653e-01 5.57876945e-01 -1.55395433e-01 -3.75943482... | [6.654112339019775, 3.4895687103271484] |
21bf810f-e019-4320-8532-1ca99ef73239 | scannet-richly-annotated-3d-reconstructions | 1702.04405 | null | http://arxiv.org/abs/1702.04405v2 | http://arxiv.org/pdf/1702.04405v2.pdf | ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes | A key requirement for leveraging supervised deep learning methods is the
availability of large, labeled datasets. Unfortunately, in the context of RGB-D
scene understanding, very little data is available -- current datasets cover a
small range of scene views and have limited semantic annotations. To address
this issue,... | ['Matthias Nießner', 'Thomas Funkhouser', 'Manolis Savva', 'Maciej Halber', 'Angel X. Chang', 'Angela Dai'] | 2017-02-14 | scannet-richly-annotated-3d-reconstructions-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Dai_ScanNet_Richly-Annotated_3D_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Dai_ScanNet_Richly-Annotated_3D_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-object-classification'] | ['computer-vision'] | [ 1.76991150e-01 1.68667361e-01 -2.33517066e-01 -8.86166930e-01
-1.18912768e+00 -8.64758611e-01 2.57009000e-01 -3.36227193e-02
-7.98332617e-02 1.63804159e-01 7.86692873e-02 -1.35300308e-01
1.84575111e-01 -6.87875688e-01 -1.01519167e+00 -5.96726276e-02
4.58561778e-01 8.71544838e-01 5.90994895e-01 6.63487241... | [8.355395317077637, -2.9064533710479736] |
8ad6ba6d-d6c8-406e-9b01-d3356c36ab01 | a-dual-attention-hierarchical-recurrent | 1810.09154 | null | https://arxiv.org/abs/1810.09154v3 | https://arxiv.org/pdf/1810.09154v3.pdf | A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification | Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition. In this paper, we propose a dual-attention hierarchical recurrent neural network for DA classification. Our model is partially inspired by the observation that conversational utt... | ['Guanyi Chen', 'Matthew Collinson', 'Ruizhe Li', 'Xiao Li', 'Chenghua Lin'] | 2018-10-22 | a-dual-attention-hierarchical-recurrent-1 | https://aclanthology.org/K19-1036 | https://aclanthology.org/K19-1036.pdf | conll-2019-11 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 1.57732859e-01 5.25186002e-01 1.53908413e-02 -4.31448907e-01
-3.17578197e-01 -3.19815934e-01 1.36558342e+00 1.76365107e-01
-3.19627851e-01 5.76417387e-01 9.11325812e-01 -6.49212450e-02
3.17162335e-01 -6.59143627e-01 -1.20320186e-01 -7.34787405e-01
1.39728457e-01 9.05068517e-01 2.27140501e-01 -4.52083170... | [12.698081970214844, 7.695603847503662] |
a1888134-2907-42a3-9367-0caf9bd0f4aa | safe-critical-modular-deep-reinforcement | 2109.02791 | null | https://arxiv.org/abs/2109.02791v7 | https://arxiv.org/pdf/2109.02791v7.pdf | Safety-Critical Learning of Robot Control with Temporal Logic Specifications | Reinforcement learning (RL) is a promising approach. However, success is limited to real-world applications, because ensuring safe exploration and facilitating adequate exploitation is a challenge for controlling robotic systems with unknown models and measurement uncertainties. The learning problem becomes even more d... | ['Cristian-Ioan Vasile', 'Mingyu Cai'] | 2021-09-07 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.44236123e-02 4.96824324e-01 -4.05526191e-01 2.33426958e-01
-1.01122260e+00 -6.55039966e-01 6.46831214e-01 2.49030553e-02
-3.40212971e-01 9.99307275e-01 -1.02851465e-01 -5.27071238e-01
-6.61302209e-01 -6.34369016e-01 -1.14859104e+00 -8.54095578e-01
-6.35701239e-01 1.30302995e-01 2.88206726e-01 -2.78517753... | [4.662838459014893, 2.136873483657837] |
5a9331d9-8313-45b2-ad02-103c5cd74c74 | fast-and-incremental-loop-closure-detection-1 | 2010.11703 | null | https://arxiv.org/abs/2010.11703v2 | https://arxiv.org/pdf/2010.11703v2.pdf | Fast and Incremental Loop Closure Detection with Deep Features and Proximity Graphs | In recent years, the robotics community has extensively examined methods concerning the place recognition task within the scope of simultaneous localization and mapping applications.This article proposes an appearance-based loop closure detection pipeline named ``FILD++" (Fast and Incremental Loop closure Detection).Fi... | ['Antonios Gasteratos', 'Konstantinos A. Tsintotas', 'Dong Wei', 'Haogang Zhu', 'Shan An'] | 2020-09-29 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-1.93117969e-02 -8.87002144e-03 -7.90420100e-02 -1.57698423e-01
-7.46510446e-01 -5.73307633e-01 6.90615416e-01 8.10625851e-01
-9.57286477e-01 4.71718431e-01 -4.08833563e-01 -2.57555127e-01
-2.20025912e-01 -9.60310936e-01 -8.59155655e-01 -3.71402651e-01
-4.35776889e-01 3.56404454e-01 4.98542696e-01 -9.07995626... | [7.502892971038818, -1.979942798614502] |
56366352-095e-4b59-94bf-419af92cb38b | a-real-time-hand-gesture-recognition-and | 1704.07296 | null | http://arxiv.org/abs/1704.07296v1 | http://arxiv.org/pdf/1704.07296v1.pdf | A Real-time Hand Gesture Recognition and Human-Computer Interaction System | In this project, we design a real-time human-computer interaction system
based on hand gesture. The whole system consists of three components: hand
detection, gesture recognition and human-computer interaction (HCI) based on
recognition; and realizes the robust control of mouse and keyboard events with
a higher accurac... | ['Pei Xu'] | 2017-04-24 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [ 1.28565222e-01 -6.55502021e-01 4.49087247e-02 -5.27184978e-02
8.64485875e-02 -6.13667965e-01 5.94491601e-01 -4.87897485e-01
-7.91142106e-01 2.29006052e-01 -2.50763297e-01 -4.00786489e-01
-1.27939686e-01 -4.76686865e-01 -1.88096404e-01 -7.31401443e-01
3.30792785e-01 1.43427074e-01 4.46761310e-01 1.37124076... | [6.475927829742432, -0.2591780424118042] |
561b834d-8feb-4753-8ade-a6e44e94d626 | valuing-player-actions-in-counter-strike | 2011.01324 | null | https://arxiv.org/abs/2011.01324v2 | https://arxiv.org/pdf/2011.01324v2.pdf | Valuing Player Actions in Counter-Strike: Global Offensive | Esports, despite its expanding interest, lacks fundamental sports analytics resources such as accessible data or proven and reproducible analytical frameworks. Even Counter-Strike: Global Offensive (CSGO), the second most popular esport, suffers from these problems. Thus, quantitative evaluation of CSGO players, a task... | ['Claudio Silva', 'Harish Doraiswamy', 'Peter Xenopoulos'] | 2020-11-02 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-4.71623242e-01 -1.08449511e-01 -6.12200856e-01 -7.33915344e-02
-1.01943099e+00 -7.81834602e-01 1.30231082e-01 5.59699595e-01
-5.34764886e-01 8.14590275e-01 4.20461953e-01 -9.21172127e-02
-6.73519969e-01 -1.10460210e+00 -4.48099226e-01 7.74467438e-02
-3.21076453e-01 8.21143448e-01 5.54134548e-01 -6.17679238... | [6.5778326988220215, 0.332614928483963] |
701051f4-1084-497c-9e20-797c7d378093 | speech-bandwidth-extension-with-wavenet | 1907.04927 | null | https://arxiv.org/abs/1907.04927v1 | https://arxiv.org/pdf/1907.04927v1.pdf | Speech bandwidth extension with WaveNet | Large-scale mobile communication systems tend to contain legacy transmission channels with narrowband bottlenecks, resulting in characteristic "telephone-quality" audio. While higher quality codecs exist, due to the scale and heterogeneity of the networks, transmitting higher sample rate audio with modern high-quality ... | ['Thomas C. Walters', 'Yannis Assael', 'Brendan Shillingford', 'Archit Gupta'] | 2019-07-05 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 3.47493887e-01 2.88585901e-01 9.63552892e-02 1.49745420e-01
-1.34443450e+00 -2.87037015e-01 5.68921790e-02 2.59474441e-02
-3.36858243e-01 7.03589022e-01 3.12074840e-01 -5.47223687e-01
-2.13089541e-01 -5.01036704e-01 -7.08034635e-01 -5.48905015e-01
-6.44653320e-01 1.24101639e-02 2.89866924e-01 -3.20799291... | [15.277820587158203, 5.909379959106445] |
41f84b1a-1068-4e3a-a497-a47628e82507 | exoskeleton-for-the-mind-exploring-strategies | 2304.08759 | null | https://arxiv.org/abs/2304.08759v1 | https://arxiv.org/pdf/2304.08759v1.pdf | Exoskeleton for the Mind: Exploring Strategies Against Misinformation with a Metacognitive Agent | Misinformation is a global problem in modern social media platforms with few solutions known to be effective. Social media platforms have offered tools to raise awareness of information, but these are closed systems that have not been empirically evaluated. Others have developed novel tools and strategies, but most hav... | ['Yuto Sawa', 'Jacqueline Urakami', 'Hiroki Oura', 'Katie Seaborn', 'Takane Ueno', 'Yeongdae Kim'] | 2023-04-18 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-2.24280745e-01 5.87840319e-01 5.76008819e-02 -7.63399377e-02
-2.14614198e-01 -8.04288447e-01 1.09463167e+00 7.68639565e-01
-8.67843986e-01 3.93436998e-01 6.67521000e-01 -6.36560977e-01
1.00800551e-01 -4.19651300e-01 -2.45545357e-01 -7.14892820e-02
-1.37124196e-01 2.30816342e-02 1.74518630e-01 -4.85122353... | [9.204875946044922, 6.5169477462768555] |
2a242e01-ffa9-4d74-9763-42b2061236b6 | recognizing-complex-gestures-on-minimalistic | 2303.10336 | null | https://arxiv.org/abs/2303.10336v1 | https://arxiv.org/pdf/2303.10336v1.pdf | Recognizing Complex Gestures on Minimalistic Knitted Sensors: Toward Real-World Interactive Systems | Developments in touch-sensitive textiles have enabled many novel interactive techniques and applications. Our digitally-knitted capacitive active sensors can be manufactured at scale with little human intervention. Their sensitive areas are created from a single conductive yarn, and they require only few connections to... | ['Ali Shokoufandeh', 'Genevieve Dion', 'Lev Saunders', 'Richard Valett', 'Denisa Qori McDonald'] | 2023-03-18 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 8.83150280e-01 -1.75289482e-01 -1.11051239e-01 -1.70571759e-01
-1.72307879e-01 -8.23088050e-01 -1.82132833e-02 -3.81839186e-01
-4.33689326e-01 4.69173938e-01 -1.65787831e-01 -2.69989848e-01
-2.18633637e-02 -8.22447360e-01 -4.21620756e-01 -4.53838915e-01
-1.32032841e-01 -1.85791031e-01 6.31582737e-01 -2.19309554... | [6.408310890197754, -0.36324843764305115] |
5739d953-2b2b-4ab2-a6ff-2398e08140f0 | surveillance-face-anti-spoofing | 2301.00975 | null | https://arxiv.org/abs/2301.00975v1 | https://arxiv.org/pdf/2301.00975v1.pdf | Surveillance Face Anti-spoofing | Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant... | ['Zhen Lei', 'Stan Z. Li', 'Xu Zhang', 'Chenxu Zhao', 'Sergio Escalera', 'Jun Wan', 'Ajian Liu', 'Hao Fang'] | 2023-01-03 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 7.63885558e-01 -5.68917394e-01 -1.88446388e-01 -3.02834153e-01
-5.30981362e-01 -4.40667957e-01 4.98678476e-01 -6.11944735e-01
-1.35705277e-01 4.76134151e-01 1.07804246e-01 -3.09696287e-01
-3.56645823e-01 -8.69488895e-01 -6.48381352e-01 -9.44772482e-01
-2.20635355e-01 -4.62506026e-01 9.42974314e-02 -3.27540904... | [12.95838737487793, 1.2064987421035767] |
69c11eb8-d030-4ada-a33c-0d18aa4a0116 | eye-of-the-beholder-improved-relation | 2106.05387 | null | https://arxiv.org/abs/2106.05387v2 | https://arxiv.org/pdf/2106.05387v2.pdf | Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents | Text-based games (TBGs) have become a popular proving ground for the demonstration of learning-based agents that make decisions in quasi real-world settings. The crux of the problem for a reinforcement learning agent in such TBGs is identifying the objects in the world, and those objects' relations with that world. Whi... | ['Kartik Talamadupula', 'Subhajit Chaudhury', 'Keerthiram Murugesan'] | 2021-06-09 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 3.47389765e-02 1.09537579e-01 1.64215378e-02 -1.26454130e-01
-3.79427522e-01 -6.74525082e-01 9.94331658e-01 2.52915353e-01
-5.68535388e-01 6.01822376e-01 1.19048320e-01 -3.36534381e-01
-1.67981803e-01 -1.05695450e+00 -7.96929061e-01 -4.40497667e-01
-3.05675417e-01 1.07185268e+00 6.78157151e-01 -7.69309640... | [4.154480457305908, 1.1345064640045166] |
e379c14e-2944-4976-b302-075060cc486c | revisiting-pretraining-with-adapters | null | null | https://aclanthology.org/2021.repl4nlp-1.11 | https://aclanthology.org/2021.repl4nlp-1.11.pdf | Revisiting Pretraining with Adapters | Pretrained language models have served as the backbone for many state-of-the-art NLP results. These models are large and expensive to train. Recent work suggests that continued pretraining on task-specific data is worth the effort as pretraining leads to improved performance on downstream tasks. We explore alternatives... | ['Jonathan Hilgart', 'Nathan Susanj', 'Alex Shum', 'Seungwon Kim'] | null | null | null | null | acl-repl4nlp-2021-8 | ['continual-pretraining'] | ['methodology'] | [ 3.18582624e-01 2.30108872e-01 -3.66687626e-01 -5.63175976e-01
-1.03225052e+00 -8.81047547e-01 7.37659156e-01 -5.95996939e-02
-1.03247142e+00 8.30364585e-01 5.26571751e-01 -6.76026762e-01
-6.43389747e-02 -5.62038958e-01 -7.95118332e-01 -3.02986503e-01
2.52965331e-01 8.29816282e-01 1.12483904e-01 -2.98737824... | [10.741288185119629, 8.511872291564941] |
84272800-49cc-4d54-a2a7-9dd0879a8a05 | intercode-standardizing-and-benchmarking | 2306.14898 | null | https://arxiv.org/abs/2306.14898v2 | https://arxiv.org/pdf/2306.14898v2.pdf | InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback | Humans write code in a fundamentally interactive manner and rely on constant execution feedback to correct errors, resolve ambiguities, and decompose tasks. While LLMs have recently exhibited promising coding capabilities, current coding benchmarks mostly consider a static instruction-to-code sequence transduction proc... | ['Shunyu Yao', 'Karthik Narasimhan', 'Akshara Prabhakar', 'John Yang'] | 2023-06-26 | null | null | null | null | ['code-generation', 'benchmarking', 'benchmarking'] | ['computer-code', 'miscellaneous', 'robots'] | [-1.47866160e-01 1.00001171e-01 -1.51094258e-01 -3.43674630e-01
-8.95332456e-01 -1.15052903e+00 4.74114299e-01 1.91525653e-01
9.89606902e-02 4.59012121e-01 1.49396166e-01 -9.86018896e-01
2.42365554e-01 -6.81745827e-01 -9.28407431e-01 -2.32984573e-01
-2.97423273e-01 3.36592376e-01 1.05296999e-01 -4.40756083... | [7.8725504875183105, 7.727171897888184] |
0c515d7d-1d1f-4bc4-801e-4a7b3e6ac97c | make-the-most-out-of-your-net-alternating | 2303.15792 | null | https://arxiv.org/abs/2303.15792v1 | https://arxiv.org/pdf/2303.15792v1.pdf | Make the Most Out of Your Net: Alternating Between Canonical and Hard Datasets for Improved Image Demosaicing | Image demosaicing is an important step in the image processing pipeline for digital cameras, and it is one of the many tasks within the field of image restoration. A well-known characteristic of natural images is that most patches are smooth, while high-content patches like textures or repetitive patterns are much rare... | ['Tomer Peleg', 'Raz Z. Nossek', 'Yuval Becker'] | 2023-03-28 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 6.15855455e-01 -1.12121426e-01 -3.20748352e-02 -3.52381855e-01
-4.14015621e-01 -5.80205768e-02 4.84082758e-01 -3.56348231e-02
-4.67487693e-01 2.87106782e-01 3.34965765e-01 -3.22148740e-01
-1.30307436e-01 -8.45493078e-01 -8.02110076e-01 -9.50914502e-01
3.08736395e-02 1.16341032e-01 3.87903541e-01 -2.87815213... | [11.393596649169922, -2.0415005683898926] |
4d41bffd-564f-4ab4-8085-dafd0ac28fd9 | tab-iais-flexible-table-recognition-and | 2105.11879 | null | https://arxiv.org/abs/2105.11879v2 | https://arxiv.org/pdf/2105.11879v2.pdf | Flexible Table Recognition and Semantic Interpretation System | Table extraction is an important but still unsolved problem. In this paper, we introduce a flexible and modular table extraction system. We develop two rule-based algorithms that perform the complete table recognition process, including table detection and segmentation, and support the most frequent table formats. More... | ['Joachim köhler', 'Sven Behnke', 'Alexander M. Esser', 'Marcin Namysl'] | 2021-05-25 | null | null | null | null | ['table-recognition', 'table-detection', 'table-extraction'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [ 3.72844398e-01 1.67942002e-01 -4.16062176e-01 -3.65928411e-01
-1.17835033e+00 -8.49638343e-01 2.45148808e-01 8.54313016e-01
-1.08895525e-01 8.42562020e-01 4.15940806e-02 -4.45623159e-01
6.00247420e-02 -1.18714345e+00 -7.25336969e-01 6.65674731e-02
-2.26688068e-02 9.00876582e-01 4.42348421e-01 -2.19011004... | [9.593180656433105, 7.833480358123779] |
6bbd2737-cf25-46b6-8f9d-eeb76418cb23 | m2trec-metadata-aware-multi-task-transformer | 2209.11824 | null | https://arxiv.org/abs/2209.11824v1 | https://arxiv.org/pdf/2209.11824v1.pdf | M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations | Session-based recommender systems (SBRSs) have shown superior performance over conventional methods. However, they show limited scalability on large-scale industrial datasets since most models learn one embedding per item. This leads to a large memory requirement (of storing one vector per item) and poor performance on... | ['Xiquan Cui', 'Srijan Kumar', 'Amir Afsharinejad', 'Sejoon Oh', 'Walid Shalaby'] | 2022-09-23 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-1.13303676e-01 -4.98285443e-01 -6.27150893e-01 -4.09806490e-01
-7.09508121e-01 -4.94443923e-01 2.52359778e-01 1.51656643e-01
-2.12239817e-01 5.60019672e-01 5.33021331e-01 1.61715969e-02
-4.33151335e-01 -6.83758557e-01 -8.34475517e-01 -6.53120220e-01
-2.42145076e-01 5.30033767e-01 3.65966186e-02 -1.29586816... | [10.123784065246582, 5.563226222991943] |
d8bb9be3-788e-4d0c-8443-741629997949 | multivariate-time-series-classification-a | 2307.02253 | null | https://arxiv.org/abs/2307.02253v1 | https://arxiv.org/pdf/2307.02253v1.pdf | Multivariate Time Series Classification: A Deep Learning Approach | This paper investigates different methods and various neural network architectures applicable in the time series classification domain. The data is obtained from a fleet of gas sensors that measure and track quantities such as oxygen and sound. With the help of this data, we can detect events such as occupancy in a spe... | ['Aida Farahani', 'Julien Vitay', 'Mohamed Abouelnaga'] | 2023-07-05 | null | null | null | null | ['classification-1', 'time-series-classification'] | ['methodology', 'time-series'] | [ 4.63033617e-02 -5.57781696e-01 -5.79187907e-02 -3.47126275e-01
3.07018571e-02 -4.39396769e-01 6.41117036e-01 4.00986642e-01
-7.90852666e-01 5.44902146e-01 1.53391138e-01 -3.95768851e-01
-5.55866480e-01 -1.19779384e+00 -5.25742590e-01 -7.59767771e-01
-5.77600896e-01 2.51456797e-01 5.86002842e-02 -1.41025946... | [6.995502471923828, 2.842916488647461] |
4c48826d-6582-40b6-b9e6-79a6dc0bd4a9 | hrpose-real-time-high-resolution-6d-pose | 2204.09429 | null | https://arxiv.org/abs/2204.09429v1 | https://arxiv.org/pdf/2204.09429v1.pdf | HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation | Real-time 6D object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely High-Resolution 6D Pose Estimation Network (HRPose). We ado... | ['Shibei Xue', 'Zihao Sheng', 'Qi Guan'] | 2022-04-20 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation', 'robotic-grasping'] | ['computer-vision', 'computer-vision', 'robots'] | [-1.38477772e-01 -3.55373472e-02 -7.89154917e-02 -2.62040466e-01
-6.44656599e-01 -1.93698615e-01 7.48218317e-03 -6.09244227e-01
-3.18130672e-01 3.04538041e-01 -2.67256588e-01 3.39742377e-02
-2.22236007e-01 -6.95626259e-01 -1.04120076e+00 -5.14002383e-01
-5.01175374e-02 6.66852534e-01 2.52349824e-01 -1.86963439... | [7.158291339874268, -2.424743890762329] |
646bb901-daa0-48a7-9bd9-d9d3f72c6bc3 | learning-from-web-data-the-benefit-of | 1812.09232 | null | http://arxiv.org/abs/1812.09232v1 | http://arxiv.org/pdf/1812.09232v1.pdf | Learning from Web Data: the Benefit of Unsupervised Object Localization | Annotating a large number of training images is very time-consuming. In this
background, this paper focuses on learning from easy-to-acquire web data and
utilizes the learned model for fine-grained image classification in labeled
datasets. Currently, the performance gain from training with web data is
incremental, like... | ['Jufeng Yang', 'Yu-Kun Lai', 'Xiaoxiao Sun', 'Liang Zheng'] | 2018-12-21 | null | null | null | null | ['unsupervised-object-localization'] | ['computer-vision'] | [ 1.31082565e-01 -1.56501845e-01 -1.74492493e-01 -6.15736783e-01
-9.87087548e-01 -8.40676904e-01 3.89138132e-01 1.14714213e-01
-3.91963422e-01 5.13794661e-01 -8.41163695e-02 9.92849916e-02
-2.44356632e-01 -8.77258658e-01 -1.00503504e+00 -7.59759188e-01
2.60674208e-01 4.19434875e-01 6.56517684e-01 2.17279226... | [9.579238891601562, 2.5419437885284424] |
9d4d7ac3-5389-42c9-a00d-bf7d847f91c3 | soft-prompt-tuning-to-predict-lung-cancer | 2303.15846 | null | https://arxiv.org/abs/2303.15846v1 | https://arxiv.org/pdf/2303.15846v1.pdf | Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes | We investigate different natural language processing (NLP) approaches based on contextualised word representations for the problem of early prediction of lung cancer using free-text patient medical notes of Dutch primary care physicians. Because lung cancer has a low prevalence in primary care, we also address the prob... | ['Iacer Calixto', 'Ameen Abu-Hanna', 'Iacopo Vagliano', 'Auke Elfrink'] | 2023-03-28 | null | null | null | null | ['contextualised-word-representations'] | ['natural-language-processing'] | [ 1.55063644e-01 3.95106733e-01 -6.11229539e-01 -2.10886598e-01
-1.23664272e+00 -1.05744824e-01 3.40979666e-01 1.02359474e+00
-8.63204122e-01 7.36218750e-01 9.37831402e-01 -6.33320391e-01
-4.10121650e-01 -8.19144309e-01 -2.26019993e-01 -4.76581573e-01
1.38865430e-02 9.41789448e-01 1.19521931e-01 -2.40403950... | [8.479984283447266, 8.391029357910156] |
7b7ee84c-380d-4349-b664-9cbca4b221bc | deep-multi-modal-networks-for-book-genre | 2011.07658 | null | https://arxiv.org/abs/2011.07658v1 | https://arxiv.org/pdf/2011.07658v1.pdf | Deep multi-modal networks for book genre classification based on its cover | Book covers are usually the very first impression to its readers and they often convey important information about the content of the book. Book genre classification based on its cover would be utterly beneficial to many modern retrieval systems, considering that the complete digitization of books is an extremely expen... | ['Lukun Zheng', 'Chandra Kundu'] | 2020-11-15 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [-8.63243043e-02 -6.25444353e-01 -3.50209832e-01 -2.05303803e-01
-7.13539064e-01 -6.61001563e-01 8.20254683e-01 -7.59587809e-02
-5.86457849e-02 5.97798944e-01 1.89162835e-01 1.33637175e-01
-1.71716079e-01 -1.01958203e+00 -6.70010865e-01 -5.48017025e-01
5.05307794e-01 7.71303177e-01 5.40853590e-02 -5.42060852... | [11.645183563232422, 2.2861409187316895] |
139ff613-8b65-4918-acd7-8da8c6326d8d | online-real-time-recurrent-learning-using | 2302.05326 | null | https://arxiv.org/abs/2302.05326v2 | https://arxiv.org/pdf/2302.05326v2.pdf | Scalable Real-Time Recurrent Learning Using Sparse Connections and Selective Learning | State construction from sensory observations is an important component of a reinforcement learning agent. One solution for state construction is to use recurrent neural networks. Back-propagation through time (BPTT), and real-time recurrent learning (RTRL) are two popular gradient-based methods for recurrent learning. ... | ['Martha White', 'Rich Sutton', 'Haseeb Shah', 'Khurram Javed'] | 2023-01-20 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-2.15356484e-01 -9.79335159e-02 -4.73074734e-01 -1.26858652e-01
-4.16995943e-01 -5.92642665e-01 6.21702135e-01 1.44102782e-01
-9.46269691e-01 9.43067014e-01 -1.09846126e-02 -5.51011920e-01
-1.15070734e-02 -5.83922029e-01 -9.18429613e-01 -6.33599699e-01
-6.97939634e-01 2.80535609e-01 5.81854999e-01 -3.76272351... | [4.117514133453369, 1.9309636354446411] |
aad136da-d94d-43e4-82f1-de653a5cbf18 | an-adaptive-and-scalable-ann-based-model | 2203.10515 | null | https://arxiv.org/abs/2203.10515v1 | https://arxiv.org/pdf/2203.10515v1.pdf | An Adaptive and Scalable ANN-based Model-Order-Reduction Method for Large-Scale TO Designs | Topology Optimization (TO) provides a systematic approach for obtaining structure design with optimum performance of interest. However, the process requires numerical evaluation of objective function and constraints at each iteration, which is computational expensive especially for large-scale design. Deep learning-bas... | ['Wenjing Ye', 'Dan Xu', 'Chao Qian', 'Ren Kai Tan'] | 2022-03-20 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [-1.16339602e-01 -2.66289823e-02 5.60438819e-02 -4.39626537e-02
-3.39405864e-01 -2.21970826e-01 1.98074616e-02 -1.37245897e-02
-1.62211299e-01 9.64577258e-01 -2.89317638e-01 -1.57827646e-01
-3.76907647e-01 -1.14389169e+00 -8.29703748e-01 -7.19206750e-01
1.80041730e-01 1.03511298e+00 8.18854719e-02 -4.04899120... | [6.2143096923828125, 3.3647282123565674] |
0e96b7a2-a2b0-4f25-8c2c-930b36d01e4f | superline3d-self-supervised-line-segmentation | 2208.01925 | null | https://arxiv.org/abs/2208.01925v1 | https://arxiv.org/pdf/2208.01925v1.pdf | SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud | Poles and building edges are frequently observable objects on urban roads, conveying reliable hints for various computer vision tasks. To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description... | ['Yong liu', 'Mingyang Li', 'Teng Ma', 'Jun Chen', 'Tianxin Huang', 'Sheng Yang', 'Xiangrui Zhao'] | 2022-08-03 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-9.46160108e-02 1.54686440e-02 -2.45161355e-01 -6.48048103e-01
-1.06903148e+00 -4.97682661e-01 6.33431315e-01 8.00514035e-03
-2.71154791e-01 2.48178601e-01 -4.69908416e-01 -3.62949878e-01
3.73813212e-01 -9.88179445e-01 -9.54483449e-01 -1.00205839e-01
1.12952352e-01 9.91926789e-01 7.15476573e-01 -1.29511327... | [8.063271522521973, -3.0945751667022705] |
482fb6a9-eefe-4d97-bbb8-7e8820c94f9b | backdoor-vulnerabilities-in-normally-trained | 2211.15929 | null | https://arxiv.org/abs/2211.15929v1 | https://arxiv.org/pdf/2211.15929v1.pdf | Backdoor Vulnerabilities in Normally Trained Deep Learning Models | We conduct a systematic study of backdoor vulnerabilities in normally trained Deep Learning models. They are as dangerous as backdoors injected by data poisoning because both can be equally exploited. We leverage 20 different types of injected backdoor attacks in the literature as the guidance and study their correspon... | ['Xiangyu Zhang', 'Yunshu Mao', 'Zhuo Zhang', 'Guangyu Shen', 'Yingqi Liu', 'Shengwei An', 'Shiqing Ma', 'Siyuan Cheng', 'Zhenting Wang', 'Guanhong Tao'] | 2022-11-29 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-2.47403100e-01 -1.12669908e-01 -7.07218289e-01 1.37393653e-01
-5.37320852e-01 -1.69589174e+00 9.19746637e-01 2.29289606e-02
-3.29930574e-01 4.71640557e-01 -1.19447090e-01 -1.17027891e+00
1.91126823e-01 -9.91193056e-01 -1.32033277e+00 -6.51744902e-01
-2.23738268e-01 -1.46153063e-01 6.19000673e-01 -1.11941926... | [5.798958778381348, 7.7354865074157715] |
abd2f5b1-dfa6-4ae4-8697-5720f3f7591d | set-transformer-beamsnet-for-auv-velocity | 2212.11671 | null | https://arxiv.org/abs/2212.11671v1 | https://arxiv.org/pdf/2212.11671v1.pdf | Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios | Autonomous underwater vehicles (AUVs) are regularly used for deep ocean applications. Commonly, the autonomous navigation task is carried out by a fusion between two sensors: the inertial navigation system and the Doppler velocity log (DVL). The DVL operates by transmitting four acoustic beams to the sea floor, and onc... | ['Itzik Klein', 'Zeev Yampolsky', 'Nadav Cohen'] | 2022-12-22 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-3.94643731e-02 1.49872497e-01 4.97521192e-01 -2.13689227e-02
-3.60822409e-01 -5.77037334e-01 4.77719873e-01 7.64261857e-02
-1.01864934e+00 9.16684806e-01 -1.63591295e-01 -3.28229338e-01
-2.62470514e-01 -9.71532106e-01 -9.28760529e-01 -9.60736871e-01
-3.40953499e-01 2.01202005e-01 3.82509828e-01 -5.00193536... | [7.4899373054504395, -1.8051968812942505] |
e4c514fa-2c95-4e52-9eb5-c7bd8c6499a3 | qi-tts-questioning-intonation-control-for | 2303.07682 | null | https://arxiv.org/abs/2303.07682v1 | https://arxiv.org/pdf/2303.07682v1.pdf | QI-TTS: Questioning Intonation Control for Emotional Speech Synthesis | Recent expressive text to speech (TTS) models focus on synthesizing emotional speech, but some fine-grained styles such as intonation are neglected. In this paper, we propose QI-TTS which aims to better transfer and control intonation to further deliver the speaker's questioning intention while transferring emotion fro... | ['Jing Xiao', 'Ning Cheng', 'Jianzong Wang', 'xulong Zhang', 'Haobin Tang'] | 2023-03-14 | null | null | null | null | ['emotional-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 1.08611137e-01 5.85480966e-03 -1.89036131e-01 -5.16731441e-01
-4.53432918e-01 -3.56779009e-01 1.27083823e-01 -3.49373877e-01
-1.52667642e-01 4.21577811e-01 8.86621356e-01 -2.48674408e-01
4.98492450e-01 -6.15067780e-01 -1.64642528e-01 -4.66453612e-01
5.52821100e-01 -1.69999301e-01 -2.38443419e-01 -6.04662716... | [14.626646995544434, 6.3648152351379395] |
f732c75e-e4c8-4372-9f5b-729b3aa13899 | paco-provocation-involving-action-culture-and | 2303.12808 | null | https://arxiv.org/abs/2303.12808v1 | https://arxiv.org/pdf/2303.12808v1.pdf | PACO: Provocation Involving Action, Culture, and Oppression | In India, people identify with a particular group based on certain attributes such as religion. The same religious groups are often provoked against each other. Previous studies show the role of provocation in increasing tensions between India's two prominent religious groups: Hindus and Muslims. With the advent of the... | ['Munindar P. Singh', 'Ganning Xu', 'Vaibhav Garg'] | 2023-03-19 | null | null | null | null | ['culture'] | ['speech'] | [-7.67440945e-02 5.60764909e-01 -3.78200948e-01 -3.76907736e-01
-1.18514657e+00 -8.58559728e-01 1.15678573e+00 7.04155922e-01
-3.59029174e-01 5.93819857e-01 1.36672235e+00 -1.85388505e-01
2.09537651e-02 -8.55573475e-01 -1.31408110e-01 -4.48622346e-01
1.67195365e-01 6.28365755e-01 1.15653602e-02 -1.02272856... | [8.782393455505371, 10.405388832092285] |
f5d9a122-2f69-4fe4-a44b-438886b2beb1 | a-complex-network-approach-to-time-series | 2108.06920 | null | https://arxiv.org/abs/2108.06920v1 | https://arxiv.org/pdf/2108.06920v1.pdf | A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders | Electromyography (EMG) refers to a biomedical signal indicating neuromuscular activity and muscle morphology. Experts accurately diagnose neuromuscular disorders using this time series. Modern data analysis techniques have recently led to introducing novel approaches for mapping time series data to graphs and complex n... | ['Behnaz Ansari', 'Nasser Ghadiri', 'Samaneh Samiei'] | 2021-08-16 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 3.71668577e-01 -6.68274760e-02 -1.08596392e-01 2.45057717e-02
1.55860977e-03 -2.56455511e-01 1.15136094e-01 4.98507060e-02
-4.35607612e-01 8.40033174e-01 -1.76399812e-01 1.92447621e-02
-8.24758589e-01 -7.20055401e-01 -2.62939215e-01 -6.34628773e-01
-5.33084631e-01 3.29756021e-01 1.59920633e-01 -2.10650995... | [6.876959323883057, 0.23905296623706818] |
9e4cd924-22ac-4c62-a0be-26cd77e77b18 | plug-and-play-controller-for-story-completion | null | null | https://aclanthology.org/2022.in2writing-1.6 | https://aclanthology.org/2022.in2writing-1.6.pdf | Plug-and-Play Controller for Story Completion: A Pilot Study toward Emotion-aware Story Writing Assistance | Emotions are essential for storytelling and narrative generation, and as such, the relationship between stories and emotions has been extensively studied. The authors of this paper, including a professional novelist, have examined the use of natural language processing to address the problems of novelists from the pers... | ['Tatsuya Harada', 'Ryohei Shimizu', 'Hiroaki Yamane', 'Yusuke Mori'] | null | null | null | null | in2writing-acl-2022-5 | ['story-completion'] | ['natural-language-processing'] | [ 3.82076830e-01 5.38309932e-01 2.03140110e-01 -3.58270377e-01
-3.38685036e-01 -6.67419612e-01 6.82637513e-01 1.20402746e-01
-3.73439454e-02 7.61484265e-01 7.19934344e-01 -6.73253015e-02
1.44879475e-01 -8.32318246e-01 -3.57009977e-01 -1.69196278e-01
2.89832830e-01 5.43596268e-01 -1.62891805e-01 -5.01701713... | [11.722502708435059, 8.860037803649902] |
5188d410-758d-44c3-ab0d-775d878ee496 | the-stoic2021-covid-19-ai-challenge-applying | 2306.10484 | null | https://arxiv.org/abs/2306.10484v2 | https://arxiv.org/pdf/2306.10484v2.pdf | The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data | Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which ... | ['Marie-Pierre Revel Dubois', 'Bram van Ginneken', 'Nikos Paragios', 'Razvan Ionasec', 'Christoph Russ', 'Alexander Lemm', 'James A. Meakin', 'Marco Aiello', 'Pasquale Borrelli', 'Mario Verdicchio', 'Reda Bouadjenek', 'Imran Razzak', 'Ngoc Dung Huynh', 'Panagiotis Gonidakis', 'Nikos Deligiannis', 'Hichem Sahli', 'Jef V... | 2023-06-18 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [-3.28413993e-02 8.63363296e-02 -8.05726871e-02 -4.49210346e-01
-1.34300303e+00 -6.11190319e-01 6.34888709e-02 2.14778602e-01
-6.96856320e-01 6.34411216e-01 4.07864824e-02 -7.84458220e-01
-4.42145467e-01 -5.17949104e-01 -9.08946931e-01 -6.13471448e-01
-4.22051847e-01 7.86772728e-01 7.96401948e-02 4.73416984... | [15.344478607177734, -1.8945430517196655] |
e204a7aa-b7fc-4bc5-9c7a-ba8908d36f63 | centermask-real-time-anchor-free-instance-3 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Lee_CenterMask_Real-Time_Anchor-Free_Instance_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_CenterMask_Real-Time_Anchor-Free_Instance_Segmentation_CVPR_2020_paper.pdf | CenterMask: Real-Time Anchor-Free Instance Segmentation | We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein with Mask R-CNN. Plugged into the FCOS object detector, the SAG-Mask branch predicts a segmentation m... | [' Jongyoul Park', 'Youngwan Lee'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 1.94223776e-01 5.42567015e-01 -2.32856035e-01 -2.46353000e-01
-7.00301826e-01 -3.93551797e-01 1.12954795e-01 -6.45503581e-01
-4.80483621e-01 6.45899713e-01 -1.65773287e-01 -3.23875666e-01
2.38817707e-01 -4.80581701e-01 -9.92846131e-01 -5.99394679e-01
2.14867964e-01 2.78840452e-01 7.74623096e-01 2.05027312... | [9.555655479431152, 0.07854526489973068] |
bf8ed96b-def1-47b1-a6b8-7c7bbd0d0990 | boltzmann-machine-learning-and-regularization | 1909.05006 | null | https://arxiv.org/abs/1909.05006v4 | https://arxiv.org/pdf/1909.05006v4.pdf | Boltzmann machine learning and regularization methods for inferring evolutionary fields and couplings from a multiple sequence alignment | The inverse Potts problem to infer a Boltzmann distribution for homologous protein sequences from their single-site and pairwise amino acid frequencies recently attracts a great deal of attention in the studies of protein structure and evolution. We study regularization and learning methods and how to tune regularizati... | ['Sanzo Miyazawa'] | 2019-09-10 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 3.42910945e-01 3.28757688e-02 4.39739525e-02 -6.79521382e-01
-9.27691281e-01 -4.93569285e-01 2.94772416e-01 2.28755116e-01
-8.04248095e-01 1.34104395e+00 -1.69728190e-01 -1.91712290e-01
-3.65783498e-02 -5.22316098e-01 -1.09440434e+00 -1.46896267e+00
-3.24077010e-01 7.47422516e-01 2.38410324e-01 -2.63641983... | [4.792689800262451, 5.380179405212402] |
3d3a78c7-c0b6-4d74-adc2-9bd12d401d38 | ideal-improved-dense-local-contrastive | 2210.15075 | null | https://arxiv.org/abs/2210.15075v2 | https://arxiv.org/pdf/2210.15075v2.pdf | IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation | Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To th... | ['Rammohan Mallipeddi', 'Sayan Nag', 'Rohit Kundu', 'Soumitri Chattopadhyay', 'Hritam Basak'] | 2022-10-26 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 3.28451872e-01 3.16619515e-01 -2.33743683e-01 -7.91274607e-01
-1.14540267e+00 -1.08741753e-01 5.59774280e-01 1.82710737e-02
-5.31295598e-01 7.08456695e-01 3.12608600e-01 -1.71110734e-01
-1.90501645e-01 -5.82191229e-01 -5.79144657e-01 -7.65290141e-01
-2.60824598e-02 3.66124779e-01 5.52265532e-02 1.14061445... | [14.5757417678833, -2.1508188247680664] |
1832ebf3-fb0b-4a1b-a260-e0dfaf115b6b | residual-lstm-design-of-a-deep-recurrent | 1701.03360 | null | http://arxiv.org/abs/1701.03360v3 | http://arxiv.org/pdf/1701.03360v3.pdf | Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition | In this paper, a novel architecture for a deep recurrent neural network,
residual LSTM is introduced. A plain LSTM has an internal memory cell that can
learn long term dependencies of sequential data. It also provides a temporal
shortcut path to avoid vanishing or exploding gradients in the temporal domain.
The residua... | ['Mostafa El-Khamy', 'Jungwon Lee', 'Jaeyoung Kim'] | 2017-01-10 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [-1.40652815e-02 4.96739417e-01 7.63212666e-02 -2.42280319e-01
-1.19389728e-01 -1.25751346e-01 3.04489315e-01 -2.83910006e-01
-7.23283052e-01 5.35556257e-01 1.36021271e-01 -6.70935035e-01
3.64372104e-01 -7.06669748e-01 -6.23850048e-01 -7.99596488e-01
8.71210396e-02 -2.10598931e-01 5.60344756e-01 -4.10395563... | [10.876084327697754, 6.310694694519043] |
d4fe6532-12f9-4eca-8bc0-f9e41fdb3d12 | english-malay-cross-lingual-embedding | null | null | https://aclanthology.org/2022.acl-srw.16 | https://aclanthology.org/2022.acl-srw.16.pdf | English-Malay Cross-Lingual Embedding Alignment using Bilingual Lexicon Augmentation | As high-quality Malay language resources are still a scarcity, cross lingual word embeddings make it possible for richer English resources to be leveraged for downstream Malay text classification tasks. This paper focuses on creating an English-Malay cross-lingual word embeddings using embedding alignment by exploiting... | ['Jasy Suet Yan Liew', 'Ying Hao Lim'] | null | null | null | null | acl-2022-5 | ['cross-lingual-word-embeddings'] | ['natural-language-processing'] | [-2.49435261e-01 7.38724768e-02 -2.74520487e-01 -3.65607083e-01
-1.01182079e+00 -6.82521045e-01 8.65769684e-01 1.76514789e-01
-1.11709440e+00 6.73834562e-01 7.51580417e-01 -7.48935342e-01
1.57815337e-01 -9.14754152e-01 -5.14568448e-01 -4.54700172e-01
1.54770672e-01 5.73739290e-01 1.94107909e-02 -6.03212535... | [11.172316551208496, 10.113343238830566] |
3a112783-d94e-4dd4-8807-1751adfdf0fe | cnn-based-rgb-d-salient-object-detection | 1909.09309 | null | https://arxiv.org/abs/1909.09309v1 | https://arxiv.org/pdf/1909.09309v1.pdf | CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse | The goal of this work is to present a systematic solution for RGB-D salient object detection, which addresses the following three aspects with a unified framework: modal-specific representation learning, complementary cue selection and cross-modal complement fusion. To learn discriminative modal-specific features, we p... | ['Youfu Li', 'Hao Chen'] | 2019-09-20 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 4.45262879e-01 -1.19487613e-01 -3.55132192e-01 -2.06904218e-01
-9.31159914e-01 -8.51886868e-02 5.23750603e-01 6.68509752e-02
-2.02548578e-01 5.73214650e-01 4.98245567e-01 2.20373929e-01
-2.45687813e-01 -4.61803615e-01 -6.16893351e-01 -9.88105655e-01
1.73748344e-01 -2.36160368e-01 7.20027208e-01 -4.13787007... | [9.7916259765625, -0.7383522987365723] |
516de9a5-9e34-4288-a84a-1331205cbeb5 | mocanet-motion-retargeting-in-the-wild-via | 2112.10082 | null | https://arxiv.org/abs/2112.10082v2 | https://arxiv.org/pdf/2112.10082v2.pdf | MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks | We present a novel framework that brings the 3D motion retargeting task from controlled environments to in-the-wild scenarios. In particular, our method is capable of retargeting body motion from a character in a 2D monocular video to a 3D character without using any motion capture system or 3D reconstruction procedure... | ['Chen Change Loy', 'Yizhou Wang', 'Wayne Wu', 'Ziang Di', 'Zhuoqian Yang', 'Wentao Zhu'] | 2021-12-19 | null | null | null | null | ['action-analysis', 'motion-retargeting'] | ['computer-vision', 'computer-vision'] | [ 1.35597825e-01 -1.24833196e-01 -5.37175119e-01 -1.21431937e-02
-6.09079421e-01 -8.14328194e-01 4.54772562e-01 -8.16784322e-01
-3.21613491e-01 3.71762842e-01 6.27328575e-01 2.67875791e-01
3.47549260e-01 -3.53522569e-01 -6.73581362e-01 -7.57413208e-01
1.25159770e-01 4.42643106e-01 1.14000067e-01 -1.48166955... | [7.234744548797607, -0.5343773365020752] |
3924e078-abeb-411e-903f-beea299d2553 | foreground-aware-semantic-representations-for | 2006.00809 | null | https://arxiv.org/abs/2006.00809v1 | https://arxiv.org/pdf/2006.00809v1.pdf | Foreground-aware Semantic Representations for Image Harmonization | Image harmonization is an important step in photo editing to achieve visual consistency in composite images by adjusting the appearances of foreground to make it compatible with background. Previous approaches to harmonize composites are based on training of encoder-decoder networks from scratch, which makes it challen... | ['Polina Popenova', 'Konstantin Sofiiuk', 'Anton Konushin'] | 2020-06-01 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 6.02788448e-01 -1.36315733e-01 3.77333164e-02 -3.20164412e-01
-8.37990761e-01 -2.34822378e-01 6.27687931e-01 -1.28327563e-01
-3.96979190e-02 5.16560555e-01 2.72893697e-01 -2.57790461e-02
4.11398977e-01 -8.11107218e-01 -1.20131075e+00 -6.22631371e-01
3.96713763e-01 4.23524082e-02 2.62691438e-01 -2.54242331... | [11.233782768249512, -1.2829240560531616] |
764a66d1-9a70-42dc-9766-43e1d965bb20 | simple-primitives-with-feasibility-and | 2211.02895 | null | https://arxiv.org/abs/2211.02895v1 | https://arxiv.org/pdf/2211.02895v1.pdf | Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning | The task of Compositional Zero-Shot Learning (CZSL) is to recognize images of novel state-object compositions that are absent during the training stage. Previous methods of learning compositional embedding have shown effectiveness in closed-world CZSL. However, in Open-World CZSL (OW-CZSL), their performance tends to d... | ['Yi Yang', 'XiaoJun Wu', 'Wei Fang', 'Xiaojun Chang', 'Lina Yao', 'Yun Li', 'Zhe Liu'] | 2022-11-05 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 4.49340641e-01 2.43239645e-02 -2.53535390e-01 -1.78333044e-01
-1.24999061e-01 -5.75020730e-01 8.48100126e-01 -1.62376985e-01
-2.09599525e-01 5.22201002e-01 2.98401803e-01 -2.83693016e-01
-8.61461312e-02 -9.56311941e-01 -1.00281799e+00 -8.09000552e-01
2.34958947e-01 5.44274867e-01 3.22014838e-01 -2.24402502... | [10.261046409606934, 2.1553537845611572] |
9202c343-f565-46d0-a400-107c529d9c59 | enhancing-mr-image-segmentation-with | 2108.03429 | null | https://arxiv.org/abs/2108.03429v3 | https://arxiv.org/pdf/2108.03429v3.pdf | Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation | The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impractical due to data sharing and privacy issues. To address this challen... | ['Huaqi Qiu', 'Shuo Wang', 'Zeju Li', 'Daniel Rueckert', 'Wenjia Bai', 'Giacomo Tarroni', 'Liang Chen', 'Cheng Ouyang', 'Chen Qin', 'Chen Chen'] | 2021-08-07 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 8.90488446e-01 6.43057346e-01 -1.46673098e-01 -7.07933962e-01
-8.72297525e-01 -6.64707482e-01 3.11399758e-01 1.46780089e-01
-8.58850420e-01 6.77157521e-01 -2.93399900e-01 -3.54952753e-01
2.68264651e-01 -7.31078446e-01 -8.12342167e-01 -8.72352660e-01
5.13015315e-03 8.18308294e-01 -1.89626619e-01 1.58054695... | [14.56381893157959, -2.117736339569092] |
ef9b19bc-2582-4dd2-880a-1491bf3f0899 | neural-bee-colony-optimization-a-case-study | 2306.00720 | null | https://arxiv.org/abs/2306.00720v1 | https://arxiv.org/pdf/2306.00720v1.pdf | Neural Bee Colony Optimization: A Case Study in Public Transit Network Design | In this work we explore the combination of metaheuristics and learned neural network solvers for combinatorial optimization. We do this in the context of the transit network design problem, a uniquely challenging combinatorial optimization problem with real-world importance. We train a neural network policy to perform ... | ['Gregory Dudek', 'Andrew Holliday'] | 2023-05-18 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 4.77528006e-01 2.20795423e-01 -4.51825529e-01 2.47899681e-01
-6.12857878e-01 -6.35731041e-01 4.30262685e-01 1.40843075e-02
-6.50347888e-01 1.21170151e+00 -3.48187611e-02 -5.43889821e-01
-7.28079975e-01 -1.06627166e+00 -6.43536925e-01 -9.18835044e-01
-3.41895521e-01 9.96236861e-01 2.19558422e-02 -5.62621295... | [5.1963701248168945, 3.0047450065612793] |
d774e223-2115-48db-884e-971c47853c34 | comparison-of-possibilistic-fuzzy-local | 1904.01014 | null | http://arxiv.org/abs/1904.01014v1 | http://arxiv.org/pdf/1904.01014v1.pdf | Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation | Synthetic aperture sonar (SAS) imagery can generate high resolution images of
the seafloor. Thus, segmentation algorithms can be used to partition the images
into different seafloor environments. In this paper, we compare two
possibilistic segmentation approaches. Possibilistic approaches allow for the
ability to detec... | ['Matthew Cook', 'James Keller', 'Joshua Peeples', 'Alina Zare', 'Daniel Suen'] | 2019-04-01 | null | null | null | null | ['landmine'] | ['computer-vision'] | [ 4.23075795e-01 -1.06557623e-01 3.46805066e-01 -3.69519889e-01
-5.44836760e-01 -7.01159060e-01 4.90692526e-01 2.36526638e-01
-5.39806306e-01 8.51194024e-01 -3.07904959e-01 -3.51301283e-01
-6.79459333e-01 -1.11299968e+00 -2.42148042e-01 -8.96606386e-01
-9.50821266e-02 3.66882592e-01 5.06770790e-01 -1.40950173... | [9.5677490234375, -1.7487760782241821] |
1a45f9e1-c30e-42ad-b613-fc02676c9929 | joint-adaptive-sparsity-and-low-rankness-on | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Wen_Joint_Adaptive_Sparsity_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Wen_Joint_Adaptive_Sparsity_ICCV_2017_paper.pdf | Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising | Recent works on adaptive sparse and low-rank signal modeling have demonstrated their usefulness, especially in image/video processing applications. While a patch-based sparse model imposes local structure, low-rankness of the grouped patches exploits non-local correlation. Applying either approach alone usually limits ... | ['Bihan Wen', 'Yoram Bresler', 'Luke Pfister', 'Yanjun Li'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['video-denoising'] | ['computer-vision'] | [ 3.44825327e-01 -6.64231420e-01 4.34463248e-02 -1.70469239e-01
-9.04419422e-01 -2.66455382e-01 2.16154516e-01 -2.46320054e-01
-2.05182791e-01 2.71284074e-01 3.98553938e-01 -2.97911447e-02
-3.60363662e-01 -2.55013198e-01 -7.04304516e-01 -9.27008867e-01
-3.85910749e-01 -7.86476210e-02 1.94081351e-01 -1.16624288... | [11.48567008972168, -2.100799560546875] |
8f52f992-4e11-4880-9c2f-3a49ac1c4127 | block-simultaneous-direction-method-of | 1708.09066 | null | http://arxiv.org/abs/1708.09066v1 | http://arxiv.org/pdf/1708.09066v1.pdf | Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraints | We introduce a generalization of the linearized Alternating Direction Method
of Multipliers to optimize a real-valued function $f$ of multiple arguments
with potentially multiple constraints $g_\circ$ on each of them. The function
$f$ may be nonconvex as long as it is convex in every argument, while the
constraints $g_... | ['Fred Moolekamp', 'Peter Melchior'] | 2017-08-30 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 1.77271992e-01 -7.85200670e-02 -9.10126269e-02 -1.54171541e-01
-8.32195282e-01 -7.39428580e-01 1.46710023e-01 -1.97054803e-01
-3.45113367e-01 7.98576891e-01 -1.47618741e-01 -5.68653226e-01
-5.42923689e-01 -5.76384366e-01 -9.67311561e-01 -1.02988017e+00
-8.26823488e-02 2.78055221e-01 -7.38554597e-01 -4.22534168... | [6.97024393081665, 4.480668067932129] |
8acccc69-bd5b-4dc6-8893-5b40bd82b872 | differentiable-agent-based-epidemiology | 2207.09714 | null | https://arxiv.org/abs/2207.09714v2 | https://arxiv.org/pdf/2207.09714v2.pdf | Differentiable Agent-based Epidemiology | Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments. Agent-based models (ABMs) are an increasingly popular simulation paradigm that can represent the heterogeneity of contact interactions wi... | ['Balaji Krishnamurthy', 'Arnau Quera-Bofarull', 'Ramesh Raskar', 'B. Aditya Prakash', 'Jayakumar Subramanian', 'Alexander Rodríguez', 'Ayush Chopra'] | 2022-07-20 | null | null | null | null | ['epidemiology'] | ['medical'] | [-3.61966729e-01 -5.29870868e-01 -1.72450289e-01 9.71520618e-02
-3.30678970e-01 -4.16973203e-01 6.60668015e-01 4.23653871e-01
-2.48481870e-01 1.18144667e+00 -8.15837011e-02 -8.03805232e-01
-2.86416173e-01 -7.75329411e-01 -6.72940016e-01 -5.08951902e-01
-9.76907909e-01 1.24290347e+00 -6.61676675e-02 -4.11146373... | [6.046048164367676, 4.353994369506836] |
eddfaa14-695a-4009-ab23-820dd14947df | contextual-squeeze-and-excitation-for | 2206.09843 | null | https://arxiv.org/abs/2206.09843v3 | https://arxiv.org/pdf/2206.09843v3.pdf | Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification | Recent years have seen a growth in user-centric applications that require effective knowledge transfer across tasks in the low-data regime. An example is personalization, where a pretrained system is adapted by learning on small amounts of labeled data belonging to a specific user. This setting requires high accuracy u... | ['Richard E. Turner', 'Sebastian Nowozin', 'Katja Hofmann', 'Aliaksandra Shysheya', 'John Bronskill', 'Massimiliano Patacchiola'] | 2022-06-20 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 4.94932562e-01 3.56560163e-02 -2.31414735e-01 -5.69049776e-01
-7.09895432e-01 -3.24237943e-01 6.34611726e-01 1.81984276e-01
-1.05931330e+00 6.27276659e-01 1.82465225e-01 -5.04181720e-02
-3.34971130e-01 -3.67858499e-01 -9.33220804e-01 -7.66266644e-01
9.83564332e-02 6.62546217e-01 3.10463160e-01 -4.86629099... | [9.840343475341797, 2.9364845752716064] |
24bb5656-5f6e-4143-b729-23ee4d9844d0 | vani-very-lightweight-accent-controllable-tts | 2303.07578 | null | https://arxiv.org/abs/2303.07578v1 | https://arxiv.org/pdf/2303.07578v1.pdf | VANI: Very-lightweight Accent-controllable TTS for Native and Non-native speakers with Identity Preservation | We introduce VANI, a very lightweight multi-lingual accent controllable speech synthesis system. Our model builds upon disentanglement strategies proposed in RADMMM and supports explicit control of accent, language, speaker and fine-grained $F_0$ and energy features for speech synthesis. We utilize the Indic languages ... | ['Bryan Catanzaro', 'Boris Ginsburg', 'João Felipe Santos', 'Kevin J. Shih', 'Rafael Valle', 'Subhankar Ghosh', 'Akshit Arora', 'Rohan Badlani'] | 2023-03-14 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [-2.70349890e-01 5.83723187e-02 -5.11835575e-01 -4.25078899e-01
-1.13561785e+00 -8.40510488e-01 7.37783551e-01 -6.90469086e-01
-3.92272502e-01 7.29448974e-01 7.88460553e-01 -5.17758846e-01
3.20765406e-01 -1.17817029e-01 -6.90551996e-01 -3.89463753e-01
-6.89093024e-02 4.54415858e-01 -4.48644459e-01 -3.13320071... | [14.641429901123047, 6.888149261474609] |
b871a9a0-89f7-4129-8c75-ca9fe198837b | a-biomedical-knowledge-graph-for-biomarker | 2302.04737 | null | https://arxiv.org/abs/2302.04737v2 | https://arxiv.org/pdf/2302.04737v2.pdf | A Biomedical Knowledge Graph for Biomarker Discovery in Cancer | Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A dom... | ['Stefan Decker', 'Lina Molinas Comet', 'Dietrich Rebholz-Schuhmann', 'Oya Beyan', 'Md. Rezaul Karim'] | 2023-02-09 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-3.48031104e-01 5.16292512e-01 -6.72865093e-01 -2.61719555e-01
-4.62379485e-01 -5.03353417e-01 1.87212259e-01 9.71990824e-01
3.24407220e-02 1.25784659e+00 4.98695850e-01 -4.14998710e-01
-6.13948941e-01 -1.20229864e+00 -7.41627932e-01 -4.17002112e-01
-4.28861044e-02 5.29447317e-01 4.61436510e-01 -3.17800134... | [8.731971740722656, 8.319466590881348] |
0e755ec6-95df-4995-ba79-27564f9d62b6 | end-to-end-deep-learning-based-adaptation-1 | 2306.02450 | null | https://arxiv.org/abs/2306.02450v1 | https://arxiv.org/pdf/2306.02450v1.pdf | End-To-End Deep Learning-based Adaptation Control for Linear Acoustic Echo Cancellation | The attenuation of acoustic loudspeaker echoes remains to be one of the open challenges to achieve pleasant full-duplex hands free speech communication. In many modern signal enhancement interfaces, this problem is addressed by a linear acoustic echo canceler which subtracts a loudspeaker echo estimate from the recorde... | ['Walter Kellermann', 'Andreas Brendel', 'Thomas Haubner'] | 2023-06-04 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.72979975e-01 -1.05873868e-01 4.93464351e-01 -1.58198297e-01
-8.47375333e-01 -4.56888795e-01 1.25241324e-01 -3.38439852e-01
-5.09194195e-01 5.46046257e-01 3.17116529e-01 -2.06250459e-01
-3.57263058e-01 -3.12438995e-01 -4.75900173e-01 -8.72667730e-01
1.24965020e-01 5.82192540e-02 -1.31943608e-02 -2.21909985... | [15.047626495361328, 5.909576892852783] |
1c247c0d-21f3-4698-8ed8-91672fa6de20 | adversarial-training-for-multi-context-joint | 1808.06876 | null | http://arxiv.org/abs/1808.06876v3 | http://arxiv.org/pdf/1808.06876v3.pdf | Adversarial training for multi-context joint entity and relation extraction | Adversarial training (AT) is a regularization method that can be used to
improve the robustness of neural network methods by adding small perturbations
in the training data. We show how to use AT for the tasks of entity recognition
and relation extraction. In particular, we demonstrate that applying AT to a
general pur... | ['Giannis Bekoulis', 'Johannes Deleu', 'Chris Develder', 'Thomas Demeester'] | 2018-08-21 | adversarial-training-for-multi-context-joint-1 | https://aclanthology.org/D18-1307 | https://aclanthology.org/D18-1307.pdf | emnlp-2018-10 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 1.77818224e-01 6.22959554e-01 -2.76024789e-01 -3.55684131e-01
-6.62380636e-01 -5.94355047e-01 6.31156147e-01 1.53351918e-01
-7.23042130e-01 9.27155197e-01 2.29363814e-01 -3.69461119e-01
3.78746241e-01 -9.31275785e-01 -1.01831996e+00 -2.96894372e-01
-3.40413719e-01 3.96773815e-01 9.33280662e-02 -4.49018151... | [9.433311462402344, 9.084041595458984] |
d9f96017-6fe3-4202-a9ea-62c53c69e83e | wadenet-wavelet-decomposition-based-cnn-for | 2011.05594 | null | https://arxiv.org/abs/2011.05594v1 | https://arxiv.org/pdf/2011.05594v1.pdf | WaDeNet: Wavelet Decomposition based CNN for Speech Processing | Existing speech processing systems consist of different modules, individually optimized for a specific task such as acoustic modelling or feature extraction. In addition to not assuring optimality of the system, the disjoint nature of current speech processing systems make them unsuitable for ubiquitous health applicat... | ['Abhijith Ragav', 'Prithvi Suresh'] | 2020-11-11 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 2.52900839e-01 2.89484084e-01 2.34302774e-01 -4.33472484e-01
-8.04849565e-01 4.94705513e-02 4.01792563e-02 1.04270272e-01
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-1.83707848e-01 -1.31040560e-02 1.15658231e-01 -1.81895331... | [14.558507919311523, 5.904572010040283] |
3888debe-cf74-4e49-9d8f-b9df1571a2a1 | document-level-relation-extraction-via-pair | null | null | https://aclanthology.org/2022.coling-1.213 | https://aclanthology.org/2022.coling-1.213.pdf | Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning | Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article. Recent methods achieve considerable performance but still suffer from two challenges: a) the relational entity pairs are sparse, b) the representation of entity pairs is insufficient. In this paper,... | ['Zuyu Zhao', 'Weijian Sun', 'Kang Liu', 'Jun Zhao', 'Yubo Chen', 'Hang Yang', 'Xiusheng Huang'] | null | null | null | null | coling-2022-10 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-4.80051078e-02 3.31830055e-01 -5.88713467e-01 -2.31607080e-01
-8.20510447e-01 -3.81978571e-01 6.07831240e-01 5.00408590e-01
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-9.69753265e-02 5.82117856e-01 3.78136158e-01 -2.89563209... | [9.18833065032959, 8.58006477355957] |
bfaafa55-4048-453b-b9ff-1a69eda99353 | topic-driven-adaptive-network-for-cross | 2111.14094 | null | https://arxiv.org/abs/2111.14094v2 | https://arxiv.org/pdf/2111.14094v2.pdf | Topic Driven Adaptive Network for Cross-Domain Sentiment Classification | Cross-domain sentiment classification has been a hot spot these years, which aims to learn a reliable classifier using labeled data from a source domain and evaluate it on a target domain. In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space. To furt... | ['Yanghui Rao', 'Fu Lee Wang', 'Qingyuan Wu', 'Yiqiao Qiu', 'Yicheng Zhu'] | 2021-11-28 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 5.80176413e-02 -3.55243832e-02 -3.20557982e-01 -8.84039044e-01
-7.96883523e-01 -5.32521844e-01 6.36583865e-01 1.04124904e-01
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-1.03082947e-01 -7.38888502e-01 -3.10441285e-01 -5.86930990e-01
3.43679756e-01 3.23748350e-01 2.30172306e-01 -4.43040341... | [11.228562355041504, 6.6540446281433105] |
201cd48d-82f3-4d46-981c-b7cf9f234368 | faceinpainter-high-fidelity-face-adaptation | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper.pdf | FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains | In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains. Concretely, by explicitly disentangling foreground and background of the target face, the first stage focuses on adaptive face fitting to the fixed b... | ['Ran He', 'Xingguang Song', 'Jie Cao', 'Zhaoyang Li', 'Jia Li'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['facial-inpainting'] | ['computer-vision'] | [ 5.09085119e-01 4.07916218e-01 1.67822331e-01 -3.40173006e-01
-7.35143304e-01 -4.02108014e-01 3.23993593e-01 -7.83703864e-01
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4.93942440e-01 5.51072955e-01 -1.92899659e-01 -1.14214920... | [12.800138473510742, -0.09517697989940643] |
e48c892e-feb5-4960-84b5-8585afb08cca | post-processing-networks-method-for | 2207.12185 | null | https://arxiv.org/abs/2207.12185v1 | https://arxiv.org/pdf/2207.12185v1.pdf | Post-processing Networks: Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning | Many studies have proposed methods for optimizing the dialogue performance of an entire pipeline task-oriented dialogue system by jointly training modules in the system using reinforcement learning. However, these methods are limited in that they can only be applied to modules implemented using trainable neural-based m... | ['Ryuichiro Higashinaka', 'Atsumoto Ohashi'] | 2022-07-25 | null | https://aclanthology.org/2022.sigdial-1.1 | https://aclanthology.org/2022.sigdial-1.1.pdf | sigdial-acl-2022-9 | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [-2.69825369e-01 8.45138490e-01 3.34139347e-01 -5.65604508e-01
-3.31863701e-01 -7.96872735e-01 6.39474571e-01 2.49259714e-02
-5.39781511e-01 5.76649129e-01 1.02102138e-01 -3.98748219e-01
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1.37654785e-02 7.32164562e-01 5.09483635e-01 -8.29381526... | [12.949519157409668, 7.9753875732421875] |
f39332ec-f169-40cb-9b75-ea911c6aec96 | self-supervised-learning-of-object-parts-for | 2204.13101 | null | https://arxiv.org/abs/2204.13101v2 | https://arxiv.org/pdf/2204.13101v2.pdf | Self-Supervised Learning of Object Parts for Semantic Segmentation | Progress in self-supervised learning has brought strong general image representation learning methods. Yet so far, it has mostly focused on image-level learning. In turn, tasks such as unsupervised image segmentation have not benefited from this trend as they require spatially-diverse representations. However, learning... | ['Yuki M. Asano', 'Adrian Ziegler'] | 2022-04-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 5.31783819e-01 2.28447035e-01 -1.01371676e-01 -3.90344173e-01
-7.17830718e-01 -7.31149673e-01 6.88759506e-01 5.10855794e-01
-3.61528367e-01 3.46767455e-01 9.62411016e-02 -3.02700307e-02
-2.13859856e-01 -8.11233759e-01 -8.00399542e-01 -8.05854619e-01
1.69681362e-03 7.44269371e-01 7.80491114e-01 -9.29301158... | [9.573381423950195, 0.928826093673706] |
cab329a7-5b70-4763-882a-9b919fcbd229 | learning-by-applying-a-general-framework-for | 2302.05717 | null | https://arxiv.org/abs/2302.05717v1 | https://arxiv.org/pdf/2302.05717v1.pdf | Learning by Applying: A General Framework for Mathematical Reasoning via Enhancing Explicit Knowledge Learning | Mathematical reasoning is one of the crucial abilities of general artificial intelligence, which requires machines to master mathematical logic and knowledge from solving problems. However, existing approaches are not transparent (thus not interpretable) in terms of what knowledge has been learned and applied in the re... | ['Qi Liu', 'ChengXiang Zhai', 'Zhenya Huang', 'Jiayu Liu'] | 2023-02-11 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 1.96242914e-01 3.92178357e-01 -2.82554120e-01 -4.42389905e-01
1.53715964e-02 -5.57062089e-01 5.01150846e-01 4.06744555e-02
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-5.22041857e-01 -1.56090271e+00 -1.08414304e+00 -3.10566753e-01
2.59431958e-01 4.13278252e-01 3.82981211e-01 -4.93230253... | [9.589482307434082, 7.521488189697266] |
0abe2055-a4af-438e-8721-6638d225e09d | hirl-a-general-framework-for-hierarchical | 2205.13159 | null | https://arxiv.org/abs/2205.13159v1 | https://arxiv.org/pdf/2205.13159v1.pdf | HIRL: A General Framework for Hierarchical Image Representation Learning | Learning self-supervised image representations has been broadly studied to boost various visual understanding tasks. Existing methods typically learn a single level of image semantics like pairwise semantic similarity or image clustering patterns. However, these methods can hardly capture multiple levels of semantic in... | ['Bingbing Ni', 'Yi Xu', 'Jian Tang', 'Zhenbang Sun', 'Jiawen Li', 'Xuanyu Zhu', 'Yuanfan Guo', 'Minghao Xu'] | 2022-05-26 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 4.85355914e-01 2.33392920e-02 -6.34346008e-01 -7.81422913e-01
-4.85130817e-01 -6.02512598e-01 6.24517679e-01 4.35391873e-01
-2.21419007e-01 4.24604714e-01 4.32827324e-01 2.70756762e-02
-2.36349046e-01 -8.29372048e-01 -9.35126662e-01 -6.62239552e-01
4.29350957e-02 2.83620000e-01 4.18870032e-01 -1.36924326... | [9.753029823303223, 1.9323415756225586] |
8eefc1c1-a413-4e36-b795-eb49622e9ed0 | probabilistic-monocular-3d-human-pose | 2107.13788 | null | https://arxiv.org/abs/2107.13788v2 | https://arxiv.org/pdf/2107.13788v2.pdf | Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows | 3D human pose estimation from monocular images is a highly ill-posed problem due to depth ambiguities and occlusions. Nonetheless, most existing works ignore these ambiguities and only estimate a single solution. In contrast, we generate a diverse set of hypotheses that represents the full posterior distribution of fea... | ['Bastian Wandt', 'Bodo Rosenhahn', 'Marco Rudolph', 'Tom Wehrbein'] | 2021-07-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wehrbein_Probabilistic_Monocular_3D_Human_Pose_Estimation_With_Normalizing_Flows_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wehrbein_Probabilistic_Monocular_3D_Human_Pose_Estimation_With_Normalizing_Flows_ICCV_2021_paper.pdf | iccv-2021-1 | ['multi-hypotheses-3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-7.66106993e-02 1.72981784e-01 -5.12187853e-02 -3.74706239e-01
-7.89335430e-01 -6.75621629e-01 4.84217703e-01 -3.89658540e-01
-5.81652761e-01 8.81010175e-01 2.05877200e-01 1.15111075e-01
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7.89794698e-02 9.03284132e-01 4.61891919e-01 -4.58257645... | [7.034970760345459, -1.0044233798980713] |
1f8e52e8-77fa-4418-b0f6-de15a6048e2b | speaker-separation-using-speaker-inventories | 2010.10556 | null | https://arxiv.org/abs/2010.10556v1 | https://arxiv.org/pdf/2010.10556v1.pdf | Speaker Separation Using Speaker Inventories and Estimated Speech | We propose speaker separation using speaker inventories and estimated speech (SSUSIES), a framework leveraging speaker profiles and estimated speech for speaker separation. SSUSIES contains two methods, speaker separation using speaker inventories (SSUSI) and speaker separation using estimated speech (SSUES). SSUSI per... | ['Yifan Gong', 'Jinyu Li', 'DeLiang Wang', 'Zhuo Chen', 'Peidong Wang'] | 2020-10-20 | null | null | null | null | ['speech-extraction', 'speaker-separation'] | ['speech', 'speech'] | [ 3.32087547e-01 -1.51095435e-01 -1.97103590e-01 -5.21160543e-01
-1.15261602e+00 -6.76078856e-01 6.57329082e-01 -2.43644387e-01
-5.53363934e-02 4.87965047e-01 4.87084836e-01 -4.21024859e-01
-4.50822443e-01 3.18916291e-02 -2.13953257e-01 -7.19329834e-01
-2.31771380e-01 2.11912110e-01 -1.44475415e-01 -1.28368497... | [14.643508911132812, 6.055769443511963] |
224dedb8-017f-4056-9a38-32e6cd14146f | instance-similarity-deep-hashing-for-multi | 1803.02987 | null | https://arxiv.org/abs/1803.02987v3 | https://arxiv.org/pdf/1803.02987v3.pdf | Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval | Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional feature-learning-based methods. Most of these methods examine the pairwise similarity on the semanti... | ['Song Wang', 'Yuewei Lin', 'Qin Zou', 'Long Chen', 'Zheng Zhang'] | 2018-03-08 | null | null | null | null | ['multi-label-image-retrieval'] | ['computer-vision'] | [ 5.59188938e-03 -4.68996674e-01 -4.26440299e-01 -6.84381902e-01
-1.22182405e+00 -3.92912716e-01 3.10432881e-01 8.10731471e-01
-4.80314404e-01 4.65071023e-01 3.35303843e-02 3.62375438e-01
-3.88933659e-01 -7.79708862e-01 -4.43740338e-01 -1.04603362e+00
-3.14906836e-02 4.98927891e-01 2.16627061e-01 1.36269256... | [11.368840217590332, 0.9497243762016296] |
0d503f9f-e3ef-4a76-a829-c02dde92049b | mention-annotations-alone-enable-efficient | 2210.07602 | null | https://arxiv.org/abs/2210.07602v2 | https://arxiv.org/pdf/2210.07602v2.pdf | Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution | Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, transferring these models to new target domains containing out-of-vocabulary spans and requiring differing annotation schemes remains challenging. Typical approaches involve continued training on annotat... | ['Emma Strubell', 'Anjalie Field', 'Nupoor Gandhi'] | 2022-10-14 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 4.07254040e-01 7.30438828e-01 -5.20217180e-01 -5.18965304e-01
-1.64842832e+00 -1.06718802e+00 2.77367800e-01 1.71636626e-01
-7.25841403e-01 1.10607350e+00 7.10157931e-01 -1.58125997e-01
-3.90445776e-02 -1.95673272e-01 -7.29811907e-01 -3.73569489e-01
8.34781826e-02 1.39428091e+00 2.21994951e-01 -2.03767300... | [9.31106948852539, 9.538646697998047] |
683841d8-8b0d-48d9-9b42-81f58170fbab | abcd-a-graph-framework-to-convert-complex | 2106.12027 | null | https://arxiv.org/abs/2106.12027v1 | https://arxiv.org/pdf/2106.12027v1.pdf | ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences | Atomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering. Previous work mainly relies on rule-based methods ... | ['Rebecca J. Passonneau', 'Huang', 'Ting-Hao', 'Yanjun Gao'] | 2021-06-22 | null | https://aclanthology.org/2021.acl-long.303 | https://aclanthology.org/2021.acl-long.303.pdf | acl-2021-5 | ['discourse-parsing'] | ['natural-language-processing'] | [ 5.75567007e-01 9.68551636e-01 1.58575550e-02 -6.08923733e-01
-9.86019254e-01 -8.15902650e-01 4.58017111e-01 7.84005344e-01
-2.66377389e-01 8.14360738e-01 8.25422943e-01 -5.54250777e-01
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-1.99270174e-01 5.79783618e-01 2.94347256e-01 -5.36078870... | [12.316582679748535, 9.437493324279785] |
df78a80c-6c4f-40a5-a06c-7eabc0030f1b | pointwise-shape-adaptive-dct-for-high-quality | null | null | https://ieeexplore.ieee.org/document/7071698?arnumber=7071698 | https://www.cs.tut.fi/~foi/papers/Eusipco2006-SA-DCT_Deblocking.pdf | Pointwise shape-adaptive DCT for high-quality deblocking of compressed color images | We present an high-quality image deblocking algorithm based on the shape-adaptive DCT (SA-DCT). The SA-DCT is a low-complexity transform which can be computed on a support of arbitrary shape. This transform has been adopted by the MPEG-4 standard and it is found implemented in modern video hardware. The use of this sha... | ['Karen Egiazarian', 'Vladimir Katkovnik', 'Alessandro Foi'] | 2006-09-04 | null | null | null | null | ['image-deblocking'] | ['computer-vision'] | [ 5.29289842e-01 -5.43934524e-01 2.73391247e-01 1.86193697e-02
-3.49448353e-01 -3.09337884e-01 4.14782941e-01 -1.67639926e-01
-3.44254851e-01 6.63914323e-01 3.76473874e-01 -6.88812882e-02
-3.73452231e-02 -6.17303669e-01 -2.29696333e-01 -1.12158203e+00
-1.97538763e-01 -1.34059757e-01 5.62373996e-01 -2.67672420... | [11.332501411437988, -2.2705605030059814] |
b7825748-26d9-4bbb-ac40-69cc9043843c | language-guided-networks-for-cross-modal | 2006.10457 | null | https://arxiv.org/abs/2006.10457v2 | https://arxiv.org/pdf/2006.10457v2.pdf | Language Guided Networks for Cross-modal Moment Retrieval | We address the challenging task of cross-modal moment retrieval, which aims to localize a temporal segment from an untrimmed video described by a natural language query. It poses great challenges over the proper semantic alignment between vision and linguistic domains. Existing methods independently extract the feature... | ['Kun Liu', 'Huadong Ma', 'Chuang Gan'] | 2020-06-18 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-3.76052074e-02 -5.16653895e-01 -3.27049375e-01 -2.95144469e-01
-1.06192648e+00 -5.05595565e-01 7.39030719e-01 -4.65137094e-01
-5.28312862e-01 2.74404526e-01 2.53270090e-01 1.16175957e-01
-1.02767639e-01 -3.01387072e-01 -7.19669700e-01 -8.92351747e-01
1.14057176e-01 -3.53747219e-01 2.33492002e-01 -1.32361546... | [10.260483741760254, 0.849033534526825] |
7389cc4b-31fb-4460-a354-3a19d5d1b462 | dynamic-range-independent-image-quality | null | null | https://resources.mpi-inf.mpg.de/hdr/vis_metric/ | https://resources.mpi-inf.mpg.de/hdr/vis_metric/aydin_sg08.pdf | Dynamic Range Independent Image Quality Assessment | The diversity of display technologies and introduction of high dynamic range imagery introduces the necessity of comparing images of radically different dynamic ranges. Current quality assessment metrics are not suitable for this task, as they assume that both reference and test images have the same dynamic range. Imag... | ['Hans-Peter Seidel', 'Karol Myszkowski', 'Rafal Mantiuk', 'Tunç O. Aydın'] | 2008-08-01 | null | null | null | siggraph-2008-8 | ['image-quality-assessment', 'tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 8.37009966e-01 -4.33590561e-01 3.57832938e-01 -5.15140295e-01
-3.55873644e-01 -6.72606111e-01 6.62967443e-01 -4.49028276e-02
-3.36109519e-01 5.16093731e-01 -7.41897374e-02 -3.98962110e-01
-3.65170926e-01 -6.55417144e-01 -2.91597515e-01 -6.31486475e-01
8.24820474e-02 -2.13202998e-01 5.15026748e-01 -4.46544558... | [11.529112815856934, -2.1039369106292725] |
8d0501b0-0454-411e-bece-b28ffcac95d4 | spatiotemporal-attention-based-semantic | 2305.12796 | null | https://arxiv.org/abs/2305.12796v1 | https://arxiv.org/pdf/2305.12796v1.pdf | Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition | This paper studies the computational offloading of video action recognition in edge computing. To achieve effective semantic information extraction and compression, following semantic communication we propose a novel spatiotemporal attention-based autoencoder (STAE) architecture, including a frame attention module and ... | ['Qi Zhang', 'Alexandros Iosifidis', 'Mehdi Bennis', 'Nan Li'] | 2023-05-22 | null | null | null | null | ['video-recognition', 'action-recognition-in-videos', 'action-recognition', 'edge-computing'] | ['computer-vision', 'computer-vision', 'computer-vision', 'time-series'] | [ 2.75663584e-01 -8.01603571e-02 -1.37851223e-01 -2.16994494e-01
-6.23033643e-01 6.87859952e-02 1.86461508e-01 -3.86558533e-01
-5.25887012e-01 5.59585869e-01 5.42050481e-01 -3.34358513e-01
8.57124031e-02 -6.90641582e-01 -1.26684737e+00 -5.95109522e-01
-1.53258637e-01 -1.27879947e-01 2.80880064e-01 1.79759100... | [11.068805694580078, -1.610788345336914] |
effc9667-9a93-450e-ba09-5398a9fe3c0d | measuring-feature-diversity-in-native | null | null | https://aclanthology.org/W15-0606 | https://aclanthology.org/W15-0606.pdf | Measuring Feature Diversity in Native Language Identification | null | ['Aoife Cahill', 'Shervin Malmasi'] | 2015-06-01 | null | null | null | ws-2015-6 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.233047008514404, 3.7484772205352783] |
b7df65b3-7981-4a63-a19b-fc366ab326ea | towards-supervised-extractive-text | 1911.06121 | null | https://arxiv.org/abs/1911.06121v1 | https://arxiv.org/pdf/1911.06121v1.pdf | Towards Supervised Extractive Text Summarization via RNN-based Sequence Classification | This article briefly explains our submitted approach to the DocEng'19 competition on extractive summarization. We implemented a recurrent neural network based model that learns to classify whether an article's sentence belongs to the corresponding extractive summary or not. We bypass the lack of large annotated news co... | ['Max Lübbering', 'Eduardo Brito', 'David Biesner', 'Christian Bauckhage', 'Lars Patrick Hillebrand'] | 2019-11-13 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 4.85546440e-01 8.17867279e-01 -5.09173155e-01 -3.08888823e-01
-1.12898445e+00 -5.26730180e-01 6.71689093e-01 5.99830925e-01
-6.61514580e-01 1.20941305e+00 1.47966099e+00 -1.38324812e-01
2.34887376e-01 -4.53456223e-01 -7.65829802e-01 -1.21005699e-01
1.96253225e-01 3.14944118e-01 -2.79553145e-01 -3.40023369... | [12.577139854431152, 9.524531364440918] |
42905805-5608-4e32-96fc-5f7c568b7a46 | locate-end-to-end-localization-of-actions-in | 2203.10719 | null | https://arxiv.org/abs/2203.10719v1 | https://arxiv.org/pdf/2203.10719v1.pdf | LocATe: End-to-end Localization of Actions in 3D with Transformers | Understanding a person's behavior from their 3D motion is a fundamental problem in computer vision with many applications. An important component of this problem is 3D Temporal Action Localization (3D-TAL), which involves recognizing what actions a person is performing, and when. State-of-the-art 3D-TAL methods employ ... | ['Arjun Chandrasekaran', 'Michael J. Black', 'Bolei Zhou', 'Jiankai Sun'] | 2022-03-21 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 1.40822157e-01 -5.39431453e-01 -1.55284300e-01 -2.75775999e-01
-6.71476185e-01 -2.47371376e-01 5.87961495e-01 -3.74889940e-01
-4.33182389e-01 2.83918709e-01 5.32365799e-01 6.35912642e-02
1.84073552e-01 -3.00510854e-01 -4.76485223e-01 -6.40393555e-01
-2.77442098e-01 2.21284270e-01 2.93231428e-01 6.15268908... | [8.233120918273926, 0.4600500464439392] |
22c86d4d-ef58-4694-a1c2-9890b2f22797 | sentiment-classification-using-document | null | null | https://aclanthology.org/P19-2057 | https://aclanthology.org/P19-2057.pdf | Sentiment Classification Using Document Embeddings Trained with Cosine Similarity | In document-level sentiment classification, each document must be mapped to a fixed length vector. Document embedding models map each document to a dense, low-dimensional vector in continuous vector space. This paper proposes training document embeddings using cosine similarity instead of dot product. Experiments on th... | ['Tan Thongtan', 'Tanasanee Phienthrakul'] | 2019-07-01 | null | null | null | acl-2019-7 | ['document-embedding'] | ['methodology'] | [-4.16828722e-01 -3.95356536e-01 -5.66459477e-01 -5.95616043e-01
-4.75970298e-01 -5.26423037e-01 8.36687088e-01 8.21177781e-01
-7.59162843e-01 1.26506761e-01 5.78185976e-01 -4.25491542e-01
-1.38172776e-01 -8.41289520e-01 -7.32344538e-02 -4.67861980e-01
-4.73658415e-03 2.30914950e-01 -1.22016087e-01 -2.76567489... | [10.489649772644043, 8.612469673156738] |
046eec6a-7a60-4cfd-bc15-77e908dbc9be | psychology-guided-controllable-story-1 | 2210.07493 | null | https://arxiv.org/abs/2210.07493v1 | https://arxiv.org/pdf/2210.07493v1.pdf | Psychology-guided Controllable Story Generation | Controllable story generation is a challenging task in the field of NLP, which has attracted increasing research interest in recent years. However, most existing works generate a whole story conditioned on the appointed keywords or emotions, ignoring the psychological changes of the protagonist. Inspired by psychology ... | ['Wei Peng', 'Luxi Xing', 'Guanqun Bi', 'Yunpeng Li', 'Yue Hu', 'Yuqiang Xie'] | 2022-10-14 | psychology-guided-controllable-story | https://aclanthology.org/2022.coling-1.564 | https://aclanthology.org/2022.coling-1.564.pdf | coling-2022-10 | ['story-generation'] | ['natural-language-processing'] | [-1.29152257e-02 1.59740284e-01 -2.39663601e-01 -4.35204118e-01
-5.99842295e-02 -4.03984219e-01 6.92500055e-01 2.01909006e-01
1.29888773e-01 5.43970168e-01 7.34958053e-01 4.44872022e-01
-3.61757837e-02 -9.55944777e-01 -4.93425548e-01 -4.28242058e-01
4.84839708e-01 4.27705884e-01 -2.92049646e-02 -5.51733851... | [11.791192054748535, 8.833076477050781] |
3c33aa59-e043-4fc0-9e64-a9deefe9e1ba | can-x2vec-save-lives-integrating-graph-and | 2001.01126 | null | https://arxiv.org/abs/2001.01126v1 | https://arxiv.org/pdf/2001.01126v1.pdf | Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health Classification | Graph and language embedding models are becoming commonplace in large scale analyses given their ability to represent complex sparse data densely in low-dimensional space. Integrating these models' complementary relational and communicative data may be especially helpful if predicting rare events or classifying members... | ['Alexander Ruch'] | 2020-01-04 | null | null | null | null | ['activity-prediction', 'document-embedding', 'activity-prediction'] | ['computer-vision', 'methodology', 'time-series'] | [-4.51775156e-02 4.50627863e-01 -4.54583287e-01 -1.12204589e-01
-1.72467634e-01 -4.87820923e-01 4.01919246e-01 8.60675633e-01
-4.19495851e-01 4.63312894e-01 7.82839715e-01 -3.44386697e-01
-3.66527706e-01 -1.15737605e+00 2.55609840e-01 -1.84126720e-01
-5.36693156e-01 6.11282229e-01 -2.51106441e-01 -1.24922365... | [7.1880950927734375, 6.232656478881836] |
84889524-dd49-44f2-9bf1-9aa53f804647 | memory-enhanced-embedding-learning-for-cross | 2103.15686 | null | https://arxiv.org/abs/2103.15686v1 | https://arxiv.org/pdf/2103.15686v1.pdf | Memory Enhanced Embedding Learning for Cross-Modal Video-Text Retrieval | Cross-modal video-text retrieval, a challenging task in the field of vision and language, aims at retrieving corresponding instance giving sample from either modality. Existing approaches for this task all focus on how to design encoding model through a hard negative ranking loss, leaving two key problems unaddressed d... | ['Jiebo Luo', 'Hongtao Xie', 'Zheng-Jun Zha', 'Kecheng Zheng', 'Rui Zhao'] | 2021-03-29 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 1.23643979e-01 -5.16128838e-01 -2.84108937e-01 -2.81402320e-01
-8.57315242e-01 -1.17898747e-01 5.25036514e-01 1.11097917e-01
-7.24515080e-01 3.77179086e-01 -1.52902365e-01 1.24560699e-01
-1.29151270e-01 -6.08367860e-01 -6.43407583e-01 -9.04259086e-01
1.13081336e-01 2.38422662e-01 1.40352935e-01 1.37007311... | [10.610312461853027, 1.0826624631881714] |
160887fe-cd60-4741-8b8f-539b5c1f763b | the-lmu-munich-system-for-the-wmt-2020 | 2010.13192 | null | https://arxiv.org/abs/2010.13192v1 | https://arxiv.org/pdf/2010.13192v1.pdf | The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task | This paper describes the submission of LMU Munich to the WMT 2020 unsupervised shared task, in two language directions, German<->Upper Sorbian. Our core unsupervised neural machine translation (UNMT) system follows the strategy of Chronopoulou et al. (2020), using a monolingual pretrained language generation model (on ... | ['Alexander Fraser', 'Viktor Hangya', 'Dario Stojanovski', 'Alexandra Chronopoulou'] | 2020-10-25 | null | https://aclanthology.org/2020.wmt-1.128 | https://aclanthology.org/2020.wmt-1.128.pdf | wmt-emnlp-2020-11 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.25518274e-01 3.83106351e-01 -1.17545836e-01 -3.96193534e-01
-1.32935917e+00 -8.32202315e-01 9.07383442e-01 -2.95019954e-01
-8.03250074e-01 1.26045179e+00 4.30430442e-01 -7.43729770e-01
3.05480599e-01 -5.42714834e-01 -7.88415670e-01 -4.04597074e-01
4.68359828e-01 1.24872971e+00 -3.68221968e-01 -6.87105238... | [11.55778694152832, 10.426965713500977] |
ca4e39c7-e7c1-4cf9-b29c-187735e38463 | catch-a-waveform-learning-to-generate-audio | 2106.06426 | null | https://arxiv.org/abs/2106.06426v2 | https://arxiv.org/pdf/2106.06426v2.pdf | Catch-A-Waveform: Learning to Generate Audio from a Single Short Example | Models for audio generation are typically trained on hours of recordings. Here, we illustrate that capturing the essence of an audio source is typically possible from as little as a few tens of seconds from a single training signal. Specifically, we present a GAN-based generative model that can be trained on one short ... | ['Tomer Michaeli', 'Tamar Rott Shaham', 'Gal Greshler'] | 2021-06-11 | null | http://proceedings.neurips.cc/paper/2021/hash/af21d0c97db2e27e13572cbf59eb343d-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/af21d0c97db2e27e13572cbf59eb343d-Paper.pdf | neurips-2021-12 | ['audio-generation'] | ['audio'] | [ 6.55454099e-01 3.21356535e-01 3.52215379e-01 -1.82033852e-02
-1.22264838e+00 -8.82310271e-01 4.36905921e-01 -7.51675218e-02
6.59312233e-02 9.22511160e-01 2.02588528e-01 7.52478465e-02
1.01908460e-01 -6.17967010e-01 -9.43747044e-01 -6.46434963e-01
-4.93317991e-02 2.57538378e-01 5.39010838e-02 -3.94104242... | [15.597638130187988, 5.7985687255859375] |
44942d8c-1e92-49d8-a062-f69e1670d5dd | changepoint-detection-in-noisy-data-using-a | null | null | https://doi.org/10.3390/brainsci12050525 | https://www.mdpi.com/2076-3425/12/5/525/pdf?version=1650772641 | Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs | An important problem in many fields dealing with noisy time series, such as psychophysiological single trial data during learning or monitoring treatment effects over time, is detecting a change in the model underlying a time series. Here, we present a new method for detecting a single changepoint in a linear time seri... | ['Grażyna Ślusarczyk', 'Anna Bereś', 'Jeremi Ochab', 'Piotr Fabian', 'Krzysztof Kotowski', 'Grzegorz Kończak', 'Katarzyna Stapor', 'Werner Sommer'] | 2022-04-21 | null | null | null | brain-sciences-2022-4 | ['time-series-regression'] | ['time-series'] | [ 4.53878224e-01 -4.19942856e-01 5.99468090e-02 -2.26735502e-01
-6.31884873e-01 -6.20185792e-01 3.86729658e-01 3.99078935e-01
-7.09760666e-01 7.74855018e-01 -1.24549232e-01 -2.90275127e-01
-7.37277448e-01 -2.25429103e-01 -7.12941647e-01 -7.56901979e-01
-4.99081403e-01 -3.98204587e-02 1.47060439e-01 -7.51130357... | [13.038705825805664, 3.3905794620513916] |
451b3629-62c5-4812-966e-f9c6f4748015 | visual-chain-of-thought-bridging-logical-gaps | 2305.02317 | null | https://arxiv.org/abs/2305.02317v1 | https://arxiv.org/pdf/2305.02317v1.pdf | Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings | Recent advances in large language models elicit reasoning in a chain of thought that allows models to decompose problems in a human-like fashion. Though this paradigm improves multi-step reasoning ability in language models, it is limited by being unimodal and applied mainly to question-answering tasks. We claim that i... | ['William Yang Wang', 'Diba Mirza', 'Chinmay Sonar', 'Michael Saxon', 'Yujie Lu', 'Alex Mei', 'Ryan He', 'Andy Ouyang', 'Vaishnavi Himakunthala', 'Daniel Rose'] | 2023-05-03 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.33782277e-01 7.46587813e-01 -3.93962227e-02 -1.28574520e-01
-7.76043415e-01 -9.56662238e-01 1.00706089e+00 4.23504978e-01
-8.56387615e-02 4.19125825e-01 1.08540320e+00 -8.96255195e-01
-1.35253847e-01 -6.48114741e-01 -7.17652380e-01 3.83966975e-02
3.56935292e-01 8.38577151e-01 -1.15061678e-01 -5.97887814... | [10.872467994689941, 1.8985121250152588] |
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