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77865b2e-d071-484d-a801-42e8af5bdda9 | on-hyperspectral-unmixing | 2106.14177 | null | https://arxiv.org/abs/2106.14177v1 | https://arxiv.org/pdf/2106.14177v1.pdf | On Hyperspectral Unmixing | In this article the author reviews Jos\'e Bioucas-Dias' key contributions to hyperspectral unmixing (HU), in memory of him as an influential scholar and for his many beautiful ideas introduced to the hyperspectral community. Our story will start with vertex component analysis (VCA) -- one of the most celebrated HU algo... | ['Wing-Kin Ma'] | 2021-06-27 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.91064489e-01 -1.24542803e-01 -2.23532513e-01 5.26459850e-02
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3f9f8185-7df6-4262-a475-57ae9b2941d1 | dynamic-aggregated-network-for-gait | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ma_Dynamic_Aggregated_Network_for_Gait_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ma_Dynamic_Aggregated_Network_for_Gait_Recognition_CVPR_2023_paper.pdf | Dynamic Aggregated Network for Gait Recognition | Gait recognition is beneficial for a variety of applications, including video surveillance, crime scene investigation, and social security, to mention a few. However, gait recognition often suffers from multiple exterior factors in real scenes, such as carrying conditions, wearing overcoats, and diverse viewing ang... | ['Yongzhen Huang', 'Xuecai Hu', 'Chunshui Cao', 'Dezhi Zheng', 'Ying Fu', 'Kang Ma'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['gait-recognition'] | ['computer-vision'] | [-2.47998595e-01 -1.03982460e+00 -2.57349819e-01 -2.07436100e-01
-2.35463336e-01 2.66718380e-02 1.15934238e-01 -1.94867343e-01
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-3.49703163e-01 -1.43750012e-01 5.67825735e-01 -3.12939942... | [14.295323371887207, 1.4185725450515747] |
c60c4aaf-bcad-4f92-9f75-52d470fb1183 | splinecnn-fast-geometric-deep-learning-with | 1711.0892 | null | http://arxiv.org/abs/1711.08920v2 | http://arxiv.org/pdf/1711.08920v2.pdf | SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels | We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant
of deep neural networks for irregular structured and geometric input, e.g.,
graphs or meshes. Our main contribution is a novel convolution operator based
on B-splines, that makes the computation time independent from the kernel size
due to th... | ['Heinrich Müller', 'Matthias Fey', 'Jan Eric Lenssen', 'Frank Weichert'] | 2017-11-24 | splinecnn-fast-geometric-deep-learning-with-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Fey_SplineCNN_Fast_Geometric_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Fey_SplineCNN_Fast_Geometric_CVPR_2018_paper.pdf | cvpr-2018-6 | ['superpixel-image-classification'] | ['computer-vision'] | [ 5.40474616e-03 3.44403803e-01 3.29939388e-02 -3.64474058e-01
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-5.35076201e-01 3.35297734e-01 2.81670690e-01 -2.59025842... | [8.27674388885498, -3.668534994125366] |
95981c75-1fa5-4afd-b11b-d2679f354b0e | discrete-opinion-tree-induction-for-aspect | null | null | https://aclanthology.org/2022.acl-long.145 | https://aclanthology.org/2022.acl-long.145.pdf | Discrete Opinion Tree Induction for Aspect-based Sentiment Analysis | Dependency trees have been intensively used with graph neural networks for aspect-based sentiment classification. Though being effective, such methods rely on external dependency parsers, which can be unavailable for low-resource languages or perform worse in low-resource domains. In addition, dependency trees are also... | ['Yue Zhang', 'Zhongqing Wang', 'Zhiyang Teng', 'Chenhua Chen'] | null | null | null | null | acl-2022-5 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-6.95747510e-02 1.29210249e-01 -6.52737856e-01 -7.33047187e-01
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1.84427455e-01 6.59686267e-01 2.66787186e-02 -2.73416609... | [11.440799713134766, 6.68006706237793] |
270fcbcc-26a5-46ff-bc44-cdb0bca68e7a | wasserstein-auto-encoders-of-merge-trees-and | 2307.02509 | null | https://arxiv.org/abs/2307.02509v1 | https://arxiv.org/pdf/2307.02509v1.pdf | Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams) | This paper presents a computational framework for the Wasserstein auto-encoding of merge trees (MT-WAE), a novel extension of the classical auto-encoder neural network architecture to the Wasserstein metric space of merge trees. In contrast to traditional auto-encoders which operate on vectorized data, our formulation ... | ['Julien Tierny', 'Mahieu Pont'] | 2023-07-05 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 4.41543132e-01 1.98522970e-01 3.50530297e-01 -3.61168295e-01
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-5.57343066e-01 4.69850272e-01 -2.75546432e-01 -1.64642930... | [8.117996215820312, 4.049621105194092] |
953b1846-3ae1-4dfc-ad39-8b7958e3cce0 | the-uncertainty-based-retrieval-framework-for | null | null | https://aclanthology.org/2022.lt4hala-1.25 | https://aclanthology.org/2022.lt4hala-1.25.pdf | The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS | Automatic analysis for modern Chinese has greatly improved the accuracy of text mining in related fields, but the study of ancient Chinese is still relatively rare. Ancient text division and lexical annotation are important parts of classical literature comprehension, and previous studies have tried to construct auxili... | ['Zhichen Ren', 'Pengyu Wang'] | null | null | null | null | lt4hala-lrec-2022-6 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 2.92471759e-02 -9.30153802e-02 -4.84083951e-01 -3.88403088e-01
-6.95049345e-01 -4.83018577e-01 3.92338663e-01 1.39915673e-02
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3.96187395e-01 -7.39712536e-01 -2.95894951e-01 -5.08635461e-01
3.87522638e-01 8.40632200e-01 3.80772114e-01 -2.33192772... | [10.016508102416992, 10.108893394470215] |
1e1b9f40-405c-4625-b576-c5a09aa7e63f | entity-agnostic-representation-learning-for | 2302.01849 | null | https://arxiv.org/abs/2302.01849v1 | https://arxiv.org/pdf/2302.01849v1.pdf | Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding | We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs. Conventional knowledge graph embedding methods map elements in a knowledge graph, including entities and relations, into continuous vector spaces by assigni... | ['Huajun Chen', 'Jeff Z. Pan', 'Yang Gao', 'Yushan Zhu', 'Zhen Yao', 'Wen Zhang', 'Mingyang Chen'] | 2023-02-03 | null | null | null | null | ['knowledge-graph-embedding', 'entity-embeddings'] | ['graphs', 'methodology'] | [-4.00553077e-01 5.42187393e-01 -7.83734500e-01 -1.76978260e-01
-3.05049658e-01 -7.40235448e-01 2.82569170e-01 4.66884732e-01
-3.92421067e-01 5.43639660e-01 2.78653115e-01 -3.08323741e-01
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-4.41314012e-01 5.54133654e-01 2.99162537e-01 -9.92817711... | [8.754498481750488, 7.9013566970825195] |
dd360a10-fed2-4661-91a0-0cdf62546cee | quantitative-argument-summarization-and | 2010.05369 | null | https://arxiv.org/abs/2010.05369v1 | https://arxiv.org/pdf/2010.05369v1.pdf | Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis | When summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence. Work on multi-document summarization has traditionally focused on creating textual summaries, which lack this quantitative aspect. Recent... | ['Noam Slonim', 'Dan Lahav', 'Roni Friedman', 'Lilach Eden', 'Yoav Kantor', 'Roy Bar-Haim'] | 2020-10-11 | null | https://aclanthology.org/2020.emnlp-main.3 | https://aclanthology.org/2020.emnlp-main.3.pdf | emnlp-2020-11 | ['key-point-matching'] | ['natural-language-processing'] | [ 4.05993283e-01 6.36721909e-01 -7.05286503e-01 -1.71380192e-01
-1.48031139e+00 -8.49677563e-01 1.23072445e+00 1.27994967e+00
-2.30422974e-01 7.79997349e-01 1.25383115e+00 -3.68345529e-01
-2.84740418e-01 -6.15482628e-01 -4.17339474e-01 -1.38105184e-01
3.57636809e-01 4.88192379e-01 1.52488440e-01 -4.92794007... | [12.151368141174316, 9.560551643371582] |
dfbbbe9b-1641-4b23-836f-d8fdef0a0c49 | a-semi-supervised-object-detection-algorithm | 2306.04834 | null | https://arxiv.org/abs/2306.04834v1 | https://arxiv.org/pdf/2306.04834v1.pdf | A Semi-supervised Object Detection Algorithm for Underwater Imagery | Detection of artificial objects from underwater imagery gathered by Autonomous Underwater Vehicles (AUVs) is a key requirement for many subsea applications. Real-world AUV image datasets tend to be very large and unlabelled. Furthermore, such datasets are typically imbalanced, containing few instances of objects of int... | ['Stefan B. Williams', 'Oscar Pizarro', 'Suraj Bijjahalli'] | 2023-06-07 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 2.92133957e-01 1.08924761e-01 6.68378413e-01 -4.14638907e-01
-1.02345657e+00 -4.78627235e-01 4.02287960e-01 2.81318843e-01
-7.24417686e-01 4.71229583e-01 -2.83482343e-01 1.79805905e-01
-2.29825988e-01 -9.14842010e-01 -9.80261743e-01 -1.06095123e+00
-4.99158561e-01 4.92980599e-01 4.76392448e-01 -4.69579361... | [7.698256492614746, 2.230764389038086] |
08c6f90a-37f0-43db-bdb3-a4d89b7eb49b | least-square-value-iteration-is-robust-under | 2306.10694 | null | https://arxiv.org/abs/2306.10694v1 | https://arxiv.org/pdf/2306.10694v1.pdf | Least Square Value Iteration is Robust Under Locally Bounded Misspecification Error | The success of reinforcement learning heavily relies on the function approximation of policy, value or models, where misspecification (a mismatch between the ground-truth and best function approximators) naturally occurs especially when the ground-truth is complex. As misspecification error does not vanish even with in... | ['Lin Yang', 'Yunfan Li'] | 2023-06-19 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 1.50060020e-02 4.95583057e-01 -5.39198875e-01 8.16806853e-02
-1.12065673e+00 -7.51456797e-01 1.11636370e-01 1.95566714e-01
-7.54476070e-01 1.33621407e+00 -2.78756112e-01 -5.55324852e-01
-6.21862650e-01 -8.74196708e-01 -1.06664371e+00 -9.00524855e-01
-3.19558531e-01 4.46383893e-01 -5.41834533e-03 -1.88053221... | [4.299725532531738, 2.847247362136841] |
e4c7da36-50ee-406c-85d3-f5e73058ba66 | an-underwater-image-enhancement-benchmark | 1901.05495 | null | https://arxiv.org/abs/1901.05495v2 | https://arxiv.org/pdf/1901.05495v2.pdf | An Underwater Image Enhancement Benchmark Dataset and Beyond | Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected re... | ['DaCheng Tao', 'Sam Kwong', 'Runmin Cong', 'Chunle Guo', 'Wenqi Ren', 'Junhui Hou', 'Chongyi Li'] | 2019-01-11 | null | null | null | null | ['underwater-image-restoration'] | ['computer-vision'] | [ 4.05594438e-01 -6.53394386e-02 9.04238760e-01 -4.27741647e-01
-4.46894884e-01 -1.16439581e-01 2.12294802e-01 -7.69104287e-02
-1.28507376e+00 6.54468536e-01 1.92958727e-01 2.55032703e-02
-1.42873982e-02 -1.01760721e+00 -8.28198850e-01 -8.87663722e-01
-4.73815709e-01 -5.84233224e-01 3.37390661e-01 -6.73533559... | [10.687390327453613, -3.5251107215881348] |
80d1ceef-27ae-446a-9bf1-0b38649f82f5 | leveraging-procedural-generation-to-benchmark | 1912.01588 | null | https://arxiv.org/abs/1912.01588v2 | https://arxiv.org/pdf/1912.01588v2.pdf | Leveraging Procedural Generation to Benchmark Reinforcement Learning | We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like environments designed to benchmark both sample efficiency and generalization in reinforcement learning. We believe that the community will benefit from increased access to high quality training environments, and we provide detailed experimen... | ['Karl Cobbe', 'John Schulman', 'Jacob Hilton', 'Christopher Hesse'] | 2019-12-03 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/2971-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/2971-Paper.pdf | icml-2020-1 | ['procgen-hard-100m'] | ['playing-games'] | [ 6.50041178e-03 -1.02482274e-01 -7.77327269e-03 -2.34715790e-02
-8.65863025e-01 -8.48874629e-01 5.87975860e-01 1.15930391e-02
-9.23452020e-01 1.06597972e+00 1.68369964e-01 -3.82254869e-01
-3.20632085e-02 -9.30615544e-01 -7.45201886e-01 -3.57670873e-01
-6.42550647e-01 4.59763467e-01 3.43626082e-01 -2.46585310... | [3.929730176925659, 1.5652248859405518] |
93d7467d-a572-4a1f-8388-23866a20bea1 | reference-guided-image-inpainting-using | 2301.08044 | null | https://arxiv.org/abs/2301.08044v1 | https://arxiv.org/pdf/2301.08044v1.pdf | Reference Guided Image Inpainting using Facial Attributes | Image inpainting is a technique of completing missing pixels such as occluded region restoration, distracting objects removal, and facial completion. Among these inpainting tasks, facial completion algorithm performs face inpainting according to the user direction. Existing approaches require delicate and well controll... | ['Hanseok Ko', 'Youngsaeng Jin', 'David Han', 'Yuanming Li', 'Jeonggi Kwak', 'Dongsik Yoon'] | 2023-01-19 | null | null | null | null | ['facial-inpainting', 'image-inpainting', 'ms-ssim'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.06935298e-01 1.59723371e-01 5.68703935e-02 -7.03425527e-01
-9.39259887e-01 -2.86012292e-01 3.76750112e-01 -4.15807456e-01
-2.21004814e-01 9.23212707e-01 8.27415213e-02 2.02726275e-01
2.98349500e-01 -5.92124760e-01 -1.01024878e+00 -5.60179412e-01
4.80694950e-01 5.28220654e-01 -2.97859639e-01 -2.00317562... | [12.599967002868652, -0.2276587039232254] |
8133c4af-62a5-4d12-b814-867b84c8cfdd | the-guide-and-the-explorer-smart-agents-for | null | null | https://openreview.net/forum?id=G9JXCpShpni | https://openreview.net/pdf?id=G9JXCpShpni | The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning | Iterated batch reinforcement learning (RL) is a growing subfield fueled by the demand from systems engineers for intelligent control solutions that they can apply within their technical and organizational constraints. Model-based RL (MBRL) suits this scenario well for its sample efficiency and modularity. Recent MBRL t... | ['Gabriel Hurtado', 'Othman Gaizi', 'Balázs Kégl', 'Albert Thomas'] | 2021-09-29 | null | null | null | null | ['acrobot'] | ['playing-games'] | [ 1.50164083e-01 4.44305927e-01 -5.82454443e-01 2.26072341e-01
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23437eb7-9e32-479a-a2b8-88086a92bf91 | mixed-integer-optimal-control-via | 2305.01461 | null | https://arxiv.org/abs/2305.01461v1 | https://arxiv.org/pdf/2305.01461v1.pdf | Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Vehicle Energy Management | Many optimal control problems require the simultaneous output of continuous and discrete control variables. Such problems are usually formulated as mixed-integer optimal control (MIOC) problems, which are challenging to solve due to the complexity of the solution space. Numerical methods such as branch-and-bound are co... | ['Yuan Lin', 'Jinming Xu'] | 2023-05-02 | null | null | null | null | ['q-learning', 'energy-management'] | ['methodology', 'time-series'] | [-2.93905944e-01 1.31694302e-01 -7.06949651e-01 1.28890231e-01
-9.28550899e-01 -4.47832137e-01 3.27419400e-01 8.58079940e-02
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-1.11766376e-01 6.66452289e-01 -1.53811961e-01 -3.39377999... | [5.282005786895752, 2.2994890213012695] |
a4c61a36-79d8-40c1-b260-a97d19026d69 | bilinear-cnn-models-for-fine-grained-visual | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Lin_Bilinear_CNN_Models_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Lin_Bilinear_CNN_Models_ICCV_2015_paper.pdf | Bilinear CNN Models for Fine-Grained Visual Recognition | We propose bilinear models, a recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an image descriptor. This architecture can model local pairwise feature interactions in a translationally invariant manner wh... | ['Subhransu Maji', 'Aruni RoyChowdhury', 'Tsung-Yu Lin'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [-8.80729780e-02 -5.79145253e-01 -1.20950356e-01 -7.16532588e-01
-5.67401171e-01 -7.54377246e-01 8.95105124e-01 1.37463599e-01
-6.94851160e-01 3.68924797e-01 -1.80879936e-01 -2.55338877e-01
-2.74459302e-01 -6.44374430e-01 -7.66459644e-01 -7.93204665e-01
-2.46148854e-01 3.24030429e-01 2.98950672e-01 -9.26601216... | [9.406373023986816, 1.757855772972107] |
57f5e2ff-9333-4ed6-a8d4-0b279ef58dc5 | client-driven-lightweight-method-to-generate | null | null | https://www.scitepress.org/Link.aspx?doi=10.5220/0011335300003289 | https://www.scitepress.org/Link.aspx?doi=10.5220/0011335300003289 | Client-driven Lightweight Method to Generate Artistic Media for Feature-length Sports Videos | This paper proposes a lightweight methodology to attract users and increase views of videos through personalized artistic media i.e., static thumbnails and animated Graphics Interchange Format (GIF) images. The proposed method analyzes lightweight thumbnail containers (LTC) using computational resources of the client d... | ['Eun-Seok Ryu', 'Jaehyuk Choi', 'Ghulam Mujtaba'] | 2022-07-01 | null | null | null | conference-2022-7 | ['animated-gif-generation', 'sports-analytics', 'video-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.39207223e-01 -4.88643557e-01 -3.08334976e-02 -3.88986394e-02
-3.23035538e-01 -6.65695071e-01 1.75077870e-01 5.36094084e-02
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-2.00054068e-02 1.69739619e-01 9.06755626e-01 1.55803382... | [10.595227241516113, -1.0071988105773926] |
890da00f-80b7-46ca-af7a-f0ff95b64a33 | multimodal-data-integration-for-oncology-in | 2303.06471 | null | https://arxiv.org/abs/2303.06471v1 | https://arxiv.org/pdf/2303.06471v1.pdf | Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review | Cancer has relational information residing at varying scales, modalities, and resolutions of the acquired data, such as radiology, pathology, genomics, proteomics, and clinical records. Integrating diverse data types can improve the accuracy and reliability of cancer diagnosis and treatment. There can be disease-relate... | ['Ghulam Rasool', 'Paul Stewart', 'Ravi P. Ramachandran', 'Aakash Tripathi', 'Asim Waqas'] | 2023-03-11 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 3.66241515e-01 -4.66754660e-02 -6.11235261e-01 -1.86887588e-02
-9.29584086e-01 -1.95595786e-01 2.01722220e-01 9.98799264e-01
-1.60754487e-01 6.82068944e-01 4.44834888e-01 -5.25667608e-01
-6.34905457e-01 -7.16401339e-01 -2.13509753e-01 -1.00869846e+00
-8.14770833e-02 4.08489496e-01 -4.56813246e-01 -4.89398807... | [15.162378311157227, -2.782594919204712] |
918c57c9-abcc-4a7d-96f9-ddcadef8a67e | kernel-spectral-clustering-and-applications | 1505.00477 | null | http://arxiv.org/abs/1505.00477v1 | http://arxiv.org/pdf/1505.00477v1.pdf | Kernel Spectral Clustering and applications | In this chapter we review the main literature related to kernel spectral
clustering (KSC), an approach to clustering cast within a kernel-based
optimization setting. KSC represents a least-squares support vector machine
based formulation of spectral clustering described by a weighted kernel PCA
objective. Just as in th... | ['Johan A. K. Suykens', 'Carlos Alzate', 'Rocco Langone', 'Raghvendra Mall'] | 2015-05-03 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 5.60300723e-02 -2.27820277e-01 -3.50054115e-01 -2.28969604e-01
-6.95370138e-01 -5.80286205e-01 2.35383600e-01 2.14482948e-01
-2.03547671e-01 4.65624511e-01 -2.26141378e-01 -1.32654980e-01
-8.00435066e-01 -3.66286397e-01 -4.56561506e-01 -1.26546037e+00
-3.86585534e-01 6.17013633e-01 -8.59367624e-02 3.28552157... | [7.742588520050049, 4.351927757263184] |
98491218-ae57-4091-9539-9f36ab5338c0 | meta-tuning-loss-functions-and-data | 2304.12161 | null | https://arxiv.org/abs/2304.12161v1 | https://arxiv.org/pdf/2304.12161v1.pdf | Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection | Few-shot object detection, the problem of modelling novel object detection categories with few training instances, is an emerging topic in the area of few-shot learning and object detection. Contemporary techniques can be divided into two groups: fine-tuning based and meta-learning based approaches. While meta-learning... | ['Ramazan Gokberk Cinbis', 'Orhun Buğra Baran', 'Berkan Demirel'] | 2023-04-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Demirel_Meta-Tuning_Loss_Functions_and_Data_Augmentation_for_Few-Shot_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Demirel_Meta-Tuning_Loss_Functions_and_Data_Augmentation_for_Few-Shot_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['few-shot-object-detection'] | ['computer-vision'] | [ 2.18508944e-01 -3.95193091e-03 -4.35576439e-01 -2.91962355e-01
-8.18862736e-01 3.97443138e-02 9.22448754e-01 3.46655160e-01
-5.83458900e-01 4.94991213e-01 -5.35215922e-02 3.63071114e-01
-1.55234948e-01 -9.54386294e-01 -7.27698207e-01 -7.51082599e-01
2.24355415e-01 2.99997807e-01 7.58007526e-01 -2.71181434... | [9.848987579345703, 2.4686851501464844] |
85ca2069-832d-476a-93df-7688707c1089 | the-yule-frisch-waugh-lovell-theorem | 2307.00369 | null | https://arxiv.org/abs/2307.00369v1 | https://arxiv.org/pdf/2307.00369v1.pdf | The Yule-Frisch-Waugh-Lovell Theorem | This paper traces the historical and analytical development of what is known in the econometrics literature as the Frisch-Waugh-Lovell theorem. This theorem demonstrates that the coefficients on any subset of covariates in a multiple regression is equal to the coefficients in a regression of the residualized outcome va... | ['Deepankar Basu'] | 2023-07-01 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-3.64447534e-02 7.83637837e-02 -5.21783710e-01 -4.34355289e-01
-6.09542489e-01 -3.89361203e-01 3.22085798e-01 -1.88864186e-01
-3.56109560e-01 9.27069485e-01 2.15044469e-01 -8.96323562e-01
-6.33444786e-01 -3.43848795e-01 -6.30325556e-01 -7.07791388e-01
-5.74388914e-02 -9.99064893e-02 -2.57435024e-01 -1.55404717... | [7.816800594329834, 5.083799839019775] |
e34566fb-c7aa-4685-a137-de82a7ec01a8 | dude-dual-decoder-multilingual-asr-for-indian | 2210.16739 | null | https://arxiv.org/abs/2210.16739v1 | https://arxiv.org/pdf/2210.16739v1.pdf | DuDe: Dual-Decoder Multilingual ASR for Indian Languages using Common Label Set | In a multilingual country like India, multilingual Automatic Speech Recognition (ASR) systems have much scope. Multilingual ASR systems exhibit many advantages like scalability, maintainability, and improved performance over the monolingual ASR systems. However, building multilingual systems for Indian languages is cha... | ['Umesh S', 'Mudit Batra', 'Arunkumar A'] | 2022-10-30 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-3.00655216e-01 -4.57604825e-01 -2.17556283e-01 -5.16780257e-01
-1.16486669e+00 -8.88646960e-01 5.51216364e-01 -1.75816178e-01
-3.27066302e-01 6.56913280e-01 3.01976621e-01 -7.85666585e-01
5.05448997e-01 -4.66502160e-01 -6.03514135e-01 -2.07549453e-01
6.50839567e-01 4.45112884e-01 1.88488260e-01 -4.90114689... | [14.336803436279297, 7.033246994018555] |
7757203a-35c2-4622-8c78-00ce6297d58b | bridging-distributional-and-risk-sensitive | 2210.14051 | null | https://arxiv.org/abs/2210.14051v2 | https://arxiv.org/pdf/2210.14051v2.pdf | Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds | We study the regret guarantee for risk-sensitive reinforcement learning (RSRL) via distributional reinforcement learning (DRL) methods. In particular, we consider finite episodic Markov decision processes whose objective is the entropic risk measure (EntRM) of return. We identify a key property of the EntRM, the monoto... | ['Zhi-Quan Luo', 'Hao Liang'] | 2022-10-25 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [ 1.42230064e-01 3.68379027e-01 -1.89358994e-01 -2.09874973e-01
-1.15100658e+00 -5.74147105e-01 -1.04448915e-01 2.91847676e-01
-1.04002130e+00 1.15406692e+00 -2.56055534e-01 -6.38677001e-01
-7.81431377e-01 -9.45390284e-01 -9.45301414e-01 -1.12450039e+00
-5.68546355e-01 2.30820671e-01 9.11225379e-02 -2.87419558... | [4.373906135559082, 2.9331655502319336] |
fe0ecf29-eddd-49ee-a9ca-023843d2b205 | exploiting-topic-based-twitter-sentiment-for | null | null | https://aclanthology.org/P13-2005 | https://aclanthology.org/P13-2005.pdf | Exploiting Topic based Twitter Sentiment for Stock Prediction | null | ['Huayi Li', 'Xiaotie Deng', 'Bing Liu', 'Qing Li', 'Jianfeng Si', 'Arjun Mukherjee'] | 2013-08-01 | null | null | null | acl-2013-8 | ['stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-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... | [-7.326651573181152, 3.7653591632843018] |
6366af3c-5591-4d31-a85d-de06ca7e5f88 | multilingual-short-text-responses-clustering | null | null | https://aclanthology.org/W18-3723 | https://aclanthology.org/W18-3723.pdf | Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration | Text clustering is a powerful technique to detect topics from document corpora, so as to provide information browsing, analysis, and organization. On the other hand, the Instant Response System (IRS) has been widely used in recent years to enhance student engagement in class and thus improve their learning effectivenes... | ['Tsung-Yen Li', 'Chun-Yen Chang', 'Yu-Ta Chien', 'Lung-Hao Lee', 'Yuen-Hsien Tseng'] | 2018-07-01 | null | null | null | ws-2018-7 | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-1.67444140e-01 -2.29402483e-01 -3.34866382e-02 -4.11629975e-01
-3.17322969e-01 -5.90822577e-01 5.53459167e-01 6.55553043e-01
-3.13149482e-01 4.49255377e-01 5.66453263e-02 -5.07728040e-01
-4.25508916e-01 -8.78519833e-01 3.26028951e-02 -6.18476927e-01
4.17710990e-01 2.20001608e-01 6.01907015e-01 -2.52722293... | [10.39604377746582, 7.284470558166504] |
c7ad3b7f-1622-434d-a74d-ebdaacaa35f4 | 1e2caecoea-e343a-c-14e2a-ae2e-acoustic-echo-1 | null | null | https://aclanthology.org/O17-1018 | https://aclanthology.org/O17-1018.pdf | 改進的向量空間可適性濾波器用於聲學回聲消除 (Acoustic Echo Cancellation Using an Improved Vector-Space-Based Adaptive Filtering Algorithm) [In Chinese] | null | ['Ying-Ren Chien', 'Jin Li-You', 'Yu Tsao'] | 2017-11-01 | 1e2caecoea-e343a-c-14e2a-ae2e-acoustic-echo | https://aclanthology.org/O17-3005 | https://aclanthology.org/O17-3005.pdf | roclingijclclp-2017-11 | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [-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... | [-7.233340740203857, 3.6774749755859375] |
58f411d6-bae4-46de-87e5-4f7e8b9acaa3 | optimizing-multi-domain-performance-with | 2304.06277 | null | https://arxiv.org/abs/2304.06277v1 | https://arxiv.org/pdf/2304.06277v1.pdf | Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies | Improving performance in multiple domains is a challenging task, and often requires significant amounts of data to train and test models. Active learning techniques provide a promising solution by enabling models to select the most informative samples for labeling, thus reducing the amount of labeled data required to a... | ['Rakshitha Panduranga', 'Royston Mascarenhas', 'Akshay Gulati', 'Aayush Shah', 'Anand Gokul Mahalingam'] | 2023-04-13 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 2.77185738e-01 -4.65032309e-02 -8.81335676e-01 -8.02156031e-01
-1.54902732e+00 -5.55420816e-01 5.60640275e-01 4.75714177e-01
-5.89289904e-01 6.63079441e-01 3.51536907e-02 -3.12324595e-02
1.31960228e-01 -5.52274108e-01 -5.95974982e-01 -4.94020104e-01
2.03597322e-01 7.97146976e-01 5.64296246e-01 1.32242382... | [9.632560729980469, 4.171031475067139] |
7f247823-cc73-45f2-b4e0-2a9291a21f90 | unsupervised-quality-prediction-for-improved | 2307.01464 | null | https://arxiv.org/abs/2307.01464v1 | https://arxiv.org/pdf/2307.01464v1.pdf | Unsupervised Quality Prediction for Improved Single-Frame and Weighted Sequential Visual Place Recognition | While substantial progress has been made in the absolute performance of localization and Visual Place Recognition (VPR) techniques, it is becoming increasingly clear from translating these systems into applications that other capabilities like integrity and predictability are just as important, especially for safety- o... | ['Michael Milford', 'Jason J. Ford', 'Helen Carson'] | 2023-07-04 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 3.22984606e-01 -2.36666813e-01 -4.56157923e-01 -6.18400693e-01
-8.48851502e-01 -7.67884135e-01 7.88670540e-01 4.55293983e-01
-6.07444942e-01 4.58565682e-01 2.53873527e-01 -6.61512733e-01
-9.14058536e-02 -4.61396217e-01 -6.97901309e-01 -1.12644501e-01
-5.41136563e-01 2.56374866e-01 6.87688589e-01 -2.83792764... | [7.518453121185303, -1.8674038648605347] |
f32a8e1b-c31e-49df-9380-33164a1aa32d | chinese-grammatical-errors-diagnosis-system | null | null | https://aclanthology.org/2020.nlptea-1.14 | https://aclanthology.org/2020.nlptea-1.14.pdf | Chinese Grammatical Errors Diagnosis System Based on BERT at NLPTEA-2020 CGED Shared Task | In the process of learning Chinese, second language learners may have various grammatical errors due to the negative transfer of native language. This paper describes our submission to the NLPTEA 2020 shared task on CGED. We present a hybrid system that utilizes both detection and correction stages. The detection stage... | ['Yingjie Han', 'yuke wang', 'Yingjie Yan', 'Haotian Huang', 'Yangchao Han', 'Hongying Zan'] | null | null | null | null | aacl-nlp-tea-2020-12-1 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 5.32541703e-03 7.13350698e-02 2.99991608e-01 -6.63628221e-01
-7.60792196e-01 -1.65411934e-01 5.31930886e-02 4.12470549e-01
-1.05665803e+00 9.79613841e-01 3.23765337e-01 -5.99860966e-01
7.88503170e-01 -8.08955669e-01 -5.92399836e-01 -1.15104534e-01
2.20576525e-01 2.87784159e-01 5.32896817e-01 -6.60475075... | [11.033177375793457, 10.800220489501953] |
fdd664b1-2b51-4ab7-b88a-5efaec783286 | interactive-submodular-bandit | null | null | http://papers.nips.cc/paper/6619-interactive-submodular-bandit | http://papers.nips.cc/paper/6619-interactive-submodular-bandit.pdf | Interactive Submodular Bandit | In many machine learning applications, submodular functions have been used as a model for evaluating the utility or payoff of a set such as news items to recommend, sensors to deploy in a terrain, nodes to influence in a social network, to name a few. At the heart of all these applications is the assumption that the un... | ['Lin Chen', 'Andreas Krause', 'Amin Karbasi'] | 2017-12-01 | null | null | null | neurips-2017-12 | ['movie-recommendation', 'data-summarization'] | ['miscellaneous', 'miscellaneous'] | [ 2.04699650e-01 5.87122679e-01 -7.14789987e-01 -4.70718592e-01
-8.65401149e-01 -9.10247922e-01 2.06375182e-01 1.48319140e-01
-4.81137633e-01 9.58926737e-01 3.32297772e-01 -3.52880031e-01
-6.40898645e-01 -7.85953462e-01 -1.09956896e+00 -8.89185607e-01
-4.83359188e-01 7.01211870e-01 -3.30501884e-01 -6.38632625... | [4.7088117599487305, 3.454927921295166] |
ea79cf11-793c-46a5-b626-fba0eed4dc85 | open-set-representation-learning-through | 2106.15278 | null | https://arxiv.org/abs/2106.15278v3 | https://arxiv.org/pdf/2106.15278v3.pdf | Open-Set Representation Learning through Combinatorial Embedding | Visual recognition tasks are often limited to dealing with a small subset of classes simply because the labels for the remaining classes are unavailable. We are interested in identifying novel concepts in a dataset through representation learning based on both labeled and unlabeled examples, and extending the horizon o... | ['Bohyung Han', 'Junoh Kang', 'Geeho Kim'] | 2021-06-29 | open-set-representation-learning-through-1 | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_Open-Set_Representation_Learning_Through_Combinatorial_Embedding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_Open-Set_Representation_Learning_Through_Combinatorial_Embedding_CVPR_2023_paper.pdf | cvpr-2023-1 | ['novel-class-discovery', 'image-categorization', 'novel-class-discovery', 'novel-concepts'] | ['computer-vision', 'computer-vision', 'methodology', 'reasoning'] | [ 5.43105364e-01 1.03317983e-02 -5.67984879e-01 -6.25131607e-01
-9.84630704e-01 -8.04366291e-01 7.68183649e-01 5.64574182e-01
-2.13271663e-01 7.32047617e-01 8.07966739e-02 1.56147435e-01
-5.15842736e-01 -6.25595033e-01 -5.63044250e-01 -1.02730405e+00
-1.40387073e-01 6.66709840e-01 -2.73933876e-02 2.87074417... | [9.653130531311035, 2.9196720123291016] |
635b0cd3-2889-49e7-b160-198526a3a7c6 | cat-nerf-constancy-aware-tx-2-former-for | 2304.07915 | null | https://arxiv.org/abs/2304.07915v1 | https://arxiv.org/pdf/2304.07915v1.pdf | CAT-NeRF: Constancy-Aware Tx$^2$Former for Dynamic Body Modeling | This paper addresses the problem of human rendering in the video with temporal appearance constancy. Reconstructing dynamic body shapes with volumetric neural rendering methods, such as NeRF, requires finding the correspondence of the points in the canonical and observation space, which demands understanding human body... | ['Ram Nevatia', 'Wanrong Zheng', 'Zhaoheng Zheng', 'Haidong Zhu'] | 2023-04-16 | null | null | null | null | ['neural-rendering'] | ['computer-vision'] | [-1.12126902e-01 -1.13466583e-01 -3.56119387e-02 -4.22588855e-01
1.48687571e-01 -2.65423328e-01 3.00501198e-01 -6.05759799e-01
-1.92610145e-01 4.29812580e-01 2.48957679e-01 3.56414735e-01
2.09430411e-01 -5.41059732e-01 -8.57577443e-01 -5.56454778e-01
1.90251525e-02 2.73339182e-01 2.88568318e-01 -4.70280468... | [11.79651165008545, -0.8711130619049072] |
c28a37d5-985c-42a1-b9cc-e28ba4cec03f | a-generic-self-supervised-learning-ssl | 2306.15836 | null | https://arxiv.org/abs/2306.15836v1 | https://arxiv.org/pdf/2306.15836v1.pdf | A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery | Remote sensing data has been widely used for various Earth Observation (EO) missions such as land use and cover classification, weather forecasting, agricultural management, and environmental monitoring. Most existing remote sensing data-based models are based on supervised learning that requires large and representati... | ['Liangxiu Han', 'Xin Zhang'] | 2023-06-27 | null | null | null | null | ['self-supervised-learning', 'weather-forecasting'] | ['computer-vision', 'miscellaneous'] | [ 9.61066008e-01 -2.35836640e-01 -3.23449165e-01 -5.49114823e-01
-6.20786011e-01 -4.07808304e-01 5.96087992e-01 2.39296794e-01
-4.55949247e-01 8.87209117e-01 -3.78523052e-01 -7.03655899e-01
-3.40234995e-01 -1.32320213e+00 -3.02942902e-01 -1.03418374e+00
-1.28383517e-01 1.39636963e-04 -1.95654914e-01 -6.76548541... | [9.689863204956055, -1.4769763946533203] |
ce2c10e8-c84f-433b-b5f9-b7587753440f | detr-with-additional-global-aggregation-for | 2304.07082 | null | https://arxiv.org/abs/2304.07082v1 | https://arxiv.org/pdf/2304.07082v1.pdf | DETR with Additional Global Aggregation for Cross-domain Weakly Supervised Object Detection | This paper presents a DETR-based method for cross-domain weakly supervised object detection (CDWSOD), aiming at adapting the detector from source to target domain through weak supervision. We think DETR has strong potential for CDWSOD due to an insight: the encoder and the decoder in DETR are both based on the attentio... | ['Yi Yang', 'Si Liu', 'Yifan Sun', 'Zongheng Tang'] | 2023-04-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tang_DETR_With_Additional_Global_Aggregation_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tang_DETR_With_Additional_Global_Aggregation_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2023_paper.pdf | cvpr-2023-1 | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 1.50971800e-01 7.05613717e-02 -5.29262722e-01 -5.09563565e-01
-1.13092113e+00 -5.32099605e-01 5.31703174e-01 -5.63266277e-02
-2.92406231e-01 4.85579163e-01 3.24921235e-02 3.21588106e-02
3.12358290e-01 -6.16003335e-01 -1.01143384e+00 -6.43665314e-01
3.33448946e-01 5.41141748e-01 1.04969347e+00 -2.00140566... | [9.407964706420898, 1.3528774976730347] |
c3004d17-7b6c-4f9d-930a-04a59ee63c05 | graph-context-attention-networks-for-size | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Jiang_Graph-Context_Attention_Networks_for_Size-Varied_Deep_Graph_Matching_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Jiang_Graph-Context_Attention_Networks_for_Size-Varied_Deep_Graph_Matching_CVPR_2022_paper.pdf | Graph-Context Attention Networks for Size-Varied Deep Graph Matching | Deep learning for graph matching has received growing interest and developed rapidly in the past decade. Although recent deep graph matching methods have shown excellent performance on matching between graphs of equal size in the computer vision area, the size-varied graph matching problem, where the number of keyp... | ['Bryan M. Williams', 'Sue Black', 'Plamen Angelov', 'Hossein Rahmani', 'Zheheng Jiang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['graph-matching'] | ['graphs'] | [ 9.25223008e-02 1.23686567e-01 -2.48530149e-01 -3.44990641e-01
-7.03370094e-01 -4.40939009e-01 4.49710965e-01 7.53037870e-01
-2.56966442e-01 8.26637521e-02 -1.82537958e-01 -3.77874583e-01
-4.99887884e-01 -9.07242179e-01 -6.49770498e-01 -5.25299191e-01
-2.68680245e-01 5.55008352e-01 2.28197336e-01 -1.85889360... | [7.134178161621094, 6.3704633712768555] |
47aa5316-c0eb-4f4a-8538-444216ce984e | shapes2toon-generating-cartoon-characters | 2211.02141 | null | https://arxiv.org/abs/2211.02141v1 | https://arxiv.org/pdf/2211.02141v1.pdf | Shapes2Toon: Generating Cartoon Characters from Simple Geometric Shapes | Cartoons are an important part of our entertainment culture. Though drawing a cartoon is not for everyone, creating it using an arrangement of basic geometric primitives that approximates that character is a fairly frequent technique in art. The key motivation behind this technique is that human bodies - as well as car... | ['Md. Faraz Kabir Khan', 'Mohtasim Hossain Shovon', 'Md. Hasib Al Zadid', 'Md. Shafiur Rahman', 'Mohammad Imrul Jubair', 'Simanta Deb Turja'] | 2022-11-03 | null | null | null | null | ['culture'] | ['speech'] | [ 3.15529555e-01 1.51697934e-01 5.33002913e-01 -1.57186076e-01
9.61612910e-02 -7.93840766e-01 6.81186020e-01 -1.94873407e-01
9.16806702e-03 6.07072353e-01 -1.42836779e-01 -3.31293136e-01
4.10155147e-01 -1.34056902e+00 -9.51686084e-01 -2.32482955e-01
2.71403164e-01 5.87494314e-01 4.44308251e-01 -5.16907692... | [11.720819473266602, -0.41440901160240173] |
65204952-c7a4-4327-8c4e-5bd42c140954 | deep-learning-for-monaural-speech-separation | null | null | https://ieeexplore.ieee.org/document/6853860 | https://minjekim.com/papers/icassp2014_phuang.pdf | Deep learning for monaural speech separation | Monaural source separation is useful for many real-world applications though it is a challenging problem. In this paper, we study deep learning for monaural speech separation. We propose the joint optimization of the deep learning models (deep neural networks and recurrent neural networks) with an extra masking layer, ... | ['Mark Hasegawa-Johnson', 'Po-Sen Huang', 'Paris Smaragdis', 'Minje Kim'] | 2014-05-04 | null | null | null | icassp-2014-5 | ['multi-speaker-source-separation'] | ['speech'] | [ 7.50205070e-02 -2.45192811e-01 5.23507781e-02 -2.02661112e-01
-1.15871882e+00 -2.35763326e-01 2.73081541e-01 -2.49419659e-01
-1.90224722e-01 6.59582317e-01 5.30454636e-01 -3.02875578e-01
-1.09727062e-01 -1.17009126e-01 -5.38632691e-01 -9.58015025e-01
2.97494382e-01 -1.48598805e-01 2.83000502e-03 -1.22678950... | [14.954517364501953, 5.860958099365234] |
a54ce8b2-6ae2-498a-a210-165114ed6e6f | a-k-nearest-neighbor-approach-towards-multi | null | null | https://aclanthology.org/N19-2019 | https://aclanthology.org/N19-2019.pdf | A k-Nearest Neighbor Approach towards Multi-level Sequence Labeling | In this paper we present a new method for intent recognition for complex dialog management in low resource situations. Complex dialog management is required because our target domain is real world mixed initiative food ordering between agents and their customers, where individual customer utterances may contain multipl... | ['Yue Chen', 'John Chen'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['intent-recognition'] | ['natural-language-processing'] | [ 1.92831516e-01 4.25394833e-01 -4.38456088e-02 -7.88329244e-01
-4.69102293e-01 -6.60711706e-01 5.53321183e-01 3.11449111e-01
-8.55794966e-01 1.05735755e+00 4.03341353e-01 -3.37622732e-01
3.72317165e-01 -5.57437956e-01 -2.54704773e-01 -3.93351614e-01
3.23105454e-02 1.04314649e+00 1.84780002e-01 -6.33287191... | [12.767327308654785, 7.813595294952393] |
38292d46-3b2b-4a8e-a3b0-277e9d74ebb9 | beyond-controlled-environments-3d-camera-re | 2008.02004 | null | https://arxiv.org/abs/2008.02004v1 | https://arxiv.org/pdf/2008.02004v1.pdf | Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes | Long-term camera re-localization is an important task with numerous computer vision and robotics applications. Whilst various outdoor benchmarks exist that target lighting, weather and seasonal changes, far less attention has been paid to appearance changes that occur indoors. This has led to a mismatch between popular... | ['Federico Tombari', 'Tommaso Cavallari', 'Torsten Sattler', 'Johanna Wald', 'Stuart Golodetz'] | 2020-08-05 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/287_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520460.pdf | eccv-2020-8 | ['camera-relocalization'] | ['computer-vision'] | [-1.39023559e-02 -8.51831794e-01 -1.70944668e-02 -4.43007082e-01
-5.91214180e-01 -9.25628126e-01 6.55533910e-01 1.54334486e-01
-6.79009199e-01 5.34111619e-01 2.00227946e-01 3.42683047e-02
2.76270453e-02 -4.74742860e-01 -9.23054218e-01 -6.01005197e-01
4.26256768e-02 -9.51773077e-02 5.27826130e-01 -2.56133080... | [7.623378276824951, -2.034660816192627] |
0e56b7e1-07f2-43d3-b294-7e4d386ce6e6 | beyond-normal-on-the-evaluation-of-mutual | 2306.11078 | null | https://arxiv.org/abs/2306.11078v1 | https://arxiv.org/pdf/2306.11078v1.pdf | Beyond Normal: On the Evaluation of Mutual Information Estimators | Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology. However, mutual information estimators are typically evaluated on simple families of probability distributions, namely multivariate normal d... | ['Alexander Marx', 'Niko Beerenwinkel', 'Julia E. Vogt', 'Frederic Grabowski', 'Paweł Czyż'] | 2023-06-19 | null | null | null | null | ['domain-generalization', 'mutual-information-estimation', 'benchmarking', 'benchmarking'] | ['methodology', 'methodology', 'miscellaneous', 'robots'] | [ 1.37562945e-01 -1.09163336e-01 -3.36829454e-01 -6.58882976e-01
-5.34366369e-01 -3.93407851e-01 5.64311743e-01 2.84556776e-01
-4.04412478e-01 1.36860859e+00 1.88913316e-01 -1.38549671e-01
-8.29493642e-01 -7.75628388e-01 -3.80329281e-01 -7.52218723e-01
-5.01313031e-01 7.04101503e-01 -2.18656838e-01 2.18531668... | [7.467804431915283, 4.202160358428955] |
4b315624-98e1-4705-947e-6f94e46d5ac8 | on-the-robustness-of-counterfactual | 2201.09051 | null | https://arxiv.org/abs/2201.09051v3 | https://arxiv.org/pdf/2201.09051v3.pdf | On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations | Counterfactual explanations (CEs) are a powerful means for understanding how decisions made by algorithms can be changed. Researchers have proposed a number of desiderata that CEs should meet to be practically useful, such as requiring minimal effort to enact, or complying with causal models. We consider a further aspe... | ['Saverio Fracaros', 'Marco Virgolin'] | 2022-01-22 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.19272959e-01 3.79581869e-01 -3.75883013e-01 -3.09386849e-01
-6.04488969e-01 -7.27263689e-01 8.42938244e-01 1.34604067e-01
-2.98217237e-01 1.04043317e+00 5.45849323e-01 -6.92193747e-01
-4.20053095e-01 -6.36458695e-01 -9.91206288e-01 -5.55387497e-01
-8.91006738e-02 2.54783213e-01 -1.11182801e-01 -1.69722866... | [8.64247989654541, 5.599459171295166] |
6911310b-d6d3-494a-a085-1235b9323aa2 | benchmarking-foundation-models-with-language | 2306.04181 | null | https://arxiv.org/abs/2306.04181v1 | https://arxiv.org/pdf/2306.04181v1.pdf | Benchmarking Foundation Models with Language-Model-as-an-Examiner | Numerous benchmarks have been established to assess the performance of foundation models on open-ended question answering, which serves as a comprehensive test of a model's ability to understand and generate language in a manner similar to humans. Most of these works focus on proposing new datasets, however, we see two... | ['Lei Hou', 'Juanzi Li', 'Jiayin Zhang', 'Haozhe Lyu', 'Yijia Xiao', 'Kaisheng Zeng', 'Jifan Yu', 'Xiaozhi Wang', 'Yuze He', 'Xin Lv', 'Yixin Cao', 'Jiahao Ying', 'Yushi Bai'] | 2023-06-07 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-1.31979227e-01 1.75583199e-01 9.38825011e-02 -6.39295220e-01
-1.55476630e+00 -1.16770554e+00 3.84253353e-01 2.16142386e-01
-4.09385532e-01 4.61443186e-01 2.21293926e-01 -5.23527563e-01
-1.91429138e-01 -7.66809702e-01 -6.98208869e-01 1.07940003e-01
5.84788263e-01 5.69806516e-01 5.39287031e-01 -4.31520671... | [11.350000381469727, 8.094339370727539] |
42fcfc92-41ec-43bf-a6f7-7ba504f4be74 | spatial-and-modal-optimal-transport-for-fast | 2305.02774 | null | https://arxiv.org/abs/2305.02774v1 | https://arxiv.org/pdf/2305.02774v1.pdf | Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction | Multi-modal Magnetic Resonance Imaging (MRI) plays an important role in clinical medicine. However, the acquisitions of some modalities, such as the T2-weighted modality, need a long time and they are always accompanied by motion artifacts. On the other hand, the T1-weighted image (T1WI) shares the same underlying info... | ['Shihui Ying', 'Dinggang Shen', 'Qian Wang', 'Jun Shi', 'Zhijie Wen', 'Qi Wang'] | 2023-05-04 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 4.11292344e-01 5.81053570e-02 1.04970224e-01 -4.71190885e-02
-8.92824769e-01 -2.12008446e-01 4.95305151e-01 -4.15473849e-01
-3.40773255e-01 5.61552703e-01 3.64545614e-01 6.66309800e-03
-5.53314090e-01 -6.20117903e-01 -5.15431881e-01 -1.03593576e+00
2.89416108e-02 4.24063593e-01 4.65784997e-01 -1.05232045... | [13.560517311096191, -2.37461256980896] |
926126e7-bcce-4a74-8f0d-cd6ab51d5863 | dynamic-3d-gaze-from-afar-deep-gaze | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Nonaka_Dynamic_3D_Gaze_From_Afar_Deep_Gaze_Estimation_From_Temporal_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Nonaka_Dynamic_3D_Gaze_From_Afar_Deep_Gaze_Estimation_From_Temporal_CVPR_2022_paper.pdf | Dynamic 3D Gaze From Afar: Deep Gaze Estimation From Temporal Eye-Head-Body Coordination | We introduce a novel method and dataset for 3D gaze estimation of a freely moving person from a distance, typically in surveillance views. Eyes cannot be clearly seen in such cases due to occlusion and lacking resolution. Existing gaze estimation methods suffer or fall back to approximating gaze with head pose as t... | ['Ko Nishino', 'Shohei Nobuhara', 'Soma Nonaka'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['gaze-estimation'] | ['computer-vision'] | [ 1.24451621e-02 2.07978189e-01 8.75747704e-04 -8.05122674e-01
-8.60632583e-02 -4.49188977e-01 4.12960708e-01 -4.88737255e-01
-3.28630120e-01 3.56568635e-01 3.04413885e-01 5.20368526e-03
2.55699366e-01 1.17393106e-01 -6.61815286e-01 -6.18096411e-01
-5.04827015e-02 -5.40301539e-02 9.45878625e-02 2.08380312... | [14.119274139404297, 0.08621423691511154] |
1b086bff-8f99-44e1-b180-6b3d9c644790 | decor-defy-knowledge-forgetting-by-predicting | 2305.18441 | null | https://arxiv.org/abs/2305.18441v1 | https://arxiv.org/pdf/2305.18441v1.pdf | DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes | Lifelong audio feature extraction involves learning new sound classes incrementally, which is essential for adapting to new data distributions over time. However, optimizing the model only on new data can lead to catastrophic forgetting of previously learned tasks, which undermines the model's ability to perform well o... | ['Nima Mesgarani', 'Yinghao Aaron Li', 'Xilin Jiang'] | 2023-05-29 | null | null | null | null | ['acoustic-scene-classification', 'scene-classification'] | ['audio', 'computer-vision'] | [ 1.99176744e-01 -3.33874524e-01 9.77008417e-03 -5.09672999e-01
-1.06240082e+00 -4.85021144e-01 2.97083735e-01 5.11861205e-01
-5.03075898e-01 9.96593833e-01 2.15759918e-01 2.76025273e-02
-1.18581541e-01 -6.65269673e-01 -7.39161789e-01 -5.79823792e-01
-3.10592502e-01 2.33675599e-01 4.97338176e-01 6.55860826... | [9.973753929138184, 3.5700104236602783] |
f97b2f3e-b877-4687-bd12-6e67421bdaa9 | rhetorical-structure-approach-for-online | null | null | https://aclanthology.org/2022.lrec-1.635 | https://aclanthology.org/2022.lrec-1.635.pdf | Rhetorical Structure Approach for Online Deception Detection: A Survey | Most information is passed on in the form of language. Therefore, research on how people use language to inform and misinform, and how this knowledge may be automatically extracted from large amounts of text is surely relevant. This survey provides first-hand experiences and a comprehensive review of rhetorical-level s... | ['Thiago Pardo', 'Fabrício Benevenuto', 'Zohar Rabinovich', 'Jonas D‘Alessandro', 'Francielle Vargas'] | null | null | null | null | lrec-2022-6 | ['deception-detection'] | ['miscellaneous'] | [ 1.92758337e-01 5.48412025e-01 -9.10329759e-01 8.56088754e-03
-7.83587694e-01 -1.05966210e+00 1.09052753e+00 7.63513684e-01
-1.74846843e-01 1.04400301e+00 9.61934149e-01 -6.24767482e-01
3.88111711e-01 -4.24767852e-01 -3.29440594e-01 -2.37397119e-01
3.48857552e-01 8.98719952e-02 1.95433289e-01 -6.87481165... | [8.223597526550293, 10.251309394836426] |
5d681c7d-66b1-4a97-9cd5-77050c01b637 | salt-and-pepper-noise-removal-method-based-on | 2110.09113 | null | https://arxiv.org/abs/2110.09113v9 | https://arxiv.org/pdf/2110.09113v9.pdf | Salt and pepper noise removal method based on stationary Framelet transform with non-convex sparsity regularization | Salt and pepper noise removal is a common inverse problem in image processing. Traditional denoising methods have two limitations. First, noise characteristics are often not described accurately. For example, the noise location information is often ignored and the sparsity of the salt and pepper noise is often describe... | ['Yanping Xu', 'Chaoqun Yu', 'Yuming Huang', 'Jianhua Song', 'Huiying Huang', 'Lingzhi Wang', 'Yingpin Chen'] | 2021-10-18 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 2.93892086e-01 -6.28643572e-01 1.74084470e-01 7.29720965e-02
-3.53949666e-01 1.73261575e-02 4.26100455e-02 -2.35074386e-01
-3.55615854e-01 6.08301938e-01 1.98847651e-01 2.17828408e-01
-1.67228088e-01 -6.87763691e-01 -1.35124773e-01 -1.30122185e+00
3.52926224e-01 -5.00156999e-01 9.62688401e-02 -2.10136995... | [11.239115715026855, -2.470210313796997] |
be6f6535-3460-4fa5-933b-e0ac9c1a7471 | abstractive-text-summarization-by | 1812.05407 | null | http://arxiv.org/abs/1812.05407v1 | http://arxiv.org/pdf/1812.05407v1.pdf | Abstractive Text Summarization by Incorporating Reader Comments | In neural abstractive summarization field, conventional sequence-to-sequence
based models often suffer from summarizing the wrong aspect of the document
with respect to the main aspect. To tackle this problem, we propose the task of
reader-aware abstractive summary generation, which utilizes the reader comments
to help... | ['Shen Gao', 'Piji Li', 'Zhaochun Ren', 'Xiuying Chen', 'Rui Yan', 'Dongyan Zhao', 'Lidong Bing'] | 2018-12-13 | null | null | null | null | ['reader-aware-summarization'] | ['natural-language-processing'] | [ 4.78908956e-01 3.94719958e-01 -2.88845152e-02 -1.28243297e-01
-1.33834195e+00 -5.51130116e-01 8.02821517e-01 2.34328121e-01
-2.11462811e-01 9.28645611e-01 9.95185733e-01 -5.72374016e-02
4.39842999e-01 -4.96787906e-01 -7.40544081e-01 -3.58392179e-01
4.30761516e-01 4.79383916e-01 2.35670373e-01 -3.69940966... | [12.512392044067383, 9.416106224060059] |
8edaef41-e018-43e3-b150-e75e4f5cba11 | a-study-of-n-gram-and-embedding | null | null | https://aclanthology.org/W17-5026 | https://aclanthology.org/W17-5026.pdf | A study of N-gram and Embedding Representations for Native Language Identification | We report on our experiments with N-gram and embedding based feature representations for Native Language Identification (NLI) as a part of the NLI Shared Task 2017 (team name: NLI-ISU). Our best performing system on the test set for written essays had a macro F1 of 0.8264 and was based on word uni, bi and trigram featu... | ['Sowmya Vajjala', 'Sagnik Banerjee'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [-2.70177215e-01 -4.38876227e-02 -4.66892332e-01 -1.90158896e-02
-7.17934370e-01 -7.80742586e-01 9.98672187e-01 4.07909364e-01
-7.50312746e-01 4.63575572e-01 9.19978559e-01 -7.46309042e-01
-1.92743838e-01 -5.13772190e-01 4.15457599e-02 -9.19545442e-02
7.55662844e-02 4.20041561e-01 -4.30753648e-01 1.09876283... | [10.452022552490234, 10.282004356384277] |
7b97bf20-d29b-4de7-882a-8d4851216614 | learning-equational-theorem-proving | 2102.05547 | null | https://arxiv.org/abs/2102.05547v1 | https://arxiv.org/pdf/2102.05547v1.pdf | Learning Equational Theorem Proving | We develop Stratified Shortest Solution Imitation Learning (3SIL) to learn equational theorem proving in a deep reinforcement learning (RL) setting. The self-trained models achieve state-of-the-art performance in proving problems generated by one of the top open conjectures in quasigroup theory, the Abelian Inner Mappi... | ['Josef Urban', 'Mikoláš Janota', 'Tom Heskes', 'Jelle Piepenbrock'] | 2021-02-10 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [-3.52308713e-02 8.26388538e-01 6.91070408e-02 1.39734417e-01
-1.06083214e+00 -7.15684474e-01 6.69463813e-01 -2.69513279e-01
-1.52566954e-01 1.19874072e+00 -4.56443787e-01 -1.14000821e+00
-1.82557508e-01 -7.53747821e-01 -1.34331608e+00 -2.94201791e-01
-5.00528216e-01 6.90158606e-01 1.31255090e-01 -5.59519470... | [8.92287826538086, 7.067637920379639] |
465def8d-5bb9-4f69-bc87-24097437796f | unsupervised-heart-rate-estimation-in | 1708.05356 | null | http://arxiv.org/abs/1708.05356v1 | http://arxiv.org/pdf/1708.05356v1.pdf | Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout | Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
dire... | ['Nikil Dutt', 'Siebren Schaafsma', 'Raj Thilak Rajan', 'Jeffrey L. Krichmar', 'Willemijn Groenendaal', 'Prathyusha Adiraju', 'Paruthi Pradhapan', 'Chris Van Hoof', 'Francky Catthoor', 'Anup Das'] | 2017-07-18 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 5.46216309e-01 -2.59545505e-01 2.96876937e-01 -1.27767503e-01
-1.91018730e-01 -4.99226421e-01 -3.41386050e-02 5.14071941e-01
-6.06045485e-01 9.73137558e-01 -3.43079150e-01 1.26329333e-01
-1.61300585e-01 -6.96287215e-01 -5.99404991e-01 -8.49849224e-01
-3.48818868e-01 3.68454486e-01 2.33366072e-01 3.64642553... | [13.892457008361816, 3.164400100708008] |
9aff249b-94f7-4dfb-afdf-7b0d2be1c50c | an-ensemble-quadratic-echo-state-network-for | 1708.05094 | null | http://arxiv.org/abs/1708.05094v1 | http://arxiv.org/pdf/1708.05094v1.pdf | An Ensemble Quadratic Echo State Network for Nonlinear Spatio-Temporal Forecasting | Spatio-temporal data and processes are prevalent across a wide variety of
scientific disciplines. These processes are often characterized by nonlinear
time dynamics that include interactions across multiple scales of spatial and
temporal variability. The data sets associated with many of these processes are
increasing ... | ['Christopher K. Wikle', 'Patrick L. McDermott'] | 2017-08-16 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-5.14306054e-02 -5.15048563e-01 2.23665163e-01 -1.98971346e-01
-3.69434685e-01 -7.65599370e-01 1.03068578e+00 4.47743565e-01
-2.97273666e-01 9.97491896e-01 1.55930951e-01 -7.79015124e-01
-8.11723113e-01 -8.21230412e-01 -3.08424026e-01 -6.60045087e-01
-7.72641838e-01 6.04994416e-01 2.79847980e-01 -2.73349434... | [6.580275058746338, 3.3432230949401855] |
17dc13d0-0640-4fe9-b306-a10ba47191d6 | detailed-region-adaptive-normalization-for | 2109.14525 | null | https://arxiv.org/abs/2109.14525v4 | https://arxiv.org/pdf/2109.14525v4.pdf | DRAN: Detailed Region-Adaptive Normalization for Conditional Image Synthesis | In recent years, conditional image synthesis has attracted growing attention due to its controllability in the image generation process. Although recent works have achieved realistic results, most of them have difficulty handling fine-grained styles with subtle details. To address this problem, a novel normalization mo... | ['Xu Wang', 'Bo Peng', 'Jing Dong', 'Jingna Sun', 'Peibin Chen', 'Yueming Lyu'] | 2021-09-29 | null | null | null | null | ['texture-synthesis', 'facial-makeup-transfer'] | ['computer-vision', 'computer-vision'] | [ 1.17230058e-01 -5.57317436e-01 -2.31872261e-01 -6.38234317e-01
-5.16507566e-01 -6.28684461e-01 5.65277517e-01 -3.55664253e-01
-2.09819257e-01 7.38589942e-01 3.39615732e-01 1.73897877e-01
2.66953796e-01 -1.05148077e+00 -8.18426728e-01 -6.52738631e-01
5.78965545e-01 9.75097492e-02 2.91480720e-01 -4.60581064... | [11.475278854370117, -0.7550219893455505] |
ad080fc6-3bb8-4daa-a7a7-0ba1903aa969 | learning-entity-representations-for-few-shot | null | null | https://openreview.net/forum?id=BJgum4Qgu4 | https://openreview.net/pdf?id=BJgum4Qgu4 | Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories | Language modeling tasks, in which words are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases. Motivated by the observation that efforts to code
world knowledge into machine readable knowledge bases tend to be entity-centric... | ['Tom Kwiatkowski', 'David Weiss', 'Livio Baldini Soares', 'Nicholas FitzGerald', 'Jeffrey Ling'] | 2019-03-20 | null | null | null | iclr-workshop-lld-2019 | ['learning-word-embeddings'] | ['methodology'] | [-3.56789827e-01 3.51740330e-01 -4.69363242e-01 -3.99001956e-01
-5.40171802e-01 -5.23788869e-01 7.88128436e-01 8.59836876e-01
-7.48754084e-01 7.28509486e-01 7.05332816e-01 -4.00583178e-01
1.01720780e-01 -1.21560931e+00 -8.69936466e-01 -5.53303547e-02
-2.59259820e-01 4.91821617e-01 1.12776995e-01 -3.84982795... | [9.950591087341309, 8.735747337341309] |
7bc12d23-3455-4629-b743-6dd74e60e502 | explore-contextual-information-for-3d-scene | 2210.0624 | null | https://arxiv.org/abs/2210.06240v2 | https://arxiv.org/pdf/2210.06240v2.pdf | Explore Contextual Information for 3D Scene Graph Generation | 3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we p... | ['Xin Yang', 'BaoCai Yin', 'Qiang Zhang', 'Bokai Liu', 'Zhaoxuan Zhang', 'Chengjiang Long', 'Yuanyuan Liu'] | 2022-10-12 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 3.44728827e-01 3.98339421e-01 -3.98325473e-01 -4.85369623e-01
-4.76732731e-01 -3.51143219e-02 6.79264605e-01 4.71512586e-01
2.91737199e-01 5.43052375e-01 4.23414297e-02 -2.49563262e-01
-3.10608327e-01 -1.11442959e+00 -1.69663936e-01 -4.10710782e-01
5.65343946e-02 7.44917810e-01 5.35240769e-01 1.72636602... | [10.323127746582031, 1.6714763641357422] |
eb7824ac-5654-442e-b8ef-29d5cb50cdda | improving-single-image-defocus-deblurring-how | 2108.05251 | null | https://arxiv.org/abs/2108.05251v2 | https://arxiv.org/pdf/2108.05251v2.pdf | Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning | Many camera sensors use a dual-pixel (DP) design that operates as a rudimentary light field providing two sub-aperture views of a scene in a single capture. The DP sensor was developed to improve how cameras perform autofocus. Since the DP sensor's introduction, researchers have found additional uses for the DP data, s... | ['Michael S. Brown', 'Mahmoud Afifi', 'Abdullah Abuolaim'] | 2021-08-11 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 5.01480997e-01 -3.78155559e-01 4.38186899e-02 -3.36802691e-01
-8.86794865e-01 -6.55545354e-01 4.66642976e-01 -8.59943926e-01
-2.05324784e-01 5.49115121e-01 6.91668928e-01 -2.44362324e-01
1.57131970e-01 -3.81898075e-01 -1.01391423e+00 -9.81074750e-01
4.80105788e-01 -1.79307103e-01 8.48687589e-02 1.90575883... | [11.190256118774414, -2.7131805419921875] |
76ecba2a-d753-4a15-923b-e7d7e594c23a | tacticzero-learning-to-prove-theorems-from | 2102.09756 | null | https://arxiv.org/abs/2102.09756v2 | https://arxiv.org/pdf/2102.09756v2.pdf | TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning | We propose a novel approach to interactive theorem-proving (ITP) using deep reinforcement learning. The proposed framework is able to learn proof search strategies as well as tactic and arguments prediction in an end-to-end manner. We formulate the process of ITP as a Markov decision process (MDP) in which each state r... | ['Amir Dezfouli', 'Christian Walder', 'Michael Norrish', 'Minchao Wu'] | 2021-02-19 | null | http://proceedings.neurips.cc/paper/2021/hash/4dea382d82666332fb564f2e711cbc71-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/4dea382d82666332fb564f2e711cbc71-Paper.pdf | neurips-2021-12 | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.58410579e-01 7.32675493e-01 -2.76664943e-01 2.95310169e-02
-9.02054846e-01 -1.07149208e+00 8.01207960e-01 9.36024934e-02
-1.65178627e-01 9.38075423e-01 -3.73019904e-01 -1.30901957e+00
-2.62565017e-01 -1.03102469e+00 -1.05709994e+00 -1.21568024e-01
-3.84515107e-01 7.26055324e-01 4.91477311e-01 5.63870966... | [8.920601844787598, 7.082557201385498] |
8760c806-129a-4da4-98de-3ddc900ac3a3 | rrwavenet-a-compact-end-to-end-multi-scale | 2208.08672 | null | https://arxiv.org/abs/2208.08672v2 | https://arxiv.org/pdf/2208.08672v2.pdf | RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation | Respiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events such as heart disease, lung disease, and sleep disorders. Unfortunately, standard manual RR counting is prone to human error and cannot be performed continuously. This study proposes a method for continuously estimating RR, ... | ['Theerawit Wilaiprasitporn', 'Emmanuel Mignot', 'Proadpran Punyabukkana', 'Tanut Choksatchawathi', 'Narin Kunaseth', 'Thee Mateepithaktham', 'Kawisara Ueafuea', 'Punnawish Thuwajit', 'Guntitat Sawadwuthikul', 'Pongpanut Osathitporn'] | 2022-08-18 | null | null | null | null | ['photoplethysmography-ppg', 'respiratory-rate-estimation'] | ['medical', 'medical'] | [ 8.25948492e-02 -1.01565197e-01 3.87001820e-02 -2.75083870e-01
-7.50451148e-01 -1.81104258e-01 -3.36548418e-01 -7.25835413e-02
-6.42551899e-01 9.91135657e-01 -4.26707640e-02 -4.68193471e-01
-1.04255840e-01 -5.21426439e-01 -3.69158059e-01 -6.28504097e-01
-2.51235247e-01 1.83460675e-02 -2.10544795e-01 3.85454327... | [13.927268981933594, 2.9805960655212402] |
6c469607-b67f-4bfa-b8a3-850ae8c74520 | iedit-localised-text-guided-image-editing | 2305.05947 | null | https://arxiv.org/abs/2305.05947v1 | https://arxiv.org/pdf/2305.05947v1.pdf | iEdit: Localised Text-guided Image Editing with Weak Supervision | Diffusion models (DMs) can generate realistic images with text guidance using large-scale datasets. However, they demonstrate limited controllability in the output space of the generated images. We propose a novel learning method for text-guided image editing, namely \texttt{iEdit}, that generates images conditioned on... | ['Loris Bazzani', 'Michael Donoser', 'Tae-Kyun Kim', 'Binod Bhattarai', 'Erhan Gundogdu', 'Rumeysa Bodur'] | 2023-05-10 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 9.35960233e-01 3.29320699e-01 1.87898144e-01 -3.38912159e-01
-7.11437702e-01 -7.14726925e-01 9.58941221e-01 -2.93129355e-01
-4.08923507e-01 7.64843822e-01 -5.39144613e-02 -1.42714173e-01
-7.65718669e-02 -7.23141968e-01 -1.11917114e+00 -6.74615085e-01
2.76092798e-01 5.65207958e-01 8.95096287e-02 -6.43807128... | [11.508194923400879, -0.4864347577095032] |
2ce20c98-72cd-4522-bdfb-3e18afe19553 | supercaustics-real-time-open-source | 2107.11008 | null | https://arxiv.org/abs/2107.11008v2 | https://arxiv.org/pdf/2107.11008v2.pdf | SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications | Transparent objects are a very challenging problem in computer vision. They are hard to segment or classify due to their lack of precise boundaries, and there is limited data available for training deep neural networks. As such, current solutions for this problem employ rigid synthetic datasets, which lack flexibility ... | ['Rolando Estrada', 'Mehdi Mousavi'] | 2021-07-23 | null | null | null | null | ['transparent-objects', 'transparent-object-depth-estimation', 'transparent-object-detection', 'physical-simulations'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 2.92407662e-01 6.29128441e-02 5.55099785e-01 -1.62536219e-01
-4.92219806e-01 -4.79660362e-01 4.66880471e-01 -2.26097584e-01
-6.94127604e-02 5.56054533e-01 -4.10440713e-01 -4.72279191e-01
4.32732671e-01 -9.57679093e-01 -9.40723419e-01 -7.04385042e-01
-2.98592560e-02 6.59759223e-01 9.67293561e-01 -1.15544759... | [10.379950523376465, -1.2811797857284546] |
f6dbb6be-16f8-4f89-af2f-e9c852363b34 | a-new-twitter-verb-lexicon-for-natural | null | null | https://aclanthology.org/L12-1641 | https://aclanthology.org/L12-1641.pdf | A New Twitter Verb Lexicon for Natural Language Processing | We describe in-progress work on the creation of a new lexical resource that contains a list of 486 verbs annotated with quantified temporal durations for the events that they describe. This resource is being compiled from more than 14 million tweets from the Twitter microblogging site. We are creating this lexicon of v... | ['Graham Katz', 'Jennifer Williams'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['game-of-chess'] | ['playing-games'] | [-1.43518165e-01 5.28695956e-02 -4.56795007e-01 -3.25926960e-01
-5.58195174e-01 -9.42537963e-01 9.19394553e-01 1.01399612e+00
-7.80758083e-01 8.93724680e-01 1.05519950e+00 -1.59972891e-01
1.89332232e-01 -8.38820636e-01 -2.27548912e-01 3.63817848e-02
-2.02419341e-01 2.34619841e-01 2.52241164e-01 -4.14622992... | [9.098980903625488, 9.448710441589355] |
c4d40367-7b07-44eb-b216-f2987f58dd31 | robot-task-planning-and-situation-handling-in | 2210.01287 | null | https://arxiv.org/abs/2210.01287v1 | https://arxiv.org/pdf/2210.01287v1.pdf | Robot Task Planning and Situation Handling in Open Worlds | Automated task planning algorithms have been developed to help robots complete complex tasks that require multiple actions. Most of those algorithms have been developed for "closed worlds" assuming complete world knowledge is provided. However, the real world is generally open, and the robots frequently encounter unfor... | ['Shiqi Zhang', 'Chad Esselink', 'Hao Yang', 'Nieqing Cao', 'Saeid Amiri', 'Xiaohan Zhang', 'Yan Ding'] | 2022-10-04 | null | null | null | null | ['common-sense-reasoning', 'robot-task-planning'] | ['reasoning', 'robots'] | [ 4.20352906e-01 5.58513463e-01 -6.61173910e-02 -3.31878096e-01
-7.64249325e-01 -7.43340492e-01 4.99636799e-01 8.86847377e-02
-5.05838692e-01 1.01793754e+00 2.16305554e-01 -3.17153007e-01
-3.46852392e-01 -5.20345271e-01 -7.83079445e-01 -2.69374937e-01
-3.47400576e-01 1.18126225e+00 3.70863318e-01 -6.54504836... | [4.429544448852539, 0.910805881023407] |
b343ae6d-53ed-4c6c-97eb-7904ece8a4a9 | generalizing-fingerprint-spoof-detector | 1901.03918 | null | http://arxiv.org/abs/1901.03918v2 | http://arxiv.org/pdf/1901.03918v2.pdf | Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier | Prevailing fingerprint recognition systems are vulnerable to spoof attacks.
To mitigate these attacks, automated spoof detectors are trained to distinguish
a set of live or bona fide fingerprints from a set of known spoof fingerprints.
Despite their success, spoof detectors remain vulnerable when exposed to
attacks fro... | ['Joshua J. Engelsma', 'Anil K. Jain'] | 2019-01-13 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 1.11741340e+00 -1.40768737e-01 -1.91099405e-01 -2.14355052e-01
-3.15319031e-01 -1.07268357e+00 6.18444383e-01 -2.08491132e-01
4.28373972e-03 5.25639713e-01 -5.46952963e-01 -4.32119876e-01
-3.87687907e-02 -1.13925028e+00 -1.24985111e+00 -7.11814404e-01
-7.54609704e-02 4.49396908e-01 2.79413432e-01 -1.97324641... | [12.982288360595703, 1.122490406036377] |
38ec22d7-d3bc-4a95-8e67-e343605ec9d6 | biscuit-causal-representation-learning-from | 2306.09643 | null | https://arxiv.org/abs/2306.09643v1 | https://arxiv.org/pdf/2306.09643v1.pdf | BISCUIT: Causal Representation Learning from Binary Interactions | Identifying the causal variables of an environment and how to intervene on them is of core value in applications such as robotics and embodied AI. While an agent can commonly interact with the environment and may implicitly perturb the behavior of some of these causal variables, often the targets it affects remain unkn... | ['Efstratios Gavves', 'Taco Cohen', 'Yuki M. Asano', 'Sindy Löwe', 'Sara Magliacane', 'Phillip Lippe'] | 2023-06-16 | null | null | null | null | ['causal-discovery', 'causal-identification'] | ['knowledge-base', 'reasoning'] | [ 4.27151442e-01 3.76126915e-01 -3.58784378e-01 -5.73012186e-03
-5.14822314e-03 -8.27558339e-01 7.75920033e-01 1.59608677e-01
-2.91163743e-01 1.20430136e+00 3.54085594e-01 -2.75525659e-01
-7.43314505e-01 -7.86628246e-01 -1.35097754e+00 -1.02890873e+00
-4.44706470e-01 6.77493036e-01 -1.50861636e-01 1.14989206... | [7.872957706451416, 5.270256042480469] |
5a2db3ee-bdc8-4baf-b695-67ee01e5cfbb | learning-discriminative-motion-features | 1812.04172 | null | http://arxiv.org/abs/1812.04172v1 | http://arxiv.org/pdf/1812.04172v1.pdf | Learning Discriminative Motion Features Through Detection | Despite huge success in the image domain, modern detection models such as
Faster R-CNN have not been used nearly as much for video analysis. This is
arguably due to the fact that detection models are designed to operate on
single frames and as a result do not have a mechanism for learning motion
representations directl... | ['Gedas Bertasius', 'Du Tran', 'Lorenzo Torresani', 'Jianbo Shi', 'Christoph Feichtenhofer'] | 2018-12-11 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 1.37522161e-01 -1.94032356e-01 -5.64177632e-01 -1.51782051e-01
-6.03080273e-01 -4.39741075e-01 2.86264598e-01 -2.62012243e-01
-6.06058300e-01 2.62390494e-01 4.96155947e-01 2.88416684e-01
2.13876113e-01 -5.11670530e-01 -8.45757365e-01 -4.89799857e-01
-2.92471856e-01 -5.63339964e-02 4.03880596e-01 -1.62410825... | [8.099140167236328, 0.24702244997024536] |
99e5e5ba-9bf6-4adf-8880-5a8ac49acfb7 | do-not-sleep-on-linear-models-simple-and | 2207.07753 | null | https://arxiv.org/abs/2207.07753v3 | https://arxiv.org/pdf/2207.07753v3.pdf | Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring | Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches achieved only marginal improvements, often at the expense of requiring more data and more expensive training procedures. Despite all these ef... | ['Nicolas Vandenbussche', 'Sofie Van Hoecke', 'Gilles Vandewiele', 'Michael Rademaker', 'Emiel Deprost', 'Jonas Van Der Donckt', 'Jeroen Van Der Donckt'] | 2022-07-15 | null | null | null | null | ['sleep-stage-detection', 'multimodal-sleep-stage-detection', 'sleep-staging', 'automatic-sleep-stage-classification'] | ['medical', 'medical', 'medical', 'medical'] | [ 2.16970250e-01 2.47550830e-01 -4.12229806e-01 -5.32002270e-01
-7.81855643e-01 -3.57442290e-01 3.92728627e-01 5.12942791e-01
-6.72079563e-01 7.40948856e-01 2.66441941e-01 -5.51398695e-01
-2.46794954e-01 -3.34158808e-01 -2.14219466e-01 -5.83806872e-01
2.12358451e-03 4.68391478e-01 7.01752082e-02 -1.19653039... | [13.53534984588623, 3.5360617637634277] |
d3aecd41-23a8-44a1-a8bc-335c77200cd6 | an-empirical-study-of-multitask-learning-to | 2304.08115 | null | https://arxiv.org/abs/2304.08115v1 | https://arxiv.org/pdf/2304.08115v1.pdf | An Empirical Study of Multitask Learning to Improve Open Domain Dialogue Systems | Autoregressive models used to generate responses in open-domain dialogue systems often struggle to take long-term context into account and to maintain consistency over a dialogue. Previous research in open-domain dialogue generation has shown that the use of \emph{auxiliary tasks} can introduce inductive biases that en... | ['Richard Johansson', 'Mehrdad Farahani'] | 2023-04-17 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 5.27093112e-02 9.23033357e-01 1.98617682e-01 -6.41995192e-01
-8.35238338e-01 -5.68911016e-01 1.25250828e+00 -9.47581083e-02
-4.78514612e-01 1.28130758e+00 8.50665808e-01 -1.84969127e-01
5.98809905e-02 -4.82421517e-01 -2.11524710e-01 -3.27747703e-01
1.29909292e-01 1.15624011e+00 -1.04247265e-01 -8.98174047... | [12.711736679077148, 8.131341934204102] |
dc738d86-f11b-4e14-8068-7f7ac87053e8 | icdar-2019-robust-reading-challenge-on | 1912.09641 | null | https://arxiv.org/abs/1912.09641v1 | https://arxiv.org/pdf/1912.09641v1.pdf | ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard | Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, Chinese has more than 6000 commonly used characters and Chinesecharacters can be arranged in various layouts with numerous fonts. The Chinese signboards in street view are... | ['Baoguang Shi', 'Qi Song', 'Mingkun Yang', 'Kai Zhou', 'Yongsheng Zhou', 'C. V. Jawahar', 'Shijian Lu', 'Rui Zhang', 'Nan Li', 'Minghui Liao', 'Dong Wang', 'Xi Liu', 'Xiang Bai', 'Qianyi Jiang', 'Lei Wang', 'Dimosthenis Karatzas'] | 2019-12-20 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 2.33974665e-01 -6.58736467e-01 -4.27378193e-02 -2.84075439e-01
-7.24975407e-01 -6.92558527e-01 4.87329036e-01 -3.02640051e-01
-5.68022370e-01 5.49499393e-01 2.85938978e-01 -2.46071860e-01
3.31977129e-01 -2.37498686e-01 -5.31651199e-01 -6.25029683e-01
4.83215898e-01 2.67877489e-01 3.83944154e-01 3.44699733... | [11.951560020446777, 2.23593807220459] |
7b4236f0-7ae7-408b-9f7b-18ec605dff25 | efficient-video-scene-text-spotting-unifying | 1903.03299 | null | https://arxiv.org/abs/1903.03299v3 | https://arxiv.org/pdf/1903.03299v3.pdf | You Only Recognize Once: Towards Fast Video Text Spotting | Video text spotting is still an important research topic due to its various real-applications. Previous approaches usually fall into the four-staged pipeline: text detection in individual images, framewisely recognizing localized text regions, tracking text streams and generating final results with complicated post-pro... | ['ShiLiang Pu', 'Zhanzhan Cheng', 'Jing Lu', 'Shuigeng Zhou', 'Yi Niu', 'Fei Wu'] | 2019-03-08 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 4.43354607e-01 -9.29177821e-01 -1.35245427e-01 -1.69094950e-01
-9.02366042e-01 -2.71018147e-01 4.71831143e-01 -2.25532934e-01
-3.96067321e-01 4.74806875e-02 2.12774947e-01 -1.24607556e-01
1.33765310e-01 -6.05643928e-01 -5.72774291e-01 -7.40920126e-01
5.03002226e-01 4.50491428e-01 5.36790371e-01 2.61306018... | [11.986751556396484, 2.148066759109497] |
fadb4694-c94e-453a-bf47-0219cd1bd463 | graph-encoder-embedding | 2109.13098 | null | https://arxiv.org/abs/2109.13098v3 | https://arxiv.org/pdf/2109.13098v3.pdf | One-Hot Graph Encoder Embedding | In this paper we propose a lightning fast graph embedding method called one-hot graph encoder embedding. It has a linear computational complexity and the capacity to process billions of edges within minutes on standard PC -- making it an ideal candidate for huge graph processing. It is applicable to either adjacency ma... | ['Carey E. Priebe', 'Qizhe Wang', 'Cencheng Shen'] | 2021-09-27 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 3.81720960e-02 6.24403298e-01 -5.39051771e-01 7.02886134e-02
-3.42710644e-01 -7.36949086e-01 4.65423346e-01 5.77742755e-01
-2.18002528e-01 4.03215557e-01 -1.44972175e-01 -8.13081741e-01
1.58175513e-01 -1.09693062e+00 -8.47487271e-01 -6.04457498e-01
-6.93624914e-01 8.35030615e-01 9.65673029e-02 -7.31634498... | [7.1660027503967285, 5.852138519287109] |
bfac278c-c999-4553-83d7-cb3f250040ba | evaluating-generalization-in-classical-and | 2201.0877 | null | https://arxiv.org/abs/2201.08770v3 | https://arxiv.org/pdf/2201.08770v3.pdf | Generalization Metrics for Practical Quantum Advantage in Generative Models | As the quantum computing community gravitates towards understanding the practical benefits of quantum computers, having a clear definition and evaluation scheme for assessing practical quantum advantage in the context of specific applications is paramount. Generative modeling, for example, is a widely accepted natural ... | ['Alejandro Perdomo-Ortiz', 'Marta Mauri', 'Kaitlin Gili'] | 2022-01-21 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 3.64037573e-01 3.50825995e-01 6.94393143e-02 -1.43752530e-01
-1.11046481e+00 -5.18127143e-01 8.55108559e-01 -2.01009855e-01
-2.04262674e-01 9.80800688e-01 -1.39212400e-01 -4.66237158e-01
-3.77870321e-01 -1.28226709e+00 -5.70244849e-01 -1.23180223e+00
1.35403976e-01 6.67207897e-01 -3.96858782e-01 -4.20045316... | [5.605358600616455, 4.977115631103516] |
98c27969-0e2e-4706-a393-f19562b14537 | end-to-end-lane-detection-with-one-to-several | 2305.00675 | null | https://arxiv.org/abs/2305.00675v4 | https://arxiv.org/pdf/2305.00675v4.pdf | End-to-End Lane detection with One-to-Several Transformer | Although lane detection methods have shown impressive performance in real-world scenarios, most of methods require post-processing which is not robust enough. Therefore, end-to-end detectors like DEtection TRansformer(DETR) have been introduced in lane detection.However, one-to-one label assignment in DETR can degrade ... | ['Rui Zhou', 'Kunyang Zhou'] | 2023-05-01 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [-1.88585743e-02 -1.16564564e-01 -4.13544089e-01 -6.10304832e-01
-8.96666169e-01 -4.21247095e-01 1.48380399e-01 -1.61692545e-01
-4.90728259e-01 4.67892468e-01 -2.20527370e-02 -4.22807097e-01
1.69011652e-01 -5.72393656e-01 -7.21568823e-01 -5.90291083e-01
1.80692002e-01 -2.69607361e-02 1.05993783e+00 -1.85310006... | [8.114404678344727, -1.3732094764709473] |
ea7543ad-f71e-405e-b0cc-f9d01a1b6ca1 | how-does-generative-retrieval-scale-to | 2305.11841 | null | https://arxiv.org/abs/2305.11841v1 | https://arxiv.org/pdf/2305.11841v1.pdf | How Does Generative Retrieval Scale to Millions of Passages? | Popularized by the Differentiable Search Index, the emerging paradigm of generative retrieval re-frames the classic information retrieval problem into a sequence-to-sequence modeling task, forgoing external indices and encoding an entire document corpus within a single Transformer. Although many different approaches ha... | ['Vinh Q. Tran', 'Donald Metzler', 'Jimmy Lin', 'Honglei Zhuang', 'Adam D. Lelkes', 'Jai Gupta', 'Kai Hui', 'Ronak Pradeep'] | 2023-05-19 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 3.64041984e-01 -2.22471088e-01 -4.47989069e-02 -1.60998851e-02
-1.68277323e+00 -7.82955587e-01 1.09623957e+00 7.19748586e-02
-4.16588902e-01 7.40062177e-01 5.03221214e-01 -3.94533783e-01
-4.64434415e-01 -5.78219056e-01 -7.02692866e-01 -5.40605307e-01
-1.13081131e-02 8.46663296e-01 1.59780398e-01 -4.96722609... | [11.461040496826172, 7.6132378578186035] |
8ea15346-24ed-48d5-aabe-a13cbaca3b01 | neural-attribution-for-semantic-bug | null | null | http://papers.nips.cc/paper/9358-neural-attribution-for-semantic-bug-localization-in-student-programs | http://papers.nips.cc/paper/9358-neural-attribution-for-semantic-bug-localization-in-student-programs.pdf | Neural Attribution for Semantic Bug-Localization in Student Programs | Providing feedback is an integral part of teaching. Most open online courses on programming make use of automated grading systems to support programming assignments and give real-time feedback. These systems usually rely on test results to quantify the programs' functional correctness. They return failing tests to the ... | ['Aditya Kanade', 'Rahul Gupta', 'Shirish Shevade'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['fault-localization'] | ['computer-code'] | [-2.67079651e-01 -2.51216181e-02 -4.13294494e-01 -5.82117379e-01
-7.30533957e-01 -7.09360540e-01 -3.52700561e-01 7.41224170e-01
2.07384571e-01 2.48579860e-01 -4.00430501e-01 -1.05161238e+00
8.64948556e-02 -1.15401888e+00 -1.18164361e+00 -8.21924303e-03
1.21954679e-02 1.43679976e-01 5.85011244e-01 -3.97606909... | [7.772510528564453, 7.721486568450928] |
f73c4ccc-547e-45de-a097-1738b741cf92 | membership-inference-on-word-embedding-and | 2106.11384 | null | https://arxiv.org/abs/2106.11384v1 | https://arxiv.org/pdf/2106.11384v1.pdf | Membership Inference on Word Embedding and Beyond | In the text processing context, most ML models are built on word embeddings. These embeddings are themselves trained on some datasets, potentially containing sensitive data. In some cases this training is done independently, in other cases, it occurs as part of training a larger, task-specific model. In either case, it... | ['Marcello Hasegawa', 'Esha Ghosh', 'Melissa Chase', 'Huseyin A. Inan', 'Saeed Mahloujifar'] | 2021-06-21 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.64582956e-01 4.29048032e-01 -9.92515683e-03 -1.14850923e-01
-9.02315438e-01 -1.09099233e+00 9.53474879e-01 6.64573848e-01
-5.44273496e-01 3.52274656e-01 2.01502562e-01 -9.40267086e-01
3.34393919e-01 -1.15859091e+00 -8.92540812e-01 -7.66283751e-01
-1.49249196e-01 2.92007983e-01 3.40989262e-01 -7.42247924... | [5.978590965270996, 7.713857650756836] |
cf1a3cd4-4cd4-42d6-bb6a-77ff455fe0aa | attribute-based-classification-for-zero-shot | null | null | https://ieeexplore.ieee.org/document/6571196 | https://hannes.nickisch.org/papers/articles/lampert13attributes.pdf | Attribute-Based Classification for Zero-Shot Visual Object Categorization | We study the problem of object recognition for categories for which we have no training examples, a task also called
zero-data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently;
the world contains tens of thousands of different object classes, ... | ['Stefan Harmeling', 'Hannes Nickisch', 'Christoph H. Lampert'] | 2013-07-30 | null | null | null | ieee-transactions-on-pattern-analysis-and-17 | ['object-categorization'] | ['computer-vision'] | [ 6.51560426e-01 6.67325500e-03 -2.11999178e-01 -7.77121723e-01
-3.14088523e-01 -5.58771849e-01 7.61801422e-01 7.45250463e-01
-5.48624992e-01 6.59899533e-01 -3.38572323e-01 9.68247205e-02
-2.06918105e-01 -9.79294896e-01 -5.76099455e-01 -8.72221649e-01
-7.43252337e-02 9.16677356e-01 3.11959207e-01 -6.50445595... | [9.704559326171875, 2.5364162921905518] |
3c1703a3-c0b3-4874-934c-8ec57f14f808 | rigid-soft-interactive-learning-for-robust | 2003.01584 | null | https://arxiv.org/abs/2003.01584v1 | https://arxiv.org/pdf/2003.01584v1.pdf | Rigid-Soft Interactive Learning for Robust Grasping | Inspired by widely used soft fingers on grasping, we propose a method of rigid-soft interactive learning, aiming at reducing the time of data collection. In this paper, we classify the interaction categories into Rigid-Rigid, Rigid-Soft, Soft-Rigid according to the interaction surface between grippers and target object... | ['Xiaobo Liu', 'Fang Wan', 'Linhan Yang', 'Yujia Liu', 'Jia Pan', 'Haokun Wang', 'Chaoyang Song'] | 2020-02-29 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-2.59180605e-01 -1.72452509e-01 -1.16170704e-01 -2.59060621e-01
-1.47117972e-01 -8.55080366e-01 1.31676480e-01 -6.66799366e-01
-4.52767134e-01 4.39083159e-01 -4.95177984e-01 3.07273138e-02
-7.61733413e-01 -6.00737810e-01 -9.15537298e-01 -1.03197730e+00
-3.30262631e-01 8.24090123e-01 6.00727260e-01 -2.93740749... | [5.8287763595581055, -0.8778322339057922] |
8e0f81b1-7bf4-48b8-a25a-10eef30718d3 | knowledge-graph-informed-fake-news | 2110.10457 | null | https://arxiv.org/abs/2110.10457v2 | https://arxiv.org/pdf/2110.10457v2.pdf | Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles | Increasing amounts of freely available data both in textual and relational form offers exploration of richer document representations, potentially improving the model performance and robustness. An emerging problem in the modern era is fake news detection -- many easily available pieces of information are not necessari... | ['Blaž Škrlj', 'Senja Pollak', 'Marko Robnik-Šikonja', 'Timen Stepišnik-Perdih', 'Boshko Koloski'] | 2021-10-20 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [ 1.42329514e-01 2.06949085e-01 -5.84433973e-01 -2.28947788e-01
-8.86795104e-01 -6.40503645e-01 9.46113288e-01 8.55478823e-01
1.41426297e-02 6.37578726e-01 4.73739624e-01 -5.07494688e-01
-2.94066817e-01 -1.03245032e+00 -7.82339573e-01 -2.22488776e-01
1.27946436e-02 7.33308375e-01 1.95303142e-01 -7.23033965... | [8.183954238891602, 10.237748146057129] |
57362925-bfa4-4e3b-a007-158715d4250f | sonnet-generation-by-training-on-non-poetic | null | null | https://openreview.net/forum?id=6wKqI-x0Vb | https://openreview.net/pdf?id=6wKqI-x0Vb | Sonnet Generation by Training on Non-poetic Texts with Discourse-level Coherence and Poetic Features | Poetry generation, and creative language generation in general, usually suffers from the lack of large training data. In this paper, we present a novel framework to generate sonnets that does not require training on poems. We design a hierarchical framework which plans the poem sketch before decoding. Specifically, a c... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['sonnet-generation'] | ['natural-language-processing'] | [ 2.90807098e-01 4.78962898e-01 9.29595530e-02 3.36795785e-02
-6.73825085e-01 -5.51842391e-01 1.09297431e+00 -4.25551981e-01
-1.19069135e-02 6.80870593e-01 8.20588946e-01 -3.02518271e-02
3.28853160e-01 -1.04500246e+00 -4.06679213e-01 -4.11970645e-01
3.74374419e-01 7.47226894e-01 4.65953834e-02 -6.01754725... | [11.591136932373047, 9.377100944519043] |
6e6f544f-c61a-467a-84aa-4747a5687a66 | sparse-representation-based-classification | 1607.04942 | null | http://arxiv.org/abs/1607.04942v1 | http://arxiv.org/pdf/1607.04942v1.pdf | Sparse Representation-Based Classification: Orthogonal Least Squares or Orthogonal Matching Pursuit? | Spare representation of signals has received significant attention in recent
years. Based on these developments, a sparse representation-based
classification (SRC) has been proposed for a variety of classification and
related tasks, including face recognition. Recently, a class dependent variant
of SRC was proposed to ... | ['Saurabh Prasad', 'Minshan Cui'] | 2016-07-18 | null | null | null | null | ['sparse-representation-based-classification', 'remote-sensing-image-classification'] | ['computer-vision', 'miscellaneous'] | [ 6.18205369e-01 -3.95265937e-01 -1.52484626e-01 -4.95904744e-01
-5.73977888e-01 -1.44028604e-01 2.80705124e-01 -5.68465330e-02
-1.16949817e-02 7.39761233e-01 -2.17289627e-02 -4.24102247e-02
-3.88159961e-01 -5.75688064e-01 -2.94272363e-01 -1.00056612e+00
-1.12945147e-01 -1.95513010e-01 -7.56463408e-02 -1.46093350... | [12.44079303741455, 0.40752479434013367] |
8b4f24d2-6279-442a-9d2b-563a2f7f1616 | multimodal-relation-extraction-with-cross | 2305.16166 | null | https://arxiv.org/abs/2305.16166v1 | https://arxiv.org/pdf/2305.16166v1.pdf | Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis | Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair. Existing retrieval-augmented approaches mainly focused on modeling the retrieved textual knowledge, but this may not be able to accurately identify complex rel... | ['Philip S. Yu', 'Irwin King', 'Zhiyang Teng', 'Zhijiang Guo', 'Xuming Hu'] | 2023-05-25 | null | null | null | null | ['cross-modal-retrieval', 'relation-extraction'] | ['miscellaneous', 'natural-language-processing'] | [ 2.30544254e-01 -1.91579103e-01 -3.76109689e-01 -3.19146305e-01
-1.04385459e+00 -3.43715847e-01 8.78360808e-01 8.23541045e-01
-2.95523196e-01 6.19283140e-01 3.16210270e-01 -6.82582408e-02
-2.79038936e-01 -7.03591645e-01 -4.96398300e-01 -2.24943697e-01
2.59489745e-01 1.90851659e-01 4.83464360e-01 1.41548580... | [10.709062576293945, 1.4351511001586914] |
6e4a8cac-48cc-4c83-b846-36426e63cb49 | boundary-detection-and-categorization-of | null | null | https://aclanthology.org/2022.argmining-1.12 | https://aclanthology.org/2022.argmining-1.12.pdf | Boundary Detection and Categorization of Argument Aspects via Supervised Learning | Aspect-based argument mining (ABAM) is the task of automatic _detection_ and _categorization_ of argument aspects, i.e. the parts of an argumentative text that contain the issue-specific key rationale for its conclusion. From empirical data, overlapping but not congruent sets of aspect categories can be derived for dif... | ['Gregor Wiedemann', 'Mattes Ruckdeschel'] | null | null | null | null | argmining-acl-2022-10 | ['boundary-detection', 'argument-mining'] | ['computer-vision', 'natural-language-processing'] | [ 1.77580297e-01 8.44201982e-01 -5.17542481e-01 -4.85000253e-01
-8.88000071e-01 -8.15126359e-01 1.02942300e+00 1.12912500e+00
-3.63711238e-01 7.12648630e-01 8.35985422e-01 -8.56085300e-01
-2.59598196e-01 -6.36438847e-01 -3.83018464e-01 -4.10230577e-01
2.36666694e-01 7.06917524e-01 2.41659686e-01 -6.81506246... | [9.432713508605957, 9.629117012023926] |
f7f193f7-4864-4f22-b094-216aba03bf74 | learning-where-to-learn-gradient-sparsity-in | 2110.14402 | null | https://arxiv.org/abs/2110.14402v1 | https://arxiv.org/pdf/2110.14402v1.pdf | Learning where to learn: Gradient sparsity in meta and continual learning | Finding neural network weights that generalize well from small datasets is difficult. A promising approach is to learn a weight initialization such that a small number of weight changes results in low generalization error. We show that this form of meta-learning can be improved by letting the learning algorithm decide ... | ['João Sacramento', 'Nicolas Zucchet', 'Massimo Caccia', 'Simon Schug', 'Seijin Kobayashi', 'Dominic Zhao', 'Johannes von Oswald'] | 2021-10-27 | null | http://proceedings.neurips.cc/paper/2021/hash/2a10665525774fa2501c2c8c4985ce61-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/2a10665525774fa2501c2c8c4985ce61-Paper.pdf | neurips-2021-12 | ['sparse-learning'] | ['methodology'] | [ 3.43312293e-01 1.05463207e-01 -5.20002067e-01 -4.96111661e-01
-3.50779861e-01 -4.52665031e-01 6.71781063e-01 1.51893228e-01
-5.95526576e-01 7.65256166e-01 3.65176022e-01 -1.20354502e-03
-4.54310924e-01 -9.24275577e-01 -7.14421809e-01 -8.14025104e-01
-1.70868948e-01 2.24410877e-01 1.70924410e-03 -6.09239221... | [8.887541770935059, 3.2533509731292725] |
5ecefef2-fa4b-4b18-838b-36191d1e93c1 | reference-based-oct-angiogram-super | 2305.05835 | null | https://arxiv.org/abs/2305.05835v1 | https://arxiv.org/pdf/2305.05835v1.pdf | Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation | Optical coherence tomography angiography (OCTA) is a new imaging modality to visualize retinal microvasculature and has been readily adopted in clinics. High-resolution OCT angiograms are important to qualitatively and quantitatively identify potential biomarkers for different retinal diseases accurately. However, one ... | ['Hao Chen', 'Carol Y. Cheung', 'An Ran Ran', 'Ziqi Tang', 'Dawei Yang', 'Yuyan Ruan'] | 2023-05-10 | null | null | null | null | ['texture-synthesis', 'reference-based-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 4.06073272e-01 -1.27482623e-01 1.21635027e-01 -1.80533215e-01
-6.32690132e-01 -2.09467590e-01 2.17929170e-01 -1.35492593e-01
-2.49989659e-01 8.10353339e-01 -1.67117402e-01 -3.02315563e-01
-1.22341178e-01 -9.63051796e-01 -4.17761296e-01 -8.45628262e-01
3.00415128e-01 1.87469080e-01 5.48394442e-01 1.42818570... | [15.73158073425293, -3.9257242679595947] |
e4600439-17c7-4e1e-b644-fefe9f865196 | accuracy-privacy-trade-off-in-deep-ensemble | 2105.05381 | null | https://arxiv.org/abs/2105.05381v4 | https://arxiv.org/pdf/2105.05381v4.pdf | Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective | Deep ensemble learning has been shown to improve accuracy by training multiple neural networks and averaging their outputs. Ensemble learning has also been suggested to defend against membership inference attacks that undermine privacy. In this paper, we empirically demonstrate a trade-off between these two goals, name... | ['Xin Liu', 'Zubair Shafiq', 'Shahbaz Rezaei'] | 2021-05-12 | accuracy-privacy-trade-off-in-deep-ensemble-a | https://openreview.net/forum?id=wxVpa5z4DU1 | https://openreview.net/pdf?id=wxVpa5z4DU1 | null | ['membership-inference-attack'] | ['computer-vision'] | [ 6.88057840e-02 -2.29736254e-01 3.31227839e-01 -5.02558470e-01
-6.77655995e-01 -1.12181413e+00 4.71492738e-01 7.17864197e-04
-4.95691329e-01 8.77103508e-01 -6.38142228e-02 -6.92738175e-01
-2.70713747e-01 -8.10103953e-01 -7.88547277e-01 -8.65901887e-01
-3.35580975e-01 -1.11459307e-01 -4.35623646e-01 -2.47859687... | [5.99124813079834, 7.094431400299072] |
9ec450d1-02f1-41af-97cb-046a7a72418e | sherlock-scalable-fact-learning-in-images | 1511.04891 | null | http://arxiv.org/abs/1511.04891v4 | http://arxiv.org/pdf/1511.04891v4.pdf | Sherlock: Scalable Fact Learning in Images | We study scalable and uniform understanding of facts in images. Existing
visual recognition systems are typically modeled differently for each fact type
such as objects, actions, and interactions. We propose a setting where all
these facts can be modeled simultaneously with a capacity to understand
unbounded number of ... | ['Walter Chang', 'Mohamed Elhoseiny', 'Brian Price', 'Ahmed Elgammal', 'Scott Cohen'] | 2015-11-16 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [-1.67600840e-01 2.51877010e-01 -2.83619225e-01 -6.93852544e-01
-6.56580687e-01 -7.56450891e-01 7.94215918e-01 3.37465584e-01
-1.30948182e-02 8.41477931e-01 4.06640291e-01 -2.45298773e-01
-1.35874733e-01 -9.13996994e-01 -1.60118842e+00 -6.47976875e-01
-3.62813622e-01 6.33388162e-01 1.22430986e-02 -1.14320092... | [10.453036308288574, 1.692186713218689] |
57627e2a-59e5-4336-8457-e67d89964e76 | deepcontour-a-deep-convolutional-feature | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Shen_DeepContour_A_Deep_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Shen_DeepContour_A_Deep_2015_CVPR_paper.pdf | DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection | Contour detection serves as the basis of a variety of computer vision tasks such as image segmentation and object recognition. The mainstream works to address this problem focus on designing engineered gradient features. In this work, we show that contour detection accuracy can be improved by instead making the use of ... | ['Zhijiang Zhang', 'Wei Shen', 'Xinggang Wang', 'Yan Wang', 'Xiang Bai'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['contour-detection'] | ['computer-vision'] | [ 2.28442580e-01 8.19493234e-02 -4.84221101e-01 -7.74631023e-01
-5.00513971e-01 -3.70338321e-01 2.69258887e-01 9.55766067e-02
-7.23058641e-01 4.60159421e-01 -2.98441827e-01 -1.72211424e-01
2.68470824e-01 -1.10785210e+00 -7.16024399e-01 -8.16262782e-01
8.38002041e-02 6.06679767e-02 6.36457682e-01 7.56677845... | [9.562115669250488, 0.42912471294403076] |
8c7738d1-2221-4193-8386-92f29db7bd7b | technology-report-smartphone-based-pedestrian | 2301.03471 | null | https://arxiv.org/abs/2301.03471v1 | https://arxiv.org/pdf/2301.03471v1.pdf | Technology Report : Smartphone-Based Pedestrian Dead Reckoning Integrated with Data-Fusion-Adopted Visible Light Positioning | Pedestrian dead-reckoning (PDR) is a potential indoor localization technology that obtains location estimation with the inertial measurement unit (IMU). However, one of its most significant drawbacks is the accumulation of its measurement error. This paper proposes a visible light positioning (VLP)-integrated PDR syste... | ['Xuecong Fang', 'Yingcong Chen', 'Danlan Yuan', 'Ziyang Ge', 'ShangSheng Wen'] | 2023-01-06 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 3.78650911e-02 -6.49263918e-01 9.90898609e-02 -1.64720237e-01
-6.10828221e-01 -4.83410656e-01 1.99920744e-01 -2.11063512e-02
-6.73233926e-01 1.01251137e+00 5.92794095e-04 -4.13856298e-01
3.36937994e-01 -9.63541508e-01 -5.64161479e-01 -6.40566230e-01
4.59227026e-01 -2.82733947e-01 1.76980004e-01 -6.00758642... | [6.432774066925049, 0.8467540144920349] |
f58ed07e-fdf1-41fc-829a-b90a395411f5 | traffic-sign-detection-with-event-cameras-and | 2207.13345 | null | https://arxiv.org/abs/2207.13345v1 | https://arxiv.org/pdf/2207.13345v1.pdf | Traffic Sign Detection With Event Cameras and DCNN | In recent years, event cameras (DVS - Dynamic Vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions -- parameters that are parti... | ['Tomasz Kryjak', 'Piotr Wzorek'] | 2022-07-27 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 9.48983356e-02 -8.13501701e-02 4.30571824e-01 -2.03584418e-01
-2.91632742e-01 -1.90786988e-01 9.50897157e-01 2.65542150e-01
-1.06573355e+00 6.75481915e-01 -5.36860466e-01 -8.62753019e-02
-7.70578831e-02 -7.74283350e-01 -5.33645511e-01 -8.51320744e-01
1.93456784e-01 2.39540607e-01 9.15753961e-01 -3.10540468... | [8.434290885925293, -1.0616436004638672] |
36242baf-e461-4e82-8df9-95ced55ba066 | enhancing-data-security-against-cyberattacks | 2305.11652 | null | https://arxiv.org/abs/2305.11652v1 | https://arxiv.org/pdf/2305.11652v1.pdf | Enhancing data security against cyberattacks in artificial intelligence based smartgrid systems with crypto agility | A new paradigm of electricity generation at the distribution level, with renewable and alternative sources, is possible with microgrids. The main idea is to have microgrids deployed on low- or medium-voltage active distribution networks. They can be advantageous in many different ways, such as improving the energy effi... | ['Wilson Lopes', 'Emmanuel Anti', 'Sayawu Diaba', 'Mazaher Karimi', 'Mike Mekkanen', 'Tero Vartiainen', 'Mohammed Elmusrati', 'Marcelo Simoes'] | 2023-05-19 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.86148125e-01 -2.91671753e-01 1.23686576e-02 1.91203147e-01
3.46678764e-01 -1.01329112e+00 3.36637437e-01 2.35485241e-01
4.36855406e-01 1.32062399e+00 -6.23700731e-02 -1.49846911e-01
-5.15833020e-01 -9.74361062e-01 9.56328064e-02 -1.19140780e+00
-5.81413209e-01 5.09839058e-01 2.57984921e-02 -4.55312371... | [5.776973724365234, 2.568315267562866] |
81d6e3fd-1214-4427-8b10-af9ed767a04a | learning-visual-n-grams-from-web-data | 1612.09161 | null | http://arxiv.org/abs/1612.09161v2 | http://arxiv.org/pdf/1612.09161v2.pdf | Learning Visual N-Grams from Web Data | Real-world image recognition systems need to recognize tens of thousands of
classes that constitute a plethora of visual concepts. The traditional approach
of annotating thousands of images per class for training is infeasible in such
a scenario, prompting the use of webly supervised data. This paper explores the
train... | ['Laurens van der Maaten', 'Allan Jabri', 'Ang Li', 'Armand Joulin'] | 2016-12-29 | learning-visual-n-grams-from-web-data-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Li_Learning_Visual_N-Grams_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Learning_Visual_N-Grams_ICCV_2017_paper.pdf | iccv-2017-10 | ['zero-shot-transfer-image-classification'] | ['computer-vision'] | [ 3.75951082e-01 -1.17149040e-01 -2.48217732e-01 -4.07234371e-01
-8.93222988e-01 -6.47447050e-01 6.91690266e-01 3.72083724e-01
-4.68663126e-01 4.63908672e-01 2.65961707e-01 -6.56019866e-01
4.26664054e-01 -6.99596584e-01 -9.68560874e-01 -3.41049731e-01
5.46207242e-02 3.78428996e-01 2.99142743e-03 1.19934157... | [10.380906105041504, 1.8809481859207153] |
5c461c84-e394-49c9-af78-75ef9dd4049f | multi-task-learning-framework-for-extracting | 2211.03742 | null | https://arxiv.org/abs/2211.03742v1 | https://arxiv.org/pdf/2211.03742v1.pdf | Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations | Predicting emotions expressed in text is a well-studied problem in the NLP community. Recently there has been active research in extracting the cause of an emotion expressed in text. Most of the previous work has done causal emotion entailment in documents. In this work, we propose neural models to extract emotion caus... | ['Ashutosh Modi', 'Ashwani Bhat'] | 2022-11-07 | null | null | null | null | ['causal-emotion-entailment'] | ['natural-language-processing'] | [ 2.46192813e-01 3.72020870e-01 -2.47838661e-01 -9.67064857e-01
-8.33804131e-01 -5.25151014e-01 5.63927531e-01 9.82859284e-02
-1.41255185e-01 9.92785454e-01 1.08674848e+00 7.69505790e-03
2.13243634e-01 -5.72011173e-01 -7.77214348e-01 -3.09414148e-01
2.02192262e-01 1.76443189e-01 -7.16580868e-01 -2.00733945... | [12.753632545471191, 6.288743019104004] |
7e2b7532-d259-4ea9-9b09-0134ac63f90d | energy-efficient-deployment-of-multiple-uavs | 2003.05668 | null | http://arxiv.org/abs/2003.05668v1 | http://arxiv.org/pdf/2003.05668v1.pdf | Energy-efficient Deployment of Multiple UAVs Using Ellipse Clustering to Establish Base Stations | The demand for future wireless communication systems is being satisfied for
various circumstances through unmanned aerial vehicles (UAVs), which act as
flying base stations (BSs). In this letter, we propose an ellipse clustering
algorithm that maximizes the user coverage probability of UAV- BSs and avoids
inter-cell in... | [] | 2020-03-12 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [-2.30622470e-01 1.78056791e-01 -2.60290027e-01 4.62248176e-01
4.51110393e-01 -1.00928009e+00 9.20052733e-03 -1.93885162e-01
-1.68768704e-01 1.05510855e+00 -4.49197888e-01 -6.94045782e-01
-3.98377627e-01 -8.89831781e-01 -1.57977507e-01 -1.06699359e+00
-4.23959166e-01 -2.04034612e-01 2.33517408e-01 4.41566743... | [5.973454475402832, 1.4558496475219727] |
4874766c-b8fd-4e33-a95b-00d5cf4609c8 | leveraging-denoised-abstract-meaning | 2307.02127 | null | https://arxiv.org/abs/2307.02127v1 | https://arxiv.org/pdf/2307.02127v1.pdf | Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction | Grammatical Error Correction (GEC) is the task of correcting errorful sentences into grammatically correct, semantically consistent, and coherent sentences. Popular GEC models either use large-scale synthetic corpora or use a large number of human-designed rules. The former is costly to train, while the latter requires... | ['Dongyan Zhao', 'Hejing Cao'] | 2023-07-05 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 2.61655331e-01 3.03140253e-01 5.21765351e-01 -7.97297478e-01
-1.28904057e+00 -3.35074514e-01 3.87171298e-01 2.00961843e-01
-4.66557890e-01 8.37698579e-01 3.81817073e-01 -1.44405156e-01
4.07075793e-01 -7.36300707e-01 -9.83994782e-01 -2.35998169e-01
5.49599946e-01 4.15461868e-01 3.89075801e-02 -4.22994941... | [11.081777572631836, 10.705124855041504] |
52d49634-0909-40e2-b72e-db475d5794f1 | xtarnet-learning-to-extract-task-adaptive | 2003.08561 | null | https://arxiv.org/abs/2003.08561v2 | https://arxiv.org/pdf/2003.08561v2.pdf | XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning | Learning novel concepts while preserving prior knowledge is a long-standing challenge in machine learning. The challenge gets greater when a novel task is given with only a few labeled examples, a problem known as incremental few-shot learning. We propose XtarNet, which learns to extract task-adaptive representation (T... | ['Do-Yeon Kim', 'Jun Seo', 'Sung Whan Yoon', 'Jaekyun Moon'] | 2020-03-19 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6928-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6928-Paper.pdf | icml-2020-1 | ['novel-concepts'] | ['reasoning'] | [ 4.73701119e-01 1.07385777e-01 -3.18092316e-01 -3.40927422e-01
-8.80928636e-01 -6.47243187e-02 5.82361698e-01 1.33244425e-01
-5.95143557e-01 6.58174217e-01 9.02861208e-02 5.21072984e-01
-7.78708160e-02 -7.27894127e-01 -5.92880487e-01 -7.41781056e-01
-2.13577986e-01 3.12719703e-01 6.61076486e-01 -2.14016765... | [9.98175048828125, 3.1111693382263184] |
21f3cedc-d4f1-4bf1-a9b9-4e540ba1f5bb | multi-granularity-chinese-word-embedding | null | null | https://aclanthology.org/D16-1100 | https://aclanthology.org/D16-1100.pdf | Multi-Granularity Chinese Word Embedding | null | ['Rongchao Yin', 'Peng Li', 'Rui Li', 'Bin Wang', 'Quan Wang'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['learning-word-embeddings'] | ['methodology'] | [-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... | [-7.3876447677612305, 3.8253791332244873] |
f721465e-3d04-4981-8fbe-2d6898cc5422 | sense-a-shared-encoder-network-for-scene-flow-1 | 1910.12361 | null | https://arxiv.org/abs/1910.12361v1 | https://arxiv.org/pdf/1910.12361v1.pdf | SENSE: a Shared Encoder Network for Scene-flow Estimation | We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion estimation, and semantic segmentation. Our key insight is that sharing features makes the network m... | ['Erik Learned-Miller', 'Deqing Sun', 'Zhaoyang Lv', 'Huaizu Jiang', 'Jan Kautz', 'Varun Jampani'] | 2019-10-27 | sense-a-shared-encoder-network-for-scene-flow | http://openaccess.thecvf.com/content_ICCV_2019/html/Jiang_SENSE_A_Shared_Encoder_Network_for_Scene-Flow_Estimation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Jiang_SENSE_A_Shared_Encoder_Network_for_Scene-Flow_Estimation_ICCV_2019_paper.pdf | iccv-2019-10 | ['occlusion-estimation', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [-7.08222166e-02 -8.38075131e-02 -6.49966598e-01 -5.49063981e-01
-4.91850615e-01 -5.19061446e-01 3.84719610e-01 -2.60888368e-01
-4.67689306e-01 8.65995049e-01 6.48522615e-01 -3.52698378e-02
1.61447570e-01 -7.78371334e-01 -7.47323632e-01 -3.91696066e-01
-1.04465999e-01 3.53263825e-01 2.87501872e-01 1.28497601... | [8.726972579956055, -1.9060944318771362] |
d3f4b7d1-8e9d-46e8-9e65-43da364576f2 | cross-domain-human-parsing-via-adversarial | 1801.0126 | null | http://arxiv.org/abs/1801.01260v2 | http://arxiv.org/pdf/1801.01260v2.pdf | Cross-domain Human Parsing via Adversarial Feature and Label Adaptation | Human parsing has been extensively studied recently due to its wide
applications in many important scenarios. Mainstream fashion parsing models
focus on parsing the high-resolution and clean images. However, directly
applying the parsers trained on benchmarks to a particular application scenario
in the wild, e.g., a ca... | ['Defa Zhu', 'Yu Chen', 'Si Liu', 'Jiashi Feng', 'Guanghui Ren', 'Yao Sun', 'Jizhong Han'] | 2018-01-04 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 0.4325786 0.04377211 0.20091961 -0.69627786 -0.9310189 -0.73504484
0.22315875 -0.27156672 -0.30263332 0.6445311 0.01289222 -0.02663219
0.09622125 -0.6717509 -0.7946985 -0.63776046 0.390803 0.25382444
0.41609102 -0.13574961 -0.15880157 0.21283558 -1.1690885 0.19749686
0.9863909 0.79947555 0.... | [9.25375747680664, 0.7842438817024231] |
5694a2f0-2089-4d9d-973b-e2c57c897fbb | clusterq-semantic-feature-distribution | 2205.00179 | null | https://arxiv.org/abs/2205.00179v2 | https://arxiv.org/pdf/2205.00179v2.pdf | Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization | To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require o... | ['Shuicheng Yan', 'Jicong Fan', 'Haijun Zhang', 'Richang Hong', 'Zhao Zhang', 'Yangcheng Gao'] | 2022-04-30 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 1.76441576e-02 -3.94644320e-01 -1.82456806e-01 -6.14388108e-01
-6.08586609e-01 -3.02670896e-01 3.50326419e-01 1.30555689e-01
-7.10871339e-01 7.73450315e-01 -2.32091114e-01 -2.42142305e-01
-1.26570016e-01 -1.08939826e+00 -8.19244504e-01 -8.61644387e-01
1.87687173e-01 2.29781091e-01 2.87057072e-01 -5.89318424... | [8.70576000213623, 3.0287702083587646] |
1d52ff78-2480-4f03-b0b2-4877eb81c12a | foveation-based-deep-video-compression | 2203.1649 | null | https://arxiv.org/abs/2203.16490v1 | https://arxiv.org/pdf/2203.16490v1.pdf | Foveation-based Deep Video Compression without Motion Search | The requirements of much larger file sizes, different storage formats, and immersive viewing conditions of VR pose significant challenges to the goals of acquiring, transmitting, compressing, and displaying high-quality VR content. At the same time, the great potential of deep learning to advance progress on the video ... | ['Alan C. Bovik', 'Richard Webb', 'Meixu Chen'] | 2022-03-30 | null | null | null | null | ['foveation'] | ['computer-vision'] | [ 2.79934347e-01 -1.98407874e-01 3.00722364e-02 -7.55642578e-02
-3.72014970e-01 -3.47734183e-01 3.49375039e-01 1.82041118e-03
-5.96371889e-01 3.84630293e-01 1.52542248e-01 -3.31470162e-01
-7.13290945e-02 -6.77166879e-01 -8.14272106e-01 -5.79966009e-01
-2.58984596e-01 -3.74536514e-01 3.15357298e-01 1.98648330... | [11.366376876831055, -1.6833715438842773] |
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