paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
54ab50b1-b9ef-47f5-8bcc-320cf527f74a | lb-simtsc-an-efficient-similarity-aware-graph | 2301.04838 | null | https://arxiv.org/abs/2301.04838v2 | https://arxiv.org/pdf/2301.04838v2.pdf | LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification | Time series classification is an important data mining task that has received a lot of interest in the past two decades. Due to the label scarcity in practice, semi-supervised time series classification with only a few labeled samples has become popular. Recently, Similarity-aware Time Series Classification (SimTSC) is... | ['Jessica Lin', 'Li Zhang', 'Arnav Jain', 'Wenjie Xi'] | 2023-01-12 | null | null | null | null | ['semi-supervised-time-series-classification', 'dynamic-time-warping'] | ['time-series', 'time-series'] | [ 7.08870739e-02 -3.10472369e-01 -1.86335310e-01 -5.24613619e-01
-4.48361844e-01 -7.18145907e-01 3.92708629e-01 7.68884122e-01
-5.68552732e-01 5.06956398e-01 -3.31544161e-01 -5.51571369e-01
-6.11147046e-01 -8.59017789e-01 -4.78063166e-01 -7.13188052e-01
-9.53124702e-01 4.97077614e-01 -5.18280501e-03 -1.88399807... | [7.312307357788086, 3.432619094848633] |
35c43d49-1573-4671-970f-42dbd8c1b76f | pointresnet-residual-network-for-3d-point | 2211.11040 | null | https://arxiv.org/abs/2211.11040v1 | https://arxiv.org/pdf/2211.11040v1.pdf | PointResNet: Residual Network for 3D Point Cloud Segmentation and Classification | Point cloud segmentation and classification are some of the primary tasks in 3D computer vision with applications ranging from augmented reality to robotics. However, processing point clouds using deep learning-based algorithms is quite challenging due to the irregular point formats. Voxelization or 3D grid-based repre... | ['Shanmuganathan Raman', 'Seema Kumari', 'Saagar Parikh', 'Aadesh Desai'] | 2022-11-20 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 2.99755111e-03 -1.73949525e-01 4.44057281e-04 -3.84015977e-01
-5.19827068e-01 -2.18861938e-01 5.74607491e-01 3.03023588e-02
-4.01304364e-01 1.45873874e-01 -4.24134672e-01 -4.97851372e-01
1.89510345e-01 -8.92429471e-01 -8.90683532e-01 -6.09746933e-01
-2.22230971e-01 7.74396718e-01 3.72627556e-01 -9.64373350... | [7.951545715332031, -3.4316680431365967] |
6b414684-4784-4a30-8ff6-be55793d9dac | pfns-are-flexible-models-for-real-world | 2305.17535 | null | https://arxiv.org/abs/2305.17535v4 | https://arxiv.org/pdf/2305.17535v4.pdf | PFNs4BO: In-Context Learning for Bayesian Optimization | In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible surrogate for Bayesian Optimization (BO). PFNs are neural processes that are trained to approximate the posterior predictive distribution (PPD) through in-context learning on any prior distribution that can be efficiently sampled from. We describe ho... | ['Frank Hutter', 'Noah Hollmann', 'Matthias Feurer', 'Samuel Müller'] | 2023-05-27 | null | null | null | null | ['automl', 'hyperparameter-optimization', 'bayesian-optimization'] | ['methodology', 'methodology', 'methodology'] | [-1.81168213e-01 3.86259347e-01 -9.05699804e-02 -2.95749843e-01
-6.36823356e-01 -4.57695276e-01 7.34847188e-01 -2.51603544e-01
-2.83718169e-01 8.65997076e-01 2.98299551e-01 -2.79949397e-01
-4.18075502e-01 -5.67786098e-01 -1.02298188e+00 -8.28648031e-01
-2.03738526e-01 9.03620481e-01 5.32371178e-02 2.04916209... | [6.959787845611572, 3.8718695640563965] |
bd24c53a-0fda-4679-a85d-2e05936e1902 | advanced-feature-learning-on-point-clouds | 2205.09962 | null | https://arxiv.org/abs/2205.09962v1 | https://arxiv.org/pdf/2205.09962v1.pdf | Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling | Existing point cloud feature learning networks often incorporate sequences of sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation to learn high-semantic point features that represent the global context of a point cloud. Unfortunately, the compounded loss of information concernin... | ['Seung-Hyun Kong', 'Dong-Hee Paek', 'Kevin Tirta Wijaya'] | 2022-05-20 | null | null | null | null | ['3d-point-cloud-classification'] | ['computer-vision'] | [-5.11544406e-01 -3.38187903e-01 5.48171513e-02 -6.44384444e-01
-8.36846471e-01 -4.65444535e-01 4.22349185e-01 4.46239829e-01
-2.10702047e-01 2.69551337e-01 -1.65148273e-01 3.18351299e-01
-5.77875137e-01 -1.30538535e+00 -8.45366001e-01 -6.94504559e-01
-1.66422606e-01 2.22945586e-01 3.77856374e-01 -1.79372892... | [7.910632133483887, -3.4492123126983643] |
5d010847-5e44-4894-b749-b16c4f650122 | degree-aware-based-adversarial-graph | null | null | https://www.sciencedirect.com/science/article/pii/S0925231222001448 | https://www.sciencedirect.com/science/article/pii/S0925231222001448/pdfft?md5=1791f3ca52941e00736316a5605ed3ff&pid=1-s2.0-S0925231222001448-main.pdf | Degree aware based adversarial graph convolutional networks for entity alignment in heterogeneous knowledge graph | Entity alignment, as the vital technique for knowledge graph construction and integration, aims to match entities that refer to the same real-world identity in different knowledge graphs (KGs). Recently, much effort has been devoted to embedding-based methods for entity alignment. For most of such methods, the entity w... | ['Tao Luo', 'Jianfeng Li', 'Yining Wang', 'Hanchen Wang'] | 2022-04-28 | null | null | null | neurocomputing-2022-4 | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-2.83408463e-01 3.53150189e-01 -7.45905191e-02 -6.26194775e-02
-8.40754882e-02 -6.76715851e-01 4.95574951e-01 5.13148844e-01
-1.92402765e-01 6.17975414e-01 9.29025039e-02 -1.39933825e-01
-3.10455471e-01 -1.42325282e+00 -6.43738210e-01 -5.87258637e-01
-7.28067830e-02 3.10772240e-01 1.60644606e-01 -4.37950432... | [8.729898452758789, 7.9181742668151855] |
faf4c410-eb63-4d88-9297-b85069bca5f0 | secseq-semantic-coding-for-sequence-to | null | null | https://openreview.net/forum?id=B1x5GPUOTX | https://openreview.net/pdf?id=B1x5GPUOTX | SeCSeq: Semantic Coding for Sequence-to-Sequence based Extreme Multi-label Classification | Extreme multi-label classification (XMC) aims at assigning to an instance the most relevant subset of labels from a colossal label set. There has been some success in formulating the multi-label problem as sequence-to-sequence (Seq2Seq) learning, where the positive class labels of each input instance are used as the co... | ['Yiming Yang', 'Inderjit S. Dhillon', 'Hsiang-Fu Yu', 'Wei-Cheng Chang'] | 2018-11-13 | null | null | null | nips-workshop-cdnnria-2018 | ['extreme-multi-label-classification'] | ['methodology'] | [ 1.13004339e+00 9.22711343e-02 -4.07611161e-01 -8.77859890e-01
-1.30043209e+00 -7.69588530e-01 4.01341528e-01 -3.48197185e-02
-5.20225763e-01 7.53385603e-01 1.28899664e-01 -2.71730155e-01
1.05828427e-01 -3.36120516e-01 -5.78811288e-01 -7.31704116e-01
2.94023722e-01 6.35538578e-01 -1.99833184e-01 3.00636262... | [9.565849304199219, 4.412591457366943] |
e9dab7af-f06b-4bec-811c-f5ac0458fa63 | robustfill-neural-program-learning-under | 1703.07469 | null | http://arxiv.org/abs/1703.07469v1 | http://arxiv.org/pdf/1703.07469v1.pdf | RobustFill: Neural Program Learning under Noisy I/O | The problem of automatically generating a computer program from some
specification has been studied since the early days of AI. Recently, two
competing approaches for automatic program learning have received significant
attention: (1) neural program synthesis, where a neural network is conditioned
on input/output (I/O)... | ['Abdel-rahman Mohamed', 'Surya Bhupatiraju', 'Rishabh Singh', 'Jacob Devlin', 'Pushmeet Kohli', 'Jonathan Uesato'] | 2017-03-21 | robustfill-neural-program-learning-under-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=661 | http://proceedings.mlr.press/v70/devlin17a/devlin17a.pdf | icml-2017-8 | ['program-induction'] | ['computer-code'] | [ 6.08476996e-01 3.16674203e-01 -4.85396832e-01 -4.51792657e-01
-8.66168559e-01 -5.62066734e-01 5.68185031e-01 2.61495948e-01
-8.00251365e-02 4.53043342e-01 -3.23411711e-02 -9.19865191e-01
3.47956032e-01 -1.06178796e+00 -1.34557366e+00 -1.24898545e-01
-2.34531667e-02 3.62157136e-01 9.68994871e-02 -1.14530995... | [8.101204872131348, 7.51939582824707] |
4a0a549f-dc50-45be-9f77-90d774c5d1d3 | geometric-change-detection-in-digital-twins | 2103.08201 | null | https://arxiv.org/abs/2103.08201v1 | https://arxiv.org/pdf/2103.08201v1.pdf | Geometric Change Detection in Digital Twins using 3D Machine Learning | Digital twins are meant to bridge the gap between real-world physical systems and virtual representations. Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations of objects in the digital replica. Digital twin applications are required to rapidly update in... | ['Omer San', 'Mandar Tabib', 'Adil Rasheed', 'Julia Maria Graham', 'Tiril Sundby'] | 2021-03-15 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 3.59352201e-01 -4.21397328e-01 1.19259253e-01 3.09289575e-01
-1.82918996e-01 -3.83382946e-01 3.26720834e-01 -1.85772240e-01
-2.50237763e-01 9.55264345e-02 -6.72380745e-01 -1.98791400e-01
1.56034887e-01 -8.88603210e-01 -7.34292388e-01 -7.06301212e-01
-1.32699430e-01 2.03439489e-01 7.87283778e-01 7.49135688... | [6.947773456573486, -2.2430646419525146] |
c0834ad4-1e6a-46ba-8a9f-58f637584116 | boosting-knowledge-graph-generation-from | null | null | https://link.springer.com/chapter/10.1007/978-3-031-33455-9_29 | https://oa.upm.es/73463/1/_2023___ESWC__RML_Tabular_Views.pdf | Boosting Knowledge Graph Generation from Tabular Data with RML Views | A large amount of data is available in tabular form. RML is commonly used to declare how such data can be transformed into RDF. However, RML presents limitations that lead, in many cases, to the need for additional preprocessing using scripting. Although some proposed extensions (e.g., FnO or RML fields) address some o... | ['Oscar Corcho', 'María S. Pérez', 'María Navas-Loro', 'Ahmad Alobaid', 'Julián Arenas-Guerrero'] | 2023-05-22 | null | null | null | extended-semantic-web-conference-2023-5 | ['knowledge-graphs-data-curation', 'data-integration'] | ['knowledge-base', 'knowledge-base'] | [-1.81760326e-01 4.54931796e-01 -1.63204357e-01 -7.88532913e-01
-2.99226969e-01 -8.58286738e-01 7.06152022e-01 4.83000308e-01
-5.51893339e-02 7.14219868e-01 -2.55928282e-03 -5.98236978e-01
-4.26248997e-01 -1.42249274e+00 -5.80846548e-01 2.47361228e-01
5.11578023e-02 8.22568774e-01 6.32757425e-01 -5.96597612... | [9.141754150390625, 7.745604991912842] |
a9faf60f-8194-40d0-bd38-9cfd1c668061 | lift-yourself-up-retrieval-augmented-text | 2305.02437 | null | https://arxiv.org/abs/2305.02437v2 | https://arxiv.org/pdf/2305.02437v2.pdf | Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory | With direct access to human-written reference as memory, retrieval-augmented generation has achieved much progress in a wide range of text generation tasks. Since better memory would typically prompt better generation~(we define this as primal problem). The traditional approach for memory retrieval involves selecting m... | ['Rui Yan', 'Dongyan Zhao', 'Lemao Liu', 'Xiuying Chen', 'Di Luo', 'Xin Cheng'] | 2023-05-03 | null | null | null | null | ['dialogue-generation', 'abstractive-text-summarization', 'text-summarization', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 5.42838037e-01 2.66361088e-01 -3.84447485e-01 4.12051566e-02
-1.20647752e+00 -5.10497272e-01 9.57330525e-01 1.24188885e-01
-3.13182592e-01 1.06636608e+00 6.40827179e-01 -3.06979388e-01
2.14503005e-01 -9.35948730e-01 -5.82824171e-01 -4.22308266e-01
3.98414552e-01 7.33502567e-01 -9.45494976e-03 -4.81357336... | [12.067597389221191, 9.02849006652832] |
b5699ebb-2afd-4555-8bed-053041580c23 | low-complexity-three-dimensional-discrete | 2206.00124 | null | https://arxiv.org/abs/2206.00124v1 | https://arxiv.org/pdf/2206.00124v1.pdf | Low-complexity Three-dimensional Discrete Hartley Transform Approximations for Medical Image Compression | The discrete Hartley transform (DHT) is a useful tool for medical image coding. The three-dimensional DHT (3D DHT) can be employed to compress medical image data, such as magnetic resonance and X-ray angiography. However, the computation of the 3D DHT involves several multiplications by irrational quantities, which req... | ['R. J. Cintra', 'F. M. Bayer', 'V. A. Coutinho'] | 2022-05-31 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 6.01841807e-01 5.46995476e-02 1.03359818e-01 -3.84607241e-02
-4.27837878e-01 1.08355545e-01 3.04491132e-01 6.40511513e-01
-7.70481229e-01 5.49511373e-01 -7.09432811e-02 -5.04742742e-01
-2.45567992e-01 -8.46977234e-01 -4.34328884e-01 -5.63879132e-01
-4.24472868e-01 2.70361930e-01 2.46222332e-01 -1.13265701... | [11.483774185180664, -2.2972264289855957] |
a73ddbfe-42a5-44f1-9dcf-4e9a3536fc7c | pose-guided-human-animation-from-a-single | 2012.03796 | null | https://arxiv.org/abs/2012.03796v2 | https://arxiv.org/pdf/2012.03796v2.pdf | Pose-Guided Human Animation from a Single Image in the Wild | We present a new pose transfer method for synthesizing a human animation from a single image of a person controlled by a sequence of body poses. Existing pose transfer methods exhibit significant visual artifacts when applying to a novel scene, resulting in temporal inconsistency and failures in preserving the identity... | ['Christian Theobalt', 'Hyun Soo Park', 'Kripasindhu Sarkar', 'Vladislav Golyanik', 'Lingjie Liu', 'Jae Shin Yoon'] | 2020-12-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yoon_Pose-Guided_Human_Animation_From_a_Single_Image_in_the_Wild_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yoon_Pose-Guided_Human_Animation_From_a_Single_Image_in_the_Wild_CVPR_2021_paper.pdf | cvpr-2021-1 | ['pose-transfer'] | ['computer-vision'] | [ 4.30147201e-01 2.86276966e-01 3.41149956e-01 -2.73781717e-01
-3.82845968e-01 -6.72866821e-01 6.76664829e-01 -4.86792624e-01
-1.10496152e-02 5.69478631e-01 -2.64734887e-02 3.55094731e-01
4.78204995e-01 -5.85175216e-01 -1.08182204e+00 -6.56430542e-01
1.03306711e-01 5.88105142e-01 2.61196405e-01 -2.28954121... | [11.578238487243652, -0.7588024139404297] |
33f9acfe-fb9e-4dcf-b31d-bb0cb723675f | swde-a-sub-word-and-document-embedding-based | 1808.00957 | null | http://arxiv.org/abs/1808.00957v1 | http://arxiv.org/pdf/1808.00957v1.pdf | SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection | In order to expand their reach and increase website ad revenue, media outlets
have started using clickbait techniques to lure readers to click on articles on
their digital platform. Having successfully enticed the user to open the
article, the article fails to satiate his curiosity serving only to boost
click-through r... | ['Manish Shrivastava', 'Yash Kumar Lal', 'Vasudeva Varma', 'Vaibhav Kumar', 'Mrinal Dhar', 'Dhruv Khattar', 'Abhimanshu Mishra'] | 2018-08-02 | null | null | null | null | ['document-embedding', 'clickbait-detection'] | ['methodology', 'natural-language-processing'] | [-5.12239672e-02 1.52471542e-01 -1.74688041e-01 -3.08590710e-01
-7.15864778e-01 -6.90108955e-01 9.27842081e-01 4.98472601e-01
-7.40588605e-01 3.50199372e-01 4.35370743e-01 -6.89031422e-01
1.66398525e-01 -8.59008908e-01 -9.48190272e-01 -1.49178386e-01
1.23078480e-01 3.15063596e-01 2.60751098e-01 -3.55434746... | [7.805327415466309, 9.753251075744629] |
b0bceaa4-dfbc-4f57-aa77-ee7addb5cbca | born-for-auto-tagging-faster-and-better-with | 2206.07264 | null | https://arxiv.org/abs/2206.07264v1 | https://arxiv.org/pdf/2206.07264v1.pdf | Born for Auto-Tagging: Faster and better with new objective functions | Keyword extraction is a task of text mining. It is applied to increase search volume in SEO and ads. Implemented in auto-tagging, it makes tagging on a mass scale of online articles and photos efficiently and accurately. BAT is invented for auto-tagging which served as awoo's AI marketing platform (AMP). awoo AMP not o... | ['Huang-Ting Shieh', 'Chiung-ju Liu'] | 2022-06-15 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [-9.38351974e-02 1.06001452e-01 -3.82821441e-01 -2.20961899e-01
-7.42445469e-01 -3.30529690e-01 2.16792434e-01 5.60295917e-02
-4.53407139e-01 4.02985901e-01 -1.13918617e-01 -2.18047157e-01
-3.59851986e-01 -7.48199046e-01 -3.71251404e-01 -6.10466897e-01
-3.73303086e-01 2.68910855e-01 2.11313263e-01 -2.92528719... | [10.054065704345703, 5.684659957885742] |
0627e47e-09f5-42be-a672-d372b7056d66 | multiscale-fields-of-patterns | 1406.0924 | null | http://arxiv.org/abs/1406.0924v3 | http://arxiv.org/pdf/1406.0924v3.pdf | Multiscale Fields of Patterns | We describe a framework for defining high-order image models that can be used
in a variety of applications. The approach involves modeling local patterns in
a multiscale representation of an image. Local properties of a coarsened image
reflect non-local properties of the original image. In the case of binary
images loc... | ['Pedro F. Felzenszwalb', 'John G. Oberlin'] | 2014-06-04 | multiscale-fields-of-patterns-1 | http://papers.nips.cc/paper/5283-multiscale-fields-of-patterns | http://papers.nips.cc/paper/5283-multiscale-fields-of-patterns.pdf | neurips-2014-12 | ['contour-detection'] | ['computer-vision'] | [ 6.15804434e-01 -9.39819813e-02 -3.59475702e-01 -3.38955790e-01
-5.48648715e-01 -2.84017712e-01 8.42201054e-01 3.58835489e-01
-4.82099146e-01 6.51470065e-01 -5.77124916e-02 1.45203099e-01
-1.75004795e-01 -1.08213937e+00 -5.57141483e-01 -9.34615791e-01
-2.04365849e-02 4.28367794e-01 1.00479877e+00 3.04323249... | [11.280797004699707, -2.41402006149292] |
ea5de8df-cabb-4c11-ae06-2ae23614769f | ncsu_sas_sam-deep-encoding-and-reconstruction | null | null | https://aclanthology.org/W15-4323 | https://aclanthology.org/W15-4323.pdf | NCSU\_SAS\_SAM: Deep Encoding and Reconstruction for Normalization of Noisy Text | null | ['Samuel Leeman-Munk', 'James Lester', 'James Cox'] | 2015-07-01 | null | null | null | ws-2015-7 | ['lexical-normalization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.312983512878418, 3.8664746284484863] |
b2bfb07b-9ae0-458f-8129-6e921af4f067 | multilingual-epidemiological-text | null | null | https://aclanthology.org/2020.coling-main.543 | https://aclanthology.org/2020.coling-main.543.pdf | Multilingual Epidemiological Text Classification: A Comparative Study | In this paper, we approach the multilingual text classification task in the context of the epidemiological field. Multilingual text classification models tend to perform differently across different languages (low- or high-resourced), more particularly when the dataset is highly imbalanced, which is the case for epidem... | ['Moses Odeo', 'Ga{\\"e}l Lejeune', 'Adam Jatowt', 'Antoine Doucet', 'Emanuela Boros', 'Stephen Mutuvi'] | 2020-12-01 | null | null | null | coling-2020-8 | ['multilingual-text-classification'] | ['miscellaneous'] | [-4.05193388e-01 -1.35151535e-01 -5.03256142e-01 -3.73882912e-02
-3.18512648e-01 -5.18844426e-01 1.04191494e+00 8.08708429e-01
-8.85194182e-01 5.27605772e-01 8.51802289e-01 -5.16252100e-01
-1.37471735e-01 -6.86167777e-01 -5.47074258e-01 -4.12798107e-01
7.58513734e-02 8.16792309e-01 -1.01954989e-01 -4.64909196... | [10.093439102172852, 9.920912742614746] |
d07e78a0-5298-4001-b795-42d10782012d | audio-captioning-using-pre-trained-large | 2012.07331 | null | https://arxiv.org/abs/2012.07331v1 | https://arxiv.org/pdf/2012.07331v1.pdf | Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval | The goal of audio captioning is to translate input audio into its description using natural language. One of the problems in audio captioning is the lack of training data due to the difficulty in collecting audio-caption pairs by crawling the web. In this study, to overcome this problem, we propose to use a pre-trained... | ['Masahiro Yasuda', 'Daiki Takeuchi', 'Daisuke Niizumi', 'Yasunori Ohishi', 'Yuma Koizumi'] | 2020-12-14 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 6.06914163e-01 4.18316156e-01 4.21116799e-01 -1.96641058e-01
-1.66231167e+00 -6.03030264e-01 4.75145519e-01 1.22853518e-01
-7.79848844e-02 8.88341129e-01 5.06128788e-01 -1.07596204e-01
2.22575322e-01 -5.63172638e-01 -1.11023498e+00 -3.07418108e-01
1.69292703e-01 7.85506666e-01 1.39418349e-01 -1.95728093... | [15.281113624572754, 4.882950305938721] |
93fc7bf6-cc9c-4455-aed1-782335c1c8f5 | super-resolution-method-for-coherent-doa | 2103.03271 | null | https://arxiv.org/abs/2103.03271v1 | https://arxiv.org/pdf/2103.03271v1.pdf | Super-resolution Method for Coherent DOA Estimation of Multiple Wideband Sources | We focus on coherent direction of arrival estimation of wideband sources based on spatial sparsity. This area of research is encountered in many applications such as passive radar, sonar, mining, and communication problems, in which an increasing attention has been devoted to improving the estimation accuracy and robus... | ['Mohammad Hossein Kahaei', 'Milad Javadzadeh Jirhandeh'] | 2021-03-04 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 1.24586493e-01 -3.63128394e-01 1.60241202e-01 6.69872835e-02
-7.02246726e-01 -2.36297995e-01 2.68606663e-01 -5.26341647e-02
-2.73021907e-01 9.74499345e-01 3.22741508e-01 2.93855011e-01
-5.72443128e-01 -9.34170187e-01 -2.82909155e-01 -1.02907491e+00
-2.76193321e-01 -1.49416059e-01 8.17149356e-02 -1.79500014... | [6.476180553436279, 1.3352352380752563] |
84b5ac8d-6880-41d7-be97-724ceec09647 | the-re-label-method-for-data-centric-machine | 2302.04391 | null | https://arxiv.org/abs/2302.04391v3 | https://arxiv.org/pdf/2302.04391v3.pdf | The Re-Label Method For Data-Centric Machine Learning | In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. I... | ['Tong Guo'] | 2023-02-09 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-1.66458368e-01 -3.01409692e-01 3.96552794e-02 -7.34396935e-01
-7.14547515e-01 -5.89405775e-01 2.58550644e-01 2.22246513e-01
-6.44613206e-01 1.14748037e+00 9.95189473e-02 -1.10243171e-01
3.23842019e-01 -6.07110322e-01 -5.59874892e-01 -4.21309710e-01
1.09391645e-01 5.21024466e-01 4.82582927e-01 -7.80265555... | [9.38431167602539, 3.9376440048217773] |
8aa162c9-6a58-42c3-b2de-31adca0ce4c1 | advances-in-black-box-vi-normalizing-flows | 2006.10343 | null | https://arxiv.org/abs/2006.10343v2 | https://arxiv.org/pdf/2006.10343v2.pdf | Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization | Recent research has seen several advances relevant to black-box VI, but the current state of automatic posterior inference is unclear. One such advance is the use of normalizing flows to define flexible posterior densities for deep latent variable models. Another direction is the integration of Monte-Carlo methods to s... | ['Abhinav Agrawal', 'Daniel Sheldon', 'Justin Domke'] | 2020-06-18 | null | http://proceedings.neurips.cc/paper/2020/hash/c91e3483cf4f90057d02aa492d2b25b1-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/c91e3483cf4f90057d02aa492d2b25b1-Paper.pdf | neurips-2020-12 | ['variational-monte-carlo'] | ['miscellaneous'] | [ 7.04474328e-03 -1.81907967e-01 -1.94339648e-01 -2.30938405e-01
-9.60279882e-01 -5.11923850e-01 9.51790333e-01 -2.01347575e-01
-3.10792297e-01 1.08439493e+00 1.14869200e-01 -2.95092195e-01
-2.79339522e-01 -5.88596284e-01 -5.14475346e-01 -9.60037589e-01
2.61437356e-01 9.20482397e-01 3.38688702e-03 1.26951605... | [6.956808567047119, 3.867215633392334] |
2417aa81-de52-49ef-90b1-0be6b7cb86b6 | uncertainty-aware-contour-proposal-networks | null | null | https://openreview.net/forum?id=YtgRjBw-7GJ | https://openreview.net/pdf?id=YtgRjBw-7GJ | Uncertainty-Aware Contour Proposal Networks for Cell Segmentation in Multi-Modality High-Resolution Microscopy Images | We present a simple framework for cell segmentation, based on uncertainty-aware Contour Proposal Networks (CPNs).
It is designed to provide high segmentation accuracy while remaining computationally efficient, which makes it an ideal solution for high throughput microscopy applications. Each predicted cell is provided... | ['Timo Dickscheid', 'Katrin Amunts', 'Stefan Harmeling', 'Eric Upschulte'] | 2022-11-30 | null | null | null | neurips-cellseg-2022-2022-11 | ['cell-segmentation'] | ['medical'] | [ 4.14674342e-01 2.07128093e-01 -4.47567226e-03 -2.45242700e-01
-1.09117115e+00 -6.56072557e-01 3.90737623e-01 4.91292208e-01
-8.11939478e-01 1.19536245e+00 -5.05114436e-01 -3.34813476e-01
1.85342893e-01 -2.57012516e-01 -7.77250051e-01 -1.09982145e+00
1.08983874e-01 8.83118570e-01 4.41045672e-01 3.35775733... | [14.516863822937012, -3.114067554473877] |
f66a726a-801c-414f-9f41-11b95ba23773 | deepwriting-making-digital-ink-editable-via | 1801.08379 | null | http://arxiv.org/abs/1801.08379v1 | http://arxiv.org/pdf/1801.08379v1.pdf | DeepWriting: Making Digital Ink Editable via Deep Generative Modeling | Digital ink promises to combine the flexibility and aesthetics of handwriting
and the ability to process, search and edit digital text. Character recognition
converts handwritten text into a digital representation, albeit at the cost of
losing personalized appearance due to the technical difficulties of separating
the ... | ['Fabrizio Pece', 'Emre Aksan', 'Otmar Hilliges'] | 2018-01-25 | null | null | null | null | ['handwritten-word-generation', 'handwriting-generation'] | ['computer-vision', 'computer-vision'] | [ 5.92351675e-01 -5.80315292e-02 2.92147905e-01 -2.12452292e-01
-1.17897697e-01 -1.23525691e+00 6.37495816e-01 3.93971987e-02
-1.16522104e-01 4.43983525e-01 9.41662937e-02 -3.58667940e-01
4.95595932e-02 -8.40191722e-01 -5.41003108e-01 -4.46409255e-01
4.55008775e-01 4.44242418e-01 1.30063698e-01 -1.13919392... | [11.642932891845703, -0.054519400000572205] |
95135a80-62e7-41c0-a2a4-597ec02c55be | low-complexity-deep-video-compression-with-a | 2303.11599 | null | https://arxiv.org/abs/2303.11599v2 | https://arxiv.org/pdf/2303.11599v2.pdf | Low-complexity Deep Video Compression with A Distributed Coding Architecture | Prevalent predictive coding-based video compression methods rely on a heavy encoder to reduce temporal redundancy, which makes it challenging to deploy them on resource-constrained devices. Since the 1970s, distributed source coding theory has indicated that independent encoding and joint decoding with side information... | ['Jun Zhang', 'Jiawei Shao', 'Xinjie Zhang'] | 2023-03-21 | null | null | null | null | ['motion-estimation'] | ['computer-vision'] | [ 1.38191476e-01 -2.78976738e-01 -4.56905425e-01 -2.10544407e-01
-9.24749255e-01 7.26753324e-02 1.49902269e-01 -1.64949875e-02
8.29546228e-02 4.56249088e-01 6.23492956e-01 -1.81967914e-01
-1.55853242e-01 -7.60753989e-01 -7.31386542e-01 -7.93136537e-01
-4.75429952e-01 4.22759838e-02 2.74096221e-01 3.41021866... | [11.334227561950684, -1.6301425695419312] |
62b81e6b-0458-4429-b7f1-6fded56b49b9 | auxiliary-signal-guided-knowledge-encoder | 2006.03744 | null | https://arxiv.org/abs/2006.03744v1 | https://arxiv.org/pdf/2006.03744v1.pdf | Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation | Beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image featu... | ['Xiaodan Liang', 'Xiaojun Chang', 'Mingjie Li', 'Fuyu Wang'] | 2020-06-06 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 5.12169302e-01 7.65116453e-01 -2.08795428e-01 -3.71353567e-01
-9.31599200e-01 -1.66573420e-01 5.45928001e-01 1.38162911e-01
-2.08915733e-02 7.81960130e-01 5.10973513e-01 -2.14863971e-01
3.09387427e-02 -9.43466663e-01 -8.91156852e-01 -7.71379173e-01
3.62553209e-01 3.63656193e-01 1.51458994e-01 -9.28350091... | [15.055386543273926, -1.3882713317871094] |
227bda08-dac6-47ee-87a5-cecb71779f27 | a-new-approach-for-trading-based-on-long | 2001.03333 | null | https://arxiv.org/abs/2001.03333v1 | https://arxiv.org/pdf/2001.03333v1.pdf | A new approach for trading based on Long Short Term Memory technique | The stock market prediction has always been crucial for stakeholders, traders and investors. We developed an ensemble Long Short Term Memory (LSTM) model that includes two-time frequencies (annual and daily parameters) in order to predict the next-day Closing price (one step ahead). Based on a four-step approach, this ... | ['Saaid Achchab', 'Zineb Lanbouri'] | 2020-01-10 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-6.50712371e-01 -3.25656414e-01 -7.31664374e-02 -1.25129402e-01
-1.92229077e-01 -6.33044243e-01 7.97678173e-01 -1.53809354e-01
-5.24803102e-01 1.14507210e+00 2.78198630e-01 -6.31452799e-01
-1.70593694e-01 -1.19340241e+00 -1.62986130e-01 -4.43306565e-01
-4.91267949e-01 2.49504447e-01 7.86638185e-02 -4.80404973... | [4.52601957321167, 4.198631763458252] |
d7e7b5fd-7d9e-4fd1-a081-b37917f965b5 | shadowformer-global-context-helps-image | 2302.01650 | null | https://arxiv.org/abs/2302.01650v1 | https://arxiv.org/pdf/2302.01650v1.pdf | ShadowFormer: Global Context Helps Image Shadow Removal | Recent deep learning methods have achieved promising results in image shadow removal. However, most of the existing approaches focus on working locally within shadow and non-shadow regions, resulting in severe artifacts around the shadow boundaries as well as inconsistent illumination between shadow and non-shadow regi... | ['Bihan Wen', 'Hao Cheng', 'Ding Liu', 'Siyu Huang', 'Lanqing Guo'] | 2023-02-03 | null | null | null | null | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 3.86915088e-01 -2.64754564e-01 3.30439746e-01 -4.84198004e-01
-4.78516966e-01 -1.18079573e-01 4.64349121e-01 -5.47118306e-01
2.80003510e-02 7.08472431e-01 5.91718197e-01 -4.70066547e-01
3.68006974e-01 -5.05080104e-01 -5.65877020e-01 -1.00520301e+00
3.28130990e-01 -7.27893189e-02 6.49309397e-01 -1.36607900... | [10.830449104309082, -4.076090335845947] |
3235960f-bd36-4a7f-9cb3-931369761a7f | towards-fully-automated-segmentation-of-rat | 2109.04188 | null | https://arxiv.org/abs/2109.04188v1 | https://arxiv.org/pdf/2109.04188v1.pdf | Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks | Automated segmentation of human cardiac magnetic resonance datasets has been steadily improving during recent years. However, these methods are not directly applicable in preclinical context due to limited datasets and lower image resolution. Successful application of deep architectures for rat cardiac segmentation, al... | ['Leif Hultin', 'Patrik Kagelid', 'Peter Konings', 'Magdalena Zurek', 'Arijit Patra', 'Harris Vince', 'Andrea Gondova', 'Daniel Fernandez-Llaneza'] | 2021-09-09 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.60784775e-01 8.43526050e-02 1.84497818e-01 -3.88651311e-01
-6.33800626e-01 -7.09822059e-01 3.21655065e-01 6.29895270e-01
-7.13759303e-01 7.41240799e-01 -3.30884844e-01 -4.89292741e-01
-1.44933537e-01 -5.62552154e-01 -4.72345740e-01 -6.65044188e-01
-3.21217895e-01 1.02591777e+00 4.74116206e-01 2.79686093... | [14.134992599487305, -2.508458137512207] |
057cc273-bc0d-4125-ae35-6bbade13de37 | factorization-of-multi-agent-sampling-based | 2304.00342 | null | https://arxiv.org/abs/2304.00342v1 | https://arxiv.org/pdf/2304.00342v1.pdf | Factorization of Multi-Agent Sampling-Based Motion Planning | Modern robotics often involves multiple embodied agents operating within a shared environment. Path planning in these cases is considerably more challenging than in single-agent scenarios. Although standard Sampling-based Algorithms (SBAs) can be used to search for solutions in the robots' joint space, this approach qu... | ['Emilio Frazzoli', 'Andrea Censi', 'Pietro Zullo', 'Alessandro Zanardi'] | 2023-04-01 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 7.28921220e-02 4.03291374e-01 4.19409983e-02 1.57565251e-01
-6.54514849e-01 -9.17468786e-01 2.27057815e-01 9.35968459e-02
-4.70281094e-01 1.04109037e+00 -1.29388899e-01 -3.37442100e-01
-7.50939727e-01 -9.44604158e-01 -8.57903004e-01 -8.26676488e-01
-4.58689392e-01 1.13336349e+00 2.29485899e-01 -9.53752100... | [4.865657806396484, 1.7048355340957642] |
78b010e6-2b2d-4455-881d-6b9fe6e571da | a-clarification-of-misconceptions-myths-and | 2008.05607 | null | https://arxiv.org/abs/2008.05607v1 | https://arxiv.org/pdf/2008.05607v1.pdf | A clarification of misconceptions, myths and desired status of artificial intelligence | The field artificial intelligence (AI) has been founded over 65 years ago. Starting with great hopes and ambitious goals the field progressed though various stages of popularity and received recently a revival in the form of deep neural networks. Some problems of AI are that so far neither 'intelligence' nor the goals ... | ['Olli Yli-Harja', 'Frank Emmert-Streib', 'Matthias Dehmer'] | 2020-08-03 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 2.11437613e-01 4.04400140e-01 -1.97185636e-01 -5.47846854e-01
-1.21852346e-02 -3.00200194e-01 1.03894532e+00 4.30751182e-02
-4.48409766e-01 8.93734217e-01 2.42210925e-01 -4.64326531e-01
-4.25663054e-01 -7.44639575e-01 -1.67005658e-01 -6.19667530e-01
-2.78011709e-01 6.18360162e-01 -2.65771121e-01 -4.91167396... | [9.039913177490234, 6.399143218994141] |
c3413361-2e2b-4087-a5f2-2e46467caf0e | affective-decoding-for-empathetic-response | 2108.08102 | null | https://arxiv.org/abs/2108.08102v3 | https://arxiv.org/pdf/2108.08102v3.pdf | Affective Decoding for Empathetic Response Generation | Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose a simple technique called Affective Decoding for empathetic response generation. Our method can effectively incorporate emotion signals dur... | ['Chengkun Zeng', 'Zhigang Chen', 'Ruizhe Li', 'Chenghua Lin', 'Guanyi Chen'] | 2021-08-18 | null | https://aclanthology.org/2021.inlg-1.37 | https://aclanthology.org/2021.inlg-1.37.pdf | inlg-acl-2021-8 | ['empathetic-response-generation'] | ['natural-language-processing'] | [-3.25076729e-01 7.11108506e-01 9.07209665e-02 -7.97889948e-01
-6.17200255e-01 -2.87036330e-01 4.95511472e-01 -3.06011885e-02
-3.68390232e-01 7.62215614e-01 1.06760490e+00 4.20525521e-01
4.57017928e-01 -5.95915079e-01 1.56978548e-01 -4.61079329e-01
3.68441314e-01 4.82844502e-01 -9.61978257e-01 -1.08594978... | [13.142956733703613, 7.6380696296691895] |
a3e054b8-0c82-4da9-be6c-4d61a2649278 | the-effect-of-information-type-on-human | 2302.09069 | null | https://arxiv.org/abs/2302.09069v1 | https://arxiv.org/pdf/2302.09069v1.pdf | The Effect of Information Type on Human Cognitive Augmentation | When performing a task alone, humans achieve a certain level of performance. When humans are assisted by a tool or automation to perform the same task, performance is enhanced (augmented). Recently developed cognitive systems are able to perform cognitive processing at or above the level of a human in some domains. Whe... | ['Samuel McGaha', 'Ron Fulbright'] | 2023-02-15 | null | null | null | null | ['type'] | ['speech'] | [ 2.99143314e-01 4.50432420e-01 5.23112059e-01 -1.56700000e-01
1.02576762e-01 -6.98794365e-01 6.14266634e-01 6.68652594e-01
-5.04178643e-01 5.18597424e-01 2.86910057e-01 -2.53555447e-01
-6.86792731e-01 -8.35531414e-01 -1.18958749e-01 -1.81689054e-01
2.99386263e-01 5.35702050e-01 2.94575065e-01 -4.44245726... | [9.028462409973145, 6.351901054382324] |
9220fd04-369f-473e-a7f4-effd53e1015a | low-rank-representation-of-head-impact | 2004.12979 | null | https://arxiv.org/abs/2004.12979v1 | https://arxiv.org/pdf/2004.12979v1.pdf | Low-rank representation of head impact kinematics: A data-driven emulator | Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real-t... | ['Kaveh Laksari', 'Hessam Babaee', 'Nima Toosizadeh', 'Patricio Arrue'] | 2020-04-27 | null | null | null | null | ['injury-prediction'] | ['playing-games'] | [-2.58318126e-01 -1.53774306e-01 -1.62455216e-02 -2.65703145e-02
-6.45498633e-01 -2.34286532e-01 1.59842908e-01 5.87454718e-03
-5.45855105e-01 7.14280248e-01 5.42096615e-01 -2.23696470e-01
-4.69730049e-01 -5.30726790e-01 -5.89344800e-01 -8.65776300e-01
-4.27870125e-01 4.72977668e-01 5.08732259e-01 -2.49064758... | [14.068229675292969, -1.885719895362854] |
dc37164c-314d-41a4-bfb9-d0ee9a4818b3 | adaptive-template-enhancement-for-improved | 2201.01218 | null | https://arxiv.org/abs/2201.01218v1 | https://arxiv.org/pdf/2201.01218v1.pdf | Adaptive Template Enhancement for Improved Person Recognition using Small Datasets | A novel instance-based method for the classification of electroencephalography (EEG) signals is presented and evaluated in this paper. The non-stationary nature of the EEG signals, coupled with the demanding task of pattern recognition with limited training data as well as the potentially noisy signal acquisition condi... | ['Farzin Deravi', 'Sanaul Hoque', 'Su Yang'] | 2022-01-03 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 7.64083624e-01 -4.25458670e-01 4.41688299e-01 -4.54689741e-01
-8.49655628e-01 -3.34835768e-01 3.49543184e-01 3.64323944e-01
-6.90299094e-01 1.09654260e+00 -3.38460058e-01 3.06255311e-01
-8.15219879e-01 -3.71032417e-01 -2.71935552e-01 -1.11532581e+00
-2.73710102e-01 1.68591157e-01 -3.39980394e-01 1.09069027... | [13.250313758850098, 3.3363819122314453] |
e010470c-98fa-4d9c-9efe-f9137d94983a | monocular-bev-perception-of-road-scenes-via | 2211.08144 | null | https://arxiv.org/abs/2211.08144v1 | https://arxiv.org/pdf/2211.08144v1.pdf | Monocular BEV Perception of Road Scenes via Front-to-Top View Projection | HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of th... | ['Jia Pan', 'Shengfeng He', 'Yuexin Ma', 'Yuanlong Yu', 'Jiaxin Cai', 'Weixiang Yang', 'Qi Li', 'Wenxi Liu'] | 2022-11-15 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 1.07082218e-01 -1.27810091e-01 -1.57775268e-01 -7.78881013e-01
-5.84416330e-01 -5.00627041e-01 4.93873417e-01 -3.73396456e-01
-2.60172188e-01 3.60519469e-01 -6.60730526e-02 -3.57228398e-01
2.90923446e-01 -1.37724125e+00 -1.17912030e+00 -4.72321332e-01
6.28511667e-01 4.76185590e-01 9.51882541e-01 -1.89395934... | [8.197169303894043, -2.1952428817749023] |
165b7fdb-bc2c-4410-b81e-3dc93256e959 | crisp-curriculum-inducing-primitive-informed | 2304.03535 | null | https://arxiv.org/abs/2304.03535v1 | https://arxiv.org/pdf/2304.03535v1.pdf | CRISP: Curriculum inducing Primitive Informed Subgoal Prediction for Hierarchical Reinforcement Learning | Hierarchical reinforcement learning is a promising approach that uses temporal abstraction to solve complex long horizon problems. However, simultaneously learning a hierarchy of policies is unstable as it is challenging to train higher-level policy when the lower-level primitive is non-stationary. In this paper, we pr... | ['Vinay P Namboodiri', 'Utsav Singh'] | 2023-04-07 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 2.16564819e-01 3.84242296e-01 -3.09111118e-01 -2.19742022e-02
-1.00746906e+00 -8.29392731e-01 5.29998302e-01 9.44214091e-02
-6.64660871e-01 1.16689026e+00 4.40995432e-02 -4.57679331e-01
-4.06286359e-01 -6.47663057e-01 -8.84170711e-01 -7.71801949e-01
-7.10658371e-01 7.19275892e-01 6.19456053e-01 -2.91720212... | [4.209881782531738, 1.4591076374053955] |
69aafbfd-3e35-4d5d-bf04-4bb8bd6be7c7 | multi-attribute-open-set-recognition | 2208.06809 | null | https://arxiv.org/abs/2208.06809v1 | https://arxiv.org/pdf/2208.06809v1.pdf | Multi-Attribute Open Set Recognition | Open Set Recognition (OSR) extends image classification to an open-world setting, by simultaneously classifying known classes and identifying unknown ones. While conventional OSR approaches can detect Out-of-Distribution (OOD) samples, they cannot provide explanations indicating which underlying visual attribute(s) (e.... | ['Volker Fischer', 'Mauricio Munoz', 'Claudia Blaiotta', 'Chaithanya Kumar Mummadi', 'Piyapat Saranrittichai'] | 2022-08-14 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.67949957e-01 6.73680156e-02 -3.96619171e-01 -6.02592409e-01
-9.96759474e-01 -9.48995709e-01 7.61647403e-01 3.39003980e-01
5.17921820e-02 6.49278641e-01 -1.36168674e-01 -1.94468215e-01
-1.99900325e-02 -7.19561756e-01 -1.00393236e+00 -6.12103164e-01
-8.69621187e-02 7.19846427e-01 1.01378791e-01 1.87844455... | [9.71666431427002, 2.8500876426696777] |
d4490687-93e2-4f24-a492-b704adf0ec5f | zero-shot-personalized-speech-enhancement | 2105.03542 | null | https://arxiv.org/abs/2105.03542v1 | https://arxiv.org/pdf/2105.03542v1.pdf | Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection | This paper presents a novel zero-shot learning approach towards personalized speech enhancement through the use of a sparsely active ensemble model. Optimizing speech denoising systems towards a particular test-time speaker can improve performance and reduce run-time complexity. However, test-time model adaptation may ... | ['Minje Kim', 'Aswin Sivaraman'] | 2021-05-08 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 1.54123649e-01 1.82097375e-01 1.74952269e-01 -4.89607960e-01
-1.25218678e+00 -4.14303571e-01 1.41605362e-01 -1.47722647e-01
-3.84402305e-01 2.74208695e-01 6.18445694e-01 4.13370207e-02
-2.15971276e-01 -4.76792693e-01 -3.17824453e-01 -1.00542092e+00
2.46346984e-02 7.26162255e-01 -1.10288702e-01 -2.57803142... | [14.608619689941406, 6.166618824005127] |
3ebd2b95-fcaa-4d83-832c-dfee0e6b023c | explainable-machine-learning-for-hydrocarbon | 2212.07563 | null | https://arxiv.org/abs/2212.07563v1 | https://arxiv.org/pdf/2212.07563v1.pdf | Explainable Machine Learning for Hydrocarbon Prospect Risking | Hydrocarbon prospect risking is a critical application in geophysics predicting well outcomes from a variety of data including geological, geophysical, and other information modalities. Traditional routines require interpreters to go through a long process to arrive at the probability of success of specific outcomes. A... | ['Ghassan AlRegib', 'Ahmad Mustafa'] | 2022-12-15 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 1.45175263e-01 8.88688564e-01 -1.50148928e-01 -8.28921318e-01
-6.27466679e-01 -4.72870171e-01 8.07119548e-01 6.26996100e-01
1.97620362e-01 6.13272071e-01 4.37074900e-01 -1.18279719e+00
-2.58017004e-01 -9.99639273e-01 -8.95607829e-01 -3.26324761e-01
-3.33207101e-01 7.88703024e-01 -5.96384471e-03 -1.96138978... | [8.750967025756836, 5.839333534240723] |
68dc7561-3950-42d2-a39b-884a9bbe4707 | self-distillation-for-unsupervised-3d-domain | 2210.08226 | null | https://arxiv.org/abs/2210.08226v1 | https://arxiv.org/pdf/2210.08226v1.pdf | Self-Distillation for Unsupervised 3D Domain Adaptation | Point cloud classification is a popular task in 3D vision. However, previous works, usually assume that point clouds at test time are obtained with the same procedure or sensor as those at training time. Unsupervised Domain Adaptation (UDA) instead, breaks this assumption and tries to solve the task on an unlabeled tar... | ['Luigi Di Stefano', 'Samuele Salti', 'Pierluigi Zama Ramirez', 'Riccardo Spezialetti', 'Adriano Cardace'] | 2022-10-15 | null | null | null | null | ['point-cloud-reconstruction', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 3.47300023e-01 3.87931354e-02 -2.56201386e-01 -4.94080752e-01
-7.67257035e-01 -7.75832236e-01 6.08510315e-01 2.18703866e-01
-1.02912314e-01 1.89304188e-01 -5.45513690e-01 -2.91713327e-01
4.42916062e-03 -7.35703886e-01 -1.02822340e+00 -6.10079110e-01
2.58098125e-01 1.12678850e+00 4.80645031e-01 1.53724089... | [8.012557029724121, -3.1193437576293945] |
cf03782b-05bc-4227-94eb-71cffd0ad687 | convergence-of-uncertainty-estimates-in | 2301.12649 | null | https://arxiv.org/abs/2301.12649v2 | https://arxiv.org/pdf/2301.12649v2.pdf | Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery | Sparse model identification enables nonlinear dynamical system discovery from data. However, the control of false discoveries for sparse model identification is challenging, especially in the low-data and high-noise limit. In this paper, we perform a theoretical study on ensemble sparse model discovery, which shows emp... | ['J. Nathan Kutz', 'Steven L. Brunton', 'Urban Fasel', 'L. Mars Gao'] | 2023-01-30 | null | null | null | null | ['variable-selection', 'model-discovery'] | ['methodology', 'miscellaneous'] | [ 1.58021688e-01 -1.35589868e-01 -3.85752171e-02 -3.74702960e-02
-9.94889975e-01 -3.64624619e-01 2.85062790e-01 -2.57039696e-01
1.13754958e-01 1.38127816e+00 -2.06416368e-01 -1.36703223e-01
-4.84181464e-01 -5.54494262e-01 -8.76835704e-01 -1.03998458e+00
-4.12551254e-01 7.65315175e-01 -3.41219634e-01 2.19165370... | [7.094308853149414, 4.435424327850342] |
69dbcee6-e3e8-4005-8e95-10d6f8b17216 | htmot-hierarchical-topic-modelling-over-time | 2112.03104 | null | https://arxiv.org/abs/2112.03104v2 | https://arxiv.org/pdf/2112.03104v2.pdf | HTMOT : Hierarchical Topic Modelling Over Time | Over the years, topic models have provided an efficient way of extracting insights from text. However, while many models have been proposed, none are able to model topic temporality and hierarchy jointly. Modelling time provide more precise topics by separating lexically close but temporally distinct topics while model... | ['Ashwin Ittoo', 'Judicael Poumay'] | 2021-11-22 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-2.05056369e-01 2.74127632e-01 -3.23185116e-01 -3.32355380e-01
-9.94083703e-01 -3.68912935e-01 1.21869576e+00 2.53465772e-01
-1.52014911e-01 6.54765725e-01 5.75486779e-01 -1.72928542e-01
-5.79822622e-02 -9.52718735e-01 -2.25324512e-01 -5.83233297e-01
-4.87299412e-01 6.94165170e-01 5.29392123e-01 6.78127781... | [10.371562957763672, 7.026930332183838] |
3517314e-3ccd-4d72-94b3-f1cf050cf4ea | chbias-bias-evaluation-and-mitigation-of | 2305.11262 | null | https://arxiv.org/abs/2305.11262v1 | https://arxiv.org/pdf/2305.11262v1.pdf | CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models | \textit{\textbf{\textcolor{red}{Warning}:} This paper contains content that may be offensive or upsetting.} Pretrained conversational agents have been exposed to safety issues, exhibiting a range of stereotypical human biases such as gender bias. However, there are still limited bias categories in current research, and... | ['Mykola Pechenizkiy', 'Ling Chen', 'Yitong Li', 'Zijing Shi', 'Meng Fang', 'Jiaxu Zhao'] | 2023-05-18 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [-1.88409500e-02 3.13696802e-01 -1.76599368e-01 -6.11830175e-01
2.70567685e-02 -4.44866776e-01 9.35925961e-01 -1.68880582e-01
-4.47417766e-01 9.97085273e-01 6.83023453e-01 -3.55724424e-01
4.21576649e-01 -7.80228913e-01 -1.98436767e-01 -7.38354802e-01
5.33625841e-01 3.61603558e-01 -2.53235877e-01 -9.30443108... | [9.1924467086792, 10.245473861694336] |
688d25e9-62be-4240-a911-b1620d01d98c | ba-net-dense-bundle-adjustment-network | 1806.04807 | null | https://arxiv.org/abs/1806.04807v3 | https://arxiv.org/pdf/1806.04807v3.pdf | BA-Net: Dense Bundle Adjustment Network | This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error. The whole pipeline is differentiable so that the network can learn suitable features tha... | ['Chengzhou Tang', 'Ping Tan'] | 2018-06-13 | null | null | null | null | ['depth-and-camera-motion'] | ['computer-vision'] | [ 0.00583111 0.33326465 0.10432972 -0.70674634 -0.845899 -0.3932679
0.5960693 -0.39422062 -0.38360098 0.3168311 0.23172316 0.14866479
0.08201252 -1.0263184 -0.9454983 -0.6004654 0.21335925 0.5290956
0.2515009 -0.18724403 0.50399023 0.5983545 -1.5412257 0.14118655
0.71549904 1.2946031 0.52... | [8.601398468017578, -2.6735174655914307] |
c473d621-639e-4ccd-aaef-8c5788dfaf13 | multivariate-time-series-classification-with-1 | 2010.05649 | null | https://arxiv.org/abs/2010.05649v2 | https://arxiv.org/pdf/2010.05649v2.pdf | Multivariate Time Series Classification with Hierarchical Variational Graph Pooling | With the advancement of sensing technology, multivariate time series classification (MTSC) has recently received considerable attention. Existing deep learning-based MTSC techniques, which mostly rely on convolutional or recurrent neural networks, are primarily concerned with the temporal dependency of single time seri... | ['Zhongbin Xu', 'Ziheng Duan', 'Wei Wang', 'Yizhou Sun', 'Yueyang Wang', 'Anni Ren', 'Yida Huang', 'Haoyan Xu'] | 2020-10-12 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 5.65470904e-02 -5.09914458e-02 -1.15010403e-01 -2.61005223e-01
-5.21376014e-01 -2.71292090e-01 5.11033058e-01 4.12678719e-01
5.65879159e-02 4.59424317e-01 9.33029503e-02 -2.92471170e-01
-1.82168931e-01 -1.00729787e+00 -7.82479525e-01 -8.07728052e-01
-4.94319409e-01 1.33999540e-02 1.86121151e-01 -1.93644818... | [6.818709373474121, 2.8210859298706055] |
4ebc83cd-ef1b-4a09-9f27-5ac100500d37 | improving-policy-learning-via-language | 2210.00066 | null | https://arxiv.org/abs/2210.00066v1 | https://arxiv.org/pdf/2210.00066v1.pdf | Improving Policy Learning via Language Dynamics Distillation | Recent work has shown that augmenting environments with language descriptions improves policy learning. However, for environments with complex language abstractions, learning how to ground language to observations is difficult due to sparse, delayed rewards. We propose Language Dynamics Distillation (LDD), which pretra... | ['Tim Rocktäschel', 'Edward Grefenstette', 'Luke Zettlemoyer', 'Jesse Mu', 'Victor Zhong'] | 2022-09-30 | null | null | null | null | ['nethack'] | ['playing-games'] | [-2.33526364e-01 -9.56076533e-02 -2.04324380e-01 -1.90795869e-01
-6.26462817e-01 -8.58879983e-01 8.14257979e-01 7.53380731e-02
-7.66608119e-01 1.01313436e+00 5.17851233e-01 -5.06171048e-01
1.08785003e-01 -4.27986503e-01 -1.15130222e+00 -4.29492354e-01
-6.38722956e-01 7.74896622e-01 -3.81666310e-02 -4.27069455... | [4.186910629272461, 1.3837281465530396] |
f90f2046-fe80-476b-9d9b-1e8145020ef6 | attack-agnostic-adversarial-detection | 2206.00489 | null | https://arxiv.org/abs/2206.00489v1 | https://arxiv.org/pdf/2206.00489v1.pdf | Attack-Agnostic Adversarial Detection | The growing number of adversarial attacks in recent years gives attackers an advantage over defenders, as defenders must train detectors after knowing the types of attacks, and many models need to be maintained to ensure good performance in detecting any upcoming attacks. We propose a way to end the tug-of-war between ... | ['Wael AbdAlmageed', 'Jay Billa', 'Mohamed Hussein', 'Jiaxin Cheng'] | 2022-06-01 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [-1.41884252e-01 -1.22964554e-01 1.88552514e-01 -4.42261398e-02
-7.61324704e-01 -1.37424457e+00 6.85961843e-01 4.03428040e-02
-3.73117268e-01 3.24341267e-01 -6.36993200e-02 -5.24059117e-01
1.23319395e-01 -7.28683829e-01 -6.94402635e-01 -7.12768197e-01
-5.75191677e-01 1.80126145e-01 4.06285286e-01 -4.78973776... | [5.749630928039551, 7.822292804718018] |
2c9b34f2-ec18-4292-bf5f-3ead57d5e6e4 | towards-semi-supervised-learning-of-automatic | 2204.03896 | null | https://arxiv.org/abs/2204.03896v1 | https://arxiv.org/pdf/2204.03896v1.pdf | Towards Semi-Supervised Learning of Automatic Post-Editing: Data-Synthesis by Infilling Mask with Erroneous Tokens | Semi-supervised learning that leverages synthetic training data has been widely adopted in the field of Automatic post-editing (APE) to overcome the lack of human-annotated training data. In that context, data-synthesis methods to create high-quality synthetic data have also received much attention. Considering that AP... | ['Jong-Hyeok Lee', 'Baikjin Jung', 'Seong-Hwan Heo', 'WonKee Lee'] | 2022-04-08 | null | null | null | null | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 9.10637558e-01 3.98328841e-01 9.88323167e-02 -3.37976187e-01
-1.34293735e+00 -4.84293818e-01 9.19894576e-01 2.73125201e-01
-5.97660065e-01 9.46819365e-01 3.99127334e-01 -3.02786976e-01
2.38545522e-01 -5.85387945e-01 -8.85476291e-01 -3.34123909e-01
5.57201445e-01 3.61442804e-01 -1.32028773e-01 -2.85259813... | [11.584450721740723, 9.74827766418457] |
51ffcdad-b72c-4085-815e-0712ac3a11e9 | geometry-aware-reference-synthesis-for-multi | 2207.08601 | null | https://arxiv.org/abs/2207.08601v2 | https://arxiv.org/pdf/2207.08601v2.pdf | Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution | Recent multi-view multimedia applications struggle between high-resolution (HR) visual experience and storage or bandwidth constraints. Therefore, this paper proposes a Multi-View Image Super-Resolution (MVISR) task. It aims to increase the resolution of multi-view images captured from the same scene. One solution is t... | ['Chenxi Ma', 'Weimin Tan', 'Bo Yan', 'Yuqi Sun', 'Ri Cheng'] | 2022-07-18 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 5.50360084e-01 -3.95777225e-01 -2.15208396e-01 -1.40567228e-01
-1.06015933e+00 -2.89813310e-01 2.16568395e-01 -8.88479531e-01
2.48971004e-02 7.56202877e-01 4.37063366e-01 2.40390331e-01
-3.46432701e-02 -8.84733558e-01 -6.25795305e-01 -6.78058863e-01
4.84249920e-01 -3.19487780e-01 5.45052230e-01 -3.62185776... | [10.808774948120117, -2.139523983001709] |
ec21df2c-03e5-4dac-80f9-123576a7aaed | automated-segmentation-of-hip-and-thigh | 1906.11484 | null | https://arxiv.org/abs/1906.11484v1 | https://arxiv.org/pdf/1906.11484v1.pdf | Automated Segmentation of Hip and Thigh Muscles in Metal Artifact-Contaminated CT using Convolutional Neural Network-Enhanced Normalized Metal Artifact Reduction | In total hip arthroplasty, analysis of postoperative medical images is important to evaluate surgical outcome. Since Computed Tomography (CT) is most prevalent modality in orthopedic surgery, we aimed at the analysis of CT image. In this work, we focus on the metal artifact in postoperative CT caused by the metallic im... | ['Masaki Takao', 'Yoshito Otake', 'Yoshinobu Sato', 'Nobuhiko Sugano', 'Yuta Hiasa', 'Mitsuki Sakamoto', 'Yuki Suzuki'] | 2019-06-27 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 3.09661180e-02 2.61408985e-01 3.58198524e-01 2.30296031e-01
-7.29775071e-01 8.35739002e-02 -2.34592050e-01 -1.85973570e-01
-6.78752363e-01 6.95211351e-01 7.28463158e-02 -6.56129047e-02
-8.32819864e-02 -6.39678657e-01 -8.38438570e-01 -6.59272909e-01
-3.22831810e-01 5.09538770e-01 5.92346013e-01 -9.10119563... | [13.614556312561035, -2.5695197582244873] |
9a77047b-9547-4cec-8086-7527f0ef0c0f | bidirectional-inference-networks-a-class-of | 1902.02037 | null | http://arxiv.org/abs/1902.02037v1 | http://arxiv.org/pdf/1902.02037v1.pdf | Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling | We consider the problem of inferring the values of an arbitrary set of
variables (e.g., risk of diseases) given other observed variables (e.g.,
symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images
or EEG). This is a common problem in healthcare since variables of interest
often differ for dif... | ['Ming-Min Zhao', 'Tommi S. Jaakkola', 'Chengzhi Mao', 'Hao Wang', 'Dina Katabi', 'Hao He'] | 2019-02-06 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [ 4.16202813e-01 2.54252583e-01 -3.72614831e-01 -7.43897736e-01
-6.18213713e-01 6.00092001e-02 3.50599289e-01 1.33263394e-01
-4.56807613e-01 1.35429156e+00 2.09021702e-01 -1.83386266e-01
-6.86819613e-01 -7.61847973e-01 -7.57075012e-01 -8.76729608e-01
-1.26921922e-01 9.11010742e-01 2.61961762e-02 4.83550012... | [7.813964366912842, 5.239936828613281] |
f2f5af39-c536-499f-9d1c-6adb3f6d5d8f | epipolar-guided-deep-object-matching-for | 2007.15540 | null | https://arxiv.org/abs/2007.15540v1 | https://arxiv.org/pdf/2007.15540v1.pdf | Epipolar-Guided Deep Object Matching for Scene Change Detection | This paper describes a viewpoint-robust object-based change detection network (OBJ-CDNet). Mobile cameras such as drive recorders capture images from different viewpoints each time due to differences in camera trajectory and shutter timing. However, previous methods for pixel-wise change detection are vulnerable to the... | ['Ken Sakurada', 'Shun Iwase', 'Ryuhei Hamaguchi', 'Yutaka Matsuo', 'Rio Yokota', 'Kento Doi'] | 2020-07-30 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [ 1.20376691e-01 -5.22213995e-01 1.58406779e-01 -4.14573133e-01
1.59690320e-01 -5.59544981e-01 4.13219631e-01 -2.70497531e-01
-3.38562638e-01 2.37202004e-01 -2.42848918e-01 -1.25485405e-01
1.05876334e-01 -9.66384947e-01 -8.57693434e-01 -3.50661993e-01
1.88445091e-01 -1.29557252e-01 7.58988321e-01 -2.22979099... | [8.715157508850098, -1.8501040935516357] |
f4c0cf70-be89-468c-a27c-b49e4726d6b6 | towards-adaptable-and-interactive-image | 2306.03500 | null | https://arxiv.org/abs/2306.03500v1 | https://arxiv.org/pdf/2306.03500v1.pdf | Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memory | Interactive machine learning (IML) is a beneficial learning paradigm in cases of limited data availability, as human feedback is incrementally integrated into the training process. In this paper, we present an IML pipeline for image captioning which allows us to incrementally adapt a pre-trained image captioning model ... | ['Daniel Sonntag', 'Mareike Hartmann', 'Aliki Anagnostopoulou'] | 2023-06-06 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 5.48183799e-01 5.30275047e-01 -1.22707233e-01 -4.44048554e-01
-4.55099702e-01 -5.98885596e-01 5.91834843e-01 3.94677132e-01
-5.99460006e-01 7.29301810e-01 2.58884251e-01 -3.94523889e-01
4.37359273e-01 -5.40479183e-01 -9.97684538e-01 -2.93415546e-01
-4.94184606e-02 8.47803771e-01 4.31735188e-01 -1.52378445... | [10.28110408782959, 1.8547966480255127] |
f184ae9f-9990-4e8c-a2ed-a1ac7d1e762f | an-aiot-enabled-autonomous-dementia | 2207.00804 | null | https://arxiv.org/abs/2207.00804v1 | https://arxiv.org/pdf/2207.00804v1.pdf | An AIoT-enabled Autonomous Dementia Monitoring System | An autonomous Artificial Internet of Things (AIoT) system for elderly dementia patients monitoring in a smart home is presented. The system mainly implements two functions based on the activity inference of the sensor data, which are real time abnormal activity monitoring and trend prediction of disease related activit... | ['Jinyang Li', 'Xingyu Wu'] | 2022-07-02 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 3.00040513e-01 2.56178260e-01 -4.65212852e-01 -5.83249867e-01
-5.75619899e-02 3.74035299e-01 5.74107766e-01 1.52930841e-01
-4.60933834e-01 1.16204417e+00 6.09049380e-01 -3.72986972e-01
-3.37130010e-01 -1.07927465e+00 2.11432166e-02 -8.84166718e-01
-3.04612011e-01 2.48345181e-01 2.03705132e-02 3.50762337... | [7.415951251983643, 0.8565438985824585] |
75549586-3a0e-4833-ad0a-b592b7388136 | accurate-spectral-super-resolution-from | 1806.03575 | null | http://arxiv.org/abs/1806.03575v3 | http://arxiv.org/pdf/1806.03575v3.pdf | Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN | Different from traditional hyperspectral super-resolution approaches that
focus on improving the spatial resolution, spectral super-resolution aims at
producing a high-resolution hyperspectral image from the RGB observation with
super-resolution in spectral domain. However, it is challenging to accurately
reconstruct a... | ['Yanning Zhang', 'Jun Li', 'Wei Wei', 'Lei Zhang', 'Yiqi Yan'] | 2018-06-10 | null | null | null | null | ['spectral-reconstruction', 'spectral-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 1.21513426e+00 -3.55399609e-01 1.07947126e-01 -2.53649235e-01
-8.51076841e-01 -5.69225371e-01 1.51784703e-01 -3.67883772e-01
-8.76481924e-03 9.41755354e-01 1.24910586e-01 -1.18531249e-01
-4.74546909e-01 -1.24117994e+00 -6.69334412e-01 -1.09454060e+00
3.43264222e-01 -1.07577205e-01 -4.24942553e-01 -1.67359054... | [10.210756301879883, -2.0200018882751465] |
6a0e9ba3-daab-4895-9a7e-2d15c4922f2d | morel-multi-omics-relational-learning-1 | 2203.08149 | null | https://arxiv.org/abs/2203.08149v1 | https://arxiv.org/pdf/2203.08149v1.pdf | MoReL: Multi-omics Relational Learning | Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems. One of critical challenges when dealing with real-world multi-omics data is that they may m... | ['Xiaoning Qian', 'Nick Duffield', 'Ehsan Hajiramezanali', 'Arman Hasanzadeh'] | 2022-03-15 | morel-multi-omics-relational-learning | https://openreview.net/forum?id=DnG75_KyHjX | https://openreview.net/pdf?id=DnG75_KyHjX | iclr-2022-4 | ['relational-reasoning'] | ['natural-language-processing'] | [ 1.62896365e-01 -3.79467495e-02 -1.97397158e-01 -2.68612355e-01
-4.27084953e-01 -7.19253957e-01 3.73160958e-01 3.16607475e-01
3.52921993e-01 8.22875738e-01 6.24182940e-01 -1.27613187e-01
-4.69310433e-01 -7.74573267e-01 -7.50529051e-01 -1.19992900e+00
5.85962981e-02 9.43738878e-01 -1.60692230e-01 3.09627980... | [6.0380635261535645, 5.705058574676514] |
733eef2f-165b-46b0-9a9f-44a7dd58a996 | symbiotic-message-passing-model-for-transfer | 2304.07017 | null | https://arxiv.org/abs/2304.07017v1 | https://arxiv.org/pdf/2304.07017v1.pdf | Symbiotic Message Passing Model for Transfer Learning between Anti-Fungal and Anti-Bacterial Domains | Machine learning, and representation learning in particular, has the potential to facilitate drug discovery by screening billions of compounds. For example, a successful approach is representing the molecules as a graph and utilizing graph neural networks (GNN). Yet, these approaches still require experimental measurem... | ['Yonatan Savir', 'Tanya Wasserman', 'Ronen Taub'] | 2023-04-14 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 5.7374442e-01 -1.9788805e-02 -3.8155919e-01 9.9390402e-02
-4.0749002e-01 -7.5315917e-01 5.1812607e-01 6.5088117e-01
-2.0480128e-01 1.2177567e+00 -3.8263279e-01 -8.5250986e-01
-1.3073683e-01 -1.0947503e+00 -1.1074167e+00 -6.7091995e-01
-2.6967984e-02 7.6712430e-01 3.6775929e-01 -1.8389726e-01
2.8616217e-01... | [5.348282337188721, 5.80991792678833] |
2a3cfc46-61da-426e-bd1e-cd369cf7d870 | interactive-segmentation-as-gaussion-process | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_Interactive_Segmentation_As_Gaussion_Process_Classification_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_Interactive_Segmentation_As_Gaussion_Process_Classification_CVPR_2023_paper.pdf | Interactive Segmentation As Gaussion Process Classification | Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. For this task, most of the current deep learning (DL)-based methods mainly follow the general pipelines of semantic segmentation. Albeit achieving promising performance, they do not fully and explicitly utilize and ... | ['Yefeng Zheng', 'Deyu Meng', 'Yawen Huang', 'Yuexiang Li', 'Qian Zhao', 'Hong Wang', 'Minghao Zhou'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['interactive-segmentation'] | ['computer-vision'] | [ 7.33113810e-02 1.00951627e-01 -3.01960558e-01 -2.79496223e-01
-1.06409252e+00 -3.89625400e-01 4.19620216e-01 -2.13702649e-01
-2.80053914e-01 6.44287705e-01 -3.12733471e-01 -2.44548872e-01
-5.22034802e-02 -6.87865078e-01 -8.29299867e-01 -9.82396603e-01
5.51138341e-01 3.15835893e-01 5.33411264e-01 3.95489872... | [9.482627868652344, -0.0005439297528937459] |
beafcf57-bda5-44cb-9a48-c4cdce746ba0 | personalized-image-aesthetics-assessment-via | null | null | https://ieeexplore.ieee.org/abstract/document/9115059 | https://ieeexplore.ieee.org/abstract/document/9115059 | Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient Optimization | Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an ``average'' user. However, such generic aesthetics models neglect the fact that users' aesthetic preferences vary significantly depending on their unique preferences. Therefore, it is essential to tackle the issue for person... | ['and Guangming Shi', 'Guiguang Ding', 'Sicheng Zhao', 'Jinjian Wu', 'Leida Li', 'Hancheng Zhu'] | 2020-06-11 | null | null | null | ieee-transactions-on-cybernetics-2020-6 | ['aesthetics-quality-assessment'] | ['computer-vision'] | [ 4.89903167e-02 -8.21486413e-02 1.86341032e-01 -4.43143278e-01
-9.14570332e-01 -1.51510477e-01 1.82508856e-01 -5.19511104e-02
-3.11192602e-01 1.41272262e-01 -3.57178599e-02 1.73108906e-01
-2.17674121e-01 -7.89896071e-01 -4.45186764e-01 -7.28937864e-01
2.42220059e-01 6.10424817e-01 -1.28040865e-01 -3.57827276... | [11.52184772491455, -1.055650234222412] |
2b4c78a9-eb83-44f1-878c-52804edf5044 | hyperspectral-pansharpening-a-review | 1504.04531 | null | http://arxiv.org/abs/1504.04531v1 | http://arxiv.org/pdf/1504.04531v1.pdf | Hyperspectral pansharpening: a review | Pansharpening aims at fusing a panchromatic image with a multispectral one,
to generate an image with the high spatial resolution of the former and the
high spectral resolution of the latter. In the last decade, many algorithms
have been presented in the literature for pansharpening using multispectral
data. With the i... | ['Jean-Yves Tourneret', 'Miguel Simões', 'José M. Bioucas-Dias', 'Xavier Briottet', 'Qi Wei', 'Naoto Yokoya', 'Giorgio A. Licciardi', 'Nicolas Dobigeon', 'Laetitia Loncan', 'Sophie Fabre', 'Luis B. Almeida', 'Jocelyn Chanussot', 'Gemine Vivone', 'Wenzhi Liao', 'Miguel A. Veganzones'] | 2015-04-17 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 9.75735247e-01 -6.13607109e-01 5.09081185e-02 3.02702207e-02
-5.97721159e-01 -6.32513404e-01 5.31536162e-01 -2.06005171e-01
-2.95664340e-01 8.42930615e-01 -2.72767730e-02 -5.46343885e-02
-9.87683773e-01 -1.04649723e+00 1.29793361e-02 -1.20330536e+00
2.77640760e-01 2.30905071e-01 4.28838916e-02 -4.02072757... | [10.08407974243164, -2.102548837661743] |
9190845b-7aaa-46b7-a129-9af5c997fceb | coarse-to-fine-recursive-speech-separation | 2203.16054 | null | https://arxiv.org/abs/2203.16054v1 | https://arxiv.org/pdf/2203.16054v1.pdf | Coarse-to-Fine Recursive Speech Separation for Unknown Number of Speakers | The vast majority of speech separation methods assume that the number of speakers is known in advance, hence they are specific to the number of speakers. By contrast, a more realistic and challenging task is to separate a mixture in which the number of speakers is unknown. This paper formulates the speech separation wi... | ['Xiangdong Su', 'Xiang Hao', 'Zhenhao Jin'] | 2022-03-30 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 2.40406901e-01 -1.46949738e-01 1.74417660e-01 -1.53180003e-01
-1.03845227e+00 -5.66849411e-01 6.43547833e-01 -1.15442865e-01
-2.72691786e-01 5.83523452e-01 2.86813289e-01 -2.73104191e-01
1.14761107e-02 -2.48721421e-01 -2.98596710e-01 -9.88563657e-01
1.59047306e-01 4.70706999e-01 4.01658714e-01 -1.59084901... | [14.860955238342285, 5.907175064086914] |
faf41f38-b8bb-495e-8882-7f8c885d6065 | take-5-interpretable-image-classification | 2303.13166 | null | https://arxiv.org/abs/2303.13166v1 | https://arxiv.org/pdf/2303.13166v1.pdf | Take 5: Interpretable Image Classification with a Handful of Features | Deep Neural Networks use thousands of mostly incomprehensible features to identify a single class, a decision no human can follow. We propose an interpretable sparse and low dimensional final decision layer in a deep neural network with measurable aspects of interpretability and demonstrate it on fine-grained image cla... | ['Bodo Rosenhahn', 'Marco Rudolph', 'Thomas Norrenbrock'] | 2023-03-23 | null | null | null | null | ['fine-grained-image-classification', 'interpretable-machine-learning'] | ['computer-vision', 'methodology'] | [ 3.36449057e-01 7.15436816e-01 -9.42948982e-02 -8.74541163e-01
-3.67861032e-01 -5.60703516e-01 5.47507107e-01 1.75761133e-01
-2.52541900e-01 5.87807953e-01 2.29509488e-01 -6.10240817e-01
-1.75534293e-01 -5.29774070e-01 -7.38171518e-01 -6.56714618e-01
5.56464531e-02 7.36449182e-01 -2.20963165e-01 3.14501852... | [8.923942565917969, 5.676281929016113] |
f1796795-c34e-42a1-8f88-957c89a333ad | a-primer-on-getting-neologisms-from-foreign | 2304.10495 | null | https://arxiv.org/abs/2304.10495v1 | https://arxiv.org/pdf/2304.10495v1.pdf | A primer on getting neologisms from foreign languages to under-resourced languages | Mainly due to lack of support, most under-resourced languages have a reduced lexicon in most realms and domains of increasing importance, then their speakers need to significantly augment it. Although neologisms should arise from the languages themselves, external sources are widely accepted. However, we dispute the "c... | ['Luis Camacho'] | 2023-03-07 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-3.30139369e-01 2.58789897e-01 -3.41896743e-01 -2.47251481e-01
-1.27690896e-01 -8.60691190e-01 9.45291519e-01 -1.91914603e-01
-8.73644471e-01 1.02285290e+00 7.76613772e-01 -9.21083689e-01
-9.95117053e-02 -6.32939517e-01 -2.96392977e-01 -3.88045549e-01
3.21600050e-01 3.69819999e-01 2.18106255e-01 -1.01565206... | [10.451817512512207, 9.90169906616211] |
bd47f959-7fa4-4c81-81c3-7d119d3f0dec | exploring-text-representations-for-generative | null | null | https://aclanthology.org/2022.clinicalnlp-1.12 | https://aclanthology.org/2022.clinicalnlp-1.12.pdf | Exploring Text Representations for Generative Temporal Relation Extraction | Sequence-to-sequence models are appealing because they allow both encoder and decoder to be shared across many tasks by formulating those tasks as text-to-text problems. Despite recently reported successes of such models, we find that engineering input/output representations for such text-to-text models is challenging.... | ['Guergana Savova', 'Timothy Miller', 'Steven Bethard', 'Dmitriy Dligach'] | null | null | null | null | naacl-clinicalnlp-2022-7 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 8.95065248e-01 6.60179436e-01 -5.22679687e-01 -5.68163216e-01
-9.86285686e-01 -7.19641805e-01 9.41301227e-01 6.91634893e-01
-3.35551083e-01 1.08511400e+00 7.02629387e-01 -9.53811288e-01
-3.74015540e-01 -6.14665926e-01 -5.45159340e-01 -3.42257112e-01
-3.53345394e-01 7.47412264e-01 5.58060408e-02 -3.26618701... | [8.505514144897461, 8.918716430664062] |
0ba28b81-cb2e-4e1c-b016-e1f0866c9031 | rep-predicting-the-time-course-of-drug | 1907.11911 | null | https://arxiv.org/abs/1907.11911v1 | https://arxiv.org/pdf/1907.11911v1.pdf | REP: Predicting the Time-Course of Drug Sensitivity | The biological processes involved in a drug's mechanisms of action are oftentimes dynamic, complex and difficult to discern. Time-course gene expression data is a rich source of information that can be used to unravel these complex processes, identify biomarkers of drug sensitivity and predict the response to a drug. H... | ['Amin Emad', 'Cheng Qian', 'Nicholas D. Sidiropoulos'] | 2019-07-27 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 3.52422267e-01 -7.08424628e-01 -4.38059896e-01 -2.43635967e-01
-3.12822193e-01 -5.98297298e-01 4.96136755e-01 5.47694504e-01
-1.82043210e-01 6.30600452e-01 1.46853551e-01 -3.81716430e-01
-6.65253937e-01 -7.08350480e-01 -2.46269912e-01 -1.19173455e+00
-5.78371584e-01 6.86344802e-01 -1.94571838e-01 -2.94391006... | [6.033640384674072, 5.608170509338379] |
54eb687d-19b7-4870-80ba-162153e4b677 | don-t-stop-self-supervision-accent-adaptation | 2307.00453 | null | https://arxiv.org/abs/2307.00453v1 | https://arxiv.org/pdf/2307.00453v1.pdf | Don't Stop Self-Supervision: Accent Adaptation of Speech Representations via Residual Adapters | Speech representations learned in a self-supervised fashion from massive unlabeled speech corpora have been adapted successfully toward several downstream tasks. However, such representations may be skewed toward canonical data characteristics of such corpora and perform poorly on atypical, non-native accented speaker ... | ['Katrin Kirchhoff', 'Sravan Bodapati', 'Karthik Gopalakrishnan', 'Saket Dingliwal', 'Sanchit Sinha', 'Anshu Bhatia'] | 2023-07-02 | null | null | null | null | ['speech-recognition', 'automatic-speech-recognition'] | ['speech', 'speech'] | [ 1.89628109e-01 6.03172719e-01 8.96708760e-03 -8.16041708e-01
-1.25276971e+00 -6.42841578e-01 6.07732594e-01 -1.98372900e-01
-5.94749033e-01 6.97935939e-01 7.03529298e-01 -3.87900203e-01
2.94949621e-01 -2.31195822e-01 -6.02845073e-01 -5.80359638e-01
1.69123739e-01 7.20917642e-01 -1.08777411e-01 -5.25583863... | [14.385852813720703, 6.723005294799805] |
91180242-2cf0-44e1-96fb-8a403945c447 | accurate-and-efficient-stereo-matching-via | 2209.12699 | null | https://arxiv.org/abs/2209.12699v2 | https://arxiv.org/pdf/2209.12699v2.pdf | Accurate and Efficient Stereo Matching via Attention Concatenation Volume | Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method, named attention concatenation volume (ACV), wh... | ['Xin Yang', 'Jinhui Tang', 'Junda Cheng', 'Yun Wang', 'Gangwei Xu'] | 2022-09-23 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-1.38662560e-02 -4.83474374e-01 -5.11564966e-03 -1.03370331e-01
-3.61005694e-01 -6.73745526e-04 3.60837758e-01 -3.60466987e-01
-5.48604190e-01 7.44599342e-01 2.80298263e-01 -1.55107081e-01
-2.60736018e-01 -1.01701295e+00 -7.16504276e-01 -5.32548428e-01
1.46106735e-01 4.11328673e-01 6.69307470e-01 -3.54967862... | [8.916604042053223, -2.2264482975006104] |
d9877afb-2320-407a-ba6c-ca4468b8afdc | spiking-sampling-network-for-image-sparse | 2211.04166 | null | https://arxiv.org/abs/2211.04166v1 | https://arxiv.org/pdf/2211.04166v1.pdf | Spiking sampling network for image sparse representation and dynamic vision sensor data compression | Sparse representation has attracted great attention because it can greatly save storage re- sources and find representative features of data in a low-dimensional space. As a result, it may be widely applied in engineering domains including feature extraction, compressed sensing, signal denoising, picture clustering, an... | ['Yilei Zhang', 'Chunming Jiang'] | 2022-11-08 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 5.63312411e-01 -5.73401511e-01 1.36654660e-01 -5.73202111e-02
-1.84260145e-01 -2.31045306e-01 1.78498238e-01 2.48146690e-02
-4.96616960e-01 6.77680314e-01 7.54194781e-02 3.60362619e-01
-8.11435133e-02 -7.82569230e-01 -5.45243204e-01 -1.07281625e+00
1.71795383e-01 -6.74642669e-03 4.12954777e-01 1.58182129... | [11.105327606201172, -1.7430795431137085] |
977cd459-8338-4bd5-8d35-29a1e87b4b70 | efficient-micro-structured-weight-unification | 2106.08301 | null | https://arxiv.org/abs/2106.08301v2 | https://arxiv.org/pdf/2106.08301v2.pdf | Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression | Compressing Deep Neural Network (DNN) models to alleviate the storage and computation requirements is essential for practical applications, especially for resource limited devices. Although capable of reducing a reasonable amount of model parameters, previous unstructured or structured weight pruning methods can hardly... | ['Songnan Li', 'Shan Liu', 'Yanzhi Wang', 'Kaidi Xu', 'Wei Wang', 'Wei Jiang', 'Sheng Lin'] | 2021-06-15 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 4.04920310e-01 -9.22373980e-02 -4.78353240e-02 -4.28432643e-01
-1.97288617e-02 1.41968295e-01 1.24896348e-01 -9.54492614e-02
-8.43402565e-01 7.46086419e-01 -2.43026853e-01 -2.94400334e-01
-3.01478177e-01 -9.28421676e-01 -6.91178322e-01 -9.29677725e-01
1.69622242e-01 2.05133334e-01 1.99701980e-01 5.99495247... | [8.53030776977539, 3.071462631225586] |
d2ad1c1b-916c-4519-aaf5-1aa10177220a | differentiable-hierarchical-graph-grouping | 2007.11864 | null | https://arxiv.org/abs/2007.11864v1 | https://arxiv.org/pdf/2007.11864v1.pdf | Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation | Multi-person pose estimation is challenging because it localizes body keypoints for multiple persons simultaneously. Previous methods can be divided into two streams, i.e. top-down and bottom-up methods. The top-down methods localize keypoints after human detection, while the bottom-up methods localize keypoints direct... | ['Sheng Jin', 'Wentao Liu', 'Ping Luo', 'Chen Qian', 'Wenhai Wang', 'Wanli Ouyang', 'Enze Xie'] | 2020-07-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/386_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520698.pdf | eccv-2020-8 | ['2d-human-pose-estimation'] | ['computer-vision'] | [-0.18000409 0.02465307 -0.189461 -0.12481438 -0.40891722 -0.3211083
0.21108074 0.31311077 -0.44392437 0.15857895 -0.01135644 0.28218958
-0.01619366 -0.69465214 -0.6862085 -0.36471966 -0.31623834 0.71108264
0.50052226 -0.11927637 -0.15263726 0.41020784 -1.4487704 -0.10573889
0.77237314 0.72879535 0.1... | [7.119457721710205, -0.7864536046981812] |
5e0f9a69-3c89-41b7-9fd0-745db0638291 | multi-view-keypoints-for-reliable-6d-object | 2303.16833 | null | https://arxiv.org/abs/2303.16833v1 | https://arxiv.org/pdf/2303.16833v1.pdf | Multi-View Keypoints for Reliable 6D Object Pose Estimation | 6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment. It is particularly challenging in bin-picking applications, where many objects are low-feature and reflective, and self-occlusion between objects of the same type is common. We propose a novel multi-vie... | ['Angela P. Schoellig', 'Alan Li'] | 2023-03-29 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 1.04198970e-01 -1.13909230e-01 2.86557049e-01 -7.80285522e-02
-7.76126683e-01 -8.89997363e-01 5.07463038e-01 3.41135204e-01
-6.13347411e-01 4.14790571e-01 -4.01602626e-01 1.81437612e-01
-3.45062256e-01 -4.17868972e-01 -9.61142242e-01 -4.67132568e-01
-1.52105121e-02 1.07534432e+00 6.70972705e-01 9.68992390... | [7.255337715148926, -2.3009254932403564] |
f6d3dae9-5f5b-4ce0-b6cf-b3dd0855774e | a-dense-material-segmentation-dataset-for | 2207.10614 | null | https://arxiv.org/abs/2207.10614v1 | https://arxiv.org/pdf/2207.10614v1.pdf | A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing | A key algorithm for understanding the world is material segmentation, which assigns a label (metal, glass, etc.) to each pixel. We find that a model trained on existing data underperforms in some settings and propose to address this with a large-scale dataset of 3.2 million dense segments on 44,560 indoor and outdoor i... | ['Ransen Niu', 'Paul Upchurch'] | 2022-07-21 | null | null | null | null | ['scene-parsing', 'material-classification', 'material-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.05892658e-01 2.62501091e-02 -2.97490120e-01 -5.34012318e-01
-1.09589553e+00 -1.03821361e+00 3.25326800e-01 -1.59123372e-02
-1.68037593e-01 4.31780457e-01 1.51980430e-01 -2.00789660e-01
4.91429418e-01 -9.62390304e-01 -1.26562572e+00 -3.29378933e-01
4.02862698e-01 5.02924681e-01 6.28771424e-01 1.38348639... | [9.65163516998291, 0.3722502887248993] |
363745f2-d294-4826-89af-43fd7a0091d8 | antisymmetricrnn-a-dynamical-system-view-on | 1902.09689 | null | http://arxiv.org/abs/1902.09689v1 | http://arxiv.org/pdf/1902.09689v1.pdf | AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks | Recurrent neural networks have gained widespread use in modeling sequential
data. Learning long-term dependencies using these models remains difficult
though, due to exploding or vanishing gradients. In this paper, we draw
connections between recurrent networks and ordinary differential equations. A
special form of rec... | ['Ed H. Chi', 'Eldad Haber', 'Bo Chang', 'Minmin Chen'] | 2019-02-26 | antisymmetricrnn-a-dynamical-system-view-on-1 | https://openreview.net/forum?id=ryxepo0cFX | https://openreview.net/pdf?id=ryxepo0cFX | iclr-2019-5 | ['sequential-image-classification'] | ['computer-vision'] | [-1.20928951e-01 3.24076451e-02 -1.67565718e-01 -1.63458109e-01
-3.14167179e-02 -4.25473839e-01 6.90118551e-01 -4.73363340e-01
-4.98356521e-01 8.48876953e-01 1.94043607e-01 -5.60301304e-01
-3.66971530e-02 -3.09276342e-01 -6.59606218e-01 -7.38862336e-01
-3.04015011e-01 1.92008689e-01 1.50174834e-02 -6.31317914... | [7.6716718673706055, 3.4292569160461426] |
dd860527-b366-4f68-be6b-5f236ad3306b | takelab-at-semeval-2017-task-6 | null | null | https://aclanthology.org/S17-2066 | https://aclanthology.org/S17-2066.pdf | TakeLab at SemEval-2017 Task 6: \#RankingHumorIn4Pages | This paper describes our system for humor ranking in tweets within the SemEval 2017 Task 6: {\#}HashtagWars (6A and 6B). For both subtasks, we use an off-the-shelf gradient boosting model built on a rich set of features, handcrafted to provide the model with the external knowledge needed to better predict the humor in ... | ['Jan {\\v{S}}najder', "Domagoj Alagi{\\'c}", "Antonio {\\v{S}}ajatovi{\\'c}", "Ivan Mr{\\v{s}}i{\\'c}", 'Marin Kukova{\\v{c}}ec', 'Juraj Malenica'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['humor-detection'] | ['natural-language-processing'] | [-7.40589261e-01 -9.41855684e-02 -1.14132658e-01 -2.01300308e-01
-5.86012602e-01 -5.23232758e-01 9.40133154e-01 4.30417806e-01
-4.13497627e-01 8.00673366e-01 8.06287050e-01 -3.53309810e-01
1.60032585e-01 -6.65739357e-01 -3.72114718e-01 -2.54666984e-01
-8.28874707e-02 4.77485955e-01 3.00435096e-01 -1.27188861... | [8.841609954833984, 11.05250072479248] |
7a1d2f19-d479-43a3-a81b-bd7cf967049a | a-novel-method-using-machine-learning-to | 2301.12340 | null | https://arxiv.org/abs/2301.12340v1 | https://arxiv.org/pdf/2301.12340v1.pdf | A novel method using machine learning to integrate features from lung and epicardial adipose tissue for detecting the severity of COVID-19 infection | Objectives: To investigate the value of radiomics features of epicardial adipose tissue (EAT) combined with lung for detecting the severity of Coronavirus Disease 2019 (COVID-19) infection. Methods: The retrospective study included data from 515 COVID-19 patients (Cohort1: 415, cohort2: 100) from the two centers betwee... | ['Weihua Zhou', 'Neng Dai', 'Fubao Zhu', 'Chuang Han', 'Yanting Li', 'Alair Augusto Sarmet Moreira Damas dos Santos', 'Wolney de Andrade Martins', 'Claudio Tinoco Mesquita', 'Chen Zhao', 'Daniel Gama das Neves', 'Yanhui Tian', 'Ni Yao'] | 2023-01-29 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-1.82498530e-01 -2.37040445e-01 -2.81625569e-01 1.24308892e-01
-7.38032997e-01 -8.01864922e-01 1.12388112e-01 3.14045936e-01
-3.91700029e-01 6.51941180e-01 3.86286154e-02 -4.83981729e-01
-1.98603630e-01 -6.50316834e-01 -1.96732730e-01 -7.20385671e-01
-5.06581724e-01 7.75181115e-01 1.97737113e-01 5.71960151... | [15.457290649414062, -1.844844102859497] |
48df79b5-bbd6-454b-beb7-f4b2b04d13fc | if-net-an-illumination-invariant-feature | 2008.03897 | null | https://arxiv.org/abs/2008.03897v1 | https://arxiv.org/pdf/2008.03897v1.pdf | IF-Net: An Illumination-invariant Feature Network | Feature descriptor matching is a critical step is many computer vision applications such as image stitching, image retrieval and visual localization. However, it is often affected by many practical factors which will degrade its performance. Among these factors, illumination variations are the most influential one, and... | ['Kuan-Wen Chen', 'Zu-Kuan Huang', 'Zhao-Xu Luo', 'Po-Heng Chen', 'Chun Yang'] | 2020-08-10 | null | null | null | null | ['image-stitching', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [ 1.87551886e-01 -8.69587958e-01 -3.43969584e-01 -1.89535379e-01
-5.20288825e-01 -3.60214978e-01 5.25044143e-01 4.80374880e-02
-3.17725092e-01 3.78327698e-01 -7.80602843e-02 7.59134665e-02
-3.74160528e-01 -6.70103133e-01 -5.95007718e-01 -8.54780316e-01
2.15412840e-01 1.17826588e-01 4.16396677e-01 -1.91425487... | [10.821693420410156, 0.47993242740631104] |
4b4f1e70-2007-453d-87ec-5a3f05b6b195 | i-see-dead-people-gray-box-adversarial-attack | 2306.07591 | null | https://arxiv.org/abs/2306.07591v1 | https://arxiv.org/pdf/2306.07591v1.pdf | I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models | Modern image-to-text systems typically adopt the encoder-decoder framework, which comprises two main components: an image encoder, responsible for extracting image features, and a transformer-based decoder, used for generating captions. Taking inspiration from the analysis of neural networks' robustness against adversa... | ['Moshe Sipper', 'Raz Lapid'] | 2023-06-13 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 7.08676875e-01 3.86157244e-01 3.92408103e-01 -1.64025620e-01
-1.00821185e+00 -1.08448267e+00 7.82912970e-01 -5.41200399e-01
-3.29478830e-02 4.42390591e-01 -9.57389101e-02 -5.74172616e-01
5.09295166e-01 -7.08118141e-01 -1.48823822e+00 -5.83312750e-01
2.32355997e-01 2.35885620e-01 -5.89500926e-02 -2.92927742... | [5.7564239501953125, 7.819818019866943] |
6d655468-451c-4aca-b114-86d9e3a26bd8 | point-bert-pre-training-3d-point-cloud | 2111.14819 | null | https://arxiv.org/abs/2111.14819v2 | https://arxiv.org/pdf/2111.14819v2.pdf | Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling | We present Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud. Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers. Specifically, we first divide a point cloud into several local point patches, and a point cloud Token... | ['Jiwen Lu', 'Jie zhou', 'Tiejun Huang', 'Yongming Rao', 'Lulu Tang', 'Xumin Yu'] | 2021-11-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yu_Point-BERT_Pre-Training_3D_Point_Cloud_Transformers_With_Masked_Point_Modeling_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yu_Point-BERT_Pre-Training_3D_Point_Cloud_Transformers_With_Masked_Point_Modeling_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-point-cloud-linear-classification', 'few-shot-point-cloud-classification', 'few-shot-3d-point-cloud-classification', 'point-cloud-segmentation', 'point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.95371449e-01 1.67273253e-01 -1.10408038e-01 -2.15314075e-01
-1.21213007e+00 -5.10130048e-01 6.84707999e-01 -3.59578013e-01
1.71747789e-01 1.37635693e-01 -3.14115435e-01 -2.79225767e-01
1.70747131e-01 -1.10409713e+00 -1.49375606e+00 -5.88312745e-01
-6.83076233e-02 9.01409209e-01 1.27266183e-01 -2.12515563... | [8.082572937011719, -3.447631359100342] |
77684837-08b0-48bc-a578-b81da20409ae | program-induction-by-rationale-generation | 1705.04146 | null | http://arxiv.org/abs/1705.04146v3 | http://arxiv.org/pdf/1705.04146v3.pdf | Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems | Solving algebraic word problems requires executing a series of arithmetic
operations---a program---to obtain a final answer. However, since programs can
be arbitrarily complicated, inducing them directly from question-answer pairs
is a formidable challenge. To make this task more feasible, we solve these
problems by ge... | ['Wang Ling', 'Chris Dyer', 'Phil Blunsom', 'Dani Yogatama'] | 2017-05-11 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 2.51582444e-01 2.37186745e-01 -2.29750276e-01 -8.59133363e-01
-1.03653216e+00 -9.88970637e-01 3.94309819e-01 4.06278312e-01
-6.84392676e-02 5.38011968e-01 2.40644207e-03 -1.06354320e+00
1.64694980e-01 -1.36400604e+00 -1.02482283e+00 1.33211643e-01
6.18736446e-02 4.37497199e-01 2.86398470e-01 -3.91071171... | [9.584199905395508, 7.43862771987915] |
3601d06d-e150-4633-bef9-9f5f35f90a74 | collaborative-neural-rendering-using-anime | 2207.05378 | null | https://arxiv.org/abs/2207.05378v5 | https://arxiv.org/pdf/2207.05378v5.pdf | Collaborative Neural Rendering using Anime Character Sheets | Drawing images of characters with desired poses is an essential but laborious task in anime production. Assisting artists to create is a research hotspot in recent years. In this paper, we present the Collaborative Neural Rendering (CoNR) method, which creates new images for specified poses from a few reference images ... | ['Zhewei Huang', 'Ailin Huang', 'Zuzeng Lin'] | 2022-07-12 | null | null | null | null | ['image-to-video', 'image-to-3d'] | ['computer-vision', 'computer-vision'] | [ 8.77821967e-02 -2.05191284e-01 2.14128733e-01 -3.03409308e-01
-3.15304369e-01 -6.18433714e-01 5.83559930e-01 -5.39037883e-01
-3.39648165e-02 5.18737555e-01 2.63872415e-01 1.07777402e-01
2.20046118e-01 -9.41785932e-01 -6.10941172e-01 -3.71050656e-01
3.85553509e-01 4.62796420e-01 7.22190365e-02 -4.99943048... | [11.829695701599121, -0.48429644107818604] |
67bf78ab-6dfc-4b82-b609-4ac967e1b2f1 | incremental-natural-language-processing | null | null | https://aclanthology.org/C18-1253 | https://aclanthology.org/C18-1253.pdf | Incremental Natural Language Processing: Challenges, Strategies, and Evaluation | Incrementality is ubiquitous in human-human interaction and beneficial for human-computer interaction. It has been a topic of research in different parts of the NLP community, mostly with focus on the specific topic at hand even though incremental systems have to deal with similar challenges regardless of domain. In th... | ['Arne K{\\"o}hn'] | 2018-08-01 | incremental-natural-language-processing-2 | https://aclanthology.org/C18-1253 | https://aclanthology.org/C18-1253.pdf | coling-2018-8 | ['dialogue-understanding'] | ['natural-language-processing'] | [ 1.70889303e-01 1.29263118e-01 -2.55283922e-01 -4.53676552e-01
-3.21126610e-01 -9.33299780e-01 9.35109138e-01 5.86172462e-01
-3.94112915e-01 3.67987037e-01 2.23551840e-01 -2.86255687e-01
-5.96452296e-01 -3.30349445e-01 -4.97225635e-02 -2.87683427e-01
-9.04229060e-02 8.32260072e-01 3.17258507e-01 -4.08156157... | [11.211568832397461, 8.860475540161133] |
6ed035b3-4193-4175-b187-ac26dc7abe79 | flexible-job-classification-with-zero-shot | 2209.12678 | null | https://arxiv.org/abs/2209.12678v1 | https://arxiv.org/pdf/2209.12678v1.pdf | Flexible Job Classification with Zero-Shot Learning | Using a taxonomy to organize information requires classifying objects (documents, images, etc) with appropriate taxonomic classes. The flexible nature of zero-shot learning is appealing for this task because it allows classifiers to naturally adapt to taxonomy modifications. This work studies zero-shot multi-label docu... | ['Thom Lake'] | 2022-08-30 | null | null | null | null | ['document-classification', 'job-classification', 'taxonomy-expansion'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.63813853e-01 4.52869236e-02 -4.18364584e-01 -3.61538619e-01
-7.32883215e-01 -5.90099394e-01 5.95999956e-01 5.26206195e-01
-6.29853249e-01 4.11625922e-01 2.14780688e-01 -2.00245559e-01
-5.68190873e-01 -7.35595882e-01 7.16618747e-02 -4.50966030e-01
5.30774612e-03 6.29333913e-01 2.83652753e-01 -5.27845994... | [10.04108715057373, 3.4288876056671143] |
2a483645-9adb-4381-8f60-e5acb153b965 | crformer-a-cross-region-transformer-for | 2207.01600 | null | https://arxiv.org/abs/2207.01600v1 | https://arxiv.org/pdf/2207.01600v1.pdf | CRFormer: A Cross-Region Transformer for Shadow Removal | Aiming to restore the original intensity of shadow regions in an image and make them compatible with the remaining non-shadow regions without a trace, shadow removal is a very challenging problem that benefits many downstream image/video-related tasks. Recently, transformers have shown their strong capability in variou... | ['Song Wang', 'Zhihao Liu', 'Xinyi Wu', 'Zhenyao Wu', 'Hui Yin', 'Jin Wan'] | 2022-07-04 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 7.04842746e-01 -1.66281268e-01 3.07117611e-01 -2.33165771e-01
-2.61427999e-01 -1.69918254e-01 4.04883415e-01 -3.94164324e-01
-3.64260077e-02 6.98138237e-01 2.67365575e-01 -3.91609579e-01
-5.21428809e-02 -6.42416060e-01 -6.44616067e-01 -1.32831824e+00
4.01371062e-01 9.24618170e-02 1.00418913e+00 -4.82917398... | [10.844086647033691, -4.086328506469727] |
9222b2d3-b914-4437-8e51-0437fb315765 | discriminative-region-based-multi-label-zero | 2108.09301 | null | https://arxiv.org/abs/2108.09301v1 | https://arxiv.org/pdf/2108.09301v1.pdf | Discriminative Region-based Multi-Label Zero-Shot Learning | Multi-label zero-shot learning (ZSL) is a more realistic counter-part of standard single-label ZSL since several objects can co-exist in a natural image. However, the occurrence of multiple objects complicates the reasoning and requires region-specific processing of visual features to preserve their contextual cues. We... | ['Mubarak Shah', 'Ling Shao', 'Fahad Shahbaz Khan', 'Salman Khan', 'Akshita Gupta', 'Sanath Narayan'] | 2021-08-20 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Narayan_Discriminative_Region-Based_Multi-Label_Zero-Shot_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Narayan_Discriminative_Region-Based_Multi-Label_Zero-Shot_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['multi-label-zero-shot-learning'] | ['computer-vision'] | [ 5.09980321e-01 -3.85787152e-02 -3.95296663e-01 -3.60447735e-01
-1.05925643e+00 -5.09552479e-01 8.19717824e-01 6.14463747e-01
-3.85386407e-01 4.74902838e-01 9.50969830e-02 2.83880174e-01
-1.46545783e-01 -7.48641491e-01 -6.38207734e-01 -8.56455922e-01
2.80588120e-01 6.90447092e-02 7.87583888e-01 -6.87642545... | [9.846894264221191, 2.506770372390747] |
eea1a8d8-e558-4b25-9ba0-bc8c568e9f99 | imagination-is-all-you-need-curved | 2211.07591 | null | https://arxiv.org/abs/2211.07591v2 | https://arxiv.org/pdf/2211.07591v2.pdf | Imagination is All You Need! Curved Contrastive Learning for Abstract Sequence Modeling Utilized on Long Short-Term Dialogue Planning | Inspired by the curvature of space-time (Einstein, 1921), we introduce Curved Contrastive Learning (CCL), a novel representation learning technique for learning the relative turn distance between utterance pairs in multi-turn dialogues. The resulting bi-encoder models can guide transformers as a response ranking model ... | ['Gerasimos Spanakis', 'Stefan Schaffer', 'Justus-Jonas Erker'] | 2022-11-14 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 1.52118713e-01 7.32304752e-01 -8.35154504e-02 -5.86866260e-01
-8.90787125e-01 -9.18555021e-01 1.25723505e+00 1.60383329e-01
-2.30663568e-01 4.66911793e-01 1.21162581e+00 -4.74719524e-01
-2.78460622e-01 -6.09638870e-01 -4.76626694e-01 -6.71336412e-01
-4.41081226e-01 6.11499071e-01 -4.18854058e-01 -7.03234255... | [12.727585792541504, 7.959446907043457] |
4cc49707-e7a3-47aa-902a-1903da53555a | emergency-action-termination-for-immediate | 2211.06351 | null | https://arxiv.org/abs/2211.06351v1 | https://arxiv.org/pdf/2211.06351v1.pdf | Emergency action termination for immediate reaction in hierarchical reinforcement learning | Hierarchical decomposition of control is unavoidable in large dynamical systems. In reinforcement learning (RL), it is usually solved with subgoals defined at higher policy levels and achieved at lower policy levels. Reaching these goals can take a substantial amount of time, during which it is not verified whether the... | ['Tomasz Trzciński', 'Artur Grudkowski', 'Mateusz Ostaszewski', 'Paweł Wawrzyński', 'Jakub Łyskawa', 'Michał Bortkiewicz'] | 2022-11-11 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 5.13405129e-02 6.29525632e-02 -1.34815797e-01 1.95575163e-01
-2.18174294e-01 -6.74390376e-01 5.42587459e-01 5.37126660e-01
-6.20709658e-01 1.50234330e+00 -1.16198100e-01 -2.72015721e-01
-3.44483078e-01 -9.51683283e-01 -6.58800781e-01 -1.02608216e+00
-2.34294057e-01 5.97887695e-01 6.97096467e-01 -4.54062343... | [4.329390525817871, 2.0075623989105225] |
15d3b112-26be-4c6e-8500-9d05f2e74280 | improving-feature-generalizability-with | 2204.12915 | null | https://arxiv.org/abs/2204.12915v1 | https://arxiv.org/pdf/2204.12915v1.pdf | Improving Feature Generalizability with Multitask Learning in Class Incremental Learning | Many deep learning applications, like keyword spotting, require the incorporation of new concepts (classes) over time, referred to as Class Incremental Learning (CIL). The major challenge in CIL is catastrophic forgetting, i.e., preserving as much of the old knowledge as possible while learning new tasks. Various techn... | ['Cecilia Mascolo', 'Chi Ian Tang', 'Dong Ma'] | 2022-04-26 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 2.09508866e-01 -1.67664006e-01 -3.23441118e-01 -2.47416988e-01
-6.68274283e-01 -4.40666378e-01 5.33126593e-01 3.29633266e-01
-6.55172884e-01 9.63282824e-01 -5.05036004e-02 -1.89911142e-01
-4.79956977e-02 -7.10476279e-01 -8.54878843e-01 -6.38821661e-01
2.11173803e-01 2.82866329e-01 4.64043647e-01 7.57211447... | [9.759343147277832, 3.5003929138183594] |
cdc340e9-1b7d-40bd-8827-ed46b488418f | 3d-consistent-robust-segmentation-of-cardiac | 1804.09400 | null | http://arxiv.org/abs/1804.09400v1 | http://arxiv.org/pdf/1804.09400v1.pdf | 3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation | We propose a method based on deep learning to perform cardiac segmentation on
short axis MRI image stacks iteratively from the top slice (around the base) to
the bottom slice (around the apex). At each iteration, a novel variant of U-net
is applied to propagate the segmentation of a slice to the adjacent slice below
it... | ['Nicholas Ayache', 'Hervé Delingette', 'Qiao Zheng', 'Nicolas Duchateau'] | 2018-04-25 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.72847921e-01 4.02005434e-01 1.24134436e-01 -4.32788223e-01
-7.56231904e-01 -4.58347142e-01 4.25436109e-01 5.32894969e-01
-6.85894489e-01 8.99133384e-01 2.03760520e-01 -1.89144999e-01
-3.69024813e-01 -6.35594845e-01 -6.03300512e-01 -7.74051070e-01
-4.82023388e-01 8.93765271e-01 7.88789272e-01 1.11592129... | [14.25141429901123, -2.374495506286621] |
1fd3aba0-af60-448f-9f56-cf93d064d58a | wisenetmd-motion-detection-using-dynamic | 1805.09277 | null | http://arxiv.org/abs/1805.09277v1 | http://arxiv.org/pdf/1805.09277v1.pdf | WisenetMD: Motion Detection Using Dynamic Background Region Analysis | Motion detection algorithms that can be applied to surveillance cameras such
as CCTV (Closed Circuit Television) have been studied extensively. Motion
detection algorithm is mostly based on background subtraction. One main issue
in this technique is that false positives of dynamic backgrounds such as wind
shaking trees... | ['Jeong-Eun Lim', 'Jin-Wook Shim', 'Soon-Chul Kwon', 'Sang-Ha Lee', 'Jisang Yoo'] | 2018-05-23 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 3.76143247e-01 -8.51183653e-01 1.85037673e-01 5.93621358e-02
7.08782896e-02 -7.18973279e-01 2.63813227e-01 -1.25215203e-01
-6.92829967e-01 8.46367002e-01 -8.03217366e-02 -6.30571187e-01
5.14055490e-01 -7.70297647e-01 -2.50921309e-01 -8.73205304e-01
4.41620201e-02 -2.02316448e-01 1.21334910e+00 5.99375851... | [8.835376739501953, -0.945231020450592] |
b75ebc4e-683e-4b6b-9585-fa103d281def | shielded-decision-making-in-mdps | 1807.06096 | null | https://arxiv.org/abs/1807.06096v2 | https://arxiv.org/pdf/1807.06096v2.pdf | Safe Reinforcement Learning via Probabilistic Shields | This paper targets the efficient construction of a safety shield for decision making in scenarios that incorporate uncertainty. Markov decision processes (MDPs) are prominent models to capture such planning problems. Reinforcement learning (RL) is a machine learning technique to determine near-optimal policies in MDPs ... | ['Bettina Könighofer', 'Roderick Bloem', 'Nils Jansen', 'Alexandru C. Serban', 'Sebastian Junges'] | 2018-07-16 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.90908119e-01 7.44327247e-01 -2.86495775e-01 -1.84304282e-01
-9.47147965e-01 -5.74350238e-01 4.19701874e-01 4.49107498e-01
-4.69968557e-01 1.06258798e+00 -4.05081436e-02 -8.51252556e-01
-5.98130584e-01 -9.42237377e-01 -8.55514824e-01 -5.60027957e-01
-8.87606978e-01 7.27459013e-01 4.28160727e-01 -2.12486267... | [4.541505336761475, 2.104837417602539] |
ae6fc0e5-9556-46b5-a0a4-97e0a65098c4 | polyphone-disambiguation-and-accent | 2201.09427 | null | https://arxiv.org/abs/2201.09427v1 | https://arxiv.org/pdf/2201.09427v1.pdf | Polyphone disambiguation and accent prediction using pre-trained language models in Japanese TTS front-end | Although end-to-end text-to-speech (TTS) models can generate natural speech, challenges still remain when it comes to estimating sentence-level phonetic and prosodic information from raw text in Japanese TTS systems. In this paper, we propose a method for polyphone disambiguation (PD) and accent prediction (AP). The pr... | ['Toshiyuki Kumakura', 'Toshiyuki Sekiya', 'Emiru Tsunoo', 'Chie Kamada', 'Masaki Hamada', 'Rem Hida'] | 2022-01-24 | null | null | null | null | ['morphological-analysis', 'polyphone-disambiguation'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.53101367e-03 4.09658343e-01 2.64133543e-01 -5.01036584e-01
-1.28762591e+00 -5.03336191e-01 3.88539910e-01 -1.34759858e-01
-3.36394519e-01 7.62615919e-01 6.01753891e-01 -2.88701981e-01
3.64683628e-01 -2.13442415e-01 -3.85458976e-01 -6.04697049e-01
3.20221752e-01 2.12101921e-01 2.04542354e-01 -2.98618287... | [14.732780456542969, 6.678366661071777] |
fc10e311-d11a-4e1e-950e-b89cfc6a37a2 | 1st-solution-places-for-cvpr-2023-ug-2 | 2306.09379 | null | https://arxiv.org/abs/2306.09379v1 | https://arxiv.org/pdf/2306.09379v1.pdf | 1st Solution Places for CVPR 2023 UG$^2$+ Challenge Track 2.2-Coded Target Restoration through Atmospheric Turbulence | In this technical report, we briefly introduce the solution of our team VIELab-HUST for coded target restoration through atmospheric turbulence in CVPR 2023 UG$^2$+ Track 2.2. In this task, we propose an efficient multi-stage framework to restore a high quality image from distorted frames. Specifically, each distorted ... | ['Luxin Yan', 'Yi Chang', 'Xueyao Xiao', 'Haoyue Liu', 'Shuning Cao', 'Shengqi Xu'] | 2023-06-15 | null | null | null | null | ['deblurring', 'image-registration'] | ['computer-vision', 'computer-vision'] | [ 5.35046935e-01 -5.96573710e-01 4.35787588e-01 -1.27045900e-01
-9.28240180e-01 -7.05275714e-01 3.30749065e-01 -4.28208441e-01
-1.19785979e-01 7.36941695e-01 2.37849027e-01 -1.63589641e-01
-8.19863975e-02 -4.15962636e-01 -5.21888137e-01 -9.31403875e-01
-2.76651774e-02 -3.41544330e-01 1.14950493e-01 -1.58660159... | [11.231415748596191, -2.237999677658081] |
9e18ed22-e7cf-45d7-89b3-45365a0b86fa | learning-with-noisy-labels-over-imbalanced | 2211.08722 | null | https://arxiv.org/abs/2211.08722v1 | https://arxiv.org/pdf/2211.08722v1.pdf | Learning with Noisy Labels over Imbalanced Subpopulations | Learning with Noisy Labels (LNL) has attracted significant attention from the research community. Many recent LNL methods rely on the assumption that clean samples tend to have "small loss". However, this assumption always fails to generalize to some real-world cases with imbalanced subpopulations, i.e., training subpo... | ['Jianhua Yao', 'Bingzhe Wu', 'Zongbo Han', 'Bing He', 'Yu Zhao', 'Mingcai Chen'] | 2022-11-16 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.26152807e-01 -1.85293376e-01 -4.57328498e-01 -6.03053927e-01
-1.39535713e+00 -4.32151705e-01 3.08011323e-01 5.02933860e-01
-2.86562651e-01 9.54754412e-01 2.55451817e-02 -5.01498394e-02
-8.24754089e-02 -6.87974632e-01 -6.70379460e-01 -1.13052130e+00
3.86698961e-01 3.34862202e-01 -1.61379918e-01 3.01958740... | [9.439525604248047, 3.990262269973755] |
51a741e3-7d36-4436-8c59-ee30eb7b2c90 | learning-based-robust-speaker-counting-and | 2303.06867 | null | https://arxiv.org/abs/2303.06867v1 | https://arxiv.org/pdf/2303.06867v1.pdf | Learning-based Robust Speaker Counting and Separation with the Aid of Spatial Coherence | A two-stage approach is proposed for speaker counting and speech separation in noisy and reverberant environments. A spatial coherence matrix (SCM) is computed using whitened relative transfer functions (wRTFs) across time frames. The global activity functions of each speaker are estimated on the basis of a simplex con... | ['Mingsian Bai', 'Yicheng Hsu'] | 2023-03-13 | null | null | null | null | ['speech-separation', 'speaker-separation'] | ['speech', 'speech'] | [ 2.53924757e-01 -3.61235648e-01 2.83708900e-01 -1.89283162e-01
-8.93860579e-01 -3.46640825e-01 3.67579788e-01 -1.25156865e-01
-3.26223254e-01 4.47171539e-01 5.78052282e-01 1.23316415e-01
-3.14573109e-01 -9.80915874e-02 -6.52892143e-02 -1.11858773e+00
-4.50993001e-01 5.06423227e-02 -6.05048798e-02 1.75218970... | [14.912898063659668, 5.799304008483887] |
c09ec03a-6a05-47c5-9d91-59d3bd796c1f | on-systematic-style-differences-between | null | null | https://openreview.net/forum?id=mRUrRIL-jXh | https://openreview.net/pdf?id=mRUrRIL-jXh | On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation | Modern unsupervised machine translation (MT) systems reach reasonable translation quality under clean and controlled data conditions. As the performance gap between supervised and unsupervised MT narrows, it is interesting to ask whether the different training methods result in systematically different output beyond wh... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.15772736e-01 4.52893227e-01 -4.54611897e-01 -5.08262277e-01
-1.15438879e+00 -1.07643366e+00 9.79743958e-01 1.71038464e-01
-3.42774361e-01 1.08618736e+00 7.93681920e-01 -7.65185833e-01
1.73029765e-01 -4.91486013e-01 -4.38455433e-01 -2.94902593e-01
5.86203277e-01 8.83098900e-01 -2.25105137e-01 -5.43161094... | [11.581535339355469, 10.202765464782715] |
8f7281d5-0acd-4bc6-9073-e247d97b5f72 | towards-accurate-instance-segmentation-in | 2307.02877 | null | https://arxiv.org/abs/2307.02877v1 | https://arxiv.org/pdf/2307.02877v1.pdf | Towards accurate instance segmentation in large-scale LiDAR point clouds | Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods strug... | ['Konrad Schindler', 'Rasmus Astrup', 'Stefano Puliti', 'Frawa Vetterli', 'Theodora Kontogianni', 'Torben Peters', 'Binbin Xiang'] | 2023-07-06 | null | null | null | null | ['panoptic-segmentation', 'instance-segmentation', 'scene-understanding', 'clustering', 'management'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 3.85779113e-01 4.70101684e-02 -4.26047146e-01 -4.35350090e-01
-3.78431469e-01 -7.92656898e-01 6.34773195e-01 4.36588943e-01
-1.20991617e-01 4.11236286e-01 -3.97624448e-02 -5.15458107e-01
-4.96332794e-01 -1.26510012e+00 -5.06375253e-01 -6.24041915e-01
-2.21931368e-01 7.33298957e-01 4.03046131e-01 -2.01868154... | [8.34992790222168, -2.522951602935791] |
80bef646-700e-498d-a95f-bc8be226ea5c | 190508611 | 1905.08611 | null | https://arxiv.org/abs/1905.08611v1 | https://arxiv.org/pdf/1905.08611v1.pdf | Machine learning approach for segmenting glands in colon histology images using local intensity and texture features | Colon Cancer is one of the most common types of cancer. The treatment is planned to depend on the grade or stage of cancer. One of the preconditions for grading of colon cancer is to segment the glandular structures of tissues. Manual segmentation method is very time-consuming, and it leads to life risk for the patient... | ['Soumick Chatterjee', 'Rupali Khatun'] | 2019-05-15 | null | null | null | null | ['colorectal-gland-segmentation'] | ['medical'] | [ 2.68125802e-01 -2.45439019e-02 -1.91755101e-01 -3.47571224e-01
-2.15030253e-01 -4.95210677e-01 2.44232133e-01 6.91186905e-01
-5.79414010e-01 6.03035748e-01 -2.05833316e-01 -3.45566124e-01
-1.01669870e-01 -1.13633633e+00 1.11438315e-02 -9.49120760e-01
-6.35041222e-02 8.10710013e-01 5.75895667e-01 1.00006349... | [15.170065879821777, -2.902688503265381] |
adc82b24-6660-42fe-99be-376870420f93 | porous-lattice-based-transformer-encoder-for | 1911.02733 | null | https://arxiv.org/abs/1911.02733v3 | https://arxiv.org/pdf/1911.02733v3.pdf | Porous Lattice-based Transformer Encoder for Chinese NER | Incorporating lattices into character-level Chinese named entity recognition is an effective method to exploit explicit word information. Recent works extend recurrent and convolutional neural networks to model lattice inputs. However, due to the DAG structure or the variable-sized potential word set for lattice inputs... | ['Zhang Yue', 'Yu Bowen', 'Xue Mengge', 'Wang Bin', 'Meng Erli', 'Liu Tingwen'] | 2019-11-07 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-2.03601778e-01 -2.20955640e-01 -2.31949046e-01 -1.57450601e-01
-6.44737482e-01 -4.44332838e-01 2.34723136e-01 1.30760461e-01
-5.18907070e-01 6.22563601e-01 6.31734550e-01 -5.76406419e-01
3.87331367e-01 -9.84740078e-01 -6.80288136e-01 -7.85719812e-01
-8.12233835e-02 2.30867296e-01 3.21018577e-01 -1.19292818... | [9.839982986450195, 9.791244506835938] |
714abf11-2c9d-4561-9476-3705bc7dc830 | semantic-hypergraphs | 1908.10784 | null | https://arxiv.org/abs/1908.10784v2 | https://arxiv.org/pdf/1908.10784v2.pdf | Semantic Hypergraphs | Approaches to Natural language processing (NLP) may be classified along a double dichotomy open/opaque - strict/adaptive. The former axis relates to the possibility of inspecting the underlying processing rules, the latter to the use of fixed or adaptive rules. We argue that many techniques fall into either the open-st... | ['Camille Roth', 'Telmo Menezes'] | 2019-08-28 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 4.16800201e-01 6.47586107e-01 -4.20422643e-01 -1.96994632e-01
-4.33274090e-01 -1.00611782e+00 1.01379001e+00 5.38272083e-01
-1.09321982e-01 6.53627217e-01 4.95406687e-01 -9.67836380e-01
-6.97284639e-01 -1.03757191e+00 -1.04797386e-01 -3.84681582e-01
-4.46167216e-02 8.15019846e-01 4.58359450e-01 -6.13819540... | [9.852252960205078, 8.755498886108398] |
fc7a2064-d887-4e3b-851d-4f1ea0da9d19 | modelling-customer-churn-for-the-retail | 2304.00575 | null | https://arxiv.org/abs/2304.00575v1 | https://arxiv.org/pdf/2304.00575v1.pdf | Modelling customer churn for the retail industry in a deep learning based sequential framework | As retailers around the world increase efforts in developing targeted marketing campaigns for different audiences, predicting accurately which customers are most likely to churn ahead of time is crucial for marketing teams in order to increase business profits. This work presents a deep survival framework to predict wh... | ['Berthold Lausen', 'Maged Ali', 'Henrik Nordmark', 'Juan Pablo Equihua'] | 2023-04-02 | null | null | null | null | ['feature-engineering', 'marketing'] | ['methodology', 'miscellaneous'] | [-7.67445043e-02 -5.86528145e-02 -3.91532838e-01 -9.15353894e-01
-5.41054726e-01 -2.53779948e-01 2.00512558e-01 3.90623748e-01
-3.46374810e-01 1.35245726e-01 6.98599517e-02 -6.17498875e-01
-1.50892347e-01 -9.79761064e-01 -5.40721655e-01 -4.87673700e-01
-3.77905369e-01 8.81314814e-01 -4.94629711e-01 -4.51680154... | [9.393022537231445, 5.881396770477295] |
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