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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8c8ffc82-eb59-4ee6-9cba-f488b5fed1bb | lung-nodule-segmentation-and-low-confidence | 2303.08416 | null | https://arxiv.org/abs/2303.08416v4 | https://arxiv.org/pdf/2303.08416v4.pdf | Lung Nodule Segmentation and Low-Confidence Region Prediction with Uncertainty-Aware Attention Mechanism | Radiologists have different training and clinical experiences, which may result in various segmentation annotations for lung nodules, causing segmentation uncertainty. Conventional methods usually select a single annotation as the learning target or try to learn a latent space of various annotations, but these approach... | ['S. Kevin Zhou', 'Xiaohong Zhang', 'Chen Liu', 'Zhulin An', 'Yue Zhang', 'Qiuli Wang', 'Han Yang'] | 2023-03-15 | null | null | null | null | ['lung-nodule-segmentation'] | ['medical'] | [ 1.03918649e-01 6.09717429e-01 -4.23396617e-01 -4.70238328e-01
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4.48024511e-01 5.70808828e-01 8.44516098e-01 4.63143080... | [14.722505569458008, -2.124925374984741] |
fb5960bf-6885-4e6c-a922-ace6f2d348d0 | prediction-based-one-shot-dynamic-parking | 2208.14231 | null | https://arxiv.org/abs/2208.14231v1 | https://arxiv.org/pdf/2208.14231v1.pdf | Prediction-based One-shot Dynamic Parking Pricing | Many U.S. metropolitan cities are notorious for their severe shortage of parking spots. To this end, we present a proactive prediction-driven optimization framework to dynamically adjust parking prices. We use state-of-the-art deep learning technologies such as neural ordinary differential equations (NODEs) to design o... | ['Noseong Park', 'Jeongwhan Choi', 'Heejoo Shin', 'Seoyoung Hong'] | 2022-08-30 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-7.40671337e-01 -1.24861397e-01 -5.90300918e-01 -4.44200367e-01
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-1.09727025e-01 6.01990283e-01 -2.01396309e-02 -4.68901157... | [6.00666618347168, 1.6874703168869019] |
902d6c43-1bc7-4e1c-b251-28769e5f4b54 | grammatical-error-detection-using-error-and | null | null | https://aclanthology.org/I17-1005 | https://aclanthology.org/I17-1005.pdf | Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings | In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns. Most existing algorithms for learning word embeddings usually model only the syntactic context of words so that classifiers treat erroneous and correct words as similar inputs. We address t... | ['Yuya Sakaizawa', 'Masahiro Kaneko', 'Mamoru Komachi'] | 2017-11-01 | grammatical-error-detection-using-error-and-1 | https://aclanthology.org/I17-1005 | https://aclanthology.org/I17-1005.pdf | ijcnlp-2017-11 | ['learning-word-embeddings', 'grammatical-error-detection'] | ['methodology', 'natural-language-processing'] | [-1.76478669e-01 5.45576913e-03 -1.39394194e-01 -6.28643692e-01
-7.15061426e-01 -2.39159748e-01 -4.18933816e-02 1.06533158e+00
-1.08800125e+00 4.84423786e-01 3.26169401e-01 -7.42500544e-01
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9.83201340e-02 2.04149872e-01 -1.33342281e-01 -3.42191666... | [11.00619125366211, 10.707874298095703] |
89754d1b-9c0c-42d9-bd71-ab1229c1029c | zero-shot-object-detection | 1804.04340 | null | http://arxiv.org/abs/1804.04340v2 | http://arxiv.org/pdf/1804.04340v2.pdf | Zero-Shot Object Detection | We introduce and tackle the problem of zero-shot object detection (ZSD),
which aims to detect object classes which are not observed during training. We
work with a challenging set of object classes, not restricting ourselves to
similar and/or fine-grained categories as in prior works on zero-shot
classification. We pre... | ['Gaurav Sharma', 'Ankan Bansal', 'Ajay Divakaran', 'Karan Sikka', 'Rama Chellappa'] | 2018-04-12 | zero-shot-object-detection-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Ankan_Bansal_Zero-Shot_Object_Detection_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Ankan_Bansal_Zero-Shot_Object_Detection_ECCV_2018_paper.pdf | eccv-2018-9 | ['zero-shot-object-detection'] | ['computer-vision'] | [ 5.56941748e-01 2.33017839e-02 -2.22627833e-01 -2.39817828e-01
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-1.38263941e-01 -6.37552917e-01 -3.89404923e-01 -7.77817786e-01
1.32953346e-01 4.13275957e-01 9.34056342e-01 1.87665567... | [9.436176300048828, 1.6066113710403442] |
f8f6d1b3-e748-4614-a34c-6629bc4189f8 | a-massive-scale-semantic-similarity-dataset | 2306.17810 | null | https://arxiv.org/abs/2306.17810v1 | https://arxiv.org/pdf/2306.17810v1.pdf | A Massive Scale Semantic Similarity Dataset of Historical English | A diversity of tasks use language models trained on semantic similarity data. While there are a variety of datasets that capture semantic similarity, they are either constructed from modern web data or are relatively small datasets created in the past decade by human annotators. This study utilizes a novel source, newl... | ['Melissa Dell', 'Emily Silcock'] | 2023-06-30 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.97829972e-02 -3.28404635e-01 -4.10968155e-01 -3.99730295e-01
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3.76738161e-01 4.69688773e-01 2.25358218e-01 -5.32503605... | [10.979652404785156, 8.976202964782715] |
2326461c-adf2-4c28-83fa-25007641d559 | adaptive-multi-source-predictor-for-zero-shot | 2303.10383 | null | https://arxiv.org/abs/2303.10383v1 | https://arxiv.org/pdf/2303.10383v1.pdf | Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation | Both static and moving objects usually exist in real-life videos. Most video object segmentation methods only focus on exacting and exploiting motion cues to perceive moving objects. Once faced with static objects frames, moving object predictors may predict failed results caused by uncertain motion information, such a... | ['Huchuan Lu', 'Lihe Zhang', 'Jiaxing Yang', 'Youwei Pang', 'Shijie Chang', 'Xiaoqi Zhao'] | 2023-03-18 | null | null | null | null | ['video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.47794709e-01 -3.85360360e-01 -2.42932737e-01 -2.00382471e-01
-4.01756644e-01 -1.13961466e-01 2.08500683e-01 -2.05120519e-01
-4.40414160e-01 6.09908402e-01 1.31178305e-01 1.49217799e-01
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3.41095269e-01 -1.14020124e-01 1.14834082e+00 2.58196555... | [9.39942455291748, -0.3724464774131775] |
e1f72492-3571-4f4d-a15f-dda60075ab85 | citecaselaw-citation-worthiness-detection-in | 2305.03508 | null | https://arxiv.org/abs/2305.03508v1 | https://arxiv.org/pdf/2305.03508v1.pdf | CiteCaseLAW: Citation Worthiness Detection in Caselaw for Legal Assistive Writing | In legal document writing, one of the key elements is properly citing the case laws and other sources to substantiate claims and arguments. Understanding the legal domain and identifying appropriate citation context or cite-worthy sentences are challenging tasks that demand expensive manual annotation. The presence of ... | ['Ponnurangam Kumaraguru', 'Rajiv Ratn Shah', 'Yaman Kumar', 'Reshma Sheik', 'Gitansh Satija', 'Pritish Wadhwa', 'Mann Khatri'] | 2023-05-03 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 2.25798979e-01 -5.44128940e-02 -6.48416638e-01 -1.93603292e-01
-1.40317273e+00 -7.43448973e-01 8.70711744e-01 2.87420571e-01
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5.43237448e-01 4.68075901e-01 -1.10674329e-01 -2.51909882... | [9.941329956054688, 9.28042221069336] |
c01ae2e7-c04c-4cd0-bbec-a501429ad327 | sustainability-using-renewable-electricity | 2202.13101 | null | https://arxiv.org/abs/2202.13101v1 | https://arxiv.org/pdf/2202.13101v1.pdf | Sustainability using Renewable Electricity (SuRE) towards NetZero Emissions | Demand for energy has increased significantly across the globe due to increase in population and economic growth. Growth in energy demand poses serious threat to the environment since majority of the energy sources are non-renewable and based on fossil fuels, which leads to emission of harmful greenhouse gases. Organiz... | ['Rengaraj Ramasubbu', 'Tamal Bhattacharyya', 'Sandeep Vaity', 'Harshit Sampgaon', 'Rajesh kumar Palani', 'Gopali Contractor', 'Sreedhar Seetharam', 'Bhushan Jagyasi', 'Pallavi Gawade', 'Saurabh Pashine', 'Jinu Jayan'] | 2022-02-26 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-5.27582988e-02 2.10094512e-01 -1.76492810e-01 2.44819090e-01
-2.89478987e-01 -7.62149811e-01 4.82426316e-01 4.49687801e-02
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1.16795816e-01 -1.37604821e+00 8.64112750e-03 -6.40457869e-01
5.47100544e-01 2.70614833e-01 -3.83842468e-01 -2.19645336... | [5.750117301940918, 2.5645477771759033] |
469a40de-fd38-4cde-96af-3d35bc1c535f | diff-nst-diffusion-interleaving-for | 2307.04157 | null | https://arxiv.org/abs/2307.04157v2 | https://arxiv.org/pdf/2307.04157v2.pdf | DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer | Neural Style Transfer (NST) is the field of study applying neural techniques to modify the artistic appearance of a content image to match the style of a reference style image. Traditionally, NST methods have focused on texture-based image edits, affecting mostly low level information and keeping most image structures ... | ['John Collomosse', 'Nicholas Kolkin', 'Eli Shechtman', 'Andrew Gilbert', 'Gemma Canet Tarrés', 'Dan Ruta'] | 2023-07-09 | null | null | null | null | ['style-transfer', 'image-generation'] | ['computer-vision', 'computer-vision'] | [ 6.75882518e-01 3.55505466e-01 2.07623243e-01 -1.42125651e-01
7.23979101e-02 -8.70811522e-01 8.23753715e-01 -2.46951342e-01
-3.26992631e-01 6.75467730e-01 1.48428753e-01 1.06474772e-01
8.54239613e-02 -1.11250770e+00 -9.00208116e-01 -7.39196956e-01
1.78488657e-01 2.52091438e-01 4.22193527e-01 -5.80019474... | [11.583467483520508, -0.42698201537132263] |
b6036d68-89ed-4277-8fda-88916fa1e62c | criticality-as-it-could-be-organizational | 1704.05255 | null | http://arxiv.org/abs/1704.05255v3 | http://arxiv.org/pdf/1704.05255v3.pdf | Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents | This paper outlines a methodological approach for designing adaptive agents
driving themselves near points of criticality. Using a synthetic approach we
construct a conceptual model that, instead of specifying mechanistic
requirements to generate criticality, exploits the maintenance of an
organizational structure capa... | ['Manuel G. Bedia', 'Miguel Aguilera'] | 2017-04-18 | null | null | null | null | ['acrobot'] | ['playing-games'] | [ 2.14726895e-01 4.64905828e-01 9.37164649e-02 2.06437990e-01
1.16685897e-01 -4.30781454e-01 1.11737013e+00 2.50426918e-01
-4.95429754e-01 9.39260781e-01 -1.76339433e-01 -9.78418738e-02
-5.28981268e-01 -6.62694573e-01 -7.41998315e-01 -1.27154422e+00
-4.47369426e-01 4.81864125e-01 4.91471499e-01 -8.77456427... | [5.563549041748047, 4.131024360656738] |
41545c02-57d0-467c-9135-3b1aa81bba62 | data-free-quantization-via-mixed-precision | 2307.00498 | null | https://arxiv.org/abs/2307.00498v1 | https://arxiv.org/pdf/2307.00498v1.pdf | Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning | Neural network quantization is a very promising solution in the field of model compression, but its resulting accuracy highly depends on a training/fine-tuning process and requires the original data. This not only brings heavy computation and time costs but also is not conducive to privacy and sensitive information pro... | ['Yong liu', 'Guanzhong Tian', 'Mengmeng Wang', 'Tianxin Huang', 'Shipeng Bai', 'Jun Chen'] | 2023-07-02 | null | null | null | null | ['data-free-quantization', 'quantization', 'data-free-quantization', 'model-compression'] | ['computer-vision', 'methodology', 'methodology', 'methodology'] | [ 3.44766766e-01 -1.72436163e-01 -3.18980545e-01 -3.50892127e-01
-9.20861959e-01 -2.20516007e-02 4.66626585e-01 3.66085500e-01
-4.32539701e-01 7.45766342e-01 -1.12246215e-01 -1.28797710e-01
6.86762109e-02 -1.20452535e+00 -1.14738882e+00 -6.86369896e-01
2.57808000e-01 7.91897625e-02 2.80242950e-01 -1.48547247... | [8.729589462280273, 2.9730257987976074] |
5974d1ba-4698-44a5-8bbc-f6a7d21752d7 | an-examination-of-wearable-sensors-and-video | 2307.04516 | null | https://arxiv.org/abs/2307.04516v1 | https://arxiv.org/pdf/2307.04516v1.pdf | An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification | Wearable sensors such as Inertial Measurement Units (IMUs) are often used to assess the performance of human exercise. Common approaches use handcrafted features based on domain expertise or automatically extracted features using time series analysis. Multiple sensors are required to achieve high classification accurac... | ['Georgiana Ifrim', 'Brian Caulfield', "Martin O'Reilly", 'Darragh Whelan', 'Thach Le Nguyen', 'Timilehin B. Aderinola', 'Antonio Bevilacqua', 'Ashish Singh'] | 2023-07-10 | null | null | null | null | ['feature-engineering', 'time-series'] | ['methodology', 'time-series'] | [ 4.12077725e-01 -5.43721735e-01 -2.35868514e-01 -9.76492371e-03
-6.57914221e-01 -6.59734964e-01 6.04674257e-02 3.74970585e-01
-7.30587423e-01 4.70171362e-01 4.19951342e-02 -6.48874464e-03
-3.26162241e-02 -5.61229885e-01 -6.57029867e-01 -4.36567813e-01
8.36055726e-02 -6.53744042e-02 2.13423833e-01 -6.20589033... | [7.432644844055176, 0.4443418085575104] |
583c3eef-882d-48f4-a0cf-eac4685e2b57 | a-score-level-fusion-method-for-eye-movement | 1601.03333 | null | http://arxiv.org/abs/1601.03333v1 | http://arxiv.org/pdf/1601.03333v1.pdf | A Score-level Fusion Method for Eye Movement Biometrics | This paper proposes a novel framework for the use of eye movement patterns
for biometric applications. Eye movements contain abundant information about
cognitive brain functions, neural pathways, etc. In the proposed method, eye
movement data is classified into fixations and saccades. Features extracted
from fixations ... | ['Aurobinda Routray', 'Anjith George'] | 2016-01-13 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.78078711e-01 -3.06888968e-01 1.40670445e-02 3.10234027e-03
6.84023619e-01 -4.21242803e-01 5.68300903e-01 -9.53355134e-02
-5.85807383e-01 7.63650417e-01 -6.03562631e-02 -4.06628698e-01
-3.40999335e-01 -2.28026763e-01 -1.42244488e-01 -6.60783589e-01
3.90868038e-01 -2.90448993e-01 1.30947009e-01 -1.56717762... | [13.770920753479004, 0.49560460448265076] |
a610886a-9744-448d-8c61-91185be3f56e | best-sources-forward-domain-generalization | 1806.05810 | null | http://arxiv.org/abs/1806.05810v1 | http://arxiv.org/pdf/1806.05810v1.pdf | Best sources forward: domain generalization through source-specific nets | A long standing problem in visual object categorization is the ability of
algorithms to generalize across different testing conditions. The problem has
been formalized as a covariate shift among the probability distributions
generating the training data (source) and the test data (target) and several
domain adaptation ... | ['Samuel Rota Bulò', 'Massimiliano Mancini', 'Barbara Caputo', 'Elisa Ricci'] | 2018-06-15 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 4.98503953e-01 6.29916862e-02 -2.14440599e-01 -7.31984556e-01
-4.07441825e-01 -7.21987545e-01 7.48876691e-01 2.34140962e-01
-1.97353840e-01 8.31950843e-01 -3.61924060e-02 -8.16207156e-02
-1.24071039e-01 -8.52566779e-01 -8.26601624e-01 -7.98724949e-01
7.41280988e-02 5.45444250e-01 6.17335856e-01 5.32341376... | [9.946327209472656, 2.8199901580810547] |
8b2c6eba-26ff-4273-a88a-c7bd6544eba7 | a-real-time-and-high-precision-method-for | null | null | https://link.springer.com/article/10.1007/s00521-021-06526-1 | https://link.springer.com/content/pdf/10.1007/s00521-021-06526-1.pdf | A real-time and high-precision method for small traffic-signs recognition | As a fundamental element of the traffic system, traffic signs reduce the risk of accidents by providing essential information about the road condition to drivers, pedestrians, etc. With the rapid progress of computer vision and artificial intelligence, traffic-signs recognition systems have been applied for the advance... | ['Ronghui Zhang', 'Zhihan Lv', 'Wenquan Chen', 'Kunkun Jia', 'Junzhou Chen'] | 2021-09-25 | null | null | null | neural-computing-and-applications-2021-9 | ['traffic-sign-recognition', 'traffic-sign-detection'] | ['computer-vision', 'computer-vision'] | [-7.09093660e-02 -2.36979365e-01 -2.90727854e-01 -2.94368178e-01
-2.95397252e-01 1.16832338e-01 4.30419087e-01 -4.72612441e-01
-3.44493538e-01 3.49427968e-01 -5.84416278e-02 -3.37851137e-01
-5.18509671e-02 -6.89538896e-01 -3.50142062e-01 -9.40233111e-01
5.03419816e-01 5.76473735e-02 8.95291567e-01 -4.27554309... | [7.99085807800293, -0.8021739721298218] |
981995f4-815d-449c-bcea-82bb0252313c | layoutlmv2-multi-modal-pre-training-for | 2012.14740 | null | https://arxiv.org/abs/2012.14740v4 | https://arxiv.org/pdf/2012.14740v4.pdf | LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding | Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents. We propose LayoutLMv2 architecture with new pre-training tasks to model the interaction among... | ['Lidong Zhou', 'Min Zhang', 'Wanxiang Che', 'Cha Zhang', 'Dinei Florencio', 'Yijuan Lu', 'Guoxin Wang', 'Furu Wei', 'Lei Cui', 'Tengchao Lv', 'Yiheng Xu', 'Yang Xu'] | 2020-12-29 | null | https://aclanthology.org/2021.acl-long.201 | https://aclanthology.org/2021.acl-long.201.pdf | acl-2021-5 | ['document-image-classification', 'document-layout-analysis', 'semantic-entity-labeling', 'key-information-extraction'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-1.00082465e-01 -2.47249305e-02 3.39252427e-02 -3.56404632e-01
-9.71712053e-01 -7.62663722e-01 6.37669146e-01 -4.50322405e-02
-1.58714026e-01 2.70891100e-01 2.93666452e-01 -6.28078938e-01
2.97882929e-02 -7.27765441e-01 -1.12306714e+00 -3.82550091e-01
2.74620503e-01 5.71104527e-01 1.75230309e-01 -3.04739654... | [11.425856590270996, 2.1499826908111572] |
f33c50fe-3c62-47a7-99e2-04e074554692 | a-flexible-extensible-software-framework-for | 2005.07786 | null | https://arxiv.org/abs/2005.07786v1 | https://arxiv.org/pdf/2005.07786v1.pdf | A flexible, extensible software framework for model compression based on the LC algorithm | We propose a software framework based on the ideas of the Learning-Compression (LC) algorithm, that allows a user to compress a neural network or other machine learning model using different compression schemes with minimal effort. Currently, the supported compressions include pruning, quantization, low-rank methods (i... | ['Miguel Á. Carreira-Perpiñán', 'Yerlan Idelbayev'] | 2020-05-15 | null | null | null | null | ['low-rank-compression'] | ['computer-code'] | [ 3.80123287e-01 1.10139124e-01 -2.52670974e-01 -3.35030496e-01
-4.55737084e-01 -3.32559764e-01 4.91349131e-01 3.02104294e-01
-6.05266809e-01 4.18734580e-01 -2.97505081e-01 -5.63159466e-01
-2.00992107e-01 -9.66517627e-01 -7.80133486e-01 -7.11574674e-01
-5.25326952e-02 6.44194603e-01 5.27527511e-01 1.78091228... | [8.54240894317627, 3.32901930809021] |
c9361820-564f-406c-a5a2-5b8745d64417 | early-recognition-of-human-activities-from | 1406.5309 | null | http://arxiv.org/abs/1406.5309v2 | http://arxiv.org/pdf/1406.5309v2.pdf | Early Recognition of Human Activities from First-Person Videos Using Onset Representations | In this paper, we propose a methodology for early recognition of human
activities from videos taken with a first-person viewpoint. Early recognition,
which is also known as activity prediction, is an ability to infer an ongoing
activity at its early stage. We present an algorithm to perform recognition of
activities ta... | ['Thomas J. Fuchs', 'M. S. Ryoo', 'J. K. Aggarwal', 'Lu Xia', 'Larry Matthies'] | 2014-06-20 | null | null | null | null | ['person-recognition', 'activity-prediction', 'activity-prediction'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 7.26187944e-01 -2.27142438e-01 -4.34673689e-02 -2.35214740e-01
-2.15032488e-01 -4.02415991e-01 8.69832516e-01 1.01822875e-01
-4.83302057e-01 4.54316348e-01 5.05495369e-01 3.17043126e-01
-1.98021501e-01 -3.19623768e-01 -5.40990531e-01 -7.88372874e-01
-6.18659377e-01 7.49625713e-02 4.69802618e-01 3.09638143... | [8.191540718078613, 0.35192158818244934] |
70006695-0241-4887-b2d1-c24b6f8726e1 | a-sentiment-and-emotion-aware-multimodal | null | null | https://aclanthology.org/2022.coling-1.587 | https://aclanthology.org/2022.coling-1.587.pdf | A Sentiment and Emotion Aware Multimodal Multiparty Humor Recognition in Multilingual Conversational Setting | In this paper, we hypothesize that humor is closely related to sentiment and emotions. Also, due to the tremendous growth in multilingual content, there is a great demand for building models and systems that support multilingual information access. To end this, we first extend the recently released Multimodal Multipart... | ['Pushpak Bhattacharyya', 'Asif Ekbal', 'Aseem Arora', 'Gopendra Vikram Singh', 'Dushyant Singh Chauhan'] | null | null | null | null | coling-2022-10 | ['humor-detection'] | ['natural-language-processing'] | [-4.32566077e-01 -2.28129998e-01 2.45816261e-01 -5.69649339e-01
-9.25189078e-01 -5.47204614e-01 6.17021382e-01 -2.04550717e-02
-4.85385686e-01 6.03012264e-01 7.31311023e-01 1.57618746e-01
4.08285260e-01 -3.35851818e-01 -4.28407490e-01 -4.90214229e-01
3.38698149e-01 2.01347932e-01 -3.43280703e-01 -7.22186625... | [13.064263343811035, 5.36147403717041] |
22867581-4bf8-402d-abc7-2e28ebc7f8ed | evaluating-multi-focus-natural-language | null | null | https://aclanthology.org/L12-1468 | https://aclanthology.org/L12-1468.pdf | Evaluating Multi-focus Natural Language Queries over Data Services | Natural language interfaces to data services will be a key technology to guarantee access to huge data repositories in an effortless way. This involves solving the complex problem of recognizing a relevant service or service composition given an ambiguous, potentially ungrammatical natural language question. As a first... | ['Pietro La Torre', 'Vincenzo Guerrisi', 'Silvia Quarteroni'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['service-composition'] | ['miscellaneous'] | [ 2.52988815e-01 3.16940665e-01 -4.92736511e-02 -5.80596864e-01
-9.12659347e-01 -8.00279915e-01 1.05291498e+00 4.35253501e-01
-4.84664857e-01 4.06983286e-01 5.87798715e-01 -6.50664687e-01
-4.57510263e-01 -6.26981020e-01 -1.50222585e-01 -1.35338411e-01
2.46508762e-01 9.40848887e-01 7.64160395e-01 -6.09955788... | [9.883530616760254, 7.964869499206543] |
26e237d2-2e8a-4eca-993e-c5438bf65a64 | a-method-of-generating-measurable-panoramic | 2010.14270 | null | https://arxiv.org/abs/2010.14270v1 | https://arxiv.org/pdf/2010.14270v1.pdf | A Method of Generating Measurable Panoramic Image for Indoor Mobile Measurement System | This paper designs a technique route to generate high-quality panoramic image with depth information, which involves two critical research hotspots: fusion of LiDAR and image data and image stitching. For the fusion of 3D points and image data, since a sparse depth map can be firstly generated by projecting LiDAR point... | ['Sheng Yang', 'Xiaodong Gong', 'Zemin Wang', 'Dong Xu', 'Hongyu Qiu', 'Zhirong Hu', 'Jingbin Liu', 'Hao Ma'] | 2020-10-27 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 4.92993832e-01 -3.21356714e-01 4.35306542e-02 -2.92763203e-01
-3.03858697e-01 -2.10846767e-01 3.15667003e-01 -5.12469828e-01
-4.03834671e-01 2.66535878e-01 -1.63054559e-02 2.59750690e-02
-2.32769102e-01 -1.14770925e+00 -5.37831128e-01 -6.11031651e-01
5.80905080e-01 2.83359647e-01 3.93863380e-01 -1.84654355... | [9.044339179992676, -2.463674306869507] |
5229ee30-a780-4b3f-bbef-b02433d5d236 | learning-scene-geometry-for-visual | null | null | https://hal.archives-ouvertes.fr/hal-02057378/document | https://hal.archives-ouvertes.fr/hal-02057378/document | Learning Scene Geometry for Visual Localization in Challenging Conditions | We propose a new approach for outdoor large scale image based localization that can deal with challenging scenarios like cross-season, cross-weather, day/night and longterm localization. The key component of our method is a new learned global image descriptor, that can effectively benefit from scene geometry informatio... | ['Valerie Gouet-Brunet，Cedric Demonceaux1', 'Nathan Piasco', 'Desire Sidibe'] | 2019-05-01 | null | null | null | international-conference-on-robotics-and-2 | ['image-based-localization'] | ['computer-vision'] | [-1.73136726e-01 -6.22239113e-01 2.53707051e-01 -7.00940549e-01
-1.33922601e+00 -1.06556547e+00 5.04841745e-01 2.98126161e-01
-8.87576580e-01 6.97088420e-01 -1.25877231e-01 1.57788441e-01
-9.73774195e-02 -8.01078677e-01 -8.37229609e-01 -6.27385259e-01
-3.01438957e-01 2.30437964e-01 4.69714969e-01 -2.29239091... | [7.621814727783203, -2.016674280166626] |
a15ca712-a097-4c82-825f-e608062e00bc | a-study-on-the-integration-of-pre-trained-ssl | 2211.05869 | null | https://arxiv.org/abs/2211.05869v1 | https://arxiv.org/pdf/2211.05869v1.pdf | A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding | Collecting sufficient labeled data for spoken language understanding (SLU) is expensive and time-consuming. Recent studies achieved promising results by using pre-trained models in low-resource scenarios. Inspired by this, we aim to ask: which (if any) pre-training strategies can improve performance across SLU benchmar... | ['Shinji Watanabe', 'Xuankai Chang', 'Siddharth Dalmia', 'Karthik Ganesan', 'Sujay Kumar', 'Yushi Ueda', 'Yosuke Higuchi', 'Siddhant Arora', 'Yifan Peng'] | 2022-11-10 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 4.06872869e-01 2.68922389e-01 -1.67891532e-01 -7.21396327e-01
-1.31394386e+00 -5.87287366e-01 6.74719334e-01 6.06902912e-02
-6.41593993e-01 6.45967662e-01 7.09842265e-01 -5.77038825e-01
6.23170614e-01 -4.04502600e-01 -7.22338855e-01 -3.19964252e-02
8.88918936e-02 3.77249569e-01 -1.16724648e-01 -3.19167674... | [13.990569114685059, 6.955099105834961] |
84232544-dbca-423d-a60c-194b9b60f114 | dudonet-dual-domain-network-for-ct-metal-1 | 1907.00273 | null | https://arxiv.org/abs/1907.00273v1 | https://arxiv.org/pdf/1907.00273v1.pdf | DuDoNet: Dual Domain Network for CT Metal Artifact Reduction | Computed tomography (CT) is an imaging modality widely used for medical diagnosis and treatment. CT images are often corrupted by undesirable artifacts when metallic implants are carried by patients, which creates the problem of metal artifact reduction (MAR). Existing methods for reducing the artifacts due to metallic... | ['Shaohua Kevin Zhou', 'Wei-An Lin', 'Jiebo Luo', 'Jingdan Zhang', 'Rama Chellappa', 'Xiaohang Sun', 'Haofu Liao', 'Cheng Peng'] | 2019-06-29 | dudonet-dual-domain-network-for-ct-metal | http://openaccess.thecvf.com/content_CVPR_2019/html/Lin_DuDoNet_Dual_Domain_Network_for_CT_Metal_Artifact_Reduction_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lin_DuDoNet_Dual_Domain_Network_for_CT_Metal_Artifact_Reduction_CVPR_2019_paper.pdf | cvpr-2019-6 | ['metal-artifact-reduction'] | ['medical'] | [ 3.77739400e-01 -3.58459353e-02 1.76549405e-01 -3.30938160e-01
-1.01473987e+00 -1.33386068e-02 1.96003750e-01 -2.19710365e-01
-3.24151963e-01 7.16846883e-01 2.24943325e-01 -2.02503294e-01
-1.77875906e-01 -7.51240909e-01 -6.68986619e-01 -7.15061247e-01
2.17692226e-01 4.93590146e-01 5.09106100e-01 -4.71291542... | [13.483664512634277, -2.5426745414733887] |
b9fb0732-56d3-45a5-97eb-91a325a27e77 | hypergraph-convolutional-network-based-weakly | 2211.01174 | null | https://arxiv.org/abs/2211.01174v1 | https://arxiv.org/pdf/2211.01174v1.pdf | Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations | Point cloud segmentation with scene-level annotations is a promising but challenging task. Currently, the most popular way is to employ the class activation map (CAM) to locate discriminative regions and then generate point-level pseudo labels from scene-level annotations. However, these methods always suffer from the ... | ['Zhiyong Su', 'Weiqing Li', 'Yuewei Dai', 'Peng Zhang', 'Zhuheng Lu'] | 2022-11-02 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 2.31563553e-01 2.86506772e-01 -2.48573467e-01 -5.03039002e-01
-8.30402434e-01 -1.86334148e-01 1.71310544e-01 -5.36105707e-02
-2.36973122e-01 2.37379104e-01 -4.17315781e-01 2.04147156e-02
5.01246043e-02 -1.04887521e+00 -9.61418808e-01 -5.87278187e-01
1.90447688e-01 7.50104070e-01 7.90787101e-01 -1.19542060... | [8.063189506530762, -3.1577274799346924] |
ff8e5365-d229-4d32-ad81-48b96ca5db16 | graph-matching-with-bi-level-noisy | 2212.04085 | null | https://arxiv.org/abs/2212.04085v2 | https://arxiv.org/pdf/2212.04085v2.pdf | Graph Matching with Bi-level Noisy Correspondence | In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence (ENC). In brief, on the one hand, due to the poor recognizability and viewpoint differences between i... | ['Xi Peng', 'Changqing Zhang', 'Peng Hu', 'Jun Yu', 'Mouxing Yang', 'Yijie Lin'] | 2022-12-08 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 9.61165354e-02 2.93526542e-03 1.07692992e-02 -2.59955168e-01
-9.37966883e-01 -2.10607156e-01 4.06738341e-01 9.82659906e-02
-1.44650057e-01 4.24700141e-01 8.64117816e-02 3.71467099e-02
-2.34775037e-01 -5.79875112e-01 -6.74782872e-01 -8.80905271e-01
6.33373559e-02 2.58599669e-01 2.15054378e-01 -1.11435495... | [7.238724708557129, 6.429582595825195] |
63b297fb-d546-4ec4-9a08-5141f0ea5093 | candidate-scoring-using-web-based-measure-for | null | null | https://aclanthology.org/W13-4420 | https://aclanthology.org/W13-4420.pdf | Candidate Scoring Using Web-Based Measure for Chinese Spelling Error Correction | null | ['Chung-Hsien Wu', 'Chao-Hong Liu', 'Liang-Chih Yu'] | 2013-10-01 | null | null | null | ws-2013-10 | ['csc'] | ['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... | [-7.430234432220459, 3.865859031677246] |
853bad7a-072b-4e8b-9dfd-30b02b365b6d | certified-reasoning-with-language-models | 2306.04031 | null | https://arxiv.org/abs/2306.04031v1 | https://arxiv.org/pdf/2306.04031v1.pdf | Certified Reasoning with Language Models | Language models often achieve higher accuracy when reasoning step-by-step in complex tasks. However, their reasoning can be unsound, inconsistent, or rely on undesirable prior assumptions. To tackle these issues, we introduce a class of tools for language models called guides that use state and incremental constraints ... | ['Noah D. Goodman', 'Eric Zelikman', 'Kanishk Gandhi', 'Gabriel Poesia'] | 2023-06-06 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.83905568e-02 9.08464968e-01 -2.23229527e-01 -3.90310436e-01
-7.68601596e-01 -7.65722930e-01 7.71288455e-01 -1.96323812e-01
1.57787725e-01 8.14968586e-01 1.35490939e-01 -1.01088715e+00
8.75925422e-02 -1.04490590e+00 -7.44543374e-01 3.13696824e-02
1.51860848e-01 8.88179421e-01 3.17641348e-01 -4.24044311... | [9.245381355285645, 7.2210845947265625] |
988b48c4-ac62-41c8-acdf-61c3c517d466 | a-conditional-random-field-model-for-context | 1906.07383 | null | https://arxiv.org/abs/1906.07383v1 | https://arxiv.org/pdf/1906.07383v1.pdf | A Conditional Random Field Model for Context Aware Cloud Detection in Sky Images | A conditional random field (CRF) model for cloud detection in ground based sky images is presented. We show that very high cloud detection accuracy can be achieved by combining a discriminative classifier and a higher order clique potential in a CRF framework. The image is first divided into homogeneous regions using a... | ['Jeffrey J. Rodriguez', 'Alexander D. Cronin', 'Vijai T. Jayadevan'] | 2019-06-18 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 2.98987478e-01 -3.52115899e-01 6.38230816e-02 -5.45862138e-01
-8.51982117e-01 -6.43464565e-01 7.90771246e-01 1.35908708e-01
-2.85602480e-01 6.82161152e-01 -4.93574888e-01 -1.88852519e-01
-1.41427055e-01 -7.84921050e-01 -4.43625450e-01 -1.03569388e+00
-1.89451367e-01 8.94050717e-01 6.76304996e-01 3.23912919... | [9.73434829711914, -1.7646949291229248] |
08765102-930d-426b-aa92-c645d5aeea4d | do-different-tracking-tasks-require-different | 2107.02156 | null | https://arxiv.org/abs/2107.02156v2 | https://arxiv.org/pdf/2107.02156v2.pdf | Do Different Tracking Tasks Require Different Appearance Models? | Tracking objects of interest in a video is one of the most popular and widely applicable problems in computer vision. However, with the years, a Cambrian explosion of use cases and benchmarks has fragmented the problem in a multitude of different experimental setups. As a consequence, the literature has fragmented too,... | ['Luca Bertinetto', 'Philip H. S. Torr', 'Shengjin Wang', 'Ya-Li Li', 'Hengshuang Zhao', 'Zhongdao Wang'] | 2021-07-05 | null | http://proceedings.neurips.cc/paper/2021/hash/06997f04a7db92466a2baa6ebc8b872d-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/06997f04a7db92466a2baa6ebc8b872d-Paper.pdf | neurips-2021-12 | ['multiple-people-tracking', 'video-instance-segmentation', 'online-multi-object-tracking', 'video-object-tracking', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.43848115e-01 -2.00014755e-01 -1.69798825e-02 -1.75690055e-01
-2.78758913e-01 -6.15939856e-01 8.58446836e-01 -3.30066681e-03
-5.06623447e-01 5.00135362e-01 -4.69490409e-01 6.07622676e-02
-2.35158786e-01 -2.88906783e-01 -5.95072687e-01 -9.39093769e-01
2.00021043e-02 7.37082362e-01 9.04095113e-01 -1.85044214... | [8.66832447052002, -0.7928475737571716] |
9a16504a-161c-46ca-9c22-7148ef92f95f | north-sami-dialect-identification-with-self | 2305.11864 | null | https://arxiv.org/abs/2305.11864v1 | https://arxiv.org/pdf/2305.11864v1.pdf | North Sámi Dialect Identification with Self-supervised Speech Models | The North S\'{a}mi (NS) language encapsulates four primary dialectal variants that are related but that also have differences in their phonology, morphology, and vocabulary. The unique geopolitical location of NS speakers means that in many cases they are bilingual in S\'{a}mi as well as in the dominant state language:... | ['Katri Hiovain-Asikainen', 'Sofoklis Kakouros'] | 2023-05-19 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [-4.07539815e-01 -2.12453768e-01 -3.09560120e-01 -3.52577865e-01
-4.12443042e-01 -8.49137664e-01 7.12389946e-01 -8.82975459e-02
-4.41791713e-01 4.37428594e-01 5.03979266e-01 -5.73152065e-01
-1.21732615e-01 -6.45387650e-01 -1.11060657e-01 -6.20019734e-01
4.75082397e-02 4.29917544e-01 1.58492744e-01 -9.42936301... | [14.321491241455078, 6.746435642242432] |
b921cc74-7387-4c0c-8fa4-6af46135cba3 | a-machine-learning-approach-to-doa-estimation | 2009.12858 | null | https://arxiv.org/abs/2009.12858v2 | https://arxiv.org/pdf/2009.12858v2.pdf | A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling | In this paper, we study the problem of direction of arrival estimation and model order selection for systems employing subarray sampling. Thereby, we focus on scenarios, where the number of active sources is not smaller than the number of simultaneously sampled antenna elements. For this purpose, we propose new schemes... | ['Wolfgang Utschick', 'Andreas Barthelme'] | 2020-09-27 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 2.91787028e-01 -4.09370631e-01 6.62005246e-02 -2.99604505e-01
-7.99706638e-01 -5.62510490e-01 2.50034124e-01 9.25408974e-02
-4.37478364e-01 1.02509952e+00 -2.59160876e-01 -4.63548034e-01
-7.25521147e-01 -7.27751732e-01 -2.38939315e-01 -9.46368814e-01
-4.50282633e-01 2.18495414e-01 -1.68779925e-01 6.61285594... | [6.538086414337158, 1.308972716331482] |
74730f9d-88af-4ba4-9892-81873f9c5473 | towards-explainable-in-the-wild-video-quality | 2305.12726 | null | https://arxiv.org/abs/2305.12726v1 | https://arxiv.org/pdf/2305.12726v1.pdf | Towards Explainable In-the-Wild Video Quality Assessment: a Database and a Language-Prompted Approach | The proliferation of in-the-wild videos has greatly expanded the Video Quality Assessment (VQA) problem. Unlike early definitions that usually focus on limited distortion types, VQA on in-the-wild videos is especially challenging as it could be affected by complicated factors, including various distortions and diverse ... | ['Weisi Lin', 'Qiong Yan', 'Wenxiu Sun', 'Annan Wang', 'Jingwen Hou', 'Chaofeng Chen', 'Liang Liao', 'Erli Zhang', 'HaoNing Wu'] | 2023-05-22 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [-3.11983705e-01 -6.56914353e-01 -1.74598113e-01 -5.35215974e-01
-9.06103194e-01 -9.35520470e-01 2.83631772e-01 -1.22324452e-02
-6.35386109e-02 4.27712023e-01 6.57562256e-01 1.69218909e-02
-2.12950334e-01 -6.11682951e-01 -6.36487782e-01 -4.63531494e-01
-1.43675208e-02 -2.17859551e-01 6.61078915e-02 -1.73851714... | [11.776507377624512, -1.7992511987686157] |
26c9d415-2186-4b7d-83f7-4e1fefb4742b | multi-modal-forest-optimization-algorithm | null | null | https://doi.org/10.1007/s00521-019-04113-z | https://link.springer.com/article/10.1007%2Fs00521-019-04113-z | Multi-Modal Forest Optimization Algorithm | Multi-modal optimization algorithms are one of the most challenging issues in the field of optimization. Most real-world problems have more than one solution; therefore, the potential role of multi-modal optimization algorithms is rather significant. Multi-modal problems consider several global and local optima. Theref... | ['Taymaz Rahkar-Farshi', 'Mohammad-Reza Feizi-Derakhshi', 'Mohanna Orujpour'] | 2019-03-05 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 2.34815236e-02 -5.12367666e-01 -1.36797741e-01 1.77233204e-01
-2.65521884e-01 -2.24784002e-01 1.98306352e-01 1.13073364e-01
-3.75245363e-01 7.95625508e-01 1.08406600e-02 1.09992675e-01
-6.25340402e-01 -1.23979676e+00 -1.45717055e-01 -1.40909290e+00
1.47396728e-01 7.95754075e-01 2.20020965e-01 -2.77676731... | [5.754266262054443, 3.529407501220703] |
80df35ee-a3be-4892-95d0-5479c8f5e542 | guaranteed-bounds-for-posterior-inference-in | 2204.02948 | null | https://arxiv.org/abs/2204.02948v2 | https://arxiv.org/pdf/2204.02948v2.pdf | Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming | We propose a new method to approximate the posterior distribution of probabilistic programs by means of computing guaranteed bounds. The starting point of our work is an interval-based trace semantics for a recursive, higher-order probabilistic programming language with continuous distributions. Taking the form of (sup... | ['Fabian Zaiser', 'Luke Ong', 'Raven Beutner'] | 2022-04-06 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 1.25115499e-01 2.48056531e-01 -2.06956297e-01 -3.62844259e-01
-1.00531018e+00 -8.06263089e-01 5.38227379e-01 3.91500086e-01
-8.54298323e-02 7.30470598e-01 -1.27896473e-01 -7.16850698e-01
-2.40626335e-01 -1.07038379e+00 -9.28858042e-01 -5.45927465e-01
-3.14073354e-01 9.48991060e-01 8.81981850e-01 2.73274988... | [8.379842758178711, 6.444100856781006] |
45619806-acc6-492c-84b2-86680f763763 | group-emotion-recognition-using-machine | 1905.01118 | null | https://arxiv.org/abs/1905.01118v1 | https://arxiv.org/pdf/1905.01118v1.pdf | Group Emotion Recognition Using Machine Learning | Automatic facial emotion recognition is a challenging task that has gained significant scientific interest over the past few years, but the problem of emotion recognition for a group of people has been less extensively studied. However, it is slowly gaining popularity due to the massive amount of data available on soci... | ['Samanyou Garg'] | 2019-05-03 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [-9.94410664e-02 7.95156881e-02 1.94223374e-01 -9.79479432e-01
-1.07868031e-01 -1.69116899e-01 5.10363042e-01 2.08720993e-02
-4.60040778e-01 3.57211083e-01 1.57994125e-02 4.37544465e-01
5.37722185e-02 -5.15890360e-01 -3.08208942e-01 -6.78401649e-01
-1.62310690e-01 1.93865746e-01 -2.81707831e-02 -1.01978965... | [13.509827613830566, 2.1185038089752197] |
f8eef892-9932-4752-aa0a-0dd593b4057f | contournet-taking-a-further-step-toward | 2004.04940 | null | https://arxiv.org/abs/2004.04940v1 | https://arxiv.org/pdf/2004.04940v1.pdf | ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection | Scene text detection has witnessed rapid development in recent years. However, there still exists two main challenges: 1) many methods suffer from false positives in their text representations; 2) the large scale variance of scene texts makes it hard for network to learn samples. In this paper, we propose the ContourNe... | ['Zheng-Jun Zha', 'Mengting Xing', 'Hongtao Xie', 'Yuxin Wang', 'Yongdong Zhang', 'Zilong Fu'] | 2020-04-10 | contournet-taking-a-further-step-toward-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_ContourNet_Taking_a_Further_Step_Toward_Accurate_Arbitrary-Shaped_Scene_Text_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_ContourNet_Taking_a_Further_Step_Toward_Accurate_Arbitrary-Shaped_Scene_Text_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-text-detection'] | ['computer-vision'] | [ 2.79111952e-01 -1.70843363e-01 -9.58373621e-02 -3.70976448e-01
-6.83993042e-01 -3.73071551e-01 7.29124248e-01 -1.62935495e-01
-1.16871566e-01 4.18493599e-01 1.70554847e-01 3.40280756e-02
3.42983544e-01 -8.85697544e-01 -6.27477407e-01 -8.42247009e-01
3.38297665e-01 5.95933855e-01 8.57335210e-01 -1.81279808... | [12.07292366027832, 2.26259708404541] |
61a3a4d2-e0d1-496f-815d-e138430ff6a0 | pointwise-paraphrase-appraisal-is-potentially | 2005.11996 | null | https://arxiv.org/abs/2005.11996v2 | https://arxiv.org/pdf/2005.11996v2.pdf | Pointwise Paraphrase Appraisal is Potentially Problematic | The prevailing approach for training and evaluating paraphrase identification models is constructed as a binary classification problem: the model is given a pair of sentences, and is judged by how accurately it classifies pairs as either paraphrases or non-paraphrases. This pointwise-based evaluation method does not ma... | ['Yangfeng Ji', 'David Evans', 'Hannah Chen'] | 2020-05-25 | pointwise-paraphrase-appraisal-is-potentially-1 | https://aclanthology.org/2020.acl-srw.20 | https://aclanthology.org/2020.acl-srw.20.pdf | acl-2020-6 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 3.74298602e-01 -9.35008749e-02 -1.97337210e-01 -6.65825188e-01
-8.91838372e-01 -8.60785007e-01 7.33650684e-01 5.12791932e-01
-3.39815795e-01 6.81127012e-01 2.29223534e-01 -5.21396458e-01
7.35927699e-03 -6.72450840e-01 -6.19529188e-01 -4.22536522e-01
3.39342654e-01 7.36568153e-01 1.62245110e-01 -3.82281303... | [11.323047637939453, 9.16833209991455] |
1e96424a-aaa8-4fed-9e42-66896468c28c | add-2022-the-first-audio-deep-synthesis | 2202.08433 | null | https://arxiv.org/abs/2202.08433v2 | https://arxiv.org/pdf/2202.08433v2.pdf | ADD 2022: the First Audio Deep Synthesis Detection Challenge | Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 includes three tracks: low-quality f... | ['Bin Liu', 'Zheng Lian', 'Haizhou Li', 'Zhengqi Wen', 'Le Xu', 'Xinrui Yan', 'Shuai Zhang', 'Shiming Wang', 'Shan Liang', 'Cunhang Fan', 'Ye Bai', 'Zhengkun Tian', 'Tao Wang', 'Chenglong Wang', 'Haoxin Ma', 'Shuai Nie', 'JianHua Tao', 'Ruibo Fu', 'Jiangyan Yi'] | 2022-02-17 | null | null | null | null | ['audio-generation'] | ['audio'] | [-3.99603844e-02 -1.27570421e-01 1.94348976e-01 1.54916555e-01
-1.62395549e+00 -4.99621928e-01 3.88651162e-01 -2.00126216e-01
1.80565044e-01 5.73996603e-01 6.04712665e-01 1.59708455e-01
3.74064118e-01 -1.95948347e-01 -6.09999418e-01 -5.64640403e-01
-2.02421039e-01 1.25073373e-01 5.07019937e-01 -3.43138784... | [14.153407096862793, 5.741184234619141] |
49675a62-331f-4d12-9a3e-baf25debaf70 | food-recognition-using-fusion-of-classifiers | 1709.04864 | null | http://arxiv.org/abs/1709.04864v1 | http://arxiv.org/pdf/1709.04864v1.pdf | Food Recognition using Fusion of Classifiers based on CNNs | With the arrival of convolutional neural networks, the complex problem of
food recognition has experienced an important improvement in recent years. The
best results have been obtained using methods based on very deep convolutional
neural networks, which show that the deeper the model,the better the
classification accu... | ['Marc Bolaños', 'Petia Radeva', 'Eduardo Aguilar'] | 2017-09-14 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [-1.55200273e-01 -2.94410974e-01 -2.83083439e-01 -4.69928205e-01
-1.60787582e-01 -3.24736118e-01 3.09637219e-01 6.90362692e-01
-3.18701655e-01 6.45809889e-01 3.24429244e-01 2.47477423e-02
-4.20162156e-02 -1.27283680e+00 -9.53878880e-01 -6.26628101e-01
-2.48295978e-01 3.15348879e-02 8.46939906e-03 -4.06584978... | [11.563477516174316, 4.38442850112915] |
34d444ee-7c65-4bf1-b563-0089657d2e5b | ds-1000-a-natural-and-reliable-benchmark-for | 2211.11501 | null | https://arxiv.org/abs/2211.11501v1 | https://arxiv.org/pdf/2211.11501v1.pdf | DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation | We introduce DS-1000, a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas. Compared to prior works, DS-1000 incorporates three core features. First, our problems reflect diverse, realistic, and practical use cases since we collected them from Stack... | ['Tao Yu', 'Sida Wang', 'Daniel Fried', 'Scott Wen-tau Yih', 'Luke Zettlemoyer', 'Ruiqi Zhong', 'Tianyi Zhang', 'Yiming Wang', 'Chengxi Li', 'Yuhang Lai'] | 2022-11-18 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-1.50468796e-01 -9.30276886e-02 -2.55253911e-01 -4.45475340e-01
-1.13721430e+00 -1.02729404e+00 1.66427448e-01 5.66093884e-02
-6.45187348e-02 8.04464579e-01 1.10646822e-01 -8.66954088e-01
1.60687089e-01 -8.25679779e-01 -9.67224538e-01 -1.30608037e-01
-1.56599477e-01 6.25060648e-02 4.81379509e-01 -6.55146986... | [7.798279285430908, 7.718754291534424] |
8af0cdd3-7400-46dc-a50b-164cebf49851 | algorithms-for-weighted-pushdown-automata | 2210.06884 | null | https://arxiv.org/abs/2210.06884v3 | https://arxiv.org/pdf/2210.06884v3.pdf | Algorithms for Weighted Pushdown Automata | Weighted pushdown automata (WPDAs) are at the core of many natural language processing tasks, like syntax-based statistical machine translation and transition-based dependency parsing. As most existing dynamic programming algorithms are designed for context-free grammars (CFGs), algorithms for PDAs often resort to a PD... | ['David Chiang', 'Ryan Cotterell', 'Tim Vieira', 'Brian DuSell', 'Alexandra Butoi'] | 2022-10-13 | null | null | null | null | ['dependency-parsing', 'transition-based-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.98631334e-01 1.34542659e-01 -6.77844062e-02 -1.49845704e-01
-7.67450690e-01 -7.31527209e-01 5.78015983e-01 7.08015501e-01
-6.85222924e-01 2.71851927e-01 -2.10102364e-01 -1.17706001e+00
6.54861480e-02 -1.37648737e+00 -5.51287830e-01 -3.59658748e-01
-3.44230831e-01 6.70715630e-01 9.59824860e-01 -4.67589378... | [10.360260963439941, 9.610509872436523] |
7334ab49-761b-46f1-ba52-d5ce6b9bce5d | transformer-unet-raw-image-processing-with | 2109.08417 | null | https://arxiv.org/abs/2109.08417v1 | https://arxiv.org/pdf/2109.08417v1.pdf | Transformer-Unet: Raw Image Processing with Unet | Medical image segmentation have drawn massive attention as it is important in biomedical image analysis. Good segmentation results can assist doctors with their judgement and further improve patients' experience. Among many available pipelines in medical image analysis, Unet is one of the most popular neural networks a... | ['Lei Hu', 'Xuquan Ji', 'Yonghong Zhang', 'Youyang Sha'] | 2021-09-17 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 5.38249910e-01 1.24336466e-01 -1.92219522e-02 -4.61545855e-01
-4.86111194e-01 -3.09788287e-01 2.92576730e-01 3.24897408e-01
-7.24427402e-01 3.91103894e-01 -5.15707098e-02 -3.18870217e-01
1.95538420e-02 -7.58915007e-01 -5.76379418e-01 -6.81980312e-01
1.19870171e-01 6.32728994e-01 6.31479442e-01 -2.83134338... | [14.56950855255127, -2.6020796298980713] |
a5aacd11-ff94-436b-b65a-d0848b11bd68 | everybody-is-unique-towards-unbiased-human | 2107.06239 | null | https://arxiv.org/abs/2107.06239v1 | https://arxiv.org/pdf/2107.06239v1.pdf | Everybody Is Unique: Towards Unbiased Human Mesh Recovery | We consider the problem of obese human mesh recovery, i.e., fitting a parametric human mesh to images of obese people. Despite obese person mesh fitting being an important problem with numerous applications (e.g., healthcare), much recent progress in mesh recovery has been restricted to images of non-obese people. In t... | ['Ziyan Wu', 'Terrence Chen', 'Srikrishna Karanam', 'Meng Zheng', 'Ren Li'] | 2021-07-13 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [ 3.39024693e-01 1.79517761e-01 -1.09496824e-01 -1.98987409e-01
-8.06666315e-01 -1.57794997e-01 2.52739906e-01 1.07238255e-01
-2.60832936e-01 5.36109626e-01 6.31614998e-02 1.67617425e-01
3.40406559e-02 -7.08207190e-01 -7.15693831e-01 -2.66805410e-01
5.92126511e-02 9.85679030e-01 3.10027450e-01 -4.30577725... | [7.08579683303833, -1.1219227313995361] |
9a189336-cc40-415d-b7bc-6b9caf6189bf | all-about-structure-adapting-structural | 1903.12212 | null | http://arxiv.org/abs/1903.12212v1 | http://arxiv.org/pdf/1903.12212v1.pdf | All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation | In this paper we tackle the problem of unsupervised domain adaptation for the
task of semantic segmentation, where we attempt to transfer the knowledge
learned upon synthetic datasets with ground-truth labels to real-world images
without any annotation. With the hypothesis that the structural content of
images is the m... | ['Wei-Chen Chiu', 'Wen-Hsiao Peng', 'Hui-Po Wang', 'Wei-Lun Chang'] | 2019-03-26 | all-about-structure-adapting-structural-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Chang_All_About_Structure_Adapting_Structural_Information_Across_Domains_for_Boosting_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_All_About_Structure_Adapting_Structural_Information_Across_Domains_for_Boosting_CVPR_2019_paper.pdf | cvpr-2019-6 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 7.38174677e-01 1.26615763e-01 -3.74845237e-01 -5.67609072e-01
-7.12746561e-01 -7.49917090e-01 5.84747195e-01 -1.91316009e-01
-3.77932131e-01 7.23745763e-01 -1.61766320e-01 -5.81668317e-02
4.78452221e-02 -7.71094501e-01 -8.75880241e-01 -7.40613401e-01
4.54541177e-01 7.44684577e-01 4.46730942e-01 -5.16802520... | [9.745046615600586, 1.1554534435272217] |
8c8409e0-8e75-4b6b-8905-65f4e0627965 | bof-ucb-a-bayesian-optimistic-frequentist | 2307.03587 | null | https://arxiv.org/abs/2307.03587v1 | https://arxiv.org/pdf/2307.03587v1.pdf | BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits | We propose a novel Bayesian-Optimistic Frequentist Upper Confidence Bound (BOF-UCB) algorithm for stochastic contextual linear bandits in non-stationary environments. This unique combination of Bayesian and frequentist principles enhances adaptability and performance in dynamic settings. The BOF-UCB algorithm utilizes ... | ['Melih Kandemir', 'Abdullah Akgül', 'Nicklas Werge'] | 2023-07-07 | null | null | null | null | ['multi-armed-bandits', 'decision-making'] | ['miscellaneous', 'reasoning'] | [-1.72266439e-02 1.47931064e-02 -1.00352848e+00 -1.53806776e-01
-9.42225575e-01 -3.60463679e-01 4.82427925e-01 1.13267034e-01
-5.35422325e-01 1.56557441e+00 -6.77736178e-02 -7.90823042e-01
-6.58898592e-01 -5.81167519e-01 -9.29648221e-01 -7.33124077e-01
-4.68627572e-01 7.46243417e-01 2.62468189e-01 1.90799534... | [4.500918388366699, 3.196922540664673] |
2139081a-bf36-455b-85be-9e8784d52276 | converting-the-point-of-view-of-messages | 2010.02600 | null | https://arxiv.org/abs/2010.02600v2 | https://arxiv.org/pdf/2010.02600v2.pdf | Converting the Point of View of Messages Spoken to Virtual Assistants | Virtual Assistants can be quite literal at times. If the user says "tell Bob I love him," most virtual assistants will extract the message "I love him" and send it to the user's contact named Bob, rather than properly converting the message to "I love you." We designed a system to allow virtual assistants to take a voi... | ['Jack G. M. FitzGerald', 'Dennis Liang', 'Sai Srujana Buddi', 'Vera Zu', 'Isabelle G. Lee'] | 2020-10-06 | null | https://aclanthology.org/2020.findings-emnlp.15 | https://aclanthology.org/2020.findings-emnlp.15.pdf | findings-of-the-association-for-computational | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.02167241e-01 4.25796181e-01 -9.67185199e-02 -6.86458349e-01
-1.05410397e+00 -8.16937149e-01 8.81558120e-01 -1.62254229e-01
-5.84639907e-01 7.90049434e-01 5.53241313e-01 -8.04318547e-01
4.92098600e-01 -6.94406748e-01 -5.13531804e-01 -1.68148503e-01
5.73495448e-01 8.24536204e-01 -2.66469926e-01 -5.00652015... | [14.374934196472168, 7.243810653686523] |
ee8c0be8-92ce-4516-8dc1-4d8d722884c7 | lexglue-a-benchmark-dataset-for-legal | 2110.00976 | null | https://arxiv.org/abs/2110.00976v4 | https://arxiv.org/pdf/2110.00976v4.pdf | LexGLUE: A Benchmark Dataset for Legal Language Understanding in English | Laws and their interpretations, legal arguments and agreements\ are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) te... | ['Nikolaos Aletras', 'Daniel Martin Katz', 'Ion Androutsopoulos', 'Michael Bommarito', 'Dirk Hartung', 'Abhik Jana', 'Ilias Chalkidis'] | 2021-10-03 | null | https://aclanthology.org/2022.acl-long.297 | https://aclanthology.org/2022.acl-long.297.pdf | acl-2022-5 | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 1.54914588e-01 2.09068358e-01 -8.68510425e-01 -5.39669037e-01
-1.28733325e+00 -9.04532909e-01 8.76323164e-01 2.65449524e-01
-3.12624127e-01 9.00699079e-01 5.98386049e-01 -8.46730649e-01
-2.96637118e-01 -4.00263846e-01 -5.77057660e-01 3.84467803e-02
2.13619247e-01 7.34819829e-01 7.55284503e-02 -4.45744544... | [9.933937072753906, 9.232254981994629] |
41621940-5205-4835-a2cf-e9553de540bd | bi-stride-multi-scale-graph-neural-network | 2210.02573 | null | https://arxiv.org/abs/2210.02573v4 | https://arxiv.org/pdf/2210.02573v4.pdf | Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN | Learning the physical simulation on large-scale meshes with flat Graph Neural Networks (GNNs) and stacking Message Passings (MPs) is challenging due to the scaling complexity w.r.t. the number of nodes and over-smoothing. There has been growing interest in the community to introduce \textit{multi-scale} structures to G... | ['Chenfanfu Jiang', 'Minchen Li', 'Menglei Chai', 'Yadi Cao'] | 2022-10-05 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-4.75840531e-02 6.90067792e-03 2.55491287e-01 2.29973018e-01
-4.89652038e-01 -2.50975102e-01 2.47769982e-01 3.54538441e-01
-4.32703495e-01 1.07346249e+00 -3.52110744e-01 -3.29685867e-01
-5.30668020e-01 -1.32558715e+00 -1.07510328e+00 -6.84226692e-01
-5.00344753e-01 2.74954319e-01 7.19047546e-01 -2.52451181... | [6.3353986740112305, 3.3200008869171143] |
1c9130e9-c6d3-4629-90d8-1111bcd57966 | celltrack-r-cnn-a-novel-end-to-end-deep | 2102.10377 | null | https://arxiv.org/abs/2102.10377v1 | https://arxiv.org/pdf/2102.10377v1.pdf | CellTrack R-CNN: A Novel End-To-End Deep Neural Network for Cell Segmentation and Tracking in Microscopy Images | Cell segmentation and tracking in microscopy images are of great significance to new discoveries in biology and medicine. In this study, we propose a novel approach to combine cell segmentation and cell tracking into a unified end-to-end deep learning based framework, where cell detection and segmentation are performed... | ['Weidong Cai', 'Wojciech Chrzanowski', "Lauren O'Donnell", 'Fan Zhang', 'Chaoyi Zhang', 'Yang song', 'Yuqian Chen'] | 2021-02-20 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-1.29582405e-01 -3.54392439e-01 1.43634006e-01 -3.14981528e-02
-5.68694711e-01 -6.60777271e-01 3.66374373e-01 5.19692957e-01
-1.09461796e+00 8.25884521e-01 -3.68030846e-01 -1.51161179e-01
3.73394668e-01 -5.13867736e-01 -6.05842054e-01 -9.37578738e-01
1.19930923e-01 6.39933288e-01 7.27964699e-01 2.42279306... | [14.604710578918457, -3.2330551147460938] |
a869c912-c114-4c7b-9529-993aed1622be | spp-net-deep-absolute-pose-regression-with | 1712.03452 | null | http://arxiv.org/abs/1712.03452v1 | http://arxiv.org/pdf/1712.03452v1.pdf | SPP-Net: Deep Absolute Pose Regression with Synthetic Views | Image based localization is one of the important problems in computer vision
due to its wide applicability in robotics, augmented reality, and autonomous
systems. There is a rich set of methods described in the literature how to
geometrically register a 2D image w.r.t.\ a 3D model. Recently, methods based
on deep (and ... | ['Christopher Zach', 'Pulak Purkait', 'Cheng Zhao'] | 2017-12-09 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [-1.28981739e-01 -1.28336787e-01 -1.95709616e-01 -4.16944206e-01
-6.00909352e-01 -1.49202764e-01 4.09635305e-01 -1.00808382e-01
-4.73068506e-01 4.88039583e-01 -2.73587015e-02 1.99890420e-01
-1.99273184e-01 -8.06433678e-01 -8.43515694e-01 -4.93374139e-01
-2.66268048e-02 4.74499524e-01 9.01225433e-02 -3.27421308... | [7.794301986694336, -2.0841474533081055] |
2b1c7e7f-f002-4f0c-ab7f-4253f4ba2049 | hospital-length-of-stay-prediction-based-on | 2303.09817 | null | https://arxiv.org/abs/2303.09817v1 | https://arxiv.org/pdf/2303.09817v1.pdf | Hospital Length of Stay Prediction Based on Multi-modal Data towards Trustworthy Human-AI Collaboration in Radiomics | To what extent can the patient's length of stay in a hospital be predicted using only an X-ray image? We answer this question by comparing the performance of machine learning survival models on a novel multi-modal dataset created from 1235 images with textual radiology reports annotated by humans. Although black-box mo... | ['Przemysław Biecek', 'Patryk Szatkowski', 'Przemysław Bombiński', 'Bartlomiej Sobieski', 'Hubert Baniecki'] | 2023-03-17 | null | null | null | null | ['length-of-stay-prediction'] | ['medical'] | [ 1.12073310e-02 7.78336763e-01 -4.87603813e-01 -7.59893417e-01
-1.12951994e+00 -4.26882237e-01 2.82488525e-01 8.10443878e-01
-3.41892719e-01 7.81474292e-01 5.05475581e-01 -9.76449490e-01
-4.21068192e-01 -5.07313013e-01 -4.88123626e-01 -5.36629558e-01
-1.91229954e-01 8.51496160e-01 -8.29684064e-02 1.78669408... | [8.460079193115234, 5.721365451812744] |
2d6e6b66-01ae-4b48-b3f4-69c35749eb9c | conversational-answer-generation-and | 2103.06500 | null | https://arxiv.org/abs/2103.06500v1 | https://arxiv.org/pdf/2103.06500v1.pdf | Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering | Question answering (QA) is an important use case on voice assistants. A popular approach to QA is extractive reading comprehension (RC) which finds an answer span in a text passage. However, extractive answers are often unnatural in a conversational context which results in suboptimal user experience. In this work, we ... | ['Vikas Bhardwaj', 'Hakan Inan', 'Debojeet Chatterjee', 'Barlas Oguz', 'Stan Peshterliev'] | 2021-03-11 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 2.42058218e-01 7.14102745e-01 4.32853669e-01 -2.46897489e-01
-1.63547981e+00 -8.17931294e-01 7.78045416e-01 2.49978542e-01
-1.46526456e-01 1.16094959e+00 1.06058574e+00 -3.94255966e-01
-1.20200925e-01 -5.90012968e-01 -3.60737473e-01 4.06958647e-02
4.32624429e-01 9.51434314e-01 6.94270134e-02 -8.62702906... | [11.798077583312988, 8.09407901763916] |
e6e0c3ce-db19-4992-a472-bac3e250f4d4 | a-novel-disparity-transformation-algorithm | 1808.02837 | null | http://arxiv.org/abs/1808.02837v1 | http://arxiv.org/pdf/1808.02837v1.pdf | A Novel Disparity Transformation Algorithm for Road Segmentation | The disparity information provided by stereo cameras has enabled advanced
driver assistance systems to estimate road area more accurately and
effectively. In this paper, a novel disparity transformation algorithm is
proposed to extract road areas from dense disparity maps by making the
disparity value of the road pixel... | ['Mohammud Junaid Bocus', 'Naim Dahnoun', 'Rui Fan'] | 2018-08-08 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 3.12054157e-01 -4.73928563e-02 -2.89187372e-01 -5.08041441e-01
-5.03622890e-02 -6.25888780e-02 4.67632085e-01 -8.96714106e-02
-6.26928031e-01 8.91353607e-01 -3.92877050e-02 -4.95164573e-01
2.21260846e-01 -1.06064463e+00 -3.92976552e-01 -6.47508681e-01
6.77089453e-01 1.04100453e-02 5.26748240e-01 4.11003120... | [8.85372543334961, -2.3170382976531982] |
7916a54f-2ff7-4c20-afb5-333b53384beb | revisiting-self-supervised-visual | 1901.09005 | null | http://arxiv.org/abs/1901.09005v1 | http://arxiv.org/pdf/1901.09005v1.pdf | Revisiting Self-Supervised Visual Representation Learning | Unsupervised visual representation learning remains a largely unsolved
problem in computer vision research. Among a big body of recently proposed
approaches for unsupervised learning of visual representations, a class of
self-supervised techniques achieves superior performance on many challenging
benchmarks. A large nu... | ['Alexander Kolesnikov', 'Lucas Beyer', 'Xiaohua Zhai'] | 2019-01-25 | revisiting-self-supervised-visual-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kolesnikov_Revisiting_Self-Supervised_Visual_Representation_Learning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kolesnikov_Revisiting_Self-Supervised_Visual_Representation_Learning_CVPR_2019_paper.pdf | cvpr-2019-6 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 5.81471145e-01 5.71093597e-02 -6.52225733e-01 -3.67248714e-01
-3.18800837e-01 -6.29342318e-01 8.59043002e-01 1.91570491e-01
-2.37407878e-01 5.18784523e-01 3.64479303e-01 -2.14705184e-01
-1.71803877e-01 -4.24952239e-01 -6.43988431e-01 -7.08208025e-01
1.86886583e-02 2.14061588e-01 9.87901911e-02 -2.51161039... | [9.50986385345459, 2.412519693374634] |
6c373075-6c2d-4fd8-8ffa-b633e58b7141 | latent-odes-for-irregularly-sampled-time | 1907.03907 | null | https://arxiv.org/abs/1907.03907v1 | https://arxiv.org/pdf/1907.03907v1.pdf | Latent ODEs for Irregularly-Sampled Time Series | Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs). We generalize RNNs to have continuous-time hidden dynamics defined by ordinary differential equations (ODEs), a model we call ODE-RNNs. Furthermore, we use ODE-RNNs to replace t... | ['Yulia Rubanova', 'David Duvenaud', 'Ricky T. Q. Chen'] | 2019-07-08 | null | null | null | null | ['multivariate-time-series-imputation'] | ['time-series'] | [-1.11887738e-01 -5.23636006e-02 -1.36900619e-01 9.26598981e-02
-1.03937939e-01 -3.37998837e-01 7.52923548e-01 -4.41464692e-01
-2.10607916e-01 8.16275716e-01 1.59479275e-01 -5.05285442e-01
-6.13241270e-02 -7.67107427e-01 -4.00281847e-01 -8.15889597e-01
-3.59165549e-01 6.34566844e-01 -1.32920578e-01 -1.19037740... | [6.978858947753906, 3.3885955810546875] |
63f76444-a722-42fd-b7dc-8c085daa7a58 | global-counterfactual-explainer-for-graph | 2210.11695 | null | https://arxiv.org/abs/2210.11695v2 | https://arxiv.org/pdf/2210.11695v2.pdf | Global Counterfactual Explainer for Graph Neural Networks | Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box machine learning models. One way to address this is counterfactu... | ['Ambuj Singh', 'Sayan Ranu', 'Sourav Medya', 'Zexi Huang', 'Mert Kosan'] | 2022-10-21 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.13692981e-01 1.10179484e+00 -5.88705719e-01 -1.63601398e-01
-1.46849761e-02 -1.74519807e-01 6.41501844e-01 2.42951527e-01
9.57509503e-02 1.18684435e+00 1.97625056e-01 -9.17474091e-01
-7.26771057e-01 -1.25222909e+00 -1.08074105e+00 -3.77092153e-01
-1.51073679e-01 5.56871057e-01 -1.42084092e-01 -3.93759429... | [8.430273056030273, 5.8806610107421875] |
7d11aad1-ff6e-4ad8-b3bd-7a1ee32ed74b | native-language-identification-on-text-and | 1707.07182 | null | http://arxiv.org/abs/1707.07182v1 | http://arxiv.org/pdf/1707.07182v1.pdf | Native Language Identification on Text and Speech | This paper presents an ensemble system combining the output of multiple SVM
classifiers to native language identification (NLI). The system was submitted
to the NLI Shared Task 2017 fusion track which featured students essays and
spoken responses in form of audio transcriptions and iVectors by non-native
English speake... | ['Marcos Zampieri', 'Alina Maria Ciobanu', 'Liviu P. Dinu'] | 2017-07-22 | native-language-identification-on-text-and-1 | https://aclanthology.org/W17-5045 | https://aclanthology.org/W17-5045.pdf | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [ 1.88530520e-01 -1.16048142e-01 -4.98284727e-01 -4.98006642e-01
-1.15565217e+00 -1.21355891e+00 6.44527316e-01 3.12242270e-01
-6.19688570e-01 7.16497660e-01 2.31038392e-01 -6.59077048e-01
9.50135663e-02 -1.88993052e-01 -3.64654392e-01 -4.68827076e-02
6.05361342e-01 5.74133575e-01 -2.24862099e-01 -1.25984743... | [10.41236400604248, 10.512048721313477] |
e9a4d7b0-1e07-400c-b4ad-ea7f76cfa498 | on-the-surprising-effectiveness-of | 2209.07474 | null | https://arxiv.org/abs/2209.07474v3 | https://arxiv.org/pdf/2209.07474v3.pdf | On the Surprising Effectiveness of Transformers in Low-Labeled Video Recognition | Recently vision transformers have been shown to be competitive with convolution-based methods (CNNs) broadly across multiple vision tasks. The less restrictive inductive bias of transformers endows greater representational capacity in comparison with CNNs. However, in the image classification setting this flexibility c... | ['Zsolt Kira', 'Ömer Mubarek', 'Farrukh Rahman'] | 2022-09-15 | null | null | null | null | ['video-classification', 'video-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.72571236e-01 1.21086404e-01 -2.56292433e-01 -3.42388988e-01
-5.18626511e-01 -7.57041216e-01 9.06679749e-01 -5.16422391e-01
-6.67690814e-01 4.33044165e-01 3.82832557e-01 -5.45414388e-01
-1.54115455e-02 -5.57475507e-01 -8.17688048e-01 -5.97898543e-01
-2.71623749e-02 1.93161577e-01 2.41623923e-01 -2.97690406... | [9.342842102050781, 1.3416926860809326] |
65ee3759-b648-435c-9676-0476f8fe8c9e | toward-a-universal-model-for-shape-from | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Verbin_Toward_a_Universal_Model_for_Shape_From_Texture_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Verbin_Toward_a_Universal_Model_for_Shape_From_Texture_CVPR_2020_paper.pdf | Toward a Universal Model for Shape From Texture | We consider the shape from texture problem, where the input is a single image of a curved, textured surface, and the texture and shape are both a priori unknown. We formulate this task as a three-player game between a shape process, a texture process, and a discriminator. The discriminator adapts a set of non-linear fi... | [' Todd Zickler', 'Dor Verbin'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['shape-from-texture'] | ['computer-vision'] | [ 7.52833366e-01 6.79533556e-02 6.26260221e-01 -1.16835594e-01
-6.94158673e-01 -8.21561635e-01 6.77930653e-01 -2.73890585e-01
-1.01395240e-02 3.53113741e-01 -2.69875705e-01 -9.39996168e-02
1.54266477e-01 -9.42283094e-01 -6.08615220e-01 -1.23724973e+00
2.26643175e-01 8.31127107e-01 5.39682508e-01 -2.23471880... | [9.798440933227539, -2.960512161254883] |
65155cc1-df8f-467a-92cc-d61a98ce5a21 | a-cognition-based-attention-model-for | null | null | https://aclanthology.org/D17-1048 | https://aclanthology.org/D17-1048.pdf | A Cognition Based Attention Model for Sentiment Analysis | Attention models are proposed in sentiment analysis because some words are more important than others. However,most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading... | ['Chu-Ren Huang', 'Minglei Li', 'Rong Xiang', 'Yunfei Long', 'Qin Lu'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['product-recommendation'] | ['miscellaneous'] | [ 7.83711523e-02 -5.20476475e-02 -1.72042295e-01 -7.89230585e-01
-4.35327262e-01 -1.74606219e-01 6.35320902e-01 5.68700016e-01
-6.35448635e-01 3.99395198e-01 7.05485523e-01 -2.73524612e-01
1.13027066e-01 -6.16923332e-01 -4.91039485e-01 -4.10185575e-01
6.06853187e-01 8.99314582e-02 3.26555341e-01 -4.82548356... | [11.437372207641602, 6.650262355804443] |
8ab5e246-1597-4b88-854e-1bd6d2f2edf1 | near-optimal-multi-agent-learning-for-safe | 2210.06380 | null | https://arxiv.org/abs/2210.06380v1 | https://arxiv.org/pdf/2210.06380v1.pdf | Near-Optimal Multi-Agent Learning for Safe Coverage Control | In multi-agent coverage control problems, agents navigate their environment to reach locations that maximize the coverage of some density. In practice, the density is rarely known $\textit{a priori}$, further complicating the original NP-hard problem. Moreover, in many applications, agents cannot visit arbitrary locati... | ['Andreas Krause', 'Melanie N. Zeilinger', 'Matteo Turchetta', 'Manish Prajapat'] | 2022-10-12 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.63480306e-01 7.17252493e-01 -5.82770944e-01 1.45807475e-01
-1.02541375e+00 -9.68342483e-01 5.37249818e-02 5.31487525e-01
-5.76041400e-01 1.42500103e+00 -1.73423082e-01 -2.66057789e-01
-5.04093826e-01 -1.06871951e+00 -1.12724555e+00 -1.04099560e+00
-6.20096147e-01 7.92524517e-01 -7.94464201e-02 -5.46611100... | [4.4004974365234375, 2.8118948936462402] |
cb6d902c-7765-42ed-be3d-56bacef34ef1 | learning-a-self-supervised-tone-mapping | 2110.09866 | null | https://arxiv.org/abs/2110.09866v1 | https://arxiv.org/pdf/2110.09866v1.pdf | Learning a self-supervised tone mapping operator via feature contrast masking loss | High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies. Nevertheless, the dynamic range of common display devices is still limited, therefore tone mapping (TM) remains a key challenge for image visualization. Recent work has demonstrated that neural networks can ach... | ['Ana Serrano', 'Karol Myszkowski', 'Hans-Peter Seidel', 'Bin Chen', 'Chao Wang'] | 2021-10-19 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 3.93684298e-01 -4.32285428e-01 -1.11161023e-01 -2.83326477e-01
-5.17738402e-01 -4.60871398e-01 5.92050016e-01 1.04332112e-01
-5.38324594e-01 6.38944924e-01 -2.10909247e-01 -3.21884789e-02
-2.43380666e-01 -7.62585163e-01 -6.25904918e-01 -7.12195814e-01
-1.69556528e-01 -5.84612601e-03 5.75516880e-01 -4.77461040... | [11.321791648864746, -2.0205507278442383] |
9f200856-c6f0-4af2-aefe-1faf70fa6e04 | perspective-reconstruction-of-human-faces-by | 2208.07142 | null | https://arxiv.org/abs/2208.07142v1 | https://arxiv.org/pdf/2208.07142v1.pdf | Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression | Even though 3D face reconstruction has achieved impressive progress, most orthogonal projection-based face reconstruction methods can not achieve accurate and consistent reconstruction results when the face is very close to the camera due to the distortion under the perspective projection. In this paper, we propose to ... | ['Jiankang Deng', 'Alexandros Lattas', 'Jinke Yu', 'Jia Guo'] | 2022-08-15 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-2.08283458e-02 2.62424767e-01 -4.27435264e-02 -4.48207051e-01
-5.03950238e-01 -5.25020123e-01 4.24512148e-01 -9.90767717e-01
2.80875087e-01 2.64607549e-01 1.73270091e-01 3.28609860e-03
2.44602084e-01 -4.27124918e-01 -6.62819743e-01 -3.22134674e-01
1.09789968e-01 9.10737634e-01 -3.62711519e-01 1.57964323... | [13.17502212524414, 0.020670443773269653] |
cb8d1482-a9e2-4beb-ad3d-5e9dbb22bc2a | better-datastore-better-translation | 2212.08822 | null | https://arxiv.org/abs/2212.08822v1 | https://arxiv.org/pdf/2212.08822v1.pdf | Better Datastore, Better Translation: Generating Datastores from Pre-Trained Models for Nearest Neural Machine Translation | Nearest Neighbor Machine Translation (kNNMT) is a simple and effective method of augmenting neural machine translation (NMT) with a token-level nearest neighbor retrieval mechanism. The effectiveness of kNNMT directly depends on the quality of retrieved neighbors. However, original kNNMT builds datastores based on repr... | ['ShuJian Huang', 'Mingxuan Wang', 'Zewei Sun', 'Shanbo Cheng', 'Jiahuan Li'] | 2022-12-17 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 1.09648444e-01 -1.94716215e-01 -7.22473323e-01 -2.55751610e-01
-1.36604261e+00 -7.15017200e-01 7.88965404e-01 -1.07143089e-01
-6.36493027e-01 8.98826540e-01 6.29615366e-01 -6.75371230e-01
9.03987437e-02 -7.52709091e-01 -1.00441909e+00 -2.30735973e-01
3.06759089e-01 8.06900799e-01 -3.06093335e-01 -7.81428993... | [11.67357349395752, 10.198083877563477] |
2c8310ae-9aed-452a-b9c4-39ee149ffe09 | optimizing-kernel-target-alignment-for-cloud | 2306.14515 | null | https://arxiv.org/abs/2306.14515v1 | https://arxiv.org/pdf/2306.14515v1.pdf | Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images | The optimization of Kernel-Target Alignment (TA) has been recently proposed as a way to reduce the number of hardware resources in quantum classifiers. It allows to exchange highly expressive and costly circuits to moderate size, task oriented ones. In this work we propose a simple toy model to study the optimization l... | ['Jakub Nalepa', 'Bertrand Le Saux', 'Bartosz Grabowski', 'Grzegorz Czelusta', 'Filip Szczepanek', 'Jakub Mielczarek', 'Artur Miroszewski'] | 2023-06-26 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 5.04697382e-01 -2.16772467e-01 -2.69997269e-01 -3.10999900e-01
-6.64037228e-01 -4.87408459e-01 4.43752766e-01 4.75629479e-01
-5.13263643e-01 6.48219287e-01 -5.15949190e-01 -4.29626554e-01
-3.63155633e-01 -9.03970480e-01 -7.06757009e-01 -1.01644981e+00
-7.43697658e-02 2.32172534e-01 1.13137908e-01 -3.42669547... | [5.628072738647461, 4.943429946899414] |
3c6e36a0-bb55-40ad-9110-2e8d60f8c6ba | segmental-spatiotemporal-cnns-for-fine | 1602.02995 | null | http://arxiv.org/abs/1602.02995v4 | http://arxiv.org/pdf/1602.02995v4.pdf | Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation | Joint segmentation and classification of fine-grained actions is important
for applications of human-robot interaction, video surveillance, and human
skill evaluation. However, despite substantial recent progress in large-scale
action classification, the performance of state-of-the-art fine-grained action
recognition a... | ['Austin Reiter', 'Colin Lea', 'Rene Vidal', 'Gregory D. Hager'] | 2016-02-09 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 4.24298316e-01 -2.65189528e-01 -4.90570366e-01 -4.76911098e-01
-7.49516308e-01 -5.09207845e-01 7.73032844e-01 2.06991687e-01
-5.38048089e-01 4.60075617e-01 5.14797390e-01 -6.95391670e-02
7.16327950e-02 -5.15040696e-01 -8.51212323e-01 -5.04666686e-01
-3.03662151e-01 3.20503354e-01 7.88200140e-01 -2.28410698... | [8.221253395080566, 0.5229828953742981] |
93368f36-d583-416b-adb2-cfed99a88ef4 | robust-object-detection-under-occlusion-with | 2005.11643 | null | https://arxiv.org/abs/2005.11643v2 | https://arxiv.org/pdf/2005.11643v2.pdf | Robust Object Detection under Occlusion with Context-Aware CompositionalNets | Detecting partially occluded objects is a difficult task. Our experimental results show that deep learning approaches, such as Faster R-CNN, are not robust at object detection under occlusion. Compositional convolutional neural networks (CompositionalNets) have been shown to be robust at classifying occluded objects by... | ['Yihong Sun', 'Adam Kortylewski', 'Angtian Wang', 'Alan Yuille'] | 2020-05-24 | robust-object-detection-under-occlusion-with-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Robust_Object_Detection_Under_Occlusion_With_Context-Aware_CompositionalNets_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Robust_Object_Detection_Under_Occlusion_With_Context-Aware_CompositionalNets_CVPR_2020_paper.pdf | cvpr-2020-6 | ['robust-object-detection'] | ['computer-vision'] | [ 1.28596708e-01 2.65446454e-01 -7.29930848e-02 -4.04960841e-01
-3.54610473e-01 -6.55690134e-01 5.61071992e-01 -9.07607675e-02
-4.93735790e-01 3.85599345e-01 -1.19563602e-01 -3.64328116e-01
5.15259087e-01 -6.87961698e-01 -1.00248599e+00 -5.66622078e-01
3.52618955e-02 3.67872357e-01 8.90588701e-01 -7.53443968... | [9.250589370727539, 0.6244004368782043] |
b437ac16-0a81-41fd-bcd4-86a90243d1fd | autotsg-learning-and-synthesis-for-incident | 2205.13457 | null | https://arxiv.org/abs/2205.13457v1 | https://arxiv.org/pdf/2205.13457v1.pdf | AutoTSG: Learning and Synthesis for Incident Troubleshooting | Incident management is a key aspect of operating large-scale cloud services. To aid with faster and efficient resolution of incidents, engineering teams document frequent troubleshooting steps in the form of Troubleshooting Guides (TSGs), to be used by on-call engineers (OCEs). However, TSGs are siloed, unstructured, a... | ['Anurag Gupta', 'Arjun Radhakrishna', 'Sai Pramod Upadhyayula', 'Chetan Bansal', 'Manish Shetty'] | 2022-05-26 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-1.46548152e-01 -2.32782945e-01 1.72915637e-01 -3.49443436e-01
-7.83083558e-01 -5.63494742e-01 -1.58518061e-01 5.89429319e-01
-8.35294649e-02 4.21783984e-01 1.82147503e-01 -9.86505330e-01
-3.17907959e-01 -7.40500391e-01 -4.16817605e-01 4.48167436e-02
8.96545574e-02 2.83186674e-01 3.79666500e-02 -1.12632640... | [7.9870524406433105, 7.01692533493042] |
3427e0ca-ce45-41c0-9cd5-e036153b5ff2 | automatic-trade-off-adaptation-in-offline-rl | 2306.09744 | null | https://arxiv.org/abs/2306.09744v1 | https://arxiv.org/pdf/2306.09744v1.pdf | Automatic Trade-off Adaptation in Offline RL | Recently, offline RL algorithms have been proposed that remain adaptive at runtime. For example, the LION algorithm \cite{lion} provides the user with an interface to set the trade-off between behavior cloning and optimality w.r.t. the estimated return at runtime. Experts can then use this interface to adapt the policy... | ['Thomas Runkler', 'Steffen Udluft', 'Phillip Swazinna'] | 2023-06-16 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.87811577e-01 1.85814038e-01 -4.69890207e-01 -3.68251711e-01
-5.60846984e-01 -1.06785858e+00 1.53082564e-01 1.66315764e-01
-7.99107134e-01 6.35819972e-01 -2.58398443e-01 -5.65789998e-01
-5.81629634e-01 -7.57300735e-01 -2.28999242e-01 -5.81371903e-01
-1.66308418e-01 5.39126158e-01 2.16892809e-01 -1.20643690... | [4.110484600067139, 2.322951078414917] |
978dce74-e8ee-4d34-8bb8-d2d726f5f455 | improving-factuality-of-abstractive | 2305.14981 | null | https://arxiv.org/abs/2305.14981v1 | https://arxiv.org/pdf/2305.14981v1.pdf | Improving Factuality of Abstractive Summarization without Sacrificing Summary Quality | Improving factual consistency of abstractive summarization has been a widely studied topic. However, most of the prior works on training factuality-aware models have ignored the negative effect it has on summary quality. We propose EFACTSUM (i.e., Effective Factual Summarization), a candidate summary generation and ran... | ['Muhao Chen', 'Fei Wang', 'Tanay Dixit'] | 2023-05-24 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 1.99638575e-01 4.21066940e-01 -5.72780669e-01 -3.58006269e-01
-1.21173513e+00 -5.98221362e-01 1.00889909e+00 6.75285280e-01
-3.78783196e-01 1.12658930e+00 8.99997652e-01 -6.62833229e-02
-1.65826201e-01 -6.76726937e-01 -8.23290050e-01 -1.37920201e-01
1.77856222e-01 -4.25380170e-02 1.71710998e-01 -3.12281758... | [12.364200592041016, 9.372175216674805] |
3ade9224-9d55-4a64-bf8b-cee6eaae5cf0 | challenges-in-the-knowledge-base-population | null | null | https://aclanthology.org/L12-1104 | https://aclanthology.org/L12-1104.pdf | Challenges in the Knowledge Base Population Slot Filling Task | The Knowledge Based Population (KBP) evaluation track of the Text Analysis Conferences (TAC) has been held for the past 3 years. One of the two tasks of KBP is slot filling: finding within a large corpus the values of a set of attributes of given people and organizations. This task has proven very challenging, with top... | ['Ralph Grishman', 'Bonan Min'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['knowledge-base-population'] | ['natural-language-processing'] | [ 6.33229911e-02 6.00053847e-01 -1.47941768e-01 -3.68747264e-01
-9.46177542e-01 -6.39450669e-01 5.76956511e-01 6.91819608e-01
-7.59323537e-01 1.34185779e+00 4.27923083e-01 -2.66017258e-01
-5.95983446e-01 -6.02390528e-01 -4.89586473e-01 -1.72289893e-01
3.89287949e-01 1.20765996e+00 4.90425110e-01 -2.39698797... | [9.34967041015625, 9.168758392333984] |
e2d0a22a-015e-4587-8c8d-42ab56ea8f29 | building-a-public-domain-voice-database-for | null | null | https://doi.org/10.1145/3487553.3524931 | https://wikiworkshop.org/2022/papers/WikiWorkshop2022_paper_13.pdf | Building a Public Domain Voice Database for Odia | Projects like Mozilla Common Voice were born to address the challenges of unavailability of voice data or the high cost of available data for use in speech technology such as Automatic Speech Recognition (ASR) research and application development. The pilot detailed in this paper is about creating a large freely-licens... | ['Subhashish Panigrahi'] | 2022-08-16 | null | null | null | www-22-companion-proceedings-of-the-web | ['automatic-speech-recognition'] | ['speech'] | [-2.71858931e-01 2.08549440e-01 2.58287638e-01 -2.01199517e-01
-1.17381823e+00 -7.37649322e-01 4.65837121e-01 -8.15438852e-03
-5.53939223e-01 6.06808662e-01 7.02458680e-01 -8.40117574e-01
-9.49924663e-02 -2.99007386e-01 -1.17838368e-01 -4.51076299e-01
2.23873660e-01 6.28188133e-01 -2.88032647e-02 -4.66353476... | [14.245469093322754, 6.987270355224609] |
eee0135b-e983-45cd-87d3-8443eed4bd5b | a-video-based-end-to-end-pipeline-for-non | 2303.16867 | null | https://arxiv.org/abs/2303.16867v1 | https://arxiv.org/pdf/2303.16867v1.pdf | A Video-based End-to-end Pipeline for Non-nutritive Sucking Action Recognition and Segmentation in Young Infants | We present an end-to-end computer vision pipeline to detect non-nutritive sucking (NNS) -- an infant sucking pattern with no nutrition delivered -- as a potential biomarker for developmental delays, using off-the-shelf baby monitor video footage. One barrier to clinical (or algorithmic) assessment of NNS stems from its... | ['Sarah Ostadabbas', 'Emily Zimmerman', 'Rebecca A. Schwartz-Mette', 'Marie J. Hayes', 'Matthew S. Goodwin', 'Emma C. Grace', 'Cassandra B. Rowan', 'Cholpady Vikram Kamath', 'Samuel Zlota', 'Kashish Jain', 'Elaheh Hatamimajoumerd', 'Michael Wan', 'Shaotong Zhu'] | 2023-03-29 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 6.37450457e-01 4.35119122e-01 -6.02239788e-01 -5.34592390e-01
-1.04706049e+00 -7.37823248e-01 -2.47447342e-01 5.61235487e-01
-2.73182690e-01 -1.42520860e-01 1.29560158e-01 -5.69758900e-02
-7.86914900e-02 -4.23489362e-01 -1.21767986e+00 -4.31572616e-01
-2.91989952e-01 1.70731336e-01 3.79008502e-01 3.58524621... | [14.057308197021484, -2.289034605026245] |
0d239958-e8cc-411d-9b37-5fd85a3276b8 | rubik-s-cube-operator-a-plug-and-play | 2203.12921 | null | https://arxiv.org/abs/2203.12921v1 | https://arxiv.org/pdf/2203.12921v1.pdf | Rubik's Cube Operator: A Plug And Play Permutation Module for Better Arranging High Dimensional Industrial Data in Deep Convolutional Processes | The convolutional neural network (CNN) has been widely applied to process the industrial data based tensor input, which integrates data records of distributed industrial systems from the spatial, temporal, and system dynamics aspects. However, unlike images, information in the industrial data based tensor is not necess... | ['Zijun Zhang', 'Zhong Zheng', 'Luoxiao Yang'] | 2022-03-24 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [-7.02831894e-02 -5.18543541e-01 3.82767431e-02 -1.95632756e-01
1.74928352e-01 -4.87031460e-01 2.66961575e-01 -1.95403621e-01
-2.36438140e-01 4.76702988e-01 -1.02934673e-01 -3.72554451e-01
-9.53475535e-01 -7.54274070e-01 -7.60136545e-01 -1.08509529e+00
-4.98678029e-01 4.89203967e-02 -2.29357809e-01 -1.54416248... | [7.005475997924805, 2.748657703399658] |
2faeb445-10ab-487e-8bc0-7dee1df149fb | comparative-analysis-of-automatic-skin-lesion | 1904.03075 | null | http://arxiv.org/abs/1904.03075v1 | http://arxiv.org/pdf/1904.03075v1.pdf | Comparative Analysis of Automatic Skin Lesion Segmentation with Two Different Implementations | Lesion segmentation from the surrounding skin is the first task for
developing automatic Computer-Aided Diagnosis of skin cancer. Variant features
of lesion like uneven distribution of color, irregular shape, border and
texture make this task challenging. The contribution of this paper is to
present and compare two dif... | ['Md. Kamrul Hasan', 'Fakrul Islam Tushar', 'Basel Alyafi'] | 2019-04-05 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 8.48494589e-01 5.37416525e-02 1.79520085e-01 1.07469605e-02
-8.29287469e-01 -7.11564541e-01 5.66003978e-01 6.96940958e-01
-6.88593388e-01 5.90055227e-01 -2.73266405e-01 -1.79946244e-01
7.32825398e-02 -5.75817227e-01 -2.29688752e-02 -9.40809727e-01
1.04317024e-01 2.72502005e-01 8.38021040e-01 5.95488176... | [15.503442764282227, -3.024406671524048] |
9ebb9100-b8cf-4e41-a75e-30f0c50c2109 | juman-a-morphological-analysis-toolkit-for | null | null | https://aclanthology.org/D18-2010 | https://aclanthology.org/D18-2010.pdf | Juman++: A Morphological Analysis Toolkit for Scriptio Continua | We present a three-part toolkit for developing morphological analyzers for languages without natural word boundaries. The first part is a C++11/14 lattice-based morphological analysis library that uses a combination of linear and recurrent neural net language models for analysis. The other parts are a tool for exposing... | ['Sadao Kurohashi', 'Daisuke Kawahara', 'Arseny Tolmachev'] | 2018-11-01 | null | null | null | emnlp-2018-11 | ['art-analysis'] | ['computer-vision'] | [-8.01275447e-02 -6.72679022e-02 -1.19213670e-01 -1.90759808e-01
-9.41216111e-01 -7.61050880e-01 7.42267221e-02 2.53588855e-01
-8.46128702e-01 6.25734150e-01 3.72984350e-01 -1.19345641e+00
3.50813717e-01 -7.77190208e-01 -1.26560137e-01 -3.73085976e-01
-9.12675932e-02 6.37969255e-01 3.25807154e-01 -4.00578052... | [10.430002212524414, 10.094178199768066] |
175ecc1e-8cdf-408c-a302-d89dc137aff6 | graph-neural-network-based-log-anomaly | 2307.00527 | null | https://arxiv.org/abs/2307.00527v1 | https://arxiv.org/pdf/2307.00527v1.pdf | Graph Neural Network based Log Anomaly Detection and Explanation | Event logs are widely used to record the status of high-tech systems, making log anomaly detection important for monitoring those systems. Most existing log anomaly detection methods take a log event count matrix or log event sequences as input, exploiting quantitative and/or sequential relationships between log events... | ['Matthijs van Leeuwen', 'Jiayang Shi', 'Zhong Li'] | 2023-07-02 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 3.01314071e-02 4.29928638e-02 -1.79015547e-01 -9.04608332e-03
2.77335197e-02 -4.56098318e-01 5.72786391e-01 1.11587071e+00
2.91566253e-01 2.15093136e-01 -4.11631260e-03 -7.65186310e-01
-3.39227945e-01 -1.16325498e+00 -6.19132996e-01 -2.22212166e-01
-7.67911553e-01 3.83125901e-01 4.94738519e-01 -1.87112704... | [6.640012264251709, 5.772704601287842] |
6efb5d7c-c6b9-4ecf-9e66-08532724f0aa | brain2word-decoding-brain-activity-for | 2009.04765 | null | https://arxiv.org/abs/2009.04765v3 | https://arxiv.org/pdf/2009.04765v3.pdf | Brain2Word: Decoding Brain Activity for Language Generation | Brain decoding, understood as the process of mapping brain activities to the stimuli that generated them, has been an active research area in the last years. In the case of language stimuli, recent studies have shown that it is possible to decode fMRI scans into an embedding of the word a subject is reading. However, s... | ['Beni Egressy', 'Roger Wattenhofer', 'Damian Pascual', 'Nicolas Affolter'] | 2020-09-10 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 6.77807033e-01 3.68749350e-01 1.30695477e-01 -4.53784794e-01
-4.83776659e-01 -4.19846833e-01 1.14558280e+00 2.54482538e-01
-8.73466969e-01 6.31446421e-01 5.15801132e-01 -2.24794626e-01
2.98310250e-01 -6.62261546e-01 -7.84948528e-01 -6.71893060e-01
1.09382153e-01 4.99561727e-01 -4.54405509e-02 -3.78080197... | [10.896322250366211, 2.4996275901794434] |
fdcdf584-5f2e-49c5-abf0-2f04e0c3b1b1 | ukp-square-an-online-platform-for-question | 2203.13693 | null | https://arxiv.org/abs/2203.13693v2 | https://arxiv.org/pdf/2203.13693v2.pdf | UKP-SQUARE: An Online Platform for Question Answering Research | Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e.g., extractive, abstractive), require different model architectures (e.g., generative, discriminative), and setups (e.g., with or without retrieval). Despite having a large numb... | ['Iryna Gurevych', 'Gözde Gül Şahin', 'Nils Reimers', 'Jonas Pfeiffer', 'Leonardo F. R. Ribeiro', 'Haritz Puerto', 'Hannah Sterz', 'Clifton Poth', 'Gregor Geigle', 'Max Eichler', 'Rachneet Sachdeva', 'Kexin Wang', 'Tim Baumgärtner'] | 2022-03-25 | null | https://aclanthology.org/2022.acl-demo.2 | https://aclanthology.org/2022.acl-demo.2.pdf | acl-2022-5 | ['explainable-models'] | ['computer-vision'] | [-4.64056879e-01 -4.21093673e-01 2.48620212e-01 -4.68578905e-01
-1.02246451e+00 -1.23696148e+00 4.09775227e-01 3.98869187e-01
-1.92154646e-01 1.61483824e-01 5.07034361e-02 -6.90241873e-01
-5.97587883e-01 -8.96340787e-01 -4.85300541e-01 -2.57547289e-01
5.10122955e-01 1.00344825e+00 4.11407143e-01 -2.97214806... | [10.860869407653809, 8.330595016479492] |
a1297aaa-8b21-4664-b127-fc75072561b7 | stochastic-precision-ensemble-self-knowledge | 2009.14502 | null | https://arxiv.org/abs/2009.14502v1 | https://arxiv.org/pdf/2009.14502v1.pdf | Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks | The quantization of deep neural networks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve the performance of quantized networks. In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ). SPEQ is a knowledge... | ['Sungho Shin', 'Yoonho Boo', 'Jungwook Choi', 'Wonyong Sung'] | 2020-09-30 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [ 1.88117445e-01 -2.94647133e-03 -2.66435921e-01 -6.49294198e-01
-7.87980020e-01 -4.11461920e-01 1.82079598e-01 1.18535087e-01
-8.59288752e-01 8.96479070e-01 -3.35448116e-01 -5.16534805e-01
-1.45716488e-01 -1.00407112e+00 -9.65138555e-01 -1.03771269e+00
3.54120672e-01 1.77442178e-01 4.67766434e-01 3.81757841... | [8.71484088897705, 3.010143518447876] |
672ea043-83d6-4e48-915a-b380e9a1046a | are-cars-just-3d-boxes-jointly-estimating-the | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Zia_Are_Cars_Just_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Zia_Are_Cars_Just_2014_CVPR_paper.pdf | Are Cars Just 3D Boxes? - Jointly Estimating the 3D Shape of Multiple Objects | Current systems for scene understanding typically represent objects as 2D or 3D bounding boxes. While these representations have proven robust in a variety of applications, they provide only coarse approximations to the true 2D and 3D extent of objects. As a result, object-object interactions, such as occlusions or gro... | ['Michael Stark', 'Muhammad Zeeshan Zia', 'Konrad Schindler'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['3d-shape-modeling'] | ['computer-vision'] | [-8.16950649e-02 3.82027566e-01 4.76739071e-02 -5.13863206e-01
-3.08740258e-01 -7.57416368e-01 8.46276939e-01 4.42481250e-01
1.64931387e-01 1.23528153e-01 2.14876458e-01 -2.91043043e-01
2.92166360e-02 -9.87920642e-01 -8.79867733e-01 -1.48311988e-01
1.10691287e-01 1.11376023e+00 6.10945404e-01 -8.57340023... | [8.288081169128418, -2.799570083618164] |
9e9e1314-2751-4c0a-8ca7-582bf807f7d9 | recnet-early-attention-guided-feature | 2302.09409 | null | https://arxiv.org/abs/2302.09409v1 | https://arxiv.org/pdf/2302.09409v1.pdf | RecNet: Early Attention Guided Feature Recovery | Uncertainty in sensors results in corrupted input streams and hinders the performance of Deep Neural Networks (DNN), which focus on deducing information from data. However, for sensors with multiple input streams, the relevant information among the streams correlates and hence contains mutual information. This paper ut... | ['Bashima Islam', 'Subrata Biswas'] | 2023-02-18 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [ 4.30035979e-01 1.56174570e-01 5.29424429e-01 -3.15607637e-01
-8.61520529e-01 -3.45601857e-01 2.95081347e-01 1.81909978e-01
-6.17515802e-01 8.27883005e-01 6.72705054e-01 1.28009677e-01
-5.26725173e-01 -5.92966676e-01 -9.35671747e-01 -8.30145776e-01
-1.29407763e-01 -8.68149325e-02 -7.56936520e-02 4.78801101... | [15.089173316955566, 5.822480201721191] |
03642d6a-3ba3-484a-b9fe-cf86b1ea7f9a | physics-guided-problem-decomposition-for | 2202.05994 | null | https://arxiv.org/abs/2202.05994v2 | https://arxiv.org/pdf/2202.05994v2.pdf | Physics-Guided Problem Decomposition for Scaling Deep Learning of High-dimensional Eigen-Solvers: The Case of Schrödinger's Equation | Given their ability to effectively learn non-linear mappings and perform fast inference, deep neural networks (NNs) have been proposed as a viable alternative to traditional simulation-driven approaches for solving high-dimensional eigenvalue equations (HDEs), which are the foundation for many scientific applications. ... | ['Anish Arora', 'Viktor Podolskiy', 'Anuj Karpatne', 'Wei-Cheng Lee', 'Samuel Olin', 'Sangeeta Srivastava'] | 2022-02-12 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [ 7.83923268e-02 7.37279505e-02 2.06659168e-01 -2.27448314e-01
-6.45440757e-01 -4.25526053e-01 3.20728093e-01 -1.26843438e-01
-5.20545542e-01 9.27603185e-01 -2.42989212e-01 -4.77693439e-01
-4.09453243e-01 -8.34865987e-01 -9.70647454e-01 -1.09355438e+00
1.93569168e-01 5.31396866e-01 -1.29576370e-01 -2.68740684... | [5.468612194061279, 4.93288516998291] |
522ef1d6-8874-49b5-9291-923db6743dc9 | multivariate-time-series-imputation-with | null | null | http://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks | http://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks.pdf | Multivariate Time Series Imputation with Generative Adversarial Networks | Multivariate time series usually contain a large number of missing values, which hinders the application of advanced analysis methods on multivariate time series data. Conventional approaches to addressing the challenge of missing values, including mean/zero imputation, case deletion, and matrix factorization-based imp... | ['Yonghong Luo', 'Yuan Xiaojie', 'Jun Xu', 'Ying Zhang', 'Xiangrui Cai'] | 2018-12-01 | null | null | null | neurips-2018-12 | ['multivariate-time-series-imputation'] | ['time-series'] | [ 4.28351015e-01 -2.86749214e-01 -1.98470745e-02 -3.45387101e-01
-9.01498318e-01 -4.82323080e-01 4.04586107e-01 -3.80474299e-01
1.16063818e-01 1.18799567e+00 4.09361959e-01 -1.03043333e-01
-7.59336129e-02 -7.40062237e-01 -1.03991210e+00 -9.38237906e-01
-6.94313571e-02 1.52541935e-01 -8.76445651e-01 -2.16532215... | [7.054409503936768, 3.2663919925689697] |
fb054a45-65a2-4abc-8a2a-52d1f0ad76e2 | deep-generative-quantile-copula-models-for | 1907.10697 | null | https://arxiv.org/abs/1907.10697v1 | https://arxiv.org/pdf/1907.10697v1.pdf | Deep Generative Quantile-Copula Models for Probabilistic Forecasting | We introduce a new category of multivariate conditional generative models and demonstrate its performance and versatility in probabilistic time series forecasting and simulation. Specifically, the output of quantile regression networks is expanded from a set of fixed quantiles to the whole Quantile Function by a univar... | ['Ruofeng Wen', 'Kari Torkkola'] | 2019-07-24 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-4.24948454e-01 -4.77912910e-02 -3.46169740e-01 -6.54726982e-01
-1.00118434e+00 -7.73939788e-01 8.73350441e-01 -1.83936417e-01
2.04960123e-01 1.13538659e+00 1.43417209e-01 -3.83830607e-01
-3.87108207e-01 -1.19993055e+00 -8.63825500e-01 -9.74680662e-01
-5.36702573e-01 1.18235958e+00 -3.61084342e-01 8.97141919... | [7.170009613037109, 3.7383525371551514] |
3e40d991-a516-4349-99da-c1ceda0d4a43 | bio-inspired-spike-based-hippocampus-and | 2305.12892 | null | https://arxiv.org/abs/2305.12892v1 | https://arxiv.org/pdf/2305.12892v1.pdf | Bio-inspired spike-based Hippocampus and Posterior Parietal Cortex models for robot navigation and environment pseudo-mapping | The brain has a great capacity for computation and efficient resolution of complex problems, far surpassing modern computers. Neuromorphic engineering seeks to mimic the basic principles of the brain to develop systems capable of achieving such capabilities. In the neuromorphic field, navigation systems are of great in... | ['Fernando Perez-Pena', 'Gabriel Jimenez-Moreno', 'Angel Jimenez-Fernandez', 'Juan P. Dominguez-Morales', 'Alvaro Ayuso-Martinez', 'Daniel Casanueva-Morato'] | 2023-05-22 | null | null | null | null | ['robot-navigation'] | ['robots'] | [ 2.91398279e-02 -1.15827452e-02 6.08565271e-01 7.99859315e-02
6.47619188e-01 -3.48184973e-01 6.68693721e-01 -7.25153387e-02
-6.68838739e-01 5.86895704e-01 -5.19695640e-01 1.10624842e-01
-1.83354869e-01 -1.00029039e+00 -7.84602821e-01 -8.67824435e-01
-3.46996844e-01 4.34913754e-01 8.48813713e-01 -5.43972135... | [8.15259075164795, 2.505573272705078] |
d876f97e-89a9-46a9-99fb-27f1b1b5ec2f | sparse-filtered-sirt-for-electron-tomography | 1608.01686 | null | http://arxiv.org/abs/1608.01686v2 | http://arxiv.org/pdf/1608.01686v2.pdf | Sparse Filtered SIRT for Electron Tomography | Electron tomographic reconstruction is a method for obtaining a
three-dimensional image of a specimen with a series of two dimensional
microscope images taken from different viewing angles. Filtered backprojection,
one of the most popular tomographic reconstruction methods, does not work well
under the existence of ima... | ['Chiwoo Park', 'Chen Mu'] | 2016-08-04 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 4.03623939e-01 -4.26990718e-01 7.48940587e-01 -1.22418433e-01
-4.65868920e-01 1.84113204e-01 2.01997772e-01 -4.04210240e-01
-8.18329990e-01 5.63923240e-01 1.34077340e-01 -5.00178747e-02
-2.47408748e-01 -6.05878115e-01 -3.29097956e-01 -9.82519507e-01
3.24989736e-01 2.49961480e-01 5.57971478e-01 5.12488410... | [12.648848533630371, -2.742842435836792] |
a925f99d-09ca-4282-8397-7b927bf2c0a5 | discriminative-region-attention-and | 2204.13323 | null | https://arxiv.org/abs/2204.13323v1 | https://arxiv.org/pdf/2204.13323v1.pdf | Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification | Vehicle re-identification (Re-ID) is urgently demanded to alleviate thepressure caused by the increasingly onerous task of urban traffic management. Multiple challenges hamper the applications of vision-based vehicle Re-ID methods: (1) The appearances of different vehicles of the same brand/model are often similar; How... | ['Li Ge', 'Lin Wang', 'Ying WEI', 'Yuefeng Wang', 'Huadong Li'] | 2022-04-28 | null | null | null | null | ['vehicle-re-identification'] | ['computer-vision'] | [-2.91030049e-01 -4.18843716e-01 -2.88719743e-01 -5.27579010e-01
-7.22184300e-01 -6.30116463e-01 8.80229831e-01 -3.45446825e-01
-1.70803681e-01 4.21330005e-01 -2.38689221e-02 -1.01077497e-01
1.37543827e-01 -6.00318670e-01 -6.99151933e-01 -8.31263781e-01
4.87639010e-01 3.71936321e-01 3.53572130e-01 -2.42486104... | [8.125836372375488, -1.0086445808410645] |
85d54fda-8328-4547-8e4a-4f3491131c3c | language-matters-a-weakly-supervised-pre | 2203.03911 | null | https://arxiv.org/abs/2203.03911v3 | https://arxiv.org/pdf/2203.03911v3.pdf | Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting | Recently, Vision-Language Pre-training (VLP) techniques have greatly benefited various vision-language tasks by jointly learning visual and textual representations, which intuitively helps in Optical Character Recognition (OCR) tasks due to the rich visual and textual information in scene text images. However, these me... | ['Shijian Lu', 'Wenqing Zhang', 'Song Bai', 'Philip Torr', 'Yu Hao', 'Chuhui Xue'] | 2022-03-08 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 7.11598277e-01 -2.07691580e-01 -2.46454656e-01 -2.54992247e-01
-7.07009315e-01 -4.50672448e-01 7.02165365e-01 7.81094597e-04
-6.75880909e-01 3.89962375e-01 2.07540959e-01 -2.26328313e-01
5.44567168e-01 -4.95914429e-01 -1.19331694e+00 -5.94511867e-01
5.80993772e-01 4.14132178e-01 2.93143183e-01 1.51779145... | [11.830986976623535, 2.2103750705718994] |
1de088e0-69fd-4e8e-8de3-956b9ea327a2 | sequence-to-sequence-models-for-cache | null | null | https://aclanthology.org/P18-1171 | https://aclanthology.org/P18-1171.pdf | Sequence-to-sequence Models for Cache Transition Systems | In this paper, we present a sequence-to-sequence based approach for mapping natural language sentences to AMR semantic graphs. We transform the sequence to graph mapping problem to a word sequence to transition action sequence problem using a special transition system called a cache transition system. To address the sp... | ['Xiaochang Peng', 'Giorgio Satta', 'Linfeng Song', 'Daniel Gildea'] | 2018-07-01 | null | null | null | acl-2018-7 | ['hard-attention'] | ['methodology'] | [ 7.01622188e-01 3.94219279e-01 -4.97362465e-01 -5.67156017e-01
-9.66451883e-01 -4.87581879e-01 4.37287867e-01 1.92175925e-01
-3.51229638e-01 1.97345853e-01 7.24820614e-01 -8.48566413e-01
5.71670115e-01 -8.36041689e-01 -9.73532498e-01 6.74771219e-02
2.42753118e-01 5.77863336e-01 2.34523252e-01 -2.16899246... | [10.438475608825684, 9.082802772521973] |
b7ca4a80-1865-4212-b9ae-704416e02e73 | sketch-based-3d-shape-retrieval-using | 1504.03504 | null | http://arxiv.org/abs/1504.03504v1 | http://arxiv.org/pdf/1504.03504v1.pdf | Sketch-based 3D Shape Retrieval using Convolutional Neural Networks | Retrieving 3D models from 2D human sketches has received considerable
attention in the areas of graphics, image retrieval, and computer vision.
Almost always in state of the art approaches a large amount of "best views" are
computed for 3D models, with the hope that the query sketch matches one of
these 2D projections ... | ['Yi Li', 'Le Kang', 'Fang Wang'] | 2015-04-14 | sketch-based-3d-shape-retrieval-using-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Wang_Sketch-Based_3D_Shape_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Wang_Sketch-Based_3D_Shape_2015_CVPR_paper.pdf | cvpr-2015-6 | ['3d-shape-retrieval'] | ['computer-vision'] | [-5.90439048e-03 -3.50795865e-01 -1.99172333e-01 -4.24633086e-01
-7.57974625e-01 -8.56820941e-01 8.32237005e-01 -3.05647820e-01
-2.04455480e-01 5.82268760e-02 6.13253228e-02 -4.31298763e-02
-2.05033809e-01 -7.14784026e-01 -4.79949534e-01 -5.05706251e-01
1.85297921e-01 6.51057005e-01 2.41128162e-01 -1.22626528... | [8.568814277648926, -3.4970202445983887] |
0b4ef07f-8ffd-47fd-9e06-6f61bc9ac715 | hierarchical-confusion-matrix-for | 2306.09461 | null | https://arxiv.org/abs/2306.09461v1 | https://arxiv.org/pdf/2306.09461v1.pdf | Hierarchical confusion matrix for classification performance evaluation | In this work we propose a novel concept of a hierarchical confusion matrix, opening the door for popular confusion matrix based (flat) evaluation measures from binary classification problems, while considering the peculiarities of hierarchical classification problems. We develop the concept to a generalized form and pr... | ['Martin Hemberg', 'Michael Neunteufel', 'Kevin Riehl'] | 2023-06-15 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 3.04314733e-01 2.50428736e-01 -5.11056818e-02 -2.57501125e-01
-4.60403651e-01 -8.23160052e-01 5.25077105e-01 6.56298578e-01
-2.11928114e-01 8.55829000e-01 -1.01611063e-01 -5.29222310e-01
-1.02067578e+00 -8.82210553e-01 3.30315232e-01 -7.54455566e-01
-5.10286808e-01 7.35622764e-01 6.21850193e-01 -1.92062691... | [7.6675705909729, 4.618402004241943] |
10a9b152-158e-488c-9292-659d47bd8612 | bridging-the-covid-19-data-and-the-1 | 2301.13692 | null | https://arxiv.org/abs/2301.13692v1 | https://arxiv.org/pdf/2301.13692v1.pdf | Bridging the Covid-19 Data and the Epidemiological Model using Time-Varying Parameter SIRD Model | This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modeling structure designed for the ... | ['Yasin Simsek', 'Cem Cakmakli'] | 2023-01-31 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 5.83564676e-02 -1.05961792e-01 -3.60471934e-01 -2.00532779e-01
-6.14574611e-01 -4.63219941e-01 6.92826033e-01 1.32459655e-01
-4.16104048e-01 5.65267980e-01 6.28037274e-01 -6.18354499e-01
-5.10157228e-01 -8.03574562e-01 -2.05641493e-01 -6.53434694e-01
-5.16685724e-01 6.87859118e-01 -1.33202061e-01 -1.06597468... | [6.011052131652832, 4.389005184173584] |
51a7f7a4-dc28-41ca-8188-8ec1b799721d | rankcse-unsupervised-sentence-representations | 2305.16726 | null | https://arxiv.org/abs/2305.16726v1 | https://arxiv.org/pdf/2305.16726v1.pdf | RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank | Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning has been widely adopted which derives high-quality sentence representations by pulling similar semantics closer and pushing dissimilar ones... | ['Rui Yan', 'Kai Chen', 'Dongyan Zhao', 'Yunsen Xian', 'Wei Wu', 'Jingang Wang', 'Qifan Wang', 'Jiahao Liu', 'Jiduan Liu'] | 2023-05-26 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 6.57461107e-01 -2.22953379e-01 -1.08911484e-01 -8.71165931e-01
-1.00193810e+00 -4.70202833e-01 7.20346630e-01 8.41473639e-01
-7.68173277e-01 5.50876439e-01 7.53084421e-01 -6.35770187e-02
-3.15298915e-01 -6.16415620e-01 -3.40468675e-01 -6.04074836e-01
3.15735638e-01 4.24586982e-01 3.52000266e-01 -5.05083084... | [11.046395301818848, 8.52261734008789] |
9c6ccf10-6de0-4e0e-a344-1d8281b7df4a | the-causal-neural-connection-expressiveness | 2107.00793 | null | https://arxiv.org/abs/2107.00793v3 | https://arxiv.org/pdf/2107.00793v3.pdf | The Causal-Neural Connection: Expressiveness, Learnability, and Inference | One of the central elements of any causal inference is an object called structural causal model (SCM), which represents a collection of mechanisms and exogenous sources of random variation of the system under investigation (Pearl, 2000). An important property of many kinds of neural networks is universal approximabilit... | ['Elias Bareinboim', 'Yoshua Bengio', 'Kai-Zhan Lee', 'Kevin Xia'] | 2021-07-02 | null | http://proceedings.neurips.cc/paper/2021/hash/5989add1703e4b0480f75e2390739f34-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/5989add1703e4b0480f75e2390739f34-Paper.pdf | neurips-2021-12 | ['causal-identification'] | ['reasoning'] | [ 4.70952123e-01 6.06740654e-01 -6.17893457e-01 -2.37690762e-01
1.42493173e-01 -5.52755713e-01 9.98874784e-01 2.03908041e-01
-1.14454843e-01 1.12124181e+00 3.66026700e-01 -7.43691206e-01
-8.70049477e-01 -1.09744728e+00 -1.26459324e+00 -6.87190413e-01
-5.15184283e-01 2.22356901e-01 -1.86492786e-01 -7.18220994... | [8.136590003967285, 5.437344074249268] |
68c0bd20-760c-4cf9-9441-24904927f22b | tinysiamese-network-for-biometric-analysis | 2307.00578 | null | https://arxiv.org/abs/2307.00578v1 | https://arxiv.org/pdf/2307.00578v1.pdf | TinySiamese Network for Biometric Analysis | Biometric recognition is the process of verifying or classifying human characteristics in images or videos. It is a complex task that requires machine learning algorithms, including convolutional neural networks (CNNs) and Siamese networks. Besides, there are several limitations to consider when using these algorithms ... | ['Adel M. ALIMI', 'Habib Chabchoub', 'Tarek M. Hamdani', 'Islem Jarraya'] | 2023-07-02 | null | null | null | null | ['face-verification'] | ['computer-vision'] | [-1.48398150e-02 -3.78497601e-01 6.56705871e-02 -3.20699722e-01
-9.54942033e-02 -3.00749153e-01 8.00741613e-02 -3.22121888e-01
-7.17664361e-01 5.15115440e-01 -7.56497085e-01 -3.21734190e-01
-8.83295164e-02 -8.87784004e-01 -6.10486865e-01 -7.83612728e-01
-1.73783749e-01 1.66604757e-01 8.84199291e-02 -1.42602682... | [13.302651405334473, 0.9484583139419556] |
a5826382-126e-4c74-959c-cadc1b1a1a8a | tmr-text-to-motion-retrieval-using | 2305.00976 | null | https://arxiv.org/abs/2305.00976v1 | https://arxiv.org/pdf/2305.00976v1.pdf | TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesis | In this paper, we present TMR, a simple yet effective approach for text to 3D human motion retrieval. While previous work has only treated retrieval as a proxy evaluation metric, we tackle it as a standalone task. Our method extends the state-of-the-art text-to-motion synthesis model TEMOS, and incorporates a contrasti... | ['Gül Varol', 'Michael J. Black', 'Mathis Petrovich'] | 2023-05-02 | null | null | null | null | ['motion-synthesis', 'moment-retrieval', 'text-to-3d'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.58274223e-02 -5.35583496e-01 -4.53217953e-01 9.38486960e-03
-1.35623527e+00 -7.11462319e-01 1.02787590e+00 -2.34053940e-01
-5.45866370e-01 3.73061657e-01 6.74142659e-01 6.60138801e-02
-1.51847541e-01 -2.40462720e-01 -5.00966012e-01 -6.86478317e-01
-7.12386519e-02 6.81412399e-01 4.90362734e-01 -2.06705406... | [10.151657104492188, 0.8134397864341736] |
257f2cd6-c672-417a-a3c5-e603ce902d77 | maximum-volume-inscribed-ellipsoid-a-new | 1708.02883 | null | http://arxiv.org/abs/1708.02883v3 | http://arxiv.org/pdf/1708.02883v3.pdf | Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization | Consider a structured matrix factorization model where one factor is
restricted to have its columns lying in the unit simplex. This
simplex-structured matrix factorization (SSMF) model and the associated
factorization techniques have spurred much interest in research topics over
different areas, such as hyperspectral u... | ['Chong-Yung Chi', 'Wing-Kin Ma', 'Chia-Hsiang Lin', 'Yue Wang', 'Ruiyuan Wu'] | 2017-08-09 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 6.43368244e-01 2.20193341e-01 -1.32957339e-01 1.63879186e-01
-4.03326064e-01 -7.56226063e-01 2.98257768e-01 -3.57572764e-01
-1.32584810e-01 8.06652784e-01 8.74982476e-02 -4.28950340e-01
-7.22421288e-01 -6.32771254e-01 -6.75279558e-01 -1.33718038e+00
-1.24901086e-01 6.49106026e-01 -3.77626479e-01 -2.01038703... | [10.073305130004883, -1.9274266958236694] |
32049717-af72-48a8-8e1f-c0733ff07be2 | hotgp-higher-order-typed-genetic-programming | 2304.03200 | null | https://arxiv.org/abs/2304.03200v1 | https://arxiv.org/pdf/2304.03200v1.pdf | HOTGP -- Higher-Order Typed Genetic Programming | Program synthesis is the process of generating a computer program following a set of specifications, which can be a high-level description of the problem and/or a set of input-output examples. The synthesis can be modeled as a search problem in which the search space is the set of all the programs valid under a grammar... | ['Emilio Francesquini', 'Fabrício Olivetti de França', 'Matheus Campos Fernandes'] | 2023-04-06 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 2.36951739e-01 2.70932317e-01 -4.71716732e-01 -3.91061604e-01
-5.29720306e-01 -7.91692734e-01 3.60750854e-01 -3.29996599e-03
1.37395084e-01 6.85143113e-01 -3.76301467e-01 -8.25836658e-01
9.14682895e-02 -1.30625093e+00 -8.98121953e-01 -2.91521519e-01
-3.27256799e-01 5.48771262e-01 3.91983151e-01 -3.02925557... | [8.131832122802734, 7.313465118408203] |
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