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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d4329c61-d3e5-400f-9298-80d18c374a84 | raft-rationale-adaptor-for-few-shot-abusive | 2211.17046 | null | https://arxiv.org/abs/2211.17046v1 | https://arxiv.org/pdf/2211.17046v1.pdf | RAFT: Rationale adaptor for few-shot abusive language detection | Abusive language is a concerning problem in online social media. Past research on detecting abusive language covers different platforms, languages, demographies, etc. However, models trained using these datasets do not perform well in cross-domain evaluation settings. To overcome this, a common strategy is to use a few... | ['Animesh Mukherjee', 'Binny Mathew', 'Kushal Kedia', 'Divyanshu Sheth', 'Punyajoy Saha'] | 2022-11-30 | null | null | null | null | ['cross-domain-few-shot', 'abusive-language'] | ['computer-vision', 'natural-language-processing'] | [ 7.72881880e-02 -1.57269686e-01 -5.58040440e-01 -2.10021734e-01
-1.17373192e+00 -4.60989684e-01 7.63587713e-01 3.29188645e-01
-3.82226944e-01 6.26711369e-01 3.84336710e-01 -1.51816845e-01
2.25613520e-01 -4.73134518e-01 -3.40052783e-01 -1.97889075e-01
4.07662749e-01 5.16255856e-01 4.08440053e-01 -6.71970725... | [8.836777687072754, 10.443801879882812] |
c3c1cdbb-9ef5-4593-bda2-630bb5d9cfc0 | conformal-prediction-set-for-time-series | 2206.07851 | null | https://arxiv.org/abs/2206.07851v1 | https://arxiv.org/pdf/2206.07851v1.pdf | Conformal prediction set for time-series | When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods. In this paper, we develop Ensemble Regularized Adaptive Prediction Set (ERAPS) to... | ['Yao Xie', 'Chen Xu'] | 2022-06-15 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 4.51288968e-01 -7.07379282e-02 -1.89142495e-01 -8.15907300e-01
-8.54535401e-01 -6.70131087e-01 4.11183804e-01 3.62446196e-02
4.47928486e-03 1.26747870e+00 -2.15835452e-01 -4.90129203e-01
-6.51990712e-01 -8.89565825e-01 -5.77944517e-01 -7.40323305e-01
-2.23172441e-01 2.07018405e-01 -1.13955610e-01 1.04279615... | [7.408056735992432, 3.974581003189087] |
f36d515b-d419-478c-a788-557ab615f9de | dynamic-local-geometry-capture-in-3d | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9565556 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9565556 | Dynamic Local Geometry Capture in 3D PointCloud Classification | With the advent of PointNet, the popularity of deep neural networks has increased in point cloud analysis. PointNet's successor, PointNet++, partitions the input point cloud and recursively applies PointNet to capture local geometry. PointNet++ model uses ball querying for local geometry capture in its set abstraction ... | ['Chandra Kambhamettu', 'Shivanand Venkanna Sheshappanavar'] | 2021-10-19 | null | null | null | ieee-4th-international-conference-on | ['3d-classification'] | ['computer-vision'] | [-6.85196519e-01 -2.53022552e-01 5.04972041e-02 -2.08925322e-01
-4.45187449e-01 -5.33838034e-01 4.28802937e-01 9.05242190e-02
-1.39813647e-01 1.13730289e-01 -3.69535059e-01 -4.58037406e-01
-2.02062845e-01 -1.39340305e+00 -8.91650259e-01 -2.13961303e-01
-2.82275856e-01 9.77656007e-01 6.52047217e-01 -4.14148241... | [7.900228500366211, -3.509323835372925] |
90fe825a-3e2f-4da9-bbfa-43174c6a2652 | combining-residual-networks-with-lstms-for | 1703.04105 | null | http://arxiv.org/abs/1703.04105v4 | http://arxiv.org/pdf/1703.04105v4.pdf | Combining Residual Networks with LSTMs for Lipreading | We propose an end-to-end deep learning architecture for word-level visual
speech recognition. The system is a combination of spatiotemporal
convolutional, residual and bidirectional Long Short-Term Memory networks. We
train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging
database of 500-size targ... | ['Georgios Tzimiropoulos', 'Themos Stafylakis'] | 2017-03-12 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.70573846e-01 -7.94794932e-02 -6.67023957e-01 -2.25879833e-01
-1.26378167e+00 -3.17454904e-01 4.91535097e-01 -4.85559136e-01
-6.44722223e-01 3.66936535e-01 5.20154059e-01 -8.08382452e-01
6.74326122e-01 9.97510403e-02 -9.80788291e-01 -6.78505301e-01
-6.36471957e-02 -1.98148116e-01 1.31878391e-01 1.95322067... | [14.337918281555176, 5.020552158355713] |
28a65515-6c79-4147-b5d8-aa9d54d6d82e | emoberta-speaker-aware-emotion-recognition-in | 2108.12009 | null | https://arxiv.org/abs/2108.12009v1 | https://arxiv.org/pdf/2108.12009v1.pdf | EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa | We present EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa, a simple yet expressive scheme of solving the ERC (emotion recognition in conversation) task. By simply prepending speaker names to utterances and inserting separation tokens between the utterances in a dialogue, EmoBERTa can learn int... | ['Piek Vossen', 'Taewoon Kim'] | 2021-08-26 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-3.87013882e-01 5.47067225e-02 2.70419955e-01 -9.99777019e-01
-8.62182856e-01 -6.50263965e-01 5.11973500e-01 -2.13085413e-01
-1.53463289e-01 3.32255512e-01 6.37276828e-01 -1.39828861e-01
4.20630455e-01 1.10407956e-01 -1.13041513e-01 -2.58196622e-01
-3.56495023e-01 3.89528841e-01 -5.26759028e-01 -5.46467066... | [13.018585205078125, 6.206577301025391] |
e8b7a90a-4d27-4912-ab80-e75b3d2736e9 | fast-fourier-color-constancy-and-grayness | 1908.02076 | null | https://arxiv.org/abs/1908.02076v2 | https://arxiv.org/pdf/1908.02076v2.pdf | Fast Fourier Color Constancy and Grayness Index for ISPA Illumination Estimation Challenge | We briefly introduce two submissions to the Illumination Estimation Challenge, in the Int'l Workshop on Color Vision, affiliated to the 11th Int'l Symposium on Image and Signal Processing and Analysis. The Fourier-transform-based submission is ranked 3rd, and the statistical Gray-pixel-based one ranked 6th. | ['Yanlin Qian', 'Ke Chen', 'Huanglin Yu'] | 2019-08-06 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 3.22756290e-01 -6.49091482e-01 1.38437614e-01 -3.82943511e-01
-8.97479773e-01 -4.35341179e-01 3.78630906e-01 -8.93299356e-02
-5.20738661e-01 3.91570568e-01 -2.35524416e-01 3.87888253e-02
3.48572075e-01 -4.63186204e-02 -3.43884021e-01 -7.37374067e-01
-3.13867569e-01 -6.12474918e-01 1.24241719e-02 2.34740600... | [10.506363868713379, -2.564770460128784] |
1b7c7f30-afcf-4d66-8fef-a75abd0a9caa | approaching-sign-language-gloss-translation | null | null | https://aclanthology.org/2021.mtsummit-at4ssl.7 | https://aclanthology.org/2021.mtsummit-at4ssl.7.pdf | Approaching Sign Language Gloss Translation as a Low-Resource Machine Translation Task | A cascaded Sign Language Translation system first maps sign videos to gloss annotations and then translates glosses into a spoken languages. This work focuses on the second-stage gloss translation component, which is challenging due to the scarcity of publicly available parallel data. We approach gloss translation as a... | ['Kevin Duh', 'Xuan Zhang'] | null | null | null | null | mtsummit-2021-8 | ['sign-language-translation'] | ['computer-vision'] | [ 4.81080174e-01 -2.70548612e-01 -5.00595212e-01 -4.65930015e-01
-1.31691003e+00 -7.90392816e-01 7.80828416e-01 -8.77474487e-01
-6.87084198e-01 7.10684597e-01 7.31587470e-01 -3.16381454e-01
2.85653830e-01 -1.36388347e-01 -5.73892534e-01 -5.02946198e-01
4.06998008e-01 9.15688276e-01 4.04029340e-02 -2.34573528... | [9.214421272277832, -6.5424113273620605] |
d7078d42-268d-451e-832a-9371b0c6b06a | how-to-choose-good-samples-for-text-data | 2302.00894 | null | https://arxiv.org/abs/2302.00894v1 | https://arxiv.org/pdf/2302.00894v1.pdf | How to choose "Good" Samples for Text Data Augmentation | Deep learning-based text classification models need abundant labeled data to obtain competitive performance. Unfortunately, annotating large-size corpus is time-consuming and laborious. To tackle this, multiple researches try to use data augmentation to expand the corpus size. However, data augmentation may potentially... | ['Shengyi Jiang', 'Ziyu Yang', 'Yingwen Fu', 'Nankai Lin', 'Xiaotian Lin'] | 2023-02-02 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 3.28680366e-01 6.32866612e-03 -2.45258778e-01 -5.95073402e-01
-5.93066096e-01 -3.76884155e-02 4.40455228e-01 3.62875581e-01
-8.25490713e-01 8.36094856e-01 2.51220107e-01 -6.90932199e-02
-7.61399046e-02 -8.93748999e-01 -1.57677561e-01 -6.76437676e-01
3.73107314e-01 4.48667884e-01 -1.68811366e-01 -1.90300643... | [10.543789863586426, 7.52340030670166] |
bbcca804-b77c-432e-a5e9-8a716e73b155 | recipenlg-a-cooking-recipes-dataset-for-semi | null | null | https://aclanthology.org/2020.inlg-1.4 | https://aclanthology.org/2020.inlg-1.4.pdf | RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation | Semi-structured text generation is a non-trivial problem. Although last years have brought lots of improvements in natural language generation, thanks to the development of neural models trained on large scale datasets, these approaches still struggle with producing structured, context- and commonsense-aware texts. Mor... | ['Agnieszka Ławrynowicz', 'Dawid Wiśniewski', 'Wojciech Taisner', 'Martyna Maciejewska', 'Michał Gilski', 'Michał Bień'] | 2020-12-15 | null | null | null | null | ['recipe-generation'] | ['miscellaneous'] | [ 3.95403802e-01 2.38172710e-01 1.89685315e-01 -2.84723520e-01
-7.25675762e-01 -7.46077597e-01 7.13625371e-01 2.40592301e-01
-1.24457903e-01 1.03790498e+00 7.55116165e-01 1.78441405e-01
2.18154520e-01 -1.20749950e+00 -7.01733351e-01 -3.05259466e-01
4.50937241e-01 5.63098490e-01 -1.73156768e-01 -6.41903996... | [11.502715110778809, 4.65281867980957] |
b6b4c504-2c6d-4561-a413-149426ad8b39 | data-augmentation-for-low-resource-quechua | 2207.06872 | null | https://arxiv.org/abs/2207.06872v1 | https://arxiv.org/pdf/2207.06872v1.pdf | Data Augmentation for Low-Resource Quechua ASR Improvement | Automatic Speech Recognition (ASR) is a key element in new services that helps users to interact with an automated system. Deep learning methods have made it possible to deploy systems with word error rates below 5% for ASR of English. However, the use of these methods is only available for languages with hundreds or t... | ['Jordi Luque', 'Mireia Farrús', 'Guillermo Cámbara', 'Nuria Bel', 'Rodolfo Zevallos'] | 2022-07-14 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 1.55393213e-01 3.10390174e-01 3.56714219e-01 -4.51775014e-01
-1.07792866e+00 -2.80898958e-01 5.21644235e-01 -7.25784376e-02
-7.31261253e-01 7.67922997e-01 3.91152024e-01 -6.90622211e-01
4.50150728e-01 -5.39843261e-01 -4.52438533e-01 -2.61174947e-01
2.62533575e-01 8.72075558e-01 1.84919015e-01 -8.39796722... | [14.392953872680664, 6.855511665344238] |
da912806-d326-4df4-a81d-9a6bca453699 | improving-diffusion-models-for-scene-text | 2304.05568 | null | https://arxiv.org/abs/2304.05568v1 | https://arxiv.org/pdf/2304.05568v1.pdf | Improving Diffusion Models for Scene Text Editing with Dual Encoders | Scene text editing is a challenging task that involves modifying or inserting specified texts in an image while maintaining its natural and realistic appearance. Most previous approaches to this task rely on style-transfer models that crop out text regions and feed them into image transfer models, such as GANs. However... | ['Shiyu Chang', 'Brian Price', 'Zhifei Zhang', 'Bairu Hou', 'Zhaowen Wang', 'Guanhua Zhang', 'Jiabao Ji'] | 2023-04-12 | null | null | null | null | ['scene-text-editing'] | ['computer-vision'] | [ 7.26583481e-01 3.15831900e-02 2.65203696e-02 -3.87742281e-01
-2.97263861e-01 -6.84433043e-01 8.44356537e-01 -4.88834381e-01
-2.77524710e-01 6.29495800e-01 1.86005250e-01 -3.13713729e-01
5.92915714e-01 -8.06777894e-01 -9.45124090e-01 -6.57831132e-01
7.72250712e-01 3.27150106e-01 3.63348454e-01 -3.17069024... | [11.46506118774414, -0.2872617244720459] |
f14f1005-cf2f-48e5-a86e-16f7079a3800 | gamma-and-vega-hedging-using-deep | 2205.05614 | null | https://arxiv.org/abs/2205.05614v4 | https://arxiv.org/pdf/2205.05614v4.pdf | Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning | We show how D4PG can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset. We assume that the trader makes the portfolio delta neutral at the end of each day by taking a position in the u... | ['Jun Yuan', 'Zeyu Wang', 'Zissis Poulos', 'John Hull', 'Soroush Farghadani', 'Jacky Chen', 'Jay Cao'] | 2022-05-10 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-6.33683980e-01 3.80935147e-02 -2.92584822e-02 -3.47542129e-02
-4.63028997e-01 -9.21470702e-01 2.39638269e-01 -1.37178704e-01
-3.22286636e-01 8.87046576e-01 -1.15433529e-01 -6.23613358e-01
-1.47787005e-01 -1.18362939e+00 -2.34677926e-01 -5.21705508e-01
-6.12310506e-02 6.79483771e-01 2.13989213e-01 -2.24232748... | [4.887043476104736, 3.935810089111328] |
3d6ea531-c75d-4875-b359-5859f0560b05 | graphs-constraints-and-search-for-the | 2210.09880 | null | https://arxiv.org/abs/2210.09880v2 | https://arxiv.org/pdf/2210.09880v2.pdf | Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus | The Abstraction and Reasoning Corpus (ARC) aims at benchmarking the performance of general artificial intelligence algorithms. The ARC's focus on broad generalization and few-shot learning has made it difficult to solve using pure machine learning. A more promising approach has been to perform program synthesis within ... | ['Scott Sanner', 'Elias B. Khalil', 'Yudong Xu'] | 2022-10-18 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 3.29969317e-01 5.90464115e-01 -4.18046057e-01 -1.16528518e-01
-5.42724013e-01 -4.05324161e-01 7.78162479e-01 4.10861135e-01
1.36860430e-01 4.27938461e-01 -6.02872521e-02 -7.63836443e-01
-1.58651829e-01 -9.55033422e-01 -6.70551956e-01 -2.18793720e-01
-1.23531818e-01 7.34189749e-01 4.35497910e-01 -1.81898803... | [8.551314353942871, 7.229255676269531] |
339c93ac-407c-4972-9fb8-d907545889ca | unest-local-spatial-representation-learning | 2209.14378 | null | https://arxiv.org/abs/2209.14378v1 | https://arxiv.org/pdf/2209.14378v1.pdf | UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation | Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realize global communication via the self-attention mechanism. Ho... | ['Yucheng Tang', 'Bennett A. Landman', 'Yuankai Huo', 'Zizhao Zhang', 'Richard G. Abramson', 'Thomas A. Lasko', 'Zhoubing Xu', 'Shunxing Bao', 'Thomas Li', 'Ho Hin Lee', 'Riqiang Gao', 'Leon Y. Cai', 'Yinchi Zhou', 'Qi Yang', 'Xin Yu'] | 2022-09-28 | null | null | null | null | ['brain-segmentation'] | ['medical'] | [ 4.59713578e-01 4.91266638e-01 -2.30083853e-01 -2.22188130e-01
-8.76623988e-01 -3.97056311e-01 2.67494500e-01 5.52971996e-02
-1.78303316e-01 6.08046293e-01 2.18147993e-01 -1.80324748e-01
-2.32523695e-01 -6.75914764e-01 -9.16303396e-01 -8.95117342e-01
-4.14380461e-01 7.23609686e-01 3.76691014e-01 8.83374512... | [14.584197998046875, -2.5007331371307373] |
d0d1945d-8178-470c-8390-b3162b6a8383 | enhancing-self-disclosure-in-neural-dialog | 2109.05090 | null | https://arxiv.org/abs/2109.05090v2 | https://arxiv.org/pdf/2109.05090v2.pdf | Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking | Neural language modelling has progressed the state-of-the-art in different downstream Natural Language Processing (NLP) tasks. One such area is of open-domain dialog modelling, neural dialog models based on GPT-2 such as DialoGPT have shown promising performance in single-turn conversation. However, such (neural) dialo... | ['Vincent Wade', 'Benjamin Cowan', 'Mayank Soni'] | 2021-09-10 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [ 5.55375293e-02 1.18001032e+00 -7.57415816e-02 -7.00417280e-01
-3.43358070e-01 -2.67720729e-01 1.13635659e+00 1.55338094e-01
-1.43087670e-01 1.13622034e+00 1.08090889e+00 -6.63000643e-02
3.14567722e-02 -7.75397301e-01 1.95886195e-01 -3.78746033e-01
2.84427971e-01 8.40321600e-01 -4.64927219e-02 -7.75488257... | [12.835681915283203, 7.986604690551758] |
029a0d97-db7d-432a-8889-a360833459b5 | using-neural-machine-translation-methods-for | null | null | https://aclanthology.org/2022.acl-srw.21 | https://aclanthology.org/2022.acl-srw.21.pdf | Using Neural Machine Translation Methods for Sign Language Translation | We examine methods and techniques, proven to be helpful for the text-to-text translation of spoken languages in the context of gloss-to-text translation systems, where the glosses are the written representation of the signs. We present one of the first works that include experiments on both parallel corpora of the Germ... | ['Sebastian Möller', 'Eleftherios Avramidis', 'Galina Angelova'] | null | null | null | null | acl-2022-5 | ['sign-language-translation'] | ['computer-vision'] | [ 4.24758077e-01 2.58645833e-01 -1.93344057e-02 -5.32871187e-01
-1.19962680e+00 -6.88776374e-01 1.08479321e+00 -4.01020169e-01
-6.81614876e-01 8.10509801e-01 6.92140639e-01 -4.30395722e-01
3.53911370e-01 -1.78794667e-01 -5.09734273e-01 -7.04827785e-01
3.44173461e-01 1.20610642e+00 2.01259568e-01 -4.34259534... | [9.244807243347168, -6.571361064910889] |
efdcc9b1-0d4e-45b2-ab8b-d08498e692fe | clamp-contrastive-language-music-pre-training | 2304.11029 | null | https://arxiv.org/abs/2304.11029v3 | https://arxiv.org/pdf/2304.11029v3.pdf | CLaMP: Contrastive Language-Music Pre-training for Cross-Modal Symbolic Music Information Retrieval | We introduce CLaMP: Contrastive Language-Music Pre-training, which learns cross-modal representations between natural language and symbolic music using a music encoder and a text encoder trained jointly with a contrastive loss. To pre-train CLaMP, we collected a large dataset of 1.4 million music-text pairs. It employe... | ['Maosong Sun', 'Xu Tan', 'Dingyao Yu', 'Shangda Wu'] | 2023-04-21 | null | null | null | null | ['music-classification', 'music-information-retrieval'] | ['music', 'music'] | [ 3.99236411e-01 -2.12051958e-01 -2.33299434e-01 -1.98487461e-01
-1.21635377e+00 -8.89848411e-01 4.48842287e-01 4.79789414e-02
-5.11790633e-01 2.96432972e-01 3.78963023e-01 2.13910472e-02
-1.39800131e-01 -6.02622926e-01 -9.38336968e-01 -1.79603815e-01
2.03899503e-01 5.79839468e-01 -1.34744108e-01 -1.05164438... | [15.841741561889648, 5.318155765533447] |
650e1ba6-9d39-4268-9503-5a266b326ce0 | conda-a-contextual-dual-annotated-dataset-for | 2106.06213 | null | https://arxiv.org/abs/2106.06213v2 | https://arxiv.org/pdf/2106.06213v2.pdf | CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection | Traditional toxicity detection models have focused on the single utterance level without deeper understanding of context. We introduce CONDA, a new dataset for in-game toxic language detection enabling joint intent classification and slot filling analysis, which is the core task of Natural Language Understanding (NLU).... | ['Soyeon Caren Han', 'Josiah Poon', 'Siqu Long', 'Xinghong Guo', 'Kunze Wang', 'Tongshu Zhang', 'Jean Lee', 'Guanghao Huang', 'Henry Weld'] | 2021-06-11 | null | https://aclanthology.org/2021.findings-acl.213 | https://aclanthology.org/2021.findings-acl.213.pdf | findings-acl-2021-8 | ['dota-2'] | ['playing-games'] | [ 6.21724725e-02 -4.09041584e-01 -2.14075133e-01 -8.79996866e-02
-1.34201813e+00 -1.08820558e+00 5.95930994e-01 4.07820344e-01
-3.26829523e-01 7.69725740e-01 8.36163461e-01 -2.70512164e-01
-2.96862006e-01 -7.26137102e-01 -2.91059881e-01 -2.97353208e-01
-1.90420657e-01 3.46116513e-01 3.60489249e-01 -4.76549745... | [9.047736167907715, 10.433893203735352] |
f2c51885-9913-4642-9888-ab5811934bce | personalized-state-anxiety-detection-an | 2304.09928 | null | https://arxiv.org/abs/2304.09928v1 | https://arxiv.org/pdf/2304.09928v1.pdf | Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline | Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing work trains models on an entire group of participants, failing to capt... | ['Laura E. Barnes', 'Mehdi Boukhechba', 'Bethany A. Teachman', 'Congyu Wu', 'Mark Rucker', 'Emma R. Toner', 'Maria A. Larrazabal', 'Mingyue Tang', 'Zhiyuan Wang'] | 2023-04-19 | null | null | null | null | ['anxiety-detection'] | ['medical'] | [ 4.17813092e-01 3.39528054e-01 -2.88029253e-01 -8.84780407e-01
-1.10341477e+00 -5.76499701e-01 1.77969739e-01 7.99921870e-01
-1.93331018e-01 2.01322943e-01 4.85427082e-01 1.53929442e-01
-2.00754076e-01 -5.80064595e-01 -5.82256652e-02 2.77158529e-01
-4.24532086e-01 1.18427522e-01 -2.49336764e-01 -2.09700376... | [8.786940574645996, 10.35095500946045] |
ca0b153b-e078-420c-b21a-538ebc8b63a7 | timemae-self-supervised-representations-of | 2303.00320 | null | https://arxiv.org/abs/2303.00320v3 | https://arxiv.org/pdf/2303.00320v3.pdf | TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders | Enhancing the expressive capacity of deep learning-based time series models with self-supervised pre-training has become ever-increasingly prevalent in time series classification. Even though numerous efforts have been devoted to developing self-supervised models for time series data, we argue that the current methods ... | ['Enhong Chen', 'Rujiao Zhang', 'Hao Zhang', 'Zhiding Liu', 'Qi Liu', 'Mingyue Cheng'] | 2023-03-01 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 2.97427118e-01 -6.82900473e-02 -4.21724059e-02 -2.98205495e-01
-4.80735630e-01 -6.07475579e-01 6.35689497e-01 4.56363186e-02
-2.71411240e-01 3.50717753e-01 3.82633865e-01 -3.60076934e-01
-1.54573232e-01 -9.91396785e-01 -7.21437275e-01 -9.27996814e-01
-6.02185011e-01 6.85696080e-02 -2.76618898e-02 -3.51193845... | [7.246419906616211, 3.0421650409698486] |
29a0b056-b162-4bec-b18b-631302b8f0df | wide-range-mri-artifact-removal-with | 2210.07976 | null | https://arxiv.org/abs/2210.07976v2 | https://arxiv.org/pdf/2210.07976v2.pdf | Wide Range MRI Artifact Removal with Transformers | Artifacts on magnetic resonance scans are a serious challenge for both radiologists and computer-aided diagnosis systems. Most commonly, artifacts are caused by motion of the patients, but can also arise from device-specific abnormalities such as noise patterns. Irrespective of the source, artifacts can not only render... | ['Kevin Smith', 'Lennart Alexander Van der Goten'] | 2022-10-14 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 3.96283329e-01 3.26892018e-01 1.47043973e-01 -6.32833391e-02
-5.88335216e-01 -1.25548005e-01 2.91673809e-01 1.19331680e-01
-2.75572419e-01 9.12646234e-01 2.13002607e-01 -4.47553694e-01
-3.59376699e-01 -6.39706135e-01 -8.30841184e-01 -6.99540615e-01
-1.27867147e-01 5.57699978e-01 5.20827115e-01 8.63488689... | [13.571914672851562, -2.519087076187134] |
65d4a0b4-4c37-455f-b21b-1e0409761be2 | confronting-ambiguity-in-6d-object-pose | 2305.15873 | null | https://arxiv.org/abs/2305.15873v1 | https://arxiv.org/pdf/2305.15873v1.pdf | Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3) | Addressing accuracy limitations and pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge, particularly due to object symmetries or occlusions. In response, we introduce a novel score-based diffusion method applied to the $SE(3)$ group, marking the first application of diff... | ['Chun-Yi Lee', 'Hsuan-Kung Yang', 'Hao-Wei Chen', 'Tsu-Ching Hsiao'] | 2023-05-25 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 5.05261011e-02 -7.21141621e-02 2.53399014e-01 -2.34984696e-01
-1.11060154e+00 -7.07646370e-01 2.91673213e-01 -2.25996330e-01
-5.22312462e-01 3.45488369e-01 -7.08187670e-02 3.20654631e-01
-5.01740277e-01 -2.84332752e-01 -5.72318792e-01 -7.35580981e-01
-1.67077124e-01 6.35644138e-01 -1.29842060e-02 1.19000554... | [7.2125654220581055, -2.3331751823425293] |
475b2018-4718-4f71-8a19-9ca24db08c77 | self-supervised-machine-learning-model-for | 2203.13875 | null | https://arxiv.org/abs/2203.13875v2 | https://arxiv.org/pdf/2203.13875v2.pdf | Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images | In the field of materials science, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine learning methods that can automate the analysis and interpretation of microscopy images. Typically training of machine learning models re... | ['Arthi Jayaraman', 'Todd Emrick', 'Brian Montz', 'Shizhao Lu'] | 2022-03-25 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [ 8.18100393e-01 1.26036450e-01 1.34438887e-01 -6.20617926e-01
-5.91266215e-01 -8.24310124e-01 3.04800242e-01 4.45945770e-01
-7.43144870e-01 7.10849047e-01 -5.59304774e-01 -6.52757704e-01
2.82728702e-01 -8.16425025e-01 -8.97527456e-01 -9.10220325e-01
2.62388825e-01 9.64547932e-01 3.55420887e-01 2.32426792... | [14.247958183288574, -2.9848368167877197] |
2446638f-9ee1-4899-af06-7ea1208b512e | a-deep-learning-based-pipeline-for-efficient | 1910.10549 | null | https://arxiv.org/abs/1910.10549v3 | https://arxiv.org/pdf/1910.10549v3.pdf | A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images | Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose a fully automated pipeline for oral cancer detection on whole slide cytology images. The pipeline consists of fully convolutional regression... | ['Jan-Michaél Hirsch', 'Nataša Sladoje', 'Jiahao Lu', 'Christina Runow Stark', 'Joakim Lindblad', 'Eva Darai Ramqvist'] | 2019-10-23 | null | null | null | null | ['oral-cancer-classification'] | ['medical'] | [ 2.26004943e-01 -2.08520025e-01 -5.86281955e-01 9.82135311e-02
-1.56242275e+00 -4.65429574e-01 2.09289446e-01 7.94349194e-01
-7.09205329e-01 6.39440417e-01 -2.96783098e-03 -6.45586669e-01
3.88700634e-01 -8.34725380e-01 -2.95941561e-01 -1.14417124e+00
3.24024975e-01 5.80831707e-01 2.52837360e-01 2.15445474... | [15.126811027526855, -3.1064934730529785] |
183d6377-4b0f-444d-8fc3-bddc504c13ff | temporally-consistent-horizon-lines | 1907.10014 | null | https://arxiv.org/abs/1907.10014v2 | https://arxiv.org/pdf/1907.10014v2.pdf | Temporally Consistent Horizon Lines | The horizon line is an important geometric feature for many image processing and scene understanding tasks in computer vision. For instance, in navigation of autonomous vehicles or driver assistance, it can be used to improve 3D reconstruction as well as for semantic interpretation of dynamic environments. While both a... | ['Bodo Rosenhahn', 'Florian Kluger', 'Michael Ying Yang', 'Hanno Ackermann'] | 2019-07-23 | null | null | null | null | ['horizon-line-estimation'] | ['computer-vision'] | [ 2.96790600e-01 -2.75145829e-01 -1.54958859e-01 -6.98047519e-01
-4.49024469e-01 -2.58392125e-01 6.05237126e-01 -1.71509594e-01
-7.43695855e-01 5.19106328e-01 -2.98476726e-01 -3.85865510e-01
-2.78410107e-01 -6.78070009e-01 -1.02200019e+00 -5.33423603e-01
-2.40032062e-01 3.31873633e-02 5.97952127e-01 -1.02220662... | [8.357603073120117, -1.7098510265350342] |
692bd1ab-ebce-4118-b10e-210071c6cdfd | depth-from-semi-calibrated-stereo-and-defocus | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Depth_From_Semi-Calibrated_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Depth_From_Semi-Calibrated_CVPR_2016_paper.pdf | Depth From Semi-Calibrated Stereo and Defocus | In this work, we propose a multi-camera system where we combine a main high-quality camera with two low-res auxiliary cameras. The auxiliary cameras are well calibrated and act as a passive depth sensor by generating disparity maps. The main camera has an interchangeable lens and can produce good quality images at high... | ['Ting-Chun Wang', 'Ravi Ramamoorthi', 'Manohar Srikanth'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['stereo-matching'] | ['computer-vision'] | [ 4.44943756e-01 -2.29102075e-01 2.80494690e-01 -2.48801317e-02
-7.10553348e-01 -5.98885417e-01 1.70277163e-01 -4.15715665e-01
-5.49076557e-01 6.21030927e-01 -1.57509476e-01 -6.74157962e-02
4.33629423e-01 -9.64793682e-01 -6.53259397e-01 -8.39423835e-01
9.82080758e-01 1.97450787e-01 7.46287167e-01 7.81641155... | [9.2568359375, -2.5644049644470215] |
fce42baa-6ac1-466e-81a8-65c4c5390a05 | centroid-based-text-summarization-through | null | null | https://aclanthology.org/W17-1003 | https://aclanthology.org/W17-1003.pdf | Centroid-based Text Summarization through Compositionality of Word Embeddings | The textual similarity is a crucial aspect for many extractive text summarization methods. A bag-of-words representation does not allow to grasp the semantic relationships between concepts when comparing strongly related sentences with no words in common. To overcome this issue, in this paper we propose a centroid-base... | ['Pierpaolo Basile', 'Giovanni Semeraro', 'Gaetano Rossiello'] | 2017-04-01 | null | null | null | ws-2017-4 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 9.17896181e-02 1.90180540e-01 -2.02921540e-01 -2.88046032e-01
-5.88054180e-01 -2.44127855e-01 1.02603590e+00 1.00589037e+00
-7.78172016e-01 7.60524511e-01 1.03382325e+00 -6.47311807e-02
-3.08851331e-01 -6.99419975e-01 -2.77248204e-01 -6.45498812e-01
1.58897445e-01 4.48706746e-01 2.59394765e-01 -5.60682237... | [12.39920425415039, 9.479446411132812] |
fe990d25-188b-4a21-8806-dffe9ed52f22 | global-autoregressive-models-for-data | 1909.07063 | null | https://arxiv.org/abs/1909.07063v2 | https://arxiv.org/pdf/1909.07063v2.pdf | Global Autoregressive Models for Data-Efficient Sequence Learning | Standard autoregressive seq2seq models are easily trained by max-likelihood, but tend to show poor results under small-data conditions. We introduce a class of seq2seq models, GAMs (Global Autoregressive Models), which combine an autoregressive component with a log-linear component, allowing the use of global \textit{a... | ['Jean-Marc Andreoli', 'Marc Dymetman', 'Tetiana Parshakova'] | 2019-09-16 | global-autoregressive-models-for-data-1 | https://aclanthology.org/K19-1084 | https://aclanthology.org/K19-1084.pdf | conll-2019-11 | ['small-data'] | ['computer-vision'] | [ 7.68787637e-02 3.04758549e-01 2.11588308e-01 -5.71986556e-01
-9.94184196e-01 -5.44480324e-01 7.99090147e-01 -2.33714655e-01
-5.96091747e-01 8.97490919e-01 5.19917309e-01 -4.66299951e-01
1.94048166e-01 -8.26285362e-01 -7.00172901e-01 -8.39190662e-01
1.02455206e-02 7.79553294e-01 9.45444405e-02 -2.42558613... | [11.917976379394531, 9.128934860229492] |
0b01496a-777b-4b5a-8b97-6a5b8230470d | a-deep-optimization-approach-for-image | 1904.07516 | null | http://arxiv.org/abs/1904.07516v1 | http://arxiv.org/pdf/1904.07516v1.pdf | A Deep Optimization Approach for Image Deconvolution | In blind image deconvolution, priors are often leveraged to constrain the
solution space, so as to alleviate the under-determinacy. Priors which are
trained separately from the task of deconvolution tend to be instable, or
ineffective. We propose the Golf Optimizer, a novel but simple form of network
that learns deep p... | ['Zhijian Luo', 'Siyu Chen', 'Yuntao Qian'] | 2019-04-16 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [-1.81917965e-01 2.22637519e-01 5.93493059e-02 -2.04814836e-01
-5.56984186e-01 -2.91808277e-01 2.72602916e-01 -8.17294419e-01
-3.68319213e-01 6.85795605e-01 8.72103095e-01 -3.22390914e-01
3.13286334e-02 -2.56229013e-01 -9.50804114e-01 -7.72529125e-01
1.41135976e-01 1.74187034e-01 3.54668908e-02 -4.19298820... | [11.637998580932617, -2.6745567321777344] |
244a9c6a-7eca-4475-a0b7-0cd7b3b6ec79 | large-scale-visual-speech-recognition | 1807.05162 | null | http://arxiv.org/abs/1807.05162v3 | http://arxiv.org/pdf/1807.05162v3.pdf | Large-Scale Visual Speech Recognition | This work presents a scalable solution to open-vocabulary visual speech
recognition. To achieve this, we constructed the largest existing visual speech
recognition dataset, consisting of pairs of text and video clips of faces
speaking (3,886 hours of video). In tandem, we designed and trained an
integrated lipreading s... | ['Andrew Senior', 'Lorrayne Bennett', 'Hank Liao', 'Utsav Prabhu', 'Cían Hughes', 'Nando de Freitas', 'Ben Coppin', 'Matthew W. Hoffman', 'Kanishka Rao', 'Hasim Sak', 'Ben Laurie', 'Yannis Assael', 'Thomas Paine', 'Marie Mulville', 'Brendan Shillingford'] | 2018-07-13 | large-scale-visual-speech-recognition-1 | https://openreview.net/forum?id=HJxpDiC5tX | https://openreview.net/pdf?id=HJxpDiC5tX | iclr-2019-5 | ['lipreading'] | ['computer-vision'] | [ 1.37309432e-01 5.83285242e-02 -2.06696004e-01 -2.78495997e-01
-1.21182001e+00 -3.97329003e-01 6.66250288e-01 -3.57829690e-01
-4.94029760e-01 4.98719335e-01 4.80161756e-01 -3.83081526e-01
7.68262863e-01 -6.01697676e-02 -7.81013668e-01 -5.97408354e-01
3.59567314e-01 8.74199811e-03 1.82545766e-01 3.12193125... | [14.3401517868042, 5.008251190185547] |
7b1ba5b0-08bf-4701-b17c-a92be2b9ee44 | an-ensemble-of-convolution-based-methods-for | 2305.05532 | null | https://arxiv.org/abs/2305.05532v1 | https://arxiv.org/pdf/2305.05532v1.pdf | An ensemble of convolution-based methods for fault detection using vibration signals | This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig. Various traditional machine learning and deep learning methods have been proposed for multivariate time-series classification, including distance-based, functiona... | ['Chetan Gupta', 'Ahmed Farahat', 'Aniruddha Rajendra Rao', 'Lasitha Vidyaratne', 'Aman Kumar', 'Xian Yeow Lee'] | 2023-05-05 | null | null | null | null | ['fault-detection', 'time-series-classification'] | ['miscellaneous', 'time-series'] | [-2.88782954e-01 -7.02782214e-01 4.11845297e-01 -1.55433184e-02
-1.31044075e-01 7.17654638e-03 1.31512448e-01 -1.75586328e-01
-2.62494028e-01 5.15853643e-01 -2.23942861e-01 -3.07856768e-01
-7.32480884e-01 -8.19369018e-01 -4.27777499e-01 -5.46413958e-01
-9.48369741e-01 2.95946091e-01 3.52427334e-01 -5.80414236... | [6.832170486450195, 2.372307777404785] |
718384b7-5230-4bf8-9fe7-fb09a8d3fbd6 | self-supervised-point-cloud-completion-on | 2203.10569 | null | https://arxiv.org/abs/2203.10569v1 | https://arxiv.org/pdf/2203.10569v1.pdf | Self-supervised Point Cloud Completion on Real Traffic Scenes via Scene-concerned Bottom-up Mechanism | Real scans always miss partial geometries of objects due to the self-occlusions, external-occlusions, and limited sensor resolutions. Point cloud completion aims to refer the complete shapes for incomplete 3D scans of objects. Current deep learning-based approaches rely on large-scale complete shapes in the training pr... | ['Yuexin Ma', 'Xinge Zhu', 'Peishan Cong', 'Yiming Ren'] | 2022-03-20 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-2.08257630e-01 -3.12209390e-02 1.41829193e-01 -6.90827429e-01
-6.61234081e-01 -3.84476930e-01 4.78420436e-01 -3.08345854e-01
-2.76102647e-02 2.52076149e-01 -2.82482475e-01 -3.00203949e-01
1.51044950e-01 -1.06670403e+00 -1.21300399e+00 -3.55931699e-01
1.06400348e-01 9.41100955e-01 7.30480850e-01 -2.68879116... | [8.16415786743164, -3.08120059967041] |
873f515f-37a1-4fd6-b17c-b1700a49b38f | computing-the-ensemble-spread-from | 2205.09182 | null | https://arxiv.org/abs/2205.09182v1 | https://arxiv.org/pdf/2205.09182v1.pdf | Computing the ensemble spread from deterministic weather predictions using conditional generative adversarial networks | Ensemble prediction systems are an invaluable tool for weather forecasting. Practically, ensemble predictions are obtained by running several perturbations of the deterministic control forecast. However, ensemble prediction is associated with a high computational cost and often involves statistical post-processing step... | ['Alex Bihlo', 'Rüdiger Brecht'] | 2022-05-18 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 6.90571815e-02 -1.80662990e-01 4.17186171e-01 -7.73929477e-01
-7.90990949e-01 -7.70117342e-01 8.97095382e-01 -4.33436073e-02
-9.20515433e-02 1.07577169e+00 1.08133078e-01 -6.57220721e-01
-1.88422240e-02 -9.54065204e-01 -5.92485905e-01 -1.14765465e+00
-2.50007480e-01 4.67878103e-01 -3.74977559e-01 -4.49806333... | [6.580016613006592, 3.0071358680725098] |
424df873-513a-4f4e-a501-ef2d97430311 | learning-deep-context-aware-features-over | 1710.06555 | null | http://arxiv.org/abs/1710.06555v1 | http://arxiv.org/pdf/1710.06555v1.pdf | Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification | Person Re-identification (ReID) is to identify the same person across
different cameras. It is a challenging task due to the large variations in
person pose, occlusion, background clutter, etc How to extract powerful
features is a fundamental problem in ReID and is still an open problem today.
In this paper, we design ... | ['Zhang Zhang', 'Xiaotang Chen', 'Kaiqi Huang', 'Dangwei Li'] | 2017-10-18 | learning-deep-context-aware-features-over-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Li_Learning_Deep_Context-Aware_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Learning_Deep_Context-Aware_CVPR_2017_paper.pdf | cvpr-2017-7 | ['person-identification'] | ['computer-vision'] | [-3.11670661e-01 -6.86008215e-01 3.12222809e-01 -6.06006742e-01
-3.74843568e-01 -4.33798552e-01 5.12466967e-01 -3.96634191e-01
-6.88404977e-01 7.42714167e-01 4.76106405e-01 5.21610618e-01
1.06072584e-02 -4.84046966e-01 -8.06015253e-01 -7.22479582e-01
1.21988140e-01 4.20555919e-01 1.96549252e-01 -2.70992100... | [14.66459846496582, 0.8996713757514954] |
2aab9a82-518e-44fc-9b90-70ce1919e1c7 | improving-speech-related-facial-action-unit | 1706.10197 | null | http://arxiv.org/abs/1706.10197v1 | http://arxiv.org/pdf/1706.10197v1.pdf | Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion | It is challenging to recognize facial action unit (AU) from spontaneous
facial displays, especially when they are accompanied by speech. The major
reason is that the information is extracted from a single source, i.e., the
visual channel, in the current practice. However, facial activity is highly
correlated with voice... | ['Yan Tong', 'Zibo Meng', 'Ping Liu', 'Shizhong Han'] | 2017-06-29 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 1.31542355e-01 -1.07816290e-02 2.27497548e-01 -3.01027238e-01
-5.76221645e-01 -1.10206150e-01 4.34564948e-01 -3.48000646e-01
-2.43358254e-01 8.37886155e-01 3.12783927e-01 4.32918280e-01
2.22279951e-01 4.84566838e-02 -5.13468802e-01 -1.09615123e+00
2.10259557e-01 -2.70432651e-01 -2.01044083e-01 1.51643142... | [14.349449157714844, 4.975132942199707] |
b237c53e-d20c-4394-a0f7-2fe43ff1402b | semi-supervised-anomaly-detection-using | 2001.03674 | null | https://arxiv.org/abs/2001.03674v1 | https://arxiv.org/pdf/2001.03674v1.pdf | Semi-supervised Anomaly Detection using AutoEncoders | Anomaly detection refers to the task of finding unusual instances that stand out from the normal data. In several applications, these outliers or anomalous instances are of greater interest compared to the normal ones. Specifically in the case of industrial optical inspection and infrastructure asset management, findin... | ['Manpreet Singh Minhas', 'John Zelek'] | 2020-01-06 | semi-supervised-anomaly-detection-using-1 | https://openjournals.uwaterloo.ca/index.php/vsl/article/view/1654 | https://openjournals.uwaterloo.ca/index.php/vsl/article/view/1654/2021 | journal-of-computational-vision-and-imaging | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 6.05640769e-01 2.72527754e-01 6.97934628e-01 -3.60804021e-01
-4.33255643e-01 -1.59761146e-01 3.10784757e-01 6.15077019e-01
-1.47584200e-01 3.25702637e-01 -5.51101685e-01 -1.58235565e-01
-1.94315817e-02 -6.45068228e-01 -6.67962909e-01 -9.78937268e-01
-3.29306036e-01 4.08541024e-01 4.43545491e-01 -1.86745122... | [7.491888999938965, 2.0817506313323975] |
82e3c928-d9ac-4833-a965-5db8c8d260f8 | streaming-submodular-maximization-under-a-k-1 | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1126-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1126-Paper.pdf | Streaming Submodular Maximization under a k-Set System Constraint | In this paper, we propose a novel framework that converts streaming algorithms for monotone submodular maximization into streaming algorithms for non-monotone submodular maximization. This reduction readily leads to the currently tightest deterministic approximation ratio for submodular maximization subject to a $k$-ma... | ['Amin Karbasi', 'Moran Feldman', 'Ran Haba', 'Ehsan Kazemi'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1126-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1126-Paper.pdf | icml-2020-1 | ['movie-recommendation', 'data-summarization'] | ['miscellaneous', 'miscellaneous'] | [ 1.30683750e-01 5.20406187e-01 -5.86611032e-01 -3.57498944e-01
-9.15114880e-01 -9.13211644e-01 -3.27735811e-01 4.44299787e-01
-2.26755321e-01 8.25328231e-01 3.17837536e-01 5.42791337e-02
-7.97680199e-01 -9.67562139e-01 -7.86276042e-01 -5.85525751e-01
-6.30194902e-01 7.77420759e-01 -2.34863162e-02 -3.98371965... | [6.603572845458984, 4.949862480163574] |
c19f007c-b511-459b-a56a-fab2dd5631b5 | design-implementation-and-evaluation-of-an | 2305.04226 | null | https://arxiv.org/abs/2305.04226v1 | https://arxiv.org/pdf/2305.04226v1.pdf | Design, Implementation and Evaluation of an External Pose-Tracking System for Underwater Cameras | In order to advance underwater computer vision and robotics from lab environments and clear water scenarios to the deep dark ocean or murky coastal waters, representative benchmarks and realistic datasets with ground truth information are required. In particular, determining the camera pose is essential for many underw... | ['Kevin Köser', 'Felix Woelk', 'David Nakath', 'Birger Winkel'] | 2023-05-07 | null | null | null | null | ['pose-tracking', 'simultaneous-localization-and-mapping'] | ['computer-vision', 'computer-vision'] | [ 8.47873464e-02 1.50605798e-01 1.19964659e+00 -3.81925553e-01
-4.23045754e-01 -8.43904018e-01 2.46707007e-01 -1.11391641e-01
-1.09264290e+00 7.22909868e-01 -2.72689253e-01 7.02579916e-02
-2.78664321e-01 -6.33164704e-01 -8.11084688e-01 -7.54581213e-01
-2.26864934e-01 5.20600915e-01 4.21062231e-01 -4.26220328... | [7.502319812774658, -1.7634952068328857] |
62cbecdb-252f-4b29-b856-3678e4061e00 | cdf-transform-shift-an-effective-way-to-deal | 1810.02897 | null | https://arxiv.org/abs/1810.02897v3 | https://arxiv.org/pdf/1810.02897v3.pdf | CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities | The problem of inhomogeneous cluster densities has been a long-standing issue for distance-based and density-based algorithms in clustering and anomaly detection. These algorithms implicitly assume that all clusters have approximately the same density. As a result, they often exhibit a bias towards dense clusters in th... | ['Maia Angelova', 'Ye Zhu', 'Mark Carman', 'Kai Ming Ting'] | 2018-10-05 | null | null | null | null | ['clustering-algorithms-evaluation'] | ['methodology'] | [-1.86533958e-01 -4.13477607e-02 -9.16893873e-03 -2.86799759e-01
-3.75224292e-01 -5.52583575e-01 4.98952597e-01 3.90119880e-01
-3.10149103e-01 4.93167371e-01 -4.07295637e-02 -1.34760380e-01
-3.55926216e-01 -8.83645594e-01 -3.28788280e-01 -1.00854301e+00
-3.21809612e-02 7.81815529e-01 7.49962747e-01 6.28342256... | [7.546825885772705, 4.571656227111816] |
81fd8e97-4c0a-42eb-94c8-975b34e5c3c1 | avoid-overfitting-user-specific-information | 2206.08864 | null | https://arxiv.org/abs/2206.08864v1 | https://arxiv.org/pdf/2206.08864v1.pdf | Avoid Overfitting User Specific Information in Federated Keyword Spotting | Keyword spotting (KWS) aims to discriminate a specific wake-up word from other signals precisely and efficiently for different users. Recent works utilize various deep networks to train KWS models with all users' speech data centralized without considering data privacy. Federated KWS (FedKWS) could serve as a solution ... | ['De-Chuan Zhan', 'Le Gan', 'Yunfeng Shao', 'Yinchuan Li', 'Bingshuai Li', 'Shaoming Song', 'Jin-Lin Tang', 'Xin-Chun Li'] | 2022-06-17 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-3.14697176e-01 1.99431833e-02 -1.88427597e-01 -6.69153333e-01
-9.26194310e-01 -5.44788539e-01 2.70561606e-01 -6.07548654e-01
-3.82371932e-01 6.74894929e-01 3.43914747e-01 -3.35249901e-01
1.16508221e-02 -5.14880002e-01 -5.29253840e-01 -8.14861655e-01
2.23381653e-01 7.50759467e-02 4.43851613e-02 -1.62487209... | [5.804357528686523, 6.387543201446533] |
040b5bbf-de78-4e4f-8114-25b9725a9f43 | spelling-error-correction-with-soft-masked | 2005.07421 | null | https://arxiv.org/abs/2005.07421v1 | https://arxiv.org/pdf/2005.07421v1.pdf | Spelling Error Correction with Soft-Masked BERT | Spelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability. Without loss of generality we consider Chinese spelling error correction (CSC) in this paper. A state-of-the-art method for the task selects a character from... | ['Shaohua Zhang', 'Hang Li', 'Haoran Huang', 'Jicong Liu'] | 2020-05-15 | spelling-error-correction-with-soft-masked-1 | https://aclanthology.org/2020.acl-main.82 | https://aclanthology.org/2020.acl-main.82.pdf | acl-2020-6 | ['csc'] | ['natural-language-processing'] | [ 5.01014411e-01 -3.19326997e-01 2.43498400e-01 -1.79457992e-01
-7.50849068e-01 -1.91281885e-01 3.26977760e-01 4.97833282e-01
-8.21915090e-01 8.71279716e-01 5.57514839e-02 -5.62606871e-01
3.49797189e-01 -6.36963367e-01 -6.93915486e-01 -5.93037128e-01
3.98373634e-01 1.95249781e-01 6.21536434e-01 -4.13113922... | [10.956887245178223, 10.80453109741211] |
a175efa5-c9da-4f11-b2c3-1a976ec7cb8f | gradient-surgery-for-one-shot-unlearning-on | 2307.04550 | null | https://arxiv.org/abs/2307.04550v1 | https://arxiv.org/pdf/2307.04550v1.pdf | Gradient Surgery for One-shot Unlearning on Generative Model | Recent regulation on right-to-be-forgotten emerges tons of interest in unlearning pre-trained machine learning models. While approximating a straightforward yet expensive approach of retrain-from-scratch, recent machine unlearning methods unlearn a sample by updating weights to remove its influence on the weight parame... | ['Woohyung Lim', 'Hyemin Jung', 'Seoyoon Kim', 'Seohui Bae'] | 2023-07-10 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 4.85541701e-01 3.81204069e-01 -2.79310226e-01 -3.14766914e-01
-5.23058712e-01 -5.49917758e-01 7.80539274e-01 -2.85235584e-01
-7.22076058e-01 8.39404941e-01 5.66975057e-01 -6.30060881e-02
-2.44512245e-01 -5.35895050e-01 -1.07929909e+00 -9.38813806e-01
3.44501823e-01 4.33566719e-01 -4.12887335e-02 -5.06867953... | [8.398063659667969, 3.590689182281494] |
2766a0bd-36ff-4dbf-8ca7-c09fed1bf715 | human-machine-knowledge-hybrid-augmentation | 2304.13963 | null | https://arxiv.org/abs/2304.13963v2 | https://arxiv.org/pdf/2304.13963v2.pdf | Human-machine knowledge hybrid augmentation method for surface defect detection based few-data learning | Visual-based defect detection is a crucial but challenging task in industrial quality control. Most mainstream methods rely on large amounts of existing or related domain data as auxiliary information. However, in actual industrial production, there are often multi-batch, low-volume manufacturing scenarios with rapidly... | ['Xiaoqiao Wang', 'Yu Gong', 'ChiChun Zhou'] | 2023-04-27 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.74828583e-01 -1.50245249e-01 1.15140177e-01 3.92714189e-03
-5.45930505e-01 -1.31274208e-01 6.38313890e-02 4.97144401e-01
-1.39445558e-01 5.60956240e-01 -4.49990958e-01 5.33505082e-02
8.70828703e-02 -6.20033026e-01 -4.42332566e-01 -8.01764071e-01
3.75160456e-01 4.24631476e-01 3.57861727e-01 -2.30489448... | [7.392986297607422, 1.917964220046997] |
58626b85-a44f-457c-99dd-02409f97eb82 | the-dots-have-their-values-exploiting-the | null | null | https://aclanthology.org/2020.findings-emnlp.409 | https://aclanthology.org/2020.findings-emnlp.409.pdf | The Dots Have Their Values: Exploiting the Node-Edge Connections in Graph-based Neural Models for Document-level Relation Extraction | The goal of Document-level Relation Extraction (DRE) is to recognize the relations between entity mentions that can span beyond sentence boundary. The current state-of-the-art method for this problem has involved the graph-based edge-oriented model where the entity mentions, entities, and sentences in the documents are... | ['Thien Huu Nguyen', 'Minh Trung Nguyen', 'Hieu Minh Tran'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 5.30414470e-02 4.62296993e-01 -4.85173345e-01 -4.29497331e-01
-4.37583566e-01 -3.08550566e-01 6.13597989e-01 7.04198360e-01
-3.22398990e-02 3.60795438e-01 4.94382799e-01 -4.17584032e-01
-1.12362340e-01 -1.15553296e+00 -5.03179610e-01 -4.12709743e-01
-3.79488647e-01 1.54582754e-01 2.28636459e-01 -2.91487157... | [9.280449867248535, 8.614015579223633] |
3b83031e-c08e-4e99-bff9-0ecb0d5c91d5 | lip-listening-mixing-senses-to-understand | 2207.05692 | null | https://arxiv.org/abs/2207.05692v1 | https://arxiv.org/pdf/2207.05692v1.pdf | Lip-Listening: Mixing Senses to Understand Lips using Cross Modality Knowledge Distillation for Word-Based Models | In this work, we propose a technique to transfer speech recognition capabilities from audio speech recognition systems to visual speech recognizers, where our goal is to utilize audio data during lipreading model training. Impressive progress in the domain of speech recognition has been exhibited by audio and audio-vis... | ['Hesham M. Eraqi', 'Nourhan Sakr', 'Omar Abugabal', 'Hadeel Mabrouk'] | 2022-06-05 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 3.90809834e-01 1.70302033e-01 -3.51076484e-01 -3.03915180e-02
-9.37610686e-01 -2.94500887e-01 7.00187445e-01 -1.95185691e-01
-4.30305839e-01 5.41567743e-01 3.97279829e-01 -5.92474163e-01
3.68031234e-01 -2.45456155e-02 -7.20521629e-01 -6.85613990e-01
3.12946737e-01 1.64934248e-01 3.83527994e-01 -7.79350102... | [14.324458122253418, 5.021787166595459] |
1c9e2410-fe57-4b5f-ba4e-1b6f279810d5 | nerv-neural-representations-for-videos | 2110.13903 | null | https://arxiv.org/abs/2110.13903v1 | https://arxiv.org/pdf/2110.13903v1.pdf | NeRV: Neural Representations for Videos | We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Given a frame index, NeRV outputs the corresponding RGB image. Video encoding ... | ['Abhinav Shrivastava', 'Ser-Nam Lim', 'Yixuan Ren', 'Hanyu Wang', 'Bo He', 'Hao Chen'] | 2021-10-26 | null | http://proceedings.neurips.cc/paper/2021/hash/b44182379bf9fae976e6ae5996e13cd8-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/b44182379bf9fae976e6ae5996e13cd8-Paper.pdf | neurips-2021-12 | ['video-denoising', 'neural-network-compression', 'neural-network-compression'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 4.97822464e-01 8.24481994e-02 -1.68379828e-01 -3.16319346e-01
-2.67376125e-01 -1.06792659e-01 2.72442847e-01 -3.87054086e-01
-5.18321812e-01 4.55553085e-01 1.75222337e-01 -2.39745900e-01
2.25818396e-01 -1.05149651e+00 -1.20651066e+00 -6.59967184e-01
-3.07506998e-04 -2.31258914e-01 1.91864781e-02 5.43646924... | [11.299532890319824, -1.543127417564392] |
8658c786-f26d-44bc-92e1-f0b32f0b6df5 | unsupervised-domain-adaptation-for-semantic-3 | 2112.03241 | null | https://arxiv.org/abs/2112.03241v1 | https://arxiv.org/pdf/2112.03241v1.pdf | Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey | Semantic segmentation plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. Yet, the state-of-the-art models rely on large amount of annotated samples, which are more expensive to obtain than in tasks such as image classification... | ['Boris Chidlovskii', 'Riccardo Volpi', 'Gabriela Csurka'] | 2021-12-06 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 6.62560821e-01 -2.75859684e-02 -5.69668531e-01 -6.50714219e-01
-7.25932419e-01 -8.13357472e-01 2.18525201e-01 4.70284373e-02
-5.36105692e-01 7.03134835e-01 -2.41668582e-01 -1.03925079e-01
8.00443515e-02 -6.15715683e-01 -4.83678758e-01 -7.13790476e-01
2.48633534e-01 8.55687976e-01 7.35844016e-01 -1.34381726... | [9.644550323486328, 1.2861918210983276] |
03e2a412-d73c-4305-8ad7-efa7bb0cb2b7 | nesy4vrd-a-multifaceted-resource-for | 2305.13258 | null | https://arxiv.org/abs/2305.13258v1 | https://arxiv.org/pdf/2305.13258v1.pdf | NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection | NeSy4VRD is a multifaceted resource designed to support the development of neurosymbolic AI (NeSy) research. NeSy4VRD re-establishes public access to the images of the VRD dataset and couples them with an extensively revised, quality-improved version of the VRD visual relationship annotations. Crucially, NeSy4VRD provi... | ['Tillman Weyde', 'Giacomo Tarroni', 'Ernesto Jiménez-Ruiz', 'David Herron'] | 2023-05-22 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [-3.14863533e-01 5.23521304e-01 -5.91196775e-01 -3.86850983e-01
8.02805871e-02 -4.77286100e-01 5.32255471e-01 3.32774788e-01
-1.84897944e-01 4.79381412e-01 5.68239927e-01 -9.35866609e-02
-4.92168933e-01 -7.34417617e-01 -3.73008937e-01 7.19189271e-02
-1.63105130e-01 8.19462299e-01 5.09323418e-01 -5.19191444... | [9.03248119354248, 7.928737163543701] |
906a3215-4cba-48ad-9cca-948225f17c82 | purepos-20-a-hybrid-tool-for-morphological | null | null | https://aclanthology.org/R13-1071 | https://aclanthology.org/R13-1071.pdf | PurePos 2.0: a hybrid tool for morphological disambiguation | null | ["Attila Nov{\\'a}k", 'Gy{\\"o}rgy Orosz'] | 2013-09-01 | purepos-20-a-hybrid-tool-for-morphological-1 | https://aclanthology.org/R13-1071 | https://aclanthology.org/R13-1071.pdf | ranlp-2013-9 | ['morphological-disambiguation'] | ['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... | [-7.259118556976318, 3.5106022357940674] |
5f005039-bda1-411a-8693-357306d43f6d | revisiting-the-roles-of-text-in-text-games-1 | 2210.08384 | null | https://arxiv.org/abs/2210.08384v1 | https://arxiv.org/pdf/2210.08384v1.pdf | Revisiting the Roles of "Text" in Text Games | Text games present opportunities for natural language understanding (NLU) methods to tackle reinforcement learning (RL) challenges. However, recent work has questioned the necessity of NLU by showing random text hashes could perform decently. In this paper, we pursue a fine-grained investigation into the roles of text ... | ['Mo Yu', 'Joshua B. Tenenbaum', 'Chuang Gan', 'Shunyu Yao', 'Yi Gu'] | 2022-10-15 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.40335724e-01 6.64339483e-01 -1.50708109e-01 2.04265624e-01
-1.09612596e+00 -8.51990283e-01 9.79229510e-01 1.66781858e-01
-8.11351001e-01 8.17272544e-01 5.53254306e-01 -5.77590287e-01
-1.03379376e-01 -7.91105032e-01 -8.22831690e-01 -5.95607221e-01
1.09658107e-01 8.98881257e-01 1.38035208e-01 -5.66333473... | [3.8234121799468994, 1.38559091091156] |
69b23e40-0cc4-4ddc-a812-4b8de38c39c3 | extracting-and-modeling-durations-for-habits | null | null | https://aclanthology.org/P12-2044 | https://aclanthology.org/P12-2044.pdf | Extracting and modeling durations for habits and events from Twitter | null | ['Graham Katz', 'Jennifer Williams'] | 2012-07-01 | null | null | null | acl-2012-7 | ['game-of-chess'] | ['playing-games'] | [-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... | [-7.466550350189209, 3.7846498489379883] |
8f60f843-373b-4b85-89f1-7a8294640011 | srp-efficient-class-aware-embedding-learning | 1811.03166 | null | http://arxiv.org/abs/1811.03166v1 | http://arxiv.org/pdf/1811.03166v1.pdf | SRP: Efficient class-aware embedding learning for large-scale data via supervised random projections | Supervised dimensionality reduction strategies have been of great interest.
However, current supervised dimensionality reduction approaches are difficult
to scale for situations characterized by large datasets given the high
computational complexities associated with such methods. While stochastic
approximation strateg... | ['Ali Ghodsi', 'Amir-Hossein Karimi', 'Alexander Wong'] | 2018-11-07 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 2.30994359e-01 -1.96417391e-01 9.00228880e-03 -1.71266437e-01
-6.68727398e-01 -4.76786822e-01 7.32211947e-01 2.72599105e-02
-3.49831074e-01 4.43204582e-01 2.48956829e-01 -2.29041219e-01
-5.59707582e-01 -7.16582417e-01 -1.44846290e-01 -1.19596159e+00
-6.84095128e-03 4.01801199e-01 -8.34122449e-02 1.15878791... | [7.8506855964660645, 4.176077365875244] |
b4233166-0ca2-4fae-ba6a-9b0ca6623e29 | using-reinforcement-learning-to-learn-how-to | 1801.01999 | null | http://arxiv.org/abs/1801.01999v1 | http://arxiv.org/pdf/1801.01999v1.pdf | Using reinforcement learning to learn how to play text-based games | The ability to learn optimal control policies in systems where action space
is defined by sentences in natural language would allow many interesting
real-world applications such as automatic optimisation of dialogue systems.
Text-based games with multiple endings and rewards are a promising platform for
this task, sinc... | ['Mikuláš Zelinka'] | 2018-01-06 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-1.62741859e-02 4.42687660e-01 -2.67964542e-01 -2.74720013e-01
-5.15642524e-01 -8.66341650e-01 1.18435800e+00 -5.88595010e-02
-7.97657490e-01 1.08804488e+00 4.00070488e-01 -6.50568128e-01
-6.18615039e-02 -8.75496089e-01 -2.60999173e-01 -3.57382327e-01
-1.92187771e-01 9.76565301e-01 5.32241046e-01 -1.02416837... | [3.7843902111053467, 1.4621535539627075] |
6655c3b2-1fba-4dc5-8e89-f203e01d3a06 | a-simple-generative-model-of-logical | 2305.11098 | null | https://arxiv.org/abs/2305.11098v1 | https://arxiv.org/pdf/2305.11098v1.pdf | A Simple Generative Model of Logical Reasoning and Statistical Learning | Statistical learning and logical reasoning are two major fields of AI expected to be unified for human-like machine intelligence. Most existing work considers how to combine existing logical and statistical systems. However, there is no theory of inference so far explaining how basic approaches to statistical learning ... | ['Hiroyuki Kido'] | 2023-05-18 | null | null | null | null | ['bayesian-inference', 'logical-reasoning', 'formal-logic'] | ['methodology', 'reasoning', 'reasoning'] | [ 4.24757041e-02 7.58712888e-01 -2.30499059e-01 -7.02811062e-01
-2.35370591e-01 -3.53357941e-01 1.11253619e+00 1.05116628e-01
-4.33272749e-01 9.49730098e-01 1.57057300e-01 -7.53156483e-01
-9.65582848e-01 -8.79154146e-01 -8.60250413e-01 -5.92974544e-01
7.72527456e-02 8.94378185e-01 2.89967299e-01 -8.02547857... | [8.66307544708252, 6.58110237121582] |
79708990-a2d4-4b74-bfdf-83517f946f51 | multi-modal-learning-for-au-detection-based | 2203.11441 | null | https://arxiv.org/abs/2203.11441v1 | https://arxiv.org/pdf/2203.11441v1.pdf | Multi-Modal Learning for AU Detection Based on Multi-Head Fused Transformers | Multi-modal learning has been intensified in recent years, especially for applications in facial analysis and action unit detection whilst there still exist two main challenges in terms of 1) relevant feature learning for representation and 2) efficient fusion for multi-modalities. Recently, there are a number of works... | ['Lijun Yin', 'Xiang Zhang'] | 2022-03-22 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 3.22594255e-01 -1.80274129e-01 -1.24141708e-01 7.21382443e-03
-1.24497426e+00 3.28231230e-02 5.52038014e-01 -1.03019148e-01
-4.22509074e-01 2.15763018e-01 4.26764488e-01 5.49445033e-01
2.95491368e-01 -8.02600324e-01 -6.01141334e-01 -8.66944790e-01
2.99204826e-01 7.24751363e-03 3.64421457e-01 -2.83996940... | [13.605030059814453, 1.6230545043945312] |
c224c7a6-b746-4b95-88aa-2bed1457370a | visual-entailment-task-for-visually-grounded | 1811.10582 | null | http://arxiv.org/abs/1811.10582v2 | http://arxiv.org/pdf/1811.10582v2.pdf | Visual Entailment Task for Visually-Grounded Language Learning | We introduce a new inference task - Visual Entailment (VE) - which differs
from traditional Textual Entailment (TE) tasks whereby a premise is defined by
an image, rather than a natural language sentence as in TE tasks. A novel
dataset SNLI-VE (publicly available at https://github.com/necla-ml/SNLI-VE) is
proposed for ... | ['Ning Xie', 'Derek Doran', 'Farley Lai', 'Asim Kadav'] | 2018-11-26 | null | null | null | null | ['grounded-language-learning', 'visual-entailment'] | ['natural-language-processing', 'reasoning'] | [ 3.98931792e-03 2.73528963e-01 -4.58579212e-02 -6.76532149e-01
-6.86725199e-01 -7.85236955e-01 9.47440565e-01 -1.52405009e-01
-2.88225085e-01 4.30372953e-01 4.38924253e-01 -9.48727131e-01
3.59244823e-01 -7.35132277e-01 -1.22499883e+00 7.61784315e-02
3.28377247e-01 6.12971187e-01 -2.29515716e-01 -2.03981206... | [10.845710754394531, 1.746693730354309] |
a8a83837-0b99-4fc3-a2e4-c8879f086656 | block-wise-partitioning-for-extreme-multi | 1811.01305 | null | http://arxiv.org/abs/1811.01305v1 | http://arxiv.org/pdf/1811.01305v1.pdf | Block-wise Partitioning for Extreme Multi-label Classification | Extreme multi-label classification aims to learn a classifier that annotates
an instance with a relevant subset of labels from an extremely large label set.
Many existing solutions embed the label matrix to a low-dimensional linear
subspace, or examine the relevance of a test instance to every label via a
linear scan. ... | ['Thomas C. M. Lee', 'Cho-Jui Hsieh', 'Yuefeng Liang'] | 2018-11-04 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 5.98462045e-01 4.13170271e-02 -6.87958479e-01 -6.63564086e-01
-1.13631499e+00 -9.92855966e-01 5.77373058e-02 3.97529334e-01
-3.49731185e-02 5.61885238e-01 -4.15575296e-01 -9.78922322e-02
-4.40490454e-01 -5.92382073e-01 -1.94425732e-01 -1.12960875e+00
2.11807713e-01 9.90965426e-01 -1.83403686e-01 6.00644171... | [9.46960735321045, 4.31952428817749] |
44f32ed0-73ce-4fae-a648-1b3d269fb598 | massive-a-1m-example-multilingual-natural | 2204.08582 | null | https://arxiv.org/abs/2204.08582v2 | https://arxiv.org/pdf/2204.08582v2.pdf | MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages | We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation. MASSIVE contains 1M realistic, parallel, labeled virtual assistant utterances spanning 51 languages, 18 domains, 60 intents, and 55 slots. MASSIVE was created by ta... | ['Prem Natarajan', 'Gokhan Tur', 'Wouter Leeuwis', 'Misha Britan', 'Laurie Crist', 'Swetha Ranganath', 'Richa Singh', 'Vishesh Kakarala', 'Liam Urbach', 'Aaron Nash', 'Ana Sanchez', 'Kay Rottmann', 'Scott Mackie', 'Charith Peris', 'Christopher Hench', 'Jack FitzGerald'] | 2022-04-18 | null | null | null | null | ['zero-shot-slot-filling', 'xlm-r', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.27579682e-02 1.03541188e-01 -8.54034722e-01 -4.30208087e-01
-1.20582652e+00 -9.66663241e-01 4.84445274e-01 1.35583058e-01
-7.60588109e-01 9.06333327e-01 5.55877209e-01 -8.61581147e-01
3.44548076e-01 -9.56324711e-02 -5.00904441e-01 4.26359653e-01
4.18217212e-01 1.43549347e+00 -1.96075186e-01 -3.76618862... | [12.192140579223633, 8.619315147399902] |
dd0b2e5d-a542-45b6-821b-dc8a1c4d19aa | unsupervised-contrastive-learning-based | 2205.00122 | null | https://arxiv.org/abs/2205.00122v1 | https://arxiv.org/pdf/2205.00122v1.pdf | Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection | Early detection of lung nodules with computed tomography (CT) is critical for the longer survival of lung cancer patients and better quality of life. Computer-aided detection/diagnosis (CAD) is proven valuable as a second or concurrent reader in this context. However, accurate detection of lung nodules remains a challe... | ['Ge Wang', 'Chuang Niu'] | 2022-04-30 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 1.50453463e-01 1.06348895e-01 -2.38545388e-01 -2.00274084e-02
-9.76393104e-01 -4.04588223e-01 3.52147639e-01 1.05798647e-01
-2.78398544e-01 8.43259878e-03 1.63196400e-01 -7.63111591e-01
2.62705889e-02 -9.56304729e-01 -5.01529455e-01 -6.01146638e-01
-1.38931200e-01 7.90015578e-01 9.03387904e-01 2.90919393... | [15.400002479553223, -2.1304943561553955] |
7b250b60-dfd9-4f05-abaf-deab4468c724 | a-cascaded-approach-for-ultraly-high | 2306.16036 | null | https://arxiv.org/abs/2306.16036v1 | https://arxiv.org/pdf/2306.16036v1.pdf | A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans | Liver cancer has high morbidity and mortality rates in the world. Multi-phase CT is a main medical imaging modality for detecting/identifying and diagnosing liver tumors. Automatically detecting and classifying liver lesions in CT images have the potential to improve the clinical workflow. This task remains challenging... | ['Ling Zhang', 'Chien-Hung Liao', 'Le Lu', 'Min Wu', 'Ke Yan', 'Chien-Wei Peng', 'Chi-Tung Cheng', 'Fakai Wang'] | 2023-06-28 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-1.21862572e-02 -2.31124699e-01 -1.65775567e-01 -5.02598137e-02
-1.03401685e+00 -7.31453776e-01 4.18571234e-01 4.81232315e-01
-1.97174907e-01 1.78921476e-01 1.10229701e-01 -5.84219694e-01
-1.09506719e-01 -5.26533306e-01 -1.62966847e-01 -1.03628862e+00
-5.29860914e-01 9.34233069e-01 5.84100425e-01 6.30807161... | [14.553059577941895, -2.6781444549560547] |
89c07b29-c0f2-4c4e-8316-57e28e486c60 | toponym-detection-in-the-bio-medical-domain-a | null | null | https://aclanthology.org/R19-1106 | https://aclanthology.org/R19-1106.pdf | Toponym Detection in the Bio-Medical Domain: A Hybrid Approach with Deep Learning | This paper compares how different machine learning classifiers can be used together with simple string matching and named entity recognition to detect locations in texts. We compare five different state-of-the-art machine learning classifiers in order to predict whether a sentence contains a location or not. Following ... | ['Tharindu Ranasinghe', 'Alistair Plum', 'Constantin Orasan'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['toponym-resolution'] | ['natural-language-processing'] | [ 3.22241604e-01 2.83405147e-02 -2.26494476e-01 -3.69388372e-01
-8.33750486e-01 -6.30142808e-01 6.00951135e-01 1.36796141e+00
-1.18489277e+00 8.87824416e-01 3.12525064e-01 -3.51833493e-01
-1.38511389e-01 -7.24791050e-01 -4.70599174e-01 -3.42363924e-01
1.81571245e-01 8.97615969e-01 2.76081979e-01 -2.49626517... | [8.642224311828613, 8.908021926879883] |
4f37c18a-c8df-47a3-abcf-22f3694c3047 | a-unified-object-counting-network-with-object | 2212.14193 | null | https://arxiv.org/abs/2212.14193v3 | https://arxiv.org/pdf/2212.14193v3.pdf | A Unified Object Counting Network with Object Occupation Prior | The counting task, which plays a fundamental role in numerous applications (e.g., crowd counting, traffic statistics), aims to predict the number of objects with various densities. Existing object counting tasks are designed for a single object class. However, it is inevitable to encounter newly coming data with new cl... | ['Qingshan Liu', 'Yuankai Qi', 'Fengna Cheng', 'Qing Wang', 'Shengqin Jiang'] | 2022-12-29 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.52113780e-01 -3.50094259e-01 -2.72903562e-01 -6.25817418e-01
-1.20946437e-01 -1.75464779e-01 4.41318631e-01 1.48796946e-01
-9.02152598e-01 9.66031373e-01 -1.90390840e-01 -1.25505686e-01
2.24506557e-01 -1.28001666e+00 -6.24521255e-01 -6.75293744e-01
5.24924472e-02 8.71667087e-01 8.67910326e-01 1.83016181... | [9.037168502807617, 0.4983205795288086] |
a9ead0e6-0634-4713-9cc5-9b0327bf8b02 | blind-identification-of-ambisonic-reduced | 2305.03558 | null | https://arxiv.org/abs/2305.03558v2 | https://arxiv.org/pdf/2305.03558v2.pdf | Blind identification of Ambisonic reduced room impulse response | Recently proposed Generalized Time-domain Velocity Vector (GTVV) is a generalization of relative room impulse response in spherical harmonic (aka Ambisonic) domain that allows for blind estimation of early-echo parameters: the directions and relative delays of individual reflections. However, the derived closed-form ex... | ['Jérôme Daniel', 'Srđan Kitić'] | 2023-05-05 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 0.25711167 -0.21046227 0.6912693 0.03600211 -0.36007532 -0.73130494
0.43300608 -0.07471137 -0.52347636 0.6447013 0.28385648 -0.29970434
-0.75560933 -0.5651467 -0.24226633 -1.095391 -0.05163706 -0.21074061
-0.20340395 -0.14116812 0.20823732 0.6840656 -1.5122162 -0.46901593
0.9433666 0.7684253 0.... | [15.173714637756348, 5.7023725509643555] |
f5ad2924-f031-4926-9453-ab41d74355fb | svldl-improved-speaker-age-estimation-using | 2210.09524 | null | https://arxiv.org/abs/2210.09524v2 | https://arxiv.org/pdf/2210.09524v2.pdf | SVLDL: Improved Speaker Age Estimation Using Selective Variance Label Distribution Learning | Estimating age from a single speech is a classic and challenging topic. Although Label Distribution Learning (LDL) can represent adjacent indistinguishable ages well, the uncertainty of the age estimate for each utterance varies from person to person, i.e., the variance of the age distribution is different. To address ... | ['Jing Xiao', 'Junqing Peng', 'Jianzong Wang', 'Zuheng Kang'] | 2022-10-18 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-3.60420704e-01 -4.44448441e-02 -1.85394138e-01 -9.41066742e-01
-9.18104649e-01 -3.35584521e-01 4.77476835e-01 9.94743630e-02
-5.45732081e-01 6.31401181e-01 3.35611224e-01 -6.70595933e-03
2.63587505e-01 -4.01408345e-01 -2.54500955e-01 -8.78671885e-01
3.54222625e-01 3.58537465e-01 9.95163918e-02 2.07967490... | [14.15589427947998, 6.023719787597656] |
53f8d7f3-9b23-41be-bdba-91227e6a5139 | euler-detecting-network-lateral-movement-via | null | null | https://www.ndss-symposium.org/ndss-paper/auto-draft-227/ | https://www.ndss-symposium.org/wp-content/uploads/2022-107A-paper.pdf | Euler: Detecting Network Lateral Movement via Scalable Temporal Link Prediction | Lateral movement is a key stage of system compromise used by advanced persistent threats. Detecting it is no
simple task. When network host logs are abstracted into discrete temporal graphs, the problem can be reframed as anomalous edge detection in an evolving network. Research in modern deep graph learning technique... | ['H. Howie Huang', 'Isaiah J. King'] | 2022-04-24 | null | null | null | ndss-2022-4 | ['edge-detection', 'dynamic-link-prediction'] | ['computer-vision', 'graphs'] | [ 1.65137619e-01 4.02413681e-02 -1.90751523e-01 -3.95443849e-02
2.56047137e-02 -5.66233277e-01 5.54792583e-01 6.27537608e-01
-1.58617407e-01 1.23228543e-01 -2.90105700e-01 -1.04569852e+00
-1.75197557e-01 -9.74372566e-01 -5.52737296e-01 -2.45449081e-01
-9.13931966e-01 7.34375000e-01 7.36878574e-01 -4.59796488... | [6.596451282501221, 5.969913959503174] |
33b9a1b6-7a8a-4cb9-8f6b-434c9fe9836f | discovering-human-object-interaction-concepts | 2203.14272 | null | https://arxiv.org/abs/2203.14272v2 | https://arxiv.org/pdf/2203.14272v2.pdf | Discovering Human-Object Interaction Concepts via Self-Compositional Learning | A comprehensive understanding of human-object interaction (HOI) requires detecting not only a small portion of predefined HOI concepts (or categories) but also other reasonable HOI concepts, while current approaches usually fail to explore a huge portion of unknown HOI concepts (i.e., unknown but reasonable combination... | ['DaCheng Tao', 'Baosheng Yu', 'Zhi Hou'] | 2022-03-27 | null | null | null | null | ['affordance-recognition', 'human-object-interaction-concept-discovery'] | ['computer-vision', 'computer-vision'] | [ 2.44330242e-01 1.72037550e-03 -1.75009489e-01 -2.60899425e-01
-5.43951929e-01 -4.96061176e-01 4.24968690e-01 3.36608350e-01
-2.53239095e-01 6.17559135e-01 -1.47973597e-01 -8.97474438e-02
-2.80860871e-01 -4.55253601e-01 -8.63779366e-01 -2.99610853e-01
-1.85600817e-01 6.64557815e-01 3.56575191e-01 9.38748792... | [9.615500450134277, 1.5424422025680542] |
b35bed02-b912-4024-aaa1-320f9ae14c58 | seed-self-supervised-distillation-for-visual-1 | 2101.04731 | null | https://arxiv.org/abs/2101.04731v2 | https://arxiv.org/pdf/2101.04731v2.pdf | SEED: Self-supervised Distillation For Visual Representation | This paper is concerned with self-supervised learning for small models. The problem is motivated by our empirical studies that while the widely used contrastive self-supervised learning method has shown great progress on large model training, it does not work well for small models. To address this problem, we propose a... | ['Zicheng Liu', 'Yezhou Yang', 'Lei Zhang', 'Lijuan Wang', 'JianFeng Wang', 'Zhiyuan Fang'] | 2021-01-12 | seed-self-supervised-distillation-for-visual | https://openreview.net/forum?id=AHm3dbp7D1D | https://openreview.net/pdf?id=AHm3dbp7D1D | iclr-2021-1 | ['unsupervised-pre-training'] | ['methodology'] | [ 2.94170737e-01 5.76176405e-01 -5.89610279e-01 -7.59825349e-01
-7.90314198e-01 -4.54084426e-01 7.43180990e-01 -5.15105091e-02
-7.78864145e-01 7.59603918e-01 2.37651080e-01 -4.83980030e-01
4.16451633e-01 -6.30152106e-01 -1.06368959e+00 -4.40959543e-01
-5.51330075e-02 7.33314395e-01 3.47677946e-01 -1.33779109... | [9.46528434753418, 2.7484960556030273] |
7897b1bc-6ca2-4470-ac5c-fc41acfa27a7 | retain-an-interpretable-predictive-model-for | 1608.05745 | null | http://arxiv.org/abs/1608.05745v4 | http://arxiv.org/pdf/1608.05745v4.pdf | RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism | Accuracy and interpretability are two dominant features of successful
predictive models. Typically, a choice must be made in favor of complex black
box models such as recurrent neural networks (RNN) for accuracy versus less
accurate but more interpretable traditional models such as logistic regression.
This tradeoff po... | ['Walter F. Stewart', 'Joshua A. Kulas', 'Mohammad Taha Bahadori', 'Jimeng Sun', 'Edward Choi', 'Andy Schuetz'] | 2016-08-19 | retain-an-interpretable-predictive-model-for-1 | http://papers.nips.cc/paper/6321-retain-an-interpretable-predictive-model-for-healthcare-using-reverse-time-attention-mechanism | http://papers.nips.cc/paper/6321-retain-an-interpretable-predictive-model-for-healthcare-using-reverse-time-attention-mechanism.pdf | neurips-2016-12 | ['disease-trajectory-forecasting'] | ['medical'] | [ 3.70138437e-01 5.65663338e-01 -4.71655101e-01 -7.40507782e-01
-7.06642866e-01 -1.16119079e-01 -2.98527945e-02 5.73045254e-01
-2.72498578e-01 5.64076126e-01 8.72020781e-01 -9.20417309e-01
-6.52032256e-01 -4.92622197e-01 -5.54299355e-01 -9.20595080e-02
-2.19398990e-01 9.63411093e-01 -9.32909548e-01 5.79100437... | [7.971344470977783, 6.274102687835693] |
86f26a85-bd00-48c7-8631-70fe5117d1fd | caco-both-positive-and-negative-samples-are | 2203.14370 | null | https://arxiv.org/abs/2203.14370v1 | https://arxiv.org/pdf/2203.14370v1.pdf | CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive Learning | As a representative self-supervised method, contrastive learning has achieved great successes in unsupervised training of representations. It trains an encoder by distinguishing positive samples from negative ones given query anchors. These positive and negative samples play critical roles in defining the objective to ... | ['Guo-Jun Qi', 'Dan Zeng', 'Yuhang Huang', 'Xiao Wang'] | 2022-03-27 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.45804822e-01 2.91253865e-01 -4.74020481e-01 -6.50804102e-01
-9.98361766e-01 -6.42070591e-01 5.50340891e-01 -1.43285498e-01
-7.00709641e-01 7.54920244e-01 -5.58498278e-02 9.47259590e-02
3.73232454e-01 -6.55161381e-01 -1.14881706e+00 -6.98994696e-01
-3.93858016e-01 4.44877207e-01 1.89295691e-02 -2.80269533... | [9.551514625549316, 2.572080373764038] |
624f9b8e-6dd2-45a0-b9b5-6a4a9a8d3809 | neural-network-extrapolations-with-g | null | null | https://openreview.net/forum?id=7t1FcJUWhi3 | https://openreview.net/pdf?id=7t1FcJUWhi3 | Neural Network Extrapolations with G-invariances from a Single Environment | Despite —or maybe because of— their astonishing capacity to fit data, neural networks are widely believed to be unable to extrapolate beyond training data distribution. This work shows that, for extrapolations based on transformation groups, a model’s inability to extrapolate is unrelated to its capacity. Rather, the s... | ['Bruno Ribeiro', 'S Chandra Mouli'] | 2021-01-01 | null | null | null | iclr-2021-1 | ['counterfactual-inference'] | ['miscellaneous'] | [ 6.44009829e-01 5.25106072e-01 -3.71145278e-01 -5.06387651e-01
-3.85503590e-01 -5.42551100e-01 9.39366996e-01 -2.11613238e-01
-6.43682837e-01 1.15247929e+00 1.05036817e-01 -8.06014240e-01
-4.20641840e-01 -7.76229918e-01 -1.38761353e+00 -5.83909452e-01
-9.31333285e-03 1.47427097e-01 5.01192510e-02 -8.58774036... | [8.5984468460083, 5.432613849639893] |
caf4aa7e-b4d1-40df-906c-e7979d2cdaf2 | beyond-frontal-faces-improving-person | 1501.05703 | null | http://arxiv.org/abs/1501.05703v2 | http://arxiv.org/pdf/1501.05703v2.pdf | Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues | We explore the task of recognizing peoples' identities in photo albums in an
unconstrained setting. To facilitate this, we introduce the new People In Photo
Albums (PIPA) dataset, consisting of over 60000 instances of 2000 individuals
collected from public Flickr photo albums. With only about half of the person
images ... | ['Rob Fergus', 'Manohar Paluri', 'Yaniv Taigman', 'Ning Zhang', 'Lubomir Bourdev'] | 2015-01-23 | beyond-frontal-faces-improving-person-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Beyond_Frontal_Faces_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Beyond_Frontal_Faces_2015_CVPR_paper.pdf | cvpr-2015-6 | ['person-recognition'] | ['computer-vision'] | [ 1.82267874e-01 -4.53572929e-01 3.53254974e-01 -5.21251202e-01
-5.24258494e-01 -7.35696852e-01 7.94692576e-01 -8.79145682e-01
-3.70915353e-01 4.92450386e-01 3.31916660e-01 7.74571598e-01
2.15334252e-01 -3.80373120e-01 -6.19186759e-01 -7.31279731e-01
-4.17215005e-02 5.07101834e-01 -4.39776182e-01 -1.15785450... | [14.30441951751709, 0.9856342077255249] |
a2affe0e-7603-4e8e-8068-fa50c0967e98 | qasr-qcri-aljazeera-speech-resource-a-large | 2106.13000 | null | https://arxiv.org/abs/2106.13000v1 | https://arxiv.org/pdf/2106.13000v1.pdf | QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus | We introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. This multi-dialect speech dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news channel. The dataset is released with lightly supervised transcriptions, aligned with the audio segments. Un... | ['Ahmed Ali', 'Shammur Absar Chowdhury', 'Amir Hussein', 'Hamdy Mubarak'] | 2021-06-24 | null | null | null | null | ['dialect-identification', 'punctuation-restoration', 'speaker-identification'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.51301488e-01 2.76677907e-01 1.35009795e-01 -7.74584293e-01
-1.69526029e+00 -6.91843510e-01 3.03057432e-01 6.69192374e-02
-3.79202753e-01 3.39824855e-01 7.23991990e-01 -5.85101128e-01
2.33218700e-01 -2.15697512e-01 -5.46874106e-01 -6.29608691e-01
-1.18805356e-01 7.36952603e-01 9.59389210e-02 -6.50598526... | [14.398089408874512, 6.824253559112549] |
e1836370-5f9e-4593-8c21-9ec30a4013b4 | uiu-net-u-net-in-u-net-for-infrared-small | 2212.00968 | null | https://arxiv.org/abs/2212.00968v1 | https://arxiv.org/pdf/2212.00968v1.pdf | UIU-Net: U-Net in U-Net for Infrared Small Object Detection | Learning-based infrared small object detection methods currently rely heavily on the classification backbone network. This tends to result in tiny object loss and feature distinguishability limitations as the network depth increases. Furthermore, small objects in infrared images are frequently emerged bright and dark, ... | ['Jocelyn Chanussot', 'Danfeng Hong', 'Xin Wu'] | 2022-12-02 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.30206782e-01 -2.43412748e-01 -3.08682412e-01 -1.86999708e-01
-6.07061028e-01 -1.18547574e-01 3.00798696e-02 -4.41236764e-01
-2.99784571e-01 4.84345138e-01 -1.49761230e-01 -1.81187794e-01
6.63986802e-02 -1.02032626e+00 -9.39542174e-01 -9.37313139e-01
8.78133699e-02 -3.41829687e-01 4.02133942e-01 -2.07568407... | [9.090545654296875, -0.889919102191925] |
cb15001f-5da4-4bd0-a673-5141352ec924 | effect-of-word-embedding-variable-parameters | 2101.02906 | null | https://arxiv.org/abs/2101.02906v1 | https://arxiv.org/pdf/2101.02906v1.pdf | Effect of Word Embedding Variable Parameters on Arabic Sentiment Analysis Performance | Social media such as Twitter, Facebook, etc. has led to a generated growing number of comments that contains users opinions. Sentiment analysis research deals with these comments to extract opinions which are positive or negative. Arabic language is a rich morphological language; thus, classical techniques of English s... | ['Nursal ARICI', 'Anwar Alnawas'] | 2021-01-08 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.84327111e-01 -2.11627722e-01 -2.73080379e-01 -4.77964729e-01
1.57984480e-01 -5.89528620e-01 4.63631600e-01 9.10035789e-01
-6.56593800e-01 5.77915907e-01 4.42717642e-01 -4.37116057e-01
1.71154156e-01 -1.02454937e+00 4.01189178e-01 -6.70695007e-01
-3.76821905e-02 5.68015426e-02 2.46236399e-01 -9.74696398... | [11.018199920654297, 6.911370277404785] |
cf563b3e-ab30-42fc-9c43-79411f2b5119 | joint-constrained-learning-for-event-event | 2010.06727 | null | https://arxiv.org/abs/2010.06727v2 | https://arxiv.org/pdf/2010.06727v2.pdf | Joint Constrained Learning for Event-Event Relation Extraction | Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. In this process, one can induce event complexes that organize multi-granular events with temporal order and membership relations interweaving among them. Due to the lack of jointly label... | ['Dan Roth', 'Hongming Zhang', 'Muhao Chen', 'Haoyu Wang'] | 2020-10-13 | null | https://aclanthology.org/2020.emnlp-main.51 | https://aclanthology.org/2020.emnlp-main.51.pdf | emnlp-2020-11 | ['temporal-relation-extraction', 'event-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.47035256e-01 2.84077555e-01 -4.58147466e-01 -5.70929527e-01
-9.87329960e-01 -7.87640512e-01 9.76547778e-01 7.69072950e-01
-5.76175153e-01 9.31100070e-01 6.19878173e-01 -2.44906694e-01
-3.07818592e-01 -8.18563759e-01 -7.44597077e-01 -1.69793442e-01
-6.07679605e-01 7.50607431e-01 2.44653150e-01 2.85104632... | [9.112794876098633, 9.158293724060059] |
a42c9abd-eba1-4cb0-b5a7-a4043242089c | expert-agnostic-ultrasound-image-quality | 2307.02462 | null | https://arxiv.org/abs/2307.02462v2 | https://arxiv.org/pdf/2307.02462v2.pdf | Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering | Ultrasound imaging is a commonly used modality for several diagnostic and therapeutic procedures. However, the diagnosis by ultrasound relies heavily on the quality of images assessed manually by sonographers, which diminishes the objectivity of the diagnosis and makes it operator-dependent. The supervised learning-bas... | ['Subir Kumar Saha', 'Richard Voyles', 'SH Chandrashekhara', 'Dimitrios Ntentia', 'Deepak Raina'] | 2023-07-05 | null | null | null | null | ['image-quality-assessment', 'clustering'] | ['computer-vision', 'methodology'] | [ 7.20745549e-02 4.49510217e-02 2.55370200e-01 -4.79145110e-01
-7.79128671e-01 -4.23024058e-01 -9.93144140e-02 5.24648130e-01
-5.18323243e-01 1.59581169e-01 5.50283790e-02 -8.87631252e-02
-7.50479698e-01 -6.46162271e-01 -1.52814195e-01 -1.11656260e+00
-3.84870410e-01 3.49647969e-01 -3.27487551e-02 2.63632655... | [14.582500457763672, -2.3132903575897217] |
610dc925-23ff-4cc8-88ef-81112f162367 | lut-gce-lookup-table-global-curve-estimation | 2306.07083 | null | https://arxiv.org/abs/2306.07083v2 | https://arxiv.org/pdf/2306.07083v2.pdf | LUT-GCE: Lookup Table Global Curve Estimation for Fast Low-light Image Enhancement | We present an effective and efficient approach for low-light image enhancement, named Lookup Table Global Curve Estimation (LUT-GCE). In contrast to existing curve-based methods with pixel-wise adjustment, we propose to estimate a global curve for the entire image that allows corrections for both under- and over-exposu... | ['Jinhui Tang', 'Jiangxin Dong', 'Changguang Wu'] | 2023-06-12 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 0.3318722 -0.4908849 0.17607233 -0.2590123 -0.8661816 -0.30246097
0.14384443 0.17313679 -0.680025 0.6483503 -0.24458934 -0.02842506
-0.11889499 -0.8996953 -0.8493633 -0.9305729 0.48734862 -0.06163694
0.54475343 -0.14202502 0.4768046 0.6355823 -1.4945544 -0.19963694
1.1550622 1.2268101 0.... | [10.811840057373047, -2.4935972690582275] |
c3d4344b-69f9-4880-b0a0-7d9837ec7c6f | a-unified-framework-for-sparse-relaxed | 1807.05411 | null | http://arxiv.org/abs/1807.05411v4 | http://arxiv.org/pdf/1807.05411v4.pdf | A Unified Framework for Sparse Relaxed Regularized Regression: SR3 | Regularized regression problems are ubiquitous in statistical modeling,
signal processing, and machine learning. Sparse regression in particular has
been instrumental in scientific model discovery, including compressed sensing
applications, variable selection, and high-dimensional analysis. We propose a
broad framework... | ['J. Nathan Kutz', 'Aleksandr Y. Aravkin', 'Travis Askham', 'Peng Zheng', 'Steven L. Brunton'] | 2018-07-14 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 4.77965057e-01 -2.58980989e-01 -4.19606179e-01 -2.35970601e-01
-1.13342226e+00 -1.41830519e-01 -6.06874749e-02 -1.06533632e-01
-8.63429829e-02 1.06586385e+00 4.41119462e-01 -6.52005747e-02
-4.13219959e-01 -3.42590809e-01 -7.34816074e-01 -8.71027589e-01
-3.73811066e-01 2.76361555e-01 -6.54770195e-01 -1.01477973... | [6.974621772766113, 4.437622547149658] |
88b8ee25-a5b2-4de5-a62b-6966ef782813 | practical-stereo-matching-via-cascaded | 2203.11483 | null | https://arxiv.org/abs/2203.11483v1 | https://arxiv.org/pdf/2203.11483v1.pdf | Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation | With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress. However, it remains a great challenge to accurately extract disparities from real-world image pairs taken by consumer-level devices like smartphones, due to practical complicating factors such as thin ... | ['Shuaicheng Liu', 'Haoqiang Fan', 'Jiangyu Liu', 'Lei Yang', 'Ziwei Yan', 'Tao Cai', 'Pengfei Xiong', 'Peisen Wang', 'Jiankun Li'] | 2022-03-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Practical_Stereo_Matching_via_Cascaded_Recurrent_Network_With_Adaptive_Correlation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Practical_Stereo_Matching_via_Cascaded_Recurrent_Network_With_Adaptive_Correlation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['stereo-matching-1'] | ['computer-vision'] | [ 4.64400589e-01 -2.46644884e-01 9.43837687e-02 -4.44752872e-01
-6.65119410e-01 -2.18906417e-01 4.91341293e-01 -2.58135498e-01
-4.57268655e-01 6.62878335e-01 4.05669153e-01 -6.39887080e-02
-5.51551543e-02 -6.55707181e-01 -8.33346963e-01 -3.61581206e-01
2.30427295e-01 8.89292359e-02 4.66541052e-01 -3.59885573... | [8.826929092407227, -2.2811875343322754] |
32b21582-2e3f-4e5d-a2e4-b9866465d6e4 | neural-voting-field-for-camera-space-3d-hand | 2305.04328 | null | https://arxiv.org/abs/2305.04328v1 | https://arxiv.org/pdf/2305.04328v1.pdf | Neural Voting Field for Camera-Space 3D Hand Pose Estimation | We present a unified framework for camera-space 3D hand pose estimation from a single RGB image based on 3D implicit representation. As opposed to recent works, most of which first adopt holistic or pixel-level dense regression to obtain relative 3D hand pose and then follow with complex second-stage operations for 3D ... | ['Zicheng Liu', 'Junsong Yuan', 'Lijuan Wang', 'Lin Liang', 'Kevin Lin', 'Chung-Ching Lin', 'Lin Huang'] | 2023-05-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Neural_Voting_Field_for_Camera-Space_3D_Hand_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Neural_Voting_Field_for_Camera-Space_3D_Hand_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-hand-pose-estimation', 'hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'computer-vision', 'graphs'] | [-1.48543924e-01 -5.51818490e-01 -4.11354899e-01 -1.56942278e-01
-1.11292315e+00 -4.46480781e-01 1.94421366e-01 -4.55919415e-01
-6.06670320e-01 2.80612230e-01 4.69057828e-01 9.53218266e-02
-2.44413689e-03 -3.40972364e-01 -6.50311887e-01 -6.61716878e-01
3.30229312e-01 9.78110254e-01 -7.19811916e-02 -2.07394548... | [6.56567907333374, -0.8334240913391113] |
b843c5b6-1a02-44a3-948c-37a07a4fb123 | one-step-knowledge-distillation-and-fine | 2305.17394 | null | https://arxiv.org/abs/2305.17394v2 | https://arxiv.org/pdf/2305.17394v2.pdf | One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification | The application of speech self-supervised learning (SSL) models has achieved remarkable performance in speaker verification (SV). However, there is a computational cost hurdle in employing them, which makes development and deployment difficult. Several studies have simply compressed SSL models through knowledge distill... | ['Ha-Jin Yu', 'Hyun-seo Shin', 'Ju-ho Kim', 'Chan-yeong Lim', 'Jungwoo Heo'] | 2023-05-27 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-1.04539551e-01 2.09234402e-01 -1.62267819e-01 -7.18043327e-01
-9.78107214e-01 -4.16435301e-01 3.80466491e-01 -6.07894287e-02
-5.53418934e-01 6.72831416e-01 3.42533708e-01 -5.73320627e-01
4.79656868e-02 -3.34875554e-01 -4.69098210e-01 -4.99356061e-01
2.79895157e-01 9.31282714e-02 2.76993960e-02 4.92620375... | [14.316469192504883, 6.190550327301025] |
b01f0c9d-c917-4988-b5f2-7cda9574d3ad | fast-and-flexible-indoor-scene-synthesis-via | 1811.12463 | null | http://arxiv.org/abs/1811.12463v1 | http://arxiv.org/pdf/1811.12463v1.pdf | Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models | We present a new, fast and flexible pipeline for indoor scene synthesis that
is based on deep convolutional generative models. Our method operates on a
top-down image-based representation, and inserts objects iteratively into the
scene by predicting their category, location, orientation and size with
separate neural ne... | ['Yu-an Lin', 'Kai Wang', 'Daniel Ritchie'] | 2018-11-29 | fast-and-flexible-indoor-scene-synthesis-via-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ritchie_Fast_and_Flexible_Indoor_Scene_Synthesis_via_Deep_Convolutional_Generative_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ritchie_Fast_and_Flexible_Indoor_Scene_Synthesis_via_Deep_Convolutional_Generative_CVPR_2019_paper.pdf | cvpr-2019-6 | ['indoor-scene-synthesis'] | ['computer-vision'] | [ 1.38822034e-01 -4.78791073e-02 6.15288615e-01 -5.59917271e-01
-2.46729180e-01 -6.11801505e-01 7.09969282e-01 -2.42771208e-01
1.60193115e-01 5.40496945e-01 4.57979470e-01 -1.79916501e-01
1.26985952e-01 -1.34728527e+00 -1.12153566e+00 -2.25817278e-01
2.61217654e-01 6.09696329e-01 4.29902762e-01 -1.60652190... | [9.204151153564453, -3.0983028411865234] |
119f8ed2-abbe-4fd5-bf41-a73cc7023efb | object-guided-instance-segmentation-for | 1911.09199 | null | https://arxiv.org/abs/1911.09199v1 | https://arxiv.org/pdf/1911.09199v1.pdf | Object-Guided Instance Segmentation for Biological Images | Instance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the objects, make instance segmentation a non-trivial task. Current box-free instance segmentation methods typically rely on local pixel-level inf... | ['Daniel J. Hoeppner', 'Dimitris N. Metaxas', 'Jingru Yi', 'Wei Fan', 'Lianyi Han', 'Bo Liu', 'Pengxiang Wu', 'Hui Tang'] | 2019-11-20 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 4.41387177e-01 6.72615916e-02 -1.41944483e-01 -3.00655931e-01
-5.84397972e-01 -3.86291325e-01 2.38088921e-01 6.86171234e-01
-3.68368208e-01 5.47276556e-01 -7.35854566e-01 1.93080425e-01
-3.87337734e-03 -8.13219726e-01 -6.33541584e-01 -1.19827938e+00
3.53656292e-01 6.02912307e-01 7.09878027e-01 2.90447205... | [9.641822814941406, 0.18241679668426514] |
ae74e055-9e23-4cc8-9674-5fb526683899 | clip-nav-using-clip-for-zero-shot-vision-and | 2211.16649 | null | https://arxiv.org/abs/2211.16649v1 | https://arxiv.org/pdf/2211.16649v1.pdf | CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation | Household environments are visually diverse. Embodied agents performing Vision-and-Language Navigation (VLN) in the wild must be able to handle this diversity, while also following arbitrary language instructions. Recently, Vision-Language models like CLIP have shown great performance on the task of zero-shot object re... | ['Gaurav S. Sukhatme', 'Jesse Thomason', 'Robinson Piramuthu', 'Gunnar Sigurdsson', 'Vishnu Sashank Dorbala'] | 2022-11-30 | null | null | null | null | ['vision-and-language-navigation'] | ['robots'] | [ 1.31320551e-01 -2.45997578e-01 8.91278535e-02 -3.48598540e-01
-7.16824651e-01 -6.37338221e-01 1.05707884e+00 2.49227211e-02
-8.53253722e-01 6.15048885e-01 3.50507170e-01 -4.12234753e-01
-1.00199521e-01 -6.36910558e-01 -7.65106678e-01 -5.74406803e-01
1.83101613e-02 4.20606673e-01 4.08917189e-01 -7.03978062... | [4.452682971954346, 0.6675182580947876] |
281f2c56-6902-4b04-86a3-aa46426c9063 | imitrob-imitation-learning-dataset-for | 2209.07976 | null | https://arxiv.org/abs/2209.07976v3 | https://arxiv.org/pdf/2209.07976v3.pdf | Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators | This paper introduces a dataset for training and evaluating methods for 6D pose estimation of hand-held tools in task demonstrations captured by a standard RGB camera. Despite the significant progress of 6D pose estimation methods, their performance is usually limited for heavily occluded objects, which is a common cas... | ['Matus Tuna', 'Jan K. Behrens', 'Radoslav Skoviera', 'Robert Babuska', 'Josef Sivic', 'Gabriela Sejnova', 'Karla Stepanova', 'Jiri Sedlar'] | 2022-09-16 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.00935608e-01 -2.60532141e-01 -1.90772936e-01 4.43435926e-03
-4.65671569e-01 -5.36025345e-01 6.57717228e-01 -6.25697494e-01
-4.26939934e-01 5.04742920e-01 -3.24431062e-01 1.10052861e-01
-7.18253031e-02 1.27866030e-01 -8.30118477e-01 -5.15933931e-01
-3.14359018e-03 9.41212893e-01 4.11733419e-01 1.00297704... | [6.371726989746094, -0.9351359605789185] |
b352813b-baf1-4916-8c31-1f92538aca1c | automatic-ischemic-stroke-lesion-segmentation | 2007.03294 | null | https://arxiv.org/abs/2007.03294v1 | https://arxiv.org/pdf/2007.03294v1.pdf | Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks | Ischemic stroke lesion segmentation from Computed Tomography Perfusion (CTP) images is important for accurate diagnosis of stroke in acute care units. However, it is challenged by low image contrast and resolution of the perfusion parameter maps, in addition to the complex appearance of the lesion. To deal with this pr... | ['Ning Huang', 'Tao Song', 'Mei Cui', 'Guotai Wang', 'Qiang Dong', 'Shaoting Zhang'] | 2020-07-07 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [ 2.68301070e-01 -2.73687363e-01 -2.60892451e-01 -5.20894229e-01
-1.17794359e+00 -4.95621204e-01 3.42282653e-01 4.29374166e-02
-6.83974504e-01 7.19832599e-01 4.54452366e-01 -2.49679849e-01
-1.41682059e-01 -8.08320701e-01 -5.01452088e-01 -8.20835233e-01
-2.69090474e-01 4.46959645e-01 6.60130143e-01 1.78805977... | [14.374393463134766, -2.1077678203582764] |
53fd165b-04d8-486d-9372-af26515a5918 | necessary-and-sufficient-polynomial | 1912.11987 | null | https://arxiv.org/abs/1912.11987v1 | https://arxiv.org/pdf/1912.11987v1.pdf | Necessary and Sufficient Polynomial Constraints on Compatible Triplets of Essential Matrices | The essential matrix incorporates relative rotation and translation parameters of two calibrated cameras. The well-known algebraic characterization of essential matrices, i.e. necessary and sufficient conditions under which an arbitrary matrix (of rank two) becomes essential, consists of a unique matrix equation of deg... | ['E. V. Martyushev'] | 2019-12-15 | null | null | null | null | ['camera-auto-calibration'] | ['computer-vision'] | [ 5.31776026e-02 -5.59622757e-02 4.94937040e-02 -3.06783170e-01
1.59819354e-03 -7.64550626e-01 6.21360481e-01 -1.58703998e-01
-2.04483867e-01 4.12790805e-01 -1.16196252e-01 -2.19219342e-01
-4.21790481e-01 -8.65688026e-02 -5.97753227e-01 -6.83344483e-01
2.66693980e-01 6.05355442e-01 -1.19756728e-01 -4.73293275... | [7.963305473327637, -2.3238656520843506] |
dc6db2aa-1247-4be9-adf4-6a6b7ad8082b | captain-comprehensive-composition-assistance | 1811.04184 | null | http://arxiv.org/abs/1811.04184v1 | http://arxiv.org/pdf/1811.04184v1.pdf | CAPTAIN: Comprehensive Composition Assistance for Photo Taking | Many people are interested in taking astonishing photos and sharing with
others. Emerging hightech hardware and software facilitate ubiquitousness and
functionality of digital photography. Because composition matters in
photography, researchers have leveraged some common composition techniques to
assess the aesthetic q... | ['James Z. Wang', 'Mohammad Mahdi Kamani', 'Farshid Farhat'] | 2018-11-10 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 4.33408707e-01 -1.93192199e-01 -1.40445381e-01 -4.68168557e-01
-5.22732794e-01 -6.11702442e-01 4.31655496e-01 -2.01156214e-01
-4.68621626e-02 2.89520193e-02 7.10947514e-01 2.90428549e-01
-2.68391967e-01 -5.01154780e-01 -3.77094507e-01 -4.05776173e-01
3.65136206e-01 7.50129372e-02 4.87513132e-02 -2.98035979... | [11.469855308532715, -0.9798357486724854] |
379338a6-0ea1-4d32-b35a-1774683c66a9 | on-graph-based-reentrancy-free-semantic | 2302.07679 | null | https://arxiv.org/abs/2302.07679v1 | https://arxiv.org/pdf/2302.07679v1.pdf | On graph-based reentrancy-free semantic parsing | We propose a novel graph-based approach for semantic parsing that resolves two problems observed in the literature: (1) seq2seq models fail on compositional generalization tasks; (2) previous work using phrase structure parsers cannot cover all the semantic parses observed in treebanks. We prove that both MAP inference... | ['Caio Corro', 'Alban Petit'] | 2023-02-15 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 4.36279386e-01 6.46631956e-01 -5.24042070e-01 -7.00165033e-01
-1.42631352e+00 -9.77942407e-01 9.52684507e-03 3.74980599e-01
-3.10769141e-01 1.03156877e+00 2.71213770e-01 -8.14617634e-01
-2.87031353e-01 -8.47629726e-01 -8.58650446e-01 -2.99019724e-01
-2.21111819e-01 1.02630556e+00 6.39575481e-01 -1.01121038... | [10.460500717163086, 9.413471221923828] |
f178ae46-c012-4814-a556-35d76bd0ad10 | learning-classifiers-of-prototypes-and | 2212.08355 | null | https://arxiv.org/abs/2212.08355v1 | https://arxiv.org/pdf/2212.08355v1.pdf | Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation | Universal Domain Adaptation aims to transfer the knowledge between the datasets by handling two shifts: domain-shift and category-shift. The main challenge is correctly distinguishing the unknown target samples while adapting the distribution of known class knowledge from source to target. Most existing methods approac... | ['In So Kweon', 'Sanghyun Woo', 'KwanYong Park', 'Inkyu Shin', 'Sungsu Hur'] | 2022-12-16 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 3.69249433e-01 -1.10308200e-01 -4.98867452e-01 -5.53351760e-01
-1.13122058e+00 -8.13441455e-01 5.58035135e-01 2.24093273e-01
-3.86692822e-01 8.27624917e-01 -1.97240040e-01 7.38144442e-02
-3.65346342e-01 -6.77959800e-01 -7.28235602e-01 -9.00826395e-01
1.41524091e-01 7.47046888e-01 4.76777077e-01 1.77532569... | [10.223665237426758, 3.12904953956604] |
d23bb5cf-f2bf-4eb9-8f62-a58fbd54c28a | data-centric-learning-from-unlabeled-graphs | 2303.10108 | null | https://arxiv.org/abs/2303.10108v1 | https://arxiv.org/pdf/2303.10108v1.pdf | Data-Centric Learning from Unlabeled Graphs with Diffusion Model | Graph property prediction tasks are important and numerous. While each task offers a small size of labeled examples, unlabeled graphs have been collected from various sources and at a large scale. A conventional approach is training a model with the unlabeled graphs on self-supervised tasks and then fine-tuning the mod... | ['Meng Jiang', 'Tengfei Luo', 'Jiaxin Xu', 'Tong Zhao', 'Eric Inae', 'Gang Liu'] | 2023-03-17 | null | null | null | null | ['graph-property-prediction'] | ['graphs'] | [ 4.02412295e-01 7.36750066e-01 -5.33342540e-01 -6.06887400e-01
-4.65698987e-01 -4.62637007e-01 4.77929503e-01 1.09537400e-01
1.45643637e-01 9.80208516e-01 2.46172696e-01 -7.28249922e-02
-5.51126786e-02 -8.35940659e-01 -6.32273793e-01 -5.67207277e-01
4.25679283e-03 8.77884150e-01 3.51293772e-01 -2.90004741... | [7.374345302581787, 6.149233818054199] |
3e8450d7-ad06-462a-9984-2111129d30ec | milan-sky-survey-a-dataset-of-raw-deep-sky | null | null | https://www.sciencedirect.com/science/article/pii/S2352340923002524 | https://www.sciencedirect.com/science/article/pii/S2352340923002524/pdfft?md5=64b4209de72fd1893bd8da68fa7fa5cd&pid=1-s2.0-S2352340923002524-main.pdf | MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope | Modern automated telescopes allow to capture astronomical images in a reproducible way. During the MILAN research project (MachIne Learning for AstroNomy), we have observed deep sky with a Stellina observation station for twelve months from the Luxembourg Greater Region. Thus, we have captured raw images of more than 1... | ['Benoît Vandame', 'Christophe Destruel', 'Gilles Krebs', 'Pierrick Bruneau', 'Patrik Hitzelberger', 'Olivier Parisot'] | 2023-04-11 | null | null | null | data-in-brief-2023-4 | ['astronomy'] | ['miscellaneous'] | [-3.93876940e-01 -9.78167802e-02 1.45191401e-01 -1.18056804e-01
-8.57758150e-02 -9.74367201e-01 1.16115165e+00 -4.92652357e-01
-5.78382671e-01 6.43351316e-01 -1.24477901e-01 -3.05283129e-01
7.70326555e-02 -6.13408446e-01 -2.80113846e-01 -1.02481234e+00
1.08134173e-01 6.15203559e-01 3.35129529e-01 2.73550004... | [7.679409027099609, 3.0558016300201416] |
26dd6ef3-5198-436c-809e-70940332b7a7 | unified-interactive-image-matting | 2205.08324 | null | https://arxiv.org/abs/2205.08324v2 | https://arxiv.org/pdf/2205.08324v2.pdf | Unified Interactive Image Matting | Recent image matting studies are developing towards proposing trimap-free or interactive methods for complete complex image matting tasks. Although avoiding the extensive labors of trimap annotation, existing methods still suffer from two limitations: (1) For the single image with multiple objects, it is essential to p... | ['Stephen D. H. Yang', 'Conghui He', 'Yiqi Lin', 'Weijia Li', 'Bin Wang'] | 2022-05-17 | null | null | null | null | ['transparent-objects', 'image-matting', 'foreground-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.59852552e-01 2.52583846e-02 5.12170978e-02 -3.99399817e-01
-6.04023635e-01 -4.67761427e-01 2.25485906e-01 -5.33672869e-01
-7.24394321e-02 3.41642708e-01 -1.65455624e-01 -3.75603825e-01
1.69805348e-01 -5.83128691e-01 -8.16161036e-01 -8.30659091e-01
5.62004805e-01 4.56609666e-01 5.36871672e-01 -5.71163893... | [10.612481117248535, -0.9170144200325012] |
e650fae8-6706-4dd7-a55d-801dcb60d109 | self-supervised-representation-learning-for-5 | 2101.12482 | null | https://arxiv.org/abs/2101.12482v4 | https://arxiv.org/pdf/2101.12482v4.pdf | Self-Supervised Pretraining for RGB-D Salient Object Detection | Existing CNNs-Based RGB-D salient object detection (SOD) networks are all required to be pretrained on the ImageNet to learn the hierarchy features which helps provide a good initialization. However, the collection and annotation of large-scale datasets are time-consuming and expensive. In this paper, we utilize self-s... | ['Xiang Ruan', 'Huchuan Lu', 'Lihe Zhang', 'Youwei Pang', 'Xiaoqi Zhao'] | 2021-01-29 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 6.73306882e-02 -2.12896895e-02 -2.07833380e-01 -5.81375718e-01
-8.03883195e-01 -2.62894601e-01 4.52181935e-01 4.30317260e-02
-4.20661062e-01 3.32816213e-01 6.16598055e-02 -1.18505999e-01
1.53483614e-01 -7.95876205e-01 -7.32803762e-01 -7.86478460e-01
3.05305928e-01 -9.77651924e-02 5.84332347e-01 -3.29425991... | [9.598716735839844, -0.9307129979133606] |
0698b585-c0e4-4d58-af8d-468509ce6cf1 | anaphora-resolution-for-machine-translation | null | null | https://aclanthology.org/F13-2023 | https://aclanthology.org/F13-2023.pdf | Anaphora Resolution for Machine Translation (R\'esolution d'anaphores et traitement des pronoms en traduction automatique \`a base de r\`egles) [in French] | null | ["Sharid Lo{\\'a}iciga"] | 2013-06-01 | anaphora-resolution-for-machine-translation-1 | https://aclanthology.org/F13-2023 | https://aclanthology.org/F13-2023.pdf | jeptalnrecital-2013-6 | ['abstract-anaphora-resolution'] | ['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... | [-7.372808456420898, 3.732131004333496] |
1e623bb4-7d44-4397-9de1-83bb72fd0fae | automated-evaluation-for-student | 2205.04083 | null | https://arxiv.org/abs/2205.04083v1 | https://arxiv.org/pdf/2205.04083v1.pdf | Automated Evaluation for Student Argumentative Writing: A Survey | This paper surveys and organizes research works in an under-studied area, which we call automated evaluation for student argumentative writing. Unlike traditional automated writing evaluation that focuses on holistic essay scoring, this field is more specific: it focuses on evaluating argumentative essays and offers sp... | ['Juneyoung Park', 'Yohan Lee', 'Xinyu Wang'] | 2022-05-09 | null | null | null | null | ['automated-writing-evaluation'] | ['natural-language-processing'] | [-1.47284448e-01 3.86653841e-01 -6.00793302e-01 -4.17900264e-01
-6.64006472e-01 -8.48640501e-01 6.10613465e-01 9.71397996e-01
-2.93297380e-01 1.10720825e+00 4.49718654e-01 -7.73366094e-01
-5.20575285e-01 -7.39260435e-01 -2.14893743e-01 -1.01422541e-01
6.18864357e-01 5.76751709e-01 4.74459976e-02 -6.65946066... | [11.275352478027344, 9.310837745666504] |
0173552f-d5ac-4015-a7fc-5cbf1f7e6bcb | uzbektagger-the-rule-based-pos-tagger-for | 2301.12711 | null | https://arxiv.org/abs/2301.12711v2 | https://arxiv.org/pdf/2301.12711v2.pdf | UzbekTagger: The rule-based POS tagger for Uzbek language | This research paper presents a part-of-speech (POS) annotated dataset and tagger tool for the low-resource Uzbek language. The dataset includes 12 tags, which were used to develop a rule-based POS-tagger tool. The corpus text used in the annotation process was made sure to be balanced over 20 different fields in order ... | ['Ogabek Sobirov', 'Ollabergan Yuldashev', 'Elmurod Kuriyozov', 'Maksud Sharipov'] | 2023-01-30 | null | null | null | null | ['text-to-speech-synthesis'] | ['speech'] | [-6.29684404e-02 7.24152429e-03 -2.66205315e-02 -3.43946099e-01
-4.65206444e-01 -8.97921264e-01 6.45003974e-01 4.63244110e-01
-5.58921814e-01 8.49582911e-01 3.58417451e-01 -7.82846153e-01
-9.84208807e-02 -6.84642911e-01 -1.86963186e-01 -6.40080333e-01
2.52775550e-02 9.93209004e-01 2.87142247e-01 -6.17271245... | [10.377514839172363, 10.242680549621582] |
a7b9768c-b427-476e-9e82-08b3a0d90a0a | variational-autoencoder-for-anti-cancer-drug | 2008.09763 | null | https://arxiv.org/abs/2008.09763v7 | https://arxiv.org/pdf/2008.09763v7.pdf | Variational Autoencoder for Anti-Cancer Drug Response Prediction | Cancer is a primary cause of human death, but discovering drugs and tailoring cancer therapies are expensive and time-consuming. We seek to facilitate the discovery of new drugs and treatment strategies for cancer using variational autoencoders (VAEs) and multi-layer perceptrons (MLPs) to predict anti-cancer drug respo... | ['Jiaqing Xie', 'Zhi Jing', 'Hongyuan Dong', 'Dexin Ren'] | 2020-08-22 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 5.20880446e-02 -2.21581366e-02 -3.54322970e-01 -1.86302215e-02
-9.16974247e-01 -2.31067017e-01 4.14079696e-01 2.14801669e-01
-4.03542876e-01 1.16197944e+00 -7.16184974e-02 -5.41880846e-01
-1.76359247e-02 -9.57503617e-01 -8.22549343e-01 -1.20233798e+00
2.57358044e-01 4.81696814e-01 -1.39055356e-01 -2.00399667... | [5.918603420257568, 5.732357978820801] |
c091f07e-57d4-4bca-a643-4a083e3dede0 | ramp-retrieval-and-attribute-marking-enhanced | 2305.17131 | null | https://arxiv.org/abs/2305.17131v1 | https://arxiv.org/pdf/2305.17131v1.pdf | RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation | Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of translation outputs. While ACT has garnered attention in recent years due to its usefulness in real-world applications, progress in the task is currently... | ['Maria Nadejde', 'Georgiana Dinu', 'Anna Currey', 'Benjamin Hsu', 'Xing Niu', 'Phu Mon Htut', 'Gabriele Sarti'] | 2023-05-26 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.14924884e-01 -9.04652104e-02 -7.46284783e-01 -4.25309598e-01
-1.48189914e+00 -9.14726734e-01 1.34131372e+00 3.48184526e-01
-4.53952521e-01 8.57852757e-01 4.41746205e-01 -5.22248685e-01
1.01538725e-01 -3.95031601e-01 -4.58833724e-01 -2.65248567e-01
3.72558773e-01 8.33586812e-01 -8.06836039e-02 -4.73305166... | [11.566768646240234, 10.211501121520996] |
efc983fe-97f3-4dca-9dd4-06dd0130ba4e | a-novel-plsa-based-traffic-signs | 1503.06643 | null | http://arxiv.org/abs/1503.06643v1 | http://arxiv.org/pdf/1503.06643v1.pdf | A novel pLSA based Traffic Signs Classification System | In this work we developed a novel and fast traffic sign recognition system, a
very important part for advanced driver assistance system and for autonomous
driving. Traffic signs play a very vital role in safe driving and avoiding
accident. We have used image processing and topic discovery model pLSA to
tackle this chal... | ['Mrinal Haloi'] | 2015-03-23 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 1.16745643e-01 -4.32437837e-01 -3.41381192e-01 -7.90560901e-01
-4.72895443e-01 -3.98069322e-01 1.02524233e+00 -3.02461892e-01
-6.02550983e-01 5.61490417e-01 4.55089718e-01 -5.86395562e-01
-4.68914628e-01 -5.32591760e-01 -2.59880930e-01 -9.83308792e-01
3.07529628e-01 5.04688561e-01 6.47935569e-01 -2.21131489... | [7.995189189910889, -0.8101916909217834] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.