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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5594b0c5-c2e3-4cd2-8721-def0e9d5fda8 | automated-attribution-and-intertextual | 1405.0616 | null | http://arxiv.org/abs/1405.0616v1 | http://arxiv.org/pdf/1405.0616v1.pdf | Automated Attribution and Intertextual Analysis | In this work, we employ quantitative methods from the realm of statistics and
machine learning to develop novel methodologies for author attribution and
textual analysis. In particular, we develop techniques and software suitable
for applications to Classical study, and we illustrate the efficacy of our
approach in sev... | ['James Brofos', 'Ajay Kannan', 'Rui Shu'] | 2014-05-03 | null | null | null | null | ['author-attribution'] | ['natural-language-processing'] | [ 3.83682828e-03 1.94423467e-01 -1.97334036e-01 1.41735584e-01
-1.06803924e-01 -5.79542935e-01 1.26688778e+00 5.53490162e-01
-6.93114281e-01 8.21265757e-01 4.89036560e-01 -6.92591965e-01
-4.91468310e-01 -6.11337721e-01 -9.12420750e-02 -3.54817212e-01
8.49482417e-02 5.99771857e-01 -2.60294646e-01 -5.34591317... | [9.58317756652832, 10.551403999328613] |
d0b271a7-4e52-4977-b778-393621e6b68a | dota-a-large-scale-dataset-for-object | 1711.10398 | null | https://arxiv.org/abs/1711.10398v3 | https://arxiv.org/pdf/1711.10398v3.pdf | DOTA: A Large-scale Dataset for Object Detection in Aerial Images | Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on... | ['Jiebo Luo', 'Zhen Zhu', 'Gui-Song Xia', 'Serge Belongie', 'Mihai Datcu', 'Jian Ding', 'Marcello Pelillo', 'Liangpei Zhang', 'Xiang Bai'] | 2017-11-28 | dota-a-large-scale-dataset-for-object-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Xia_DOTA_A_Large-Scale_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Xia_DOTA_A_Large-Scale_CVPR_2018_paper.pdf | cvpr-2018-6 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 9.53350440e-02 -4.96020436e-01 4.49861884e-01 -1.90520123e-01
-2.68011689e-01 -9.72263694e-01 2.91763544e-01 1.21869422e-01
-3.64977419e-01 1.90362021e-01 -4.76857156e-01 -1.43705145e-01
-3.84293571e-02 -1.07539296e+00 -5.26793182e-01 -5.38888693e-01
-5.09867668e-01 1.52099028e-01 6.41893446e-01 -3.47013354... | [8.78760051727295, -0.8174564838409424] |
44f0c305-10d6-4d5f-8c7b-4af6821f8a03 | detection-of-malicious-android-applications | 2103.00637 | null | https://arxiv.org/abs/2103.00637v1 | https://arxiv.org/pdf/2103.00637v1.pdf | Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering | Today anti-malware community is facing challenges due to the ever-increasing sophistication and volume of malware attacks developed by adversaries. Traditional malware detection mechanisms are not able to cope-up with next-generation malware attacks. Therefore in this paper, we propose effective and efficient Android m... | ['Mohit Sewak', 'Shivin Thukral', 'Sanjay K. Sahay', 'Hemant Rathore'] | 2021-02-28 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 3.90789546e-02 -5.26746571e-01 -2.06119806e-01 -7.90205672e-02
-2.03042179e-01 -6.09691799e-01 7.33816564e-01 -1.02846198e-01
-2.47921288e-01 4.04138714e-01 -3.57795954e-01 -7.49139726e-01
-1.90367371e-01 -8.52390528e-01 -4.64760125e-01 -5.32119453e-01
-5.36114395e-01 3.44919086e-01 2.14508131e-01 -6.60314634... | [14.424654006958008, 9.681934356689453] |
b711d0ea-cd0a-4348-9d81-8d61fd00508b | contrastive-multi-view-textual-visual | 2211.12926 | null | https://arxiv.org/abs/2211.12926v1 | https://arxiv.org/pdf/2211.12926v1.pdf | Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification | In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging than traditionally-studied 'closed-set' and 'large-scale training samples per category' logo recognition settings. We propose a novel multi-... | ['Anand Mishra', 'Abhirama S. Penamakuri', 'Nakul Sharma'] | 2022-11-23 | null | null | null | null | ['logo-recognition'] | ['computer-vision'] | [ 2.70649552e-01 -5.51613688e-01 -3.79272819e-01 -2.03549147e-01
-1.36308980e+00 -1.00130260e+00 4.82557923e-01 -1.06724881e-01
1.13191292e-01 -1.31410047e-01 -9.82382242e-03 5.10893129e-02
-1.71833262e-02 -3.93657386e-01 -1.05465305e+00 -6.65996194e-01
-3.51029001e-02 7.20224857e-01 1.38981253e-01 -1.22128628... | [9.32852840423584, 1.3282842636108398] |
48c2e491-45fb-4e70-9ac5-c00312335977 | spsql-step-by-step-parsing-based-framework | 2305.11061 | null | https://arxiv.org/abs/2305.11061v1 | https://arxiv.org/pdf/2305.11061v1.pdf | SPSQL: Step-by-step Parsing Based Framework for Text-to-SQL Generation | Converting text into the structured query language (Text2SQL) is a research hotspot in the field of natural language processing (NLP), which has broad application prospects. In the era of big data, the use of databases has penetrated all walks of life, in which the collected data is large in scale, diverse in variety, ... | ['Han Jiang', 'Liangfeng Jin', 'Yiling Li', 'Hao Shen', 'Gang Sun', 'Ran Shen'] | 2023-05-10 | null | null | null | null | ['text-to-sql', 'marketing'] | ['computer-code', 'miscellaneous'] | [-9.44228917e-02 -4.64548320e-02 -1.72476023e-02 -6.00437522e-01
-6.11851394e-01 -5.37007689e-01 2.28467003e-01 4.42636460e-01
-4.34052974e-01 7.39263713e-01 2.85805285e-01 -3.35044295e-01
1.28137589e-01 -1.17522430e+00 -5.00463068e-01 -1.59380138e-01
5.87468863e-01 6.99186504e-01 4.01539087e-01 -3.84729117... | [9.887860298156738, 7.887744426727295] |
46fdd58d-4883-47d9-a0fc-dc29f987401d | a-systematic-literature-review-of-automated-2 | 2108.09646 | null | https://arxiv.org/abs/2108.09646v2 | https://arxiv.org/pdf/2108.09646v2.pdf | A Systematic Review of Automated Query Reformulations in Source Code Search | Fixing software bugs and adding new features are two of the major maintenance tasks. Software bugs and features are reported as change requests. Developers consult these requests and often choose a few keywords from them as an ad hoc query. Then they execute the query with a search engine to find the exact locations wi... | ['Chanchal K. Roy', 'Mohammad Masudur Rahman'] | 2021-08-22 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.09632060e-01 -2.59966075e-01 -5.48331976e-01 -1.23588823e-01
-7.46161938e-01 -8.08690071e-01 -9.93042141e-02 4.26189125e-01
-2.74283886e-01 3.18615049e-01 2.05410749e-01 -5.08110166e-01
-4.65091765e-01 -4.30580258e-01 -3.55437994e-01 2.57383227e-01
5.06249487e-01 -2.97606647e-01 4.03191686e-01 -2.15432227... | [7.662139415740967, 7.967586517333984] |
7a9784f7-8b3b-4d92-a1a4-c266900b5fc4 | intrinsic-decomposition-of-image-sequences | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Laffont_Intrinsic_Decomposition_of_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Laffont_Intrinsic_Decomposition_of_ICCV_2015_paper.pdf | Intrinsic Decomposition of Image Sequences From Local Temporal Variations | We present a method for intrinsic image decomposition, which aims to decompose images into reflectance and shading layers. Our input is a sequence of images with varying illumination acquired by a static camera, e.g. an indoor scene with a moving light source or an outdoor timelapse. We leverage the local color variati... | ['Jean-Charles Bazin', 'Pierre-Yves Laffont'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 7.08410323e-01 -4.48156118e-01 5.18648684e-01 -4.05528784e-01
-5.46135545e-01 -7.94049680e-01 4.24845129e-01 -4.10997897e-01
-1.81410134e-01 4.30000931e-01 -6.30480722e-02 2.15877146e-01
9.66896936e-02 -5.97812176e-01 -6.22661531e-01 -1.00149560e+00
4.68030274e-01 3.19855511e-02 1.91004634e-01 -1.14682701... | [9.840376853942871, -2.9884462356567383] |
cf1f3ccc-9b43-4c53-ba49-37ba7e27785f | editorial-introduction-to-the-issue-on-deep | 2102.06531 | null | https://arxiv.org/abs/2102.06531v1 | https://arxiv.org/pdf/2102.06531v1.pdf | Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression | Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods. Hence, the state of the art in image restoration and compression is getting redefined. This special issue covers the state of the art in learned image/vide... | ['Chao Dong', 'Radu Timofte', 'Michele Covell', 'A. Murat Tekalp'] | 2021-02-09 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [ 6.45842791e-01 -2.17148602e-01 -4.44865465e-01 -3.53780985e-01
-5.55504620e-01 4.79213297e-01 3.78134102e-01 -1.83326364e-01
-1.82737604e-01 6.35514200e-01 5.62761009e-01 1.46456584e-02
-3.42953473e-01 -6.96767867e-01 -6.98445201e-01 -8.49439740e-01
-2.56685674e-01 -2.10407853e-01 -2.72217214e-01 -1.99139461... | [11.380339622497559, -1.663252830505371] |
6e5c922b-0bcb-47a7-a848-8e01defe0c39 | deepsdf-learning-continuous-signed-distance | 1901.05103 | null | http://arxiv.org/abs/1901.05103v1 | http://arxiv.org/pdf/1901.05103v1.pdf | DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation | Computer graphics, 3D computer vision and robotics communities have produced
multiple approaches to representing 3D geometry for rendering and
reconstruction. These provide trade-offs across fidelity, efficiency and
compression capabilities. In this work, we introduce DeepSDF, a learned
continuous Signed Distance Funct... | ['Steven Lovegrove', 'Richard Newcombe', 'Peter Florence', 'Jeong Joon Park', 'Julian Straub'] | 2019-01-16 | deepsdf-learning-continuous-signed-distance-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Park_DeepSDF_Learning_Continuous_Signed_Distance_Functions_for_Shape_Representation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Park_DeepSDF_Learning_Continuous_Signed_Distance_Functions_for_Shape_Representation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-shape-representation'] | ['computer-vision'] | [ 2.15454891e-01 4.03194457e-01 2.65169084e-01 -3.10493529e-01
-5.18285036e-01 -6.44718707e-01 8.24081182e-01 4.94934618e-01
6.14444055e-02 3.22275490e-01 4.44181226e-02 -2.92607605e-01
7.71236941e-02 -1.18044460e+00 -8.69743347e-01 -4.30310369e-01
-3.49438488e-01 8.40319812e-01 2.90561974e-01 -1.19047761... | [8.645515441894531, -3.644989013671875] |
7df41fd1-66be-4be7-b2d8-1b36c161d4d3 | provable-convergence-of-variational-monte | 2303.10599 | null | https://arxiv.org/abs/2303.10599v1 | https://arxiv.org/pdf/2303.10599v1.pdf | Provable Convergence of Variational Monte Carlo Methods | The Variational Monte Carlo (VMC) is a promising approach for computing the ground state energy of many-body quantum problems and attracts more and more interests due to the development of machine learning. The recent paradigms in VMC construct neural networks as trial wave functions, sample quantum configurations usin... | ['Zaiwen Wen', 'Huajie Chen', 'Fan Chen', 'Tianyou Li'] | 2023-03-19 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [-3.30133811e-02 -4.08844873e-02 7.98127577e-02 8.60987883e-03
-8.75626266e-01 -2.60024786e-01 1.53630406e-01 1.17139630e-01
-9.45649385e-01 1.01184285e+00 -5.36725760e-01 -6.50174797e-01
-2.42227674e-01 -9.21635211e-01 -9.95079815e-01 -1.29289353e+00
-1.20019004e-01 5.17705321e-01 1.48927286e-01 -1.09759688... | [6.027612209320068, 4.7420268058776855] |
208f4081-17c2-429f-bcdd-9ffe73598ee1 | chatgpt-as-an-attack-tool-stealthy-textual | 2304.14475 | null | https://arxiv.org/abs/2304.14475v1 | https://arxiv.org/pdf/2304.14475v1.pdf | ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger | Textual backdoor attacks pose a practical threat to existing systems, as they can compromise the model by inserting imperceptible triggers into inputs and manipulating labels in the training dataset. With cutting-edge generative models such as GPT-4 pushing rewriting to extraordinary levels, such attacks are becoming e... | ['Chaowei Xiao', 'V. G. Vinod Vydiswaran', 'Zhuofeng Wu', 'Yijin Yang', 'Jiazhao Li'] | 2023-04-27 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 2.42368832e-01 1.88269034e-01 -7.28516281e-02 9.27660912e-02
-6.79517806e-01 -1.44270802e+00 1.19824386e+00 -2.03687593e-01
1.06554911e-01 3.29473019e-01 -1.19257616e-02 -1.02094543e+00
1.86863050e-01 -7.50774741e-01 -7.13258445e-01 -4.00088161e-01
-9.66098905e-02 6.92087263e-02 9.87425372e-02 -4.12270963... | [5.947137832641602, 7.766709327697754] |
c8bb4d5d-d265-450e-893d-28ad999ad98c | improving-the-performance-of-eeg-decoding | 2011.14694 | null | https://arxiv.org/abs/2011.14694v4 | https://arxiv.org/pdf/2011.14694v4.pdf | Anchored-STFT and GNAA: An extension of STFT in conjunction with an adversarial data augmentation technique for the decoding of neural signals | Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meanin... | ['Christian Klaes', 'Ioannis Iossifidis', 'Tobias Glasmachers', 'Susanne Dyck', 'Muhammad Saif-ur-Rehman', 'Omair Ali'] | 2020-11-30 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 3.86569679e-01 -2.30820939e-01 4.28202450e-01 -1.74478874e-01
-4.72456455e-01 -2.23767295e-01 5.49359858e-01 -3.20978403e-01
-5.10704637e-01 1.01928592e+00 1.01376235e-01 -1.15634285e-01
-9.89132598e-02 -4.94242340e-01 -8.07440341e-01 -7.98274398e-01
-1.78683460e-01 -1.49458602e-01 -1.00856692e-01 -2.06548572... | [13.158563613891602, 3.4660208225250244] |
6c78fbcc-4fe9-4eb7-9f95-27777fedb31f | unsupervised-feature-based-algorithms-for | 2305.01429 | null | https://arxiv.org/abs/2305.01429v1 | https://arxiv.org/pdf/2305.01429v1.pdf | Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression | Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to the regressor series. The TSER archive for comparing algorithms was released in 2022 with 19 problems. We increase the size of this archive to... | ['Anthony Bagnall', 'Diego Furtado Silva', 'Guilherme Arcencio', 'Matthew Middlehurst', 'David Guijo-Rubio'] | 2023-05-02 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 4.47994769e-01 -3.89528126e-01 -3.99215311e-01 -3.59926969e-01
-7.26053953e-01 -5.71689069e-01 1.17293751e+00 -1.91935435e-01
-3.03028792e-01 9.81058836e-01 1.11648478e-01 -6.05551898e-01
-3.12405288e-01 -6.49061143e-01 -4.35692132e-01 -8.81905735e-01
-4.03271854e-01 3.47297132e-01 1.59919590e-01 -5.33445835... | [7.154778957366943, 3.1803407669067383] |
6615c078-5e0a-4271-ae99-4ab2ef29ac96 | societal-biases-in-retrieved-contents | 2104.13640 | null | https://arxiv.org/abs/2104.13640v2 | https://arxiv.org/pdf/2104.13640v2.pdf | Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers | Societal biases resonate in the retrieved contents of information retrieval (IR) systems, resulting in reinforcing existing stereotypes. Approaching this issue requires established measures of fairness in respect to the representation of various social groups in retrieval results, as well as methods to mitigate such bi... | ['Markus Schedl', 'Simone Kopeinik', 'Navid Rekabsaz'] | 2021-04-28 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [-2.14567363e-01 -3.11518759e-02 -3.35344404e-01 -4.71852273e-01
-1.01454926e+00 -8.04025233e-01 1.04515493e+00 4.53700721e-01
-8.22881162e-01 7.43134439e-01 7.79156685e-01 -5.08284383e-02
-4.07712936e-01 -8.27971518e-01 -4.83470917e-01 -3.90807271e-01
1.01780370e-02 4.39608544e-01 -2.10759312e-01 -7.23103166... | [9.055594444274902, 5.273565769195557] |
aed092c5-aef8-4b32-87ec-613c55bc3691 | bayesian-optimisation-for-active-monitoring | 2202.07595 | null | https://arxiv.org/abs/2202.07595v1 | https://arxiv.org/pdf/2202.07595v1.pdf | Bayesian Optimisation for Active Monitoring of Air Pollution | Air pollution is one of the leading causes of mortality globally, resulting in millions of deaths each year. Efficient monitoring is important to measure exposure and enforce legal limits. New low-cost sensors can be deployed in greater numbers and in more varied locations, motivating the problem of efficient automated... | ['Nigel H. Goddard', 'Christopher G. Lucas', 'Sigrid Passano Hellan'] | 2022-02-15 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 3.13731968e-01 -1.89454675e-01 3.38922068e-02 -1.48219869e-01
-7.24123359e-01 -5.36710918e-01 2.36914068e-01 5.82653761e-01
-7.00802684e-01 1.10806882e+00 2.44441211e-01 -5.20182967e-01
-8.07337224e-01 -1.04262769e+00 -3.23987901e-01 -7.66463935e-01
-1.30893275e-01 5.38903654e-01 3.03314149e-01 5.77499084... | [6.2088189125061035, 2.619499921798706] |
f425fb4f-9325-41af-8406-27021af619f7 | end-to-end-attention-based-text-dependent | 1701.00562 | null | http://arxiv.org/abs/1701.00562v1 | http://arxiv.org/pdf/1701.00562v1.pdf | End-to-End Attention based Text-Dependent Speaker Verification | A new type of End-to-End system for text-dependent speaker verification is
presented in this paper. Previously, using the phonetically
discriminative/speaker discriminative DNNs as feature extractors for speaker
verification has shown promising results. The extracted frame-level (DNN
bottleneck, posterior or d-vector) ... | ['Jinyu Li', 'Shi-Xiong Zhang', 'Zhuo Chen', 'Yifan Gong', 'Yong Zhao'] | 2017-01-03 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 1.30675331e-01 -2.12502539e-01 1.78593304e-02 -1.04593956e+00
-1.47510958e+00 -2.78921157e-01 4.02196795e-01 -2.17425764e-01
-5.58383882e-01 2.25400552e-01 4.20642287e-01 -2.64753461e-01
3.10480803e-01 -6.52726963e-02 -3.63712609e-01 -1.03422451e+00
1.57582983e-01 3.46897066e-01 -3.92002106e-01 1.80857643... | [14.43547248840332, 6.069706439971924] |
b3bb2d16-7700-4df8-8353-e6c215aead82 | video-similarity-and-alignment-learning-on | 2108.01817 | null | https://arxiv.org/abs/2108.01817v1 | https://arxiv.org/pdf/2108.01817v1.pdf | Video Similarity and Alignment Learning on Partial Video Copy Detection | Existing video copy detection methods generally measure video similarity based on spatial similarities between key frames, neglecting the latent similarity in temporal dimension, so that the video similarity is biased towards spatial information. There are methods modeling unified video similarity in an end-to-end way,... | ['Yiliang Lv', 'Mingqian Tang', 'Xiangteng He', 'Zhen Han'] | 2021-08-04 | null | null | null | null | ['partial-video-copy-detection', 'video-similarity'] | ['computer-vision', 'computer-vision'] | [ 2.31905356e-02 -4.57985044e-01 -6.62621915e-01 -1.58318907e-01
-7.80806303e-01 -5.08708060e-01 4.11571354e-01 -2.52496563e-02
-8.34901780e-02 2.25983053e-01 5.23200333e-01 -3.14296260e-02
2.26131384e-03 -5.06667495e-01 -9.65372562e-01 -6.51874721e-01
-2.45400921e-01 -9.86299962e-02 8.08163345e-01 1.06456667... | [9.936110496520996, 0.6057008504867554] |
b8bc39c3-ecab-4d2b-a5a3-495c9b9962de | holistic-interaction-transformer-network-for | 2210.12686 | null | https://arxiv.org/abs/2210.12686v2 | https://arxiv.org/pdf/2210.12686v2.pdf | Holistic Interaction Transformer Network for Action Detection | Actions are about how we interact with the environment, including other people, objects, and ourselves. In this paper, we propose a novel multi-modal Holistic Interaction Transformer Network (HIT) that leverages the largely ignored, but critical hand and pose information essential to most human actions. The proposed "H... | ['Shang-Hong Lai', 'Min-Hung Chen', 'Gueter Josmy Faure'] | 2022-10-23 | null | null | null | null | ['fine-grained-action-detection'] | ['computer-vision'] | [ 4.23482098e-02 -2.79756427e-01 -2.68754065e-02 -3.40271175e-01
-7.07542181e-01 -3.77296031e-01 6.74015284e-01 -1.40144557e-01
-3.66543382e-01 5.05391300e-01 7.50352561e-01 4.35406774e-01
-4.85429280e-02 -5.27085543e-01 -5.05192757e-01 -6.91421866e-01
2.20508441e-01 4.55261111e-01 4.95657504e-01 -2.58395225... | [8.177556991577148, 0.5216231346130371] |
0477b25e-f877-4635-89be-593b920a82b0 | fast-and-multi-aspect-mining-of-complex-time | 2303.03789 | null | https://arxiv.org/abs/2303.03789v2 | https://arxiv.org/pdf/2303.03789v2.pdf | Fast and Multi-aspect Mining of Complex Time-stamped Event Streams | Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), and local mobility activities: (pick-up and drop-off locations, time), how can we summarize large, dynamic high-order tensor streams? How can we see any hidden patterns, rules, and ano... | ['Yasushi Sakurai', 'Yuichiro Wada', 'Yuhei Umeda', 'Koki Kawabata', 'Yasuko Matsubara', 'Kota Nakamura'] | 2023-03-07 | null | null | null | null | ['data-compression'] | ['time-series'] | [-2.54272461e-01 -7.16270983e-01 -1.50608197e-01 9.30331089e-03
-2.39444867e-01 -6.58134460e-01 5.24825037e-01 7.83662736e-01
3.16514015e-01 2.81752646e-01 3.10437858e-01 -2.64702708e-01
-6.73978984e-01 -7.59862423e-01 -5.09825587e-01 -7.95996070e-01
-1.18720353e+00 6.47594750e-01 5.75403631e-01 -3.12185168... | [7.250403881072998, 2.8772335052490234] |
e7be82ce-c09c-41d2-a631-1a8e92dc47b0 | flow-guided-semi-supervised-video-object | 2301.10492 | null | https://arxiv.org/abs/2301.10492v1 | https://arxiv.org/pdf/2301.10492v1.pdf | Flow-guided Semi-supervised Video Object Segmentation | We propose an optical flow-guided approach for semi-supervised video object segmentation. Optical flow is usually exploited as additional guidance information in unsupervised video object segmentation. However, its relevance in semi-supervised video object segmentation has not been fully explored. In this work, we foll... | ['Michael Felsberg', 'Maria Magnusson', 'Andreas Robinson', 'Yushan Zhang'] | 2023-01-25 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.11507934e-01 2.35290527e-02 -6.80338085e-01 -3.95808309e-01
-4.10756677e-01 -4.99544650e-01 3.64353478e-01 -3.14932376e-01
-6.58105373e-01 6.28605306e-01 1.92522705e-01 -8.12583789e-02
4.47572112e-01 -4.31886435e-01 -8.54481697e-01 -5.89453042e-01
1.83050066e-01 1.49927884e-01 5.22756517e-01 2.52380759... | [9.015715599060059, -0.16930800676345825] |
b3a5b48e-463b-4776-926d-aa1eea026f19 | continuous-sign-language-recognition-based-on | 2303.06820 | null | https://arxiv.org/abs/2303.06820v1 | https://arxiv.org/pdf/2303.06820v1.pdf | Continuous sign language recognition based on cross-resolution knowledge distillation | The goal of continuous sign language recognition(CSLR) research is to apply CSLR models as a communication tool in real life, and the real-time requirement of the models is important. In this paper, we address the model real-time problem through cross-resolution knowledge distillation. In our study, we found that keepi... | ['Quan Gan', 'Fei Yuan', 'Jing Li', 'Qidan Zhu'] | 2023-03-13 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-2.76304875e-02 -4.20124233e-01 4.40012924e-02 -2.64677852e-01
-6.03239298e-01 -1.81684375e-01 3.05968314e-01 -7.64322162e-01
-9.53585863e-01 6.80468380e-01 -1.99126620e-02 -2.03123108e-01
-9.96346697e-02 -8.49644542e-01 -6.24344170e-01 -8.05622220e-01
1.24061935e-01 -5.05041406e-02 7.68078029e-01 3.48344073... | [9.234880447387695, -6.4615254402160645] |
e166fd4a-444d-4b6a-9996-b093e6d3b206 | low-resource-multilingual-and-zero-shot | 2210.12223 | null | https://arxiv.org/abs/2210.12223v1 | https://arxiv.org/pdf/2210.12223v1.pdf | Low-Resource Multilingual and Zero-Shot Multispeaker TTS | While neural methods for text-to-speech (TTS) have shown great advances in modeling multiple speakers, even in zero-shot settings, the amount of data needed for those approaches is generally not feasible for the vast majority of the world's over 6,000 spoken languages. In this work, we bring together the tasks of zero-... | ['Ngoc Thang Vu', 'Julia Koch', 'Florian Lux'] | 2022-10-21 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 1.14089474e-01 2.74816722e-01 -5.25561906e-03 -5.20323277e-01
-1.27537656e+00 -4.75334972e-01 5.27922750e-01 -4.04630572e-01
-2.90288329e-01 6.82944477e-01 3.48557651e-01 -5.18989563e-01
4.01661396e-01 -1.74711481e-01 -5.72890222e-01 -3.98724109e-01
-1.16836689e-02 5.04530251e-01 -4.64317761e-02 -3.83558184... | [14.796573638916016, 6.6967244148254395] |
6957bbc1-196b-4969-a8e6-1c7724978942 | continuous-descriptor-based-control-for-deep | 2302.13542 | null | https://arxiv.org/abs/2302.13542v1 | https://arxiv.org/pdf/2302.13542v1.pdf | Continuous descriptor-based control for deep audio synthesis | Despite significant advances in deep models for music generation, the use of these techniques remains restricted to expert users. Before being democratized among musicians, generative models must first provide expressive control over the generation, as this conditions the integration of deep generative models in creati... | ['Philippe Esling', 'David Genova', 'Sarah Nabi', 'Nils Demerlé', 'Ninon Devis'] | 2023-02-27 | null | null | null | null | ['music-generation', 'music-generation', 'continuous-control'] | ['audio', 'music', 'playing-games'] | [ 1.89364448e-01 2.51953661e-01 4.63169575e-01 9.82465371e-02
-5.48920810e-01 -1.33358777e+00 9.43328202e-01 -2.66368717e-01
-1.80335999e-01 5.77349126e-01 2.18170285e-01 6.94699585e-02
-2.69793123e-01 -8.67592096e-01 -6.49410665e-01 -5.23514390e-01
-7.14405626e-02 3.50599259e-01 -5.37522808e-02 -2.42395774... | [15.753579139709473, 5.818224906921387] |
8feb7862-53d1-438a-ab12-7c0c02df78d0 | bayesian-approach-to-gaussian-process | 2305.11586 | null | https://arxiv.org/abs/2305.11586v2 | https://arxiv.org/pdf/2305.11586v2.pdf | Bayesian approach to Gaussian process regression with uncertain inputs | Conventional Gaussian process regression exclusively assumes the existence of noise in the output data of model observations. In many scientific and engineering applications, however, the input locations of observational data may also be compromised with uncertainties owing to modeling assumptions, measurement errors, ... | ['Mengwu Guo', 'Dongwei Ye'] | 2023-05-19 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 1.64281175e-01 -9.28094462e-02 2.54188210e-01 -3.81899357e-01
-6.94434464e-01 -3.81921262e-01 8.12556863e-01 4.61097620e-02
-2.26049781e-01 1.24691784e+00 -7.62224048e-02 -4.58925247e-01
-5.92394948e-01 -1.01394153e+00 -6.57496691e-01 -1.08830917e+00
3.09533894e-01 4.20608550e-01 8.98896679e-02 2.28804022... | [6.539185523986816, 3.505702257156372] |
72073b49-932d-4a2b-b217-aaff5383d222 | an-investigation-of-feature-selection-and | 2010.10025 | null | https://arxiv.org/abs/2010.10025v1 | https://arxiv.org/pdf/2010.10025v1.pdf | An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification | SigNet is a state of the art model for feature representation used for handwritten signature verification (HSV). This representation is based on a Deep Convolutional Neural Network (DCNN) and contains 2048 dimensions. When transposed to a dissimilarity space generated by the dichotomy transformation (DT), related to th... | ['Robert Sabourin', 'Rafael M. O. Cruz', 'Adriano L. I. Oliveira', 'Victor L. F. Souza'] | 2020-10-19 | null | null | null | null | ['2048'] | ['playing-games'] | [ 1.46620244e-01 -2.94842720e-01 2.82002330e-01 -2.35009834e-01
5.39864600e-02 -2.05786467e-01 1.04722941e+00 1.36865720e-01
-7.52778411e-01 9.17507231e-01 -2.75740594e-01 -2.90599819e-02
-9.44479823e-01 -9.63836908e-01 -2.77974665e-01 -1.13771629e+00
-3.06591913e-02 6.43874645e-01 1.73144966e-01 -3.86929661... | [7.970636367797852, 3.5600521564483643] |
7195d768-d95c-4a90-8308-03b59f6d0093 | mlprune-multi-layer-pruning-for-automated | null | null | https://openreview.net/forum?id=r1g5b2RcKm | https://openreview.net/pdf?id=r1g5b2RcKm | MLPrune: Multi-Layer Pruning for Automated Neural Network Compression | Model compression can significantly reduce the computation and memory footprint of large neural networks. To achieve a good trade-off between model size and accuracy, popular compression techniques usually rely on hand-crafted heuristics and
require manually setting the compression ratio of each layer. This process is ... | ['Raquel Urtasun', 'Wenyuan Zeng'] | 2018-09-27 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 1.00211024e-01 -2.67530471e-04 -1.99816152e-01 -3.52916151e-01
-5.35160959e-01 -3.92654747e-01 2.49960944e-01 2.01743305e-01
-9.17195797e-01 4.57185745e-01 -3.53492230e-01 -6.56586528e-01
-2.30518058e-01 -8.98793757e-01 -8.77549410e-01 -5.14575660e-01
1.05560720e-01 4.33738381e-01 3.64056110e-01 -1.42966509... | [8.592823028564453, 3.230581045150757] |
4c469e4a-d695-4f82-8d4e-afad8f4f603d | evaluating-the-utility-of-gan-generated | 2306.13929 | null | https://arxiv.org/abs/2306.13929v1 | https://arxiv.org/pdf/2306.13929v1.pdf | Evaluating the Utility of GAN Generated Synthetic Tabular Data for Class Balancing and Low Resource Settings | The present study aimed to address the issue of imbalanced data in classification tasks and evaluated the suitability of SMOTE, ADASYN, and GAN techniques in generating synthetic data to address the class imbalance and improve the performance of classification models in low-resource settings. The study employed the Gen... | ['Bharath Kumar Bolla', 'Nagarjuna Chereddy'] | 2023-06-24 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 5.29521763e-01 1.61689863e-01 -5.73708236e-01 -4.98131454e-01
-9.98263717e-01 -1.36421517e-01 5.70667207e-01 6.87780902e-02
-3.87018740e-01 9.64582384e-01 2.39590019e-01 -1.85164616e-01
1.54124409e-01 -8.73601079e-01 -3.38617444e-01 -5.70974767e-01
4.36261445e-01 5.76389611e-01 -4.68474597e-01 -1.57178313... | [8.794302940368652, 4.3289055824279785] |
dbc5ffed-44fa-4321-a5ca-d585b06ffa2a | surveying-generative-ai-s-economic | 2305.02823 | null | https://arxiv.org/abs/2305.02823v2 | https://arxiv.org/pdf/2305.02823v2.pdf | Surveying Generative AI's Economic Expectations | I introduce a survey of economic expectations formed by querying a large language model (LLM)'s expectations of various financial and macroeconomic variables based on a sample of news articles from the Wall Street Journal between 1984 and 2021. I find the resulting expectations closely match existing surveys including ... | ['Leland Bybee'] | 2023-05-04 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-4.32815850e-01 4.15523708e-01 -5.23249388e-01 -5.17429411e-01
-6.64700210e-01 -7.58307099e-01 9.84826505e-01 5.56667984e-01
-3.43035549e-01 7.61134982e-01 9.50975239e-01 -1.03803039e+00
-1.47894129e-01 -8.30382884e-01 -7.34612226e-01 -7.26844789e-03
2.19282582e-01 3.83700222e-01 -2.96454549e-01 -3.74311864... | [4.458001136779785, 4.330584526062012] |
358b75bc-29b2-469d-a281-095027d34411 | auto-gait-automatic-ataxia-risk-assessment | 2203.08215 | null | https://arxiv.org/abs/2203.08215v2 | https://arxiv.org/pdf/2203.08215v2.pdf | Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision on Gait Task Videos | In this paper, we investigated whether we can 1) detect participants with ataxia-specific gait characteristics (risk-prediction), and 2) assess severity of ataxia from gait (severity-assessment) using computer vision. We created a dataset of 155 videos from 89 participants, 24 controls and 65 diagnosed with (or are pre... | ['Ehsan Hoque', 'Tetsuo Ashizawa', 'Phillip Yang', 'Abdelrahman Abdelkader', 'Jeet Thaker', 'Titilayo Olubajo', 'Md Saiful Islam', 'Masum Hasan', 'Wasifur Rahman'] | 2022-03-15 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [-2.54234344e-01 -2.07028091e-01 -3.47321965e-02 -1.77070439e-01
-8.70217562e-01 -5.21323144e-01 -5.51381409e-02 2.37013146e-01
-7.51174450e-01 7.96657383e-01 7.06346691e-01 4.65218499e-02
-2.42712080e-01 -6.31439447e-01 -2.73945212e-01 -4.83933061e-01
-6.69400692e-01 4.57729727e-01 5.40552676e-01 -2.00233564... | [7.089312553405762, 0.3288057744503021] |
e50fc29c-8f74-4f2e-aa42-c42460be5497 | shapechanger-environments-for-transfer | 1709.05070 | null | http://arxiv.org/abs/1709.05070v1 | http://arxiv.org/pdf/1709.05070v1.pdf | Shapechanger: Environments for Transfer Learning | We present Shapechanger, a library for transfer reinforcement learning
specifically designed for robotic tasks. We consider three types of knowledge
transfer---from simulation to simulation, from simulation to real, and from
real to real---and a wide range of tasks with continuous states and actions.
Shapechanger is un... | ['Francisco J. Valero-Cuevas', 'Théo-Tim J. Denisart', 'Sébastien M. R. Arnold', 'Tsam Kiu Pun'] | 2017-09-15 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-5.47892272e-01 1.67792905e-02 -2.17779949e-01 -2.41641551e-01
-5.14921546e-01 -7.71384120e-01 6.97139204e-01 -3.58832031e-01
-2.99516380e-01 1.34438419e+00 -6.77070543e-02 -5.42717695e-01
-1.91838428e-01 -8.65582168e-01 -1.05129695e+00 -4.18248713e-01
-4.16337758e-01 6.01658642e-01 4.78171915e-01 -7.59874523... | [4.319726943969727, 1.2340573072433472] |
f5d6f9f5-660a-41f5-b5a6-89eb37f4fc99 | leveraging-unlabeled-data-to-track | 2212.04461 | null | https://arxiv.org/abs/2212.04461v1 | https://arxiv.org/pdf/2212.04461v1.pdf | Leveraging Unlabeled Data to Track Memorization | Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, called susceptibility, to gauge such memorization for neural netwo... | ['Patrick Thiran', 'Hanie Sedghi', 'Mahsa Forouzesh'] | 2022-12-08 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.79586822e-01 -1.63200572e-01 -2.46325079e-02 -7.03402936e-01
-7.48530149e-01 -8.11581731e-01 5.91000795e-01 3.64045501e-01
-7.25473881e-01 9.09972072e-01 -1.36458933e-01 -3.40635836e-01
-8.42614174e-02 -4.93967146e-01 -8.37417722e-01 -7.26824403e-01
3.21267135e-02 -6.51184618e-02 -1.94143996e-01 1.77517757... | [9.25161075592041, 3.875567674636841] |
54afdb0b-d2d6-43b3-812d-54cb27673e86 | neuralnetwork-viterbi-a-framework-for-weakly | 1805.06875 | null | http://arxiv.org/abs/1805.06875v1 | http://arxiv.org/pdf/1805.06875v1.pdf | NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning | Video learning is an important task in computer vision and has experienced
increasing interest over the recent years. Since even a small amount of videos
easily comprises several million frames, methods that do not rely on a
frame-level annotation are of special importance. In this work, we propose a
novel learning alg... | ['Hilde Kuehne', 'Ahsan Iqbal', 'Alexander Richard', 'Juergen Gall'] | 2018-05-17 | neuralnetwork-viterbi-a-framework-for-weakly-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Richard_NeuralNetwork-Viterbi_A_Framework_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Richard_NeuralNetwork-Viterbi_A_Framework_CVPR_2018_paper.pdf | cvpr-2018-6 | ['weakly-supervised-action-segmentation'] | ['computer-vision'] | [ 3.87820810e-01 -2.85591627e-03 -6.08045220e-01 -3.89274895e-01
-9.50232983e-01 -5.65455258e-01 5.13967156e-01 2.57524431e-01
-8.39989066e-01 7.72856236e-01 -7.58337453e-02 -1.96047962e-01
4.40371454e-01 -3.62473905e-01 -9.91596580e-01 -5.78877330e-01
-1.64740682e-01 3.08969378e-01 1.04772663e+00 1.49240687... | [8.721572875976562, 0.2756616175174713] |
cfa1419f-56ae-4735-82b9-b311aa430a79 | reliability-and-sharpness-in-border-crossing | 1711.04848 | null | http://arxiv.org/abs/1711.04848v1 | http://arxiv.org/pdf/1711.04848v1.pdf | Reliability and Sharpness in Border Crossing Traffic Interval Prediction | Short-term traffic volume prediction models have been extensively studied in
the past few decades. However, most of the previous studies only focus on
single-value prediction. Considering the uncertain and chaotic nature of the
transportation system, an accurate and reliable prediction interval with upper
and lower bou... | ['Adel Sadek', 'Lei Lin', 'John Handley'] | 2017-11-13 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-4.54304785e-01 -1.20548427e-01 -2.12975308e-01 -9.95104909e-02
-2.11140528e-01 -1.90645725e-01 3.09450477e-01 3.99266154e-01
-1.61225319e-01 1.42038572e+00 -5.26917279e-01 -4.13175374e-01
-9.09344375e-01 -1.19074118e+00 -3.73790979e-01 -6.92687452e-01
-2.20232964e-01 7.75677979e-01 4.73296195e-01 -4.06900108... | [6.125596046447754, 3.4681694507598877] |
aca73d94-ecd5-419d-8a8a-3ff875fece3e | qa-gnn-reasoning-with-language-models-and | 2104.06378 | null | https://arxiv.org/abs/2104.06378v5 | https://arxiv.org/pdf/2104.06378v5.pdf | QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering | The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. In t... | ['Jure Leskovec', 'Percy Liang', 'Antoine Bosselut', 'Hongyu Ren', 'Michihiro Yasunaga'] | 2021-04-13 | null | https://aclanthology.org/2021.naacl-main.45 | https://aclanthology.org/2021.naacl-main.45.pdf | naacl-2021-4 | ['multi-hop-question-answering', 'riddle-sense'] | ['knowledge-base', 'natural-language-processing'] | [ 2.88072795e-01 8.59596908e-01 5.58199063e-02 -3.27009171e-01
-1.09312677e+00 -6.09228849e-01 3.50190103e-01 6.42862499e-01
-2.69009233e-01 8.85351896e-01 6.92558110e-01 -6.02712989e-01
-2.88529038e-01 -1.09329510e+00 -7.86618531e-01 6.32863343e-02
2.67518073e-01 9.71616924e-01 4.69216704e-01 -5.80920994... | [10.588459014892578, 7.876356601715088] |
6d0f83ae-cb50-4e6d-acb1-3c6811975d54 | storygan-a-sequential-conditional-gan-for | 1812.02784 | null | http://arxiv.org/abs/1812.02784v2 | http://arxiv.org/pdf/1812.02784v2.pdf | StoryGAN: A Sequential Conditional GAN for Story Visualization | We propose a new task, called Story Visualization. Given a multi-sentence
paragraph, the story is visualized by generating a sequence of images, one for
each sentence. In contrast to video generation, story visualization focuses
less on the continuity in generated images (frames), but more on the global
consistency acr... | ['Jianfeng Gao', 'Lawrence Carin', 'Yelong Shen', 'Yu Cheng', 'Jingjing Liu', 'Zhe Gan', 'Yitong Li', 'Yuexin Wu', 'David Carlson'] | 2018-12-06 | storygan-a-sequential-conditional-gan-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_StoryGAN_A_Sequential_Conditional_GAN_for_Story_Visualization_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_StoryGAN_A_Sequential_Conditional_GAN_for_Story_Visualization_CVPR_2019_paper.pdf | cvpr-2019-6 | ['story-visualization'] | ['computer-vision'] | [ 3.56678188e-01 1.54771328e-01 1.44108638e-01 -2.27649525e-01
-7.01067984e-01 -5.83647728e-01 1.03103900e+00 -2.09500790e-01
1.19051419e-01 8.21296751e-01 5.28273821e-01 -5.77228293e-02
4.41526413e-01 -5.38686752e-01 -8.50491464e-01 -5.96279979e-01
2.41411746e-01 8.84818956e-02 3.59680265e-01 -5.94184957... | [11.171338081359863, 0.522719144821167] |
56914967-c3e6-468c-a421-b340fb99dc31 | sparsity-aware-ssaf-algorithm-with-individual | 2009.08593 | null | https://arxiv.org/abs/2009.08593v1 | https://arxiv.org/pdf/2009.08593v1.pdf | Sparsity-Aware SSAF Algorithm with Individual Weighting Factors for Acoustic Echo Cancellation | In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to e... | ['Yi Yu', 'Tao Yang', 'Yingsong Li', 'Rodrigo C. de Lamare', 'Hongyang Chen'] | 2020-09-18 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 4.21961278e-01 -3.75025481e-01 5.01201153e-01 -2.14677721e-01
-5.22611082e-01 -3.90636295e-01 1.20070599e-01 -3.66828799e-01
-3.44385743e-01 4.04803574e-01 5.89342773e-01 -4.33327258e-01
-6.22346997e-01 -5.12229353e-02 -3.87070537e-01 -9.46754277e-01
-3.54364336e-01 -5.84244549e-01 1.41617909e-01 -1.07827179... | [15.104939460754395, 5.767660617828369] |
945c081a-d1d1-4184-8487-910fad61682c | icdar-2023-video-text-reading-competition-for | 2304.04376 | null | https://arxiv.org/abs/2304.04376v1 | https://arxiv.org/pdf/2304.04376v1.pdf | ICDAR 2023 Video Text Reading Competition for Dense and Small Text | Recently, video text detection, tracking, and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenarios, while ignoring extreme video text challenges, i.e., dense... | ['Xiang Bai', 'Dimosthenis Karatzas', 'Umapada Pal', 'Mike Zheng Shou', 'Jiahong Li', 'Zhuang Li', 'Yuzhong Zhao', 'Weijia Wu'] | 2023-04-10 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 2.45837644e-01 -5.94189107e-01 4.32833880e-02 -6.61044866e-02
-5.45552909e-01 -3.69868040e-01 7.88541019e-01 -1.54310063e-01
-5.72729230e-01 4.34668243e-01 3.02681655e-01 -7.71069974e-02
1.63938344e-01 -2.88668394e-01 -7.60822654e-01 -7.48069525e-01
1.91980019e-01 5.40642798e-01 5.89471877e-01 1.83703527... | [11.961438179016113, 2.190186023712158] |
a517a8ef-b621-42b2-8c98-fa1a23bc92a0 | pointnet-deep-hierarchical-feature-learning | 1706.02413 | null | http://arxiv.org/abs/1706.02413v1 | http://arxiv.org/pdf/1706.02413v1.pdf | PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space | Few prior works study deep learning on point sets. PointNet by Qi et al. is a
pioneer in this direction. However, by design PointNet does not capture local
structures induced by the metric space points live in, limiting its ability to
recognize fine-grained patterns and generalizability to complex scenes. In this
work,... | ['Leonidas J. Guibas', 'Li Yi', 'Hao Su', 'Charles R. Qi'] | 2017-06-07 | pointnet-deep-hierarchical-feature-learning-1 | http://papers.nips.cc/paper/7095-pointnet-deep-hierarchical-feature-learning-on-point-sets-in-a-metric-space | http://papers.nips.cc/paper/7095-pointnet-deep-hierarchical-feature-learning-on-point-sets-in-a-metric-space.pdf | neurips-2017-12 | ['3d-part-segmentation', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-3.61962408e-01 -3.96519154e-01 -4.44357432e-02 -3.46161276e-01
-4.00374055e-01 -4.62442160e-01 6.88779712e-01 3.10128331e-01
-4.15851265e-01 4.06273633e-01 -1.83084577e-01 -4.77222614e-02
-3.65657359e-01 -1.25025272e+00 -1.07314551e+00 -4.07019377e-01
-3.39876831e-01 8.14892054e-01 3.41604233e-01 -1.76894605... | [7.931834697723389, -3.6423943042755127] |
291250a1-2d2c-4b23-9026-4813bc5c38a4 | multifaceted-domain-specific-document | null | null | https://aclanthology.org/2021.naacl-demos.9 | https://aclanthology.org/2021.naacl-demos.9.pdf | Multifaceted Domain-Specific Document Embeddings | Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms. Further, they transform each document into a single embedding vector, making it hard to capture different notions of document similar... | ['Ralf Krestel', 'Philipp Hager', 'Julian Risch'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['document-embedding'] | ['methodology'] | [-1.37779281e-01 -1.62312135e-01 -5.74929237e-01 -3.42986137e-01
-7.00286031e-01 -8.84228230e-01 1.01547050e+00 5.47189116e-01
-4.25116926e-01 4.68230546e-01 6.61890805e-01 -3.36007237e-01
-2.96859831e-01 -6.86767936e-01 -5.79274297e-01 -2.78876245e-01
-1.47868842e-01 6.71219230e-01 8.51248726e-02 -3.10095429... | [10.449792861938477, 8.513272285461426] |
94f6e627-207c-4c8c-bc90-7bc28995b88b | automatic-extraction-of-parallel-speech | null | null | https://aclanthology.org/W17-2506 | https://aclanthology.org/W17-2506.pdf | Automatic Extraction of Parallel Speech Corpora from Dubbed Movies | This paper presents a methodology to extract parallel speech corpora based on any language pair from dubbed movies, together with an application framework in which some corresponding prosodic parameters are extracted. The obtained parallel corpora are especially suitable for speech-to-speech translation applications wh... | ["Mireia Farr{\\'u}s", 'Alp {\\"O}ktem', 'Leo Wanner'] | 2017-08-01 | null | null | null | ws-2017-8 | ['speech-to-speech-translation'] | ['speech'] | [ 1.78157583e-01 5.16514853e-03 -2.09347069e-01 -4.71248209e-01
-9.37424839e-01 -6.70352995e-01 6.72871113e-01 1.53358608e-01
-2.82648712e-01 1.03719401e+00 2.69020498e-01 -2.63506889e-01
2.43843287e-01 -3.87590766e-01 -2.39008695e-01 -5.36016226e-01
2.43517831e-01 7.07593620e-01 3.96276355e-01 -7.00155020... | [14.639245986938477, 6.775047779083252] |
b6bb40d1-50b1-48e8-a23d-73fadeaa650e | the-multilingual-amazon-reviews-corpus | 2010.02573 | null | https://arxiv.org/abs/2010.02573v1 | https://arxiv.org/pdf/2010.02573v1.pdf | The Multilingual Amazon Reviews Corpus | We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019. Each record in the dataset contains the review te... | ['Noah A. Smith', 'György Szarvas', 'Yichao Lu', 'Phillip Keung'] | 2020-10-06 | null | https://aclanthology.org/2020.emnlp-main.369 | https://aclanthology.org/2020.emnlp-main.369.pdf | emnlp-2020-11 | ['multilingual-text-classification'] | ['miscellaneous'] | [-5.31411886e-01 -1.69922441e-01 -5.91009617e-01 -6.80528343e-01
-1.13886833e+00 -8.47471058e-01 9.04685318e-01 4.65860397e-01
-8.46767247e-01 8.10572147e-01 3.30414712e-01 -2.77652174e-01
4.99106973e-01 -5.02857864e-01 -4.75827694e-01 -2.34234497e-01
4.57580030e-01 4.19905841e-01 -2.50771493e-01 -2.00169310... | [11.301886558532715, 6.922852993011475] |
eb41030c-5b3d-4921-b047-95a433be6247 | multiple-granularity-analysis-for-fine | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Ni_Multiple_Granularity_Analysis_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Ni_Multiple_Granularity_Analysis_2014_CVPR_paper.pdf | Multiple Granularity Analysis for Fine-grained Action Detection | We propose to decompose the fine-grained human activity analysis problem into two sequential tasks with increasing granularity. Firstly, we infer the coarse interaction status, i.e., which object is being manipulated and where it is. Knowing that the major challenge is frequent mutual occlusions during manipulation, we... | ['Bingbing Ni', 'Vignesh R. Paramathayalan', 'Pierre Moulin'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['fine-grained-action-detection'] | ['computer-vision'] | [ 5.32320678e-01 -4.64225352e-01 -3.73431951e-01 2.05580126e-02
-3.63341808e-01 -5.84724665e-01 5.27162313e-01 1.23946555e-01
-2.06290260e-01 5.66365480e-01 5.77458978e-01 3.18307310e-01
-2.52354890e-01 -4.08805311e-01 -6.00794613e-01 -8.54424417e-01
-7.90585950e-02 1.66767538e-01 6.75952196e-01 5.98262921... | [8.153820991516113, 0.4843553602695465] |
fe9053f4-28b6-44be-b2cd-1f256720d24c | adversarial-attacks-on-graph-classifiers-via | null | null | http://proceedings.neurips.cc/paper/2021/hash/38811c5285e34e2e3319ab7d9f2cfa5b-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/38811c5285e34e2e3319ab7d9f2cfa5b-Paper.pdf | Adversarial Attacks on Graph Classifiers via Bayesian Optimisation | Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to analysing adversar... | ['Xiaowen Dong', 'Michael Osborne', 'Arno Blaas', 'Robin Ru', 'Henry Kenlay', 'Xingchen Wan'] | 2021-12-01 | null | https://openreview.net/forum?id=5j_lH4OpZBl | https://openreview.net/pdf?id=5j_lH4OpZBl | neurips-2021-12 | ['bayesian-optimisation'] | ['methodology'] | [ 6.66250050e-01 1.80422977e-01 -3.87980863e-02 1.15763426e-01
-3.63315821e-01 -9.78684723e-01 7.49785304e-01 6.02060676e-01
-1.25627279e-01 7.83542514e-01 -3.33053201e-01 -7.77892053e-01
-5.42922616e-01 -9.49145973e-01 -6.72205210e-01 -8.88889194e-01
-5.87747812e-01 5.66178083e-01 4.99473602e-01 -3.53340805... | [6.035343170166016, 7.424300670623779] |
073fa75d-f83a-4b15-b1df-012d08b52e72 | enriched-robust-multi-view-kernel-subspace | 2205.10495 | null | https://arxiv.org/abs/2205.10495v1 | https://arxiv.org/pdf/2205.10495v1.pdf | Enriched Robust Multi-View Kernel Subspace Clustering | Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity... | ['Kai Liu', 'Mengyuan Zhang'] | 2022-05-21 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.44065547e-01 -6.89614832e-01 -6.04224950e-02 -9.57318842e-02
-6.26969814e-01 -7.33996987e-01 2.43383735e-01 -2.79581577e-01
-1.80987462e-01 2.79160470e-01 2.57495672e-01 3.48834146e-04
-4.18593138e-01 -2.83731610e-01 -3.07673723e-01 -1.13124073e+00
3.37098271e-01 5.50608754e-01 2.64998287e-01 2.76440561... | [8.104527473449707, 4.571674823760986] |
13ba7024-c878-4a8c-a521-37446b3de3b9 | a-study-on-cross-population-age-estimation | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Guo_A_Study_on_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Guo_A_Study_on_2014_CVPR_paper.pdf | A Study on Cross-Population Age Estimation | We study the problem of cross-population age estimation. Human aging is determined by the genes and influenced by many factors. Different populations, e.g., males and females, Caucasian and Asian, may age differently. Previous research has discovered the aging difference among different populations, and reported large ... | ['Guodong Guo', 'Chao Zhang'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['human-aging'] | ['miscellaneous'] | [-2.10698068e-01 -3.44321102e-01 -1.17724061e-01 -5.01481235e-01
-3.71349007e-01 -4.48863953e-02 2.11291179e-01 9.31561086e-03
-5.29450953e-01 9.43803966e-01 1.99211881e-01 2.88330764e-01
1.04186602e-01 -8.35412264e-01 -3.03281844e-01 -8.11017275e-01
-3.44650239e-01 4.88403589e-01 -2.22037524e-01 -7.10621625... | [13.520681381225586, 0.8318542838096619] |
9ad96411-3fc9-4ba9-aae0-04ff3e050743 | distracting-downpour-adversarial-weather | 2305.06716 | null | https://arxiv.org/abs/2305.06716v1 | https://arxiv.org/pdf/2305.06716v1.pdf | Distracting Downpour: Adversarial Weather Attacks for Motion Estimation | Current adversarial attacks on motion estimation, or optical flow, optimize small per-pixel perturbations, which are unlikely to appear in the real world. In contrast, adverse weather conditions constitute a much more realistic threat scenario. Hence, in this work, we present a novel attack on motion estimation that ex... | ['Andrés Bruhn', 'Lukas Mehl', 'Jenny Schmalfuss'] | 2023-05-11 | null | null | null | null | ['motion-estimation'] | ['computer-vision'] | [ 1.90136507e-01 -2.38162071e-01 4.48101401e-01 2.55911142e-01
-3.08834016e-01 -8.82773817e-01 8.07467282e-01 -2.83145785e-01
-3.23543042e-01 1.06450760e+00 -3.93305793e-02 -2.52703905e-01
5.93304813e-01 -9.23866749e-01 -8.43621552e-01 -9.11207914e-01
-2.62953103e-01 -9.57630575e-02 3.90244424e-01 -3.04916471... | [5.417754650115967, 7.932959079742432] |
8aee5116-0433-464b-a13e-f2ae955cfc52 | deep-learning-of-segment-level-feature | 2302.02419 | null | https://arxiv.org/abs/2302.02419v1 | https://arxiv.org/pdf/2302.02419v1.pdf | deep learning of segment-level feature representation for speech emotion recognition in conversations | Accurately detecting emotions in conversation is a necessary yet challenging task due to the complexity of emotions and dynamics in dialogues. The emotional state of a speaker can be influenced by many different factors, such as interlocutor stimulus, dialogue scene, and topic. In this work, we propose a conversational... | ['Joshua Reiss', 'Huy Phan', 'Jiachen Luo'] | 2023-02-05 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [ 6.93392679e-02 -1.90762877e-01 3.25206399e-01 -6.74937785e-01
-7.27185011e-01 -4.54457194e-01 5.12513757e-01 -2.11565513e-02
-1.16378047e-01 4.73687917e-01 8.55200768e-01 3.01966518e-01
1.56144187e-01 -1.99340641e-01 -1.34344071e-01 -6.54083908e-01
-1.83069915e-01 1.26607031e-01 -1.30369857e-01 -5.76750219... | [13.164347648620605, 5.9398627281188965] |
58f3c642-1985-49e5-b41f-4d85183476e8 | ictcas-ucas-tal-submission-to-the-ava | null | null | http://research.google.com/ava/2021/S1_ICTCAS-UCAS-TAL.pdf | http://research.google.com/ava/2021/S1_ICTCAS-UCAS-TAL.pdf | ICTCAS-UCAS-TAL Submission to the AVA-ActiveSpeaker Task at ActivityNet Challenge 2021 | This report presents a brief description of our method for the AVA Active Speaker Detection (ASD) task at ActivityNet
Challenge 2021. Our solution, the Extended Unified Context Network (Extended UniCon) is based on a novel Unified
Context Network (UniCon) designed for robust ASD, which combines multiple types of cont... | ['Shiguang Shan', 'Zhongqin Wu', 'Xiao Liu', 'Shuang Yang', 'Susan Liang', 'Yuanhang Zhang'] | 2021-06-01 | null | null | null | the-activitynet-large-scale-activity | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 1.75868526e-01 2.41644308e-02 -2.41306409e-01 -4.12298411e-01
-1.53661788e+00 -5.27610302e-01 5.52924573e-01 -3.07449877e-01
-4.06349212e-01 4.64446723e-01 5.94093263e-01 -2.21792072e-01
-1.99822947e-01 1.46538839e-01 -2.16409549e-01 -6.60897851e-01
-2.51357615e-01 3.80675793e-01 4.74230289e-01 -2.50875264... | [14.398632049560547, 5.919618606567383] |
b9171c1c-6006-4233-b8d2-c195c3963bdc | collecting-the-public-perception-of-ai-and | 2008.01339 | null | https://arxiv.org/abs/2008.01339v1 | https://arxiv.org/pdf/2008.01339v1.pdf | Collecting the Public Perception of AI and Robot Rights | Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities." Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1... | ['Changyeon Kim', 'Chihyung Jeon', 'Seungho Ryu', 'Meeyoung Cha', 'Gabriel Lima'] | 2020-08-04 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-1.99490175e-01 1.10072911e+00 -4.19223011e-01 -2.89835364e-01
1.31272271e-01 -6.94349945e-01 1.06420243e+00 2.30201054e-02
-1.09859300e+00 7.45758593e-01 7.09920108e-01 -8.58552098e-01
1.26944587e-01 -5.30847490e-01 -4.16696191e-01 -3.27294320e-01
3.50070864e-01 1.90152764e-01 -3.69690239e-01 -4.40288842... | [9.117410659790039, 6.33119010925293] |
7946dea7-0c29-4164-80f9-b23a495fda02 | do-backdoors-assist-membership-inference | 2303.12589 | null | https://arxiv.org/abs/2303.12589v1 | https://arxiv.org/pdf/2303.12589v1.pdf | Do Backdoors Assist Membership Inference Attacks? | When an adversary provides poison samples to a machine learning model, privacy leakage, such as membership inference attacks that infer whether a sample was included in the training of the model, becomes effective by moving the sample to an outlier. However, the attacks can be detected because inference accuracy deteri... | ['Naoto Yanai', 'Toshiki Shibahara', 'Nami Ashizawa', 'Yumeki Goto'] | 2023-03-22 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 1.40414461e-01 1.75292209e-01 8.30345452e-02 -2.42524177e-01
-8.99976134e-01 -1.23393655e+00 3.89783144e-01 3.63245040e-01
-5.13926268e-01 9.55966055e-01 -4.86594439e-01 -4.90680158e-01
1.38259828e-01 -8.76259744e-01 -1.54294229e+00 -9.30753529e-01
-5.68059878e-03 7.64423236e-02 5.96987829e-02 2.95391083... | [5.889516830444336, 7.2586846351623535] |
8c37b59a-5ff8-4951-a534-c340e3a43836 | incentivizing-combinatorial-bandit | 2206.00494 | null | https://arxiv.org/abs/2206.00494v1 | https://arxiv.org/pdf/2206.00494v1.pdf | Incentivizing Combinatorial Bandit Exploration | Consider a bandit algorithm that recommends actions to self-interested users in a recommendation system. The users are free to choose other actions and need to be incentivized to follow the algorithm's recommendations. While the users prefer to exploit, the algorithm can incentivize them to explore by leveraging the in... | ['Zhiwei Steven Wu', 'Aleksandrs Slivkins', 'Dung Daniel Ngo', 'Xinyan Hu'] | 2022-06-01 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.18271777e-01 5.48828065e-01 -1.11969256e+00 -1.80709571e-01
-6.17839217e-01 -7.90603697e-01 3.77985954e-01 -3.19978863e-01
-4.18950975e-01 1.01683593e+00 3.27469230e-01 -6.68133199e-01
-6.56852782e-01 -9.28844094e-01 -6.79806709e-01 -7.72823393e-01
-3.16910028e-01 1.12987792e+00 -4.03779179e-01 5.28506041... | [4.500825881958008, 3.2531042098999023] |
c6093cdc-8ba7-4c91-935f-fcf453ed8e2d | neural-cross-lingual-entity-linking | 1712.01813 | null | http://arxiv.org/abs/1712.01813v1 | http://arxiv.org/pdf/1712.01813v1.pdf | Neural Cross-Lingual Entity Linking | A major challenge in Entity Linking (EL) is making effective use of
contextual information to disambiguate mentions to Wikipedia that might refer
to different entities in different contexts. The problem exacerbates with
cross-lingual EL which involves linking mentions written in non-English
documents to entries in the ... | ['Wael Hamza', 'Gourab Kundu', 'Radu Florian', 'Avirup Sil'] | 2017-12-05 | null | null | null | null | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-7.12406039e-01 -1.29218891e-01 -2.85228997e-01 -1.17192388e-01
-1.17559206e+00 -8.61872911e-01 8.35635185e-01 7.59317756e-01
-1.08995473e+00 7.39294291e-01 6.10606670e-01 -1.54475644e-01
-6.40118122e-02 -9.17596042e-01 -7.91024029e-01 -5.28014302e-02
-1.16420992e-01 5.74116051e-01 1.53092757e-01 -5.34468174... | [9.588529586791992, 8.923338890075684] |
f2468152-46f5-44b2-8f0d-83cfded7f63e | codified-audio-language-modeling-learns | 2107.05677 | null | https://arxiv.org/abs/2107.05677v1 | https://arxiv.org/pdf/2107.05677v1.pdf | Codified audio language modeling learns useful representations for music information retrieval | We demonstrate that language models pre-trained on codified (discretely-encoded) music audio learn representations that are useful for downstream MIR tasks. Specifically, we explore representations from Jukebox (Dhariwal et al. 2020): a music generation system containing a language model trained on codified audio from ... | ['Percy Liang', 'Chris Donahue', 'Rodrigo Castellon'] | 2021-07-12 | null | null | null | null | ['music-generation', 'genre-classification', 'music-generation', 'music-information-retrieval'] | ['audio', 'computer-vision', 'music', 'music'] | [ 2.71064669e-01 3.56429696e-01 -1.30163416e-01 -2.38872748e-02
-1.29690897e+00 -8.52025807e-01 7.23746955e-01 1.10474855e-01
-3.56482983e-01 2.51519382e-01 1.12943769e+00 -1.63878649e-01
-6.02765791e-02 -5.28045177e-01 -5.21456122e-01 -1.68778300e-01
-1.32871076e-01 7.06720799e-02 1.34104099e-02 -3.45526963... | [15.68427848815918, 5.245269775390625] |
30b5c3ed-9b3c-482e-8cc2-2b8dbf7378d1 | self-supervised-prototypical-transfer | 2006.11325 | null | https://arxiv.org/abs/2006.11325v1 | https://arxiv.org/pdf/2006.11325v1.pdf | Self-Supervised Prototypical Transfer Learning for Few-Shot Classification | Most approaches in few-shot learning rely on costly annotated data related to the goal task domain during (pre-)training. Recently, unsupervised meta-learning methods have exchanged the annotation requirement for a reduction in few-shot classification performance. Simultaneously, in settings with realistic domain shift... | ['Arnout Devos', 'Carlos Medina', 'Matthias Grossglauser'] | 2020-06-19 | null | null | null | null | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 3.82069409e-01 2.32993811e-01 -7.03700423e-01 -5.99630833e-01
-1.02532446e+00 -1.94216058e-01 7.99664021e-01 4.25898522e-01
-5.47924936e-01 6.67651057e-01 3.33140343e-01 2.76685268e-01
-6.76516518e-02 -6.54922009e-01 -6.02306306e-01 -4.03689384e-01
9.62550635e-04 7.41576791e-01 3.56850982e-01 -3.05924416... | [9.934575080871582, 3.022298812866211] |
77760999-e047-4bc4-b919-8a142fabfad8 | named-entity-recognition-for-norwegian | null | null | https://aclanthology.org/W19-6123 | https://aclanthology.org/W19-6123.pdf | Named-Entity Recognition for Norwegian | NER is the task of recognizing and demarcating the segments of a document that are part of a name and which type of name it is. We use 4 different categories of names: Locations (LOC), miscellaneous (MISC), organizations (ORG), and persons (PER). Even though we employ state of the art methods—including sub-word embeddi... | ['Bjarte Johansen'] | null | null | null | null | ws-nodalida-2019-9 | ['miscellaneous'] | ['miscellaneous'] | [-7.00797617e-01 4.49987035e-03 -5.75007834e-02 -8.46552327e-02
-6.28144860e-01 -9.47248757e-01 8.95431995e-01 3.77589554e-01
-1.10054100e+00 8.04431021e-01 8.89207900e-01 -5.43280184e-01
-1.71072707e-01 -7.86534488e-01 -3.22238684e-01 -2.81481951e-01
3.19516927e-01 5.78992248e-01 -3.12873535e-02 -2.33633012... | [9.832271575927734, 9.70787525177002] |
a477409c-ef05-40df-a40d-32bcf8383a19 | large-scale-artificial-neural-network-1 | 1510.02709 | null | http://arxiv.org/abs/1510.02709v1 | http://arxiv.org/pdf/1510.02709v1.pdf | Large-scale Artificial Neural Network: MapReduce-based Deep Learning | Faced with continuously increasing scale of data, original back-propagation
neural network based machine learning algorithm presents two non-trivial
challenges: huge amount of data makes it difficult to maintain both efficiency
and accuracy; redundant data aggravates the system workload. This project is
mainly focused ... | ['Ruizhi Li', 'Xu Wei', 'Gengtao Jia', 'Risheng Wang', 'Kairan Sun'] | 2015-10-09 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-3.22375327e-01 -5.63972354e-01 1.19029224e-01 -6.20855093e-01
1.46797776e-01 -2.77629107e-01 -5.12125343e-03 -2.89414525e-01
-3.80884886e-01 4.55937147e-01 -2.13287920e-01 -4.67370600e-01
-2.38497034e-01 -1.15858054e+00 -3.54155838e-01 -7.54028618e-01
9.99455974e-02 3.59881967e-01 1.68730989e-01 -1.13019362... | [11.80608081817627, 2.6552648544311523] |
725fdcfb-1da2-4081-bdbe-bfabf771c137 | adversarial-unsupervised-domain-adaptation-1 | 2102.06864 | null | https://arxiv.org/abs/2102.06864v1 | https://arxiv.org/pdf/2102.06864v1.pdf | Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection | Face Presentation Attack Detection (PAD) has drawn increasing attentions to secure the face recognition systems that are widely used in many applications. Conventional face anti-spoofing methods have been proposed, assuming that testing is from the same domain used for training, and so cannot generalize well on unseen ... | ['Hani Mahdi', 'Mohamed N. Moustafa', 'Yomna Safaa El-Din'] | 2021-02-13 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.62791538e-01 -2.29826272e-01 -2.38951921e-01 -3.83262068e-01
-3.67286742e-01 -6.81152344e-01 4.50090677e-01 -1.80052131e-01
-4.25532125e-02 5.43574154e-01 -2.83176214e-01 -6.46668300e-02
-1.99792862e-01 -7.81044006e-01 -5.66151619e-01 -1.04562891e+00
-1.50597557e-01 3.29468161e-01 8.25795755e-02 -1.39057219... | [13.07801628112793, 1.1740694046020508] |
614549fc-9975-49c9-9306-b0721c8c7ab8 | designing-ecg-monitoring-healthcare-system | 2105.12497 | null | https://arxiv.org/abs/2105.12497v2 | https://arxiv.org/pdf/2105.12497v2.pdf | Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI | Deep learning play a vital role in classifying different arrhythmias using the electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and it can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a sing... | ['Shujun Li', 'Ludovic Koehl', 'Kim Phuc Tran', 'Ali Raza'] | 2021-05-26 | null | null | null | null | ['arrhythmia-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [-4.79033925e-02 3.38061452e-01 1.81120113e-01 -6.85091555e-01
-4.51107323e-01 -2.40129158e-01 -1.96728274e-01 4.10124987e-01
-2.15790406e-01 8.81788492e-01 -1.50441587e-01 -5.79845786e-01
-3.58842969e-01 -7.47330964e-01 -6.13093376e-01 -6.25364304e-01
-1.99525863e-01 3.67772788e-01 -8.60845804e-01 3.37053627... | [14.276062965393066, 3.307525634765625] |
89acdc69-98f1-41b5-8a35-cb318d72c4c3 | embedding-representation-of-academic | 2210.03290 | null | https://arxiv.org/abs/2210.03290v1 | https://arxiv.org/pdf/2210.03290v1.pdf | Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning | Academic networks in the real world can usually be portrayed as heterogeneous information networks (HINs) with multi-type, universally connected nodes and multi-relationships. Some existing studies for the representation learning of homogeneous information networks cannot be applicable to heterogeneous information netw... | ['Ang Li', 'Meiyu Liang', 'Yawen Li', 'Junfu Wang'] | 2022-10-07 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-7.01587081e-01 1.18256360e-01 -3.49513859e-01 4.50810194e-02
-1.30458027e-02 -3.81748080e-01 5.15986562e-01 4.04774159e-01
2.77511831e-02 3.88189018e-01 2.30232298e-01 -4.33713436e-01
-6.78469360e-01 -1.52372468e+00 -2.89704829e-01 -5.07384062e-01
-3.03176701e-01 4.67894644e-01 1.57281965e-01 -4.50361520... | [7.299123287200928, 6.232067108154297] |
2e999653-53ea-4889-9b92-0187446a5e74 | blind-image-deconvolution-by-automatic | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Gong_Blind_Image_Deconvolution_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Gong_Blind_Image_Deconvolution_CVPR_2016_paper.pdf | Blind Image Deconvolution by Automatic Gradient Activation | Blind image deconvolution is an ill-posed inverse problem which is often addressed through the application of appropriate prior. Although some priors are informative in general, many images do not strictly conform to this, leading to degraded performance in the kernel estimation. More critically, real images may be con... | ['Anton Van Den Hengel', 'Yanning Zhang', 'Dong Gong', 'Qinfeng Shi', 'Mingkui Tan'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['image-deconvolution'] | ['computer-vision'] | [ 2.72445560e-01 -4.23934877e-01 2.73787260e-01 -3.19148868e-01
-3.53784174e-01 -3.55457634e-01 3.28590780e-01 -2.84584850e-01
-4.52902228e-01 7.82497644e-01 5.97336441e-02 1.61432743e-01
-3.53035361e-01 -3.37676853e-01 -5.29495895e-01 -1.03947103e+00
1.88291907e-01 -1.22063234e-01 3.57060999e-01 1.05015874... | [11.466014862060547, -2.637230634689331] |
15117032-214d-4f14-9522-b64bc7f8b783 | the-bayesian-brain-with-a-bit-less-bayes | 2111.09063 | null | https://arxiv.org/abs/2111.09063v1 | https://arxiv.org/pdf/2111.09063v1.pdf | The "Bayesian" brain, with a bit less Bayes | The idea that the brain is a probabilistic (Bayesian) inference machine, continuously trying to figure out the hidden causes of its inputs, has become very influential in cognitive (neuro)science over recent decades. Here I present a relatively straightforward generalization of this idea: the primary computational task... | ['Eelke Spaak'] | 2021-11-17 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 2.81214148e-01 4.58367229e-01 -3.68445143e-02 -3.42868209e-01
1.63799286e-01 -2.63890505e-01 1.14417005e+00 2.23854899e-01
-4.76549447e-01 7.26403713e-01 6.96066856e-01 -4.47009146e-01
-4.75547701e-01 -5.89436114e-01 -5.26643038e-01 -9.57284749e-01
-1.93182439e-01 1.53397143e-01 1.15613155e-01 -3.41011941... | [8.665550231933594, 6.156114101409912] |
c7a04346-f021-41d4-a123-9cf3a1aeeba1 | sparse-subspace-clustering-friendly-deep | 2111.13920 | null | https://arxiv.org/abs/2111.13920v1 | https://arxiv.org/pdf/2111.13920v1.pdf | Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification | Subspace clustering techniques have shown promise in hyperspectral image segmentation. The fundamental assumption in subspace clustering is that the samples belonging to different clusters/segments lie in separable subspaces. What if this condition does not hold? We surmise that even if the condition does not hold in t... | ['Angshul Majumdar', 'Anurag Goel'] | 2021-11-27 | null | null | null | null | ['hyperspectral-image-segmentation', 'image-clustering'] | ['computer-vision', 'computer-vision'] | [ 5.26059270e-01 -2.56685317e-01 3.01237870e-02 -2.99244553e-01
-4.21960056e-01 -9.55829084e-01 1.70160070e-01 -1.67948782e-01
-6.59563299e-03 2.55411208e-01 8.14230144e-02 -3.06754619e-01
-4.19902235e-01 -5.41308105e-01 -5.26350737e-01 -1.36187804e+00
1.93930089e-01 5.83117127e-01 -3.56311351e-01 2.48510584... | [8.072644233703613, 4.131118297576904] |
68e9fb4a-d3a8-4587-be9a-7dcc34ae7c85 | gcrdn-global-context-driven-residual-dense | null | null | https://ieeexplore.ieee.org/abstract/document/10115440 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10115440&tag=1 | GCRDN: Global Context-Driven Residual Dense Network for Remote Sensing Image Superresolution | Superresolution (SR) of remote sensing images aims to restore high-quality information from low-resolution images. Recently, it has witnessed great strides with the rapid development of deep learning (DL) techniques. Despite their good performance, these DL-based models are often ineffective in balancing global and loc... | ['Man-on Pun', 'Xiaokang Zhang', 'Xianping Ma', 'Jialu Sui'] | 2023-05-04 | null | null | null | jounal-2023-5 | ['image-reconstruction', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 4.92542624e-01 -1.75938785e-01 3.38109061e-02 -4.67350125e-01
-9.83624041e-01 2.14500621e-01 4.80963439e-01 -3.17096531e-01
-2.53280066e-02 6.45694613e-01 5.01335382e-01 2.35606402e-01
-3.09801549e-01 -8.19771528e-01 -5.67186475e-01 -9.74966347e-01
4.95880693e-02 -3.07967335e-01 -1.10066071e-01 -3.19683790... | [10.686222076416016, -2.0424749851226807] |
0f95ecdd-1fd6-405e-ab48-db23cecd0f57 | submission-to-generic-event-boundary | 2206.15268 | null | https://arxiv.org/abs/2206.15268v1 | https://arxiv.org/pdf/2206.15268v1.pdf | Submission to Generic Event Boundary Detection Challenge@CVPR 2022: Local Context Modeling and Global Boundary Decoding Approach | Generic event boundary detection (GEBD) is an important yet challenging task in video understanding, which aims at detecting the moments where humans naturally perceive event boundaries. In this paper, we present a local context modeling and global boundary decoding approach for GEBD task. Local context modeling sub-ne... | ['LiMin Wang', 'Wayne Wu', 'Chen Qian', 'Jing Tan', 'Zhaoyang Liu', 'Jiaqi Tang'] | 2022-06-30 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.72732383e-01 8.35582316e-02 -3.19861263e-01 -2.80278146e-01
-7.67100334e-01 -3.68093640e-01 5.21937549e-01 9.84143019e-02
-2.04879135e-01 3.75554055e-01 5.88853538e-01 1.67171042e-02
5.02266586e-01 -4.94307488e-01 -8.05260301e-01 -3.28447878e-01
-1.73410058e-01 -2.89609618e-02 4.68345135e-01 2.34272167... | [9.399123191833496, 0.5773455500602722] |
b2531e58-cfe3-46b1-aaf1-8dea287ee3bc | accurate-rgb-d-salient-object-detection-via | 2007.11782 | null | https://arxiv.org/abs/2007.11782v1 | https://arxiv.org/pdf/2007.11782v1.pdf | Accurate RGB-D Salient Object Detection via Collaborative Learning | Benefiting from the spatial cues embedded in depth images, recent progress on RGB-D saliency detection shows impressive ability on some challenge scenarios. However, there are still two limitations. One hand is that the pooling and upsampling operations in FCNs might cause blur object boundaries. On the other hand, usi... | ['Yongri Piao', 'Wei Ji', 'Miao Zhang', 'Jingjing Li', 'Huchuan Lu'] | 2020-07-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2916_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630052.pdf | eccv-2020-8 | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.06646055e-01 6.36989158e-03 -2.00735882e-01 -3.86748224e-01
-1.70201525e-01 -1.47744700e-01 2.09844068e-01 -8.18427280e-03
-2.75524229e-01 6.07131064e-01 1.75155684e-01 1.23825070e-04
-1.87279046e-01 -7.64626741e-01 -4.70432997e-01 -8.40009212e-01
1.00040942e-01 -4.14379507e-01 8.93068492e-01 6.23094791... | [9.66472339630127, -0.7755324244499207] |
e3a6cbf7-48db-4f2d-984e-ecd8190f6a6d | robust-control-for-dynamical-systems-with-non | 2301.01526 | null | https://arxiv.org/abs/2301.01526v1 | https://arxiv.org/pdf/2301.01526v1.pdf | Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions | Controllers for dynamical systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise in a dynamical system, and common assumptions are that the underlying distributions are known and/or Gaussian. In practice, however, these assumptions... | ['Nils Jansen', 'Marielle Stoelinga', 'Hasan A. Poonawala', 'David Parker', 'Alessandro Abate', 'Licio Romao', 'Thom Badings'] | 2023-01-04 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 2.73230493e-01 2.48213112e-01 -1.11924887e-01 3.09912533e-01
-7.33658612e-01 -8.46711993e-01 6.81023300e-01 2.33804971e-01
3.28440927e-02 7.82021463e-01 -4.55212802e-01 -7.29191303e-01
-2.15950802e-01 -9.85258639e-01 -8.66370082e-01 -7.21484303e-01
-2.74660945e-01 5.46866477e-01 7.14472294e-01 -6.47557825... | [4.807823181152344, 2.2441632747650146] |
b2d8af69-6548-4a8f-beec-3db4c699d538 | l-spex-localized-target-speaker-extraction | 2202.09995 | null | https://arxiv.org/abs/2202.09995v1 | https://arxiv.org/pdf/2202.09995v1.pdf | L-SpEx: Localized Target Speaker Extraction | Speaker extraction aims to extract the target speaker's voice from a multi-talker speech mixture given an auxiliary reference utterance. Recent studies show that speaker extraction benefits from the location or direction of the target speaker. However, these studies assume that the target speaker's location is known in... | ['Haizhou Li', 'Jianwu Dang', 'Eng Siong Chng', 'Longbiao Wang', 'Chenglin Xu', 'Meng Ge'] | 2022-02-21 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [-7.57409036e-02 -2.24898070e-01 1.08919002e-01 -4.57838774e-01
-1.48598635e+00 -5.78388333e-01 3.30009490e-01 -2.06816256e-01
-2.62884915e-01 3.14695865e-01 5.76561391e-01 4.26098555e-02
2.12489024e-01 2.11206954e-02 -5.46889901e-01 -9.99074399e-01
9.44252461e-02 -2.14924559e-01 -1.73615245e-03 1.99983940... | [14.704360961914062, 5.561341762542725] |
0ca16b5a-be02-4a6a-b920-3863a74c43ce | applying-transfer-learning-for-improving | 2101.02351 | null | https://arxiv.org/abs/2101.02351v1 | https://arxiv.org/pdf/2101.02351v1.pdf | Applying Transfer Learning for Improving Domain-Specific Search Experience Using Query to Question Similarity | Search is one of the most common platforms used to seek information. However, users mostly get overloaded with results whenever they use such a platform to resolve their queries. Nowadays, direct answers to queries are being provided as a part of the search experience. The question-answer (QA) retrieval process plays a... | ['Sohom Ghosh', 'Shruti Agrawal', 'Ankush Chopra'] | 2021-01-07 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [-1.37486398e-01 -3.60385925e-01 -2.23554373e-01 -3.93500745e-01
-1.01872015e+00 -7.80421674e-01 7.19232917e-01 5.87287009e-01
-9.29689825e-01 1.72100440e-01 1.23375870e-01 -4.28070158e-01
-5.90723991e-01 -1.05853283e+00 -1.84671536e-01 -7.13095590e-02
4.47342396e-01 7.06049740e-01 6.89869225e-01 -9.05487001... | [11.244067192077637, 8.103750228881836] |
77d6cf64-d625-479a-9e2a-5eec297a4865 | visbert-hidden-state-visualizations-for | 2011.04507 | null | https://arxiv.org/abs/2011.04507v1 | https://arxiv.org/pdf/2011.04507v1.pdf | VisBERT: Hidden-State Visualizations for Transformers | Explainability and interpretability are two important concepts, the absence of which can and should impede the application of well-performing neural networks to real-world problems. At the same time, they are difficult to incorporate into the large, black-box models that achieve state-of-the-art results in a multitude ... | ['Felix A. Gers', 'Alexander Löser', 'Benjamin Winter', 'Betty van Aken'] | 2020-11-09 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 3.21342498e-01 6.97021246e-01 6.96167815e-03 -4.52296644e-01
-5.25662243e-01 -7.17671335e-01 6.75299466e-01 3.17685187e-01
4.95332368e-02 3.38278651e-01 6.31496489e-01 -8.83050799e-01
-2.15370595e-01 -6.97042346e-01 -6.99018717e-01 -1.37056798e-01
2.59800673e-01 6.54376030e-01 2.46028945e-01 -4.25476909... | [9.745911598205566, 7.3175249099731445] |
27e1ddb7-443b-4a4c-9c88-a9fa36dbaab4 | learning-sparse-adversarial-dictionaries-for | 1712.00640 | null | http://arxiv.org/abs/1712.00640v1 | http://arxiv.org/pdf/1712.00640v1.pdf | Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification | Audio events are quite often overlapping in nature, and more prone to noise
than visual signals. There has been increasing evidence for the superior
performance of representations learned using sparse dictionaries for
applications like audio denoising and speech enhancement. This paper
concentrates on modifying the tra... | ['Puranjoy Bhattacharya', 'Vaisakh Shaj'] | 2017-12-02 | null | null | null | null | ['audio-denoising'] | ['audio'] | [ 3.48496079e-01 -1.08512402e-01 -1.59887094e-02 -1.94960609e-01
-1.04027104e+00 -4.94412035e-01 3.04073185e-01 3.11773330e-01
-2.41880253e-01 7.29527354e-01 3.91955197e-01 1.58445314e-01
1.13116674e-01 -7.40171731e-01 -5.23932278e-01 -1.12083185e+00
-8.11814219e-02 3.28273267e-01 -1.18900970e-01 -1.31827325... | [15.360363960266113, 5.595808982849121] |
ba2389f7-d4eb-4099-80c8-f42a057a6a59 | mime-mimicking-emotions-for-empathetic | 2010.01454 | null | https://arxiv.org/abs/2010.01454v1 | https://arxiv.org/pdf/2010.01454v1.pdf | MIME: MIMicking Emotions for Empathetic Response Generation | Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. ... | ['Soujanya Poria', 'Rada Mihalcea', 'Alexander Gelbukh', 'Deepanway Ghosal', 'Jiankun Lu', 'Shanshan Peng', 'Pengfei Hong', 'Navonil Majumder'] | 2020-10-04 | null | https://aclanthology.org/2020.emnlp-main.721 | https://aclanthology.org/2020.emnlp-main.721.pdf | emnlp-2020-11 | ['empathetic-response-generation'] | ['natural-language-processing'] | [-3.65099519e-01 2.38253132e-01 2.77221352e-02 -4.54812348e-01
-4.99544561e-01 -7.36329496e-01 7.22475827e-01 1.83525309e-02
-3.02265257e-01 7.87818253e-01 8.06702554e-01 2.32854962e-01
2.11213991e-01 -5.95385790e-01 9.00074095e-03 -5.52606642e-01
5.05616307e-01 5.16189098e-01 -4.59153444e-01 -7.50174761... | [13.143977165222168, 7.659074306488037] |
b559fd5b-9fd5-4bc2-86fc-9e964142b833 | testing-the-dark-confined-landscape-from | 2012.11614 | null | https://arxiv.org/abs/2012.11614v3 | https://arxiv.org/pdf/2012.11614v3.pdf | Testing the dark SU(N) Yang-Mills theory Confined Landscape: From the Lattice to Gravitational Waves | We pave the way for future gravitational-wave detection experiments, such as the Big Bang Observer and DECIGO, to constrain dark sectors made of SU(N) Yang-Mills confined theories. We go beyond the state-of-the-art by combining first principle lattice results and effective field theory approaches to infer essential inf... | ['Zhi-Wei Wang', 'Francesco Sannino', 'Manuel Reichert', 'Wei-Chih Huang'] | 2020-12-21 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-2.49840729e-02 4.53455262e-02 -4.98336777e-02 1.32184401e-01
1.52309418e-01 -2.41289571e-01 1.05273330e+00 -4.71649379e-01
-1.41476467e-01 6.25577748e-01 2.62631834e-01 -7.70532489e-01
1.08372360e-01 -9.67281461e-01 -4.10036862e-01 -1.15816712e+00
-1.03176750e-01 5.66151500e-01 5.89037597e-01 -4.19633716... | [5.772940158843994, 4.6955389976501465] |
55f07732-928a-4e18-bf2c-2d14deafe443 | semi-supervised-time-series-classification | 2006.11031 | null | https://arxiv.org/abs/2006.11031v1 | https://arxiv.org/pdf/2006.11031v1.pdf | Semi-supervised time series classification method for quantum computing | In this paper we develop methods to solve two problems related to time series (TS) analysis using quantum computing: reconstruction and classification. We formulate the task of reconstructing a given TS from a training set of data as an unconstrained binary optimization (QUBO) problem, which can be solved by both quant... | ['Andrii Kleshchonok', 'Sheir Yarkoni', 'Marc Hilbert', 'Yury Dzerin', 'Florian Neukart'] | 2020-06-19 | null | null | null | null | ['semi-supervised-time-series-classification'] | ['time-series'] | [ 1.07337475e+00 5.94598614e-03 -1.25379309e-01 -5.02299964e-01
-1.21670544e+00 -8.41208339e-01 6.23455167e-01 9.31745246e-02
-6.28065526e-01 9.41023171e-01 -3.88905466e-01 -4.85317677e-01
-1.20366020e-02 -1.00731981e+00 -6.09105110e-01 -1.02997446e+00
1.89287946e-01 7.45414853e-01 -3.62846069e-02 -3.75464290... | [5.576687812805176, 4.923187255859375] |
9c16edb4-0804-4261-9afd-8f5c160fd842 | from-image-to-imuge-immunized-image | 2110.14196 | null | https://arxiv.org/abs/2110.14196v1 | https://arxiv.org/pdf/2110.14196v1.pdf | From Image to Imuge: Immunized Image Generation | We introduce Imuge, an image tamper resilient generative scheme for image self-recovery. The traditional manner of concealing image content within the image are inflexible and fragile to diverse digital attack, i.e. image cropping and JPEG compression. To address this issue, we jointly train a U-Net backboned encoder, ... | ['Siyi Li', 'Xinpeng Zhang', 'Haisheng Xu', 'Hang Zhou', 'Zhenxing Qian', 'Qichao Ying'] | 2021-10-27 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 7.15314031e-01 5.19256704e-02 -1.90781057e-01 2.45717913e-01
-9.89212215e-01 -9.91863847e-01 2.81832349e-02 -2.68733710e-01
-2.09210813e-01 4.35134858e-01 1.37629047e-01 -4.69787598e-01
6.53916478e-01 -7.58831203e-01 -1.29545069e+00 -7.95045614e-01
-1.45664230e-01 -2.98168719e-01 5.60181402e-02 3.11053302... | [4.595645904541016, 7.984776496887207] |
6a0fc8a7-67e6-43fa-83ee-48d0ab635ae1 | tinkering-under-the-hood-interactive-zero | 1612.04901 | null | http://arxiv.org/abs/1612.04901v1 | http://arxiv.org/pdf/1612.04901v1.pdf | Tinkering Under the Hood: Interactive Zero-Shot Learning with Net Surgery | We consider the task of visual net surgery, in which a CNN can be
reconfigured without extra data to recognize novel concepts that may be omitted
from the training set. While most prior work make use of linguistic cues for
such "zero-shot" learning, we do so by using a pictorial language
representation of the training ... | ['Vivek Krishnan', 'Deva Ramanan'] | 2016-12-15 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 9.58626568e-02 4.03276145e-01 1.97244555e-01 -5.64491749e-01
3.01648527e-01 -1.12905622e+00 6.27872467e-01 1.37567341e-01
-7.21339822e-01 3.97810608e-01 -1.28114909e-01 -4.50795174e-01
2.73679197e-01 -8.56508493e-01 -1.12038112e+00 -3.42845559e-01
-3.01242948e-01 1.67052865e-01 4.81055617e-01 -5.46799600... | [9.969602584838867, 1.968381643295288] |
24b1ce6d-52a7-4b3b-bed4-63e0c8eefb08 | pseudo-supervised-metrics-evaluating | 2303.10310 | null | https://arxiv.org/abs/2303.10310v1 | https://arxiv.org/pdf/2303.10310v1.pdf | Pseudo Supervised Metrics: Evaluating Unsupervised Image to Image Translation Models In Unsupervised Cross-Domain Classification Frameworks | The ability to classify images accurately and efficiently is dependent on having access to large labeled datasets and testing on data from the same domain that the model is trained on. Classification becomes more challenging when dealing with new data from a different domain, where collecting a large labeled dataset an... | ['Ying Sun', 'Han Hu', 'Teresa Wu', 'Md Mahfuzur Rahman Siddiquee', 'Firas Al-Hindawi'] | 2023-03-18 | null | null | null | null | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 6.04425848e-01 6.19500764e-02 -2.51977354e-01 -5.00452757e-01
-8.00516963e-01 -7.91959405e-01 6.39364719e-01 2.60077804e-01
-2.89248496e-01 7.23015070e-01 -2.95308948e-01 -1.32657483e-01
-1.28160343e-01 -6.19642735e-01 -5.72458386e-01 -6.17284775e-01
3.24167937e-01 6.82895124e-01 1.18931629e-01 9.29112211... | [9.73633098602295, 2.531639337539673] |
bee9a1fb-2dd7-40ef-b91c-584c8fb482dc | context-autoencoder-for-self-supervised | 2202.03026 | null | https://arxiv.org/abs/2202.03026v2 | https://arxiv.org/pdf/2202.03026v2.pdf | Context Autoencoder for Self-Supervised Representation Learning | We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. The goal is to pretrain an encoder by solving the pretext task: estimate the masked patches from the visible patches in an image. Our approach first feeds the visible patches into the enco... | ['Jingdong Wang', 'Gang Zeng', 'Ping Luo', 'Shumin Han', 'Yunhao Wang', 'Shentong Mo', 'Ying Xin', 'Xiaodi Wang', 'Mingyu Ding', 'Xiaokang Chen'] | 2022-02-07 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 9.40059841e-01 9.65691030e-01 -9.32422876e-02 -4.50296193e-01
-7.12051630e-01 -3.09711576e-01 6.26636028e-01 -7.92343095e-02
-1.77196369e-01 2.83462673e-01 4.81136054e-01 5.14885373e-02
1.78961664e-01 -6.27759814e-01 -1.19138193e+00 -7.01225042e-01
1.00731246e-01 3.59460860e-01 5.19355051e-02 1.00869406... | [9.981844902038574, 1.5598187446594238] |
4c5308ab-7dbe-4f56-8b84-3c93edef894c | multicenter-automatic-detection-of-invasive | 2301.06789 | null | https://arxiv.org/abs/2301.06789v1 | https://arxiv.org/pdf/2301.06789v1.pdf | Multicenter automatic detection of invasive carcinoma on breast whole slide images | Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology... | ['Sophie Prévot', 'Marie Sockeel', 'Elisabeth Lanteri', 'Loris Guichard', 'Thomas Depoilly', 'Christophe Bontoux', 'Yoan Ditchi', 'Claire Bocciarelli', 'Julien Adam', 'Solène-Florence Kammerer-Jacquet', 'Stéphane Sockeel', 'Nicolas Pozin', 'Rémy Peyret'] | 2023-01-17 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.80239105e-01 2.46095434e-01 -3.01790774e-01 -8.60928521e-02
-1.00279522e+00 -4.65893269e-01 1.27171978e-01 6.57697439e-01
-5.49585521e-01 8.15759361e-01 -6.61649466e-01 -3.97829950e-01
-2.25226283e-01 -1.00886524e+00 -5.12861431e-01 -1.00516462e+00
2.77695395e-02 7.43262351e-01 3.24782073e-01 2.11483270... | [15.118449211120605, -2.948206663131714] |
250a7bfd-85dc-48e2-94a4-e1cb2e467ba6 | recurrent-neural-network-for-text | 1605.05101 | null | http://arxiv.org/abs/1605.05101v1 | http://arxiv.org/pdf/1605.05101v1.pdf | Recurrent Neural Network for Text Classification with Multi-Task Learning | Neural network based methods have obtained great progress on a variety of
natural language processing tasks. However, in most previous works, the models
are learned based on single-task supervised objectives, which often suffer from
insufficient training data. In this paper, we use the multi-task learning
framework to ... | ['Xuanjing Huang', 'Xipeng Qiu', 'Pengfei Liu'] | 2016-05-17 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 3.18732202e-01 -2.91204304e-01 -3.76591504e-01 -6.40776396e-01
-6.99868262e-01 9.10316855e-02 6.04396522e-01 1.39874727e-01
-5.60640931e-01 7.05018997e-01 2.99105018e-01 -2.80236118e-02
4.57000366e-04 -3.86087596e-01 -3.00160259e-01 -4.88900423e-01
5.34277976e-01 3.08891416e-01 2.53094286e-01 -3.18981975... | [10.728341102600098, 7.7174882888793945] |
b008109a-6aec-4c8f-89fa-4629898c2fbe | from-spelling-to-grammar-a-new-framework-for | 2211.01625 | null | https://arxiv.org/abs/2211.01625v1 | https://arxiv.org/pdf/2211.01625v1.pdf | From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction | Chinese Grammatical Error Correction (CGEC) aims to generate a correct sentence from an erroneous sequence, where different kinds of errors are mixed. This paper divides the CGEC task into two steps, namely spelling error correction and grammatical error correction. Specifically, we propose a novel zero-shot approach f... | ['Yunfang Wu', 'Xiuyu Wu'] | 2022-11-03 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 4.79775906e-01 9.21185613e-02 3.86952788e-01 -5.70159853e-01
-9.90877926e-01 -1.77008882e-01 1.29302070e-01 5.50926208e-01
-5.77136934e-01 8.20455968e-01 2.82212347e-01 -3.23458433e-01
5.08977294e-01 -6.94390953e-01 -9.55588162e-01 -2.40022212e-01
6.97837353e-01 1.93090200e-01 2.99366146e-01 -3.10318679... | [11.022086143493652, 10.745335578918457] |
91480f83-9f50-4329-964e-a7984e6dbe78 | pairwise-symmetry-reasoning-for-multi-agent | 2103.07116 | null | https://arxiv.org/abs/2103.07116v1 | https://arxiv.org/pdf/2103.07116v1.pdf | Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search | Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks us to plan collision-free paths for a team of cooperative agents. In this work, we show that one of the reasons why MAPF is so hard to solve is due to a phenomenon called pairwise symmetry, which occurs when two agents have many different ... | ['Sven Koenig', 'Peter J. Stuckey', 'Daniel Harabor', 'Jiaoyang Li'] | 2021-03-12 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.02400593e-01 3.90113175e-01 1.27840608e-01 1.17142670e-01
-8.07427347e-01 -9.67160344e-01 5.97961068e-01 6.40157163e-01
-3.64187300e-01 1.06529045e+00 -3.25131379e-02 -4.84322757e-01
-8.47968459e-01 -9.43711340e-01 -7.40718246e-01 -6.42919242e-01
-7.91132450e-01 1.30587983e+00 9.11790848e-01 -6.74540639... | [4.958359718322754, 1.8085863590240479] |
fc238214-8d1e-4fd8-86c5-03e82a2d01c5 | a-similarity-preserving-neural-network | 2102.05503 | null | https://arxiv.org/abs/2102.05503v1 | https://arxiv.org/pdf/2102.05503v1.pdf | A Similarity-preserving Neural Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit | Learning to detect content-independent transformations from data is one of the central problems in biological and artificial intelligence. An example of such problem is unsupervised learning of a visual motion detector from pairs of consecutive video frames. Rao and Ruderman formulated this problem in terms of learning... | ['Dmitri B. Chklovskii', 'Anirvan M. Sengupta', 'Yanis Bahroun'] | 2021-02-10 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 5.25224626e-01 1.05514526e-01 -1.17913134e-01 -2.51519054e-01
3.14978715e-05 -4.50334221e-01 8.42671514e-01 -1.33044809e-01
-8.07359278e-01 5.04453361e-01 1.41259506e-01 -1.68394744e-02
-1.60168305e-01 -5.75058937e-01 -9.29370165e-01 -9.88227785e-01
9.10330340e-02 1.04291186e-01 3.91851008e-01 2.50622332... | [8.972643852233887, -0.35700756311416626] |
2d506c98-eef7-46e3-8eda-fc825254235c | renoir-a-dataset-for-real-low-light-image | 1409.8230 | null | http://arxiv.org/abs/1409.8230v9 | http://arxiv.org/pdf/1409.8230v9.pdf | RENOIR - A Dataset for Real Low-Light Image Noise Reduction | Image denoising algorithms are evaluated using images corrupted by artificial
noise, which may lead to incorrect conclusions about their performances on real
noise. In this paper we introduce a dataset of color images corrupted by
natural noise due to low-light conditions, together with spatially and
intensity-aligned ... | ['Adrian Barbu', 'Josue Anaya'] | 2014-09-29 | null | null | null | null | ['color-image-denoising', 'noise-estimation'] | ['computer-vision', 'medical'] | [ 4.72476244e-01 -5.95768273e-01 6.55345976e-01 -2.49205843e-01
-1.05390406e+00 -3.13526779e-01 5.30262828e-01 -2.43116114e-02
-1.00016093e+00 6.36507809e-01 1.60127163e-01 9.69138816e-02
-2.23443627e-01 -8.87799859e-01 -5.85609198e-01 -1.39882612e+00
7.38083273e-02 1.52498513e-01 3.91643286e-01 1.23317935... | [11.477592468261719, -2.420574188232422] |
ca56bde9-12ba-431b-a82f-531e7c72a68b | a-tale-of-two-laws-of-semantic-change | 2305.19143 | null | https://arxiv.org/abs/2305.19143v1 | https://arxiv.org/pdf/2305.19143v1.pdf | A Tale of Two Laws of Semantic Change: Predicting Synonym Changes with Distributional Semantic Models | Lexical Semantic Change is the study of how the meaning of words evolves through time. Another related question is whether and how lexical relations over pairs of words, such as synonymy, change over time. There are currently two competing, apparently opposite hypotheses in the historical linguistic literature regardin... | ['Pascal Denis', 'Mikaela Keller', 'Bastien Liétard'] | 2023-05-30 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 1.15743965e-01 -2.85616815e-01 -4.36491519e-01 -2.97052413e-01
8.03865120e-02 -9.72986281e-01 1.05804133e+00 5.28109610e-01
-7.85346270e-01 6.86129332e-01 5.12643516e-01 -4.65103507e-01
-2.11754575e-01 -8.37153673e-01 -2.01879650e-01 -4.63029951e-01
2.60151267e-01 3.77171457e-01 5.23759186e-01 -6.93245947... | [10.266857147216797, 9.115123748779297] |
f61f5a81-e8f2-4dc9-a1f5-cd092d81fae1 | wild-patterns-ten-years-after-the-rise-of | 1712.03141 | null | http://arxiv.org/abs/1712.03141v2 | http://arxiv.org/pdf/1712.03141v2.pdf | Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning | Learning-based pattern classifiers, including deep networks, have shown
impressive performance in several application domains, ranging from computer
vision to cybersecurity. However, it has also been shown that adversarial input
perturbations carefully crafted either at training or at test time can easily
subvert their... | ['Battista Biggio', 'Fabio Roli'] | 2017-12-08 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 5.91831982e-01 3.25900197e-01 2.32541487e-01 -2.35378012e-01
-1.92132115e-01 -1.13817716e+00 8.36246014e-01 1.03603654e-01
-4.48306590e-01 5.05803525e-01 -3.35485697e-01 -7.90942609e-01
-1.35367781e-01 -8.98560405e-01 -9.34638321e-01 -1.03821540e+00
-3.68732601e-01 1.08338306e-02 1.72855750e-01 -3.58004302... | [5.580699443817139, 7.690971851348877] |
d089a3f8-f357-4fc7-b521-2cc6fdc81fc4 | a-comprehensive-modeling-approach-for-crop | 2306.10121 | null | https://arxiv.org/abs/2306.10121v1 | https://arxiv.org/pdf/2306.10121v1.pdf | A Comprehensive Modeling Approach for Crop Yield Forecasts using AI-based Methods and Crop Simulation Models | Numerous solutions for yield estimation are either based on data-driven models, or on crop-simulation models (CSMs). Researchers tend to build data-driven models using nationwide crop information databases provided by agencies such as the USDA. On the opposite side of the spectrum, CSMs require fine data that may be ha... | ['Priscilla Barreira Avegliano', 'Bruno Silva', 'Renato Luiz de Freitas Cunha'] | 2023-06-16 | null | null | null | null | ['management'] | ['miscellaneous'] | [-2.71434244e-02 -1.31311081e-03 -3.45285416e-01 -1.62743106e-01
-2.87871331e-01 -6.05694532e-01 2.47549042e-01 1.00412619e+00
7.20977113e-02 6.34594560e-01 -2.15818793e-01 -9.88902688e-01
-4.39249218e-01 -1.40074801e+00 -6.01482511e-01 -4.82046992e-01
-1.62368506e-01 3.35326791e-01 6.34192675e-02 -6.77252293... | [9.35716438293457, -1.60745370388031] |
8477cc7e-37ce-4b73-b6fc-475911b6c98a | cross-modal-fine-tuning-align-then-refine | 2302.05738 | null | https://arxiv.org/abs/2302.05738v2 | https://arxiv.org/pdf/2302.05738v2.pdf | Cross-Modal Fine-Tuning: Align then Refine | Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP. However, similar gains have not been observed in many other modalities due to a lack of relevant pretrained models. In this work, we propose ORCA, a general cross-modal fine-tuning framework that ... | ['Ameet Talwalkar', 'Graham Neubig', 'Mikhail Khodak', 'Corey Staten', 'Lucio M. Dery', 'Liam Li', 'Junhong Shen'] | 2023-02-11 | null | null | null | null | ['automl'] | ['methodology'] | [ 5.19335568e-01 -5.38331605e-02 -3.73398632e-01 -5.07059157e-01
-1.04645455e+00 -7.02837408e-01 7.70217717e-01 -3.09715360e-01
-5.71543336e-01 4.84568864e-01 7.09712803e-01 2.06580743e-01
-8.43992978e-02 -4.30808991e-01 -8.30468237e-01 -5.91466188e-01
2.83984035e-01 3.49425763e-01 2.78211702e-02 -1.27106503... | [10.573726654052734, 1.5783884525299072] |
07bd73e8-e140-424e-b9fc-6ab1a1386e4f | motion-compensation-via-epipolar-consistency | 2303.00449 | null | https://arxiv.org/abs/2303.00449v1 | https://arxiv.org/pdf/2303.00449v1.pdf | Motion Compensation via Epipolar Consistency for In-Vivo X-Ray Microscopy | Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are characteristic of osteoporosis. The complexity of this method stems from the requirement for high-quality 3D reconstructions of the murine bone... | ['Andreas Maier', 'Silke Christiansen', 'Georg Schett', 'Stefan Uderhardt', 'Georgiana Neag', 'Daniela Weidner', 'Oliver Aust', 'Sabrina Pechmann', 'Yixing Huang', 'Mingxuan Gu', 'Fabian Wagner', 'Mareike Thies'] | 2023-03-01 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 2.08350256e-01 -3.08557242e-01 2.90594429e-01 -1.46582380e-01
-5.82553506e-01 -1.24654904e-01 2.45916456e-01 1.40302598e-01
-9.03991878e-01 6.98446989e-01 -4.64289859e-02 -2.06341982e-01
-3.20619047e-01 -6.60867274e-01 -5.54259956e-01 -8.34562898e-01
-5.13097197e-02 8.29284668e-01 7.89508402e-01 -2.53925938... | [13.105095863342285, -2.7437455654144287] |
2f3e2e51-c5db-4a58-97c3-20b78292d89a | change-point-detection-in-time-series-data-by | 1203.0453 | null | https://arxiv.org/abs/1203.0453v2 | https://arxiv.org/pdf/1203.0453v2.pdf | Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation | The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the rela... | ['Masashi Sugiyama', 'Nigel Collier', 'Makoto Yamada', 'Song Liu'] | 2012-03-02 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.59780070e-01 -4.10229623e-01 -2.08380714e-01 -3.47024977e-01
-8.69801223e-01 -5.21369874e-01 7.60549486e-01 4.75842804e-01
-3.24960083e-01 1.03021908e+00 5.74104190e-02 -1.49399027e-01
-2.55792916e-01 -7.62502134e-01 -5.38423777e-01 -5.29040277e-01
-8.54619682e-01 4.35891114e-02 3.82816464e-01 1.09410144... | [7.060250282287598, 3.459113597869873] |
025add83-862e-43ac-aace-1e43b58d0b4f | synthetic-to-real-unsupervised-domain-1 | 2009.01766 | null | https://arxiv.org/abs/2009.01766v1 | https://arxiv.org/pdf/2009.01766v1.pdf | Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wild | Deep learning-based scene text detection can achieve preferable performance, powered with sufficient labeled training data. However, manual labeling is time consuming and laborious. At the extreme, the corresponding annotated data are unavailable. Exploiting synthetic data is a very promising solution except for domain... | ['Enze Xie', 'Weijia Wu', 'Ning Lu'] | 2020-09-03 | null | null | null | null | ['adversarial-text', 'scene-text-detection'] | ['adversarial', 'computer-vision'] | [ 5.71533442e-01 -1.79907337e-01 1.14171140e-01 -4.24262494e-01
-8.53959560e-01 -6.16719306e-01 6.60368204e-01 3.65899168e-02
-4.42328036e-01 7.16887712e-01 6.00391552e-02 3.66887748e-02
3.56793344e-01 -7.11717904e-01 -7.01080859e-01 -6.57359898e-01
5.98998964e-01 5.19558847e-01 5.04412472e-01 -1.02227010... | [11.820219993591309, 2.1256515979766846] |
87ea92a9-0e58-4390-b386-59ee069230a5 | query-performance-prediction-from-ad-hoc-to | 2305.10923 | null | https://arxiv.org/abs/2305.10923v1 | https://arxiv.org/pdf/2305.10923v1.pdf | Query Performance Prediction: From Ad-hoc to Conversational Search | Query performance prediction (QPP) is a core task in information retrieval. The QPP task is to predict the retrieval quality of a search system for a query without relevance judgments. Research has shown the effectiveness and usefulness of QPP for ad-hoc search. Recent years have witnessed considerable progress in conv... | ['Maarten de Rijke', 'Mohammad Aliannejadi', 'Negar Arabzadeh', 'Chuan Meng'] | 2023-05-18 | null | null | null | null | ['passage-retrieval', 'conversational-search'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.15201989e-01 -2.37106025e-01 -5.49501419e-01 -1.01933032e-01
-1.57079542e+00 -7.88723350e-01 9.44714427e-01 3.83716434e-01
-4.63030040e-01 3.42892587e-01 7.05389857e-01 -3.77297640e-01
-6.41501844e-01 -3.98920894e-01 -2.69412786e-01 -3.76469433e-01
-1.22498227e-02 7.68760502e-01 3.08080792e-01 -6.59065783... | [12.022924423217773, 7.78770112991333] |
457cde9b-0d5e-4313-8f36-5e6120248c86 | stochastic-bandit-models-for-delayed | 1706.09186 | null | http://arxiv.org/abs/1706.09186v3 | http://arxiv.org/pdf/1706.09186v3.pdf | Stochastic Bandit Models for Delayed Conversions | Online advertising and product recommendation are important domains of
applications for multi-armed bandit methods. In these fields, the reward that
is immediately available is most often only a proxy for the actual outcome of
interest, which we refer to as a conversion. For instance, in web advertising,
clicks can be ... | ['Olivier Cappé', 'Vianney Perchet', 'Claire Vernade'] | 2017-06-28 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 8.81263092e-02 2.27233469e-02 -5.74202180e-01 -1.66788608e-01
-7.56681502e-01 -7.60954261e-01 5.40149748e-01 3.46915245e-01
-7.02260137e-01 9.65601385e-01 -1.13765150e-01 -7.81445146e-01
-5.35087407e-01 -8.50690126e-01 -1.09508443e+00 -7.85972238e-01
-2.22039148e-01 8.64303529e-01 6.20331988e-02 4.36537378... | [4.546941757202148, 3.3217833042144775] |
e06db993-e4f2-4f22-bcae-e58ce92026ba | generating-classical-chinese-poems-from | 1909.00279 | null | https://arxiv.org/abs/1909.00279v1 | https://arxiv.org/pdf/1909.00279v1.pdf | Generating Classical Chinese Poems from Vernacular Chinese | Classical Chinese poetry is a jewel in the treasure house of Chinese culture. Previous poem generation models only allow users to employ keywords to interfere the meaning of generated poems, leaving the dominion of generation to the model. In this paper, we propose a novel task of generating classical Chinese poems fro... | ['Elena Suet-Ying Chiu', 'Zhichao Yang', 'Weijiang Feng', 'Yansong Feng', 'Pengshan Cai', 'Hong Yu', 'Fei Li'] | 2019-08-31 | generating-classical-chinese-poems-from-1 | https://aclanthology.org/D19-1637 | https://aclanthology.org/D19-1637.pdf | ijcnlp-2019-11 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.01543444e-01 3.51148456e-01 9.65069234e-02 1.41803445e-02
-9.13143992e-01 -8.95211399e-01 8.67137253e-01 -1.70330450e-01
-3.69429022e-01 1.29059839e+00 4.08918381e-01 -2.38241985e-01
1.76736802e-01 -1.20693362e+00 -4.65109587e-01 -2.13578761e-01
7.34102666e-01 9.17449892e-01 1.31716400e-01 -6.72147930... | [11.61719799041748, 9.3589506149292] |
948e9115-f249-43da-bc17-73923b9a9770 | automatic-segmentation-of-left-ventricle-in | 2201.12805 | null | https://arxiv.org/abs/2201.12805v1 | https://arxiv.org/pdf/2201.12805v1.pdf | Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images | Segmentation of the left ventricle in cardiac magnetic resonance imaging MRI scans enables cardiologists to calculate the volume of the left ventricle and subsequently its ejection fraction. The ejection fraction is a measurement that expresses the percentage of blood leaving the heart with each contraction. Cardiologi... | ['J. R. Harish Kumar', 'J. H. Gagan', 'Garvit Chhabra'] | 2022-01-30 | null | null | null | null | ['template-matching', 'cardiac-segmentation'] | ['computer-vision', 'medical'] | [-1.80645883e-01 8.71696323e-02 1.13964483e-01 -4.17172700e-01
-4.61572766e-01 -7.02767491e-01 -1.03121705e-01 2.28830904e-01
-6.74424410e-01 5.84514618e-01 4.85901013e-02 -2.26798147e-01
6.29666597e-02 -3.90423805e-01 3.31816152e-02 -6.59113109e-01
-3.78929436e-01 9.44102943e-01 3.26338261e-01 3.54859054... | [14.14999771118164, -2.4763684272766113] |
720a8e0c-33c8-4749-9f48-06a23b6e815c | puzzling-machines-a-challenge-on-learning | 2004.13161 | null | https://arxiv.org/abs/2004.13161v1 | https://arxiv.org/pdf/2004.13161v1.pdf | PuzzLing Machines: A Challenge on Learning From Small Data | Deep neural models have repeatedly proved excellent at memorizing surface patterns from large datasets for various ML and NLP benchmarks. They struggle to achieve human-like thinking, however, because they lack the skill of iterative reasoning upon knowledge. To expose this problem in a new light, we introduce a challe... | ['Gözde Gül Şahin', 'Iryna Gurevych', 'Phillip Rust', 'Yova Kementchedjhieva'] | 2020-04-27 | puzzling-machines-a-challenge-on-learning-1 | https://aclanthology.org/2020.acl-main.115 | https://aclanthology.org/2020.acl-main.115.pdf | acl-2020-6 | ['small-data'] | ['computer-vision'] | [ 1.40478825e-02 4.15836424e-01 -1.37380630e-01 -2.40207016e-01
-8.59144330e-01 -8.80268455e-01 5.93929112e-01 3.63604963e-01
-1.84612602e-01 9.34852839e-01 1.31398246e-01 -7.21273005e-01
-2.83035278e-01 -1.05675495e+00 -1.03890741e+00 -3.51558685e-01
-1.17310785e-01 8.52620661e-01 4.38080169e-02 -5.67845106... | [9.516555786132812, 7.31996488571167] |
3591a1fa-a4ee-4016-a0a6-f61ee9aeda9e | a-regularization-method-to-improve | 2110.09759 | null | https://arxiv.org/abs/2110.09759v2 | https://arxiv.org/pdf/2110.09759v2.pdf | A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification | Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the human heart. By using deep neural networks (DNNs), interpretation of ECG signals can be fully automated for the identification of potential abnormalities in a patient's heart in a fraction of a second. Studies have shown tha... | ['Liang Liang', 'Linhai Ma'] | 2021-10-19 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 4.93917882e-01 -1.75739914e-01 4.76632684e-01 -2.75327832e-01
-7.57490575e-01 -6.72487557e-01 -2.22507671e-01 1.90575063e-01
-3.46892506e-01 8.40987921e-01 -1.84738934e-01 -2.99503535e-01
-1.52109355e-01 -6.86384320e-01 -5.01254976e-01 -7.78327465e-01
-1.28886595e-01 -1.49572149e-01 -9.55844074e-02 -4.04157415... | [14.30118179321289, 3.179476022720337] |
ba11c51f-8bc5-4d12-8926-7f4f47ffcd5f | fastlts-non-autoregressive-end-to-end | 2207.03800 | null | https://arxiv.org/abs/2207.03800v2 | https://arxiv.org/pdf/2207.03800v2.pdf | FastLTS: Non-Autoregressive End-to-End Unconstrained Lip-to-Speech Synthesis | Unconstrained lip-to-speech synthesis aims to generate corresponding speeches from silent videos of talking faces with no restriction on head poses or vocabulary. Current works mainly use sequence-to-sequence models to solve this problem, either in an autoregressive architecture or a flow-based non-autoregressive archi... | ['Zhou Zhao', 'Yongqi Wang'] | 2022-07-08 | null | null | null | null | ['lip-to-speech-synthesis'] | ['computer-vision'] | [ 6.13889471e-02 -5.00490405e-02 -9.22568142e-02 -9.44017991e-02
-9.75013793e-01 -1.86537489e-01 3.72952282e-01 -6.19759500e-01
3.59007046e-02 6.19576454e-01 4.83366281e-01 -4.48322088e-01
4.17397052e-01 -5.80780566e-01 -6.50231659e-01 -5.59135199e-01
3.37417752e-01 2.67009556e-01 3.04931134e-01 3.88703831... | [13.268961906433105, -0.3931049108505249] |
4a6bf798-3552-476e-9c22-fc31aa7113f4 | imposing-connectome-derived-topology-on-an | 2201.09359 | null | https://arxiv.org/abs/2201.09359v1 | https://arxiv.org/pdf/2201.09359v1.pdf | Imposing Connectome-Derived Topology on an Echo State Network | Can connectome-derived constraints inform computation? In this paper we investigate the contribution of a fruit fly connectome's topology on the performance of an Echo State Network (ESN) -- a subset of Reservoir Computing which is state of the art in chaotic time series prediction. Specifically, we replace the reservo... | ['Mark Daley', 'Jacob Morra'] | 2022-01-23 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.14709269e-02 -7.78460652e-02 2.05513328e-01 6.00728355e-02
4.46441859e-01 -6.79340720e-01 9.28053737e-01 -1.86195448e-01
-2.93363065e-01 5.54532230e-01 -1.42353782e-02 -3.47754359e-01
-5.19979179e-01 -6.33245587e-01 -6.51042879e-01 -7.44220018e-01
-1.16527963e+00 6.66465640e-01 4.72464859e-01 -5.62929928... | [6.7003326416015625, 3.534294366836548] |
11815499-af09-4ebf-9f5f-9d0bd66600ec | gretel-a-unified-framework-for-graph | 2206.02957 | null | https://arxiv.org/abs/2206.02957v1 | https://arxiv.org/pdf/2206.02957v1.pdf | GRETEL: A unified framework for Graph Counterfactual Explanation Evaluation | Machine Learning (ML) systems are a building part of the modern tools which impact our daily life in several application domains. Due to their black-box nature, those systems are hardly adopted in application domains (e.g. health, finance) where understanding the decision process is of paramount importance. Explanation... | ['Giovanni Stilo', 'Mario Alfonso Prado-Romero'] | 2022-06-07 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.33530357e-01 7.43296683e-01 -6.60526991e-01 -2.53224730e-01
2.23351885e-02 -3.09526384e-01 1.07269764e+00 4.98402625e-01
2.24543020e-01 1.05697763e+00 1.99833065e-01 -9.35487032e-01
-6.27308547e-01 -8.26613069e-01 -6.79005682e-01 -2.65983999e-01
-3.32007021e-01 6.98773801e-01 1.28866443e-02 2.44924589... | [8.669771194458008, 5.739274024963379] |
322b2da6-c53f-4e82-a810-88f83a9783c3 | analyse-automatique-de-lancien-armenien | null | null | https://aclanthology.org/2022.digitam-1.3 | https://aclanthology.org/2022.digitam-1.3.pdf | Analyse Automatique de l’Ancien Arménien. Évaluation d’une méthode hybride « dictionnaire » et « réseau de neurones » sur un Extrait de l’Adversus Haereses d’Irénée de Lyon | The aim of this paper is to evaluate a lexical analysis (mainly lemmatization and POS-tagging) of a sample of the Ancient Armenian version of the Adversus Haereses by Irenaeus of Lyons (2nd c.) by using hybrid approach based on digital dictionaries on the one hand, and on Recurrent Neural Network (RNN) on the other han... | ['Gabriel Kepeklian', 'Bastien Kindt'] | null | null | null | null | digitam-lrec-2022-6 | ['lemmatization', 'lexical-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [-9.07356963e-02 3.02393109e-01 5.04913442e-02 6.18033856e-02
-4.83038247e-01 -7.07453609e-01 9.04100358e-01 4.39482719e-01
-1.01408398e+00 1.07320535e+00 2.72943586e-01 -5.32889485e-01
-4.79514524e-02 -8.99218738e-01 -1.28128305e-01 -5.78897238e-01
2.68372953e-01 7.43603826e-01 4.47345003e-02 -7.02340245... | [10.337142944335938, 10.259321212768555] |
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