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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
57dc3ecd-3dbb-49db-b84d-c4da58f208c2 | hopretriever-retrieve-hops-over-wikipedia-to | 2012.15534 | null | https://arxiv.org/abs/2012.15534v1 | https://arxiv.org/pdf/2012.15534v1.pdf | HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions | Collecting supporting evidence from large corpora of text (e.g., Wikipedia) is of great challenge for open-domain Question Answering (QA). Especially, for multi-hop open-domain QA, scattered evidence pieces are required to be gathered together to support the answer extraction. In this paper, we propose a new retrieval ... | ['Bingquan Liu', 'Zhenzhou Ji', 'Chengjie Sun', 'Qun Liu', 'Xin Jiang', 'Lifeng Shang', 'Xiaoguang Li', 'Shaobo Li'] | 2020-12-31 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-3.83961231e-01 8.04023445e-01 -4.44917083e-01 -4.32625934e-02
-1.34139633e+00 -7.85907030e-01 6.13213897e-01 9.25583959e-01
-3.63611609e-01 1.08059180e+00 6.56513691e-01 -4.15813178e-01
-6.26661122e-01 -8.48044097e-01 -1.16424549e+00 -2.88009405e-01
1.55456081e-01 7.16398716e-01 7.64977038e-01 -4.50744241... | [10.810441017150879, 7.902409076690674] |
1d9c0360-49b3-41ec-8f3a-d8da687fd0fc | ask-adaptively-selecting-key-local-features | 2110.07703 | null | https://arxiv.org/abs/2110.07703v1 | https://arxiv.org/pdf/2110.07703v1.pdf | ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition | Indoor scene images usually contain scattered objects and various scene layouts, which make RGB-D scene classification a challenging task. Existing methods still have limitations for classifying scene images with great spatial variability. Thus, how to extract local patch-level features effectively using only image lab... | ['Qi Wang', 'Yuan Yuan', 'Zhitong Xiong'] | 2021-10-14 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 2.70138204e-01 -6.89816535e-01 -2.58030504e-01 -6.90648019e-01
-1.01775289e+00 -4.38390225e-01 4.65509444e-01 1.08896419e-01
-2.46195018e-01 2.78086156e-01 7.44064059e-03 1.50061667e-01
-5.67297518e-01 -8.41627955e-01 -2.90444672e-01 -1.16073608e+00
1.75718501e-01 -1.98637858e-01 2.99657285e-01 9.65368822... | [9.574338912963867, -0.9518435001373291] |
5052bf52-4796-499d-9d38-39fa4c3d7ccb | learning-based-quality-control-for-cardiac-mr | 1803.09354 | null | http://arxiv.org/abs/1803.09354v2 | http://arxiv.org/pdf/1803.09354v2.pdf | Learning-Based Quality Control for Cardiac MR Images | The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends
on the ability of the operator to correctly tune the acquisition parameters to
the subject being scanned and on the potential occurrence of imaging artefacts
such as cardiac and respiratory motion. In the clinical practice, a quality
control st... | ["Declan P. O'Regan", 'Jonathan Passerat-Palmbach', 'Wenjia Bai', 'Stuart Cook', 'Hideaki Suzuki', 'Giacomo Tarroni', 'Daniel Rueckert', 'Antonio de Marvao', 'Paul M. Matthews', 'Ozan Oktay', 'Andreas Schuh', 'Ben Glocker'] | 2018-03-25 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 5.50045609e-01 1.08080372e-01 2.93662399e-01 -3.67123395e-01
-9.61743414e-01 -8.95434916e-01 3.09004128e-01 7.95193493e-01
-6.69213057e-01 7.01685965e-01 -6.97373599e-02 -4.82003719e-01
-4.56535637e-01 -4.55944955e-01 -2.82472402e-01 -6.77552879e-01
-3.07659090e-01 9.86369491e-01 7.52678752e-01 6.45644665... | [14.076196670532227, -2.521127700805664] |
8375fc97-294c-4b4b-9bee-aa4f9fc35370 | performance-optimization-using-multimodal | 2304.12568 | null | https://arxiv.org/abs/2304.12568v2 | https://arxiv.org/pdf/2304.12568v2.pdf | Performance Optimization using Multimodal Modeling and Heterogeneous GNN | Growing heterogeneity and configurability in HPC architectures has made auto-tuning applications and runtime parameters on these systems very complex. Users are presented with a multitude of options to configure parameters. In addition to application specific solutions, a common approach is to use general purpose searc... | ['Ali Jannesari', 'Eduardo Cesar', 'Anna Sikora', 'Ali TehraniJamsaz', 'Jordi Alcaraz', 'Akash Dutta'] | 2023-04-25 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [-7.02497423e-01 -8.18034470e-01 -2.22778141e-01 -3.54251444e-01
-5.16222119e-01 -6.24925494e-01 2.25785226e-01 3.61168653e-01
-1.77196845e-01 3.26022714e-01 7.84620643e-02 -5.32615781e-01
-2.32579887e-01 -8.58945608e-01 -6.79940999e-01 -7.51016855e-01
-1.04775667e-01 7.50310481e-01 1.08558536e-01 -4.51959461... | [6.266903877258301, 3.738947868347168] |
1a3c9013-f467-4d52-b848-acf3304feb01 | monocular-visual-inertial-depth-estimation | 2303.12134 | null | https://arxiv.org/abs/2303.12134v1 | https://arxiv.org/pdf/2303.12134v1.pdf | Monocular Visual-Inertial Depth Estimation | We present a visual-inertial depth estimation pipeline that integrates monocular depth estimation and visual-inertial odometry to produce dense depth estimates with metric scale. Our approach performs global scale and shift alignment against sparse metric depth, followed by learning-based dense alignment. We evaluate o... | ['Vladlen Koltun', 'Matthias Müller', 'René Ranftl', 'Diana Wofk'] | 2023-03-21 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [-4.72489744e-02 1.90736979e-01 -1.60497382e-01 -4.80311841e-01
-1.09466422e+00 -4.73504871e-01 5.54934561e-01 -1.21212058e-01
-4.88300890e-01 8.52860808e-01 5.57451487e-01 2.47524399e-03
3.01598072e-01 -7.61278152e-01 -9.41368043e-01 -2.12298617e-01
-9.34819803e-02 8.55553150e-01 2.44942009e-01 -7.17649534... | [8.315435409545898, -2.3638577461242676] |
b4c8cb71-c5f3-448d-ba19-96a47a3cf860 | document-enhancement-system-using-auto | null | null | https://openreview.net/forum?id=S1Mnzp9qLB | https://openreview.net/pdf?id=S1Mnzp9qLB | Document Enhancement System Using Auto-encoders | The conversion of scanned documents to digital forms is performed using an Optical Character Recognition (OCR) software. This work focuses on improving the quality of scanned documents in order to improve the OCR output. We create an end-to-end document enhancement pipeline which takes in a set of noisy documents and p... | ['Nigel P. Duffy', 'Hamid Motahari', 'Sridhar V. Dasaratha', 'Sunil R. Tiyyagura', 'Mehrdad J. Gangeh'] | 2019-09-14 | null | null | null | neurips-workshop-document-intelligen-2019-12 | ['document-enhancement'] | ['computer-vision'] | [ 9.89584923e-01 -3.80333662e-01 5.32435060e-01 -5.80018103e-01
-5.94511867e-01 -5.50983369e-01 5.01892626e-01 -1.66329816e-01
-5.82661510e-01 1.45224094e-01 4.79030639e-01 -1.61010906e-01
7.41507765e-03 -6.88938618e-01 -5.74201345e-01 -2.66226888e-01
1.79528415e-01 -1.26545608e-01 1.54300332e-01 -2.38889009... | [11.818465232849121, 2.4665098190307617] |
04ed567e-4c19-41ba-98f0-d102fb09cb92 | visualization-of-emergency-department | 1907.11039 | null | https://arxiv.org/abs/1907.11039v1 | https://arxiv.org/pdf/1907.11039v1.pdf | Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping | Visual summarization of clinical data collected on patients contained within the electronic health record (EHR) may enable precise and rapid triage at the time of patient presentation to an emergency department (ED). The triage process is critical in the appropriate allocation of resources and in anticipating eventual ... | ['Bobak J. Mortazavi', 'R. Andrew Taylor', 'Nathan C. Hurley', 'Adrian D. Haimovich'] | 2019-07-05 | null | null | null | null | ['patient-phenotyping'] | ['medical'] | [ 6.84704185e-02 -2.58357860e-02 7.21492320e-02 -4.54263389e-02
-6.37669861e-01 -6.25520289e-01 3.30627292e-01 8.54793787e-01
-1.50191069e-01 2.26412326e-01 7.26590633e-01 -4.26476836e-01
-6.73968375e-01 -3.69507134e-01 1.03746086e-01 -8.56791496e-01
-6.91047013e-01 8.48228574e-01 -6.22165203e-01 2.27145031... | [7.095866680145264, 5.424148082733154] |
71459493-0ee6-4bd5-bcec-b9ddbe941b63 | the-bayesian-low-rank-determinantal-point | 1608.04245 | null | http://arxiv.org/abs/1608.04245v2 | http://arxiv.org/pdf/1608.04245v2.pdf | The Bayesian Low-Rank Determinantal Point Process Mixture Model | Determinantal point processes (DPPs) are an elegant model for encoding
probabilities over subsets, such as shopping baskets, of a ground set, such as
an item catalog. They are useful for a number of machine learning tasks,
including product recommendation. DPPs are parametrized by a positive
semi-definite kernel matrix... | ['Mike Gartrell', 'Noam Koenigstein', 'Ulrich Paquet'] | 2016-08-15 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 8.61665830e-02 -2.13735640e-01 -6.22864068e-01 -1.08958229e-01
-1.00386107e+00 -6.87444627e-01 7.81505942e-01 8.65971204e-03
9.07480344e-02 4.94447827e-01 4.43423122e-01 -5.31355202e-01
-3.44097942e-01 -7.78709710e-01 -8.62219155e-01 -8.10178816e-01
-1.29080296e-01 1.07668328e+00 2.29374290e-01 6.91361874... | [7.46917724609375, 4.404806137084961] |
37a536ec-a0e7-4097-aa95-3079261df966 | open-relation-modeling-learning-to-define | 2108.09241 | null | https://arxiv.org/abs/2108.09241v2 | https://arxiv.org/pdf/2108.09241v2.pdf | Open Relation Modeling: Learning to Define Relations between Entities | Relations between entities can be represented by different instances, e.g., a sentence containing both entities or a fact in a Knowledge Graph (KG). However, these instances may not well capture the general relations between entities, may be difficult to understand by humans, even may not be found due to the incomplete... | ['Wen-mei Hwu', 'JinJun Xiong', 'Kevin Chen-Chuan Chang', 'Jie Huang'] | 2021-08-20 | null | https://aclanthology.org/2022.findings-acl.26 | https://aclanthology.org/2022.findings-acl.26.pdf | findings-acl-2022-5 | ['open-relation-modeling'] | ['natural-language-processing'] | [ 1.04082115e-01 9.76740241e-01 -2.72950172e-01 -4.98157173e-01
-4.03701901e-01 -6.73647046e-01 4.17697728e-01 6.22518718e-01
1.56375989e-01 9.42638278e-01 2.68123657e-01 -3.68878722e-01
-2.28341788e-01 -1.55711675e+00 -8.64868760e-01 1.33559287e-01
-1.86725318e-01 8.53593886e-01 2.18415409e-01 -3.20313245... | [9.180152893066406, 8.202777862548828] |
f1f3ad30-0979-4403-9d87-883c97586f45 | lightweight-adapter-tuning-for-multilingual | 2106.01463 | null | https://arxiv.org/abs/2106.01463v2 | https://arxiv.org/pdf/2106.01463v2.pdf | Lightweight Adapter Tuning for Multilingual Speech Translation | Adapter modules were recently introduced as an efficient alternative to fine-tuning in NLP. Adapter tuning consists in freezing pretrained parameters of a model and injecting lightweight modules between layers, resulting in the addition of only a small number of task-specific trainable parameters. While adapter tuning ... | ['Laurent Besacier', 'Didier Schwab', 'Jiatao Gu', 'Changhan Wang', 'Juan Pino', 'Hang Le'] | 2021-06-02 | null | https://aclanthology.org/2021.acl-short.103 | https://aclanthology.org/2021.acl-short.103.pdf | acl-2021-5 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.16802403e-01 3.16849858e-01 -1.84656084e-01 -4.80643839e-01
-1.33684576e+00 -8.24130535e-01 7.68303275e-01 -2.41064966e-01
-6.37659132e-01 7.75490642e-01 1.81335568e-01 -8.50064278e-01
4.20536011e-01 -3.79573405e-01 -1.18089032e+00 -3.58188510e-01
3.10026646e-01 1.09325433e+00 -7.32551217e-02 -4.15975600... | [11.579437255859375, 10.265050888061523] |
08bd2b49-61d3-4aa4-b294-7748247b6141 | gender-classification-from-iris-texture | 1905.00372 | null | http://arxiv.org/abs/1905.00372v1 | http://arxiv.org/pdf/1905.00372v1.pdf | Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features | Soft biometric information such as gender can contribute to many applications
like as identification and security. This paper explores the use of a Binary
Statistical Features (BSIF) algorithm for classifying gender from iris texture
images captured with NIR sensors. It uses the same pipeline for iris
recognition syste... | ['Juan Tapia', 'Claudia Arellano'] | 2019-05-01 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 4.94096905e-01 2.82455474e-01 3.60472687e-02 -7.43942797e-01
-1.86734945e-02 -3.23565304e-01 6.09970987e-01 1.49298366e-02
-5.67864597e-01 6.62776172e-01 -9.72743258e-02 -2.50981480e-01
-3.15352082e-01 -4.82025504e-01 -2.39395857e-01 -9.90545511e-01
3.93935889e-01 5.03431082e-01 -5.62206749e-03 4.91819195... | [3.7431323528289795, -3.6314902305603027] |
f675a13e-9b10-4c66-bcab-9b9aca772071 | online-fairness-aware-learning-with | 2108.06231 | null | https://arxiv.org/abs/2108.06231v1 | https://arxiv.org/pdf/2108.06231v1.pdf | Online Fairness-Aware Learning with Imbalanced Data Streams | Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc. The underlying data distribution might evolve over time calling for model adaptation as new instances arrive and old instances becom... | ['Eirini Ntoutsi', 'Wenbin Zhang', 'Vasileios Iosifidis'] | 2021-08-13 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [ 1.64864659e-01 -2.99687505e-01 -5.90906382e-01 -6.82434380e-01
-5.45206368e-01 -4.16925043e-01 4.69336629e-01 6.63206279e-01
-6.13382518e-01 1.14684117e+00 -3.45261723e-01 -3.74682367e-01
-2.32716992e-01 -9.23952103e-01 -4.70009536e-01 -6.67026520e-01
-3.30011278e-01 6.58732474e-01 3.34135801e-01 -2.78914958... | [8.861295700073242, 4.269344806671143] |
ed1eb385-3124-47b4-ab0c-c2f4d7e1363e | does-the-brain-represent-words-an-evaluation | 1806.00591 | null | http://arxiv.org/abs/1806.00591v1 | http://arxiv.org/pdf/1806.00591v1.pdf | Does the brain represent words? An evaluation of brain decoding studies of language understanding | Language decoding studies have identified word representations which can be
used to predict brain activity in response to novel words and sentences
(Anderson et al., 2016; Pereira et al., 2018). The unspoken assumption of these
studies is that, during processing, linguistic information is transformed into
some shared s... | ['Anna Ivanova', 'Jon Gauthier'] | 2018-06-02 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 5.27961314e-01 5.99263832e-02 1.89163629e-02 -4.04699355e-01
-2.26833478e-01 -8.60939264e-01 9.82649505e-01 4.76611584e-01
-6.57793283e-01 3.14929485e-01 8.08309019e-01 -7.50973940e-01
6.85510337e-02 -6.21775150e-01 -3.96094739e-01 -2.10062206e-01
2.42928341e-01 2.65935808e-01 -4.83463332e-02 -3.83071184... | [10.262152671813965, 8.52951717376709] |
7974689f-4558-4ebb-8abc-ca084bdff234 | blemish-aware-and-progressive-face-retouching | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xie_Blemish-Aware_and_Progressive_Face_Retouching_With_Limited_Paired_Data_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xie_Blemish-Aware_and_Progressive_Face_Retouching_With_Limited_Paired_Data_CVPR_2023_paper.pdf | Blemish-Aware and Progressive Face Retouching With Limited Paired Data | Face retouching aims to remove facial blemishes, while at the same time maintaining the textual details of a given input image. The main challenge lies in distinguishing blemishes from the facial characteristics, such as moles. Training an image-to-image translation network with pixel-wise supervision suffers from ... | ['Hau San Wong', 'Zhiwen Yu', 'Si Wu', 'Zhen Xu', 'Wen Xue', 'Lianxin Xie'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'miscellaneous'] | [ 5.04628658e-01 2.72192091e-01 3.07719670e-02 -2.42102563e-01
-7.78288305e-01 -3.22427005e-01 4.83867288e-01 -2.22912982e-01
-2.44128346e-01 5.92195809e-01 2.55373746e-01 5.01315445e-02
2.90104479e-01 -7.29560971e-01 -8.70236278e-01 -8.67188036e-01
5.01535296e-01 -8.39402899e-03 -1.71907485e-01 -1.66475266... | [12.684867858886719, -0.11263102293014526] |
952059a8-3ab0-4e49-bf23-d193b9655bb0 | dasvdd-deep-autoencoding-support-vector-data | 2106.05410 | null | https://arxiv.org/abs/2106.05410v2 | https://arxiv.org/pdf/2106.05410v2.pdf | DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection | Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. With recent advancements in deep learning, researchers have designed efficient deep anomaly detection methods. Existing works commonly use neural networks to map the data into a more informative ... | ['Narges Armanfard', 'Hadi Hojjati'] | 2021-06-09 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [-1.89999446e-01 3.37656811e-02 5.61014712e-01 -4.00929451e-01
-1.17216108e-03 -1.12685606e-01 5.19450128e-01 3.78534377e-01
-2.51852870e-01 1.10137269e-01 -1.59053847e-01 -1.53612480e-01
-2.74365153e-02 -9.40634012e-01 -6.54194772e-01 -9.91469145e-01
-1.48099899e-01 7.42881000e-01 2.04054397e-02 -5.82111739... | [7.614740371704102, 2.3569207191467285] |
349481fe-83b5-4e35-bfed-a8ff73e55c83 | are-deep-policy-gradient-algorithms-truly | 1811.02553 | null | https://arxiv.org/abs/1811.02553v4 | https://arxiv.org/pdf/1811.02553v4.pdf | A Closer Look at Deep Policy Gradients | We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. To this end, we propose a fine-grained analysis of state-of-the-art methods based on key elements of this framework: gradient estimation, value prediction, and optimization landscapes. Our result... | ['Firdaus Janoos', 'Shibani Santurkar', 'Aleksander Madry', 'Andrew Ilyas', 'Larry Rudolph', 'Logan Engstrom', 'Dimitris Tsipras'] | 2018-11-06 | null | https://openreview.net/forum?id=ryxdEkHtPS | https://openreview.net/pdf?id=ryxdEkHtPS | iclr-2020-1 | ['value-prediction'] | ['computer-code'] | [-1.16826624e-01 -2.31606618e-01 -9.55472052e-01 -3.87575150e-01
-6.38467669e-01 -8.54231656e-01 9.09518003e-01 1.20494790e-01
-7.13445961e-01 1.13125992e+00 5.63125014e-01 -6.86169386e-01
-2.03818783e-01 -4.98685151e-01 -7.99193382e-01 -3.92529249e-01
-7.28387907e-02 2.90605754e-01 7.51975998e-02 -3.68773431... | [4.100866317749023, 2.283757209777832] |
36d15a46-6723-4b2f-878d-126feb488235 | sdxl-improving-latent-diffusion-models-for | 2307.01952 | null | https://arxiv.org/abs/2307.01952v1 | https://arxiv.org/pdf/2307.01952v1.pdf | SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis | We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We... | ['Robin Rombach', 'Joe Penna', 'Jonas Müller', 'Tim Dockhorn', 'Andreas Blattmann', 'Kyle Lacey', 'Zion English', 'Dustin Podell'] | 2023-07-04 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 2.41221040e-01 3.36138606e-01 -1.50375128e-01 -2.28732809e-01
-9.78814185e-01 -5.18370926e-01 1.05231762e+00 -3.78034413e-01
-1.78299010e-01 5.97426057e-01 6.61359549e-01 -3.06146175e-01
3.08366627e-01 -7.42163718e-01 -9.22103465e-01 -5.38143098e-01
3.14205199e-01 5.21249592e-01 9.18336436e-02 -9.01548117... | [11.346586227416992, -0.22454309463500977] |
f9f7d280-4b0b-4705-886b-7122b8ab7518 | autonomous-systems-autonomous-systems-indoor | 2304.08893 | null | https://arxiv.org/abs/2304.08893v1 | https://arxiv.org/pdf/2304.08893v1.pdf | Autonomous Systems: Autonomous Systems: Indoor Drone Navigation | Drones are a promising technology for autonomous data collection and indoor sensing. In situations when human-controlled UAVs may not be practical or dependable, such as in uncharted or dangerous locations, the usage of autonomous UAVs offers flexibility, cost savings, and reduced risk. The system creates a simulated q... | ['Manoj Kumar Rajagopal', 'Naren M', 'Santosh Narayan', 'Aswin Iyer'] | 2023-04-18 | null | null | null | null | ['drone-navigation', 'autonomous-navigation'] | ['computer-vision', 'computer-vision'] | [-1.66665599e-01 -1.23466335e-01 6.74750030e-01 -2.99011409e-01
3.60708058e-01 -9.40921366e-01 6.01638496e-01 -1.70528844e-01
-5.84326744e-01 8.94252658e-01 -7.44373381e-01 -7.02078640e-01
-2.85936564e-01 -1.03884780e+00 -2.73135900e-01 -3.84715170e-01
-4.51318115e-01 3.44084799e-01 4.64176923e-01 -1.00496900... | [7.275373458862305, -1.8777130842208862] |
13cbb61a-309e-4cb2-a1f1-b0c14ea5e790 | fairness-in-representation-for-multilingual | null | null | https://openreview.net/forum?id=-llS6TiOew | https://openreview.net/pdf?id=-llS6TiOew | Fairness in Representation for Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling | We perform systematically and fairly controlled experiments with the 6-layer Transformer to investigate whether languages which have been traditionally considered morphologically rich (AR and RU) and poor (ZH) are equally hard to conditional-language-model. We evaluate through statistical comparisons across 30 possible... | ['Ada Wan'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['multilingual-nlp'] | ['natural-language-processing'] | [ 6.90364093e-02 -1.68884933e-01 -2.94901222e-01 -2.92685270e-01
-5.32549739e-01 -7.14521110e-01 6.89557433e-01 5.13428807e-01
-9.97276843e-01 5.47917128e-01 8.09226751e-01 -1.13244724e+00
-2.01132208e-01 -7.05818057e-01 -4.28183109e-01 -3.84750456e-01
1.14749305e-01 4.24558938e-01 -1.80610031e-01 -4.11908031... | [10.563170433044434, 9.958029747009277] |
c70d0ea5-2f28-4b42-909a-ff139aeadd3e | fedabc-targeting-fair-competition-in | 2302.07450 | null | https://arxiv.org/abs/2302.07450v1 | https://arxiv.org/pdf/2302.07450v1.pdf | FedABC: Targeting Fair Competition in Personalized Federated Learning | Federated learning aims to collaboratively train models without accessing their client's local private data. The data may be Non-IID for different clients and thus resulting in poor performance. Recently, personalized federated learning (PFL) has achieved great success in handling Non-IID data by enforcing regularizati... | ['DaCheng Tao', 'Yonggang Wen', 'Kehua Su', 'Han Hu', 'Yong Luo', 'Li Shen', 'Dui Wang'] | 2023-02-15 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-1.94858953e-01 -3.63985360e-01 -3.12709481e-01 -9.27683532e-01
-1.06650686e+00 -2.26482064e-01 4.49748747e-02 1.48445240e-03
-1.20296150e-01 9.45864260e-01 -2.93629128e-03 -1.25662491e-01
-2.44181484e-01 -8.01586151e-01 -6.34432912e-01 -1.11988366e+00
2.76385307e-01 4.47924078e-01 -1.05295248e-01 2.33627617... | [5.8370747566223145, 6.304182529449463] |
4214ba75-d1e6-454f-9162-af43cb845592 | unsupervised-object-level-video-summarization | 1801.00543 | null | http://arxiv.org/abs/1801.00543v2 | http://arxiv.org/pdf/1801.00543v2.pdf | Unsupervised Object-Level Video Summarization with Online Motion Auto-Encoder | Unsupervised video summarization plays an important role on digesting,
browsing, and searching the ever-growing videos every day, and the underlying
fine-grained semantic and motion information (i.e., objects of interest and
their key motions) in online videos has been barely touched. In this paper, we
investigate a pi... | ['Yu-jia Zhang', 'Min Tan', 'Dingwen Zhang', 'Xiaodan Liang', 'Eric P. Xing'] | 2018-01-02 | null | null | null | null | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 2.87551105e-01 -3.50098401e-01 -6.08001411e-01 -2.81154811e-01
-4.64031994e-01 -4.03744012e-01 5.02894461e-01 -1.00101260e-02
-2.06628680e-01 3.84845197e-01 9.25333858e-01 1.99343845e-01
5.79090677e-02 -3.56967866e-01 -6.17071629e-01 -5.97070813e-01
-8.32621455e-02 -2.21531048e-01 7.90189028e-01 1.57475069... | [10.351653099060059, 0.4733276963233948] |
2307fa24-4846-457d-baaa-e59de5208728 | diffcollage-parallel-generation-of-large | 2303.17076 | null | https://arxiv.org/abs/2303.17076v1 | https://arxiv.org/pdf/2303.17076v1.pdf | DiffCollage: Parallel Generation of Large Content with Diffusion Models | We present DiffCollage, a compositional diffusion model that can generate large content by leveraging diffusion models trained on generating pieces of the large content. Our approach is based on a factor graph representation where each factor node represents a portion of the content and a variable node represents their... | ['Ming-Yu Liu', 'Yongxin Chen', 'Xun Huang', 'Jiaming Song', 'Qinsheng Zhang'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_DiffCollage_Parallel_Generation_of_Large_Content_With_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_DiffCollage_Parallel_Generation_of_Large_Content_With_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['infinite-image-generation'] | ['computer-vision'] | [ 3.42744470e-01 5.62317312e-01 8.24411213e-03 2.41953194e-01
-9.08408284e-01 -6.38324797e-01 1.30776358e+00 -2.80850857e-01
7.69001767e-02 7.20704496e-01 8.36742997e-01 -4.16269869e-01
4.21605855e-01 -1.14284313e+00 -8.53380203e-01 -6.76271081e-01
6.12585172e-02 5.23827672e-01 2.24468663e-01 -3.74925822... | [11.133367538452148, -0.2509036660194397] |
e76b88ec-5366-482a-acfc-1d4de5b9f566 | mining-both-commonality-and-specificity-from | 2303.02677 | null | https://arxiv.org/abs/2303.02677v1 | https://arxiv.org/pdf/2303.02677v1.pdf | Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization | The multi-document summarization task requires the designed summarizer to generate a short text that covers the important information of original documents and satisfies content diversity. This paper proposes a multi-document summarization approach based on hierarchical clustering of documents. It utilizes the construc... | ['Bing Ma'] | 2023-03-05 | null | null | null | null | ['multi-document-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.98059613e-01 1.60992965e-01 -3.73856783e-01 -1.90161660e-01
-1.13398123e+00 -7.16224194e-01 5.82873821e-01 7.82343745e-01
-1.07067507e-02 1.07289124e+00 1.08506310e+00 1.87984228e-01
-3.68917555e-01 -5.26137471e-01 -2.01113924e-01 -4.71785128e-01
2.67038997e-02 5.22547662e-01 4.05069083e-01 -2.19611466... | [12.580982208251953, 9.573553085327148] |
63ed4671-2ac3-4d1a-b17e-1c8ed0ddfc97 | clear-contrastive-learning-for-sentence | 2012.15466 | null | https://arxiv.org/abs/2012.15466v1 | https://arxiv.org/pdf/2012.15466v1.pdf | CLEAR: Contrastive Learning for Sentence Representation | Pre-trained language models have proven their unique powers in capturing implicit language features. However, most pre-training approaches focus on the word-level training objective, while sentence-level objectives are rarely studied. In this paper, we propose Contrastive LEArning for sentence Representation (CLEAR), w... | ['Hao Ma', 'Fei Sun', 'Madian Khabsa', 'Jiatao Gu', 'Sinong Wang', 'Zhuofeng Wu'] | 2020-12-31 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 5.65086067e-01 -8.36101621e-02 -3.78710896e-01 -5.55818141e-01
-1.02720737e+00 -4.56898212e-01 6.31298065e-01 5.33339202e-01
-7.56097257e-01 8.37240934e-01 6.74046159e-01 -6.31860614e-01
3.42682689e-01 -4.85589504e-01 -4.99002457e-01 -2.49230400e-01
3.50292623e-02 1.18464734e-02 -6.69924170e-02 -7.23992407... | [10.91950798034668, 8.751779556274414] |
0835b737-05bc-4593-b481-2866fddac7c6 | position-offset-label-prediction-for | null | null | https://aclanthology.org/2022.coling-1.480 | https://aclanthology.org/2022.coling-1.480.pdf | Position Offset Label Prediction for Grammatical Error Correction | We introduce a novel position offset label prediction subtask to the encoder-decoder architecture for grammatical error correction (GEC) task. To keep the meaning of the input sentence unchanged, only a few words should be inserted or deleted during correction, and most of tokens in the erroneous sentence appear in the... | ['Yunfang Wu', 'Xu sun', 'Jingsong Yu', 'Xiuyu Wu'] | null | null | null | null | coling-2022-10 | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 3.55180740e-01 2.27846295e-01 -2.48797052e-02 -5.29963315e-01
-9.79457319e-01 -1.80112924e-02 1.46743685e-01 2.75124222e-01
-5.94945371e-01 8.66413951e-01 3.91119003e-01 -3.40649277e-01
6.99303150e-01 -3.46736580e-01 -1.06400418e+00 -3.92009676e-01
5.52841961e-01 2.49823779e-02 1.35654256e-01 -2.90405929... | [11.040520668029785, 10.727279663085938] |
923a07a5-1dad-41d7-ba5e-62aee3bb3bda | learning-inter-superpoint-affinity-for-weakly | 2210.05534 | null | https://arxiv.org/abs/2210.05534v1 | https://arxiv.org/pdf/2210.05534v1.pdf | Learning Inter-Superpoint Affinity for Weakly Supervised 3D Instance Segmentation | Due to the few annotated labels of 3D point clouds, how to learn discriminative features of point clouds to segment object instances is a challenging problem. In this paper, we propose a simple yet effective 3D instance segmentation framework that can achieve good performance by annotating only one point for each insta... | ['Jin Xie', 'Le Hui', 'Linghua Tang'] | 2022-10-11 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-6.21291548e-02 4.04991806e-01 -4.33114350e-01 -6.44735694e-01
-1.02085674e+00 -5.50699055e-01 3.42802644e-01 3.31927001e-01
1.92293003e-02 9.85414386e-02 -7.14234769e-01 -1.39581844e-01
-3.04660052e-01 -8.63733351e-01 -1.02134252e+00 -4.45006728e-01
-2.52752513e-01 1.30516458e+00 9.20107126e-01 2.61891961... | [8.021469116210938, -3.159083604812622] |
8c6a9aa0-dece-44f3-8555-52cb499a8ac5 | sprt-based-efficient-best-arm-identification | 2207.11158 | null | https://arxiv.org/abs/2207.11158v3 | https://arxiv.org/pdf/2207.11158v3.pdf | SPRT-based Efficient Best Arm Identification in Stochastic Bandits | This paper investigates the best arm identification (BAI) problem in stochastic multi-armed bandits in the fixed confidence setting. The general class of the exponential family of bandits is considered. The existing algorithms for the exponential family of bandits face computational challenges. To mitigate these challe... | ['Ali Tajer', 'Arpan Mukherjee'] | 2022-07-22 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 3.25691968e-01 -2.98448980e-01 -9.58813250e-01 -1.35832027e-01
-1.36052549e+00 -9.30933297e-01 1.43274188e-01 -1.19095884e-01
-1.33288339e-01 9.64355767e-01 -2.72622287e-01 -1.02427804e+00
-8.50601435e-01 -6.67464435e-01 -7.49659419e-01 -8.42489719e-01
-2.38198578e-01 7.75025725e-01 -1.76010579e-01 2.27008134... | [4.557054042816162, 3.309588670730591] |
42cf4dd9-ab0b-4925-bf51-91d5b670a490 | which-clustering-do-you-want-inducing-your | 1401.5389 | null | http://arxiv.org/abs/1401.5389v1 | http://arxiv.org/pdf/1401.5389v1.pdf | Which Clustering Do You Want? Inducing Your Ideal Clustering with Minimal Feedback | While traditional research on text clustering has largely focused on grouping
documents by topic, it is conceivable that a user may want to cluster documents
along other dimensions, such as the authors mood, gender, age, or sentiment.
Without knowing the users intention, a clustering algorithm will only group
documents... | ['Sajib Dasgupta', 'Vincent Ng'] | 2014-01-16 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [ 6.77251071e-02 -7.14885518e-02 -1.45532459e-01 -6.18570805e-01
-5.60187459e-01 -8.06736887e-01 4.97705281e-01 8.93439174e-01
-6.15186155e-01 -7.34144002e-02 3.35439652e-01 -4.57612634e-01
-2.46217951e-01 -7.50595689e-01 2.03271642e-01 -6.44361734e-01
1.52022168e-01 6.99620187e-01 -4.10346836e-02 6.88324869... | [10.42912483215332, 7.2179694175720215] |
8b980edd-f6d3-4df0-8984-eacee5bff446 | dpm-clustering-sensitive-data-through | 2307.02969 | null | https://arxiv.org/abs/2307.02969v1 | https://arxiv.org/pdf/2307.02969v1.pdf | DPM: Clustering Sensitive Data through Separation | Privacy-preserving clustering groups data points in an unsupervised manner whilst ensuring that sensitive information remains protected. Previous privacy-preserving clustering focused on identifying concentration of point clouds. In this paper, we take another path and focus on identifying appropriate separators that s... | ['Esfandiar Mohammadi', 'Florian Thaeter', 'Marcel Gehrke', 'Tanya Braun', 'Johannes Liebenow', 'Yara Schütt'] | 2023-07-06 | null | null | null | null | ['clustering'] | ['methodology'] | [-5.08322567e-03 7.53075629e-02 -9.90750194e-02 -3.28389853e-01
-1.19093335e+00 -1.02395570e+00 6.10317700e-02 6.62910223e-01
-5.33148468e-01 5.19664466e-01 -3.02003384e-01 -3.08516622e-01
-2.57953912e-01 -1.06414580e+00 -8.21256936e-01 -1.01524758e+00
-4.66437697e-01 7.01464593e-01 2.30713516e-01 4.02696371... | [6.074069499969482, 6.6207594871521] |
1df4eabd-85d0-4b26-b57b-5083e49a42af | a-feasible-framework-for-arbitrary-shaped | 1912.04561 | null | https://arxiv.org/abs/1912.04561v2 | https://arxiv.org/pdf/1912.04561v2.pdf | A Feasible Framework for Arbitrary-Shaped Scene Text Recognition | Deep learning based methods have achieved surprising progress in Scene Text Recognition (STR), one of classic problems in computer vision. In this paper, we propose a feasible framework for multi-lingual arbitrary-shaped STR, including instance segmentation based text detection and language model based attention mechan... | ['Wei Wang', 'Qingjie Liu', 'Di Huang', 'Yunhong Wang', 'Jinjin Zhang'] | 2019-12-10 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 0.21448414 -0.34704524 -0.08425187 -0.3341143 -1.0419716 -0.7428026
0.7701414 0.15851323 -0.6041505 0.16657433 0.41549432 -0.6941401
0.40079 -0.47817153 -0.69268703 -0.30145866 0.9657074 0.7557792
0.09508628 -0.18067384 0.6440099 0.30716074 -0.93252355 0.6336561
0.87089777 0.6321896 0.4095... | [11.972616195678711, 2.2877774238586426] |
5f372c3f-7dd8-4dda-abdc-51c7b15ba303 | psycholinguistic-features-for-deceptive-role | null | null | https://aclanthology.org/N16-1047 | https://aclanthology.org/N16-1047.pdf | Psycholinguistic Features for Deceptive Role Detection in Werewolf | null | ['Eyal Amir', 'Roxana Girju', 'Codruta Girlea'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2285027503967285, 3.659602642059326] |
68da04b3-8711-4872-b19c-bb346a58aaca | streetsurf-extending-multi-view-implicit | 2306.04988 | null | https://arxiv.org/abs/2306.04988v1 | https://arxiv.org/pdf/2306.04988v1.pdf | StreetSurf: Extending Multi-view Implicit Surface Reconstruction to Street Views | We present a novel multi-view implicit surface reconstruction technique, termed StreetSurf, that is readily applicable to street view images in widely-used autonomous driving datasets, such as Waymo-perception sequences, without necessarily requiring LiDAR data. As neural rendering research expands rapidly, its integra... | ['Yikang Li', 'Dongliang Wang', 'Chenjing Ding', 'Chiyu Wang', 'Botian Shi', 'Yeqi Bai', 'Xinyang Li', 'Nianchen Deng', 'Jianfei Guo'] | 2023-06-08 | null | null | null | null | ['neural-rendering', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 2.56807417e-01 -5.61501905e-02 2.38807052e-01 -3.01288694e-01
-8.28674674e-01 -6.75035954e-01 8.46863091e-01 -4.68627065e-01
-2.23336563e-01 7.04723239e-01 -3.24997976e-02 -5.24639010e-01
1.43394753e-01 -1.10855782e+00 -8.78103971e-01 -4.28037912e-01
3.37918252e-01 8.80263925e-01 4.23438966e-01 -3.32737535... | [8.977422714233398, -2.963477611541748] |
79d7c6e0-aeb3-47a5-add2-17eefa1ef7fe | d2-decentralized-training-over-decentralized | null | null | https://icml.cc/Conferences/2018/Schedule?showEvent=2485 | http://proceedings.mlr.press/v80/tang18a/tang18a.pdf | $D^2$: Decentralized Training over Decentralized Data |
While training a machine learning model using multiple workers, each of which collects data from its own data source, it would be useful when the data collected from different workers are unique and different. Ironically, recent analysis of decentralized parallel stochastic gradient descent (D-PSGD) relies on the ... | ['Ce Zhang', 'Ming Yan', 'Hanlin Tang', 'Ji Liu', 'Xiangru Lian'] | 2018-07-01 | null | null | null | icml-2018-7 | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.18120629e-01 -2.06137687e-01 1.32803336e-01 -5.23264945e-01
-8.27985227e-01 -4.55773175e-01 2.04841290e-02 2.06672117e-01
-1.01232433e+00 6.95014060e-01 -4.03568864e-01 -6.34729147e-01
-2.99219042e-01 -7.79187560e-01 -6.15075469e-01 -8.73969555e-01
-2.79576987e-01 5.27172446e-01 2.14861080e-01 -1.78155914... | [6.343105792999268, 4.760767936706543] |
859dc5f6-b49c-4d14-9e1b-382d755b5d87 | carlane-a-lane-detection-benchmark-for | 2206.08083 | null | https://arxiv.org/abs/2206.08083v3 | https://arxiv.org/pdf/2206.08083v3.pdf | CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains | Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains. While Unsupervised Domain Adaptation has been applied to a wide variety of complex vision tasks, only few works focus on lane detection for autonomous dri... | ['Johann Haselberger', 'Bonifaz Stuhr', 'Julian Gebele'] | 2022-06-16 | null | null | null | null | ['lane-detection', '2d-semantic-segmentation', 'unsupervised-pre-training'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 8.32542330e-02 -1.44736797e-01 -6.67840600e-01 -7.31275916e-01
-9.01509583e-01 -8.32716405e-01 7.85502970e-01 -3.00162464e-01
-4.86253053e-01 9.02423739e-01 1.54326215e-01 -3.33490402e-01
3.02308321e-01 -4.51993018e-01 -5.89279652e-01 -6.58716977e-01
7.57499486e-02 5.78711987e-01 6.23133123e-01 -3.44173938... | [8.19431209564209, -1.6727945804595947] |
df335934-cc96-4f49-aff7-aaa356affa25 | expobench-benchmarking-surrogate-based | 2106.04618 | null | https://arxiv.org/abs/2106.04618v2 | https://arxiv.org/pdf/2106.04618v2.pdf | EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions | Surrogate algorithms such as Bayesian optimisation are especially designed for black-box optimisation problems with expensive objectives, such as hyperparameter tuning or simulation-based optimisation. In the literature, these algorithms are usually evaluated with synthetic benchmarks which are well established but hav... | ['Mathijs de Weerdt', 'Sicco Verwer', 'Rickard Karlsson', 'Arthur Guijt', 'Laurens Bliek'] | 2021-06-08 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 3.58447462e-01 -1.55805618e-01 -9.58128348e-02 -3.22993606e-01
-1.07988417e+00 -6.16131604e-01 7.85182834e-01 3.59269053e-01
-6.67685986e-01 1.18142724e+00 7.27494359e-02 -3.69388342e-01
-7.85585165e-01 -6.33522689e-01 -5.04259646e-01 -1.02711177e+00
-2.08102524e-01 7.53205001e-01 -8.71159360e-02 -3.79421748... | [6.274319648742676, 3.7876124382019043] |
d9304b26-467e-4a80-b23c-bfb02fad8ffe | graph-attention-recurrent-neural-networks-for | 2103.10760 | null | https://arxiv.org/abs/2103.10760v2 | https://arxiv.org/pdf/2103.10760v2.pdf | Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting -- Full version | We consider a setting where multiple entities inter-act with each other over time and the time-varying statuses of the entities are represented as multiple correlated time series. For example, speed sensors are deployed in different locations in a road network, where the speed of a specific location across time is capt... | ['Bin Yang', 'Chenjuan Guo', 'Razvan-Gabriel Cirstea'] | 2021-03-19 | null | null | null | null | ['correlated-time-series-forecasting'] | ['time-series'] | [-2.73867130e-01 -2.23178208e-01 -1.29589915e-01 -3.10672611e-01
-5.38128018e-02 -3.57337683e-01 3.38006824e-01 6.03319287e-01
-3.61139059e-01 4.55452025e-01 3.63794684e-01 -2.59153903e-01
-4.53285486e-01 -1.14523780e+00 -8.70892107e-01 -3.77606988e-01
-6.73492491e-01 2.24429399e-01 1.06364138e-01 -3.69933754... | [6.661051273345947, 2.6201932430267334] |
b6157fb4-7df4-4b18-87bd-aac6fefa8581 | unsupervised-pcfg-induction-for-grounded | null | null | https://aclanthology.org/D12-1040 | https://aclanthology.org/D12-1040.pdf | Unsupervised PCFG Induction for Grounded Language Learning with Highly Ambiguous Supervision | null | ['Raymond Mooney', 'Joohyun Kim'] | 2012-07-01 | null | null | null | emnlp-2012-7 | ['grounded-language-learning'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.135559558868408, 3.5461292266845703] |
acaffd22-8264-454a-8967-272c27d78953 | tabmixer-excavating-label-distribution | 2210.13852 | null | https://arxiv.org/abs/2210.13852v1 | https://arxiv.org/pdf/2210.13852v1.pdf | TabMixer: Excavating Label Distribution Learning with Small-scale Features | Label distribution learning (LDL) differs from multi-label learning which aims at representing the polysemy of instances by transforming single-label values into descriptive degrees. Unfortunately, the feature space of the label distribution dataset is affected by human factors and the inductive bias of the feature ext... | ['Xiuyi Jia', 'Zhuoran Zheng', 'Weiyi Cong'] | 2022-10-25 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 3.30514610e-01 2.21490622e-01 -4.55357641e-01 -7.66914725e-01
-7.10171580e-01 -5.37766516e-01 2.88764626e-01 1.76154040e-02
-2.03155905e-01 7.92917013e-01 2.52192199e-01 -5.40532209e-02
-2.33946919e-01 -5.84712505e-01 -7.04518855e-01 -1.12058449e+00
2.89846271e-01 6.38503730e-01 -5.78661680e-01 3.40491384... | [9.526296615600586, 3.889653205871582] |
475ab367-5177-40ea-9d01-abaeca827ec2 | discrete-time-risk-sensitive-portfolio | 2201.02828 | null | https://arxiv.org/abs/2201.02828v1 | https://arxiv.org/pdf/2201.02828v1.pdf | Discrete-time risk sensitive portfolio optimization with proportional transaction costs | In this paper we consider a discrete-time risk sensitive portfolio optimization over a long time horizon with proportional transaction costs. We show that within the log-return i.i.d. framework the solution to a suitable Bellman equation exists under minimal assumptions and can be used to characterize the optimal strat... | ['Łukasz Stettner', 'Marcin Pitera'] | 2022-01-08 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-3.06755632e-01 2.57387310e-01 -1.85887173e-01 -1.98146105e-01
-7.71154404e-01 -9.85011816e-01 7.30733946e-02 1.24670900e-01
-4.95613128e-01 8.92034411e-01 -3.60145628e-01 -5.41490316e-01
-7.34910607e-01 -7.85454512e-01 -3.86219025e-01 -7.35396743e-01
-5.44652760e-01 4.59535956e-01 -3.85430001e-04 -9.53527391... | [4.898488521575928, 3.919424057006836] |
4e2fd861-2c79-427c-8bba-58a01839b39c | assessing-demographic-bias-transfer-from | 2205.10049 | null | https://arxiv.org/abs/2205.10049v1 | https://arxiv.org/pdf/2205.10049v1.pdf | Assessing Demographic Bias Transfer from Dataset to Model: A Case Study in Facial Expression Recognition | The increasing amount of applications of Artificial Intelligence (AI) has led researchers to study the social impact of these technologies and evaluate their fairness. Unfortunately, current fairness metrics are hard to apply in multi-class multi-demographic classification problems, such as Facial Expression Recognitio... | ['Mikel Galar', 'Daniel Paternain', 'Iris Dominguez-Catena'] | 2022-05-20 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.15624705e-01 1.94499224e-01 -3.53639275e-01 -8.18106174e-01
-1.13712654e-01 -3.62189591e-01 7.02144146e-01 1.18367203e-01
-7.64608204e-01 8.58565867e-01 2.51319230e-01 -9.64820385e-03
6.39609620e-03 -6.14786625e-01 -2.68408775e-01 -5.49526095e-01
2.06179589e-01 3.36463541e-01 -2.85421193e-01 -3.79763722... | [13.022562026977539, 1.361531376838684] |
cd4c5987-502c-4cba-9267-890f17f00b05 | on-diffusion-modeling-for-anomaly-detection | 2305.18593 | null | https://arxiv.org/abs/2305.18593v1 | https://arxiv.org/pdf/2305.18593v1.pdf | On Diffusion Modeling for Anomaly Detection | Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probab... | ['Siamak Ravanbakhsh', 'Yashar Hezaveh', 'Vineet Jain', 'Victor Livernoche'] | 2023-05-29 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [-2.23599762e-01 -1.67918339e-01 8.29847455e-02 -1.94490626e-01
-6.80685461e-01 -3.84433001e-01 1.04515338e+00 5.90776384e-01
-2.69378036e-01 1.64118335e-01 2.81220436e-01 -4.94907618e-01
-1.40945956e-01 -9.66435254e-01 -5.11744916e-01 -6.15351439e-01
-5.07524908e-01 8.68437409e-01 2.71566838e-01 2.74605453... | [7.619528293609619, 2.4099254608154297] |
f76289ac-4a79-434c-8152-7fe245172e05 | preventing-errors-in-person-detection-a-part | 2307.04533 | null | https://arxiv.org/abs/2307.04533v1 | https://arxiv.org/pdf/2307.04533v1.pdf | Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework | The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications. Especially for detecting humans, which is often a fundamental task in safety-critical applications, it is vital to prevent errors. To address this challenge, we propose a self-monitoring fr... | ['Stephan Günnemann', 'Karsten Roscher', 'Andrea Matic', 'Franziska Schwaiger'] | 2023-07-10 | null | null | null | null | ['object-detection', 'human-detection'] | ['computer-vision', 'computer-vision'] | [ 2.15198755e-01 2.40701124e-01 4.28149134e-01 -2.32598275e-01
-4.45146233e-01 -3.63295019e-01 4.53151584e-01 3.76605541e-01
-6.77886426e-01 3.44941586e-01 -2.75405586e-01 2.80577373e-02
4.08762962e-01 -6.23360395e-01 -8.51428390e-01 -3.33543330e-01
-1.50306374e-01 3.89692396e-01 7.74535120e-01 -6.14837669... | [7.907919406890869, -0.011560487560927868] |
05163292-42b1-4164-b6e8-403f16f6c80b | weakly-supervised-arrhythmia-detection-based | 2012.05641 | null | https://arxiv.org/abs/2012.05641v1 | https://arxiv.org/pdf/2012.05641v1.pdf | Weakly Supervised Arrhythmia Detection Based on Deep Convolutional Neural Network | Supervised deep learning has been widely used in the studies of automatic ECG classification, which largely benefits from sufficient annotation of large datasets. However, most of the existing large ECG datasets are roughly annotated, so the classification model trained on them can only detect the existence of abnormal... | ['Henggui Zhang', 'Yongfeng Yuan', 'Runnan He', 'Qince Li', 'Kuanquan Wang', 'Yang Liu'] | 2020-12-10 | null | null | null | null | ['arrhythmia-detection', 'ecg-classification'] | ['medical', 'medical'] | [ 1.83974072e-01 -1.10622339e-01 -1.52194396e-01 -4.77145582e-01
-6.80710614e-01 -4.95685577e-01 -2.89784789e-01 4.03718710e-01
-4.92325239e-02 7.16555238e-01 -3.94063406e-02 -3.77785772e-01
-2.23435447e-01 -7.94654667e-01 -3.75004113e-01 -7.79909968e-01
-4.31202114e-01 2.72222757e-01 -2.02282578e-01 3.41360539... | [14.282023429870605, 3.2607674598693848] |
bda7e8d5-baef-40ad-a506-c6afe636a1a6 | deep-signal-recovery-with-one-bit | 1812.00797 | null | http://arxiv.org/abs/1812.00797v1 | http://arxiv.org/pdf/1812.00797v1.pdf | Deep Signal Recovery with One-Bit Quantization | Machine learning, and more specifically deep learning, have shown remarkable
performance in sensing, communications, and inference. In this paper, we
consider the application of the deep unfolding technique in the problem of
signal reconstruction from its one-bit noisy measurements. Namely, we propose a
model-based mac... | ['Naveed Naimipour', 'Shahin Khobahi', 'Yonina C. Eldar', 'Mojtaba Soltanalian'] | 2018-11-30 | null | null | null | null | ['inference-optimization'] | ['audio'] | [ 5.57905018e-01 -1.55266419e-01 4.76389192e-02 -1.47427887e-01
-1.04476023e+00 -8.58096629e-02 3.12489301e-01 -2.88934380e-01
-2.08409801e-01 7.87194967e-01 1.53694645e-01 -6.45405948e-01
-3.72236729e-01 -4.79725242e-01 -8.03614676e-01 -9.37727332e-01
-2.90222883e-01 1.10384319e-02 -7.70016432e-01 2.59256512... | [6.515263557434082, 1.4637624025344849] |
7240f75d-f2d8-44ca-978c-66e3574e019a | sltunet-a-simple-unified-model-for-sign-1 | 2305.01778 | null | https://arxiv.org/abs/2305.01778v1 | https://arxiv.org/pdf/2305.01778v1.pdf | SLTUNET: A Simple Unified Model for Sign Language Translation | Despite recent successes with neural models for sign language translation (SLT), translation quality still lags behind spoken languages because of the data scarcity and modality gap between sign video and text. To address both problems, we investigate strategies for cross-modality representation sharing for SLT. We pro... | ['Rico Sennrich', 'Mathias Müller', 'Biao Zhang'] | 2023-05-02 | sltunet-a-simple-unified-model-for-sign | https://openreview.net/forum?id=EBS4C77p_5S | https://openreview.net/pdf?id=EBS4C77p_5S | international-conference-on-learning-2 | ['sign-language-translation'] | ['computer-vision'] | [ 1.89239189e-01 -2.13024750e-01 -4.81231391e-01 -2.96601623e-01
-1.28138375e+00 -6.23834252e-01 8.52253973e-01 -6.04331195e-01
-5.69263101e-01 5.34662902e-01 7.85827398e-01 -3.75652313e-01
2.81834066e-01 -1.08672395e-01 -6.87094808e-01 -3.48903567e-01
2.43308112e-01 6.20415628e-01 -1.38930082e-01 -2.92518705... | [9.252911567687988, -6.570659160614014] |
1da18deb-9c01-4ff8-bfa6-3917eb074311 | ditto-nerf-diffusion-based-iterative-text-to | 2304.02827 | null | https://arxiv.org/abs/2304.02827v1 | https://arxiv.org/pdf/2304.02827v1.pdf | DITTO-NeRF: Diffusion-based Iterative Text To Omni-directional 3D Model | The increasing demand for high-quality 3D content creation has motivated the development of automated methods for creating 3D object models from a single image and/or from a text prompt. However, the reconstructed 3D objects using state-of-the-art image-to-3D methods still exhibit low correspondence to the given image ... | ['Se Young Chun', 'Gwanghyun Kim', 'Hayeon Kim', 'Hoigi Seo'] | 2023-04-06 | null | null | null | null | ['3d-object-reconstruction', 'image-to-3d', 'object-reconstruction', 'text-to-3d'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.92506388e-01 1.72577858e-01 2.23874643e-01 -1.81944072e-01
-1.15108442e+00 -5.02879500e-01 7.49615967e-01 -4.66571599e-01
3.04787718e-02 4.39227700e-01 2.84063220e-01 8.87474194e-02
1.99760824e-01 -5.38676560e-01 -9.52377141e-01 -3.99363220e-01
4.20544416e-01 9.58354831e-01 5.32454073e-01 -6.13064878... | [9.236065864562988, -3.1199660301208496] |
418f672d-81af-4781-8531-d26bb8325b33 | treeflow-probabilistic-programming-and | 2211.05220 | null | https://arxiv.org/abs/2211.05220v1 | https://arxiv.org/pdf/2211.05220v1.pdf | TreeFlow: probabilistic programming and automatic differentiation for phylogenetics | Probabilistic programming frameworks are powerful tools for statistical modelling and inference. They are not immediately generalisable to phylogenetic problems due to the particular computational properties of the phylogenetic tree object. TreeFlow is a software library for probabilistic programming and automatic diff... | ['Alexei Drummond', 'Frederick A Matsen IV', 'Marc A Suchard', 'Hassan Nasif', 'Xiang Ji', 'Mathieu Fourment', 'Christiaan Swanepoel'] | 2022-11-09 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 2.02129215e-01 -5.04377067e-01 -1.92141309e-02 -6.09186530e-01
-4.03965473e-01 -9.26937342e-01 5.14091969e-01 2.70241827e-01
-4.28975672e-01 7.82103479e-01 -3.01396191e-01 -8.65503728e-01
-4.20350820e-01 -7.71282375e-01 -1.49425000e-01 -1.03709745e+00
-4.26064372e-01 9.74897623e-01 5.36904335e-01 2.78816164... | [4.9459452629089355, 5.0653204917907715] |
535a1123-d0e4-4a71-a4a2-2946a5175d12 | instant-neural-graphics-primitives-with-a | 2201.05989 | null | https://arxiv.org/abs/2201.05989v2 | https://arxiv.org/pdf/2201.05989v2.pdf | Instant Neural Graphics Primitives with a Multiresolution Hash Encoding | Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We reduce this cost with a versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access ope... | ['Alexander Keller', 'Christoph Schied', 'Alex Evans', 'Thomas Müller'] | 2022-01-16 | null | null | null | null | ['neural-radiance-caching'] | ['computer-vision'] | [-6.88100010e-02 -2.32529957e-02 7.00097084e-02 -2.26888761e-01
-6.84196591e-01 -3.94576937e-01 4.06264335e-01 6.04176462e-01
-1.00757062e+00 5.26807666e-01 -2.45208055e-01 -5.64951122e-01
2.82760531e-01 -1.40029216e+00 -9.37956154e-01 -6.19949520e-01
-2.51850247e-01 3.95529419e-01 5.68762183e-01 -9.10661444... | [8.50973129272461, 2.9681661128997803] |
b9b3c1d9-b79c-4515-ae96-a5c4d4a392fa | safe-motion-planning-with-environment | 2305.06004 | null | https://arxiv.org/abs/2305.06004v1 | https://arxiv.org/pdf/2305.06004v1.pdf | Safe motion planning with environment uncertainty | We present an approach for safe motion planning under robot state and environment (obstacle and landmark location) uncertainties. To this end, we first develop an approach that accounts for the landmark uncertainties during robot localization. Existing planning approaches assume that the landmark locations are well kno... | ['Marco Baglietto', 'Fulvio Mastrogiovanni', 'Antony Thomas'] | 2023-05-10 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 9.39433351e-02 4.28793550e-01 4.90594096e-02 -2.01751500e-01
-1.04247308e+00 -4.85692352e-01 5.79831183e-01 4.32825744e-01
-8.11728120e-01 1.01153004e+00 -1.59858137e-01 -1.93779439e-01
-4.05393153e-01 -1.03060210e+00 -9.97683823e-01 -7.20340133e-01
-2.33214974e-01 8.54402184e-01 5.84519446e-01 -8.01676586... | [5.062306880950928, 1.5190446376800537] |
d5a628f5-a344-4b5b-8f36-5a5ff5867563 | an-attention-and-prediction-guided-visual | 2104.13018 | null | https://arxiv.org/abs/2104.13018v7 | https://arxiv.org/pdf/2104.13018v7.pdf | Attention and Prediction Guided Motion Detection for Low-Contrast Small Moving Targets | Small target motion detection within complex natural environments is an extremely challenging task for autonomous robots. Surprisingly, the visual systems of insects have evolved to be highly efficient in detecting mates and tracking prey, even though targets occupy as small as a few degrees of their visual fields. The... | ['Cheng Hu', 'Shigang Yue', 'Jigen Peng', 'Huatian Wang', 'Jiannan Zhao', 'Hongxin Wang'] | 2021-04-27 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 5.57992101e-01 -4.04729247e-01 -4.78563793e-02 5.91995604e-02
2.16856599e-01 -4.23348099e-01 5.57882845e-01 -7.54624754e-02
-6.72880173e-01 3.87883544e-01 -3.63791615e-01 1.08911499e-01
3.65287215e-01 -7.83905029e-01 -4.10825819e-01 -9.24179494e-01
-2.59656936e-01 1.37097999e-01 1.20571470e+00 -8.56026635... | [8.405806541442871, -0.8109832406044006] |
0f8f5182-4a42-486c-b52d-e92635cfed7a | bytecover3-accurate-cover-song-identification | 2303.11692 | null | https://arxiv.org/abs/2303.11692v1 | https://arxiv.org/pdf/2303.11692v1.pdf | ByteCover3: Accurate Cover Song Identification on Short Queries | Deep learning based methods have become a paradigm for cover song identification (CSI) in recent years, where the ByteCover systems have achieved state-of-the-art results on all the mainstream datasets of CSI. However, with the burgeon of short videos, many real-world applications require matching short music excerpts ... | ['Zejun Ma', 'Bilei Zhu', 'Huidong Liang', 'Xia Liang', 'Zijie Wang', 'Xingjian Du'] | 2023-03-21 | null | null | null | null | ['cover-song-identification'] | ['music'] | [ 1.23269431e-01 -8.32197070e-01 -2.21663088e-01 -1.03610501e-01
-1.32635379e+00 -7.62281179e-01 3.47487837e-01 -4.32569087e-02
-4.23339754e-01 3.40089738e-01 1.44533977e-01 1.92953125e-01
-3.13771546e-01 -6.70562506e-01 -7.81184256e-01 -5.28525591e-01
-1.88518599e-01 6.13687456e-01 1.52968541e-01 -3.89768444... | [15.682795524597168, 5.226885795593262] |
f63fef42-20e6-4077-a5e4-60f649080460 | merging-datasets-for-aggressive-text | null | null | https://aclanthology.org/W18-4416 | https://aclanthology.org/W18-4416.pdf | Merging Datasets for Aggressive Text Identification | This paper presents the approach of the team {``}groutar{''} to the shared task on Aggression Identification, considering the test sets in English, both from Facebook and general Social Media. This experiment aims to test the effect of merging new datasets in the performance of classification models. We followed a stan... | ["S{\\'e}rgio Nunes", "Jos{\\'e} Ferreira", 'Luiz Pires', 'Paula Fortuna', 'Guilherme Routar'] | 2018-08-01 | null | null | null | coling-2018-8 | ['aggression-identification'] | ['natural-language-processing'] | [-3.04170609e-01 2.14436620e-01 -1.40565950e-02 -3.93652231e-01
-1.12134591e-01 -2.28919953e-01 6.13468945e-01 8.29894781e-01
-1.02670717e+00 9.02059257e-01 1.71115056e-01 -1.40961990e-01
-6.38198793e-01 -8.71692359e-01 -2.71964431e-01 -3.65343332e-01
-2.19268844e-01 6.30184472e-01 3.16207349e-01 -6.78713679... | [8.816535949707031, 10.64580249786377] |
890ffa7c-2b27-4d16-90e4-f9fc4f111855 | passive-defense-against-3d-adversarial-point | 2205.08738 | null | https://arxiv.org/abs/2205.08738v3 | https://arxiv.org/pdf/2205.08738v3.pdf | 3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds | 3D deep models consuming point clouds have achieved sound application effects in computer vision. However, recent studies have shown they are vulnerable to 3D adversarial point clouds. In this paper, we regard these malicious point clouds as 3D steganography examples and present a new perspective, 3D steganalysis, to c... | ['Xiaohua Xie', 'Yi Zhou', 'Zixuan Chen', 'Huajun Zhou', 'Jiahao Zhu'] | 2022-05-18 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 2.39513323e-01 2.76531249e-01 2.75699288e-01 4.80001360e-01
-7.32339442e-01 -1.17452908e+00 8.92991245e-01 -3.14089417e-01
-3.16324793e-02 -4.24314700e-02 -6.90416694e-01 -5.74523926e-01
5.76458454e-01 -1.05392635e+00 -1.14087641e+00 -8.03683043e-01
-1.39181197e-01 5.00004768e-01 5.51637053e-01 -5.61996400... | [7.702873229980469, -4.467500686645508] |
7235d322-a13a-4726-b53b-1807af840006 | discrete-graph-structure-learning-for-1 | 2101.06861 | null | https://arxiv.org/abs/2101.06861v3 | https://arxiv.org/pdf/2101.06861v3.pdf | Discrete Graph Structure Learning for Forecasting Multiple Time Series | Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model. When using deep neural networks as forecasting model... | ['Jinbo Bi', 'Jie Chen', 'Chao Shang'] | 2021-01-18 | discrete-graph-structure-learning-for | https://openreview.net/forum?id=WEHSlH5mOk | https://openreview.net/pdf?id=WEHSlH5mOk | iclr-2021-1 | ['graph-structure-learning'] | ['graphs'] | [-1.82941154e-01 3.00563514e-01 -2.95439720e-01 -3.73382211e-01
-1.15044661e-01 -7.11126089e-01 7.84806192e-01 3.14643800e-01
1.72587439e-01 5.34794629e-01 3.28876346e-01 -6.78962231e-01
-3.94949675e-01 -1.01652551e+00 -9.88172948e-01 -6.10642910e-01
-9.24631119e-01 7.36271322e-01 -4.34632450e-01 -1.08984992... | [6.7967705726623535, 2.946995258331299] |
6099f91b-130c-46df-994a-7d6877808770 | a-deep-insight-into-measuring-face-image | 2110.11111 | null | https://arxiv.org/abs/2110.11111v2 | https://arxiv.org/pdf/2110.11111v2.pdf | A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metrics | Quality scores provide a measure to evaluate the utility of biometric samples for biometric recognition. Biometric recognition systems require high-quality samples to achieve optimal performance. This paper focuses on face images and the measurement of face image utility with general and face-specific image quality met... | ['Naser Damer', 'Olaf Henniger', 'Cong Chen', 'Biying Fu'] | 2021-10-21 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 1.24391474e-01 -9.53630731e-02 6.54251799e-02 -8.74166727e-01
-6.77462161e-01 -5.36130190e-01 5.39277732e-01 -3.90854061e-01
-2.52399385e-01 3.93402517e-01 1.66025668e-01 1.84389710e-01
-4.86317247e-01 -7.76190877e-01 -5.49266189e-02 -5.89783609e-01
-5.18224835e-02 3.21514726e-01 -6.23052776e-01 -1.99208930... | [13.062430381774902, 0.8888477683067322] |
19946a13-f062-4032-be77-59ca0387bd8b | magnetic-resonance-fingerprinting-using | 1812.08155 | null | http://arxiv.org/abs/1812.08155v1 | http://arxiv.org/pdf/1812.08155v1.pdf | Magnetic Resonance Fingerprinting using Recurrent Neural Networks | Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative
magnetic resonance imaging that allows simultaneous measurement of multiple
tissue properties in a single, time-efficient acquisition. Standard MRF
reconstructs parametric maps using dictionary matching and lacks scalability
due to computational ... | ['Claudia Prieto', 'Nicolo Fuin', 'Ilkay Oksuz', 'Rene M. Botnar', 'James Clough', 'Gastao Cruz', 'Aurelien Bustin', 'Julia A. Schnabel', 'Andrew P. King'] | 2018-12-19 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 5.63024163e-01 -1.52409732e-01 -2.32640564e-01 -3.68980914e-01
-1.08172691e+00 -3.34746629e-01 1.85860783e-01 1.56715557e-01
-4.69876319e-01 6.69038355e-01 2.41268054e-01 -9.06021819e-02
-4.65473741e-01 -5.63797414e-01 -6.29075885e-01 -6.70347810e-01
-4.32047635e-01 8.01011622e-01 4.11495179e-01 4.23619859... | [13.488677024841309, -2.4192278385162354] |
d5c0d21c-daad-4943-bdf0-4905b903f8db | learning-with-latent-group-sparsity-via-heat | 2201.08326 | null | https://arxiv.org/abs/2201.08326v1 | https://arxiv.org/pdf/2201.08326v1.pdf | Learning with latent group sparsity via heat flow dynamics on networks | Group or cluster structure on explanatory variables in machine learning problems is a very general phenomenon, which has attracted broad interest from practitioners and theoreticians alike. In this work we contribute an approach to learning under such group structure, that does not require prior information on the grou... | ['Soumendu Sundar Mukherjee', 'Subhroshekhar Ghosh'] | 2022-01-20 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 4.53786612e-01 3.32105517e-01 -1.37320319e-02 9.88085493e-02
-2.55684137e-01 -7.33771503e-01 7.60686278e-01 5.72822571e-01
-3.86852652e-01 7.86382854e-01 -2.30760500e-01 -4.59339917e-01
-8.27821255e-01 -9.70755398e-01 -5.56175888e-01 -1.25972521e+00
-5.25016844e-01 4.69089955e-01 2.38640774e-02 -2.49134302... | [6.950779438018799, 5.138308048248291] |
7bacaedd-8019-4531-a25c-eac4d08d36ee | acquiring-background-knowledge-to-improve | 1709.05467 | null | http://arxiv.org/abs/1709.05467v1 | http://arxiv.org/pdf/1709.05467v1.pdf | Acquiring Background Knowledge to Improve Moral Value Prediction | In this paper, we address the problem of detecting expressions of moral
values in tweets using content analysis. This is a particularly challenging
problem because moral values are often only implicitly signaled in language,
and tweets contain little contextual information due to length constraints. To
address these ob... | ['Marlon Mooijman', 'Heng Ji', 'Morteza Dehghani', 'Joe Hoover', 'Ying Lin'] | 2017-09-16 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 4.12270635e-01 5.40238202e-01 -6.84257567e-01 -5.82189560e-01
-1.25161380e-01 -2.13762388e-01 4.48721468e-01 8.52202237e-01
-9.58300829e-01 9.90430057e-01 3.82180333e-01 -7.60674104e-02
6.99402243e-02 -1.01960063e+00 -1.59591958e-01 -2.89029926e-01
2.68188536e-01 4.31234896e-01 8.08558017e-02 -5.48610091... | [9.221970558166504, 10.080038070678711] |
dc7a1e62-e93a-4bc1-b718-1d4c8f93dff0 | structurenet-hierarchical-graph-networks-for | 1908.00575 | null | https://arxiv.org/abs/1908.00575v1 | https://arxiv.org/pdf/1908.00575v1.pdf | StructureNet: Hierarchical Graph Networks for 3D Shape Generation | The ability to generate novel, diverse, and realistic 3D shapes along with associated part semantics and structure is central to many applications requiring high-quality 3D assets or large volumes of realistic training data. A key challenge towards this goal is how to accommodate diverse shape variations, including bot... | ['Paul Guerrero', 'Peter Wonka', 'Li Yi', 'Leonidas J. Guibas', 'Niloy Mitra', 'Kaichun Mo', 'Hao Su'] | 2019-08-01 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 3.72402787e-01 3.86134446e-01 2.79245287e-01 -3.89200956e-01
-4.25815523e-01 -9.08796310e-01 6.88586593e-01 2.79099226e-01
2.98136741e-01 4.87106711e-01 3.68237615e-01 -9.99115035e-02
-2.41407558e-01 -1.31552708e+00 -9.06405985e-01 -4.56729144e-01
-1.04529686e-01 1.08093703e+00 2.24388123e-01 -3.54069680... | [8.823758125305176, -3.6563656330108643] |
7b258e57-c60d-4a40-b64c-5422eedf9a95 | dimensionality-reduction-as-probabilistic | 2304.07658 | null | https://arxiv.org/abs/2304.07658v2 | https://arxiv.org/pdf/2304.07658v2.pdf | Dimensionality Reduction as Probabilistic Inference | Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data. DR is a critical step in many analysis pipelines as it enables visualisation, noise reduction and efficient downstream processing of the data. In this work, we... | ['Neil D. Lawrence', 'Vidhi Lalchand', 'Francisco Vargas', 'Aditya Ravuri'] | 2023-04-15 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [-2.34838858e-01 3.15807819e-01 3.01534086e-01 -4.24256772e-02
-6.45427883e-01 -8.62543046e-01 1.18198895e+00 -3.44088897e-02
1.17141694e-01 2.68310249e-01 4.44605887e-01 -4.32111561e-01
-7.16995120e-01 -8.70357037e-01 -5.95473945e-01 -9.34186220e-01
-2.45950613e-02 8.42650890e-01 5.80317639e-02 2.82524824... | [6.90895938873291, 3.791161298751831] |
34cb9680-e218-4eb0-94b8-d86da62612d3 | a-wearable-eeg-system-for-closed-loop | 2212.11273 | null | https://arxiv.org/abs/2212.11273v1 | https://arxiv.org/pdf/2212.11273v1.pdf | A Wearable EEG System for Closed-Loop Neuromodulation of High-Frequency Sleep-Related Oscillations | In healthy sleepers, cortical alpha oscillations are present during the transition from wakefulness to sleep, and dissipate at sleep onset. For individuals with insomnia, alpha power is elevated during the wake-sleep transition and can persist throughout the night. Neuromodulation techniques using phase-locked stimulat... | ['David Wang', 'Ryan Yost', 'Heather Read', 'Ryan Neely', 'Scott Bressler'] | 2022-12-21 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [ 1.47978231e-01 -3.40467006e-01 2.37807304e-01 -1.59626435e-02
-2.96034217e-01 -4.44968343e-01 -1.14981458e-01 3.20370942e-01
-7.53924608e-01 7.98469126e-01 3.55146199e-01 -1.71518072e-01
-8.38747546e-02 -2.92954355e-01 -6.57059550e-02 -6.55266047e-01
-5.45149922e-01 1.26713216e-01 3.72981690e-02 -1.42711371... | [13.484414100646973, 3.42234206199646] |
ce71c654-9db3-477e-bf20-53e8c06ba4b4 | federated-learning-enables-big-data-for-rare | 2204.10836 | null | https://arxiv.org/abs/2204.10836v2 | https://arxiv.org/pdf/2204.10836v2.pdf | Federated Learning Enables Big Data for Rare Cancer Boundary Detection | Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) du... | ['Carmen Balana', 'Spyridon Bakas', 'Jason Martin', 'Jill S Barnholtz-Sloan', 'Bjoern Menze', 'Prashant Shah', 'Charles Apgar', 'Lisa Cimino', 'Cynthia Price', 'Sailaja Marella', 'Brian Bialecki', 'Kendall Schmidt', 'Deepak Kattil Veettil', 'James Gimpel', 'Michael A Boss', 'Fabio Y Moraes', 'Danielle Cutler', 'Anh Tra... | 2022-04-22 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.96317539e-01 2.87374616e-01 -5.16920745e-01 -2.63304204e-01
-1.81028509e+00 -3.95583600e-01 1.36614978e-01 4.80315328e-01
-7.52725482e-01 9.88485634e-01 6.89977765e-01 -7.08129168e-01
-1.78589836e-01 -3.49142075e-01 -4.77464139e-01 -7.34108269e-01
-2.05377176e-01 6.73583746e-01 -2.40764558e-01 4.15977418... | [14.943219184875488, -2.666996717453003] |
26037ead-6d1f-4a2e-9c8a-ae7eacdfe7a6 | photoelectric-factor-prediction-using | 2206.08950 | null | https://arxiv.org/abs/2206.08950v1 | https://arxiv.org/pdf/2206.08950v1.pdf | Photoelectric Factor Prediction Using Automated Learning and Uncertainty Quantification | The photoelectric factor (PEF) is an important well logging tool to distinguish between different types of reservoir rocks because PEF measurement is sensitive to elements with high atomic number. Furthermore, the ratio of rock minerals could be determined by combining PEF log with other well logs. However, PEF log cou... | ['Abdulazeez Abdulraheem', 'Salaheldin Elkatatny', 'Ahmed Farid Ibrahim', 'Khalid L. Alsamadony'] | 2022-06-17 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-2.02675536e-01 1.21980309e-01 2.77946115e-01 -2.19344541e-01
-2.56375432e-01 5.91343492e-02 3.97371233e-01 3.57100427e-01
-4.55561638e-01 1.04483223e+00 -1.70283571e-01 -1.59091458e-01
-4.67820108e-01 -1.41892898e+00 -5.15038311e-01 -7.66398489e-01
-4.41654474e-02 9.90935504e-01 5.73009253e-01 -1.26278639... | [6.297726631164551, 3.2250866889953613] |
92ddbde4-dab7-496b-b957-c11215279805 | mwe-as-wsd-solving-multiword-expression | 2303.06623 | null | https://arxiv.org/abs/2303.06623v1 | https://arxiv.org/pdf/2303.06623v1.pdf | MWE as WSD: Solving Multiword Expression Identification with Word Sense Disambiguation | Recent work in word sense disambiguation (WSD) utilizes encodings of the sense gloss (definition text), in addition to the input words and context, to improve performance. In this work we demonstrate that this approach can be adapted for use in multiword expression (MWE) identification by training a Bi-encoder model wh... | ['Jacob Hoffman', 'Joshua Tanner'] | 2023-03-12 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 6.23597503e-01 7.01746196e-02 -1.54372126e-01 -4.27585602e-01
-1.15699232e+00 -8.15939903e-01 6.40005350e-01 3.57248515e-01
-1.14326465e+00 6.40087903e-01 4.86971498e-01 -4.01779741e-01
1.58014074e-02 -5.16023457e-01 -2.37334192e-01 -1.00760534e-01
4.34906036e-02 5.97462416e-01 2.09918007e-01 -7.87048042... | [10.356470108032227, 9.4617280960083] |
420d6065-f31e-4ba6-a44b-e96ada343249 | gafx-a-general-audio-feature-extractor | 2207.09145 | null | https://arxiv.org/abs/2207.09145v1 | https://arxiv.org/pdf/2207.09145v1.pdf | GAFX: A General Audio Feature eXtractor | Most machine learning models for audio tasks are dealing with a handcrafted feature, the spectrogram. However, it is still unknown whether the spectrogram could be replaced with deep learning based features. In this paper, we answer this question by comparing the different learnable neural networks extracting features ... | ['Xiaohu Zhu', 'Hanhaodi Zhang', 'Zhaoyang Bu'] | 2022-07-19 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 2.95067549e-01 -3.21468413e-02 3.34743142e-01 -2.02149227e-01
-9.37199235e-01 -6.44474864e-01 5.04862309e-01 -2.03349382e-01
-2.49639079e-01 5.66826701e-01 5.22943139e-01 -1.48803145e-01
-3.68311465e-01 -5.96135914e-01 -6.96588635e-01 -5.14042199e-01
-2.42602900e-01 8.69025569e-03 -5.40917516e-02 -2.43344635... | [15.594429969787598, 5.24717378616333] |
8592102e-2020-4db8-9012-ecc86bea5323 | bloom-library-multimodal-datasets-in-300 | 2210.14712 | null | https://arxiv.org/abs/2210.14712v1 | https://arxiv.org/pdf/2210.14712v1.pdf | Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks | We present Bloom Library, a linguistically diverse set of multimodal and multilingual datasets for language modeling, image captioning, visual storytelling, and speech synthesis/recognition. These datasets represent either the most, or among the most, multilingual datasets for each of the included downstream tasks. In ... | ['Daniel Whitenack', 'Abraham Owodunni', 'Anna Filighera', 'Jacob Mansdorfer', 'Joshua Nemecek', 'Colin Leong'] | 2022-10-26 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [-6.65846542e-02 1.11910403e-01 -6.08565271e-01 -2.14193210e-01
-1.26393235e+00 -9.05433834e-01 1.05081427e+00 -2.66066402e-01
-3.52170885e-01 6.81234181e-01 8.69115233e-01 -4.53705549e-01
5.38645089e-01 -2.23952815e-01 -7.69069135e-01 -3.82077366e-01
1.22546710e-01 8.98840785e-01 -3.38574558e-01 -3.87511700... | [11.21796703338623, 1.5863239765167236] |
5730c260-d6ea-41e5-9f8d-452ed7fa8760 | perceptual-kalman-filters-online-state | 2306.02400 | null | https://arxiv.org/abs/2306.02400v1 | https://arxiv.org/pdf/2306.02400v1.pdf | Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint | Many practical settings call for the reconstruction of temporal signals from corrupted or missing data. Classic examples include decoding, tracking, signal enhancement and denoising. Since the reconstructed signals are ultimately viewed by humans, it is desirable to achieve reconstructions that are pleasing to human pe... | ['Ron Meir', 'Tomer Michaeli', 'Dror Freirich'] | 2023-06-04 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 6.79555953e-01 6.19302131e-02 3.20045948e-01 4.76911850e-02
-3.96544188e-01 -5.83541930e-01 4.82678175e-01 -4.04526778e-02
-4.62725222e-01 6.75932825e-01 2.17075408e-01 -2.30359361e-01
-4.39849287e-01 -4.58827406e-01 -7.77240038e-01 -1.00735962e+00
-1.35457456e-01 -3.26450586e-01 1.32696226e-01 -7.37680346... | [11.438981056213379, -2.0996625423431396] |
e1979097-3054-4e4b-b132-293f8a273b5f | from-solution-synthesis-to-student-attempt | 2205.01265 | null | https://arxiv.org/abs/2205.01265v3 | https://arxiv.org/pdf/2205.01265v3.pdf | From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Tasks | Block-based visual programming environments are increasingly used to introduce computing concepts to beginners. Given that programming tasks are open-ended and conceptual, novice students often struggle when learning in these environments. AI-driven programming tutors hold great promise in automatically assisting strug... | ['Nikitas Theodoropoulos', 'Adish Singla'] | 2022-05-03 | null | null | null | null | ['program-synthesis', 'misconceptions'] | ['computer-code', 'miscellaneous'] | [ 2.74935156e-01 3.16114187e-01 -4.37969826e-02 -4.22957778e-01
-4.03069079e-01 -7.72696197e-01 5.18050015e-01 6.84650302e-01
-1.45297199e-01 1.57173783e-01 -2.69288033e-01 -1.06212831e+00
1.00124761e-01 -9.37951505e-01 -7.92246163e-01 -2.60807633e-01
2.38059476e-01 4.78299439e-01 2.65231252e-01 -3.85635763... | [9.413805961608887, 7.371584415435791] |
67f32829-5b32-403f-bf8a-d0391e0d153b | enhancing-building-semantic-segmentation | 2307.04101 | null | https://arxiv.org/abs/2307.04101v1 | https://arxiv.org/pdf/2307.04101v1.pdf | Enhancing Building Semantic Segmentation Accuracy with Super Resolution and Deep Learning: Investigating the Impact of Spatial Resolution on Various Datasets | The development of remote sensing and deep learning techniques has enabled building semantic segmentation with high accuracy and efficiency. Despite their success in different tasks, the discussions on the impact of spatial resolution on deep learning based building semantic segmentation are quite inadequate, which mak... | ['Ryosuke Shibasaki', 'Jinyue Yan', 'Xiaoya Song', 'Dou Huang', 'Haoran Zhang', 'Xiaodan Shi', 'Zhiling Guo'] | 2023-07-09 | null | null | null | null | ['semantic-segmentation', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 2.81106611e-03 -2.58959532e-01 1.93761359e-03 -3.51719767e-01
-7.07173586e-01 -2.98819765e-02 3.55682880e-01 -1.12326875e-01
-3.92128050e-01 8.56789351e-01 1.44435510e-01 -3.75105321e-01
-4.20105785e-01 -1.65562093e+00 -3.78309697e-01 -8.23623300e-01
-1.53061554e-01 3.49278837e-01 2.08140194e-01 -2.87477434... | [9.33131217956543, -1.3891724348068237] |
0e20fde9-acb7-424e-918a-bcdf593badcd | exactly-optimal-quickest-change-detection-of | 2303.13778 | null | https://arxiv.org/abs/2303.13778v1 | https://arxiv.org/pdf/2303.13778v1.pdf | Exactly Optimal Quickest Change Detection of Markov Chains | This paper establishes that an exactly optimal rule for Bayesian Quickest Change Detection (QCD) of Markov chains is a threshold test on the no change posterior. We also provide a computationally efficient scalar filter for the no change posterior whose effort is independent of the dimension of the chains. We establish... | ['Aaron McFadyen', 'Jasmin James', 'Caitlin Tompkins', 'Justin M. Kennedy', 'Jason J. Ford'] | 2023-03-24 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 1.90718725e-01 -5.11226878e-02 9.88212451e-02 -1.06679529e-01
-7.04443872e-01 -6.71895027e-01 7.03972161e-01 2.01054234e-02
-6.30885124e-01 1.13339257e+00 -2.39299744e-01 -6.29645586e-01
8.74099061e-02 -4.77474719e-01 -6.52874708e-01 -1.13147533e+00
-3.06261808e-01 4.72130150e-01 7.72246361e-01 5.35987392... | [6.908048629760742, 4.0222063064575195] |
770de7dc-6ecc-4de0-843b-612b4c973ba1 | iterated-learning-for-emergent-systematicity-1 | 2105.01119 | null | https://arxiv.org/abs/2105.01119v1 | https://arxiv.org/pdf/2105.01119v1.pdf | Iterated learning for emergent systematicity in VQA | Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice. When instead learning layouts and modules jointly, compositionality does not arise automatically and an explicit pressure is necessary for the emergence of la... | ['Aaron Courville', 'Eeshan Dhekane', 'Yuchen Lu', 'Max Schwarzer', 'Ankit Vani'] | 2021-05-03 | iterated-learning-for-emergent-systematicity | https://openreview.net/forum?id=Pd_oMxH8IlF | https://openreview.net/pdf?id=Pd_oMxH8IlF | iclr-2021-1 | ['systematic-generalization'] | ['reasoning'] | [ 1.89339295e-01 5.65944552e-01 -1.36727570e-02 -1.31695643e-01
-1.89968973e-01 -9.70452249e-01 7.08810568e-01 1.61359515e-02
-1.87095478e-01 2.03727990e-01 1.81701273e-01 -7.20673680e-01
-1.69061124e-01 -8.74757588e-01 -1.25111723e+00 -3.86164337e-01
-2.66509920e-01 4.95647252e-01 2.08553210e-01 -2.63731033... | [9.541594505310059, 6.968441963195801] |
518c9b75-f362-40e8-b09a-67b4b57c8980 | model-based-image-adjustment-for-a-successful | 2103.03062 | null | https://arxiv.org/abs/2103.03062v1 | https://arxiv.org/pdf/2103.03062v1.pdf | Model-based image adjustment for a successful pansharpening | A new model-based image adjustment for the enhancement of multi-resolution image fusion or pansharpening is proposed. Such image adjustment is needed for most pansharpening methods using panchromatic band and/or intensity image (calculated as a weighted sum of multispectral bands) as an input. Due various reasons, e.g.... | ['Gintautas Palubinskas'] | 2021-03-04 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 9.11729455e-01 -7.50422120e-01 -8.89104530e-02 -1.14520214e-01
-6.68279707e-01 -6.87578022e-01 3.73865664e-01 5.35280518e-02
-3.88458192e-01 7.60239780e-01 -1.87308207e-01 3.67978401e-02
-5.08433521e-01 -1.33537149e+00 -1.96314320e-01 -1.15850222e+00
3.94607991e-01 1.14725977e-01 6.67703748e-02 -4.58043665... | [10.07407283782959, -2.0995922088623047] |
54519b26-2ad5-4cfa-8df9-06169cac7d70 | on-the-possibility-of-rewarding-structure | 1910.04023 | null | https://arxiv.org/abs/1910.04023v4 | https://arxiv.org/pdf/1910.04023v4.pdf | On the Possibility of Rewarding Structure Learning Agents: Mutual Information on Linguistic Random Sets | We present a first attempt to elucidate a theoretical and empirical approach to design the reward provided by a natural language environment to some structure learning agent. To this end, we revisit the Information Theory of unsupervised induction of phrase-structure grammars to characterize the behavior of simulated a... | ['J. Anibal Arias-Aguilar', 'Mauricio Carrasco-Ruíz', 'Ignacio Arroyo-Fernández'] | 2019-10-09 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 4.86592680e-01 1.23572159e+00 -1.68814912e-01 -5.64584255e-01
-4.98062313e-01 -9.39227164e-01 9.44817126e-01 4.86997105e-02
-5.93133688e-01 7.41264284e-01 1.85646594e-01 -6.15658522e-01
-2.22726256e-01 -1.00156701e+00 -8.75194669e-01 -5.76709092e-01
-2.63951808e-01 1.20226228e+00 2.05024719e-01 -4.20484617... | [4.10689640045166, 1.3673323392868042] |
0a94d5a0-7a9a-4d0d-a210-c2f30636f60a | multi-task-learning-network-for-emotion | 2003.01478 | null | https://arxiv.org/abs/2003.01478v2 | https://arxiv.org/pdf/2003.01478v2.pdf | Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition | Conversational emotion recognition (CER) has attracted increasing interests in the natural language processing (NLP) community. Different from the vanilla emotion recognition, effective speaker-sensitive utterance representation is one major challenge for CER. In this paper, we exploit speaker identification (SI) as an... | ['Yijiang Liu', 'Meishan Zhang', 'Donghong Ji', 'Jingye Li'] | 2020-03-03 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 3.73764634e-01 -6.27123564e-02 1.06342316e-01 -7.96700001e-01
-1.18966687e+00 -3.19013417e-01 6.32375062e-01 -2.43296325e-01
-4.71609592e-01 6.84839904e-01 7.21342385e-01 2.63335072e-02
3.77249151e-01 -8.09633285e-02 -1.24546401e-01 -7.84301996e-01
6.56138733e-02 -5.80709949e-02 -2.44685322e-01 -3.81151438... | [13.188431739807129, 5.939605236053467] |
6b7ee39e-5a44-4c99-887a-db092c46eb49 | defix-detecting-and-fixing-failure-scenarios | 2210.16567 | null | https://arxiv.org/abs/2210.16567v1 | https://arxiv.org/pdf/2210.16567v1.pdf | DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving | Safely navigating through an urban environment without violating any traffic rules is a crucial performance target for reliable autonomous driving. In this paper, we present a Reinforcement Learning (RL) based methodology to DEtect and FIX (DeFIX) failures of an Imitation Learning (IL) agent by extracting infraction sp... | ['Nazim Kemal Ure', 'Ferhat Yurdakul', 'Halil Durmus', 'Feyza Eksen', 'Resul Dagdanov'] | 2022-10-29 | null | null | null | null | ['carla-map-leaderboard'] | ['robots'] | [-4.07181717e-02 2.78438449e-01 -1.08024783e-01 -2.70069122e-01
-6.82473600e-01 -6.44351304e-01 7.94155598e-01 -3.39636300e-03
-7.21858263e-01 9.64718878e-01 -5.39083838e-01 -8.12114656e-01
-5.35720102e-02 -8.09696913e-01 -1.19383264e+00 -6.92166865e-01
-3.59474212e-01 7.73337185e-01 6.07956707e-01 -4.97129560... | [5.093986511230469, 1.2593127489089966] |
e53008f7-01fd-4ac4-a0c4-b0978f5a0022 | transformer-based-models-and-hardware | 2304.10891 | null | https://arxiv.org/abs/2304.10891v1 | https://arxiv.org/pdf/2304.10891v1.pdf | Transformer-based models and hardware acceleration analysis in autonomous driving: A survey | Transformer architectures have exhibited promising performance in various autonomous driving applications in recent years. On the other hand, its dedicated hardware acceleration on portable computational platforms has become the next critical step for practical deployment in real autonomous vehicles. This survey paper ... | ['Xi Chen', 'Zheng Liu', 'Juan Zhong'] | 2023-04-21 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [ 1.39260516e-01 6.85486523e-03 -4.04741406e-01 -5.73953211e-01
-3.69668871e-01 -4.66042727e-01 4.77479249e-01 -2.41619244e-01
-3.67720127e-01 2.13890880e-01 -2.40226865e-01 -8.52544665e-01
2.03514516e-01 -7.85437942e-01 -7.81369984e-01 -5.81884086e-01
-6.94988817e-02 2.80495018e-01 5.35957634e-01 -5.19751728... | [8.10883903503418, -1.2386804819107056] |
de9bb5fa-1a68-4064-8bb5-04a6f2f985b5 | dare-slam-degeneracy-aware-and-resilient-loop | 2102.05117 | null | https://arxiv.org/abs/2102.05117v1 | https://arxiv.org/pdf/2102.05117v1.pdf | DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments | Enabling fully autonomous robots capable of navigating and exploring large-scale, unknown and complex environments has been at the core of robotics research for several decades. A key requirement in autonomous exploration is building accurate and consistent maps of the unknown environment that can be used for reliable ... | ['Ali-akbar Agha-mohammadi', 'Curtis Padgett', 'Sally Wood', 'Matteo Palieri', 'Kamak Ebadi'] | 2021-02-09 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 1.49297580e-01 -3.33829343e-01 2.91053474e-01 -4.43050057e-01
-5.93361259e-01 -9.49719667e-01 3.45034748e-01 4.84246999e-01
-5.85728168e-01 1.05649936e+00 -2.45395303e-01 -2.64128834e-01
-5.03920436e-01 -5.73425114e-01 -8.08559000e-01 -4.44161773e-01
-5.49586058e-01 8.27203214e-01 5.42881310e-01 -5.03837645... | [7.231740951538086, -2.028764009475708] |
9aadc27f-1ec3-4349-be7e-5f9d25fcbbd7 | distilling-knowledge-from-language-models-for | 2210.05991 | null | https://arxiv.org/abs/2210.05991v2 | https://arxiv.org/pdf/2210.05991v2.pdf | Text-Derived Knowledge Helps Vision: A Simple Cross-modal Distillation for Video-based Action Anticipation | Anticipating future actions in a video is useful for many autonomous and assistive technologies. Most prior action anticipation work treat this as a vision modality problem, where the models learn the task information primarily from the video features in the action anticipation datasets. However, knowledge about action... | ['Niranjan Balasubramanian', 'Minh Hoai', 'Tanvi Aggarwal', 'Sayontan Ghosh'] | 2022-10-12 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 4.98831600e-01 4.81823027e-01 -5.46113789e-01 -4.12451148e-01
-4.31922257e-01 -3.66563231e-01 1.00408089e+00 -4.84489024e-01
-5.26498377e-01 3.56929183e-01 6.66557014e-01 -3.45529243e-02
3.66234392e-01 -1.51856616e-01 -9.36725855e-01 -3.61600518e-01
-7.85245374e-02 2.28029281e-01 2.44147152e-01 -1.96363330... | [8.230287551879883, 0.5417092442512512] |
a01c24a5-0086-4703-b61a-17d6215ca9ae | self-supervised-learning-of-pretext-invariant | 1912.01991 | null | https://arxiv.org/abs/1912.01991v1 | https://arxiv.org/pdf/1912.01991v1.pdf | Self-Supervised Learning of Pretext-Invariant Representations | The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations for a large training set of images. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, ... | ['Laurens van der Maaten', 'Ishan Misra'] | 2019-12-04 | self-supervised-learning-of-pretext-invariant-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.pdf | cvpr-2020-6 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 7.63815641e-01 3.88628393e-01 -2.68545926e-01 -5.24684727e-01
-5.25245249e-01 -5.43074310e-01 9.72309232e-01 1.04160786e-01
-3.35624874e-01 2.45343283e-01 4.18633968e-01 2.77369060e-02
-1.89355373e-01 -9.06419873e-01 -1.18346024e+00 -5.44534087e-01
1.55117765e-01 4.45394397e-01 2.42543846e-01 -4.80615675... | [9.656173706054688, 2.1529197692871094] |
24dd34c7-e0ae-468d-a7bf-c5ff4844e9a5 | structured-pruning-for-multi-task-deep-neural | 2304.06840 | null | https://arxiv.org/abs/2304.06840v1 | https://arxiv.org/pdf/2304.06840v1.pdf | Structured Pruning for Multi-Task Deep Neural Networks | Although multi-task deep neural network (DNN) models have computation and storage benefits over individual single-task DNN models, they can be further optimized via model compression. Numerous structured pruning methods are already developed that can readily achieve speedups in single-task models, but the pruning of mu... | ['Hui Guan', 'Lijun Zhang', 'Siddhant Garg'] | 2023-04-13 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 1.96808100e-01 -7.42319003e-02 7.10686296e-02 -5.21952629e-01
-4.99110550e-01 -1.16599761e-01 3.06677729e-01 6.83478266e-02
-8.80498588e-01 6.77960575e-01 -3.85204777e-02 -3.11994284e-01
-3.50169212e-01 -4.84687537e-01 -5.90911746e-01 -4.46272641e-01
1.72121063e-01 6.41172945e-01 7.64669001e-01 5.95881753... | [8.608660697937012, 3.3013458251953125] |
dddc1c6b-cc3a-4713-9f89-766c000a2177 | training-neural-networks-based-on-imperialist | 1704.04095 | null | http://arxiv.org/abs/1704.04095v1 | http://arxiv.org/pdf/1704.04095v1.pdf | Training Neural Networks Based on Imperialist Competitive Algorithm for Predicting Earthquake Intensity | In this study we determined neural network weights and biases by Imperialist
Competitive Algorithm (ICA) in order to train network for predicting earthquake
intensity in Richter. For this reason, we used dependent parameters like
earthquake occurrence time, epicenter's latitude and longitude in degree, focal
depth in k... | ['Mohsen Moradi'] | 2017-02-13 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [-3.38432461e-01 -2.38749627e-02 -8.16345401e-03 3.80113982e-02
-1.84306890e-01 -3.83330017e-01 2.39600509e-01 -1.65272817e-01
-9.88919556e-01 1.35816026e+00 4.56265695e-02 -4.23961490e-01
-3.22807580e-01 -1.03087735e+00 -6.72999263e-01 -9.43733513e-01
-3.61466736e-01 4.38147098e-01 9.92305428e-02 -3.88681203... | [6.367530822753906, 3.0615179538726807] |
caa30eea-e92e-416b-85d9-f5ec594d88b6 | toward-moire-free-and-detail-preserving | 2305.08585 | null | https://arxiv.org/abs/2305.08585v1 | https://arxiv.org/pdf/2305.08585v1.pdf | Toward Moiré-Free and Detail-Preserving Demosaicking | 3D convolutions are commonly employed by demosaicking neural models, in the same way as solving other image restoration problems. Counter-intuitively, we show that 3D convolutions implicitly impede the RGB color spectra from exchanging complementary information, resulting in spectral-inconsistent inference of the local... | ['Zitong An', 'Haoyuan Shi', 'Bo Zhao', 'Yan Niu', 'Xuanchen Li'] | 2023-05-15 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 3.17301422e-01 -2.68445939e-01 4.06443834e-01 -1.49996772e-01
-5.53880036e-01 -3.96141350e-01 3.44988316e-01 -2.99685031e-01
-3.51472229e-01 6.91642582e-01 4.78076398e-01 -1.42886981e-01
-3.74210298e-01 -8.98534119e-01 -7.24643409e-01 -1.05214107e+00
-8.58920589e-02 -2.55286932e-01 -1.65180907e-01 -3.15063745... | [11.161294937133789, -2.361258029937744] |
a2a0cbe7-af6d-4b06-8f80-8d54599afba1 | contextual-dictionary-lookup-for-knowledge | 2306.07719 | null | https://arxiv.org/abs/2306.07719v1 | https://arxiv.org/pdf/2306.07719v1.pdf | Contextual Dictionary Lookup for Knowledge Graph Completion | Knowledge graph completion (KGC) aims to solve the incompleteness of knowledge graphs (KGs) by predicting missing links from known triples, numbers of knowledge graph embedding (KGE) models have been proposed to perform KGC by learning embeddings. Nevertheless, most existing embedding models map each relation into a un... | ['Yuren Zhou', 'Zibin Zheng', 'Chuan Chen', 'Yining Wang', 'YouMing Liu', 'Delai Qiu', 'Jining Wang'] | 2023-06-13 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding', 'knowledge-graph-completion', 'knowledge-graphs'] | ['graphs', 'graphs', 'knowledge-base', 'knowledge-base'] | [-1.89425141e-01 1.97806090e-01 -5.48173130e-01 -3.09891582e-01
-2.46470317e-01 -4.07377601e-01 5.69864810e-01 6.48310542e-01
-1.86548293e-01 6.69718444e-01 4.18156624e-01 -3.25815938e-02
-4.56927508e-01 -1.25020099e+00 -7.24760890e-01 -6.05387926e-01
4.43452820e-02 7.14921594e-01 1.39283732e-01 -2.35016912... | [8.771209716796875, 7.8449296951293945] |
3a087759-77a2-4bf4-bfd0-097d496c3cad | reasoning-about-goals-steps-and-temporal | 2009.07690 | null | https://arxiv.org/abs/2009.07690v2 | https://arxiv.org/pdf/2009.07690v2.pdf | Reasoning about Goals, Steps, and Temporal Ordering with WikiHow | We propose a suite of reasoning tasks on two types of relations between procedural events: goal-step relations ("learn poses" is a step in the larger goal of "doing yoga") and step-step temporal relations ("buy a yoga mat" typically precedes "learn poses"). We introduce a dataset targeting these two relations based on ... | ['Chris Callison-Burch', 'Li Zhang', 'Qing Lyu'] | 2020-09-16 | null | https://aclanthology.org/2020.emnlp-main.374 | https://aclanthology.org/2020.emnlp-main.374.pdf | emnlp-2020-11 | ['cloze-test'] | ['natural-language-processing'] | [ 2.70272762e-01 4.25748944e-01 -2.31473714e-01 -2.31385469e-01
-9.34760034e-01 -6.22878015e-01 9.51715946e-01 4.69002694e-01
-2.12787867e-01 7.84200907e-01 4.87998277e-01 -4.74129707e-01
-2.02843100e-01 -1.07998407e+00 -9.72406328e-01 -2.52550840e-01
2.55613446e-01 7.16624856e-01 5.32480836e-01 -7.87987411... | [10.759053230285645, 8.464929580688477] |
5b11672c-28e7-4607-9554-bddc79db9e03 | 3d-siamese-voxel-to-bev-tracker-for-sparse | 2111.04426 | null | https://arxiv.org/abs/2111.04426v2 | https://arxiv.org/pdf/2111.04426v2.pdf | 3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds | 3D object tracking in point clouds is still a challenging problem due to the sparsity of LiDAR points in dynamic environments. In this work, we propose a Siamese voxel-to-BEV tracker, which can significantly improve the tracking performance in sparse 3D point clouds. Specifically, it consists of a Siamese shape-aware f... | ['Jian Yang', 'Jin Xie', 'Mingmei Cheng', 'Lingpeng Wang', 'Le Hui'] | 2021-11-08 | null | http://proceedings.neurips.cc/paper/2021/hash/f0fcf351df4eb6786e9bb6fc4e2dee02-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/f0fcf351df4eb6786e9bb6fc4e2dee02-Paper.pdf | neurips-2021-12 | ['3d-object-tracking'] | ['computer-vision'] | [-3.59251618e-01 -3.31644505e-01 -2.12601572e-01 -2.36213535e-01
-7.71674514e-01 -4.99697685e-01 4.88691926e-01 -1.57212108e-01
-4.23102736e-01 1.31958127e-01 -2.84908950e-01 1.12573773e-01
-5.90430573e-02 -6.55005217e-01 -9.21439707e-01 -6.88492298e-01
-2.43206322e-01 7.98110306e-01 5.13304770e-01 8.82244557... | [6.6672682762146, -2.391969680786133] |
2d9251ce-8758-411d-81ee-6268b74df0b0 | gd-vdm-generated-depth-for-better-diffusion | 2306.11173 | null | https://arxiv.org/abs/2306.11173v1 | https://arxiv.org/pdf/2306.11173v1.pdf | GD-VDM: Generated Depth for better Diffusion-based Video Generation | The field of generative models has recently witnessed significant progress, with diffusion models showing remarkable performance in image generation. In light of this success, there is a growing interest in exploring the application of diffusion models to other modalities. One such challenge is the generation of cohere... | ['Ethan Fetaya', 'Lior Bracha', 'Idan Achituve', 'Ariel Lapid'] | 2023-06-19 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 2.52953768e-01 7.08200857e-02 3.60084146e-01 -1.24066779e-02
-8.20203900e-01 -5.57152748e-01 1.27191436e+00 -4.64175105e-01
-1.20160311e-01 6.77033126e-01 6.33525670e-01 1.64882302e-01
1.05259024e-01 -8.75749409e-01 -5.05587339e-01 -6.49912238e-01
-7.58555755e-02 4.48601276e-01 2.50538945e-01 -2.10536554... | [11.004794120788574, -0.29501327872276306] |
0675a295-3b9d-490b-b460-d2b01e0f95e5 | deepprog-a-transformer-based-framework-for | 2104.03642 | null | https://arxiv.org/abs/2104.03642v3 | https://arxiv.org/pdf/2104.03642v3.pdf | CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting | In medical applications, deep learning methods are built to automate diagnostic tasks. However, a clinically relevant question that practitioners usually face, is how to predict the future trajectory of a disease (prognosis). Current methods for such a problem often require domain knowledge, and are complicated to appl... | ['Aleksei Tiulpin', 'Matthew B. Blaschko', 'Simo Saarakkala', 'Huy Hoang Nguyen'] | 2021-04-08 | null | null | null | null | ['disease-trajectory-forecasting'] | ['medical'] | [-1.01964444e-01 1.73546538e-01 -2.79815495e-01 -2.91420668e-01
-1.01927054e+00 -1.40781552e-01 3.50571662e-01 3.38880479e-01
-8.34073722e-02 6.96617842e-01 4.48303878e-01 -3.51537436e-01
-4.67267722e-01 -6.00367844e-01 -5.04121184e-01 -9.27457333e-01
-3.86589706e-01 9.20066237e-01 1.74587011e-01 6.55896664... | [14.846692085266113, -1.9904801845550537] |
fea10599-c3f9-41b8-9fa0-1b843fd41409 | olkavs-an-open-large-scale-korean-audio | 2301.06375 | null | https://arxiv.org/abs/2301.06375v1 | https://arxiv.org/pdf/2301.06375v1.pdf | OLKAVS: An Open Large-Scale Korean Audio-Visual Speech Dataset | Inspired by humans comprehending speech in a multi-modal manner, various audio-visual datasets have been constructed. However, most existing datasets focus on English, induce dependencies with various prediction models during dataset preparation, and have only a small number of multi-view videos. To mitigate the limita... | ['Hyung-Min Park', 'Rae-Hong Park', 'Jun Hwan Ahn', 'Seung-Hyun Lee', 'Kwanghee Choi', 'Jung-Wook Hwang', 'Jeongkyun Park'] | 2023-01-16 | null | null | null | null | ['speaker-recognition', 'audio-visual-speech-recognition'] | ['speech', 'speech'] | [-2.59118021e-01 -3.59494060e-01 -3.60569000e-01 -5.79049230e-01
-1.30453277e+00 -4.67936069e-01 3.95099491e-01 -4.34174567e-01
-4.28342745e-02 1.75560579e-01 8.19438577e-01 -1.94500551e-01
4.83014822e-01 3.38908359e-02 -6.24919474e-01 -6.18293703e-01
2.94997424e-01 4.98951524e-02 5.78885563e-02 1.34950101... | [14.283489227294922, 5.070558071136475] |
52cd30c3-3ea8-4697-a881-7df75eb8f7e6 | deep-transport-network-for-unsupervised-video | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_Deep_Transport_Network_for_Unsupervised_Video_Object_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_Deep_Transport_Network_for_Unsupervised_Video_Object_Segmentation_ICCV_2021_paper.pdf | Deep Transport Network for Unsupervised Video Object Segmentation | The popular unsupervised video object segmentation methods fuse the RGB frame and optical flow via a two-stream network. However, they cannot handle the distracting noises in each input modality, which may vastly deteriorate the model performance. We propose to establish the correspondence between the input modalit... | ['Bo Liu', 'Qingshan Liu', 'Dong Liu', 'Zicheng Zhao', 'Kaihua Zhang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 1.76917717e-01 -2.24980295e-01 -1.84241682e-01 -5.22939622e-01
-5.47375739e-01 -5.27043283e-01 2.64903247e-01 -4.52820390e-01
-6.29197717e-01 3.20484847e-01 -4.25450280e-02 4.71950844e-02
-5.68360835e-02 -6.20831370e-01 -8.64065170e-01 -8.14355612e-01
2.08454773e-01 -1.28164485e-01 4.95798886e-01 1.97104484... | [9.181207656860352, -0.24208083748817444] |
db2bf765-aa2c-4987-a347-866119b607b0 | understanding-bloom-an-empirical-study-on | 2211.14865 | null | https://arxiv.org/abs/2211.14865v2 | https://arxiv.org/pdf/2211.14865v2.pdf | Understanding BLOOM: An empirical study on diverse NLP tasks | We view the landscape of large language models (LLMs) through the lens of the recently released BLOOM model to understand the performance of BLOOM and other decoder-only LLMs compared to BERT-style encoder-only models. We achieve this by evaluating the smaller BLOOM model variants (\textit{350m/560m} and \textit{1b3/1b... | ['Preethi Raghavan', 'SaiKrishna Rallabandi', 'Parag Pravin Dakle'] | 2022-11-27 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [-1.50550857e-01 3.69956106e-01 -2.66962796e-01 1.23482354e-01
-1.36128318e+00 -8.41643155e-01 1.12327468e+00 1.88780665e-01
-4.57263887e-01 9.09281731e-01 6.84671283e-01 -6.95000768e-01
-1.00850008e-01 -5.53597093e-01 -1.00634372e+00 -3.62500936e-01
1.21056847e-02 9.71102953e-01 1.07349895e-01 -6.51789606... | [11.556554794311523, 8.88492202758789] |
0f5c2495-7d7e-4d78-bfef-d6b1545490ca | reasoning-structural-relation-for-occlusion | 2112.10087 | null | https://arxiv.org/abs/2112.10087v1 | https://arxiv.org/pdf/2112.10087v1.pdf | Reasoning Structural Relation for Occlusion-Robust Facial Landmark Localization | In facial landmark localization tasks, various occlusions heavily degrade the localization accuracy due to the partial observability of facial features. This paper proposes a structural relation network (SRN) for occlusion-robust landmark localization. Unlike most existing methods that simply exploit the shape constrai... | ['Weiqin Tong', 'Songmin Dai', 'Jide Li', 'Xiaoqiang Li', 'Congcong Zhu'] | 2021-12-19 | null | null | null | null | ['face-alignment', 'landmark-tracking'] | ['computer-vision', 'computer-vision'] | [-7.09584579e-02 1.78758577e-01 -4.64064389e-01 -6.26662016e-01
-4.50896710e-01 -1.32713288e-01 3.83265942e-01 -5.86412549e-01
1.60039570e-02 2.43314341e-01 1.61132976e-01 -5.74402250e-02
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-5.04921265e-02 1.79283731e-02 -1.55793661e-02 -1.30265660... | [13.400725364685059, 0.44058793783187866] |
da8fc742-db99-449c-a733-a2716c877056 | cs-um6p-at-semeval-2021-task-1-a-deep | null | null | https://aclanthology.org/2021.semeval-1.73 | https://aclanthology.org/2021.semeval-1.73.pdf | CS-UM6P at SemEval-2021 Task 1: A Deep Learning Model-based Pre-trained Transformer Encoder for Lexical Complexity | Lexical Complexity Prediction (LCP) involves assigning a difficulty score to a particular word or expression, in a text intended for a target audience. In this paper, we introduce a new deep learning-based system for this challenging task. The proposed system consists of a deep learning model, based on pre-trained tran... | ['Ismail Berrada', 'Kabil Essefar', 'Abdellah El Mekki', 'Abdelkader El Mahdaouy', 'Nabil El Mamoun'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [ 3.42396051e-01 2.59132981e-01 -2.14967858e-02 -5.33397317e-01
-1.05251288e+00 -1.70905128e-01 3.68847430e-01 3.54413897e-01
-7.40945399e-01 3.00971448e-01 4.40789431e-01 -4.89610583e-01
2.25993574e-01 -7.18165874e-01 -6.54861093e-01 -2.27507815e-01
3.75177294e-01 2.57307678e-01 2.35271510e-02 -1.33005947... | [10.826109886169434, 8.632277488708496] |
34ab7a78-c716-4bd3-913a-9935a793629f | emergence-of-self-reproducing-metabolisms-as | 2103.08245 | null | https://arxiv.org/abs/2103.08245v3 | https://arxiv.org/pdf/2103.08245v3.pdf | Emergence of Self-Reproducing Metabolisms as Recursive Algorithms in an Artificial Chemistry | One of the main goals of Artificial Life is to research the conditions for the emergence of life, not necessarily as it is, but as it could be. Artificial Chemistries are one of the most important tools for this purpose because they provide us with a basic framework to investigate under which conditions metabolisms cap... | ['Tomas Mikolov', 'Germán Kruszewski'] | 2021-03-15 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 4.09051389e-01 2.17557564e-01 2.49759972e-01 2.37430513e-01
6.10681295e-01 -9.62511837e-01 9.46874321e-01 2.04508066e-01
6.25391826e-02 9.60293114e-01 -2.25912333e-01 -4.96101737e-01
-1.20338239e-02 -1.03498900e+00 -7.70410717e-01 -1.15839219e+00
-3.35169017e-01 4.95438725e-01 2.66249567e-01 -7.99698114... | [5.5981245040893555, 4.175902843475342] |
0e4cf482-6f05-4753-9ccb-c3ab5f0089ab | risk-sharing-measuring-variability-and | 2302.04034 | null | https://arxiv.org/abs/2302.04034v1 | https://arxiv.org/pdf/2302.04034v1.pdf | Risk sharing, measuring variability, and distortion riskmetrics | We address the problem of sharing risk among agents with preferences modelled by a general class of comonotonic additive and law-based functionals that need not be either monotone or convex. Such functionals are called distortion riskmetrics, which include many statistical measures of risk and variability used in portf... | ['Ruodu Wang', 'Liyuan Lin', 'Jean-Gabriel Lauzier'] | 2023-02-08 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.65576261e-01 2.21731439e-01 -4.81715471e-01 -2.90964574e-01
-6.02607310e-01 -1.00195181e+00 6.13442242e-01 7.79981613e-02
-5.17099977e-01 9.13767040e-01 5.49807191e-01 -2.70789146e-01
-1.05778635e+00 -8.00957382e-01 -1.45909891e-01 -9.42491353e-01
-4.23289001e-01 5.85874438e-01 -1.29596740e-01 -1.89415842... | [4.968071460723877, 3.8927197456359863] |
73d22a27-89c8-4b40-9cc5-589bdc1f0c3f | graph-convolutional-networks-for-event | null | null | https://aclanthology.org/2021.naacl-main.273 | https://aclanthology.org/2021.naacl-main.273.pdf | Graph Convolutional Networks for Event Causality Identification with Rich Document-level Structures | We study the problem of Event Causality Identification (ECI) to detect causal relation between event mention pairs in text. Although deep learning models have recently shown state-of-the-art performance for ECI, they are limited to the intra-sentence setting where event mention pairs are presented in the same sentences... | ['Thien Huu Nguyen', 'Minh Tran Phu'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['event-causality-identification'] | ['natural-language-processing'] | [ 3.04328889e-01 5.28080881e-01 -2.56514311e-01 -4.64541316e-01
-4.16400582e-01 -4.77908701e-01 1.01537108e+00 9.31110620e-01
-6.64389208e-02 6.19773686e-01 1.02156436e+00 -5.48486352e-01
-3.52682084e-01 -1.06135893e+00 -9.00086701e-01 1.16069555e-01
-5.86926997e-01 5.02256572e-01 2.90733248e-01 -1.68868899... | [9.078768730163574, 9.064620971679688] |
a5ed1d4c-5c73-4e0c-b8f4-fccc35b47c2d | neural-guided-program-synthesis-of | null | null | https://aclanthology.org/2022.pandl-1.10 | https://aclanthology.org/2022.pandl-1.10.pdf | Neural-Guided Program Synthesis of Information Extraction Rules Using Self-Supervision | We propose a neural-based approach for rule synthesis designed to help bridge the gap between the interpretability, precision and maintainability exhibited by rule-based information extraction systems with the scalability and convenience of statistical information extraction systems. This is achieved by avoiding placin... | ['Marco A. Valenzuela-Escárcega', 'Gus Hahn-Powell', 'Robert Vacareanu', 'Enrique Noriega-Atala'] | null | null | null | null | pandl-coling-2022-10 | ['program-synthesis'] | ['computer-code'] | [ 4.03536230e-01 4.07282412e-01 -2.53794253e-01 -6.24462962e-01
-8.62268090e-01 -6.96728170e-01 7.61994421e-01 5.45297444e-01
-4.57008690e-01 8.22954357e-01 -5.31132296e-02 -7.86194861e-01
-4.94210660e-01 -7.96907604e-01 -5.55572033e-01 -1.73263222e-01
-2.31775999e-01 5.15344322e-01 1.73186630e-01 -2.96850711... | [9.752248764038086, 7.863102912902832] |
0730a86a-c878-4768-8ddf-84bafb82c0fb | improving-the-morphological-analysis-of | null | null | https://aclanthology.org/W16-3715 | https://aclanthology.org/W16-3715.pdf | Improving the Morphological Analysis of Classical Sanskrit | The paper describes a new tagset for the morphological disambiguation of Sanskrit, and compares the accuracy of two machine learning methods (Conditional Random Fields, deep recurrent neural networks) for this task, with a special focus on how to model the lexicographic information. It reports a significant improvement... | ['Oliver Hellwig'] | 2016-12-01 | null | null | null | ws-2016-12 | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 8.85512009e-02 -4.80349511e-02 -3.50390106e-01 -1.84833854e-01
-5.72399139e-01 -6.87251985e-01 8.05681467e-01 4.38603848e-01
-9.33431208e-01 8.93074274e-01 9.23903942e-01 -7.28394687e-01
-4.14941549e-01 -6.01006150e-01 9.31622460e-02 -6.25276625e-01
-2.42338359e-01 1.13883162e+00 -5.52978925e-02 -5.70276320... | [10.177136421203613, 9.903135299682617] |
503ea157-3e5f-4743-9fcb-19a13ae43596 | recurrent-chunking-mechanisms-for-long-text | 2005.08056 | null | https://arxiv.org/abs/2005.08056v2 | https://arxiv.org/pdf/2005.08056v2.pdf | Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension | In this paper, we study machine reading comprehension (MRC) on long texts, where a model takes as inputs a lengthy document and a question and then extracts a text span from the document as an answer. State-of-the-art models tend to use a pretrained transformer model (e.g., BERT) to encode the joint contextual informat... | ['Hongyu Gong', 'Yelong Shen', 'Jianshu Chen', 'Dian Yu', 'Dong Yu'] | 2020-05-16 | recurrent-chunking-mechanisms-for-long-text-1 | https://aclanthology.org/2020.acl-main.603 | https://aclanthology.org/2020.acl-main.603.pdf | acl-2020-6 | ['triviaqa'] | ['miscellaneous'] | [ 4.64146256e-01 3.53688836e-01 -2.49402702e-01 -3.86685312e-01
-1.07486546e+00 -7.15126932e-01 1.55167669e-01 5.57476223e-01
-5.64837158e-01 7.54080951e-01 3.78840655e-01 -6.49611115e-01
-8.60032998e-03 -1.13710880e+00 -9.24925089e-01 -2.53196001e-01
5.28016925e-01 5.52677691e-01 6.15581810e-01 -2.80453622... | [11.410008430480957, 8.123052597045898] |
9dcc7233-615c-4594-89ee-16440eabda6f | scaling-up-discourse-quality-annotation-for | null | null | https://aclanthology.org/2022.lrec-1.353 | https://aclanthology.org/2022.lrec-1.353.pdf | Scaling up Discourse Quality Annotation for Political Science | The empirical quantification of the quality of a contribution to a political discussion is at the heart of deliberative theory, the subdiscipline of political science which investigates decision-making in deliberative democracy. Existing annotation on deliberative quality is time-consuming and carried out by experts, t... | ['Gabriella Lapesa', 'Neele Falk'] | null | null | null | null | lrec-2022-6 | ['argument-mining'] | ['natural-language-processing'] | [ 1.65172875e-01 7.42870808e-01 -5.57180703e-01 -4.04347479e-01
-9.99330759e-01 -9.66437876e-01 1.19418526e+00 8.68981600e-01
-4.34157848e-01 7.96677768e-01 1.10649633e+00 -1.09274483e+00
-3.84378523e-01 -8.93896759e-01 -3.42194647e-01 -2.95724183e-01
4.98920858e-01 7.48471200e-01 -3.22611481e-01 -3.36502582... | [9.354303359985352, 9.712018966674805] |
c6b56c7f-42b6-4e52-bd05-a0b701571e9f | measuring-hidden-bias-within-face-recognition | 2110.09839 | null | https://arxiv.org/abs/2110.09839v1 | https://arxiv.org/pdf/2110.09839v1.pdf | Measuring Hidden Bias within Face Recognition via Racial Phenotypes | Recent work reports disparate performance for intersectional racial groups across face recognition tasks: face verification and identification. However, the definition of those racial groups has a significant impact on the underlying findings of such racial bias analysis. Previous studies define these groups based on e... | ['Toby P. Breckon', 'Noura Al Moubayed', 'Furkan Tektas', 'Seyma Yucer'] | 2021-10-19 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 4.17415857e-01 -4.78302911e-02 -4.77851301e-01 -8.30078304e-01
-1.94672152e-01 -6.26685381e-01 7.30920613e-01 7.55289719e-02
-2.48487443e-01 6.20486617e-01 2.23866209e-01 -6.57666564e-01
-4.32723492e-01 -6.27870917e-01 -1.80827752e-01 -8.58784616e-01
-1.55206725e-01 1.05683334e-01 -6.78569317e-01 4.27477844... | [13.03630542755127, 1.2332451343536377] |
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