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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3204f9d3-4091-45ea-825f-6e94d70e5569 | unpaired-multi-view-graph-clustering-with | 2307.03476 | null | https://arxiv.org/abs/2307.03476v1 | https://arxiv.org/pdf/2307.03476v1.pdf | Unpaired Multi-View Graph Clustering with Cross-View Structure Matching | Multi-view clustering (MVC), which effectively fuses information from multiple views for better performance, has received increasing attention. Most existing MVC methods assume that multi-view data are fully paired, which means that the mappings of all corresponding samples between views are pre-defined or given in adv... | ['Xinwang Liu', 'Xinhang Wan', 'Ke Liang', 'Weixuan Liang', 'Qing Liao', 'Siwei Wang', 'Yi Wen'] | 2023-07-07 | null | null | null | null | ['graph-clustering', 'clustering'] | ['graphs', 'methodology'] | [ 8.30152705e-02 -3.90741140e-01 -1.64576590e-01 -1.37745544e-01
-5.34327447e-01 -6.52355373e-01 4.02243763e-01 8.65023881e-02
9.45887864e-02 1.72848284e-01 1.39581427e-01 8.29542652e-02
-4.44855660e-01 -6.48412287e-01 -4.63666677e-01 -7.59700358e-01
4.66931850e-01 2.90890872e-01 3.68796498e-01 -7.72139356... | [8.239069938659668, 4.626129627227783] |
93373cea-05bd-4aed-bb8a-1b1cdee4f95a | ensemble-learning-of-colorectal-cancer | 1409.0788 | null | http://arxiv.org/abs/1409.0788v1 | http://arxiv.org/pdf/1409.0788v1.pdf | Ensemble Learning of Colorectal Cancer Survival Rates | In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. We build on
existing research... | ['John Scholefield', 'Uwe Aickelin', 'Chris Roadknight', 'Lindy Durrant'] | 2014-09-02 | null | null | null | null | ['tumour-classification'] | ['medical'] | [ 1.88585445e-01 -2.05056090e-02 -3.39500040e-01 -2.54745752e-01
-7.84493029e-01 -3.51218909e-01 5.66516161e-01 8.59000385e-01
-6.02648556e-01 6.73753023e-01 6.38674200e-01 -6.58871710e-01
-8.51559639e-01 -3.56998205e-01 2.82388270e-01 -1.24741876e+00
-3.43855739e-01 6.64606988e-01 -3.12244952e-01 -4.39471573... | [15.138965606689453, -3.0889194011688232] |
a17f3bf7-cee8-4d19-9f10-b346b902b9b4 | a-comparison-of-temporal-encoders-for | 2301.09962 | null | https://arxiv.org/abs/2301.09962v1 | https://arxiv.org/pdf/2301.09962v1.pdf | A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons | With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a "wake-up" mechanism for subsequent computationally expensive speech recognition. One promising approach is the use of neuromorphic sensors and spiking neural networks (SNNs) implemented in neuromorphi... | ['Fredrik Sandin', 'Elisabetta Chicca', 'Foteini Liwicki', 'Lyes Khacef', 'Ton Juny Pina', 'Mattias Nilsson'] | 2023-01-24 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 5.16806483e-01 -2.84402996e-01 2.16270298e-01 -4.23839629e-01
-1.80806100e-01 -3.58747274e-01 7.00953841e-01 -1.03651449e-01
-8.82728696e-01 6.91259146e-01 -1.18456788e-01 -1.54191852e-01
-2.40794197e-01 -7.52010763e-01 -6.21779621e-01 -8.72496843e-01
-5.31850636e-01 2.44789422e-01 5.66722989e-01 -1.86008751... | [8.229387283325195, 2.461663246154785] |
f173f153-0b4c-4875-9b1a-26badb75ca74 | simultaneity-in-binary-outcome-models-with-an | 2207.07343 | null | https://arxiv.org/abs/2207.07343v2 | https://arxiv.org/pdf/2207.07343v2.pdf | Simultaneity in Binary Outcome Models with an Application to Employment for Couples | Two of Peter Schmidt's many contributions to econometrics have been to introduce a simultaneous logit model for bivariate binary outcomes and to study estimation of dynamic linear fixed effects panel data models using short panels. In this paper, we study a dynamic panel data version of the bivariate model introduced i... | ['Martin Weidner', 'Ekaterini Kyriazidou', 'Luojia Hu', 'Bo E. Honoré'] | 2022-07-15 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-2.32558295e-01 -8.00718963e-02 -7.43976831e-01 -3.73410821e-01
-3.58585864e-01 -5.81113875e-01 5.80756187e-01 -4.80372719e-02
-2.21390292e-01 1.24358702e+00 7.28458941e-01 -7.64167249e-01
-4.59851325e-01 -6.96983099e-01 -2.72185415e-01 -5.07050753e-01
-1.46821365e-01 2.73679942e-01 -2.30498806e-01 1.60368070... | [7.877408981323242, 5.149297714233398] |
fbbb2c4f-1a39-4a68-8336-606c0bf86f64 | spectrogram-frame-linear-network-and | null | null | https://www.sciencedirect.com/science/article/abs/pii/S1574954119303206?via%3Dihub | https://www.sciencedirect.com/science/article/abs/pii/S1574954119303206?via%3Dihub | Spectrogram-frame linear network and continuous frame sequence for bird sound classification | Inspired by that bird sound has various frequency distributions and continuous time-varying properties, a novel method is proposed for the classification of bird sound based on continuous frame sequence and spectrogram-frame linear network (SFLN). In order to form a continuous frame sequence as the standard input for S... | ['Xiaohu Qiang', 'Xibei Huang', 'Zhiqiang Zhang', 'Guoxiong Zhou', 'Aibin Chen', 'Xin Zhang'] | 2019-11-01 | null | null | null | ecological-informatics-2019-11 | ['sound-classification'] | ['audio'] | [-1.51303649e-01 -7.00131118e-01 -2.11847238e-02 -9.34217125e-02
-7.91688710e-02 -2.73567945e-01 6.52173236e-02 6.62817061e-02
-4.89763856e-01 1.37117714e-01 -8.71071517e-02 -2.47878388e-01
-1.64757892e-02 -7.42067337e-01 -2.57947981e-01 -8.17593634e-01
-4.18771118e-01 -7.37972498e-01 4.44849938e-01 1.50526389... | [15.243314743041992, 5.3104658126831055] |
7b66ab57-d546-4081-940f-776187b4719d | bures-wasserstein-means-of-graphs | 2305.19738 | null | https://arxiv.org/abs/2305.19738v1 | https://arxiv.org/pdf/2305.19738v1.pdf | Bures-Wasserstein Means of Graphs | Finding the mean of sampled data is a fundamental task in machine learning and statistics. However, in cases where the data samples are graph objects, defining a mean is an inherently difficult task. We propose a novel framework for defining a graph mean via embeddings in the space of smooth graph signal distributions,... | ['Pascal Frossard', 'Isabel Haasler'] | 2023-05-31 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 3.46826315e-01 1.56756371e-01 -1.26411483e-01 -3.50112468e-01
-3.59524578e-01 -5.54131746e-01 4.07234818e-01 5.13538301e-01
-1.72791526e-01 1.34439215e-01 2.07853932e-02 -3.37803252e-02
-4.52383041e-01 -6.41283751e-01 -6.07017100e-01 -8.69096577e-01
-6.96329057e-01 3.52378756e-01 9.82678160e-02 1.58588052... | [7.132145404815674, 5.426761627197266] |
c12c1d9c-2373-45f1-9935-2d62158f02d5 | multi-channel-end-to-end-neural-network-for | 2206.09728 | null | https://arxiv.org/abs/2206.09728v1 | https://arxiv.org/pdf/2206.09728v1.pdf | Multi-channel end-to-end neural network for speech enhancement, source localization, and voice activity detection | Speech enhancement and source localization has been active research for several decades with a wide range of real-world applications. Recently, the Deep Complex Convolution Recurrent network (DCCRN) has yielded impressive enhancement performance for single-channel systems. In this study, a neural beamformer consisting ... | ['Mingsian R. Bai', 'Yicheng Hsu', 'Yuan Chen'] | 2022-06-20 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 1.27248302e-01 -5.52160144e-01 1.74922422e-01 5.95439598e-02
-7.37174451e-01 -9.84113589e-02 1.92846850e-01 -2.84127682e-01
-4.40499306e-01 5.23140550e-01 7.98060954e-01 -2.19761834e-01
-1.60830110e-01 -4.34443921e-01 -1.09659925e-01 -1.13169920e+00
4.31243405e-02 -7.67666876e-01 -1.44318007e-02 -1.50266305... | [15.045753479003906, 5.87208366394043] |
1826054b-e2ad-4378-bbe5-6715b507d618 | image-semantic-relation-generation | 2210.11253 | null | https://arxiv.org/abs/2210.11253v1 | https://arxiv.org/pdf/2210.11253v1.pdf | Image Semantic Relation Generation | Scene graphs provide structured semantic understanding beyond images. For downstream tasks, such as image retrieval, visual question answering, visual relationship detection, and even autonomous vehicle technology, scene graphs can not only distil complex image information but also correct the bias of visual models usi... | ['Mingzhe Du'] | 2022-10-19 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 5.20261765e-01 4.96140182e-01 5.99616319e-02 -3.35416406e-01
-5.54720819e-01 -5.13854980e-01 9.47781622e-01 1.13082215e-01
-3.18854749e-01 5.30495942e-01 1.69458389e-02 -4.72661883e-01
1.25675857e-01 -8.18658412e-01 -9.03579414e-01 -5.67267358e-01
4.57834691e-01 6.25965595e-01 5.17553389e-01 -9.69742537... | [10.405935287475586, 1.5999559164047241] |
c03c66f2-dc70-4bc9-a8f5-fe025e0f138a | getting-topology-and-point-cloud-generation | 1912.03787 | null | https://arxiv.org/abs/1912.03787v1 | https://arxiv.org/pdf/1912.03787v1.pdf | Getting Topology and Point Cloud Generation to Mesh | In this work, we explore the idea that effective generative models for point clouds under the autoencoding framework must acknowledge the relationship between a continuous surface, a discretized mesh, and a set of points sampled from the surface. This view motivates a generative model that works by progressively deform... | ['Chun-Liang Li', 'Songwei Ge', 'Eunsu Kang', 'Austin Dill'] | 2019-12-08 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 1.42077237e-01 5.72089553e-01 4.35408622e-01 -1.78914577e-01
-5.50134063e-01 -4.68898207e-01 7.52130926e-01 -4.35019433e-01
3.46872509e-01 6.48913145e-01 -7.57184811e-03 -1.10082105e-01
1.40449449e-01 -1.59234798e+00 -1.18005705e+00 -5.75862527e-01
2.28280239e-02 1.01394176e+00 -1.97008371e-01 -3.25476646... | [8.822527885437012, -3.6738955974578857] |
1b22bf71-cab6-43f9-a695-830e62a6f705 | ankh-optimized-protein-language-model-unlocks | 2301.06568 | null | https://arxiv.org/abs/2301.06568v1 | https://arxiv.org/pdf/2301.06568v1.pdf | Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling | As opposed to scaling-up protein language models (PLMs), we seek improving performance via protein-specific optimization. Although the proportionality between the language model size and the richness of its learned representations is validated, we prioritize accessibility and pursue a path of data-efficient, cost-reduc... | ['Burkhard Rost', 'Charlotte Rochereau', 'Mohamed Elkerdawy', 'Walid Moustafa', 'Wafaa Salah-Eldin', 'Hazem Essam', 'Ahmed Elnaggar'] | 2023-01-16 | null | null | null | null | ['protein-language-model', 'protein-function-prediction'] | ['medical', 'medical'] | [ 4.26698029e-01 2.83797771e-01 -2.23050773e-01 -3.54224592e-01
-7.42677629e-01 -6.56331897e-01 2.20069230e-01 4.57080454e-01
-6.17250025e-01 9.80024636e-01 1.95516184e-01 -9.28442776e-01
-2.12336760e-02 -2.85933197e-01 -1.08157253e+00 -5.32024205e-01
-2.51339823e-01 5.10236144e-01 9.20828655e-02 -3.54499817... | [4.754044532775879, 5.678895950317383] |
5c6c4370-0091-4b2c-8912-a593e4283bd5 | query-dependent-video-representation-for | 2303.13874 | null | https://arxiv.org/abs/2303.13874v1 | https://arxiv.org/pdf/2303.13874v1.pdf | Query-Dependent Video Representation for Moment Retrieval and Highlight Detection | Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as the demand for video understanding is drastically increased. The key objective of MR/HD is to localize the moment and estimate clip-wise accordance level, i.e., saliency score, to the given text query. Although the recent transfor... | ['Jae-Pil Heo', 'Dongchan Park', 'Sanguk Park', 'Sangeek Hyun', 'WonJun Moon'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Moon_Query-Dependent_Video_Representation_for_Moment_Retrieval_and_Highlight_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Moon_Query-Dependent_Video_Representation_for_Moment_Retrieval_and_Highlight_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['highlight-detection', 'moment-retrieval', 'video-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.32414803e-01 -3.74209702e-01 -4.11174566e-01 -2.27776945e-01
-7.87468374e-01 -3.55570167e-01 6.10882521e-01 -1.24375120e-01
-2.08278477e-01 1.86195418e-01 3.83571208e-01 1.78728417e-01
1.56626552e-01 -4.99258757e-01 -8.58040035e-01 -4.71767724e-01
1.74160358e-02 -7.07817301e-02 7.05603361e-01 -2.93987274... | [10.027645111083984, 0.6331753134727478] |
57262b16-5e21-4d17-8577-27c5650f8eb6 | fraunhofer-sit-at-checkthat-2023-tackling | 2307.02377 | null | https://arxiv.org/abs/2307.02377v1 | https://arxiv.org/pdf/2307.02377v1.pdf | Fraunhofer SIT at CheckThat! 2023: Tackling Classification Uncertainty Using Model Souping on the Example of Check-Worthiness Classification | This paper describes the second-placed approach developed by the Fraunhofer SIT team in the CLEF-2023 CheckThat! lab Task 1B for English. Given a text snippet from a political debate, the aim of this task is to determine whether it should be assessed for check-worthiness. Detecting check-worthy statements aims to facil... | ['Jeong-Eun Choi', 'Inna Vogel', 'Raphael Frick'] | 2023-07-03 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 7.82422442e-03 5.82542300e-01 -3.06842953e-01 -1.82623044e-01
-1.20404184e+00 -7.59557307e-01 9.69019413e-01 9.21196163e-01
-5.85768044e-01 8.70622039e-01 4.00668263e-01 -9.64330196e-01
-2.10393220e-01 -5.95172346e-01 -6.16556823e-01 -6.89387470e-02
4.49470907e-01 6.11336529e-01 3.17485869e-01 -2.73684561... | [8.949235916137695, 9.585911750793457] |
e1f0618d-0bb3-4b3e-af53-f8bafb082432 | kochet-a-korean-cultural-heritage-corpus-for | 2209.00367 | null | https://arxiv.org/abs/2209.00367v2 | https://arxiv.org/pdf/2209.00367v2.pdf | KoCHET: a Korean Cultural Heritage corpus for Entity-related Tasks | As digitized traditional cultural heritage documents have rapidly increased, resulting in an increased need for preservation and management, practical recognition of entities and typification of their classes has become essential. To achieve this, we propose KoCHET - a Korean cultural heritage corpus for the typical en... | ['Heuiseok Lim', 'Junyoung Son', 'Jinsung Kim', 'Gyeongmin Kim'] | 2022-09-01 | null | https://aclanthology.org/2022.coling-1.308 | https://aclanthology.org/2022.coling-1.308.pdf | coling-2022-10 | ['entity-typing'] | ['natural-language-processing'] | [-4.37729776e-01 2.04596650e-02 -2.25871116e-01 -8.56168568e-02
-7.82939076e-01 -7.04007745e-01 4.48556572e-01 4.46394205e-01
-7.63550639e-01 1.03755212e+00 6.22538328e-01 -1.83911547e-01
-1.35241225e-01 -1.19175351e+00 -3.16619515e-01 -4.56271350e-01
-9.83645208e-03 3.34569722e-01 -4.22496349e-02 -2.17769250... | [9.67652416229248, 9.478724479675293] |
6b2d2872-ed2d-4ae4-8f84-0568a8c246c0 | query-based-summarization-using-mdl-principle | null | null | https://aclanthology.org/W17-1004 | https://aclanthology.org/W17-1004.pdf | Query-based summarization using MDL principle | Query-based text summarization is aimed at extracting essential information that answers the query from original text. The answer is presented in a minimal, often predefined, number of words. In this paper we introduce a new unsupervised approach for query-based extractive summarization, based on the minimum descriptio... | ['Marina Litvak', 'Natalia Vanetik'] | 2017-04-01 | null | null | null | ws-2017-4 | ['query-based-extractive-summarization'] | ['natural-language-processing'] | [ 5.96196234e-01 3.12726200e-01 -4.05812979e-01 -8.19441751e-02
-1.17678368e+00 -5.88015795e-01 5.88856518e-01 1.10893893e+00
-6.32808506e-01 1.13328290e+00 1.09897017e+00 3.27137917e-01
-4.76158410e-01 -7.59646177e-01 -2.49144703e-01 -1.86482146e-01
6.36212751e-02 5.54182231e-01 2.82761902e-01 -4.62187767... | [12.516560554504395, 9.5604829788208] |
595342fa-e935-48f9-8424-d551595653d6 | an-efficient-mini-batch-method-via-partial | 2108.09645 | null | https://arxiv.org/abs/2108.09645v4 | https://arxiv.org/pdf/2108.09645v4.pdf | Improving Mini-batch Optimal Transport via Partial Transportation | Mini-batch optimal transport (m-OT) has been widely used recently to deal with the memory issue of OT in large-scale applications. Despite their practicality, m-OT suffers from misspecified mappings, namely, mappings that are optimal on the mini-batch level but are partially wrong in the comparison with the optimal tra... | ['Tung Pham', 'The-Anh Vu-Le', 'Nhat Ho', 'Dang Nguyen', 'Khai Nguyen'] | 2021-08-22 | improving-mini-batch-optimal-transport-via | https://openreview.net/forum?id=9Sf8fbue1br | https://openreview.net/pdf?id=9Sf8fbue1br | null | ['partial-domain-adaptation'] | ['methodology'] | [-3.67042780e-01 -2.94451326e-01 3.66625888e-03 -3.43267530e-01
-6.30385280e-01 -4.19958681e-01 3.45807940e-01 -2.88406402e-01
-2.32006148e-01 9.28800941e-01 1.08489546e-03 -2.74967790e-01
-2.91783690e-01 -8.04089904e-01 -1.05412722e+00 -6.91307008e-01
4.30324720e-03 5.30793428e-01 3.30547631e-01 -1.58291817... | [9.683389663696289, 1.827862024307251] |
b791aa47-efb9-400d-95fb-f0886616acc2 | detection-and-analysis-of-drive-by-download | null | null | https://sites.cs.ucsb.edu/~chris/research/doc/www10_jsand.pdf | https://sites.cs.ucsb.edu/~chris/research/doc/www10_jsand.pdf | Detection and Analysis of Drive-by-Download Attacks and Malicious JavaScript Code | JavaScript is a browser scripting language that allows developers
to create sophisticated client-side interfaces for web applications.
However, JavaScript code is also used to carry out attacks against
the user’s browser and its extensions. These attacks usually result
in the download of additional malware that tak... | ['Giovanni Vigna', 'Christopher Kruegel', 'Marco Cova'] | 2010-04-26 | null | null | null | www-10-proceedings-of-the-19th-international | ['anomaly-detection'] | ['methodology'] | [-1.48135712e-02 -3.02353978e-01 -9.60926265e-02 -5.15152253e-02
-4.23307866e-01 -1.32811046e+00 7.88445532e-01 3.47461700e-01
-3.97359729e-02 -6.17828593e-02 -4.32422638e-01 -1.06199133e+00
2.84420997e-01 -8.83661866e-01 -2.84066767e-01 -5.42295277e-01
-1.88907772e-01 2.25016046e-02 1.00423598e+00 -5.85734360... | [14.379332542419434, 9.66339111328125] |
886be60b-1525-4ab0-b679-55a74b7fc24f | erasing-concepts-from-diffusion-models | 2303.07345 | null | https://arxiv.org/abs/2303.07345v3 | https://arxiv.org/pdf/2303.07345v3.pdf | Erasing Concepts from Diffusion Models | Motivated by recent advancements in text-to-image diffusion, we study erasure of specific concepts from the model's weights. While Stable Diffusion has shown promise in producing explicit or realistic artwork, it has raised concerns regarding its potential for misuse. We propose a fine-tuning method that can erase a vi... | ['David Bau', 'Jaden Fiotto-Kaufman', 'Joanna Materzynska', 'Rohit Gandikota'] | 2023-03-13 | null | null | null | null | ['text-based-image-editing'] | ['computer-vision'] | [ 7.08491266e-01 2.20222995e-01 5.61907515e-02 -2.58495271e-01
-3.03375244e-01 -9.69614029e-01 9.48032141e-01 -3.08025807e-01
-4.00280297e-01 5.81596971e-01 1.26241818e-01 -4.18222457e-01
1.67695820e-01 -5.33368468e-01 -7.38479376e-01 -6.60568178e-01
1.15661800e-01 3.71850014e-01 1.16543189e-01 -4.72830757... | [11.394994735717773, -0.23991671204566956] |
8cb2f8b6-a8b3-43da-908b-45c2028a7577 | xpqa-cross-lingual-product-question-answering | 2305.09249 | null | https://arxiv.org/abs/2305.09249v1 | https://arxiv.org/pdf/2305.09249v1.pdf | xPQA: Cross-Lingual Product Question Answering across 12 Languages | Product Question Answering (PQA) systems are key in e-commerce applications to provide responses to customers' questions as they shop for products. While existing work on PQA focuses mainly on English, in practice there is need to support multiple customer languages while leveraging product information available in Eng... | ['Adrià De Gispert', 'Bill Byrne', 'Akari Asai', 'Xiaoyu Shen'] | 2023-05-16 | null | null | null | null | ['answer-generation'] | ['natural-language-processing'] | [ 7.36487582e-02 -1.30756408e-01 -1.82058096e-01 -5.62905312e-01
-1.73548305e+00 -1.21731627e+00 2.15789825e-01 2.04024166e-01
-2.73673594e-01 5.72177470e-01 8.42741579e-02 -1.03530073e+00
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4.15578932e-01 1.38473153e+00 1.07598521e-01 -8.50242436... | [11.322874069213867, 8.353824615478516] |
17ad83c8-a0e0-427c-8a3d-7433ef5167fa | hade-hierarchical-affective-dialog-encoder | null | null | https://openreview.net/forum?id=cpMUiyD9EA | https://openreview.net/pdf?id=cpMUiyD9EA | HADE: Hierarchical Affective Dialog Encoder for Personality Recognition in Conversation | Personality recognition in conversation aims to determine the personality traits of speakers through the dialogue content, which is of great importance in designing personalized conversational AI. Existing methods that use only linguistic patterns in utterances limit their performance. To fill in the gap, we investigat... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['personality-recognition-in-conversation'] | ['natural-language-processing'] | [-4.57682788e-01 3.11597735e-01 1.72598466e-01 -8.45582783e-01
-6.09269366e-02 -3.28777224e-01 5.55187225e-01 -2.69692719e-01
-6.29743338e-02 6.27854466e-01 7.88471937e-01 4.27698255e-01
2.08110705e-01 -3.92658859e-01 2.51070201e-01 -6.66704893e-01
-2.48411875e-02 5.57022393e-01 -4.78793591e-01 -4.25212026... | [13.003846168518066, 6.041062355041504] |
a00f438d-fc0e-45b9-8db5-0682f56ebf86 | event-based-stereo-depth-estimation-from-ego | 2210.08927 | null | https://arxiv.org/abs/2210.08927v1 | https://arxiv.org/pdf/2210.08927v1.pdf | Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion | Event cameras are bio-inspired sensors that mimic the human retina by responding to brightness changes in the scene. They generate asynchronous spike-based outputs at microsecond resolution, providing advantages over traditional cameras like high dynamic range, low motion blur and power efficiency. Most event-based ste... | ['Guillermo Gallego', 'Suman Ghosh'] | 2022-10-17 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 1.50696486e-01 -5.62419116e-01 3.36911470e-01 -2.30249658e-01
-5.05303562e-01 -6.04337156e-01 5.21839917e-01 -9.20942426e-02
-7.11805761e-01 1.00268221e+00 2.75415570e-01 3.58642280e-01
2.28266194e-01 -3.68380398e-01 -7.31879354e-01 -7.28937328e-01
1.51543885e-01 -1.42257854e-01 5.42355895e-01 5.79474986... | [8.66585922241211, -1.2806909084320068] |
b46c9161-5528-4b40-932b-44b9b4ef0635 | content-based-medical-image-retrieval-with | 2211.15371 | null | https://arxiv.org/abs/2211.15371v1 | https://arxiv.org/pdf/2211.15371v1.pdf | Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss | Broadspread use of medical imaging devices with digital storage has paved the way for curation of substantial data repositories. Fast access to image samples with similar appearance to suspected cases can help establish a consulting system for healthcare professionals, and improve diagnostic procedures while minimizing... | ['Tolga Cukur', 'Emin Celik', 'Şaban Öztürk'] | 2022-11-22 | null | null | null | null | ['medical-image-retrieval', 'content-based-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'computer-vision', 'medical'] | [ 2.89380282e-01 -3.52324754e-01 -3.69640142e-01 -4.98681307e-01
-1.58627439e+00 -4.32735890e-01 3.55891556e-01 8.15699935e-01
-7.01358795e-01 1.97561473e-01 6.94335103e-02 -2.72569805e-01
-6.91967010e-01 -4.48469460e-01 -3.48334104e-01 -7.32450187e-01
-1.97178617e-01 4.76561785e-01 3.07316542e-01 8.23722035... | [14.391144752502441, -1.5766748189926147] |
e7774629-67ed-43a4-8ba1-620d9f4a2a7f | purpose-and-polarity-of-citation-towards-nlp | null | null | https://aclanthology.org/N13-1067 | https://aclanthology.org/N13-1067.pdf | Purpose and Polarity of Citation: Towards NLP-based Bibliometrics | null | ['Dragomir Radev', 'Amjad Abu-Jbara', 'Jefferson Ezra'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['citation-intent-classification'] | ['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... | [-7.2034831047058105, 3.695701837539673] |
51533747-2dba-4914-9015-41517d4bbe30 | what-the-language-you-tweet-says-about-your | 1701.06233 | null | http://arxiv.org/abs/1701.06233v1 | http://arxiv.org/pdf/1701.06233v1.pdf | What the Language You Tweet Says About Your Occupation | Many aspects of people's lives are proven to be deeply connected to their
jobs. In this paper, we first investigate the distinct characteristics of major
occupation categories based on tweets. From multiple social media platforms, we
gather several types of user information. From users' LinkedIn webpages, we
learn thei... | ['Thuy-vy Thi Nguyen', 'Haoyuan Xiao', 'Jiebo Luo', 'Tianran Hu'] | 2017-01-22 | null | null | null | null | ['job-prediction'] | ['natural-language-processing'] | [-1.70175388e-01 4.12228405e-02 -4.51786548e-01 -7.88506866e-01
-3.72605294e-01 -2.39070058e-01 6.61112905e-01 4.56028849e-01
-5.43006241e-01 5.11904716e-01 6.94747448e-01 -7.44805858e-02
-2.01631173e-01 -8.58916521e-01 7.62161389e-02 -3.36476594e-01
5.14369011e-01 2.63415635e-01 -2.81820446e-01 -3.79798681... | [9.429332733154297, 10.315314292907715] |
54b97711-ecaa-4d02-a75a-8316158fe598 | non-negative-bregman-divergence-minimization | 2006.06979 | null | https://arxiv.org/abs/2006.06979v3 | https://arxiv.org/pdf/2006.06979v3.pdf | Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation | Density ratio estimation (DRE) is at the core of various machine learning tasks such as anomaly detection and domain adaptation. In existing studies on DRE, methods based on Bregman divergence (BD) minimization have been extensively studied. However, BD minimization when applied with highly flexible models, such as dee... | ['Takeshi Teshima', 'Masahiro Kato'] | 2020-06-12 | null | https://openreview.net/forum?id=PGmqOzKEPZN | https://openreview.net/pdf?id=PGmqOzKEPZN | null | ['density-ratio-estimation'] | ['methodology'] | [-1.67406082e-01 -2.76047885e-01 1.10228874e-01 -5.36307275e-01
-7.04083979e-01 1.50392121e-02 2.76019216e-01 2.86325783e-01
-5.48901498e-01 9.26598668e-01 -2.02079356e-01 -2.47898474e-01
-2.41518125e-01 -6.61630034e-01 -9.23904240e-01 -7.53104091e-01
2.18053520e-01 7.13190660e-02 2.58081228e-01 1.17800340... | [7.615871429443359, 3.4821417331695557] |
200cca13-e13d-4d7a-b0bb-c7d649f13545 | ji-yu-ping-xing-jiao-hu-zhu-yi-li-wang-luo-de | null | null | https://aclanthology.org/2022.ccl-1.21 | https://aclanthology.org/2022.ccl-1.21.pdf | 基于平行交互注意力网络的中文电子病历实体及关系联合抽取(Parallel Interactive Attention Network for Joint Entity and Relation Extraction Based on Chinese Electronic Medical Record) | “基于电子病历构建医学知识图谱对医疗技术的发展具有重要意义,实体和关系抽取是构建知识图谱的关键技术。本文针对目前实体关系联合抽取中存在的特征交互不充分的问题,提出了一种平行交互注意力网络(PIAN)以充分挖掘实体与关系的相关性,在多个标准的医学和通用数据集上取得最优结果;当前中文医学实体及关系标注数据集较少,本文基于中文电子病历构建了实体和关系抽取数据集(CEMRIE),与医学专家共同制定了语料标注规范,并基于所提出的模型实验得出基准结果。” | ['Yuan Guanghui', 'Xueyang Qin', 'Zehao Wang', 'Lishuang Li'] | null | null | null | null | ccl-2022-10 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-8.11145782e-01 -7.50582039e-01 6.79321051e-01 3.93725753e-01
2.50153631e-01 -1.34203017e+00 2.88557887e-01 6.44162655e-01
1.11585759e-01 1.20174348e+00 2.47544453e-01 -2.04209730e-01
-5.73490381e-01 -9.45285380e-01 -1.80191413e-01 -1.14906859e+00
-2.07024276e-01 1.40600336e+00 3.11161757e-01 -3.08903933... | [-3.316035270690918, 6.907709121704102] |
ea44ca46-ff10-4924-a2b1-c8b92b867edb | hypernyms-under-siege-linguistically | 1612.04460 | null | http://arxiv.org/abs/1612.04460v2 | http://arxiv.org/pdf/1612.04460v2.pdf | Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection | The fundamental role of hypernymy in NLP has motivated the development of
many methods for the automatic identification of this relation, most of which
rely on word distribution. We investigate an extensive number of such
unsupervised measures, using several distributional semantic models that differ
by context type an... | ['Dominik Schlechtweg', 'Enrico Santus', 'Vered Shwartz'] | 2016-12-14 | hypernyms-under-siege-linguistically-1 | https://aclanthology.org/E17-1007 | https://aclanthology.org/E17-1007.pdf | eacl-2017-4 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 9.30759385e-02 1.69854477e-01 -4.32263017e-01 -4.00590748e-01
-1.32025257e-01 -7.64151692e-01 9.89931762e-01 7.68722832e-01
-9.06405747e-01 8.77056956e-01 2.52204537e-01 -1.25458002e-01
-6.44681752e-01 -1.13013470e+00 5.97801991e-02 -7.35212624e-01
2.88851321e-01 9.62846875e-01 4.85169947e-01 -5.56342542... | [10.110187530517578, 8.839445114135742] |
d22af100-1b11-4985-a442-fdb77f22848f | taming-lagrangian-chaos-with-multi-objective | 2212.09612 | null | https://arxiv.org/abs/2212.09612v1 | https://arxiv.org/pdf/2212.09612v1.pdf | Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning | We consider the problem of two active particles in 2D complex flows with the multi-objective goals of minimizing both the dispersion rate and the energy consumption of the pair. We approach the problem by means of Multi Objective Reinforcement Learning (MORL), combining scalarization techniques together with a Q-learni... | ['Massimo Cencini', 'Antonio Celani', 'Francesco Borra', 'Luca Biferale', 'Chiara Calascibetta'] | 2022-12-19 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-2.03984916e-01 8.29458889e-03 -8.77203643e-02 4.99405712e-01
-5.60802460e-01 -7.62821138e-01 1.54695272e-01 4.30037349e-01
-8.93285275e-01 1.44975007e+00 -3.59963000e-01 -1.32700771e-01
-8.99214268e-01 -7.48916507e-01 -4.72803980e-01 -1.18124926e+00
-5.99269331e-01 6.34896219e-01 1.34316340e-01 -5.14994681... | [4.426708221435547, 2.2566940784454346] |
e5d939b5-b92a-4c44-95bd-2b4723cae64b | cardiac-segmentation-using-transfer-learning | 2209.09714 | null | https://arxiv.org/abs/2209.09714v1 | https://arxiv.org/pdf/2209.09714v1.pdf | Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts | Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues. While there has been significant efforts on improving the quality of the algorithms, few works have ta... | ['Kevin McGuinness', "Noel E. O'Connor", 'Kathleen M. Curran', 'Eric Arazo', 'Carles Garcia-Cabrera'] | 2022-09-20 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.71290898e-01 3.73643160e-01 3.01234394e-01 -3.98420483e-01
-3.75463694e-01 -2.86051840e-01 2.13926449e-01 -7.57135004e-02
-4.25829232e-01 7.84859657e-01 2.50662565e-01 -2.52514422e-01
-1.58539549e-01 -3.44372451e-01 -4.95973706e-01 -6.12284005e-01
-4.19350356e-01 2.44713202e-01 4.50658560e-01 -2.66312361... | [14.150760650634766, -2.3356621265411377] |
24716363-74b1-40a6-9c90-b94f55160a3d | multimodal-analysis-informed-content | 2104.13276 | null | https://arxiv.org/abs/2104.13276v3 | https://arxiv.org/pdf/2104.13276v3.pdf | MULTIMODAL ANALYSIS: Informed content estimation and audio source separation | This dissertation proposes the study of multimodal learning in the context of musical signals. Throughout, we focus on the interaction between audio signals and text information. Among the many text sources related to music that can be used (e.g. reviews, metadata, or social network feedback), we concentrate on lyrics.... | ['Gabriel Meseguer-Brocal'] | 2021-04-27 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 5.54113686e-02 -2.34601930e-01 -4.30780381e-01 1.34071603e-01
-9.12640452e-01 -8.92944336e-01 3.95947248e-01 4.45360869e-01
-1.18585331e-02 4.61033374e-01 8.01147163e-01 2.81702280e-01
-5.69482088e-01 -3.63483757e-01 -1.92813799e-01 -6.65013313e-01
2.50363145e-02 -2.40777239e-01 -1.62357643e-01 -3.43196809... | [15.890008926391602, 5.252040863037109] |
15b6a364-dcee-4d68-b4a0-940337b2c8fd | breaking-character-are-subwords-good-enough-1 | 2204.04748 | null | https://arxiv.org/abs/2204.04748v1 | https://arxiv.org/pdf/2204.04748v1.pdf | Breaking Character: Are Subwords Good Enough for MRLs After All? | Large pretrained language models (PLMs) typically tokenize the input string into contiguous subwords before any pretraining or inference. However, previous studies have claimed that this form of subword tokenization is inadequate for processing morphologically-rich languages (MRLs). We revisit this hypothesis by pretra... | ['Omer Levy', 'Reut Tsarfaty', 'Tal Avinari', 'Omri Keren'] | 2022-04-10 | null | null | null | null | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 3.00839189e-02 2.49320358e-01 -1.01489507e-01 -3.75111073e-01
-8.71222615e-01 -1.03824759e+00 4.52834874e-01 6.87557638e-01
-1.24634135e+00 7.17132270e-01 1.63592264e-01 -9.30374086e-01
1.69370815e-01 -8.49663079e-01 -7.03179717e-01 -1.06260709e-01
7.66604245e-02 4.80886281e-01 3.45406622e-01 -3.55003476... | [10.438570022583008, 10.001030921936035] |
14a1b62f-d85f-43db-a2f2-bab57cc2d9b6 | covost-a-diverse-multilingual-speech-to-text | 2002.01320 | null | https://arxiv.org/abs/2002.01320v2 | https://arxiv.org/pdf/2002.01320v2.pdf | CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus | Spoken language translation has recently witnessed a resurgence in popularity, thanks to the development of end-to-end models and the creation of new corpora, such as Augmented LibriSpeech and MuST-C. Existing datasets involve language pairs with English as a source language, involve very specific domains or are low re... | ['Juan Pino', 'Jiatao Gu', 'Anne Wu', 'Changhan Wang'] | 2020-02-04 | covost-a-diverse-multilingual-speech-to-text-1 | https://aclanthology.org/2020.lrec-1.517 | https://aclanthology.org/2020.lrec-1.517.pdf | lrec-2020-5 | ['speech-to-text-translation'] | ['natural-language-processing'] | [-4.21478450e-01 -1.21868700e-01 -4.14082527e-01 -6.86062634e-01
-1.47563064e+00 -8.57999742e-01 8.67960691e-01 -2.73380548e-01
-3.71064603e-01 1.11154866e+00 6.94611728e-01 -5.60098171e-01
4.96937364e-01 -1.39112547e-01 -6.58713818e-01 -1.15189001e-01
7.25236684e-02 1.11283219e+00 -9.10462290e-02 -6.98513269... | [14.28817081451416, 7.290187835693359] |
eebe3b3e-a859-466e-a278-914d2999d0d0 | left-ventricular-wall-motion-estimation-by | 2008.04615 | null | https://arxiv.org/abs/2008.04615v1 | https://arxiv.org/pdf/2008.04615v1.pdf | Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection | Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion o... | ['Junaid Malik', 'Aysen Degerli', 'Serkan Kiranyaz', 'Ridha Hamila', 'Rashid Mazhar', 'Tahir Hamid', 'Rayyan Ahmed', 'Moncef Gabbouj', 'Rayaan Abouhasera', 'Morteza Zabihi'] | 2020-08-11 | null | null | null | null | ['myocardial-infarction-detection'] | ['medical'] | [-1.83632135e-01 -4.13747221e-01 -1.24625929e-01 2.22588688e-01
-6.70465529e-01 -7.99365759e-01 -3.19012910e-01 7.75962621e-02
-7.30178144e-04 7.44689584e-01 -7.37782940e-02 -5.84570885e-01
-3.40330750e-01 -4.14009243e-01 1.45187616e-01 -8.49207461e-01
-8.07226241e-01 4.82737750e-01 1.84761479e-01 3.26054543... | [14.129775047302246, -2.378782033920288] |
e438de8e-1796-4ec4-a5e1-116ee38accac | joint-activity-delay-detection-and-channel | 2305.12372 | null | https://arxiv.org/abs/2305.12372v1 | https://arxiv.org/pdf/2305.12372v1.pdf | Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access | Most existing studies on joint activity detection and channel estimation for grant-free massive random access (RA) systems assume perfect synchronization among all active users, which is hard to achieve in practice. Therefore, this paper considers asynchronous grant-free massive RA systems and develops novel algorithms... | ['Jun Zhang', 'Yuyi Mao', 'Xinyu Bian'] | 2023-05-21 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 4.32463475e-02 -3.03740621e-01 -1.90881401e-01 3.08199823e-01
-7.72822499e-01 -2.09331736e-02 2.57782400e-01 -3.46154198e-02
-5.76687276e-01 1.00635552e+00 -6.33399189e-02 -7.12310791e-01
4.68110144e-02 -5.50055325e-01 -1.17356300e-01 -8.88648212e-01
-6.60853446e-01 2.84152683e-02 -4.00057696e-02 8.85634050... | [6.207645416259766, 1.4027677774429321] |
bc49096f-0940-4864-bfa9-8ce4af5f3d4a | answer-mining-from-a-pool-of-images-towards | 2306.16713 | null | https://arxiv.org/abs/2306.16713v1 | https://arxiv.org/pdf/2306.16713v1.pdf | Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering | We study visual question answering in a setting where the answer has to be mined from a pool of relevant and irrelevant images given as a context. For such a setting, a model must first retrieve relevant images from the pool and answer the question from these retrieved images. We refer to this problem as retrieval-base... | ['Anand Mishra', 'Mithun Das Gupta', 'Manish Gupta', 'Abhirama Subramanyam Penamakuri'] | 2023-06-29 | null | null | null | null | ['visual-question-answering-1', 'retrieval', 'question-answering', 'answer-generation'] | ['computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [ 2.55945981e-01 1.44022062e-01 1.26300707e-01 -4.26557332e-01
-1.52849400e+00 -7.28242874e-01 6.73300982e-01 -9.91594940e-02
-3.61454427e-01 6.17630541e-01 1.91001892e-01 -2.43195310e-01
8.42220709e-02 -7.94043839e-01 -9.71467972e-01 -3.73890311e-01
6.94097817e-01 6.68339252e-01 3.74824405e-01 -2.84146428... | [10.85375690460205, 1.5849360227584839] |
bb4f1612-2f02-4b4f-8161-eb410040c8d8 | on-implicit-bias-in-overparameterized-bilevel | 2212.14032 | null | https://arxiv.org/abs/2212.14032v1 | https://arxiv.org/pdf/2212.14032v1.pdf | On Implicit Bias in Overparameterized Bilevel Optimization | Many problems in machine learning involve bilevel optimization (BLO), including hyperparameter optimization, meta-learning, and dataset distillation. Bilevel problems consist of two nested sub-problems, called the outer and inner problems, respectively. In practice, often at least one of these sub-problems is overparam... | ['Roger Grosse', 'David Duvenaud', 'Fabian Pedregosa', 'Jonathan Lorraine', 'Paul Vicol'] | 2022-12-28 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-3.29133004e-01 3.88433374e-02 -3.37563157e-01 -4.27868456e-01
-7.21145332e-01 -4.95772243e-01 4.63231593e-01 2.27903947e-01
-7.61063576e-01 8.52389991e-01 9.05333310e-02 -3.71688008e-01
-5.78627467e-01 -4.78969663e-01 -7.42527366e-01 -1.05675268e+00
-1.57174826e-01 5.92489064e-01 -3.49448711e-01 -3.54315817... | [7.0304741859436035, 4.078549861907959] |
9af93b81-7733-4b12-9687-e9c4f6c060ce | time-conditioned-action-anticipation-in-one | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Ke_Time-Conditioned_Action_Anticipation_in_One_Shot_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ke_Time-Conditioned_Action_Anticipation_in_One_Shot_CVPR_2019_paper.pdf | Time-Conditioned Action Anticipation in One Shot | The goal of human action anticipation is to predict future actions. Ideally, in real-world applications such as video surveillance and self-driving systems, future actions should not only be predicted with high accuracy but also at arbitrary and variable time-horizons ranging from short- to long-term predictions. Curre... | [' Bernt Schiele', ' Mario Fritz', 'Qiuhong Ke'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['action-anticipation'] | ['computer-vision'] | [ 4.89604741e-01 7.53585547e-02 -3.49590212e-01 -7.15348840e-01
-5.18560231e-01 -1.38713211e-01 6.97895885e-01 -6.20334037e-02
-4.31737751e-01 6.79139733e-01 5.11465847e-01 -1.15160853e-01
-2.49413237e-01 -6.91785634e-01 -4.91986305e-01 -3.76096874e-01
-5.41007102e-01 2.05746189e-01 5.22372603e-01 -1.94991589... | [8.006952285766602, 0.5253226161003113] |
69be3e07-36dd-4db1-976b-f0ed8b29e413 | open-world-pose-transfer-via-sequential-test | 2303.10945 | null | https://arxiv.org/abs/2303.10945v1 | https://arxiv.org/pdf/2303.10945v1.pdf | Open-World Pose Transfer via Sequential Test-Time Adaption | Pose transfer aims to transfer a given person into a specified posture, has recently attracted considerable attention. A typical pose transfer framework usually employs representative datasets to train a discriminative model, which is often violated by out-of-distribution (OOD) instances. Recently, test-time adaption (... | ['Liang Lin', 'Jinshan Pan', 'Yukai Shi', 'Yongyi Lu', 'Tianshui Chen', 'Zhijing Yang', 'Xiaoyu Xian', 'Junyang Chen'] | 2023-03-20 | null | null | null | null | ['person-re-identification', 'pose-transfer'] | ['computer-vision', 'computer-vision'] | [ 1.43198580e-01 -6.47033425e-03 -2.03298137e-01 -6.78838372e-01
-5.79395950e-01 -5.36835551e-01 5.06665111e-01 -5.42550743e-01
-2.92711735e-01 6.73404038e-01 1.70797884e-01 3.94612461e-01
6.02963753e-02 -7.08197653e-01 -7.35821307e-01 -5.68677902e-01
1.86121881e-01 8.96647036e-01 1.27373904e-01 -3.01722705... | [6.988305568695068, -0.996613621711731] |
d042e807-93d7-41d9-9795-02ca02296c96 | resdiff-combining-cnn-and-diffusion-model-for | 2303.08714 | null | https://arxiv.org/abs/2303.08714v2 | https://arxiv.org/pdf/2303.08714v2.pdf | ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution | Adapting the Diffusion Probabilistic Model (DPM) for direct image super-resolution is wasteful, given that a simple Convolutional Neural Network (CNN) can recover the main low-frequency content. Therefore, we present ResDiff, a novel Diffusion Probabilistic Model based on Residual structure for Single Image Super-Resol... | ['Jinglin Zhang', 'Guangxing Liu', 'Zhengyang Shan', 'Shuyao Shang'] | 2023-03-15 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 2.43188486e-01 -4.76207733e-02 -1.06538087e-01 5.52211069e-02
-8.98541152e-01 1.82256892e-01 2.79756427e-01 -5.81406951e-01
-5.33805974e-02 7.13653803e-01 6.26513124e-01 2.87578404e-01
-2.68859684e-01 -1.01011825e+00 -4.26801234e-01 -9.53121901e-01
2.84735620e-01 -1.61970973e-01 4.34756517e-01 -2.23095253... | [11.16004467010498, -2.0530831813812256] |
e7a6de43-d678-4c6e-b614-936e9a25ea2d | towards-black-box-adversarial-example | 2306.02021 | null | https://arxiv.org/abs/2306.02021v1 | https://arxiv.org/pdf/2306.02021v1.pdf | Towards Black-box Adversarial Example Detection: A Data Reconstruction-based Method | Adversarial example detection is known to be an effective adversarial defense method. Black-box attack, which is a more realistic threat and has led to various black-box adversarial training-based defense methods, however, does not attract considerable attention in adversarial example detection. In this paper, we fill ... | ['Jitao Sang', 'Yunfan Yang', 'Zhiyu Lin', 'YiFei Gao'] | 2023-06-03 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 4.14162397e-01 -1.03431717e-01 6.80979267e-02 2.69082487e-01
-9.26375985e-01 -8.68544817e-01 9.98671830e-01 -7.53865018e-02
-1.24778070e-01 3.53907049e-01 -1.04717381e-01 -4.03327078e-01
1.05371736e-01 -9.52248096e-01 -5.83113790e-01 -7.20350266e-01
-2.41344705e-01 7.51172192e-03 1.54578120e-01 -4.30745989... | [5.614711284637451, 7.908064365386963] |
ea534c45-f6a2-431e-b983-0df89faa38a9 | improving-image-clustering-through-sample | 2209.12621 | null | https://arxiv.org/abs/2209.12621v1 | https://arxiv.org/pdf/2209.12621v1.pdf | Improving Image Clustering through Sample Ranking and Its Application to remote--sensing images | Image clustering is a very useful technique that is widely applied to various areas, including remote sensing. Recently, visual representations by self-supervised learning have greatly improved the performance of image clustering. To further improve the well-trained clustering models, this paper proposes a novel method... | ['Guoping Qiu', 'Qinglin Li'] | 2022-09-26 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 2.96202153e-01 -2.56963134e-01 -3.49654913e-01 -5.74899733e-01
-7.89352059e-01 -1.28771558e-01 3.93716186e-01 2.64876664e-01
-3.28412950e-01 2.77426928e-01 -3.52531709e-02 -2.58489549e-01
-2.36641571e-01 -1.01247299e+00 -3.02973777e-01 -1.02795410e+00
-3.51840556e-01 2.78649271e-01 2.77018510e-02 4.10433322... | [9.765454292297363, -1.3093961477279663] |
b92e9227-da0d-4950-aae5-411a1f1e60c1 | active-exploration-based-on-information-gain | 2211.10934 | null | https://arxiv.org/abs/2211.10934v2 | https://arxiv.org/pdf/2211.10934v2.pdf | Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation | Autonomous robots need to learn the categories of various places by exploring their environments and interacting with users. However, preparing training datasets with linguistic instructions from users is time-consuming and labor-intensive. Moreover, effective exploration is essential for appropriate concept formation ... | ['Tadahiro Taniguchi', 'Yoshinobu Hagiwara', 'Lotfi El Hafi', 'Tomochika Ishikawa', 'Yoshiki Tabuchi', 'Akira Taniguchi'] | 2022-11-20 | null | null | null | null | ['language-acquisition', 'sequential-bayesian-inference'] | ['natural-language-processing', 'time-series'] | [ 1.37633562e-01 2.47697309e-01 -7.34052211e-02 -4.33329463e-01
-5.13432860e-01 -4.29303616e-01 5.03521085e-01 3.02509695e-01
-8.26044083e-01 9.69132304e-01 -1.15729067e-02 -2.62217015e-01
-5.58305323e-01 -1.24521935e+00 -8.37661088e-01 -9.16541159e-01
-2.64617175e-01 7.76413202e-01 2.50033230e-01 -1.93277404... | [4.636216163635254, 0.6566154956817627] |
6e47c530-bf78-4d62-aced-4647a2c20be5 | experimental-results-on-the-native-language | null | null | https://aclanthology.org/W13-1710 | https://aclanthology.org/W13-1710.pdf | Experimental Results on the Native Language Identification Shared Task | null | ['Dragomir Radev', 'Amjad Abu-Jbara', 'Rahul Jha', 'Eric Morley'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['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... | [-7.2285614013671875, 3.5001909732818604] |
44588fc3-c4c0-4acb-b522-0095dc99a599 | prokd-an-unsupervised-prototypical-knowledge | 2301.08855 | null | https://arxiv.org/abs/2301.08855v2 | https://arxiv.org/pdf/2301.08855v2.pdf | ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition | For named entity recognition (NER) in zero-resource languages, utilizing knowledge distillation methods to transfer language-independent knowledge from the rich-resource source languages to zero-resource languages is an effective means. Typically, these approaches adopt a teacher-student architecture, where the teacher... | ['Chunming Hu', 'Jihong Liu', 'Hong Zhang', 'Guanghui Ma', 'Ling Ge'] | 2023-01-21 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-3.91169302e-02 4.23193090e-02 -4.06662643e-01 -4.61724311e-01
-4.43113148e-01 -6.91086173e-01 4.41957951e-01 -7.61714205e-02
-7.99716830e-01 6.46748304e-01 3.21849324e-02 -3.95279348e-01
1.44142106e-01 -8.87525618e-01 -5.17009974e-01 -4.57527190e-01
2.42782980e-01 5.45348763e-01 1.80407166e-01 -5.24378777... | [10.057045936584473, 9.619327545166016] |
a4b33c7b-ac7b-43ec-b520-411e28de76f2 | learning-instance-and-task-aware-dynamic | 2112.03494 | null | https://arxiv.org/abs/2112.03494v2 | https://arxiv.org/pdf/2112.03494v2.pdf | Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning | Learning and generalizing to novel concepts with few samples (Few-Shot Learning) is still an essential challenge to real-world applications. A principle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task. Dynamic networks have been shown capable of learning co... | ['Tom Drummond', 'Tianyu Zhu', 'Mehrtash Harandi', 'Yan Zuo', 'Gil Avraham', 'Pengfei Fang', 'Rongkai Ma'] | 2021-12-07 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 1.43482670e-01 -3.91008407e-01 -9.74003077e-02 -4.64055568e-01
-5.42958021e-01 -2.65326381e-01 6.96460724e-01 5.86767420e-02
-7.46937811e-01 3.54851067e-01 -7.33201532e-03 2.10427985e-01
-3.31796221e-02 -6.98599100e-01 -7.48021543e-01 -5.35334349e-01
-2.23503456e-01 2.61101127e-01 9.31026459e-01 -3.95862520... | [9.936655044555664, 2.7700436115264893] |
7e3bd489-b936-4e72-80bd-ef6e1f94e8b2 | stratified-transformer-for-3d-point-cloud | 2203.14508 | null | https://arxiv.org/abs/2203.14508v1 | https://arxiv.org/pdf/2203.14508v1.pdf | Stratified Transformer for 3D Point Cloud Segmentation | 3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies. In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization abil... | ['Jiaya Jia', 'Xiaojuan Qi', 'Shu Liu', 'Hengshuang Zhao', 'LiWei Wang', 'Li Jiang', 'Jianhui Liu', 'Xin Lai'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lai_Stratified_Transformer_for_3D_Point_Cloud_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lai_Stratified_Transformer_for_3D_Point_Cloud_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-segmentation'] | ['computer-vision'] | [-2.62684107e-01 -5.57367563e-01 -2.42597654e-01 -4.19742018e-01
-4.66317058e-01 -5.13933957e-01 5.15704930e-01 2.93458015e-01
-3.44441086e-01 2.69142807e-01 2.08922531e-02 -9.35977846e-02
-2.30946407e-01 -1.02855563e+00 -7.45877266e-01 -6.40793383e-01
-5.74295335e-02 2.99857646e-01 6.30423069e-01 -2.41042189... | [7.956449031829834, -3.51674747467041] |
d6c0c8ad-944e-4edb-9b56-24cb338c7b0d | simmc-simple-masked-contrastive-learning-of | 2204.09826 | null | https://arxiv.org/abs/2204.09826v4 | https://arxiv.org/pdf/2204.09826v4.pdf | SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-Identification | Recent advances in skeleton-based person re-identification (re-ID) obtain impressive performance via either hand-crafted skeleton descriptors or skeleton representation learning with deep learning paradigms. However, they typically require skeletal pre-modeling and label information for training, which leads to limited... | ['Chunyan Miao', 'Haocong Rao'] | 2022-04-21 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 3.05684835e-01 -3.53188872e-01 -3.49614978e-01 -3.00424069e-01
-6.14220738e-01 -2.26551965e-01 6.53948426e-01 -1.27806544e-01
-4.76050943e-01 4.02400434e-01 5.38037121e-01 4.21514481e-01
-1.42307598e-02 -5.77367604e-01 -4.58365053e-01 -5.92154026e-01
-4.15803231e-02 4.77925330e-01 5.39338104e-02 -3.33162472... | [14.676630973815918, 1.0256463289260864] |
379cce60-62f8-4234-bf64-4b58d41f86af | adaptive-scene-category-discovery-with | 1502.00374 | null | http://arxiv.org/abs/1502.00374v1 | http://arxiv.org/pdf/1502.00374v1.pdf | Adaptive Scene Category Discovery with Generative Learning and Compositional Sampling | This paper investigates a general framework to discover categories of
unlabeled scene images according to their appearances (i.e., textures and
structures). We jointly solve the two coupled tasks in an unsupervised manner:
(i) classifying images without pre-determining the number of categories, and
(ii) pursuing genera... | ['Liang Lin', 'Xiaohua Duan', 'Ruimao Zhang'] | 2015-02-02 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 2.59834766e-01 4.30227024e-03 -1.67655021e-01 -3.87792438e-01
-4.65600729e-01 -5.30747056e-01 7.48519957e-01 3.17800716e-02
-6.87222853e-02 8.14220011e-02 -3.25721949e-01 -1.57764763e-01
-8.39113593e-02 -9.46333528e-01 -5.50285637e-01 -1.16767395e+00
2.45693475e-01 8.23060930e-01 2.80737787e-01 4.23093766... | [8.118592262268066, 4.500980377197266] |
ea494ad1-6893-4c93-86fb-492605a5bb40 | detection-and-classification-of-cardiac | null | null | https://doi.org/10.1016/j.isci.2020.100886 | https://www.cell.com/action/showPdf?pii=S2589-0042%2820%2930070-5 | Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model | Electrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs). They are also being used to develop computer-assisted methods for heart disease diagnosis. We have developed a convolution neural network model to detect and classify CAs, using a large 12-lead ECG dataset (6,877 recordings) provi... | ['Ming-Jing Hwang', 'Yu-Feng Hu', 'Chih-Han Huang', 'Tsai-Min Chen', 'Edward S.C. Shih'] | 2020-02-04 | null | null | null | iscience-2020-2 | ['arrhythmia-detection'] | ['medical'] | [ 2.46421158e-01 -2.59466887e-01 1.18539371e-01 -3.27302456e-01
-9.47368026e-01 -6.74086273e-01 -3.21159661e-02 3.61300170e-01
-4.71039504e-01 5.63799441e-01 5.70877921e-03 -6.79560542e-01
-3.19074899e-01 -1.49007306e-01 -4.16964650e-01 -4.34412539e-01
-6.40550792e-01 2.93857813e-01 -5.96633554e-01 2.91378409... | [14.341334342956543, 3.3006155490875244] |
f7943e0a-c319-464a-a167-eb3d8deed810 | testing-deep-neural-network-based-image | 1905.07831 | null | https://arxiv.org/abs/1905.07831v3 | https://arxiv.org/pdf/1905.07831v3.pdf | Testing DNN Image Classifiers for Confusion & Bias Errors | Image classifiers are an important component of today's software, from consumer and business applications to safety-critical domains. The advent of Deep Neural Networks (DNNs) is the key catalyst behind such wide-spread success. However, wide adoption comes with serious concerns about the robustness of software systems... | ['Vicente Ordonez', 'Gail Kaiser', 'Baishakhi Ray', 'Ziyuan Zhong', 'Yuchi Tian'] | 2019-05-20 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 3.64086807e-01 -1.62370101e-01 -9.80428532e-02 -7.41037309e-01
-3.62321734e-01 -8.03339541e-01 2.79726803e-01 2.42363173e-03
-3.46065015e-01 5.58875263e-01 -5.12972474e-01 -7.28243470e-01
7.01771975e-02 -6.58129752e-01 -1.18676174e+00 -4.65405792e-01
2.22594157e-01 1.39875673e-02 6.13879085e-01 -1.90976262... | [6.5707688331604, 7.665940284729004] |
80fdafb0-9425-43f3-a583-e71a64370002 | reinforcement-causal-structure-learning-on | 2211.12151 | null | https://arxiv.org/abs/2211.12151v1 | https://arxiv.org/pdf/2211.12151v1.pdf | Reinforcement Causal Structure Learning on Order Graph | Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed data, and non-identifiability of causal graph, it is almost impossible to infer a single precise DAG. Some methods approximate the posterior d... | ['Maozu Guo', 'Zhengtian Wu', 'Jun Wang', 'Guoxian Yu', 'Dezhi Yang'] | 2022-11-22 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-2.00112522e-01 1.71245918e-01 -5.01686633e-01 -3.91964585e-01
-5.36738992e-01 -5.06636441e-01 3.77301753e-01 6.69461563e-02
3.01801115e-02 1.03905189e+00 3.06180716e-01 -5.26863515e-01
-5.03095329e-01 -9.82056439e-01 -1.04607689e+00 -5.64327419e-01
-8.13591957e-01 1.09978020e+00 4.89495873e-01 4.47818577... | [7.814145565032959, 5.324918746948242] |
78f1a494-edbb-4758-ba12-c25859f0b859 | talkclip-talking-head-generation-with-text | 2304.00334 | null | https://arxiv.org/abs/2304.00334v1 | https://arxiv.org/pdf/2304.00334v1.pdf | TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles | In order to produce facial-expression-specified talking head videos, previous audio-driven one-shot talking head methods need to use a reference video with a matching speaking style (i.e., facial expressions). However, finding videos with a desired style may not be easy, potentially restricting their application. In th... | ['Xin Yu', 'Zhidong Deng', 'Zhipeng Hu', 'Changjie Fan', 'Tangjie Lv', 'Bowen Ma', 'Yu Ding', 'Suzhen Wang', 'Yifeng Ma'] | 2023-04-01 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 1.48175150e-01 -3.39710265e-02 -2.49879315e-01 -1.01680064e+00
-8.42891812e-01 -5.23632288e-01 6.03715360e-01 -8.04289103e-01
5.32241203e-02 4.34459299e-01 7.17822909e-01 3.84538978e-01
4.66593057e-01 -2.63761818e-01 -6.32512450e-01 -7.44546950e-01
3.04143339e-01 1.61059901e-01 -4.37520176e-01 -9.22607183... | [13.201016426086426, -0.4103944003582001] |
6fbec2f4-666b-4ec7-bd6d-a2a8446422fe | federated-learning-for-breast-density | 2009.01871 | null | https://arxiv.org/abs/2009.01871v3 | https://arxiv.org/pdf/2009.01871v3.pdf | Federated Learning for Breast Density Classification: A Real-World Implementation | Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to... | ['Jayashree Kalpathy-Cramer', 'Mona Flores', 'Ram C. Naidu', 'Evan Leibovitz', 'Behrooz Hashemian', 'Jesse Tetreault', 'Matheus Mendonça', 'Felipe Kitamura', 'Vikash Gupta', 'Yuhong Wen', 'Nir Neumark', 'Meesam Shah', 'Katharina V. Hoebel', 'Bryan Chen', 'B. Min Yun', 'Alvin Ihsani', 'Vitor Lavor', 'Thomas Schultz', 'S... | 2020-09-03 | null | null | null | null | ['breast-density-classification'] | ['medical'] | [-1.60442770e-01 3.64763945e-01 -5.10021210e-01 -8.97696257e-01
-1.55665648e+00 -2.13245019e-01 2.76330113e-01 3.34928989e-01
-4.56081808e-01 7.66475201e-01 3.27581704e-01 -9.07116711e-01
-2.57418990e-01 -7.43510783e-01 -8.04429591e-01 -6.88287079e-01
-3.31063777e-01 8.55024278e-01 -2.48049855e-01 3.44632089... | [6.126986503601074, 6.4747700691223145] |
1c92af2a-3eb2-4f89-a6f7-c632d0642e15 | on-the-risk-of-misinformation-pollution-with | 2305.13661 | null | https://arxiv.org/abs/2305.13661v1 | https://arxiv.org/pdf/2305.13661v1.pdf | On the Risk of Misinformation Pollution with Large Language Models | In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly Open-Domain Question Answering (ODQA) systems. We establish a threat model and simulate ... | ['William Yang Wang', 'Min-Yen Kan', 'Preslav Nakov', 'Wenhu Chen', 'Liangming Pan', 'Yikang Pan'] | 2023-05-23 | null | null | null | null | ['misinformation', 'open-domain-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [ 8.61938149e-02 4.26205963e-01 3.81955169e-02 -1.19176500e-01
-1.08650017e+00 -1.01597846e+00 1.08110976e+00 6.34049118e-01
-3.53057295e-01 4.76306707e-01 6.73813879e-01 -1.11266649e+00
7.61428252e-02 -1.04651964e+00 -5.92620134e-01 -2.12300912e-01
3.31235826e-01 8.91803205e-02 4.90326822e-01 -7.05573440... | [8.399974822998047, 9.930487632751465] |
14958994-ed50-4ff7-9a50-4a0e7903f1cf | actions-speak-louder-than-goalsvaluing-player | null | null | https://www.researchgate.net/publication/323302738_Actions_Speak_Louder_Than_Goals_Valuing_Player_Actions_in_Soccer | https://arxiv.org/pdf/1802.07127.pdf | Actions Speak Louder than Goals:Valuing Player Actions in Soccer | Assessing the impact of the individual actions performed by soccerplayers during games is a crucial aspect of the player recruitmentprocess. Unfortunately, most traditional metrics fall short in ad-dressing this task as they either focus on rare actions like shotsand goals alone or fail to account for the context in wh... | ['Jesse Davis', 'Lotte Bransen', 'Tom Decroos', 'Jan Van Haaren'] | 2019-07-10 | null | null | null | published-in-kdd-2018-2019-7 | ['football-action-valuation'] | ['playing-games'] | [-2.19280422e-02 -1.18653968e-01 -3.65128756e-01 1.47393290e-02
-8.39051783e-01 -8.93408298e-01 6.78452551e-01 4.34414655e-01
-9.21953976e-01 8.33691895e-01 5.63379109e-01 -7.14507923e-02
-6.18951917e-01 -7.02224791e-01 -3.13105196e-01 -6.64110541e-01
3.75071406e-01 5.86367249e-01 4.16323036e-01 -6.74860299... | [6.488515377044678, 0.40102824568748474] |
b8055988-f4cb-491a-ba6f-10929c1909e3 | brdf-estimation-of-complex-materials-with | 1811.09131 | null | http://arxiv.org/abs/1811.09131v1 | http://arxiv.org/pdf/1811.09131v1.pdf | BRDF Estimation of Complex Materials with Nested Learning | The estimation of the optical properties of a material from RGB-images is an
important but extremely ill-posed problem in Computer Graphics. While recent
works have successfully approached this problem even from just a single
photograph, significant simplifications of the material model are assumed,
limiting the usabil... | ['Jorge Lopez-Moreno', 'Dan Casas', 'Raquel Vidaurre', 'Elena Garces'] | 2018-11-22 | null | null | null | null | ['brdf-estimation'] | ['computer-vision'] | [ 6.57364011e-01 -6.19071536e-03 7.67895639e-01 -3.66322190e-01
-2.84897804e-01 -4.74416971e-01 4.26280349e-01 -3.17406207e-01
-1.28782541e-01 7.46019900e-01 -2.88202018e-01 -2.69237906e-01
-3.16799313e-01 -8.32942128e-01 -7.98283815e-01 -7.89078832e-01
1.06822483e-01 6.29079282e-01 1.66162834e-01 -2.13326991... | [9.776284217834473, -2.9623265266418457] |
03fa0e15-d9c1-4f71-bc02-deea154408b1 | ersam-neural-architecture-search-for-energy | 2303.10727 | null | https://arxiv.org/abs/2303.10727v2 | https://arxiv.org/pdf/2303.10727v2.pdf | ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement | Social ambiance describes the context in which social interactions happen, and can be measured using speech audio by counting the number of concurrent speakers. This measurement has enabled various mental health tracking and human-centric IoT applications. While on-device Socal Ambiance Measure (SAM) is highly desirabl... | ['Yingyan Lin', 'Ashutosh Sabharwal', 'Jiayi Yuan', 'Wenwan Chen', 'Chaojian Li'] | 2023-03-19 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 4.01040643e-01 1.47214085e-02 -3.56909722e-01 -3.12936962e-01
-6.46914601e-01 -3.62617254e-01 1.16688445e-01 1.30154997e-01
-6.56200290e-01 5.96072614e-01 1.29638240e-01 -4.38756496e-01
-1.00721180e-01 -6.37811184e-01 -3.67718399e-01 -6.34586751e-01
5.87665401e-02 1.26090497e-01 -3.29268456e-01 2.10534126... | [14.408002853393555, 5.461910247802734] |
3b65f4d3-bfe5-40a4-a083-f2f6599ccf57 | graph-based-automatic-domain-term-extraction | null | null | https://aclanthology.org/2020.icon-termtraction.1 | https://aclanthology.org/2020.icon-termtraction.1.pdf | Graph Based Automatic Domain Term Extraction | We present a Graph Based Approach to automatically extract domain specific terms from technical domains like Biochemistry, Communication, Computer Science and Law. Our approach is similar to TextRank with an extra post-processing step to reduce the noise. We performed our experiments on the mentioned domains provided b... | ['Dipti Sharma', 'Hema Ala'] | null | null | null | null | icon-2020-12 | ['term-extraction'] | ['natural-language-processing'] | [ 8.73953328e-02 2.14262381e-01 -5.47367394e-01 -1.66326135e-01
-8.48252416e-01 -8.67853165e-01 1.09376633e+00 8.18110585e-01
-5.88049233e-01 1.20890689e+00 5.32397628e-01 -6.24289155e-01
-6.67744815e-01 -5.39496899e-01 -2.72812843e-01 -1.24274686e-01
-2.19742566e-01 8.24316323e-01 5.95424175e-01 -6.04263365... | [9.133244514465332, 8.638927459716797] |
901a9d3c-0f3c-4f15-92b6-e200d02be010 | molcpt-molecule-continuous-prompt-tuning-to | 2212.10614 | null | https://arxiv.org/abs/2212.10614v1 | https://arxiv.org/pdf/2212.10614v1.pdf | MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning | Molecular representation learning is crucial for the problem of molecular property prediction, where graph neural networks (GNNs) serve as an effective solution due to their structure modeling capabilities. Since labeled data is often scarce and expensive to obtain, it is a great challenge for GNNs to generalize in the... | ['Xia Hu', 'Xiao Huang', 'Kaixiong Zhou', 'Cameron Diao'] | 2022-12-20 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 6.35636806e-01 -2.48541772e-01 -7.07540512e-01 -5.24660707e-01
-3.02485436e-01 -6.39722466e-01 2.02623695e-01 6.22305572e-01
-1.76774021e-02 9.91664410e-01 -7.97652528e-02 -7.27500618e-01
-2.35687554e-01 -1.10991192e+00 -9.27042246e-01 -8.44752371e-01
-1.55507877e-01 1.63573742e-01 -2.01225560e-02 -3.15959543... | [5.115235328674316, 5.88067102432251] |
fac4f04d-7dfc-4569-8102-4e5f9bacc9b7 | polarstream-streaming-lidar-object-detection | 2106.07545 | null | https://arxiv.org/abs/2106.07545v2 | https://arxiv.org/pdf/2106.07545v2.pdf | PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars | Recent works recognized lidars as an inherently streaming data source and showed that the end-to-end latency of lidar perception models can be reduced significantly by operating on wedge-shaped point cloud sectors rather then the full point cloud. However, due to use of cartesian coordinate systems these methods repres... | ['Oscar Beijbom', 'Sourabh Vora', 'Qi Chen'] | 2021-06-14 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.56894112e-01 -1.50411889e-01 -2.23993771e-02 -5.45979202e-01
-7.99521029e-01 -7.08672106e-01 6.65433764e-01 3.72680783e-01
-4.82253522e-01 5.25889635e-01 5.89512326e-02 -3.45464379e-01
1.93802845e-02 -1.21087325e+00 -1.07564533e+00 -3.25023532e-01
-2.95727044e-01 4.03848112e-01 9.03120339e-01 5.47428466... | [8.150337219238281, -2.841381549835205] |
5ed8756c-096f-41e1-827b-984dca81f8f8 | space-partitioning-ransac | 2111.12385 | null | https://arxiv.org/abs/2111.12385v2 | https://arxiv.org/pdf/2111.12385v2.pdf | Space-Partitioning RANSAC | A new algorithm is proposed to accelerate RANSAC model quality calculations. The method is based on partitioning the joint correspondence space, e.g., 2D-2D point correspondences, into a pair of regular grids. The grid cells are mapped by minimal sample models, estimated within RANSAC, to reject correspondences that ar... | ['Gabor Valasek', 'Daniel Barath'] | 2021-11-24 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 5.41892871e-02 -2.62778789e-01 3.21216397e-02 -1.65240526e-01
-7.73064137e-01 -1.04799640e+00 7.70673871e-01 -1.42679125e-01
-1.46368086e-01 5.02676785e-01 -1.81082822e-02 -1.65800035e-01
-1.73670501e-01 -6.19357169e-01 -6.77558124e-01 -6.44577980e-01
4.01778162e-01 1.10705960e+00 1.84831396e-01 -3.02430928... | [7.954090118408203, -2.399183750152588] |
ec3056c5-e5e5-4972-8f51-d2945988f7a2 | ghost-at-semeval-2021-task-5-is-explanation | null | null | https://aclanthology.org/2021.semeval-1.114 | https://aclanthology.org/2021.semeval-1.114.pdf | GHOST at SemEval-2021 Task 5: Is explanation all you need? | This paper discusses different approaches to the Toxic Spans Detection task. The problem posed by the task was to determine which words contribute mostly to recognising a document as toxic. As opposed to binary classification of entire texts, word-level assessment could be of great use during comment moderation, also a... | ['Hanna Klimczak', "Kamil Pluci{\\'n}ski"] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 6.06237173e-01 6.18237793e-01 -4.09617990e-01 -3.83399785e-01
-8.01879764e-01 -2.59360939e-01 9.69526350e-01 6.09926701e-01
-2.43279070e-01 5.90918899e-01 5.23949444e-01 -5.88568687e-01
-3.59039634e-01 -6.04256153e-01 -1.98958918e-01 -6.88515306e-01
2.02507555e-01 6.75755024e-01 -2.37846673e-01 -1.97330758... | [8.966512680053711, 10.42581844329834] |
c3484dae-51c8-4961-9bd7-6074d039723c | illumination-adaptive-person-re | 1905.04525 | null | https://arxiv.org/abs/1905.04525v2 | https://arxiv.org/pdf/1905.04525v2.pdf | Illumination-Adaptive Person Re-identification | Most person re-identification (ReID) approaches assume that person images are captured under relatively similar illumination conditions. In reality, long-term person retrieval is common, and person images are often captured under different illumination conditions at different times across a day. In this situation, the ... | ["Shin'ichi Satoh", 'Yung-Yu Chuang', 'Zheng Wang', 'Zelong Zeng', 'Yinqiang Zheng', 'Zhixiang Wang'] | 2019-05-11 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [ 1.14656463e-02 -8.79088998e-01 1.30671665e-01 -5.44964850e-01
-2.53709048e-01 -7.99040616e-01 6.46124899e-01 -3.04304034e-01
-6.02887154e-01 7.33110845e-01 2.09791556e-01 2.61625051e-01
-1.55447125e-01 -3.86068314e-01 -2.30678573e-01 -7.40885854e-01
2.45616421e-01 3.24225277e-01 -5.33186734e-01 1.11327745... | [14.744973182678223, 1.0339698791503906] |
1b5489b8-c334-48a6-b817-ed1d96caef14 | explicit-occlusion-modeling-for-3d-object | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Zia_Explicit_Occlusion_Modeling_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Zia_Explicit_Occlusion_Modeling_2013_CVPR_paper.pdf | Explicit Occlusion Modeling for 3D Object Class Representations | Despite the success of current state-of-the-art object class detectors, severe occlusion remains a major challenge. This is particularly true for more geometrically expressive 3D object class representations. While these representations have attracted renewed interest for precise object pose estimation, the focus has m... | ['Michael Stark', 'M. Zeeshan Zia', 'Konrad Schindler'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['occlusion-estimation', '3d-object-reconstruction'] | ['computer-vision', 'computer-vision'] | [-4.00755592e-02 2.48119682e-01 -1.45835862e-01 -4.74958330e-01
-7.61094928e-01 -6.17310166e-01 7.08301425e-01 1.19996309e-01
-1.27939373e-01 1.90518424e-01 2.76557297e-01 -6.09380230e-02
1.22298278e-01 -6.05990887e-01 -9.29479182e-01 -3.99275392e-01
1.69138983e-01 8.13828230e-01 4.63023990e-01 -7.94700012... | [7.739974021911621, -2.649872064590454] |
d6b73676-11bc-4057-ac0f-cb34d96f10ba | conversational-semantic-role-labeling-with | 2210.03037 | null | https://arxiv.org/abs/2210.03037v1 | https://arxiv.org/pdf/2210.03037v1.pdf | Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph | Conversational semantic role labeling (CSRL) is a newly proposed task that uncovers the shallow semantic structures in a dialogue text. Unfortunately several important characteristics of the CSRL task have been overlooked by the existing works, such as the structural information integration, near-neighbor influence. In... | ['Donghong Ji', 'Yafeng Ren', 'Meishan Zhang', 'Shengqiong Wu', 'Hao Fei'] | 2022-10-06 | null | null | null | null | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 4.37921584e-01 9.53510821e-01 -3.54431540e-01 -6.67795956e-01
-8.86646032e-01 -7.06315219e-01 7.22450376e-01 4.36591327e-01
-1.59466952e-01 7.56984353e-01 1.08521783e+00 -2.85845071e-01
-1.33299261e-01 -2.38454476e-01 -3.75145018e-01 -6.79829836e-01
-6.58191964e-02 6.94629431e-01 3.50722134e-01 -4.78604704... | [12.393465042114258, 8.052603721618652] |
3ce59a90-a96d-42d0-8896-e565893ebfd4 | last-layer-fairness-fine-tuning-is-simple-and | 2304.03935 | null | https://arxiv.org/abs/2304.03935v1 | https://arxiv.org/pdf/2304.03935v1.pdf | Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks | As machine learning has been deployed ubiquitously across applications in modern data science, algorithmic fairness has become a great concern and varieties of fairness criteria have been proposed. Among them, imposing fairness constraints during learning, i.e. in-processing fair training, has been a popular type of tr... | ['James Zou', 'Kenji Kawaguchi', 'Ting Ye', 'Huaxiu Yao', 'Zhun Deng', 'Yuzhen Mao'] | 2023-04-08 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-4.22145948e-02 -9.79141444e-02 -3.74691665e-01 -1.02578855e+00
-2.77457148e-01 -2.14021564e-01 4.15229827e-01 2.88292039e-02
-8.91367614e-01 8.83205891e-01 -1.22892313e-01 -4.87911075e-01
-1.70953795e-01 -7.95324147e-01 -6.03069127e-01 -4.06669915e-01
1.32179007e-01 1.14319764e-01 -1.74896464e-01 -1.97077125... | [8.95401382446289, 5.168385028839111] |
9cafe3ba-4836-4f9a-9927-3c1e8fad90a2 | dual-contrastive-attributed-graph-clustering | 2206.07897 | null | https://arxiv.org/abs/2206.07897v2 | https://arxiv.org/pdf/2206.07897v2.pdf | NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering | Attributed graph clustering is one of the most fundamental tasks among graph learning field, the goal of which is to group nodes with similar representations into the same cluster without human annotations. Recent studies based on graph contrastive learning method have achieved remarkable results when exploit graph-str... | ['Junhua Wu', 'Zhenquan Zhang', 'Qijia He', 'Guanyu Yang', 'Tong Wang'] | 2022-06-16 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 2.72478852e-02 1.88015759e-01 -4.40662205e-01 -3.02460015e-01
-2.66421914e-01 -1.42782077e-01 5.72000086e-01 4.25709605e-01
-9.38560665e-02 1.45134240e-01 1.43911973e-01 7.44659379e-02
-3.09560895e-01 -8.19753408e-01 -3.61915231e-01 -1.09450579e+00
-2.51980782e-01 1.62820697e-01 -5.36488742e-02 -1.79790810... | [7.4129838943481445, 6.029924392700195] |
5bdb9ef4-2f71-41c9-95b7-439aa1f0e5c4 | multi-components-system-for-automatic-arabic | null | null | https://link.springer.com/chapter/10.1007/978-3-030-45439-5_23 | https://link.springer.com/content/pdf/10.1007%2F978-3-030-45439-5_23.pdf | Multi-components System for Automatic Arabic Diacritization | In this paper, we propose an approach to tackle the problem of the automatic restoration of Arabic diacritics that includes three components stacked in a pipeline: a deep learning model which is a multi-layer recurrent neural network with LSTM and Dense layers, a character-level rule-based corrector which applies deter... | ['Shengwu Xiong', 'Hamza Abbad'] | 2020-04-08 | null | null | null | null | ['arabic-text-diacritization'] | ['natural-language-processing'] | [ 1.65806800e-01 -8.39455128e-02 6.43127561e-01 -4.80742902e-01
-7.23128438e-01 -5.69190562e-01 4.74751651e-01 5.04266858e-01
-8.92673373e-01 6.59075141e-01 1.64319336e-01 -5.89842677e-01
1.39065057e-01 -9.19100165e-01 -1.00652862e+00 -8.12555969e-01
1.87316492e-01 4.23296720e-01 3.58790517e-01 -9.16523397... | [10.842432975769043, 10.422711372375488] |
91391434-d4fc-4037-ac89-089aa11be0c5 | neural-machine-translation-for-query | 1806.10478 | null | http://arxiv.org/abs/1806.10478v2 | http://arxiv.org/pdf/1806.10478v2.pdf | Neural Machine Translation for Query Construction and Composition | Research on question answering with knowledge base has recently seen an
increasing use of deep architectures. In this extended abstract, we study the
application of the neural machine translation paradigm for question parsing. We
employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph
query lang... | ['André Valdestilhas', 'Gustavo Publio', 'Diego Esteves', 'Edgard Marx', 'Tommaso Soru', 'Diego Moussallem'] | 2018-06-27 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 5.43187559e-01 1.13276565e+00 -2.93935500e-02 -6.67649746e-01
-9.77446020e-01 -9.22666728e-01 7.21708000e-01 2.08824471e-01
5.14914514e-03 6.19581580e-01 4.12269026e-01 -9.12559927e-01
1.32929206e-01 -1.57744658e+00 -1.19931209e+00 3.87218535e-01
1.95690468e-01 9.30669487e-01 5.11816621e-01 -7.80672669... | [10.221617698669434, 7.891458988189697] |
4e6344b2-24a9-4501-91d6-cb5b520cfdc2 | real-time-pear-fruit-detection-and-counting | null | null | https://www.mdpi.com/1188352 | https://www.mdpi.com/1424-8220/21/14/4803/pdf | Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT | This study aimed to produce a robust real-time pear fruit counter for mobile applications using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and the multiple object-tracking algorithm Deep SORT. This study also provided a systematic and pragmatic methodology for choosing the most s... | ['Tofael Ahamed', 'Addie Ira Borja Parico'] | 2021-07-14 | null | null | null | sensors-2021-7 | ['object-counting'] | ['computer-vision'] | [-7.69281611e-02 -3.21054369e-01 1.13420961e-02 2.70797640e-01
-3.27928662e-01 -8.20177734e-01 2.23180681e-01 6.19181633e-01
-5.59553742e-01 3.64370912e-01 -1.08607733e+00 -3.04582119e-01
-2.99140126e-01 -9.96485651e-01 -4.89949971e-01 -7.50671148e-01
8.28363374e-02 3.88789058e-01 7.60861099e-01 7.10278228... | [9.165655136108398, -1.452631950378418] |
f98e78cd-f5da-4b49-a3eb-52dadd91538d | relating-eeg-recordings-to-speech-using | 2303.06435 | null | https://arxiv.org/abs/2303.06435v1 | https://arxiv.org/pdf/2303.06435v1.pdf | Relating EEG recordings to speech using envelope tracking and the speech-FFR | During speech perception, a listener's electroencephalogram (EEG) reflects acoustic-level processing as well as higher-level cognitive factors such as speech comprehension and attention. However, decoding speech from EEG recordings is challenging due to the low signal-to-noise ratios of EEG signals. We report on an app... | ['Tobias Reichenbach', 'Danilo Mandic', 'Mike Thornton'] | 2023-03-11 | null | null | null | null | ['eeg-decoding', 'eeg', 'eeg', 'eeg-decoding'] | ['medical', 'methodology', 'time-series', 'time-series'] | [ 4.01685238e-01 -1.17788631e-02 8.87584269e-01 -3.75941366e-01
-1.10394061e+00 -2.73563325e-01 2.98090190e-01 3.75352591e-01
-5.10830343e-01 7.33759999e-01 5.98901987e-01 -3.09149235e-01
-1.20790459e-01 -2.59072602e-01 -3.60829473e-01 -5.81095874e-01
-2.90827245e-01 8.41866955e-02 1.53917983e-01 -6.68325424... | [13.234691619873047, 3.4472708702087402] |
ea363674-e222-49ad-9836-ad073fddf5da | balancing-the-trade-off-between-cost-and | 2207.09089 | null | https://arxiv.org/abs/2207.09089v1 | https://arxiv.org/pdf/2207.09089v1.pdf | Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method | The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifical... | ['Zhenzhou Tang', 'Qian Hu', 'Fangyi Xu', 'Yingying Xu', 'Long Chen'] | 2022-07-19 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [-7.19565675e-02 -9.89276096e-02 -1.17376179e-01 4.59515043e-02
-2.25987628e-01 -2.95382947e-01 -2.67353863e-01 2.95826316e-01
-5.15608788e-01 8.34191084e-01 -2.83685297e-01 -1.94833070e-01
-7.21306980e-01 -8.42317641e-01 -4.06551749e-01 -1.30957317e+00
-3.73780221e-01 2.34949201e-01 9.92923453e-02 -4.51642215... | [5.932121276855469, 1.5911647081375122] |
e85b86fa-e633-4132-90be-aa9d73525876 | a-semi-supervised-deep-transfer-learning | null | null | https://ieeexplore.ieee.org/document/9552475 | https://ieeexplore.ieee.org/document/9552475 | A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction | Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the availability of labeled historical data but also on the careful samples selection. ... | ['Mohamed Benbouzid', 'Toufik Bentrcia', 'Leila-Hayet Mouss', 'Tarek Berghout'] | 2021-09-29 | null | null | null | ieee-transaction-on-energy-conversion-2021-9 | ['interpretability-techniques-for-deep-learning'] | ['miscellaneous'] | [ 2.16232911e-01 -3.94849181e-01 -6.54748902e-02 -3.27565700e-01
-7.07226023e-02 -2.78603807e-02 1.88330755e-01 5.42983532e-01
-4.60287452e-01 9.74367559e-01 -1.58673674e-01 -2.10372180e-01
-6.58565760e-01 -1.17365944e+00 -6.33115292e-01 -1.10871327e+00
-3.87912929e-01 8.62634599e-01 1.40223093e-02 -4.17043477... | [6.760011196136475, 2.4990832805633545] |
273d5553-b506-45b9-97e2-f28a504931fe | unveiling-covid-19-from-chest-x-ray-with-deep | 2004.05405 | null | https://arxiv.org/abs/2004.05405v1 | https://arxiv.org/pdf/2004.05405v1.pdf | Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data | The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep-learning to COVID classifica... | ['Carlo Alberto Barbano', 'Enzo Tartaglione', 'Claudio Berzovini', 'Marco Grangetto', 'Marco Calandri'] | 2020-04-11 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.92551687e-01 -3.07582259e-01 6.22633621e-02 -3.73349130e-01
-7.87796378e-01 -6.96100771e-01 3.85078937e-01 6.49604321e-01
-8.55890453e-01 7.43435919e-01 1.07119612e-01 -8.46268415e-01
-3.93036485e-01 -6.96226716e-01 -6.83370173e-01 -5.65540671e-01
-3.47005039e-01 9.35895860e-01 1.75632298e-01 4.69108485... | [15.424230575561523, -1.8149809837341309] |
0348bdfa-8fcb-4492-a362-eaa328d196b8 | evaluating-the-acquisition-of-semantic | null | null | https://aclanthology.org/2021.cmcl-1.24 | https://aclanthology.org/2021.cmcl-1.24.pdf | Evaluating the Acquisition of Semantic Knowledge from Cross-situational Learning in Artificial Neural Networks | When learning their native language, children acquire the meanings of words and sentences from highly ambiguous input without much explicit supervision. One possible learning mechanism is cross-situational learning, which has been successfully tested in laboratory experiments with children. Here we use Artificial Neura... | ['Abdellah Fourtassi', 'Mitja Nikolaus'] | null | null | null | null | naacl-cmcl-2021-6 | ['language-acquisition'] | ['natural-language-processing'] | [ 3.68491650e-01 -8.41199830e-02 1.93021953e-01 -6.95062101e-01
-2.74757713e-01 -6.80913091e-01 6.70914292e-01 7.01681733e-01
-1.02667081e+00 5.79474568e-01 1.69517204e-01 3.30593735e-02
-1.24014750e-01 -8.27436268e-01 -1.05471468e+00 -4.00650650e-01
-2.19732508e-01 4.01051462e-01 4.43578035e-01 -2.28199467... | [10.208179473876953, 8.644620895385742] |
5cfc75dd-69c0-4712-a4c8-4f86a6520680 | syreanet-a-physically-guided-underwater-image | 2302.08269 | null | https://arxiv.org/abs/2302.08269v2 | https://arxiv.org/pdf/2302.08269v2.pdf | SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images | Underwater image enhancement (UIE) is vital for high-level vision-related underwater tasks. Although learning-based UIE methods have made remarkable achievements in recent years, it's still challenging for them to consistently deal with various underwater conditions, which could be caused by: 1) the use of the simplifi... | ['Ben M. Chen', 'Lihua Dou', 'Zhi Gao', 'Ruixin Yan', 'Zhenjun Zhao', 'Jinqiang Cui', 'Junjie Wen'] | 2023-02-16 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 3.44423562e-01 1.94057584e-01 6.86336577e-01 -2.48741686e-01
-6.26829028e-01 -1.68790027e-01 3.23801190e-01 -5.85686505e-01
-5.49751401e-01 9.75778282e-01 1.24172442e-01 4.60946783e-02
-2.17789605e-01 -8.21151733e-01 -7.92800367e-01 -1.08684397e+00
-2.05958867e-03 6.68046921e-02 2.23694474e-01 -5.17298222... | [10.7001953125, -3.552147388458252] |
5af035b2-c8f4-4eea-b06c-351adaa96dd9 | end-to-end-dense-video-captioning-as-sequence | null | null | https://openreview.net/forum?id=dWjknwjRtZ0 | https://openreview.net/pdf?id=dWjknwjRtZ0 | End-to-end Dense Video Captioning as Sequence Generation | Dense video captioning aims to identify the events of interest in an input video, and generate descriptive captions for each event. Previous approaches usually follow a two-stage generative process, which first proposes a segment for each event, then renders a caption for each identified segment. Recent advances in lar... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['dense-video-captioning'] | ['computer-vision'] | [ 5.32797217e-01 2.45082274e-01 -1.49616420e-01 -3.80518049e-01
-1.11576939e+00 -4.38973784e-01 8.98427010e-01 -2.58730769e-01
-1.00091904e-01 1.00218987e+00 7.43432820e-01 3.97122698e-03
7.63702869e-01 -3.22875232e-01 -1.02111292e+00 -4.79151785e-01
1.08206244e-02 9.12554502e-01 6.81997091e-02 -1.12045528... | [10.52307415008545, 0.7012279629707336] |
5aece3ac-b542-4807-95c6-08a80afb7d50 | a-comprehensive-study-of-cotton-price | 2212.01584 | null | https://arxiv.org/abs/2212.01584v2 | https://arxiv.org/pdf/2212.01584v2.pdf | A comprehensive study of cotton price fluctuations using multiple Econometric and LSTM neural network models | This paper proposes a new coherent model for a comprehensive study of the cotton price using econometrics and Long-Short term memory neural network (LSTM) methodologies. We call a simple cotton price trend and then assumed conjectures in structural method (ARMA), Markov switching dynamic regression, simultaneous equati... | ['Mohammad Hemami', 'Mehdi Ghasemi Meymandi', 'Morteza Tahami Pour Zarandi'] | 2022-12-03 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-2.71029770e-01 -3.44431609e-01 -4.07434940e-01 -1.60694242e-01
1.38351917e-01 -5.39061546e-01 5.49559891e-01 -3.78698885e-01
8.99605220e-04 6.55069172e-01 2.90597491e-02 -1.25490451e+00
-3.34252626e-01 -9.24974620e-01 -7.11312175e-01 -1.01443124e+00
-3.92153889e-01 1.71135023e-01 -5.70787311e-01 -2.47152612... | [4.684666156768799, 4.130308628082275] |
69fb63a5-fa3a-4cb1-8b73-93adeefa02bf | interpretable-deep-models-for-cardiac | 2006.13811 | null | https://arxiv.org/abs/2006.13811v2 | https://arxiv.org/pdf/2006.13811v2.pdf | Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction | Advances in deep learning (DL) have resulted in impressive accuracy in some medical image classification tasks, but often deep models lack interpretability. The ability of these models to explain their decisions is important for fostering clinical trust and facilitating clinical translation. Furthermore, for many probl... | ['Christopher A. Rinaldi', 'Baldeep S. Sidhu', 'Esther Puyol-Antón', 'Mark Elliott', 'James R. Clough', 'Daniel Rueckert', 'Chen Chen', 'Vishal Mehta', 'Justin Gould', 'Bram Ruijsink', 'Bradley Porter', 'Andrew P. King'] | 2020-06-24 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 2.44889930e-01 6.69346750e-01 -3.19675118e-01 -3.61783981e-01
-4.78592485e-01 -4.60872889e-01 2.89183527e-01 2.01338947e-01
4.19851206e-02 9.47780490e-01 4.91805613e-01 -5.89097321e-01
-4.33425218e-01 -5.38222790e-01 -6.53559983e-01 -1.02229905e+00
-4.23326902e-02 6.62400126e-01 -3.84039342e-01 1.17471181... | [8.535085678100586, 5.699045181274414] |
1dc8031d-99e9-42b8-814a-db9f36a34d45 | x-risawoz-high-quality-end-to-end | 2306.17674 | null | https://arxiv.org/abs/2306.17674v1 | https://arxiv.org/pdf/2306.17674v1.pdf | X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents | Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost, we apply manual editing to automatically translated data. We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese Ri... | ['Monica S. Lam', 'Deyi Xiong', 'Chaobin You', 'Aditya Yadavalli', 'Michael Sun', 'Manish Shrivastava', 'Vivek Seshadri', 'Jiwon Seo', 'Sina J. Semnani', 'Nasredine Semmar', 'Ponnurangam Kumaraguru', 'Prashant Kodali', 'Sungkyun Kim', 'Anmol Goel', 'Gaël de Chalendar', 'Monojit Choudhury', 'Kalika Bali', 'Tianhao Shen'... | 2023-06-30 | null | null | null | null | ['entity-alignment', 'machine-translation', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-2.57131517e-01 1.50467709e-01 -1.23590127e-01 -3.62976581e-01
-1.22238469e+00 -1.00324488e+00 7.86517441e-01 -2.30609462e-01
-7.58957028e-01 1.33183587e+00 5.46819448e-01 -5.11845231e-01
5.40043235e-01 -5.59365809e-01 -3.39190483e-01 9.10153147e-03
3.00201982e-01 1.26159561e+00 -7.17900470e-02 -1.01803935... | [12.46104621887207, 8.45144271850586] |
7aa2a951-7b4a-4d3d-9429-8e9f17dfd67b | k-arma-models-for-clustering-time-series-data | 2207.00039 | null | https://arxiv.org/abs/2207.00039v1 | https://arxiv.org/pdf/2207.00039v1.pdf | K-ARMA Models for Clustering Time Series Data | We present an approach to clustering time series data using a model-based generalization of the K-Means algorithm which we call K-Models. We prove the convergence of this general algorithm and relate it to the hard-EM algorithm for mixture modeling. We then apply our method first with an AR($p$) clustering example and ... | ['Martin T. Wells', 'David S. Matteson', 'Derek O. Hoare'] | 2022-06-30 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-4.03577298e-01 -3.34960073e-01 3.57793421e-01 -4.25820380e-01
-8.98417413e-01 -5.51339746e-01 3.89183730e-01 2.31926411e-01
-3.92428756e-01 1.79511085e-01 -2.87023425e-01 -6.33646011e-01
-5.65234482e-01 -5.26326537e-01 -5.78396976e-01 -9.10730898e-01
-7.40562201e-01 9.02301073e-01 1.83450788e-01 1.88758165... | [7.140988349914551, 3.783130407333374] |
b54d0706-7ab4-4f11-9716-f83b1ca70b6c | affective-reasoning-at-utterance-level-in | 2305.02615 | null | https://arxiv.org/abs/2305.02615v1 | https://arxiv.org/pdf/2305.02615v1.pdf | Affective Reasoning at Utterance Level in Conversations: A Causal Discovery Approach | The affective reasoning task is a set of emerging affect-based tasks in conversation, including Emotion Recognition in Conversation (ERC),Emotion-Cause Pair Extraction (ECPE), and Emotion-Cause Span Recognition (ECSR). Existing methods make various assumptions on the apparent relationship while neglecting the essential... | ['Wenjing Zhu', 'Xinyu Yang', 'Jing Luo', 'Hang Chen'] | 2023-05-04 | null | null | null | null | ['causal-discovery', 'emotion-recognition-in-conversation', 'emotion-cause-pair-extraction'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [ 4.50558752e-01 7.14483857e-01 -8.46108422e-02 -7.21235454e-01
-4.61428523e-01 -3.12748104e-01 9.15143728e-01 -1.49678186e-01
4.86730546e-01 9.38912749e-01 1.19897985e+00 -5.90948649e-02
-3.49484116e-01 -4.72921848e-01 -5.65957963e-01 -5.20322204e-01
-4.86959338e-01 4.45372969e-01 -4.69584405e-01 -1.73424527... | [12.78210735321045, 6.37459135055542] |
2463a202-0299-4dc2-83f7-8b3bda2cd0fa | cooperative-tuning-of-multi-agent-optimal | 2209.12017 | null | https://arxiv.org/abs/2209.12017v1 | https://arxiv.org/pdf/2209.12017v1.pdf | Cooperative Tuning of Multi-Agent Optimal Control Systems | This paper investigates the problem of cooperative tuning of multi-agent optimal control systems, where a network of agents (i.e. multiple coupled optimal control systems) adjusts parameters in their dynamics, objective functions, or controllers in a coordinated way to minimize the sum of their loss functions. Differen... | ['Brian D. O. Anderson', 'Shaoshuai Mou', 'Wanxin Jin', 'Zehui Lu'] | 2022-09-24 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-4.55100775e-01 2.01802611e-01 -5.51437319e-04 9.82779935e-02
-3.08738083e-01 -7.70551980e-01 1.34105608e-01 4.41345781e-01
-4.78912801e-01 9.33880627e-01 -2.69018292e-01 1.50696978e-01
-7.30893075e-01 -5.95220506e-01 -4.35588270e-01 -1.14772904e+00
-1.56917423e-01 6.40473008e-01 1.08365536e-01 -6.22974813... | [4.777555465698242, 2.5609188079833984] |
46e97d2c-ad1d-41fe-a71b-3ebd95d05b22 | event-based-visual-odometry-with-full | 2306.01188 | null | https://arxiv.org/abs/2306.01188v1 | https://arxiv.org/pdf/2306.01188v1.pdf | Event-based Visual Odometry with Full Temporal Resolution via Continuous-time Gaussian Process Regression | Event-based cameras asynchronously capture individual visual changes in a scene. This makes them more robust than traditional frame-based cameras to highly dynamic motions and poor illumination. It also means that every measurement in a scene can occur at a unique time. Handling these different measurement times is a m... | ['Jonathan D. Gammell', 'Jianeng Wang'] | 2023-06-01 | null | null | null | null | ['visual-odometry'] | ['robots'] | [ 1.26480594e-01 -6.10713184e-01 2.34279186e-01 -1.59889936e-01
-8.34198117e-01 -6.67420626e-01 7.78159142e-01 4.66155149e-02
-6.49041355e-01 5.25950551e-01 9.61568803e-02 4.48776260e-02
2.02409223e-01 -3.88543487e-01 -8.82316291e-01 -6.84501767e-01
6.23658970e-02 3.15591872e-01 8.15357983e-01 4.11896586... | [8.334868431091309, -1.7996472120285034] |
caa0fa6f-6358-4365-ad29-f35a5e15b789 | item-silk-road-recommending-items-from | 1706.03205 | null | http://arxiv.org/abs/1706.03205v1 | http://arxiv.org/pdf/1706.03205v1.pdf | Item Silk Road: Recommending Items from Information Domains to Social Users | Online platforms can be divided into information-oriented and social-oriented
domains. The former refers to forums or E-commerce sites that emphasize
user-item interactions, like Trip.com and Amazon; whereas the latter refers to
social networking services (SNSs) that have rich user-user connections, such as
Facebook an... | ['Tat-Seng Chua', 'Xiang Wang', 'Liqiang Nie', 'Xiangnan He'] | 2017-06-10 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [-3.07107538e-01 1.31420210e-01 -4.93411183e-01 -3.67837936e-01
-1.04122028e-01 -4.32707340e-01 6.72612965e-01 2.85969526e-01
-3.11861426e-01 6.31819844e-01 4.16561753e-01 -1.25361070e-01
-4.75122929e-01 -1.08058727e+00 -2.08172143e-01 -2.64155418e-01
-2.03060955e-02 3.24039459e-01 5.16887486e-01 -6.79108441... | [10.183971405029297, 5.609234809875488] |
9e266e21-501a-437e-b81c-eab6e108d5db | adaptive-graph-signal-processing-algorithms | 1709.03726 | null | http://arxiv.org/abs/1709.03726v1 | http://arxiv.org/pdf/1709.03726v1.pdf | Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies | The goal of this paper is to propose novel strategies for adaptive learning
of signals defined over graphs, which are observed over a (randomly
time-varying) subset of vertices. We recast two classical adaptive algorithms
in the graph signal processing framework, namely, the least mean squares (LMS)
and the recursive l... | ['Paolo Di Lorenzo', 'Geert Leus', 'Sergio Barbarossa', 'Paolo Banelli', 'Elvin Isufi'] | 2017-09-12 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 5.09222388e-01 2.74773568e-01 1.18995808e-01 -2.60502193e-02
-7.33377457e-01 -3.28984410e-01 1.88922614e-01 2.88887143e-01
-9.61084962e-02 5.49312055e-01 -3.12526256e-01 -1.43739477e-01
-7.35254884e-01 -5.25195837e-01 -5.04824817e-01 -9.69366550e-01
-8.02723229e-01 -3.35947536e-02 1.32510349e-01 2.48075053... | [6.578417778015137, 1.6094863414764404] |
34ac8f59-ecdd-4531-b234-d10dfacd6ed6 | compodiff-versatile-composed-image-retrieval | 2303.11916 | null | https://arxiv.org/abs/2303.11916v1 | https://arxiv.org/pdf/2303.11916v1.pdf | CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion | This paper proposes a novel diffusion-based model, CompoDiff, for solving Composed Image Retrieval (CIR) with latent diffusion and presents a newly created dataset of 18 million reference images, conditions, and corresponding target image triplets to train the model. CompoDiff not only achieves a new zero-shot state-of... | ['Sangdoo Yun', 'Yoohoon Kang', 'HeeJae Jun', 'Wonjae Kim', 'Sanghyuk Chun', 'Geonmo Gu'] | 2023-03-21 | null | null | null | null | ['composed-image-retrieval'] | ['computer-vision'] | [ 7.88083393e-03 -7.48976707e-01 -4.85349387e-01 -1.22644745e-01
-8.77993345e-01 -6.06188536e-01 7.56842971e-01 -6.11570835e-01
-3.10250551e-01 2.91480660e-01 3.49988729e-01 -8.67191106e-02
-2.00070977e-01 -2.74430186e-01 -3.53511602e-01 -9.08421218e-01
5.96199818e-02 8.63561034e-02 9.75044817e-02 -1.00184664... | [10.361820220947266, 1.0765408277511597] |
d207a4e9-1801-4982-8f41-43463ade46f5 | pruning-the-search-space-of-the-wolof-lfg | null | null | https://aclanthology.org/L14-1497 | https://aclanthology.org/L14-1497.pdf | Pruning the Search Space of the Wolof LFG Grammar Using a Probabilistic and a Constraint Grammar Parser | This paper presents a method for greatly reducing parse times in LFG by integrating a Constraint Grammar parser into a probabilistic context-free grammar. The CG parser is used in the pre-processing phase to reduce morphological and lexical ambiguity. Similarly, the c-structure pruning mechanism of XLE is used in the p... | ['Cheikh M. Bamba Dione'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 3.45537633e-01 6.58280432e-01 1.24441236e-01 -5.92490315e-01
-1.05411100e+00 -8.17095459e-01 2.69745085e-02 6.52316391e-01
-6.43718064e-01 7.66041934e-01 4.09268551e-02 -7.88020670e-01
-7.30182976e-02 -8.99623394e-01 -2.30614215e-01 -3.54383171e-01
2.96423375e-03 6.38281286e-01 6.01034164e-01 -4.85607684... | [10.351118087768555, 9.67233657836914] |
d00b3122-b3a7-4bc4-83c5-bbc7a1d22caa | in-field-high-throughput-grapevine | 2104.06945 | null | https://arxiv.org/abs/2104.06945v1 | https://arxiv.org/pdf/2104.06945v1.pdf | In-field high throughput grapevine phenotyping with a consumer-grade depth camera | Plant phenotyping, that is, the quantitative assessment of plant traits including growth, morphology, physiology, and yield, is a critical aspect towards efficient and effective crop management. Currently, plant phenotyping is a manually intensive and time consuming process, which involves human operators making measur... | ['Giulio Reina', 'Antonio Petitti', 'Roberto Marani', 'Annalisa Milella'] | 2021-04-14 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 2.30697259e-01 -2.79809535e-01 -1.73522085e-01 2.71826804e-01
2.57467866e-01 -1.16474712e+00 -2.26088315e-01 1.01863229e+00
1.51278893e-03 3.90013814e-01 -9.83040988e-01 -9.49967802e-01
-1.08133048e-01 -9.56131995e-01 3.60213891e-02 -6.50001585e-01
8.50494578e-02 2.04838291e-01 1.03839032e-01 -7.50177279... | [9.09396743774414, -1.6344586610794067] |
3897179b-2a78-4e60-946b-cc45049b4f4d | vector-quantized-neural-networks-for-acoustic | 2005.09409 | null | https://arxiv.org/abs/2005.09409v2 | https://arxiv.org/pdf/2005.09409v2.pdf | Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge | In this paper, we explore vector quantization for acoustic unit discovery. Leveraging unlabelled data, we aim to learn discrete representations of speech that separate phonetic content from speaker-specific details. We propose two neural models to tackle this challenge - both use vector quantization to map continuous f... | ['Leanne Nortje', 'Herman Kamper', 'Benjamin van Niekerk'] | 2020-05-19 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 8.58911425e-02 3.41920197e-01 -1.49357975e-01 -4.60860580e-01
-1.32080770e+00 -7.07659364e-01 7.06288993e-01 -1.84967414e-01
-2.61720836e-01 5.35251439e-01 5.36689758e-01 -6.83767498e-01
3.23191255e-01 -4.41575915e-01 -7.65671015e-01 -4.96649086e-01
-7.92450011e-02 4.93110567e-01 1.65718660e-01 -2.78379440... | [14.708041191101074, 6.592362403869629] |
fccab0ef-5774-456d-aaf4-d72e670ba74a | humanbench-towards-general-human-centric | 2303.05675 | null | https://arxiv.org/abs/2303.05675v1 | https://arxiv.org/pdf/2303.05675v1.pdf | HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining | Human-centric perceptions include a variety of vision tasks, which have widespread industrial applications, including surveillance, autonomous driving, and the metaverse. It is desirable to have a general pretrain model for versatile human-centric downstream tasks. This paper forges ahead along this path from the aspec... | ['Wanli Ouyang', 'Rui Zhao', 'Li Yi', 'Haiyang Yang', 'Feng Zhu', 'Lei Bai', 'Yuanzheng Ci', 'Yizhou Wang', 'Meilin Chen', 'Qingsong Xie', 'Cheng Chen', 'Shixiang Tang'] | 2023-03-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tang_HumanBench_Towards_General_Human-Centric_Perception_With_Projector_Assisted_Pretraining_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tang_HumanBench_Towards_General_Human-Centric_Perception_With_Projector_Assisted_Pretraining_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pedestrian-attribute-recognition', 'pedestrian-detection', 'human-parsing'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.40786716e-01 1.00145871e-02 1.10240981e-01 -6.87545717e-01
-6.14094853e-01 -4.29124475e-01 5.58470607e-01 -1.18597277e-01
-6.69806421e-01 7.10752368e-01 1.84658002e-02 -2.40836248e-01
1.95615008e-01 -6.21195674e-01 -1.03950703e+00 -7.18067110e-01
4.47376743e-02 7.10279584e-01 4.91474330e-01 -3.26079577... | [7.685159206390381, -0.6607102155685425] |
f5893faf-1e9c-4e7d-9e4b-16e8c83b6c80 | fedhil-heterogeneity-resilient-federated | 2307.01780 | null | https://arxiv.org/abs/2307.01780v1 | https://arxiv.org/pdf/2307.01780v1.pdf | FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices | Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments.... | ['Sudeep Pasricha', 'Danish Gufran'] | 2023-07-04 | null | null | null | null | ['indoor-localization', 'federated-learning', 'management'] | ['computer-vision', 'methodology', 'miscellaneous'] | [-2.73335904e-01 -7.08693624e-01 -1.88541025e-01 -3.02295148e-01
-9.48544145e-01 -7.11476505e-01 4.75351587e-02 2.21326634e-01
-3.45174372e-01 8.04514170e-01 2.00609684e-01 -6.03643298e-01
-2.11083636e-01 -6.61869884e-01 -8.38761091e-01 -5.50041199e-01
-1.64151058e-01 -1.80770427e-01 6.98293597e-02 3.32821578... | [6.417831897735596, 0.8933756947517395] |
ecfbd9bb-ccf6-41cb-8625-0297374fb2ef | learning-without-forgetting-for-vision | 2305.19270 | null | https://arxiv.org/abs/2305.19270v1 | https://arxiv.org/pdf/2305.19270v1.pdf | Learning without Forgetting for Vision-Language Models | Class-Incremental Learning (CIL) or continual learning is a desired capability in the real world, which requires a learning system to adapt to new tasks without forgetting former ones. While traditional CIL methods focus on visual information to grasp core features, recent advances in Vision-Language Models (VLM) have ... | ['Ziwei Liu', 'De-Chuan Zhan', 'Han-Jia Ye', 'Jingyi Ning', 'Yuanhan Zhang', 'Da-Wei Zhou'] | 2023-05-30 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 2.63138175e-01 -2.39273861e-01 -1.63447872e-01 -2.58459032e-01
-3.40223581e-01 -2.88472235e-01 6.37064815e-01 -1.73523724e-01
-4.99164820e-01 6.24655008e-01 9.86139104e-02 5.30679971e-02
1.29035234e-01 -5.33004105e-01 -9.03195679e-01 -5.71691453e-01
4.25427020e-01 3.93721282e-01 3.54040235e-01 -9.03428197... | [9.896248817443848, 3.2875967025756836] |
44f44abd-19bf-4969-ab5b-f8531ff16c95 | quasi-balanced-self-training-on-noise-aware | 2203.03833 | null | https://arxiv.org/abs/2203.03833v2 | https://arxiv.org/pdf/2203.03833v2.pdf | Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap | Semantic analyses of object point clouds are largely driven by releasing of benchmarking datasets, including synthetic ones whose instances are sampled from object CAD models. However, learning from synthetic data may not generalize to practical scenarios, where point clouds are typically incomplete, non-uniformly dist... | ['Kui Jia', 'Ke Chen', 'Longkun Zou', 'ZiHao Wang', 'Yongwei Chen'] | 2022-03-08 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 1.89291954e-01 -2.04418480e-01 -1.77769363e-02 -5.34805059e-01
-1.13488996e+00 -5.86407423e-01 6.26377583e-01 -2.45604649e-01
1.79210231e-01 8.23499084e-01 -2.65784055e-01 -4.58403956e-03
-1.44485474e-01 -1.05374742e+00 -1.07928491e+00 -8.15803409e-01
1.54085353e-01 1.04869854e+00 3.03574860e-01 -8.90301839... | [8.284819602966309, -3.053553342819214] |
6edad42b-706e-418d-a7d5-23229458db7e | deepsynth-program-synthesis-for-automatic | 1911.10244 | null | https://arxiv.org/abs/1911.10244v5 | https://arxiv.org/pdf/1911.10244v5.pdf | DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning | This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorithm for synthesis of c... | ['Mohammadhosein Hasanbeig', 'Alessandro Abate', 'Tom Melham', 'Daniel Kroening', 'Natasha Yogananda Jeppu'] | 2019-11-22 | null | null | null | null | ['probabilistic-deep-learning', 'montezumas-revenge'] | ['computer-vision', 'playing-games'] | [ 7.49474317e-02 3.12194586e-01 -2.60557801e-01 8.87816623e-02
-6.90201521e-01 -6.74144804e-01 7.29299128e-01 2.01261088e-01
-7.06000865e-01 1.10867095e+00 1.03863887e-01 -3.69113058e-01
-1.92614615e-01 -8.23273122e-01 -7.59063661e-01 -7.70458817e-01
-5.09095132e-01 9.54952538e-01 2.28487372e-01 -3.09262484... | [4.092466354370117, 1.7611104249954224] |
8098bbda-b577-4968-aab3-d9b2c3e9e0ab | mcf-mutual-correction-framework-for-semi | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf | MCF: Mutual Correction Framework for Semi-Supervised Medical Image Segmentation | Semi-supervised learning is a promising method for medical image segmentation under limited annotation. However, the model cognitive bias impairs the segmentation performance, especially for edge regions. Furthermore, current mainstream semi-supervised medical image segmentation (SSMIS) methods lack designs to hand... | ['Xinbo Gao', 'Weisheng Li', 'Xiuli Bi', 'Bin Xiao', 'Yongchao Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['semi-supervised-medical-image-segmentation', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 5.07061362e-01 4.69340563e-01 -5.55321038e-01 -6.61961734e-01
-5.72315693e-01 1.43003939e-02 1.38102826e-02 -1.73794397e-03
-5.86952806e-01 6.71244502e-01 -1.39327109e-01 -2.25948468e-01
6.51479587e-02 -4.54326451e-01 -5.23116171e-01 -8.19966435e-01
2.40127623e-01 4.87724990e-01 6.94924831e-01 6.70155883... | [14.654088973999023, -2.1079001426696777] |
ab9a6253-335c-4245-b7c7-48a6ef12efe2 | data-driven-news-generation-for-automated | null | null | https://aclanthology.org/W17-3528 | https://aclanthology.org/W17-3528.pdf | Data-Driven News Generation for Automated Journalism | Despite increasing amounts of data and ever improving natural language generation techniques, work on automated journalism is still relatively scarce. In this paper, we explore the field and challenges associated with building a journalistic natural language generation system. We present a set of requirements that shou... | ['Mark Granroth-Wilding', 'Leo Lepp{\\"a}nen', 'Myriam Munezero', 'Hannu Toivonen'] | 2017-09-01 | null | null | null | ws-2017-9 | ['news-generation'] | ['natural-language-processing'] | [ 8.10113847e-02 6.70507312e-01 -3.75845551e-01 -1.94536313e-01
-6.50318384e-01 -9.40703034e-01 1.41966915e+00 3.08123261e-01
-4.51069891e-01 1.29654634e+00 8.86649907e-01 -7.76059270e-01
-7.58391768e-02 -6.24088526e-01 -4.16009218e-01 4.94925588e-01
4.61243629e-01 5.18910348e-01 -2.67593652e-01 -6.48694813... | [11.741381645202637, 9.080975532531738] |
4ff086e3-dc1b-4b38-9973-05d5cf154efb | lsdm-long-short-diffeomorphic-motion-for | 2301.04748 | null | https://arxiv.org/abs/2301.04748v2 | https://arxiv.org/pdf/2301.04748v2.pdf | LSDM: Long-Short Diffeomorphic Motion for Weakly-Supervised Ultrasound Landmark Tracking | Accurate tracking of an anatomical landmark over time has been of high interests for disease assessment such as minimally invasive surgery and tumor radiation therapy. Ultrasound imaging is a promising modality benefiting from low-cost and real-time acquisition. However, generating a precise landmark tracklet is very c... | ['Huiyu Zhou', 'Xuejun Ni', 'Yan Shen', 'Bin Yang', 'Zhihua Liu'] | 2023-01-11 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [ 1.06307484e-01 -3.02562467e-03 -3.75087947e-01 -2.32203305e-01
-1.25795329e+00 -6.58865035e-01 3.70844334e-01 -2.30447754e-01
-4.54871356e-01 4.15263802e-01 2.95263112e-01 -2.19639376e-01
-4.50562954e-01 -2.47347280e-01 -5.65617621e-01 -1.00960946e+00
-8.98165256e-02 5.26236057e-01 4.97701019e-01 1.85128197... | [14.207191467285156, -2.6191647052764893] |
1be615b0-ea69-4689-a320-b655dae79fea | audio-visual-face-reenactment | 2210.02755 | null | https://arxiv.org/abs/2210.02755v1 | https://arxiv.org/pdf/2210.02755v1.pdf | Audio-Visual Face Reenactment | This work proposes a novel method to generate realistic talking head videos using audio and visual streams. We animate a source image by transferring head motion from a driving video using a dense motion field generated using learnable keypoints. We improve the quality of lip sync using audio as an additional input, he... | ['C V Jawahar', 'Vinay Namboodiri', 'Rudrabha Mukhopadhyay', 'Madhav Agarwal'] | 2022-10-06 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 5.32158390e-02 2.51449019e-01 5.80227235e-03 -4.83167440e-01
-9.90871668e-01 -5.91359735e-01 6.54282510e-01 -7.11548209e-01
-6.11224696e-02 6.67491794e-01 5.19206822e-01 2.16568306e-01
4.45920289e-01 -4.50235218e-01 -7.72084832e-01 -6.94339275e-01
1.87434431e-03 3.50248516e-01 8.20155591e-02 -1.06992505... | [13.219442367553711, -0.41698533296585083] |
1cd318b6-74c8-4433-aedd-57c1b23f8bf2 | ambisep-ambisonic-to-ambisonic-reverberant | 2206.06184 | null | https://arxiv.org/abs/2206.06184v1 | https://arxiv.org/pdf/2206.06184v1.pdf | AmbiSep: Ambisonic-to-Ambisonic Reverberant Speech Separation Using Transformer Networks | Consider a multichannel Ambisonic recording containing a mixture of several reverberant speech signals. Retreiving the reverberant Ambisonic signals corresponding to the individual speech sources blindly from the mixture is a challenging task as it requires to estimate multiple signal channels for each source. In this ... | ['Emanuël A. P. Habets', 'Srikanth Raj Chetupalli', 'Adrian Herzog'] | 2022-06-13 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 2.07544416e-01 -7.45229006e-01 6.52697802e-01 -4.43698540e-02
-1.40682077e+00 -6.53138816e-01 2.16725320e-01 -6.14157617e-01
-2.97074616e-01 5.90997398e-01 6.77138329e-01 -3.72470796e-01
-2.49394909e-01 -1.13416344e-01 -5.33274770e-01 -1.01526690e+00
-1.33561343e-01 -2.27362424e-01 -1.94797352e-01 -1.43869519... | [15.008684158325195, 5.797251224517822] |
ca93de5d-dab2-48d2-af85-5ba5c6f30b6f | re-evaluating-adem-a-deeper-look-at-scoring | 1902.08832 | null | http://arxiv.org/abs/1902.08832v1 | http://arxiv.org/pdf/1902.08832v1.pdf | Re-evaluating ADEM: A Deeper Look at Scoring Dialogue Responses | Automatically evaluating the quality of dialogue responses for unstructured
domains is a challenging problem. ADEM(Lowe et al. 2017) formulated the
automatic evaluation of dialogue systems as a learning problem and showed that
such a model was able to predict responses which correlate significantly with
human judgement... | ['Mithun Das Gupta', 'Mitesh M. Khapra', 'Ananya B. Sai', 'Mukundhan Srinivasan'] | 2019-02-23 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 2.40346074e-01 8.12496543e-01 4.12727892e-01 -6.18828177e-01
-9.07086551e-01 -1.09037387e+00 9.85938549e-01 -6.54846802e-02
-5.38152754e-01 7.81753361e-01 4.82429951e-01 -6.59378350e-01
5.24173118e-03 -6.03902042e-01 -1.77809134e-01 -2.52564311e-01
8.84253755e-02 7.77162850e-01 1.92569688e-01 -1.14661312... | [12.706365585327148, 8.183653831481934] |
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