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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c6e0a0f9-2504-4f79-bbc0-4edf2babd547 | learning-using-privileged-information-for | 2206.08632 | null | https://arxiv.org/abs/2206.08632v2 | https://arxiv.org/pdf/2206.08632v2.pdf | Learning Using Privileged Information for Zero-Shot Action Recognition | Zero-Shot Action Recognition (ZSAR) aims to recognize video actions that have never been seen during training. Most existing methods assume a shared semantic space between seen and unseen actions and intend to directly learn a mapping from a visual space to the semantic space. This approach has been challenged by the s... | ['Zihui Guo', 'Yonghong Hou', 'Bin Yu', 'Wanqing Li', 'Zhiyi Gao'] | 2022-06-17 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 4.29904938e-01 2.15377584e-01 -3.07351679e-01 -4.21606392e-01
-3.67020935e-01 -1.42246574e-01 7.86455274e-01 -2.43167222e-01
-5.86387277e-01 7.66076624e-01 4.01021421e-01 1.98386922e-01
1.87412016e-02 -4.93658096e-01 -7.45706499e-01 -5.42607784e-01
1.58308204e-02 2.03604504e-01 6.55605376e-01 -1.62389517... | [8.555285453796387, 0.9188034534454346] |
f0fbbc07-f7f0-46e2-b422-4c4d5f66b129 | drug-repurposing-targeting-covid-19-3cl | 2305.18088 | null | https://arxiv.org/abs/2305.18088v3 | https://arxiv.org/pdf/2305.18088v3.pdf | Drug Repurposing Targeting COVID-19 3CL Protease using Molecular Docking and Machine Learning Regression Approach | The COVID-19 pandemic has created a global health crisis, driving the need for the rapid identification of potential therapeutics. To meet this challenge, drug repurposing is the only solution with saving cost and time. In this study, we used the Zinc database to screen the world-approved including FDA-approved 5903 dr... | ['Abdul Majid', 'Imra Aqeel'] | 2023-05-25 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-1.35079375e-03 -5.50153971e-01 -2.09011614e-01 5.31601347e-02
-5.16749084e-01 -6.46464825e-01 9.55685135e-03 5.20724773e-01
-3.10417265e-01 1.46133912e+00 -9.99874026e-02 -6.84867263e-01
-2.18573257e-01 -5.60563922e-01 -5.71006596e-01 -8.09595585e-01
-2.11031348e-01 5.70220709e-01 -2.12445050e-01 -2.53960460... | [4.727728366851807, 5.2632246017456055] |
9e77b6bb-f615-4012-b9c6-b1bf6520dbb0 | an-empirical-study-of-end-to-end-temporal | 2204.02932 | null | https://arxiv.org/abs/2204.02932v1 | https://arxiv.org/pdf/2204.02932v1.pdf | An Empirical Study of End-to-End Temporal Action Detection | Temporal action detection (TAD) is an important yet challenging task in video understanding. It aims to simultaneously predict the semantic label and the temporal interval of every action instance in an untrimmed video. Rather than end-to-end learning, most existing methods adopt a head-only learning paradigm, where th... | ['Xiang Bai', 'Song Bai', 'Xiaolong Liu'] | 2022-04-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_An_Empirical_Study_of_End-to-End_Temporal_Action_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_An_Empirical_Study_of_End-to-End_Temporal_Action_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-classification'] | ['computer-vision'] | [ 1.07227489e-01 -2.30221704e-01 -6.40673220e-01 -4.02131855e-01
-1.00980341e+00 -4.08616334e-01 3.46598327e-01 -2.44881451e-01
-5.54637372e-01 1.43448904e-01 2.92683929e-01 -2.84731597e-01
2.01214537e-01 -1.97071716e-01 -7.56519973e-01 -3.86626393e-01
-4.55171943e-01 -1.21805586e-01 5.44302940e-01 2.79012501... | [8.473756790161133, 0.37611404061317444] |
ebabe482-2865-4ff2-b279-92a18c52679d | paracrawl-web-scale-acquisition-of-parallel | null | null | https://aclanthology.org/2020.acl-main.417 | https://aclanthology.org/2020.acl-main.417.pdf | ParaCrawl: Web-Scale Acquisition of Parallel Corpora | We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software. We empirically compare alternative methods and publish benchmark data sets for sentence alignment and sentence pair filtering. We also describe the parallel corpora released and evaluate their... | ['Pin-zhen Chen', 'Brian Thompson', "Elsa Sarr{\\'\\i}as", "Gema Ram{\\'\\i}rez-S{\\'a}nchez", 'Miquel Espl{\\`a}-Gomis', "Marta Ba{\\~n}{\\'o}n", 'William Waites', 'Mikel L. Forcada', 'Jaume Zaragoza', 'Hieu Hoang', 'Barry Haddow', 'Kenneth Heafield', 'Sergio Ortiz Rojas', 'Philipp Koehn', 'Marek Strelec', 'Leopoldo P... | 2020-07-01 | null | null | null | acl-2020-6 | ['parallel-corpus-mining'] | ['natural-language-processing'] | [ 2.36818954e-01 -2.60719627e-01 -2.81441659e-01 -4.56317127e-01
-1.69633019e+00 -1.06758428e+00 8.02431762e-01 2.48444989e-01
-5.85101902e-01 1.15521443e+00 5.02805769e-01 -5.38649440e-01
2.16272563e-01 -4.68172997e-01 -5.27759850e-01 -1.29534587e-01
6.77836835e-02 1.45202470e+00 3.90600920e-01 -9.34880376... | [11.466346740722656, 10.377930641174316] |
e90728a9-d7d6-4412-bbf4-2e78c2ddf1e8 | diachronic-parsing-of-pre-standard-irish | null | null | https://aclanthology.org/2022.cltw-1.2 | https://aclanthology.org/2022.cltw-1.2.pdf | Diachronic Parsing of Pre-Standard Irish | Irish underwent a major spelling standardization in the 1940’s and 1950’s, and as a result it can be challenging to apply language technologies designed for the modern language to older, “pre-standard” texts. Lemmatization, tagging, and parsing of these pre-standard texts play an important role in a number of applicati... | ['Kevin Scannell'] | null | null | null | null | cltw-lrec-2022-6 | ['lemmatization'] | ['natural-language-processing'] | [ 1.00626417e-01 -1.01629332e-01 -2.21508726e-01 -4.14195746e-01
-8.20296049e-01 -1.08114445e+00 6.77945554e-01 4.71344590e-01
-9.45148528e-01 7.69870043e-01 7.22025514e-01 -5.92160583e-01
8.37972835e-02 -4.48242873e-01 -5.21392263e-02 -2.53850013e-01
5.81857003e-03 5.53232372e-01 8.56967643e-02 -5.13448596... | [10.402688980102539, 10.124384880065918] |
b669fd9c-50bd-46fa-8e9b-f184cc5dbd3e | efficient-multi-grained-knowledge-reuse-for | 2306.02027 | null | https://arxiv.org/abs/2306.02027v1 | https://arxiv.org/pdf/2306.02027v1.pdf | Efficient Multi-Grained Knowledge Reuse for Class Incremental Segmentation | Class Incremental Semantic Segmentation (CISS) has been a trend recently due to its great significance in real-world applications. Although the existing CISS methods demonstrate remarkable performance, they either leverage the high-level knowledge (feature) only while neglecting the rich and diverse knowledge in the lo... | ['Xinchao Wang', 'Shuicheng Yan', 'Zhihe Lu'] | 2023-06-03 | null | null | null | null | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 3.80786061e-01 -2.98681529e-03 -1.14691675e-01 -3.70105535e-01
-8.29366148e-01 -4.89626586e-01 4.75339860e-01 1.94002420e-01
-6.49609447e-01 5.22825778e-01 -9.36734006e-02 5.35421148e-02
-1.61094218e-01 -8.79977047e-01 -8.76606882e-01 -6.29207194e-01
1.27631247e-01 5.14958845e-03 1.07230914e+00 -2.84158528... | [9.642404556274414, 0.17618820071220398] |
d47905d7-e92b-4396-819e-53e3d124fa92 | algebraic-learning-towards-interpretable | 2203.06690 | null | https://arxiv.org/abs/2203.06690v1 | https://arxiv.org/pdf/2203.06690v1.pdf | Algebraic Learning: Towards Interpretable Information Modeling | Along with the proliferation of digital data collected using sensor technologies and a boost of computing power, Deep Learning (DL) based approaches have drawn enormous attention in the past decade due to their impressive performance in extracting complex relations from raw data and representing valuable information. M... | ['Tong Owen Yang'] | 2022-03-13 | null | null | null | null | ['abstract-algebra'] | ['reasoning'] | [ 2.69016683e-01 6.01133704e-01 -3.44257504e-01 -2.66501009e-01
-2.11812973e-01 -3.90217096e-01 6.72161639e-01 4.18396413e-01
-6.25367556e-03 7.18427360e-01 -1.21045321e-01 -5.09532213e-01
-6.63551927e-01 -7.31560171e-01 -5.81496179e-01 -7.87161946e-01
-1.27370238e-01 1.68952733e-01 -3.61021876e-01 -2.56400019... | [8.567501068115234, 5.163493633270264] |
fb0b2c87-9a22-4730-813d-9650ea4d2a8b | interpretability-and-transparency-driven | 2307.01225 | null | https://arxiv.org/abs/2307.01225v1 | https://arxiv.org/pdf/2307.01225v1.pdf | Interpretability and Transparency-Driven Detection and Transformation of Textual Adversarial Examples (IT-DT) | Transformer-based text classifiers like BERT, Roberta, T5, and GPT-3 have shown impressive performance in NLP. However, their vulnerability to adversarial examples poses a security risk. Existing defense methods lack interpretability, making it hard to understand adversarial classifications and identify model vulnerabi... | ['Sharif Abuadbba', 'M. Ali Babar', 'Bushra Sabir'] | 2023-07-03 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 3.03049207e-01 9.28495377e-02 -1.63477138e-02 -1.68468550e-01
-7.67344117e-01 -1.39570415e+00 7.97716379e-01 3.59691232e-01
6.12811297e-02 2.30622426e-01 4.27460790e-01 -7.35776246e-01
5.69892339e-02 -7.65340209e-01 -5.48602581e-01 -3.11400682e-01
8.18032175e-02 2.88881898e-01 -2.82876104e-01 -4.05016243... | [5.994237422943115, 8.067666053771973] |
1bb632fb-6a00-4b12-8fc8-62f12f952e2f | how-far-are-we-from-solving-the-2d-3d-face | 1703.07332 | null | http://arxiv.org/abs/1703.07332v3 | http://arxiv.org/pdf/1703.07332v3.pdf | How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) | This paper investigates how far a very deep neural network is from attaining
close to saturating performance on existing 2D and 3D face alignment datasets.
To this end, we make the following 5 contributions: (a) we construct, for the
first time, a very strong baseline by combining a state-of-the-art architecture
for la... | ['Georgios Tzimiropoulos', 'Adrian Bulat'] | 2017-03-21 | how-far-are-we-from-solving-the-2d-3d-face-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Bulat_How_Far_Are_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Bulat_How_Far_Are_ICCV_2017_paper.pdf | iccv-2017-10 | ['head-pose-estimation'] | ['computer-vision'] | [-2.41128072e-01 1.95746347e-01 -5.48261357e-03 -6.67242169e-01
-7.58470297e-01 -3.86748523e-01 7.76393950e-01 -3.85704011e-01
-3.84594470e-01 2.01251864e-01 2.66465902e-01 -1.77999541e-01
6.83445781e-02 -3.25013906e-01 -6.90071583e-01 -3.96959841e-01
-2.19422296e-01 8.17820847e-01 -1.89931557e-01 -3.89720410... | [13.440896034240723, 0.36690250039100647] |
56c443ac-8358-4258-bad1-7a10a8e0c58c | improving-spoken-language-identification-with | 2302.08229 | null | https://arxiv.org/abs/2302.08229v1 | https://arxiv.org/pdf/2302.08229v1.pdf | Improving Spoken Language Identification with Map-Mix | The pre-trained multi-lingual XLSR model generalizes well for language identification after fine-tuning on unseen languages. However, the performance significantly degrades when the languages are not very distinct from each other, for example, in the case of dialects. Low resource dialect classification remains a chall... | ['Eng Siong Chng', 'Tarun Gupta', 'Swaraj Dalmia', 'Kriti Anandan', 'Shangeth Rajaa'] | 2023-02-16 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-1.27551690e-01 -4.10782546e-01 -5.93336821e-01 -5.81491232e-01
-1.25448406e+00 -1.05103648e+00 7.16706991e-01 -7.56636932e-02
-4.77864772e-01 5.53090632e-01 4.68498617e-01 -5.39034069e-01
3.08097184e-01 -5.99503577e-01 -7.60096788e-01 -4.94441241e-01
3.35594267e-01 6.65906906e-01 -2.67369777e-01 -2.32777253... | [10.99407958984375, 9.96546459197998] |
835c0e5f-d257-4d83-80c3-db12ece8d66c | one-general-teacher-for-multi-data-multi-task | null | null | https://openreview.net/forum?id=z5IgrlFV_e | https://openreview.net/pdf?id=z5IgrlFV_e | One General Teacher for Multi-Data Multi-Task: A New Knowledge Distillation Framework for Discourse Relation Analysis | Automatically identifying the discourse relations can help many downstream NLP tasks such as reading comprehension. It can be categorized into explicit and implicit discourse relation recognition (EDRR and IDRR). Due to the lack of connectives, IDRR remains to be a big challenge. A good number of methods have been dev... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['relation-classification'] | ['natural-language-processing'] | [ 2.41479948e-01 7.52018631e-01 -3.62713456e-01 -5.21588743e-01
-7.82896161e-01 -4.61140722e-01 7.17022955e-01 2.06148952e-01
-3.65703017e-01 8.33618939e-01 3.33062947e-01 -6.24606669e-01
-2.94495344e-01 -6.55884564e-01 -5.18001199e-01 -7.90278316e-01
2.06947237e-01 5.40920973e-01 2.85561889e-01 -2.05782309... | [10.689582824707031, 9.282344818115234] |
574904dc-2d57-418d-8491-40293b319e98 | robust-pose-transfer-with-dynamic-details | 2106.14132 | null | https://arxiv.org/abs/2106.14132v3 | https://arxiv.org/pdf/2106.14132v3.pdf | Robust Pose Transfer with Dynamic Details using Neural Video Rendering | Pose transfer of human videos aims to generate a high fidelity video of a target person imitating actions of a source person. A few studies have made great progress either through image translation with deep latent features or neural rendering with explicit 3D features. However, both of them rely on large amounts of tr... | ['Lin Gao', 'Wei Liu', 'Yu-Kun Lai', 'Xuan Wang', 'Hao-Zhi Huang', 'Yang-tian Sun'] | 2021-06-27 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 3.66398335e-01 -1.47123873e-01 2.03123704e-01 -1.34385183e-01
-5.31746447e-01 -3.53089154e-01 7.87357390e-01 -6.59817815e-01
-9.60651636e-02 7.55212367e-01 2.34137416e-01 2.36658975e-01
3.73799652e-01 -6.35390162e-01 -1.03081179e+00 -7.85550833e-01
-4.35280707e-03 -7.08408579e-02 1.53056264e-01 -1.63259447... | [11.313522338867188, -1.1458417177200317] |
0b54367b-e0f2-42ac-8cab-4d7708ff33b7 | spac-net-synthetic-pose-aware-animal | 2305.17845 | null | https://arxiv.org/abs/2305.17845v2 | https://arxiv.org/pdf/2305.17845v2.pdf | SPAC-Net: Synthetic Pose-aware Animal ControlNet for Enhanced Pose Estimation | Animal pose estimation has become a crucial area of research, but the scarcity of annotated data is a significant challenge in developing accurate models. Synthetic data has emerged as a promising alternative, but it frequently exhibits domain discrepancies with real data. Style transfer algorithms have been proposed t... | ['Sarah Ostadabbas', 'Le Jiang'] | 2023-05-29 | null | null | null | null | ['style-transfer', 'pose-estimation', 'edge-detection', 'animal-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.93200961e-01 1.24702707e-01 3.93379778e-01 -3.32930684e-01
-7.45962679e-01 -5.61089575e-01 5.68225622e-01 -1.62294880e-02
-6.08393610e-01 7.52368569e-01 -2.22180095e-02 3.62063348e-01
2.16184556e-01 -7.07736552e-01 -1.23475087e+00 -3.90858322e-01
3.89281064e-02 5.97139418e-01 5.39027631e-01 -3.60648572... | [7.592082977294922, -1.0702061653137207] |
5f5e8fd4-df13-4606-b04f-616c951f2a1e | multivariate-time-series-anomaly-detection | 2009.02040 | null | https://arxiv.org/abs/2009.02040v1 | https://arxiv.org/pdf/2009.02040v1.pdf | Multivariate Time-series Anomaly Detection via Graph Attention Network | Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications. Recent approaches have achieved significant progress in this topic, but there is remaining limitations. One major limitation is that they do not capture the relationships between different time... | ['Yujing Wang', 'Yunhai Tong', 'Congrui Huang', 'Hang Zhao', 'Jing Bai', 'Defu Cao', 'Bixiong Xu', 'Qi Zhang', 'Juanyong Duan', 'Jie Tong'] | 2020-09-04 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [ 6.94195703e-02 -2.43158624e-01 -8.12604465e-03 -3.86518806e-01
-4.63834871e-03 -1.42767340e-01 3.54503393e-01 7.66976237e-01
1.39552634e-02 1.28139034e-01 -1.02710359e-01 -4.47762221e-01
-3.49335670e-01 -7.08608329e-01 -4.37685490e-01 -4.89733726e-01
-6.71626747e-01 2.89869934e-01 2.79972553e-01 -2.02192321... | [7.24104642868042, 2.772202730178833] |
616e63c7-13ca-4aa7-8f00-78ce8c7039b8 | assignment-space-based-multi-object-tracking | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Choudhuri_Assignment-Space-Based_Multi-Object_Tracking_and_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Choudhuri_Assignment-Space-Based_Multi-Object_Tracking_and_Segmentation_ICCV_2021_paper.pdf | Assignment-Space-Based Multi-Object Tracking and Segmentation | Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data. Existing methods perform well on multi-object detection and segmentation for independent video frames, but tracking of objects over time remains a challenge. MOTS methods formulate tracking locally, i.e., fram... | ['Alexander G. Schwing', 'Girish Chowdhary', 'Anwesa Choudhuri'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 2.43631572e-01 -3.83050263e-01 -1.28547713e-01 3.80232073e-02
-8.89896333e-01 -8.32762063e-01 -8.69552791e-02 1.67354494e-01
-5.47430933e-01 3.89833391e-01 -5.23581505e-01 5.16652539e-02
-1.01826623e-01 -4.18394983e-01 -1.09670210e+00 -7.70564377e-01
-1.76365852e-01 6.93035662e-01 1.28287244e+00 1.70382902... | [6.437530517578125, -2.031482219696045] |
43d8e0a7-9bd2-4137-b23c-45c04c01e615 | dense-3d-point-cloud-reconstruction-using-a | 1901.08906 | null | http://arxiv.org/abs/1901.08906v1 | http://arxiv.org/pdf/1901.08906v1.pdf | Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network | Reconstructing a high-resolution 3D model of an object is a challenging task
in computer vision. Designing scalable and light-weight architectures is
crucial while addressing this problem. Existing point-cloud based
reconstruction approaches directly predict the entire point cloud in a single
stage. Although this techn... | ['R. Venkatesh Babu', 'Priyanka Mandikal'] | 2019-01-25 | null | null | null | null | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.05284609e-01 -1.52525976e-01 2.13396266e-01 -3.67030531e-01
-8.68520319e-01 -3.53831798e-01 5.36710560e-01 4.09908332e-02
1.43500492e-01 2.66961902e-01 -2.08243549e-01 -2.82971971e-02
2.17763111e-01 -1.21947050e+00 -1.15505993e+00 -2.18825117e-01
6.67058676e-02 1.08117366e+00 6.44418836e-01 -1.19974725... | [8.406600952148438, -3.5586838722229004] |
30ee8969-81bc-4bd3-8481-83e4e722d5bb | gred-graph-regularized-3d-shape | 1309.4426 | null | http://arxiv.org/abs/1309.4426v1 | http://arxiv.org/pdf/1309.4426v1.pdf | GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images | Analysis of microscopy images can provide insight into many biological
processes. One particularly challenging problem is cell nuclear segmentation in
highly anisotropic and noisy 3D image data. Manually localizing and segmenting
each and every cell nuclei is very time consuming, which remains a bottleneck
in large sca... | ['Gunnar Rätsch', 'Xinghua Lou', 'Christian Widmer', 'Stephanie Heinrich', 'Philipp Drewe', 'Shefali Umrania'] | 2013-09-17 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 2.49452284e-03 -3.55867416e-01 4.18229669e-01 -2.61023670e-01
-5.47500253e-01 -7.21656501e-01 1.06246792e-01 6.57559752e-01
-1.07030308e+00 7.57703543e-01 -6.50868475e-01 -3.70836169e-01
2.89759487e-01 -5.42035639e-01 -2.30857536e-01 -1.03203464e+00
1.81425229e-01 1.09059870e+00 8.52738976e-01 1.96529716... | [14.399277687072754, -3.1540346145629883] |
292af0a0-ee56-4e88-bd57-0a3051123bfa | adversarial-attacks-on-knowledge-graph-1 | 2111.03120 | null | https://arxiv.org/abs/2111.03120v1 | https://arxiv.org/pdf/2111.03120v1.pdf | Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods | Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about the security vulnerabilities that might disrupt their intended behaviour. We study data poisoning attacks against KGE models for link prediction. These attacks craft adversarial additions or deletions at training time to cause model f... | ["Declan O'Sullivan", 'Luca Costabello', 'John Kelleher', 'Peru Bhardwaj'] | 2021-11-04 | adversarial-attacks-on-knowledge-graph | https://aclanthology.org/2021.emnlp-main.648 | https://aclanthology.org/2021.emnlp-main.648.pdf | emnlp-2021-11 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [ 5.26059158e-02 6.83309138e-01 -5.20210087e-01 -1.73670538e-02
-5.01774430e-01 -7.89963365e-01 6.97070837e-01 4.58722651e-01
-1.72830030e-01 8.18362951e-01 -5.62779233e-02 -6.50544226e-01
-1.67191505e-01 -1.32142806e+00 -1.23184395e+00 -4.37845021e-01
-4.51261610e-01 6.58252358e-01 5.36200404e-01 -1.29844651... | [6.199313163757324, 7.372837543487549] |
f21a3644-14d0-46a4-a1e7-2f56057a2dbb | gp-unit-generative-prior-for-versatile | 2306.04636 | null | https://arxiv.org/abs/2306.04636v1 | https://arxiv.org/pdf/2306.04636v1.pdf | GP-UNIT: Generative Prior for Versatile Unsupervised Image-to-Image Translation | Recent advances in deep learning have witnessed many successful unsupervised image-to-image translation models that learn correspondences between two visual domains without paired data. However, it is still a great challenge to build robust mappings between various domains especially for those with drastic visual discr... | ['Chen Change Loy', 'Ziwei Liu', 'Liming Jiang', 'Shuai Yang'] | 2023-06-07 | null | null | null | null | ['unsupervised-image-to-image-translation', 'image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 3.13891172e-01 7.99140707e-02 -1.56643018e-01 -3.26622128e-01
-8.93115520e-01 -6.54260159e-01 6.89168155e-01 -5.65285981e-01
1.20786160e-01 7.37203956e-01 4.91788005e-03 1.19792476e-01
2.35887557e-01 -8.55074704e-01 -9.26568210e-01 -6.84322476e-01
6.88489854e-01 6.19639635e-01 1.93387493e-01 -3.44424129... | [11.728214263916016, -0.393639475107193] |
fc7c575c-0863-42c4-b925-5793fd9faf05 | an-evaluation-of-log-parsing-with-chatgpt | 2306.01590 | null | https://arxiv.org/abs/2306.01590v1 | https://arxiv.org/pdf/2306.01590v1.pdf | An Evaluation of Log Parsing with ChatGPT | Software logs play an essential role in ensuring the reliability and maintainability of large-scale software systems, as they are often the sole source of runtime information. Log parsing, which converts raw log messages into structured data, is an important initial step towards downstream log analytics. In recent stud... | ['Hongyu Zhang', 'Van-Hoang Le'] | 2023-06-02 | null | null | null | null | ['log-parsing'] | ['computer-code'] | [ 4.33990099e-02 2.40513142e-02 -3.09516072e-01 -2.47513637e-01
-1.07801723e+00 -6.84502304e-01 3.17420542e-01 6.26099646e-01
8.33486021e-02 1.14001736e-01 2.09201559e-01 -1.06624532e+00
1.98504031e-01 -5.82835257e-01 -3.91487479e-01 3.23740095e-01
-4.19696122e-01 3.25105727e-01 7.07882941e-01 -7.28250667... | [7.941205978393555, 7.009542465209961] |
32398f74-f56b-4b24-8a34-f8c13af1affe | the-conditional-cauchy-schwarz-divergence | 2301.08970 | null | https://arxiv.org/abs/2301.08970v1 | https://arxiv.org/pdf/2301.08970v1.pdf | The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making | The Cauchy-Schwarz (CS) divergence was developed by Pr\'{i}ncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be simply estimated by a kernel density estimator from given sampl... | ['José C. Príncipe', 'Robert Jenssen', 'Sigurd Løkse', 'Hongming Li', 'Shujian Yu'] | 2023-01-21 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-7.16443658e-02 -1.03970289e-01 -2.87309382e-02 -4.94991720e-01
-9.28354204e-01 -5.15854359e-01 4.87864941e-01 1.45083547e-01
-3.78756613e-01 1.09608734e+00 -3.61865819e-01 -3.28363866e-01
-5.80284595e-01 -4.20444995e-01 -4.27669287e-01 -9.36836898e-01
-6.11083865e-01 4.36432719e-01 1.51557073e-01 1.91310644... | [7.221261978149414, 4.088985919952393] |
e1dcc2c7-6d5d-4632-b706-6bcd30a20f32 | enhancement-of-noisy-speech-with-low-speech | 1802.05125 | null | http://arxiv.org/abs/1802.05125v1 | http://arxiv.org/pdf/1802.05125v1.pdf | Enhancement of Noisy Speech with Low Speech Distortion Based on Probabilistic Geometric Spectral Subtraction | A speech enhancement method based on probabilistic geometric approach to
spectral subtraction (PGA) performed on short time magnitude spectrum is
presented in this paper. A confidence parameter of noise estimation is
introduced in the gain function of the proposed method to prevent subtraction
of the overestimated and ... | [] | 2018-02-13 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 6.36069894e-01 -8.45174417e-02 5.41340888e-01 -1.33666426e-01
-6.64546967e-01 -4.15848911e-01 2.49675199e-01 8.71121585e-02
-3.87434304e-01 7.95824289e-01 4.69997108e-01 -1.07528962e-01
-2.60483772e-01 -5.31405747e-01 -5.91643006e-02 -1.04166532e+00
2.24760354e-01 -5.22438705e-01 3.85898769e-01 -1.71742067... | [15.00145435333252, 5.779177188873291] |
10f99de3-5069-415b-8a40-ebde9d08a7b1 | real-time-joint-semantic-segmentation-and | 1809.04766 | null | http://arxiv.org/abs/1809.04766v2 | http://arxiv.org/pdf/1809.04766v2.pdf | Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations | Deployment of deep learning models in robotics as sensory information
extractors can be a daunting task to handle, even using generic GPU cards.
Here, we address three of its most prominent hurdles, namely, i) the adaptation
of a single model to perform multiple tasks at once (in this work, we consider
depth estimation... | ['Vladimir Nekrasov', 'Tom Drummond', 'Chunhua Shen', 'Andrew Spek', 'Thanuja Dharmasiri', 'Ian Reid'] | 2018-09-13 | null | null | null | null | ['surface-normals-estimation'] | ['computer-vision'] | [ 2.47402415e-01 3.68308365e-01 3.94950271e-01 -2.67440677e-01
-7.56942749e-01 -6.99289501e-01 3.01056623e-01 2.59694427e-01
-8.81603301e-01 6.11600757e-01 -2.97098964e-01 -3.87731284e-01
6.55905753e-02 -7.97637820e-01 -1.01293576e+00 -6.81546926e-01
9.66619998e-02 7.42806613e-01 7.87031114e-01 -2.99480353... | [8.484028816223145, -2.3378190994262695] |
9c76825d-f7f0-434e-8bf4-2032eaf3c271 | butknot-at-semeval-2016-task-5-supervised | null | null | https://aclanthology.org/S16-1048 | https://aclanthology.org/S16-1048.pdf | BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection | null | ["Jakub Mach{\\'a}{\\v{c}}ek"] | 2016-06-01 | null | null | null | semeval-2016-6 | ['aspect-category-detection'] | ['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... | [-7.350883960723877, 3.7655811309814453] |
109f428d-f691-4f67-8c41-d0a132d4e401 | supervised-learning-in-the-presence-of-noise | 2103.07808 | null | https://arxiv.org/abs/2103.07808v1 | https://arxiv.org/pdf/2103.07808v1.pdf | Supervised Learning in the Presence of Noise: Application in ICD-10 Code Classification | ICD coding is the international standard for capturing and reporting health conditions and diagnosis for revenue cycle management in healthcare. Manually assigning ICD codes is prone to human error due to the large code vocabulary and the similarities between codes. Since machine learning based approaches require groun... | ['Javed Aslam', 'Amir Tahmasebi', 'Bingyang Ye', 'Cheng Li', 'Youngwoo Kim'] | 2021-03-13 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 6.84114471e-02 8.42672884e-02 -4.72251862e-01 -5.03394842e-01
-7.99745321e-01 -7.05269992e-01 -8.91227217e-04 6.85154796e-01
-1.61029339e-01 4.97573644e-01 3.61488551e-01 -6.19164288e-01
-3.28975946e-01 -7.14937508e-01 -3.75590533e-01 -2.66376883e-01
1.81965694e-01 5.82926869e-01 -2.64095873e-01 2.42371067... | [8.01131534576416, 6.8083014488220215] |
bf6ceb8e-83c6-4805-a7c0-330907b80aea | blind-image-deblurring-with-unknown-kernel | 2208.09483 | null | https://arxiv.org/abs/2208.09483v1 | https://arxiv.org/pdf/2208.09483v1.pdf | Blind Image Deblurring with Unknown Kernel Size and Substantial Noise | Blind image deblurring (BID) has been extensively studied in computer vision and adjacent fields. Modern methods for BID can be grouped into two categories: single-instance methods that deal with individual instances using statistical inference and numerical optimization, and data-driven methods that train deep-learnin... | ['Ju Sun', 'Hengkang Wang', 'Taihui Li', 'Zhong Zhuang'] | 2022-08-18 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [-6.87024817e-02 -4.12469268e-01 2.52797380e-02 -1.54212657e-02
-7.65897036e-01 -4.16438311e-01 5.23683906e-01 -7.26742744e-01
-1.98461354e-01 9.22749162e-01 1.04135640e-01 -3.55626494e-01
-4.70884442e-01 -3.22445899e-01 -7.03176498e-01 -1.06855059e+00
8.84753540e-02 1.23303838e-01 1.12070166e-01 -1.21914662... | [11.606806755065918, -2.640920639038086] |
cac3bc1c-f227-4b8c-80fd-263594955cb5 | zerokbc-a-comprehensive-benchmark-for-zero | 2212.03091 | null | https://arxiv.org/abs/2212.03091v1 | https://arxiv.org/pdf/2212.03091v1.pdf | ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion | Knowledge base completion (KBC) aims to predict the missing links in knowledge graphs. Previous KBC tasks and approaches mainly focus on the setting where all test entities and relations have appeared in the training set. However, there has been limited research on the zero-shot KBC settings, where we need to deal with... | ['Jianshu Chen', 'Dong Yu', 'Dian Yu', 'Xiaoman Pan', 'Hongming Zhang', 'Wenlin Yao', 'Pei Chen'] | 2022-12-06 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-1.32525638e-01 4.99123961e-01 -5.93484044e-01 -2.13932265e-02
-5.10098994e-01 -3.93654495e-01 5.38112104e-01 2.06718609e-01
-2.33746752e-01 1.21613169e+00 9.06831324e-02 -3.34370762e-01
-5.21297157e-01 -9.43815947e-01 -7.43311107e-01 -2.56975085e-01
-4.20486152e-01 9.09351587e-01 7.31699765e-01 -6.51019573... | [8.930124282836914, 8.08023452758789] |
fd70e0d9-a374-41d9-b6f8-f6d5c0afef82 | joint-chinese-word-segmentation-and-span | 2211.01638 | null | https://arxiv.org/abs/2211.01638v2 | https://arxiv.org/pdf/2211.01638v2.pdf | Joint Chinese Word Segmentation and Span-based Constituency Parsing | In constituency parsing, span-based decoding is an important direction. However, for Chinese sentences, because of their linguistic characteristics, it is necessary to utilize other models to perform word segmentation first, which introduces a series of uncertainties and generally leads to errors in the computation of ... | ['Cong Liu', 'Tianyu Shi', 'Zhicheng Wang'] | 2022-11-03 | null | null | null | null | ['constituency-parsing', 'chinese-word-segmentation'] | ['natural-language-processing', 'natural-language-processing'] | [-2.55037230e-02 -1.02734543e-01 -9.00404751e-02 -7.37435579e-01
-9.59896386e-01 -7.36348212e-01 -6.33255094e-02 4.51764494e-01
-4.97229755e-01 9.21206892e-01 3.61892432e-01 -9.14827526e-01
5.27753532e-01 -9.14701998e-01 -2.62010098e-01 -3.98473918e-01
3.79559904e-01 2.93348908e-01 4.80853945e-01 -1.55737147... | [10.111282348632812, 10.101997375488281] |
e80729ad-5ffd-4515-a1b5-8725ed7a37d2 | hierarchical-multi-resolution-mesh-networks | 1607.07695 | null | http://arxiv.org/abs/1607.07695v2 | http://arxiv.org/pdf/1607.07695v2.pdf | Hierarchical Multi-resolution Mesh Networks for Brain Decoding | We propose a new framework, called Hierarchical Multi-resolution Mesh
Networks (HMMNs), which establishes a set of brain networks at multiple time
resolutions of fMRI signal to represent the underlying cognitive process. The
suggested framework, first, decomposes the fMRI signal into various frequency
subbands using wa... | ['Mete Ozay', 'Fatos Tunay Yarman Vural', 'Itir Onal Ertugrul'] | 2016-07-12 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 4.18663211e-02 4.00019661e-02 3.39987833e-04 -2.21972585e-01
9.22644958e-02 -5.69303513e-01 3.65629613e-01 1.60896063e-01
-5.55603728e-02 5.76371014e-01 3.18892211e-01 1.04322545e-01
-1.04444170e+00 -1.04724526e+00 -5.30833662e-01 -6.79449916e-01
-7.25714207e-01 3.54412287e-01 2.59200990e-01 -2.52406090... | [12.479047775268555, 3.389512062072754] |
c232e78e-405f-46c9-b265-d2bfa364fbf3 | placing-historical-events-on-a-timeline-a | null | null | https://openreview.net/forum?id=zMLzqW4AC20 | https://openreview.net/pdf?id=zMLzqW4AC20 | Placing (Historical) Events on a Timeline: A Classification cum Co-ref Resolution Approach | The event timeline provides one of the most effective ways to visualize the important historical events that occurred over a period of time, presenting the insights that may not be so apparent from reading the equivalent information in textual form. By leveraging generative adversarial learning for important event clas... | ['Anonymous'] | 2021-11-16 | null | https://openreview.net/forum?id=Y5tTolyfhoP | https://openreview.net/pdf?id=Y5tTolyfhoP | acl-arr-september-2021-9 | ['timeline-summarization'] | ['natural-language-processing'] | [ 5.29423177e-01 2.13621736e-01 1.06668629e-01 -1.28605410e-01
-1.00652075e+00 -8.31782997e-01 1.41118741e+00 7.21328437e-01
-2.24448875e-01 1.01440847e+00 1.15225232e+00 -5.29109776e-01
-1.73830971e-01 -8.02555084e-01 -5.14003158e-01 -5.45437515e-01
-3.32938850e-01 4.51952010e-01 -4.03513424e-02 -4.47760552... | [11.265583992004395, 9.0396146774292] |
21a0340a-0cb0-4ac2-80ff-4f46ac54ad91 | initialization-and-regularization-of-1 | 2105.01029 | null | https://arxiv.org/abs/2105.01029v2 | https://arxiv.org/pdf/2105.01029v2.pdf | Initialization and Regularization of Factorized Neural Layers | Factorized layers--operations parameterized by products of two or more matrices--occur in a variety of deep learning contexts, including compressed model training, certain types of knowledge distillation, and multi-head self-attention architectures. We study how to initialize and regularize deep nets containing such la... | ['Nicolò Fusi', 'Lester Mackey', 'Neil Tenenholtz', 'Mikhail Khodak'] | 2021-05-03 | initialization-and-regularization-of | https://openreview.net/forum?id=KTlJT1nof6d | https://openreview.net/pdf?id=KTlJT1nof6d | iclr-2021-1 | ['unsupervised-pre-training'] | ['methodology'] | [ 3.54382336e-01 2.17792317e-01 -3.78001541e-01 -3.15411925e-01
-6.39118791e-01 -4.86088306e-01 7.50493884e-01 -7.87085891e-02
-6.23326421e-01 3.34717929e-01 7.78171837e-01 -4.80807036e-01
-2.69123226e-01 -3.98873955e-01 -9.69423771e-01 -7.28472054e-01
-1.91544235e-01 4.34437573e-01 -3.32061112e-01 -2.90951997... | [8.401971817016602, 3.5988972187042236] |
802ccce4-a4dc-427d-9a4e-4d06d5701b98 | tax2vec-constructing-interpretable-features | 1902.00438 | null | https://arxiv.org/abs/1902.00438v3 | https://arxiv.org/pdf/1902.00438v3.pdf | tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification | The use of background knowledge is largely unexploited in text classification tasks. This paper explores word taxonomies as means for constructing new semantic features, which may improve the performance and robustness of the learned classifiers. We propose tax2vec, a parallel algorithm for constructing taxonomy-based ... | ['Senja Pollak', 'Nada Lavrač', 'Jan Kralj', 'Matej Martinc', 'Blaž Škrlj'] | 2019-02-01 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 1.03616714e-01 1.94337904e-01 -5.76610744e-01 -4.57706034e-01
-4.74143296e-01 -3.02824706e-01 1.09691000e+00 7.54215896e-01
-7.55856454e-01 8.50382328e-01 6.36228681e-01 -1.63145244e-01
-3.66504550e-01 -7.74394691e-01 -9.13084745e-02 -6.21534586e-01
-1.06717581e-02 5.48171222e-01 -3.92680205e-02 -4.54956353... | [10.545265197753906, 7.829506874084473] |
9c28e550-f2f8-4d61-b372-523bc4afcbf6 | docformerv2-local-features-for-document | 2306.01733 | null | https://arxiv.org/abs/2306.01733v1 | https://arxiv.org/pdf/2306.01733v1.pdf | DocFormerv2: Local Features for Document Understanding | We propose DocFormerv2, a multi-modal transformer for Visual Document Understanding (VDU). The VDU domain entails understanding documents (beyond mere OCR predictions) e.g., extracting information from a form, VQA for documents and other tasks. VDU is challenging as it needs a model to make sense of multiple modalities... | ['R. Manmatha', 'Yichu Zhou', 'Nishant Sankaran', 'Qi Dong', 'Peng Tang', 'Srikar Appalaraju'] | 2023-06-02 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 3.19357038e-01 2.52545416e-01 4.78388220e-02 -4.28783268e-01
-1.07516265e+00 -1.01032627e+00 1.21493518e+00 4.08257544e-02
-1.46776959e-01 2.35089034e-01 6.93482637e-01 -6.33542001e-01
2.20275670e-01 -4.69677001e-01 -1.07520616e+00 -2.32651204e-01
3.25958788e-01 9.05665219e-01 1.48109183e-01 -1.56467631... | [11.238581657409668, 1.9897083044052124] |
76b06b18-5801-496f-83cf-17b8f4c394e1 | improving-candidate-generation-for-low | 2003.01343 | null | https://arxiv.org/abs/2003.01343v1 | https://arxiv.org/pdf/2003.01343v1.pdf | Improving Candidate Generation for Low-resource Cross-lingual Entity Linking | Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches... | ['Shruti Rijhawani', 'Jaime Carbonell', 'Shuyan Zhou', 'John Wieting', 'Graham Neubig'] | 2020-03-03 | improving-candidate-generation-for-low-1 | https://aclanthology.org/2020.tacl-1.8 | https://aclanthology.org/2020.tacl-1.8.pdf | tacl-2020-1 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-4.86599624e-01 5.74789643e-01 -4.99554217e-01 -5.97084314e-02
-1.87525809e+00 -7.97532976e-01 6.49645567e-01 3.82644981e-01
-7.79745698e-01 1.34054756e+00 4.95471925e-01 -2.46312976e-01
3.91119681e-02 -7.65941501e-01 -1.04937375e+00 4.41565886e-02
-3.78456600e-02 9.56147909e-01 6.21710658e-01 -4.94247347... | [9.550453186035156, 8.956287384033203] |
59fb53a2-74a7-40f4-a5f7-d0d510c47dba | enhancing-underexposed-photos-using | 1907.10992 | null | https://arxiv.org/abs/1907.10992v3 | https://arxiv.org/pdf/1907.10992v3.pdf | Enhancing Underexposed Photos using Perceptually Bidirectional Similarity | Although remarkable progress has been made, existing methods for enhancing underexposed photos tend to produce visually unpleasing results due to the existence of visual artifacts (e.g., color distortion, loss of details and uneven exposure). We observed that this is because they fail to ensure the perceptual consisten... | ['Wei-Shi Zheng', 'Yongwei Nie', 'Chunxia Xiao', 'Lei Zhu', 'Qing Zhang'] | 2019-07-25 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 6.93618000e-01 -2.70624340e-01 3.48785132e-01 -2.56337792e-01
-7.17953682e-01 -2.61560023e-01 4.46288824e-01 -2.70680726e-01
-1.60600901e-01 6.65481985e-01 7.74726868e-02 1.66881979e-01
-2.39847302e-01 -5.61254978e-01 -6.97819948e-01 -9.76612031e-01
2.88194418e-01 -6.24501765e-01 1.90010220e-01 -7.84194618... | [10.802980422973633, -2.5109128952026367] |
0a25e256-d443-477f-8642-db02206504d2 | multi-stage-feature-selection-based | 1708.08750 | null | http://arxiv.org/abs/1708.08750v1 | http://arxiv.org/pdf/1708.08750v1.pdf | Multi-Stage Feature Selection Based Intelligent Classifier for Classification of Incipient Stage Fire in Building | In this study, an early fire detection algorithm has been proposed based on
low cost array sensing system, utilizing gas sensors, dust particles and
ambient sensors such as temperature and humidity sensor. The odor or
smell-print emanated from various fire sources and building construction
materials at early stage are ... | ['Shaharil Mad Saad', 'Allan Melvin Andrew', 'Ammar Zakaria', 'Ali Yeon Md Shakaff'] | 2017-08-12 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 5.42338014e-01 -9.29337263e-01 3.83838385e-01 -5.94932400e-02
1.42540798e-01 -6.40704632e-01 2.54257381e-01 3.93854856e-01
-4.38653916e-01 6.08498216e-01 -1.10500038e-01 2.13402733e-01
-7.66287863e-01 -1.00958562e+00 2.03040298e-02 -9.24160480e-01
-1.90155208e-01 3.80888492e-01 -2.83374876e-01 2.98949098... | [9.884026527404785, -1.5238770246505737] |
312d1f31-771e-4ed3-9c28-72743bf9ece4 | cov-ti-net-transferred-initialization-with | 2209.09556 | null | https://arxiv.org/abs/2209.09556v1 | https://arxiv.org/pdf/2209.09556v1.pdf | CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis | This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very complicated and time-consuming process because of the complexity of im... | ['Saeid Nahavandi', 'Abbas Khorsavi', 'Shady Mohamed', 'Farzin Tabarsinezhad', 'Keshav Kumar', 'Houshyar Asadi', 'Abadhan S. Sabyasachi', 'H M Dipu Kabir', 'Subrota Kumar Mondal', 'Mohammad Reza Chalak Qazani', 'Sadia Khanam'] | 2022-09-20 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 1.49362564e-01 1.21869184e-01 -5.86419329e-02 -1.70871377e-01
-3.41940284e-01 -1.34596294e-02 3.34422916e-01 -2.01459378e-02
-1.06461966e+00 9.10490692e-01 -4.50654060e-01 -4.14318621e-01
-2.79081523e-01 -8.27222168e-01 -6.57105863e-01 -7.91154027e-01
1.78045645e-01 7.09172010e-01 3.54856104e-01 -2.67470896... | [14.924219131469727, -2.5939557552337646] |
61847e5b-33df-4591-9463-24c3222ad999 | resource-constrained-neural-networks-for-5g | 2107.11070 | null | https://arxiv.org/abs/2107.11070v1 | https://arxiv.org/pdf/2107.11070v1.pdf | Resource Constrained Neural Networks for 5G Direction-of-Arrival Estimation in Micro-controllers | With the introduction of shared spectrum sensing and beam-forming based multi-antenna transceivers, 5G networks demand spectrum sensing to identify opportunities in time, frequency, and spatial domains. Narrow beam-forming makes it difficult to have spatial sensing (direction-of-arrival, DoA, estimation) in a centraliz... | ['Hem-Dutt Dabral', 'Danilo Pau', 'S. J. Darak', 'Shivam Chandhok', 'Romesh Rajoria', 'Piyush Sahoo'] | 2021-07-23 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 7.94275627e-02 -3.45990777e-01 -1.54391870e-01 -6.75110966e-02
-2.21018210e-01 -3.98405492e-01 -2.86741555e-02 -3.20474535e-01
-1.30761817e-01 8.75086188e-01 -5.27494699e-02 -6.96114779e-01
-6.52092934e-01 -8.68987918e-01 2.03381255e-01 -6.86729550e-01
-3.89945865e-01 1.45777306e-02 8.94431025e-02 7.56443888... | [6.333780765533447, 1.2005078792572021] |
a9de4016-1d8f-4a58-a576-29a50252b2cb | segment-anything-model-sam-for-digital | 2304.04155 | null | https://arxiv.org/abs/2304.04155v1 | https://arxiv.org/pdf/2304.04155v1.pdf | Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging | The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks).... | ['Yuankai Huo', 'Yucheng Tang', 'Haichun Yang', 'Agnes B. Fogo', 'Shilin Zhao', 'Yaohong Wang', 'Keith T. Wilson', 'Lori A. Coburn', 'Lee E. Wheless', 'Bennett A. Landman', 'Shunxing Bao', 'Lucas W. Remedios', 'Tianyuan Yao', 'Quan Liu', 'Can Cui', 'Ruining Deng'] | 2023-04-09 | null | null | null | null | ['zero-shot-segmentation', 'tumor-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.16285121e-01 4.66771454e-01 -4.72455502e-01 -4.77204949e-01
-1.46532822e+00 -5.64907432e-01 2.85920501e-03 2.87259698e-01
-5.30932367e-01 3.38640958e-01 2.05578711e-02 -7.03247011e-01
1.78896058e-02 -4.01885301e-01 -3.68537605e-01 -8.25707972e-01
2.62282729e-01 5.23171842e-01 5.97591817e-01 2.84523696... | [14.740694046020508, -2.2630302906036377] |
36a0a1cd-ec43-40b2-b6c6-61071979456a | robust-deep-auc-maximization-a-new-surrogate | 2012.03173 | null | https://arxiv.org/abs/2012.03173v2 | https://arxiv.org/pdf/2012.03173v2.pdf | Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification | Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset. Most previous works of AUC maximization focus on the perspective of optimization by designing efficient stochastic algorithms, and studies on generalization performance of large-scale ... | ['Tianbao Yang', 'Milan Sonka', 'Yan Yan', 'Zhuoning Yuan'] | 2020-12-06 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yuan_Large-Scale_Robust_Deep_AUC_Maximization_A_New_Surrogate_Loss_and_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yuan_Large-Scale_Robust_Deep_AUC_Maximization_A_New_Surrogate_Loss_and_ICCV_2021_paper.pdf | iccv-2021-1 | ['graph-property-prediction'] | ['graphs'] | [ 3.17987263e-01 1.52193204e-01 -3.22334528e-01 -6.51493251e-01
-1.23623145e+00 -1.53304756e-01 8.31387118e-02 4.46973711e-01
-7.97465742e-01 7.43516982e-01 -1.79974154e-01 -5.40154994e-01
-1.36254802e-01 -4.78116840e-01 -6.93992913e-01 -7.55929232e-01
-3.41337562e-01 2.07955137e-01 7.89221302e-02 2.31638879... | [14.984713554382324, -2.4747369289398193] |
9010a684-68f0-469d-bf20-2e5fa2683136 | image-generation-network-for-covert | 2207.10292 | null | https://arxiv.org/abs/2207.10292v1 | https://arxiv.org/pdf/2207.10292v1.pdf | Image Generation Network for Covert Transmission in Online Social Network | Online social networks have stimulated communications over the Internet more than ever, making it possible for secret message transmission over such noisy channels. In this paper, we propose a Coverless Image Steganography Network, called CIS-Net, that synthesizes a high-quality image directly conditioned on the secret... | ['Xinpeng Zhang', 'Zhenxing Qian', 'Sheng Li', 'Qichao Ying', 'Zhengxin You'] | 2022-07-21 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 1.18195164e+00 7.95745850e-01 2.65585780e-01 2.61995941e-02
-1.36061370e-01 -5.86659968e-01 8.07610691e-01 -6.73486412e-01
-2.33105138e-01 7.78913200e-01 -8.94973278e-02 -4.29037720e-01
2.53247142e-01 -1.19269395e+00 -8.02090466e-01 -9.61600244e-01
-3.79796028e-01 -1.59068689e-01 7.36686811e-02 -5.68231642... | [4.325167655944824, 8.045863151550293] |
60b4e0f5-18a4-437c-8a5b-2a4aa7dd0649 | instance-dependent-noisy-label-learning-via | 2209.00906 | null | https://arxiv.org/abs/2209.00906v1 | https://arxiv.org/pdf/2209.00906v1.pdf | Instance-Dependent Noisy Label Learning via Graphical Modelling | Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them. There are many types of label noise, such as symmetric, asymmetric and instance-dependent noise (IDN), with IDN being the only type that depends on image information. Such dependence on image informati... | ['Gustavo Carneiro', 'Thanh-Toan Do', 'Rafael Felix', 'Cuong Nguyen', 'Arpit Garg'] | 2022-09-02 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 4.47688133e-01 -4.06389460e-02 2.54536215e-02 -6.04007065e-01
-1.02951872e+00 -6.34234548e-01 8.13344657e-01 -1.31218195e-01
-3.49013776e-01 7.61478782e-01 -1.10915944e-01 2.38473788e-02
-1.55870944e-01 -5.28891504e-01 -6.70027792e-01 -1.07480824e+00
3.23877752e-01 7.12667167e-01 2.10843105e-02 1.43158033... | [9.426602363586426, 3.868515729904175] |
06982017-eaec-47b8-92bd-b98a5fbf4d16 | a-framework-for-semi-automated-web-service | 1311.6709 | null | http://arxiv.org/abs/1311.6709v1 | http://arxiv.org/pdf/1311.6709v1.pdf | A Framework for Semi-automated Web Service Composition in Semantic Web | Number of web services available on Internet and its usage are increasing
very fast. In many cases, one service is not enough to complete the business
requirement; composition of web services is carried out. Autonomous composition
of web services to achieve new functionality is generating considerable
attention in sema... | ['Archana Chougule', 'Debajyoti Mukhopadhyay'] | 2013-11-26 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 1.21235967e-01 -4.30745631e-02 1.22579597e-01 -7.23043442e-01
-2.66304493e-01 -8.59776199e-01 5.96657157e-01 -6.31318390e-02
-1.18827380e-01 4.63484406e-01 1.72459394e-01 -3.31090420e-01
-4.01311427e-01 -1.11570823e+00 -2.76260916e-02 -4.89210367e-01
2.62927234e-01 9.00280654e-01 7.49295533e-01 -6.74116015... | [8.689482688903809, 7.021985054016113] |
1ebc3c55-317f-417b-ae69-a548c689c8ac | mugs-a-multi-granular-self-supervised | 2203.14415 | null | https://arxiv.org/abs/2203.14415v1 | https://arxiv.org/pdf/2203.14415v1.pdf | Mugs: A Multi-Granular Self-Supervised Learning Framework | In self-supervised learning, multi-granular features are heavily desired though rarely investigated, as different downstream tasks (e.g., general and fine-grained classification) often require different or multi-granular features, e.g.~fine- or coarse-grained one or their mixture. In this work, for the first time, we p... | ['Shuicheng Yan', 'Teck Khim Ng', 'Weihao Yu', 'Chenyang Si', 'Yichen Zhou', 'Pan Zhou'] | 2022-03-27 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 2.31635794e-01 5.16548567e-02 -5.94311118e-01 -6.00131691e-01
-9.31080639e-01 -5.28904617e-01 4.34783548e-01 3.93079102e-01
-2.26555154e-01 6.55245125e-01 -2.26754010e-01 -2.44679347e-01
-1.88252911e-01 -1.19447744e+00 -9.00031090e-01 -9.01413321e-01
-1.06537797e-01 3.37221533e-01 5.14916003e-01 -7.50322491... | [9.501462936401367, 1.9890329837799072] |
7f52fbdc-7020-4bb9-9951-07c0bfb10355 | optimization-of-robot-trajectory-planning | 2206.03651 | null | https://arxiv.org/abs/2206.03651v1 | https://arxiv.org/pdf/2206.03651v1.pdf | Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms | We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble techniques, as well as path relinking for solution refinement. The core optimization module consists of a biased random-key genetic algorith... | ['Helmut G. Katzgraber', 'Mauricio G. C. Resende', 'Andre Luckow', 'Philipp Ross', 'Johannes Klepsch', 'Yannick van Dijk', 'Henry Montagu', 'J. Kyle Brubaker', 'Martin J. A. Schuetz'] | 2022-06-08 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 2.62366086e-01 1.26371801e-01 -1.35768220e-01 -1.50324091e-01
-1.34587455e+00 -8.78836036e-01 5.89007318e-01 -2.81603099e-03
-5.66414058e-01 7.39733160e-01 -6.23587593e-02 -6.70674801e-01
-3.86603564e-01 -9.48454678e-01 -8.99397671e-01 -8.91490996e-01
-2.06430539e-01 1.03837061e+00 -1.73961371e-02 -9.38131034... | [5.593486309051514, 4.781510829925537] |
46c2ec95-14df-44d3-95cf-3d13dfaf3c4d | explainable-artificial-intelligence-in-2 | 2212.07058 | null | https://arxiv.org/abs/2212.07058v1 | https://arxiv.org/pdf/2212.07058v1.pdf | Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases | Explainable Artificial Intelligence (AI) in the form of an interpretable and semiautomatic approach to stage grading ocular pathologies such as Diabetic retinopathy, Hypertensive retinopathy, and other retinopathies on the backdrop of major systemic diseases. The experimental study aims to evaluate an explainable stage... | ['Dr Prakash Kamaraj', 'Meghna Kulkarni', 'Rajkumar Vaghashiya', 'Ayushi Raj Bhatt'] | 2022-12-14 | null | null | null | null | ['optic-disc-detection'] | ['medical'] | [ 2.54188031e-01 9.80116367e-01 1.41039446e-01 -8.04410398e-01
5.97256906e-02 -3.71565998e-01 3.32101911e-01 2.24253666e-02
-1.99447781e-01 5.39209366e-01 2.98037261e-01 -8.36693883e-01
-5.21637380e-01 -5.19117951e-01 -3.79536897e-02 -4.80442643e-01
3.06972861e-01 8.18457723e-01 -1.28963590e-01 6.84978664... | [15.832551956176758, -4.0009846687316895] |
f051a52d-f7ac-4321-8cd9-1119f3228744 | explainable-machine-learning-for-categorical | 2305.18437 | null | https://arxiv.org/abs/2305.18437v1 | https://arxiv.org/pdf/2305.18437v1.pdf | Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization | Building accurate and interpretable Machine Learning (ML) models for heterogeneous/mixed data is a long-standing challenge for algorithms designed for numeric data. This work focuses on developing numeric coding schemes for non-numeric attributes for ML algorithms to support accurate and explainable ML models, methods ... | ['Elijah McCoy', 'Boris Kovalerchuk'] | 2023-05-29 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-1.45491973e-01 5.65725029e-01 -3.59041780e-01 -8.03994358e-01
-1.73746850e-02 -6.28583968e-01 5.87715626e-01 5.42383850e-01
3.61808866e-01 7.63626695e-01 -6.48302585e-02 -1.04008007e+00
-8.07791948e-01 -6.41311169e-01 -2.30667844e-01 -3.59732181e-01
-5.23536742e-01 1.00492597e+00 -5.07757902e-01 5.65899834... | [8.0935697555542, 4.68079137802124] |
bc9efeaa-0991-4e7d-bd45-f2abf1b89267 | icanet-a-method-of-short-video-emotion | 2208.11346 | null | https://arxiv.org/abs/2208.11346v1 | https://arxiv.org/pdf/2208.11346v1.pdf | ICANet: A Method of Short Video Emotion Recognition Driven by Multimodal Data | With the fast development of artificial intelligence and short videos, emotion recognition in short videos has become one of the most important research topics in human-computer interaction. At present, most emotion recognition methods still stay in a single modality. However, in daily life, human beings will usually d... | ['Lanhang Zhai', 'Mengmeng Tian', 'Xuecheng Wu'] | 2022-08-24 | null | null | null | null | ['video-emotion-recognition'] | ['computer-vision'] | [-7.20389336e-02 -4.97722358e-01 5.32823429e-02 -1.38598457e-01
-1.35799065e-01 -3.32707494e-01 4.61095691e-01 -1.91979483e-01
-5.63542843e-01 6.92986131e-01 9.71223041e-02 2.41572276e-01
9.38649997e-02 -2.56757230e-01 -9.67601463e-02 -7.97263503e-01
2.51961142e-01 -1.46383092e-01 -2.42863402e-01 -1.38071001... | [13.303299903869629, 4.85098123550415] |
a8bcf8fe-5081-46d0-b848-fa3f167d12ac | look-into-person-self-supervised-structure | 1703.05446 | null | http://arxiv.org/abs/1703.05446v2 | http://arxiv.org/pdf/1703.05446v2.pdf | Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing | Human parsing has recently attracted a lot of research interests due to its
huge application potentials. However existing datasets have limited number of
images and annotations, and lack the variety of human appearances and the
coverage of challenging cases in unconstrained environment. In this paper, we
introduce a ne... | ['Xiaodan Liang', 'Liang Lin', 'Ke Gong', 'Xiaohui Shen', 'Dongyu Zhang'] | 2017-03-16 | look-into-person-self-supervised-structure-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Gong_Look_Into_Person_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Gong_Look_Into_Person_CVPR_2017_paper.pdf | cvpr-2017-7 | ['human-parsing'] | ['computer-vision'] | [ 2.70162255e-01 2.24803746e-01 -2.37675861e-01 -5.58870971e-01
-8.70289028e-01 -5.43024480e-01 3.60542923e-01 -5.25784135e-01
-3.38203788e-01 4.12246764e-01 3.42898369e-01 2.35229939e-01
3.10918003e-01 -4.30153906e-01 -6.53163314e-01 -5.09775579e-01
1.26670897e-01 5.35586119e-01 5.71924865e-01 -2.70421892... | [8.194456100463867, -0.2464849054813385] |
92a69380-579f-44fd-9b65-d51abecc2b8a | prefix-projection-global-constraint-for | 1504.07877 | null | http://arxiv.org/abs/1504.07877v2 | http://arxiv.org/pdf/1504.07877v2.pdf | Prefix-Projection Global Constraint for Sequential Pattern Mining | Sequential pattern mining under constraints is a challenging data mining
task. Many efficient ad hoc methods have been developed for mining sequential
patterns, but they are all suffering from a lack of genericity. Recent works
have investigated Constraint Programming (CP) methods, but they are not still
effective beca... | ['Amina Kemmar', 'Yahia Lebbah', 'Thierry Charnois', 'Samir Loudni', 'Patrice Boizumault'] | 2015-04-29 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 2.22728521e-01 -2.75096595e-01 -4.81082797e-01 -4.07403678e-01
-2.11915281e-02 -2.13473693e-01 3.64926517e-01 8.68908837e-02
-3.19690555e-01 8.52451444e-01 -1.02804057e-01 -1.69650286e-01
-5.55790842e-01 -1.03101397e+00 -2.42654622e-01 -4.63752866e-01
-1.31314054e-01 6.86757147e-01 9.10887063e-01 -1.64342627... | [8.33487606048584, 6.305901050567627] |
f3c14040-7a92-4628-bb53-ea789bf1ba74 | search-to-capture-long-range-dependency-with | 2302.08671 | null | https://arxiv.org/abs/2302.08671v1 | https://arxiv.org/pdf/2302.08671v1.pdf | Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification | In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task. Currently, shallow GNNs are more common due to the well-known over-smoothing problem facing deeper GNNs. However, they are sub-optimal without utilizing the information from distant nodes, i.e., the long-range dependencies... | ['Quanming Yao', 'Huan Zhao', 'Zhiqiang He', 'Lanning Wei'] | 2023-02-17 | null | null | null | null | ['graph-structure-learning', 'graph-classification'] | ['graphs', 'graphs'] | [-2.95074545e-02 5.27138561e-02 -2.18946338e-01 -3.58158618e-01
1.83428854e-01 -2.60739494e-02 2.34643593e-01 -3.25177051e-02
-3.90875340e-01 3.44046533e-01 1.87952116e-01 -2.67676711e-01
-4.43629265e-01 -1.09714639e+00 -5.08818030e-01 -8.66990030e-01
-2.31734440e-02 -1.62533686e-01 4.49423492e-01 -3.26076150... | [7.267803192138672, 6.2573747634887695] |
29405175-8711-4daa-8bb4-daac07c17930 | lexicon-infused-phrase-embeddings-for-named | 1404.5367 | null | http://arxiv.org/abs/1404.5367v1 | http://arxiv.org/pdf/1404.5367v1.pdf | Lexicon Infused Phrase Embeddings for Named Entity Resolution | Most state-of-the-art approaches for named-entity recognition (NER) use semi
supervised information in the form of word clusters and lexicons. Recently
neural network-based language models have been explored, as they as a byproduct
generate highly informative vector representations for words, known as word
embeddings. ... | ['Andrew McCallum', 'Vineet Kumar', 'Alexandre Passos'] | 2014-04-22 | lexicon-infused-phrase-embeddings-for-named-1 | https://aclanthology.org/W14-1609 | https://aclanthology.org/W14-1609.pdf | ws-2014-6 | ['learning-word-embeddings'] | ['methodology'] | [-3.44841897e-01 9.19328630e-02 -3.54827821e-01 -3.37546587e-01
-1.18951762e+00 -7.49181390e-01 5.88068724e-01 3.74195635e-01
-1.10867500e+00 5.70813477e-01 7.09470809e-01 -2.65401751e-01
1.07990399e-01 -7.62745202e-01 -2.32220963e-01 -3.05884182e-01
3.68157998e-02 7.83696949e-01 -3.53699550e-02 -2.35684261... | [9.744842529296875, 9.579429626464844] |
9d6401f9-44ec-4bf1-a2b3-51da492e3a07 | collective-knowledge-graph-completion-with | 2305.15895 | null | https://arxiv.org/abs/2305.15895v1 | https://arxiv.org/pdf/2305.15895v1.pdf | Collective Knowledge Graph Completion with Mutual Knowledge Distillation | Knowledge graph completion (KGC), the task of predicting missing information based on the existing relational data inside a knowledge graph (KG), has drawn significant attention in recent years. However, the predictive power of KGC methods is often limited by the completeness of the existing knowledge graphs from diffe... | ['Yi-Ke Guo', 'Jiahao Sun', 'Ovidiu Serban', 'Weihang Zhang'] | 2023-05-25 | null | null | null | null | ['knowledge-graph-completion'] | ['knowledge-base'] | [-2.77781636e-01 7.70573199e-01 -5.50851822e-01 -2.08551392e-01
-5.93072116e-01 -4.70097423e-01 4.79785532e-01 4.40046817e-01
-8.82404745e-02 9.86168087e-01 3.14444304e-01 -2.72643447e-01
-3.12048614e-01 -1.11808014e+00 -1.23273206e+00 -3.30212235e-01
-1.95977598e-01 5.41189909e-01 7.30676726e-02 -2.50445843... | [8.972822189331055, 8.060175895690918] |
7a411484-4e99-4317-9aad-08716fa475c3 | on-the-benefit-of-syntactic-supervision-for | null | null | https://aclanthology.org/2021.emnlp-main.503 | https://aclanthology.org/2021.emnlp-main.503.pdf | On the Benefit of Syntactic Supervision for Cross-lingual Transfer in Semantic Role Labeling | Although recent developments in neural architectures and pre-trained representations have greatly increased state-of-the-art model performance on fully-supervised semantic role labeling (SRL), the task remains challenging for languages where supervised SRL training data are not abundant. Cross-lingual learning can impr... | ['Eduard Hovy', 'Emma Strubell', 'Zhisong Zhang'] | null | null | null | null | emnlp-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 4.33784366e-01 2.37848386e-01 -9.21636343e-01 -7.00077951e-01
-1.15202677e+00 -8.15122724e-01 7.66256332e-01 7.02925622e-02
-8.46620619e-01 9.01665032e-01 7.95392931e-01 -1.64823323e-01
1.34841248e-01 -2.45692343e-01 -7.72242725e-01 -1.57542676e-01
1.50520146e-01 5.69700122e-01 8.10067430e-02 -5.26013434... | [10.456666946411133, 9.5051908493042] |
4a2cd9b5-dd20-4319-8cd5-b1c8dfdc3439 | the-best-of-both-worlds-accurate-global-and | 2301.08968 | null | https://arxiv.org/abs/2301.08968v2 | https://arxiv.org/pdf/2301.08968v2.pdf | The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation | Heterogeneity of data distributed across clients limits the performance of global models trained through federated learning, especially in the settings with highly imbalanced class distributions of local datasets. In recent years, personalized federated learning (pFL) has emerged as a potential solution to the challeng... | ['Haris Vikalo', 'Wang', 'Johnny', 'Huancheng Chen'] | 2023-01-21 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-5.02177298e-01 3.16663124e-02 -4.91756499e-01 -4.81120676e-01
-1.24670553e+00 -5.73672891e-01 3.69272798e-01 -1.30925432e-01
8.10683370e-02 8.61483216e-01 3.43179613e-01 -8.30763131e-02
-1.60853595e-01 -8.53670359e-01 -8.58081341e-01 -1.02818656e+00
1.55046538e-01 9.80840027e-01 7.09702000e-02 1.75547078... | [5.835948944091797, 6.3117804527282715] |
399104eb-d64f-4a34-b39e-a39450170eaf | towards-building-a-crowd-sourced-sky-map | 1406.1528 | null | http://arxiv.org/abs/1406.1528v1 | http://arxiv.org/pdf/1406.1528v1.pdf | Towards building a Crowd-Sourced Sky Map | We describe a system that builds a high dynamic-range and wide-angle image of
the night sky by combining a large set of input images. The method makes use of
pixel-rank information in the individual input images to improve a "consensus"
pixel rank in the combined image. Because it only makes use of ranks and the
comple... | ['Bernhard Scholkopf', 'Dustin Lang', 'David W. Hogg'] | 2014-06-05 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 3.72685552e-01 -8.88251662e-02 7.25907683e-01 -1.76349372e-01
-6.15256131e-01 -1.18090391e+00 6.58389688e-01 -1.07348815e-01
-3.94948691e-01 5.71667552e-01 -1.84768021e-01 -6.17726624e-01
-6.21797815e-02 -7.69934535e-01 -6.39813542e-01 -8.02792132e-01
-1.19610270e-02 2.72903740e-01 7.54871070e-01 -2.12947026... | [10.511492729187012, -2.501208543777466] |
4852b351-3a4e-44fa-9c17-34b28a9bd8a6 | neurst-neural-speech-translation-toolkit | 2012.10018 | null | https://arxiv.org/abs/2012.10018v3 | https://arxiv.org/pdf/2012.10018v3.pdf | NeurST: Neural Speech Translation Toolkit | NeurST is an open-source toolkit for neural speech translation. The toolkit mainly focuses on end-to-end speech translation, which is easy to use, modify, and extend to advanced speech translation research and products. NeurST aims at facilitating the speech translation research for NLP researchers and building reliabl... | ['Lei LI', 'Rong Ye', 'Qianqian Dong', 'Mingxuan Wang', 'Chengqi Zhao'] | 2020-12-18 | null | https://aclanthology.org/2021.acl-demo.7 | https://aclanthology.org/2021.acl-demo.7.pdf | acl-2021-5 | ['speech-to-text-translation'] | ['natural-language-processing'] | [-4.96018827e-02 -1.36245981e-01 -4.61889237e-01 -4.21665162e-01
-1.31349194e+00 -6.22236788e-01 7.25558698e-01 -5.97212255e-01
-3.58612180e-01 6.84171915e-01 4.45904225e-01 -8.78572702e-01
6.44588947e-01 -4.17924345e-01 -7.76141942e-01 -6.48954332e-01
4.24925029e-01 9.01777089e-01 4.08699252e-02 -5.18130481... | [14.475046157836914, 7.166932582855225] |
920babf4-8a51-4cc7-9290-e5e308eabfc6 | the-trajectory-of-voice-onset-time-with-vocal | 1810.07030 | null | http://arxiv.org/abs/1810.07030v1 | http://arxiv.org/pdf/1810.07030v1.pdf | The Trajectory of Voice Onset Time with Vocal Aging | Vocal aging, a universal process of human aging, can largely affect one's
language use, possibly including some subtle acoustic features of one's
utterances like Voice Onset Time. To figure out the time effects, Queen
Elizabeth's Christmas speeches are documented and analyzed in the long-term
trend. We build statistica... | ['Jian Hu', 'Xuanda Chen', 'Ziyu Xiong'] | 2018-10-15 | null | null | null | null | ['human-aging'] | ['miscellaneous'] | [-3.17112505e-01 -5.30800410e-02 -3.71378273e-01 -1.06546283e-02
-2.66470194e-01 -1.54233292e-01 5.69719374e-01 -1.39705002e-01
-5.73979676e-01 8.85039091e-01 9.29429054e-01 -4.37061697e-01
-1.53449342e-01 -3.50153893e-01 -4.64760453e-01 -6.99004769e-01
-3.51393640e-01 -2.04390064e-01 6.00041598e-02 -3.26714575... | [14.304320335388184, 6.170373439788818] |
c59a7241-9b7a-4a26-b896-bb4a822c539a | a-bi-lstm-autoencoder-framework-for-anomaly | 2303.09703 | null | https://arxiv.org/abs/2303.09703v1 | https://arxiv.org/pdf/2303.09703v1.pdf | A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset | Anomalies refer to data points or events that deviate from normal and homogeneous events, which can include fraudulent activities, network infiltrations, equipment malfunctions, process changes, or other significant but infrequent events. Prompt detection of such events can prevent potential losses in terms of finances... | ['Imtiaz Ahmed', 'Ahmed Shoyeb Raihan'] | 2023-03-17 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [-1.87151115e-02 -5.63451707e-01 3.51211905e-01 -1.85149163e-02
2.24300042e-01 -2.12826863e-01 3.47961068e-01 6.29727185e-01
-2.94910818e-01 4.21223223e-01 -1.74767613e-01 -4.60689515e-01
-2.59695411e-01 -9.81619239e-01 -4.21902567e-01 -8.03372204e-01
-5.19541025e-01 -5.39552271e-02 -3.51973460e-03 -1.77606180... | [7.08329963684082, 2.7227303981781006] |
fe80e8b2-f56a-4d66-b454-fa1d833e839f | otre-where-optimal-transport-guided-unpaired | 2302.03003 | null | https://arxiv.org/abs/2302.03003v4 | https://arxiv.org/pdf/2302.03003v4.pdf | OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing | Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal image quality is mandated for accurate medical diagnoses and automated... | ['Jacob M. Sobczak', 'Yalin Wang', 'Keshav Nandakumar', 'Zhangsihao Yang', 'Mohammad Farazi', 'Oana M. Dumitrascu', 'Peijie Qiu', 'Wenhui Zhu'] | 2023-02-06 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 4.35768127e-01 5.46409003e-02 -8.86269007e-03 -5.22865832e-01
-7.66473651e-01 -1.92660391e-01 1.79026261e-01 -3.04256827e-01
-4.41185594e-01 6.47639096e-01 1.57952815e-01 -5.59693336e-01
-2.66456693e-01 -4.48446900e-01 -6.57509327e-01 -7.97284663e-01
2.62725651e-01 -1.47771046e-01 2.77745515e-01 2.22414523... | [15.769476890563965, -3.95688796043396] |
fa2aabae-3257-4d9e-9ea3-716bef1724fe | mutual-information-alleviates-hallucinations | 2210.13210 | null | https://arxiv.org/abs/2210.13210v2 | https://arxiv.org/pdf/2210.13210v2.pdf | Mutual Information Alleviates Hallucinations in Abstractive Summarization | Despite significant progress in the quality of language generated from abstractive summarization models, these models still exhibit the tendency to hallucinate, i.e., output content not supported by the source document. A number of works have tried to fix--or at least uncover the source of--the problem with limited suc... | ['Clara Meister', 'Ryan Cotterell', 'Liam van der Poel'] | 2022-10-24 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 2.98793644e-01 8.25041831e-01 -3.05273265e-01 -9.55486372e-02
-1.10233092e+00 -4.68813092e-01 6.83198333e-01 5.19739985e-01
-1.15032412e-01 1.07204270e+00 9.59374726e-01 -2.75985509e-01
4.99690436e-02 -5.33950746e-01 -6.07241511e-01 -5.28927922e-01
2.22852901e-02 6.10662580e-01 -2.20569327e-01 -9.78873149... | [11.880477905273438, 9.14864444732666] |
bb6ebccb-cff2-4623-accf-2cd35d61de05 | automatic-lesion-detection-system-alds-for | 2003.06276 | null | https://arxiv.org/abs/2003.06276v1 | https://arxiv.org/pdf/2003.06276v1.pdf | Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers | Technology aided platforms provide reliable tools in almost every field these days. These tools being supported by computational power are significant for applications that need sensitive and precise data analysis. One such important application in the medical field is Automatic Lesion Detection System (ALDS) for skin ... | ['Rana Hammad Raza', 'Muhammad Aatif Mobeen Azhar', 'Muhammad Ali Farooq'] | 2020-03-13 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 7.39446700e-01 1.60503238e-02 -1.54885799e-01 -2.59875923e-01
-6.77533448e-01 -5.62321782e-01 4.12879139e-01 8.62773776e-01
-5.76910794e-01 7.23708153e-01 -1.59010321e-01 -3.65985781e-01
-3.04099023e-01 -7.92214334e-01 2.00165972e-01 -9.26475286e-01
3.77425224e-01 6.60710037e-01 5.17929256e-01 1.73608333... | [15.466198921203613, -3.0252604484558105] |
2ae891d3-19ad-44b1-90a8-c825c34de9da | soft-layer-selection-with-meta-learning-for | 2107.09840 | null | https://arxiv.org/abs/2107.09840v1 | https://arxiv.org/pdf/2107.09840v1.pdf | Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer | Multilingual pre-trained contextual embedding models (Devlin et al., 2019) have achieved impressive performance on zero-shot cross-lingual transfer tasks. Finding the most effective fine-tuning strategy to fine-tune these models on high-resource languages so that it transfers well to the zero-shot languages is a non-tr... | ['Saab Mansour', 'Jason Krone', 'Batool Haider', 'Weijia Xu'] | 2021-07-21 | null | https://aclanthology.org/2021.metanlp-1.2 | https://aclanthology.org/2021.metanlp-1.2.pdf | acl-metanlp-2021-8 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [-2.30044156e-01 4.18913290e-02 -5.05012989e-01 -6.43387794e-01
-1.35039091e+00 -5.96489608e-01 8.84077847e-01 -2.08393171e-01
-8.42064857e-01 9.73770022e-01 5.25950313e-01 -5.20832598e-01
2.62044221e-01 -6.78628922e-01 -1.06231356e+00 -3.63002777e-01
1.26279309e-01 7.50144958e-01 7.69885024e-03 -4.54317480... | [10.995960235595703, 9.658278465270996] |
082af2a5-ca03-455a-96ae-2c4f1554cc88 | hybridformer-improving-squeezeformer-with | 2303.08636 | null | https://arxiv.org/abs/2303.08636v1 | https://arxiv.org/pdf/2303.08636v1.pdf | HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanism | SqueezeFormer has recently shown impressive performance in automatic speech recognition (ASR). However, its inference speed suffers from the quadratic complexity of softmax-attention (SA). In addition, limited by the large convolution kernel size, the local modeling ability of SqueezeFormer is insufficient. In this pap... | ['Heng Lu', 'Lei Ma', 'Jiangyu Han', 'JingJing Yin', 'Yu Pan', 'Yuguang Yang'] | 2023-03-15 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 2.22818866e-01 3.44161168e-02 5.80105707e-02 -4.65645850e-01
-9.45448816e-01 -1.55926362e-01 2.81192452e-01 -3.81250560e-01
-4.33573961e-01 4.04721111e-01 2.65472025e-01 -7.37508953e-01
2.53493004e-02 -2.67597467e-01 -6.20211244e-01 -7.37945199e-01
3.84843320e-01 2.48925705e-02 5.39960042e-02 -1.50885105... | [14.565415382385254, 6.331838130950928] |
c20642cc-3b31-4a83-a8ac-aefb685f1000 | using-deep-cross-modal-hashing-and-error | 1902.04139 | null | http://arxiv.org/abs/1902.04139v1 | http://arxiv.org/pdf/1902.04139v1.pdf | Using Deep Cross Modal Hashing and Error Correcting Codes for Improving the Efficiency of Attribute Guided Facial Image Retrieval | With benefits of fast query speed and low storage cost, hashing-based image
retrieval approaches have garnered considerable attention from the research
community. In this paper, we propose a novel Error-Corrected Deep Cross Modal
Hashing (CMH-ECC) method which uses a bitmap specifying the presence of certain
facial att... | ['Matthew C. Valenti', 'Nasser M. Nasrabadi', 'Veeru Talreja', 'Fariborz Taherkhani'] | 2019-02-11 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [-7.89154544e-02 -2.53237933e-01 -1.77613467e-01 -7.56324053e-01
-1.15094090e+00 -2.17077285e-01 5.13783276e-01 2.62000620e-01
-2.46368214e-01 2.87337691e-01 1.60505250e-01 2.65614718e-01
-2.05339849e-01 -9.35430408e-01 -6.43814027e-01 -9.20485198e-01
-1.17588453e-01 3.92481625e-01 -1.87116444e-01 -2.38096267... | [11.457079887390137, 0.9090246558189392] |
c51c39b5-ccae-4a56-a4dd-d361d6894d2f | zero-shot-end-to-end-spoken-language | 2305.12793 | null | https://arxiv.org/abs/2305.12793v1 | https://arxiv.org/pdf/2305.12793v1.pdf | Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training | End-to-end (E2E) spoken language understanding (SLU) is constrained by the cost of collecting speech-semantics pairs, especially when label domains change. Hence, we explore \textit{zero-shot} E2E SLU, which learns E2E SLU without speech-semantics pairs, instead using only speech-text and text-semantics pairs. Previous... | ['Jinglun Cai', 'Haoqi Li', 'Kaisheng Yao', 'Julian Salazar', 'Jianfeng He'] | 2023-05-22 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 5.62041044e-01 3.70561153e-01 -1.94024235e-01 -6.70829773e-01
-1.58487368e+00 -5.07193387e-01 4.60087836e-01 2.61484161e-02
-4.39170688e-01 4.77502078e-01 5.74184120e-01 4.35826145e-02
2.15546833e-03 -2.08840206e-01 -7.18890429e-01 -4.44570273e-01
2.44159460e-01 9.16937470e-01 5.33198528e-02 -1.11894906... | [13.81321907043457, 7.035414695739746] |
045fae45-396e-46a7-8dec-d2a5bf4c58a1 | a-continual-development-methodology-for-large | 2209.07326 | null | https://arxiv.org/abs/2209.07326v3 | https://arxiv.org/pdf/2209.07326v3.pdf | A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems | The traditional Machine Learning (ML) methodology requires to fragment the development and experimental process into disconnected iterations whose feedback is used to guide design or tuning choices. This methodology has multiple efficiency and scalability disadvantages, such as leading to spend significant resources in... | ['Andrea Gesmundo'] | 2022-09-15 | null | null | null | null | ['scene-classification', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [ 2.95266300e-01 3.02847862e-01 1.34697482e-01 -3.96620363e-01
-6.86530530e-01 -5.06771922e-01 6.28941834e-01 4.57170635e-01
-4.96293098e-01 6.49214983e-01 -4.40347135e-01 -4.38828439e-01
-7.07371354e-01 -4.84142691e-01 -5.73319733e-01 -5.45076847e-01
-1.14821270e-01 9.64963555e-01 4.36618954e-01 6.57719895... | [8.346049308776855, 4.363644599914551] |
2211d902-36b0-4e41-8fce-265dc74859c2 | reference-based-autoencoder-for-surface | 2211.10060 | null | https://arxiv.org/abs/2211.10060v2 | https://arxiv.org/pdf/2211.10060v2.pdf | Normal Reference Attention and Defective Feature Perception Network for Surface Defect Detection | Visual anomaly detection plays a significant role in the development of industrial automatic product quality inspection. As a result of the utmost imbalance in the amount of normal and abnormal data, growing attention has been given to unsupervised methods for defect detection. Although existing reconstruction-based me... | ['Wenyong Yu', 'Haiming Yao', 'Wei Luo'] | 2022-11-18 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 6.82249248e-01 9.73347500e-02 2.84390867e-01 -2.49396279e-01
-5.19069612e-01 1.67383865e-01 1.05376795e-01 2.83653319e-01
1.97527364e-01 1.03849664e-01 -3.53034675e-01 4.36257338e-03
-1.05795227e-01 -8.04859936e-01 -4.46261287e-01 -1.00655591e+00
2.99123824e-01 -2.04095361e-03 5.60473025e-01 -3.12003046... | [7.496016025543213, 1.8929733037948608] |
8e0daa68-fefc-4256-a1d9-e845e609cfd0 | gencomparesum-a-hybrid-unsupervised | null | null | https://aclanthology.org/2022.bionlp-1.22 | https://aclanthology.org/2022.bionlp-1.22.pdf | GenCompareSum: a hybrid unsupervised summarization method using salience | Text summarization (TS) is an important NLP task. Pre-trained Language Models (PLMs) have been used to improve the performance of TS. However, PLMs are limited by their need of labelled training data and by their attention mechanism, which often makes them unsuitable for use on long documents. To this end, we propose a... | ['Sophia Ananiadou', 'Qianqian Xie', 'Jennifer Bishop'] | null | null | null | null | bionlp-acl-2022-5 | ['extractive-summarization'] | ['natural-language-processing'] | [ 6.32914126e-01 4.65126783e-01 -1.76545829e-01 -2.29931742e-01
-1.21788216e+00 -5.80073655e-01 8.88154328e-01 6.81992471e-01
-4.83436435e-01 9.76684809e-01 8.83870959e-01 3.01134512e-02
-2.81826202e-02 -5.36379635e-01 -4.99325603e-01 -5.77349365e-01
2.90404528e-01 8.42500687e-01 3.06485593e-01 -2.35745996... | [12.480443954467773, 9.492705345153809] |
44a45a50-164f-4157-82df-8a357ab7875e | towards-a-unified-view-on-visual-parameter | 2210.00788 | null | https://arxiv.org/abs/2210.00788v2 | https://arxiv.org/pdf/2210.00788v2.pdf | Towards a Unified View on Visual Parameter-Efficient Transfer Learning | Parameter efficient transfer learning (PETL) aims at making good use of the representation knowledge in the pre-trained large models by fine-tuning a small number of parameters. Recently, taking inspiration from the natural language processing (NLP) domain, popular PETL techniques such as prompt-tuning and Adapter have... | ['Chang Wen Chen', 'Qi Tian', 'Lingbo Liu', 'Jianlong Chang', 'Bruce X. B. Yu'] | 2022-10-03 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [-5.15556335e-02 -2.03253537e-01 -1.48038238e-01 -3.51655960e-01
-7.06103623e-01 -5.63552499e-01 7.32548594e-01 -1.55170739e-01
-7.83187628e-01 4.95962918e-01 1.62911564e-01 -3.29591393e-01
-2.30570763e-01 -4.51256424e-01 -9.03620064e-01 -7.86997736e-01
3.63697708e-01 4.49849039e-01 4.76610631e-01 -2.05038756... | [10.176244735717773, 1.9558099508285522] |
07bac89c-7d88-458e-a1cc-2e0f2b5ff6cc | opp-miner-order-preserving-sequential-pattern | 2202.03140 | null | https://arxiv.org/abs/2202.03140v2 | https://arxiv.org/pdf/2202.03140v2.pdf | OPP-Miner: Order-preserving sequential pattern mining | A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, and weather forecast. To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format... | ['Xindong Wu', 'Xingquan Zhu', 'Lei Guo', 'Yan Li', 'Qian Hu', 'Youxi Wu'] | 2022-01-09 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 9.17155147e-02 -7.82656193e-01 -2.22416341e-01 -1.52948156e-01
3.18618417e-01 -5.60835898e-01 3.38700980e-01 4.78801519e-01
-9.40041840e-02 5.89582324e-01 2.64456511e-01 -3.86785179e-01
-8.34689438e-01 -1.04007053e+00 -1.32723823e-01 -5.83384395e-01
-6.75073504e-01 3.47442210e-01 3.11834514e-01 -2.08598375... | [7.313724994659424, 3.375871419906616] |
62c66527-a0b9-4b9b-8945-d88b64f51ae2 | from-clozing-to-comprehending-retrofitting | 2212.04755 | null | https://arxiv.org/abs/2212.04755v2 | https://arxiv.org/pdf/2212.04755v2.pdf | From Clozing to Comprehending: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine Reader | We present Pre-trained Machine Reader (PMR), a novel method for retrofitting pre-trained masked language models (MLMs) to pre-trained machine reading comprehension (MRC) models without acquiring labeled data. PMR can resolve the discrepancy between model pre-training and downstream fine-tuning of existing MLMs. To buil... | ['Lidong Bing', 'Luo Si', 'Wai Lam', 'Meng Zhou', 'Wenxuan Zhang', 'Xin Li', 'Weiwen Xu'] | 2022-12-09 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 6.15876973e-01 1.05534744e+00 -2.12287590e-01 -4.28490371e-01
-1.23058712e+00 -4.52354550e-01 4.59441423e-01 4.49839681e-01
-4.22723204e-01 8.25782835e-01 6.16231740e-01 -9.25817430e-01
-1.17062837e-01 -5.94833255e-01 -9.55016315e-01 2.60563940e-01
2.69210339e-01 4.68810737e-01 8.71173069e-02 -3.23859841... | [11.024075508117676, 8.110283851623535] |
4dd08158-edbc-4815-a535-76bb0bbd4428 | graph-neural-networks-go-forward-forward | 2302.05282 | null | https://arxiv.org/abs/2302.05282v1 | https://arxiv.org/pdf/2302.05282v1.pdf | Graph Neural Networks Go Forward-Forward | We present the Graph Forward-Forward (GFF) algorithm, an extension of the Forward-Forward procedure to graphs, able to handle features distributed over a graph's nodes. This allows training graph neural networks with forward passes only, without backpropagation. Our method is agnostic to the message-passing scheme, and... | ['François Fleuret', 'Bálint Máté', 'Mathieu Alain', 'Daniele Paliotta'] | 2023-02-10 | null | null | null | null | ['graph-property-prediction'] | ['graphs'] | [ 4.41195875e-01 4.31628823e-01 -1.83009543e-02 -3.85252208e-01
3.38013798e-01 -3.28314155e-01 8.70561182e-01 5.10648668e-01
-5.75192869e-01 7.65933871e-01 -2.37068504e-01 -7.16276765e-01
-1.95537239e-01 -1.35613000e+00 -1.16082871e+00 -6.65780246e-01
-8.98005962e-01 4.82220501e-01 5.19691169e-01 -3.04242104... | [6.927856922149658, 6.178539752960205] |
3666a733-1a37-474d-8527-4daeca71d8ee | 3d-object-recognition-with-ensemble-learning | 1904.08159 | null | https://arxiv.org/abs/1904.08159v2 | https://arxiv.org/pdf/1904.08159v2.pdf | 3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models | In this study, we present an analysis of model-based ensemble learning for 3D point-cloud object classification and detection. An ensemble of multiple model instances is known to outperform a single model instance, but there is little study of the topic of ensemble learning for 3D point clouds. First, an ensemble of mu... | ['Tarek El-Gaaly', 'Łukasz Chechliński', 'Daniel Koguciuk'] | 2019-04-17 | null | null | null | null | ['3d-object-recognition', '3d-classification'] | ['computer-vision', 'computer-vision'] | [-2.79816717e-01 -2.80804873e-01 2.82905877e-01 -3.11048627e-01
-5.72475433e-01 -5.05500615e-01 3.36732119e-01 -5.09218052e-02
-4.58658248e-01 3.77498478e-01 -1.13040340e+00 -6.91002846e-01
-3.41305614e-01 -9.30378556e-01 -1.13514698e+00 -7.22812653e-01
-5.18257320e-01 8.80599916e-01 1.89314067e-01 -3.14539135... | [7.925760746002197, -3.4585959911346436] |
13fa5cb3-e157-4cd9-8db5-affcf2b09288 | multi-organ-cancer-classification-and | 1606.00897 | null | http://arxiv.org/abs/1606.00897v2 | http://arxiv.org/pdf/1606.00897v2.pdf | Multi-Organ Cancer Classification and Survival Analysis | Accurate and robust cell nuclei classification is the cornerstone for a wider
range of tasks in digital and Computational Pathology. However, most machine
learning systems require extensive labeling from expert pathologists for each
individual problem at hand, with no or limited abilities for knowledge transfer
between... | ['Thomas Fuchs', 'Peter Schüffler', 'Peter Wild', 'Stefan Bauer', 'Joachim M. Buhmann', 'Nicolas Carion'] | 2016-06-02 | null | null | null | null | ['nuclei-classification'] | ['medical'] | [ 1.21544331e-01 8.45541209e-02 -2.88080901e-01 -9.84986946e-02
-8.82044792e-01 -6.25710666e-01 4.09080565e-01 5.63147485e-01
-5.87670565e-01 1.05450380e+00 -1.76204279e-01 -4.26544636e-01
6.25960827e-02 -8.52346778e-01 -4.14871782e-01 -1.12053776e+00
1.86504964e-02 7.53479958e-01 1.71940774e-02 3.10728569... | [15.061395645141602, -3.0759661197662354] |
eb459a0c-c58c-4d72-b93c-74d438b7794a | cross-domain-3d-hand-pose-estimation-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Cross-Domain_3D_Hand_Pose_Estimation_With_Dual_Modalities_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Cross-Domain_3D_Hand_Pose_Estimation_With_Dual_Modalities_CVPR_2023_paper.pdf | Cross-Domain 3D Hand Pose Estimation With Dual Modalities | Recent advances in hand pose estimation have shed light on utilizing synthetic data to train neural networks, which however inevitably hinders generalization to real-world data due to domain gaps. To solve this problem, we present a framework for cross-domain semi-supervised hand pose estimation and target the chal... | ['Angela Yao', 'Linlin Yang', 'Qiuxia Lin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-hand-pose-estimation', 'keypoint-detection', 'hand-pose-estimation', '3d-hand-pose-estimation', 'pseudo-label'] | ['computer-vision', 'computer-vision', 'computer-vision', 'graphs', 'miscellaneous'] | [ 3.10607284e-01 -1.09780088e-01 -3.66882682e-01 -2.40601555e-01
-1.14655542e+00 -6.92398250e-01 3.35690230e-01 -4.00068581e-01
-5.57999492e-01 8.71802926e-01 3.11147302e-01 2.51782052e-02
2.42459383e-02 -4.78676826e-01 -9.05063927e-01 -6.18964672e-01
3.94003838e-01 8.40568841e-01 1.84345454e-01 -1.28711909... | [6.7218828201293945, -0.8396722674369812] |
9d0c893f-0f9f-4852-ac76-ff30d0a8bae5 | domain-agnostic-learning-with-disentangled | 1904.12347 | null | http://arxiv.org/abs/1904.12347v1 | http://arxiv.org/pdf/1904.12347v1.pdf | Domain Agnostic Learning with Disentangled Representations | Unsupervised model transfer has the potential to greatly improve the
generalizability of deep models to novel domains. Yet the current literature
assumes that the separation of target data into distinct domains is known as a
priori. In this paper, we propose the task of Domain-Agnostic Learning (DAL):
How to transfer k... | ['Zijun Huang', 'Kate Saenko', 'Xingchao Peng', 'Ximeng Sun'] | 2019-04-28 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 3.91514450e-01 1.95348859e-01 -1.37598649e-01 -5.52944064e-01
-8.39932501e-01 -9.09433722e-01 7.88599432e-01 -3.48749787e-01
-3.19152415e-01 1.00244641e+00 -6.94943294e-02 -1.71923652e-01
-7.61053562e-02 -7.05228150e-01 -6.90265119e-01 -7.02028692e-01
6.80851936e-02 8.92772675e-01 -1.11692391e-01 -1.60687685... | [10.261934280395508, 3.0280961990356445] |
32b62954-edfd-48a7-bc24-1d115358365c | estimating-treatment-effects-using | 2211.04370 | null | https://arxiv.org/abs/2211.04370v3 | https://arxiv.org/pdf/2211.04370v3.pdf | NESTER: An Adaptive Neurosymbolic Method for Treatment Effect Estimation | Treatment effect estimation from observational data is a central problem in causal inference. Methods based on potential outcomes framework solve this problem by exploiting inductive biases and heuristics from causal inference. Each of these methods addresses a specific aspect of treatment effect estimation, such as co... | ['Vineeth N Balasubramanian', 'Abbavaram Gowtham Reddy'] | 2022-11-08 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 4.41746384e-01 2.70624936e-01 -1.32446587e+00 -5.33489227e-01
-6.18767381e-01 -1.85241207e-01 5.28361559e-01 2.67533213e-01
-2.02322543e-01 1.27464426e+00 1.03491998e+00 -8.18151593e-01
-6.39172912e-01 -1.15668309e+00 -9.68816936e-01 -5.73819041e-01
-2.08478183e-01 3.33896816e-01 -3.60093594e-01 1.86601907... | [8.037425994873047, 5.434096336364746] |
8f99a276-d0f8-4da4-8a8b-4772d24eb8ad | parallel-attention-network-with-sequence | 2105.08481 | null | https://arxiv.org/abs/2105.08481v1 | https://arxiv.org/pdf/2105.08481v1.pdf | Parallel Attention Network with Sequence Matching for Video Grounding | Given a video, video grounding aims to retrieve a temporal moment that semantically corresponds to a language query. In this work, we propose a Parallel Attention Network with Sequence matching (SeqPAN) to address the challenges in this task: multi-modal representation learning, and target moment boundary prediction. W... | ['Rick Siow Mong Goh', 'Joey Tianyi Zhou', 'Liangli Zhen', 'Wei Jing', 'Aixin Sun', 'Hao Zhang'] | 2021-05-18 | null | https://aclanthology.org/2021.findings-acl.69 | https://aclanthology.org/2021.findings-acl.69.pdf | findings-acl-2021-8 | ['video-grounding'] | ['computer-vision'] | [ 2.37912416e-01 -2.57232696e-01 -6.12079322e-01 -2.73086667e-01
-8.87167811e-01 -3.64519864e-01 7.13629901e-01 -1.57728568e-01
-4.33491945e-01 1.63798794e-01 7.84644842e-01 3.70775089e-02
1.92919046e-01 -4.83292520e-01 -7.44425833e-01 -2.06518307e-01
-5.70102669e-02 2.95877486e-01 3.73979539e-01 -1.18393280... | [10.117977142333984, 0.8568747639656067] |
5537cdae-9abc-4595-8bde-bcd7a1274144 | saliency-based-segmentation-of-dermoscopic | 2011.13179 | null | https://arxiv.org/abs/2011.13179v3 | https://arxiv.org/pdf/2011.13179v3.pdf | Saliency-based segmentation of dermoscopic images using color information | Skin lesion segmentation is one of the crucial steps for an efficient non-invasive computer-aided early diagnosis of melanoma. This paper investigates how color information, besides saliency, can be used to determine the pigmented lesion region automatically. Unlike most existing segmentation methods using only the sal... | ['Giuliana Ramella'] | 2020-11-26 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 9.32743549e-01 5.31378239e-02 -1.45490184e-01 -1.81662038e-01
-5.05921125e-01 -2.06913501e-01 4.71622378e-01 6.97590292e-01
-6.84826672e-01 5.53044558e-01 -6.01025075e-02 -2.74580598e-01
-2.54677206e-01 -4.27398562e-01 -1.06502399e-01 -8.95194471e-01
3.86619359e-01 5.30727841e-02 7.19251692e-01 -4.22787815... | [15.612696647644043, -2.999173641204834] |
2ffa66ff-f3dc-4d82-857c-a2417ec0c764 | prix-lm-pretraining-for-multilingual-1 | null | null | https://openreview.net/forum?id=y1DoH6Y75rK | https://openreview.net/pdf?id=y1DoH6Y75rK | Prix-LM: Pretraining for Multilingual Knowledge Base Construction | Knowledge bases (KBs) contain plenty of structured world and commonsense knowledge. As such, they often complement distributional text-based information and facilitate various downstream tasks. Since their manual construction is resource- and time-intensive, recent efforts have tried leveraging large pretrained languag... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-6.20127320e-01 2.08124325e-01 -9.40706074e-01 -2.10796788e-01
-1.05943680e+00 -7.17959523e-01 6.07758045e-01 3.92465174e-01
-6.20166421e-01 1.51780760e+00 6.56196654e-01 -4.57236916e-01
1.39848337e-01 -9.05861437e-01 -1.14106572e+00 -2.02583708e-02
2.19449192e-01 6.51587844e-01 1.06323116e-01 -6.46346092... | [9.49140739440918, 8.800393104553223] |
84468633-0e55-40d9-bd73-19dc2bdf10b2 | gnmr-a-provable-one-line-algorithm-for-low | 2106.12933 | null | https://arxiv.org/abs/2106.12933v3 | https://arxiv.org/pdf/2106.12933v3.pdf | GNMR: A provable one-line algorithm for low rank matrix recovery | Low rank matrix recovery problems, including matrix completion and matrix sensing, appear in a broad range of applications. In this work we present GNMR -- an extremely simple iterative algorithm for low rank matrix recovery, based on a Gauss-Newton linearization. On the theoretical front, we derive recovery guarantees... | ['Boaz Nadler', 'Pini Zilber'] | 2021-06-24 | null | null | null | null | ['low-rank-matrix-completion'] | ['methodology'] | [ 6.17582321e-01 -3.83423641e-02 -2.87140399e-01 1.61066279e-01
-1.04258525e+00 -5.98251045e-01 2.69814700e-01 -1.75094102e-02
-2.22833514e-01 7.27837861e-01 5.47704637e-01 -4.51757908e-01
-7.50689924e-01 -2.47745782e-01 -6.86478436e-01 -7.01016188e-01
-5.73950648e-01 3.23263377e-01 -3.37947100e-01 -4.83120263... | [6.965213775634766, 4.644918441772461] |
af27652d-ed75-47b1-9d89-9d7789776568 | direct-and-residual-subspace-decomposition-of | 2207.09733 | null | https://arxiv.org/abs/2207.09733v2 | https://arxiv.org/pdf/2207.09733v2.pdf | Direct and Residual Subspace Decomposition of Spatial Room Impulse Responses | Psychoacoustic experiments have shown that directional properties of the direct sound, salient reflections, and the late reverberation of an acoustic room response can have a distinct influence on the auditory perception of a given room. Spatial room impulse responses (SRIRs) capture those properties and thus are used ... | ['Jens Ahrens', 'Paul Calamia', 'Sebastià V. Amengual Garí', 'Thomas Deppisch'] | 2022-07-20 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 3.61135691e-01 -6.11203671e-01 1.09807277e+00 -5.38329147e-02
-8.49085331e-01 -4.68740791e-01 3.00811440e-01 -1.39441201e-02
-3.55065018e-01 2.74375826e-01 8.45500231e-01 -1.23437770e-01
-3.00621629e-01 -4.52890277e-01 -1.97059304e-01 -1.06547034e+00
-2.42132515e-01 -2.81609744e-01 2.56805867e-01 -4.00373966... | [15.139379501342773, 5.747859001159668] |
b68e30fb-f085-4650-aeb2-7ed66cea745b | network-comparison-study-of-deep-activation | 2202.03695 | null | https://arxiv.org/abs/2202.03695v1 | https://arxiv.org/pdf/2202.03695v1.pdf | Network Comparison Study of Deep Activation Feature Discriminability with Novel Objects | Feature extraction has always been a critical component of the computer vision field. More recently, state-of-the-art computer visions algorithms have incorporated Deep Neural Networks (DNN) in feature extracting roles, creating Deep Convolutional Activation Features (DeCAF). The transferability of DNN knowledge domain... | ['Alper Yilmaz', 'Michael Karnes'] | 2022-02-08 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [ 8.80279690e-02 -3.13221127e-01 -1.03499912e-01 -4.78179723e-01
1.72718287e-01 -8.22999716e-01 9.41630840e-01 -1.09310150e-01
-5.53752005e-01 5.10415614e-01 -1.48566544e-01 8.73760879e-02
-7.43165672e-01 -6.26982152e-01 -4.22564775e-01 -7.64134049e-01
-2.56362826e-01 2.00108096e-01 2.72140771e-01 -4.16714549... | [9.51385498046875, 2.3220107555389404] |
d28a9ba7-eab0-4e14-83c8-83fe8ca93030 | fast-fourier-color-constancy | 1611.07596 | null | https://arxiv.org/abs/1611.07596v3 | https://arxiv.org/pdf/1611.07596v3.pdf | Fast Fourier Color Constancy | We present Fast Fourier Color Constancy (FFCC), a color constancy algorithm which solves illuminant estimation by reducing it to a spatial localization task on a torus. By operating in the frequency domain, FFCC produces lower error rates than the previous state-of-the-art by 13-20% while being 250-3000 times faster. T... | ['Yun-Ta Tsai', 'Jonathan T. Barron'] | 2016-11-23 | fast-fourier-color-constancy-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Barron_Fast_Fourier_Color_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Barron_Fast_Fourier_Color_CVPR_2017_paper.pdf | cvpr-2017-7 | ['color-constancy'] | ['computer-vision'] | [ 1.92235280e-02 -7.81611204e-01 6.79855198e-02 2.30135992e-01
-6.42308295e-01 -6.38796866e-01 3.85940611e-01 -2.60939449e-01
-2.31818587e-01 7.73930252e-01 1.69082075e-01 -4.79477257e-01
3.96312177e-01 -4.84267175e-01 -3.41103703e-01 -7.20719516e-01
-4.42730412e-02 -2.09466025e-01 3.42687547e-01 3.18873450... | [10.424629211425781, -2.6374261379241943] |
966338ee-75b6-4839-97f2-0b52c3ea323c | deep-neural-networks-for-covid-19-detection | 2012.07655 | null | https://arxiv.org/abs/2012.07655v4 | https://arxiv.org/pdf/2012.07655v4.pdf | Deep Neural Networks for COVID-19 Detection and Diagnosis using Images and Acoustic-based Techniques: A Recent Review | The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus. In t... | ['Ali Narin', 'Walid Hariri'] | 2020-12-10 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 5.42056337e-02 -5.47638178e-01 -1.62813246e-01 1.66482061e-01
1.03473170e-02 -3.66279483e-01 3.40235353e-01 1.33443370e-01
-6.31267846e-01 7.69473553e-01 -1.34474248e-01 -2.27336258e-01
-9.76705402e-02 -9.68729854e-01 -2.42124483e-01 -1.03696477e+00
-1.45548552e-01 1.07210052e+00 2.13255852e-01 1.01919182... | [15.589373588562012, -1.6785900592803955] |
06facda6-56b2-47c5-ace5-21412f6dbf24 | spirit-diffusion-self-consistency-driven | 2304.05060 | null | https://arxiv.org/abs/2304.05060v1 | https://arxiv.org/pdf/2304.05060v1.pdf | SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI | Diffusion models are a leading method for image generation and have been successfully applied in magnetic resonance imaging (MRI) reconstruction. Current diffusion-based reconstruction methods rely on coil sensitivity maps (CSM) to reconstruct multi-coil data. However, it is difficult to accurately estimate CSMs in pra... | ['Yanjie Zhu', 'Dong Liang', 'Hairong Zheng', 'Sen Jia', 'Jing Cheng', 'Chentao Cao', 'Zhuo-Xu Cui'] | 2023-04-11 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 5.21583483e-02 -1.97015733e-01 3.51386726e-01 -3.45082551e-01
-4.48106527e-01 -2.46081114e-01 4.36210841e-01 -2.46372759e-01
-3.74445617e-01 5.13319373e-01 3.44257593e-01 -2.25643679e-01
-3.62251937e-01 -4.58924025e-01 -1.96810573e-01 -1.12908936e+00
-1.13770738e-01 3.93550962e-01 4.28912878e-01 -2.87391152... | [13.534701347351074, -2.3960635662078857] |
53bfa387-6990-4a0b-a9bb-8a04162a9ad0 | 3dfacefill-an-analysis-by-synthesis-approach | 2110.10395 | null | https://arxiv.org/abs/2110.10395v1 | https://arxiv.org/pdf/2110.10395v1.pdf | 3DFaceFill: An Analysis-By-Synthesis Approach to Face Completion | Existing face completion solutions are primarily driven by end-to-end models that directly generate 2D completions of 2D masked faces. By having to implicitly account for geometric and photometric variations in facial shape and appearance, such approaches result in unrealistic completions, especially under large variat... | ['Vishnu Boddeti', 'Rahul Dey'] | 2021-10-20 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 1.74600855e-01 2.76215136e-01 4.58471894e-01 -4.36706603e-01
-6.70622766e-01 -6.41451240e-01 7.18102276e-01 -4.82018471e-01
5.21658808e-02 4.75178003e-01 4.01608855e-01 5.97145744e-02
2.94604689e-01 -5.49303830e-01 -7.82936633e-01 -7.36744761e-01
8.80087465e-02 2.02993587e-01 -3.65800798e-01 -1.00084916... | [12.82443618774414, -0.22173534333705902] |
b2e89043-b462-4f73-b3e9-1a5832250183 | randomly-projected-additive-gaussian | 1912.12834 | null | https://arxiv.org/abs/1912.12834v1 | https://arxiv.org/pdf/1912.12834v1.pdf | Randomly Projected Additive Gaussian Processes for Regression | Gaussian processes (GPs) provide flexible distributions over functions, with inductive biases controlled by a kernel. However, in many applications Gaussian processes can struggle with even moderate input dimensionality. Learning a low dimensional projection can help alleviate this curse of dimensionality, but introduc... | ['Ian A. Delbridge', 'Andrew Gordon Wilson', 'David S. Bindel'] | 2019-12-30 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/4272-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/4272-Paper.pdf | icml-2020-1 | ['small-data'] | ['computer-vision'] | [ 1.24404199e-01 9.30516049e-02 6.92299157e-02 -4.41601090e-02
-9.78957415e-01 -9.01472032e-01 7.50259399e-01 -2.18616068e-01
-2.81117737e-01 8.72820437e-01 1.01640271e-02 -4.95227814e-01
-3.68065476e-01 -1.08164835e+00 -8.89349878e-01 -1.23279774e+00
5.67846559e-02 9.10340965e-01 3.51883508e-02 2.20744565... | [7.26861572265625, 3.8154168128967285] |
6e30ca52-d6ee-4bb5-b19d-bfc4195f1908 | unsupervised-contrastive-photo-to-caricature | 2011.04965 | null | https://arxiv.org/abs/2011.04965v1 | https://arxiv.org/pdf/2011.04965v1.pdf | Unsupervised Contrastive Photo-to-Caricature Translation based on Auto-distortion | Photo-to-caricature translation aims to synthesize the caricature as a rendered image exaggerating the features through sketching, pencil strokes, or other artistic drawings. Style rendering and geometry deformation are the most important aspects in photo-to-caricature translation task. To take both into consideration,... | ['Ran He', 'Aihua Zheng', 'Mandi Luo', 'Xin Ma', 'Yuhe Ding'] | 2020-11-10 | null | null | null | null | ['photo-to-caricature-translation', 'caricature'] | ['computer-vision', 'computer-vision'] | [ 6.42157376e-01 1.28814518e-01 1.38903618e-01 -3.51861626e-01
-5.00744343e-01 -6.90337658e-01 8.01001906e-01 -6.05049074e-01
1.93375826e-01 6.21849895e-01 1.27586693e-01 -3.24346386e-02
2.34830841e-01 -7.49330401e-01 -1.02274823e+00 -5.26148617e-01
7.51731336e-01 4.18591321e-01 -1.64040431e-01 -1.86271101... | [12.116382598876953, -0.3787212371826172] |
e7fa2663-e008-4ab9-b170-f6f0a39cbded | redi-efficient-learning-free-diffusion | 2302.02285 | null | https://arxiv.org/abs/2302.02285v1 | https://arxiv.org/pdf/2302.02285v1.pdf | ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval | Diffusion models show promising generation capability for a variety of data. Despite their high generation quality, the inference for diffusion models is still time-consuming due to the numerous sampling iterations required. To accelerate the inference, we propose ReDi, a simple yet learning-free Retrieval-based Diffus... | ['Lei LI', 'William Yang Wang', 'Xianjun Yang', 'Kexun Zhang'] | 2023-02-05 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 1.31266654e-01 -5.48882931e-02 -3.93330485e-01 2.19552130e-01
-1.16179240e+00 -7.73077905e-01 8.36760044e-01 3.34334113e-02
-1.55878127e-01 9.23659980e-01 1.95969984e-01 -2.33323947e-01
-1.65250853e-01 -1.22758281e+00 -7.88649321e-01 -5.62347829e-01
1.05085358e-01 9.47649717e-01 3.74257028e-01 -5.83182648... | [11.143110275268555, -0.37346193194389343] |
03baa82a-9a22-43e6-ab0b-5f95e2207980 | coreface-sample-guided-contrastive | 2304.11668 | null | https://arxiv.org/abs/2304.11668v1 | https://arxiv.org/pdf/2304.11668v1.pdf | CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition | The discriminability of feature representation is the key to open-set face recognition. Previous methods rely on the learnable weights of the classification layer that represent the identities. However, the evaluation process learns no identity representation and drops the classifier from training. This inconsistency c... | ['Feng Wang', 'Youzhe Song'] | 2023-04-23 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 3.37591201e-01 -6.42188266e-02 -9.35492143e-02 -7.44444251e-01
-5.10790706e-01 -4.05997276e-01 4.89621997e-01 -6.55922949e-01
-2.30573416e-01 4.50007766e-01 -5.40185571e-02 1.90266445e-01
-2.67920345e-01 -5.73180676e-01 -7.79142916e-01 -9.97184873e-01
2.09790036e-01 5.21425717e-02 -3.65334064e-01 -9.54024643... | [13.219524383544922, 0.5464556217193604] |
a690903b-c917-4015-8589-433664273daf | detecting-vanishing-points-using-global-image | 1608.05684 | null | http://arxiv.org/abs/1608.05684v1 | http://arxiv.org/pdf/1608.05684v1.pdf | Detecting Vanishing Points using Global Image Context in a Non-Manhattan World | We propose a novel method for detecting horizontal vanishing points and the
zenith vanishing point in man-made environments. The dominant trend in existing
methods is to first find candidate vanishing points, then remove outliers by
enforcing mutual orthogonality. Our method reverses this process: we propose a
set of h... | ['Menghua Zhai', 'Scott Workman', 'Nathan Jacobs'] | 2016-08-19 | detecting-vanishing-points-using-global-image-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhai_Detecting_Vanishing_Points_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhai_Detecting_Vanishing_Points_CVPR_2016_paper.pdf | cvpr-2016-6 | ['horizon-line-estimation'] | ['computer-vision'] | [-1.66349262e-01 -2.59236008e-01 1.16121352e-01 -3.95167232e-01
-5.04720688e-01 -6.11021638e-01 8.34763765e-01 1.45741418e-01
-4.56257015e-01 1.45570427e-01 9.98629704e-02 -1.54769018e-01
1.51874840e-01 -8.53671908e-01 -6.84754372e-01 -4.74464655e-01
-1.28815398e-01 3.34247261e-01 7.75479198e-01 -6.15529954... | [8.025334358215332, -2.038414716720581] |
178be14a-c474-4c88-a0a8-2a6a1e3c5a19 | dart-distribution-aware-retinal-transform-for | 1710.10800 | null | http://arxiv.org/abs/1710.10800v3 | http://arxiv.org/pdf/1710.10800v3.pdf | DART: Distribution Aware Retinal Transform for Event-based Cameras | We introduce a generic visual descriptor, termed as distribution aware
retinal transform (DART), that encodes the structural context using log-polar
grids for event cameras. The DART descriptor is applied to four different
problems, namely object classification, tracking, detection and feature
matching: (1) The DART fe... | ['Shihao Zhang', 'Bharath Ramesh', 'Garrick Orchard', 'Cheng Xiang', 'Ngoc Anh Le Thi', 'Hong Yang'] | 2017-10-30 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 1.04529308e-02 -4.44627434e-01 -1.13376021e-01 -1.58199981e-01
-8.98536921e-01 -7.62370586e-01 9.34496343e-01 3.73071283e-01
-6.35983288e-01 4.75843459e-01 -1.41123846e-01 2.31640771e-01
-4.92585599e-01 -3.13380361e-01 -6.48444593e-01 -8.78572881e-01
-4.20291543e-01 2.71001965e-01 5.65129519e-01 1.75989315... | [6.437562465667725, -2.1005494594573975] |
053c62ab-84cc-491b-b2c4-e48c73bb90c1 | separate-and-diffuse-using-a-pretrained | 2301.10752 | null | https://arxiv.org/abs/2301.10752v2 | https://arxiv.org/pdf/2301.10752v2.pdf | Separate And Diffuse: Using a Pretrained Diffusion Model for Improving Source Separation | The problem of speech separation, also known as the cocktail party problem, refers to the task of isolating a single speech signal from a mixture of speech signals. Previous work on source separation derived an upper bound for the source separation task in the domain of human speech. This bound is derived for determini... | ['Lior Wolf', 'Eliya Nachmani', 'Shahar Lutati'] | 2023-01-25 | null | null | null | null | ['audio-source-separation', 'speech-separation', 'multi-speaker-source-separation'] | ['audio', 'speech', 'speech'] | [ 4.38185543e-01 3.02803725e-01 2.88376331e-01 -6.28805608e-02
-1.24706674e+00 -8.12681317e-01 7.10315883e-01 -2.01953188e-01
-5.64018339e-02 2.93105364e-01 4.16079283e-01 -3.35935563e-01
-5.95939830e-02 -1.47244528e-01 -6.23103440e-01 -1.08054852e+00
-8.07525888e-02 5.13771117e-01 2.17972264e-01 -1.48997515... | [15.224556922912598, 5.765683650970459] |
9db1baf0-db33-447a-b453-c0cb8e9311ad | a-graph-neural-network-approach-to | 2303.13773 | null | https://arxiv.org/abs/2303.13773v1 | https://arxiv.org/pdf/2303.13773v1.pdf | A Graph Neural Network Approach to Nanosatellite Task Scheduling: Insights into Learning Mixed-Integer Models | This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNN). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the goal is to find the optimal schedule for tasks to be carried out in orbit while taking into account Quality-of-Service (QoS) considerations suc... | ['Leandro dos Santos Coelho', 'Eduardo Augusto Bezerra', 'Eduardo Camponogara', 'Cezar Antônio Rigo', 'Laio Oriel Seman', 'Bruno Machado Pacheco'] | 2023-03-24 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 3.78546476e-01 3.37714702e-01 -3.19579989e-01 -1.20276049e-01
-1.73650101e-01 -2.83438146e-01 1.30299523e-01 2.79827237e-01
-3.62962008e-01 9.56459999e-01 -3.59818667e-01 -4.43775445e-01
-1.00360382e+00 -7.86677122e-01 -9.54478443e-01 -9.57221031e-01
-6.06846154e-01 8.26075435e-01 -6.18809462e-01 -2.93138117... | [5.218470573425293, 2.889234781265259] |
27836d8e-800a-45f0-ab90-1611d36eb434 | diverse-projection-ensembles-for | 2306.07124 | null | https://arxiv.org/abs/2306.07124v1 | https://arxiv.org/pdf/2306.07124v1.pdf | Diverse Projection Ensembles for Distributional Reinforcement Learning | In contrast to classical reinforcement learning, distributional reinforcement learning algorithms aim to learn the distribution of returns rather than their expected value. Since the nature of the return distribution is generally unknown a priori or arbitrarily complex, a common approach finds approximations within a s... | ['Matthijs T. J. Spaan', 'Wendelin Böhmer', 'Moritz A. Zanger'] | 2023-06-12 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [ 2.52119862e-02 9.28971693e-02 -1.67820349e-01 -5.99131584e-01
-9.96926248e-01 -8.20990562e-01 7.22928703e-01 1.20125808e-01
-6.57780766e-01 1.14679360e+00 2.74379373e-01 -4.71773654e-01
-3.68186116e-01 -8.87471855e-01 -8.30520511e-01 -8.98128867e-01
-1.15759134e-01 8.71840417e-01 -1.98384255e-01 -2.07666472... | [4.123377323150635, 2.5773816108703613] |
555cb637-c4c7-4920-aefe-e8eb66e32620 | automated-metrics-for-medical-multi-document | 2305.13693 | null | https://arxiv.org/abs/2305.13693v1 | https://arxiv.org/pdf/2305.13693v1.pdf | Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations | Evaluating multi-document summarization (MDS) quality is difficult. This is especially true in the case of MDS for biomedical literature reviews, where models must synthesize contradicting evidence reported across different documents. Prior work has shown that rather than performing the task, models may exploit shortcu... | ['Byron C. Wallace', 'Erin Bransom', 'Bailey E. Kuehl', 'Thinh Hung Truong', 'Jay DeYoung', 'Yulia Otmakhova', 'Lucy Lu Wang'] | 2023-05-23 | null | null | null | null | ['multi-document-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.56334120e-01 3.16095650e-01 -4.70200151e-01 -2.49011904e-01
-1.50555933e+00 -1.04605591e+00 6.24420524e-01 1.17986095e+00
-4.76089180e-01 9.66871202e-01 1.12458837e+00 -3.27101588e-01
-4.09720004e-01 -3.14730376e-01 -9.61635783e-02 -1.07894629e-01
3.42795402e-01 5.25967598e-01 -6.72473907e-02 1.57635678... | [12.30379581451416, 9.564273834228516] |
f14de9d4-cf87-447b-83ff-d6fe31e267b4 | overview-generalizations-of-multi-agent-path | 1702.05515 | null | http://arxiv.org/abs/1702.05515v1 | http://arxiv.org/pdf/1702.05515v1.pdf | Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios | Multi-agent path finding (MAPF) is well-studied in artificial intelligence,
robotics, theoretical computer science and operations research. We discuss
issues that arise when generalizing MAPF methods to real-world scenarios and
four research directions that address them. We emphasize the importance of
addressing these ... | ['Tansel Uras', 'Sven Koenig', 'Nora Ayanian', 'Wolfgang Hoenig', 'Liron Cohen', 'Craig Tovey', 'T. K. Satish Kumar', 'Hong Xu', 'Guni Sharon', 'Hang Ma'] | 2017-02-17 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 7.47645125e-02 6.50239214e-02 -3.33360434e-01 -1.30291581e-01
-2.48010635e-01 -6.10003948e-01 7.49219298e-01 4.60387170e-01
-7.59740055e-01 1.03991318e+00 -5.54966442e-02 -5.84415019e-01
-7.50865400e-01 -1.03162611e+00 -4.55013275e-01 -4.18252379e-01
-9.24475610e-01 8.91012907e-01 5.31018496e-01 -4.56423253... | [4.977100372314453, 1.7156450748443604] |
1302acb7-52f4-465b-97b1-1a88f4154915 | bayesian-neural-networks-essentials | 2106.13594 | null | https://arxiv.org/abs/2106.13594v1 | https://arxiv.org/pdf/2106.13594v1.pdf | Bayesian Neural Networks: Essentials | Bayesian neural networks utilize probabilistic layers that capture uncertainty over weights and activations, and are trained using Bayesian inference. Since these probabilistic layers are designed to be drop-in replacement of their deterministic counter parts, Bayesian neural networks provide a direct and natural way t... | ['Daniel T. Chang'] | 2021-06-22 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-3.79388332e-01 3.93767238e-01 -4.40147035e-02 -8.48351181e-01
-3.72923315e-01 -4.15436208e-01 7.05011189e-01 -6.34050667e-01
-3.49486977e-01 6.89790308e-01 1.59104243e-01 -6.22930646e-01
-4.73488599e-01 -6.83031380e-01 -8.29384327e-01 -6.00973248e-01
-2.07641855e-01 6.13222361e-01 4.31800544e-01 4.20944124... | [7.267587661743164, 3.875962018966675] |
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