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e6c43d56-ed4c-4773-bba7-3749b3237e94 | a-simple-approach-to-adversarial-robustness-1 | 2204.05432 | null | https://arxiv.org/abs/2204.05432v1 | https://arxiv.org/pdf/2204.05432v1.pdf | A Simple Approach to Adversarial Robustness in Few-shot Image Classification | Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years. However, the classifiers are vulnerable to adversarial examples, posing a question regarding their generalization capabilities. Recent works have tried to combine meta-learning appr... | ['Hamed Pirsiavash', 'Akshayvarun Subramanya'] | 2022-04-11 | a-simple-approach-to-adversarial-robustness | https://openreview.net/forum?id=__ObYt4753c | https://openreview.net/pdf?id=__ObYt4753c | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 1.64509758e-01 4.78114747e-02 -5.08134067e-02 -3.75611216e-01
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6faee8dc-7142-46e3-8fd3-27a8aed05b94 | a-fused-gromov-wasserstein-framework-for | 2305.06574 | null | https://arxiv.org/abs/2305.06574v1 | https://arxiv.org/pdf/2305.06574v1.pdf | A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment | Entity alignment is the task of identifying corresponding entities across different knowledge graphs (KGs). Although recent embedding-based entity alignment methods have shown significant advancements, they still struggle to fully utilize KG structural information. In this paper, we introduce FGWEA, an unsupervised ent... | ['Jia Li', 'Kangfei Zhao', 'Jianheng Tang'] | 2023-05-11 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-3.93517278e-02 2.60736376e-01 -6.33382320e-01 -4.04900730e-01
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d3ed9783-cf93-4b24-a0dd-08d745a9dd64 | srtr-self-reasoning-transformer-with-visual | 2212.09329 | null | https://arxiv.org/abs/2212.09329v1 | https://arxiv.org/pdf/2212.09329v1.pdf | SrTR: Self-reasoning Transformer with Visual-linguistic Knowledge for Scene Graph Generation | Objects in a scene are not always related. The execution efficiency of the one-stage scene graph generation approaches are quite high, which infer the effective relation between entity pairs using sparse proposal sets and a few queries. However, they only focus on the relation between subject and object in triplet set ... | ['Shuai Wang', 'Zhenbo Liu', 'Yuxiang Zhang'] | 2022-12-19 | null | null | null | null | ['scene-graph-generation', 'relational-reasoning'] | ['computer-vision', 'natural-language-processing'] | [ 2.50132740e-01 5.13594031e-01 -3.01926672e-01 -5.60280502e-01
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72528dc1-f483-4bd8-ba6d-56b38dd313d1 | spatiality-guided-transformer-for-3d-dense | 2204.10688 | null | https://arxiv.org/abs/2204.10688v1 | https://arxiv.org/pdf/2204.10688v1.pdf | Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds | Dense captioning in 3D point clouds is an emerging vision-and-language task involving object-level 3D scene understanding. Apart from coarse semantic class prediction and bounding box regression as in traditional 3D object detection, 3D dense captioning aims at producing a further and finer instance-level label of natu... | ['Weidong Cai', 'Jianhui Yu', 'Chaoyi Zhang', 'Heng Wang'] | 2022-04-22 | null | null | null | null | ['dense-captioning', '3d-dense-captioning'] | ['computer-vision', 'computer-vision'] | [ 5.20374253e-02 2.95116335e-01 -1.71286345e-01 -7.15550184e-01
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9.73697230e-02 8.50789607e-01 2.92283624e-01 9.66008604... | [8.200602531433105, -3.2937395572662354] |
97318698-cca9-41bf-bc66-0987108a9f95 | 190600823 | 1906.00823 | null | https://arxiv.org/abs/1906.00823v3 | https://arxiv.org/pdf/1906.00823v3.pdf | Data-driven Estimation of Sinusoid Frequencies | Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy. The goal is to estimate the frequency of each component in a multisinusoidal signal from a finite number of noisy samples. A recent machine-learning approach u... | ['Carlos Fernandez-Granda', 'Gautier Izacard', 'Sreyas Mohan'] | 2019-06-03 | data-driven-estimation-of-sinusoid | http://papers.nips.cc/paper/8756-data-driven-estimation-of-sinusoid-frequencies | http://papers.nips.cc/paper/8756-data-driven-estimation-of-sinusoid-frequencies.pdf | neurips-2019-12 | ['seismic-imaging'] | ['miscellaneous'] | [ 4.96187299e-01 -8.30664784e-02 6.03050962e-02 -2.62792617e-01
-9.89838779e-01 -1.04447588e-01 3.19735140e-01 -6.99883793e-03
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-6.12292588e-01 -2.46938579e-02 8.99991617e-02 -4.66317348... | [15.165475845336914, 5.545777797698975] |
75558096-eebf-4510-b367-7edc88e8a2ae | start-small-think-big-on-hyperparameter | 2207.04979 | null | https://arxiv.org/abs/2207.04979v1 | https://arxiv.org/pdf/2207.04979v1.pdf | Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings | Knowledge graph embedding (KGE) models are an effective and popular approach to represent and reason with multi-relational data. Prior studies have shown that KGE models are sensitive to hyperparameter settings, however, and that suitable choices are dataset-dependent. In this paper, we explore hyperparameter optimizat... | ['Rainer Gemulla', 'Fritz Niesel', 'Adrian Kochsiek'] | 2022-07-11 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.22616470e-01 2.16001943e-01 -2.61402994e-01 -7.16862381e-02
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-3.66298467e-01 9.20825839e-01 3.33522290e-01 -1.08510353... | [8.673002243041992, 7.644240856170654] |
92d1a7ec-a916-4026-96a9-32afbc290c03 | prognet-a-transferable-deep-network-for | null | null | https://www.mdpi.com/2226-4310/10/1/10 | https://www.mdpi.com/2226-4310/10/1/10 | ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight Conditions | Machine learning prognosis for condition monitoring of safety-critical systems, such as aircraft engines, continually faces challenges of data unavailability, complexity, and drift. Consequently, this paper overcomes these challenges by introducing adaptive deep transfer learning methodologies, strengthened with robust... | ['Mohamed Benbouzid', 'Leïla-Hayet Mouss', 'Mohamed-Djamel Mouss', 'Tarek Berghout'] | 2022-12-23 | null | null | null | mdpi-aerospace-2022-12 | ['feature-engineering'] | ['methodology'] | [-1.94906116e-01 -4.35131073e-01 -6.97324798e-03 -6.97663650e-02
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-4.03162032e-01 5.19167721e-01 -6.62866002e-03 -1.77500337... | [6.796926021575928, 2.477292060852051] |
d264838f-5349-4f71-8129-45b2380d1070 | open-set-automatic-target-recognition | 2211.05883 | null | https://arxiv.org/abs/2211.05883v1 | https://arxiv.org/pdf/2211.05883v1.pdf | Open-Set Automatic Target Recognition | Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. ATR algorithms are extensively used in real-world scenarios such as military and surveillance applications. Existing ATR algorithms are developed for traditional cl... | ['Vishal M. Patel', 'Shuowen Hu', 'Celso M. de Melo', 'Vibashan VS', 'Bardia Safaei'] | 2022-11-10 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.68120933e-01 -2.61968315e-01 -1.60621136e-01 -5.63391805e-01
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-9.20334011e-02 7.36900926e-01 4.05549824e-01 -6.48362190... | [9.49856948852539, 1.9759944677352905] |
b521b223-23e6-403c-bf1f-774209860bf9 | integrating-generative-artificial | 2305.17137 | null | https://arxiv.org/abs/2305.17137v1 | https://arxiv.org/pdf/2305.17137v1.pdf | Integrating Generative Artificial Intelligence in Intelligent Vehicle Systems | This paper aims to serve as a comprehensive guide for researchers and practitioners, offering insights into the current state, potential applications, and future research directions for generative artificial intelligence and foundation models within the context of intelligent vehicles. As the automotive industry progre... | ['Björn W. Schuller', 'Nicolas Flores-Herr', 'Hans-Jörg Vögel', 'Serena Striegel', 'Jeremy Dillmann', 'Lukas Stappen'] | 2023-05-15 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 7.88525939e-02 6.58057034e-01 -1.95084378e-01 -2.59639263e-01
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2.47056082e-01 1.85543001e-01 -3.35198790e-01 -6.42233670... | [5.700277805328369, 1.0554478168487549] |
80579f6a-6fb5-4cf6-b9c8-3ea690594b03 | an-analysis-of-mixed-initiative-and | 2005.12340 | null | https://arxiv.org/abs/2005.12340v1 | https://arxiv.org/pdf/2005.12340v1.pdf | An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues | The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system. How to achieve this is poorly understood. We propose a set of unsupervised metrics, termed ConversationShape, that highlights the role each of the conversation participants plays by comparing the di... | ['Svitlana Vakulenko', 'Maarten de Rijke', 'Evangelos Kanoulas'] | 2020-05-25 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 7.35500902e-02 5.30456960e-01 -3.10302079e-01 -5.73753834e-01
-6.40468240e-01 -9.44225729e-01 1.44071937e+00 1.09606028e-01
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4.00231570e-01 1.03469467e+00 -7.35271871e-02 -7.07626581... | [12.33432388305664, 7.884649753570557] |
2b4c89a0-d2e8-4ef1-b6b5-9f12d27cba86 | streaming-min-max-hypergraph-partitioning | null | null | http://papers.nips.cc/paper/5897-streaming-min-max-hypergraph-partitioning | http://papers.nips.cc/paper/5897-streaming-min-max-hypergraph-partitioning.pdf | Streaming Min-max Hypergraph Partitioning | In many applications, the data is of rich structure that can be represented by a hypergraph, where the data items are represented by vertices and the associations among items are represented by hyperedges. Equivalently, we are given an input bipartite graph with two types of vertices: items, and associations (which we ... | ['Dan Alistarh', 'Milan Vojnovic', 'Jennifer Iglesias'] | 2015-12-01 | null | null | null | neurips-2015-12 | ['hypergraph-partitioning'] | ['graphs'] | [ 2.26587459e-01 1.95876192e-02 -3.59324932e-01 -5.33552803e-02
-2.44495630e-01 -7.67667055e-01 -7.39575317e-03 7.39316702e-01
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-5.19497156e-01 -1.06750131e+00 -8.07707071e-01 -8.36695910e-01
-5.35348475e-01 9.67561305e-01 3.50696474e-01 1.63626671... | [6.8960747718811035, 5.122836112976074] |
7b67eb44-779c-4567-8694-c97fcc991d0f | dense-and-low-rank-gaussian-crfs-using-deep | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Chandra_Dense_and_Low-Rank_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Chandra_Dense_and_Low-Rank_ICCV_2017_paper.pdf | Dense and Low-Rank Gaussian CRFs Using Deep Embeddings | In this work we introduce a structured prediction model that endows the Deep Gaussian Conditional Random Field (G-CRF) with a densely connected graph structure. We keep memory and computational complexity under control by expressing the pairwise interactions as inner products of low-dimensional, learnable embeddings. T... | ['Siddhartha Chandra', 'Nicolas Usunier', 'Iasonas Kokkinos'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['human-part-segmentation'] | ['computer-vision'] | [-1.10847965e-01 2.60635376e-01 -5.40382005e-02 -4.47096765e-01
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-2.07502231e-01 6.84731066e-01 1.90610647e-01 1.60309419... | [9.646337509155273, 0.4085260033607483] |
bdfb7837-0314-4262-b2e4-7e6146473049 | complex-knowledge-base-question-answering-a | 2108.06688 | null | https://arxiv.org/abs/2108.06688v5 | https://arxiv.org/pdf/2108.06688v5.pdf | Complex Knowledge Base Question Answering: A Survey | Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performance on complex questions is still far from satisfactory. Therefore, in recent years, researchers propose a... | ['Ji-Rong Wen', 'Wayne Xin Zhao', 'Jing Jiang', 'Jinhao Jiang', 'Gaole He', 'Yunshi Lan'] | 2021-08-15 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.03567946e-01 2.82393724e-01 -6.35067895e-02 -5.95333695e-01
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-4.54862863e-01 -1.19368720e+00 -5.36951661e-01 -2.36131042e-01
3.37213695e-01 9.18742418e-01 6.54698312e-01 -9.08577263... | [10.662154197692871, 7.94125509262085] |
e692d9f9-b166-476d-a255-40eb2a0c6e38 | procedural-content-generation-better | 2105.14780 | null | https://arxiv.org/abs/2105.14780v1 | https://arxiv.org/pdf/2105.14780v1.pdf | Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning | The idea of transfer in reinforcement learning (TRL) is intriguing: being able to transfer knowledge from one problem to another problem without learning everything from scratch. This promises quicker learning and learning more complex methods. To gain an insight into the field and to detect emerging trends, we perform... | ['Aske Plaat', 'Mike Preuss', 'Matthias Müller-Brockhausen'] | 2021-05-31 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-8.71377364e-02 -2.19991252e-01 -3.05411190e-01 -1.89386800e-01
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-3.21284115e-01 -9.50023472e-01 -8.87105405e-01 -3.79850447e-01
-3.25060725e-01 6.48819804e-01 3.77671689e-01 -6.78532064... | [4.077471733093262, 1.5128012895584106] |
13ada109-c880-416e-b388-e56f765e57f8 | an-electra-model-for-latin-token-tagging | null | null | https://aclanthology.org/2022.lt4hala-1.30 | https://aclanthology.org/2022.lt4hala-1.30.pdf | An ELECTRA Model for Latin Token Tagging Tasks | This report describes the KU Leuven / Brepols-CTLO submission to EvaLatin 2022. We present the results of our current small Latin ELECTRA model, which will be expanded to a larger model in the future. For the lemmatization task, we combine a neural token-tagging approach with the in-house rule-based lemma lists from Br... | ['Alek Keersmaekers', 'Wouter Mercelis'] | null | null | null | null | lt4hala-lrec-2022-6 | ['lemmatization', 'morphological-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-1.82261273e-01 3.08676600e-01 -1.35978594e-01 -4.37623858e-01
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3.52146327e-02 7.21134484e-01 1.72920585e-01 -1.80956617... | [10.436720848083496, 10.114312171936035] |
408f9c2d-a55b-49fb-bcde-5a9cb680f16b | high-resolution-synthesis-of-high-density | 2209.09809 | null | https://arxiv.org/abs/2209.09809v2 | https://arxiv.org/pdf/2209.09809v2.pdf | High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection | Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensitivity of mammography for breast cancer detection can be reduced by mo... | ['Karim Lekadir', 'Laura Igual', 'Fredrik Strand', 'Maciej Bobowicz', 'Javier del Riego', 'Alessandro Catanese', 'Oliver Diaz', 'Richard Osuala', 'Kaisar Kushibar', 'Lidia Garrucho'] | 2022-09-20 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 6.20787501e-01 9.31640744e-01 -4.88568723e-01 -4.16738361e-01
-1.00346243e+00 2.30721772e-01 3.82994503e-01 -6.51253089e-02
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-1.47393644e-01 8.65159392e-01 3.32250178e-01 1.70967758... | [15.167534828186035, -2.4325995445251465] |
33b6022f-c7aa-4e61-9721-0ab45c5dc600 | enlarging-instance-specific-and-class | 2303.15467 | null | https://arxiv.org/abs/2303.15467v1 | https://arxiv.org/pdf/2303.15467v1.pdf | Enlarging Instance-specific and Class-specific Information for Open-set Action Recognition | Open-set action recognition is to reject unknown human action cases which are out of the distribution of the training set. Existing methods mainly focus on learning better uncertainty scores but dismiss the importance of feature representations. We find that features with richer semantic diversity can significantly imp... | ['Qifeng Chen', 'Yingya Zhang', 'Zhiwu Qing', 'Yixuan Pei', 'Xiang Wang', 'Shiwei Zhang', 'Jun Cen'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cen_Enlarging_Instance-Specific_and_Class-Specific_Information_for_Open-Set_Action_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cen_Enlarging_Instance-Specific_and_Class-Specific_Information_for_Open-Set_Action_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-set-action-recognition', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 4.69240308e-01 -1.27874002e-01 -5.15836477e-01 -5.30774474e-01
-8.61170530e-01 -3.89285028e-01 2.76199102e-01 -1.40322790e-01
-2.12690935e-01 8.01788092e-01 2.62253523e-01 2.45891243e-01
-5.31157494e-01 -4.53884184e-01 -7.16622710e-01 -8.25503528e-01
-6.06614053e-02 2.34568059e-01 5.43755829e-01 -3.81469093... | [8.48003101348877, 0.8739152550697327] |
a3f5cc44-41de-4aa4-a008-877bcfad1b02 | license-plate-recognition-lpr-a-review-with | 1401.5559 | null | http://arxiv.org/abs/1401.5559v1 | http://arxiv.org/pdf/1401.5559v1.pdf | License Plate Recognition (LPR): A Review with Experiments for Malaysia Case Study | Most vehicle license plate recognition use neural network techniques to
enhance its computing capability. The image of the vehicle license plate is
captured and processed to produce a textual output for further processing. This
paper reviews image processing and neural network techniques applied at
different stages whi... | ['Mohd Adili Norasikin', 'Norazira A Jalil', 'Emaliana Kasmuri', 'Nuzulha Khilwani Ibrahim', 'Sazilah Salam', 'Mohamad Riduwan Md Nawawi'] | 2014-01-22 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 4.10753518e-01 -5.55018783e-01 1.39599487e-01 -4.12511647e-01
1.36825129e-01 -5.80348253e-01 1.82687834e-01 -4.55716252e-01
-6.55486882e-01 4.91858184e-01 -7.53074735e-02 -7.57167518e-01
7.34470636e-02 -9.61967647e-01 -2.10554257e-01 -5.20592093e-01
7.83339858e-01 2.03888059e-01 2.03199670e-01 -1.00826405... | [9.799010276794434, -4.995089530944824] |
cf50d4f7-efa4-4dee-8cd7-30e9b8b216a3 | uncertainty-quantification-in-synthetic | 2210.05026 | null | https://arxiv.org/abs/2210.05026v2 | https://arxiv.org/pdf/2210.05026v2.pdf | Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption | We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions or estimators in settings with staggered treatment adoption, offering precise non-asymptotic coverage probability guarantees. From a methodological perspective, we provide a detailed discussion of di... | ['Rocio Titiunik', 'Filippo Palomba', 'Yingjie Feng', 'Matias D. Cattaneo'] | 2022-10-10 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 4.96971279e-01 4.38295603e-01 -1.13479638e+00 -3.83482367e-01
-1.01948929e+00 -6.53835356e-01 7.66618192e-01 2.50263602e-01
-2.56981373e-01 1.24687445e+00 8.19469035e-01 -6.16028070e-01
-6.07884645e-01 -7.68740535e-01 -8.73315632e-01 -6.50266290e-01
-1.69931933e-01 5.32491028e-01 -4.65637058e-01 5.49891710... | [7.9633331298828125, 5.27595329284668] |
71e0234e-2e77-4633-9ac0-f69a24d6a31a | relational-temporal-graph-reasoning-for-dual | 2306.09114 | null | https://arxiv.org/abs/2306.09114v1 | https://arxiv.org/pdf/2306.09114v1.pdf | Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding | Dual-task dialog language understanding aims to tackle two correlative dialog language understanding tasks simultaneously via leveraging their inherent correlations. In this paper, we put forward a new framework, whose core is relational temporal graph reasoning.We propose a speaker-aware temporal graph (SATG) and a du... | ['Ivor W. Tsang', 'Bowen Xing'] | 2023-06-15 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-2.27834806e-01 4.98458654e-01 -2.05045536e-01 -6.65555358e-01
-6.22242749e-01 -5.26831090e-01 8.71541917e-01 -2.51612753e-01
5.71395755e-02 3.71427536e-01 6.63071632e-01 -4.40868318e-01
-2.10811600e-01 -6.57155693e-01 -3.58860552e-01 -4.70197082e-01
1.99283585e-01 7.92143583e-01 1.80252463e-01 -7.64652908... | [12.486075401306152, 7.6388258934021] |
26a24a96-7bd5-4ea7-80d2-9fdb18b38505 | ice-hockey-player-identification-via | 2111.11535 | null | https://arxiv.org/abs/2111.11535v2 | https://arxiv.org/pdf/2111.11535v2.pdf | Ice hockey player identification via transformers and weakly supervised learning | Identifying players in video is a foundational step in computer vision-based sports analytics. Obtaining player identities is essential for analyzing the game and is used in downstream tasks such as game event recognition. Transformers are the existing standard in Natural Language Processing (NLP) and are swiftly gaini... | ['John S. Zelek', 'David A. Clausi', 'Pascale Walters', 'William McNally', 'Kanav Vats'] | 2021-11-22 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 2.51145095e-01 -3.48844886e-01 -2.83978164e-01 -1.62625149e-01
-9.05897439e-01 -9.22862053e-01 4.57881421e-01 1.82071716e-01
-9.21362162e-01 3.47802967e-01 2.28708804e-01 -1.05057769e-01
7.66793564e-02 -9.24802601e-01 -8.71400118e-01 -4.57952648e-01
-1.11441268e-02 5.85839450e-01 8.92048538e-01 -3.45153123... | [7.840733528137207, 0.1669655293226242] |
5517ce5b-0604-4255-8e21-3633bae9191c | on-the-safety-of-vulnerable-road-users-by | 2004.11909 | null | https://arxiv.org/abs/2004.11909v1 | https://arxiv.org/pdf/2004.11909v1.pdf | On the safety of vulnerable road users by cyclist orientation detection using Deep Learning | In this work, orientation detection using Deep Learning is acknowledged for a particularly vulnerable class of road users,the cyclists. Knowing the cyclists' orientation is of great relevance since it provides a good notion about their future trajectory, which is crucial to avoid accidents in the context of intelligent... | ['Carlos A. Carballo-Monsivais', 'Diego A. Mercado-Ravell', 'Marichelo Garcia-Venegas'] | 2020-04-25 | null | null | null | null | ['2d-cyclist-detection'] | ['computer-vision'] | [-2.60897487e-01 -1.29889429e-01 -3.56880993e-01 -2.98627585e-01
-4.79124606e-01 -2.60445058e-01 4.81408775e-01 -5.23496307e-02
-7.43157148e-01 4.96154249e-01 -2.15423539e-01 -5.36430776e-01
-1.22193679e-01 -1.32062602e+00 -7.33494282e-01 -6.38185859e-01
-2.31895313e-01 1.98578686e-01 8.04920614e-01 -5.76040089... | [8.044533729553223, -0.8729920983314514] |
2b3554cc-5490-427a-b04d-fdc45d2c93d8 | robust-training-under-label-noise-by-over | 2202.14026 | null | https://arxiv.org/abs/2202.14026v2 | https://arxiv.org/pdf/2202.14026v2.pdf | Robust Training under Label Noise by Over-parameterization | Recently, over-parameterized deep networks, with increasingly more network parameters than training samples, have dominated the performances of modern machine learning. However, when the training data is corrupted, it has been well-known that over-parameterized networks tend to overfit and do not generalize. In this wo... | ['Chong You', 'Qing Qu', 'Zhihui Zhu', 'Sheng Liu'] | 2022-02-28 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.55249619e-01 2.29847953e-02 -1.30397335e-01 -4.39977914e-01
-1.03127205e+00 -4.76854831e-01 3.58405501e-01 -3.62110347e-01
-3.88783455e-01 7.45764136e-01 3.87025863e-01 2.89383203e-01
-3.39893430e-01 -3.92266423e-01 -1.01124465e+00 -1.19961917e+00
8.95103514e-02 5.74362993e-01 -3.54706854e-01 6.40600920... | [9.110742568969727, 3.5687689781188965] |
34f4aacf-723d-4147-85ce-dbaff4aee3e4 | a-graph-constrained-changepoint-learning | 2102.01319 | null | https://arxiv.org/abs/2102.01319v2 | https://arxiv.org/pdf/2102.01319v2.pdf | A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection | This study presents a new viewpoint on ECG signal analysis by applying a graph-based changepoint detection model to locate R-peak positions. This model is based on a new graph learning algorithm to learn the constraint graph given the labeled ECG data. The proposed learning algorithm starts with a simple initial graph ... | ['Fatemeh Afghah', 'Toby Hocking', 'Atiyeh Fotoohinasab'] | 2021-02-02 | null | null | null | null | ['qrs-complex-detection'] | ['medical'] | [ 2.46692851e-01 2.32331961e-01 -2.29442671e-01 -3.65328223e-01
-7.07022250e-01 -3.30477715e-01 -2.67082274e-01 4.95743364e-01
-1.91555396e-01 6.88662946e-01 -3.51309955e-01 -2.23345160e-01
-3.59714329e-01 -3.86672914e-01 -1.68934852e-01 -5.48376322e-01
-8.23809862e-01 1.38564274e-01 2.64020473e-01 1.94655314... | [14.230029106140137, 3.171272039413452] |
9e42578c-0c5d-411e-8273-e935103319e3 | graph-embedding-augmented-skill-rating-system | 2304.08257 | null | https://arxiv.org/abs/2304.08257v1 | https://arxiv.org/pdf/2304.08257v1.pdf | Graph Embedding Augmented Skill Rating System | This paper presents a framework for learning player embeddings in competitive games and events. Players and their win-loss relationships are modeled as a skill gap graph, which is an undirected weighted graph. The player embeddings are learned from the graph using a random walk-based graph embedding method and can refl... | ['Jiasheng Wang'] | 2023-04-17 | null | null | null | null | ['graph-embedding'] | ['graphs'] | [-6.30359709e-01 4.73259091e-02 -3.29554349e-01 5.88904060e-02
-1.38358116e-01 -6.76962495e-01 3.71245474e-01 5.30742764e-01
-8.67088735e-01 1.92174956e-01 4.50906068e-01 -4.76137642e-03
-7.58223414e-01 -1.37725127e+00 -2.81743314e-02 -2.74598390e-01
-5.14476597e-01 9.58273172e-01 6.20329916e-01 -8.26880932... | [6.650767803192139, 0.36705076694488525] |
51894445-ce2a-4069-8b57-1b23fbce73bd | trecvid-2020-a-comprehensive-campaign-for | 2104.13473 | null | https://arxiv.org/abs/2104.13473v1 | https://arxiv.org/pdf/2104.13473v1.pdf | TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domains | The TREC Video Retrieval Evaluation (TRECVID) is a TREC-style video analysis and retrieval evaluation with the goal of promoting progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation. Over the last twenty years this effort h... | ['Georges Quenot', 'Wessel Kraaij', 'Gareth J. F. Jones', 'Yvette Graham', 'Alan F. Smeaton', 'Jeffrey Liu', 'Lukas Diduch', 'Baptiste Chocot', 'Eliot Godard', 'Jesse Zhang', 'Andrew Delgado', 'Yooyoung Lee', 'Afzal Godil', 'Jonathan Fiscus', 'Keith Curtis', 'Asad A. Butt', 'George Awad'] | 2021-04-27 | null | null | null | null | ['instance-search', 'ad-hoc-video-search'] | ['computer-vision', 'computer-vision'] | [ 3.73945296e-01 -6.44843578e-01 -4.59812954e-02 -3.44733953e-01
-1.62519920e+00 -9.41052794e-01 9.93938148e-01 6.55281544e-01
-8.04086149e-01 4.93024915e-01 7.89929271e-01 1.56936660e-01
-1.31602734e-01 -6.19171523e-02 -1.68424234e-01 -3.99026513e-01
-1.95057601e-01 3.62562001e-01 6.54071093e-01 -1.78082466... | [10.44188404083252, 0.7328755855560303] |
51d603df-29fb-4c9a-8e2c-d381d8aa0537 | subword-based-cross-lingual-transfer-of-1 | null | null | https://aclanthology.org/2022.sigmorphon-1.7 | https://aclanthology.org/2022.sigmorphon-1.7.pdf | Subword-based Cross-lingual Transfer of Embeddings from Hindi to Marathi and Nepali | Word embeddings are growing to be a crucial resource in the field of NLP for any language. This work introduces a novel technique for static subword embeddings transfer for Indic languages from a relatively higher resource language to a genealogically related low resource language. We primarily work with HindiMarathi, ... | ['Zdeněk Žabokrtský', 'Niyata Bafna'] | null | null | null | null | naacl-sigmorphon-2022-7 | ['word-similarity'] | ['natural-language-processing'] | [-2.37770051e-01 -1.92037627e-01 -3.98078561e-01 -3.01656812e-01
-8.29185247e-01 -7.97335029e-01 9.70691860e-01 3.73340577e-01
-1.03295827e+00 6.88380897e-01 7.21215725e-01 -6.59124076e-01
-2.16997936e-01 -7.72266626e-01 -6.02376938e-01 -4.43359971e-01
8.74986351e-02 1.21636975e+00 1.53343538e-02 -8.48140895... | [10.857433319091797, 9.957269668579102] |
5fb29603-96e1-40b3-80ac-4f3d7b19f324 | an-enhanced-conv-tasnet-model-for-speech | 2205.13657 | null | https://arxiv.org/abs/2205.13657v3 | https://arxiv.org/pdf/2205.13657v3.pdf | An enhanced Conv-TasNet model for speech separation using a speaker distance-based loss function | This work addresses the problem of speech separation in the Spanish Language using pre-trained deep learning models. As with many speech processing tasks, large databases in other languages different from English are scarce. Therefore this work explores different training strategies using the Conv-TasNet model as a ben... | ['Julián D. Arias-Londoño', 'Jose A. Arango-Sánchez'] | 2022-05-26 | null | null | null | null | ['speech-separation'] | ['speech'] | [-9.75318253e-02 1.93739370e-01 2.11457372e-01 -3.58195841e-01
-7.78387189e-01 -1.17032997e-01 5.17697811e-01 2.12859347e-01
-6.31467342e-01 3.17582250e-01 1.66182995e-01 -4.54557002e-01
-9.22043696e-02 -1.85593516e-01 -2.75466084e-01 -8.42712641e-01
-1.92008987e-01 2.26676296e-02 7.84169063e-02 -2.52281159... | [14.76084041595459, 5.989994049072266] |
28aa3de7-6559-4609-ba43-ce38b8f29294 | dexdeform-dexterous-deformable-object | 2304.03223 | null | https://arxiv.org/abs/2304.03223v1 | https://arxiv.org/pdf/2304.03223v1.pdf | DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics | In this work, we aim to learn dexterous manipulation of deformable objects using multi-fingered hands. Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics interaction with deformable objects. At the same time, previous trajectory opti... | ['Chuang Gan', 'Joshua B. Tenenbaum', 'Hao Su', 'Tao Du', 'Tao Chen', 'Zhiao Huang', 'Sizhe Li'] | 2023-03-27 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-6.59875050e-02 1.54537439e-01 2.65955031e-01 -6.84012920e-02
-2.62168288e-01 -1.02865005e+00 5.21674156e-01 -4.84108806e-01
-3.48452866e-01 8.28955650e-01 1.70658574e-01 -8.09560269e-02
-5.58142722e-01 -5.26159704e-01 -9.28043842e-01 -3.98958087e-01
-3.67107719e-01 7.69615829e-01 3.31611663e-01 -6.64567351... | [4.7901387214660645, 0.557355523109436] |
2894bce0-1a1e-4279-a4ef-ee11c838d2a6 | rosko-row-skipping-outer-products-for-sparse | 2307.03930 | null | https://arxiv.org/abs/2307.03930v1 | https://arxiv.org/pdf/2307.03930v1.pdf | Rosko: Row Skipping Outer Products for Sparse Matrix Multiplication Kernels | We propose Rosko -- row skipping outer products -- for deriving sparse matrix multiplication (SpMM) kernels in reducing computation and memory access requirements of deep neural networks (DNNs). Rosko allows skipping of entire row computations during program execution with low sparsity-management overheads. We analytic... | ['Mark Ting', 'H. T. Kung', 'Andrew Sabot', 'Vikas Natesh'] | 2023-07-08 | null | null | null | null | ['management'] | ['miscellaneous'] | [-1.45535424e-01 -1.01522617e-01 -3.40148509e-01 -4.68172371e-01
-8.22252557e-02 -3.62567514e-01 7.16294795e-02 1.49775609e-01
-6.62521303e-01 1.36672333e-01 3.40784639e-02 -7.24559784e-01
-8.25407952e-02 -6.85167789e-01 -8.66459072e-01 -6.48957789e-01
-1.43095493e-01 1.83497578e-01 5.70614077e-02 9.33987871... | [8.38585090637207, 3.031419515609741] |
9b1ef057-a774-4fbf-9c18-c2036bd1e4e9 | building-an-invisible-shield-for-your | 2305.12881 | null | https://arxiv.org/abs/2305.12881v1 | https://arxiv.org/pdf/2305.12881v1.pdf | Building an Invisible Shield for Your Portrait against Deepfakes | The issue of detecting deepfakes has garnered significant attention in the research community, with the goal of identifying facial manipulations for abuse prevention. Although recent studies have focused on developing generalized models that can detect various types of deepfakes, their performance is not always be reli... | ['Youjian Zhao', 'Errui Ding', 'Chengbin Quan', 'Lirui Deng', 'Zhizhi Guo', 'Hang Zhou', 'Tianshu Hu', 'Jiazhi Guan'] | 2023-05-22 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 5.72601438e-01 -1.81426182e-01 -1.40740871e-01 -2.39783436e-01
-6.69151306e-01 -6.99324489e-01 8.51431370e-01 2.46444289e-02
-4.75573599e-01 3.68968248e-01 -1.75215960e-01 -2.13324204e-01
4.51939106e-02 -7.03636050e-01 -7.30161488e-01 -8.94498706e-01
-1.40209764e-01 -3.00999671e-01 -4.95035574e-02 -1.65508613... | [12.689583778381348, 1.0220118761062622] |
5e11b42c-ee28-45f2-8c0e-2990e3db6614 | panoptic-scene-graph-generation | 2207.11247 | null | https://arxiv.org/abs/2207.11247v1 | https://arxiv.org/pdf/2207.11247v1.pdf | Panoptic Scene Graph Generation | Existing research addresses scene graph generation (SGG) -- a critical technology for scene understanding in images -- from a detection perspective, i.e., objects are detected using bounding boxes followed by prediction of their pairwise relationships. We argue that such a paradigm causes several problems that impede t... | ['Ziwei Liu', 'Wayne Zhang', 'Kaiyang Zhou', 'Zujin Guo', 'Yi Zhe Ang', 'Jingkang Yang'] | 2022-07-22 | null | null | null | null | ['scene-graph-generation', 'panoptic-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 5.64546525e-01 2.47832865e-01 -3.05441972e-02 -5.41026056e-01
-6.86219990e-01 -7.13497221e-01 6.95829928e-01 1.06306598e-01
-5.27131185e-03 2.43601918e-01 1.47321209e-01 -3.76159698e-01
1.67258114e-01 -8.18930745e-01 -8.84537637e-01 -5.45107782e-01
1.01936303e-01 4.81179029e-01 6.23287261e-01 -2.92020679... | [10.392077445983887, 1.6250725984573364] |
5df84b88-6ad3-4633-8ff8-b7074d350220 | rewarded-soups-towards-pareto-optimal | 2306.04488 | null | https://arxiv.org/abs/2306.04488v1 | https://arxiv.org/pdf/2306.04488v1.pdf | Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards | Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further align the network with the intended usage. Yet the imperfections in the proxy reward may hinder the training and lead to suboptimal results; ... | ['Matthieu Cord', 'Laure Soulier', 'Jean-Baptiste Gaya', 'Corentin Dancette', 'Mustafa Shukor', 'Guillaume Couairon', 'Alexandre Rame'] | 2023-06-07 | null | null | null | null | ['visual-grounding', 'image-captioning', 'text-summarization'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.16999251e-01 4.64630485e-01 -3.29384625e-01 -3.99878830e-01
-7.47549772e-01 -7.13727951e-01 7.99767613e-01 6.69597611e-02
-4.99884605e-01 9.75270092e-01 5.28639197e-01 -1.98777765e-01
-2.01372534e-01 -5.36405921e-01 -8.06702793e-01 -5.70581913e-01
1.41244367e-01 7.14461744e-01 -2.60707080e-01 -3.49876910... | [11.751151084899902, 8.970925331115723] |
782d02c6-eb83-4461-8375-1e0e3f916faa | sadi-a-self-adaptive-decomposed-interpretable | 2306.08299 | null | https://arxiv.org/abs/2306.08299v1 | https://arxiv.org/pdf/2306.08299v1.pdf | SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events | Accurate prediction of electric load is crucial in power grid planning and management. In this paper, we solve the electric load forecasting problem under extreme events such as scorching heats. One challenge for accurate forecasting is the lack of training samples under extreme conditions. Also load usually changes dr... | ['Liang Sun', 'Qingsong Wen', 'Yi Wang', 'Rui Xia', 'Tian Zhou', 'Linxiao Yang', 'Ziqing Ma', 'Hengbo Liu'] | 2023-06-14 | null | null | null | null | ['load-forecasting', 'management'] | ['miscellaneous', 'miscellaneous'] | [-2.84106821e-01 -5.40595353e-01 -9.70127881e-02 -7.09277809e-01
-3.94231409e-01 -3.07225436e-01 2.28472620e-01 8.17524083e-03
3.44539583e-01 1.00350821e+00 3.31551403e-01 -3.63957673e-01
-3.27992916e-01 -9.17144477e-01 -2.29824692e-01 -1.12021005e+00
-1.19482912e-01 3.55041623e-01 -5.91667473e-01 -2.70078897... | [6.372968673706055, 2.8883302211761475] |
3da313ce-cbc2-413e-ad3e-c44c20891e81 | from-real-to-synthetic-and-back-synthesizing | 2006.02110 | null | https://arxiv.org/abs/2006.02110v1 | https://arxiv.org/pdf/2006.02110v1.pdf | From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding | We present a method for synthesizing naturally looking images of multiple people interacting in a specific scenario. These images benefit from the advantages of synthetic data: being fully controllable and fully annotated with any type of standard or custom-defined ground truth. To reduce the synthetic-to-real domain g... | ['Nadav Bhonker', 'Igor Kviatkovsky', 'Gerard Medioni'] | 2020-06-03 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 8.87451708e-01 6.26150787e-01 6.08412027e-01 -6.28171325e-01
-1.08980024e+00 -7.81597614e-01 7.88349569e-01 -4.94177312e-01
-2.46160612e-01 7.21458972e-01 1.35895953e-01 -3.97868194e-02
5.17414868e-01 -1.01870739e+00 -1.06351411e+00 -4.92622435e-01
1.85923442e-01 8.27607989e-01 2.25885078e-01 -1.75980344... | [11.387032508850098, -0.3872469961643219] |
0368b205-dd27-45f8-9174-73833300a285 | end-to-end-video-matting-with-trimap | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_End-to-End_Video_Matting_With_Trimap_Propagation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_End-to-End_Video_Matting_With_Trimap_Propagation_CVPR_2023_paper.pdf | End-to-End Video Matting With Trimap Propagation | The research of video matting mainly focuses on temporal coherence and has gained significant improvement via neural networks. However, matting usually relies on user-annotated trimaps to estimate alpha values, which is a labor-intensive issue. Although recent studies exploit video object segmentation methods to pr... | ['Ming-Sui Lee', 'Wei-Lun Huang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-matting', 'video-matting', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-8.62127170e-02 -2.60526806e-01 -2.14081883e-01 -2.09047690e-01
-7.92683601e-01 -1.30380511e-01 2.85660952e-01 -5.43481708e-01
-2.88630158e-01 2.59082675e-01 1.87550504e-02 -1.38773426e-01
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3.05045575e-01 4.42122877e-01 4.04822856e-01 1.26687407... | [10.605337142944336, -0.8950015902519226] |
ba57e31e-31d4-412e-87e5-ab6dc34c90bb | a-heat-diffusion-perspective-on-geodesic | 2305.19043 | null | https://arxiv.org/abs/2305.19043v1 | https://arxiv.org/pdf/2305.19043v1.pdf | A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction | Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold s... | ['Smita Krishnaswamy', 'Ian Adelstein', 'Guy Wolf', 'Yanlei Zhang', 'Edward De Brouwer', 'Alexander Tong', 'Guillaume Huguet'] | 2023-05-30 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-2.47389957e-01 3.48283239e-02 1.64715722e-01 -1.54390410e-01
-6.94468498e-01 -7.04230964e-01 7.80372322e-01 3.12881857e-01
-4.69752729e-01 5.85808992e-01 3.62342566e-01 -1.02213971e-01
-7.43498743e-01 -7.62055695e-01 -4.85136509e-01 -1.19955361e+00
-3.34382892e-01 4.53940630e-01 -7.84915015e-02 -2.31641203... | [7.718908786773682, 4.073135852813721] |
2c8b3901-3716-4e59-98c3-8bc07a98b502 | universal-domain-adaptation-via-compressive | 2304.11862 | null | https://arxiv.org/abs/2304.11862v2 | https://arxiv.org/pdf/2304.11862v2.pdf | Universal Domain Adaptation via Compressive Attention Matching | Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain without any prior knowledge about the label set. The challenge lies in how to determine whether the target samples belong to common categories. The mainstream methods make judgments based on the sample features, w... | ['Chao Wu', 'Kun Kuang', 'Yunfeng Shao', 'Zexi Li', 'Junkun Yuan', 'Yincuan Li', 'Didi Zhu'] | 2023-04-24 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 5.38198948e-01 -4.23379362e-01 -3.18698227e-01 -2.24183902e-01
-8.67003858e-01 -2.33557299e-01 6.78067207e-01 -2.22590715e-01
-1.40072078e-01 3.72124106e-01 2.37086013e-01 1.10878289e-01
-5.38767725e-02 -5.15340388e-01 -6.82254493e-01 -1.07935774e+00
8.04155052e-01 8.64783376e-02 1.84472412e-01 5.98137565... | [9.689459800720215, 1.8517128229141235] |
b7848b79-fd0f-4fe9-92ab-f4c28ebb3e6a | robust-and-accurate-depth-estimation-by | 2207.06139 | null | https://arxiv.org/abs/2207.06139v1 | https://arxiv.org/pdf/2207.06139v1.pdf | Robust and accurate depth estimation by fusing LiDAR and Stereo | Depth estimation is one of the key technologies in some fields such as autonomous driving and robot navigation. However, the traditional method of using a single sensor is inevitably limited by the performance of the sensor. Therefore, a precision and robust method for fusing the LiDAR and stereo cameras is proposed. T... | ['Rui Guo', 'Xiaoyu Long', 'En Li', 'Junfeng Fan', 'Guangyao Xu'] | 2022-07-13 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 1.15924411e-01 -6.22034252e-01 -1.56962778e-02 -4.13529336e-01
-2.19495475e-01 -2.48333402e-02 3.26001197e-01 -1.87859815e-02
-6.55985057e-01 7.56840408e-01 -2.85922050e-01 -1.47597799e-02
2.24273026e-01 -1.26776040e+00 -4.75208849e-01 -7.30215549e-01
6.37024164e-01 2.65254438e-01 8.52649391e-01 -1.15717150... | [8.713748931884766, -2.4095938205718994] |
73179ba7-f9fe-477b-a766-9a648469b698 | feasibility-of-universal-anomaly-detection | 2307.00750 | null | https://arxiv.org/abs/2307.00750v1 | https://arxiv.org/pdf/2307.00750v1.pdf | Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images | Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction... | ['Yuankai Huo', 'Bennett A. Landman', 'Keith T. Wilson', 'Lori A. Coburn', 'Qi Liu', 'Ken S. Lau', 'Joseph T. Roland', 'Zuhayr Asad', 'Lucas W. Remedios', 'Ruining Deng', 'Yucheng Tang', 'Shunxing Bao', 'Yaohong Wang', 'Can Cui'] | 2023-07-03 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 3.01757008e-01 -1.01235524e-01 2.56339937e-01 -2.30951875e-01
-5.86385906e-01 -3.28570455e-01 4.23479378e-01 5.58534205e-01
-4.43368554e-01 6.15198493e-01 -3.91875595e-01 -4.11489189e-01
-1.10963866e-01 -5.97469807e-01 -5.45295298e-01 -1.20075238e+00
-2.92377919e-01 3.66486788e-01 1.19313605e-01 1.44828305... | [7.6322021484375, 2.227351427078247] |
1f8ad830-6142-4350-8676-f32e96bda8be | scanpath-prediction-on-information | 2112.02340 | null | https://arxiv.org/abs/2112.02340v2 | https://arxiv.org/pdf/2112.02340v2.pdf | Scanpath Prediction on Information Visualisations | We propose Unified Model of Saliency and Scanpaths (UMSS) -- a model that learns to predict visual saliency and scanpaths (i.e. sequences of eye fixations) on information visualisations. Although scanpaths provide rich information about the importance of different visualisation elements during the visual exploration pr... | ['Andreas Bulling', 'Mihai Bâce', 'Yao Wang'] | 2021-12-04 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [ 3.60391289e-01 8.52208138e-02 -2.27713004e-01 -3.26745629e-01
-3.65161866e-01 -6.26735866e-01 6.27148092e-01 3.03945780e-01
-5.56006655e-02 4.05221045e-01 4.66933578e-01 -4.94348049e-01
-3.73568624e-01 -3.85880880e-02 -5.43070376e-01 -3.25998664e-01
-2.31312364e-01 6.54765964e-02 7.34590650e-01 -2.40003809... | [10.127998352050781, 1.2762490510940552] |
dd41e1bf-88a9-4176-acff-203683c54adb | demonstration-of-interactive-teaching-for-end | null | null | https://aclanthology.org/W17-5511 | https://aclanthology.org/W17-5511.pdf | Demonstration of interactive teaching for end-to-end dialog control with hybrid code networks | This is a demonstration of interactive teaching for practical end-to-end dialog systems driven by a recurrent neural network. In this approach, a developer teaches the network by interacting with the system and providing on-the-spot corrections. Once a system is deployed, a developer can also correct mistakes in logged... | ['Lars Liden', 'Jason D. Williams'] | 2017-08-01 | null | null | null | ws-2017-8 | ['dialog-learning'] | ['natural-language-processing'] | [-3.68727326e-01 5.47297597e-01 2.84526587e-01 -8.76389325e-01
-2.27400720e-01 -8.37001324e-01 3.17428142e-01 3.24920416e-01
-2.27470443e-01 5.52219808e-01 -5.79579324e-02 -8.18394125e-01
-5.84046505e-02 -6.32514656e-01 -2.13125646e-01 -6.44971803e-02
6.41516820e-02 8.05035293e-01 3.31623673e-01 -1.12051570... | [12.995356559753418, 7.975066184997559] |
59dbf956-c227-4476-a21f-d93d0e81ca4a | an-efficient-and-scalable-collection-of-fly | 2109.10986 | null | https://arxiv.org/abs/2109.10986v1 | https://arxiv.org/pdf/2109.10986v1.pdf | An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments | State-of-the-art visual place recognition performance is currently being achieved utilizing deep learning based approaches. Despite the recent efforts in designing lightweight convolutional neural network based models, these can still be too expensive for the most hardware restricted robot applications. Low-overhead VP... | ['Shoaib Ehsan', 'Klaus D. McDonald-Maier', 'Michael Milford', 'Bruno Ferrarini', 'Bruno Arcanjo'] | 2021-09-22 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-7.66701698e-02 -2.74497867e-01 -8.91090259e-02 -3.23483676e-01
-2.58043438e-01 -6.55791998e-01 7.55394936e-01 7.61378258e-02
-6.97403729e-01 4.46704805e-01 -3.92714441e-01 -1.66546002e-01
-1.06245093e-01 -6.57654405e-01 -6.79511547e-01 -4.43290085e-01
-2.63506562e-01 2.05176339e-01 5.66641748e-01 -2.66398847... | [7.6713361740112305, -1.812486171722412] |
14dd93e4-d9bb-43dc-a12f-290adec9b241 | drivingstereo-a-large-scale-dataset-for | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_DrivingStereo_A_Large-Scale_Dataset_for_Stereo_Matching_in_Autonomous_Driving_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_DrivingStereo_A_Large-Scale_Dataset_for_Stereo_Matching_in_Autonomous_Driving_CVPR_2019_paper.pdf | DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios | Great progress has been made on estimating disparity maps from stereo images. However, with the limited stereo data available in the existing datasets and unstable ranging precision of current stereo methods, industry-level stereo matching in autonomous driving remains challenging. In this paper, we construct a novel l... | [' Bolei Zhou', ' Jianping Shi', ' Zhidong Deng', ' Chaoqin Huang', ' Xiao Song', 'Guorun Yang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['stereo-matching'] | ['computer-vision'] | [-4.29364257e-02 -3.47012192e-01 -4.92416061e-02 -9.44189847e-01
-8.36449146e-01 -5.55787504e-01 6.23663068e-01 -3.21002573e-01
-4.29626793e-01 5.99966943e-01 7.24024400e-02 -1.93592772e-01
9.95805934e-02 -9.70357478e-01 -7.35498190e-01 -4.05246198e-01
2.47112781e-01 4.83522356e-01 6.95301414e-01 -5.84757507... | [8.501482009887695, -2.300100326538086] |
ec6cb8ea-2103-4165-8e71-b9251ba4eb9a | selective-transfer-learning-with-adversarial | null | null | https://www.tandfonline.com/doi/full/10.1080/09540091.2021.2021143 | https://www.tandfonline.com/doi/full/10.1080/09540091.2021.2021143 | Selective transfer learning with adversarial training for stock movement prediction | Stock movement prediction is a critical issue in the field of financial investment. It is very challenging since a stock usually shows highly stochastic property in price and has complex relationships with other stocks. Most existing approaches cannot jointly take the above two issues into account and thus cannot yield... | ['Zibin Zheng', 'Hong-Ning Dai', 'Yang Li'] | 2021-09-30 | null | null | null | taylor-francis-online-2021-9 | ['stock-prediction'] | ['time-series'] | [-3.67810786e-01 -4.51523334e-01 -3.10855120e-01 -3.19449753e-01
-8.56699467e-01 -5.67683935e-01 6.64854825e-01 -2.03335807e-01
-5.46127081e-01 1.00018227e+00 1.25675008e-01 -5.23047894e-02
5.93474656e-02 -1.23301017e+00 -9.91714060e-01 -6.25591755e-01
-1.47755712e-01 5.20777345e-01 6.95745409e-01 -6.17937148... | [4.454220294952393, 4.230938911437988] |
1ce768fd-da86-401f-b221-6c0d1179de25 | selective-communication-for-cooperative | 2305.17181 | null | https://arxiv.org/abs/2305.17181v1 | https://arxiv.org/pdf/2305.17181v1.pdf | Selective Communication for Cooperative Perception in End-to-End Autonomous Driving | The reliability of current autonomous driving systems is often jeopardized in situations when the vehicle's field-of-view is limited by nearby occluding objects. To mitigate this problem, vehicle-to-vehicle communication to share sensor information among multiple autonomous driving vehicles has been proposed. However, ... | ['Stephen F. Smith', 'Hsu-kuang Chiu'] | 2023-05-26 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.33104363e-01 5.36935449e-01 -9.11255479e-02 -4.68021840e-01
-5.11021078e-01 -4.79165643e-01 5.36398411e-01 5.29444098e-01
-6.58660531e-01 1.02670860e+00 -2.02914998e-01 -4.51144844e-01
-3.83490354e-01 -1.02752221e+00 -5.27903259e-01 -7.82577515e-01
-3.52984756e-01 4.77159083e-01 8.43215346e-01 -4.39924508... | [5.50058126449585, 1.4211496114730835] |
6cd822f3-dfa2-4ed8-9717-566823c7f06f | understanding-augmentation-based-self | 2306.00788 | null | https://arxiv.org/abs/2306.00788v1 | https://arxiv.org/pdf/2306.00788v1.pdf | Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation | Good data augmentation is one of the key factors that lead to the empirical success of self-supervised representation learning such as contrastive learning and masked language modeling, yet theoretical understanding of its role in learning good representations remains limited. Recent work has built the connection betwe... | ['Pradeep Ravikumar', 'Zico Kolter', 'Andrej Risteski', 'Bingbin Liu', 'Runtian Zhai'] | 2023-06-01 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [ 2.82613695e-01 6.34398341e-01 -2.35882789e-01 -9.47957337e-02
-9.30683196e-01 -5.65348208e-01 5.73335826e-01 3.94894838e-01
-3.02953333e-01 4.79563147e-01 4.29111481e-01 -5.22598267e-01
-1.88286945e-01 -7.03787088e-01 -1.19464767e+00 -9.67631400e-01
-4.12941098e-01 2.75024831e-01 -2.18661845e-01 -2.24992409... | [7.8938069343566895, 4.031744003295898] |
eac268d7-742c-4bdc-836f-8a8044a11140 | diffava-personalized-text-to-audio-generation | 2305.12903 | null | https://arxiv.org/abs/2305.12903v1 | https://arxiv.org/pdf/2305.12903v1.pdf | DiffAVA: Personalized Text-to-Audio Generation with Visual Alignment | Text-to-audio (TTA) generation is a recent popular problem that aims to synthesize general audio given text descriptions. Previous methods utilized latent diffusion models to learn audio embedding in a latent space with text embedding as the condition. However, they ignored the synchronization between audio and visual ... | ['Yapeng Tian', 'Jing Shi', 'Shentong Mo'] | 2023-05-22 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 1.09976463e-01 -1.17422044e-01 -1.45302102e-01 -1.98572978e-01
-1.27071762e+00 -3.76722544e-01 7.53601372e-01 -5.27628124e-01
1.21395677e-01 1.26152873e-01 9.45197344e-01 1.65763780e-01
2.74340630e-01 -3.13296288e-01 -8.23277116e-01 -7.11707234e-01
3.77195239e-01 1.45490602e-01 -3.51070091e-02 2.11596027... | [15.259737014770508, 5.194042205810547] |
871c286f-397f-4137-8d2a-cb75657b34c4 | pdvn-a-patch-based-dual-view-network-for-face | null | null | https://ieeexplore.ieee.org/document/10007998 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10007998 | PDVN: A Patch-based Dual-view Network for Face Liveness Detection using Light Field Focal Stack | Light Field Focal Stack (LFFS) can be efficiently rendered from a light field (LF) image captured by plenoptic cameras. Differences in the 3D surface and texture of biometric samples are internally reflected in the defocus blur and local patterns between the rendered slices of LFFS. This unique property makes LFFS quit... | ['Yunlong Wang; Mupei Li; Zhengquan Luo; Zhenan Sun'] | 2023-01-17 | null | null | null | 2022-ieee-international-joint-conference-on | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.27716833e-01 -2.20088363e-01 -1.86310038e-01 -2.09728241e-01
-2.75783062e-01 -6.25616431e-01 4.63138044e-01 -7.65776992e-01
3.07801187e-01 4.01733518e-01 2.15343073e-01 -2.11089719e-02
-4.03608829e-02 -7.14453697e-01 -4.20178115e-01 -1.06825566e+00
2.81684428e-01 -2.68785089e-01 -7.04533458e-02 4.75237481... | [13.01268196105957, 1.1194846630096436] |
70cba9e5-3759-4e79-83d4-b26293a7ac54 | domain-constraints-in-feature-space | 2205.15128 | null | https://arxiv.org/abs/2205.15128v3 | https://arxiv.org/pdf/2205.15128v3.pdf | Level Up with RealAEs: Leveraging Domain Constraints in Feature Space to Strengthen Robustness of Android Malware Detection | The vulnerability to adversarial examples remains one major obstacle for Machine Learning (ML)-based Android malware detection. Realistic attacks in the Android malware domain create Realizable Adversarial Examples (RealAEs), i.e., AEs that satisfy the domain constraints of Android malware. Recent studies have shown th... | ['Zhuoran Liu', 'Veelasha Moonsamy', 'Zhengyu Zhao', 'Hamid Bostani'] | 2022-05-30 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 3.47132683e-01 8.07975128e-04 -2.31840715e-01 -3.51080857e-02
-6.10699654e-01 -1.08649313e+00 7.47643530e-01 -2.57490396e-01
3.19276415e-02 5.43493807e-01 -2.44685978e-01 -8.96209121e-01
-2.69440293e-01 -8.47392797e-01 -9.64631438e-01 -4.68881994e-01
-5.40498734e-01 1.76752135e-01 6.77015781e-02 -6.12915456... | [14.383382797241211, 9.662261962890625] |
e8825a11-96ca-4c8a-b3b8-24931c509a2b | rewritenet-realistic-scene-text-image | 2107.11041 | null | https://arxiv.org/abs/2107.11041v2 | https://arxiv.org/pdf/2107.11041v2.pdf | RewriteNet: Reliable Scene Text Editing with Implicit Decomposition of Text Contents and Styles | Scene text editing (STE), which converts a text in a scene image into the desired text while preserving an original style, is a challenging task due to a complex intervention between text and style. In this paper, we propose a novel STE model, referred to as RewriteNet, that decomposes text images into content and styl... | ['Sungrae Park', 'Gayoung Lee', 'Seung Shin', 'Moonbin Yim', 'Seonghyeon Kim', 'Yoonsik Kim', 'Junyeop Lee'] | 2021-07-23 | null | null | null | null | ['scene-text-recognition', 'scene-text-editing'] | ['computer-vision', 'computer-vision'] | [ 7.21111774e-01 -2.08904833e-01 6.94423243e-02 -5.07913053e-01
-2.99711049e-01 -6.26854360e-01 8.87443006e-01 -5.10196090e-01
-8.76471251e-02 5.76021254e-01 2.15592965e-01 -4.19191383e-02
3.96181822e-01 -6.53381765e-01 -8.29960883e-01 -4.47763264e-01
8.94516766e-01 2.06600204e-01 1.70362934e-01 -2.89913207... | [11.599202156066895, -0.27018916606903076] |
e78dce3b-9ea3-4c4b-8e99-9c51cda58849 | randomized-schur-complement-views-for-graph | 2306.04004 | null | https://arxiv.org/abs/2306.04004v1 | https://arxiv.org/pdf/2306.04004v1.pdf | Randomized Schur Complement Views for Graph Contrastive Learning | We introduce a randomized topological augmentor based on Schur complements for Graph Contrastive Learning (GCL). Given a graph laplacian matrix, the technique generates unbiased approximations of its Schur complements and treats the corresponding graphs as augmented views. We discuss the benefits of our approach, provi... | ['Vignesh Kothapalli'] | 2023-06-06 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 3.19457263e-01 6.12617016e-01 -5.29092431e-01 -1.30942157e-02
-6.27830148e-01 -7.87492871e-01 8.85626197e-01 2.24773914e-01
7.70371556e-02 4.31768864e-01 2.93739200e-01 -6.49065912e-01
1.29458392e-02 -7.36317754e-01 -8.10383677e-01 -5.31490028e-01
-6.94586933e-01 5.33552051e-01 1.85102299e-01 -1.40302569... | [7.0989670753479, 6.2496562004089355] |
7165a35e-b559-48c2-afb6-1a52d7229f12 | an-overlapping-free-leaf-segmentation-method | 1908.04018 | null | https://arxiv.org/abs/1908.04018v1 | https://arxiv.org/pdf/1908.04018v1.pdf | An overlapping-free leaf segmentation method for plant point clouds | Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are so... | ['Siyuan Yan', 'Yang Chen', 'Yan Cao', 'Sifan Wang', 'Guoliang Shi', 'Xin Cai', 'Dawei Li'] | 2019-08-12 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [-3.10642682e-02 -2.03839079e-01 2.27706656e-02 1.09390847e-01
-2.76400030e-01 -1.02518320e+00 -1.71698555e-02 4.96100694e-01
3.06027830e-01 2.91277111e-01 -7.47429609e-01 -4.44987446e-01
-1.72757626e-01 -1.08721209e+00 -2.04210281e-01 -9.84062731e-01
1.66260794e-01 6.19162500e-01 5.84776044e-01 2.51458496... | [9.075273513793945, -1.6372984647750854] |
400429be-1cd5-4456-a849-4ffe2be65dda | dynamic-context-guided-capsule-network-for | 2009.02016 | null | https://arxiv.org/abs/2009.02016v1 | https://arxiv.org/pdf/2009.02016v1.pdf | Dynamic Context-guided Capsule Network for Multimodal Machine Translation | Multimodal machine translation (MMT), which mainly focuses on enhancing text-only translation with visual features, has attracted considerable attention from both computer vision and natural language processing communities. Most current MMT models resort to attention mechanism, global context modeling or multimodal joi... | ['Jiebo Luo', 'Jie zhou', 'Yubin Ge', 'Yongjing Yin', 'Jinsong Su', 'Fandong Meng', 'Zhengyuan Yang', 'Huan Lin'] | 2020-09-04 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 2.42548794e-01 -5.50829351e-01 -3.17311734e-01 -2.75402993e-01
-8.96432459e-01 -5.51830947e-01 6.80686831e-01 -1.34098738e-01
-2.99547374e-01 4.96025085e-01 4.14801866e-01 -5.38583338e-01
4.04591709e-01 -3.52581382e-01 -6.70679569e-01 -7.47378767e-01
6.00681841e-01 1.79556623e-01 -2.72010446e-01 -9.08055529... | [11.47145938873291, 1.518404245376587] |
69761244-41a3-4e20-b712-5070796c3178 | correlative-preference-transfer-with | 2211.11191 | null | https://arxiv.org/abs/2211.11191v4 | https://arxiv.org/pdf/2211.11191v4.pdf | Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation | Advanced recommender systems usually involve multiple domains (such as scenarios or categories) for various marketing strategies, and users interact with them to satisfy diverse demands. The goal of multi-domain recommendation (MDR) is to improve the recommendation performance of all domains simultaneously. Conventiona... | ['Liang Wang', 'Weimin Zhang', 'Bo Zheng', 'Shaoguo Liu', 'Penghui Wei', 'Zixuan Xu'] | 2022-11-21 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-1.23043917e-01 -1.42660871e-01 -5.42637944e-01 -5.68646491e-01
-2.28530005e-01 -5.35663605e-01 -6.14353642e-02 7.38586709e-02
-7.63635412e-02 6.64182961e-01 4.91517276e-01 -2.44252935e-01
-7.41951168e-01 -1.12313998e+00 -4.49837357e-01 -4.05901015e-01
-2.61405289e-01 6.72691107e-01 2.06875861e-01 -7.16231704... | [10.21165657043457, 5.611721992492676] |
9e5efa49-8c81-408d-8989-112530ef9f7d | egocentric-scene-context-for-human-centric | 2207.11365 | null | https://arxiv.org/abs/2207.11365v2 | https://arxiv.org/pdf/2207.11365v2.pdf | EgoEnv: Human-centric environment representations from egocentric video | First-person video highlights a camera-wearer's activities in the context of their persistent environment. However, current video understanding approaches reason over visual features from short video clips that are detached from the underlying physical space and capture only what is immediately visible. We present an a... | ['Kristen Grauman', 'James Hillis', 'Ruta Desai', 'Santhosh Kumar Ramakrishnan', 'Tushar Nagarajan'] | 2022-07-22 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [-2.55755633e-02 -2.68927306e-01 -4.23534252e-02 -1.13601297e-01
-1.43835738e-01 -5.30528486e-01 5.98056197e-01 -4.35682446e-01
-3.52026552e-01 3.72159481e-01 4.90694493e-01 3.44840556e-01
2.16424465e-01 -2.48722002e-01 -1.05727112e+00 -3.44901294e-01
-4.50882494e-01 2.91269813e-02 2.11287364e-01 -4.54286598... | [8.244117736816406, 0.46269160509109497] |
cf293d68-71f1-4788-9c49-0014b1bbabb8 | drug-drug-interaction-extraction-from | 1701.08303 | null | http://arxiv.org/abs/1701.08303v2 | http://arxiv.org/pdf/1701.08303v2.pdf | Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network | Simultaneous administration of multiple drugs can have synergistic or
antagonistic effects as one drug can affect activities of other drugs.
Synergistic effects lead to improved therapeutic outcomes, whereas,
antagonistic effects can be life-threatening, may lead to increased healthcare
cost, or may even cause death. T... | ['Sunil Kumar Sahu', 'Ashish Anand'] | 2017-01-28 | null | null | null | null | ['medical-relation-extraction', 'drug-drug-interaction-extraction'] | ['medical', 'natural-language-processing'] | [ 2.90771425e-01 -2.12255865e-01 -6.25366688e-01 -7.24799857e-02
-6.23458087e-01 -3.70191842e-01 7.47725964e-01 4.42356467e-01
-4.81991529e-01 1.04538095e+00 1.63820431e-01 -4.46778893e-01
-2.62692094e-01 -6.36797369e-01 -5.39333701e-01 -7.75252223e-01
-2.08320677e-01 4.10053879e-01 -6.75705820e-02 -5.03389351... | [8.312841415405273, 8.631037712097168] |
f4f4ac5e-ad84-4812-b907-e0e0f66f05f4 | illuminant-chromaticity-estimation-from | 1906.05526 | null | https://arxiv.org/abs/1906.05526v1 | https://arxiv.org/pdf/1906.05526v1.pdf | Illuminant Chromaticity Estimation from Interreflections | Reliable estimation of illuminant chromaticity is crucial for simulating color constancy and for white balancing digital images. However, estimating illuminant chromaticity from a single image is an ill-posed task, in general, and existing solutions typically employ a variety of assumptions and heuristics. In this pape... | ['Eytan Lifshitz', 'Dani Lischinski'] | 2019-06-13 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 6.14941657e-01 -5.75740218e-01 3.84080261e-01 -1.30891293e-01
-1.81830123e-01 -7.35345364e-01 2.80866951e-01 -3.00564021e-01
-1.73779000e-02 6.11794055e-01 -3.13121021e-01 -1.63876638e-01
1.71780646e-01 -6.22244179e-01 -5.43924212e-01 -9.20094371e-01
2.47184843e-01 1.78538486e-01 1.87626123e-01 -2.04552248... | [10.308075904846191, -2.7133560180664062] |
fe6b5786-e8b9-4c1f-aa9a-967f413ed5d1 | wave-physics-as-an-analog-recurrent-neural | 1904.12831 | null | https://arxiv.org/abs/1904.12831v2 | https://arxiv.org/pdf/1904.12831v2.pdf | Wave Physics as an Analog Recurrent Neural Network | Analog machine learning hardware platforms promise to be faster and more energy-efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate for building analog processors for time-varying signals. Here we identify a mapping between the dynamics of wave physics, and ... | ['Momchil Minkov', 'Shanhui Fan', 'Tyler W. Hughes', 'Ian A. D. Williamson'] | 2019-04-29 | null | null | null | null | ['vowel-classification'] | ['audio'] | [ 1.87216416e-01 -3.43180984e-01 4.30753767e-01 -9.13917366e-03
-3.97202283e-01 -6.18686736e-01 3.31533998e-01 -8.42771456e-02
-2.75028110e-01 4.16581035e-01 -1.14304908e-02 -3.62495691e-01
-2.27714464e-01 -1.02914822e+00 -6.06639564e-01 -8.18187535e-01
-6.86241329e-01 2.68082350e-01 3.07430863e-01 -1.96843579... | [8.05434513092041, 2.7429840564727783] |
d316ff6a-05c8-427a-9ef4-eda9298746b8 | synthesis-of-sparse-linear-arrays-via-low | 2209.04577 | null | https://arxiv.org/abs/2209.04577v1 | https://arxiv.org/pdf/2209.04577v1.pdf | Synthesis of Sparse Linear Arrays via Low-Rank Hankel Matrix Completion | In the process of realizing the synthesis of the antenna array synthesis, it is of practical significance to arrange the nonuniform array and reduce the number of elements. According to the Matrix Pencil Method (MPM), we proposed an improved nonuniform array algorithm for reducing the number of array elements. We desig... | ['Xuejing Zhang'] | 2022-09-10 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.41198683e-01 -1.14791706e-01 5.78362763e-01 4.11890820e-02
4.70821634e-02 -6.65663600e-01 -1.62682123e-03 -4.39041406e-01
-7.60140568e-02 5.25255859e-01 3.99247319e-01 -4.58414227e-01
-5.93733191e-01 -8.57949018e-01 -1.61594436e-01 -1.02229857e+00
3.68160158e-02 1.56431401e-03 -2.92035103e-01 -2.75109023... | [6.425793647766113, 1.3409645557403564] |
09a00120-1432-4751-90e7-e0d3a7b537ec | analysis-of-machine-learning-for-link-quality | 1812.08856 | null | https://arxiv.org/abs/1812.08856v9 | https://arxiv.org/pdf/1812.08856v9.pdf | Machine Learning for Wireless Link Quality Estimation: A Survey | Since the emergence of wireless communication networks, a plethora of research papers focus their attention on the quality aspects of wireless links. The analysis of the rich body of existing literature on link quality estimation using models developed from data traces indicates that the techniques used for modeling li... | ['Mihael Mohorčič', 'Gregor Cerar', 'Carolina Fortuna', 'Halil Yetgin'] | 2018-12-07 | null | null | null | null | ['link-quality-estimation'] | ['miscellaneous'] | [-2.50140995e-01 -2.99810860e-02 -8.70627999e-01 -3.21332306e-01
-9.22617257e-01 -4.10605878e-01 -1.86113045e-01 2.50281662e-01
-8.89238063e-03 1.22429860e+00 -1.91108182e-01 -8.16660941e-01
-8.53554785e-01 -1.03739047e+00 -5.87785304e-01 -2.13639155e-01
-7.91707635e-01 3.26427788e-01 9.70032215e-02 8.24258327... | [6.142945289611816, 1.6218605041503906] |
a16cd49c-8dd7-4081-b19b-c187fb9b7e19 | a-review-of-machine-learning-applications-in-1 | 2003.00646 | null | https://arxiv.org/abs/2003.00646v2 | https://arxiv.org/pdf/2003.00646v2.pdf | A review of machine learning applications in wildfire science and management | Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a sco... | ['Sean C P Coogan', 'Piyush Jain', 'Mike D Flannigan', 'Mark Crowley', 'Steve Taylor', 'Sriram Ganapathi Subramanian'] | 2020-03-02 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 4.01102185e-01 -5.58409631e-01 -7.02278852e-01 2.18811035e-01
7.89936259e-02 -6.46346629e-01 5.28262317e-01 3.30322146e-01
-5.96590400e-01 1.05280459e+00 3.65128636e-01 -1.04403436e+00
-6.43697917e-01 -1.31933939e+00 -3.33789140e-01 -7.85885334e-01
-6.61731303e-01 2.62021095e-01 -2.04265401e-01 -2.23892912... | [9.293952941894531, -1.3820558786392212] |
32577114-2afe-4eb9-8b4d-abad63068a5e | mdldroid-a-chainsgd-reduce-approach-to-mobile | 2002.02897 | null | https://arxiv.org/abs/2002.02897v2 | https://arxiv.org/pdf/2002.02897v2.pdf | MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing | Personal mobile sensing is fast permeating our daily lives to enable activity monitoring, healthcare and rehabilitation. Combined with deep learning, these applications have achieved significant success in recent years. Different from conventional cloud-based paradigms, running deep learning on devices offers several a... | ['Tao Gu', 'Yu Zhang', 'Xi Zhang'] | 2020-02-07 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-8.54005292e-02 -8.92357081e-02 -5.20660818e-01 -2.96069801e-01
-8.98726881e-01 -3.74380112e-01 1.32862506e-02 -1.74003437e-01
-5.49445033e-01 7.91128457e-01 7.12189451e-02 -3.41357350e-01
-7.07765147e-02 -8.92274916e-01 -7.40629733e-01 -5.83887994e-01
9.51050594e-02 3.01592261e-01 3.87042835e-02 1.91241696... | [5.986800670623779, 6.082214832305908] |
fb10b42c-7e03-4d92-b9a6-1831844c1a5f | modeling-empathic-similarity-in-personal | 2305.14246 | null | https://arxiv.org/abs/2305.14246v1 | https://arxiv.org/pdf/2305.14246v1.pdf | Modeling Empathic Similarity in Personal Narratives | The most meaningful connections between people are often fostered through expression of shared vulnerability and emotional experiences in personal narratives. We introduce a new task of identifying similarity in personal stories based on empathic resonance, i.e., the extent to which two people empathize with each other... | ['Cynthia Breazeal', 'Hae Won Park', 'Pedro Colon-Hernandez', 'Maarten Sap', 'Jocelyn Shen'] | 2023-05-23 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-5.81532657e-01 2.47495294e-01 -1.30499646e-01 -2.96582818e-01
-1.80452451e-01 -4.51629907e-01 8.09797287e-01 7.94862211e-01
-4.63773042e-01 5.82618535e-01 1.37923694e+00 6.80197060e-01
-5.08438647e-01 -6.18223965e-01 2.93267250e-01 7.76825622e-02
5.60938530e-02 5.06713510e-01 -3.48516703e-01 -8.38072181... | [13.155315399169922, 7.629956245422363] |
cf56750f-9084-4d39-9ecf-54f19aa9538a | a-policy-gradient-framework-for-stochastic | 2302.05816 | null | https://arxiv.org/abs/2302.05816v2 | https://arxiv.org/pdf/2302.05816v2.pdf | A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee | We consider policy gradient methods for stochastic optimal control problem in continuous time. In particular, we analyze the gradient flow for the control, viewed as a continuous time limit of the policy gradient method. We prove the global convergence of the gradient flow and establish a convergence rate under some re... | ['Jianfeng Lu', 'Mo Zhou'] | 2023-02-11 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-3.53215903e-01 1.88418955e-01 -5.07384479e-01 2.69676894e-01
-4.58482593e-01 -6.93432629e-01 1.66406944e-01 5.57287522e-02
-5.63873172e-01 1.20028794e+00 2.73010641e-01 -5.59082627e-01
-2.59059489e-01 -4.19016093e-01 -8.71345282e-01 -8.35781157e-01
-5.77040277e-02 -2.34302387e-01 -5.98565675e-02 -3.38045090... | [4.410010814666748, 2.6706485748291016] |
38ee4299-fad3-42cf-b9ac-ba35169e51a1 | model-agnostic-counterfactual-reasoning-for | 2010.15363 | null | https://arxiv.org/abs/2010.15363v2 | https://arxiv.org/pdf/2010.15363v2.pdf | Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System | The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e., fitting a recommender model to recover the user behavior data with pointwise or pairwise loss, makes the model biased towards popular item... | ['Xiangnan He', 'JinFeng Yi', 'Ziwei Wu', 'Jiawei Chen', 'Fuli Feng', 'Tianxin Wei'] | 2020-10-29 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 1.71636860e-03 8.26437026e-02 -6.60597444e-01 -1.97413921e-01
-2.54249692e-01 -4.88924354e-01 2.97073364e-01 -5.21718375e-02
-2.91670382e-01 8.10723603e-01 5.05041361e-01 -5.85292339e-01
-6.04115188e-01 -1.02576947e+00 -1.05296266e+00 -6.92372918e-01
-9.19321105e-02 3.36229175e-01 1.35036779e-03 -2.17326880... | [9.796257019042969, 5.564734935760498] |
02ba376e-bfd7-470c-b53e-4bdad32f112c | an-empirical-analysis-of-formality-in-online | null | null | https://aclanthology.org/Q16-1005 | https://aclanthology.org/Q16-1005.pdf | An Empirical Analysis of Formality in Online Communication | This paper presents an empirical study of linguistic formality. We perform an analysis of humans{'} perceptions of formality in four different genres. These findings are used to develop a statistical model for predicting formality, which is evaluated under different feature settings and genres. We apply our model to an... | ['Ellie Pavlick', 'Joel Tetreault'] | 2016-01-01 | null | null | null | tacl-2016-1 | ['automatic-writing'] | ['natural-language-processing'] | [-1.71066299e-01 4.24131513e-01 -2.58156747e-01 -4.08327550e-01
-3.14646691e-01 -8.03423345e-01 8.53702784e-01 5.87179422e-01
-3.06557834e-01 5.34505546e-01 8.46434414e-01 -7.88204253e-01
-4.73917127e-01 -1.95028245e-01 -2.72229016e-01 8.88614580e-02
-1.94806695e-01 -1.83536485e-02 4.18287925e-02 -2.92748451... | [10.085942268371582, 9.659077644348145] |
bd36f7b8-e04b-40e3-9878-2ae85625ea3a | eye-tracked-virtual-reality-a-comprehensive | 2305.14080 | null | https://arxiv.org/abs/2305.14080v1 | https://arxiv.org/pdf/2305.14080v1.pdf | Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges | Latest developments in computer hardware, sensor technologies, and artificial intelligence can make virtual reality (VR) and virtual spaces an important part of human everyday life. Eye tracking offers not only a hands-free way of interaction but also the possibility of a deeper understanding of human visual attention ... | ['Enkelejda Kasneci', 'Eakta Jain', 'Kevin Butler', 'Hong Gao', 'Brendan David-John', 'Mengdi Wang', 'Süleyman Özdel', 'Efe Bozkir'] | 2023-05-23 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-1.69250622e-01 8.82942751e-02 -1.76554639e-03 -2.45215043e-01
1.73706576e-01 -7.82670677e-01 5.06289583e-03 1.91504851e-01
-6.02126062e-01 6.84968591e-01 -8.96736905e-02 -4.14475113e-01
-7.48684615e-05 -3.47871482e-01 -4.07927223e-02 -3.45054716e-01
1.08045466e-01 -8.31847966e-01 -1.84711635e-01 -1.11051716... | [14.085490226745605, 0.1290014684200287] |
fb3f8ddb-0c6d-4201-9c47-e96e472476b4 | ethicist-targeted-training-data-extraction | 2307.04401 | null | https://arxiv.org/abs/2307.04401v1 | https://arxiv.org/pdf/2307.04401v1.pdf | Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation | Large pre-trained language models achieve impressive results across many tasks. However, recent works point out that pre-trained language models may memorize a considerable fraction of their training data, leading to the privacy risk of information leakage. In this paper, we propose a method named Ethicist for targeted... | ['Minlie Huang', 'Jiaxin Wen', 'Zhexin Zhang'] | 2023-07-10 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.75961429e-01 9.79542267e-03 -5.75793207e-01 -4.12646800e-01
-1.28596044e+00 -8.96580756e-01 3.38528574e-01 6.10816538e-01
-8.33028316e-01 7.40872800e-01 2.16311231e-01 -4.19522226e-01
4.49836284e-01 -7.15518892e-01 -1.13134325e+00 -7.55851686e-01
7.74079412e-02 1.81500718e-01 -2.76222438e-01 3.08684230... | [5.972390174865723, 7.2681732177734375] |
548d5970-2f41-46d4-9bf8-40fe5d3cb1f6 | fortunately-discourse-markers-can-enhance | 2201.02026 | null | https://arxiv.org/abs/2201.02026v2 | https://arxiv.org/pdf/2201.02026v2.pdf | Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis | In recent years, pretrained language models have revolutionized the NLP world, while achieving state of the art performance in various downstream tasks. However, in many cases, these models do not perform well when labeled data is scarce and the model is expected to perform in the zero or few shot setting. Recently, se... | ['Noam Slonim', 'Ranit Aharonov', 'Lena Dankin', 'Artem Spector', 'Ilya Shnayderman', 'Liat Ein-Dor'] | 2022-01-06 | null | null | null | null | ['continual-pretraining'] | ['methodology'] | [ 1.67184293e-01 1.45742223e-01 -4.64082837e-01 -4.86741632e-01
-9.58562315e-01 -5.43198347e-01 7.90027261e-01 1.34306580e-01
-5.63264966e-01 8.03151608e-01 5.41094482e-01 -3.53605896e-01
4.90886629e-01 -6.90564036e-01 -4.99840796e-01 -5.16058922e-01
4.17854190e-01 6.76642060e-01 8.62425119e-02 -5.17596364... | [10.706154823303223, 8.347957611083984] |
da7089e7-26b3-43a7-8357-a1ea159fdb57 | tan-ntm-topic-attention-networks-for-neural | 2012.01524 | null | https://arxiv.org/abs/2012.01524v2 | https://arxiv.org/pdf/2012.01524v2.pdf | TAN-NTM: Topic Attention Networks for Neural Topic Modeling | Topic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or sequence of tokens as input followed by variational inference and BoW reconstruction to learn topic-word distributio... | ['Balaji Krishnamurthy', 'Milan Aggarwal', 'Shashank Shailabh', 'Madhur Panwar'] | 2020-12-02 | null | https://aclanthology.org/2021.acl-long.299 | https://aclanthology.org/2021.acl-long.299.pdf | acl-2021-5 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 9.52693969e-02 3.67994010e-01 -7.35981226e-01 -5.98277926e-01
-1.26808882e+00 -4.43661392e-01 1.17753232e+00 3.08807433e-01
-7.90316239e-03 7.50514567e-01 8.37469697e-01 -3.12267661e-01
5.83951958e-02 -9.08626974e-01 -8.35717142e-01 -8.04031372e-01
-4.14884686e-02 7.23387003e-01 7.14457110e-02 -1.12576433... | [10.399821281433105, 6.954836845397949] |
c6e5fdb3-d3e8-4f3f-bb90-44d7d5b55d36 | a-critical-re-evaluation-of-neural-methods | null | null | https://dl.acm.org/doi/abs/10.14778/3529337.3529355 | https://www.vldb.org/pvldb/vol15/p1712-arora.pdf | A Critical Re-evaluation of Neural Methods for Entity Alignment | Neural methods have become the de-facto choice for the vast majority of data analysis tasks, and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA methods have been published since 2017. However, surprisingly, an analysis of the differences between neural and non-neural EA method... | ['Robert West', 'Alberto García-Durán', 'Akhil Arora', 'Stefano Huber', 'Manuel Leone'] | 2022-04-01 | null | null | null | pvldb-2022-4 | ['entity-resolution'] | ['natural-language-processing'] | [ 1.08611874e-01 1.20195009e-01 -3.29993278e-01 -3.91168118e-01
-7.61736572e-01 -5.61914623e-01 5.02593696e-01 3.02548707e-01
-7.57094860e-01 7.13681638e-01 4.64255780e-01 -4.88125503e-01
-4.78054702e-01 -7.93281436e-01 -9.22982037e-01 -3.03650171e-01
-2.03761294e-01 6.83020413e-01 -2.06196323e-01 -2.44352356... | [9.522855758666992, 8.577093124389648] |
d14734d9-66ce-4fde-ae7c-d1da5f053c68 | deepcapture-image-spam-detection-using-deep | 2006.08885 | null | https://arxiv.org/abs/2006.08885v1 | https://arxiv.org/pdf/2006.08885v1.pdf | DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation | Image spam emails are often used to evade text-based spam filters that detect spam emails with their frequently used keywords. In this paper, we propose a new image spam email detection tool called DeepCapture using a convolutional neural network (CNN) model. There have been many efforts to detect image spam emails, bu... | ['Hyoungshick Kim', 'Bedeuro Kim', 'Sharif Abuadbba'] | 2020-06-16 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.29209653e-01 -4.82138246e-01 2.84474313e-01 -3.61221135e-01
-2.07143217e-01 -2.01311350e-01 8.07885408e-01 -1.71461865e-01
-5.00812531e-01 3.81529331e-01 -2.14271650e-01 -6.21928990e-01
2.99506783e-01 -8.48342717e-01 -7.58196115e-01 -4.00872380e-01
3.31517160e-01 2.47987717e-01 5.65444946e-01 -1.66793853... | [7.795183181762695, 9.961892127990723] |
bbe33703-4a68-4ed8-957c-5acbe5d951f8 | reconnet-non-iterative-reconstruction-of | 1601.06892 | null | http://arxiv.org/abs/1601.06892v2 | http://arxiv.org/pdf/1601.06892v2.pdf | ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements | The goal of this paper is to present a non-iterative and more importantly an
extremely fast algorithm to reconstruct images from compressively sensed (CS)
random measurements. To this end, we propose a novel convolutional neural
network (CNN) architecture which takes in CS measurements of an image as input
and outputs ... | ['Suhas Lohit', 'Amit Ashok', 'Pavan Turaga', 'Ronan Kerviche', 'Kuldeep Kulkarni'] | 2016-01-26 | null | null | null | cvpr-2016 | ['real-time-visual-tracking'] | ['computer-vision'] | [ 9.35007036e-01 -1.32976443e-01 1.85582578e-01 -1.46057770e-01
-9.13858533e-01 -5.40615261e-01 4.04153228e-01 -2.96306759e-01
-4.70279515e-01 3.57797652e-01 6.57739192e-02 -4.04603750e-01
-1.34880975e-01 -5.91491222e-01 -1.31874514e+00 -5.31227350e-01
-2.39117533e-01 -2.83989925e-02 1.77294806e-01 -9.15622711... | [10.934943199157715, -2.2638156414031982] |
d768a83d-42b4-4144-a1cf-6d7852fc094d | stylemask-disentangling-the-style-space-of | 2209.13375 | null | https://arxiv.org/abs/2209.13375v1 | https://arxiv.org/pdf/2209.13375v1.pdf | StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment | In this paper we address the problem of neural face reenactment, where, given a pair of a source and a target facial image, we need to transfer the target's pose (defined as the head pose and its facial expressions) to the source image, by preserving at the same time the source's identity characteristics (e.g., facial ... | ['Georgios Tzimiropoulos', 'Ioannis Patras', 'Vasileios Argyriou', 'Christos Tzelepis', 'Stella Bounareli'] | 2022-09-27 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 3.51777554e-01 2.39578754e-01 6.42588288e-02 -3.33941787e-01
-5.17509341e-01 -7.42032707e-01 6.41501546e-01 -5.17016351e-01
-1.20313480e-01 6.57930195e-01 1.71735913e-01 1.61625862e-01
2.09882692e-01 -6.13792896e-01 -9.26375449e-01 -9.12685812e-01
2.17307314e-01 4.25060362e-01 -3.88912618e-01 -3.22376043... | [12.744640350341797, 0.0028673922643065453] |
b457c285-97c3-4a5c-8e2a-6d5d63cb694f | ask-to-understand-question-generation-for | 2203.09073 | null | https://arxiv.org/abs/2203.09073v1 | https://arxiv.org/pdf/2203.09073v1.pdf | Ask to Understand: Question Generation for Multi-hop Question Answering | Multi-hop Question Answering (QA) requires the machine to answer complex questions by finding scattering clues and reasoning from multiple documents. Graph Network (GN) and Question Decomposition (QD) are two common approaches at present. The former uses the "black-box" reasoning process to capture the potential relati... | ['Yizhe Yang', 'Yang Gao', 'Mucheng Ren', 'Jiawei Li'] | 2022-03-17 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [-1.57385215e-01 7.49620318e-01 4.53615457e-01 -3.30077082e-01
-8.53962421e-01 -6.61447167e-01 5.27456403e-01 3.37440073e-01
1.20718300e-01 6.97605312e-01 5.14304876e-01 -6.52644455e-01
-6.55560195e-01 -1.21722722e+00 -4.10315514e-01 -4.52149212e-02
2.85124898e-01 7.89687634e-01 6.18203700e-01 -8.47894490... | [10.982002258300781, 7.939665794372559] |
aa1931a1-4c4a-4f30-9058-150492b7cd32 | few-shot-nlu-with-vector-projection-distance | 2112.04999 | null | https://arxiv.org/abs/2112.04999v1 | https://arxiv.org/pdf/2112.04999v1.pdf | Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF | Data sparsity problem is a key challenge of Natural Language Understanding (NLU), especially for a new target domain. By training an NLU model in source domains and applying the model to an arbitrary target domain directly (even without fine-tuning), few-shot NLU becomes crucial to mitigate the data scarcity issue. In ... | ['Kai Yu', 'Qingliang Miao', 'Zhi Chen', 'Ruisheng Cao', 'Lu Chen', 'Su Zhu'] | 2021-12-09 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 4.68482405e-01 1.18022725e-01 -8.56756270e-01 -6.54139519e-01
-9.62050200e-01 -2.48546511e-01 5.82315445e-01 -1.13761552e-01
-5.74142814e-01 7.58693397e-01 5.79384327e-01 -2.52045155e-01
2.66246587e-01 -8.15001607e-01 -6.43733919e-01 -2.43286535e-01
3.07776749e-01 8.26463580e-01 -1.05663143e-01 -1.22070149... | [10.799788475036621, 7.911838054656982] |
b3c9f973-3f0d-4832-9211-9b1fd6c66252 | deterministic-point-cloud-registration-via | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Deterministic_Point_Cloud_Registration_via_Novel_Transformation_Decomposition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Deterministic_Point_Cloud_Registration_via_Novel_Transformation_Decomposition_CVPR_2022_paper.pdf | Deterministic Point Cloud Registration via Novel Transformation Decomposition | Given a set of putative 3D-3D point correspondences, we aim to remove outliers and estimate rigid transformation with 6 degrees of freedom (DOF). Simultaneously estimating these 6 DOF is time-consuming due to high-dimensional parameter space. To solve this problem, it is common to decompose 6 DOF, i.e. independentl... | ['Yun-hui Liu', 'Qiang Nie', 'Haoang Li', 'Wen Chen'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['point-cloud-registration'] | ['computer-vision'] | [-2.18612775e-01 -1.21948369e-01 -3.44265372e-01 2.37926379e-01
-8.92900288e-01 -7.80059636e-01 3.77457917e-01 -1.50382295e-01
-2.64464498e-01 4.60473955e-01 1.55594558e-01 -9.53352600e-02
-2.53836304e-01 -5.10109305e-01 -7.26522565e-01 -6.58476889e-01
-1.79599635e-02 7.77917624e-01 2.15277597e-01 8.07635952... | [7.716597080230713, -2.711590051651001] |
503dce1d-154b-4fd4-b89a-bc853fdd5f6d | aiparsing-anchor-free-instance-level-human | 2207.06854 | null | https://arxiv.org/abs/2207.06854v1 | https://arxiv.org/pdf/2207.06854v1.pdf | AIParsing: Anchor-free Instance-level Human Parsing | Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level. To address these two issues, we have designed an instance-level human parsing network which is anchor-free and solvable... | ['Jie zhou', 'Zhanjie Song', 'Guo-Jun Qi', 'Xiaochun Cao', 'Sanyi Zhang'] | 2022-07-14 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 3.67176682e-01 7.12068796e-01 -1.24841563e-01 -4.31919783e-01
-1.11719394e+00 -2.91859925e-01 2.42108852e-03 2.12372780e-01
-4.36905682e-01 3.63522321e-01 -3.83906513e-01 -2.45574564e-01
3.11346859e-01 -8.32284749e-01 -8.35791528e-01 -5.10208845e-01
1.02630220e-01 5.09121418e-01 1.26865447e+00 -3.45293805... | [8.661726951599121, 0.007779398001730442] |
9d54954f-185b-4f16-a3e6-f3cb2a9a8942 | diverse-sample-generation-pushing-the-limit | 2109.00212 | null | https://arxiv.org/abs/2109.00212v3 | https://arxiv.org/pdf/2109.00212v3.pdf | Diverse Sample Generation: Pushing the Limit of Generative Data-free Quantization | Generative data-free quantization emerges as a practical compression approach that quantizes deep neural networks to low bit-width without accessing the real data. This approach generates data utilizing batch normalization (BN) statistics of the full-precision networks to quantize the networks. However, it always faces... | ['Jiakai Wang', 'Jiwen Lu', 'Xianglong Liu', 'Xiangguo Zhang', 'Yifu Ding', 'Haotong Qin'] | 2021-09-01 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 3.24081242e-01 -3.14922661e-01 -1.97637945e-01 -5.39438307e-01
-5.03793716e-01 -2.55580664e-01 4.23077673e-01 -1.33733183e-01
-5.62457323e-01 8.47277701e-01 1.63185731e-01 -1.28594652e-01
-1.28913403e-01 -9.57456350e-01 -7.48930752e-01 -1.21532500e+00
2.76185870e-01 6.27353340e-02 2.21002549e-01 -6.01627752... | [8.707697868347168, 3.0109636783599854] |
98ba119b-a7b9-40d5-8a9e-017553e098b1 | learning-the-dynamics-of-physical-systems-1 | 2203.08852 | null | https://arxiv.org/abs/2203.08852v1 | https://arxiv.org/pdf/2203.08852v1.pdf | Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks | We propose a new method for spatio-temporal forecasting on arbitrarily distributed points. Assuming that the observed system follows an unknown partial differential equation, we derive a continuous-time model for the dynamics of the data via the finite element method. The resulting graph neural network estimates the in... | ['Stephan Günnemann', 'Marten Lienen'] | 2022-03-16 | learning-the-dynamics-of-physical-systems | https://openreview.net/forum?id=HFmAukZ-k-2 | https://openreview.net/pdf?id=HFmAukZ-k-2 | iclr-2022-4 | ['interpretability-techniques-for-deep-learning', 'spatio-temporal-forecasting'] | ['miscellaneous', 'time-series'] | [ 2.84228344e-02 1.77365229e-01 4.77181040e-02 2.14072186e-02
-1.54529572e-01 -7.83753991e-01 9.43482995e-01 8.00579041e-03
-8.95514991e-03 8.06289613e-01 3.81154567e-01 -6.00083709e-01
-3.12779844e-01 -1.02326465e+00 -8.78343225e-01 -8.94761682e-01
-4.61912483e-01 5.07779658e-01 -4.22029048e-02 -3.85637134... | [6.5795207023620605, 3.3248114585876465] |
ff31e5a5-ed22-4a47-827d-791e8b9e0907 | improving-energy-management-of-hybrid | 2302.13157 | null | https://arxiv.org/abs/2302.13157v1 | https://arxiv.org/pdf/2302.13157v1.pdf | Improving Energy Management of Hybrid Electric Vehicles by Considering Battery Electric-Thermal Model | This article proposes an offline Energy Management System (EMS) for Parallel Hybrid Electric Vehicles (PHEVs). Dividing the torque between the Electric Motor (EM) and the Internal Combustion Engine (ICE) requires a suitable EMS. Batteries are vital to HEVs and significantly impact overall vehicle cost and performance. ... | ['Arash Mousaei'] | 2023-02-25 | null | null | null | null | ['energy-management'] | ['time-series'] | [-4.80126143e-01 -2.79557735e-01 -3.18442225e-01 7.34413341e-02
2.39497155e-01 -4.78782207e-01 3.42192769e-01 -1.37189552e-01
-4.10774082e-01 9.63075578e-01 -8.03850293e-01 -3.23998898e-01
-1.97650135e-01 -7.10446954e-01 -7.84732521e-01 -1.28910077e+00
3.48896623e-01 2.55976111e-01 2.64167041e-01 -2.20283009... | [5.617627143859863, 2.230302333831787] |
13338419-13c6-4e8e-95d3-0dcf8b71350d | gotta-catch-em-all-using-concealed-trapdoors | 1904.08554 | null | https://arxiv.org/abs/1904.08554v6 | https://arxiv.org/pdf/1904.08554v6.pdf | Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks | Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to compute adversarial examples that exploit them. In our work, we explore a new "honeypot" approach to protect DNN models. We intentiona... | ['Hai-Tao Zheng', 'Emily Wenger', 'Bo Li', 'Ben Y. Zhao', 'Bolun Wang', 'Shawn Shan'] | 2019-04-18 | null | null | null | null | ['traffic-sign-recognition', 'adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'computer-vision', 'knowledge-base'] | [ 2.77553737e-01 2.91662682e-02 7.91134834e-02 -7.63825476e-02
-5.29470563e-01 -1.30251849e+00 5.89242935e-01 -2.77441800e-01
-4.14167911e-01 5.37118435e-01 -2.39632711e-01 -6.58233762e-01
-7.52669498e-02 -1.05401087e+00 -9.37484443e-01 -6.91598117e-01
-3.63957375e-01 6.02644533e-02 4.08802450e-01 -4.10914987... | [5.69915771484375, 7.663370609283447] |
80c45b11-b230-452d-b8a2-deb2c3fd6ca0 | taxocom-topic-taxonomy-completion-with | 2201.06771 | null | https://arxiv.org/abs/2201.06771v2 | https://arxiv.org/pdf/2201.06771v2.pdf | TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters | Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering. Recently, several unsupervised methods have been developed to automatically construct the topic taxonomy from a te... | ['Hwanjo Yu', 'Jiawei Han', 'Susik Yoon', 'SeongKu Kang', 'Jiaming Shen', 'Dongha Lee'] | 2022-01-18 | null | null | null | null | ['topic-coverage'] | ['natural-language-processing'] | [-5.45483120e-02 1.86408296e-01 -4.51148897e-01 -4.10029739e-01
-8.44818711e-01 -7.18284607e-01 8.61902714e-01 5.14471352e-01
-1.14153074e-02 3.17054480e-01 6.89029098e-01 -3.78537215e-02
-4.58708405e-01 -9.37690675e-01 -2.21754640e-01 -8.56476665e-01
-2.13442639e-01 7.55731046e-01 3.19175482e-01 2.51800299... | [10.364582061767578, 6.894242763519287] |
d0a78022-efdf-4ca4-aa64-a0591db20bf1 | pre-training-multi-modal-dense-retrievers-for | 2306.16478 | null | https://arxiv.org/abs/2306.16478v1 | https://arxiv.org/pdf/2306.16478v1.pdf | Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering | This paper studies a category of visual question answering tasks, in which accessing external knowledge is necessary for answering the questions. This category is called outside-knowledge visual question answering (OK-VQA). A major step in developing OK-VQA systems is to retrieve relevant documents for the given multi-... | ['Hamed Zamani', 'Mahta Rafiee', 'Alireza Salemi'] | 2023-06-28 | null | null | null | null | ['visual-question-answering-1', 'retrieval', 'question-answering', 'passage-retrieval'] | ['computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [-1.81186527e-01 -6.01704232e-02 -7.80982804e-03 -1.46466702e-01
-1.63428950e+00 -6.06298506e-01 8.31334889e-01 3.70483994e-01
-4.92501080e-01 4.75131154e-01 4.66615915e-01 -1.66334003e-01
2.22334508e-02 -8.42301190e-01 -6.57615781e-01 -4.49629754e-01
4.42020684e-01 8.71783376e-01 6.33645058e-01 -5.44572234... | [10.90993595123291, 1.6333801746368408] |
0dd93087-d04e-47ad-9698-757a4d243e64 | kimera-multi-robust-distributed-dense-metric | 2106.14386 | null | https://arxiv.org/abs/2106.14386v2 | https://arxiv.org/pdf/2106.14386v2.pdf | Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems | This paper presents Kimera-Multi, the first multi-robot system that (i) is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures resulting from perceptual aliasing, (ii) is fully distributed and only relies on local (peer-to-peer) communication to achieve distributed localization... | ['Luca Carlone', 'Jonathan P. How', 'Carlos Nieto-Granda', 'Fernando Herrera Arias', 'Yun Chang', 'Yulun Tian'] | 2021-06-28 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [-2.72775084e-01 1.12044379e-01 2.38762289e-01 -4.02936079e-02
-9.45659041e-01 -8.01930308e-01 3.30265880e-01 8.06910336e-01
-4.18506652e-01 4.77221251e-01 -5.88300705e-01 -4.25037146e-02
-3.68171692e-01 -8.41312051e-01 -1.33512831e+00 -4.87587184e-01
-5.76898873e-01 1.42729056e+00 5.70058167e-01 -3.09813824... | [7.218970775604248, -2.126558780670166] |
6380fcc8-d386-4258-87e0-81356bbc361f | effective-convolutional-attention-network-for | null | null | https://aclanthology.org/2021.emnlp-main.481 | https://aclanthology.org/2021.emnlp-main.481.pdf | Effective Convolutional Attention Network for Multi-label Clinical Document Classification | Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels. In this paper, we present an effective convolutional attention network for the MLDC problem with a focus on medical code prediction from clinical documen... | ['Thomas Schaaf', 'Matthew R. Gormley', 'Russell Klopfer', 'Hua Cheng', 'Yang Liu'] | null | null | null | null | emnlp-2021-11 | ['medical-code-prediction'] | ['medical'] | [ 5.44090390e-01 1.60633802e-01 -2.98096091e-01 -6.20051444e-01
-1.69127190e+00 -3.38740170e-01 2.88472474e-01 4.76269335e-01
-4.35278535e-01 5.98916888e-01 6.44347012e-01 -3.25381786e-01
-2.39004701e-01 -2.98495501e-01 -4.80307728e-01 -6.24466419e-01
-9.92258415e-02 6.16778851e-01 -2.48719707e-01 -2.44891737... | [8.09335994720459, 6.760948657989502] |
837c60c9-97e6-4915-8680-7823b84180fb | energy-efficient-task-adaptation-for-nlp-edge | 2303.16100 | null | https://arxiv.org/abs/2303.16100v2 | https://arxiv.org/pdf/2303.16100v2.pdf | Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures | Executing machine learning inference tasks on resource-constrained edge devices requires careful hardware-software co-design optimizations. Recent examples have shown how transformer-based deep neural network models such as ALBERT can be used to enable the execution of natural language processing (NLP) inference on mob... | ['Marco Donato', 'Aleksandre Avaliani', 'Zirui Fu'] | 2023-03-25 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 2.76275516e-01 2.30575904e-01 -3.87066096e-01 -4.29035395e-01
-4.50101316e-01 -1.41266584e-01 5.47615826e-01 2.49524757e-01
-5.65179229e-01 3.87549520e-01 5.68772666e-02 -8.15044880e-01
-1.02653116e-01 -1.00414467e+00 -8.64896834e-01 -4.68243420e-01
7.18497410e-02 4.82263565e-01 -5.42300344e-02 5.37699014... | [8.429266929626465, 2.929877996444702] |
b43ef6cb-2e7f-48f9-9339-3a0c847c8987 | learning-garment-densepose-for-robust-warping | 2303.17688 | null | https://arxiv.org/abs/2303.17688v1 | https://arxiv.org/pdf/2303.17688v1.pdf | Learning Garment DensePose for Robust Warping in Virtual Try-On | Virtual try-on, i.e making people virtually try new garments, is an active research area in computer vision with great commercial applications. Current virtual try-on methods usually work in a two-stage pipeline. First, the garment image is warped on the person's pose using a flow estimation network. Then in the second... | ['Antoine Toisoul', 'Tao Xiang', 'Sen He', 'Aiyu Cui'] | 2023-03-30 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 8.58020596e-03 3.43720466e-02 -9.44284722e-02 -1.45810530e-01
-2.42418289e-01 -3.07188332e-01 5.42112410e-01 -5.53273320e-01
-4.09483761e-01 4.70026612e-01 1.91358566e-01 5.75030521e-02
3.35729957e-01 -8.15400779e-01 -7.14041770e-01 -3.32024157e-01
1.34328723e-01 7.43583322e-01 2.96765685e-01 -7.36414671... | [11.805594444274902, -0.8584553003311157] |
a3fa8d50-54a3-4eea-b2a8-99cdca921ce6 | spatio-temporal-relation-learning-for-video | 2209.13116 | null | https://arxiv.org/abs/2209.13116v1 | https://arxiv.org/pdf/2209.13116v1.pdf | Spatio-Temporal Relation Learning for Video Anomaly Detection | Anomaly identification is highly dependent on the relationship between the object and the scene, as different/same object actions in same/different scenes may lead to various degrees of normality and anomaly. Therefore, object-scene relation actually plays a crucial role in anomaly detection but is inadequately explore... | ['Jian Yang', 'Biao Wang', 'Zhen Cui', 'Hui Lv'] | 2022-09-27 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [ 0.06569105 -0.38036066 0.0948521 -0.20471267 0.19425543 -0.05244778
0.7276564 0.34862348 -0.03827053 0.13465698 0.22082487 -0.05823952
-0.33991095 -0.7037551 -0.6600447 -0.80173206 -0.21152571 0.01340644
0.5223906 -0.09996746 0.24684559 0.5452448 -1.4710704 0.18760568
0.7958113 1.2585261 0.... | [7.913832664489746, 1.550997257232666] |
b5929123-8ae5-41e7-8c06-7baec30d0bc3 | abpn-adaptive-blend-pyramid-network-for-real | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lei_ABPN_Adaptive_Blend_Pyramid_Network_for_Real-Time_Local_Retouching_of_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lei_ABPN_Adaptive_Blend_Pyramid_Network_for_Real-Time_Local_Retouching_of_CVPR_2022_paper.pdf | ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo | Photo retouching finds many applications in various fields. However, most existing methods are designed for global retouching and seldom pay attention to the local region, while the latter is actually much more tedious and time-consuming in photography pipelines. In this paper, we propose a novel adaptive blend pyr... | ['Di Huang', 'Xuansong Xie', 'Miaomiao Cui', 'Hongyu Yang', 'Xiefan Guo', 'Biwen Lei'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['photo-retouching'] | ['computer-vision'] | [ 2.15427756e-01 -3.92309636e-01 -1.82211041e-01 3.47737968e-02
-5.82108200e-01 -1.79572403e-02 3.21315110e-01 -2.42904335e-01
-3.88203412e-01 5.46618521e-01 1.36685535e-01 -8.16645622e-02
1.06657371e-01 -1.02501881e+00 -9.21078026e-01 -9.19525385e-01
5.47729671e-01 -1.69788867e-01 8.89663637e-01 -2.13066742... | [10.973577499389648, -1.8129193782806396] |
84b4db99-8d02-4c0f-bbb3-074ee34b58ba | glimpse-clouds-human-activity-recognition | 1802.07898 | null | http://arxiv.org/abs/1802.07898v4 | http://arxiv.org/pdf/1802.07898v4.pdf | Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points | We propose a method for human activity recognition from RGB data that does
not rely on any pose information during test time and does not explicitly
calculate pose information internally. Instead, a visual attention module
learns to predict glimpse sequences in each frame. These glimpses correspond to
interest points i... | ['Christian Wolf', 'Graham W. Taylor', 'Julien Mille', 'Fabien Baradel'] | 2018-02-22 | glimpse-clouds-human-activity-recognition-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Baradel_Glimpse_Clouds_Human_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Baradel_Glimpse_Clouds_Human_CVPR_2018_paper.pdf | cvpr-2018-6 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 1.23342246e-01 -2.82203376e-01 -2.64998555e-01 -6.47392124e-02
-6.35029614e-01 -4.86991614e-01 4.24956083e-01 -1.46130249e-01
-6.29317701e-01 5.47281921e-01 3.73786598e-01 2.05860198e-01
9.14644152e-02 -3.06636810e-01 -6.92090571e-01 -9.07103658e-01
-9.07540172e-02 6.04357004e-01 4.33094889e-01 1.90203577... | [8.129939079284668, 0.4376958906650543] |
7476ec08-40e8-4882-9cf1-21dfa0822eaf | nodis-neural-ordinary-differential-scene | 2001.04735 | null | https://arxiv.org/abs/2001.04735v3 | https://arxiv.org/pdf/2001.04735v3.pdf | NODIS: Neural Ordinary Differential Scene Understanding | Semantic image understanding is a challenging topic in computer vision. It requires to detect all objects in an image, but also to identify all the relations between them. Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretatio... | ['Cong Yuren', 'Wentong Liao', 'Michael Ying Yang', 'Hanno Ackermann', 'Bodo Rosenhahn'] | 2020-01-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3577_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650630.pdf | eccv-2020-8 | ['visual-relationship-detection'] | ['computer-vision'] | [ 7.29247212e-01 5.09646475e-01 -6.72166571e-02 -4.91414130e-01
-1.41938850e-01 -6.90035820e-01 5.75157464e-01 5.36687136e-01
-4.89966981e-02 4.23111141e-01 -3.79088670e-01 -2.15928495e-01
-2.67109931e-01 -7.86453426e-01 -1.06141078e+00 -5.30690253e-01
6.69451803e-03 6.42373025e-01 3.44154626e-01 1.67509019... | [10.33183765411377, 1.6137068271636963] |
48a6a6e3-586b-47ef-a295-4c5ab4b124e1 | natural-evolution-strategy-for-mixed-integer | 2304.10724 | null | https://arxiv.org/abs/2304.10724v1 | https://arxiv.org/pdf/2304.10724v1.pdf | Natural Evolution Strategy for Mixed-Integer Black-Box Optimization | This paper proposes a natural evolution strategy (NES) for mixed-integer black-box optimization (MI-BBO) that appears in real-world problems such as hyperparameter optimization of machine learning and materials design. This problem is difficult to optimize because plateaus where the values do not change appear when the... | ['Isao Ono', 'Koki Ikeda'] | 2023-04-21 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 5.87147996e-02 1.50066763e-01 -2.05516070e-01 3.11858237e-01
-4.58506465e-01 -2.55511671e-01 9.12028626e-02 2.06237286e-01
-6.74741864e-01 1.31789768e+00 -4.56427097e-01 -1.23453796e-01
-6.79419816e-01 -7.93530285e-01 -6.69132769e-01 -1.00803614e+00
-4.65186983e-01 4.65478718e-01 3.10089365e-02 -7.18390703... | [5.674839496612549, 3.4961276054382324] |
8ec2937c-a694-4b4a-8124-984acea3bad6 | squib-effects-of-cognitive-effort-on-the | null | null | https://aclanthology.org/J17-2006 | https://aclanthology.org/J17-2006.pdf | Squib: Effects of Cognitive Effort on the Resolution of Overspecified Descriptions | Studies in referring expression generation (REG) have shown different effects of referential overspecification on the resolution of certain descriptions. To further investigate effects of this kind, this article reports two eye-tracking experiments that measure the time required to recognize target objects based on dif... | ["Fl{\\'a}vio Luiz Coutinho", "Matheus Mendes de Sant{'}Ana", "r{\\'e}", 'Iv Paraboni', 'Alex Gwo Jen Lan'] | 2017-06-01 | null | null | null | cl-2017-6 | ['referring-expression-generation'] | ['computer-vision'] | [ 6.10275716e-02 1.68061957e-01 -3.71437743e-02 -6.72676086e-01
-6.78261638e-01 -3.57224584e-01 5.88355720e-01 6.82801664e-01
-4.93329257e-01 3.87946218e-01 8.18525910e-01 -2.66623229e-01
-1.77390411e-01 -5.60421348e-01 -2.50266790e-01 -3.76814753e-01
3.02684844e-01 2.74488837e-01 2.12033972e-01 -3.33160043... | [10.32080364227295, 8.938133239746094] |
92409433-5716-46c4-914d-855d75be3363 | latent-alignment-and-variational-attention | 1807.03756 | null | http://arxiv.org/abs/1807.03756v2 | http://arxiv.org/pdf/1807.03756v2.pdf | Latent Alignment and Variational Attention | Neural attention has become central to many state-of-the-art models in
natural language processing and related domains. Attention networks are an
easy-to-train and effective method for softly simulating alignment; however,
the approach does not marginalize over latent alignments in a probabilistic
sense. This property ... | ['Yoon Kim', 'Justin Chiu', 'Alexander M. Rush', 'Yuntian Deng', 'Demi Guo'] | 2018-07-10 | latent-alignment-and-variational-attention-1 | http://papers.nips.cc/paper/8179-latent-alignment-and-variational-attention | http://papers.nips.cc/paper/8179-latent-alignment-and-variational-attention.pdf | neurips-2018-12 | ['hard-attention'] | ['methodology'] | [ 1.95771664e-01 5.17492533e-01 -3.54834855e-01 -3.37379336e-01
-1.29482985e+00 -5.54705381e-01 8.51139426e-01 -5.40274754e-02
-3.51943403e-01 7.20659316e-01 2.44474277e-01 -4.90333021e-01
1.43420264e-01 -5.04385531e-01 -8.91139269e-01 -7.34270155e-01
3.36161256e-01 1.18291390e+00 -3.96740213e-02 6.23591207... | [11.632735252380371, 9.03283405303955] |
eff20b9c-f7cc-422a-b20a-c022a5706cdc | online-cyber-attack-detection-in-smart-grid-a | 1809.05258 | null | http://arxiv.org/abs/1809.05258v1 | http://arxiv.org/pdf/1809.05258v1.pdf | Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach | Early detection of cyber-attacks is crucial for a safe and reliable operation
of the smart grid. In the literature, outlier detection schemes making
sample-by-sample decisions and online detection schemes requiring perfect
attack models have been proposed. In this paper, we formulate the online
attack/anomaly detection... | ['Xiaodong Wang', 'Oyetunji Ogundijo', 'Mehmet Necip Kurt', 'Chong Li'] | 2018-09-14 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-2.40395755e-01 6.30498379e-02 -1.69752259e-02 -1.97630785e-02
-7.94679224e-01 -3.80478680e-01 2.97361106e-01 9.32407260e-01
-1.80395782e-01 5.72952807e-01 -1.43639207e-01 -6.32083535e-01
-2.98979759e-01 -6.93205655e-01 -2.63257951e-01 -9.53091860e-01
-7.54516423e-01 3.42354387e-01 4.21138912e-01 1.48059875... | [4.668497562408447, 2.454172372817993] |
bd45d911-1c2f-45f7-9a97-5ac477aedb8a | inversemv-composing-piano-scores-with-a | 2112.15320 | null | https://arxiv.org/abs/2112.15320v1 | https://arxiv.org/pdf/2112.15320v1.pdf | InverseMV: Composing Piano Scores with a Convolutional Video-Music Transformer | Many social media users prefer consuming content in the form of videos rather than text. However, in order for content creators to produce videos with a high click-through rate, much editing is needed to match the footage to the music. This posts additional challenges for more amateur video makers. Therefore, we propos... | ['Mu Yang', 'Chin-Tung Lin'] | 2021-12-31 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.43775839e-01 -2.66617507e-01 -2.08546117e-01 1.12990819e-01
-1.07578957e+00 -6.50804281e-01 2.67207891e-01 -4.91860449e-01
3.98822986e-02 2.92752445e-01 5.73352993e-01 2.05591708e-01
-1.52076825e-01 -2.88102359e-01 -8.71180415e-01 -3.09345365e-01
3.37160490e-02 -1.80211775e-02 8.98763239e-02 -1.12273611... | [15.660262107849121, 5.349071979522705] |
da7a4f68-2720-449e-87c4-2a40742ea273 | unmasking-deepfakes-masked-autoencoding | 2306.06881 | null | https://arxiv.org/abs/2306.06881v1 | https://arxiv.org/pdf/2306.06881v1.pdf | Unmasking Deepfakes: Masked Autoencoding Spatiotemporal Transformers for Enhanced Video Forgery Detection | We present a novel approach for the detection of deepfake videos using a pair of vision transformers pre-trained by a self-supervised masked autoencoding setup. Our method consists of two distinct components, one of which focuses on learning spatial information from individual RGB frames of the video, while the other l... | ['Ali Etemad', 'Will Hickie', 'Levent Özparlak', 'Mojtaba Kolahdouzi', 'Sayantan Das'] | 2023-06-12 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-1.04160786e-01 -3.13765019e-01 -9.69732702e-02 -3.98682892e-01
-5.72775424e-01 -4.70077902e-01 7.21284509e-01 -5.69197595e-01
-4.54604357e-01 4.95427072e-01 7.26192966e-02 1.25751406e-01
-1.12638555e-01 -7.18597829e-01 -9.79394257e-01 -6.07500672e-01
-4.07283813e-01 3.63184154e-01 3.33563596e-01 -2.12932914... | [13.265040397644043, 1.1701290607452393] |
e435ca42-af2e-4d5e-86d6-1978c728e8ca | semi-supervised-acoustic-modelling-for-five | 2004.06480 | null | https://arxiv.org/abs/2004.06480v1 | https://arxiv.org/pdf/2004.06480v1.pdf | Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech | This paper considers the impact of automatic segmentation on the fully-automatic, semi-supervised training of automatic speech recognition (ASR) systems for five-lingual code-switched (CS) speech. Four automatic segmentation techniques were evaluated in terms of the recognition performance of an ASR system trained on t... | ['E. van der Westhuizen', 'E. Yılmaz', 'T. R. Niesler', 'A. Biswas', 'F. de Wet', 'N. Wilkinson'] | 2020-04-08 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 3.86735231e-01 3.30103487e-01 1.22578219e-02 -5.62109947e-01
-1.20952415e+00 -4.44885194e-01 5.84472954e-01 -2.46150300e-01
-6.25839829e-01 3.21750462e-01 3.02827507e-01 -7.66751826e-01
4.53124404e-01 -1.42503548e-02 -3.46362442e-01 -8.72413337e-01
2.25111976e-01 4.76103932e-01 2.57875115e-01 -1.42097235... | [14.478891372680664, 6.5719075202941895] |
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