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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1a7ab7b4-5d3e-42da-955c-aef2a917badf | molecular-dipole-moment-learning-via | 2205.15510 | null | https://arxiv.org/abs/2205.15510v1 | https://arxiv.org/pdf/2205.15510v1.pdf | Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning | This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree-Fock computations. A molecular-orbital-based (MOB) pairwise decomposition of the correlation part of the dipole moment... | ['Thomas F. Miller III', 'Lixue Cheng', 'Jiace Sun'] | 2022-05-31 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.60566920e-01 -1.36674002e-01 -1.40320465e-01 -4.01802599e-01
-1.01589060e+00 -2.01654032e-01 4.79157060e-01 4.48550284e-01
-3.60008627e-01 1.15936625e+00 -2.54062712e-01 -3.47944051e-01
-3.16059321e-01 -6.82314336e-01 -7.08618045e-01 -1.42378008e+00
-4.57657427e-01 6.17508888e-01 -1.57691523e-01 -2.01915547... | [5.163029193878174, 5.355057239532471] |
e7b2eb01-2f9f-4bee-b273-aae8e0c7191b | an-image-quality-assessment-dataset-for | 2304.05772 | null | https://arxiv.org/abs/2304.05772v1 | https://arxiv.org/pdf/2304.05772v1.pdf | An Image Quality Assessment Dataset for Portraits | Year after year, the demand for ever-better smartphone photos continues to grow, in particular in the domain of portrait photography. Manufacturers thus use perceptual quality criteria throughout the development of smartphone cameras. This costly procedure can be partially replaced by automated learning-based methods f... | ['Jean Ponce', 'Sira Ferradans', 'Theo Cayla', 'Davide Garcia-Civiero', 'Ana-Stefania Calarasanu', 'Nicolas Chahine'] | 2023-04-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chahine_An_Image_Quality_Assessment_Dataset_for_Portraits_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chahine_An_Image_Quality_Assessment_Dataset_for_Portraits_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-quality-assessment'] | ['computer-vision'] | [ 3.33598882e-01 -1.56071231e-01 -2.35237986e-01 -5.18203318e-01
-1.07452571e+00 -7.68978417e-01 5.50948024e-01 1.35584781e-02
-1.38805613e-01 5.32852292e-01 1.86715290e-01 -6.50510192e-02
-1.00756191e-01 -5.13829112e-01 -5.72328269e-01 -4.47812557e-01
3.72990191e-01 9.03477818e-02 -1.19036593e-01 2.30712327... | [11.8433198928833, -1.8169310092926025] |
5ded4fec-fab9-4d37-9416-32466fbb81f1 | homography-estimation-with-convolutional | 2010.01041 | null | https://arxiv.org/abs/2010.01041v2 | https://arxiv.org/pdf/2010.01041v2.pdf | Homography Estimation with Convolutional Neural Networks Under Conditions of Variance | Planar homography estimation is foundational to many computer vision problems, such as Simultaneous Localization and Mapping (SLAM) and Augmented Reality (AR). However, conditions of high variance confound even the state-of-the-art algorithms. In this report, we analyze the performance of two recently published methods... | ['Avinash Kak', 'David Niblick'] | 2020-10-02 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 1.27068996e-01 -3.02631110e-01 1.91591024e-01 -2.96297610e-01
-6.31314576e-01 -5.67214072e-01 5.73441803e-01 -2.25638971e-01
-5.44617712e-01 4.68423158e-01 -4.97042574e-03 -1.89942077e-01
-4.28651050e-02 -8.44237566e-01 -1.03913438e+00 -4.36677933e-01
-9.86295100e-03 1.67983934e-01 1.21951833e-01 -5.99933803... | [7.772150993347168, -2.0093822479248047] |
03e046d4-dd28-4367-8169-82900456af24 | assessing-the-effectiveness-of-gpt-3-in | 2306.08190 | null | https://arxiv.org/abs/2306.08190v1 | https://arxiv.org/pdf/2306.08190v1.pdf | Assessing the Effectiveness of GPT-3 in Detecting False Political Statements: A Case Study on the LIAR Dataset | The detection of political fake statements is crucial for maintaining information integrity and preventing the spread of misinformation in society. Historically, state-of-the-art machine learning models employed various methods for detecting deceptive statements. These methods include the use of metadata (W. Wang et al... | ['Mars Gokturk Buchholz'] | 2023-06-14 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-3.22092354e-01 2.86239505e-01 -6.62009776e-01 -1.87526330e-01
-1.02819729e+00 -5.87099493e-01 1.20184815e+00 6.90740407e-01
-5.22839725e-01 7.51489878e-01 6.12891197e-01 -6.80265248e-01
2.29015082e-01 -8.25150013e-01 -5.38529277e-01 -2.12071855e-02
1.80569172e-01 1.29916683e-01 3.24818671e-01 -4.72913414... | [8.267525672912598, 10.19655990600586] |
698adc4a-9880-4454-a1d2-f53fbb5bc03e | driving-digital-engineering-integration-and | 2206.10454 | null | https://arxiv.org/abs/2206.10454v1 | https://arxiv.org/pdf/2206.10454v1.pdf | Driving Digital Engineering Integration and Interoperability Through Semantic Integration of Models with Ontologies | Engineered solutions are becoming more complex and multi-disciplinary in nature. This evolution requires new techniques to enhance design and analysis tasks that incorporate data integration and interoperability across various engineering tool suites spanning multiple domains at different abstraction levels. Semantic W... | ['Zhongyuan Yu', 'Dinesh Verma', 'Benjamin Kruse', 'Steven Hespelt', 'John Dzielski', 'Mark Blackburn', 'Thomas Hagedorn', 'Daniel Dunbar'] | 2022-06-08 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-2.11997434e-01 9.51945111e-02 2.51120955e-01 -3.41861069e-01
-1.04751222e-01 -8.00744712e-01 5.44073880e-01 2.73809433e-01
2.48988360e-01 1.16719224e-01 2.49277562e-01 -3.75473887e-01
-1.11494005e+00 -1.10136223e+00 -2.53721289e-02 3.21135223e-01
3.37281615e-01 5.07483006e-01 5.10249674e-01 -7.01665461... | [9.062145233154297, 7.666627883911133] |
e29d2da3-3aae-41d7-98cb-07db1949c2c3 | a-comprehensive-study-on-the-robustness-of | 2306.12111 | null | https://arxiv.org/abs/2306.12111v1 | https://arxiv.org/pdf/2306.12111v1.pdf | A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking | Deep neural networks (DNNs) have found widespread applications in interpreting remote sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are vulnerable to different types of noises, particularly adversarial noises. Surprisingly, there has been a lack of comprehensive studies on the robu... | ['Lap-Pui Chau', 'Mingyang Ma', 'Yuru Su', 'Xiaofei Wang', 'Jiawei Lian', 'Shaohui Mei'] | 2023-06-21 | null | null | null | null | ['adversarial-robustness', 'benchmarking', 'benchmarking'] | ['adversarial', 'miscellaneous', 'robots'] | [ 4.66078222e-01 -4.89244074e-01 3.34127396e-01 -2.36078829e-01
-6.23695135e-01 -9.47927296e-01 6.38126612e-01 -1.55494377e-01
-4.06333327e-01 3.70777667e-01 1.83654413e-01 -6.66282058e-01
-2.03941077e-01 -8.30301046e-01 -6.68120146e-01 -8.34281027e-01
-3.13570678e-01 -4.26850289e-01 -5.86234815e-02 -5.74136734... | [5.607115268707275, 7.875911235809326] |
80680571-611c-4ce5-950d-809980270979 | faithful-knowledge-distillation | 2306.04431 | null | https://arxiv.org/abs/2306.04431v2 | https://arxiv.org/pdf/2306.04431v2.pdf | Faithful Knowledge Distillation | Knowledge distillation (KD) has received much attention due to its success in compressing networks to allow for their deployment in resource-constrained systems. While the problem of adversarial robustness has been studied before in the KD setting, previous works overlook what we term the relative calibration of the st... | ['Krishnamurthy Dj Dvijotham', 'Francisco Eiras', 'Philip H. S. Torr', 'M. Pawan Kumar', 'Rudy Brunel', 'Tom A. Lamb'] | 2023-06-07 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [ 1.47116020e-01 7.13158071e-01 -3.73411924e-01 -2.10202277e-01
-6.34399235e-01 -1.02295315e+00 4.73760903e-01 2.95119673e-01
-5.39403975e-01 7.82790959e-01 1.58126913e-02 -4.92628813e-01
-4.23514038e-01 -7.95333266e-01 -1.12644124e+00 -6.87851131e-01
-1.29123852e-01 5.74992537e-01 1.84437633e-01 -1.57962903... | [5.623227596282959, 7.834003448486328] |
f111f4f0-ab15-4fbc-a78b-9886e32ef755 | feature-transformation-for-cross-domain-few | 2203.02270 | null | https://arxiv.org/abs/2203.02270v1 | https://arxiv.org/pdf/2203.02270v1.pdf | Feature Transformation for Cross-domain Few-shot Remote Sensing Scene Classification | Effectively classifying remote sensing scenes is still a challenge due to the increasing spatial resolution of remote imaging and large variances between remote sensing images. Existing research has greatly improved the performance of remote sensing scene classification (RSSC). However, these methods are not applicable... | ['Wei Luo', 'Zhihao Chen', 'Qiaoling Chen'] | 2022-03-04 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 6.38780355e-01 -3.85072201e-01 -1.08168185e-01 -6.72217667e-01
-8.37104559e-01 -6.18366957e-01 6.23983741e-01 -1.94828138e-01
-3.91482353e-01 7.42519498e-01 -1.74207807e-01 -2.88036227e-01
-6.72214210e-01 -1.09094787e+00 -3.78907442e-01 -8.50694299e-01
-3.61873917e-02 2.39664197e-01 2.22500727e-01 -4.16113764... | [9.670011520385742, -1.3688151836395264] |
ab619110-5729-4ca6-8e94-4ecd3726c668 | the-ability-of-image-language-explainable | 2209.09310 | null | https://arxiv.org/abs/2209.09310v1 | https://arxiv.org/pdf/2209.09310v1.pdf | The Ability of Image-Language Explainable Models to Resemble Domain Expertise | Recent advances in vision and language (V+L) models have a promising impact in the healthcare field. However, such models struggle to explain how and why a particular decision was made. In addition, model transparency and involvement of domain expertise are critical success factors for machine learning models to make a... | ['Ujjwal Ratan', 'Anna Zapaishchykova', 'Petrus Werner'] | 2022-09-19 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 1.71931200e-02 6.73322618e-01 -2.16098636e-01 -5.12719810e-01
-4.46295142e-01 -3.88500869e-01 5.30772984e-01 3.14263672e-01
-1.37631312e-01 5.87005794e-01 3.21668088e-01 -8.28890026e-01
-6.57187626e-02 -3.58377188e-01 -7.07475185e-01 -2.82240629e-01
3.84720415e-01 5.92786312e-01 -4.05656308e-01 -1.96180400... | [8.935934066772461, 5.5338239669799805] |
0131001b-b052-4b6e-897b-c18c6370fec3 | deductive-verification-of-chain-of-thought | 2306.03872 | null | https://arxiv.org/abs/2306.03872v2 | https://arxiv.org/pdf/2306.03872v2.pdf | Deductive Verification of Chain-of-Thought Reasoning | Large Language Models (LLMs) significantly benefit from Chain-of-Thought (CoT) prompting in performing various reasoning tasks. While CoT allows models to produce more comprehensive reasoning processes, its emphasis on intermediate reasoning steps can inadvertently introduce hallucinations and accumulated errors, there... | ['Hao Su', 'Roland Memisevic', 'Mingu Lee', 'Zhiao Huang', 'Xuanlin Li', 'Yunhao Fang', 'Zhan Ling'] | 2023-06-06 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-2.50855219e-02 8.01081240e-01 8.68778825e-02 -4.03306365e-01
-3.09330344e-01 -6.24508977e-01 8.23592186e-01 1.88824102e-01
2.06111534e-03 6.43155575e-01 2.58663356e-01 -9.33143795e-01
-1.36318430e-01 -1.02035320e+00 -3.79497498e-01 1.20991752e-01
4.57392246e-01 4.39828336e-01 6.85099736e-02 -3.26200396... | [9.515277862548828, 7.367082118988037] |
04f90199-131d-453f-8606-f152f7860d3d | learning-selective-self-mutual-attention-for | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Learning_Selective_Self-Mutual_Attention_for_RGB-D_Saliency_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Learning_Selective_Self-Mutual_Attention_for_RGB-D_Saliency_Detection_CVPR_2020_paper.pdf | Learning Selective Self-Mutual Attention for RGB-D Saliency Detection | Saliency detection on RGB-D images is receiving more and more research interests recently. Previous models adopt the early fusion or the result fusion scheme to fuse the input RGB and depth data or their saliency maps, which incur the problem of distribution gap or information loss. Some other models use the feature fu... | [' Junwei Han', ' Ni Zhang', 'Nian Liu'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.85900137e-01 -6.58846349e-02 -7.91539773e-02 -5.28934717e-01
-6.57146037e-01 3.99223641e-02 3.95160913e-01 2.21106857e-01
-3.78446847e-01 4.51029211e-01 4.77906376e-01 1.58796191e-01
1.61443919e-01 -5.95160484e-01 -7.29020834e-01 -7.31853962e-01
5.56413591e-01 -4.25992817e-01 9.77233529e-01 -2.06718996... | [9.712965965270996, -0.7540121674537659] |
b42a16fb-590b-4439-b295-2cd89ff5d813 | unravela-decipherment-toolkit | null | null | https://aclanthology.org/P15-2090 | https://aclanthology.org/P15-2090.pdf | UNRAVEL---A Decipherment Toolkit | null | ['Malte Nuhn', 'Hermann Ney', 'Julian Schamper'] | 2015-07-01 | unravel-a-decipherment-toolkit | https://aclanthology.org/P15-2090 | https://aclanthology.org/P15-2090.pdf | ijcnlp-2015-7 | ['decipherment'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.277866840362549, 3.6416878700256348] |
2d2f6b2a-ee85-420b-a02e-53fbafabcb5d | can-open-domain-qa-reader-utilize-external | 2211.12707 | null | https://arxiv.org/abs/2211.12707v1 | https://arxiv.org/pdf/2211.12707v1.pdf | Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans? | Recent state-of-the-art open-domain QA models are typically based on a two stage retriever-reader approach in which the retriever first finds the relevant knowledge/passages and the reader then leverages that to predict the answer. Prior work has shown that the performance of the reader usually tends to improve with th... | ['Chitta Baral', 'Man Luo', 'Neeraj Varshney'] | 2022-11-23 | null | null | null | null | ['triviaqa', 'open-domain-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [-1.79114968e-01 4.15608704e-01 -6.03938885e-02 -1.29846230e-01
-1.50237596e+00 -1.04562461e+00 4.56281900e-01 2.39395186e-01
-6.39553428e-01 9.75522459e-01 6.33025467e-02 -5.77791989e-01
-2.44733840e-01 -1.07197547e+00 -8.59671414e-01 -1.78059623e-01
6.84634566e-01 1.13190186e+00 7.33663619e-01 -6.80207670... | [11.17094612121582, 7.974584102630615] |
41819506-fa79-4249-8d95-53b018d802f6 | voxelnet-end-to-end-learning-for-point-cloud | 1711.06396 | null | http://arxiv.org/abs/1711.06396v1 | http://arxiv.org/pdf/1711.06396v1.pdf | VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection | Accurate detection of objects in 3D point clouds is a central problem in many
applications, such as autonomous navigation, housekeeping robots, and
augmented/virtual reality. To interface a highly sparse LiDAR point cloud with
a region proposal network (RPN), most existing efforts have focused on
hand-crafted feature r... | ['Yin Zhou', 'Oncel Tuzel'] | 2017-11-17 | voxelnet-end-to-end-learning-for-point-cloud-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhou_VoxelNet_End-to-End_Learning_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_VoxelNet_End-to-End_Learning_CVPR_2018_paper.pdf | cvpr-2018-6 | ['birds-eye-view-object-detection'] | ['computer-vision'] | [-2.44943440e-01 -1.53461561e-01 -2.83921659e-02 -5.21977425e-01
-4.96534944e-01 -4.02058154e-01 5.95613182e-01 1.10605940e-01
-4.90187168e-01 1.48158416e-01 -2.71271944e-01 -4.64511395e-01
2.40228668e-01 -1.05590141e+00 -1.01324880e+00 -3.64664972e-01
-1.06997930e-01 7.64179528e-01 7.27331996e-01 -1.80766404... | [7.744967937469482, -2.8010919094085693] |
d28977e4-95d4-4310-8c95-df076e2a4276 | dsc-iit-ism-at-semeval-2020-task-6-boosting | 2009.08180 | null | https://arxiv.org/abs/2009.08180v1 | https://arxiv.org/pdf/2009.08180v1.pdf | DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction | We explore the performance of Bidirectional Encoder Representations from Transformers (BERT) at definition extraction. We further propose a joint model of BERT and Text Level Graph Convolutional Network so as to incorporate dependencies into the model. Our proposed model produces better results than BERT and achieves c... | ['Priyanshu Kumar', 'Aman Sinha', 'Aadarsh Singh'] | 2020-09-17 | null | https://aclanthology.org/2020.semeval-1.93 | https://aclanthology.org/2020.semeval-1.93.pdf | semeval-2020 | ['definition-extraction'] | ['natural-language-processing'] | [ 2.39656731e-01 6.60706580e-01 -3.11470479e-01 -4.55228478e-01
-6.68749392e-01 -8.55274081e-01 8.69689286e-01 2.56539404e-01
-3.27466995e-01 9.11423206e-01 7.50366747e-01 -1.20364511e+00
3.57864380e-01 -1.32134509e+00 -8.58025253e-01 2.60578454e-01
-1.28281638e-01 4.56216186e-01 3.32442105e-01 -5.74425220... | [9.968160629272461, 9.0113525390625] |
b6471f4b-485f-4446-8725-e93b92ff70cf | how-to-be-fair-and-diverse | 1610.07183 | null | http://arxiv.org/abs/1610.07183v1 | http://arxiv.org/pdf/1610.07183v1.pdf | How to be Fair and Diverse? | Due to the recent cases of algorithmic bias in data-driven decision-making,
machine learning methods are being put under the microscope in order to
understand the root cause of these biases and how to correct them. Here, we
consider a basic algorithmic task that is central in machine learning:
subsampling from a large ... | ['Tarun Kathuria', 'Amit Deshpande', 'L. Elisa Celis', 'Nisheeth K. Vishnoi'] | 2016-10-23 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 5.79910874e-01 2.85896808e-01 -3.15279603e-01 -6.45311594e-01
-5.13700604e-01 -3.22982907e-01 7.30295777e-01 3.53942305e-01
-5.84626377e-01 1.07782936e+00 4.45077032e-01 -1.95380241e-01
-3.56285632e-01 -7.23399818e-01 -3.07495147e-01 -9.82778668e-01
4.41876143e-01 4.26869601e-01 -6.59118816e-02 -2.16770962... | [8.668256759643555, 5.202047824859619] |
c460c5da-3c24-4c7e-a0db-9b8c2f2393ea | variational-inference-posterior-threshold | 2301.04771 | null | https://arxiv.org/abs/2301.04771v1 | https://arxiv.org/pdf/2301.04771v1.pdf | Variational Inference: Posterior Threshold Improves Network Clustering Accuracy in Sparse Regimes | Variational inference has been widely used in machine learning literature to fit various Bayesian models. In network analysis, this method has been successfully applied to solve the community detection problems. Although these results are promising, their theoretical support is only for relatively dense networks, an as... | ['Can M. Le', 'Xuezhen Li'] | 2023-01-12 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 1.73178986e-01 2.51304537e-01 -1.45113423e-01 -2.75055505e-02
-4.31102276e-01 -3.48977089e-01 3.37622136e-01 2.84522176e-01
-4.22577143e-01 8.85301590e-01 -4.91342604e-01 -8.70868564e-02
-3.43534201e-01 -9.10869777e-01 -5.61327875e-01 -1.14461541e+00
-1.35945708e-01 8.69143784e-01 2.75558978e-01 2.35913396... | [6.947624206542969, 5.183107852935791] |
02c8445f-7630-4300-8dca-397d63a10a66 | optimal-quadratic-binding-for-relational | 2204.07186 | null | https://arxiv.org/abs/2204.07186v1 | https://arxiv.org/pdf/2204.07186v1.pdf | Optimal quadratic binding for relational reasoning in vector symbolic neural architectures | Binding operation is fundamental to many cognitive processes, such as cognitive map formation, relational reasoning, and language comprehension. In these processes, two different modalities, such as location and objects, events and their contextual cues, and words and their roles, need to be bound together, but little ... | ['Haim Sompolinsky', 'Naoki Hiratani'] | 2022-04-14 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 1.81934386e-01 -1.26863644e-01 2.38373484e-02 -2.60176033e-01
-2.83643454e-01 -5.92881143e-01 6.27479494e-01 4.83007967e-01
-7.71019161e-01 7.42992520e-01 1.71108872e-01 -2.91208386e-01
-3.67813438e-01 -1.02036858e+00 -7.97146082e-01 -6.48869753e-01
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1f48f27c-f954-44ee-934f-6c0520a90974 | rlas-biabc-a-reinforcement-learning-based | 2301.02807 | null | https://arxiv.org/abs/2301.02807v1 | https://arxiv.org/pdf/2301.02807v1.pdf | RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm | Answer selection (AS) is a critical subtask of the open-domain question answering (QA) problem. The present paper proposes a method called RLAS-BIABC for AS, which is established on attention mechanism-based long short-term memory (LSTM) and the bidirectional encoder representations from transformers (BERT) word embedd... | ['Saeed Shiry Ghidary', 'Azam Bastanfard', 'Javad Mohammadzadeh', 'Hamid Gharagozlou'] | 2023-01-07 | null | null | null | null | ['imbalanced-classification', 'open-domain-question-answering', 'answer-selection'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 3.17970484e-01 2.69481480e-01 -2.56376982e-01 -3.94705892e-01
-4.83623445e-01 -1.37017861e-01 -1.08842999e-02 2.84404546e-01
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-2.55714595e-01 -1.18396544e+00 -6.26105607e-01 -9.49794412e-01
2.24012583e-01 7.17364013e-01 5.47870696e-01 -4.96134877... | [11.065961837768555, 8.071281433105469] |
cafb469b-ca59-4f74-96da-1eb426b997ce | identification-of-twitter-bots-based-on-an | 2112.04913 | null | https://arxiv.org/abs/2112.04913v2 | https://arxiv.org/pdf/2112.04913v2.pdf | Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study | Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured. It provides a simple usage framework with short messages and an efficient application programming interface (API) enabling the research community to study and analyze several... | ['Sotiris Ioannidis', 'Despoina Antonakaki', 'Christos Tzagkarakis', 'Alexander Shevtsov'] | 2021-12-08 | null | null | null | null | ['twitter-bot-detection'] | ['miscellaneous'] | [-2.84080863e-01 2.88636744e-01 -6.54289901e-01 1.67005673e-01
1.32494822e-01 -4.95606661e-01 1.10187948e+00 2.46426046e-01
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1.00134753e-01 -1.12507200e+00 4.91018519e-02 -6.48642838e-01
9.31053907e-02 4.79239374e-01 2.38466620e-01 -7.57005453... | [8.086346626281738, 10.130343437194824] |
e79f7384-8536-4839-b84a-6460a55ec3d7 | a-stock-prediction-model-based-on-dcnn | 2009.03239 | null | https://arxiv.org/abs/2009.03239v1 | https://arxiv.org/pdf/2009.03239v1.pdf | A Stock Prediction Model Based on DCNN | The prediction of a stock price has always been a challenging issue, as its volatility can be affected by many factors such as national policies, company financial reports, industry performance, and investor sentiment etc.. In this paper, we present a prediction model based on deep CNN and the candle charts, the contin... | ['Ningning Liu', 'Qiao Zhou'] | 2020-09-07 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-9.51278925e-01 -8.66572738e-01 -2.13196918e-01 -3.71539772e-01
6.75931275e-02 -4.76362169e-01 5.70566833e-01 -2.16709867e-01
-3.13471615e-01 7.35606670e-01 1.15882210e-01 -5.55348575e-01
-3.11582424e-02 -1.23634744e+00 -3.15589368e-01 -6.36629522e-01
-9.93178859e-02 -3.91818024e-02 1.39330938e-01 -5.95870793... | [4.451918125152588, 4.227987289428711] |
09cab97a-d075-466c-801f-f97bf3df31a1 | box-supervised-instance-segmentation-with | 2207.09055 | null | https://arxiv.org/abs/2207.09055v1 | https://arxiv.org/pdf/2207.09055v1.pdf | Box-supervised Instance Segmentation with Level Set Evolution | In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In this paper, we propose a novel single-shot box-supervised instance segmentation approach, which inte... | ['Lei Zhang', 'Xiansheng Hua', 'Miaomiao Cui', 'Jianke Zhu', 'Wenyu Liu', 'Wentong Li'] | 2022-07-19 | null | null | null | null | ['box-supervised-instance-segmentation'] | ['computer-vision'] | [ 3.70471865e-01 3.35514218e-01 -2.97726333e-01 -7.06254900e-01
-9.11357760e-01 -2.80815274e-01 3.64225239e-01 7.95553327e-02
-4.80828702e-01 5.83829105e-01 -4.69032437e-01 1.11711606e-01
-1.61849856e-02 -8.03654194e-01 -8.17048609e-01 -7.90690303e-01
1.92797974e-01 5.89381278e-01 4.66124237e-01 5.44695482... | [9.601234436035156, 0.27757441997528076] |
6a767f1e-30d1-484b-857a-d422a1f6c486 | duet-2d-structured-and-approximately | 2306.16058 | null | https://arxiv.org/abs/2306.16058v2 | https://arxiv.org/pdf/2306.16058v2.pdf | DUET: 2D Structured and Approximately Equivariant Representations | Multiview Self-Supervised Learning (MSSL) is based on learning invariances with respect to a set of input transformations. However, invariance partially or totally removes transformation-related information from the representations, which might harm performance for specific downstream tasks that require such informatio... | ['Luca Zappella', 'Dan Busbridge', 'Jason Ramapuram', 'Chen Huang', 'Arno Blaas', 'T. Anderson Keller', 'Federico Danieli', 'Xavier Suau'] | 2023-06-28 | null | null | null | null | ['self-supervised-learning', 'transfer-learning'] | ['computer-vision', 'miscellaneous'] | [ 2.65855581e-01 3.44886541e-01 -4.01481390e-01 -4.69942510e-01
-4.64562088e-01 -1.02917314e+00 9.52506959e-01 1.14319600e-01
1.06169209e-01 5.27421415e-01 7.28662133e-01 -7.81130453e-04
-2.73959249e-01 -6.55733883e-01 -8.86367977e-01 -5.05444705e-01
6.57356950e-03 3.19775522e-01 -1.03645004e-01 -4.05619830... | [9.050681114196777, 2.7041268348693848] |
8825e9b8-0e75-4e1d-85ec-ef8452f3eae5 | magneto-an-efficient-deep-learning-method-for-1 | 2011.04349 | null | https://arxiv.org/abs/2011.04349v1 | https://arxiv.org/pdf/2011.04349v1.pdf | MAGNeto: An Efficient Deep Learning Method for the Extractive Tags Summarization Problem | In this work, we study a new image annotation task named Extractive Tags Summarization (ETS). The goal is to extract important tags from the context lying in an image and its corresponding tags. We adjust some state-of-the-art deep learning models to utilize both visual and textual information. Our proposed solution co... | ['Ngoc C. Lê', 'Trung Thanh Tran', 'Giang Nam Ngo', 'Lam Thanh Do', 'Tung Dinh Nguyen', 'Anh Tuan Vu', 'Hieu Trong Phung'] | 2020-11-09 | magneto-an-efficient-deep-learning-method-for | https://arxiv.org/abs/2011.04349 | https://arxiv.org/pdf/2011.04349 | null | ['extractive-tags-summarization'] | ['natural-language-processing'] | [ 1.63234979e-01 5.83481900e-02 -3.10135335e-02 -5.94974339e-01
-1.03387308e+00 -2.37556607e-01 4.64118391e-01 2.24473789e-01
-7.53492773e-01 6.01136684e-01 3.39112252e-01 9.05173272e-02
2.56009281e-01 -5.08010209e-01 -8.61177027e-01 -7.67441094e-01
2.44546443e-01 -4.14080210e-02 3.22937936e-01 -4.10813652... | [9.76876449584961, 0.35662877559661865] |
938d876a-127c-4daa-8ba9-0f0ae2a9096d | analysis-of-hydrological-and-suspended | 1911.12466 | null | https://arxiv.org/abs/1911.12466v2 | https://arxiv.org/pdf/1911.12466v2.pdf | Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering | Hydrological storm events are a primary driver for transporting water quality constituents such as turbidity, suspended sediments and nutrients. Analyzing the concentration (C) of these water quality constituents in response to increased streamflow discharge (Q), particularly when monitored at high temporal resolution ... | ['Byung Suk Lee', 'Ali Javed', 'Scott D. Hamshaw', 'Donna M. Rizzo'] | 2019-11-28 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-7.05160871e-02 -5.74795425e-01 1.77600220e-01 -3.11698675e-01
-3.24407727e-01 -8.79218221e-01 5.55027187e-01 6.96328163e-01
-2.04212397e-01 7.55266786e-01 5.13062656e-01 -6.31419957e-01
-7.19970405e-01 -1.10805643e+00 -2.12524757e-01 -9.83613670e-01
-8.07778776e-01 2.46929064e-01 -3.76208201e-02 -4.46085006... | [6.494519233703613, 3.120260238647461] |
161ea0ee-7641-4731-a2c1-5f8ae63aa280 | icdar-2021-competition-on-scientific | 2106.14616 | null | https://arxiv.org/abs/2106.14616v1 | https://arxiv.org/pdf/2106.14616v1.pdf | ICDAR 2021 Competition on Scientific Literature Parsing | Scientific literature contain important information related to cutting-edge innovations in diverse domains. Advances in natural language processing have been driving the fast development in automated information extraction from scientific literature. However, scientific literature is often available in unstructured PDF... | ['Douglas Burdick', 'Xu Zhong', 'Antonio Jimeno Yepes'] | 2021-06-08 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 2.04771250e-01 -6.61700889e-02 -7.82948285e-02 -1.54172868e-01
-1.06672812e+00 -1.17669213e+00 6.36170685e-01 8.37503850e-01
-2.30246902e-01 8.01408410e-01 -3.58912423e-02 -5.00458181e-01
-1.75396770e-01 -7.67547607e-01 -1.17300439e+00 -2.96088994e-01
-1.11280652e-02 5.95518768e-01 -2.39950512e-02 2.98178077... | [11.670382499694824, 2.840211868286133] |
924fcd22-1091-413e-9176-b5605b260fdc | road-genome-a-topology-reasoning-benchmark | 2304.10440 | null | https://arxiv.org/abs/2304.10440v2 | https://arxiv.org/pdf/2304.10440v2.pdf | OpenLane-V2: A Topology Reasoning Benchmark for Scene Understanding in Autonomous Driving | Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute accurate judgments. However, existing benchmarks tend to oversimplify the scene by solely focusing on lane perception tasks. Observing that human drivers rely on both lanes and traffic signals to operate their vehicle... | ['Wei zhang', 'Junchi Yan', 'Ping Luo', 'Bangjun Wang', 'Peijin Jia', 'Chonghao Sima', 'Li Chen', 'Tianyu Li', 'Hongyang Li', 'Hang Xu', 'Feng Wen', 'Shengyin Jiang', 'Yuting Wang', 'Yang Li', 'Zhenbo Liu', 'Huijie Wang'] | 2023-04-20 | null | null | null | null | ['3d-lane-detection', 'lane-detection'] | ['computer-vision', 'computer-vision'] | [-1.94989443e-01 3.16818029e-01 -1.65828168e-01 -9.59097207e-01
-2.92131275e-01 -6.89119041e-01 8.75237703e-01 7.75797591e-02
-2.04873960e-02 3.52411687e-01 1.86170414e-01 -8.93557727e-01
-2.13650465e-02 -9.03597355e-01 -7.06680298e-01 -1.11450672e-01
-2.15822935e-01 6.92880392e-01 8.35008562e-01 -5.99328756... | [8.014110565185547, -1.6064603328704834] |
e5399e18-5a9e-4872-bf3c-5db7dc5f8d83 | discriminatory-and-orthogonal-feature | 2210.11519 | null | https://arxiv.org/abs/2210.11519v1 | https://arxiv.org/pdf/2210.11519v1.pdf | Discriminatory and orthogonal feature learning for noise robust keyword spotting | Keyword Spotting (KWS) is an essential component in a smart device for alerting the system when a user prompts it with a command. As these devices are typically constrained by computational and energy resources, the KWS model should be designed with a small footprint. In our previous work, we developed lightweight dyna... | ['Hanseok Ko', 'David K. Han', 'Kyungdeuk Ko', 'Donghyeon Kim'] | 2022-10-20 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 4.19370115e-01 -1.87064022e-01 6.81810081e-02 -3.53970110e-01
-7.80601859e-01 -3.32654655e-01 2.81037241e-01 9.03444588e-02
-5.31392813e-01 4.46534723e-01 5.51236495e-02 -3.46175700e-01
-2.70012796e-01 -3.72002006e-01 -4.39173549e-01 -8.54541004e-01
-3.14609185e-02 -5.41850448e-01 2.58232117e-01 7.35432357... | [14.577825546264648, 6.050532817840576] |
08456197-4d76-4e1a-8e07-2138ea01a454 | bivariate-beta-lstm | 1905.10521 | null | https://arxiv.org/abs/1905.10521v3 | https://arxiv.org/pdf/1905.10521v3.pdf | Bivariate Beta-LSTM | Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function. However, due to the graduality of the sigmoid function, the sigmoid gate is not flexible in representing m... | ['Il-Chul Moon', 'JoonHo Jang', 'Seung jae Shin', 'Kyungwoo Song'] | 2019-05-25 | null | null | null | null | ['music-modeling'] | ['music'] | [ 1.05319194e-01 1.20406106e-01 -1.61988571e-01 -3.62974137e-01
-2.95250535e-01 -4.21207368e-01 5.97061932e-01 -2.72111874e-02
-4.29711670e-01 8.56063485e-01 2.93441772e-01 -2.94296890e-01
1.08598046e-01 -1.10451853e+00 -1.00244284e+00 -1.00158143e+00
6.23659603e-02 1.02974288e-03 2.11342767e-01 -5.32699190... | [10.760590553283691, 6.490073204040527] |
ae516642-6e5c-41da-8e31-c61aa9bc40da | bridgeformer-bridging-video-text-retrieval | 2201.04850 | null | https://arxiv.org/abs/2201.04850v2 | https://arxiv.org/pdf/2201.04850v2.pdf | Bridging Video-text Retrieval with Multiple Choice Questions | Pre-training a model to learn transferable video-text representation for retrieval has attracted a lot of attention in recent years. Previous dominant works mainly adopt two separate encoders for efficient retrieval, but ignore local associations between videos and texts. Another line of research uses a joint encoder t... | ['Ping Luo', 'XiaoHu Qie', 'Ying Shan', 'Dian Li', 'Xihui Liu', 'Yixiao Ge', 'Yuying Ge'] | 2022-01-13 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ge_Bridging_Video-Text_Retrieval_With_Multiple_Choice_Questions_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ge_Bridging_Video-Text_Retrieval_With_Multiple_Choice_Questions_CVPR_2022_paper.pdf | cvpr-2022-1 | ['zero-shot-action-recognition', 'video-text-retrieval', 'text-to-video-search'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 1.19668171e-01 -5.30525923e-01 -5.22337437e-01 -1.95885420e-01
-1.23232996e+00 -6.27561092e-01 8.57807159e-01 -3.90160680e-01
-6.14994824e-01 3.64854574e-01 5.82720160e-01 1.00336611e-01
-1.05168089e-01 -4.34978634e-01 -9.05152082e-01 -6.85293317e-01
2.06324220e-01 2.94917017e-01 3.24875861e-01 -1.59993902... | [10.304606437683105, 0.9250437617301941] |
640e7cf9-afca-45f4-8084-7540d31ac890 | compact-model-training-by-low-rank-projection | 2204.05566 | null | https://arxiv.org/abs/2204.05566v2 | https://arxiv.org/pdf/2204.05566v2.pdf | Compact Model Training by Low-Rank Projection with Energy Transfer | Low-rankness plays an important role in traditional machine learning, but is not so popular in deep learning. Most previous low-rank network compression methods compress the networks by approximating pre-trained models and re-training. However, the optimal solution in the Euclidean space may be quite different from the... | ['Xiangmin Xu', 'Fang Liu', 'Xiaofen Xing', 'Zhenquan Lin', 'Kailing Guo'] | 2022-04-12 | null | null | null | null | ['low-rank-compression'] | ['computer-code'] | [ 1.8316190e-01 1.8989526e-01 -4.0042838e-01 -2.8684407e-01
-3.0512387e-01 -5.3609982e-02 3.0964038e-01 -3.0939299e-01
-7.3633265e-01 6.4889121e-01 3.2377955e-01 -2.2637258e-01
-4.3253082e-01 -6.7882907e-01 -1.0573913e+00 -7.0954722e-01
2.8787235e-02 5.2599704e-01 1.4878546e-01 2.5196031e-02
-6.3461192e-02... | [8.496973037719727, 3.255361318588257] |
5d52943a-b54b-4883-9cfd-2d4be550e9b3 | sc2-supervised-compression-for-split | 2203.08875 | null | https://arxiv.org/abs/2203.08875v2 | https://arxiv.org/pdf/2203.08875v2.pdf | SC2 Benchmark: Supervised Compression for Split Computing | With the increasing demand for deep learning models on mobile devices, splitting neural network computation between the device and a more powerful edge server has become an attractive solution. However, existing split computing approaches often underperform compared to a naive baseline of remote computation on compress... | ['Stephan Mandt', 'Marco Levorato', 'Ruihan Yang', 'Yoshitomo Matsubara'] | 2022-03-16 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 4.85361814e-01 -1.45270318e-01 -7.37375200e-01 -7.78741837e-01
-8.27234149e-01 -1.80806339e-01 3.17613602e-01 3.75640988e-02
-6.81155264e-01 4.46579516e-01 1.21246874e-01 -3.73136938e-01
-5.97187579e-02 -7.08181441e-01 -8.65526497e-01 -5.64862311e-01
7.07143545e-02 4.18999463e-01 7.11752698e-02 6.35451555... | [8.557530403137207, 3.0465469360351562] |
87e60a34-70ad-4270-915f-1a4ce9855550 | ignore-previous-prompt-attack-techniques-for | 2211.09527 | null | https://arxiv.org/abs/2211.09527v1 | https://arxiv.org/pdf/2211.09527v1.pdf | Ignore Previous Prompt: Attack Techniques For Language Models | Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing applications. However, studies that explore their vulnerabilities emerging from malicious user interaction are scarce. By proposing PromptInject, a prosaic alignment framework for mask-... | ['Ian Ribeiro', 'Fábio Perez'] | 2022-11-17 | null | null | null | null | ['real-world-adversarial-attack', 'adversarial-text'] | ['adversarial', 'adversarial'] | [ 2.48673875e-02 2.98593193e-01 -2.74506390e-01 -4.12546359e-02
-1.04635942e+00 -1.25914168e+00 8.17436337e-01 -1.40480444e-01
-1.93793830e-02 5.04268408e-01 1.54795080e-01 -9.63133037e-01
3.13994735e-01 -5.29661357e-01 -6.99877858e-01 -4.90968674e-01
-3.09573743e-03 5.24440050e-01 -2.54972100e-01 -4.92508858... | [6.062373638153076, 8.04647445678711] |
9639f085-fe12-4d29-a5f7-9ef4e12fe2d2 | mutual-information-divergence-a-unified | 2205.13445 | null | https://arxiv.org/abs/2205.13445v1 | https://arxiv.org/pdf/2205.13445v1.pdf | Mutual Information Divergence: A Unified Metric for Multimodal Generative Models | Text-to-image generation and image captioning are recently emerged as a new experimental paradigm to assess machine intelligence. They predict continuous quantity accompanied by their sampling techniques in the generation, making evaluation complicated and intractable to get marginal distributions. Based on a recent tr... | ['Sang-Woo Lee', 'Kang Min Yoo', 'Jiyoung Lee', 'Yunji Kim', 'Jin-Hwa Kim'] | 2022-05-25 | null | null | null | null | ['human-judgment-correlation', 'human-judgment-classification'] | ['reasoning', 'reasoning'] | [ 6.60687208e-01 3.76441538e-01 -1.37830526e-01 -5.52464247e-01
-1.19720912e+00 -6.46260798e-01 1.24019384e+00 -1.25591457e-01
-4.71516103e-01 7.94182777e-01 3.84504914e-01 -5.71843572e-02
-3.65150981e-02 -4.82616186e-01 -7.63770401e-01 -8.23372602e-01
2.57890731e-01 5.94714105e-01 -3.53712887e-01 8.05563703... | [11.07171630859375, 0.8958476185798645] |
bada5dc0-6ce8-4c3f-aaea-8c137db3497d | disentangled-phonetic-representation-for | 2305.14783 | null | https://arxiv.org/abs/2305.14783v1 | https://arxiv.org/pdf/2305.14783v1.pdf | Disentangled Phonetic Representation for Chinese Spelling Correction | Chinese Spelling Correction (CSC) aims to detect and correct erroneous characters in Chinese texts. Although efforts have been made to introduce phonetic information (Hanyu Pinyin) in this task, they typically merge phonetic representations with character representations, which tends to weaken the representation effect... | ['Qifan Wang', 'Xiaojun Quan', 'Zihong Liang'] | 2023-05-24 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 5.48909664e-01 -1.30824402e-01 -1.26140207e-01 -5.07848859e-01
-6.61879659e-01 -3.69583249e-01 5.45203686e-01 1.43757671e-01
-5.96953571e-01 5.03354073e-01 4.55835730e-01 -5.27436018e-01
5.09900570e-01 -6.04122519e-01 -5.61026037e-01 -6.33547664e-01
6.75878584e-01 7.50468969e-02 1.56884998e-01 7.57212043... | [10.872296333312988, 10.719463348388672] |
ffae0a86-1058-4da7-be4c-c0b7ebbaee2d | pragmatics-in-grounded-language-learning | 2211.08371 | null | https://arxiv.org/abs/2211.08371v2 | https://arxiv.org/pdf/2211.08371v2.pdf | Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches | People rely heavily on context to enrich meaning beyond what is literally said, enabling concise but effective communication. To interact successfully and naturally with people, user-facing artificial intelligence systems will require similar skills in pragmatics: relying on various types of context -- from shared ling... | ['Aida Nematzadeh', 'Roma Patel', 'Jennifer Hu', 'Nicholas Tomlin', 'Daniel Fried'] | 2022-11-15 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [ 8.86217132e-02 4.25298721e-01 -1.50809288e-01 -5.79949260e-01
-2.82731742e-01 -6.98180318e-01 7.90457726e-01 1.64742202e-01
-3.53270739e-01 7.01960385e-01 1.30728436e+00 -2.65249908e-01
-2.74386823e-01 -4.24846768e-01 1.89481229e-01 -4.52438369e-02
6.85350969e-02 2.28327468e-01 -2.85076797e-01 -8.09688926... | [9.315995216369629, 6.8219757080078125] |
c25f00bf-cebd-4131-ace8-cf904df4706e | eformer-edge-enhancement-based-transformer | 2109.08044 | null | https://arxiv.org/abs/2109.08044v2 | https://arxiv.org/pdf/2109.08044v2.pdf | Eformer: Edge Enhancement based Transformer for Medical Image Denoising | In this work, we present Eformer - Edge enhancement based transformer, a novel architecture that builds an encoder-decoder network using transformer blocks for medical image denoising. Non-overlapping window-based self-attention is used in the transformer block that reduces computational requirements. This work further... | ['Santosh Yadav', 'Abhishek Iyer', 'Tanish Mittal', 'Harsh Sulakhe', 'Achleshwar Luthra'] | 2021-09-16 | null | null | null | null | ['medical-image-denoising'] | ['computer-vision'] | [ 2.73189783e-01 1.64524227e-01 1.48524314e-01 -4.58659738e-01
-1.27851677e+00 1.55517340e-01 6.49254471e-02 5.70604652e-02
-7.36721814e-01 4.49133962e-01 4.18425500e-01 -3.91543329e-01
2.40292177e-02 -5.84309101e-01 -7.54177809e-01 -9.77040529e-01
-2.97924995e-01 -4.10666913e-01 1.97749466e-01 -2.89703190... | [13.491364479064941, -2.5111496448516846] |
3d3701b0-7a76-420c-a4b0-a1b4d698a448 | metagad-learning-to-meta-transfer-for-few | 2305.10668 | null | https://arxiv.org/abs/2305.10668v1 | https://arxiv.org/pdf/2305.10668v1.pdf | MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection | Graph anomaly detection has long been an important problem in various domains pertaining to information security such as financial fraud, social spam, network intrusion, etc. The majority of existing methods are performed in an unsupervised manner, as labeled anomalies in a large scale are often too expensive to acquir... | ['Kai Shu', 'Canyu Chen', 'Kaize Ding', 'Xiongxiao Xu'] | 2023-05-18 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 3.92720103e-01 1.06752120e-01 9.71296951e-02 -2.11109176e-01
-2.59579718e-01 -3.86450976e-01 4.40948248e-01 7.37531364e-01
9.92036536e-02 6.24955297e-01 -4.73594636e-01 -3.59095782e-01
-9.09992903e-02 -1.08911538e+00 -6.39834344e-01 -6.09476805e-01
-3.75096411e-01 3.39133620e-01 3.72823745e-01 -2.06931934... | [6.624269962310791, 5.76399040222168] |
0bf47dd9-99db-4803-bc89-dd6817cfce07 | mobile-user-interface-element-detection-via-1 | 2305.09699 | null | https://arxiv.org/abs/2305.09699v1 | https://arxiv.org/pdf/2305.09699v1.pdf | Mobile User Interface Element Detection Via Adaptively Prompt Tuning | Recent object detection approaches rely on pretrained vision-language models for image-text alignment. However, they fail to detect the Mobile User Interface (MUI) element since it contains additional OCR information, which describes its content and function but is often ignored. In this paper, we develop a new MUI ele... | ['Weiqiang Wang', 'Changhua Meng', 'Jun Lan', 'Haoxing Chen', 'Zhuoer Xu', 'Zhangxuan Gu'] | 2023-05-16 | mobile-user-interface-element-detection-via | http://openaccess.thecvf.com//content/CVPR2023/html/Gu_Mobile_User_Interface_Element_Detection_via_Adaptively_Prompt_Tuning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gu_Mobile_User_Interface_Element_Detection_via_Adaptively_Prompt_Tuning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['optical-character-recognition'] | ['computer-vision'] | [ 2.76003748e-01 -5.04946828e-01 -4.61965591e-01 -1.64061084e-01
-8.92775118e-01 -6.72291338e-01 6.35491073e-01 4.14241292e-02
-3.95851433e-01 9.90359858e-02 1.50540844e-01 -3.61788720e-01
2.96198487e-01 -2.32842058e-01 -6.91594899e-01 -2.85643756e-01
4.62621510e-01 3.42749953e-01 4.55046326e-01 -7.95025155... | [10.866461753845215, 1.9094696044921875] |
efc52455-20e1-4c79-8e79-3820b03f47ab | a-joint-model-for-graph-based-chinese | null | null | https://aclanthology.org/2020.ccl-1.76 | https://aclanthology.org/2020.ccl-1.76.pdf | A Joint Model for Graph-based Chinese Dependency Parsing | In Chinese dependency parsing, the joint model of word segmentation, POS tagging and dependency parsing has become the mainstream framework because it can eliminate error propagation and share knowledge, where the transition-based model with feature templates maintains the best performance. Recently, the graph-based jo... | ['Yufeng Chen', 'Jinan Xu', 'Yujie Zhang', 'Mingtong Liu', 'Xingchen Li'] | null | null | null | null | ccl-2020-10 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [-3.90578300e-01 1.72738299e-01 -1.95990279e-01 -2.11932406e-01
-7.89136946e-01 -4.59887028e-01 5.12377024e-02 1.21495388e-01
-6.33859456e-01 7.59467542e-01 3.17712665e-01 -6.68618441e-01
3.62280339e-01 -7.85453141e-01 -4.10169750e-01 -6.69041395e-01
1.99110359e-01 2.54378617e-01 8.60247076e-01 -5.84496818... | [10.009744644165039, 10.058658599853516] |
a4c1022d-4cb1-413c-8799-434473d1482e | multi-head-cascaded-swin-transformers-with | 2207.08412 | null | https://arxiv.org/abs/2207.08412v2 | https://arxiv.org/pdf/2207.08412v2.pdf | Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction | Global correlations are widely seen in human anatomical structures due to similarity across tissues and bones. These correlations are reflected in magnetic resonance imaging (MRI) scans as a result of close-range proton density and T1/T2 parameters. Furthermore, to achieve accelerated MRI, k-space data are undersampled... | ['Zhaolin Chen', 'Gary Egan', 'Mehrtash Harandi', 'Kamlesh Pawar', 'Mevan Ekanayake'] | 2022-07-18 | null | null | null | null | ['de-aliasing'] | ['computer-vision'] | [ 1.28071234e-01 -2.99988016e-02 -3.69214304e-02 -3.10796738e-01
-9.62515593e-01 8.71409103e-03 2.75518298e-01 -1.61235947e-02
-3.12840581e-01 5.12964189e-01 6.81386709e-01 7.57874101e-02
-5.81496418e-01 -6.16664767e-01 -7.11615026e-01 -9.76995707e-01
-4.74960804e-01 3.10905188e-01 4.07934666e-01 -3.87807578... | [13.614700317382812, -2.428156614303589] |
081dd35a-91c5-48f0-ac05-527d3d70ff1e | evaluating-diversity-of-multiword-expressions | null | null | https://aclanthology.org/2022.coling-1.290 | https://aclanthology.org/2022.coling-1.290.pdf | Evaluating Diversity of Multiword Expressions in Annotated Text | Diversity can be decomposed into three distinct concepts, namely: variety, balance and disparity. This paper borrows from the extensive formalization and measures of diversity developed in ecology in order to evaluate the variety and balance of multiword expression annotation produced by automatic annotation systems. T... | ['Jean-Yves Antoine', 'Agata Savary', 'Yagmur Ozturk', 'Adam Lion-Bouton'] | null | null | null | null | coling-2022-10 | ['lemmatization'] | ['natural-language-processing'] | [ 1.10446543e-01 -4.81197946e-02 -3.27798054e-02 -3.42270494e-01
-6.30275130e-01 -1.02994978e+00 7.65927792e-01 4.02228236e-01
-9.71688330e-01 1.01409912e+00 6.10635936e-01 -1.74932793e-01
-1.71724379e-01 -5.67898095e-01 -2.43638664e-01 -6.05723262e-01
-3.02463789e-02 2.80362457e-01 -7.36771477e-03 -5.13203800... | [10.441834449768066, 10.087299346923828] |
2bad04b6-afbd-4afb-b1f7-a6daa4de239a | a-lightweight-and-detector-free-3d-single | 2203.04232 | null | https://arxiv.org/abs/2203.04232v2 | https://arxiv.org/pdf/2203.04232v2.pdf | A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds | Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking. However, it is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and in... | ['Uwe Stilla', 'Antoni B. Chan', 'Wei Li', 'Qiangqiang Wu', 'Yan Xia'] | 2022-03-08 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-3.33104968e-01 -5.73587894e-01 -3.00337642e-01 -1.17601082e-01
-5.88277221e-01 -7.06871331e-01 5.03241241e-01 -1.16522811e-01
-5.50728559e-01 2.23634288e-01 -5.29276252e-01 -4.02455032e-01
2.98586726e-01 -6.09758854e-01 -6.35341525e-01 -5.58368146e-01
7.62713179e-02 6.04133189e-01 1.08478856e+00 6.49685264... | [6.663346767425537, -2.2389767169952393] |
0593b9ce-2fe0-4b52-bff3-fece0e91c39f | p-tree-programming | 1707.03744 | null | http://arxiv.org/abs/1707.03744v1 | http://arxiv.org/pdf/1707.03744v1.pdf | P-Tree Programming | We propose a novel method for automatic program synthesis. P-Tree Programming
represents the program search space through a single probabilistic prototype
tree. From this prototype tree we form program instances which we evaluate on a
given problem. The error values from the evaluations are propagated through the
proto... | ['Christian Oesch'] | 2017-07-12 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 3.85815233e-01 1.48872152e-01 -8.15500379e-01 -4.05478984e-01
-7.23413110e-01 -4.83544111e-01 2.45095491e-01 4.01995808e-01
-1.02536418e-01 8.52302849e-01 -4.79047179e-01 -5.23599803e-01
-3.00725013e-01 -1.03940582e+00 -8.44262183e-01 -5.20411730e-01
-1.38128236e-01 7.88773119e-01 5.88090301e-01 -7.31931254... | [8.205163955688477, 7.181873798370361] |
a319a2db-a678-497a-bbee-bf2acab3f1f4 | unsupervised-learning-of-3d-scene-flow-from | 2206.03673 | null | https://arxiv.org/abs/2206.03673v1 | https://arxiv.org/pdf/2206.03673v1.pdf | Unsupervised Learning of 3D Scene Flow from Monocular Camera | Scene flow represents the motion of points in the 3D space, which is the counterpart of the optical flow that represents the motion of pixels in the 2D image. However, it is difficult to obtain the ground truth of scene flow in the real scenes, and recent studies are based on synthetic data for training. Therefore, how... | ['Hesheng Wang', 'Ruiqi Ding', 'Xiaoyu Tian', 'Guangming Wang'] | 2022-06-08 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.10221151e-02 -4.67450410e-01 -1.66970402e-01 -3.18998396e-01
-1.88771114e-01 -4.61488366e-01 2.81054497e-01 -4.24075484e-01
-6.29885435e-01 6.71166718e-01 8.39602947e-02 -3.77422012e-02
7.11368024e-02 -8.99113655e-01 -6.05575621e-01 -8.30970228e-01
2.15140253e-01 1.52228624e-01 4.72149938e-01 2.04995587... | [8.592412948608398, -2.031944513320923] |
3291b837-d0b1-4ab0-8693-b712b0095fb7 | slsg-industrial-image-anomaly-detection-by | 2305.00398 | null | https://arxiv.org/abs/2305.00398v1 | https://arxiv.org/pdf/2305.00398v1.pdf | SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification | Industrial image anomaly detection under the setting of one-class classification has significant practical value. However, most existing models struggle to extract separable feature representations when performing feature embedding and struggle to build compact descriptions of normal features when performing one-class ... | ['Zhaoyang Wu', 'Zhiwei Yang', 'Jing Liu', 'Minghui Yang'] | 2023-04-30 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 8.68952498e-02 2.31159270e-01 1.33765206e-01 -4.31198150e-01
1.08312324e-01 4.92200814e-02 5.04612803e-01 2.95215189e-01
2.54803807e-01 8.32131132e-02 -2.16471665e-02 -3.39572847e-01
-2.15710372e-01 -1.09197819e+00 -5.93685508e-01 -6.65315270e-01
-5.44029772e-01 7.25686252e-02 2.34138951e-01 -2.41335273... | [6.635772228240967, 5.772824287414551] |
12928a98-32a8-4603-b591-edcdd98bae70 | nndetection-a-self-configuring-method-for | 2106.00817 | null | https://arxiv.org/abs/2106.00817v2 | https://arxiv.org/pdf/2106.00817v2.pdf | nnDetection: A Self-configuring Method for Medical Object Detection | Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e.g. pixels. For this task, the cumbersome and iterative process of method configuration const... | ['Klaus H. Maier-Hein', 'Fabian Isensee', 'Paul F. Jaeger', 'Michael Baumgartner'] | 2021-06-01 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [ 2.75315970e-01 6.14196844e-02 -1.68360144e-01 -4.47699100e-01
-8.98752928e-01 -5.56549013e-01 3.95677924e-01 5.28281629e-01
-7.71537483e-01 2.91596472e-01 -3.28704983e-01 -5.54375112e-01
6.12095371e-02 -4.64230537e-01 -2.54271656e-01 -6.31257653e-01
-4.48537432e-02 7.86056221e-01 5.00307918e-01 1.03159592... | [14.972787857055664, -2.42423677444458] |
8738a3a3-72b2-45bf-9cd2-0e8e61eb46c9 | lifted-symmetry-detection-and-breaking-for | null | null | http://papers.nips.cc/paper/5691-lifted-symmetry-detection-and-breaking-for-map-inference | http://papers.nips.cc/paper/5691-lifted-symmetry-detection-and-breaking-for-map-inference.pdf | Lifted Symmetry Detection and Breaking for MAP Inference | Symmetry breaking is a technique for speeding up propositional satisfiability testing by adding constraints to the theory that restrict the search space while preserving satisfiability. In this work, we extend symmetry breaking to the problem of model finding in weighted and unweighted relational theories, a class of p... | ['Parag Singla', 'Henry Kautz', 'Timothy Kopp'] | 2015-12-01 | null | null | null | neurips-2015-12 | ['symmetry-detection'] | ['computer-vision'] | [ 6.89666033e-01 7.41934299e-01 -7.92965829e-01 -4.63966638e-01
-6.28506839e-01 -6.27989948e-01 3.81647527e-01 9.73285958e-02
2.04234004e-01 6.41842186e-01 1.71416372e-01 -7.69015968e-01
-9.22060907e-01 -1.26924443e+00 -1.17629015e+00 -2.54649967e-01
-5.82353652e-01 1.02351880e+00 7.39054024e-01 -3.36619139... | [8.633851051330566, 6.737736701965332] |
3904a971-4dec-406b-b132-b32ae3061e15 | generic-event-boundary-detection-in-video | 2301.04288 | null | https://arxiv.org/abs/2301.04288v1 | https://arxiv.org/pdf/2301.04288v1.pdf | Generic Event Boundary Detection in Video with Pyramid Features | Generic event boundary detection (GEBD) aims to split video into chunks at a broad and diverse set of actions as humans naturally perceive event boundaries. In this study, we present an approach that considers the correlation between neighbor frames with pyramid feature maps in both spatial and temporal dimensions to c... | ['Soo-Hyung Kim', 'Guee-Sang Lee', 'Hyung-Jeong Yang', 'Van Thong Huynh'] | 2023-01-11 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.14209376e-01 -4.84517306e-01 -9.63794813e-02 -2.35173523e-01
-4.51656729e-01 -1.71446323e-01 4.70598400e-01 -2.94984411e-02
-5.11222959e-01 2.82396227e-01 8.50183070e-01 5.10133207e-01
2.42392477e-02 -7.10768700e-01 -6.88084841e-01 -3.58561158e-01
-5.23781657e-01 -1.81445837e-01 1.03456843e+00 -1.82409868... | [8.470132827758789, 0.40288248658180237] |
f0b74be6-e522-4a70-a0bd-fc4219d45a77 | microscopy-image-restoration-with-deep-wiener | 1911.10989 | null | https://arxiv.org/abs/1911.10989v3 | https://arxiv.org/pdf/1911.10989v3.pdf | Microscopy Image Restoration with Deep Wiener-Kolmogorov filters | Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise. In this work, we propose a unifying framework of algorithms for Gaussian image deblurring and denoising. These... | ['Stamatios Lefkimmiatis', 'Valeriya Pronina', 'Dmitry V. Dylov', 'Filippos Kokkinos'] | 2019-11-25 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3405_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650188.pdf | eccv-2020-8 | ['image-deconvolution'] | ['computer-vision'] | [ 3.92419606e-01 -3.52128953e-01 3.95707637e-01 5.25766723e-02
-5.60814977e-01 -2.29111746e-01 6.31363213e-01 1.71813756e-01
-7.85513699e-01 8.19025338e-01 -1.93875268e-01 -2.41765812e-01
-2.17659891e-01 -3.18229526e-01 -6.28926516e-01 -1.41584945e+00
7.83981159e-02 2.63313204e-01 1.65539667e-01 2.51439214... | [12.102730751037598, -2.5872907638549805] |
d3bf6cb8-8eb4-45e3-afbd-3c4134f4ffa6 | contrastive-video-question-answering-via | 2302.13668 | null | https://arxiv.org/abs/2302.13668v2 | https://arxiv.org/pdf/2302.13668v2.pdf | Contrastive Video Question Answering via Video Graph Transformer | We propose to perform video question answering (VideoQA) in a Contrastive manner via a Video Graph Transformer model (CoVGT). CoVGT's uniqueness and superiority are three-fold: 1) It proposes a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations and dynamics,... | ['Tat-Seng Chua', 'Shuicheng Yan', 'Richang Hong', 'Yicong Li', 'Angela Yao', 'Pan Zhou', 'Junbin Xiao'] | 2023-02-27 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [-2.03687042e-01 -7.48242736e-02 -3.29621173e-02 -1.26219228e-01
-9.26872790e-01 -6.70372784e-01 5.15082717e-01 -3.47922355e-01
-1.09235317e-01 3.68002892e-01 6.65741026e-01 -2.95334041e-01
-2.08580837e-01 -6.38013005e-01 -1.01580703e+00 -4.09580320e-01
-3.07524484e-02 6.26564622e-01 3.07856172e-01 -3.74090463... | [10.328262329101562, 1.0474095344543457] |
edf1ffbb-e48b-44de-a03c-32bc01537de9 | identifying-relevant-positions-in-proteins-by | 1503.03815 | null | http://arxiv.org/abs/1503.03815v2 | http://arxiv.org/pdf/1503.03815v2.pdf | Identifying relevant positions in proteins by Critical Variable Selection | Evolution in its course found a variety of solutions to the same optimisation
problem. The advent of high-throughput genomic sequencing has made available
extensive data from which, in principle, one can infer the underlying structure
on which biological functions rely. In this paper, we present a new method
aimed at e... | [] | 2016-01-19 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 8.08556616e-01 -2.94378281e-01 -4.16218936e-02 -2.55797982e-01
-6.22550309e-01 -9.85054314e-01 3.83995801e-01 5.69944322e-01
-4.85840499e-01 1.28483725e+00 1.71380296e-01 -4.52052295e-01
-5.57014287e-01 -4.61233467e-01 -6.80995941e-01 -1.28576076e+00
-3.44628513e-01 7.45118380e-01 5.02718151e-01 -5.07147133... | [4.871285438537598, 5.213534355163574] |
9ee703d8-7347-4e9f-9a0a-f96e173085ac | modeling-long-and-short-term-temporal | 1703.07015 | null | http://arxiv.org/abs/1703.07015v3 | http://arxiv.org/pdf/1703.07015v3.pdf | Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks | Multivariate time series forecasting is an important machine learning problem
across many domains, including predictions of solar plant energy output,
electricity consumption, and traffic jam situation. Temporal data arise in
these real-world applications often involves a mixture of long-term and
short-term patterns, f... | ['Yiming Yang', 'Wei-Cheng Chang', 'Hanxiao Liu', 'Guokun Lai'] | 2017-03-21 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [ 5.69204465e-02 -5.70930064e-01 1.59654282e-02 -3.03460270e-01
-3.78827512e-01 -4.08037871e-01 8.65194559e-01 -1.44156426e-01
1.54282168e-01 6.56914651e-01 2.03998685e-01 -6.90945864e-01
-3.49442810e-01 -8.53483677e-01 -6.96196198e-01 -8.43924284e-01
-4.44851220e-01 8.18084031e-02 -4.84271646e-02 -1.29606098... | [6.862717628479004, 2.9417078495025635] |
f020f776-359d-4f3b-9ffc-bae955c88fa0 | sooner-than-expected-hitting-the-wall-of | 1609.07722 | null | http://arxiv.org/abs/1609.07722v1 | http://arxiv.org/pdf/1609.07722v1.pdf | Sooner than Expected: Hitting the Wall of Complexity in Evolution | In evolutionary robotics an encoding of the control software, which maps
sensor data (input) to motor control values (output), is shaped by stochastic
optimization methods to complete a predefined task. This approach is assumed to
be beneficial compared to standard methods of controller design in those cases
where no a... | ['Thomas Schmickl', 'Payam Zahadat', 'Heiko Hamann'] | 2016-09-25 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 5.06375372e-01 3.25082868e-01 5.68697035e-01 -1.15270108e-01
2.08055004e-01 -5.01263678e-01 7.10595369e-01 -2.38521978e-01
-4.29068297e-01 9.23916399e-01 -3.77680600e-01 -2.65402466e-01
-6.01695001e-01 -9.48240101e-01 -7.68940330e-01 -8.15171421e-01
-1.48524180e-01 4.70443070e-01 3.22771341e-01 -1.00165677... | [5.683590412139893, 3.8805885314941406] |
020f7e8a-40c3-4be3-93eb-30e11f6d3b7d | cluster-based-deep-ensemble-learning-for | 2302.08343 | null | https://arxiv.org/abs/2302.08343v1 | https://arxiv.org/pdf/2302.08343v1.pdf | Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes | Memes have gained popularity as a means to share visual ideas through the Internet and social media by mixing text, images and videos, often for humorous purposes. Research enabling automated analysis of memes has gained attention in recent years, including among others the task of classifying the emotion expressed in ... | ['Arkaitz Zubiaga', 'Jing Ma', 'XIAOYU GUO'] | 2023-02-16 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [-3.70480955e-01 -3.31810772e-01 1.35169283e-01 -1.74688771e-01
-2.60148615e-01 -4.25216347e-01 9.17546332e-01 2.86175936e-01
-3.09853524e-01 3.09784502e-01 5.58144629e-01 2.98234284e-01
2.44694799e-01 -4.99060422e-01 -2.27961391e-01 -5.77884078e-01
6.20367229e-02 -5.30598462e-02 -3.27815592e-01 -2.97953695... | [8.490242004394531, 10.688573837280273] |
4267c762-fc87-48d8-b913-3d6995232732 | person-search-by-text-attribute-query-as-zero | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Dong_Person_Search_by_Text_Attribute_Query_As_Zero-Shot_Learning_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Dong_Person_Search_by_Text_Attribute_Query_As_Zero-Shot_Learning_ICCV_2019_paper.pdf | Person Search by Text Attribute Query As Zero-Shot Learning | Existing person search methods predominantly assume the availability of at least one-shot imagery sample of the queried person. This assumption is limited in circumstances where only a brief textual (or verbal) description of the target person is available. In this work, we present a deep learning method for attribute ... | [' Xiatian Zhu', ' Shaogang Gong', 'Qi Dong'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['person-search'] | ['computer-vision'] | [ 3.09140861e-01 -3.12301099e-01 -3.27499539e-01 -7.92444944e-01
-1.03601575e+00 -3.99012536e-01 1.25628233e+00 3.57037961e-01
-6.27912343e-01 5.95137060e-01 5.60594559e-01 2.92435765e-01
-2.84923106e-01 -7.51919389e-01 -4.07194972e-01 -4.82467145e-01
1.35149479e-01 9.69913721e-01 -7.08591193e-02 -1.90082733... | [14.624531745910645, 0.9259564876556396] |
fcd00e02-9da1-42f2-8516-d58c1c19ff76 | reranking-overgenerated-responses-for-end-to | 2211.03648 | null | https://arxiv.org/abs/2211.03648v2 | https://arxiv.org/pdf/2211.03648v2.pdf | Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems | End-to-end (E2E) task-oriented dialogue (ToD) systems are prone to fall into the so-called "likelihood trap", resulting in generated responses which are dull, repetitive, and often inconsistent with dialogue history. Comparing ranked lists of multiple generated responses against the "gold response" (from evaluation dat... | ['Anna Korhonen', 'Fangyu Liu', 'Ivan Vulić', 'Songbo Hu'] | 2022-11-07 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 2.74675190e-01 3.13778102e-01 1.08936712e-01 -4.56692278e-01
-1.30211866e+00 -7.81875730e-01 6.23859107e-01 2.27728724e-01
-6.45061851e-01 1.03718531e+00 4.59985077e-01 -2.93742977e-02
-4.25219417e-01 -5.92084825e-01 -6.91678971e-02 -5.64106762e-01
2.51100451e-01 1.01865423e+00 5.14121056e-01 -7.20232069... | [12.636003494262695, 8.096800804138184] |
102f31f8-82da-43cb-9f41-8203fad3737a | leti-learning-to-generate-from-textual | 2305.10314 | null | https://arxiv.org/abs/2305.10314v1 | https://arxiv.org/pdf/2305.10314v1.pdf | LeTI: Learning to Generate from Textual Interactions | Finetuning pre-trained language models (LMs) enhances the models' capabilities. Prior techniques fine-tune a pre-trained LM on input-output pairs (e.g., instruction fine-tuning), or with numerical rewards that gauge the quality of its outputs (e.g., reinforcement learning from human feedback). We explore LMs' potential... | ['Heng Ji', 'Reyhaneh Jabbarvand', 'Hao Peng', 'Xingyao Wang'] | 2023-05-17 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 3.43733996e-01 3.74792576e-01 -4.20837402e-01 -3.20704430e-01
-1.30133665e+00 -9.49500263e-01 5.74753284e-01 1.88785091e-01
-2.61162013e-01 7.22228289e-01 1.27683237e-01 -9.50971067e-01
3.96609247e-01 -9.17421162e-01 -1.39216721e+00 -5.20507395e-02
1.61275752e-02 3.06349993e-01 2.50731975e-01 -1.82307318... | [7.9670562744140625, 7.6883158683776855] |
07997bda-77be-4f86-8ea0-74862bc8e237 | forget-free-continual-learning-with-soft | 2303.14962 | null | https://arxiv.org/abs/2303.14962v1 | https://arxiv.org/pdf/2303.14962v1.pdf | Forget-free Continual Learning with Soft-Winning SubNetworks | Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that competitive smooth (non-binary) subnetworks exist within a dense network in continual learning tasks, we investigate two proposed architecture-based continual learning methods which sequentially learn and select adaptive binary- (WSN) and non-b... | ['Chang D. Yoo', 'Sung Ju Hwang', 'Sultan Rizky Madjid', 'Jaehong Yoon', 'Haeyong Kang'] | 2023-03-27 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning'] | ['computer-vision', 'methodology'] | [ 1.10096073e+00 5.07942379e-01 6.63797744e-03 -2.35922411e-01
1.77540123e-01 -1.24687046e-01 6.15076900e-01 -1.49379596e-01
-7.26699054e-01 1.20129931e+00 -3.17425281e-01 1.16182044e-01
-7.18668401e-01 -8.48292172e-01 -1.04121625e+00 -9.83009934e-01
-7.25748360e-01 5.44161379e-01 1.01080382e+00 -6.56597763... | [9.80077075958252, 3.3889567852020264] |
b34884bf-d4ef-4031-8396-ab3197196be7 | multimodal-behavioral-markers-exploring | null | null | https://dl.acm.org/doi/abs/10.1145/3340555.3353718 | https://dl.acm.org/doi/pdf/10.1145/3340555.3353718 | Multimodal Behavioral Markers Exploring Suicidal Intent in Social Media Videos | Suicide is one of the leading causes of death in the modern world. In this digital age, individuals are increasingly using social media to express themselves and often use these platforms to express suicidal intent. Various studies have inspected suicidal intent behavioral markers in controlled environments but it is s... | ['Jeffrey M. Girard', 'Louis Philippe Morency', 'Vaibhav Vaibhav', 'Mahmoud Al Ismail', 'Ankit Parag Shah', 'Vasu Sharma'] | 2019-10-01 | null | null | null | international-conference-on-multimodal | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.92243859e-01 -7.02669546e-02 -1.01674698e-01 -2.31508657e-01
-8.89104068e-01 -2.28752345e-01 6.34381831e-01 4.62476432e-01
-5.84222317e-01 4.19627547e-01 9.30331528e-01 4.76305693e-01
1.04626231e-02 -2.67108738e-01 2.71209359e-01 -3.77729565e-01
-3.41132164e-01 -8.46078843e-02 -1.97784662e-01 -2.68393874... | [13.379636764526367, 2.2185497283935547] |
0138c26c-601f-4f10-a69b-eaa680a7100a | sos-stereo-matching-in-o-1-with-slanted | null | null | https://ieeexplore.ieee.org/document/8593800 | https://ieeexplore.ieee.org/document/8593800 | SOS: Stereo Matching in O(1) with Slanted Support Windows | Depth cameras have accelerated research in many areas of computer vision. Most triangulation-based depth cameras, whether structured light systems like the Kinect or active (assisted) stereo systems, are based on the principle of stereo matching. Depth from stereo is an active research topic dating back 30 years. Despi... | [] | 2018-01-01 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 4.22970712e-01 -2.44000331e-01 -7.28633776e-02 -2.91360080e-01
-8.28212023e-01 -7.04833746e-01 5.03286660e-01 1.52737692e-01
-7.14723706e-01 3.79659474e-01 -7.35927448e-02 -1.96884483e-01
2.74921477e-01 -8.40181470e-01 -6.59260869e-01 -6.93270624e-01
2.17144877e-01 4.68323976e-01 6.23569906e-01 -3.01787946... | [8.879694938659668, -2.566859245300293] |
52574bbb-0abd-45df-8d04-00e5ed5c6c3a | precision-aware-latency-and-energy-balancing | 2306.05060 | null | https://arxiv.org/abs/2306.05060v1 | https://arxiv.org/pdf/2306.05060v1.pdf | Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference | The need to execute Deep Neural Networks (DNNs) at low latency and low power at the edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) encapsulating a diverse set of hardware accelerators. How to optimally map a DNN onto such multi-accelerator systems is an open problem. We propose ODiMO, a h... | ['Daniele Jahier Pagliari', 'Marian Verhelst', 'Massimo Poncino', 'Enrico Macii', 'Luca Benini', 'Giuseppe Maria Sarda', 'Alessio Burrello', 'Matteo Risso'] | 2023-06-08 | null | null | null | null | ['quantization'] | ['methodology'] | [-1.80914775e-01 -1.05950803e-01 -2.02380717e-01 -5.23282409e-01
-4.06930834e-01 -5.67148566e-01 3.26396644e-01 2.12169439e-01
-6.83581650e-01 5.53775847e-01 -3.95545773e-02 -4.12539870e-01
-1.91938534e-01 -9.01826918e-01 -8.36442471e-01 -5.69104552e-01
1.10873789e-01 5.07863939e-01 3.33435148e-01 -2.20299736... | [8.386846542358398, 2.876842975616455] |
8cc33119-3df2-4e5a-9719-d2f4c12d51f2 | efficient-personalized-federated-learning-via | 2305.02776 | null | https://arxiv.org/abs/2305.02776v2 | https://arxiv.org/pdf/2305.02776v2.pdf | Efficient Personalized Federated Learning via Sparse Model-Adaptation | Federated Learning (FL) aims to train machine learning models for multiple clients without sharing their own private data. Due to the heterogeneity of clients' local data distribution, recent studies explore the personalized FL that learns and deploys distinct local models with the help of auxiliary global models. Howe... | ['Yaliang Li', 'Bolin Ding', 'Dawei Gao', 'Liuyi Yao', 'Daoyuan Chen'] | 2023-05-04 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-2.51218468e-01 -1.74349263e-01 -7.68012702e-01 -5.60519040e-01
-1.04239511e+00 -2.27600813e-01 1.93475962e-01 -4.49591994e-01
3.28613780e-02 6.44292355e-01 1.91350892e-01 2.11067662e-01
-3.42774570e-01 -9.18668389e-01 -5.78027546e-01 -9.49319184e-01
9.78382900e-02 9.68751729e-01 5.62946610e-02 2.24989235... | [5.820129871368408, 6.254515171051025] |
31fbee3d-1c72-48d1-bbf4-631b76bc08cf | cp-cnn-core-periphery-principle-guided | 2304.10515 | null | https://arxiv.org/abs/2304.10515v1 | https://arxiv.org/pdf/2304.10515v1.pdf | CP-CNN: Core-Periphery Principle Guided Convolutional Neural Network | The evolution of convolutional neural networks (CNNs) can be largely attributed to the design of its architecture, i.e., the network wiring pattern. Neural architecture search (NAS) advances this by automating the search for the optimal network architecture, but the resulting network instance may not generalize well in... | ['Tianming Liu', 'Dajiang Zhu', 'Zihao Wu', 'Haixing Dai', 'Lin Zhao'] | 2023-03-27 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 2.08232149e-01 1.84257969e-01 1.17512830e-01 -3.80047202e-01
6.94131494e-01 -5.15452504e-01 4.45476413e-01 -4.27299470e-01
-2.76290119e-01 3.70190978e-01 6.55191690e-02 -3.11111987e-01
-3.67077619e-01 -6.84287608e-01 -5.91599464e-01 -6.65556908e-01
2.60858238e-01 5.36241662e-03 8.20330828e-02 -4.78399754... | [8.458636283874512, 3.1661124229431152] |
41b54be7-068b-4ec8-8149-da79b175816f | contour-integration-using-graph-cut-and-non | 2010.14561 | null | https://arxiv.org/abs/2010.14561v2 | https://arxiv.org/pdf/2010.14561v2.pdf | Contour Integration using Graph-Cut and Non-Classical Receptive Field | Many edge and contour detection algorithms give a soft-value as an output and the final binary map is commonly obtained by applying an optimal threshold. In this paper, we propose a novel method to detect image contours from the extracted edge segments of other algorithms. Our method is based on an undirected graphical... | ['Zahra Mousavi Kouzehkanan', 'Babak Nadjar Araabi', 'Reshad Hosseini'] | 2020-10-27 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 2.60258645e-01 -2.37063747e-02 -2.88981825e-01 -2.14316264e-01
2.21580446e-01 -3.45465988e-01 3.08362424e-01 2.18359932e-01
-6.01017952e-01 3.47746342e-01 8.16449150e-02 -3.94490287e-02
-1.42231464e-01 -9.54846919e-01 -1.91551924e-01 -6.61141038e-01
-6.28387854e-02 -4.52225387e-01 8.60718429e-01 -1.37206286... | [10.943737030029297, -2.4188239574432373] |
2e167487-ac23-4405-8958-b292442c73a9 | prediction-guided-multi-objective | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1114-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1114-Paper.pdf | Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control | Many real-world control problems involve conflicting objectives where we desire a dense and high-quality set of control policies that are optimal for different objective preferences (called Pareto-optimal). While extensive research in multi-objective reinforcement learning (MORL) has been conducted to tackle such probl... | ['Daniela Rus', 'Pingchuan Ma', 'Yunsheng Tian', 'Wojciech Matusik', 'Jie Xu', 'Shinjiro Sueda'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1114-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1114-Paper.pdf | icml-2020-1 | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-1.39511809e-01 -3.37361664e-01 -4.78152186e-01 -5.20254113e-02
-8.00110698e-01 -1.43060774e-01 4.80787791e-02 2.42786452e-01
-5.05010188e-01 1.34457636e+00 -2.92036738e-02 3.12464833e-02
-8.19873393e-01 -5.53494513e-01 -6.18816555e-01 -8.30359399e-01
-3.71189862e-01 9.35105324e-01 1.93395272e-01 -4.56783652... | [4.280002593994141, 2.378115653991699] |
69e9437d-314b-4632-bdca-aba0cdd0fd35 | balanced-and-hierarchical-relation-learning | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_Balanced_and_Hierarchical_Relation_Learning_for_One-Shot_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_Balanced_and_Hierarchical_Relation_Learning_for_One-Shot_Object_Detection_CVPR_2022_paper.pdf | Balanced and Hierarchical Relation Learning for One-Shot Object Detection | Instance-level feature matching is significantly important to the success of modern one-shot object detectors. Recently, the methods based on the metric-learning paradigm have achieved an impressive process. Most of these works only measure the relations between query and target objects on a single level, resulting... | ['Yu Zhang', 'Yong Tang', 'Xian-Sheng Hua', 'Jianqiang Huang', 'Bing Deng', 'Hualian Sheng', 'Sijia Cai', 'Hanqing Yang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['one-shot-object-detection'] | ['computer-vision'] | [ 6.77125752e-02 -2.77567863e-01 -3.87359619e-01 -6.04726553e-01
-9.25093412e-01 -8.43214989e-02 5.74837863e-01 4.36288834e-01
-5.01984179e-01 3.06276172e-01 -1.80510730e-01 3.43561918e-01
-1.51035354e-01 -6.44970477e-01 -6.00154281e-01 -7.27166355e-01
9.21131596e-02 1.57590583e-01 9.03375268e-01 -1.78321868... | [9.368559837341309, 1.2142620086669922] |
f780a1d8-93c5-44b8-8557-d4f2f5ebb5f8 | simple-yet-effective-code-switching-language | 2305.19759 | null | https://arxiv.org/abs/2305.19759v1 | https://arxiv.org/pdf/2305.19759v1.pdf | Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning | Code-switching, also called code-mixing, is the linguistics phenomenon where in casual settings, multilingual speakers mix words from different languages in one utterance. Due to its spontaneous nature, code-switching is extremely low-resource, which makes it a challenging problem for language and speech processing tas... | ['Bismarck Odoom', 'Tianjian Li', 'Cihan Xiao', 'Shuyue Stella Li'] | 2023-05-31 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 1.90426111e-01 -4.39480692e-01 -1.92073286e-01 -3.89025003e-01
-1.17161834e+00 -6.83405578e-01 3.51088017e-01 -1.69543233e-02
-2.78836310e-01 2.11591899e-01 1.34053081e-01 -9.83546257e-01
5.34779310e-01 -6.18810095e-02 -6.31811678e-01 -4.67257679e-01
1.43360049e-01 4.09848511e-01 3.82421575e-02 -3.27383399... | [14.349710464477539, 6.959644794464111] |
13c79135-8630-4354-b600-33dc8a95465f | multi-view-matrix-completion-for-multi-label | 1904.03901 | null | http://arxiv.org/abs/1904.03901v1 | http://arxiv.org/pdf/1904.03901v1.pdf | Multi-View Matrix Completion for Multi-Label Image Classification | There is growing interest in multi-label image classification due to its
critical role in web-based image analytics-based applications, such as
large-scale image retrieval and browsing. Matrix completion has recently been
introduced as a method for transductive (semi-supervised) multi-label
classification, and has seve... | ['DaCheng Tao', 'Yong Luo', 'Tongliang Liu', 'Chao Xu'] | 2019-04-08 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.20550609e-01 -5.69193721e-01 -2.64707565e-01 -4.89605814e-01
-1.08312464e+00 -4.81153071e-01 4.08237010e-01 2.63847679e-01
-4.49499607e-01 4.58129048e-01 -2.17042223e-01 6.61908388e-02
-3.72005433e-01 -5.06832540e-01 -5.28249621e-01 -1.16348386e+00
4.85920250e-01 1.54975504e-01 -1.57044873e-01 1.17498182... | [8.681797981262207, 4.427880764007568] |
ca0e968b-005b-413a-8761-32fc273b3492 | building-on-huang-et-al-glossbert-for-word | 2112.07089 | null | https://arxiv.org/abs/2112.07089v1 | https://arxiv.org/pdf/2112.07089v1.pdf | Building on Huang et al. GlossBERT for Word Sense Disambiguation | We propose to take on the problem ofWord Sense Disambiguation (WSD). In language, words of the same form can take different meanings depending on context. While humans easily infer the meaning or gloss of such words by their context, machines stumble on this task.As such, we intend to replicated and expand upon the res... | ['Yichun Yu', 'Apoorva Sharma', 'Kanika Jindal', 'James Hale', 'Nikhil Patel'] | 2021-12-14 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 2.83220112e-01 8.57532993e-02 -1.90984443e-01 -2.72449464e-01
-4.69307393e-01 -9.16912615e-01 9.93142605e-01 5.29786706e-01
-7.58745015e-01 7.86591709e-01 5.94599366e-01 -4.90859747e-01
-2.48149052e-01 -6.31960154e-01 -9.44264680e-02 -4.03910249e-01
3.37209761e-01 4.21198249e-01 6.73315674e-02 -5.81586897... | [10.28553581237793, 9.039926528930664] |
abec81a9-c351-40ad-9f02-9441883d8112 | lgnn-a-context-aware-line-segment-detector | 2008.05892 | null | https://arxiv.org/abs/2008.05892v2 | https://arxiv.org/pdf/2008.05892v2.pdf | LGNN: A Context-aware Line Segment Detector | We present a novel real-time line segment detection scheme called Line Graph Neural Network (LGNN). Existing approaches require a computationally expensive verification or postprocessing step. Our LGNN employs a deep convolutional neural network (DCNN) for proposing line segment directly, with a graph neural network (G... | ['Qiang Hu', 'Quan Meng', 'Jingyi Yu', 'Xuming He', 'Jiakai Zhang'] | 2020-08-13 | null | null | null | null | ['line-segment-detection'] | ['computer-vision'] | [ 2.10516840e-01 2.68935770e-01 -2.27255121e-01 -2.47309402e-01
-3.64129692e-01 -6.27510130e-01 2.50446290e-01 3.09492946e-01
1.64280906e-01 -4.95477405e-04 -3.63716990e-01 -7.61027634e-01
5.05022099e-03 -1.21000838e+00 -1.10098219e+00 1.54918388e-01
-4.71797585e-01 4.18798476e-01 4.23324049e-01 -2.23014832... | [8.10633373260498, -1.901037335395813] |
1380061a-6874-4bb3-b220-50a8c298a189 | enriching-the-e2e-dataset | null | null | https://aclanthology.org/2021.inlg-1.18 | https://aclanthology.org/2021.inlg-1.18.pdf | Enriching the E2E dataset | This study introduces an enriched version of the E2E dataset, one of the most popular language resources for data-to-text NLG. We extract intermediate representations for popular pipeline tasks such as discourse ordering, text structuring, lexicalization and referring expression generation, enabling researchers to rapi... | ['Adriana Pagano', 'Brian Davis', 'Helena Vaz', 'Thiago castro Ferreira'] | null | null | null | null | inlg-acl-2021-8 | ['referring-expression-generation'] | ['computer-vision'] | [ 2.75786489e-01 8.81379545e-01 -4.13945079e-01 -4.18768972e-01
-6.80292904e-01 -7.96558142e-01 1.11242008e+00 7.33801067e-01
-4.26173121e-01 8.09723139e-01 1.32831085e+00 -2.36727074e-01
1.33398727e-01 -7.39461362e-01 -3.76743793e-01 1.89084843e-01
4.41979229e-01 6.58553541e-01 -1.55256882e-01 -5.92522800... | [11.041111946105957, 9.08449649810791] |
cb6e97f4-bbed-4f3a-be4a-96bddcf1cfc3 | face-sketch-synthesis-with-style-transfer | 2009.08679 | null | https://arxiv.org/abs/2009.08679v1 | https://arxiv.org/pdf/2009.08679v1.pdf | Face Sketch Synthesis with Style Transfer using Pyramid Column Feature | In this paper, we propose a novel framework based on deep neural networks for face sketch synthesis from a photo. Imitating the process of how artists draw sketches, our framework synthesizes face sketches in a cascaded manner. A content image is first generated that outlines the shape of the face and the key facial fe... | ['Kwan-Yee K. Wong', 'Xiao Tan', 'Chaofeng Chen'] | 2020-09-18 | null | null | null | null | ['face-sketch-synthesis'] | ['computer-vision'] | [ 1.64688259e-01 -1.50296584e-01 4.61981632e-02 -3.93747360e-01
-2.33333722e-01 -6.38691902e-01 8.12376201e-01 -6.71437919e-01
1.81385309e-01 5.32948196e-01 2.49522045e-01 8.84066597e-02
3.10049027e-01 -1.11695647e+00 -8.23970795e-01 -3.37359130e-01
5.17291069e-01 9.46908444e-02 -2.20665932e-02 -2.32141435... | [12.348952293395996, -0.12106359004974365] |
7df1fac5-20f5-4567-ad21-1d7cfa143d65 | end-to-end-trainable-self-attentive-shallow | 2008.06146 | null | https://arxiv.org/abs/2008.06146v1 | https://arxiv.org/pdf/2008.06146v1.pdf | End-to-End Trainable Self-Attentive Shallow Network for Text-Independent Speaker Verification | Generalized end-to-end (GE2E) model is widely used in speaker verification (SV) fields due to its expandability and generality regardless of specific languages. However, the long-short term memory (LSTM) based on GE2E has two limitations: First, the embedding of GE2E suffers from vanishing gradient, which leads to perf... | ['Jungbae Park', 'Sang Wan Lee', 'Hyeonmook Park'] | 2020-08-14 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-3.25995870e-02 1.57920159e-02 1.16685636e-01 -5.96008241e-01
-8.57483327e-01 -2.33555049e-01 3.08835834e-01 -1.83884740e-01
-4.97242242e-01 3.95048082e-01 3.33628654e-01 -5.00896156e-01
2.62396544e-01 -2.01343372e-01 -3.52852285e-01 -6.51579738e-01
-1.63492244e-02 -2.69994438e-01 1.58144124e-02 -2.47789323... | [14.340799331665039, 6.07287073135376] |
17c57ed4-b0ff-4071-b457-4f1dec04a4d4 | soil-moisture-estimation-from-sentinel-1 | 2210.10665 | null | https://arxiv.org/abs/2210.10665v1 | https://arxiv.org/pdf/2210.10665v1.pdf | Soil moisture estimation from Sentinel-1 interferometric observations over arid regions | We present a methodology based on interferometric synthetic aperture radar (InSAR) time series analysis that can provide surface (top 5 cm) soil moisture (SSM) estimations. The InSAR time series analysis consists of five processing steps. A co-registered Single Look Complex (SLC) SAR stack as well as meteorological inf... | ['Vassilia Karathanassi', 'Kleanthis Karamvasis'] | 2022-10-18 | null | null | null | null | ['soil-moisture-estimation'] | ['computer-vision'] | [ 4.59808975e-01 -2.39171416e-01 4.36201006e-01 -1.26622275e-01
-6.97224140e-01 -5.48679709e-01 5.75598598e-01 2.10270450e-01
-2.28304267e-01 1.32554436e+00 -1.80562407e-01 -5.27802646e-01
-4.73636180e-01 -1.18795919e+00 -3.21442276e-01 -1.05634356e+00
-7.41118550e-01 4.96776551e-01 -1.59429051e-02 -7.57503927... | [9.471580505371094, -1.662549376487732] |
e1a33e71-b058-483d-a780-a662f9effe56 | run-time-monitors-design-for-adaptive-radar | 2302.09985 | null | https://arxiv.org/abs/2302.09985v1 | https://arxiv.org/pdf/2302.09985v1.pdf | Run-Time Monitors Design for Adaptive Radar Systems: A Practical Framework | Adaptivity in multi-function radar systems is rapidly increasing, especially when moving towards fully adaptive, cognitive radar systems. However, the large number of available system configurations makes the rigorous verification and certification process during the testing phase, deployment, and after hardware and so... | ['Laura Anitori', 'Ahmad Mouri Sardarabadi', 'Giuseppe Papari', 'Mario Coutino', 'Pepijn Cox'] | 2023-02-20 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [ 2.35142484e-01 -2.17333600e-01 1.76431447e-01 -5.33202648e-01
-2.67113373e-02 -1.05098307e+00 5.02777338e-01 2.40818813e-01
-5.68293873e-03 5.69732308e-01 -5.07440865e-01 -8.31821442e-01
-6.52257621e-01 -8.56328309e-01 -4.58354264e-01 -4.69102412e-01
-4.16340977e-01 6.97580278e-01 3.14380407e-01 -2.16010734... | [5.035825729370117, 2.2367210388183594] |
d63a1f64-359f-4794-a4d7-2709f032d63d | greedy-offset-guided-keypoint-grouping-for | 2107.03098 | null | https://arxiv.org/abs/2107.03098v2 | https://arxiv.org/pdf/2107.03098v2.pdf | Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation | We propose a simple yet reliable bottom-up approach with a good trade-off between accuracy and efficiency for the problem of multi-person pose estimation. Given an image, we employ an Hourglass Network to infer all the keypoints from different persons indiscriminately as well as the guiding offsets connecting the adjac... | ['Zengfu Wang', 'Jiwei Chen', 'Linhua Xiang', 'Jia Li'] | 2021-07-07 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-7.16926754e-02 -1.05001532e-01 -8.87062103e-02 -2.95907646e-01
-9.51735556e-01 -4.03563321e-01 5.49467266e-01 4.42665517e-02
-4.76321518e-01 4.95666534e-01 3.63118023e-01 3.79256248e-01
-2.11934507e-01 -3.78385127e-01 -8.95809114e-01 -5.72310627e-01
-2.35677138e-01 6.04950607e-01 1.58661366e-01 -5.00694588... | [7.197500705718994, -0.7557703256607056] |
5c8493fe-acf4-401a-abf7-7565b4047b7c | deepbeat-a-multi-task-deep-learning-approach | 2001.00155 | null | https://arxiv.org/abs/2001.00155v2 | https://arxiv.org/pdf/2001.00155v2.pdf | DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices | Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements like step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprie... | ['Euan Ashley', 'Jessica Torres Soto'] | 2020-01-01 | null | null | null | null | ['heart-rate-variability', 'arrhythmia-detection'] | ['medical', 'medical'] | [ 3.12044442e-01 -3.08106542e-01 1.92868114e-01 -2.39490569e-01
-1.12108171e+00 -8.17668259e-01 -2.09602699e-01 9.24688056e-02
-3.75406146e-01 1.00974739e+00 2.15467319e-01 -4.25145775e-01
-1.56390712e-01 -3.58785331e-01 -5.65068483e-01 -7.31635332e-01
-4.77494091e-01 7.66996369e-02 -6.79009080e-01 2.52614468... | [14.15150260925293, 3.1740121841430664] |
62abec11-1a5f-43e2-abb0-aa3dbeb00753 | d2im-net-learning-detail-disentangled | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_D2IM-Net_Learning_Detail_Disentangled_Implicit_Fields_From_Single_Images_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_D2IM-Net_Learning_Detail_Disentangled_Implicit_Fields_From_Single_Images_CVPR_2021_paper.pdf | D2IM-Net: Learning Detail Disentangled Implicit Fields From Single Images | We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit fi... | ['Hao Zhang', 'Manyi Li'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 3.75783086e-01 4.28541839e-01 2.28152975e-01 -3.86806160e-01
-9.26449180e-01 -5.58515370e-01 6.33731782e-01 -8.27755854e-02
-6.50217980e-02 5.13088226e-01 3.81756157e-01 2.27148846e-01
-4.33133990e-02 -1.03128600e+00 -1.05045879e+00 -9.86540735e-01
1.95152715e-01 7.32581675e-01 -5.93096428e-02 9.80595946... | [8.844468116760254, -3.420180559158325] |
7835b4b3-4607-4fe1-b024-2b9d16e5d420 | leveraging-language-identification-to-enhance | 2306.04964 | null | https://arxiv.org/abs/2306.04964v1 | https://arxiv.org/pdf/2306.04964v1.pdf | Leveraging Language Identification to Enhance Code-Mixed Text Classification | The usage of more than one language in the same text is referred to as Code Mixed. It is evident that there is a growing degree of adaption of the use of code-mixed data, especially English with a regional language, on social media platforms. Existing deep-learning models do not take advantage of the implicit language ... | ['Mukta S. Takalikar', 'Raviraj Joshi', 'Aryan Patil', 'Varad Patwardhan', 'Abhishek Phaltankar', 'Gauri Takawane'] | 2023-06-08 | null | null | null | null | ['hate-speech-detection', 'sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [-2.52237350e-01 -2.56582797e-01 -1.95100367e-01 -3.93749446e-01
-7.20812857e-01 -5.46972930e-01 4.85273659e-01 4.91167486e-01
-7.38346934e-01 2.48137340e-01 2.23504156e-01 -7.02101231e-01
5.01448333e-01 -4.02469456e-01 -3.49888444e-01 -1.52865842e-01
1.62564367e-02 -1.92561485e-02 -2.80439883e-01 -6.17764652... | [9.411457061767578, 10.45862102508545] |
8370e049-b965-4584-af7f-49c3b5fee77c | faceverse-a-fine-grained-and-detail | 2203.14057 | null | https://arxiv.org/abs/2203.14057v3 | https://arxiv.org/pdf/2203.14057v3.pdf | FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset | We present FaceVerse, a fine-grained 3D Neural Face Model, which is built from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K high-fidelity 3D head scan models. A novel coarse-to-fine structure is proposed to take better advantage of our hybrid dataset. In the coarse module, we generate a base... | ['Yebin Liu', 'Liang Li', 'Chenguang Ma', 'Tao Yu', 'ZhiYuan Chen', 'Lizhen Wang'] | 2022-03-26 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_FaceVerse_A_Fine-Grained_and_Detail-Controllable_3D_Face_Morphable_Model_From_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_FaceVerse_A_Fine-Grained_and_Detail-Controllable_3D_Face_Morphable_Model_From_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-face-reconstruction', 'face-model', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.98867077e-01 3.38333726e-01 1.99130550e-01 -8.40303004e-01
-4.74871725e-01 -3.08094341e-02 5.36479414e-01 -1.06695330e+00
1.66159689e-01 2.91761875e-01 8.33853558e-02 1.97590753e-01
8.90398324e-02 -9.43173349e-01 -8.04818749e-01 -6.05323315e-01
1.90124914e-01 7.34527290e-01 -2.11917937e-01 -3.38702470... | [12.994573593139648, -0.10028346627950668] |
439990e1-9cc3-41e4-922b-578156822843 | unscene3d-unsupervised-3d-instance | 2303.14541 | null | https://arxiv.org/abs/2303.14541v1 | https://arxiv.org/pdf/2303.14541v1.pdf | UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes | 3D instance segmentation is fundamental to geometric understanding of the world around us. Existing methods for instance segmentation of 3D scenes rely on supervision from expensive, manual 3D annotations. We propose UnScene3D, the first fully unsupervised 3D learning approach for class-agnostic 3D instance segmentatio... | ['Angela Dai', 'Or Litany', 'David Rozenberszki'] | 2023-03-25 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 4.66073602e-01 5.64496696e-01 -3.81852627e-01 -5.20981550e-01
-9.97778535e-01 -8.41622531e-01 6.29606307e-01 3.05750400e-01
-8.03487077e-02 4.84692417e-02 -2.91641593e-01 -4.78996724e-01
8.65026414e-02 -7.95778215e-01 -8.78783762e-01 -7.95606673e-02
-1.55317754e-01 1.31503260e+00 6.70028925e-01 2.26634651... | [7.9958577156066895, -3.25665020942688] |
509efe49-df0f-4e5a-9c7e-e1e46acc2cfd | transferring-textual-knowledge-for-visual | 2207.01297 | null | https://arxiv.org/abs/2207.01297v4 | https://arxiv.org/pdf/2207.01297v4.pdf | Revisiting Classifier: Transferring Vision-Language Models for Video Recognition | Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is an important topic in computer vision research. Along with the growth of computational capacity, we now have open-source vision-language pre-trained models in large scales of the model architecture and amount of data. In this stud... | ['Wanli Ouyang', 'Zhun Sun', 'Wenhao Wu'] | 2022-07-04 | null | null | null | null | ['zero-shot-action-recognition', 'action-classification'] | ['computer-vision', 'computer-vision'] | [ 8.93017054e-02 -3.95498693e-01 -3.88181686e-01 -3.67867291e-01
-7.30855107e-01 -4.77525860e-01 5.67711532e-01 -5.19683242e-01
-6.31376565e-01 3.23479831e-01 1.11743324e-01 -3.77671659e-01
4.12263900e-01 -5.39778173e-01 -1.01710761e+00 -7.53111243e-01
3.31311464e-01 5.57149984e-02 5.24312675e-01 -3.29600088... | [9.751955032348633, 1.3264479637145996] |
0868da6e-927a-4d9b-bdae-dd244f420b1e | learning-canonical-representations-for-scene | 1912.07414 | null | https://arxiv.org/abs/1912.07414v5 | https://arxiv.org/pdf/1912.07414v5.pdf | Learning Canonical Representations for Scene Graph to Image Generation | Generating realistic images of complex visual scenes becomes challenging when one wishes to control the structure of the generated images. Previous approaches showed that scenes with few entities can be controlled using scene graphs, but this approach struggles as the complexity of the graph (the number of objects and ... | ['Roei Herzig', 'Amir Globerson', 'Gal Chechik', 'Amir Bar', 'Trevor Darrell', 'Huijuan Xu'] | 2019-12-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5328_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710205.pdf | eccv-2020-8 | ['layout-to-image-generation', 'scene-generation'] | ['computer-vision', 'computer-vision'] | [ 3.17434788e-01 2.77185827e-01 1.37211353e-01 -1.49737746e-01
-1.73092753e-01 -8.79516959e-01 7.62395263e-01 2.36485481e-01
-1.16556369e-01 5.84335804e-01 2.10073829e-01 -1.78487629e-01
1.07010081e-01 -1.09515798e+00 -8.41853917e-01 -1.21707879e-01
1.55595150e-02 4.74357903e-01 4.99984831e-01 -3.11794788... | [10.51976203918457, 1.4424207210540771] |
be1e1ae7-feaf-4ef2-a565-7fd9da2555ba | deep-attention-based-supernovae | 2201.08482 | null | https://arxiv.org/abs/2201.08482v3 | https://arxiv.org/pdf/2201.08482v3.pdf | Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves | In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multi-band light-curves is a challenging task due to the highly irregular cadence, long time gaps, missing-values, fe... | ['Francisco Förster', 'Pablo A. Estévez', 'Óscar Pimentel'] | 2022-01-20 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-1.26505420e-02 -5.16116858e-01 1.94972128e-01 -2.25065887e-01
-6.91835940e-01 -2.66519547e-01 8.78757238e-01 -2.70209253e-01
-5.36298990e-01 8.07406545e-01 -4.13114965e-01 -4.42000091e-01
-4.35549527e-01 -7.99066722e-01 -7.83222497e-01 -1.05966616e+00
1.52652457e-01 4.44524378e-01 2.40420908e-01 -1.12838484... | [7.5849127769470215, 3.123342752456665] |
5fcabfa0-4747-4b59-ab83-fcbd1a9b085d | reactive-human-to-robot-handovers-of | 2011.08961 | null | https://arxiv.org/abs/2011.08961v2 | https://arxiv.org/pdf/2011.08961v2.pdf | Reactive Human-to-Robot Handovers of Arbitrary Objects | Human-robot object handovers have been an actively studied area of robotics over the past decade; however, very few techniques and systems have addressed the challenge of handing over diverse objects with arbitrary appearance, size, shape, and rigidity. In this paper, we present a vision-based system that enables react... | ['Dieter Fox', 'Maya Cakmak', 'Yu-Wei Chao', 'Arsalan Mousavian', 'Chris Paxton', 'Wei Yang'] | 2020-11-17 | null | null | null | null | ['grasp-generation'] | ['computer-vision'] | [-2.02965364e-01 -1.28543392e-01 1.24013133e-01 -2.03857988e-01
-2.95678198e-01 -9.64844942e-01 9.99580026e-02 -1.60558328e-01
-8.23152885e-02 5.04172504e-01 -1.68367520e-01 -5.29664047e-02
-2.25770354e-01 -1.93325609e-01 -6.81633770e-01 -5.06042004e-01
-3.82250369e-01 8.85418892e-01 6.36525512e-01 -5.73404431... | [5.653860092163086, -0.6644056439399719] |
727fa164-b697-4045-9932-cc7e66d404ef | white-box-membership-attack-against-machine | 2206.03584 | null | https://arxiv.org/abs/2206.03584v1 | https://arxiv.org/pdf/2206.03584v1.pdf | White-box Membership Attack Against Machine Learning Based Retinopathy Classification | The advances in machine learning (ML) have greatly improved AI-based diagnosis aid systems in medical imaging. However, being based on collecting medical data specific to individuals induces several security issues, especially in terms of privacy. Even though the owner of the images like a hospital put in place strict ... | ['Gouenou Coatrieux', 'Gwenolé Quellec', 'Reda Bellafqira', 'Mounia Hamidouche'] | 2022-05-30 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 5.64162552e-01 4.76450980e-01 -1.65330797e-01 -5.81286848e-01
-2.93649554e-01 -5.15212715e-01 3.73365402e-01 1.79272011e-01
-5.12186944e-01 6.55588090e-01 -2.78338641e-01 -6.03996813e-01
-8.17868561e-02 -9.65827346e-01 -8.33393216e-01 -6.78703547e-01
-1.32445887e-01 5.71721256e-01 -3.02393019e-01 6.03484869... | [5.975453853607178, 7.121121883392334] |
d1fbb0a8-22b8-40d0-87f6-65b3f377f951 | searching-with-consistent-prioritization-for | 1812.06356 | null | http://arxiv.org/abs/1812.06356v1 | http://arxiv.org/pdf/1812.06356v1.pdf | Searching with Consistent Prioritization for Multi-Agent Path Finding | We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing
prioritized MAPF algorithms depend on rule-of-thumb heuristics and random
assignment to determine a fixed total priority ordering of all agents a priori.
We instead explore the space of all possible partial priority orderings as part
of a novel... | ['Peter J. Stuckey', 'Sven Koenig', 'Daniel Harabor', 'Jiaoyang Li', 'Hang Ma'] | 2018-12-15 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 3.46062928e-01 3.36921930e-01 -4.24303353e-01 -1.24544285e-01
-6.60466969e-01 -8.35534275e-01 5.68665385e-01 3.02391469e-01
-4.12174731e-01 1.19103265e+00 2.45136380e-01 -5.62536180e-01
-1.08855486e+00 -1.00588822e+00 -3.70647609e-01 -3.55230361e-01
-8.37691486e-01 1.21058381e+00 8.41437936e-01 -5.55583417... | [4.94943904876709, 1.8612700700759888] |
4b69c814-96bb-4453-ad93-7ac735d1ac2d | margin-based-few-shot-class-incremental | 2210.04524 | null | https://arxiv.org/abs/2210.04524v1 | https://arxiv.org/pdf/2210.04524v1.pdf | Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation | Few-shot class-incremental learning (FSCIL) is designed to incrementally recognize novel classes with only few training samples after the (pre-)training on base classes with sufficient samples, which focuses on both base-class performance and novel-class generalization. A well known modification to the base-class train... | ['Ruixuan Li', 'Yuhua Li', 'Shanghang Zhang', 'Yixiong Zou'] | 2022-10-10 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 4.03945327e-01 5.18834405e-02 -2.42120221e-01 -5.87812662e-01
-5.50111175e-01 -1.93861827e-01 4.27941620e-01 1.23670772e-01
-3.97037029e-01 6.04145408e-01 -3.04271221e-01 -3.46365720e-01
-3.76179099e-01 -7.87952781e-01 -6.63925171e-01 -7.46585190e-01
-2.77987365e-02 1.30708262e-01 7.63538003e-01 -1.50843039... | [9.831647872924805, 3.280817747116089] |
ee0d861d-03a0-45ca-b1ad-302510188a8a | robust-semi-supervised-anomaly-detection-via | 2303.03925 | null | https://arxiv.org/abs/2303.03925v1 | https://arxiv.org/pdf/2303.03925v1.pdf | Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption | Anomaly detection is the task of recognising novel samples which deviate significantly from pre-establishednormality. Abnormal classes are not present during training meaning that models must learn effective rep-resentations solely across normal class data samples. Deep Autoencoders (AE) have been widely used foranomal... | ['Toby P Breckon', 'Yona Falinie A Gaus', 'Neelanjan Bhowmik', 'Jack W Barker'] | 2023-03-02 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 3.75508726e-01 1.07932463e-01 4.27685916e-01 -1.09742023e-01
-6.39659882e-01 -8.01142156e-01 8.46092403e-01 1.53454095e-01
-3.32521379e-01 7.03136265e-01 -2.82944918e-01 -4.59696293e-01
-1.60830006e-01 -8.76023054e-01 -9.76239741e-01 -9.73302782e-01
-3.19289416e-01 1.04631722e-01 1.04404669e-02 -2.30704889... | [7.632523536682129, 2.35868501663208] |
fd0923f3-ff24-4e35-ab4b-225d8886ef9e | bisenet-bilateral-segmentation-network-for | 1808.00897 | null | http://arxiv.org/abs/1808.00897v1 | http://arxiv.org/pdf/1808.00897v1.pdf | BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation | Semantic segmentation requires both rich spatial information and sizeable
receptive field. However, modern approaches usually compromise spatial
resolution to achieve real-time inference speed, which leads to poor
performance. In this paper, we address this dilemma with a novel Bilateral
Segmentation Network (BiSeNet).... | ['Changxin Gao', 'Jingbo Wang', 'Changqian Yu', 'Nong Sang', 'Gang Yu', 'Chao Peng'] | 2018-08-02 | bisenet-bilateral-segmentation-network-for-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Changqian_Yu_BiSeNet_Bilateral_Segmentation_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Changqian_Yu_BiSeNet_Bilateral_Segmentation_ECCV_2018_paper.pdf | eccv-2018-9 | ['thermal-image-segmentation', 'dichotomous-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 8.49010199e-02 -4.93031293e-01 1.05442330e-01 -4.25974756e-01
-4.83146757e-01 -4.63340789e-01 2.44080007e-01 7.15889037e-02
-8.08056891e-01 5.81685066e-01 -8.18955153e-02 -2.40728423e-01
6.32861331e-02 -1.00616741e+00 -5.36566794e-01 -6.76913738e-01
3.43730748e-01 -1.37465611e-01 8.81380618e-01 1.14265725... | [9.350322723388672, -0.5574501752853394] |
0be5f031-d131-4493-b254-92a6113c1a2d | kfnet-learning-temporal-camera-relocalization | 2003.10629 | null | https://arxiv.org/abs/2003.10629v1 | https://arxiv.org/pdf/2003.10629v1.pdf | KFNet: Learning Temporal Camera Relocalization using Kalman Filtering | Temporal camera relocalization estimates the pose with respect to each video frame in sequence, as opposed to one-shot relocalization which focuses on a still image. Even though the time dependency has been taken into account, current temporal relocalization methods still generally underperform the state-of-the-art one... | ['Long Quan', 'Lei Zhou', 'Yao Yao', 'Mingmin Zhen', 'Zixin Luo', 'Tianwei Shen', 'Tian Fang', 'Jiahui Zhang'] | 2020-03-24 | kfnet-learning-temporal-camera-relocalization-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_KFNet_Learning_Temporal_Camera_Relocalization_Using_Kalman_Filtering_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_KFNet_Learning_Temporal_Camera_Relocalization_Using_Kalman_Filtering_CVPR_2020_paper.pdf | cvpr-2020-6 | ['camera-relocalization'] | ['computer-vision'] | [-1.63147524e-01 -3.21087509e-01 -2.16684356e-01 -3.29599202e-01
-7.65132964e-01 -4.46527481e-01 5.30952871e-01 -1.69846028e-01
-6.10276341e-01 4.71648395e-01 1.09085172e-01 1.06901474e-01
-3.46479058e-01 -4.76911098e-01 -8.70351076e-01 -5.00715196e-01
2.51924515e-01 1.06291175e-01 5.08272886e-01 1.32786348... | [8.090576171875, -2.1282448768615723] |
1ca1a9dd-0de6-4c77-8cb6-1f05b9a9963e | non-intrusive-load-monitoring-in-chaotic | 1801.05363 | null | http://arxiv.org/abs/1801.05363v1 | http://arxiv.org/pdf/1801.05363v1.pdf | Non Intrusive Load Monitoring in Chaotic Switching Networks | In this work, a non intrusive load disaggregation scheme is proposed. By
using a kernel based nonlinear regression strategy, the switching dynamic of an
electric network, simulated as a set of RLC circuits with chaotic switching, is
approximated using a time series of the total power consumption. The results
suggest th... | [] | 2018-01-12 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-1.09471850e-01 -8.40697140e-02 4.80698934e-03 3.02783726e-03
1.86496034e-01 -8.40411246e-01 5.09223282e-01 -7.87339360e-02
2.03348976e-02 9.79630768e-01 -4.97211754e-01 -4.15073395e-01
-3.35178524e-01 -6.00409985e-01 -2.27021743e-02 -1.20167339e+00
-1.89073026e-01 2.78316051e-01 -1.83174610e-02 -4.77086693... | [5.811440944671631, 2.805062770843506] |
5b6cdec3-de4a-44ac-84df-e12ba33f74f2 | improving-segmentation-of-objects-with | 2304.06229 | null | https://arxiv.org/abs/2304.06229v1 | https://arxiv.org/pdf/2304.06229v1.pdf | Improving Segmentation of Objects with Varying Sizes in Biomedical Images using Instance-wise and Center-of-Instance Segmentation Loss Function | In this paper, we propose a novel two-component loss for biomedical image segmentation tasks called the Instance-wise and Center-of-Instance (ICI) loss, a loss function that addresses the instance imbalance problem commonly encountered when using pixel-wise loss functions such as the Dice loss. The Instance-wise compon... | ['Henrik Skibbe', 'Charissa Poon', 'Muhammad Febrian Rachmadi'] | 2023-04-13 | null | null | null | null | ['lesion-segmentation'] | ['medical'] | [ 2.96188533e-01 1.87895596e-01 -1.16146013e-01 -4.85193998e-01
-1.03679645e+00 -4.75831151e-01 2.97697604e-01 5.17221570e-01
-6.61714137e-01 7.24865735e-01 -4.54621017e-01 -1.48919657e-01
-1.15426399e-01 -5.69223881e-01 -6.40929639e-01 -8.07063878e-01
-2.44869798e-01 1.42441303e-01 4.26948339e-01 1.93627626... | [14.656875610351562, -2.357570171356201] |
7d1601c8-da18-4553-8f8e-911a02c6256c | unleashing-the-power-of-neural-discourse-1 | null | null | https://aclanthology.org/2020.coling-main.337 | https://aclanthology.org/2020.coling-main.337.pdf | Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining | RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining. In this paper, we demonstrate a simple, yet highly accurate discourse parser, incorporating recent contextual language models. Our parser establishes the new state-o... | ['Giuseppe Carenini', 'Patrick Huber', 'Grigorii Guz'] | 2020-12-01 | null | null | null | coling-2020-8 | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.41964990e-01 9.31521416e-01 -5.35267353e-01 -3.93976718e-01
-1.35460329e+00 -7.41772354e-01 8.59166682e-01 5.26999831e-01
-4.24423873e-01 1.08431816e+00 1.06746447e+00 -8.82155061e-01
3.27010512e-01 -6.08497262e-01 -5.18105268e-01 -3.93587649e-01
-2.13186800e-01 5.74527681e-01 5.22848964e-01 -5.84992111... | [10.81224250793457, 9.471895217895508] |
08c83109-2fd2-4abf-92cd-f8fed1077338 | expand-rerank-and-retrieve-query-reranking | 2305.17080 | null | https://arxiv.org/abs/2305.17080v1 | https://arxiv.org/pdf/2305.17080v1.pdf | Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering | We propose EAR, a query Expansion And Reranking approach for improving passage retrieval, with the application to open-domain question answering. EAR first applies a query expansion model to generate a diverse set of queries, and then uses a query reranker to select the ones that could lead to better retrieval results.... | ['James Glass', 'Wen-tau Yih', 'Shang-Wen Li', 'Wei Fang', 'Yung-Sung Chuang'] | 2023-05-26 | null | null | null | null | ['passage-retrieval', 'open-domain-question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.07960704e-02 -2.37329140e-01 -4.21016932e-01 8.85151401e-02
-1.87287366e+00 -7.22480834e-01 4.53755528e-01 4.23078537e-01
-5.44435143e-01 7.78467953e-01 6.33980811e-01 -1.60059333e-01
-5.50321937e-01 -7.59202600e-01 -5.44999957e-01 -1.52638137e-01
-5.58358058e-02 1.19367540e+00 5.09704947e-01 -7.24175870... | [11.504715919494629, 7.653847694396973] |
75350e10-369f-4466-9062-8d831bf01c3b | combining-strategic-learning-and-tactical | 1709.03480 | null | http://arxiv.org/abs/1709.03480v1 | http://arxiv.org/pdf/1709.03480v1.pdf | Combining Strategic Learning and Tactical Search in Real-Time Strategy Games | A commonly used technique for managing AI complexity in real-time strategy
(RTS) games is to use action and/or state abstractions. High-level abstractions
can often lead to good strategic decision making, but tactical decision quality
may suffer due to lost details. A competing method is to sample the search
space whic... | ['Marius Stanescu', 'Michael Buro', 'Nicolas A. Barriga'] | 2017-09-11 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [ 3.71467680e-01 2.62510896e-01 -1.33852765e-01 2.19865441e-02
-4.81119603e-01 -6.34041548e-01 6.15985334e-01 -1.36512548e-01
-7.33875096e-01 8.11328888e-01 2.39305589e-02 -4.72540855e-01
-2.75428981e-01 -1.02110076e+00 -2.36916736e-01 -4.15467620e-01
-4.60774988e-01 1.07552457e+00 7.06662714e-01 -1.15928698... | [3.5574915409088135, 1.475885272026062] |
964cd64b-4892-4a38-8849-9422a50240b0 | knowledge-distillation-for-detection | 2211.08071 | null | https://arxiv.org/abs/2211.08071v2 | https://arxiv.org/pdf/2211.08071v2.pdf | Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling | DETR is a novel end-to-end transformer architecture object detector, which significantly outperforms classic detectors when scaling up the model size. In this paper, we focus on the compression of DETR with knowledge distillation. While knowledge distillation has been well-studied in classic detectors, there is a lack ... | ['Errui Ding', 'Junyu Han', 'Haocheng Feng', 'Gang Zhang', 'Wanping Zhang', 'Fukui Yang', 'Shengzhao Wen', 'Xin Li', 'Yu Wang'] | 2022-11-15 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-3.51407170e-01 -8.09528977e-02 -1.41849175e-01 -2.42245778e-01
-6.28054321e-01 -3.50491792e-01 5.45441151e-01 -1.62083451e-02
-6.67657256e-01 5.12221456e-01 -2.48152450e-01 -2.35015005e-01
1.21108837e-01 -8.63086522e-01 -9.39213395e-01 -3.34794313e-01
2.60156929e-01 5.58831155e-01 7.90402174e-01 -1.87379763... | [9.258536338806152, 1.2587190866470337] |
f9751d14-9083-4164-8620-0a4a947448c1 | inference-on-extreme-quantiles-of-unobserved | 2210.08524 | null | https://arxiv.org/abs/2210.08524v3 | https://arxiv.org/pdf/2210.08524v3.pdf | Inference on Extreme Quantiles of Unobserved Individual Heterogeneity | We develop a methodology for conducting inference on extreme quantiles of unobserved individual heterogeneity (heterogeneous coefficients, heterogeneous treatment effects, etc.) in a panel data or meta-analysis setting. Inference in such settings is challenging: only noisy estimates of unobserved heterogeneity are avai... | ['Vladislav Morozov'] | 2022-10-16 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 1.19185142e-01 2.48609826e-01 -8.46809447e-01 -2.72642702e-01
-8.02964568e-01 -5.65300941e-01 2.55953789e-01 5.58579206e-01
-1.72521159e-01 1.23859155e+00 2.27638558e-01 -6.04038477e-01
-6.15168154e-01 -7.35593677e-01 -5.76560378e-01 -6.13725364e-01
-2.35851243e-01 3.72922033e-01 -2.37511337e-01 2.37315908... | [7.619297027587891, 4.82524299621582] |
8562eabe-23eb-4782-ba4c-db0564b84947 | tribe-or-not-critical-inspection-of-group | 2303.09664 | null | https://arxiv.org/abs/2303.09664v1 | https://arxiv.org/pdf/2303.09664v1.pdf | Tribe or Not? Critical Inspection of Group Differences Using TribalGram | With the rise of AI and data mining techniques, group profiling and group-level analysis have been increasingly used in many domains including policy making and direct marketing. In some cases, the statistics extracted from data may provide insights to a group's shared characteristics; in others, the group-level analys... | ['Rebecca Hwa', 'Wen-Ting Chung', 'Yu-Ru Lin', 'Muheng Yan', 'Yongsu Ahn'] | 2023-03-16 | null | null | null | null | ['interpretable-machine-learning', 'marketing'] | ['methodology', 'miscellaneous'] | [ 2.11027607e-01 7.44336009e-01 -6.55868709e-01 -7.18298554e-01
1.33145601e-01 -3.00713897e-01 2.48091981e-01 1.02635276e+00
2.84620225e-02 2.36980781e-01 8.58640611e-01 -8.54576468e-01
-5.92908263e-01 -4.79799628e-01 1.20021641e-01 -2.39919037e-01
-5.31732328e-02 3.30881655e-01 -5.39196432e-01 2.05888879... | [8.90078353881836, 5.678644180297852] |
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