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8021eefa-c552-4013-8a44-cdcf9a37d6a7 | a-light-rule-based-approach-to-english | null | null | https://aclanthology.org/Y15-2040 | https://aclanthology.org/Y15-2040.pdf | A Light Rule-based Approach to English Subject-Verb Agreement Errors on the Third Person Singular Forms | null | ['Yuzhu Wang', 'Hai Zhao'] | 2015-10-01 | a-light-rule-based-approach-to-english-1 | https://aclanthology.org/Y15-2040 | https://aclanthology.org/Y15-2040.pdf | paclic-2015-10 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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9e016405-ec18-4f07-962a-1b75fe258c2e | joint-visual-and-wireless-signal-feature | 2008.08790 | null | https://arxiv.org/abs/2008.08790v1 | https://arxiv.org/pdf/2008.08790v1.pdf | Joint Visual and Wireless Signal Feature based Approach for High-Precision Indoor Localization | The existing localization systems for indoor applications basically rely on wireless signal. With the massive deployment of low-cost cameras, the visual image based localization become attractive as well. However, in the existing literature, the hybrid visual and wireless approaches simply combine the above schemes in ... | ['Shugong Xu', 'Shunqing Zhang', 'Chenlu Xiang', 'Guangbing Zhou', 'Yu Wang'] | 2020-08-20 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [-1.50381178e-01 -6.87592745e-01 -1.35193184e-01 -3.14261585e-01
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-8.55925530e-02 -4.03946251e-01 4.04943943e-01 1.38577864... | [6.339590549468994, 0.9888256788253784] |
ba42c4a9-7bc8-472a-afcf-fc630822fe72 | sliced-optimal-partial-transport | 2212.08049 | null | https://arxiv.org/abs/2212.08049v8 | https://arxiv.org/pdf/2212.08049v8.pdf | Sliced Optimal Partial Transport | Optimal transport (OT) has become exceedingly popular in machine learning, data science, and computer vision. The core assumption in the OT problem is the equal total amount of mass in source and target measures, which limits its application. Optimal Partial Transport (OPT) is a recently proposed solution to this limit... | ['Berhnard Schmitzer', 'Soheil Kolouri', 'Mathew Thorpe', 'Yikun Bai'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bai_Sliced_Optimal_Partial_Transport_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bai_Sliced_Optimal_Partial_Transport_CVPR_2023_paper.pdf | cvpr-2023-1 | ['point-cloud-registration'] | ['computer-vision'] | [ 3.37647647e-02 -2.54923254e-01 1.28332049e-01 -9.65298414e-02
-7.63123751e-01 -3.18633199e-01 2.58373857e-01 2.45913222e-01
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f702a492-6490-4b9b-b4f5-64192f010491 | is-your-perspective-also-my-perspective | null | null | https://aclanthology.org/2022.argmining-1.11 | https://aclanthology.org/2022.argmining-1.11.pdf | Is Your Perspective Also My Perspective? Enriching Prediction with Subjectivity | Although argumentation can be highly subjective, the common practice with supervised machine learning is to construct and learn from an aggregated ground truth formed from individual judgments by majority voting, averaging, or adjudication. This approach leads to a neglect of individual, but potentially important persp... | ['Julia Romberg'] | null | null | null | null | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 3.48731816e-01 9.32886183e-01 -2.85123348e-01 -5.05137086e-01
-7.69033313e-01 -8.17510188e-01 1.18348491e+00 9.25868332e-01
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7.83707500e-01 7.22977936e-01 7.14096874e-02 -4.87738341... | [9.454183578491211, 9.688672065734863] |
ffa8fa52-d736-4971-b1d0-e88956bb4e77 | automatic-tuning-of-loss-trade-offs-without | 2305.16699 | null | https://arxiv.org/abs/2305.16699v1 | https://arxiv.org/pdf/2305.16699v1.pdf | Automatic Tuning of Loss Trade-offs without Hyper-parameter Search in End-to-End Zero-Shot Speech Synthesis | Recently, zero-shot TTS and VC methods have gained attention due to their practicality of being able to generate voices even unseen during training. Among these methods, zero-shot modifications of the VITS model have shown superior performance, while having useful properties inherited from VITS. However, the performanc... | ['Tae-Hyun Oh', 'Bohyung Kim', 'Seongyeon Park'] | 2023-05-26 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.64438352e-01 2.06543550e-01 -1.51228622e-01 -6.23403937e-02
-1.20923257e+00 -4.55798477e-01 6.10842705e-01 -5.20796001e-01
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3.60849112e-01 4.69434142e-01 3.47964764e-01 -1.82461157... | [15.081082344055176, 6.203067302703857] |
cd87ac0e-8ac4-42e8-b2e9-0a1b27a35977 | vector-quantized-diffusion-model-with | 2208.09141 | null | https://arxiv.org/abs/2208.09141v2 | https://arxiv.org/pdf/2208.09141v2.pdf | Vector Quantized Diffusion Model with CodeUnet for Text-to-Sign Pose Sequences Generation | Sign Language Production (SLP) aims to translate spoken languages into sign sequences automatically. The core process of SLP is to transform sign gloss sequences into their corresponding sign pose sequences (G2P). Most existing G2P models usually perform this conditional long-range generation in an autoregressive manne... | ['Xiaohui Hu', 'Yao Du', 'Hao Tang', 'Zexian Li', 'Qipeng Zhang', 'Pan Xie'] | 2022-08-19 | null | null | null | null | ['sign-language-production'] | ['natural-language-processing'] | [ 2.21259013e-01 -8.14559758e-02 -7.96835497e-02 -1.74955949e-01
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3.53899837e-01 6.25408590e-01 1.79748669e-01 -2.86412835... | [9.201066970825195, -6.508727550506592] |
f3416e01-8872-416b-8694-f564d2e6f779 | dlow-diversifying-latent-flows-for-diverse | 2003.08386 | null | https://arxiv.org/abs/2003.08386v2 | https://arxiv.org/pdf/2003.08386v2.pdf | DLow: Diversifying Latent Flows for Diverse Human Motion Prediction | Deep generative models are often used for human motion prediction as they are able to model multi-modal data distributions and characterize diverse human behavior. While much care has been taken into designing and learning deep generative models, how to efficiently produce diverse samples from a deep generative model a... | ['Ye Yuan', 'Kris Kitani'] | 2020-03-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/794_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540324.pdf | eccv-2020-8 | ['human-pose-forecasting'] | ['computer-vision'] | [ 7.10836798e-02 -1.77475259e-01 -5.46461523e-01 -2.54327327e-01
-6.43017590e-01 -4.15927082e-01 6.08697295e-01 -7.36753464e-01
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3.11508209e-01 8.32305551e-01 1.73336595e-01 7.21859112... | [7.220428943634033, -0.02221449837088585] |
3272713f-bc63-41f9-81cc-865a365035a5 | host-based-network-intrusion-detection-via | 2306.09451 | null | https://arxiv.org/abs/2306.09451v1 | https://arxiv.org/pdf/2306.09451v1.pdf | Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier | Network Intrusion Detection Systems (NIDS) have been extensively investigated by monitoring real network traffic and analyzing suspicious activities. However, there are limitations in detecting specific types of attacks with NIDS, such as Advanced Persistent Threats (APT). Additionally, NIDS is restricted in observing ... | ['Petar Djukic', 'Mehran Bagheri', 'Burak Kantarci', 'Murat Simsek', 'Zhiyan Chen'] | 2023-06-15 | null | null | null | null | ['intrusion-detection', 'network-intrusion-detection'] | ['miscellaneous', 'miscellaneous'] | [ 4.47718315e-02 -6.42807662e-01 -4.72358346e-01 -1.81916475e-01
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-3.51960450e-01 -1.10856533e+00 6.67561870e-03 -4.70931381e-01
-3.40321422e-01 4.71626639e-01 4.83885407e-01 2.02657934... | [5.26702880859375, 7.200207233428955] |
c12476df-d398-498e-9ab9-e5c30814fff3 | pixel-level-explanation-of-multiple-instance | 2303.08632 | null | https://arxiv.org/abs/2303.08632v1 | https://arxiv.org/pdf/2303.08632v1.pdf | Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images | Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-making. Multiple instance learning with attention pooling provides instance-level explainability, however for many clinical applications a deeper, pixel-level explanation is desirable, but missing so far. In this work, we inve... | ['Carsten Marr', 'Nassir Navab', 'Daniel Rueckert', 'Rudolf Matthias Hehr', 'Peter Lienemann', 'Ashkan Khakzar', 'Oleksandra Adonkina', 'Ario Sadafi'] | 2023-03-15 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 6.45010173e-01 1.05877411e+00 -3.57822776e-01 -6.93162024e-01
-8.20994020e-01 7.01859891e-02 1.75099105e-01 7.06401706e-01
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1.62576899e-01 8.43023419e-01 -6.23307787e-02 1.15544729... | [15.156295776367188, -2.6191391944885254] |
9e75a4fb-7d36-41e1-841c-79f3cd4bd037 | training-data-generating-networks-linking-3d-1 | 2010.08276 | null | https://arxiv.org/abs/2010.08276v2 | https://arxiv.org/pdf/2010.08276v2.pdf | Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization | We propose a novel 3d shape representation for 3d shape reconstruction from a single image. Rather than predicting a shape directly, we train a network to generate a training set which will be fed into another learning algorithm to define the shape. The nested optimization problem can be modeled by bi-level optimizatio... | ['Peter Wonka', 'Biao Zhang'] | 2020-10-16 | training-data-generating-networks-shape | https://openreview.net/forum?id=dDo8druYppX | https://openreview.net/pdf?id=dDo8druYppX | iclr-2022-4 | ['3d-shape-representation'] | ['computer-vision'] | [ 2.95448065e-01 1.04845673e-01 -2.53085732e-01 -4.45494592e-01
-9.66275275e-01 -4.65238422e-01 6.36457920e-01 1.29876360e-01
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9.02837589e-02 -1.17265880e+00 -6.61375761e-01 -4.10600156e-01
2.96396255e-01 8.27908218e-01 7.61022195e-02 -2.12016076... | [8.455249786376953, -3.22452712059021] |
27ca032c-bb09-41d6-ab9f-8cb37b430f13 | minimum-delay-moving-object-detection | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Lao_Minimum_Delay_Moving_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Lao_Minimum_Delay_Moving_CVPR_2017_paper.pdf | Minimum Delay Moving Object Detection | We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon it moves in an online manner. Due to unreliability of motion between frames... | ['Dong Lao', 'Ganesh Sundaramoorthi'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['moving-object-detection'] | ['computer-vision'] | [ 2.09070116e-01 -2.35454053e-01 -3.29701751e-01 -1.07417852e-02
-4.28669959e-01 -7.06040919e-01 5.65130077e-02 1.62379459e-01
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-6.42346323e-01 2.62321502e-01 1.35107231e+00 3.72955859... | [8.883326530456543, -0.5604375004768372] |
62a9f1bf-1078-478a-884e-be9d64793990 | integrating-machine-learning-concepts-into | 2211.06491 | null | https://arxiv.org/abs/2211.06491v1 | https://arxiv.org/pdf/2211.06491v1.pdf | Integrating machine learning concepts into undergraduate classes | In this innovative practice work-in-progress paper, we compare two different methods to teach machine learning concepts to undergraduate students in Electrical Engineering. While machine learning is now being offered as a senior-level elective in several curricula, this does not mean all students are exposed to it. Exp... | ['Mahesh K. Banavar', 'Blaine Ayotte', 'Chinmay Sahu'] | 2022-11-09 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 3.53670985e-01 4.30489838e-01 -1.12370312e-01 -4.95805442e-01
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2.46357787e-02 4.05704945e-01 3.32042098e-01 -5.80513954... | [8.542141914367676, 4.584677696228027] |
d813f2bc-b709-4a05-8bb9-854b787a7b78 | invariant-grounding-for-video-question-1 | 2206.02349 | null | https://arxiv.org/abs/2206.02349v1 | https://arxiv.org/pdf/2206.02349v1.pdf | Invariant Grounding for Video Question Answering | Video Question Answering (VideoQA) is the task of answering questions about a video. At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer. In leading VideoQA models, the typical learning objective, empirical risk minimization (ERM), latches ... | ['Tat-Seng Chua', 'Wei Ji', 'Junbin Xiao', 'Xiang Wang', 'Yicong Li'] | 2022-06-06 | invariant-grounding-for-video-question | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-question-answering'] | ['computer-vision'] | [ 1.06771022e-01 3.01605463e-01 3.56039032e-02 -3.14990491e-01
-7.35869110e-01 -5.88191211e-01 5.65007567e-01 -1.00590199e-01
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2.07146585e-01 -8.01040977e-02 4.92868096e-01 -1.76779240... | [10.447587013244629, 1.2689754962921143] |
939e37be-9859-4fcf-9fc2-16824d95bfe5 | wavelet-based-reflection-symmetry-detection | 1707.02931 | null | http://arxiv.org/abs/1707.02931v4 | http://arxiv.org/pdf/1707.02931v4.pdf | Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms | Symmetry is one of the significant visual properties inside an image plane,
to identify the geometrically balanced structures through real-world objects.
Existing symmetry detection methods rely on descriptors of the local image
features and their neighborhood behavior, resulting incomplete symmetrical axis
candidates ... | ['Philippe Colantoni', 'Olivier Alata', 'Cecile Barat', 'Christophe Ducottet', 'Mohamed Elawady'] | 2017-07-10 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 8.87018219e-02 -4.24714386e-01 -3.56938422e-01 -3.16407502e-01
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-8.02653670e-01 -8.46581697e-01 -1.99301884e-01 -7.22903132e-01
-1.56247631e-01 2.34178111e-01 6.43987954e-01 -2.07272291... | [8.99336051940918, -1.990265965461731] |
aae62a96-456d-45cc-9d4c-c35425843f6a | vertex-centric-visual-programming-for-graph | null | null | https://dl.acm.org/doi/10.1145/3448016.3452770 | https://dl.acm.org/doi/pdf/10.1145/3448016.3452770 | Vertex-Centric Visual Programming for Graph Neural Networks | Graph neural networks (GNNs) have achieved remarkable performance in many graph analytics tasks such as node classification, link prediction and graph clustering. Existing GNN systems (e.g., PyG and DGL) adopt a tensor-centric programming model and train GNNs with manually written operators. Such design results in poor... | ['Fan Yu', 'Bo Tang', 'Yufei Cai', 'Peiqi Yin', 'Xiao Yan', 'James Cheng', 'Tatiana Jin', 'Yuntao Gui', 'Yidi Wu'] | 2021-06-09 | null | null | null | proceedings-of-the-2021-international | ['graph-clustering'] | ['graphs'] | [-4.23654258e-01 6.43033534e-02 -2.45683342e-01 -2.44796336e-01
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-4.06215280e-01 5.26970685e-01 3.39815259e-01 -3.61633241... | [7.052546977996826, 5.8206892013549805] |
2e6378cf-ec85-4e90-9224-c8dcac45287b | evaluating-covid-19-sequence-data-using | 2211.10546 | null | https://arxiv.org/abs/2211.10546v2 | https://arxiv.org/pdf/2211.10546v2.pdf | Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model | The SARS-CoV-2 coronavirus is the cause of the COVID-19 disease in humans. Like many coronaviruses, it can adapt to different hosts and evolve into different lineages. It is well-known that the major SARS-CoV-2 lineages are characterized by mutations that happen predominantly in the spike protein. Understanding the spi... | ['Sarwan Ali'] | 2022-11-19 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 3.66216660e-01 -3.60090554e-01 -1.97628457e-02 -1.88949153e-01
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-4.89982426e-01 8.58596325e-01 -2.12350432e-02 -3.18020284... | [5.019580841064453, 5.321118354797363] |
bf7b9c9a-c1fc-4c89-b231-df2c2fb7c518 | subsidiary-prototype-alignment-for-universal | 2210.15909 | null | https://arxiv.org/abs/2210.15909v1 | https://arxiv.org/pdf/2210.15909v1.pdf | Subsidiary Prototype Alignment for Universal Domain Adaptation | Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift. The goal is to categorize unlabeled target samples, either into one of the "known" categories or into a single "unknown" category. A major problem in UniDA is negative trans... | ['R. Venkatesh Babu', 'Varun Jampani', 'Hiran Sarkar', 'Akshay Kulkarni', 'Suvaansh Bhambri', 'Jogendra Nath Kundu'] | 2022-10-28 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 3.96335512e-01 6.16685003e-02 -1.18102930e-01 -4.09668416e-01
-8.03301215e-01 -8.09254467e-01 8.29526246e-01 1.58283785e-01
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9.83937830e-02 5.15523255e-01 2.03293189e-01 -2.89102018... | [10.114795684814453, 2.7938222885131836] |
2c9fe0b2-d78c-40c9-be80-ba50ddec3913 | edin-an-end-to-end-benchmark-and-pipeline-for | 2205.12570 | null | https://arxiv.org/abs/2205.12570v1 | https://arxiv.org/pdf/2205.12570v1.pdf | EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing | Existing work on Entity Linking mostly assumes that the reference knowledge base is complete, and therefore all mentions can be linked. In practice this is hardly ever the case, as knowledge bases are incomplete and because novel concepts arise constantly. This paper created the Unknown Entity Discovery and Indexing (E... | ['Nicola Cancedda', 'Sebastian Riedel', 'Mikhail Plekhanov', 'Fabio Petroni', 'Nora Kassner'] | 2022-05-25 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [-5.04108012e-01 5.13313055e-01 -4.00235742e-01 -1.07420571e-01
-8.73272598e-01 -1.00811803e+00 5.90253294e-01 9.49282050e-01
-5.84696174e-01 1.03161895e+00 5.27242482e-01 1.06177978e-01
-2.66104043e-01 -9.55391049e-01 -6.89805984e-01 -1.75794899e-01
-2.73906350e-01 9.64397848e-01 7.01901615e-01 -2.39832178... | [9.352926254272461, 8.741158485412598] |
d0be8972-7abb-45be-8167-04bf7413f429 | 190910145 | 1909.10145 | null | https://arxiv.org/abs/1909.10145v1 | https://arxiv.org/pdf/1909.10145v1.pdf | PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs | Physics-informed neural networks (PINNs) encode physical conservation laws and prior physical knowledge into the neural networks, ensuring the correct physics is represented accurately while alleviating the need for supervised learning to a great degree. While effective for relatively short-term time integration, when ... | ['George Em. Karniadakis', 'Xuhui Meng', 'Zhen Li', 'Dongkun Zhang'] | 2019-09-23 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-1.10907622e-01 -1.80924982e-02 2.89942861e-01 -1.94210699e-03
-5.12855351e-01 -2.39183068e-01 2.89546490e-01 1.07660808e-01
-4.79866058e-01 1.22783601e+00 -4.83320475e-01 -4.54245061e-01
-3.19220275e-01 -1.06476033e+00 -8.12595904e-01 -9.49517250e-01
-2.56975651e-01 7.67390490e-01 2.37091854e-01 -1.22955449... | [6.490522861480713, 3.4266889095306396] |
cde4a356-4d85-4cfb-a184-7bb761d4f7d6 | prediction-of-drug-effectiveness-in | 2210.08016 | null | https://arxiv.org/abs/2210.08016v3 | https://arxiv.org/pdf/2210.08016v3.pdf | Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms | Rheumatoid arthritis (RA) is an autoimmune condition caused when patients' immune system mistakenly targets their own tissue. Machine learning (ML) has the potential to identify patterns in patient electronic health records (EHR) to forecast the best clinical treatment to improve patient outcomes. This study introduced... | ['Jacopo Cirrone', 'Valay Shah', 'Woodward B. Galbraith', 'Nikunj Gupta', 'Shengjia Chen'] | 2022-10-14 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 3.22688341e-01 -3.22004765e-01 -5.72259724e-01 -3.85003865e-01
-1.06222069e+00 -3.36799204e-01 4.19017673e-01 7.03280926e-01
-1.52179435e-01 7.61373401e-01 6.17031991e-01 -3.80512476e-01
-6.21847570e-01 -8.56803238e-01 7.55178332e-02 -5.37241638e-01
-2.28162676e-01 1.35473073e+00 -2.94957042e-01 2.31038421... | [7.7790913581848145, 6.252460956573486] |
7a2ebab0-9589-4b2d-931e-b616693c6bcb | graphcfc-a-directed-graph-based-cross-modal | 2207.12261 | null | https://arxiv.org/abs/2207.12261v3 | https://arxiv.org/pdf/2207.12261v3.pdf | GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition | Emotion Recognition in Conversation (ERC) plays a significant part in Human-Computer Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can mitigate the drawbacks of uni-modal approaches. Recently, Graph Neural Networks (GNNs) have been widely used in a variety of fields due to their sup... | ['Zhigang Zeng', 'Guoqing Lv', 'XiaoPing Wang', 'Jiang Li'] | 2022-07-06 | null | null | null | null | ['multimodal-emotion-recognition', 'emotion-classification', 'emotion-recognition-in-conversation', 'emotion-classification', 'multimodal-emotion-recognition'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 2.62091845e-01 -1.07624449e-01 -9.35044438e-02 -3.34275156e-01
-7.76086628e-01 -3.51660848e-01 3.40420932e-01 1.81373790e-01
-3.33790660e-01 5.38458169e-01 4.66470718e-01 -1.13397680e-01
-2.61327922e-01 -6.67655051e-01 -2.84863502e-01 -8.82950306e-01
4.29493859e-02 1.27382532e-01 -2.41406634e-01 -6.47166193... | [13.1229248046875, 5.1522040367126465] |
7fed3843-6d32-4377-b9fe-cda740cc4226 | link-prediction-with-attention-applied-on | 2302.06229 | null | https://arxiv.org/abs/2302.06229v1 | https://arxiv.org/pdf/2302.06229v1.pdf | Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models | Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a vector space to predict new links, scoring them according to geometric criteria. Relations in the graph may follow patterns that can be lea... | ['Steffen Staab', 'Daniel Hernández', 'Mojtaba Nayyeri', 'Cosimo Gregucci'] | 2023-02-13 | null | null | null | null | ['knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'methodology'] | [-2.67062455e-01 4.16195571e-01 -7.05778182e-01 -9.14678350e-02
-1.48798153e-01 -8.25528800e-01 4.08244193e-01 4.35781330e-01
2.04364643e-01 5.51320910e-01 3.01582754e-01 -3.60868782e-01
-5.90114713e-01 -1.33252227e+00 -8.77225816e-01 -3.47649813e-01
-2.14474842e-01 7.37441897e-01 6.76292241e-01 -4.53001708... | [8.6832857131958, 7.781783580780029] |
cbb5f7a5-1257-443d-8f6c-d4e8c00d0890 | confidence-measure-guided-single-image-de | 1909.04207 | null | https://arxiv.org/abs/1909.04207v1 | https://arxiv.org/pdf/1909.04207v1.pdf | Confidence Measure Guided Single Image De-raining | Single image de-raining is an extremely challenging problem since the rainy images contain rain streaks which often vary in size, direction and density. This varying characteristic of rain streaks affect different parts of the image differently. Previous approaches have attempted to address this problem by leveraging s... | ['Rajeev Yasarla', 'Vishal M. Patel'] | 2019-09-10 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.17013901e-01 -5.56479812e-01 4.14019406e-01 -7.02433288e-01
-3.42237860e-01 -3.34956974e-01 -1.70117900e-01 -2.38750309e-01
-2.58549482e-01 1.00270355e+00 -9.09963176e-02 -1.81008186e-02
3.88830528e-02 -9.41792488e-01 -6.46877706e-01 -1.23233271e+00
-1.97069287e-01 -1.17985673e-01 3.33001137e-01 -3.05254489... | [10.916572570800781, -3.2546162605285645] |
2cb5ea9a-7012-4465-9145-d9b461ebe606 | every-pixel-counts-unsupervised-geometry | 1806.10556 | null | http://arxiv.org/abs/1806.10556v2 | http://arxiv.org/pdf/1806.10556v2.pdf | Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding | Learning to estimate 3D geometry in a single image by watching unlabeled
videos via deep convolutional network has made significant process recently.
Current state-of-the-art (SOTA) methods, are based on the learning framework of
rigid structure-from-motion, where only 3D camera ego motion is modeled for
geometry estim... | ['Wei Xu', 'Peng Wang', 'Yang Wang', 'Ram Nevatia', 'Zhenheng Yang'] | 2018-06-27 | null | null | null | null | ['depth-and-camera-motion', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [ 3.51523384e-02 -1.07451133e-01 -1.67471930e-01 -3.73332739e-01
-6.54934466e-01 -8.31140280e-01 3.45226228e-01 -7.31315613e-01
-3.61168355e-01 2.37906739e-01 4.70908433e-02 -1.42478347e-01
2.67443359e-01 -6.91839278e-01 -1.19639659e+00 -6.62661374e-01
2.53835261e-01 4.52366501e-01 4.67064410e-01 2.38452524... | [8.505105972290039, -2.0151023864746094] |
a60f6f10-c261-4744-9911-607fcc15fde2 | direct-comparative-analysis-of-nature | 2212.10797 | null | https://arxiv.org/abs/2212.10797v1 | https://arxiv.org/pdf/2212.10797v1.pdf | Direct Comparative Analysis of Nature-inspired Optimization Algorithms on Community Detection Problem in Social Networks | Nature-inspired optimization Algorithms (NIOAs) are nowadays a popular choice for community detection in social networks. Community detection problem in social network is treated as optimization problem, where the objective is to either maximize the connection within the community or minimize connections between the co... | ['Anupam Biswas', 'Alberto Tonda', 'Bijita Singha', 'Soumita Das'] | 2022-12-21 | null | null | null | null | ['community-detection'] | ['graphs'] | [-1.25947120e-02 -1.20463341e-01 5.93751967e-02 1.95063099e-01
3.22316766e-01 -5.59537411e-01 3.67631704e-01 5.67294240e-01
-4.46527362e-01 7.79331803e-01 -2.35222101e-01 2.99523994e-02
-5.78109741e-01 -1.32298458e+00 6.47432217e-03 -7.13638842e-01
-6.83112621e-01 5.92933178e-01 3.29851210e-01 -4.00547892... | [6.998391151428223, 5.301792144775391] |
2382d8dd-5ace-44e0-b29c-7ba5b9d91e58 | a-domain-knowledge-enhanced-pre-trained | null | null | https://aclanthology.org/2022.coling-1.85 | https://aclanthology.org/2022.coling-1.85.pdf | A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal Products | We present a biomedical knowledge enhanced pre-trained language model for medicinal product vertical search. Following ELECTRA’s replaced token detection (RTD) pre-training, we leverage biomedical entity masking (EM) strategy to learn better contextual word representations. Furthermore, we propose a novel pre-training ... | ['Feifei Lyu', 'Jianhui Jiang', 'Kesong Liu'] | null | null | null | null | coling-2022-10 | ['intent-classification'] | ['natural-language-processing'] | [ 5.86393595e-01 2.14216337e-01 -9.87621725e-01 -1.84257746e-01
-1.24644125e+00 -4.81051594e-01 3.68161500e-01 6.47502422e-01
-7.06214249e-01 6.34486258e-01 4.61755365e-01 -8.56960058e-01
1.12772532e-01 -7.83219337e-01 -8.44495714e-01 -3.69114906e-01
1.28872082e-01 1.36184916e-01 -3.10891956e-01 1.80719241... | [8.470491409301758, 8.701309204101562] |
3e5105a0-741e-4df9-9a1d-4124a38b60c3 | physiological-parameter-monitoring-from | null | null | https://ieeexplore.ieee.org/document/5963704 | https://ieeexplore.ieee.org/document/5963704/ | Physiological Parameter Monitoring from Optical Recordings with a Mobile Phone | We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor. We confirm the accuracy of measurements of breathing rate, cardiac R-R intervals, and blood oxy... | ['Senior Member', 'and Ki H. Chon', 'Yitzhak Mendelson', 'Member', 'Domhnull Granquist-Fraser', 'Alexander M. Gorbach', 'Joseph Meyer', 'Jinseok Lee', 'IEEE', 'Student Member', 'Christopher G. Scully'] | 2011-07-29 | null | null | null | null | ['spo2-estimation', 'heart-rate-variability', 'heart-rate-estimation'] | ['medical', 'medical', 'medical'] | [ 7.34215200e-01 -4.37304109e-01 1.44822985e-01 -2.07612917e-01
2.51240790e-01 -5.24596870e-01 -2.54751593e-01 2.34171167e-01
-4.38758701e-01 1.17981863e+00 -1.14577055e-01 -3.74774516e-01
1.36329830e-01 -5.33181131e-01 3.09156746e-01 -6.12336218e-01
-1.79172337e-01 -1.76252648e-01 -1.32714003e-01 3.09966981... | [13.921407699584961, 2.9155805110931396] |
2f40743c-7ed9-4771-9a9a-7156b40d67ce | improving-punctuation-restoration-for-speech | 2110.00560 | null | https://arxiv.org/abs/2110.00560v1 | https://arxiv.org/pdf/2110.00560v1.pdf | Improving Punctuation Restoration for Speech Transcripts via External Data | Automatic Speech Recognition (ASR) systems generally do not produce punctuated transcripts. To make transcripts more readable and follow the expected input format for downstream language models, it is necessary to add punctuation marks. In this paper, we tackle the punctuation restoration problem specifically for the n... | ['Simon Corston-Oliver', 'Shashi Bhushan TN', 'Md Tahmid Rahman Laskar', 'Cheng Chen', 'Xue-Yong Fu'] | 2021-10-01 | null | https://aclanthology.org/2021.wnut-1.19 | https://aclanthology.org/2021.wnut-1.19.pdf | wnut-acl-2021-11 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 4.91169602e-01 1.79653496e-01 4.11221981e-02 -6.01402044e-01
-1.32988036e+00 -6.44312799e-01 3.98919553e-01 -1.60859197e-01
-3.53867650e-01 7.88421631e-01 6.63841903e-01 -6.38056695e-01
3.99596602e-01 -3.40965241e-01 -5.78700662e-01 -5.34968913e-01
4.82370168e-01 2.31006995e-01 5.56319952e-02 -2.99313962... | [14.368659973144531, 6.937809467315674] |
ee181499-4168-42a5-bb9a-86a4c4f5f1cb | syntax-guided-program-reduction-for | 2205.14374 | null | https://arxiv.org/abs/2205.14374v2 | https://arxiv.org/pdf/2205.14374v2.pdf | Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models | Neural code intelligence (CI) models are opaque black-boxes and offer little insight on the features they use in making predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption in safety-critical applications. Recently, input program reduction techniques have been proposed to i... | ['Mohammad Amin Alipour', 'Aftab Hussain', 'Md Rafiqul Islam Rabin'] | 2022-05-28 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [ 3.34829658e-01 4.74892408e-01 -2.59628892e-01 -2.80055851e-01
-3.38416070e-01 -9.11615014e-01 3.53253156e-01 2.00903147e-01
-5.41469939e-02 3.33856970e-01 -1.11788884e-02 -1.03358114e+00
3.17320704e-01 -1.07817829e+00 -1.05409694e+00 -1.67937502e-01
-6.24098480e-02 -5.91828749e-02 3.29333007e-01 -2.74803698... | [7.426300525665283, 7.714366436004639] |
0af919c8-9eb1-4360-bd8f-dbb98850f226 | semantic-invariant-multi-view-clustering-with | 2305.12743 | null | https://arxiv.org/abs/2305.12743v1 | https://arxiv.org/pdf/2305.12743v1.pdf | Semantic Invariant Multi-view Clustering with Fully Incomplete Information | Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly affect real-world multi-view applications. Existing approaches heavily rely on paired samples to realign or impute defective ones, but such pr... | ['Xi Peng', 'Peng Hu', 'Changqing Zhang', 'Yiding Lu', 'Mouxing Yang', 'Pengxin Zeng'] | 2023-05-22 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 2.09067896e-01 -2.44259968e-01 -1.37822643e-01 -5.60701787e-01
-1.10377395e+00 -7.91135848e-01 3.93743575e-01 -6.94994000e-04
-1.43377393e-01 6.13190413e-01 1.41566858e-01 2.71035254e-01
-3.52965117e-01 -4.57186401e-01 -8.51155460e-01 -9.45362449e-01
1.91974327e-01 6.23035669e-01 5.83651699e-02 2.61066407... | [8.39802360534668, 4.539137363433838] |
165bc4a9-26ed-4f80-bb30-7dc3d47bbf92 | uncertainty-based-offline-reinforcement | 2110.01548 | null | https://arxiv.org/abs/2110.01548v2 | https://arxiv.org/pdf/2110.01548v2.pdf | Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble | Offline reinforcement learning (offline RL), which aims to find an optimal policy from a previously collected static dataset, bears algorithmic difficulties due to function approximation errors from out-of-distribution (OOD) data points. To this end, offline RL algorithms adopt either a constraint or a penalty term tha... | ['Hyun Oh Song', 'Jang-Hyun Kim', 'Seungyong Moon', 'Gaon An'] | 2021-10-04 | null | http://proceedings.neurips.cc/paper/2021/hash/3d3d286a8d153a4a58156d0e02d8570c-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/3d3d286a8d153a4a58156d0e02d8570c-Paper.pdf | neurips-2021-12 | ['value-prediction', 'd4rl'] | ['computer-code', 'robots'] | [-1.89389527e-01 6.06556907e-02 -3.70090991e-01 -1.53044894e-01
-1.00851572e+00 -6.37694299e-01 2.85606354e-01 2.25651398e-01
-6.03132129e-01 1.07333052e+00 -3.48093361e-01 -3.76650035e-01
-3.88281405e-01 -6.12009108e-01 -9.96075392e-01 -8.90166402e-01
-1.22676000e-01 5.60498774e-01 -1.09083787e-01 -5.78217246... | [4.171765327453613, 2.3870530128479004] |
089934a1-5441-4525-bed0-2690cff1d4d3 | sparsity-agnostic-depth-completion | 2212.00790 | null | https://arxiv.org/abs/2212.00790v1 | https://arxiv.org/pdf/2212.00790v1.pdf | Sparsity Agnostic Depth Completion | We present a novel depth completion approach agnostic to the sparsity of depth points, that is very likely to vary in many practical applications. State-of-the-art approaches yield accurate results only when processing a specific density and distribution of input points, i.e. the one observed during training, narrowing... | ['Stefano Mattoccia', 'Matteo Poggi', 'Andrea Conti'] | 2022-12-01 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 2.02172086e-01 1.07006215e-01 -1.05161250e-01 -7.83313587e-02
-4.26216513e-01 -4.38380808e-01 6.08859599e-01 1.95735201e-01
-6.01237059e-01 9.26987112e-01 7.85155296e-02 -1.52801380e-01
-2.44403541e-01 -1.01041758e+00 -8.66781712e-01 -5.73226571e-01
-2.91530043e-01 8.59474003e-01 6.57815158e-01 -2.64639944... | [8.675382614135742, -2.5309674739837646] |
801fc086-7c4f-4b63-a4fb-4769638c4afc | slot-vae-object-centric-scene-generation-with | 2306.06997 | null | https://arxiv.org/abs/2306.06997v1 | https://arxiv.org/pdf/2306.06997v1.pdf | Slot-VAE: Object-Centric Scene Generation with Slot Attention | Slot attention has shown remarkable object-centric representation learning performance in computer vision tasks without requiring any supervision. Despite its object-centric binding ability brought by compositional modelling, as a deterministic module, slot attention lacks the ability to generate novel scenes. In this ... | ['Justin Dauwels', 'Letao Liu', 'Yanbo Wang'] | 2023-06-12 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 6.20706022e-01 6.55019939e-01 3.83934565e-02 -4.44619596e-01
-8.77868116e-01 -2.94507980e-01 1.13546479e+00 -3.55194718e-01
1.56592280e-01 6.16920590e-01 5.66059947e-01 -2.61022419e-01
8.72042552e-02 -9.71772969e-01 -1.14633417e+00 -7.48405337e-01
4.93682683e-01 8.05116117e-01 -2.05401871e-02 -1.76930770... | [10.834663391113281, -0.07489997893571854] |
4ee4656f-0718-4b8a-9349-a50abe647cd4 | leveraging-deep-learning-approaches-for | 2304.01908 | null | https://arxiv.org/abs/2304.01908v1 | https://arxiv.org/pdf/2304.01908v1.pdf | Leveraging Deep Learning Approaches for Deepfake Detection: A Review | Conspicuous progression in the field of machine learning and deep learning have led the jump of highly realistic fake media, these media oftentimes referred as deepfakes. Deepfakes are fabricated media which are generated by sophisticated AI that are at times very difficult to set apart from the real media. So far, thi... | ['Mounika Vanamala', 'Rushit Dave', 'Aniruddha Tiwari'] | 2023-04-04 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-1.44683838e-01 3.76662433e-01 -2.15741441e-01 7.40322247e-02
-2.14789212e-01 -4.85740632e-01 1.14421499e+00 1.82907566e-01
-2.90851057e-01 6.86087966e-01 8.80709141e-02 -4.02198315e-01
1.82561517e-01 -1.08110440e+00 -9.36824501e-01 -2.75575519e-01
1.13674410e-01 4.60077882e-01 3.45343381e-01 -5.92411816... | [8.187281608581543, 10.260335922241211] |
6113c8da-7a1f-4bfa-a880-c65a6d9add34 | attention-based-models-for-text-dependent | 1710.10470 | null | http://arxiv.org/abs/1710.10470v3 | http://arxiv.org/pdf/1710.10470v3.pdf | Attention-Based Models for Text-Dependent Speaker Verification | Attention-based models have recently shown great performance on a range of
tasks, such as speech recognition, machine translation, and image captioning
due to their ability to summarize relevant information that expands through the
entire length of an input sequence. In this paper, we analyze the usage of
attention mec... | ['Li Wan', 'Ignacio Lopez Moreno', 'F A Rezaur Rahman Chowdhury', 'Quan Wang'] | 2017-10-28 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 6.08055115e-01 1.25264153e-01 -1.22184053e-01 -4.70051944e-01
-1.32643974e+00 -2.31463075e-01 5.27258277e-01 -1.93900794e-01
-5.36447406e-01 5.93767703e-01 9.11923885e-01 -5.74423373e-01
6.08584464e-01 1.48041993e-01 -7.08516002e-01 -5.25358617e-01
2.22127706e-01 9.65447351e-02 -1.10970788e-01 4.41536419... | [14.407130241394043, 6.9199299812316895] |
cdd16f8a-7dc8-4695-ba46-a75cbf0581f2 | adaptive-low-rank-and-sparse-decomposition-of | 1302.1610 | null | http://arxiv.org/abs/1302.1610v2 | http://arxiv.org/pdf/1302.1610v2.pdf | Adaptive low rank and sparse decomposition of video using compressive sensing | We address the problem of reconstructing and analyzing surveillance videos
using compressive sensing. We develop a new method that performs video
reconstruction by low rank and sparse decomposition adaptively. Background
subtraction becomes part of the reconstruction. In our method, a background
model is used in which ... | ['Wei Deng', 'Fei Yang', 'Dimitris Metaxas', 'Zuowei Shen', 'Hong Jiang'] | 2013-02-06 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 7.83128440e-01 -7.22043633e-01 -1.01020552e-01 -1.20135218e-01
-4.00583684e-01 -4.15575147e-01 2.67341137e-01 -6.96964502e-01
-1.19653843e-01 5.93811750e-01 3.46739262e-01 -3.37187350e-01
2.92921156e-01 -4.93830323e-01 -7.00134099e-01 -8.86387527e-01
-1.26890391e-01 -3.21817219e-01 5.25534868e-01 6.98122233... | [9.035405158996582, -0.833404541015625] |
4796a0b4-af4d-4680-88fa-df7670933101 | deep-successor-reinforcement-learning | 1606.02396 | null | http://arxiv.org/abs/1606.02396v1 | http://arxiv.org/pdf/1606.02396v1.pdf | Deep Successor Reinforcement Learning | Learning robust value functions given raw observations and rewards is now
possible with model-free and model-based deep reinforcement learning
algorithms. There is a third alternative, called Successor Representations
(SR), which decomposes the value function into two components -- a reward
predictor and a successor ma... | ['Simanta Gautam', 'Samuel J. Gershman', 'Ardavan Saeedi', 'Tejas D. Kulkarni'] | 2016-06-08 | null | null | null | null | ['game-of-doom', 'fps-games'] | ['playing-games', 'playing-games'] | [ 7.61744156e-02 1.92952439e-01 -3.31275731e-01 -3.02193701e-01
-5.73551595e-01 -4.45670485e-01 8.37038696e-01 3.97797674e-01
-8.83255720e-01 1.22248125e+00 2.77681082e-01 -1.08818322e-01
-3.64856690e-01 -8.95948529e-01 -7.58040786e-01 -6.96548581e-01
-7.88367093e-01 3.81990075e-01 4.61816996e-01 -5.87745667... | [4.138786792755127, 1.7570451498031616] |
582473fe-9922-4735-82f9-e449da7f740b | indices-matter-learning-to-index-for-deep | 1908.00672 | null | https://arxiv.org/abs/1908.00672v1 | https://arxiv.org/pdf/1908.00672v1.pdf | Indices Matter: Learning to Index for Deep Image Matting | We show that existing upsampling operators can be unified with the notion of the index function. This notion is inspired by an observation in the decoding process of deep image matting where indices-guided unpooling can recover boundary details much better than other upsampling operators such as bilinear interpolation.... | ['Hao Lu', 'Chunhua Shen', 'Songcen Xu', 'Yutong Dai'] | 2019-08-02 | indices-matter-learning-to-index-for-deep-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Lu_Indices_Matter_Learning_to_Index_for_Deep_Image_Matting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Lu_Indices_Matter_Learning_to_Index_for_Deep_Image_Matting_ICCV_2019_paper.pdf | iccv-2019-10 | ['semantic-image-matting'] | ['computer-vision'] | [ 3.85259420e-01 5.66301823e-01 -1.39182448e-01 -3.69997591e-01
-6.37084424e-01 -1.77595079e-01 6.28326833e-01 -3.11162651e-01
-3.30539286e-01 4.73566502e-01 5.00988364e-01 -3.64052027e-01
2.81761438e-01 -9.91207719e-01 -1.36765087e+00 -4.64166671e-01
1.53641412e-02 3.67124349e-01 1.40715048e-01 -3.04348469... | [10.70920467376709, -0.6176631450653076] |
eb7e30dd-380e-40bd-ad0c-f78f0a2d5043 | minihack-the-planet-a-sandbox-for-open-ended | 2109.13202 | null | https://arxiv.org/abs/2109.13202v2 | https://arxiv.org/pdf/2109.13202v2.pdf | MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research | Progress in deep reinforcement learning (RL) is heavily driven by the availability of challenging benchmarks used for training agents. However, benchmarks that are widely adopted by the community are not explicitly designed for evaluating specific capabilities of RL methods. While there exist environments for assessing... | ['Tim Rocktäschel', 'Edward Grefenstette', 'Heinrich Küttler', 'Fabio Petroni', 'Eric Hambro', 'Minqi Jiang', 'Jack Parker-Holder', 'Vitaly Kurin', 'Robert Kirk', 'Mikayel Samvelyan'] | 2021-09-27 | null | null | null | null | ['nethack'] | ['playing-games'] | [-6.07218385e-01 -1.04718663e-01 8.77742190e-03 -1.27395794e-01
-6.17717981e-01 -8.42011034e-01 7.24289715e-01 -3.14362533e-02
-7.40906477e-01 9.46528137e-01 2.86152840e-01 -4.70030427e-01
1.09450510e-02 -8.64502549e-01 -4.50847775e-01 -4.23707604e-01
-4.92267072e-01 7.43699789e-01 3.43679100e-01 -7.11237013... | [4.048752784729004, 1.291099190711975] |
20812b7c-5546-4e6f-8fb7-11b6f0928f97 | action-anticipation-for-collaborative | 1910.00714 | null | https://arxiv.org/abs/1910.00714v2 | https://arxiv.org/pdf/1910.00714v2.pdf | Action Anticipation for Collaborative Environments: The Impact of Contextual Information and Uncertainty-Based Prediction | To interact with humans in collaborative environments, machines need to be able to predict (i.e., anticipate) future events, and execute actions in a timely manner. However, the observation of the human limb movements may not be sufficient to anticipate their actions unambiguously. In this work, we consider two additio... | ['José Santos-Victor', 'Raquel Vassallo', 'Plinio Moreno', 'Clebeson Canuto', 'Jorge Samatelo'] | 2019-10-01 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 6.30813420e-01 1.61390543e-01 -6.50841966e-02 -5.17483652e-01
-4.42280948e-01 -4.31372285e-01 7.94563353e-01 1.68646365e-01
-6.67824447e-01 5.75271785e-01 3.96890253e-01 -1.23247489e-01
-3.36850643e-01 -2.56302625e-01 -5.13741374e-01 -4.70068187e-01
-2.80136704e-01 3.79622877e-01 3.09555322e-01 -9.40512493... | [7.9503631591796875, 0.5170770287513733] |
5037b394-d97e-4a14-b993-310dd9a0e5a5 | fine-tuning-convolutional-neural-networks-for-1 | null | null | https://www.sciencedirect.com/science/article/pii/S0957417418304421?casa_token=SuXq40lwuksAAAAA:Q73-Pe0oYbRuzuyqljxps1qkZScWER4-FTKgukOhLZ1hKYWPogAOYsFoNIihz9hw7PgKtJEHaA | https://www.sciencedirect.com/science/article/abs/pii/S0957417418304421 | Fine-tuning Convolutional Neural Networks for fine art classification | The increasing availability of large digitized fine art collections opens new research perspectives in the intersection of artificial intelligence and art history. Motivated by the successful performance of Convolutional Neural Networks (CNN) for a wide variety of computer vision tasks, in this paper we explore their a... | ['Tomislav Lipic', 'Sonja Grgic', 'Eva Cetinic'] | 2018-12-30 | null | null | null | expert-systems-with-applications-2018-12 | ['scene-recognition', 'artistic-style-classification', 'genre-classification', 'artist-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.40085471e-01 -3.94000769e-01 -8.27304199e-02 -3.09466362e-01
-2.63856620e-01 -9.82736945e-01 1.06511557e+00 3.23456496e-01
-6.04094446e-01 5.06049275e-01 2.47554377e-01 8.70095938e-02
-6.09581470e-01 -1.02819920e+00 -6.57237470e-01 -3.44331026e-01
2.84887075e-01 6.31807446e-01 8.51076096e-03 -4.15929943... | [11.164783477783203, 0.40535402297973633] |
8f9592e0-00ed-48fe-a468-166e254002af | intelligent-diagnostic-scheme-for-lung-cancer | 2303.06340 | null | https://arxiv.org/abs/2303.06340v1 | https://arxiv.org/pdf/2303.06340v1.pdf | Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning | Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, par... | ['Cong Wang', 'Shi-Ju Ran', 'Gang Su', 'Xiao-Dong Han', 'Cheng-en Wang', 'Xiao-Guang Li', 'Lin Cheng', 'Sheng-Chen Bai', 'Yu-Jia An'] | 2023-03-11 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.53468984e-01 3.92851293e-01 -1.80551380e-01 -2.79380977e-01
-3.14520955e-01 -3.24061006e-01 2.26535633e-01 4.59456503e-01
-1.66009679e-01 8.38768184e-01 -2.97591180e-01 -5.29240131e-01
-5.34436941e-01 -9.20462608e-01 -3.13143015e-01 -1.17689633e+00
-2.28953864e-02 5.45386910e-01 -8.91894475e-02 8.15508887... | [8.691813468933105, 5.510343074798584] |
8b7e2d81-cd92-4ebd-809f-2dadfef940a3 | music-transcription-based-on-bayesian-piece | 1908.06969 | null | https://arxiv.org/abs/1908.06969v2 | https://arxiv.org/pdf/1908.06969v2.pdf | Musical Rhythm Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions | Most work on musical score models (a.k.a. musical language models) for music transcription has focused on describing the local sequential dependence of notes in musical scores and failed to capture their global repetitive structure, which can be a useful guide for transcribing music. Focusing on rhythm, we formulate se... | ['Eita Nakamura', 'Kazuyoshi Yoshii'] | 2019-08-18 | null | null | null | null | ['music-transcription'] | ['music'] | [ 3.27807724e-01 -3.08901034e-02 -7.45341405e-02 -1.13372002e-02
-9.36482906e-01 -9.18089390e-01 3.51527929e-01 -2.03326583e-01
1.17587648e-01 4.57481891e-01 5.02623796e-01 1.48688734e-01
-7.73854196e-01 -5.11281133e-01 -3.99347126e-01 -7.67430663e-01
-7.28969872e-02 5.16666234e-01 1.06280476e-01 -1.20341837... | [15.900578498840332, 5.416536331176758] |
99fe3c6a-8d3a-4cb4-bcbe-882ea6640bde | contrastive-semi-supervised-learning-for-1 | 2303.09101 | null | https://arxiv.org/abs/2303.09101v4 | https://arxiv.org/pdf/2303.09101v4.pdf | Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank | Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress. In this work, we propose a mean-teacher based Semi-supervised Underwater Image Restoration (Semi-UIR) framework to incorporate the unlabeled data into network tr... | ['Yunsong Li', 'Jun Chen', 'Huan Liu', 'Keyan Wang', 'Shirui Huang'] | 2023-03-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Contrastive_Semi-Supervised_Learning_for_Underwater_Image_Restoration_via_Reliable_Bank_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Contrastive_Semi-Supervised_Learning_for_Underwater_Image_Restoration_via_Reliable_Bank_CVPR_2023_paper.pdf | cvpr-2023-1 | ['underwater-image-restoration'] | ['computer-vision'] | [ 1.72065109e-01 1.34036809e-01 1.64129511e-01 -6.29319310e-01
-1.01235664e+00 -8.83930698e-02 6.24785163e-02 -2.84842432e-01
-5.23559809e-01 9.41063821e-01 1.52160019e-01 -2.31093794e-01
-1.92651391e-01 -5.92467666e-01 -9.22025502e-01 -1.07453740e+00
2.34635860e-01 1.13327123e-01 9.49213728e-02 -1.42574996... | [10.710356712341309, -3.52913236618042] |
98702eb8-b067-4a3a-8be0-29872832f3f4 | a-novel-improved-fuzzy-support-vector-machine | 1801.00681 | null | http://arxiv.org/abs/1801.00681v1 | http://arxiv.org/pdf/1801.00681v1.pdf | A novel improved fuzzy support vector machine based stock price trend forecast model | Application of fuzzy support vector machine in stock price forecast. Support
vector machine is a new type of machine learning method proposed in 1990s. It
can deal with classification and regression problems very successfully. Due to
the excellent learning performance of support vector machine, the technology
has becom... | ['Guohao Li', 'Yifan Bao', 'Shuheng Wang'] | 2018-01-02 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-6.25885904e-01 -6.82087481e-01 -2.79903203e-01 -2.16047093e-01
5.01511872e-01 -4.45253849e-01 2.67114818e-01 5.31821884e-03
-3.26361805e-01 7.81109869e-01 -4.09998566e-01 -3.62917483e-01
-2.24897891e-01 -1.13579023e+00 2.04394739e-02 -5.53946972e-01
1.02899089e-01 4.08202976e-01 5.63649893e-01 -1.00270474... | [4.88771390914917, 4.015379905700684] |
7cf7d5dd-ba20-4d3f-9172-24e507724268 | mix-em-unsupervised-image-classification | 2007.09502 | null | https://arxiv.org/abs/2007.09502v2 | https://arxiv.org/pdf/2007.09502v2.pdf | MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings | We present MIX'EM, a novel solution for unsupervised image classification. MIX'EM generates representations that by themselves are sufficient to drive a general-purpose clustering algorithm to deliver high-quality classification. This is accomplished by building a mixture of embeddings module into a contrastive visual ... | ['Tinne Tuytelaars', 'Ali Varamesh'] | 2020-07-18 | null | null | null | null | ['unsupervised-image-classification'] | ['computer-vision'] | [ 2.13723511e-01 1.73990000e-02 -9.60038900e-02 -3.21139902e-01
-7.76529551e-01 -5.86296201e-01 6.76326573e-01 1.17618419e-01
-4.65205938e-01 3.05853754e-01 7.91905746e-02 -2.85438359e-01
-9.60160717e-02 -6.64270520e-01 -5.05836189e-01 -1.03281975e+00
1.40256837e-01 4.34786379e-01 -1.52847350e-01 1.92085013... | [9.322752952575684, 3.0423238277435303] |
7d48d01d-2d5a-47f9-975b-0c3ef7894479 | crime-scene-classification-from-skeletal | 2207.01687 | null | https://arxiv.org/abs/2207.01687v1 | https://arxiv.org/pdf/2207.01687v1.pdf | Crime scene classification from skeletal trajectory analysis in surveillance settings | Video anomaly analysis is a core task actively pursued in the field of computer vision, with applications extending to real-world crime detection in surveillance footage. In this work, we address the task of human-related crime classification. In our proposed approach, the human body in video frames, represented as ske... | ['Maya Aghaei', 'Estefania Talavera', 'Alina-Daniela Matei'] | 2022-07-04 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 4.69952345e-01 5.98073900e-02 -2.04170533e-02 -2.30510086e-01
-4.96253759e-01 -3.07541877e-01 8.50756347e-01 8.72815624e-02
-4.48077798e-01 3.00309211e-01 2.13931859e-01 -7.18192384e-02
-1.28152698e-01 -6.10322714e-01 -5.57124138e-01 -6.42025888e-01
-3.08855414e-01 1.39404669e-01 3.50379497e-01 -9.18338224... | [8.024195671081543, 0.9282089471817017] |
f4f99b4e-a5e3-417d-9b4f-480bb00b3cd4 | dynamic-refinement-network-for-oriented-and | 2005.09973 | null | https://arxiv.org/abs/2005.09973v2 | https://arxiv.org/pdf/2005.09973v2.pdf | Dynamic Refinement Network for Oriented and Densely Packed Object Detection | Object detection has achieved remarkable progress in the past decade. However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and ali... | ['Wei-Ming Dong', 'Kekai Sheng', 'Xiaowei Guo', 'Yuqiang Ren', 'Haolei Yuan', 'Chongyang Ma', 'Changsheng Xu', 'Xingjia Pan'] | 2020-05-20 | dynamic-refinement-network-for-oriented-and-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Dynamic_Refinement_Network_for_Oriented_and_Densely_Packed_Object_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Pan_Dynamic_Refinement_Network_for_Oriented_and_Densely_Packed_Object_Detection_CVPR_2020_paper.pdf | cvpr-2020-6 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 1.44745614e-02 -2.58188754e-01 -7.82150850e-02 -4.66370970e-01
-4.41946626e-01 -4.77535039e-01 3.92067820e-01 -1.76928744e-01
-4.85823989e-01 4.21098113e-01 9.69407111e-02 -1.00274958e-01
1.15955167e-01 -6.77752972e-01 -7.44784832e-01 -7.69646883e-01
3.01897395e-02 2.66408384e-01 8.91548634e-01 -1.29410893... | [9.074153900146484, 0.280362606048584] |
a7691f11-9715-411b-940e-4bd0c0075040 | disentangled-contrastive-collaborative | 2305.02759 | null | https://arxiv.org/abs/2305.02759v2 | https://arxiv.org/pdf/2305.02759v2.pdf | Disentangled Contrastive Collaborative Filtering | Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastive learning (GCL) has exhibited powerful performance in addressing the supervision label shortage issue by learning augmented user and item r... | ['Chao Huang', 'Dawei Yin', 'Jiashu Zhao', 'Lianghao Xia', 'Xubin Ren'] | 2023-05-04 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 3.62416863e-01 1.20846450e-01 -4.40017045e-01 -4.26059991e-01
-2.58014530e-01 -4.15739864e-01 7.09501386e-01 -5.54178171e-02
-6.97459327e-03 6.01504624e-01 8.15282762e-01 -2.45944247e-01
-5.11778951e-01 -8.43816519e-01 -5.33466339e-01 -4.56645668e-01
-9.68563706e-02 1.60958976e-01 -4.40313488e-01 -3.77302170... | [10.216425895690918, 5.5940752029418945] |
ae375951-555c-4f78-ab66-ed9a5112b1c7 | ss-cxr-multitask-representation-learning | 2211.12944 | null | https://arxiv.org/abs/2211.12944v2 | https://arxiv.org/pdf/2211.12944v2.pdf | SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model | Chest X-rays (CXRs) are a widely used imaging modality for the diagnosis and prognosis of lung disease. The image analysis tasks vary. Examples include pathology detection and lung segmentation. There is a large body of work where machine learning algorithms are developed for specific tasks. A significant recent exampl... | ['Marius George Linguraru', 'Josef Kitler', 'Gustavo Nino', 'Muhammad Awais', 'Sara Atito', 'Abhijeet Parida', 'Syed Muhammad Anwar'] | 2022-11-23 | null | null | null | null | ['covid-19-detection', 'pneumonia-detection'] | ['medical', 'medical'] | [ 4.88753259e-01 -3.93665433e-02 -3.28785181e-01 -3.81448418e-01
-8.98888350e-01 -5.60867965e-01 3.16973627e-01 3.07748079e-01
-4.34566677e-01 5.94384432e-01 5.99540435e-02 -4.97667074e-01
-3.29102635e-01 -6.28846049e-01 -5.97608209e-01 -8.85242522e-01
3.38369235e-02 7.93395102e-01 6.88144207e-01 3.04896623... | [15.10559368133545, -2.008033275604248] |
8a565271-60da-4798-a230-d482d0ed964e | nested-named-entity-recognition-as-corpus | null | null | https://aclanthology.org/2022.coling-1.218 | https://aclanthology.org/2022.coling-1.218.pdf | Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing | As a fundamental natural language processing task and one of core knowledge extraction techniques, named entity recognition (NER) is widely used to extract information from texts for downstream tasks. Nested NER is a branch of NER in which the named entities (NEs) are nested with each other. However, most of the previo... | ['Hai Zhao', 'Zuchao Li', 'Yifei Yang'] | null | null | null | null | coling-2022-10 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-3.74746043e-03 9.40935910e-02 -3.48754376e-01 -4.05753851e-01
-8.58252168e-01 -7.14089751e-01 4.54086363e-01 4.87092763e-01
-6.29156351e-01 7.61955082e-01 7.66461253e-01 -4.51611787e-01
9.10529401e-03 -9.25772667e-01 -5.98239362e-01 -2.49774575e-01
-1.61735788e-01 1.08978838e-01 3.49163204e-01 -5.27225792... | [9.642755508422852, 9.495841979980469] |
ba92756d-e3e5-4a6f-92c7-b687e172c54e | unsupervised-dependency-parsing-with | null | null | https://aclanthology.org/P14-1126 | https://aclanthology.org/P14-1126.pdf | Unsupervised Dependency Parsing with Transferring Distribution via Parallel Guidance and Entropy Regularization | null | ['Fei Xia', 'Xuezhe Ma'] | 2014-06-01 | null | null | null | acl-2014-6 | ['unsupervised-dependency-parsing'] | ['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... | [-7.280282974243164, 3.7558233737945557] |
f26b36e4-f1d4-40ed-8b08-cb70cae28b86 | text2video-text-driven-talking-head-video | 2104.14631 | null | https://arxiv.org/abs/2104.14631v3 | https://arxiv.org/pdf/2104.14631v3.pdf | Text2Video: Text-driven Talking-head Video Synthesis with Personalized Phoneme-Pose Dictionary | With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic. In this paper, we present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversarial network (GAN) to ... | ['Liangjun Zhang', 'Miao Liao', 'Jiahong Yuan', 'Sibo Zhang'] | 2021-04-29 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 4.14786249e-01 -2.52869842e-03 2.36565605e-01 -2.86164105e-01
-1.04077601e+00 -3.64080489e-01 7.86891282e-01 -4.67445165e-01
-1.66615248e-02 9.15713906e-01 3.63496512e-01 1.21164024e-01
2.42724746e-01 -8.81801963e-01 -9.20049608e-01 -7.70460486e-01
2.07475260e-01 2.71053642e-01 1.11878425e-01 -1.55728042... | [13.134787559509277, -0.3573201298713684] |
45a74616-e120-4b96-9df6-3a003d694561 | enhanced-memory-network-the-novel-network | 2110.03392 | null | https://arxiv.org/abs/2110.03392v1 | https://arxiv.org/pdf/2110.03392v1.pdf | Enhanced Memory Network: The novel network structure for Symbolic Music Generation | Symbolic melodies generation is one of the essential tasks for automatic music generation. Recently, models based on neural networks have had a significant influence on generating symbolic melodies. However, the musical context structure is complicated to capture through deep neural networks. Although long short-term m... | ['Lan Wang', 'Nan Yan', 'Haibin Liu', 'Jin Li'] | 2021-10-07 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.58234686e-01 -3.05975914e-01 3.60826738e-02 1.42708672e-02
-4.64137346e-01 -2.70487875e-01 3.11614960e-01 -2.26620302e-01
-3.24165553e-01 7.13308811e-01 2.51241922e-01 1.63508728e-02
-2.11539388e-01 -8.67663205e-01 -5.55779517e-01 -8.18599582e-01
1.33139893e-01 -1.21843733e-01 4.77462634e-02 -4.29041147... | [15.968387603759766, 5.5294365882873535] |
c0d15580-9862-446e-b005-751d9a71f617 | structured-siamese-network-for-real-time | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Yunhua_Zhang_Structured_Siamese_Network_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yunhua_Zhang_Structured_Siamese_Network_ECCV_2018_paper.pdf | Structured Siamese Network for Real-Time Visual Tracking | Local structure of target objects are essential for robust tracking. However, existing methods based on deep neural networks mostly describe the target appearance from the global view, leading to high sensitivity to non-rigid appearance change and partial occlusion. In this paper, we circumvent this issue by proposing ... | ['Mengyang Feng', 'Lijun Wang', 'Huchuan Lu', 'Dong Wang', 'Yunhua Zhang', 'Jinqing Qi'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['real-time-visual-tracking'] | ['computer-vision'] | [ 1.06826060e-01 -4.21294659e-01 -3.67803395e-01 -3.73196751e-01
-3.47461700e-01 -7.04901218e-01 8.04179013e-01 1.98241353e-01
-4.63175535e-01 3.44683975e-01 -3.57599825e-01 3.35186310e-02
9.62191634e-03 -6.16576076e-01 -8.31629217e-01 -7.70983815e-01
-4.02319655e-02 4.24578846e-01 6.93101883e-01 3.42643172... | [6.379543781280518, -2.142549753189087] |
c0ff3504-dbed-4534-bae9-2485aa26d719 | neural-bandits-for-data-mining-searching-for | 2212.05190 | null | https://arxiv.org/abs/2212.05190v3 | https://arxiv.org/pdf/2212.05190v3.pdf | Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy | Polypharmacy, most often defined as the simultaneous consumption of five or more drugs at once, is a prevalent phenomenon in the older population. Some of these polypharmacies, deemed inappropriate, may be associated with adverse health outcomes such as death or hospitalization. Considering the combinatorial nature of ... | ['Caroline Sirois', 'Richard Khoury', 'Audrey Durand', 'Alexandre Larouche'] | 2022-12-10 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.23945439e-01 -2.54107237e-01 -2.84006327e-01 -4.10857145e-03
-7.38067329e-01 -5.60050249e-01 1.39499351e-01 8.33207786e-01
-4.30817991e-01 1.31411219e+00 6.58673272e-02 -5.67390025e-01
-5.60009837e-01 -8.40493798e-01 -6.76031351e-01 -3.61359864e-01
-3.75674218e-01 9.58933532e-01 -1.81214765e-01 6.18978031... | [8.102019309997559, 5.351780891418457] |
8205e873-a7b4-4a64-847e-f7cd78f5480f | multimodal-emotion-recognition-using | 1602.08225 | null | http://arxiv.org/abs/1602.08225v1 | http://arxiv.org/pdf/1602.08225v1.pdf | Multimodal Emotion Recognition Using Multimodal Deep Learning | To enhance the performance of affective models and reduce the cost of
acquiring physiological signals for real-world applications, we adopt
multimodal deep learning approach to construct affective models from multiple
physiological signals. For unimodal enhancement task, we indicate that the best
recognition accuracy o... | ['Bao-liang Lu', 'Wei-Long Zheng', 'Wei Liu'] | 2016-02-26 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-2.78204888e-01 -1.40386075e-01 3.70965958e-01 -3.78421098e-01
-6.95640266e-01 -1.79239601e-01 3.37056905e-01 -2.39106581e-01
-3.35029304e-01 1.00700641e+00 3.42557281e-02 4.38667625e-01
-1.32294027e-02 -5.30311823e-01 -4.91738737e-01 -9.69741523e-01
-3.85421142e-02 -3.58617567e-02 -6.61527216e-01 -1.85968325... | [13.161837577819824, 3.4838550090789795] |
e3251a2d-128e-40c0-abf1-e1f9c74da38c | the-lmu-munich-unsupervised-machine | null | null | https://aclanthology.org/W18-6428 | https://aclanthology.org/W18-6428.pdf | The LMU Munich Unsupervised Machine Translation Systems | We describe LMU Munich{'}s unsupervised machine translation systems for English↔German translation. These systems were used to participate in the WMT18 news translation shared task and more specifically, for the unsupervised learning sub-track. The systems are trained on English and German monolingual data only and e... | ['er', 'Viktor Hangya', 'Alex Fraser', 'Matthias Huck', 'Dario Stojanovski'] | 2018-10-01 | null | null | null | ws-2018-10 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 1.14536785e-01 9.14066061e-02 -6.45692289e-01 -4.07611251e-01
-1.18349135e+00 -6.76373005e-01 8.54678631e-01 -4.88139801e-02
-9.49986637e-01 1.07006919e+00 4.84404176e-01 -1.01828825e+00
1.83605000e-01 -3.13353509e-01 -5.76453328e-01 -4.22647983e-01
4.33396310e-01 1.23071623e+00 -5.26808560e-01 -6.72417819... | [11.554022789001465, 10.42821216583252] |
afbdd65f-7615-4fd8-9313-9d48581e1bd5 | image-specific-information-suppression-and | 2208.14365 | null | https://arxiv.org/abs/2208.14365v1 | https://arxiv.org/pdf/2208.14365v1.pdf | Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search | Text-based person search is a challenging task that aims to search pedestrian images with the same identity from the image gallery given a query text description. In recent years, text-based person search has made good progress, and state-of-the-art methods achieve superior performance by learning local fine-grained co... | ['Jinhui Tang', 'Liyan Zhang', 'Hao Tang', 'Shuanglin Yan'] | 2022-08-30 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 1.45298988e-01 -5.33628643e-01 -2.86846399e-01 -5.10448515e-01
-7.08284616e-01 -1.34151042e-01 8.67729008e-01 -2.97247916e-01
-6.86198354e-01 3.48287404e-01 3.91521543e-01 1.81873068e-01
-2.25112990e-01 -6.43491626e-01 -5.26672602e-01 -6.86369121e-01
8.41336429e-01 4.89600152e-01 4.21749890e-01 -1.30023733... | [14.667048454284668, 0.8369172215461731] |
a845fe61-fb8e-4b03-89b0-aa854a5c32c3 | an-ensemble-of-transfer-semi-supervised-and | 1806.06506 | null | http://arxiv.org/abs/1806.06506v2 | http://arxiv.org/pdf/1806.06506v2.pdf | An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification | In this work, we propose an ensemble of classifiers to distinguish between
various degrees of abnormalities of the heart using Phonocardiogram (PCG)
signals acquired using digital stethoscopes in a clinical setting, for the
INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats
SubChallenge. Our primary c... | ['Shabnam Ghaffarzadegan', 'Md. Tauhiduzzaman Khan', 'Ahmed Imtiaz Humayun', 'Taufiq Hasan', 'Zhe Feng'] | 2018-06-18 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 5.10047913e-01 6.59430474e-02 3.36249709e-01 -2.48920530e-01
-1.19949663e+00 -3.41080368e-01 1.24776043e-01 1.10746503e-01
-3.46543550e-01 3.34493369e-01 3.36356819e-01 -4.61734682e-01
-3.11005235e-01 -5.55059433e-01 -2.44042233e-01 -6.98412180e-01
-3.68243456e-01 2.27965713e-01 -4.19387132e-01 -5.73240295... | [14.320817947387695, 3.319998025894165] |
5f789216-5df9-4b02-af2c-2f8df94d10b2 | deep-convolutional-neural-network-for-1 | 2007.02393 | null | https://arxiv.org/abs/2007.02393v2 | https://arxiv.org/pdf/2007.02393v2.pdf | Deep Convolutional Neural Network for Identifying Seam-Carving Forgery | Seam carving is a representative content-aware image retargeting approach to adjust the size of an image while preserving its visually prominent content. To maintain visually important content, seam-carving algorithms first calculate the connected path of pixels, referred to as the seam, according to a defined cost fun... | ['Heung-Kyu Lee', 'Myung-Joon Kwon', 'In-Jae Yu', 'Seung-Hun Nam', 'Minseok Son', 'Wonhyuk Ahn'] | 2020-07-05 | null | null | null | null | ['image-retargeting', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 4.64886010e-01 -1.89854562e-01 1.18316151e-01 -1.23977661e-01
-6.15187287e-01 -3.34328681e-01 3.15398455e-01 5.86462803e-02
-3.42900872e-01 3.42520326e-01 -8.44140798e-02 -2.23571703e-01
8.22955519e-02 -8.96886230e-01 -6.96098506e-01 -8.01912010e-01
5.98925538e-02 -5.70139170e-01 6.10990047e-01 -1.84091419... | [11.193918228149414, -1.2751120328903198] |
318e106f-8705-4a7e-bfe4-465b21b9db86 | manga109dialog-a-large-scale-dialogue-dataset | 2306.17469 | null | https://arxiv.org/abs/2306.17469v1 | https://arxiv.org/pdf/2306.17469v1.pdf | Manga109Dialog A Large-scale Dialogue Dataset for Comics Speaker Detection | The expanding market for e-comics has spurred interest in the development of automated methods to analyze comics. For further understanding of comics, an automated approach is needed to link text in comics to characters speaking the words. Comics speaker detection research has practical applications, such as automatic ... | ['Yusuke Matsui', 'Kiyoharu Aizawa', 'Yingxuan Li'] | 2023-06-30 | null | null | null | null | ['scene-graph-generation', 'graph-generation'] | ['computer-vision', 'graphs'] | [-1.29320934e-01 -2.09673241e-01 5.88029549e-02 -3.49333256e-01
-9.20448601e-01 -6.13078296e-01 7.87207127e-01 3.02085251e-01
-5.62959835e-02 2.34753460e-01 5.04088163e-01 -1.17722474e-01
3.21105301e-01 -8.34919393e-01 -6.38730407e-01 -2.71871239e-01
4.21943009e-01 7.39790678e-01 3.04800242e-01 -2.19677631... | [11.946514129638672, 2.255078077316284] |
29aa5349-5150-407b-b730-d714197a982a | a-laboratory-created-dataset-with-ground | 1902.08347 | null | http://arxiv.org/abs/1902.08347v1 | http://arxiv.org/pdf/1902.08347v1.pdf | A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation | Spectral unmixing is an important and challenging problem in hyperspectral
data processing. This topic has been extensively studied and a variety of
unmixing algorithms have been proposed in the literature. However, the lack of
publicly available dataset with ground-truth makes it difficult to evaluate and
compare the ... | ['Zhe He', 'Jie Chen', 'Min Zhao'] | 2019-02-22 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 7.36297607e-01 -7.21833110e-01 7.65515566e-02 3.20070684e-02
-4.39450860e-01 -6.60628319e-01 5.06173909e-01 -3.52966413e-02
-1.60512611e-01 8.53329003e-01 -1.75153077e-01 -1.36757642e-01
-3.49227846e-01 -7.83344328e-01 -2.73001939e-01 -1.09965158e+00
9.45695564e-02 3.29407036e-01 -3.03634703e-02 -1.45728439... | [10.073735237121582, -2.0680179595947266] |
221b7460-b434-4bd8-9b8e-8f5b7f8b4a4f | randomized-3d-scene-generation-for | 2306.04237 | null | https://arxiv.org/abs/2306.04237v1 | https://arxiv.org/pdf/2306.04237v1.pdf | Randomized 3D Scene Generation for Generalizable Self-supervised Pre-training | Capturing and labeling real-world 3D data is laborious and time-consuming, which makes it costly to train strong 3D models. To address this issue, previous works generate randomized 3D scenes and pre-train models on generated data. Although the pre-trained models gain promising performance boosts, previous works have t... | ['Michael Heizmann', 'Lanxiao Li'] | 2023-06-07 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 4.00098979e-01 2.37064496e-01 1.95952475e-01 -3.65451783e-01
-7.72048295e-01 -5.79830050e-01 7.85051703e-01 -2.78422475e-01
-1.68166146e-01 3.59192491e-01 -1.07578794e-02 -1.91748068e-01
2.62624413e-01 -1.09867990e+00 -8.19791436e-01 -6.71329796e-01
3.92589837e-01 9.26356852e-01 4.41336840e-01 -5.20713776... | [8.18388843536377, -2.910064458847046] |
ffe000b9-572d-4e6e-b1d0-5cebf7241e94 | leveraging-distributional-semantics-for-multi | 1709.05976 | null | http://arxiv.org/abs/1709.05976v3 | http://arxiv.org/pdf/1709.05976v3.pdf | Leveraging Distributional Semantics for Multi-Label Learning | We present a novel and scalable label embedding framework for large-scale
multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using
Distributional Semantics). Our approach draws inspiration from ideas rooted in
distributional semantics, specifically the Skip Gram Negative Sampling (SGNS)
approach, widely us... | ['Nagarajan Natarajan', 'Vivek Gupta', 'Rahul Wadbude', 'Prateek Jain', 'Piyush Rai', 'Harish Karnick'] | 2017-09-18 | null | null | null | null | ['document-embedding'] | ['methodology'] | [ 2.94319451e-01 -1.85469568e-01 -5.95771730e-01 -6.06259465e-01
-1.20047712e+00 -6.24873817e-01 6.60790265e-01 7.29901910e-01
-6.17411137e-01 4.24903929e-01 3.70673150e-01 -1.87371433e-01
-8.62508565e-02 -5.14727235e-01 -2.95829177e-01 -8.17923665e-01
-3.64339119e-03 5.75605869e-01 -5.84043711e-02 3.55472992... | [9.580645561218262, 4.344039440155029] |
2f27a73a-ae7f-4804-86be-dad202e0c227 | consecutive-question-generation-via-dynamic | 2211.08850 | null | https://arxiv.org/abs/2211.08850v1 | https://arxiv.org/pdf/2211.08850v1.pdf | Consecutive Question Generation via Dynamic Multitask Learning | In this paper, we propose the task of consecutive question generation (CQG), which generates a set of logically related question-answer pairs to understand a whole passage, with a comprehensive consideration of the aspects including accuracy, coverage, and informativeness. To achieve this, we first examine the four key... | ['Xing Shi', 'Sujian Li', 'Yunji Li'] | 2022-11-16 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 1.60442069e-01 3.64448935e-01 2.89139450e-01 -3.62424284e-01
-1.65687215e+00 -8.46889794e-01 5.55894673e-01 3.41054380e-01
-3.00472856e-01 1.15469122e+00 6.43434584e-01 -4.21380669e-01
-2.55475402e-01 -8.43006015e-01 -7.27932990e-01 -1.67886361e-01
3.60850483e-01 7.26751387e-01 4.28049415e-01 -4.76150662... | [11.416350364685059, 8.083335876464844] |
f641703c-df23-4cff-a0ca-b8c815d905ad | variational-graph-generator-for-multi-view | 2210.07011 | null | https://arxiv.org/abs/2210.07011v2 | https://arxiv.org/pdf/2210.07011v2.pdf | Variational Graph Generator for Multi-View Graph Clustering | Multi-view graph clustering (MGC) methods are increasingly being studied due to the explosion of multi-view data with graph structural information. The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views. However, existing works have an inher... | ['Lifang He', 'Philip S. Yu', 'Zhifeng Hao', 'Xiaorong Pu', 'Shudong Huang', 'Yazhou Ren', 'Jie Xu', 'Yawen Ling', 'Jianpeng Chen'] | 2022-10-13 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-2.13409290e-01 1.12095661e-01 -5.31128049e-02 -2.13843137e-01
-7.63883173e-01 -7.11733162e-01 4.01496381e-01 3.51331197e-02
3.23044389e-01 2.49008611e-01 2.88138807e-01 1.63526833e-01
-4.45706308e-01 -6.23134851e-01 -5.42418122e-01 -9.27229941e-01
-3.25137861e-02 3.45569313e-01 -9.02872831e-02 -5.79223456... | [8.052624702453613, 4.860507965087891] |
28dbff23-06a2-4a3e-a511-49cbdef3d166 | neuralmind-unicamp-at-2022-trec-neuclir-large | 2303.16145 | null | https://arxiv.org/abs/2303.16145v1 | https://arxiv.org/pdf/2303.16145v1.pdf | NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval | This paper reports on a study of cross-lingual information retrieval (CLIR) using the mT5-XXL reranker on the NeuCLIR track of TREC 2022. Perhaps the biggest contribution of this study is the finding that despite the mT5 model being fine-tuned only on query-document pairs of the same language it proved to be viable for... | ['Rodrigo Nogueira', 'Roberto Lotufo', 'Vitor Jeronymo'] | 2023-03-28 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-4.34628695e-01 -3.80108267e-01 -5.63423336e-01 7.83704445e-02
-1.61297715e+00 -1.00539529e+00 1.18111241e+00 5.22247732e-01
-1.05876839e+00 5.27437985e-01 6.03489935e-01 -4.88318354e-01
-6.74762845e-01 -3.78968939e-02 -1.92332134e-01 -1.02768719e-01
-7.13730231e-02 6.69034183e-01 2.12569714e-01 -7.10984826... | [11.399214744567871, 9.82288932800293] |
44cb024c-e971-4eed-a0e1-20c2af354d1e | image-classifiers-leak-sensitive-attributes | 2303.09289 | null | https://arxiv.org/abs/2303.09289v2 | https://arxiv.org/pdf/2303.09289v2.pdf | Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations | Neural network-based image classifiers are powerful tools for computer vision tasks, but they inadvertently reveal sensitive attribute information about their classes, raising concerns about their privacy. To investigate this privacy leakage, we introduce the first Class Attribute Inference Attack (CAIA), which leverag... | ['Kristian Kersting', 'Patrick Schramowski', 'Manuel Brack', 'Felix Friedrich', 'Dominik Hintersdorf', 'Lukas Struppek'] | 2023-03-16 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 9.43395257e-01 3.61305863e-01 -1.36317506e-01 -9.36907053e-01
-7.56373167e-01 -1.09011936e+00 6.21746361e-01 3.75527814e-02
-2.62698621e-01 6.84211552e-01 -2.26946592e-01 -3.83936584e-01
2.41752550e-01 -8.28209281e-01 -9.11179662e-01 -8.82242560e-01
-1.78774484e-02 1.13264456e-01 -4.04529572e-01 2.46405482... | [12.724774360656738, 0.8469222187995911] |
4f525ffe-351b-48c3-b6ed-e5d41eb7d232 | baseline-method-for-the-sport-task-of | 2302.02752 | null | https://arxiv.org/abs/2302.02752v1 | https://arxiv.org/pdf/2302.02752v1.pdf | Baseline Method for the Sport Task of MediaEval 2022 with 3D CNNs using Attention Mechanisms | This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2022 benchmark. This task proposes two subtasks: stroke classification from trimmed videos, and stroke detection from untrimmed videos. This baseline addresses both subtasks. We propose two types of 3D-CNN architectures to ... | ['Pierre-Etienne Martin'] | 2023-02-06 | null | null | null | null | ['action-classification', 'stroke-classification'] | ['computer-vision', 'methodology'] | [-3.56932804e-02 -5.95456995e-02 -2.16264814e-01 -2.47534681e-02
-6.45150304e-01 -7.34260499e-01 7.01660335e-01 -3.64188790e-01
-9.81999993e-01 3.50599200e-01 3.65508884e-01 -3.79884362e-01
3.83325577e-01 -3.34467322e-01 -7.92023599e-01 -3.09840620e-01
1.22925498e-01 7.23021552e-02 7.70426035e-01 3.82755250... | [7.954560279846191, 0.16818839311599731] |
5f2bf354-cc11-4b2d-a026-0fd207131638 | a-visual-slam-with-moving-object-trajectory | 2303.02257 | null | https://arxiv.org/abs/2303.02257v1 | https://arxiv.org/pdf/2303.02257v1.pdf | A Visual SLAM with Moving Object Trajectory Prediction | Visual Simultaneous Localization and Mapping (SLAM) has received significant attention in recent years due to its ability to estimate camera trajectory and create an environment map using visual data alone, making a substantial contribution to autonomous driving applications, in particular, a real-world scenario with m... | ['Wenbin Li', 'Siyuan Gou', 'Qi Zhang'] | 2023-03-03 | null | null | null | null | ['trajectory-prediction', 'simultaneous-localization-and-mapping'] | ['computer-vision', 'computer-vision'] | [-3.34226906e-01 -5.06384432e-01 -5.81437014e-02 -4.06652689e-01
-2.33401790e-01 -5.82018137e-01 6.31251276e-01 1.08907133e-01
-6.64648950e-01 6.85986459e-01 -1.59288839e-01 -3.01209450e-01
1.70310766e-01 -6.88184977e-01 -5.29155135e-01 -4.82872009e-01
-1.39929846e-01 5.66708505e-01 9.54688907e-01 -1.08868279... | [7.172585964202881, -2.1037724018096924] |
448527f1-6a7e-4cce-8632-fcff814eea4e | make-one-shot-video-object-segmentation-1 | 2012.01866 | null | https://arxiv.org/abs/2012.01866v1 | https://arxiv.org/pdf/2012.01866v1.pdf | Make One-Shot Video Object Segmentation Efficient Again | Video object segmentation (VOS) describes the task of segmenting a set of objects in each frame of a video. In the semi-supervised setting, the first mask of each object is provided at test time. Following the one-shot principle, fine-tuning VOS methods train a segmentation model separately on each given object mask. H... | ['Laura Leal-Taixe', 'Tim Meinhardt'] | 2020-12-03 | make-one-shot-video-object-segmentation | http://proceedings.neurips.cc/paper/2020/hash/781397bc0630d47ab531ea850bddcf63-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/781397bc0630d47ab531ea850bddcf63-Paper.pdf | neurips-2020-12 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.12374566e-01 -1.01326577e-01 -2.55234629e-01 -4.16138738e-01
-9.92375731e-01 -7.46173143e-01 1.70643106e-01 -1.71392754e-01
-6.33463979e-01 3.60725522e-01 -3.95592272e-01 -2.17448041e-01
3.01194668e-01 -4.58317131e-01 -1.00993431e+00 -4.93214607e-01
2.03259185e-01 5.74627817e-01 8.34414661e-01 1.93139240... | [9.225085258483887, -0.056011658161878586] |
6286d588-f731-4cbd-b084-b265b7218dbf | bisyn-gat-bi-syntax-aware-graph-attention | 2204.03117 | null | https://arxiv.org/abs/2204.03117v2 | https://arxiv.org/pdf/2204.03117v2.pdf | BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to align aspects and corresponding sentiments for aspect-specific sentiment polarity inference. It is challenging because a sentence may contain multiple aspects or complicated (e.g., conditional, coordinating, or adversative) re... | ['Zhiyong He', 'Fei Wang', 'Xian-Ling Mao', 'Wei Wei', 'Shuo Liang'] | 2022-04-06 | null | https://aclanthology.org/2022.findings-acl.144 | https://aclanthology.org/2022.findings-acl.144.pdf | findings-acl-2022-5 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 4.48158793e-02 1.62007511e-01 -3.42892408e-01 -7.71672845e-01
-3.70756328e-01 -7.26380706e-01 5.02648950e-01 4.59916830e-01
6.16461635e-02 3.80872428e-01 5.87372363e-01 -4.61996526e-01
3.17694694e-01 -8.95139635e-01 -7.08560467e-01 -4.12648290e-01
3.43445629e-01 3.72499883e-01 -7.83145577e-02 -6.59172952... | [11.497518539428711, 6.629034042358398] |
e5c3b69a-d77b-44b4-8ba0-25736ae3e842 | inherent-consistent-learning-for-accurate | 2303.14175 | null | https://arxiv.org/abs/2303.14175v4 | https://arxiv.org/pdf/2303.14175v4.pdf | Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation | Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations. In this paper, we propose a novel Inherent Consistent Learning (ICL) method, aims to learn robust semantic category representations through the semantic consistency guidance of ... | ['Ruimao Zhang', 'Si-Qi Liu', 'Jie Yang', 'Ye Zhu'] | 2023-03-24 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 1.84905663e-01 4.55545992e-01 -4.57613260e-01 -7.30603158e-01
-8.42085898e-01 -1.73581645e-01 2.35007629e-01 1.60522070e-02
-3.48323286e-01 6.63477421e-01 -1.01646975e-01 -1.25714034e-01
2.45348308e-02 -4.53022838e-01 -5.68965077e-01 -6.40088618e-01
3.92332166e-01 4.92013544e-01 4.03735429e-01 3.02694082... | [9.667867660522461, 1.0996897220611572] |
f2bb5527-8c2b-481e-bae6-d2b6e1a1bfbf | learning-joint-multilingual-sentence | 1704.04154 | null | http://arxiv.org/abs/1704.04154v2 | http://arxiv.org/pdf/1704.04154v2.pdf | Learning Joint Multilingual Sentence Representations with Neural Machine Translation | In this paper, we use the framework of neural machine translation to learn
joint sentence representations across six very different languages. Our aim is
that a representation which is independent of the language, is likely to
capture the underlying semantics. We define a new cross-lingual similarity
measure, compare u... | ['Matthijs Douze', 'Holger Schwenk'] | 2017-04-13 | learning-joint-multilingual-sentence-1 | https://aclanthology.org/W17-2619 | https://aclanthology.org/W17-2619.pdf | ws-2017-8 | ['joint-multilingual-sentence-representations'] | ['natural-language-processing'] | [-9.64141041e-02 -7.21132085e-02 -3.48160356e-01 -9.27994370e-01
-1.03187025e+00 -8.03822160e-01 9.42905962e-01 6.01903260e-01
-5.28581440e-01 8.41750681e-01 8.79654884e-01 -3.28473806e-01
-8.60944390e-02 -7.51078427e-01 -7.68500328e-01 -2.98898425e-02
1.68937631e-02 4.78257507e-01 -1.74647644e-01 -7.09896147... | [10.983830451965332, 9.7974271774292] |
e00b9874-4e04-4a73-9d9d-05d17f3ae20f | trace-table-reconstruction-aligned-to-corner | 2305.00630 | null | https://arxiv.org/abs/2305.00630v1 | https://arxiv.org/pdf/2305.00630v1.pdf | TRACE: Table Reconstruction Aligned to Corner and Edges | A table is an object that captures structured and informative content within a document, and recognizing a table in an image is challenging due to the complexity and variety of table layouts. Many previous works typically adopt a two-stage approach; (1) Table detection(TD) localizes the table region in an image and (2)... | ['Seonghyeon Kim', 'Seung Shin', 'Jaeheung Surh', 'Daehyun Nam', 'Youngmin Baek'] | 2023-05-01 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 2.65817165e-01 -1.47863597e-01 -1.28393963e-01 -2.28928208e-01
-7.98810065e-01 -7.76774108e-01 3.57045501e-01 6.34303629e-01
-8.59271362e-03 3.62761945e-01 2.43941426e-01 -1.17808424e-01
1.13402411e-01 -8.96629512e-01 -9.45385158e-01 -6.09394729e-01
-3.07543408e-02 5.35668373e-01 5.03301740e-01 -4.01363820... | [11.695273399353027, 3.0375070571899414] |
597ccb79-3cff-4ab5-8627-e498d6b8dd55 | rangeseg-range-aware-real-time-segmentation | 2205.01570 | null | https://arxiv.org/abs/2205.01570v1 | https://arxiv.org/pdf/2205.01570v1.pdf | RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds | Semantic outdoor scene understanding based on 3D LiDAR point clouds is a challenging task for autonomous driving due to the sparse and irregular data structure. This paper takes advantages of the uneven range distribution of different LiDAR laser beams to propose a range aware instance segmentation network, RangeSeg. R... | ['Tian Sheuan Chang', 'Tzu-Hsuan Chen'] | 2022-05-02 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 8.12914595e-02 -2.50179827e-01 -1.21330740e-02 -9.34880793e-01
-7.12471068e-01 -4.90525573e-01 2.95843184e-01 2.88151167e-02
-8.08445156e-01 5.60736537e-01 -6.29320741e-01 -3.62966239e-01
2.72134561e-02 -1.19326866e+00 -1.03537929e+00 -3.58058900e-01
9.56072360e-02 1.05613101e+00 1.23155224e+00 -3.18882279... | [8.148159980773926, -2.5941426753997803] |
062b602a-e57c-4ce3-b7f8-6856002e5500 | universal-proposition-bank-2-0 | null | null | https://aclanthology.org/2022.lrec-1.181 | https://aclanthology.org/2022.lrec-1.181.pdf | Universal Proposition Bank 2.0 | Semantic role labeling (SRL) represents the meaning of a sentence in the form of predicate-argument structures. Such shallow semantic analysis is helpful in a wide range of downstream NLP tasks and real-world applications. As treebanks enabled the development of powerful syntactic parsers, the accurate predicate-argume... | ['Yunyao Li', 'Huaiyu Zhu', 'Khoi-Nguyen Tran', 'Huyen Nguyen', 'Ha Linh', 'Michał Ulewicz', 'Alexandre Rademaker', 'Ishan Jindal'] | null | null | null | null | lrec-2022-6 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.32193780e-02 5.80240607e-01 -5.33963084e-01 -4.92883921e-01
-1.22439337e+00 -9.02013659e-01 4.63573635e-01 4.66191798e-01
-4.75853324e-01 1.29443383e+00 6.98718011e-01 -4.88126069e-01
2.12741703e-01 -7.56607413e-01 -7.34464526e-01 -2.66514599e-01
-1.02391643e-02 5.17505825e-01 5.25244296e-01 -6.77796125... | [10.3731107711792, 9.4784574508667] |
cc0a1c82-580a-4c67-aea4-b6d5fd4ce0fa | masked-reconstruction-contrastive-learning | 2211.09013 | null | https://arxiv.org/abs/2211.09013v1 | https://arxiv.org/pdf/2211.09013v1.pdf | Masked Reconstruction Contrastive Learning with Information Bottleneck Principle | Contrastive learning (CL) has shown great power in self-supervised learning due to its ability to capture insight correlations among large-scale data. Current CL models are biased to learn only the ability to discriminate positive and negative pairs due to the discriminative task setting. However, this bias would lead ... | ['Xuecheng Nie', 'Tiande Guo', 'Congying Han', 'Bonan Li', 'Ziwen Liu'] | 2022-11-15 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 7.47958779e-01 -2.26877872e-02 -5.58810174e-01 -4.69571084e-01
-6.65331423e-01 -2.72159368e-01 3.30429077e-01 8.03034306e-02
-4.66382414e-01 6.24963820e-01 3.96635085e-02 -4.69894201e-01
-3.40210140e-01 -6.03032172e-01 -5.45793056e-01 -8.59294832e-01
1.31659672e-01 -1.77638251e-02 9.37602669e-02 -1.04402024... | [9.348588943481445, 3.0815393924713135] |
292d7577-0c70-4b8e-87d5-e738a9f2711f | graph-information-aggregation-cross-domain | null | null | https://doi.org/10.1109/TNNLS.2022.3185795 | https://doi.org/10.1109/TNNLS.2022.3185795 | Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification | Most domain adaptation (DA) methods in cross-scene hyperspectral image classification focus on cases where source data (SD) and target data (TD) with the same classes are obtained by the same sensor. However, the classification performance is significantly reduced when there are new classes in TD. In addition, domain a... | ['Qian Du', 'Ran Tao', 'Shuai Wang', 'Mengmeng Zhang', 'Wei Li', 'Yuxiang Zhang'] | 2022-06-30 | null | null | null | ieee-transactions-on-neural-networks-and-15 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.71878797e-01 -4.87744749e-01 -2.10621715e-01 -3.45950097e-01
-6.47118270e-01 -4.16007727e-01 3.40123415e-01 3.47165674e-01
7.55806640e-02 7.63011813e-01 7.45575689e-03 8.78814831e-02
-8.01498652e-01 -1.05451477e+00 -2.39785030e-01 -1.31292629e+00
1.11741513e-01 2.73612261e-01 3.19854617e-01 -1.57061934... | [10.038907051086426, -1.727795958518982] |
37c9b5b2-ea8b-4a0c-9a80-7dcaa72cf200 | decoupling-knowledge-from-memorization | 2205.14704 | null | https://arxiv.org/abs/2205.14704v3 | https://arxiv.org/pdf/2205.14704v3.pdf | Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning | Prompt learning approaches have made waves in natural language processing by inducing better few-shot performance while they still follow a parametric-based learning paradigm; the oblivion and rote memorization problems in learning may encounter unstable generalization issues. Specifically, vanilla prompt learning may ... | ['Huajun Chen', 'Luo Si', 'Fei Huang', 'Chuanqi Tan', 'Shumin Deng', 'Xiaozhuan Liang', 'Ningyu Zhang', 'Lei LI', 'Xiang Chen'] | 2022-05-29 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 4.91702221e-02 -7.36035919e-03 -3.65966916e-01 -2.63504714e-01
-6.08241618e-01 -4.75373060e-01 6.28029466e-01 4.85567659e-01
-8.02614987e-01 5.50537288e-01 1.67850927e-01 -3.44136804e-01
-2.59311378e-01 -9.83908117e-01 -6.12549186e-01 -5.48231304e-01
9.23465416e-02 2.49579817e-01 2.11944446e-01 -2.03839719... | [10.718913078308105, 7.8835930824279785] |
3016302f-fc73-4d91-9d3b-10915d3b0d05 | bayesian-inference-for-jump-diffusion | 2304.06592 | null | https://arxiv.org/abs/2304.06592v1 | https://arxiv.org/pdf/2304.06592v1.pdf | Bayesian Inference for Jump-Diffusion Approximations of Biochemical Reaction Networks | Biochemical reaction networks are an amalgamation of reactions where each reaction represents the interaction of different species. Generally, these networks exhibit a multi-scale behavior caused by the high variability in reaction rates and abundances of species. The so-called jump-diffusion approximation is a valuabl... | ['Heinz Koeppl', 'Bastian Alt', 'Derya Altıntan'] | 2023-04-13 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.65640199e-01 -1.64857298e-01 1.05631940e-01 1.50515959e-01
-6.08115345e-02 -3.48674476e-01 1.13680160e+00 4.92520779e-01
-4.89146799e-01 1.03928232e+00 -2.20571458e-01 -3.76308620e-01
-2.14322074e-03 -1.04911458e+00 -6.01141274e-01 -1.24711978e+00
-1.27682045e-01 1.03297544e+00 4.61185426e-01 8.32067877... | [6.217855453491211, 4.18162202835083] |
06f168f3-bc7a-47a3-9416-1a3d5e6829ee | skeleton-prototype-contrastive-learning-with | 2208.11814 | null | https://arxiv.org/abs/2208.11814v1 | https://arxiv.org/pdf/2208.11814v1.pdf | Skeleton Prototype Contrastive Learning with Multi-Level Graph Relation Modeling for Unsupervised Person Re-Identification | Person re-identification (re-ID) via 3D skeletons is an important emerging topic with many merits. Existing solutions rarely explore valuable body-component relations in skeletal structure or motion, and they typically lack the ability to learn general representations with unlabeled skeleton data for person re-ID. This... | ['Chunyan Miao', 'Haocong Rao'] | 2022-08-25 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.08219255e-02 1.44633457e-01 -5.18828392e-01 -2.74919063e-01
-1.95139334e-01 6.50643110e-02 5.63628972e-01 -1.35874197e-01
-2.60419957e-02 3.35205048e-01 7.32608259e-01 6.16996109e-01
-2.37072021e-01 -9.50998485e-01 -2.46276349e-01 -3.17507625e-01
-1.80653259e-01 9.34272230e-01 1.72716275e-01 -2.97511786... | [14.632935523986816, 1.0475571155548096] |
eae5939a-6781-4f05-9582-fb46fe804959 | complex-word-identification-using-character-n | null | null | https://aclanthology.org/W18-0541 | https://aclanthology.org/W18-0541.pdf | Complex Word Identification Using Character n-grams | This paper investigates the use of character n-gram frequencies for identifying complex words in English, German and Spanish texts. The approach is based on the assumption that complex words are likely to contain different character sequences than simple words. The multinomial Naive Bayes classifier was used with n-gra... | ["Maja Popovi{\\'c}"] | 2018-06-01 | null | null | null | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-1.14454828e-01 -1.60799399e-01 -1.72755376e-01 -7.87988212e-03
-8.74953985e-01 -9.58441257e-01 9.89077508e-01 6.34927571e-01
-1.15518296e+00 8.19474578e-01 1.09719098e-01 -6.61669791e-01
9.11127478e-02 -4.07369852e-01 -1.57615431e-02 -3.81156296e-01
-9.85700861e-02 5.38462460e-01 2.26851955e-01 -2.78234899... | [10.560651779174805, 10.502967834472656] |
6479f570-a87e-4007-aa89-d32b559e45c3 | pv3d-a-3d-generative-model-for-portrait-video | 2212.06384 | null | https://arxiv.org/abs/2212.06384v3 | https://arxiv.org/pdf/2212.06384v3.pdf | PV3D: A 3D Generative Model for Portrait Video Generation | Recent advances in generative adversarial networks (GANs) have demonstrated the capabilities of generating stunning photo-realistic portrait images. While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extend... | ['Zhongcong Xu', 'Mike Zheng Shou', 'Jiashi Feng', 'Song Bai', 'Wenqing Zhang', 'Jun Hao Liew', 'Jianfeng Zhang'] | 2022-12-13 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 3.08954686e-01 -3.32261585e-02 1.72051731e-02 -6.22225553e-03
-4.59353268e-01 -8.86538625e-01 8.40403616e-01 -1.01969039e+00
3.95578414e-01 7.93493152e-01 1.57686055e-01 -1.35371611e-01
2.32353956e-01 -9.94757891e-01 -8.95434141e-01 -7.81599402e-01
3.41404468e-01 5.87620996e-02 -9.01660323e-02 -2.31411546... | [12.29703140258789, -0.46961429715156555] |
046b16f2-15f7-4810-9e1c-51bccb827ccf | document-image-layout-analysis-via-explicit | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0020025521007106 | http://zhengyingbin.cc/index_files/e3net.pdf | Document Image Layout Analysis via Explicit Edge Embedding Network | Layout analysis from a document image plays an important role in document content understanding and information extraction systems. While many existing methods focus on learning knowledge with convolutional networks directly from color channels, we argue the importance of highfrequency structures in document images, e... | ['Liang He', 'Hao Ye', 'Tianlong Ma', 'Yingbin Zheng', 'Xingjiao Wu'] | 2021-10-01 | null | null | null | information-sciences-2021-10 | ['document-layout-analysis'] | ['computer-vision'] | [ 2.18959272e-01 -2.02001080e-01 -1.44530565e-01 -3.27155143e-01
-2.21967667e-01 -4.83296275e-01 6.67461157e-01 5.82421906e-02
-2.14157507e-01 3.61274481e-01 4.10864562e-01 -3.45232904e-01
-1.71794429e-01 -8.62924695e-01 -7.64450133e-01 -6.37009919e-01
6.60793856e-02 -3.54178220e-01 -3.35043520e-02 -7.52900727... | [11.591004371643066, 2.335472822189331] |
c22328ee-c1f2-495c-a047-5b07e285d323 | hitachi-at-semeval-2022-task-2-on-the | null | null | https://aclanthology.org/2022.semeval-1.15 | https://aclanthology.org/2022.semeval-1.15.pdf | Hitachi at SemEval-2022 Task 2: On the Effectiveness of Span-based Classification Approaches for Multilingual Idiomaticity Detection | In this paper, we describe our system for SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding. The task aims at detecting idiomaticity in an input sequence (Subtask A) and modeling representation of sentences that contain potential idiomatic multiword expressions (MWEs) (Subtask B) in three ... | ['Yasuhiro Sogawa', 'Hiroaki Ozaki', 'Gaku Morio', 'Atsuki Yamaguchi'] | null | null | null | null | semeval-naacl-2022-7 | ['xlm-r'] | ['natural-language-processing'] | [-5.77876531e-02 -8.22472498e-02 -6.87030077e-01 -4.27149922e-01
-8.40895295e-01 -7.51511574e-01 7.39657462e-01 -2.53212273e-01
-3.64610463e-01 5.08129001e-01 6.70068800e-01 -5.44841468e-01
8.58206451e-02 -4.99498218e-01 -2.04383194e-01 -1.42700344e-01
-2.43382290e-01 9.03072059e-01 -3.91602039e-01 -9.73415196... | [10.802519798278809, 9.652938842773438] |
e2ae8e9b-3e7d-4a8c-9ef5-bc592f5ac44d | beyond-admm-a-unified-client-variance-reduced | 2212.01519 | null | https://arxiv.org/abs/2212.01519v3 | https://arxiv.org/pdf/2212.01519v3.pdf | Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework | As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and communication resources. Various client-variance-reduction schemes and client sampling strategies have been respectively introduced to impro... | ['Defeng Sun', 'Tony Q. S. Quek', 'Tsung-Hui Chang', 'Zhiguo Wang', 'Yanqing Xu', 'Shuai Wang'] | 2022-12-03 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [-2.84951091e-01 -3.26976001e-01 -3.58733684e-01 -2.96999276e-01
-8.28283727e-01 -4.23203379e-01 4.29741263e-01 -2.18476534e-01
-4.60428884e-03 8.50919783e-01 1.27156489e-02 -3.78781855e-01
-5.84425569e-01 -7.02306569e-01 -6.15091264e-01 -1.25995457e+00
-8.11586156e-02 5.25001049e-01 -7.40354136e-02 -6.27178326... | [5.859445571899414, 6.25146484375] |
40ad0acc-a287-4415-955a-dd3654e30172 | speech-enhancement-with-multi-granularity | 2302.08342 | null | https://arxiv.org/abs/2302.08342v1 | https://arxiv.org/pdf/2302.08342v1.pdf | Speech Enhancement with Multi-granularity Vector Quantization | With advances in deep learning, neural network based speech enhancement (SE) has developed rapidly in the last decade. Meanwhile, the self-supervised pre-trained model and vector quantization (VQ) have achieved excellent performance on many speech-related tasks, while they are less explored on SE. As it was shown in ou... | ['Jie Zhang', 'Qiu-Shi Zhu', 'Xiao-Ying Zhao'] | 2023-02-16 | null | null | null | null | ['speech-enhancement', 'speech-denoising'] | ['speech', 'speech'] | [ 2.89426655e-01 1.02857187e-01 1.43179089e-01 -4.36849743e-01
-7.07646132e-01 5.80814183e-02 5.53910553e-01 1.93014696e-01
-7.71543980e-01 4.45610344e-01 6.38024092e-01 5.49879894e-02
-1.46174893e-01 -6.05178833e-01 -5.92980683e-01 -9.23088074e-01
-8.75744224e-02 -4.58023071e-01 1.51433378e-01 -4.64540124... | [14.978838920593262, 5.981487274169922] |
96470085-76af-4242-b11a-2274f28b2812 | weakly-supervised-positional-contrastive | 2307.04617 | null | https://arxiv.org/abs/2307.04617v1 | https://arxiv.org/pdf/2307.04617v1.pdf | Weakly-supervised positional contrastive learning: application to cirrhosis classification | Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e.g., radiological scores). Access to high-confidence labels, such as histology-based diagnoses, is rare and costly. Pretraining strategies, like contrastive learning (CL) methods, can leverage unlabeled or weakly-anno... | ['Isabelle Bloch', 'Pietro Gori', 'Marc-Michel Rohé', 'Alexandre Bône', 'Emma Sarfati'] | 2023-07-10 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'classification-1'] | ['computer-vision', 'methodology', 'methodology'] | [ 5.30820563e-02 1.76546797e-01 -4.69721794e-01 -4.96179312e-01
-1.45791280e+00 -6.14903808e-01 2.09364489e-01 7.67031670e-01
-4.29271787e-01 9.64220107e-01 1.20803080e-01 -5.18331528e-01
-1.59137994e-02 -5.95808208e-01 -6.71364844e-01 -1.05311775e+00
-2.81118721e-01 7.17181981e-01 3.64401191e-01 5.05447865... | [14.663549423217773, -2.362980604171753] |
0bbc0d74-8be4-4eea-89ab-022bf9e811cf | any-to-any-generation-via-composable | 2305.11846 | null | https://arxiv.org/abs/2305.11846v1 | https://arxiv.org/pdf/2305.11846v1.pdf | Any-to-Any Generation via Composable Diffusion | We present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities. Unlike existing generative AI systems, CoDi can generate multiple modalities in parallel and its input is not l... | ['Mohit Bansal', 'Michael Zeng', 'Chenguang Zhu', 'ZiYi Yang', 'Zineng Tang'] | 2023-05-19 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 5.27980685e-01 1.58614621e-01 7.71776661e-02 9.93820205e-02
-8.76973033e-01 -1.10844684e+00 1.15719819e+00 -4.56142545e-01
6.64056465e-02 7.52931416e-01 4.21380728e-01 -2.89330259e-02
1.34206293e-02 -7.57003009e-01 -7.41351306e-01 -4.96351182e-01
1.88259393e-01 4.98560071e-01 -1.02878518e-01 -1.96873054... | [11.150273323059082, -0.10158813744783401] |
fcb7ae9c-f83c-407c-83f9-470ad663be5c | penelopie-enabling-open-information | 2103.15075 | null | https://arxiv.org/abs/2103.15075v1 | https://arxiv.org/pdf/2103.15075v1.pdf | PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation | In this paper we present our submission for the EACL 2021 SRW; a methodology that aims at bridging the gap between high and low-resource languages in the context of Open Information Extraction, showcasing it on the Greek language. The goals of this paper are twofold: First, we build Neural Machine Translation (NMT) mod... | ['Nikolaos Matsatsinis', 'Nikolaos Papadakis', 'Dimitris Papadopoulos'] | 2021-03-28 | null | https://aclanthology.org/2021.eacl-srw.4 | https://aclanthology.org/2021.eacl-srw.4.pdf | eacl-2021-2 | ['open-information-extraction'] | ['natural-language-processing'] | [ 4.23945487e-01 4.71802026e-01 -1.86814010e-01 -4.29915860e-02
-1.45328164e+00 -7.28402078e-01 7.64137506e-01 1.53623641e-01
-4.98129010e-01 1.13303816e+00 5.89728892e-01 -8.19821596e-01
1.93144053e-01 -1.00517094e+00 -1.09885824e+00 3.21443826e-01
5.14136255e-01 9.72064793e-01 1.85960516e-01 -7.72700191... | [10.800636291503906, 9.564441680908203] |
13b6e13b-f794-400d-9090-2bd9666024fc | knowledge-graph-is-in-rescue-task-oriented | null | null | https://openreview.net/forum?id=UQk8XMFAE2u | https://openreview.net/pdf?id=UQk8XMFAE2u | Knowledge Graph is in Rescue: Task Oriented Dialogue System for Response Generation without NLU and DM | Natural language understanding (NLU) and dialogue management (DM) are the standard prerequisites for response generation in a task-oriented dialogue system. In the existing literature, NLU and DM have been tackled as two independent tasks, requiring separate labeled data. Besides this problem of additional data require... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['dialogue-management'] | ['natural-language-processing'] | [ 4.17554945e-01 6.09123170e-01 3.42428386e-01 -5.94460130e-01
-5.14172733e-01 -6.29239917e-01 8.42276752e-01 1.46799967e-01
-3.82722765e-01 9.68457639e-01 4.25817907e-01 -5.45030832e-01
2.08017126e-01 -1.01500261e+00 -2.22859457e-01 -2.11376280e-01
4.30665344e-01 9.28296208e-01 1.61680222e-01 -7.43753314... | [12.68867015838623, 8.09184741973877] |
76297b21-5eca-4ec8-a800-a77278cafa0a | russiansuperglue-a-russian-language | 2010.15925 | null | https://arxiv.org/abs/2010.15925v2 | https://arxiv.org/pdf/2010.15925v2.pdf | RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark | In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE. Recent advances in the field of universal language models and transformers require the development of a methodology for their broad diagnostics and testing for general intellectual skills - detection of n... | ['Andrey Evlampiev', 'Andrey Chertok', 'Maria Tikhonova', 'Vladislav Mikhailov', 'Valentin Malykh', 'Ekaterina Artemova', 'Denis Shevelev', 'Anton Emelyanov', 'Alena Fenogenova', 'Tatiana Shavrina'] | 2020-10-29 | null | https://aclanthology.org/2020.emnlp-main.381 | https://aclanthology.org/2020.emnlp-main.381.pdf | emnlp-2020-11 | ['logical-reasoning-question-ansering'] | ['natural-language-processing'] | [-7.32482821e-02 3.97407055e-01 1.53080344e-01 -3.27580214e-01
-6.82604253e-01 -6.62482917e-01 7.50504315e-01 3.64363223e-01
-4.43920046e-01 8.13082099e-01 9.06080008e-02 -6.30412638e-01
-4.49816942e-01 -8.42556417e-01 -5.38031101e-01 3.52427065e-02
4.31157440e-01 1.15335441e+00 1.19273216e-01 -6.86445594... | [9.741512298583984, 7.444939136505127] |
60b39bb1-dc85-485a-bf5d-258ed5be2155 | the-munich-biovoice-corpus-effects-of | null | null | https://aclanthology.org/L14-1491 | https://aclanthology.org/L14-1491.pdf | The Munich Biovoice Corpus: Effects of Physical Exercising, Heart Rate, and Skin Conductance on Human Speech Production | We introduce a spoken language resource for the analysis of impact that physical exercising has on human speech production. In particular, the database provides heart rate and skin conductance measurement information alongside the audio recordings. It contains recordings from 19 subjects in a relaxed state and after ex... | ['Bj{\\"o}rn Schuller', 'Florian Eyben', 'Felix Friedmann'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['heart-rate-estimation'] | ['medical'] | [ 4.02108431e-01 2.98305482e-01 -2.70047411e-02 -2.60467172e-01
-8.67475331e-01 -3.60124826e-01 3.59766245e-01 3.00636649e-01
-4.19429392e-01 4.87827182e-01 6.68551385e-01 3.02817896e-02
3.95327844e-02 -2.95658529e-01 2.34684333e-01 -8.43391359e-01
-2.37589866e-01 -1.73518732e-01 -3.81640613e-01 6.15012972... | [13.787335395812988, 3.1227710247039795] |
524192e1-c47b-4946-8014-02565c484980 | english-to-bengali-multimodal-neural-machine | null | null | https://aclanthology.org/2022.wat-1.14 | https://aclanthology.org/2022.wat-1.14.pdf | English to Bengali Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation | Automatic translation of one natural language to another is a popular task of natural language processing. Although the deep learning-based technique known as neural machine translation (NMT) is a widely accepted machine translation approach, it needs an adequate amount of training data, which is a challenging issue fo... | ['Sivaji Bandyopadhyay', 'Partha Pakray', 'Riyanka Manna', 'Pankaj Dadure', 'Sahinur Rahman Laskar'] | null | null | null | null | wat-2022-10 | ['transliteration'] | ['natural-language-processing'] | [ 3.19411844e-01 -2.41637468e-01 -7.72548020e-02 -1.90443411e-01
-1.53934205e+00 -8.47786725e-01 8.92581522e-01 -1.39696002e-01
-4.80508208e-01 1.03767312e+00 1.61906347e-01 -5.78563571e-01
6.19249880e-01 -5.13745904e-01 -9.80689764e-01 -5.13236463e-01
6.61666095e-01 1.04875469e+00 -3.47427726e-01 -4.71419126... | [11.499488830566406, 1.5460001230239868] |
809029cc-1e4d-485c-9daa-b2f79ae5b032 | recursions-are-all-you-need-towards-efficient | 2305.05505 | null | https://arxiv.org/abs/2305.05505v1 | https://arxiv.org/pdf/2305.05505v1.pdf | Recursions Are All You Need: Towards Efficient Deep Unfolding Networks | The use of deep unfolding networks in compressive sensing (CS) has seen wide success as they provide both simplicity and interpretability. However, since most deep unfolding networks are iterative, this incurs significant redundancies in the network. In this work, we propose a novel recursion-based framework to enhance... | ['Ali Al-Shaikhi', 'Hamzah Luqman', 'Motaz Alfarraj', 'Rawwad Alhejaili'] | 2023-05-09 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 2.90033996e-01 1.43674821e-01 -3.74789420e-03 -3.07695597e-01
-5.89290082e-01 -4.80460674e-01 2.89300948e-01 -2.72859931e-01
-3.29181880e-01 3.74028116e-01 2.64228195e-01 -6.82276011e-01
-1.62989050e-01 -7.08816767e-01 -7.83637166e-01 -7.35971928e-01
-1.48466602e-01 -1.00201368e-01 -7.38795027e-02 -5.27259558... | [11.0884428024292, -1.91536283493042] |
c23e93da-4457-4678-aa71-0938341bbcd6 | an-open-framework-for-remote-ppg-methods-and | null | null | https://ieeexplore.ieee.org/document/9272290 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9272290 | An Open Framework for Remote-PPG Methods and their Assessment | This paper presents a comprehensive framework for studying methods of pulse rate estimation relying on remote photoplethysmography (rPPG). There has been a remarkable development of rPPG techniques in recent years, and the publication of several surveys too, yet a sound assessment of their performance has been overlook... | ['Raffaella Lanzarotti', 'Giuliano Grossi', 'Alessandro D’Amelio', 'Vittorio Cuculo', 'Donatello Conte', 'Giuseppe Boccignone'] | 2020-11-26 | null | null | null | null | ['physiological-computing', 'photoplethysmography-ppg', 'heart-rate-estimation'] | ['computer-vision', 'medical', 'medical'] | [ 2.15582445e-01 -2.41895363e-01 7.67881945e-02 -2.37356335e-01
-3.75429839e-01 -5.46759963e-01 5.16256571e-01 8.72485936e-02
-4.72140342e-01 7.14740992e-01 8.57066810e-02 -2.13290110e-01
-1.74385071e-01 -4.22734022e-01 -2.27435321e-01 -9.42022145e-01
-9.15475860e-02 1.55020282e-01 4.13220972e-01 1.45271778... | [13.872936248779297, 2.786458730697632] |
58fe8d3f-1137-45f7-b9f5-239cb510ed38 | ai-augmentation-of-radiologist-performance-in | null | null | https://pubs.rsna.org/doi/full/10.1148/radiol.2020201491 | https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020201491 | AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT | Background
COVID-19 and pneumonia of other etiology share similar CT characteristics, contributing to the challenges in differentiating them with high accuracy.
Purpose
To establish and evaluate an artificial intelligence (AI) system in differentiating COVID-19 and other pneumonia on chest CT and assess radiologis... | ['Wei-Hua Liao', 'Qi-Zhi Yu', 'Raymond Y. Huang', 'Fei-Xian Fu', 'Yi-Hui Li', 'Ping-Feng Hu', 'Qiu-Hua Zeng', 'Xiao-Long Jiang', 'Lin-Bo Shi', 'Dong-Cui Wang', 'Zeng Xiong', 'Thi My Linh Tran', 'Ji Whae Choi', 'Ben Hsieh', 'Kasey Halsey', 'Ji Mei', 'Ronnie Sebro', 'Robin Wang', 'Michael K. Atalay', 'Ken Chang', 'Ian Pa... | 2020-04-27 | null | null | null | rsna-2020-4 | ['covid-19-image-segmentation'] | ['computer-vision'] | [ 1.69209167e-01 1.63951945e-02 -3.49069983e-01 -1.00003548e-01
-9.01298046e-01 -7.90892184e-01 9.04059038e-03 4.27960008e-01
-7.06100047e-01 6.28837228e-01 2.11879417e-01 -9.57328439e-01
-4.03043360e-01 -6.16559327e-01 -5.68245471e-01 -6.08341992e-01
-2.33269423e-01 1.05826783e+00 3.11473399e-01 9.43307638... | [15.483450889587402, -1.9082891941070557] |
28675b52-d205-4825-87d1-5c5f4db963f3 | extracting-narrative-timelines-as-temporal | null | null | https://aclanthology.org/P12-1010 | https://aclanthology.org/P12-1010.pdf | Extracting Narrative Timelines as Temporal Dependency Structures | null | ['Marie-Francine Moens', 'R', 'Oleks Kolomiyets', 'Steven Bethard'] | 2012-07-01 | null | null | null | acl-2012-7 | ['temporal-information-extraction'] | ['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... | [-7.321430683135986, 3.6746392250061035] |
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