paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
200c03de-4f3c-4ec1-b59a-0addc3aa2d3f | scale-scaling-up-the-complexity-for-advanced | 2306.09237 | null | https://arxiv.org/abs/2306.09237v1 | https://arxiv.org/pdf/2306.09237v1.pdf | SCALE: Scaling up the Complexity for Advanced Language Model Evaluation | Recent strides in Large Language Models (LLMs) have saturated many NLP benchmarks (even professional domain-specific ones), emphasizing the need for novel, more challenging novel ones to properly assess LLM capabilities. In this paper, we introduce a novel NLP benchmark that poses challenges to current LLMs across four... | ['Joel Niklaus', 'Daniel E. Ho', 'Ilias Chalkidis', 'Matthias Stürmer', 'Veton Matoshi', 'Ronja Stern', 'Vishvaksenan Rasiah'] | 2023-06-15 | null | null | null | null | ['text-classification', 'information-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.93291926e-02 3.96052003e-02 -7.43994772e-01 -2.17412710e-01
-1.96946156e+00 -1.01481926e+00 1.09036899e+00 3.60358924e-01
-7.18854070e-01 1.24090803e+00 7.19668746e-01 -6.26051009e-01
-2.39502057e-01 -2.32785285e-01 -7.89873123e-01 -2.02961028e-01
1.65126130e-01 8.73516023e-01 -1.55233383e-01 -3.13584268... | [10.380867004394531, 9.367769241333008] |
0db61c43-9f1d-4e3c-9a0f-b37ca6fbb9e1 | decoupled-variational-embedding-for-signed | 2008.12450 | null | https://arxiv.org/abs/2008.12450v1 | https://arxiv.org/pdf/2008.12450v1.pdf | Decoupled Variational Embedding for Signed Directed Networks | Node representation learning for signed directed networks has received considerable attention in many real-world applications such as link sign prediction, node classification and node recommendation. The challenge lies in how to adequately encode the complex topological information of the networks. Recent studies main... | ['Yan-Feng Wang', 'Xu Chen', 'Ya zhang', 'Jiangchao Yao', 'Maosen Li'] | 2020-08-28 | null | null | null | null | ['link-sign-prediction'] | ['graphs'] | [-9.95899886e-02 2.40291297e-01 -5.07306814e-01 -5.53545713e-01
1.37720719e-01 -6.58378482e-01 5.95751286e-01 1.92080200e-01
1.76947176e-01 4.07503158e-01 3.86472464e-01 -2.83410847e-01
-7.53262699e-01 -1.11490393e+00 -5.70586681e-01 -6.21554554e-01
-6.68954372e-01 3.20147991e-01 8.05967376e-02 -3.04404348... | [7.220211982727051, 6.182987213134766] |
a76544c2-a0a6-48e8-ac48-2e1b8c180282 | flocks-of-stochastic-parrots-differentially | 2305.15594 | null | https://arxiv.org/abs/2305.15594v1 | https://arxiv.org/pdf/2305.15594v1.pdf | Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models | Large language models (LLMs) are excellent in-context learners. However, the sensitivity of data contained in prompts raises privacy concerns. Our work first shows that these concerns are valid: we instantiate a simple but highly effective membership inference attack against the data used to prompt LLMs. To address thi... | ['Franziska Boenisch', 'Nicolas Papernot', 'Adam Dziedzic', 'Haonan Duan'] | 2023-05-24 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [-7.70723075e-02 1.57380298e-01 6.94939569e-02 -7.01244473e-01
-1.39861238e+00 -1.20557702e+00 4.87651169e-01 2.21941352e-01
-8.38121593e-01 8.91912341e-01 -1.15152217e-01 -6.53058052e-01
2.84695923e-01 -7.26823628e-01 -1.10179436e+00 -8.11382115e-01
-4.07145992e-02 1.14737131e-01 1.75778314e-01 -4.40454787... | [5.977313041687012, 6.856266975402832] |
bf793681-c474-468b-8501-59c91712089b | stock-price-prediction-using-principle | 1803.05075 | null | http://arxiv.org/abs/1803.05075v1 | http://arxiv.org/pdf/1803.05075v1.pdf | Stock Price Prediction using Principle Components | The literature provides strong evidence that stock prices can be predicted
from past price data. Principal component analysis (PCA) is a widely used
mathematical technique for dimensionality reduction and analysis of data by
identifying a small number of principal components to explain the variation
found in a data set... | [] | 2018-03-13 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-4.69785780e-01 -4.41247314e-01 8.68506655e-02 -1.48152292e-01
-2.25623518e-01 -7.83658385e-01 5.57862461e-01 -5.82580388e-01
-9.39333066e-02 5.61043680e-01 4.74906713e-01 -3.38539988e-01
-4.78226423e-01 -8.86250079e-01 -1.30175427e-01 -7.93941975e-01
-8.04357305e-02 3.63500923e-01 -1.05175495e-01 -9.43563208... | [4.76621150970459, 4.1323018074035645] |
9baf4213-50a1-4b3d-87df-de72913e3e7f | offline-policy-optimization-in-rl-with | 2212.14405 | null | https://arxiv.org/abs/2212.14405v1 | https://arxiv.org/pdf/2212.14405v1.pdf | Offline Policy Optimization in RL with Variance Regularizaton | Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy RL algorithms suffer from distributional shift, due to mismatch between dataset and the target policy, leading to high variance and over-es... | ['Doina Precup', 'Lihong Li', 'Zhaoran Wang', 'Animesh Garg', 'Zhuoran Yang', 'Samin Yeasar Arnob', 'Homanga Bharadhwaj', 'Samarth Sinha', 'Riashat Islam'] | 2022-12-29 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 4.89612762e-03 1.54216588e-01 -6.66791320e-01 1.21012695e-01
-1.04060364e+00 -8.02806199e-01 4.47518378e-01 2.43128985e-01
-7.27082133e-01 1.25799465e+00 1.54361457e-01 -5.64887702e-01
-3.45198095e-01 -3.84835392e-01 -9.45029557e-01 -7.99347997e-01
-6.86530545e-02 4.63285089e-01 -1.55004233e-01 -1.72417253... | [4.139175891876221, 2.4229421615600586] |
aac571f0-1b32-4765-a9a5-26c7786aa73d | cosst-multi-organ-segmentation-with-partially | 2304.14030 | null | https://arxiv.org/abs/2304.14030v2 | https://arxiv.org/pdf/2304.14030v2.pdf | COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training | Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sample size and only partially labeled, i.e., only a subset of organs are annotated. Therefore, it is c... | ['Sasa Grbic', 'Ipek Oguz', 'Guillaume Chabin', 'Jianing Wang', 'Hao Li', 'Riqiang Gao', 'Zhoubing Xu', 'Han Liu'] | 2023-04-27 | null | null | null | null | ['outlier-detection', 'pseudo-label'] | ['methodology', 'miscellaneous'] | [ 5.03810942e-01 3.36232364e-01 -5.20394564e-01 -6.00227773e-01
-1.41806829e+00 -4.90181267e-01 5.02102897e-02 9.49822143e-02
-3.66787404e-01 7.07799554e-01 -2.08035335e-02 4.65315320e-02
8.90645310e-02 -2.75670707e-01 -7.11462736e-01 -1.03766501e+00
3.56783867e-01 7.45034814e-01 2.56848305e-01 5.10558903... | [14.685197830200195, -2.081237316131592] |
ca61db9e-df98-479f-8b2b-8fcb3d15069f | context-aware-emotion-recognition-networks | 1908.05913 | null | https://arxiv.org/abs/1908.05913v1 | https://arxiv.org/pdf/1908.05913v1.pdf | Context-Aware Emotion Recognition Networks | Traditional techniques for emotion recognition have focused on the facial expression analysis only, thus providing limited ability to encode context that comprehensively represents the emotional responses. We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facia... | ['Seungryong Kim', 'Jungin Park', 'Sunok Kim', 'Kwanghoon Sohn', 'Jiyoung Lee'] | 2019-08-16 | context-aware-emotion-recognition-networks-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Lee_Context-Aware_Emotion_Recognition_Networks_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Lee_Context-Aware_Emotion_Recognition_Networks_ICCV_2019_paper.pdf | iccv-2019-10 | ['emotion-recognition-in-context'] | ['natural-language-processing'] | [ 2.33577907e-01 -4.51921284e-01 -1.52800474e-02 -8.05559039e-01
-3.71459872e-01 -3.35102051e-01 3.95453304e-01 -2.59950072e-01
-4.30299848e-01 4.64497656e-01 4.76141274e-01 3.20822090e-01
1.49803326e-01 -5.03421426e-01 -4.33384597e-01 -8.79157364e-01
-2.91781962e-01 -4.28184301e-01 -5.90018094e-01 -5.82728386... | [13.581469535827637, 1.7915948629379272] |
e1af1d48-2785-42d8-a00c-a879360bbbda | multi-modal-cross-domain-alignment-network | 2209.11572 | null | https://arxiv.org/abs/2209.11572v2 | https://arxiv.org/pdf/2209.11572v2.pdf | Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval | As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given language query. Most previous methods depend heavily on numerous manual annotations (i.e., moment boundaries), which are extremely expensive t... | ['Yuchong Hu', 'Pan Zhou', 'Daizong Liu', 'Xiang Fang'] | 2022-09-23 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 1.49239108e-01 -4.80958790e-01 -4.16919261e-01 -2.90946901e-01
-1.17872119e+00 -7.10403919e-01 6.28319085e-01 -2.96606123e-02
-4.15024936e-01 3.72130394e-01 1.20216578e-01 3.05491656e-01
3.48719880e-02 -5.76330185e-01 -7.20327914e-01 -5.44713676e-01
2.81108856e-01 3.78363580e-01 5.03448427e-01 -9.56938341... | [10.27379322052002, 0.7949258089065552] |
592f8466-240d-45e1-81b2-fe601421eccb | differential-covariance-a-new-class-of | 1706.02451 | null | http://arxiv.org/abs/1706.02451v1 | http://arxiv.org/pdf/1706.02451v1.pdf | Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings | With our ability to record more neurons simultaneously, making sense of these
data is a challenge. Functional connectivity is one popular way to study the
relationship between multiple neural signals. Correlation-based methods are a
set of currently well-used techniques for functional connectivity estimation.
However, ... | ['Terrence J. Sejnowski', 'Maxim Bazhenov', 'Giri P. Krishnan', 'Anup Das', 'Tiger W. Lin'] | 2017-06-08 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [ 2.25357264e-01 -6.83214307e-01 3.62580627e-01 -1.04319714e-01
-6.40724003e-01 -7.40508318e-01 4.56140727e-01 1.02198854e-01
-7.13157535e-01 1.25168574e+00 -2.74632186e-01 -7.70013705e-02
-1.63132057e-01 -5.19057810e-01 -8.02395701e-01 -9.59702373e-01
-2.89348334e-01 2.09709346e-01 2.91453600e-01 2.55268544... | [7.925047397613525, 3.0851285457611084] |
3a46e82b-f039-4bc6-9fea-e6c3266f7512 | improvising-the-learning-of-neural-networks | 2109.14746 | null | https://arxiv.org/abs/2109.14746v2 | https://arxiv.org/pdf/2109.14746v2.pdf | Improvising the Learning of Neural Networks on Hyperspherical Manifold | The impact of convolution neural networks (CNNs) in the supervised settings provided tremendous increment in performance. The representations learned from CNN's operated on hyperspherical manifold led to insightful outcomes in face recognition, face identification, and other supervised tasks. A broad range of activatio... | ['Madhu G', 'Akshay Patel Shilhora', 'Sai Vardhan Kanumolu', 'Lalith Bharadwaj Baru'] | 2021-09-29 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 9.29084271e-02 5.64072967e-01 2.43269414e-01 -7.19643652e-01
2.16244772e-01 -2.73196083e-02 4.32725132e-01 -6.26397550e-01
-3.00468892e-01 7.63528883e-01 -6.52540848e-02 -5.05687118e-01
-4.59559679e-01 -9.56657946e-01 -8.96863520e-01 -8.13100040e-01
-6.25586927e-01 1.42966777e-01 -6.57315493e-01 -2.25927800... | [13.177252769470215, 0.7900658845901489] |
47c71a02-db91-4d93-be36-d35a0617d063 | transformer-based-sar-image-despeckling | 2201.09355 | null | https://arxiv.org/abs/2201.09355v1 | https://arxiv.org/pdf/2201.09355v1.pdf | Transformer-based SAR Image Despeckling | Synthetic Aperture Radar (SAR) images are usually degraded by a multiplicative noise known as speckle which makes processing and interpretation of SAR images difficult. In this paper, we introduce a transformer-based network for SAR image despeckling. The proposed despeckling network comprises of a transformer-based en... | ['Vishal M. Patel', 'Jeya Maria Jose Valanarasu', 'Wele Gedara Chaminda Bandara', 'Malsha V. Perera'] | 2022-01-23 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 8.14431906e-01 -2.32104018e-01 5.52432120e-01 -6.88648343e-01
-8.91542852e-01 -3.08843374e-01 5.27165055e-01 -7.14779615e-01
-4.19988483e-01 5.57140291e-01 4.22409803e-01 -1.84640139e-01
-2.62434065e-01 -8.56808364e-01 -6.93602562e-01 -8.02304447e-01
-4.38779108e-02 1.38557926e-01 -6.02533668e-02 -1.67838633... | [10.427117347717285, -2.256582498550415] |
c138f03e-7f9f-4590-8523-474e5a48afe1 | exploring-cross-lingual-transfer-learning | null | null | https://aclanthology.org/2021.findings-acl.177 | https://aclanthology.org/2021.findings-acl.177.pdf | Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation | null | ['Hui Jiang', 'Thi Ngoc Quynh Do', 'Judith Gaspers', 'Chao Wang'] | null | null | null | null | findings-acl-2021-8 | ['unsupervised-machine-translation'] | ['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... | [-7.2134318351745605, 3.650367498397827] |
1c730ca0-002e-4ec6-a28c-a99ad49b6d22 | cir-net-cross-modality-interaction-and | 2210.02843 | null | https://arxiv.org/abs/2210.02843v1 | https://arxiv.org/pdf/2210.02843v1.pdf | CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection | Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement. For the cross-modality interaction, 1) a progressiv... | ['Yao Zhao', 'Qingming Huang', 'Xiaochun Cao', 'Chongyi Li', 'Chen Zhang', 'Qinwei Lin', 'Runmin Cong'] | 2022-10-06 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 3.07376713e-01 2.21364498e-01 -1.03617176e-01 -2.50332326e-01
-6.21722043e-01 1.53611526e-01 3.50073785e-01 1.49044588e-01
-4.30075288e-01 2.47890264e-01 5.52596867e-01 3.06607895e-02
2.92390317e-01 -5.96655905e-01 -6.52042866e-01 -7.08379805e-01
3.95563573e-01 -1.81750879e-01 1.01600325e+00 -3.20530951... | [9.730170249938965, -0.7172547578811646] |
78a25396-1f0a-4581-81fc-4263fcd47a8b | learnings-from-data-integration-for-augmented | 2304.04576 | null | https://arxiv.org/abs/2304.04576v1 | https://arxiv.org/pdf/2304.04576v1.pdf | Learnings from Data Integration for Augmented Language Models | One of the limitations of large language models is that they do not have access to up-to-date, proprietary or personal data. As a result, there are multiple efforts to extend language models with techniques for accessing external data. In that sense, LLMs share the vision of data integration systems whose goal is to pr... | ['Jane Dwivedi-Yu', 'Alon Halevy'] | 2023-04-10 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-6.53827429e-01 1.78271994e-01 -8.53824317e-01 -4.85452712e-01
-5.79358339e-01 -7.63130844e-01 7.83247411e-01 6.17136776e-01
-3.53089303e-01 4.86106575e-01 5.70121109e-01 -4.47572887e-01
-2.78741449e-01 -7.31796026e-01 -3.82624790e-02 3.98697406e-01
1.90029249e-01 3.62168223e-01 2.72230297e-01 -6.04054034... | [9.153058052062988, 7.845519065856934] |
953426e4-dec9-4171-98f6-1a995eb40710 | continual-active-learning-using-pseudo | 2111.13069 | null | https://arxiv.org/abs/2111.13069v2 | https://arxiv.org/pdf/2111.13069v2.pdf | Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics | Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies resulting in a heterogeneous set of acquisition settings. When training a deep learning model on an initial static training set, model performance and reliability suffer from changes of acquisi... | ['Georg Langs', 'Helmut Prosch', 'Christian Herold', 'Johannes Hofmanninger', 'Matthias Perkonigg'] | 2021-11-25 | null | null | null | null | ['age-estimation', 'cardiac-segmentation', 'lung-nodule-detection', 'age-estimation'] | ['computer-vision', 'medical', 'medical', 'miscellaneous'] | [ 7.47470081e-01 3.41502100e-01 -2.42544994e-01 -6.62198126e-01
-8.63532305e-01 -3.08514714e-01 3.87370348e-01 5.17951727e-01
-1.00852859e+00 5.75591922e-01 -2.82858431e-01 -1.66683823e-01
-2.21682966e-01 -3.60177487e-01 -5.71750224e-01 -7.33606279e-01
-2.24064305e-01 1.06099606e+00 6.99323177e-01 3.84626359... | [14.733572959899902, -2.19905948638916] |
db371a54-1d50-4b22-bbdc-2ff4c7538027 | robust-handwriting-recognition-with-limited | 2008.08148 | null | https://arxiv.org/abs/2008.08148v1 | https://arxiv.org/pdf/2008.08148v1.pdf | Robust Handwriting Recognition with Limited and Noisy Data | Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved. Most existing approaches focus on handwriting datasets that have clearly written text and carefully segmented labels. In this paper, we instead focus on learning handwritten characters from maintenanc... | ['Tzu-Hsiang Lin', 'Saket Dingliwal', 'Zhuo Li', 'Collin McCormack', 'Amrith Setlur', 'Jae Lim', 'Hai Pham', 'Kang Huang', 'Tam Vu', 'Barnabas Poczos'] | 2020-08-18 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.50905716e-01 -4.82762605e-01 -5.40659010e-01 -5.54953337e-01
-5.74996948e-01 -8.32945645e-01 5.99240601e-01 -1.35449115e-02
-6.98496282e-01 4.37594503e-01 -2.23008506e-02 -5.63556671e-01
3.10366541e-01 -3.07283670e-01 -3.51008326e-01 -5.89016974e-01
5.98322451e-01 7.73772836e-01 3.03879380e-01 1.04532972... | [11.835637092590332, 2.4263901710510254] |
8ace22fd-7f4b-4901-ab86-1abc70769ce0 | stage-spatio-temporal-attention-on-graph | 1912.04316 | null | https://arxiv.org/abs/1912.04316v3 | https://arxiv.org/pdf/1912.04316v3.pdf | Video action detection by learning graph-based spatio-temporal interactions | Action Detection is a complex task that aims to detect and classify human actions in video clips. Typically, it has been addressed by processing fine-grained features extracted from a video classification backbone. Recently, thanks to the robustness of object and people detectors, a deeper focus has been added on relat... | ['Lorenzo Baraldi', 'Simone Bronzin', 'Rita Cucchiara', 'Matteo Tomei', 'Simone Calderara'] | 2019-12-09 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [-6.36662357e-03 3.26620750e-02 -2.58087903e-01 -3.07627141e-01
-2.28086963e-01 -3.15251827e-01 7.90613651e-01 2.58750528e-01
-6.29146934e-01 2.78596699e-01 5.01740515e-01 3.84732813e-01
-1.33715615e-01 -5.34441531e-01 -6.36010408e-01 -3.57912689e-01
-5.83328009e-01 1.63106754e-01 5.70281386e-01 -1.12073667... | [8.201815605163574, 0.5228443741798401] |
c4293ffe-103e-47f6-915e-7ee224917660 | multi-granularity-hierarchical-attention | 1811.11934 | null | http://arxiv.org/abs/1811.11934v1 | http://arxiv.org/pdf/1811.11934v1.pdf | Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering | This paper describes a novel hierarchical attention network for reading
comprehension style question answering, which aims to answer questions for a
given narrative paragraph. In the proposed method, attention and fusion are
conducted horizontally and vertically across layers at different levels of
granularity between ... | ['Wei Wang', 'Ming Yan', 'Chen Wu'] | 2018-11-29 | multi-granularity-hierarchical-attention-1 | https://aclanthology.org/P18-1158 | https://aclanthology.org/P18-1158.pdf | acl-2018-7 | ['triviaqa'] | ['miscellaneous'] | [ 5.08447662e-02 2.26456523e-01 3.56976539e-02 -4.97988522e-01
-1.28016877e+00 -6.20958745e-01 4.55302447e-01 4.22961444e-01
-2.62367100e-01 5.37146270e-01 9.48729396e-01 -3.31164777e-01
-2.83191651e-01 -8.74691486e-01 -7.50089884e-01 -1.03251323e-01
4.53907013e-01 5.62980711e-01 2.24307641e-01 -5.90992630... | [11.2422456741333, 8.042315483093262] |
f96df501-88e4-4474-85ae-b67d546d6e54 | factored-attention-and-embedding-for | 2203.06458 | null | https://arxiv.org/abs/2203.06458v1 | https://arxiv.org/pdf/2203.06458v1.pdf | Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation | Echocardiography is widely used to clinical practice for diagnosis and treatment, e.g., on the common congenital heart defects. The traditional manual manipulation is error-prone due to the staff shortage, excess workload, and less experience, leading to the urgent requirement of an automated computer-aided reporting s... | ['Yue Gao', 'Xiaojing Ma', 'Shengchuang Zhang', 'Xuri Ge', 'Chengpeng Dai', 'Rongrong Ji', 'Fuhai Chen'] | 2022-03-12 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 1.03203833e-01 4.28908542e-02 1.37840092e-01 -3.27826798e-01
-9.76668596e-01 -4.87023443e-01 6.52651712e-02 3.05073857e-02
5.68698645e-02 3.52737308e-01 6.46602392e-01 -2.65389353e-01
-3.85506153e-01 -4.94192868e-01 -2.27035895e-01 -7.92932689e-01
6.59698397e-02 4.45644617e-01 -4.57037613e-02 9.18156877... | [15.020888328552246, -1.4602466821670532] |
c278fc1e-b1de-413a-b68b-0e032482c6dd | avoiding-negative-side-effects-and-promoting | null | null | https://openreview.net/forum?id=HJe7bxBYvr | https://openreview.net/pdf?id=HJe7bxBYvr | Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning | With the recent proliferation of the usage of reinforcement learning (RL) agents for solving real-world tasks, safety emerges as a necessary ingredient for their successful application. In this paper, we focus on ensuring the safety of the agent while making sure that the agent does not cause any unnecessary disrupti... | ['Benjamin Eysenbach', 'Dhruv Ramani'] | 2019-09-25 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.59117514e-01 5.97517908e-01 -5.21448180e-02 -1.88774783e-02
-3.80972624e-01 -9.00252640e-01 7.85557389e-01 1.79643229e-01
-7.50582874e-01 9.54938769e-01 -6.80095479e-02 -7.28025913e-01
-2.99547166e-01 -1.04798520e+00 -6.78106546e-01 -7.44449198e-01
-3.33266526e-01 3.79752010e-01 3.80599141e-01 -3.46200675... | [4.418636322021484, 2.0260825157165527] |
6fb146c9-9803-4e6e-aaab-d8708721a204 | intensity-scan-context-coding-intensity-and | 2003.05656 | null | https://arxiv.org/abs/2003.05656v1 | https://arxiv.org/pdf/2003.05656v1.pdf | Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection | Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM). It is often tackled with light detection and ranging (LiDAR) sensor due to its view-point and illumination invariant properties. Existing works on 3D loop closure detection often leverage the matching of loca... | ['Lihua Xie', 'Han Wang', 'Chen Wang'] | 2020-03-12 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 4.25435871e-01 -7.73288786e-01 -3.44491273e-01 -5.95769584e-01
-8.59927237e-01 -5.68375528e-01 7.51267552e-01 4.41192806e-01
-5.85668802e-01 3.87163758e-01 -1.10288203e-01 -3.68285835e-01
-3.38653266e-01 -9.02729332e-01 -4.51253057e-01 -4.51968312e-01
8.97859596e-03 5.03124833e-01 4.11310434e-01 -6.76482543... | [7.467704772949219, -2.18833589553833] |
e88d42b6-b705-4426-8ea3-ecf43d368230 | self-distillation-with-meta-learning-for-1 | 2305.12209 | null | https://arxiv.org/abs/2305.12209v1 | https://arxiv.org/pdf/2305.12209v1.pdf | Self-Distillation with Meta Learning for Knowledge Graph Completion | In this paper, we propose a selfdistillation framework with meta learning(MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddings and tackle the longtail samples. Specifically, we first propose a dynamic pruning technique to obtain a small pruned model from a large s... | ['Min Yang', 'Chengming Li', 'Junhao Liu', 'Yunshui Li'] | 2023-05-20 | self-distillation-with-meta-learning-for | https://aclanthology.org/2022.findings-emnlp.149/ | https://aclanthology.org/2022.findings-emnlp.149.pdf | findings-of-the-association-for-computational-2 | ['knowledge-graph-completion'] | ['knowledge-base'] | [ 2.16097817e-01 3.97112995e-01 -5.38639367e-01 1.65547252e-01
-2.44470283e-01 -1.96104884e-01 2.56366700e-01 4.20477241e-01
-6.00283623e-01 6.26334846e-01 2.03314781e-01 -1.07841007e-01
-2.24989846e-01 -1.21694553e+00 -8.30469131e-01 -6.30842984e-01
-1.13414444e-01 5.61532855e-01 2.63168037e-01 -9.49002951... | [9.541646957397461, 3.5645415782928467] |
f338840b-c87d-426b-87ae-3d84cf75bc6e | knowledge-distillation-for-neural-transducer | 2305.15971 | null | https://arxiv.org/abs/2305.15971v1 | https://arxiv.org/pdf/2305.15971v1.pdf | Knowledge Distillation for Neural Transducer-based Target-Speaker ASR: Exploiting Parallel Mixture/Single-Talker Speech Data | Neural transducer (RNNT)-based target-speaker speech recognition (TS-RNNT) directly transcribes a target speaker's voice from a multi-talker mixture. It is a promising approach for streaming applications because it does not incur the extra computation costs of a target speech extraction frontend, which is a critical ba... | ['Taichi Asami', 'Atsunori Ogawa', 'Ryo Masumura', 'Tomohiro Tanaka', 'Kohei Matsuura', 'Takanori Ashihara', 'Marc Delcroix', 'Tsubasa Ochiai', 'Hiroshi Sato', 'Takafumi Moriya'] | 2023-05-25 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 3.49176377e-01 1.27380952e-01 -1.23415023e-01 -4.26060110e-01
-1.56241798e+00 -5.43172836e-01 5.06850958e-01 -5.83458006e-01
-2.15476513e-01 3.08020532e-01 4.72858459e-01 -6.93957448e-01
5.87427914e-01 -6.79342970e-02 -4.41245645e-01 -9.54486012e-01
2.69344479e-01 4.38475549e-01 -1.03217445e-01 2.72217742... | [14.595677375793457, 6.343209743499756] |
3fb2247a-6828-4552-a648-1458d6275f83 | analysis-of-scheduling-schemes-based-on | 2307.02368 | null | https://arxiv.org/abs/2307.02368v1 | https://arxiv.org/pdf/2307.02368v1.pdf | Analysis of Scheduling schemes based on Carrier Aggregation in LTE-Advanced and their improvement | In this paper I focused on resource scheduling in the downlink of LTE-Advanced with aggregation of multiple Component Carriers (CCs). When Carrier Aggregation (CA) is applied, a well-designed resource scheduling scheme is essential to the LTE-A system. Joint User Scheduling (JUS), Separated Random User Scheduling (SRUS... | ['Sajjad Emdadi Mahdimahalleh'] | 2023-07-05 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [-6.50565187e-03 -1.86739132e-01 -3.58290613e-01 -1.13606289e-01
-2.20089749e-01 -2.43210688e-01 6.46767169e-02 -2.13221028e-01
-3.93218130e-01 1.62429249e+00 1.53533310e-01 -5.83313167e-01
-2.46901661e-01 -4.38989520e-01 7.99709707e-02 -8.66973341e-01
-7.00570345e-01 1.26201645e-01 5.99161983e-01 -5.22669435... | [6.000781059265137, 1.5616461038589478] |
f1325beb-6828-4705-81c3-fd59f4f473ee | on-the-choice-of-perception-loss-function-for | 2305.19301 | null | https://arxiv.org/abs/2305.19301v1 | https://arxiv.org/pdf/2305.19301v1.pdf | On the Choice of Perception Loss Function for Learned Video Compression | We study causal, low-latency, sequential video compression when the output is subjected to both a mean squared-error (MSE) distortion loss as well as a perception loss to target realism. Motivated by prior approaches, we consider two different perception loss functions (PLFs). The first, PLF-JD, considers the joint dis... | ['Ashish Khisti', 'Wei Yu', 'Jun Chen', 'Buu Phan', 'Sadaf Salehkalaibar'] | 2023-05-30 | null | null | null | null | ['video-compression'] | ['computer-vision'] | [ 5.35235107e-01 7.44856447e-02 -1.35471627e-01 -4.45211902e-02
-8.74150872e-01 -2.31296986e-01 4.87746239e-01 2.43523285e-01
-4.53520834e-01 6.20341539e-01 3.69673878e-01 -1.96656048e-01
-3.38784099e-01 -6.42060399e-01 -1.04754925e+00 -8.03932846e-01
-4.16941583e-01 1.03708491e-01 2.69421995e-01 1.64480712... | [11.473199844360352, -1.845371961593628] |
23aca737-e4e9-465e-bc4f-8515d213a63f | unifiedabsa-a-unified-absa-framework-based-on | 2211.10986 | null | https://arxiv.org/abs/2211.10986v1 | https://arxiv.org/pdf/2211.10986v1.pdf | UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning | Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level sentiment information. There are many ABSA tasks, and the current dominant paradigm is to train task-specific models for each task. However, application scenarios of ABSA tasks are often diverse. This solution usually requires a large amou... | ['Jianfei Yu', 'Rui Xia', 'Zengzhi Wang'] | 2022-11-20 | null | null | null | null | ['aspect-term-extraction-and-sentiment', 'aspect-extraction', 'aspect-based-sentiment-analysis', 'aspect-oriented-opinion-extraction', 'aspect-category-opinion-sentiment-quadruple', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.32773926e-02 -6.02284670e-01 -3.61800879e-01 -5.99534929e-01
-1.18841982e+00 -3.99590164e-01 5.38324058e-01 2.48318855e-02
-1.30741403e-01 4.26510155e-01 -1.15105510e-02 -4.16441619e-01
2.25351915e-01 -7.38378108e-01 -6.26258373e-01 -6.00734770e-01
5.10334432e-01 5.59647024e-01 4.50368881e-01 -5.49888372... | [11.474257469177246, 6.683948516845703] |
5e9c97c4-91b8-4f7f-a7ae-6e7b9ee0522e | noisy-universal-domain-adaptation-via | 2304.10333 | null | https://arxiv.org/abs/2304.10333v1 | https://arxiv.org/pdf/2304.10333v1.pdf | Noisy Universal Domain Adaptation via Divergence Optimization for Visual Recognition | To transfer the knowledge learned from a labeled source domain to an unlabeled target domain, many studies have worked on universal domain adaptation (UniDA), where there is no constraint on the label sets of the source domain and target domain. However, the existing UniDA methods rely on source samples with correct an... | ['Yoshitaka Ushiku', 'Atsushi Hashimoto', 'Qing Yu'] | 2023-04-20 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [-2.52008140e-02 -1.82205766e-01 -1.06525667e-01 -5.61475217e-01
-1.12179124e+00 -7.23280489e-01 3.76334310e-01 -1.80892408e-01
-2.39685610e-01 1.09526277e+00 -2.25749910e-01 3.84003483e-02
1.92598626e-01 -6.20097637e-01 -6.60583258e-01 -8.54478896e-01
5.29305696e-01 7.26049542e-01 1.86047375e-01 -3.66808325... | [10.388797760009766, 3.137843608856201] |
bcdd6765-9896-4949-8560-2155457c6264 | maximum-entropy-model-based-reinforcement | 2112.01195 | null | https://arxiv.org/abs/2112.01195v1 | https://arxiv.org/pdf/2112.01195v1.pdf | Maximum Entropy Model-based Reinforcement Learning | Recent advances in reinforcement learning have demonstrated its ability to solve hard agent-environment interaction tasks on a super-human level. However, the application of reinforcement learning methods to practical and real-world tasks is currently limited due to most RL state-of-art algorithms' sample inefficiency,... | ['Aleksei Shpilman', 'Oleg Svidchenko'] | 2021-12-02 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-1.68723240e-01 1.88459858e-01 -9.39766318e-02 2.29411915e-01
-6.77908540e-01 -2.03492403e-01 4.40738469e-01 5.53221293e-02
-8.41936767e-01 1.37913322e+00 -3.03352982e-01 -3.42114568e-01
-3.94163042e-01 -7.12504804e-01 -5.34714818e-01 -6.76134050e-01
-4.93141353e-01 9.04041350e-01 2.44411007e-01 -5.81109703... | [3.9573445320129395, 1.7723671197891235] |
53d92600-0619-44f0-b5f7-84f40131e14e | composite-task-completion-dialogue-policy | 1704.03084 | null | http://arxiv.org/abs/1704.03084v3 | http://arxiv.org/pdf/1704.03084v3.pdf | Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning | Building a dialogue agent to fulfill complex tasks, such as travel planning,
is challenging because the agent has to learn to collectively complete multiple
subtasks. For example, the agent needs to reserve a hotel and book a flight so
that there leaves enough time for commute between arrival and hotel check-in.
This p... | ['Kam-Fai Wong', 'Jianfeng Gao', 'Xiujun Li', 'Asli Celikyilmaz', 'Sungjin Lee', 'Lihong Li', 'Baolin Peng'] | 2017-04-10 | composite-task-completion-dialogue-policy-1 | https://aclanthology.org/D17-1237 | https://aclanthology.org/D17-1237.pdf | emnlp-2017-9 | ['task-completion-dialogue-policy-learning'] | ['natural-language-processing'] | [-1.44367395e-02 6.55053318e-01 -1.29961416e-01 -6.52299523e-01
-7.52328634e-01 -7.70178139e-01 8.75808597e-01 2.22955793e-01
-5.65955877e-01 1.02275646e+00 4.64318901e-01 -5.81328571e-01
-1.43352106e-01 -7.32291937e-01 -2.03778699e-01 -5.69294155e-01
-2.41359308e-01 1.08774650e+00 3.58822376e-01 -7.46610343... | [13.148064613342285, 7.994983673095703] |
e2c4c75e-92e8-421c-9135-fd6c4eb4cd9e | human-motion-detection-using-sharpened | 2202.11667 | null | https://arxiv.org/abs/2202.11667v1 | https://arxiv.org/pdf/2202.11667v1.pdf | Human Motion Detection Using Sharpened Dimensionality Reduction and Clustering | Sharpened dimensionality reduction (SDR), which belongs to the class of multidimensional projection techniques, has recently been introduced to tackle the challenges in the exploratory and visual analysis of high-dimensional data. SDR has been applied to various real-world datasets, such as human activity sensory data ... | ['Jos B. T. M. Roerdink', 'Youngjoo Kim', 'Jeewon Heo'] | 2022-02-23 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 4.63126525e-02 -4.91181910e-01 7.72794038e-02 -2.25772962e-01
-4.05667543e-01 -8.19692135e-01 6.07172430e-01 1.41995400e-02
-1.25651494e-01 1.25459179e-01 6.86972976e-01 -2.18505561e-01
-5.18074870e-01 -5.71349561e-01 -7.95404017e-02 -7.39289284e-01
-3.19342136e-01 5.66715717e-01 1.10794343e-01 2.68966138... | [7.678572654724121, 4.527169227600098] |
856c40ed-440d-47e7-ad14-2a887c4a891c | reformulating-dover-lap-label-mapping-as-a | 2104.01954 | null | https://arxiv.org/abs/2104.01954v2 | https://arxiv.org/pdf/2104.01954v2.pdf | Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem | We recently proposed DOVER-Lap, a method for combining overlap-aware speaker diarization system outputs. DOVER-Lap improved upon its predecessor DOVER by using a label mapping method based on globally-informed greedy search. In this paper, we analyze this label mapping in the framework of a maximum orthogonal graph par... | ['Sanjeev Khudanpur', 'Desh Raj'] | 2021-04-05 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 1.61597505e-01 2.57826239e-01 -1.82984576e-01 -4.93761867e-01
-1.74400342e+00 -7.94707954e-01 1.75251719e-02 4.93025295e-02
-2.94049144e-01 6.26468658e-01 1.43870890e-01 -4.57746744e-01
-4.06866044e-01 -2.67455250e-01 -5.42247176e-01 -4.71708208e-01
-3.19235355e-01 9.26147997e-01 3.47816288e-01 -6.83142245... | [14.348014831542969, 6.154474258422852] |
cf3e7e31-54d4-4584-a46d-a329b313c9e9 | abi-neural-ensemble-model-for-gender | 1902.08856 | null | http://arxiv.org/abs/1902.08856v1 | http://arxiv.org/pdf/1902.08856v1.pdf | ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction | We present our system for the CLIN29 shared task on cross-genre gender
detection for Dutch. We experimented with a multitude of neural models (CNN,
RNN, LSTM, etc.), more "traditional" models (SVM, RF, LogReg, etc.), different
feature sets as well as data pre-processing. The final results suggested that
using tokenized... | ['Eva Vanmassenhove', 'Dimitar Shterionov', 'Andy Way', 'Amit Moryossef', 'Alberto Poncelas'] | 2019-02-23 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-2.34636948e-01 1.59262940e-01 -1.78253561e-01 -5.33818007e-01
-8.11118960e-01 -4.78410125e-01 8.74387741e-01 2.39824146e-01
-9.20418680e-01 9.13442314e-01 4.30818468e-01 -4.12673533e-01
-3.88240665e-02 -7.07801104e-01 -2.92673439e-01 -6.91689551e-01
-6.13961257e-02 8.66301835e-01 -7.33367354e-02 -5.90500772... | [9.470843315124512, 10.346303939819336] |
ff68052f-fa56-43fc-a45e-2aeb56e3a411 | enhancing-pre-trained-models-with-text | 2209.04179 | null | https://arxiv.org/abs/2209.04179v1 | https://arxiv.org/pdf/2209.04179v1.pdf | Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation | Today the pre-trained language models achieve great success for question generation (QG) task and significantly outperform traditional sequence-to-sequence approaches. However, the pre-trained models treat the input passage as a flat sequence and are thus not aware of the text structure of input passage. For QG task, w... | ['Yunfang Wu', 'Fanyi Qu', 'Xin Jia', 'Zichen Wu'] | 2022-09-09 | null | https://aclanthology.org/2022.coling-1.571 | https://aclanthology.org/2022.coling-1.571.pdf | coling-2022-10 | ['question-generation'] | ['natural-language-processing'] | [ 1.31399810e-01 5.54745078e-01 -6.86807698e-03 -2.40783423e-01
-1.00988960e+00 -6.67172134e-01 6.22091413e-01 7.89862499e-02
-4.59526330e-01 8.42197001e-01 6.61499619e-01 -7.13203430e-01
4.56079274e-01 -9.59934235e-01 -7.76721358e-01 1.76875722e-02
4.87459391e-01 6.01383746e-01 6.49657369e-01 -6.73027277... | [11.41395092010498, 8.174013137817383] |
3e4e7c99-3f30-4d13-b849-a474687d35be | fetmrqc-automated-quality-control-for-fetal | 2304.05879 | null | https://arxiv.org/abs/2304.05879v1 | https://arxiv.org/pdf/2304.05879v1.pdf | FetMRQC: Automated Quality Control for fetal brain MRI | Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where large and unpredictable fetal motion can lead to substantial artifacts in the acquired images. Existing methods for fetal brain quality assessment operate... | ['Meritxell Bach Cuadra', 'Elisenda Eixarch', 'Yvan Gomez', 'Oscar Esteban', 'Thomas Sanchez'] | 2023-04-12 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 1.09737411e-01 2.14722499e-01 3.56732100e-01 -7.56886780e-01
-9.59697008e-01 -6.08683050e-01 8.78165588e-02 4.47002828e-01
-2.30865613e-01 5.22058070e-01 1.63905263e-01 -2.91529119e-01
-6.20865464e-01 -5.07849574e-01 -5.09070337e-01 -5.28853238e-01
-3.80120009e-01 5.71671665e-01 2.30763316e-01 3.29918116... | [14.079511642456055, -2.3374481201171875] |
80573398-4eb2-4181-a071-ed1bbf557db9 | stochastic-subgraph-neighborhood-pooling-for | 2304.08556 | null | https://arxiv.org/abs/2304.08556v1 | https://arxiv.org/pdf/2304.08556v1.pdf | Stochastic Subgraph Neighborhood Pooling for Subgraph Classification | Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph. Subgraph classification has applications such as predicting the cellular function of a group of proteins or identifying rare diseases given a collection of phen... | ['Amirali Salehi-Abari', 'Paul Louis', 'Shweta Ann Jacob'] | 2023-04-17 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 3.04460168e-01 3.51006836e-01 -4.97272909e-01 -2.43728638e-01
-4.19295907e-01 -6.20619595e-01 4.49012935e-01 6.73219562e-01
3.51935215e-02 5.50424755e-01 3.14010456e-02 -5.38655281e-01
2.75293496e-02 -1.16300678e+00 -7.50372469e-01 -6.73504651e-01
-3.73365134e-01 3.47088575e-01 3.10792685e-01 3.71466838... | [7.014598369598389, 6.246481895446777] |
83cc901f-dbc5-4965-8928-6fe4f1b9aa24 | a-spatio-temporal-multilayer-perceptron-for | 2204.11511 | null | https://arxiv.org/abs/2204.11511v2 | https://arxiv.org/pdf/2204.11511v2.pdf | A Spatio-Temporal Multilayer Perceptron for Gesture Recognition | Gesture recognition is essential for the interaction of autonomous vehicles with humans. While the current approaches focus on combining several modalities like image features, keypoints and bone vectors, we present neural network architecture that delivers state-of-the-art results only with body skeleton input data. W... | ['Vasileios Belagiannis', 'Klaus Dietmayer', 'Youssef Dawoud', 'Alexander Tsaregorodtsev', 'Adrian Holzbock'] | 2022-04-25 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 1.47121519e-01 -1.11172006e-01 -4.07767862e-01 -4.75850284e-01
-2.61284381e-01 -2.79074367e-02 1.09166992e+00 -4.92614418e-01
-9.08352137e-01 1.12620279e-01 1.22750990e-01 -1.37097150e-01
-1.52938887e-01 -5.29989183e-01 -6.51022911e-01 -7.15826035e-01
-5.49600899e-01 4.41089272e-01 3.91461670e-01 -2.65769541... | [7.157289028167725, -0.13081710040569305] |
4af10b58-98de-41c1-94ce-12a07ec81997 | neural-network-surgery-with-sets | 1912.06719 | null | https://arxiv.org/abs/1912.06719v2 | https://arxiv.org/pdf/1912.06719v2.pdf | Neural Network Surgery with Sets | The cost to train machine learning models has been increasing exponentially, making exploration and research into the correct features and architecture a costly or intractable endeavor at scale. However, using a technique named "surgery" OpenAI Five was continuously trained to play the game DotA 2 over the course of 10... | ['Susan Zhang', 'Jonathan Raiman', 'Christy Dennison'] | 2019-12-13 | null | null | null | null | ['dota-2'] | ['playing-games'] | [ 2.59888113e-01 -6.54253885e-02 1.03829548e-01 -3.65274876e-01
-1.18280776e-01 -9.33177292e-01 3.89634043e-01 -8.95860270e-02
-6.18738234e-01 5.17050385e-01 -1.70046329e-01 -6.49510503e-01
-4.75068808e-01 -5.51539421e-01 -8.11388195e-01 -5.43203473e-01
-2.61632234e-01 5.18527806e-01 3.79946083e-01 -3.77735496... | [8.542799949645996, 3.313526153564453] |
a16de9aa-6d83-419b-be9c-bd34961af785 | single-reference-image-based-scene-relighting | 1708.07066 | null | http://arxiv.org/abs/1708.07066v1 | http://arxiv.org/pdf/1708.07066v1.pdf | Single Reference Image based Scene Relighting via Material Guided Filtering | Image relighting is to change the illumination of an image to a target
illumination effect without known the original scene geometry, material
information and illumination condition. We propose a novel outdoor scene
relighting method, which needs only a single reference image and is based on
material constrained layer ... | ['Xiao-Dong Li', 'Xin Jin', 'Xianggang Jiang', 'Ningning Liu', 'Yannan Li', 'Shiming Ge', 'Chaoen Xiao'] | 2017-08-23 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 9.55470085e-01 -1.73841313e-01 1.98898911e-01 -7.36937597e-02
-1.91691995e-01 -5.43864191e-01 2.84266055e-01 -7.14430034e-01
-2.07945034e-01 6.69210315e-01 1.08846631e-02 9.97852883e-04
3.46751422e-01 -8.56744766e-01 -9.08396959e-01 -1.05661488e+00
9.14191484e-01 -3.40042502e-01 3.22535485e-01 -2.23505080... | [10.0889253616333, -2.6765153408050537] |
6ed95b03-06c7-4db2-a511-282c3fe02a6a | virtual-vs-reality-external-validation-of | 2203.03074 | null | https://arxiv.org/abs/2203.03074v1 | https://arxiv.org/pdf/2203.03074v1.pdf | Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography | Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with numerous applications of deep learning for COVID-19 diagnosis. Virtual imaging trials (VITs) could provide a solution for objective evaluat... | ['Joseph Y. Lo', 'Ehsan Samei', 'W. Paul Segars', 'Maciej A. Mazurowski', 'Rafael B. Fricks', 'Saman Sotoudeh-Paima', 'Ehsan Abadi', 'Fakrul Islam Tushar'] | 2022-03-07 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-1.28570542e-01 1.61358565e-01 3.76239861e-03 -2.01285928e-01
-1.10330594e+00 -5.14190912e-01 1.93810448e-01 1.71845838e-01
-5.97841561e-01 4.52861726e-01 8.32800195e-03 -7.81780064e-01
-4.02526706e-01 -5.26497304e-01 -5.31597197e-01 -7.21814573e-01
-5.64610124e-01 9.91529882e-01 2.96801418e-01 3.52477163... | [15.119707107543945, -2.005183458328247] |
69379f3d-ae55-49cb-9604-9e155739e261 | consistentnerf-enhancing-neural-radiance | 2305.11031 | null | https://arxiv.org/abs/2305.11031v1 | https://arxiv.org/pdf/2305.11031v1.pdf | ConsistentNeRF: Enhancing Neural Radiance Fields with 3D Consistency for Sparse View Synthesis | Neural Radiance Fields (NeRF) has demonstrated remarkable 3D reconstruction capabilities with dense view images. However, its performance significantly deteriorates under sparse view settings. We observe that learning the 3D consistency of pixels among different views is crucial for improving reconstruction quality in ... | ['Ziwei Liu', 'Gim Hee Lee', 'Zhenguo Li', 'Tianyang Hu', 'Lanqing Hong', 'Longhui Yu', 'Kaiyu Li', 'Kaichen Zhou', 'Shoukang Hu'] | 2023-05-18 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 8.57913122e-02 -4.78496552e-01 -1.96127340e-01 -4.54231411e-01
-7.99115300e-01 -6.79389656e-01 4.40082252e-01 -3.87475818e-01
8.20793658e-02 4.38282937e-01 4.68468815e-01 -3.97801809e-02
3.17011066e-02 -7.95099437e-01 -9.81660903e-01 -7.05848694e-01
1.58185050e-01 -2.12436572e-01 7.51853064e-02 -2.18162537... | [9.146186828613281, -2.7036595344543457] |
5b27abd0-021b-475f-9165-13ddcb8fede9 | selective-memory-recursive-least-squares | 2211.07909 | null | https://arxiv.org/abs/2211.07909v1 | https://arxiv.org/pdf/2211.07909v1.pdf | Selective Memory Recursive Least Squares: Uniformly Allocated Approximation Capabilities of RBF Neural Networks in Real-Time Learning | When performing real-time learning tasks, the radial basis function neural network (RBFNN) is expected to make full use of the training samples such that its learning accuracy and generalization capability are guaranteed. Since the approximation capability of the RBFNN is finite, training methods with forgetting mechan... | ['Yanan Li', 'Jiangang Li', 'Yiming Fei'] | 2022-11-15 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-1.28568932e-01 -7.44266063e-02 -1.00544713e-01 -2.18375877e-01
3.38338204e-02 -6.05750494e-02 1.68410882e-01 -2.79114336e-01
-3.81786734e-01 1.29217124e+00 -5.69126010e-01 -1.64654404e-01
-4.32129472e-01 -1.09273791e+00 -6.31339192e-01 -1.06648302e+00
3.95759255e-01 9.98211056e-02 3.86115462e-01 -2.09814370... | [9.807040214538574, 3.4316442012786865] |
ff5bd7b3-2501-4021-9d00-3c10dd621a90 | massive-migration-from-the-steppe-is-a-source | 1502.02783 | null | http://arxiv.org/abs/1502.02783v1 | http://arxiv.org/pdf/1502.02783v1.pdf | Massive migration from the steppe is a source for Indo-European languages in Europe | We generated genome-wide data from 69 Europeans who lived between 8,000-3,000
years ago by enriching ancient DNA libraries for a target set of almost four
hundred thousand polymorphisms. Enrichment of these positions decreases the
sequencing required for genome-wide ancient DNA analysis by a median of around
250-fold, ... | [] | 2015-02-10 | null | null | null | null | ['dna-analysis'] | ['medical'] | [ 1.30133390e-01 1.24012664e-01 1.88561976e-01 -9.90467612e-03
-1.13138862e-01 -6.29249990e-01 7.67207682e-01 2.30126441e-01
-9.82887745e-01 9.41065669e-01 3.59838665e-01 -4.66974437e-01
9.83717367e-02 -1.15002513e+00 -2.63442278e-01 -6.41548038e-01
-3.97212088e-01 7.43658125e-01 1.71132222e-01 -5.79782963... | [5.1537017822265625, 4.800164222717285] |
32e3d5fe-d0ad-4a08-b86e-5a29e921fdc2 | parallelisable-existential-rules-a-story-of | 2107.06054 | null | https://arxiv.org/abs/2107.06054v1 | https://arxiv.org/pdf/2107.06054v1.pdf | Parallelisable Existential Rules: a Story of Pieces | In this paper, we consider existential rules, an expressive formalism well suited to the representation of ontological knowledge and data-to-ontology mappings in the context of ontology-based data integration. The chase is a fundamental tool to do reasoning with existential rules as it computes all the facts entailed b... | ['Michaël Thomazo', 'Marie-Laure Mugnier', 'Maxime Buron'] | 2021-07-13 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 2.66757697e-01 7.21483052e-01 1.23850606e-01 -2.74549901e-01
-1.26304373e-01 -7.96488762e-01 8.71071875e-01 3.34152102e-01
-2.66701162e-01 5.86271584e-01 -7.35829771e-02 -6.60185933e-01
-6.61173999e-01 -1.62941742e+00 -6.08721614e-01 -3.36524010e-01
-2.52316773e-01 7.29686439e-01 9.47926521e-01 -9.32449579... | [8.675952911376953, 6.83144474029541] |
bdb8ce9d-bd85-406c-a7ee-a86ffb95cc8a | swissalps-at-semeval-2017-task-3-attention | null | null | https://aclanthology.org/S17-2054 | https://aclanthology.org/S17-2054.pdf | SwissAlps at SemEval-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering | In this paper we propose a system for reranking answers for a given question. Our method builds on a siamese CNN architecture which is extended by two attention mechanisms. The approach was evaluated on the datasets of the SemEval-2017 competition for Community Question Answering (cQA), where it achieved 7th place obta... | ['Mark Cieliebak', 'Jan Milan Deriu'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [-1.42369524e-01 1.42963290e-01 1.25525445e-01 -2.14645743e-01
-1.33612120e+00 -4.03357089e-01 6.56322837e-01 7.80622840e-01
-1.03418803e+00 4.31240261e-01 7.95231938e-01 -4.07114029e-01
-1.07613243e-01 -3.21555197e-01 -5.21865368e-01 1.53246999e-01
9.75282341e-02 8.35282207e-01 7.04622746e-01 -6.20158792... | [11.39137077331543, 8.0326566696167] |
74ccbdc5-393f-4076-8e15-e2a7cf63a736 | coreference-aware-double-channel-attention | 2305.08348 | null | https://arxiv.org/abs/2305.08348v2 | https://arxiv.org/pdf/2305.08348v2.pdf | Coreference-aware Double-channel Attention Network for Multi-party Dialogue Reading Comprehension | We tackle Multi-party Dialogue Reading Comprehension (abbr., MDRC). MDRC stands for an extractive reading comprehension task grounded on a batch of dialogues among multiple interlocutors. It is challenging due to the requirement of understanding cross-utterance contexts and relationships in a multi-turn multi-party con... | ['Yu Hong', 'Mengxing Dong', 'Yifan Fan', 'Bowei Zou', 'Yanling Li'] | 2023-05-15 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [ 2.95368701e-01 5.10406017e-01 1.44054800e-01 -6.28094375e-01
-1.02403617e+00 -5.91761053e-01 5.04730046e-01 2.71946311e-01
-1.73982680e-01 4.58933473e-01 7.96486199e-01 -4.26076621e-01
-1.08591832e-01 -6.12906098e-01 -5.66955447e-01 -4.75663334e-01
1.15209751e-01 7.84671605e-01 1.40051633e-01 -8.19376647... | [12.272010803222656, 7.890283584594727] |
14096639-abfb-4503-baec-570b27799829 | boxgraph-semantic-place-recognition-and-pose | 2206.15154 | null | https://arxiv.org/abs/2206.15154v1 | https://arxiv.org/pdf/2206.15154v1.pdf | BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR | This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching. We model 3D point clouds as fully-connected graphs of semantically identified components where each vertex corresponds to an object instance and encodes its shape. Optimal vertex... | ['Paul Newman', 'Matthew Gadd', 'Daniele De Martini', 'Georgi Pramatarov'] | 2022-06-30 | null | null | null | null | ['graph-matching'] | ['graphs'] | [-1.43372655e-01 3.86179149e-01 -4.04936112e-02 -2.14935899e-01
-1.05188906e+00 -9.08728778e-01 5.81123829e-01 5.26174009e-01
-4.59466010e-01 3.25981736e-01 -3.54015082e-01 -2.16158867e-01
-2.46588230e-01 -7.75691986e-01 -1.23822832e+00 -2.13663220e-01
-5.75256288e-01 1.31198716e+00 5.68249881e-01 1.72187418... | [7.426711559295654, -2.2696187496185303] |
05d639b3-7ced-4c51-832c-70261f52fdd1 | mmfn-multi-modal-fusion-net-for-end-to-end | null | null | https://github.com/Kin-Zhang/mmfn | https://github.com/Kin-Zhang/mmfn | MMFN: Multi-Modal Fusion Net for End-to-End Autonomous Driving | Under review | ['Lujia Wang', 'Ren Xin', 'Feiyi Chen', 'Ruoyu Geng', 'Mingkai Tang', 'Qingwen Zhang'] | 2022-03-01 | null | null | null | iros-in-submission-2022-3 | ['carla-map-leaderboard'] | ['robots'] | [ 6.63066685e-01 1.78641975e-01 -1.05959380e+00 -1.42309278e-01
-3.76402855e-01 -6.69434905e-01 2.63007939e-01 -2.65344501e-01
-3.11054438e-01 1.14149046e+00 -3.14230680e-01 -8.33182931e-01
-1.04852647e-01 -7.15872407e-01 -6.89421237e-01 -1.04359233e+00
-8.32765937e-01 -6.09926209e-02 2.16621369e-01 -3.59781951... | [-7.255091190338135, 3.738337278366089] |
0799b3d4-cabb-47e8-a3fb-933e69abbfad | business-taxonomy-construction-using-concept | 1906.09694 | null | https://arxiv.org/abs/1906.09694v1 | https://arxiv.org/pdf/1906.09694v1.pdf | Business Taxonomy Construction Using Concept-Level Hierarchical Clustering | Business taxonomies are indispensable tools for investors to do equity research and make professional decisions. However, to identify the structure of industry sectors in an emerging market is challenging for two reasons. First, existing taxonomies are designed for mature markets, which may not be the appropriate class... | ['Win-Bin Huang', 'Frank Z. Xing', 'Haodong Bai', 'Erik Cambria'] | 2019-06-24 | business-taxonomy-construction-using-concept-1 | https://aclanthology.org/W19-5501 | https://aclanthology.org/W19-5501.pdf | ws-2019-8 | ['business-taxonomy-construction'] | ['miscellaneous'] | [-7.23970771e-01 -1.75287619e-01 -6.27980053e-01 -1.09053634e-01
6.95209950e-02 -6.60821140e-01 5.93232572e-01 -2.74735279e-02
-8.71145427e-02 4.16672170e-01 1.63947418e-01 -7.47297525e-01
-1.70200825e-01 -1.04892242e+00 -5.88947125e-02 -3.17594916e-01
2.38325745e-01 6.45388246e-01 2.40901038e-01 -3.49444687... | [4.59147310256958, 4.300631523132324] |
1effb89f-8672-4fc5-8891-ee60005bc7c2 | image-clustering-using-an-augmented | 2011.04094 | null | https://arxiv.org/abs/2011.04094v1 | https://arxiv.org/pdf/2011.04094v1.pdf | Image Clustering using an Augmented Generative Adversarial Network and Information Maximization | Image clustering has recently attracted significant attention due to the increased availability of unlabelled datasets. The efficiency of traditional clustering algorithms heavily depends on the distance functions used and the dimensionality of the features. Therefore, performance degradation is often observed when tac... | ['Spencer A. Thomas', 'Yaochu Jin', 'Foivos Ntelemis'] | 2020-11-08 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 3.29032809e-01 -1.44190285e-02 2.34214500e-01 -3.34557384e-01
-9.31478620e-01 -3.97975296e-01 5.88653207e-01 -5.92504581e-03
-5.40191889e-01 4.49683398e-01 -1.56452745e-01 1.69362351e-01
-1.64832696e-01 -6.24095738e-01 -5.12562513e-01 -1.39751196e+00
2.21283033e-01 4.91279900e-01 -1.39427766e-01 2.49879628... | [9.27463436126709, 3.1198601722717285] |
27026765-1a12-41e2-9bfa-5b9ba9a43725 | semi-supervised-object-detection-with-1 | 2107.05031 | null | https://arxiv.org/abs/2107.05031v1 | https://arxiv.org/pdf/2107.05031v1.pdf | Semi-Supervised Object Detection with Adaptive Class-Rebalancing Self-Training | This study delves into semi-supervised object detection (SSOD) to improve detector performance with additional unlabeled data. State-of-the-art SSOD performance has been achieved recently by self-training, in which training supervision consists of ground truths and pseudo-labels. In current studies, we observe that cla... | ['Bin Wang', 'Tianxiang Pan', 'Fangyuan Zhang'] | 2021-07-11 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.86185318e-01 6.58290740e-03 -3.19304049e-01 -5.50932765e-01
-1.05897248e+00 -4.32218313e-01 5.12560785e-01 5.47235124e-02
-8.01565170e-01 6.32262111e-01 -5.77629767e-02 -2.15122730e-01
6.09961033e-01 -6.53391778e-01 -9.85264361e-01 -6.87032461e-01
4.10892695e-01 3.73389691e-01 7.85938680e-01 1.63188264... | [9.19808292388916, 1.2785786390304565] |
39d67976-a4ad-4474-af12-424d56f73779 | energy-minimization-in-ris-assisted-uav | 2208.08639 | null | https://arxiv.org/abs/2208.08639v1 | https://arxiv.org/pdf/2208.08639v1.pdf | Energy Minimization in RIS-Assisted UAV-Enabled Wireless Power Transfer Systems | Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) systems offer significant advantages in coverage and deployment flexibility, but suffer from endurance limitations due to the limited onboard energy. This paper proposes to improve the energy efficiency of UAV-enabled WPT systems with multiple ground s... | ['Cunhua Pan', 'Li Li', 'Zhangjie Peng', 'Zhenkun Zhang', 'Hong Ren'] | 2022-08-18 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 4.26482975e-01 1.91422328e-01 -2.84848567e-02 3.36247325e-01
-1.93460494e-01 -7.88341701e-01 1.41884643e-03 -1.28461227e-01
-2.33591169e-01 8.99817467e-01 -3.34544063e-01 -1.89057603e-01
-8.46869290e-01 -1.16704655e+00 -4.83076930e-01 -1.30563736e+00
-2.55480677e-01 -4.88253564e-01 -2.60941893e-01 -2.85191417... | [5.9599409103393555, 1.5146898031234741] |
9a637d3e-6bd7-4bb8-a355-870f4e109bf5 | deciphering-undersegmented-ancient-scripts | 2010.11054 | null | https://arxiv.org/abs/2010.11054v1 | https://arxiv.org/pdf/2010.11054v1.pdf | Deciphering Undersegmented Ancient Scripts Using Phonetic Prior | Most undeciphered lost languages exhibit two characteristics that pose significant decipherment challenges: (1) the scripts are not fully segmented into words; (2) the closest known language is not determined. We propose a decipherment model that handles both of these challenges by building on rich linguistic constrain... | ['Regina Barzilay', 'Yuan Cao', 'Enrico Santus', 'Frederik Hartmann', 'Jiaming Luo'] | 2020-10-21 | null | null | null | null | ['decipherment'] | ['natural-language-processing'] | [-3.29921618e-02 -1.38540650e-02 -1.50368288e-01 -1.97411627e-01
-5.48234403e-01 -9.24777508e-01 1.01183486e+00 7.74264634e-02
-6.58856273e-01 5.61568022e-01 5.88482440e-01 -5.92008829e-01
-8.60909745e-02 -7.20677495e-01 -6.67302728e-01 -4.08688635e-01
4.39590737e-02 7.03750134e-01 -9.08154622e-02 -2.84931034... | [10.734383583068848, 10.047224044799805] |
c63d52cc-7479-4a5b-adae-abfef8a44a02 | sageformer-series-aware-graph-enhanced | 2307.01616 | null | https://arxiv.org/abs/2307.01616v1 | https://arxiv.org/pdf/2307.01616v1.pdf | SageFormer: Series-Aware Graph-Enhanced Transformers for Multivariate Time Series Forecasting | Multivariate time series forecasting plays a critical role in diverse domains. While recent advancements in deep learning methods, especially Transformers, have shown promise, there remains a gap in addressing the significance of inter-series dependencies. This paper introduces SageFormer, a Series-aware Graph-enhanced... | ['Yuantao Gu', 'Xin Wang', 'Zhenwei Zhang'] | 2023-07-04 | null | null | null | null | ['time-series-forecasting', 'multivariate-time-series-forecasting'] | ['time-series', 'time-series'] | [ 1.07480317e-01 -5.88467956e-01 -1.87577456e-02 -2.16427490e-01
-5.20363867e-01 -6.10439956e-01 4.17691112e-01 3.89381558e-01
3.05288553e-01 4.54513252e-01 3.13076437e-01 -5.25056601e-01
-4.95836467e-01 -6.44285440e-01 -5.41351676e-01 -4.35722947e-01
-1.03477120e+00 1.33829162e-01 9.54712257e-02 -4.46938246... | [7.0049004554748535, 2.8619415760040283] |
6b554815-6681-4837-8d72-f89e13ad1460 | scicap-a-knowledge-augmented-dataset-to-study | 2306.03491 | null | https://arxiv.org/abs/2306.03491v1 | https://arxiv.org/pdf/2306.03491v1.pdf | SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure Captioning | In scholarly documents, figures provide a straightforward way of communicating scientific findings to readers. Automating figure caption generation helps move model understandings of scientific documents beyond text and will help authors write informative captions that facilitate communicating scientific findings. Unli... | ['Naoaki Okazaki', 'Hideki Tanaka', 'Raj Dabre', 'Zhishen Yang'] | 2023-06-06 | null | null | null | null | ['optical-character-recognition', 'image-captioning'] | ['computer-vision', 'computer-vision'] | [ 4.77080941e-01 4.41994637e-01 -4.13914137e-02 -3.09271663e-01
-1.32609510e+00 -1.01100731e+00 9.74069595e-01 1.08984284e-01
-1.49335086e-01 8.80589664e-01 5.87192833e-01 -6.20380759e-01
5.32228708e-01 -5.75904191e-01 -1.40483129e+00 -1.63003519e-01
4.99495864e-01 3.52594614e-01 -2.21593842e-01 6.15566298... | [10.969822883605957, 1.2227375507354736] |
b35dbd43-bf6e-4fcf-92b0-f96e5147d742 | demand-side-scheduling-based-on-deep-actor | 2005.01979 | null | https://arxiv.org/abs/2005.01979v2 | https://arxiv.org/pdf/2005.01979v2.pdf | Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids | We consider the problem of demand-side energy management, where each household is equipped with a smart meter that is able to schedule home appliances online. The goal is to minimize the overall cost under a real-time pricing scheme. While previous works have introduced centralized approaches in which the scheduling al... | ['Wenbo Wang', 'Joash Lee', 'Dusit Niyato'] | 2020-05-05 | null | null | null | null | ['distributional-reinforcement-learning', 'smart-grid-prediction'] | ['methodology', 'miscellaneous'] | [-5.36031604e-01 3.77299905e-01 3.85305309e-03 -1.34532601e-01
-6.67568624e-01 -6.40675187e-01 3.14342439e-01 1.57707632e-01
-4.03341889e-01 9.02535737e-01 -1.34288192e-01 -7.72644058e-02
6.14009984e-02 -1.10525131e+00 -5.13273776e-01 -1.23471308e+00
-3.01928341e-01 7.71676779e-01 -3.98377240e-01 6.94901124... | [5.537351608276367, 2.5537099838256836] |
50d16a48-06d6-458f-8531-87e6a14d66b7 | um-cam-uncertainty-weighted-multi-resolution | 2306.11490 | null | https://arxiv.org/abs/2306.11490v1 | https://arxiv.org/pdf/2306.11490v1.pdf | UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentation | Accurate segmentation of the fetal brain from Magnetic Resonance Image (MRI) is important for prenatal assessment of fetal development. Although deep learning has shown the potential to achieve this task, it requires a large fine annotated dataset that is difficult to collect. To address this issue, weakly-supervised s... | ['Guotai Wang', 'Shaoting Zhang', 'Tao Lu', 'Jia Fu'] | 2023-06-20 | null | null | null | null | ['weakly-supervised-segmentation', 'brain-segmentation'] | ['computer-vision', 'medical'] | [ 4.83619094e-01 6.16680264e-01 -3.70484710e-01 -8.54131460e-01
-8.65066290e-01 -4.54309702e-01 1.84577212e-01 2.30574116e-01
-2.95014054e-01 5.45547366e-01 7.46375173e-02 -4.58417740e-03
-2.96434984e-02 -7.59333074e-01 -8.71719539e-01 -8.02390814e-01
4.17274423e-02 5.94871938e-01 4.78922278e-01 1.68737009... | [14.605692863464355, -2.1579952239990234] |
e606a74f-f815-4f5e-9f8b-f132717c2392 | retrospective-motion-correction-in-gradient | 2303.17239 | null | https://arxiv.org/abs/2303.17239v1 | https://arxiv.org/pdf/2303.17239v1.pdf | Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNs | Magnetic Resonance Imaging allows high resolution data acquisition with the downside of motion sensitivity due to relatively long acquisition times. Even during the acquisition of a single 2D slice, motion can severely corrupt the image. Retrospective motion correction strategies do not interfere during acquisition tim... | ['Bernadette N. Hahn', 'Mathias S. Feinler'] | 2023-03-30 | null | null | null | null | ['motion-compensation', 'motion-estimation'] | ['computer-vision', 'computer-vision'] | [ 8.55696619e-01 3.23562890e-01 1.60648167e-01 -3.10882211e-01
-6.80242240e-01 -4.99987602e-01 4.49657738e-01 -3.78336310e-01
-6.97519362e-01 8.24637651e-01 2.89369583e-01 -1.00730188e-01
-1.37891397e-01 -3.46603423e-01 -7.97047973e-01 -9.09022927e-01
-2.50196666e-01 2.96686351e-01 1.89508662e-01 -4.48414721... | [13.524894714355469, -2.4490268230438232] |
66cae12e-e8dd-4415-b90a-cad4316ac57d | a-pipeline-for-creative-visual-storytelling | 1807.08077 | null | http://arxiv.org/abs/1807.08077v1 | http://arxiv.org/pdf/1807.08077v1.pdf | A Pipeline for Creative Visual Storytelling | Computational visual storytelling produces a textual description of events
and interpretations depicted in a sequence of images. These texts are made
possible by advances and cross-disciplinary approaches in natural language
processing, generation, and computer vision. We define a computational creative
visual storytel... | ['Stephanie M. Lukin', 'Reginald Hobbs', 'Clare R. Voss'] | 2018-07-21 | a-pipeline-for-creative-visual-storytelling-1 | https://aclanthology.org/W18-1503 | https://aclanthology.org/W18-1503.pdf | ws-2018-6 | ['visual-storytelling'] | ['natural-language-processing'] | [ 5.08684039e-01 3.30133706e-01 4.02078927e-01 -3.30100209e-01
-2.12021202e-01 -9.86456633e-01 1.47330928e+00 1.17069162e-01
7.60329291e-02 5.06773710e-01 8.78826141e-01 -9.89583731e-02
2.48941332e-02 -5.43522120e-01 -4.74538326e-01 -1.47001117e-01
1.71110600e-01 6.78098977e-01 3.96637857e-01 -3.43615830... | [11.204222679138184, 0.8640230894088745] |
97325127-3700-4047-ae29-fee6913e2217 | zero-knowledge-zero-shot-learning-for-novel | 2302.04427 | null | https://arxiv.org/abs/2302.04427v1 | https://arxiv.org/pdf/2302.04427v1.pdf | Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery | Generalized Zero-Shot Learning (GZSL) and Open-Set Recognition (OSR) are two mainstream settings that greatly extend conventional visual object recognition. However, the limitations of their problem settings are not negligible. The novel categories in GZSL require pre-defined semantic labels, making the problem setting... | ['Hongfu Liu', 'Zhaonan Li'] | 2023-02-09 | null | null | null | null | ['object-recognition', 'generalized-zero-shot-learning', 'generalized-zero-shot-learning', 'open-set-learning'] | ['computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 4.95486557e-01 1.63297325e-01 -2.70149171e-01 -5.07265568e-01
-7.56454468e-01 -4.59023654e-01 4.43083972e-01 -1.19675770e-01
1.23659037e-01 4.51915532e-01 2.25860640e-01 1.79218933e-01
-3.43037188e-01 -7.03965187e-01 -6.41641557e-01 -9.32553828e-01
3.40611696e-01 4.55283374e-01 2.10650608e-01 -3.99634056... | [9.878239631652832, 2.400954246520996] |
018df2bc-f46d-46ef-925e-ec5ac404a11e | rapid-retrofitting-ieee-802-11ay-access | 2109.04819 | null | https://arxiv.org/abs/2109.04819v3 | https://arxiv.org/pdf/2109.04819v3.pdf | RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing | In this work we present RAPID, the first joint communication and radar system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. Unlike existing approaches for human sensing at millimeter-wave frequencies, which rely on special-purpose radars, RAPID achieves radar-level sensing accuracy ... | ['Joerg Widmer', 'Enver Bashirov', 'Francesca Meneghello', 'Michele Rossi', 'Jesus Omar Lacruz', 'Jacopo Pegoraro'] | 2021-09-10 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 2.08872333e-01 -3.87615785e-02 2.81454786e-03 -2.81569362e-01
-8.86615157e-01 -4.44039643e-01 2.12140232e-01 -2.57925630e-01
-4.37635601e-01 8.92885089e-01 -1.80940568e-01 -2.89897293e-01
-3.23457986e-01 -1.11852574e+00 -2.63589323e-01 -6.64929390e-01
-5.49311459e-01 2.22315580e-01 -3.39424968e-01 2.91349590... | [6.644696235656738, 0.7478752732276917] |
beedd3d1-9eab-4871-9632-b29a38bcd720 | leurn-learning-explainable-univariate-rules | 2303.14937 | null | https://arxiv.org/abs/2303.14937v1 | https://arxiv.org/pdf/2303.14937v1.pdf | LEURN: Learning Explainable Univariate Rules with Neural Networks | In this paper, we propose LEURN: a neural network architecture that learns univariate decision rules. LEURN is a white-box algorithm that results into univariate trees and makes explainable decisions in every stage. In each layer, LEURN finds a set of univariate rules based on an embedding of the previously checked rul... | ['Caglar Aytekin'] | 2023-03-27 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 1.43496946e-01 6.32397175e-01 -7.97780991e-01 -7.54420280e-01
-2.82612056e-01 -3.63539129e-01 6.52781546e-01 2.39891946e-01
1.61109671e-01 8.91350448e-01 1.00556307e-01 -3.85969967e-01
-5.56743264e-01 -1.18076074e+00 -7.84651756e-01 -4.88401800e-01
-1.46082431e-01 1.09605038e+00 -5.22425659e-02 -3.00643090... | [8.824024200439453, 6.011087894439697] |
030e2877-4dd3-4213-9e1d-e04aa53661ae | continual-transformers-redundancy-free | 2201.06268 | null | https://arxiv.org/abs/2201.06268v3 | https://arxiv.org/pdf/2201.06268v3.pdf | Continual Transformers: Redundancy-Free Attention for Online Inference | Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a time. Consequently, their use during online inference on time-series data entails considerable redundancy due to the overlap in successive token sequences. In this work, we propose novel formulatio... | ['Alexandros Iosifidis', 'Arian Bakhtiarnia', 'Lukas Hedegaard'] | 2022-01-17 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 2.41500154e-01 -1.67035520e-01 -2.12291911e-01 -3.40302765e-01
-8.28497112e-01 -5.68754911e-01 6.89074159e-01 3.76226634e-01
-4.82309192e-01 6.11145616e-01 -2.13343557e-03 -7.58841217e-01
1.83587074e-01 -8.33365679e-01 -9.22250032e-01 -4.89600658e-01
-2.59665668e-01 2.04945281e-01 2.37467527e-01 -1.79889783... | [7.226963043212891, 3.0623860359191895] |
1bd106b5-289c-45de-9795-6e8221f87288 | structural-scaffolds-for-citation-intent | 1904.01608 | null | https://arxiv.org/abs/1904.01608v2 | https://arxiv.org/pdf/1904.01608v2.pdf | Structural Scaffolds for Citation Intent Classification in Scientific Publications | Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose structural scaffolds, a multitask model to incorporate structural informa... | ['Waleed Ammar', 'Madeleine van Zuylen', 'Field Cady', 'Arman Cohan'] | 2019-04-02 | structural-scaffolds-for-citation-intent-1 | https://aclanthology.org/N19-1361 | https://aclanthology.org/N19-1361.pdf | naacl-2019-6 | ['citation-intent-classification'] | ['natural-language-processing'] | [-2.27503836e-01 -1.57556891e-01 -7.00270712e-01 -8.29402953e-02
-1.38415742e+00 -1.18227994e+00 1.03937483e+00 4.27043498e-01
-4.05949086e-01 8.60492945e-01 5.13158202e-01 -8.87351930e-01
-3.04863989e-01 -4.06553149e-01 -9.25177932e-01 -1.17884584e-01
4.50644612e-01 5.03972232e-01 -1.60435408e-01 3.74204874... | [9.658754348754883, 8.273748397827148] |
9b9eea30-e7d2-4112-89d6-8924b371160e | patch-craft-video-denoising-by-deep-modeling | 2103.13767 | null | https://arxiv.org/abs/2103.13767v2 | https://arxiv.org/pdf/2103.13767v2.pdf | Patch Craft: Video Denoising by Deep Modeling and Patch Matching | The non-local self-similarity property of natural images has been exploited extensively for solving various image processing problems. When it comes to video sequences, harnessing this force is even more beneficial due to the temporal redundancy. In the context of image and video denoising, many classically-oriented al... | ['Peyman Milanfar', 'Michael Elad', 'Gregory Vaksman'] | 2021-03-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Vaksman_Patch_Craft_Video_Denoising_by_Deep_Modeling_and_Patch_Matching_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Vaksman_Patch_Craft_Video_Denoising_by_Deep_Modeling_and_Patch_Matching_ICCV_2021_paper.pdf | iccv-2021-1 | ['color-image-denoising', 'video-denoising', 'patch-matching'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.26090479e-01 -7.00908303e-02 2.12125346e-01 -3.32875967e-01
-3.32336068e-01 -3.13244432e-01 7.28426576e-01 2.16018498e-01
-5.53838193e-01 4.56725001e-01 2.77209401e-01 1.83331564e-01
-1.54304698e-01 -8.19790065e-01 -8.87268841e-01 -9.76629853e-01
-1.41025603e-01 -3.32383841e-01 3.90877187e-01 -5.59252381... | [11.481825828552246, -2.271467685699463] |
bc9d55e3-40d8-4af1-9f4a-b93c0b2fa4c2 | synthetic-data-generation-and-multi-task | null | null | https://aclanthology.org/2021.wnut-1.29 | https://aclanthology.org/2021.wnut-1.29.pdf | Synthetic Data Generation and Multi-Task Learning for Extracting Temporal Information from Health-Related Narrative Text | Extracting temporal information is critical to process health-related text. Temporal information extraction is a challenging task for language models because it requires processing both texts and numbers. Moreover, the fundamental challenge is how to obtain a large-scale training dataset. To address this, we propose a ... | ['Bart Vanrumste', 'Stijn Luca', 'Dietwig Lowet', 'Heereen Shim'] | null | null | null | null | wnut-acl-2021-11 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 5.73366404e-01 -9.07365829e-02 -1.99442953e-01 -3.75165969e-01
-1.00886977e+00 -1.26337111e-01 7.28375435e-01 4.10398960e-01
-7.65822291e-01 7.66496778e-01 3.31928223e-01 -4.81169745e-02
-2.26872489e-01 -4.78629082e-01 -5.15621722e-01 -5.29129922e-01
-2.85317838e-01 3.22114438e-01 1.92092448e-01 -2.70613194... | [9.499263763427734, 8.933911323547363] |
1d0dddb5-afd3-4a42-8e64-5e7c9f489f0c | capacitance-resistance-model-and-recurrent | 2109.08779 | null | https://arxiv.org/abs/2109.08779v1 | https://arxiv.org/pdf/2109.08779v1.pdf | Capacitance Resistance Model and Recurrent Neural Network for Well Connectivity Estimation : A Comparison Study | In this report, two commonly used data-driven models for predicting well production under a waterflood setting: the capacitance resistance model (CRM) and recurrent neural networks (RNN) are compared. Both models are completely data-driven and are intended to learn the reservoir behavior during a water flood from histo... | ['Deepthi Sen'] | 2021-09-17 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [-1.79627553e-01 -2.03559309e-01 -1.84535190e-01 -2.38280356e-01
-1.36668965e-01 -1.56773254e-01 3.01493287e-01 3.60594183e-01
-1.46843880e-01 7.21150577e-01 5.92027426e-01 -7.33286858e-01
-2.49532118e-01 -1.28015566e+00 -5.18490791e-01 -5.44088185e-01
-6.55922413e-01 -1.42248021e-02 -2.50650376e-01 -7.47072160... | [6.507472515106201, 3.0090324878692627] |
ce11d37d-e2c8-45d8-9734-5732b16eed49 | peer-a-comprehensive-and-multi-task-benchmark | 2206.02096 | null | https://arxiv.org/abs/2206.02096v2 | https://arxiv.org/pdf/2206.02096v2.pdf | PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding | We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field. In this paper, we propose such a benchmark ca... | ['Jian Tang', 'Runcheng Liu', 'Chang Ma', 'Yangtian Zhang', 'Zhaocheng Zhu', 'Jiarui Lu', 'Zuobai Zhang', 'Minghao Xu'] | 2022-06-05 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 2.84120739e-01 -3.77762467e-01 -2.65744269e-01 -4.97232676e-01
-1.00982559e+00 -5.22197783e-01 1.98450133e-01 3.83313745e-01
-3.28666180e-01 1.08346868e+00 -1.76614765e-02 -3.64336759e-01
1.16264641e-01 -2.39877164e-01 -1.14552617e+00 -9.11447287e-01
5.35360500e-02 7.55110085e-01 3.18533748e-01 -1.61231697... | [4.758534908294678, 5.685074329376221] |
da4ce8fe-ac37-4761-a100-d9d4655b2056 | bridging-unpaired-facial-photos-and-sketches | 2102.00635 | null | https://arxiv.org/abs/2102.00635v3 | https://arxiv.org/pdf/2102.00635v3.pdf | Bridging Unpaired Facial Photos And Sketches By Line-drawings | In this paper, we propose a novel method to learn face sketch synthesis models by using unpaired data. Our main idea is bridging the photo domain $\mathcal{X}$ and the sketch domain $Y$ by using the line-drawing domain $\mathcal{Z}$. Specially, we map both photos and sketches to line-drawings by using a neural style tr... | ['Lingna Dai', 'Jingjie Zhu', 'Xiang Li', 'Meimei Shang', 'Fei Gao'] | 2021-02-01 | null | null | null | null | ['face-sketch-synthesis'] | ['computer-vision'] | [ 5.87379217e-01 -9.82585456e-03 2.13221572e-02 -5.38929343e-01
-7.20513344e-01 -7.68465102e-01 4.15806770e-01 -4.66482133e-01
-1.78473726e-01 7.66580403e-01 -4.75813240e-01 -6.25777692e-02
-1.71736553e-01 -1.29930365e+00 -1.13790154e+00 -5.30701578e-01
4.04633015e-01 3.91285509e-01 -3.03757370e-01 -1.23161629... | [12.315714836120605, -0.22364094853401184] |
994bc695-57ef-44c5-a9c3-18ecd1059695 | mitigating-dataset-harms-requires-stewardship | 2108.02922 | null | https://arxiv.org/abs/2108.02922v2 | https://arxiv.org/pdf/2108.02922v2.pdf | Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers | Machine learning datasets have elicited concerns about privacy, bias, and unethical applications, leading to the retraction of prominent datasets such as DukeMTMC, MS-Celeb-1M, and Tiny Images. In response, the machine learning community has called for higher ethical standards in dataset creation. To help inform these ... | ['Arvind Narayanan', 'Arunesh Mathur', 'Kenny Peng'] | 2021-08-06 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 2.20689207e-01 4.53444451e-01 -2.11143255e-01 -7.77170897e-01
-4.25497293e-01 -6.64539456e-01 5.83688915e-01 -4.15537618e-02
-6.52280569e-01 8.85650277e-01 4.99142945e-01 -5.87561548e-01
-1.48100987e-01 -4.27282870e-01 -7.23490715e-01 -2.56818861e-01
6.37073398e-01 -2.06946030e-01 -6.41522884e-01 2.37504974... | [12.936300277709961, 1.3327046632766724] |
1535ebfd-7ec5-4a7d-b749-16eb963227f8 | ethically-aligned-deep-learning-unbiased | 2111.05149 | null | https://arxiv.org/abs/2111.05149v1 | https://arxiv.org/pdf/2111.05149v1.pdf | Ethically aligned Deep Learning: Unbiased Facial Aesthetic Prediction | Facial beauty prediction (FBP) aims to develop a machine that automatically makes facial attractiveness assessment. In the past those results were highly correlated with human ratings, therefore also with their bias in annotating. As artificial intelligence can have racist and discriminatory tendencies, the cause of sk... | ['Matthias Rätsch', 'Xueping Su', 'Tobias Gerlach', 'Leping Peng', 'Thomas Weber', 'Michael Danner'] | 2021-11-09 | null | null | null | null | ['facial-beauty-prediction'] | ['computer-vision'] | [ 8.08958523e-03 6.69318080e-01 -5.61769456e-02 -9.40126479e-01
1.24028929e-01 -1.76143155e-01 5.14948368e-01 1.56979397e-01
-4.59688038e-01 6.90033317e-01 7.53280520e-02 2.10913923e-02
-7.69627988e-02 -8.67275417e-01 -3.79453361e-01 -3.31268370e-01
-2.09913291e-02 5.96022487e-01 -3.21634710e-01 -5.26392698... | [13.144914627075195, 1.2307897806167603] |
d4a24b13-2b42-4e1d-ae80-f468b450d52b | towards-emotion-aided-multi-modal-dialogue | null | null | https://aclanthology.org/2020.acl-main.402 | https://aclanthology.org/2020.acl-main.402.pdf | Towards Emotion-aided Multi-modal Dialogue Act Classification | The task of Dialogue Act Classification (DAC) that purports to capture communicative intent has been studied extensively. But these studies limit themselves to text. Non-verbal features (change of tone, facial expressions etc.) can provide cues to identify DAs, thus stressing the benefit of incorporating multi-modal in... | ['Sriparna Saha', 'Aditya Patra', 'Tulika Saha', 'Pushpak Bhattacharyya'] | 2020-07-01 | null | null | null | acl-2020-6 | ['dialogue-act-classification'] | ['natural-language-processing'] | [-1.32588357e-01 -6.10599108e-02 1.58735782e-01 -5.58984876e-01
-5.22464871e-01 -5.44503629e-01 9.58070517e-01 -1.96521968e-01
-5.41571736e-01 6.77306056e-01 6.30531192e-01 2.60133773e-01
2.14481562e-01 -4.00664121e-01 1.57538708e-02 -7.47881413e-01
2.09469140e-01 6.20900273e-01 -3.20603281e-01 -7.20300376... | [13.048405647277832, 6.177439212799072] |
1654cd7b-a7db-48fb-99eb-bc48698c4f99 | adversarial-audio-super-resolution-with | null | null | https://openreview.net/forum?id=H1eH4n09KX | https://openreview.net/pdf?id=H1eH4n09KX | Adversarial Audio Super-Resolution with Unsupervised Feature Losses | Neural network-based methods have recently demonstrated state-of-the-art results on image synthesis and super-resolution tasks, in particular by using variants of generative adversarial networks (GANs) with supervised feature losses. Nevertheless, previous feature loss formulations rely on the availability of large aux... | ['Visvesh Sathe', 'Sung Kim'] | 2018-09-27 | null | null | null | null | ['audio-super-resolution', 'audio-super-resolution'] | ['audio', 'music'] | [ 6.83056295e-01 2.50573188e-01 1.09208770e-01 -1.46871299e-01
-1.15511513e+00 -4.06369776e-01 6.70926750e-01 -5.74490309e-01
-6.40512258e-02 8.63495648e-01 4.15885776e-01 9.64202657e-02
2.39666611e-01 -9.00145173e-01 -7.34370768e-01 -7.12164342e-01
-9.09730047e-03 1.65332437e-01 1.57141387e-02 -4.17968899... | [11.55235767364502, -0.47712475061416626] |
b419d3f7-c834-446a-9261-063a4d216fe0 | harmonic-quantum-neural-networks | 2212.07462 | null | https://arxiv.org/abs/2212.07462v1 | https://arxiv.org/pdf/2212.07462v1.pdf | Harmonic (Quantum) Neural Networks | Harmonic functions are abundant in nature, appearing in limiting cases of Maxwell's, Navier-Stokes equations, the heat and the wave equation. Consequently, there are many applications of harmonic functions, spanning applications from industrial process optimisation to robotic path planning and the calculation of first ... | ['Vincent E. Elfving', 'Jeong-il Kye', 'Brad Kim', 'Yunjun Choi', 'Hyukgeun Cha', 'Seong-hyok Kim', 'Chul Lee', 'Mario Dagrada', 'Antonio A. Gentile', 'Atiyo Ghosh'] | 2022-12-14 | null | null | null | null | ['robot-navigation'] | ['robots'] | [ 5.81273019e-01 4.16535318e-01 3.00464816e-02 -2.35877529e-01
-3.23989242e-01 -4.75423962e-01 8.48435998e-01 -8.96714479e-02
-6.21137440e-01 9.34013486e-01 -2.02392682e-01 -4.07915890e-01
-5.48177004e-01 -1.19343615e+00 -6.63308322e-01 -1.13230336e+00
-4.00012076e-01 6.99667454e-01 -8.58711228e-02 -6.35592163... | [6.017858982086182, 4.201018333435059] |
bdc81b91-6edb-45e6-8c76-55374e57b022 | meta-learning-enabled-score-based-generative | 2305.02509 | null | https://arxiv.org/abs/2305.02509v1 | https://arxiv.org/pdf/2305.02509v1.pdf | Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI | Magnetic resonance imaging (MRI) is known to have reduced signal-to-noise ratios (SNR) at lower field strengths, leading to signal degradation when producing a low-field MRI image from a high-field one. Therefore, reconstructing a high-field-like image from a low-field MRI is a complex problem due to the ill-posed natu... | ['Dong Liang', 'Haifeng Wang', 'Yanjie Zhu', 'Qingyong Zhu', 'Jing Cheng', 'Yuanyuan Liu', 'Chentao Cao', 'Congcong Liu', 'Zhuo-Xu Cui'] | 2023-05-04 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 7.45159149e-01 -9.40207615e-02 3.12278420e-03 -4.96411443e-01
-1.27354658e+00 -1.39908120e-01 3.85143727e-01 -1.32602677e-01
-6.20830178e-01 8.68182003e-01 -1.72868535e-01 -1.93084881e-01
-5.97148120e-01 -6.27463639e-01 -1.02608848e+00 -9.91541862e-01
-4.12545562e-01 5.41555405e-01 2.62865663e-01 5.54589294... | [13.531881332397461, -2.395874261856079] |
8a6ab022-ef84-4ec3-be87-16aeeb845928 | domain-adaptation-through-synthesis-for | 1804.10094 | null | http://arxiv.org/abs/1804.10094v1 | http://arxiv.org/pdf/1804.10094v1.pdf | Domain Adaptation through Synthesis for Unsupervised Person Re-identification | Drastic variations in illumination across surveillance cameras make the
person re-identification problem extremely challenging. Current large scale
re-identification datasets have a significant number of training subjects, but
lack diversity in lighting conditions. As a result, a trained model requires
fine-tuning to b... | ['Jean-Francois Lalonde', 'Peter Carr', 'Slawomir Bak'] | 2018-04-26 | domain-adaptation-through-synthesis-for-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Slawomir_Bak_Domain_Adaptation_through_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Slawomir_Bak_Domain_Adaptation_through_ECCV_2018_paper.pdf | eccv-2018-9 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 2.36713797e-01 -5.12544930e-01 2.86233932e-01 -6.30779803e-01
-4.72441077e-01 -8.33448827e-01 7.04406559e-01 -4.02570784e-01
-4.21358585e-01 8.44669044e-01 8.21833760e-02 1.58667892e-01
3.71914893e-01 -4.62386221e-01 -5.97891271e-01 -5.51114619e-01
5.68648458e-01 6.07778132e-01 3.85646261e-02 3.77426334... | [14.703268051147461, 0.9893621206283569] |
91b31a04-8c94-4b14-8b59-7e40b394b95e | interactive-evaluation-of-dialog-track-at | 2207.14403 | null | https://arxiv.org/abs/2207.14403v1 | https://arxiv.org/pdf/2207.14403v1.pdf | Interactive Evaluation of Dialog Track at DSTC9 | The ultimate goal of dialog research is to develop systems that can be effectively used in interactive settings by real users. To this end, we introduced the Interactive Evaluation of Dialog Track at the 9th Dialog System Technology Challenge. This track consisted of two sub-tasks. The first sub-task involved building ... | ['Maxine Eskenazi', 'David Traum', 'Seyed Hossein Alavi', 'Carla Gordon', 'Yulan Feng', 'Shikib Mehri'] | 2022-07-28 | null | https://aclanthology.org/2022.lrec-1.616 | https://aclanthology.org/2022.lrec-1.616.pdf | lrec-2022-6 | ['interactive-evaluation-of-dialog', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-1.76201820e-01 6.07609272e-01 1.90709025e-01 -6.87232554e-01
-6.84011042e-01 -1.33772933e+00 1.02519894e+00 3.27171721e-02
-2.79660404e-01 9.20594871e-01 8.23265851e-01 -4.73890126e-01
2.42891148e-01 -3.80212784e-01 3.24752003e-01 3.65399092e-01
2.52027124e-01 1.26211464e+00 3.67749244e-01 -1.10965121... | [12.920735359191895, 8.033636093139648] |
14806eb7-3aae-4585-aaac-a5bf2238b19d | a-novel-local-binary-pattern-based-blind | 2101.06383 | null | https://arxiv.org/abs/2101.06383v1 | https://arxiv.org/pdf/2101.06383v1.pdf | A Novel Local Binary Pattern Based Blind Feature Image Steganography | Steganography methods in general terms tend to embed more and more secret bits in the cover images. Most of these methods are designed to embed secret information in such a way that the change in the visual quality of the resulting stego image is not detectable. There exists some methods which preserve the global struc... | ['Anand Singh Jalal', 'Soumendu Chakraborty'] | 2021-01-16 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 6.67401671e-01 1.04373433e-02 8.89257491e-02 -1.20965587e-02
2.17217654e-01 -3.67111415e-01 3.04037690e-01 -1.98192999e-01
-1.26556367e-01 6.41303539e-01 6.01412952e-02 -1.70294866e-01
1.54369222e-02 -1.18060768e+00 -5.64337134e-01 -1.00819492e+00
-2.04044640e-01 -3.88373882e-01 4.62583899e-01 -5.46307683... | [4.29630708694458, 8.051518440246582] |
d6eb50df-7b2f-4cc7-abee-f4a0b6db3e1c | lgpsolver-solving-logic-grid-puzzles | null | null | https://aclanthology.org/2020.findings-emnlp.100 | https://aclanthology.org/2020.findings-emnlp.100.pdf | LGPSolver - Solving Logic Grid Puzzles Automatically | Logic grid puzzle (LGP) is a type of word problem where the task is to solve a problem in logic. Constraints for the problem are given in the form of textual clues. Once these clues are transformed into formal logic, a deductive reasoning process provides the solution. Solving logic grid puzzles in a fully automatic ma... | ['Selma Tekir', 'Elgun Jabrayilzade'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['logic-grid-puzzle', 'formal-logic'] | ['miscellaneous', 'reasoning'] | [ 1.48577899e-01 1.96849167e-01 -2.81009942e-01 -2.07829490e-01
-8.98137987e-01 -1.12705946e+00 3.34268004e-01 4.70712870e-01
-8.31803493e-03 7.11374938e-01 -5.46747968e-02 -6.42694831e-01
-5.97969592e-01 -1.22352755e+00 -5.93752861e-01 -2.77372807e-01
1.99813530e-01 9.48596537e-01 6.62447631e-01 -6.31220460... | [8.972189903259277, 7.113369464874268] |
363e5c78-a6e7-4c58-9215-44bfe5d41373 | encoding-carbon-emission-flow-in-energy | 2305.13538 | null | https://arxiv.org/abs/2305.13538v1 | https://arxiv.org/pdf/2305.13538v1.pdf | Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach | Decarbonizing the energy supply is essential and urgent to mitigate the increasingly visible climate change. Its basis is identifying emission responsibility during power allocation by the carbon emission flow (CEF) model. However, the main challenge of CEF application is the intractable nonlinear relationship between ... | ['Hongbin Sun', 'Yinliang Xu', 'Linwei Sang'] | 2023-05-22 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 8.77784193e-02 -8.10067728e-02 -7.41989970e-01 -7.27543458e-02
-4.28209782e-01 -5.75060248e-01 2.02600673e-01 -3.68999183e-01
-2.42164377e-02 1.06649876e+00 3.04855049e-01 -6.64732873e-01
-1.01423991e+00 -1.00346172e+00 -6.58879757e-01 -9.76568341e-01
4.19025160e-02 2.04315692e-01 -1.05195868e+00 -1.22560887... | [5.618373394012451, 2.5897669792175293] |
217ba661-66ea-4f6e-9b11-a5267ab4f1e4 | psa-det3d-pillar-set-abstraction-for-3d | 2210.10983 | null | https://arxiv.org/abs/2210.10983v2 | https://arxiv.org/pdf/2210.10983v2.pdf | PSA-Det3D: Pillar Set Abstraction for 3D object Detection | Small object detection for 3D point cloud is a challenging problem because of two limitations: (1) Perceiving small objects is much more diffcult than normal objects due to the lack of valid points. (2) Small objects are easily blocked which breaks the shape of their meshes in 3D point cloud. In this paper, we propose ... | ['Haifeng Hu', 'Dihu Chena', 'Zhijie Zheng', 'Jingwen Zhao', 'Zhicong Huang'] | 2022-10-20 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 1.12904459e-01 -2.32532844e-01 3.51091951e-01 -1.68435648e-01
-3.85088712e-01 -4.58972603e-01 4.27376747e-01 6.31442666e-02
-1.76374555e-01 -3.42120938e-02 -5.01822114e-01 -2.11982548e-01
3.71109992e-01 -8.80731404e-01 -8.68712962e-01 -6.80026293e-01
1.36916474e-01 6.36768818e-01 1.27414334e+00 -4.69130948... | [7.781625270843506, -2.666860580444336] |
64c9de21-7007-44b9-9d7f-a167d5efca8d | data-augmentation-with-adversarial-training | null | null | https://aclanthology.org/2021.acl-long.401 | https://aclanthology.org/2021.acl-long.401.pdf | Data Augmentation with Adversarial Training for Cross-Lingual NLI | Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a cross-lingual model that can then be applied to multiple new languages. In practice, however, we still face the problem of scarce labeled data, leading to subpar results. In this pape... | ['Gerard de Melo', 'Dongkuan Xu', 'Zuohui Fu', 'Yaxin Zhu', 'Xin Dong'] | 2021-08-01 | null | null | null | acl-2021-5 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 2.75019467e-01 1.96854487e-01 -3.55137616e-01 -4.92804199e-01
-1.18687570e+00 -9.20534790e-01 8.06670189e-01 -1.13080367e-01
-2.56447762e-01 1.16058493e+00 2.09354430e-01 -4.44824070e-01
3.29703480e-01 -8.32111061e-01 -9.42874014e-01 -4.56594408e-01
3.13878328e-01 4.26930070e-01 -2.21196860e-01 -3.33352715... | [11.182607650756836, 9.980642318725586] |
5c172387-1f7e-4ce4-bb73-981650c4470a | template-free-prompt-tuning-for-few-shot-ner-1 | null | null | https://openreview.net/forum?id=ocsgIiRIxxO | https://openreview.net/pdf?id=ocsgIiRIxxO | Template-free Prompt Tuning for Few-shot NER | Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words. However, when applied to token-level labeling tasks such as NER, it would be time-consuming to enumerate the template queries over all potential entity sp... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['few-shot-ner'] | ['natural-language-processing'] | [ 2.68267810e-01 7.26415738e-02 4.68473695e-03 -3.38352978e-01
-1.03989995e+00 -4.09898251e-01 4.43948776e-01 2.44194716e-01
-9.80848372e-01 8.88189435e-01 1.61776811e-01 -3.35613489e-01
-1.11274011e-01 -8.26970398e-01 -3.36715460e-01 -8.22620690e-01
3.33085001e-01 3.99073750e-01 3.95668179e-01 -1.14771731... | [9.840083122253418, 9.451677322387695] |
a1e059c1-b2cb-452f-a356-5750eb7f4928 | a-dataset-of-reverberant-spatial-sound-scenes | 2006.01919 | null | https://arxiv.org/abs/2006.01919v1 | https://arxiv.org/pdf/2006.01919v1.pdf | A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection | This report presents the dataset and the evaluation setup of the Sound Event Localization & Detection (SELD) task for the DCASE 2020 Challenge. The SELD task refers to the problem of trying to simultaneously classify a known set of sound event classes, detect their temporal activations, and estimate their spatial direc... | ['Archontis Politis', 'Sharath Adavanne', 'Tuomas Virtanen'] | 2020-06-02 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 2.85553843e-01 -6.36252284e-01 6.60198331e-01 -1.84819072e-01
-1.23051047e+00 -7.62113392e-01 5.81960201e-01 -8.53916109e-02
-4.28129703e-01 2.50041932e-01 5.90530574e-01 1.02356516e-01
8.15857053e-02 -4.49164242e-01 -7.18107939e-01 -7.39537477e-01
-4.23675656e-01 -6.83420748e-02 5.17099679e-01 -9.97587480... | [15.11795711517334, 5.194571495056152] |
3d987284-68ad-4493-97f0-2c0b6ab4fb3f | on-the-state-of-german-abstractive-text | 2301.07095 | null | https://arxiv.org/abs/2301.07095v1 | https://arxiv.org/pdf/2301.07095v1.pdf | On the State of German (Abstractive) Text Summarization | With recent advancements in the area of Natural Language Processing, the focus is slowly shifting from a purely English-centric view towards more language-specific solutions, including German. Especially practical for businesses to analyze their growing amount of textual data are text summarization systems, which trans... | ['Michael Gertz', 'Jing Fan', 'Dennis Aumiller'] | 2023-01-17 | null | null | null | null | ['abstractive-text-summarization', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.36313003e-01 1.74023449e-01 -2.07894832e-01 -2.24515930e-01
-1.23976219e+00 -9.13097203e-01 7.24618793e-01 6.77696347e-01
-5.78408480e-01 1.05530131e+00 8.92768562e-01 -5.02115965e-01
-2.35858828e-01 -4.02711183e-01 -4.40188378e-01 -2.30303064e-01
3.59992683e-01 5.26471198e-01 -2.37872731e-02 -3.73241544... | [12.284221649169922, 9.47409439086914] |
e49f7209-e9c5-4070-8545-0f7d3218b060 | tax-free-3dmm-conditional-face-generation | 2305.13460 | null | https://arxiv.org/abs/2305.13460v2 | https://arxiv.org/pdf/2305.13460v2.pdf | 'Tax-free' 3DMM Conditional Face Generation | 3DMM conditioned face generation has gained traction due to its well-defined controllability; however, the trade-off is lower sample quality: Previous works such as DiscoFaceGAN and 3D-FM GAN show a significant FID gap compared to the unconditional StyleGAN, suggesting that there is a quality tax to pay for controllabi... | ['James Tompkin', 'Yue Wang', 'Xinjie Yi', 'Zhiqiu Yu', 'Yiwen Huang'] | 2023-05-22 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 4.85490859e-01 6.07059896e-01 -3.19183081e-01 -8.93340334e-02
-4.57297355e-01 -6.04462028e-01 6.91851616e-01 -9.38619554e-01
2.94950128e-01 1.01786625e+00 2.34692961e-01 -1.49439111e-01
-2.59070843e-01 -9.73211944e-01 -5.08500576e-01 -8.46186042e-01
3.37073445e-01 2.89110452e-01 -5.15876591e-01 -2.14037627... | [11.796252250671387, -0.45622357726097107] |
817510cc-4b57-454b-a16b-27653ada0333 | clvos23-a-long-video-object-segmentation | 2304.04259 | null | https://arxiv.org/abs/2304.04259v1 | https://arxiv.org/pdf/2304.04259v1.pdf | CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning | Continual learning in real-world scenarios is a major challenge. A general continual learning model should have a constant memory size and no predefined task boundaries, as is the case in semi-supervised Video Object Segmentation (VOS), where continual learning challenges particularly present themselves in working on l... | ['Paul Fieguth', 'Zeyad Moustafa', 'Amir Nazemi'] | 2023-04-09 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.31348118e-01 -1.92237958e-01 -5.11011839e-01 -2.68739164e-01
-8.37292492e-01 -4.95897800e-01 2.61390328e-01 -1.28538579e-01
-6.33947611e-01 4.72350866e-01 -2.25729018e-01 -3.28770399e-01
-1.44254211e-02 -2.73236066e-01 -1.19844592e+00 -2.76222855e-01
-1.87491000e-01 3.43626529e-01 6.53921366e-01 1.86886907... | [9.168906211853027, 0.1859457641839981] |
9021ed4d-86cc-4156-b076-00595659237e | wavelet-domain-residual-network-wavresnet-for | 1703.01383 | null | http://arxiv.org/abs/1703.01383v1 | http://arxiv.org/pdf/1703.01383v1.pdf | Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction | Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT
are computationally complex because of the repeated use of the forward and
backward projection. Inspired by this success of deep learning in computer
vision applications, we recently proposed a deep convolutional neural network
(CNN) for low-d... | ['Jong Chul Ye', 'Junhong Min', 'Eunhee Kang'] | 2017-03-04 | null | null | null | null | ['low-dose-x-ray-ct-reconstruction'] | ['medical'] | [ 2.57651240e-01 -6.02177206e-05 1.97688699e-01 -2.96863586e-01
-9.19486225e-01 2.12531060e-01 2.15221524e-01 -1.62610412e-01
-5.49144685e-01 4.36383128e-01 6.26565039e-01 -2.07725003e-01
-3.33907425e-01 -8.90004694e-01 -4.99302447e-01 -1.03808618e+00
-4.62044356e-03 -5.87881682e-03 2.29447275e-01 -2.22654954... | [13.468148231506348, -2.5394105911254883] |
65dce070-9171-4990-9729-4bd020023806 | regression-trees-and-random-forest-based | 1606.07578 | null | http://arxiv.org/abs/1606.07578v1 | http://arxiv.org/pdf/1606.07578v1.pdf | Regression Trees and Random forest based feature selection for malaria risk exposure prediction | This paper deals with prediction of anopheles number, the main vector of
malaria risk, using environmental and climate variables. The variables
selection is based on an automatic machine learning method using regression
trees, and random forests combined with stratified two levels cross validation.
The minimum threshol... | ['Bienvenue Kouwayè'] | 2016-06-24 | null | null | null | null | ['malaria-risk-exposure-prediction'] | ['medical'] | [ 1.99868113e-01 -1.07580952e-01 -2.50401914e-01 -6.08209014e-01
-3.08642834e-01 -2.14996397e-01 5.31676292e-01 3.75325859e-01
-5.57170093e-01 1.46641326e+00 6.09854907e-02 -3.52119505e-01
-4.16176140e-01 -1.08353674e+00 -1.34246781e-01 -1.20152843e+00
-7.74101257e-01 7.68385112e-01 -3.03172231e-01 -6.08195439... | [7.804529190063477, 4.815837860107422] |
7e9a7163-72b7-4800-b897-d0a98ef7f948 | action-classification-with-locality | 1408.3810 | null | http://arxiv.org/abs/1408.3810v2 | http://arxiv.org/pdf/1408.3810v2.pdf | Action Classification with Locality-constrained Linear Coding | We propose an action classification algorithm which uses Locality-constrained
Linear Coding (LLC) to capture discriminative information of human body
variations in each spatiotemporal subsequence of a video sequence. Our proposed
method divides the input video into equally spaced overlapping spatiotemporal
subsequences... | ['Ajmal Mian', 'Arif Mahmood', 'Hossein Rahmani', 'Du Huynh'] | 2014-08-17 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 3.91239762e-01 -5.95556736e-01 -6.83439374e-01 -1.12553872e-01
-6.16919577e-01 -1.29329279e-01 3.21401298e-01 -9.30543989e-02
-4.44094032e-01 6.99217856e-01 6.02115571e-01 4.12849665e-01
2.35694766e-01 -3.16936731e-01 -7.48420775e-01 -8.36921811e-01
-4.88639742e-01 -2.79866666e-01 5.74939072e-01 2.55849630... | [8.324971199035645, 0.48735684156417847] |
a523db1d-8d23-4c82-b698-4cadef8504eb | sequential-randomized-smoothing-for-1 | 2112.03000 | null | https://arxiv.org/abs/2112.03000v2 | https://arxiv.org/pdf/2112.03000v2.pdf | Sequential Randomized Smoothing for Adversarially Robust Speech Recognition | While Automatic Speech Recognition has been shown to be vulnerable to adversarial attacks, defenses against these attacks are still lagging. Existing, naive defenses can be partially broken with an adaptive attack. In classification tasks, the Randomized Smoothing paradigm has been shown to be effective at defending mo... | ['Bhiksha Raj', 'Raphael Olivier'] | 2021-11-05 | sequential-randomized-smoothing-for | https://aclanthology.org/2021.emnlp-main.514 | https://aclanthology.org/2021.emnlp-main.514.pdf | emnlp-2021-11 | ['robust-speech-recognition'] | ['speech'] | [ 4.72042561e-01 1.17955416e-01 2.26334363e-01 -1.32533893e-01
-1.13547897e+00 -1.17222512e+00 9.26783741e-01 -2.36761704e-01
-3.39178860e-01 2.50328302e-01 3.01044345e-01 -9.73506510e-01
1.29650667e-01 -3.95929277e-01 -5.38856089e-01 -7.23028719e-01
-2.14686170e-01 -1.68706290e-02 5.11132598e-01 -6.89736664... | [14.00989818572998, 5.835207462310791] |
65fb116c-ec31-47eb-90ab-703415529e7a | ms-gwnn-multi-scale-graph-wavelet-neural | 2012.14619 | null | https://arxiv.org/abs/2012.14619v1 | https://arxiv.org/pdf/2012.14619v1.pdf | MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis | Breast cancer is one of the most common cancers in women worldwide, and early detection can significantly reduce the mortality rate of breast cancer. It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer. And thus, it is the key to design an accurate computer-a... | ['Quanzheng Li', 'Mo Zhang'] | 2020-12-29 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 3.40058625e-01 -5.08174598e-02 -2.48123422e-01 -2.12286845e-01
-9.04823542e-01 -1.56203195e-01 1.19370535e-01 7.08421230e-01
-1.10202201e-01 1.78137094e-01 1.99405178e-01 -5.27206242e-01
-3.55769157e-01 -1.04006827e+00 -2.24139005e-01 -1.07223356e+00
-1.90333068e-01 -1.61258608e-01 1.34604186e-01 -3.43994141... | [15.168581008911133, -2.913496732711792] |
22ea2a4d-5b8c-4423-89b8-b51d8c02cc24 | a-general-framework-for-multi-step-ahead | 2207.14219 | null | https://arxiv.org/abs/2207.14219v6 | https://arxiv.org/pdf/2207.14219v6.pdf | A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting | The exponential growth of machine learning (ML) has prompted a great deal of interest in quantifying the uncertainty of each prediction for a user-defined level of confidence since nowadays ML is increasingly being used in high-stakes settings. Reliable ML via prediction intervals (PIs) that take into account jointly t... | ['José Moreira', 'Ana Maria Tomé', 'Martim Sousa'] | 2022-07-28 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-1.40644327e-01 1.25733256e-01 -8.08422565e-02 -6.30656660e-01
-1.29658115e+00 -5.85797548e-01 7.55864918e-01 3.57004642e-01
-3.46447200e-01 9.91842806e-01 6.93440586e-02 -5.38993657e-01
-5.15002191e-01 -7.10022569e-01 -8.25959682e-01 -9.10580277e-01
-7.19062760e-02 5.45870900e-01 -7.37359598e-02 1.97763234... | [7.219130992889404, 3.7436845302581787] |
322ca9a9-75ba-44a6-8004-8f00e8c0eee3 | transfer-reinforcement-learning-under | 2003.04427 | null | https://arxiv.org/abs/2003.04427v1 | https://arxiv.org/pdf/2003.04427v1.pdf | Transfer Reinforcement Learning under Unobserved Contextual Information | In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a demonstrator agent that has access to a context-aware policy and can generate transition and reward data based on that policy. These data const... | ['Yan Zhang', 'Michael M. Zavlanos'] | 2020-03-09 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 1.86815649e-01 3.85910094e-01 -3.85939568e-01 -1.33038551e-01
-6.94999158e-01 -5.00880063e-01 3.25469166e-01 3.34424347e-01
-8.38453293e-01 1.29084504e+00 -2.07865685e-01 -2.35269696e-01
-2.35180974e-01 -9.21618938e-01 -1.06942999e+00 -9.35344934e-01
-3.45444530e-01 4.13720727e-01 1.68781966e-01 -1.24558866... | [4.321567058563232, 1.9891828298568726] |
aa00a4a4-2dd2-4d9c-aff1-76447066fe45 | stargraph-a-coarse-to-fine-representation | 2205.14209 | null | https://arxiv.org/abs/2205.14209v2 | https://arxiv.org/pdf/2205.14209v2.pdf | StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph | Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood. We propose a method named StarGraph, which gives a novel way to utilize the neighborhood information for large-scale knowledge graphs to ob... | ['Yuhui Yin', 'Yafeng Deng', 'Linhui Feng', 'Xiangrui Gao', 'Hongzhu Li'] | 2022-05-27 | null | null | null | null | ['entity-embeddings'] | ['methodology'] | [-5.64670742e-01 5.35496116e-01 -7.19297409e-01 -6.09397404e-02
-4.91778702e-01 -4.18295085e-01 3.09612304e-01 4.73203026e-02
-9.12758261e-02 9.25061166e-01 3.79369706e-01 -1.92182869e-01
-4.36125040e-01 -1.33641279e+00 -8.45184267e-01 -6.62809789e-01
-3.26275975e-01 5.43379068e-01 1.19016975e-01 -2.00004533... | [8.781804084777832, 7.950326442718506] |
9e79fac0-9298-4848-9ba2-1f92de1f7466 | generalised-agent-for-solving-higher-board | 2212.12252 | null | https://arxiv.org/abs/2212.12252v1 | https://arxiv.org/pdf/2212.12252v1.pdf | Generalised agent for solving higher board states of tic tac toe using Reinforcement Learning | Tic Tac Toe is amongst the most well-known games. It has already been shown that it is a biased game, giving more chances to win for the first player leaving only a draw or a loss as possibilities for the opponent, assuming both the players play optimally. Thus on average majority of the games played result in a draw. ... | ['Bhavuk Kalra'] | 2022-12-23 | null | null | null | null | ['board-games'] | ['playing-games'] | [-1.75643861e-01 2.07104087e-01 -8.95608887e-02 1.91668242e-01
-3.11043680e-01 -5.64970255e-01 4.92978990e-02 -2.96797842e-01
-4.35380042e-01 1.17313337e+00 -4.89434302e-01 -6.37638509e-01
-7.72461116e-01 -1.03168929e+00 -4.86270279e-01 -7.81937540e-01
-2.50948548e-01 9.70524728e-01 4.16080743e-01 -9.33097780... | [3.441865921020508, 1.4810246229171753] |
250d284f-6cec-4a9f-a497-c0beb36b9330 | rebuild-and-ensemble-exploring-defense-1 | 2203.14207 | null | https://arxiv.org/abs/2203.14207v2 | https://arxiv.org/pdf/2203.14207v2.pdf | Text Adversarial Purification as Defense against Adversarial Attacks | Adversarial purification is a successful defense mechanism against adversarial attacks without requiring knowledge of the form of the incoming attack. Generally, adversarial purification aims to remove the adversarial perturbations therefore can make correct predictions based on the recovered clean samples. Despite the... | ['Xipeng Qiu', 'Demin Song', 'Linyang Li'] | 2022-03-27 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 4.72316921e-01 -4.91269492e-03 5.24380267e-01 1.70726001e-01
-8.38103056e-01 -1.27959502e+00 8.97848070e-01 -8.63518789e-02
-3.48896027e-01 5.63538074e-01 2.36342087e-01 -4.51065332e-01
2.87230521e-01 -9.97556090e-01 -7.28212953e-01 -1.02544487e+00
1.49265915e-01 4.42235351e-01 2.32716985e-02 -5.19377768... | [5.9304304122924805, 8.003902435302734] |
b5e407c5-ee34-492b-9088-5da1a7cb03cc | biomedical-multi-hop-question-answering-using | 2211.05351 | null | https://arxiv.org/abs/2211.05351v1 | https://arxiv.org/pdf/2211.05351v1.pdf | Biomedical Multi-hop Question Answering Using Knowledge Graph Embeddings and Language Models | Biomedical knowledge graphs (KG) are heterogenous networks consisting of biological entities as nodes and relations between them as edges. These entities and relations are extracted from millions of research papers and unified in a single resource. The goal of biomedical multi-hop question-answering over knowledge grap... | ['Mukta A. Paliwal', 'Shraddha S. Mane', 'Dattaraj J. Rao'] | 2022-11-10 | null | null | null | null | ['knowledge-graph-embeddings', 'multi-hop-question-answering', 'knowledge-graph-embeddings'] | ['graphs', 'knowledge-base', 'methodology'] | [-1.41000703e-01 5.97416401e-01 -9.28580910e-02 -2.74446338e-01
-2.61572331e-01 -4.98942375e-01 -1.85412653e-02 8.59965861e-01
-2.45714784e-01 1.13821149e+00 3.22440207e-01 -4.17804897e-01
-5.13671696e-01 -1.29920399e+00 -5.78639925e-01 -2.85447985e-01
-5.40281832e-02 6.11862540e-01 3.00071895e-01 -5.27308345... | [8.414650917053223, 8.192179679870605] |
d57cb622-45f8-465b-9a2e-44d41613ebb9 | cross-version-defect-prediction-with-class | 2212.14404 | null | https://arxiv.org/abs/2212.14404v1 | https://arxiv.org/pdf/2212.14404v1.pdf | Cross Version Defect Prediction with Class Dependency Embeddings | Software Defect Prediction aims at predicting which software modules are the most probable to contain defects. The idea behind this approach is to save time during the development process by helping find bugs early. Defect Prediction models are based on historical data. Specifically, one can use data collected from pas... | ['Rami Puzis', 'Lior Rokach', 'Moti Cohen'] | 2022-12-29 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-1.73075035e-01 1.30182758e-01 -1.67266279e-01 -2.52276868e-01
-1.11332864e-01 -3.36631715e-01 3.22837055e-01 1.04323256e+00
-1.19828142e-01 4.69285771e-02 1.02964759e-01 -3.52999836e-01
-4.87860441e-02 -1.10855329e+00 -2.67479151e-01 -1.85341567e-01
-3.54619056e-01 5.99110164e-02 5.02313614e-01 -2.38572389... | [7.394968509674072, 7.739782810211182] |
19b1cc54-73f1-4c63-9cdf-4d8684fc7a8e | evaluation-of-self-taught-learning-based | 2204.12624 | null | https://arxiv.org/abs/2204.12624v1 | https://arxiv.org/pdf/2204.12624v1.pdf | Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition | This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER). The idea is to create complementary representations promoting diversity by varying the autoencoders' initialization, architecture, and training dat... | ['Alessandro L. Koerich', 'Jean Paul Barddal', 'Alceu de S. Britto Jr.', 'Leonardo L. Veras', 'Bruna Delazeri'] | 2022-04-26 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 1.01990178e-01 1.70333445e-01 -8.53088871e-02 -8.75563025e-01
-1.97213173e-01 5.11216149e-02 8.02066445e-01 -1.99318424e-01
-4.35854763e-01 1.05896401e+00 3.11619937e-01 3.65878493e-01
-3.27168435e-01 -6.28029227e-01 -1.96355447e-01 -1.04086578e+00
-2.69120514e-01 4.89798784e-01 -3.27528238e-01 -5.70614994... | [13.553196907043457, 1.8158053159713745] |
82af3470-e34c-48c7-969f-5b6da1222aad | drvertgraduate-uncertainty-aware-deep | 1910.11777 | null | https://arxiv.org/abs/1910.11777v2 | https://arxiv.org/pdf/1910.11777v2.pdf | DR$\vert$GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images | Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and follow up of patients, but the screening process can be tiresome and prone to errors. Deep learning approaches have shown promising performance as computer-aided diagnosis(CAD) systems, but their black-box behaviour hinders the clini... | ['Aurélio Campilho', 'Ana Maria Mendonça', 'Ângela Carneiro', 'Luís Mendonça', 'Teresa Araújo', 'Carolina Maia', 'Susana Penas', 'Guilherme Aresta'] | 2019-10-25 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-1.51410070e-03 4.98105288e-01 -1.13546878e-01 -7.75285602e-01
-1.09598100e+00 -2.15437725e-01 1.10966474e-01 3.06101263e-01
-2.26917028e-01 6.53816640e-01 -1.21828265e-01 -3.89882147e-01
-6.02470934e-01 -7.91198730e-01 -4.91837054e-01 -7.41161466e-01
1.01808561e-02 6.61111653e-01 2.11435929e-03 2.87593722... | [15.674602508544922, -3.7103593349456787] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.