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ffe90c6e-cb6f-441e-aba7-6ddc593daf9f | zero-shot-language-transfer-vs-iterative-back | 2104.00106 | null | https://arxiv.org/abs/2104.00106v1 | https://arxiv.org/pdf/2104.00106v1.pdf | Zero-Shot Language Transfer vs Iterative Back Translation for Unsupervised Machine Translation | This work focuses on comparing different solutions for machine translation on low resource language pairs, namely, with zero-shot transfer learning and unsupervised machine translation. We discuss how the data size affects the performance of both unsupervised MT and transfer learning. Additionally we also look at how t... | ['Har Simrat Singh', 'Chengzhi Huang', 'Aviral Joshi'] | 2021-03-31 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 9.39082280e-02 1.63619936e-01 -6.65115654e-01 -3.73807549e-01
-1.37091327e+00 -6.09978080e-01 8.74787569e-01 -1.10152485e-02
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2.12896228e-01 -4.51473981e-01 -6.70684338e-01 -3.60996485e-01
3.54586095e-01 9.22613025e-01 -2.67763045e-02 -5.32877445... | [11.505293846130371, 10.27646541595459] |
d4e8abdb-6987-42e9-b267-c1819761ad6f | simoap-improve-coherence-and-consistency-in | 2305.11130 | null | https://arxiv.org/abs/2305.11130v2 | https://arxiv.org/pdf/2305.11130v2.pdf | SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation | Language models trained on large-scale corpora can generate remarkably fluent results in open-domain dialogue. However, for the persona-based dialogue generation task, consistency and coherence are also key factors, which are great challenges for language models. Existing works mainly focus on valuable data filtering, ... | ['Xueqi Cheng', 'HuaWei Shen', 'Liang Pang', 'Junkai Zhou'] | 2023-05-18 | null | null | null | null | ['dialogue-generation', 'response-generation', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 2.28652898e-02 6.55327961e-02 -1.07820690e-01 -6.49929881e-01
-1.28549457e+00 -5.04883647e-01 7.10436165e-01 6.69323504e-02
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3.91586363e-01 1.18243814e+00 2.32452422e-01 -7.63065994... | [12.576828002929688, 8.248071670532227] |
61f2b6d7-b815-4f9f-9242-8cb705167cf5 | decoding-kinetic-features-of-hand-motor | null | null | https://onlinelibrary.wiley.com/doi/abs/10.1111/ejn.14936 | https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejn.14936 | Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networks | Building accurate movement decoding models from brain signals is crucial for many biomedical applications. Predicting specific movement features, such as speed and force, before movement execution may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attem... | ['José Biurrun Manresa', 'Mads Jochumsen', 'Luciano Schiaffino', 'Yanina Atum', 'Ramiro Gatti'] | 2020-08-11 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 3.13148797e-01 -3.14053297e-01 -2.64206320e-01 -2.44664162e-01
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-4.49984938e-01 2.49918476e-01 8.44079703e-02 -3.28909636... | [12.990964889526367, 3.4084746837615967] |
f6fb58f5-42e2-463d-bfab-78f4fe38e5eb | tinyml-tools-applications-challenges-and | 2303.13569 | null | https://arxiv.org/abs/2303.13569v1 | https://arxiv.org/pdf/2303.13569v1.pdf | TinyML: Tools, Applications, Challenges, and Future Research Directions | In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the desired accuracy, which has limited their use mainly to high-capability devices such as network nod... | ['Onel L. A. López', 'Sridhar Iyer', 'Prasoon Raghuwanshi', 'Krishna Pai', 'Rakhee Kallimani'] | 2023-03-23 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-1.06856890e-01 1.19335242e-01 -3.73516738e-01 -5.09807050e-01
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ddc58d10-3c9f-45b5-8bb0-47ffdff8338d | pybibx-a-python-library-for-bibliometric-and | 2304.14516 | null | https://arxiv.org/abs/2304.14516v1 | https://arxiv.org/pdf/2304.14516v1.pdf | pyBibX -- A Python Library for Bibliometric and Scientometric Analysis Powered with Artificial Intelligence Tools | Bibliometric and Scientometric analyses offer invaluable perspectives on the complex research terrain and collaborative dynamics spanning diverse academic disciplines. This paper presents pyBibX, a python library devised to conduct comprehensive bibliometric and scientometric analyses on raw data files sourced from Sco... | ['Carlos Henrique Tarjano Santos', 'Marcio Pereira Basilio', 'Valdecy Pereira'] | 2023-04-27 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [-5.54645717e-01 -2.06771240e-01 -6.16919339e-01 4.29756969e-01
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-1.82652399e-01 6.10005617e-01 -6.28723800e-01 -2.69024342... | [9.60920238494873, 8.214582443237305] |
4b01185d-4017-4491-9242-6fda6e38ba6d | dmix-adaptive-distance-aware-interpolative | null | null | https://aclanthology.org/2022.acl-short.67 | https://aclanthology.org/2022.acl-short.67.pdf | DMix: Adaptive Distance-aware Interpolative Mixup | Interpolation-based regularisation methods such as Mixup, which generate virtual training samples, have proven to be effective for various tasks and modalities.We extend Mixup and propose DMix, an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space. DMix leve... | ['Lucie Flek', 'Diyi Yang', 'Di Jin', 'Ritesh Soun', 'Shrey Pandit', 'Megh Thakkar', 'Ramit Sawhney'] | null | null | null | null | acl-2022-5 | ['sentence-classification'] | ['natural-language-processing'] | [ 1.90441310e-01 -1.62917115e-02 -2.69770771e-01 -4.69731331e-01
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7.02610472e-03 2.10180208e-01 -2.33194754e-01 -3.04476619... | [10.76498031616211, 8.40109634399414] |
6ee667c6-d6f3-4cfc-aa7a-066d08ffe087 | understanding-the-effect-of-the-long-tail-on | 2306.06238 | null | https://arxiv.org/abs/2306.06238v3 | https://arxiv.org/pdf/2306.06238v3.pdf | Understanding the Effect of the Long Tail on Neural Network Compression | Network compression is now a mature sub-field of neural network research: over the last decade, significant progress has been made towards reducing the size of models and speeding up inference, while maintaining the classification accuracy. However, many works have observed that focusing on just the overall accuracy ca... | ['Ganesh Gopalakrishnan', 'Michael Garland', 'Saurav Muralidharan', 'Aditya Bhaskara', 'Vinu Joseph', 'Harvey Dam'] | 2023-06-09 | null | null | null | null | ['neural-network-compression', 'neural-network-compression', 'memorization'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [ 8.65128875e-01 2.45224237e-01 -2.77609766e-01 -4.32650387e-01
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8.89183953e-02 3.69410396e-01 2.28457171e-02 5.12334555... | [8.469149589538574, 3.453639268875122] |
7a0ae689-015e-4687-b9a0-8cd38948e842 | a-multi-level-contextual-model-for-person | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Li_A_Multi-Level_Contextual_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Li_A_Multi-Level_Contextual_CVPR_2016_paper.pdf | A Multi-Level Contextual Model For Person Recognition in Photo Albums | In this work, we present a new framework for person recognition in photo albums that exploits contextual cues at multiple levels, spanning individual persons, individual photos, and photo groups. Through experiments, we show that the information available at each of these distinct contextual levels provides complement... | ['Xiaohui Shen', 'Haoxiang Li', 'Zhe Lin', 'Jonathan Brandt', 'Gang Hua'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['person-recognition'] | ['computer-vision'] | [ 4.48732108e-01 -3.29037845e-01 -5.12163676e-02 -5.91704667e-01
-6.16615891e-01 -7.57876158e-01 8.55785787e-01 8.33679065e-02
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2.15354949e-01 1.76182181e-01 -2.32053310e-01 5.25920950... | [14.515748977661133, 0.9530503153800964] |
cd635de9-7a9f-4085-956e-76ca5f6fea7e | uit-hwdb-using-transferring-method-to | 2211.05407 | null | https://arxiv.org/abs/2211.05407v1 | https://arxiv.org/pdf/2211.05407v1.pdf | UIT-HWDB: Using Transferring Method to Construct A Novel Benchmark for Evaluating Unconstrained Handwriting Image Recognition in Vietnamese | Recognizing handwriting images is challenging due to the vast variation in writing style across many people and distinct linguistic aspects of writing languages. In Vietnamese, besides the modern Latin characters, there are accent and letter marks together with characters that draw confusion to state-of-the-art handwri... | ['Kiet Van Nguyen', 'Duong T. D. Vo', 'Nghia Hieu Nguyen'] | 2022-11-10 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 1.21732458e-01 -7.22838640e-01 -2.02623438e-02 -3.13425094e-01
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5.34816802e-01 4.21072811e-01 5.61414771e-02 -3.73050958... | [11.865046501159668, 2.5406997203826904] |
36232faf-c9ff-4b51-91f5-2f86533041e2 | ctap-complementary-temporal-action-proposal | 1807.04821 | null | http://arxiv.org/abs/1807.04821v2 | http://arxiv.org/pdf/1807.04821v2.pdf | CTAP: Complementary Temporal Action Proposal Generation | Temporal action proposal generation is an important task, akin to object
proposals, temporal action proposals are intended to capture "clips" or
temporal intervals in videos that are likely to contain an action. Previous
methods can be divided to two groups: sliding window ranking and actionness
score grouping. Sliding... | ['Jiyang Gao', 'Ram Nevatia', 'Kan Chen'] | 2018-07-12 | ctap-complementary-temporal-action-proposal-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Jiyang_Gao_CTAP_Complementary_Temporal_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Jiyang_Gao_CTAP_Complementary_Temporal_ECCV_2018_paper.pdf | eccv-2018-9 | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 5.82261086e-01 7.77751021e-03 -6.97284520e-01 -3.96194637e-01
-9.57062125e-01 -3.44629079e-01 7.81748295e-01 -6.43813331e-03
-4.80522454e-01 4.79342878e-01 5.66857517e-01 2.91347057e-01
-6.09828904e-02 -5.33297956e-01 -6.15690351e-01 -6.15822315e-01
-3.67463440e-01 1.19640650e-02 1.31331694e+00 5.53024374... | [8.353755950927734, 0.43458056449890137] |
dd68563b-cac9-4e81-9b26-d862e62859d8 | approaches-toward-physical-and-general-video | 2112.07661 | null | https://arxiv.org/abs/2112.07661v1 | https://arxiv.org/pdf/2112.07661v1.pdf | Approaches Toward Physical and General Video Anomaly Detection | In recent years, many works have addressed the problem of finding never-seen-before anomalies in videos. Yet, most work has been focused on detecting anomalous frames in surveillance videos taken from security cameras. Meanwhile, the task of anomaly detection (AD) in videos exhibiting anomalous mechanical behavior, has... | ['Niv Cohen', 'Laura Kart'] | 2021-12-14 | null | null | null | null | ['physical-video-anomaly-detection', 'general-action-video-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 4.38230574e-01 -2.05627248e-01 1.24115370e-01 -9.89917200e-03
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-5.85751891e-01 -4.60567549e-02 6.53081834e-01 -1.75750209... | [7.8846564292907715, 1.5533655881881714] |
9d267713-13f8-4891-aa15-eb27a0b05899 | semi-supervised-chinese-word-segmentation | null | null | https://aclanthology.org/D14-1010 | https://aclanthology.org/D14-1010.pdf | Semi-Supervised Chinese Word Segmentation Using Partial-Label Learning With Conditional Random Fields | null | ['Paul Vozila', 'Fan Yang'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['partial-label-learning'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.340752601623535, 3.701204776763916] |
835daf30-7f76-4ce4-bce6-333aaf75f722 | feature-learning-for-fault-detection-in-high | 1810.05550 | null | https://arxiv.org/abs/1810.05550v2 | https://arxiv.org/pdf/1810.05550v2.pdf | Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals | Complex industrial systems are continuously monitored by a large number of heterogeneous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns. The paper proposes an integrated automatic unsupervised featu... | ['Thomas Palmé', 'Gabriel Michau', 'Yang Hu', 'Olga Fink'] | 2018-10-12 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 2.38596573e-01 4.60313521e-02 4.71462905e-01 -1.79104149e-01
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-8.80642235e-01 -8.21151614e-01 -3.07555974e-01 -1.23674154e+00
-4.39204752e-01 8.83736014e-01 8.00136011e-03 -1.55870706... | [6.747382640838623, 2.4039876461029053] |
08afe1de-b485-4480-a30f-083a67aa343a | learning-to-selectively-learn-for-weakly-1 | null | null | https://aclanthology.org/2022.naacl-main.99 | https://aclanthology.org/2022.naacl-main.99.pdf | Learning to Selectively Learn for Weakly Supervised Paraphrase Generation with Model-based Reinforcement Learning | Paraphrase generation is an important language generation task attempting to interpret user intents and systematically generate new phrases of identical meanings to the given ones. However, the effectiveness of paraphrase generation is constrained by the access to the golden labeled data pairs where both the amount and... | ['Ping Li', 'Dingcheng Li', 'Haiyan Yin'] | null | null | null | null | naacl-2022-7 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 5.89337111e-01 3.48414451e-01 -6.27456009e-01 -3.32837939e-01
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4.33708012e-01 7.58558273e-01 -2.06641078e-01 -5.93297422... | [11.628988265991211, 9.060684204101562] |
68e2e7a6-7c92-4699-8573-4f172c0cb480 | qvhighlights-detecting-moments-and-highlights | 2107.09609 | null | https://arxiv.org/abs/2107.09609v2 | https://arxiv.org/pdf/2107.09609v2.pdf | QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries | Detecting customized moments and highlights from videos given natural language (NL) user queries is an important but under-studied topic. One of the challenges in pursuing this direction is the lack of annotated data. To address this issue, we present the Query-based Video Highlights (QVHIGHLIGHTS) dataset. It consists... | ['Mohit Bansal', 'Tamara L. Berg', 'Jie Lei'] | 2021-07-20 | null | null | null | null | ['highlight-detection', 'moment-retrieval'] | ['computer-vision', 'computer-vision'] | [ 2.36042425e-01 -1.60798728e-01 -4.54799861e-01 -3.74867648e-01
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-2.38326013e-01 2.51497626e-02 5.41716993e-01 -2.47254878... | [10.131299018859863, 0.6682005524635315] |
597df21d-072f-46bd-8df1-1089eea3b718 | 0-1-deep-neural-networks-via-block-coordinate | 2206.09379 | null | https://arxiv.org/abs/2206.09379v1 | https://arxiv.org/pdf/2206.09379v1.pdf | 0/1 Deep Neural Networks via Block Coordinate Descent | The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs). As it counts 1 for positive variables and 0 for others, its intrinsic characteristics (e.g., discontinuity and no viable information of subgradients) impede its development for several decades. Even if there ... | ['Naihua Xiu', 'Geoffrey Ye Li', 'Shenglong Zhou', 'HUI ZHANG'] | 2022-06-19 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [-3.06377187e-02 -7.20557645e-02 -4.01065618e-01 -4.78249609e-01
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5.62340580e-02 2.24001974e-01 -8.68374333e-02 -1.66629270... | [8.005598068237305, 3.599343776702881] |
cc7afce9-2880-49db-89d8-06b4b1b3c367 | distilprotbert-a-distilled-protein-language | null | null | https://www.biorxiv.org/content/10.1101/2022.05.09.491157v1 | https://www.biorxiv.org/content/10.1101/2022.05.09.491157v1.full.pdf | DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts | Recently, Deep Learning models, initially developed in the field of Natural Language Processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameters needed to be fitted and the amount of computational resources they require. R... | ['Ron Unger', 'Yanay Ofran', 'Yaron Geffen'] | 2022-05-10 | null | null | null | biorxiv-2022-5 | ['protein-language-model', 'protein-secondary-structure-prediction'] | ['medical', 'medical'] | [ 3.42396975e-01 2.43354797e-01 1.79731086e-01 -4.81521875e-01
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4b20e3d7-3eb0-4a03-91e5-ba0fd1e43b99 | a-sourceful-twist-emoji-prediction-based-on | 2103.07833 | null | https://arxiv.org/abs/2103.07833v1 | https://arxiv.org/pdf/2103.07833v1.pdf | A `Sourceful' Twist: Emoji Prediction Based on Sentiment, Hashtags and Application Source | We widely use emojis in social networking to heighten, mitigate or negate the sentiment of the text. Emoji suggestions already exist in many cross-platform applications but an emoji is predicted solely based a few prominent words instead of understanding the subject and substance of the text. Through this paper, we sho... | ['Patrick Dudas', 'Shomir Wilson', 'Kenneth Huang', 'Rahul Katiki', 'Chi-Yang Hsu', 'Zeba Karishma', 'Pranav Venkit'] | 2021-03-14 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-3.52977484e-01 4.95467670e-02 -6.85850158e-02 -5.41621268e-01
2.62659490e-01 -2.69909561e-01 5.29104769e-01 4.02628511e-01
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2.43330039e-02 -8.64056647e-01 -6.31094798e-02 -1.86747894e-01
1.73979104e-01 -1.75502390e-01 2.50139654e-01 -7.60649621... | [11.288226127624512, 6.913079738616943] |
45b17767-e00f-4ec7-8015-22f7ddff9105 | time-contrastive-networks-self-supervised | 1704.06888 | null | http://arxiv.org/abs/1704.06888v3 | http://arxiv.org/pdf/1704.06888v3.pdf | Time-Contrastive Networks: Self-Supervised Learning from Video | We propose a self-supervised approach for learning representations and
robotic behaviors entirely from unlabeled videos recorded from multiple
viewpoints, and study how this representation can be used in two robotic
imitation settings: imitating object interactions from videos of humans, and
imitating human poses. Imit... | ['Yevgen Chebotar', 'Stefan Schaal', 'Eric Jang', 'Sergey Levine', 'Corey Lynch', 'Pierre Sermanet', 'Jasmine Hsu'] | 2017-04-23 | null | null | null | null | ['video-alignment'] | ['computer-vision'] | [-1.50662825e-01 3.63699608e-02 -1.30036235e-01 -1.25517502e-01
-1.99373156e-01 -8.34114373e-01 4.50730026e-01 -5.94749212e-01
-4.03315604e-01 7.35077441e-01 3.97247598e-02 5.56834757e-01
8.58806521e-02 -8.31346214e-02 -1.24806798e+00 -7.53822625e-01
-4.17226911e-01 5.27824581e-01 8.08691606e-02 -1.92028731... | [4.625287055969238, 0.7261697053909302] |
46929d78-f949-43dc-9640-3bf42bbf404f | series-photo-selection-via-multi-view-graph | 2203.09736 | null | https://arxiv.org/abs/2203.09736v1 | https://arxiv.org/pdf/2203.09736v1.pdf | Series Photo Selection via Multi-view Graph Learning | Series photo selection (SPS) is an important branch of the image aesthetics quality assessment, which focuses on finding the best one from a series of nearly identical photos. While a great progress has been observed, most of the existing SPS approaches concentrate solely on extracting features from the original image,... | ['Yilong Yin', 'Xiushan Nie', 'Jian Zhang', 'Yongshun Gong', 'Lu Zhang', 'Jin Huang'] | 2022-03-18 | null | null | null | null | ['aesthetics-quality-assessment'] | ['computer-vision'] | [ 1.98235020e-01 -2.42904186e-01 -6.93760961e-02 -3.04380417e-01
-6.79657221e-01 -1.80036008e-01 2.87837386e-01 7.53160641e-02
-1.54996673e-02 2.09017709e-01 2.62575299e-01 3.52976799e-01
-2.10806310e-01 -6.05715156e-01 -5.82374513e-01 -7.73025513e-01
1.82747126e-01 -1.77583754e-01 2.45870203e-01 -3.13882440... | [11.472626686096191, -0.9995842576026917] |
dea82dae-659d-41ac-b5a3-bf237b64f125 | semantically-tied-paired-cycle-consistency | 1903.03372 | null | http://arxiv.org/abs/1903.03372v1 | http://arxiv.org/pdf/1903.03372v1.pdf | Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval | Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer
vision, allowing to retrieve natural images relevant to sketch queries that
might not been seen in the training phase. Existing works either require
aligned sketch-image pairs or inefficient memory fusion layer for mapping the
visual informat... | ['Zeynep Akata', 'Anjan Dutta'] | 2019-03-08 | semantically-tied-paired-cycle-consistency-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Dutta_Semantically_Tied_Paired_Cycle_Consistency_for_Zero-Shot_Sketch-Based_Image_Retrieval_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Dutta_Semantically_Tied_Paired_Cycle_Consistency_for_Zero-Shot_Sketch-Based_Image_Retrieval_CVPR_2019_paper.pdf | cvpr-2019-6 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.93662632e-01 -2.18350172e-01 -1.04067259e-01 -3.11128914e-01
-1.10807753e+00 -6.28298163e-01 1.14023471e+00 -2.46253058e-01
-6.79580197e-02 3.56580287e-01 2.83732936e-02 1.14830844e-01
-1.01586483e-01 -8.41106415e-01 -8.13733935e-01 -5.75057089e-01
3.83795321e-01 5.62428415e-01 3.33203286e-01 -2.36429960... | [11.611011505126953, 0.6764500141143799] |
68258055-af86-45c7-9980-6d787c05cd7a | jseegraph-joint-structured-event-extraction | 2306.14633 | null | https://arxiv.org/abs/2306.14633v1 | https://arxiv.org/pdf/2306.14633v1.pdf | JSEEGraph: Joint Structured Event Extraction as Graph Parsing | We propose a graph-based event extraction framework JSEEGraph that approaches the task of event extraction as general graph parsing in the tradition of Meaning Representation Parsing. It explicitly encodes entities and events in a single semantic graph, and further has the flexibility to encode a wider range of additio... | ['Lilja Øvrelid', 'Samia Touileb', 'Huiling You'] | 2023-06-26 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 2.43115798e-01 6.99493110e-01 -2.83513993e-01 -3.92306328e-01
-6.37264848e-01 -8.88857782e-01 6.80404544e-01 7.58556485e-01
-3.61311674e-01 7.34430075e-01 5.71420789e-01 -3.31304610e-01
-4.95566353e-02 -1.08736265e+00 -7.91262090e-01 -1.37215897e-01
-5.73037624e-01 5.14004171e-01 6.60038769e-01 1.10105425... | [9.067686080932617, 9.164521217346191] |
55b51326-b311-41eb-82e6-599ff31cce68 | improving-human-robot-collaboration-via | 2303.11425 | null | https://arxiv.org/abs/2303.11425v1 | https://arxiv.org/pdf/2303.11425v1.pdf | Improving Human-Robot Collaboration via Computational Design | When robots entered our day-to-day life, the shared space surrounding humans and robots is critical for effective Human-Robot collaboration. The design of shared space should satisfy humans' preferences and robots' efficiency. This work uses kitchen design as an example to illustrate the importance of good space design... | ['Jyh-Ming Lien', 'Jixuan Zhi'] | 2023-03-20 | null | null | null | null | ['motion-planning'] | ['robots'] | [-4.90834415e-01 3.22390586e-01 -1.29967883e-01 -1.50094822e-01
-1.05506115e-01 -3.68901342e-01 7.85226822e-02 -1.50259927e-01
-5.33405542e-01 8.50563645e-01 1.80485249e-01 -1.85410097e-01
-3.02373707e-01 -6.17245257e-01 -2.32724234e-01 -3.10348004e-01
-2.00777650e-01 8.39852333e-01 5.18425889e-02 -3.94283891... | [4.892285346984863, 1.15488600730896] |
7f5ecc67-9d7a-4ed1-848e-7c600a29b2f3 | character-aware-neural-networks-for-arabic | null | null | https://aclanthology.org/W16-3703 | https://aclanthology.org/W16-3703.pdf | Character-Aware Neural Networks for Arabic Named Entity Recognition for Social Media | Named Entity Recognition (NER) is the task of classifying or labelling atomic elements in the text into categories such as Person, Location or Organisation. For Arabic language, recognizing named entities is a challenging task because of the complexity and the unique characteristics of this language. In addition, most ... | ['Mourad Gridach'] | 2016-12-01 | null | null | null | ws-2016-12 | ['text-clustering'] | ['natural-language-processing'] | [-2.61120081e-01 -2.39406317e-01 1.41960770e-01 -3.51028621e-01
-5.00123620e-01 -6.24901116e-01 7.15968966e-01 3.35781544e-01
-9.91778672e-01 8.85532618e-01 3.27960610e-01 -2.51680702e-01
2.75483787e-01 -1.08030272e+00 -4.36935604e-01 -5.43435276e-01
-2.17666611e-01 4.20330465e-01 2.23157242e-01 -7.43041754... | [9.810142517089844, 9.799256324768066] |
a485fcae-54b5-4198-ad3a-03b6998b4829 | joint-feature-distribution-alignment-learning | 2204.11434 | null | https://arxiv.org/abs/2204.11434v1 | https://arxiv.org/pdf/2204.11434v1.pdf | Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition | Face recognition for visible light (VIS) images achieve high accuracy thanks to the recent development of deep learning. However, heterogeneous face recognition (HFR), which is a face matching in different domains, is still a difficult task due to the domain discrepancy and lack of large HFR dataset. Several methods ha... | ['Hitoshi Imaoka', 'Akinori F. Ebihara', 'Akihiro Hayasaka', 'Hiroshi Hashimoto', 'Takaya Miyamoto'] | 2022-04-25 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 2.55173799e-02 -6.61190510e-01 -3.34525853e-02 -4.66727108e-01
-1.04979968e+00 -1.81255832e-01 7.04558134e-01 -6.62793398e-01
-5.51823005e-02 8.16953242e-01 9.14098769e-02 8.76247808e-02
-3.05072665e-01 -5.82877100e-01 -5.33924758e-01 -9.44236577e-01
4.04006511e-01 3.32304835e-01 -2.19963774e-01 -3.52426052... | [13.160563468933105, 0.5355408191680908] |
e15ea447-3d17-44b9-8ccf-71e9c54f2c88 | pymicetracking-an-open-source-toolbox-for | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Menezes_PyMiceTracking_An_Open-Source_Toolbox_for_Real-Time_Behavioral_Neuroscience_Experiments_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Menezes_PyMiceTracking_An_Open-Source_Toolbox_for_Real-Time_Behavioral_Neuroscience_Experiments_CVPR_2022_paper.pdf | PyMiceTracking: An Open-Source Toolbox for Real-Time Behavioral Neuroscience Experiments | The development of computational tools allows the advancement of research in behavioral neuroscience and elevates the limits of experiment design. Many behavioral experiments need to determine the animal's position from its tracking, which is crucial for real-time decision-making and further analysis of experimenta... | ['Helton Maia', 'Aron de Miranda', 'Richardson Menezes'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['contour-detection'] | ['computer-vision'] | [ 4.95824814e-01 -6.95933342e-01 2.86146462e-01 -2.54854679e-01
4.15600091e-01 -4.75413352e-01 1.39399469e-01 5.65348923e-01
-9.61708128e-01 4.70227152e-01 -5.61740756e-01 -3.35659087e-01
-2.56515015e-02 -4.60641712e-01 -1.99133024e-01 -8.63160014e-01
-7.91379064e-03 3.29000279e-02 3.49547088e-01 2.43571356... | [12.935050964355469, 0.2744884490966797] |
cf183ad3-58ce-4510-8bce-1a722601c816 | a-unified-approach-to-discourse-relation | null | null | https://aclanthology.org/2021.disrpt-1.5 | https://aclanthology.org/2021.disrpt-1.5.pdf | A Unified Approach to Discourse Relation Classification in nine Languages | This paper presents efforts to solve the shared task on discourse relation classification (disrpt task 3). The intricate prediction task aims to predict a large number of classes from the Rhetorical Structure Theory (RST) framework for nine target languages. Labels include discourse relations such as background, condit... | ['Franziska Pannach', 'Hanna Varachkina'] | null | null | null | null | emnlp-disrpt-2021-11 | ['relation-classification'] | ['natural-language-processing'] | [ 2.43445233e-01 6.69594705e-01 -5.07033646e-01 -4.67758745e-01
-7.45257020e-01 -6.69725955e-01 1.30007112e+00 6.93794906e-01
-4.68009114e-01 7.53506780e-01 1.04136479e+00 -8.22433829e-01
3.51719595e-02 -3.67909342e-01 -1.95551485e-01 -3.94479573e-01
-6.45390525e-02 5.42248905e-01 2.01132759e-01 -7.76769161... | [10.821365356445312, 9.30653190612793] |
19174371-4dde-443a-91b1-e3d58f495244 | incorporating-uncertain-segmentation | 2004.06384 | null | https://arxiv.org/abs/2004.06384v2 | https://arxiv.org/pdf/2004.06384v2.pdf | Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text | Chinese word segmentation is necessary to provide word-level information for Chinese named entity recognition (NER) systems. However, segmentation error propagation is a challenge for Chinese NER while processing colloquial data like social media text. In this paper, we propose a model (UIcwsNN) that specializes in ide... | ['Shengbin Jia', 'Yang Xiang', 'Xiaojun Chen', 'Shijia E', 'Ling Ding'] | 2020-04-14 | incorporating-uncertain-segmentation-1 | https://aclanthology.org/2020.socialnlp-1.7 | https://aclanthology.org/2020.socialnlp-1.7.pdf | ws-2020-7 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-1.09891044e-02 4.43332680e-02 -2.10789099e-01 -4.79695290e-01
-7.41984129e-01 -5.24405658e-01 -3.68715562e-02 2.42583573e-01
-1.10377932e+00 5.54790378e-01 4.54277158e-01 -6.70100987e-01
4.38057303e-01 -8.90084803e-01 -3.71266186e-01 -3.90705675e-01
2.41232380e-01 2.85621643e-01 3.24421436e-01 -1.20228752... | [9.847980499267578, 9.909340858459473] |
323ee2ca-35c5-4fed-9fcd-58e575e0e2c8 | constructing-self-motivated-pyramid | 1908.09547 | null | https://arxiv.org/abs/1908.09547v1 | https://arxiv.org/pdf/1908.09547v1.pdf | Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach | We propose a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic source domains to real target domains. Our approach draws on an insight connecting two existing works: curriculum domain adaptation and self... | ['Boqing Gong', 'Qing Lian', 'Lixin Duan', 'Fengmao Lv'] | 2019-08-26 | constructing-self-motivated-pyramid-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Lian_Constructing_Self-Motivated_Pyramid_Curriculums_for_Cross-Domain_Semantic_Segmentation_A_Non-Adversarial_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Lian_Constructing_Self-Motivated_Pyramid_Curriculums_for_Cross-Domain_Semantic_Segmentation_A_Non-Adversarial_ICCV_2019_paper.pdf | iccv-2019-10 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 5.71222305e-01 4.88277286e-01 -2.68442929e-01 -6.50128961e-01
-6.24924481e-01 -8.28674614e-01 4.41703767e-01 -6.99802116e-02
-5.50794244e-01 7.04486191e-01 -1.63861632e-01 -5.63224107e-02
2.22091287e-01 -9.58345115e-01 -1.03405488e+00 -6.53264046e-01
3.35986257e-01 8.15679371e-01 5.68057001e-01 -4.01161104... | [9.744067192077637, 1.364626169204712] |
bf63b985-fa65-4752-b887-342369d78c9d | divided-spectro-temporal-attention-for-sound | 2306.02591 | null | https://arxiv.org/abs/2306.02591v1 | https://arxiv.org/pdf/2306.02591v1.pdf | Divided spectro-temporal attention for sound event localization and detection in real scenes for DCASE2023 challenge | Localizing sounds and detecting events in different room environments is a difficult task, mainly due to the wide range of reflections and reverberations. When training neural network models with sounds recorded in only a few room environments, there is a tendency for the models to become overly specialized to those sp... | ['Jung-Woo Choi', 'Byeong-Yun Ko', 'Yusun Shul'] | 2023-06-05 | null | null | null | null | ['sound-event-detection', 'sound-event-localization-and-detection'] | ['audio', 'audio'] | [ 3.12179446e-01 -4.54698503e-01 7.97795117e-01 -2.06056312e-01
-8.61146867e-01 -3.60824257e-01 3.07876587e-01 1.84050784e-01
-5.21862745e-01 2.54189253e-01 6.13612294e-01 -2.31383920e-01
9.95180160e-02 -5.67603648e-01 -7.16790438e-01 -6.34457886e-01
-7.40566179e-02 -6.93407118e-01 2.20222414e-01 -1.08418480... | [15.18979263305664, 5.4107561111450195] |
b1e2ad5a-b0f5-452f-b0c4-4d42fc359ba0 | nanopublication-based-semantic-publishing-and | 2203.01608 | null | https://arxiv.org/abs/2203.01608v1 | https://arxiv.org/pdf/2203.01608v1.pdf | Nanopublication-Based Semantic Publishing and Reviewing: A Field Study with Formalization Papers | With the rapidly increasing amount of scientific literature,it is getting continuously more difficult for researchers in different disciplines to be updated with the recent findings in their field of study.Processing scientific articles in an automated fashion has been proposed as a solution to this problem,but the acc... | ['Jacco van Ossenbruggen', 'Davide Ceolin', 'Tobias Kuhn', 'Cristina-Iulia Bucur'] | 2022-03-03 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [-8.81628022e-02 5.28361857e-01 -6.50898814e-02 -2.05248192e-01
-3.30963403e-01 -8.58120382e-01 7.54933357e-01 6.77894354e-01
-4.58638757e-01 1.02138269e+00 2.03863814e-01 -6.58101439e-01
-4.02816951e-01 -8.73807251e-01 -9.20778334e-01 1.52676746e-01
2.19273895e-01 5.53217292e-01 4.46075886e-01 -1.52103946... | [9.429861068725586, 8.169365882873535] |
e9622451-dd54-44a7-9cff-9555f65c46e5 | local-and-global-contextual-features-fusion | 2305.01111 | null | https://arxiv.org/abs/2305.01111v1 | https://arxiv.org/pdf/2305.01111v1.pdf | Local and Global Contextual Features Fusion for Pedestrian Intention Prediction | Autonomous vehicles (AVs) are becoming an indispensable part of future transportation. However, safety challenges and lack of reliability limit their real-world deployment. Towards boosting the appearance of AVs on the roads, the interaction of AVs with pedestrians including "prediction of the pedestrian crossing inten... | ['Chenghao Qian', 'Tanveer Hussain', 'Mahdi Rezaei', 'Mohsen Azarmi'] | 2023-05-01 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 1.87301952e-02 -2.10208073e-01 -3.23283613e-01 -7.23818898e-01
-4.48803455e-01 -6.92681149e-02 8.90605390e-01 1.20143734e-01
-6.04038179e-01 5.65973043e-01 2.32625023e-01 -2.77139008e-01
2.03509361e-01 -7.92416513e-01 -5.69704533e-01 -7.69456685e-01
-1.91878881e-02 -1.73627958e-02 7.01544940e-01 -3.80855322... | [7.717977523803711, -0.5460496544837952] |
7a8f76c1-abe8-4b6b-a7e8-8c8af5d05eb2 | deep-reinforcement-learning-framework-for | 1704.02532 | null | http://arxiv.org/abs/1704.02532v1 | http://arxiv.org/pdf/1704.02532v1.pdf | Deep Reinforcement Learning framework for Autonomous Driving | Reinforcement learning is considered to be a strong AI paradigm which can be
used to teach machines through interaction with the environment and learning
from their mistakes. Despite its perceived utility, it has not yet been
successfully applied in automotive applications. Motivated by the successful
demonstrations of... | ['Senthil Yogamani', 'Mohammed Abdou', 'Etienne Perot', 'Ahmad El Sallab'] | 2017-04-08 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-2.28412867e-01 3.75699013e-01 -7.39336163e-02 -3.69982868e-01
-2.83779740e-01 -2.18234748e-01 7.95463324e-01 -2.69929647e-01
-6.70121014e-01 7.04423845e-01 -2.38318488e-01 -7.32386529e-01
6.90098405e-02 -9.68650460e-01 -9.62689102e-01 -5.80422103e-01
-1.53411493e-01 5.21780312e-01 6.08490348e-01 -8.51340353... | [5.368793487548828, 1.140724539756775] |
4c88957d-7142-45b6-9769-9040b9939fac | upb-at-semeval-2021-task-5-virtual | 2104.08635 | null | https://arxiv.org/abs/2104.08635v1 | https://arxiv.org/pdf/2104.08635v1.pdf | UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detection | The real-world impact of polarization and toxicity in the online sphere marked the end of 2020 and the beginning of this year in a negative way. Semeval-2021, Task 5 - Toxic Spans Detection is based on a novel annotation of a subset of the Jigsaw Unintended Bias dataset and is the first language toxicity detection task... | ['Mihai Dascalu', 'Dumitru-Clementin Cercel', 'Andrei Paraschiv'] | 2021-04-17 | null | https://aclanthology.org/2021.semeval-1.26 | https://aclanthology.org/2021.semeval-1.26.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 1.47330686e-01 2.01690838e-01 -5.09368181e-02 1.28728211e-01
-1.40550053e+00 -1.03828490e+00 9.65910316e-01 6.29271150e-01
-3.21267337e-01 1.03667998e+00 5.22549450e-01 -4.84713465e-01
2.26909611e-02 -7.04843998e-01 -8.18476260e-01 -3.12074542e-01
-9.15640965e-02 4.14700896e-01 1.88500941e-01 -1.90246642... | [8.947410583496094, 10.62563419342041] |
869c333c-d62e-49e8-be2c-b7e105ef055c | residual-3d-scene-flow-learning-with-context | 2109.04685 | null | https://arxiv.org/abs/2109.04685v2 | https://arxiv.org/pdf/2109.04685v2.pdf | Residual 3D Scene Flow Learning with Context-Aware Feature Extraction | Scene flow estimation is the task to predict the point-wise or pixel-wise 3D displacement vector between two consecutive frames of point clouds or images, which has important application in fields such as service robots and autonomous driving. Although many previous works have explored greatly on scene flow estimation ... | ['Hesheng Wang', 'Xinrui Wu', 'Yunzhe Hu', 'Guangming Wang'] | 2021-09-10 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 4.37901840e-02 -4.26905364e-01 1.45548001e-01 -5.03664136e-01
2.96506472e-02 -3.00874352e-01 5.73177218e-01 -9.81284901e-02
-5.57735085e-01 5.03091931e-01 -1.03320241e-01 -1.31514579e-01
-2.50706136e-01 -7.93638825e-01 -7.34338999e-01 -6.18952394e-01
-2.91881561e-01 2.76492894e-01 6.88295484e-01 -3.94830018... | [8.533295631408691, -1.9839069843292236] |
49128314-905d-485a-ad89-701c8afe5dcb | hyperspectral-compressive-wavefront-sensing | 2303.03555 | null | https://arxiv.org/abs/2303.03555v1 | https://arxiv.org/pdf/2303.03555v1.pdf | Hyperspectral Compressive Wavefront Sensing | Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilised for the snapshot compressive imaging reconstruction due to its parameter efficiency and... | ['Andreas Doepp', 'Peter Norreys', 'Robin H. W. Wang', 'Jannik Esslinger', 'Sunny Howard'] | 2023-03-06 | null | null | null | null | ['unrolling'] | ['computer-vision'] | [ 7.85342872e-01 -2.30140418e-01 4.18576658e-01 -1.98329672e-01
-6.93497419e-01 -3.48699003e-01 4.75506485e-01 -5.88070273e-01
-5.01383305e-01 6.88344598e-01 1.03913620e-01 -3.35858047e-01
-6.23050690e-01 -4.12307352e-01 -5.41884303e-01 -9.44434762e-01
-5.78208447e-01 3.47311169e-01 -2.34001756e-01 -1.41930908... | [11.256460189819336, -2.4292893409729004] |
78340be4-33ff-45f4-942f-13220c84e593 | video-compressive-sensing-for-dynamic-mri | 1401.7715 | null | http://arxiv.org/abs/1401.7715v2 | http://arxiv.org/pdf/1401.7715v2.pdf | Video Compressive Sensing for Dynamic MRI | We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compress... | ['Wotao Yin', 'Jianing V. Shi', 'Richard G. Baraniuk', 'Aswin C. Sankaranarayanan'] | 2014-01-30 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 7.36401796e-01 -4.53787483e-02 -1.68688759e-01 2.02579126e-01
-7.13570952e-01 -2.51910329e-01 2.30080619e-01 -4.72592562e-01
-3.86168897e-01 3.83070141e-01 2.63839602e-01 -2.78559178e-01
-4.08284873e-01 -3.20837013e-02 -7.98220277e-01 -8.71273756e-01
-2.51535535e-01 1.25257269e-01 -2.12592736e-01 -3.19456495... | [11.713347434997559, -2.2345871925354004] |
75968e3d-b942-4160-8cf7-a851f63fe257 | a-review-of-deep-learning-for-video | 2304.11431 | null | https://arxiv.org/abs/2304.11431v1 | https://arxiv.org/pdf/2304.11431v1.pdf | A Review of Deep Learning for Video Captioning | Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work in the fields of computer vision, natural language processing (NLP), linguistics, and human-computer interaction. In essence, VC involves understanding a video and describing it with language. Captioning is used in a host of a... | ['Fatih Porikli', 'Erik Cambria', 'Abbas Khosravi', 'Abduallah Mohamed', 'Shuicheng Yan', 'Mohammad Ghavamzadeh', 'Daniel McDuff', 'Farhad Pourpanah', 'Swaraja Kuraparthi', 'Meenakshi Kollati', 'Moloud Abdar'] | 2023-04-22 | null | null | null | null | ['video-captioning', 'dense-video-captioning', 'video-question-answering', 'video-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.49526310e-01 3.40736806e-01 -1.70704082e-01 -3.01616527e-02
-7.91407049e-01 -7.90468931e-01 6.19124472e-01 2.44294330e-01
-1.93373114e-01 6.49776638e-01 6.09620094e-01 -4.42304343e-01
1.64879799e-01 -5.10934174e-01 -9.10080492e-01 -1.43388122e-01
-5.25029562e-02 5.39589524e-01 1.98591575e-01 -3.79129231... | [10.492877960205078, 1.0178673267364502] |
48b1d0f2-be2b-444c-8222-5e2c7604f1c8 | determinantal-point-processes-implicitly | 2011.06964 | null | https://arxiv.org/abs/2011.06964v2 | https://arxiv.org/pdf/2011.06964v2.pdf | Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems | Semi-parametric regression models are used in several applications which require comprehensibility without sacrificing accuracy. Typical examples are spline interpolation in geophysics, or non-linear time series problems, where the system includes a linear and non-linear component. We discuss here the use of a finite D... | ['Johan A. K. Suykens', 'Joachim Schreurs', 'Michaël Fanuel'] | 2020-11-13 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [-6.72983751e-02 1.98767245e-01 1.19497173e-01 -5.08200288e-01
-1.00797951e+00 -1.00354098e-01 6.24269247e-01 -8.08807462e-02
-3.59770447e-01 1.03480232e+00 -2.01769490e-02 -1.86334372e-01
-2.86322385e-01 -7.29441226e-01 -7.73708999e-01 -8.77562463e-01
-1.23516046e-01 4.15027440e-01 -3.35707469e-03 -1.33759663... | [6.928389072418213, 3.986034393310547] |
9cbf5c73-7edc-4144-86e1-1d181b6c5c9c | the-regretful-navigation-agent-for-vision-and | null | null | https://arxiv.org/abs/1903.01602 | https://arxiv.org/pdf/1903.01602.pdf | The Regretful Navigation Agent for Vision-and-Language Navigation | As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making. Specifically, the Vision and Language Navigation (VLN) task involves navigating to a goal purely from language instructions and visual information without explici... | ['Chih-Yao Ma', 'Zsolt Kira', 'Ghassan AlRegib', 'Caiming Xiong', 'Zuxuan Wu'] | 2019-03-05 | null | null | null | cvpr-2019-oral-2019-3 | ['vision-language-navigation'] | ['computer-vision'] | [ 6.37289807e-02 8.08300897e-02 -2.75861263e-01 -2.91215450e-01
-6.91149533e-01 -5.03664017e-01 7.28007853e-01 1.32473111e-01
-9.34558451e-01 7.18707025e-01 1.94707572e-01 -6.10342324e-01
-1.68466475e-02 -6.12705648e-01 -7.50782430e-01 -5.56722105e-01
-3.18176746e-01 4.53627050e-01 4.82576102e-01 -2.04455107... | [4.494775772094727, 0.5466209053993225] |
5794b8ed-2f89-42c0-bb89-128338c0b3f4 | aideveloper-deep-learning-image | null | null | https://www.biorxiv.org/content/10.1101/2020.03.03.975250v1 | https://www.biorxiv.org/content/10.1101/2020.03.03.975250v1.full.pdf | AIDeveloper: deep learning image classification in life science and beyond | Publications on artificial intelligence (AI)-based image analysis have increased drastically in recent years. However, all applications use individual solutions highly specialized for a particular task. Here, we present an easy-to-use, adaptable, open source software, called AIDeveloper (AID) to train neural nets (NN) ... | ['Thomas Krüger', 'Martin Kräter', 'Shada Abuhattum', 'Despina Soteriou', 'Angela Jacobi', 'Maik Herbig', 'Jochen Guck'] | 2020-03-05 | null | null | null | biorxiv-2020-3 | ['blood-cell-count'] | ['computer-vision'] | [ 2.16478676e-01 -2.83966213e-01 6.22473359e-02 -3.13537151e-01
-4.02390540e-01 -5.35223126e-01 1.89239547e-01 4.31566179e-01
-8.57513607e-01 6.94637239e-01 -3.85231495e-01 -3.79290760e-01
3.28836411e-01 -9.86218333e-01 -5.09328365e-01 -9.30916905e-01
1.23081012e-02 9.46049869e-01 8.71431977e-02 -9.51355398... | [14.806970596313477, -3.097543478012085] |
f374584a-c6fa-49ab-a86f-f8b9c725815e | nurse-care-activity-recognition-challenge | null | null | https://doi.org/10.1145/3341162.3345577 | http://delivery.acm.org/10.1145/3350000/3345577/p746-lago.pdf | Nurse care activity recognition challenge: summary and results | Although activity recognition has been studied for a long time now, research and applications have focused on physical activity recognition. Even if many application domains require the recognition of more complex activities, research on such activities has attracted less attention. One reason for this gap is the lack ... | ['http://delivery.acm.org/10.1145/3350000/3345577/p746-lago.pdf'] | 2019-09-09 | null | null | null | ubicompiswc-19-proceedings-of-the-2019-acm | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 5.38942575e-01 2.05167532e-02 -6.22466683e-01 -4.80317444e-01
-3.62250417e-01 -6.65384233e-02 5.10690093e-01 2.85942644e-01
-5.67451298e-01 8.97667468e-01 9.10857022e-01 -1.50540918e-01
-4.23061907e-01 -5.13460577e-01 -1.76581681e-01 -7.42215335e-01
-2.09496468e-01 1.54378504e-01 -8.80387425e-02 1.75870925... | [7.393789291381836, 0.7194857001304626] |
44fd1279-786d-442b-b6fd-534be4c604b2 | a-pointer-network-architecture-for-joint | null | null | https://aclanthology.org/2020.findings-emnlp.391 | https://aclanthology.org/2020.findings-emnlp.391.pdf | A Pointer Network Architecture for Joint Morphological Segmentation and Tagging | Morphologically Rich Languages (MRLs) such as Arabic, Hebrew and Turkish often require Morphological Disambiguation (MD), i.e., the prediction of morphological decomposition of tokens into morphemes, early in the pipeline. Neural MD may be addressed as a simple pipeline, where segmentation is followed by sequence taggi... | ['Reut Tsarfaty', 'Amit Seker'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['morphological-disambiguation'] | ['natural-language-processing'] | [-1.06051922e-01 3.28792721e-01 1.50423851e-02 -3.32022130e-01
-7.19295025e-01 -1.16378355e+00 2.84537584e-01 4.60370272e-01
-7.85728455e-01 6.04403973e-01 1.50871336e-01 -9.28859949e-01
2.69915402e-01 -8.64473999e-01 -5.70003390e-01 -4.47761297e-01
-2.25384876e-01 8.33939254e-01 4.34789330e-01 -2.78609574... | [10.388566970825195, 10.06127643585205] |
140d2ca5-eb9b-4115-b9b0-2daee29fa041 | av-transpeech-audio-visual-robust-speech-to | 2305.15403 | null | https://arxiv.org/abs/2305.15403v1 | https://arxiv.org/pdf/2305.15403v1.pdf | AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation | Direct speech-to-speech translation (S2ST) aims to convert speech from one language into another, and has demonstrated significant progress to date. Despite the recent success, current S2ST models still suffer from distinct degradation in noisy environments and fail to translate visual speech (i.e., the movement of lip... | ['Zhou Zhao', 'Xiang Yin', 'Jinglin Liu', 'Lichao Zhang', 'Jinzheng He', 'Zhenhui Ye', 'Linjun Li', 'Yi Ren', 'Xize Cheng', 'Huadai Liu', 'Rongjie Huang'] | 2023-05-24 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 4.32516009e-01 2.52464935e-02 -1.96046144e-01 -1.10445678e-01
-1.61233878e+00 -7.11052716e-01 7.06717908e-01 -2.12096259e-01
-6.05321070e-03 3.25325787e-01 5.74675918e-01 -7.80697584e-01
7.09685326e-01 -4.28070650e-02 -7.49286532e-01 -5.74331999e-01
5.31395555e-01 2.27329835e-01 1.92171648e-01 -2.44618893... | [14.488015174865723, 5.402403354644775] |
d85c459b-0d7b-4e94-b0e4-78ddf2542cae | enhancing-real-world-adversarial-patches-with | 2102.05334 | null | https://arxiv.org/abs/2102.05334v2 | https://arxiv.org/pdf/2102.05334v2.pdf | Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenes | Adversarial examples have proven to be a concerning threat to deep learning models, particularly in the image domain. However, while many studies have examined adversarial examples in the real world, most of them relied on 2D photos of the attack scene. As a result, the attacks proposed may have limited effectiveness w... | ['Yuval Elovici', 'Lior Rokach', 'Yael Mathov'] | 2021-02-10 | null | null | null | null | ['real-world-adversarial-attack'] | ['adversarial'] | [ 1.54857442e-01 -8.61692652e-02 4.34385240e-01 2.74406988e-02
-4.46917474e-01 -1.10615432e+00 8.07796538e-01 -3.98443878e-01
-4.84830767e-01 4.61334854e-01 -2.81215608e-01 -4.95854914e-01
2.32596606e-01 -1.16895354e+00 -1.05988479e+00 -5.89829087e-01
-2.17174992e-01 1.76825032e-01 4.73826289e-01 -5.13338506... | [5.461292266845703, 7.849300861358643] |
0b242795-0217-4d13-86dc-89fdea44a444 | one-sided-box-filter-for-edge-preserving | 2108.05021 | null | https://arxiv.org/abs/2108.05021v1 | https://arxiv.org/pdf/2108.05021v1.pdf | One-Sided Box Filter for Edge Preserving Image Smoothing | Image smoothing is a fundamental task in signal processing. For such task, box filter is well-known. However, box filter can not keep some features of the signal, such as edges, corners and the jump in the step function. In this paper, we present a one-sided box filter that can smooth the signal but keep the discontinu... | ['Yuanhao Gong'] | 2021-08-11 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 1.58743218e-01 -1.41905412e-01 3.30486357e-01 -2.36256778e-01
-4.59442198e-01 -2.64389932e-01 1.37015963e-02 1.47069827e-01
-7.02907979e-01 5.12863517e-01 3.23381828e-04 -1.55306488e-01
1.80071384e-01 -7.90668309e-01 -6.30646348e-01 -6.62671149e-01
-4.75265056e-01 -6.83352411e-01 1.00174534e+00 -2.64375687... | [11.068683624267578, -2.533808708190918] |
46a00611-851a-4535-9a82-518a3a2fc2c4 | cnn-based-synthesis-of-realistic-high | 1907.00787 | null | https://arxiv.org/abs/1907.00787v2 | https://arxiv.org/pdf/1907.00787v2.pdf | CNN-based synthesis of realistic high-resolution LiDAR data | This paper presents a novel CNN-based approach for synthesizing high-resolution LiDAR point cloud data. Our approach generates semantically and perceptually realistic results with guidance from specialized loss-functions. First, we utilize a modified per-point loss that addresses missing LiDAR point measurements. Secon... | ['J. Marius Zöllner', 'Christoph B. Rist', 'Larissa T. Triess', 'David Peter', 'Markus Enzweiler'] | 2019-06-28 | null | null | null | null | ['depth-image-upsampling', 'point-set-upsampling', 'point-cloud-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.57552677e-01 1.39609352e-01 9.29929838e-02 -6.61098480e-01
-1.40680373e+00 -6.29857242e-01 3.75742465e-01 5.10504901e-01
-3.37510318e-01 6.49180830e-01 -3.01369041e-01 -1.14204206e-01
1.23392045e-01 -1.27801347e+00 -1.22906685e+00 6.55671209e-02
-6.82423711e-02 8.79688978e-01 5.36112905e-01 -1.41712308... | [8.332454681396484, -3.0319571495056152] |
b020dfe4-b6d6-475a-a000-ccfc78f3704f | a-dependable-hybrid-machine-learning-model | 2212.04546 | null | https://arxiv.org/abs/2212.04546v2 | https://arxiv.org/pdf/2212.04546v2.pdf | A Dependable Hybrid Machine Learning Model for Network Intrusion Detection | Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data is a recognized issu... | ['Mohammad Abu Yousuf', 'Mohammad Ali Moni', 'Fares Alharbi', 'Arnisha Akhter', 'Md Ashraf Uddin', 'Md. Manowarul Islam', 'Khondokar Fida Hasan', 'Md. Alamin Talukder'] | 2022-12-08 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-2.57846296e-01 -6.22017145e-01 1.48558587e-01 -4.55116123e-01
1.31723642e-01 -2.93128580e-01 5.58451116e-01 5.42845607e-01
-5.44390678e-01 5.67719042e-01 -6.68998361e-01 -6.45015419e-01
-6.35548890e-01 -1.00267684e+00 -8.59165117e-02 -4.44553584e-01
-2.88131833e-01 7.97903597e-01 4.20975447e-01 -2.67873138... | [5.2505669593811035, 7.18936014175415] |
36d03122-77ca-4779-afb5-b3b6d642c7ed | a-representation-learning-framework-for-multi | null | null | https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12236 | https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12236/12016 | A Representation Learning Framework for Multi-Source Transfer Parsing | Cross-lingual model transfer has been a promising approach for inducing dependency parsers for low-resource languages where annotated treebanks are not available. The major obstacles for the model transfer approach are two-fold: 1. Lexical features are not directly transferable across languages; 2. Target language-spec... | ['Ting Liu', 'Haifeng Wang', 'David Yarowsky', 'Wanxiang Che', 'Jiang Guo'] | 2016-03-05 | null | null | null | null | ['cross-lingual-zero-shot-dependency-parsing'] | ['natural-language-processing'] | [-5.54481447e-02 3.60970587e-01 -4.23397064e-01 -5.70603132e-01
-1.76243854e+00 -8.67516279e-01 1.50968835e-01 1.08385742e-01
-5.34716070e-01 1.16444492e+00 4.16523695e-01 -4.21602815e-01
5.03391862e-01 -6.00885272e-01 -8.83855700e-01 -1.16258807e-01
6.47182465e-02 6.23610914e-01 2.62032747e-01 -4.58399534... | [10.535527229309082, 9.817673683166504] |
d7c5ca83-1b27-4999-a0a8-eddc383be55e | double-sided-information-aided-temporal | 2205.07494 | null | https://arxiv.org/abs/2205.07494v1 | https://arxiv.org/pdf/2205.07494v1.pdf | Double-Sided Information Aided Temporal-Correlated Massive Access | This letter considers temporal-correlated massive access, where each device, once activated, is likely to transmit continuously over several consecutive frames. Motivated by that the device activity at each frame is correlated to not only its previous frame but also its next frame, we propose a double-sided information... | ['Yunfeng Guan', 'Meixia Tao', 'Weifeng Zhu'] | 2022-05-16 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 8.16195309e-01 -1.49136499e-01 -5.86551547e-01 1.13066159e-01
-7.19457150e-01 -2.22565889e-01 2.56966978e-01 1.23464905e-01
-4.02590722e-01 1.17957890e+00 1.30671725e-01 -3.49678099e-01
2.47664511e-01 -2.75791883e-01 -4.03508186e-01 -9.06938672e-01
-7.45548189e-01 -2.51699418e-01 4.32351142e-01 5.43080866... | [6.2332563400268555, 1.4035987854003906] |
5ab16ada-2004-4dd4-8999-667189b08be9 | smap-single-shot-multi-person-absolute-3d | 2008.11469 | null | https://arxiv.org/abs/2008.11469v1 | https://arxiv.org/pdf/2008.11469v1.pdf | SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation | Recovering multi-person 3D poses with absolute scales from a single RGB image is a challenging problem due to the inherent depth and scale ambiguity from a single view. Addressing this ambiguity requires to aggregate various cues over the entire image, such as body sizes, scene layouts, and inter-person relationships. ... | ['Wei Jiang', 'Jianan Zhen', 'Hujun Bao', 'Wentao Liu', 'Xiaowei Zhou', 'Qi Fang', 'Jiaming Sun'] | 2020-08-26 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2395_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600545.pdf | eccv-2020-8 | ['3d-multi-person-pose-estimation-absolute', '3d-depth-estimation', '3d-multi-person-pose-estimation-root-relative', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.66506672e-01 -1.70358688e-01 6.80454588e-03 -4.47513372e-01
-7.04498351e-01 -5.69679439e-01 2.60169357e-01 4.96118935e-03
-3.26221138e-01 1.96489364e-01 3.48284125e-01 4.56825465e-01
5.00061251e-02 -6.29082143e-01 -6.24500275e-01 -5.38724780e-01
1.65614888e-01 7.79164433e-01 6.13552213e-01 -1.71484008... | [7.077182769775391, -1.0500280857086182] |
1ae2f7c7-ea3c-430c-9a33-f3f3f1c50c4a | inno-at-semeval-2020-task-11-leveraging-pure | 2008.11584 | null | https://arxiv.org/abs/2008.11584v2 | https://arxiv.org/pdf/2008.11584v2.pdf | Inno at SemEval-2020 Task 11: Leveraging Pure Transformer for Multi-Class Propaganda Detection | The paper presents the solution of team "Inno" to a SEMEVAL 2020 task 11 "Detection of propaganda techniques in news articles". The goal of the second subtask is to classify textual segments that correspond to one of the 18 given propaganda techniques in news articles dataset. We tested a pure Transformer-based model w... | ['Vladimir Ivanov', 'Dmitry Grigorev'] | 2020-08-26 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [ 8.09833333e-02 4.14020531e-02 -3.90467912e-01 -1.16928697e-01
-8.56004179e-01 -7.28214502e-01 1.51648510e+00 2.33745128e-01
-4.88644928e-01 5.42597473e-01 6.14733756e-01 -6.51515603e-01
-2.03974858e-01 -6.08465910e-01 -5.55490911e-01 -3.47088009e-01
2.18069088e-02 5.20603657e-01 2.61905551e-01 -5.52818000... | [8.476266860961914, 10.681117057800293] |
c1793c37-7002-4089-8964-681213052484 | global-optimality-and-finite-sample-analysis | 2111.02997 | null | https://arxiv.org/abs/2111.02997v3 | https://arxiv.org/pdf/2111.02997v3.pdf | Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch | In this paper, we establish the global optimality and convergence rate of an off-policy actor critic algorithm in the tabular setting without using density ratio to correct the discrepancy between the state distribution of the behavior policy and that of the target policy. Our work goes beyond existing works on the opt... | ['Romain Laroche', 'Remi Tachet', 'Shangtong Zhang'] | 2021-11-04 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-2.44701907e-01 3.19675624e-01 -6.57399058e-01 1.24227814e-01
-7.01596439e-01 -6.87196255e-01 4.99611616e-01 1.16327535e-02
-7.61799395e-01 1.20611644e+00 2.64009923e-01 -6.58353150e-01
-8.55367035e-02 -4.07465577e-01 -7.91169882e-01 -8.84757698e-01
1.74261168e-01 6.90882683e-01 2.23739132e-01 -3.09730262... | [4.270388603210449, 2.6068952083587646] |
903710d2-804e-4ec8-bfc4-3c4ca7c9d1ea | nlp_hz-at-semeval-2018-task-9-a-nearest | null | null | https://aclanthology.org/S18-1148 | https://aclanthology.org/S18-1148.pdf | NLP\_HZ at SemEval-2018 Task 9: a Nearest Neighbor Approach | Hypernym discovery aims to discover the hypernym word sets given a hyponym word and proper corpus. This paper proposes a simple but effective method for the discovery of hypernym sets based on word embedding, which can be used to measure the contextual similarities between words. Given a test hyponym word, we get its h... | ['Wei Qiu', 'Mosha Chen', 'Luo Si', 'Linlin Li'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 2.42911726e-02 2.75455296e-01 -3.07807863e-01 -8.54942352e-02
2.40862697e-01 -5.55427492e-01 6.85661435e-01 7.45983422e-01
-9.98025715e-01 4.94235963e-01 4.82580721e-01 -2.79284716e-01
-6.43136919e-01 -1.18611157e+00 -2.87548192e-02 -7.18324006e-01
-1.17192201e-01 7.97697484e-01 1.04272105e-01 -6.27179325... | [9.874410629272461, 8.75112533569336] |
3e6faaa5-48b9-435e-80f5-f3d92953d977 | expansion-of-visual-hints-for-improved | 2211.00392 | null | https://arxiv.org/abs/2211.00392v1 | https://arxiv.org/pdf/2211.00392v1.pdf | Expansion of Visual Hints for Improved Generalization in Stereo Matching | We introduce visual hints expansion for guiding stereo matching to improve generalization. Our work is motivated by the robustness of Visual Inertial Odometry (VIO) in computer vision and robotics, where a sparse and unevenly distributed set of feature points characterizes a scene. To improve stereo matching, we propos... | ['Juho Kannala', 'Arno Solin', 'Niki Loppi', 'Yuxin Hou', 'Andrea Pilzer'] | 2022-11-01 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-6.24668747e-02 7.35827610e-02 -3.59707624e-01 1.73399244e-02
-1.51245028e-01 -6.17560744e-01 7.33659863e-01 -4.01924038e-03
-3.88936341e-01 4.81245935e-01 1.61979407e-01 -3.91047597e-01
-4.59398776e-02 -5.39635599e-01 -9.02382135e-01 -3.96536440e-01
-1.78504631e-01 5.05399346e-01 5.91268241e-01 -2.26083428... | [7.8836669921875, -2.202723264694214] |
efb63804-d67e-41b5-855d-d5cdad56e965 | explicit-feature-interaction-aware-uplift | 2306.00315 | null | https://arxiv.org/abs/2306.00315v1 | https://arxiv.org/pdf/2306.00315v1.pdf | Explicit Feature Interaction-aware Uplift Network for Online Marketing | As a key component in online marketing, uplift modeling aims to accurately capture the degree to which different treatments motivate different users, such as coupons or discounts, also known as the estimation of individual treatment effect (ITE). In an actual business scenario, the options for treatment may be numerous... | ['Xiuqiang He', 'Fuyuan Lyu', 'Han Gao', 'Xing Tang', 'Dugang Liu'] | 2023-06-01 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 1.69538125e-01 -9.71063748e-02 -8.49362493e-01 -8.39984596e-01
-2.05041096e-01 -3.65041465e-01 4.18470591e-01 2.95413971e-01
-1.63556799e-01 3.19692105e-01 4.25325334e-01 -1.62727118e-01
-4.02425259e-01 -1.06220925e+00 -6.12906218e-01 -5.58518708e-01
-4.75796312e-02 4.03396785e-01 -1.57821909e-01 -2.99896002... | [9.678936958312988, 5.400411128997803] |
f4c16ac0-1e8d-4bd4-bc1b-0f599c293577 | deep-adaptive-attention-for-joint-facial | 1803.05588 | null | http://arxiv.org/abs/1803.05588v2 | http://arxiv.org/pdf/1803.05588v2.pdf | Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment | Facial action unit (AU) detection and face alignment are two highly
correlated tasks since facial landmarks can provide precise AU locations to
facilitate the extraction of meaningful local features for AU detection. Most
existing AU detection works often treat face alignment as a preprocessing and
handle the two tasks... | ['Zhilei Liu', 'Jianfei Cai', 'Zhiwen Shao', 'Lizhuang Ma'] | 2018-03-15 | deep-adaptive-attention-for-joint-facial-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Zhiwen_Shao_Deep_Adaptive_Attention_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhiwen_Shao_Deep_Adaptive_Attention_ECCV_2018_paper.pdf | eccv-2018-9 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [-9.08606648e-02 -6.85249045e-02 -1.07736140e-01 -4.85087216e-01
-1.01858056e+00 -2.50395626e-01 4.15168941e-01 -2.15226740e-01
-3.19127709e-01 -1.53650679e-02 3.13547790e-01 4.78723973e-01
4.36799169e-01 -7.54548550e-01 -5.16137719e-01 -9.01216745e-01
2.04648852e-01 8.09986219e-02 1.25269309e-01 -1.07328467... | [13.619840621948242, 1.467418909072876] |
411381c0-49a8-41be-b28b-e0aa4351a355 | affordance-learning-in-direct-perception-for | 1903.08746 | null | http://arxiv.org/abs/1903.08746v1 | http://arxiv.org/pdf/1903.08746v1.pdf | Affordance Learning In Direct Perception for Autonomous Driving | Recent development in autonomous driving involves high-level computer vision
and detailed road scene understanding. Today, most autonomous vehicles are
using mediated perception approach for path planning and control, which highly
rely on high-definition 3D maps and real time sensors. Recent research efforts
aim to sub... | ['Jean M. Uwabeza Vianney', 'Chen Sun', 'Dongpu Cao'] | 2019-03-20 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 1.43101737e-01 3.75745296e-01 -5.86432330e-02 -8.62211287e-01
-1.93946674e-01 -2.39384085e-01 7.56486475e-01 4.84369975e-03
-5.14359713e-01 5.07442892e-01 -6.06203489e-02 -7.81101942e-01
-2.51189083e-01 -1.20353401e+00 -8.76977742e-01 -2.91046828e-01
2.67584771e-02 5.20657420e-01 4.26711619e-01 -7.99146116... | [8.004969596862793, -1.9281450510025024] |
d7a532e2-30f6-4b81-a3d3-599b924530de | x-torch-differentiable-scientific-computing | 2010.01921 | null | https://arxiv.org/abs/2010.01921v1 | https://arxiv.org/pdf/2010.01921v1.pdf | $ξ$-torch: differentiable scientific computing library | Physics-informed learning has shown to have a better generalization than learning without physical priors. However, training physics-informed deep neural networks requires some aspect of physical simulations to be written in a differentiable manner. Unfortunately, some operations and functionals commonly used in physic... | ['Sam M. Vinko', 'Muhammad F. Kasim'] | 2020-10-05 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-6.81997657e-01 -2.06852555e-01 1.60615176e-01 -5.10762453e-01
-5.87304354e-01 -4.51369375e-01 1.59271225e-01 -1.93399251e-01
-3.19804549e-01 1.38084102e+00 -5.74386954e-01 -5.80460846e-01
-2.63946295e-01 -7.85305262e-01 -1.06492567e+00 -1.06493914e+00
-3.39562684e-01 3.05849135e-01 9.77787003e-02 -2.22730786... | [6.4570746421813965, 3.4948158264160156] |
f2aeb634-6a8d-46c7-9d3a-fd39b83ef022 | star-net-action-recognition-using-spatio | 1902.10024 | null | http://arxiv.org/abs/1902.10024v1 | http://arxiv.org/pdf/1902.10024v1.pdf | STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection | While depth cameras and inertial sensors have been frequently leveraged for
human action recognition, these sensing modalities are impractical in many
scenarios where cost or environmental constraints prohibit their use. As such,
there has been recent interest on human action recognition using low-cost,
readily-availab... | ['William McNally', 'Alexander Wong', 'John McPhee'] | 2019-02-26 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 2.49124765e-01 -2.81754136e-01 -3.10647078e-02 -4.33503360e-01
-3.50133657e-01 -8.43228102e-02 3.87900651e-01 -4.73287642e-01
-6.87721431e-01 4.86745954e-01 4.87962067e-01 -7.01892599e-02
1.43874630e-01 -5.89296520e-01 -7.48011827e-01 -4.80745703e-01
6.32891506e-02 1.01898305e-01 -2.97933985e-02 -4.86154482... | [7.822884559631348, 0.400326669216156] |
1d92914d-fee5-4daa-9bab-23d84c0d879d | esg-valued-portfolio-optimization-and-dynamic | 2206.02854 | null | https://arxiv.org/abs/2206.02854v1 | https://arxiv.org/pdf/2206.02854v1.pdf | ESG-Valued Portfolio Optimization and Dynamic Asset Pricing | ESG ratings provide a quantitative measure for socially responsible investment. We present a unified framework for incorporating numeric ESG ratings into dynamic pricing theory. Specifically, we introduce an ESG-valued return that is a linearly constrained transformation of financial return and ESG score. This leads to... | ['Svetlozar T. Rachev', 'Stefan Mittnik', 'W. Brent Lindquist', 'Davide Lauria'] | 2022-06-06 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-5.47528207e-01 4.56401259e-01 -3.52549374e-01 -4.46715891e-01
-3.79950732e-01 -8.20905507e-01 3.51855576e-01 -3.79137963e-01
-4.20156568e-01 7.46062338e-01 2.75721192e-01 -6.08127773e-01
-7.13898599e-01 -1.08742416e+00 3.74941863e-02 -3.18980336e-01
-3.25686902e-01 2.30318964e-01 -4.15891409e-02 -2.77433097... | [4.9518866539001465, 3.9434919357299805] |
62c28e48-1010-4de3-adbc-791f75436983 | signed-directed-graph-contrastive-learning | 2301.05163 | null | https://arxiv.org/abs/2301.05163v1 | https://arxiv.org/pdf/2301.05163v1.pdf | Signed Directed Graph Contrastive Learning with Laplacian Augmentation | Graph contrastive learning has become a powerful technique for several graph mining tasks. It learns discriminative representation from different perspectives of augmented graphs. Ubiquitous in our daily life, singed-directed graphs are the most complex and tricky to analyze among various graph types. That is why singe... | ['Chong-Kwon Kim', 'Yoonhyuk Choi', 'Taewook Ko'] | 2023-01-12 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 1.54832467e-01 3.21457475e-01 -3.22633743e-01 -1.01624422e-01
-3.21467042e-01 -6.17874503e-01 7.72717178e-01 2.34042794e-01
5.46149537e-02 5.29857576e-01 -3.48578952e-02 -2.33368307e-01
-5.22845149e-01 -8.39878142e-01 -6.83668911e-01 -8.82908046e-01
-5.64232171e-01 3.98965716e-01 1.38520852e-01 -4.36265051... | [7.241724967956543, 6.172756671905518] |
fd3a9fc8-5ada-4667-a722-b31b49d0c6f4 | eventhpe-event-based-3d-human-pose-and-shape | 2108.06819 | null | https://arxiv.org/abs/2108.06819v1 | https://arxiv.org/pdf/2108.06819v1.pdf | EventHPE: Event-based 3D Human Pose and Shape Estimation | Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals. Events, on the other hand, have their unique challenges: rather than capturing static body postures, the event signals are best at capturi... | ['Li Cheng', 'Minglun Gong', 'Shoushun Chen', 'Xiaoqin Hu', 'Pengyu Wang', 'Sen Wang', 'Xinxin Zuo', 'Chuan Guo', 'Shihao Zou'] | 2021-08-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zou_EventHPE_Event-Based_3D_Human_Pose_and_Shape_Estimation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zou_EventHPE_Event-Based_3D_Human_Pose_and_Shape_Estimation_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-2.54658479e-02 -2.01746881e-01 1.48193344e-01 -1.89930081e-01
1.93552691e-02 -2.89340943e-01 4.37187642e-01 -1.82193145e-01
-3.66185129e-01 4.57628161e-01 6.26589179e-01 4.57641721e-01
2.42741376e-01 -6.71681166e-01 -6.07464612e-01 -3.54875118e-01
-2.94048488e-01 3.31229240e-01 4.01979804e-01 -8.28908011... | [7.336039066314697, -0.6248089671134949] |
af614ad4-f8ac-42e0-91d3-82a72d1f76ff | can-spoofing-countermeasure-and-speaker | 2303.07073 | null | https://arxiv.org/abs/2303.07073v3 | https://arxiv.org/pdf/2303.07073v3.pdf | Can spoofing countermeasure and speaker verification systems be jointly optimised? | Spoofing countermeasure (CM) and automatic speaker verification (ASV) sub-systems can be used in tandem with a backend classifier as a solution to the spoofing aware speaker verification (SASV) task. The two sub-systems are typically trained independently to solve different tasks. While our previous work demonstrated t... | ['Nicholas Evans', 'Massimiliano Todisco', 'Hemlata Tak', 'Wanying Ge'] | 2023-03-13 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 3.60591710e-01 2.68797636e-01 8.28418136e-02 -5.14885604e-01
-1.26386726e+00 -7.95367658e-01 1.05090225e+00 1.56735796e-02
-4.50292587e-01 4.51364458e-01 2.24002391e-01 -9.02758837e-01
1.59999862e-01 -6.53863996e-02 -4.46812361e-01 -5.68243682e-01
-1.95973098e-01 3.75671208e-01 2.95191184e-02 -5.28757870... | [14.125195503234863, 5.933498859405518] |
c230f1a1-32bc-4633-853a-606ba594f312 | hero-roberta-and-longformer-hebrew-language | 2304.11077 | null | https://arxiv.org/abs/2304.11077v1 | https://arxiv.org/pdf/2304.11077v1.pdf | HeRo: RoBERTa and Longformer Hebrew Language Models | In this paper, we fill in an existing gap in resources available to the Hebrew NLP community by providing it with the largest so far pre-train dataset HeDC4, a state-of-the-art pre-trained language model HeRo for standard length inputs and an efficient transformer LongHeRo for long input sequences. The HeRo model was e... | ['Harel Haskey', 'Vitaly Shalumov'] | 2023-04-18 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [-4.91231568e-02 2.28837449e-02 -2.47120395e-01 -4.94044811e-01
-1.03653908e+00 -7.99153745e-01 7.04222739e-01 2.99425781e-01
-8.63930285e-01 8.02772403e-01 3.69067311e-01 -4.83207375e-01
-8.89474228e-02 -5.36904156e-01 -4.34181720e-01 -1.64736465e-01
7.53298402e-02 9.81904387e-01 1.22918263e-01 -4.99535471... | [10.32907485961914, 9.526021957397461] |
1f5727f0-13c1-45db-a15c-2be39886d02d | can-pre-trained-vision-and-language-models | 2302.11713 | null | https://arxiv.org/abs/2302.11713v2 | https://arxiv.org/pdf/2302.11713v2.pdf | Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? | Large language models have demonstrated an emergent capability in answering knowledge intensive questions. With recent progress on web-scale visual and language pre-training, do these models also understand how to answer visual information seeking questions? To answer this question, we present InfoSeek, a Visual Questi... | ['Ming-Wei Chang', 'Alan Ritter', 'Soravit Changpinyo', 'Haitian Sun', 'Yi Luan', 'Hexiang Hu', 'Yang Chen'] | 2023-02-23 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 5.64863123e-02 4.02012169e-01 -1.45226941e-01 -2.43262857e-01
-1.04783618e+00 -1.19087946e+00 7.22785354e-01 2.92629540e-01
-3.38545829e-01 2.40935564e-01 3.61736655e-01 -8.61640215e-01
-1.18045621e-01 -4.01522458e-01 -6.58783555e-01 7.01075187e-03
3.71807277e-01 9.06364202e-01 5.53393364e-01 -3.99902701... | [10.945703506469727, 1.7703304290771484] |
24b492fb-cdc7-40e0-955e-f4420d2f36bf | meev-body-mesh-estimation-on-egocentric-video | 2210.14165 | null | https://arxiv.org/abs/2210.14165v1 | https://arxiv.org/pdf/2210.14165v1.pdf | MEEV: Body Mesh Estimation On Egocentric Video | This technical report introduces our solution, MEEV, proposed to the EgoBody Challenge at ECCV 2022. Captured from head-mounted devices, the dataset consists of human body shape and motion of interacting people. The EgoBody dataset has challenges such as occluded body or blurry image. In order to overcome the challenge... | ['Dongyoon Wee', 'Nicolas Monet'] | 2022-10-21 | null | null | null | null | ['3d-pose-estimation', '3d-human-pose-estimation', '3d-human-pose-and-shape-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-5.38642645e-01 9.86501947e-02 6.44430965e-02 -1.85577959e-01
-4.44116980e-01 -4.05903697e-01 4.66509789e-01 -7.30938494e-01
-2.38471285e-01 5.20921588e-01 7.25193620e-01 5.60353935e-01
3.53886813e-01 -3.17023426e-01 -3.57636362e-01 -4.06440884e-01
1.23027295e-01 2.54731804e-01 1.09995350e-01 -1.03384823... | [6.991231918334961, -0.918549120426178] |
fe56b032-150a-400c-9e3d-017a5e0a906c | multiple-sequence-alignment-for-short | 1510.09037 | null | http://arxiv.org/abs/1510.09037v2 | http://arxiv.org/pdf/1510.09037v2.pdf | Multiple sequence alignment for short sequences | Multiple sequence alignment (MSA) has been one of the most important problems
in bioinformatics for more decades and it is still heavily examined by many
mathematicians and biologists. However, mostly because of the practical
motivation of this problem, the research on this topic is focused on aligning
long sequences. ... | [] | 2015-11-15 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 7.20865011e-01 -1.01711787e-01 -1.03063636e-01 -2.83851892e-01
-5.35147607e-01 -9.43778872e-01 -5.75157255e-02 5.14445662e-01
-4.89488423e-01 1.09843016e+00 -2.92651236e-01 -5.91886163e-01
-9.37176794e-02 -7.22190738e-01 -4.42458898e-01 -1.08364677e+00
-2.59363949e-01 6.72448456e-01 4.80439305e-01 -3.88610572... | [4.891394138336182, 5.142265319824219] |
62e09f35-275b-4f63-8504-14fa8ac3c991 | cloud-based-deep-learning-end-to-end-full | 2304.13506 | null | https://arxiv.org/abs/2304.13506v1 | https://arxiv.org/pdf/2304.13506v1.pdf | Cloud-Based Deep Learning: End-To-End Full-Stack Handwritten Digit Recognition | Herein, we present Stratus, an end-to-end full-stack deep learning application deployed on the cloud. The rise of productionized deep learning necessitates infrastructure in the cloud that can provide such service (IaaS). In this paper, we explore the use of modern cloud infrastructure and micro-services to deliver acc... | ['Terry Luo', 'Ashwin Kumar', 'Aadarsh Jha', 'Ruida Zeng'] | 2023-02-01 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-8.32070827e-01 -2.51523405e-01 5.73033750e-01 -7.33407140e-01
-2.98562765e-01 -7.39517570e-01 2.83097893e-01 -2.50685036e-01
-2.45776728e-01 2.93003380e-01 -3.94987077e-01 -8.05822551e-01
-1.33968070e-01 -8.85563076e-01 -6.53768361e-01 -6.08486712e-01
-8.75602812e-02 8.07993650e-01 1.62882246e-02 -3.08141019... | [8.522989273071289, 2.913975238800049] |
5499b59a-d5e9-437f-984a-acddf08144d8 | quantifying-quality-of-class-conditional | 2210.07617 | null | https://arxiv.org/abs/2210.07617v1 | https://arxiv.org/pdf/2210.07617v1.pdf | Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain | Generative models are designed to address the data scarcity problem. Even with the exploding amount of data, due to computational advancements, some applications (e.g., health care, weather forecast, fault detection) still suffer from data insufficiency, especially in the time-series domain. Thus generative models are ... | ['Sheraz Ahmed', 'Andreas Dengel', 'Peter Schichtel', 'Sankrutyayan Thota', 'Maria Walch', 'Alireza Koochali'] | 2022-10-14 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.74533546e-01 -3.44113737e-01 1.94877550e-01 -4.02137727e-01
-8.87830973e-01 -4.91538554e-01 7.81922817e-01 -2.09791347e-01
-1.95690840e-01 6.46330535e-01 -9.12301540e-02 -2.62065947e-01
-5.04894614e-01 -7.63476133e-01 -2.78066486e-01 -9.73723888e-01
3.04115657e-02 3.21147949e-01 8.42092335e-02 -1.21955328... | [7.332810401916504, 2.5183444023132324] |
fbad6b40-6742-44d9-b52e-e000d6000b6f | automated-audio-captioning-with-recurrent | 1706.10006 | null | http://arxiv.org/abs/1706.10006v2 | http://arxiv.org/pdf/1706.10006v2.pdf | Automated Audio Captioning with Recurrent Neural Networks | We present the first approach to automated audio captioning. We employ an
encoder-decoder scheme with an alignment model in between. The input to the
encoder is a sequence of log mel-band energies calculated from an audio file,
while the output is a sequence of words, i.e. a caption. The encoder is a
multi-layered, bi-... | ['Sharath Adavanne', 'Tuomas Virtanen', 'Konstantinos Drossos'] | 2017-06-30 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 7.82040656e-01 2.76597738e-01 2.79558063e-01 -2.96862692e-01
-1.04703259e+00 -3.01630497e-01 3.38138074e-01 -7.19924364e-03
-1.66191474e-01 6.49673760e-01 7.25437462e-01 -7.25417361e-02
4.40752745e-01 -3.51798356e-01 -9.83491778e-01 -6.23864651e-01
-5.88922389e-02 3.01476985e-01 8.32951590e-02 -3.11727002... | [15.281448364257812, 4.9098052978515625] |
700c7328-763f-4a86-986a-8959293647e8 | debiased-pseudo-labeling-in-self-training | 2202.07136 | null | https://arxiv.org/abs/2202.07136v5 | https://arxiv.org/pdf/2202.07136v5.pdf | Debiased Self-Training for Semi-Supervised Learning | Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets. Yet these datasets are time-consuming and labor-exhaustive to obtain on realistic tasks. To mitigate the requirement for labeled data, self-training is widely used in semi-supervised learning by i... | ['Mingsheng Long', 'Jianmin Wang', 'Pengfei Wan', 'Ximei Wang', 'Junguang Jiang', 'Baixu Chen'] | 2022-02-15 | null | null | null | null | ['semi-supervised-image-classification', 'texture-classification'] | ['computer-vision', 'computer-vision'] | [ 4.49602813e-01 1.72065943e-01 -3.33957404e-01 -7.13163316e-01
-7.47690380e-01 -6.47047162e-01 3.52234930e-01 -1.90074280e-01
-6.20794594e-01 9.75877464e-01 -4.00678068e-01 -3.01295161e-01
2.65082508e-01 -6.19873047e-01 -9.95838165e-01 -8.21080923e-01
3.29535395e-01 5.75543880e-01 6.76612630e-02 -6.22990308... | [9.460481643676758, 3.6106793880462646] |
266337a6-c385-452d-bd71-39eac3730042 | enhanced-meta-learning-for-cross-lingual | 1911.06161 | null | https://arxiv.org/abs/1911.06161v2 | https://arxiv.org/pdf/1911.06161v2.pdf | Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources | For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER). While all existing methods directly transfer from source-learned model to a target language, in this paper, we propose to fine-tune the learned model with a few sim... | ['Chin-Yew Lin', 'Börje F. Karlsson', 'Qianhui Wu', 'Hui Chen', 'Zijia Lin', 'Guoxin Wang', 'Biqing Huang'] | 2019-11-14 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [ 3.04956716e-02 -1.51849777e-01 -2.95988828e-01 -8.72505426e-01
-1.08258522e+00 -5.88119864e-01 3.46786439e-01 1.05299696e-01
-8.05083990e-01 7.27248430e-01 2.70969361e-01 -1.18929371e-01
2.66063124e-01 -5.50429642e-01 -6.71292067e-01 -1.57417893e-01
8.12764317e-02 4.47172731e-01 6.23982251e-02 -2.79722691... | [9.946247100830078, 9.640213012695312] |
4b354729-8e42-4a60-aec4-19e432aa0f29 | quick-algorithms-for-independent-vector | 1910.10242 | null | https://arxiv.org/abs/1910.10242v2 | https://arxiv.org/pdf/1910.10242v2.pdf | Algorithm for Independent Vector Extraction Based on Semi-Time-Variant Mixing Model | A new algorithm for dynamic independent vector extraction is proposed. It is based on the mixing model where mixing parameters related to the source-of-interest (SOI) are time-variant while the separating parameters are time-invariant. A contrast function based on the quasi-likelihood approach is optimized using the Ne... | ['Jaroslav Čmejla', 'Tomáš Kounovský', 'Václav Kautský', 'Zbyněk Koldovský'] | 2019-10-22 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 1.14634730e-01 -3.95819336e-01 2.76055753e-01 -9.48077142e-02
-8.33928287e-01 -5.93741655e-01 4.70631242e-01 -2.03392923e-01
-5.35611808e-01 6.34215415e-01 1.52755529e-01 -2.29017466e-01
-5.98916948e-01 -1.47064850e-01 -2.38203481e-01 -1.19161832e+00
-6.17901325e-01 1.90873966e-01 -5.45196533e-02 -1.44865289... | [15.150936126708984, 5.69337797164917] |
2508892c-dca6-481c-880f-f9946baca9dd | heterogeneous-branch-collaborative-learning | 2303.11621 | null | https://arxiv.org/abs/2303.11621v1 | https://arxiv.org/pdf/2303.11621v1.pdf | Heterogeneous-Branch Collaborative Learning for Dialogue Generation | With the development of deep learning, advanced dialogue generation methods usually require a greater amount of computational resources. One promising approach to obtaining a high-performance and lightweight model is knowledge distillation, which relies heavily on the pre-trained powerful teacher. Collaborative learnin... | ['Kan Li', 'Bin Sun', 'Shaoxiong Feng', 'Yiwei Li'] | 2023-03-21 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 1.30024433e-01 4.60393518e-01 -2.64021993e-01 -4.68509823e-01
-5.94734430e-01 -4.97008413e-01 7.28916585e-01 2.29168624e-01
-5.23821294e-01 1.30351305e+00 3.56262326e-01 -2.89035320e-01
-1.42555803e-01 -1.03755772e+00 -2.01341003e-01 -8.50366235e-01
2.88758755e-01 9.54025745e-01 2.41444632e-01 -6.23815358... | [12.591045379638672, 8.100469589233398] |
8f38738d-d472-4b5c-95d7-9063cf36ab36 | genesis-generative-scene-inference-and | 1907.13052 | null | https://arxiv.org/abs/1907.13052v4 | https://arxiv.org/pdf/1907.13052v4.pdf | GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations | Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning. Yet, even though tasks in these domains typically involve distinct objects, most state-of-the-art generative models do not explicitly capture the compositional nature of visual scenes. Two recent exceptions, MONet ... | ['Oiwi Parker Jones', 'Martin Engelcke', 'Ingmar Posner', 'Adam R. Kosiorek'] | 2019-07-30 | null | https://openreview.net/forum?id=BkxfaTVFwH | https://openreview.net/pdf?id=BkxfaTVFwH | iclr-2020-1 | ['scene-generation', 'unsupervised-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.54413086e-01 9.73883793e-02 1.26105130e-01 -2.77091593e-01
-5.00700712e-01 -7.84384727e-01 1.46089303e+00 -4.62048650e-01
-1.31293666e-02 5.09763539e-01 2.67856777e-01 -7.08603719e-03
-1.96937263e-01 -7.89155066e-01 -8.91679764e-01 -9.75227416e-01
1.40379369e-01 8.72540534e-01 3.58349383e-02 2.66827941... | [10.06421184539795, 0.2712155878543854] |
0c3f9f0a-daaa-46de-a4f0-29711dc18e3e | cuni-system-for-wmt16-automatic-post-editing | 1606.07481 | null | http://arxiv.org/abs/1606.07481v1 | http://arxiv.org/pdf/1606.07481v1.pdf | CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks | Neural sequence to sequence learning recently became a very promising
paradigm in machine translation, achieving competitive results with statistical
phrase-based systems. In this system description paper, we attempt to utilize
several recently published methods used for neural sequential learning in order
to build sys... | ['Ondřej Bojar', 'Marek Tlustý', 'Jindřich Helcl', 'Jindřich Libovický', 'Pavel Pecina'] | 2016-06-23 | cuni-system-for-wmt16-automatic-post-editing-1 | https://aclanthology.org/W16-2361 | https://aclanthology.org/W16-2361.pdf | ws-2016-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 7.87316978e-01 -2.76966602e-01 -5.36591828e-01 -2.96277493e-01
-1.28074586e+00 -4.64388251e-01 9.40023959e-01 6.13007061e-02
-7.44288862e-01 1.15986896e+00 4.38545793e-01 -8.64374697e-01
4.36936557e-01 1.12940006e-01 -8.65688622e-01 -2.09351599e-01
3.77159894e-01 9.75934029e-01 -2.24688947e-01 -6.24239326... | [11.60059642791748, 10.382320404052734] |
92aa6117-fc85-492f-867e-670bac0e66a4 | personalized-federated-learning-with-hidden | 2211.10684 | null | https://arxiv.org/abs/2211.10684v2 | https://arxiv.org/pdf/2211.10684v2.pdf | Personalized Federated Learning with Hidden Information on Personalized Prior | Federated learning (FL for simplification) is a distributed machine learning technique that utilizes global servers and collaborative clients to achieve privacy-preserving global model training without direct data sharing. However, heterogeneous data problem, as one of FL's main problems, makes it difficult for the glo... | ['Jiancheng Lv', 'Qing Ye', 'Yuhao Zhou', 'Mingjia Shi'] | 2022-11-19 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-7.20593095e-01 -1.73357710e-01 -4.14491296e-01 -4.86671627e-01
-8.88528228e-01 -4.49564159e-01 2.08254442e-01 2.41631083e-03
-3.47945035e-01 8.70770812e-01 1.08403914e-01 -3.21548194e-01
-2.44116843e-01 -8.55844021e-01 -7.80565083e-01 -1.19204521e+00
1.43457711e-01 5.83644271e-01 -4.41563129e-03 2.10335612... | [5.837806224822998, 6.3294901847839355] |
ec0dfbf2-f602-41df-b011-e66b6efa35f2 | sentence-constituent-aware-aspect-category | 2010.01461 | null | https://arxiv.org/abs/2010.01461v1 | https://arxiv.org/pdf/2010.01461v1.pdf | Sentence Constituent-Aware Aspect-Category Sentiment Analysis with Graph Attention Networks | Aspect category sentiment analysis (ACSA) aims to predict the sentiment polarities of the aspect categories discussed in sentences. Since a sentence usually discusses one or more aspect categories and expresses different sentiments toward them, various attention-based methods have been developed to allocate the appropr... | ['Sheng-hua Zhong', 'Cunxiang Yin', 'Yuncong Li'] | 2020-10-04 | null | null | null | null | ['aspect-category-detection'] | ['natural-language-processing'] | [ 1.42451972e-01 3.82641047e-01 -2.86921173e-01 -7.31818140e-01
-4.94717747e-01 -5.16366303e-01 3.98882866e-01 3.65930945e-01
-3.97155248e-02 1.31560102e-01 5.28923929e-01 -4.90975708e-01
2.34608352e-01 -1.16608572e+00 -3.06938678e-01 -5.59089124e-01
4.24150288e-01 4.02975500e-01 2.90944517e-01 -6.20467842... | [11.483041763305664, 6.626908302307129] |
37d36f81-99ba-43b9-b61a-68c8f80c925b | ls-net-fast-single-shot-line-segment-detector | 1912.09532 | null | https://arxiv.org/abs/1912.09532v2 | https://arxiv.org/pdf/1912.09532v2.pdf | LS-Net: Fast Single-Shot Line-Segment Detector | In low-altitude Unmanned Aerial Vehicle (UAV) flights, power lines are considered as one of the most threatening hazards and one of the most difficult obstacles to avoid. In recent years, many vision-based techniques have been proposed to detect power lines to facilitate self-driving UAVs and automatic obstacle avoidan... | ['Davide Roverso', 'Van Nhan Nguyen', 'Robert Jenssen'] | 2019-12-19 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 1.05713224e-02 -3.87758374e-01 2.59472191e-01 1.22334838e-01
-1.60795912e-01 -8.28168452e-01 3.30548167e-01 7.92032480e-02
-2.97597855e-01 5.92648029e-01 -7.18492150e-01 -6.80885911e-01
-1.33136109e-01 -1.03533781e+00 -4.05608475e-01 -4.94911641e-01
-2.02295497e-01 5.22487098e-03 6.22659743e-01 -6.17305458... | [8.791184425354004, -0.9968221187591553] |
a38c65d4-a1c7-4b68-a7fb-0778e61dced5 | multi-task-learning-improves-performance-in | 2307.01401 | null | https://arxiv.org/abs/2307.01401v1 | https://arxiv.org/pdf/2307.01401v1.pdf | Multi-Task Learning Improves Performance In Deep Argument Mining Models | The successful analysis of argumentative techniques from user-generated text is central to many downstream tasks such as political and market analysis. Recent argument mining tools use state-of-the-art deep learning methods to extract and annotate argumentative techniques from various online text corpora, however each ... | ['Marco Morucci', 'Isaac Mehlhaff', 'Shashank Shekhar', 'Amirhossein Farzam'] | 2023-07-03 | null | null | null | null | ['multi-task-learning', 'argument-mining'] | ['methodology', 'natural-language-processing'] | [ 1.05287530e-01 6.59001827e-01 -7.48686373e-01 -4.87066865e-01
-1.26917183e+00 -9.79471207e-01 1.15557325e+00 7.89594293e-01
-5.68066001e-01 7.86486149e-01 7.88292646e-01 -1.12576449e+00
-4.30053115e-01 -7.23332345e-01 -8.09034646e-01 -8.00148621e-02
1.76538914e-01 9.39461708e-01 4.41561863e-02 -4.63191092... | [9.586353302001953, 9.613236427307129] |
b93e8a89-8926-4e17-b2e0-8f7f2d99e65a | paddlespeech-an-easy-to-use-all-in-one-speech-1 | 2205.12007 | null | https://arxiv.org/abs/2205.12007v1 | https://arxiv.org/pdf/2205.12007v1.pdf | PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit | PaddleSpeech is an open-source all-in-one speech toolkit. It aims at facilitating the development and research of speech processing technologies by providing an easy-to-use command-line interface and a simple code structure. This paper describes the design philosophy and core architecture of PaddleSpeech to support sev... | ['Liang Huang', 'Yanjun Ma', 'dianhai yu', 'Xiaoguang Hu', 'Zeyu Chen', 'Enlei Gong', 'Xiaojie Chen', 'Yuxin Huang', 'Renjie Zheng', 'Xintong Li', 'Junkun Chen', 'Tian Yuan', 'HUI ZHANG'] | 2022-05-20 | paddlespeech-an-easy-to-use-all-in-one-speech | https://aclanthology.org/2022.naacl-demo.12 | https://aclanthology.org/2022.naacl-demo.12.pdf | naacl-acl-2022-7 | ['environmental-sound-classification', 'speech-to-text-translation', 'keyword-spotting', 'speaker-identification'] | ['audio', 'natural-language-processing', 'speech', 'speech'] | [-2.97641009e-01 -1.71158053e-02 -1.01712465e-01 -6.24382854e-01
-1.10157466e+00 -5.50916493e-01 7.68170774e-01 -1.51578948e-01
-2.16917500e-01 2.89098889e-01 4.37001914e-01 -9.02910173e-01
3.89197528e-01 -2.66196281e-01 -1.74480975e-01 -5.79129398e-01
1.60119981e-01 6.13421023e-01 2.80156642e-01 -3.42697918... | [14.400145530700684, 6.890883922576904] |
bd739298-2658-45d1-932c-3d346f160615 | sleep-apnea-detection-from-single-lead-ecg-a | null | null | https://ieeexplore.ieee.org/abstract/document/9714370 | https://ieeexplore.ieee.org/abstract/document/9714370 | Sleep Apnea Detection From Single-Lead ECG: A Comprehensive Analysis of Machine Learning and Deep Learning Algorithms | https://ieeexplore.ieee.org/abstract/document/9714370 | ['Mohamad', 'Mahsa ; Forouzanfar', 'Bahrami'] | 2022-02-15 | null | null | null | ieee-transactions-on-instrumentation-and-1 | ['sleep-apnea-detection'] | ['medical'] | [-3.13157916e-01 -2.12861970e-02 -3.29496741e-01 -8.66282284e-02
-6.42045856e-01 1.75915539e-01 -2.73249522e-02 8.82882550e-02
-3.50012392e-01 1.08797061e+00 3.42663795e-01 -2.71520108e-01
-7.25315139e-02 -7.58162916e-01 -1.54720386e-02 -8.51412416e-01
-1.62162296e-02 2.78586954e-01 -1.84649095e-01 1.31346747... | [6.396810531616211, 2.775449275970459] |
3865fa1e-38bb-4dc4-8e23-bcc128259b7e | proceedings-first-workshop-on-causal | 1608.07398 | null | http://arxiv.org/abs/1608.07398v1 | http://arxiv.org/pdf/1608.07398v1.pdf | Proceedings First Workshop on Causal Reasoning for Embedded and safety-critical Systems Technologies | Formal approaches for automated causality analysis, fault localization,
explanation of events, accountability and blaming have been proposed
independently by several communities --- in particular, AI, concurrency,
model-based diagnosis, formal methods. Work on these topics has significantly
gained speed during the last... | ['Gregor Gössler', 'Oleg Sokolsky'] | 2016-08-26 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-6.86806813e-02 3.56170624e-01 -2.35054776e-01 -2.80337423e-01
-5.09461582e-01 -5.13382256e-01 7.22599685e-01 5.49454391e-01
2.91344553e-01 7.91346490e-01 2.21177295e-01 -7.93887198e-01
-5.22199214e-01 -4.52449262e-01 -4.46610630e-01 -1.51232436e-01
-8.59758556e-01 5.67676425e-01 7.24516273e-01 -6.60872087... | [8.319266319274902, 6.107024192810059] |
f9b132c1-8c91-4001-b3ad-20dacf6eff72 | fast-and-interpretable-nonlocal-neural | 2306.01950 | null | https://arxiv.org/abs/2306.01950v1 | https://arxiv.org/pdf/2306.01950v1.pdf | Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning | Nonlocal self-similarity within natural images has become an increasingly popular prior in deep-learning models. Despite their successful image restoration performance, such models remain largely uninterpretable due to their black-box construction. Our previous studies have shown that interpretable construction of a fu... | ['Yao Wang', 'Adeen Flinker', 'Amirhossein Khalilian-Gourtani', 'Nikola Janjušević'] | 2023-06-02 | null | null | null | null | ['image-restoration', 'grayscale-image-denoising', 'dictionary-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 5.08565187e-01 3.96110922e-01 -7.83639774e-03 -5.57478428e-01
-7.22527921e-01 -2.93080956e-01 7.17093468e-01 -1.71443716e-01
-2.35543162e-01 5.68178892e-01 4.90365654e-01 -3.69757235e-01
-2.01676011e-01 -6.57442987e-01 -1.24971783e+00 -8.61606240e-01
-8.24899226e-03 1.23724878e-01 -2.31804177e-01 -2.25869775... | [11.395543098449707, -2.096381902694702] |
4a9c1933-5b33-4005-b1a1-7038126d9093 | stochastic-transformer-networks-with-linear | 2109.13318 | null | https://arxiv.org/abs/2109.13318v2 | https://arxiv.org/pdf/2109.13318v2.pdf | Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation | Automating sign language translation (SLT) is a challenging real world application. Despite its societal importance, though, research progress in the field remains rather poor. Crucially, existing methods that yield viable performance necessitate the availability of laborious to obtain gloss sequence groundtruth. In th... | ['Sotirios Chatzis', 'Dimitris N. Metaxas', 'Dimitrios Kosmopoulos', 'Konstantinos P. Panousis', 'Andreas Voskou'] | 2021-09-01 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Voskou_Stochastic_Transformer_Networks_With_Linear_Competing_Units_Application_To_End-to-End_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Voskou_Stochastic_Transformer_Networks_With_Linear_Competing_Units_Application_To_End-to-End_ICCV_2021_paper.pdf | iccv-2021-1 | ['sign-language-translation'] | ['computer-vision'] | [ 7.38827169e-01 1.15827799e-01 -1.83225974e-01 -2.25913629e-01
-1.23785186e+00 -4.26687866e-01 7.10147679e-01 -1.45745739e-01
-8.82894933e-01 5.72200954e-01 3.17821175e-01 -4.83411670e-01
1.70321926e-01 -5.01998603e-01 -1.00298059e+00 -7.37819135e-01
4.42773700e-01 8.50863039e-01 3.14644754e-01 -6.24020286... | [9.209833145141602, -6.530070781707764] |
d3c9f3d1-923c-4f1e-9faa-831b66dfe984 | stylized-data-to-text-generation-a-case-study | 2305.03256 | null | https://arxiv.org/abs/2305.03256v1 | https://arxiv.org/pdf/2305.03256v1.pdf | Stylized Data-to-Text Generation: A Case Study in the E-Commerce Domain | Existing data-to-text generation efforts mainly focus on generating a coherent text from non-linguistic input data, such as tables and attribute-value pairs, but overlook that different application scenarios may require texts of different styles. Inspired by this, we define a new task, namely stylized data-to-text gene... | ['Liqiang Nie', 'Wei Zhou', 'Zhongzhou Zhao', 'Xuming Lin', 'Xuemeng Song', 'Liqiang Jing'] | 2023-05-05 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.16762918e-01 1.66876465e-01 -1.30209982e-01 -4.97462034e-01
-7.63355076e-01 -5.59574366e-01 6.08988822e-01 -1.96919575e-01
2.23585650e-01 8.60225797e-01 4.98531342e-01 -6.28087372e-02
8.50108042e-02 -1.05868506e+00 -5.47048509e-01 -4.33142036e-01
6.53496981e-01 5.60373008e-01 -1.95218742e-01 -4.44227219... | [11.713399887084961, 8.956891059875488] |
f97efb85-7a4a-4bee-8ba5-826bb8ddd87c | m-3vsnet-unsupervised-multi-metric-multi-view | 2005.00363 | null | https://arxiv.org/abs/2005.00363v2 | https://arxiv.org/pdf/2005.00363v2.pdf | M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network | The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised m... | ['Xiao Liu', 'Yijia He', 'Hongwei Yi', 'Can Huang', 'Jingbin Liu', 'Baichuan Huang'] | 2020-04-30 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [-1.55774549e-01 -2.65197188e-01 -1.25572652e-01 -6.44402206e-01
-8.34583342e-01 -3.31923991e-01 3.88646036e-01 -2.99003869e-01
-1.61136135e-01 6.01631343e-01 1.18095227e-01 7.38748815e-03
-3.07711363e-01 -8.47363532e-01 -9.62012351e-01 -6.78719461e-01
4.29929465e-01 5.43747008e-01 4.17875081e-01 -2.41875023... | [8.623281478881836, -2.6853835582733154] |
7ebb9b2a-a308-49af-b2a1-b6a0bac4f866 | panacea-an-automated-misinformation-detection | 2303.01241 | null | https://arxiv.org/abs/2303.01241v1 | https://arxiv.org/pdf/2303.01241v1.pdf | PANACEA: An Automated Misinformation Detection System on COVID-19 | In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art appro... | ['Yulan He', 'Maria Liakata', 'Rob Procter', 'Arkaitz Zubiaga', 'Lin Gui', 'Elena Kochkina', 'Lixing Zhu', 'Miguel Arana-Catania', 'Runcong Zhao'] | 2023-02-28 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-4.06383395e-01 6.43098056e-01 -5.26561022e-01 1.96105242e-01
-3.45375687e-01 -5.16659498e-01 1.04855573e+00 1.11756039e+00
-2.29843959e-01 6.31052971e-01 6.01341963e-01 -7.25445449e-01
2.17608176e-02 -1.28696084e+00 -3.89247686e-01 1.50542423e-01
-2.12643921e-01 6.28243685e-01 5.94271302e-01 -9.71700430... | [8.24092960357666, 10.07502269744873] |
4b4dbfda-8f69-452a-bc49-af7e4cfe916f | towards-automated-covid-19-presence-and | 2305.08660 | null | https://arxiv.org/abs/2305.08660v1 | https://arxiv.org/pdf/2305.08660v1.pdf | Towards Automated COVID-19 Presence and Severity Classification | COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of intensive care units, predicting the future severity of a COVID-19 patient is crucial. The presented approach follows state-of-theart techni... | ['Frank Kramer', 'Elisabeth André', 'Bernhard Bauer', 'Wolfgang Reif', 'Miriam Elia', 'Fabio Hellmann', 'Silvan Mertes', 'Niklas Schröter', 'Dominik Müller'] | 2023-05-15 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 2.05654472e-01 8.57515819e-03 7.41700754e-02 -3.14836830e-01
-3.88747960e-01 -1.77542523e-01 2.58995861e-01 6.16333306e-01
-7.61725366e-01 8.50284219e-01 1.27350643e-01 -4.40912515e-01
-6.22631311e-01 -7.13187397e-01 -1.59264684e-01 -6.48883820e-01
-3.88249129e-01 1.10854232e+00 1.68487340e-01 6.68166056... | [15.449665069580078, -1.8050180673599243] |
72a2bb61-7ab4-439a-9f7d-0cd47e76ac2f | entity-resolution-with-hierarchical-graph | null | null | https://dl.acm.org/doi/10.1145/3514221.3517872 | https://dl.acm.org/doi/pdf/10.1145/3514221.3517872 | Entity Resolution with Hierarchical Graph Attention Networks | Entity Resolution (ER) links entities that refer to the same real-world entity from different sources. Existing work usually takes pairs of entities as input and judges those pairs independently. However, there is often interdependence between different pairs of ER decisions, e.g., the entities from the same data sourc... | ['Xinqiao Lv', 'Hai Jin', 'Gao Cong', 'Yuhong Gu', 'Dezhong Yao'] | 2022-06-01 | null | null | null | sigmod-pods-2022-6 | ['entity-resolution'] | ['natural-language-processing'] | [-3.89262199e-01 2.13245109e-01 -2.88669854e-01 -3.10033083e-01
-5.15363216e-01 -3.24945688e-01 4.59624439e-01 6.92596972e-01
-4.61759031e-01 5.20529091e-01 5.25986612e-01 7.80872777e-02
-1.52596742e-01 -1.05688608e+00 -5.42547464e-01 -4.29500431e-01
-2.64493134e-02 6.14568651e-01 3.61486524e-01 -3.05201948... | [8.889159202575684, 8.148289680480957] |
9b6cec66-28ee-4cf4-b346-45af79bd9d17 | on-the-local-cache-update-rules-in-streaming | 2303.16340 | null | https://arxiv.org/abs/2303.16340v1 | https://arxiv.org/pdf/2303.16340v1.pdf | On the Local Cache Update Rules in Streaming Federated Learning | In this study, we address the emerging field of Streaming Federated Learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capacity. Traditional federated learning relies on fixed data sets, whereas in SFL, data is streamed, and its distribution changes over time, lea... | ['Jie Xu', 'Jieming Bian', 'Heqiang Wang'] | 2023-03-28 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-3.27748597e-01 -6.90418482e-01 -6.88142180e-01 -5.73202550e-01
-7.59088695e-01 -3.37110668e-01 2.47004390e-01 4.34028178e-01
-4.45350051e-01 8.86492491e-01 1.74086824e-01 -3.21252227e-01
-4.32944715e-01 -9.77191269e-01 -7.84087718e-01 -7.88684666e-01
-2.25268334e-01 4.10795301e-01 8.37058187e-01 2.65363846... | [5.845589637756348, 6.2722249031066895] |
a1efd442-d002-4256-952c-8afca91c229c | share-with-thy-neighbors-single-view | 2204.10310 | null | https://arxiv.org/abs/2204.10310v3 | https://arxiv.org/pdf/2204.10310v3.pdf | Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency | Approaches for single-view reconstruction typically rely on viewpoint annotations, silhouettes, the absence of background, multiple views of the same instance, a template shape, or symmetry. We avoid all such supervision and assumptions by explicitly leveraging the consistency between images of different object instanc... | ['Mathieu Aubry', 'Alexei A. Efros', 'Matthew Fisher', 'Tom Monnier'] | 2022-04-21 | null | null | null | null | ['single-view-3d-reconstruction', '3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.24016750e-01 1.28977790e-01 4.83337753e-02 -5.01654327e-01
-9.68034565e-01 -7.92667747e-01 9.10515606e-01 -8.10600668e-02
-2.15286195e-01 5.61084807e-01 -2.13711902e-01 7.99975172e-02
1.61302656e-01 -5.05462348e-01 -1.25328755e+00 -6.98816299e-01
1.57038346e-01 1.00700307e+00 5.94106197e-01 -1.31795928... | [8.44853401184082, -2.9495432376861572] |
4b2563ba-f06b-40f1-a6ca-9b1011d0ae0e | co-attention-hierarchical-network-generating | 1911.08648 | null | https://arxiv.org/abs/1911.08648v1 | https://arxiv.org/pdf/1911.08648v1.pdf | Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension | In reading comprehension, generating sentence-level distractors is a significant task, which requires a deep understanding of the article and question. The traditional entity-centered methods can only generate word-level or phrase-level distractors. Although recently proposed neural-based methods like sequence-to-seque... | ['Yunfang Wu', 'Senlin Luo', 'Xiaorui Zhou'] | 2019-11-20 | null | null | null | null | ['distractor-generation'] | ['natural-language-processing'] | [ 1.08078532e-02 2.27369666e-01 1.91372499e-01 -5.69453202e-02
-8.03107679e-01 -4.32506651e-01 6.33561790e-01 2.78668310e-02
-3.73039961e-01 7.71140873e-01 8.99708629e-01 -1.78029969e-01
2.91983426e-01 -7.57025123e-01 -7.55900085e-01 -3.64395827e-01
6.92514956e-01 3.85258079e-01 4.17927951e-01 -6.60607994... | [11.654434204101562, 8.392521858215332] |
d4fe6635-ec38-40ee-9f26-64cf125f6454 | deep-learning-for-cancer-prognosis-prediction | 2306.14596 | null | https://arxiv.org/abs/2306.14596v2 | https://arxiv.org/pdf/2306.14596v2.pdf | Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding | Survival prediction for cancer patients is critical for optimal treatment selection and patient management. Current patient survival prediction methods typically extract survival information from patients' clinical record data or biological and imaging data. In practice, experienced clinicians can have a preliminary as... | ['Yixing Huang', 'Florian Putz', 'Christoph Bert', 'Rainer Fietkau', 'Andreas Maier', 'Dominik Kornek', 'Ahmed Gomaa', 'Amr Hagag'] | 2023-06-26 | null | null | null | null | ['survival-analysis', 'management'] | ['miscellaneous', 'miscellaneous'] | [ 4.20017183e-01 4.15542543e-01 -3.73632133e-01 -3.56061310e-01
-8.33959401e-01 -2.27576882e-01 5.29127598e-01 9.35314521e-02
-2.81855613e-01 8.38509202e-01 5.50074458e-01 -1.12591453e-01
5.32895140e-03 -9.32332337e-01 -8.62387121e-02 -1.24956238e+00
-1.15673445e-01 2.10253671e-01 -6.27200425e-01 -3.02603059... | [15.278616905212402, -2.811782121658325] |
29ca0700-eb96-47b4-ac01-b211b57edcf7 | investigating-neighborhood-modeling-and | 2112.11734 | null | https://arxiv.org/abs/2112.11734v2 | https://arxiv.org/pdf/2112.11734v2.pdf | D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning | Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs). Prior work in DRL is largely constrained (e.g., limited to directed acyclic graphs), or has poor generalizability across tasks (e.g., evaluated solely on one task). Most Graph Neural Networks (GNNs) exhibit p... | ['Mubbasir Kapadia', 'Gerard de Melo', 'Zuohui Fu', 'Samuel S. Sohn', 'Advith Chegu', 'Honglu Zhou'] | 2021-12-22 | null | null | null | null | ['link-property-prediction'] | ['graphs'] | [ 1.74910761e-02 4.67756897e-01 -5.06009877e-01 -1.16344266e-01
-7.84609541e-02 -5.92177749e-01 7.31784284e-01 4.30888325e-01
2.03294784e-01 6.04737043e-01 4.69531566e-01 -7.11889446e-01
-6.36538386e-01 -1.34722650e+00 -3.97291273e-01 -3.28046441e-01
-6.68942869e-01 7.60483563e-01 1.59354091e-01 -2.43070647... | [7.018763065338135, 6.224867343902588] |
d9ce3be7-aa76-444b-9a25-e903fc35e5cb | district-dialogue-state-tracking-with | 2212.02851 | null | https://arxiv.org/abs/2212.02851v1 | https://arxiv.org/pdf/2212.02851v1.pdf | DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning | Dialogue State Tracking (DST), a key component of task-oriented conversation systems, represents user intentions by determining the values of pre-defined slots in an ongoing dialogue. Existing approaches use hand-crafted templates and additional slot information to fine-tune and prompt large pre-trained language models... | ['Vatche Isahagian', 'Evelyn Duesterwald', 'Praveen Venkateswaran'] | 2022-12-06 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 3.01564962e-01 3.36209536e-01 -3.97777945e-01 -5.97319901e-01
-7.88673520e-01 -8.07044089e-01 9.80480433e-01 1.41976118e-01
-4.96752113e-01 9.38113689e-01 6.25125945e-01 -2.91369110e-01
5.00169657e-02 -4.79086101e-01 2.50688076e-01 -1.12336963e-01
1.90023437e-01 1.13640749e+00 6.60307705e-01 -9.17373657... | [12.896936416625977, 7.853987693786621] |
54cbb521-0bab-4c15-9f5c-174dbb0ee8c9 | single-domain-generalization-for-lidar | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_Single_Domain_Generalization_for_LiDAR_Semantic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_Single_Domain_Generalization_for_LiDAR_Semantic_Segmentation_CVPR_2023_paper.pdf | Single Domain Generalization for LiDAR Semantic Segmentation | With the success of the 3D deep learning models, various perception technologies for autonomous driving have been developed in the LiDAR domain. While these models perform well in the trained source domain, they struggle in unseen domains with a domain gap. In this paper, we propose a single domain generalization m... | ['Kuk-Jin Yoon', 'Changgyoon Oh', 'Yoonsu Kang', 'Hyeonseong Kim'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.76883751e-01 4.66919206e-02 -1.67020395e-01 -7.20457137e-01
-5.60596943e-01 -5.51617324e-01 4.79693919e-01 -1.63205668e-01
-2.45304585e-01 5.96366227e-01 -1.25777468e-01 3.75254378e-02
-5.83933927e-02 -7.95454144e-01 -8.93561721e-01 -5.43545365e-01
3.50555718e-01 7.34102190e-01 6.80401087e-01 -1.52130798... | [8.201592445373535, -2.5778722763061523] |
35e3c1e2-2d2a-46a7-8b00-7b7de23ba737 | bert2code-can-pretrained-language-models-be | 2104.08017 | null | https://arxiv.org/abs/2104.08017v1 | https://arxiv.org/pdf/2104.08017v1.pdf | BERT2Code: Can Pretrained Language Models be Leveraged for Code Search? | Millions of repetitive code snippets are submitted to code repositories every day. To search from these large codebases using simple natural language queries would allow programmers to ideate, prototype, and develop easier and faster. Although the existing methods have shown good performance in searching codes when the... | ['Rifat Shahriyar', 'Anindya Iqbal', 'Tahmid Hasan', 'Tanveer Muttaqueen', 'Kazi Sajeed Mehrab', 'Md. Mahim Anjum Haque', 'Masum Hasan', 'Abdullah Al Ishtiaq'] | 2021-04-16 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-2.21965566e-01 -5.31090572e-02 -5.75481594e-01 -2.26079315e-01
-5.26408434e-01 -7.66577840e-01 3.39494944e-01 5.42210042e-01
-2.52814829e-01 -1.14394732e-01 5.17642021e-01 -7.50734448e-01
-1.86841339e-02 -6.96806252e-01 -6.18088722e-01 6.59585893e-02
-2.16625735e-01 6.41479343e-02 2.42052913e-01 -2.65750289... | [7.55244779586792, 8.068450927734375] |
ac5936c7-54c6-4834-bfac-12c784eac5b4 | graph-enhanced-dual-attention-network-for | null | null | https://aclanthology.org/2020.coling-main.136 | https://aclanthology.org/2020.coling-main.136.pdf | Graph Enhanced Dual Attention Network for Document-Level Relation Extraction | Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relational facts. To improve inter-sentence reasoning, we propose to characterize the complex interaction between sentences and potential relation instances via a Graph Enhan... | ['Shikun Zhang', 'Xiangyu Xi', 'Rui Xie', 'Zhonghao Sheng', 'Wei Ye', 'Bo Li'] | 2020-12-01 | null | null | null | coling-2020-8 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 0.34779376 1.0581323 -0.20508957 -0.55965227 -0.76069313 -0.48929685
0.6950808 0.5085981 0.06197954 0.73289686 0.67418957 -0.6260191
-0.23261647 -1.2833298 -0.87063223 -0.0651768 -0.03494391 0.47755867
0.13653105 -0.6422836 -0.22699055 0.22620451 -0.88766325 0.6061486
0.8993934 0.8968155 0.00... | [9.269532203674316, 8.582245826721191] |
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