paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
71c1e820-427b-4b7f-887a-d38ad810e33b | 3d-future-3d-furniture-shape-with-texture | 2009.09633 | null | https://arxiv.org/abs/2009.09633v1 | https://arxiv.org/pdf/2009.09633v1.pdf | 3D-FUTURE: 3D Furniture shape with TextURE | The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories. Thus, they typically have insufficient geometric details and less informative textures, making them less attractive for comprehensive and subtle research in areas such as high-quality 3D mesh and texture recovery. This paper... | ['DaCheng Tao', 'Mingming Gong', 'Steve Maybank', 'Lin Gao', 'Huan Fu', 'Rongfei Jia', 'Binqiang Zhao'] | 2020-09-21 | null | null | null | null | ['3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image'] | ['computer-vision', 'computer-vision'] | [ 1.50944851e-02 -4.24039304e-01 1.28063455e-01 -3.48794162e-01
-1.01445699e+00 -6.32059991e-01 2.88736880e-01 7.60425702e-02
5.02206028e-01 -7.71790277e-03 -1.55413419e-01 -7.33914152e-02
-1.52844861e-01 -9.62020397e-01 -9.71714616e-01 -4.19597387e-01
1.31039992e-01 1.32803571e+00 8.47887695e-02 -2.49711558... | [8.384509086608887, -3.2472434043884277] |
15346d9e-8c53-4f4f-ba67-b712a49ccc17 | sardino-ultra-fast-dynamic-ensemble-for | 2204.08189 | null | https://arxiv.org/abs/2204.08189v4 | https://arxiv.org/pdf/2204.08189v4.pdf | Sardino: Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile Edge | Adversarial example attack endangers the mobile edge systems such as vehicles and drones that adopt deep neural networks for visual sensing. This paper presents {\em Sardino}, an active and dynamic defense approach that renews the inference ensemble at run time to develop security against the adaptive adversary who tri... | ['Rui Tan', 'Wenjie Luo', 'Zhenyu Yan', 'Qun Song'] | 2022-04-18 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 2.90085912e-01 2.64297843e-01 1.57674044e-01 -4.33133803e-02
-6.84390128e-01 -8.74509573e-01 7.05268264e-01 -6.36344731e-01
-6.82117701e-01 6.66799545e-01 -5.53396046e-01 -8.93492937e-01
1.11856172e-02 -9.07075465e-01 -7.61533439e-01 -7.45312989e-01
-2.50007123e-01 2.21399844e-01 5.61980426e-01 -5.90914190... | [5.4260454177856445, 7.846906661987305] |
878d0818-f423-489b-bee5-ea944bced71a | worth-of-knowledge-in-deep-learning | 2307.00712 | null | https://arxiv.org/abs/2307.00712v1 | https://arxiv.org/pdf/2307.00712v1.pdf | Worth of knowledge in deep learning | Knowledge constitutes the accumulated understanding and experience that humans use to gain insight into the world. In deep learning, prior knowledge is essential for mitigating shortcomings of data-driven models, such as data dependence, generalization ability, and compliance with constraints. To enable efficient evalu... | ['Dongxiao Zhang', 'Yuntian Chen', 'Hao Xu'] | 2023-07-03 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-4.24569286e-02 2.95243412e-01 -5.58596134e-01 -6.16460741e-01
5.96557260e-02 -7.28930593e-01 6.07959926e-01 2.42398649e-01
-4.58028525e-01 5.99404991e-01 3.38510931e-01 -5.47423124e-01
-5.93989432e-01 -8.07259917e-01 -7.57245898e-01 -2.44083777e-01
1.44624099e-01 2.64319330e-01 8.26342925e-02 -1.23616733... | [9.181829452514648, 6.5734734535217285] |
1d359948-2aa0-4daa-8bb9-201a4e97b1b0 | towards-deep-learning-powered-ivf-a-large | 2203.00531 | null | https://arxiv.org/abs/2203.00531v2 | https://arxiv.org/pdf/2203.00531v2.pdf | Towards deep learning-powered IVF: A large public benchmark for morphokinetic parameter prediction | An important limitation to the development of Artificial Intelligence (AI)-based solutions for In Vitro Fertilization (IVF) is the absence of a public reference benchmark to train and evaluate deep learning (DL) models. In this work, we describe a fully annotated dataset of 704 videos of developing embryos, for a total... | ['Harold Mouchère', 'Thomas Fréour', 'Perrine Paul-Gilloteaux', 'Laurent David', 'Nicolas Normand', 'Magalie Feyeux', 'Tristan Gomez'] | 2022-03-01 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 1.44015148e-01 5.39336145e-01 1.02756366e-01 -2.66876370e-01
-2.83023477e-01 -6.63236856e-01 3.70233119e-01 3.65956575e-01
-3.16369236e-01 8.43149185e-01 -4.47534099e-02 -4.01522666e-01
-7.32627362e-02 -8.04481447e-01 -9.17480528e-01 -6.68332160e-01
-3.35810423e-01 8.29548717e-01 -4.74156022e-01 -2.33650416... | [14.630830764770508, -3.1673731803894043] |
de9af1d6-7995-42c1-a8de-09c8fcb1b135 | improving-constituency-parsing-with-span | 2010.07543 | null | https://arxiv.org/abs/2010.07543v1 | https://arxiv.org/pdf/2010.07543v1.pdf | Improving Constituency Parsing with Span Attention | Constituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task. N-grams, which is a conventional type of feature for contextual information, have been demonstrated to be useful in many tasks, and thus could also be be... | ['Tong Zhang', 'Fei Xia', 'Yan Song', 'Yuanhe Tian'] | 2020-10-15 | null | https://aclanthology.org/2020.findings-emnlp.153 | https://aclanthology.org/2020.findings-emnlp.153.pdf | findings-of-the-association-for-computational | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.86074436e-01 1.14751354e-01 -4.41726983e-01 -5.53314149e-01
-8.70323777e-01 -5.64273238e-01 8.46056715e-02 5.19869506e-01
-2.42057055e-01 5.10921001e-01 9.15980697e-01 -6.07340872e-01
4.15350050e-01 -1.02831721e+00 -6.18038058e-01 -5.18062532e-01
4.45158184e-02 -1.90660924e-01 2.13223442e-01 -4.66461152... | [10.541061401367188, 9.532485961914062] |
53f2e6b3-e043-44d6-9d68-3d04c85edd32 | unsupervised-multimodal-neural-machine | 2005.03119 | null | https://arxiv.org/abs/2005.03119v1 | https://arxiv.org/pdf/2005.03119v1.pdf | Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting | Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. However, it is still challenging to associate source-target sentences in the latent space. As people speak different languages biologically share similar visual systems, the potential of achieving better alignm... | ['Alexander Hauptmann', 'Po-Yao Huang', 'Xiaojun Chang', 'Junjie Hu'] | 2020-05-06 | unsupervised-multimodal-neural-machine-1 | https://aclanthology.org/2020.acl-main.731 | https://aclanthology.org/2020.acl-main.731.pdf | acl-2020-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 1.45902902e-01 2.52828151e-02 -5.11782110e-01 -2.44343281e-01
-1.02666306e+00 -8.04749370e-01 1.16800272e+00 -2.58716017e-01
-3.55931371e-01 6.25781357e-01 4.99236465e-01 -3.47752392e-01
4.17153269e-01 -9.41737518e-02 -8.06777775e-01 -8.05025399e-01
3.26473117e-01 8.07935297e-01 -3.19260985e-01 -8.92064348... | [11.411373138427734, 1.5015501976013184] |
f8335da8-1cb1-4a7d-8787-a962dd24bf98 | first-order-methods-with-markovian-noise-from | 2305.15938 | null | https://arxiv.org/abs/2305.15938v1 | https://arxiv.org/pdf/2305.15938v1.pdf | First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities | This paper delves into stochastic optimization problems that involve Markovian noise. We present a unified approach for the theoretical analysis of first-order gradient methods for stochastic optimization and variational inequalities. Our approach covers scenarios for both non-convex and strongly convex minimization pr... | ['Eric Moulines', 'Alexey Naumov', 'Alexander Gasnikov', 'Marina Sheshukova', 'Sergey Samsonov', 'Aleksandr Beznosikov'] | 2023-05-25 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 1.53347701e-01 -1.96700543e-01 -2.75191031e-02 -5.48041612e-02
-1.34685493e+00 -8.10485840e-01 3.17620337e-01 -3.90288755e-02
-6.28556311e-01 8.78476143e-01 9.16919112e-02 -5.86148977e-01
-1.77411303e-01 -6.64459527e-01 -8.81314397e-01 -1.02658165e+00
1.88578233e-01 3.61282587e-01 1.06062703e-01 -3.30574811... | [6.794434547424316, 4.312807083129883] |
3202cf74-bed5-497e-8774-5d72994378d9 | automatic-brain-structures-segmentation-using | 1811.04312 | null | http://arxiv.org/abs/1811.04312v1 | http://arxiv.org/pdf/1811.04312v1.pdf | Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net | Brain image segmentation is used for visualizing and quantifying anatomical
structures of the brain. We present an automated ap-proach using 2D deep
residual dilated networks which captures rich context information of different
tissues for the segmentation of eight brain structures. The proposed system was
evaluated in... | ['Bjoern Menze', 'Andrii Zhygallo', 'Hongwei Li'] | 2018-11-10 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [-3.00680641e-02 1.04011469e-01 3.01334023e-01 -6.31112397e-01
-5.32711446e-01 -5.26328504e-01 2.21199512e-01 1.99559882e-01
-9.34855938e-01 6.95756018e-01 1.39622137e-01 4.44268920e-02
-2.49871120e-01 -2.66589165e-01 -1.19011641e-01 -6.04250729e-01
-6.33846104e-01 3.32924247e-01 2.80839831e-01 1.82243749... | [14.18640422821045, -2.336777925491333] |
915c47ac-7b4f-44be-84c4-37a7962cd214 | efficient-and-low-overhead-website | 2302.13763 | null | https://arxiv.org/abs/2302.13763v1 | https://arxiv.org/pdf/2302.13763v1.pdf | Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic | Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several website fingerprinting attacks based on machine learning and deep learning tend to use the most typical features to achieve a satisfactory perf... | ['Zhe Liu', 'Liming Fang', 'Chunpeng Ge', 'Yuwen Qian', 'Ming Ding', 'Chuan Ma', 'Guodong Huang'] | 2023-02-27 | null | null | null | null | ['website-fingerprinting-attacks'] | ['adversarial'] | [-5.45173734e-02 -8.04737210e-01 -2.21244469e-01 -2.83103138e-01
-4.04306620e-01 -8.29689264e-01 4.47589427e-01 -9.15015936e-02
-4.44650561e-01 4.43488091e-01 -3.35430056e-01 -8.14645708e-01
-3.08222860e-01 -1.19541943e+00 -3.40938091e-01 -5.74820220e-01
-4.07798737e-02 -1.53136507e-01 6.19289935e-01 -5.30230254... | [5.242466926574707, 7.214937686920166] |
2bc310fd-8422-451e-810c-aa392f453142 | conversational-analysis-using-utterance-level | 1805.06242 | null | http://arxiv.org/abs/1805.06242v2 | http://arxiv.org/pdf/1805.06242v2.pdf | Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks | Recent approaches for dialogue act recognition have shown that context from
preceding utterances is important to classify the subsequent one. It was shown
that the performance improves rapidly when the context is taken into account.
We propose an utterance-level attention-based bidirectional recurrent neural
network (U... | ['Cornelius Weber', 'Sven Magg', 'Stefan Wermter', 'Chandrakant Bothe'] | 2018-05-16 | null | null | null | null | ['dialog-act-classification', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.64649826e-01 3.21733713e-01 -9.65852141e-02 -7.53594637e-01
-4.63752896e-01 -2.18062982e-01 9.64288890e-01 5.10602117e-01
-6.86929047e-01 8.29684496e-01 8.80132198e-01 -3.91845018e-01
1.06240220e-01 -6.07675314e-01 -1.86355636e-01 -6.79569125e-01
1.25602335e-01 4.83714283e-01 7.04508126e-02 -7.91836739... | [12.802143096923828, 7.688101768493652] |
75fb5c4b-099d-423c-9e0b-28cf9fbb5627 | precision-psychiatry-predicting | 2306.12462 | null | https://arxiv.org/abs/2306.12462v1 | https://arxiv.org/pdf/2306.12462v1.pdf | Precision psychiatry: predicting predictability | Precision psychiatry is an ermerging field that aims to provide individualized approaches to mental health care. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much em... | ['Edwin van Dellen'] | 2023-06-21 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 5.14508545e-01 1.09925777e-01 -9.45302546e-01 -5.88649035e-01
-4.48077500e-01 -1.50139987e-01 1.75220147e-01 8.13684106e-01
-5.86084962e-01 6.95624530e-01 4.08240169e-01 -5.62838912e-01
-6.36142373e-01 -3.08816344e-01 -1.46661261e-02 -2.35048562e-01
-2.17992246e-01 7.17383027e-01 -5.38652718e-01 2.01527029... | [8.091586112976074, 5.531695365905762] |
b366f811-0f0a-4b24-bab1-c71d16e21779 | emotional-responses-in-artificial-agent-based | 1401.2121 | null | http://arxiv.org/abs/1401.2121v1 | http://arxiv.org/pdf/1401.2121v1.pdf | Emotional Responses in Artificial Agent-Based Systems: Reflexivity and Adaptation in Artificial Life | The current work addresses a virtual environment with self-replicating agents
whose decisions are based on a form of "somatic computation" (soma - body) in
which basic emotional responses, taken in parallelism to actual living
organisms, are introduced as a way to provide the agents with greater reflexive
abilities. Th... | ['Carlos Pedro Gonçalves'] | 2014-01-09 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-1.55961454e-01 5.30314684e-01 2.39358664e-01 2.36928225e-01
1.09104788e+00 -5.25586843e-01 8.46106112e-01 2.85663344e-02
-3.69575739e-01 1.03681540e+00 -2.43089363e-01 2.09335625e-01
2.90108800e-01 -1.02474880e+00 -1.96925163e-01 -9.22624588e-01
-1.21520467e-01 3.13352406e-01 -1.62149265e-01 -1.14695525... | [5.580928325653076, 4.1436920166015625] |
c82fc1ea-b79d-404c-bac8-3fb8345eb481 | density-ratio-estimation-based-bayesian | 2305.15612 | null | https://arxiv.org/abs/2305.15612v1 | https://arxiv.org/pdf/2305.15612v1.pdf | Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning | Bayesian optimization has attracted huge attention from diverse research areas in science and engineering, since it is capable of finding a global optimum of an expensive-to-evaluate black-box function efficiently. In general, a probabilistic regression model, e.g., Gaussian processes, random forests, and Bayesian neur... | ['Jungtaek Kim'] | 2023-05-24 | null | null | null | null | ['density-ratio-estimation', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-2.14482933e-01 -1.70627967e-01 -4.27493125e-01 -7.04163730e-01
-9.71537471e-01 -1.88351139e-01 3.56458426e-01 2.02706486e-01
-4.15287822e-01 1.17708516e+00 -3.52222770e-01 -1.86486259e-01
-3.30850840e-01 -8.14055264e-01 -6.13457680e-01 -1.10363042e+00
2.89144307e-01 7.55118847e-01 1.59326851e-01 4.43579316... | [6.8430585861206055, 3.825211763381958] |
80a5aeb3-cac1-4be3-a7b0-9249bcf539f4 | automated-speech-tools-for-helping | 2204.07272 | null | https://arxiv.org/abs/2204.07272v2 | https://arxiv.org/pdf/2204.07272v2.pdf | Automated speech tools for helping communities process restricted-access corpora for language revival efforts | Many archival recordings of speech from endangered languages remain unannotated and inaccessible to community members and language learning programs. One bottleneck is the time-intensive nature of annotation. An even narrower bottleneck occurs for recordings with access constraints, such as language that must be vetted... | ['Tolúlopé Ògúnrèmí', 'Dan Jurafsky', 'Jane Simpson', 'Roy Barker', 'Michael Higgins', 'Ruben Thompson', 'Alison Mount', 'Martijn Bartelds', 'Nay San'] | 2022-04-15 | null | https://aclanthology.org/2022.computel-1.6 | https://aclanthology.org/2022.computel-1.6.pdf | computel-acl-2022-5 | ['activity-detection', 'spoken-language-identification'] | ['computer-vision', 'speech'] | [ 1.10413477e-01 2.61019856e-01 3.53688926e-01 -3.13724160e-01
-1.65539479e+00 -1.31626010e+00 2.45960996e-01 5.27004540e-01
-8.74591887e-01 7.43712425e-01 7.32188463e-01 -6.73859775e-01
2.03004658e-01 -1.21087618e-01 -3.48843783e-01 -2.69535720e-01
3.06504577e-01 7.10614145e-01 5.18580899e-02 -1.79988854... | [14.12572193145752, 6.912771701812744] |
3c2bd2e3-1849-43df-ada4-3f21b127ca4a | g-matt-single-step-retrosynthesis-prediction | 2305.03153 | null | https://arxiv.org/abs/2305.03153v1 | https://arxiv.org/pdf/2305.03153v1.pdf | G-MATT: Single-step Retrosynthesis Prediction using Molecular Grammar Tree Transformer | In recent years, several reaction templates-based and template-free approaches have been reported for single-step retrosynthesis prediction. Even though many of these approaches perform well from traditional data-driven metrics standpoint, there is a disconnect between model architectures used and underlying chemistry ... | ['Venkat Venkatasubramanian', 'Vipul Mann', 'Kevin Zhang'] | 2023-05-04 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 4.62268472e-01 7.46428147e-02 -3.56885612e-01 -1.23333961e-01
-7.31744528e-01 -9.34391260e-01 7.27659047e-01 6.42875433e-01
1.60111450e-02 8.68485391e-01 3.59306931e-01 -6.27315938e-01
2.07546026e-01 -6.73711121e-01 -8.05133641e-01 -7.51282454e-01
1.03075571e-01 3.32062602e-01 9.59606245e-02 -4.73858476... | [4.507802486419678, 6.108270168304443] |
103fa6e9-6583-4264-a5d7-5688b5cd44bd | learnable-expansion-and-compression-network | 2104.02281 | null | https://arxiv.org/abs/2104.02281v1 | https://arxiv.org/pdf/2104.02281v1.pdf | Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning | Few-shot class-incremental learning (FSCIL), which targets at continuously expanding model's representation capacity under few supervisions, is an important yet challenging problem. On the one hand, when fitting new tasks (novel classes), features trained on old tasks (old classes) could significantly drift, causing ca... | ['Qixiang Ye', 'Rongrong Ji', 'Chang Liu', 'Mengying Fu', 'Binghao Liu', 'Mingbao Lin', 'Boyu Yang'] | 2021-04-06 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.28835225e-01 2.63686091e-01 -5.32533834e-03 -1.69592157e-01
-2.69363701e-01 -1.30665712e-02 2.12540179e-01 -1.30739450e-01
-5.73999226e-01 8.59655619e-01 -2.34713510e-01 1.05035625e-01
-2.03658864e-01 -6.23569608e-01 -9.58326221e-01 -7.83994138e-01
8.25384334e-02 4.87020046e-01 4.99865234e-01 -1.17394120... | [9.82400131225586, 3.391835927963257] |
7a811abe-2d9b-4797-816d-d2bfca9fdf7d | learning-audio-visual-dereverberation | 2106.07732 | null | https://arxiv.org/abs/2106.07732v2 | https://arxiv.org/pdf/2106.07732v2.pdf | Learning Audio-Visual Dereverberation | Reverberation not only degrades the quality of speech for human perception, but also severely impacts the accuracy of automatic speech recognition. Prior work attempts to remove reverberation based on the audio modality only. Our idea is to learn to dereverberate speech from audio-visual observations. The visual enviro... | ['Kristen Grauman', 'David Harwath', 'Wei Sun', 'Changan Chen'] | 2021-06-14 | learning-audio-visual-dereverberation-1 | https://openreview.net/forum?id=ExJ4lMbZcqa | https://openreview.net/pdf?id=ExJ4lMbZcqa | null | ['speaker-identification'] | ['speech'] | [ 3.40089887e-01 -2.98119605e-01 1.17281449e+00 -3.22991788e-01
-1.40799832e+00 -6.60534918e-01 2.44867131e-01 -1.26623234e-03
-5.22870198e-02 3.00830364e-01 8.40249836e-01 -2.26779088e-01
1.61494493e-01 -1.97460547e-01 -6.95499241e-01 -7.75273979e-01
-1.55443832e-01 -1.66405991e-01 7.97991455e-03 -2.46515080... | [15.06653881072998, 5.737292289733887] |
3091842e-9051-45fd-bc9b-c8b259543c4b | sleep-arousal-detection-from-polysomnography | 1810.08875 | null | http://arxiv.org/abs/1810.08875v1 | http://arxiv.org/pdf/1810.08875v1.pdf | Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent Neural Networks | Sleep disorders are implicated in a growing number of health problems. In
this paper, we present a signal-processing/machine learning approach to
detecting arousals in the multi-channel polysomnographic recordings of the
Physionet/CinC Challenge2018 dataset.
Methods: Our network architecture consists of two component... | ['Masun Nabhan Homsi', 'Philip Warrick'] | 2018-10-21 | null | null | null | null | ['sleep-arousal-detection'] | ['medical'] | [ 6.72696054e-01 4.96262237e-02 2.34275073e-01 -4.56802547e-01
-7.57111430e-01 -2.67121553e-01 1.27515554e-01 1.99855506e-01
-6.82390869e-01 9.71297145e-01 1.95514694e-01 5.25189517e-03
-1.71784967e-01 -2.01520175e-01 -2.10574538e-01 -7.97748983e-01
-4.89595622e-01 -1.58119708e-01 -8.55752975e-02 -7.59696141... | [13.511641502380371, 3.5056896209716797] |
b9a1a149-535d-478e-a398-f885c5f770ca | pre-training-language-models-for-comparative | 2305.14457 | null | https://arxiv.org/abs/2305.14457v1 | https://arxiv.org/pdf/2305.14457v1.pdf | Pre-training Language Models for Comparative Reasoning | In this paper, we propose a novel framework to pre-train language models for enhancing their abilities of comparative reasoning over texts. While recent research has developed models for NLP tasks that require comparative reasoning, they suffer from costly manual data labeling and limited generalizability to different ... | ['Meng Jiang', 'Wenhao Yu', 'Zhihan Zhang', 'Mengxia Yu'] | 2023-05-23 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 1.05529226e-01 5.03130853e-01 -3.07793051e-01 -4.58156109e-01
-1.60870123e+00 -7.69488156e-01 9.60518420e-01 6.49774551e-01
-8.57587159e-01 7.87567258e-01 6.82018220e-01 -8.28684092e-01
-1.55115083e-01 -7.61098862e-01 -5.09661555e-01 3.34662765e-01
3.11935782e-01 9.48130786e-01 3.69843334e-01 -7.00563669... | [10.834507942199707, 8.371488571166992] |
524ac8d8-0be3-44cb-9d29-4d265a87f135 | a-text-guided-protein-design-framework | 2302.04611 | null | https://arxiv.org/abs/2302.04611v1 | https://arxiv.org/pdf/2302.04611v1.pdf | A Text-guided Protein Design Framework | Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format describing proteins' high-level properties. Yet, whether the incorporation of such text data can help protein design tasks has not been expl... | ['Anima Anandkumar', 'Hongyu Guo', 'Jian Tang', 'Chaowei Xiao', 'Anthony Gitter', 'Weili Nie', 'Zhao Xu', 'Jiarui Lu', 'Yutao Zhu', 'Shengchao Liu'] | 2023-02-09 | null | null | null | null | ['protein-design'] | ['medical'] | [ 7.42392063e-01 1.71317697e-01 -1.83562100e-01 -4.65481192e-01
-9.43363845e-01 -6.23154640e-01 3.13937962e-01 5.20708740e-01
-2.51073271e-01 1.19143283e+00 4.66120452e-01 -2.62704849e-01
2.41045699e-01 -4.77331936e-01 -1.13533032e+00 -7.54112124e-01
3.43351126e-01 6.76300108e-01 7.05095232e-02 -1.32200792... | [4.694322109222412, 5.6420464515686035] |
d5230020-cc92-4d87-83b7-79c62b383922 | a-unified-model-for-video-understanding-and | 2211.10624 | null | https://arxiv.org/abs/2211.10624v2 | https://arxiv.org/pdf/2211.10624v2.pdf | A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset | Video understanding is an important task in short video business platforms and it has a wide application in video recommendation and classification. Most of the existing video understanding works only focus on the information that appeared within the video content, including the video frames, audio and text. However, i... | ['Zhongyuan Wang', 'Ruiji Fu', 'Size Li', 'Fan Yang', 'Gaofeng Meng', 'Ximan Liu', 'Xiangyu Wu', 'Haojie Pan', 'Dong Shen', 'Jiaxin Deng'] | 2022-11-19 | null | null | null | null | ['video-understanding', 'knowledge-graph-embedding', 'common-sense-reasoning'] | ['computer-vision', 'graphs', 'reasoning'] | [-1.69734418e-01 -1.76444650e-01 -7.50133097e-01 -7.14922845e-02
-4.76490617e-01 -4.51135546e-01 3.25673103e-01 -4.82476801e-02
-2.37418070e-01 4.06443626e-01 6.26621246e-01 -7.99016804e-02
-5.28387785e-01 -6.00566804e-01 -8.82615149e-01 -2.73267895e-01
1.04235016e-01 9.70381871e-02 3.97830635e-01 -1.72831967... | [10.155856132507324, 0.9570338726043701] |
b631b57a-42be-4af6-9f9d-deacf12ddd5a | efficient-diffusion-policies-for-offline | 2305.20081 | null | https://arxiv.org/abs/2305.20081v1 | https://arxiv.org/pdf/2305.20081v1.pdf | Efficient Diffusion Policies for Offline Reinforcement Learning | Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL significantly boosts the performance of offline RL by representing a policy with a diffusion model, whose success relies on a parametri... | ['Shuicheng Yan', 'Tianyu Pang', 'Chao Du', 'Xiao Ma', 'Bingyi Kang'] | 2023-05-31 | null | null | null | null | ['policy-gradient-methods', 'offline-rl', 'd4rl'] | ['methodology', 'playing-games', 'robots'] | [-3.87271106e-01 -7.63094425e-02 -7.60481656e-01 1.12349883e-01
-9.41354871e-01 -7.10135937e-01 5.68859160e-01 -1.99456617e-01
-8.45852554e-01 1.12740600e+00 1.26302257e-01 -8.51260960e-01
1.30713237e-02 -6.72217846e-01 -8.47338438e-01 -8.39921772e-01
-7.36379251e-02 7.16368258e-01 2.50895768e-01 -1.51023835... | [4.079835891723633, 2.148141860961914] |
c482c15b-f824-4381-9425-cf838f55ffbf | behavior-driven-synthesis-of-human-dynamics | 2103.04677 | null | https://arxiv.org/abs/2103.04677v2 | https://arxiv.org/pdf/2103.04677v2.pdf | Behavior-Driven Synthesis of Human Dynamics | Generating and representing human behavior are of major importance for various computer vision applications. Commonly, human video synthesis represents behavior as sequences of postures while directly predicting their likely progressions or merely changing the appearance of the depicted persons, thus not being able to ... | ['Björn Ommer', 'Michael Dorkenwald', 'Timo Milbich', 'Andreas Blattmann'] | 2021-03-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Blattmann_Behavior-Driven_Synthesis_of_Human_Dynamics_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Blattmann_Behavior-Driven_Synthesis_of_Human_Dynamics_CVPR_2021_paper.pdf | cvpr-2021-1 | ['human-dynamics'] | ['computer-vision'] | [ 3.13578188e-01 -4.39459272e-02 -3.68810967e-02 -2.99959838e-01
-1.51174575e-01 -6.97484493e-01 7.02806890e-01 -5.85324951e-02
-1.08997762e-01 6.26481116e-01 2.87168801e-01 2.40293518e-01
2.63795435e-01 -6.99853301e-01 -1.06364977e+00 -5.94132900e-01
1.56373858e-01 6.31625950e-01 1.06390461e-01 -1.30601600... | [10.831832885742188, -0.7788289189338684] |
c43f69ce-b879-4b47-8558-88e388a246b8 | precise-stock-price-prediction-for-robust | 2201.05570 | null | https://arxiv.org/abs/2201.05570v1 | https://arxiv.org/pdf/2201.05570v1.pdf | Precise Stock Price Prediction for Robust Portfolio Design from Selected Sectors of the Indian Stock Market | Stock price prediction is a challenging task and a lot of propositions exist in the literature in this area. Portfolio construction is a process of choosing a group of stocks and investing in them optimally to maximize the return while minimizing the risk. Since the time when Markowitz proposed the Modern Portfolio The... | ['Praveen Varukolu', 'Koushik Tulasi', 'Kaushik Muthukrishnan', 'Geetha Joseph', 'Ashwin Kumar R S', 'Jaydip Sen'] | 2022-01-14 | null | null | null | null | ['portfolio-optimization', 'stock-price-prediction'] | ['time-series', 'time-series'] | [-1.66629881e-01 3.56317535e-02 3.22194882e-02 -2.31180146e-01
-1.41900435e-01 -5.88696837e-01 2.64859349e-01 -1.44316301e-01
-2.64080554e-01 8.14959586e-01 1.02699451e-01 -5.35788298e-01
-7.63954580e-01 -1.33484638e+00 -4.06489283e-01 -5.18281460e-01
-1.57472983e-01 6.74713969e-01 2.53592193e-01 -3.14606607... | [4.658111095428467, 4.075068950653076] |
53535010-e660-467e-b4b6-7d996b31046e | model-driven-engineering-method-to-support | 2307.04495 | null | https://arxiv.org/abs/2307.04495v1 | https://arxiv.org/pdf/2307.04495v1.pdf | Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML | Methods: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the identification and integration of various data sources, the required definition of semantic ... | ['Stefanie Rinderle-Ma', 'Juergen Mangler', 'Simon Raedler'] | 2023-07-10 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 1.60335541e-01 4.16360468e-01 7.39408582e-02 -5.09874463e-01
2.03215018e-01 -6.76887810e-01 5.67029357e-01 8.15113604e-01
-4.69323844e-02 2.80315489e-01 -5.11159897e-01 -7.58463621e-01
-7.90655851e-01 -9.66167271e-01 -3.47372055e-01 -1.29830763e-01
9.30207148e-02 6.25750422e-01 -5.82216196e-02 -1.13647923... | [8.86557388305664, 6.175148010253906] |
5bf1ac8d-3e57-4a02-bdaf-d505591be2b8 | collaborative-policy-learning-for-dynamic | 2307.00541 | null | https://arxiv.org/abs/2307.00541v1 | https://arxiv.org/pdf/2307.00541v1.pdf | Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning | In this paper, we examine cloud-edge-terminal IoT networks, where edges undertake a range of typical dynamic scheduling tasks. In these IoT networks, a central policy for each task can be constructed at a cloud server. The central policy can be then used by the edges conducting the task, thereby mitigating the need for... | ['Hyun-Suk Lee', 'Ji-Wan Kim', 'Da-Eun Lee', 'Do-Yup Kim'] | 2023-07-02 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [-2.74731010e-01 1.72844887e-01 -4.64559972e-01 -1.34835839e-01
-1.47315398e-01 -6.35954797e-01 1.06011249e-01 -2.87976533e-01
-5.66208124e-01 1.05295813e+00 -1.12858914e-01 -3.42387170e-01
-6.01266205e-01 -8.04098785e-01 -5.12251556e-01 -1.07850087e+00
-1.38490230e-01 6.37649477e-01 2.21970126e-01 3.07630561... | [5.858726501464844, 1.8429381847381592] |
40640991-0bf9-43ea-bb6e-1dff07354209 | lensid-a-cnn-rnn-based-framework-towards-lens | 2107.00875 | null | https://arxiv.org/abs/2107.00875v1 | https://arxiv.org/pdf/2107.00875v1.pdf | LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos | A critical complication after cataract surgery is the dislocation of the lens implant leading to vision deterioration and eye trauma. In order to reduce the risk of this complication, it is vital to discover the risk factors during the surgery. However, studying the relationship between lens dislocation and its suspici... | ['Klaus Schoeffmann', 'Yosuf El-Shabrawi', 'Stephanie Sarny', 'Doris Putzgruber-Adamitsch', 'Mario Taschwer', 'Negin Ghamsarian'] | 2021-07-02 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 3.57161522e-01 4.46213819e-02 -1.85716040e-02 -9.84787848e-03
-4.23501760e-01 -4.69860464e-01 2.70057470e-01 -3.43234763e-02
-3.53674829e-01 4.24485266e-01 2.82449961e-01 -4.00605500e-01
-4.83340412e-01 -3.25167954e-01 -4.84379441e-01 -8.33466947e-01
1.79110110e-01 2.50203609e-01 2.34776273e-01 3.19159329... | [15.668631553649902, -3.902482509613037] |
be24fb17-d247-48ea-a1a8-edb900ce3b22 | fundamental-limits-of-two-layer-autoencoders | 2212.13468 | null | https://arxiv.org/abs/2212.13468v1 | https://arxiv.org/pdf/2212.13468v1.pdf | Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods | Autoencoders are a popular model in many branches of machine learning and lossy data compression. However, their fundamental limits, the performance of gradient methods and the features learnt during optimization remain poorly understood, even in the two-layer setting. In fact, earlier work has considered either linear... | ['Marco Mondelli', 'Hamed Hassani', 'Kevin Kögler', 'Alexander Shevchenko'] | 2022-12-27 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 6.01231083e-02 4.94442135e-01 -1.26407474e-01 -2.56658043e-03
-4.50549722e-01 -2.14378566e-01 4.13275391e-01 2.84637660e-01
-5.11348307e-01 6.96136892e-01 3.44943047e-01 -1.02510877e-01
-4.52067196e-01 -8.70438814e-01 -1.04882121e+00 -1.11077142e+00
-4.23218668e-01 4.72041845e-01 -2.11165681e-01 -3.97083223... | [7.777393817901611, 3.644230842590332] |
395facfc-6b2d-4168-8867-b1d2f06ff216 | automatic-negation-and-speculation-detection | null | null | https://aclanthology.org/U17-1008 | https://aclanthology.org/U17-1008.pdf | Automatic Negation and Speculation Detection in Veterinary Clinical Text | null | ['Timothy Baldwin', 'Katherine Cheng', 'Karin Verspoor'] | 2017-12-01 | automatic-negation-and-speculation-detection-1 | https://aclanthology.org/U17-1008 | https://aclanthology.org/U17-1008.pdf | alta-2017-12 | ['speculation-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.405374050140381, 3.7354230880737305] |
83085097-8b4f-4449-91b7-0e3d81fcf4cd | masked-discrimination-for-self-supervised | 2203.11183 | null | https://arxiv.org/abs/2203.11183v2 | https://arxiv.org/pdf/2203.11183v2.pdf | Masked Discrimination for Self-Supervised Learning on Point Clouds | Masked autoencoding has achieved great success for self-supervised learning in the image and language domains. However, mask based pretraining has yet to show benefits for point cloud understanding, likely due to standard backbones like PointNet being unable to properly handle the training versus testing distribution m... | ['Yong Jae Lee', 'Mu Cai', 'Haotian Liu'] | 2022-03-21 | null | null | null | null | ['3d-shape-retrieval', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 2.66290605e-01 1.07834384e-01 -2.56781340e-01 -3.16553652e-01
-1.10672855e+00 -6.71244979e-01 5.09266138e-01 1.37822568e-01
-1.95601910e-01 2.70340085e-01 -2.29810894e-01 -4.08311993e-01
2.52365261e-01 -9.23969030e-01 -1.35674286e+00 -5.88075340e-01
-8.26723948e-02 9.03328776e-01 4.29671913e-01 1.61944538... | [7.976362705230713, -3.3786840438842773] |
c3e20f00-06f2-438c-861e-f78be0841c9b | learn-from-structural-scope-improving-aspect | 2204.12784 | null | https://arxiv.org/abs/2204.12784v1 | https://arxiv.org/pdf/2204.12784v1.pdf | Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks | Aspect-level sentiment analysis aims to determine the sentiment polarity towards a specific target in a sentence. The main challenge of this task is to effectively model the relation between targets and sentiments so as to filter out noisy opinion words from irrelevant targets. Most recent efforts capture relations thr... | ['Jiawei Peng', 'Ming Cai', 'Jianwang Wu', 'Xiaoxuan Pang', 'Lvxiaowei Xu'] | 2022-04-27 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 6.00668132e-01 5.48987091e-01 -5.86387455e-01 -9.94097710e-01
-9.36841965e-01 -9.09909964e-01 4.29060549e-01 6.59012556e-01
-1.50645360e-01 3.88732612e-01 9.05797064e-01 -3.83467883e-01
3.32398504e-01 -9.86278474e-01 -4.83025014e-01 -2.72700131e-01
2.17242047e-01 2.55497336e-01 -7.24559696e-03 -7.49662101... | [11.474905967712402, 6.7704572677612305] |
a6ffa53e-75ee-47f3-aff4-3d8f2284219f | spatio-temporal-fusion-based-convolutional | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_Spatio-Temporal_Fusion_Based_Convolutional_Sequence_Learning_for_Lip_Reading_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Spatio-Temporal_Fusion_Based_Convolutional_Sequence_Learning_for_Lip_Reading_ICCV_2019_paper.pdf | Spatio-Temporal Fusion Based Convolutional Sequence Learning for Lip Reading | Current state-of-the-art approaches for lip reading are based on sequence-to-sequence architectures that are designed for natural machine translation and audio speech recognition. Hence, these methods do not fully exploit the characteristics of the lip dynamics, causing two main drawbacks. First, the short-range tempor... | [' Shilin Wang', ' Feng Cheng', 'Xingxuan Zhang'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['lipreading'] | ['computer-vision'] | [ 1.09456733e-01 -1.84711769e-01 -3.78110409e-01 -3.26523334e-02
-7.94054985e-01 -2.25983247e-01 5.64782143e-01 -9.84822214e-02
-4.50259477e-01 5.31388283e-01 3.53944182e-01 -9.89037454e-02
2.53936946e-01 -2.65751362e-01 -5.86485982e-01 -7.91593730e-01
2.09404826e-01 -3.65934640e-01 5.74141562e-01 -7.48837888... | [14.327265739440918, 4.98912239074707] |
91df46e9-6909-4a05-a296-7eb1f20f3bdc | online-neural-coreference-resolution-with | null | null | https://aclanthology.org/2022.crac-1.2 | https://aclanthology.org/2022.crac-1.2.pdf | Online Neural Coreference Resolution with Rollback | Humans process natural language online, whether reading a document or participating in multiparty dialogue. Recent advances in neural coreference resolution have focused on offline approaches that assume the full communication history as input. This is neither realistic nor sufficient if we wish to support dialogue und... | ['Benjamin Van Durme', 'Patrick Xia'] | null | null | null | null | coling-crac-2022-10 | ['dialogue-understanding', 'coreference-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.62920594e-01 6.23315156e-01 -2.47930408e-01 -7.72599280e-01
-1.16213000e+00 -1.13905430e+00 1.21982276e+00 1.68355405e-01
-9.25300062e-01 9.06487167e-01 7.07007945e-01 -4.52275336e-01
2.72688251e-02 -3.56936604e-01 -5.38811862e-01 -1.24170013e-01
1.08904898e-01 1.08382452e+00 1.88112795e-01 -3.32299739... | [12.748037338256836, 7.984209060668945] |
fcda4c77-10d1-497b-99ed-6fdfb89f7e50 | counterfactual-vision-and-language-navigation-2 | null | null | http://proceedings.neurips.cc/paper/2020/hash/39016cfe079db1bfb359ca72fcba3fd8-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/39016cfe079db1bfb359ca72fcba3fd8-Paper.pdf | Counterfactual Vision-and-Language Navigation: Unravelling the Unseen | The task of vision-and-language navigation (VLN) requires an agent to follow text instructions to find its way through simulated household environments. A prominent challenge is to train an agent capable of generalising to new environments at test time, rather than one that simply memorises trajectories and visual deta... | ['Anton Van Den Hengel', 'Qinfeng Shi', 'Damien Teney', 'Ehsan Abbasnejad', 'Amin Parvaneh'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['embodied-question-answering'] | ['computer-vision'] | [ 4.76089239e-01 3.66982698e-01 4.39098299e-01 -4.80232328e-01
-5.96445084e-01 -6.87365890e-01 9.91524041e-01 -1.04825415e-01
-6.82144523e-01 1.07846773e+00 3.11588377e-01 -5.84005117e-01
6.78382739e-02 -8.43413293e-01 -1.25965178e+00 -5.58404386e-01
-3.16493005e-01 6.33200407e-01 2.95039937e-02 -3.25026065... | [4.485801696777344, 0.6416134238243103] |
97087fbb-dd3a-4782-ab17-b566bfd4a759 | automatic-and-manual-web-annotations-in-an | null | null | https://aclanthology.org/L18-1384 | https://aclanthology.org/L18-1384.pdf | Automatic and Manual Web Annotations in an Infrastructure to handle Fake News and other Online Media Phenomena | null | ['Julian Moreno-Schneider', 'Peter Bourgonje', 'Georg Rehm'] | 2018-05-01 | automatic-and-manual-web-annotations-in-an-1 | https://aclanthology.org/L18-1384 | https://aclanthology.org/L18-1384.pdf | lrec-2018-5 | ['rumour-detection', 'news-annotation'] | ['natural-language-processing', 'natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.247804164886475, 3.5127599239349365] |
6bdc77a0-094e-493b-8173-1143945c6675 | thematic-context-vector-association-based-on | 2304.01423 | null | https://arxiv.org/abs/2304.01423v1 | https://arxiv.org/pdf/2304.01423v1.pdf | Thematic context vector association based on event uncertainty for Twitter | Keyword extraction is a crucial process in text mining. The extraction of keywords with respective contextual events in Twitter data is a big challenge. The challenging issues are mainly because of the informality in the language used. The use of misspelled words, acronyms, and ambiguous terms causes informality. The e... | ['Parag Kulkarni', 'Swapnil Mane', 'Vaibhav Khatavkar'] | 2023-04-04 | null | null | null | null | ['sarcasm-detection', 'keyword-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.39241582e-02 -2.14468911e-01 -1.30255610e-01 -9.50757861e-02
-5.30077934e-01 -5.27697742e-01 8.77331436e-01 9.99922276e-01
-7.77414203e-01 8.23075593e-01 6.60889804e-01 2.54947115e-02
-4.46098179e-01 -6.03264153e-01 -8.38379487e-02 -6.73742950e-01
1.74560145e-01 2.66995788e-01 1.50284201e-01 -4.14687455... | [10.669188499450684, 7.382303714752197] |
f405f3f2-3163-4aa7-8880-3f13268be368 | 190505700 | 1905.05700 | null | https://arxiv.org/abs/1905.05700v1 | https://arxiv.org/pdf/1905.05700v1.pdf | Learning meters of Arabic and English poems with Recurrent Neural Networks: a step forward for language understanding and synthesis | Recognizing a piece of writing as a poem or prose is usually easy for the majority of people; however, only specialists can determine which meter a poem belongs to. In this paper, we build Recurrent Neural Network (RNN) models that can classify poems according to their meters from plain text. The input text is encoded ... | ['Omar M. Ibrahime', 'Taha M. Madbouly', 'Waleed A. Yousef', 'Moustafa A. Mahmoud'] | 2019-05-07 | null | null | null | null | ['poem-meters-classification'] | ['natural-language-processing'] | [ 1.48627564e-01 -1.97505634e-02 -1.93517238e-01 4.77435924e-02
-5.93093097e-01 -7.55313218e-01 6.83233917e-01 -1.49877921e-01
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2.74783641e-01 6.58020437e-01 -2.56660908e-01 -6.58186018... | [10.877154350280762, 9.99445915222168] |
fdda595e-2d1c-4bc2-b952-4408f6d2017b | data-driven-stochastic-motion-evaluation-and | 2302.05041 | null | https://arxiv.org/abs/2302.05041v1 | https://arxiv.org/pdf/2302.05041v1.pdf | Data-Driven Stochastic Motion Evaluation and Optimization with Image by Spatially-Aligned Temporal Encoding | This paper proposes a probabilistic motion prediction method for long motions. The motion is predicted so that it accomplishes a task from the initial state observed in the given image. While our method evaluates the task achievability by the Energy-Based Model (EBM), previous EBMs are not designed for evaluating the c... | ['Norimichi Ukita', 'Takeru Oba'] | 2023-02-10 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 1.37824640e-01 -3.92650850e-02 -6.43836915e-01 -1.25026211e-01
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-1.19197838e-01 -3.49493921e-01 -6.53039157e-01 -1.01831162e+00
-7.22263604e-02 5.16182650e-03 1.89453363e-01 3.48703444... | [10.710487365722656, -0.9015041589736938] |
0fa00393-6468-4c08-bf57-89d054da438c | speck-a-smart-event-based-vision-sensor-with | 2304.06793 | null | https://arxiv.org/abs/2304.06793v1 | https://arxiv.org/pdf/2304.06793v1.pdf | Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline | Edge computing solutions that enable the extraction of high level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smart devices that require sensory processing for their application on the edge. To tackle this problem, we present a smart vision sensor System... | ['Ning Qiao', 'Tugba Demirci', 'Sadique Sheik', 'Qian Liu', 'Yudi Ren', 'Roberto Cattaneo', 'Merkourios Katsimpris', 'Carsten Nielsen', 'Michele De Marchi', 'Yannan Xing', 'Ole Richter'] | 2023-04-13 | null | null | null | null | ['event-based-vision', 'edge-computing'] | ['computer-vision', 'time-series'] | [ 5.59865475e-01 5.25172763e-02 6.11068964e-01 -1.56614497e-01
3.78096290e-02 -2.61423647e-01 4.38934624e-01 3.26439202e-01
-7.77563453e-01 3.72131914e-01 -1.49267107e-01 2.51057088e-01
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1.85322419e-01 -4.90803644e-02 7.25623310e-01 1.34166375... | [8.22390365600586, 2.3703548908233643] |
51401a43-f8ea-46d9-91d8-180dcb8309e2 | local-search-for-integer-linear-programming | 2305.00188 | null | https://arxiv.org/abs/2305.00188v3 | https://arxiv.org/pdf/2305.00188v3.pdf | New Characterizations and Efficient Local Search for General Integer Linear Programming | Integer linear programming (ILP) models a wide range of practical combinatorial optimization problems and has significant impacts in industry and management sectors. This work proposes new characterizations of ILP with the concept of boundary solutions. Motivated by the new characterizations, we develop an efficient lo... | ['JinKun Lin', 'Mengchuan Zou', 'Shaowei Cai', 'Peng Lin'] | 2023-04-29 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 8.25237408e-02 1.17558941e-01 -8.81209195e-01 -5.05974889e-02
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-8.16423357e-01 -9.40541029e-01 -7.96911240e-01 -8.33445787e-01
-1.25037879e-01 9.18669581e-01 1.43144354e-01 -7.03304484... | [5.220143795013428, 2.9553050994873047] |
c9a5613d-115b-4885-972a-c1ea69af9224 | singapore-soundscape-site-selection-survey-s5 | 2206.03112 | null | https://arxiv.org/abs/2206.03112v1 | https://arxiv.org/pdf/2206.03112v1.pdf | Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering | The ecological validity of soundscape studies usually rests on a choice of soundscapes that are representative of the perceptual space under investigation. For example, a soundscape pleasantness study might investigate locations with soundscapes ranging from "pleasant" to "annoying". The choice of soundscapes is typica... | ['Woon-Seng Gan', 'Zhen-Ting Ong', 'Karn N. Watcharasupat', 'Joo Young Hong', 'Bhan Lam', 'Kenneth Ooi'] | 2022-06-07 | null | null | null | null | ['unsupervised-spatial-clustering'] | ['time-series'] | [-2.09284127e-01 -7.45698869e-01 4.01710749e-01 -6.18011132e-02
-8.90272856e-01 -8.45100403e-01 1.23716936e-01 6.93654895e-01
-6.92311406e-01 1.96953610e-01 6.99513853e-01 -3.72547477e-01
-6.82081461e-01 -5.41227698e-01 -1.40164837e-01 -5.02794564e-01
-9.56135914e-02 -3.59077215e-01 -1.95631981e-01 2.08060965... | [15.141338348388672, 5.56298828125] |
4bb8891c-c110-49f4-b14e-39cdbd544d9c | which-contrast-does-matter-towards-a-deep | 1905.04105 | null | https://arxiv.org/abs/1905.04105v1 | https://arxiv.org/pdf/1905.04105v1.pdf | Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN | Thanks to the recent success of generative adversarial network (GAN) for image synthesis, there are many exciting GAN approaches that successfully synthesize MR image contrast from other images with different contrasts. These approaches are potentially important for image imputation problems, where complete set of data... | ['Won-Jin Moon', 'Dongwook Lee', 'Jong Chul Ye'] | 2019-05-10 | null | null | null | null | ['image-imputation'] | ['computer-vision'] | [ 5.99697053e-01 1.81320667e-01 1.46169037e-01 -2.56654412e-01
-7.54045069e-01 -4.84254599e-01 3.56838495e-01 -4.27263558e-01
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-1.27416953e-01 -6.86350167e-01 -8.02456498e-01 -1.19768798e+00
1.84714068e-02 3.61993015e-01 4.00354564e-02 -2.86199778... | [13.958343505859375, -2.100517988204956] |
d2472a6e-dfe3-4293-b334-84abda3b6039 | distributional-reinforcement-learning-with-4 | 2106.03228 | null | https://arxiv.org/abs/2106.03228v3 | https://arxiv.org/pdf/2106.03228v3.pdf | Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks | The distributional reinforcement learning (RL) approach advocates for representing the complete probability distribution of the random return instead of only modelling its expectation. A distributional RL algorithm may be characterised by two main components, namely the representation of the distribution together with ... | ['Damien Ernst', 'Gilles Louppe', 'Adrien Bolland', 'Antoine Wehenkel', 'Thibaut Théate'] | 2021-06-06 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.08101392e-01 1.63645327e-01 -9.67196897e-02 -3.68988752e-01
-6.06271744e-01 -4.04903442e-01 7.17005312e-01 3.95058244e-01
-8.35439682e-01 9.54356194e-01 6.57853782e-02 -2.71103889e-01
-8.47571015e-01 -1.01945066e+00 -4.66005147e-01 -8.81127596e-01
-2.67940670e-01 6.10863984e-01 -6.23955727e-02 -1.92641914... | [4.125988483428955, 2.6053431034088135] |
8969c80f-80ec-4b4a-9e27-b6a76087dd03 | stereo-matching-with-color-and-monochrome | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Jeon_Stereo_Matching_With_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Jeon_Stereo_Matching_With_CVPR_2016_paper.pdf | Stereo Matching With Color and Monochrome Cameras in Low-Light Conditions | Consumer devices with stereo cameras have become popular because of their low-cost depth sensing capability. However, those systems usually suffer from low imaging quality and inaccurate depth acquisition under low-light conditions. To address the problem, we present a new stereo matching method with a color and monoch... | ['Joon-Young Lee', 'Hae-Gon Jeon', 'In So Kweon', 'Hyowon Ha', 'Sunghoon Im'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['stereo-matching'] | ['computer-vision'] | [ 5.62539279e-01 -5.56861162e-01 -1.34794684e-02 -3.81071597e-01
-4.81009126e-01 -3.18052620e-01 3.14718425e-01 -4.15286005e-01
-4.29265320e-01 6.43848121e-01 3.94650511e-02 8.52258950e-02
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7.27975190e-01 -3.75983771e-03 5.31040609e-01 8.84647220... | [9.286445617675781, -2.5661263465881348] |
6bae08fb-13ed-4fd1-91a0-e7c68bc73ec6 | mutual-mean-teaching-pseudo-label-refinery-1 | 2001.01526 | null | https://arxiv.org/abs/2001.01526v2 | https://arxiv.org/pdf/2001.01526v2.pdf | Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification | Person re-identification (re-ID) aims at identifying the same persons' images across different cameras. However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one. State-of-the-art unsupervised domain adaptation methods for person ... | ['Dapeng Chen', 'Yixiao Ge', 'Hongsheng Li'] | 2020-01-06 | null | https://openreview.net/forum?id=rJlnOhVYPS | https://openreview.net/pdf?id=rJlnOhVYPS | iclr-2020-1 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 4.84830067e-02 -1.42767489e-01 -1.09029435e-01 -6.79868817e-01
-7.85883784e-01 -4.46026385e-01 6.18907511e-01 -1.75998867e-01
-6.37800515e-01 8.94730926e-01 1.00099497e-01 3.34202021e-01
-1.60226837e-01 -4.69345987e-01 -5.22849381e-01 -6.98710859e-01
3.69147509e-01 7.75814474e-01 -1.19356692e-01 -3.02325916... | [14.796370506286621, 1.0752228498458862] |
70ae81dc-07c3-4d63-bc5d-e4dc5efd8985 | lepard-learning-partial-point-cloud-matching | 2111.12591 | null | https://arxiv.org/abs/2111.12591v2 | https://arxiv.org/pdf/2111.12591v2.pdf | Lepard: Learning partial point cloud matching in rigid and deformable scenes | We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes. The key characteristics are the following techniques that exploit 3D positional knowledge for point cloud matching: 1) An architecture that disentangles point cloud representation into feature space and 3D posi... | ['Tatsuya Harada', 'Yang Li'] | 2021-11-24 | lepard-learning-partial-point-cloud-matching-1 | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-feature-matching', '3d-point-cloud-matching', 'partial-point-cloud-matching'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.43979111e-01 -3.08420956e-01 -1.84070528e-01 -2.72769421e-01
-1.09744298e+00 -8.03265452e-01 8.17518353e-01 1.24433741e-01
-4.40599978e-01 1.27875403e-01 1.30873203e-01 -1.44235007e-02
-2.77858108e-01 -6.39451742e-01 -8.64367783e-01 -5.67869961e-01
-2.53171414e-01 1.16047490e+00 3.32054049e-01 -3.26118439... | [7.724968910217285, -2.8950247764587402] |
56f36a20-d25b-4fd2-9b7e-a3ac03c61344 | batch-monte-carlo-tree-search | 2104.04278 | null | https://arxiv.org/abs/2104.04278v1 | https://arxiv.org/pdf/2104.04278v1.pdf | Batch Monte Carlo Tree Search | Making inferences with a deep neural network on a batch of states is much faster with a GPU than making inferences on one state after another. We build on this property to propose Monte Carlo Tree Search algorithms using batched inferences. Instead of using either a search tree or a transposition table we propose to us... | ['Tristan Cazenave'] | 2021-04-09 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-1.67498097e-01 2.24510223e-01 1.06016316e-01 -2.97520280e-01
-4.29792643e-01 -4.54705238e-01 6.41374588e-01 2.17052460e-01
-9.68105674e-01 1.01041436e+00 -1.41325593e-01 -8.10703456e-01
-3.24109048e-01 -1.35312653e+00 -8.62333000e-01 -6.84317172e-01
-1.19229004e-01 9.16104615e-01 4.86945599e-01 -6.23867698... | [3.6497156620025635, 1.616476058959961] |
db778b84-f23d-414a-afd9-d06a8e8a84bf | dwrseg-dilation-wise-residual-network-for | 2212.01173 | null | https://arxiv.org/abs/2212.01173v1 | https://arxiv.org/pdf/2212.01173v1.pdf | DWRSeg: Dilation-wise Residual Network for Real-time Semantic Segmentation | Real-time semantic segmentation has played an important role in intelligent vehicle scenarios. Recently, numerous networks have incorporated information from multi-size receptive fields to facilitate feature extraction in real-time semantic segmentation tasks. However, these methods preferentially adopt massive recepti... | ['Xiangyang Xu', 'Yaping Dai', 'Zhongjian Dai', 'Shouchun Xu', 'Xu Liu', 'Haoran Wei'] | 2022-12-02 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 1.95411175e-01 7.69788772e-02 6.08520880e-02 -4.94584471e-01
-6.29637837e-01 -3.77192199e-01 4.48010981e-01 -2.68181026e-01
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2.17926562e-01 -8.83035064e-01 -8.14805269e-01 -7.91765869e-01
1.06997155e-01 1.55220166e-01 7.65731692e-01 -2.52994657... | [9.211313247680664, -0.5522753000259399] |
78a08103-c6fd-4d02-909a-d7b0c38cf957 | jpg-jointly-learn-to-align-automated-disease | null | null | https://aclanthology.org/2022.coling-1.523 | https://aclanthology.org/2022.coling-1.523.pdf | JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation | Automated radiology report generation aims to generate paragraphs that describe fine-grained visual differences among cases, especially those between the normal and the diseased. Existing methods seldom consider the cross-modal alignment between textual and visual features and tend to ignore disease tags as an auxiliar... | ['Kenji Suzuki', 'Manabu Okumura', 'Dongyuan Li', 'Jingyi You'] | null | null | null | null | coling-2022-10 | ['medical-report-generation', 'disease-prediction'] | ['medical', 'medical'] | [ 3.44416916e-01 2.21720859e-01 -2.94529796e-01 -2.65852422e-01
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9.38555971e-02 4.14236218e-01 -1.80069461e-01 1.55213684... | [15.037494659423828, -1.403349757194519] |
be9c0636-64c5-48aa-ab2c-07cd91a7efec | adversarial-samples-for-deep-monocular-6d | 2203.00302 | null | https://arxiv.org/abs/2203.00302v2 | https://arxiv.org/pdf/2203.00302v2.pdf | Adversarial samples for deep monocular 6D object pose estimation | Estimating 6D object pose from an RGB image is important for many real-world applications such as autonomous driving and robotic grasping. Recent deep learning models have achieved significant progress on this task but their robustness received little research attention. In this work, for the first time, we study adver... | ['Jihong Zhu', 'Hao Wang', 'Shuang Liang', 'Weiming Li', 'Jinlai Zhang'] | 2022-03-01 | null | null | null | null | ['6d-pose-estimation', 'robotic-grasping'] | ['computer-vision', 'robots'] | [-2.31990702e-02 1.42505877e-02 2.00410932e-01 -3.17523003e-01
-8.78226519e-01 -9.66381848e-01 3.99504006e-01 -3.27283531e-01
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1.44991530e-02 3.60856652e-01 1.78680107e-01 -1.39455229... | [7.707076072692871, -4.462747573852539] |
9e53acca-a586-4103-b330-f9b6bc3802d7 | diagnostic-spatio-temporal-transformer-with | 2305.17149 | null | https://arxiv.org/abs/2305.17149v1 | https://arxiv.org/pdf/2305.17149v1.pdf | Diagnostic Spatio-temporal Transformer with Faithful Encoding | This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency. The key technical challenge is to extract actionable insights from the dependency tensor characterizing high-order interactions among temporal and spatial indices. We formalize t... | ['Xabier De Carlos', 'Ekhi Zugasti', 'Pin-Yu Chen', 'Tsuyoshi Idé', 'Jokin Labaien'] | 2023-05-26 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 2.58490860e-01 -4.14361060e-01 4.38424870e-02 -1.11211948e-01
-3.31052721e-01 -5.83979964e-01 3.86727631e-01 1.79782882e-01
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-1.18955982e+00 -5.79925060e-01 -3.99280638e-01 -9.45124924e-01
-1.16164839e+00 1.72735468e-01 3.04329395e-01 -3.62940371... | [7.261014461517334, 2.908790349960327] |
65253701-b384-472c-b5ee-9b41769421e6 | expressing-high-level-scientific-claims-with | 2109.12907 | null | https://arxiv.org/abs/2109.12907v3 | https://arxiv.org/pdf/2109.12907v3.pdf | Expressing High-Level Scientific Claims with Formal Semantics | The use of semantic technologies is gaining significant traction in science communication with a wide array of applications in disciplines including the Life Sciences, Computer Science, and the Social Sciences. Languages like RDF, OWL, and other formalisms based on formal logic are applied to make scientific knowledge ... | ['Jacco van Ossenbruggen', 'Davide Ceolin', 'Tobias Kuhn', 'Cristina-Iulia Bucur'] | 2021-09-27 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 1.13764383e-01 6.44635916e-01 -2.11418360e-01 -3.50918323e-01
-5.51591367e-02 -6.69776320e-01 8.24603021e-01 6.84597254e-01
-3.29351902e-01 7.99654365e-01 1.04527138e-01 -6.49040341e-01
-6.05740607e-01 -1.12613940e+00 -6.08184338e-01 -8.94751474e-02
3.53696406e-01 5.74549317e-01 6.74865067e-01 -2.49704450... | [9.162849426269531, 7.748493194580078] |
fccb1324-6a8c-4dd6-bb96-334473eea562 | planning-irregular-object-packing-via | 2211.09382 | null | https://arxiv.org/abs/2211.09382v1 | https://arxiv.org/pdf/2211.09382v1.pdf | Planning Irregular Object Packing via Hierarchical Reinforcement Learning | Object packing by autonomous robots is an im-portant challenge in warehouses and logistics industry. Most conventional data-driven packing planning approaches focus on regular cuboid packing, which are usually heuristic and limit the practical use in realistic applications with everyday objects. In this paper, we propo... | ['Jiwen Lu', 'Jie zhou', 'Ziwei Wang', 'Sichao Huang'] | 2022-11-17 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-4.22203213e-01 4.76537526e-01 -1.89495087e-01 -2.70440936e-01
-3.16519896e-03 -4.95155245e-01 -2.12248921e-01 6.02309465e-01
-2.98807055e-01 9.35597539e-01 -1.94036111e-01 -1.65824771e-01
-4.51207817e-01 -1.18795550e+00 -1.22912133e+00 -8.14834118e-01
-6.88802719e-01 1.49748445e+00 2.79951602e-01 -3.97479296... | [4.9604878425598145, 2.6645185947418213] |
5f806eba-2ddf-4d97-9895-340b5a7afcff | mulco-recognizing-chinese-nested-named | 2211.10854 | null | https://arxiv.org/abs/2211.10854v1 | https://arxiv.org/pdf/2211.10854v1.pdf | Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopes | Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition. NNER is where one entity may be part of a longer entity, and this may happen on multiple levels, as the term nested suggests. These nested structures make traditional sequence label... | ['Yu Xu', 'Di Niu', 'Jerry Chen', 'Weidong Guo', 'Jinwen Luo', 'Jiuding Yang'] | 2022-11-20 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-1.78372547e-01 -2.46537685e-01 -1.85753569e-01 -3.54490161e-01
-5.94307661e-01 -7.86733806e-01 5.12787960e-02 -1.01460122e-01
-6.68533623e-01 9.20806766e-01 5.64552128e-01 -3.90670687e-01
3.83877814e-01 -7.62061000e-01 -6.66841626e-01 -2.80203432e-01
-1.06436655e-01 2.65761942e-01 4.86393213e-01 -2.13773578... | [9.6849365234375, 9.618857383728027] |
41e2264f-f518-4456-9086-2157698020b6 | a-near-optimal-algorithm-for-bilevel | 2302.08766 | null | https://arxiv.org/abs/2302.08766v2 | https://arxiv.org/pdf/2302.08766v2.pdf | A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization | Bilevel optimization problems, which are problems where two optimization problems are nested, have more and more applications in machine learning. In many practical cases, the upper and the lower objectives correspond to empirical risk minimization problems and therefore have a sum structure. In this context, we propos... | ['Pierre Ablin', 'Samuel Vaiter', 'Thomas Moreau', 'Mathieu Dagréou'] | 2023-02-17 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-1.25653908e-01 1.29476279e-01 -1.12037174e-01 -2.27641955e-01
-1.18865740e+00 -5.58222830e-01 3.90720107e-02 2.59958506e-01
-7.73232698e-01 8.29409957e-01 -4.84281749e-01 -6.34723246e-01
-5.88071823e-01 -6.62177324e-01 -8.11170578e-01 -9.29581642e-01
-3.48264873e-01 5.42786062e-01 -1.85885698e-01 4.55695699... | [6.554422855377197, 4.477138996124268] |
8590188d-6ba7-4cd8-b49f-ae3d14a15f30 | learning-self-game-play-agents-for | 1903.03674 | null | https://arxiv.org/abs/1903.03674v2 | https://arxiv.org/pdf/1903.03674v2.pdf | Learning Self-Game-Play Agents for Combinatorial Optimization Problems | Recent progress in reinforcement learning (RL) using self-game-play has shown remarkable performance on several board games (e.g., Chess and Go) as well as video games (e.g., Atari games and Dota2). It is plausible to consider that RL, starting from zero knowledge, might be able to gradually approximate a winning strat... | ['Karl Lieberherr', 'Ruiyang Xu'] | 2019-03-08 | null | null | null | null | ['board-games'] | ['playing-games'] | [-4.82586920e-02 5.46514392e-01 -1.99750662e-01 4.38691080e-02
-9.20297503e-01 -5.70173740e-01 5.14328480e-01 -1.33938476e-01
-5.90071201e-01 1.15133190e+00 6.75961897e-02 -6.38695896e-01
-3.22178155e-01 -1.31331038e+00 -9.19538200e-01 -5.20859003e-01
-2.30192259e-01 7.44742155e-01 3.51182640e-01 -7.81525254... | [3.6504180431365967, 1.5296553373336792] |
9bf461ca-d555-41c4-a126-7a1d84db1f77 | biomedical-interpretable-entity | 2106.09502 | null | https://arxiv.org/abs/2106.09502v1 | https://arxiv.org/pdf/2106.09502v1.pdf | Biomedical Interpretable Entity Representations | Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable. This can be a barrier to model uptake in important domains such as biomedicine. There has been recent work on general interpretable repre... | ['Kush R. Varshney', 'Byron C. Wallace', 'Joydeep Ghosh', 'Ioana Baldini', 'Yasumasa Onoe', 'Diego Garcia-Olano'] | 2021-06-17 | null | https://aclanthology.org/2021.findings-acl.311 | https://aclanthology.org/2021.findings-acl.311.pdf | findings-acl-2021-8 | ['entity-disambiguation'] | ['natural-language-processing'] | [ 3.22523415e-01 9.62674797e-01 -4.04188901e-01 -4.96787846e-01
-6.80558801e-01 -4.27418321e-01 2.72423178e-01 6.97557330e-01
-4.82125819e-01 1.18822145e+00 6.56057417e-01 -5.29677868e-01
-2.05368355e-01 -7.62881398e-01 -8.71708572e-01 -2.95052409e-01
-1.25966594e-01 1.15040767e+00 -4.42483902e-01 -1.57952964... | [8.57308578491211, 8.707049369812012] |
9a52988f-0f5e-4fd9-b91f-8c9657ee3d65 | coupled-learning-for-facial-deblur | 1904.08671 | null | http://arxiv.org/abs/1904.08671v1 | http://arxiv.org/pdf/1904.08671v1.pdf | Coupled Learning for Facial Deblur | Blur in facial images significantly impedes the efficiency of recognition
approaches. However, most existing blind deconvolution methods cannot generate
satisfactory results due to their dependence on strong edges, which are
sufficient in natural images but not in facial images. In this paper, we
represent point spread... | ['DaCheng Tao', 'Dayong Tian'] | 2019-04-18 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 1.27289966e-01 -6.08927429e-01 3.82725775e-01 -3.62657636e-01
-4.67223436e-01 -4.49360311e-01 3.25089157e-01 -9.41765249e-01
-1.43505007e-01 8.10514271e-01 1.97080538e-01 1.31487280e-01
-3.55183691e-01 -4.18605238e-01 -4.20025021e-01 -1.14680469e+00
2.54922271e-01 -1.58185996e-02 -1.07877441e-01 -3.09331659... | [12.887382507324219, 0.16406957805156708] |
ebadb889-16f7-4054-9e0e-c8ffa9880e23 | contrastive-attention-mechanism-for | 1910.13114 | null | https://arxiv.org/abs/1910.13114v2 | https://arxiv.org/pdf/1910.13114v2.pdf | Contrastive Attention Mechanism for Abstractive Sentence Summarization | We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence. The proposed contrastive attention mechanism accommodates two categories of attention: one is the conventional attenti... | ['Yue Zhang', 'Mingming Yin', 'Weihua Luo', 'Xiangyu Duan', 'Min Zhang', 'Hoongfei Yu'] | 2019-10-29 | contrastive-attention-mechanism-for-1 | https://aclanthology.org/D19-1301 | https://aclanthology.org/D19-1301.pdf | ijcnlp-2019-11 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 5.38344383e-01 5.44730246e-01 7.72298723e-02 -3.05430591e-01
-8.04956377e-01 -2.54238188e-01 5.89613974e-01 4.47129935e-01
-4.69941676e-01 8.45790029e-01 9.00085151e-01 -2.10921139e-01
3.59717846e-01 -5.09616852e-01 -7.00007558e-01 -5.90462744e-01
2.70559072e-01 1.62963286e-01 2.72370338e-01 -5.50446212... | [12.502352714538574, 9.456235885620117] |
f9fc0e79-ab60-4767-be90-99040ddbeb27 | stag-a-stable-fiducial-marker-system | 1707.06292 | null | https://arxiv.org/abs/1707.06292v2 | https://arxiv.org/pdf/1707.06292v2.pdf | STag: A Stable Fiducial Marker System | Fiducial markers provide better-defined features than the ones naturally available in the scene. For this reason, they are widely utilized in computer vision applications where reliable pose estimation is required. Factors such as imaging noise and subtle changes in illumination induce jitter on the estimated pose. Jit... | ['Cuneyt Akinlar', 'Burak Benligiray', 'Cihan Topal'] | 2017-07-19 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.39094010e-01 -3.18785727e-01 -1.93896014e-02 1.05409756e-01
-4.39137310e-01 -8.59890461e-01 5.10171473e-01 -1.43075615e-01
-3.50753069e-01 5.42363048e-01 -3.35018516e-01 3.19720022e-02
1.25638261e-01 -3.68481539e-02 -4.94433582e-01 -7.18490660e-01
-7.62397423e-02 -1.28975864e-02 4.03039604e-01 1.03316158... | [7.874627113342285, -2.1310338973999023] |
9fbf2505-0443-4b4e-b5b7-741975c70ee7 | automatic-correction-of-human-translations | 2206.08593 | null | https://arxiv.org/abs/2206.08593v1 | https://arxiv.org/pdf/2206.08593v1.pdf | Automatic Correction of Human Translations | We introduce translation error correction (TEC), the task of automatically correcting human-generated translations. Imperfections in machine translations (MT) have long motivated systems for improving translations post-hoc with automatic post-editing. In contrast, little attention has been devoted to the problem of aut... | ['John DeNero', 'Joern Wuebker', 'Aditya Shastry', 'Geza Kovacs', 'Jessy Lin'] | 2022-06-17 | null | https://aclanthology.org/2022.naacl-main.36 | https://aclanthology.org/2022.naacl-main.36.pdf | naacl-2022-7 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 5.50807536e-01 3.30902547e-01 -3.78895253e-02 -5.85325420e-01
-1.29400527e+00 -8.11245143e-01 7.36267090e-01 -9.42719169e-03
-4.94584948e-01 1.08103800e+00 4.04598087e-01 -8.35705340e-01
4.54167038e-01 -1.98888227e-01 -8.74565005e-01 3.26727957e-01
6.62872553e-01 1.05922031e+00 -2.05278352e-01 -8.29239368... | [11.590827941894531, 10.27652645111084] |
cd1f66c9-a371-486e-b76f-924a09a1ec6f | causal-identification-under-markov | 1812.06209 | null | http://arxiv.org/abs/1812.06209v1 | http://arxiv.org/pdf/1812.06209v1.pdf | Causal Identification under Markov Equivalence | Assessing the magnitude of cause-and-effect relations is one of the central
challenges found throughout the empirical sciences. The problem of
identification of causal effects is concerned with determining whether a causal
effect can be computed from a combination of observational data and substantive
knowledge about t... | ['Amin Jaber', 'Jiji Zhang', 'Elias Bareinboim'] | 2018-12-15 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 5.93671978e-01 1.98183358e-01 -6.58875585e-01 -3.43393713e-01
-3.22091669e-01 -7.58113801e-01 8.97291839e-01 5.95108390e-01
1.15354948e-01 9.68418658e-01 4.59231794e-01 -6.89937949e-01
-8.59355509e-01 -1.02915537e+00 -9.45313275e-01 -7.35339761e-01
-5.31456709e-01 3.94468069e-01 1.07363954e-01 7.08618462... | [7.861738681793213, 5.372500896453857] |
26a69ee7-3a93-4d8f-96ca-6fde186492cd | first-go-then-post-explore-the-benefits-of | 2212.03251 | null | https://arxiv.org/abs/2212.03251v2 | https://arxiv.org/pdf/2212.03251v2.pdf | First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation | Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that successful exploration requires an agent to first return to an interesting state ('Go'), and only then explore into unknown terrain ('Explore'). We refer to such expl... | ['Aske Plaat', 'Mike Preuss', 'Thomas M. Moerland', 'Zhao Yang'] | 2022-12-06 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-7.25600868e-02 2.56572962e-01 -1.27142072e-01 -2.89327390e-02
-8.72529328e-01 -7.29246080e-01 4.16760564e-01 2.84152497e-02
-6.89869106e-01 1.38358104e+00 1.74699165e-02 -5.70770264e-01
-4.63751853e-01 -8.69345307e-01 -6.95325553e-01 -8.33476305e-01
-8.59837115e-01 6.30927444e-01 1.49788782e-01 -6.66651964... | [3.9809482097625732, 1.7398675680160522] |
54fd0cd7-d29a-48b4-9bd2-8ef4f97e4a90 | compound-figure-separation-of-biomedical-1 | 2208.14357 | null | https://arxiv.org/abs/2208.14357v1 | https://arxiv.org/pdf/2208.14357v1.pdf | Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning | With the rapid development of self-supervised learning (e.g., contrastive learning), the importance of having large-scale images (even without annotations) for training a more generalizable AI model has been widely recognized in medical image analysis. However, collecting large-scale task-specific unannotated data at s... | ['Yuankai Huo', 'Catie Chang', 'Haichun Yang', 'Bennett A. Landman', 'Agnes B. Fogo', 'Mengyang Zhao', 'Shunxing Bao', 'Zuhayr Asad', 'Aadarsh Jha', 'Jiachen Xu', 'Yuanhan Tian', 'Ruining Deng', 'Quan Liu', 'Jun Long', 'Chang Qu', 'Tianyuan Yao'] | 2022-08-30 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.05507922e-01 2.49283776e-01 -2.37301871e-01 -3.40614498e-01
-1.18894625e+00 -6.49453938e-01 2.10069925e-01 3.41853261e-01
-5.03712118e-01 4.72854942e-01 -3.77456635e-01 -5.14273882e-01
6.37457520e-02 -5.32457232e-01 -1.10761940e+00 -5.94205618e-01
-4.67780866e-02 3.66611511e-01 2.08717704e-01 -4.53007072... | [15.004556655883789, -2.6381890773773193] |
68e0b27a-f256-4020-a8b4-5b05556a4768 | delta-keyword-transformer-bringing | 2204.03479 | null | https://arxiv.org/abs/2204.03479v1 | https://arxiv.org/pdf/2204.03479v1.pdf | Delta Keyword Transformer: Bringing Transformers to the Edge through Dynamically Pruned Multi-Head Self-Attention | Multi-head self-attention forms the core of Transformer networks. However, their quadratically growing complexity with respect to the input sequence length impedes their deployment on resource-constrained edge devices. We address this challenge by proposing a dynamic pruning method, which exploits the temporal stabilit... | ['Marian Verhelst', 'Zuzana Jelčicová'] | 2022-03-20 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.85526416e-01 -3.96701694e-03 -3.34684938e-01 -4.99181859e-02
-8.48828733e-01 -3.56824607e-01 2.25825191e-01 3.45940232e-01
-6.97935283e-01 3.71758610e-01 6.52143732e-02 -8.53226662e-01
8.55354667e-02 -7.06897438e-01 -7.09090471e-01 -4.06083226e-01
-5.95529266e-02 1.95342466e-01 4.64321285e-01 1.18420936... | [8.701839447021484, 3.493722677230835] |
33e6ea0b-8d11-4dd6-8d7c-c34fa117ef73 | large-language-models-are-frame-level | 2305.14330 | null | https://arxiv.org/abs/2305.14330v2 | https://arxiv.org/pdf/2305.14330v2.pdf | Large Language Models are Frame-level Directors for Zero-shot Text-to-Video Generation | In the paradigm of AI-generated content (AIGC), there has been increasing attention in extending pre-trained text-to-image (T2I) models to text-to-video (T2V) generation. Despite their effectiveness, these frameworks face challenges in maintaining consistent narratives and handling rapid shifts in scene composition or ... | ['Seungryong Kim', 'Heeseong Shin', 'Sunghwan Hong', 'Junyoung Seo', 'Susung Hong'] | 2023-05-23 | null | null | null | null | ['video-generation', 'zero-shot-text-to-video-generation', 'text-to-video-generation'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.31982756e-01 7.41353408e-02 -1.45449415e-01 -3.35398197e-01
-8.45869720e-01 -3.73661965e-01 9.33096588e-01 -2.03025341e-01
-1.00713268e-01 6.32299542e-01 5.41490316e-01 -5.49957231e-02
4.12480831e-01 -4.87348348e-01 -1.11628878e+00 -2.91386753e-01
3.29858810e-01 -1.27319202e-01 2.66677022e-01 -4.08873074... | [10.84661865234375, -0.3382839560508728] |
f8e7147a-242f-45e9-920e-34da3b477d4d | online-data-selection-for-federated-learning | 2209.00195 | null | https://arxiv.org/abs/2209.00195v4 | https://arxiv.org/pdf/2209.00195v4.pdf | To Store or Not? Online Data Selection for Federated Learning with Limited Storage | Machine learning models have been deployed in mobile networks to deal with massive data from different layers to enable automated network management and intelligence on devices. To overcome high communication cost and severe privacy concerns of centralized machine learning, federated learning (FL) has been proposed to ... | ['Fan Wu', 'Guihai Chen', 'Yunfeng Shao', 'Bingshuai Li', 'Zhenzhe Zheng', 'Chen Gong'] | 2022-09-01 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 8.24073926e-02 3.33450884e-02 -6.19670570e-01 -6.09187782e-01
-7.26031184e-01 -3.57450694e-01 -1.10497996e-01 5.56386821e-02
-2.57414967e-01 9.23231065e-01 -6.03093505e-01 -4.32013541e-01
-7.26835549e-01 -8.15674782e-01 -7.75909245e-01 -7.56694317e-01
-1.85743853e-01 3.25206697e-01 -1.13518976e-01 3.56196165... | [5.946731090545654, 6.1466498374938965] |
b3654006-0c9e-433d-92eb-73695f61a1f8 | ecnu-at-semeval-2016-task-3-exploring | null | null | https://aclanthology.org/S16-1135 | https://aclanthology.org/S16-1135.pdf | ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering | null | ['Man Lan', 'Guoshun Wu'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['question-similarity'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.287500858306885, 3.6192221641540527] |
7132c428-048c-4283-9d4b-67ece33ab750 | an-algebraic-framework-for-stock-flow | 2211.01290 | null | https://arxiv.org/abs/2211.01290v3 | https://arxiv.org/pdf/2211.01290v3.pdf | A Categorical Framework for Modeling with Stock and Flow Diagrams | Stock and flow diagrams are already an important tool in epidemiology, but category theory lets us go further and treat these diagrams as mathematical entities in their own right. In this chapter we use communicable disease models created with our software, StockFlow.jl, to explain the benefits of the categorical appro... | ['Eric Redekopp', 'Nathaniel D. Osgood', 'Xiaoyan Li', 'John C. Baez', 'Sophie Libkind'] | 2022-11-01 | null | null | null | null | ['epidemiology'] | ['medical'] | [-2.93105811e-01 5.55958807e-01 -1.84920177e-01 -3.07425112e-01
2.93899506e-01 -6.00106239e-01 9.10037518e-01 6.84880674e-01
1.41976699e-01 4.47177976e-01 5.73412120e-01 -1.09038472e+00
-7.88802624e-01 -1.00236142e+00 -4.59183343e-02 -1.24673031e-01
-7.70176053e-01 3.25475901e-01 3.49256009e-01 -3.17095101... | [7.811157703399658, 5.375862121582031] |
7b4146de-72e6-4d50-a1a7-70f258bdabe9 | multi-objective-design-of-multilayer | 2101.10858 | null | https://arxiv.org/abs/2101.10858v1 | https://arxiv.org/pdf/2101.10858v1.pdf | Multi-objective design of multilayer microwave dielectric filters using artificial bee colony algorithm | Artificial bee colony algorithm (ABC) developed by inspiring the foraging phenomena of the natural honey bees is a simple and powerful metaheuristic optimization algorithm. The performance of single objective ABC performance has been well demonstrated by implemented to different design optimization problems from elec-t... | ['Abdurrahim Toktas'] | 2021-01-22 | null | null | null | null | ['metaheuristic-optimization', 'electrical-engineering'] | ['methodology', 'miscellaneous'] | [-1.09210461e-01 -2.79922128e-01 4.49746072e-01 -7.83807263e-02
-1.57101348e-01 -1.97681189e-01 4.04760502e-02 -3.16361576e-01
-3.01308125e-01 1.19179678e+00 -8.12006555e-03 -2.19438925e-01
-1.45560312e+00 -1.08654773e+00 -2.82530934e-01 -1.23912489e+00
-1.15762331e-01 4.77016538e-01 -3.26247901e-01 -2.92053789... | [5.701632499694824, 3.4871819019317627] |
32873f79-e175-4b24-827c-e93705d4ea8c | uper-boosting-multi-document-summarization | null | null | https://aclanthology.org/2022.coling-1.550 | https://aclanthology.org/2022.coling-1.550.pdf | UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor | Multi-Document Summarization (MDS) commonly employs the 2-stage extract-then-abstract paradigm, which first extracts a relatively short meta-document, then feeds it into the deep neural networks to generate an abstract. Previous work usually takes the ROUGE score as the label for training a scoring model to evaluate so... | ['Jian-Yun Nie', 'Lei Hou', 'Juanzi Li', 'Fangwei Zhu', 'Jifan Yu', 'Shangqing Tu'] | null | null | null | null | coling-2022-10 | ['document-summarization'] | ['natural-language-processing'] | [ 2.66229689e-01 2.63556570e-01 -3.39819759e-01 -3.32584977e-01
-1.03260255e+00 -6.11007392e-01 7.88548827e-01 4.58573997e-01
-4.61213410e-01 5.65208018e-01 7.96572328e-01 -2.20868677e-01
1.20584734e-01 -6.25586212e-01 -6.45500481e-01 -3.89437020e-01
2.21417889e-01 4.40724880e-01 5.26635833e-02 -2.07085162... | [12.18823528289795, 9.216728210449219] |
be028a5e-ae7f-4d2d-abc8-b332aee7c658 | the-power-of-subsampling-in-submodular | 2104.02772 | null | https://arxiv.org/abs/2104.02772v1 | https://arxiv.org/pdf/2104.02772v1.pdf | The Power of Subsampling in Submodular Maximization | We propose subsampling as a unified algorithmic technique for submodular maximization in centralized and online settings. The idea is simple: independently sample elements from the ground set, and use simple combinatorial techniques (such as greedy or local search) on these sampled elements. We show that this approach ... | ['Amin Karbasi', 'Moran Feldman', 'Ehsan Kazemi', 'Christopher Harshaw'] | 2021-04-06 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 1.27397720e-02 2.97788709e-01 -7.10586131e-01 4.54723947e-02
-1.21864092e+00 -8.64416242e-01 -3.18164676e-01 4.09018368e-01
-4.06911463e-01 6.81020021e-01 2.10578889e-01 -9.63404402e-02
-5.20252287e-01 -8.60440075e-01 -1.22982132e+00 -6.89034641e-01
-7.07488596e-01 7.85180748e-01 3.61126196e-03 -1.24428801... | [6.514891624450684, 4.876201629638672] |
a220b4f8-7e9a-4809-86d6-1a8189434306 | msa-transformer | null | null | https://www.biorxiv.org/content/10.1101/2021.02.12.430858v1 | https://www.biorxiv.org/content/10.1101/2021.02.12.430858v1.full.pdf | MSA Transformer | Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins. Protein language models studied to date have been trained to perform inference from individual sequences. The longstanding approach in computational biology has been to make inferences from a fami... | ['Alexander Rives', 'Tom Sercu', 'Pieter Abbeel', 'John F. Canny', 'Joshua Meier', 'Robert Verkuil', 'Jason Liu', 'Roshan Rao'] | 2021-02-13 | null | null | null | null | ['protein-language-model', 'multiple-sequence-alignment'] | ['medical', 'medical'] | [ 6.54933751e-01 2.01237127e-01 -2.03749865e-01 -5.73280156e-01
-7.53758729e-01 -7.27148533e-01 5.65502346e-01 5.05722880e-01
-5.30311406e-01 9.98678744e-01 4.17696871e-02 -6.58712864e-01
2.83986688e-01 -2.48341694e-01 -1.04527485e+00 -8.69233072e-01
-3.67586091e-02 9.03443396e-01 3.33940953e-01 -2.08382428... | [4.699473857879639, 5.664149761199951] |
97aec89b-8b30-4920-b664-2344376cd035 | grab-a-dataset-of-whole-body-human-grasping | 2008.11200 | null | https://arxiv.org/abs/2008.11200v1 | https://arxiv.org/pdf/2008.11200v1.pdf | GRAB: A Dataset of Whole-Body Human Grasping of Objects | Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time. While "grasping" is commonly thought of as a single hand stably lifting an object, we capture the motio... | ['Michael J. Black', 'Dimitrios Tzionas', 'Omid Taheri', 'Nima Ghorbani'] | 2020-08-25 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2534_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490562.pdf | eccv-2020-8 | ['human-object-interaction-motion-tracking', 'grasp-generation', 'human-grasp-contact-prediction'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [-2.41433144e-01 -2.34203964e-01 4.93693538e-02 -1.60050690e-01
-1.73193797e-01 -7.68663108e-01 2.18410686e-01 -4.12874311e-01
-2.51646526e-02 3.48837197e-01 4.28380352e-03 8.02668482e-02
-7.55136386e-02 -5.52575290e-01 -1.04633963e+00 -6.50179148e-01
-2.45977238e-01 1.16659868e+00 1.23601504e-01 -1.65878385... | [5.95949125289917, -0.901121199131012] |
c8a287cc-0ad9-41d0-9f8e-5d92295b8eb8 | topological-map-construction-and-scene | null | null | https://link.springer.com/article/10.1007%2Fs10514-017-9638-9#Sec2 | https://link.springer.com/article/10.1007%2Fs10514-017-9638-9#Sec2 | Topological map construction and scene recognition for vehicle localization | This paper presents a vehicle localization method
to assist vehicle navigation based on topological map construction
and scene recognition. A topological map is constructed
using omni-directional image sequences, and the
node information of the topological map is used for place
recognition and derivation of vehicl... | ['Huei-Yung Lin'] | 2018-01-01 | null | null | null | conference-2018-1 | ['scene-change-detection'] | ['computer-vision'] | [-4.08135653e-02 -7.66968012e-01 -1.58032216e-02 -7.39712298e-01
-3.23660731e-01 -6.27996743e-01 9.38340068e-01 1.89934954e-01
-6.39033020e-01 5.64242244e-01 -2.98873782e-01 -5.87811470e-01
-3.96976769e-01 -1.23922622e+00 -3.52436870e-01 -4.52067077e-01
-1.02447141e-02 2.99220949e-01 4.93898183e-01 -2.41215184... | [7.469056129455566, -2.0875189304351807] |
2a37cdcd-8032-4f3a-80b3-570c80911701 | body-gesture-recognition-to-control-a-social | 2206.07538 | null | https://arxiv.org/abs/2206.07538v1 | https://arxiv.org/pdf/2206.07538v1.pdf | Body Gesture Recognition to Control a Social Robot | In this work, we propose a gesture based language to allow humans to interact with robots using their body in a natural way. We have created a new gesture detection model using neural networks and a custom dataset of humans performing a set of body gestures to train our network. Furthermore, we compare body gesture com... | ['Anaís Garrell', 'Alberto Sanfeliu', 'Ramón Romero', 'Joan Jaume Oliver', 'Javier Laplaza'] | 2022-06-15 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 5.02486229e-02 5.24022758e-01 3.91199179e-02 -4.55061138e-01
6.33450329e-01 -4.79384400e-02 8.27923834e-01 -5.24645746e-01
-9.27358866e-01 7.25796580e-01 2.63242394e-01 2.12049305e-01
-8.00154358e-02 -6.12864375e-01 -4.24143523e-01 -5.30028880e-01
-5.27852237e-01 9.17446673e-01 2.78080672e-01 -6.03094518... | [5.778250694274902, 0.13110682368278503] |
57df312f-9cd3-44a4-86a1-6bdf2ac3ca2d | image-free-multi-character-recognition | 2112.10587 | null | https://arxiv.org/abs/2112.10587v1 | https://arxiv.org/pdf/2112.10587v1.pdf | Image-free multi-character recognition | The recently developed image-free sensing technique maintains the advantages of both the light hardware and software, which has been applied in simple target classification and motion tracking. In practical applications, however, there usually exist multiple targets in the field of view, where existing trials fail to p... | ['Liheng Bian', 'Chunli Zhu', 'Huayi Wang'] | 2021-12-20 | null | null | null | null | ['license-plate-detection'] | ['computer-vision'] | [ 6.54821873e-01 -7.90982187e-01 6.75250217e-02 -9.02044326e-02
-7.80432343e-01 -4.59971577e-01 3.37179452e-01 -3.28678727e-01
-5.21403193e-01 5.13061583e-01 -5.57456851e-01 -2.31147245e-01
3.95254120e-02 -6.96510255e-01 -6.79946065e-01 -1.11574090e+00
5.44132292e-01 -4.30238433e-02 6.47651255e-01 1.11770488... | [9.8368501663208, -4.8779144287109375] |
3b17c440-262d-4366-a890-d48095f1f57b | serving-graph-neural-networks-with | 2307.01684 | null | https://arxiv.org/abs/2307.01684v1 | https://arxiv.org/pdf/2307.01684v1.pdf | Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services | Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. To render GNN-based service for IoT-driven smart applications, traditional model serving paradigms usually resort to the cloud by fully uplo... | ['Zhi Zhou', 'Xiaoxi Zhang', 'Ke Luo', 'Peng Huang', 'Xu Chen', 'Liekang Zeng'] | 2023-07-04 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-5.02451718e-01 8.77550468e-02 -7.48830512e-02 -2.19700068e-01
1.12282999e-01 -4.31727469e-01 2.49438614e-01 -2.77873039e-01
1.79469705e-01 5.44483423e-01 2.46454835e-01 -5.42392731e-01
-4.09959406e-01 -1.34154069e+00 -3.73832315e-01 -6.20889008e-01
-5.59007704e-01 8.53895366e-01 2.25845173e-01 -2.14961380... | [7.037622451782227, 5.467007637023926] |
1bde6372-ed09-43ba-b650-44617e6e4307 | multi-source-contrastive-learning-from | 2302.07077 | null | https://arxiv.org/abs/2302.07077v2 | https://arxiv.org/pdf/2302.07077v2.pdf | Multi-Source Contrastive Learning from Musical Audio | Contrastive learning constitutes an emerging branch of self-supervised learning that leverages large amounts of unlabeled data, by learning a latent space, where pairs of different views of the same sample are associated. In this paper, we propose musical source association as a pair generation strategy in the context ... | ['Petros Maragos', 'Athanasia Zlatintsi', 'Christos Garoufis'] | 2023-02-14 | null | null | null | null | ['genre-classification', 'music-auto-tagging'] | ['computer-vision', 'music'] | [ 6.09864533e-01 -2.54880637e-02 -3.14259261e-01 -3.40596914e-01
-1.06521976e+00 -1.09721386e+00 6.83656156e-01 9.82357040e-02
-1.76954806e-01 5.15223682e-01 4.96354461e-01 3.08092237e-01
-4.54772294e-01 -4.49460059e-01 -5.63309431e-01 -7.46401250e-01
-1.00324541e-01 4.49236035e-01 -1.24832585e-01 -6.35615969... | [15.610803604125977, 5.204279899597168] |
9923ce39-9c39-460b-9751-ff0a51de44e8 | ensemble-classifier-approach-in-breast-cancer | 1704.03801 | null | http://arxiv.org/abs/1704.03801v1 | http://arxiv.org/pdf/1704.03801v1.pdf | Ensemble classifier approach in breast cancer detection and malignancy grading- A review | The diagnosed cases of Breast cancer is increasing annually and unfortunately
getting converted into a high mortality rate. Cancer, at the early stages, is
hard to detect because the malicious cells show similar properties (density) as
shown by the non-malicious cells. The mortality ratio could have been minimized
if t... | ['Deepti Ameta'] | 2017-04-11 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [-4.73095179e-02 5.78576811e-02 -1.49165422e-01 -1.09437458e-01
-1.46619514e-01 -2.12260000e-02 4.12783533e-01 7.98968315e-01
-2.08502129e-01 9.04181421e-01 -6.53495416e-02 -4.85885262e-01
-1.36665687e-01 -9.90929902e-01 -5.57401180e-02 -1.02556074e+00
1.41825706e-01 1.08437765e+00 4.45181251e-01 -9.72955450... | [15.368152618408203, -2.7766849994659424] |
63158a18-6f47-42f6-b075-9da482d5afd1 | raild-towards-leveraging-relation-features | 2211.11407 | null | https://arxiv.org/abs/2211.11407v1 | https://arxiv.org/pdf/2211.11407v1.pdf | RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphs | Due to the open world assumption, Knowledge Graphs (KGs) are never complete. In order to address this issue, various Link Prediction (LP) methods are proposed so far. Some of these methods are inductive LP models which are capable of learning representations for entities not seen during training. However, to the best o... | ['Mehwish Alam', 'Harald Sack', 'Genet Asefa Gesese'] | 2022-11-21 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [-1.08431697e-01 8.96829307e-01 -5.02403557e-01 -2.22002670e-01
-3.85667801e-01 -2.98261166e-01 7.83655465e-01 5.22509098e-01
1.25319228e-01 1.13082945e+00 2.36954004e-01 -3.77360612e-01
-4.12123710e-01 -1.36796176e+00 -9.05934751e-01 -1.10642567e-01
-3.92333299e-01 7.59330153e-01 4.87183779e-01 -4.54888076... | [8.965865135192871, 8.083586692810059] |
43cfda6b-6e2a-48d6-bc3f-4b2996107518 | non-neural-models-matter-a-re-evaluation-of | 2203.08274 | null | https://arxiv.org/abs/2203.08274v1 | https://arxiv.org/pdf/2203.08274v1.pdf | Non-neural Models Matter: A Re-evaluation of Neural Referring Expression Generation Systems | In recent years, neural models have often outperformed rule-based and classic Machine Learning approaches in NLG. These classic approaches are now often disregarded, for example when new neural models are evaluated. We argue that they should not be overlooked, since, for some tasks, well-designed non-neural approaches ... | ['Kees Van Deemter', 'Guanyi Chen', 'Fahime Same'] | 2022-03-15 | null | https://aclanthology.org/2022.acl-long.380 | https://aclanthology.org/2022.acl-long.380.pdf | acl-2022-5 | ['referring-expression-generation'] | ['computer-vision'] | [ 1.42635763e-01 4.89343584e-01 -2.15306312e-01 -6.77034736e-01
-5.75195789e-01 -4.53801155e-01 9.09877896e-01 2.52468407e-01
-8.77161443e-01 1.10048509e+00 3.77620220e-01 -5.62893212e-01
-1.22846842e-01 -1.01410890e+00 -5.62740088e-01 -2.68753976e-01
2.11434603e-01 6.46736920e-01 2.93311924e-02 -5.83822846... | [10.685511589050293, 8.995511054992676] |
0ad590ae-8b87-4321-b357-23cf66c87366 | perception-oriented-stereo-image-super | 2207.06617 | null | https://arxiv.org/abs/2207.06617v1 | https://arxiv.org/pdf/2207.06617v1.pdf | Perception-Oriented Stereo Image Super-Resolution | Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR. However, existing StereoSR models mainly concentrate on improving quantitative evaluation metrics and neglect the visual quality of super-resolved stereo images. To improve the perceptual performanc... | ['Xuhao Jiang', 'Weimin Tan', 'Bo Yan', 'Chenxi Ma'] | 2022-07-14 | null | null | null | null | ['disparity-estimation', 'stereo-image-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 2.41200939e-01 -1.82565793e-01 4.66735363e-02 -4.73720551e-01
-9.94262815e-01 1.20268121e-01 2.34774977e-01 -3.42334598e-01
-1.08895019e-01 8.46449137e-01 5.35551012e-01 1.91326350e-01
-7.48490691e-02 -9.94248867e-01 -5.03520072e-01 -5.58360815e-01
2.83586472e-01 -1.27707243e-01 5.57887673e-01 -4.43490356... | [10.687559127807617, -2.1641006469726562] |
525a2765-da7c-40a6-9501-9cfbba660200 | early-covid-19-diagnosis-from-lung-ultrasound | null | null | https://ieeexplore.ieee.org/document/9756430 | https://ieeexplore.ieee.org/document/9756430 | Early COVID-19 Diagnosis from Lung Ultrasound Images Combining RIULBP-TP and 3D-DenseNet | The pandemic of COVID-19 has affected the world with the high deaths rate. Early diagnosis of this disease is the bottleneck to the patient's health recovery. Its symptoms appear through the wide range of experiments especially accompany with the severe lung lesions. These lesions could be spotted on the lung ultrasoun... | ['Seyed Omid Shahdi', 'Mahmood Mohassel Feghhi', 'Vida Esmaeili'] | 2022-04-19 | null | null | null | 9th-iranian-joint-congress-on-fuzzy-and | ['covid-19-detection'] | ['medical'] | [-5.58007285e-02 -7.69298196e-01 2.72770096e-02 3.30481291e-01
-5.85365653e-01 -3.06960136e-01 3.71326983e-01 -2.63747931e-01
-3.04359198e-01 7.94597566e-01 -6.36783689e-02 -1.48566991e-01
-1.99580401e-01 -6.27730191e-01 -1.79526120e-01 -1.03606141e+00
5.79456836e-02 4.93731678e-01 7.19757140e-01 -1.34110510... | [15.560016632080078, -1.7199786901474] |
ea281b64-0744-4e90-b547-c4df09567ebc | coarse-to-fine-cascaded-networks-with-smooth | 2203.13052 | null | https://arxiv.org/abs/2203.13052v4 | https://arxiv.org/pdf/2203.13052v4.pdf | Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition | Facial expression recognition plays an important role in human-computer interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with Smooth Predicting (CFC-SP) to improve the performance of facial expression recognition. CFC-SP contains two core components, namely Coarse-to-Fine Cascaded networks (CF... | ['Guodong Guo', 'Zhongsong Ma', 'Yu Zhu', 'Zichang Tan', 'Fanglei Xue'] | 2022-03-24 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-6.50520846e-02 -2.89556295e-01 -2.04706132e-01 -8.63409698e-01
-2.69158989e-01 -1.21173725e-01 3.90565485e-01 -3.05902362e-01
-1.20361246e-01 4.43666637e-01 2.59203851e-01 3.12555939e-01
1.80775702e-01 -4.42289740e-01 -3.65468472e-01 -7.62425601e-01
-1.43501326e-01 -6.39109462e-02 -2.30798632e-01 -6.19285941... | [13.622647285461426, 1.7188304662704468] |
7705d5e1-9c1f-4e2b-9ecc-f44095496701 | singlish-message-paraphrasing-a-joint-task-of | null | null | https://aclanthology.org/2022.coling-1.345 | https://aclanthology.org/2022.coling-1.345.pdf | Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization | Within the natural language processing community, English is by far the most resource-rich language. There is emerging interest in conducting translation via computational approaches to conform its dialects or creole languages back to standard English. This computational approach paves the way to leverage generic Engli... | ['Nancy F. Chen', 'Ai Ti Aw', 'Shikang Ni', 'Zhengyuan Liu'] | null | null | null | null | coling-2022-10 | ['stance-detection'] | ['natural-language-processing'] | [ 5.88305652e-01 -7.52484575e-02 -4.02835608e-01 -4.42031085e-01
-1.12041426e+00 -9.88064766e-01 7.25014865e-01 2.80069470e-01
-4.41656709e-01 6.49823248e-01 7.00138927e-01 -4.70449597e-01
4.27692920e-01 -6.26922727e-01 -6.60235167e-01 -2.45752543e-01
4.07204241e-01 5.56546867e-01 5.14021665e-02 -8.24948192... | [11.365999221801758, 10.144684791564941] |
73367228-1491-42ce-b3ff-04fdff88a472 | adapt-at-semeval-2018-task-9-skip-gram-word | null | null | https://aclanthology.org/S18-1151 | https://aclanthology.org/S18-1151.pdf | ADAPT at SemEval-2018 Task 9: Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora | This paper describes a simple but competitive unsupervised system for hypernym discovery. The system uses skip-gram word embeddings with negative sampling, trained on specialised corpora. Candidate hypernyms for an input word are predicted based based on cosine similarity scores. Two sets of word embedding models were ... | ['Filip Klubi{\\v{c}}ka', 'Alfredo Maldonado'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 2.11095572e-01 5.80727935e-01 -2.83687115e-01 -1.42360985e-01
-1.65151298e-01 -2.81883895e-01 6.66014552e-01 7.40791082e-01
-1.33032632e+00 4.08904672e-01 4.57985312e-01 -2.54429936e-01
-5.03726065e-01 -8.51180494e-01 5.01293302e-01 -5.34401059e-01
-2.20127285e-01 1.11379457e+00 3.82320106e-01 -6.52389050... | [9.87692642211914, 8.73806381225586] |
42a71a7c-c40d-44e6-9601-0a142e0de576 | phonemic-transcription-of-low-resource-tonal | null | null | https://aclanthology.org/U17-1006 | https://aclanthology.org/U17-1006.pdf | Phonemic Transcription of Low-Resource Tonal Languages | null | ['Alexis Michaud', 'Trevor Cohn', 'Oliver Adams', 'Graham Neubig'] | 2017-12-01 | phonemic-transcription-of-low-resource-tonal-1 | https://aclanthology.org/U17-1006 | https://aclanthology.org/U17-1006.pdf | alta-2017-12 | ['acoustic-modelling'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2693634033203125, 3.825343132019043] |
228bb6d2-fc37-486c-b7df-83369a694b8a | why-an-android-app-is-classified-as-malware | 2004.11516 | null | https://arxiv.org/abs/2004.11516v2 | https://arxiv.org/pdf/2004.11516v2.pdf | Why an Android App is Classified as Malware? Towards Malware Classification Interpretation | Machine learning (ML) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly-used features. In practice, most of the ML classifications only provide a binary label to mobile users and app security analysts. However, stakeh... | ['Weiping Wen', 'Michael R. Lyu', 'Bozhi Wu', 'Yang Liu', 'Sen Chen', 'Cuiyun Gao', 'Lingling Fan'] | 2020-04-24 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 3.29740077e-01 -6.36977926e-02 -7.93209493e-01 -3.10020715e-01
-2.03361273e-01 -3.66851687e-01 4.11399990e-01 -3.62548605e-02
2.20123336e-01 4.06976044e-01 -9.56980586e-02 -9.93641555e-01
-2.73379628e-02 -5.67895353e-01 -7.46337950e-01 -3.55168998e-01
1.06943689e-01 1.08877167e-01 -2.31250320e-02 -3.86782847... | [14.410722732543945, 9.674978256225586] |
54e22e53-6582-4f4e-9811-d499331492a0 | anomaly-detection-with-inexact-labels | 1909.04807 | null | https://arxiv.org/abs/1909.04807v1 | https://arxiv.org/pdf/1909.04807v1.pdf | Anomaly Detection with Inexact Labels | We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measur... | ['Shotaro Tora', 'Machiko Toyoda', 'Tomoharu Iwata', 'Naonori Ueda'] | 2019-09-11 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 1.16320916e-01 -5.31084538e-02 1.54582366e-01 -7.90124416e-01
-5.66480517e-01 -4.18208331e-01 3.03694874e-01 5.92150927e-01
-2.60725528e-01 2.97831655e-01 -2.80123621e-01 -1.91388950e-01
-2.33509228e-01 -8.21341097e-01 -6.11968875e-01 -6.75549984e-01
-4.18202966e-01 5.11653423e-01 1.65884092e-01 1.54148608... | [7.586719036102295, 2.5070066452026367] |
9065801b-d5ae-4a8b-a628-cf9d4888c780 | improving-convergence-for-nonconvex-composite | 2009.10629 | null | https://arxiv.org/abs/2009.10629v4 | https://arxiv.org/pdf/2009.10629v4.pdf | Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex Penalties | Nesterov's accelerated gradient (AG) is a popular technique to optimize objective functions comprising two components: a convex loss and a penalty function. While AG methods perform well for convex penalties, such as the LASSO, convergence issues may arise when it is applied to nonconvex penalties, such as SCAD. A rece... | ['Masoud Asgharian', 'Sahir Bhatnagar', 'Kai Yang'] | 2020-09-22 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-7.91488513e-02 -2.50706196e-01 -2.41147488e-01 -3.83787125e-01
-1.11160612e+00 -2.86250561e-01 2.35728715e-02 1.88513808e-02
-4.81950730e-01 9.36855614e-01 1.16489336e-01 -2.19918385e-01
-2.80541927e-01 -3.05724829e-01 -7.05288410e-01 -9.84610796e-01
-2.40662619e-01 2.14541674e-01 -1.50664032e-01 -1.47594780... | [6.964200496673584, 4.427789688110352] |
a3cd2945-b104-4006-9abf-f6cbdcf7c69a | qos-aware-big-service-composition-using | null | null | https://onlinelibrary.wiley.com/doi/10.1002/cpe.6362 | https://onlinelibrary.wiley.com/share/author/NKUW9XJGQQ4WEXWI9XZI?target=10.1002/cpe.6362 | QoS-aware Big Service Composition using Distributed Co-Evolutionary Algorithm | Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service compositi... | ['Ugo Fiore', 'G R Gangadharan', 'Chandrashekar Jatoth', 'Avik Dutta'] | 2021-09-14 | null | null | null | concurrency-and-computation-practice-and | ['service-composition'] | ['miscellaneous'] | [-4.75924343e-01 -7.18726516e-01 2.27345139e-01 -4.63129580e-01
-4.99496073e-01 -3.86326879e-01 -6.63067624e-02 -5.11175036e-01
9.29656327e-02 5.66076875e-01 2.14124694e-01 1.45642087e-01
-8.18830967e-01 -1.06694698e+00 -6.18590750e-02 -9.16502297e-01
-1.26824882e-02 1.14146340e+00 3.47388506e-01 -3.61886948... | [8.58079719543457, 6.939772129058838] |
ba1f3b0f-7e09-4a8a-ac11-6280bd659173 | optimal-counterfactual-explanations-in-tree | 2106.06631 | null | https://arxiv.org/abs/2106.06631v2 | https://arxiv.org/pdf/2106.06631v2.pdf | Optimal Counterfactual Explanations in Tree Ensembles | Counterfactual explanations are usually generated through heuristics that are sensitive to the search's initial conditions. The absence of guarantees of performance and robustness hinders trustworthiness. In this paper, we take a disciplined approach towards counterfactual explanations for tree ensembles. We advocate f... | ['Thibaut Vidal', 'Axel Parmentier'] | 2021-06-11 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 5.30041933e-01 6.81932092e-01 -6.42332733e-01 -2.39403099e-01
-9.14343774e-01 -7.22652435e-01 6.27360702e-01 2.02427953e-01
-9.95773673e-02 1.20537150e+00 3.79096158e-02 -7.80887842e-01
-8.38697314e-01 -7.04731166e-01 -6.62754834e-01 -5.32042503e-01
-1.39398172e-01 7.23444402e-01 -1.72699362e-01 2.60742515... | [8.64020824432373, 5.519951820373535] |
c5453116-7cbb-4f08-a2db-9f33b92ee381 | exploring-the-effectiveness-of-dataset | 2306.11763 | null | https://arxiv.org/abs/2306.11763v1 | https://arxiv.org/pdf/2306.11763v1.pdf | Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards | Deep object detection models have achieved notable successes in recent years, but one major obstacle remains: the requirement for a large amount of training data. Obtaining such data is a tedious process and is mainly time consuming, leading to the exploration of new research avenues like synthetic data generation tech... | ['Klaas Dijkstra', 'Maya Aghaei', 'Alexander van Meekeren'] | 2023-06-20 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'prompt-engineering'] | ['medical', 'miscellaneous', 'natural-language-processing'] | [ 4.53269541e-01 9.72824171e-02 2.09157571e-01 -1.06628530e-01
-6.53933704e-01 -6.05286777e-01 5.28871119e-01 2.32977718e-01
-2.69645452e-01 2.31206343e-01 -6.86330557e-01 -3.15043241e-01
4.11004633e-01 -1.02685952e+00 -8.56706262e-01 -5.17681897e-01
1.49788149e-02 4.74059165e-01 7.96882629e-01 5.17859794... | [8.573263168334961, -0.9910483956336975] |
967c60e6-ac3d-4de2-9418-d19bb11b6350 | online-clustering-of-bandits | 1401.8257 | null | http://arxiv.org/abs/1401.8257v3 | http://arxiv.org/pdf/1401.8257v3.pdf | Online Clustering of Bandits | We introduce a novel algorithmic approach to content recommendation based on
adaptive clustering of exploration-exploitation ("bandit") strategies. We
provide a sharp regret analysis of this algorithm in a standard stochastic
noise setting, demonstrate its scalability properties, and prove its
effectiveness on a number... | ['Giovanni Zappella', 'Shuai Li', 'Claudio Gentile'] | 2014-01-31 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 8.71071294e-02 -1.16417661e-01 -9.77679074e-01 -2.31718972e-01
-1.31800878e+00 -5.73035479e-01 3.59224260e-01 -1.14971131e-01
-2.52447873e-01 1.09029484e+00 4.40830857e-01 -7.38686144e-01
-8.29002559e-01 -4.90374267e-01 -9.83730078e-01 -7.32227862e-01
-1.92837268e-01 7.82978833e-01 1.29661174e-03 -4.18752953... | [4.532822132110596, 3.2901010513305664] |
329fdded-6dae-44f9-8a74-7288f65c327c | scaling-spherical-cnns | 2306.05420 | null | https://arxiv.org/abs/2306.05420v1 | https://arxiv.org/pdf/2306.05420v1.pdf | Scaling Spherical CNNs | Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical convolutions is in the spectral domain (via the convolution theorem), which is still costlier than the usual planar convolutions. For this rea... | ['Ameesh Makadia', 'Jean-Jacques Slotine', 'Carlos Esteves'] | 2023-06-08 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-1.62104532e-01 3.32843453e-01 9.63201001e-02 -1.83538631e-01
-4.67921168e-01 -7.38054514e-01 4.24449682e-01 -1.35599114e-02
-4.26593035e-01 6.29952133e-01 1.26205355e-01 -8.42445314e-01
3.42119098e-01 -9.77022350e-01 -9.64289725e-01 -7.11077332e-01
-3.47017765e-01 2.50877887e-01 3.81266654e-01 -6.70980573... | [6.886926174163818, 6.078395366668701] |
3c2dc417-04a5-4977-9ec5-07f312b3a0b0 | the-first-international-ancient-chinese-word | null | null | https://aclanthology.org/2022.lt4hala-1.19 | https://aclanthology.org/2022.lt4hala-1.19.pdf | The First International Ancient Chinese Word Segmentation and POS Tagging Bakeoff: Overview of the EvaHan 2022 Evaluation Campaign | This paper presents the results of the First Ancient Chinese Word Segmentation and POS Tagging Bakeoff (EvaHan), which was held at the Second Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) 2022, in the context of the 13th Edition of the Language Resources and Evaluation Conference (LRE... | ['Dongbo Wang', 'Weiguang Qu', 'Chao Xu', 'Minxuan Feng', 'Jingya Lu', 'Yiguo Yuan', 'Bin Li'] | null | null | null | null | lt4hala-lrec-2022-6 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [-2.59469569e-01 1.50327891e-01 -4.51999425e-04 -7.85252079e-03
-1.02911377e+00 -9.84924376e-01 4.01266903e-01 2.85230011e-01
-1.22005975e+00 7.07828224e-01 2.96489030e-01 -6.28012359e-01
4.64344531e-01 -3.65242928e-01 -1.79951802e-01 -3.58705372e-01
3.42259288e-01 5.43242157e-01 5.07698834e-01 -2.38128364... | [10.494804382324219, 10.141814231872559] |
6326ed40-7b4d-4f1c-a8d9-8ad389b6955c | scene-parsing-via-dense-recurrent-neural | 1811.04778 | null | http://arxiv.org/abs/1811.04778v1 | http://arxiv.org/pdf/1811.04778v1.pdf | Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection | Recurrent neural networks (RNNs) have shown the ability to improve scene
parsing through capturing long-range dependencies among image units. In this
paper, we propose dense RNNs for scene labeling by exploring various long-range
semantic dependencies among image units. Different from existing RNN based
approaches, our... | ['Heng Fan', 'Haibin Ling', 'Longin Jan Latecki', 'Peng Chu'] | 2018-11-09 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 4.85543400e-01 3.44505578e-01 -2.84934223e-01 -7.08617210e-01
-2.71964312e-01 -2.25863829e-01 4.20365214e-01 -1.73327282e-01
-5.69898844e-01 4.40706611e-01 7.40507722e-01 -1.83659717e-01
1.71850815e-01 -8.86477292e-01 -9.42542017e-01 -5.70403695e-01
1.84204042e-01 2.45805368e-01 3.98192972e-01 -1.34241223... | [9.585456848144531, 0.40741223096847534] |
2ad551aa-bcf5-480f-9cd0-be20bce0541f | inference-in-sparse-graphs-with-pairwise | 1703.02728 | null | http://arxiv.org/abs/1703.02728v3 | http://arxiv.org/pdf/1703.02728v3.pdf | Inference in Sparse Graphs with Pairwise Measurements and Side Information | We consider the statistical problem of recovering a hidden "ground truth"
binary labeling for the vertices of a graph up to low Hamming error from noisy
edge and vertex measurements. We present new algorithms and a sharp
finite-sample analysis for this problem on trees and sparse graphs with poor
expansion properties s... | ['Dylan J. Foster', 'Daniel Reichman', 'Karthik Sridharan'] | 2017-03-08 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 5.39114833e-01 7.25669086e-01 -1.96458116e-01 2.07292184e-01
-1.11106920e+00 -6.21660769e-01 2.55169153e-01 4.49579209e-01
2.92186961e-02 9.22328115e-01 -1.95729211e-01 -3.92575592e-01
-4.09494996e-01 -1.20052612e+00 -9.56519246e-01 -1.07290316e+00
-9.30871844e-01 8.08184385e-01 2.99294740e-01 -4.57135737... | [6.85534143447876, 5.092423915863037] |
614a4119-6c28-460b-9a73-55f3d5680a48 | understanding-metrics-for-paraphrasing | 2205.13119 | null | https://arxiv.org/abs/2205.13119v1 | https://arxiv.org/pdf/2205.13119v1.pdf | Understanding Metrics for Paraphrasing | Paraphrase generation is a difficult problem. This is not only because of the limitations in text generation capabilities but also due that to the lack of a proper definition of what qualifies as a paraphrase and corresponding metrics to measure how good it is. Metrics for evaluation of paraphrasing quality is an on go... | ['Tarun Joshi', 'Rahul Singh', 'Omkar Patil'] | 2022-05-26 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.59071311e-01 -2.40373593e-02 -8.63350406e-02 -2.72001714e-01
-5.59313059e-01 -7.04407930e-01 8.45639050e-01 5.25523841e-01
-5.17976284e-02 7.48794317e-01 7.29089677e-01 -1.30275503e-01
-4.21514094e-01 -8.26005101e-01 -3.43936533e-01 -2.21651308e-02
5.61890364e-01 4.43139106e-01 1.97539181e-02 -5.82968533... | [11.442992210388184, 9.145482063293457] |
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