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10cf09ee-b8a7-41ca-82e4-d0466e7c0536 | extracting-events-with-informal-temporal | null | null | https://aclanthology.org/P13-2145 | https://aclanthology.org/P13-2145.pdf | Extracting Events with Informal Temporal References in Personal Histories in Online Communities | null | ["Carolyn Penstein Ros{\\'e}", 'Hyeju Jang', 'Guang Xiang', 'Zeyu Zheng', 'Miaomiao Wen'] | 2013-08-01 | null | null | null | acl-2013-8 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.374553203582764, 3.7077677249908447] |
8192f97d-b2ab-4c9f-80a6-505901d03670 | patient-specific-3d-volumetric-reconstruction | 1810.03270 | null | http://arxiv.org/abs/1810.03270v1 | http://arxiv.org/pdf/1810.03270v1.pdf | Patient-Specific 3D Volumetric Reconstruction of Bioresorbable Stents: A Method to Generate 3D Geometries for Computational Analysis of Coronaries Treated with Bioresorbable Stents | As experts continue to debate the optimal surgery practice for coronary
disease - percutaneous coronary intervention (PCI) or coronary aortic bypass
graft (CABG) - computational tools may provide a quantitative assessment of
each option. Computational fluid dynamics (CFD) has been used to assess the
interplay between h... | ['Alessandro Veneziani', 'Yasir Bouchi', 'Boyi Yang', 'Habib Samady', 'Bill Gogas', 'Don Giddens', 'Tianli Han', 'Marina Piccinelli', 'Gaetano Esposito'] | 2018-10-08 | null | null | null | null | ['3d-volumetric-reconstruction'] | ['computer-vision'] | [ 2.59831455e-03 2.45574815e-03 1.55108973e-01 3.18458796e-01
-4.47877407e-01 -7.51971722e-01 1.55643195e-01 4.51658249e-01
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-5.02442300e-01 8.95730078e-01 5.01843214e-01 -6.53738007... | [14.044008255004883, -2.5936434268951416] |
3d472a89-b5c3-48c0-8ad8-ec8878121c34 | a-gis-aided-approach-for-geolocalizing-an | 2208.12251 | null | https://arxiv.org/abs/2208.12251v1 | https://arxiv.org/pdf/2208.12251v1.pdf | A Gis Aided Approach for Geolocalizing an Unmanned Aerial System Using Deep Learning | The Global Positioning System (GPS) has become a part of our daily life with the primary goal of providing geopositioning service. For an unmanned aerial system (UAS), geolocalization ability is an extremely important necessity which is achieved using Inertial Navigation System (INS) with the GPS at its heart. Without ... | ['Alper Yilmaz', 'Deniz Karakay', 'Jianli Wei'] | 2022-08-25 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 4.07249741e-02 -3.20409358e-01 2.88489699e-01 -2.56460130e-01
-7.34300375e-01 -7.38269091e-01 4.21766281e-01 -1.62064731e-01
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-1.94528684e-01 2.30218440e-01 1.16548486e-01 -4.95635927... | [7.411804676055908, -1.9582751989364624] |
f8ac7da6-cc34-4201-a9f0-0dec62acee95 | emotion-twenty-questions-dialog-system-for | 2210.02400 | null | https://arxiv.org/abs/2210.02400v1 | https://arxiv.org/pdf/2210.02400v1.pdf | Emotion Twenty Questions Dialog System for Lexical Emotional Intelligence | This paper presents a web-based demonstration of Emotion Twenty Questions (EMO20Q), a dialog game whose purpose is to study how people describe emotions. EMO20Q can also be used to develop artificially intelligent dialog agents that can play the game. In previous work, an EMO20Q agent used a sequential Bayesian machine... | ['Nie', 'Huihui', 'Adedamola Sanusi', 'Abe Kazemzadeh'] | 2022-10-05 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [-6.68556988e-01 5.40942967e-01 7.51663685e-01 -8.36828113e-01
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8.77786204e-02 6.98242784e-01 4.13705230e-01 -8.92718196... | [13.081067085266113, 7.780576705932617] |
35b75a76-398f-4553-b400-9e7ea05f7c19 | on-the-multidimensional-augmentation-of | 2211.10642 | null | https://arxiv.org/abs/2211.10642v1 | https://arxiv.org/pdf/2211.10642v1.pdf | On the Multidimensional Augmentation of Fingerprint Data for Indoor Localization in A Large-Scale Building Complex Based on Multi-Output Gaussian Process | Wi-Fi fingerprinting becomes a dominant solution for large-scale indoor localization due to its major advantage of not requiring new infrastructure and dedicated devices. The number and the distribution of Reference Points (RPs) for the measurement of localization fingerprints like RSSI during the offline phase, howeve... | ['Jeremy Smith', 'Kyeong Soo Kim', 'Sihao Li', 'Zhe Tang'] | 2022-11-19 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 1.77923683e-02 -3.24485987e-01 3.21643412e-01 -2.05740958e-01
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-4.27319445e-02 3.28852713e-01 3.86766605e-02 1.24502622... | [6.402682781219482, 0.9253554940223694] |
737e0159-d4fa-458c-b59f-618d2a651ec6 | data-mining-in-clinical-trial-text | 2001.11268 | null | https://arxiv.org/abs/2001.11268v1 | https://arxiv.org/pdf/2001.11268v1.pdf | Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks | This research on data extraction methods applies recent advances in natural language processing to evidence synthesis based on medical texts. Texts of interest include abstracts of clinical trials in English and in multilingual contexts. The main focus is on information characterized via the Population, Intervention, C... | ['Julian P. T. Higgins', 'Julie Weeds', 'Lena Schmidt'] | 2020-01-30 | null | null | null | null | ['pico', 'entity-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.69791460e-01 8.32745373e-01 -6.67115569e-01 -3.10745299e-01
-1.15236485e+00 -2.33353525e-01 5.28357506e-01 1.06088686e+00
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-4.73040104e-01 -4.95891064e-01 -5.58350503e-01 -3.51490706e-01
-1.15330480e-01 4.10306782e-01 -2.76704341e-01 -2.13834018... | [8.492820739746094, 8.705096244812012] |
6fc220c3-d998-4b0b-9e85-b2c68f0fb557 | fine-grained-identity-preserving-landmark | 2110.04708 | null | https://arxiv.org/abs/2110.04708v2 | https://arxiv.org/pdf/2110.04708v2.pdf | Fine-grained Identity Preserving Landmark Synthesis for Face Reenactment | Recent face reenactment works are limited by the coarse reference landmarks, leading to unsatisfactory identity preserving performance due to the distribution gap between the manipulated landmarks and those sampled from a real person. To address this issue, we propose a fine-grained identity-preserving landmark-guided ... | ['Bin Fu', 'Gang Yu', 'Tao Chen', 'Weixi Zhang', 'Youcheng Ben', 'Haichao Zhang'] | 2021-10-10 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 2.39652544e-01 4.18192148e-02 1.09543823e-01 -6.50516570e-01
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-5.84658496e-02 7.99710989e-01 2.75045276e-01 5.76376021e-01
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1.23171769e-01 2.46036783e-01 -2.45232731e-02 -3.04789841... | [12.764107704162598, -0.03203541040420532] |
e51c2330-30ba-4016-9318-8b1ec0f806b6 | td3-with-reverse-kl-regularizer-for-offline | 2212.02125 | null | https://arxiv.org/abs/2212.02125v1 | https://arxiv.org/pdf/2212.02125v1.pdf | TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets | We consider an offline reinforcement learning (RL) setting where the agent need to learn from a dataset collected by rolling out multiple behavior policies. There are two challenges for this setting: 1) The optimal trade-off between optimizing the RL signal and the behavior cloning (BC) signal changes on different stat... | ['TieYan Liu', 'Tao Qin', 'Jiang Bian', 'Lei Song', 'Xuyun Zhang', 'Wei Shen', 'Li Zhao', 'Chuheng Zhang', 'Yuanying Cai'] | 2022-12-05 | null | null | null | null | ['d4rl'] | ['robots'] | [-9.70742032e-02 -3.06646258e-01 -5.53846657e-01 -1.37671024e-01
-6.40383244e-01 -7.29450524e-01 4.91057783e-01 -4.09099571e-02
-7.79378414e-01 9.57691848e-01 1.22680888e-01 -8.59380588e-02
-3.32630008e-01 -6.42372847e-01 -8.21223617e-01 -1.14527738e+00
-5.13055205e-01 4.82006520e-01 2.67839372e-01 -2.97134101... | [4.078127384185791, 2.1850500106811523] |
4eb43d0e-fce4-468f-90d0-3470054083a8 | same-scenario-adaptive-mixture-of-experts-for | 2112.13747 | null | https://arxiv.org/abs/2112.13747v6 | https://arxiv.org/pdf/2112.13747v6.pdf | MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction | Promotions are becoming more important and prevalent in e-commerce to attract customers and boost sales, leading to frequent changes of occasions, which drives users to behave differently. In such situations, most existing Click-Through Rate (CTR) models can't generalize well to online serving due to distribution uncer... | ['Yang Huang', 'Xu He', 'Bo Cao', 'Chengjun Mao', 'Hong Wen', 'Jing Zhang', 'Yibin Shen', 'Xiaofeng Pan'] | 2021-12-27 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.23272985e-01 -5.53601742e-01 -2.26224005e-01 -7.50674129e-01
-3.93038154e-01 -1.85862198e-01 5.70066333e-01 -1.41465053e-01
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-5.40112376e-01 -7.48782814e-01 -5.86328685e-01 -6.31898582e-01
-5.49313203e-02 1.31932870e-01 5.20685650e-02 -4.00842756... | [10.133788108825684, 5.558138847351074] |
d641b181-1109-43d3-bc18-5cab37f75aa7 | neural-models-for-factual-inconsistency | 2306.08872 | null | https://arxiv.org/abs/2306.08872v1 | https://arxiv.org/pdf/2306.08872v1.pdf | Neural models for Factual Inconsistency Classification with Explanations | Factual consistency is one of the most important requirements when editing high quality documents. It is extremely important for automatic text generation systems like summarization, question answering, dialog modeling, and language modeling. Still, automated factual inconsistency detection is rather under-studied. Exi... | ['Vasudeva Varma', 'Manish Gupta', 'KV Aditya Srivatsa', 'Harshit Gupta', 'Abhinav Menon', 'Mukund Choudhary', 'Tathagata Raha'] | 2023-06-15 | null | null | null | null | ['natural-language-inference'] | ['natural-language-processing'] | [ 2.07999721e-01 5.47926426e-01 -6.17156386e-01 -4.37361747e-01
-9.62241948e-01 -4.49320465e-01 9.24080074e-01 5.64769506e-01
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-1.06171884e-01 -6.80805802e-01 -8.09165359e-01 8.09732154e-02
3.37699085e-01 7.23398864e-01 2.06762299e-01 -4.37560469... | [9.203853607177734, 9.447497367858887] |
3766e74b-0130-43be-b503-2d0bbc24006d | on-modality-bias-recognition-and-reduction | 2202.12690 | null | https://arxiv.org/abs/2202.12690v2 | https://arxiv.org/pdf/2202.12690v2.pdf | On Modality Bias Recognition and Reduction | Making each modality in multi-modal data contribute is of vital importance to learning a versatile multi-modal model. Existing methods, however, are often dominated by one or few of modalities during model training, resulting in sub-optimal performance. In this paper, we refer to this problem as modality bias and attem... | ['Alberto del Bimbo', 'Mohan Kankanhalli', 'Zhiyong Cheng', 'Harry Cheng', 'Liqiang Nie', 'Yangyang Guo'] | 2022-02-25 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [ 3.70836765e-01 -1.69409037e-01 -4.43611532e-01 -3.34920138e-01
-1.04379559e+00 -5.17254353e-01 9.62558985e-01 -2.58861519e-02
-4.22635674e-01 5.98838329e-01 2.47111276e-01 -2.44542584e-01
-1.20421790e-01 -3.94403428e-01 -8.94851208e-01 -7.95929015e-01
2.62257338e-01 2.73092210e-01 1.20869800e-01 3.12426928... | [10.549508094787598, 1.664299726486206] |
e7b1bb04-744d-4085-bc08-aee82ca31b33 | hyperspectral-unmixing-with-endmember | 1703.06151 | null | http://arxiv.org/abs/1703.06151v1 | http://arxiv.org/pdf/1703.06151v1.pdf | Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation | A semi-supervised Partial Membership Latent Dirichlet Allocation approach is
developed for hyperspectral unmixing and endmember estimation while accounting
for spectral variability and spatial information. Partial Membership Latent
Dirichlet Allocation is an effective approach for spectral unmixing while
representing s... | ['Hao Sun', 'Alina Zare', 'Sheng Zou'] | 2017-03-17 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 3.87738913e-01 -4.24230725e-01 -6.03627741e-01 -2.04123318e-01
-6.46631539e-01 -7.34308183e-01 5.55476964e-01 -1.17519014e-01
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-2.30550557e-01 -8.29426646e-01 1.10582300e-01 -1.19260001e+00
2.07508773e-01 7.44794548e-01 -5.97105622e-01 5.36348403... | [10.033674240112305, -2.021247386932373] |
11ca1c67-b175-4a65-96a1-c2003cfe2faa | semi-supervised-domain-adaptation-via-2 | 2305.02693 | null | https://arxiv.org/abs/2305.02693v2 | https://arxiv.org/pdf/2305.02693v2.pdf | Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning | In semi-supervised domain adaptation (SSDA), a few labeled target samples of each class help the model to transfer knowledge representation from the fully labeled source domain to the target domain. Many existing methods ignore the benefits of making full use of the labeled target samples from multi-level. To make bett... | ['Wenkai Chen', 'Chuang Zhu', 'Xinyang Huang'] | 2023-05-04 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 1.65605381e-01 -1.27483308e-01 -7.90736020e-01 -7.90635586e-01
-9.97274578e-01 -6.78610623e-01 4.59160477e-01 -3.50021422e-02
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2.91223705e-01 6.74564600e-01 4.95074362e-01 7.11345747... | [10.33215618133545, 3.02047061920166] |
a426a2ae-3d23-4e1b-9128-dccc3ddcc475 | neural-symbolic-entangled-framework-for | 2209.08779 | null | https://arxiv.org/abs/2209.08779v1 | https://arxiv.org/pdf/2209.08779v1.pdf | Neural-Symbolic Entangled Framework for Complex Query Answering | Answering complex queries over knowledge graphs (KG) is an important yet challenging task because of the KG incompleteness issue and cascading errors during reasoning. Recent query embedding (QE) approaches to embed the entities and relations in a KG and the first-order logic (FOL) queries into a low dimensional space,... | ['Huajun Chen', 'Hui Chen', 'Peng Ye', 'Wen Zhang', 'Zezhong Xu'] | 2022-09-19 | null | null | null | null | ['complex-query-answering'] | ['knowledge-base'] | [-3.54145467e-01 5.44803977e-01 -3.89494002e-01 -1.97758913e-01
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f6374b3b-2c20-4cd3-8e65-a1ff75e0f8e7 | meta-causal-learning-for-single-domain | 2304.03709 | null | https://arxiv.org/abs/2304.03709v1 | https://arxiv.org/pdf/2304.03709v1.pdf | Meta-causal Learning for Single Domain Generalization | Single domain generalization aims to learn a model from a single training domain (source domain) and apply it to multiple unseen test domains (target domains). Existing methods focus on expanding the distribution of the training domain to cover the target domains, but without estimating the domain shift between the sou... | ['Jiebo Luo', 'Xinxiao wu', 'Zhi Gao', 'Jin Chen'] | 2023-04-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Meta-Causal_Learning_for_Single_Domain_Generalization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Meta-Causal_Learning_for_Single_Domain_Generalization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.46201766e-01 2.31905058e-01 -5.23003519e-01 -5.93978763e-01
-6.44114435e-01 -6.96990132e-01 6.83179498e-01 -2.08522722e-01
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3.45431775e-01 8.16096127e-01 4.17331576e-01 -9.40984339... | [10.338838577270508, 3.0937559604644775] |
fd843473-2cb2-4a68-933a-b25931eb68f1 | fcns-in-the-wild-pixel-level-adversarial-and | 1612.02649 | null | http://arxiv.org/abs/1612.02649v1 | http://arxiv.org/pdf/1612.02649v1.pdf | FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation | Fully convolutional models for dense prediction have proven successful for a
wide range of visual tasks. Such models perform well in a supervised setting,
but performance can be surprisingly poor under domain shifts that appear mild
to a human observer. For example, training on one city and testing on another
in a diff... | ['Judy Hoffman', 'Fisher Yu', 'Trevor Darrell', 'Dequan Wang'] | 2016-12-08 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 7.16812193e-01 2.22106595e-02 -1.23227470e-01 -5.58529377e-01
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2.69935638e-01 8.62012267e-01 8.06129336e-01 -2.31748998... | [9.800701141357422, 1.4420305490493774] |
dfb0d38e-343e-4369-99d7-88f67a82f2c0 | on-the-contribution-of-word-embeddings-to | null | null | https://aclanthology.org/C16-1265 | https://aclanthology.org/C16-1265.pdf | On the contribution of word embeddings to temporal relation classification | Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities. This occurs frequently in inter-sentential relations, whose entities are not connected via direct syntactic relations making classification even more difficult. I... | ['Paramita Mirza', 'Sara Tonelli'] | 2016-12-01 | on-the-contribution-of-word-embeddings-to-1 | https://aclanthology.org/C16-1265 | https://aclanthology.org/C16-1265.pdf | coling-2016-12 | ['temporal-relation-classification', 'implicit-relations'] | ['natural-language-processing', 'natural-language-processing'] | [-3.12740505e-02 1.65520459e-01 -3.95220608e-01 -5.98912060e-01
-1.26007006e-01 -8.28422368e-01 1.12018871e+00 1.29584277e+00
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-6.52855814e-01 4.47037965e-01 3.26905996e-01 -3.71130824... | [9.165438652038574, 9.192998886108398] |
468cfba7-27fe-4b3d-b93f-ef8a668ea805 | deep-correlation-analysis-for-audio-eeg | 2105.08492 | null | https://arxiv.org/abs/2105.08492v2 | https://arxiv.org/pdf/2105.08492v2.pdf | Deep Correlation Analysis for Audio-EEG Decoding | The electroencephalography (EEG), which is one of the easiest modes of recording brain activations in a non-invasive manner, is often distorted due to recording artifacts which adversely impacts the stimulus-response analysis. The most prominent techniques thus far attempt to improve the stimulus-response correlations ... | ['Sriram Ganapathy', 'Jaswanth Reddy Katthi'] | 2021-05-18 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 7.76491836e-02 -3.85477066e-01 5.36952794e-01 -5.25766432e-01
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-5.42437196e-01 7.66749203e-01 2.18892559e-01 2.96974540e-01
-4.06582385e-01 -2.71748930e-01 -5.65708101e-01 -7.61576831e-01
-5.26905239e-01 -7.87129328e-02 -1.35927543e-01 -8.35679173... | [13.162996292114258, 3.4216299057006836] |
da9ce070-7590-4038-aac5-e37f59d2d65e | understanding-the-impact-of-culture-in | 2305.04836 | null | https://arxiv.org/abs/2305.04836v1 | https://arxiv.org/pdf/2305.04836v1.pdf | Understanding the Impact of Culture in Assessing Helpfulness of Online Reviews | Online reviews have become essential for users to make informed decisions in everyday tasks ranging from planning summer vacations to purchasing groceries and making financial investments. A key problem in using online reviews is the overabundance of online that overwhelms the users. As a result, recommendation systems... | ['Shivakant Mishra', 'Maram Kurdi', 'Omar Hammad', 'Nuha Albadi', 'Khaled Alanezi'] | 2023-04-27 | null | null | null | null | ['culture'] | ['speech'] | [-4.84912127e-01 -2.35632136e-01 -4.80987608e-01 -4.79900748e-01
-7.19556178e-04 -6.32511854e-01 6.76648498e-01 6.30120814e-01
-4.78949070e-01 3.73699307e-01 5.69789529e-01 -4.92451131e-01
-8.62371325e-02 -5.60281456e-01 -3.68092284e-02 -3.23492110e-01
6.37721717e-01 5.40905073e-02 -3.14590335e-02 -9.59387839... | [10.946427345275879, 6.838450908660889] |
8df7743a-6e21-4a14-a406-897bbcc7ae83 | weakly-supervised-learning-of-rigid-3d-scene | 2102.08945 | null | https://arxiv.org/abs/2102.08945v1 | https://arxiv.org/pdf/2102.08945v1.pdf | Weakly Supervised Learning of Rigid 3D Scene Flow | We propose a data-driven scene flow estimation algorithm exploiting the observation that many 3D scenes can be explained by a collection of agents moving as rigid bodies. At the core of our method lies a deep architecture able to reason at the \textbf{object-level} by considering 3D scene flow in conjunction with other... | ['Tolga Birdal', 'Leonidas J. Guibas', 'Andreas Wieser', 'Or Litany', 'Zan Gojcic'] | 2021-02-17 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Gojcic_Weakly_Supervised_Learning_of_Rigid_3D_Scene_Flow_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Gojcic_Weakly_Supervised_Learning_of_Rigid_3D_Scene_Flow_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-flow-estimation'] | ['computer-vision'] | [ 9.81834531e-03 2.17804372e-01 7.75638819e-02 -5.50088644e-01
-2.24909484e-01 -8.34025264e-01 9.40343738e-01 -2.79850550e-02
-3.31852049e-01 3.40237737e-01 2.77377129e-01 -3.81860703e-01
1.11121029e-01 -6.80682838e-01 -9.55827236e-01 -4.42938983e-01
-3.82147892e-03 6.39270961e-01 6.84354007e-01 -4.12239939... | [8.52578353881836, -1.9497125148773193] |
ff47e1b2-3cbe-4fdb-9ec7-f531a12c3b22 | robust-benchmarking-for-machine-learning-of | 2007.16127 | null | https://arxiv.org/abs/2007.16127v1 | https://arxiv.org/pdf/2007.16127v1.pdf | Robust Benchmarking for Machine Learning of Clinical Entity Extraction | Clinical studies often require understanding elements of a patient's narrative that exist only in free text clinical notes. To transform notes into structured data for downstream use, these elements are commonly extracted and normalized to medical vocabularies. In this work, we audit the performance of and indicate are... | ['David Sontag', "Chloe O'Connell", 'Yasmin Fatemi', 'Monica Agrawal', 'Ariel Levy'] | 2020-07-31 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 3.44926447e-01 4.63463813e-01 -4.05589730e-01 -5.13955534e-01
-1.18195939e+00 -8.81156862e-01 2.58863389e-01 1.21188474e+00
-7.73147702e-01 8.89288247e-01 7.05061018e-01 -3.35879236e-01
-3.43711376e-01 -3.36463839e-01 -1.18525147e-01 -2.51529187e-01
1.77046135e-01 5.90427816e-01 -1.66526154e-01 -7.49819651... | [8.414449691772461, 8.663819313049316] |
e2b511d8-15b4-4ed6-bfa9-3849bc08d053 | interactive-image-manipulation-with-complex | 2211.15352 | null | https://arxiv.org/abs/2211.15352v1 | https://arxiv.org/pdf/2211.15352v1.pdf | Interactive Image Manipulation with Complex Text Instructions | Recently, text-guided image manipulation has received increasing attention in the research field of multimedia processing and computer vision due to its high flexibility and controllability. Its goal is to semantically manipulate parts of an input reference image according to the text descriptions. However, most of the... | ['Jinjia Zhou', 'Man M. Ho', 'Zhiqiang Zhang', 'Ryugo Morita'] | 2022-11-25 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 7.44108379e-01 -2.04310209e-01 8.68135393e-02 -2.20021904e-01
-1.06451577e-02 -5.89435279e-01 3.25596303e-01 -1.21475615e-01
-6.13123000e-01 3.39848459e-01 -2.98339069e-01 -7.30546638e-02
-1.41247511e-01 -8.54340136e-01 -6.72101021e-01 -7.33986795e-01
3.52679431e-01 2.60848701e-01 8.08663666e-01 -4.42290187... | [11.235116004943848, -1.089045524597168] |
ef729db5-0e5e-4d09-aebc-903c20770d4f | radfusion-benchmarking-performance-and | 2111.11665 | null | https://arxiv.org/abs/2111.11665v2 | https://arxiv.org/pdf/2111.11665v2.pdf | RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR | Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i.e., they only learn features from pixel-level information. Recent research revealing how race ... | ['Matthew P. Lungren', 'Nigam Shah', 'Lei Xing', 'Daniel Rubin', 'Imon Banerjee', 'Marcello Chang', 'Timothy J. Amrhein', 'Alaa Youssef', 'Jason Alan Fries', 'Shih-Cheng Huang', 'Yuyin Zhou'] | 2021-11-23 | null | null | null | null | ['pulmonary-embolism-detection'] | ['medical'] | [ 3.30856055e-01 1.18526824e-01 -6.47762060e-01 -7.29818642e-01
-1.25469947e+00 -5.32498360e-01 3.68895203e-01 6.49645329e-01
-5.65724969e-01 7.06681013e-01 8.76576662e-01 -8.31400573e-01
-1.12310544e-01 -6.18494749e-01 -5.14803946e-01 -6.59816206e-01
-8.84641260e-02 4.84713972e-01 -6.22494578e-01 5.44245124... | [15.028770446777344, -2.13090443611145] |
992831d0-c988-4bc1-8e48-03f86a061d3c | greedy-infomax-for-biologically-plausible | 1905.11786 | null | https://arxiv.org/abs/1905.11786v3 | https://arxiv.org/pdf/1905.11786v3.pdf | Putting An End to End-to-End: Gradient-Isolated Learning of Representations | We propose a novel deep learning method for local self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order in data instead. Inspired by the observation that biological neural networks appear to learn without backpropagating a global error signal,... | ["Peter O'Connor", 'Sindy Löwe', 'Bastiaan S. Veeling'] | 2019-05-28 | putting-an-end-to-end-to-end-gradient | http://papers.nips.cc/paper/8568-putting-an-end-to-end-to-end-gradient-isolated-learning-of-representations | http://papers.nips.cc/paper/8568-putting-an-end-to-end-to-end-gradient-isolated-learning-of-representations.pdf | neurips-2019-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.33179796e-01 6.10673070e-01 -9.47416425e-02 -5.09423673e-01
-4.17335689e-01 -6.12245977e-01 5.62916338e-01 3.03765982e-01
-6.65213645e-01 8.19638908e-01 2.38401055e-01 2.42388751e-02
-4.49315179e-03 -7.52439857e-01 -1.11795592e+00 -8.26987147e-01
-2.51800954e-01 3.49755108e-01 2.37457097e-01 -2.14816281... | [9.424918174743652, 2.727818727493286] |
34ad50ba-926a-41e5-8692-09ceb8413a72 | coper-continuous-patient-state-perceiver | 2208.03196 | null | https://arxiv.org/abs/2208.03196v2 | https://arxiv.org/pdf/2208.03196v2.pdf | COPER: Continuous Patient State Perceiver | In electronic health records (EHRs), irregular time-series (ITS) occur naturally due to patient health dynamics, reflected by irregular hospital visits, diseases/conditions and the necessity to measure different vitals signs at each visit etc. ITS present challenges in training machine learning algorithms which mostly ... | ['David A. Clifton', "Odhran O'Donoghue", 'Anshul Thakur', 'Vinod Kumar Chauhan'] | 2022-08-05 | null | null | null | null | ['mortality-prediction', 'irregular-time-series'] | ['medical', 'time-series'] | [ 9.50873713e-04 3.10456641e-02 1.33770317e-01 -3.43776554e-01
-5.00154436e-01 -1.90036863e-01 -6.37958646e-02 5.61505198e-01
-8.85277838e-02 5.47737122e-01 5.38044095e-01 -2.57758647e-01
-4.33471739e-01 -5.61026931e-01 -3.83766621e-01 -4.03673828e-01
-4.71829087e-01 2.23300755e-01 -6.52669430e-01 -2.48506576... | [7.962932109832764, 6.2210307121276855] |
6b87cd1e-f4a7-41b0-b72e-9c8ffdc2f3e7 | multimodal-unsupervised-image-to-image | 1804.04732 | null | http://arxiv.org/abs/1804.04732v2 | http://arxiv.org/pdf/1804.04732v2.pdf | Multimodal Unsupervised Image-to-Image Translation | Unsupervised image-to-image translation is an important and challenging
problem in computer vision. Given an image in the source domain, the goal is to
learn the conditional distribution of corresponding images in the target
domain, without seeing any pairs of corresponding images. While this
conditional distribution i... | ['Ming-Yu Liu', 'Serge Belongie', 'Jan Kautz', 'Xun Huang'] | 2018-04-12 | multimodal-unsupervised-image-to-image-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Xun_Huang_Multimodal_Unsupervised_Image-to-image_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xun_Huang_Multimodal_Unsupervised_Image-to-image_ECCV_2018_paper.pdf | eccv-2018-9 | ['multimodal-unsupervised-image-to-image'] | ['computer-vision'] | [ 7.21894443e-01 -1.42148614e-01 -1.08276501e-01 -6.75669730e-01
-7.34961629e-01 -9.87166524e-01 6.51046991e-01 -3.82675409e-01
-1.14993013e-01 6.29848421e-01 -1.30226508e-01 -1.70558542e-02
1.61341444e-01 -7.58792281e-01 -1.03034270e+00 -8.43537569e-01
7.33345628e-01 7.12677419e-01 -9.70441103e-02 -2.01744940... | [11.661535263061523, -0.3345124125480652] |
48de4ec3-8d86-4e50-aaa6-d5759062ce05 | a-short-review-on-applications-of-deep | 1812.06292 | null | http://arxiv.org/abs/1812.06292v2 | http://arxiv.org/pdf/1812.06292v2.pdf | A short review on Applications of Deep learning for Cyber security | Deep learning is an advanced model of traditional machine learning. This has
the capability to extract optimal feature representation from raw input
samples. This has been applied towards various use cases in cyber security such
as intrusion detection, malware classification, android malware detection, spam
and phishin... | ['Soman Kp', 'Vinayakumar R', 'Mohammed Harun Babu R'] | 2018-12-15 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 6.19868599e-02 -2.60303736e-01 -5.62654376e-01 -1.35819539e-01
3.92951757e-01 -5.77806532e-01 6.68290496e-01 2.33426586e-01
-1.17494568e-01 5.75521767e-01 -2.59681791e-01 -1.10202527e+00
-5.95861785e-02 -6.55212462e-01 -9.85398516e-02 -4.05948550e-01
-2.69283712e-01 1.56753421e-01 6.42821416e-02 -3.34009349... | [14.425592422485352, 9.681670188903809] |
db2835b2-4ace-4438-a103-3eee4d4f450e | lambada-backward-chaining-for-automated | 2212.13894 | null | https://arxiv.org/abs/2212.13894v2 | https://arxiv.org/pdf/2212.13894v2.pdf | LAMBADA: Backward Chaining for Automated Reasoning in Natural Language | Remarkable progress has been made on automated reasoning with natural text, by using Language Models (LMs) and methods such as Chain-of-Thought and Selection-Inference. These techniques search for proofs in the forward direction from axioms to the conclusion, which suffers from a combinatorial explosion of the search s... | ['Mehran Kazemi', 'Deepak Ramachandran', 'Xin Xu', 'Deepti Bhatia', 'Najoung Kim'] | 2022-12-20 | null | null | null | null | ['lambada', 'logical-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 2.89712518e-01 6.85580134e-01 -3.88916641e-01 -5.52603826e-02
-8.52570653e-01 -8.34086955e-01 7.97155857e-01 3.98977876e-01
1.70253664e-02 7.70040274e-01 1.24022849e-01 -1.36242139e+00
-4.02022660e-01 -1.12494385e+00 -8.71364415e-01 -2.34692767e-02
-1.40187830e-01 6.41956031e-01 4.32386577e-01 -1.21764310... | [9.144688606262207, 7.182344436645508] |
5e490658-4421-40ec-9e21-282c17954bb3 | learning-non-metric-visual-similarity-for | 1709.01353 | null | http://arxiv.org/abs/1709.01353v2 | http://arxiv.org/pdf/1709.01353v2.pdf | Learning Non-Metric Visual Similarity for Image Retrieval | Measuring visual similarity between two or more instances within a data
distribution is a fundamental task in image retrieval. Theoretically,
non-metric distances are able to generate a more complex and accurate
similarity model than metric distances, provided that the non-linear data
distribution is precisely captured... | ['George Vogiatzis', 'Noa Garcia'] | 2017-09-05 | learning-non-metric-visual-similarity-for-1 | https://openreview.net/forum?id=Skvd-myR- | https://openreview.net/pdf?id=Skvd-myR- | iclr-2018-1 | ['instance-search'] | ['computer-vision'] | [ 2.89008975e-01 -3.40849340e-01 -1.14696033e-01 -9.57300484e-01
-7.85895526e-01 -6.40467525e-01 8.29783201e-01 2.99775243e-01
-6.50998831e-01 4.03605849e-02 1.17838494e-01 -1.59827933e-01
-6.87743664e-01 -7.31435061e-01 -6.69409752e-01 -3.58908594e-01
-2.16271922e-01 5.39569318e-01 6.82489127e-02 -3.26163411... | [9.822032928466797, 2.1884400844573975] |
175ceffe-c870-48c7-b071-7a126846c57c | localization-guided-learning-for-pedestrian | 1808.09102 | null | http://arxiv.org/abs/1808.09102v1 | http://arxiv.org/pdf/1808.09102v1.pdf | Localization Guided Learning for Pedestrian Attribute Recognition | Pedestrian attribute recognition has attracted many attentions due to its
wide applications in scene understanding and person analysis from surveillance
videos. Existing methods try to use additional pose, part or viewpoint
information to complement the global feature representation for attribute
classification. Howeve... | ['Xihui Liu', 'Jing Shao', 'Pengze Liu', 'Junjie Yan'] | 2018-08-28 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [-1.80780590e-01 -3.85196418e-01 -1.67884931e-01 -9.27738011e-01
-4.61418420e-01 -3.59697282e-01 7.90560782e-01 4.51506555e-01
-5.14803588e-01 6.90593541e-01 3.60577911e-01 2.99214482e-01
2.80435886e-02 -8.84675682e-01 -4.83745933e-01 -6.83895230e-01
-8.78427699e-02 5.19275427e-01 5.37412763e-01 -6.45275638... | [14.40774154663086, 0.9984171986579895] |
db97ffc0-06fd-4c72-b95b-19ead1c9baf6 | grey-box-models-for-wave-loading-prediction | 2105.13813 | null | https://arxiv.org/abs/2105.13813v2 | https://arxiv.org/pdf/2105.13813v2.pdf | Grey-box models for wave loading prediction | The quantification of wave loading on offshore structures and components is a crucial element in the assessment of their useful remaining life. In many applications the well-known Morison's equation is employed to estimate the forcing from waves with assumed particle velocities and accelerations. This paper develops a ... | ['Elizabeth J Cross', 'Ulf T Tygesen', 'Timothy J Rogers', 'Daniel J Pitchforth'] | 2021-05-10 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.02962762e-01 9.53683257e-02 7.77564883e-01 1.24700837e-01
-7.63604581e-01 -4.28186357e-01 6.03251636e-01 3.39589387e-01
-2.83699065e-01 9.05605078e-01 9.93338227e-02 -6.10565484e-01
-1.09430480e+00 -8.83835793e-01 -3.80297929e-01 -1.23903000e+00
-2.87490129e-01 5.76729536e-01 5.08376122e-01 -4.15015608... | [6.406726360321045, 3.280604600906372] |
2f02887a-8c2a-46ff-a09a-e6466dc430b5 | physics-inspired-spatiotemporal-graph-ai | 2306.15728 | null | https://arxiv.org/abs/2306.15728v1 | https://arxiv.org/pdf/2306.15728v1.pdf | Physics-inspired spatiotemporal-graph AI ensemble for gravitational wave detection | We introduce a novel method for gravitational wave detection that combines: 1) hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational wave signals; and 2) graph neural networks to capture spatial correlations among gravitational wave ob... | ['Huihuo Zheng', 'E. A. Huerta', 'Minyang Tian'] | 2023-06-27 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-3.26127797e-01 -2.10494488e-01 4.65282440e-01 1.95775449e-01
-5.85847199e-01 -6.00531518e-01 8.29108417e-01 -5.10940313e-01
-3.77783030e-01 1.39734462e-01 -3.13981891e-01 -8.00456762e-01
-7.40865096e-02 -1.03183198e+00 -4.42404181e-01 -8.77600729e-01
-7.08225667e-01 8.56374681e-01 4.40600872e-01 -5.74763156... | [7.6314005851745605, 3.1263036727905273] |
3a8e6845-48ea-4cf4-a7c3-08b13aa74937 | transferable-curricula-through-difficulty | 2306.13028 | null | https://arxiv.org/abs/2306.13028v1 | https://arxiv.org/pdf/2306.13028v1.pdf | Transferable Curricula through Difficulty Conditioned Generators | Advancements in reinforcement learning (RL) have demonstrated superhuman performance in complex tasks such as Starcraft, Go, Chess etc. However, knowledge transfer from Artificial "Experts" to humans remain a significant challenge. A promising avenue for such transfer would be the use of curricula. Recent methods in cu... | ['Pradeep Varakantham', 'Sidney Tio'] | 2023-06-22 | null | null | null | null | ['transfer-learning', 'starcraft'] | ['miscellaneous', 'playing-games'] | [-5.11786602e-02 2.71860927e-01 7.70663545e-02 -6.91784993e-02
-3.44506353e-01 -8.05184603e-01 3.74002665e-01 3.35348129e-01
-8.14423800e-01 9.50200438e-01 -5.19602410e-02 -2.32049689e-01
-4.45149451e-01 -1.11967242e+00 -9.46123719e-01 -5.82439363e-01
-2.11430788e-01 6.25390589e-01 3.23298454e-01 -7.48414338... | [4.027013301849365, 1.4789998531341553] |
f433a4a3-6fc8-4b75-a001-fa205e04678b | when-vision-fails-text-attacks-against-vit | 2306.07033 | null | https://arxiv.org/abs/2306.07033v1 | https://arxiv.org/pdf/2306.07033v1.pdf | When Vision Fails: Text Attacks Against ViT and OCR | While text-based machine learning models that operate on visual inputs of rendered text have become robust against a wide range of existing attacks, we show that they are still vulnerable to visual adversarial examples encoded as text. We use the Unicode functionality of combining diacritical marks to manipulate encode... | ['Nicolas Papernot', 'Ross Anderson', 'Ilia Shumailov', 'Jenny Blessing', 'Nicholas Boucher'] | 2023-06-12 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 7.81021774e-01 5.03587902e-01 1.59149170e-01 -7.81459138e-02
-5.23743868e-01 -1.26451457e+00 9.48993564e-01 -1.46390137e-03
-2.00129434e-01 4.24616098e-01 -1.87714532e-01 -7.66911030e-01
5.41427910e-01 -8.66993070e-01 -1.08106565e+00 -2.65508592e-01
-6.32694513e-02 5.81698492e-02 1.29998639e-01 -2.96665847... | [5.923095226287842, 8.0703706741333] |
b82d6796-73d6-4c34-a9ae-55c3f1faea8f | topology-aware-loss-for-aorta-and-great | 2307.03137 | null | https://arxiv.org/abs/2307.03137v1 | https://arxiv.org/pdf/2307.03137v1.pdf | Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed Tomography Images | Segmentation networks are not explicitly imposed to learn global invariants of an image, such as the shape of an object and the geometry between multiple objects, when they are trained with a standard loss function. On the other hand, incorporating such invariants into network training may help improve performance for ... | ['Cigdem Gunduz-Demir', 'Rustu Turkay', 'Ilke Ali Gurses', 'Sinan Unver', 'Seher Ozcelik'] | 2023-07-06 | null | null | null | null | ['computed-tomography-ct', 'anatomy'] | ['methodology', 'miscellaneous'] | [ 1.45417780e-01 5.06457508e-01 -1.91342577e-01 -2.87704140e-01
-4.67804037e-02 -3.76325607e-01 5.12334466e-01 3.95482600e-01
-6.13417804e-01 5.73381007e-01 -3.46323788e-01 -1.63420841e-01
-2.53366381e-01 -9.70773220e-01 -7.61173546e-01 -7.77538538e-01
-3.95559072e-01 7.97964633e-01 5.59417069e-01 -1.24485180... | [14.221311569213867, -2.5754659175872803] |
f9f39121-6068-4a99-ab34-1948912f8fb6 | extensions-to-brahmic-script-processing | null | null | https://aclanthology.org/2022.lrec-1.692 | https://aclanthology.org/2022.lrec-1.692.pdf | Extensions to Brahmic script processing within the Nisaba library: new scripts, languages and utilities | The Brahmic family of scripts is used to record some of the most spoken languages in the world and is arguably the most diverse family of writing systems. In this work, we present several substantial extensions to Brahmic script functionality within the open-source Nisaba library of finite-state script normalization an... | ['Brian Roark', 'Lawrence Wolf-Sonkin', 'Raiomond Doctor', 'Cibu Johny', 'Alexander Gutkin'] | null | null | null | null | lrec-2022-6 | ['transliteration'] | ['natural-language-processing'] | [ 2.96824753e-01 -3.46468300e-01 4.75279205e-02 -6.15289032e-01
-3.95199955e-01 -1.24531639e+00 8.61807883e-01 -1.99008241e-01
-5.86408019e-01 3.24703395e-01 4.38772082e-01 -6.91240907e-01
1.76448628e-01 -5.44692695e-01 -8.29201266e-02 -2.65411556e-01
3.90271097e-01 6.62412226e-01 3.66251320e-01 -8.02707016... | [10.535441398620605, 10.380023956298828] |
cbc00d0f-c325-483c-a359-526a7207fad2 | self-paced-learning-with-adaptive-deep-visual | 1807.09200 | null | http://arxiv.org/abs/1807.09200v1 | http://arxiv.org/pdf/1807.09200v1.pdf | Self-Paced Learning with Adaptive Deep Visual Embeddings | Selecting the most appropriate data examples to present a deep neural network
(DNN) at different stages of training is an unsolved challenge. Though
practitioners typically ignore this problem, a non-trivial data scheduling
method may result in a significant improvement in both convergence and
generalization performanc... | ['Vithursan Thangarasa', 'Graham W. Taylor'] | 2018-07-24 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 8.22174847e-02 -2.55894721e-01 -3.34755093e-01 -7.75256753e-01
-7.49734402e-01 -3.25278252e-01 5.22411287e-01 1.81192741e-01
-7.49874830e-01 5.59988320e-01 6.30687922e-02 -1.58948570e-01
-3.21032912e-01 -6.12075984e-01 -7.41208792e-01 -7.50425518e-01
-6.39996529e-02 3.69954884e-01 -7.32046813e-02 2.55794793... | [9.601006507873535, 2.96291446685791] |
a3172c97-277a-4ac2-a054-c03fb992c2a7 | semantic-similarity-computing-for-scientific | 2203.12593 | null | https://arxiv.org/abs/2203.12593v1 | https://arxiv.org/pdf/2203.12593v1.pdf | Semantic Similarity Computing for Scientific Academic Conferences fused with domain features | Aiming at the problem that the current general-purpose semantic text similarity calculation methods are difficult to use the semantic information of scientific academic conference data, a semantic similarity calculation algorithm for scientific academic conferences by fusion with domain features is proposed. First, the... | ['Ang Li', 'Yawen Li', 'Runyu Yu'] | 2022-03-21 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [-3.43455434e-01 -5.20709574e-01 -7.25470558e-02 -3.68466169e-01
-2.90139079e-01 -2.09415048e-01 5.80140829e-01 3.70370388e-01
-7.49417305e-01 7.75036633e-01 9.56458375e-02 1.19186461e-01
-8.52949202e-01 -1.05665183e+00 4.95778508e-02 -4.61151600e-01
1.56054869e-01 6.74425602e-01 4.33488369e-01 -3.05824131... | [9.62845516204834, 8.436957359313965] |
72bab474-f9bf-4d31-9011-e04ab661e565 | ruber-an-unsupervised-method-for-automatic | 1701.03079 | null | http://arxiv.org/abs/1701.03079v2 | http://arxiv.org/pdf/1701.03079v2.pdf | RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems | Open-domain human-computer conversation has been attracting increasing
attention over the past few years. However, there does not exist a standard
automatic evaluation metric for open-domain dialog systems; researchers usually
resort to human annotation for model evaluation, which is time- and
labor-intensive. In this ... | ['Lili Mou', 'Chongyang Tao', 'Rui Yan', 'Dongyan Zhao'] | 2017-01-11 | null | null | null | null | ['dialogue-evaluation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-2.28439227e-01 1.85951754e-01 3.59417498e-02 -7.19782948e-01
-9.28192437e-01 -8.86249781e-01 6.65387571e-01 4.64168042e-02
-5.34104943e-01 7.43220866e-01 3.89829159e-01 -1.46162003e-01
-5.48370667e-02 -6.31425261e-01 2.90920824e-01 -4.15684253e-01
5.91652036e-01 9.82106268e-01 4.99897510e-01 -5.94107509... | [12.79433822631836, 7.990901947021484] |
00e2c2b5-a796-40fb-bfae-9407fe615108 | grassmann-averages-for-scalable-robust-pca | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Hauberg_Grassmann_Averages_for_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Hauberg_Grassmann_Averages_for_2014_CVPR_paper.pdf | Grassmann Averages for Scalable Robust PCA | As the collection of large datasets becomes increasingly automated, the occurrence of outliers will increase -- "big data" implies "big outliers''. While principal component analysis (PCA) is often used to reduce the size of data, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the ... | ['Michael J. Black', 'Soren Hauberg', 'Aasa Feragen'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['shadow-removal', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 6.18773550e-02 -4.63862300e-01 3.74526292e-01 -1.10328675e-03
-9.60566700e-01 -7.32618034e-01 5.15082955e-01 -6.81784227e-02
-2.42112637e-01 3.32917958e-01 2.05715030e-01 -1.11776225e-01
-3.38079721e-01 -4.44587052e-01 -5.51497579e-01 -1.11672604e+00
-2.69058228e-01 2.67826051e-01 1.14111312e-01 -7.33739287... | [7.611686706542969, 4.258675575256348] |
d28c073a-3e9b-44fd-8d8a-fb644b1eba02 | 3d-human-tongue-reconstruction-from-single-in | 2106.12302 | null | https://arxiv.org/abs/2106.12302v1 | https://arxiv.org/pdf/2106.12302v1.pdf | 3D human tongue reconstruction from single "in-the-wild" images | 3D face reconstruction from a single image is a task that has garnered increased interest in the Computer Vision community, especially due to its broad use in a number of applications such as realistic 3D avatar creation, pose invariant face recognition and face hallucination. Since the introduction of the 3D Morphable... | ['Stefanos Zafeiriou', 'Vasileios Triantafyllou', 'Stylianos Moschoglou', 'Stylianos Ploumpis'] | 2021-06-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ploumpis_3D_Human_Tongue_Reconstruction_From_Single_In-the-Wild_Images_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ploumpis_3D_Human_Tongue_Reconstruction_From_Single_In-the-Wild_Images_CVPR_2022_paper.pdf | cvpr-2022-1 | ['robust-face-recognition', '3d-face-reconstruction', 'face-hallucination', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.35162398e-01 4.45057958e-01 2.72048593e-01 -3.69129449e-01
-7.05311954e-01 -5.34850359e-01 6.38560474e-01 -3.20527375e-01
-6.98784590e-02 3.75312954e-01 3.22994024e-01 1.44084752e-01
1.92911282e-01 -4.87295806e-01 -7.42107034e-01 -7.28388488e-01
2.88854897e-01 8.43140781e-01 -1.52459264e-01 -2.31851578... | [12.759668350219727, -0.28265926241874695] |
54c30328-72b8-4b54-89b6-3e5daa0014dd | vision-transformers-for-small-histological | 2305.17370 | null | https://arxiv.org/abs/2305.17370v1 | https://arxiv.org/pdf/2305.17370v1.pdf | Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation | Computational Pathology (CPATH) systems have the potential to automate diagnostic tasks. However, the artifacts on the digitized histological glass slides, known as Whole Slide Images (WSIs), may hamper the overall performance of CPATH systems. Deep Learning (DL) models such as Vision Transformers (ViTs) may detect and... | ['Kjersti Engan', 'Tahlita CM Zuiverloon', 'Farbod Khoraminia', 'Trygve Eftestol', 'Neel Kanwal'] | 2023-05-27 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.55139649e-01 3.50106746e-01 2.94710129e-01 -1.26266584e-01
-1.16648245e+00 -3.57218266e-01 3.16603631e-01 3.56672585e-01
-4.60040957e-01 4.68609273e-01 -3.15676391e-01 -5.86211205e-01
-8.41565952e-02 -6.65720940e-01 -7.34762371e-01 -1.10772228e+00
3.22755665e-01 2.33035192e-01 5.60557067e-01 3.20168346... | [15.030715942382812, -2.8591551780700684] |
c3b516ba-d043-4960-95a0-b7f0d447c48b | farspredict-a-benchmark-dataset-for-link | 2303.14647 | null | https://arxiv.org/abs/2303.14647v1 | https://arxiv.org/pdf/2303.14647v1.pdf | Farspredict: A benchmark dataset for link prediction | Link prediction with knowledge graph embedding (KGE) is a popular method for knowledge graph completion. Furthermore, training KGEs on non-English knowledge graph promote knowledge extraction and knowledge graph reasoning in the context of these languages. However, many challenges in non-English KGEs pose to learning a... | ['Mohsen Jahanshahi', 'Behrouz Minaei-Bidgoli', 'Najmeh Torabian'] | 2023-03-26 | null | null | null | null | ['knowledge-graph-embedding', 'knowledge-graph-completion'] | ['graphs', 'knowledge-base'] | [-7.16049254e-01 9.19959724e-01 -5.54374933e-01 -6.13185279e-02
1.30245611e-01 -4.99306977e-01 3.96723330e-01 3.55426848e-01
-5.53767860e-01 9.35786068e-01 3.64721924e-01 -2.97440588e-01
-6.18155956e-01 -1.59935534e+00 -6.76347911e-01 4.10679681e-03
-4.49365437e-01 9.58267808e-01 4.57940876e-01 -6.31173313... | [8.843306541442871, 7.9552154541015625] |
085c29d6-40a2-47ba-8c66-3891f3f2de3a | a-multibias-mitigated-and-sentiment-knowledge | 2207.08104 | null | https://arxiv.org/abs/2207.08104v1 | https://arxiv.org/pdf/2207.08104v1.pdf | A Multibias-mitigated and Sentiment Knowledge Enriched Transformer for Debiasing in Multimodal Conversational Emotion Recognition | Multimodal emotion recognition in conversations (mERC) is an active research topic in natural language processing (NLP), which aims to predict human's emotional states in communications of multiple modalities, e,g., natural language and facial gestures. Innumerable implicit prejudices and preconceptions fill human lang... | ['Dawei Song', 'Yazhou Zhang', 'Fang Ma', 'Jinglin Wang'] | 2022-07-17 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-1.36986107e-01 1.66016862e-01 -2.30798185e-01 -8.39404821e-01
-1.31313801e-01 -2.26395905e-01 6.70487940e-01 -1.01630658e-01
-2.48807669e-01 7.12400496e-01 8.46996725e-01 1.49281081e-02
3.27657044e-01 -6.64109230e-01 -3.13965142e-01 -7.50122666e-01
4.75459903e-01 1.02908360e-02 -5.12516499e-01 -6.76365077... | [13.091179847717285, 5.301527500152588] |
054844f3-ce01-45ab-9422-d92d442477a6 | internet-of-things-fault-detection-and | 2307.01234 | null | https://arxiv.org/abs/2307.01234v1 | https://arxiv.org/pdf/2307.01234v1.pdf | Internet of Things Fault Detection and Classification via Multitask Learning | This paper presents a comprehensive investigation into developing a fault detection and classification system for real-world IIoT applications. The study addresses challenges in data collection, annotation, algorithm development, and deployment. Using a real-world IIoT system, three phases of data collection simulate 1... | ['Mohammad Arif Ul Alam'] | 2023-07-03 | null | null | null | null | ['classification-1', 'fault-detection', 'specificity'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-9.22233537e-02 -2.74659663e-01 -2.43286774e-01 -2.19218820e-01
-2.34307185e-01 -2.92325795e-01 1.04985945e-03 2.03775272e-01
3.83839123e-02 6.24562442e-01 -4.31479037e-01 -5.59103072e-01
-2.21809939e-01 -6.79500103e-01 1.28014326e-01 -2.99868882e-01
-1.93260834e-01 8.69317591e-01 6.85185611e-01 1.87195554... | [7.002501964569092, 2.4330663681030273] |
36841194-a826-40c4-874a-1c7eceae6fa4 | zero-shot-next-item-recommendation-using | 2304.03153 | null | https://arxiv.org/abs/2304.03153v1 | https://arxiv.org/pdf/2304.03153v1.pdf | Zero-Shot Next-Item Recommendation using Large Pretrained Language Models | Large language models (LLMs) have achieved impressive zero-shot performance in various natural language processing (NLP) tasks, demonstrating their capabilities for inference without training examples. Despite their success, no research has yet explored the potential of LLMs to perform next-item recommendations in the ... | ['Ee-Peng Lim', 'Lei Wang'] | 2023-04-06 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 1.60946220e-01 -1.94159389e-01 -7.11860657e-01 -5.74067235e-01
-7.38300264e-01 -4.68920201e-01 5.63796043e-01 -8.20481032e-02
-3.77986223e-01 3.54685247e-01 5.00228405e-01 -4.56672013e-01
-3.55542004e-01 -7.92258680e-01 -5.42331874e-01 -2.17967734e-01
4.85948287e-02 4.21535641e-01 2.07565263e-01 -4.74876136... | [10.190428733825684, 5.7052812576293945] |
f47cfc09-ae34-40e2-a94c-ea5e6f5f2f64 | a-multi-stream-convolutional-neural-network-1 | 2011.03756 | null | https://arxiv.org/abs/2011.03756v2 | https://arxiv.org/pdf/2011.03756v2.pdf | A Multi-stream Convolutional Neural Network for Micro-expression Recognition Using Optical Flow and EVM | Micro-expression (ME) recognition plays a crucial role in a wide range of applications, particularly in public security and psychotherapy. Recently, traditional methods rely excessively on machine learning design and the recognition rate is not high enough for its practical application because of its short duration and... | ['Li Zhao', 'Baolin Song', 'Ke Li', 'Jinming Liu'] | 2020-11-07 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 4.14411984e-02 -6.72128797e-01 -3.45880501e-02 -5.31339049e-01
-1.81842089e-01 2.59428490e-02 2.06481710e-01 -2.38372475e-01
-5.40128827e-01 5.38457811e-01 -2.60908734e-02 -7.31615797e-02
-5.31183416e-03 -7.94463634e-01 -2.30772451e-01 -7.45446503e-01
2.08612740e-01 -2.16248900e-01 1.17759623e-01 -1.36596724... | [13.571454048156738, 1.7451860904693604] |
34f4a50f-6f8d-418a-a9d0-fcaf2a7d81b1 | deep-learning-based-mitosis-detection-in | 2109.00816 | null | https://arxiv.org/abs/2109.00816v1 | https://arxiv.org/pdf/2109.00816v1.pdf | Deep Learning-based mitosis detection in breast cancer histologic samples | This is the submission for mitosis detection in the context of the MIDOG 2021 challenge. It is based on the two-stage objection model Faster RCNN as well as DenseNet as a backbone for the neural network architecture. It achieves a F1-score of 0.6645 on the Preliminary Test Phase Leaderboard. | ['Sylvain Berlemont', 'Hippolyte Heuberger', 'Michel Halmes'] | 2021-09-02 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 4.12651487e-02 7.59433389e-01 -9.17766690e-01 -1.96098328e-01
-8.58907938e-01 -2.70918667e-01 6.98831320e-01 2.19033927e-01
-8.19748580e-01 1.13830411e+00 4.87862319e-01 -4.28895772e-01
1.19450025e-01 -4.12397355e-01 -5.62121451e-01 -7.29261994e-01
-1.33953661e-01 7.28905380e-01 2.46688843e-01 -7.26234391... | [15.086214065551758, -3.0942044258117676] |
12b05019-fafc-40ad-80ce-4e9ef394d051 | capacity-and-bias-of-learned-geometric | null | null | http://proceedings.neurips.cc/paper/2021/hash/88d25099b103efd638163ecb40a55589-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/88d25099b103efd638163ecb40a55589-Paper.pdf | Capacity and Bias of Learned Geometric Embeddings for Directed Graphs | A wide variety of machine learning tasks such as knowledge base completion, ontology alignment, and multi-label classification can benefit from incorporating into learning differentiable representations of graphs or taxonomies. While vectors in Euclidean space can theoretically represent any graph, much recent work sh... | ['Andrew McCallum', 'Kenneth Clarkson', 'Luke Vilnis', 'Nicholas Monath', 'Dongxu Zhang', 'Michael Boratko'] | 2021-12-01 | null | https://openreview.net/forum?id=0IqTX6FcZWv | https://openreview.net/pdf?id=0IqTX6FcZWv | neurips-2021-12 | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 6.67542173e-03 7.35857725e-01 -3.12493414e-01 -2.80878246e-01
-1.58603653e-01 -7.42506027e-01 7.43535399e-01 7.48588264e-01
-9.26499069e-02 3.35454375e-01 3.96279544e-01 -7.49900997e-01
-6.64201081e-01 -1.11903441e+00 -3.99823815e-01 -4.03624624e-01
-9.61026430e-01 6.06744111e-01 1.13069721e-01 -3.54452789... | [7.134638786315918, 6.076377868652344] |
3647965f-5403-427f-947a-d208fe3bd2f3 | beyond-triplet-leveraging-the-most-data-for | 2212.10313 | null | https://arxiv.org/abs/2212.10313v1 | https://arxiv.org/pdf/2212.10313v1.pdf | Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation | Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. Previous MMT systems mainly focus on better access and use of visual information and tend to validate their methods on image-related datasets. These studies face two challenges. F... | ['Mingxuan Wang', 'Liwei Wu', 'YuYang Huang', 'Shanbo Cheng', 'Zewei Sun', 'Yaoming Zhu'] | 2022-12-20 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.31803560e-01 -4.15739894e-01 -5.61624169e-01 -2.21649259e-01
-1.01483727e+00 -7.19590664e-01 9.10879731e-01 -6.05598152e-01
-5.67538023e-01 7.84913778e-01 1.59717903e-01 -6.14128888e-01
5.65342963e-01 -4.37417865e-01 -6.71414018e-01 -4.81470972e-01
9.21648502e-01 6.51644945e-01 -6.22490719e-02 -5.42322636... | [11.483672142028809, 1.5571210384368896] |
b72a7645-99c0-42c1-9bb2-f962343d2006 | graph-transformer-gans-for-graph-constrained | 2303.08225 | null | https://arxiv.org/abs/2303.08225v1 | https://arxiv.org/pdf/2303.08225v1.pdf | Graph Transformer GANs for Graph-Constrained House Generation | We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolut... | ['Luc van Gool', 'Radu Timofte', 'Nicu Sebe', 'Ling Shao', 'Bo Li', 'Humphrey Shi', 'Zhenyu Zhang', 'Hao Tang'] | 2023-03-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tang_Graph_Transformer_GANs_for_Graph-Constrained_House_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tang_Graph_Transformer_GANs_for_Graph-Constrained_House_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['house-generation'] | ['computer-vision'] | [ 4.82720695e-02 4.85069692e-01 1.89582482e-01 -1.70768157e-01
-4.19418335e-01 -4.26499516e-01 5.85616827e-01 -1.19655736e-01
5.86724043e-01 5.93706787e-01 8.63709897e-02 3.65605764e-03
-3.66598889e-02 -1.70648909e+00 -1.00260806e+00 -5.96238673e-01
-2.38434449e-01 2.44706064e-01 1.14589646e-01 -5.77981055... | [11.647628784179688, -0.5151149034500122] |
daa54eef-25d7-4609-baa8-afda1e4a8c54 | free-lunch-robust-cross-lingual-transfer-via | 2305.16834 | null | https://arxiv.org/abs/2305.16834v1 | https://arxiv.org/pdf/2305.16834v1.pdf | Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint Averaging | Massively multilingual language models have displayed strong performance in zero-shot (ZS-XLT) and few-shot (FS-XLT) cross-lingual transfer setups, where models fine-tuned on task data in a source language are transferred without any or with only a few annotated instances to the target language(s). However, current wor... | ['Goran Glavaš', 'Ivan Vulić', 'Fabian David Schmidt'] | 2023-05-26 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 2.31650434e-02 -3.93868506e-01 -2.03852862e-01 -4.03935760e-01
-1.62313414e+00 -9.20722008e-01 6.85262501e-01 7.73652792e-02
-7.35055804e-01 1.08340693e+00 -2.49422103e-01 -5.41246831e-01
-1.17600068e-01 -3.26480210e-01 -8.39057088e-01 -4.87673074e-01
2.55322665e-01 8.68582606e-01 1.83507174e-01 -4.06491131... | [11.230829238891602, 9.942513465881348] |
a95222d4-69b4-417f-b701-248bdafd850e | guided-patch-wise-nonlocal-sar-despeckling | 1811.11872 | null | http://arxiv.org/abs/1811.11872v1 | http://arxiv.org/pdf/1811.11872v1.pdf | Guided patch-wise nonlocal SAR despeckling | We propose a new method for SAR image despeckling which leverages information
drawn from co-registered optical imagery. Filtering is performed by plain
patch-wise nonlocal means, operating exclusively on SAR data. However, the
filtering weights are computed by taking into account also the optical guide,
which is much c... | ['Sergio Vitale', 'Luisa Verdoliva', 'Giuseppe Scarpa', 'Davide Cozzolino', 'Giovanni Poggi'] | 2018-11-28 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 7.45272994e-01 -8.30428079e-02 3.31713289e-01 -2.12928727e-01
-6.77105486e-01 -4.96940941e-01 7.20409989e-01 -2.88650781e-01
-5.81453681e-01 8.91620874e-01 4.98624057e-01 -6.23991117e-02
-5.51417291e-01 -8.19298804e-01 -2.45304704e-01 -1.26965845e+00
1.00852393e-01 -2.86267810e-02 1.83583692e-01 -2.83731550... | [10.503205299377441, -2.1574974060058594] |
3c0d0372-6acf-419b-a428-53bab17d9baf | multi-agent-reinforcement-learning-with-graph | 2008.08808 | null | https://arxiv.org/abs/2008.08808v4 | https://arxiv.org/pdf/2008.08808v4.pdf | BGC: Multi-Agent Group Belief with Graph Clustering | Recent advances have witnessed that value decomposed-based multi-agent reinforcement learning methods make an efficient performance in coordination tasks. Most current methods assume that agents can make communication to assist decisions, which is impractical in some situations. In this paper, we propose a semi-communi... | ['Tianze Zhou', 'Chenfei Wang', 'Pan Tang', 'Fubiao Zhang'] | 2020-08-20 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-3.15861225e-01 1.38261616e-01 -6.08274154e-02 -3.30959052e-01
-2.18135864e-01 -9.42987725e-02 6.76333368e-01 6.39727294e-01
-3.95747930e-01 8.36072505e-01 1.09074071e-01 3.61262709e-01
-5.01606941e-01 -1.07171071e+00 -3.56904298e-01 -1.12083733e+00
-2.05866188e-01 6.01116478e-01 4.17954117e-01 -4.40625429... | [3.7770769596099854, 1.9880543947219849] |
2e213923-b2b0-424f-8cb0-66c12309b565 | one-stage-3d-whole-body-mesh-recovery-with | 2303.16160 | null | https://arxiv.org/abs/2303.16160v1 | https://arxiv.org/pdf/2303.16160v1.pdf | One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer | Whole-body mesh recovery aims to estimate the 3D human body, face, and hands parameters from a single image. It is challenging to perform this task with a single network due to resolution issues, i.e., the face and hands are usually located in extremely small regions. Existing works usually detect hands and faces, enla... | ['Yu Li', 'Lei Zhang', 'Haoqian Wang', 'Ailing Zeng', 'Jing Lin'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_One-Stage_3D_Whole-Body_Mesh_Recovery_With_Component_Aware_Transformer_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_One-Stage_3D_Whole-Body_Mesh_Recovery_With_Component_Aware_Transformer_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-estimation', '3d-human-reconstruction', '3d-multi-person-mesh-recovery'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.07680827e-02 2.00473085e-01 -9.94450673e-02 -4.12387192e-01
-7.50951111e-01 -2.20364869e-01 1.88864127e-01 -5.26021242e-01
7.15763047e-02 3.87841702e-01 4.80299503e-01 5.93528450e-01
2.55470365e-01 -6.36499822e-01 -7.46082008e-01 -4.39703792e-01
2.70747542e-01 8.45090091e-01 2.43161038e-01 -1.89882651... | [7.174017906188965, -1.0479793548583984] |
c3e8c237-86b1-41c0-b890-a9159be6c0c3 | multi-view-orthonormalized-partial-least | 2007.05028 | null | https://arxiv.org/abs/2007.05028v1 | https://arxiv.org/pdf/2007.05028v1.pdf | Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions | We establish a family of subspace-based learning method for multi-view learning using the least squares as the fundamental basis. Specifically, we investigate orthonormalized partial least squares (OPLS) and study its important properties for both multivariate regression and classification. Building on the least square... | ['Wen-Wei', 'Ren-cang Li', 'Li Wang'] | 2020-07-09 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 6.51901513e-02 -5.28983414e-01 -4.10603315e-01 -4.73895341e-01
-9.26111341e-01 -6.83845222e-01 7.70739436e-01 -5.74505210e-01
-1.10007025e-01 4.71799284e-01 4.83623266e-01 8.85418281e-02
-4.66786921e-01 -5.13713360e-01 -6.49571717e-01 -9.25080061e-01
2.78923273e-01 -1.07264135e-03 -2.27290228e-01 -1.11717418... | [8.38999080657959, 4.556726932525635] |
b6554c84-fd82-40b7-8586-f959fbf3f668 | meta-learning-for-low-resource-neural-machine | 1808.08437 | null | http://arxiv.org/abs/1808.08437v1 | http://arxiv.org/pdf/1808.08437v1.pdf | Meta-Learning for Low-Resource Neural Machine Translation | In this paper, we propose to extend the recently introduced model-agnostic
meta-learning algorithm (MAML) for low-resource neural machine translation
(NMT). We frame low-resource translation as a meta-learning problem, and we
learn to adapt to low-resource languages based on multilingual high-resource
language tasks. W... | ['Jiatao Gu', 'Yun Chen', 'Kyunghyun Cho', 'Yong Wang', 'Victor O. K. Li'] | 2018-08-25 | meta-learning-for-low-resource-neural-machine-1 | https://aclanthology.org/D18-1398 | https://aclanthology.org/D18-1398.pdf | emnlp-2018-10 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.42180692e-02 -1.35215253e-01 -5.07955015e-01 7.48940883e-03
-1.53069198e+00 -5.97775280e-01 1.06358922e+00 -1.15345992e-01
-9.62626517e-01 1.47300148e+00 -6.39167940e-03 -9.06023026e-01
2.56718367e-01 -3.88362259e-01 -1.05124056e+00 -1.56093314e-01
4.37334239e-01 8.42108905e-01 -1.82791784e-01 -6.57376409... | [11.642589569091797, 10.342035293579102] |
db79e814-97ca-41cf-a5ba-75386346698b | docile-benchmark-for-document-information | 2302.05658 | null | https://arxiv.org/abs/2302.05658v2 | https://arxiv.org/pdf/2302.05658v2.pdf | DocILE Benchmark for Document Information Localization and Extraction | This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition. It contains 6.7k annotated business documents, 100k synthetically generated documents, and nearly~1M unlabeled documents for unsupervised pre-... | ['Dimosthenis Karatzas', 'Mickaël Coustaty', 'Antoine Doucet', 'Jiří Matas', 'Matyáš Skalický', 'Matěj Kocián', 'Ahmed Hamdi', 'Yash Patel', 'Michal Uřičář', 'Milan Šulc', 'Štěpán Šimsa'] | 2023-02-11 | null | null | null | null | ['unsupervised-pre-training', 'key-information-extraction'] | ['methodology', 'natural-language-processing'] | [ 2.59937216e-02 -5.12244068e-02 -4.65785384e-01 -9.40867960e-02
-1.21084380e+00 -9.78354692e-01 8.81743848e-01 5.66178381e-01
-1.44379482e-01 8.71078074e-01 4.10400748e-01 -1.65427163e-01
-2.72196442e-01 -4.96255934e-01 -9.01382208e-01 -4.58726406e-01
-2.49676526e-01 9.87411261e-01 2.91966230e-01 -2.02186406... | [11.696913719177246, 2.7929861545562744] |
ccacdca9-19e3-498b-815f-47899fe29848 | graph-based-methods-coupled-with-specific | 2306.00042 | null | https://arxiv.org/abs/2306.00042v1 | https://arxiv.org/pdf/2306.00042v1.pdf | Graph-based methods coupled with specific distributional distances for adversarial attack detection | Artificial neural networks are prone to being fooled by carefully perturbed inputs which cause an egregious misclassification. These \textit{adversarial} attacks have been the focus of extensive research. Likewise, there has been an abundance of research in ways to detect and defend against them. We introduce a novel a... | ['Michel Dojat', 'Sophie Achard', 'Martial Mermillod', 'Lucrezia Carboni', 'Dwight Nwaigwe'] | 2023-05-31 | null | null | null | null | ['adversarial-attack', 'adversarial-attack-detection', 'adversarial-attack-detection'] | ['adversarial', 'computer-vision', 'knowledge-base'] | [ 7.30739474e-01 6.23119712e-01 -3.25936154e-02 -2.89151520e-01
-5.22663593e-02 -1.10022140e+00 8.96243870e-01 2.69951195e-01
1.26312941e-01 4.99107927e-01 -8.07962567e-02 -7.10337102e-01
-2.85563059e-02 -1.04758120e+00 -1.06727660e+00 -6.40248179e-01
-3.37268829e-01 8.57825484e-03 1.94694832e-01 -2.94230521... | [5.834445953369141, 7.740427494049072] |
ac12f707-1f90-489d-85bc-799626a9acd5 | universal-litmus-patterns-revealing-backdoor | 1906.10842 | null | https://arxiv.org/abs/1906.10842v2 | https://arxiv.org/pdf/1906.10842v2.pdf | Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs | The unprecedented success of deep neural networks in many applications has made these networks a prime target for adversarial exploitation. In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs). We introduce the concept of Univ... | ['Hamed Pirsiavash', 'Soheil Kolouri', 'Heiko Hoffmann', 'Aniruddha Saha'] | 2019-06-26 | universal-litmus-patterns-revealing-backdoor-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Kolouri_Universal_Litmus_Patterns_Revealing_Backdoor_Attacks_in_CNNs_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Kolouri_Universal_Litmus_Patterns_Revealing_Backdoor_Attacks_in_CNNs_CVPR_2020_paper.pdf | cvpr-2020-6 | ['traffic-sign-recognition'] | ['computer-vision'] | [ 2.40420312e-01 -2.55766571e-01 -1.30649418e-01 -3.53599973e-02
-2.74050891e-01 -1.25453210e+00 6.22267306e-01 -4.27453339e-01
-5.07661045e-01 4.69633192e-01 -4.72488701e-01 -1.23794365e+00
3.30058336e-01 -8.37247610e-01 -1.12737954e+00 -7.69858003e-01
-2.34735250e-01 -5.07683754e-01 5.22763848e-01 -1.67769611... | [5.6721649169921875, 7.773167133331299] |
c9dde8ca-2221-40f2-9c2b-c6f04ba22b44 | a-deep-convolutional-neural-network-for-2 | 2110.15956 | null | https://arxiv.org/abs/2110.15956v1 | https://arxiv.org/pdf/2110.15956v1.pdf | A deep convolutional neural network for classification of Aedes albopictus mosquitoes | Monitoring the spread of disease-carrying mosquitoes is a first and necessary step to control severe diseases such as dengue, chikungunya, Zika or yellow fever. Previous citizen science projects have been able to obtain large image datasets with linked geo-tracking information. As the number of international collaborat... | ['David Masip', 'Mohammad Mahdi Dehshibi', 'Gereziher Adhane'] | 2021-10-29 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.04452851e-02 -1.28062069e-01 3.46348941e-01 -4.81537253e-01
9.05979145e-03 -6.56774580e-01 7.05289006e-01 2.24051118e-01
-8.12461257e-01 6.32372379e-01 -1.29209206e-01 -4.92823690e-01
-6.90342188e-02 -8.43400240e-01 -4.85508978e-01 -7.40390062e-01
-6.35863245e-01 5.87984979e-01 4.22505802e-03 -2.42474347... | [9.118902206420898, -1.1104358434677124] |
0eb9f318-0636-48fe-99be-f6301447ae22 | learning-a-depth-covariance-function | 2303.12157 | null | https://arxiv.org/abs/2303.12157v1 | https://arxiv.org/pdf/2303.12157v1.pdf | Learning a Depth Covariance Function | We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define priors over depth functions, predictive distributions given observations, and methods for active point selection. We leverage these techniques fo... | ['Andrew J. Davison', 'Eric Dexheimer'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Dexheimer_Learning_a_Depth_Covariance_Function_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dexheimer_Learning_a_Depth_Covariance_Function_CVPR_2023_paper.pdf | cvpr-2023-1 | ['depth-completion', 'visual-odometry'] | ['computer-vision', 'robots'] | [ 1.52859524e-01 4.23393816e-01 -4.15306389e-01 -7.70674527e-01
-9.24258649e-01 -8.19061875e-01 8.02604258e-01 1.37239501e-01
-7.00959444e-01 4.51206714e-01 3.72773468e-01 -2.31707215e-01
4.19899113e-02 -6.32134676e-01 -5.99100173e-01 -5.20642281e-01
1.58519268e-01 8.18329573e-01 2.16543868e-01 1.81251988... | [8.498863220214844, -2.6918468475341797] |
47ac896b-0ef5-4a1b-8f85-8eccb16f137f | learning-sparse-nonlinear-dynamics-via-mixed | 2206.00176 | null | https://arxiv.org/abs/2206.00176v1 | https://arxiv.org/pdf/2206.00176v1.pdf | Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization | Discovering governing equations of complex dynamical systems directly from data is a central problem in scientific machine learning. In recent years, the sparse identification of nonlinear dynamics (SINDy) framework, powered by heuristic sparse regression methods, has become a dominant tool for learning parsimonious mo... | ['Wes Gurnee', 'Dimitris Bertsimas'] | 2022-06-01 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 4.34941612e-02 -4.01336163e-01 -2.57623881e-01 1.66719660e-01
-6.48400784e-01 -6.53024018e-01 4.94189262e-01 -2.34918967e-01
-1.93645563e-02 1.20057762e+00 -2.03570560e-01 -2.45363891e-01
-7.26849139e-01 3.80465612e-02 -5.16794205e-01 -8.95695806e-01
-3.18124563e-01 8.55912209e-01 -3.13611180e-01 -7.32563362... | [6.544191360473633, 3.5302634239196777] |
ec359be5-26c4-4762-bc7c-1c19c939b769 | analysis-and-utilization-of-entrainment-on | 2212.03398 | null | https://arxiv.org/abs/2212.03398v1 | https://arxiv.org/pdf/2212.03398v1.pdf | Analysis and Utilization of Entrainment on Acoustic and Emotion Features in User-agent Dialogue | Entrainment is the phenomenon by which an interlocutor adapts their speaking style to align with their partner in conversations. It has been found in different dimensions as acoustic, prosodic, lexical or syntactic. In this work, we explore and utilize the entrainment phenomenon to improve spoken dialogue systems for v... | ['Trevor Wood', 'Agis Oikonomou Filandras', 'Jonas Rohnke', 'Marek Strelec', 'Antonio Bonafonte', 'Constantinos Papayiannis', 'David McHardy', 'Nikos Kargas', 'Daxin Tan'] | 2022-12-07 | null | null | null | null | ['spoken-dialogue-systems'] | ['speech'] | [-3.40320349e-01 3.99097055e-01 1.89242765e-01 -4.80948150e-01
-3.77337784e-01 -6.18811071e-01 7.60166764e-01 -1.11769296e-01
-1.31474286e-01 7.19879925e-01 7.37393260e-01 -2.39652246e-01
-1.57534033e-02 -2.30453417e-01 -2.06191242e-01 -5.93649626e-01
1.18445560e-01 3.00697803e-01 -4.98263352e-02 -6.21281087... | [14.569896697998047, 6.642016410827637] |
486d83ba-df29-4dab-a4b8-9375a7397584 | deep-reinforcement-learning-for-multi-agent-2 | 2208.01769 | null | https://arxiv.org/abs/2208.01769v1 | https://arxiv.org/pdf/2208.01769v1.pdf | Deep Reinforcement Learning for Multi-Agent Interaction | The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Towards this goal, the Autonomous Agents Research Group develops novel machine learning algorithms for autonomous systems control, with a speci... | ['Stefano V. Albrecht', 'Cheng Wang', 'Giuseppe Vecchio', 'Massimiliano Tamborski', 'Lukas Schäfer', 'Arrasy Rahman', 'Georgios Papoudakis', 'Trevor McInroe', 'Balint Gyevnar', 'Shangmin Guo', 'Samuel Garcin', 'Elliot Fosong', 'Mhairi Dunion', 'Filippos Christianos', 'Ignacio Carlucho', 'Cillian Brewitt', 'Ibrahim H. A... | 2022-08-02 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [-1.87475264e-01 3.18643332e-01 -5.02241552e-01 -2.70126790e-01
-1.29460424e-01 -2.06365824e-01 1.07033157e+00 2.66110063e-01
-3.34392190e-01 1.01033974e+00 1.57670453e-01 -1.29182532e-01
-3.83345217e-01 -8.03752303e-01 -4.69053298e-01 -8.34821165e-01
-5.70289493e-01 9.27639067e-01 6.53962493e-02 -1.53574511... | [3.806912660598755, 2.0264902114868164] |
db6d9e82-e4dc-4264-b0e0-06a48d173206 | sapnet-segmentation-aware-progressive-network | 2111.08892 | null | https://arxiv.org/abs/2111.08892v2 | https://arxiv.org/pdf/2111.08892v2.pdf | SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Deraining | Deep learning algorithms have recently achieved promising deraining performances on both the natural and synthetic rainy datasets. As an essential low-level pre-processing stage, a deraining network should clear the rain streaks and preserve the fine semantic details. However, most existing methods only consider low-le... | ['Gaurav Gupta', 'Yuxiong Wu', 'Changjie Lu', 'Shen Zheng'] | 2021-11-17 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 3.19141299e-01 -1.42955288e-01 1.99895397e-01 -5.52947223e-01
-5.04825950e-01 -2.92071730e-01 1.83898166e-01 -4.03602839e-01
-4.42363173e-01 7.88472652e-01 -2.80022055e-01 -1.33384511e-01
3.12402308e-01 -1.10230625e+00 -9.50055301e-01 -1.12099278e+00
1.46295175e-01 1.71208784e-01 5.55533528e-01 -2.43660703... | [10.903042793273926, -3.191300392150879] |
59beb649-2a34-4acf-884e-ab48442ef0ef | diversifying-joint-vision-language | 2306.03421 | null | https://arxiv.org/abs/2306.03421v2 | https://arxiv.org/pdf/2306.03421v2.pdf | Diversifying Joint Vision-Language Tokenization Learning | Building joint representations across images and text is an essential step for tasks such as Visual Question Answering and Video Question Answering. In this work, we find that the representations must not only jointly capture features from both modalities but should also be diverse for better generalization performance... | ['Anelia Angelova', 'AJ Piergiovanni', 'Vardaan Pahuja'] | 2023-06-06 | null | null | null | null | ['visual-question-answering-1', 'video-question-answering'] | ['computer-vision', 'computer-vision'] | [-1.87055543e-02 -1.36809379e-01 -4.76799071e-01 -4.80296612e-01
-1.00698662e+00 -6.44861400e-01 8.69994164e-01 5.36644049e-02
-5.83928287e-01 5.52666008e-01 4.84344959e-01 -2.54477024e-01
2.18293414e-01 -4.71675366e-01 -8.11084569e-01 -3.94254029e-01
2.49358460e-01 2.24699602e-01 1.20499462e-01 1.84775233... | [10.71689224243164, 1.4633948802947998] |
2511059c-81e7-4bee-bbc0-f449bef9549e | road-damages-detection-and-classification | 2211.00091 | null | https://arxiv.org/abs/2211.00091v1 | https://arxiv.org/pdf/2211.00091v1.pdf | Road Damages Detection and Classification with YOLOv7 | Maintaining the roadway infrastructure is one of the essential factors in enabling a safe, economic, and sustainable transportation system. Manual roadway damage data collection is laborious and unsafe for humans to perform. This area is poised to benefit from the rapid advance and diffusion of artificial intelligence ... | ['Christopher Donan', 'Du Nguyen', 'Vung Pham'] | 2022-10-31 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [ 8.03187303e-03 -1.01965681e-01 -2.39395387e-02 -3.35287780e-01
-8.74218345e-01 -3.66782337e-01 5.59312761e-01 7.10160881e-02
-3.33122015e-01 6.12370133e-01 3.02627325e-01 -2.69862384e-01
-4.39762957e-02 -1.37744522e+00 -6.32571638e-01 -7.72070050e-01
1.69715434e-01 5.18580005e-02 2.77630985e-01 -2.06056550... | [7.407608509063721, 1.1035584211349487] |
fe93fb61-4fd5-4c10-8b2b-1b5eba342852 | modelling-neuronal-behaviour-with-time-series | 2107.06762 | null | https://arxiv.org/abs/2107.06762v1 | https://arxiv.org/pdf/2107.06762v1.pdf | Modelling Neuronal Behaviour with Time Series Regression: Recurrent Neural Networks on C. Elegans Data | Given the inner complexity of the human nervous system, insight into the dynamics of brain activity can be gained from understanding smaller and simpler organisms, such as the nematode C. Elegans. The behavioural and structural biology of these organisms is well-known, making them prime candidates for benchmarking mode... | ['L. Miguel Silveira', 'Arlindo L. Oliveira', 'Ruxandra Barbulescu', 'Gonçalo Mestre'] | 2021-07-01 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 3.78449202e-01 -2.00201556e-01 5.55790246e-01 -4.94204648e-02
3.73123825e-01 -4.17354614e-01 8.55943859e-01 9.80884768e-03
-6.92209840e-01 6.87311888e-01 -2.84763813e-01 -2.54077584e-01
-4.24114019e-02 -4.87114161e-01 -7.13488519e-01 -8.75051618e-01
-4.12430137e-01 3.77670318e-01 5.15080094e-01 -4.63639647... | [8.065876960754395, 2.924891948699951] |
66ba059b-7fa2-45bc-ad4d-fc32581f496e | self-supervised-eeg-representation-learning | 2110.15278 | null | https://arxiv.org/abs/2110.15278v3 | https://arxiv.org/pdf/2110.15278v3.pdf | Self-supervised EEG Representation Learning for Automatic Sleep Staging | Background: Deep learning models have shown great success in automating tasks in sleep medicine by learning from carefully annotated Electroencephalogram (EEG) data. However, effectively utilizing a large amount of raw EEG remains a challenge. Objective: In this paper, we aim to learn robust vector representations from... | ['Jimeng Sun', 'M. Brandon Westover', 'Danica Xiao', 'Chaoqi Yang'] | 2021-10-27 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 4.26672429e-01 -4.84718150e-03 -1.51913956e-01 -8.13527107e-01
-8.27296734e-01 -1.97699904e-01 1.43034816e-01 1.04703344e-02
-4.95765030e-01 1.06063044e+00 4.59409952e-01 6.47351593e-02
-2.78154850e-01 -2.27314472e-01 -4.34125155e-01 -7.68925965e-01
-3.02103311e-01 2.55339414e-01 -3.10443908e-01 -1.09020598... | [13.34559440612793, 3.520479202270508] |
a1bccc15-fa1f-46cc-8016-b565556dcd3e | over-the-air-gaussian-process-regression | 2210.02204 | null | https://arxiv.org/abs/2210.02204v2 | https://arxiv.org/pdf/2210.02204v2.pdf | Over-the-Air Gaussian Process Regression Based on Product of Experts | This paper proposes a distributed Gaussian process regression (GPR) with over-the-air computation, termed AirComp GPR, for communication- and computation-efficient data analysis over wireless networks. GPR is a non-parametric regression method that can model the target flexibly. However, its computational complexity an... | ['Koya Sato'] | 2022-10-05 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 4.09882367e-02 2.64288992e-01 3.23407799e-01 -2.00894699e-01
-1.37241066e+00 4.55605499e-02 9.52341184e-02 2.64818668e-01
-3.87833148e-01 8.62913668e-01 -4.40102100e-01 -4.71150309e-01
-3.74851346e-01 -1.19398916e+00 -7.26543486e-01 -1.11191499e+00
-8.86769533e-01 1.02807784e+00 1.51671935e-02 2.41530418... | [6.332211494445801, 1.2526854276657104] |
25493510-a0d1-44a9-9718-66738102ad53 | bootstrapping-meaning-through-listening | 2210.12857 | null | https://arxiv.org/abs/2210.12857v1 | https://arxiv.org/pdf/2210.12857v1.pdf | Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings | Inducing semantic representations directly from speech signals is a highly challenging task but has many useful applications in speech mining and spoken language understanding. This study tackles the unsupervised learning of semantic representations for spoken utterances. Through converting speech signals into hidden u... | ['Chia-wen Lo', 'Cong Zhang', 'Yadong Liu', 'Zuoyu Tian', 'Jian Zhu'] | 2022-10-23 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'spoken-language-understanding', 'spoken-language-understanding', 'acoustic-unit-discovery'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'speech', 'speech'] | [ 3.73332381e-01 8.84180248e-01 -1.44497871e-01 -9.15815175e-01
-6.15473509e-01 -2.69143015e-01 7.64237404e-01 4.05266732e-02
-4.09188449e-01 4.81463224e-01 6.79171264e-01 -2.86513060e-01
2.31364682e-01 -6.03265345e-01 -6.69077039e-01 -5.45272768e-01
6.23483993e-02 6.06105208e-01 -9.58915353e-02 -1.83874562... | [14.06570053100586, 6.894286155700684] |
9d69651a-c5db-49a6-ba86-f990e9a60d5b | translation-enhanced-multilingual-text-to | 2305.19216 | null | https://arxiv.org/abs/2305.19216v1 | https://arxiv.org/pdf/2305.19216v1.pdf | Translation-Enhanced Multilingual Text-to-Image Generation | Research on text-to-image generation (TTI) still predominantly focuses on the English language due to the lack of annotated image-caption data in other languages; in the long run, this might widen inequitable access to TTI technology. In this work, we thus investigate multilingual TTI (termed mTTI) and the current pote... | ['Anna Korhonen', 'Ivan Vulić', 'Stephen Rawls', 'Ching-Yun Chang', 'Yaoyiran Li'] | 2023-05-30 | null | null | null | null | ['nmt', 'crosslingual-text-to-image-generation', 'multilingual-text-to-image-generation', 'cross-lingual-text-to-image-generation', 'multi-lingual-text-to-image-generation'] | ['computer-code', 'computer-vision', 'computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 4.35555369e-01 1.45635903e-01 -1.57769978e-01 -1.37876496e-01
-1.28294671e+00 -6.24562144e-01 1.15849245e+00 -6.87566757e-01
-3.12606901e-01 9.18694258e-01 5.39229214e-01 -5.76056242e-01
1.81806341e-01 -4.69219804e-01 -1.00025356e+00 -4.55504566e-01
4.23925459e-01 6.66532576e-01 -3.06186080e-01 -2.63587266... | [11.409706115722656, 1.5168007612228394] |
00ce6b85-0f99-48d1-a8b3-f2ee5ed31583 | bilinear-cnns-for-fine-grained-visual | 1504.07889 | null | http://arxiv.org/abs/1504.07889v6 | http://arxiv.org/pdf/1504.07889v6.pdf | Bilinear CNNs for Fine-grained Visual Recognition | We present a simple and effective architecture for fine-grained visual
recognition called Bilinear Convolutional Neural Networks (B-CNNs). These
networks represent an image as a pooled outer product of features derived from
two CNNs and capture localized feature interactions in a translationally
invariant manner. B-CNN... | ['Tsung-Yu Lin', 'Subhransu Maji', 'Aruni RoyChowdhury'] | 2015-04-29 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 1.02984190e-01 -4.01354402e-01 7.41791278e-02 -4.22071725e-01
-4.60973620e-01 -5.00531793e-01 6.19006097e-01 -2.96616882e-01
-3.68148446e-01 4.81041282e-01 -2.53662735e-01 -3.40220839e-01
-8.93749222e-02 -6.02857471e-01 -1.11500466e+00 -7.18597770e-01
-1.84676245e-01 2.66319245e-01 3.51206422e-01 -3.70717943... | [9.281630516052246, 1.5489684343338013] |
2fe8d3dd-9fbf-4ed7-a087-5e3193565d21 | pair-based-joint-encoding-with-relational | 2212.01844 | null | https://arxiv.org/abs/2212.01844v1 | https://arxiv.org/pdf/2212.01844v1.pdf | Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction | Emotion-cause pair extraction (ECPE) aims to extract emotion clauses and corresponding cause clauses, which have recently received growing attention. Previous methods sequentially encode features with a specified order. They first encode the emotion and cause features for clause extraction and then combine them for pai... | ['Qianli Ma', 'Xichen Shang', 'Junlong Liu'] | 2022-12-04 | null | null | null | null | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [ 1.41276717e-01 3.88500005e-01 -1.28680348e-01 -7.81251371e-01
-8.40482056e-01 -4.56410468e-01 5.44043422e-01 3.01909596e-01
-3.24223330e-03 5.37222743e-01 4.16090518e-01 2.51172751e-01
-1.41063511e-01 -1.00081527e+00 -5.02985001e-01 -5.34502268e-01
-2.41576105e-01 3.67679387e-01 -2.33211920e-01 -4.48620945... | [12.579793930053711, 6.202013969421387] |
b774f63d-b5ba-4027-9264-6d2a3a8f2389 | a-unified-front-end-framework-for-english | 2305.10666 | null | https://arxiv.org/abs/2305.10666v1 | https://arxiv.org/pdf/2305.10666v1.pdf | a unified front-end framework for english text-to-speech synthesis | The front-end is a critical component of English text-to-speech (TTS) systems, responsible for extracting linguistic features that are essential for a text-to-speech model to synthesize speech, such as prosodies and phonemes. The English TTS front-end typically consists of a text normalization (TN) module, a prosody wo... | ['Yuxuan Wang', 'Yuping Wang', 'YuanYuan Huo', 'Qiuqiang Kong', 'Yu Dong', 'Chen Li', 'Zelin Ying'] | 2023-05-18 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 1.50206149e-01 3.77719961e-02 -1.32789597e-01 -4.87337530e-01
-9.53157008e-01 -4.48319823e-01 3.90133619e-01 4.62539755e-02
-3.29924047e-01 3.35073799e-01 4.51859176e-01 -6.38322234e-01
5.30654490e-01 -3.82505953e-01 -2.84253299e-01 -4.84206527e-01
5.06416678e-01 1.30452618e-01 3.13374490e-01 -3.88885409... | [14.734979629516602, 6.722599029541016] |
3fb1234e-747d-4b45-ae2d-c034949859ec | sleeppriorcl-contrastive-representation | 2110.09966 | null | https://arxiv.org/abs/2110.09966v1 | https://arxiv.org/pdf/2110.09966v1.pdf | SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging | The objective of this paper is to learn semantic representations for sleep stage classification from raw physiological time series. Although supervised methods have gained remarkable performance, they are limited in clinical situations due to the requirement of fully labeled data. Self-supervised learning (SSL) based o... | ['Youfang Lin', 'Jiaoxue Deng', 'Qinfeng Xiao', 'Jing Wang', 'Hongjun Zhang'] | 2021-10-15 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 4.17847961e-01 -2.69918442e-02 -4.08203274e-01 -7.08272159e-01
-7.31812239e-01 -2.06089944e-01 2.81802654e-01 5.33613265e-01
-4.31101680e-01 1.00496304e+00 2.39107877e-01 2.36050576e-01
-7.16197640e-02 -4.05549198e-01 -4.41600084e-01 -8.40058506e-01
3.14171538e-02 2.55968362e-01 2.13572085e-01 -1.22806676... | [13.374597549438477, 3.4759485721588135] |
c4358b79-c20b-43ce-9bc7-3d7f8650c409 | segment-anything-in-non-euclidean-domains | 2304.11595 | null | https://arxiv.org/abs/2304.11595v1 | https://arxiv.org/pdf/2304.11595v1.pdf | Segment Anything in Non-Euclidean Domains: Challenges and Opportunities | The recent work known as Segment Anything (SA) has made significant strides in pushing the boundaries of semantic segmentation into the era of foundation models. The impact of SA has sparked extremely active discussions and ushered in an encouraging new wave of developing foundation models for the diverse tasks in the ... | ['DaCheng Tao', 'Xinchao Wang', 'Yongcheng Jing'] | 2023-04-23 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 7.41923690e-01 2.98638552e-01 -2.78297096e-01 -4.63694990e-01
-3.67642343e-01 -5.42262137e-01 5.09788036e-01 1.96153671e-01
9.66698602e-02 2.28986621e-01 2.45134272e-02 -4.66916829e-01
-6.11254394e-01 -9.33210611e-01 -5.20303369e-01 -6.99353516e-01
-2.22190320e-01 4.68541503e-01 9.16629732e-02 -2.81954825... | [7.07454252243042, 6.200143814086914] |
245887b1-b800-46ec-a10c-5308f272a5dc | open-source-fpga-ml-codesign-for-the-mlperf | 2206.11791 | null | https://arxiv.org/abs/2206.11791v1 | https://arxiv.org/pdf/2206.11791v1.pdf | Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark | We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms. We use the open-source hls4ml and FINN workflows, which aim to democratize AI-hardware codesign of optimized neural networks on FPGAs. We present the design and implementat... | ['Michaela Blott', 'Aidan Yokuda', 'Olivia Weng', 'Yaman Umuroglu', 'Nhan Tran', 'Rushil Roy', 'Tai Nguyen', 'Jules Muhizi', 'Andres Meza', 'Jason Liang', 'Ryan Kastner', 'Shih-Chieh Hsu', 'Scott Hauck', 'Ben Hawks', 'Nicolò Ghielmetti', 'Javier Duarte', 'Giuseppe Di Guglielmo', 'Hendrik Borras'] | 2022-06-23 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.99497536e-01 -1.72123685e-01 -1.07986346e-01 -8.76302242e-01
-1.54931039e-01 -4.45523530e-01 2.46441975e-01 2.80397952e-01
-6.89994276e-01 5.86196840e-01 -3.78237039e-01 -6.19646549e-01
-2.84182400e-01 -8.03814352e-01 -8.55270028e-01 -3.18367422e-01
-2.68256843e-01 3.76984864e-01 6.50049597e-02 -9.37639177... | [8.392123222351074, 2.900789737701416] |
13dfe6c0-e204-4add-98fb-c17ff39ee8b5 | dense-retrieval-adaptation-using-target | 2307.02740 | null | https://arxiv.org/abs/2307.02740v1 | https://arxiv.org/pdf/2307.02740v1.pdf | Dense Retrieval Adaptation using Target Domain Description | In information retrieval (IR), domain adaptation is the process of adapting a retrieval model to a new domain whose data distribution is different from the source domain. Existing methods in this area focus on unsupervised domain adaptation where they have access to the target document collection or supervised (often f... | ['W. Bruce Croft', 'Edgar Meij', 'Srivas Prasad', 'Sachith Sri Ram Kothur', 'Yong Zhuang', 'Helia Hashemi'] | 2023-07-06 | null | null | null | null | ['domain-adaptation', 'unsupervised-domain-adaptation', 'retrieval', 'information-retrieval'] | ['methodology', 'methodology', 'methodology', 'natural-language-processing'] | [ 4.33647066e-01 6.14279322e-03 -5.73674083e-01 -5.04370809e-01
-1.31026733e+00 -8.93390238e-01 9.93255258e-01 2.89391428e-01
-3.99171501e-01 7.51814604e-01 2.76663065e-01 2.01040640e-01
-3.74113023e-01 -6.42813921e-01 -3.45620155e-01 -3.86666447e-01
3.03867608e-01 1.34403682e+00 4.41831142e-01 -6.18763030... | [11.186606407165527, 7.840414524078369] |
ee4942cd-53f1-48ed-9dc5-a74f34745580 | the-skill-task-matching-model-mechanism-model | 2306.12176 | null | https://arxiv.org/abs/2306.12176v1 | https://arxiv.org/pdf/2306.12176v1.pdf | The Skill-Task Matching Model: Mechanism, Model Form and implications | We propose the iteration mechanism as a supplement to the price mechanism in microeconomics. We hold that firms set expected profits in the beginning of each producing period, then try to achieve them. The ability to achieve target number is not born. Firms continuously trial and error, let their actual profits increas... | ['WeiGuo Yang', 'Da Xie'] | 2023-06-21 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-1.12502865e-01 4.04030651e-01 -6.19256616e-01 1.34297505e-01
9.89064574e-02 -4.05047208e-01 5.61955929e-01 -5.13653517e-01
-6.00560486e-01 7.70573854e-01 -1.33915022e-01 -5.39887011e-01
-6.70173407e-01 -8.23136687e-01 -3.19225967e-01 -4.89104658e-01
2.87051499e-01 5.92967212e-01 -4.38076347e-01 -2.57749707... | [4.383849143981934, 3.1656956672668457] |
dc400202-97b0-44ed-b855-a248e8e56b35 | is-kalman-filter-optimal-for-fault-detection | 2301.11573 | null | https://arxiv.org/abs/2301.11573v2 | https://arxiv.org/pdf/2301.11573v2.pdf | On the optimality of Kalman Filter for Fault Detection | Kalman filter is widely used for residual generation in fault detection. It leads to optimality in fault detection using some performance indices and also leads to statistically sound residual evaluation and threshold setting. This paper shows that these nice features do not necessarily imply an optimal fault detection... | ['Yucai Zhu', 'Jinming Zhou'] | 2023-01-27 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-2.19171584e-01 -1.97757140e-01 3.30601871e-01 2.02504243e-03
-4.19082642e-01 -7.07161203e-02 4.22175735e-01 1.37311816e-01
-2.55367339e-01 1.13527167e+00 -2.28289917e-01 -5.23806036e-01
-8.00391316e-01 -6.28525853e-01 -1.21363625e-01 -9.28333163e-01
-3.98294777e-01 1.68255568e-01 4.94063973e-01 -5.23036607... | [6.3239336013793945, 2.6717512607574463] |
7224c809-490e-40be-a325-66eb8bba4fa9 | sneaky-spikes-uncovering-stealthy-backdoor | 2302.06279 | null | https://arxiv.org/abs/2302.06279v2 | https://arxiv.org/pdf/2302.06279v2.pdf | Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data | Deep neural networks (DNNs) have demonstrated remarkable performance across various tasks, including image and speech recognition. However, maximizing the effectiveness of DNNs requires meticulous optimization of numerous hyperparameters and network parameters through training. Moreover, high-performance DNNs entail ma... | ['Aitor Urbieta', 'Stjepan Picek', 'Oguzhan Ersoy', 'Gorka Abad'] | 2023-02-13 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 5.89351773e-01 -2.22185954e-01 1.74635842e-01 1.39696762e-01
-2.06249669e-01 -9.62022960e-01 5.92993855e-01 -2.31420055e-01
-7.65138328e-01 6.62216663e-01 -3.41697067e-01 -4.15048122e-01
3.99345793e-02 -7.74161160e-01 -9.56590116e-01 -1.12667000e+00
-4.43910211e-01 -4.09860164e-01 3.45712334e-01 -1.75992340... | [5.605057716369629, 7.774829387664795] |
12ab29ba-0a35-4e56-9719-265958f5fa0a | aakos-aspect-adaptive-knowledge-based-opinion | 2306.05537 | null | https://arxiv.org/abs/2306.05537v1 | https://arxiv.org/pdf/2306.05537v1.pdf | AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization | The rapid growth of information on the Internet has led to an overwhelming amount of opinions and comments on various activities, products, and services. This makes it difficult and time-consuming for users to process all the available information when making decisions. Text summarization, a Natural Language Processing... | ['Quan Bai', 'Edmund M-K. Lai', 'Weihua Li', 'Guan Wang'] | 2023-05-26 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 3.96662146e-01 2.33863339e-01 -4.06924099e-01 -4.00964111e-01
-8.68927598e-01 -4.97739881e-01 4.58777338e-01 8.10222983e-01
-1.09425345e-02 7.30765104e-01 7.38986552e-01 -1.10799810e-02
1.92894503e-01 -6.42447233e-01 -1.48769855e-01 -3.21362376e-01
2.76926696e-01 5.16962826e-01 6.33893162e-02 -5.21624386... | [12.459211349487305, 9.372713088989258] |
53e0ccb2-db8c-4fb3-a912-5d115b201e40 | predictive-modelling-of-football-injuries | 1609.07480 | null | http://arxiv.org/abs/1609.07480v1 | http://arxiv.org/pdf/1609.07480v1.pdf | Predictive modelling of football injuries | The goal of this thesis is to investigate the potential of predictive
modelling for football injuries. This work was conducted in close collaboration
with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation
of Wolverhampton Wanderers (WW).
Three investigations were conducted:
1. Predicting the... | ['Stylianos Kampakis'] | 2016-09-20 | null | null | null | null | ['injury-prediction', 'game-of-football'] | ['playing-games', 'playing-games'] | [ 7.12025259e-03 -4.42930698e-01 -2.13287994e-01 1.74802423e-01
-4.03162688e-01 -1.61924511e-01 -2.62328058e-01 2.15604499e-01
-6.93106413e-01 5.94659865e-01 3.84996921e-01 -1.86527506e-01
-9.22273338e-01 -9.39367771e-01 -6.76288247e-01 -5.65683901e-01
-3.43956828e-01 5.13199091e-01 3.65645289e-01 -3.13307256... | [6.851314067840576, 0.394118994474411] |
958bb299-604d-450f-b03f-33389444b43d | improving-unsupervised-neural-aspect | 2006.09766 | null | https://arxiv.org/abs/2006.09766v1 | https://arxiv.org/pdf/2006.09766v1.pdf | Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification | Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling. While models such as neural attention-based aspect extraction (ABAE) have been successfully applied to user-generated texts, they are less c... | ['Anton Alekseev', 'Elena Tutubalina', 'Sergey Nikolenko', 'Valentin Malykh'] | 2020-06-17 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 2.28634372e-01 7.27081358e-01 -1.38994619e-01 -5.69640815e-01
-9.31570828e-01 -1.82224870e-01 1.09938681e+00 7.79822886e-01
-7.47826755e-01 6.38832629e-01 9.77790833e-01 -7.81343430e-02
-1.16032161e-01 -9.58586514e-01 -5.63984931e-01 -5.00514448e-01
1.24039866e-01 6.71423376e-01 2.36731485e-01 -2.35598564... | [11.366394996643066, 6.753102779388428] |
d67ef436-5a26-4667-932a-34d4724799e2 | code-switching-language-modeling-using-syntax | 1805.12070 | null | http://arxiv.org/abs/1805.12070v2 | http://arxiv.org/pdf/1805.12070v2.pdf | Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning | Lack of text data has been the major issue on code-switching language
modeling. In this paper, we introduce multi-task learning based language model
which shares syntax representation of languages to leverage linguistic
information and tackle the low resource data issue. Our model jointly learns
both language modeling ... | ['Chien-Sheng Wu', 'Pascale Fung', 'Genta Indra Winata', 'Andrea Madotto'] | 2018-05-30 | code-switching-language-modeling-using-syntax-1 | https://aclanthology.org/W18-3207 | https://aclanthology.org/W18-3207.pdf | ws-2018-7 | ['syntax-representation'] | ['natural-language-processing'] | [-4.45051134e-01 -1.31878555e-01 -6.74961209e-01 -3.98882270e-01
-1.40303230e+00 -4.14253801e-01 1.86386555e-01 3.30630213e-01
-3.18937391e-01 5.00432074e-01 3.88494998e-01 -7.68211961e-01
1.13738045e-01 -2.62166917e-01 -5.69154799e-01 -1.21147707e-01
-1.66239530e-01 2.50134736e-01 1.26201496e-01 -2.87911773... | [10.498238563537598, 9.72341537475586] |
ebe8410b-88f3-4160-9203-9f55853d5f65 | llm-rm-at-semeval-2023-task-2-multilingual | 2305.03300 | null | https://arxiv.org/abs/2305.03300v1 | https://arxiv.org/pdf/2305.03300v1.pdf | LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER using XLM-RoBERTa | Named Entity Recognition(NER) is a task of recognizing entities at a token level in a sentence. This paper focuses on solving NER tasks in a multilingual setting for complex named entities. Our team, LLM-RM participated in the recently organized SemEval 2023 task, Task 2: MultiCoNER II,Multilingual Complex Named Entity... | ['Vasudeva Varma', 'Rahul Mehta'] | 2023-05-05 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [-7.83563793e-01 -1.24455497e-01 3.07337474e-02 -4.17595088e-01
-1.02785063e+00 -9.63357151e-01 5.28906882e-01 2.35263839e-01
-1.23890233e+00 1.52406406e+00 6.62892282e-01 -2.76947588e-01
2.81580597e-01 -4.65533495e-01 -4.85543132e-01 1.35962099e-01
-2.77135342e-01 5.62194943e-01 -1.48740411e-01 -2.76985884... | [9.84539794921875, 9.81165599822998] |
4f139ba0-5b43-417e-8677-95f16cd0c7be | activenet-a-computer-vision-based-approach-to | 2010.13714 | null | https://arxiv.org/abs/2010.13714v1 | https://arxiv.org/pdf/2010.13714v1.pdf | ActiveNet: A computer-vision based approach to determine lethargy | The outbreak of COVID-19 has forced everyone to stay indoors, fabricating a significant drop in physical activeness. Our work is constructed upon the idea to formulate a backbone mechanism, to detect levels of activeness in real-time, using a single monocular image of a target person. The scope can be generalized under... | ['Aadit Agarwal', 'Aitik Gupta'] | 2020-10-26 | null | null | null | null | ['activeness-detection'] | ['computer-vision'] | [ 4.94522095e-01 1.84858248e-01 1.36900678e-01 -2.66244680e-01
1.47971064e-01 -7.87698627e-01 5.55615127e-01 2.74892479e-01
-6.43024623e-01 6.23254240e-01 -4.10216749e-02 -1.00758284e-01
-3.50191861e-01 -7.95334637e-01 -7.66581818e-02 -7.38989532e-01
-2.35401854e-01 4.21273977e-01 6.12912253e-02 -1.02630325... | [8.523344993591309, -0.6538624167442322] |
ccc29c36-b963-4c41-9739-4e0a64fe922a | prompt-based-zero-shot-relation-1 | null | null | https://openreview.net/forum?id=OULoKDV7CO | https://openreview.net/pdf?id=OULoKDV7CO | Prompt-based Zero-shot Relation Classification with Semantic Knowledge Augmentation | In relation classification, recognizing unseen (new) relations for which there are no training instances is a challenging task. We propose a prompt-based model with semantic knowledge augmentation (ZS-SKA) to recognize unseen relations under the zero-shot setting. We present a new word-level sentence translation rule a... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['relation-classification'] | ['natural-language-processing'] | [ 5.67884326e-01 1.02844691e+00 -3.94274294e-01 -6.02401555e-01
-5.89306474e-01 -4.10740525e-01 7.43305087e-01 -1.79999415e-02
-6.32164925e-02 9.40866053e-01 3.07646424e-01 -3.71175855e-01
-2.48622403e-01 -1.15262079e+00 -6.63624585e-01 -2.33285055e-01
3.12980026e-01 9.82006073e-01 1.07445531e-01 -6.70919657... | [9.296186447143555, 8.48508071899414] |
c0a9c6dd-da37-41de-a945-555f7a5efb44 | relational-learning-for-joint-head-and-human | 1909.10674 | null | https://arxiv.org/abs/1909.10674v1 | https://arxiv.org/pdf/1909.10674v1.pdf | Relational Learning for Joint Head and Human Detection | Head and human detection have been rapidly improved with the development of deep convolutional neural networks. However, these two tasks are often studied separately without considering their inherent correlation, leading to that 1) head detection is often trapped in more false positives, and 2) the performance of huma... | ['Stan Z. Li', 'Xudong Zou', 'Shifeng Zhang', 'Junliang Xing', 'Zhen Lei', 'Cheng Chi'] | 2019-09-24 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-2.59399265e-01 3.02371740e-01 1.22975573e-01 -3.00937802e-01
-3.36053491e-01 -4.33753915e-02 4.39797580e-01 -1.04228683e-01
-5.63519597e-01 5.82448602e-01 2.16607988e-01 4.29075122e-01
4.89849538e-01 -5.98538458e-01 -4.54452425e-01 -8.32458615e-01
-1.12255737e-01 3.89902800e-01 7.88672388e-01 -3.46561265... | [7.98602294921875, -0.5849825143814087] |
b35b5fde-faf1-49f1-94eb-d9cd1f230531 | view-to-label-multi-view-consistency-for-self | 2305.17972 | null | https://arxiv.org/abs/2305.17972v1 | https://arxiv.org/pdf/2305.17972v1.pdf | View-to-Label: Multi-View Consistency for Self-Supervised 3D Object Detection | For autonomous vehicles, driving safely is highly dependent on the capability to correctly perceive the environment in 3D space, hence the task of 3D object detection represents a fundamental aspect of perception. While 3D sensors deliver accurate metric perception, monocular approaches enjoy cost and availability adva... | ['Francesca Odone', 'Federico Tombari', 'Fabian Manhardt', 'Nikolas Brasch', 'Issa Mouawad'] | 2023-05-29 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [ 2.64581859e-01 -1.57648569e-03 -2.94064164e-01 -7.19231308e-01
-5.99189341e-01 -7.20614433e-01 7.15674639e-01 -6.77450150e-02
-5.66704869e-01 4.95007068e-01 -4.53248888e-01 -4.60697263e-01
2.15816110e-01 -4.70804036e-01 -8.32681298e-01 -5.87513447e-01
2.40357235e-01 5.56047320e-01 6.94841385e-01 -1.61497355... | [7.870424747467041, -2.525078296661377] |
271d979f-f3d0-4b94-bee7-5d7cd069e099 | investigation-of-synthetic-speech-detection | 1610.03009 | null | http://arxiv.org/abs/1610.03009v1 | http://arxiv.org/pdf/1610.03009v1.pdf | Investigation of Synthetic Speech Detection Using Frame- and Segment-Specific Importance Weighting | Speaker verification systems are vulnerable to spoofing attacks which
presents a major problem in their real-life deployment. To date, most of the
proposed synthetic speech detectors (SSDs) have weighted the importance of
different segments of speech equally. However, different attack methods have
different strengths a... | ['Cenk Demiroglu', 'Ali Khodabakhsh'] | 2016-10-10 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 1.36057362e-01 -4.74084355e-02 3.89117569e-01 -1.60702944e-01
-9.11571145e-01 -6.88526630e-01 6.84501588e-01 2.84484550e-02
-2.01323628e-01 6.11091912e-01 2.05895767e-01 -5.15054822e-01
1.92374557e-01 -2.81523705e-01 -5.86304963e-01 -8.01482677e-01
-1.22880429e-01 -4.41922881e-02 7.23253369e-01 -1.33000195... | [14.103872299194336, 5.864459991455078] |
e747ac8b-a26e-4fb2-9fac-58ddd8b8be4d | structured-light-dark-field-microscope | 2202.05357 | null | https://arxiv.org/abs/2202.05357v1 | https://arxiv.org/pdf/2202.05357v1.pdf | Structured light dark-field microscope | A resolution-enhanced dark-field microscope by structured light illumination is proposed to improve resolution and contrast. A set of phase-shifted fringes are projected to the sample plane at large angle to capture modulated dark-field images, from which resolution- and contrast-enhanced dark-field image, as well as s... | ['Rongguang Liang', 'Bofan Song', 'Shaobai Li'] | 2022-02-10 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.08817446e+00 -4.08896297e-01 3.62936795e-01 -2.73612320e-01
-3.57549012e-01 -8.55977014e-02 2.64688465e-03 -3.29340667e-01
-5.29058158e-01 1.03399968e+00 -3.05277318e-01 8.82833675e-02
-5.13177700e-02 -5.31392038e-01 4.12174165e-02 -1.20152223e+00
1.65321127e-01 9.55649093e-02 6.73262179e-01 1.45667091... | [11.388802528381348, -2.630215644836426] |
11b9469f-29fd-4c93-82b3-88d656f77918 | latent-optimal-paths-by-gumbel-propagation | 2306.02568 | null | https://arxiv.org/abs/2306.02568v1 | https://arxiv.org/pdf/2306.02568v1.pdf | Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming | We propose a unified approach to obtain structured sparse optimal paths in the latent space of a variational autoencoder (VAE) using dynamic programming and Gumbel propagation. We solve the classical optimal path problem by a probability softening solution, called the stochastic optimal path, and transform a wide range... | ['Charles Patrick Martin', 'Jing Zhang', 'Christian Walder', 'Xinlei Niu'] | 2023-06-05 | null | null | null | null | ['bayesian-inference', 'singing-voice-synthesis'] | ['methodology', 'speech'] | [ 4.22499403e-02 6.84822321e-01 -1.78998813e-01 -1.90119237e-01
-8.11478555e-01 -5.24042070e-01 7.17012107e-01 -3.48645598e-01
-8.63565803e-02 8.02095890e-01 2.76582867e-01 -1.96815938e-01
-3.66141856e-01 -8.31810892e-01 -1.04104519e+00 -8.91500354e-01
-8.57687145e-02 9.03820753e-01 1.60705596e-01 3.75845954... | [6.886633396148682, 3.8600316047668457] |
67cd68fa-3fb8-49da-a70e-4f042e07310a | interpreting-deep-forest-through-feature | 2305.00805 | null | https://arxiv.org/abs/2305.00805v1 | https://arxiv.org/pdf/2305.00805v1.pdf | Interpreting Deep Forest through Feature Contribution and MDI Feature Importance | Deep forest is a non-differentiable deep model which has achieved impressive empirical success across a wide variety of applications, especially on categorical/symbolic or mixed modeling tasks. Many of the application fields prefer explainable models, such as random forests with feature contributions that can provide l... | ['Yuan Jiang', 'Shen-Huan Lyu', 'Yi-Xiao He'] | 2023-05-01 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 1.82833642e-01 3.36772978e-01 -3.97058487e-01 -7.54311144e-01
-2.61143167e-02 -1.51773999e-02 6.12637639e-01 -9.86022651e-02
3.42209816e-01 1.16351748e+00 2.76715726e-01 -3.39103609e-01
-3.57962519e-01 -1.09862530e+00 -6.83212221e-01 -8.02169740e-01
-1.61403731e-01 6.03013992e-01 7.71941384e-03 -1.35528883... | [8.881723403930664, 5.616302967071533] |
015fba58-cdc5-43cd-8ed4-69b85a826f46 | learning-policies-from-human-data-for-skat | 1905.10907 | null | https://arxiv.org/abs/1905.10907v1 | https://arxiv.org/pdf/1905.10907v1.pdf | Learning Policies from Human Data for Skat | Decision-making in large imperfect information games is difficult. Thanks to recent success in Poker, Counterfactual Regret Minimization (CFR) methods have been at the forefront of research in these games. However, most of the success in large games comes with the use of a forward model and powerful state abstractions.... | ['Christopher Solinas', 'Michael Buro', 'Douglas Rebstock'] | 2019-05-27 | null | null | null | null | ['card-games'] | ['playing-games'] | [-3.09925407e-01 3.09132546e-01 -1.10055715e-01 -6.58872500e-02
-6.54058337e-01 -7.23586380e-01 5.29720783e-01 -3.11266392e-01
-1.00005352e+00 1.23258960e+00 1.23700388e-01 -6.98010027e-01
-4.23772484e-01 -7.52215683e-01 -6.88538909e-01 -4.26782489e-01
-4.02648509e-01 8.59056175e-01 2.83131570e-01 -9.14708674... | [3.6492295265197754, 1.6597737073898315] |
804844fc-7d76-41d9-a011-35a64796e81b | scanbank-a-benchmark-dataset-for-figure | 2106.15320 | null | https://arxiv.org/abs/2106.15320v1 | https://arxiv.org/pdf/2106.15320v1.pdf | ScanBank: A Benchmark Dataset for Figure Extraction from Scanned Electronic Theses and Dissertations | We focus on electronic theses and dissertations (ETDs), aiming to improve access and expand their utility, since more than 6 million are publicly available, and they constitute an important corpus to aid research and education across disciplines. The corpus is growing as new born-digital documents are included, and sin... | ['Jian Wu', 'Edward A. Fox', 'William A. Ingram', 'Sampanna Yashwant Kahu'] | 2021-06-23 | null | null | null | null | ['table-extraction'] | ['miscellaneous'] | [-4.93717194e-02 4.22579229e-01 -1.56030282e-01 -1.81072131e-01
-9.67691362e-01 -7.70746589e-01 5.19420147e-01 3.53336871e-01
-5.27015030e-01 8.32833827e-01 1.61678419e-02 -6.91219628e-01
-3.30635458e-02 -1.09808397e+00 -1.09750640e+00 -1.07918411e-01
2.60369092e-01 6.63572431e-01 1.41532188e-02 1.06178232... | [11.655952453613281, 2.806732654571533] |
cbfe3ff9-cb0f-4f13-833d-fa0546ed1772 | exploring-the-trade-offs-unified-large | 2304.09138 | null | https://arxiv.org/abs/2304.09138v1 | https://arxiv.org/pdf/2304.09138v1.pdf | Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task | Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguis... | ['Tianming Liu', 'Dajiang Zhu', 'Xiang Li', 'Dinggang Shen', 'Quanzheng Li', 'Wei Liu', 'Gang Li', 'Lin Zhao', 'Zhengliang Liu', 'Chong Ma', 'Haixing Dai', 'Xiaowei Yu', 'Chao Cao', 'Lu Zhang', 'Zihao Wu'] | 2023-04-18 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-8.40340108e-02 4.43358272e-01 -2.74627090e-01 -4.14578617e-01
-1.28748798e+00 -4.26262498e-01 3.77653867e-01 4.53712106e-01
-5.13456464e-01 6.39575660e-01 4.29649323e-01 -7.77044594e-01
-6.25235558e-01 -6.17154777e-01 -2.04929337e-01 -1.82952777e-01
-1.46271855e-01 1.08637559e+00 1.35636061e-01 -2.10032821... | [8.8742036819458, 8.50212574005127] |
24c60e53-cf8a-43c7-b7f0-6a18eb28134b | bdis-bayesian-dense-inverse-searching-method | 2205.03133 | null | https://arxiv.org/abs/2205.03133v1 | https://arxiv.org/pdf/2205.03133v1.pdf | BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching | In stereoscope-based Minimally Invasive Surgeries (MIS), dense stereo matching plays an indispensable role in 3D shape recovery, AR, VR, and navigation tasks. Although numerous Deep Neural Network (DNN) approaches are proposed, the conventional prior-free approaches are still popular in the industry because of the lack... | ['Maani Ghaffari', 'Jianyu Lin', 'Qiuchen Zhu', 'Jingwei Song'] | 2022-05-06 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 1.88835546e-01 2.08795890e-02 4.37246226e-02 -8.76391828e-02
-1.00645542e+00 3.29842642e-02 1.30602598e-01 -3.82823683e-02
-6.68250024e-01 6.24991000e-01 1.23713687e-01 -3.67265463e-01
-1.03698045e-01 -6.79890752e-01 -6.61336541e-01 -1.05522525e+00
4.11954910e-01 3.95205736e-01 4.38470811e-01 -1.86494030... | [13.773874282836914, -3.066627264022827] |
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