paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bab063e5-c6d9-4e05-9b38-85853dd5015b | level-up-the-deepfake-detection-a-method-to | 2303.00608 | null | https://arxiv.org/abs/2303.00608v1 | https://arxiv.org/pdf/2303.00608v1.pdf | Level Up the Deepfake Detection: a Method to Effectively Discriminate Images Generated by GAN Architectures and Diffusion Models | The image deepfake detection task has been greatly addressed by the scientific community to discriminate real images from those generated by Artificial Intelligence (AI) models: a binary classification task. In this work, the deepfake detection and recognition task was investigated by collecting a dedicated dataset of ... | ['Sebastiano Battiato', 'Oliver Giudice', 'Luca Guarnera'] | 2023-03-01 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 4.98270690e-01 1.62032932e-01 1.53520554e-01 1.15937971e-01
-5.33167839e-01 -6.19733870e-01 1.30419254e+00 -4.93554682e-01
-2.63274521e-01 9.10991907e-01 -2.32603446e-01 -1.49215072e-01
1.52421728e-01 -8.94956768e-01 -6.45452917e-01 -1.02550197e+00
2.47218788e-01 7.10293412e-01 6.70383498e-02 -1.40346751... | [12.46102523803711, 1.074063777923584] |
5856db94-4f39-4826-8659-eba41e31f996 | type-enhanced-ensemble-triple-representation | 2305.01556 | null | https://arxiv.org/abs/2305.01556v1 | https://arxiv.org/pdf/2305.01556v1.pdf | Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment | Entity alignment(EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs(KGs), which aims to discover entities referring to the same real-world object from different KGs. Most existing methods generate aligning entity representation by mining the relevance of triple elements via embedding-... | ['Min Yang', 'Xueyan Zhao', 'Haihang Wang', 'Chengxiang Tan', 'Zhishuo Zhang'] | 2023-05-02 | null | null | null | null | ['entity-alignment', 'specificity', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-0.31905043 0.12525897 -0.50471383 -0.40628967 -0.75003153 -0.5653738
0.500777 0.3637303 -0.45931542 0.688959 0.54784167 0.01414781
-0.4663602 -0.95503193 -0.9050132 -0.5292895 0.09514716 0.5043149
0.20721108 -0.63851714 -0.04171581 -0.10849088 -1.3793038 0.2843405
1.4671763 0.8263725 -0.061... | [8.760917663574219, 7.978930473327637] |
d078f676-6139-46e1-a2c8-c82b307ef855 | algorithmic-trading-with-fitted-q-iteration | 1805.07478 | null | http://arxiv.org/abs/1805.07478v1 | http://arxiv.org/pdf/1805.07478v1.pdf | Algorithmic Trading with Fitted Q Iteration and Heston Model | We present the use of the fitted Q iteration in algorithmic trading. We show
that the fitted Q iteration helps alleviate the dimension problem that the
basic Q-learning algorithm faces in application to trading. Furthermore, we
introduce a procedure including model fitting and data simulation to enrich
training data as... | [] | 2018-05-18 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-4.94615495e-01 8.80105942e-02 -3.12502861e-01 -1.44463718e-01
-4.48627740e-01 -7.22799480e-01 4.75171715e-01 -4.61655766e-01
-7.35929966e-01 1.30657125e+00 -3.78385186e-01 -8.25901449e-01
-1.45911783e-01 -8.95517945e-01 -6.55892849e-01 -4.22551572e-01
-3.84173751e-01 7.45930135e-01 2.64716297e-01 -2.80885577... | [4.445273399353027, 3.8426642417907715] |
945535b0-49a1-4543-bdfd-0270d84a3195 | half-real-half-fake-distillation-for-class | 2104.00875 | null | https://arxiv.org/abs/2104.00875v1 | https://arxiv.org/pdf/2104.00875v1.pdf | Half-Real Half-Fake Distillation for Class-Incremental Semantic Segmentation | Despite their success for semantic segmentation, convolutional neural networks are ill-equipped for incremental learning, \ie, adapting the original segmentation model as new classes are available but the initial training data is not retained. Actually, they are vulnerable to catastrophic forgetting problem. We try to ... | ['Xian-Sheng Hua', 'Wenyu Liu', 'Jianqiang Huang', 'Mingyuan Tao', 'Xinggang Wang', 'Wentian Hao', 'Zilong Huang'] | 2021-04-02 | null | null | null | null | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 7.85773993e-01 2.41982996e-01 -4.82358485e-02 -6.27723336e-01
-4.77321863e-01 -5.68702221e-01 3.69142234e-01 -1.96738884e-01
-6.51317120e-01 9.12981749e-01 -4.20423597e-01 -1.30814224e-01
3.26270163e-01 -1.05291319e+00 -1.15295005e+00 -9.64949310e-01
3.97648394e-01 4.60068434e-01 7.49034286e-01 1.57897174... | [9.446645736694336, 1.9165418148040771] |
4db80b99-f933-460e-b31f-8a8341fff1ab | seeing-is-believing-pedestrian-trajectory | null | null | http://irtizahasan.com/ | http://irtizahasan.com/WACV_2018_Seeing_is_believing.pdf | “Seeing is Believing”: Pedestrian Trajectory Forecasting Using Visual Frustum of Attention | In this paper we show the importance of the head
pose estimation in the task of trajectory forecasting. This
cue, when produced by an oracle and injected in a novel
socially-based energy minimization approach, allows to get
state-of-the-art performances on four different forecasting
benchmarks, without relying on ... | ['Alessio Del Bue', 'Theodore Tsesmelis', 'Irtiza Hasan', 'Fabio Galasso', 'Marco Cristani', 'Francesco Setti'] | 2018-03-12 | null | null | null | ieee-winter-conference-on-applications-of-2 | ['head-pose-estimation'] | ['computer-vision'] | [-3.29532713e-01 5.17654181e-01 8.41672868e-02 -3.32448632e-01
-5.99532485e-01 -4.81991619e-01 8.89458656e-01 4.33415532e-01
-6.76159263e-01 6.53518438e-01 2.54611462e-01 6.30068481e-02
-1.56027332e-01 -6.50060415e-01 -1.04849958e+00 -8.30380738e-01
-1.77656293e-01 8.50458741e-01 4.59586233e-01 -3.82656217... | [13.74754810333252, 0.33239343762397766] |
e17a12dc-77bc-4661-a8c9-b2b6a15456a0 | inferring-gender-of-a-twitter-user-using | 1405.6667 | null | http://arxiv.org/abs/1405.6667v1 | http://arxiv.org/pdf/1405.6667v1.pdf | Inferring gender of a Twitter user using celebrities it follows | This paper addresses the task of user gender classification in social media,
with an application to Twitter. The approach automatically predicts gender by
leveraging observable information such as the tweet behavior, linguistic
content of the user's Twitter feed and the celebrities followed by the user.
This paper firs... | ['Puneet Singh Ludu'] | 2014-05-26 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-5.12433887e-01 2.78704464e-01 -6.51720524e-01 -6.37344241e-01
-2.66599149e-01 -5.31418443e-01 1.00240791e+00 1.20294869e+00
-8.39745343e-01 8.31293583e-01 4.83586520e-01 -5.34938015e-02
1.24935791e-01 -1.10426605e+00 -3.59083191e-02 -2.43478641e-01
-3.62282336e-01 3.95803660e-01 -8.42631608e-02 -6.72727108... | [9.449480056762695, 10.345113754272461] |
b8c02ddb-f846-4530-9ba9-f876863fbedf | mrnet-multiple-input-receptive-field-network | 2301.12972 | null | https://arxiv.org/abs/2301.12972v3 | https://arxiv.org/pdf/2301.12972v3.pdf | Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene | This paper proposes EyeNet, a novel semantic segmentation network for point clouds that addresses the critical yet often overlooked parameter of coverage area size. Inspired by human peripheral vision, EyeNet overcomes the limitations of conventional networks by introducing a simple but efficient multi-contour input an... | ['Gunho Sohn', 'Maryam Jameela', 'Yeongjeong Jeong', 'Sunghwan Yoo'] | 2023-01-30 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 9.52669084e-02 2.09869854e-02 2.01276094e-01 -1.49913281e-01
-4.76737395e-02 -5.71905911e-01 3.96799862e-01 2.42686003e-01
-7.82844901e-01 5.91337979e-01 -6.60599947e-01 -3.99797350e-01
-4.69357856e-02 -1.05082333e+00 -5.97442985e-01 -2.71033049e-01
-8.22092220e-02 5.27903616e-01 1.18543661e+00 -2.11261973... | [7.985296249389648, -3.1652908325195312] |
cf7434fb-456d-441d-9691-02ff7ca863dd | learning-preferences-for-referring-expression | null | null | https://aclanthology.org/W12-1503 | https://aclanthology.org/W12-1503.pdf | Learning Preferences for Referring Expression Generation: Effects of Domain, Language and Algorithm | null | ['Mari{\\"e}t Theune', 'Emiel Krahmer', 'Ruud Koolen'] | 2012-05-01 | null | null | null | ws-2012-5 | ['referring-expression-generation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.357715129852295, 3.6744203567504883] |
572b390b-3f5a-48e1-ba1b-b185ed19df03 | gan-prior-embedded-network-for-blind-face | 2105.06070 | null | https://arxiv.org/abs/2105.06070v1 | https://arxiv.org/pdf/2105.06070v1.pdf | GAN Prior Embedded Network for Blind Face Restoration in the Wild | Blind face restoration (BFR) from severely degraded face images in the wild is a very challenging problem. Due to the high illness of the problem and the complex unknown degradation, directly training a deep neural network (DNN) usually cannot lead to acceptable results. Existing generative adversarial network (GAN) ba... | ['Lei Zhang', 'Xuansong Xie', 'Peiran Ren', 'Tao Yang'] | 2021-05-13 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yang_GAN_Prior_Embedded_Network_for_Blind_Face_Restoration_in_the_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_GAN_Prior_Embedded_Network_for_Blind_Face_Restoration_in_the_CVPR_2021_paper.pdf | cvpr-2021-1 | ['blind-face-restoration'] | ['computer-vision'] | [ 2.22048119e-01 -3.27031724e-02 4.21154141e-01 -1.74698144e-01
-7.00604737e-01 -2.49254942e-01 2.86004096e-01 -1.02635550e+00
2.63390899e-01 8.30819309e-01 4.69177127e-01 -2.85478476e-02
3.87719572e-01 -9.66651022e-01 -7.60973454e-01 -1.09472299e+00
4.80030239e-01 6.50953874e-02 -2.68812984e-01 -3.13443750... | [12.811806678771973, -0.08628809452056885] |
35eb19b6-c201-4d5e-8d60-663d24019be6 | acid-action-conditional-implicit-visual | 2203.06856 | null | https://arxiv.org/abs/2203.06856v3 | https://arxiv.org/pdf/2203.06856v3.pdf | ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation | Manipulating volumetric deformable objects in the real world, like plush toys and pizza dough, bring substantial challenges due to infinite shape variations, non-rigid motions, and partial observability. We introduce ACID, an action-conditional visual dynamics model for volumetric deformable objects based on structured... | ['Yuke Zhu', 'Anima Anandkumar', 'Silvio Savarese', 'Leonidas J. Guibas', 'Christopher Choy', 'Zhenyu Jiang', 'Bokui Shen'] | 2022-03-14 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-1.68756694e-01 -8.25370029e-02 1.56968143e-02 -6.31444454e-02
-6.75106883e-01 -8.19634318e-01 6.15527034e-01 -3.50898534e-01
-1.10698164e-01 6.06550336e-01 2.73940355e-01 7.35405236e-02
-7.02219009e-02 -7.01378107e-01 -1.04617298e+00 -8.14396918e-01
-3.78534406e-01 7.55744636e-01 4.40332651e-01 -3.18858087... | [4.9885382652282715, 0.29269570112228394] |
40fbfd47-5c29-4398-b786-99638d2111c9 | perplexity-from-plm-is-unreliable-for | 2210.05892 | null | https://arxiv.org/abs/2210.05892v2 | https://arxiv.org/pdf/2210.05892v2.pdf | Perplexity from PLM Is Unreliable for Evaluating Text Quality | Recently, amounts of works utilize perplexity~(PPL) to evaluate the quality of the generated text. They suppose that if the value of PPL is smaller, the quality(i.e. fluency) of the text to be evaluated is better. However, we find that the PPL referee is unqualified and it cannot evaluate the generated text fairly for ... | ['Xuying Meng', 'Aixin Sun', 'Jiawen Deng', 'Yequan Wang'] | 2022-10-12 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-2.10923642e-01 -2.23739091e-02 -3.65499891e-02 -2.30803922e-01
-7.86203325e-01 -8.60483587e-01 4.00196493e-01 3.46290499e-01
-3.57803911e-01 9.79024708e-01 4.31879610e-01 -5.77060103e-01
-8.33653882e-02 -7.16957211e-01 -3.55723143e-01 -3.63918126e-01
5.05792797e-01 3.60459030e-01 3.37870300e-01 -1.90128461... | [11.594110488891602, 9.461009979248047] |
8c09f9f8-6c7d-4c9a-a9d7-cb6b077f865d | pneumonia-detection-in-chest-radiographs | 1811.08939 | null | http://arxiv.org/abs/1811.08939v1 | http://arxiv.org/pdf/1811.08939v1.pdf | Pneumonia Detection in Chest Radiographs | In this work, we describe our approach to pneumonia classification and
localization in chest radiographs. This method uses only \emph{open-source}
deep learning object detection and is based on CoupleNet, a fully convolutional
network which incorporates global and local features for object detection. Our
approach achie... | ['The DeepRadiology Team'] | 2018-11-21 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 2.86647379e-01 5.84319904e-02 3.76460521e-04 -1.47645846e-01
-1.16074586e+00 -4.58344817e-01 1.69505626e-01 3.49456221e-01
-7.02230692e-01 5.08130789e-01 2.21795321e-01 -5.60692847e-01
2.08129082e-02 -4.43077415e-01 -7.13891625e-01 -3.77113998e-01
-1.63011625e-01 5.86618423e-01 8.43506396e-01 -1.33996666... | [15.228771209716797, -2.108893394470215] |
eedfc39e-fa3e-4e09-990b-a26bd451d136 | an-attention-driven-two-stage-clustering | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5955_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730018.pdf | An Attention-driven Two-stage Clustering Method for Unsupervised Person Re-Identification | The progressive clustering method and its variants, which iteratively generate pseudo labels for unlabeled data and perform feature learning, have shown great process in unsupervised person re-identification (re-id). However, they have an intrinsic problem of modeling the in-camera variability of images successfully, t... | ['Xiao Liu', 'Zilong Ji', 'Xiaohan Lin', 'Si Wu', 'Xiaolong Zou', 'Tiejun Huang'] | null | null | null | null | eccv-2020-8 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 4.10293788e-02 -3.48215699e-01 1.00665607e-01 -3.61436576e-01
-3.68736863e-01 -3.63763183e-01 6.72331452e-01 -2.37237085e-02
-5.50566971e-01 4.53900516e-01 2.91245878e-01 2.88412571e-01
-2.14869659e-02 -5.41085303e-01 -2.96804041e-01 -1.06314754e+00
3.29883873e-01 6.42167807e-01 3.00232768e-01 3.10292631... | [14.860269546508789, 1.1668992042541504] |
d7997301-a37c-48b7-8569-31848cf2f1fb | can-locational-disparity-of-prosumer-energy | 2207.10248 | null | https://arxiv.org/abs/2207.10248v3 | https://arxiv.org/pdf/2207.10248v3.pdf | Can locational disparity of prosumer energy optimization due to inverter rules be limited? | To mitigate issues related to the growth of variable smart loads and distributed generation, distribution system operators (DSO) now make it binding for prosumers with inverters to operate under pre-set rules. In particular, the maximum active and reactive power set points for prosumers are based on local voltage measu... | ['Deepjyoti Deka', 'Dirk Van Hertem', 'Ana Bušić', 'Md Umar Hashmi'] | 2022-07-21 | null | null | null | null | ['energy-management'] | ['time-series'] | [-2.47341707e-01 1.77518204e-01 -7.85608739e-02 1.66520223e-01
-3.61951917e-01 -1.39351594e+00 1.95547760e-01 4.72497970e-01
3.84850085e-01 1.18751383e+00 -2.11968288e-01 -5.09957969e-01
-7.09647655e-01 -1.04312861e+00 -1.82154760e-01 -1.00350928e+00
-2.24209622e-01 3.76270562e-01 -4.31936443e-01 -2.88724899... | [5.66974401473999, 2.5290186405181885] |
70d11641-6b3c-4d95-ae7c-1a0155f4225a | local-shrunk-discriminant-analysis-lsda | 1705.01206 | null | http://arxiv.org/abs/1705.01206v1 | http://arxiv.org/pdf/1705.01206v1.pdf | Local Shrunk Discriminant Analysis (LSDA) | Dimensionality reduction is a crucial step for pattern recognition and data
mining tasks to overcome the curse of dimensionality. Principal component
analysis (PCA) is a traditional technique for unsupervised dimensionality
reduction, which is often employed to seek a projection to best represent the
data in a least-sq... | ['Guotai Zhang', 'Hua Zhang', 'Feiping Nie', 'Zan Gao'] | 2017-05-03 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-2.17034101e-01 -5.41354775e-01 -1.20813839e-01 -1.94886759e-01
-3.01971883e-01 -4.58126903e-01 3.08325469e-01 -1.67402685e-01
-8.62455368e-02 4.08245265e-01 3.76668960e-01 -9.20350924e-02
-4.91677046e-01 -5.88661790e-01 1.17675647e-01 -1.22939074e+00
1.19279206e-01 3.40770304e-01 4.24018763e-02 -7.18462691... | [7.911818027496338, 4.262103080749512] |
b6abcc62-f7d8-40ed-84fc-f3c219757ad9 | from-independent-prediction-to-re-ordered | 1906.01230 | null | https://arxiv.org/abs/1906.01230v1 | https://arxiv.org/pdf/1906.01230v1.pdf | From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification | Emotion cause identification aims at identifying the potential causes that lead to a certain emotion expression in text. Several techniques including rule based methods and traditional machine learning methods have been proposed to address this problem based on manually designed rules and features. More recently, some ... | ['Zixiang Ding', 'Rui Xia', 'Huihui He', 'Mengran Zhang'] | 2019-06-04 | null | null | null | null | ['emotion-cause-extraction'] | ['natural-language-processing'] | [ 1.38464928e-01 -8.26263726e-02 -2.82761157e-01 -6.53931022e-01
-3.70554656e-01 -2.78329909e-01 6.10595822e-01 3.55700940e-01
-2.00538948e-01 3.48934710e-01 6.17533982e-01 5.74989170e-02
-3.49756420e-01 -7.39975691e-01 -4.54312414e-01 -6.65631652e-01
-3.17479335e-02 1.95950195e-01 -1.65967658e-01 -3.20780724... | [12.627114295959473, 6.215687274932861] |
f4eaa925-4d9e-458c-9c31-bb8af201a7a6 | validation-of-massively-parallel-adaptive | 2305.01334 | null | https://arxiv.org/abs/2305.01334v1 | https://arxiv.org/pdf/2305.01334v1.pdf | Validation of massively-parallel adaptive testing using dynamic control matching | A/B testing is a widely-used paradigm within marketing optimization because it promises identification of causal effects and because it is implemented out of the box in most messaging delivery software platforms. Modern businesses, however, often run many A/B/n tests at the same time and in parallel, and package many c... | ['Schaun Wheeler'] | 2023-05-02 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 4.24482465e-01 -1.96666569e-01 -3.41634363e-01 -4.15344328e-01
-3.87173295e-01 -8.52474570e-01 2.62110591e-01 6.15171194e-01
-4.14306134e-01 7.68857598e-01 -5.50312048e-04 -5.93186438e-01
-5.89334548e-01 -9.57366943e-01 -1.04876757e+00 -5.17208338e-01
-1.59531921e-01 1.09838963e+00 4.29900825e-01 -3.28353882... | [4.771016597747803, 2.544118881225586] |
d9f587ea-2e20-4c96-b3ba-bec835ff2b3c | privacy-preserving-adversarial-facial | 2305.05391 | null | https://arxiv.org/abs/2305.05391v1 | https://arxiv.org/pdf/2305.05391v1.pdf | Privacy-preserving Adversarial Facial Features | Face recognition service providers protect face privacy by extracting compact and discriminative facial features (representations) from images, and storing the facial features for real-time recognition. However, such features can still be exploited to recover the appearance of the original face by building a reconstruc... | ['Kui Ren', 'Kaixin Liu', 'Wei Yuan', 'Peng Sun', 'Yan Wang', 'Jiahui Hu', 'Wenwen Zhang', 'Shuaifan Jin', 'He Wang', 'Zhibo Wang'] | 2023-05-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Privacy-Preserving_Adversarial_Facial_Features_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Privacy-Preserving_Adversarial_Facial_Features_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-recognition'] | ['computer-vision'] | [ 2.41938144e-01 1.52466938e-01 3.11349273e-01 -7.38643587e-01
-5.62282145e-01 -1.08969975e+00 3.09264898e-01 -6.99138999e-01
-1.30858511e-01 3.39783311e-01 -1.04059726e-01 -1.30676687e-01
2.14490399e-01 -1.03077638e+00 -8.85466874e-01 -1.12418437e+00
-1.27374455e-01 -3.86673123e-01 -2.46923387e-01 4.64566574... | [12.815507888793945, 0.9517271518707275] |
2198132b-5853-42a7-9649-ce0664b15bf8 | unsupervised-monocular-depth-reconstruction | 2012.15680 | null | https://arxiv.org/abs/2012.15680v3 | https://arxiv.org/pdf/2012.15680v3.pdf | Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes | Monocular depth reconstruction of complex and dynamic scenes is a highly challenging problem. While for rigid scenes learning-based methods have been offering promising results even in unsupervised cases, there exists little to no literature addressing the same for dynamic and deformable scenes. In this work, we presen... | ['Luc van Gool', 'Martin R. Oswald', 'Ajad Chhatkuli', 'Thomas Probst', 'Danda Pani Paudel', 'Ayça Takmaz'] | 2020-12-31 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 1.93843886e-01 7.30517581e-02 -3.80637459e-02 -3.66991758e-01
-4.83542293e-01 -6.66029751e-01 4.75719422e-01 -6.28466070e-01
-2.88845748e-01 6.30898178e-01 2.57369101e-01 1.56807318e-01
-2.10108254e-02 -6.11281753e-01 -6.73139989e-01 -8.65813315e-01
3.82216752e-01 9.00490224e-01 3.71107727e-01 3.35164249... | [8.628686904907227, -2.2203540802001953] |
b149d7eb-bce6-4b76-abab-096ffa564d52 | global-priors-guided-modulation-network-for | 2208.06885 | null | https://arxiv.org/abs/2208.06885v2 | https://arxiv.org/pdf/2208.06885v2.pdf | Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping | Joint super-resolution and inverse tone-mapping (SR-ITM) aims to enhance the visual quality of videos that have quality deficiencies in resolution and dynamic range. This problem arises when using 4K high dynamic range (HDR) TVs to watch a low-resolution standard dynamic range (LR SDR) video. Previous methods that rely... | ['Yurong Dai', 'Xing Wen', 'Ming Sun', 'Jinjia Zhou', 'Chang Wu', 'Li Xu', 'Shaoyi Long', 'Gang He'] | 2022-08-14 | null | null | null | null | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 5.39687216e-01 -5.80776095e-01 -1.75478086e-01 -1.44553274e-01
-7.84535587e-01 -4.38739061e-02 2.25382283e-01 -7.61640310e-01
-1.82670802e-01 6.67274058e-01 3.14573854e-01 -1.59376740e-01
-2.14114755e-01 -7.21484780e-01 -5.37035763e-01 -7.34140754e-01
-1.28563613e-01 -6.17783070e-01 6.01048172e-01 -3.43140662... | [11.041099548339844, -2.0616400241851807] |
c42d2d91-b309-4462-a0ee-ca9bb9d45b42 | nsnet-non-saliency-suppression-sampler-for | 2207.10388 | null | https://arxiv.org/abs/2207.10388v1 | https://arxiv.org/pdf/2207.10388v1.pdf | NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition | It is challenging for artificial intelligence systems to achieve accurate video recognition under the scenario of low computation costs. Adaptive inference based efficient video recognition methods typically preview videos and focus on salient parts to reduce computation costs. Most existing works focus on complex netw... | ['Wanli Ouyang', 'Xiaoran Fan', 'Haosen Yang', 'Dongliang He', 'Rui Su', 'Haoran Wang', 'Wenhao Wu', 'Boyang xia'] | 2022-07-21 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 5.56927025e-01 -2.65065610e-01 -7.31737196e-01 -3.45096588e-01
-5.20055592e-01 2.04957295e-02 3.16167414e-01 -1.97944880e-01
-3.93421918e-01 8.17287922e-01 1.74880400e-01 6.42493740e-02
2.60768473e-01 -4.97630715e-01 -7.78342605e-01 -7.64878988e-01
3.32162380e-02 -2.39254758e-01 7.27600634e-01 -3.50577161... | [9.516412734985352, -0.31090736389160156] |
21dd5f9e-6fc2-45cb-8e71-cdd8a436d684 | dual-teacher-exploiting-intra-domain-and | 2101.02375 | null | https://arxiv.org/abs/2101.02375v1 | https://arxiv.org/pdf/2101.02375v1.pdf | Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation | Annotation scarcity is a long-standing problem in medical image analysis area. To efficiently leverage limited annotations, abundant unlabeled data are additionally exploited in semi-supervised learning, while well-established cross-modality data are investigated in domain adaptation. In this paper, we aim to explore t... | ['Pheng-Ann Heng', 'Lequan Yu', 'Shujun Wang', 'Kang Li'] | 2021-01-07 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 4.39392924e-01 4.69326556e-01 -4.66539025e-01 -4.56459880e-01
-9.83796120e-01 -5.59501946e-01 2.50428677e-01 -1.41858971e-02
-5.08641481e-01 9.55692291e-01 -1.35694453e-02 -1.01985477e-01
-2.16872641e-03 -5.55479467e-01 -6.45888388e-01 -8.60389292e-01
2.85858333e-01 4.37987566e-01 4.30410802e-01 9.79163423... | [14.650004386901855, -2.026695966720581] |
2a937af6-e9c0-4976-b24c-86d376145a94 | predicting-gender-from-iris-texture-may-be | 1811.10066 | null | http://arxiv.org/abs/1811.10066v1 | http://arxiv.org/pdf/1811.10066v1.pdf | Predicting Gender from Iris Texture May Be Harder Than It Seems | Predicting gender from iris images has been reported by several researchers
as an application of machine learning in biometrics. Recent works on this topic
have suggested that the preponderance of the gender cues is located in the
periocular region rather than in the iris texture itself. This paper focuses on
teasing o... | ['Kevin Bowyer', 'Andrey Kuehlkamp'] | 2018-11-25 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [ 5.10207079e-02 2.91672498e-01 -5.46506643e-01 -5.03655314e-01
-1.94435809e-02 -4.94178832e-01 5.74928939e-01 3.84439856e-01
-2.49107167e-01 4.19338822e-01 4.32489365e-01 -3.54453623e-01
-9.54245105e-02 -4.24811304e-01 -3.24649483e-01 -1.12064457e+00
1.00164071e-01 3.36956084e-01 -3.33243221e-01 1.53990716... | [3.7451012134552, -3.6296439170837402] |
5f85ddb1-0d77-4fd2-8c1f-4e389a5095a7 | clinical-concept-extraction-for-document | 1906.03380 | null | https://arxiv.org/abs/1906.03380v1 | https://arxiv.org/pdf/1906.03380v1.pdf | Clinical Concept Extraction for Document-Level Coding | The text of clinical notes can be a valuable source of patient information and clinical assessments. Historically, the primary approach for exploiting clinical notes has been information extraction: linking spans of text to concepts in a detailed domain ontology. However, recent work has demonstrated the potential of s... | ['Sarah Wiegreffe', 'Sherry Yan', 'Jimeng Sun', 'Jacob Eisenstein', 'Edward Choi'] | 2019-06-08 | clinical-concept-extraction-for-document-1 | https://aclanthology.org/W19-5028 | https://aclanthology.org/W19-5028.pdf | ws-2019-8 | ['clinical-concept-extraction'] | ['medical'] | [ 4.97145683e-01 6.68926716e-01 -4.51317608e-01 -3.68797511e-01
-9.09510374e-01 -5.28333545e-01 4.11937207e-01 1.26896679e+00
-3.87483627e-01 6.77393317e-01 9.08346117e-01 -3.93531919e-01
-3.90894294e-01 -6.75484300e-01 -1.29787400e-02 -4.88156348e-01
4.38108146e-02 4.23294455e-01 -1.37439653e-01 1.35966837... | [8.393926620483398, 8.575933456420898] |
b5e70bf2-c2a4-4ec6-abbb-ff6c6ac519e2 | physical-pooling-functions-in-graph-neural | 2207.13779 | null | https://arxiv.org/abs/2207.13779v1 | https://arxiv.org/pdf/2207.13779v1.pdf | Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction | Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a standard pooling function to pre... | ['Alexander Mitsos', 'Kai Leonhard', 'Manuel Dahmen', 'Martin Grohe', 'Jana M. Weber', 'Jan G. Rittig', 'Artur M. Schweidtmann'] | 2022-07-27 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 3.78893465e-01 -1.92815781e-01 -3.21594119e-01 -2.38759682e-01
-3.98415148e-01 -7.43205190e-01 4.87064898e-01 7.15432823e-01
-4.44825560e-01 1.19661987e+00 1.01380534e-01 -2.57686824e-01
-4.72096741e-01 -1.14835274e+00 -1.00661540e+00 -9.20777977e-01
-4.43900615e-01 -2.56969094e-01 3.81661624e-01 -3.37334335... | [5.143467903137207, 5.646833896636963] |
ab7e3951-9a58-4cbe-9ce7-845300d13af9 | deep-perceptual-preprocessing-for-video | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chadha_Deep_Perceptual_Preprocessing_for_Video_Coding_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chadha_Deep_Perceptual_Preprocessing_for_Video_Coding_CVPR_2021_paper.pdf | Deep Perceptual Preprocessing for Video Coding | We introduce the concept of rate-aware deep perceptual preprocessing (DPP) for video encoding. DPP makes a single pass over each input frame in order to enhance its visual quality when the video is to be compressed with any codec at any bitrate. The resulting bitstreams can be decoded and displayed at the client si... | ['Yiannis Andreopoulos', 'Aaron Chadha'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 5.98154724e-01 2.71520354e-02 -2.11214915e-01 -4.21070993e-01
-8.46984804e-01 -2.61159390e-01 4.10281479e-01 3.24605331e-02
-3.79828364e-01 3.96922916e-01 2.67062902e-01 -5.30059993e-01
1.46107867e-01 -8.06534410e-01 -1.04896259e+00 -4.71973121e-01
-3.15643102e-01 6.12990698e-03 3.22418630e-01 -2.85390288... | [11.382092475891113, -1.596691370010376] |
3236009b-7361-46ec-b0da-32d79215797e | tvnet-temporal-voting-network-for-action | 2201.00434 | null | https://arxiv.org/abs/2201.00434v1 | https://arxiv.org/pdf/2201.00434v1.pdf | TVNet: Temporal Voting Network for Action Localization | We propose a Temporal Voting Network (TVNet) for action localization in untrimmed videos. This incorporates a novel Voting Evidence Module to locate temporal boundaries, more accurately, where temporal contextual evidence is accumulated to predict frame-level probabilities of start and end action boundaries. Our action... | ['Toby Perrett', 'Majid Mirmehdi', 'Dima Damen', 'Hanyuan Wang'] | 2022-01-02 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 1.80170998e-01 5.26799336e-02 -7.08109736e-01 -1.68278694e-01
-8.57571065e-01 -7.32743800e-01 8.04471731e-01 -2.94999599e-01
-6.46488905e-01 7.30231822e-01 5.77697992e-01 3.32718855e-03
1.90048888e-01 -3.75023514e-01 -4.57063109e-01 -2.71927148e-01
-3.51059675e-01 -1.06803201e-01 1.09571719e+00 1.95166320... | [8.271923065185547, 0.3623332977294922] |
f4619326-c4b7-4182-a52e-d0a2a411e6ea | dynamic-object-tracking-and-masking-for | 2008.00072 | null | https://arxiv.org/abs/2008.00072v1 | https://arxiv.org/pdf/2008.00072v1.pdf | Dynamic Object Tracking and Masking for Visual SLAM | In dynamic environments, performance of visual SLAM techniques can be impaired by visual features taken from moving objects. One solution is to identify those objects so that their visual features can be removed for localization and mapping. This paper presents a simple and fast pipeline that uses deep neural networks,... | ['François Michaud', 'Pier-Marc Comtois-Rivet', 'François Grondin', 'Jean-Samuel Lauzon', 'Mathieu Labbé', 'Jonathan Vincent'] | 2020-07-31 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-3.72492701e-01 -3.67409557e-01 1.16056427e-01 -2.43656561e-01
-4.10330407e-02 -6.83924019e-01 7.31961966e-01 2.72639424e-01
-1.09446049e+00 7.23935068e-01 -3.87041807e-01 -2.31221154e-01
-3.73135917e-02 -3.45597893e-01 -8.37363899e-01 -4.05684382e-01
-6.56613469e-01 8.47756982e-01 9.45214212e-01 -4.36597615... | [7.366080284118652, -2.0457417964935303] |
49f7ab05-d2c3-4370-ae43-2c9889d4d48e | federatednilm-a-distributed-and-privacy | 2108.03591 | null | https://arxiv.org/abs/2108.03591v1 | https://arxiv.org/pdf/2108.03591v1.pdf | FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning | Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumptions, can help to analyze electricity consumption behaviours of users and enable practical smart energy and smart grid appli... | ['Xizhong Chen', 'Qian Wang', 'Fanlin Meng', 'Shuang Dai'] | 2021-08-08 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-4.43498999e-01 -2.42379621e-01 -2.77173519e-01 -7.62041211e-01
-7.47054458e-01 -4.01149958e-01 6.12754762e-01 -1.57314941e-01
3.76406424e-02 7.90634274e-01 2.73363709e-01 -4.52435583e-01
1.78242251e-01 -1.02841592e+00 -2.95925945e-01 -1.11014700e+00
-2.29785413e-01 2.46044829e-01 -7.25156963e-01 3.51143926... | [5.8642659187316895, 2.779656410217285] |
41bfba85-1fc6-4494-817b-b26660f86001 | virtualpose-learning-generalizable-3d-human | 2207.09949 | null | https://arxiv.org/abs/2207.09949v1 | https://arxiv.org/pdf/2207.09949v1.pdf | VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data | While monocular 3D pose estimation seems to have achieved very accurate results on the public datasets, their generalization ability is largely overlooked. In this work, we perform a systematic evaluation of the existing methods and find that they get notably larger errors when tested on different cameras, human poses ... | ['Yizhou Wang', 'Wenjun Zeng', 'Xiaoxuan Ma', 'Chunyu Wang', 'Jiajun Su'] | 2022-07-20 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation-absolute', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-5.62102441e-03 5.15907705e-02 -2.41831057e-02 -3.38811457e-01
-7.59660721e-01 -6.74321115e-01 6.10691786e-01 -5.45824528e-01
-1.17149279e-01 4.80016738e-01 1.06534339e-01 -1.34901881e-01
2.42869183e-01 -5.33406675e-01 -1.06028366e+00 -6.64766073e-01
2.52312779e-01 4.72387075e-01 1.56476647e-01 -1.91290714... | [7.9364752769470215, -2.298448085784912] |
aa680ae7-0436-44dc-a69a-9d35597bac92 | ell-2-norm-flow-diffusion-in-near-linear-time | 2105.14629 | null | https://arxiv.org/abs/2105.14629v2 | https://arxiv.org/pdf/2105.14629v2.pdf | $\ell_2$-norm Flow Diffusion in Near-Linear Time | Diffusion is a fundamental graph procedure and has been a basic building block in a wide range of theoretical and empirical applications such as graph partitioning and semi-supervised learning on graphs. In this paper, we study computationally efficient diffusion primitives beyond random walk. We design an $\widetilde{... | ['Di Wang', 'Richard Peng', 'Li Chen'] | 2021-05-30 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 1.65706560e-01 4.56606477e-01 -3.63301843e-01 4.94020917e-02
-4.40987796e-01 -5.31212270e-01 2.53002322e-03 3.48339289e-01
-1.53693795e-01 6.56366825e-01 -2.17214897e-02 -4.39511627e-01
-5.79085827e-01 -1.12867343e+00 -4.83209848e-01 -7.46403039e-01
-6.25571609e-01 7.11934686e-01 1.17008276e-01 -2.92014003... | [7.06540060043335, 5.151228904724121] |
7b4b19ad-3143-456a-85bf-b6fad3adfeba | vlab-enhancing-video-language-pre-training-by | 2305.13167 | null | https://arxiv.org/abs/2305.13167v1 | https://arxiv.org/pdf/2305.13167v1.pdf | VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending | Large-scale image-text contrastive pre-training models, such as CLIP, have been demonstrated to effectively learn high-quality multimodal representations. However, there is limited research on learning video-text representations for general video multimodal tasks based on these powerful features. Towards this goal, we ... | ['Jiashi Feng', 'Jing Liu', 'Yi Yang', 'Dongmei Fu', 'Zikang Liu', 'Xiaojie Jin', 'Zhicheng Huang', 'Fan Ma', 'Sihan Chen', 'Xingjian He'] | 2023-05-22 | null | null | null | null | ['video-captioning', 'video-text-retrieval', 'video-question-answering', 'video-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.87872365e-01 -5.43233216e-01 -3.56351346e-01 -3.54542464e-01
-1.01695919e+00 -4.28786427e-01 6.57464266e-01 -3.27590168e-01
-3.70135039e-01 4.59211469e-01 3.52235734e-01 -1.53278321e-01
8.28426480e-02 -2.48959452e-01 -1.00956345e+00 -6.35913730e-01
2.58100808e-01 6.40801266e-02 1.27872944e-01 -1.47817269... | [10.290420532226562, 0.8875619173049927] |
da22bc51-ccdd-4b45-b05e-b60e59e344d4 | delving-deep-into-regularity-a-simple-but | 2204.05544 | null | https://arxiv.org/abs/2204.05544v2 | https://arxiv.org/pdf/2204.05544v2.pdf | Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition | Recent years have witnessed the improving performance of Chinese Named Entity Recognition (NER) from proposing new frameworks or incorporating word lexicons. However, the inner composition of entity mentions in character-level Chinese NER has been rarely studied. Actually, most mentions of regular types have strong nam... | ['Nicholas Jing Yuan', 'Baoxing Huai', 'Yi Zheng', 'Zhefeng Wang', 'Xiaoye Qu', 'Yingjie Gu'] | 2022-04-12 | null | https://aclanthology.org/2022.findings-naacl.143 | https://aclanthology.org/2022.findings-naacl.143.pdf | findings-naacl-2022-7 | ['type-prediction', 'chinese-named-entity-recognition'] | ['computer-code', 'natural-language-processing'] | [-3.15254569e-01 -1.73227966e-01 -2.05968142e-01 -2.51099706e-01
-4.70670253e-01 -3.82770687e-01 1.37420923e-01 3.06660891e-01
-6.12973034e-01 6.08010232e-01 5.06314158e-01 -1.11555889e-01
-8.45407248e-02 -8.94179702e-01 -2.49536440e-01 -6.59168661e-01
1.42691553e-01 5.63576408e-02 1.22665830e-01 -1.49630755... | [9.566482543945312, 9.583312034606934] |
4cd958b9-4f48-4272-94af-4502149db193 | dpmc-weighted-model-counting-by-dynamic | 2008.08748 | null | https://arxiv.org/abs/2008.08748v1 | https://arxiv.org/pdf/2008.08748v1.pdf | DPMC: Weighted Model Counting by Dynamic Programming on Project-Join Trees | We propose a unifying dynamic-programming framework to compute exact literal-weighted model counts of formulas in conjunctive normal form. At the center of our framework are project-join trees, which specify efficient project-join orders to apply additive projections (variable eliminations) and joins (clause multiplica... | ['Vu H. N. Phan', 'Jeffrey M. Dudek', 'Moshe Y. Vardi'] | 2020-08-20 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 3.08984458e-01 2.44818076e-01 -5.54828882e-01 -2.21279025e-01
-7.95269191e-01 -5.86088777e-01 4.11590546e-01 4.96770889e-01
-1.77234322e-01 4.34183478e-01 -3.98272723e-02 -8.93461943e-01
-1.12237290e-01 -1.31008124e+00 -5.54160297e-01 -3.94452177e-02
-2.55005628e-01 1.35429931e+00 7.09824800e-01 -2.02761125... | [8.624218940734863, 6.772404670715332] |
4dfb5320-c0ac-4adb-82fe-98e752f33621 | learning-based-heuristic-for-combinatorial | 2306.03434 | null | https://arxiv.org/abs/2306.03434v1 | https://arxiv.org/pdf/2306.03434v1.pdf | Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks | A dominating set of a graph $\mathcal{G=(V, E)}$ is a subset of vertices $S\subseteq\mathcal{V}$ such that every vertex $v\in \mathcal{V} \setminus S$ outside the dominating set is adjacent to a vertex $u\in S$ within the set. The minimum dominating set problem seeks to find a dominating set of minimum cardinality and ... | ['Xenofon Koutsoukos', 'Mudassir Shabbir', 'Abihith Kothapalli'] | 2023-06-06 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 4.90387157e-02 3.01152945e-01 -7.80190229e-02 -2.63321489e-01
-5.11553466e-01 -8.93543661e-01 -2.93944299e-01 7.00174332e-01
-4.12516505e-01 5.93766928e-01 -6.86400831e-01 -5.85806847e-01
-6.94054902e-01 -1.51284897e+00 -1.03076839e+00 -5.44992507e-01
-7.53678679e-01 7.36976504e-01 1.21445365e-01 -1.91020653... | [6.868957042694092, 5.664711952209473] |
cb733028-f775-49d3-8efd-873514430600 | competency-aware-neural-machine-translation | 2211.13865 | null | https://arxiv.org/abs/2211.13865v1 | https://arxiv.org/pdf/2211.13865v1.pdf | Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality? | Neural machine translation (NMT) is often criticized for failures that happen without awareness. The lack of competency awareness makes NMT untrustworthy. This is in sharp contrast to human translators who give feedback or conduct further investigations whenever they are in doubt about predictions. To fill this gap, we... | ['Jun Xie', 'Luo Si', 'Kai Fan', 'Dayiheng Liu', 'Haoran Wei', 'Baosong Yang', 'Pei Zhang'] | 2022-11-25 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 3.06446612e-01 2.74685055e-01 -2.56419390e-01 -3.99082482e-01
-1.20255458e+00 -7.27288425e-01 6.99583530e-01 1.60197496e-01
-4.71377760e-01 8.98100972e-01 2.20912844e-01 -4.65730309e-01
1.44868031e-01 -4.03980732e-01 -9.64061797e-01 -1.55709028e-01
6.02503359e-01 7.46127784e-01 -2.95746982e-01 -3.87553990... | [11.634576797485352, 10.122751235961914] |
932821e2-d685-48d8-aa76-8614205f9b94 | emulation-of-physical-processes-with-emukit | 2110.13293 | null | https://arxiv.org/abs/2110.13293v1 | https://arxiv.org/pdf/2110.13293v1.pdf | Emulation of physical processes with Emukit | Decision making in uncertain scenarios is an ubiquitous challenge in real world systems. Tools to deal with this challenge include simulations to gather information and statistical emulation to quantify uncertainty. The machine learning community has developed a number of methods to facilitate decision making, but so f... | ['Javier Gonzalez', 'Neil D. Lawrence', 'Cliff McCollum', 'Maren Mahsereci', 'Mark Pullin', 'Andrei Paleyes'] | 2021-10-25 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-5.30387223e-01 -1.56312332e-01 8.38588476e-02 -4.95426774e-01
-8.70645642e-01 -5.09125948e-01 8.15380454e-01 2.47063398e-01
-1.34553120e-01 8.53925407e-01 -2.80459914e-02 -8.70926917e-01
-5.34757257e-01 -8.37027133e-01 -2.49837667e-01 -5.84429741e-01
-2.16893807e-01 8.82493436e-01 1.13730349e-01 -1.24510065... | [6.15205717086792, 3.66279935836792] |
dfe4aff7-646f-42f8-a9bb-492292057879 | orthogonal-subspace-decomposition-a-new | null | null | https://openreview.net/forum?id=sr68jSUakP | https://openreview.net/pdf?id=sr68jSUakP | Orthogonal Subspace Decomposition: A New Perspective of Learning Discriminative Features for Face Clustering | Face clustering is an important task, due to its wide applications in practice. Graph-based face clustering methods have recently made a great progress and achieved new state-of-the-art results. Learning discriminative node features is the key to further improve the performance of graph-based face... | ['Zhongchao shi', 'Thomas Lukasiewicz', 'JianFeng Wang'] | 2021-01-01 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-3.51645231e-01 -3.15895855e-01 -3.12243164e-01 -3.93688709e-01
-2.95821697e-01 -1.39617130e-01 5.04373908e-01 -8.25075582e-02
4.15275656e-02 1.19621724e-01 2.18866378e-01 1.91102132e-01
-2.61526525e-01 -5.71457148e-01 -1.93429977e-01 -1.03114188e+00
-7.34084323e-02 3.41697276e-01 1.35912746e-01 1.23426877... | [13.468725204467773, 1.0762628316879272] |
efb4f6d5-6504-430a-bed3-f255c2d07b08 | hand-gesture-recognition-of-dumb-person-using | 2201.12622 | null | https://arxiv.org/abs/2201.12622v1 | https://arxiv.org/pdf/2201.12622v1.pdf | Hand Gesture Recognition of Dumb Person Using one Against All Neural Network | We propose a new technique for recognition of dumb person hand gesture in real world environment. In this technique, the hand image containing the gesture is preprocessed and then hand region is segmented by convergent the RGB color image to L.a.b color space. Only few statistical features are used to classify the segm... | ['Sajjad Ahmed', 'Lan Hong', 'Muhammad Asim Khan'] | 2022-01-29 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 3.74973625e-01 -7.36219764e-01 -4.17543799e-01 -4.66493428e-01
-2.02261768e-02 -9.34352398e-01 3.53388578e-01 -3.82270813e-01
-8.84572566e-01 6.34984255e-01 -3.04739505e-01 -3.57094020e-01
2.07931057e-01 -7.37070560e-01 -4.93077450e-02 -8.68013322e-01
3.57186288e-01 5.88868380e-01 4.09821689e-01 1.79080665... | [6.480772972106934, -0.295287549495697] |
21401265-6205-4841-800d-bd40266cf7d7 | enhanced-neural-beamformer-with-spatial | 2306.15942 | null | https://arxiv.org/abs/2306.15942v1 | https://arxiv.org/pdf/2306.15942v1.pdf | Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction | Recently, deep learning-based beamforming algorithms have shown promising performance in target speech extraction tasks. However, most systems do not fully utilize spatial information. In this paper, we propose a target speech extraction network that utilizes spatial information to enhance the performance of neural bea... | ['Yujun Wang', 'Dazhi Gao', 'Qinwen Guo', 'Wenbo Zhu', 'Peng Gao', 'Junnan Wu', 'Aoqi Guo'] | 2023-06-28 | null | null | null | null | ['dimensionality-reduction', 'speech-separation', 'speech-extraction'] | ['methodology', 'speech', 'speech'] | [-3.73158976e-03 -1.62721619e-01 4.09250818e-02 -4.09559876e-01
-9.15447056e-01 -1.55184686e-01 1.46885052e-01 -5.45135438e-01
-2.25175321e-01 3.64615411e-01 8.48259985e-01 -2.64444351e-01
-3.17492574e-01 -5.49975336e-01 -3.98396015e-01 -8.23551238e-01
3.02624077e-01 -2.19560921e-01 1.70829311e-01 4.98114191... | [14.896368026733398, 5.866588592529297] |
7c5eef92-cbd8-4cb0-92ba-c27db0c921d0 | robust-breast-cancer-detection-in-mammography | 1912.11027 | null | https://arxiv.org/abs/1912.11027v2 | https://arxiv.org/pdf/1912.11027v2.pdf | Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach | Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018. To achieve earlier breast cancer detection, screening x-ray mammography is recommended by health organizations worldwide and has been estimated to decrease breast cancer mortality by 20-40%. Nevertheless, significant false positiv... | ['Jerrold L. Boxerman', 'Jorge Onieva Onieva', 'William Lotter', 'A. Gregory Sorensen', 'Abdul Rahman Diab', 'Mack Bandler', 'Bryan Haslam', 'Meiyun Wang', 'Kevin Wu', 'Jiye G. Kim', 'Gopal Vijayaraghavan', 'Eric Wu', 'Giorgia Grisot'] | 2019-12-23 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 3.91494930e-01 5.65269232e-01 -4.58546162e-01 -6.27014816e-01
-1.53035641e+00 -1.32900268e-01 2.71877926e-02 7.36040354e-01
-5.22063971e-01 4.95923728e-01 5.52427284e-02 -9.79627609e-01
5.87912090e-02 -9.65185642e-01 -8.26043844e-01 -4.76920068e-01
-2.63170391e-01 6.98852777e-01 2.99552411e-01 2.62526840... | [15.211962699890137, -2.506608724594116] |
09d07679-e54e-4eef-a8da-72a9874b83fd | benchmarking-robustness-in-object-detection | 1907.07484 | null | https://arxiv.org/abs/1907.07484v2 | https://arxiv.org/pdf/1907.07484v2.pdf | Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming | The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving. We here provide an easy-to-use benchmark to assess how object detection models perform when image quality degrades. The three resulting benchmark datasets,... | ['Wieland Brendel', 'Robert Geirhos', 'Oliver Bringmann', 'Matthias Bethge', 'Benjamin Mitzkus', 'Evgenia Rusak', 'Claudio Michaelis', 'Alexander S. Ecker'] | 2019-07-17 | null | https://openreview.net/forum?id=ryljMpNtwr | https://openreview.net/pdf?id=ryljMpNtwr | null | ['robust-object-detection'] | ['computer-vision'] | [ 2.50708014e-01 -3.92502725e-01 1.70759499e-01 -4.44943994e-01
-8.57005239e-01 -6.87601805e-01 7.87156284e-01 -1.22588105e-01
-6.53658628e-01 5.11905372e-01 -3.35003078e-01 -3.90231609e-01
3.86276245e-01 -4.10767525e-01 -1.01311481e+00 -6.43999338e-01
-3.49518299e-01 -2.05935556e-02 5.19627094e-01 -2.58046716... | [8.156719207763672, -1.4017155170440674] |
f2fef493-2a35-4950-989d-bc7c4aa03b98 | generalizable-person-re-identification-via-1 | 2212.02398 | null | https://arxiv.org/abs/2212.02398v1 | https://arxiv.org/pdf/2212.02398v1.pdf | Generalizable Person Re-Identification via Viewpoint Alignment and Fusion | In the current person Re-identification (ReID) methods, most domain generalization works focus on dealing with style differences between domains while largely ignoring unpredictable camera view change, which we identify as another major factor leading to a poor generalization of ReID methods. To tackle the viewpoint ch... | ['Yanning Zhang', 'Peng Wang', 'Shizhou Zhang', 'Ruiqi Wu', 'Guosheng Lin', 'Liying Gao', 'Lingqiao Liu', 'Bingliang Jiao'] | 2022-12-05 | null | null | null | null | ['person-re-identification', 'generalizable-person-re-identification'] | ['computer-vision', 'computer-vision'] | [-1.76217884e-01 -3.27306271e-01 1.13732517e-01 -3.92065883e-01
-2.42506489e-01 -5.00466168e-01 4.39475209e-01 -5.11005938e-01
-2.06137285e-01 4.97710109e-01 3.01585019e-01 2.54026264e-01
3.55017453e-01 -5.92417955e-01 -6.31159246e-01 -7.78942883e-01
6.35819554e-01 2.44373128e-01 2.57139951e-01 -3.56011927... | [14.706416130065918, 0.9547355771064758] |
1a5c8b98-b735-4a72-8334-8d2bc4afb54e | towards-modern-card-games-with-large-scale | 2206.12700 | null | https://arxiv.org/abs/2206.12700v2 | https://arxiv.org/pdf/2206.12700v2.pdf | Towards Modern Card Games with Large-Scale Action Spaces Through Action Representation | Axie infinity is a complicated card game with a huge-scale action space. This makes it difficult to solve this challenge using generic Reinforcement Learning (RL) algorithms. We propose a hybrid RL framework to learn action representations and game strategies. To avoid evaluating every action in the large feasible acti... | ['Yan Zhang', 'Huan Lu', 'Xiongjie Xie', 'Yuanyuan Qin', 'Yiting Xie', 'Site Li', 'Tianyu Shi', 'Zhiyuan Yao'] | 2022-06-25 | null | null | null | null | ['card-games'] | ['playing-games'] | [-2.99211424e-02 1.42313600e-01 -5.53326666e-01 3.30517352e-01
-9.61427450e-01 -6.46145284e-01 5.06363988e-01 -4.87719387e-01
-8.36714804e-01 1.01887393e+00 3.79292905e-01 -2.16367573e-01
-3.19370955e-01 -9.22087789e-01 -4.16617334e-01 -5.46488166e-01
-4.03263457e-02 6.49932086e-01 3.49153608e-01 -5.31161189... | [3.8327620029449463, 1.611063838005066] |
72fd2c65-5e41-4ec6-844a-258af88d07f4 | signet-convolutional-siamese-network-for | 1707.02131 | null | http://arxiv.org/abs/1707.02131v2 | http://arxiv.org/pdf/1707.02131v2.pdf | SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification | Offline signature verification is one of the most challenging tasks in
biometrics and document forensics. Unlike other verification problems, it needs
to model minute but critical details between genuine and forged signatures,
because a skilled falsification might often resembles the real signature with
small deformati... | ['Umapada Pal', 'J. Ignacio Toledo', 'Anjan Dutta', 'Sounak Dey', 'Josep Llados', 'Suman K. Ghosh'] | 2017-07-07 | null | null | null | null | ['handwriting-verification'] | ['computer-vision'] | [ 4.21447903e-01 -2.15652153e-01 2.54864812e-01 -7.58878291e-01
-4.39643353e-01 -8.25860262e-01 7.73181617e-01 -3.57049495e-01
-2.56971270e-01 3.66824061e-01 -1.45632282e-01 -1.06111526e-01
-3.81295353e-01 -4.92519975e-01 -5.71820021e-01 -8.55609715e-01
-2.85715330e-02 5.15197694e-01 -8.25067014e-02 -2.49570131... | [12.74160099029541, 1.2266607284545898] |
4f2a5bb5-cbd5-4a7c-9d42-0e9a8c15dc65 | ciao-a-contrastive-adaptation-mechanism-for | 2208.07221 | null | https://arxiv.org/abs/2208.07221v1 | https://arxiv.org/pdf/2208.07221v1.pdf | CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition | Current facial expression recognition systems demand an expensive re-training routine when deployed to different scenarios than they were trained for. Biasing them towards learning specific facial characteristics, instead of performing typical transfer learning methods, might help these systems to maintain high perform... | ['Alessandra Sciutti', 'Pablo Barros'] | 2022-08-10 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 3.28991294e-01 4.86386828e-02 -1.62962928e-01 -1.20356798e+00
-1.44771621e-01 -2.96151549e-01 3.95757079e-01 -3.70489031e-01
-4.00781304e-01 5.03982365e-01 2.10181668e-01 4.32031125e-01
1.54724166e-01 -4.55385625e-01 -4.48928505e-01 -6.81539476e-01
-2.01863080e-01 1.93795204e-01 -6.52434528e-01 -5.47181547... | [13.568479537963867, 1.7688807249069214] |
16421c30-aabc-4b86-8c80-a34a7c59a2a6 | adapting-to-continuous-covariate-shift-via | 2302.02552 | null | https://arxiv.org/abs/2302.02552v1 | https://arxiv.org/pdf/2302.02552v1.pdf | Adapting to Continuous Covariate Shift via Online Density Ratio Estimation | Dealing with distribution shifts is one of the central challenges for modern machine learning. One fundamental situation is the \emph{covariate shift}, where the input distributions of data change from training to testing stages while the input-conditional output distribution remains unchanged. In this paper, we initia... | ['Masashi Sugiyama', 'Peng Zhao', 'Zhen-Yu Zhang', 'Yu-Jie Zhang'] | 2023-02-06 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 2.96299368e-01 6.97673932e-02 -2.70146072e-01 -4.89521891e-01
-8.97252262e-01 -5.31630337e-01 1.14172086e-01 1.32217094e-01
-3.73958409e-01 1.12140429e+00 -3.78208816e-01 -4.04177547e-01
-5.38439751e-01 -6.23676956e-01 -9.63127851e-01 -8.69411409e-01
-8.35770667e-02 5.16265094e-01 -1.00341812e-01 2.31069311... | [8.164678573608398, 3.866518974304199] |
452908fb-8bc8-47ad-9c22-ddc2b47aafb6 | how-much-can-clip-benefit-vision-and-language | 2107.06383 | null | https://arxiv.org/abs/2107.06383v1 | https://arxiv.org/pdf/2107.06383v1.pdf | How Much Can CLIP Benefit Vision-and-Language Tasks? | Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world. However, it has been observed that large-scale pretraining usually can result in better generalization performance,... | ['Kurt Keutzer', 'Zhewei Yao', 'Kai-Wei Chang', 'Anna Rohrbach', 'Mohit Bansal', 'Hao Tan', 'Liunian Harold Li', 'Sheng Shen'] | 2021-07-13 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [-3.09484396e-02 6.96474388e-02 -1.44736782e-01 -3.17988932e-01
-9.12328243e-01 -7.73782969e-01 7.67026365e-01 1.23852283e-01
-6.34772122e-01 2.90657282e-01 2.39087418e-01 -5.62561035e-01
6.09231055e-01 -4.92882311e-01 -1.29030466e+00 -1.77779570e-01
3.90337467e-01 3.09978873e-01 3.59917521e-01 -2.52198845... | [10.75590705871582, 1.6552002429962158] |
7b3c0005-a92f-4dec-aae6-710eb3654b94 | partition-based-stability-of-coalitional | 2304.10651 | null | https://arxiv.org/abs/2304.10651v1 | https://arxiv.org/pdf/2304.10651v1.pdf | Partition-based Stability of Coalitional Games | We are concerned with the stability of a coalitional game, i.e., a transferable-utility (TU) cooperative game. First, the concept of core can be weakened so that the blocking of changes is limited to only those with multilateral backings. This principle of consensual blocking, as well as the traditional core-defining p... | ['Jian Yang'] | 2023-04-20 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-1.55608803e-01 6.87652826e-01 -1.51363626e-01 3.68601531e-01
-3.54975373e-01 -1.12267363e+00 5.26041090e-01 -3.14755812e-02
-4.97210205e-01 1.27445924e+00 3.46686423e-01 -5.61716318e-01
-9.05548692e-01 -1.06219053e+00 -1.77205011e-01 -1.10354555e+00
-2.03462616e-01 8.38328063e-01 3.63768190e-01 -7.92032659... | [4.216564655303955, 2.7838425636291504] |
7e52b570-18f9-4385-99c4-c8382d9be702 | perceptual-quality-assessment-of-smartphone | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.pdf | Perceptual Quality Assessment of Smartphone Photography | As smartphones become people's primary cameras to take photos, the quality of their cameras and the associated computational photography modules has become a de facto standard in evaluating and ranking smartphones in the consumer market. We conduct so far the most comprehensive study of perceptual quality assessment of... | [' Zhou Wang', ' Kede Ma', ' Yan Zeng', ' Hanwei Zhu', 'Yuming Fang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 1.27623543e-01 -6.83270693e-01 -4.30875607e-02 -4.82062936e-01
-7.85175800e-01 -6.38161302e-01 2.32580423e-01 -5.90175251e-03
-3.36725652e-01 4.36908603e-01 2.99735039e-01 -3.95783961e-01
-1.83809176e-02 -5.62426090e-01 -7.38628685e-01 -5.52498996e-01
3.78585577e-01 -2.08098024e-01 9.60433856e-03 -1.11704409... | [11.805438995361328, -1.8460917472839355] |
2ab59c8a-e501-4d1a-a3e2-c9c190826887 | learning-where-to-learn | null | null | https://openreview.net/forum?id=RLp_rHS4LMV | https://openreview.net/pdf?id=RLp_rHS4LMV | Learning where to learn | Finding neural network weights that generalize well from small datasets is difficult. A promising approach is to (meta-)learn a weight initialization from a collection of tasks, such that a small number of weight changes results in low generalization error. We show that this form of meta-learning can be improved b... | ['Johannes von Oswald', 'Joao Sacramento', 'Nicolas Zucchet', 'Dominic Zhao'] | 2021-03-13 | null | null | null | iclr-workshop-learning-to-learn-2021-5 | ['sparse-learning'] | ['methodology'] | [ 4.09850806e-01 6.72514662e-02 -2.58011490e-01 -3.99983168e-01
-1.64532304e-01 -5.16923904e-01 3.76897097e-01 2.49179244e-01
-6.92388296e-01 6.74219608e-01 2.99333811e-01 4.88729998e-02
-2.97550976e-01 -7.68827736e-01 -7.89722800e-01 -8.43238652e-01
-2.29629904e-01 2.07673818e-01 3.51380110e-01 -3.25948864... | [8.632560729980469, 3.3115792274475098] |
aae5615c-988f-48b1-8b94-be4dff6d7227 | intra-batch-supervision-for-panoptic-1 | 2304.08222 | null | https://arxiv.org/abs/2304.08222v1 | https://arxiv.org/pdf/2304.08222v1.pdf | Intra-Batch Supervision for Panoptic Segmentation on High-Resolution Images | Unified panoptic segmentation methods are achieving state-of-the-art results on several datasets. To achieve these results on high-resolution datasets, these methods apply crop-based training. In this work, we find that, although crop-based training is advantageous in general, it also has a harmful side-effect. Specifi... | ['Gijs Dubbelman', 'Daan de Geus'] | 2023-04-17 | intra-batch-supervision-for-panoptic | https://openaccess.thecvf.com/content/WACV2023/html/de_Geus_Intra-Batch_Supervision_for_Panoptic_Segmentation_on_High-Resolution_Images_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/de_Geus_Intra-Batch_Supervision_for_Panoptic_Segmentation_on_High-Resolution_Images_WACV_2023_paper.pdf | ieee-cvf-winter-conference-on-applications-of-2 | ['panoptic-segmentation'] | ['computer-vision'] | [ 1.26639515e-01 -2.15868264e-01 -3.04794997e-01 -4.83048320e-01
-1.04787517e+00 -6.44880772e-01 4.39273208e-01 3.82086379e-03
-3.38131100e-01 6.37996733e-01 -2.77671218e-01 -3.92403215e-01
-4.16229181e-02 -8.92431915e-01 -6.21935904e-01 -8.59230697e-01
-6.55490309e-02 2.66205728e-01 4.25214261e-01 -1.67975336... | [9.520251274108887, -1.3613134622573853] |
5ca0fcec-2ad1-445c-af2a-d4b0fd6e92d8 | learning-3d-semantics-from-pose-noisy-2d | 2204.08084 | null | https://arxiv.org/abs/2204.08084v3 | https://arxiv.org/pdf/2204.08084v3.pdf | Learning 3D Semantics from Pose-Noisy 2D Images with Hierarchical Full Attention Network | We propose a novel framework to learn 3D point cloud semantics from 2D multi-view image observations containing pose error. On the one hand, directly learning from the massive, unstructured and unordered 3D point cloud is computationally and algorithmically more difficult than learning from compactly-organized and cont... | ['Long Chen', 'Junkun Xie', 'Lin Chen', 'Yuhang He'] | 2022-04-17 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 9.47056487e-02 1.07882001e-01 -6.82088286e-02 -5.31207502e-01
-7.05064416e-01 -4.32863772e-01 2.03747511e-01 -6.26915181e-03
-1.53028548e-01 2.55446553e-01 -2.08227888e-01 -9.64294523e-02
4.96657565e-02 -7.81674743e-01 -1.09663892e+00 -5.80412388e-01
4.44127917e-01 6.54958487e-01 4.47976500e-01 -1.06162801... | [8.17686939239502, -3.0482428073883057] |
e0e730f6-ecfa-40a9-9470-5db0188f430e | 190807519 | 1908.07519 | null | https://arxiv.org/abs/1908.07519v1 | https://arxiv.org/pdf/1908.07519v1.pdf | Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing | In a human-centered intelligent manufacturing system, sensing and understanding of the worker's activity are the primary tasks. In this paper, we propose a novel multi-modal approach for worker activity recognition by leveraging information from different sensors and in different modalities. Specifically, a smart armba... | ['Zhaozheng Yin', 'Ming C. Leu', 'Wenjin Tao'] | 2019-08-20 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 6.18699253e-01 -3.32554311e-01 -1.92692116e-01 -1.25992611e-01
-4.52403456e-01 -3.48924428e-01 4.49896693e-01 -2.52934694e-01
-2.37443388e-01 4.18488920e-01 1.29764304e-01 2.10205093e-01
-5.65790534e-01 -4.82566476e-01 -6.65277243e-01 -8.05408955e-01
3.16900373e-01 1.23362258e-01 -2.98330467e-02 2.22098306... | [7.866721153259277, 0.4866173565387726] |
a4fcf4d3-0635-49bd-a244-881704bb6f20 | toward-a-realistic-model-of-speech-processing | 2206.01685 | null | https://arxiv.org/abs/2206.01685v2 | https://arxiv.org/pdf/2206.01685v2.pdf | Toward a realistic model of speech processing in the brain with self-supervised learning | Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input. These algorithms, however, remain largely implausible: they require (1) extraordinarily large amounts of data, (2) unobtainable supervised labels, (3) textual rather than raw sensor... | ['Jean-Remi King', 'Christophe Pallier', 'Ewan Dunbar', 'Alexandre Gramfort', 'Yves Boubenec', 'Pierre Orhan', 'Charlotte Caucheteux', 'Juliette Millet'] | 2022-06-03 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 2.88221419e-01 2.69526571e-01 1.14973478e-01 -5.60162127e-01
-3.43766600e-01 -5.84021688e-01 7.78948486e-01 1.77618831e-01
-6.04053974e-01 5.16437650e-01 5.71285307e-01 -3.80231559e-01
-9.43822786e-02 -5.90294600e-01 -6.90418184e-01 -3.83585781e-01
-2.09906548e-01 4.58631217e-01 1.14829145e-01 -3.68382365... | [10.333003044128418, 8.51891040802002] |
375f0ee9-f94b-459f-825a-667aaefc94ed | r2-d2-color-inspired-convolutional-neural | 1705.04448 | null | http://arxiv.org/abs/1705.04448v5 | http://arxiv.org/pdf/1705.04448v5.pdf | R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections | The influence of Deep Learning on image identification and natural language
processing has attracted enormous attention globally. The convolution neural
network that can learn without prior extraction of features fits well in
response to the rapid iteration of Android malware. The traditional solution
for detecting And... | ['Hung-Yu Kao', 'TonTon Hsien-De Huang'] | 2017-05-12 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 6.04560077e-02 -5.50809205e-01 -2.45642781e-01 -3.55971009e-01
-3.06340545e-01 -5.82196712e-01 2.67370403e-01 -4.22508448e-01
-5.26189864e-01 1.77514642e-01 -4.30472434e-01 -8.73001873e-01
3.26468527e-01 -7.31995583e-01 -6.13947093e-01 -3.05911839e-01
-2.40959704e-01 -2.90552080e-01 7.85027742e-02 -9.17876810... | [14.421274185180664, 9.6759033203125] |
61aa7bc3-41bf-45e5-8d9e-7c1054db69ac | bi-directional-recurrent-neural-ordinary | 2112.12809 | null | https://arxiv.org/abs/2112.12809v1 | https://arxiv.org/pdf/2112.12809v1.pdf | Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification | Classification of posts in social media such as Twitter is difficult due to the noisy and short nature of texts. Sequence classification models based on recurrent neural networks (RNN) are popular for classifying posts that are sequential in nature. RNNs assume the hidden representation dynamics to evolve in a discrete... | ['P. K. Srijith', 'Srinivas Anumasa', 'Maunika Tamire'] | 2021-12-23 | null | https://aclanthology.org/2022.wit-1.3 | https://aclanthology.org/2022.wit-1.3.pdf | wit-acl-2022-5 | ['rumour-detection'] | ['natural-language-processing'] | [ 1.51032507e-01 -2.38212094e-01 -2.39373147e-01 -2.71533728e-01
3.15580308e-01 -4.03893203e-01 6.67012513e-01 1.14977941e-01
-4.70471054e-01 7.24260151e-01 3.23617816e-01 -5.41520536e-01
2.69921154e-01 -9.78411853e-01 -3.92978132e-01 -6.83993638e-01
-1.47209093e-01 2.44762734e-01 6.35746345e-02 -6.70810819... | [7.127151966094971, 3.4479501247406006] |
6a0861f0-fda8-4381-804f-65a4ee6c82f5 | toward-a-corpus-of-cantonese-verbal-comments | null | null | https://aclanthology.org/Y15-2002 | https://aclanthology.org/Y15-2002.pdf | Toward a Corpus of Cantonese Verbal Comments and their Classification by Multi-dimensional Analysis | null | ['Oi Yee Kwong'] | 2015-10-01 | toward-a-corpus-of-cantonese-verbal-comments-1 | https://aclanthology.org/Y15-2002 | https://aclanthology.org/Y15-2002.pdf | paclic-2015-10 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.482332706451416, 3.583852767944336] |
721e33c7-6def-4f81-9439-6e8616e4ee25 | structure-sensitive-graph-dictionary | 2306.10505 | null | https://arxiv.org/abs/2306.10505v1 | https://arxiv.org/pdf/2306.10505v1.pdf | Structure-Sensitive Graph Dictionary Embedding for Graph Classification | Graph structure expression plays a vital role in distinguishing various graphs. In this work, we propose a Structure-Sensitive Graph Dictionary Embedding (SS-GDE) framework to transform input graphs into the embedding space of a graph dictionary for the graph classification task. Instead of a plain use of a base graph ... | ['Zhen Cui', 'Chuanwei Zhou', 'Wenting Zhao', 'Xudong Wang', 'Tong Zhang', 'Guangbu Liu'] | 2023-06-18 | null | null | null | null | ['graph-classification', 'classification-1'] | ['graphs', 'methodology'] | [-1.35334674e-02 -4.25418280e-02 -6.61087334e-02 -2.82679170e-01
-3.37919176e-01 -4.80714470e-01 4.03222978e-01 1.36844724e-01
-2.07324088e-01 3.30938697e-01 2.52487510e-01 -2.69974113e-01
-2.54029989e-01 -1.06079412e+00 -4.71529335e-01 -1.01669347e+00
1.45606786e-01 2.12814718e-01 1.02338240e-01 -3.28522742... | [7.280083179473877, 6.296138763427734] |
33c00c17-4816-4ec8-b14d-6a912123348c | orca-a-few-shot-benchmark-for-chinese | 2302.13619 | null | https://arxiv.org/abs/2302.13619v1 | https://arxiv.org/pdf/2302.13619v1.pdf | Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension | The conversational machine reading comprehension (CMRC) task aims to answer questions in conversations, which has been a hot research topic in recent years because of its wide applications. However, existing CMRC benchmarks in which each conversation is assigned a static passage are inconsistent with real scenarios. Th... | ['Jia Li', 'Baoyuan Wang', 'Jiaxing Zhang', 'Ruyi Gan', 'Jianfeng Liu', 'Qi Yang', 'Xinshi Lin', 'Junqing He', 'Yinan Bao', 'Hongguang Li', 'Nuo Chen'] | 2023-02-27 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.39323854e-01 1.14955544e-01 7.37384260e-02 -5.50812840e-01
-1.16339636e+00 -5.62136114e-01 8.48587632e-01 1.16190389e-01
-2.97913820e-01 7.90729761e-01 7.59910524e-01 -2.52640158e-01
1.39757991e-01 -5.89183807e-01 -5.03890812e-01 -3.78234357e-01
3.19414139e-01 6.66941285e-01 3.15191329e-01 -6.71426475... | [11.999550819396973, 8.075698852539062] |
8c455773-8122-4320-959b-ddf5d9dc8217 | spatial-language-representation-with-multi | 2008.09236 | null | https://arxiv.org/abs/2008.09236v1 | https://arxiv.org/pdf/2008.09236v1.pdf | Spatial Language Representation with Multi-Level Geocoding | We present a multi-level geocoding model (MLG) that learns to associate texts to geographic locations. The Earth's surface is represented using space-filling curves that decompose the sphere into a hierarchy of similarly sized, non-overlapping cells. MLG balances generalization and accuracy by combining losses across m... | ['Li Zhang', 'Eugene Ie', 'Jason Baldridge', 'Sayali Kulkarni', 'Shailee Jain', 'Mohammad Javad Hosseini'] | 2020-08-21 | null | null | null | null | ['toponym-resolution'] | ['natural-language-processing'] | [-4.48364675e-01 2.98467368e-01 -6.26805604e-01 -1.06978215e-01
-9.69231725e-01 -8.85535479e-01 6.33539915e-01 5.75058699e-01
-3.96873295e-01 8.54072273e-01 6.52758300e-01 -2.13909745e-01
-2.89673030e-01 -1.19948339e+00 -9.23446953e-01 -1.02505431e-01
-1.36060432e-01 8.98551106e-01 3.38370204e-01 -1.91035643... | [9.326783180236816, 8.92342758178711] |
3c45a20c-982a-408f-9f07-428cf00e5fd2 | minervas-massive-interior-environments | 2107.06149 | null | https://arxiv.org/abs/2107.06149v4 | https://arxiv.org/pdf/2107.06149v4.pdf | MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis | With the rapid development of data-driven techniques, data has played an essential role in various computer vision tasks. Many realistic and synthetic datasets have been proposed to address different problems. However, there are lots of unresolved challenges: (1) the creation of dataset is usually a tedious process wit... | ['Hujun Bao', 'Yuchi Huo', 'Rui Wang', 'Rui Tang', 'Jiaxiang Zheng', 'Jia Zheng', 'Hao Zhang', 'Haocheng Ren'] | 2021-07-13 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 2.42513761e-01 -7.94353902e-01 4.74180162e-01 -4.15110141e-01
-3.78914535e-01 -8.47910941e-01 4.91148740e-01 -2.74782032e-01
-2.58824736e-01 3.92081022e-01 -1.23244546e-01 -3.79156590e-01
7.10505322e-02 -9.13382888e-01 -6.06635869e-01 -6.02275670e-01
2.86314309e-01 5.09564638e-01 6.13649845e-01 -1.80867746... | [9.205510139465332, -3.071375608444214] |
54c066b4-739d-4d33-8b1f-23f9d807a84d | transformation-invariant-network-for-few-shot | 2303.06817 | null | https://arxiv.org/abs/2303.06817v2 | https://arxiv.org/pdf/2303.06817v2.pdf | Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images | Object detection in remote sensing images relies on a large amount of labeled data for training. However, the increasing number of new categories and class imbalance make exhaustive annotation impractical. Few-shot object detection (FSOD) addresses this issue by leveraging meta-learning on seen base classes and fine-tu... | ['Heng-Chao Li', 'Zongxin Gan', 'Turgay Celik', 'Xun Xu', 'Nanqing Liu'] | 2023-03-13 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 0.46006462 -0.4478689 -0.2242193 -0.4087382 -0.8394173 -0.43982652
0.51224214 0.10564432 -0.41909558 0.37379724 -0.11261969 -0.06446829
-0.3208784 -0.8854872 -0.5269628 -0.7746474 0.05490774 -0.04815278
0.6553715 -0.07412183 0.13948011 0.7078745 -1.8427715 0.02084668
0.8994558 0.9964376 0.... | [9.031524658203125, -0.8801636099815369] |
1d77dfd9-ff9f-42b5-ac19-6729547093c8 | xai-in-computational-linguistics | 2305.04631 | null | https://arxiv.org/abs/2305.04631v1 | https://arxiv.org/pdf/2305.04631v1.pdf | XAI in Computational Linguistics: Understanding Political Leanings in the Slovenian Parliament | The work covers the development and explainability of machine learning models for predicting political leanings through parliamentary transcriptions. We concentrate on the Slovenian parliament and the heated debate on the European migrant crisis, with transcriptions from 2014 to 2020. We develop both classical machine ... | ['Senja Pollak', 'Bojan Evkoski'] | 2023-05-08 | null | null | null | null | ['unity'] | ['computer-vision'] | [-5.70575111e-02 8.75096858e-01 -6.66752100e-01 -5.41305661e-01
-4.22469527e-01 -6.90108538e-01 9.88991022e-01 3.67396951e-01
-4.87264752e-01 6.84486628e-01 1.55532861e+00 -1.19127059e+00
-1.90837517e-01 -8.40216279e-01 -3.95974696e-01 -5.75540066e-01
3.48129988e-01 7.49598861e-01 -8.19211721e-01 -7.36625731... | [9.098142623901367, 9.882983207702637] |
5f7a762f-d816-4081-bf77-8f1e504d39a7 | funqg-molecular-representation-learning-via | 2207.08597 | null | https://arxiv.org/abs/2207.08597v2 | https://arxiv.org/pdf/2207.08597v2.pdf | FunQG: Molecular Representation Learning Via Quotient Graphs | Learning expressive molecular representations is crucial to facilitate the accurate prediction of molecular properties. Despite the significant advancement of graph neural networks (GNNs) in molecular representation learning, they generally face limitations such as neighbors-explosion, under-reaching, over-smoothing, a... | ['Yavar Taheri Yeganeh', 'Ali Hojatnia', 'Zahra Taheri', 'Hossein Hajiabolhassan'] | 2022-07-18 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 4.48581636e-01 -7.22035468e-02 -5.49886584e-01 -1.17907882e-01
-4.49338883e-01 -3.24391216e-01 3.53702903e-01 7.00437963e-01
-1.57850683e-01 1.08728433e+00 5.93847558e-02 -4.46667016e-01
-2.46865109e-01 -1.21294594e+00 -8.45676780e-01 -1.03051257e+00
-1.50011435e-01 3.05721819e-01 1.91540435e-01 -3.85723770... | [5.142967700958252, 5.907784938812256] |
f3d69b3b-79ef-4d6f-a712-d5d205e05f94 | elegansnet-a-brief-scientific-report-and | 2304.13538 | null | https://arxiv.org/abs/2304.13538v1 | https://arxiv.org/pdf/2304.13538v1.pdf | ElegansNet: a brief scientific report and initial experiments | This research report introduces ElegansNet, a neural network that mimics real-world neuronal network circuitry, with the goal of better understanding the interplay between connectome topology and deep learning systems. The proposed approach utilizes the powerful representational capabilities of living beings' neuronal ... | ['Roberto Tagliaferri', 'Pietro Liò', 'Andrea Terlizzi', 'Francesco Bardozzo'] | 2023-04-06 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.88602671e-01 1.57358691e-01 3.46090808e-03 -7.26101622e-02
7.09682345e-01 -4.43316519e-01 5.72501421e-01 -2.34915972e-01
-4.76373911e-01 1.02070189e+00 -1.35875285e-01 -3.07044864e-01
-5.75297892e-01 -8.80855560e-01 -8.68700564e-01 -7.29605258e-01
-6.12060905e-01 5.15746653e-01 3.42631072e-01 -4.23650563... | [9.274300575256348, 2.6163768768310547] |
633799e2-8d63-4d4e-b3e2-33cee3ab9ca9 | rgb-t-object-trackingbenchmark-and-baseline | 1805.08982 | null | http://arxiv.org/abs/1805.08982v1 | http://arxiv.org/pdf/1805.08982v1.pdf | RGB-T Object Tracking:Benchmark and Baseline | RGB-Thermal (RGB-T) object tracking receives more and more attention due to
the strongly complementary benefits of thermal information to visible data.
However, RGB-T research is limited by lacking a comprehensive evaluation
platform. In this paper, we propose a large-scale video benchmark dataset for
RGB-T tracking.It... | ['Jin Tang', 'Xinyan Liang', 'Yijuan Lu', 'Nan Zhao', 'Chenglong Li'] | 2018-05-23 | null | null | null | null | ['rgb-t-tracking'] | ['computer-vision'] | [-7.30640143e-02 -5.91839314e-01 -1.83288142e-01 -1.59354165e-01
-5.07989645e-01 -3.88081074e-01 2.14976028e-01 -3.25447887e-01
-4.20175284e-01 3.53730440e-01 -4.58256528e-02 6.59081759e-03
7.94712454e-02 -4.29095745e-01 -5.78576028e-01 -1.05172133e+00
5.11449538e-02 5.14094122e-02 5.29801011e-01 -8.29864070... | [6.345104217529297, -2.210825204849243] |
9f1db407-0afb-4b04-a13a-e4c75dd1b9be | video-summarization-with-attention-based | 1708.09545 | null | http://arxiv.org/abs/1708.09545v2 | http://arxiv.org/pdf/1708.09545v2.pdf | Video Summarization with Attention-Based Encoder-Decoder Networks | This paper addresses the problem of supervised video summarization by
formulating it as a sequence-to-sequence learning problem, where the input is a
sequence of original video frames, the output is a keyshot sequence. Our key
idea is to learn a deep summarization network with attention mechanism to mimic
the way of se... | ['Xuelong. Li', 'Yanwei Pang', 'Kailin Xiong', 'Zhong Ji'] | 2017-08-31 | null | null | null | null | ['supervised-video-summarization'] | ['computer-vision'] | [ 6.45198107e-01 4.41627670e-03 -2.57115364e-01 -2.59953409e-01
-8.76390278e-01 6.53843358e-02 5.21159768e-01 -9.94535536e-02
-3.98464829e-01 7.91699886e-01 8.30563962e-01 4.66404529e-03
4.42788392e-01 -2.51370430e-01 -1.05489957e+00 -7.34530449e-01
7.78004751e-02 -2.82783508e-01 3.62911582e-01 -9.93439630... | [10.42518138885498, 0.4338070750236511] |
a9d723dd-4ece-4330-8344-c306c680d7ab | meta-generative-flow-networks-with | 2306.09742 | null | https://arxiv.org/abs/2306.09742v1 | https://arxiv.org/pdf/2306.09742v1.pdf | Meta Generative Flow Networks with Personalization for Task-Specific Adaptation | Multi-task reinforcement learning and meta-reinforcement learning have been developed to quickly adapt to new tasks, but they tend to focus on tasks with higher rewards and more frequent occurrences, leading to poor performance on tasks with sparse rewards. To address this issue, GFlowNets can be integrated into meta-l... | ['Yinchuan Li', 'Olga Gadyatskaya', 'Haozhi Wang', 'Wei Xi', 'Xu Zhang', 'Xinyuan Ji'] | 2023-06-16 | null | null | null | null | ['meta-learning'] | ['methodology'] | [-3.09978537e-02 -5.01069315e-02 -3.34229052e-01 7.05235824e-03
-7.78516531e-01 5.99585250e-02 3.49749267e-01 1.67356715e-01
-7.12979734e-01 1.26863039e+00 -1.32479146e-01 1.45045802e-01
-3.86002719e-01 -4.93596941e-01 -6.93598032e-01 -7.72069097e-01
-1.26582190e-01 5.68006873e-01 3.33724976e-01 -1.42748609... | [3.963625431060791, 2.1230554580688477] |
19fd7783-a597-48b6-ac89-7ead8a2c537c | human-centric-image-cropping-with-partition | 2207.10269 | null | https://arxiv.org/abs/2207.10269v1 | https://arxiv.org/pdf/2207.10269v1.pdf | Human-centric Image Cropping with Partition-aware and Content-preserving Features | Image cropping aims to find visually appealing crops in an image, which is an important yet challenging task. In this paper, we consider a specific and practical application: human-centric image cropping, which focuses on the depiction of a person. To this end, we propose a human-centric image cropping method with two ... | ['Liqing Zhang', 'Xing Zhao', 'Li Niu', 'Bo Zhang'] | 2022-07-21 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 3.40060592e-01 -5.54592982e-02 -1.65247675e-02 -3.39926593e-02
-4.55620408e-01 -5.53806484e-01 3.51331979e-01 3.73338252e-01
6.42426684e-03 1.49965987e-01 1.85248032e-01 -9.49428454e-02
2.70477235e-01 -9.30099249e-01 -8.52590322e-01 -6.24037802e-01
2.22117588e-01 -5.37991188e-02 1.60339430e-01 -1.91563711... | [11.331032752990723, -1.0477385520935059] |
f11bbcf9-3752-4ca6-8aa4-e80083b33bc0 | code-mvp-learning-to-represent-source-code | 2205.02029 | null | https://arxiv.org/abs/2205.02029v1 | https://arxiv.org/pdf/2205.02029v1.pdf | CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training | Recent years have witnessed increasing interest in code representation learning, which aims to represent the semantics of source code into distributed vectors. Currently, various works have been proposed to represent the complex semantics of source code from different views, including plain text, Abstract Syntax Tree (... | ['Jin Liu', 'Hao Wu', 'Li Li', 'Pingyi Zhou', 'Jiawei Wang', 'Yao Wan', 'Yasheng Wang', 'Xin Wang'] | 2022-05-04 | null | https://aclanthology.org/2022.findings-naacl.80 | https://aclanthology.org/2022.findings-naacl.80.pdf | findings-naacl-2022-7 | ['defect-detection'] | ['computer-vision'] | [-3.80813591e-02 -4.08199131e-01 -5.89356303e-01 -4.42235708e-01
-8.49448264e-01 -8.21299732e-01 4.56109881e-01 5.18453121e-01
1.74035758e-01 -1.80207014e-01 4.61070985e-01 -3.72656584e-01
2.03160256e-01 -7.77996242e-01 -7.61092722e-01 -2.53268480e-01
2.11394802e-01 -2.06875399e-01 2.09436148e-01 -2.51814902... | [7.503148555755615, 7.9898505210876465] |
98486603-11fc-4095-9fa3-32bdb0431451 | self-supervised-learning-for-few-shot-image | 1911.06045 | null | https://arxiv.org/abs/1911.06045v3 | https://arxiv.org/pdf/1911.06045v3.pdf | Self-Supervised Learning For Few-Shot Image Classification | Few-shot image classification aims to classify unseen classes with limited labelled samples. Recent works benefit from the meta-learning process with episodic tasks and can fast adapt to class from training to testing. Due to the limited number of samples for each task, the initial embedding network for meta-learning b... | ['Hui Xue', 'Yuan He', 'Yuhong Li', 'Yuefeng Chen', 'Da Chen', 'Feng Mao'] | 2019-11-14 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.01971216e-01 -6.08723015e-02 -5.05151212e-01 -3.73678714e-01
-7.46512413e-01 -6.28492087e-02 6.04489684e-01 -6.35195300e-02
-4.98286366e-01 6.62817419e-01 1.53693045e-02 1.22164048e-01
-5.19129187e-02 -7.71022856e-01 -6.04577482e-01 -7.35711694e-01
1.19733311e-01 2.04845443e-01 5.09475172e-01 -2.42264315... | [10.009986877441406, 2.9817123413085938] |
7bd01f26-a994-4d1b-9baf-29ab1951429f | relaxed-forced-choice-improves-performance-of | 2305.00220 | null | https://arxiv.org/abs/2305.00220v1 | https://arxiv.org/pdf/2305.00220v1.pdf | Relaxed forced choice improves performance of visual quality assessment methods | In image quality assessment, a collective visual quality score for an image or video is obtained from the individual ratings of many subjects. One commonly used format for these experiments is the two-alternative forced choice method. Two stimuli with the same content but differing visual quality are presented sequenti... | ['Dietmar Saupe', 'Raouf Hamzaoui', 'Ulf-Dietrich Reips', 'Harald Reiterer', 'Johannes Zagermann', 'Mohsen Jenadeleh'] | 2023-04-29 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 9.31717604e-02 -2.67642200e-01 1.92452490e-01 -4.67902362e-01
-6.47253335e-01 -7.76821733e-01 1.94255203e-01 3.57007116e-01
-7.69010484e-01 7.43083775e-01 6.75512031e-02 -2.70211130e-01
-1.31502792e-01 -5.13706028e-01 -4.71043944e-01 -6.19127035e-01
3.63419652e-01 2.15477064e-01 3.51433039e-01 -2.69537624... | [11.802483558654785, -1.8525745868682861] |
1637addd-cf03-4510-b765-5722b1e60eba | simple-thermal-noise-estimation-of-switched-1 | 1908.08109 | null | http://arxiv.org/abs/1908.08109v1 | http://arxiv.org/pdf/1908.08109v1.pdf | Simple Thermal Noise Estimation of Switched Capacitor Circuits Based on OTAs -- Part II: SC Filters | In Part I of this paper, we have shown how to calculate the thermal noise
voltage variances in switched-capacitor (SC) circuits using operational
transconductance amplifiers (OTAs) with capacitive feedback by using the
extended Bode theorem. The method allows a precise estimation of the thermal
noise voltage variances ... | [] | 2019-08-21 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 2.91196436e-01 -3.12796831e-01 3.09888393e-01 7.86868706e-02
-2.87677199e-01 -7.89898038e-01 1.45823866e-01 1.48037314e-01
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1.00237586e-01 -1.83869481e-01 5.44727802e-01 -3.26810777... | [13.935396194458008, 3.2039825916290283] |
3d071943-a96b-4d21-8197-4c488d875eaa | single-underwater-image-restoration-by | 2103.09697 | null | https://arxiv.org/abs/2103.09697v2 | https://arxiv.org/pdf/2103.09697v2.pdf | Single Underwater Image Restoration by Contrastive Learning | Underwater image restoration attracts significant attention due to its importance in unveiling the underwater world. This paper elaborates on a novel method that achieves state-of-the-art results for underwater image restoration based on the unsupervised image-to-image translation framework. We design our method by lev... | ['Mohammad Ali Armin', 'Lars Petersson', 'Ran Wei', 'Saeed Anwar', 'Janet Anstee', 'Elizabeth Botha', 'Tim Malthus', 'Mehrdad Shoeiby', 'Junlin Han'] | 2021-03-17 | null | null | null | null | ['underwater-image-restoration'] | ['computer-vision'] | [ 7.09686399e-01 2.48208493e-01 7.41903245e-01 -5.94358385e-01
-9.00777519e-01 -2.04887778e-01 3.82761240e-01 -3.58774036e-01
-8.02750587e-01 6.86232984e-01 3.65517169e-01 -1.25099584e-01
-7.75762424e-02 -9.34066951e-01 -1.07098949e+00 -9.54743445e-01
-3.16851109e-01 -1.89560696e-01 -1.19029976e-01 -3.88301462... | [10.695380210876465, -3.5464460849761963] |
6fe7d085-4d06-4f8d-8df5-209ad9d66537 | domain-aligned-prefix-averaging-for-domain | 2305.16820 | null | https://arxiv.org/abs/2305.16820v2 | https://arxiv.org/pdf/2305.16820v2.pdf | Domain Aligned Prefix Averaging for Domain Generalization in Abstractive Summarization | Domain generalization is hitherto an underexplored area applied in abstractive summarization. Moreover, most existing works on domain generalization have sophisticated training algorithms. In this paper, we propose a lightweight, weight averaging based, Domain Aligned Prefix Averaging approach to domain generalization ... | ['Pradeepika Verma', 'Sukomal Pal', 'Pranav Ajit Nair'] | 2023-05-26 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 6.12903476e-01 1.16825037e-01 -7.56961107e-01 -3.51799488e-01
-9.94370818e-01 -7.76906967e-01 8.11192334e-01 8.34206045e-01
-3.23039770e-01 1.12931836e+00 8.99643898e-01 1.54117391e-01
-1.97291553e-01 -6.67345941e-01 -5.40504038e-01 -2.93520629e-01
-1.36744855e-02 8.74474585e-01 3.97648394e-01 -2.06035241... | [12.40915584564209, 9.420913696289062] |
96a6ede5-8178-45ab-b48f-56160d3e6d92 | identity-masking-effectiveness-and-gesture | 2301.08408 | null | https://arxiv.org/abs/2301.08408v1 | https://arxiv.org/pdf/2301.08408v1.pdf | Identity masking effectiveness and gesture recognition: Effects of eye enhancement in seeing through the mask | Face identity masking algorithms developed in recent years aim to protect the privacy of people in video recordings. These algorithms are designed to interfere with identification, while preserving information about facial actions. An important challenge is to preserve subtle actions in the eye region, while obscuring ... | ["Alice J. O'Toole", 'Thomas Karnowski', 'Madeline Rachow'] | 2023-01-20 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 6.60827816e-01 -1.48624152e-01 3.07761133e-01 -3.71571481e-01
-1.41470805e-01 -7.87791908e-01 3.92629266e-01 -1.98436573e-01
-8.88691664e-01 4.87195760e-01 1.47778749e-01 -4.16886151e-01
1.34490713e-01 -3.62620920e-01 -3.84298176e-01 -7.37697959e-01
8.16607624e-02 -7.28462279e-01 2.20364362e-01 -7.74895598... | [13.042136192321777, 1.046260952949524] |
40180553-e186-4416-a5e5-2268e8264fa4 | single-image-reflection-suppression | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Arvanitopoulos_Single_Image_Reflection_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Arvanitopoulos_Single_Image_Reflection_CVPR_2017_paper.pdf | Single Image Reflection Suppression | Reflections are a common artifact in images taken through glass windows. Automatically removing the reflection artifacts after the picture is taken is an ill-posed problem. Attempts to solve this problem using optimization schemes therefore rely on various prior assumptions from the physical world. Instead of removing ... | ['Sabine Susstrunk', 'Radhakrishna Achanta', 'Nikolaos Arvanitopoulos'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['reflection-removal'] | ['computer-vision'] | [ 8.92651320e-01 -1.05883837e-01 6.33020461e-01 -2.70413041e-01
-7.09210694e-01 5.86812152e-03 4.65444535e-01 -4.05282348e-01
-4.66966718e-01 5.93866289e-01 2.40084350e-01 -9.18961614e-02
5.75340800e-02 -4.40729052e-01 -5.00924587e-01 -9.08045053e-01
2.37345561e-01 -4.24752682e-01 2.43404865e-01 7.48674348... | [10.576534271240234, -2.753453493118286] |
98b34394-f497-4d76-a0e0-3a2d86aaeb91 | a-temporal-knowledge-graph-completion-method | 2108.13024 | null | https://arxiv.org/abs/2108.13024v2 | https://arxiv.org/pdf/2108.13024v2.pdf | A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution | Completion through the embedding representation of the knowledge graph (KGE) has been a research hotspot in recent years. Realistic knowledge graphs are mostly related to time, while most of the existing KGE algorithms ignore the time information. A few existing methods directly or indirectly encode the time informatio... | ['Yuhong Zhang', 'Kangzheng Liu'] | 2021-08-30 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-4.11334008e-01 -2.20281139e-01 -6.95382357e-01 -1.25763029e-01
2.01699004e-01 -4.94154245e-01 4.64990944e-01 4.95714068e-01
-3.84893864e-01 5.47195673e-01 2.89707780e-01 -1.68870687e-01
-6.82533324e-01 -1.34241164e+00 -3.60683322e-01 -5.19141138e-01
-4.78373677e-01 1.65624157e-01 7.08156288e-01 -1.37042567... | [8.56711196899414, 7.901711940765381] |
8f8101c8-5a74-4557-81ef-c4287f6441a0 | forms-of-anaphoric-reference-to | null | null | https://aclanthology.org/W18-2406 | https://aclanthology.org/W18-2406.pdf | Forms of Anaphoric Reference to Organisational Named Entities: Hoping to widen appeal, they diversified | Proper names of organisations are a special case of collective nouns. Their meaning can be conceptualised as a collective unit or as a plurality of persons, allowing for different morphological marking of coreferent anaphoric pronouns. This paper explores the variability of references to organisation names with 1) a co... | ["Sharid Lo{\\'a}iciga", 'Luca Bevacqua', 'Christian Hardmeier', 'Hannah Rohde'] | 2018-07-01 | null | null | null | ws-2018-7 | ['story-continuation'] | ['computer-vision'] | [ 2.54079886e-02 3.65695983e-01 -1.38447016e-01 -3.61558944e-01
-4.99742180e-01 -8.70653450e-01 1.30435073e+00 3.91980708e-01
-8.72000635e-01 9.93658185e-01 1.02464795e+00 -1.70229405e-01
-3.93609911e-01 -7.01037824e-01 -2.46074006e-01 -7.14191437e-01
3.46245110e-01 8.35513175e-01 5.00998616e-01 -5.67672729... | [10.171425819396973, 9.300381660461426] |
bc9dbb94-4f14-4dec-992e-0484f07edd62 | effect-of-different-splitting-criteria-on-the | 2210.14501 | null | https://arxiv.org/abs/2210.14501v1 | https://arxiv.org/pdf/2210.14501v1.pdf | Effect of different splitting criteria on the performance of speech emotion recognition | Traditional speech emotion recognition (SER) evaluations have been performed merely on a speaker-independent condition; some of them even did not evaluate their result on this condition. This paper highlights the importance of splitting training and test data for SER by script, known as sentence-open or text-independen... | ['Akira Sasou', 'Bagus Tris Atmaja'] | 2022-10-26 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-3.24154622e-03 -3.05575937e-01 5.28925300e-01 -6.92043722e-01
-9.92760420e-01 -6.72049224e-01 3.49979848e-01 -4.30759527e-02
-7.38538504e-01 6.09456241e-01 3.74339968e-01 -3.83810282e-01
1.79982241e-02 7.50815794e-02 -1.95324510e-01 -7.20016241e-01
2.48489454e-02 1.22602157e-01 4.86943759e-02 -5.26370645... | [13.96747875213623, 5.884884357452393] |
bf493986-b525-49a5-9b34-4e229a4192cb | causal-order-identification-to-address | 2108.04947 | null | https://arxiv.org/abs/2108.04947v2 | https://arxiv.org/pdf/2108.04947v2.pdf | Causal Order Identification to Address Confounding: Binary Variables | This paper considers an extension of the linear non-Gaussian acyclic model (LiNGAM) that determines the causal order among variables from a dataset when the variables are expressed by a set of linear equations, including noise. In particular, we assume that the variables are binary. The existing LiNGAM assumes that no ... | ['Yusuke Inaoka', 'Joe Suzuki'] | 2021-08-10 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 9.61593091e-02 -2.02435739e-02 -1.91484168e-01 -3.24469596e-01
-3.10470581e-01 -4.08074319e-01 2.70582944e-01 -2.33682580e-02
-4.81988102e-01 8.51731360e-01 -1.09415529e-02 -3.70027751e-01
-9.16427076e-01 -9.22899067e-01 -5.60980380e-01 -9.40505326e-01
-4.03784037e-01 4.20198143e-01 -1.00683749e-01 2.05383599... | [7.73684549331665, 4.9553608894348145] |
9fd2d631-a6bf-42d8-8d55-d5f17dbee520 | infinite-time-horizon-safety-of-bayesian | 2111.03165 | null | https://arxiv.org/abs/2111.03165v1 | https://arxiv.org/pdf/2111.03165v1.pdf | Infinite Time Horizon Safety of Bayesian Neural Networks | Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction. We consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with infinite time horizon systems. Compared to the existin... | ['Thomas A. Henzinger', 'Krishnendu Chatterjee', 'Đorđe Žikelić', 'Mathias Lechner'] | 2021-11-04 | null | http://proceedings.neurips.cc/paper/2021/hash/544defa9fddff50c53b71c43e0da72be-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/544defa9fddff50c53b71c43e0da72be-Paper.pdf | neurips-2021-12 | ['safe-exploration'] | ['robots'] | [ 3.01526666e-01 7.89045513e-01 -3.31813961e-01 -1.54726774e-01
-6.83725893e-01 -6.17249012e-01 3.67679983e-01 5.35274250e-03
-4.18496639e-01 9.45925832e-01 -2.74869144e-01 -9.05424178e-01
-6.60533309e-01 -9.83391702e-01 -1.31529725e+00 -8.00777435e-01
-6.06202364e-01 4.22386706e-01 4.37241048e-01 -9.49737430... | [4.5551228523254395, 2.225177049636841] |
7794d63f-34f8-4ec2-8fcb-e498a13df0ca | seetheseams-localized-detection-of-seam | 2108.12534 | null | https://arxiv.org/abs/2108.12534v1 | https://arxiv.org/pdf/2108.12534v1.pdf | SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery | Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or insert/remove roads in a satellite image. This paper proposes a novel approach for detecting and localizing seams in such images. While there ... | ['B. S. Manjunath', 'Shivkumar Chandrasekaran', 'Lakshmanan Nataraj', 'Erik Rosten', 'Chandrakanth Gudavalli'] | 2021-08-28 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 6.05549395e-01 -2.11204827e-01 -3.96227390e-02 -2.73174703e-01
-8.61364186e-01 -8.61847878e-01 8.13710392e-01 1.67277399e-02
-4.43745911e-01 6.19108558e-01 2.42647007e-01 -1.08722113e-01
2.26206146e-03 -6.89340055e-01 -7.79377520e-01 -7.25291193e-01
1.49079293e-01 -3.14600527e-01 6.69135511e-01 -2.87460417... | [11.148996353149414, -1.1957578659057617] |
65f9ea5b-64c4-4dde-a09b-cfd061b00fa4 | limitations-of-deep-neural-networks-a | 2012.15754 | null | https://arxiv.org/abs/2012.15754v1 | https://arxiv.org/pdf/2012.15754v1.pdf | Limitations of Deep Neural Networks: a discussion of G. Marcus' critical appraisal of deep learning | Deep neural networks have triggered a revolution in artificial intelligence, having been applied with great results in medical imaging, semi-autonomous vehicles, ecommerce, genetics research, speech recognition, particle physics, experimental art, economic forecasting, environmental science, industrial manufacturing, a... | ['Stefanos Tsimenidis'] | 2020-12-22 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 1.44102469e-01 3.58037889e-01 -2.74827212e-01 -2.84312308e-01
-1.19261913e-01 -1.84682116e-01 5.72869956e-01 3.91721427e-02
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-3.52767348e-01 -7.63527632e-01 -4.57566023e-01 -8.12245846e-01
-2.78506503e-02 2.38417342e-01 -6.81556314e-02 -1.60808042... | [8.985457420349121, 6.430929660797119] |
e75c7908-4c97-4e0d-b03e-6e1aa7477622 | illumination-invariant-active-camera | 2204.06580 | null | https://arxiv.org/abs/2204.06580v1 | https://arxiv.org/pdf/2204.06580v1.pdf | Illumination-Invariant Active Camera Relocalization for Fine-Grained Change Detection in the Wild | Active camera relocalization (ACR) is a new problem in computer vision that significantly reduces the false alarm caused by image distortions due to camera pose misalignment in fine-grained change detection (FGCD). Despite the fruitful achievements that ACR can support, it still remains a challenging problem caused by ... | ['Qian Zhang', 'Wei Feng', 'Nan Li'] | 2022-04-13 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 4.26657736e-01 -3.38160485e-01 9.99113545e-02 -2.31451720e-01
-7.18775928e-01 -7.04309881e-01 1.99134022e-01 -1.73416317e-01
-6.20844424e-01 4.27313656e-01 1.43570155e-01 2.30717972e-01
-3.00068762e-02 -6.04639471e-01 -8.01209688e-01 -9.62540627e-01
3.17405522e-01 1.69852786e-02 5.43990612e-01 -2.69618124... | [7.7593278884887695, -2.1599037647247314] |
cb644fd7-81b3-4ede-90b3-36134f1f42fe | a-unified-framework-for-domain-adaptive-pose | 2204.00172 | null | https://arxiv.org/abs/2204.00172v3 | https://arxiv.org/pdf/2204.00172v3.pdf | A Unified Framework for Domain Adaptive Pose Estimation | While pose estimation is an important computer vision task, it requires expensive annotation and suffers from domain shift. In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a synthetic source domain to a target domain without supervision. While several ... | ['Stan Sclaroff', 'Margrit Betke', 'Kate Saenko', 'Kaihong Wang', 'Donghyun Kim'] | 2022-04-01 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [ 1.05139539e-01 6.89813197e-02 -2.19301492e-01 -4.21164155e-01
-9.18077648e-01 -7.23038793e-01 4.26917523e-01 -3.03738475e-01
-4.42355692e-01 6.87308311e-01 -6.89965934e-02 2.67885655e-01
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2.54202902e-01 8.95450413e-01 5.69587529e-01 -2.66798586... | [7.207331657409668, -0.9849103689193726] |
cd23c570-4686-4f05-8052-d71667319fa5 | backdoor-defense-via-deconfounded | 2303.06818 | null | https://arxiv.org/abs/2303.06818v1 | https://arxiv.org/pdf/2303.06818v1.pdf | Backdoor Defense via Deconfounded Representation Learning | Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples into the training dataset. While extensive efforts have been made to detect and remove backdoors from backdoored DNNs, it is still not clear w... | ['Qingyong Hu', 'Zepu Lu', 'Zhicai Wang', 'Qi Liu', 'Zaixi Zhang'] | 2023-03-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Backdoor_Defense_via_Deconfounded_Representation_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Backdoor_Defense_via_Deconfounded_Representation_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['backdoor-attack'] | ['adversarial'] | [ 1.81396365e-01 5.76400682e-02 -3.65782261e-01 6.75366260e-03
-4.65599984e-01 -9.98972774e-01 7.73605525e-01 -6.96679950e-02
-3.17819156e-02 7.38301694e-01 7.45978579e-02 -7.19879866e-01
-6.72255382e-02 -8.67863357e-01 -1.09855926e+00 -1.02927005e+00
-1.64738402e-01 -1.79719433e-01 1.68428659e-01 -1.08388782... | [5.855088233947754, 7.683503150939941] |
857358be-f92f-4dcc-8ff0-a92e3616e37f | which-has-better-visual-quality-the-clear | null | null | https://ieeexplore.ieee.org/document/8489929 | https://www.researchgate.net/publication/328240901_Which_Has_Better_Visual_Quality_The_Clear_Blue_Sky_or_a_Blurry_Animal | Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal? | Image content variation is a typical and challenging problem in no-reference image quality assessment (NR-IQA). This work pays special attention to the impact of image content variation on NR-IQA methods. To better analyze this impact, we focus on blur-dominated distortions to exclude the impacts of distortion-type var... | ['Weisi Lin', 'Tingting Jiang', 'Ming Jiang', 'Dingquan Li'] | 2018-10-11 | null | null | null | ieee-transactions-on-multimedia-2018-10 | ['image-quality-estimation', 'blind-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.22200498e-01 -7.37473965e-01 8.62105712e-02 -2.83264339e-01
-8.07851791e-01 -2.65480936e-01 4.23797041e-01 -3.35975051e-01
-1.70263588e-01 6.27185225e-01 4.06021267e-01 5.24088144e-02
-4.85607028e-01 -6.33344293e-01 -7.28936195e-01 -9.29070175e-01
4.72360514e-02 -3.46205473e-01 4.94299531e-02 -1.72360912... | [11.7786865234375, -1.916977047920227] |
bd4f03e3-92ad-42cd-bea3-cd8277931d31 | multi-class-cell-detection-using-spatial-1 | 2110.04886 | null | https://arxiv.org/abs/2110.04886v2 | https://arxiv.org/pdf/2110.04886v2.pdf | Multi-Class Cell Detection Using Spatial Context Representation | In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks. Classifying cells into subtypes, such as tumor cells, lymphocytes or stromal cells is particularly challenging. Existing methods focus on morphological appearance of individual cells, whereas in... | ['Chao Chen', 'Joel Saltz', 'Dimitris Samaras', 'Tahsin Kurc', 'Rajarsi Gupta', 'Eric Yee', 'Felicia Allard', 'John Van Arnam', 'David Belinsky', 'Shahira Abousamra'] | 2021-10-10 | multi-class-cell-detection-using-spatial | http://openaccess.thecvf.com//content/ICCV2021/html/Abousamra_Multi-Class_Cell_Detection_Using_Spatial_Context_Representation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Abousamra_Multi-Class_Cell_Detection_Using_Spatial_Context_Representation_ICCV_2021_paper.pdf | iccv-2021-1 | ['cell-detection'] | ['computer-vision'] | [ 7.38846585e-02 -2.97637135e-01 -3.06982458e-01 -2.95264542e-01
-1.23434460e+00 -5.67092478e-01 7.04037249e-01 9.84766483e-01
-3.91025275e-01 6.44112885e-01 7.67464936e-02 -3.42311949e-01
1.46875978e-02 -9.05626237e-01 -2.10425884e-01 -1.22833824e+00
2.52004154e-02 8.59474123e-01 2.14981675e-01 3.02664012... | [14.999223709106445, -3.0634548664093018] |
a593602e-563d-4529-8bfc-1883cc31fc57 | thurstonian-boltzmann-machines-learning-from | 1408.0055 | null | http://arxiv.org/abs/1408.0055v1 | http://arxiv.org/pdf/1408.0055v1.pdf | Thurstonian Boltzmann Machines: Learning from Multiple Inequalities | We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture
that can naturally incorporate a wide range of data inputs at the same time.
Our motivation rests in the Thurstonian view that many discrete data types can
be considered as being generated from a subset of underlying latent continuous
variables, ... | ['Svetha Venkatesh', 'Truyen Tran', 'Dinh Phung'] | 2014-08-01 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.88727841e-01 1.72732010e-01 -5.81363142e-01 -7.71135628e-01
-1.34650961e-01 -5.48391521e-01 1.03543139e+00 2.94969324e-02
-5.11851907e-01 1.12552130e+00 3.11388284e-01 -6.71815455e-01
-7.64899433e-01 -1.12500370e+00 -7.08088458e-01 -6.24694109e-01
-3.18963766e-01 1.14749396e+00 -3.47491622e-01 1.60912409... | [7.731123924255371, 4.278194904327393] |
892bf451-9107-4b9d-8896-bdb355d0c165 | conditional-augmentation-for-aspect-term | 2004.14769 | null | https://arxiv.org/abs/2004.14769v2 | https://arxiv.org/pdf/2004.14769v2.pdf | Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation | Aspect term extraction aims to extract aspect terms from review texts as opinion targets for sentiment analysis. One of the big challenges with this task is the lack of sufficient annotated data. While data augmentation is potentially an effective technique to address the above issue, it is uncontrollable as it may cha... | ['Yan Song', 'Qing Ling', 'Kun Li', 'Chengbo Chen', 'Xiaojun Quan'] | 2020-04-30 | conditional-augmentation-for-aspect-term-1 | https://aclanthology.org/2020.acl-main.631 | https://aclanthology.org/2020.acl-main.631.pdf | acl-2020-6 | ['extract-aspect'] | ['natural-language-processing'] | [ 6.21046245e-01 4.31610674e-01 -4.83662069e-01 -3.73498082e-01
-9.07186925e-01 -8.88288975e-01 6.47080123e-01 2.17967495e-01
-1.69129953e-01 9.19585228e-01 3.28386545e-01 -4.15923983e-01
5.10219812e-01 -7.51275182e-01 -4.41217184e-01 -5.11706531e-01
3.69824469e-01 3.22540790e-01 -1.21630564e-01 -6.27232194... | [11.420455932617188, 6.738393783569336] |
742dacc8-b901-41aa-91bf-0aedcf20354b | federated-learning-based-active | 2104.07158 | null | https://arxiv.org/abs/2104.07158v1 | https://arxiv.org/pdf/2104.07158v1.pdf | Federated Learning-based Active Authentication on Mobile Devices | User active authentication on mobile devices aims to learn a model that can correctly recognize the enrolled user based on device sensor information. Due to lack of negative class data, it is often modeled as a one-class classification problem. In practice, mobile devices are connected to a central server, e.g, all and... | ['Vishal M. Patel', 'Poojan Oza'] | 2021-04-14 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 2.70747751e-01 -2.94774741e-01 -4.05764639e-01 6.26711398e-02
-1.01480913e+00 -7.59673893e-01 3.66481155e-01 3.37633133e-01
-3.30523312e-01 7.19841301e-01 -4.41928923e-01 -8.03345680e-01
-3.91373158e-01 -7.97709227e-01 -5.62833548e-01 -9.00048852e-01
-1.15270019e-01 1.28563866e-01 1.73962042e-01 1.08296461... | [5.893285751342773, 6.3076171875] |
747cd456-4f96-46ef-a2ef-e1a794b3cfc6 | deep-neural-networks-architectures-from-the | 2306.03406 | null | https://arxiv.org/abs/2306.03406v1 | https://arxiv.org/pdf/2306.03406v1.pdf | Deep neural networks architectures from the perspective of manifold learning | Despite significant advances in the field of deep learning in ap-plications to various areas, an explanation of the learning pro-cess of neural network models remains an important open ques-tion. The purpose of this paper is a comprehensive comparison and description of neural network architectures in terms of ge-ometr... | ['German Magai'] | 2023-06-06 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [-1.06743865e-01 3.08163851e-01 3.05269361e-01 -2.98209786e-01
4.49679315e-01 -6.30098343e-01 9.03187454e-01 2.41605014e-01
1.09197438e-01 3.33317339e-01 1.32188439e-01 -5.95909953e-01
-5.17878652e-01 -1.06929326e+00 -9.73623335e-01 -4.80601519e-01
-7.65832841e-01 7.15365410e-01 8.55063275e-02 -7.48750329... | [6.801983833312988, 5.9997100830078125] |
25416736-a047-44be-ac91-4131daf02cc3 | identification-of-ischemic-heart-disease-by | 2010.15893 | null | https://arxiv.org/abs/2010.15893v1 | https://arxiv.org/pdf/2010.15893v1.pdf | Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability | The diagnosis of heart diseases is a difficult task generally addressed by an appropriate examination of patients clinical data. Recently, the use of heart rate variability (HRV) analysis as well as of some machine learning algorithms, has proved to be a valuable support in the diagnosis process. However, till now, isc... | ['Agostino Accardo', 'Gianfranco Sinagra', 'Miloš Ajčević', 'Aleksandar Miladinović', 'Beatrice De Paola', 'Luca Restivo', 'Marco Merlo', 'Giulia Silveri'] | 2020-10-29 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 1.14471495e-01 3.55069875e-03 8.07661489e-02 -2.92656839e-01
2.28626817e-01 -3.27529371e-01 4.25640270e-02 5.95413327e-01
-6.75312459e-01 1.03266430e+00 -4.31783944e-01 -5.03330529e-01
-4.46007103e-01 -7.16769040e-01 2.89407492e-01 -7.28525519e-01
-3.72672111e-01 8.39069963e-01 -1.06310584e-01 7.99241662... | [14.111681938171387, 3.1528944969177246] |
2694bb2b-7b0e-4742-9cdd-70b2c62e9e28 | skin-lesion-segmentation-and-classification-2 | 1908.05730 | null | https://arxiv.org/abs/1908.05730v1 | https://arxiv.org/pdf/1908.05730v1.pdf | Skin Lesion Segmentation and Classification for ISIC 2018 by Combining Deep CNN and Handcrafted Features | This short report describes our submission to the ISIC 2018 Challenge in Skin Lesion Analysis Towards Melanoma Detection for Task1 and Task 3. This work has been accomplished by a team of researchers at the University of Dayton Signal and Image Processing Lab. Our proposed approach is computationally efficient are comb... | ['Redha Ali', 'Temesguen Messay Kebede', 'Russell C. Hardie', 'Manawaduge Supun De Silva'] | 2019-08-14 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 7.67444968e-01 -2.78238207e-01 -3.42352211e-01 -4.28901374e-01
-9.57834899e-01 -3.05076182e-01 5.84404171e-01 1.79868549e-01
-6.70859873e-01 5.99653840e-01 1.22762166e-01 -2.80174673e-01
-5.18625304e-02 -3.78765047e-01 -4.00739536e-02 -7.48092830e-01
1.54712826e-01 -5.08260250e-01 2.62590796e-01 -3.08264587... | [15.690048217773438, -2.97685170173645] |
5f8fb1ea-77d2-4172-aa4d-5f5db14bbf80 | a-novel-mask-r-cnn-model-to-segment | 2204.01201 | null | https://arxiv.org/abs/2204.01201v1 | https://arxiv.org/pdf/2204.01201v1.pdf | A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtraction | The segmentation of diseases is a popular topic explored by researchers in the field of machine learning. Brain tumors are extremely dangerous and require the utmost precision to segment for a successful surgery. Patients with tumors usually take 4 MRI scans, T1, T1gd, T2, and FLAIR, which are then sent to radiologists... | ['Sanskriti Singh'] | 2022-04-04 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 4.81774747e-01 6.54026151e-01 -8.92700776e-02 -5.17672539e-01
-9.12779093e-01 -3.15478146e-01 3.64845365e-01 2.18939230e-01
-8.71048868e-01 5.74853361e-01 1.90368101e-01 -5.44580579e-01
1.31455347e-01 -5.39349258e-01 -5.27358651e-01 -7.13821769e-01
7.38055557e-02 8.04977596e-01 6.16282701e-01 1.88413501... | [14.539889335632324, -2.3694849014282227] |
8d74a741-ea94-4200-87ae-f2cacc0b8ff3 | trans2k-unlocking-the-power-of-deep-models | 2210.03436 | null | https://arxiv.org/abs/2210.03436v1 | https://arxiv.org/pdf/2210.03436v1.pdf | Trans2k: Unlocking the Power of Deep Models for Transparent Object Tracking | Visual object tracking has focused predominantly on opaque objects, while transparent object tracking received very little attention. Motivated by the uniqueness of transparent objects in that their appearance is directly affected by the background, the first dedicated evaluation dataset has emerged recently. We contri... | ['Matej Kristan', 'Jiri Matas', 'Ziga Trojer', 'Alan Lukezic'] | 2022-10-07 | null | null | null | null | ['transparent-objects', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 1.36989981e-01 -9.18855071e-02 2.45114695e-03 -1.93070158e-01
-4.76390541e-01 -1.02172506e+00 6.57119155e-01 -2.22252190e-01
-2.41699785e-01 5.45693457e-01 2.55659539e-02 -2.16478571e-01
3.66579324e-01 -2.13404506e-01 -8.57791424e-01 -5.84829271e-01
-2.86755443e-01 5.74555039e-01 7.63325512e-01 1.50264129... | [6.362607955932617, -2.0277228355407715] |
d71e91c1-da63-4c87-ae28-b7586613e4d4 | variational-quantum-circuits-for-quantum | 1912.07286 | null | https://arxiv.org/abs/1912.07286v2 | https://arxiv.org/pdf/1912.07286v2.pdf | Variational Quantum Circuits for Quantum State Tomography | Quantum state tomography is a key process in most quantum experiments. In this work, we employ quantum machine learning for state tomography. Given an unknown quantum state, it can be learned by maximizing the fidelity between the output of a variational quantum circuit and this state. The number of parameters of the v... | ['He-Liang Huang', 'Anqi Huang', 'Xiang Fu', 'Junjie Wu', 'Xuejun Yang', 'Shichuan Xue', 'Xiaogang Qiang', 'Ping Xu', 'Yong Liu', 'Mingtang Deng', 'Dongyang Wang', 'Chu Guo'] | 2019-12-16 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 4.53794956e-01 -4.95212190e-02 -3.46607082e-02 -1.08399428e-01
-7.99350381e-01 -7.22623110e-01 3.11623931e-01 7.50426948e-02
-5.14842093e-01 7.90772259e-01 -4.43756193e-01 -7.87908494e-01
1.33779868e-01 -1.20460498e+00 -6.85471952e-01 -1.00069284e+00
-5.96488453e-02 6.34943604e-01 -8.99009481e-02 -2.78669089... | [5.6367106437683105, 4.869591236114502] |
ce7373db-c763-42f5-87f5-dc3f4b4a8582 | incremental-loop-closure-verification-by | 1509.07611 | null | http://arxiv.org/abs/1509.07611v1 | http://arxiv.org/pdf/1509.07611v1.pdf | Incremental Loop Closure Verification by Guided Sampling | Loop closure detection, the task of identifying locations revisited by a
robot in a sequence of odometry and perceptual observations, is typically
formulated as a combination of two subtasks: (1) bag-of-words image retrieval
and (2) post-verification using RANSAC geometric verification. The main
contribution of this st... | ['Kanji Tanaka'] | 2015-09-25 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 2.82269210e-01 1.00010149e-01 -1.60102308e-01 -2.64349520e-01
-6.73428297e-01 -5.58657527e-01 9.40832615e-01 4.85300332e-01
-4.73373652e-01 6.09588385e-01 -1.48385227e-01 -3.64359140e-01
-4.61731225e-01 -5.67640305e-01 -8.32779586e-01 -4.83965456e-01
-2.04598442e-01 8.90503824e-01 5.20867646e-01 -3.14100325... | [7.324501037597656, -2.0479114055633545] |
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