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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
98020190-7121-4dbd-a562-e64a923ad4b9 | identifying-recurring-patterns-with-deep | 1806.05229 | null | https://arxiv.org/abs/1806.05229v3 | https://arxiv.org/pdf/1806.05229v3.pdf | Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising | Image denoising methods must effectively model, implicitly or explicitly, the vast diversity of patterns and textures that occur in natural images. This is challenging, even for modern methods that leverage deep neural networks trained to regress to clean images from noisy inputs. One recourse is to rely on "internal" ... | ['Zhihao Xia', 'Ayan Chakrabarti'] | 2018-06-13 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 5.56049883e-01 -4.36148733e-01 4.57439482e-01 -5.57688117e-01
-1.01255810e+00 -4.25119460e-01 3.55012864e-01 -1.52840972e-01
-3.46366078e-01 3.74699235e-01 3.02661788e-02 1.21407714e-02
7.76974410e-02 -8.62124920e-01 -9.21731353e-01 -1.17987418e+00
1.80295277e-02 -1.92238986e-01 -4.40548845e-02 -3.46709698... | [11.50780200958252, -2.2942845821380615] |
bff8dda1-c963-4b51-8211-e9ac4ca42da2 | identity-sensitive-knowledge-propagation-for | 2208.12023 | null | https://arxiv.org/abs/2208.12023v1 | https://arxiv.org/pdf/2208.12023v1.pdf | Identity-Sensitive Knowledge Propagation for Cloth-Changing Person Re-identification | Cloth-changing person re-identification (CC-ReID), which aims to match person identities under clothing changes, is a new rising research topic in recent years. However, typical biometrics-based CC-ReID methods often require cumbersome pose or body part estimators to learn cloth-irrelevant features from human biometric... | ['Jingwen Guo', 'Hao Tang', 'Wei Shi', 'Hong Liu', 'Jianbing Wu'] | 2022-08-25 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 1.46621495e-01 -2.42489323e-01 1.19213142e-01 -6.77735388e-01
-5.13099849e-01 -3.84938717e-01 1.43858373e-01 -4.23446476e-01
-4.46436912e-01 6.05298281e-01 1.17392272e-01 2.65845358e-01
-1.91003655e-03 -4.43343371e-01 -6.76424146e-01 -7.89383888e-01
5.51389754e-01 -8.66035074e-02 -1.46680534e-01 -6.00238629... | [14.697482109069824, 0.9426100254058838] |
57a746a2-689e-48d3-9084-bf93a866b4e0 | rethinking-crowdsourcing-annotation-partial | 2109.02688 | null | https://arxiv.org/abs/2109.02688v1 | https://arxiv.org/pdf/2109.02688v1.pdf | Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification | Annotated images are required for both supervised model training and evaluation in image classification. Manually annotating images is arduous and expensive, especially for multi-labeled images. A recent trend for conducting such laboursome annotation tasks is through crowdsourcing, where images are annotated by volunt... | ['Z. Jane Wang', 'Tianze Yu', 'Jianzhe Lin'] | 2021-09-06 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.37816721e-01 3.99659723e-01 -2.76804745e-01 -5.02903759e-01
-9.12622690e-01 -9.01223838e-01 1.48452967e-01 4.38625693e-01
-7.94630349e-01 7.50293970e-01 -4.64243323e-01 1.44174233e-01
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2.56020069e-01 6.79668367e-01 5.95257342e-01 -1.39879003... | [9.599787712097168, 4.274991989135742] |
833f2cbf-f36a-4567-a099-300c5dda750d | exploring-the-limits-of-indiscriminate-data | 2303.03592 | null | https://arxiv.org/abs/2303.03592v3 | https://arxiv.org/pdf/2303.03592v3.pdf | Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks | Indiscriminate data poisoning attacks aim to decrease a model's test accuracy by injecting a small amount of corrupted training data. Despite significant interest, existing attacks remain relatively ineffective against modern machine learning (ML) architectures. In this work, we introduce the notion of model poisoning ... | ['Gautam Kamath', 'YaoLiang Yu', 'Yiwei Lu'] | 2023-03-07 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 1.46580279e-01 -2.30550706e-01 -5.13776302e-01 1.97522208e-01
-8.69601309e-01 -1.11961520e+00 7.72773683e-01 4.90617454e-01
-5.40845692e-01 5.53455055e-01 -5.34891598e-02 -7.32169986e-01
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-3.05861861e-01 3.39630127e-01 2.19934642e-01 -7.84292072... | [5.835359573364258, 7.5794525146484375] |
f6f4cb92-c940-45ba-9f8e-db91c19cf8b5 | domain-shifts-in-dermoscopic-skin-cancer | 2304.06968 | null | https://arxiv.org/abs/2304.06968v3 | https://arxiv.org/pdf/2304.06968v3.pdf | Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation | The limited ability of Convolutional Neural Networks to generalize to images from previously unseen domains is a major limitation, in particular, for safety-critical clinical tasks such as dermoscopic skin cancer classification. In order to translate CNN-based applications into the clinic, it is essential that they are... | ['Titus J. Brinker', 'Julia Niebling', 'Roman C. Maron', 'Sireesha Chamarthi', 'Katharina Fogelberg'] | 2023-04-14 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 4.36905921e-01 -2.44785160e-01 -8.31687357e-03 -3.41377050e-01
-3.70781273e-01 -9.35287774e-01 4.33757544e-01 4.99214113e-01
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-2.52273530e-01 -7.26472080e-01 -8.31380546e-01 -5.74974954e-01
5.30322045e-02 3.36920768e-01 3.69487703e-01 -1.87870506... | [15.4923734664917, -2.9318130016326904] |
f7c4358b-f7f9-4fa1-b8ce-240e61094325 | overlapping-community-detection-using-dynamic | 2210.11174 | null | https://arxiv.org/abs/2210.11174v1 | https://arxiv.org/pdf/2210.11174v1.pdf | Overlapping Community Detection using Dynamic Dilated Aggregation in Deep Residual GCN | Overlapping community detection is a key problem in graph mining. Some research has considered applying graph convolutional networks (GCN) to tackle the problem. However, it is still challenging to incorporate deep graph convolutional networks in the case of general irregular graphs. In this study, we design a deep dyn... | ['Md Saidur Rahman', 'Md Iqbal Hossain', 'Md Nurul Muttakin'] | 2022-10-20 | null | null | null | null | ['graph-mining', 'community-detection'] | ['graphs', 'graphs'] | [-3.0486293e-03 3.3583844e-01 2.2348334e-01 3.0376082e-02
-2.9274660e-01 -4.2394441e-01 3.6528021e-01 2.4616629e-01
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-5.8716965e-01 7.4326313e-01 3.2109234e-01 1.2573995e-01
6.7692801e-02... | [6.942113399505615, 6.085938453674316] |
99804930-fe8d-4843-95d7-943ef9662cbe | high-impedance-fault-detection-through-quasi | 2212.09989 | null | https://arxiv.org/abs/2212.09989v1 | https://arxiv.org/pdf/2212.09989v1.pdf | High Impedance Fault Detection Through Quasi-Static State Estimation: A Parameter Error Modeling Approach | This paper presents a model for detecting high-impedance faults (HIFs) using parameter error modeling and a two-step per-phase weighted least squares state estimation (SE) process. The proposed scheme leverages the use of phasor measurement units and synthetic measurements to identify per-phase power flow and injection... | ['Newton G. Bretas', 'Sean Meyn', 'Arturo Bretas', 'Austin Cooper'] | 2022-12-20 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 8.65647718e-02 -1.97210997e-01 -9.12975818e-02 3.42156813e-02
-1.01461709e+00 -5.08598506e-01 2.42123023e-01 3.26895982e-01
4.69107032e-01 1.10897553e+00 -2.36713231e-01 -4.33587670e-01
-9.10987794e-01 -4.27006632e-01 -3.00920129e-01 -9.41092193e-01
-8.29882503e-01 4.55530196e-01 -1.21974573e-01 -2.90363915... | [6.053831100463867, 2.5664336681365967] |
40972be2-cff9-4d27-bef7-816e363b23e0 | context-sensitive-temporal-feature-learning-1 | 2204.03270 | null | https://arxiv.org/abs/2204.03270v2 | https://arxiv.org/pdf/2204.03270v2.pdf | Context-Sensitive Temporal Feature Learning for Gait Recognition | Although gait recognition has drawn increasing research attention recently, it remains challenging to learn discriminative temporal representation, since the silhouette differences are quite subtle in spatial domain. Inspired by the observation that human can distinguish gaits of different subjects by adaptively focusi... | ['Bin Feng', 'Wenyu Liu', 'Botao He', 'Bo Yang', 'Hao Wang', 'Xinggang Wang', 'Duowang Zhu', 'Xiaohu Huang'] | 2022-04-07 | context-sensitive-temporal-feature-learning | http://openaccess.thecvf.com//content/ICCV2021/html/Huang_Context-Sensitive_Temporal_Feature_Learning_for_Gait_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Huang_Context-Sensitive_Temporal_Feature_Learning_for_Gait_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['multiview-gait-recognition'] | ['computer-vision'] | [-7.84118846e-02 -7.07422078e-01 -3.73233616e-01 -3.07912618e-01
-5.83578646e-01 -7.26759881e-02 1.75475046e-01 -8.16627219e-02
-2.47680962e-01 5.78730822e-01 2.51019210e-01 3.60600263e-01
-3.45568806e-01 -6.38762772e-01 -1.84992492e-01 -8.78794134e-01
-6.94448590e-01 -3.20571624e-02 5.40155292e-01 -2.34295532... | [14.281294822692871, 1.4163490533828735] |
df68e75e-a546-459a-9fa9-36fad633cb3d | a-priority-map-for-vision-and-language | 2207.11717 | null | https://arxiv.org/abs/2207.11717v4 | https://arxiv.org/pdf/2207.11717v4.pdf | A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues | In a busy city street, a pedestrian surrounded by distractions can pick out a single sign if it is relevant to their route. Artificial agents in outdoor Vision-and-Language Navigation (VLN) are also confronted with detecting supervisory signal on environment features and location in inputs. To boost the prominence of r... | ['Rico Sennrich', 'Leonardo Impett', 'Jason Armitage'] | 2022-07-24 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 3.20492685e-01 -4.50403988e-02 2.02377081e-01 -5.67556262e-01
-1.06077564e+00 -5.58085620e-01 9.83890474e-01 2.46509328e-01
-8.94682705e-01 3.08922768e-01 6.39807999e-01 -5.87695897e-01
-3.46000135e-01 -6.29783869e-01 -5.45458078e-01 -2.43823737e-01
-5.15254997e-02 5.87001026e-01 3.92704725e-01 -4.65397954... | [6.222151279449463, 0.5793972611427307] |
a4b1d23b-3d67-4647-8936-a7a8307b1084 | a-multi-view-joint-learning-framework-for | 2301.11608 | null | https://arxiv.org/abs/2301.11608v1 | https://arxiv.org/pdf/2301.11608v1.pdf | A Multi-View Joint Learning Framework for Embedding Clinical Codes and Text Using Graph Neural Networks | Learning to represent free text is a core task in many clinical machine learning (ML) applications, as clinical text contains observations and plans not otherwise available for inference. State-of-the-art methods use large language models developed with immense computational resources and training data; however, applyi... | ['Yixin Chen', 'Bradley Fritz', 'Christopher King', 'Lecheng Kong'] | 2023-01-27 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 1.28734216e-01 3.23490620e-01 -5.78065336e-01 -3.79901350e-01
-8.42906237e-01 -3.80445540e-01 3.73551399e-01 8.18578899e-01
-3.60832989e-01 4.57854569e-01 8.06006253e-01 -7.42500663e-01
-3.41218293e-01 -6.98047042e-01 -5.11761129e-01 -3.78902018e-01
-3.26361567e-01 1.13993990e+00 -3.81733477e-01 -6.04028627... | [7.948308944702148, 6.743780136108398] |
dd834058-51cf-4c5d-ba8d-544df564a54b | what-makes-sound-event-localization-and | 2107.10469 | null | https://arxiv.org/abs/2107.10469v2 | https://arxiv.org/pdf/2107.10469v2.pdf | What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis | Sound event localization and detection (SELD) is an emerging research topic that aims to unify the tasks of sound event detection and direction-of-arrival estimation. As a result, SELD inherits the challenges of both tasks, such as noise, reverberation, interference, polyphony, and non-stationarity of sound sources. Fu... | ['Woon Seng Gan', 'Douglas L. Jones', 'Ngoc Khanh Nguyen', 'Zhen Jian Lee', 'Karn N. Watcharasupat', 'Thi Ngoc Tho Nguyen'] | 2021-07-22 | null | null | null | null | ['direction-of-arrival-estimation', 'sound-event-localization-and-detection'] | ['audio', 'audio'] | [-3.11162502e-01 -7.14910686e-01 8.07230949e-01 9.00061056e-02
-1.25068605e+00 -5.45317769e-01 9.32440013e-02 1.36063725e-01
-2.26873815e-01 4.94291812e-01 3.02157968e-01 -6.72461838e-02
-2.55994368e-02 -3.05283993e-01 -5.48050761e-01 -4.70931947e-01
-2.33167484e-01 -5.41732349e-02 4.23867136e-01 -9.21855643... | [15.18349552154541, 5.319964408874512] |
a1eb4efa-3f50-49ef-9bf9-bb9809377065 | decision-making-in-non-stationary | 2202.13003 | null | https://arxiv.org/abs/2202.13003v1 | https://arxiv.org/pdf/2202.13003v1.pdf | Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search | Decision-making under uncertainty (DMU) is present in many important problems. An open challenge is DMU in non-stationary environments, where the dynamics of the environment can change over time. Reinforcement Learning (RL), a popular approach for DMU problems, learns a policy by interacting with a model of the environ... | ['Abhishek Dubey', 'Ayan Mukhopadhyay', 'Geoffrey Pettet'] | 2022-02-25 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 1.58389226e-01 4.19720709e-02 -3.09051991e-01 8.38179886e-02
-8.53053570e-01 -7.80232966e-01 6.56301022e-01 2.65079767e-01
-8.27124417e-01 1.23568034e+00 1.27607867e-01 -5.49295843e-01
-1.65168896e-01 -8.92972052e-01 -7.74932265e-01 -6.59806728e-01
-3.74302983e-01 7.75987267e-01 5.10029793e-01 -1.97187468... | [4.261341571807861, 2.0276641845703125] |
44d93468-4231-4928-ba02-ac3fee91012e | hybrid-subspace-learning-for-high-dimensional | 1808.01687 | null | http://arxiv.org/abs/1808.01687v1 | http://arxiv.org/pdf/1808.01687v1.pdf | Hybrid Subspace Learning for High-Dimensional Data | The high-dimensional data setting, in which p >> n, is a challenging
statistical paradigm that appears in many real-world problems. In this setting,
learning a compact, low-dimensional representation of the data can
substantially help distinguish signal from noise. One way to achieve this goal
is to perform subspace le... | ['Micol Marchetti-Bowick', 'Benjamin J. Lengerich', 'Ankur P. Parikh', 'Eric P. Xing'] | 2018-08-05 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 1.35000214e-01 -3.42414260e-01 -1.01109460e-01 -1.92273736e-01
-7.23482311e-01 -7.94124007e-01 4.65430439e-01 -3.45089942e-01
-1.36521384e-01 5.70579708e-01 3.80572408e-01 4.64670062e-02
-3.54974806e-01 -3.21826696e-01 -5.22712171e-01 -1.17906857e+00
9.50279552e-03 3.23011220e-01 -1.18664637e-01 1.05573505... | [7.746199607849121, 4.294505596160889] |
451e5f92-6153-4131-a51d-abe260332662 | dropkey-for-vision-transformer | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_DropKey_for_Vision_Transformer_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_DropKey_for_Vision_Transformer_CVPR_2023_paper.pdf | DropKey for Vision Transformer | In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of Vision Transformer, which is important while surprisingly ignored by prior works. In particular, we conduct researches on three core questions: First, what to drop in self-attention layers? Different from dropping ... | ['Luoqi Liu', 'Tiande Guo', 'Xiangjian Jiang', 'Congying Han', 'Xuecheng Nie', 'Yinhan Hu', 'Bonan Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [ 6.28599972e-02 5.70381880e-02 1.18607311e-02 -1.72825456e-01
-9.13106278e-02 -2.11593300e-01 -1.47638544e-02 -1.75322801e-01
-6.13932312e-01 5.53278506e-01 5.55361509e-02 -1.25841796e-02
-1.21533265e-02 -6.26086950e-01 -1.03008282e+00 -8.00671458e-01
3.20246458e-01 -6.80225715e-02 7.48026013e-01 -3.29262882... | [9.855834007263184, 0.21868382394313812] |
c9a77000-7269-4f82-aece-d76b3a47ab17 | ca-pmg-channel-attention-and-progressive | null | null | https://www.researchgate.net/publication/351338170_CA-PMG_Channel_attention_and_progressive_multi-granularity_training_network_for_fine-grained_visual_classification | https://www.researchgate.net/publication/351338170_CA-PMG_Channel_attention_and_progressive_multi-granularity_training_network_for_fine-grained_visual_classification | CA-PMG: Channel attention and progressive multi-granularity training network for fine-grained visual classification | Fine-grained visual classification is challenging due to the inherently subtle intra-class object variations. To solve this issue, a novel framework named channel attention and progressive multi-granularity training network, is proposed. It first exploits meaningful feature maps through the channel attention module and... | ['Maoguo Gong', 'Ruyi Liu', 'Xiangzeng Liu', 'Hang Yao', 'Qiguang Miao', 'Peipei Zhao'] | 2021-04-21 | null | null | null | iet-image-processing-2021-4 | ['fine-grained-visual-recognition', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [-7.86607936e-02 -2.85393745e-01 -2.94199377e-01 -4.40737098e-01
-1.04778802e+00 -4.54070300e-01 4.76758510e-01 -3.73458229e-02
-2.24776760e-01 5.59121966e-01 1.47649646e-01 -1.08511224e-01
-8.55994746e-02 -5.71793437e-01 -8.78644288e-01 -5.04119873e-01
1.25500029e-02 3.22077721e-02 3.22182417e-01 1.55016007... | [9.588634490966797, 2.030710458755493] |
02729033-275c-430e-8cf5-7a032e21c104 | maked-multi-lingual-automatic-keyword | null | null | https://aclanthology.org/2022.lrec-1.664 | https://aclanthology.org/2022.lrec-1.664.pdf | MAKED: Multi-lingual Automatic Keyword Extraction Dataset | Keyword extraction is an integral task for many downstream problems like clustering, recommendation, search and classification. Development and evaluation of keyword extraction techniques require an exhaustive dataset; however, currently, the community lacks large-scale multi-lingual datasets. In this paper, we present... | ['Dwaipayan Roy', 'Adam Jatowt', 'Sriparna Saha', 'Anubhav Jangra', 'Yash Verma'] | null | null | null | null | lrec-2022-6 | ['keyword-extraction'] | ['natural-language-processing'] | [-5.69095314e-01 -4.25864130e-01 -7.77628601e-01 -1.31417781e-01
-1.41439056e+00 -1.15819383e+00 8.56388867e-01 4.69330221e-01
-8.18236709e-01 9.09748197e-01 8.14876199e-01 -4.78631109e-01
-3.04042846e-01 -3.77251476e-01 -5.69006920e-01 -4.64754969e-01
8.09692033e-03 4.35230345e-01 3.90221268e-01 -1.48779646... | [10.19558048248291, 9.622690200805664] |
c6b18734-29b3-4380-953f-35688183a35d | a-general-method-for-amortizing-variational | 1811.05090 | null | http://arxiv.org/abs/1811.05090v1 | http://arxiv.org/pdf/1811.05090v1.pdf | A General Method for Amortizing Variational Filtering | We introduce the variational filtering EM algorithm, a simple,
general-purpose method for performing variational inference in dynamical latent
variable models using information from only past and present variables, i.e.
filtering. The algorithm is derived from the variational objective in the
filtering setting and cons... | ['Yisong Yue', 'Joseph Marino', 'Milan Cvitkovic'] | 2018-11-13 | a-general-method-for-amortizing-variational-1 | http://papers.nips.cc/paper/8011-a-general-method-for-amortizing-variational-filtering | http://papers.nips.cc/paper/8011-a-general-method-for-amortizing-variational-filtering.pdf | neurips-2018-12 | ['inference-optimization'] | ['audio'] | [-1.16842195e-01 -8.96443501e-02 -7.33900070e-02 -2.04232246e-01
-8.41434956e-01 -5.44003367e-01 1.15206885e+00 -4.80873734e-01
-4.06634629e-01 8.04034472e-01 1.63825721e-01 -2.85293549e-01
1.09749973e-01 -8.25047374e-01 -7.73944676e-01 -8.84399712e-01
5.92528060e-02 8.73039365e-01 4.25956585e-02 2.32264712... | [6.906907081604004, 3.8281140327453613] |
b5266bac-6756-42a3-9cd6-fa6cb42b36fd | fs6d-few-shot-6d-pose-estimation-of-novel | 2203.14628 | null | https://arxiv.org/abs/2203.14628v1 | https://arxiv.org/pdf/2203.14628v1.pdf | FS6D: Few-Shot 6D Pose Estimation of Novel Objects | 6D object pose estimation networks are limited in their capability to scale to large numbers of object instances due to the close-set assumption and their reliance on high-fidelity object CAD models. In this work, we study a new open set problem; the few-shot 6D object poses estimation: estimating the 6D pose of an unk... | ['Qifeng Chen', 'Jian Sun', 'Haoqiang Fan', 'Yao Wang', 'Yisheng He'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/He_FS6D_Few-Shot_6D_Pose_Estimation_of_Novel_Objects_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/He_FS6D_Few-Shot_6D_Pose_Estimation_of_Novel_Objects_CVPR_2022_paper.pdf | cvpr-2022-1 | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.80498117e-02 1.68120250e-01 -6.55674860e-02 -5.55288494e-01
-5.80387115e-01 -5.02755702e-01 3.34346741e-01 -4.39229161e-01
-5.89134283e-02 2.76327759e-01 -1.90016136e-01 1.89430356e-01
-2.34110251e-01 -6.06173217e-01 -9.88358676e-01 -5.11764109e-01
2.56509155e-01 8.08424771e-01 3.31202835e-01 -9.50350910... | [7.676603317260742, -2.7748820781707764] |
f5257cc7-86d7-456b-891e-e2c96212e968 | mapping-process-for-the-task-wikidata | 2210.12659 | null | https://arxiv.org/abs/2210.12659v1 | https://arxiv.org/pdf/2210.12659v1.pdf | Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences | Acknowledged as one of the most successful online cooperative projects in human society, Wikipedia has obtained rapid growth in recent years and desires continuously to expand content and disseminate knowledge values for everyone globally. The shortage of volunteers brings to Wikipedia many issues, including developing... | ['Grigori Sidorov', 'Alexander Gelbukha', 'Hoang Thang Ta'] | 2022-10-23 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 2.61745062e-02 5.71009278e-01 7.66253918e-02 -1.36208400e-01
-7.09149241e-01 -4.16077882e-01 6.46152496e-01 5.79017699e-01
-4.66528475e-01 1.05909085e+00 7.91373551e-01 -2.27093875e-01
1.56696245e-01 -1.13442886e+00 -2.78559148e-01 -1.08309865e-01
2.67615378e-01 2.53057629e-01 1.27695993e-01 -6.82344377... | [12.144311904907227, 9.470080375671387] |
c9e2095e-4614-447c-8367-064a492f01a3 | neighbortrack-improving-single-object | 2211.06663 | null | https://arxiv.org/abs/2211.06663v2 | https://arxiv.org/pdf/2211.06663v2.pdf | NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets | We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requires no additional data or retraining. Instead, it uses the confidence score predicted by the backbone SOT network to automatically derive ne... | ['Hong-Yuan Mark Liao', 'Yung-Yu Chuang', 'Youn-Long Lin', 'Hung-Shuo Chang', 'Cheng-Yun Yang', 'Chien-Yao Wang', 'Yu-Hsi Chen'] | 2022-11-12 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-2.58356631e-01 -2.82083213e-01 -2.76582748e-01 -1.97623730e-01
-7.42915273e-01 -9.30638313e-01 1.64482638e-01 -1.85201854e-01
-4.51344818e-01 6.68860495e-01 -1.57360911e-01 -1.09744363e-01
-7.55584985e-02 -5.34708440e-01 -1.01047778e+00 -4.74622011e-01
-4.10078406e-01 4.69325602e-01 7.89600253e-01 2.55542696... | [6.353831768035889, -2.1097798347473145] |
1891a55c-98e9-445b-bb4e-f8a664c04b0b | l0learn-a-scalable-package-for-sparse | 2202.04820 | null | https://arxiv.org/abs/2202.04820v2 | https://arxiv.org/pdf/2202.04820v2.pdf | L0Learn: A Scalable Package for Sparse Learning using L0 Regularization | We present L0Learn: an open-source package for sparse linear regression and classification using $\ell_0$ regularization. L0Learn implements scalable, approximate algorithms, based on coordinate descent and local combinatorial optimization. The package is built using C++ and has user-friendly R and Python interfaces. L... | ['Tim Nonet', 'Rahul Mazumder', 'Hussein Hazimeh'] | 2022-02-10 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-8.84058297e-01 -2.82097340e-01 -3.64425272e-01 -5.07608950e-01
-1.38916433e+00 -4.31478262e-01 1.48436517e-01 2.35915333e-01
-1.68368459e-01 6.77131176e-01 4.43843603e-01 -1.83082357e-01
7.97568038e-02 -6.59906149e-01 -7.31663465e-01 -7.46331871e-01
-2.88587600e-01 5.09928048e-01 -3.04307908e-01 5.14300577... | [7.2239580154418945, 4.414794445037842] |
296d7d08-4377-4637-b8e4-75b72f7ee0b8 | risa-net-rotation-invariant-structure-aware | 2010.00973 | null | https://arxiv.org/abs/2010.00973v1 | https://arxiv.org/pdf/2010.00973v1.pdf | RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval | Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to be capable of representing detailed geometric information to discriminate shapes with globally similar structures. Moreover, 3D objects can be ... | ['Lin Gao', 'Yu-Kun Lai', 'Fang-Lue Zhang', 'Jiawei Sun', 'Jie Yang', 'Rao Fu'] | 2020-10-02 | null | null | null | null | ['3d-object-retrieval'] | ['computer-vision'] | [-4.03934598e-01 -4.35187668e-01 -1.06473304e-01 -4.51746881e-01
-9.97588992e-01 -1.00303102e+00 9.37097609e-01 4.02268410e-01
1.92779407e-01 -2.76723281e-02 3.15727502e-01 1.94729820e-01
-5.49831867e-01 -1.20971060e+00 -5.89372039e-01 -6.15635753e-01
-5.98567910e-03 1.15292156e+00 9.26403478e-02 -9.50270593... | [8.141122817993164, -3.850538730621338] |
ee7bab5f-1f1b-487b-b81d-adf6a79106c2 | convolutional-lstms-for-cloud-robust | 1811.02471 | null | http://arxiv.org/abs/1811.02471v2 | http://arxiv.org/pdf/1811.02471v2.pdf | Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagery | Clouds frequently cover the Earth's surface and pose an omnipresent challenge
to optical Earth observation methods. The vast majority of remote sensing
approaches either selectively choose single cloud-free observations or employ a
pre-classification strategy to identify and mask cloudy pixels. We follow a
different st... | ['Marco Körner', 'Marc Rußwurm'] | 2018-10-28 | null | null | null | null | ['segmentation-of-remote-sensing-imagery'] | ['miscellaneous'] | [ 4.91368830e-01 -4.88074332e-01 3.44756573e-01 -3.82145762e-01
-2.81684995e-01 -1.09952283e+00 6.88546896e-01 2.08031222e-01
-3.53185236e-01 6.82371259e-01 -3.56726319e-01 -7.26408601e-01
1.41191289e-01 -8.36350620e-01 -6.58147395e-01 -8.72206509e-01
-3.63409132e-01 1.02456763e-01 4.98548299e-02 -2.22955763... | [9.631141662597656, -1.6334415674209595] |
3bd07adf-4c90-4990-8ac2-0da1059026aa | counterfactual-explanation-with-missing | 2304.14606 | null | https://arxiv.org/abs/2304.14606v1 | https://arxiv.org/pdf/2304.14606v1.pdf | Counterfactual Explanation with Missing Values | Counterfactual Explanation (CE) is a post-hoc explanation method that provides a perturbation for altering the prediction result of a classifier. Users can interpret the perturbation as an "action" to obtain their desired decision results. Existing CE methods require complete information on the features of an input ins... | ['Yuichi Ike', 'Ken Kobayashi', 'Takuya Takagi', 'Kentaro Kanamori'] | 2023-04-28 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.33195978e-01 5.05570412e-01 -5.51316082e-01 -4.91873503e-01
-8.44609022e-01 -4.29261416e-01 3.80710453e-01 -2.67071091e-02
-9.24287811e-02 1.22475350e+00 4.29704905e-01 -3.10080141e-01
-3.86141479e-01 -6.82195544e-01 -1.21325111e+00 -8.09572041e-01
2.28910401e-01 3.88979435e-01 -6.23375118e-01 2.23159477... | [8.666192054748535, 5.58976411819458] |
8bfcb154-a056-45d3-9436-0ac1f34c8ed3 | image-pre-compensation-balancing-contrast-and | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Ji_Image_Pre-compensation_Balancing_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Ji_Image_Pre-compensation_Balancing_2014_CVPR_paper.pdf | Image Pre-compensation: Balancing Contrast and Ringing | The goal of image pre-compensation is to process an image such that after being convolved with a known kernel, will appear close to the sharp reference image. In a practical setting, the pre-compensated image has significantly higher dynamic range than the latent image. As a result, some form of tone mapping is needed.... | ['Jinwei Ye', 'Yu Ji', 'Sing Bing Kang', 'Jingyi Yu'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['tone-mapping'] | ['computer-vision'] | [ 6.01149559e-01 -1.35230169e-01 1.35666579e-01 -1.97893187e-01
-4.40525413e-01 -5.41896343e-01 3.15007716e-01 7.75851160e-02
-4.89193052e-01 6.36263371e-01 2.93289185e-01 -2.35732660e-01
-7.08203614e-02 -6.75926149e-01 -6.65796041e-01 -7.40930736e-01
2.28151619e-01 -3.57454926e-01 5.08243203e-01 -7.65597522... | [10.875617980957031, -2.4571712017059326] |
e1088f9d-16fa-4251-b1b0-dac2b89d94f7 | use-of-a-taxonomy-of-empathetic-response-1 | 2305.10096 | null | https://arxiv.org/abs/2305.10096v1 | https://arxiv.org/pdf/2305.10096v1.pdf | Use of a Taxonomy of Empathetic Response Intents to Control and Interpret Empathy in Neural Chatbots | A recent trend in the domain of open-domain conversational agents is enabling them to converse empathetically to emotional prompts. Current approaches either follow an end-to-end approach or condition the responses on similar emotion labels to generate empathetic responses. But empathy is a broad concept that refers to... | ['Pearl Pu', 'Anuradha Welivita'] | 2023-05-17 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [-2.50264853e-01 4.68120754e-01 2.06575155e-01 -7.75179982e-01
-1.11703709e-01 -4.68895018e-01 8.05302918e-01 -1.07702993e-01
-1.67250603e-01 8.85462642e-01 6.78801298e-01 2.54392296e-01
3.14363912e-02 -6.22015774e-01 4.20053184e-01 -2.78544843e-01
3.85939479e-01 7.87070036e-01 -4.30281699e-01 -9.82406378... | [13.121760368347168, 7.676385879516602] |
0509cf77-0b7c-40bf-839e-54913a3183e4 | amelioration-de-la-qualite-d-images-avec-un | 2303.07151 | null | https://arxiv.org/abs/2303.07151v1 | https://arxiv.org/pdf/2303.07151v1.pdf | Amélioration de la qualité d'images avec un algorithme d'optimisation inspirée par la nature | Reproducible images preprocessing is important in the field of computer vision, for efficient algorithms comparison or for new images corpus preparation. In this paper, we propose a method to obtain an explicit and ordered sequence of transformations that improves a given image: the computation is performed via a natur... | ['Thomas Tamisier', 'Olivier Parisot'] | 2023-03-13 | null | null | null | null | ['nature-inspired-optimization-algorithm'] | ['computer-code'] | [ 3.28625828e-01 -1.66663870e-01 4.05935109e-01 -4.96758670e-01
-4.71301466e-01 -5.26845515e-01 8.57053101e-01 6.55449033e-01
-9.25105333e-01 3.85431617e-01 5.24339303e-02 7.66810551e-02
9.90714785e-03 -9.86547410e-01 -7.90820062e-01 -5.10696530e-01
9.20089558e-02 3.75222951e-01 5.67347407e-02 -3.53350639... | [14.103784561157227, 13.31803035736084] |
35916c05-abae-4823-a8d0-655b493a6092 | a-multi-domain-framework-for-textual | null | null | https://aclanthology.org/L18-1432 | https://aclanthology.org/L18-1432.pdf | A Multi-Domain Framework for Textual Similarity. A Case Study on Question-to-Question and Question-Answering Similarity Tasks | null | ['Hern', 'Amir Hazem', 'Basma El Amal Boussaha', 'Nicolas ez'] | 2018-05-01 | a-multi-domain-framework-for-textual-1 | https://aclanthology.org/L18-1432 | https://aclanthology.org/L18-1432.pdf | lrec-2018-5 | ['question-similarity'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.185973167419434, 3.7379775047302246] |
2764d4b5-e911-44cc-9361-1957c3acb996 | a-simple-strategy-to-provable-invariance-via | 2209.11916 | null | https://arxiv.org/abs/2209.11916v1 | https://arxiv.org/pdf/2209.11916v1.pdf | A Simple Strategy to Provable Invariance via Orbit Mapping | Many applications require robustness, or ideally invariance, of neural networks to certain transformations of input data. Most commonly, this requirement is addressed by training data augmentation, using adversarial training, or defining network architectures that include the desired invariance by design. In this work,... | ['Michael Moeller', 'Adam Czapliński', 'Zorah Lähner', 'Jonas Geiping', 'Kanchana Vaishnavi Gandikota'] | 2022-09-24 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 4.61527348e-01 3.17342103e-01 -2.30204463e-02 -3.44299287e-01
1.18828215e-01 -8.97453725e-01 8.04550529e-01 -1.49228290e-01
-4.00637001e-01 5.03504217e-01 -7.08879456e-02 -4.23447669e-01
-3.53108406e-01 -9.12586689e-01 -1.14030910e+00 -7.87363589e-01
-2.18505815e-01 2.92747498e-01 -4.37566824e-03 -4.10043061... | [8.863134384155273, 2.5939319133758545] |
bcdb092a-9e45-40cd-b6c5-0c39d3417696 | multi-source-diffusion-models-for | 2302.02257 | null | https://arxiv.org/abs/2302.02257v3 | https://arxiv.org/pdf/2302.02257v3.pdf | Multi-Source Diffusion Models for Simultaneous Music Generation and Separation | In this work, we define a diffusion-based generative model capable of both music synthesis and source separation by learning the score of the joint probability density of sources sharing a context. Alongside the classic total inference tasks (i.e., generating a mixture, separating the sources), we also introduce and ex... | ['Emanuele Rodolà', 'Luca Cosmo', 'Michele Mancusi', 'Emilian Postolache', 'Irene Tallini', 'Giorgio Mariani'] | 2023-02-04 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.44906718e-01 1.28778443e-01 8.89262557e-03 1.53699249e-01
-1.34706414e+00 -8.38392317e-01 9.92976248e-01 -2.10337430e-01
4.94522676e-02 7.38666713e-01 6.63199484e-01 1.19945139e-01
-3.36661249e-01 -6.10770583e-01 -7.75327802e-01 -9.77284253e-01
-1.94904059e-01 7.82402337e-01 -6.56695217e-02 -5.92212565... | [15.653407096862793, 5.67043924331665] |
0c769b16-0cf7-49f0-ae68-a3fb0dea6de6 | toward-leveraging-pre-trained-self-supervised | 2306.12714 | null | https://arxiv.org/abs/2306.12714v1 | https://arxiv.org/pdf/2306.12714v1.pdf | Toward Leveraging Pre-Trained Self-Supervised Frontends for Automatic Singing Voice Understanding Tasks: Three Case Studies | Automatic singing voice understanding tasks, such as singer identification, singing voice transcription, and singing technique classification, benefit from data-driven approaches that utilize deep learning techniques. These approaches work well even under the rich diversity of vocal and noisy samples owing to their rep... | ['Yuya Yamamoto'] | 2023-06-22 | null | null | null | null | ['self-supervised-learning', 'singer-identification', 'music-classification'] | ['computer-vision', 'music', 'music'] | [ 5.54560646e-02 -4.73280847e-01 -2.78491586e-01 -3.55300933e-01
-1.03924417e+00 -5.85069895e-01 2.64713645e-01 -4.23849136e-01
-1.81603253e-01 3.58192593e-01 2.74663389e-01 -1.24989748e-01
-7.36475512e-02 -6.54145554e-02 -3.76656443e-01 -6.55507624e-01
1.85572095e-02 3.34780037e-01 -9.87014025e-02 4.31849249... | [15.260927200317383, 6.09234619140625] |
ea8155cf-0ce1-4831-a8a1-ef99854ac48c | training-neural-networks-for-aspect | null | null | https://openreview.net/forum?id=HyxoAoxOwN | https://openreview.net/pdf?id=HyxoAoxOwN | Training Neural Networks for Aspect Extraction Using Descriptive Keywords Only | Aspect extraction in online product reviews is a key task in sentiment analysis and opinion mining. Training supervised neural networks for aspect extraction is not possible when ground truth aspect labels are not available, while the unsupervised neural topic models fail to capture the particular aspects of interest. ... | ['Luis Gravano', 'Daniel Hsu', 'Giannis Karamanolakis'] | 2019-03-14 | null | null | null | iclr-workshop-lld-2019 | ['aspect-extraction', 'topic-models'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.87480682e-01 4.44759995e-01 -6.37609661e-01 -5.61649024e-01
-6.83593035e-01 -7.45260656e-01 6.27759695e-01 7.18262196e-01
-4.79066819e-01 5.94208419e-01 1.02094404e-01 -4.51660037e-01
2.04879194e-01 -8.93997431e-01 -6.82254553e-01 -7.39433467e-01
2.14425430e-01 3.61570954e-01 -3.55667397e-02 -2.81579107... | [11.337011337280273, 6.722313404083252] |
e2ead0c3-8269-4b47-a8d4-df728ad92951 | dealing-with-typos-for-bert-based-passage | 2108.12139 | null | https://arxiv.org/abs/2108.12139v2 | https://arxiv.org/pdf/2108.12139v2.pdf | Dealing with Typos for BERT-based Passage Retrieval and Ranking | Passage retrieval and ranking is a key task in open-domain question answering and information retrieval. Current effective approaches mostly rely on pre-trained deep language model-based retrievers and rankers. These methods have been shown to effectively model the semantic matching between queries and passages, also i... | ['Guido Zuccon', 'Shengyao Zhuang'] | 2021-08-27 | null | https://aclanthology.org/2021.emnlp-main.225 | https://aclanthology.org/2021.emnlp-main.225.pdf | emnlp-2021-11 | ['passage-ranking', 'passage-re-ranking'] | ['natural-language-processing', 'natural-language-processing'] | [-1.48375764e-01 -4.77155268e-01 -1.01621680e-01 -7.86032621e-03
-1.41926825e+00 -7.39892423e-01 7.23517120e-01 6.30030096e-01
-6.19182229e-01 5.17686665e-01 5.06297946e-01 -1.36415482e-01
-5.91197670e-01 -7.18272507e-01 -9.20808733e-01 -1.12424660e-02
9.58423167e-02 8.23397458e-01 6.09026730e-01 -7.71998525... | [11.53713607788086, 7.679036617279053] |
ec1cc8f4-8017-42d8-ba8f-bcc402c56018 | gode-integrating-biochemical-knowledge-graph | 2306.01631 | null | https://arxiv.org/abs/2306.01631v1 | https://arxiv.org/pdf/2306.01631v1.pdf | Gode -- Integrating Biochemical Knowledge Graph into Pre-training Molecule Graph Neural Network | The precise prediction of molecular properties holds paramount importance in facilitating the development of innovative treatments and comprehending the intricate interplay between chemicals and biological systems. In this study, we propose a novel approach that integrates graph representations of individual molecular ... | ['Pengcheng Jiang'] | 2023-06-02 | null | null | null | null | ['knowledge-graphs', 'property-prediction'] | ['knowledge-base', 'medical'] | [ 6.30518258e-01 2.86544655e-02 -7.06981897e-01 -2.97407806e-01
-7.03821599e-01 -5.61463594e-01 5.31601727e-01 9.52856660e-01
-6.10905588e-02 1.15992677e+00 2.24034309e-01 -4.71556634e-01
-3.94898653e-01 -8.31612229e-01 -1.07145917e+00 -5.66872418e-01
-1.68459624e-01 2.76531696e-01 1.51373938e-01 -8.56344998... | [5.103039741516113, 5.89339017868042] |
d5567898-8bad-4f37-887c-470a7a7d10f3 | learning-word-embeddings-from-speech | 1711.01515 | null | http://arxiv.org/abs/1711.01515v1 | http://arxiv.org/pdf/1711.01515v1.pdf | Learning Word Embeddings from Speech | In this paper, we propose a novel deep neural network architecture,
Sequence-to-Sequence Audio2Vec, for unsupervised learning of fixed-length
vector representations of audio segments excised from a speech corpus, where
the vectors contain semantic information pertaining to the segments, and are
close to other vectors i... | ['Yu-An Chung', 'James Glass'] | 2017-11-05 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [ 4.52470660e-01 -6.16789237e-02 -1.19733579e-01 -3.86416286e-01
-8.81628394e-01 -4.23494756e-01 3.00626427e-01 2.17502266e-01
-5.98307192e-01 4.20851469e-01 7.23933697e-01 -1.71177402e-01
1.84505597e-01 -5.08167267e-01 -5.43605804e-01 -7.02573836e-01
-6.56224042e-02 4.24103327e-02 3.48331444e-02 -1.11851722... | [15.138367652893066, 5.090996742248535] |
b0ad9b2d-024f-4804-a0b4-090e438882d4 | a-decentralized-spike-based-learning | 2305.16748 | null | https://arxiv.org/abs/2305.16748v1 | https://arxiv.org/pdf/2305.16748v1.pdf | A Decentralized Spike-based Learning Framework for Sequential Capture in Discrete Perimeter Defense Problem | This paper proposes a novel Decentralized Spike-based Learning (DSL) framework for the discrete Perimeter Defense Problem (d-PDP). A team of defenders is operating on the perimeter to protect the circular territory from radially incoming intruders. At first, the d-PDP is formulated as a spatio-temporal multi-task assig... | ['Shirin Dora', 'Suresh Sundaram', 'Shridhar Velhal', 'Mohammed Thousif'] | 2023-05-26 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 1.99827775e-01 -2.03069866e-01 2.72734553e-01 1.87455386e-01
-7.13165998e-01 -1.23526394e+00 2.55963832e-01 2.87679434e-01
-7.25551486e-01 8.03517878e-01 -7.90236235e-01 -1.34371355e-01
-4.67901021e-01 -7.48133838e-01 -6.18457496e-01 -1.43227446e+00
-1.97687685e-01 8.59305322e-01 5.19089162e-01 -1.30435020... | [3.811410903930664, 2.307469367980957] |
83555a4a-bb33-4271-a9e5-77dab1e4dd7b | constructing-category-specific-models-for | 1802.09292 | null | http://arxiv.org/abs/1802.09292v1 | http://arxiv.org/pdf/1802.09292v1.pdf | Constructing Category-Specific Models for Monocular Object-SLAM | We present a new paradigm for real-time object-oriented SLAM with a monocular
camera. Contrary to previous approaches, that rely on object-level models, we
construct category-level models from CAD collections which are now widely
available. To alleviate the need for huge amounts of labeled data, we develop a
rendering ... | ['J. Krishna Murthy', 'Rishabh Khawad', 'Parv Parkhiya', 'Brojeshwar Bhowmick', 'K. Madhava Krishna'] | 2018-02-26 | null | null | null | null | ['object-slam'] | ['computer-vision'] | [-8.94985721e-02 -2.98926562e-01 1.56842638e-02 -5.31927228e-01
-8.22026849e-01 -6.09126806e-01 6.21719539e-01 1.15248457e-01
-2.02208161e-01 5.00537574e-01 -3.31733108e-01 1.20690405e-01
-1.86749637e-01 -6.44534767e-01 -7.91288435e-01 -4.12916839e-01
6.45038784e-02 1.17713392e+00 4.22732174e-01 -9.89811495... | [7.3274102210998535, -2.3462979793548584] |
2b980dde-fdf9-4c30-8db9-2ddb2bd820ee | shaddr-real-time-example-based-geometry-and | 2306.04889 | null | https://arxiv.org/abs/2306.04889v1 | https://arxiv.org/pdf/2306.04889v1.pdf | ShaDDR: Real-Time Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering | We present ShaDDR, an example-based deep generative neural network which produces a high-resolution textured 3D shape through geometry detailization and conditional texture generation applied to an input coarse voxel shape. Trained on a small set of detailed and textured exemplar shapes, our method learns to detailize ... | ['Hao Zhang', 'Hang Zhou', 'Zhiqin Chen', 'Qimin Chen'] | 2023-06-08 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 3.96616608e-01 5.49597979e-01 5.59352458e-01 -2.17824563e-01
-7.61614263e-01 -7.39106953e-01 8.27036321e-01 -4.24786121e-01
5.60645878e-01 6.56621635e-01 1.73777834e-01 -2.38085026e-03
1.88897122e-02 -1.41650510e+00 -1.13018751e+00 -5.96323669e-01
-1.48471281e-01 8.38305652e-01 -1.48716941e-01 -2.16889575... | [8.996967315673828, -3.576079845428467] |
ab7daef9-78c8-48a2-8061-3c46fc464572 | graph-based-stock-recommendation-by-time | null | null | https://dl.acm.org/doi/10.1145/3451397 | https://dl.acm.org/doi/pdf/10.1145/3451397 | Graph-Based Stock Recommendation by Time-Aware Relational Attention Network | The stock market investors aim at maximizing their investment returns. Stock recommendation task is to recommend stocks with higher return ratios for the investors. Most stock prediction methods study the historical sequence patterns to predict stock trend or price in the near future. In fact, the future price of a sto... | ['Zhao Li', 'Shichao Zhang', 'Jianxin Wang', 'Cong Xu', 'Xiaoting Ying', 'Jianliang Gao'] | 2022-02-01 | null | null | null | acm-transactions-on-knowledge-discovery-from-3 | ['stock-prediction'] | ['time-series'] | [-7.66406178e-01 -4.08732086e-01 -5.07734358e-01 -2.92912662e-01
3.82375687e-01 -5.93906641e-01 5.73322415e-01 2.50813048e-02
-1.71772867e-01 3.11702549e-01 8.27186644e-01 -3.63853693e-01
-4.72637266e-01 -1.54986167e+00 -5.45252323e-01 -3.37250799e-01
-3.44476998e-01 2.71922559e-01 5.28237462e-01 -6.77038670... | [4.312275409698486, 4.340070724487305] |
11c75bda-9e11-413f-a35d-789857cc841f | high-order-interaction-for-weakly-supervised | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0925231221013060 | https://www.sciencedirect.com/science/article/abs/pii/S0925231221013060 | High-Order-Interaction for weakly supervised Fine-Grained Visual Categorization | Fine-Grained Visual Categorization (FGVC) is a challenging task due to the large intra-subcategory and small inter-subcategory variances. Recent studies tackle this task through a weakly supervised manner without using the part annotation from the experts. Of those, methods based on bilinear pooling are one of the main... | ['Shaozi Li', 'Zhun Zhong', 'Zhimin Luo', 'Junzhen Wang', 'Nanyu Li'] | 2021-11-13 | high-order-interaction-for-weakly-supervised-1 | https://github.com/puallee/HOI-Net | https://github.com/puallee/HOI-Net | neurocomputing-2021-11 | ['fine-grained-image-recognition', 'fine-grained-image-classification', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.87471533e-01 -5.58952868e-01 8.93994793e-02 -5.44216514e-01
-6.90362692e-01 -4.05001909e-01 6.54307961e-01 -2.32205540e-03
-4.29160774e-01 4.49256718e-01 2.17910886e-01 1.04220480e-01
-3.03618908e-01 -6.79997087e-01 -6.41473591e-01 -8.87342513e-01
1.49734125e-01 -1.66180491e-01 5.47255039e-01 -3.50399539... | [9.672012329101562, 1.976745843887329] |
03f16acd-0d90-4ee3-a78f-27954f0dae7c | smc-uda-structure-modal-constraint-for | 2306.08213 | null | https://arxiv.org/abs/2306.08213v1 | https://arxiv.org/pdf/2306.08213v1.pdf | SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation | Medical image segmentation based on deep learning often fails when deployed on images from a different domain. The domain adaptation methods aim to solve domain-shift challenges, but still face some problems. The transfer learning methods require annotation on the target domain, and the generative unsupervised domain a... | ['Zhicheng Jiao', 'Harrison Bai', 'Xinbo Gao', 'Rama Chellappa', 'Ihab Kamel', 'Li Yang', 'Lulu Bi', 'Jie Li', 'Zhusi Zhong'] | 2023-06-14 | null | null | null | null | ['unsupervised-domain-adaptation', 'self-learning'] | ['methodology', 'natural-language-processing'] | [ 4.48455215e-01 2.85203695e-01 -4.04592991e-01 -6.16719544e-01
-1.05399859e+00 -5.27163804e-01 3.46830398e-01 -1.28622964e-01
-1.55033335e-01 5.86791933e-01 2.90241331e-01 -5.38599305e-02
-2.36294195e-01 -8.51873994e-01 -6.52769446e-01 -1.10613251e+00
2.65612662e-01 9.77236807e-01 4.60989028e-01 -3.38490978... | [14.576336860656738, -1.9771854877471924] |
ebe5c9a4-424a-48b2-866b-8bb98536bfe4 | mediar-harmony-of-data-centric-and-model | 2212.03465 | null | https://arxiv.org/abs/2212.03465v1 | https://arxiv.org/pdf/2212.03465v1.pdf | MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy | Cell segmentation is a fundamental task for computational biology analysis. Identifying the cell instances is often the first step in various downstream biomedical studies. However, many cell segmentation algorithms, including the recently emerging deep learning-based methods, still show limited generality under the mu... | ['Se-Young Yun', 'Joonkee Kim', 'Sangmook Kim', 'Gihun Lee'] | 2022-12-07 | null | null | null | null | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [ 9.44042951e-02 -2.96205610e-01 -1.61353230e-01 -2.05898672e-01
-1.25981247e+00 -5.82580745e-01 3.47802013e-01 4.34853464e-01
-7.10398376e-01 1.08480942e+00 -3.19615692e-01 -3.35451066e-01
7.18174577e-02 -5.41627347e-01 -6.33711040e-01 -1.07814538e+00
4.55907315e-01 6.71019197e-01 3.68098229e-01 1.99662864... | [14.549108505249023, -3.1437408924102783] |
b2ec9f12-f13b-4a40-8910-28ee27544fcf | leveraging-machine-learning-for-multichain | 2306.07972 | null | https://arxiv.org/abs/2306.07972v1 | https://arxiv.org/pdf/2306.07972v1.pdf | Leveraging Machine Learning for Multichain DeFi Fraud Detection | Since the inception of permissionless blockchains with Bitcoin in 2008, it became apparent that their most well-suited use case is related to making the financial system and its advantages available to everyone seamlessly without depending on any trusted intermediaries. Smart contracts across chains provide an ecosyste... | ['Leandros Tassiulas', 'Iason Ofeidis', 'Sandro Scherrers', 'Georgios Palaiokrassas'] | 2023-05-17 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-5.01691520e-01 -1.53328493e-01 -2.50513703e-01 -3.94058861e-02
-3.42161059e-01 -9.91970778e-01 9.27623272e-01 4.02821004e-01
-5.95544755e-01 1.07395136e+00 4.72326726e-02 -5.25456250e-01
-9.83412340e-02 -9.26649570e-01 -4.29283082e-01 -5.26740849e-01
-3.53303969e-01 7.30915189e-01 2.17235371e-01 -3.03388834... | [6.393923759460449, 6.40379524230957] |
69bac929-5704-4d07-8531-4e84f92c2c7a | from-disfluency-detection-to-intent-detection | 2209.08359 | null | https://arxiv.org/abs/2209.08359v1 | https://arxiv.org/pdf/2209.08359v1.pdf | From Disfluency Detection to Intent Detection and Slot Filling | We present the first empirical study investigating the influence of disfluency detection on downstream tasks of intent detection and slot filling. We perform this study for Vietnamese -- a low-resource language that has no previous study as well as no public dataset available for disfluency detection. First, we extend ... | ['Dat Quoc Nguyen', 'Thinh Hung Truong', 'Mai Hoang Dao'] | 2022-09-17 | null | null | null | null | ['xlm-r', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [-1.57009766e-01 5.06864116e-02 -3.45817477e-01 -1.28946543e-01
-6.80339754e-01 -7.30992019e-01 6.20793939e-01 1.19135313e-01
-8.33828509e-01 7.69628704e-01 9.24685180e-01 -8.75098884e-01
5.45611501e-01 -4.43492651e-01 -4.27577972e-01 -8.22887421e-02
-2.03216374e-02 5.18126965e-01 1.90033403e-03 -4.42389250... | [10.701452255249023, 9.88488483428955] |
309fb2ec-17a5-4782-aac1-426928717888 | serial-contrastive-knowledge-distillation-for | 2305.06616 | null | https://arxiv.org/abs/2305.06616v1 | https://arxiv.org/pdf/2305.06616v1.pdf | Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction | Continual few-shot relation extraction (RE) aims to continuously train a model for new relations with few labeled training data, of which the major challenges are the catastrophic forgetting of old relations and the overfitting caused by data sparsity. In this paper, we propose a new model, namely SCKD, to accomplish t... | ['Wei Hu', 'Zitao Wang', 'Xinyi Wang'] | 2023-05-11 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [-2.89810617e-02 3.28612179e-01 -5.33652186e-01 -2.44093344e-01
-1.34656399e-01 1.70601681e-01 4.30296987e-01 2.09087551e-01
-4.18103427e-01 1.10186708e+00 5.60138002e-02 -1.04293628e-02
-2.78176069e-01 -8.73447895e-01 -4.38927978e-01 -3.93531948e-01
1.49270847e-01 7.42467940e-01 5.48697770e-01 -4.20474738... | [9.145071983337402, 8.522132873535156] |
001bfc3f-cfe0-4af1-8b94-9347e3c51a6c | benchmarking-robustness-of-ai-enabled-multi | 2306.03454 | null | https://arxiv.org/abs/2306.03454v1 | https://arxiv.org/pdf/2306.03454v1.pdf | Benchmarking Robustness of AI-enabled Multi-sensor Fusion Systems: Challenges and Opportunities | Multi-Sensor Fusion (MSF) based perception systems have been the foundation in supporting many industrial applications and domains, such as self-driving cars, robotic arms, and unmanned aerial vehicles. Over the past few years, the fast progress in data-driven artificial intelligence (AI) has brought a fast-increasing ... | ['Baowen Xu', 'Zhenyu Chen', 'Lei Ma', 'Yang Feng', 'Zhijie Wang', 'Xinyu Gao'] | 2023-06-06 | null | null | null | null | ['self-driving-cars', 'object-tracking', 'depth-completion'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.28849423e-01 -1.16193667e-01 -4.94693108e-02 -4.50282902e-01
-4.70609784e-01 -4.79442686e-01 7.55350828e-01 8.84694234e-02
-1.82912692e-01 2.61522710e-01 -1.09192543e-01 -2.70949543e-01
-2.44334221e-01 -9.29460287e-01 -8.14858913e-01 -7.24240899e-01
-1.38641313e-01 7.35451058e-02 4.96596247e-01 -5.91909289... | [7.857624530792236, -1.3868157863616943] |
7efd4258-5f7f-4701-9fe1-2868ec581830 | samossa-multivariate-singular-spectrum | 2305.16491 | null | https://arxiv.org/abs/2305.16491v1 | https://arxiv.org/pdf/2305.16491v1.pdf | SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise | The well-established practice of time series analysis involves estimating deterministic, non-stationary trend and seasonality components followed by learning the residual stochastic, stationary components. Recently, it has been shown that one can learn the deterministic non-stationary components accurately using multiv... | ['Devavrat Shah', 'Sean Mann', 'Munther Dahleh', 'Abdullah Alomar'] | 2023-05-25 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 4.56222713e-01 -3.79598081e-01 1.28476411e-01 8.99277702e-02
-1.09550130e+00 -8.98332715e-01 5.90552866e-01 1.02878541e-01
4.95850714e-03 5.01343369e-01 3.97411287e-02 -5.57262421e-01
-5.68085790e-01 -2.68772542e-01 -9.06414270e-01 -1.04810977e+00
-3.87177944e-01 1.14211567e-01 -1.97816879e-01 -4.18224186... | [6.901791095733643, 3.76599383354187] |
a6acd7e3-eea0-435e-95da-82cb7834d6f0 | importance-aware-learning-for-neural-headline | 1912.01114 | null | https://arxiv.org/abs/1912.01114v1 | https://arxiv.org/pdf/1912.01114v1.pdf | Importance-Aware Learning for Neural Headline Editing | Many social media news writers are not professionally trained. Therefore, social media platforms have to hire professional editors to adjust amateur headlines to attract more readers. We propose to automate this headline editing process through neural network models to provide more immediate writing support for these s... | ['Ying Zeng', 'Lei LI', 'Qingyang Wu', 'Hao Zhou', 'Zhou Yu'] | 2019-11-25 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 2.27545306e-01 3.36704671e-01 -2.09968552e-01 -6.61315918e-01
-8.88026893e-01 -3.23055476e-01 7.41494954e-01 5.04237786e-02
-6.84379339e-01 7.34352887e-01 6.72152817e-01 -1.45462081e-01
3.10167074e-01 -6.90484107e-01 -1.00173342e+00 1.45414129e-01
8.09193373e-01 3.80851716e-01 -8.51962566e-02 -4.42867249... | [12.131884574890137, 9.15505313873291] |
0568b137-a85a-4199-9d18-c143145327f0 | lsdnet-trainable-modification-of-lsd | 2209.04642 | null | https://arxiv.org/abs/2209.04642v1 | https://arxiv.org/pdf/2209.04642v1.pdf | LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection | As of today, the best accuracy in line segment detection (LSD) is achieved by algorithms based on convolutional neural networks - CNNs. Unfortunately, these methods utilize deep, heavy networks and are slower than traditional model-based detectors. In this paper we build an accurate yet fast CNN- based detector, LSDNet... | ['Evgeny Shvets', 'Leonid Erlygin', 'Lev Teplyakov'] | 2022-09-10 | null | null | null | null | ['line-segment-detection', 'line-detection'] | ['computer-vision', 'computer-vision'] | [-2.41228208e-01 1.41148925e-01 -2.39459246e-01 -7.61796348e-03
-6.39746785e-01 -5.71394145e-01 3.23718607e-01 3.56180102e-01
-6.10424340e-01 4.65025097e-01 -1.82623655e-01 -4.31109577e-01
3.48296016e-01 -6.52251601e-01 -9.36178029e-01 -1.64284557e-01
-9.96007249e-02 5.17004356e-02 1.00996053e+00 -4.46799099... | [8.305620193481445, -1.6451045274734497] |
2c8d7ff2-5ba7-4669-b5f6-8ab49bef141e | transferring-deep-reinforcement-learning-with | 1809.00770 | null | http://arxiv.org/abs/1809.00770v1 | http://arxiv.org/pdf/1809.00770v1.pdf | Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation | In the past few years, deep reinforcement learning has been proven to solve
problems which have complex states like video games or board games. The next
step of intelligent agents would be able to generalize between tasks, and using
prior experience to pick up new skills more quickly. However, most
reinforcement learni... | ['Bing-Yu Chen', 'I-Chao Shen', 'Shu-Hsuan Hsu'] | 2018-09-04 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 7.03694150e-02 -1.50168180e-01 -1.68573067e-01 3.02954484e-02
-5.05520225e-01 -7.26265728e-01 7.08389819e-01 2.67871097e-02
-9.26150203e-01 1.37745500e+00 -1.09962314e-01 -1.61103681e-01
-1.98094770e-01 -8.28389347e-01 -7.24130988e-01 -5.72521329e-01
-2.41950065e-01 8.68414044e-01 6.56209290e-01 -6.48311555... | [4.046187400817871, 1.5248554944992065] |
515a7622-3bf2-4304-ad20-63bdef50e2e9 | empirical-study-incorporating-linguistic | 2210.07559 | null | https://arxiv.org/abs/2210.07559v1 | https://arxiv.org/pdf/2210.07559v1.pdf | Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech Synthesis | We present a comprehensive empirical study for personalized spontaneous speech synthesis on the basis of linguistic knowledge. With the advent of voice cloning for reading-style speech synthesis, a new voice cloning paradigm for human-like and spontaneous speech synthesis is required. We, therefore, focus on personaliz... | ['Hiroshi Saruwatari', 'Shinnosuke Takamichi', 'Takaaki Saeki', 'Yuta Matsunaga'] | 2022-10-14 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 4.54322219e-01 5.30687928e-01 -2.10602283e-01 -3.27431887e-01
-6.86741233e-01 -5.20570815e-01 4.10816759e-01 -2.18571782e-01
-1.28914386e-01 5.19996107e-01 7.77982414e-01 -4.18314815e-01
1.58627793e-01 -3.78859460e-01 -3.91785055e-01 -3.83109957e-01
4.42472875e-01 1.97870553e-01 -1.69346228e-01 -2.73729026... | [14.7843656539917, 6.742637634277344] |
d824afbf-f080-4821-b303-012265d1b254 | stride-scene-text-recognition-in-device | 2105.07795 | null | https://arxiv.org/abs/2105.07795v1 | https://arxiv.org/pdf/2105.07795v1.pdf | STRIDE : Scene Text Recognition In-Device | Optical Character Recognition (OCR) systems have been widely used in various applications for extracting semantic information from images. To give the user more control over their privacy, an on-device solution is needed. The current state-of-the-art models are too heavy and complex to be deployed on-device. We develop... | ['Gopi Ramena', 'Sukumar Moharana', 'Nikhil Arora', 'Arun D Prabhu', 'Rachit S Munjal'] | 2021-05-17 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 3.83738518e-01 -4.17771906e-01 -9.96553749e-02 -4.92773414e-01
-4.44512933e-01 -3.35088521e-01 3.22326958e-01 4.89242189e-02
-7.76325643e-01 1.74813852e-01 -3.73613179e-01 -6.85296178e-01
2.85478204e-01 -6.46858573e-01 -6.64532959e-01 -4.17783111e-01
7.54517615e-01 2.31793433e-01 3.12032998e-01 2.31690526... | [11.940177917480469, 2.2237861156463623] |
6cab085c-e52c-4581-9d58-7a49397e6528 | streaming-sparse-linear-regression | 2211.06039 | null | https://arxiv.org/abs/2211.06039v2 | https://arxiv.org/pdf/2211.06039v2.pdf | Online Linearized LASSO | Sparse regression has been a popular approach to perform variable selection and enhance the prediction accuracy and interpretability of the resulting statistical model. Existing approaches focus on offline regularized regression, while the online scenario has rarely been studied. In this paper, we propose a novel onlin... | ['Qiang Sun', 'Xiuneng Zhu', 'Yuhao Yan', 'Shuoguang Yang'] | 2022-11-11 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 2.76796073e-01 -1.01877317e-01 -3.93901050e-01 -2.97369570e-01
-1.08647811e+00 -2.98851520e-01 -3.37578177e-01 5.60601532e-01
-5.93626678e-01 9.02686417e-01 -3.85055542e-01 -4.59412932e-01
-3.87545526e-01 -6.14344478e-01 -8.55770648e-01 -9.07252133e-01
-4.53487188e-01 1.39906317e-01 -1.14795893e-01 8.78687650... | [6.679098129272461, 4.514091968536377] |
efba3ee7-0fcb-4767-bd14-92afbe7e2ec6 | atom-search-optimization-with-simulated | 2005.08642 | null | https://arxiv.org/abs/2005.08642v1 | https://arxiv.org/pdf/2005.08642v1.pdf | Atom Search Optimization with Simulated Annealing -- a Hybrid Metaheuristic Approach for Feature Selection | 'Hybrid meta-heuristics' is one of the most interesting recent trends in the field of optimization and feature selection (FS). In this paper, we have proposed a binary variant of Atom Search Optimization (ASO) and its hybrid with Simulated Annealing called ASO-SA techniques for FS. In order to map the real values used ... | ['Kushal Kanti Ghosh', 'Soulib Ghosh', 'Ram Sarkar', 'Suman Kumar Bera', 'Ritam Guha'] | 2020-05-10 | null | null | null | null | ['facial-emotion-recognition', 'handwritten-digit-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.58728296e-01 -2.61420250e-01 -2.68095970e-01 -4.28835452e-01
-3.21146965e-01 -2.72975177e-01 4.36639607e-01 3.62008631e-01
-4.88228500e-01 1.09777331e+00 2.87697781e-02 2.59631108e-02
-6.37457490e-01 -8.43996644e-01 -2.30958849e-01 -8.03708792e-01
-1.30691960e-01 7.03569233e-01 3.18124920e-01 -4.18230087... | [7.856736660003662, 3.726134777069092] |
ebb5f802-9cc6-4875-b14c-62c2a5538902 | learning-multi-scale-representations-for | 1408.2938 | null | http://arxiv.org/abs/1408.2938v1 | http://arxiv.org/pdf/1408.2938v1.pdf | Learning Multi-Scale Representations for Material Classification | The recent progress in sparse coding and deep learning has made unsupervised
feature learning methods a strong competitor to hand-crafted descriptors. In
computer vision, success stories of learned features have been predominantly
reported for object recognition tasks. In this paper, we investigate if and how
feature l... | ['Wenbin Li', 'Mario Fritz'] | 2014-08-13 | null | null | null | null | ['material-classification', 'material-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.43525192e-01 -4.12216514e-01 -3.13499480e-01 -4.56436485e-01
-7.94903159e-01 -4.65099365e-01 7.81782568e-01 2.47294143e-01
-1.75172597e-01 6.10435188e-01 3.70530009e-01 1.44163311e-01
-3.79845679e-01 -6.74263060e-01 -7.28247046e-01 -7.58174002e-01
-1.80541654e-03 3.39634329e-01 1.79397717e-01 8.76836702... | [9.4940185546875, 2.26234769821167] |
b96cf205-21da-4930-9ba4-e583d0e4e1d9 | adjointnet-constraining-machine-learning | 2109.03956 | null | https://arxiv.org/abs/2109.03956v1 | https://arxiv.org/pdf/2109.03956v1.pdf | AdjointNet: Constraining machine learning models with physics-based codes | Physics-informed Machine Learning has recently become attractive for learning physical parameters and features from simulation and observation data. However, most existing methods do not ensure that the physics, such as balance laws (e.g., mass, momentum, energy conservation), are constrained. Some recent works (e.g., ... | ['Maruti K. Mudunuru', 'Bulbul Ahmmed', 'Satish Karra'] | 2021-09-08 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-3.33601683e-01 1.47907129e-02 -2.44112834e-02 -1.63317800e-01
-1.31158248e-01 -5.92461288e-01 4.66073990e-01 2.00880975e-01
-3.80023420e-01 9.79557633e-01 -2.90845186e-01 -5.08501470e-01
-5.87154448e-01 -9.37466383e-01 -8.11287582e-01 -8.06907892e-01
-4.11956310e-01 6.49796546e-01 -2.52615921e-02 -1.36607781... | [6.435507297515869, 3.441756248474121] |
4865cec8-f730-4c6b-9704-d32932c973c3 | graph-fuzzy-system-concepts-models-and | 2210.16730 | null | https://arxiv.org/abs/2210.16730v1 | https://arxiv.org/pdf/2210.16730v1.pdf | Graph Fuzzy System: Concepts, Models and Algorithms | Fuzzy systems (FSs) have enjoyed wide applications in various fields, including pattern recognition, intelligent control, data mining and bioinformatics, which is attributed to the strong interpretation and learning ability. In traditional application scenarios, FSs are mainly applied to model Euclidean space data and ... | ['Shitong Wang', 'Kup-Sze Choi', 'Zhenping Xie', 'Zhaohong Deng', 'Fuping Hu'] | 2022-10-30 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.55801937e-01 -1.48688152e-01 -3.78507487e-02 -2.81736225e-01
4.79322106e-01 -1.43490642e-01 1.98161393e-01 3.81963313e-01
-7.18550682e-02 5.01412451e-01 -4.85048354e-01 -5.14313161e-01
-7.57681847e-01 -1.29710162e+00 -3.88326496e-01 -5.45500636e-01
-1.50466532e-01 2.53091156e-01 6.21059299e-01 -6.28839791... | [7.561838150024414, 4.690603256225586] |
83e70d42-d326-4efc-ab8a-f8dff149ad1f | developing-personalized-models-of-blood | 2007.12802 | null | https://arxiv.org/abs/2007.12802v1 | https://arxiv.org/pdf/2007.12802v1.pdf | Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks | Blood pressure monitoring is an essential component of hypertension management and in the prediction of associated comorbidities. Blood pressure is a dynamic vital sign with frequent changes throughout a given day. Capturing blood pressure remotely and frequently (also known as ambulatory blood pressure monitoring) has... | ['Harlan M. Krumholz', 'Bassem Ibrahim', 'Nathan C. Hurley', 'Bobak J. Mortazavi', 'Roozbeh Jafari', 'Lida Zhang', 'Erica Spatz'] | 2020-07-24 | null | null | null | null | ['blood-pressure-estimation'] | ['medical'] | [ 1.94301739e-01 2.22829223e-01 3.91802862e-02 -4.58135098e-01
-7.17419267e-01 -2.81171978e-01 -1.41904771e-01 3.80765855e-01
-5.93767643e-01 1.31061804e+00 -2.99689174e-01 -5.08801460e-01
-1.95073903e-01 -1.03346050e+00 -8.17808390e-01 -4.82176453e-01
-5.73605895e-01 3.09026450e-01 -2.95709997e-01 1.43090054... | [14.067906379699707, 2.9746549129486084] |
4b6b37d2-1b08-40f4-881f-36d85dc661f8 | set-the-scene-global-local-training-for | 2303.13450 | null | https://arxiv.org/abs/2303.13450v1 | https://arxiv.org/pdf/2303.13450v1.pdf | Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes | Recent breakthroughs in text-guided image generation have led to remarkable progress in the field of 3D synthesis from text. By optimizing neural radiance fields (NeRF) directly from text, recent methods are able to produce remarkable results. Yet, these methods are limited in their control of each object's placement o... | ['Daniel Cohen-Or', 'Raja Giryes', 'Gal Metzer', 'Elad Richardson', 'Dana Cohen-Bar'] | 2023-03-23 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 5.58342934e-01 -9.73653495e-02 2.27980003e-01 -2.83668935e-01
-3.56305391e-01 -8.44627023e-01 7.37091422e-01 4.95466553e-02
-5.62616512e-02 4.84925717e-01 1.39517263e-01 1.43342558e-02
7.66759664e-02 -1.03600240e+00 -7.94948995e-01 -8.07842135e-01
5.98764122e-01 5.68563342e-01 3.12579870e-01 -5.70556819... | [9.304643630981445, -3.0763027667999268] |
47246374-bb6f-402d-a36a-0904ea655b9a | nilut-conditional-neural-implicit-3d-lookup | 2306.11920 | null | https://arxiv.org/abs/2306.11920v1 | https://arxiv.org/pdf/2306.11920v1.pdf | NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement | 3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D L... | ['Radu Timofte', 'Michael S. Brown', 'Javier Vazquez-Corral', 'Marcos V. Conde'] | 2023-06-20 | null | null | null | null | ['color-manipulation', 'photo-retouching', 'image-enhancement', 'tone-mapping'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.13702583e-01 -4.34964985e-01 -1.75655082e-01 -4.77346301e-01
-6.04280412e-01 -7.08077312e-01 3.81928027e-01 -1.13859951e-01
-3.12506557e-01 3.46221119e-01 -1.85073957e-01 -7.20320404e-01
3.28016758e-01 -7.29368091e-01 -9.63470697e-01 -3.81942153e-01
1.26530245e-01 1.41426280e-01 3.06674361e-01 -7.70941675... | [10.553651809692383, -2.0437729358673096] |
fe14a2af-c106-41a8-8f4e-2b0f715a4e45 | estimate-then-optimize-versus-integrated | 2304.06833 | null | https://arxiv.org/abs/2304.06833v1 | https://arxiv.org/pdf/2304.06833v1.pdf | Estimate-Then-Optimize Versus Integrated-Estimation-Optimization: A Stochastic Dominance Perspective | In data-driven stochastic optimization, model parameters of the underlying distribution need to be estimated from data in addition to the optimization task. Recent literature suggests the integration of the estimation and optimization processes, by selecting model parameters that lead to the best empirical objective pe... | ['Yunfan Zhao', 'Haofeng Zhang', 'Henry Lam', 'Adam N. Elmachtoub'] | 2023-04-13 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 4.64297645e-02 1.17746331e-01 -4.81048375e-01 -3.44940960e-01
-1.19841325e+00 -8.07645917e-01 3.36508870e-01 6.49635494e-02
-4.65379536e-01 9.13891435e-01 2.23027825e-01 -2.06833467e-01
-6.84362829e-01 -2.46187881e-01 -6.95683002e-01 -9.48745131e-01
1.45376891e-01 6.38280988e-01 -2.72202700e-01 7.06173554... | [6.578854560852051, 4.119256973266602] |
8c567b30-931d-4aef-8b5b-2e1aecaee78d | prosodic-enhanced-siamese-convolutional | 1808.01026 | null | http://arxiv.org/abs/1808.01026v1 | http://arxiv.org/pdf/1808.01026v1.pdf | Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification | In this paper a novel cross-device text-independent speaker verification
architecture is proposed. Majority of the state-of-the-art deep architectures
that are used for speaker verification tasks consider Mel-frequency cepstral
coefficients. In contrast, our proposed Siamese convolutional neural network
architecture us... | ['Nasser M. Nasrabadi', 'Sobhan Soleymani', 'Hadi Kazemi', 'Jeremy Dawson', 'Ali Dabouei', 'Seyed Mehdi Iranmanesh'] | 2018-07-31 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-4.58060093e-02 -2.79983133e-01 7.34375045e-02 -5.97095728e-01
-8.82327914e-01 -3.90072137e-01 2.47657925e-01 2.16527686e-01
-4.26854312e-01 4.25884485e-01 2.58647442e-01 -2.69172192e-01
-4.86464910e-02 -1.58972025e-01 -2.84636170e-01 -7.41874218e-01
-1.69330820e-01 -2.16741800e-01 -2.85596669e-01 -2.75963396... | [14.423074722290039, 6.039051532745361] |
455009ad-e691-4aad-a32e-d18c8a8b90b5 | predicate-argument-alignment-using-a-global | null | null | https://aclanthology.info/papers/N15-1002/n15-1002 | https://www.aclweb.org/anthology/N15-1002 | Predicate Argument Alignment using a Global Coherence Model | null | ['Mark Dredze', 'Benjamin Van Durme', 'Travis Wolfe'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5391852855682373, 15.869174003601074] |
5088f3d6-4957-404a-a250-595381771e4b | grf-learning-a-general-radiance-field-for-3d-1 | 2010.04595 | null | https://arxiv.org/abs/2010.04595v3 | https://arxiv.org/pdf/2010.04595v3.pdf | GRF: Learning a General Radiance Field for 3D Representation and Rendering | We present a simple yet powerful neural network that implicitly represents and renders 3D objects and scenes only from 2D observations. The network models 3D geometries as a general radiance field, which takes a set of 2D images with camera poses and intrinsics as input, constructs an internal representation for each p... | ['Bo Yang', 'Alex Trevithick'] | 2020-10-09 | grf-learning-a-general-radiance-field-for-3d | http://openaccess.thecvf.com//content/ICCV2021/html/Trevithick_GRF_Learning_a_General_Radiance_Field_for_3D_Representation_and_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Trevithick_GRF_Learning_a_General_Radiance_Field_for_3D_Representation_and_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 2.71682531e-01 -3.70574966e-02 2.04628125e-01 -4.58805174e-01
-3.36982936e-01 -7.52298355e-01 7.27700412e-01 -4.34837252e-01
9.16314572e-02 2.60330409e-01 -2.25682883e-03 -1.29766598e-01
2.34768853e-01 -1.03477848e+00 -1.10939109e+00 -4.53440577e-01
2.43492082e-01 4.66754138e-01 6.46392331e-02 2.89239623... | [8.839462280273438, -2.9981157779693604] |
47cc835b-f5a2-4213-ba4c-c9f5737b6226 | modelling-multi-issue-bargaining-dialogues | null | null | https://aclanthology.org/L16-1500 | https://aclanthology.org/L16-1500.pdf | Modelling Multi-issue Bargaining Dialogues: Data Collection, Annotation Design and Corpus | The paper describes experimental dialogue data collection activities, as well semantically annotated corpus creation undertaken within EU-funded METALOGUE project(www.metalogue.eu). The project aims to develop a dialogue system with flexible dialogue management to enable system{'}s adaptive, reactive, interactive and p... | ['Volha Petukhova', 'Harmen de Weerd', 'Fokie Cnossen', 'Niels Taatgen', 'Andrei Malchanau', 'Christopher Stevens'] | 2016-05-01 | modelling-multi-issue-bargaining-dialogues-1 | https://aclanthology.org/L16-1500 | https://aclanthology.org/L16-1500.pdf | lrec-2016-5 | ['dialogue-management'] | ['natural-language-processing'] | [ 0.12178647 0.7899332 0.23188695 -0.6167947 -0.3578375 -0.8684339
1.2352629 0.26653305 -0.5558646 1.0011463 0.7017243 -0.16023932
-0.3153593 -0.7497026 0.47146255 -0.21530883 0.14333962 1.4049481
0.2415939 -0.95996463 0.62572336 0.42056257 -1.1677452 0.60586566
0.5127109 0.29336557 0.43... | [12.944530487060547, 7.963223457336426] |
1fedcb59-a066-4060-afe7-b382da6b30cb | director-field-model-of-the-primary-visual | 1310.1341 | null | http://arxiv.org/abs/1310.1341v2 | http://arxiv.org/pdf/1310.1341v2.pdf | Director Field Model of the Primary Visual Cortex for Contour Detection | We aim to build the simplest possible model capable of detecting long, noisy
contours in a cluttered visual scene. For this, we model the neural dynamics in
the primate primary visual cortex in terms of a continuous director field that
describes the average rate and the average orientational preference of active
neuron... | ['Ilya Nemenman', 'Rebecca Butterfield', 'Vijay Singh', 'Martin Tchernookov'] | 2013-10-04 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 2.18063563e-01 -2.90146470e-01 1.18062392e-01 -2.19636276e-01
1.67314738e-01 -6.94250584e-01 8.44762921e-01 -3.43055695e-01
-7.90359318e-01 6.21107757e-01 4.28062379e-02 1.71868727e-01
-1.64400443e-01 -5.35023332e-01 -3.98955166e-01 -1.17584920e+00
-3.42129081e-01 4.50495422e-01 6.51075065e-01 1.05432644... | [9.486054420471191, 2.3166518211364746] |
c8c90ef7-29df-4915-b359-c98a0bf12ef0 | discovery-of-natural-language-concepts-in | 1902.07249 | null | http://arxiv.org/abs/1902.07249v2 | http://arxiv.org/pdf/1902.07249v2.pdf | Discovery of Natural Language Concepts in Individual Units of CNNs | Although deep convolutional networks have achieved improved performance in
many natural language tasks, they have been treated as black boxes because they
are difficult to interpret. Especially, little is known about how they
represent language in their intermediate layers. In an attempt to understand
the representatio... | ['Dong-Hyun Lee', 'Yo Joong Choe', 'Seil Na', 'Gunhee Kim'] | 2019-02-18 | discovery-of-natural-language-concepts-in-1 | https://openreview.net/forum?id=S1EERs09YQ | https://openreview.net/pdf?id=S1EERs09YQ | iclr-2019-5 | ['concept-alignment'] | ['computer-vision'] | [ 3.94360781e-01 2.22932268e-02 -1.99826345e-01 -5.91509759e-01
-5.73602617e-02 -8.31136346e-01 7.88339913e-01 4.45998251e-01
-4.50848460e-01 4.24352139e-01 6.37179732e-01 -4.76843446e-01
3.40656847e-01 -8.46960306e-01 -7.21492827e-01 -1.45718738e-01
1.57204375e-01 6.03193402e-01 -7.08202273e-02 -4.89476234... | [10.61548900604248, 8.846811294555664] |
98ad51a4-eaad-4c17-9fde-6fbb52a3cdd2 | rqdia-regularizing-q-value-distributions-with | null | null | https://openreview.net/forum?id=rqcLsG8Kme9 | https://openreview.net/pdf?id=rqcLsG8Kme9 | rQdia: Regularizing Q-Value Distributions With Image Augmentation | rQdia (pronounced “Arcadia”) regularizes Q-value distributions with augmented images in pixel-based deep reinforcement learning. With a simple auxiliary loss, that equalizes these distributions via MSE, rQdia boosts DrQ and SAC on 9/12 and 10/12 tasks respectively in the MuJoCo Continuous Control Suite from pixels, and... | ['Chenliang Xu', 'Jing Bi', 'Samuel Lerman'] | 2021-09-29 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [-1.22315660e-01 -2.23886650e-02 -4.33315128e-01 -2.90809602e-01
-1.16930389e+00 -4.79286432e-01 8.93758237e-01 -2.83423662e-01
-9.81996715e-01 1.00837231e+00 1.35261742e-02 -3.38185102e-01
-5.89147322e-02 -7.33837903e-01 -1.06953394e+00 -7.94022858e-01
-4.99784321e-01 2.47323319e-01 6.91418201e-02 -3.52109790... | [4.190040588378906, 1.5809446573257446] |
45873268-157a-4f1c-a606-72f262ae7c47 | cs-mlgcn-multiplex-graph-convolutional | 2210.08811 | null | https://arxiv.org/abs/2210.08811v1 | https://arxiv.org/pdf/2210.08811v1.pdf | CS-MLGCN : Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks | Community Search (CS) is one of the fundamental tasks in network science and has attracted much attention due to its ability to discover personalized communities with a wide range of applications. Given any query nodes, CS seeks to find a densely connected subgraph containing query nodes. Most existing approaches usual... | ['Farnoosh Hashemi', 'Ali Behrouz'] | 2022-10-17 | null | null | null | null | ['community-search'] | ['graphs'] | [-0.08506904 0.0367949 -0.41213173 -0.16823293 0.1469012 -0.8577075
0.4956891 0.41481018 0.1877222 0.29252484 0.20757274 -0.14063318
-0.6012214 -1.3368144 -0.5142181 -0.4187539 -0.45879415 0.7088097
0.63771105 -0.27572972 0.01164853 0.39384824 -0.8861392 0.16728164
0.81235206 0.55293393 0.16... | [7.212950706481934, 6.024726390838623] |
9ea1ed1f-27d3-4f2e-8954-7b5e04d69a6c | contrastive-losses-are-natural-criteria-for | 2211.10056 | null | https://arxiv.org/abs/2211.10056v1 | https://arxiv.org/pdf/2211.10056v1.pdf | Contrastive Losses Are Natural Criteria for Unsupervised Video Summarization | Video summarization aims to select the most informative subset of frames in a video to facilitate efficient video browsing. Unsupervised methods usually rely on heuristic training objectives such as diversity and representativeness. However, such methods need to bootstrap the online-generated summaries to compute the o... | ['Hajime Nagahara', 'Mayu Otani', 'Yuta Nakashima', 'Zongshang Pang'] | 2022-11-18 | null | null | null | null | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 2.94646740e-01 -1.26754031e-01 -5.14111817e-01 -3.45205933e-01
-1.18967772e+00 -4.38340485e-01 5.86389303e-01 4.32188585e-02
-2.52006412e-01 6.45349681e-01 6.86235607e-01 1.91812977e-01
-9.48354229e-02 -3.09167773e-01 -7.88197756e-01 -5.69696665e-01
-3.56419683e-01 1.30231455e-01 3.40871364e-02 1.19619787... | [10.201072692871094, 0.4518008232116699] |
29f4d872-36f1-4c0f-b89f-52671f709e6a | a-question-entailment-approach-to-question | 1901.08079 | null | http://arxiv.org/abs/1901.08079v1 | http://arxiv.org/pdf/1901.08079v1.pdf | A Question-Entailment Approach to Question Answering | One of the challenges in large-scale information retrieval (IR) is to develop
fine-grained and domain-specific methods to answer natural language questions.
Despite the availability of numerous sources and datasets for answer retrieval,
Question Answering (QA) remains a challenging problem due to the difficulty of
the ... | ['Dina Demner-Fushman', 'Asma Ben Abacha'] | 2019-01-23 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [ 2.86638170e-01 2.78894603e-01 5.19070029e-02 -4.05313492e-01
-1.93783152e+00 -7.45761216e-01 5.12887359e-01 6.85937226e-01
-6.80120647e-01 6.80587649e-01 7.18084276e-01 -3.07365775e-01
-7.32060552e-01 -5.71273506e-01 -5.55961967e-01 -1.06945552e-01
2.62087464e-01 9.66261566e-01 3.22299808e-01 -7.06332028... | [8.788662910461426, 8.579341888427734] |
3ac55d30-5459-479c-ba15-e45d8543116a | captainglove-capacitive-and-inertial-fusion | 2306.04319 | null | https://arxiv.org/abs/2306.04319v1 | https://arxiv.org/pdf/2306.04319v1.pdf | CaptAinGlove: Capacitive and Inertial Fusion-Based Glove for Real-Time on Edge Hand Gesture Recognition for Drone Control | We present CaptAinGlove, a textile-based, low-power (1.15Watts), privacy-conscious, real-time on-the-edge (RTE) glove-based solution with a tiny memory footprint (2MB), designed to recognize hand gestures used for drone control. We employ lightweight convolutional neural networks as the backbone models and a hierarchic... | ['Paul Lukowicz', 'Bo Zhou', 'Lala Ray', 'Daniel Geißler', 'Sungho Suh', 'Hymalai Bello'] | 2023-06-07 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.21422291e-01 -3.39400172e-01 -3.71657759e-01 -1.24827027e-01
-2.90179491e-01 -8.15711141e-01 -3.45320702e-02 -5.97711504e-01
-7.35249996e-01 4.64409649e-01 3.01186014e-02 -5.14680386e-01
-1.53277904e-01 -4.52356756e-01 -4.96185869e-01 -5.58931231e-01
-1.09946288e-01 -3.07130575e-01 1.08029116e-02 1.90920115... | [6.554998397827148, -0.16574181616306305] |
c9599a47-12ff-402d-ba6c-6935cfdc7ac3 | machine-learning-in-asset-management-part-1 | null | null | https://jfds.pm-research.com/content/2/1/10/tab-article-info | https://vixra.org/pdf/1912.0492v1.pdf | Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies | This is the first in a series of arti-cles dealing with machine learning in asset management. Asset management can be broken into the following tasks: (1) portfolio construction, (2) risk management, (3) capital management, (4) infra-structure and deployment, and (5) sales and marketing. This article focuses on portfol... | ['Derek Snow'] | 2020-02-10 | null | null | null | journal-of-financial-data-science-2020-2 | ['algorithmic-trading'] | ['time-series'] | [-8.58649686e-02 -3.61864008e-02 -1.86957344e-01 -1.62950203e-01
-3.98478180e-01 -8.47732425e-01 5.67584097e-01 4.51717943e-01
-1.52300581e-01 6.14418030e-01 7.49175102e-02 -1.16219306e+00
-5.39491713e-01 -1.01902521e+00 -2.19159096e-01 -5.54672539e-01
-5.19414097e-02 1.03146851e+00 -1.00556791e-01 1.34538058... | [4.616764068603516, 4.108184337615967] |
28355508-beb7-4117-8621-7eb07c4e081c | improving-the-gap-in-visual-speech | 2305.14203 | null | https://arxiv.org/abs/2305.14203v1 | https://arxiv.org/pdf/2305.14203v1.pdf | Improving the Gap in Visual Speech Recognition Between Normal and Silent Speech Based on Metric Learning | This paper presents a novel metric learning approach to address the performance gap between normal and silent speech in visual speech recognition (VSR). The difference in lip movements between the two poses a challenge for existing VSR models, which exhibit degraded accuracy when applied to silent speech. To solve this... | ['Shigeo Morishima', 'Qi Feng', 'Keitaro Tanaka', 'Sara Kashiwagi'] | 2023-05-23 | null | null | null | null | ['metric-learning', 'metric-learning', 'visual-speech-recognition'] | ['computer-vision', 'methodology', 'speech'] | [ 4.11507636e-01 4.52517495e-02 -3.60842794e-01 -3.30662996e-01
-1.06150627e+00 -6.38866663e-01 8.04521620e-01 -2.74714679e-01
-2.63639987e-01 4.16909337e-01 5.42340994e-01 -4.02398974e-01
4.78851534e-02 -2.00973704e-01 -5.49737453e-01 -7.10711837e-01
3.26987803e-01 1.77851334e-01 8.55649933e-02 -4.72293720... | [14.33244800567627, 5.054844379425049] |
8f7882e9-9d04-4aa8-9d28-134af752e556 | regularized-weight-aggregation-in-networked | 2301.12617 | null | https://arxiv.org/abs/2301.12617v1 | https://arxiv.org/pdf/2301.12617v1.pdf | Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation | In federated learning (FL), the global model at the server requires an efficient mechanism for weight aggregation and a systematic strategy for collaboration selection to manage and optimize communication payload. We introduce a practical and cost-efficient method for regularized weight aggregation and propose a labors... | ['Mojtaba Jafaritadi', 'Suleiman A. Khan', 'Elina Kontio', 'Esa Alhoniemi', 'Mohammad Ayyaz Azeem', 'Muhammad Irfan Khan'] | 2023-01-30 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [-1.79959044e-01 2.67445028e-01 -3.88771206e-01 -3.83195192e-01
-1.23810506e+00 -2.54460424e-01 5.00218332e-01 2.75379390e-01
-4.38090593e-01 9.10177350e-01 5.47444046e-01 -5.65776944e-01
-6.95852876e-01 -7.26633072e-01 -1.30374566e-01 -9.33613777e-01
-2.15564236e-01 6.96769357e-01 8.10045749e-02 -3.26443389... | [6.069920063018799, 6.4277496337890625] |
532eb426-2e8e-41d3-90bb-6c5492edd8e6 | playing-it-safe-information-constrains | 2304.08976 | null | https://arxiv.org/abs/2304.08976v2 | https://arxiv.org/pdf/2304.08976v2.pdf | Playing it safe: information constrains collective betting strategies | Every interaction of a living organism with its environment involves the placement of a bet. Armed with partial knowledge about a stochastic world, the organism must decide its next step or near-term strategy, an act that implicitly or explicitly involves the assumption of a model of the world. Better information about... | ['Vijay Balasubramanian', 'Philipp Fleig'] | 2023-04-18 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 6.18719935e-01 2.27208763e-01 2.63288748e-02 -2.53863316e-02
-8.89003798e-02 -3.51217359e-01 5.95831692e-01 1.45942941e-01
-6.97708666e-01 1.13970876e+00 -2.01461807e-01 -4.72887754e-01
-4.01670039e-01 -1.04526663e+00 -8.35179210e-01 -1.14901996e+00
-5.55701591e-02 5.44412911e-01 2.04591274e-01 -1.79111853... | [6.000269889831543, 4.249303817749023] |
0e0157ae-2e91-4226-b137-be3122ef5ba4 | uncertainty-quantification-in-imaging-and | 2004.00227 | null | https://arxiv.org/abs/2004.00227v3 | https://arxiv.org/pdf/2004.00227v3.pdf | Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach | In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a Bayesian framework allows for a principled way of studying uncertainty by solving for the model posterior distribution. Imaging, however, t... | ['Gabrio Rizzuti', 'Felix J. Herrmann', 'Ali Siahkoohi'] | 2020-04-01 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 2.65288740e-01 1.97690830e-01 4.17800367e-01 -3.50638062e-01
-8.72437656e-01 -4.63724613e-01 7.17407405e-01 -3.55866030e-02
-5.90368390e-01 8.34626079e-01 7.10488260e-02 -2.21614406e-01
-5.00184000e-01 -1.19922197e+00 -8.58452380e-01 -9.34613764e-01
-1.45621672e-01 6.77216411e-01 2.13621780e-01 1.82317972... | [6.8192853927612305, 3.3954017162323] |
59622d76-77bb-4152-a052-69d94fa13c39 | on-the-robustness-of-chatgpt-an-adversarial | 2302.12095 | null | https://arxiv.org/abs/2302.12095v4 | https://arxiv.org/pdf/2302.12095v4.pdf | On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective | ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing attention over the past few months. While evaluations of various aspects of ChatGPT have been done, its robustness, i.e., the performance to unexpected inputs, is still unclear to the public. Robustness is of particular concern in respon... | ['Xing Xie', 'Yue Zhang', 'Binxin Jiao', 'Xiubo Geng', 'Wei Ye', 'Haojun Huang', 'Linyi Yang', 'Yidong Wang', 'Runkai Zheng', 'Hao Chen', 'Wenxin Hou', 'Xixu Hu', 'Jindong Wang'] | 2023-02-22 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [-1.55107349e-01 4.67731237e-01 3.65967937e-02 -2.69575506e-01
-1.10256457e+00 -8.29250991e-01 7.07535326e-01 4.47943294e-03
-1.87321499e-01 8.70505273e-01 5.46807408e-01 -5.20926356e-01
-3.70211154e-02 -4.43051457e-01 -4.94465262e-01 -4.11071867e-01
5.44337481e-02 6.21408820e-01 -2.60346243e-03 -7.11664975... | [6.221107482910156, 8.151312828063965] |
4bae97a0-39b9-4e39-9944-0747e1d4db18 | fuxi-a-cascade-machine-learning-forecasting | 2306.12873 | null | https://arxiv.org/abs/2306.12873v2 | https://arxiv.org/pdf/2306.12873v2.pdf | FuXi: A cascade machine learning forecasting system for 15-day global weather forecast | Over the past few years, due to the rapid development of machine learning (ML) models for weather forecasting, state-of-the-art ML models have shown superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES) in 10-day forecasts at a spatial resoluti... | ['Hao Li', 'Yuan Qi', 'Yinghui Xu', 'Yuan Cheng', 'Feng Zhang', 'Xiaohui Zhong', 'Lei Chen'] | 2023-06-22 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-5.03339529e-01 -4.14303750e-01 2.16680676e-01 -3.58833760e-01
-5.22145271e-01 -4.97610927e-01 1.00264728e+00 -1.11926096e-02
-1.76286265e-01 1.17903841e+00 1.74246997e-01 -1.10864127e+00
-2.41991088e-01 -9.80224371e-01 -2.61582946e-03 -8.35025847e-01
-6.10171199e-01 4.78253551e-02 -1.64390057e-01 -6.94761932... | [6.518044948577881, 2.93401837348938] |
ce97253a-70a1-4a1a-a64b-fb17a33d2d05 | how-good-are-variational-autoencoders-at | 2304.10767 | null | https://arxiv.org/abs/2304.10767v1 | https://arxiv.org/pdf/2304.10767v1.pdf | How good are variational autoencoders at transfer learning? | Variational autoencoders (VAEs) are used for transfer learning across various research domains such as music generation or medical image analysis. However, there is no principled way to assess before transfer which components to retrain or whether transfer learning is likely to help on a target task. We propose to expl... | ['Marek Grzes', 'Lisa Bonheme'] | 2023-04-21 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 4.49752748e-01 2.17330977e-01 -5.80029935e-02 -1.19556025e-01
-7.44821370e-01 -5.51846862e-01 6.20971560e-01 -2.56135702e-01
-3.64018559e-01 5.91546893e-01 5.30979693e-01 -4.45231766e-01
-2.66130805e-01 -6.03186190e-01 -9.82780874e-01 -6.23764694e-01
2.70077825e-01 2.57792383e-01 -1.42020255e-01 -3.36098932... | [9.788146018981934, 2.522564172744751] |
206ad65b-d715-446d-9eb8-ac1c85bdd4d1 | neural-voice-puppetry-audio-driven-facial | 1912.05566 | null | https://arxiv.org/abs/1912.05566v2 | https://arxiv.org/pdf/1912.05566v2.pdf | Neural Voice Puppetry: Audio-driven Facial Reenactment | We present Neural Voice Puppetry, a novel approach for audio-driven facial video synthesis. Given an audio sequence of a source person or digital assistant, we generate a photo-realistic output video of a target person that is in sync with the audio of the source input. This audio-driven facial reenactment is driven by... | ['Matthias Nießner', 'Justus Thies', 'Christian Theobalt', 'Mohamed Elgharib', 'Ayush Tewari'] | 2019-12-11 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2619_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610698.pdf | eccv-2020-8 | ['talking-head-generation', 'talking-face-generation'] | ['computer-vision', 'computer-vision'] | [ 4.23326135e-01 4.76323992e-01 2.97511697e-01 -3.43136817e-01
-9.37975228e-01 -5.11143804e-01 7.92398751e-01 -1.02499461e+00
2.32844248e-01 4.31762010e-01 6.28168702e-01 2.35588863e-01
4.80534077e-01 -3.56170684e-01 -1.01682949e+00 -6.48190379e-01
4.45351191e-02 4.69669104e-01 -9.43287760e-02 -9.09256637... | [13.227287292480469, -0.4366714656352997] |
fa40f5e6-dcde-4653-8626-ecf6a758ddcc | solar-second-order-loss-and-attention-for | 2001.08972 | null | https://arxiv.org/abs/2001.08972v5 | https://arxiv.org/pdf/2001.08972v5.pdf | SOLAR: Second-Order Loss and Attention for Image Retrieval | Recent works in deep-learning have shown that second-order information is beneficial in many computer-vision tasks. Second-order information can be enforced both in the spatial context and the abstract feature dimensions. In this work, we explore two second-order components. One is focused on second-order spatial infor... | ['Krystian Mikolajczyk', 'Vassileios Balntas', 'Tony Ng', 'Yurun Tian'] | 2020-01-24 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4963_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700256.pdf | eccv-2020-8 | ['patch-matching'] | ['computer-vision'] | [ 6.58320114e-02 -4.32613105e-01 -3.75451446e-01 -3.92315239e-01
-7.99411774e-01 -3.09282005e-01 8.23802292e-01 4.94393289e-01
-6.23438239e-01 2.27657899e-01 1.32362172e-01 -2.66067740e-02
-4.44335788e-01 -7.07132161e-01 -5.05815089e-01 -7.36205816e-01
-1.84608951e-01 6.18556961e-02 5.34041941e-01 -2.43612349... | [10.624214172363281, 0.6810358762741089] |
0b156a0c-3ea3-4485-b0fd-93797da7af00 | real-robot-challenge-using-deep-reinforcement | 2109.15233 | null | https://arxiv.org/abs/2109.15233v3 | https://arxiv.org/pdf/2109.15233v3.pdf | Solving the Real Robot Challenge using Deep Reinforcement Learning | This paper details our winning submission to Phase 1 of the 2021 Real Robot Challenge; a challenge in which a three-fingered robot must carry a cube along specified goal trajectories. To solve Phase 1, we use a pure reinforcement learning approach which requires minimal expert knowledge of the robotic system, or of rob... | ['David Cordova Bulens', 'Qiang Wang', 'Stephen J. Redmond', "Noel O'Connor", 'Kevin McGuinness', 'Francisco Roldan Sanchez', 'Robert McCarthy'] | 2021-09-30 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-6.13554940e-02 3.53472918e-01 -2.62750220e-02 -1.29265189e-02
-7.49404788e-01 -1.06537127e+00 3.69485140e-01 -1.67845711e-01
-5.21412313e-01 9.42281485e-01 -2.16729417e-01 -3.47344846e-01
-5.45505643e-01 -3.99936318e-01 -1.24531174e+00 -9.14577782e-01
-5.12394369e-01 8.98529053e-01 1.34880826e-01 -5.25305986... | [4.594848155975342, 0.8425165414810181] |
a7a4cba0-b868-4508-901c-2a43d734debe | conformalizing-machine-translation-evaluation | 2306.06221 | null | https://arxiv.org/abs/2306.06221v1 | https://arxiv.org/pdf/2306.06221v1.pdf | Conformalizing Machine Translation Evaluation | Several uncertainty estimation methods have been recently proposed for machine translation evaluation. While these methods can provide a useful indication of when not to trust model predictions, we show in this paper that the majority of them tend to underestimate model uncertainty, and as a result they often produce m... | ['André F. T. Martins', 'Chrysoula Zerva'] | 2023-06-09 | null | null | null | null | ['conformal-prediction', 'machine-translation', 'conformal-prediction'] | ['computer-vision', 'natural-language-processing', 'reasoning'] | [ 1.99992046e-01 4.50017422e-01 -5.99694550e-01 -5.78516483e-01
-1.60819077e+00 -9.98319805e-01 7.25095093e-01 4.19747084e-01
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-1.37446091e-01 -7.53655374e-01 -8.74311626e-01 -3.84457707e-01
2.76759982e-01 9.54682887e-01 2.09484488e-01 1.93336532... | [7.898689270019531, 4.4471259117126465] |
2c67c9bc-0d7c-43d9-9d15-e20fc6283ac1 | improvement-of-e-commerce-recommendation | null | null | https://www.dbc.wroc.pl/publication/148614 | https://sciendo.com/en/article/10.15611/eada.2020.3.03?tab=anteprima-pdf | Improvement of e-commerce recommendation systems with deep hybrid collaborative filtering with content: A case study | This paper presents a proposition to utilize flexible neural network architecture called Deep Hybrid Collaborative Filtering with Content (DHCF) as a product recommendation engine. Its main goal is to provide better shopping suggestions for customers on the e-commerce platform. The system was tested on 2018 Amazon Revi... | ['Michał Górnik', 'Filip Wójcik'] | 2020-11-24 | null | null | null | econometrics-ekonometria-advances-in-applied | ['collaborative-filtering'] | ['miscellaneous'] | [-6.10332131e-01 -3.93280029e-01 1.94592252e-01 -5.46371162e-01
6.45418093e-02 -6.13689184e-01 6.74784720e-01 3.31341535e-01
-6.03551507e-01 5.26931703e-01 2.65697479e-01 -4.27385837e-01
-7.33053982e-01 -9.62200522e-01 -3.69715154e-01 -4.53592420e-01
-2.97040790e-01 8.75395313e-02 -1.00838505e-01 -5.25413692... | [10.023388862609863, 5.744769096374512] |
034961e3-f2a2-4233-863f-92f9a15cdc13 | joint-multi-frame-detection-and-segmentation | 1906.10886 | null | https://arxiv.org/abs/1906.10886v1 | https://arxiv.org/pdf/1906.10886v1.pdf | Joint Multi-frame Detection and Segmentation for Multi-cell Tracking | Tracking living cells in video sequence is difficult, because of cell morphology and high similarities between cells. Tracking-by-detection methods are widely used in multi-cell tracking. We perform multi-cell tracking based on the cell centroid detection, and the performance of the detector has high impact on tracking... | ['Zibin Zhou', 'Peng Gao', 'Fei Wang', 'Chengkang He', 'Wenjuan Xi', 'Huaying Chen'] | 2019-06-26 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [-8.42157304e-02 -7.63249815e-01 -6.75759017e-02 5.03195167e-01
-5.90053618e-01 -6.98702216e-01 2.12418199e-01 2.91048467e-01
-7.46553481e-01 8.68963540e-01 -3.02012891e-01 2.19867066e-01
4.74697202e-01 -7.31793225e-01 -1.65451407e-01 -1.22339821e+00
1.03092059e-01 3.45761925e-01 8.88440251e-01 2.37223864... | [14.515825271606445, -3.223742961883545] |
bfe5e058-c3f0-4f46-bb20-5e9f3edfdafb | underwater-fish-detection-using-deep-learning | 1811.01494 | null | http://arxiv.org/abs/1811.01494v1 | http://arxiv.org/pdf/1811.01494v1.pdf | Underwater Fish Detection using Deep Learning for Water Power Applications | Clean energy from oceans and rivers is becoming a reality with the
development of new technologies like tidal and instream turbines that generate
electricity from naturally flowing water. These new technologies are being
monitored for effects on fish and other wildlife using underwater video.
Methods for automated anal... | ['Shari Matzner', 'Wenwei Xu'] | 2018-11-05 | null | null | null | null | ['fish-detection'] | ['computer-vision'] | [-2.77138166e-02 -3.89389470e-02 7.28684664e-01 -4.47286546e-01
-2.98603982e-01 -6.70175970e-01 1.88479081e-01 1.21603578e-01
-8.13754141e-01 7.21703708e-01 7.35526681e-02 -6.58773705e-02
-3.08472142e-02 -1.07857096e+00 -7.89022028e-01 -7.45108128e-01
-6.21058822e-01 2.37031564e-01 4.43052918e-01 -2.17740655... | [8.484566688537598, -1.2336680889129639] |
188f7f43-96fb-4d1c-8443-9c0ec3137546 | spectral-clustering-via-orthogonalization | 2305.10356 | null | https://arxiv.org/abs/2305.10356v1 | https://arxiv.org/pdf/2305.10356v1.pdf | Spectral Clustering via Orthogonalization-Free Methods | Graph Signal Filter used as dimensionality reduction in spectral clustering usually requires expensive eigenvalue estimation. We analyze the filter in an optimization setting and propose to use four orthogonalization-free methods by optimizing objective functions as dimensionality reduction in spectral clustering. The ... | ['Haizhao Yang', 'Qiyuan Pang'] | 2023-05-16 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 3.70503850e-02 1.00115508e-01 3.29900116e-01 3.40826482e-01
-4.88269776e-01 -6.94248259e-01 -6.74304040e-03 1.27111124e-02
-2.73930877e-01 1.08495623e-01 1.54253349e-01 -2.03290552e-01
-6.57982230e-01 -5.75414956e-01 -3.96158367e-01 -8.72140646e-01
-7.03458965e-01 3.00887674e-01 -4.28275950e-02 -1.58852056... | [7.072559356689453, 4.854666709899902] |
8304f694-1940-4a3a-a8f7-4b5103818909 | multilingual-bidirectional-unsupervised | 2209.02821 | null | https://arxiv.org/abs/2209.02821v4 | https://arxiv.org/pdf/2209.02821v4.pdf | Multilingual Bidirectional Unsupervised Translation Through Multilingual Finetuning and Back-Translation | We propose a two-stage approach for training a single NMT model to translate unseen languages both to and from English. For the first stage, we initialize an encoder-decoder model to pretrained XLM-R and RoBERTa weights, then perform multilingual fine-tuning on parallel data in 40 languages to English. We find this mod... | ['Mohammad Sadegh Rasooli', 'Chris Callison-Burch', 'Ajay Patel', 'Bryan Li'] | 2022-09-06 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [ 1.60881311e-01 1.06471367e-02 -5.39290547e-01 -4.48495805e-01
-1.71131980e+00 -9.37976122e-01 9.33165908e-01 -5.85988462e-01
-4.95591968e-01 1.21876907e+00 4.54173505e-01 -9.18554366e-01
7.04964459e-01 -4.68577057e-01 -1.07844555e+00 -3.35147113e-01
4.42281693e-01 9.68962848e-01 -4.75168467e-01 -5.97264171... | [11.65914249420166, 10.24752140045166] |
1ccb0fbc-69d7-49c8-b081-5cfd76d91968 | principles-and-guidelines-for-evaluating | 2306.16740 | null | https://arxiv.org/abs/2306.16740v1 | https://arxiv.org/pdf/2306.16740v1.pdf | Principles and Guidelines for Evaluating Social Robot Navigation Algorithms | A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms that tackle social navigation remains hard because it involves no... | ['Roberto Martin-Martin', 'Alexander Toshev', 'Naoki Yokoyama', 'Peng Xu', 'Xuesu Xiao', 'Marynel Vazquez', 'Nathan Tsoi', 'Peter Trautman', 'Ada V. Taylor', 'Peter Stone', 'Phani Teja Singamaneni', 'Soeren Pirk', 'Reuth Mirksy', 'Luis J. Manso', 'Tsang-Wei Edward Lee', 'Haresh Karnan', 'Jonathan P. How', 'Justin Hart'... | 2023-06-29 | null | null | null | null | ['benchmarking', 'social-navigation', 'benchmarking', 'robot-navigation'] | ['miscellaneous', 'robots', 'robots', 'robots'] | [-6.14921264e-02 3.24142337e-01 1.84730917e-01 -5.03184438e-01
-9.48924944e-02 -6.92536592e-01 8.76107156e-01 1.26641124e-01
-9.22233939e-01 7.62007415e-01 3.64095062e-01 -3.34448963e-01
-3.47576499e-01 -4.04724628e-01 -2.51137376e-01 -3.84283155e-01
-2.01324999e-01 3.06687266e-01 1.46242395e-01 -8.43404770... | [4.822906970977783, 0.9852423071861267] |
1e391622-2420-4891-9bab-d11ccf1de402 | a-comparative-analysis-of-latent-regressor | 2302.13678 | null | https://arxiv.org/abs/2302.13678v1 | https://arxiv.org/pdf/2302.13678v1.pdf | A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversion | Previous research has shown that established techniques for spoken voice conversion (VC) do not perform as well when applied to singing voice conversion (SVC). We propose an alternative loss component in a loss function that is otherwise well-established among VC tasks, which has been shown to improve our model's SVC p... | ['Simon Dixon', "Brendan O'Connor"] | 2023-02-27 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 3.40307169e-02 -8.57407153e-02 3.15592080e-01 -1.24811582e-01
-9.24698830e-01 -7.81057537e-01 5.79709232e-01 -2.30576500e-01
-6.70487821e-01 4.87857819e-01 7.37712026e-01 -1.44295141e-01
-1.50288031e-01 -4.37032789e-01 -4.99140620e-01 -6.34507895e-01
-1.14675857e-01 2.96826363e-01 -9.38730780e-03 -3.61244321... | [15.462530136108398, 6.018396377563477] |
f174b5e6-0119-4b6d-ad58-791f49a92150 | gentus-simulating-user-behaviour-and-language | 2208.10817 | null | https://arxiv.org/abs/2208.10817v1 | https://arxiv.org/pdf/2208.10817v1.pdf | GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers | User simulators (USs) are commonly used to train task-oriented dialogue systems (DSs) via reinforcement learning. The interactions often take place on semantic level for efficiency, but there is still a gap from semantic actions to natural language, which causes a mismatch between training and deployment environment. I... | ['Milica Gašić', 'Michael Heck', 'Carel van Niekerk', 'Nurul Lubis', 'Shutong Feng', 'Christian Geishauser', 'Hsien-Chin Lin'] | 2022-08-23 | null | https://aclanthology.org/2022.sigdial-1.28 | https://aclanthology.org/2022.sigdial-1.28.pdf | sigdial-acl-2022-9 | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 1.98806107e-01 6.89548373e-01 3.15285981e-01 -4.26678717e-01
-3.50276619e-01 -6.23284757e-01 9.64279950e-01 -1.15262598e-01
-4.18156445e-01 8.08202744e-01 3.29772413e-01 -3.53988826e-01
1.99721202e-01 -1.14953256e+00 -8.24513793e-01 -2.15192407e-01
2.18343809e-01 8.97336841e-01 3.69130313e-01 -1.03131926... | [13.074106216430664, 8.033596992492676] |
bb722414-5cca-44a4-b0b4-aff27d722ec2 | using-classifier-features-to-determine | null | null | https://aclanthology.org/N18-4009 | https://aclanthology.org/N18-4009.pdf | Using Classifier Features to Determine Language Transfer on Morphemes | The aim of this thesis is to perform a Native Language Identification (NLI) task where we identify an English learner{'}s native language background based only on the learner{'}s English writing samples. We focus on the use of English grammatical morphemes across four proficiency levels. The outcome of the computationa... | ['ra', 'Alex Lavrentovich'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['cross-corpus', 'native-language-identification'] | ['computer-vision', 'natural-language-processing'] | [ 8.76792073e-02 -1.39336765e-01 -4.07030821e-01 -3.02607834e-01
-6.16928577e-01 -8.87826204e-01 4.99355018e-01 5.11343837e-01
-8.33395302e-01 2.26769149e-01 3.96661848e-01 -9.59576190e-01
-2.04641491e-01 -5.95236778e-01 -5.45369565e-01 -5.65993786e-02
3.29266489e-01 5.91208003e-02 -1.34493142e-01 -2.68821895... | [10.703539848327637, 10.140847206115723] |
f824a596-b1c1-4045-8084-33fa48ac51f0 | understanding-plasticity-in-neural-networks | 2303.01486 | null | https://arxiv.org/abs/2303.01486v2 | https://arxiv.org/pdf/2303.01486v2.pdf | Understanding plasticity in neural networks | Plasticity, the ability of a neural network to quickly change its predictions in response to new information, is essential for the adaptability and robustness of deep reinforcement learning systems. Deep neural networks are known to lose plasticity over the course of training even in relatively simple learning problems... | ['Will Dabney', 'Razvan Pascanu', 'Bernardo Avila Pires', 'Evgenii Nikishin', 'Zeyu Zheng', 'Clare Lyle'] | 2023-03-02 | null | null | null | null | ['atari-games'] | ['playing-games'] | [ 4.88455266e-01 4.89124889e-03 -3.28594521e-02 -2.10618630e-01
-3.11939213e-02 -5.32620668e-01 4.09328997e-01 1.98893636e-01
-6.98281884e-01 7.06129372e-01 2.36998454e-01 -1.51606023e-01
-3.91607374e-01 -6.11804605e-01 -1.09068453e+00 -8.75456929e-01
-1.30179390e-01 -5.47194146e-02 2.14114860e-01 -3.85524392... | [8.42251205444336, 3.2987732887268066] |
c540d395-49fd-4230-80a9-9df49b47e8e0 | jet-tagging-in-the-lund-plane-with-graph | 2012.08526 | null | https://arxiv.org/abs/2012.08526v2 | https://arxiv.org/pdf/2012.08526v2.pdf | Jet tagging in the Lund plane with graph networks | The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation... | ['Huilin Qu', 'Frédéric A. Dreyer'] | 2020-12-15 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-2.40073711e-01 -2.75903032e-03 -2.02545121e-01 -4.21202004e-01
-4.61284339e-01 -5.80715060e-01 8.68336976e-01 3.32111835e-01
-3.52534145e-01 5.96958756e-01 5.70294112e-02 -7.04861104e-01
-2.45185927e-01 -8.57553780e-01 -6.24797583e-01 -8.50147545e-01
-4.35596049e-01 1.30264175e+00 7.48574078e-01 -1.85819596... | [15.698441505432129, 2.919255018234253] |
ea6274f4-47d6-43a5-8a15-3d451e6d69d3 | guide-the-learner-controlling-product-of | 2302.02852 | null | https://arxiv.org/abs/2302.02852v1 | https://arxiv.org/pdf/2302.02852v1.pdf | Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution Similarities | Several proposals have been put forward in recent years for improving out-of-distribution (OOD) performance through mitigating dataset biases. A popular workaround is to train a robust model by re-weighting training examples based on a secondary biased model. Here, the underlying assumption is that the biased model res... | ['Mohammad Taher Pilehvar', 'Hossein Amirkhani', 'Ali Modarressi'] | 2023-02-06 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [ 3.87570083e-01 5.68633854e-01 -6.14468038e-01 -8.81637096e-01
-6.37630761e-01 -5.58590174e-01 7.60984421e-01 3.64680529e-01
-1.38058946e-01 5.08174479e-01 2.71179616e-01 -3.70551229e-01
5.12797236e-02 -8.27211916e-01 -8.27587903e-01 -4.71818596e-01
4.52140033e-01 5.30184925e-01 2.52799302e-01 9.39579234... | [9.040609359741211, 4.624279022216797] |
50076b8b-7993-42a6-a553-00beb0365a49 | native-language-identification-using | null | null | https://aclanthology.org/C12-1027 | https://aclanthology.org/C12-1027.pdf | Native Language Identification using Recurring $n$-grams -- Investigating Abstraction and Domain Dependence | null | ['Detmar Meurers', 'Serhiy Bykh'] | 2012-12-01 | native-language-identification-using-1 | https://aclanthology.org/C12-1027 | https://aclanthology.org/C12-1027.pdf | coling-2012-12 | ['native-language-identification'] | ['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... | [-7.340203762054443, 3.6877286434173584] |
a3af051f-d8b4-412d-bf8b-6055529d8fb0 | conversational-text-to-sql-an-odyssey-into | 2302.11054 | null | https://arxiv.org/abs/2302.11054v1 | https://arxiv.org/pdf/2302.11054v1.pdf | Conversational Text-to-SQL: An Odyssey into State-of-the-Art and Challenges Ahead | Conversational, multi-turn, text-to-SQL (CoSQL) tasks map natural language utterances in a dialogue to SQL queries. State-of-the-art (SOTA) systems use large, pre-trained and finetuned language models, such as the T5-family, in conjunction with constrained decoding. With multi-tasking (MT) over coherent tasks with disc... | ['Dilek Hakkani-Tur', 'Lu Zeng', 'Sree Hari Krishnan Parthasarathi'] | 2023-02-21 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 1.96619585e-01 5.49389362e-01 -1.08097434e-01 -9.04453456e-01
-1.76406109e+00 -9.51658964e-01 7.34257638e-01 3.31880212e-01
-1.31129399e-01 4.73462939e-01 7.03411877e-01 -6.41903877e-01
1.38905719e-01 -6.63044155e-01 -1.10040176e+00 2.27002561e-01
1.47624344e-01 1.21726012e+00 2.17757732e-01 -7.09332705... | [10.066301345825195, 7.867446422576904] |
a2ccc9b1-b27c-44c8-81d2-3f94bad9f163 | searching-for-the-holy-grail-of-sponsorship | 2305.09473 | null | https://arxiv.org/abs/2305.09473v1 | https://arxiv.org/pdf/2305.09473v1.pdf | Searching for the "Holy Grail" of sponsorship-linked marketing: A generalizable sponsorship ROI model | Marketers routinely allocate a significant portion of their budget to sponsorship. However, isolating the return on investment from such efforts has remained a challenge. Thus, a dataset of more than 5,800 sponsorships is analyzed using survival analysis approaches that utilizes the sponsor's renewal of the sponsorship... | ['Jonathan A. Jensen'] | 2023-04-11 | null | null | null | null | ['marketing', 'survival-analysis'] | ['miscellaneous', 'miscellaneous'] | [ 1.67158574e-01 4.53265399e-01 -7.40753293e-01 -1.50042385e-01
-5.29413521e-01 -3.70539218e-01 3.37753743e-01 5.37630260e-01
-3.66241097e-01 4.78640646e-01 4.16499853e-01 -6.63747787e-01
-4.24960017e-01 -7.85842180e-01 -4.77642953e-01 -4.39138651e-01
5.11588752e-02 4.73512448e-02 -3.92004520e-01 8.57948512... | [9.170112609863281, 5.871751308441162] |
e1915189-0b57-4e1b-8d07-d6018a2b6160 | sugar-efficient-subgraph-level-training-via | 2202.00075 | null | https://arxiv.org/abs/2202.00075v3 | https://arxiv.org/pdf/2202.00075v3.pdf | SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning | Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition. Nevertheless, resource-efficient GNN learning is a rarely explored topic despite its many benefits for edge computing and Internet of Things (Io... | ['Radu Marculescu', 'Mengtian Yang', 'Yuedong Yang', 'Zihui Xue'] | 2022-01-31 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-3.22382078e-02 4.82488945e-02 -6.35900199e-01 -1.29186705e-01
7.53953308e-02 -2.64993101e-01 2.35966712e-01 2.26529926e-01
-1.67010233e-01 6.77906752e-01 -1.77965626e-01 -9.16471899e-01
-3.25154632e-01 -1.33656049e+00 -5.34882903e-01 -6.55755937e-01
-4.85115588e-01 4.63046640e-01 3.41971725e-01 6.85182363... | [7.021803855895996, 5.758882999420166] |
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