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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c5d1461e-6438-4683-b7e0-b8f39ed1b1ea | sounding-video-generator-a-unified-framework | 2303.16541 | null | https://arxiv.org/abs/2303.16541v1 | https://arxiv.org/pdf/2303.16541v1.pdf | Sounding Video Generator: A Unified Framework for Text-guided Sounding Video Generation | As a combination of visual and audio signals, video is inherently multi-modal. However, existing video generation methods are primarily intended for the synthesis of visual frames, whereas audio signals in realistic videos are disregarded. In this work, we concentrate on a rarely investigated problem of text guided sou... | ['Jing Liu', 'Xinxin Zhu', 'Sihan Chen', 'Weining Wang', 'Jiawei Liu'] | 2023-03-29 | null | null | null | null | ['audio-generation', 'video-generation'] | ['audio', 'computer-vision'] | [ 3.02232981e-01 -1.52970433e-01 9.98295844e-02 5.14662564e-02
-1.26872599e+00 -3.65275383e-01 9.49460745e-01 -4.61924851e-01
1.13874249e-01 7.38963902e-01 5.00692487e-01 1.17704861e-01
4.66109365e-01 -5.53516030e-01 -9.97971058e-01 -8.29407454e-01
2.77476996e-01 -1.13687985e-01 4.83563207e-02 -8.99541974... | [15.332815170288086, 5.0997138023376465] |
6dadb733-6c99-4530-8ba0-dcd26aafb19a | differences-in-boundary-behavior-in-the-3d | 2306.03987 | null | https://arxiv.org/abs/2306.03987v1 | https://arxiv.org/pdf/2306.03987v1.pdf | Differences in boundary behavior in the 3D vertex and Voronoi models | An important open question in the modeling of biological tissues is how to identify the right scale for coarse-graining, or equivalently, the right number of degrees of freedom. For confluent biological tissues, both vertex and Voronoi models, which differ only in their representation of the degrees of freedom, have ef... | ['M. Lisa Manning', 'Tao Zhang', 'Elizabeth Lawson-Keister'] | 2023-06-06 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.65251771e-02 -4.02822904e-02 4.77303788e-02 4.52358574e-01
-5.76239079e-02 -6.73724532e-01 8.54597926e-01 4.52828377e-01
-2.70277262e-01 7.85307825e-01 1.90289766e-01 -2.24007994e-01
-2.18327075e-01 -6.71795368e-01 -2.88347900e-01 -1.21586537e+00
-2.03297630e-01 7.44706869e-01 6.49272323e-01 -3.42163771... | [13.608821868896484, -3.0536720752716064] |
7c32657a-125d-4d76-a0d5-57a2af4e91a0 | deep-contextualized-word-representations | 1802.05365 | null | http://arxiv.org/abs/1802.05365v2 | http://arxiv.org/pdf/1802.05365v2.pdf | Deep contextualized word representations | We introduce a new type of deep contextualized word representation that
models both (1) complex characteristics of word use (e.g., syntax and
semantics), and (2) how these uses vary across linguistic contexts (i.e., to
model polysemy). Our word vectors are learned functions of the internal states
of a deep bidirectiona... | ['Luke Zettlemoyer', 'Christopher Clark', 'Kenton Lee', 'Matthew E. Peters', 'Matt Gardner', 'Mohit Iyyer', 'Mark Neumann'] | 2018-02-15 | deep-contextualized-word-representations-1 | https://aclanthology.org/N18-1202 | https://aclanthology.org/N18-1202.pdf | naacl-2018-6 | ['conversational-response-selection', 'citation-intent-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.17319652e-01 9.15665403e-02 -3.39089900e-01 -6.19980395e-01
-5.58840632e-01 -8.96668613e-01 8.15990448e-01 3.93861085e-01
-6.17229640e-01 3.05395514e-01 7.72196054e-01 -9.52588201e-01
3.10313463e-01 -8.17545056e-01 -8.74322236e-01 -1.64495155e-01
2.53098428e-01 3.33248556e-01 -2.61250213e-02 -5.40704668... | [10.672410011291504, 8.75184154510498] |
275d3c90-6b42-486f-a483-aeaeab04f5c6 | category-level-6d-object-pose-estimation-with | 2212.04632 | null | https://arxiv.org/abs/2212.04632v2 | https://arxiv.org/pdf/2212.04632v2.pdf | Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation | In this paper, we propose a novel 3D graph convolution based pipeline for category-level 6D pose and size estimation from monocular RGB-D images. The proposed method leverages an efficient 3D data augmentation and a novel vector-based decoupled rotation representation. Specifically, we first design an orientation-aware... | ['Ales Leonardis', 'Jinming Duan', 'Linlin Shen', 'Hyung Jin Chang', 'Zhongqun Zhang', 'Xi Jia', 'Wei Chen'] | 2022-12-09 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [-2.87301242e-01 -1.25766367e-01 -3.37685317e-01 -3.52274507e-01
-1.58861712e-01 -5.21056592e-01 5.44289529e-01 -2.68828928e-01
-3.08119774e-01 -4.15766127e-02 2.90099919e-01 -1.75422862e-01
-2.15564612e-02 -6.67718649e-01 -9.35406268e-01 -8.34363341e-01
1.77305820e-03 9.43270698e-02 6.24146648e-02 -8.32431242... | [7.67245626449585, -2.8845996856689453] |
b31e54fc-c1cc-47d2-9d7b-8ecfe01a9bcb | using-self-training-to-improve-back | 2006.02876 | null | https://arxiv.org/abs/2006.02876v3 | https://arxiv.org/pdf/2006.02876v3.pdf | Enhanced back-translation for low resource neural machine translation using self-training | Improving neural machine translation (NMT) models using the back-translations of the monolingual target data (synthetic parallel data) is currently the state-of-the-art approach for training improved translation systems. The quality of the backward system - which is trained on the available parallel data and used for t... | ['Bashir Shehu Galadanci', 'Abubakar Isa', 'Idris Abdulmumin'] | 2020-06-04 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 4.00840759e-01 2.11892530e-01 -2.49387160e-01 -4.75644350e-01
-1.21661639e+00 -7.46001899e-01 9.38194096e-01 -1.81760192e-01
-7.35750735e-01 1.32313907e+00 2.38615155e-01 -8.04118514e-01
7.14513600e-01 -5.14982224e-01 -1.06492579e+00 -4.85225320e-01
6.20628476e-01 1.06281126e+00 -2.75737315e-01 -8.26855838... | [11.52930736541748, 10.41143798828125] |
b45a695b-e2f5-471e-89d4-9647cf1baeb4 | initial-results-for-pairwise-causal-discovery | 2212.01279 | null | https://arxiv.org/abs/2212.01279v1 | https://arxiv.org/pdf/2212.01279v1.pdf | Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow | Pairwise Causal Discovery is the task of determining causal, anticausal, confounded or independence relationships from pairs of variables. Over the last few years, this challenging task has promoted not only the discovery of novel machine learning models aimed at solving the task, but also discussions on how learning t... | ['Flavio Figueiredo', 'Felipe Giori'] | 2022-12-02 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 2.68331736e-01 -3.47239785e-02 -4.55350578e-01 -2.94458628e-01
-4.41544712e-01 -8.76089871e-01 1.20675373e+00 4.44604874e-01
-7.60011747e-03 9.93211150e-01 3.85577023e-01 -8.57088149e-01
-4.67387289e-01 -6.81035638e-01 -6.64899945e-01 -5.76299548e-01
-9.22913492e-01 -1.46882102e-01 3.68211791e-02 8.61124247... | [7.874087810516357, 5.3662543296813965] |
e5122aac-30f5-4f28-86bf-bb536797c010 | unsupervised-traffic-scene-generation-with | 2303.08473 | null | https://arxiv.org/abs/2303.08473v1 | https://arxiv.org/pdf/2303.08473v1.pdf | Unsupervised Traffic Scene Generation with Synthetic 3D Scene Graphs | Image synthesis driven by computer graphics achieved recently a remarkable realism, yet synthetic image data generated this way reveals a significant domain gap with respect to real-world data. This is especially true in autonomous driving scenarios, which represent a critical aspect for overcoming utilizing synthetic ... | ['Federico Tombari', 'Nassir Navab', 'Rachid Ellouze', 'Artem Savkin'] | 2023-03-15 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 9.28636789e-01 3.31275463e-01 2.33606353e-01 -4.46101099e-01
-3.46969187e-01 -3.27945799e-01 1.04584968e+00 -4.50637251e-01
-1.02086700e-01 8.08814704e-01 2.42851406e-01 -4.56746042e-01
1.04961051e-02 -1.30968940e+00 -1.04872191e+00 -4.07754540e-01
4.51343507e-01 4.01079208e-01 5.26132323e-02 -6.78926766... | [11.252035140991211, -0.3876481056213379] |
22177242-2027-47c4-9c03-c5e981f74111 | how-attentive-are-graph-attention-networks | 2105.14491 | null | https://arxiv.org/abs/2105.14491v3 | https://arxiv.org/pdf/2105.14491v3.pdf | How Attentive are Graph Attention Networks? | Graph Attention Networks (GATs) are one of the most popular GNN architectures and are considered as the state-of-the-art architecture for representation learning with graphs. In GAT, every node attends to its neighbors given its own representation as the query. However, in this paper we show that GAT computes a very li... | ['Eran Yahav', 'Uri Alon', 'Shaked Brody'] | 2021-05-30 | how-attentive-are-graph-attention-networks-1 | https://openreview.net/forum?id=F72ximsx7C1 | https://openreview.net/pdf?id=F72ximsx7C1 | iclr-2022-4 | ['graph-property-prediction'] | ['graphs'] | [-2.20774099e-01 3.38805497e-01 -2.72540420e-01 -2.40183622e-01
-2.99147516e-01 -5.34412682e-01 5.68885148e-01 2.63615698e-01
-3.42523664e-01 4.40528035e-01 2.10781261e-01 -5.60678840e-01
-3.99442911e-02 -1.13984334e+00 -8.89515817e-01 -5.43388188e-01
-2.57697225e-01 7.89499819e-01 3.36711645e-01 -3.49074394... | [6.980874538421631, 6.241896629333496] |
8d6f09d2-f01b-4940-b37f-b173440f4900 | woodbury-transformations-for-deep-generative | 2002.12229 | null | https://arxiv.org/abs/2002.12229v3 | https://arxiv.org/pdf/2002.12229v3.pdf | Woodbury Transformations for Deep Generative Flows | Normalizing flows are deep generative models that allow efficient likelihood calculation and sampling. The core requirement for this advantage is that they are constructed using functions that can be efficiently inverted and for which the determinant of the function's Jacobian can be efficiently computed. Researchers h... | ['You Lu', 'Bert Huang'] | 2020-02-27 | null | http://proceedings.neurips.cc/paper/2020/hash/3fb04953d95a94367bb133f862402bce-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/3fb04953d95a94367bb133f862402bce-Paper.pdf | neurips-2020-12 | ['normalising-flows'] | ['methodology'] | [-1.14815079e-01 -1.99792355e-01 -7.13808239e-02 -3.29631299e-01
-4.00019318e-01 -5.95075488e-01 9.23164964e-01 -5.30241072e-01
-4.87159610e-01 9.71219182e-01 4.93966758e-01 -4.28137779e-01
-8.83001387e-02 -1.03580928e+00 -5.24213612e-01 -6.91385329e-01
-2.01210529e-01 2.20416605e-01 3.04608494e-02 7.39026889... | [7.156661510467529, 3.805534839630127] |
b4ba5bda-97d9-4b64-8179-3fdfc976a95c | deep-clustering-survival-machines-with | 2301.11826 | null | https://arxiv.org/abs/2301.11826v3 | https://arxiv.org/pdf/2301.11826v3.pdf | Deep Clustering Survival Machines with Interpretable Expert Distributions | Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. In this study, we propose a hybrid survival analysis method, referred to as deep clustering survival machines, that combines the discriminati... | ['Yong Fan', 'Hao Zheng', 'Zhen Zhou', 'Zhicheng Jiao', 'Hongming Li', 'BoJian Hou'] | 2023-01-27 | null | null | null | null | ['survival-analysis', 'deep-clustering', 'deep-clustering'] | ['miscellaneous', 'miscellaneous', 'natural-language-processing'] | [-3.94753724e-01 -1.68315127e-01 -4.93671000e-01 -6.92994416e-01
-9.47659373e-01 -3.77785951e-01 5.94324231e-01 2.51956791e-01
-2.61864960e-02 7.82389462e-01 2.54171818e-01 -1.70560971e-01
-5.10455489e-01 -6.33767128e-01 -1.82504967e-01 -1.22941959e+00
-2.47680113e-01 1.15738511e+00 -7.69612193e-02 3.50114495... | [7.7277445793151855, 5.598430633544922] |
070af0c2-05b8-4ba3-9ba7-bd546404d254 | depth-aware-object-segmentation-and-grasp | 2111.11114 | null | https://arxiv.org/abs/2111.11114v1 | https://arxiv.org/pdf/2111.11114v1.pdf | Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks | In this paper, we present a novel deep neural network architecture for joint class-agnostic object segmentation and grasp detection for robotic picking tasks using a parallel-plate gripper. We introduce depth-aware Coordinate Convolution (CoordConv), a method to increase accuracy for point proposal based object instanc... | ['Friedrich Fraundorfer', 'Stephan Weiss', 'Rohit Dhakate', 'Christoph Böhm', 'Stefan Ainetter'] | 2021-11-22 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 1.15755990e-01 -6.89828098e-02 1.92259535e-01 -5.21591246e-01
-5.86448669e-01 -1.05252182e+00 1.46590516e-01 4.01397258e-01
-2.82410443e-01 -1.08466648e-01 -6.82965040e-01 1.90503731e-01
-3.34066421e-01 -7.04899788e-01 -1.05659330e+00 -5.31154394e-01
-2.29555607e-01 8.07194769e-01 3.03571105e-01 -7.72754848... | [5.7822957038879395, -0.86263507604599] |
b06db2b6-6d6c-4abd-8b6d-97712f63859b | factor-augmented-regularized-model-for-hazard | 2210.01067 | null | https://arxiv.org/abs/2210.01067v1 | https://arxiv.org/pdf/2210.01067v1.pdf | Factor-Augmented Regularized Model for Hazard Regression | A prevalent feature of high-dimensional data is the dependence among covariates, and model selection is known to be challenging when covariates are highly correlated. To perform model selection for the high-dimensional Cox proportional hazards model in presence of correlated covariates with factor structure, we propose... | ['Jianqing Fan', 'Pierre Bayle'] | 2022-10-03 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 1.77378982e-01 -4.07789141e-01 -6.79632246e-01 -5.67456365e-01
-1.28080654e+00 -9.85167921e-03 2.88712353e-01 4.60212864e-02
-3.12741488e-01 1.05444312e+00 5.24852335e-01 -3.38522047e-01
-5.68733692e-01 -6.44790947e-01 -3.44235897e-01 -8.91784966e-01
-6.65101290e-01 8.49944651e-01 -9.88276079e-02 1.51522651... | [7.748204231262207, 5.307884693145752] |
3267af87-2d5e-4054-833e-12c5fa7f7289 | atrial-fibrillation-detection-using-rr | 2302.07648 | null | https://arxiv.org/abs/2302.07648v1 | https://arxiv.org/pdf/2302.07648v1.pdf | Atrial Fibrillation Detection Using RR-Intervals for Application in Photoplethysmographs | Atrial Fibrillation is a common form of irregular heart rhythm that can be very dangerous. Our primary goal is to analyze Atrial Fibrillation data within ECGs to develop a model based only on RR-Intervals, or the length between heart-beats, to create a real time classification model for Atrial Fibrillation to be implem... | ['Yishi Wang', 'Georgia Smith'] | 2023-02-13 | null | null | null | null | ['atrial-fibrillation-detection'] | ['medical'] | [ 2.64736891e-01 -3.11187178e-01 5.10116667e-02 -1.36084527e-01
-3.53083044e-01 -7.64808536e-01 -1.69123471e-01 1.07535571e-01
-3.11990470e-01 8.98893237e-01 -3.35947484e-01 -9.23535168e-01
-1.54037386e-01 -5.40702939e-01 1.00253047e-02 -4.98937994e-01
-4.91290808e-01 4.50049847e-01 -2.58709341e-01 6.14467524... | [14.2445650100708, 3.2504122257232666] |
31c24fa5-6dd9-4543-8275-aff4a85f1d91 | robust-head-pose-estimation-based-on | 1603.09732 | null | http://arxiv.org/abs/1603.09732v3 | http://arxiv.org/pdf/1603.09732v3.pdf | Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions | Head-pose estimation has many applications, such as social event analysis,
human-robot and human-computer interaction, driving assistance, and so forth.
Head-pose estimation is challenging because it must cope with changing
illumination conditions, variabilities in face orientation and in appearance,
partial occlusions... | ['Silèye Ba', 'Antoine Deleforge', 'Vincent Drouard', 'Radu Horaud', 'Georgios Evangelidis'] | 2016-03-31 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-2.41549909e-01 2.07693025e-01 -1.22414790e-01 -8.65440190e-01
-8.60791802e-01 -1.53204978e-01 5.67949235e-01 -2.02360451e-01
-3.32119286e-01 6.89487219e-01 4.94626999e-01 4.17449713e-01
3.32228793e-03 -1.78936586e-01 -6.39618397e-01 -7.43002772e-01
-8.78182650e-02 6.73798382e-01 -2.02263311e-01 -4.90246974... | [13.55349063873291, 0.2861662209033966] |
3d9efea2-ad38-4c51-9deb-8481173ca215 | imbalanced-multi-label-classification-for | 2306.07046 | null | https://arxiv.org/abs/2306.07046v1 | https://arxiv.org/pdf/2306.07046v1.pdf | Imbalanced Multi-label Classification for Business-related Text with Moderately Large Label Spaces | In this study, we compared the performance of four different methods for multi label text classification using a specific imbalanced business dataset. The four methods we evaluated were fine tuned BERT, Binary Relevance, Classifier Chains, and Label Powerset. The results show that fine tuned BERT outperforms the other ... | ['Christophe Cruz', 'Muhammad Arslan'] | 2023-06-12 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-3.08930017e-02 -2.45289430e-01 -9.17102277e-01 -5.45312822e-01
-8.99930477e-01 -5.04665911e-01 5.55202305e-01 1.16580307e+00
-4.60561991e-01 7.49454558e-01 -4.57883701e-02 -4.06539202e-01
-5.56833684e-01 -4.22249794e-01 1.16872169e-01 -5.55602670e-01
3.69357139e-01 9.61375833e-01 1.01755876e-02 -3.22887659... | [9.350797653198242, 4.681680202484131] |
265fb321-7297-4749-a2dc-9b4acdcea86e | tempeval-3-evaluating-events-time-expressions | 1206.5333 | null | http://arxiv.org/abs/1206.5333v2 | http://arxiv.org/pdf/1206.5333v2.pdf | TempEval-3: Evaluating Events, Time Expressions, and Temporal Relations | We describe the TempEval-3 task which is currently in preparation for the
SemEval-2013 evaluation exercise. The aim of TempEval is to advance research on
temporal information processing. TempEval-3 follows on from previous TempEval
events, incorporating: a three-part task structure covering event, temporal
expression a... | ['James Allen', 'Naushad UzZaman', 'Marc Verhagen', 'Leon Derczynski', 'Hector Llorens', 'James Pustejovsky'] | 2012-06-22 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-5.92178181e-02 1.01751581e-01 -4.31853354e-01 -6.61115289e-01
-9.64126587e-01 -6.77724302e-01 1.24034023e+00 5.45483828e-01
-7.28594959e-01 9.48711097e-01 9.02926505e-01 -3.37492228e-02
-2.08596572e-01 -2.85556823e-01 -4.10358936e-01 1.98184457e-02
-9.03497219e-01 5.87886572e-01 6.35886431e-01 -2.14071825... | [9.098223686218262, 9.237205505371094] |
ad9c3382-565e-4ed5-ab47-ac2b04ce41ae | ismallnet-densely-nested-network-with-label | 2210.16561 | null | https://arxiv.org/abs/2210.16561v2 | https://arxiv.org/pdf/2210.16561v2.pdf | iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection | Small targets are often submerged in cluttered backgrounds of infrared images. Conventional detectors tend to generate false alarms, while CNN-based detectors lose small targets in deep layers. To this end, we propose iSmallNet, a multi-stream densely nested network with label decoupling for infrared small object detec... | ['Mingqiang Wei', 'Haoran Xie', 'Jie Qin', 'Peng Li', 'Yongzhen Wang', 'Zhiheng Hu'] | 2022-10-29 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 1.14168525e-01 -4.98715639e-02 1.49708584e-01 -2.90512294e-01
-4.50178832e-01 -6.58365965e-01 3.98545086e-01 -5.24572358e-02
-4.65983510e-01 3.23462069e-01 -1.58887506e-01 -1.72643468e-01
2.17136294e-01 -8.32562149e-01 -5.62299728e-01 -1.02796257e+00
1.51738212e-01 4.53911684e-02 9.48877573e-01 -1.67046949... | [8.830071449279785, -0.7442511916160583] |
6d63700c-5dde-4239-99c8-11e66f9c4ad1 | comparative-study-of-parameter-selection-for | 2302.00592 | null | https://arxiv.org/abs/2302.00592v1 | https://arxiv.org/pdf/2302.00592v1.pdf | Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation | Magnitude-based pruning is a technique used to optimise deep learning models for edge inference. We have achieved over 75% model size reduction with a higher accuracy than the original multi-output regression model for head-pose estimation. | ['Pratheepan Yogarajah', 'Pradeepa Samarasinghe', 'Shyam Reyal', 'Nuwan Kodagoda', 'Asiri Lindamulage'] | 2022-12-28 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-8.83234069e-02 6.49083912e-01 -8.84801894e-02 -8.32435906e-01
-8.27826321e-01 2.51126915e-01 9.26671084e-03 1.52309045e-01
-7.05059350e-01 7.92954445e-01 1.81108937e-01 -3.20964664e-01
1.32727861e-01 -4.74019885e-01 -7.55852044e-01 -3.57991070e-01
-2.87272364e-01 6.12219393e-01 -6.66313693e-02 -6.41685538... | [13.650168418884277, 0.3008947968482971] |
6eb8942c-0b73-4dcb-8e2c-9866ece0037a | a-sparse-sampling-sensor-front-end-ic-for-low | 2208.06698 | null | https://arxiv.org/abs/2208.06698v1 | https://arxiv.org/pdf/2208.06698v1.pdf | A Sparse Sampling Sensor Front-end IC for Low Power Continuous SpO$_2$ \& HR Monitoring | Photoplethysmography (PPG) is an attractive method to acquire vital signs such as heart rate and blood oxygenation and is frequently used in clinical and at-home settings. Continuous operation of health monitoring devices demands a low power sensor that does not restrict the device battery life. Silicon photodiodes (PD... | ['Rikky Muller', 'Ana Claudia Arias', 'Jonathan Ting', 'Cem Yalcin', 'Jasmine Jan', 'Sina Faraji Alamouti'] | 2022-08-13 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 4.77078974e-01 4.12695706e-01 8.29466339e-03 -1.96722016e-01
-6.86776266e-02 -3.46206725e-01 -4.62023526e-01 2.51839459e-01
-3.12963694e-01 8.35329831e-01 -1.44058987e-01 1.24451868e-01
2.53046960e-01 -5.91638267e-01 -9.25350264e-02 -7.63426602e-01
2.65651107e-01 -1.49345741e-01 4.20020968e-02 3.89940619... | [13.9420166015625, 3.0739176273345947] |
ba96f94b-d25e-4f6a-b176-2f6f3d8f791f | expats-a-toolkit-for-explainable-automated | 2104.03364 | null | https://arxiv.org/abs/2104.03364v1 | https://arxiv.org/pdf/2104.03364v1.pdf | EXPATS: A Toolkit for Explainable Automated Text Scoring | Automated text scoring (ATS) tasks, such as automated essay scoring and readability assessment, are important educational applications of natural language processing. Due to their interpretability of models and predictions, traditional machine learning (ML) algorithms based on handcrafted features are still in wide use... | ['Masato Hagiwara', 'Hitoshi Manabe'] | 2021-04-07 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-4.02174622e-01 5.02468925e-03 -9.81379375e-02 -6.46054506e-01
-6.33013546e-01 -9.12882149e-01 3.08848768e-01 4.46201414e-01
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-8.44896510e-02 -6.07245505e-01 -2.82151908e-01 5.61361872e-02
3.35919142e-01 4.78031844e-01 9.71474946e-02 -2.81878442... | [11.144932746887207, 9.139872550964355] |
9df156fe-90f9-417c-bace-e4b267a9d962 | stl-based-synthesis-of-feedback-controllers | 2212.01022 | null | https://arxiv.org/abs/2212.01022v1 | https://arxiv.org/pdf/2212.01022v1.pdf | STL-Based Synthesis of Feedback Controllers Using Reinforcement Learning | Deep Reinforcement Learning (DRL) has the potential to be used for synthesizing feedback controllers (agents) for various complex systems with unknown dynamics. These systems are expected to satisfy diverse safety and liveness properties best captured using temporal logic. In RL, the reward function plays a crucial rol... | ['Indranil Saha', 'Nikhil Kumar Singh'] | 2022-12-02 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 8.40084478e-02 2.18479797e-01 -4.64157552e-01 -4.65434082e-02
-4.30976778e-01 -6.37245476e-01 8.33642125e-01 1.11760929e-01
-1.48075953e-01 9.93355751e-01 -1.10899836e-01 -5.69676697e-01
-3.55738819e-01 -9.32983816e-01 -8.39199364e-01 -5.97345650e-01
-4.50862437e-01 2.36795664e-01 5.73642790e-01 -6.29309297... | [4.502504348754883, 2.06162691116333] |
c595ea1d-4b4a-4c7f-b2e8-6d828b4f9016 | contextual-gradient-scaling-for-few-shot | 2110.10353 | null | https://arxiv.org/abs/2110.10353v1 | https://arxiv.org/pdf/2110.10353v1.pdf | Contextual Gradient Scaling for Few-Shot Learning | Model-agnostic meta-learning (MAML) is a well-known optimization-based meta-learning algorithm that works well in various computer vision tasks, e.g., few-shot classification. MAML is to learn an initialization so that a model can adapt to a new task in a few steps. However, since the gradient norm of a classifier (hea... | ['Byung Cheol Song', 'SeungHyun Lee', 'Sanghyuk Lee'] | 2021-10-20 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 1.03344560e-01 -2.18558505e-01 -2.72976458e-01 -5.22565544e-01
-5.69014966e-01 -2.56000049e-02 3.97865921e-01 6.95095286e-02
-5.73279619e-01 4.32902902e-01 2.23133955e-02 3.03357989e-01
-1.37748607e-02 -6.06720090e-01 -5.86445034e-01 -9.42681551e-01
1.83505177e-01 1.03718758e-01 5.46573997e-01 -3.64069194... | [9.981729507446289, 3.1196887493133545] |
8888b53d-42b9-4779-9172-bdd28d0838a2 | challenges-in-generalization-in-open-domain | 2109.01156 | null | https://arxiv.org/abs/2109.01156v3 | https://arxiv.org/pdf/2109.01156v3.pdf | Challenges in Generalization in Open Domain Question Answering | Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions. However, it is unclear which aspects of novel questions make them challenging. Drawing upon studies on systematic generaliza... | ['Pontus Stenetorp', 'Sebastian Riedel', 'Patrick Lewis', 'Linqing Liu'] | 2021-09-02 | null | https://aclanthology.org/2022.findings-naacl.155 | https://aclanthology.org/2022.findings-naacl.155.pdf | findings-naacl-2022-7 | ['triviaqa', 'systematic-generalization'] | ['miscellaneous', 'reasoning'] | [-9.07583311e-02 2.89278537e-01 2.18398079e-01 -4.28017288e-01
-1.39125776e+00 -1.15873134e+00 5.56115746e-01 3.74356598e-01
-4.79277045e-01 8.98859441e-01 3.71271402e-01 -5.37592292e-01
-5.56023121e-01 -6.22079194e-01 -6.77724957e-01 -4.86693494e-02
3.33357841e-01 8.09136569e-01 5.67088604e-01 -6.37138546... | [11.271682739257812, 7.982008457183838] |
8067ba3e-4590-47f5-a523-b77150c75558 | benefits-of-semantics-on-web-service | 1305.0191 | null | http://arxiv.org/abs/1305.0191v1 | http://arxiv.org/pdf/1305.0191v1.pdf | Benefits of Semantics on Web Service Composition from a Complex Network Perspective | The number of publicly available Web services (WS) is continuously growing,
and in parallel, we are witnessing a rapid development in semantic-related web
technologies. The intersection of the semantic web and WS allows the
development of semantic WS. In this work, we adopt a complex network
perspective to perform a co... | ['Jean-François Santucci', 'Vincent Labatut', 'Chantal Cherifi'] | 2013-05-01 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-9.36415792e-02 2.75313675e-01 7.58174211e-02 -5.50482392e-01
2.78380662e-01 -8.85843694e-01 9.22709465e-01 5.59744298e-01
-1.06102504e-01 6.14498973e-01 3.72977108e-01 -7.93747529e-02
-7.56295741e-01 -1.29677701e+00 -3.00109182e-02 -2.16879413e-01
-6.08093739e-01 4.82059568e-01 1.15528286e+00 -7.05810249... | [8.69305419921875, 7.057613849639893] |
f1f8bb44-80c4-4a1a-8ffa-bf1212c76113 | large-ai-model-based-semantic-communications | 2307.03492 | null | https://arxiv.org/abs/2307.03492v1 | https://arxiv.org/pdf/2307.03492v1.pdf | Large AI Model-Based Semantic Communications | Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed-reality, and the Internet of everything. However, in current SC systems, the construction of the knowledge base (KB) faces several issues, including limited knowledge representation,... | ['Xiaohu You', 'Cunhua Pan', 'Kun Yang', 'Kezhi Wang', 'Li Dong', 'Yubo Peng', 'Feibo Jiang'] | 2023-07-07 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [ 2.15195268e-01 1.83771282e-01 -3.55766356e-01 -3.56276393e-01
-3.46670717e-01 -1.89032659e-01 2.12337330e-01 9.58756506e-02
-3.19976538e-01 8.03929269e-01 2.60996342e-01 1.08116247e-01
-5.37124574e-01 -1.13892066e+00 -5.59186935e-01 -4.72679555e-01
2.49241978e-01 2.79108822e-01 7.89583683e-01 -3.24909508... | [8.76678466796875, 7.714876174926758] |
0f7716d9-35b0-437a-9b1e-6b4db6370f79 | visualizing-convolutional-neural-networks-to | 1809.03851 | null | http://arxiv.org/abs/1809.03851v1 | http://arxiv.org/pdf/1809.03851v1.pdf | Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification | Because of their state-of-the-art performance in computer vision, CNNs are
becoming increasingly popular in a variety of fields, including medicine.
However, as neural networks are black box function approximators, it is
difficult, if not impossible, for a medical expert to reason about their
output. This could potenti... | ['Bert Vankeirsbilck', 'Pieter Van Molle', 'Pieter Simoens', 'Bart Dhoedt', 'Tim Verbelen', 'Miguel De Strooper'] | 2018-09-11 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.61320297e-02 6.10527456e-01 -8.24442431e-02 -3.38400036e-01
1.30988762e-01 -5.01299500e-01 1.12720400e-01 5.75373113e-01
-2.40896761e-01 6.77885652e-01 -1.03004932e-01 -7.63562560e-01
7.41085187e-02 -1.01122105e+00 -5.54813504e-01 -6.18452132e-01
3.22646022e-01 2.03759015e-01 -5.03862873e-02 -6.29819632... | [8.850595474243164, 5.543475151062012] |
69380c33-ef7c-40a7-beb7-54d301d14cc0 | beyond-pick-and-place-tackling-robotic | 2110.06192 | null | https://arxiv.org/abs/2110.06192v2 | https://arxiv.org/pdf/2110.06192v2.pdf | Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes | We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple "pick-and-place" solution. Our method is a reinforcement learning (RL) approach combined with vision-based interactive pol... | ['Francesco Nori', 'Raia Hadsell', 'Martin Riedmiller', 'Federico Casarini', 'Stefano Saliceti', 'Antoine Laurens', 'Michael Neunert', 'Rae Jeong', 'Akhil Raju', 'Jose Enrique Chen', 'Claudio Fantacci', 'David Khosid', 'Nimrod Gileadi', 'Abbas Abdolmaleki', 'Arunkumar Byravan', 'Jost Tobias Springenberg', 'Konstantinos... | 2021-10-12 | null | null | null | null | ['skill-generalization', 'skill-mastery'] | ['robots', 'robots'] | [-9.15607885e-02 3.42978351e-02 1.01191200e-01 -1.32078752e-01
-7.05142677e-01 -9.89075661e-01 5.85522592e-01 -2.10641220e-01
-4.74533558e-01 1.05523837e+00 -9.14339796e-02 -4.48444545e-01
-2.68163949e-01 -4.80430514e-01 -1.26326001e+00 -6.75149262e-01
-2.37961441e-01 1.03384435e+00 4.24824029e-01 -5.57076037... | [4.636603355407715, 0.7419630885124207] |
5a4294ee-f68f-45ec-ae1a-047db0570f88 | what-can-you-do-with-a-rock-affordance | 1703.03429 | null | http://arxiv.org/abs/1703.03429v1 | http://arxiv.org/pdf/1703.03429v1.pdf | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embed... | ['Daniel Ricks', 'Nancy Fulda', 'David Wingate', 'Ben Murdoch'] | 2017-03-09 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [ 1.55421078e-01 7.93409199e-02 -3.69568259e-01 -8.20024684e-02
-2.71934569e-01 -7.07608879e-01 7.24265993e-01 3.58449876e-01
-1.22702539e+00 7.69011438e-01 3.52477461e-01 -1.89669147e-01
-1.74249828e-01 -8.50868106e-01 -3.18998992e-01 -5.90445280e-01
-2.70107448e-01 3.65785897e-01 2.99971104e-01 -4.17012513... | [4.109218120574951, 1.344769835472107] |
512c9520-c873-442b-bd22-1cb0342e8f5a | traffic-sign-detection-and-classification-in | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf | Traffic-Sign Detection and Classification in the Wild | Although promising results have been achieved in the areas of traffic-sign detection and classification, few works have provided simultaneous solutions to these two tasks for realistic real world images. We make two contributions to this problem. Firstly, we have created a large traffic-sign benchmark from 100000 Tence... | ['Shimin Hu', 'Baoli Li', 'Xiaolei Huang', 'SongHai Zhang', 'Dun Liang', 'Zhe Zhu'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['traffic-sign-detection'] | ['computer-vision'] | [ 3.91454905e-01 -5.44534326e-01 -1.54248178e-01 -4.11286980e-01
-4.30035681e-01 -4.81759608e-01 5.32398045e-01 -8.30865562e-01
-3.37330282e-01 4.71221894e-01 -3.97211581e-01 -5.23561537e-01
1.02474429e-01 -6.42757058e-01 -6.45036995e-01 -6.14905655e-01
-1.58591330e-01 2.47688353e-01 7.49370337e-01 -2.11778283... | [7.967419624328613, -0.833193838596344] |
c300f699-ebf2-4a1b-bd30-16813c26b139 | cv-hazop-introducing-test-data-validation-for | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zendel_CV-HAZOP_Introducing_Test_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zendel_CV-HAZOP_Introducing_Test_ICCV_2015_paper.pdf | CV-HAZOP: Introducing Test Data Validation for Computer Vision | Test data plays an important role in computer vision (CV) but is plagued by two questions: Which situations should be covered by the test data and have we tested enough to reach a conclusion? In this paper we propose a new solution answering these questions using a standard procedure devised by the safety community to ... | ['Markus Murschitz', 'Wolfgang Herzner', 'Oliver Zendel', 'Martin Humenberger'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['stereo-matching'] | ['computer-vision'] | [ 9.64338630e-02 -6.61117882e-02 4.48333174e-01 -2.01046944e-01
-7.05131590e-01 -6.79986954e-01 7.90926993e-01 5.05918205e-01
-2.77247071e-01 4.83185649e-01 -1.07708964e-02 -4.05449390e-01
-7.49993622e-01 -6.67679310e-01 -6.51737273e-01 -5.86150944e-01
1.14481837e-01 5.12667716e-01 7.99393594e-01 -3.35644752... | [6.939948558807373, 0.9789062738418579] |
fe7b527a-7e94-4c73-adf9-b3d8c443a4b9 | unfused-unsupervised-finetuning-using-self | 2303.05668 | null | https://arxiv.org/abs/2303.05668v2 | https://arxiv.org/pdf/2303.05668v2.pdf | UNFUSED: UNsupervised Finetuning Using SElf supervised Distillation | In this paper, we introduce UnFuSeD, a novel approach to leverage self-supervised learning and reduce the need for large amounts of labeled data for audio classification. Unlike prior works, which directly fine-tune a self-supervised pre-trained encoder on a target dataset, we use the encoder to generate pseudo-labels ... | ['Dinesh Manocha', 'S. Umesh', 'Sreyan Ghosh', 'Ashish Seth'] | 2023-03-10 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 5.13723195e-01 9.87897217e-02 -1.10610567e-01 -6.37860775e-01
-1.36389291e+00 -7.79416263e-01 3.31599444e-01 -1.15873285e-01
-4.73373264e-01 7.26828516e-01 2.94480205e-01 -9.66047123e-02
7.70935193e-02 -7.45454550e-01 -8.87225032e-01 -4.78595525e-01
-7.72184879e-02 6.07892632e-01 6.22133352e-02 1.21899895... | [15.32943058013916, 5.205358505249023] |
fa8f3c9d-07bf-4bb1-a4c8-49b52f7fb1f4 | learning-and-using-the-arrow-of-time | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Wei_Learning_and_Using_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wei_Learning_and_Using_CVPR_2018_paper.pdf | Learning and Using the Arrow of Time | We seek to understand the arrow of time in videos -- what makes videos look like they are playing forwards or backwards? Can we visualize the cues? Can the arrow of time be a supervisory signal useful for activity analysis? To this end, we build three large-scale video datasets and apply a learning-based approach to th... | ['Andrew Zisserman', 'Donglai Wei', 'William T. Freeman', 'Joseph J. Lim'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['video-forensics', 'self-supervised-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.01826900e-01 -2.34292358e-01 -4.63328242e-01 -4.59881246e-01
-1.48344576e-01 -6.75991774e-01 5.39020479e-01 2.21162681e-02
-3.00254971e-01 2.65752971e-01 4.56875324e-01 -1.39374152e-01
-2.17366870e-02 -3.61995548e-01 -9.15051997e-01 -6.57046676e-01
-7.44053245e-01 -1.83704108e-01 5.98641396e-01 2.88204104... | [8.607853889465332, 0.7229812741279602] |
643ad215-fcdf-4d82-bf29-dbcc22996043 | bird-s-eye-view-panoptic-segmentation-using | 2108.03227 | null | https://arxiv.org/abs/2108.03227v3 | https://arxiv.org/pdf/2108.03227v3.pdf | Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images | Bird's-Eye-View (BEV) maps have emerged as one of the most powerful representations for scene understanding due to their ability to provide rich spatial context while being easy to interpret and process. Such maps have found use in many real-world tasks that extensively rely on accurate scene segmentation as well as ob... | ['Abhinav Valada', 'Nikhil Gosala'] | 2021-08-06 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 4.03580427e-01 -3.75623643e-01 5.25978133e-02 -6.45238519e-01
-2.27961987e-01 -1.01412487e+00 8.02167177e-01 1.05410494e-01
-1.01778388e-01 2.63956547e-01 -1.15990914e-01 -3.68452698e-01
-2.85181403e-01 -1.00721884e+00 -7.58083105e-01 -6.94329321e-01
1.22943372e-01 4.39394325e-01 6.52066767e-01 -2.62749732... | [9.371573448181152, -0.5817340016365051] |
f0b60d52-c77c-4ba9-a78e-f41f07f645c3 | incorporating-global-visual-features-into-1 | null | null | https://aclanthology.org/D17-1105 | https://aclanthology.org/D17-1105.pdf | Incorporating Global Visual Features into Attention-based Neural Machine Translation. | We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. Global image features are extracted using a pre-trained convolutional neural network and are incorporated (i) as words in the source sentence, (ii... | ['Qun Liu', 'Iacer Calixto'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['video-description'] | ['computer-vision'] | [ 3.44008178e-01 2.48078778e-01 -1.14654727e-01 -1.63634345e-01
-1.17485368e+00 -4.22858655e-01 1.19092178e+00 -8.00277665e-02
-9.03197587e-01 8.80444825e-01 4.38604206e-01 -4.72656995e-01
5.42895734e-01 -5.06897748e-01 -1.32803786e+00 -4.96247292e-01
4.03743416e-01 8.90963674e-01 1.37957126e-01 -2.36589059... | [11.43750286102295, 1.507404088973999] |
315e1eec-6843-4516-880b-079a35b54d60 | provable-robustness-for-streaming-models-with | 2303.16308 | null | https://arxiv.org/abs/2303.16308v1 | https://arxiv.org/pdf/2303.16308v1.pdf | Provable Robustness for Streaming Models with a Sliding Window | The literature on provable robustness in machine learning has primarily focused on static prediction problems, such as image classification, in which input samples are assumed to be independent and model performance is measured as an expectation over the input distribution. Robustness certificates are derived for indiv... | ['Soheil Feizi', 'Vinu Sankar Sadasivan', 'Aounon Kumar'] | 2023-03-28 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 5.35010457e-01 5.94998188e-02 -3.13973486e-01 -1.55850887e-01
-6.77043200e-01 -7.74452090e-01 4.10266876e-01 5.55479109e-01
-5.24750590e-01 4.17237610e-01 -1.24085061e-01 -4.86100584e-01
-3.19074243e-02 -8.38822603e-01 -1.39364851e+00 -7.05074310e-01
-6.69466734e-01 1.12853847e-01 2.20680371e-01 1.06725261... | [5.633861541748047, 7.690343856811523] |
c8c92913-a592-4458-ad42-ecd76fe6af2d | gain-missing-data-imputation-using-generative | 1806.02920 | null | http://arxiv.org/abs/1806.02920v1 | http://arxiv.org/pdf/1806.02920v1.pdf | GAIN: Missing Data Imputation using Generative Adversarial Nets | We propose a novel method for imputing missing data by adapting the
well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call
our method Generative Adversarial Imputation Nets (GAIN). The generator (G)
observes some components of a real data vector, imputes the missing components
conditioned on what ... | ['James Jordon', 'Mihaela van der Schaar', 'Jinsung Yoon'] | 2018-06-07 | gain-missing-data-imputation-using-generative-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2025 | http://proceedings.mlr.press/v80/yoon18a/yoon18a.pdf | icml-2018-7 | ['multivariate-time-series-imputation'] | ['time-series'] | [ 4.02032584e-01 4.09175128e-01 -1.74020201e-01 -3.49279284e-01
-1.01803327e+00 -7.47728050e-01 4.91317421e-01 -2.45105550e-01
-6.37673587e-02 1.20888996e+00 3.42135996e-01 -5.23286834e-02
2.25698724e-01 -1.09064341e+00 -1.31252611e+00 -1.01283669e+00
2.00850680e-01 8.19661140e-01 -6.26487851e-01 1.07187785... | [11.662676811218262, -0.2409038096666336] |
dd2616f3-eb48-41e5-9b55-350fb67df29a | support-set-based-cross-supervision-for-video | 2108.10576 | null | https://arxiv.org/abs/2108.10576v1 | https://arxiv.org/pdf/2108.10576v1.pdf | Support-Set Based Cross-Supervision for Video Grounding | Current approaches for video grounding propose kinds of complex architectures to capture the video-text relations, and have achieved impressive improvements. However, it is hard to learn the complicated multi-modal relations by only architecture designing in fact. In this paper, we introduce a novel Support-set Based C... | ['Xinbo Gao', 'Mingqian Tang', 'Ziyuan Huang', 'Xiaomeng Li', 'De Cheng', 'Shiwei Zhang', 'Nannan Wang', 'Xinpeng Ding'] | 2021-08-24 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ding_Support-Set_Based_Cross-Supervision_for_Video_Grounding_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ding_Support-Set_Based_Cross-Supervision_for_Video_Grounding_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-grounding'] | ['computer-vision'] | [ 0.1442402 0.12616742 -0.37134168 -0.32012948 -0.8201831 -0.15437023
0.64646447 -0.12743014 -0.26583916 0.6643051 0.27537382 -0.02143876
0.03205769 -0.72948354 -1.1705977 -0.6453041 0.03349091 0.4556702
0.46471465 -0.3446368 -0.41308263 -0.06303996 -1.5278523 0.7709614
0.87564194 1.0326614 0.26... | [10.27218246459961, 1.2040289640426636] |
90b9766a-7701-4249-8db5-ea3e3212bae7 | uncertainty-detection-in-eeg-neural-decoding | 2201.00627 | null | https://arxiv.org/abs/2201.00627v2 | https://arxiv.org/pdf/2201.00627v2.pdf | Uncertainty Detection and Reduction in Neural Decoding of EEG Signals | EEG decoding systems based on deep neural networks have been widely used in decision making of brain computer interfaces (BCI). Their predictions, however, can be unreliable given the significant variance and noise in EEG signals. Previous works on EEG analysis mainly focus on the exploration of noise pattern in the so... | ['Hui Yang', 'Sargur N. Srihari', 'Sheng Liu', 'Zhenyi Wang', 'Tiehang Duan'] | 2021-12-28 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 2.93649852e-01 6.57046288e-02 5.86089790e-01 -6.45875633e-01
-7.59836972e-01 -1.29682824e-01 3.86548400e-01 -1.25861466e-02
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-5.76943338e-01 -3.81483734e-01 -9.86307263e-01 -7.23481119e-01
-1.53130710e-01 4.28829521e-01 2.39491239e-02 1.37990475... | [13.119057655334473, 3.421414613723755] |
6863d4ed-003a-4eb1-8032-bf77e7591996 | dgraph-a-large-scale-financial-dataset-for | 2207.03579 | null | https://arxiv.org/abs/2207.03579v4 | https://arxiv.org/pdf/2207.03579v4.pdf | DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection | Graph Anomaly Detection (GAD) has recently become a hot research spot due to its practicability and theoretical value. Since GAD emphasizes the application and the rarity of anomalous samples, enriching the varieties of its datasets is fundamental work. Thus, this paper present DGraph, a real-world dynamic graph in the... | ['Michalis Vazirgiannis', 'Lei Chen', 'Jiarong Xu', 'Zhisheng Zhang', 'Chunping Wang', 'Yang Wang', 'Yang Yang', 'Xuanwen Huang'] | 2022-06-30 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [-3.12410980e-01 1.26856729e-01 -3.29953939e-01 -1.19452439e-02
1.99610054e-01 -5.94370484e-01 4.64463711e-01 1.95605353e-01
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-2.25859210e-01 -1.06279159e+00 -1.34176120e-01 -6.02974176e-01
-7.90330291e-01 4.66741413e-01 5.16980469e-01 -3.45694751... | [6.715132236480713, 5.818628311157227] |
1d74e5fa-090f-4e23-8c30-4a71b85bce7b | improvements-in-remote-cardiopulmonary | null | null | http://affect.media.mit.edu/pdfs/14.McDuff_etal_Improvements.pdf | http://affect.media.mit.edu/pdfs/14.McDuff_etal_Improvements.pdf | Improvements in Remote Cardiopulmonary Measurement Using a Five Band Digital Camera | Remote measurement of the blood volume pulse via photoplethysmography (PPG) using digital cameras and ambient light has great potential for healthcare and affective computing. However, traditional RGB cameras have limited frequency resolution. We present results of PPG measurements from a novel five band camera and sho... | ['R', 'and Picard', 'S.', 'Gontarek', 'D.', 'McDuff'] | 2014-05-14 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-variability'] | ['medical', 'medical'] | [ 1.17930360e-01 -2.43754506e-01 1.68196782e-01 -3.69830847e-01
-3.30636144e-01 -5.22276700e-01 -7.69355372e-02 -8.35174546e-02
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2.52073288e-01 -4.29982603e-01 -3.14337760e-01 1.28714964... | [13.888586044311523, 2.848926544189453] |
73b5d808-db6e-48ac-b52b-e210bc1a3df4 | toward-connecting-speech-acts-and-search | 2305.04858 | null | https://arxiv.org/abs/2305.04858v1 | https://arxiv.org/pdf/2305.04858v1.pdf | Toward Connecting Speech Acts and Search Actions in Conversational Search Tasks | Conversational search systems can improve user experience in digital libraries by facilitating a natural and intuitive way to interact with library content. However, most conversational search systems are limited to performing simple tasks and controlling smart devices. Therefore, there is a need for systems that can a... | ['Chirag Shah', 'Satanu Ghosh', 'Souvick Ghosh'] | 2023-05-08 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.16819531e-01 3.75561059e-01 -1.69340581e-01 -5.16737401e-01
-5.55808902e-01 -5.04991353e-01 9.13231611e-01 9.06530172e-02
-3.07293445e-01 3.11048478e-01 8.80475342e-01 -5.81032932e-01
-7.64718428e-02 -4.41308856e-01 -6.84174225e-02 -1.15932167e-01
4.24318552e-01 5.13477921e-01 -3.28818299e-02 -5.40328562... | [12.344806671142578, 7.809362888336182] |
bf6a6dfc-c114-4068-852a-e7a0465dda4e | stg-mtl-scalable-task-grouping-for-multi-task | 2307.03374 | null | https://arxiv.org/abs/2307.03374v1 | https://arxiv.org/pdf/2307.03374v1.pdf | STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map | Multi-Task Learning (MTL) is a powerful technique that has gained popularity due to its performance improvement over traditional Single-Task Learning (STL). However, MTL is often challenging because there is an exponential number of possible task groupings, which can make it difficult to choose the best one, and some g... | ['Mohamed ElHelw', 'Mustafa Elattar', 'Abubakar Abid', 'Ammar Sherif'] | 2023-07-07 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 3.45437258e-01 -5.77841401e-01 -2.29378328e-01 -2.95731843e-01
-9.70840812e-01 -4.73891824e-01 4.43140417e-01 2.22230598e-01
-4.15241331e-01 7.24097788e-01 -4.18084040e-02 -1.75173268e-01
-4.72459823e-01 -8.57730210e-02 -3.88984412e-01 -7.71377981e-01
-2.44148016e-01 4.75535899e-01 4.27428037e-01 -4.56325747... | [9.286399841308594, 3.9107987880706787] |
17108744-347e-44e4-8c25-09d0a8bddf8b | translating-embeddings-in-document-for | null | null | https://openreview.net/forum?id=BHUCb6_xBde | https://openreview.net/pdf?id=BHUCb6_xBde | Translating Embeddings in Document for Modeling Multi-relational Graphs | Document-level relation extraction (RE) aims at extracting heterogeneous relational graphs upon entities in document, which has been handled with graph neural networks (GNNs) and pre-trained language models (PLMs) effectively.
However, a crucial problem is that most GNNs adopt a task-independent pseudo graph convoluat... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.10574672e-02 5.76101959e-01 -2.16577142e-01 -1.66749880e-01
-2.29301006e-01 -3.29095900e-01 7.35455453e-01 2.42539898e-01
-5.76480031e-01 6.04367912e-01 2.45326787e-01 -4.75824237e-01
-1.04048751e-01 -1.31180668e+00 -8.71808767e-01 -2.87106067e-01
-1.99238062e-01 4.38956976e-01 3.80240768e-01 -2.46391207... | [9.045134544372559, 8.174911499023438] |
46a33434-25ab-4db2-a9db-b4ed2fa4bf12 | dynamic-full-field-optical-coherence | 2301.12888 | null | https://arxiv.org/abs/2301.12888v1 | https://arxiv.org/pdf/2301.12888v1.pdf | Dynamic Full-Field Optical Coherence Tomography module adapted to commercial microscopes for longitudinal in vitro cell culture study | Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as a label-free imaging tool, capable of resolving cell types and organelles within 3D live samples, whilst monitoring their activity at tens of milliseconds resolution. Here, a D-FFOCT module design is presented which can be coupled to a co... | ['Kate Grieve', 'Olivier Thouvenin', 'Sacha Reichman', 'Olivier Goureau', 'Serge Picaud', 'Valerie Forster', 'Amélie Slembrouck-Brec', 'Marilou Clémençon', 'Jeremy Brogard', 'Salvatore Azzollini', 'Tual Monfort'] | 2023-01-30 | null | null | null | null | ['culture'] | ['speech'] | [ 3.16921204e-01 -2.14211032e-01 3.92727852e-01 1.89956933e-01
-6.29985690e-01 -9.10642982e-01 5.16649485e-02 4.27513480e-01
-7.43485987e-01 7.90762484e-01 -9.07520205e-02 -1.26775563e-01
2.19811931e-01 -8.33813772e-02 -2.51342446e-01 -9.22504783e-01
-1.22593269e-01 3.44853640e-01 4.63276267e-01 5.80646276... | [13.734869956970215, -3.074584484100342] |
55e9ab69-bf5e-4bb5-9db6-27f1070ef90d | on-hallucination-and-predictive-uncertainty | 2103.15025 | null | https://arxiv.org/abs/2103.15025v1 | https://arxiv.org/pdf/2103.15025v1.pdf | On Hallucination and Predictive Uncertainty in Conditional Language Generation | Despite improvements in performances on different natural language generation tasks, deep neural models are prone to hallucinating facts that are incorrect or nonexistent. Different hypotheses are proposed and examined separately for different tasks, but no systematic explanations are available across these tasks. In t... | ['William Yang Wang', 'Yijun Xiao'] | 2021-03-28 | null | https://aclanthology.org/2021.eacl-main.236 | https://aclanthology.org/2021.eacl-main.236.pdf | eacl-2021-2 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 1.02332957e-01 8.99967968e-01 1.54941007e-02 -4.59481746e-01
-7.05189049e-01 -3.57469469e-01 1.21384406e+00 -4.33872417e-02
-1.33322328e-01 1.26378751e+00 7.85534859e-01 -1.75115928e-01
-1.11511491e-01 -8.49831998e-01 -6.25722706e-01 -3.95315975e-01
3.35253358e-01 5.85527897e-01 -7.69053921e-02 -9.85279586... | [11.54520320892334, 8.895874977111816] |
e48db7cc-94ca-4b35-b921-4a43a58d79f0 | motion-based-camera-localization-system-in | 2012.01690 | null | https://arxiv.org/abs/2012.01690v3 | https://arxiv.org/pdf/2012.01690v3.pdf | Motion-based Camera Localization System in Colonoscopy Videos | Optical colonoscopy is an essential diagnostic and prognostic tool for many gastrointestinal diseases, including cancer screening and staging, intestinal bleeding, diarrhea, abdominal symptom evaluation, and inflammatory bowel disease assessment. Automated assessment of colonoscopy is of interest considering the subjec... | ['Zijun Gao', 'Kayvan Najarian', 'Jonathan Gryak', 'Ryan W. Stidham', 'Heming Yao'] | 2020-12-03 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [ 5.89958914e-02 -1.00042380e-01 -2.60524541e-01 -7.01183900e-02
-6.91554546e-01 -9.70513344e-01 1.54937625e-01 6.12747908e-01
-7.11392641e-01 1.45518780e-01 1.84382945e-01 -4.48075652e-01
-1.31623417e-01 -4.78376865e-01 -5.95463157e-01 -7.71975696e-01
-3.16799134e-01 -7.18505085e-02 4.54518087e-02 4.09589380... | [14.043543815612793, -3.1441948413848877] |
3beeaf57-c520-4a69-8698-867469eace4d | no-reference-quality-assessment-of | null | null | https://www.mdpi.com/2313-433X/8/6/173 | https://www.mdpi.com/2313-433X/8/6/173 | No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features | With the development of digital imaging techniques, image quality assessment methods are receiving more attention in the literature. Since distortion-free versions of camera images in many practical, everyday applications are not available, the need for effective no-reference image quality assessment algorithms is grow... | ['Domonkos Varga'] | 2022-06-19 | null | null | null | journal-of-imaging-2022-6 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.69625551e-01 -8.47338021e-01 1.98706940e-01 -4.16630924e-01
-1.01881647e+00 -3.43766183e-01 5.42924106e-01 3.45634282e-01
-5.17202020e-01 5.36047578e-01 9.70028266e-02 2.44482920e-01
-6.81932926e-01 -6.68010533e-01 -1.95012569e-01 -9.74551260e-01
1.50064882e-02 -3.80232841e-01 3.31312656e-01 -4.10353661... | [11.76627254486084, -1.9315699338912964] |
7ee98526-ccb1-496d-b809-69af41431675 | shattering-the-agent-environment-interface | 2305.11455 | null | https://arxiv.org/abs/2305.11455v1 | https://arxiv.org/pdf/2305.11455v1.pdf | Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models | A centerpiece of the ever-popular reinforcement learning from human feedback (RLHF) approach to fine-tuning autoregressive language models is the explicit training of a reward model to emulate human feedback, distinct from the language model itself. This reward model is then coupled with policy-gradient methods to dram... | ['Benjamin Van Roy', 'Dilip Arumugam', 'Shi Dong', 'Wanqiao Xu'] | 2023-05-19 | null | null | null | null | ['policy-gradient-methods', 'efficient-exploration'] | ['methodology', 'methodology'] | [ 6.90969452e-02 6.56485677e-01 -1.45761296e-01 -2.02747971e-01
-8.17519784e-01 -6.62808776e-01 1.15352821e+00 2.28831604e-01
-6.08699739e-01 9.79897976e-01 5.37092268e-01 -4.60516721e-01
-2.62832463e-01 -6.23153746e-01 -5.86746931e-01 -6.27175391e-01
-1.22842379e-01 5.00427663e-01 -7.51489028e-02 -6.01531446... | [4.098508834838867, 1.7248536348342896] |
6476b834-0670-42bd-b2f1-fe8311e548f7 | linear-covariance-loss-for-end-to-end | 2303.11516 | null | https://arxiv.org/abs/2303.11516v1 | https://arxiv.org/pdf/2303.11516v1.pdf | Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation | Most modern image-based 6D object pose estimation methods learn to predict 2D-3D correspondences, from which the pose can be obtained using a PnP solver. Because of the non-differentiable nature of common PnP solvers, these methods are supervised via the individual correspondences. To address this, several methods have... | ['Mathieu Salzmann', 'Yinlin Hu', 'Fulin Liu'] | 2023-03-21 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.33413002e-01 2.13366151e-01 -1.09637819e-01 -2.33808711e-01
-1.06512487e+00 -4.90239561e-01 3.87109429e-01 -2.24805340e-01
-2.91850626e-01 4.84427780e-01 1.64664030e-01 2.20669717e-01
-1.30147725e-01 -4.96263415e-01 -1.08635712e+00 -6.68341041e-01
2.11354718e-01 7.82217622e-01 -6.88966364e-03 1.43923223... | [7.564841270446777, -2.593414306640625] |
d1cce1fc-12e0-44d7-93f2-8c1e6d6e5d0f | rediscovering-the-slavic-continuum-in | 2010.11973 | null | https://arxiv.org/abs/2010.11973v1 | https://arxiv.org/pdf/2010.11973v1.pdf | Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification | Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification. In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to inv... | ['Dietrich Klakow', 'Bernd Möbius', 'Tania Avgustinova', 'Jacek Kudera', 'Badr M. Abdullah'] | 2020-10-22 | null | https://aclanthology.org/2020.vardial-1.12 | https://aclanthology.org/2020.vardial-1.12.pdf | vardial-coling-2020-12 | ['spoken-language-identification'] | ['speech'] | [-3.87021273e-01 -1.48944125e-01 -2.49015361e-01 -5.01073718e-01
-2.87258089e-01 -7.46223450e-01 8.33093822e-01 2.06664518e-01
-6.76049054e-01 1.73981667e-01 7.81924307e-01 -4.22612160e-01
-1.18390232e-01 -2.52864093e-01 -2.71706611e-01 -4.14286941e-01
2.15652492e-02 4.02966738e-01 -5.04066825e-01 -5.48686802... | [10.590606689453125, 8.91005802154541] |
423b0e4c-fdfc-40d6-b0bb-7d08f8e2f1bd | co-optimizing-distributed-energy-resources-in | 2208.09781 | null | https://arxiv.org/abs/2208.09781v2 | https://arxiv.org/pdf/2208.09781v2.pdf | Co-optimizing Distributed Energy Resources in Linear Complexity under Net Energy Metering | The co-optimization of behind-the-meter distributed energy resources is considered for prosumers under the net energy metering tariff. The distributed energy resources considered include renewable generations, flexible demands, and battery energy storage systems. An energy management system schedules the consumptions a... | ['Qing Zhao', 'Lang Tong', 'Ahmed S. Alahmed'] | 2022-08-21 | null | null | null | null | ['energy-management'] | ['time-series'] | [-5.80200672e-01 8.78923535e-02 -6.01041019e-01 -2.23406944e-02
-6.61285162e-01 -1.00499606e+00 3.36463034e-01 1.16074361e-01
-2.26031885e-01 1.29671443e+00 -6.54490590e-02 -1.91805780e-01
-5.05248189e-01 -9.59451616e-01 -3.41675997e-01 -1.22225344e+00
-2.39593759e-01 5.80095589e-01 -6.51919365e-01 -2.94155851... | [5.619233131408691, 2.5426666736602783] |
9ea172e0-4874-4a64-81d3-f1e4efd5fa2b | code-execution-with-pre-trained-language | 2305.05383 | null | https://arxiv.org/abs/2305.05383v1 | https://arxiv.org/pdf/2305.05383v1.pdf | Code Execution with Pre-trained Language Models | Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code. However, most pre-trained models for code intelligence ignore the execution trace and only rely on source code and syntactic structures. In this paper, we investigate how well pre-trained models can un... | ['Nan Duan', 'Neel Sundaresan', 'Shengyu Fu', 'Alexey Svyatkovskiy', 'Daxin Jiang', 'Weizhu Chen', 'Shuai Lu', 'Chenxiao Liu'] | 2023-05-08 | null | null | null | null | ['text-to-code-generation', 'code-search', 'code-search'] | ['computer-code', 'computer-code', 'computer-vision'] | [ 2.64975041e-01 -7.22486619e-03 -5.07886350e-01 -5.26322722e-01
-5.25638938e-01 -8.51748168e-01 4.41962093e-01 3.75798136e-01
1.40800253e-01 -1.35825992e-01 2.87839711e-01 -9.71119463e-01
3.18446159e-01 -9.03808594e-01 -9.27178144e-01 1.91929892e-01
-1.78622574e-01 6.78394735e-02 1.71364963e-01 -3.09905112... | [7.681135177612305, 7.874302864074707] |
4642f8aa-b681-43f6-b948-09b7871fc550 | metamax-improved-open-set-deep-neural | 2211.10872 | null | https://arxiv.org/abs/2211.10872v1 | https://arxiv.org/pdf/2211.10872v1.pdf | MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration | Open-set recognition refers to the problem in which classes that were not seen during training appear at inference time. This requires the ability to identify instances of novel classes while maintaining discriminative capability for closed-set classification. OpenMax was the first deep neural network-based approach to... | ['William J. Beksi', 'Nolan B. Gutierrez', 'Zongyao Lyu'] | 2022-11-20 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.92781550e-01 1.53733507e-01 -2.22642615e-01 -7.37621546e-01
-8.08350444e-01 -9.75378335e-01 7.52707779e-01 1.41646564e-01
-5.52895248e-01 7.46387482e-01 -4.47994232e-01 -2.85684407e-01
-5.33254385e-01 -8.16716671e-01 -9.93780017e-01 -3.76134485e-01
-2.37926558e-01 7.93613076e-01 -7.98343271e-02 3.99248302... | [9.578736305236816, 2.894629955291748] |
ed1d8edf-4ae7-459e-b383-4db144a844a9 | neural-architecture-search-for-effective | 2303.09639 | null | https://arxiv.org/abs/2303.09639v1 | https://arxiv.org/pdf/2303.09639v1.pdf | Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models | Large pre-trained language models have achieved state-of-the-art results on a variety of downstream tasks. Knowledge Distillation (KD) of a smaller student model addresses their inefficiency, allowing for deployment in resource-constraint environments. KD however remains ineffective, as the student is manually selected... | ['Bishwaranjan Bhattacharjee', 'Yousef El-Kurdi', 'Rameswar Panda', 'Michele Merler', 'Takuma Udagawa', 'Aashka Trivedi'] | 2023-03-16 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-7.67397061e-02 2.51893878e-01 -1.85036406e-01 -1.46065846e-01
-1.05470467e+00 -7.99722910e-01 4.75269824e-01 1.95486218e-01
-7.48901963e-01 5.91577590e-01 -1.11973593e-02 -6.57217503e-01
-3.63711789e-02 -7.40300536e-01 -8.20708096e-01 -6.93067670e-01
3.46344441e-01 9.82807338e-01 4.26332891e-01 -9.27543193... | [8.765533447265625, 3.6538772583007812] |
b5aa0d1c-fcd8-429d-9814-baadb4e7f36c | multi-label-product-categorization-using | 1907.00420 | null | https://arxiv.org/abs/1907.00420v2 | https://arxiv.org/pdf/1907.00420v2.pdf | Multi-Label Product Categorization Using Multi-Modal Fusion Models | In this study, we investigated multi-modal approaches using images, descriptions, and titles to categorize e-commerce products on Amazon. Specifically, we examined late fusion models, where the modalities are fused at the decision level. Products were each assigned multiple labels, and the hierarchy in the labels were ... | ['Pasawee Wirojwatanakul', 'Artit Wangperawong'] | 2019-06-30 | null | null | null | null | ['product-categorization'] | ['miscellaneous'] | [ 6.85878992e-02 -9.72998813e-02 -4.11131799e-01 -4.75129038e-01
-9.81483996e-01 -8.59250546e-01 5.87306440e-01 2.33341843e-01
-4.26762581e-01 2.66126961e-01 -7.67134363e-03 2.74717230e-02
1.29076153e-01 -7.35085428e-01 -4.71935362e-01 -5.08780777e-01
4.12288249e-01 2.45649442e-01 4.87086400e-02 -1.33233920... | [9.860387802124023, 3.95865797996521] |
8e154058-a854-476e-9f5a-74925d1f96ed | iiitt-dravidian-codemix-fire2021 | 2111.07906 | null | https://arxiv.org/abs/2111.07906v1 | https://arxiv.org/pdf/2111.07906v1.pdf | IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languages | Sentiment analysis of social media posts and comments for various marketing and emotional purposes is gaining recognition. With the increasing presence of code-mixed content in various native languages, there is a need for ardent research to produce promising results. This research paper bestows a tiny contribution to ... | ['Senthil Kumar B', 'Bharathi B', 'Karthik Puranik'] | 2021-11-15 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-4.68632579e-01 -2.83943892e-01 -1.41368136e-01 -3.70309949e-01
-8.29982758e-01 -8.24411154e-01 6.90186024e-01 1.86471045e-01
-6.12770617e-01 4.99673396e-01 3.64619613e-01 -6.81569695e-01
2.06793383e-01 -1.25606611e-01 -1.71918571e-01 -4.21512991e-01
-2.60634944e-02 2.59977907e-01 -3.41020554e-01 -9.74993587... | [9.389785766601562, 10.313033103942871] |
e6f39d36-1a2e-42ac-8485-c3d18559f53b | dynamically-updating-event-representations | null | null | https://aclanthology.org/2020.findings-emnlp.121 | https://aclanthology.org/2020.findings-emnlp.121.pdf | Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning | Temporal relation classification is the pair-wise task for identifying the relation of a temporal link (TLINKs) between two mentions, i.e. event, time and document creation time (DCT). It leads to two crucial limits: 1) Two TLINKs involving a common mention do not share information. 2) Existing models with independent ... | ['Sadao Kurohashi', 'Ichiro Kobayashi', 'Masayuki Asahara', 'Fei Cheng'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['temporal-relation-classification'] | ['natural-language-processing'] | [-2.43860468e-01 4.14497778e-02 -6.54351175e-01 -2.81593174e-01
-9.37606096e-01 -7.05298543e-01 1.29744160e+00 6.08791173e-01
-4.40693527e-01 6.81444526e-01 3.73516083e-01 -3.06293577e-01
-2.02834815e-01 -6.98392868e-01 -7.58537173e-01 -3.84788245e-01
-6.04197800e-01 5.35653889e-01 6.24644816e-01 -2.82383561... | [9.1070556640625, 9.18227767944336] |
03b205f9-a080-4b14-972e-8f46f4f0f8f2 | deepstack-expert-level-artificial | 1701.01724 | null | http://arxiv.org/abs/1701.01724v3 | http://arxiv.org/pdf/1701.01724v3.pdf | DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker | Artificial intelligence has seen several breakthroughs in recent years, with
games often serving as milestones. A common feature of these games is that
players have perfect information. Poker is the quintessential game of imperfect
information, and a longstanding challenge problem in artificial intelligence.
We introdu... | ['Nolan Bard', 'Viliam Lisý', 'Neil Burch', 'Matej Moravčík', 'Michael Bowling', 'Kevin Waugh', 'Trevor Davis', 'Martin Schmid', 'Dustin Morrill', 'Michael Johanson'] | 2017-01-06 | null | null | null | null | ['game-of-poker'] | ['playing-games'] | [-3.16841424e-01 4.96543467e-01 -4.68130440e-01 6.97572716e-03
-5.62011898e-01 -7.92432785e-01 3.13846290e-01 -4.28132005e-02
-8.38415563e-01 9.18308854e-01 2.98285335e-01 -8.05908978e-01
-6.97313964e-01 -7.18771040e-01 -4.30191129e-01 -4.20299321e-01
-7.53704309e-02 9.45421875e-01 2.01076850e-01 -7.39499867... | [3.5436348915100098, 1.5563435554504395] |
f3055732-0c30-45c7-9fa4-b0e835484e7f | visual-motion-analysis-of-the-player-s-finger | 2303.12697 | null | https://arxiv.org/abs/2303.12697v1 | https://arxiv.org/pdf/2303.12697v1.pdf | Visual motion analysis of the player's finger | This work is about the extraction of the motion of fingers, in their three articulations, of a keyboard player from a video sequence. The relevance of the problem involves several aspects, in fact, the extraction of the movements of the fingers may be used to compute the keystroke efficiency and individual joint contri... | ['Marco Costanzo'] | 2023-02-24 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.62394780e-01 -4.99316335e-01 8.75098929e-02 3.31365734e-01
-1.66802585e-01 -7.61860728e-01 3.80107582e-01 3.50017333e-03
-8.41509044e-01 3.23284626e-01 4.97684255e-02 -2.00003237e-01
-6.25637174e-01 -3.85838598e-01 -1.67661741e-01 -7.12961137e-01
1.38804868e-01 2.86691457e-01 7.70113826e-01 -3.28441739... | [15.913712501525879, 5.220574855804443] |
10cb75e2-bec8-4d38-9ce4-8781d89d1e14 | audioclip-extending-clip-to-image-text-and | 2106.13043 | null | https://arxiv.org/abs/2106.13043v1 | https://arxiv.org/pdf/2106.13043v1.pdf | AudioCLIP: Extending CLIP to Image, Text and Audio | In the past, the rapidly evolving field of sound classification greatly benefited from the application of methods from other domains. Today, we observe the trend to fuse domain-specific tasks and approaches together, which provides the community with new outstanding models. In this work, we present an extension of the ... | ['Andreas Dengel', 'Jörn Hees', 'Federico Raue', 'Andrey Guzhov'] | 2021-06-24 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 1.57218531e-01 -4.01958227e-01 3.02840352e-01 -2.94981837e-01
-1.55042505e+00 -6.38350606e-01 5.57837248e-01 6.60168976e-02
-3.78111303e-01 3.86968613e-01 2.76329890e-02 1.49565727e-01
-1.76519319e-01 -5.63991547e-01 -6.58220410e-01 -7.78954268e-01
-1.50752962e-01 2.57622182e-01 7.00461566e-01 -1.95147499... | [15.182211875915527, 5.167586803436279] |
30681068-f98e-482d-815b-ce84da37cbda | more-informed-random-sample-consensus | 2011.09116 | null | https://arxiv.org/abs/2011.09116v1 | https://arxiv.org/pdf/2011.09116v1.pdf | More Informed Random Sample Consensus | Random sample consensus (RANSAC) is a robust model-fitting algorithm. It is widely used in many fields including image-stitching and point cloud registration. In RANSAC, data is uniformly sampled for hypothesis generation. However, this uniform sampling strategy does not fully utilize all the information on many proble... | ['YangQuan Chen', 'Guoxiang Zhang'] | 2020-11-18 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 4.97456901e-02 -5.81645250e-01 -9.26164091e-02 -3.08303714e-01
-6.63216174e-01 -6.38698220e-01 4.09096897e-01 1.30347311e-01
-3.41075361e-01 5.46709836e-01 1.22531980e-01 -5.91998659e-02
-1.90915391e-01 -6.50109112e-01 -4.89213496e-01 -8.01154077e-01
3.82426083e-01 9.13501203e-01 3.36114943e-01 8.78173634... | [7.987942695617676, -2.4273455142974854] |
09aa3939-3f99-4297-b2b9-af58328a4043 | learning-to-generate-code-comments-from-class | 2103.13426 | null | https://arxiv.org/abs/2103.13426v2 | https://arxiv.org/pdf/2103.13426v2.pdf | Learning to Generate Code Comments from Class Hierarchies | Descriptive code comments are essential for supporting code comprehension and maintenance. We propose the task of automatically generating comments for overriding methods. We formulate a novel framework which accommodates the unique contextual and linguistic reasoning that is required for performing this task. Our appr... | ['Milos Gligoric', 'Junyi Jessy Li', 'Raymond J. Mooney', 'Pengyu Nie', 'Sheena Panthaplackel', 'Jiyang Zhang'] | 2021-03-24 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 6.62894666e-01 6.42667055e-01 -1.83270857e-01 -4.99275953e-01
-4.21986163e-01 -5.73672533e-01 7.83584714e-01 5.66110849e-01
-1.82837411e-03 6.00023091e-01 4.62448955e-01 -8.06801915e-01
1.02059305e-01 -7.51239359e-01 -3.98492694e-01 -3.03248078e-01
6.51288852e-02 1.24366909e-01 1.43625438e-01 -2.28405401... | [7.63087272644043, 7.866621017456055] |
34f5ccfe-efe1-4bbe-835b-f79373642374 | fair-k-centers-via-maximum-matching | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/4221-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/4221-Paper.pdf | Fair k-Centers via Maximum Matching | The field of algorithms has seen a push for fairness, or the removal of inherent bias, in recent history. In data summarization, where a much smaller subset of a data set is chosen to represent the whole of the data, fairness can be introduced by guaranteeing each "demographic group" a specific portion of the represent... | ['Thy Nguyen', 'Huy Nguyen', 'Matthew Jones'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/4221-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/4221-Paper.pdf | icml-2020-1 | ['data-summarization'] | ['miscellaneous'] | [ 3.71724889e-02 4.39811826e-01 -6.11489296e-01 -5.06375432e-01
-7.35228658e-01 -5.28737426e-01 3.51000160e-01 7.82404840e-01
-5.99590659e-01 8.17269742e-01 5.82320809e-01 -1.61104396e-01
-4.90131289e-01 -8.22510421e-01 -4.22317594e-01 -7.42932022e-01
-2.41833881e-01 9.61532593e-01 1.88501954e-01 -6.35194480... | [6.6814775466918945, 4.927112579345703] |
4e1d602a-e279-484a-be7a-578ab7a1d8de | de-biasing-facial-detection-system-using-vae | 2204.09556 | null | https://arxiv.org/abs/2204.09556v1 | https://arxiv.org/pdf/2204.09556v1.pdf | De-biasing facial detection system using VAE | Bias in AI/ML-based systems is a ubiquitous problem and bias in AI/ML systems may negatively impact society. There are many reasons behind a system being biased. The bias can be due to the algorithm we are using for our problem or may be due to the dataset we are using, having some features over-represented in it. In t... | ['Prof. Tanuja Pattanshetti', 'Kajal Kumbharkar', 'Siddhant V. Kandge', 'Vedant V. Kandge'] | 2022-04-16 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 3.08775187e-01 2.40258917e-01 -2.20510304e-01 -4.54522222e-01
1.30707175e-01 -4.36432868e-01 7.67315209e-01 -7.79155940e-02
-3.20743948e-01 8.15142572e-01 3.20986241e-01 1.69420236e-04
-1.75731540e-01 -1.14757740e+00 -7.90817857e-01 -7.07185686e-01
8.33860412e-02 5.31696558e-01 1.23757599e-02 -4.58898783... | [8.965996742248535, 4.735591411590576] |
0501b2bd-5c9c-40b7-9820-7c6e07e49d14 | resolution-robust-large-mask-inpainting-with | 2109.07161 | null | https://arxiv.org/abs/2109.07161v2 | https://arxiv.org/pdf/2109.07161v2.pdf | Resolution-robust Large Mask Inpainting with Fourier Convolutions | Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function. To allevia... | ['Victor Lempitsky', 'Kiwoong Park', 'Harshith Goka', 'Naejin Kong', 'Aleksei Silvestrov', 'Arsenii Ashukha', 'Anastasia Remizova', 'Anton Mashikhin', 'Elizaveta Logacheva', 'Roman Suvorov'] | 2021-09-15 | null | null | null | null | ['seeing-beyond-the-visible'] | ['computer-vision'] | [ 2.64751822e-01 1.02061266e-02 -9.85263381e-03 -4.82333452e-02
-1.00407171e+00 -4.20595586e-01 4.70876604e-01 -3.22378844e-01
-3.57681096e-01 7.91882992e-01 4.11758542e-01 -1.11262307e-01
1.34354800e-01 -7.16186285e-01 -1.09428275e+00 -5.74554563e-01
2.04745635e-01 7.32279643e-02 3.99606049e-01 -3.56134176... | [11.198492050170898, -0.9549143314361572] |
204f487e-addb-419c-8c17-4deb976915a5 | modselect-automatic-modality-selection-for | 2208.09414 | null | https://arxiv.org/abs/2208.09414v1 | https://arxiv.org/pdf/2208.09414v1.pdf | ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization | Modality selection is an important step when designing multimodal systems, especially in the case of cross-domain activity recognition as certain modalities are more robust to domain shift than others. However, selecting only the modalities which have a positive contribution requires a systematic approach. We tackle th... | ['Rainer Stiefelhagen', 'David Schneider', 'Alina Roitberg', 'Zdravko Marinov'] | 2022-08-19 | null | null | null | null | ['cross-domain-activity-recognition'] | ['computer-vision'] | [ 3.96845847e-01 -1.47502214e-01 -3.25640410e-01 -3.51481616e-01
-8.76203954e-01 -9.27863896e-01 8.52724493e-01 3.19583774e-01
-6.17057323e-01 8.40827167e-01 4.07904804e-01 9.95050147e-02
-1.88495830e-01 -5.50726831e-01 -5.29043615e-01 -7.06370473e-01
1.47016451e-01 4.56810385e-01 3.49103510e-01 -1.08017333... | [10.319201469421387, 3.1275908946990967] |
37d817e3-4716-4ee3-99e7-eb315e11f832 | adversarial-examples-in-deep-learning-for | 2009.11911 | null | https://arxiv.org/abs/2009.11911v1 | https://arxiv.org/pdf/2009.11911v1.pdf | Adversarial Examples in Deep Learning for Multivariate Time Series Regression | Multivariate time series (MTS) regression tasks are common in many real-world data mining applications including finance, cybersecurity, energy, healthcare, prognostics, and many others. Due to the tremendous success of deep learning (DL) algorithms in various domains including image recognition and computer vision, re... | ['Gautam Raj Mode', 'Khaza Anuarul Hoque'] | 2020-09-24 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 4.49332632e-02 -2.93032289e-01 1.22139826e-02 3.27172987e-02
-5.04014611e-01 -4.40257967e-01 4.87397492e-01 1.33499026e-01
-1.83447644e-01 8.12906027e-01 -2.32398540e-01 -8.14754605e-01
-7.92236701e-02 -1.06398892e+00 -9.18225288e-01 -8.09983611e-01
-5.94759107e-01 -1.18062664e-02 -2.46372689e-02 -5.63449204... | [5.467544078826904, 7.517639636993408] |
1797759f-368f-4304-bd71-a9814b9654a7 | a-robust-and-generalized-framework-for | 2105.10651 | null | https://arxiv.org/abs/2105.10651v1 | https://arxiv.org/pdf/2105.10651v1.pdf | A Robust and Generalized Framework for Adversarial Graph Embedding | Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs. However, most existing methods usually randomly select the negative samples from the o... | ['Lifang He', 'Philip S. Yu', 'Qingyun Sun', 'Shijie Zhu', 'Senzhang Wang', 'Hao Peng', 'Xingcheng Fu', 'JianXin Li'] | 2021-05-22 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 1.61947221e-01 4.11350608e-01 -3.21363419e-01 -1.64249748e-01
-7.02901557e-02 -5.46799481e-01 4.38037217e-01 1.94360226e-04
1.15130335e-01 6.77136660e-01 -3.34414579e-02 -2.95671910e-01
-2.28169560e-01 -1.53190327e+00 -4.87430841e-01 -7.03715861e-01
-2.32862249e-01 3.86795133e-01 1.84457079e-01 -3.95949602... | [7.094965934753418, 6.36529016494751] |
683e9c87-9b15-496b-bc77-a9b710fddccd | a-groupwise-multilinear-correspondence | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Bolkart_A_Groupwise_Multilinear_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Bolkart_A_Groupwise_Multilinear_ICCV_2015_paper.pdf | A Groupwise Multilinear Correspondence Optimization for 3D Faces | Multilinear face models are widely used to model the space of human faces with expressions. For databases of 3D human faces of different identities performing multiple expressions, these statistical shape models decouple identity and expression variations. To compute a high-quality multilinear face model, the quality o... | ['Stefanie Wuhrer', 'Timo Bolkart'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['3d-face-modeling'] | ['computer-vision'] | [-1.67964593e-01 1.02986231e-01 -8.94551054e-02 -6.03256881e-01
-8.49299848e-01 -6.15186870e-01 4.78526443e-01 -2.50081986e-01
-2.94021338e-01 4.27509010e-01 -1.48852989e-01 2.54852861e-01
-1.22096539e-01 -4.34920400e-01 -6.79770231e-01 -6.84962451e-01
2.78079938e-02 7.88215280e-01 -4.06005323e-01 -1.39897630... | [13.185270309448242, 0.11683144420385361] |
8a71de7c-75b4-4da7-9c5a-f3877e54429e | facies-classification-from-well-logs-using-an | 1706.00613 | null | http://arxiv.org/abs/1706.00613v1 | http://arxiv.org/pdf/1706.00613v1.pdf | Facies classification from well logs using an inception convolutional network | The idea to use automated algorithms to determine geological facies from well
logs is not new (see e.g Busch et al. (1987); Rabaute (1998)) but the recent
and dramatic increase in research in the field of machine learning makes it a
good time to revisit the topic. Following an exercise proposed by Dubois et al.
(2007) ... | ['Mathieu Rodriguez', 'Matthias Delescluse', 'Janis Keuper', 'Valentin Tschannen'] | 2017-06-02 | null | null | null | null | ['facies-classification'] | ['miscellaneous'] | [-6.00534193e-02 3.04464549e-01 5.90716302e-02 -4.26702231e-01
-5.00182390e-01 -6.94556773e-01 8.77190292e-01 -9.00283828e-02
-5.48961699e-01 9.36787546e-01 4.45093075e-03 -7.50580847e-01
-3.61161172e-01 -1.11562026e+00 -5.29180646e-01 -6.05221570e-01
-6.20614588e-01 5.00477612e-01 3.49479556e-01 -3.89461488... | [7.10626745223999, 2.299971580505371] |
159052dc-26be-476c-a4fd-d2a9832cfc68 | rethinking-style-transformer-by-energy-based | null | null | https://openreview.net/forum?id=rqONayCTQKl | https://openreview.net/pdf?id=rqONayCTQKl | Rethinking Style Transformer by Energy-based Interpretation: Adversarial Unsupervised Style Transfer using Pretrained Model | Style control, content preservation, and fluency determine the quality of text style transfer models. To train on a nonparallel corpus, several existing approaches aim to deceive the style discriminator with an adversarial loss. However, adversarial training significantly degrades fluency compared to the other two metr... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 2.11644918e-01 9.34019499e-03 -2.22963821e-02 -4.15955037e-01
-5.20044804e-01 -9.29581344e-01 7.19371736e-01 -3.52906436e-01
-5.78202248e-01 7.25073755e-01 2.98754543e-01 -6.50658086e-02
7.04345524e-01 -7.21594572e-01 -7.23276615e-01 -4.72863555e-01
4.71304446e-01 4.41845596e-01 -7.36978278e-02 -3.53841513... | [11.762456893920898, 9.590755462646484] |
26d81d5d-12f9-47a0-96cd-f031c231b33e | tackling-the-cocktail-fork-problem-for | 2212.07327 | null | https://arxiv.org/abs/2212.07327v1 | https://arxiv.org/pdf/2212.07327v1.pdf | Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks | Emulating the human ability to solve the cocktail party problem, i.e., focus on a source of interest in a complex acoustic scene, is a long standing goal of audio source separation research. Much of this research investigates separating speech from noise, speech from speech, musical instruments from each other, or soun... | ['Jonathan Le Roux', 'Zhong-Qiu Wang', 'Aswin Shanmugam Subramanian', 'Gordon Wichern', 'Darius Petermann'] | 2022-12-14 | null | null | null | null | ['audio-tagging', 'audio-source-separation', 'activity-detection'] | ['audio', 'audio', 'computer-vision'] | [ 4.23052132e-01 -5.17627060e-01 1.79923043e-01 1.17837086e-01
-1.50632167e+00 -9.01151061e-01 3.92172098e-01 1.51209325e-01
-1.95495650e-01 3.42549324e-01 8.04636478e-01 -2.27387637e-01
-3.82025063e-01 -1.78413138e-01 -5.06345510e-01 -1.00525820e+00
-1.17594125e-02 -1.99798152e-01 6.15115613e-02 -5.70705719... | [15.214704513549805, 5.671285629272461] |
399c58df-8c73-4055-961e-a05b02b6a708 | instructions-and-guide-causal-insights-for | 2208.12610 | null | https://arxiv.org/abs/2208.12610v2 | https://arxiv.org/pdf/2208.12610v2.pdf | NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education | In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data. The first is to identify the causal relationships between different constructs, where a construct is defined as the smallest element of learning. The second challenge ... | ['Cheng Zhang', 'Joel Jennings', 'Nick Pawlowski', 'Simon Woodhead', 'Craig Barton', 'Zichao Wang', 'Digory Smith', 'Wenbo Gong'] | 2022-08-17 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 3.16414088e-02 3.29028010e-01 1.21337429e-01 -2.61096627e-01
-3.54295999e-01 -7.59650171e-01 5.41760206e-01 8.67486477e-01
-4.17470038e-01 6.65268660e-01 2.18314812e-01 -9.53218281e-01
-8.39810789e-01 -1.06500292e+00 -9.98098731e-01 -1.36393249e-01
-5.33537924e-01 2.94752479e-01 3.32752138e-01 -6.60114825... | [10.12828540802002, 7.202239513397217] |
fbe3ac12-3ca7-4f66-af4f-8d0ccce39aa9 | subsampling-generative-adversarial-networks | 1909.10670 | null | https://arxiv.org/abs/1909.10670v5 | https://arxiv.org/pdf/1909.10670v5.pdf | Subsampling Generative Adversarial Networks: Density Ratio Estimation in Feature Space with Softplus Loss | Filtering out unrealistic images from trained generative adversarial networks (GANs) has attracted considerable attention recently. Two density ratio based subsampling methods---Discriminator Rejection Sampling (DRS) and Metropolis-Hastings GAN (MH-GAN)---were recently proposed, and their effectiveness in improving GAN... | ['Xin Ding', 'Z. Jane Wang', 'William J. Welch'] | 2019-09-24 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 8.73836875e-02 -3.56035330e-03 -1.55491987e-02 -2.80547500e-01
-1.13688314e+00 -3.02167267e-01 6.96566224e-01 -6.34541214e-01
-3.97135228e-01 1.26726687e+00 3.80670689e-02 -1.88549578e-01
1.63826391e-01 -1.14772213e+00 -7.63215959e-01 -9.77114856e-01
5.13557076e-01 6.10532105e-01 4.59997281e-02 -1.55018821... | [11.618304252624512, -0.17994503676891327] |
5d404ea0-aa9e-4911-b8c0-47a66b407dc7 | convolutional-recurrent-neural-networks-for-1 | 1609.04243 | null | http://arxiv.org/abs/1609.04243v3 | http://arxiv.org/pdf/1609.04243v3.pdf | Convolutional Recurrent Neural Networks for Music Classification | We introduce a convolutional recurrent neural network (CRNN) for music
tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local
feature extraction and recurrent neural networks for temporal summarisation of
the extracted features. We compare CRNN with three CNN structures that have
been used for ... | ['Kyunghyun Cho', 'Keunwoo Choi', 'Mark Sandler', 'George Fazekas'] | 2016-09-14 | null | null | null | null | ['music-classification'] | ['music'] | [ 1.47166073e-01 -1.29612964e-02 -3.07103425e-01 8.95456523e-02
-5.79641163e-01 -6.16415262e-01 6.40904129e-01 1.36064827e-01
-6.47634208e-01 2.04245493e-01 7.30262518e-01 7.97716305e-02
-3.32828045e-01 -6.21534467e-01 -2.13084236e-01 -4.21316952e-01
-2.78718352e-01 -6.53320700e-02 2.31014803e-01 -2.42058396... | [15.81241226196289, 5.270679473876953] |
fea23833-0eaa-4306-8c6a-b425a91e8b86 | knowsemlm-a-knowledge-infused-semantic | null | null | https://aclanthology.org/K19-1051 | https://aclanthology.org/K19-1051.pdf | KnowSemLM: A Knowledge Infused Semantic Language Model | Story understanding requires developing expectations of what events come next in text. Prior knowledge {--} both statistical and declarative {--} is essential in guiding such expectations. While existing semantic language models (SemLM) capture event co-occurrence information by modeling event sequences as semantic fra... | ['Dan Roth', 'Haoruo Peng', 'Qiang Ning'] | 2019-11-01 | null | null | null | conll-2019-11 | ['cloze-test'] | ['natural-language-processing'] | [ 3.69735092e-01 5.38995206e-01 -5.45292616e-01 -7.31117606e-01
-5.10826707e-01 -3.34811985e-01 1.10569596e+00 6.99073255e-01
-3.13931197e-01 1.04356802e+00 9.94002163e-01 -1.50963992e-01
-1.21928915e-01 -1.01456344e+00 -1.03273177e+00 8.13759677e-03
1.95525363e-02 5.42511642e-01 3.65757167e-01 8.17436948... | [10.844182968139648, 8.916668891906738] |
92daea72-517d-4491-90c8-b301dd42a923 | aspect-based-sentiment-analysis-as-machine | null | null | https://aclanthology.org/2022.coling-1.217 | https://aclanthology.org/2022.coling-1.217.pdf | Aspect-based Sentiment Analysis as Machine Reading Comprehension | Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation. Instead, we propose a novel end-to-end framework, MRCOOL: MRC-PrOmpt mOdeL framework, where numerous sentiment aspects are elicited by a machine reading comprehensi... | ['Hai Zhao', 'Yifei Yang'] | null | null | null | null | coling-2022-10 | ['aspect-based-sentiment-analysis', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.23025584e-01 2.43104592e-01 -2.33362228e-01 -9.46522057e-01
-1.08173513e+00 -5.16122758e-01 7.95328677e-01 2.69014746e-01
-4.59407508e-01 4.34659898e-01 5.77995241e-01 -3.93026441e-01
2.50873119e-01 -6.52143538e-01 -6.95261300e-01 -2.77673423e-01
6.62399590e-01 5.35618424e-01 -1.43916070e-01 -4.67218786... | [11.478500366210938, 6.6630425453186035] |
3c381de6-b1fb-4636-8f3d-e18a6e1f1bb0 | hard-non-monotonic-attention-for-character | 1808.10024 | null | https://arxiv.org/abs/1808.10024v2 | https://arxiv.org/pdf/1808.10024v2.pdf | Hard Non-Monotonic Attention for Character-Level Transduction | Character-level string-to-string transduction is an important component of various NLP tasks. The goal is to map an input string to an output string, where the strings may be of different lengths and have characters taken from different alphabets. Recent approaches have used sequence-to-sequence models with an attentio... | ['Pamela Shapiro', 'Shijie Wu', 'Ryan Cotterell'] | 2018-08-29 | hard-non-monotonic-attention-for-character-1 | https://aclanthology.org/D18-1473 | https://aclanthology.org/D18-1473.pdf | emnlp-2018-10 | ['hard-attention'] | ['methodology'] | [ 1.06285596e+00 1.98140576e-01 -1.14760250e-01 -4.23548609e-01
-1.01592684e+00 -1.03031778e+00 6.87752426e-01 7.96052292e-02
-4.31664795e-01 7.31326401e-01 2.56233573e-01 -6.39683425e-01
3.26860428e-01 -7.45495319e-01 -1.35715830e+00 -7.95598447e-01
2.06480354e-01 8.84437084e-01 2.70581275e-01 -2.26574779... | [11.20093059539795, 8.82163143157959] |
9ba82f0e-302a-4316-ba92-176caf261a0a | leverage-points-in-modality-shifts-comparing | 2306.02348 | null | https://arxiv.org/abs/2306.02348v1 | https://arxiv.org/pdf/2306.02348v1.pdf | Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word Representations | Multimodal embeddings aim to enrich the semantic information in neural representations of language compared to text-only models. While different embeddings exhibit different applicability and performance on downstream tasks, little is known about the systematic representation differences attributed to the visual modali... | ['Denis Paperno', 'Lisa Bylinina', 'Aleksey Tikhonov'] | 2023-06-04 | null | null | null | null | ['visual-grounding', 'word-embeddings'] | ['computer-vision', 'methodology'] | [-2.93737262e-01 -3.42922732e-02 -2.92264581e-01 -3.91861081e-01
-3.58685225e-01 -8.87675107e-01 1.21432984e+00 7.86438107e-01
-9.72052395e-01 2.47090608e-01 1.02146566e+00 -2.42562041e-01
-1.23946648e-02 -5.92301369e-01 -4.34039503e-01 -4.88017172e-01
7.04734474e-02 2.91127115e-01 -2.39578754e-01 -4.53615427... | [10.688928604125977, 1.9298986196517944] |
be235c20-bd0a-403c-8adf-2b70d85f49b6 | few-shot-dialogue-summarization-via-skeleton | 2305.12077 | null | https://arxiv.org/abs/2305.12077v1 | https://arxiv.org/pdf/2305.12077v1.pdf | Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer | In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To efficiently learn from few-shot samples, previous works have utilized massive annotated data from other downstream tasks and then performed prompt... | ['Mark Riedl', 'Ani Nenkova', 'Kanak Mahadik', 'Ruiyi Zhang', 'Handong Zhao', 'Junda Wu', 'Haoliang Wang', 'Tong Yu', 'Kaige Xie'] | 2023-05-20 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 0.48066258 0.5848817 -0.29713798 -0.34262708 -1.2591066 -0.5243595
0.67154676 0.14378257 -0.34071606 1.0019839 0.9173627 -0.16599321
0.40584046 -0.3974321 -0.50522584 -0.20957811 0.28538308 0.62643886
0.40668097 -0.44972393 0.25805622 -0.18897511 -0.9195706 0.5639096
1.2929357 0.37385455 0.32... | [12.66293716430664, 8.215937614440918] |
bc9c938a-12a6-4079-b682-1ef3ee6ad8ab | a-survey-on-deep-learning-for-skin-lesion | 2206.00356 | null | https://arxiv.org/abs/2206.00356v3 | https://arxiv.org/pdf/2206.00356v3.pdf | A Survey on Deep Learning for Skin Lesion Segmentation | Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrins... | ['Catarina Barata', 'Alceu Bissoto', 'Kumar Abhishek', 'Ghassan Hamarneh', 'M. Emre Celebi', 'Eduardo Valle', 'Sandra Avila', 'Zahra Mirikharaji'] | 2022-06-01 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.86124659e-01 3.34008560e-02 -3.23834062e-01 -9.20213163e-02
-7.35395730e-01 -5.07014036e-01 2.30269551e-01 1.89559191e-01
-2.06066951e-01 5.18071473e-01 -2.01966137e-01 -1.98828295e-01
1.08320996e-01 -7.70546675e-01 -2.88553923e-01 -9.64839637e-01
2.10326910e-01 -9.62856635e-02 2.65625298e-01 1.16030417... | [15.60517406463623, -2.9752984046936035] |
dd95a0f0-3878-4261-ac48-0a4beae08abe | benchmarking-aggression-identification-in | null | null | https://aclanthology.org/W18-4401 | https://aclanthology.org/W18-4401.pdf | Benchmarking Aggression Identification in Social Media | In this paper, we present the report and findings of the Shared Task on Aggression Identification organised as part of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC - 1) at COLING 2018. The task was to develop a classifier that could discriminate between Overtly Aggressive, Covertly Aggressive, and... | ['Marcos Zampieri', 'Atul Kr. Ojha', 'Shervin Malmasi', 'Ritesh Kumar'] | 2018-08-01 | null | null | null | coling-2018-8 | ['aggression-identification'] | ['natural-language-processing'] | [-4.42068368e-01 2.63108313e-01 3.40483129e-01 -4.21999842e-01
-7.66037941e-01 -3.94265652e-01 5.45655251e-01 3.73391986e-01
-6.97618425e-01 7.91083694e-01 5.11203885e-01 1.50797009e-01
-3.28166515e-01 -2.96873897e-01 1.49194136e-01 -3.83911729e-01
-1.70201704e-01 6.34627461e-01 3.77787262e-01 -7.86545396... | [8.791690826416016, 10.759329795837402] |
1a5c64bd-241c-4168-9065-4ce0c729fc35 | graph-anomaly-detection-with-unsupervised | 2210.09535 | null | https://arxiv.org/abs/2210.09535v2 | https://arxiv.org/pdf/2210.09535v2.pdf | Graph Anomaly Detection with Unsupervised GNNs | Graph-based anomaly detection finds numerous applications in the real-world. Thus, there exists extensive literature on the topic that has recently shifted toward deep detection models due to advances in deep learning and graph neural networks (GNNs). A vast majority of prior work focuses on detecting node/edge/subgrap... | ['Leman Akoglu', 'Arvind Srinivasan', 'Saurabh Sawlani', 'Lingxiao Zhao'] | 2022-10-18 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 1.56798437e-01 1.66294098e-01 1.64314434e-02 -6.26580492e-02
-3.39934140e-01 -3.66458416e-01 6.75034285e-01 6.88415647e-01
-7.46660531e-02 1.36965126e-01 2.35354938e-02 -6.03464305e-01
-1.80859327e-01 -1.04904771e+00 -4.55662400e-01 -5.99807322e-01
-9.38650787e-01 4.17379528e-01 3.35937709e-01 -2.44786471... | [6.669644832611084, 5.8443756103515625] |
814226dc-f945-4d11-94ea-a1fa4fb73023 | museclir-a-multiple-senses-and-cross-lingual | null | null | https://aclanthology.org/2022.coling-1.96 | https://aclanthology.org/2022.coling-1.96.pdf | MuSeCLIR: A Multiple Senses and Cross-lingual Information Retrieval Dataset | This paper addresses a deficiency in existing cross-lingual information retrieval (CLIR) datasets and provides a robust evaluation of CLIR systems’ disambiguation ability. CLIR is commonly tackled by combining translation and traditional IR. Due to translation ambiguity, the problem of ambiguity is worse in CLIR than i... | ['David Weir', 'Julie Weeds', 'Wing Yan Li'] | null | null | null | null | coling-2022-10 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-3.43994141e-01 -2.34978318e-01 -6.14111900e-01 -7.74855018e-02
-1.34349144e+00 -1.08403611e+00 9.34013605e-01 2.34924629e-01
-1.12766218e+00 1.02163398e+00 5.34020543e-01 -3.74636441e-01
-2.58129567e-01 -3.61212045e-01 -3.03676724e-01 -1.69682220e-01
4.51708794e-01 9.60007250e-01 9.35179144e-02 -7.53070772... | [11.327770233154297, 9.830466270446777] |
90a46c1b-77cb-43c7-9a70-c10200cbfa66 | ustc-nelslip-at-semeval-2023-task-2 | 2305.02517 | null | https://arxiv.org/abs/2305.02517v1 | https://arxiv.org/pdf/2305.02517v1.pdf | USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER | This paper describes the system developed by the USTC-NELSLIP team for SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II). A method named Statistical Construction and Dual Adaptation of Gazetteer (SCDAG) is proposed for Multilingual Complex NER. The method first utilizes a statistics-base... | ['Xiaoyi Zhao', 'Quan Liu', 'Zhen-Hua Ling', 'Jiajun Qi', 'Jia-Chen Gu', 'Jun-Yu Ma'] | 2023-05-04 | null | null | null | null | ['named-entity-recognition-ner', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [-3.71467084e-01 6.68658540e-02 1.17783345e-01 -6.16402268e-01
-9.63934720e-01 -5.97036600e-01 5.88191748e-01 -1.94341466e-01
-1.09015155e+00 9.52106059e-01 3.19986552e-01 -3.32758009e-01
2.41519630e-01 -2.27904916e-01 -6.08892500e-01 -2.05550194e-01
-4.41245623e-02 4.91057128e-01 7.47515112e-02 -3.72136444... | [9.856324195861816, 9.746881484985352] |
130509a5-a37a-4000-8326-cc02c96f31a5 | recurrent-trend-predictive-neural-network-for | null | null | https://ieeexplore.ieee.org/document/9451553 | https://ieeexplore.ieee.org/document/9451553 | Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection | We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN perform... | ['Osman Yildiz', 'Cüneyt Güzeliş', 'Mert Nakıp'] | 2021-06-10 | null | null | null | ieee-access-2021-6 | ['fire-detection', 'time-series-regression'] | ['time-series', 'time-series'] | [ 2.59151459e-01 -3.11790138e-01 -1.99396700e-01 -2.80702382e-01
-5.23098230e-01 -1.30567521e-01 5.88821411e-01 6.05643928e-01
-2.57590979e-01 6.13649607e-01 1.60523802e-01 -3.95286381e-01
-4.16820228e-01 -1.09828842e+00 -5.69316328e-01 -8.05491865e-01
-4.27572578e-01 2.63147414e-01 6.07916176e-01 2.62978785... | [6.907099723815918, 2.816312313079834] |
c429ffd9-2d81-415c-9fdd-1691fcfd3eef | rethinking-video-vits-sparse-video-tubes-for | 2212.03229 | null | https://arxiv.org/abs/2212.03229v1 | https://arxiv.org/pdf/2212.03229v1.pdf | Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning | We present a simple approach which can turn a ViT encoder into an efficient video model, which can seamlessly work with both image and video inputs. By sparsely sampling the inputs, the model is able to do training and inference from both inputs. The model is easily scalable and can be adapted to large-scale pre-traine... | ['Anelia Angelova', 'Weicheng Kuo', 'AJ Piergiovanni'] | 2022-12-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Piergiovanni_Rethinking_Video_ViTs_Sparse_Video_Tubes_for_Joint_Image_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Piergiovanni_Rethinking_Video_ViTs_Sparse_Video_Tubes_for_Joint_Image_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-classification', 'action-recognition-in-videos-2'] | ['computer-vision', 'computer-vision'] | [ 1.72923997e-01 2.19354749e-01 -4.61225748e-01 -3.93001318e-01
-7.53939450e-01 -6.92820489e-01 4.70385134e-01 -9.12805378e-01
-2.16481894e-01 5.71080685e-01 2.96409220e-01 -6.45529032e-01
5.13714314e-01 -7.30050623e-01 -1.20935607e+00 -3.22199583e-01
1.85950667e-01 3.86374652e-01 3.49677712e-01 -5.77196814... | [9.458285331726074, 0.9271924495697021] |
0cc8afaa-df1b-4341-a357-918aaae00d93 | distribution-aligned-multimodal-and-multi | 2006.01431 | null | https://arxiv.org/abs/2006.01431v1 | https://arxiv.org/pdf/2006.01431v1.pdf | Distribution Aligned Multimodal and Multi-Domain Image Stylization | Multimodal and multi-domain stylization are two important problems in the field of image style transfer. Currently, there are few methods that can perform both multimodal and multi-domain stylization simultaneously. In this paper, we propose a unified framework for multimodal and multi-domain style transfer with the su... | ['Wei-Ming Dong', 'Minxuan Lin', 'Fan Tang', 'Xiao Li', 'Chongyang Ma', 'Changsheng Xu'] | 2020-06-02 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 4.65551764e-01 -2.87227839e-01 2.74742711e-02 1.09714679e-02
-7.13797450e-01 -1.14753509e+00 8.68630707e-01 -3.46868873e-01
2.09951848e-02 8.70633483e-01 -6.01224303e-02 1.26238659e-01
-1.75801173e-01 -8.37190330e-01 -5.52759886e-01 -6.50961995e-01
7.14141667e-01 5.72731733e-01 2.53368855e-01 -4.89617169... | [11.721649169921875, -0.5113092660903931] |
0564f40c-7fe6-4920-8b19-2f1b8835d58e | exif-as-language-learning-cross-modal | 2301.04647 | null | https://arxiv.org/abs/2301.04647v4 | https://arxiv.org/pdf/2301.04647v4.pdf | EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata | We learn a visual representation that captures information about the camera that recorded a given photo. To do this, we train a multimodal embedding between image patches and the EXIF metadata that cameras automatically insert into image files. Our model represents this metadata by simply converting it to text and then... | ['Andrew Owens', 'Ayush Shrivastava', 'Chenhao Zheng'] | 2023-01-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_EXIF_As_Language_Learning_Cross-Modal_Associations_Between_Images_and_Camera_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_EXIF_As_Language_Learning_Cross-Modal_Associations_Between_Images_and_Camera_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-forensics'] | ['computer-vision'] | [ 1.69686407e-01 1.48037225e-01 -4.81732339e-01 -6.12531185e-01
-1.07719779e+00 -1.04761922e+00 6.68179989e-01 3.25620204e-01
-3.37289512e-01 2.43000109e-02 3.85637850e-01 -1.90603986e-01
2.38714352e-01 -4.47958440e-01 -1.30438733e+00 -6.32900596e-01
2.81111330e-01 2.15855852e-01 -1.39955744e-01 5.43510854... | [12.125144004821777, 1.0543708801269531] |
e08c7ed0-2755-4f3e-9f9b-77e7eab72054 | ffdnet-toward-a-fast-and-flexible-solution | 1710.04026 | null | http://arxiv.org/abs/1710.04026v2 | http://arxiv.org/pdf/1710.04026v2.pdf | FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising | Due to the fast inference and good performance, discriminative learning
methods have been widely studied in image denoising. However, these methods
mostly learn a specific model for each noise level, and require multiple models
for denoising images with different noise levels. They also lack flexibility to
deal with sp... | ['WangMeng Zuo', 'Lei Zhang', 'Kai Zhang'] | 2017-10-11 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 1.19430110e-01 -8.33353698e-01 2.13817924e-01 -3.25615942e-01
-6.22072637e-01 -2.38678753e-01 3.24405670e-01 -2.37408236e-01
-4.75909323e-01 4.51059550e-01 2.50496000e-01 -5.21430224e-02
-2.42733642e-01 -9.76009727e-01 -4.20129329e-01 -1.29954982e+00
1.44746423e-01 -1.03811920e-01 3.50573659e-01 -2.58483708... | [11.47974967956543, -2.3912267684936523] |
d384c582-4b7f-4cc8-9bfd-91b460496011 | lens-to-lens-bokeh-effect-transformation | null | null | https://openaccess.thecvf.com/content/CVPR2023W/NTIRE/html/Conde_Lens-to-Lens_Bokeh_Effect_Transformation._NTIRE_2023_Challenge_Report_CVPRW_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023W/NTIRE/papers/Conde_Lens-to-Lens_Bokeh_Effect_Transformation._NTIRE_2023_Challenge_Report_CVPRW_2023_paper.pdf | Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report | We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge. Recent advancements of mobile photography aim to reach the visual quality of full-frame cameras. Now, a goal in computational photography is to op... | ['JiXiang Niu', 'Yiqing Xu', 'Baoliang Chen', 'Yuxuan Zhao', 'Thomas B. Schön', 'Jens Sjölund', 'Zheng Zhao', 'Fredrik K. Gustafsson', 'Ziwei Luo', 'Vishal Monga', 'Amirsaeed Yazdani', 'Trung Hoang', 'Haichuan Zhang', 'Siyuan Lai', 'Wenyi Lian', 'Zhihao Yang', 'Xinchao Wang', 'Michael Bi Mi', 'Yongcheng Jing', 'Songhua... | 2023-06-01 | null | null | null | cvprw-2023-6 | ['bokeh-effect-rendering'] | ['computer-vision'] | [ 4.30285156e-01 -6.43809885e-03 3.21563035e-01 -4.13316816e-01
-1.37327224e-01 -2.42211848e-01 6.79972053e-01 -7.04198122e-01
-4.86805975e-01 5.59882998e-01 2.03238413e-01 -1.27148151e-01
2.66951472e-02 -6.77844107e-01 -1.07630372e+00 -6.85356498e-01
3.37797910e-01 -3.32529731e-02 1.90015882e-01 -2.89771974... | [10.69456958770752, -2.267122745513916] |
240e5379-fd42-4de4-b5de-40d514878901 | wukong-reader-multi-modal-pre-training-for | 2212.09621 | null | https://arxiv.org/abs/2212.09621v1 | https://arxiv.org/pdf/2212.09621v1.pdf | Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding | Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-training objectives are studied in existing solutions, the document textline, as an intrinsic granularity in VDU, has seldom been explored so ... | ['Qun Liu', 'Xin Jiang', 'Jiansheng Wei', 'Lu Hou', 'Liangwei Wang', 'Rongfu Zheng', 'Nian Xie', 'Shuang Liu', 'Wentao Li', 'Xiaojun Meng', 'Zhiguang Liu', 'Haoli Bai'] | 2022-12-19 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 1.51756898e-01 -4.63404715e-01 -4.57723171e-01 -3.44112217e-01
-5.93730032e-01 -8.08532119e-01 8.51639092e-01 3.99089158e-01
9.56713036e-02 1.68644488e-01 4.22051400e-01 -3.73128325e-01
1.17828883e-01 -5.99589109e-01 -6.99466884e-01 -3.26510996e-01
3.72893780e-01 2.57287502e-01 1.88385576e-01 -1.48839643... | [11.609916687011719, 2.316042900085449] |
75e3caf7-8321-4707-9ca8-e6433148c7f1 | saliendet-a-saliency-based-feature | 2305.06940 | null | https://arxiv.org/abs/2305.06940v2 | https://arxiv.org/pdf/2305.06940v2.pdf | SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving | Object detection (OD) is crucial to autonomous driving. On the other hand, unknown objects, which have not been seen in training sample set, are one of the reasons that hinder autonomous vehicles from driving beyond the operational domain. To addresss this issue, we propose a saliency-based OD algorithm (SalienDet) to ... | ['Azim Eskandarian', 'Ce Zhang', 'Ning Ding'] | 2023-05-11 | null | null | null | null | ['object-proposal-generation', 'incremental-learning'] | ['computer-vision', 'methodology'] | [-1.78534746e-01 1.26845419e-01 -2.87484795e-01 -2.29281962e-01
-4.88907456e-01 -4.18203205e-01 5.51848948e-01 -1.61161814e-02
-3.59869152e-01 5.37444115e-01 -9.54374894e-02 -3.21795732e-01
2.01547295e-02 -7.54997313e-01 -7.16510773e-01 -3.46473724e-01
2.00467944e-01 3.02689910e-01 9.16053176e-01 -3.41810375... | [8.524320602416992, -0.8235548138618469] |
7219c313-eea6-465d-81d7-11a76887b2c0 | conversational-memory-network-for-emotion | null | null | https://aclanthology.org/N18-1193 | https://aclanthology.org/N18-1193.pdf | Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos | Emotion recognition in conversations is crucial for the development of empathetic machines. Present methods mostly ignore the role of inter-speaker dependency relations while classifying emotions in conversations. In this paper, we address recognizing utterance-level emotions in dyadic conversational videos. We propose... | ['Louis-Philippe Morency', 'Erik Cambria', 'Soujanya Poria', 'Roger Zimmermann', 'Devamanyu Hazarika', 'Amir Zadeh'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.36420250e-01 1.01460077e-01 -1.16670519e-01 -7.20038116e-01
-7.49648809e-01 -2.73867190e-01 5.62896609e-01 -1.24864325e-01
-1.85920641e-01 8.09545517e-01 9.61532772e-01 3.39470625e-01
3.39048147e-01 -4.17401910e-01 -4.25347179e-01 -6.36739910e-01
-8.85067880e-02 1.35088518e-01 -5.73545754e-01 -4.22925085... | [13.10676097869873, 5.893034934997559] |
2188cc46-d696-4a39-b5c0-9775c6666d36 | exploring-word-segmentation-and-medical | null | null | https://aclanthology.org/2021.bionlp-1.23 | https://aclanthology.org/2021.bionlp-1.23.pdf | Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts | Chinese word segmentation (CWS) and medical concept recognition are two fundamental tasks to process Chinese electronic medical records (EMRs) and play important roles in downstream tasks for understanding Chinese EMRs. One challenge to these tasks is the lack of medical domain datasets with high-quality annotations, e... | ['Yan Song', 'Xiang Wan', 'Song Wu', 'Tsung-Hui Chang', 'Yuanhe Tian', 'Yang Liu'] | null | null | null | null | naacl-bionlp-2021-6 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 6.12240791e-01 5.51148131e-02 -2.81488687e-01 -3.68811846e-01
-1.35611737e+00 -3.04312676e-01 3.63211595e-02 1.92811161e-01
-9.57529604e-01 4.36290413e-01 3.71637642e-01 -8.87951732e-01
2.08782285e-01 -3.14551920e-01 5.92958219e-02 -5.31999409e-01
1.94934785e-01 8.38469684e-01 2.37280354e-01 3.77796553... | [8.561278343200684, 8.83786392211914] |
2ed9999f-fbb4-44eb-845f-b1c6b8babc05 | weakly-supervised-video-moment-retrieval-via | 1911.08199 | null | https://arxiv.org/abs/1911.08199v3 | https://arxiv.org/pdf/1911.08199v3.pdf | Weakly-Supervised Video Moment Retrieval via Semantic Completion Network | Video moment retrieval is to search the moment that is most relevant to the given natural language query. Existing methods are mostly trained in a fully-supervised setting, which requires the full annotations of temporal boundary for each query. However, manually labeling the annotations is actually time-consuming and ... | ['Qi. Wang', 'Huasheng Liu', 'Zhijie Lin', 'Zhou Zhao', 'Zhu Zhang'] | 2019-11-19 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 1.41701773e-01 -3.02849352e-01 -6.30291641e-01 -5.61004400e-01
-1.19106221e+00 -5.24326086e-01 5.83450139e-01 2.61664242e-01
-6.24503672e-01 4.77332264e-01 3.89321804e-01 2.74839282e-01
7.79966190e-02 -4.52611327e-01 -5.14789701e-01 -4.36996132e-01
-1.19718008e-01 3.44599843e-01 8.46718252e-01 1.80259705... | [9.85183334350586, 0.7014656662940979] |
fa1e554e-a56e-41d8-870b-d6ce54472115 | koniq-10k-an-ecologically-valid-database-for | 1910.06180 | null | https://arxiv.org/abs/1910.06180v2 | https://arxiv.org/pdf/1910.06180v2.pdf | KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment | Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content and annotating it accurately. We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dat... | ['Vlad Hosu', 'Tamas Sziranyi', 'Hanhe Lin', 'Dietmar Saupe'] | 2019-10-14 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-3.18810105e-01 -2.82484621e-01 3.08730125e-01 -2.38262385e-01
-1.22740066e+00 -6.14219904e-01 5.09977639e-01 -1.05737736e-02
-5.51967084e-01 7.06657350e-01 2.96323210e-01 9.68980715e-02
-2.68659502e-01 -8.35305929e-01 -7.80479670e-01 -3.34952652e-01
-1.46280035e-01 3.93653989e-01 2.12745085e-01 -3.34074914... | [11.877026557922363, -1.7828257083892822] |
2f5fa5d1-77f1-4db2-9ba5-2751b9f4edae | improving-cross-lingual-word-embeddings-by | 1808.08780 | null | http://arxiv.org/abs/1808.08780v1 | http://arxiv.org/pdf/1808.08780v1.pdf | Improving Cross-Lingual Word Embeddings by Meeting in the Middle | Cross-lingual word embeddings are becoming increasingly important in
multilingual NLP. Recently, it has been shown that these embeddings can be
effectively learned by aligning two disjoint monolingual vector spaces through
linear transformations, using no more than a small bilingual dictionary as
supervision. In this w... | ['Steven Schockaert', 'Luis Espinosa-Anke', 'Jose Camacho-Collados', 'Yerai Doval'] | 2018-08-27 | improving-cross-lingual-word-embeddings-by-1 | https://aclanthology.org/D18-1027 | https://aclanthology.org/D18-1027.pdf | emnlp-2018-10 | ['multilingual-nlp'] | ['natural-language-processing'] | [-3.32284302e-01 -1.49706721e-01 -4.59318310e-01 -2.16759980e-01
-8.04471672e-01 -1.03709626e+00 9.28482056e-01 5.33004105e-01
-8.89666021e-01 7.12622762e-01 4.49110389e-01 -3.50849092e-01
1.16193764e-01 -4.57872987e-01 -8.01639199e-01 -5.89238524e-01
1.49745852e-01 4.50442374e-01 -8.22355747e-02 -4.55539405... | [11.03411865234375, 10.075485229492188] |
4b64909d-1174-4e96-ad0e-f6bfc9ad57db | netgpt-generative-pretrained-transformer-for | 2304.09513 | null | https://arxiv.org/abs/2304.09513v2 | https://arxiv.org/pdf/2304.09513v2.pdf | NetGPT: Generative Pretrained Transformer for Network Traffic | All data on the Internet are transferred by network traffic, thus accurately modeling network traffic can help improve network services quality and protect data privacy. Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic, and generate disting... | ['Yujun Zhang', 'Yequan Wang', 'Chungang Lin', 'Xuying Meng'] | 2023-04-19 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 3.99225563e-01 -4.56509501e-01 -4.28426176e-01 -5.35269022e-01
-3.88110936e-01 -8.74267697e-01 4.50806230e-01 -4.27730769e-01
3.53173725e-03 4.61453825e-01 -7.41073564e-02 -1.26908576e+00
-8.31386447e-02 -1.00906754e+00 -5.41658223e-01 -2.36956626e-01
5.06497920e-02 7.05137134e-01 4.01501775e-01 -2.40763605... | [5.078881740570068, 7.2417097091674805] |
dda06661-9e73-4cb2-ad51-e75adc3f2d48 | a-survey-on-using-gaze-behaviour-for-natural | 2112.15471 | null | https://arxiv.org/abs/2112.15471v2 | https://arxiv.org/pdf/2112.15471v2.pdf | A Survey on Using Gaze Behaviour for Natural Language Processing | Gaze behaviour has been used as a way to gather cognitive information for a number of years. In this paper, we discuss the use of gaze behaviour in solving different tasks in natural language processing (NLP) without having to record it at test time. This is because the collection of gaze behaviour is a costly task, bo... | ['Pushpak Bhattacharyya', 'Abhijit Mishra', 'Diptesh Kanojia', 'Sandeep Mathias'] | 2021-12-21 | null | null | null | null | ['complex-word-identification'] | ['natural-language-processing'] | [ 2.55975157e-01 2.94411361e-01 1.35493532e-01 -3.29279333e-01
-1.82273373e-01 -6.63639367e-01 3.41699064e-01 7.51829088e-01
-7.35428751e-01 6.92722738e-01 1.80545021e-02 -6.04001641e-01
-3.01386654e-01 -1.49062112e-01 -6.69067353e-02 -5.33774972e-01
5.10333478e-01 3.11580628e-01 3.69095981e-01 -3.30217510... | [11.15761661529541, 9.337653160095215] |
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