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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2ea4023a-6737-486c-b042-3e2504f103eb | gaussian-membership-inference-privacy | 2306.07273 | null | https://arxiv.org/abs/2306.07273v1 | https://arxiv.org/pdf/2306.07273v1.pdf | Gaussian Membership Inference Privacy | We propose a new privacy notion called $f$-Membership Inference Privacy ($f$-MIP), which explicitly considers the capabilities of realistic adversaries under the membership inference attack threat model. By doing so $f$-MIP offers interpretable privacy guarantees and improved utility (e.g., better classification accura... | ['Gjergji Kasneci', 'Martin Pawelczyk', 'Tobias Leemann'] | 2023-06-12 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 2.21756268e-02 4.87038791e-02 -7.27059543e-02 -5.18064618e-01
-1.21652317e+00 -1.11206102e+00 2.67620951e-01 3.04768067e-02
-5.71835995e-01 6.73552752e-01 -3.85151953e-01 -9.99405324e-01
-1.51434526e-01 -7.81651318e-01 -1.08227921e+00 -6.87305987e-01
-5.07781148e-01 5.10932207e-02 -1.88261852e-01 1.41816929... | [5.940936088562012, 7.022603988647461] |
194445b5-d1ea-4383-9ac4-acb67477e028 | efficient-rule-learning-with-template | 2003.06071 | null | https://arxiv.org/abs/2003.06071v2 | https://arxiv.org/pdf/2003.06071v2.pdf | Towards Learning Instantiated Logical Rules from Knowledge Graphs | Efficiently inducing high-level interpretable regularities from knowledge graphs (KGs) is an essential yet challenging task that benefits many downstream applications. In this work, we present GPFL, a probabilistic rule learner optimized to mine instantiated first-order logic rules from KGs. Instantiated rules contain ... | ['Yu Guan', 'Yulong Gu', 'Paolo Missier'] | 2020-03-13 | null | null | null | null | ['inductive-knowledge-graph-completion'] | ['knowledge-base'] | [ 3.42669845e-01 9.47060525e-01 -7.16822624e-01 -2.90976048e-01
-6.98073387e-01 -7.68036783e-01 5.71426153e-01 1.67794719e-01
2.27444723e-01 8.88026118e-01 1.52059495e-01 -6.96877182e-01
-5.70868492e-01 -1.19999337e+00 -1.23520064e+00 -2.36990571e-01
-3.10817808e-01 7.69181788e-01 5.96711874e-01 -9.03581232... | [8.969869613647461, 7.516846656799316] |
12ba881a-7e5f-44e1-ac9d-3812d27a1ea2 | convolutional-neural-network-array-for-sign | 2004.11836 | null | https://arxiv.org/abs/2004.11836v1 | https://arxiv.org/pdf/2004.11836v1.pdf | Convolutional Neural Network Array for Sign Language Recognition using Wearable IMUs | Advancements in gesture recognition algorithms have led to a significant growth in sign language translation. By making use of efficient intelligent models, signs can be recognized with precision. The proposed work presents a novel one-dimensional Convolutional Neural Network (CNN) array architecture for recognition of... | ['Rinki Gupta', 'Karush Suri'] | 2020-04-21 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 3.98023754e-01 -1.24111705e-01 -2.00212643e-01 -4.07089829e-01
-3.59218985e-01 -5.59856296e-01 6.15645051e-01 -4.93072003e-01
-8.64918232e-01 4.80366915e-01 3.23094368e-01 -2.74541825e-01
1.14703029e-01 -2.82323450e-01 -5.08478105e-01 -8.15171421e-01
3.88448238e-01 3.07179708e-02 -4.72864695e-02 -2.16780864... | [9.069401741027832, -6.361203670501709] |
355d43ce-9af8-4d65-ae13-7da976db5a95 | context-aware-feature-generation-for-zero | 2008.06893 | null | https://arxiv.org/abs/2008.06893v1 | https://arxiv.org/pdf/2008.06893v1.pdf | Context-aware Feature Generation for Zero-shot Semantic Segmentation | Existing semantic segmentation models heavily rely on dense pixel-wise annotations. To reduce the annotation pressure, we focus on a challenging task named zero-shot semantic segmentation, which aims to segment unseen objects with zero annotations. This task can be accomplished by transferring knowledge across categori... | ['Liqing Zhang', 'Zihan Zhao', 'Siyuan Zhou', 'Li Niu', 'Zhangxuan Gu'] | 2020-08-16 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 2.88899511e-01 2.38688529e-01 -3.03515315e-01 -6.86174452e-01
-7.82615423e-01 -3.11415017e-01 3.27094972e-01 2.18787968e-01
-5.34860671e-01 2.30219468e-01 3.69321287e-01 1.13030553e-01
2.73919195e-01 -1.00138950e+00 -6.61485970e-01 -6.12010598e-01
4.81901944e-01 1.60034329e-01 6.57326519e-01 -1.67138338... | [9.65347957611084, 1.0164978504180908] |
2ed9b638-8cc2-462b-9403-7ca8e66adfff | contrastive-hierarchical-discourse-graph-for | 2306.00177 | null | https://arxiv.org/abs/2306.00177v1 | https://arxiv.org/pdf/2306.00177v1.pdf | Contrastive Hierarchical Discourse Graph for Scientific Document Summarization | The extended structural context has made scientific paper summarization a challenging task. This paper proposes CHANGES, a contrastive hierarchical graph neural network for extractive scientific paper summarization. CHANGES represents a scientific paper with a hierarchical discourse graph and learns effective sentence ... | ['Jiawei Zhang', 'Xiao Liu', 'Haopeng Zhang'] | 2023-05-31 | null | null | null | null | ['scientific-article-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.92264664e-01 1.11837530e+00 -4.69522178e-01 -8.18527117e-02
-6.14532590e-01 -3.94185543e-01 6.20177329e-01 9.92938280e-01
1.13771237e-01 8.24744761e-01 1.31823516e+00 -2.45086372e-01
-2.49633998e-01 -6.95356250e-01 -1.07632565e+00 -2.10285991e-01
-2.71155417e-01 4.33710039e-01 -1.99109942e-01 5.65411709... | [12.592289924621582, 9.551342964172363] |
ecc49ef7-8b42-4313-bb8a-f5a4a0e5a881 | hand-object-interaction-and-precise | 1511.03814 | null | http://arxiv.org/abs/1511.03814v2 | http://arxiv.org/pdf/1511.03814v2.pdf | Hand-Object Interaction and Precise Localization in Transitive Action Recognition | Action recognition in still images has seen major improvement in recent years
due to advances in human pose estimation, object recognition and stronger
feature representations produced by deep neural networks. However, there are
still many cases in which performance remains far from that of humans. A major
difficulty a... | ['Shimon Ullman', 'Amir Rosenfeld'] | 2015-11-12 | null | null | null | null | ['action-recognition-in-still-images'] | ['computer-vision'] | [ 5.82788348e-01 7.40210572e-03 -8.22751038e-03 -2.93611258e-01
-5.95031977e-01 -5.04716754e-01 7.17423201e-01 -6.64918199e-02
-4.58131850e-01 5.42835951e-01 3.57136965e-01 4.36145306e-01
-1.47357881e-01 -2.43155986e-01 -6.00939929e-01 -6.07713461e-01
-2.10912600e-02 7.39605069e-01 4.91978705e-01 5.72754256... | [7.936575889587402, 0.2556283473968506] |
428d2ce1-4c3d-449b-b460-189bcfeaa7b9 | multi-scale-embedded-cnn-for-music-tagging | 1906.06746 | null | https://arxiv.org/abs/1906.06746v1 | https://arxiv.org/pdf/1906.06746v1.pdf | Multi-scale Embedded CNN for Music Tagging (MsE-CNN) | Convolutional neural networks (CNN) recently gained notable attraction in a variety of machine learning tasks: including music classification and style tagging. In this work, we propose implementing intermediate connections to the CNN architecture to facilitate the transfer of multi-scale/level knowledge between differ... | ['Nima Hamidi', 'Mohsen Vahidzadeh', 'Stephen Baek'] | 2019-06-16 | null | null | null | null | ['music-classification'] | ['music'] | [ 1.44170016e-01 -3.12779784e-01 -2.82670826e-01 -1.17209613e-01
-4.60406929e-01 -6.40427530e-01 4.14705515e-01 1.66820616e-01
-5.29643059e-01 4.55199182e-01 3.84152561e-01 1.18869498e-01
-3.71248163e-02 -6.23247862e-01 -4.39325929e-01 -2.09133908e-01
-1.68356806e-01 5.12350313e-02 -3.40624174e-05 -1.06421851... | [15.786416053771973, 5.2700276374816895] |
51a8a8ec-2516-4e72-ab70-c036cc68c7cf | sleep-quality-prediction-in-caregivers-using | null | null | https://www.sciencedirect.com/science/article/pii/S001048251930160X | https://www.sciencedirect.com/science/article/pii/S001048251930160X | Sleep quality prediction in caregivers using physiological signals | Most caregivers of people with dementia (CPWD) experience a high degree of stress due to the demands of providing care, especially when addressing unpredictable behavioral and psychological symptoms of dementia. Such challenging responsibilities make caregivers susceptible to poor sleep quality with detrimental effects... | ['Jennifer C. Hughes', 'Reza Sadeghi', 'Tanvi Banerjee', 'Larry W. Lawhorne'] | 2019-05-20 | null | null | null | computers-in-biology-and-medicine-2019-5 | ['heart-rate-variability', 'sleep-quality-prediction-1'] | ['medical', 'medical'] | [-2.02469751e-01 -3.87622952e-01 -2.08386287e-01 -4.10067737e-01
2.16363501e-02 -1.32819235e-01 -2.58997798e-01 8.65950435e-02
-6.25615597e-01 1.11316264e+00 4.49020505e-01 -1.02599762e-01
-3.46496776e-02 -3.74106050e-01 4.25495803e-01 -8.20935309e-01
-1.79491177e-01 -1.16876699e-01 -7.97008798e-02 4.40009497... | [13.560246467590332, 3.3915083408355713] |
1dcef78b-d14d-4758-9f40-d5c268a936c2 | structure-aware-face-clustering-on-a-large-1 | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Shen_Structure-Aware_Face_Clustering_on_a_Large-Scale_Graph_With_107_Nodes_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Shen_Structure-Aware_Face_Clustering_on_a_Large-Scale_Graph_With_107_Nodes_CVPR_2021_paper.pdf | Structure-Aware Face Clustering on a Large-Scale Graph With 107 Nodes | Face clustering is a promising method for annotating unlabeled face images. Recent supervised approaches have boosted the face clustering accuracy greatly, however their performance is still far from satisfactory. These methods can be roughly divided into global-based and local-based ones. Global-based methods suff... | ['Jie zhou', 'Jiwen Lu', 'Dalong Du', 'Guan Huang', 'Zheng Zhu', 'Wanhua Li', 'Shuai Shen'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['face-clustering'] | ['computer-vision'] | [-8.83313268e-02 2.18771681e-01 -3.74038309e-01 -6.04835153e-01
-8.63668561e-01 -3.43848944e-01 3.83269072e-01 -2.13289469e-01
-6.36387318e-02 3.84044439e-01 4.33174595e-02 -1.15237996e-01
-1.32478669e-01 -7.30921626e-01 -5.90198934e-01 -8.21016133e-01
-1.10099494e-01 6.78535402e-01 1.67086974e-01 1.96689948... | [13.509328842163086, 0.9461907744407654] |
f004fdd8-098a-4df6-9cb9-d0a931667c24 | remote-task-oriented-grasp-area-teaching-by | 2303.10195 | null | https://arxiv.org/abs/2303.10195v1 | https://arxiv.org/pdf/2303.10195v1.pdf | Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot Learning | A robot operating in unstructured environments must be able to discriminate between different grasping styles depending on the prospective manipulation task. Having a system that allows learning from remote non-expert demonstrations can very feasibly extend the cognitive skills of a robot for task-oriented grasping. We... | ['Eckehard Steinbach', 'Shaobo Zhou', 'Sudarshan Rajagopalan', 'Furkan Kaynar'] | 2023-03-17 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 4.36891019e-01 1.38381585e-01 6.28461763e-02 -5.63367009e-01
-7.03536868e-01 -6.16886437e-01 1.65899694e-01 1.42538734e-02
-5.45264840e-01 4.09556806e-01 -6.01468801e-01 4.32465598e-02
-2.36019507e-01 -7.63461113e-01 -1.14084697e+00 -8.86968911e-01
-3.48826855e-01 1.08894408e+00 6.10375822e-01 -2.17151895... | [5.76169490814209, -0.766557514667511] |
da654107-0e10-4485-a334-0d5372475bb2 | novel-predator-prey-model-admitting-exact | 2208.02457 | null | https://arxiv.org/abs/2208.02457v2 | https://arxiv.org/pdf/2208.02457v2.pdf | Novel predator-prey model admitting exact analytical solution | The Lotka-Volterra predator-prey model still represents the paradigm for the description of the competition in population dynamics. Despite its extreme simplicity, it does not admit an analytical solution, and for this reason, numerical integration methods are usually adopted to apply it to various fields of science. T... | ['G. Kaniadakis'] | 2022-08-04 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-2.86207408e-01 -4.15015072e-01 1.90682039e-01 1.74498379e-01
2.47001931e-01 -3.67670298e-01 5.56826711e-01 4.47648793e-01
-5.81247330e-01 9.57652271e-01 -7.39293814e-01 -8.15155432e-02
-6.04397297e-01 -8.28287601e-01 -2.40652859e-01 -1.21128488e+00
-3.49271357e-01 4.43118155e-01 2.83826947e-01 -7.08469868... | [5.971914768218994, 4.303834438323975] |
63185bc8-a137-4915-beb8-47e54f0f6fa4 | mutual-information-regularized-offline | 2210.07484 | null | https://arxiv.org/abs/2210.07484v1 | https://arxiv.org/pdf/2210.07484v1.pdf | Mutual Information Regularized Offline Reinforcement Learning | Offline reinforcement learning (RL) aims at learning an effective policy from offline datasets without active interactions with the environment. The major challenge of offline RL is the distribution shift that appears when out-of-distribution actions are queried, which makes the policy improvement direction biased by e... | ['Shuicheng Yan', 'Min Lin', 'Zhongwen Xu', 'Bingyi Kang', 'Xiao Ma'] | 2022-10-14 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.12527698e-01 1.69092983e-01 -8.25722218e-01 -6.35029972e-02
-7.48712003e-01 -7.01312006e-01 4.92859662e-01 2.46138260e-01
-8.18462729e-01 8.86555314e-01 2.76033849e-01 -5.16856492e-01
-2.99856484e-01 -6.37995780e-01 -9.51953173e-01 -9.10130143e-01
-2.37357050e-01 5.02176583e-01 -1.25212327e-01 -1.14960812... | [4.0716423988342285, 2.2066099643707275] |
4c781e21-6ad8-40e6-b5e0-aeafdf4f1dbf | sms-spam-filtering-using-probabilistic-topic | 1606.05554 | null | http://arxiv.org/abs/1606.05554v1 | http://arxiv.org/pdf/1606.05554v1.pdf | SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder | In This paper we present a novel approach to spam filtering and demonstrate
its applicability with respect to SMS messages. Our approach requires minimum
features engineering and a small set of la- belled data samples. Features are
extracted using topic modelling based on latent Dirichlet allocation, and then
a compreh... | ['A. Stephen McGough', 'Peter Matthews', 'Toby Breckon', 'Noura Al Moubayed'] | 2016-06-17 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 2.04648033e-01 1.93721145e-01 4.04255956e-01 -5.57938278e-01
-4.05707896e-01 -5.87719418e-02 1.39109135e+00 3.16476643e-01
-4.48244035e-01 3.96549940e-01 3.09789330e-01 -3.19231987e-01
-2.54857570e-01 -9.23364818e-01 -6.36615679e-02 -1.06016695e+00
-6.76305667e-02 1.09408212e+00 4.54392910e-01 -3.23010325... | [7.93793249130249, 10.008736610412598] |
fa3b6ce5-0bd3-4ff2-954d-17ffc41ead4e | learning-adaptive-control-flow-in | null | null | https://openreview.net/forum?id=v8IbnUesFpE | https://openreview.net/pdf?id=v8IbnUesFpE | Learning Adaptive Control Flow in Transformers for Improved Systematic Generalization | Despite successes across a broad range of applications, Transformers have limited capability in systematic generalization. The situation is especially frustrating for algorithmic tasks, where they often fail to find intuitive solutions that can be simply expressed in terms of attention patterns. Here we propose two mod... | ['Jürgen Schmidhuber', 'Kazuki Irie', 'Róbert Csordás'] | 2021-10-08 | null | null | null | neurips-workshop-aiplans-2021-12 | ['systematic-generalization'] | ['reasoning'] | [ 2.96869040e-01 1.98808312e-01 -1.94594741e-01 -4.30558145e-01
-4.21522737e-01 -8.69727254e-01 4.98731285e-01 2.86552131e-01
8.48917067e-02 8.21095526e-01 3.89522195e-01 -8.30014586e-01
-4.30522084e-01 -7.56566703e-01 -6.16054118e-01 -2.49614894e-01
-9.15685818e-02 7.96531737e-01 5.23856608e-03 -5.61894834... | [9.476909637451172, 7.159472465515137] |
eb06d6fc-5bc3-4047-86ef-e9cd49658481 | towards-global-optimality-in-cooperative-marl | 2207.11143 | null | https://arxiv.org/abs/2207.11143v3 | https://arxiv.org/pdf/2207.11143v3.pdf | Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework | Decentralized execution is one core demand in cooperative multi-agent reinforcement learning (MARL). Recently, most popular MARL algorithms have adopted decentralized policies to enable decentralized execution and use gradient descent as their optimizer. However, there is hardly any theoretical analysis of these algori... | ['Chongjie Zhang', 'Jianhao Wang', 'Chenghao Li', 'Jianing Ye'] | 2022-07-12 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-6.57832980e-01 1.40382405e-02 -6.16899908e-01 8.25228915e-02
-8.43391955e-01 -3.91909957e-01 4.23438072e-01 3.41852382e-02
-7.93188095e-01 1.37100315e+00 4.63602841e-02 -5.16139686e-01
-3.34086210e-01 -4.32889700e-01 -7.24302471e-01 -1.16708314e+00
-3.92329097e-01 8.02561224e-01 9.38562974e-02 -4.96854842... | [3.7733094692230225, 2.0581250190734863] |
3fcdcdce-0f55-48e1-bcc3-701cbeb638c6 | pedestrian-attribute-recognition-in-video | 2106.06485 | null | https://arxiv.org/abs/2106.06485v1 | https://arxiv.org/pdf/2106.06485v1.pdf | Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization | Pedestrian attribute recognition in surveillance scenarios is still a challenging task due to inaccurate localization of specific attributes. In this paper, we propose a novel view-attribute localization method based on attention (VALA), which relies on the strong relevance between attributes and views to capture speci... | ['Linlin Ou', 'Xinyi Yu', 'Weichen Chen'] | 2021-06-11 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [ 2.17334218e-02 -1.02648355e-01 3.23297717e-02 -8.85262847e-01
-6.32233143e-01 -3.47535014e-01 5.49795270e-01 -4.59437966e-02
-2.74125814e-01 4.83690709e-01 4.34024304e-01 1.05600528e-01
4.33900580e-02 -9.47981417e-01 -8.34225833e-01 -8.38098347e-01
1.77409753e-01 2.26467207e-01 4.58921224e-01 -7.30145648... | [14.403976440429688, 0.9907699823379517] |
e3ef052c-7c52-4a25-aad4-7b4a12767278 | quantum-machine-learning-and-quantum | 2004.12076 | null | https://arxiv.org/abs/2004.12076v2 | https://arxiv.org/pdf/2004.12076v2.pdf | Quantum machine learning and quantum biomimetics: A perspective | Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on the one hand, to carry out more efficient machine learning calculations by means of quantum devices, while, on the other hand, to employ machine learning techniques to better control quantum systems... | ['Lucas Lamata'] | 2020-04-25 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.52094990e-01 2.04936668e-01 8.02130848e-02 1.35804638e-01
5.72334826e-02 -4.24642444e-01 8.38625014e-01 2.71793276e-01
-5.47748148e-01 9.81775641e-01 -3.78820926e-01 -2.95259029e-01
9.26050842e-02 -1.45624411e+00 -7.21090555e-01 -1.16012907e+00
-2.33956408e-02 4.88819033e-01 4.72072922e-02 -6.61196709... | [5.524615287780762, 4.925134658813477] |
cd62af12-1764-4022-8c47-acc7946a0694 | multimodal-representation-learning-via | 2103.04537 | null | https://arxiv.org/abs/2103.04537v5 | https://arxiv.org/pdf/2103.04537v5.pdf | Multimodal Representation Learning via Maximization of Local Mutual Information | We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn useful image representations by taking advantage of the rich information contained in the free text that describes the findings in the image... | ['William M. Wells', 'Polina Golland', 'Steven Horng', 'Seth Berkowitz', 'Keegan Quigley', 'Miriam Cha', 'Daniel Moyer', 'Ruizhi Liao'] | 2021-03-08 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 5.90500653e-01 2.29579195e-01 -3.12257707e-01 -7.63625503e-01
-9.48590338e-01 -3.67537439e-01 1.05586398e+00 2.27994159e-01
-5.26198566e-01 5.45679629e-01 6.07365549e-01 -1.32611739e-02
-1.60389364e-01 -6.04024529e-01 -6.47183061e-01 -6.98316276e-01
-9.65477824e-02 1.76292434e-01 -2.76462823e-01 2.21969381... | [9.540130615234375, 2.67230486869812] |
2121940a-100e-476c-ab99-0ba00695b175 | saral-a-low-resource-cross-lingual-domain | null | null | https://aclanthology.org/P19-3004 | https://aclanthology.org/P19-3004.pdf | SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage | With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In thi... | ['Banriskhem Kayang Khonglah', 'Chester Palen-Michel', 'Marjorie Freedman', 'Thamme Gowda', 'Scott Miller', 'Constantine Lignos', 'Jayadev Billa', 'Srikanth Madikeri', 'Elizabeth Boschee', 'Michael Pust', 'Jonathan May', 'Joel Barry'] | 2019-07-01 | null | null | null | acl-2019-7 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.17111576e-02 2.14615837e-02 -3.20695937e-01 9.88590866e-02
-2.33261800e+00 -8.98766756e-01 5.45335233e-01 6.81752980e-01
-7.22561240e-01 8.69477153e-01 9.43582654e-01 -2.73984253e-01
-3.03249627e-01 -1.81852892e-01 -3.87521446e-01 -3.31151001e-02
1.71691179e-01 8.21307957e-01 -3.05880271e-02 -6.25615656... | [14.345773696899414, 7.288815975189209] |
ffb6ab9f-a9ea-4819-b8c7-d7074f026423 | state-of-the-art-in-human-scanpath-prediction | 2102.12239 | null | https://arxiv.org/abs/2102.12239v2 | https://arxiv.org/pdf/2102.12239v2.pdf | State-of-the-Art in Human Scanpath Prediction | The last years have seen a surge in models predicting the scanpaths of fixations made by humans when viewing images. However, the field is lacking a principled comparison of those models with respect to their predictive power. In the past, models have usually been evaluated based on comparing human scanpaths to scanpat... | ['Matthias Bethge', 'Matthias Kümmerer'] | 2021-02-24 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [ 3.96728516e-01 1.24558896e-01 -2.01449826e-01 -4.53045756e-01
3.62892523e-02 -4.25480753e-01 7.06510484e-01 3.79778683e-01
-5.52900016e-01 6.49626672e-01 6.92271441e-02 -3.77809376e-01
-3.63119006e-01 -4.95958745e-01 -6.61382973e-01 -5.49759746e-01
-6.34856448e-02 6.43777430e-01 7.52913177e-01 -2.30502620... | [10.02005672454834, 1.5453217029571533] |
eaf8e101-5bcb-4556-8ca9-6eb3d9d3d0db | what-can-i-do-here-leveraging-deep-3d | 1812.00889 | null | http://arxiv.org/abs/1812.00889v1 | http://arxiv.org/pdf/1812.00889v1.pdf | What can I do here? Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detection | This paper develops and evaluates a novel method that allows for the
detection of affordances in a scalable and multiple-instance manner on visually
recovered pointclouds. Our approach has many advantages over alternative
methods, as it is based on highly parallelizable, one-shot learning that is
fast in commodity hard... | ['Walterio Mayol-Cuevas', 'Eduardo Ruiz'] | 2018-12-03 | null | null | null | null | ['multiple-affordance-detection', 'affordance-detection'] | ['computer-vision', 'computer-vision'] | [ 1.11280113e-01 -4.64935862e-02 1.71185955e-01 -1.82576999e-01
-5.28747320e-01 -3.18012387e-01 7.51484096e-01 3.82099450e-01
-5.47417402e-01 3.26896012e-01 3.54212373e-01 -1.97937950e-01
-1.19335979e-01 -5.76616824e-01 -8.47664118e-01 -4.63029236e-01
-1.33234948e-01 6.68166697e-01 9.01759267e-01 -4.04652774... | [7.771450042724609, -1.8089286088943481] |
63cdfea1-a39a-445e-b8b7-f5b066f12cae | lemma-a-multi-view-dataset-for-learning-multi | 2007.15781 | null | https://arxiv.org/abs/2007.15781v1 | https://arxiv.org/pdf/2007.15781v1.pdf | LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task Activities | Understanding and interpreting human actions is a long-standing challenge and a critical indicator of perception in artificial intelligence. However, a few imperative components of daily human activities are largely missed in prior literature, including the goal-directed actions, concurrent multi-tasks, and collaborati... | ['Song-Chun Zhu', 'Yixin Zhu', 'Baoxiong Jia', 'Siyuan Huang', 'Yixin Chen'] | 2020-07-31 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5581_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710766.pdf | eccv-2020-8 | ['action-understanding'] | ['computer-vision'] | [ 6.95227623e-01 5.99978529e-02 -3.06454837e-01 -3.99184614e-01
-4.61537153e-01 -6.27210140e-01 1.10812473e+00 -8.86627883e-02
-1.99473709e-01 5.84507346e-01 9.40223217e-01 -7.04811560e-03
-1.95835501e-01 -2.86609888e-01 -6.91888571e-01 -4.18575138e-01
-4.84415501e-01 6.78993165e-01 1.62600726e-01 -1.86746180... | [8.250495910644531, 0.5404900312423706] |
47ca57a2-0446-4b19-b8f2-830ec1bd37d1 | speak-foreign-languages-with-your-own-voice | 2303.03926 | null | https://arxiv.org/abs/2303.03926v1 | https://arxiv.org/pdf/2303.03926v1.pdf | Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling | We propose a cross-lingual neural codec language model, VALL-E X, for cross-lingual speech synthesis. Specifically, we extend VALL-E and train a multi-lingual conditional codec language model to predict the acoustic token sequences of the target language speech by using both the source language speech and the target la... | ['Furu Wei', 'Sheng Zhao', 'Lei He', 'Jinyu Li', 'Huaming Wang', 'Yanqing Liu', 'Zhuo Chen', 'Shujie Liu', 'Yu Wu', 'Sanyuan Chen', 'Chengyi Wang', 'Long Zhou', 'Ziqiang Zhang'] | 2023-03-07 | null | null | null | null | ['text-to-speech-synthesis', 'speech-to-speech-translation', 'speech-synthesis'] | ['speech', 'speech', 'speech'] | [-1.77136019e-01 1.47136897e-01 -2.49855787e-01 -5.57257533e-01
-1.47482252e+00 -4.46497560e-01 3.33619416e-01 -3.76147270e-01
-4.86535877e-02 5.07429004e-01 3.97092402e-01 -6.33629620e-01
6.34902775e-01 -3.70266706e-01 -8.09728265e-01 -5.60114086e-01
4.25296098e-01 1.81558922e-01 -1.07126452e-01 -2.53680199... | [14.685746192932129, 6.883449077606201] |
4324f686-f9c6-4c63-835f-08c57956c2f3 | metaiqa-deep-meta-learning-for-no-reference | 2004.05508 | null | https://arxiv.org/abs/2004.05508v1 | https://arxiv.org/pdf/2004.05508v1.pdf | MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment | Recently, increasing interest has been drawn in exploiting deep convolutional neural networks (DCNNs) for no-reference image quality assessment (NR-IQA). Despite of the notable success achieved, there is a broad consensus that training DCNNs heavily relies on massive annotated data. Unfortunately, IQA is a typical smal... | ['Jinjian Wu', 'Hancheng Zhu', 'Leida Li', 'Guangming Shi', 'Weisheng Dong'] | 2020-04-11 | metaiqa-deep-meta-learning-for-no-reference-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_MetaIQA_Deep_Meta-Learning_for_No-Reference_Image_Quality_Assessment_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhu_MetaIQA_Deep_Meta-Learning_for_No-Reference_Image_Quality_Assessment_CVPR_2020_paper.pdf | cvpr-2020-6 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 2.22472981e-01 -4.16835546e-01 -1.46785393e-01 -4.61857110e-01
-1.02822590e+00 -2.51962483e-01 4.46334183e-01 -2.32477352e-01
-2.21288949e-01 4.14652795e-01 1.75231352e-01 -5.94065785e-02
-3.28053087e-01 -7.91762710e-01 -6.49403870e-01 -6.16873264e-01
-7.29916198e-03 -4.90227118e-02 -1.62921533e-01 -3.00165445... | [11.886719703674316, -1.8961541652679443] |
cdbbf1ad-56e7-468f-9849-a3b94c5e69b2 | composition-of-relational-features-with-an | 2206.00738 | null | https://arxiv.org/abs/2206.00738v2 | https://arxiv.org/pdf/2206.00738v2.pdf | Composition of Relational Features with an Application to Explaining Black-Box Predictors | Relational machine learning programs like those developed in Inductive Logic Programming (ILP) offer several advantages: (1) The ability to model complex relationships amongst data instances; (2) The use of domain-specific relations during model construction; and (3) The models constructed are human-readable, which is ... | ['Devanshu Shah', 'Tirtharaj Dash', 'A Baskar', 'Ashwin Srinivasan'] | 2022-06-01 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 3.96235466e-01 8.05075228e-01 -1.08438417e-01 -6.49057150e-01
-1.19224399e-01 -4.12091345e-01 6.62984788e-01 2.59211659e-01
6.73752874e-02 7.02712297e-01 -8.14620927e-02 -9.27449346e-01
-6.19757652e-01 -1.36452270e+00 -1.15891612e+00 -4.40256774e-01
-6.85923815e-01 5.88614643e-01 2.16130942e-01 -4.89861488... | [8.882976531982422, 6.9422783851623535] |
f36c5fc9-821c-41f5-9900-7b848cf1137e | policyclustergcn-identifying-efficient | 2306.14357 | null | https://arxiv.org/abs/2306.14357v1 | https://arxiv.org/pdf/2306.14357v1.pdf | PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks | Graph convolutional networks (GCNs) have achieved huge success in several machine learning (ML) tasks on graph-structured data. Recently, several sampling techniques have been proposed for the efficient training of GCNs and to improve the performance of GCNs on ML tasks. Specifically, the subgraph-based sampling approa... | ['Srinivasan Parthasarathy', 'Balaraman Ravindran', 'Shaileshh Bojja Venkatakrishnan', 'Saket Gurukar'] | 2023-06-25 | null | null | null | null | ['node-classification', 'graph-partitioning'] | ['graphs', 'graphs'] | [-1.21336348e-01 5.42918205e-01 -7.97796726e-01 -3.90798271e-01
-5.29768050e-01 -1.34482384e-01 5.34376919e-01 3.17577362e-01
-3.34292293e-01 6.02060854e-01 -2.23076701e-01 -5.51659644e-01
-1.95152223e-01 -1.09330118e+00 -8.87139559e-01 -8.49696815e-01
-4.12424505e-01 9.23217356e-01 -1.00886106e-01 2.57707626... | [7.331878185272217, 6.215417385101318] |
455db635-bbca-4fa7-9d8c-e6b531e89465 | hierarchical-federated-learning | 2307.00233 | null | https://arxiv.org/abs/2307.00233v1 | https://arxiv.org/pdf/2307.00233v1.pdf | Hierarchical Federated Learning Incentivization for Gas Usage Estimation | Accurately estimating gas usage is essential for the efficient functioning of gas distribution networks and saving operational costs. Traditional methods rely on centralized data processing, which poses privacy risks. Federated learning (FL) offers a solution to this problem by enabling local data processing on each pa... | ['Han Yu', 'Zengxiang Li', 'Qijie Ding', 'Xiuli Wang', 'Zhenpeng Yu', 'Chengyi Yang', 'Xiaoli Tang', 'Has Sun'] | 2023-07-01 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [-4.00276452e-01 3.75248939e-01 -3.44742537e-01 -2.91848123e-01
-3.11834604e-01 -6.09546125e-01 2.05709562e-01 3.30821782e-01
-4.24914688e-01 8.45903337e-01 -1.39990747e-01 -4.40698355e-01
-4.02946204e-01 -1.51578176e+00 -1.36872873e-01 -1.04276145e+00
-1.97241873e-01 3.55959177e-01 -3.54524374e-01 2.67955512... | [5.834280490875244, 6.044119834899902] |
265871dd-7437-4fb6-a839-896e77beb2ba | crowdsensing-based-road-damage-detection | 2211.11362 | null | https://arxiv.org/abs/2211.11362v1 | https://arxiv.org/pdf/2211.11362v1.pdf | Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022) | This paper summarizes the Crowdsensing-based Road Damage Detection Challenge (CRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2022. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data co... | ['Yoshihide Sekimoto', 'Takehiro Kashiyama', 'Hiroshi Omata', 'Durga Toshniwal', 'Sanjay Kumar Ghosh', 'Hiroya Maeda', 'Deeksha Arya'] | 2022-11-21 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-3.34783852e-01 -6.97065890e-02 2.67467380e-01 1.29740849e-01
-1.26780915e+00 -4.98856097e-01 7.15109646e-01 1.16638236e-01
-5.28819621e-01 7.32187867e-01 6.34328783e-01 2.20591232e-01
1.25678435e-01 -7.96018362e-01 -5.25934279e-01 -6.55724645e-01
-5.52785695e-02 3.22908282e-01 5.00587285e-01 -4.45928395... | [7.403700351715088, 1.0739742517471313] |
d0c0b60e-a52d-4b9e-93ba-b3e04c9c8e13 | gcdf1-a-goal-and-context-driven-f-score-for | null | null | https://aclanthology.org/2021.eancs-1.2 | https://aclanthology.org/2021.eancs-1.2.pdf | GCDF1: A Goal- and Context- Driven F-Score for Evaluating User Models | The evaluation of dialogue systems in interaction with simulated users has been proposed to improve turn-level, corpus-based metrics which can only evaluate test cases encountered in a corpus and cannot measure system’s ability to sustain multi-turn interactions. Recently, little emphasis was put on automatically asses... | ['Bill Byrne', 'Bo-Hsiang Tseng', 'Alexandru Coca'] | null | null | null | null | eancs-2021-11 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-9.36880037e-02 5.58593333e-01 3.88275236e-01 -5.49492955e-01
-9.61170793e-01 -9.12172914e-01 1.19374096e+00 2.73572862e-01
-3.51365268e-01 8.09600532e-01 5.27436435e-01 -4.28182274e-01
-7.94267282e-02 -4.75354016e-01 -8.16474259e-02 -1.34767279e-01
-3.12845744e-02 8.97832394e-01 3.62064183e-01 -5.64672887... | [12.871386528015137, 8.05367660522461] |
ea522497-631a-4f02-bebf-fad3739a5912 | domain-mismatch-doesn-t-always-prevent-cross | 2211.16671 | null | https://arxiv.org/abs/2211.16671v1 | https://arxiv.org/pdf/2211.16671v1.pdf | Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning | Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc. However, some recent publications have claimed that domain mismatch prevents cross-lingual transfer,... | ['Noah A. Smith', 'Phillip Keung', 'Daniel Edmiston'] | 2022-11-30 | null | null | null | null | ['word-similarity', 'unsupervised-machine-translation', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-4.39894572e-03 -1.92300856e-01 -5.02766609e-01 -3.50107700e-01
-1.44455945e+00 -9.51753974e-01 8.50764990e-01 3.10228765e-02
-8.04554760e-01 1.15442002e+00 4.21739012e-01 -5.82368910e-01
3.17756832e-01 -4.05535907e-01 -9.43936229e-01 -4.04510647e-01
4.77357775e-01 1.04275453e+00 7.28224590e-02 -4.85010028... | [11.032645225524902, 9.944483757019043] |
7ad46b73-bde8-4c11-b234-d31fb1429572 | a-neural-method-for-goal-oriented-dialog | null | null | https://openreview.net/forum?id=ByhthReRb | https://openreview.net/pdf?id=ByhthReRb | A Neural Method for Goal-Oriented Dialog Systems to interact with Named Entities | Many goal-oriented dialog tasks, especially ones in which the dialog system has to interact with external knowledge sources such as databases, have to handle a large number of Named Entities (NEs). There are at least two challenges in handling NEs using neural methods in such settings: individual NEs may occur only rar... | ['Satinder Singh', 'Xiaoxiao Guo', 'Mo Yu', 'Jatin Ganhotra', 'Janarthanan Rajendran'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-2.50690728e-01 3.76645714e-01 1.54319704e-01 -5.94513059e-01
-5.02810717e-01 -8.39122951e-01 4.83823836e-01 2.63967842e-01
-7.55967140e-01 1.12438703e+00 3.20898414e-01 -3.71099204e-01
-9.39725339e-02 -9.65631127e-01 -2.94782072e-01 -2.86444817e-02
-7.71950856e-02 1.20685244e+00 3.79399389e-01 -9.39067304... | [12.67417049407959, 7.979498386383057] |
5fc13877-66b0-4f94-9a49-075a4dedfd5e | knowledge-graph-augmented-abstractive | 2005.01159 | null | https://arxiv.org/abs/2005.01159v1 | https://arxiv.org/pdf/2005.01159v1.pdf | Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward | Sequence-to-sequence models for abstractive summarization have been studied extensively, yet the generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, the summarizer should acquire semantic interpretation over input, e.g., via str... | ['Lu Wang', 'Lingfei Wu', 'Luyang Huang'] | 2020-05-03 | knowledge-graph-augmented-abstractive-1 | https://aclanthology.org/2020.acl-main.457 | https://aclanthology.org/2020.acl-main.457.pdf | acl-2020-6 | ['cloze-test'] | ['natural-language-processing'] | [ 3.36139441e-01 6.81549191e-01 -1.65167570e-01 -4.41738307e-01
-1.00972450e+00 -6.46944344e-01 5.41534424e-01 5.36307752e-01
-3.96741629e-01 8.83308291e-01 1.09304118e+00 -1.35712117e-01
7.08843768e-02 -7.11592197e-01 -1.00893605e+00 -1.09762579e-01
1.83377713e-01 6.05773270e-01 -4.62810602e-03 -3.68562311... | [12.279693603515625, 9.250367164611816] |
f9f6089b-b05e-4590-af40-aed93d8aff1f | lilgym-natural-language-visual-reasoning-with | 2211.01994 | null | https://arxiv.org/abs/2211.01994v3 | https://arxiv.org/pdf/2211.01994v3.pdf | lilGym: Natural Language Visual Reasoning with Reinforcement Learning | We present lilGym, a new benchmark for language-conditioned reinforcement learning in visual environments. lilGym is based on 2,661 highly-compositional human-written natural language statements grounded in an interactive visual environment. We introduce a new approach for exact reward computation in every possible wor... | ['Yoav Artzi', 'Noriyuki Kojima', 'Kianté Brantley', 'Anne Wu'] | 2022-11-03 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-1.62096903e-01 -1.41366795e-01 -2.84314662e-01 -1.13436371e-01
-8.21660638e-01 -1.02565491e+00 5.41985393e-01 2.08802834e-01
-4.98166174e-01 1.06068814e+00 6.55999482e-02 -9.47982132e-01
3.65788341e-01 -5.07807910e-01 -9.06447709e-01 -4.84130710e-01
-4.49619979e-01 6.15691483e-01 3.79481912e-01 -7.41443187... | [4.029333591461182, 1.5427740812301636] |
42ffb2ec-2d75-47f5-a143-85ecf125e0cc | diffload-uncertainty-quantification-in-load | 2306.01001 | null | https://arxiv.org/abs/2306.01001v1 | https://arxiv.org/pdf/2306.01001v1.pdf | DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model | Electrical load forecasting is of great significance for the decision makings in power systems, such as unit commitment and energy management. In recent years, various self-supervised neural network-based methods have been applied to electrical load forecasting to improve forecasting accuracy and capture uncertainties.... | ['Yi Wang', 'Liang Sun', 'Chaoli Zhang', 'Qingsong Wen', 'Zhixian Wang'] | 2023-05-31 | null | null | null | null | ['load-forecasting', 'energy-management'] | ['miscellaneous', 'time-series'] | [-1.08632166e-02 -1.79492176e-01 -6.85152085e-03 -5.25449276e-01
-6.51665390e-01 -4.99081671e-01 5.71781695e-01 2.58715898e-01
-1.52859524e-01 1.19975662e+00 2.07196966e-01 -3.84892672e-01
-4.62576509e-01 -9.81354892e-01 -5.88110685e-01 -8.23703110e-01
-1.24262288e-01 4.65511143e-01 -4.05475870e-02 2.15195283... | [6.1752142906188965, 2.9737255573272705] |
6bf38a7e-4dc5-453e-b584-6cc672bdf19c | long-range-constraints-for-neural-texture | 2211.11137 | null | https://arxiv.org/abs/2211.11137v1 | https://arxiv.org/pdf/2211.11137v1.pdf | Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Loss | In the past decade, exemplar-based texture synthesis algorithms have seen strong gains in performance by matching statistics of deep convolutional neural networks. However, these algorithms require regularization terms or user-added spatial tags to capture long range constraints in images. Having access to a user-added... | ['Albert Chua', 'Liping Yin'] | 2022-11-21 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 2.06261098e-01 -2.50252128e-01 2.60002941e-01 -4.60561723e-01
-6.01178586e-01 -2.46749595e-01 6.29899979e-01 5.22420555e-02
-3.83757293e-01 8.70049536e-01 -2.12802216e-01 -7.32515529e-02
-5.91512382e-01 -9.40317929e-01 -8.95861328e-01 -8.23094368e-01
2.64731497e-02 3.90864998e-01 3.73843282e-01 -1.94006130... | [11.468525886535645, -0.5854077935218811] |
f1e001e4-c3f8-46eb-a6c2-618cf2fead60 | can-learning-from-natural-image-denoising-be | 1902.10379 | null | https://arxiv.org/abs/1902.10379v3 | https://arxiv.org/pdf/1902.10379v3.pdf | Can learning from natural image denoising be used for seismic data interpolation? | We propose a convolutional neural network (CNN) denoising based method for seismic data interpolation. It provides a simple and efficient way to break though the lack problem of geophysical training labels that are often required by deep learning methods. The new method consists of two steps: (1) Train a set of CNN den... | ['Xiuyan Yang', 'Hao Zhang', 'Jianwei Ma'] | 2019-02-27 | null | null | null | null | ['de-aliasing'] | ['computer-vision'] | [-4.79556955e-02 4.27120514e-02 7.94171572e-01 -3.59000295e-01
-9.14434195e-01 -7.10892230e-02 4.68427896e-01 -1.24199502e-01
-6.61634982e-01 8.50449204e-01 1.53367847e-01 -1.61694556e-01
-4.04771119e-01 -9.91720378e-01 -1.03800690e+00 -9.06762719e-01
-2.12277129e-01 1.18494324e-01 6.26815036e-02 -5.90723932... | [11.53306770324707, -2.305431842803955] |
7551251a-e704-4c38-a6c6-0fd7281771a1 | save-spectral-shift-aware-adaptation-of-image | 2305.18670 | null | https://arxiv.org/abs/2305.18670v1 | https://arxiv.org/pdf/2305.18670v1.pdf | SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-guided Video Editing | Text-to-Image (T2I) diffusion models have achieved remarkable success in synthesizing high-quality images conditioned on text prompts. Recent methods have tried to replicate the success by either training text-to-video (T2V) models on a very large number of text-video pairs or adapting T2I models on text-video pairs in... | ['Nazanin Rahnavard', 'Chen Chen', 'Mohsen Joneidi', 'Umar Khalid', 'Nazmul Karim'] | 2023-05-30 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 6.14692152e-01 -2.56964326e-01 -2.04585288e-02 -2.86230862e-01
-5.19410491e-01 -5.93847334e-01 4.99189794e-01 -1.79698035e-01
-6.50600076e-01 5.52225292e-01 7.27254748e-02 -1.67597368e-01
-4.44274880e-02 -4.18579787e-01 -7.99309254e-01 -8.85675073e-01
1.87838599e-01 1.50308490e-01 3.38600844e-01 -1.67647954... | [11.114380836486816, -0.8145363926887512] |
4e49634c-35db-4418-a373-4dd8d6fc7fe6 | retrogan-a-cyclic-post-specialization-system | 2108.12941 | null | https://arxiv.org/abs/2108.12941v1 | https://arxiv.org/pdf/2108.12941v1.pdf | RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations | Retrofitting is a technique used to move word vectors closer together or further apart in their space to reflect their relationships in a Knowledge Base (KB). However, retrofitting only works on concepts that are present in that KB. RetroGAN uses a pair of Generative Adversarial Networks (GANs) to learn a one-to-one ma... | ['Peter Chin', 'Cynthia Breazeal', 'Catherine Havasi', 'Henry Lieberman', 'Yida Xin', 'Pedro Colon-Hernandez'] | 2021-08-30 | null | https://aclanthology.org/2021.findings-acl.183 | https://aclanthology.org/2021.findings-acl.183.pdf | findings-acl-2021-8 | ['word-similarity'] | ['natural-language-processing'] | [ 3.06299597e-01 7.50068128e-02 -1.63762663e-02 -2.19241232e-01
-7.51854062e-01 -7.92810857e-01 4.50773507e-01 9.17730927e-02
-7.63342977e-01 9.34580624e-01 5.73477745e-01 -3.23254168e-01
6.14974126e-02 -1.07816625e+00 -8.26302588e-01 -5.66911161e-01
4.24349099e-01 5.11024952e-01 -1.23653397e-01 -1.07791126... | [10.605355262756348, 8.704318046569824] |
0a2a36d8-4b64-45e5-aa55-3806d83f80fd | discovering-object-masks-with-transformers-1 | 2206.06363 | null | https://arxiv.org/abs/2206.06363v1 | https://arxiv.org/pdf/2206.06363v1.pdf | Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation | The task of unsupervised semantic segmentation aims to cluster pixels into semantically meaningful groups. Specifically, pixels assigned to the same cluster should share high-level semantic properties like their object or part category. This paper presents MaskDistill: a novel framework for unsupervised semantic segmen... | ['Luc van Gool', 'Simon Vandenhende', 'Wouter Van Gansbeke'] | 2022-06-13 | discovering-object-masks-with-transformers | https://arxiv.org/abs/2206.06363 | https://arxiv.org/pdf/2206.06363.pdf | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 6.49699867e-01 3.43808889e-01 -1.19552299e-01 -4.87985551e-01
-6.90746307e-01 -6.49610400e-01 5.82478642e-01 3.96170586e-01
-5.31939268e-01 3.60375911e-01 -1.19664706e-02 -5.31763136e-02
2.38349184e-01 -8.37633669e-01 -7.48066187e-01 -6.18843317e-01
3.95156413e-01 3.92668277e-01 7.69155502e-01 1.30156651... | [9.527420043945312, 0.5319823026657104] |
d6de6e86-4d33-4981-98a1-d5321d868220 | segmentation-mask-guided-end-to-end-person | 1908.10179 | null | https://arxiv.org/abs/1908.10179v1 | https://arxiv.org/pdf/1908.10179v1.pdf | Segmentation Mask Guided End-to-End Person Search | Person search aims to search for a target person among multiple images recorded by multiple surveillance cameras, which faces various challenges from both pedestrian detection and person re-identification. Besides the large intra-class variations owing to various illumination conditions, occlusions and varying poses, b... | ['Kai-Zhu Huang', 'Yao Zhao', 'Jimin Xiao', 'Dingyuan Zheng'] | 2019-08-27 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.48834661e-01 -9.21964228e-01 1.19022802e-01 -2.10189924e-01
-5.40196240e-01 -6.02506399e-01 3.82358938e-01 -3.06924451e-02
-8.66092622e-01 6.66492641e-01 8.54365006e-02 2.43294135e-01
3.24643165e-01 -5.17447591e-01 -4.15170848e-01 -8.75135362e-01
6.42626286e-02 2.53572017e-01 5.57758987e-01 3.07463378... | [14.781792640686035, 0.8371515274047852] |
52b991e1-ff4e-4a2a-9b07-3c0acacba52a | deep-unsupervised-anomaly-detection | null | null | https://openaccess.thecvf.com/content/WACV2021/papers/Li_Deep_Unsupervised_Anomaly_Detection_WACV_2021_paper.pdf | https://openaccess.thecvf.com/content/WACV2021/papers/Li_Deep_Unsupervised_Anomaly_Detection_WACV_2021_paper.pdf | Deep unsupervised anomaly detection | This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting. The key idea behind our algorithm is to learn the representation underlying normal data. To this end, we leverage the latest clustering technique suitable for handling high dimensional data. This hypothesis prov... | ['Wen-Yan Lin', 'Siying Liu', 'Zheng Wang', 'Tangqing Li'] | 2021-01-05 | null | null | null | winter-conference-on-applications-of-computer-4 | ['unsupervised-anomaly-detection-with-specified-5', 'unsupervised-anomaly-detection-with-specified-4', 'unsupervised-anomaly-detection-with-specified-7', 'unsupervised-anomaly-detection-with-specified-6', 'unsupervised-anomaly-detection-with-specified'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.42714083e-01 1.35070896e-02 -4.56761196e-03 -4.86177683e-01
-4.73477632e-01 -2.68348247e-01 5.47404706e-01 4.18631941e-01
-2.06640705e-01 1.51888788e-01 4.48043011e-02 -7.18855113e-02
-3.46468091e-01 -9.57970977e-01 -5.09034395e-01 -1.04965520e+00
-3.03822219e-01 9.03880298e-01 2.33021099e-02 6.19083419... | [7.590263366699219, 2.428600549697876] |
72b08e62-a539-4a42-8afe-36046fd08ace | generalized-source-free-domain-adaptation | 2108.01614 | null | https://arxiv.org/abs/2108.01614v2 | https://arxiv.org/pdf/2108.01614v2.pdf | Generalized Source-free Domain Adaptation | Domain adaptation (DA) aims to transfer the knowledge learned from a source domain to an unlabeled target domain. Some recent works tackle source-free domain adaptation (SFDA) where only a source pre-trained model is available for adaptation to the target domain. However, those methods do not consider keeping source pe... | ['Shangling Jui', 'Luis Herranz', 'Joost Van de Weijer', 'Yaxing Wang', 'Shiqi Yang'] | 2021-08-03 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yang_Generalized_Source-Free_Domain_Adaptation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_Generalized_Source-Free_Domain_Adaptation_ICCV_2021_paper.pdf | iccv-2021-1 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 7.52646700e-02 -2.18749255e-01 -4.65155900e-01 -4.02665406e-01
-7.42079973e-01 -6.40850723e-01 6.00667477e-01 -8.59805495e-02
-2.61522144e-01 8.23118985e-01 1.99580610e-01 1.19976841e-01
1.38009742e-01 -6.43760741e-01 -7.11567402e-01 -7.44783878e-01
3.13936949e-01 7.39569545e-01 4.23710465e-01 -1.83094412... | [10.358477592468262, 3.0133755207061768] |
bf4eaa65-cb04-4e47-8a64-5e1bbc946e4e | deep-learning-and-traffic-classification | 2104.03182 | null | https://arxiv.org/abs/2104.03182v2 | https://arxiv.org/pdf/2104.03182v2.pdf | Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications | The increasing success of Machine Learning (ML) and Deep Learning (DL) has recently re-sparked interest towards traffic classification. While classification of known traffic is a well investigated subject with supervised classification tools (such as ML and DL models) are known to provide satisfactory performance, dete... | ['Dario Rossi', 'Feng Jun', 'Alessandro Finamore', 'Lixuan Yang'] | 2021-04-07 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 4.32214364e-02 -5.77495635e-01 -3.95211309e-01 -2.09718138e-01
-7.40822852e-01 -5.78794420e-01 7.24332154e-01 1.81747258e-01
-1.35226622e-01 8.43860507e-01 -4.67018366e-01 -9.94231164e-01
-3.73444796e-01 -1.00390100e+00 -4.43306983e-01 -7.32121944e-01
-1.26223490e-01 7.38422215e-01 5.51211357e-01 -1.14237629... | [5.064985752105713, 7.233597755432129] |
1cfaa8dd-91f8-4e38-b769-a028c4f4aed6 | decoupled-diffusion-models-with-explicit | 2306.13720 | null | https://arxiv.org/abs/2306.13720v1 | https://arxiv.org/pdf/2306.13720v1.pdf | Decoupled Diffusion Models with Explicit Transition Probability | Recent diffusion probabilistic models (DPMs) have shown remarkable abilities of generated content, however, they often suffer from complex forward processes, resulting in inefficient solutions for the reversed process and prolonged sampling times. In this paper, we aim to address the aforementioned challenges by focusi... | ['Kai Xu', 'Xinwang Liu', 'Zheng Qin', 'Yuhang Huang'] | 2023-06-23 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 3.96598399e-01 -6.52953163e-02 3.37528169e-01 1.10087715e-01
-7.03583777e-01 -4.75945622e-01 8.07816148e-01 -3.37505072e-01
-3.24191779e-01 6.49329901e-01 -2.88638640e-02 -3.38003218e-01
-3.54471982e-01 -8.96403074e-01 -6.37396693e-01 -1.41050529e+00
1.89348578e-01 4.28483665e-01 2.74165243e-01 -1.86162010... | [11.230767250061035, -0.548655092716217] |
c3915dab-e9b1-4a30-8662-3f4f6ddec31c | deep-snake-for-real-time-instance | 2001.01629 | null | https://arxiv.org/abs/2001.01629v3 | https://arxiv.org/pdf/2001.01629v3.pdf | Deep Snake for Real-Time Instance Segmentation | This paper introduces a novel contour-based approach named deep snake for real-time instance segmentation. Unlike some recent methods that directly regress the coordinates of the object boundary points from an image, deep snake uses a neural network to iteratively deform an initial contour to match the object boundary,... | ['Xiuli Li', 'Sida Peng', 'Hujun Bao', 'Wen Jiang', 'Xiaowei Zhou', 'Huaijin Pi'] | 2020-01-06 | deep-snake-for-real-time-instance-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Peng_Deep_Snake_for_Real-Time_Instance_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Peng_Deep_Snake_for_Real-Time_Instance_Segmentation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['real-time-instance-segmentation'] | ['computer-vision'] | [-2.38834694e-01 6.76656738e-02 6.50042593e-02 -3.49325359e-01
-5.54119408e-01 -6.32514656e-01 2.58900076e-01 4.39153790e-01
-6.18228495e-01 9.56104398e-02 -4.64784384e-01 -2.94438094e-01
4.58752900e-01 -9.23013449e-01 -5.27212620e-01 -5.54984152e-01
-3.18870038e-01 4.51705098e-01 7.24408448e-01 -1.33435220... | [9.514043807983398, 0.14285610616207123] |
47f318a5-b24f-4cfc-8b9c-7ec17e17cac7 | dynamic-causal-graph-convolutional-network | 2306.07019 | null | https://arxiv.org/abs/2306.07019v1 | https://arxiv.org/pdf/2306.07019v1.pdf | Dynamic Causal Graph Convolutional Network for Traffic Prediction | Modeling complex spatiotemporal dependencies in correlated traffic series is essential for traffic prediction. While recent works have shown improved prediction performance by using neural networks to extract spatiotemporal correlations, their effectiveness depends on the quality of the graph structures used to represe... | ['Chen Zhang', 'Rui Zhao', 'Lei Bai', 'Zhishuai Li', 'Ziyue Li', 'Junpeng Lin'] | 2023-06-12 | null | null | null | null | ['traffic-prediction'] | ['time-series'] | [-1.92936331e-01 -1.87145397e-01 -2.53443211e-01 -3.74705702e-01
-1.86332483e-02 -9.51969549e-02 5.97025335e-01 -4.17296141e-01
3.58376026e-01 8.34430635e-01 1.95082158e-01 -8.52767289e-01
-3.93068552e-01 -1.16000426e+00 -7.26977289e-01 -1.91635087e-01
-4.28307593e-01 3.88964653e-01 8.39480758e-01 -3.63946348... | [6.459692001342773, 2.056347608566284] |
cbb8dd7f-dcc7-4f13-94bb-62a213e18d3c | what-makes-a-top-performing-precision | 2006.02785 | null | https://arxiv.org/abs/2006.02785v2 | https://arxiv.org/pdf/2006.02785v2.pdf | What Makes a Top-Performing Precision Medicine Search Engine? Tracing Main System Features in a Systematic Way | From 2017 to 2019 the Text REtrieval Conference (TREC) held a challenge task on precision medicine using documents from medical publications (PubMed) and clinical trials. Despite lots of performance measurements carried out in these evaluation campaigns, the scientific community is still pretty unsure about the impact ... | ['Udo Hahn', 'Michel Oleynik', 'Erik Faessler'] | 2020-06-04 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [ 3.97970736e-01 -3.34010988e-01 -3.92837554e-01 -1.02436557e-01
-1.32670736e+00 -7.55328894e-01 1.01817942e+00 1.01327538e+00
-1.17789578e+00 5.31007767e-01 5.84356368e-01 -7.24528849e-01
-7.01060951e-01 -2.78287202e-01 -2.84983993e-01 -2.74287790e-01
-2.52631098e-01 6.26251161e-01 3.23958844e-01 -1.34454072... | [8.806992530822754, 8.687763214111328] |
aa2650f3-175d-4d41-a84b-7cdc77dcf858 | soft-anchor-point-object-detection | 1911.12448 | null | https://arxiv.org/abs/1911.12448v2 | https://arxiv.org/pdf/1911.12448v2.pdf | Soft Anchor-Point Object Detection | Recently, anchor-free detection methods have been through great progress. The major two families, anchor-point detection and key-point detection, are at opposite edges of the speed-accuracy trade-off, with anchor-point detectors having the speed advantage. In this work, we boost the performance of the anchor-point dete... | ['Zhiqiang Shen', 'Fangyi Chen', 'Marios Savvides', 'Chenchen Zhu'] | 2019-11-27 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/721_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540086.pdf | eccv-2020-8 | ['dense-object-detection'] | ['computer-vision'] | [-1.05809897e-01 -2.13766754e-01 -2.44201973e-01 -5.50616905e-02
-1.40737712e+00 -4.06000286e-01 2.73049682e-01 4.61789995e-01
-3.63352001e-01 -8.49722326e-02 -2.54479665e-02 -3.01334430e-02
8.24417844e-02 -4.13845062e-01 -7.08959937e-01 -7.05872893e-01
-3.76162171e-01 -1.02323875e-01 1.05161297e+00 -2.19619662... | [8.710086822509766, -0.39567244052886963] |
a97dcfcf-9868-4e27-abb9-0ba8378f2a41 | learning-with-a-mole-transferable-latent | 2306.03857 | null | https://arxiv.org/abs/2306.03857v1 | https://arxiv.org/pdf/2306.03857v1.pdf | Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction | Agents navigating in 3D environments require some form of memory, which should hold a compact and actionable representation of the history of observations useful for decision taking and planning. In most end-to-end learning approaches the representation is latent and usually does not have a clearly defined interpretati... | ['Christian Wolf', 'Gianluca Monaci', 'Assem Sadek', 'Leonid Antsfeld', 'Guillaume Bono'] | 2023-06-06 | null | null | null | null | ['navigate'] | ['reasoning'] | [ 1.94454387e-01 8.01003635e-01 -2.99720801e-02 -3.70783120e-01
-6.73514426e-01 -7.61019111e-01 8.56256366e-01 2.30359778e-01
-5.69107652e-01 6.95798159e-01 3.44859034e-01 -2.67502785e-01
-3.84529829e-01 -8.87972295e-01 -1.12540889e+00 -8.49568188e-01
-3.76097590e-01 1.13880169e+00 1.06466062e-01 -2.50930041... | [4.665308475494385, 0.7159745693206787] |
3fdda359-bdae-424f-9306-2ba411c0b816 | attribute-recognition-by-joint-recurrent | 1709.08553 | null | http://arxiv.org/abs/1709.08553v1 | http://arxiv.org/pdf/1709.08553v1.pdf | Attribute Recognition by Joint Recurrent Learning of Context and Correlation | Recognising semantic pedestrian attributes in surveillance images is a
challenging task for computer vision, particularly when the imaging quality is
poor with complex background clutter and uncontrolled viewing conditions, and
the number of labelled training data is small. In this work, we formulate a
Joint Recurrent ... | ['Jingya Wang', 'Shaogang Gong', 'Xiatian Zhu', 'Wei Li'] | 2017-09-25 | attribute-recognition-by-joint-recurrent-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Wang_Attribute_Recognition_by_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Wang_Attribute_Recognition_by_ICCV_2017_paper.pdf | iccv-2017-10 | ['pedestrian-attribute-recognition', 'multi-label-image-classification'] | ['computer-vision', 'computer-vision'] | [ 4.42722172e-01 -3.29455256e-01 -1.39220757e-02 -1.01612282e+00
-8.29188168e-01 -3.95100266e-01 6.41740561e-01 1.65586531e-01
-4.51528490e-01 5.52808166e-01 4.17860597e-01 5.88971563e-03
1.09474510e-01 -5.14015138e-01 -8.82851541e-01 -7.66846418e-01
-6.48104772e-02 6.42579019e-01 -1.32320225e-01 1.92009628... | [14.464241981506348, 0.9811956882476807] |
05efa87c-5316-468a-8f76-bdf646b2635f | co-search-covid-19-information-retrieval-with | 2006.09595 | null | https://arxiv.org/abs/2006.09595v1 | https://arxiv.org/pdf/2006.09595v1.pdf | CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization | The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. As of May 2020, 128,000 coronavirus-related publications have been collected through the COVID-1... | ['Anuprit Kale', 'Richard Socher', 'Kazuma Hashimoto', 'Andre Esteva', 'Dragomir Radev', 'Wenpeng Yin', 'Romain Paulus'] | 2020-06-17 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 4.00673412e-02 -1.35122895e-01 -5.71596384e-01 -1.17035367e-01
-1.36708260e+00 -7.67154038e-01 5.22929788e-01 8.09493303e-01
-5.94049811e-01 7.99690664e-01 8.07327330e-01 -3.86032581e-01
-4.38302130e-01 -6.60789192e-01 -8.17023039e-01 -1.01660296e-01
4.63369042e-02 1.07936776e+00 -8.46488178e-02 -2.26029187... | [8.691701889038086, 8.709358215332031] |
93a7cd5c-bec7-4092-8f5c-08ad6705f2df | deriving-explanation-of-deep-visual-saliency | 2109.03575 | null | https://arxiv.org/abs/2109.03575v1 | https://arxiv.org/pdf/2109.03575v1.pdf | Deriving Explanation of Deep Visual Saliency Models | Deep neural networks have shown their profound impact on achieving human level performance in visual saliency prediction. However, it is still unclear how they learn the task and what it means in terms of understanding human visual system. In this work, we develop a technique to derive explainable saliency models from ... | ['Santanu Chaudhury', 'Chaker Larabi', 'Jayanta Mukhopadhyay', 'Sai Phani Kumar Malladi'] | 2021-09-08 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 4.05270785e-01 5.57166040e-01 -5.34130037e-02 -2.42074832e-01
1.23354107e-01 -4.67640981e-02 6.56201303e-01 -2.74777338e-02
1.26046941e-01 7.23753154e-01 3.24392706e-01 -2.42086798e-01
-1.10678010e-01 -5.63888073e-01 -1.07810235e+00 -4.24816132e-01
4.24673930e-02 9.83769223e-02 8.32619607e-01 -5.16089916... | [10.055135726928711, 1.61247718334198] |
3279b072-074d-4e8d-bf3e-5d0932074586 | chatgpt-is-not-enough-enhancing-large | 2306.11489 | null | https://arxiv.org/abs/2306.11489v1 | https://arxiv.org/pdf/2306.11489v1.pdf | ChatGPT is not Enough: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling | Recently, ChatGPT, a representative large language model (LLM), has gained considerable attention due to its powerful emergent abilities. Some researchers suggest that LLMs could potentially replace structured knowledge bases like knowledge graphs (KGs) and function as parameterized knowledge bases. However, while LLMs... | ['Xindong Wu', 'Xiao Ding', 'Zhao Li', 'Hongyang Chen', 'Linyao Yang'] | 2023-06-20 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-3.25029224e-01 9.95120108e-01 -4.30540770e-01 5.40869758e-02
-4.91832912e-01 -5.28752089e-01 7.70806253e-01 2.76614904e-01
-1.42904043e-01 1.04360747e+00 5.28052866e-01 -5.34411371e-01
-1.97053030e-01 -1.44392335e+00 -7.58756220e-01 -3.58220190e-02
-7.44709745e-02 7.45434642e-01 4.98081565e-01 -2.67842174... | [10.256875038146973, 8.024232864379883] |
f0b77972-912b-452d-bc82-5697c2f06334 | aspect-extraction-with-automated-prior | null | null | https://aclanthology.org/P14-1033 | https://aclanthology.org/P14-1033.pdf | Aspect Extraction with Automated Prior Knowledge Learning | null | ['Zhiyuan Chen', 'Bing Liu', 'Arjun Mukherjee'] | 2014-06-01 | null | null | null | acl-2014-6 | ['aspect-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.269987106323242, 3.704164743423462] |
f2d5d1a4-30df-4fe5-89c9-f1ebc198f7ae | micro-expression-spotting-a-new-benchmark | 2007.12421 | null | https://arxiv.org/abs/2007.12421v2 | https://arxiv.org/pdf/2007.12421v2.pdf | Micro-expression spotting: A new benchmark | Micro-expressions (MEs) are brief and involuntary facial expressions that occur when people are trying to hide their true feelings or conceal their emotions. Based on psychology research, MEs play an important role in understanding genuine emotions, which leads to many potential applications. Therefore, ME analysis has... | ['Quang-Nhat Vo', 'Thuong-Khanh Tran', 'Xiaopeng Hong', 'Xiaobai Li', 'Guoying Zhao'] | 2020-07-24 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 2.21234411e-01 -3.16710591e-01 -3.45246106e-01 -4.76144850e-01
-4.83317047e-01 -1.19198114e-01 4.03998256e-01 -3.21909547e-01
-3.35067332e-01 3.80165368e-01 2.65683550e-02 3.27132702e-01
1.10008404e-01 -3.56563300e-01 -1.40190855e-01 -9.85242724e-01
-1.10553652e-01 -1.02652349e-01 -2.47192442e-01 -3.01162213... | [13.603320121765137, 1.8062536716461182] |
893720dd-5d0a-47ce-899e-4c66c3d6480f | safe-deep-rl-for-intraoperative-planning-of | 2305.05354 | null | https://arxiv.org/abs/2305.05354v2 | https://arxiv.org/pdf/2305.05354v2.pdf | Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement | Spinal fusion surgery requires highly accurate implantation of pedicle screw implants, which must be conducted in critical proximity to vital structures with a limited view of anatomy. Robotic surgery systems have been proposed to improve placement accuracy, however, state-of-the-art systems suffer from the limitations... | ['Philipp Fuernstahl', 'Andreas Krause', 'Benjamin F. Grewe', 'Mazda Farshad', 'Yarden As', 'Fabio Carrillo', 'Hooman Esfandiari', 'Yunke Ao'] | 2023-05-09 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-8.77450109e-02 7.66875148e-01 -3.18274319e-01 5.66032454e-02
-8.35570097e-01 -4.27683860e-01 2.60310173e-01 3.10572416e-01
-5.37019908e-01 7.83303916e-01 3.05978566e-01 -6.69693351e-01
-5.32019079e-01 -6.05009735e-01 -1.03821576e+00 -3.18781883e-01
-3.82180154e-01 9.28687096e-01 3.09604526e-01 -3.36551040... | [13.824329376220703, -3.0089073181152344] |
30699e4f-099d-48fb-bf9e-b875342780b2 | data-efficient-paraphrase-generation-to | null | null | https://aclanthology.org/2020.coling-industry.2 | https://aclanthology.org/2020.coling-industry.2.pdf | Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems | Recent progress through advanced neural models pushed the performance of task-oriented dialog systems to almost perfect accuracy on existing benchmark datasets for intent classification and slot labeling. However, in evolving real-world dialog systems, where new functionality is regularly added, a major additional chal... | ['Daniil Sorokin', 'Caglar Tirkaz', 'Tobias Falke', 'Shailza Jolly'] | 2020-12-01 | null | null | null | coling-2020-8 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 2.72411942e-01 3.47348779e-01 -1.88818462e-02 -6.83166146e-01
-8.36296916e-01 -8.21276963e-01 7.26768672e-01 3.64259899e-01
-5.88369191e-01 1.01238143e+00 5.98252296e-01 -3.26720893e-01
3.48023891e-01 -5.55180848e-01 -2.23995924e-01 -7.40606114e-02
3.85197431e-01 1.21140385e+00 3.28633815e-01 -7.44208336... | [12.762572288513184, 7.9821672439575195] |
ac96e2f9-0641-405c-9fad-f867df4a2027 | tabbie-pretrained-representations-of-tabular | 2105.02584 | null | https://arxiv.org/abs/2105.02584v1 | https://arxiv.org/pdf/2105.02584v1.pdf | TABBIE: Pretrained Representations of Tabular Data | Existing work on tabular representation learning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as BERT. While this joint pretraining improves tasks involving paired tables and text (e.g., answering questions about tables), we show that i... | ['Mohit Iyyer', 'Varun Manjunatha', 'Dung Thai', 'Hiroshi Iida'] | 2021-05-06 | null | https://aclanthology.org/2021.naacl-main.270 | https://aclanthology.org/2021.naacl-main.270.pdf | naacl-2021-4 | ['cell-detection', 'table-annotation', 'table-annotation', 'column-type-annotation'] | ['computer-vision', 'knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [ 3.79602350e-02 4.99540716e-01 -4.65515286e-01 -3.27665478e-01
-1.05576110e+00 -8.92326415e-01 6.27962470e-01 1.14999247e+00
4.78105694e-02 8.99132490e-01 6.09195352e-01 -6.61391914e-01
2.55524218e-01 -1.16732657e+00 -1.24679959e+00 -1.32979617e-01
-1.74318492e-01 1.05282295e+00 -1.31456837e-01 -3.91392946... | [9.659161567687988, 7.80434513092041] |
a1af1e56-fc82-4d38-a34b-fa166344bbbf | let-s-not-quote-out-of-context-unified-vision | 2306.00931 | null | https://arxiv.org/abs/2306.00931v1 | https://arxiv.org/pdf/2306.00931v1.pdf | "Let's not Quote out of Context": Unified Vision-Language Pretraining for Context Assisted Image Captioning | Well-formed context aware image captions and tags in enterprise content such as marketing material are critical to ensure their brand presence and content recall. Manual creation and updates to ensure the same is non trivial given the scale and the tedium towards this task. We propose a new unified Vision-Language (VL)... | ['Sumit Shekhar', 'Niyati Chhaya', 'Pushpak Bhattacharyya', 'Abisek Rajakumar Kalarani'] | 2023-06-01 | null | null | null | null | ['image-captioning', 'marketing', 'keyword-extraction', 'visual-entailment'] | ['computer-vision', 'miscellaneous', 'natural-language-processing', 'reasoning'] | [ 6.61486566e-01 -2.04090811e-02 -3.26876819e-01 -4.77792591e-01
-1.26492584e+00 -8.03486884e-01 1.09477174e+00 1.03046671e-01
-5.47445357e-01 4.25866634e-01 4.82504219e-01 -4.99290377e-01
4.21016634e-01 -1.55618131e-01 -1.22726059e+00 -4.07686710e-01
3.94935399e-01 4.88802075e-01 1.08792372e-01 -3.13871145... | [10.976485252380371, 1.1512154340744019] |
ff43cf0f-3512-4ea7-9a01-b4394b19f488 | piks-a-technique-to-identify-actionable | 2304.02208 | null | https://arxiv.org/abs/2304.02208v1 | https://arxiv.org/pdf/2304.02208v1.pdf | PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data | With calls for increasing transparency, governments are releasing greater amounts of data in multiple domains including finance, education and healthcare. The efficient exploratory analysis of healthcare data constitutes a significant challenge. Key concerns in public health include the quick identification and analysi... | ['Hang Peng', 'Soumyabrata Dey', 'Subrata Garai', 'A. Ravishankar Rao'] | 2023-04-05 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [-1.25840560e-01 -5.41261137e-02 -3.47833097e-01 -6.32979929e-01
-8.94408226e-01 -1.76210463e-01 1.55368581e-01 7.87587464e-01
-3.38580012e-01 6.26507342e-01 6.87373221e-01 -8.22325706e-01
-2.77739853e-01 -7.78037429e-01 -6.38634086e-01 -5.43379009e-01
-1.83600426e-01 4.92930084e-01 -2.37120315e-01 1.19930945... | [7.861851692199707, 5.9829301834106445] |
bb45b692-ec37-4dde-a812-efc712879222 | learning-pixel-adaptive-weights-for-portrait | 2112.03536 | null | https://arxiv.org/abs/2112.03536v1 | https://arxiv.org/pdf/2112.03536v1.pdf | Learning Pixel-Adaptive Weights for Portrait Photo Retouching | Portrait photo retouching is a photo retouching task that emphasizes human-region priority and group-level consistency. The lookup table-based method achieves promising retouching performance by learning image-adaptive weights to combine 3-dimensional lookup tables (3D LUTs) and conducting pixel-to-pixel color transfor... | ['Yongqiang Zhao', 'Dawei Yan', 'Chengzhe Lu', 'Binglu Wang'] | 2021-12-07 | null | null | null | null | ['photo-retouching'] | ['computer-vision'] | [ 3.93218398e-01 -2.07116634e-01 -2.39541888e-01 -1.92075819e-01
-5.16566992e-01 -5.34293115e-01 1.87816739e-01 -1.33116513e-01
-2.93551266e-01 6.56822383e-01 -3.65870558e-02 -1.64435955e-03
1.80646658e-01 -6.80054605e-01 -9.43870842e-01 -1.03788435e+00
6.10153615e-01 -3.57823074e-01 3.96784574e-01 -2.08670333... | [11.331398963928223, -1.1540988683700562] |
6e8b0ade-27b0-4658-b29d-9cff6a414ff5 | a-novel-approach-to-vehicle-pose-estimation | 2107.09607 | null | https://arxiv.org/abs/2107.09607v1 | https://arxiv.org/pdf/2107.09607v1.pdf | A Novel Approach to Vehicle Pose Estimation using Automotive Radar | This paper presents a set of novel scan-matching techniques for vehicle pose estimation using automotive radar measurements. The proposed approach modifies the Normal Distributions Transform (NDT) -- a state-of-the-art scan-matching SLAM technique, widely used in lidar-based localization -- to account for particular as... | ['Alexander Yarovoy', 'Nikita Petrov', 'Martijn Heller'] | 2021-07-20 | null | null | null | null | ['vehicle-pose-estimation'] | ['computer-vision'] | [ 2.67833173e-01 -5.47467209e-02 3.67669851e-01 -6.62621558e-01
-6.05221391e-01 -1.28193691e-01 6.93608880e-01 1.77442078e-02
-8.20763528e-01 9.73270833e-01 -4.06906188e-01 -3.72169018e-01
-8.66295159e-01 -1.04912460e+00 -5.34701407e-01 -7.82854140e-01
-1.99142590e-01 1.01536191e+00 1.84375286e-01 -3.17647010... | [6.728953838348389, 0.9547088146209717] |
22325d73-dbd8-4ca5-81b8-480822e713cf | document-level-relation-extraction-with | 2010.11304 | null | https://arxiv.org/abs/2010.11304v3 | https://arxiv.org/pdf/2010.11304v3.pdf | Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling | Document-level relation extraction (RE) poses new challenges compared to its sentence-level counterpart. One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multiple possible relations. In this paper, we propose two novel techniques, adaptive t... | ['Jing Huang', 'Tengyu Ma', 'Kevin Huang', 'Wenxuan Zhou'] | 2020-10-21 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 4.04220909e-01 1.12404227e-01 -4.20172989e-01 -4.20963436e-01
-1.35095525e+00 -5.96575558e-01 3.98560524e-01 8.16676974e-01
-7.59456217e-01 9.23429132e-01 1.97314978e-01 -1.08725771e-01
-1.15338355e-01 -6.15515947e-01 -3.96621794e-01 -5.90151548e-01
8.24180618e-02 5.33055544e-01 1.67701244e-01 1.23285010... | [9.151293754577637, 8.726655006408691] |
5096a77e-1d3e-409f-ac6c-92f55a5ec322 | real-time-3d-head-pose-and-facial-landmark | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Papazov_Real-Time_3D_Head_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Papazov_Real-Time_3D_Head_2015_CVPR_paper.pdf | Real-Time 3D Head Pose and Facial Landmark Estimation From Depth Images Using Triangular Surface Patch Features | We present a real-time system for 3D head pose estimation and facial landmark localization using a commodity depth sensor. We introduce a novel triangular surface patch (TSP) descriptor, which encodes the shape of the 3D surface of the face within a triangular area. The proposed descriptor is viewpoint invariant, and i... | ['Tim K. Marks', 'Chavdar Papazov', 'Michael Jones'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['head-pose-estimation'] | ['computer-vision'] | [-1.56843720e-03 5.79160489e-02 7.89598823e-02 -7.45043337e-01
-1.11894655e+00 -3.04646343e-01 4.94014442e-01 1.56236216e-01
-4.34880167e-01 1.48582697e-01 2.25896001e-01 6.42300546e-01
2.14720994e-01 -5.89732230e-01 -5.99124610e-01 -5.67805886e-01
-6.82358295e-02 1.04301262e+00 3.53800684e-01 -1.65445656... | [13.500707626342773, 0.16287916898727417] |
ce9bdcdd-456e-4b6b-b4df-fadb33e35da6 | mo-gym-a-library-of-multi-objective | null | null | https://bnaic2022.uantwerpen.be/wp-content/uploads/BNAICBeNeLearn_2022_submission_6485.pdf | https://bnaic2022.uantwerpen.be/wp-content/uploads/BNAICBeNeLearn_2022_submission_6485.pdf | MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments | We introduce MO-Gym, an extensible library containing a diverse set of multi-objective reinforcement learning environments. It introduces a standardized API that facilitates conducting experiments and performance analyses of algorithms designed to interact with multi-objective Markov decision processes. Importantly, it... | ['Bruno C. da Silva', 'Ana L. C. Bazzan', 'Ann Nowé', 'Grégoire Danoy', 'El-Ghazali Talbi', 'Florian Felten', 'Lucas N. Alegre'] | 2022-11-30 | null | null | null | benelux-conference-on-artificial-intelligence-1 | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-5.71453750e-01 -1.83274373e-01 -2.14958966e-01 -1.00634418e-01
-8.15867722e-01 -5.00457168e-01 3.40689510e-01 7.47734010e-02
-4.59056169e-01 1.00747657e+00 -1.38341516e-01 -3.98776740e-01
-4.50953454e-01 -8.37148428e-01 -5.43468595e-01 -9.18943167e-01
-3.55290383e-01 7.09804296e-01 2.62809634e-01 -4.06469494... | [3.9273064136505127, 1.4718669652938843] |
2cafd96c-188a-4c03-b026-0d0b27d54930 | edge-but-not-least-cross-view-graph-pooling | 2109.11796 | null | https://arxiv.org/abs/2109.11796v1 | https://arxiv.org/pdf/2109.11796v1.pdf | Edge but not Least: Cross-View Graph Pooling | Graph neural networks have emerged as a powerful model for graph representation learning to undertake graph-level prediction tasks. Various graph pooling methods have been developed to coarsen an input graph into a succinct graph-level representation through aggregating node embeddings obtained via graph convolution. H... | ['Ivor W. Tsang', 'Jie Yin', 'Xiaowei Zhou'] | 2021-09-24 | null | null | null | null | ['graph-regression'] | ['graphs'] | [-1.07998222e-01 3.47147614e-01 -4.49182421e-01 -3.15193206e-01
-1.43869847e-01 -4.29840982e-01 6.65912032e-01 5.86633027e-01
1.10206723e-01 3.64812702e-01 4.52579856e-01 -1.31410509e-01
-1.11847915e-01 -1.28171957e+00 -5.84201515e-01 -6.17491126e-01
-4.01446760e-01 -1.90640256e-01 7.76625797e-02 -1.51432052... | [7.091124057769775, 6.290483474731445] |
0c400778-60d3-4751-80eb-615ccbf8531b | chinese-paragraph-level-discourse-parsing | null | null | https://aclanthology.org/2020.coling-main.506 | https://aclanthology.org/2020.coling-main.506.pdf | Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading | Discourse structure tree construction is the fundamental task of discourse parsing and most previous work focused on English. Due to the cultural and linguistic differences, existing successful methods on English discourse parsing cannot be transformed into Chinese directly, especially in paragraph level suffering from... | ['Qiaoming Zhu', 'Fang Kong', 'Peifeng Li', 'Xiaomin Chu', 'Feng Jiang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.54003042e-01 5.84500372e-01 -4.42660689e-01 -2.91345954e-01
-5.87266982e-01 -6.56810045e-01 5.19031703e-01 4.86023575e-02
-2.26413846e-01 9.30584848e-01 1.10737884e+00 -8.22777808e-01
6.69373512e-01 -9.56973076e-01 -2.58694172e-01 -6.84812307e-01
3.33703518e-01 6.18357696e-02 5.39187551e-01 -4.85598654... | [10.832134246826172, 9.504203796386719] |
41b56495-89ea-47c2-9f74-b2c9a9724edf | diffusionct-latent-diffusion-model-for-ct | 2301.08815 | null | https://arxiv.org/abs/2301.08815v2 | https://arxiv.org/pdf/2301.08815v2.pdf | DiffusionCT: Latent Diffusion Model for CT Image Standardization | Computed tomography (CT) is one of the modalities for effective lung cancer screening, diagnosis, treatment, and prognosis. The features extracted from CT images are now used to quantify spatial and temporal variations in tumors. However, CT images obtained from various scanners with customized acquisition protocols ma... | ['Jin Chen', 'Ge Wang', 'Michael A. Brooks', 'Jie Zhang', 'Md Selim'] | 2023-01-20 | null | null | null | null | ['image-harmonization', 'lung-cancer-diagnosis'] | ['computer-vision', 'medical'] | [ 4.54904169e-01 5.46095893e-02 -4.60574448e-01 -3.01026791e-01
-1.20248008e+00 -1.87504604e-01 3.44632775e-01 -9.21239778e-02
-3.00736874e-01 3.88034850e-01 3.93415123e-01 -1.96778789e-01
1.16290651e-01 -8.64238918e-01 -4.32899624e-01 -1.17639005e+00
2.70937324e-01 6.42981529e-01 3.47020149e-01 2.39593193... | [14.022889137268066, -2.30000901222229] |
05aa9e99-e336-402a-840f-ab64c3b8d16b | sublabel-accurate-convex-relaxation-of | 1604.01980 | null | http://arxiv.org/abs/1604.01980v2 | http://arxiv.org/pdf/1604.01980v2.pdf | Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies | Convex relaxations of nonconvex multilabel problems have been demonstrated to
produce superior (provably optimal or near-optimal) solutions to a variety of
classical computer vision problems. Yet, they are of limited practical use as
they require a fine discretization of the label space, entailing a huge demand
in memo... | ['Daniel Cremers', 'Thomas Möllenhoff', 'Michael Moeller', 'Jan Lellmann', 'Emanuel Laude'] | 2016-04-07 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [-3.67082097e-02 -2.44308282e-02 -3.67350459e-01 -4.43202078e-01
-1.14295864e+00 -6.95032895e-01 -1.86340306e-02 -1.34555167e-02
-4.26617384e-01 1.04435158e+00 9.17542130e-02 -2.38798440e-01
-9.06379670e-02 -3.19702029e-01 -7.79505610e-01 -9.03122187e-01
1.63013726e-01 5.47668934e-01 -3.16258341e-01 -1.08618604... | [6.978598594665527, 4.350953102111816] |
a4291695-520a-4835-9df6-e898f96a2c8f | glodyne-global-topology-preserving-dynamic | 2008.01935 | null | https://arxiv.org/abs/2008.01935v4 | https://arxiv.org/pdf/2008.01935v4.pdf | GloDyNE: Global Topology Preserving Dynamic Network Embedding | Learning low-dimensional topological representation of a network in dynamic environments is attracting much attention due to the time-evolving nature of many real-world networks. The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology a... | ['Ke Tang', 'Han Zhang', 'Chengbin Hou', 'Shan He'] | 2020-08-05 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-2.86779225e-01 3.26242633e-02 -1.75179020e-01 5.91115877e-02
6.86621852e-03 -6.16979361e-01 6.28383696e-01 4.05393600e-01
-1.86635450e-01 4.53130007e-01 3.29594880e-01 -1.80668890e-01
-5.07537961e-01 -1.11711609e+00 -4.41393882e-01 -8.74276400e-01
-5.72269499e-01 5.77215612e-01 5.51694274e-01 -3.44246447... | [7.172929286956787, 6.1025309562683105] |
9274aee1-f861-430a-9053-2f5bc1916603 | towards-explainable-music-emotion-recognition | 1907.03572 | null | https://arxiv.org/abs/1907.03572v1 | https://arxiv.org/pdf/1907.03572v1.pdf | Towards Explainable Music Emotion Recognition: The Route via Mid-level Features | Emotional aspects play an important part in our interaction with music. However, modelling these aspects in MIR systems have been notoriously challenging since emotion is an inherently abstract and subjective experience, thus making it difficult to quantify or predict in the first place, and to make sense of the predic... | ['Shreyan Chowdhury', 'Verena Haunschmid', 'Gerhard Widmer', 'Andreu Vall'] | 2019-07-08 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 8.83230940e-02 3.67229998e-01 3.26582432e-01 -3.31359148e-01
-4.11066152e-02 -6.14244103e-01 5.29788077e-01 3.19012582e-01
-5.91550693e-02 3.60693187e-01 5.39334714e-01 4.92360555e-02
-4.30572659e-01 -6.34794712e-01 -4.52686638e-01 -3.01139444e-01
-2.42046028e-01 2.09053040e-01 -1.62504748e-01 -4.89333749... | [15.868280410766602, 5.394870281219482] |
97130c44-60f6-4f8a-a05d-4eccae96d5a6 | a-dynamic-graph-interactive-framework-with | 2211.04023 | null | https://arxiv.org/abs/2211.04023v1 | https://arxiv.org/pdf/2211.04023v1.pdf | A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding | Multi-intent detection and slot filling joint models are gaining increasing traction since they are closer to complicated real-world scenarios. However, existing approaches (1) focus on identifying implicit correlations between utterances and one-hot encoded labels in both tasks while ignoring explicit label characteri... | ['Yuexian Zou', 'Tengtao Song', 'Xuxin Cheng', 'Weiyuan Xu', 'Zhihong Zhu'] | 2022-11-08 | null | null | null | null | ['spoken-language-understanding', 'intent-detection', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 4.07580495e-01 6.55656517e-01 -5.41088998e-01 -6.60538852e-01
-7.97776282e-01 -1.74599186e-01 4.48783785e-01 2.90128767e-01
-2.83138216e-01 5.76498747e-01 3.99053782e-01 -1.99946299e-01
1.52426928e-01 -4.94259745e-01 -4.78740126e-01 -3.17453504e-01
1.12719357e-01 7.04278469e-01 4.09584224e-01 5.03816828... | [12.514104843139648, 7.2993388175964355] |
dbe03d0d-efb2-4e3a-a70f-eec78fd611cd | an-experimental-study-in-real-time-facial-1 | 2304.03064 | null | https://arxiv.org/abs/2304.03064v1 | https://arxiv.org/pdf/2304.03064v1.pdf | An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset | Although real-time facial emotion recognition is a hot topic research domain in the field of human-computer interaction, state-of the-art available datasets still suffer from various problems, such as some unrelated photos such as document photos, unbalanced numbers of photos in each class, and misleading images that c... | ['Khloud Al Jallad', 'Tarek Barhoum', 'Rasha Albezreh', 'Rouaa Alaraj', 'Lana Ahmad Abdullah', 'Rahmeh Abou Zafra'] | 2023-04-06 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [-1.25081643e-01 -5.76415919e-02 -2.22707298e-02 -6.30801260e-01
-9.00254026e-02 -1.65232435e-01 3.91830772e-01 -8.00367072e-02
-4.74719524e-01 9.19588327e-01 -1.02697715e-01 4.16484773e-01
2.25387573e-01 -3.91165227e-01 -2.87374854e-01 -7.90450275e-01
-2.66261697e-02 -1.50741696e-01 -2.13091969e-01 -4.73584682... | [13.569962501525879, 1.8432459831237793] |
8585a96c-c578-4a38-a50d-a87e57f55ec1 | beyond-contrastive-learning-a-variational | 2212.10726 | null | https://arxiv.org/abs/2212.10726v2 | https://arxiv.org/pdf/2212.10726v2.pdf | Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval | Contrastive learning has been successfully used for retrieval of semantically aligned sentences, but it often requires large batch sizes or careful engineering to work well. In this paper, we instead propose a generative model for learning multilingual text embeddings which can be used to retrieve or score sentence pai... | ['Taylor Berg-Kirkpatrick', 'Graham Neubig', 'William W. Cohen', 'Jonathan H. Clark', 'John Wieting'] | 2022-12-21 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [-2.24084668e-02 -2.65235603e-01 -1.99473932e-01 -4.77659792e-01
-1.75327551e+00 -9.06494737e-01 9.20608461e-01 2.70385087e-01
-7.01330900e-01 5.57834804e-01 6.33925140e-01 -5.56279004e-01
2.59002368e-03 -3.20377737e-01 -7.64944851e-01 -4.71619248e-01
2.42246598e-01 8.93921793e-01 -1.01698495e-01 -4.96503562... | [11.141907691955566, 9.849842071533203] |
dc202d4b-3a11-44f2-98e7-09b3cd5677d6 | a-brief-survey-of-recent-edge-preserving | 1503.07297 | null | http://arxiv.org/abs/1503.07297v1 | http://arxiv.org/pdf/1503.07297v1.pdf | A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images | Edge preserving filters preserve the edges and its information while blurring
an image. In other words they are used to smooth an image, while reducing the
edge blurring effects across the edge like halos, phantom etc. They are
nonlinear in nature. Examples are bilateral filter, anisotropic diffusion
filter, guided fil... | ['Amlan Chakrabarti', 'Chandrajit Pal', 'Ranjan Ghosh'] | 2015-03-25 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 2.28660986e-01 -4.49095249e-01 6.14635944e-01 -1.53307423e-01
2.19896823e-01 -6.62662745e-01 4.84930843e-01 -6.63412139e-02
-5.02671063e-01 9.02932525e-01 6.02252126e-01 -3.90032679e-02
-1.74772754e-01 -7.65113115e-01 -4.37484682e-01 -7.23555505e-01
-1.11401305e-02 -1.25091344e-01 5.21820784e-01 -3.72127593... | [11.12946891784668, -2.5562186241149902] |
2a720965-bf39-4e7b-ba73-9cb6a6d296d7 | rare-and-zero-shot-word-sense-disambiguation | null | null | https://aclanthology.org/2022.acl-long.323 | https://aclanthology.org/2022.acl-long.323.pdf | Rare and Zero-shot Word Sense Disambiguation using Z-Reweighting | Word sense disambiguation (WSD) is a crucial problem in the natural language processing (NLP) community. Current methods achieve decent performance by utilizing supervised learning and large pre-trained language models. However, the imbalanced training dataset leads to poor performance on rare senses and zero-shot sens... | ['Tong Zhang', 'Yangqiu Song', 'Hongming Zhang', 'Ying Su'] | null | null | null | null | acl-2022-5 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 6.55100942e-02 -2.06670657e-01 -6.57368422e-01 -2.93052882e-01
-4.29103613e-01 -2.57407576e-01 3.83988529e-01 7.46339262e-01
-9.50131536e-01 6.61032617e-01 4.25364316e-01 -3.02460313e-01
-2.27283284e-01 -9.89102900e-01 7.37068430e-02 -4.51428771e-01
-9.19328183e-02 3.43580335e-01 2.55124897e-01 -7.82783568... | [10.265185356140137, 8.96411418914795] |
2cd8efe0-ec58-4c68-8031-22917928a158 | financial-trading-model-with-stock-bar-chart | 1903.04610 | null | http://arxiv.org/abs/1903.04610v1 | http://arxiv.org/pdf/1903.04610v1.pdf | Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks | Even though computational intelligence techniques have been extensively
utilized in financial trading systems, almost all developed models use the time
series data for price prediction or identifying buy-sell points. However, in
this study we decided to use 2-D stock bar chart images directly without
introducing any ad... | ['Omer Berat Sezer', 'Ahmet Murat Ozbayoglu'] | 2019-03-11 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-7.25487351e-01 -1.63339153e-01 2.29812726e-01 -2.16636121e-01
-3.81128639e-01 -8.64008009e-01 8.81888986e-01 -1.12564139e-01
-4.29436237e-01 4.80323136e-01 -1.29983261e-01 -7.16283321e-01
-1.84422076e-01 -1.14456820e+00 -5.94842672e-01 -2.64694512e-01
-5.43736696e-01 5.54269433e-01 2.66003937e-01 -6.90100133... | [4.366585731506348, 4.283901214599609] |
7b124d0f-f9df-48d4-b1d2-adcc5d162785 | toward-reliable-human-pose-forecasting-with | 2304.06707 | null | https://arxiv.org/abs/2304.06707v1 | https://arxiv.org/pdf/2304.06707v1.pdf | Toward Reliable Human Pose Forecasting with Uncertainty | Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited uncertainty analysis have hindered progress in the field. To add... | ['Alexandre Alahi', 'Taylor Mordan', 'Zahra Tehraninasab', 'Amirhossein Alimohammadi', 'Yashar Zoroofchi Benisi', 'Parham Saremi', 'Matin Daghyani', 'Mehrshad Mirmohammadi', 'Saeed Saadatnejad'] | 2023-04-13 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [-4.97412644e-02 3.05132717e-01 7.13155642e-02 -6.40175045e-01
-7.89001405e-01 -4.37445939e-01 6.91616952e-01 1.48050785e-01
-3.07246923e-01 7.10160911e-01 4.91424769e-01 1.05851352e-01
-3.02322209e-01 -5.33841193e-01 -7.93983519e-01 -5.42783618e-01
-1.62676245e-01 7.84035623e-01 2.60979354e-01 1.35024518... | [7.14342737197876, -0.861072301864624] |
60c5fafa-b996-41b1-9732-44cde740a7d1 | beyond-lexical-a-semantic-retrieval-framework-1 | null | null | https://openreview.net/forum?id=H1xvMrHqAE | https://openreview.net/pdf?id=H1xvMrHqAE | Beyond Lexical: A Semantic Retrieval Framework for Textual Search Engine | Search engine has become a fundamental component in various web and mobile applications. Retrieving relevant documents from the massive datasets is challenging for a search engine system, especially when faced with verbose or tail queries. In this paper, we explore a vector space search framework for document retrieval... | ['Anonymous'] | 2019-06-09 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [-2.05720738e-01 -6.40506864e-01 -6.10803902e-01 -2.93957412e-01
-1.12623370e+00 -7.32839227e-01 8.37122977e-01 2.30325237e-02
-5.07676959e-01 6.58356324e-02 4.38854784e-01 -2.11069360e-01
-8.47342551e-01 -8.72195482e-01 -2.82701463e-01 -1.69086277e-01
6.77759945e-03 8.33116829e-01 3.66885900e-01 -4.48640674... | [11.356941223144531, 7.467426776885986] |
ccad82a1-daa8-4958-bba0-68b3eed741ae | invero-making-semantic-role-labeling | null | null | https://aclanthology.org/2020.emnlp-demos.11 | https://aclanthology.org/2020.emnlp-demos.11.pdf | InVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles | Semantic Role Labeling (SRL) is deeply dependent on complex linguistic resources and sophisticated neural models, which makes the task difficult to approach for non-experts. To address this issue we present a new platform named Intelligible Verbs and Roles (InVeRo). This platform provides access to a new verb resource,... | ['Roberto Navigli', 'Davide Zanfardino', 'Fabrizio Brignone', 'Simone Conia'] | 2020-10-01 | null | null | null | emnlp-2020-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 1.55627295e-01 4.30795550e-01 -4.93178487e-01 -4.17098552e-01
-4.36986595e-01 -1.03398681e+00 5.87618768e-01 4.02484417e-01
-6.38301969e-01 8.64910722e-01 7.06473053e-01 -1.66133001e-01
-1.91627413e-01 -7.02519476e-01 -3.36314768e-01 -2.63855457e-01
1.15316279e-01 8.53235781e-01 2.45255679e-01 -8.34128201... | [10.275971412658691, 9.42556381225586] |
19b62514-dd6a-4b63-8f0f-4f66de71d50b | improving-statistical-fidelity-for-neural | 2301.11189 | null | https://arxiv.org/abs/2301.11189v2 | https://arxiv.org/pdf/2301.11189v2.pdf | Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models | Lossy image compression aims to represent images in as few bits as possible while maintaining fidelity to the original. Theoretical results indicate that optimizing distortion metrics such as PSNR or MS-SSIM necessarily leads to a discrepancy in the statistics of original images from those of reconstructions, in partic... | ['Jakob Verbeek', 'Hervé Jégou', 'Karen Ullrich', 'Alaaeldin El-Nouby', 'Matthew J. Muckley'] | 2023-01-26 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 3.81505787e-01 -1.94034472e-01 8.51461291e-02 -3.31121653e-01
-9.41189170e-01 -4.97303486e-01 4.42931533e-01 -3.44890982e-01
-1.89973757e-01 7.54446447e-01 4.35153186e-01 -2.11408928e-01
9.65105668e-02 -7.61051118e-01 -9.53438997e-01 -7.44281590e-01
-1.50381342e-01 -1.27267197e-01 -3.58520359e-01 -2.32741330... | [11.445757865905762, -1.5955421924591064] |
bfeffae5-5cd4-4919-9776-1c0d17a73e90 | indoor-localization-with-robust-global | 2210.06294 | null | https://arxiv.org/abs/2210.06294v1 | https://arxiv.org/pdf/2210.06294v1.pdf | Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach | Fingerprinting-based positioning significantly improves the indoor localization performance in non-line-of-sight-dominated areas. However, its deployment and maintenance is cost-intensive as it needs ground-truth reference systems for both the initial training and the adaption to environmental changes. In contrast, cha... | ['Christopher Mutschler', 'Bjoern M. Eskofier', 'Tobias Feigl', 'George Yammine', 'Maximilian Stahlke'] | 2022-10-07 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 5.32424897e-02 -1.35769501e-01 5.51806912e-02 -4.32105094e-01
-1.16340959e+00 -8.09433341e-01 9.83389989e-02 2.34920651e-01
-6.11808419e-01 8.93562138e-01 -2.25114986e-01 -5.84685981e-01
-3.95957470e-01 -6.39183342e-01 -9.76989508e-01 -8.12564552e-01
-6.30415082e-01 3.39994341e-01 -1.19385138e-01 7.85254985... | [6.34637975692749, 0.955189049243927] |
e2be40e1-3f4b-4de7-931a-c2daf274512a | extending-adversarial-attacks-and-defenses-to | 1901.03006 | null | https://arxiv.org/abs/1901.03006v4 | https://arxiv.org/pdf/1901.03006v4.pdf | Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers | 3D object classification and segmentation using deep neural networks has been extremely successful. As the problem of identifying 3D objects has many safety-critical applications, the neural networks have to be robust against adversarial changes to the input data set. There is a growing body of research on generating h... | ['Ronald Yu', 'Hao Su', 'Daniel Liu'] | 2019-01-10 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [ 3.58441211e-02 3.30601223e-02 4.36097711e-01 -3.82241398e-01
-2.52342790e-01 -1.14466131e+00 7.25031137e-01 -2.13475332e-01
-3.01037282e-01 1.01301089e-01 -6.10835969e-01 -8.13993275e-01
1.50560737e-01 -9.66130793e-01 -1.16341257e+00 -7.23103762e-01
-4.24716443e-01 3.12732935e-01 4.96840030e-01 -4.10212725... | [7.704028129577637, -4.45789909362793] |
f9a0d9f8-a381-4d0c-b741-15dd50e84acd | overview-of-the-ninth-dialog-system | 2011.06486 | null | https://arxiv.org/abs/2011.06486v1 | https://arxiv.org/pdf/2011.06486v1.pdf | Overview of the Ninth Dialog System Technology Challenge: DSTC9 | This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9). This edition of the DSTC focuses on applying end-to-end dialog technologies for four distinct tasks in dialog systems, namely, 1. Task-oriented dialog Modeling with unstructured knowledge access, 2. Multi-domain task-oriented dialog, 3. Intera... | ['Rajen Subba', 'Shivani Poddar', 'Seungwhan Moon', 'Satwik Kottur', 'Alborz Geramifard', 'Ankita De', 'Paul A. Crook', 'Cho', 'Eunjoon', 'Ahmad Beirami', 'Maxine Eskenazi', 'David Traum', 'Seyed Hossein Alavi', 'Carla Gordon', 'Yulan Feng', 'Shikib Mehri', 'Jianfeng Gao', 'Minlie Huang', 'Swadheen Shukla', 'Zheng Zhan... | 2020-11-12 | null | null | null | null | ['interactive-evaluation-of-dialog'] | ['natural-language-processing'] | [-5.35132051e-01 5.99785388e-01 1.77348182e-01 -6.83765829e-01
-7.07590222e-01 -1.13311911e+00 1.09261668e+00 -2.04409152e-01
-4.72179800e-01 9.11120713e-01 8.16445410e-01 -3.01201612e-01
1.35458902e-01 8.67742673e-02 3.18319231e-01 -1.46083027e-01
2.38792270e-01 1.70119572e+00 4.91070002e-01 -1.08424592... | [12.82015609741211, 7.997310638427734] |
063f2e4c-1290-4957-abdd-775eac2c1d71 | prompt-engineering-for-healthcare | 2304.14670 | null | https://arxiv.org/abs/2304.14670v1 | https://arxiv.org/pdf/2304.14670v1.pdf | Prompt Engineering for Healthcare: Methodologies and Applications | This review will introduce the latest advances in prompt engineering in the field of natural language processing (NLP) for the medical domain. First, we will provide a brief overview of the development of prompt engineering and emphasize its significant contributions to healthcare NLP applications such as question-answ... | ['Shu Zhang', 'Tianming Liu', 'Dinggang Shen', 'Yixuan Yuan', 'Dajiang Zhu', 'Bao Ge', 'Xiang Li', 'Yiheng Liu', 'Haiyang Zhang', 'Chenxi Yue', 'Huawen Hu', 'Jinru Wu', 'Yanqing Kang', 'Qiushi Yang', 'Haixing Dai', 'Chong Ma', 'Zihao Wu', 'Sigang Yu', 'Enze Shi', 'Jiaqi Wang'] | 2023-04-28 | null | null | null | null | ['prompt-engineering', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.07062006e-01 6.87376559e-01 -5.37641227e-01 -3.62281591e-01
-1.08412218e+00 -2.49683216e-01 3.49394143e-01 1.16384649e+00
-4.33765292e-01 9.45435762e-01 9.86489236e-01 -3.30256492e-01
-4.70883399e-01 -5.31572580e-01 -1.16880432e-01 -4.08036888e-01
5.34117483e-02 6.26767218e-01 -2.25127473e-01 -2.95652121... | [12.234389305114746, 9.436200141906738] |
c312eb98-2e09-4e30-8cd9-dc05f784f975 | fame-vil-multi-tasking-vision-language-model | 2303.02483 | null | https://arxiv.org/abs/2303.02483v1 | https://arxiv.org/pdf/2303.02483v1.pdf | FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks | In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image captioning. They differ drastically in each individual input/output format and dataset size. It has been common to design a task-specific ... | ['Tao Xiang', 'Yi-Zhe Song', 'Li Zhang', 'Licheng Yu', 'Xiatian Zhu', 'Xiao Han'] | 2023-03-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Han_FAME-ViL_Multi-Tasking_Vision-Language_Model_for_Heterogeneous_Fashion_Tasks_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Han_FAME-ViL_Multi-Tasking_Vision-Language_Model_for_Heterogeneous_Fashion_Tasks_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multi-modal-classification'] | ['miscellaneous'] | [ 8.63936618e-02 -4.41314071e-01 -3.36513668e-01 -2.57137299e-01
-1.22252488e+00 -7.44448721e-01 6.09788477e-01 -2.17242897e-01
-4.30234104e-01 4.37897563e-01 2.03411326e-01 -1.14805900e-01
4.15728800e-02 -3.48120570e-01 -8.25554311e-01 -5.14086187e-01
4.38515037e-01 5.49468279e-01 1.63897842e-01 -2.26442859... | [10.688091278076172, 1.4691765308380127] |
cd9b6434-1876-47ab-843c-35a79f1c04ee | beamlearning-an-end-to-end-deep-learning | 2104.13347 | null | https://arxiv.org/abs/2104.13347v1 | https://arxiv.org/pdf/2104.13347v1.pdf | BeamLearning: an end-to-end Deep Learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data | Sound sources localization using multichannel signal processing has been a subject of active research for decades. In recent years, the use of deep learning in audio signal processing has allowed to drastically improve performances for machine hearing. This has motivated the scientific community to also develop machine... | ['Alexandre Garcia', 'Éric Bavu', 'Hadrien Pujol'] | 2021-04-27 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 2.79262692e-01 -8.07155252e-01 9.22504783e-01 -2.77604401e-01
-1.31219769e+00 -5.28849304e-01 2.87012368e-01 2.50244558e-01
-4.94580746e-01 5.94209015e-01 2.88855106e-01 -2.08438650e-01
-4.59910363e-01 -6.36846721e-01 -5.49221516e-01 -9.64392483e-01
-6.28733695e-01 7.66410828e-02 8.39577839e-02 1.93958320... | [15.153164863586426, 5.7441534996032715] |
394961fa-1b63-43d9-9d2c-0de347c95e18 | vice-self-supervised-visual-concept | 2111.12460 | null | https://arxiv.org/abs/2111.12460v3 | https://arxiv.org/pdf/2111.12460v3.pdf | ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment | Recent self-supervised models have demonstrated equal or better performance than supervised methods, opening for AI systems to learn visual representations from practically unlimited data. However, these methods are typically classification-based and thus ineffective for learning high-resolution feature maps that prese... | ['Kazuya Takeda', 'Kento Ohtani', 'Alexander Carballo', 'Keisuke Fujii', 'Tomoki Hayashi', 'Robin Karlsson'] | 2021-11-24 | null | null | null | null | ['unsupervised-semantic-segmentation', 'learning-word-embeddings'] | ['computer-vision', 'methodology'] | [ 1.88524768e-01 3.02740782e-01 -4.29607809e-01 -4.86425847e-01
-6.99659467e-01 -6.16386473e-01 6.77113652e-01 3.98421198e-01
-8.40405047e-01 6.08157039e-01 2.36206606e-01 1.32974666e-02
1.00787049e-02 -8.01056564e-01 -6.50991976e-01 -6.49245441e-01
-3.26698750e-01 3.74235034e-01 4.95130926e-01 1.24566495... | [9.70186996459961, 1.1673661470413208] |
bd32add4-36d1-4683-a2db-6d3b94627eed | abstractive-summarization-as-augmentation-for | 2305.18023 | null | https://arxiv.org/abs/2305.18023v1 | https://arxiv.org/pdf/2305.18023v1.pdf | Abstractive Summarization as Augmentation for Document-Level Event Detection | Transformer-based models have consistently produced substantial performance gains across a variety of NLP tasks, compared to shallow models. However, deep models are orders of magnitude more computationally expensive than shallow models, especially on tasks with large sequence lengths, such as document-level event dete... | ['Domagoj Pluščec', 'Filip Karlo Došilović', 'Janko Vidaković'] | 2023-05-29 | null | null | null | null | ['abstractive-text-summarization', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.49342394e-01 9.48730484e-02 -3.40758473e-01 -2.21762791e-01
-1.52764606e+00 -5.56372464e-01 9.65431929e-01 5.97382724e-01
-5.13730466e-01 1.02110779e+00 7.10427463e-01 -3.27875018e-01
3.02917928e-01 -6.32286191e-01 -6.72068655e-01 -5.66665351e-01
9.68156978e-02 4.14947957e-01 8.46704766e-02 -1.19942501... | [12.251676559448242, 9.259209632873535] |
0f7b2787-0a8f-432d-a874-5e525b363980 | joint-non-parametric-point-process-model-for | 2209.04142 | null | https://arxiv.org/abs/2209.04142v6 | https://arxiv.org/pdf/2209.04142v6.pdf | Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes | A treatment policy defines when and what treatments are applied to affect some outcome of interest. Data-driven decision-making requires the ability to predict what happens if a policy is changed. Existing methods that predict how the outcome evolves under different scenarios assume that the tentative sequences of futu... | ['Pekka Marttinen', 'Kirsi Pietiläinen', 'Tuure Saarinen', 'Anne Juuti', 'ST John', 'Çağlar Hızlı'] | 2022-09-09 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 5.15422165e-01 7.26304427e-02 -5.98672569e-01 -3.32147211e-01
-3.55690897e-01 -6.05499387e-01 8.10890019e-01 4.95828718e-01
-4.64260340e-01 1.29321492e+00 6.97140276e-01 -8.06919396e-01
-3.83320570e-01 -1.03097188e+00 -8.36975157e-01 -6.84468806e-01
-1.87222794e-01 9.61567700e-01 -1.09788924e-01 4.00088727... | [8.037847518920898, 5.340810298919678] |
b7fc8ea6-562d-42a7-9af4-9f197c90ae13 | knowledge-driven-slot-constraints-for-goal | null | null | https://aclanthology.org/2021.naacl-main.266 | https://aclanthology.org/2021.naacl-main.266.pdf | Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems | In goal-oriented dialogue systems, users provide information through slot values to achieve specific goals. Practically, some combinations of slot values can be invalid according to external knowledge. For example, a combination of {``}cheese pizza{''} (a menu item) and {``}oreo cookies{''} (a topping) from an input ut... | ['Saab Mansour', 'Daniele Bonadiman', 'Piyawat Lertvittayakumjorn'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 2.06308559e-01 5.07235110e-01 4.33366224e-02 -7.32437372e-01
-4.94410098e-01 -7.70118833e-01 3.98890615e-01 6.18869841e-01
-3.42749774e-01 7.52830923e-01 5.43021113e-02 -6.01818383e-01
-3.48369092e-01 -7.88044274e-01 -4.42434847e-01 -7.29762316e-02
1.14001758e-01 1.08876657e+00 6.84544981e-01 -8.99309635... | [12.777647018432617, 7.7884721755981445] |
0ff71c0e-fc21-4d57-be2f-764829b07bd3 | knowledge-bridged-causal-interaction-network | 2212.02995 | null | https://arxiv.org/abs/2212.02995v1 | https://arxiv.org/pdf/2212.02995v1.pdf | Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment | Causal Emotion Entailment aims to identify causal utterances that are responsible for the target utterance with a non-neutral emotion in conversations. Previous works are limited in thorough understanding of the conversational context and accurate reasoning of the emotion cause. To this end, we propose Knowledge-Bridge... | ['Bing Qin', 'Zhuojun Li', 'Yanyan Zhao', 'Weixiang Zhao'] | 2022-12-06 | null | null | null | null | ['causal-emotion-entailment'] | ['natural-language-processing'] | [ 8.74680430e-02 8.85260761e-01 -2.51023322e-01 -7.66069055e-01
-5.61258614e-01 -3.75187427e-01 6.65771425e-01 1.60617679e-01
4.00917590e-01 6.69854879e-01 1.14304161e+00 -6.44336343e-02
-2.61604115e-02 -5.16647160e-01 -6.12842262e-01 -3.39783996e-01
-1.32830173e-01 1.58296302e-01 -2.24070743e-01 -5.01238406... | [12.838027000427246, 6.392460823059082] |
8b85c77a-add3-4089-9341-7119af555b18 | graph-masked-autoencoder-for-sequential | 2305.04619 | null | https://arxiv.org/abs/2305.04619v3 | https://arxiv.org/pdf/2305.04619v3.pdf | Graph Masked Autoencoder for Sequential Recommendation | While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios. To address the issue of insufficient labe... | ['Chao Huang', 'Lianghao Xia', 'Yaowen Ye'] | 2023-05-08 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 2.43623003e-01 -1.02384597e-01 -5.65470397e-01 -3.60330343e-01
-3.11594278e-01 -4.85722214e-01 5.25371313e-01 -4.54522111e-02
-7.54439011e-02 4.25222337e-01 5.74321210e-01 -3.86423290e-01
-1.52982593e-01 -7.11159885e-01 -6.29506528e-01 -5.78737974e-01
2.13828668e-01 4.01431233e-01 -3.79700035e-01 -4.94098336... | [10.195484161376953, 5.585234642028809] |
98a7e15d-293e-4ae0-990c-57daec0150e8 | domain-adaptation-in-reinforcement-learning | 2102.05714 | null | https://arxiv.org/abs/2102.05714v2 | https://arxiv.org/pdf/2102.05714v2.pdf | Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation | Despite the recent success of deep reinforcement learning (RL), domain adaptation remains an open problem. Although the generalization ability of RL agents is critical for the real-world applicability of Deep RL, zero-shot policy transfer is still a challenging problem since even minor visual changes could make the tra... | ['Jeffrey L. Krichmar', 'Emre Neftci', 'Xinyun Zou', 'Kexin Chen', 'Takashi Nagata', 'Jinwei Xing'] | 2021-02-10 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 1.66842192e-01 1.05687365e-01 -2.82397598e-01 -1.76440358e-01
-7.62053967e-01 -7.35376954e-01 8.43468368e-01 -3.24285865e-01
-6.81603253e-01 1.08910263e+00 -4.67687882e-02 -6.63247555e-02
2.91211516e-01 -4.69188958e-01 -9.81915116e-01 -7.09029317e-01
9.32188481e-02 6.87822402e-01 5.47302127e-01 -5.03292322... | [4.467667102813721, 1.0221922397613525] |
c837b439-bd90-4465-94da-5aaaa3e82b09 | exploring-the-role-of-audio-in-video | 2306.12559 | null | https://arxiv.org/abs/2306.12559v1 | https://arxiv.org/pdf/2306.12559v1.pdf | Exploring the Role of Audio in Video Captioning | Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M. Though these approaches have achieved significant improvement, the audio modality is often ignored in vide... | ['Heng Wang', 'Ehsan Elhamifar', 'Haichao Yu', 'Longyin Wen', 'Linjie Yang', 'YuHan Shen'] | 2023-06-21 | null | null | null | null | ['video-captioning', 'automatic-speech-recognition'] | ['computer-vision', 'speech'] | [ 5.59709549e-01 1.80259228e-01 5.14239296e-02 -3.91495228e-01
-1.51006055e+00 -7.00273812e-01 6.69267595e-01 -2.50897646e-01
-2.60641932e-01 5.60552895e-01 6.38741255e-01 -2.69920230e-01
3.90698612e-01 -1.88679919e-01 -1.05018663e+00 -5.07577658e-01
9.97501165e-02 5.94160929e-02 1.06673770e-01 2.89201252... | [15.129231452941895, 4.866677761077881] |
890427d8-c87a-4e4b-bfdd-dd8760cc55ae | measuring-intelligence-through-games | 1109.1314 | null | https://arxiv.org/abs/1109.1314v1 | https://arxiv.org/pdf/1109.1314v1.pdf | Measuring Intelligence through Games | Artificial general intelligence (AGI) refers to research aimed at tackling the full problem of artificial intelligence, that is, create truly intelligent agents. This sets it apart from most AI research which aims at solving relatively narrow domains, such as character recognition, motion planning, or increasing player... | ['Jürgen Schmidhuber', 'Julian Togelius', 'Tom Schaul'] | 2011-09-06 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 3.16265523e-01 4.68207270e-01 8.69191624e-03 2.36090776e-02
-1.43605247e-01 -6.47176504e-01 8.08832169e-01 -2.02755153e-01
-5.44143915e-01 9.15777922e-01 -2.34952867e-01 -6.46439016e-01
-5.97330749e-01 -1.27347124e+00 -2.18647107e-01 -6.58444285e-01
-2.23059151e-02 1.01121724e+00 5.50965965e-01 -8.16601276... | [3.510091781616211, 1.4715774059295654] |
d614a1fd-3e32-479a-b347-54c6fe7bd6ed | ynu-hpcc-at-semeval-2022-task-6-transformer | null | null | https://aclanthology.org/2022.semeval-1.134 | https://aclanthology.org/2022.semeval-1.134.pdf | YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and Arabic | In this paper, we (a YNU-HPCC team) describe the system we built in the SemEval-2022 competition. As participants in Task 6 (titled “iSarcasmEval: Intended Sarcasm Detection In English and Arabic”), we implement the sentiment system for all three subtasks in English and Arabic. All subtasks involve the detection of sar... | ['Xuejie Zhang', 'Jin Wang', 'Guangmin Zheng'] | null | null | null | null | semeval-naacl-2022-7 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 1.82231650e-01 2.16479808e-01 -1.39649466e-01 -7.10156083e-01
-1.25496602e+00 -7.08971024e-01 4.95125562e-01 3.88076097e-01
-7.94583142e-01 5.85617185e-01 4.59385216e-01 -5.01578748e-01
4.41492289e-01 -2.29813471e-01 -4.54402834e-01 -5.45700312e-01
3.90536398e-01 5.78268170e-01 2.63260137e-02 -6.63679183... | [9.185791969299316, 10.595486640930176] |
1f94f2d3-19d7-44d7-a887-e48dcd60cf68 | crosslingual-embeddings-are-essential-in-unmt | 2106.04995 | null | https://arxiv.org/abs/2106.04995v1 | https://arxiv.org/pdf/2106.04995v1.pdf | Crosslingual Embeddings are Essential in UNMT for Distant Languages: An English to IndoAryan Case Study | Recent advances in Unsupervised Neural Machine Translation (UNMT) have minimized the gap between supervised and unsupervised machine translation performance for closely related language pairs. However, the situation is very different for distant language pairs. Lack of lexical overlap and low syntactic similarities suc... | ['Pushpak Bhattacharyya', 'Rudra Murthy V', 'Tamali Banerjee'] | 2021-06-09 | null | https://aclanthology.org/2021.mtsummit-research.3 | https://aclanthology.org/2021.mtsummit-research.3.pdf | mtsummit-2021-8 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-2.53705811e-02 4.92914282e-02 -3.52513731e-01 -4.07237858e-01
-1.18538046e+00 -8.26670527e-01 7.73557186e-01 1.57942958e-02
-7.42984116e-01 8.76624584e-01 6.71270430e-01 -7.48945117e-01
4.75988865e-01 -4.53628182e-01 -7.13301778e-01 -6.21698678e-01
3.26063156e-01 6.43557847e-01 -3.86482805e-01 -4.29683149... | [11.437277793884277, 10.24424934387207] |
7730adfd-868d-4e2c-82e9-9ebe7746a3ce | wekws-a-production-first-small-footprint-end | 2210.16743 | null | https://arxiv.org/abs/2210.16743v1 | https://arxiv.org/pdf/2210.16743v1.pdf | WeKws: A production first small-footprint end-to-end Keyword Spotting Toolkit | Keyword spotting (KWS) enables speech-based user interaction and gradually becomes an indispensable component of smart devices. Recently, end-to-end (E2E) methods have become the most popular approach for on-device KWS tasks. However, there is still a gap between the research and deployment of E2E KWS methods. In this ... | ['Fuping Pan', 'Lei Xie', 'Xiao-Lei Zhang', 'BinBin Zhang', 'Jingyong Hou', 'Menglong Xu', 'Jie Wang'] | 2022-10-30 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-1.43419713e-01 -1.02252439e-01 -3.89626473e-01 -3.96683425e-01
-1.08974147e+00 -3.51173967e-01 1.71908885e-01 -5.29375732e-01
-4.34593111e-01 3.56016397e-01 3.49894524e-01 -5.99459827e-01
3.41030210e-01 -3.43689173e-01 -5.86975276e-01 -3.96596223e-01
3.81025136e-01 7.18923435e-02 2.41819128e-01 -1.02364466... | [14.228819847106934, 6.408493995666504] |
80ca41ac-b28a-4a55-8176-86c5230ebe11 | machine-learning-based-prototyping-of | 1802.02312 | null | https://arxiv.org/abs/1802.02312v2 | https://arxiv.org/pdf/1802.02312v2.pdf | Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps | It is common practice for developers of user-facing software to transform a mock-up of a graphical user interface (GUI) into code. This process takes place both at an application's inception and in an evolutionary context as GUI changes keep pace with evolving features. Unfortunately, this practice is challenging and t... | ['Denys Poshyvanyk', 'Richard Bonett', 'Michael Curcio', 'Carlos Bernal-Cárdenas', 'Kevin Moran'] | 2018-02-07 | null | null | null | null | ['component-classification'] | ['natural-language-processing'] | [ 2.14807764e-02 -1.82469815e-01 -8.98561329e-02 -2.29450718e-01
-6.44007385e-01 -9.39003229e-01 2.17951745e-01 3.00255358e-01
4.46089596e-01 -1.27126100e-02 -1.87765226e-01 -6.61184132e-01
-3.08685184e-01 -6.62378490e-01 -5.20816684e-01 1.61326349e-01
-3.88276391e-02 2.09379762e-01 2.25846678e-01 -2.99095139... | [8.223063468933105, 7.336215019226074] |
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