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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2e42119b-89c0-455a-b72a-2b09525cea40 | a-survey-on-arabic-named-entity-recognition | 2302.03512 | null | https://arxiv.org/abs/2302.03512v2 | https://arxiv.org/pdf/2302.03512v2.pdf | A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends | As more and more Arabic texts emerged on the Internet, extracting important information from these Arabic texts is especially useful. As a fundamental technology, Named entity recognition (NER) serves as the core component in information extraction technology, while also playing a critical role in many other Natural La... | ['Baoxing Huai', 'Zhefeng Wang', 'Zechang Li', 'Qingrong Xia', 'Yingjie Gu', 'Xiaoye Qu'] | 2023-02-07 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-4.90209967e-01 -1.75083086e-01 -7.85165280e-02 -1.92472219e-01
-5.21063149e-01 -7.59712279e-01 4.90808815e-01 5.85868299e-01
-6.97880566e-01 5.78434587e-01 4.24279183e-01 -1.95874095e-01
1.76806804e-02 -1.16092980e+00 3.24014798e-02 -4.47612077e-01
-2.46501312e-01 5.62160790e-01 -2.33882695e-01 -1.25095212... | [9.905879974365234, 9.851344108581543] |
dab56837-0e67-4cba-856f-1a3a74c50972 | adnet-a-deep-network-for-detecting-adverts | 1811.04115 | null | http://arxiv.org/abs/1811.04115v1 | http://arxiv.org/pdf/1811.04115v1.pdf | ADNet: A Deep Network for Detecting Adverts | Online video advertising gives content providers the ability to deliver
compelling content, reach a growing audience, and generate additional revenue
from online media. Recently, advertising strategies are designed to look for
original advert(s) in a video frame, and replacing them with new adverts. These
strategies, p... | ['François Pitié', 'Killian McCabe', 'Wei Xu', 'Soumyabrata Dev', 'Clare Conran', 'Jian Tang', 'Matthew Nicholson', 'Murhaf Hossari', 'Atul Nautiyal'] | 2018-11-09 | null | null | null | null | ['detecting-adverts'] | ['miscellaneous'] | [ 2.06944704e-01 -1.80711985e-01 -5.43175101e-01 -3.57814133e-01
-8.00664842e-01 -4.57789749e-01 4.40531254e-01 3.70845318e-01
-2.46422902e-01 2.72199452e-01 2.69753665e-01 -1.94793925e-01
2.48855054e-01 -7.69886434e-01 -9.49807644e-01 -2.91578114e-01
-1.53460264e-01 5.17225638e-02 7.31469154e-01 -1.26207545... | [9.977415084838867, 0.5492779016494751] |
16f58cdf-97b9-438e-aedb-ff4b23b290cc | self-supervised-video-similarity-learning | 2304.03378 | null | https://arxiv.org/abs/2304.03378v2 | https://arxiv.org/pdf/2304.03378v2.pdf | Self-Supervised Video Similarity Learning | We introduce S$^2$VS, a video similarity learning approach with self-supervision. Self-Supervised Learning (SSL) is typically used to train deep models on a proxy task so as to have strong transferability on target tasks after fine-tuning. Here, in contrast to prior work, SSL is used to perform video similarity learnin... | ['Symeon Papadopoulos', 'Ioannis Patras', 'Ioannis Kompatsiaris', 'Christos Tzelepis', 'Giorgos Tolias', 'Giorgos Kordopatis-Zilos'] | 2023-04-06 | null | null | null | null | ['video-similarity'] | ['computer-vision'] | [ 4.91277814e-01 -1.26431242e-01 -4.71100301e-01 -5.95141947e-01
-1.19280231e+00 -4.61977780e-01 6.89404786e-01 2.68267781e-01
-5.16401708e-01 5.09998381e-01 2.11248487e-01 4.89404909e-02
3.63760218e-02 -3.21023017e-01 -9.53847229e-01 -4.27250683e-01
-3.39415163e-01 2.67517030e-01 4.33032990e-01 -1.15381576... | [9.212698936462402, 1.3136699199676514] |
1b92e154-2301-4a29-ba6e-bc597320c835 | man-recon-manifold-learning-for | 2212.07568 | null | https://arxiv.org/abs/2212.07568v1 | https://arxiv.org/pdf/2212.07568v1.pdf | Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretation | Deep learning can extract rich data representations if provided sufficient quantities of labeled training data. For many tasks however, annotating data has significant costs in terms of time and money, owing to the high standards of subject matter expertise required, for example in medical and geophysical image interpr... | ['Ghassan AlRegib', 'Ahmad Mustafa'] | 2022-12-15 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [ 3.55073392e-01 7.51043737e-01 -8.63610357e-02 -5.70516467e-01
-1.30649900e+00 -2.16757149e-01 5.15600741e-01 3.45413387e-01
-8.27463150e-01 7.28487194e-01 1.82459712e-01 5.56197669e-03
-6.19202435e-01 -6.90574944e-01 -6.87145472e-01 -1.06386113e+00
-2.90938377e-01 7.50546634e-01 -1.42326728e-01 3.10445547... | [14.742524147033691, -2.1054201126098633] |
2d4a21c6-620b-436d-9d26-397514a048e8 | understanding-reinforcement-learning | 2304.00026 | null | https://arxiv.org/abs/2304.00026v1 | https://arxiv.org/pdf/2304.00026v1.pdf | Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization | This paper presents a review of the field of reinforcement learning (RL), with a focus on providing a comprehensive overview of the key concepts, techniques, and algorithms for beginners. RL has a unique setting, jargon, and mathematics that can be intimidating for those new to the field or artificial intelligence more... | ['Hajar Mousannif', 'Mohamed-Amine Chadi'] | 2023-03-31 | null | null | null | null | ['q-learning', 'offline-rl'] | ['methodology', 'playing-games'] | [ 1.44666154e-02 1.64715767e-01 -5.29922605e-01 5.41411564e-02
-4.51694965e-01 -6.31437123e-01 2.62138158e-01 -6.64374675e-04
-6.24696732e-01 1.09387994e+00 -2.03818172e-01 -4.32175785e-01
-4.76055115e-01 -6.99242055e-01 -2.96968251e-01 -9.22984362e-01
-3.49299788e-01 4.68587726e-01 -1.19203866e-01 -6.76797032... | [3.9201860427856445, 1.7779439687728882] |
99528c19-c62e-45a1-ae45-ae7401893934 | heuristic-weakly-supervised-3d-human-pose | 2105.10996 | null | https://arxiv.org/abs/2105.10996v3 | https://arxiv.org/pdf/2105.10996v3.pdf | Heuristic Weakly Supervised 3D Human Pose Estimation | Monocular 3D human pose estimation from RGB images has attracted significant attention in recent years. However, recent models depend on supervised training with 3D pose ground truth data or known pose priors for their target domains. 3D pose data is typically collected with motion capture devices, severely limiting th... | ['Sarah Ostadabbas', 'Michael Wan', 'Shuangjun Liu'] | 2021-05-23 | null | null | null | null | ['3d-pose-estimation', 'monocular-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-8.89523476e-02 3.38784277e-01 -3.76020908e-01 -4.39597726e-01
-9.79610503e-01 -4.30115879e-01 6.24069199e-02 -8.70428905e-02
-7.01979578e-01 5.77782452e-01 3.03276032e-01 1.45178527e-01
3.32925946e-01 -3.01806718e-01 -1.19623137e+00 -4.25638765e-01
-4.55880947e-02 9.79312658e-01 1.14743471e-01 -2.12735236... | [6.971930503845215, -0.9527939558029175] |
8c1d2022-3afd-4b7f-940f-f1e4a7dece0f | perception-matters-detecting-perception | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yuan_Perception_Matters_Detecting_Perception_Failures_of_VQA_Models_Using_Metamorphic_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yuan_Perception_Matters_Detecting_Perception_Failures_of_VQA_Models_Using_Metamorphic_CVPR_2021_paper.pdf | Perception Matters: Detecting Perception Failures of VQA Models Using Metamorphic Testing | Visual question answering (VQA) takes an image and a natural-language question as input and returns a natural-language answer. To date, VQA models are primarily assessed by their accuracy on high-level reasoning questions. Nevertheless, Given that perception tasks (e.g., recognizing objects) are the building blocks... | ['Tsong Yueh Chen', 'Mingyue Jiang', 'Shuai Wang', 'Yuanyuan Yuan'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['dnn-testing'] | ['adversarial'] | [ 1.09768234e-01 2.22121969e-01 3.29413176e-01 -4.02420402e-01
-8.12212348e-01 -1.08285272e+00 4.39009845e-01 2.03578636e-01
-1.03020720e-01 5.04800817e-03 -1.00965805e-01 -6.96532011e-01
2.68265437e-02 -1.13098645e+00 -1.17690206e+00 -9.40803587e-02
4.29998666e-01 3.65299702e-01 6.84457541e-01 -5.43136537... | [10.916337966918945, 1.8159607648849487] |
c3fccd2e-d1f9-46c3-b439-454d323e0b1d | split-federated-learning-on-micro-controllers | 2210.01961 | null | https://arxiv.org/abs/2210.01961v1 | https://arxiv.org/pdf/2210.01961v1.pdf | Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase | Nowadays, AI companies improve service quality by aggressively collecting users' data generated by edge devices, which jeopardizes data privacy. To prevent this, Federated Learning is proposed as a private learning scheme, using which users can locally train the model without collecting users' raw data to servers. Howe... | ['Runcong Kuang', 'Jingtao Li'] | 2022-10-04 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-3.01123142e-01 -7.14292750e-02 -6.57430172e-01 -5.68437755e-01
-9.71377552e-01 -7.51102030e-01 -1.06772833e-01 -2.57145822e-01
-3.43859464e-01 6.67921424e-01 -1.58794329e-01 -5.13587415e-01
2.15326384e-01 -7.66982734e-01 -7.32145250e-01 -6.37550056e-01
2.22854659e-01 1.87628925e-01 5.72986752e-02 4.53283042... | [5.8988189697265625, 6.212772846221924] |
0a28c49a-313b-4c1b-8b7c-f1bdfdbf87d9 | histoseg-quick-attention-with-multi-loss | 2209.00729 | null | https://arxiv.org/abs/2209.00729v1 | https://arxiv.org/pdf/2209.00729v1.pdf | HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images | Medical image segmentation assists in computer-aided diagnosis, surgeries, and treatment. Digitize tissue slide images are used to analyze and segment glands, nuclei, and other biomarkers which are further used in computer-aided medical applications. To this end, many researchers developed different neural networks to ... | ['Muhammad Moazam Fraz', 'Saad Wazir'] | 2022-09-01 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.36745766e-01 1.55972317e-01 -3.71341974e-01 -3.26111495e-01
-9.00733650e-01 -2.02057749e-01 4.52660657e-02 4.59508687e-01
-6.65130198e-01 6.66693866e-01 -3.73698212e-02 -2.67649055e-01
7.91421160e-02 -7.03721642e-01 -2.74884164e-01 -9.19293284e-01
-9.07129124e-02 3.87279898e-01 3.85711849e-01 -8.64859000... | [15.036906242370605, -2.861516237258911] |
3262ffa3-5f57-41a3-9c89-aef30b30a5b4 | video-based-contrastive-learning-on-decision | 2304.10073 | null | https://arxiv.org/abs/2304.10073v2 | https://arxiv.org/pdf/2304.10073v2.pdf | Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis | How can we teach a computer to recognize 10,000 different actions? Deep learning has evolved from supervised and unsupervised to self-supervised approaches. In this paper, we present a new contrastive learning-based framework for decision tree-based classification of actions, including human-human interactions (HHI) an... | ['Xin Li', 'Shuo Wang', 'Chuanbo Hu', 'Na Zhang', 'Xiangxu Yu', 'Mindi Ruan'] | 2023-04-20 | null | null | null | null | ['human-object-interaction-detection', 'symmetry-detection', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 8.19674671e-01 2.61437207e-01 -2.84971464e-02 -6.07267082e-01
-1.74683779e-01 3.01252715e-02 5.26350558e-01 -6.20565750e-02
-1.02446610e-02 2.11759150e-01 2.41862431e-01 -8.09402615e-02
-5.78209698e-01 -4.94580418e-01 -4.32448715e-01 -7.17534363e-01
-4.52730507e-01 6.16570175e-01 3.22383165e-01 -1.62841663... | [8.138569831848145, 0.8211416006088257] |
6a9080be-f479-46a9-8198-cc258740cf5c | learning-to-adapt-to-light | 2202.08098 | null | https://arxiv.org/abs/2202.08098v1 | https://arxiv.org/pdf/2202.08098v1.pdf | Learning to Adapt to Light | Light adaptation or brightness correction is a key step in improving the contrast and visual appeal of an image. There are multiple light-related tasks (for example, low-light enhancement and exposure correction) and previous studies have mainly investigated these tasks individually. However, it is interesting to consi... | ['Yong-Jie Li', 'Xian-Shi Zhang', 'Shi-Xuan Zhao', 'Cheng Cheng', 'Kai-Fu Yang'] | 2022-02-16 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 5.79779863e-01 -5.89177072e-01 2.42070109e-01 -1.28894031e-01
-1.43341869e-01 -1.33833185e-01 4.18342203e-01 -2.38763243e-01
-5.35692036e-01 7.18530536e-01 6.10959865e-02 9.59064215e-02
1.31395862e-01 -7.84454703e-01 -7.29467630e-01 -9.99250412e-01
5.38889527e-01 -8.24805319e-01 5.43105423e-01 -5.03642857... | [10.826553344726562, -2.4567484855651855] |
9ceb2410-0a9f-4c2f-b227-c0eba8645825 | icm-3d-instantiated-category-modeling-for-3d | 2108.11771 | null | https://arxiv.org/abs/2108.11771v1 | https://arxiv.org/pdf/2108.11771v1.pdf | ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation | Separating 3D point clouds into individual instances is an important task for 3D vision. It is challenging due to the unknown and varying number of instances in a scene. Existing deep learning based works focus on a two-step pipeline: first learn a feature embedding and then cluster the points. Such a two-step pipeline... | ['Lei LI', 'Lu Qi', 'Tao Kong', 'Yukang Chen', 'Ruihang Chu'] | 2021-08-26 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-6.79319873e-02 -1.51397377e-01 -1.54188797e-01 -5.65234005e-01
-8.80982399e-01 -8.36470306e-01 6.80936992e-01 2.44137608e-02
-1.32662132e-01 -8.21450949e-02 -3.14218640e-01 -4.96539295e-01
-1.47179022e-01 -6.22076690e-01 -8.81305575e-01 -3.19091439e-01
-1.19691692e-01 8.51490736e-01 3.99043858e-01 2.43786693... | [8.028265953063965, -3.238132953643799] |
6d50ec92-229c-4571-9e84-2b27bd09e057 | importance-weighting-with-a-adversarial | null | null | https://openreview.net/forum?id=rGh-L5qYqvl | https://openreview.net/pdf?id=rGh-L5qYqvl | Importance Weighting with a Adversarial Network for Large-Scale Sleep Staging | To develop a generalized automated sleep staging method based on the gold standard modality, electroencephalograms (EEGs), requires a large and accurately labeled training and test set acquired from different individuals with diverse demographics and medical conditions. However, data in the training set may exhibit cha... | ['Gari D. Clifford', 'Samaneh Nasiri'] | 2020-06-12 | null | null | null | icml-workshop-lifelongml-2020-7 | ['sleep-staging'] | ['medical'] | [ 3.14388067e-01 -1.05412573e-01 1.04514830e-01 -6.97224855e-01
-8.78027260e-01 -4.69943017e-01 1.38102472e-01 1.40287191e-01
-5.33833981e-01 1.22734177e+00 3.41395169e-01 1.60405561e-01
-3.74146312e-01 -4.29391921e-01 -6.36315107e-01 -6.38529241e-01
-2.75114983e-01 2.70557076e-01 -2.59804577e-01 -8.11516196... | [13.321730613708496, 3.4814343452453613] |
58da3ba3-dd3e-4789-be9d-0aefddd99d6f | ver-scaling-on-policy-rl-leads-to-the | 2210.05064 | null | https://arxiv.org/abs/2210.05064v1 | https://arxiv.org/pdf/2210.05064v1.pdf | VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement | We present Variable Experience Rollout (VER), a technique for efficiently scaling batched on-policy reinforcement learning in heterogenous environments (where different environments take vastly different times to generate rollouts) to many GPUs residing on, potentially, many machines. VER combines the strengths of and ... | ['Dhruv Batra', 'Irfan Essa', 'Erik Wijmans'] | 2022-10-11 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [-2.19491929e-01 -8.14489350e-02 -1.08119650e-02 2.18822479e-01
-8.11356425e-01 -1.02952921e+00 6.47002101e-01 -1.77861433e-02
-8.52513433e-01 8.99091601e-01 -1.50527051e-02 -6.82339668e-01
-4.45104316e-02 -7.80179024e-01 -1.16560006e+00 -7.17628896e-01
-5.13373613e-01 6.96709692e-01 2.55393237e-01 -6.26778781... | [4.399611473083496, 0.9661296010017395] |
2dd318bc-87ea-479c-a3af-b2a0ad81d497 | multiplayer-war-of-attrition-with-asymmetric | 2302.09427 | null | https://arxiv.org/abs/2302.09427v1 | https://arxiv.org/pdf/2302.09427v1.pdf | Multiplayer War of Attrition with Asymmetric Private Information | This paper models a multiplayer war of attrition game with asymmetric incomplete information on the private provision of one public good to investigate the effect of ex-ante asymmetry. In the unique equilibrium, asymmetry leads to a stratified behavior pattern such that one player provides the good instantly with a pos... | ['Hongcheng Li'] | 2023-02-18 | null | null | null | null | ['type'] | ['speech'] | [-4.39653486e-01 4.51866925e-01 -7.33545542e-01 2.18524978e-01
-1.43373936e-01 -6.68449044e-01 1.04915284e-01 2.51052499e-01
-9.72483277e-01 8.94677877e-01 5.77275276e-01 -5.38983703e-01
-6.13025486e-01 -7.98129678e-01 -1.41664416e-01 -7.99845219e-01
8.10567886e-02 5.38461924e-01 7.12465197e-02 -2.15745628... | [4.307759761810303, 3.0325393676757812] |
3bfa7d5d-9f8e-4c30-b224-d81ae4e0bb69 | research-on-cpi-prediction-based-on-natural | 2303.05666 | null | https://arxiv.org/abs/2303.05666v1 | https://arxiv.org/pdf/2303.05666v1.pdf | Research on CPI Prediction Based on Natural Language Processing | In the past, the seed keywords for CPI prediction were often selected based on empirical summaries of research and literature studies, which were prone to select omitted and invalid variables. In this paper, we design a keyword expansion technique for CPI prediction based on the cutting-edge NLP model, PANGU. We improv... | ['Nuo Lei', 'Xiaobin Tang'] | 2023-03-10 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [-6.49959967e-02 -1.16017222e-01 -1.08631992e+00 6.99865744e-02
-6.29528522e-01 -5.59061706e-01 5.19217134e-01 2.08510593e-01
-2.64891297e-01 9.75758255e-01 5.39405942e-01 -8.92817318e-01
-7.19486713e-01 -1.06543148e+00 -4.46824640e-01 -3.29910994e-01
9.32382233e-03 4.68307883e-01 1.37468293e-01 1.14282511... | [12.032177925109863, 8.799059867858887] |
8ca8f922-7bc4-4d4d-bad9-588177e5d090 | open-information-extraction-from-2007-to-2022 | 2208.08690 | null | https://arxiv.org/abs/2208.08690v1 | https://arxiv.org/pdf/2208.08690v1.pdf | Open Information Extraction from 2007 to 2022 -- A Survey | Open information extraction is an important NLP task that targets extracting structured information from unstructured text without limitations on the relation type or the domain of the text. This survey paper covers open information extraction technologies from 2007 to 2022 with a focus on new models not covered by pre... | ['Yue Zhang', 'Songfang Huang', 'Wenjie Dong', 'Wenyang Gao', 'Pai Liu'] | 2022-08-18 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 2.28002384e-01 7.87294865e-01 -7.91441917e-01 -1.47390142e-01
-6.45937443e-01 -9.55281973e-01 5.03555894e-01 4.76738989e-01
-4.06570852e-01 1.20704520e+00 4.17848676e-01 -1.76761851e-01
-5.68595111e-01 -7.20750451e-01 -2.83311397e-01 2.96991706e-01
1.45952955e-01 5.02028823e-01 -1.81545407e-01 -2.39277914... | [9.443408012390137, 8.718393325805664] |
db6d7dcd-ecc6-424d-a04c-c41385ac0233 | environment-agnostic-multitask-learning-for | 2003.00443 | null | https://arxiv.org/abs/2003.00443v5 | https://arxiv.org/pdf/2003.00443v5.pdf | Environment-agnostic Multitask Learning for Natural Language Grounded Navigation | Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e.g., following natural language instructions or dialog. However, existing methods tend to overfit training data in seen environments and fail to generalize well in previously unseen environments. To close the... | ['William Yang Wang', 'Zornitsa Kozareva', 'Eugene Ie', 'Xin Eric Wang', 'Vihan Jain', 'Sujith Ravi'] | 2020-03-01 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4629_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690409.pdf | eccv-2020-8 | ['vision-language-navigation'] | ['computer-vision'] | [-7.91151002e-02 -4.02306803e-02 2.32766420e-01 -5.31803310e-01
-8.27724874e-01 -8.14297080e-01 7.64765739e-01 -2.36678183e-01
-8.62764418e-01 5.66408455e-01 5.84382534e-01 -6.01692975e-01
2.00926643e-02 -5.98919094e-01 -9.29397285e-01 -5.50295770e-01
-3.42627019e-02 6.91301942e-01 4.30649638e-01 -7.41443217... | [4.4711408615112305, 0.5627771615982056] |
8e790b3e-3bad-481a-ab60-659b58838715 | computational-language-acquisition-with | 2303.01502 | null | https://arxiv.org/abs/2303.01502v1 | https://arxiv.org/pdf/2303.01502v1.pdf | Computational Language Acquisition with Theory of Mind | Unlike current state-of-the-art language models, young children actively acquire language through interactions with their surrounding environment and caretakers. One mechanism that has been argued to be critical to language learning is the ability to infer the mental states of other agents in social environments, coine... | ['Graham Neubig', 'Yonatan Bisk', 'Emmy Liu', 'Hao Zhu', 'Andy Liu'] | 2023-03-02 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 7.31038153e-02 6.13456368e-01 1.97670639e-01 -1.09521285e-01
-1.67593822e-01 -6.10598743e-01 9.10363019e-01 4.17996079e-01
-6.12732649e-01 1.96878031e-01 3.76620203e-01 -3.20917100e-01
1.97811937e-03 -8.60980153e-01 -6.29939258e-01 -3.13743085e-01
-2.28175417e-01 6.96526587e-01 5.76731935e-02 -3.84612083... | [10.291427612304688, 8.548709869384766] |
fcfc3456-5036-4773-ac20-15e612af3ac2 | towards-local-visual-modeling-for-image | 2302.06098 | null | https://arxiv.org/abs/2302.06098v1 | https://arxiv.org/pdf/2302.06098v1.pdf | Towards Local Visual Modeling for Image Captioning | In this paper, we study the local visual modeling with grid features for image captioning, which is critical for generating accurate and detailed captions. To achieve this target, we propose a Locality-Sensitive Transformer Network (LSTNet) with two novel designs, namely Locality-Sensitive Attention (LSA) and Locality-... | ['Rongrong Ji', 'Yiyi Zhou', 'Xiaoshuai Sun', 'Jiayi Ji', 'Yiwei Ma'] | 2023-02-13 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 2.53859814e-02 -2.25402638e-01 -2.44595230e-01 -5.10501623e-01
-1.06125832e+00 -3.73154968e-01 5.06100953e-01 -1.23436257e-01
-8.90367702e-02 6.37645781e-01 5.43182850e-01 -5.92541099e-02
3.44247036e-02 -4.15282190e-01 -1.31082582e+00 -6.64662063e-01
3.05331945e-01 3.33094597e-01 2.63835192e-01 -2.06414089... | [10.658991813659668, 0.8935550451278687] |
5ccbfa9a-6407-4695-9033-081a9b9f5e5c | iptr-learning-a-representation-for | null | null | https://openreview.net/forum?id=Peg7mkjzvyP | https://openreview.net/pdf?id=Peg7mkjzvyP | iPTR: Learning a representation for interactive program translation retrieval | Program translation contributes to many real world scenarios, such as porting codebases written in an obsolete or deprecated language to a modern one or re-implementing existing projects in one's preferred programming language. Existing data-driven approaches either require large amounts of training data or neglect sig... | ['Ziawasch Abedjan', 'Binger Chen'] | 2021-01-01 | null | null | null | null | ['code-translation'] | ['computer-code'] | [ 1.05845567e-03 -1.47989914e-01 -6.59439325e-01 -5.11650383e-01
-1.19157946e+00 -8.00905645e-01 2.46032253e-01 3.04842800e-01
-4.39522415e-02 1.15981705e-01 8.12453777e-02 -7.29884386e-01
1.56197533e-01 -8.94536674e-01 -8.69961619e-01 2.09380519e-02
3.30988824e-01 4.89321738e-01 3.02564204e-01 -3.73573363... | [7.595396518707275, 7.982832908630371] |
ba338cd4-271e-496f-8bf0-c7ac12be736f | upscaling-global-hourly-gpp-with-temporal | 2306.13815 | null | https://arxiv.org/abs/2306.13815v1 | https://arxiv.org/pdf/2306.13815v1.pdf | Upscaling Global Hourly GPP with Temporal Fusion Transformer (TFT) | Reliable estimates of Gross Primary Productivity (GPP), crucial for evaluating climate change initiatives, are currently only available from sparsely distributed eddy covariance tower sites. This limitation hampers access to reliable GPP quantification at regional to global scales. Prior machine learning studies on ups... | ['Yanghui Kang', 'Maoya Bassiouni', 'Alberto Todeschini', 'Puya Vahabi', 'Trevor Keenan', 'John Calzaretta', 'Mary Chau', 'Rumi Nakagawa'] | 2023-06-23 | null | null | null | null | ['feature-importance'] | ['methodology'] | [ 1.39293540e-03 -2.85514712e-01 1.71410665e-03 -1.53859332e-01
-4.83749479e-01 -7.82459199e-01 8.51168513e-01 4.54334080e-01
-1.84063256e-01 1.13568830e+00 3.84882838e-01 -8.51776958e-01
-5.18031120e-01 -9.27866518e-01 -4.21657354e-01 -9.75867331e-01
-4.53281492e-01 8.49687010e-02 6.11572489e-02 -2.97266930... | [9.440858840942383, -1.536049485206604] |
dfef57c9-02cb-4b99-8971-6bc5d3891d91 | choice-fusion-as-knowledge-for-zero-shot | 2302.13013 | null | https://arxiv.org/abs/2302.13013v1 | https://arxiv.org/pdf/2302.13013v1.pdf | Choice Fusion as Knowledge for Zero-Shot Dialogue State Tracking | With the demanding need for deploying dialogue systems in new domains with less cost, zero-shot dialogue state tracking (DST), which tracks user's requirements in task-oriented dialogues without training on desired domains, draws attention increasingly. Although prior works have leveraged question-answering (QA) data t... | ['Biing-Hwang Juang', 'Ting-Wei Wu', 'Jingfeng Yang', 'Ruolin Su'] | 2023-02-25 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 2.78791726e-01 6.98886812e-01 -1.39379546e-01 -7.04638183e-01
-1.16949332e+00 -5.59646308e-01 8.35156202e-01 -9.63354036e-02
-2.73141623e-01 8.69504154e-01 6.54006243e-01 -3.96236926e-01
1.49918512e-01 -5.28332174e-01 1.33749947e-01 -1.09544605e-01
5.04690170e-01 1.03966331e+00 4.04140681e-01 -1.14715385... | [12.81859302520752, 7.975155830383301] |
32daf045-cd3d-4576-83d7-150e3ce9e104 | a-novel-repetition-normalized-adversarial | 1902.07110 | null | http://arxiv.org/abs/1902.07110v1 | http://arxiv.org/pdf/1902.07110v1.pdf | A novel repetition normalized adversarial reward for headline generation | While reinforcement learning can effectively improve language generation
models, it often suffers from generating incoherent and repetitive phrases
\cite{paulus2017deep}. In this paper, we propose a novel repetition normalized
adversarial reward to mitigate these problems. Our repetition penalized reward
can greatly re... | ['Pascale Fung', 'Peng Xu'] | 2019-02-19 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [-7.4108146e-02 1.1836150e-01 -9.9990472e-02 1.9082128e-01
-1.1548419e+00 -8.5528004e-01 6.6542292e-01 -2.0339741e-01
-4.9103415e-01 1.3133417e+00 3.7141737e-01 -3.4398800e-01
3.1453723e-01 -9.2045116e-01 -7.7674204e-01 -3.3216089e-01
-2.7309047e-02 3.4559152e-01 -2.5774881e-01 -8.2947832e-01
1.2459966e-01... | [11.814126968383789, 9.134638786315918] |
631beb80-da85-48c5-9786-8c03fc5edaa8 | a-review-on-physical-and-data-driven-based | 2105.02959 | null | https://arxiv.org/abs/2105.02959v1 | https://arxiv.org/pdf/2105.02959v1.pdf | A review on physical and data-driven based nowcasting methods using sky images | Amongst all the renewable energy resources (RES), solar is the most popular form of energy source and is of particular interest for its widely integration into the power grid. However, due to the intermittent nature of solar source, it is of the greatest significance to forecast solar irradiance to ensure uninterrupted... | ['Wilfried Elmenreich', 'Ekanki Sharma'] | 2021-04-28 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [-1.76914334e-01 -5.85593045e-01 -2.20434472e-01 -1.38113543e-01
-1.98216349e-01 -7.36505270e-01 7.52454162e-01 1.08584957e-02
2.03248277e-01 1.36551094e+00 6.72170073e-02 -3.82899106e-01
-2.95462813e-02 -1.06132483e+00 -2.28059918e-01 -1.09225738e+00
2.69147426e-01 -1.29496664e-01 -1.33978173e-01 -4.33993250... | [6.3466386795043945, 2.6915359497070312] |
6ac83e0b-1bb0-4299-9d73-e19b7f704c46 | knowledge-guided-text-retrieval-and-reading | 1911.03868 | null | https://arxiv.org/abs/1911.03868v2 | https://arxiv.org/pdf/1911.03868v2.pdf | Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering | We introduce an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article. Our goals are to boost coverage by using knowledge-guide... | ['Sewon Min', 'Luke Zettlemoyer', 'Hannaneh Hajishirzi', 'Danqi Chen'] | 2019-11-10 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 2.09426731e-01 5.72025836e-01 -1.71546504e-01 2.51050871e-02
-1.60188913e+00 -1.01195049e+00 6.77650690e-01 1.00727987e+00
-3.87437075e-01 1.06612957e+00 9.09473300e-01 -4.02922720e-01
-4.40154105e-01 -1.31651211e+00 -1.06095517e+00 1.83941796e-02
3.19186710e-02 1.00606096e+00 9.84130979e-01 -8.42344463... | [10.944465637207031, 7.959722518920898] |
85b3cc0d-edcc-4032-9e46-7e4ed901c49a | learning-to-abstract-and-predict-human | 2008.09234 | null | https://arxiv.org/abs/2008.09234v1 | https://arxiv.org/pdf/2008.09234v1.pdf | Learning to Abstract and Predict Human Actions | Human activities are naturally structured as hierarchies unrolled over time. For action prediction, temporal relations in event sequences are widely exploited by current methods while their semantic coherence across different levels of abstraction has not been well explored. In this work we model the hierarchical struc... | ['Truyen Tran', 'Svetha Venkatesh', 'Vuong Le', 'Romero Morais'] | 2020-08-20 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 5.15887260e-01 1.26169875e-01 -6.89498246e-01 -6.71387970e-01
-3.94266069e-01 -2.69278467e-01 6.50967956e-01 8.30878690e-02
2.41131727e-02 6.54249728e-01 1.33626485e+00 1.71316564e-01
-1.33374974e-01 -3.57375771e-01 -7.02587962e-01 -2.41711691e-01
-6.36072397e-01 1.20978348e-01 5.62166870e-01 8.41334909... | [8.349591255187988, 0.6202506422996521] |
055f5aaf-9f5b-4c22-9584-b1b53b38a71f | anvaya-an-algorithm-and-case-study-on | 1511.07023 | null | http://arxiv.org/abs/1511.07023v1 | http://arxiv.org/pdf/1511.07023v1.pdf | Anvaya: An Algorithm and Case-Study on Improving the Goodness of Software Process Models generated by Mining Event-Log Data in Issue Tracking System | Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process Aware
Information Systems (PAIS) generating event-logs during the life-cycle of a bug
report. Process Mining consists of mining event logs generated from PAIS for
process model discovery, conformance and enhancement. We apply process map
discovery t... | ['Divya Kundra', 'Ashish Sureka', 'Prerna Juneja'] | 2015-11-22 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 2.93462966e-02 1.30656779e-01 5.10503590e-01 -2.83144284e-02
-2.62975365e-01 -7.85553455e-01 3.92431170e-01 8.76407921e-01
-9.23915487e-03 4.09918636e-01 1.64714992e-01 -4.30082321e-01
-9.09070969e-01 -1.01307988e+00 -2.24377438e-02 -2.80770928e-01
-6.83879673e-01 5.17305315e-01 6.73619866e-01 8.28988403... | [8.568202018737793, 6.069533348083496] |
2c7a7b44-a0ea-4727-9651-b4a3a6d1a54d | catgrasp-learning-category-level-task | 2109.09163 | null | https://arxiv.org/abs/2109.09163v2 | https://arxiv.org/pdf/2109.09163v2.pdf | CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation | Task-relevant grasping is critical for industrial assembly, where downstream manipulation tasks constrain the set of valid grasps. Learning how to perform this task, however, is challenging, since task-relevant grasp labels are hard to define and annotate. There is also yet no consensus on proper representations for mo... | ['Stefan Schaal', 'Kostas Bekris', 'Wenzhao Lian', 'Bowen Wen'] | 2021-09-19 | null | null | null | null | ['grasp-generation', 'human-grasp-contact-prediction', 'physical-simulations', 'industrial-robots', 'robot-task-planning'] | ['computer-vision', 'miscellaneous', 'miscellaneous', 'robots', 'robots'] | [ 4.24309134e-01 -1.38748944e-01 4.28620093e-02 -5.80745220e-01
-4.55462575e-01 -8.38659942e-01 8.29758272e-02 1.05324231e-01
2.41214991e-01 4.87881273e-01 -4.29813921e-01 1.28699550e-02
-6.27054989e-01 -5.49193084e-01 -8.65357459e-01 -7.45441318e-01
-3.66042346e-01 7.11712241e-01 1.27002582e-01 -6.28945678... | [5.782176494598389, -0.8737516403198242] |
fa6180e9-1f03-46e4-b8e7-a51dd858af4a | cosformer-detecting-co-salient-object-with | 2104.14729 | null | https://arxiv.org/abs/2104.14729v2 | https://arxiv.org/pdf/2104.14729v2.pdf | CoSformer: Detecting Co-Salient Object with Transformers | Co-Salient Object Detection (CoSOD) aims at simulating the human visual system to discover the common and salient objects from a group of relevant images. Recent methods typically develop sophisticated deep learning based models have greatly improved the performance of CoSOD task. But there are still two major drawback... | ['Bo Li', 'Lv Tang'] | 2021-04-30 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [-1.69197400e-03 -3.34563911e-01 -7.65464921e-03 -1.78946570e-01
-6.58606946e-01 -1.91421837e-01 6.11547589e-01 1.98651161e-02
-2.79223472e-01 1.44538701e-01 4.72234160e-01 1.60681203e-01
-1.55105531e-01 -2.32015118e-01 -7.18195915e-01 -7.06822991e-01
1.56386092e-01 -2.56967247e-01 4.84448373e-01 2.28892621... | [9.754337310791016, -0.18391746282577515] |
baf2b0b0-708b-463b-ae58-87080b27eec6 | rococo-robust-benchmark-ms-coco-to-stress | 2304.10727 | null | https://arxiv.org/abs/2304.10727v1 | https://arxiv.org/pdf/2304.10727v1.pdf | RoCOCO: Robust Benchmark MS-COCO to Stress-test Robustness of Image-Text Matching Models | Recently, large-scale vision-language pre-training models and visual semantic embedding methods have significantly improved image-text matching (ITM) accuracy on MS COCO 5K test set. However, it is unclear how robust these state-of-the-art (SOTA) models are when using them in the wild. In this paper, we propose a novel... | ['Jin Young Choi', 'Sangdoo Yun', 'Sanghyuk Chun', 'Hajung Yoon', 'Daeho Um', 'Seulki Park'] | 2023-04-21 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 1.39404625e-01 -4.16671038e-01 -1.82768106e-01 -3.59908879e-01
-8.56432974e-01 -7.97995627e-01 7.52420604e-01 -2.28103355e-01
-7.67051339e-01 3.52824122e-01 7.86269456e-03 -2.87698656e-01
1.70358658e-01 -5.93791902e-01 -9.99984920e-01 -4.16056693e-01
3.82241696e-01 1.31531745e-01 2.61688888e-01 -3.33732784... | [10.769301414489746, 1.5025659799575806] |
38d8f386-8c4e-4167-8c7a-48faefd12694 | blind-video-quality-assessment-at-the-edge | 2306.10386 | null | https://arxiv.org/abs/2306.10386v1 | https://arxiv.org/pdf/2306.10386v1.pdf | Blind Video Quality Assessment at the Edge | Owing to the proliferation of user-generated videos on the Internet, blind video quality assessment (BVQA) at the edge attracts growing attention. The usage of deep-learning-based methods is restricted by their large model sizes and high computational complexity. In light of this, a novel lightweight BVQA method called... | ['C. -C. Jay Kuo', 'Yun-Cheng Wang', 'Zhanxuan Mei'] | 2023-06-17 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [ 5.21990508e-02 -5.69460452e-01 1.09264933e-01 -3.06919575e-01
-1.22777307e+00 -2.41792798e-01 2.10246861e-01 1.14532210e-01
-1.37464225e-01 5.70282400e-01 8.09879750e-02 -2.56359816e-01
-1.16436519e-01 -5.65572858e-01 -2.19961539e-01 -7.54020393e-01
-5.48727401e-02 -3.47661734e-01 1.88422754e-01 -2.75613945... | [11.839993476867676, -1.8463002443313599] |
922ed78b-f60d-4647-976a-688dc8cb7ba5 | pyramid-attention-network-for-semantic | 1805.10180 | null | http://arxiv.org/abs/1805.10180v3 | http://arxiv.org/pdf/1805.10180v3.pdf | Pyramid Attention Network for Semantic Segmentation | A Pyramid Attention Network(PAN) is proposed to exploit the impact of global
contextual information in semantic segmentation. Different from most existing
works, we combine attention mechanism and spatial pyramid to extract precise
dense features for pixel labeling instead of complicated dilated convolution
and artific... | ['Lingxue Wang', 'Pengfei Xiong', 'Hanchao Li', 'Jie An'] | 2018-05-25 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 1.77907422e-01 -2.91055664e-02 -9.21574458e-02 -6.02581143e-01
-8.04432273e-01 -2.95930922e-01 2.75006056e-01 -7.78163448e-02
-6.86740458e-01 4.73039806e-01 2.46490076e-01 4.36141491e-02
3.40680391e-01 -9.84831452e-01 -9.42538798e-01 -4.85236704e-01
2.68311590e-01 -7.58467913e-02 8.21066976e-01 -6.88947272... | [9.57597541809082, 0.2675439119338989] |
ed60fe28-3500-4985-9168-4d43befcb779 | conversation-style-transfer-using-few-shot | 2302.08362 | null | https://arxiv.org/abs/2302.08362v1 | https://arxiv.org/pdf/2302.08362v1.pdf | Conversation Style Transfer using Few-Shot Learning | Conventional text style transfer approaches for natural language focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e.g., formality). When applying style transfer on conversations such as task-oriented dialogues, existing approaches suffer from... | ['Dan Roth', 'Saab Mansour', 'Yi Zhang', 'Elman Mansimov', 'Nikolaos Pappas', 'Raphael Shu', 'Shamik Roy'] | 2023-02-16 | null | null | null | null | ['text-style-transfoer', 'intent-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.31782210e-01 3.21778804e-01 1.02440283e-01 -9.43532109e-01
-8.04530680e-01 -7.80473173e-01 8.88398230e-01 -2.04729512e-01
-4.55957800e-01 1.00824118e+00 6.65212870e-01 -1.89935267e-01
2.86211878e-01 -6.05931878e-01 -2.93015808e-01 -3.13886613e-01
5.12585223e-01 8.44468772e-01 7.93961436e-02 -8.22641194... | [12.512354850769043, 8.300850868225098] |
abb185bf-655a-4145-91e8-b2830e430bdf | simple-thermal-noise-estimation-of-switched | 1908.08099 | null | http://arxiv.org/abs/1908.08099v1 | http://arxiv.org/pdf/1908.08099v1.pdf | Simple Thermal Noise Estimation of Switched Capacitor Circuits Based on OTAs -- Part I: Amplifiers with Capacitive Feedback | This paper presents a simple method for estimating the thermal noise voltage
variance in passive and active switched-capacitor (SC) circuits using
operational transconductance amplifiers (OTA). The proposed method is based on
the Bode theorem for passive network which is extended to active circuits based
on OTAs with c... | [] | 2019-08-21 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 5.15542567e-01 -2.20246390e-01 3.24968755e-01 -1.22204442e-02
-7.27703497e-02 -8.22584927e-01 2.31990725e-01 2.83010542e-01
-6.40124440e-01 7.06356168e-01 -5.85772097e-01 -5.95212698e-01
-3.38489532e-01 -3.95032227e-01 -1.01296254e-01 -6.29550517e-01
2.59917285e-02 -1.29158467e-01 5.36453724e-01 -3.11076641... | [13.945063591003418, 3.2016336917877197] |
c83bccfd-f5d5-462c-82aa-eb0db598afeb | multi-level-attention-for-unsupervised-person | 2201.03141 | null | https://arxiv.org/abs/2201.03141v1 | https://arxiv.org/pdf/2201.03141v1.pdf | Multi-Level Attention for Unsupervised Person Re-Identification | The attention mechanism is widely used in deep learning because of its excellent performance in neural networks without introducing additional information. However, in unsupervised person re-identification, the attention module represented by multi-headed self-attention suffers from attention spreading in the condition... | ['Yi Zheng'] | 2022-01-10 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-2.66757607e-01 -2.34373108e-01 1.17089771e-01 -3.67647350e-01
-3.35281819e-01 -1.71661794e-01 4.96948719e-01 -3.99395870e-03
-8.44053209e-01 7.70339251e-01 4.44734961e-01 1.24979444e-01
2.22744614e-01 -6.69732213e-01 -6.06734097e-01 -4.16489720e-01
3.32758397e-01 4.15878743e-01 8.16225335e-02 5.98393716... | [14.649534225463867, 0.8971444368362427] |
9058f37c-e5ff-4170-aea1-3cf45ef6b3cf | human-identity-preserved-motion-retargeting | 2204.06862 | null | https://arxiv.org/abs/2204.06862v2 | https://arxiv.org/pdf/2204.06862v2.pdf | An Identity-Preserved Framework for Human Motion Transfer | Human motion transfer (HMT) aims to generate a video clip for the target subject by imitating the source subject's motion. Although previous methods have achieved remarkable results in synthesizing good-quality videos, those methods omit the effects of individualized motion information from the source and target motion... | ['Xiaoqing Zhang', 'Shiqi Yu', 'Jingzhe Ma'] | 2022-04-14 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 1.13651715e-01 -2.44074136e-01 -2.51682848e-01 8.14562314e-04
-6.66662693e-01 -3.81860375e-01 3.57861876e-01 -1.01563740e+00
-9.14107934e-02 6.78263009e-01 5.91716707e-01 2.78617114e-01
4.36507687e-02 -5.97225249e-01 -6.86608374e-01 -7.73194969e-01
6.26239926e-02 1.35382816e-01 -1.34596452e-01 -9.21906754... | [10.875593185424805, -0.8013954758644104] |
1caad067-2e04-494c-9377-a85b2394b83c | camouflaged-object-detection-and-tracking-a | 2012.13581 | null | https://arxiv.org/abs/2012.13581v1 | https://arxiv.org/pdf/2012.13581v1.pdf | Camouflaged Object Detection and Tracking: A Survey | Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and several essential survey papers exist. However, the research on camouflage object detection and tracking limited due to ... | ['Ajoy Mondal'] | 2020-12-25 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 2.52617776e-01 -5.49059272e-01 -4.59059089e-01 4.85717773e-01
4.55084473e-01 -6.92035198e-01 4.53170091e-01 -3.94986898e-01
-2.36423939e-01 9.36955392e-01 -4.22293723e-01 -3.33587438e-01
4.26765352e-01 -4.33544636e-01 -2.84285605e-01 -1.12374389e+00
4.91126217e-02 -1.37434006e-01 8.22403133e-01 -6.17039502... | [8.245865821838379, -0.8993552327156067] |
9f5fcd63-cc5b-4165-a85e-3da0833a07e6 | learning-a-deep-convolutional-network-for | null | null | https://ojs.aaai.org//index.php/AAAI/article/view/4837 | https://dl.acm.org/doi/pdf/10.1609/aaai.v33i01.33018255 | Learning a Deep Convolutional Network for Colorization in Monochrome-Color Dual-Lens System | In the monochrome-color dual-lens system, the gray image captured by the monochrome camera has better quality than the color image from the color camera, but does not have color information. To get high-quality color images, it is desired to colorize the gray image with the color image as reference. Related works usual... | ['Yunhong Wang', 'Xiaojie Wang', 'Weixin Li', 'Xuan Dong'] | 2019-09-09 | null | null | null | proceedings-of-the-aaai-conference-on-3 | ['colorization'] | ['computer-vision'] | [ 2.21940890e-01 -6.54494047e-01 2.48575881e-02 -2.14784175e-01
-3.44584554e-01 -3.44681650e-01 -1.18154317e-01 -6.09639585e-01
-6.15193725e-01 5.84561169e-01 -8.41535255e-02 -2.18899235e-01
1.47584155e-01 -1.10156429e+00 -7.39208817e-01 -1.01046097e+00
5.97516000e-01 -8.92513022e-02 3.60413730e-01 -1.23497814... | [11.126585960388184, -1.2415879964828491] |
d2572877-99c9-44fa-8003-f480eb39fa9b | multi-agent-path-finding-using-evolutionary | 2212.02010 | null | https://arxiv.org/abs/2212.02010v1 | https://arxiv.org/pdf/2212.02010v1.pdf | Multi Agent Path Finding using Evolutionary Game Theory | In this paper, we consider the problem of path finding for a set of homogeneous and autonomous agents navigating a previously unknown stochastic environment. In our problem setting, each agent attempts to maximize a given utility function while respecting safety properties. Our solution is based on ideas from evolution... | ['Jyotirmoy V. Deshmukh', 'Sheryl Paul'] | 2022-12-05 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-2.59375591e-02 4.03267771e-01 -7.34503788e-04 5.32290697e-01
-5.04917979e-01 -9.28938627e-01 5.29326200e-01 1.96098000e-01
-8.71680737e-01 1.35751343e+00 -1.24702707e-01 -3.56343120e-01
-5.02659440e-01 -1.10377657e+00 -6.35315537e-01 -9.51362491e-01
-7.05370307e-01 7.87979066e-01 3.18957925e-01 -8.44425976... | [4.019989013671875, 2.286405086517334] |
814d3c43-23f1-473f-93b3-504ed70d0b19 | the-hypervolume-indicator-hessian-matrix | 2211.04171 | null | https://arxiv.org/abs/2211.04171v3 | https://arxiv.org/pdf/2211.04171v3.pdf | The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and Sparsity | The problem of approximating the Pareto front of a multiobjective optimization problem can be reformulated as the problem of finding a set that maximizes the hypervolume indicator. This paper establishes the analytical expression of the Hessian matrix of the mapping from a (fixed size) collection of $n$ points in the $... | ['Hao Wang', 'Michael T. M. Emmerich', 'André H. Deutz'] | 2022-11-08 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [-9.60660204e-02 -1.93517864e-01 -1.32315785e-01 -1.85527727e-01
-8.26681256e-01 -6.40310049e-01 -2.50646293e-01 5.35266027e-02
-4.56188858e-01 9.13096428e-01 -2.95775414e-01 -3.50368053e-01
-1.04959261e+00 -7.50374734e-01 -6.21366382e-01 -1.09798861e+00
-3.37690651e-01 5.29085159e-01 -3.54414672e-01 -2.51613498... | [6.528757572174072, 4.4208984375] |
c97a0c4c-ccb2-43ec-ac4c-b68a7792246d | evaluating-gan-based-image-augmentation-for | null | null | https://www.mdpi.com/2076-3417/11/1/36 | https://www.mdpi.com/2076-3417/11/1/36/pdf | Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images | The inherent imbalance in the data distribution of X-ray security images is one of the most challenging aspects of computer vision algorithms applied in this domain. Most of the prior studies in this field have ignored this aspect, limiting their application in the practical setting. This paper investigates the effect ... | ['Yong-Jin Jeong', 'Joanna Kazzandra Dumagpi'] | 2020-12-23 | null | null | null | applied-sciences-2020-12 | ['image-augmentation'] | ['computer-vision'] | [ 5.24257064e-01 1.08015411e-01 -5.66879809e-02 -2.14332238e-01
-1.20126247e+00 -3.59030217e-01 6.27238393e-01 -2.07887843e-01
-4.78713423e-01 5.98344326e-01 -3.04772556e-01 -4.89833832e-01
3.46034616e-01 -1.03545690e+00 -1.05196095e+00 -9.06306684e-01
3.36835831e-01 2.53029674e-01 5.38990758e-02 -2.74415910... | [14.178162574768066, -1.9903843402862549] |
20abac97-3d0e-45b5-bd1e-58e55fab5645 | unsupervised-parsing-with-s-diora-single-tree | null | null | https://aclanthology.org/2020.emnlp-main.392 | https://aclanthology.org/2020.emnlp-main.392.pdf | Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders | The deep inside-outside recursive autoencoder (DIORA; Drozdov et al. 2019) is a self-supervised neural model that learns to induce syntactic tree structures for input sentences *without access to labeled training data*. In this paper, we discover that while DIORA exhaustively encodes all possible binary trees of a sent... | ['Andrew McCallum', 'Mohit Iyyer', "Tim O{'}Gorman", 'Yi-Pei Chen', 'Subendhu Rongali', 'Andrew Drozdov'] | null | null | null | null | emnlp-2020-11 | ['constituency-grammar-induction', 'constituency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.26970512e-01 7.83930659e-01 -1.91690758e-01 -8.68978381e-01
-9.37613487e-01 -7.52926350e-01 -5.23720235e-02 2.58558601e-01
-2.35792071e-01 8.27594399e-01 4.25297171e-01 -7.14017749e-01
2.07335413e-01 -1.16052353e+00 -1.08923864e+00 -5.81877410e-01
-2.20886543e-01 6.67559266e-01 9.86706614e-02 -2.74997979... | [10.394278526306152, 9.550223350524902] |
ef6eb696-5fe4-46f8-9a69-f87e2eff4df4 | e-calib-a-fast-robust-and-accurate | 2306.09078 | null | https://arxiv.org/abs/2306.09078v1 | https://arxiv.org/pdf/2306.09078v1.pdf | E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras | Event cameras triggered a paradigm shift in the computer vision community delineated by their asynchronous nature, low latency, and high dynamic range. Calibration of event cameras is always essential to account for the sensor intrinsic parameters and for 3D perception. However, conventional image-based calibration tec... | ['Yahya Zweiri', 'Davide Scaramuzza', 'Lakmal Seneviratne', 'Abdelqader Abusafieh', 'Daniel Gehrig', 'Muhammad Humais', 'Abdulla Ayyad', 'Mohammed Salah'] | 2023-06-15 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 3.63533676e-01 -5.83876193e-01 3.95374537e-01 -1.15715489e-01
-5.15098572e-01 -8.56866956e-01 4.66691732e-01 4.26170714e-02
-4.14761871e-01 3.88352364e-01 -2.37415686e-01 2.26994865e-02
2.35422533e-02 -2.13280156e-01 -8.60191643e-01 -5.50090075e-01
4.85773236e-01 1.27253488e-01 5.65439701e-01 3.57915580... | [8.239412307739258, -2.126347541809082] |
d1921267-188a-4870-94ac-da2c823c6858 | signed-graph-diffusion-network-1 | 2012.14191 | null | https://arxiv.org/abs/2012.14191v1 | https://arxiv.org/pdf/2012.14191v1.pdf | Signed Graph Diffusion Network | Given a signed social graph, how can we learn appropriate node representations to infer the signs of missing edges? Signed social graphs have received considerable attention to model trust relationships. Learning node representations is crucial to effectively analyze graph data, and various techniques such as network e... | ['U Kang', 'Jaemin Yoo', 'Jinhong Jung'] | 2020-12-28 | signed-graph-diffusion-network | https://openreview.net/forum?id=YPm0fzy_z6R | https://openreview.net/pdf?id=YPm0fzy_z6R | null | ['link-sign-prediction'] | ['graphs'] | [-9.99789387e-02 6.48430169e-01 -6.20853007e-01 -6.18451953e-01
2.16680750e-01 -2.34207079e-01 6.13774598e-01 4.72200185e-01
1.75684139e-01 4.85961348e-01 1.03093393e-01 -5.41060388e-01
-4.26663488e-01 -1.18859923e+00 -3.91930878e-01 -2.00096861e-01
-7.18695045e-01 3.88824314e-01 4.12622184e-01 -4.32875156... | [7.23580265045166, 6.262538909912109] |
f95e41e4-1ce1-4395-a3b0-c676e3e287a2 | few-shot-learning-enables-population-scale | 2301.10351 | null | https://arxiv.org/abs/2301.10351v3 | https://arxiv.org/pdf/2301.10351v3.pdf | Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa | Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work,... | ['Jared Streich', 'Daniel Jacobson', 'Marie Klein', 'Jack Bailey-Bale', 'Gail Taylor', 'Kevin Flores', 'Erica M. Rutter', 'David Kainer', 'P. Doug Hyatt', 'Larry M. York', 'Hari B. Chhetri', 'Mirko Pavicic', 'John Lagergren'] | 2023-01-24 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 6.17545843e-01 -2.98935205e-01 -2.75071949e-01 -3.98972295e-02
-4.19033617e-01 -1.05685258e+00 -1.44879177e-01 6.12372398e-01
7.64213726e-02 5.17307818e-01 -5.30026734e-01 -5.55305958e-01
-4.17180091e-01 -1.19178271e+00 -4.52044874e-01 -8.04164946e-01
1.44172991e-02 3.63264591e-01 3.54197741e-01 -1.01964280... | [9.090339660644531, -1.5749030113220215] |
2d87ba0f-03d9-4b19-a7fa-af62ee54beb4 | a-unified-deep-speaker-embedding-framework | 2012.00486 | null | https://arxiv.org/abs/2012.00486v1 | https://arxiv.org/pdf/2012.00486v1.pdf | A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data | This paper proposes a unified deep speaker embedding framework for modeling speech data with different sampling rates. Considering the narrowband spectrogram as a sub-image of the wideband spectrogram, we tackle the joint modeling problem of the mixed-bandwidth data in an image classification manner. From this perspect... | ['Ming Li', 'Weicheng Cai'] | 2020-12-01 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 1.64295644e-01 -2.24847436e-01 -1.45548120e-01 -5.47675073e-01
-1.21812093e+00 -1.61040336e-01 4.40664440e-01 -3.03977937e-01
-5.22837400e-01 3.37352484e-01 2.29264915e-01 -3.69041473e-01
-1.17073394e-01 -4.71212178e-01 -6.63131833e-01 -7.44918168e-01
1.57831073e-01 -2.33036846e-01 -1.47018224e-01 7.17494413... | [14.486130714416504, 6.003144264221191] |
94a0b80f-b9dd-4f38-a002-212ddc70e755 | a-pseudo-likelihood-approach-to-community | 2303.05909 | null | https://arxiv.org/abs/2303.05909v1 | https://arxiv.org/pdf/2303.05909v1.pdf | A pseudo-likelihood approach to community detection in weighted networks | Community structure is common in many real networks, with nodes clustered in groups sharing the same connections patterns. While many community detection methods have been developed for networks with binary edges, few of them are applicable to networks with weighted edges, which are common in practice. We propose a pse... | ['Elizaveta Levina', 'Andressa Cerqueira'] | 2023-03-10 | null | null | null | null | ['stochastic-block-model', 'community-detection'] | ['graphs', 'graphs'] | [ 3.90434057e-01 3.65551353e-01 -1.95825905e-01 -1.90456912e-01
3.24641764e-01 -5.33025861e-01 2.91680545e-01 2.16450602e-01
-4.44034070e-01 8.56023610e-01 -2.69609224e-03 -3.22357595e-01
-6.28423393e-01 -9.07092273e-01 -3.04624408e-01 -5.59685290e-01
-1.02426386e+00 7.61198223e-01 3.73881370e-01 2.30894849... | [6.985806465148926, 5.218618392944336] |
eae5c69d-3094-4d4c-a90e-a43e900dd66d | joint-representation-learning-and-keypoint | null | null | https://zhunzhong.site/paper/RK_Net.pdf | https://zhunzhong.site/paper/RK_Net.pdf | Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization | In this paper, we study the cross-view geo-localization problem to match images from different viewpoints. The key motivation underpinning this task is to learn a discriminative viewpoint-invariant visual representation. Inspired by the human visual system for mining local patterns, we propose a new framework called RK... | ['Nicu Sebe', 'Yi Yang', 'Shaozi Li', 'Zhiming Luo', 'Zhun Zhong', 'Zhedong Zheng', 'Jinliang Lin'] | 2022-05-15 | null | null | null | ieee-transactions-on-image-processing-tip | ['image-based-localization', 'drone-navigation', 'drone-view-target-localization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.12998801e-01 -2.57268995e-01 -3.77357036e-01 -3.03359330e-01
-9.82372701e-01 -4.21008080e-01 5.83473384e-01 9.00458768e-02
-3.42364132e-01 3.29144150e-01 3.48753899e-01 9.57300663e-02
5.03432825e-02 -5.30781746e-01 -9.92403090e-01 -4.72685963e-01
-1.19950362e-01 -8.60273540e-02 3.22644234e-01 -8.94014090... | [7.860334873199463, -1.877717137336731] |
b9141de4-2440-4354-9662-74de72a1b05e | tape-assessing-few-shot-russian-language | 2210.12813 | null | https://arxiv.org/abs/2210.12813v1 | https://arxiv.org/pdf/2210.12813v1.pdf | TAPE: Assessing Few-shot Russian Language Understanding | Recent advances in zero-shot and few-shot learning have shown promise for a scope of research and practical purposes. However, this fast-growing area lacks standardized evaluation suites for non-English languages, hindering progress outside the Anglo-centric paradigm. To address this line of research, we propose TAPE (... | ['Vladislav Mikhailov', 'Ekaterina Artemova', 'Valentina Kurenshchikova', 'Alena Spiridonova', 'Svetlana Iordanskaia', 'Anastasiia Bashmakova', 'Oleg Zinkevich', 'Albina Akhmetgareeva', 'Maria Tikhonova', 'Nadezhda Katricheva', 'Denis Shevelev', 'Alena Fenogenova', 'Tatiana Shavrina', 'Ekaterina Taktasheva'] | 2022-10-23 | null | null | null | null | ['adversarial-text', 'logical-reasoning'] | ['adversarial', 'reasoning'] | [ 1.57162905e-01 7.03449398e-02 -1.73711643e-01 -2.63994992e-01
-1.16754055e+00 -8.45847309e-01 8.16763937e-01 1.73331290e-01
-5.76081574e-01 7.49429047e-01 6.12371802e-01 -7.30460346e-01
9.82511640e-02 -4.68545765e-01 -6.86427295e-01 -3.64426941e-01
1.65234253e-01 3.51675570e-01 -1.12309627e-01 -6.63897753... | [6.181268215179443, 8.170186042785645] |
7985465b-7e2b-446f-ab8e-753ba7881cab | towards-efficient-ecg-based-atrial | 2211.02678 | null | https://arxiv.org/abs/2211.02678v2 | https://arxiv.org/pdf/2211.02678v2.pdf | Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks | Atrial fibrillation (AF) is the most common cardiac arrhythmia and associated with a high risk for serious conditions like stroke. The use of wearable devices embedded with automatic and timely AF assessment from electrocardiograms (ECGs) has shown to be promising in preventing life-threatening situations. Although dee... | ['Wolfgang Nejdl', 'Zhao Ren', 'Leonie Basso'] | 2022-10-27 | null | null | null | null | ['atrial-fibrillation-detection'] | ['medical'] | [ 2.32626423e-01 -1.90784514e-01 7.48448223e-02 -3.09276074e-01
-5.27271390e-01 -5.21197319e-01 -2.97587633e-01 2.93753207e-01
-5.33343375e-01 7.86528349e-01 -1.15021296e-01 -7.45075226e-01
-1.52362898e-01 -6.07546270e-01 -3.50855589e-01 -5.00309110e-01
-6.09632790e-01 1.80752322e-01 -3.64736170e-01 2.03611836... | [14.26538372039795, 3.2848706245422363] |
b4f0827e-4121-47dc-aaab-3069e72608fb | iadet-simplest-human-in-the-loop-object | 2307.01582 | null | https://arxiv.org/abs/2307.01582v1 | https://arxiv.org/pdf/2307.01582v1.pdf | IAdet: Simplest human-in-the-loop object detection | This work proposes a strategy for training models while annotating data named Intelligent Annotation (IA). IA involves three modules: (1) assisted data annotation, (2) background model training, and (3) active selection of the next datapoints. Under this framework, we open-source the IAdet tool, which is specific for s... | ['Gabriele Facciolo', 'Franco Marchesoni-Acland'] | 2023-07-04 | null | null | null | null | ['object-detection'] | ['computer-vision'] | [ 2.25449443e-01 1.06633812e-01 1.15534067e-01 -4.20642823e-01
-6.95881248e-01 -5.37108362e-01 6.62220895e-01 1.06758505e-01
-6.85047686e-01 3.36625993e-01 -5.95564008e-01 -4.72636849e-01
3.36508721e-01 -5.98560870e-01 -5.16484261e-01 -5.49746871e-01
1.81842506e-01 6.14975929e-01 9.09559786e-01 8.78383219... | [8.828147888183594, 0.169268399477005] |
2d97ab3f-a333-4e5f-8557-6f38f6e59951 | locformer-enabling-transformers-to-perform | 2112.10066 | null | https://arxiv.org/abs/2112.10066v1 | https://arxiv.org/pdf/2112.10066v1.pdf | LocFormer: Enabling Transformers to Perform Temporal Moment Localization on Long Untrimmed Videos With a Feature Sampling Approach | We propose LocFormer, a Transformer-based model for video grounding which operates at a constant memory footprint regardless of the video length, i.e. number of frames. LocFormer is designed for tasks where it is necessary to process the entire long video and at its core lie two main contributions. First, our model inc... | ['Qi Wu', 'Hiroya Takamura', 'Basura Fernando', 'Edison Marrese-Taylor', 'Cristian Rodriguez-Opazo'] | 2021-12-19 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 2.00097516e-01 -9.99512002e-02 -2.72169679e-01 -6.55489415e-02
-9.97860849e-01 -3.69576126e-01 4.80235696e-01 -2.90223956e-01
-3.73843968e-01 2.79834002e-01 3.26601774e-01 -1.82297692e-01
2.36136004e-01 -6.25291944e-01 -1.15736687e+00 -6.21930897e-01
-3.30517739e-01 2.93285459e-01 6.80488586e-01 -3.17616463... | [9.17036247253418, 0.42628246545791626] |
7ab4893b-6fad-46bd-8ca8-6de170e77294 | information-design-in-multi-agent | 2305.06807 | null | https://arxiv.org/abs/2305.06807v1 | https://arxiv.org/pdf/2305.06807v1.pdf | Information Design in Multi-Agent Reinforcement Learning | Reinforcement learning (RL) mimics how humans and animals interact with the environment. The setting is somewhat idealized because, in actual tasks, other agents in the environment have their own goals and behave adaptively to the ego agent. To thrive in those environments, the agent needs to influence other agents so ... | ['Baoxiang Wang', 'Hongyuan Zha', 'Wenhao Li', 'Yue Lin'] | 2023-05-08 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.02613449e-01 3.81301373e-01 -5.65207303e-01 -2.44863406e-01
3.39859985e-02 -5.98297775e-01 3.62277895e-01 9.12420526e-02
-9.79932189e-01 1.12500441e+00 3.62692982e-01 -1.79372579e-01
-2.94149011e-01 -8.74537468e-01 -4.75943893e-01 -8.02563250e-01
-2.26462960e-01 2.51589805e-01 -3.99685651e-01 -3.10365945... | [4.195568561553955, 2.788027048110962] |
8e87312c-e398-4afa-9ee8-5b040cc1c2f2 | adaptation-of-statistical-machine-translation | null | null | https://aclanthology.org/E12-1012 | https://aclanthology.org/E12-1012.pdf | Adaptation of Statistical Machine Translation Model for Cross-Lingual Information Retrieval in a Service Context | null | ['Christof Monz', 'Vassilina Nikoulina', 'Nikolaos Lagos', 'Bogomil Kovachev'] | 2012-04-01 | null | null | null | eacl-2012-4 | ['cross-lingual-information-retrieval'] | ['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... | [-7.220370769500732, 3.7762811183929443] |
7f35438b-9a58-47d1-a842-c2e610393609 | federated-learning-over-a-wireless-network | 2307.03758 | null | https://arxiv.org/abs/2307.03758v1 | https://arxiv.org/pdf/2307.03758v1.pdf | Federated Learning over a Wireless Network: Distributed User Selection through Random Access | User selection has become crucial for decreasing the communication costs of federated learning (FL) over wireless networks. However, centralized user selection causes additional system complexity. This study proposes a network intrinsic approach of distributed user selection that leverages the radio resource competitio... | ['Lingjuan Lyu', 'Tao Cui', 'Songtao Wu', 'Ce Zheng', 'Shiyao Ma', 'Chen Sun'] | 2023-07-07 | null | null | null | null | ['fairness', 'federated-learning', 'fairness'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 3.69351804e-01 2.67590761e-01 -9.85636413e-01 -1.19278960e-01
-4.96576250e-01 -4.01717395e-01 1.29883900e-01 -1.85356468e-01
-6.55617654e-01 1.43353748e+00 -4.01929229e-01 -8.41187477e-01
-5.59913874e-01 -9.64253664e-01 -6.85059577e-02 -1.04699457e+00
-6.66726589e-01 9.65424925e-02 -2.72634011e-02 1.66122288... | [5.945969581604004, 1.634917974472046] |
466bbebd-df4b-48ac-b56e-e53034493d82 | identification-of-small-objects-in-satellite | 2209.02564 | null | https://arxiv.org/abs/2209.02564v2 | https://arxiv.org/pdf/2209.02564v2.pdf | Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery | Images in aerial datasets are very large in resolution, and each frame contains many dense and small objects. State-of-the-art detection methods fail to capture small objects, local features, and region proposals for densely overlapped objects in aerial imagery due to the high variation of object sizes in satellite ima... | ['Jelena Tešić', 'Debojyoti Biswas'] | 2022-09-06 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.02186519e-01 -7.10849822e-01 4.16719317e-02 -4.43089128e-01
-4.86196816e-01 -9.00227606e-01 3.88232708e-01 1.92607805e-01
-4.96930093e-01 6.49029553e-01 4.58541140e-02 4.37014550e-01
-4.38127786e-01 -1.00342309e+00 -5.78117311e-01 -6.75982177e-01
-4.84450936e-01 4.14372116e-01 7.26118386e-01 -3.41275096... | [8.790846824645996, -0.8268471956253052] |
d4bdb574-426c-4e5a-bd02-6a20622c619a | scut-fbp5500-a-diverse-benchmark-dataset-for | 1801.06345 | null | http://arxiv.org/abs/1801.06345v1 | http://arxiv.org/pdf/1801.06345v1.pdf | SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction | Facial beauty prediction (FBP) is a significant visual recognition problem to
make assessment of facial attractiveness that is consistent to human
perception. To tackle this problem, various data-driven models, especially
state-of-the-art deep learning techniques, were introduced, and benchmark
dataset become one of th... | ['Lingyu Liang', 'Mengru Li', 'Luojun Lin', 'Lianwen Jin', 'Duorui Xie'] | 2018-01-19 | null | null | null | null | ['facial-beauty-prediction'] | ['computer-vision'] | [-1.53042063e-01 -2.87159204e-01 -1.37736887e-01 -6.22678578e-01
-1.70559168e-01 -1.02796108e-01 6.12211645e-01 -3.15282553e-01
3.72489588e-03 4.48906779e-01 4.40570191e-02 2.09556088e-01
-3.80095720e-01 -8.67059171e-01 -3.43471587e-01 -7.96621263e-01
-1.21229321e-01 3.50176901e-01 -3.36824119e-01 -5.60627520... | [13.467999458312988, 1.0725972652435303] |
adbcae9a-daa0-4092-a5db-280eb098da76 | popularity-driven-data-integration | 2209.14049 | null | https://arxiv.org/abs/2209.14049v1 | https://arxiv.org/pdf/2209.14049v1.pdf | Popularity Driven Data Integration | More and more, with the growing focus on large scale analytics, we are confronted with the need of integrating data from multiple sources. The problem is that these data are impossible to reuse as-is. The net result is high cost, with the further drawback that the resulting integrated data will again be hardly reusable... | ['Alessio Zamboni', 'Mayukh Bagchi', 'Mattia Fumagalli', 'Simone Bocca', 'Fausto Giunchiglia'] | 2022-09-28 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-3.89838129e-01 1.51722103e-01 -1.52077422e-01 2.34269816e-02
-2.65428811e-01 -6.03794873e-01 1.12542398e-01 8.16578209e-01
-6.83484912e-01 6.94401383e-01 3.56376886e-01 2.85063731e-03
-4.25386369e-01 -1.10581374e+00 -4.20048803e-01 -2.59895772e-01
1.01959854e-01 3.28579962e-01 4.72232908e-01 -2.41158128... | [9.1970796585083, 7.887192249298096] |
1bbef120-2da4-445b-9e43-8196132afa38 | gaitref-gait-recognition-with-refined | 2304.07916 | null | https://arxiv.org/abs/2304.07916v1 | https://arxiv.org/pdf/2304.07916v1.pdf | GaitRef: Gait Recognition with Refined Sequential Skeletons | Identifying humans with their walking sequences, known as gait recognition, is a useful biometric understanding task as it can be observed from a long distance and does not require cooperation from the subject. Two common modalities used for representing the walking sequence of a person are silhouettes and joint skelet... | ['Ram Nevatia', 'Zhaoheng Zheng', 'Wanrong Zheng', 'Haidong Zhu'] | 2023-04-16 | null | null | null | null | ['gait-recognition', 'multiview-gait-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.11604050e-01 -5.19877911e-01 -1.16410799e-01 -2.86150903e-01
-3.10300678e-01 -2.14556918e-01 3.21954042e-01 -3.20854902e-01
-3.44688684e-01 7.85844028e-01 1.03930831e-01 7.61081696e-01
3.59306484e-01 -3.46011877e-01 -2.82869130e-01 -8.14253330e-01
-2.66514838e-01 5.50556719e-01 6.11881673e-01 9.73456651... | [14.265470504760742, 1.4143925905227661] |
eb2fd415-3c0a-4f1e-942c-beab48704c10 | learning-sparse-and-continuous-graph | 2201.09686 | null | https://arxiv.org/abs/2201.09686v2 | https://arxiv.org/pdf/2201.09686v2.pdf | Balanced Graph Structure Learning for Multivariate Time Series Forecasting | Accurate forecasting of multivariate time series is an extensively studied subject in finance, transportation, and computer science. Fully mining the correlation and causation between the variables in a multivariate time series exhibits noticeable results in improving the performance of a time series model. Recently, s... | ['Ran Chen', 'Feng Liu', 'Zhenglong Jia', 'Chengshuo Du', 'Yanze Wang', 'Weijun Chen'] | 2022-01-24 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-3.43357027e-01 -7.16841081e-03 -3.95899594e-01 -3.05745453e-01
-5.65536618e-02 -4.41533774e-01 6.97256744e-01 8.19106549e-02
3.22005451e-01 3.95352781e-01 4.03457582e-01 -6.72095954e-01
-3.26384544e-01 -9.31891441e-01 -6.11035049e-01 -6.30515873e-01
-6.75943553e-01 9.39689428e-02 1.23623475e-01 -4.05111492... | [6.787665843963623, 2.7934439182281494] |
aaa04a66-eba5-49bb-8703-debcc011f8cd | can-recurrent-neural-networks-learn-process | 2212.06430 | null | https://arxiv.org/abs/2212.06430v1 | https://arxiv.org/pdf/2212.06430v1.pdf | Can recurrent neural networks learn process model structure? | Various methods using machine and deep learning have been proposed to tackle different tasks in predictive process monitoring, forecasting for an ongoing case e.g. the most likely next event or suffix, its remaining time, or an outcome-related variable. Recurrent neural networks (RNNs), and more specifically long short... | ['Jochen De Weerdt', 'Seppe vanden Broucke', 'Jari Peeperkorn'] | 2022-12-13 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 2.99352854e-01 1.25800744e-01 6.91511780e-02 -1.09035417e-01
-2.52695680e-01 -2.83442050e-01 9.41151261e-01 4.54148918e-01
-5.19356728e-01 6.67991102e-01 2.50329494e-01 -5.44131875e-01
-5.41301608e-01 -8.70999217e-01 -6.01417303e-01 -5.92487574e-01
-1.10946462e-01 6.23034358e-01 2.08807066e-01 1.13208301... | [8.55530834197998, 5.937636852264404] |
b6ba53ba-7afb-4ef6-843c-fe7c397b0b3c | modern-bayesian-experimental-design | 2302.14545 | null | https://arxiv.org/abs/2302.14545v1 | https://arxiv.org/pdf/2302.14545v1.pdf | Modern Bayesian Experimental Design | Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments. However, its deployment often poses substantial computational challenges that can undermine its practical use. In this review, we outline how recent advances have transformed our ability to overcome th... | ['Freddie Bickford Smith', 'Desi R Ivanova', 'Adam Foster', 'Tom Rainforth'] | 2023-02-28 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 1.60620913e-01 -7.40403891e-01 -3.40259671e-01 -4.74345058e-01
-6.86280072e-01 -6.50306821e-01 2.81963915e-01 -6.61699772e-02
-5.95129550e-01 1.09794641e+00 -1.48525655e-01 -7.46184707e-01
-3.48795533e-01 -3.06990057e-01 -4.87749130e-01 -8.07446182e-01
-8.80020484e-02 2.16261625e-01 8.27793628e-02 3.14470381... | [6.468561172485352, 4.02844762802124] |
c0c972dc-99dc-4484-a793-b3e097ef5965 | using-text-embeddings-for-causal-inference | 1905.12741 | null | https://arxiv.org/abs/1905.12741v2 | https://arxiv.org/pdf/1905.12741v2.pdf | Adapting Text Embeddings for Causal Inference | Does adding a theorem to a paper affect its chance of acceptance? Does labeling a post with the author's gender affect the post popularity? This paper develops a method to estimate such causal effects from observational text data, adjusting for confounding features of the text such as the subject or writing quality. We... | ['David M. Blei', 'Dhanya Sridhar', 'Victor Veitch'] | 2019-05-29 | null | null | null | null | ['supervised-dimensionality-reduction', 'causal-identification'] | ['computer-vision', 'reasoning'] | [-2.28553470e-02 1.41249865e-01 -9.08507824e-01 -1.32869840e-01
-5.21150172e-01 -7.18370914e-01 1.02309132e+00 7.97853351e-01
-3.89417708e-01 7.30401933e-01 1.09619844e+00 -8.44368696e-01
-6.15806341e-01 -1.04108596e+00 -9.65938270e-01 -3.98567885e-01
-2.25389645e-01 3.63732427e-01 -4.31420296e-01 1.16634421... | [8.027848243713379, 5.395163059234619] |
957c480b-f48a-4773-a41d-fd0fa6926dca | towards-comparability-of-linguistic-graph | null | null | https://aclanthology.org/L16-1630 | https://aclanthology.org/L16-1630.pdf | Towards Comparability of Linguistic Graph Banks for Semantic Parsing | We announce a new language resource for research on semantic parsing, a large, carefully curated collection of semantic dependency graphs representing multiple linguistic traditions. This resource is called SDP{\textasciitilde}2016 and provides an update and extension to previous versions used as Semantic Dependency Pa... | ["Zde{\\v{n}}ka Ure{\\v{s}}ov{\\'a}", 'Jan Haji{\\v{c}}', "Silvie Cinkov{\\'a}", 'Yusuke Miyao', 'Stephan Oepen', 'Angelina Ivanova', 'Marco Kuhlmann', 'Daniel Zeman', 'Dan Flickinger'] | 2016-05-01 | towards-comparability-of-linguistic-graph-1 | https://aclanthology.org/L16-1630 | https://aclanthology.org/L16-1630.pdf | lrec-2016-5 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 6.57920465e-02 6.87434375e-01 -5.45907378e-01 -6.92686558e-01
-1.09941256e+00 -1.04272580e+00 5.21285772e-01 4.77017343e-01
-4.08135176e-01 7.17688859e-01 1.01507854e+00 -3.98700684e-01
-1.99584380e-01 -4.42984164e-01 -3.09958130e-01 6.07316270e-02
4.52706158e-01 6.71721458e-01 2.33427137e-01 -4.69325066... | [10.37667465209961, 9.510156631469727] |
39ea76c1-5586-49a2-9472-db309c791ec6 | cam-convs-camera-aware-multi-scale | 1904.02028 | null | http://arxiv.org/abs/1904.02028v1 | http://arxiv.org/pdf/1904.02028v1.pdf | CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth | Single-view depth estimation suffers from the problem that a network trained
on images from one camera does not generalize to images taken with a different
camera model. Thus, changing the camera model requires collecting an entirely
new training dataset. In this work, we propose a new type of convolution that
can take... | ['Benjamin Ummenhofer', 'Thomas Brox', 'Luis Montesano', 'Javier Civera', 'Huizhong Zhou', 'Jose M. Facil'] | 2019-04-03 | cam-convs-camera-aware-multi-scale-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Facil_CAM-Convs_Camera-Aware_Multi-Scale_Convolutions_for_Single-View_Depth_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Facil_CAM-Convs_Camera-Aware_Multi-Scale_Convolutions_for_Single-View_Depth_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-depth-estimation'] | ['computer-vision'] | [ 3.08232337e-01 -1.73183799e-01 -2.16480836e-01 -7.47257888e-01
7.49906227e-02 -6.71198308e-01 5.01819313e-01 -5.58731973e-01
-5.82079589e-01 6.09400630e-01 -2.13257864e-01 -1.55698610e-02
2.25874990e-01 -8.97600412e-01 -1.01874065e+00 -6.30470455e-01
4.10697669e-01 3.26332629e-01 4.71322209e-01 2.51275986... | [8.643441200256348, -2.444333553314209] |
67889da5-2113-4e2d-8cb9-c5d4663fef7c | roboflow-100-a-rich-multi-domain-object | 2211.13523 | null | https://arxiv.org/abs/2211.13523v3 | https://arxiv.org/pdf/2211.13523v3.pdf | Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark | The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that ... | ['Jacob Solawetz', 'Mark McQuade', 'Paul Guerrie', 'Francesco Saverio Zuppichini', 'Floriana Ciaglia'] | 2022-11-24 | null | null | null | null | ['thermal-infrared-object-tracking', 'object-counting', 'medical-object-detection', 'object-categorization', 'visual-object-tracking', 'small-object-detection', 'object-discovery-in-videos'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.19882591e-01 -1.46865666e-01 -1.83203772e-01 -5.01431346e-01
-6.62783504e-01 -1.04356337e+00 7.05680013e-01 -3.95351313e-02
-6.80849075e-01 7.32288539e-01 -1.76260337e-01 -1.53939575e-01
5.82564026e-02 -7.19950080e-01 -1.00912809e+00 -3.56449872e-01
-1.97207376e-01 6.52112663e-01 5.67773581e-01 1.23343684... | [9.652209281921387, 2.0035111904144287] |
d7e596b9-df1b-4b2d-a957-9fba2abc3a7e | a-comprehensive-comparison-of-neural-networks | 2210.12321 | null | https://arxiv.org/abs/2210.12321v1 | https://arxiv.org/pdf/2210.12321v1.pdf | A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection | Neural networks have long been at the center of a debate around the cognitive mechanism by which humans process inflectional morphology. This debate has gravitated into NLP by way of the question: Are neural networks a feasible account for human behavior in morphological inflection? We address that question by measurin... | ['Katharina Kann', 'Shiran Dudy', 'Adam Wiemerslage'] | 2022-10-22 | null | null | null | null | ['morphological-inflection'] | ['natural-language-processing'] | [ 1.12760305e-01 9.12904367e-02 7.41781965e-02 -5.20272315e-01
-2.19753057e-01 -8.57708156e-01 6.96186244e-01 6.47257328e-01
-1.01088452e+00 3.37543577e-01 7.15139389e-01 -7.51651824e-01
-1.13829777e-01 -1.08289301e+00 -5.53530037e-01 -2.22997084e-01
2.37588748e-01 6.74937606e-01 -7.77992904e-02 -4.54399705... | [10.632967948913574, 9.417126655578613] |
8444f0f0-e8cf-4602-9471-a5667943499b | a-multi-task-learning-framework-for-opinion | 2010.01512 | null | https://arxiv.org/abs/2010.01512v2 | https://arxiv.org/pdf/2010.01512v2.pdf | A Multi-task Learning Framework for Opinion Triplet Extraction | The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of a... | ['Benyou Wang', 'Dawei Song', 'Qiuchi Li', 'Chen Zhang'] | 2020-10-04 | null | https://aclanthology.org/2020.findings-emnlp.72 | https://aclanthology.org/2020.findings-emnlp.72.pdf | findings-of-the-association-for-computational | ['extract-aspect', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.22922966e-01 -2.19760109e-02 -2.12150499e-01 -8.50880623e-01
-1.29103506e+00 -7.77013302e-01 8.35002244e-01 4.13454890e-01
-1.97671190e-01 3.25820237e-01 2.61158943e-01 -5.09466290e-01
2.35484511e-01 -7.48686731e-01 -5.18307209e-01 -7.40947366e-01
2.32057303e-01 7.01395512e-01 -1.55938089e-01 -4.16484267... | [11.4752197265625, 6.647435188293457] |
56697732-0c44-4d60-8ee0-6c8730f60369 | non-intrusive-surrogate-modelling-using | 2212.14507 | null | https://arxiv.org/abs/2212.14507v1 | https://arxiv.org/pdf/2212.14507v1.pdf | Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis | Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthet... | ['Felix Krahmer', 'Jonas Jehle', 'Anna Veselovska', 'Maternus Herold'] | 2022-12-30 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-2.69751132e-01 -1.83508068e-01 3.16335022e-01 -2.50114530e-01
-8.32362652e-01 -1.86438739e-01 1.04251420e+00 2.30339885e-01
-2.85694063e-01 1.27469194e+00 5.02672613e-01 -2.25434899e-01
-9.04470921e-01 -6.15023971e-01 -3.06028903e-01 -8.79643500e-01
-2.40420207e-01 8.94939661e-01 -1.17232867e-01 -2.63999462... | [6.572484016418457, 3.4184937477111816] |
028a0db9-144b-4dc6-a545-3de3002bd53b | improving-graph-neural-network-expressivity | 2006.09252 | null | https://arxiv.org/abs/2006.09252v3 | https://arxiv.org/pdf/2006.09252v3.pdf | Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting | While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the structure of the underlying graph. It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler-Leman (WL) graph is... | ['Giorgos Bouritsas', 'Fabrizio Frasca', 'Stefanos Zafeiriou', 'Michael M. Bronstein'] | 2020-06-16 | null | https://openreview.net/forum?id=LT0KSFnQDWF | https://openreview.net/pdf?id=LT0KSFnQDWF | null | ['graph-regression'] | ['graphs'] | [ 3.86976629e-01 2.49805033e-01 -4.41946536e-01 -1.80110455e-01
8.47664624e-02 -8.34599614e-01 5.79191327e-01 6.25664234e-01
-2.09699646e-01 7.75081336e-01 -1.75009921e-01 -7.51437426e-01
-6.87942684e-01 -1.08835649e+00 -8.44303548e-01 -6.37169898e-01
-8.42356920e-01 4.19356018e-01 2.82526225e-01 -3.74281436... | [6.882291316986084, 6.190467357635498] |
4f9ced28-7e5c-4034-aca1-5116b7898c2f | rethinking-the-value-of-gazetteer-in-chinese | 2207.02802 | null | https://arxiv.org/abs/2207.02802v2 | https://arxiv.org/pdf/2207.02802v2.pdf | Rethinking the Value of Gazetteer in Chinese Named Entity Recognition | Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification. However, to further understand the generalizability and effectiveness of gazetteers, the NLP community still lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper, we f... | ['Daxin Jiang', 'Yang Yang', 'Bojia Lin', 'Yin Zhang', 'Jiangang Zhu', 'Xiangji Zeng', 'Qianglong Chen'] | 2022-07-06 | null | null | null | null | ['boundary-detection', 'chinese-named-entity-recognition'] | ['computer-vision', 'natural-language-processing'] | [-3.98539960e-01 -1.51108101e-01 -7.43826181e-02 -3.51358116e-01
-3.51973325e-01 -6.43436611e-01 3.37857485e-01 2.39772335e-01
-7.45907247e-01 6.18339837e-01 5.31098485e-01 -3.97473305e-01
-5.49340770e-02 -6.73868477e-01 -4.25028831e-01 -3.38075429e-01
1.73146561e-01 -7.56457448e-02 8.40715617e-02 -3.65992755... | [9.757684707641602, 9.501348495483398] |
4e6a964f-bc98-4989-ba49-6f95168feb80 | change-diffusion-change-detection-map | 2306.03424 | null | https://arxiv.org/abs/2306.03424v2 | https://arxiv.org/pdf/2306.03424v2.pdf | A Generative Change Detection Model Based on Difference-Feature Guided DDPM | Deep learning (DL) approaches, such as CNN and Transformer networks, have shown promise in bitemporal change detection (CD). However, these approaches have limitations in capturing long-range dependencies and incorporating 2D structure and spatial local information, resulting in inaccurate CD maps with discerning edges... | ['Man-on Pun', 'Xiaokang Zhang', 'Wendi Liang', 'Xianping Ma', 'Yihan Wen'] | 2023-06-06 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 2.91571140e-01 -2.49587893e-01 -1.17969073e-01 -2.17171624e-01
-7.18920290e-01 -2.45517448e-01 8.65105629e-01 8.39389488e-02
-2.47750506e-01 5.07809997e-01 4.84084785e-01 -9.20860395e-02
-2.51683205e-01 -1.26163983e+00 -5.70240438e-01 -9.54719782e-01
-1.29094034e-01 1.54819295e-01 3.77684951e-01 -2.28712082... | [9.756092071533203, -1.3812792301177979] |
bd973ebf-3edf-48e8-bc40-1da1b1a0361d | policy-driven-neural-response-generation-for-1 | null | null | https://aclanthology.org/2020.inlg-1.46 | https://aclanthology.org/2020.inlg-1.46.pdf | Policy-Driven Neural Response Generation for Knowledge-Grounded Dialog Systems | Open-domain dialog systems aim to generate relevant, informative and engaging responses. In this paper, we propose using a dialog policy to plan the content and style of target, open domain responses in the form of an action plan, which includes knowledge sentences related to the dialog context, targeted dialog acts, t... | ['Dilek Hakkani-Tur', 'Mihail Eric', 'Yang Liu', 'Seokhwan Kim', 'Karthik Gopalakrishnan', 'Behnam Hedayatnia'] | null | null | null | null | inlg-acl-2020-12 | ['open-domain-dialog'] | ['natural-language-processing'] | [ 3.37290198e-01 1.11555147e+00 1.17123485e-01 -7.38693476e-01
-8.78092349e-01 -8.35818648e-01 1.14901149e+00 -3.90252098e-02
-1.35686070e-01 1.08472741e+00 1.03568983e+00 -4.52958383e-02
2.55329728e-01 -8.04144025e-01 -2.22420231e-01 -2.83864260e-01
4.57694024e-01 1.04031193e+00 3.06260064e-02 -6.51591837... | [12.934053421020508, 8.071553230285645] |
6b2edac5-2c56-4dc2-9aac-e4dc85f97c70 | mind-the-pad-cnns-can-develop-blind-spots-1 | 2010.02178 | null | https://arxiv.org/abs/2010.02178v1 | https://arxiv.org/pdf/2010.02178v1.pdf | Mind the Pad -- CNNs can Develop Blind Spots | We show how feature maps in convolutional networks are susceptible to spatial bias. Due to a combination of architectural choices, the activation at certain locations is systematically elevated or weakened. The major source of this bias is the padding mechanism. Depending on several aspects of convolution arithmetic, t... | ['Orion Reblitz-Richardson', 'Jun Yuan', 'Vivek Miglani', 'Narine Kokhlikyan', 'Bilal Alsallakh'] | 2020-10-05 | mind-the-pad-cnns-can-develop-blind-spots | https://openreview.net/forum?id=m1CD7tPubNy | https://openreview.net/pdf?id=m1CD7tPubNy | iclr-2021-1 | ['small-object-detection'] | ['computer-vision'] | [ 4.20553833e-01 -8.71020854e-02 1.46880403e-01 -3.86136442e-01
1.87464863e-01 -8.33528161e-01 5.15251577e-01 -3.71479429e-03
-4.79743123e-01 5.72555840e-01 2.66343802e-01 -5.33242106e-01
4.83955926e-04 -8.05103719e-01 -9.44555938e-01 -7.33193517e-01
-7.21315071e-02 -3.64000529e-01 7.70592213e-01 -1.06989495... | [9.828001022338867, 2.1946513652801514] |
9056f5a1-5959-453c-8b11-272beb210e40 | a-strong-baseline-for-domain-adaptation-and | 1904.01638 | null | http://arxiv.org/abs/1904.01638v1 | http://arxiv.org/pdf/1904.01638v1.pdf | A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging | This work provides a strong baseline for the problem of multi-source
multi-target domain adaptation and generalization in medical imaging. Using a
diverse collection of ten chest X-ray datasets, we empirically demonstrate the
benefits of training medical imaging deep learning models on varied patient
populations for ge... | ['Ben Covington', 'Kevin Lyman', 'Jordan Prosky', 'Li Yao'] | 2019-04-02 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 5.42860270e-01 4.00142558e-02 -5.57563245e-01 -8.99235964e-01
-1.67901564e+00 -4.11266178e-01 7.39142969e-02 5.48873320e-02
-4.74136591e-01 8.48080635e-01 1.02592267e-01 -5.01139998e-01
-2.59497106e-01 -3.56842697e-01 -5.37767112e-01 -5.56719244e-01
-3.26922297e-01 1.08239937e+00 1.48876876e-01 5.54298423... | [14.99630069732666, -2.1907107830047607] |
2dd44971-a606-4c8f-b623-a0af76caf8a2 | unsupervised-few-shot-action-recognition-via | 2109.15317 | null | https://arxiv.org/abs/2109.15317v2 | https://arxiv.org/pdf/2109.15317v2.pdf | Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation | We present MetaUVFS as the first Unsupervised Meta-learning algorithm for Video Few-Shot action recognition. MetaUVFS leverages over 550K unlabeled videos to train a two-stream 2D and 3D CNN architecture via contrastive learning to capture the appearance-specific spatial and action-specific spatio-temporal video featur... | ['Mei Chen', 'Fuxin Li', 'Ye Yu', 'Gaurav Mittal', 'Jay Patravali'] | 2021-09-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Patravali_Unsupervised_Few-Shot_Action_Recognition_via_Action-Appearance_Aligned_Meta-Adaptation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Patravali_Unsupervised_Few-Shot_Action_Recognition_via_Action-Appearance_Aligned_Meta-Adaptation_ICCV_2021_paper.pdf | iccv-2021-1 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 3.27934891e-01 -6.95112860e-04 -7.56031811e-01 -3.49042118e-01
-9.28669691e-01 1.87764883e-01 8.42084229e-01 -2.23627567e-01
-4.22501475e-01 5.04855990e-01 5.40633321e-01 4.12212342e-01
1.19001046e-01 -5.14442623e-01 -1.00600529e+00 -6.06462181e-01
-1.32084236e-01 2.67898589e-01 5.19256532e-01 -5.97889200... | [8.631003379821777, 0.8640347123146057] |
06bb8344-512e-4baa-a48d-0993b7719276 | group-fairness-in-non-monotone-submodular | 2302.01546 | null | https://arxiv.org/abs/2302.01546v2 | https://arxiv.org/pdf/2302.01546v2.pdf | Group Fairness in Non-monotone Submodular Maximization | Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data items from a large dataset. However, data items might have sensitive attributes such as race or gender,... | ['Shaojie Tang', 'Jing Yuan'] | 2023-02-03 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 2.60619164e-01 4.53908801e-01 -8.85201871e-01 -7.48916864e-01
-5.40992141e-01 -5.22677064e-01 -1.57542959e-01 7.10555792e-01
-3.50551903e-01 9.66078818e-01 3.92022222e-01 1.18976519e-01
-3.92200559e-01 -8.89252603e-01 -5.34462810e-01 -6.35654509e-01
-1.45330235e-01 7.00300694e-01 -2.95332849e-01 -1.11773446... | [6.593654632568359, 4.941413879394531] |
6785b8f1-0661-4d9f-b6c1-7d23ba32612b | towards-cnn-map-compression-for-camera | 1703.00845 | null | http://arxiv.org/abs/1703.00845v1 | http://arxiv.org/pdf/1703.00845v1.pdf | Towards CNN Map Compression for camera relocalisation | This paper presents a study on the use of Convolutional Neural Networks for
camera relocalisation and its application to map compression. We follow state
of the art visual relocalisation results and evaluate response to different
data inputs -- namely, depth, grayscale, RGB, spatial position and combinations
of these. ... | ['Walterio Mayol-Cuevas', 'Luis Contreras'] | 2017-03-02 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 2.31070414e-01 -6.82097152e-02 -2.11732894e-01 -2.82385558e-01
5.73482960e-02 -5.25643170e-01 7.96209276e-01 3.11326712e-01
-1.17788827e+00 6.48507118e-01 3.05072039e-01 -2.68226266e-01
-3.29829574e-01 -9.35026169e-01 -8.73655319e-01 -3.32340568e-01
7.39424676e-02 3.15760404e-01 7.96406567e-01 -3.68635505... | [7.804407596588135, -1.793989896774292] |
f1b830c1-2e1b-4c83-82f8-c75d03f79bcb | promptagator-few-shot-dense-retrieval-from-8 | 2209.11755 | null | https://arxiv.org/abs/2209.11755v1 | https://arxiv.org/pdf/2209.11755v1.pdf | Promptagator: Few-shot Dense Retrieval From 8 Examples | Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is limited, with the implicit assumption that it is possible to generalize from one task to all the rest. However, this overlooks the fact that th... | ['Ming-Wei Chang', 'Keith B. Hall', 'Kelvin Guu', 'Anton Bakalov', 'Jing Lu', 'Jianmo Ni', 'Yi Luan', 'Ji Ma', 'Vincent Y. Zhao', 'Zhuyun Dai'] | 2022-09-23 | null | null | null | null | ['natural-questions'] | ['miscellaneous'] | [ 3.13044280e-01 5.52105382e-02 -4.28815931e-01 -1.32919610e-01
-1.67263472e+00 -6.90164328e-01 1.03238451e+00 -3.22507019e-03
-5.68101823e-01 7.52052665e-01 3.33527714e-01 -3.63549292e-01
-2.93393165e-01 -6.12220824e-01 -6.18796051e-01 -1.86706945e-01
1.37094855e-01 8.00646663e-01 2.55034059e-01 -6.94255650... | [11.526514053344727, 7.693319320678711] |
0994558b-1504-4004-bf71-ddcad3eb32e0 | deepstay-stay-region-extraction-from-location | 2306.06068 | null | https://arxiv.org/abs/2306.06068v1 | https://arxiv.org/pdf/2306.06068v1.pdf | DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision | Nowadays, mobile devices enable constant tracking of the user's position and location trajectories can be used to infer personal points of interest (POIs) like homes, workplaces, or stores. A common way to extract POIs is to first identify spatio-temporal regions where a user spends a significant amount of time, known ... | ['Lars Schmidt-Thieme', 'Christina Jenkins', 'Emma Andersson', 'Daniela Thyssens', 'Christian Löwens'] | 2023-06-05 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [-1.85034424e-01 -1.72375545e-01 -6.63697064e-01 -4.25722539e-01
-1.01200891e+00 -5.11919200e-01 6.60245895e-01 2.48321354e-01
-4.99596030e-01 7.61807859e-01 3.34277987e-01 -4.13347065e-01
2.59142686e-02 -1.07863247e+00 -8.21633041e-01 -3.90125245e-01
7.22948462e-02 4.58835632e-01 4.90852296e-01 -1.84578598... | [6.541368007659912, 1.964179277420044] |
e3eab5e9-e41d-4c61-9794-af294e71cc79 | thda-treasure-hunt-data-augmentation-for | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Maksymets_THDA_Treasure_Hunt_Data_Augmentation_for_Semantic_Navigation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Maksymets_THDA_Treasure_Hunt_Data_Augmentation_for_Semantic_Navigation_ICCV_2021_paper.pdf | THDA: Treasure Hunt Data Augmentation for Semantic Navigation | Can general-purpose neural models learn to navigate? For PointGoal navigation (""go to x, y""), the answer is a clear `yes' -- mapless neural models composed of task-agnostic components (CNNs and RNNs) trained with large-scale model-free reinforcement learning achieve near-perfect performance. However, for ObjectGo... | ['Dhruv Batra', 'Stefan Lee', 'Wojciech Galuba', 'Erik Wijmans', 'Aaron Gokaslan', 'Vincent Cartillier', 'Oleksandr Maksymets'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['pointgoal-navigation'] | ['robots'] | [-1.61780254e-03 3.45083684e-01 5.28030843e-02 -2.45764419e-01
-6.88490152e-01 -5.74168146e-01 4.83601451e-01 3.85021828e-02
-9.91126001e-01 8.77574027e-01 2.77401716e-01 -5.63655913e-01
-2.95685738e-01 -9.43309247e-01 -1.32519174e+00 -7.24418759e-01
-7.13953555e-01 6.18376076e-01 1.68597549e-01 -6.59916282... | [4.541376113891602, 0.688188910484314] |
b9bef0a3-706a-418f-8877-c47132ad84bd | exploring-adversarially-robust-training-for | 2202.09300 | null | https://arxiv.org/abs/2202.09300v2 | https://arxiv.org/pdf/2202.09300v2.pdf | Exploring Adversarially Robust Training for Unsupervised Domain Adaptation | Unsupervised Domain Adaptation (UDA) methods aim to transfer knowledge from a labeled source domain to an unlabeled target domain. UDA has been extensively studied in the computer vision literature. Deep networks have been shown to be vulnerable to adversarial attacks. However, very little focus is devoted to improving... | ['Vishal M. Patel', 'Shao-Yuan Lo'] | 2022-02-18 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.51342079e-01 1.05869293e-01 3.65027934e-02 -1.67503789e-01
-8.77600312e-01 -1.13798845e+00 7.88292408e-01 -2.39045128e-01
-9.87612307e-02 6.84984684e-01 -2.12214798e-01 -4.67726856e-01
1.34479493e-01 -9.68058884e-01 -9.15299118e-01 -8.45520735e-01
3.18216473e-01 3.19889218e-01 1.39029518e-01 -2.55901694... | [5.605449676513672, 7.913774490356445] |
86ac4148-0061-4500-a78d-f26c8add6dcf | unidecor-a-unified-deception-corpus-for-cross | 2306.02827 | null | https://arxiv.org/abs/2306.02827v2 | https://arxiv.org/pdf/2306.02827v2.pdf | UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection | Verbal deception has been studied in psychology, forensics, and computational linguistics for a variety of reasons, like understanding behaviour patterns, identifying false testimonies, and detecting deception in online communication. Varying motivations across research fields lead to differences in the domain choices ... | ['Roman Klinger', 'Aswathy Velutharambath'] | 2023-06-05 | null | null | null | null | ['cross-corpus', 'domain-generalization', 'deception-detection'] | ['computer-vision', 'methodology', 'miscellaneous'] | [-2.28084087e-01 -1.85411334e-01 -2.32613459e-01 -6.33250594e-01
-8.07708979e-01 -9.23083425e-01 8.57002079e-01 2.66501456e-01
-3.88322264e-01 5.69728076e-01 4.15812045e-01 -5.71365595e-01
-2.63797790e-02 -1.28544465e-01 -1.42183930e-01 -2.60291815e-01
5.80603838e-01 4.30926494e-02 -3.77443759e-03 -1.85144737... | [8.250186920166016, 10.388477325439453] |
2992a05d-2ab2-4f16-9a8e-4fe7dacff5a1 | prepaid-or-postpaid-that-is-the-question | 1706.10172 | null | http://arxiv.org/abs/1706.10172v1 | http://arxiv.org/pdf/1706.10172v1.pdf | Prepaid or Postpaid? That is the question. Novel Methods of Subscription Type Prediction in Mobile Phone Services | In this paper we investigate the behavioural differences between mobile phone
customers with prepaid and postpaid subscriptions. Our study reveals that (a)
postpaid customers are more active in terms of service usage and (b) there are
strong structural correlations in the mobile phone call network as connections
betwee... | ['Márton Karsai', 'Wei Du', 'Yongjun Liao', 'Eric Fleury', 'Martin Minnoni', 'Carlos Sarraute'] | 2017-06-30 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 1.45863652e-01 5.35309970e-01 -5.42192221e-01 -4.99232590e-01
3.52794416e-02 -2.69142538e-01 4.95629460e-01 5.01922965e-01
-2.08963871e-01 7.12730765e-01 -1.82697430e-01 -5.96458137e-01
-5.80767035e-01 -1.37873375e+00 -2.96202004e-01 -5.07016659e-01
-3.74109715e-01 1.34367990e+00 4.60499763e-01 -3.36797327... | [6.995727062225342, 5.352860450744629] |
74c9a692-a252-4fea-9f9e-52287dd1849f | neuron-pruning-for-compressing-deep-networks | 1707.06838 | null | http://arxiv.org/abs/1707.06838v1 | http://arxiv.org/pdf/1707.06838v1.pdf | Neuron Pruning for Compressing Deep Networks using Maxout Architectures | This paper presents an efficient and robust approach for reducing the size of
deep neural networks by pruning entire neurons. It exploits maxout units for
combining neurons into more complex convex functions and it makes use of a
local relevance measurement that ranks neurons according to their activation on
the traini... | ['Rene Grzeszick', 'Gernot A. Fink', 'Fernando Moya Rueda'] | 2017-07-21 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.03746362e-01 3.97266954e-01 1.71133399e-01 -4.84365433e-01
9.92425606e-02 4.87014614e-02 -2.87911892e-02 1.06592111e-01
-9.43232894e-01 6.31703854e-01 -5.31140387e-01 -4.59817499e-01
-4.13182855e-01 -7.71501303e-01 -4.83487725e-01 -7.32004523e-01
-1.73037618e-01 1.11633604e-02 4.69418839e-02 -6.78884611... | [8.509384155273438, 3.0613853931427] |
fbbeab3a-81ea-4627-9c88-8ca796940a8c | deep-tone-mapping-operator-for-high-dynamic | 1908.04197 | null | https://arxiv.org/abs/1908.04197v1 | https://arxiv.org/pdf/1908.04197v1.pdf | Deep Tone Mapping Operator for High Dynamic Range Images | A computationally fast tone mapping operator (TMO) that can quickly adapt to a wide spectrum of high dynamic range (HDR) content is quintessential for visualization on varied low dynamic range (LDR) output devices such as movie screens or standard displays. Existing TMOs can successfully tone-map only a limited number ... | ['Aljosa Smolic', 'Aakanksha Rana', 'Praveer Singh', 'Giuseppe Valenzise', 'Frederic Dufaux', 'Nikos Komodakis'] | 2019-08-12 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 4.86099690e-01 -4.78774250e-01 3.22428584e-01 -7.45377019e-02
-8.87321115e-01 -8.15095723e-01 5.14062703e-01 -6.09238803e-01
-3.84052321e-02 7.39474118e-01 1.64004967e-01 -1.97198585e-01
-6.16143309e-02 -9.32063162e-01 -7.53960311e-01 -6.80180311e-01
-1.79920211e-01 -1.88173920e-01 2.08176836e-01 -5.49223483... | [11.010347366333008, -2.1904540061950684] |
b14f34f3-aa5c-402f-9a22-8894f402bed4 | tensor-decomposition-for-minimization-of-e2e | 2306.01247 | null | https://arxiv.org/abs/2306.01247v1 | https://arxiv.org/pdf/2306.01247v1.pdf | Tensor decomposition for minimization of E2E SLU model toward on-device processing | Spoken Language Understanding (SLU) is a critical speech recognition application and is often deployed on edge devices. Consequently, on-device processing plays a significant role in the practical implementation of SLU. This paper focuses on the end-to-end (E2E) SLU model due to its small latency property, unlike a cas... | ['Shinji Watanabe', 'Emiru Tsunoo', 'Brian Yan', 'Yifan Peng', 'Shih-Lun Wu', 'Jessica Huynh', 'Hayato Futami', 'Siddhant Arora', 'Yosuke Kashiwagi'] | 2023-06-02 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [-2.59811670e-01 -2.73863375e-01 1.38218686e-01 -4.25695747e-01
-6.40756965e-01 -4.51973945e-01 1.46705970e-01 -5.10735393e-01
-5.34746051e-01 7.51826987e-02 4.25486386e-01 -8.94716620e-01
1.09177455e-01 -5.19010365e-01 -6.87628806e-01 -3.30846667e-01
1.58861682e-01 1.64463535e-01 -1.44396260e-01 -3.89904603... | [14.393120765686035, 6.511799335479736] |
d1828b17-eacc-479c-95d6-d54ebc5ed878 | online-prediction-with-history-dependent | 2008.00052 | null | https://arxiv.org/abs/2008.00052v2 | https://arxiv.org/pdf/2008.00052v2.pdf | Online Prediction With History-Dependent Experts: The General Case | We study the problem of prediction of binary sequences with expert advice in the online setting, which is a classic example of online machine learning. We interpret the binary sequence as the price history of a stock, and view the predictor as an investor, which converts the problem into a stock prediction problem. In ... | ['Jeff Calder', 'Nadejda Drenska'] | 2020-07-31 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-2.63371259e-01 6.03327870e-01 1.06603303e-03 -1.71738248e-02
-3.94644320e-01 -8.80414903e-01 -2.91875392e-01 1.22534178e-01
-1.00545037e+00 9.79076266e-01 -4.10915792e-01 -4.57383424e-01
-2.74171054e-01 -9.93091524e-01 -8.20309401e-01 -8.19016039e-01
-3.25842410e-01 5.09139299e-01 -1.38290524e-01 -4.43762511... | [4.571775913238525, 3.338637113571167] |
63c5e73f-5847-4ab7-82b1-bb4788bc47c6 | robust-dual-graph-regularized-moving-object | 2204.11939 | null | https://arxiv.org/abs/2204.11939v1 | https://arxiv.org/pdf/2204.11939v1.pdf | Robust Dual-Graph Regularized Moving Object Detection | Moving object detection and its associated background-foreground separation have been widely used in a lot of applications, including computer vision, transportation and surveillance. Due to the presence of the static background, a video can be naturally decomposed into a low-rank background and a sparse foreground. Ma... | ['Biyun Xie', 'Ruihan Zhu', 'Ruilong Shen', 'Jing Qin'] | 2022-04-25 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 4.46421117e-01 -2.30300650e-01 -7.38753676e-02 -1.91932783e-01
-2.87709147e-01 6.07792102e-02 1.43308327e-01 -4.52519566e-01
-2.41383865e-01 5.99621773e-01 5.01472764e-02 1.31904989e-01
-1.62576944e-01 -4.19835389e-01 -3.21183592e-01 -1.19669127e+00
1.33545086e-01 -8.24222267e-02 3.08609366e-01 5.95409386... | [9.050864219665527, -0.8081048727035522] |
1ad2bff5-7ef8-42dc-820c-12f4582957a1 | learning-to-transpile-amr-into-sparql-1 | null | null | https://openreview.net/forum?id=qSMK1JCZcr7 | https://openreview.net/pdf?id=qSMK1JCZcr7 | Learning to Transpile AMR into SPARQL | We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA). This allows to delegate part of the abstraction problem to a strongly pre-trained semantic parser, while learning transpiling with small amount of paired data. We departure f... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.80085728e-02 9.73554671e-01 -1.94110617e-01 -7.38914490e-01
-1.19648349e+00 -6.75783157e-01 5.22053897e-01 3.47906858e-01
-3.99424821e-01 8.31347048e-01 5.70838213e-01 -6.85600460e-01
-8.80049169e-03 -1.28803778e+00 -1.09709787e+00 -7.12717548e-02
4.16853279e-02 9.37071383e-01 5.32947600e-01 -7.28784740... | [10.275386810302734, 8.028038024902344] |
32d48fa0-ca74-4ad9-9bd3-bb81a12ade50 | statistical-mechanics-of-generalization-in-1 | 2212.13069 | null | https://arxiv.org/abs/2212.13069v2 | https://arxiv.org/pdf/2212.13069v2.pdf | Homophily modulates double descent generalization in graph convolution networks | Graph neural networks are among the most successful machine learning models for relational datasets like metabolic, transportation, and social networks. Yet the determinants of their strong generalization for diverse interactions encoded in the data are not well understood. Methods from statistical learning theory do n... | ['Ivan Dokmanić', 'Hong Hu', 'Liming Pan', 'Cheng Shi'] | 2022-12-26 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-2.24205360e-01 3.22381228e-01 -8.06196555e-02 -2.22831830e-01
4.85014230e-01 -4.58681971e-01 9.99165237e-01 4.33921695e-01
-1.12773001e-01 5.59943914e-01 1.97254345e-01 -6.50697470e-01
-5.32474339e-01 -1.21161497e+00 -1.01393306e+00 -9.80627894e-01
-5.98714471e-01 4.73350257e-01 3.96426499e-01 -7.18798339... | [6.830185413360596, 6.082275867462158] |
91f3813f-bce8-4fd8-82d7-55bd141b1a7a | a-data-driven-approach-to-the-forecasting-of | 2012.00685 | null | https://arxiv.org/abs/2012.00685v4 | https://arxiv.org/pdf/2012.00685v4.pdf | A data-driven approach to the forecasting of ground-level ozone concentration | The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied to the forecast of the day-ahe... | ['Vasco Medici', 'Davide Strepparava', 'Lorenzo Nespoli', 'Dario Marvin'] | 2020-10-14 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [ 1.32868320e-01 -9.79748443e-02 8.61724168e-02 -4.74503487e-01
-3.74565303e-01 -3.76835704e-01 7.74933755e-01 5.39279163e-01
-3.07839215e-01 1.13075984e+00 4.90218133e-01 -7.49831557e-01
-8.70424509e-01 -1.13810956e+00 -4.07214701e-01 -8.70959580e-01
-2.06640393e-01 2.45107174e-01 1.50144389e-02 -3.11877489... | [6.547499179840088, 2.9173688888549805] |
dfc6e285-2cce-4a34-91f5-b875dc09a49f | explainable-representations-for-relation | 2306.12687 | null | https://arxiv.org/abs/2306.12687v1 | https://arxiv.org/pdf/2306.12687v1.pdf | Explainable Representations for Relation Prediction in Knowledge Graphs | Knowledge graphs represent real-world entities and their relations in a semantically-rich structure supported by ontologies. Exploring this data with machine learning methods often relies on knowledge graph embeddings, which produce latent representations of entities that preserve structural and local graph neighbourho... | ['Catia Pesquita', 'Sara Silva', 'Rita T. Sousa'] | 2023-06-22 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graphs', 'knowledge-graph-embeddings'] | ['graphs', 'knowledge-base', 'methodology'] | [ 2.51727194e-01 1.05490017e+00 -8.38717222e-01 -3.26876640e-01
-5.60323484e-02 -3.24712038e-01 5.30722082e-01 8.51269484e-01
4.19665396e-01 7.89482713e-01 7.87572563e-01 -3.53385895e-01
-6.67214453e-01 -1.07634985e+00 -6.18364632e-01 -2.39407316e-01
-5.44781029e-01 8.11951458e-01 3.71107422e-02 -2.96858579... | [8.6978178024292, 7.791006088256836] |
09ce29d5-e476-45f6-8e2a-3a7ef8e3276a | automatic-metadata-extraction-incorporating | 2107.00516 | null | https://arxiv.org/abs/2107.00516v1 | https://arxiv.org/pdf/2107.00516v1.pdf | Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations | Electronic Theses and Dissertations (ETDs) contain domain knowledge that can be used for many digital library tasks, such as analyzing citation networks and predicting research trends. Automatic metadata extraction is important to build scalable digital library search engines. Most existing methods are designed for bor... | ['Edward A. Fox', 'William A. Ingram', 'Jian Wu', 'Himarsha R. Jayanetti', 'Muntabir Hasan Choudhury'] | 2021-07-01 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [-4.14925128e-01 -3.02987397e-01 -5.70417821e-01 -1.02186017e-01
-9.89500463e-01 -7.82923698e-01 8.38043034e-01 4.39833015e-01
-2.93624401e-01 9.59279180e-01 3.41462344e-01 -6.49182379e-01
-5.25185019e-02 -8.71317983e-01 -5.44908345e-01 -4.25434589e-01
4.04386491e-01 2.16673002e-01 2.57199764e-01 4.55953002... | [9.478109359741211, 8.228556632995605] |
76900728-5e0f-4a63-8560-6af75e594c9f | 6dof-pose-estimation-of-a-3d-rigid-object | 2209.08266 | null | https://arxiv.org/abs/2209.08266v1 | https://arxiv.org/pdf/2209.08266v1.pdf | 6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features | The point pair feature (PPF) is widely used for 6D pose estimation. In this paper, we propose an efficient 6D pose estimation method based on the PPF framework. We introduce a well-targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry. A pose hypothesis vali... | ['Kai Xu', 'Jia Wang', 'Chenyang Zhu', 'Lintao Zheng', 'Renjiao Yi', 'Lu Deng', 'Fei Chen', 'Chenyi Liu'] | 2022-09-17 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 1.33788839e-01 -4.13472727e-02 1.28269643e-01 -1.08801141e-01
-7.71577954e-01 -4.49519396e-01 3.11618745e-01 1.46343023e-01
-3.08707893e-01 4.08912063e-01 -1.11906998e-01 -1.72726691e-01
-4.21530962e-01 -5.18293798e-01 -5.56905925e-01 -2.42157146e-01
-3.45024794e-01 5.91219962e-01 3.40159804e-01 1.93031486... | [7.65352201461792, -2.5576999187469482] |
d5bdeaff-7041-46cc-87a0-37bbeeff3a2d | zeroth-order-stochastic-variance-reduction | 1805.10367 | null | http://arxiv.org/abs/1805.10367v2 | http://arxiv.org/pdf/1805.10367v2.pdf | Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization | As application demands for zeroth-order (gradient-free) optimization
accelerate, the need for variance reduced and faster converging approaches is
also intensifying. This paper addresses these challenges by presenting: a) a
comprehensive theoretical analysis of variance reduced zeroth-order (ZO)
optimization, b) a nove... | ['Pai-Shun Ting', 'Pin-Yu Chen', 'Sijia Liu', 'Shiyu Chang', 'Lisa Amini', 'Bhavya Kailkhura'] | 2018-05-25 | zeroth-order-stochastic-variance-reduction-1 | http://papers.nips.cc/paper/7630-zeroth-order-stochastic-variance-reduction-for-nonconvex-optimization | http://papers.nips.cc/paper/7630-zeroth-order-stochastic-variance-reduction-for-nonconvex-optimization.pdf | neurips-2018-12 | ['material-classification'] | ['computer-vision'] | [ 4.25347567e-01 -5.90316653e-02 -4.57450449e-02 -9.19636637e-02
-1.20838547e+00 -6.13517404e-01 1.33549437e-01 9.11711678e-02
-5.02987862e-01 9.96965051e-01 -5.02856553e-01 -7.37329245e-01
-3.82551163e-01 -6.88381135e-01 -1.20337677e+00 -1.10385120e+00
-2.11146161e-01 4.65857923e-01 -1.45535499e-01 -4.33932662... | [6.715270042419434, 4.1843414306640625] |
2411b0ae-283b-40b5-a8d1-44d00b205740 | ensemble-long-short-term-memory-enlstm | 2004.13562 | null | https://arxiv.org/abs/2004.13562v2 | https://arxiv.org/pdf/2004.13562v2.pdf | Ensemble long short-term memory (EnLSTM) network | In this study, we propose an ensemble long short-term memory (EnLSTM) network, which can be trained on a small dataset and process sequential data. The EnLSTM is built by combining the ensemble neural network (ENN) and the cascaded long short-term memory (C-LSTM) network to leverage their complementary strengths. In or... | ['Dongxiao Zhang', 'Yuntian Chen'] | 2020-04-26 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.20368704e-01 3.18554491e-02 4.26637262e-01 2.07937881e-03
-7.20625103e-01 1.61681429e-01 4.60211217e-01 -6.44502137e-03
-4.63896453e-01 8.48530889e-01 1.91484854e-01 -3.84073466e-01
-1.84070960e-01 -8.23073804e-01 -9.84627664e-01 -6.60519898e-01
-1.66362405e-01 2.23399907e-01 -1.18929558e-01 -2.35888869... | [6.816561698913574, 2.796184539794922] |
83f129d1-c13f-489c-843b-bc45932d907f | language-as-queries-for-referring-video | 2201.00487 | null | https://arxiv.org/abs/2201.00487v2 | https://arxiv.org/pdf/2201.00487v2.pdf | Language as Queries for Referring Video Object Segmentation | Referring video object segmentation (R-VOS) is an emerging cross-modal task that aims to segment the target object referred by a language expression in all video frames. In this work, we propose a simple and unified framework built upon Transformer, termed ReferFormer. It views the language as queries and directly atte... | ['Ping Luo', 'Zehuan Yuan', 'Peize Sun', 'Yi Jiang', 'Jiannan Wu'] | 2022-01-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wu_Language_As_Queries_for_Referring_Video_Object_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wu_Language_As_Queries_for_Referring_Video_Object_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['referring-expression-segmentation', 'video-instance-segmentation', 'referring-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.19562128e-01 -2.32759312e-01 -1.24032900e-01 -2.37623125e-01
-1.02358806e+00 -4.78137642e-01 4.28316176e-01 -4.51685369e-01
-5.78855157e-01 3.00436437e-01 1.42801166e-01 -3.80004980e-02
1.60818085e-01 -3.87037992e-01 -7.85876632e-01 -4.74236131e-01
6.31959513e-02 8.10454860e-02 9.77976501e-01 -2.25453839... | [9.314410209655762, 0.08872223645448685] |
30ea05dc-be7e-43b2-b2f1-a59612f505ab | cmsbert-clr-context-driven-modality-shifting | 2209.07424 | null | https://arxiv.org/abs/2209.07424v1 | https://arxiv.org/pdf/2209.07424v1.pdf | CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations | Multimodal sentiment analysis has become an increasingly popular research area as the demand for multimodal online content is growing. For multimodal sentiment analysis, words can have different meanings depending on the linguistic context and non-verbal information, so it is crucial to understand the meaning of the wo... | ['Jihie Kim', 'Junghun Kim'] | 2022-08-21 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 7.83905014e-02 -3.63739341e-01 -1.49656236e-01 -5.35295486e-01
-5.93045771e-01 -5.43518960e-01 4.25322354e-01 1.78870931e-01
-6.96690619e-01 2.02949718e-01 6.47988796e-01 3.79862227e-02
1.42895117e-01 -3.94081652e-01 -3.98163527e-01 -8.38385880e-01
3.36224258e-01 -1.33452401e-01 4.00830284e-02 -4.29697663... | [13.129388809204102, 5.0378265380859375] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.