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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5e086d4b-5841-4cf1-91be-baccd421bafb | on-the-convergence-of-policy-gradient-methods | 2210.08857 | null | https://arxiv.org/abs/2210.08857v1 | https://arxiv.org/pdf/2210.08857v1.pdf | On the convergence of policy gradient methods to Nash equilibria in general stochastic games | Learning in stochastic games is a notoriously difficult problem because, in addition to each other's strategic decisions, the players must also contend with the fact that the game itself evolves over time, possibly in a very complicated manner. Because of this, the convergence properties of popular learning algorithms ... | ['Emmanouil-Vasileios Vlatakis-Gkaragkounis', 'Panayotis Mertikopoulos', 'Kyriakos Lotidis', 'Angeliki Giannou'] | 2022-10-17 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-1.50914818e-01 1.35146931e-01 -2.14980721e-01 2.48502359e-01
-5.89051902e-01 -9.16143298e-01 2.59671450e-01 8.49030092e-02
-8.68149638e-01 1.26751709e+00 7.62590487e-03 -6.70842409e-01
-5.01696050e-01 -7.82478094e-01 -6.67839587e-01 -9.54347849e-01
-2.48831332e-01 4.37061369e-01 2.69115716e-01 -5.35107434... | [4.2360520362854, 2.6311147212982178] |
b88d4b47-82f9-4d05-8faf-66b9ccc90e28 | duo-segnet-adversarial-dual-views-for-semi | 2108.11154 | null | https://arxiv.org/abs/2108.11154v1 | https://arxiv.org/pdf/2108.11154v1.pdf | Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation | Segmentation of images is a long-standing challenge in medical AI. This is mainly due to the fact that training a neural network to perform image segmentation requires a significant number of pixel-level annotated data, which is often unavailable. To address this issue, we propose a semi-supervised image segmentation t... | ['Mehrtash Harandi', 'Gary Egan', 'Zhaolin Chen', 'Himashi Peiris'] | 2021-08-25 | null | null | null | null | ['semi-supervised-medical-image-segmentation', 'multi-view-learning'] | ['computer-vision', 'computer-vision'] | [ 3.23530346e-01 2.86984056e-01 -3.15211266e-01 -3.97268206e-01
-1.02341878e+00 -5.72140992e-01 2.68528331e-02 -2.96456486e-01
-5.44850469e-01 6.46615267e-01 -1.54753432e-01 -2.56639183e-01
3.43343168e-01 -6.69761479e-01 -7.56207943e-01 -8.48327100e-01
3.92991304e-01 3.62205654e-01 7.90674463e-02 -2.08781660... | [14.68226146697998, -2.0678367614746094] |
9c849682-8cac-4f5f-8526-d56df5c0049a | relation-order-histograms-as-a-network | null | null | https://link.springer.com/chapter/10.1007/978-3-030-77964-1_18 | https://www.iccs-meeting.org/archive/iccs2021/papers/127430217.pdf | Relation order histograms as a network embedding tool | In this work, we introduce a novel graph embedding technique called NERO (Network Embedding based on Relation Order histograms). Its performance is assessed using a number of well-known classification problems and a newly introduced benchmark dealing with detailed laminae venation networks. The proposed algorithm achie... | ['Michał Idzik', 'Radosław Łazaz'] | 2021-06-09 | null | null | null | international-conference-on-computational-5 | ['network-embedding'] | ['methodology'] | [ 1.65138185e-01 3.23114067e-01 -1.59996241e-01 1.48959339e-01
2.77615458e-01 -4.22032446e-01 1.04530597e+00 7.73942351e-01
-4.31640357e-01 6.02701128e-01 -1.66910142e-01 -5.01966357e-01
-7.41611898e-01 -1.14162695e+00 -2.21153870e-01 -6.65574431e-01
-4.51619536e-01 8.70390892e-01 5.00307083e-01 -4.90799129... | [7.047555923461914, 5.907862663269043] |
22026cf4-19e8-48dd-bde0-b0d97d0c367e | motion-aware-dynamic-graph-neural-network-for | 2203.00387 | null | https://arxiv.org/abs/2203.00387v1 | https://arxiv.org/pdf/2203.00387v1.pdf | Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing | Video snapshot compressive imaging (SCI) utilizes a 2D detector to capture sequential video frames and compresses them into a single measurement. Various reconstruction methods have been developed to recover the high-speed video frames from the snapshot measurement. However, most existing reconstruction methods are inc... | ['Xin Yuan', 'Bo Chen', 'Ziheng Cheng', 'Ruiying Lu'] | 2022-03-01 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 3.17389458e-01 -6.59735918e-01 -4.90750335e-02 -4.65806015e-02
-2.42942959e-01 -2.58876383e-01 2.35070124e-01 -3.11015427e-01
3.53768654e-02 4.93079424e-01 2.97949970e-01 -1.83860749e-01
-2.59008139e-01 -6.21917844e-01 -7.19628513e-01 -8.30688059e-01
-3.96280318e-01 -3.47748816e-01 3.56301516e-01 1.62083045... | [11.029342651367188, -2.068798780441284] |
292b4713-77de-4654-be14-ccaa0c99e870 | sirfyn-single-image-relighting-from-your | 2112.04497 | null | https://arxiv.org/abs/2112.04497v1 | https://arxiv.org/pdf/2112.04497v1.pdf | SIRfyN: Single Image Relighting from your Neighbors | We show how to relight a scene, depicted in a single image, such that (a) the overall shading has changed and (b) the resulting image looks like a natural image of that scene. Applications for such a procedure include generating training data and building authoring environments. Naive methods for doing this fail. One r... | ['YuXiong Wang', 'Yuanyi Zhong', 'Pranav Asthana', 'Anand Bhattad', 'D. A. Forsyth'] | 2021-12-08 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 7.31029928e-01 1.42908514e-01 7.03893185e-01 -6.96871758e-01
-3.22433710e-01 -5.76816916e-01 6.36926949e-01 -4.13277149e-01
8.25673118e-02 7.46441960e-01 2.27012664e-01 -3.58803034e-01
3.73516530e-01 -9.62023735e-01 -9.12769139e-01 -7.33340383e-01
2.25684181e-01 4.18752462e-01 1.85499251e-01 -5.02532840... | [9.821457862854004, -2.986156702041626] |
ec4f17e7-345b-4cf7-9ed1-d33c38322145 | image-restoration-using-convolutional-auto | 1606.08921 | null | http://arxiv.org/abs/1606.08921v3 | http://arxiv.org/pdf/1606.08921v3.pdf | Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections | Image restoration, including image denoising, super resolution, inpainting,
and so on, is a well-studied problem in computer vision and image processing,
as well as a test bed for low-level image modeling algorithms. In this work, we
propose a very deep fully convolutional auto-encoder network for image
restoration, wh... | ['Yu-Bin Yang', 'Xiao-Jiao Mao', 'Chunhua Shen'] | 2016-06-29 | null | null | null | null | ['jpeg-artifact-correction'] | ['computer-vision'] | [ 3.07263613e-01 -7.57953674e-02 2.71434605e-01 -4.12469745e-01
-3.64361078e-01 5.42980433e-02 3.66072148e-01 -4.61393774e-01
-2.39476621e-01 6.65872455e-01 4.57884163e-01 -5.29551171e-02
2.16155022e-01 -7.28381038e-01 -1.07234418e+00 -8.05964470e-01
2.26804018e-01 -1.72463968e-01 -3.01426109e-02 -2.65563518... | [11.281986236572266, -2.2546942234039307] |
d3af98e9-643a-4d19-aaf8-06e7727410b2 | text-only-training-for-image-captioning-using | 2211.00575 | null | https://arxiv.org/abs/2211.00575v1 | https://arxiv.org/pdf/2211.00575v1.pdf | Text-Only Training for Image Captioning using Noise-Injected CLIP | We consider the task of image-captioning using only the CLIP model and additional text data at training time, and no additional captioned images. Our approach relies on the fact that CLIP is trained to make visual and textual embeddings similar. Therefore, we only need to learn how to translate CLIP textual embeddings ... | ['Amir Globerson', 'Ron Mokady', 'David Nukrai'] | 2022-11-01 | null | null | null | null | ['semi-supervised-learning-for-image-captioning'] | ['computer-vision'] | [ 5.68847954e-01 4.52295601e-01 -2.18085766e-01 -3.95285159e-01
-9.33527231e-01 -7.24337697e-01 5.05214930e-01 -1.48199692e-01
-3.61835063e-01 6.61071897e-01 4.65416104e-01 -2.33887821e-01
6.67154968e-01 -3.47330719e-01 -1.34231925e+00 -2.65082866e-01
4.22884375e-01 3.65837336e-01 8.28542095e-03 -7.63285980... | [11.142629623413086, 0.730832576751709] |
97aab74f-c3ce-4b19-a8a8-4443401b49d8 | how-well-do-multi-hop-reading-comprehension | null | null | https://openreview.net/forum?id=Ougcw7mdk8u | https://openreview.net/pdf?id=Ougcw7mdk8u | How Well Do Multi-hop Reading Comprehension Models Understand Date Information? | Many previous works demonstrated that existing multi-hop reading comprehension datasets (e.g., HotpotQA) contain reasoning shortcuts, where the questions can be answered without performing multi-hop reasoning. Recently, several multi-hop datasets have been proposed to solve the reasoning shortcut problem or evaluate th... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [-2.13638797e-01 5.56322694e-01 8.77104606e-03 -5.21515250e-01
-8.57799113e-01 -8.13944101e-01 2.93858349e-01 4.95842546e-01
-1.35542929e-01 5.48900604e-01 8.03219974e-02 -8.54043126e-01
-8.14622819e-01 -1.30477333e+00 -8.08525741e-01 2.57662654e-01
4.26465780e-01 6.84256971e-01 5.35757184e-01 -7.84002483... | [10.939240455627441, 7.917413711547852] |
ecf8d616-59b3-4b35-9c72-08bf8a83cbfe | multilingual-lexicalized-constituency-parsing | null | null | https://aclanthology.org/E17-2053 | https://aclanthology.org/E17-2053.pdf | Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks | We introduce a constituency parser based on a bi-LSTM encoder adapted from recent work (Cross and Huang, 2016b; Kiperwasser and Goldberg, 2016), which can incorporate a lower level character biLSTM (Ballesteros et al., 2015; Plank et al., 2016). We model two important interfaces of constituency parsing with auxiliary t... | ["Beno{\\^\\i}t Crabb{\\'e}", 'Maximin Coavoux'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['morphological-tagging'] | ['natural-language-processing'] | [ 3.48433167e-01 7.33820856e-01 -3.89906794e-01 -5.11481941e-01
-1.13497174e+00 -9.56666768e-01 3.24415624e-01 5.37239671e-01
-5.31776249e-01 8.24620724e-01 5.63497007e-01 -9.23539042e-01
6.30794585e-01 -7.92564631e-01 -8.77114534e-01 -2.45162904e-01
-2.29899898e-01 1.00131541e-01 3.92593473e-01 -2.35760674... | [10.360333442687988, 9.671757698059082] |
f2a03ce8-cfce-43aa-a7ee-c72baacf4295 | collaborating-domain-shared-and-target | 2207.09767 | null | https://arxiv.org/abs/2207.09767v1 | https://arxiv.org/pdf/2207.09767v1.pdf | Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action Recognition | In this work, we consider the problem of cross-domain 3D action recognition in the open-set setting, which has been rarely explored before. Specifically, there is a source domain and a target domain that contain the skeleton sequences with different styles and categories, and our purpose is to cluster the target data b... | ['Zilei Wang', 'Qinying Liu'] | 2022-07-20 | null | null | null | null | ['online-clustering', 'video-domain-adapation', '3d-human-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.29504341e-01 -1.89884409e-01 -4.65231717e-01 -4.02227193e-01
-8.46492946e-01 -5.37269711e-01 3.60766917e-01 -4.19218391e-01
-6.00353256e-03 4.04787362e-01 2.98986018e-01 2.16126576e-01
-3.25857580e-01 -2.59917885e-01 -3.54706138e-01 -8.89907420e-01
-4.32361476e-03 5.40362656e-01 3.50954503e-01 1.20211542... | [8.381820678710938, 0.9197940230369568] |
df235761-13cb-4528-b06a-04b33b397b5b | on-the-transferability-of-pre-trained-1 | 2204.09653 | null | https://arxiv.org/abs/2204.09653v1 | https://arxiv.org/pdf/2204.09653v1.pdf | On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages | A recent study by Ahmed and Devanbu reported that using a corpus of code written in multilingual datasets to fine-tune multilingual Pre-trained Language Models (PLMs) achieves higher performance as opposed to using a corpus of code written in just one programming language. However, no analysis was made with respect to ... | ['Timofey Bryksin', 'David Lo', 'Fatemeh Fard', 'Fuxiang Chen'] | 2022-04-05 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-2.76255250e-01 -2.95170367e-01 -3.40303510e-01 -2.22924143e-01
-1.12717354e+00 -9.56961036e-01 4.81639028e-01 3.12850147e-01
-2.77422667e-01 4.29865718e-01 2.24069670e-01 -6.01280212e-01
-7.27567682e-03 -5.13445020e-01 -6.57444656e-01 -1.93234503e-01
3.29795405e-02 2.78655678e-01 1.64124250e-01 -3.35102856... | [7.628871917724609, 7.928544521331787] |
35243e67-92ee-4112-97cb-4cab35a220e1 | id-mixgcl-identity-mixup-for-graph | 2304.10045 | null | https://arxiv.org/abs/2304.10045v1 | https://arxiv.org/pdf/2304.10045v1.pdf | ID-MixGCL: Identity Mixup for Graph Contrastive Learning | Recently developed graph contrastive learning (GCL) approaches compare two different "views" of the same graph in order to learn node/graph representations. The core assumption of these approaches is that by graph augmentation, it is possible to generate several structurally different but semantically similar graph str... | ['Chuan Zhou', 'Tingwen Liu', 'Xinghua Zhang', 'Jiangxia Cao', 'Bowen Yu', 'Gehang Zhang'] | 2023-04-20 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 4.55354691e-01 4.28693295e-01 -2.61702597e-01 -1.88183516e-01
-3.06328893e-01 -7.39753425e-01 5.97832322e-01 7.17627704e-01
-6.37825057e-02 7.61176884e-01 -1.53269887e-01 -3.49252075e-01
5.80016449e-02 -1.02969623e+00 -9.35099781e-01 -9.56124544e-01
-9.91113335e-02 4.82728213e-01 2.45390818e-01 -2.77674109... | [7.191321849822998, 6.270619869232178] |
e6c2f0e2-8503-4a23-ab56-2967e128cd49 | deep-multimodal-representation-learning-from | 1704.03152 | null | http://arxiv.org/abs/1704.03152v1 | http://arxiv.org/pdf/1704.03152v1.pdf | Deep Multimodal Representation Learning from Temporal Data | In recent years, Deep Learning has been successfully applied to multimodal
learning problems, with the aim of learning useful joint representations in
data fusion applications. When the available modalities consist of time series
data such as video, audio and sensor signals, it becomes imperative to consider
their temp... | ['Palghat Ramesh', 'Jiebo Luo', 'Sriganesh Madhvanath', 'Edgar A. Bernal', 'Xitong Yang', 'Radha Chitta'] | 2017-04-11 | deep-multimodal-representation-learning-from-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Yang_Deep_Multimodal_Representation_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Deep_Multimodal_Representation_CVPR_2017_paper.pdf | cvpr-2017-7 | ['audio-visual-speech-recognition'] | ['speech'] | [ 4.10227567e-01 -5.21007240e-01 -7.64274299e-02 -3.21550429e-01
-1.06980503e+00 -2.10970193e-01 8.04325938e-01 2.63222486e-01
-5.50922751e-01 5.32960176e-01 4.93537009e-01 2.55815238e-02
-5.06582916e-01 -2.24267438e-01 -6.13619149e-01 -8.00930679e-01
-2.81863451e-01 -1.66907668e-01 2.72403866e-01 -1.12665944... | [13.340847969055176, 5.037857532501221] |
80ec76b2-f3c0-4536-af5d-68f3e3dd1830 | using-hankel-matrices-for-dynamics-based | 1506.05001 | null | http://arxiv.org/abs/1506.05001v1 | http://arxiv.org/pdf/1506.05001v1.pdf | Using Hankel Matrices for Dynamics-based Facial Emotion Recognition and Pain Detection | This paper proposes a new approach to model the temporal dynamics of a
sequence of facial expressions. To this purpose, a sequence of Face Image
Descriptors (FID) is regarded as the output of a Linear Time Invariant (LTI)
system. The temporal dynamics of such sequence of descriptors are represented
by means of a Hankel... | ['Liliana Lo Presti', 'Marco La Cascia'] | 2015-06-16 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 1.23773478e-01 -4.63960439e-01 -3.05105031e-01 -5.08094847e-01
-1.75831988e-01 -4.48940098e-01 1.00913835e+00 -2.47991383e-01
-1.79002717e-01 4.14896876e-01 -2.37459078e-01 2.92028695e-01
-3.19702834e-01 -1.44328758e-01 -1.53357461e-01 -9.13661480e-01
-6.96893394e-01 2.43515447e-02 -4.82998878e-01 -4.27804023... | [13.647475242614746, 1.8394198417663574] |
ce6ec311-ac68-4fbe-831b-6e7717bbb298 | on-exploring-undetermined-relationships-for | 1905.01595 | null | https://arxiv.org/abs/1905.01595v1 | https://arxiv.org/pdf/1905.01595v1.pdf | On Exploring Undetermined Relationships for Visual Relationship Detection | In visual relationship detection, human-notated relationships can be regarded as determinate relationships. However, there are still large amount of unlabeled data, such as object pairs with less significant relationships or even with no relationships. We refer to these unlabeled but potentially useful data as undeterm... | ['DaCheng Tao', 'Yibing Zhan', 'Jun Yu', 'Ting Yu'] | 2019-05-05 | on-exploring-undetermined-relationships-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhan_On_Exploring_Undetermined_Relationships_for_Visual_Relationship_Detection_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhan_On_Exploring_Undetermined_Relationships_for_Visual_Relationship_Detection_CVPR_2019_paper.pdf | cvpr-2019-6 | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.24479465e-01 5.35875522e-02 -5.69873512e-01 -4.74049866e-01
-8.48378316e-02 -4.70021904e-01 5.96908748e-01 7.27512240e-02
-1.46490827e-01 7.16764569e-01 -2.24226899e-02 -1.91800758e-01
-2.64892131e-01 -9.48413253e-01 -6.41004324e-01 -5.62121451e-01
1.51744746e-02 4.76854295e-01 6.12155616e-01 -2.49461353... | [10.227730751037598, 1.6997777223587036] |
a184687e-7a33-4221-8cbe-a04a4be7bc1e | tumornet-lung-nodule-characterization-using | 1703.00645 | null | http://arxiv.org/abs/1703.00645v1 | http://arxiv.org/pdf/1703.00645v1.pdf | TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process | Characterization of lung nodules as benign or malignant is one of the most
important tasks in lung cancer diagnosis, staging and treatment planning. While
the variation in the appearance of the nodules remains large, there is a need
for a fast and robust computer aided system. In this work, we propose an
end-to-end tra... | ['Sarfaraz Hussein', 'Qi Song', 'Ulas Bagci', 'Kunlin Cao', 'Robert Gillies'] | 2017-03-02 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 1.45814374e-01 6.67962357e-02 -6.62266687e-02 -2.37537339e-01
-7.54281640e-01 -4.83308375e-01 4.96801406e-01 5.04431389e-02
-2.58162498e-01 1.70011505e-01 3.30112278e-01 -3.43197703e-01
-1.52497247e-01 -7.56378293e-01 -3.55335623e-01 -9.88431275e-01
2.26371035e-01 5.93021989e-01 3.71773303e-01 7.08620772... | [15.419922828674316, -2.1384196281433105] |
8e97547e-cf24-4479-9cf6-8cefea84af01 | unlimiformer-long-range-transformers-with | 2305.01625 | null | https://arxiv.org/abs/2305.01625v2 | https://arxiv.org/pdf/2305.01625v2.pdf | Unlimiformer: Long-Range Transformers with Unlimited Length Input | Since the proposal of transformers, these models have been limited to bounded input lengths, because of their need to attend to every token in the input. In this work, we propose Unlimiformer: a general approach that wraps any existing pretrained encoder-decoder transformer, and offloads the cross-attention computation... | ['Matthew R. Gormley', 'Graham Neubig', 'Uri Alon', 'Amanda Bertsch'] | 2023-05-02 | null | null | null | null | ['multi-document-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.65504292e-01 5.80424629e-02 -1.95763648e-01 -7.07559437e-02
-1.29065442e+00 -8.80822837e-01 4.27627265e-01 4.20212090e-01
-7.41642714e-01 5.74594259e-01 5.14410257e-01 -6.09832168e-01
-7.67610967e-02 -7.39193082e-01 -1.04918051e+00 -4.94696110e-01
4.80113924e-03 8.37126791e-01 2.89011121e-01 7.39680603... | [10.921186447143555, 7.5249342918396] |
700f78ca-13bc-40a9-a188-02751bd3b3d8 | shared-coupling-bridge-for-weakly-supervised | 2212.07047 | null | https://arxiv.org/abs/2212.07047v1 | https://arxiv.org/pdf/2212.07047v1.pdf | Shared Coupling-bridge for Weakly Supervised Local Feature Learning | Sparse local feature extraction is usually believed to be of important significance in typical vision tasks such as simultaneous localization and mapping, image matching and 3D reconstruction. At present, it still has some deficiencies needing further improvement, mainly including the discrimination power of extracted ... | ['Luping Ji', 'Jiewen Zhu', 'Jiayuan Sun'] | 2022-12-14 | null | null | null | null | ['simultaneous-localization-and-mapping', 'camera-localization', 'visual-localization', 'image-matching'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-9.55046415e-02 -4.71537709e-01 -5.30405164e-01 -2.66673833e-01
-1.14297879e+00 -2.44722977e-01 5.95236123e-01 -1.26390502e-01
-3.38542610e-01 2.02438563e-01 5.19031584e-02 6.92325681e-02
-3.38451594e-01 -4.09842193e-01 -8.03691566e-01 -8.95467043e-01
-1.42748162e-01 -4.27081482e-03 2.78258145e-01 -1.25618741... | [7.8910298347473145, -1.9964605569839478] |
fd20bee2-cb5a-4735-aa9b-25232bedb55d | mad-a-scalable-dataset-for-language-grounding | 2112.00431 | null | https://arxiv.org/abs/2112.00431v2 | https://arxiv.org/pdf/2112.00431v2.pdf | MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions | The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques. In comparison, limited effort has been made at assessing the fitness of these datasets for the video-language grounding task. Recent works have begun t... | ['Bernard Ghanem', 'Silvio Giancola', 'Chen Zhao', 'Fabian Caba Heilbron', 'Juan León Alcázar', 'Alejandro Pardo', 'Mattia Soldan'] | 2021-12-01 | mad-a-scalable-dataset-for-language-grounding-1 | http://openaccess.thecvf.com//content/CVPR2022/html/Soldan_MAD_A_Scalable_Dataset_for_Language_Grounding_in_Videos_From_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Soldan_MAD_A_Scalable_Dataset_for_Language_Grounding_in_Videos_From_CVPR_2022_paper.pdf | cvpr-2022-1 | ['moment-retrieval', 'natural-language-moment-retrieval'] | ['computer-vision', 'computer-vision'] | [ 1.71205133e-01 -1.94707304e-01 -4.60466921e-01 -4.74192739e-01
-1.28399026e+00 -6.91614866e-01 7.77917087e-01 1.45903811e-01
-3.59829962e-01 4.79614943e-01 6.52835011e-01 -1.15359225e-03
2.14109957e-01 -2.73539603e-01 -8.77278090e-01 -3.17620128e-01
-2.87257582e-01 2.30096877e-01 2.43434981e-01 -8.31942111... | [10.428953170776367, 0.8274787068367004] |
d33d057c-ad18-4a5b-aa81-690a161c74b1 | simple-yet-surprisingly-effective-training | 2212.13918 | null | https://arxiv.org/abs/2212.13918v1 | https://arxiv.org/pdf/2212.13918v1.pdf | Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition | Human Activity Recognition (HAR) is one of the core research areas in mobile and wearable computing. With the application of deep learning (DL) techniques such as CNN, recognizing periodic or static activities (e.g, walking, lying, cycling, etc.) has become a well studied problem. What remains a major challenge though ... | ['Thomas Ploetz', 'Paolo Missier', 'Xin Guan', 'Yu Guan', 'Shuai Shao'] | 2022-12-23 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 5.14420509e-01 -2.12227300e-01 -3.74623328e-01 -1.61966383e-02
-1.82535276e-01 -1.45453826e-01 5.44434965e-01 -4.79162186e-02
-5.14357686e-01 8.74304056e-01 2.47920290e-01 -1.66752636e-01
-2.68003047e-01 -7.74208486e-01 -6.28636897e-01 -9.33505654e-01
-4.62743372e-01 -2.61450320e-01 4.62757915e-01 -6.93137422... | [7.5750298500061035, 0.8106054663658142] |
d3778b35-0625-4e2f-b6f1-d6209fc41af4 | a-spectral-approach-to-off-policy-evaluation | 2109.10502 | null | https://arxiv.org/abs/2109.10502v1 | https://arxiv.org/pdf/2109.10502v1.pdf | A Spectral Approach to Off-Policy Evaluation for POMDPs | We consider off-policy evaluation (OPE) in Partially Observable Markov Decision Processes, where the evaluation policy depends only on observable variables but the behavior policy depends on latent states (Tennenholtz et al. (2020a)). Prior work on this problem uses a causal identification strategy based on one-step ob... | ['Nan Jiang', 'Yash Nair'] | 2021-09-22 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 3.06183517e-01 2.94612765e-01 -6.67127550e-01 6.67100102e-02
-2.37936750e-01 -7.78705001e-01 8.58003974e-01 -1.06257468e-01
-3.68090183e-01 9.74646151e-01 4.37783182e-01 -6.73000038e-01
-6.13426745e-01 -4.36734170e-01 -3.25063646e-01 -6.97782874e-01
-2.95306176e-01 4.91259158e-01 1.07289687e-01 1.29759103... | [4.219958305358887, 2.624936103820801] |
48d6979b-fdb0-40bf-9d05-40299a505b20 | maupqa-massive-automatically-created-polish | 2305.05486 | null | https://arxiv.org/abs/2305.05486v1 | https://arxiv.org/pdf/2305.05486v1.pdf | MAUPQA: Massive Automatically-created Polish Question Answering Dataset | Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several meth... | ['Piotr Rybak'] | 2023-05-09 | null | null | null | null | ['passage-retrieval', 'open-domain-question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [-3.57095391e-01 -1.48596361e-01 2.05575526e-01 -2.66593874e-01
-1.51413870e+00 -8.88121545e-01 4.86644983e-01 5.00188470e-01
-8.77545774e-01 1.20138681e+00 3.61701190e-01 -4.11207050e-01
-5.78681864e-02 -9.94238257e-01 -6.40734017e-01 -1.63360491e-01
1.59925506e-01 9.81117785e-01 6.07644141e-01 -6.90260828... | [11.386475563049316, 7.954854488372803] |
48c4e8dc-aecd-4cef-b4c3-81280eb85b4e | advancing-volumetric-medical-image | 2306.08913 | null | https://arxiv.org/abs/2306.08913v1 | https://arxiv.org/pdf/2306.08913v1.pdf | Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder | Masked autoencoder (MAE) has emerged as a promising self-supervised pretraining technique to enhance the representation learning of a neural network without human intervention. To adapt MAE onto volumetric medical images, existing methods exhibit two challenges: first, the global information crucial for understanding t... | ['Hao Chen', 'Luyang Luo', 'Jia-Xin Zhuang'] | 2023-06-15 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 1.48511127e-01 4.12567258e-01 -8.12646970e-02 -5.88095844e-01
-6.62644267e-01 -8.34690854e-02 3.29821467e-01 2.63846755e-01
-3.00932229e-01 7.17631340e-01 2.83159643e-01 1.36250749e-01
-2.10223228e-01 -6.38006568e-01 -7.85454750e-01 -9.07455623e-01
-3.74295294e-01 5.79611063e-01 3.26259851e-01 -2.81190395... | [14.589520454406738, -2.1473593711853027] |
3defd28a-72bc-4486-b3f4-0a5ed2db189e | integrating-tick-level-data-and-periodical | 2306.17179 | null | https://arxiv.org/abs/2306.17179v1 | https://arxiv.org/pdf/2306.17179v1.pdf | Integrating Tick-level Data and Periodical Signal for High-frequency Market Making | We focus on the problem of market making in high-frequency trading. Market making is a critical function in financial markets that involves providing liquidity by buying and selling assets. However, the increasing complexity of financial markets and the high volume of data generated by tick-level trading makes it chall... | ['Can Yang', 'Cong Zheng', 'Jiafa He'] | 2023-06-19 | null | null | null | null | ['management'] | ['miscellaneous'] | [-7.86148846e-01 -5.19831240e-01 3.36427465e-02 -2.02401385e-01
-5.60936034e-01 -7.00163841e-01 5.79853117e-01 1.02509491e-01
-4.45605457e-01 9.68074560e-01 -1.02070525e-01 -6.23182356e-01
-2.95201600e-01 -1.37795317e+00 -4.27676499e-01 -5.76628566e-01
-5.74388504e-01 7.08934665e-01 -6.90649450e-02 -4.88283962... | [4.417545318603516, 3.926316022872925] |
8d6bebef-7994-439d-a85a-a5898c381587 | variational-quantum-cloning-improving | 2012.11424 | null | https://arxiv.org/abs/2012.11424v1 | https://arxiv.org/pdf/2012.11424v1.pdf | Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis | Cryptanalysis on standard quantum cryptographic systems generally involves finding optimal adversarial attack strategies on the underlying protocols. The core principle of modelling quantum attacks in many cases reduces to the adversary's ability to clone unknown quantum states which facilitates the extraction of some ... | ['Niraj Kumar', 'Elham Kashefi', 'Mina Doosti', 'Brian Coyle'] | 2020-12-21 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 4.89986241e-01 1.03685394e-01 9.84875858e-02 4.33385149e-02
-1.34555721e+00 -9.83290553e-01 4.28842396e-01 -6.55501708e-02
-5.25911987e-01 4.93439913e-01 -3.63298237e-01 -9.42830622e-01
8.50582495e-03 -1.31111348e+00 -1.00131631e+00 -1.07018089e+00
-1.42731756e-01 4.44011986e-01 -6.82345852e-02 -6.66842103... | [5.606878280639648, 4.98549747467041] |
aa87bb15-31c0-44d1-88e8-cc9e3551fe5b | unsupervised-speech-representation-learning | 1901.08810 | null | https://arxiv.org/abs/1901.08810v2 | https://arxiv.org/pdf/1901.08810v2.pdf | Unsupervised speech representation learning using WaveNet autoencoders | We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high level semantic content from the signal, e.g.\ phoneme identities, while being invariant to confounding l... | ['Aäron van den Oord', 'Samy Bengio', 'Ron J. Weiss', 'Jan Chorowski'] | 2019-01-25 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 2.70036906e-01 5.09964406e-01 1.21132873e-01 -2.90681839e-01
-9.03881788e-01 -6.59210861e-01 5.44779599e-01 7.76574761e-02
-2.68002331e-01 4.46724713e-01 6.72791183e-01 -5.15105687e-02
-9.17186029e-03 -6.73838317e-01 -9.95931923e-01 -8.94461274e-01
-4.42249402e-02 2.87075192e-01 -1.73654243e-01 6.42621517... | [14.912330627441406, 6.416104793548584] |
e492177b-d37a-4b02-ab58-8c190cdd07a9 | my-health-sensor-my-classifier-adapting-a | 2009.10799 | null | https://arxiv.org/abs/2009.10799v1 | https://arxiv.org/pdf/2009.10799v1.pdf | My Health Sensor, my Classifier: Adapting a Trained Classifier to Unlabeled End-User Data | In this work, we present an approach for unsupervised domain adaptation (DA) with the constraint, that the labeled source data are not directly available, and instead only access to a classifier trained on the source data is provided. Our solution, iteratively labels only high confidence sub-regions of the target data ... | ['Lars Aakerøy', 'Britt Øverland', 'Knut Liestøl', 'Mohan Kankanhalli', 'Stein Kristiansen', 'Sigurd Steinshamn', 'Konstantinos Nikolaidis', 'Vera Goebel', 'Thomas Plagemann', 'Harriet Akre', 'Gunn Marit Traaen'] | 2020-09-22 | null | null | null | null | ['sleep-apnea-detection'] | ['medical'] | [ 2.42969885e-01 4.18564647e-01 -2.57371545e-01 -4.45619076e-01
-6.12719655e-01 -4.07491952e-01 1.95756137e-01 6.78868115e-01
-6.72516584e-01 1.05919659e+00 2.19127964e-02 5.83858117e-02
-4.08805788e-01 -6.40289187e-01 -1.95298269e-01 -6.47558868e-01
1.24875695e-01 7.47849822e-01 4.62306827e-01 1.27841130... | [10.071670532226562, 3.277552366256714] |
dabfe3ec-fd0e-4dd8-8239-1d5b50adadeb | effective-audio-classification-network-based | 2211.02940 | null | https://arxiv.org/abs/2211.02940v4 | https://arxiv.org/pdf/2211.02940v4.pdf | Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block | Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification. While these techniques are state-of-the-art, these works' effectiveness can only be guaranteed with huge computational costs and parameters, large amounts of data aug... | ['Jianlu Shen', 'Zhen Ren', 'Yifan Huang', 'Lifang Chen', 'Zihui Yan', 'Yunjie Zhu', 'Yunhao Chen'] | 2022-11-05 | null | null | null | null | ['environmental-sound-classification', 'sound-classification', 'genre-classification'] | ['audio', 'audio', 'computer-vision'] | [ 3.97156328e-02 -1.61894321e-01 8.88071731e-02 -1.69962212e-01
-7.94904470e-01 -2.68697530e-01 7.23191425e-02 -1.70775890e-01
-4.77727413e-01 5.35265803e-01 1.51941106e-01 -1.69399217e-01
-9.38371420e-02 -8.09590280e-01 -8.52603137e-01 -5.28777421e-01
-2.14437157e-01 1.09041564e-01 1.51506290e-01 -3.39352190... | [15.159199714660645, 5.218752384185791] |
38101bb6-1266-4052-b876-ad3c5ed8ed92 | generalizing-successor-features-to-continuous | null | null | https://openreview.net/forum?id=0m4c9ZfDrDt | https://openreview.net/pdf?id=0m4c9ZfDrDt | Generalizing Successor Features to continuous domains for Multi-task Learning | The deep reinforcement learning (RL) framework has shown great promise to tackle sequential decision-making problems, where the agent learns to behave optimally through interactions with the environment and receiving rewards.
The ability of an RL agent to learn different reward functions concurrently has many benefits,... | ['Animesh Garg', 'Fabio Ramos', 'David Meger', 'Dieter Fox', 'Melissa Mozifian'] | 2021-09-29 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.77626395e-01 2.49303356e-01 -1.78497180e-01 -5.88399693e-02
-5.33148587e-01 -3.97960842e-01 7.77437627e-01 2.08252251e-01
-7.40604818e-01 1.02603936e+00 -2.25460138e-02 9.97215584e-02
-6.01054728e-01 -5.87054431e-01 -6.04925156e-01 -8.83366704e-01
-4.78979826e-01 4.96602833e-01 2.09771693e-01 -5.63972950... | [4.2016072273254395, 1.7755732536315918] |
899aa16a-fb69-485f-a318-2e3052eb517f | supertagging-combinatory-categorial-grammar | 2010.06115 | null | https://arxiv.org/abs/2010.06115v2 | https://arxiv.org/pdf/2010.06115v2.pdf | Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks | Supertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task. However, existing studies have made limited efforts to leverage contextual features except for applying powerful encoders (e... | ['Fei Xia', 'Yan Song', 'Yuanhe Tian'] | 2020-10-13 | null | https://aclanthology.org/2020.emnlp-main.487 | https://aclanthology.org/2020.emnlp-main.487.pdf | emnlp-2020-11 | ['ccg-supertagging'] | ['natural-language-processing'] | [ 2.52699554e-01 6.31958961e-01 -1.01396538e-01 -6.09173000e-01
-8.36664677e-01 -3.45711648e-01 4.84150887e-01 2.92363524e-01
-4.24699396e-01 4.95827258e-01 6.35930419e-01 -5.55126011e-01
1.39919028e-01 -9.45231616e-01 -8.12281430e-01 -4.75794613e-01
-2.23103151e-01 2.29607597e-01 2.53384486e-02 -2.09136978... | [10.505233764648438, 9.374051094055176] |
1e79d3e6-1872-44e4-8eb3-de56d30976c7 | multi-plane-program-induction-with-3d-box-1 | 2011.10007 | null | https://arxiv.org/abs/2011.10007v2 | https://arxiv.org/pdf/2011.10007v2.pdf | Multi-Plane Program Induction with 3D Box Priors | We consider two important aspects in understanding and editing images: modeling regular, program-like texture or patterns in 2D planes, and 3D posing of these planes in the scene. Unlike prior work on image-based program synthesis, which assumes the image contains a single visible 2D plane, we present Box Program Induc... | ['Joshua B. Tenenbaum', 'William T. Freeman', 'Jiajun Wu', 'Noah Snavely', 'Xiuming Zhang', 'Jiayuan Mao', 'Yikai Li'] | 2020-11-19 | multi-plane-program-induction-with-3d-box | http://proceedings.neurips.cc/paper/2020/hash/5301c4d888f5204274439e6dcf5fdb54-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/5301c4d888f5204274439e6dcf5fdb54-Paper.pdf | neurips-2020-12 | ['program-induction'] | ['computer-code'] | [ 7.75780976e-01 2.06994593e-01 -1.07626982e-01 -5.07722020e-01
-9.17802900e-02 -7.39274621e-01 6.26912415e-01 -1.01620428e-01
1.67312786e-01 -1.27484888e-01 1.67341873e-01 -5.53750038e-01
4.01157558e-01 -7.66281843e-01 -1.38886511e+00 8.67555477e-03
3.65079761e-01 3.82356763e-01 1.62383810e-01 1.25707716... | [9.100329399108887, -3.132481575012207] |
ac627e81-d7f9-45f6-892b-5fce2cbcc3c6 | generalized-reference-kernel-for-one-class | 2205.00534 | null | https://arxiv.org/abs/2205.00534v2 | https://arxiv.org/pdf/2205.00534v2.pdf | Generalized Reference Kernel for One-class Classification | In this paper, we formulate a new generalized reference kernel hoping to improve the original base kernel using a set of reference vectors. Depending on the selected reference vectors, our formulation shows similarities to approximate kernels, random mappings, and Non-linear Projection Trick. Focusing on small-scale on... | ['Alexandros Iosifidis', 'Jenni Raitoharju'] | 2022-05-01 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [-3.22548598e-02 -2.20002279e-01 -3.87309372e-01 -3.76946479e-01
-6.17664278e-01 -5.16450763e-01 6.34435356e-01 -1.99985318e-02
-2.94818759e-01 6.35515392e-01 2.94646740e-01 -1.39178231e-01
-5.43357968e-01 -6.04007959e-01 -2.75215536e-01 -6.91363215e-01
-6.35203868e-02 2.45586202e-01 5.09143591e-01 -2.84722120... | [7.7664031982421875, 3.999070405960083] |
42e4de7b-0123-42f7-b36b-1a09989b4993 | towards-a-robust-sensor-fusion-step-for-3d | 2306.07344 | null | https://arxiv.org/abs/2306.07344v1 | https://arxiv.org/pdf/2306.07344v1.pdf | Towards a Robust Sensor Fusion Step for 3D Object Detection on Corrupted Data | Multimodal sensor fusion methods for 3D object detection have been revolutionizing the autonomous driving research field. Nevertheless, most of these methods heavily rely on dense LiDAR data and accurately calibrated sensors which is often not the case in real-world scenarios. Data from LiDAR and cameras often come mis... | ['Patric Jensfelt', 'Marko Thiel', 'Viktor Karefjards', 'Maciej K. Wozniak'] | 2023-06-12 | null | null | null | null | ['3d-object-detection'] | ['computer-vision'] | [ 1.11304753e-01 -4.31049407e-01 5.20148762e-02 -5.62702775e-01
-6.29762948e-01 -6.37591183e-01 5.59739470e-01 3.53227645e-01
-4.99013305e-01 6.67817593e-01 -1.74096212e-01 -1.95327669e-01
1.78339824e-01 -7.07885385e-01 -8.79217446e-01 -5.14080763e-01
2.94621289e-01 6.85703635e-01 6.70623660e-01 -2.14139819... | [7.711618423461914, -2.3321995735168457] |
3dee9598-e815-4e35-9c70-cae20065db45 | wildrefer-3d-object-localization-in-large | 2304.05645 | null | https://arxiv.org/abs/2304.05645v1 | https://arxiv.org/pdf/2304.05645v1.pdf | WildRefer: 3D Object Localization in Large-scale Dynamic Scenes with Multi-modal Visual Data and Natural Language | We introduce the task of 3D visual grounding in large-scale dynamic scenes based on natural linguistic descriptions and online captured multi-modal visual data, including 2D images and 3D LiDAR point clouds. We present a novel method, WildRefer, for this task by fully utilizing the appearance features in images, the lo... | ['Yuexin Ma', 'Sibei Yang', 'Xinge Zhu', 'Yuenan Hou', 'Peishan Cong', 'Xidong Peng', 'Zhenxiang Lin'] | 2023-04-12 | null | null | null | null | ['visual-grounding', 'object-localization'] | ['computer-vision', 'computer-vision'] | [-4.07081217e-01 -2.46099696e-01 -2.08661467e-01 -7.68606305e-01
-4.02035087e-01 -5.37136137e-01 6.81790709e-01 -1.01389393e-01
-3.88324231e-01 3.31974149e-01 -1.79801747e-01 -6.85741007e-02
3.25755924e-01 -5.87304056e-01 -8.09160471e-01 -8.00563842e-02
-1.29852101e-01 6.93312943e-01 7.60448813e-01 -6.55597568... | [7.917912483215332, -2.2702484130859375] |
eb5a6873-7b12-436b-a10e-048cae61bc63 | automatic-spoken-language-identification | null | null | https://core.ac.uk/download/pdf/10900425.pdf | https://core.ac.uk/download/pdf/10900425.pdf | Automatic Spoken Language Identification Utilizing Acoustic and Phonetic Speech Information | Automatic spoken Language Identification (LID) is the process of identifying the language spoken within an utterance. The challenge that this task presents is that no prior information is available indicating the content of the utterance or the identity of the speaker. The trend of globalization and the pervasive popul... | ['BIT', 'BEng(Hons)', 'Kim-Yung Eddie Wong'] | 2004-06-01 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-1.38969094e-01 -3.30171734e-01 3.78159918e-02 -5.25128603e-01
-9.02081847e-01 -5.56725800e-01 6.47809565e-01 1.13224022e-01
-4.30575907e-01 3.54856193e-01 5.18958271e-01 -1.16109222e-01
-4.11853613e-03 -1.45159900e-01 1.04816422e-01 -7.34667480e-01
5.01572609e-01 3.62597257e-01 -1.91978797e-01 -2.05564961... | [14.445891380310059, 6.080620288848877] |
03f11192-11a9-4e98-9836-b5fcf712b78f | nev-ncd-negative-learning-entropy-and | 2304.07354 | null | https://arxiv.org/abs/2304.07354v1 | https://arxiv.org/pdf/2304.07354v1.pdf | NEV-NCD: Negative Learning, Entropy, and Variance regularization based novel action categories discovery | Novel Categories Discovery (NCD) facilitates learning from a partially annotated label space and enables deep learning (DL) models to operate in an open-world setting by identifying and differentiating instances of novel classes based on the labeled data notions. One of the primary assumptions of NCD is that the novel ... | ['Nirmalya Roy', 'Hyungtae Lee', 'Heesung Kwon', 'Sanjay Purushotham', 'Abu Zaher Md Faridee', 'Masud Ahmed', 'Zahid Hasan'] | 2023-04-14 | null | null | null | null | ['action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.16261405e-01 2.79754512e-02 -5.95948756e-01 -5.36281705e-01
-1.27372766e+00 -7.64280558e-01 6.31704211e-01 -2.59576797e-01
-4.28216368e-01 6.47328973e-01 3.79752159e-01 9.88028795e-02
1.47943720e-02 -1.69246256e-01 -7.66813695e-01 -5.80231965e-01
-1.12966202e-01 5.26672721e-01 8.10223892e-02 4.49526936... | [8.826106071472168, 1.0871411561965942] |
9162934c-3bb1-4c3a-802b-82f5c31e583d | data-questeval-a-referenceless-metric-for | 2104.07555 | null | https://arxiv.org/abs/2104.07555v3 | https://arxiv.org/pdf/2104.07555v3.pdf | Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation | QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions. Its adaptation to Data-to-Text tasks is not straightforward, as it requires multimodal Question Generation and Answering systems on the con... | ['Patrick Gallinari', 'Geoffrey Scoutheeten', 'Jacopo Staiano', 'Sylvain Lamprier', 'Benjamin Piwowarski', 'Laure Soulier', 'Thomas Scialom', 'Clément Rebuffel'] | 2021-04-15 | null | https://aclanthology.org/2021.emnlp-main.633 | https://aclanthology.org/2021.emnlp-main.633.pdf | emnlp-2021-11 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 8.86074081e-02 3.99631172e-01 8.78406763e-02 -4.12734389e-01
-1.42291725e+00 -7.27371633e-01 1.35029447e+00 7.24318564e-01
-6.04210556e-01 9.70051169e-01 6.20519817e-01 -4.87439819e-02
-3.47617090e-01 -5.69619656e-01 -3.78022820e-01 -1.82830229e-01
2.24560052e-01 1.21552753e+00 1.01507813e-01 -6.39179766... | [11.761666297912598, 8.431695938110352] |
3dc32b39-380d-4113-97f7-8cd98519297f | swatac-a-sentiment-analyzer-using-one-vs-rest | null | null | https://aclanthology.org/S15-2106 | https://aclanthology.org/S15-2106.pdf | SWATAC: A Sentiment Analyzer using One-Vs-Rest Logistic Regression | null | ['Yousef Alhessi', 'Richard Wicentowski'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['negation-detection'] | ['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... | [-7.193156719207764, 3.611880302429199] |
45b8bb15-b9d4-4a26-b54a-6afd189855d8 | applying-artificial-intelligence-for-age | 2201.03045 | null | https://arxiv.org/abs/2201.03045v1 | https://arxiv.org/pdf/2201.03045v1.pdf | Applying Artificial Intelligence for Age Estimation in Digital Forensic Investigations | The precise age estimation of child sexual abuse and exploitation (CSAE) victims is one of the most significant digital forensic challenges. Investigators often need to determine the age of victims by looking at images and interpreting the sexual development stages and other human characteristics. The main priority - s... | ['Harjinder Singh Lallie', 'Thomas Grubl'] | 2022-01-09 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-6.00205176e-02 2.14985356e-01 -3.21848951e-02 -5.57337821e-01
-2.81030387e-01 -2.22042054e-01 3.23380977e-01 3.36949140e-01
-8.35643113e-01 3.98165226e-01 1.50450151e-02 -1.36364043e-01
-1.54738382e-01 -7.49857306e-01 -2.62215227e-01 -5.51637948e-01
-3.48639876e-01 6.94971621e-01 -8.49420875e-02 2.32698381... | [13.1021146774292, 1.041684627532959] |
c17d91ae-f1db-4765-89d0-928ed9051caf | defuzz-deep-learning-guided-directed-fuzzing | 2010.12149 | null | https://arxiv.org/abs/2010.12149v1 | https://arxiv.org/pdf/2010.12149v1.pdf | DeFuzz: Deep Learning Guided Directed Fuzzing | Fuzzing is one of the most effective technique to identify potential software vulnerabilities. Most of the fuzzers aim to improve the code coverage, and there is lack of directedness (e.g., fuzz the specified path in a software). In this paper, we proposed a deep learning (DL) guided directed fuzzing for software vulne... | ['Yang Xiang', 'Camtepe Seyit', 'Jun Zhang', 'Sheng Wen', 'Xian Li', 'Shigang Liu', 'Xiaogang Zhu'] | 2020-10-23 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [ 7.90996477e-02 -1.27460986e-01 -2.09838405e-01 -2.80318379e-01
-5.21523833e-01 -4.63497788e-01 4.57881093e-02 9.46386904e-02
1.78069025e-01 4.81476307e-01 -6.54697418e-04 -7.39299953e-01
3.77844423e-02 -1.17642307e+00 -9.60000575e-01 -1.98828533e-01
-1.79386050e-01 -1.10884689e-01 5.49804866e-01 -2.97496080... | [7.084081649780273, 7.781159400939941] |
d22ab5d7-f9ce-4f5e-adf3-5ad947eb9bec | location-free-scene-graph-generation | 2303.10944 | null | https://arxiv.org/abs/2303.10944v1 | https://arxiv.org/pdf/2303.10944v1.pdf | Location-Free Scene Graph Generation | Scene Graph Generation (SGG) is a challenging visual understanding task. It combines the detection of entities and relationships between them in a scene. Both previous works and existing evaluation metrics rely on bounding box labels, even though many downstream scene graph applications do not need location information... | ['Benjamin Busam', 'Nassir Navab', 'Tobias Czempiel', 'Felix Holm', 'Ege Özsoy'] | 2023-03-20 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.86444724e-01 1.95640430e-01 1.50414139e-01 -3.50710690e-01
-5.17451704e-01 -8.01189005e-01 5.54838121e-01 5.14044881e-01
-1.42349511e-01 4.93039697e-01 -4.92992699e-02 -4.42157924e-01
1.40834125e-02 -1.03188205e+00 -8.73525381e-01 -4.78016615e-01
-8.65818858e-02 4.80459332e-01 5.92327118e-01 1.63100436... | [10.33240795135498, 1.63335120677948] |
60554e1e-82b1-4dcf-acf7-f0eef0b0c514 | adaptation-of-domain-specific-transformer | 2209.10966 | null | https://arxiv.org/abs/2209.10966v2 | https://arxiv.org/pdf/2209.10966v2.pdf | Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccines | Covid-19 has spread across the world and several vaccines have been developed to counter its surge. To identify the correct sentiments associated with the vaccines from social media posts, we fine-tune various state-of-the-art pre-trained transformer models on tweets associated with Covid-19 vaccines. Specifically, we ... | ['Seba Susan', 'Anubhav Sharma', 'Arjun Choudhry', 'Anmol Bansal'] | 2022-09-22 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 2.87910551e-01 1.03932194e-01 -3.58709842e-01 -6.58257127e-01
-8.71085763e-01 -5.39540231e-01 8.62430334e-01 7.03502893e-01
-2.32148379e-01 8.19974601e-01 4.40621644e-01 -5.20586431e-01
8.66329968e-02 -9.32538211e-01 -7.09243953e-01 -3.16553712e-01
-7.96021298e-02 1.04969561e+00 -1.64382905e-01 -7.69411087... | [8.558506965637207, 9.386434555053711] |
7a099e72-c0c6-4404-8297-1f61be1a30c4 | catching-both-gray-and-black-swans-open-set | 2203.14506 | null | https://arxiv.org/abs/2203.14506v1 | https://arxiv.org/pdf/2203.14506v1.pdf | Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection | Despite most existing anomaly detection studies assume the availability of normal training samples only, a few labeled anomaly examples are often available in many real-world applications, such as defect samples identified during random quality inspection, lesion images confirmed by radiologists in daily medical screen... | ['Chunhua Shen', 'Guansong Pang', 'Choubo Ding'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ding_Catching_Both_Gray_and_Black_Swans_Open-Set_Supervised_Anomaly_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ding_Catching_Both_Gray_and_Black_Swans_Open-Set_Supervised_Anomaly_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 4.86764401e-01 1.04982808e-01 1.24555439e-01 -4.91240531e-01
-8.03030729e-01 -3.34986061e-01 4.03791845e-01 5.15989125e-01
2.09377110e-01 3.37945133e-01 -5.29971607e-02 -3.32498223e-01
-2.71432668e-01 -5.35068333e-01 -5.92779636e-01 -9.59029496e-01
-4.47577149e-01 3.84891987e-01 3.04237362e-02 1.26461640... | [7.623922824859619, 2.290506362915039] |
fc7b65e5-3b06-4951-a84a-b73e8b36f67d | introduction-to-discourse-relation-parsing | null | null | https://aclanthology.org/W19-2701 | https://aclanthology.org/W19-2701.pdf | Introduction to Discourse Relation Parsing and Treebanking (DISRPT): 7th Workshop on Rhetorical Structure Theory and Related Formalisms | This overview summarizes the main contributions of the accepted papers at the 2019 workshop on Discourse Relation Parsing and Treebanking (DISRPT 2019). Co-located with NAACL 2019 in Minneapolis, the workshop{'}s aim was to bring together researchers working on corpus-based and computational approaches to discourse rel... | ['Erick Galani Maziero', 'Mikel Iruskieta', 'Juliano Antonio', 'Debopam Das', 'Amir Zeldes'] | 2019-06-01 | null | null | null | ws-2019-6 | ['connective-detection'] | ['natural-language-processing'] | [ 3.38544518e-01 1.12340057e+00 -4.58784431e-01 -4.46107835e-01
-9.60645020e-01 -8.15023243e-01 9.43493724e-01 7.25977600e-01
-3.20287526e-01 1.12515199e+00 9.00120735e-01 -7.51449525e-01
8.77027884e-02 -3.90110910e-01 -2.67977655e-01 -5.09688072e-02
-1.33438274e-01 5.43968022e-01 4.42046136e-01 -5.55193007... | [10.745706558227539, 9.488287925720215] |
9790fbb8-81b0-490d-a0e2-aac5540986b5 | winodict-probing-language-models-for-in | 2209.12153 | null | https://arxiv.org/abs/2209.12153v1 | https://arxiv.org/pdf/2209.12153v1.pdf | WinoDict: Probing language models for in-context word acquisition | We introduce a new in-context learning paradigm to measure Large Language Models' (LLMs) ability to learn novel words during inference. In particular, we rewrite Winograd-style co-reference resolution problems by replacing the key concept word with a synthetic but plausible word that the model must understand to comple... | ['William W. Cohen', 'Fangyu Liu', 'Jeremy R. Cole', 'Julian Martin Eisenschlos'] | 2022-09-25 | null | null | null | null | ['probing-language-models'] | ['natural-language-processing'] | [ 4.34754491e-01 1.46311373e-01 -2.33312264e-01 -1.29945174e-01
-9.63964939e-01 -8.28791916e-01 1.24206007e+00 3.04589868e-01
-8.38602901e-01 9.34985816e-01 4.95439649e-01 -5.19862592e-01
4.08041142e-02 -6.89786375e-01 -8.47380459e-01 -3.17852765e-01
1.26597509e-01 7.35953689e-01 6.90768659e-02 -4.55905646... | [10.713622093200684, 8.639735221862793] |
6985dfa9-2a43-4fd2-8af6-60ce541a3288 | detecting-multiword-expression-type-helps | 2005.05692 | null | https://arxiv.org/abs/2005.05692v1 | https://arxiv.org/pdf/2005.05692v1.pdf | Detecting Multiword Expression Type Helps Lexical Complexity Assessment | Multiword expressions (MWEs) represent lexemes that should be treated as single lexical units due to their idiosyncratic nature. Multiple NLP applications have been shown to benefit from MWE identification, however the research on lexical complexity of MWEs is still an under-explored area. In this work, we re-annotate ... | ['Sian Gooding', 'Ekaterina Kochmar', 'Matthew Shardlow'] | 2020-05-12 | detecting-multiword-expression-type-helps-1 | https://aclanthology.org/2020.lrec-1.545 | https://aclanthology.org/2020.lrec-1.545.pdf | lrec-2020-5 | ['complex-word-identification'] | ['natural-language-processing'] | [ 1.00568332e-01 1.04329251e-01 -2.44546518e-01 -2.43485078e-01
-6.45527065e-01 -8.02636147e-01 4.46640283e-01 5.64173698e-01
-6.88390732e-01 5.19515693e-01 6.32834554e-01 -3.98318589e-01
-1.34775057e-01 -6.74047768e-01 -3.01604509e-01 -3.00239235e-01
3.44469339e-01 2.14714840e-01 -3.38370591e-01 -3.88413906... | [10.858498573303223, 10.386322975158691] |
e736fac1-f1aa-47ff-939f-ffb317afa036 | pragmatic-competence-of-pre-trained-language | 2109.12951 | null | https://arxiv.org/abs/2109.12951v1 | https://arxiv.org/pdf/2109.12951v1.pdf | Pragmatic competence of pre-trained language models through the lens of discourse connectives | As pre-trained language models (LMs) continue to dominate NLP, it is increasingly important that we understand the depth of language capabilities in these models. In this paper, we target pre-trained LMs' competence in pragmatics, with a focus on pragmatics relating to discourse connectives. We formulate cloze-style te... | ['Allyson Ettinger', 'Yan Cong', 'Lalchand Pandia'] | 2021-09-27 | null | https://aclanthology.org/2021.conll-1.29 | https://aclanthology.org/2021.conll-1.29.pdf | conll-emnlp-2021-11 | ['implicatures'] | ['natural-language-processing'] | [-5.40447794e-02 6.87396228e-01 -7.87312239e-02 -5.16371727e-01
-6.25466228e-01 -6.98004067e-01 7.56771624e-01 3.89607906e-01
-5.81368506e-01 3.54286075e-01 6.97716832e-01 -7.95352817e-01
-3.60582680e-01 -4.51612741e-01 -4.81785893e-01 -6.50160983e-02
1.38188556e-01 4.77051228e-01 3.02382916e-01 -4.24971730... | [10.487058639526367, 8.763480186462402] |
b2e35d7d-d99a-4376-8b34-404506f3f2be | sparse-interventions-in-language-models-with | 2112.06837 | null | https://arxiv.org/abs/2112.06837v1 | https://arxiv.org/pdf/2112.06837v1.pdf | Sparse Interventions in Language Models with Differentiable Masking | There has been a lot of interest in understanding what information is captured by hidden representations of language models (LMs). Typically, interpretation methods i) do not guarantee that the model actually uses the encoded information, and ii) do not discover small subsets of neurons responsible for a considered phe... | ['Ivan Titov', 'Dieuwke Hupkes', 'Leon Schmid', 'Nicola De Cao'] | 2021-12-13 | null | null | null | null | ['gender-bias-detection', 'gender-bias-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 4.03858304e-01 5.37070572e-01 -6.95977807e-01 -4.47447479e-01
-4.62252080e-01 -3.53529364e-01 5.22383869e-01 3.05806816e-01
-4.71849203e-01 8.95845652e-01 4.63311464e-01 -3.67145419e-01
-2.36888051e-01 -6.94092929e-01 -1.08715117e+00 -7.72398233e-01
-3.95095646e-02 5.45299172e-01 -4.00905102e-01 9.24017727... | [8.995101928710938, 6.1492719650268555] |
cbc17f37-d116-4685-ab02-4ae54530be59 | drug-drug-interaction-extraction-via-1 | null | null | https://academic.oup.com/bioinformatics/article/34/5/828/4565590 | https://academic.oup.com/bioinformatics/article-pdf/34/5/828/25117737/btx659.pdf | Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths | Motivation
Adverse events resulting from drug-drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs described in the biomedical literature could further efforts for ongoing pharmacovigilance. Most of neural networks-based methods typically focus on sentence sequence to identify... | ['Wei Zheng', 'Jian Wang', 'Yijia Zhang', 'Zhihao Yang', 'Michel Dumontier', 'Hongfei Lin'] | 2017-10-25 | null | null | null | bioinformatics-2017-10 | ['drug-drug-interaction-extraction'] | ['natural-language-processing'] | [ 4.45760041e-01 -1.10987306e-01 -6.73733473e-01 -3.82789105e-01
-7.76811004e-01 -3.47060978e-01 3.07848364e-01 6.13861442e-01
-5.21760166e-01 8.54273021e-01 6.24024332e-01 -4.26383823e-01
-1.65056348e-01 -6.71714306e-01 -6.38088465e-01 -5.73381305e-01
-2.89411247e-01 9.52249989e-02 -3.45141679e-01 6.38242960... | [8.356643676757812, 8.660877227783203] |
0c93f3f7-ead7-4c55-acc1-ca7d573356bd | rfnet-recurrent-forward-network-for-dense | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Huang_RFNet_Recurrent_Forward_Network_for_Dense_Point_Cloud_Completion_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Huang_RFNet_Recurrent_Forward_Network_for_Dense_Point_Cloud_Completion_ICCV_2021_paper.pdf | RFNet: Recurrent Forward Network for Dense Point Cloud Completion | Point cloud completion is an interesting and challenging task in 3D vision, aiming to recover complete shapes from sparse and incomplete point clouds. Existing learning-based methods often require vast computation cost to achieve excellent performance, which limits their practical applications. In this paper, we pr... | ['Yong liu', 'Yifan Xu', 'Yi Yuan', 'Jiangning Zhang', 'Xiangrui Zhao', 'Mengmeng Wang', 'Xuemeng Yang', 'Jinhao Cui', 'Hao Zou', 'Tianxin Huang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [-1.68210298e-01 -4.12422776e-01 2.10716173e-01 -2.76706725e-01
-7.30782568e-01 -4.08787549e-01 4.89230901e-01 -2.57449120e-01
-1.10821120e-01 3.32574770e-02 -2.20829062e-02 -1.20014362e-02
-1.06042691e-01 -8.48099470e-01 -7.50561655e-01 -4.70120579e-01
2.09003195e-01 7.02911377e-01 5.15833437e-01 -5.56978770... | [8.319242477416992, -3.5721988677978516] |
f8754c65-020e-4085-9c49-125f61f65f82 | layoutnet-reconstructing-the-3d-room-layout | 1803.08999 | null | http://arxiv.org/abs/1803.08999v1 | http://arxiv.org/pdf/1803.08999v1.pdf | LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image | We propose an algorithm to predict room layout from a single image that
generalizes across panoramas and perspective images, cuboid layouts and more
general layouts (e.g. L-shape room). Our method operates directly on the
panoramic image, rather than decomposing into perspective images as do recent
works. Our network a... | ['Qi Shan', 'Derek Hoiem', 'Chuhang Zou', 'Alex Colburn'] | 2018-03-23 | layoutnet-reconstructing-the-3d-room-layout-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zou_LayoutNet_Reconstructing_the_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zou_LayoutNet_Reconstructing_the_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [ 3.03714752e-01 -5.36310039e-02 9.10976231e-02 -4.49832112e-01
-2.89268523e-01 -1.01411414e+00 7.07342207e-01 -9.59439129e-02
5.81120923e-02 2.82657862e-01 6.55668795e-01 -7.27290392e-01
-1.66202739e-01 -7.37554848e-01 -9.58094180e-01 -3.94663274e-01
-2.29919806e-01 5.56602597e-01 1.48419783e-01 -6.00042976... | [8.770343780517578, -2.839613676071167] |
b0ffb43c-eb75-4c78-b08e-50d8d55466cc | learning-semantic-representations-in-a-bigram | null | null | https://aclanthology.org/W13-0210 | https://aclanthology.org/W13-0210.pdf | Learning Semantic Representations in a Bigram Language Model | null | ['Jeff Mitchell'] | 2013-03-01 | null | null | null | ws-2013-3 | ['learning-semantic-representations'] | ['methodology'] | [-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... | [-7.295959949493408, 3.663486957550049] |
e223e0dd-fb0f-404c-b9e1-cac19198b502 | aasist-audio-anti-spoofing-using-integrated | 2110.01200 | null | https://arxiv.org/abs/2110.01200v1 | https://arxiv.org/pdf/2110.01200v1.pdf | AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks | Artefacts that differentiate spoofed from bona-fide utterances can reside in spectral or temporal domains. Their reliable detection usually depends upon computationally demanding ensemble systems where each subsystem is tuned to some specific artefacts. We seek to develop an efficient, single system that can detect a b... | ['Nicholas Evans', 'Ha-Jin Yu', 'Bong-Jin Lee', 'Joon Son Chung', 'Hye-jin Shim', 'Hemlata Tak', 'Hee-Soo Heo', 'Jee-weon Jung'] | 2021-10-04 | null | null | null | null | ['voice-anti-spoofing'] | ['audio'] | [ 4.67651844e-01 -2.86444008e-01 2.68015236e-01 -8.98001790e-02
-1.11035228e+00 -7.06817389e-01 7.52139866e-01 6.95373416e-02
-1.74242944e-01 2.08625108e-01 -4.98632193e-02 -6.48302913e-01
-5.30975778e-03 -2.07725212e-01 -5.86189806e-01 -5.85550010e-01
-3.11861992e-01 2.87721157e-01 7.70088971e-01 -4.96973991... | [14.03677749633789, 5.859729290008545] |
2356d00d-9e82-4032-b6d1-9f26f16de0bb | generative-gaitnet | 2201.12044 | null | https://arxiv.org/abs/2201.12044v1 | https://arxiv.org/pdf/2201.12044v1.pdf | Generative GaitNet | Understanding the relation between anatomy andgait is key to successful predictive gait simulation. Inthis paper, we present Generative GaitNet, which isa novel network architecture based on deep reinforce-ment learning for controlling a comprehensive, full-body, musculoskeletal model with 304 Hill-type mus-culotendons... | ['Jehee Lee', 'Moonseok Park', 'Jaedong Lee', 'Phil Sik Chang', 'Sehee Min', 'Jungnam Park'] | 2022-01-28 | null | null | null | null | ['gait-identification'] | ['computer-vision'] | [-3.88101816e-01 1.44955024e-01 -2.07000881e-01 2.13103727e-01
-3.53986025e-01 2.99712215e-02 2.49984398e-01 -7.04110339e-02
-2.64762163e-01 1.13067198e+00 1.55680016e-01 -2.24540561e-01
-3.36793870e-01 -1.04333937e+00 -1.00540340e+00 -6.20738506e-01
-7.04503357e-01 8.08169484e-01 9.80735794e-02 -5.97259760... | [6.927558898925781, 0.04730941727757454] |
aca47fdb-6990-4037-b9a5-5e14bd8ef2fc | progressive-cross-camera-soft-label-learning | 1908.05669 | null | https://arxiv.org/abs/1908.05669v2 | https://arxiv.org/pdf/1908.05669v2.pdf | Progressive Cross-camera Soft-label Learning for Semi-supervised Person Re-identification | In this paper, we focus on the semi-supervised person re-identification (Re-ID) case, which only has the intra-camera (within-camera) labels but not inter-camera (cross-camera) labels. In real-world applications, these intra-camera labels can be readily captured by tracking algorithms or few manual annotations, when co... | ['Yang Gao', 'Lei Qi', 'Jing Huo', 'Yinghuan Shi', 'Lei Wang'] | 2019-08-15 | null | null | null | null | ['semi-supervised-person-re-identification'] | ['computer-vision'] | [ 3.45887035e-01 -3.56854945e-01 -3.80713306e-02 -6.45660520e-01
-5.66155136e-01 -5.91938257e-01 6.77956939e-01 -1.62033856e-01
-6.16157055e-01 7.20963597e-01 1.59036174e-01 2.95947164e-01
-1.08520836e-01 -4.96533364e-01 -4.88579988e-01 -7.92366028e-01
5.85646212e-01 6.80880606e-01 -2.09645316e-01 2.76685029... | [14.77426815032959, 1.0384069681167603] |
05e67cae-c93f-441b-b897-9d31bcb9fac7 | a-fast-semi-automatic-method-for | 2004.08690 | null | https://arxiv.org/abs/2004.08690v1 | https://arxiv.org/pdf/2004.08690v1.pdf | A fast semi-automatic method for classification and counting the number and types of blood cells in an image | A novel and fast semi-automatic method for segmentation, locating and counting blood cells in an image is proposed. In this method, thresholding is used to separate the nucleus from the other parts. We also use Hough transform for circles to locate the center of white cells. Locating and counting of red cells is perfor... | ['Shahram Shirani', 'Hamed Sadeghi', 'David W. Capson'] | 2020-04-18 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [-3.08772270e-02 -3.10351044e-01 2.22620279e-01 -3.33236530e-02
-2.45345592e-01 -5.11576593e-01 1.55104339e-01 8.62610519e-01
-8.46853256e-01 6.83228314e-01 -3.15036625e-01 1.31746501e-01
3.94030243e-01 -9.98092413e-01 1.20016381e-01 -8.57063770e-01
8.34509507e-02 7.89081037e-01 6.47207201e-01 3.90255928... | [14.710326194763184, -3.053816795349121] |
14d427bd-0ff5-436f-90cc-d3af314ef531 | imagearg-a-multi-modal-tweet-dataset-for | 2209.06416 | null | https://arxiv.org/abs/2209.06416v1 | https://arxiv.org/pdf/2209.06416v1.pdf | ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining | The growing interest in developing corpora of persuasive texts has promoted applications in automated systems, e.g., debating and essay scoring systems; however, there is little prior work mining image persuasiveness from an argumentative perspective. To expand persuasiveness mining into a multi-modal realm, we present... | ['Diane Litman', 'Yue Dai', 'Meiqi Guo', 'Zhexiong Liu'] | 2022-09-14 | null | https://aclanthology.org/2022.argmining-1.1 | https://aclanthology.org/2022.argmining-1.1.pdf | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 7.98390985e-01 3.78974944e-01 -6.24227166e-01 -4.78574514e-01
-8.49444330e-01 -3.40247214e-01 1.32451749e+00 4.74499285e-01
-7.26843059e-01 5.98389447e-01 7.67140090e-01 -6.16168797e-01
-3.24012414e-02 -6.95381701e-01 -6.46601737e-01 -4.95607376e-01
4.04211760e-01 2.09580466e-01 4.27939557e-02 -4.42213356... | [8.456421852111816, 10.39653491973877] |
813325b5-e682-4f1c-ac58-8e992873cf1f | gs3d-an-efficient-3d-object-detection | 1903.10955 | null | http://arxiv.org/abs/1903.10955v2 | http://arxiv.org/pdf/1903.10955v2.pdf | GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving | We present an efficient 3D object detection framework based on a single RGB
image in the scenario of autonomous driving. Our efforts are put on extracting
the underlying 3D information in a 2D image and determining the accurate 3D
bounding box of the object without point cloud or stereo data. Leveraging the
off-the-she... | ['Wanli Ouyang', 'Buyu Li', 'Xingyu Zeng', 'Lu Sheng', 'Xiaogang Wang'] | 2019-03-26 | gs3d-an-efficient-3d-object-detection-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_GS3D_An_Efficient_3D_Object_Detection_Framework_for_Autonomous_Driving_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_GS3D_An_Efficient_3D_Object_Detection_Framework_for_Autonomous_Driving_CVPR_2019_paper.pdf | cvpr-2019-6 | ['vehicle-pose-estimation'] | ['computer-vision'] | [ 4.88044918e-02 2.38409340e-01 1.49978727e-01 -2.20282927e-01
-7.79918194e-01 -3.38578224e-01 4.41684455e-01 5.58789186e-02
-6.21204734e-01 1.17538549e-01 -5.17383099e-01 -4.21163797e-01
2.35624582e-01 -7.11372197e-01 -1.00142121e+00 -5.65671921e-01
1.31763995e-01 8.54434848e-01 9.46188092e-01 -3.83924037... | [7.682435989379883, -2.6143765449523926] |
0bfb23a8-8077-402f-b043-88b93dbb1c01 | ecml-an-ensemble-cascade-metric-learning | 2007.05720 | null | https://arxiv.org/abs/2007.05720v1 | https://arxiv.org/pdf/2007.05720v1.pdf | ECML: An Ensemble Cascade Metric Learning Mechanism towards Face Verification | Face verification can be regarded as a 2-class fine-grained visual recognition problem. Enhancing the feature's discriminative power is one of the key problems to improve its performance. Metric learning technology is often applied to address this need, while achieving a good tradeoff between underfitting and overfitti... | ['Yancheng Wang', 'Joey Tianyi Zhou', 'Zhiguo Cao', 'Fu Xiong', 'Yang Xiao', 'Jianxi Wu'] | 2020-07-11 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [-7.26959780e-02 -5.41397393e-01 1.10732829e-02 -5.44729233e-01
-6.71975851e-01 -1.90707505e-01 3.63103658e-01 -3.44187737e-01
-1.18566409e-01 4.69413429e-01 -1.40022859e-02 -1.39517233e-01
-6.22568369e-01 -6.36281013e-01 -1.83040410e-01 -1.01177609e+00
2.89027840e-01 9.45972204e-02 -1.03391752e-01 -1.70094997... | [13.12364387512207, 0.6458603739738464] |
16e463ee-c477-42f5-8841-55a23963f321 | evading-forensic-classifiers-with-attribute-1 | 2306.13091 | null | https://arxiv.org/abs/2306.13091v1 | https://arxiv.org/pdf/2306.13091v1.pdf | Evading Forensic Classifiers with Attribute-Conditioned Adversarial Faces | The ability of generative models to produce highly realistic synthetic face images has raised security and ethical concerns. As a first line of defense against such fake faces, deep learning based forensic classifiers have been developed. While these forensic models can detect whether a face image is synthetic or real ... | ['Karthik Nandakumar', 'Koushik Srivatsan', 'Fahad Shamshad'] | 2023-06-22 | evading-forensic-classifiers-with-attribute | http://openaccess.thecvf.com//content/CVPR2023/html/Shamshad_Evading_Forensic_Classifiers_With_Attribute-Conditioned_Adversarial_Faces_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shamshad_Evading_Forensic_Classifiers_With_Attribute-Conditioned_Adversarial_Faces_CVPR_2023_paper.pdf | cvpr-2023-1 | ['meta-learning'] | ['methodology'] | [ 5.94686568e-01 3.61279756e-01 1.42221510e-01 -1.65322363e-01
-7.51185954e-01 -1.04791057e+00 6.99187994e-01 -7.19876230e-01
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2.93604821e-01 -9.24571455e-01 -9.19668913e-01 -8.90863597e-01
1.96040615e-01 3.40465367e-01 -3.11661750e-01 -8.84915888... | [12.653654098510742, 0.9638271331787109] |
3aee6623-a5ec-4087-a70f-104649beb130 | gca-net-utilizing-gated-context-attention-for | 2112.04298 | null | https://arxiv.org/abs/2112.04298v3 | https://arxiv.org/pdf/2112.04298v3.pdf | GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection | Forensic analysis of manipulated pixels requires the identification of various hidden and subtle features from images. Conventional image recognition models generally fail at this task because they are biased and more attentive toward the dominant local and spatial features. In this paper, we propose a novel Gated Cont... | ['Md. Ruhul Amin', 'Md. Saiful Islam', 'Sowmen Das'] | 2021-12-08 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 3.21497083e-01 -2.39483401e-01 1.14659965e-01 -4.33854222e-01
-7.76160002e-01 -4.63326365e-01 6.28159702e-01 3.62996429e-01
-5.33553302e-01 3.86536032e-01 1.61667541e-01 -2.43425921e-01
6.34802580e-02 -6.64742470e-01 -4.92387772e-01 -6.69920266e-01
-1.19469404e-01 2.35611331e-02 3.33394140e-01 9.04599428... | [12.386143684387207, 1.0156409740447998] |
0deb9cf2-6a02-4c7f-aecb-a12da50f7f4a | data-augmentation-with-paraphrase-generation | 2205.04006 | null | https://arxiv.org/abs/2205.04006v1 | https://arxiv.org/pdf/2205.04006v1.pdf | Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System | Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. Our goal is to build Artificial Intelligence (AI) systems that can assist children in their learning process. Within such complex frameworks, Spoken Dialogue Systems (SDS) are crucial building blocks to... | ['Lama Nachman', 'Saurav Sahay', 'Eda Okur'] | 2022-05-09 | null | https://aclanthology.org/2022.lrec-1.437 | https://aclanthology.org/2022.lrec-1.437.pdf | lrec-2022-6 | ['paraphrase-generation', 'intent-recognition', 'paraphrase-generation', 'spoken-dialogue-systems'] | ['computer-code', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 3.54775071e-01 8.17534983e-01 2.34138682e-01 -5.87031126e-01
-4.74971443e-01 -6.42274618e-01 8.73300552e-01 5.50650477e-01
-5.09559333e-01 5.79767227e-01 6.13083303e-01 -5.39120257e-01
1.15506098e-01 -8.52718174e-01 -2.48507395e-01 -2.31113899e-02
1.07698783e-01 8.61330688e-01 4.29669142e-01 -8.67360294... | [12.584705352783203, 7.9947075843811035] |
1c1b8e5d-a41a-43f6-8c65-80ec1efb04f6 | image-to-image-regression-with-distribution | 2202.05265 | null | https://arxiv.org/abs/2202.05265v1 | https://arxiv.org/pdf/2202.05265v1.pdf | Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging | Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that protect against a model's mistakes and hallucinations. To address this, we develop uncertainty quantification techniques with rigorous statistic... | ['Yaniv Romano', 'Srigokul Upadhyayula', 'Thayer Alshaabi', 'Jitendra Malik', 'Michael I Jordan', 'Stephen Bates', 'Amit P Kohli', 'Anastasios N Angelopoulos'] | 2022-02-10 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 4.30726379e-01 3.46694171e-01 5.03005125e-02 -5.47703743e-01
-1.04561365e+00 -1.99310467e-01 2.51223087e-01 1.71964377e-01
-7.16396272e-01 1.18350971e+00 -5.42743087e-01 -1.19890332e-01
-7.60985166e-02 -3.47326964e-01 -9.85188305e-01 -1.04203248e+00
-1.17883980e-01 5.81105828e-01 1.10109307e-01 5.12036026... | [11.758296966552734, -1.753294587135315] |
d1b0eaa5-7d6c-4b81-ad8f-adff93b4d890 | skepxels-spatio-temporal-image-representation | 1711.05941 | null | http://arxiv.org/abs/1711.05941v4 | http://arxiv.org/pdf/1711.05941v4.pdf | Skepxels: Spatio-temporal Image Representation of Human Skeleton Joints for Action Recognition | Human skeleton joints are popular for action analysis since they can be
easily extracted from videos to discard background noises. However, current
skeleton representations do not fully benefit from machine learning with CNNs.
We propose "Skepxels" a spatio-temporal representation for skeleton sequences
to fully exploi... | ['Ajmal Mian', 'Naveed Akhtar', 'Jian Liu'] | 2017-11-16 | null | null | null | null | ['action-analysis'] | ['computer-vision'] | [ 1.42835438e-01 -2.68822968e-01 -3.79902810e-01 -1.21449515e-01
-3.89238507e-01 -1.17418267e-01 4.80250865e-01 -4.43438411e-01
-6.57236218e-01 4.02029812e-01 7.72475958e-01 5.84570169e-01
-3.57317775e-01 -5.77230573e-01 -7.96949565e-01 -7.37912595e-01
-3.49536657e-01 -7.69705558e-03 5.28841019e-01 -7.39683658... | [7.863264560699463, 0.357166588306427] |
1f925c57-e5d4-4246-abc4-3915be6f5947 | pop-mining-potential-performance-of-new | 2207.11001 | null | https://arxiv.org/abs/2207.11001v1 | https://arxiv.org/pdf/2207.11001v1.pdf | POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion | We propose a data-centric pipeline able to generate exogenous observation data for the New Fashion Product Performance Forecasting (NFPPF) problem, i.e., predicting the performance of a brand-new clothing probe with no available past observations. Our pipeline manufactures the missing past starting from a single, avail... | ['Marco Cristani', 'Geri Skenderi', 'Christian Joppi'] | 2022-07-22 | null | null | null | null | ['new-product-sales-forecasting'] | ['time-series'] | [ 6.31624386e-02 -1.94158673e-01 -4.86266375e-01 -6.19350791e-01
-6.58096969e-01 -9.49917018e-01 8.59442174e-01 3.42070907e-01
-2.21361704e-02 4.40166295e-01 3.91420841e-01 2.52957474e-02
-1.92057770e-02 -8.68658662e-01 -1.35958600e+00 -4.59336102e-01
-1.64694473e-01 5.71593106e-01 -2.07716569e-01 -2.19540507... | [7.198333740234375, 2.8070719242095947] |
e0250228-1d48-4bbc-b43e-48fc310720fa | xai-in-the-context-of-predictive-process | 2202.08265 | null | https://arxiv.org/abs/2202.08265v1 | https://arxiv.org/pdf/2202.08265v1.pdf | XAI in the context of Predictive Process Monitoring: Too much to Reveal | Predictive Process Monitoring (PPM) has been integrated into process mining tools as a value-adding task. PPM provides useful predictions on the further execution of the running business processes. To this end, machine learning-based techniques are widely employed in the context of PPM. In order to gain stakeholders tr... | ['Manfred Reichert', 'Mervat Abuelkheir', 'Ghada ElKhawaga'] | 2022-02-16 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 4.50205922e-01 6.13981247e-01 -4.45477486e-01 -5.16074300e-01
8.19102377e-02 -4.04212981e-01 1.00288880e+00 5.55044651e-01
2.38520354e-01 3.05350304e-01 4.05997545e-01 -7.29986370e-01
-7.39887834e-01 -7.87295878e-01 -3.84791851e-01 -1.46600515e-01
1.15478233e-01 4.42931831e-01 -3.33097905e-01 2.61725426... | [8.703179359436035, 5.947995662689209] |
feee920a-a7ab-4ca0-85ba-ed51333fd7a7 | finding-the-optimal-currency-composition-of | 2303.01909 | null | https://arxiv.org/abs/2303.01909v1 | https://arxiv.org/pdf/2303.01909v1.pdf | Finding the Optimal Currency Composition of Foreign Exchange Reserves with a Quantum Computer | Portfolio optimization is an inseparable part of strategic asset allocation at the Czech National Bank. Quantum computing is a new technology offering algorithms for that problem. The capabilities and limitations of quantum computers with regard to portfolio optimization should therefore be investigated. In this paper,... | ['Martin Vesely'] | 2023-03-03 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.04369426e-01 -1.60928831e-01 2.10847929e-01 -6.95950240e-02
-5.48346281e-01 -7.52447963e-01 3.75094622e-01 -2.86618173e-01
-7.57152677e-01 1.20436728e+00 -3.48952234e-01 -7.97062218e-01
-5.30357480e-01 -1.46798873e+00 4.58126329e-02 -9.31452096e-01
-4.31971103e-02 1.16148484e+00 -2.01043099e-01 -8.80668044... | [5.557889461517334, 4.926635265350342] |
a85bd6c3-0e1c-4206-8b52-243b4f8e11c6 | merry-go-round-rotate-a-frame-and-fool-a-dnn | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Thapar_Merry_Go_Round_Rotate_a_Frame_and_Fool_a_DNN_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Thapar_Merry_Go_Round_Rotate_a_Frame_and_Fool_a_DNN_CVPR_2022_paper.pdf | Merry Go Round: Rotate a Frame and Fool a DNN | A large proportion of videos captured today are first per-son videos shot from wearable cameras. Similar to other computer vision tasks, Deep Neural Networks (DNNs) are the workhorse for most state-of-the-art (SOTA) egocentric vision techniques. On the other hand DNNs are known to be susceptible to Adversarial Atta... | ['Chetan Arora', 'Aditya Nigam', 'Daksh Thapar'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['activity-detection'] | ['computer-vision'] | [ 2.43724018e-01 1.19032443e-01 3.03333431e-01 1.35111839e-01
-1.85995400e-02 -8.91083837e-01 5.18703997e-01 -5.31892836e-01
-6.26608849e-01 5.20614088e-01 1.98152199e-01 -8.93262029e-02
2.53199250e-01 -6.32425070e-01 -1.00063670e+00 -9.53216851e-01
-2.65158355e-01 -3.65564853e-01 5.24863064e-01 -3.34272921... | [5.44708776473999, 7.9567131996154785] |
485bc078-487a-4d49-8e7f-630fc07400ef | an-empirical-study-of-the-expressiveness-of | null | null | https://openreview.net/forum?id=CJmMqnXthgX | https://openreview.net/pdf?id=CJmMqnXthgX | An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks | Graph neural networks and graph kernels have achieved great success in solving machine learning problems on graphs. Recently, there has been considerable interest in determining the expressive power mainly of graph neural networks and of graph kernels, to a lesser extent. Most studies have focused on the ability of th... | ['Michalis Vazirgiannis', 'George Panagopoulos', 'Giannis Nikolentzos'] | 2021-01-01 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 1.15874233e-02 2.69333303e-01 -1.34449527e-01 -2.16445237e-01
-1.06137790e-01 -6.81272984e-01 6.64371967e-01 7.83298969e-01
-1.56160071e-01 1.72038600e-01 1.88715786e-01 -5.10122776e-01
-5.25072098e-01 -1.07212007e+00 -3.83582741e-01 -4.14279968e-01
-6.89922392e-01 4.05888259e-01 1.43033341e-01 -8.91793668... | [6.9605231285095215, 6.175890922546387] |
563211c2-7662-487a-a2fa-ac32deaad660 | grit-general-robust-image-task-benchmark | 2204.13653 | null | https://arxiv.org/abs/2204.13653v2 | https://arxiv.org/pdf/2204.13653v2.pdf | GRIT: General Robust Image Task Benchmark | Computer vision models excel at making predictions when the test distribution closely resembles the training distribution. Such models have yet to match the ability of biological vision to learn from multiple sources and generalize to new data sources and tasks. To facilitate the development and evaluation of more gene... | ['Derek Hoiem', 'Aniruddha Kembhavi', 'Ryan Marten', 'Tanmay Gupta'] | 2022-04-28 | null | null | null | null | ['surface-normals-estimation', 'object-categorization'] | ['computer-vision', 'computer-vision'] | [ 5.18688023e-01 -2.80154526e-01 2.01590806e-01 -4.75218683e-01
-6.87110722e-01 -6.17997706e-01 6.27739906e-01 2.27668285e-01
-4.19954568e-01 1.47288099e-01 -3.42432469e-01 -1.40689597e-01
-1.15721874e-01 -2.17830703e-01 -7.36288428e-01 -4.50028121e-01
2.11991578e-01 3.37242216e-01 5.63967884e-01 -1.53873593... | [9.891590118408203, 1.9254257678985596] |
beaaec90-36ed-476e-ac16-94bb378d2b14 | separation-of-line-drawings-based-on-split | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Zou_Separation_of_Line_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Zou_Separation_of_Line_2014_CVPR_paper.pdf | Separation of Line Drawings Based on Split Faces for 3D Object Reconstruction | Reconstructing 3D objects from single line drawings is often desirable in computer vision and graphics applications. If the line drawing of a complex 3D object is decomposed into primitives of simple shape, the object can be easily reconstructed. We propose an effective method to conduct the line drawing separation and... | ['Jianzhuang Liu', 'Heng Yang', 'Changqing Zou'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 1.99930951e-01 -2.17465754e-03 5.61198033e-02 -9.48248506e-02
-1.53532550e-01 -6.80226803e-01 6.23162031e-01 -5.07646203e-01
3.37244272e-01 5.15776217e-01 -1.74004808e-01 -4.25425380e-01
-5.50938211e-02 -8.76726806e-01 -4.29226339e-01 -1.91346228e-01
2.75600612e-01 9.79916811e-01 6.12139285e-01 1.01474263... | [8.780097961425781, -3.4324569702148438] |
6a560026-55f4-46d2-8406-871498dd2f01 | group-channel-pruning-and-spatial-attention | 2306.01526 | null | https://arxiv.org/abs/2306.01526v1 | https://arxiv.org/pdf/2306.01526v1.pdf | Group channel pruning and spatial attention distilling for object detection | Due to the over-parameterization of neural networks, many model compression methods based on pruning and quantization have emerged. They are remarkable in reducing the size, parameter number, and computational complexity of the model. However, most of the models compressed by such methods need the support of special ha... | ['Jiafeng Lu', 'Yongqing Chen', 'Zhuhua Hu', 'Yong Bai', 'Pu Li', 'Yun Chu'] | 2023-06-02 | null | null | null | null | ['quantization', 'model-compression'] | ['methodology', 'methodology'] | [ 0.2092469 -0.24338531 -0.13600956 -0.306323 0.2019245 -0.03241972
-0.05434289 -0.01305179 -0.78836715 0.3046437 -0.31109983 -0.07264562
-0.19400002 -1.1181128 -0.61668915 -0.827889 0.24756986 0.0262234
0.76877123 0.16317877 0.06153166 0.52743113 -1.4818175 0.30245847
0.92493385 1.4016051 0.7... | [8.54625415802002, 3.0406508445739746] |
7c11b262-c0af-4070-9af4-79c1718b046b | dermatologist-level-dermoscopy-skin-cancer | 1810.10348 | null | http://arxiv.org/abs/1810.10348v1 | http://arxiv.org/pdf/1810.10348v1.pdf | Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms | In this paper, the effectiveness and capability of convolutional neural
networks have been studied in the classification of 8 skin diseases. Different
pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152, Inception
v3, InceptionResNet v2) were used and applied on 10135 dermoscopy skin images
in total (H... | ['Amirreza Rezvantalab', 'Somayeh Karimijeshni', 'Habib Safigholi'] | 2018-10-21 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [-2.76720710e-02 2.67403752e-01 -7.12500280e-03 8.95507708e-02
4.83108386e-02 -4.12876904e-01 5.90849876e-01 1.06130771e-01
-4.98224974e-01 8.88077974e-01 -1.69973165e-01 -3.42390656e-01
-4.82938498e-01 -7.45208204e-01 2.02912614e-02 -8.11267614e-01
3.79240699e-02 2.60346830e-01 -7.27669150e-03 -1.43976107... | [15.714210510253906, -3.0189549922943115] |
0a691553-cd5c-4adb-bd07-9f3cf031b602 | context-enriched-molecule-representations | 2305.09481 | null | https://arxiv.org/abs/2305.09481v1 | https://arxiv.org/pdf/2305.09481v1.pdf | Context-enriched molecule representations improve few-shot drug discovery | A central task in computational drug discovery is to construct models from known active molecules to find further promising molecules for subsequent screening. However, typically only very few active molecules are known. Therefore, few-shot learning methods have the potential to improve the effectiveness of this critic... | ['Günter Klambauer', 'Sepp Hochreiter', 'Friedrich Rippmann', 'Daniel Kuhn', 'Lukas Friedrich', 'Philipp Seidl', 'Johannes Schimunek'] | 2023-04-24 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 4.13531333e-01 -2.69918144e-02 -4.02271122e-01 -9.95348245e-02
-6.58636451e-01 -4.54932183e-01 5.56310952e-01 4.98401970e-01
-3.42778563e-01 1.24352837e+00 1.13068022e-01 -5.73638827e-02
-3.96756321e-01 -9.65313554e-01 -8.97426665e-01 -9.32692051e-01
-3.81425805e-02 5.56007743e-01 1.13950700e-01 -2.75125623... | [5.116528034210205, 5.784980297088623] |
8f2c5091-a021-447b-95a6-71c95725542d | generalizing-graph-neural-networks-beyond | 2006.11468 | null | https://arxiv.org/abs/2006.11468v2 | https://arxiv.org/pdf/2006.11468v2.pdf | Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs | We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i.e., in networks where connected nodes may have different class labels and dissimilar features. Many popular GNNs fail to generalize to this setting, and are even outperf... | ['Mark Heimann', 'Jiong Zhu', 'Danai Koutra', 'Yujun Yan', 'Leman Akoglu', 'Lingxiao Zhao'] | 2020-06-20 | null | http://proceedings.neurips.cc/paper/2020/hash/58ae23d878a47004366189884c2f8440-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/58ae23d878a47004366189884c2f8440-Paper.pdf | neurips-2020-12 | ['node-classification-on-non-homophilic'] | ['graphs'] | [-1.89551301e-02 5.43344975e-01 -5.40357590e-01 -2.41570890e-01
3.76936138e-01 -5.24456978e-01 6.43267155e-01 2.97473937e-01
6.31574914e-02 4.73082095e-01 7.32460320e-02 -5.66281080e-01
-3.79018486e-01 -1.32672274e+00 -7.45723665e-01 -5.84161460e-01
-6.36026978e-01 5.88846922e-01 7.37711191e-02 -2.66356319... | [6.969155788421631, 6.172756195068359] |
5a50d6f7-334a-4e0c-9164-3ac0eced06b5 | vos-gan-adversarial-learning-of-visual | 1803.09092 | null | https://arxiv.org/abs/1803.09092v2 | https://arxiv.org/pdf/1803.09092v2.pdf | Adversarial Framework for Unsupervised Learning of Motion Dynamics in Videos | Human behavior understanding in videos is a complex, still unsolved problem and requires to accurately model motion at both the local (pixel-wise dense prediction) and global (aggregation of motion cues) levels. Current approaches based on supervised learning require large amounts of annotated data, whose scarce availa... | ["P. D'Oro", 'D. Giordano', 'C. Spampinato', 'S. Palazzo', 'M. Shah'] | 2018-03-24 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 3.39691758e-01 5.32030035e-03 -3.29083085e-01 -6.62923977e-02
-6.07233822e-01 -5.76125145e-01 9.11581337e-01 -1.63619965e-01
-4.17404741e-01 7.13568211e-01 6.87864050e-02 1.01169311e-01
4.60242964e-02 -8.18232775e-01 -1.01027250e+00 -9.12329912e-01
-4.93438989e-02 4.60400701e-01 5.47656894e-01 5.29595166... | [8.973320007324219, -0.09844426810741425] |
3c87761b-5173-4ffc-a58a-3ba8721d1caa | retrieval-oriented-masking-pre-training | 2210.15133 | null | https://arxiv.org/abs/2210.15133v1 | https://arxiv.org/pdf/2210.15133v1.pdf | Retrieval Oriented Masking Pre-training Language Model for Dense Passage Retrieval | Pre-trained language model (PTM) has been shown to yield powerful text representations for dense passage retrieval task. The Masked Language Modeling (MLM) is a major sub-task of the pre-training process. However, we found that the conventional random masking strategy tend to select a large number of tokens that have l... | ['Pengjun Xie', 'Guangwei Xu', 'Yanzhao Zhang', 'Dingkun Long'] | 2022-10-27 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.13134600e-01 -2.12126270e-01 -2.72920161e-01 7.17871636e-02
-1.17097712e+00 -3.76434475e-01 9.25622106e-01 6.07348800e-01
-7.59944320e-01 6.99909151e-01 4.67401743e-01 -3.80929619e-01
9.96098667e-02 -6.98864877e-01 -5.27735412e-01 -6.08605146e-01
-1.08909801e-01 1.63994655e-01 1.83648482e-01 -3.65131170... | [11.337117195129395, 7.932877063751221] |
a615a2d9-d0c0-471a-88d1-6eb98014e398 | tracking-from-patterns-learning-corresponding | 2010.10051 | null | https://arxiv.org/abs/2010.10051v1 | https://arxiv.org/pdf/2010.10051v1.pdf | Tracking from Patterns: Learning Corresponding Patterns in Point Clouds for 3D Object Tracking | A robust 3D object tracker which continuously tracks surrounding objects and estimates their trajectories is key for self-driving vehicles. Most existing tracking methods employ a tracking-by-detection strategy, which usually requires complex pair-wise similarity computation and neglects the nature of continuous object... | ['Shaojie Shen', 'Peiliang Li', 'Jieqi Shi'] | 2020-10-20 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [-3.75580072e-01 -5.18767059e-01 -2.76179582e-01 -3.13763380e-01
-4.64350313e-01 -8.56672347e-01 8.24798405e-01 1.64064635e-02
-5.31488895e-01 3.51651073e-01 -4.17744935e-01 -2.89626658e-01
1.94895267e-01 -6.77986443e-01 -5.55296838e-01 -4.15195614e-01
2.78820582e-02 9.04218674e-01 1.24001551e+00 -2.42380351... | [6.739437580108643, -2.2889902591705322] |
51017c16-9c5e-4d32-81aa-1d31b415ccac | event-based-motion-segmentation-with-spatio | 2012.08730 | null | https://arxiv.org/abs/2012.08730v3 | https://arxiv.org/pdf/2012.08730v3.pdf | Event-based Motion Segmentation with Spatio-Temporal Graph Cuts | Identifying independently moving objects is an essential task for dynamic scene understanding. However, traditional cameras used in dynamic scenes may suffer from motion blur or exposure artifacts due to their sampling principle. By contrast, event-based cameras are novel bio-inspired sensors that offer advantages to o... | ['Shaojie Shen', 'SiQi Liu', 'Xiuyuan Lu', 'Guillermo Gallego', 'Yi Zhou'] | 2020-12-16 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 5.64885259e-01 -4.40714687e-01 1.36051759e-01 -9.20784175e-02
-2.95719266e-01 -7.26044536e-01 4.03343558e-01 -5.61451167e-02
-7.06320286e-01 5.36267042e-01 -1.98761269e-01 1.29795477e-01
-1.14674591e-01 -5.16723037e-01 -7.05937445e-01 -9.18537736e-01
-9.21519250e-02 1.82345510e-01 7.51583815e-01 4.72628057... | [8.66247844696045, -1.192142128944397] |
166def54-c13e-4aa4-a2b6-96b9abb4915f | image-to-image-translation-via-hierarchical | 2103.01456 | null | https://arxiv.org/abs/2103.01456v1 | https://arxiv.org/pdf/2103.01456v1.pdf | Image-to-image Translation via Hierarchical Style Disentanglement | Recently, image-to-image translation has made significant progress in achieving both multi-label (\ie, translation conditioned on different labels) and multi-style (\ie, generation with diverse styles) tasks. However, due to the unexplored independence and exclusiveness in the labels, existing endeavors are defeated by... | ['Rongrong Ji', 'Yongjian Wu', 'Feiyue Huang', 'Xudong Mao', 'Xiaopeng Hong', 'Liujuan Cao', 'Jie Hu', 'Shengchuan Zhang', 'Xinyang Li'] | 2021-03-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_Image-to-Image_Translation_via_Hierarchical_Style_Disentanglement_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Image-to-Image_Translation_via_Hierarchical_Style_Disentanglement_CVPR_2021_paper.pdf | cvpr-2021-1 | ['multimodal-unsupervised-image-to-image'] | ['computer-vision'] | [ 4.00005221e-01 -8.75223950e-02 -4.05321211e-01 -7.03773737e-01
-7.14156449e-01 -8.70672405e-01 6.31268382e-01 -3.61980796e-01
-2.17293520e-02 6.54458523e-01 4.52768356e-01 -1.87052593e-01
1.77657545e-01 -3.52092683e-01 -4.84601945e-01 -6.42695844e-01
4.28087771e-01 4.45200831e-01 -2.71597445e-01 7.67808259... | [11.225132942199707, 0.1545376479625702] |
31df83f2-1532-4272-87ae-c0203aed05d9 | image-based-fashion-product-recommendation | 1805.08694 | null | http://arxiv.org/abs/1805.08694v2 | http://arxiv.org/pdf/1805.08694v2.pdf | Image Based Fashion Product Recommendation with Deep Learning | We develop a two-stage deep learning framework that recommends fashion images
based on other input images of similar style. For that purpose, a neural
network classifier is used as a data-driven, visually-aware feature extractor.
The latter then serves as input for similarity-based recommendations using a
ranking algor... | ['Markus Haltmeier', 'Hessel Tuinhof', 'Clemens Pirker'] | 2018-05-06 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 1.97286457e-01 -3.61134380e-01 -3.64243716e-01 -7.84153938e-01
-3.28538626e-01 -7.09701121e-01 6.44387007e-01 3.18591714e-01
-2.79136389e-01 6.06877953e-02 3.78048509e-01 -2.64459044e-01
-2.33374283e-01 -9.43334281e-01 -6.91948414e-01 -3.27459812e-01
4.16194826e-01 3.80937427e-01 -5.76831587e-02 -4.06699866... | [11.065834045410156, 0.12605157494544983] |
afb1c3d0-3b37-4715-9486-993eb37cb5e3 | experimental-study-on-reinforcement-learning | 2011.09246 | null | https://arxiv.org/abs/2011.09246v1 | https://arxiv.org/pdf/2011.09246v1.pdf | Experimental Study on Reinforcement Learning-based Control of an Acrobot | We present computational and experimental results on how artificial intelligence (AI) learns to control an Acrobot using reinforcement learning (RL). Thereby the experimental setup is designed as an embedded system, which is of interest for robotics and energy harvesting applications. Specifically, we study the control... | ['Daniel A. Duecker', 'Alexej Bespalko', 'Leo Dostal'] | 2020-11-18 | null | null | null | null | ['acrobot'] | ['playing-games'] | [ 1.63260117e-01 4.64061826e-01 -2.87647724e-01 4.60109532e-01
5.58645308e-01 -6.22283161e-01 7.36073077e-01 1.23889931e-01
-8.44111204e-01 1.13005090e+00 -2.78347939e-01 -1.45838344e-02
-2.71969855e-01 -6.94193542e-01 -5.47422588e-01 -1.36258221e+00
2.70284619e-02 2.19748318e-01 -3.47544342e-01 -3.23864698... | [4.553380012512207, 2.0878703594207764] |
f5818613-896e-416e-b025-b283d6f52398 | on-the-benefits-of-biophysical-synapses | 2303.04944 | null | https://arxiv.org/abs/2303.04944v1 | https://arxiv.org/pdf/2303.04944v1.pdf | On the Benefits of Biophysical Synapses | The approximation capability of ANNs and their RNN instantiations, is strongly correlated with the number of parameters packed into these networks. However, the complexity barrier for human understanding, is arguably related to the number of neurons and synapses in the networks, and to the associated nonlinear transfor... | ['Radu Grosu', 'Julian Lemmel'] | 2023-03-08 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 5.06264567e-01 2.93762892e-01 3.39321285e-01 1.04840770e-01
3.13828677e-01 -8.06819379e-01 6.29311085e-01 9.99612138e-02
-5.18794775e-01 8.91102910e-01 -1.83059067e-01 -4.95699316e-01
-4.23767865e-01 -6.31613791e-01 -7.87807226e-01 -8.67814422e-01
-1.11374870e-01 3.37228209e-01 2.60172367e-01 -3.04377884... | [7.8981122970581055, 3.3096773624420166] |
f3329718-d068-4e61-b227-01c42c29f391 | an-incremental-phase-mapping-approach-for-x | 2211.04011 | null | https://arxiv.org/abs/2211.04011v1 | https://arxiv.org/pdf/2211.04011v1.pdf | An Incremental Phase Mapping Approach for X-ray Diffraction Patterns using Binary Peak Representations | Despite the huge advancement in knowledge discovery and data mining techniques, the X-ray diffraction (XRD) analysis process has mostly remained untouched and still involves manual investigation, comparison, and verification. Due to the large volume of XRD samples from high-throughput XRD experiments, it has become imp... | ['Ankit Agrawal', 'Michael Bedzyk', 'Yip-Wah Chung', 'Alok Choudhary', 'Wei-keng Liao', 'Denis T. Keane', 'Justin Liao', 'Ruifeng Zhang', 'K. V. L. V. Narayanachari', 'Dipendra Jha'] | 2022-11-08 | null | null | null | null | ['x-ray-diffraction'] | ['miscellaneous'] | [ 3.84803772e-01 1.88141428e-02 -2.16688484e-01 -1.65469259e-01
-7.47261107e-01 -2.29658052e-01 4.10340160e-01 7.48778284e-01
-2.31255040e-01 6.83390737e-01 -3.88954103e-01 -4.16035563e-01
-6.03558660e-01 -9.67567682e-01 -1.88620344e-01 -8.65410209e-01
2.74978757e-01 1.09501266e+00 3.75901669e-01 1.78487763... | [5.246114730834961, 5.231610298156738] |
4071887b-bea1-446e-8aaa-4a75d0ced8c4 | a-vae-based-bayesian-bidirectional-lstm-for | 2103.12969 | null | https://arxiv.org/abs/2103.12969v2 | https://arxiv.org/pdf/2103.12969v2.pdf | A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction | The advancement of distributed generation technologies in modern power systems has led to a widespread integration of renewable power generation at customer side. However, the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties. This paper propo... | ['Adnan Anwar', 'Md. Enamul Haque', 'Md. Apel Mahmud', 'Shama Naz Islam', 'Devinder Kaur'] | 2021-03-24 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-5.48451066e-01 -1.77330717e-01 -3.44446898e-02 -3.72683823e-01
-8.90327692e-01 -4.80353385e-01 6.17196918e-01 -5.95082939e-02
1.29170671e-01 1.49362683e+00 1.32397652e-01 -3.39633286e-01
-6.17489100e-01 -1.32811797e+00 -8.01751316e-01 -1.09240103e+00
5.04373722e-02 8.07396054e-01 -3.39098603e-01 1.85745984... | [6.138530254364014, 2.864084243774414] |
62e74e1f-83dd-42e1-9016-965ecd5fa7b4 | proceedings-of-the-1st-international-workshop-6 | 2301.10062 | null | https://arxiv.org/abs/2301.10062v1 | https://arxiv.org/pdf/2301.10062v1.pdf | Proceedings of the 1st International Workshop on Reading Music Systems | The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relev... | ['Alexander Pacha', 'Jan Hajič jr.', 'Jorge Calvo-Zaragoza'] | 2022-12-01 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 4.46038187e-01 -3.97618592e-01 -1.21915758e-01 3.33743125e-01
-8.45007360e-01 -1.07128263e+00 2.12449685e-01 1.53348586e-02
-8.22027549e-02 -2.90078163e-01 2.84925699e-01 8.56997594e-02
-7.91854978e-01 -3.56102675e-01 -9.86525938e-02 -2.50565201e-01
2.58106828e-01 5.12305439e-01 6.78936318e-02 -7.15578422... | [16.00710105895996, 5.238487720489502] |
dc8ea22e-1dc9-4965-b0f9-6dfc7add1e00 | a-simple-and-efficient-deep-scanpath | 2112.04610 | null | https://arxiv.org/abs/2112.04610v1 | https://arxiv.org/pdf/2112.04610v1.pdf | A Simple and efficient deep Scanpath Prediction | Visual scanpath is the sequence of fixation points that the human gaze travels while observing an image, and its prediction helps in modeling the visual attention of an image. To this end several models were proposed in the literature using complex deep learning architectures and frameworks. Here, we explore the effici... | ['Aladine Chetouani', 'Mohamed Amine Kerkouri'] | 2021-12-08 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [-2.84984298e-02 -5.59697896e-02 -3.39404494e-01 -4.41995680e-01
2.44743258e-01 -3.98615330e-01 5.89452505e-01 -2.53694475e-01
-5.68556964e-01 4.41794902e-01 6.17877766e-02 -5.14868736e-01
-2.54399538e-01 -3.41474205e-01 -9.17467296e-01 -5.96919835e-01
-1.14336796e-01 2.97555774e-01 4.24796551e-01 -2.58982837... | [10.066036224365234, 1.4815974235534668] |
b7b9e658-da68-463e-8362-d22169aa6d59 | spike-flownet-event-based-optical-flow | 2003.06696 | null | https://arxiv.org/abs/2003.06696v3 | https://arxiv.org/pdf/2003.06696v3.pdf | Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks | Event-based cameras display great potential for a variety of tasks such as high-speed motion detection and navigation in low-light environments where conventional frame-based cameras suffer critically. This is attributed to their high temporal resolution, high dynamic range, and low-power consumption. However, conventi... | ['Kaushik Roy', 'Adarsh Kumar Kosta', 'Kenneth Chaney', 'Kostas Daniilidis', 'Alex Zihao Zhu', 'Chankyu Lee'] | 2020-03-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6736_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740358.pdf | eccv-2020-8 | ['motion-detection', 'event-based-optical-flow'] | ['computer-vision', 'computer-vision'] | [ 1.29454479e-01 -5.67454755e-01 2.31981218e-01 -1.21985644e-01
-8.78179669e-02 -3.80276144e-01 5.29820919e-01 -1.84296936e-01
-7.64634907e-01 6.81358993e-01 -3.40599529e-02 6.30548820e-02
2.45037571e-01 -5.63600421e-01 -6.91408396e-01 -6.83896363e-01
2.92107940e-01 -2.13701218e-01 6.42620981e-01 1.12813219... | [8.665432929992676, -1.1606724262237549] |
9da51c36-2be5-4a6b-99ac-ede16d9bb2a3 | all-in-1-short-text-classification-with-one | 1710.09589 | null | http://arxiv.org/abs/1710.09589v1 | http://arxiv.org/pdf/1710.09589v1.pdf | ALL-IN-1: Short Text Classification with One Model for All Languages | We present ALL-IN-1, a simple model for multilingual text classification that
does not require any parallel data. It is based on a traditional Support Vector
Machine classifier exploiting multilingual word embeddings and character
n-grams. Our model is simple, easily extendable yet very effective, overall
ranking 1st (... | ['Barbara Plank'] | 2017-10-26 | null | null | null | null | ['multilingual-word-embeddings', 'multilingual-text-classification'] | ['methodology', 'miscellaneous'] | [-6.46601260e-01 -3.33499402e-01 -7.01109052e-01 -4.22266245e-01
-9.76532161e-01 -9.08014357e-01 7.56743252e-01 9.43792522e-01
-1.04385781e+00 8.53858888e-01 5.71303606e-01 -1.05537117e+00
1.65602267e-01 -1.39712244e-01 -3.00354809e-01 -1.36485711e-01
1.84118718e-01 9.39291239e-01 -7.09674880e-02 -8.56588364... | [10.794553756713867, 9.83547306060791] |
4b3fb386-e3f0-4722-9276-eb3faadef1ed | trustguard-gnn-based-robust-and-explainable | 2306.13339 | null | https://arxiv.org/abs/2306.13339v1 | https://arxiv.org/pdf/2306.13339v1.pdf | TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support | Trust evaluation assesses trust relationships between entities and facilitates decision-making. Machine Learning (ML) shows great potential for trust evaluation owing to its learning capabilities. In recent years, Graph Neural Networks (GNNs), as a new ML paradigm, have demonstrated superiority in dealing with graph da... | ['Witold Pedrycz', 'Elisa Bertino', 'Jiahe Lan', 'Zheng Yan', 'Jie Wang'] | 2023-06-23 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-1.03584170e+00 -1.63206100e-01 -3.05671275e-01 -4.59657878e-01
4.55063015e-01 -2.71611452e-01 4.27922219e-01 5.60358942e-01
6.63459301e-02 4.19881701e-01 -8.94372091e-02 -6.96260870e-01
-1.46227181e-01 -9.07888949e-01 -3.78818899e-01 -3.21732879e-01
-7.43442833e-01 -4.41620499e-02 5.27821541e-01 -3.99250329... | [7.111410617828369, 6.420343399047852] |
ec7ab246-d86a-4833-a8cb-c2bcf9c0abb3 | multi-image-summarization-textual-summary | 2006.08686 | null | https://arxiv.org/abs/2006.08686v1 | https://arxiv.org/pdf/2006.08686v1.pdf | Multi-Image Summarization: Textual Summary from a Set of Cohesive Images | Multi-sentence summarization is a well studied problem in NLP, while generating image descriptions for a single image is a well studied problem in Computer Vision. However, for applications such as image cluster labeling or web page summarization, summarizing a set of images is also a useful and challenging task. This ... | ['Sebastian Goodman', 'Kazoo Sone', 'Radu Soricut', 'Nicholas Trieu', 'Pradyumna Narayana'] | 2020-06-15 | null | null | null | null | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 7.55552411e-01 5.29234052e-01 6.93319887e-02 -4.87943500e-01
-1.29343486e+00 -3.81861597e-01 6.67539716e-01 3.73162031e-01
-2.16618150e-01 5.39399922e-01 5.75268149e-01 1.97459459e-01
2.23575369e-01 -3.84381175e-01 -9.74096119e-01 -6.12080097e-01
2.21722543e-01 5.33072650e-01 -2.89919470e-02 6.18367083... | [11.047393798828125, 0.7834670543670654] |
15d9574e-d856-43ae-944c-f8f435c4cbcf | two-pass-discourse-segmentation-with-pairing | 1407.8215 | null | http://arxiv.org/abs/1407.8215v1 | http://arxiv.org/pdf/1407.8215v1.pdf | Two-pass Discourse Segmentation with Pairing and Global Features | Previous attempts at RST-style discourse segmentation typically adopt
features centered on a single token to predict whether to insert a boundary
before that token. In contrast, we develop a discourse segmenter utilizing a
set of pairing features, which are centered on a pair of adjacent tokens in the
sentence, by equa... | ['Graeme Hirst', 'Vanessa Wei Feng'] | 2014-07-30 | null | null | null | null | ['discourse-segmentation'] | ['natural-language-processing'] | [ 4.68869984e-01 5.87770522e-01 -3.64675790e-01 -3.66997898e-01
-1.03011084e+00 -8.15852106e-01 8.59575987e-01 7.56003976e-01
-4.55806285e-01 6.45845652e-01 3.47536922e-01 -2.23885491e-01
2.81528085e-01 -7.10802257e-01 -5.67919493e-01 -4.40470695e-01
3.43053713e-02 3.60754371e-01 6.34290874e-01 -4.09494698... | [10.782076835632324, 9.480657577514648] |
764f5ee0-310f-4d73-af88-5fefe8bfecbb | text-summarization-with-oracle-expectation | 2209.12714 | null | https://arxiv.org/abs/2209.12714v1 | https://arxiv.org/pdf/2209.12714v1.pdf | Text Summarization with Oracle Expectation | Extractive summarization produces summaries by identifying and concatenating the most important sentences in a document. Since most summarization datasets do not come with gold labels indicating whether document sentences are summary-worthy, different labeling algorithms have been proposed to extrapolate oracle extract... | ['Mirella Lapata', 'Yumo Xu'] | 2022-09-26 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 6.13944888e-01 4.65171933e-01 -5.52293897e-01 -5.97232640e-01
-1.61735332e+00 -8.52776945e-01 6.07167482e-01 4.78940278e-01
-1.97965905e-01 9.43194985e-01 7.83003509e-01 -2.80296445e-01
1.06115706e-01 -4.78303462e-01 -6.53329074e-01 -4.46297169e-01
3.14261079e-01 4.91533697e-01 -5.33587970e-02 2.32736170... | [12.434913635253906, 9.426795959472656] |
91326238-beb7-433b-91e2-d213077aa383 | overcoming-catastrophic-forgetting-by-xai | 2211.14177 | null | https://arxiv.org/abs/2211.14177v1 | https://arxiv.org/pdf/2211.14177v1.pdf | Overcoming Catastrophic Forgetting by XAI | Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life. Taking the advantages of interpretable machine learning (interpretable ML), this work proposes a novel tool called Catastrophic Forgetting Disse... | ['Giang Nguyen'] | 2022-11-25 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 7.03343451e-02 4.77139771e-01 5.92335537e-02 -1.80824891e-01
2.17750296e-01 -4.89624113e-01 4.81711239e-01 -1.10851778e-02
-2.93652087e-01 1.12712920e+00 -1.51010230e-01 -5.48780799e-01
-2.77045012e-01 -5.32308638e-01 -1.08091044e+00 -6.92484796e-01
-1.56075045e-01 4.53560084e-01 2.63831824e-01 -2.60845304... | [9.777450561523438, 3.4199485778808594] |
d81a8dd8-6798-4797-8f14-fb823ad9130b | balanced-filtering-via-non-disclosive-proxies | 2306.15083 | null | https://arxiv.org/abs/2306.15083v2 | https://arxiv.org/pdf/2306.15083v2.pdf | Balanced Filtering via Non-Disclosive Proxies | We study the problem of non-disclosively collecting a sample of data that is balanced with respect to sensitive groups when group membership is unavailable or prohibited from use at collection time. Specifically, our collection mechanism does not reveal significantly more about group membership of any individual sample... | ['Aaron Roth', 'Michael Kearns', 'Emily Diana', 'Siqi Deng'] | 2023-06-26 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 4.03479666e-01 3.44005018e-01 -8.60528886e-01 -6.67238235e-01
-1.20260990e+00 -1.33551741e+00 2.13435784e-01 6.31638169e-01
-7.56492078e-01 1.09810233e+00 5.67819551e-02 -4.26061630e-01
-8.30889121e-02 -1.05793011e+00 -9.87486839e-01 -7.31384575e-01
-5.56977727e-02 6.11674309e-01 -1.11801215e-01 5.66903532... | [5.990772247314453, 6.8665337562561035] |
7d4b611f-31b4-4b93-b706-462182904b88 | building-a-knowledge-based-dialogue-system | null | null | https://aclanthology.org/2022.sigdial-1.25 | https://aclanthology.org/2022.sigdial-1.25.pdf | Building a Knowledge-Based Dialogue System with Text Infilling | In recent years, generation-based dialogue systems using state-of-the-art (SoTA) transformer-based models have demonstrated impressive performance in simulating human-like conversations. To improve the coherence and knowledge utilization capabilities of dialogue systems, knowledge-based dialogue systems integrate retri... | ['Yasuo Ariki', 'Tetsuya Takiguchi', 'Qiang Xue'] | null | null | null | null | sigdial-acl-2022-9 | ['text-infilling'] | ['natural-language-processing'] | [-1.82565693e-02 7.53884494e-01 -2.56052073e-02 -1.67199001e-01
-6.48750186e-01 -5.64285338e-01 8.23661864e-01 4.95542623e-02
-5.16611785e-02 1.27299523e+00 7.74438143e-01 -5.24970233e-01
2.18290105e-01 -1.19887578e+00 6.51510106e-03 6.65567145e-02
5.02547085e-01 8.94063890e-01 3.99243802e-01 -9.90728915... | [12.571342468261719, 8.14500904083252] |
bb01c0a5-8724-46c7-acb0-719e87c67dc6 | counterfactually-comparing-abstaining | 2305.10564 | null | https://arxiv.org/abs/2305.10564v1 | https://arxiv.org/pdf/2305.10564v1.pdf | Counterfactually Comparing Abstaining Classifiers | Abstaining classifiers have the option to abstain from making predictions on inputs that they are unsure about. These classifiers are becoming increasingly popular in high-stake decision-making problems, as they can withhold uncertain predictions to improve their reliability and safety. When evaluating black-box abstai... | ['Aaditya Ramdas', 'Aditya Gangrade', 'Yo Joong Choe'] | 2023-05-17 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 3.86818349e-01 7.63638198e-01 -7.50643492e-01 -4.03185397e-01
-7.52613783e-01 -5.67166388e-01 6.38992786e-01 -4.54351269e-02
-5.52636385e-01 1.20402896e+00 8.87960866e-02 -1.09089243e+00
-1.72197700e-01 -6.05635941e-01 -9.88742590e-01 -7.93906152e-01
1.12991728e-01 1.96076229e-01 -2.04058215e-01 2.38767192... | [8.537556648254395, 5.456897258758545] |
f9cc362b-8757-4784-97f4-14de22c12a1d | effective-spectral-unmixing-via-robust | 1409.0685 | null | http://arxiv.org/abs/1409.0685v4 | http://arxiv.org/pdf/1409.0685v4.pdf | Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity | Hyperspectral unmixing (HU) plays a fundamental role in a wide range of
hyperspectral applications. It is still challenging due to the common presence
of outlier channels and the large solution space. To address the above two
issues, we propose a novel model by emphasizing both robust representation and
learning-based ... | ['Chunhong Pan', 'Ying Wang', 'Feiyun Zhu', 'Gaofeng Meng', 'Bin Fan'] | 2014-09-02 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 3.57224196e-01 -2.26036206e-01 -4.47170250e-02 2.97980495e-02
-7.91567862e-01 -9.14685950e-02 9.69711766e-02 -8.69836584e-02
-1.01724371e-01 7.48306274e-01 -1.09412381e-02 -2.22020783e-02
-4.89286363e-01 -9.25570726e-01 -6.38021588e-01 -1.32735527e+00
-6.71318695e-02 -1.50157824e-01 -2.51000047e-01 -1.83751255... | [10.29332160949707, -2.0657832622528076] |
7822d5eb-f1f7-458e-9fff-6cd73ae189a8 | albert-a-lite-bert-for-self-supervised | 1909.11942 | null | https://arxiv.org/abs/1909.11942v6 | https://arxiv.org/pdf/1909.11942v6.pdf | ALBERT: A Lite BERT for Self-supervised Learning of Language Representations | Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems, we present two parameter-reduction techn... | ['Zhenzhong Lan', 'Sebastian Goodman', 'Mingda Chen', 'Kevin Gimpel', 'Radu Soricut', 'Piyush Sharma'] | 2019-09-26 | null | https://openreview.net/forum?id=H1eA7AEtvS | https://openreview.net/pdf?id=H1eA7AEtvS | iclr-2020-1 | ['multi-task-language-understanding', 'multimodal-intent-recognition', 'linguistic-acceptability'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-0.1512984 -0.11824317 -0.2719647 -0.58973986 -1.1566037 -0.3328084
0.4144712 0.24489816 -0.4869962 0.76392305 0.58904785 -0.40296945
0.0963416 -0.6527961 -0.6141716 -0.49708918 -0.19847432 0.4986565
0.07836869 -0.49946976 0.39386997 0.04238322 -1.1027365 0.6366613
0.83609146 0.5944886 0.179... | [11.151619911193848, 8.559986114501953] |
8f689fa9-1922-4e3c-93f2-b05f8829717b | self-calibrating-anomaly-and-change-detection | 2209.02379 | null | https://arxiv.org/abs/2209.02379v1 | https://arxiv.org/pdf/2209.02379v1.pdf | Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots | Automatic detection of visual anomalies and changes in the environment has been a topic of recurrent attention in the fields of machine learning and computer vision over the past decades. A visual anomaly or change detection algorithm identifies regions of an image that differ from a reference image or dataset. The maj... | ['Tomi Westerlund', 'Jorge Peña Queralta', 'Sahar Salimpour'] | 2022-08-26 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 3.22085559e-01 -2.18818232e-01 5.54070652e-01 -3.61454576e-01
-2.73731858e-01 -4.07945603e-01 6.57603383e-01 5.36391497e-01
-2.65423089e-01 3.15925747e-01 -3.72711301e-01 8.63890499e-02
-5.80186769e-02 -6.09831870e-01 -1.06512046e+00 -6.59758389e-01
-1.32268086e-01 9.78223607e-02 7.56738544e-01 -2.80560076... | [7.68773078918457, 2.007030487060547] |
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