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65f7da03-e1de-447e-aed5-e950397cb90a | dionysus-recovering-scene-structures-by | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Dionysus_Recovering_Scene_Structures_by_Dividing_Into_Semantic_Pieces_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Dionysus_Recovering_Scene_Structures_by_Dividing_Into_Semantic_Pieces_CVPR_2023_paper.pdf | Dionysus: Recovering Scene Structures by Dividing Into Semantic Pieces | Most existing 3D reconstruction methods result in either detail loss or unsatisfying efficiency. However, effectiveness and efficiency are equally crucial in real-world applications, e.g., autonomous driving and augmented reality. We argue that this dilemma comes from wasted resources on valueless depth samples. Th... | ['Lei Chen', 'Likang Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 2.14905683e-02 2.98691511e-01 -2.20057145e-01 -2.64238387e-01
-9.37856138e-01 -2.65486330e-01 3.46499652e-01 1.29660107e-02
-3.61788213e-01 7.98411548e-01 -5.91791905e-02 -3.01322509e-02
-2.99096823e-01 -8.53539348e-01 -5.80930948e-01 -6.24242902e-01
3.43657620e-02 4.76363569e-01 5.61183155e-01 2.73623206... | [8.760688781738281, -2.1941099166870117] |
c68b3ae3-9619-42f5-b3c9-c1cc4f9165b6 | 3d-bounding-box-estimation-using-deep | 1612.00496 | null | http://arxiv.org/abs/1612.00496v2 | http://arxiv.org/pdf/1612.00496v2.pdf | 3D Bounding Box Estimation Using Deep Learning and Geometry | We present a method for 3D object detection and pose estimation from a single
image. In contrast to current techniques that only regress the 3D orientation
of an object, our method first regresses relatively stable 3D object properties
using a deep convolutional neural network and then combines these estimates
with geo... | ['John Flynn', 'Jana Kosecka', 'Dragomir Anguelov', 'Arsalan Mousavian'] | 2016-12-01 | 3d-bounding-box-estimation-using-deep-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Mousavian_3D_Bounding_Box_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Mousavian_3D_Bounding_Box_CVPR_2017_paper.pdf | cvpr-2017-7 | ['vehicle-pose-estimation', 'viewpoint-estimation'] | ['computer-vision', 'computer-vision'] | [-1.45129450e-02 1.70341820e-01 -6.76354915e-02 -5.02734303e-01
-8.11887026e-01 -8.16188276e-01 6.29493237e-01 3.22628021e-01
-7.49965429e-01 3.74085754e-02 -3.56603593e-01 -3.73617522e-02
2.84546584e-01 -4.73886430e-01 -1.03303194e+00 -4.12491500e-01
-8.16062838e-02 9.14793491e-01 8.19892645e-01 2.88242877... | [7.622430324554443, -2.734161376953125] |
2a140041-1ab5-479d-9ee7-6331beaed57c | do-multilingual-language-models-capture | 2203.09904 | null | https://arxiv.org/abs/2203.09904v1 | https://arxiv.org/pdf/2203.09904v1.pdf | Do Multilingual Language Models Capture Differing Moral Norms? | Massively multilingual sentence representations are trained on large corpora of uncurated data, with a very imbalanced proportion of languages included in the training. This may cause the models to grasp cultural values including moral judgments from the high-resource languages and impose them on the low-resource langu... | ['Kristian Kersting', 'Alexander Fraser', 'Jindřich Libovický', 'Patrick Schramowski', 'Björn Deiseroth', 'Katharina Hämmerl'] | 2022-03-18 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-2.56621540e-01 2.30303809e-01 -1.05229467e-01 -3.84204984e-01
-8.54870856e-01 -7.54487097e-01 7.08244383e-01 5.66934571e-02
-9.04146731e-01 1.14884996e+00 5.81634998e-01 -3.35375428e-01
2.61332095e-01 -5.82048953e-01 -7.14914620e-01 -5.29750466e-01
1.93723828e-01 7.89554417e-01 -4.24363405e-01 -3.70285243... | [9.986949920654297, 10.21242618560791] |
06ccd4f0-2f1a-4890-8796-460578b1dd05 | boosting-few-shot-text-classification-via | 2303.16764 | null | https://arxiv.org/abs/2303.16764v1 | https://arxiv.org/pdf/2303.16764v1.pdf | Boosting Few-Shot Text Classification via Distribution Estimation | Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain. However, directly applying this approach to few-shot t... | ['Xianchao Zhang', 'Hong Yu', 'Hongyang Chen', 'Xiao-Ming Wu', 'Fenglong Ma', 'Siyang Zhao', 'Xiaotong Zhang', 'Feng Zhang', 'Han Liu'] | 2023-03-26 | null | null | null | null | ['few-shot-image-classification', 'few-shot-text-classification'] | ['computer-vision', 'natural-language-processing'] | [ 3.23903680e-01 -2.12938249e-01 -4.31534350e-01 -6.02069557e-01
-8.06295455e-01 -2.22658291e-01 7.12614357e-01 1.52512029e-01
-4.67730343e-01 8.53587389e-01 7.68015087e-02 2.54001975e-01
5.68643175e-02 -7.66009867e-01 -4.85685170e-01 -9.06973481e-01
5.95548451e-01 4.84293431e-01 4.91445541e-01 3.35770249... | [10.192517280578613, 3.372305154800415] |
f2c4264d-da52-4aaa-8b92-2c7249a7da64 | a-generative-approach-to-zero-shot-and-few | 1801.09086 | null | http://arxiv.org/abs/1801.09086v1 | http://arxiv.org/pdf/1801.09086v1.pdf | A Generative Approach to Zero-Shot and Few-Shot Action Recognition | We present a generative framework for zero-shot action recognition where some
of the possible action classes do not occur in the training data. Our approach
is based on modeling each action class using a probability distribution whose
parameters are functions of the attribute vector representing that action
class. In p... | ['Anurag Mittal', 'Piyush Rai', 'Vinay Kumar Verma', 'M Shiva Krishna Reddy', 'Ashish Mishra', 'Arulkumar S'] | 2018-01-27 | null | null | null | null | ['zero-shot-action-recognition', 'few-shot-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.35808271e-01 1.00152954e-01 -6.64106607e-01 -3.75270247e-01
-5.39891005e-01 -4.18816149e-01 8.15403402e-01 -3.62177074e-01
-2.33277231e-01 7.16659367e-01 4.86616611e-01 2.46663064e-01
2.48541180e-02 -8.25948656e-01 -7.08074093e-01 -1.08159006e+00
2.10056141e-01 7.42134988e-01 4.05640811e-01 -1.16532400... | [8.59445571899414, 0.992862343788147] |
3973abee-d499-4267-8a49-b0d0cefc4523 | y-2-net-fcrn-for-acoustic-echo-and-noise | 2103.17189 | null | https://arxiv.org/abs/2103.17189v2 | https://arxiv.org/pdf/2103.17189v2.pdf | Y$^2$-Net FCRN for Acoustic Echo and Noise Suppression | In recent years, deep neural networks (DNNs) were studied as an alternative to traditional acoustic echo cancellation (AEC) algorithms. The proposed models achieved remarkable performance for the separate tasks of AEC and residual echo suppression (RES). A promising network topology is a fully convolutional recurrent n... | ['Tim Fingscheidt', 'Maximilian Strake', 'Jan Franzen', 'Ernst Seidel'] | 2021-03-31 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.82878584e-01 -1.09557472e-01 7.81643033e-01 -1.17046669e-01
-8.66399348e-01 -1.97493941e-01 5.50244749e-01 -3.35355252e-02
-7.06763327e-01 3.69854122e-01 5.41069567e-01 -3.02305400e-01
-3.11155561e-02 -2.47198179e-01 -5.19765258e-01 -9.15359616e-01
-5.14759608e-02 -3.20584744e-01 2.15341315e-01 -3.78953815... | [15.036421775817871, 5.93364143371582] |
84287a5a-12c1-4d9e-ae6a-086b3536d27f | extractive-topical-summarization-with-aspects | null | null | https://openreview.net/forum?id=dTwZya8uNnS | https://openreview.net/pdf?id=dTwZya8uNnS | Extractive Topical Summarization With Aspects | Extractive summarization is a task of highlighting the most important parts of the text. We introduce a new approach to extractive summarization task using hidden topical structure and information about aspects of the text. Experimental results on CNN/DailyMail demonstrate that our approach generates more accurate summ... | ['Anonymous'] | 2021-09-17 | null | null | null | acl-arr-september-2021-9 | ['extractive-summarization'] | ['natural-language-processing'] | [ 2.53800094e-01 5.81745088e-01 -2.90366411e-01 -8.84426683e-02
-1.17508006e+00 -5.71794927e-01 7.62031138e-01 6.54488802e-01
-4.44551080e-01 1.08722496e+00 1.50110793e+00 1.12612180e-01
1.43262178e-01 -5.23920059e-01 -5.62169671e-01 -2.26515040e-01
-7.17808958e-03 2.24151745e-01 8.79960041e-03 -5.43203175... | [12.544774055480957, 9.50643539428711] |
2cdabe5a-607c-4f0c-9605-24864fa531f7 | amigo-sparse-multi-modal-graph-transformer | 2303.00865 | null | https://arxiv.org/abs/2303.00865v2 | https://arxiv.org/pdf/2303.00865v2.pdf | AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images | Processing giga-pixel whole slide histopathology images (WSI) is a computationally expensive task. Multiple instance learning (MIL) has become the conventional approach to process WSIs, in which these images are split into smaller patches for further processing. However, MIL-based techniques ignore explicit information... | ['Ali Bashashati', 'Blake Gilks', 'Alexander Baras', 'Hossein Farahani', 'Haoyang Mi', 'Puria Azadi Moghadam', 'Ramin Nakhli'] | 2023-03-01 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.11927551e-01 1.63839430e-01 -3.52273062e-02 -1.00412793e-01
-1.36834192e+00 -4.23125178e-01 4.32668179e-01 6.39685571e-01
-3.52403075e-01 5.64562559e-01 2.24545226e-01 -3.09429795e-01
-2.23853156e-01 -8.14792871e-01 -4.92507011e-01 -1.32056129e+00
-1.56380668e-01 5.97896039e-01 3.47870976e-01 -1.53189316... | [15.116127967834473, -2.934734344482422] |
c6585cfe-94e1-4e1f-829e-aee4ad6cff9b | tsgp-two-stage-generative-prompting-for | 2211.13515 | null | https://arxiv.org/abs/2211.13515v1 | https://arxiv.org/pdf/2211.13515v1.pdf | TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering | Unsupervised commonsense question answering requires mining effective commonsense knowledge without the rely on the labeled task data. Previous methods typically retrieved from traditional knowledge bases or used pre-trained language models (PrLMs) to generate fixed types of knowledge, which have poor generalization ab... | ['Qi Shi', 'Le Qi', 'Yu Zhang', 'Yueqing Sun'] | 2022-11-24 | null | null | null | null | ['answer-generation'] | ['natural-language-processing'] | [ 2.97085226e-01 4.39662576e-01 -9.25846249e-02 -4.04764444e-01
-5.83256483e-01 -6.68661714e-01 4.97612894e-01 1.77646488e-01
-3.84780943e-01 8.89159560e-01 2.44157538e-01 -6.37181520e-01
-1.67433962e-01 -1.22538257e+00 -4.31025147e-01 -8.50974843e-02
6.58923984e-01 4.86760408e-01 3.20620179e-01 -6.68070257... | [10.105185508728027, 8.010886192321777] |
20956aa8-4983-47d3-8a83-4638a50f8524 | hst-mrf-heterogeneous-swin-transformer-with | 2304.04614 | null | https://arxiv.org/abs/2304.04614v1 | https://arxiv.org/pdf/2304.04614v1.pdf | HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation | The Transformer has been successfully used in medical image segmentation due to its excellent long-range modeling capabilities. However, patch segmentation is necessary when building a Transformer class model. This process may disrupt the tissue structure in medical images, resulting in the loss of relevant information... | ['Jin Zhang', 'Hongfang Gong', 'Xiaofei Huang'] | 2023-04-10 | null | null | null | null | ['skin-lesion-segmentation', 'lesion-segmentation', 'long-range-modeling'] | ['medical', 'medical', 'natural-language-processing'] | [ 4.62262779e-01 2.06911445e-01 -2.06169412e-01 -1.02351226e-01
-1.02027977e+00 -2.84101039e-01 7.93580040e-02 1.25511080e-01
-3.07425499e-01 3.32719356e-01 2.89149851e-01 -4.61885370e-02
-1.94608957e-01 -9.31853652e-01 -6.78994596e-01 -1.04452133e+00
-1.84021033e-02 -1.15102999e-01 7.06644416e-01 -1.67461276... | [14.68004035949707, -2.685971260070801] |
e5a74832-ba67-4250-9a44-504b8c1b59b0 | identification-of-stormwater-control | 2305.18630 | null | https://arxiv.org/abs/2305.18630v1 | https://arxiv.org/pdf/2305.18630v1.pdf | Identification of stormwater control strategies and their associated uncertainties using Bayesian Optimization | Dynamic control is emerging as an effective methodology for operating stormwater systems under stress from rapidly evolving weather patterns. Informed by rainfall predictions and real-time sensor measurements, control assets in the stormwater network can be dynamically configured to tune the behavior of the stormwater ... | ['Branko Kerkez', 'Abhiram Mullapudi'] | 2023-05-29 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [ 1.64821267e-01 9.97756049e-03 1.38587326e-01 -3.96279842e-02
-3.10314790e-04 -6.91353559e-01 3.35552007e-01 6.42395318e-01
-1.16352044e-01 9.71538424e-01 -8.52186084e-02 -5.47700226e-01
-8.75076056e-01 -1.41835058e+00 -3.65757495e-01 -9.34906006e-01
-3.39874744e-01 4.36153889e-01 4.53861594e-01 -5.12523770... | [5.5793843269348145, 2.630479097366333] |
a4c144ff-74a8-4a3e-a089-9df36df51488 | video-moment-retrieval-with-text-query | 2106.13566 | null | https://arxiv.org/abs/2106.13566v1 | https://arxiv.org/pdf/2106.13566v1.pdf | Video Moment Retrieval with Text Query Considering Many-to-Many Correspondence Using Potentially Relevant Pair | In this paper we undertake the task of text-based video moment retrieval from a corpus of videos. To train the model, text-moment paired datasets were used to learn the correct correspondences. In typical training methods, ground-truth text-moment pairs are used as positive pairs, whereas other pairs are regarded as ne... | ['Tatsuya Harada', 'Yusuke Mukuta', 'Sho Maeoki'] | 2021-06-25 | null | null | null | null | ['moment-retrieval', 'text-annotation'] | ['computer-vision', 'natural-language-processing'] | [ 2.17829108e-01 -3.57737422e-01 -4.55261648e-01 -4.72909957e-01
-1.00530958e+00 -6.23508334e-01 7.56771266e-01 3.58014435e-01
-3.94927979e-01 6.47352338e-01 3.40734184e-01 2.57793069e-01
1.16837295e-02 -6.72520697e-01 -7.01017439e-01 -6.60748541e-01
1.53251141e-01 4.28005636e-01 3.92010301e-01 -1.12594679... | [10.129617691040039, 0.7494707703590393] |
b9a20abc-0462-4f0b-87a5-52610738d74b | learning-large-margin-sparse-embeddings-for | 2307.04541 | null | https://arxiv.org/abs/2307.04541v1 | https://arxiv.org/pdf/2307.04541v1.pdf | Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis | Fueled by deep learning, computer-aided diagnosis achieves huge advances. However, out of controlled lab environments, algorithms could face multiple challenges. Open set recognition (OSR), as an important one, states that categories unseen in training could appear in testing. In medical fields, it could derive from in... | ['Jicong Zhang', 'Lu Xu', 'Mingyuan Liu'] | 2023-07-10 | null | null | null | null | ['medical-diagnosis', 'open-set-learning'] | ['medical', 'miscellaneous'] | [ 3.17594260e-01 3.10258597e-01 -3.55050951e-01 -1.79813817e-01
-5.48039079e-01 -3.25492144e-01 2.45609090e-01 3.15325022e-01
-1.49463147e-01 6.70290232e-01 8.55669379e-02 -9.51554254e-02
-3.66515905e-01 -6.75046086e-01 -3.55174989e-01 -9.41503465e-01
-6.20515551e-03 2.16751292e-01 1.18869700e-01 -7.35447556... | [9.712736129760742, 2.9170374870300293] |
7961c356-911b-469d-a015-0718ae91a77d | cmt-interpretable-model-for-rapid-recognition | 2210.16584 | null | https://arxiv.org/abs/2210.16584v1 | https://arxiv.org/pdf/2210.16584v1.pdf | CMT: Interpretable Model for Rapid Recognition Pneumonia from Chest X-Ray Images by Fusing Low Complexity Multilevel Attention Mechanism | Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based diagnostic models for pneumonia have been developed to enable computer-aided diagnosis. However, the long training and inference time make them inflexible. In... | ['Chenyang Xue', 'Mengxing Huang', 'Guanjun Wang', 'Sufen Ren', 'Shengchao Chen'] | 2022-10-29 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 1.62896484e-01 -2.53061652e-01 4.41141659e-03 -3.88310373e-01
-5.70551097e-01 -5.29498272e-02 -6.89777359e-02 -4.03510183e-02
-2.40076199e-01 5.16698241e-01 2.64466196e-01 -4.29840773e-01
-4.01104003e-01 -6.04159355e-01 -4.27332520e-01 -8.93221736e-01
1.64537385e-01 5.95664382e-01 4.62279133e-02 3.36885095... | [15.428973197937012, -1.8695292472839355] |
b85885c3-de17-4bac-994b-c9aeaddc5105 | learnable-graph-matching-a-practical-paradigm | 2303.15414 | null | https://arxiv.org/abs/2303.15414v1 | https://arxiv.org/pdf/2303.15414v1.pdf | Learnable Graph Matching: A Practical Paradigm for Data Association | Data association is at the core of many computer vision tasks, e.g., multiple object tracking, image matching, and point cloud registration. Existing methods usually solve the data association problem by network flow optimization, bipartite matching, or end-to-end learning directly. Despite their popularity, we find so... | ['Zhaoxiang Zhang', 'Naiyan Wang', 'Zehao Huang', 'JiaWei He'] | 2023-03-27 | null | null | null | null | ['point-cloud-registration', 'multiple-object-tracking', 'graph-matching'] | ['computer-vision', 'computer-vision', 'graphs'] | [-1.62032619e-01 -1.16365403e-01 -4.27017897e-01 -4.71137673e-01
-6.40826404e-01 -3.16352695e-01 6.27724379e-02 -1.90270729e-02
-4.09671664e-01 3.79531085e-01 -1.58786029e-01 -2.83894658e-01
-3.84984523e-01 -7.51407266e-01 -1.04286432e+00 -4.84277964e-01
-3.65496166e-02 7.06679523e-01 2.32031241e-01 -1.16872072... | [7.180649757385254, 6.39756441116333] |
187197f7-9889-4503-bd8f-1e0bee01b069 | the-ncte-transcripts-a-dataset-of-elementary | 2211.11772 | null | https://arxiv.org/abs/2211.11772v2 | https://arxiv.org/pdf/2211.11772v2.pdf | The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts | Classroom discourse is a core medium of instruction - analyzing it can provide a window into teaching and learning as well as driving the development of new tools for improving instruction. We introduce the largest dataset of mathematics classroom transcripts available to researchers, and demonstrate how this data can ... | ['Heather Hill', 'Dorottya Demszky'] | 2022-11-21 | null | null | null | null | ['elementary-mathematics'] | ['reasoning'] | [-1.80355027e-01 3.18582982e-01 -7.47811317e-01 -6.09209359e-01
-1.20949113e+00 -1.04145753e+00 5.82863390e-01 1.07909262e+00
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-2.26280272e-01 -7.24158347e-01 -6.38718963e-01 -2.83005267e-01
5.17788172e-01 7.83397928e-02 -9.75373238e-02 -1.52747974... | [11.384937286376953, 8.251638412475586] |
87a0ff5d-63f8-4187-835b-4a32d3c0f3b3 | an-adaptive-parameter-estimation-for-guided | 1609.01380 | null | http://arxiv.org/abs/1609.01380v1 | http://arxiv.org/pdf/1609.01380v1.pdf | An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution | Image deconvolution is still to be a challenging ill-posed problem for
recovering a clear image from a given blurry image, when the point spread
function is known. Although competitive deconvolution methods are numerically
impressive and approach theoretical limits, they are becoming more complex,
making analysis, and ... | ['Zhongbo Zhang', 'Hang Yang', 'Yujing Guan'] | 2016-09-06 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 1.89563796e-01 -5.76207519e-01 5.82474709e-01 -9.98882353e-02
-5.93742251e-01 -4.58853960e-01 3.33077013e-01 -6.57321453e-01
-3.60547870e-01 8.96566331e-01 1.84082717e-01 -1.58787087e-01
-3.65891457e-01 -1.84804246e-01 -4.45731640e-01 -1.14717841e+00
2.10578859e-01 3.31475511e-02 1.99398901e-02 2.11688615... | [11.603854179382324, -2.692882776260376] |
0e96c489-0fe1-4fd7-8498-06ff23a09809 | deep-convolutional-sparse-coding-network-for | 2103.05946 | null | https://arxiv.org/abs/2103.05946v1 | https://arxiv.org/pdf/2103.05946v1.pdf | Deep Convolutional Sparse Coding Network for Pansharpening with Guidance of Side Information | Pansharpening is a fundamental issue in remote sensing field. This paper proposes a side information partially guided convolutional sparse coding (SCSC) model for pansharpening. The key idea is to split the low resolution multispectral image into a panchromatic image related feature map and a panchromatic image irrelat... | ['Chunxia Zhang', 'Junmin Liu', 'Lu Huang', 'Zixiang Zhao', 'Kai Sun', 'Jiangshe Zhang', 'Shuang Xu'] | 2021-03-10 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 6.62233114e-01 -3.98222625e-01 -1.83290705e-01 -1.44918039e-01
-3.29664528e-01 -4.13911492e-01 3.84643108e-01 -5.75637579e-01
-2.21597865e-01 7.42151141e-01 3.72221559e-01 -2.51959443e-01
-5.28531849e-01 -1.15365529e+00 -5.97258449e-01 -1.01295650e+00
1.35373101e-01 -1.88440066e-02 -1.08959973e-01 -6.57042861... | [10.160844802856445, -1.9290993213653564] |
d1a5b951-5eaf-4a99-be93-2b469e823b15 | benchmarking-commonsense-knowledge-base | 2109.07679 | null | https://arxiv.org/abs/2109.07679v1 | https://arxiv.org/pdf/2109.07679v1.pdf | Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset | Reasoning over commonsense knowledge bases (CSKB) whose elements are in the form of free-text is an important yet hard task in NLP. While CSKB completion only fills the missing links within the domain of the CSKB, CSKB population is alternatively proposed with the goal of reasoning unseen assertions from external resou... | ['Bin He', 'Yangqiu Song', 'Hongming Zhang', 'Shibo Hao', 'Sehyun Choi', 'Weiqi Wang', 'Tianqing Fang'] | 2021-09-16 | null | https://aclanthology.org/2021.emnlp-main.705 | https://aclanthology.org/2021.emnlp-main.705.pdf | emnlp-2021-11 | ['knowledge-base-population'] | ['natural-language-processing'] | [ 1.41159177e-01 7.15781629e-01 -2.70387709e-01 -4.21669483e-01
-7.03710020e-01 -5.99950731e-01 7.27889657e-01 4.34676707e-01
-3.36767197e-01 1.14666367e+00 4.32535887e-01 -3.80266130e-01
-6.13655746e-02 -1.03519988e+00 -8.93048584e-01 -1.68806836e-01
2.42371038e-01 7.79203475e-01 4.73703623e-01 -5.71484685... | [9.905088424682617, 8.126697540283203] |
35432d63-239e-4d53-aed6-375aaa9c3444 | ceres-pretraining-of-graph-conditioned | null | null | https://openreview.net/forum?id=r_2z0-tufhS | https://openreview.net/pdf?id=r_2z0-tufhS | CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data | User sessions empower many search and recommendation tasks on a daily basis. Such session data are semi-structured, which encode heterogeneous relations between queries and products, and each item is described by the unstructured text. Despite recent advances in self-supervised learning for text or graphs, there lack o... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['session-search'] | ['natural-language-processing'] | [ 3.04909348e-01 3.27694625e-01 -7.53578126e-01 -7.10115910e-01
-4.59831536e-01 -8.35481822e-01 7.44864941e-01 5.64811766e-01
-1.09553300e-01 3.10212702e-01 5.94364405e-01 -3.68182153e-01
-2.14347348e-01 -8.27219784e-01 -8.98318052e-01 1.35080501e-01
-3.06832850e-01 9.83140171e-01 1.88862726e-01 -5.30466020... | [10.953429222106934, 7.308383941650391] |
359f6e88-3772-4bfd-bf6c-ad32c35105eb | discovering-stochastic-partial-differential | 2306.15873 | null | https://arxiv.org/abs/2306.15873v1 | https://arxiv.org/pdf/2306.15873v1.pdf | Discovering stochastic partial differential equations from limited data using variational Bayes inference | We propose a novel framework for discovering Stochastic Partial Differential Equations (SPDEs) from data. The proposed approach combines the concepts of stochastic calculus, variational Bayes theory, and sparse learning. We propose the extended Kramers-Moyal expansion to express the drift and diffusion terms of an SPDE... | ['Souvik Chakraborty', 'Rajdip Nayek', 'Tapas Tripura', 'Yogesh Chandrakant Mathpati'] | 2023-06-28 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 2.35100567e-01 -5.96472800e-01 2.90080100e-01 1.20441265e-01
-5.77487826e-01 -3.84207904e-01 6.54625475e-01 -1.41034245e-01
-1.99669182e-01 1.11665320e+00 -2.64519334e-01 -1.99313059e-01
-2.83694565e-01 -3.62952977e-01 -4.21384484e-01 -1.36624384e+00
-1.74682543e-01 3.97409469e-01 1.61505744e-01 5.19221947... | [6.647765159606934, 3.5862555503845215] |
1d434869-0bd0-497f-bc3c-1e1e0e0af9bd | a-literature-review-of-3d-face-reconstruction | 2110.09299 | null | https://arxiv.org/abs/2110.09299v2 | https://arxiv.org/pdf/2110.09299v2.pdf | A Review of 3D Face Reconstruction From a Single Image | 3D face reconstruction is a challenging problem but also an important task in the field of computer vision and graphics. Recently, many researchers put attention to the problem and a large number of articles have been published. Single image reconstruction is one of the branches of 3D face reconstruction, which has a l... | ['Hanxin Wang'] | 2021-10-13 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.71296492e-01 -2.98573315e-01 -4.13310900e-02 -3.57230872e-01
1.71390802e-01 -2.46411860e-02 3.95523101e-01 -5.94464481e-01
3.22188204e-03 3.03300232e-01 -5.84102459e-02 -2.91391145e-02
2.34502792e-01 -7.27266550e-01 -2.54508674e-01 -5.83710074e-01
2.30624110e-01 2.23350123e-01 2.03797057e-01 -8.02689940... | [13.131649017333984, 0.44553282856941223] |
8e060556-02cd-4708-9de0-1abd7f846080 | supervised-dictionary-learning-with-auxiliary | 2206.06774 | null | https://arxiv.org/abs/2206.06774v1 | https://arxiv.org/pdf/2206.06774v1.pdf | Supervised Dictionary Learning with Auxiliary Covariates | Supervised dictionary learning (SDL) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives. The goal of SDL is to learn a class-discriminative dictionary, which is a set of latent feature vectors that can well-... | ['Weixin Yao', 'Hanbaek Lyu', 'Joowon Lee'] | 2022-06-14 | null | null | null | null | ['pneumonia-detection', 'document-classification'] | ['medical', 'natural-language-processing'] | [ 1.54194251e-01 9.46771912e-03 -3.51315141e-01 -1.85528278e-01
-1.21258318e+00 -4.04853910e-01 6.64461404e-02 9.57842991e-02
-1.58275634e-01 6.52785361e-01 1.51745632e-01 -1.28097028e-01
-6.62991047e-01 -4.83307898e-01 -6.79786682e-01 -1.17990005e+00
-1.40924647e-01 5.52936137e-01 -4.87725139e-01 2.45760810... | [7.139898300170898, 4.440162181854248] |
9ab73237-0696-4934-896c-02757ac1ff70 | troika-multi-path-cross-modal-traction-for | 2303.15230 | null | https://arxiv.org/abs/2303.15230v1 | https://arxiv.org/pdf/2303.15230v1.pdf | Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning | Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs. Relying on learning the joint representation of seen compositions, these methods ignore the explicit modeling of the state and object, thus limit... | ['Donglin Wang', 'Yiliang Lv', 'Yutong Feng', 'Biao Gong', 'Siteng Huang'] | 2023-03-27 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 3.80710453e-01 -2.88953986e-02 -3.66349041e-01 -9.74091738e-02
-6.94128036e-01 -6.26059651e-01 1.20721495e+00 1.94370877e-02
-2.31860518e-01 7.09744543e-02 4.18666631e-01 -9.35827568e-02
2.66532838e-01 -5.28810143e-01 -7.06222653e-01 -6.69772983e-01
5.42150378e-01 4.02271420e-01 6.03057444e-01 -1.25825047... | [10.229567527770996, 2.1892056465148926] |
27507987-c14e-496e-bc62-49d04dd23916 | from-semi-supervised-to-omni-supervised-room | 2301.13865 | null | https://arxiv.org/abs/2301.13865v1 | https://arxiv.org/pdf/2301.13865v1.pdf | From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds | Room layout estimation is a long-existing robotic vision task that benefits both environment sensing and motion planning. However, layout estimation using point clouds (PCs) still suffers from data scarcity due to annotation difficulty. As such, we address the semi-supervised setting of this task based upon the idea of... | ['Hongbin Zha', 'Yurong Chen', 'Guyue Zhou', 'Hao Zhao', 'Xiaoxue Chen', 'Pengfei Li', 'Beiwen Tian', 'Huan-ang Gao'] | 2023-01-31 | null | null | null | null | ['set-matching', 'motion-planning'] | ['computer-vision', 'robots'] | [ 2.52140373e-01 1.88487805e-02 -1.10258400e-01 -5.20636022e-01
-8.33991528e-01 -7.66448140e-01 4.73179936e-01 2.16232777e-01
-3.70150447e-01 4.59505051e-01 1.80458769e-01 -2.14083314e-01
-3.40142518e-01 -5.54099679e-01 -8.46690655e-01 -7.54105449e-01
3.38056982e-02 6.16972983e-01 2.25837499e-01 9.54431089... | [8.174554824829102, -2.8444690704345703] |
4af85b3b-1f83-42a4-b233-3cd464137c3e | fooling-vision-and-language-models-despite | 1709.08693 | null | http://arxiv.org/abs/1709.08693v2 | http://arxiv.org/pdf/1709.08693v2.pdf | Fooling Vision and Language Models Despite Localization and Attention Mechanism | Adversarial attacks are known to succeed on classifiers, but it has been an
open question whether more complex vision systems are vulnerable. In this
paper, we study adversarial examples for vision and language models, which
incorporate natural language understanding and complex structures such as
attention, localizati... | ['Anna Rohrbach', 'Chang Liu', 'Xinyun Chen', 'Dawn Song', 'Xiaojun Xu', 'Trevor Darrell'] | 2017-09-25 | fooling-vision-and-language-models-despite-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Xu_Fooling_Vision_and_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Fooling_Vision_and_CVPR_2018_paper.pdf | cvpr-2018-6 | ['dense-captioning'] | ['computer-vision'] | [ 3.37929696e-01 4.93573517e-01 4.12885904e-01 -1.84200421e-01
-9.03387189e-01 -1.28447604e+00 9.64078605e-01 -3.44782501e-01
-1.56427562e-01 3.66985530e-01 2.26993531e-01 -8.75465453e-01
5.13107955e-01 -7.59091973e-01 -9.99623895e-01 -4.77795869e-01
2.89963633e-02 2.27764934e-01 3.76725852e-01 -3.85035604... | [5.700892448425293, 7.939897060394287] |
f9b3d17a-0194-415b-a338-4745a1a8b887 | are-we-there-yet-evaluating-state-of-the-art | 2007.07455 | null | https://arxiv.org/abs/2007.07455v1 | https://arxiv.org/pdf/2007.07455v1.pdf | Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking Platform | Geoparsing is an important task in geographic information retrieval. A geoparsing system, known as a geoparser, takes some texts as the input and outputs the recognized place mentions and their location coordinates. In June 2019, a geoparsing competition, Toponym Resolution in Scientific Papers, was held as one of the ... | ['Yingjie Hu', 'Jimin Wang'] | 2020-07-15 | null | null | null | null | ['toponym-resolution'] | ['natural-language-processing'] | [-3.71812433e-01 8.88582468e-02 -3.31934243e-01 -7.26776719e-02
-9.94036973e-01 -5.55221319e-01 9.83781397e-01 7.08504200e-01
-7.44803488e-01 9.86074626e-01 3.15587074e-01 -4.67840955e-02
-4.98877853e-01 -9.65797186e-01 -8.37951005e-01 -6.39821708e-01
-2.02030286e-01 8.52635086e-01 1.68239400e-01 -2.15520248... | [9.154876708984375, 9.034463882446289] |
6a664e89-d084-4f62-87e0-ce98d38acfbc | humble-teachers-teach-better-students-for | 2106.10456 | null | https://arxiv.org/abs/2106.10456v1 | https://arxiv.org/pdf/2106.10456v1.pdf | Humble Teachers Teach Better Students for Semi-Supervised Object Detection | We propose a semi-supervised approach for contemporary object detectors following the teacher-student dual model framework. Our method is featured with 1) the exponential moving averaging strategy to update the teacher from the student online, 2) using plenty of region proposals and soft pseudo-labels as the student's ... | ['Yuting Zhang', 'Yijun Luo', 'Weifeng Chen', 'Yihe Tang'] | 2021-06-19 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Tang_Humble_Teachers_Teach_Better_Students_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Tang_Humble_Teachers_Teach_Better_Students_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.38639654e-02 4.48456436e-01 -2.90939033e-01 -3.82560790e-01
-1.37163341e+00 -5.61230242e-01 6.85553670e-01 -2.05212999e-02
-8.31809700e-01 6.52072966e-01 -1.54582009e-01 -1.42388493e-01
4.37554091e-01 -4.25595343e-01 -9.57747281e-01 -7.82193959e-01
2.38703415e-01 8.41814101e-01 9.68287647e-01 4.81416658... | [9.21873664855957, 1.3033549785614014] |
715bdded-c458-4bd8-a30c-9eee355cb846 | training-differentially-private-graph-neural | 2301.00738 | null | https://arxiv.org/abs/2301.00738v1 | https://arxiv.org/pdf/2301.00738v1.pdf | Training Differentially Private Graph Neural Networks with Random Walk Sampling | Deep learning models are known to put the privacy of their training data at risk, which poses challenges for their safe and ethical release to the public. Differentially private stochastic gradient descent is the de facto standard for training neural networks without leaking sensitive information about the training dat... | ['Stephan Günnemann', 'Daniel Zügner', 'Lukas Gosch', 'Jan Schuchardt', 'Morgane Ayle'] | 2023-01-02 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 1.17005013e-01 5.53116202e-01 -2.51658559e-01 -5.23987830e-01
-8.48281443e-01 -9.38588440e-01 2.22129256e-01 2.22920686e-01
-4.17283952e-01 8.28572035e-01 1.41826615e-01 -6.29574299e-01
2.02051565e-01 -1.19294930e+00 -1.08806241e+00 -8.54033351e-01
-4.65867758e-01 1.97677672e-01 -1.62771538e-01 1.72504615... | [6.005266189575195, 7.031274318695068] |
832aa3f0-417c-4204-82d2-2bc4f902e208 | deep-learning-based-fast-and-accurate-3d-ct | 2304.11135 | null | https://arxiv.org/abs/2304.11135v1 | https://arxiv.org/pdf/2304.11135v1.pdf | Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer | Purpose: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is beh... | ['Wei Liu', 'Baoxin Li', 'Steven E. Schild', 'Terence T. Sio', 'Nathan Y. Yu', 'William W. Wong', 'David Liu', 'Zhengliang Liu', 'Jason Holmes', 'Yunze Yang', 'Hongying Feng', 'Yuzhen Ding'] | 2023-04-21 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 2.64344215e-02 3.29377621e-01 6.55297413e-02 -4.18938935e-01
-1.15868890e+00 -5.32597974e-02 1.61284640e-01 1.91408157e-01
-6.02015555e-01 6.27940714e-01 4.58993763e-01 -3.73149842e-01
-1.86399683e-01 -9.01449740e-01 -6.99910998e-01 -1.11213052e+00
7.33290706e-03 7.67176330e-01 2.59385675e-01 3.22167665... | [13.550982475280762, -2.5820529460906982] |
567e2018-8214-4c15-8576-7f336a7ad899 | social-media-writing-style-fingerprint | 1712.04762 | null | http://arxiv.org/abs/1712.04762v3 | http://arxiv.org/pdf/1712.04762v3.pdf | Social Media Writing Style Fingerprint | We present our approach for computer-aided social media text authorship
attribution based on recent advances in short text authorship verification. We
use various natural language techniques to create word-level and
character-level models that act as hidden layers to simulate a simple neural
network. The choice of word... | ['Juliang Li', 'Himank Yadav'] | 2017-12-11 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 2.65799791e-01 2.57879257e-01 -4.38999593e-01 -3.88794869e-01
-4.45920043e-02 -3.84711266e-01 8.34024906e-01 5.37515700e-01
-6.96112096e-01 7.76431739e-01 8.10790285e-02 -6.54719889e-01
2.67778814e-01 -7.05768466e-01 -2.52480716e-01 -1.30158141e-01
2.01371312e-01 1.04850702e-01 -1.95935354e-01 1.00915648... | [9.625229835510254, 10.565197944641113] |
a55f22ba-dfc7-453f-930e-dbaa6d909882 | patient-independent-interictal-epileptiform | 2304.13965 | null | https://arxiv.org/abs/2304.13965v2 | https://arxiv.org/pdf/2304.13965v2.pdf | Patient Independent Interictal Epileptiform Discharge Detection | Epilepsy is a highly prevalent brain condition with many serious complications arising from it. The majority of patients which present to a clinic and undergo electroencephalogram (EEG) monitoring would be unlikely to experience seizures during the examination period, thus the presence of interictal epileptiform discha... | ['Amitava Datta', 'Ghulam Mubashar Hassan', 'Hezam Albaqami', 'Matthew McDougall'] | 2023-04-27 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 1.25899836e-01 -2.75352955e-01 4.90855098e-01 -2.90369928e-01
-6.72312617e-01 -3.97158891e-01 2.38304704e-01 7.10256174e-02
-3.67030084e-01 8.23578477e-01 2.38553509e-02 -2.76513875e-01
-5.07364333e-01 -4.76456881e-01 -3.97987545e-01 -6.48309112e-01
-7.69571900e-01 2.61088550e-01 -1.79481775e-01 3.58043909... | [13.212512969970703, 3.467357873916626] |
8f73a0cd-e4dc-4c95-948e-95215f504455 | glen-general-purpose-event-detection-for | 2303.09093 | null | https://arxiv.org/abs/2303.09093v2 | https://arxiv.org/pdf/2303.09093v2.pdf | GLEN: General-Purpose Event Detection for Thousands of Types | The development of event extraction systems has been hindered by the absence of wide-coverage, large-scale datasets. To make event extraction systems more accessible, we build a general-purpose event detection dataset GLEN, which covers 3,465 different event types, making it over 20x larger in ontology than any current... | ['Jiawei Han', 'Heng Ji', 'Martha Palmer', 'Kathryn Conger', 'Sha Li', 'Qiusi Zhan'] | 2023-03-16 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [-3.77152450e-02 4.57647800e-01 -5.09314597e-01 -3.77768427e-01
-1.06785083e+00 -6.39914930e-01 6.13012671e-01 7.22973049e-01
-7.18049169e-01 9.38588381e-01 7.51184404e-01 -8.35819021e-02
-9.56596136e-02 -7.82055020e-01 -6.39398634e-01 -8.96891654e-02
-1.76782966e-01 5.36173582e-01 6.99214220e-01 -5.56320176... | [9.136513710021973, 9.209160804748535] |
43c5f0ab-2606-485e-b7ee-98ae2b814562 | improving-fairness-in-adaptive-social | 2302.09298 | null | https://arxiv.org/abs/2302.09298v2 | https://arxiv.org/pdf/2302.09298v2.pdf | Improving Fairness in Adaptive Social Exergames via Shapley Bandits | Algorithmic fairness is an essential requirement as AI becomes integrated in society. In the case of social applications where AI distributes resources, algorithms often must make decisions that will benefit a subset of users, sometimes repeatedly or exclusively, while attempting to maximize specific outcomes. How shou... | ['Jichen Zhu', 'Shahin Jabbari', 'Danielle Arigo', 'Santiago Ontañón', 'Diane H. Dallal', 'Thomas B. Fox', 'Jennifer Villareale', 'Robert C. Gray'] | 2023-02-18 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 1.20485704e-02 4.98477787e-01 -8.59704852e-01 -2.97505260e-01
-5.24706721e-01 -3.81685436e-01 -4.82167564e-02 -2.69867219e-02
-8.48313212e-01 1.34380770e+00 2.95305282e-01 -5.62521100e-01
-7.88121939e-01 -6.85697079e-01 -4.48931366e-01 -4.43028688e-01
1.62388012e-01 7.55093873e-01 -2.62545735e-01 -3.04037303... | [4.31564998626709, 3.119462490081787] |
93c194cb-0b1f-4749-8cae-96c6b30f0320 | undecimated-wavelet-transform-for-word | 2307.03679 | null | https://arxiv.org/abs/2307.03679v1 | https://arxiv.org/pdf/2307.03679v1.pdf | Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages | By combining the undecimated wavelet transform within a Word Embedded Semantic Marginal Autoencoder (WESMA), this research study provides a novel strategy for improving security measures and denoising multiple languages. The incorporation of these strategies is intended to address the issues of robustness, privacy, and... | ['Shreyanth S'] | 2023-07-06 | null | null | null | null | ['denoising', 'word-embeddings'] | ['computer-vision', 'methodology'] | [-1.46323353e-01 -4.44031149e-01 -3.15274112e-02 3.22914380e-03
-3.41137439e-01 -5.35067379e-01 6.09518051e-01 4.56431180e-01
-7.69888997e-01 2.63300002e-01 4.65784967e-01 -1.75443947e-01
-3.56305927e-01 -1.10310912e+00 -2.13958308e-01 -8.45825315e-01
-1.75477728e-01 -3.03757638e-01 -1.71929449e-01 -3.73154312... | [10.175972938537598, 8.694068908691406] |
93296783-85aa-4967-93e3-52df56dd244e | sketch-less-for-more-on-the-fly-fine-grained | 2002.10310 | null | https://arxiv.org/abs/2002.10310v4 | https://arxiv.org/pdf/2002.10310v4.pdf | Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval | Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user's query sketch. Its widespread applicability is however hindered by the fact that drawing a sketch takes time, and most people struggle to draw a complete and faithful sketch. In this paper, ... | ['Yi-Zhe Song', 'Ayan Kumar Bhunia', 'Yongxin Yang', 'Timothy M. Hospedales', 'Tao Xiang'] | 2020-02-24 | null | null | null | null | ['sketch-based-image-retrieval', 'on-the-fly-sketch-based-image-retrieval'] | ['computer-vision', 'computer-vision'] | [ 1.42891094e-01 -6.24493837e-01 -3.55883449e-01 -1.33364499e-01
-1.37236524e+00 -7.86121070e-01 7.94058502e-01 -6.95418566e-02
-3.04752499e-01 4.75950539e-01 2.60768086e-01 2.46900603e-01
-4.72501248e-01 -7.95792162e-01 -5.32536924e-01 -5.26055992e-01
3.97486657e-01 5.80332577e-01 6.96006343e-02 -4.44920287... | [11.651276588439941, 0.5857167840003967] |
ea7b53bc-79c7-43a6-aede-31692316e13b | structural-bias-for-aspect-sentiment-triplet | 2209.00820 | null | https://arxiv.org/abs/2209.00820v1 | https://arxiv.org/pdf/2209.00820v1.pdf | Structural Bias for Aspect Sentiment Triplet Extraction | Structural bias has recently been exploited for aspect sentiment triplet extraction (ASTE) and led to improved performance. On the other hand, it is recognized that explicitly incorporating structural bias would have a negative impact on efficiency, whereas pretrained language models (PLMs) can already capture implicit... | ['Dawei Song', 'Wei Wu', 'Jingang Wang', 'Fang Ma', 'Lei Ren', 'Chen Zhang'] | 2022-09-02 | null | https://aclanthology.org/2022.coling-1.585 | https://aclanthology.org/2022.coling-1.585.pdf | coling-2022-10 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 7.33883157e-02 1.95250586e-01 -3.33701462e-01 -4.39349413e-01
-9.01483119e-01 -5.91722250e-01 6.30951881e-01 2.08050847e-01
-7.54362762e-01 5.21619976e-01 4.88075763e-01 -6.09228730e-01
2.03853101e-01 -5.48669994e-01 -6.56090260e-01 -3.78169656e-01
2.97826082e-01 5.51690876e-01 3.52914274e-01 -4.50094700... | [11.357250213623047, 6.793923377990723] |
5918af26-9418-4816-bbce-990c3bc40e91 | a-general-recipe-for-the-analysis-of | 2303.06058 | null | https://arxiv.org/abs/2303.06058v1 | https://arxiv.org/pdf/2303.06058v1.pdf | A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms | In this paper we propose a general methodology to derive regret bounds for randomized multi-armed bandit algorithms. It consists in checking a set of sufficient conditions on the sampling probability of each arm and on the family of distributions to prove a logarithmic regret. As a direct application we revisit two fam... | ['Junya Honda', 'Kazuya Suzuki', 'Dorian Baudry'] | 2023-03-10 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-3.46100777e-02 1.51281267e-01 -7.21169472e-01 -2.33037889e-01
-9.06093121e-01 -1.02708924e+00 2.36400411e-01 1.95795566e-01
-3.65704536e-01 1.24309790e+00 -5.88928834e-02 -7.62586057e-01
-8.80662978e-01 -7.30941951e-01 -9.12682474e-01 -9.02187169e-01
1.11043835e-02 9.98460531e-01 8.93470719e-02 5.27673662... | [4.532642364501953, 3.2965569496154785] |
ff3485cf-eb6b-421a-bc6d-cae084080f26 | bevfusion-multi-task-multi-sensor-fusion-with | 2205.13542 | null | https://arxiv.org/abs/2205.13542v2 | https://arxiv.org/pdf/2205.13542v2.pdf | BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation | Multi-sensor fusion is essential for an accurate and reliable autonomous driving system. Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with camera features. However, the camera-to-LiDAR projection throws away the semantic density of camera features, hindering the effectiveness of s... | ['Song Han', 'Daniela Rus', 'Huizi Mao', 'Xinyu Yang', 'Alexander Amini', 'Haotian Tang', 'Zhijian Liu'] | 2022-05-26 | null | null | null | null | ['scene-segmentation', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-5.77804670e-02 -4.31333125e-01 -9.56964120e-02 -4.48129684e-01
-7.86128581e-01 -9.28248644e-01 6.20084763e-01 1.39702737e-01
-4.69253421e-01 1.50820902e-02 -1.93611339e-01 -3.30523729e-01
2.60129180e-02 -8.86053443e-01 -7.99251497e-01 -4.25487548e-01
5.66862285e-01 3.70156318e-01 8.22339892e-01 -6.07021034... | [7.834085941314697, -2.5369772911071777] |
68405ff9-f46a-408f-91d3-50da74b1366d | active-inference-in-hebbian-learning-networks | 2306.05053 | null | https://arxiv.org/abs/2306.05053v2 | https://arxiv.org/pdf/2306.05053v2.pdf | Active Inference in Hebbian Learning Networks | This work studies how brain-inspired neural ensembles equipped with local Hebbian plasticity can perform active inference (AIF) in order to control dynamical agents. A generative model capturing the environment dynamics is learned by a network composed of two distinct Hebbian ensembles: a posterior network, which infer... | ['Gert Cauwenberghs', 'Georges Gielen', 'Francky Catthoor', 'André Bourdoux', 'Ilja Ocket', 'Lars Keuninckx', 'Tim Verbelen', 'Ali Safa'] | 2023-06-08 | null | null | null | null | ['q-learning', 'openai-gym'] | ['methodology', 'playing-games'] | [ 2.28611007e-01 1.46679834e-01 2.80299067e-01 -1.19393758e-01
3.34432960e-01 -2.85856068e-01 8.12903762e-01 -2.81561673e-01
-6.23749197e-01 1.07490730e+00 2.00885981e-02 -5.10193557e-02
-5.56641877e-01 -8.86255980e-01 -1.05955470e+00 -1.21430099e+00
-1.86181724e-01 6.88496172e-01 3.53963405e-01 -3.77876401... | [4.255686283111572, 1.5647941827774048] |
d32ac06d-3a95-4242-a0c6-1987da7c443f | knowledge-guided-metric-learning-for-few-shot | 2004.01907 | null | https://arxiv.org/abs/2004.01907v1 | https://arxiv.org/pdf/2004.01907v1.pdf | Knowledge Guided Metric Learning for Few-Shot Text Classification | The training of deep-learning-based text classification models relies heavily on a huge amount of annotation data, which is difficult to obtain. When the labeled data is scarce, models tend to struggle to achieve satisfactory performance. However, human beings can distinguish new categories very efficiently with few ex... | ['Kang Liu', 'Dianbo Sui', 'Yubo Chen', 'Jun Zhao', 'Delai Qiu', 'Binjie Mao'] | 2020-04-04 | null | https://aclanthology.org/2021.naacl-main.261 | https://aclanthology.org/2021.naacl-main.261.pdf | naacl-2021-4 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 1.99571878e-01 2.05762461e-01 -1.76928803e-01 -3.56379002e-01
-6.62079602e-02 -8.00435767e-02 8.87198150e-01 3.56134355e-01
-8.22229505e-01 6.29996061e-01 3.34763080e-02 9.89531949e-02
4.65460792e-02 -1.16220403e+00 -1.93450227e-01 -4.36399788e-01
6.18134260e-01 5.93366623e-01 4.47470188e-01 -4.74687427... | [10.22469425201416, 3.585304021835327] |
49df928f-a0a0-43e6-9dad-e19fc7c65b00 | gcn-alp-addressing-matching-collisions-in | 2103.10600 | null | https://arxiv.org/abs/2103.10600v1 | https://arxiv.org/pdf/2103.10600v1.pdf | GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction | Nowadays online users prefer to join multiple social media for the purpose of socialized online service. The problem \textit{anchor link prediction} is formalized to link user data with the common ground on user profile, content and network structure across social networks. Most of the traditional works concentrated on... | ['Xueqi Cheng', 'HuaWei Shen', 'Shanshan Lyu', 'Yongqing Wang', 'Hao Gao'] | 2021-03-19 | null | null | null | null | ['anchor-link-prediction'] | ['graphs'] | [-2.96770543e-01 3.89414161e-01 -6.52097344e-01 -4.66068178e-01
6.35140240e-02 -3.67715538e-01 2.52166599e-01 6.40223384e-01
8.47882554e-02 3.77944738e-01 1.38754115e-01 -2.75460690e-01
-6.99429452e-01 -1.16259682e+00 -4.87004846e-01 -1.64576277e-01
-5.89257061e-01 3.88959557e-01 1.85800746e-01 -3.01856905... | [7.327400207519531, 6.250469207763672] |
b6d05947-8ad1-4065-952a-9fbaaecb0190 | keyphrase-extraction-with-incomplete | null | null | https://aclanthology.org/2021.wnut-1.4 | https://aclanthology.org/2021.wnut-1.4.pdf | Keyphrase Extraction with Incomplete Annotated Training Data | Extracting keyphrases that summarize the main points of a document is a fundamental task in natural language processing. Supervised approaches to keyphrase extraction(KPE) are largely developed based on the assumption that the training data is fully annotated. However, due to the difficulty of keyphrase annotating, KPE... | ['Richong Zhang', 'Guanghui Ma', 'Chunming Hu', 'Yanfei Lei'] | null | null | null | null | wnut-acl-2021-11 | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 1.56524166e-01 6.87055513e-02 -4.91394818e-01 -1.18683353e-02
-1.17579317e+00 -9.90354896e-01 7.13168025e-01 5.77514708e-01
-5.52262962e-01 8.77228916e-01 4.13685352e-01 -2.03290567e-01
-7.57767539e-03 -6.00119054e-01 -8.18430066e-01 -3.55255246e-01
1.49953872e-01 1.06528178e-01 2.73709565e-01 -9.49883536... | [12.292301177978516, 8.88737964630127] |
f2db487a-7457-4ee9-87d3-68d161a3e37d | combining-deep-learning-with-geometric | 2005.05481 | null | https://arxiv.org/abs/2005.05481v2 | https://arxiv.org/pdf/2005.05481v2.pdf | Combining Deep Learning with Geometric Features for Image based Localization in the Gastrointestinal Tract | Tracking monocular colonoscope in the Gastrointestinal tract (GI) is a challenging problem as the images suffer from deformation, blurred textures, significant changes in appearance. They greatly restrict the tracking ability of conventional geometry based methods. Even though Deep Learning (DL) can overcome these issu... | ['Jingwei Song', 'Andreas Girgensohn', 'Mitesh Patel', 'Chelhwon Kim'] | 2020-05-11 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [-2.41197750e-01 -1.23285428e-01 -2.64460415e-01 8.44750926e-02
-6.36631668e-01 -7.28197813e-01 2.85274923e-01 2.36691743e-01
-5.93879342e-01 5.59819877e-01 -6.27646744e-02 -8.71049687e-02
-6.31386414e-02 -6.36168420e-01 -6.83569789e-01 -9.15388525e-01
-1.91213161e-01 3.39252412e-01 4.34068859e-01 -2.74335649... | [13.961085319519043, -3.1316280364990234] |
d1cf0250-4112-4de0-978d-4d27e3181b08 | multi-source-survival-domain-adaptation | 2212.00424 | null | https://arxiv.org/abs/2212.00424v2 | https://arxiv.org/pdf/2212.00424v2.pdf | Multi-Source Survival Domain Adaptation | Survival analysis is the branch of statistics that studies the relation between the characteristics of living entities and their respective survival times, taking into account the partial information held by censored cases. A good analysis can, for example, determine whether one medical treatment for a group of patient... | ['Carolin Lawrence', 'Ammar Shaker'] | 2022-12-01 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 1.28898963e-01 -7.88463578e-02 -5.28433383e-01 -6.08302295e-01
-7.03996360e-01 -6.33727193e-01 3.88762295e-01 6.06596351e-01
-5.15005171e-01 1.24263704e+00 5.84144413e-01 -1.95480749e-01
-5.14635384e-01 -6.57635331e-01 -3.47295284e-01 -9.62404847e-01
-1.15656219e-01 8.10992718e-01 -9.28904265e-02 -8.80185515... | [7.7536725997924805, 5.546572208404541] |
70e923ba-d885-4b9e-9efa-2f096d8adcb9 | digit-recognition-in-handwritten-weather | 1304.6933 | null | http://arxiv.org/abs/1304.6933v2 | http://arxiv.org/pdf/1304.6933v2.pdf | Digit Recognition in Handwritten Weather Records | This paper addresses the automatic recognition of handwritten temperature
values in weather records. The localization of table cells is based on line
detection using projection profiles. Further, a stroke-preserving line removal
method which is based on gradient images is proposed. The presented digit
recognition utili... | ['Robert Sablatnig', 'Manuel Keglevic'] | 2013-04-25 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 1.15979224e-01 -6.56201363e-01 1.56066582e-01 -5.34924686e-01
-1.80363670e-01 -8.16097319e-01 5.83696425e-01 2.59763360e-01
-6.54334307e-01 6.38938010e-01 -4.35249299e-01 -3.30980897e-01
1.12338133e-01 -9.75032508e-01 -2.66813815e-01 -8.12731922e-01
-7.15672150e-02 -2.66705733e-02 2.29894057e-01 -1.11888774... | [11.841126441955566, 2.655866861343384] |
8bdcbe38-cef3-4988-977f-819ac702c7f1 | octree-transformer-autoregressive-3d-shape | 2111.12480 | null | https://arxiv.org/abs/2111.12480v1 | https://arxiv.org/pdf/2111.12480v1.pdf | Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences | Autoregressive models have proven to be very powerful in NLP text generation tasks and lately have gained popularity for image generation as well. However, they have seen limited use for the synthesis of 3D shapes so far. This is mainly due to the lack of a straightforward way to linearize 3D data as well as to scaling... | ['Leif Kobbelt', 'Gregor Kobsik', 'Moritz Ibing'] | 2021-11-24 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 3.16653848e-01 1.20644487e-01 1.50445491e-01 1.29860610e-01
-8.13092649e-01 -6.57146811e-01 9.56084907e-01 8.88808072e-02
-1.24828011e-01 7.20655680e-01 3.67576957e-01 -3.85495305e-01
8.45288113e-02 -1.08494246e+00 -5.76120019e-01 -6.03097618e-01
5.39188609e-02 9.09139514e-01 1.58875570e-01 -2.47319981... | [8.9655179977417, -3.614137649536133] |
9703bf69-c0df-43d8-abea-c1bec40d6ba2 | recommendation-systems-in-libraries-an | 2303.11746 | null | https://arxiv.org/abs/2303.11746v1 | https://arxiv.org/pdf/2303.11746v1.pdf | Recommendation Systems in Libraries: an Application with Heterogeneous Data Sources | The Reading&Machine project exploits the support of digitalization to increase the attractiveness of libraries and improve the users' experience. The project implements an application that helps the users in their decision-making process, providing recommendation system (RecSys)-generated lists of books the users might... | ['Marco Mellia', 'Luca Vassio', 'Greta Vallero', 'Alessandro Speciale'] | 2023-03-21 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-5.18143415e-01 -3.90575714e-02 -2.76174009e-01 -2.54505455e-01
-7.26889610e-01 -7.39042044e-01 9.62766349e-01 5.85444450e-01
-4.87952262e-01 4.19948757e-01 8.01966548e-01 -4.06663150e-01
-5.05276918e-01 -1.13168430e+00 -4.50542033e-01 -1.90506831e-01
2.11479217e-01 4.94983912e-01 2.80424863e-01 -5.94820678... | [10.063895225524902, 5.790229320526123] |
d69dbeb7-d8bb-4e5b-b5f9-8b5dddd4d962 | distributional-reinforcement-learning-for-1 | 1912.08517 | null | https://arxiv.org/abs/1912.08517v1 | https://arxiv.org/pdf/1912.08517v1.pdf | Distributional Reinforcement Learning for Energy-Based Sequential Models | Global Autoregressive Models (GAMs) are a recent proposal [Parshakova et al., CoNLL 2019] for exploiting global properties of sequences for data-efficient learning of seq2seq models. In the first phase of training, an Energy-Based model (EBM) over sequences is derived. This EBM has high representational power, but is u... | ['Jean-Marc Andreoli', 'Marc Dymetman', 'Tetiana Parshakova'] | 2019-12-18 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [ 4.26195830e-01 1.87897176e-01 -3.62184078e-01 -1.97671294e-01
-1.00781989e+00 -6.27704620e-01 7.45448709e-01 -3.38000990e-03
-7.64076293e-01 1.01401520e+00 5.46278596e-01 -3.72984469e-01
-3.34679335e-02 -6.68098927e-01 -5.68373561e-01 -7.29793012e-01
9.68525559e-02 3.33619535e-01 7.36424774e-02 -2.81914234... | [11.895626068115234, 9.174835205078125] |
579cd518-42c6-4539-b814-e33e20c32413 | a-case-study-for-compliance-as-code-with | 2302.01842 | null | https://arxiv.org/abs/2302.01842v1 | https://arxiv.org/pdf/2302.01842v1.pdf | A Case Study for Compliance as Code with Graphs and Language Models: Public release of the Regulatory Knowledge Graph | The paper presents a study on using language models to automate the construction of executable Knowledge Graph (KG) for compliance. The paper focuses on Abu Dhabi Global Market regulations and taxonomy, involves manual tagging a portion of the regulations, training BERT-based models, which are then applied to the rest ... | ['Vladimir Ershov'] | 2023-02-03 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 4.80402410e-02 8.82528484e-01 -2.73220241e-01 -3.93197149e-01
-3.61145049e-01 -6.41318977e-01 3.86758953e-01 4.96744990e-01
3.19170170e-02 5.98223805e-01 6.28963411e-01 -8.57297003e-01
-8.02370369e-01 -8.57011080e-01 -1.12044953e-01 2.20162526e-01
-1.52358040e-01 1.07520282e+00 4.32115681e-02 -5.75391173... | [9.397933006286621, 8.510960578918457] |
9b864e5d-39aa-4d2c-8781-34334812ae73 | woce-a-framework-for-clustering-ensemble-by | 1612.06598 | null | http://arxiv.org/abs/1612.06598v1 | http://arxiv.org/pdf/1612.06598v1.pdf | WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory | The Wisdom of Crowds (WOC), as a theory in the social science, gets a new
paradigm in computer science. The WOC theory explains that the aggregate
decision made by a group is often better than those of its individual members
if specific conditions are satisfied. This paper presents a novel framework for
unsupervised an... | ['Sheng-Jun Huang', 'Daoqiang Zhang', 'Muhammad Yousefnezhad'] | 2016-12-20 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [-8.06169659e-02 -2.98011154e-01 4.16690886e-01 -3.63941252e-01
-3.36477667e-01 -4.30319637e-01 6.69996440e-01 5.96364439e-01
-5.21745086e-01 7.35385716e-01 3.52263838e-01 3.11112881e-01
-5.09004176e-01 -7.10687041e-01 -2.29835048e-01 -1.26443243e+00
1.23269834e-01 5.49834192e-01 3.71711850e-01 4.85837571... | [7.619149208068848, 4.559760570526123] |
280ffcfd-dfc4-4a60-8b75-ac1c596466ae | self-aware-feedback-based-self-learning-in | 2205.00029 | null | https://arxiv.org/abs/2205.00029v1 | https://arxiv.org/pdf/2205.00029v1.pdf | Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI | Self-learning paradigms in large-scale conversational AI agents tend to leverage user feedback in bridging between what they say and what they mean. However, such learning, particularly in Markov-based query rewriting systems have far from addressed the impact of these models on future training where successive feedbac... | ['Chenlei Guo', 'Chengyuan Ma', 'Gustavo Aguilar', 'Clint Solomon Mathialagan', 'Pragaash Ponnusamy'] | 2022-04-29 | null | https://aclanthology.org/2022.naacl-industry.36 | https://aclanthology.org/2022.naacl-industry.36.pdf | naacl-acl-2022-7 | ['self-learning'] | ['natural-language-processing'] | [ 3.45019698e-01 4.60917920e-01 -3.95280756e-02 -4.84829128e-01
-9.25276875e-01 -7.78092802e-01 5.94151556e-01 2.46325821e-01
-2.91939169e-01 7.96291947e-01 3.66585553e-01 -4.90482897e-01
-1.09361541e-02 -6.91612780e-01 -6.41897380e-01 -4.58865225e-01
-7.12942407e-02 9.78911042e-01 1.56035230e-01 -7.26550341... | [12.868898391723633, 7.983265399932861] |
799aa1ec-2323-4c65-a6d9-27e84ca89afe | high-fidelity-direct-contrast-synthesis-from | 2212.10817 | null | https://arxiv.org/abs/2212.10817v1 | https://arxiv.org/pdf/2212.10817v1.pdf | High-fidelity Direct Contrast Synthesis from Magnetic Resonance Fingerprinting | Magnetic Resonance Fingerprinting (MRF) is an efficient quantitative MRI technique that can extract important tissue and system parameters such as T1, T2, B0, and B1 from a single scan. This property also makes it attractive for retrospectively synthesizing contrast-weighted images. In general, contrast-weighted images... | ['Michael Lustig', 'Stella X. Yu', 'Jonathan I. Tamir', 'Fei Tan', 'Ekin Karasan', 'Thomas Amthor', 'Jakob Meineke', 'Mariya Doneva', 'Ke Wang'] | 2022-12-21 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 6.38308406e-01 1.03960186e-01 1.10372856e-01 -1.77438408e-01
-7.86054373e-01 -5.59039354e-01 5.75059056e-01 -2.60038465e-01
-3.21494460e-01 8.75960231e-01 2.52039731e-03 -3.24977964e-01
-1.88915730e-01 -6.74728453e-01 -7.86145926e-01 -8.54482710e-01
-4.82528627e-01 6.03421926e-01 4.37542528e-01 -1.26264289... | [13.648326873779297, -2.3429038524627686] |
b17e91ec-93cc-48de-a703-2e70f36a77af | action-graphs-weakly-supervised-action | 2002.01449 | null | https://arxiv.org/abs/2002.01449v1 | https://arxiv.org/pdf/2002.01449v1.pdf | Action Graphs: Weakly-supervised Action Localization with Graph Convolution Networks | We present a method for weakly-supervised action localization based on graph convolutions. In order to find and classify video time segments that correspond to relevant action classes, a system must be able to both identify discriminative time segments in each video, and identify the full extent of each action. Achievi... | ['Hedvig Kjellström', 'Yong Jae Lee', 'Maheen Rashid'] | 2020-02-04 | null | null | null | null | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 2.23626092e-01 -2.08494022e-01 -6.23486221e-01 -3.85480314e-01
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7.36040547e-02 -5.44716954e-01 -5.91949940e-01 -4.39197093e-01
-5.26764512e-01 1.41053334e-01 5.39862990e-01 5.91548942... | [8.56916618347168, 0.7304630279541016] |
2d05b2e8-a7a5-4f79-8d4d-da35a82490e8 | an-original-framework-for-wheat-head | 2009.11977 | null | https://arxiv.org/abs/2009.11977v1 | https://arxiv.org/pdf/2009.11977v1.pdf | An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset | In this paper, we propose an original object detection methodology applied to Global Wheat Head Detection (GWHD) Dataset. We have been through two major architectures of object detection which are FasterRCNN and EfficientDet, in order to design a novel and robust wheat head detection model. We emphasize on optimizing t... | ['Rabah Attia', 'Wided Souidene', 'Fares Fourati'] | 2020-09-24 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [ 1.97802201e-01 1.78865552e-01 2.63611913e-01 -3.01346898e-01
-5.14270961e-01 -3.55490535e-01 4.32246655e-01 2.86378115e-01
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-9.32284519e-02 4.07603353e-01 5.47600806e-01 -4.17043239... | [9.018762588500977, -1.1284117698669434] |
27725fdb-35df-45cc-ad92-6c8952500d2f | balanced-adversarial-training-balancing | null | null | https://openreview.net/forum?id=jB4K33NvZsd | https://openreview.net/pdf?id=jB4K33NvZsd | Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models | Traditional (\emph{oversensitive}) adversarial examples involve finding a small perturbation that does not change an input's true label but confuses the classifier into outputting a different prediction. \emph{Undersensitive} adversarial examples are the opposite---the adversary's goal is to find a small perturbation t... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 6.11116946e-01 2.95857161e-01 6.75369874e-02 -4.73065525e-01
-1.08056772e+00 -1.44426441e+00 7.17786729e-01 1.54482886e-01
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2.29570210e-01 3.78437489e-01 1.03686132e-01 -5.18675745... | [5.963688850402832, 8.080445289611816] |
b728edcb-0614-48c8-9838-4f6bb998656d | unicorn-a-unified-backdoor-trigger-inversion | 2304.02786 | null | https://arxiv.org/abs/2304.02786v1 | https://arxiv.org/pdf/2304.02786v1.pdf | UNICORN: A Unified Backdoor Trigger Inversion Framework | The backdoor attack, where the adversary uses inputs stamped with triggers (e.g., a patch) to activate pre-planted malicious behaviors, is a severe threat to Deep Neural Network (DNN) models. Trigger inversion is an effective way of identifying backdoor models and understanding embedded adversarial behaviors. A challen... | ['Shiqing Ma', 'Juan Zhai', 'Kai Mei', 'Zhenting Wang'] | 2023-04-05 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 2.16678575e-01 -7.13270307e-02 -3.88334453e-01 -1.39662758e-01
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-5.48888631e-02 -1.80812761e-01 5.18701017e-01 -3.09527576... | [5.676504135131836, 7.664111137390137] |
d094ca14-e329-4951-95cc-6dfa6002a916 | user-controllable-recommendation-against | 2204.13844 | null | https://arxiv.org/abs/2204.13844v1 | https://arxiv.org/pdf/2204.13844v1.pdf | User-controllable Recommendation Against Filter Bubbles | Recommender systems usually face the issue of filter bubbles: overrecommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along the feedback loop and inadvertently narrow user interests. Existing work usually mitigates filter bubbles by incorporating objectives apart ... | ['Tat-Seng Chua', 'Liqiang Nie', 'Fuli Feng', 'Wenjie Wang'] | 2022-04-29 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [-1.26502842e-01 -5.21203466e-02 -5.65313756e-01 -4.35959369e-01
-9.36717615e-02 -9.49052095e-01 3.98037106e-01 8.19991529e-02
-3.72232646e-01 4.90172923e-01 4.47728604e-01 -6.65245295e-01
-2.37111762e-01 -1.13622689e+00 -5.48361480e-01 -2.27355540e-01
1.39184684e-01 8.89960229e-02 2.21128181e-01 -3.19040120... | [9.919707298278809, 5.650774002075195] |
3a4dff04-75a2-43d0-8556-a374d5a62a6b | conner-a-cascade-count-and-measurement | null | null | https://aclanthology.org/2021.semeval-1.176 | https://aclanthology.org/2021.semeval-1.176.pdf | CONNER: A Cascade Count and Measurement Extraction Tool for Scientific Discourse | This paper presents our wining contribution to SemEval 2021 Task 8: MeasEval. The purpose of this task is identifying the counts and measurements from clinical scientific discourse, including quantities, entities, properties, qualifiers, units, modifiers, and their mutual relations. This task can be induced to a joint ... | ['Yefeng Zheng', 'Xi Chen', 'Zhiyuan Qi', 'Yunyan Zhang', 'Yuejia Xiang', 'Jiarun Cao'] | 2021-08-01 | null | null | null | semeval-2021 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 3.45280886e-01 8.00828338e-01 -3.62516671e-01 -2.44860753e-01
-7.29077697e-01 -6.45753622e-01 7.59642780e-01 1.06260753e+00
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-4.56025690e-01 -5.35770535e-01 -3.49309325e-01 -1.75764456e-01
-1.00962557e-01 4.57103908e-01 -1.07583925e-01 1.00369819... | [8.495051383972168, 8.717028617858887] |
9fbab5e1-8819-482e-ba1f-296460664f31 | 1000-fps-hdr-video-with-a-spike-rgb-hybrid | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chang_1000_FPS_HDR_Video_With_a_Spike-RGB_Hybrid_Camera_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chang_1000_FPS_HDR_Video_With_a_Spike-RGB_Hybrid_Camera_CVPR_2023_paper.pdf | 1000 FPS HDR Video With a Spike-RGB Hybrid Camera | Capturing high frame rate and high dynamic range (HFR&HDR) color videos in high-speed scenes with conventional frame-based cameras is very challenging. The increasing frame rate is usually guaranteed by using shorter exposure time so that the captured video is severely interfered by noise. Alternating exposures cou... | ['Boxin Shi', 'Tiejun Huang', 'Chao Xu', 'Liwen Hu', 'Yuchen Hong', 'Chu Zhou', 'Yakun Chang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-reconstruction'] | ['computer-vision'] | [ 4.00679231e-01 -6.76598728e-01 3.41353536e-01 4.33096439e-02
-4.01638657e-01 -5.91663837e-01 2.13666603e-01 -8.06076169e-01
-7.04413712e-01 9.12091792e-01 -2.22783566e-01 2.69544631e-01
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1.35403365e-01 -3.97359103e-01 4.85393852e-01 -8.02998915... | [8.851215362548828, -1.2586861848831177] |
7e924ef3-ba3e-4d38-8eb4-0b47fdc1c0f6 | assisted-text-annotation-using-active | 2112.11914 | null | https://arxiv.org/abs/2112.11914v1 | https://arxiv.org/pdf/2112.11914v1.pdf | Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort | Large amounts of annotated data have become more important than ever, especially since the rise of deep learning techniques. However, manual annotations are costly. We propose a tool that enables researchers to create large, high-quality, annotated datasets with only a few manual annotations, thus strongly reducing ann... | ['Bela Gipp', 'Karsten Donnay', 'Felix Hamborg', 'Franziska Weeber'] | 2021-12-15 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 2.56362349e-01 6.62975311e-01 -5.00085592e-01 -5.84536076e-01
-1.32292020e+00 -7.04515815e-01 5.54746270e-01 5.63376248e-01
-8.53327632e-01 7.75347769e-01 3.50696415e-01 -1.61077887e-01
4.63101566e-01 -3.90067101e-01 -5.23231387e-01 -1.75496504e-01
3.34517121e-01 5.11239529e-01 4.50630099e-01 1.33203194... | [9.672682762145996, 4.620616912841797] |
4b7d0923-a357-4c27-8654-665baeff40a7 | contrastive-learning-for-self-supervised-pre | 2301.07283 | null | https://arxiv.org/abs/2301.07283v2 | https://arxiv.org/pdf/2301.07283v2.pdf | Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data | Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly. This reduction is particularly important for semantic segmentation tasks involving 3D datasets, which are often significantly smaller and more challenging to annotate than their image-based counterparts. Se... | ['Jonathan Kelly', 'Edwin G. Ng', 'Brandon Wagstaff', 'Andrej Janda'] | 2023-01-18 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 5.11152565e-01 3.05720329e-01 -1.40601639e-02 -6.86505914e-01
-8.85910690e-01 -9.22686815e-01 4.09005463e-01 5.37839532e-01
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2.70881563e-01 1.09133613e+00 7.19199061e-01 1.20600194... | [8.110700607299805, -2.827766180038452] |
3ad1bec5-c79e-4bb1-a7c3-44d54ce245dd | a-streamlit-based-artificial-intelligence | 2211.12851 | null | https://arxiv.org/abs/2211.12851v1 | https://arxiv.org/pdf/2211.12851v1.pdf | A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks | With the rapid development and integration of artificial intelligence (AI) methods in next-generation networks (NextG), AI algorithms have provided significant advantages for NextG in terms of frequency spectrum usage, bandwidth, latency, and security. A key feature of NextG is the integration of AI, i.e., self-learnin... | ['O Gueler', 'U. Cali', 'S. Sarp', 'F. O. Catak', 'M. Kuzlu'] | 2022-10-25 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-1.19049996e-01 1.80202946e-01 -6.13938808e-01 1.05219208e-01
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-5.28226495e-01 9.07070190e-02 1.37447894e-01 -2.14215264... | [5.464162349700928, 7.273461818695068] |
7d4c36a6-ff8f-48fd-88bc-f7247b285aae | integrating-unsupervised-data-generation-into | 2107.08772 | null | https://arxiv.org/abs/2107.08772v1 | https://arxiv.org/pdf/2107.08772v1.pdf | Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages | For most language combinations, parallel data is either scarce or simply unavailable. To address this, unsupervised machine translation (UMT) exploits large amounts of monolingual data by using synthetic data generation techniques such as back-translation and noising, while self-supervised NMT (SSNMT) identifies parall... | ['Cristina España-Bonet', 'Josef van Genabith', 'Dietrich Klakow', 'Dana Ruiter'] | 2021-07-19 | null | https://aclanthology.org/2021.mtsummit-research.7 | https://aclanthology.org/2021.mtsummit-research.7.pdf | mtsummit-2021-8 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.34164143e-01 1.76368237e-01 -1.37671694e-01 -2.66558290e-01
-1.42514503e+00 -1.11467946e+00 1.17588556e+00 3.04925367e-02
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2.90663213e-01 1.02170897e+00 -2.90427178e-01 -5.32433927... | [11.581031799316406, 10.369853973388672] |
9812f8e8-9ef5-4a14-bfe2-91c59a789a3c | weavenet-a-differentiable-solver-for-non | null | null | https://openreview.net/forum?id=ktHKpsbsxx | https://openreview.net/pdf?id=ktHKpsbsxx | WeaveNet: A Differentiable Solver for Non-linear Assignment Problems | Assignment, a task to match a limited number of elements, is a fundamental problem in informatics. Traditionally, non-linear assignment is discussed as a combinatorial optimization problem with its calculation complexity. On the other hand, it is often a sub-problem of image processing tasks, such as 3D point cloud mat... | ['Yoshitaka Ushiku', 'Naoya Chiba', 'rintaro yanagi', 'Jiaxin Ma', 'Atsushi Hashimoto', 'Shusaku Sone'] | 2021-09-29 | null | null | null | null | ['3d-point-cloud-matching'] | ['computer-vision'] | [ 4.61694002e-01 2.26062179e-01 -1.19081430e-01 -4.19791818e-01
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-2.72870094e-01 9.00284231e-01 -1.04796104e-01 -1.00472383... | [7.093888759613037, 6.334185600280762] |
50f38b65-043f-451c-9161-148255c6ba95 | nethack-is-hard-to-hack | 2305.19240 | null | https://arxiv.org/abs/2305.19240v1 | https://arxiv.org/pdf/2305.19240v1.pdf | NetHack is Hard to Hack | Neural policy learning methods have achieved remarkable results in various control problems, ranging from Atari games to simulated locomotion. However, these methods struggle in long-horizon tasks, especially in open-ended environments with multi-modal observations, such as the popular dungeon-crawler game, NetHack. In... | ['Rob Fergus', 'Lerrel Pinto', 'Ulyana Piterbarg'] | 2023-05-30 | null | null | null | null | ['atari-games', 'nethack'] | ['playing-games', 'playing-games'] | [-1.69598565e-01 1.15742348e-01 -3.99203360e-01 2.93671072e-01
-6.42663360e-01 -6.73485458e-01 7.12955594e-01 -1.82986632e-01
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-4.37657773e-01 -4.61196810e-01 -9.13758516e-01 -6.68431520e-01
-6.19820058e-01 6.67693377e-01 4.97456491e-01 -9.02204216... | [3.813220500946045, 1.5439258813858032] |
7cf737af-53dd-4691-bab5-8835ccc4a082 | connector-0-5-a-unified-framework-for-graph | 2304.13195 | null | https://arxiv.org/abs/2304.13195v1 | https://arxiv.org/pdf/2304.13195v1.pdf | Connector 0.5: A unified framework for graph representation learning | Graph representation learning models aim to represent the graph structure and its features into low-dimensional vectors in a latent space, which can benefit various downstream tasks, such as node classification and link prediction. Due to its powerful graph data modelling capabilities, various graph embedding models an... | ['O-Joun Lee', 'Van Thuy Hoang', 'Jooho Lee', 'Thanh Sang Nguyen'] | 2023-04-25 | null | null | null | null | ['graph-embedding'] | ['graphs'] | [-4.56557184e-01 4.66879666e-01 -4.54641908e-01 -2.40550280e-01
2.63123941e-02 -4.45208013e-01 5.66940904e-01 5.27972400e-01
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-9.94449556e-02 -1.41190958e+00 -2.05378383e-01 -4.67858166e-01
-5.24233878e-01 4.61942375e-01 4.33214828e-02 -2.21433803... | [7.169623851776123, 6.277251720428467] |
ba67ae5e-a1aa-4447-92a1-2595362b7ee8 | understanding-the-role-of-affect-dimensions | 2105.03983 | null | https://arxiv.org/abs/2105.03983v1 | https://arxiv.org/pdf/2105.03983v1.pdf | Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach | We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimension... | ['Niloy Ganguly', 'Soham Dasgupta', 'Sriyash Poddar', 'Atharva Naik', 'Rajdeep Mukherjee'] | 2021-05-09 | null | null | null | null | ['subjectivity-analysis'] | ['natural-language-processing'] | [-2.10917518e-01 -1.03327416e-01 -8.90466571e-02 -6.56705141e-01
-1.12016010e+00 -5.17331481e-01 4.45021421e-01 3.92049462e-01
-5.05024791e-01 5.20690858e-01 3.28291893e-01 2.26512030e-01
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-1.38397887e-01 1.37399212e-01 -5.53501964e-01 -4.83455032... | [13.104931831359863, 5.78048038482666] |
5c9217ba-3625-47ef-b716-f9e09cd469a9 | review-highlights-opinion-mining-on-reviews-a | null | null | https://dl.acm.org/citation.cfm?id=3158385 | http://vixra.org/pdf/1910.0514v1.pdf | Review highlights: opinion mining on reviews: a hybrid model for rule selection in aspect extraction | This paper proposes a methodology to extract key insights from user generated reviews. This work is based on Aspect Based Sentiment Analysis (ABSA) which predicts the sentiment of aspects mentioned in the text documents. The extracted aspects are fine-grained for the presentation form known as Review Highlights.
The... | ['Amit Kushwaha', 'Shubham Chaudhary'] | 2017-10-18 | null | null | null | iml-17-october-1718-2017-liverpool-united | ['aspect-extraction', 'extract-aspect', 'extract-aspect-polarity-tuple'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.96262026e-01 5.00574052e-01 -4.02272582e-01 -5.68874061e-01
-6.40626788e-01 -7.53946662e-01 5.31690419e-01 6.13303602e-01
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-1.26799136e-01 -8.34557116e-01 -2.98941523e-01 -3.14117938e-01
4.40022618e-01 4.49169368e-01 2.14867026e-01 -4.16318893... | [11.172515869140625, 6.8369011878967285] |
660e5c61-fe7e-4fb2-a1f3-1c330123fe2e | lidar-range-image-compression-with-deep-delta | null | null | https://openreview.net/forum?id=nzqZufLU1v | https://openreview.net/pdf?id=nzqZufLU1v | Lidar Range Image Compression with Deep Delta Encoding | Lidars are widely used in applications such as autonomous driving and augmented reality. However, the large volume of data produced by lidars can lead to high cost in data storage and transmission. Besides rising standards in point cloud compression from the MPEG (G-PCC and V-PCC), recent works also explore using deep ... | ['Dragomir Anguelov', 'Yin Zhou', 'Charles R. Qi', 'Xuanyu Zhou'] | 2021-09-29 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 4.40421999e-01 -1.97526321e-01 -2.59355187e-01 -5.24883151e-01
-5.56591630e-01 -1.70924336e-01 3.59583765e-01 1.86311573e-01
-3.08079302e-01 5.55777252e-01 1.22429952e-01 -1.78498358e-01
-9.70157385e-02 -1.41985714e+00 -1.11935401e+00 -3.13631892e-01
-8.24449286e-02 4.62211818e-01 3.24708611e-01 -2.12694749... | [8.297162055969238, -2.9792494773864746] |
57ea74f5-6312-40f6-85f6-bbc97bb1ea65 | self-supervised-cnn-for-unconstrained-3d | 1808.05323 | null | https://arxiv.org/abs/1808.05323v3 | https://arxiv.org/pdf/1808.05323v3.pdf | 3D Face From X: Learning Face Shape from Diverse Sources | We present a novel method to jointly learn a 3D face parametric model and 3D face reconstruction from diverse sources. Previous methods usually learn 3D face modeling from one kind of source, such as scanned data or in-the-wild images. Although 3D scanned data contain accurate geometric information of face shapes, the ... | ['Juyong Zhang', 'Yudong Guo', 'Lin Cai'] | 2018-08-16 | null | null | null | null | ['3d-face-modeling'] | ['computer-vision'] | [-2.17709422e-01 -4.37743478e-02 -1.36735111e-01 -8.12749445e-01
-7.85383284e-01 -2.81142086e-01 1.12484030e-01 -9.81550336e-01
1.00395024e-01 2.60401785e-01 -2.01144084e-01 2.98069328e-01
4.27977964e-02 -8.89655709e-01 -7.91841865e-01 -5.43521881e-01
3.02367270e-01 6.23606026e-01 -2.26275757e-01 8.39501247... | [13.144681930541992, 0.06212303787469864] |
46473f47-25ba-42a0-81c5-a97f6589bbdb | sls-at-semeval-2016-task-3-neural-based | null | null | https://aclanthology.org/S16-1128 | https://aclanthology.org/S16-1128.pdf | SLS at SemEval-2016 Task 3: Neural-based Approaches for Ranking in Community Question Answering | null | ['Wei-Ning Hsu', 'Tao Lei', 'Scott Cyphers', 'Mitra Mohtarami', 'Jim Glass', 'Kfir Bar', 'Yu Zhang', 'Yonatan Belinkov'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['question-similarity'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.37662410736084, 3.6809213161468506] |
3e585b09-8750-46a0-9e9a-56852fa959b2 | critical-overview-of-privacy-preserving | 2004.09612 | null | https://arxiv.org/abs/2004.09612v6 | https://arxiv.org/pdf/2004.09612v6.pdf | A Critical Overview of Privacy-Preserving Approaches for Collaborative Forecasting | Cooperation between different data owners may lead to an improvement in forecast quality - for instance by benefiting from spatial-temporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection questions, said data owners might be unwilling to share the... | ['Carla Gonçalves', 'Pierre Pinson', 'Ricardo J. Bessa'] | 2020-04-20 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 4.05261032e-02 1.02706827e-01 4.97573875e-02 -7.12084353e-01
-7.44371116e-01 -1.20394313e+00 5.24891734e-01 3.38907242e-01
-2.46031478e-01 7.65432179e-01 2.95283556e-01 -6.28914654e-01
-2.83874542e-01 -8.83433402e-01 -5.59600174e-01 -1.07234967e+00
-2.44263873e-01 2.46279225e-01 -3.64808947e-01 -7.17973039... | [5.950911998748779, 6.619917869567871] |
7a7a4a25-7652-4c5c-991c-2ad399f1fba4 | unsupervised-representation-learning-by | 1708.01246 | null | http://arxiv.org/abs/1708.01246v1 | http://arxiv.org/pdf/1708.01246v1.pdf | Unsupervised Representation Learning by Sorting Sequences | We present an unsupervised representation learning approach using videos
without semantic labels. We leverage the temporal coherence as a supervisory
signal by formulating representation learning as a sequence sorting task. We
take temporally shuffled frames (i.e., in non-chronological order) as inputs
and train a conv... | ['Ming-Hsuan Yang', 'Jia-Bin Huang', 'Hsin-Ying Lee', 'Maneesh Singh'] | 2017-08-03 | unsupervised-representation-learning-by-3 | http://openaccess.thecvf.com/content_iccv_2017/html/Lee_Unsupervised_Representation_Learning_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Lee_Unsupervised_Representation_Learning_ICCV_2017_paper.pdf | iccv-2017-10 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 8.17201197e-01 -2.51370788e-01 -6.23863816e-01 -6.65368378e-01
-3.39085042e-01 -6.03813291e-01 8.72833490e-01 -1.45483598e-01
-5.44160724e-01 5.03640711e-01 5.88789880e-01 -1.37783699e-02
-8.48071426e-02 -4.99952793e-01 -1.01725948e+00 -6.22331202e-01
-4.25186664e-01 1.99261487e-01 3.21691394e-01 8.17779526... | [8.561161994934082, 0.7285941243171692] |
1423bed4-95da-48e6-8b0a-f46d669d618a | a-hierarchical-deep-architecture-and-mini | 1806.07987 | null | http://arxiv.org/abs/1806.07987v2 | http://arxiv.org/pdf/1806.07987v2.pdf | A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection | Traffic light and sign detectors on autonomous cars are integral for road
scene perception. The literature is abundant with deep learning networks that
detect either lights or signs, not both, which makes them unsuitable for
real-life deployment due to the limited graphics processing unit (GPU) memory
and power availab... | ['Steven L. Waslander', 'Oles Andrienko', 'Ali Harakeh', 'Alex D. Pon'] | 2018-06-20 | null | null | null | null | ['traffic-sign-recognition', 'traffic-sign-detection'] | ['computer-vision', 'computer-vision'] | [ 7.57974312e-02 -3.36484581e-01 -1.70795098e-01 -4.89152253e-01
-5.95044374e-01 -4.13411230e-01 5.84546566e-01 -6.31827414e-01
-6.09603226e-01 3.55825037e-01 -5.80147445e-01 -9.58115637e-01
2.19568297e-01 -7.56246567e-01 -6.65362060e-01 -7.52204895e-01
3.55594635e-01 4.01685864e-01 8.99832129e-01 -2.94927984... | [8.019814491271973, -0.8958208560943604] |
2da50702-3299-4999-8963-52e88edcbda8 | on-translation-invariance-in-cnns | 2003.07064 | null | https://arxiv.org/abs/2003.07064v2 | https://arxiv.org/pdf/2003.07064v2.pdf | On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location | In this paper we challenge the common assumption that convolutional layers in modern CNNs are translation invariant. We show that CNNs can and will exploit the absolute spatial location by learning filters that respond exclusively to particular absolute locations by exploiting image boundary effects. Because modern CNN... | ['Jan C. van Gemert', 'Osman Semih Kayhan'] | 2020-03-16 | on-translation-invariance-in-cnns-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Kayhan_On_Translation_Invariance_in_CNNs_Convolutional_Layers_Can_Exploit_Absolute_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Kayhan_On_Translation_Invariance_in_CNNs_Convolutional_Layers_Can_Exploit_Absolute_CVPR_2020_paper.pdf | cvpr-2020-6 | ['small-data', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [ 2.12913498e-01 -5.28653041e-02 -5.18087268e-01 -4.76446480e-01
-1.65716261e-01 -8.29294741e-01 6.50511384e-01 -3.29437435e-01
-6.74653351e-01 4.47034478e-01 1.83348194e-01 -4.08790350e-01
2.22411379e-01 -8.28256428e-01 -1.18596971e+00 -5.32645166e-01
1.07104005e-02 -2.53076226e-01 3.98299992e-01 -2.14346126... | [9.255118370056152, 2.157003402709961] |
05317035-3b7d-4971-ae3b-b3975c2ac168 | rps-portfolio-asset-selection-using-graph | 2111.15634 | null | https://arxiv.org/abs/2111.15634v1 | https://arxiv.org/pdf/2111.15634v1.pdf | RPS: Portfolio Asset Selection using Graph based Representation Learning | Portfolio optimization is one of the essential fields of focus in finance. There has been an increasing demand for novel computational methods in this area to compute portfolios with better returns and lower risks in recent years. We present a novel computational method called Representation Portfolio Selection (RPS) b... | ['Erfan Loghmani', 'Ali Owfi', 'Parsa Alian', 'Mohammadamin Fazli'] | 2021-11-28 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.05266818e-01 -2.98466682e-01 5.36921397e-02 -3.65823299e-01
-5.28145015e-01 -7.13374734e-01 3.86474133e-01 -1.85735822e-01
6.36016801e-02 8.21007907e-01 3.55804622e-01 -3.35394412e-01
-1.14092326e+00 -1.41389203e+00 1.40237466e-01 -6.80387139e-01
-1.83069393e-01 6.82452381e-01 -1.71335340e-02 -3.28049183... | [4.83065128326416, 4.006330490112305] |
6e1c0022-a77e-427b-8b96-bf33e036b8bb | learning-adaptive-dense-event-stereo-from-the | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cho_Learning_Adaptive_Dense_Event_Stereo_From_the_Image_Domain_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cho_Learning_Adaptive_Dense_Event_Stereo_From_the_Image_Domain_CVPR_2023_paper.pdf | Learning Adaptive Dense Event Stereo From the Image Domain | Recently, event-based stereo matching has been studied due to its robustness in poor light conditions. However, existing event-based stereo networks suffer severe performance degradation when domains shift. Unsupervised domain adaptation (UDA) aims at resolving this problem without using the target domain ground-tr... | ['Kuk-Jin Yoon', 'Jegyeong Cho', 'Hoonhee Cho'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-reconstruction', 'stereo-matching-1', 'unsupervised-domain-adaptation'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 4.38368887e-01 -2.67774135e-01 1.60596937e-01 -3.70216191e-01
-4.06026661e-01 -1.64332256e-01 5.73072672e-01 -2.76630193e-01
-5.74649811e-01 8.67429078e-01 1.83860779e-01 4.03930902e-01
-1.21260293e-01 -9.14135516e-01 -9.05570090e-01 -8.57492447e-01
6.11497700e-01 1.29588738e-01 6.55095458e-01 -1.19237326... | [9.11130428314209, -2.2925126552581787] |
e47a9eee-0fa4-4732-8568-21cdd199f547 | multimorbidity-content-based-medical-image | 2211.12185 | null | https://arxiv.org/abs/2211.12185v1 | https://arxiv.org/pdf/2211.12185v1.pdf | Multimorbidity Content-Based Medical Image Retrieval Using Proxies | Content-based medical image retrieval is an important diagnostic tool that improves the explainability of computer-aided diagnosis systems and provides decision making support to healthcare professionals. Medical imaging data, such as radiology images, are often multimorbidity; a single sample may have more than one pa... | ['ZongYuan Ge', 'Tom Drummond', 'Mehrtash Harandi', 'Benjamin J. Meyer', 'Yunyan Xing'] | 2022-11-22 | null | null | null | null | ['medical-image-retrieval', 'content-based-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'computer-vision', 'medical'] | [ 5.21146953e-01 -5.53930402e-02 -6.12170339e-01 -5.70822835e-01
-1.50176561e+00 -5.04913449e-01 4.63422567e-01 8.37629318e-01
-1.29281864e-01 3.39933723e-01 4.82855499e-01 -2.20484763e-01
-6.57394886e-01 -7.17486024e-01 -2.03723252e-01 -7.30131745e-01
-5.70843779e-02 9.20382679e-01 -1.40853941e-01 2.94709265... | [14.451086044311523, -1.630953073501587] |
c489686d-3825-4f07-8cf8-6cca3c4c434b | smaug-sparse-masked-autoencoder-for-efficient | 2211.11446 | null | https://arxiv.org/abs/2211.11446v3 | https://arxiv.org/pdf/2211.11446v3.pdf | SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training | Video-language pre-training is crucial for learning powerful multi-modal representation. However, it typically requires a massive amount of computation. In this paper, we develop SMAUG, an efficient pre-training framework for video-language models. The foundation component in SMAUG is masked autoencoders. Different fro... | ['Cihang Xie', 'Alan Yuille', 'Huiyu Wang', 'Chen Wei', 'Yuanze Lin'] | 2022-11-21 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [-4.16399427e-02 -4.82476771e-01 -4.69570875e-01 -3.41587216e-01
-1.25814950e+00 -3.98429662e-01 4.88747507e-01 1.34741768e-01
-7.36059129e-01 1.55862868e-01 2.74661392e-01 -5.35248876e-01
4.87705797e-01 -5.63305259e-01 -9.01366353e-01 -5.92324674e-01
1.31909475e-01 -2.16369703e-02 2.96160460e-01 6.40135854... | [9.99386215209961, 0.9151372313499451] |
9e73dc83-c1cc-4083-8a4b-a7d59f795cfe | multi-speaker-and-wide-band-simulated | 2211.06750 | null | https://arxiv.org/abs/2211.06750v2 | https://arxiv.org/pdf/2211.06750v2.pdf | Multi-Speaker and Wide-Band Simulated Conversations as Training Data for End-to-End Neural Diarization | End-to-end diarization presents an attractive alternative to standard cascaded diarization systems because a single system can handle all aspects of the task at once. Many flavors of end-to-end models have been proposed but all of them require (so far non-existing) large amounts of annotated data for training. The comp... | ['Lukáš Burget', 'Alicia Lozano-Diez', 'Mireia Diez', 'Federico Landini'] | 2022-11-12 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-5.30452058e-02 3.57569128e-01 2.60350615e-01 -6.02162302e-01
-1.53294361e+00 -6.47358000e-01 8.92971218e-01 -4.68787402e-01
-3.60999078e-01 7.02156484e-01 5.62742710e-01 -1.40241936e-01
2.57602721e-01 -1.25145644e-01 -3.61863911e-01 -8.05787861e-01
1.44141288e-02 9.15149152e-01 2.15088278e-01 -3.44820857... | [14.773112297058105, 6.214160919189453] |
cf346c67-59d9-47df-aee9-ea60952a8322 | fir-based-future-trajectory-prediction-in | 2304.05345 | null | https://arxiv.org/abs/2304.05345v1 | https://arxiv.org/pdf/2304.05345v1.pdf | FIR-based Future Trajectory Prediction in Nighttime Autonomous Driving | The performance of the current collision avoidance systems in Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) can be drastically affected by low light and adverse weather conditions. Collisions with large animals such as deer in low light cause significant cost and damage every year. In this pape... | ['Justin Miller', 'Devesh Upadhyay', 'Navid Fallahinia', 'Alireza Rahimpour'] | 2023-03-31 | null | null | null | null | ['motion-prediction', 'trajectory-prediction'] | ['computer-vision', 'computer-vision'] | [ 2.66435921e-01 -3.92464809e-02 1.40356600e-01 -7.02297509e-01
-2.38670543e-01 1.71270333e-02 3.79987240e-01 3.45500082e-01
-9.83883321e-01 6.56920612e-01 -1.82995185e-01 -2.39443988e-01
-1.73575114e-02 -9.33033228e-01 -7.66281724e-01 -5.29349625e-01
-4.65744346e-01 2.06652865e-01 9.02073860e-01 -4.14943516... | [7.8504638671875, -1.0479689836502075] |
d5638792-095c-4de7-bd88-c14b913afdb7 | discriminative-localization-in-cnns-for | 1707.01086 | null | http://arxiv.org/abs/1707.01086v2 | http://arxiv.org/pdf/1707.01086v2.pdf | Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules | Automated detection and segmentation of pulmonary nodules on lung computed
tomography (CT) scans can facilitate early lung cancer diagnosis. Existing
supervised approaches for automated nodule segmentation on CT scans require
voxel-based annotations for training, which are labor- and time-consuming to
obtain. In this w... | ['Elsa D. Angelini', 'Xinyang Feng', 'Jie Yang', 'Andrew F. Laine'] | 2017-07-04 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 3.13692451e-01 3.72519106e-01 -7.10652947e-01 -4.87196892e-01
-1.35196900e+00 -4.78285044e-01 9.79133174e-02 -1.57910287e-01
-4.60393906e-01 2.85485238e-01 2.73770336e-02 -6.16974831e-01
3.01919371e-01 -7.19839513e-01 -5.64360082e-01 -6.53194487e-01
1.32118329e-01 1.00568879e+00 7.14860201e-01 5.89611530... | [15.412027359008789, -2.1146183013916016] |
6bce541f-ccf9-4d9d-aab6-1483667f3dec | automated-vulnerability-detection-in-source-1 | 2303.07525 | null | https://arxiv.org/abs/2303.07525v1 | https://arxiv.org/pdf/2303.07525v1.pdf | Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processing | One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. These flaws are highly likely ex-ploited and lead to system compromise, data leakage, or denial of ser-vice. C and C++ open source code are now available in order to create a large-scale,... | ['Zakirul Alam Bhuiya', 'Hossain Shahriar', 'Mst Shapna Akter'] | 2023-03-13 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-1.81386396e-01 -1.38769776e-01 -1.04013175e-01 -4.29720730e-02
-1.01512611e+00 -9.14945662e-01 2.37194791e-01 5.25749385e-01
-3.11535269e-01 2.97349453e-01 -1.37995526e-01 -9.95366037e-01
2.21540287e-01 -1.18886495e+00 -8.60588014e-01 -3.22982252e-01
-3.74233454e-01 -4.23159063e-01 3.33491087e-01 -4.07049149... | [7.054675102233887, 7.772696495056152] |
661156fd-6f2b-4a0e-af20-70701f27216f | pathology-aware-generative-adversarial | 2106.01915 | null | https://arxiv.org/abs/2106.01915v1 | https://arxiv.org/pdf/2106.01915v1.pdf | Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation | Convolutional Neural Networks (CNNs) can play a key role in Medical Image Analysis under large-scale annotated datasets. However, preparing such massive dataset is demanding. In this context, Generative Adversarial Networks (GANs) can generate realistic but novel samples, and thus effectively cover the real image distr... | ['Changhee Han'] | 2021-06-03 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 5.84906042e-01 6.12394750e-01 -2.59827197e-01 -1.03417806e-01
-7.19588459e-01 -2.33715296e-01 2.59337872e-01 -1.85323939e-01
-2.51898557e-01 1.02418756e+00 5.37218191e-02 -3.00002962e-01
1.32816032e-01 -1.00474238e+00 -7.19589412e-01 -1.16824484e+00
3.97518188e-01 5.01118004e-01 -2.13911220e-01 -1.86352208... | [14.090250968933105, -1.994767665863037] |
2ecde487-386e-48c1-9f04-d871d5354d0a | on-the-current-state-of-reproducibility-and | null | null | https://openreview.net/forum?id=f6VUHB7dzXU | https://openreview.net/pdf?id=f6VUHB7dzXU | On the current state of reproducibility and reporting of uncertainty for Aspect-based Sentiment Analysis | For the latter part of the past decade, Aspect-Based Sentiment Analysis has been a field of great interest within Natural Language Processing. Supported by the Semantic Evaluation Conferences in 2014 -- 2016, a variety of methods has been developed competing in improving performances on benchmark data sets. Exploiting ... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-4.96714897e-02 2.62816817e-01 -1.22151338e-01 -6.86755955e-01
-8.40384901e-01 -7.37238169e-01 1.02605021e+00 6.88060582e-01
-6.17839217e-01 7.02971160e-01 3.29915702e-01 -9.54444055e-03
-3.11991394e-01 -7.25731432e-01 -4.37809467e-01 -4.76615489e-01
7.74721131e-02 6.31288171e-01 1.03098884e-01 -4.40005690... | [11.225802421569824, 6.900378704071045] |
25023fbd-afd3-4a9c-94b8-123312e2f185 | r-pred-two-stage-motion-prediction-via-tube | 2211.08609 | null | https://arxiv.org/abs/2211.08609v5 | https://arxiv.org/pdf/2211.08609v5.pdf | Two-Stage Context-Aware model for Predicting Future Motion of Dynamic Agents | Predicting the future motion of dynamic agents is of paramount importance to ensuring safety and assessing risks in motion planning for autonomous robots. In this study, we propose a two-stage motion prediction method, called R-Pred, designed to effectively utilize both scene and interaction context using a cascade of ... | ['Jun Won Choi', 'Junyong Yun', 'Jungho Kim', 'Sehwan Choi'] | 2022-11-16 | null | null | null | null | ['motion-planning'] | ['robots'] | [-2.84758210e-01 1.40924782e-01 -3.63901138e-01 -2.22540408e-01
-6.84583843e-01 -2.38120615e-01 1.18134403e+00 1.19087376e-01
-7.70499229e-01 2.96915948e-01 6.62757695e-01 -3.15739572e-01
-8.33984688e-02 -8.41572881e-01 -6.56613469e-01 -7.01644301e-01
-5.82303286e-01 5.37707508e-01 8.53489101e-01 -3.84114385... | [5.889460563659668, 0.7917446494102478] |
065d5f8f-73c0-4fa6-9d1d-61ef543f8cc4 | visual-forecasting-by-imitating-dynamics-in | 1708.05827 | null | http://arxiv.org/abs/1708.05827v1 | http://arxiv.org/pdf/1708.05827v1.pdf | Visual Forecasting by Imitating Dynamics in Natural Sequences | We introduce a general framework for visual forecasting, which directly
imitates visual sequences without additional supervision. As a result, our
model can be applied at several semantic levels and does not require any domain
knowledge or handcrafted features. We achieve this by formulating visual
forecasting as an in... | ['Juan Carlos Niebles', 'De-An Huang', 'Kuo-Hao Zeng', 'William B. Shen', 'Min Sun'] | 2017-08-19 | visual-forecasting-by-imitating-dynamics-in-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Zeng_Visual_Forecasting_by_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Zeng_Visual_Forecasting_by_ICCV_2017_paper.pdf | iccv-2017-10 | ['action-anticipation'] | ['computer-vision'] | [ 3.81062239e-01 2.72671372e-01 -2.57022828e-01 -6.36602566e-02
-5.99226713e-01 -6.35209322e-01 1.28269303e+00 -2.67350167e-01
-3.32021475e-01 8.46844554e-01 4.07902688e-01 -1.63407356e-01
2.46659800e-01 -7.27790594e-01 -1.04248464e+00 -6.80223167e-01
1.87880099e-01 3.05495322e-01 1.88558906e-01 -4.81941015... | [8.316673278808594, 0.1445244699716568] |
a5eff86b-3348-4f08-837e-8db056e138db | revisiting-mid-level-patterns-for-distant | 2008.03128 | null | https://arxiv.org/abs/2008.03128v4 | https://arxiv.org/pdf/2008.03128v4.pdf | Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition | Existing few-shot learning (FSL) methods usually assume base classes and novel classes are from the same domain (in-domain setting). However, in practice, it may be infeasible to collect sufficient training samples for some special domains to construct base classes. To solve this problem, cross-domain FSL (CDFSL) is pr... | ['José M. F. Moura', 'Shanghang Zhang', 'Yixiong Zou', 'Yonghong Tian', 'JianPeng Yu'] | 2020-08-07 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 3.73001873e-01 -3.49722169e-02 -5.28676867e-01 -5.10046005e-01
-8.31874728e-01 -5.04784346e-01 5.05815089e-01 1.65488273e-01
-1.47257254e-01 1.04012656e+00 1.71522200e-01 5.93014807e-02
-4.23345238e-01 -9.29565430e-01 -6.61849022e-01 -7.56900728e-01
-4.69163693e-02 3.01312387e-01 5.22514582e-01 -2.00358465... | [10.095932006835938, 3.0800862312316895] |
4ef7753a-ed6f-4664-b08f-a9988176051e | videomae-v2-scaling-video-masked-autoencoders | 2303.16727 | null | https://arxiv.org/abs/2303.16727v2 | https://arxiv.org/pdf/2303.16727v2.pdf | VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking | Scale is the primary factor for building a powerful foundation model that could well generalize to a variety of downstream tasks. However, it is still challenging to train video foundation models with billions of parameters. This paper shows that video masked autoencoder (VideoMAE) is a scalable and general self-superv... | ['Yu Qiao', 'Yali Wang', 'Yi Wang', 'Yinan He', 'Zhan Tong', 'Zhiyu Zhao', 'Bingkun Huang', 'LiMin Wang'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_VideoMAE_V2_Scaling_Video_Masked_Autoencoders_With_Dual_Masking_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_VideoMAE_V2_Scaling_Video_Masked_Autoencoders_With_Dual_Masking_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-classification', 'action-recognition-in-videos', 'spatio-temporal-action-localization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.15675822e-02 -2.71966994e-01 -1.15927361e-01 -2.25432888e-01
-8.52529585e-01 -4.97656912e-01 6.16004944e-01 -5.14021277e-01
-4.55028862e-01 5.11609554e-01 1.21311441e-01 -4.54518139e-01
5.72165728e-01 -4.52575028e-01 -1.31283438e+00 -7.74032116e-01
-5.18488698e-02 9.81193734e-04 4.63408768e-01 -7.82761723... | [9.425082206726074, 0.8299190402030945] |
a9014f0b-0208-4b04-802f-a7f3ac73e478 | foundation-models-for-natural-language | 2302.08575 | null | https://arxiv.org/abs/2302.08575v1 | https://arxiv.org/pdf/2302.08575v1.pdf | Foundation Models for Natural Language Processing -- Pre-trained Language Models Integrating Media | This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP... | ['Sven Giesselbach', 'Gerhard Paaß'] | 2023-02-16 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 4.47049975e-01 3.34060669e-01 -3.64586711e-01 -3.01303923e-01
-5.32121360e-01 -5.70818067e-01 7.38079131e-01 5.42242490e-02
-4.44637060e-01 8.54757428e-01 1.05044760e-01 -1.60510793e-01
-8.32634270e-02 -9.70640779e-01 -5.75609028e-01 -5.34763873e-01
2.48477504e-01 6.73347771e-01 -8.92899185e-03 -5.42396367... | [11.002580642700195, 8.65248966217041] |
603a8a08-62c2-43b1-be25-96ff815056d4 | evaluation-beyond-task-performance-analyzing | 2211.14673 | null | https://arxiv.org/abs/2211.14673v1 | https://arxiv.org/pdf/2211.14673v1.pdf | Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex | AlphaZero, an approach to reinforcement learning that couples neural networks and Monte Carlo tree search (MCTS), has produced state-of-the-art strategies for traditional board games like chess, Go, shogi, and Hex. While researchers and game commentators have suggested that AlphaZero uses concepts that humans consider ... | ['Michael L. Littman', 'Ellie Pavlick', 'George Konidaris', 'Jessica Zosa Forde', 'Charles Lovering'] | 2022-11-26 | null | null | null | null | ['board-games'] | ['playing-games'] | [-1.39808938e-01 4.68976468e-01 -1.02400951e-01 -3.49285044e-02
-4.85151261e-01 -7.75999963e-01 5.45654237e-01 3.30909133e-01
-7.61915147e-01 7.30578780e-01 5.61235845e-01 -4.84303772e-01
-4.28714484e-01 -1.03057921e+00 -5.21648228e-01 -2.98540980e-01
-3.37366968e-01 7.68667638e-01 3.52126479e-01 -7.21541226... | [3.830531597137451, 1.3798218965530396] |
ba41fd5d-520c-45c9-86b3-82e73d95da20 | sparcs-recovering-low-rank-and-sparse | null | null | http://papers.nips.cc/paper/4438-sparcs-recovering-low-rank-and-sparse-matrices-from-compressive-measurements | http://papers.nips.cc/paper/4438-sparcs-recovering-low-rank-and-sparse-matrices-from-compressive-measurements.pdf | SpaRCS: Recovering low-rank and sparse matrices from compressive measurements | We consider the problem of recovering a matrix $\mathbf{M}$ that is the sum of a low-rank matrix $\mathbf{L}$ and a sparse matrix $\mathbf{S}$ from a small set of linear measurements of the form $\mathbf{y} = \mathcal{A}(\mathbf{M}) = \mathcal{A}({\bf L}+{\bf S})$. This model subsumes three important classes of signal... | ['Andrew E. Waters', 'Richard Baraniuk', 'Aswin C. Sankaranarayanan'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['video-compressive-sensing'] | ['computer-vision'] | [ 8.31546605e-01 -1.00961983e-01 2.75817573e-01 -1.97738111e-01
-1.04122531e+00 -6.91240668e-01 4.52670977e-02 -2.66642034e-01
-1.42092109e-01 5.99764407e-01 1.77048385e-01 -3.98667604e-01
-9.19834077e-01 -5.11394560e-01 -9.37952578e-01 -9.52998996e-01
-4.51026708e-01 1.26036748e-01 -5.12759566e-01 -3.38233322... | [7.235952854156494, 4.4547648429870605] |
1cf724cb-830f-48e1-9208-b183f51bf02e | gender-and-representation-bias-in-gpt-3 | null | null | https://aclanthology.org/2021.nuse-1.5 | https://aclanthology.org/2021.nuse-1.5.pdf | Gender and Representation Bias in GPT-3 Generated Stories | Using topic modeling and lexicon-based word similarity, we find that stories generated by GPT-3 exhibit many known gender stereotypes. Generated stories depict different topics and descriptions depending on GPT-3’s perceived gender of the character in a prompt, with feminine characters more likely to be associated with... | ['David Bamman', 'Li Lucy'] | null | null | null | null | naacl-nuse-2021-6 | ['word-similarity'] | ['natural-language-processing'] | [-1.90710396e-01 5.02486467e-01 -5.27506888e-01 -3.46106648e-01
-1.59353986e-01 -6.01288378e-01 9.61577535e-01 8.87766421e-01
-3.17033619e-01 6.33615792e-01 1.31889164e+00 -1.05030097e-01
-1.15128551e-02 -1.02044141e+00 -2.17301860e-01 -1.78265899e-01
3.87232691e-01 9.49018061e-01 -1.59918964e-01 -6.00414872... | [9.203031539916992, 10.215691566467285] |
17c50e4a-86bf-4d8b-b204-dd94d06196d6 | towards-personalized-preprocessing-pipeline | 2302.14329 | null | https://arxiv.org/abs/2302.14329v1 | https://arxiv.org/pdf/2302.14329v1.pdf | Towards Personalized Preprocessing Pipeline Search | Feature preprocessing, which transforms raw input features into numerical representations, is a crucial step in automated machine learning (AutoML) systems. However, the existing systems often have a very small search space for feature preprocessing with the same preprocessing pipeline applied to all the numerical feat... | ['Xia Hu', 'Qiaoyu Tan', 'Daochen Zha', 'Diego Martinez'] | 2023-02-28 | null | null | null | null | ['automl', 'deep-clustering', 'deep-clustering'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-6.25303611e-02 -2.83439755e-01 4.76774946e-02 -5.36237478e-01
-7.89893866e-01 -8.32076311e-01 1.66675851e-01 3.67890388e-01
-2.85960019e-01 -1.53726682e-01 -9.54358280e-02 -1.73551127e-01
-3.94355714e-01 -9.39848602e-01 -5.07743180e-01 -6.60903931e-01
-8.62721913e-03 5.33282876e-01 2.30395347e-01 1.10882320... | [9.122529029846191, 3.3256702423095703] |
1476b067-c974-41f5-8ebb-60f3f15556d9 | depth-neus-neural-implicit-surfaces-learning | 2303.17088 | null | https://arxiv.org/abs/2303.17088v1 | https://arxiv.org/pdf/2303.17088v1.pdf | Depth-NeuS: Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization | Recently, methods for neural surface representation and rendering, for example NeuS, have shown that learning neural implicit surfaces through volume rendering is becoming increasingly popular and making good progress. However, these methods still face some challenges. Existing methods lack a direct representation of d... | ['Conglin Wang', 'Yichao Gao', 'Yinhe Han', 'Shuai Liang', 'Runnan Chen', 'Cheng Zeng', 'Hanqi Jiang'] | 2023-03-30 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 3.04582983e-01 -2.28587762e-02 2.68582068e-02 -4.87727612e-01
-4.65789884e-01 -1.85628165e-03 3.92434835e-01 -1.58180863e-01
4.16743010e-02 6.30909264e-01 -9.87962559e-02 1.15847252e-01
2.72075355e-01 -1.25884497e+00 -8.48479092e-01 -4.01121587e-01
1.68374583e-01 4.70106661e-01 5.11525035e-01 -1.55491009... | [9.151484489440918, -3.220156669616699] |
683bfaf6-7e27-4114-b764-50fafc8a0f06 | fast-detection-of-multiple-objects-in-traffic | 1510.03125 | null | http://arxiv.org/abs/1510.03125v1 | http://arxiv.org/pdf/1510.03125v1.pdf | Fast detection of multiple objects in traffic scenes with a common detection framework | Traffic scene perception (TSP) aims to real-time extract accurate on-road
environment information, which in- volves three phases: detection of objects of
interest, recognition of detected objects, and tracking of objects in motion.
Since recognition and tracking often rely on the results from detection, the
ability to ... | ['Anton Van Den Hengel', 'Sakrapee Paisitkriangkrai', 'Chunhua Shen', 'Qichang Hu', 'Fatih Porikli'] | 2015-10-12 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 1.86711997e-01 -5.63663483e-01 -6.47635311e-02 -3.27918559e-01
-6.29488111e-01 -2.21028507e-01 6.08263254e-01 1.41974585e-03
-7.63347566e-01 4.48362201e-01 -3.11831057e-01 -3.92344445e-02
1.52578838e-02 -9.22396660e-01 -5.84334850e-01 -9.19046819e-01
1.87768504e-01 2.18121618e-01 1.18328714e+00 5.98351471... | [8.051230430603027, -0.8721227049827576] |
378db00b-36de-4c3b-a383-ca0de0f79357 | vgos-voxel-grid-optimization-for-view | 2304.13386 | null | https://arxiv.org/abs/2304.13386v2 | https://arxiv.org/pdf/2304.13386v2.pdf | VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs | Neural Radiance Fields (NeRF) has shown great success in novel view synthesis due to its state-of-the-art quality and flexibility. However, NeRF requires dense input views (tens to hundreds) and a long training time (hours to days) for a single scene to generate high-fidelity images. Although using the voxel grids to r... | ['Huaizhong Lin', 'Wei Xing', 'Lei Zhao', 'Boyan Ji', 'Guangyuan Li', 'Jiafu Chen', 'Zhanjie Zhang', 'Jiakai Sun'] | 2023-04-26 | null | null | null | null | ['novel-view-synthesis'] | ['computer-vision'] | [ 1.31506398e-01 -2.41392851e-01 3.37852746e-01 -3.30618024e-01
-7.15089798e-01 -3.78999203e-01 2.58888870e-01 -2.64825970e-01
8.01125988e-02 6.71048284e-01 2.42031813e-01 -2.80732393e-01
-6.72239736e-02 -1.06913459e+00 -9.27443326e-01 -6.63716495e-01
2.69819915e-01 1.48319155e-02 1.74423084e-01 -2.61400372... | [9.494585990905762, -2.9673984050750732] |
b081d588-133e-40ad-9831-47450d7ba873 | video-p2p-video-editing-with-cross-attention | 2303.04761 | null | https://arxiv.org/abs/2303.04761v1 | https://arxiv.org/pdf/2303.04761v1.pdf | Video-P2P: Video Editing with Cross-attention Control | This paper presents Video-P2P, a novel framework for real-world video editing with cross-attention control. While attention control has proven effective for image editing with pre-trained image generation models, there are currently no large-scale video generation models publicly available. Video-P2P addresses this lim... | ['Jiaya Jia', 'Zhe Lin', 'Wenbo Li', 'Yuechen Zhang', 'Shaoteng Liu'] | 2023-03-08 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 4.39710855e-01 -5.38958982e-02 -9.03030783e-02 2.45458446e-02
-5.83807349e-01 -3.79422456e-01 6.72203660e-01 -2.52053529e-01
-2.38230407e-01 5.75841486e-01 5.34044564e-01 1.01655819e-01
3.58832628e-01 -4.97210979e-01 -8.70725989e-01 -4.63100582e-01
4.98211056e-01 3.09811294e-01 2.31467694e-01 -3.26454103... | [10.892383575439453, -0.5981190204620361] |
8ffdf098-110d-4679-a065-cb149a59b224 | multi-perspective-document-revision | null | null | https://aclanthology.org/2022.coling-1.535 | https://aclanthology.org/2022.coling-1.535.pdf | Multi-Perspective Document Revision | This paper presents a novel multi-perspective document revision task. In conventional studies on document revision, tasks such as grammatical error correction, sentence reordering, and discourse relation classification have been performed individually; however, these tasks simultaneously should be revised to improve th... | ['Ryo Masumura', 'Tomohiro Tanaka', 'Hiroshi Sato', 'Mana Ihori'] | null | null | null | null | coling-2022-10 | ['grammatical-error-correction', 'relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.40564495e-01 3.46170276e-01 -1.26598954e-01 -5.74292362e-01
-6.79280937e-01 -2.95979649e-01 6.35693014e-01 4.57775533e-01
-4.87968475e-01 7.28445530e-01 5.76743960e-01 -4.41607624e-01
-4.30711359e-03 -5.15694916e-01 -5.21091521e-01 -1.09995835e-01
8.38423252e-01 4.62296635e-01 2.72545546e-01 -6.74490273... | [12.00517463684082, 9.351727485656738] |
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