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399077ba-8576-4317-99e1-d3cd0802b5ff | co-learning-planning-and-control-policies | 2303.01346 | null | https://arxiv.org/abs/2303.01346v1 | https://arxiv.org/pdf/2303.01346v1.pdf | Co-learning Planning and Control Policies Using Differentiable Formal Task Constraints | This paper presents a hierarchical reinforcement learning algorithm constrained by differentiable signal temporal logic. Previous work on logic-constrained reinforcement learning consider encoding these constraints with a reward function, constraining policy updates with a sample-based policy gradient. However, such te... | ['Suresh Jagannathan', 'Ahmed H. Qureshi', 'Daniel Lawson', 'Joe Eappen', 'Zikang Xiong'] | 2023-03-02 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 8.51598307e-02 1.61631301e-01 -6.54953957e-01 -1.66552380e-01
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-4.71363127e-01 5.42236686e-01 2.36670986e-01 -2.52466828... | [4.274981498718262, 1.7170690298080444] |
1fed9d60-d8ef-4035-ae17-8b41c30dfd4d | geometric-visual-similarity-learning-in-3d | 2303.00874 | null | https://arxiv.org/abs/2303.00874v1 | https://arxiv.org/pdf/2303.00874v1.pdf | Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training | Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training, due to their sharing of numerous same semantic regions. However, the lack of the semantic prior in metrics and the semantic-independent variation in 3D medical images make it challenging to get a reliable measurement for the ... | ['Shuo Li', 'Boyu Wang', 'Jean-Louis Coatrieux', 'Yang Chen', 'Rongjun Ge', 'Guanyu Yang', 'Yuting He'] | 2023-03-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/He_Geometric_Visual_Similarity_Learning_in_3D_Medical_Image_Self-Supervised_Pre-Training_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/He_Geometric_Visual_Similarity_Learning_in_3D_Medical_Image_Self-Supervised_Pre-Training_CVPR_2023_paper.pdf | cvpr-2023-1 | ['geometric-matching'] | ['computer-vision'] | [ 1.52776882e-01 -1.10048018e-01 -1.52776480e-01 -7.33080029e-01
-8.03937137e-01 -4.64913428e-01 4.61724430e-01 3.91103566e-01
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4.57138866e-02 1.72942057e-01 3.03016990e-01 -1.71950370... | [8.070387840270996, -3.1978418827056885] |
d1c0d478-0d26-4950-8e84-8fda801b021e | bangla-license-plate-recognition-using | 1809.00905 | null | http://arxiv.org/abs/1809.00905v1 | http://arxiv.org/pdf/1809.00905v1.pdf | Bangla License Plate Recognition Using Convolutional Neural Networks (CNN) | In the last few years, the deep learning technique in particular
Convolutional Neural Networks (CNNs) is using massively in the field of
computer vision and machine learning. This deep learning technique provides
state-of-the-art accuracy in different classification, segmentation, and
detection tasks on different bench... | ['M M Shaifur Rahman', 'Mst Shamima Nasrin', 'Moin Mostakim', 'Md Zahangir Alom'] | 2018-09-04 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [-5.53176522e-01 -6.92351520e-01 -1.39485031e-01 -4.34804827e-01
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4.81944472e-01 5.27961075e-01 7.59318888e-01 -4.19025600... | [9.825091361999512, -4.957208156585693] |
b98aef9e-fe95-4cf5-a2f4-502d9ebdcb60 | learning-intrinsic-image-decomposition-from | 1804.00582 | null | http://arxiv.org/abs/1804.00582v1 | http://arxiv.org/pdf/1804.00582v1.pdf | Learning Intrinsic Image Decomposition from Watching the World | Single-view intrinsic image decomposition is a highly ill-posed problem, and
so a promising approach is to learn from large amounts of data. However, it is
difficult to collect ground truth training data at scale for intrinsic images.
In this paper, we explore a different approach to learning intrinsic images:
observin... | ['Zhengqi Li', 'Noah Snavely'] | 2018-04-02 | learning-intrinsic-image-decomposition-from-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Learning_Intrinsic_Image_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Learning_Intrinsic_Image_CVPR_2018_paper.pdf | cvpr-2018-6 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 3.94685835e-01 -7.78855756e-02 8.91874880e-02 -3.29743385e-01
-9.98206079e-01 -7.65535712e-01 3.10248017e-01 -2.75134206e-01
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2.27049544e-01 4.55458254e-01 3.07410836e-01 -6.46224320... | [9.22297191619873, -2.6729371547698975] |
08e7d76a-9e19-4e65-9549-8f4fb7928ec6 | tvsum-summarizing-web-videos-using-titles | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Song_TVSum_Summarizing_Web_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Song_TVSum_Summarizing_Web_2015_CVPR_paper.pdf | TVSum: Summarizing Web Videos Using Titles | Video summarization is a challenging problem in part because knowing which part of a video is important requires prior knowledge about its main topic. We present TVSum, an unsupervised video summarization framework that uses title-based image search results to find visually important shots. We observe that a video titl... | ['Jordi Vallmitjana', 'Amanda Stent', 'Alejandro Jaimes', 'Yale Song'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 4.34132725e-01 -3.62800658e-02 -2.49727696e-01 3.61835919e-02
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3.04914918e-02 -2.96745241e-01 -1.03783834e+00 -7.86714852e-01
-1.21107325e-01 4.89798710e-02 4.01780605e-01 1.52637407... | [10.434208869934082, 0.5209068655967712] |
0eb33340-420d-4418-9407-c0fe35e71d5b | relationship-quantification-of-image | 2212.04148 | null | https://arxiv.org/abs/2212.04148v2 | https://arxiv.org/pdf/2212.04148v2.pdf | Relationship Quantification of Image Degradations | In this paper, we study two challenging but less-touched problems in image restoration, namely, i) how to quantify the relationship between different image degradations and ii) how to improve the performance on a specific degradation using the quantified relationship. To tackle the first challenge, Degradation Relation... | ['Xi Peng', 'Peng Hu', 'Yuanbiao Gou', 'Boyun Li', 'Wenxin Wang'] | 2022-12-08 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 2.18993410e-01 -5.53509057e-01 1.21131755e-01 -2.04336330e-01
-3.14649135e-01 -3.99168432e-02 2.58597255e-01 1.49162160e-02
-7.39543438e-02 6.17228389e-01 1.38346151e-01 -2.03921184e-01
-2.70043164e-01 -6.14777267e-01 -4.74334478e-01 -1.37199891e+00
2.25197613e-01 -2.82726794e-01 3.09541196e-01 -3.99952054... | [11.618261337280273, -2.0913193225860596] |
de6f5a64-c9d1-48fd-84e1-f2b8efb247b6 | on-defending-against-label-flipping-attacks | 1908.04473 | null | https://arxiv.org/abs/1908.04473v3 | https://arxiv.org/pdf/1908.04473v3.pdf | On Defending Against Label Flipping Attacks on Malware Detection Systems | Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and I... | ['Mohammad Shojafar', 'Reza Javidan', 'Rahim Taheri', 'Zahra Pooranian', 'Mauro Conti', 'Ali Miri'] | 2019-08-13 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 4.13655102e-01 -2.12346658e-01 -1.61284506e-01 -2.15305865e-01
-5.63048482e-01 -9.79701936e-01 6.99528635e-01 1.55051306e-01
-3.62775326e-01 6.46489561e-01 -3.69323373e-01 -7.80159771e-01
-2.57186979e-01 -7.55304456e-01 -7.56303549e-01 -7.73590803e-01
-3.45786572e-01 2.57756561e-01 5.48825264e-01 -2.52592087... | [14.394573211669922, 9.654362678527832] |
9d7a7668-5946-45da-b047-81cbbe41a4e3 | contributions-of-shape-texture-and-color-in | 2207.09510 | null | https://arxiv.org/abs/2207.09510v1 | https://arxiv.org/pdf/2207.09510v1.pdf | Contributions of Shape, Texture, and Color in Visual Recognition | We investigate the contributions of three important features of the human visual system (HVS)~ -- ~shape, texture, and color ~ -- ~to object classification. We build a humanoid vision engine (HVE) that explicitly and separately computes shape, texture, and color features from images. The resulting feature vectors are t... | ['Laurent Itti', 'Xingrui Wang', 'Zhi Xu', 'Yao Xiao', 'Yunhao Ge'] | 2022-07-19 | null | null | null | null | ['classification'] | ['methodology'] | [-2.22747192e-01 -3.29647124e-01 3.01606447e-01 -3.96487117e-01
-3.56064774e-02 -5.05954087e-01 4.68893021e-01 -1.95229836e-02
-5.65843523e-01 4.06176418e-01 -3.13440889e-01 7.69253746e-02
-1.23556145e-01 -8.49897027e-01 -4.64919955e-01 -5.45526564e-01
-1.67758420e-01 4.36464667e-01 3.98802400e-01 -3.80070657... | [10.118318557739258, 2.1580114364624023] |
da117d17-bd99-4fa6-8e71-945e8c801b40 | variational-inference-for-neyman-scott | 2303.03701 | null | https://arxiv.org/abs/2303.03701v1 | https://arxiv.org/pdf/2303.03701v1.pdf | Variational Inference for Neyman-Scott Processes | Neyman-Scott processes (NSPs) have been applied across a range of fields to model points or temporal events with a hierarchy of clusters. Markov chain Monte Carlo (MCMC) is typically used for posterior sampling in the model. However, MCMC's mixing time can cause the resulting inference to be slow, and thereby slow down... | ['Christian R. Shelton', 'Chengkuan Hong'] | 2023-03-07 | null | null | null | null | ['point-processes'] | ['methodology'] | [-1.21516529e-02 -3.80599648e-01 -1.42314121e-01 -4.14951324e-01
-1.14986575e+00 -3.95069778e-01 9.47579563e-01 9.14275199e-02
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-1.02597095e-01 -7.36820877e-01 -4.73775476e-01 -6.91265583e-01
-1.17851816e-01 1.11191213e+00 4.86648679e-01 5.84805429... | [6.789247512817383, 3.9176416397094727] |
219ebabe-d914-4862-a480-1d5813e232d9 | designing-an-illumination-aware-network-for | 2207.10582 | null | https://arxiv.org/abs/2207.10582v1 | https://arxiv.org/pdf/2207.10582v1.pdf | Designing An Illumination-Aware Network for Deep Image Relighting | Lighting is a determining factor in photography that affects the style, expression of emotion, and even quality of images. Creating or finding satisfying lighting conditions, in reality, is laborious and time-consuming, so it is of great value to develop a technology to manipulate illumination in an image as post-proce... | ['Ming-Ming Cheng', 'Chun-Le Guo', 'Rui-Xun Zhang', 'Zhen Li', 'Zuo-Liang Zhu'] | 2022-07-21 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 3.63254398e-01 -2.45361641e-01 1.06358089e-01 -6.21271968e-01
-2.45102242e-01 -3.14900488e-01 3.55954409e-01 -5.31731129e-01
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4.36303705e-01 -3.43293577e-01 5.06779477e-02 -3.87837321... | [10.059147834777832, -2.6702287197113037] |
21712fd8-b058-473c-b7df-337ac0fc5efb | grounded-language-image-pre-training | 2112.03857 | null | https://arxiv.org/abs/2112.03857v2 | https://arxiv.org/pdf/2112.03857v2.pdf | Grounded Language-Image Pre-training | This paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and ground... | ['Jianfeng Gao', 'Kai-Wei Chang', 'Jenq-Neng Hwang', 'Lei Zhang', 'Lu Yuan', 'Lijuan Wang', 'Yiwu Zhong', 'Chunyuan Li', 'Jianwei Yang', 'Haotian Zhang', 'Pengchuan Zhang', 'Liunian Harold Li'] | 2021-12-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Grounded_Language-Image_Pre-Training_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Grounded_Language-Image_Pre-Training_CVPR_2022_paper.pdf | cvpr-2022-1 | ['phrase-grounding'] | ['natural-language-processing'] | [ 2.78306723e-01 7.05641583e-02 -2.87821800e-01 -1.87389314e-01
-1.38134289e+00 -6.11987352e-01 8.80148709e-01 1.36464983e-02
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4.60522145e-01 -5.74026465e-01 -1.17359507e+00 -3.45937997e-01
-2.95588356e-02 3.62501442e-01 3.91477376e-01 -2.28979334... | [10.381685256958008, 1.6775542497634888] |
d72c96b6-1c92-4899-a509-304ce7832336 | iterative-scene-graph-generation-with | 2211.16636 | null | https://arxiv.org/abs/2211.16636v1 | https://arxiv.org/pdf/2211.16636v1.pdf | Iterative Scene Graph Generation with Generative Transformers | Scene graphs provide a rich, structured representation of a scene by encoding the entities (objects) and their spatial relationships in a graphical format. This representation has proven useful in several tasks, such as question answering, captioning, and even object detection, to name a few. Current approaches take a ... | ['Sathyanarayanan N. Aakur', 'Sanjoy Kundu'] | 2022-11-30 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 6.17430389e-01 4.16257262e-01 7.58473203e-02 -4.63864118e-01
-6.91477180e-01 -6.50392532e-01 9.05740499e-01 5.88488340e-01
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2.68511958e-02 -1.11797690e+00 -9.93949175e-01 -4.05639142e-01
-1.80345789e-01 6.01151466e-01 6.31337702e-01 1.77129999... | [10.378231048583984, 1.6078689098358154] |
7d450006-152c-4426-a86e-63ce780aaf57 | on-the-impact-of-object-and-sub-component | 1911.08216 | null | https://arxiv.org/abs/1911.08216v1 | https://arxiv.org/pdf/1911.08216v1.pdf | On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery | X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on the use of automated screening approaches, including the potential anomaly detection as a methodology for concealment detection within comple... | ['Yona Falinie A. Gaus', 'Neelanjan Bhowmik', 'Toby P. Breckon', 'Samet Akcay', 'Jack W. Barker'] | 2019-11-19 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 7.95043826e-01 -6.54122531e-02 1.04460798e-01 4.22719568e-02
-1.12513661e+00 -6.50017440e-01 5.84382832e-01 9.31358814e-01
-3.51490557e-01 1.95750371e-01 -3.41391772e-01 -8.90237033e-01
-2.22859859e-01 -7.42841184e-01 -8.27341855e-01 -5.16259015e-01
-2.94482589e-01 1.07582221e-02 3.07604074e-01 -1.94548398... | [7.499978065490723, 1.90800940990448] |
3672a046-8f73-4ab3-86ec-ba3434d6caa9 | gpt-finre-in-context-learning-for-financial | 2306.17519 | null | https://arxiv.org/abs/2306.17519v1 | https://arxiv.org/pdf/2306.17519v1.pdf | GPT-FinRE: In-context Learning for Financial Relation Extraction using Large Language Models | Relation extraction (RE) is a crucial task in natural language processing (NLP) that aims to identify and classify relationships between entities mentioned in text. In the financial domain, relation extraction plays a vital role in extracting valuable information from financial documents, such as news articles, earning... | ['Ankur Parikh', 'Pawan Kumar Rajpoot'] | 2023-06-30 | null | null | null | null | ['retrieval', 'relation-extraction'] | ['methodology', 'natural-language-processing'] | [ 9.56033766e-02 4.96817559e-01 -3.23530167e-01 -2.55812436e-01
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-3.00691783e-01 9.38571990e-01 1.72665507e-01 -2.74834633... | [9.37785530090332, 8.66728687286377] |
90da262c-eadd-4ec0-a737-fb1ab1f00bf6 | local-explanation-of-dialogue-response | 2106.06528 | null | https://arxiv.org/abs/2106.06528v2 | https://arxiv.org/pdf/2106.06528v2.pdf | Local Explanation of Dialogue Response Generation | In comparison to the interpretation of classification models, the explanation of sequence generation models is also an important problem, however it has seen little attention. In this work, we study model-agnostic explanations of a representative text generation task -- dialogue response generation. Dialog response gen... | ['William Yang Wang', 'Lise Getoor', 'Wenhu Chen', 'Connor Pryor', 'Yi-Lin Tuan'] | 2021-06-11 | null | http://proceedings.neurips.cc/paper/2021/hash/03b92cd507ff5870df0db7f074728830-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/03b92cd507ff5870df0db7f074728830-Paper.pdf | neurips-2021-12 | ['implicit-relations'] | ['natural-language-processing'] | [ 6.36560380e-01 1.11543715e+00 -4.70430776e-02 -6.64938986e-01
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3.73870701e-01 7.88336873e-01 6.94457348e-03 -4.27064657... | [12.5418119430542, 8.314375877380371] |
54077676-1ad2-45e6-8543-57cfc7242894 | magnetic-resonance-fingerprinting-with | 2209.08734 | null | https://arxiv.org/abs/2209.08734v1 | https://arxiv.org/pdf/2209.08734v1.pdf | Magnetic Resonance Fingerprinting with compressed sensing and distance metric learning | Magnetic Resonance Fingerprinting (MRF) is a novel technique that simultaneously estimates multiple tissue-related parameters, such as the longitudinal relaxation time T1, the transverse relaxation time T2, off resonance frequency B0 and proton density, from a scanned object in just tens of seconds. However, the MRF me... | ['Xiaogang Wang', 'Jing Yuan', 'Qinwei Zhang', 'Hongsheng Li', 'Zhe Wang'] | 2022-09-19 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 5.43800294e-01 -2.92482167e-01 -3.91005754e-01 -3.25807691e-01
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-4.09749359e-01 3.79025578e-01 3.63194197e-01 2.90367365... | [13.501082420349121, -2.413736343383789] |
4fe4566a-532f-41f5-a7e0-7d3b723a7010 | deep-learning-of-partial-graph-matching-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Deep_Learning_of_Partial_Graph_Matching_via_Differentiable_Top-K_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Deep_Learning_of_Partial_Graph_Matching_via_Differentiable_Top-K_CVPR_2023_paper.pdf | Deep Learning of Partial Graph Matching via Differentiable Top-K | Graph matching (GM) aims at discovering node matching between graphs, by maximizing the node- and edge-wise affinities between the matched elements. As an NP-hard problem, its challenge is further pronounced in the existence of outlier nodes in both graphs which is ubiquitous in practice, especially for vision prob... | ['Junchi Yan', 'Xiaokang Yang', 'Shaofei Jiang', 'Ziao Guo', 'Runzhong Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['stereo-matching-1', 'graph-matching'] | ['computer-vision', 'graphs'] | [ 6.49842843e-02 1.84024751e-01 1.26649022e-01 -2.51564354e-01
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-2.06466869e-01 -5.46892583e-01 -9.14015055e-01 -7.41691470e-01
-1.76643163e-01 5.65555751e-01 1.84295595e-01 -1.15534872... | [7.21090030670166, 6.386640548706055] |
03c31267-d028-4afa-a799-1b262f2171e4 | unitrans-unifying-model-transfer-and-data | 2007.07683 | null | https://arxiv.org/abs/2007.07683v1 | https://arxiv.org/pdf/2007.07683v1.pdf | UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data | Prior works in cross-lingual named entity recognition (NER) with no/little labeled data fall into two primary categories: model transfer based and data transfer based methods. In this paper we find that both method types can complement each other, in the sense that, the former can exploit context information via langua... | ['Jian-Guang Lou', 'Börje F. Karlsson', 'Zijia Lin', 'Qianhui Wu', 'Biqing Huang'] | 2020-07-15 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-3.63576366e-03 -3.12041622e-02 -6.38784647e-01 -4.07882631e-01
-1.16649199e+00 -8.74128938e-01 6.80256903e-01 -1.60952449e-01
-6.81206107e-01 1.07792413e+00 3.10031056e-01 -4.14320052e-01
3.58767003e-01 -5.30171156e-01 -6.31732821e-01 -4.36916292e-01
5.45731544e-01 4.58186775e-01 2.76059527e-02 -3.71842146... | [9.960225105285645, 9.654614448547363] |
acefef7f-f7ef-42c8-a269-8b9102ae2175 | improving-j-divergence-of-brain-connectivity | 2012.11240 | null | https://arxiv.org/abs/2012.11240v2 | https://arxiv.org/pdf/2012.11240v2.pdf | Improving J-divergence of brain connectivity states by graph Laplacian denoising | Functional connectivity (FC) can be represented as a network, and is frequently used to better understand the neural underpinnings of complex tasks such as motor imagery (MI) detection in brain-computer interfaces (BCIs). However, errors in the estimation of connectivity can affect the detection performances. In this w... | ['Stefania Colonnese', 'Fabrizio De Vico Fallani', 'Danielle S. Bassett', 'Marie-Constance Corsi', 'Gaetano Scarano', 'Tiziana Cattai'] | 2020-12-21 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [ 5.37956953e-01 -1.18423887e-01 2.91052818e-01 -1.58758070e-02
-1.28539607e-01 -3.48323882e-01 4.60382581e-01 -9.84452739e-02
-3.57239574e-01 6.00718975e-01 1.30304620e-01 -1.51685297e-01
-7.47051179e-01 -4.92732793e-01 -4.37685609e-01 -7.44677007e-01
-7.53417134e-01 1.12896934e-02 -2.34854385e-01 -9.70569849... | [12.51935863494873, 3.4235363006591797] |
08ad7dd6-3f94-4a31-b74d-a2ddfdbedc22 | multiple-confidence-gates-for-joint-training | 2204.00226 | null | https://arxiv.org/abs/2204.00226v1 | https://arxiv.org/pdf/2204.00226v1.pdf | Multiple Confidence Gates For Joint Training Of SE And ASR | Joint training of speech enhancement model (SE) and speech recognition model (ASR) is a common solution for robust ASR in noisy environments. SE focuses on improving the auditory quality of speech, but the enhanced feature distribution is changed, which is uncertain and detrimental to the ASR. To tackle this challenge,... | ['Shilei Zhang', 'Junlan Feng', 'Yingying Gao', 'Weibin Zhu', 'Tianrui Wang'] | 2022-04-01 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 0.07719503 -0.1609266 0.5118399 -0.37027082 -0.73096496 -0.05172543
0.04116208 -0.24807823 -0.3966903 0.41672313 0.3958122 -0.34207693
0.05006345 -0.47934324 -0.39732856 -0.68303066 0.20990512 -0.3593985
0.18527247 -0.4856685 -0.06374397 0.45238796 -1.6885871 0.37094623
0.8722075 0.9979815 0.7... | [14.843189239501953, 6.023190975189209] |
0e1d1c2b-107e-4a3c-af30-d0e2c04d660a | simple-deep-random-model-ensemble | 1305.1019 | null | http://arxiv.org/abs/1305.1019v2 | http://arxiv.org/pdf/1305.1019v2.pdf | Simple Deep Random Model Ensemble | Representation learning and unsupervised learning are two central topics of
machine learning and signal processing. Deep learning is one of the most
effective unsupervised representation learning approach. The main contributions
of this paper to the topics are as follows. (i) We propose to view the
representative deep ... | ['Xiao-Lei Zhang', 'Ji Wu'] | 2013-05-05 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [-2.14173824e-01 -1.31933033e-01 1.80574507e-02 -3.27183276e-01
-5.07841349e-01 -1.88840598e-01 5.06972909e-01 -2.38455832e-01
-1.87428400e-01 1.30716518e-01 3.98940384e-01 1.67137057e-01
-5.09606957e-01 -8.05319965e-01 -6.21569514e-01 -1.29522681e+00
-1.62400961e-01 6.96232975e-01 -3.03959638e-01 -4.55485433... | [9.138726234436035, 3.1967079639434814] |
0d90c416-152d-4974-9780-2a5f484544af | fast-searching-for-a-faster-arbitrarily | 2111.02394 | null | https://arxiv.org/abs/2111.02394v2 | https://arxiv.org/pdf/2111.02394v2.pdf | FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation | We propose an accurate and efficient scene text detection framework, termed FAST (i.e., faster arbitrarily-shaped text detector). Different from recent advanced text detectors that used complicated post-processing and hand-crafted network architectures, resulting in low inference speed, FAST has two new designs. (1) We... | ['Tong Lu', 'Enze Xie', 'Guo Chen', 'Wenhai Wang', 'Jiahao Wang', 'Ping Luo', 'Zhe Chen'] | 2021-11-03 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [-1.61306083e-01 -4.93712902e-01 2.47276314e-02 -2.68246353e-01
-5.01709819e-01 -4.99646544e-01 4.03462470e-01 -7.04559013e-02
-5.16680658e-01 -2.83676349e-02 -1.15773998e-01 -5.13934791e-01
2.95919508e-01 -7.64899433e-01 -5.54811776e-01 -4.52679962e-01
2.21907392e-01 3.13691050e-01 6.63821995e-01 1.18425995... | [12.021595001220703, 2.23667573928833] |
1219f8f3-5010-4088-98cc-2661f8a9f688 | low-resource-named-entity-recognition-with | null | null | https://aclanthology.org/I17-2016 | https://aclanthology.org/I17-2016.pdf | Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields | Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world{'}s languages it is unfeasible to obtain such annotation. In this paper, we present a transfer learnin... | ['Kevin Duh', 'Ryan Cotterell'] | 2017-11-01 | low-resource-named-entity-recognition-with-1 | https://aclanthology.org/I17-2016 | https://aclanthology.org/I17-2016.pdf | ijcnlp-2017-11 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-7.05389306e-02 1.02580048e-01 -2.62538522e-01 -5.71059346e-01
-1.05975413e+00 -7.01937258e-01 1.97308853e-01 3.58174175e-01
-1.05297899e+00 1.37801349e+00 1.02728292e-01 -2.89216995e-01
5.41591108e-01 -8.79429400e-01 -6.22013569e-01 -1.50970742e-01
-8.66386145e-02 8.71831238e-01 4.14398819e-01 1.01736635... | [10.051029205322266, 9.803346633911133] |
c7243f62-697a-4f04-94ed-177aa667c1f2 | individual-causal-inference-using-panel-data | 2306.01969 | null | https://arxiv.org/abs/2306.01969v1 | https://arxiv.org/pdf/2306.01969v1.pdf | Individual Causal Inference Using Panel Data With Multiple Outcomes | Policy evaluation in empirical microeconomics has been focusing on estimating the average treatment effect and more recently the heterogeneous treatment effects, often relying on the unconfoundedness assumption. We propose a method based on the interactive fixed effects model to estimate treatment effects at the indivi... | ['Wei Tian'] | 2023-06-03 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [-8.42817798e-02 1.45029649e-01 -1.20720279e+00 -2.48634726e-01
-5.20637155e-01 -3.23662490e-01 1.56192258e-01 2.83941060e-01
-4.67883587e-01 1.22747695e+00 6.97148442e-01 -7.30099320e-01
-2.33789518e-01 -8.28629375e-01 -6.86707258e-01 -3.56938154e-01
-3.41919243e-01 4.30331290e-01 -4.81574655e-01 4.59415495... | [7.894723892211914, 5.172478199005127] |
815344c0-3de5-49fa-bd1c-376b2ccf45bc | 190510752 | 1905.10752 | null | https://arxiv.org/abs/1905.10752v1 | https://arxiv.org/pdf/1905.10752v1.pdf | TIGS: An Inference Algorithm for Text Infilling with Gradient Search | Text infilling is defined as a task for filling in the missing part of a sentence or paragraph, which is suitable for many real-world natural language generation scenarios. However, given a well-trained sequential generative model, generating missing symbols conditioned on the context is challenging for existing greedy... | ['PengFei Liu', 'Dayiheng Liu', 'Jie Fu', 'Jiancheng Lv'] | 2019-05-26 | tigs-an-inference-algorithm-for-text | https://aclanthology.org/P19-1406 | https://aclanthology.org/P19-1406.pdf | acl-2019-7 | ['text-infilling'] | ['natural-language-processing'] | [ 6.50825739e-01 2.94528957e-02 -1.00564957e-01 -2.65499353e-01
-7.68330753e-01 -3.70628059e-01 8.54932904e-01 -1.21375434e-01
-3.37347895e-01 1.23743498e+00 4.31613833e-01 -6.57833397e-01
4.36068535e-01 -7.92319477e-01 -8.36370111e-01 -4.38514918e-01
6.35839343e-01 6.92314506e-01 3.78269702e-02 -1.12558916... | [11.969132423400879, 9.072413444519043] |
22d932a6-010d-4cd2-af8b-17a3121df171 | a-cnn-based-patent-image-retrieval-method-for | 2003.08741 | null | https://arxiv.org/abs/2003.08741v3 | https://arxiv.org/pdf/2003.08741v3.pdf | A Convolutional Neural Network-based Patent Image Retrieval Method for Design Ideation | The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we pro... | ['Jianxi Luo', 'Christopher L. Magee', 'Jie Hu', 'Shuo Jiang', 'Guillermo Ruiz Pava'] | 2020-03-10 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 5.19266464e-02 -2.70333529e-01 -8.31869125e-01 1.63888961e-01
-3.71810049e-01 -5.97851694e-01 7.06844687e-01 -3.20417173e-02
-5.32989129e-02 4.58625674e-01 -8.55325907e-02 -7.40641654e-01
-1.84532017e-01 -8.81598294e-01 -6.36928976e-01 -4.47496533e-01
4.59775716e-01 -2.37796288e-02 -2.48076811e-01 1.24368526... | [10.830292701721191, 0.5859860181808472] |
581b63ac-bb8b-4e7f-827a-6f036669aa04 | hairmapper-removing-hair-from-portraits-using | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wu_HairMapper_Removing_Hair_From_Portraits_Using_GANs_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wu_HairMapper_Removing_Hair_From_Portraits_Using_GANs_CVPR_2022_paper.pdf | HairMapper: Removing Hair From Portraits Using GANs | Removing hair from portrait images is challenging due to the complex occlusions between hair and face, as well as the lack of paired portrait data with/without hair. To this end, we present a dataset and a baseline method for removing hair from portrait images using generative adversarial networks (GANs). Our core ... | ['Xiaogang Jin', 'Yong-Liang Yang', 'Yiqian Wu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 4.65677172e-01 4.94653046e-01 1.17150076e-01 -3.61489922e-01
-5.24925828e-01 -5.89607060e-01 5.58821917e-01 -6.24451518e-01
3.10251623e-01 5.50983310e-01 1.30065903e-01 9.26911905e-02
5.31574845e-01 -8.66601467e-01 -7.36509681e-01 -6.75353408e-01
4.67823058e-01 4.01891768e-01 -4.04879957e-01 -4.31557834... | [12.429119110107422, -0.2864268720149994] |
11df8cf1-a766-4a4a-9e74-ca819438c891 | on-learning-semantic-representations-for | 2007.04101 | null | https://arxiv.org/abs/2007.04101v1 | https://arxiv.org/pdf/2007.04101v1.pdf | On Learning Semantic Representations for Million-Scale Free-Hand Sketches | In this paper, we study learning semantic representations for million-scale free-hand sketches. This is highly challenging due to the domain-unique traits of sketches, e.g., diverse, sparse, abstract, noisy. We propose a dual-branch CNNRNN network architecture to represent sketches, which simultaneously encodes both th... | ['Yi-Zhe Song', 'Tongtong Yuan', 'Timothy M. Hospedales', 'Tao Xiang', 'Peng Xu', 'Liang Wang', 'Yongye Huang'] | 2020-07-07 | null | null | null | null | ['learning-semantic-representations'] | ['methodology'] | [-2.56002605e-01 -5.93981981e-01 -4.28198308e-01 -4.51634496e-01
-7.99069762e-01 -4.78513032e-01 5.51396668e-01 -3.89001906e-01
2.24109907e-02 2.41883233e-01 4.25836027e-01 2.05746740e-01
-5.41233346e-02 -9.69111025e-01 -6.69954062e-01 -3.87300879e-01
2.22598672e-01 5.25236726e-01 -1.56740902e-03 -2.52702773... | [11.670783042907715, 0.5951008796691895] |
39458165-993b-49bc-81bd-c5492cb435b7 | tackling-morpion-solitaire-with-alphazero | 2006.07970 | null | https://arxiv.org/abs/2006.07970v1 | https://arxiv.org/pdf/2006.07970v1.pdf | Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning | Morpion Solitaire is a popular single player game, performed with paper and pencil. Due to its large state space (on the order of the game of Go) traditional search algorithms, such as MCTS, have not been able to find good solutions. A later algorithm, Nested Rollout Policy Adaptation, was able to find a new record of ... | ['Hui Wang', 'Aske Plaat', 'Mike Preuss', 'Michael Emmerich'] | 2020-06-14 | null | null | null | null | ['game-of-go', 'solitaire'] | ['playing-games', 'playing-games'] | [-4.47374791e-01 -1.48733826e-02 -2.08327979e-01 3.84060770e-01
-9.84287024e-01 -7.23428249e-01 3.33985418e-01 -9.74870399e-02
-6.98590398e-01 1.31699932e+00 -9.55207869e-02 -3.36806506e-01
-6.35136485e-01 -7.19977021e-01 -5.29840350e-01 -6.66799724e-01
-2.80351788e-01 6.59025967e-01 5.10631680e-01 -8.51190209... | [3.5836448669433594, 1.5313441753387451] |
dcc2ad54-95fc-4ebd-bc6f-0d5b83fa7290 | signal-is-harder-to-learn-than-bias-debiasing | 2305.19671 | null | https://arxiv.org/abs/2305.19671v1 | https://arxiv.org/pdf/2305.19671v1.pdf | Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss | Spurious correlations are everywhere. While humans often do not perceive them, neural networks are notorious for learning unwanted associations, also known as biases, instead of the underlying decision rule. As a result, practitioners are often unaware of the biased decision-making of their classifiers. Such a biased m... | ['Julia E. Vogt', 'Ričards Marcinkevičs', 'Laura Manduchi', 'Moritz Vandenhirtz'] | 2023-05-31 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 2.17962459e-01 1.18842065e-01 -1.32761136e-01 -4.28927034e-01
-2.93060064e-01 -4.46964771e-01 7.38814890e-01 -1.13097072e-01
-1.45415947e-01 8.45279157e-01 2.64241397e-01 -1.78480119e-01
-1.28639206e-01 -7.01855302e-01 -6.10775530e-01 -1.15328085e+00
2.23492056e-01 2.91766375e-01 -1.59922317e-01 -1.24929659... | [8.969643592834473, 4.62490701675415] |
bf3655ef-66f0-421f-92ca-b1499ac84cf7 | adaptive-memory-management-for-video-object | 2204.06626 | null | https://arxiv.org/abs/2204.06626v1 | https://arxiv.org/pdf/2204.06626v1.pdf | Adaptive Memory Management for Video Object Segmentation | Matching-based networks have achieved state-of-the-art performance for video object segmentation (VOS) tasks by storing every-k frames in an external memory bank for future inference. Storing the intermediate frames' predictions provides the network with richer cues for segmenting an object in the current frame. Howeve... | ['Charalambos Poullis', 'Ali Pourganjalikhan'] | 2022-04-13 | null | null | null | null | ['semi-supervised-video-object-segmentation'] | ['computer-vision'] | [ 8.60992223e-02 -9.85014588e-02 -5.39674520e-01 -3.39528471e-01
-3.69728714e-01 -3.77230853e-01 5.78018166e-02 -7.03617856e-02
-8.43586624e-01 4.74402308e-01 -1.73781604e-01 -3.43424320e-01
1.39972866e-01 -7.68361151e-01 -9.44233954e-01 -4.54471141e-01
-1.44455373e-01 3.52956623e-01 1.08307362e+00 4.21943069... | [9.130205154418945, -0.018118364736437798] |
34df5c10-dd7c-4b21-9125-cd60f7fd1da8 | defensive-ml-defending-architectural-side | 2302.01474 | null | https://arxiv.org/abs/2302.01474v1 | https://arxiv.org/pdf/2302.01474v1.pdf | Defensive ML: Defending Architectural Side-channels with Adversarial Obfuscation | Side-channel attacks that use machine learning (ML) for signal analysis have become prominent threats to computer security, as ML models easily find patterns in signals. To address this problem, this paper explores using Adversarial Machine Learning (AML) methods as a defense at the computer architecture layer to obfus... | ['Josep Torrellas', 'Nam Sung Kim', 'Bo Li', 'Raghavendra Pradyumna Pothukuchi', 'Hyoungwook Nam'] | 2023-02-03 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 1.78170323e-01 8.64063278e-02 -1.80549785e-01 9.72253531e-02
-6.87604487e-01 -8.74497235e-01 5.23443103e-01 5.73185831e-02
-3.36899728e-01 2.83193171e-01 -3.27474564e-01 -1.21992314e+00
1.88269451e-01 -7.77421951e-01 -5.58036566e-01 -9.04844344e-01
-8.15645754e-01 -1.24667481e-01 3.94529790e-01 -5.00481606... | [5.565083026885986, 7.519000053405762] |
46c15249-99c0-4883-b9c1-73cde9b6b61b | task-adaptive-feature-transformation-for-one | 2304.06832 | null | https://arxiv.org/abs/2304.06832v1 | https://arxiv.org/pdf/2304.06832v1.pdf | Task Adaptive Feature Transformation for One-Shot Learning | We introduce a simple non-linear embedding adaptation layer, which is fine-tuned on top of fixed pre-trained features for one-shot tasks, improving significantly transductive entropy-based inference for low-shot regimes. Our norm-induced transformation could be understood as a re-parametrization of the feature space to... | ['Ismail Ben Ayed', 'Freddy Lecue', 'Imtiaz Masud Ziko'] | 2023-04-13 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 2.13349849e-01 2.26166323e-01 -4.74647999e-01 -4.71567512e-01
-8.61384153e-01 -3.07548344e-01 9.54622388e-01 1.23995520e-01
-5.74686527e-01 6.77812874e-01 6.43218994e-01 2.02208653e-01
-3.79088074e-01 -8.29355121e-01 -5.69495559e-01 -1.05296230e+00
5.45331948e-02 4.54988182e-01 -2.67561413e-02 -3.22612911... | [9.469466209411621, 3.419149398803711] |
caf53489-76e2-4b81-a9dd-b4cb9783a691 | unsupervised-online-video-object-segmentation | 1810.03783 | null | https://arxiv.org/abs/1810.03783v2 | https://arxiv.org/pdf/1810.03783v2.pdf | Unsupervised Online Video Object Segmentation with Motion Property Understanding | Unsupervised video object segmentation aims to automatically segment moving objects over an unconstrained video without any user annotation. So far, only few unsupervised online methods have been reported in literature and their performance is still far from satisfactory, because the complementary information from futu... | ['Mohan Kankanhalli', 'Yongkang Wong', 'Zhiyong Cheng', 'Tao Zhuo', 'Peng Zhang'] | 2018-10-09 | unsupervised-online-video-object-segmentation-1 | null | null | ieee-transactions-on-image-processing-2019-8 | ['motion-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.49354893e-01 -1.44870073e-01 -3.06458235e-01 -1.89056858e-01
-6.79790497e-01 -4.48565066e-01 2.82300234e-01 1.01439551e-01
-6.60074890e-01 5.39507568e-01 -2.94295460e-01 1.30677493e-02
1.93752632e-01 -4.39884514e-01 -6.36784852e-01 -8.54289412e-01
4.54072095e-02 8.97807851e-02 8.97407413e-01 3.16411078... | [9.174880981445312, -0.35043877363204956] |
75d97281-9e41-482d-8c78-dd859b57ab47 | a-review-of-co-saliency-detection-technique | 1604.07090 | null | http://arxiv.org/abs/1604.07090v5 | http://arxiv.org/pdf/1604.07090v5.pdf | A Review of Co-saliency Detection Technique: Fundamentals, Applications, and Challenges | Co-saliency detection is a newly emerging and rapidly growing research area
in computer vision community. As a novel branch of visual saliency, co-saliency
detection refers to the discovery of common and salient foregrounds from two or
more relevant images, and can be widely used in many computer vision tasks. The
exis... | ['Xuelong. Li', 'Huazhu Fu', 'Dingwen Zhang', 'Junwei Han', 'Ali Borji'] | 2016-04-24 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [ 5.42432606e-01 -2.35910773e-01 -4.57432956e-01 1.70931686e-02
-4.43785429e-01 -1.94205135e-01 3.27901751e-01 1.39158785e-01
2.58513144e-03 4.33340997e-01 5.65664507e-02 9.50678587e-02
-1.04565747e-01 -3.35374832e-01 -4.53407824e-01 -7.68371344e-01
-9.92135480e-02 -2.16732129e-01 1.01705539e+00 -1.32244334... | [9.763233184814453, -0.37773048877716064] |
841e5600-dac6-42c6-857d-979f8577aaf1 | probing-for-targeted-syntactic-knowledge | 2210.16228 | null | https://arxiv.org/abs/2210.16228v1 | https://arxiv.org/pdf/2210.16228v1.pdf | Probing for targeted syntactic knowledge through grammatical error detection | Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information. We assert that if models robustly encode subject-verb agreement, they should be able to identify when agreement is correct and when it is incorrect. To that end, we propose grammati... | ['Paula Buttery', 'Marek Rei', 'Andrew Caines', 'Christopher Bryant', 'Christopher Davis'] | 2022-10-28 | null | null | null | null | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 1.24219768e-02 2.55253881e-01 1.70174167e-01 -7.97295749e-01
-1.32504916e+00 -6.82847679e-01 4.17670161e-01 8.71495545e-01
-7.90238976e-01 4.62923497e-01 6.46244824e-01 -6.14725828e-01
1.95725530e-01 -7.94026136e-01 -1.01495636e+00 5.46819046e-02
1.58445612e-01 4.07807082e-01 -7.20951706e-02 -3.35218132... | [10.731304168701172, 9.344642639160156] |
76c54b20-7e18-492e-b557-fa456637c294 | reinforcement-learning-with-tensor-networks | 2209.14089 | null | https://arxiv.org/abs/2209.14089v1 | https://arxiv.org/pdf/2209.14089v1.pdf | Reinforcement Learning with Tensor Networks: Application to Dynamical Large Deviations | We present a framework to integrate tensor network (TN) methods with reinforcement learning (RL) for solving dynamical optimisation tasks. We consider the RL actor-critic method, a model-free approach for solving RL problems, and introduce TNs as the approximators for its policy and value functions. Our "actor-critic w... | ['Juan P. Garrahan', 'Dominic C. Rose', 'Edward Gillman'] | 2022-09-28 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.72539517e-01 9.95624959e-02 -1.19787060e-01 3.67376804e-01
-5.78822911e-01 -3.76573026e-01 8.26536119e-01 -2.61221975e-01
-6.20904148e-01 1.20925355e+00 2.62862388e-02 -2.81954199e-01
-5.87088525e-01 -4.53784436e-01 -3.61389667e-01 -1.34951603e+00
-2.50335068e-01 9.94906843e-01 -1.71398759e-01 -8.09467793... | [4.1019697189331055, 2.165006160736084] |
e3be76c5-d35c-4c18-b4ca-68745428b387 | ssn-sparks-at-semeval-2019-task-9-mining | null | null | https://aclanthology.org/S19-2217 | https://aclanthology.org/S19-2217.pdf | SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data | This paper describes the work on mining the suggestions from online reviews and forums. Opinion mining detects whether the comments are positive, negative or neutral, while suggestion mining explores the review content for the possible tips or advice. The system developed by SSN-SPARKS team in SemEval-2019 for task 9 (... | ['S Milton Rajendram', 'Mirnalinee T T', 'Angel Suseelan', 'Rajalakshmi S'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [-1.82976097e-01 4.79299575e-01 -5.47497928e-01 -7.00820565e-01
3.07845503e-01 -2.82228500e-01 9.14814353e-01 7.16280460e-01
-2.83921748e-01 8.95441890e-01 6.36175156e-01 -1.20001781e+00
-6.81183068e-04 -8.68241966e-01 -2.05136333e-02 -2.67599106e-01
-1.70337796e-01 2.04786435e-01 2.53917021e-03 -7.85077333... | [10.98051929473877, 7.283262729644775] |
cc3a3a24-53f6-4ea8-9fc8-874d35df12ae | medtype-improving-medical-entity-linking-with | 2005.00460 | null | https://arxiv.org/abs/2005.00460v4 | https://arxiv.org/pdf/2005.00460v4.pdf | Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasets | Medical entity linking is the task of identifying and standardizing medical concepts referred to in an unstructured text. Most of the existing methods adopt a three-step approach of (1) detecting mentions, (2) generating a list of candidate concepts, and finally (3) picking the best concept among them. In this paper, w... | ['Denis Newman-Griffis', 'Ritam Dutt', 'Rishabh Joshi', 'Shikhar Vashishth', 'Carolyn Rose'] | 2020-05-01 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 1.59328759e-01 5.18511295e-01 -3.99084747e-01 -2.02704608e-01
-1.10296714e+00 -6.52421057e-01 2.84796715e-01 1.16946018e+00
-6.17029190e-01 9.64144289e-01 3.58148545e-01 -2.50973552e-01
-1.44243419e-01 -7.36250520e-01 -4.66115296e-01 -2.01469079e-01
1.00502610e-01 9.29669321e-01 4.84808296e-01 -8.23401585... | [8.776663780212402, 8.817458152770996] |
a93c9f69-d10f-4954-b66f-abb7a286d185 | lightweight-wood-panel-defect-detection | 2306.12113 | null | https://arxiv.org/abs/2306.12113v1 | https://arxiv.org/pdf/2306.12113v1.pdf | Lightweight wood panel defect detection method incorporating attention mechanism and feature fusion network | In recent years, deep learning has made significant progress in wood panel defect detection. However, there are still challenges such as low detection , slow detection speed, and difficulties in deploying embedded devices on wood panel surfaces. To overcome these issues, we propose a lightweight wood panel defect detec... | ['Yang Chen', 'You Miao', 'Cheng Bao', 'Lai Jiang', 'Fanghua Liu', 'Yongxin Cao'] | 2023-06-21 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.31560791e-01 -1.87646315e-01 1.46418795e-01 2.22775787e-01
-3.94029409e-01 -9.56915468e-02 -1.25529200e-01 1.22550964e-01
-1.58138022e-01 7.42681175e-02 5.95372766e-02 3.44899371e-02
-2.33722642e-01 -1.06491661e+00 -4.60550308e-01 -7.28181064e-01
-3.54287704e-03 -4.96799409e-01 7.94678867e-01 -3.82512286... | [7.509652614593506, 1.6713865995407104] |
e4ef7703-1fad-483b-b146-27e07eff7319 | towards-diverse-relevant-and-coherent-open | 2212.01145 | null | https://arxiv.org/abs/2212.01145v1 | https://arxiv.org/pdf/2212.01145v1.pdf | Towards Diverse, Relevant and Coherent Open-Domain Dialogue Generation via Hybrid Latent Variables | Conditional variational models, using either continuous or discrete latent variables, are powerful for open-domain dialogue response generation. However, previous works show that continuous latent variables tend to reduce the coherence of generated responses. In this paper, we also found that discrete latent variables ... | ['Kan Li', 'Yiwei Li', 'Weichao Wang', 'Fei Mi', 'Yitong Li', 'Bin Sun'] | 2022-12-02 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [-2.00100064e-01 1.98576465e-01 -3.44052404e-01 -3.77280325e-01
-9.53348637e-01 -7.09576011e-01 9.28014219e-01 -1.99313849e-01
-4.67434488e-02 1.12215555e+00 6.40761256e-01 1.26029521e-01
6.05973490e-02 -8.69669318e-01 -7.61724189e-02 -8.17621768e-01
6.16695344e-01 8.21802795e-01 -3.86508293e-02 -6.38881981... | [12.582223892211914, 8.341238021850586] |
89d7c21f-77dc-4628-9af9-2c37e209aa4c | addressing-leakage-in-self-supervised | 2204.11594 | null | https://arxiv.org/abs/2204.11594v1 | https://arxiv.org/pdf/2204.11594v1.pdf | Addressing Leakage in Self-Supervised Contextualized Code Retrieval | We address contextualized code retrieval, the search for code snippets helpful to fill gaps in a partial input program. Our approach facilitates a large-scale self-supervised contrastive training by splitting source code randomly into contexts and targets. To combat leakage between the two, we suggest a novel approach ... | ['Ulrich Schwanecke', 'Adrian Ulges', 'Viola Campos', 'Johannes Villmow'] | 2022-04-17 | null | https://aclanthology.org/2022.coling-1.84 | https://aclanthology.org/2022.coling-1.84.pdf | coling-2022-10 | ['defect-detection'] | ['computer-vision'] | [ 2.67605484e-01 -2.16746762e-01 -7.77390659e-01 -2.39136189e-01
-1.41408992e+00 -7.66723454e-01 2.45059878e-01 6.88187897e-01
8.93798769e-02 1.44726798e-01 9.42535922e-02 -7.77246416e-01
9.02104154e-02 -4.69734818e-01 -6.19269907e-01 -9.40193087e-02
-2.91716456e-01 -4.56796214e-02 5.44186413e-01 -1.84211478... | [7.555962562561035, 8.036498069763184] |
8794e3fb-21c8-4f2f-81c3-5a99c9dc544a | fac-3d-representation-learning-via-foreground | 2303.06388 | null | https://arxiv.org/abs/2303.06388v2 | https://arxiv.org/pdf/2303.06388v2.pdf | FAC: 3D Representation Learning via Foreground Aware Feature Contrast | Contrastive learning has recently demonstrated great potential for unsupervised pre-training in 3D scene understanding tasks. However, most existing work randomly selects point features as anchors while building contrast, leading to a clear bias toward background points that often dominate in 3D scenes. Also, object aw... | ['Ling Shao', 'Shijian Lu', 'Xiaoqin Zhang', 'Aoran Xiao', 'Kangcheng Liu'] | 2023-03-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_FAC_3D_Representation_Learning_via_Foreground_Aware_Feature_Contrast_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_FAC_3D_Representation_Learning_via_Foreground_Aware_Feature_Contrast_CVPR_2023_paper.pdf | cvpr-2023-1 | ['unsupervised-pre-training'] | ['methodology'] | [ 2.65185267e-01 -1.26421541e-01 -1.40893310e-01 -4.92863297e-01
-3.83857131e-01 -4.88613278e-01 6.36242032e-01 2.74747193e-01
-2.65279710e-01 1.69283405e-01 -2.16685385e-01 -6.49948791e-02
7.67317554e-03 -8.71974349e-01 -8.96611035e-01 -7.55466640e-01
4.05154936e-02 4.94256616e-01 8.39412391e-01 -2.18216673... | [7.957724571228027, -3.2642292976379395] |
6062a337-0d95-4a2d-bc57-b1b6d9fb48f3 | data-augmentation-for-personal-knowledge | 2002.10943 | null | https://arxiv.org/abs/2002.10943v2 | https://arxiv.org/pdf/2002.10943v2.pdf | Data Augmentation for Personal Knowledge Base Population | Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents. While artificial neural networks have led to significant improvements in the different tasks that are part of KBP, the overall F1 of the end-to-end system remains quite low. This problem is more acute i... | ['Hima Patel', 'Lokesh Nagalapatti', 'MN Thippeswamy', 'Lingraj S Vannur', 'Balaji Ganesan'] | 2020-02-23 | null | null | null | null | ['knowledge-base-population'] | ['natural-language-processing'] | [-2.95525998e-01 7.63107359e-01 -3.96264017e-01 -2.56178826e-01
-5.26798069e-01 -6.71899676e-01 2.30400354e-01 5.35914600e-01
-1.94893003e-01 1.48462749e+00 8.51838812e-02 -6.69096112e-02
-5.28084159e-01 -8.78435671e-01 -4.30038303e-01 -2.34733313e-01
9.20119062e-02 1.21573937e+00 4.63581026e-01 -4.56487924... | [9.296930313110352, 8.433512687683105] |
0d5393f2-be37-4f91-976a-be7e4d65341a | leveraging-unlabeled-whole-slide-images-for | 1807.11677 | null | http://arxiv.org/abs/1807.11677v1 | http://arxiv.org/pdf/1807.11677v1.pdf | Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection | Mitosis count is an important biomarker for prognosis of various cancers. At
present, pathologists typically perform manual counting on a few selected
regions of interest in breast whole-slide-images (WSIs) of patient biopsies.
This task is very time-consuming, tedious and subjective. Automated mitosis
detection method... | ['Janne Heikkilä', 'Simon Graham', 'Saad Ullah Akram', 'Talha Qaiser', 'Nasir Rajpoot', 'Juho Kannala'] | 2018-07-31 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 3.99986386e-01 3.17402780e-01 -4.87287790e-01 -1.68405920e-01
-1.39202225e+00 -6.08893514e-01 1.79261625e-01 7.57744730e-01
-7.32413888e-01 9.44737136e-01 -9.27694663e-02 -2.82584518e-01
2.81800985e-01 -6.59655213e-01 -4.19229984e-01 -1.20477998e+00
2.71072745e-01 7.98750937e-01 5.48229456e-01 3.15817982... | [14.962019920349121, -3.0587403774261475] |
e4f2321c-8ba6-475d-87a0-35a282db2f4b | joint-motion-correction-and-super-resolution | 2107.03887 | null | https://arxiv.org/abs/2107.03887v1 | https://arxiv.org/pdf/2107.03887v1.pdf | Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation | In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the limit of acquisition duration and respiratory/cardiac motion, stacks of multi-slice 2D images are acquired in clinical routine. The segmenta... | ['Wenjia Bai', 'Daniel Rueckert', 'Yike Guo', 'Stuart Cook', "Declan O'Regan", 'Chen Chen', 'Nicolo Savioli', 'Chen Qin', 'Shuo Wang'] | 2021-07-08 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 4.91308093e-01 1.21373266e-01 -6.09529279e-02 -3.79507571e-01
-1.16871941e+00 -5.00120401e-01 2.30167195e-01 -1.47541732e-01
-3.09199363e-01 6.19831681e-01 2.99038887e-01 6.69968277e-02
-4.13410813e-01 -4.57298011e-01 -2.13027969e-01 -8.68916273e-01
-1.63035721e-01 5.89243829e-01 4.50011402e-01 3.89567494... | [13.883010864257812, -2.4060428142547607] |
57569fde-517c-4fc1-b546-9de526aeb333 | a-tutorial-introduction-to-reinforcement | 2304.00803 | null | https://arxiv.org/abs/2304.00803v1 | https://arxiv.org/pdf/2304.00803v1.pdf | A Tutorial Introduction to Reinforcement Learning | In this paper, we present a brief survey of Reinforcement Learning (RL), with particular emphasis on Stochastic Approximation (SA) as a unifying theme. The scope of the paper includes Markov Reward Processes, Markov Decision Processes, Stochastic Approximation algorithms, and widely used algorithms such as Temporal Dif... | ['Mathukumalli Vidyasagar'] | 2023-04-03 | null | null | null | null | ['q-learning'] | ['methodology'] | [-2.30553553e-01 1.73140168e-01 -4.58635509e-01 -1.09013841e-01
-6.96303070e-01 -4.09392715e-01 3.32536846e-01 -1.18399680e-01
-7.89468586e-01 1.46249485e+00 -1.54157847e-01 -4.83656943e-01
-3.52134496e-01 -5.21421790e-01 -3.94110709e-01 -7.77131498e-01
-6.18267536e-01 4.76073384e-01 1.22548975e-01 -3.33697200... | [4.173821926116943, 2.3896148204803467] |
0714be03-3aff-45fe-a7dc-31bff484d1a7 | understanding-and-exploring-the-whole-set-of | 2303.16047 | null | https://arxiv.org/abs/2303.16047v1 | https://arxiv.org/pdf/2303.16047v1.pdf | Understanding and Exploring the Whole Set of Good Sparse Generalized Additive Models | In real applications, interaction between machine learning model and domain experts is critical; however, the classical machine learning paradigm that usually produces only a single model does not facilitate such interaction. Approximating and exploring the Rashomon set, i.e., the set of all near-optimal models, addres... | ['Cynthia Rudin', 'Margo Seltzer', 'Chudi Zhong', 'Zhi Chen'] | 2023-03-28 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.54648566e-01 1.03843279e-01 -1.92697048e-01 -2.20855370e-01
-9.96046901e-01 -7.44995415e-01 2.08772078e-01 -7.44484887e-02
2.62206830e-02 7.87886500e-01 -3.52355987e-01 -2.98259079e-01
-6.09403968e-01 -4.60352927e-01 -8.04707050e-01 -5.40984213e-01
-1.69128075e-01 1.16262126e+00 -7.71628916e-02 -1.78759377... | [7.202791213989258, 4.304013252258301] |
21254564-80d8-44ae-8bad-21d60e2cb12b | effective-pseudo-labeling-based-on-heatmap | 2303.05269 | null | https://arxiv.org/abs/2303.05269v1 | https://arxiv.org/pdf/2303.05269v1.pdf | Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection | Cell detection is an important task in biomedical research. Recently, deep learning methods have made it possible to improve the performance of cell detection. However, a detection network trained with training data under a specific condition (source domain) may not work well on data under other conditions (target doma... | ['Ryoma Bise', 'Kazuhide Watanabe', 'Kazuya Nishimura', 'Hyeonwoo Cho'] | 2023-03-09 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 2.44815245e-01 -2.33729854e-01 6.62976876e-02 -8.36234465e-02
-5.37267327e-01 -3.64140868e-01 3.48205268e-01 5.70667744e-01
-2.61030197e-01 9.55794871e-01 -2.51115531e-01 1.20214775e-01
2.34504163e-01 -8.58754337e-01 -8.83587301e-01 -1.34016573e+00
2.76615441e-01 7.86238492e-01 5.73134959e-01 1.20813772... | [14.773587226867676, -3.204890012741089] |
6c38255c-be82-481a-914f-f26fe51ab48b | caesynth-real-time-timbre-interpolation-and | 2111.05174 | null | https://arxiv.org/abs/2111.05174v1 | https://arxiv.org/pdf/2111.05174v1.pdf | CAESynth: Real-Time Timbre Interpolation and Pitch Control with Conditional Autoencoders | In this paper, we present a novel audio synthesizer, CAESynth, based on a conditional autoencoder. CAESynth synthesizes timbre in real-time by interpolating the reference sounds in their shared latent feature space, while controlling a pitch independently. We show that training a conditional autoencoder based on accura... | ['Sukhan Lee', 'Aaron Valero Puche'] | 2021-11-09 | caesynth-real-time-timbre-interpolation-and-1 | https://ieeexplore.ieee.org/document/9596414/keywords#keywords | https://arxiv.org/pdf/2111.05174.pdf | ieee-mlsp-2021-9 | ['pitch-control', 'timbre-interpolation'] | ['audio', 'audio'] | [ 1.41697735e-01 -5.00127859e-02 3.33240479e-01 -7.32848570e-02
-1.01935542e+00 -7.98759162e-01 4.35793430e-01 -2.44588912e-01
-9.91590396e-02 7.76166916e-01 5.19909799e-01 2.97573805e-01
-2.10366547e-01 -7.73656249e-01 -9.38192427e-01 -7.77471423e-01
-1.69008389e-01 1.64799765e-01 -2.50447392e-01 -2.99183987... | [15.671387672424316, 5.867585182189941] |
f8593254-864e-4912-bfb1-98dd7523435b | spatial-semantic-embedding-network-fast-3d | 2007.03169 | null | https://arxiv.org/abs/2007.03169v1 | https://arxiv.org/pdf/2007.03169v1.pdf | Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning | We propose spatial semantic embedding network (SSEN), a simple, yet efficient algorithm for 3D instance segmentation using deep metric learning. The raw 3D reconstruction of an indoor environment suffers from occlusions, noise, and is produced without any meaningful distinction between individual entities. For high-lev... | ['Young Min Kim', 'Sang Kyun Cha', 'Junha Chun', 'Dongsu Zhang'] | 2020-07-07 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.22910626e-01 2.92603731e-01 1.56845540e-01 -6.59494102e-01
-7.99924374e-01 -5.17479599e-01 5.11379719e-01 4.04044747e-01
-4.75317001e-01 8.08755085e-02 8.63096341e-02 -1.55936986e-01
-4.22119409e-01 -1.08124101e+00 -7.64659345e-01 -3.53949308e-01
-3.39125097e-01 9.01426494e-01 5.53893149e-01 2.65389621... | [8.09653377532959, -3.196101427078247] |
e072f61d-9c5d-48ea-9bba-7bf00c828289 | lexical-event-ordering-with-an-edge-factored | null | null | https://aclanthology.info/papers/N15-1122/n15-1122 | https://www.aclweb.org/anthology/N15-1122 | Lexical Event Ordering with an Edge-Factored Model | null | ['Omri Abend', 'Shay B. Cohen', 'Mark Steedman'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5392122268676758, 15.86922550201416] |
5948485c-e55f-48be-af22-e6cfb26f54ad | out-of-domain-human-mesh-reconstruction-via | 2111.04017 | null | https://arxiv.org/abs/2111.04017v1 | https://arxiv.org/pdf/2111.04017v1.pdf | Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation | We consider a new problem of adapting a human mesh reconstruction model to out-of-domain streaming videos, where performance of existing SMPL-based models are significantly affected by the distribution shift represented by different camera parameters, bone lengths, backgrounds, and occlusions. We tackle this problem th... | ['Xiaokang Yang', 'Bingbing Ni', 'Yunbo Wang', 'Michelle Z. He', 'Jingwei Xu', 'Shanyan Guan'] | 2021-11-07 | null | null | null | null | ['3d-absolute-human-pose-estimation'] | ['computer-vision'] | [-1.50645303e-03 -5.72807752e-02 -2.63867438e-01 -2.18180895e-01
-9.62915003e-01 -3.27670366e-01 8.53615254e-02 -1.15605652e-01
-2.96003670e-01 4.88300949e-01 2.68519074e-01 1.90119058e-01
8.55538994e-02 -4.88712043e-01 -1.03488064e+00 -4.97111738e-01
3.61859798e-02 7.07526386e-01 7.51085758e-01 -2.06153587... | [7.219239234924316, -0.7991213202476501] |
196b60a6-a699-4432-bd7d-55e7a55b6b13 | high-resolution-image-synthesis-and-semantic | 1711.11585 | null | http://arxiv.org/abs/1711.11585v2 | http://arxiv.org/pdf/1711.11585v2.pdf | High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs | We present a new method for synthesizing high-resolution photo-realistic
images from semantic label maps using conditional generative adversarial
networks (conditional GANs). Conditional GANs have enabled a variety of
applications, but the results are often limited to low-resolution and still far
from realistic. In thi... | ['Jan Kautz', 'Jun-Yan Zhu', 'Ming-Yu Liu', 'Ting-Chun Wang', 'Andrew Tao', 'Bryan Catanzaro'] | 2017-11-30 | high-resolution-image-synthesis-and-semantic-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_High-Resolution_Image_Synthesis_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_High-Resolution_Image_Synthesis_CVPR_2018_paper.pdf | cvpr-2018-6 | ['sketch-to-image-translation', 'fundus-to-angiography-generation'] | ['computer-vision', 'computer-vision'] | [ 6.67866647e-01 2.59984732e-01 3.60912859e-01 -2.23946944e-01
-8.39041770e-01 -8.41364264e-01 6.52867496e-01 -5.28358817e-01
-9.46739838e-02 1.02767169e+00 2.52135042e-02 2.42240489e-01
3.49377334e-01 -1.01291120e+00 -1.01323843e+00 -6.67591810e-01
4.74664479e-01 2.80644625e-01 3.59602809e-01 -1.76358119... | [11.678472518920898, -0.5233043432235718] |
b34c71a4-32e0-4f74-90e7-f2b75ba872b4 | humor-as-circuits-in-semantic-networks | null | null | https://aclanthology.org/P12-2030 | https://aclanthology.org/P12-2030.pdf | Humor as Circuits in Semantic Networks | null | ['Igor Labutov', 'Hod Lipson'] | 2012-07-01 | null | null | null | acl-2012-7 | ['humor-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.202491283416748, 3.781613349914551] |
d51357fc-df87-432a-9f79-5262ee2b4bea | fault-localization-for-framework-conversions | 2306.06157 | null | https://arxiv.org/abs/2306.06157v1 | https://arxiv.org/pdf/2306.06157v1.pdf | Fault Localization for Framework Conversions of Image Recognition Models | When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent of such impact, we perform and briefly present a differential analysis ... | ['Ajitha Rajan', 'José Cano', 'Perry Gibson', 'Nikolaos Louloudakis'] | 2023-06-10 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-2.07329229e-01 3.01968679e-03 4.86673489e-02 -9.92928371e-02
-3.43029261e-01 -7.68729329e-01 3.28643858e-01 -2.33951002e-01
-8.05103555e-02 2.14851916e-01 -3.97448450e-01 -1.18483388e+00
9.14564580e-02 -7.69906878e-01 -1.33956456e+00 -6.44921139e-02
4.51026373e-02 -2.11368278e-02 4.88975823e-01 3.54230171... | [7.521404266357422, 7.701530933380127] |
a3634ba7-fccf-490f-ab2b-03218b601ecc | hierarchical-temporal-transformer-for-3d-hand | 2209.09484 | null | https://arxiv.org/abs/2209.09484v4 | https://arxiv.org/pdf/2209.09484v4.pdf | Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action Recognition from Egocentric RGB Videos | Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity. To address occlusion and ambiguity, we develop a transformer-based framework to exploit temporal information for robust estimation. Noticing the different temporal granula... | ['Wenping Wang', 'Taku Komura', 'Jia Pan', 'Lei Yang', 'Hao Pan', 'Yilin Wen'] | 2022-09-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wen_Hierarchical_Temporal_Transformer_for_3D_Hand_Pose_Estimation_and_Action_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wen_Hierarchical_Temporal_Transformer_for_3D_Hand_Pose_Estimation_and_Action_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [ 4.44411263e-02 -1.86368570e-01 -4.42748755e-01 -2.65895456e-01
-6.80637658e-01 -4.95391548e-01 2.98696250e-01 -7.55319118e-01
-3.43408495e-01 6.51775777e-01 7.54773974e-01 2.42205665e-01
3.17237228e-02 -2.67246038e-01 -6.53822899e-01 -7.48377085e-01
-1.89639013e-02 3.56485158e-01 4.94084984e-01 9.27339122... | [7.720499515533447, 0.11806896328926086] |
7bb2941b-14f9-4ac6-978c-330298435456 | progressive-modality-reinforcement-for-human | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lv_Progressive_Modality_Reinforcement_for_Human_Multimodal_Emotion_Recognition_From_Unaligned_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lv_Progressive_Modality_Reinforcement_for_Human_Multimodal_Emotion_Recognition_From_Unaligned_CVPR_2021_paper.pdf | Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences | Human multimodal emotion recognition involves time-series data of different modalities, such as natural language, visual motions, and acoustic behaviors. Due to the variable sampling rates for sequences from different modalities, the collected multimodal streams are usually unaligned. The asynchrony across modaliti... | ['Guosheng Lin', 'Lixin Duan', 'Yanyong Huang', 'Xiang Chen', 'Fengmao Lv'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 3.16813201e-01 -4.05968696e-01 -4.42459919e-02 -2.82427400e-01
-9.04996157e-01 -4.84318376e-01 8.09136569e-01 1.88413888e-01
-5.13122618e-01 7.55471945e-01 5.32391965e-01 3.33617538e-01
1.09659694e-01 -3.43803138e-01 -5.02540946e-01 -1.04527080e+00
2.63154805e-01 -9.11705643e-02 -1.36197940e-01 -2.86757350... | [13.208966255187988, 5.067779541015625] |
82690220-e400-43b9-87ad-320ac9f8a6ff | temporal-film-capturing-long-range-sequence-1 | null | null | http://papers.nips.cc/paper/9217-temporal-film-capturing-long-range-sequence-dependencies-with-feature-wise-modulations | http://papers.nips.cc/paper/9217-temporal-film-capturing-long-range-sequence-dependencies-with-feature-wise-modulations.pdf | Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. | Learning representations that accurately capture long-range dependencies in sequential inputs --- including text, audio, and genomic data --- is a key problem in deep learning. Feed-forward convolutional models capture only feature interactions within finite receptive fields while recurrent architectures can be slow an... | ['Zayd Enam', 'Sawyer Birnbaum', 'Volodymyr Kuleshov', 'Pang Wei W. Koh', 'Stefano Ermon'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['audio-super-resolution', 'audio-super-resolution'] | ['audio', 'music'] | [ 6.83068514e-01 -2.20945925e-01 -1.65183023e-01 -6.71860814e-01
-6.71905220e-01 -4.43163306e-01 6.21937215e-01 -2.49797016e-01
-4.14265424e-01 5.72974324e-01 6.13027334e-01 -1.63214266e-01
6.56493679e-02 -5.68294644e-01 -7.90116251e-01 -6.67841554e-01
-2.38129377e-01 -6.05248846e-02 1.42953932e-01 -2.93842018... | [10.9157133102417, 6.539568901062012] |
ca0a8f41-ec6b-49ca-9360-d53beb6d11ef | segmentation-of-structural-parts-of-rosebush | 2012.11489 | null | https://arxiv.org/abs/2012.11489v2 | https://arxiv.org/pdf/2012.11489v2.pdf | Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods | Segmentation of structural parts of 3D models of plants is an important step for plant phenotyping, especially for monitoring architectural and morphological traits. Current state-of-the art approaches rely on hand-crafted 3D local features for modeling geometric variations in plant structures. While recent advancement... | ['David Rousseau', 'Gilles Galopin', 'Helin Dutagaci', 'Kaya Turgut'] | 2020-12-21 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [-1.91043064e-01 1.05252109e-01 1.00768536e-01 -3.11271995e-01
-3.17277312e-01 -9.00201857e-01 3.02794874e-01 4.87295806e-01
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-2.65935779e-01 -1.13942814e+00 -6.62609279e-01 -2.87829340e-01
-4.73677695e-01 1.01343131e+00 5.33801854e-01 -5.52065730... | [8.949522972106934, -1.8209409713745117] |
ff76657d-0bb6-4c73-992a-8fb3d4c5d26d | dynamic-inference-with-neural-interpreters | 2110.06399 | null | https://arxiv.org/abs/2110.06399v1 | https://arxiv.org/pdf/2110.06399v1.pdf | Dynamic Inference with Neural Interpreters | Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning and reuse of knowled... | ['Bernhard Schölkopf', 'Francesco Locatello', 'Yoshua Bengio', 'Peter Gehler', 'Shruti Joshi', 'Muhammad Waleed Gondal', 'Nasim Rahaman'] | 2021-10-12 | null | http://proceedings.neurips.cc/paper/2021/hash/5b4e9aa703d0bfa11041debaa2d1b633-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/5b4e9aa703d0bfa11041debaa2d1b633-Paper.pdf | neurips-2021-12 | ['systematic-generalization'] | ['reasoning'] | [ 4.36715394e-01 1.94154605e-01 1.29462391e-01 -5.19348323e-01
-5.74581046e-03 -8.19382846e-01 7.44029939e-01 2.49984637e-02
-4.60723251e-01 3.73068392e-01 7.32175633e-02 -6.27462089e-01
-2.60880321e-01 -1.17917943e+00 -1.25197959e+00 -5.57716548e-01
1.08671822e-01 7.48542309e-01 2.58679569e-01 -5.35977662... | [9.545156478881836, 7.121941566467285] |
72d82175-004a-43c6-abc5-9a11e74bf9e3 | a-high-speed-real-time-vision-system-for | 1812.04115 | null | http://arxiv.org/abs/1812.04115v1 | http://arxiv.org/pdf/1812.04115v1.pdf | A High-Speed, Real-Time Vision System for Texture Tracking and Thread Counting | In garment manufacturing, an automatic sewing machine is desirable to reduce
cost. To accomplish this, a high speed vision system is required to track
fabric motions and recognize repetitive weave patterns with high accuracy, from
a micro perspective near a sewing zone. In this paper, we present an innovative
framework... | [] | 2018-12-10 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 4.21137452e-01 -6.26214504e-01 -5.12025207e-02 1.78248987e-01
1.62740201e-01 -6.42524660e-01 -1.11837806e-02 2.96857119e-01
-7.47477859e-02 2.17753902e-01 -5.75922489e-01 -5.79004884e-02
-4.09284413e-01 -9.47614133e-01 -5.19572139e-01 -6.81443274e-01
7.77425840e-02 4.54896599e-01 6.14934325e-01 -1.79365784... | [8.455044746398926, -2.3744373321533203] |
e7e11b4a-81a5-4714-973b-3c75d566f74d | towards-sequence-utility-maximization-under | 2212.10452 | null | https://arxiv.org/abs/2212.10452v1 | https://arxiv.org/pdf/2212.10452v1.pdf | Towards Sequence Utility Maximization under Utility Occupancy Measure | The discovery of utility-driven patterns is a useful and difficult research topic. It can extract significant and interesting information from specific and varied databases, increasing the value of the services provided. In practice, the measure of utility is often used to demonstrate the importance, profit, or risk of... | ['Philip S. Yu', 'Wensheng Gan', 'Gengsen Huang'] | 2022-12-20 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 3.04163188e-01 -2.27757528e-01 -7.94957101e-01 -3.02413553e-01
-1.43562809e-01 -1.66486934e-01 -4.62580994e-02 1.87702939e-01
-1.94968611e-01 1.08549666e+00 6.76668808e-02 -3.26548159e-01
-7.15348959e-01 -1.09740150e+00 -1.50616944e-01 -6.67559206e-01
-4.62794989e-01 3.94728631e-01 3.62738967e-01 1.80157591... | [8.297300338745117, 6.281327247619629] |
9d37eee8-ca44-42ae-9f5f-e98cc7522bc7 | ufact-unfaithful-alien-corpora-training-for | null | null | https://aclanthology.org/2022.findings-acl.223 | https://aclanthology.org/2022.findings-acl.223.pdf | uFACT: Unfaithful Alien-Corpora Training for Semantically Consistent Data-to-Text Generation | We propose uFACT (Un-Faithful Alien Corpora Training), a training corpus construction method for data-to-text (d2t) generation models. We show that d2t models trained on uFACT datasets generate utterances which represent the semantic content of the data sources more accurately compared to models trained on the target c... | ['Bill Byrne', 'Alexandru Coca', 'Tisha Anders'] | null | null | null | null | findings-acl-2022-5 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 2.38720715e-01 8.90414059e-01 -5.90811968e-02 -2.43964821e-01
-1.14846396e+00 -8.73814046e-01 1.23757482e+00 1.65985718e-01
-1.62470639e-01 8.98240030e-01 9.23352420e-01 -6.48792908e-02
3.09202313e-01 -9.46031094e-01 -8.95707190e-01 -2.33968765e-01
3.16620767e-01 8.90669823e-01 5.02012037e-02 -6.10731304... | [11.444321632385254, 8.87647819519043] |
2d5c8d2e-38c0-475d-b49c-ecb4cdd59fc1 | nature-natural-auxiliary-text-utterances-for | 2111.05196 | null | https://arxiv.org/abs/2111.05196v2 | https://arxiv.org/pdf/2111.05196v2.pdf | NATURE: Natural Auxiliary Text Utterances for Realistic Spoken Language Evaluation | Slot-filling and intent detection are the backbone of conversational agents such as voice assistants, and are active areas of research. Even though state-of-the-art techniques on publicly available benchmarks show impressive performance, their ability to generalize to realistic scenarios is yet to be demonstrated. In t... | ['Mehdi Rezagholizadeh', 'Philippe Langlais', 'Abbas Ghaddar', 'Ahmad Rashid', 'David Alfonso-Hermelo'] | 2021-11-09 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 4.51487303e-01 4.05901432e-01 -1.41265839e-01 -6.96662366e-01
-8.75933051e-01 -7.23835766e-01 1.02065957e+00 -3.74151045e-03
-4.77003485e-01 7.85764754e-01 6.50802553e-01 -3.88038337e-01
2.40348160e-01 -3.05207819e-01 -2.22927913e-01 -2.60436505e-01
-3.21061075e-01 9.00705695e-01 4.51718479e-01 -6.74293339... | [12.747001647949219, 7.844950199127197] |
3fcdd040-5448-4e68-bdf7-e402f7ffc187 | aspect-extraction-and-sentiment | 1712.03430 | null | http://arxiv.org/abs/1712.03430v1 | http://arxiv.org/pdf/1712.03430v1.pdf | Aspect Extraction and Sentiment Classification of Mobile Apps using App-Store Reviews | Understanding of customer sentiment can be useful for product development. On
top of that if the priorities for the development order can be known, then
development procedure become simpler. This work has tried to address this issue
in the mobile app domain. Along with aspect and opinion extraction this work
has also c... | ['Sharmistha Dey'] | 2017-12-09 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [-4.12481986e-02 4.67158228e-01 -6.19938314e-01 -5.06568253e-01
2.84828972e-02 -4.21378016e-01 2.55948067e-01 6.17982686e-01
-1.14302531e-01 5.54239690e-01 2.43512496e-01 -3.78929853e-01
-6.85025454e-02 -9.50692654e-01 -3.31490003e-02 -1.85927778e-01
4.24637914e-01 2.22168580e-01 8.64956230e-02 -5.02701819... | [11.107365608215332, 6.789151191711426] |
2f286656-6f71-4a0e-baf6-287b31703f31 | sygns-a-systematic-generalization-testbed | 2106.01077 | null | https://arxiv.org/abs/2106.01077v1 | https://arxiv.org/pdf/2106.01077v1.pdf | SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics | Recently, deep neural networks (DNNs) have achieved great success in semantically challenging NLP tasks, yet it remains unclear whether DNN models can capture compositional meanings, those aspects of meaning that have been long studied in formal semantics. To investigate this issue, we propose a Systematic Generalizati... | ['Kentaro Inui', 'Koji Mineshima', 'Hitomi Yanaka'] | 2021-06-02 | null | https://aclanthology.org/2021.findings-acl.10 | https://aclanthology.org/2021.findings-acl.10.pdf | findings-acl-2021-8 | ['systematic-generalization'] | ['reasoning'] | [ 3.80419075e-01 1.95644364e-01 -1.35214701e-01 -7.58124053e-01
-2.81075388e-01 -9.21287060e-01 6.20797276e-01 2.21192330e-01
-3.07865232e-01 8.09632182e-01 4.48533088e-01 -5.21340609e-01
-1.00656226e-01 -1.21511149e+00 -6.61884069e-01 -2.82510430e-01
1.65363908e-01 5.01490891e-01 1.49554238e-01 -7.76433766... | [10.124689102172852, 8.457382202148438] |
1b199aea-de33-4e9f-b783-cf5e9be6a229 | fast-and-accurate-head-pose-estimation-via | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Lee_Fast_and_Accurate_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Lee_Fast_and_Accurate_ICCV_2015_paper.pdf | Fast and Accurate Head Pose Estimation via Random Projection Forests | In this paper, we consider the problem of estimating the gaze direction of a person from a low-resolution image. Under this condition, reliably extracting facial features is very difficult. We propose a novel head pose estimation algorithm based on compressive sensing. Head image patches are mapped to a large featur... | ['Ming-Hsuan Yang', 'Songhwai Oh', 'Donghoon Lee'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['head-pose-estimation'] | ['computer-vision'] | [ 5.22491038e-01 -2.02264741e-01 1.15430750e-01 -5.32239437e-01
-6.00161791e-01 6.03536032e-02 2.18072191e-01 -9.03375030e-01
-3.72333109e-01 9.06371415e-01 6.60984516e-01 3.90886873e-01
7.08692595e-02 -2.03131959e-01 -6.48754239e-01 -9.98371422e-01
1.00802287e-01 -1.40692070e-01 -2.79560030e-01 2.41315842... | [13.16814136505127, 0.2411925047636032] |
d38d9bd0-4dd5-4494-b8c9-f6585a70c0cf | fostering-generalization-in-single-view-3d | 2104.00476 | null | https://arxiv.org/abs/2104.00476v1 | https://arxiv.org/pdf/2104.00476v1.pdf | Fostering Generalization in Single-view 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors | Single-view 3D object reconstruction has seen much progress, yet methods still struggle generalizing to novel shapes unseen during training. Common approaches predominantly rely on learned global shape priors and, hence, disregard detailed local observations. In this work, we address this issue by learning a hierarchy ... | ['Thomas Brox', 'Volker Fischer', 'Maxim Tatarchenko', 'Jan Bechtold'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Bechtold_Fostering_Generalization_in_Single-View_3D_Reconstruction_by_Learning_a_Hierarchy_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Bechtold_Fostering_Generalization_in_Single-View_3D_Reconstruction_by_Learning_a_Hierarchy_CVPR_2021_paper.pdf | cvpr-2021-1 | ['single-view-3d-reconstruction', '3d-object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.80199805e-03 2.03178525e-01 1.05235100e-01 -3.48888874e-01
-5.44266045e-01 -8.96346509e-01 6.89641118e-01 1.91588506e-01
3.15403976e-02 4.96702403e-01 3.63122016e-01 2.06693694e-01
-1.48811817e-01 -9.49491799e-01 -7.14335382e-01 -7.09081829e-01
2.50310361e-01 8.52667332e-01 7.26662695e-01 8.86896253... | [8.501273155212402, -3.1302289962768555] |
09853cf0-a933-4102-a26d-b766d9922732 | gtlo-a-generalized-and-non-linear-multi | 2204.04988 | null | https://arxiv.org/abs/2204.04988v1 | https://arxiv.org/pdf/2204.04988v1.pdf | gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement Learning Approach | In real-world decision optimization, often multiple competing objectives must be taken into account. Following classical reinforcement learning, these objectives have to be combined into a single reward function. In contrast, multi-objective reinforcement learning (MORL) methods learn from vectors of per-objective rewa... | ['Johannes Dornheim'] | 2022-04-11 | null | null | null | null | ['multi-objective-reinforcement-learning', 'deep-sea-treasure-image-version'] | ['methodology', 'playing-games'] | [ 3.41962390e-02 -6.64780140e-02 -5.05946398e-01 -2.36777142e-01
-9.09294128e-01 -5.44239283e-01 2.86702663e-01 6.49202645e-01
-7.15146661e-01 1.27868998e+00 -1.55751914e-01 -1.53352201e-01
-8.05268645e-01 -7.41819143e-01 -6.97054148e-01 -8.65646482e-01
-1.46440327e-01 8.37240279e-01 -2.05109015e-01 -4.37853128... | [4.39596700668335, 2.42025089263916] |
d62e2227-cbad-48c4-b686-a5ab8baea0e7 | machine-translation-of-low-resource-indo | 2108.03739 | null | https://arxiv.org/abs/2108.03739v2 | https://arxiv.org/pdf/2108.03739v2.pdf | Machine Translation of Low-Resource Indo-European Languages | In this work, we investigate methods for the challenging task of translating between low-resource language pairs that exhibit some level of similarity. In particular, we consider the utility of transfer learning for translating between several Indo-European low-resource languages from the Germanic and Romance language ... | ['Muhammad Abdul-Mageed', 'Wei-Rui Chen'] | 2021-08-08 | null | https://aclanthology.org/2021.wmt-1.41 | https://aclanthology.org/2021.wmt-1.41.pdf | wmt-emnlp-2021-11 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 5.99834733e-02 -4.81756479e-02 -1.02970719e-01 -4.36495155e-01
-1.07568705e+00 -7.62822270e-01 7.92731047e-01 -1.17469430e-01
-5.04970431e-01 1.09506226e+00 3.45064551e-01 -6.45117640e-01
1.45326123e-01 -7.41778135e-01 -7.28024065e-01 -2.75909841e-01
2.63623267e-01 8.49256277e-01 7.36551285e-02 -9.05174971... | [11.438313484191895, 10.240743637084961] |
5f1e1cf5-523b-44bd-ac74-84a38310a918 | mixture-dense-regression-for-object-detection | 1912.00821 | null | https://arxiv.org/abs/1912.00821v2 | https://arxiv.org/pdf/1912.00821v2.pdf | Mixture Dense Regression for Object Detection and Human Pose Estimation | Mixture models are well-established learning approaches that, in computer vision, have mostly been applied to inverse or ill-defined problems. However, they are general-purpose divide-and-conquer techniques, splitting the input space into relatively homogeneous subsets in a data-driven manner. Not only ill-defined but ... | ['Ali Varamesh', 'Tinne Tuytelaars'] | 2019-12-02 | mixture-dense-regression-for-object-detection-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Varamesh_Mixture_Dense_Regression_for_Object_Detection_and_Human_Pose_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Varamesh_Mixture_Dense_Regression_for_Object_Detection_and_Human_Pose_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['dense-object-detection'] | ['computer-vision'] | [-1.48359880e-01 -1.19568668e-02 -1.89782605e-01 -4.19480294e-01
-6.98332071e-01 -4.03959394e-01 4.00585264e-01 -3.29132438e-01
-5.83773971e-01 4.29811746e-01 -1.81421146e-01 -6.96401000e-02
-2.24671215e-01 -5.56075275e-01 -5.61880648e-01 -9.17451084e-01
3.38880241e-01 8.80690932e-01 4.11457688e-01 1.08123660... | [7.296097755432129, -1.5730516910552979] |
66f571b5-1496-41f8-b461-812f96b75aef | feasibility-of-colon-cancer-detection-in | 1812.01464 | null | http://arxiv.org/abs/1812.01464v2 | http://arxiv.org/pdf/1812.01464v2.pdf | Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks | Histological evaluation of tissue samples is a typical approach to identify
colorectal cancer metastases in the peritoneum. For immediate assessment,
reliable and real-time in-vivo imaging would be required. For example,
intraoperative confocal laser microscopy has been shown to be suitable for
distinguishing organs an... | ['Daniel Drömann', 'Lukas Wittig', 'Nils Gessert', 'Alexander Schlaefer', 'Tobias Keck', 'David B. Ellebrecht'] | 2018-12-04 | null | null | null | null | ['colon-cancer-detection-in-confocal-laser'] | ['medical'] | [-1.48313060e-01 -5.02765961e-02 -8.27171803e-02 2.36943010e-02
-7.63486803e-01 -4.75159973e-01 1.57491982e-01 6.20743990e-01
-1.04177988e+00 7.60268927e-01 -2.32621208e-01 -7.95688272e-01
4.21229869e-01 -5.95281243e-01 -3.70605916e-01 -1.00389588e+00
-1.65772527e-01 4.09257323e-01 3.69328819e-02 5.32505289... | [15.052409172058105, -2.9366087913513184] |
f7c24821-f8ba-4ae2-b40c-6ffeb5207e24 | gcn-based-linkage-prediction-for-face | 2107.02477 | null | https://arxiv.org/abs/2107.02477v3 | https://arxiv.org/pdf/2107.02477v3.pdf | A Linkage-based Doubly Imbalanced Graph Learning Framework for Face Clustering | In recent years, benefiting from the expressive power of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in face clustering area. However, rare attention has been paid to GCN-based clustering on imbalanced data. Although imbalance problem has been extensively studied, the impact of imbalan... | ['Xingjian Chen', 'Qijie Shen', 'Rong Du', 'Fangyi Zhang', 'Huafeng Yang'] | 2021-07-06 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-7.41672069e-02 1.13157041e-01 -3.72007698e-01 -4.43900019e-01
-2.06916139e-01 -6.48258701e-02 6.18291087e-02 -9.36801806e-02
3.66701722e-01 4.51693058e-01 4.22839820e-02 -1.67516228e-02
-2.90725112e-01 -1.04003954e+00 -5.33181250e-01 -7.96483040e-01
2.55921613e-02 4.81540829e-01 -2.06379667e-02 -1.90742701... | [7.325470924377441, 5.9987897872924805] |
06fe86a4-760d-4ed7-bb69-14f88333051d | asrtrans-at-semeval-2022-task-5-transformer | null | null | https://aclanthology.org/2022.semeval-1.82 | https://aclanthology.org/2022.semeval-1.82.pdf | ASRtrans at SemEval-2022 Task 5: Transformer-based Models for Meme Classification | Women are frequently targeted online with hate speech and misogyny using tweets, memes, and other forms of communication. This paper describes our system for Task 5 of SemEval-2022: Multimedia Automatic Misogyny Identification (MAMI). We participated in both the sub-tasks, where we used transformer-based architecture t... | ['Arjun Rao', 'Ailneni Rakshitha Rao'] | null | null | null | null | semeval-naacl-2022-7 | ['meme-classification'] | ['natural-language-processing'] | [ 1.38423458e-01 2.60968149e-01 -1.15618911e-02 -2.92065233e-01
-9.44756150e-01 -5.26383817e-01 1.13153458e+00 1.44127175e-01
-6.73447013e-01 3.86349022e-01 1.01626374e-01 -4.38346015e-03
3.42801541e-01 -3.11523110e-01 -4.63160396e-01 -3.87066185e-01
2.20220283e-01 5.92907131e-01 1.68792754e-01 -1.87209159... | [8.577978134155273, 10.61656379699707] |
97fca98e-e96b-4659-81ed-33b5d339a9e5 | the-nlms-algorithm-with-time-variant-optimum | 1411.4834 | null | http://arxiv.org/abs/1411.4834v1 | http://arxiv.org/pdf/1411.4834v1.pdf | The NLMS algorithm with time-variant optimum stepsize derived from a Bayesian network perspective | In this article, we derive a new stepsize adaptation for the normalized least
mean square algorithm (NLMS) by describing the task of linear acoustic echo
cancellation from a Bayesian network perspective. Similar to the well-known
Kalman filter equations, we model the acoustic wave propagation from the
loudspeaker to th... | ['Walter Kellermann', 'Roland Maas', 'Christian Huemmer'] | 2014-11-18 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 3.37621421e-01 1.35629117e-01 4.00768071e-01 -1.63705930e-01
-3.02102000e-01 -3.11243117e-01 5.60494602e-01 -3.41730297e-01
-5.97470820e-01 3.78575593e-01 1.69721887e-01 -2.65948713e-01
-4.03945893e-01 -3.83859277e-01 -4.27041650e-01 -9.70750809e-01
2.43308619e-02 2.44930964e-02 1.55531600e-01 5.95998615... | [15.206494331359863, 5.694552898406982] |
1d1fc88f-f68c-4a3a-8964-cf05fd1c520b | overview-of-the-nlp-tea-2015-shared-task-for | null | null | https://aclanthology.org/W15-4401 | https://aclanthology.org/W15-4401.pdf | Overview of the NLP-TEA 2015 Shared Task for Chinese Grammatical Error Diagnosis | null | ['Li-Ping Chang', 'Liang-Chih Yu', 'Lung-Hao Lee'] | 2015-07-01 | null | null | null | ws-2015-7 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.274957656860352, 3.84256649017334] |
1a35bb12-fd98-402b-b13a-0aebbc5c47ad | openbrand-open-brand-value-extraction-from | null | null | https://aclanthology.org/2022.ecnlp-1.19 | https://aclanthology.org/2022.ecnlp-1.19.pdf | OpenBrand: Open Brand Value Extraction from Product Descriptions | Extracting attribute-value information from unstructured product descriptions continue to be of a vital importance in e-commerce applications. One of the most important product attributes is the brand which highly influences costumers’ purchasing behaviour. Thus, it is crucial to accurately extract brand information de... | ['Johann Gamper', 'Mouna Kacimi', 'Kassem Sabeh'] | null | openbrand-open-brand-value-extraction-from-1 | https://aclanthology.org/2022.ecnlp-1.19/ | https://aclanthology.org/2022.ecnlp-1.19/ | acl-2022-5 | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 3.39927152e-02 7.82148987e-02 -6.26922965e-01 -6.27154231e-01
-7.72920310e-01 -6.54258072e-01 4.04766917e-01 4.99608129e-01
-5.32400370e-01 6.38361633e-01 3.70086491e-01 -2.08577095e-03
-1.74623691e-02 -1.08531976e+00 -5.69397926e-01 -5.47349036e-01
-1.17797472e-01 6.60642147e-01 -1.88166693e-01 -4.44277078... | [9.962224960327148, 6.193742275238037] |
81365175-a7b0-41d9-91fa-d4163644f78c | learning-the-loss-functions-in-a | 2003.09124 | null | https://arxiv.org/abs/2003.09124v1 | https://arxiv.org/pdf/2003.09124v1.pdf | Learning the Loss Functions in a Discriminative Space for Video Restoration | With more advanced deep network architectures and learning schemes such as GANs, the performance of video restoration algorithms has greatly improved recently. Meanwhile, the loss functions for optimizing deep neural networks remain relatively unchanged. To this end, we propose a new framework for building effective lo... | ['Seonghyeon Nam', 'Younghyun Jo', 'Seoung Wug Oh', 'Seon Joo Kim', 'Peter Vajda', 'Jaeyeon Kang'] | 2020-03-20 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [ 2.22097546e-01 -2.87377119e-01 -9.79125723e-02 -3.66815537e-01
-7.97229886e-01 -2.21620753e-01 4.44884092e-01 -7.25039184e-01
1.10615075e-01 9.02604699e-01 6.83081567e-01 2.89596140e-01
3.19892261e-03 -9.48230386e-01 -9.89974380e-01 -8.49828124e-01
5.90768605e-02 -1.01111658e-01 -6.57099783e-02 -9.69056040... | [11.272512435913086, -1.911670446395874] |
ceaea01f-93b0-469b-849d-643c53cd141c | a-kernel-based-quantum-random-forest-for | 2210.02355 | null | https://arxiv.org/abs/2210.02355v2 | https://arxiv.org/pdf/2210.02355v2.pdf | A kernel-based quantum random forest for improved classification | The emergence of Quantum Machine Learning (QML) to enhance traditional classical learning methods has seen various limitations to its realisation. There is therefore an imperative to develop quantum models with unique model hypotheses to attain expressional and computational advantage. In this work we extend the linear... | ['Lloyd C. L. Hollenberg', 'Charles D. Hill', 'Maiyuren Srikumar'] | 2022-10-05 | null | null | null | null | ['classification'] | ['methodology'] | [ 5.75470924e-01 3.72096598e-01 -1.51384979e-01 -1.89799026e-01
-9.42650735e-01 -4.36874449e-01 5.91171563e-01 -1.70407280e-01
-4.10255581e-01 1.02076817e+00 -4.44122255e-01 -7.73253322e-01
-5.00235498e-01 -9.75315213e-01 -5.52569032e-01 -1.03541160e+00
-2.40034491e-01 3.46648097e-01 7.52287135e-02 -1.66251093... | [5.574934005737305, 4.953494071960449] |
4c93f5ad-88ad-4459-b0cd-3a5576861c60 | can-autism-be-diagnosed-with-ai | 2206.02787 | null | https://arxiv.org/abs/2206.02787v1 | https://arxiv.org/pdf/2206.02787v1.pdf | Can autism be diagnosed with AI? | Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical task... | ['Tamim Niazi', 'Christian Desrosiers', 'Camel Tanougast', 'Idowu Paul Okuwobi', 'Yujie Li', 'Qizong Lu', 'Jiali Li', 'Ahmad Chaddad'] | 2022-06-05 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.48846129e-01 1.20286323e-01 -1.11578658e-01 -3.95341903e-01
-1.10386282e-01 2.14212433e-01 1.99061036e-01 4.01608735e-01
-2.80223012e-01 5.25073111e-01 8.55980441e-02 1.25030696e-01
-3.37414056e-01 -8.93405557e-01 -2.10914358e-01 -8.33014071e-01
-3.45989376e-01 9.54144537e-01 1.50046319e-01 -1.63905174... | [14.129669189453125, -1.6571358442306519] |
2c4e6593-bf31-4645-9ab0-7b1cf159a78b | first-take-all-temporal-order-preserving | 1506.02184 | null | http://arxiv.org/abs/1506.02184v1 | http://arxiv.org/pdf/1506.02184v1.pdf | First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos | With the prevalence of the commodity depth cameras, the new paradigm of user
interfaces based on 3D motion capturing and recognition have dramatically
changed the way of interactions between human and computers. Human action
recognition, as one of the key components in these devices, plays an important
role to guarante... | ['Guo-Jun Qi', 'Jun Ye', 'Hao Hu', 'Kien A. Hua', 'Kai Li'] | 2015-06-06 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.80061311e-01 -6.49335682e-01 -3.28732371e-01 -6.77101463e-02
-5.88436246e-01 -4.15894777e-01 4.68054235e-01 -9.64114256e-03
-4.90680724e-01 1.57040060e-01 2.67481387e-01 7.51139596e-02
7.17374012e-02 -6.37322426e-01 -4.38528627e-01 -8.45106244e-01
-3.59475315e-01 5.07264808e-02 7.51104355e-01 7.94836134... | [8.09731388092041, 0.20388652384281158] |
4c21823f-1e26-4cf7-960f-6c594a6e7a02 | a-framework-for-combining-entity-resolution | 2303.07469 | null | https://arxiv.org/abs/2303.07469v1 | https://arxiv.org/pdf/2303.07469v1.pdf | A Framework for Combining Entity Resolution and Query Answering in Knowledge Bases | We propose a new framework for combining entity resolution and query answering in knowledge bases (KBs) with tuple-generating dependencies (tgds) and equality-generating dependencies (egds) as rules. We define the semantics of the KB in terms of special instances that involve equivalence classes of entities and sets of... | ['Federico Scafoglieri', 'Lucian Popa', 'Domenico Lembo', 'Phokion G. Kolaitis', 'Ronald Fagin'] | 2023-03-13 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-1.60420910e-01 5.06135106e-01 -2.32418239e-01 -3.86226624e-01
-6.13288403e-01 -8.10969055e-01 3.52404356e-01 5.17590761e-01
-2.13142827e-01 1.24850535e+00 -2.16409937e-02 -3.42465788e-01
-4.50776160e-01 -1.56842160e+00 -5.92884004e-01 -3.26106071e-01
-5.30716591e-02 9.53800976e-01 7.52649963e-01 -3.77425879... | [8.98937702178955, 7.380207061767578] |
41f0bccd-ad8f-498d-9d58-798341f651bd | deep-stochastic-attraction-and-repulsion | 1808.08779 | null | https://arxiv.org/abs/1808.08779v2 | https://arxiv.org/pdf/1808.08779v2.pdf | Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization | This paper tackles the problem of large-scale image-based localization (IBL) where the spatial location of a query image is determined by finding out the most similar reference images in a large database. For solving this problem, a critical task is to learn discriminative image representation that captures informative... | ['Yuchao Dai', 'Liu Liu', 'Hongdong Li'] | 2018-08-27 | stochastic-attraction-repulsion-embedding-for | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_Stochastic_Attraction-Repulsion_Embedding_for_Large_Scale_Image_Localization_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Stochastic_Attraction-Repulsion_Embedding_for_Large_Scale_Image_Localization_ICCV_2019_paper.pdf | iccv-2019-10 | ['image-based-localization'] | ['computer-vision'] | [-2.59018391e-01 -3.66552502e-01 -4.39590394e-01 -4.45301473e-01
-1.39511514e+00 -7.04078972e-01 6.36417687e-01 3.35639000e-01
-7.15626597e-01 4.57253754e-01 2.88358390e-01 9.04743522e-02
-4.81702715e-01 -4.90852028e-01 -1.05348337e+00 -6.68413222e-01
-2.80377120e-01 1.91729739e-01 3.27790305e-02 8.91276523... | [7.828031539916992, -1.8952535390853882] |
b2de16d6-63a1-46f0-9565-5044d5b0a3bb | a-hybrid-pso-ga-for-extractive-text | null | null | https://aclanthology.org/2021.paclic-1.30 | https://aclanthology.org/2021.paclic-1.30.pdf | A Hybrid PSO-GA for Extractive Text Summarization | null | ['Nguyen Thi Hoai', 'Tran Thi Dinh', 'Thi Thu Trang Nguyen Bui Thi-Mai-Anh'] | null | null | https://aclanthology.org/2021.paclic-1.79 | https://aclanthology.org/2021.paclic-1.79.pdf | paclic-2021-11 | ['extractive-document-summarization'] | ['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... | [-7.268165111541748, 3.729079008102417] |
38ae0668-042e-4f1a-8e9d-984d789fc559 | towards-playing-full-moba-games-with-deep-1 | 2011.12692 | null | https://arxiv.org/abs/2011.12692v4 | https://arxiv.org/pdf/2011.12692v4.pdf | Towards Playing Full MOBA Games with Deep Reinforcement Learning | MOBA games, e.g., Honor of Kings, League of Legends, and Dota 2, pose grand challenges to AI systems such as multi-agent, enormous state-action space, complex action control, etc. Developing AI for playing MOBA games has raised much attention accordingly. However, existing work falls short in handling the raw game comp... | ['Wei Liu', 'Lanxiao Huang', 'Wei Yang', 'Qiang Fu', 'Tengfei Shi', 'Liang Wang', 'Bei Shi', 'Yinyuting Yin', 'Hongsheng Yu', 'Fuhao Qiu', 'Zhao Liu', 'Jia Chen', 'Bo Liu', 'Bo Yuan', 'Sheng Chen', 'Wen Zhang', 'Guibin Chen', 'Deheng Ye'] | 2020-11-25 | towards-playing-full-moba-games-with-deep | http://proceedings.neurips.cc/paper/2020/hash/06d5ae105ea1bea4d800bc96491876e9-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/06d5ae105ea1bea4d800bc96491876e9-Paper.pdf | neurips-2020-12 | ['dota-2'] | ['playing-games'] | [-3.40248108e-01 -9.33082923e-02 -3.27823535e-02 3.18187088e-01
-7.17869699e-01 -7.17300832e-01 5.68821669e-01 -3.43275696e-01
-8.34951758e-01 1.20780838e+00 -2.35272869e-01 -4.58199233e-01
-4.62998182e-01 -8.64120781e-01 -7.52923310e-01 -6.83054328e-01
-3.72718811e-01 1.05735016e+00 5.34591675e-01 -1.05206120... | [3.6052610874176025, 1.5689071416854858] |
ae165e0e-b928-4020-b161-3021a57a3b37 | using-the-random-sprays-retinex-algorithm-for | 1310.0307 | null | http://arxiv.org/abs/1310.0307v2 | http://arxiv.org/pdf/1310.0307v2.pdf | Using the Random Sprays Retinex Algorithm for Global Illumination Estimation | In this paper the use of Random Sprays Retinex (RSR) algorithm for global
illumination estimation is proposed and its feasibility tested. Like other
algorithms based on the Retinex model, RSR also provides local illumination
estimation and brightness adjustment for each pixel and it is faster than other
path-wise Retin... | ['Sven Lončarić', 'Nikola Banić'] | 2013-10-01 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.47022614e-01 -5.56704223e-01 -2.41602901e-02 -3.50847334e-01
-4.44680214e-01 -5.26091456e-01 4.02641684e-01 -2.23863691e-01
-3.73502940e-01 9.57764089e-01 -2.72452712e-01 -3.82615685e-01
6.11721165e-02 -6.57893538e-01 -4.19479787e-01 -1.05290008e+00
2.73471087e-01 -1.30872028e-02 2.45126501e-01 -2.86013871... | [10.4093656539917, -2.5917160511016846] |
003bdc2a-cd44-497d-98f7-e7f735e8fe77 | n15news-a-new-dataset-for-multimodal-news | 2108.13327 | null | https://arxiv.org/abs/2108.13327v4 | https://arxiv.org/pdf/2108.13327v4.pdf | N24News: A New Dataset for Multimodal News Classification | Current news datasets merely focus on text features on the news and rarely leverage the feature of images, excluding numerous essential features for news classification. In this paper, we propose a new dataset, N24News, which is generated from New York Times with 24 categories and contains both text and image informati... | ['Jie Yang', 'Xiangxie Zhang', 'Xu Shan', 'Zhen Wang'] | 2021-08-30 | null | https://aclanthology.org/2022.lrec-1.729 | https://aclanthology.org/2022.lrec-1.729.pdf | lrec-2022-6 | ['news-classification'] | ['natural-language-processing'] | [-1.12895317e-01 -1.87189415e-01 -4.14981633e-01 -4.69791800e-01
-1.06696725e+00 -6.30552590e-01 1.18561316e+00 1.74307659e-01
-4.18586671e-01 7.68235981e-01 8.82141709e-01 1.39379818e-02
1.84666682e-02 -4.64241922e-01 -6.43270254e-01 -8.33420217e-01
1.36330947e-01 2.83457726e-01 -9.86905172e-02 -3.14231128... | [13.019491195678711, 5.232800483703613] |
619ba071-715e-4587-86a7-687021514d5d | something-old-something-new-grammar-based-ccg | 2109.10044 | null | https://arxiv.org/abs/2109.10044v2 | https://arxiv.org/pdf/2109.10044v2.pdf | Something Old, Something New: Grammar-based CCG Parsing with Transformer Models | This report describes the parsing problem for Combinatory Categorial Grammar (CCG), showing how a combination of Transformer-based neural models and a symbolic CCG grammar can lead to substantial gains over existing approaches. The report also documents a 20-year research program, showing how NLP methods have evolved o... | ['Stephen Clark'] | 2021-09-21 | null | null | null | null | ['ccg-supertagging'] | ['natural-language-processing'] | [ 5.04419692e-02 9.09770608e-01 -1.35424554e-01 -7.43758738e-01
-1.30647886e+00 -7.19931960e-01 4.11609411e-01 4.40776013e-02
-2.10711751e-02 6.02880597e-01 5.49648762e-01 -1.03862429e+00
1.50556386e-01 -8.54174137e-01 -5.79493403e-01 -4.55077350e-01
-2.42578566e-01 5.82439303e-01 1.31916910e-01 -5.46544969... | [10.457274436950684, 9.514153480529785] |
bb33486f-f043-41d5-b46e-e89a573a02fc | a-dataset-for-deep-learning-based-bone | 2306.04579 | null | https://arxiv.org/abs/2306.04579v1 | https://arxiv.org/pdf/2306.04579v1.pdf | A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty | Total hip arthroplasty (THA) is a widely used surgical procedure in orthopedics. For THA, it is of clinical significance to analyze the bone structure from the CT images, especially to observe the structure of the acetabulum and femoral head, before the surgical procedure. For such bone structure analyses, deep learnin... | ['Xifu Shang', 'Dong Liu', 'Ziyang Gan', 'Kaidong Zhang'] | 2023-06-07 | null | null | null | null | ['active-learning', 'anatomy', 'active-learning'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-1.04986414e-01 6.36895359e-01 -3.35320562e-01 -3.59137803e-01
-1.19806671e+00 -7.59121925e-02 -2.54184455e-02 2.25659043e-01
-5.61633110e-01 6.02741778e-01 1.38505384e-01 -1.80906802e-01
-1.01904839e-01 -6.49206161e-01 -6.12704873e-01 -9.05161679e-01
-1.38418674e-01 1.38181221e+00 7.36125052e-01 1.54965222... | [14.367851257324219, -2.195810556411743] |
895593ea-345c-4098-9daf-5cb2843075a3 | uncovering-the-local-hidden-community | 2112.04100 | null | https://arxiv.org/abs/2112.04100v1 | https://arxiv.org/pdf/2112.04100v1.pdf | Uncovering the Local Hidden Community Structure in Social Networks | Hidden community is a useful concept proposed recently for social network analysis. To handle the rapid growth of network scale, in this work, we explore the detection of hidden communities from the local perspective, and propose a new method that detects and boosts each layer iteratively on a subgraph sampled from the... | ['John E. Hopcroft', 'Kun He', 'Boyu Li', 'Meng Wang'] | 2021-12-08 | null | null | null | null | ['local-community-detection'] | ['graphs'] | [ 9.32652950e-02 4.09396142e-01 -1.82981089e-01 4.38008398e-01
-8.46356899e-02 -6.70397997e-01 3.21337223e-01 3.21469188e-01
-1.93923600e-02 4.39490050e-01 6.00187592e-02 1.23116402e-02
1.46799818e-01 -1.14419067e+00 -2.83056051e-01 -9.37945843e-01
-5.68238437e-01 4.51926470e-01 1.07610369e+00 -4.19054404... | [6.95444393157959, 5.230868816375732] |
ed01d1e4-5c3f-40b1-ae16-5abf293933ab | optimizing-scoring-function-of-dynamic | 1708.09097 | null | http://arxiv.org/abs/1708.09097v2 | http://arxiv.org/pdf/1708.09097v2.pdf | Optimizing scoring function of dynamic programming of pairwise profile alignment using derivative free neural network | A profile comparison method with position-specific scoring matrix (PSSM) is
one of the most accurate alignment methods. Currently, cosine similarity and
correlation coefficient are used as scoring functions of dynamic programming to
calculate similarity between PSSMs. However, it is unclear that these functions
are opt... | ['Kazunori D Yamada'] | 2017-08-30 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 6.11759722e-01 -4.87355798e-01 -1.43631682e-01 -3.95136923e-01
-3.08298796e-01 -6.01976931e-01 -4.13218178e-02 3.80404770e-01
-4.99671787e-01 8.72561753e-01 -1.13437667e-01 -2.87803620e-01
-5.22015870e-01 -6.76942647e-01 -2.18212828e-01 -1.01579034e+00
-2.12289661e-01 4.73533034e-01 4.42906320e-01 -6.30215466... | [4.8131184577941895, 5.272375106811523] |
4a945ae5-3ae7-49c7-ac15-d89a4352ba72 | best-feature-performance-in-codeswitched-hate | null | null | https://openreview.net/forum?id=Skl6peHFwS | https://openreview.net/pdf?id=Skl6peHFwS | Best feature performance in codeswitched hate speech texts | How well can hate speech concept be abstracted in order to inform automatic classification in codeswitched texts by machine learning classifiers? We explore different representations and empirically evaluate their predictiveness using both conventional and deep learning algorithms in identifying hate speech in a ~48k h... | ['Peter Wagacha', 'Lawrence Muchemi', 'Edward Ombui'] | 2019-09-25 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-6.34598583e-02 1.80905521e-01 -1.61575750e-01 -1.29300535e-01
-4.81767297e-01 -5.99545002e-01 1.26035297e+00 5.65660119e-01
-3.76841605e-01 4.72364992e-01 9.51585829e-01 -2.57283628e-01
6.83090091e-02 -4.73369718e-01 -7.12931827e-02 -7.31006682e-01
5.59239984e-02 2.60741889e-01 -3.22250217e-01 -1.96796104... | [8.7759428024292, 10.563959121704102] |
3c943201-4054-4376-b2fb-9ed7b10d1bff | computer-vision-system-for-eye-gaze-tracking | null | null | http://ijmcs.info/ | http://ijmcs.info/ | Computer Vision System for Eye Gaze Tracking | : Eye gaze tracking is a technique use for checking the usability problems in the Human Computer Interaction (HCI). Initially they are
present tracking technology and key elements. Eye gaze tracking technique is based on the behavior of the user when they are looking. It can use
for different kinds of methods i.e. “e... | ['Gyankamal J. Chhajed', 'Puja P. Sorate'] | 2017-06-03 | null | null | null | international-journal-of-modern-computer | ['gaze-estimation'] | ['computer-vision'] | [-3.60276341e-01 -1.40201285e-01 2.15499595e-01 1.36643752e-01
6.44822717e-01 -6.06018841e-01 1.19331166e-01 9.26444829e-02
-3.80729556e-01 5.75434506e-01 1.63397491e-01 -8.61145973e-01
-1.52252875e-02 9.47240293e-02 -9.60012078e-02 -3.58121246e-01
3.62388492e-01 -2.41384000e-01 4.11992073e-01 -6.64496869... | [13.95508861541748, 0.262911856174469] |
a5a5e628-b9e5-4f08-8892-ec9190be7bbd | role-of-language-relatedness-in-multilingual | 2109.10534 | null | https://arxiv.org/abs/2109.10534v1 | https://arxiv.org/pdf/2109.10534v1.pdf | Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages | We explore the impact of leveraging the relatedness of languages that belong to the same family in NLP models using multilingual fine-tuning. We hypothesize and validate that multilingual fine-tuning of pre-trained language models can yield better performance on downstream NLP applications, compared to models fine-tune... | ['Pushpak Bhattacharyya', 'Karthik Sankaranarayanan', 'Samarth Bharadwaj', 'Rudra Murthy V', 'Tejas Indulal Dhamecha'] | 2021-09-22 | null | https://aclanthology.org/2021.emnlp-main.675 | https://aclanthology.org/2021.emnlp-main.675.pdf | emnlp-2021-11 | ['multiple-choice-qa', 'transliteration'] | ['natural-language-processing', 'natural-language-processing'] | [-4.36857522e-01 1.08859867e-01 -2.69039273e-01 -3.23482126e-01
-1.21972787e+00 -1.16129661e+00 6.36766315e-01 2.74012089e-01
-7.71311820e-01 1.10370922e+00 5.88257313e-01 -9.10646081e-01
-1.81369022e-01 -7.26927340e-01 -7.35939384e-01 -4.20940042e-01
8.13989565e-02 6.61107183e-01 9.63632986e-02 -6.96310341... | [10.831401824951172, 10.04738998413086] |
1f32563f-07c7-44ed-bf48-8bc8acbf167a | infrared-and-visible-image-fusion-using | 1804.08992 | null | https://arxiv.org/abs/1804.08992v5 | https://arxiv.org/pdf/1804.08992v5.pdf | Infrared and visible image fusion using Latent Low-Rank Representation | Infrared and visible image fusion is an important problem in the field of image fusion which has been applied widely in many fields. To better preserve the useful information from source images, in this paper, we propose a novel image fusion method based on latent low-rank representation(LatLRR) which is simple and eff... | ['Xiao-Jun Wu', 'Hui Li'] | 2018-04-24 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 2.22005978e-01 -5.50697446e-01 -7.31895342e-02 -3.18246372e-02
-9.99805689e-01 -1.59904495e-01 2.57072717e-01 5.09711541e-02
-2.14841485e-01 5.38415253e-01 6.31934047e-01 8.92967284e-02
-1.90573037e-01 -7.35337734e-01 -1.02817036e-01 -1.01737201e+00
4.22026634e-01 -3.76386702e-01 3.74591947e-01 -3.56508255... | [10.538492202758789, -1.9445650577545166] |
191d3652-7573-46a6-b48f-53e1e33b2d9a | performance-accuration-method-of-machine | null | null | https://iocscience.org/ejournal/index.php/mantik/article/view/725 | https://iocscience.org/ejournal/index.php/mantik/article/view/725/482 | Performance Accuration Method of Machine Learning for Diabetes Prediction | Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning (ML) techniques allow us to obtain predictively, the dataset we are testing is pima-indian-diabetes with a dataset... | ['ALI Murtadho', 'Dwi Harini Sulistyawati'] | 2020-05-01 | null | null | null | jurnal-mantik-2020-5 | ['diabetes-prediction', 'automatic-machine-learning-model-selection'] | ['medical', 'methodology'] | [ 7.11980835e-02 1.63778916e-01 -4.66948330e-01 -7.66344130e-01
-4.15942460e-01 -2.99548358e-01 7.39196062e-01 1.46272138e-01
-3.45183253e-01 1.10178685e+00 2.74921060e-02 -7.03904331e-01
-6.63444579e-01 -6.73530936e-01 -3.18860084e-01 -6.78218305e-01
-4.32111919e-01 9.03039753e-01 3.37784104e-02 -1.06260061... | [8.384753227233887, 4.818276882171631] |
ac9a609e-78fa-4f7a-8d96-4f87342dca29 | locate-then-segment-a-strong-pipeline-for | 2103.16284 | null | https://arxiv.org/abs/2103.16284v1 | https://arxiv.org/pdf/2103.16284v1.pdf | Locate then Segment: A Strong Pipeline for Referring Image Segmentation | Referring image segmentation aims to segment the objects referred by a natural language expression. Previous methods usually focus on designing an implicit and recurrent feature interaction mechanism to fuse the visual-linguistic features to directly generate the final segmentation mask without explicitly modeling the ... | ['Tieniu Tan', 'Lei LI', 'Liang Wang', 'Wei Wang', 'Tao Kong', 'Ya Jing'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Jing_Locate_Then_Segment_A_Strong_Pipeline_for_Referring_Image_Segmentation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Jing_Locate_Then_Segment_A_Strong_Pipeline_for_Referring_Image_Segmentation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['generalized-referring-expression-segmentation'] | ['computer-vision'] | [ 3.39779407e-01 2.87450701e-01 -4.50582534e-01 -5.04772723e-01
-9.45545018e-01 -7.50475705e-01 6.83274984e-01 2.68588867e-02
-3.61580104e-01 1.84175789e-01 1.09978594e-01 -1.51732385e-01
3.51432383e-01 -5.48753202e-01 -7.82337666e-01 -6.06936753e-01
3.86601657e-01 5.06967962e-01 3.96426409e-01 -1.24491289... | [10.273747444152832, 1.218767762184143] |
2ac355c5-1fba-46d7-a441-69f5710bf964 | partial-annotation-learning-for-biomedical | 2305.13120 | null | https://arxiv.org/abs/2305.13120v1 | https://arxiv.org/pdf/2305.13120v1.pdf | Partial Annotation Learning for Biomedical Entity Recognition | Motivation: Named Entity Recognition (NER) is a key task to support biomedical research. In Biomedical Named Entity Recognition (BioNER), obtaining high-quality expert annotated data is laborious and expensive, leading to the development of automatic approaches such as distant supervision. However, manually and automat... | ['Zhixiong Zhang', 'Giovanni Colavizza', 'Liangping Ding'] | 2023-05-22 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [ 2.27197334e-02 5.61659157e-01 -4.32788342e-01 -4.59794879e-01
-1.19202721e+00 -3.63278568e-01 1.30015314e-01 6.16705894e-01
-1.03695750e+00 1.42021477e+00 2.00838208e-01 -1.16191588e-01
2.04740703e-01 -4.13973063e-01 -7.95262337e-01 -6.18097484e-01
2.83297926e-01 8.41954172e-01 1.75234675e-01 2.16054350... | [8.553701400756836, 8.836126327514648] |
8021dfa2-52b7-4cdf-9285-4a597268cebe | towards-robust-passage-re-ranking-model-by | null | null | https://openreview.net/forum?id=1wAxnkbl-1O | https://openreview.net/pdf?id=1wAxnkbl-1O | Towards Robust Passage Re-Ranking Model by Mitigating Lexical Match Bias | While deep learning models can overcome the limitations of traditional machine learning algorithms that use hand-crafted features, recent studies have shown that these models often achieve high dataset-specific accuracy by exploiting several bias without understanding deeper semantics of intended task. In this paper, w... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['passage-re-ranking'] | ['natural-language-processing'] | [ 4.07563508e-01 -1.40305623e-01 -9.84393731e-02 -4.47484702e-01
-9.87287760e-01 -8.56629074e-01 8.42621028e-01 1.35943770e-01
-6.87087655e-01 7.85093784e-01 5.09043157e-01 -1.89720199e-01
-1.33211225e-01 -7.62681425e-01 -8.00826132e-01 -1.67829067e-01
1.64318308e-01 2.84310728e-01 4.28561628e-01 -7.19795287... | [11.307647705078125, 7.6747589111328125] |
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