paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cba897af-afa7-4783-9bce-eae3c98748ff | a-model-to-measure-the-spread-power-of-rumors | 2002.07563 | null | https://arxiv.org/abs/2002.07563v5 | https://arxiv.org/pdf/2002.07563v5.pdf | A Model to Measure the Spread Power of Rumors | With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not ... | ['Taymaz Akan', 'Mohammad-Ali Balafar', 'Elnaz Zafarani-Moattar', 'Mehrdad Ranjbar-Khadivi', 'Ali-Reza Feizi-Derakhshi', 'Narjes Nikzad-Khasmakhi', 'Meysam Asgari-Chenaghlu', 'Mohammad-Reza Feizi-Derakhshi', 'Zoleikha Jahanbakhsh-Nagadeh', 'Majid Ramezani'] | 2020-02-18 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-5.35087585e-01 -2.49724067e-03 -3.02454531e-01 -1.40497699e-01
3.55391741e-01 -1.60562366e-01 1.05813909e+00 2.98959345e-01
-7.17199296e-02 8.87730598e-01 6.06960952e-01 -3.12144011e-01
-3.71454619e-02 -8.10247958e-01 -1.80295199e-01 -4.25068051e-01
-2.72002220e-01 2.03641862e-01 6.36132285e-02 -6.20342970... | [8.241090774536133, 10.15260124206543] |
67e23011-1320-43ac-8dcc-459eb3f90937 | dereverberation-in-acoustic-sensor-networks | 2301.07649 | null | https://arxiv.org/abs/2301.07649v1 | https://arxiv.org/pdf/2301.07649v1.pdf | Dereverberation in Acoustic Sensor Networks Using Weighted Prediction Error With Microphone-dependent Prediction Delays | In the last decades several multi-microphone speech dereverberation algorithms have been proposed, among which the weighted prediction error (WPE) algorithm. In the WPE algorithm, a prediction delay is required to reduce the correlation between the prediction signals and the direct component in the reference microphone... | ['Simon Doclo', 'Joerg Bitzer', 'Toon van Waterschoot', 'Anselm Lohmann'] | 2023-01-18 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 3.26696247e-01 -2.38018334e-01 6.93073809e-01 9.28558186e-02
-8.12438786e-01 -5.08935630e-01 4.01450172e-02 2.44853079e-01
-2.99637675e-01 4.81066585e-01 3.52776617e-01 -1.14970222e-01
-4.87286001e-01 -4.61349726e-01 -4.85810310e-01 -8.58102918e-01
-3.20778161e-01 -3.66802871e-01 1.66825548e-01 2.51933962... | [15.157439231872559, 5.740784645080566] |
d39480e6-be2c-4594-af9b-578d27e77e20 | high-resolution-photorealistic-image | 2105.09188 | null | https://arxiv.org/abs/2105.09188v1 | https://arxiv.org/pdf/2105.09188v1.pdf | High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network | Existing image-to-image translation (I2IT) methods are either constrained to low-resolution images or long inference time due to their heavy computational burden on the convolution of high-resolution feature maps. In this paper, we focus on speeding-up the high-resolution photorealistic I2IT tasks based on closed-form ... | ['Lei Zhang', 'Hui Zeng', 'Jie Liang'] | 2021-05-19 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liang_High-Resolution_Photorealistic_Image_Translation_in_Real-Time_A_Laplacian_Pyramid_Translation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liang_High-Resolution_Photorealistic_Image_Translation_in_Real-Time_A_Laplacian_Pyramid_Translation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['color-manipulation', 'photo-retouching'] | ['computer-vision', 'computer-vision'] | [ 4.68831390e-01 -3.98549557e-01 4.12690565e-02 5.43961441e-03
-8.08028519e-01 -4.05198395e-01 4.56893474e-01 -4.69615728e-01
-3.87163013e-01 4.55557406e-01 3.29942346e-01 -9.12466180e-03
-1.02817535e-01 -8.56079221e-01 -8.67506683e-01 -7.68365681e-01
4.57117766e-01 -2.21898165e-02 2.78244764e-01 -1.26715869... | [10.985404014587402, -1.8236578702926636] |
abd46bed-1e94-4682-b263-b840cf176ca7 | generating-features-with-increased-crop | 2304.05096 | null | https://arxiv.org/abs/2304.05096v1 | https://arxiv.org/pdf/2304.05096v1.pdf | Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection | Two-stage object detectors generate object proposals and classify them to detect objects in images. These proposals often do not contain the objects perfectly but overlap with them in many possible ways, exhibiting great variability in the difficulty levels of the proposals. Training a robust classifier against this cr... | ['Dimitris Samaras', 'Hieu Le', 'Jingyi Xu'] | 2023-04-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Generating_Features_With_Increased_Crop-Related_Diversity_for_Few-Shot_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Generating_Features_With_Increased_Crop-Related_Diversity_for_Few-Shot_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['few-shot-object-detection'] | ['computer-vision'] | [ 1.85828045e-01 -1.21905006e-01 -2.23685205e-01 -8.39480162e-02
-5.05195439e-01 -6.52056515e-01 5.25084317e-01 1.33019909e-01
-1.36798188e-01 2.34447479e-01 -5.95861375e-02 2.67836601e-01
1.48404628e-01 -1.14410782e+00 -8.56298566e-01 -9.16578889e-01
3.13661873e-01 2.92482853e-01 5.91805339e-01 -4.35603410... | [9.698585510253906, 2.0349221229553223] |
88819c49-ca83-4611-b720-ad97e6ebe426 | poison-attack-and-defense-on-deep-source-code | 2210.17029 | null | https://arxiv.org/abs/2210.17029v1 | https://arxiv.org/pdf/2210.17029v1.pdf | Poison Attack and Defense on Deep Source Code Processing Models | In the software engineering community, deep learning (DL) has recently been applied to many source code processing tasks. Due to the poor interpretability of DL models, their security vulnerabilities require scrutiny. Recently, researchers have identified an emergent security threat, namely poison attack. The attackers... | ['Xin Xia', 'Xing Hu', 'Zhi Jin', 'Ge Li', 'Huangzhao Zhang', 'Zhuo Li', 'Jia Li'] | 2022-10-31 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [-1.85080543e-01 -2.31541276e-01 -9.67923701e-02 1.68843091e-01
-4.22699571e-01 -1.15365875e+00 4.47878867e-01 3.82433742e-01
1.92537144e-01 -9.27805603e-02 -1.26121566e-01 -8.96374166e-01
3.57038975e-01 -9.88275290e-01 -8.63932908e-01 -3.80592376e-01
-3.97488147e-01 -2.04463825e-01 3.35197181e-01 -3.05303723... | [6.545122146606445, 7.851982593536377] |
bb77aeef-7a19-42aa-9ba9-5600641d0735 | boost-test-time-performance-with-closed-loop | 2203.10853 | null | https://arxiv.org/abs/2203.10853v2 | https://arxiv.org/pdf/2203.10853v2.pdf | Boost Test-Time Performance with Closed-Loop Inference | Conventional deep models predict a test sample with a single forward propagation, which, however, may not be sufficient for predicting hard-classified samples. On the contrary, we human beings may need to carefully check the sample many times before making a final decision. During the recheck process, one may refine/ad... | ['Mingkui Tan', 'YaoWei Wang', 'Peilin Zhao', 'Junzhou Huang', 'Haokun Li', 'Guanghui Xu', 'Yifan Zhang', 'Jiaxiang Wu', 'Shuaicheng Niu'] | 2022-03-21 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 2.57529795e-01 1.08390607e-01 -1.92516536e-01 -8.07897925e-01
-7.25298047e-01 -1.85852185e-01 3.02504957e-01 1.22959644e-01
-3.48228186e-01 8.16049337e-01 -4.78220642e-01 -3.50725174e-01
-7.19504505e-02 -9.48907137e-01 -8.34953547e-01 -4.80799615e-01
3.51458669e-01 7.43597627e-01 5.04196048e-01 1.06895283... | [9.368674278259277, 3.7293245792388916] |
649865c2-e6ce-4a88-a922-9d6741f21cb4 | rapid-inr-storage-efficient-cpu-free-dnn | 2306.16699 | null | https://arxiv.org/abs/2306.16699v1 | https://arxiv.org/pdf/2306.16699v1.pdf | Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representation | Implicit Neural Representation (INR) is an innovative approach for representing complex shapes or objects without explicitly defining their geometry or surface structure. Instead, INR represents objects as continuous functions. Previous research has demonstrated the effectiveness of using neural networks as INR for ima... | ['Cong Hao', 'Stephen BR Fitzmeyer', 'Hang Yang', 'Hanqiu Chen'] | 2023-06-29 | null | null | null | null | ['image-compression', 'quantization'] | ['computer-vision', 'methodology'] | [ 6.03219569e-01 -7.60450512e-02 -5.46748526e-02 -3.21703404e-01
-3.49594653e-01 -2.52303123e-01 3.21482480e-01 1.98650986e-01
-9.23391759e-01 3.79917145e-01 -1.66362539e-01 -6.60750628e-01
8.49573389e-02 -1.13000572e+00 -1.04301643e+00 -4.62403506e-01
2.13632826e-02 2.20502540e-01 2.19966367e-01 1.85683176... | [8.602421760559082, 2.917926788330078] |
e3f81c0a-c032-46d5-9fe8-7365a01edde0 | icdar-2021-competition-on-scientific-table | 2105.14426 | null | https://arxiv.org/abs/2105.14426v2 | https://arxiv.org/pdf/2105.14426v2.pdf | ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX | Tables present important information concisely in many scientific documents. Visual features like mathematical symbols, equations, and spanning cells make structure and content extraction from tables embedded in research documents difficult. This paper discusses the dataset, tasks, participants' methods, and results of... | ['Mayank Singh', 'Harsh Desai', 'Mrinal Anand', 'Pratik Kayal'] | 2021-05-30 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 6.88896626e-02 -3.04899421e-02 -9.41103324e-02 -5.72710752e-01
-1.32319558e+00 -1.08265829e+00 5.10148525e-01 5.10199368e-01
-4.27647047e-02 6.32879615e-01 2.97363065e-02 -1.94639295e-01
9.93814990e-02 -3.92647594e-01 -1.00958407e+00 -2.56604314e-01
1.97107747e-01 6.48095787e-01 -1.75264746e-01 3.15151364... | [11.687643051147461, 3.013688802719116] |
69a0893f-a406-444f-a2eb-115ddee5f88f | an-end-to-end-deep-learning-approach-for-1 | 2108.07453 | null | https://arxiv.org/abs/2108.07453v1 | https://arxiv.org/pdf/2108.07453v1.pdf | An End-to-End Deep Learning Approach for Epileptic Seizure Prediction | An accurate seizure prediction system enables early warnings before seizure onset of epileptic patients. It is extremely important for drug-refractory patients. Conventional seizure prediction works usually rely on features extracted from Electroencephalography (EEG) recordings and classification algorithms such as reg... | ['Mohamad Sawan', 'Hemmings Wu', 'Shiqi Zhao', 'Jie Yang', 'Yankun Xu'] | 2021-08-17 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-1.31124482e-01 -3.35700423e-01 2.09836483e-01 -3.60787988e-01
-4.47562516e-01 -1.19062342e-01 2.55909353e-01 3.26860398e-01
-6.06436253e-01 1.01939380e+00 -1.52001709e-01 -2.70087242e-01
-3.70598495e-01 -4.28980321e-01 -1.45866022e-01 -6.71335876e-01
-7.09561408e-01 -1.05282098e-01 1.81669131e-01 1.89119969... | [13.226664543151855, 3.5194737911224365] |
1d3bee02-6312-4e8a-a94a-d6b7d4b9a2c3 | scene-text-detection-with-scribble-lines | 2012.05030 | null | https://arxiv.org/abs/2012.05030v2 | https://arxiv.org/pdf/2012.05030v2.pdf | Scene Text Detection with Scribble Lines | Scene text detection, which is one of the most popular topics in both academia and industry, can achieve remarkable performance with sufficient training data. However, the annotation costs of scene text detection are huge with traditional labeling methods due to the various shapes of texts. Thus, it is practical and in... | ['Xiang Bai', 'Xiaolin Wei', 'Rui Zhang', 'Minghui Liao', 'Yang Qiu', 'Wenqing Zhang'] | 2020-12-09 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 2.79902041e-01 -1.74791262e-01 -1.32393077e-01 -1.74432039e-01
-4.08454090e-01 -6.34645343e-01 6.12242758e-01 3.43271524e-01
-2.53260732e-01 2.11429477e-01 -2.64032912e-02 -3.73810709e-01
5.16957700e-01 -7.56283998e-01 -3.31698149e-01 -6.58791959e-01
5.00510514e-01 3.85782093e-01 8.89240921e-01 -1.15629323... | [12.029952049255371, 2.3013153076171875] |
5e676eae-7869-4071-a9b0-0048ebae8daf | deep-bag-of-sub-emotions-for-depression | 2103.01334 | null | https://arxiv.org/abs/2103.01334v1 | https://arxiv.org/pdf/2103.01334v1.pdf | Deep Bag-of-Sub-Emotions for Depression Detection in Social Media | This paper presents the Deep Bag-of-Sub-Emotions (DeepBoSE), a novel deep learning model for depression detection in social media. The model is formulated such that it internally computes a differentiable Bag-of-Features (BoF) representation that incorporates emotional information. This is achieved by a reinterpretatio... | ['Manuel Montes-y-Gomez', 'Fabio A. Gonzalez', 'Mario Ezra Aragon', 'Juan S. Lara'] | 2021-03-01 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-3.54278415e-01 3.49053890e-01 -1.09509706e-01 -7.03898847e-01
-4.64149326e-01 1.21852033e-01 7.62122929e-01 7.43063033e-01
-5.58231652e-01 5.49235642e-01 3.37093621e-01 1.91681728e-01
-3.27095538e-01 -9.11085963e-01 -2.41816789e-01 -7.20677435e-01
-3.50568146e-01 4.36266214e-01 -3.55307251e-01 -5.38684845... | [13.133757591247559, 5.770815372467041] |
91cff4aa-004d-409c-93df-22f1d35f2707 | namer-a-node-based-multitasking-framework-for | null | null | https://aclanthology.org/2021.naacl-demos.3 | https://aclanthology.org/2021.naacl-demos.3.pdf | NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering | We present NAMER, an open-domain Chinese knowledge base question answering system based on a novel node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question. Equipped with techniques including data augme... | ['Sen Hu', 'Yinnian Lin', 'Lei Zou', 'Ruoyu Zhang', 'Minhao Zhang'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-4.03199643e-01 4.42440003e-01 -1.78127006e-01 -4.00917292e-01
-1.18784094e+00 -7.79880524e-01 1.35618061e-01 2.56108761e-01
-4.01881874e-01 9.63159919e-01 4.19879526e-01 -5.60844481e-01
-3.12999070e-01 -6.54473722e-01 -6.14004195e-01 -2.03840807e-01
3.56828451e-01 7.40874946e-01 6.32030904e-01 -7.76796579... | [10.753992080688477, 7.9877424240112305] |
c1238801-3646-48be-b108-7446c3305c4c | a-joint-framework-for-ancient-chinese-ws-and | null | null | https://aclanthology.org/2022.lt4hala-1.27 | https://aclanthology.org/2022.lt4hala-1.27.pdf | A Joint Framework for Ancient Chinese WS and POS Tagging Based on Adversarial Ensemble Learning | Ancient Chinese word segmentation and part-of-speech tagging tasks are crucial to facilitate the study of ancient Chinese and the dissemination of traditional Chinese culture. Current methods face problems such as lack of large-scale labeled data, individual task error propagation, and lack of robustness and generaliza... | ['Shuxun Yang'] | null | null | null | null | lt4hala-lrec-2022-6 | ['chinese-word-segmentation', 'culture'] | ['natural-language-processing', 'speech'] | [ 1.18132748e-01 -9.44505408e-02 1.10262394e-01 -3.64727259e-01
-8.34892631e-01 -7.42558062e-01 2.42383853e-01 -3.89107645e-01
-9.42187726e-01 7.34752655e-01 1.04164049e-01 -5.09899676e-01
5.20049751e-01 -6.35924935e-01 -4.60179448e-01 -7.13861465e-01
1.32414013e-01 1.51992321e-01 4.43664014e-01 -2.70283967... | [9.992293357849121, 10.08755111694336] |
08ad9184-d53e-4b9a-95cd-6f6b47e3c048 | transfer-learning-with-class-weighted-and | 2009.05977 | null | https://arxiv.org/abs/2009.05977v1 | https://arxiv.org/pdf/2009.05977v1.pdf | Transfer learning with class-weighted and focal loss function for automatic skin cancer classification | Skin cancer is by far in top-3 of the world's most common cancer. Among different skin cancer types, melanoma is particularly dangerous because of its ability to metastasize. Early detection is the key to success in skin cancer treatment. However, skin cancer diagnosis is still a challenge, even for experienced dermato... | ['Lua T. Ngo', 'Duyen N. T. Le', 'Hieu X. Le', 'Hoan T. Ngo'] | 2020-09-13 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 3.75779361e-01 -6.33538812e-02 -1.55091628e-01 1.72995985e-01
-7.53322244e-01 -3.69012237e-01 4.79000509e-01 4.70415175e-01
-3.60374242e-01 1.06480360e+00 9.33606625e-02 -4.31539983e-01
-1.67488873e-01 -9.06895399e-01 4.38081883e-02 -1.13441777e+00
1.83654100e-01 2.64140248e-01 2.84704119e-01 -1.51475653... | [15.674290657043457, -2.9966049194335938] |
058088d4-8d18-440b-a50a-1419b8dd50a2 | from-perspective-x-ray-imaging-to-parallax | 2003.02959 | null | https://arxiv.org/abs/2003.02959v1 | https://arxiv.org/pdf/2003.02959v1.pdf | From Perspective X-ray Imaging to Parallax-Robust Orthographic Stitching | Stitching images acquired under perspective projective geometry is a relevant topic in computer vision with multiple applications ranging from smartphone panoramas to the construction of digital maps. Image stitching is an equally prominent challenge in medical imaging, where the limited field-of-view captured by singl... | ['Nassir Navab', 'Mehran Armand', 'Mathias Unberath', 'Xingtong Liu', 'Javad Fotouhi'] | 2020-03-05 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 8.65293086e-01 9.81106311e-02 -1.74878109e-02 -4.93669026e-02
-8.34990680e-01 -4.59286749e-01 4.97716010e-01 7.30201006e-02
-3.42479557e-01 3.83023620e-01 4.56939250e-01 -2.74547517e-01
-4.25762892e-01 -4.17304277e-01 -4.26442236e-01 -9.25995827e-01
-1.11743666e-01 1.21187262e-01 -1.20146617e-01 -5.67448959... | [13.555532455444336, -2.764758586883545] |
baa4cfaa-41a0-4e2e-8e95-517bb5cadac4 | privacy-preserving-domain-adaptation-of | 2212.10520 | null | https://arxiv.org/abs/2212.10520v3 | https://arxiv.org/pdf/2212.10520v3.pdf | Privacy-Preserving Domain Adaptation of Semantic Parsers | Task-oriented dialogue systems often assist users with personal or confidential matters. For this reason, the developers of such a system are generally prohibited from observing actual usage. So how can they know where the system is failing and needs more training data or new functionality? In this work, we study ways ... | ['Jason Eisner', 'Tatsunori Hashimoto', 'Yu Su', 'Richard Shin', 'FatemehSadat Mireshghallah'] | 2022-12-20 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'task-oriented-dialogue-systems'] | ['medical', 'miscellaneous', 'natural-language-processing'] | [ 4.70783204e-01 1.12137926e+00 2.51657128e-01 -7.18441546e-01
-1.15402508e+00 -9.25610542e-01 3.58232796e-01 9.84237641e-02
-1.69098884e-01 1.14546287e+00 1.42142102e-02 -3.18531364e-01
4.84192401e-01 -7.57023752e-01 -5.75377464e-01 -1.48130283e-01
4.18296874e-01 6.18945658e-01 -1.10116318e-01 -3.78793240... | [12.4743013381958, 7.83352518081665] |
9b7e4b2f-3f9c-4adc-8eca-5c8211959403 | dilated-neighborhood-attention-transformer | 2209.15001 | null | https://arxiv.org/abs/2209.15001v3 | https://arxiv.org/pdf/2209.15001v3.pdf | Dilated Neighborhood Attention Transformer | Transformers are quickly becoming one of the most heavily applied deep learning architectures across modalities, domains, and tasks. In vision, on top of ongoing efforts into plain transformers, hierarchical transformers have also gained significant attention, thanks to their performance and easy integration into exist... | ['Humphrey Shi', 'Ali Hassani'] | 2022-09-29 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 4.58679870e-02 9.10457149e-02 -2.01627195e-01 -1.60103068e-01
-9.86327350e-01 -7.33807385e-01 7.44941175e-01 -8.16839710e-02
-8.07639360e-01 3.65895629e-01 1.33599833e-01 -3.95231664e-01
-7.97474477e-03 -8.28435421e-01 -8.52100492e-01 -5.69188535e-01
2.39864245e-01 6.00644350e-01 8.04642379e-01 -1.96667776... | [9.510196685791016, 0.5610043406486511] |
8dd63f80-59e2-4ccf-8821-086353e14825 | road-damage-detection-acquisition-system | 1909.08991 | null | https://arxiv.org/abs/1909.08991v1 | https://arxiv.org/pdf/1909.08991v1.pdf | Road Damage Detection Acquisition System based on Deep Neural Networks for Physical Asset Management | Research on damage detection of road surfaces has been an active area of re-search, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance ... | ['G Ochoa-Ruiz', 'L. M. Aguilar-Lobo', 'J. A. Vega-Fernández', 'S. Natraj', 'A. A. Angulo'] | 2019-09-19 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [ 2.78100163e-01 -2.27439269e-01 -5.70084229e-02 1.40293479e-01
-6.48324251e-01 -1.54994261e-02 2.55054444e-01 3.03120632e-02
-2.70955205e-01 8.63970101e-01 5.94746061e-02 -5.08491457e-01
-2.39260688e-01 -1.64007854e+00 -5.37797570e-01 -7.75888264e-01
-3.05116642e-03 -4.37174849e-02 6.45345390e-01 -4.34910566... | [7.419291973114014, 1.129409909248352] |
e3b9eff5-fc60-41bb-bfd5-15a84a135d6a | counterfactual-explanation-with-multi-agent | 2103.12983 | null | https://arxiv.org/abs/2103.12983v2 | https://arxiv.org/pdf/2103.12983v2.pdf | Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction | Motivation: Many high-performance DTA models have been proposed, but they are mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models more trustworthy, and can also enable scientists to distill biological knowledge from the models. Counterfactual explanation is one popular approa... | ['Truyen Tran', 'Thin Nguyen', 'Thomas P Quinn', 'Tri Minh Nguyen'] | 2021-03-24 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 5.27661681e-01 9.17580605e-01 -5.59044659e-01 -2.00895295e-01
-3.04668933e-01 -5.79620361e-01 7.47469902e-01 4.89301234e-02
-2.52064727e-02 1.60770977e+00 2.51964480e-01 -1.00144553e+00
-5.57349920e-01 -6.49433672e-01 -1.32049692e+00 -8.29164505e-01
-1.01232007e-01 9.24289286e-01 -4.36399341e-01 -1.89557657... | [8.570538520812988, 5.69355583190918] |
57b8e37b-f50c-448d-8167-8bfe9617541b | submanifold-sparse-convolutional-networks | 1706.01307 | null | http://arxiv.org/abs/1706.01307v1 | http://arxiv.org/pdf/1706.01307v1.pdf | Submanifold Sparse Convolutional Networks | Convolutional network are the de-facto standard for analysing spatio-temporal
data such as images, videos, 3D shapes, etc. Whilst some of this data is
naturally dense (for instance, photos), many other data sources are inherently
sparse. Examples include pen-strokes forming on a piece of paper, or (colored)
3D point cl... | ['Laurens van der Maaten', 'Benjamin Graham'] | 2017-06-05 | null | null | null | null | ['3d-part-segmentation'] | ['computer-vision'] | [ 3.10735106e-01 -2.04750076e-02 1.30353704e-01 -1.99861407e-01
2.70679444e-02 -5.72403014e-01 9.59335268e-01 -3.73348862e-01
-3.32261980e-01 4.85030949e-01 2.86764175e-01 -4.32681859e-01
-2.58611858e-01 -9.89707947e-01 -8.99124503e-01 -5.46361566e-01
-4.16254848e-01 5.57009876e-01 1.92973673e-01 -1.10122792... | [8.060649871826172, -3.6980648040771484] |
9744c9ac-6450-458e-923a-2a4063e84056 | evolving-mario-levels-in-the-latent-space-of | 1805.00728 | null | http://arxiv.org/abs/1805.00728v1 | http://arxiv.org/pdf/1805.00728v1.pdf | Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network | Generative Adversarial Networks (GANs) are a machine learning approach
capable of generating novel example outputs across a space of provided training
examples. Procedural Content Generation (PCG) of levels for video games could
benefit from such models, especially for games where there is a pre-existing
corpus of leve... | ['Adam Smith', 'Vanessa Volz', 'Simon M. Lucas', 'Jialin Liu', 'Sebastian Risi', 'Jacob Schrum'] | 2018-05-02 | null | null | null | null | ['snes-games'] | ['playing-games'] | [ 4.99571472e-01 1.92526177e-01 2.10258946e-01 1.54733062e-01
-8.76776695e-01 -7.66905606e-01 5.47105312e-01 -1.92059457e-01
-2.83151209e-01 8.77628744e-01 1.91579342e-01 -7.37593174e-02
-2.67094284e-01 -1.16525400e+00 -7.71254897e-01 -8.10727239e-01
1.04441643e-02 6.77116215e-01 2.07589939e-01 -6.56199515... | [3.6620688438415527, 1.554531455039978] |
6ec2d417-5a4a-4534-a7c7-e82ebf35baa9 | a-greedy-bit-flip-training-algorithm-for | null | null | https://aclanthology.org/2020.findings-emnlp.10 | https://aclanthology.org/2020.findings-emnlp.10.pdf | A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings | This paper presents a simple and effective discrete optimization method for training binarized knowledge graph embedding model B-CP. Unlike the prior work using a SGD-based method and quantization of real-valued vectors, the proposed method directly optimizes binary embedding vectors by a series of bit flipping operati... | ['Masashi Shimbo', 'Koki Kishimoto', 'Katsuhiko Hayashi'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [ 1.16258010e-01 6.93039954e-01 -6.15708590e-01 -2.93356061e-01
-4.48158979e-01 -2.32749581e-01 4.55842227e-01 4.35964912e-01
-5.31750500e-01 6.84266210e-01 -9.32917744e-02 -4.04144019e-01
-3.62374753e-01 -1.15290904e+00 -7.64901280e-01 -5.43175161e-01
-4.25184071e-01 7.89683282e-01 2.96768527e-02 -1.24677755... | [8.729273796081543, 7.859460830688477] |
bd242a3c-6f49-4106-80a3-a0ff20ebae29 | paraphrase-identification-with-deep-learning | 2212.06933 | null | https://arxiv.org/abs/2212.06933v1 | https://arxiv.org/pdf/2212.06933v1.pdf | Paraphrase Identification with Deep Learning: A Review of Datasets and Methods | The rapid advancement of AI technology has made text generation tools like GPT-3 and ChatGPT increasingly accessible, scalable, and effective. This can pose serious threat to the credibility of various forms of media if these technologies are used for plagiarism, including scientific literature and news sources. Despit... | ['Daniel E. Acuna', 'Cheng Qiu', 'Chao Zhou'] | 2022-12-13 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 2.95021713e-01 -9.46501922e-03 -3.99073362e-01 2.94937585e-02
-9.21391606e-01 -9.85770464e-01 8.54061306e-01 8.01905215e-01
-1.33213818e-01 5.63235164e-01 7.87182927e-01 -4.80040461e-01
3.19744572e-02 -7.59186983e-01 -5.47008932e-01 -1.23980314e-01
5.46073377e-01 3.70661497e-01 1.51224378e-02 -2.59690851... | [8.631721496582031, 10.0015287399292] |
97f0bec7-ef7d-49c7-9710-7b160bba50da | sequential-recommendation-with-diffusion | 2304.04541 | null | https://arxiv.org/abs/2304.04541v2 | https://arxiv.org/pdf/2304.04541v2.pdf | Sequential Recommendation with Diffusion Models | Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN), have been successfully applied in sequential recommendation. These methods require sampling from probability distributions and adopt auxiliary loss functions to optimize the model, which can capture the uncertainty of us... | ['Xiaofang Zhou', 'Pengpeng Zhao', 'Zhen Huang', 'Huanhuan Yuan', 'Hanwen Du'] | 2023-04-10 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 1.19403392e-01 -3.65515023e-01 -1.25503883e-01 -9.80488136e-02
-3.13744068e-01 -4.86849993e-01 6.75509751e-01 -5.98087311e-01
-2.14598626e-01 6.03772581e-01 3.06896210e-01 -2.77398080e-01
-1.33170441e-01 -9.67879713e-01 -7.73983777e-01 -1.05071592e+00
7.30240345e-01 2.82942235e-01 1.20877400e-01 -1.89924762... | [10.24290657043457, 5.51597785949707] |
fae7e9dc-24c7-4ea7-8b36-e3510e818db6 | a-deeper-autoregressive-approach-to-non | 2305.12510 | null | https://arxiv.org/abs/2305.12510v1 | https://arxiv.org/pdf/2305.12510v1.pdf | A Deeper (Autoregressive) Approach to Non-Convergent Discourse Parsing | Online social platforms provide a bustling arena for information-sharing and for multi-party discussions. Various frameworks for dialogic discourse parsing were developed and used for the processing of discussions and for predicting the productivity of a dialogue. However, most of these frameworks are not suitable for ... | ['Oren Tsur', 'Yoav Tulpan'] | 2023-05-21 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [-1.83428228e-01 6.17588043e-01 -2.03583650e-02 -7.00051248e-01
-5.32042921e-01 -8.65629077e-01 8.22298229e-01 5.10142267e-01
-5.55090785e-01 7.49031842e-01 5.96041083e-01 -1.87334821e-01
2.23150834e-01 -6.76006615e-01 -2.12155238e-01 -4.06609982e-01
4.01018232e-01 8.87683272e-01 1.61412314e-01 -5.07864356... | [12.530037879943848, 7.946274757385254] |
3eecf93f-7697-493c-92e8-84f8b71fc053 | hi-transformer-hierarchical-interactive | 2106.01040 | null | https://arxiv.org/abs/2106.01040v3 | https://arxiv.org/pdf/2106.01040v3.pdf | Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling | Transformer is important for text modeling. However, it has difficulty in handling long documents due to the quadratic complexity with input text length. In order to handle this problem, we propose a hierarchical interactive Transformer (Hi-Transformer) for efficient and effective long document modeling. Hi-Transformer... | ['Yongfeng Huang', 'Tao Qi', 'Fangzhao Wu', 'Chuhan Wu'] | 2021-06-02 | null | https://aclanthology.org/2021.acl-short.107 | https://aclanthology.org/2021.acl-short.107.pdf | acl-2021-5 | ['document-embedding'] | ['methodology'] | [ 1.31176442e-01 -1.96816906e-01 -2.00405777e-01 -4.40437496e-01
-7.86682904e-01 -4.25163746e-01 6.37648821e-01 2.63022959e-01
-1.95197761e-01 2.24043071e-01 7.56350875e-01 -1.94133312e-01
1.41400155e-02 -9.37210262e-01 -4.71918166e-01 -6.86821103e-01
4.34383154e-01 1.52233317e-01 2.97903150e-01 5.31073613... | [11.148287773132324, 8.597188949584961] |
ca1ccdf0-29e9-48b9-92e1-825fe83dffcf | discrete-point-flow-networks-for-efficient | 2007.10170 | null | https://arxiv.org/abs/2007.10170v1 | https://arxiv.org/pdf/2007.10170v1.pdf | Discrete Point Flow Networks for Efficient Point Cloud Generation | Generative models have proven effective at modeling 3D shapes and their statistical variations. In this paper we investigate their application to point clouds, a 3D shape representation widely used in computer vision for which, however, only few generative models have yet been proposed. We introduce a latent variable m... | ['Roman Klokov', 'Edmond Boyer', 'Jakob Verbeek'] | 2020-07-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4408_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680681.pdf | eccv-2020-8 | ['3d-shape-representation', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [-2.86597684e-02 1.52915940e-01 3.31263423e-01 -1.75258219e-01
-9.45136309e-01 -8.01094472e-01 1.11183548e+00 -1.99658185e-01
2.45786875e-01 4.90056366e-01 6.11491576e-02 -2.40895063e-01
1.83289111e-01 -1.20780027e+00 -8.29339623e-01 -5.95076621e-01
3.05442274e-01 1.31875455e+00 1.30124658e-01 -2.89166253... | [8.893821716308594, -3.638495445251465] |
ce0cc6c1-9fb1-4400-899a-18da7361f6f6 | invpt-inverted-pyramid-multi-task-transformer | 2306.04842 | null | https://arxiv.org/abs/2306.04842v1 | https://arxiv.org/pdf/2306.04842v1.pdf | InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene Understanding | Multi-task scene understanding aims to design models that can simultaneously predict several scene understanding tasks with one versatile model. Previous studies typically process multi-task features in a more local way, and thus cannot effectively learn spatially global and cross-task interactions, which hampers the m... | ['Dan Xu', 'Hanrong Ye'] | 2023-06-08 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 6.33632466e-02 -5.71005702e-01 4.27671224e-02 -4.82866764e-01
-8.66770267e-01 -3.73844802e-01 7.14250505e-01 -1.55178130e-01
-2.65390635e-01 3.15056622e-01 3.14203978e-01 1.59145996e-01
-3.86847407e-01 -4.23499554e-01 -6.98470294e-01 -6.31763816e-01
3.93297911e-01 1.15688637e-01 4.40249652e-01 -1.91410899... | [9.725807189941406, 1.259037733078003] |
bb0d23dd-d0a2-44cd-b258-be25ae0ef79c | computational-narratology-extracting-tense | null | null | https://aclanthology.org/L14-1256 | https://aclanthology.org/L14-1256.pdf | Computational Narratology: Extracting Tense Clusters from Narrative Texts | Computational Narratology is an emerging field within the Digital Humanities. In this paper, we tackle the problem of extracting temporal information as a basis for event extraction and ordering, as well as further investigations of complex phenomena in narrative texts. While most existing systems focus on news texts a... | ['Michael Gertz', 'Jannik Str{\\"o}tgen', 'Thomas B{\\"o}gel'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['morphological-tagging'] | ['natural-language-processing'] | [ 4.85611223e-02 2.79476225e-01 4.24918439e-03 -2.64742553e-01
-7.08601952e-01 -1.11361182e+00 9.88201976e-01 5.67315578e-01
-6.77735031e-01 7.07872391e-01 6.62958562e-01 -3.35965067e-01
-1.84558079e-01 -7.10659862e-01 -2.98453808e-01 -3.21224213e-01
-1.55924484e-01 5.79665601e-01 5.74536443e-01 -3.36803734... | [9.205469131469727, 9.32336711883545] |
7cf068d9-e4c2-4e40-83e5-7a4add4dc53c | a-fair-and-in-depth-evaluation-of-existing | 2305.14937 | null | https://arxiv.org/abs/2305.14937v1 | https://arxiv.org/pdf/2305.14937v1.pdf | A Fair and In-Depth Evaluation of Existing End-to-End Entity Linking Systems | Existing evaluations of entity linking systems often say little about how the system is going to perform for a particular application. There are four fundamental reasons for this: many benchmarks focus on named entities; it is hard to define which other entities to include; there are ambiguities in entity recognition a... | ['Natalie Prange', 'Matthias Hertel', 'Hannah Bast'] | 2023-05-24 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [-1.79623112e-01 1.58407688e-01 -4.09659445e-01 -4.89420146e-01
-8.70870411e-01 -9.97594833e-01 5.89502990e-01 6.09907210e-01
-4.67069387e-01 1.06217027e+00 3.57020974e-01 -2.79486775e-01
-2.28128955e-01 -6.72085106e-01 -5.41735053e-01 -5.82023989e-03
-1.20126195e-01 7.90362954e-01 6.42032087e-01 -5.02029896... | [9.397372245788574, 8.699065208435059] |
e8c12746-c7ac-4dfb-b4f3-ade57494259d | the-clickbait-challenge-2017-towards-a | 1812.10847 | null | http://arxiv.org/abs/1812.10847v1 | http://arxiv.org/pdf/1812.10847v1.pdf | The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength | Clickbait has grown to become a nuisance to social media users and social
media operators alike. Malicious content publishers misuse social media to
manipulate as many users as possible to visit their websites using clickbait
messages. Machine learning technology may help to handle this problem, giving
rise to automati... | ['Benno Stein', 'Tim Gollub', 'Matthias Hagen', 'Martin Potthast'] | 2018-12-27 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-2.30350286e-01 -1.72533616e-01 -4.40870404e-01 -3.21426272e-01
-1.07701290e+00 -9.17212665e-01 9.07844186e-01 6.29608512e-01
-6.97975636e-01 6.13108695e-01 -2.67786644e-02 -4.46950674e-01
2.29617104e-01 -4.45442379e-01 -4.78737801e-01 7.72358477e-02
3.37274224e-02 4.16181386e-01 1.16494131e+00 -6.76589832... | [7.746814727783203, 9.767853736877441] |
bb400e91-8a4e-4db6-968f-37d4d2780bf4 | himfr-a-hybrid-masked-face-recognition | 2209.08930 | null | https://arxiv.org/abs/2209.08930v1 | https://arxiv.org/pdf/2209.08930v1.pdf | HiMFR: A Hybrid Masked Face Recognition Through Face Inpainting | To recognize the masked face, one of the possible solutions could be to restore the occluded part of the face first and then apply the face recognition method. Inspired by the recent image inpainting methods, we propose an end-to-end hybrid masked face recognition system, namely HiMFR, consisting of three significant p... | ['Md Baharul Islam', 'Md Imran Hosen'] | 2022-09-19 | null | null | null | null | ['facial-inpainting', 'image-inpainting'] | ['computer-vision', 'computer-vision'] | [ 3.21861178e-01 1.18906602e-01 2.36484006e-01 -4.76278216e-01
-8.04603696e-01 -4.06297177e-01 3.43665421e-01 -1.15233195e+00
-2.05198992e-02 5.78772604e-01 2.83994135e-02 2.13761944e-02
4.44241941e-01 -7.01372564e-01 -9.90605772e-01 -7.80817091e-01
3.12879205e-01 1.98943198e-01 -4.47020866e-02 -1.57644087... | [12.873003005981445, 0.059548269957304] |
179357c4-e6c3-4d59-8cb7-6d06fe8d531f | an-edge-enhanced-hierarchical-graph-to-tree | null | null | https://aclanthology.org/2021.findings-emnlp.127 | https://aclanthology.org/2021.findings-emnlp.127.pdf | An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving | Math word problem solving has attracted considerable research interest in recent years. Previous works have shown the effectiveness of utilizing graph neural networks to capture the relationships in the problem. However, these works did not carefully take the edge label information and the long-range word relationship ... | ['Zhongyu Wei', 'Qi Zhang', 'Qinzhuo Wu'] | null | null | null | null | findings-emnlp-2021-11 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 3.17617595e-01 3.61964464e-01 7.63446167e-02 -3.73378247e-01
-5.55704236e-01 -3.14236641e-01 9.67142954e-02 3.86573434e-01
-8.45445246e-02 7.37450838e-01 3.47337961e-01 -4.12060350e-01
-1.07562393e-02 -1.23847055e+00 -7.72067606e-01 -2.08872989e-01
5.21360151e-02 3.59850943e-01 2.99028754e-01 -2.61288941... | [10.286176681518555, 8.246307373046875] |
15fbd765-da37-4189-9f12-b54524b21de6 | is-speech-pathology-a-biomarker-in-automatic | 2204.06450 | null | https://arxiv.org/abs/2204.06450v2 | https://arxiv.org/pdf/2204.06450v2.pdf | The effect of speech pathology on automatic speaker verification -- a large-scale study | With the advancements in deep learning (DL) and an increasing interest in data-driven speech processing methods, there is a major challenge in accessing pathological speech data. Public challenge data offers a potential remedy for this but may expose patient health information by re-identification attacks. Therefore, w... | ['Elmar Noeth', 'Seung Hee Yang', 'Andreas Maier', 'Maria Schuster', 'Tobias Weise', 'Soroosh Tayebi Arasteh'] | 2022-04-13 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-3.36679704e-02 1.39662221e-01 3.22329849e-01 -2.37646475e-01
-1.12937808e+00 -2.82672286e-01 3.54638338e-01 3.70996624e-01
-4.40329641e-01 4.21739697e-01 6.77180886e-01 -5.31698763e-01
-4.10490856e-02 -2.77185500e-01 -4.89765942e-01 -7.50514984e-01
3.85298356e-02 -1.80369895e-02 -8.52944031e-02 6.06329925... | [14.280558586120605, 6.0951457023620605] |
9ac93ce0-2f32-49e9-9c8c-5c698e08a791 | clara-classifying-and-disambiguating-user | 2306.10376 | null | https://arxiv.org/abs/2306.10376v3 | https://arxiv.org/pdf/2306.10376v3.pdf | CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents | In this paper, we focus on inferring whether the given user command is clear, ambiguous, or infeasible in the context of interactive robotic agents utilizing large language models (LLMs). To tackle this problem, we first present an uncertainty estimation method for LLMs to classify whether the command is certain (i.e.,... | ['Minsuk Chang', 'Sungjoon Choi', 'Youngjae Yu', 'Sangbeom Park', 'Joonhyung Lee', 'Seungwon Lim', 'Jeongeun Park'] | 2023-06-17 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 4.49425131e-01 5.08248329e-01 2.16338158e-01 -6.95434809e-01
-5.28070033e-01 -8.69703054e-01 5.53816199e-01 1.05194576e-01
-1.01128772e-01 8.77637744e-01 -3.26959267e-02 -5.41414797e-01
-3.59767407e-01 -5.05067110e-01 -8.43885303e-01 -4.89925921e-01
1.06527163e-02 6.42322183e-01 1.77031800e-01 -1.33515596... | [4.480828762054443, 0.8384336233139038] |
24dcac8a-4c6b-4594-b17c-4c80a10f9139 | algebraic-and-geometric-models-for-space | 2304.01150 | null | https://arxiv.org/abs/2304.01150v1 | https://arxiv.org/pdf/2304.01150v1.pdf | Algebraic and Geometric Models for Space Networking | In this paper we introduce some new algebraic and geometric perspectives on networked space communications. Our main contribution is a novel definition of a time-varying graph (TVG), defined in terms of a matrix with values in subsets of the real line P(R). We leverage semi-ring properties of P(R) to model multi-hop co... | ['Robert Kassouf-Short', 'Tung Lam', 'Alan Hylton', 'Brian Heller', 'Robert Green', 'Justin Curry', 'Jacob Cleveland', 'Robert Cardona', 'William Bernardoni'] | 2023-04-03 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [-1.47001401e-01 4.12246525e-01 -2.37939820e-01 6.10306039e-02
1.29464313e-01 -1.11214972e+00 1.09512186e+00 1.54777199e-01
-1.50004774e-02 9.29404438e-01 -8.17984119e-02 -7.20215440e-01
-1.06061828e+00 -1.15241671e+00 -5.83042681e-01 -8.44435871e-01
-1.56078911e+00 6.41972065e-01 5.01982868e-01 -8.82760882... | [6.934648513793945, 4.985941410064697] |
72d68bb3-d3d9-434f-b9a4-9d8960fd9445 | an-empirical-investigation-of-global-and | 1904.06834 | null | http://arxiv.org/abs/1904.06834v1 | http://arxiv.org/pdf/1904.06834v1.pdf | An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search | Globally normalized neural sequence models are considered superior to their
locally normalized equivalents because they may ameliorate the effects of label
bias. However, when considering high-capacity neural parametrizations that
condition on the whole input sequence, both model classes are theoretically
equivalent in... | ['Taylor Berg-Kirkpatrick', 'Chris Dyer', 'Kartik Goyal'] | 2019-04-15 | an-empirical-investigation-of-global-and-1 | https://aclanthology.org/N19-1171 | https://aclanthology.org/N19-1171.pdf | naacl-2019-6 | ['ccg-supertagging'] | ['natural-language-processing'] | [ 6.89272523e-01 2.04356194e-01 -3.68413329e-01 -3.32402617e-01
-9.28342342e-01 -5.81286192e-01 7.35323429e-01 7.12560117e-02
-9.55281317e-01 7.05101073e-01 4.97955889e-01 -5.10761917e-01
-3.49725746e-02 -6.86593473e-01 -9.69092429e-01 -6.63976192e-01
3.52109224e-01 4.80495155e-01 -1.48672340e-02 -4.33678389... | [11.505279541015625, 9.579493522644043] |
56a1b681-9750-4baf-95e2-753f97f4edcd | automated-essay-scoring-using-efficient | 2102.13136 | null | https://arxiv.org/abs/2102.13136v1 | https://arxiv.org/pdf/2102.13136v1.pdf | Automated essay scoring using efficient transformer-based language models | Automated Essay Scoring (AES) is a cross-disciplinary effort involving Education, Linguistics, and Natural Language Processing (NLP). The efficacy of an NLP model in AES tests it ability to evaluate long-term dependencies and extrapolate meaning even when text is poorly written. Large pretrained transformer-based langu... | ['Amir Jafari', 'Akanksha Malhotra', 'Christopher M Ormerod'] | 2021-02-25 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-1.22482002e-01 -9.89668742e-02 -1.73573911e-01 -4.18916553e-01
-1.04390788e+00 -8.75637591e-01 4.97034848e-01 4.43433642e-01
-5.80757141e-01 9.57308710e-01 5.52436352e-01 -7.56976008e-01
-2.46564075e-01 -6.38331413e-01 -7.32136548e-01 1.40361965e-01
3.65234882e-01 7.09695876e-01 9.60050300e-02 -4.72079098... | [11.234313011169434, 9.363542556762695] |
c279e444-d8e2-44fb-b769-ddd916823419 | entropy-enhanced-multimodal-attention-model | 1908.08191 | null | https://arxiv.org/abs/1908.08191v1 | https://arxiv.org/pdf/1908.08191v1.pdf | Entropy-Enhanced Multimodal Attention Model for Scene-Aware Dialogue Generation | With increasing information from social media, there are more and more videos available. Therefore, the ability to reason on a video is important and deserves to be discussed. TheDialog System Technology Challenge (DSTC7) (Yoshino et al. 2018) proposed an Audio Visual Scene-aware Dialog (AVSD) task, which contains five... | ['Lun-Wei Ku', 'Yun-Nung Chen', 'Chao-Chun Hsu', 'Kuan-Yen Lin'] | 2019-08-22 | null | null | null | null | ['scene-aware-dialogue'] | ['computer-vision'] | [ 5.81541061e-02 1.08974732e-01 -5.50201908e-02 -2.63453394e-01
-6.41378820e-01 -3.94208074e-01 6.23615444e-01 -1.85326964e-01
-4.41114962e-01 6.25901759e-01 8.80270362e-01 -1.54236667e-02
3.39159846e-01 -2.74129152e-01 -5.29745877e-01 -3.15577090e-01
3.32947046e-01 -5.28881215e-02 4.47659701e-01 -8.83768275... | [10.610542297363281, 0.920678973197937] |
e58ebc3d-5218-4611-924b-995a16e1ba95 | toward-achieving-robust-low-level-and-high | null | null | https://ieeexplore.ieee.org/document/8517116 | https://ieeexplore.ieee.org/document/8517116 | Toward Achieving Robust Low-Level and High-Level Scene Parsing | In this paper, we address the challenging task of scene segmentation. We first discuss and compare two widely used approaches to retain detailed spatial information from pre-trained convolutional context network (CNN)-“dilation” and “skip”. Then, we demonstrate that the parsing performance of “skip” network can be noti... | ['Gang Wang', 'Henghui Ding', 'Xudong Jiang', 'Ting Liu', 'Bing Shuai'] | 2019-03-01 | null | null | null | journal-2019-3 | ['scene-parsing'] | ['computer-vision'] | [ 4.82745230e-01 1.76361710e-01 -2.44973488e-02 -6.86881840e-01
-5.55855870e-01 -7.41357327e-01 3.03640872e-01 -7.59691074e-02
-6.42068326e-01 4.23818588e-01 6.12347685e-02 -4.49799925e-01
2.44142458e-01 -8.00996423e-01 -1.01155007e+00 -6.43292487e-01
1.65604427e-01 -3.02322835e-01 4.65841830e-01 -7.27331862... | [9.545391082763672, 0.2804185152053833] |
d879393f-5f7b-4540-a3f4-b7852877682b | encoding-program-as-image-evaluating-visual | 2111.01097 | null | https://arxiv.org/abs/2111.01097v3 | https://arxiv.org/pdf/2111.01097v3.pdf | Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code | There are several approaches for encoding source code in the input vectors of neural models. These approaches attempt to include various syntactic and semantic features of input programs in their encoding. In this paper, we investigate Code2Snapshot, a novel representation of the source code that is based on the snapsh... | ['Mohammad Amin Alipour', 'Md Rafiqul Islam Rabin'] | 2021-11-01 | null | null | null | null | ['code-classification', 'method-name-prediction'] | ['computer-code', 'natural-language-processing'] | [ 1.75355449e-01 1.29968703e-01 -3.83888930e-01 -5.53237438e-01
-4.45905715e-01 -5.44461787e-01 5.48716068e-01 5.75037837e-01
-2.28753075e-01 1.25633568e-01 5.48076928e-01 -5.46186030e-01
1.23592913e-01 -7.74486601e-01 -8.92944694e-01 -1.15509160e-01
2.73767877e-02 -2.95831561e-01 2.91348577e-01 -3.86593342... | [7.607367515563965, 7.897158145904541] |
7dd9cd7e-468b-4f67-9a83-f8293a4de0f4 | preliminary-study-on-using-vector | 2106.13479 | null | https://arxiv.org/abs/2106.13479v1 | https://arxiv.org/pdf/2106.13479v1.pdf | Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance | Generally speaking, the main objective when training a neural speech synthesis system is to synthesize natural and expressive speech from the output layer of the neural network without much attention given to the hidden layers. However, by learning useful latent representation, the system can be used for many more prac... | ['Junichi Yamagishi', 'Hieu-Thi Luong'] | 2021-06-25 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 2.37132147e-01 5.47401369e-01 -2.08040431e-01 -4.55941945e-01
-4.19911623e-01 -4.38845724e-01 6.19896114e-01 -1.72757953e-02
-3.47915053e-01 7.34754086e-01 4.49569851e-01 -2.52346903e-01
1.37707889e-01 -8.11245739e-01 -6.50839269e-01 -1.00428998e+00
1.27459764e-01 1.37050822e-01 2.07021143e-02 -5.96538782... | [14.872294425964355, 6.4548845291137695] |
c74e61e9-4c21-4e5c-aef0-eaa2ab4d0bc6 | mixup-mil-novel-data-augmentation-for | 2211.05862 | null | https://arxiv.org/abs/2211.05862v3 | https://arxiv.org/pdf/2211.05862v3.pdf | MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis | Multiple instance learning exhibits a powerful approach for whole slide image-based diagnosis in the absence of pixel- or patch-level annotations. In spite of the huge size of hole slide images, the number of individual slides is often rather small, leading to a small number of labeled samples. To improve training, we ... | ['Anton Hittmair', 'Gertie Janneke Oostingh', 'Sebastien Couillard-Despres', 'Christina Kreutzer', 'Lea Maria Stangassinger', 'Maximilian Tschuchnig', 'Lukas Koller', 'Michael Gadermayr'] | 2022-11-10 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 5.92062116e-01 3.27997118e-01 -3.20675075e-01 -3.15286756e-01
-1.36580479e+00 -1.25783160e-01 5.67858458e-01 5.47999442e-01
-5.37407041e-01 8.39602113e-01 -1.77767098e-01 -1.92076728e-01
-1.90275803e-01 -6.71715975e-01 -6.31498814e-01 -1.20514131e+00
2.39003062e-01 6.07902765e-01 3.88057351e-01 -2.46382207... | [15.066178321838379, -2.9501848220825195] |
8cec16e5-3006-421b-ae82-c33ea0b1976c | transflow-transformer-as-flow-learner | 2304.11523 | null | https://arxiv.org/abs/2304.11523v1 | https://arxiv.org/pdf/2304.11523v1.pdf | TransFlow: Transformer as Flow Learner | Optical flow is an indispensable building block for various important computer vision tasks, including motion estimation, object tracking, and disparity measurement. In this work, we propose TransFlow, a pure transformer architecture for optical flow estimation. Compared to dominant CNN-based methods, TransFlow demonst... | ['Dongfang Liu', 'Huaijin Chen', 'Yingjie Victor Chen', 'Tong Geng', 'Siqi Ma', 'Qifan Wang', 'Yawen Lu'] | 2023-04-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lu_TransFlow_Transformer_As_Flow_Learner_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lu_TransFlow_Transformer_As_Flow_Learner_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-object-detection', 'motion-estimation', 'self-learning'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [-3.57309401e-01 -8.38914931e-01 -3.50244373e-01 -1.30966336e-01
-2.92933434e-01 -3.17957401e-01 3.14204782e-01 -3.33510011e-01
-3.75652432e-01 7.83122540e-01 4.17530596e-01 -7.68962502e-02
2.29294032e-01 -5.77823579e-01 -5.85036874e-01 -6.39293492e-01
4.72396277e-02 -1.79496542e-01 4.78677601e-01 -8.81257877... | [8.94531536102295, -1.8159418106079102] |
18433aa5-7073-41ba-b68b-45b866f8f809 | gazeonce-real-time-multi-person-gaze | 2204.09480 | null | https://arxiv.org/abs/2204.09480v1 | https://arxiv.org/pdf/2204.09480v1.pdf | GazeOnce: Real-Time Multi-Person Gaze Estimation | Appearance-based gaze estimation aims to predict the 3D eye gaze direction from a single image. While recent deep learning-based approaches have demonstrated excellent performance, they usually assume one calibrated face in each input image and cannot output multi-person gaze in real time. However, simultaneous gaze es... | ['Feng Lu', 'Yunfei Liu', 'Mingfang Zhang'] | 2022-04-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_GazeOnce_Real-Time_Multi-Person_Gaze_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_GazeOnce_Real-Time_Multi-Person_Gaze_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['gaze-estimation'] | ['computer-vision'] | [ 8.09241310e-02 -5.04707843e-02 9.14944112e-02 -6.66272342e-01
-2.63780922e-01 -2.13542759e-01 2.14158148e-01 -5.91195107e-01
-2.42801756e-01 3.34245473e-01 -2.27486715e-01 -1.41439721e-01
3.06105614e-01 -1.39298514e-01 -5.73717892e-01 -4.97473150e-01
3.17035317e-01 2.34502062e-01 1.31264761e-01 -4.96627651... | [14.115507125854492, 0.07170946896076202] |
15b8e49e-4016-4705-88bc-69daf5579589 | zero-shot-federated-learning-with-new-classes | 2106.10019 | null | https://arxiv.org/abs/2106.10019v1 | https://arxiv.org/pdf/2106.10019v1.pdf | Zero-Shot Federated Learning with New Classes for Audio Classification | Federated learning is an effective way of extracting insights from different user devices while preserving the privacy of users. However, new classes with completely unseen data distributions can stream across any device in a federated learning setting, whose data cannot be accessed by the global server or other users.... | ['Satheesh K. Perepu', 'Gautham Krishna Gudur'] | 2021-06-18 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 1.21244445e-01 -1.76648811e-01 -1.58645973e-01 -4.37420934e-01
-1.27071953e+00 -1.14922404e+00 1.70008272e-01 2.59396523e-01
-1.41724721e-01 8.19753349e-01 1.07809663e-01 -1.10354714e-01
-1.80741683e-01 -6.24280930e-01 -6.95816934e-01 -7.82765746e-01
-2.30733603e-01 5.06110251e-01 2.95180883e-02 3.24913293... | [5.87308406829834, 6.297885894775391] |
0a7a0135-3e88-4d6e-b5df-730a9833579b | long-term-stock-prediction-based-on-financial | null | null | http://cs230.stanford.edu/projects_winter_2021/reports/70728801.pdf | http://cs230.stanford.edu/projects_winter_2021/reports/70728801.pdf | Long Term Stock Prediction based on Financial Statements | This paper proposes a model with LSTM and fully connected layers to predict long term stock trendings based on financial statements. Two data augmentation techniques are applied on structured data: 1) adding random noise to data fields; 2) erasing partial information from training examples. The performance of the propo... | ['Shujia Liu'] | 2021-11-01 | null | null | null | journal-2021-11 | ['stock-prediction'] | ['time-series'] | [-4.10778850e-01 1.98490947e-01 -2.57490277e-01 -7.26423264e-01
-2.79341429e-01 -3.64058256e-01 3.46577942e-01 2.18595594e-01
-6.48126006e-01 8.91627967e-01 5.13647735e-01 -8.15129399e-01
2.18493417e-01 -1.26874566e+00 -7.08503962e-01 -5.25311470e-01
-9.03237343e-01 9.62884575e-02 -2.80757882e-02 -2.28598982... | [4.46391487121582, 4.212879180908203] |
6117df5d-06c7-439e-bd19-595e5c9d745d | high-resolution-image-synthesis-with-latent | 2112.10752 | null | https://arxiv.org/abs/2112.10752v2 | https://arxiv.org/pdf/2112.10752v2.pdf | High-Resolution Image Synthesis with Latent Diffusion Models | By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. Howev... | ['Björn Ommer', 'Patrick Esser', 'Dominik Lorenz', 'Andreas Blattmann', 'Robin Rombach'] | 2021-12-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf | cvpr-2022-1 | ['layout-to-image-generation'] | ['computer-vision'] | [ 2.66567320e-01 8.59274864e-02 3.46433744e-02 -5.90983815e-02
-8.15553606e-01 -3.26106608e-01 8.38158548e-01 -3.31275284e-01
-3.17619294e-01 5.44293463e-01 2.82906622e-01 -7.45346844e-02
2.72388607e-01 -1.14833546e+00 -1.01309454e+00 -6.97193444e-01
3.93118858e-01 2.71673769e-01 1.86434656e-01 -2.36103803... | [11.374889373779297, -0.4910448491573334] |
9515f00c-2daa-4ff6-a410-ca648f234453 | spherical-transformer-for-lidar-based-3d | 2303.12766 | null | https://arxiv.org/abs/2303.12766v1 | https://arxiv.org/pdf/2303.12766v1.pdf | Spherical Transformer for LiDAR-based 3D Recognition | LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from information disconnection and limited receptive field, especially for the sparse distant points. In this work, we study the varying-sparsity distributio... | ['Jiaya Jia', 'Jianhui Liu', 'Fanbin Lu', 'Yukang Chen', 'Xin Lai'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lai_Spherical_Transformer_for_LiDAR-Based_3D_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lai_Spherical_Transformer_for_LiDAR-Based_3D_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['lidar-semantic-segmentation'] | ['computer-vision'] | [-1.16240092e-01 -3.74696285e-01 -2.87149549e-01 -3.93829733e-01
-7.45709598e-01 -6.70835018e-01 3.18138719e-01 1.45925835e-01
-3.17299128e-01 2.11571544e-01 1.52275283e-02 -1.08554617e-01
-1.84259370e-01 -7.45869637e-01 -7.45930791e-01 -7.15724289e-01
3.16282988e-01 5.34835875e-01 6.35291338e-01 -5.47770746... | [7.951814651489258, -3.2977027893066406] |
c777bf5d-58f5-481b-ad5d-34f8cd8311b2 | real-time-seismic-intensity-prediction-using | 2306.14336 | null | https://arxiv.org/abs/2306.14336v1 | https://arxiv.org/pdf/2306.14336v1.pdf | Real-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning | Seismic intensity prediction in a geographical area from early or initial seismic waves received by a few seismic stations is a critical component of an effective Earthquake Early Warning (EEW) system. State-of-the-art deep learning-based techniques for this task suffer from limited accuracy in the prediction and, more... | ['Mohammed Eunus Ali', 'A. F. M. Saiful Amin', 'Md. Forkan Uddin', 'Md. Anu Zakaria', 'Kazi Noshin', 'Rafid Umayer Murshed'] | 2023-06-25 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [-3.75411399e-02 1.03302975e-03 3.21169347e-01 -2.92459782e-02
-8.40976477e-01 -1.01134941e-01 3.36189181e-01 3.89984965e-01
-4.29906845e-01 3.90036494e-01 1.61673769e-01 -5.84527552e-01
-4.39064652e-01 -1.10088301e+00 -5.87676525e-01 -9.40163195e-01
-1.10026002e+00 1.80771440e-01 7.32407093e-01 -6.80331826... | [6.907083034515381, 2.64308762550354] |
682b9f20-4136-49c8-8076-b12e31f28144 | hdnet-human-depth-estimation-for-multi-person | 2007.08943 | null | https://arxiv.org/abs/2007.08943v1 | https://arxiv.org/pdf/2007.08943v1.pdf | HDNet: Human Depth Estimation for Multi-Person Camera-Space Localization | Current works on multi-person 3D pose estimation mainly focus on the estimation of the 3D joint locations relative to the root joint and ignore the absolute locations of each pose. In this paper, we propose the Human Depth Estimation Network (HDNet), an end-to-end framework for absolute root joint localization in the c... | ['Gim Hee Lee', 'Jiahao Lin'] | 2020-07-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3074_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630613.pdf | eccv-2020-8 | ['3d-multi-person-pose-estimation-absolute', '3d-multi-person-pose-estimation-root-relative'] | ['computer-vision', 'computer-vision'] | [-3.63738745e-01 1.76375553e-01 -7.36395642e-02 -3.67815048e-01
-6.24597549e-01 2.48435959e-02 3.08117270e-01 -4.04695928e-01
-6.49546862e-01 3.85543168e-01 3.50508720e-01 5.53036273e-01
3.76917988e-01 -4.71735299e-01 -5.97578824e-01 -3.20867747e-01
-6.40537962e-02 7.11909592e-01 1.58148468e-01 8.38527549... | [7.0937957763671875, -0.8029274344444275] |
558497ad-f429-444f-8852-b6b9c58f8203 | a-character-level-length-control-algorithm | 2205.14522 | null | https://arxiv.org/abs/2205.14522v2 | https://arxiv.org/pdf/2205.14522v2.pdf | A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization | Sentence summarization aims at compressing a long sentence into a short one that keeps the main gist, and has extensive real-world applications such as headline generation. In previous work, researchers have developed various approaches to improve the ROUGE score, which is the main evaluation metric for summarization, ... | ['Lili Mou', 'Xiang Zhang', 'Puyuan Liu'] | 2022-05-28 | null | null | null | null | ['headline-generation', 'abstractive-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.59098232e-01 -5.51369712e-02 -5.12921870e-01 -2.20532104e-01
-8.93487096e-01 -3.35959285e-01 4.55363393e-01 6.04840338e-01
-3.38187814e-01 1.03036714e+00 8.34016681e-01 -1.67168289e-01
1.25267029e-01 -7.34336436e-01 -2.66355723e-01 -5.15016019e-01
1.86110839e-01 7.99518749e-02 4.55627382e-01 -3.36832970... | [12.594905853271484, 9.466304779052734] |
56cc1e37-c3ef-40bf-ae05-f45fd726d733 | mitosis-detection-in-intestinal-crypt-images | 1608.07616 | null | http://arxiv.org/abs/1608.07616v1 | http://arxiv.org/pdf/1608.07616v1.pdf | Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields | Intestinal enteroendocrine cells secrete hormones that are vital for the
regulation of glucose metabolism but their differentiation from intestinal stem
cells is not fully understood. Asymmetric stem cell divisions have been linked
to intestinal stem cell homeostasis and secretory fate commitment. We monitored
cell div... | ['Anika Böttcher', 'Michael Sterr', 'Heiko Lickert', 'Lichao Wang', 'Gerda Bortsova', 'Fausto Milletari', 'Tingying Peng', 'Nassir Navab', 'Fabian Theis'] | 2016-08-26 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 3.89490962e-01 -2.18880828e-03 -5.33657335e-02 -3.64161253e-01
-5.85110486e-01 -8.56914997e-01 5.97908139e-01 1.14286745e+00
-7.07454503e-01 5.94989836e-01 2.37578556e-01 -1.51219100e-01
4.24468666e-01 -8.24221790e-01 -7.14750767e-01 -9.66501713e-01
-2.68354893e-01 9.56975698e-01 4.90534663e-01 4.37598735... | [14.612926483154297, -3.1864142417907715] |
fb50a242-437d-4794-b801-8f526bbbb466 | shrec-22-track-sketch-based-3d-shape | 2207.04945 | null | https://arxiv.org/abs/2207.04945v1 | https://arxiv.org/pdf/2207.04945v1.pdf | SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild | Sketch-based 3D shape retrieval (SBSR) is an important yet challenging task, which has drawn more and more attention in recent years. Existing approaches address the problem in a restricted setting, without appropriately simulating real application scenarios. To mimic the realistic setting, in this track, we adopt larg... | ['Hongyuan Wang', 'Ji Zhang', 'Qunying Zhou', 'Yan Wang', 'Haiqin Chen', 'Ying Tang', 'Feng Wang', 'Yang Wang', 'Zihao Xin', 'Zheng Zhang', 'Jianning Wang', 'Haoyang Luo', 'Minh-Triet Tran', 'Hai-Dang Nguyen', 'Tuan-Luc Huynh', 'Nhat-Khang Ngo', 'Thien-Tri Cao', 'Khoi-Nguyen Nguyen-Ngoc', 'Chi-Bien Chu', 'Nhat Hoang-Xu... | 2022-07-11 | null | null | null | null | ['3d-object-retrieval'] | ['computer-vision'] | [-9.74397287e-02 -6.23067379e-01 -2.81158164e-02 -2.50282496e-01
-9.04118538e-01 -1.09714580e+00 1.03371882e+00 -2.48882353e-01
-3.83971073e-02 1.58345774e-01 1.64551094e-01 -2.63287853e-02
-1.51064834e-02 -8.77073467e-01 -4.10075814e-01 -2.66979560e-02
-4.10008766e-02 8.29580903e-01 4.63477612e-01 -4.10690755... | [8.556492805480957, -3.5636508464813232] |
45a21190-341e-4963-8f58-bfbda7120061 | consensus-neural-network-for-medical-imaging | 1906.03639 | null | https://arxiv.org/abs/1906.03639v1 | https://arxiv.org/pdf/1906.03639v1.pdf | Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples | Deep neural networks have been proved efficient for medical image denoising. Current training methods require both noisy and clean images. However, clean images cannot be acquired for many practical medical applications due to naturally noisy signal, such as dynamic imaging, spectral computed tomography, arterial spin ... | ['Quanzheng Li', 'Kyungsang Kim', 'Kuang Gong', 'Dufan Wu'] | 2019-06-09 | null | null | null | null | ['medical-image-denoising'] | ['computer-vision'] | [ 6.03585780e-01 1.39157489e-01 2.20315173e-01 -6.05266988e-01
-7.81220019e-01 3.22188176e-02 1.35714829e-01 -1.04722090e-01
-6.41484618e-01 9.83639836e-01 1.15215495e-01 9.31167901e-02
-3.87906671e-01 -6.30866051e-01 -5.33205390e-01 -1.14146340e+00
-3.15166414e-01 4.22006458e-01 -4.28230762e-02 4.92800921... | [13.311063766479492, -2.486466884613037] |
5e58ccd7-27fb-4509-9e22-bb12f2aa259c | exploring-data-redundancy-in-real-world-image | 2306.14113 | null | https://arxiv.org/abs/2306.14113v1 | https://arxiv.org/pdf/2306.14113v1.pdf | Exploring Data Redundancy in Real-world Image Classification through Data Selection | Deep learning models often require large amounts of data for training, leading to increased costs. It is particularly challenging in medical imaging, i.e., gathering distributed data for centralized training, and meanwhile, obtaining quality labels remains a tedious job. Many methods have been proposed to address this ... | ['Xiaosong Wang', 'Shaoting Zhang', 'Zhenyu Tang'] | 2023-06-25 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 1.31349072e-01 -1.27650037e-01 -2.73584545e-01 -7.24916041e-01
-8.97130430e-01 -3.23891997e-01 1.83319375e-02 5.09011269e-01
-7.16251552e-01 8.87638390e-01 -3.21819365e-01 -1.05528042e-01
-7.37812221e-01 -6.69605494e-01 -4.38427567e-01 -9.64777291e-01
-1.02799274e-01 5.49772799e-01 -5.48523851e-02 1.61618561... | [6.066474914550781, 6.427331924438477] |
dfa61274-8400-4c67-8bc0-ed5c2686130d | learning-without-human-scores-for-blind-image | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Xue_Learning_without_Human_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Xue_Learning_without_Human_2013_CVPR_paper.pdf | Learning without Human Scores for Blind Image Quality Assessment | General purpose blind image quality assessment (BIQA) has been recently attracting significant attention in the fields of image processing, vision and machine learning. Stateof-the-art BIQA methods usually learn to evaluate the image quality by regression from human subjective scores of the training samples. However, t... | ['Wufeng Xue', 'Xuanqin Mou', 'Lei Zhang'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 1.40769362e-01 -5.26393652e-01 1.84574112e-01 -5.63974261e-01
-1.27957428e+00 -3.96620601e-01 8.10580030e-02 1.63011178e-01
-4.14434224e-01 5.62483370e-01 1.35181472e-01 3.01937182e-02
-5.39622188e-01 -6.45883918e-01 -3.94111961e-01 -9.45698798e-01
-3.57718728e-02 1.17973842e-01 4.84224647e-01 1.30345421... | [11.79484748840332, -1.9105793237686157] |
df972d75-25af-4eb8-a74f-df1bbc3d0a9b | an-application-of-cascaded-3d-fully | 1803.05431 | null | http://arxiv.org/abs/1803.05431v2 | http://arxiv.org/pdf/1803.05431v2.pdf | An application of cascaded 3D fully convolutional networks for medical image segmentation | Recent advances in 3D fully convolutional networks (FCN) have made it
feasible to produce dense voxel-wise predictions of volumetric images. In this
work, we show that a multi-class 3D FCN trained on manually labeled CT scans of
several anatomical structures (ranging from the large organs to thin vessels)
can achieve c... | ['Kensaku MORI', 'Kazunari Misawa', 'Yuichiro Hayashi', 'Hirohisa ODA', 'Michitaka Fujiwara', 'Holger R. Roth', 'Ying Yang', 'Xiangrong Zhou', 'Masahiro Oda', 'Natsuki Shimizu'] | 2018-03-14 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [-1.46118356e-02 2.55908459e-01 -2.04649180e-01 -4.93112415e-01
-9.23268259e-01 -6.56094968e-01 2.87041128e-01 4.93078589e-01
-3.83136332e-01 4.45823610e-01 1.89685836e-01 -5.20245671e-01
1.97856888e-01 -6.98651731e-01 -5.98915219e-01 -5.69493473e-01
-3.76169860e-01 9.19833362e-01 4.47493494e-01 2.64351964... | [14.671149253845215, -2.4317781925201416] |
1aa4e169-5132-4334-bdf5-11ce8072e1cd | neural-machine-translation-for-code | 2305.13504 | null | https://arxiv.org/abs/2305.13504v1 | https://arxiv.org/pdf/2305.13504v1.pdf | Neural Machine Translation for Code Generation | Neural machine translation (NMT) methods developed for natural language processing have been shown to be highly successful in automating translation from one natural language to another. Recently, these NMT methods have been adapted to the generation of program code. In NMT for code generation, the task is to generate ... | ['Clayton T. Morrison', 'Dharma KC'] | 2023-05-22 | null | null | null | null | ['nmt', 'code-generation', 'code-translation'] | ['computer-code', 'computer-code', 'computer-code'] | [ 6.88289106e-01 3.62142742e-01 -3.17082107e-01 -4.46089566e-01
-7.17129171e-01 -7.37112045e-01 5.57689071e-01 3.12418014e-01
2.25703850e-01 5.47390997e-01 1.68575183e-01 -8.08107555e-01
3.12536508e-01 -8.86819839e-01 -7.99900293e-01 1.85980070e-02
7.49625266e-02 3.77494335e-01 -4.57845926e-01 -3.09515446... | [7.767967224121094, 7.800108909606934] |
4cbadca4-c5dd-4eb7-915c-1342bf4e01f5 | model-based-demosaicking-for-acquisitions-by | 2306.01357 | null | https://arxiv.org/abs/2306.01357v1 | https://arxiv.org/pdf/2306.01357v1.pdf | Model-based demosaicking for acquisitions by a RGBW color filter array | Microsatellites and drones are often equipped with digital cameras whose sensing system is based on color filter arrays (CFAs), which define a pattern of color filter overlaid over the focal plane. Recent commercial cameras have started implementing RGBW patterns, which include some filters with a wideband spectral res... | ['Magnus O Ulfarsson', 'Mauro Dalla Mura', 'Daniele Picone', 'Matthieu Muller'] | 2023-06-02 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 4.77976739e-01 -3.39979023e-01 4.44838583e-01 -1.97884247e-01
-1.05829790e-01 -4.78495270e-01 4.83081490e-01 -3.03800106e-01
-9.17299092e-01 7.38265872e-01 -2.24020749e-01 5.73355854e-02
-1.55266166e-01 -8.34656835e-01 -6.60513520e-01 -9.60824013e-01
3.74759853e-01 1.34329617e-01 2.72078782e-01 2.63653956... | [10.214545249938965, -2.543914556503296] |
097e7580-edf9-4d53-985c-2eeb515fe21b | robust-and-controllable-object-centric | 2210.05519 | null | https://arxiv.org/abs/2210.05519v1 | https://arxiv.org/pdf/2210.05519v1.pdf | Robust and Controllable Object-Centric Learning through Energy-based Models | Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability to decompose low-level observations into discrete objects allows us to build a grounded abstract representation and identify the compositional structure of the world. Accordingly, it is a crucial step for machine learn... | ['Liam Paull', 'Yoshua Bengio', 'Marco Pavone', 'Renhao Wang', 'Boris Ivanovic', 'Tong Che', 'Ruixiang Zhang'] | 2022-10-11 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 4.19548631e-01 1.22013882e-01 -1.77478328e-01 -6.05804861e-01
-8.05724740e-01 -7.45563209e-01 9.50695157e-01 -1.33961961e-01
-1.10896945e-01 4.67698723e-01 2.06437781e-01 -7.42988139e-02
-6.51889741e-02 -9.10070539e-01 -1.17143118e+00 -7.00383902e-01
2.46310625e-02 8.52295995e-01 6.08633608e-02 3.38169950... | [10.005575180053711, 0.6707603931427002] |
8aa39a42-007b-4e0b-8eed-36d654d68cf4 | end-to-end-multi-view-lipreading | 1709.00443 | null | http://arxiv.org/abs/1709.00443v1 | http://arxiv.org/pdf/1709.00443v1.pdf | End-to-End Multi-View Lipreading | Non-frontal lip views contain useful information which can be used to enhance
the performance of frontal view lipreading. However, the vast majority of
recent lipreading works, including the deep learning approaches which
significantly outperform traditional approaches, have focused on frontal mouth
images. As a conseq... | ['Yujiang Wang', 'Zuwei Li', 'Maja Pantic', 'Stavros Petridis'] | 2017-09-01 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [-1.46467611e-02 -5.79628795e-02 -5.32465518e-01 -1.39449537e-01
-1.15163314e+00 -1.12780161e-01 8.16223323e-01 -2.84188449e-01
-3.77313823e-01 3.62176001e-01 4.04615521e-01 -8.29135403e-02
5.82933545e-01 -4.76448052e-02 -5.73612213e-01 -7.98515379e-01
3.92302066e-01 -4.33280831e-03 3.52385223e-01 1.26245737... | [14.326679229736328, 5.009440898895264] |
18945d10-4a4d-4267-a9ce-6d8a0c33979a | feature-fusion-vision-transformer-fine | 2107.02341 | null | https://arxiv.org/abs/2107.02341v3 | https://arxiv.org/pdf/2107.02341v3.pdf | Feature Fusion Vision Transformer for Fine-Grained Visual Categorization | The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet discriminative features. Most previous works achieve this by explicitly selecting the discriminative parts or integrating the attention mechanism via CNN-based approaches.However, these methods enhance the computational complexit... | ['Yongsheng Gao', 'Xiaohan Yu', 'Jun Wang'] | 2021-07-06 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [-1.33095145e-01 -3.93396914e-01 -2.37242997e-01 -3.52341652e-01
-6.63107753e-01 -1.47915736e-01 6.77284002e-01 4.48384993e-02
-5.84283113e-01 3.82553220e-01 1.97185263e-01 1.59985662e-01
-1.35417163e-01 -8.70764911e-01 -5.27419746e-01 -1.04611409e+00
3.82101566e-01 -2.86802109e-02 4.87128913e-01 -2.36897599... | [9.646004676818848, 1.91808021068573] |
41753141-703e-446f-bb95-7153ff70ed5f | direction-of-arrival-estimation-for-multiple | 1710.10059 | null | http://arxiv.org/abs/1710.10059v2 | http://arxiv.org/pdf/1710.10059v2.pdf | Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network | This paper proposes a deep neural network for estimating the directions of
arrival (DOA) of multiple sound sources. The proposed stacked convolutional and
recurrent neural network (DOAnet) generates a spatial pseudo-spectrum (SPS)
along with the DOA estimates in both azimuth and elevation. We avoid any
explicit feature... | ['Tuomas Virtanen', 'Archontis Politis', 'Sharath Adavanne'] | 2017-10-27 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-2.50281483e-01 -7.65590012e-01 1.10110438e+00 2.74946477e-04
-9.40111756e-01 -6.46220088e-01 3.80411893e-01 -1.45883545e-01
4.90416177e-02 5.23626387e-01 5.17392814e-01 -2.41074458e-01
-4.70274031e-01 -6.23367488e-01 -4.21101004e-01 -9.44225729e-01
-5.22089362e-01 -4.45827752e-01 -2.51710892e-01 -1.18669599... | [15.258953094482422, 5.604907989501953] |
5be65edf-0895-4360-9be4-6fb0bc24dead | espnet-se-speech-enhancement-for-robust | 2207.09514 | null | https://arxiv.org/abs/2207.09514v1 | https://arxiv.org/pdf/2207.09514v1.pdf | ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding | This paper presents recent progress on integrating speech separation and enhancement (SSE) into the ESPnet toolkit. Compared with the previous ESPnet-SE work, numerous features have been added, including recent state-of-the-art speech enhancement models with their respective training and evaluation recipes. Importantly... | ['Shinji Watanabe', 'Yanmin Qian', 'Yu Tsao', 'Zhong-Qiu Wang', 'Robin Scheibler', 'Brian Yan', 'Yoshiki Masuyama', 'Zhaoheng Ni', 'Samuele Cornell', 'Wangyou Zhang', 'Chenda Li', 'Xuankai Chang', 'Yen-Ju Lu'] | 2022-07-19 | null | null | null | null | ['spoken-language-understanding', 'robust-speech-recognition', 'speech-separation', 'spoken-language-understanding'] | ['natural-language-processing', 'speech', 'speech', 'speech'] | [ 2.30629534e-01 1.13833219e-01 3.15202683e-01 -4.06181246e-01
-1.28160310e+00 -4.34273928e-01 6.87018692e-01 -3.03870112e-01
-5.98639190e-01 5.32279193e-01 4.09741610e-01 -5.17229021e-01
2.21545711e-01 -7.21034184e-02 -5.01112163e-01 -6.32856190e-01
6.08608797e-02 1.65579364e-01 1.96928456e-01 -5.71806550... | [14.780576705932617, 6.066534042358398] |
c8e62af7-7408-49c1-ae1c-890d7a41839f | multivariate-confidence-calibration-for | 2004.13546 | null | https://arxiv.org/abs/2004.13546v1 | https://arxiv.org/pdf/2004.13546v1.pdf | Multivariate Confidence Calibration for Object Detection | Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates. Though there is a variety of methods for classification, the field of object detection has not been addressed yet. Therefore, we present a ... | ['Fabian Küppers', 'Jan Kronenberger', 'Amirhossein Shantia', 'Anselm Haselhoff'] | 2020-04-28 | null | null | null | null | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [ 6.27357438e-02 -2.61605650e-01 -5.79887331e-02 -7.05304027e-01
-6.01750135e-01 -5.84593892e-01 5.22836506e-01 3.94266516e-01
-8.28917444e-01 7.69821584e-01 -5.76817811e-01 -2.06341043e-01
-3.67718190e-02 -5.67459106e-01 -8.97947371e-01 -6.48210704e-01
1.99277118e-01 2.82902837e-01 6.97529733e-01 4.15574968... | [8.606608390808105, 2.0122761726379395] |
497fbab9-5ee2-4fcb-b2fd-70f2c6e28073 | learning-to-have-an-ear-for-face-super | 1909.12780 | null | https://arxiv.org/abs/1909.12780v3 | https://arxiv.org/pdf/1909.12780v3.pdf | Learning to Have an Ear for Face Super-Resolution | We propose a novel method to use both audio and a low-resolution image to perform extreme face super-resolution (a 16x increase of the input size). When the resolution of the input image is very low (e.g., 8x8 pixels), the loss of information is so dire that important details of the original identity have been lost and... | ['Simon Jenni', 'Paolo Favaro', 'Givi Meishvili'] | 2019-09-27 | learning-to-have-an-ear-for-face-super-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Meishvili_Learning_to_Have_an_Ear_for_Face_Super-Resolution_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Meishvili_Learning_to_Have_an_Ear_for_Face_Super-Resolution_CVPR_2020_paper.pdf | cvpr-2020-6 | ['audio-super-resolution', 'audio-super-resolution'] | ['audio', 'music'] | [ 4.15669978e-01 2.64712840e-01 1.19014084e-01 -3.75205129e-01
-9.38097894e-01 -2.93817759e-01 4.53145087e-01 -2.67834485e-01
-3.11082214e-01 6.90657496e-01 4.70452487e-01 3.80464017e-01
1.40708283e-01 -8.88433099e-01 -8.75871897e-01 -6.47047162e-01
1.13963716e-01 2.87302673e-01 1.25030577e-01 -3.03498376... | [12.848860740661621, -0.130252406001091] |
a6f0c2c6-5cfe-405c-a329-0c699f55888a | a-vessel-segmentation-based-cyclegan-for | 2306.02901 | null | https://arxiv.org/abs/2306.02901v1 | https://arxiv.org/pdf/2306.02901v1.pdf | A Vessel-Segmentation-Based CycleGAN for Unpaired Multi-modal Retinal Image Synthesis | Unpaired image-to-image translation of retinal images can efficiently increase the training dataset for deep-learning-based multi-modal retinal registration methods. Our method integrates a vessel segmentation network into the image-to-image translation task by extending the CycleGAN framework. The segmentation network... | ['Vincent Christlein', 'Andreas Maier', 'Aline Sindel'] | 2023-06-05 | null | null | null | null | ['image-registration', 'image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 5.14785051e-01 5.61491191e-01 -5.40558659e-02 -3.58761936e-01
-8.13980222e-01 -6.70596600e-01 5.29713035e-01 -5.47940612e-01
-4.28991616e-01 6.36402249e-01 1.40124317e-02 -3.67874563e-01
6.60625458e-01 -8.55103433e-01 -9.29992378e-01 -7.55026817e-01
5.75718641e-01 -7.07094520e-02 2.02038601e-01 1.01247020... | [15.565449714660645, -3.734544038772583] |
c5f20e49-51e1-4d45-b0f9-654e81779797 | itnlp-aikf-at-semeval-2016-task-3-a-quesiton | null | null | https://aclanthology.org/S16-1139 | https://aclanthology.org/S16-1139.pdf | ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository | null | ["Chang{'}e Jia"] | 2016-06-01 | null | null | null | semeval-2016-6 | ['question-similarity'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.351032257080078, 3.6671559810638428] |
2baf19f8-457c-4e79-ac3f-73efa739c375 | a-supervised-model-for-extraction-of | null | null | https://aclanthology.info/papers/W14-0802/w14-0802 | https://www.aclweb.org/anthology/W14-0802 | A Supervised Model for Extraction of Multiword Expressions, Based on Statistical Context Features | null | ['Ronaldo Martins', 'Meghdad Farahmand'] | 2014-04-01 | null | https://aclanthology.org/W14-0802 | https://aclanthology.org/W14-0802.pdf | ws-2014-4 | ['cross-lingual-information-retrieval'] | ['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... | [-1.5391768217086792, 15.86919116973877] |
e090fb15-839b-4a31-9c16-d5c15bdc4a9a | retroxpert-decompose-retrosynthesis | 2011.02893 | null | https://arxiv.org/abs/2011.02893v1 | https://arxiv.org/pdf/2011.02893v1.pdf | RetroXpert: Decompose Retrosynthesis Prediction like a Chemist | Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate or assist in the retrosynthesis analysis, various retrosynthesis prediction algorithms have been proposed. However, most of ... | ['Junzhou Huang', 'Yang Yu', 'Jinyu Yang', 'Shuangjia Zheng', 'Peilin Zhao', 'Qianggang Ding', 'Chaochao Yan'] | 2020-11-04 | null | http://proceedings.neurips.cc/paper/2020/hash/819f46e52c25763a55cc642422644317-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/819f46e52c25763a55cc642422644317-Paper.pdf | neurips-2020-12 | ['retrosynthesis'] | ['medical'] | [ 7.21802831e-01 4.18269902e-01 -5.27049184e-01 9.87346545e-02
-3.45793724e-01 -1.15162742e+00 8.35780263e-01 6.23549163e-01
3.59703489e-02 9.64506567e-01 3.42981219e-01 -7.33136952e-01
2.76242226e-01 -8.89730930e-01 -6.00990415e-01 -7.56199241e-01
1.92051485e-01 5.08356690e-01 2.92476982e-01 -4.18591410... | [4.502028942108154, 6.103488922119141] |
74a34e56-2dab-4940-9379-4072b7ca02ab | towards-personalized-cold-start | 2306.17256 | null | https://arxiv.org/abs/2306.17256v2 | https://arxiv.org/pdf/2306.17256v2.pdf | Towards Personalized Cold-Start Recommendation with Prompts | Recommender systems play a crucial role in helping users discover information that aligns with their interests based on their past behaviors. However, developing personalized recommendation systems becomes challenging when historical records of user-item interactions are unavailable, leading to what is known as the sys... | ['Ninghao Liu', 'Xiao Huang', 'Wenlin Yao', 'Huachi Zhou', 'Xuansheng Wu'] | 2023-06-29 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-1.47142053e-01 -3.50996256e-01 -6.70063615e-01 -3.60392660e-01
-2.71277457e-01 -7.56990075e-01 4.75166917e-01 2.17700839e-01
-2.89019823e-01 2.96166718e-01 4.01117593e-01 -3.86735737e-01
-8.76461416e-02 -6.43087983e-01 -2.51503468e-01 -3.11276138e-01
3.33346665e-01 1.70024097e-01 -1.40910089e-01 -5.94910502... | [10.149100303649902, 5.699491500854492] |
72f8fa8f-ab11-4283-96ec-571c0c031b6e | compressive-quantization-for-fast-object | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Yu_Compressive_Quantization_for_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Yu_Compressive_Quantization_for_ICCV_2017_paper.pdf | Compressive Quantization for Fast Object Instance Search in Videos | Most of current visual search systems focus on image-to-image (point-to-point) search such as image and object retrieval. Nevertheless, fast image-to-video (point-to-set) search is much less exploited. This paper tackles object instance search in videos, where efficient point-to-set matching is essential. Through joint... | ['Tan Yu', 'Zhenzhen Wang', 'Junsong Yuan'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['set-matching', 'instance-search'] | ['computer-vision', 'computer-vision'] | [ 2.60504067e-01 -7.50999153e-01 -6.78605020e-01 -1.75690934e-01
-9.24516916e-01 -4.74868089e-01 2.99112469e-01 4.95294273e-01
-2.85782725e-01 2.18011022e-01 2.72603091e-02 1.10386029e-01
-3.54311019e-01 -5.82049370e-01 -7.55067110e-01 -7.12217093e-01
-1.70901477e-01 1.79867610e-01 5.23828268e-01 3.68752390... | [10.121065139770508, 0.5733950734138489] |
02c1c323-a820-47a5-a7ee-c10a36a1a48e | road-redesign-technique-achieving-enhanced | 2302.07440 | null | https://arxiv.org/abs/2302.07440v1 | https://arxiv.org/pdf/2302.07440v1.pdf | Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model | Road infrastructure can affect the occurrence of road accidents. Therefore, identifying roadway features with high accident probability is crucial. Here, we introduce image inpainting that can assist authorities in achieving safe roadway design with minimal intervention in the current roadway structure. Image inpaintin... | ['Dongsoo Har', 'TaeYoung Kim', 'Medhavi Mishra', 'Sumit Mishra'] | 2023-02-15 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 6.02188051e-01 6.30255401e-01 -3.14463854e-01 -1.11640714e-01
-6.03215337e-01 -1.41709819e-01 3.77831697e-01 1.86824083e-01
-7.51191258e-01 6.65247977e-01 3.30743015e-01 -6.07262313e-01
-1.34921327e-01 -1.03022265e+00 -7.91948378e-01 -6.90335691e-01
3.05539012e-01 -3.06568980e-01 4.03549105e-01 -8.26827362... | [8.644720077514648, -1.2019010782241821] |
35b9cef4-b645-41ed-be5c-784046046eb2 | measuring-board-game-distance | 2301.03913 | null | https://arxiv.org/abs/2301.03913v1 | https://arxiv.org/pdf/2301.03913v1.pdf | Measuring Board Game Distance | This paper presents a general approach for measuring distances between board games within the Ludii general game system. These distances are calculated using a previously published set of general board game concepts, each of which represents a common game idea or shared property. Our results compare and contrast two di... | ['Cameron Browne', 'Éric Piette', 'Dennis J. N. J. Soemers', 'Matthew Stephenson'] | 2023-01-10 | null | null | null | null | ['board-games'] | ['playing-games'] | [-4.57964778e-01 2.98986193e-02 1.83113322e-01 1.36171177e-01
-3.64048928e-01 -1.03362238e+00 5.83700836e-01 3.00009459e-01
-5.52017987e-01 6.88790321e-01 1.15268536e-01 -3.58481586e-01
-7.69769251e-01 -1.13410449e+00 3.39040250e-01 -3.56277406e-01
-2.54031718e-01 3.40342999e-01 7.81983912e-01 -1.09251106... | [3.4816489219665527, 1.4325557947158813] |
abbcaa78-1b31-48d3-b78b-8f5e234c178e | weakly-supervised-unconstrained-action-unit | 1903.10143 | null | https://arxiv.org/abs/1903.10143v4 | https://arxiv.org/pdf/1903.10143v4.pdf | Unconstrained Facial Action Unit Detection via Latent Feature Domain | Facial action unit (AU) detection in the wild is a challenging problem, due to the unconstrained variability in facial appearances and the lack of accurate annotations. Most existing methods depend on either impractical labor-intensive labeling or inaccurate pseudo labels. In this paper, we propose an end-to-end uncons... | ['Xuequan Lu', 'Tat-Jen Cham', 'Zhiwen Shao', 'Jianfei Cai', 'Lizhuang Ma'] | 2019-03-25 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.37192118e-01 2.60878056e-01 -2.69606918e-01 -4.91952509e-01
-1.37271261e+00 -5.64777374e-01 3.10730666e-01 -4.59078342e-01
-3.32311749e-01 5.77714801e-01 -3.68391983e-02 3.39744568e-01
3.77373546e-01 -5.36528945e-01 -7.95391858e-01 -9.78018939e-01
1.80175382e-04 2.89643466e-01 -7.82882273e-02 -1.60530359... | [13.62904167175293, 1.539353370666504] |
8f529f89-5abe-435c-990e-da044d745059 | the-self-learning-ai-controller-for-adaptive | 2204.05227 | null | https://arxiv.org/abs/2204.05227v1 | https://arxiv.org/pdf/2204.05227v1.pdf | The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system | In this study we consider adaptive power beaming with fiber-array laser transmitter system in presence of atmospheric turbulence. For optimization of power transition through the atmosphere fiber-array is traditionally controlled by stochastic parallel gradient descent (SPGD) algorithm where control feedback is provide... | ['G. A. Filimonov', 'A. M. Vorontsov'] | 2022-04-08 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 2.84685671e-01 1.13210671e-01 3.43482822e-01 -3.17643583e-03
2.80516744e-01 -8.20856690e-01 1.20153137e-01 -4.83724684e-01
-4.89266604e-01 1.26858759e+00 -3.27407867e-01 -7.63179362e-02
-6.72433197e-01 -3.82402927e-01 -6.10971093e-01 -1.12090111e+00
-5.69041027e-03 2.98326492e-01 -3.01449865e-01 -3.31576198... | [5.465681552886963, 2.5140044689178467] |
ab6af541-a6fc-4c22-9b53-e1bf8e58307a | quantifying-morphological-computation-based | 1503.05113 | null | http://arxiv.org/abs/1503.05113v1 | http://arxiv.org/pdf/1503.05113v1.pdf | Quantifying Morphological Computation based on an Information Decomposition of the Sensorimotor Loop | The question how an agent is affected by its embodiment has attracted growing
attention in recent years. A new field of artificial intelligence has emerged,
which is based on the idea that intelligence cannot be understood without
taking into account embodiment. We believe that a formal approach to
quantifying the embo... | ['Johannes Rauh', 'Keyan Ghazi-Zahedi'] | 2015-03-17 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 2.57396609e-01 1.14989594e-01 1.90539107e-01 6.19450137e-02
6.80901229e-01 -3.99356484e-01 9.77811038e-01 3.44385177e-01
-6.25090718e-01 4.55476820e-01 4.33947533e-01 -3.41555439e-02
-5.25998116e-01 -1.09229004e+00 -2.69640386e-01 -6.95527434e-01
-1.16370618e-01 7.19308853e-02 -1.35202929e-01 -6.17527664... | [5.626086711883545, 4.174091339111328] |
993bf131-dfee-4563-bcf9-199ed32706d8 | a-transition-based-dependency-parser-using-a | null | null | https://aclanthology.org/P13-1014 | https://aclanthology.org/P13-1014.pdf | A Transition-Based Dependency Parser Using a Dynamic Parsing Strategy | null | ['Giorgio Satta', 'Francesco Sartorio', 'Joakim Nivre'] | 2013-08-01 | null | null | null | acl-2013-8 | ['transition-based-dependency-parsing'] | ['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... | [-7.295925140380859, 3.758568286895752] |
c1ff6717-9818-4f01-9fac-084d7990da02 | distilled-dual-encoder-model-for-vision | 2112.08723 | null | https://arxiv.org/abs/2112.08723v2 | https://arxiv.org/pdf/2112.08723v2.pdf | Distilled Dual-Encoder Model for Vision-Language Understanding | We propose a cross-modal attention distillation framework to train a dual-encoder model for vision-language understanding tasks, such as visual reasoning and visual question answering. Dual-encoder models have a faster inference speed than fusion-encoder models and enable the pre-computation of images and text during i... | ['Furu Wei', 'Bing Qin', 'Ming Liu', 'Haichao Zhu', 'Wenhui Wang', 'Zekun Wang'] | 2021-12-16 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [-1.12974823e-01 2.82843024e-01 2.95200776e-02 -5.76817334e-01
-8.13343167e-01 -4.33151573e-01 8.62785518e-01 -1.66740432e-01
-3.83666217e-01 2.16762543e-01 3.11095923e-01 -8.01775157e-01
3.22228372e-01 -7.75067389e-01 -1.22897565e+00 -3.82790446e-01
5.45801997e-01 5.41357100e-01 8.53156857e-03 -1.43794175... | [10.821964263916016, 1.6595840454101562] |
d87fd5b7-4ff0-4078-93c3-b6f8aa3b843a | contextualizing-argument-quality-assessment | 2305.12280 | null | https://arxiv.org/abs/2305.12280v1 | https://arxiv.org/pdf/2305.12280v1.pdf | Contextualizing Argument Quality Assessment with Relevant Knowledge | Automatic assessment of the quality of arguments has been recognized as a challenging task with significant implications for misinformation and targeted speech. While real world arguments are tightly anchored in context, existing efforts to judge argument quality analyze arguments in isolation, ultimately failing to ac... | ['Fred Morstatter', 'Filip Ilievski', 'Zhivar Sourati', 'Darshan Deshpande'] | 2023-05-20 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 3.28513592e-01 4.29413974e-01 -7.31178164e-01 -7.45783687e-01
-1.87773478e+00 -9.73963559e-01 9.83719826e-01 6.44069910e-01
-5.87427318e-01 8.15459430e-01 1.26038086e+00 -1.07771909e+00
2.63183396e-02 -7.70265520e-01 -8.53466868e-01 5.23531996e-02
6.05379701e-01 6.60434723e-01 6.35033548e-02 -3.31044585... | [9.749860763549805, 9.475616455078125] |
cf77e654-cae5-4c65-ace9-800f257c6c08 | 190600772 | 1906.00772 | null | https://arxiv.org/abs/1906.00772v1 | https://arxiv.org/pdf/1906.00772v1.pdf | Dynamic Service Composition Orchestrated by Cognitive Agents in Mobile & Pervasive Computing | Automatic service composition in mobile and pervasive computing faces many challenges due to the complex nature of the environment. Common approaches address service composition from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of... | ['Oscar J. Romero'] | 2019-05-31 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-7.73152485e-02 2.14674354e-01 -1.81244582e-01 -4.59787436e-02
2.84311250e-02 -4.83845264e-01 6.07234538e-01 -3.03964287e-01
-2.78876096e-01 6.99823201e-01 2.03121737e-01 -2.49093071e-01
-6.49795771e-01 -7.49834716e-01 7.07086995e-02 -6.89122200e-01
-2.20911548e-01 9.41760898e-01 5.67152977e-01 -6.71600819... | [8.616639137268066, 6.927917957305908] |
9c471d22-f96f-4b8d-b2ed-c7f949986be3 | modeling-label-correlations-for-ultra-fine | 2212.01581 | null | https://arxiv.org/abs/2212.01581v1 | https://arxiv.org/pdf/2212.01581v1.pdf | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | Ultra-fine entity typing (UFET) aims to predict a wide range of type phrases that correctly describe the categories of a given entity mention in a sentence. Most recent works infer each entity type independently, ignoring the correlations between types, e.g., when an entity is inferred as a president, it should also be... | ['Kewei Tu', 'Pengjun Xie', 'Weiqi Wu', 'Yong Jiang', 'Chengyue Jiang'] | 2022-12-03 | null | null | null | null | ['entity-typing', 'type'] | ['natural-language-processing', 'speech'] | [-4.13483791e-02 2.47066051e-01 -5.75301230e-01 -6.94676638e-01
-6.91667199e-01 -7.77978301e-01 4.02190119e-01 1.36585802e-01
-5.37107825e-01 1.23593140e+00 1.70319110e-01 -4.87508774e-01
1.56129375e-01 -1.15162921e+00 -1.35403204e+00 -4.56371963e-01
-1.33972168e-02 9.72264528e-01 -2.04525977e-01 1.05486922... | [9.680726051330566, 8.752251625061035] |
597a11eb-4c9b-4717-989d-810b58d14931 | krylov-methods-are-nearly-optimal-for-low | 2304.03191 | null | https://arxiv.org/abs/2304.03191v1 | https://arxiv.org/pdf/2304.03191v1.pdf | Krylov Methods are (nearly) Optimal for Low-Rank Approximation | We consider the problem of rank-$1$ low-rank approximation (LRA) in the matrix-vector product model under various Schatten norms: $$ \min_{\|u\|_2=1} \|A (I - u u^\top)\|_{\mathcal{S}_p} , $$ where $\|M\|_{\mathcal{S}_p}$ denotes the $\ell_p$ norm of the singular values of $M$. Given $\varepsilon>0$, our goal is to out... | ['Shyam Narayanan', 'Ainesh Bakshi'] | 2023-04-06 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 3.75476718e-01 2.16023773e-01 -2.38639899e-02 3.34970653e-01
-1.13083494e+00 -6.92893386e-01 -3.96410003e-02 5.47043458e-02
-7.17456758e-01 8.06115270e-01 -3.33341300e-01 -6.99065924e-01
-6.99534416e-01 -9.41476822e-01 -7.53409147e-01 -1.01517940e+00
-9.23307180e-01 2.59650469e-01 -1.47146285e-01 -5.07309973... | [6.542399883270264, 4.704171180725098] |
5ddf9838-2398-4cd9-bc22-31653d4870fd | infrared-safety-of-a-neural-net-top-tagging | 1806.01263 | null | http://arxiv.org/abs/1806.01263v2 | http://arxiv.org/pdf/1806.01263v2.pdf | Infrared Safety of a Neural-Net Top Tagging Algorithm | Neural network-based algorithms provide a promising approach to jet
classification problems, such as boosted top jet tagging. To date, NN-based top
taggers demonstrated excellent performance in Monte Carlo studies. In this
paper, we construct a top-jet tagger based on a Convolutional Neural Network
(CNN), and apply it ... | ['Suyong Choi', 'Maxim Perelstein', 'Seung J. Lee'] | 2018-06-04 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-3.62579584e-01 -7.46604130e-02 -3.36035609e-01 -3.39010715e-01
-5.95142484e-01 -7.94684947e-01 1.01232278e+00 2.16236711e-02
-3.28181773e-01 6.21864021e-01 2.77191490e-01 -4.99875724e-01
5.64624043e-03 -1.06704080e+00 -8.50890458e-01 -9.24230695e-01
-1.86479747e-01 1.02840030e+00 5.48877358e-01 -3.99880469... | [15.698616981506348, 2.920226573944092] |
83afc892-1c6b-407f-9522-ebf15da4476e | event-centric-query-expansion-in-web-search | 2305.19019 | null | https://arxiv.org/abs/2305.19019v1 | https://arxiv.org/pdf/2305.19019v1.pdf | Event-Centric Query Expansion in Web Search | In search engines, query expansion (QE) is a crucial technique to improve search experience. Previous studies often rely on long-term search log mining, which leads to slow updates and is sub-optimal for time-sensitive news searches. In this work, we present Event-Centric Query Expansion (EQE), a novel QE system that a... | ['Tianhua Zhou', 'Xiang Chen', 'Jin Ma', 'Zhe Zhang', 'Xiaoling Bai', 'Yangfan Zhang', 'Weijie Cui', 'Yanan Zhang'] | 2023-05-30 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [-2.15285629e-01 -5.39234579e-01 -5.68642914e-01 -2.04892457e-01
-1.36129642e+00 -6.49463236e-01 6.89192653e-01 3.71301591e-01
-6.56313598e-01 4.96383041e-01 6.21775329e-01 -2.42788792e-01
-3.89803797e-01 -9.09509659e-01 -7.22030222e-01 -7.43629993e-04
-1.87489226e-01 5.55330217e-01 6.68417513e-01 -5.33661008... | [11.481141090393066, 7.589568614959717] |
15f5ae8d-c189-4f8d-8c1b-6939502745e5 | counterfactual-explanation-for-fairness-in | 2307.04386 | null | https://arxiv.org/abs/2307.04386v1 | https://arxiv.org/pdf/2307.04386v1.pdf | Counterfactual Explanation for Fairness in Recommendation | Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems.Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users' trust in recommendation models. Existing fairness explanation methods suffer high computati... | ['Guandong Xu', 'Qing Li', 'Dianer Yu', 'Qian Li', 'Xiangmeng Wang'] | 2023-07-10 | null | null | null | null | ['fairness', 'causal-inference', 'counterfactual-explanation', 'fairness', 'causal-inference'] | ['computer-vision', 'knowledge-base', 'miscellaneous', 'miscellaneous', 'miscellaneous'] | [-1.33922407e-02 4.67129618e-01 -1.04067254e+00 -7.28594542e-01
1.42564671e-02 -1.04292259e-01 4.09574717e-01 8.32343940e-03
-1.68809459e-01 1.11050284e+00 6.69648588e-01 -8.33322227e-01
-5.70044518e-01 -9.72145379e-01 -2.97909766e-01 -1.52250916e-01
3.20437849e-02 2.29234576e-01 -4.93635386e-01 -1.85652927... | [9.448477745056152, 5.6040215492248535] |
e0476a50-a8fe-4bb9-a045-3284dda1b4e2 | 190503646 | 1905.03646 | null | https://arxiv.org/abs/1905.03646v3 | https://arxiv.org/pdf/1905.03646v3.pdf | TE141K: Artistic Text Benchmark for Text Effect Transfer | Text effects are combinations of visual elements such as outlines, colors and textures of text, which can dramatically improve its artistry. Although text effects are extensively utilized in the design industry, they are usually created by human experts due to their extreme complexity; this is laborious and not practic... | ['Wenjing Wang', 'Shuai Yang', 'Jiaying Liu'] | 2019-05-08 | null | null | null | null | ['text-effects-transfer'] | ['natural-language-processing'] | [ 5.30575454e-01 -4.05760258e-01 -7.28418678e-02 -1.90777764e-01
-2.65652061e-01 -5.07430971e-01 6.18893027e-01 -6.38321698e-01
9.20560062e-02 8.66021216e-01 3.70087832e-01 -1.84195060e-02
2.14749262e-01 -7.07104802e-01 -6.84155941e-01 -5.53544104e-01
5.56039453e-01 1.10044040e-01 1.79549053e-01 -3.60162348... | [11.612492561340332, -0.4409361779689789] |
3d4ef8a1-1e9d-4e25-a640-bf4611186d18 | equivariant-multi-view-networks | 1904.00993 | null | https://arxiv.org/abs/1904.00993v2 | https://arxiv.org/pdf/1904.00993v2.pdf | Equivariant Multi-View Networks | Several popular approaches to 3D vision tasks process multiple views of the input independently with deep neural networks pre-trained on natural images, achieving view permutation invariance through a single round of pooling over all views. We argue that this operation discards important information and leads to subpar... | ['Christine Allen-Blanchette', 'Kostas Daniilidis', 'Yinshuang Xu', 'Carlos Esteves'] | 2019-04-01 | equivariant-multi-view-networks-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Esteves_Equivariant_Multi-View_Networks_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Esteves_Equivariant_Multi-View_Networks_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-shape-retrieval'] | ['computer-vision'] | [ 3.13890755e-01 1.21636055e-02 1.46650240e-01 -3.92226517e-01
-4.96097267e-01 -1.12056541e+00 1.13952529e+00 -2.62477348e-04
-5.05124390e-01 -4.50623222e-02 3.46605659e-01 -1.56757161e-01
-1.97261441e-02 -8.45417857e-01 -8.62763464e-01 -6.97357595e-01
-4.40750532e-02 4.34903383e-01 1.53356403e-01 -1.61720797... | [8.861673355102539, 2.336280584335327] |
6492b47b-eec9-4694-a408-390c38dfe6b2 | a-self-attention-joint-model-for-spoken | 1905.11393 | null | https://arxiv.org/abs/1905.11393v1 | https://arxiv.org/pdf/1905.11393v1.pdf | A Self-Attention Joint Model for Spoken Language Understanding in Situational Dialog Applications | Spoken language understanding (SLU) acts as a critical component in goal-oriented dialog systems. It typically involves identifying the speakers intent and extracting semantic slots from user utterances, which are known as intent detection (ID) and slot filling (SF). SLU problem has been intensively investigated in rec... | ['Mengyang Chen', 'Jin Zeng', 'Jie Lou'] | 2019-05-27 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-1.38607189e-01 3.63317132e-01 -1.23863697e-01 -6.56592190e-01
-3.22156847e-01 -1.87243953e-01 5.22984266e-01 -1.50709718e-01
-3.59541804e-01 7.01200664e-01 4.38422561e-01 -4.27233070e-01
2.02251121e-01 -6.58868492e-01 -6.52369857e-02 -5.89869499e-01
5.98194063e-01 5.09660721e-01 3.19834441e-01 -5.12634933... | [12.735067367553711, 7.631462097167969] |
c6a41dfd-2494-40bf-a57c-22c8d0739723 | scaling-scaling-laws-with-board-games | 2104.03113 | null | https://arxiv.org/abs/2104.03113v2 | https://arxiv.org/pdf/2104.03113v2.pdf | Scaling Scaling Laws with Board Games | The largest experiments in machine learning now require resources far beyond the budget of all but a few institutions. Fortunately, it has recently been shown that the results of these huge experiments can often be extrapolated from the results of a sequence of far smaller, cheaper experiments. In this work, we show th... | ['Andy L. Jones'] | 2021-04-07 | null | null | null | null | ['board-games'] | ['playing-games'] | [-3.33644271e-01 1.72378331e-01 1.82721972e-01 -2.14937791e-01
-7.35981762e-01 -7.46662855e-01 4.27852839e-01 3.16501319e-01
-8.41710329e-01 8.63773763e-01 -2.44137168e-01 -5.20155370e-01
2.06773635e-02 -5.89829922e-01 -7.31873512e-01 -5.42427182e-01
-2.50525385e-01 7.81502187e-01 3.62363607e-01 -8.30683485... | [5.080963134765625, 2.957099199295044] |
1b8f6195-3787-49d4-8005-12deab0521bc | optimizing-feature-set-for-click-through-rate | 2301.10909 | null | https://arxiv.org/abs/2301.10909v1 | https://arxiv.org/pdf/2301.10909v1.pdf | Optimizing Feature Set for Click-Through Rate Prediction | Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Therefore, when selecting an optimal feature set, we should consider the influence of both feature and its interaction. However, most previous w... | ['Xue Liu', 'Xiuqiang He', 'Liang Chen', 'Dugang Liu', 'Xing Tang', 'Fuyuan Lyu'] | 2023-01-26 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 1.47862837e-01 -6.28911018e-01 -4.33553159e-01 -5.68937898e-01
-5.41286886e-01 -5.11313736e-01 2.43875176e-01 -1.82344560e-02
-4.33017731e-01 4.81489390e-01 1.19790219e-01 -1.77586630e-01
-4.24855292e-01 -1.06552756e+00 -3.44200760e-01 -8.67297709e-01
1.09344907e-01 2.47005261e-02 4.47296590e-01 9.27915238... | [10.119778633117676, 5.379974842071533] |
b172319b-048c-4ba7-a074-f7874bbe2a1e | cldice-a-topology-preserving-loss-function | 2003.07311 | null | https://arxiv.org/abs/2003.07311v7 | https://arxiv.org/pdf/2003.07311v7.pdf | clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation | Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic; particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely al... | ['Ulrich Bauer', 'Josien P. W. Pluim', 'Johannes C. Paetzold', 'Alexander Unger', 'Andrey Zhylka', 'Ivan Ezhov', 'Bjoern H. Menze', 'Anjany Sekuboyina', 'Suprosanna Shit'] | 2020-03-16 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-2.43475810e-02 4.17650938e-01 -1.06088318e-01 -3.49847645e-01
1.57407179e-01 -7.84170270e-01 3.96951348e-01 6.44738972e-01
-3.25515300e-01 5.28199673e-01 -1.25708461e-01 -4.92049336e-01
-1.56339526e-01 -1.08746350e+00 -7.20328212e-01 -5.26272297e-01
-3.75013828e-01 4.08456534e-01 7.49957383e-01 -5.15879616... | [14.293086051940918, -2.6550652980804443] |
6587bfe7-4311-4c62-a740-73fe55c96d66 | yes-we-can-annotating-english-modal-verbs | null | null | https://aclanthology.org/L12-1458 | https://aclanthology.org/L12-1458.pdf | Yes we can!? Annotating English modal verbs | This paper presents an annotation scheme for English modal verbs together with sense-annotated data from the news domain. We describe our annotation scheme and discuss problematic cases for modality annotation based on the inter-annotator agreement during the annotation. Furthermore, we present experiments on automatic... | ['Ines Rehbein', 'Josef Ruppenhofer'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 1.74796849e-01 7.64456093e-01 -5.37968874e-01 -4.83145386e-01
-1.05885875e+00 -1.15697479e+00 5.57457745e-01 4.61024493e-01
-7.77814031e-01 1.38667846e+00 9.47151601e-01 -1.71738997e-01
5.63221574e-02 -3.91615212e-01 -3.25674444e-01 -4.06304985e-01
2.04858467e-01 7.22553909e-01 5.54369211e-01 -6.29837394... | [10.102240562438965, 9.406415939331055] |
302020f8-e37c-44c0-8061-dc34e365b8e1 | investigation-into-the-effectiveness-of-long | 1603.07893 | null | http://arxiv.org/abs/1603.07893v3 | http://arxiv.org/pdf/1603.07893v3.pdf | Investigation Into The Effectiveness Of Long Short Term Memory Networks For Stock Price Prediction | The effectiveness of long short term memory networks trained by
backpropagation through time for stock price prediction is explored in this
paper. A range of different architecture LSTM networks are constructed trained
and tested. | ['Hengjian Jia'] | 2016-03-25 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-7.68881917e-01 -3.24234903e-01 -3.15828711e-01 -4.40764755e-01
2.62688220e-01 -3.28070283e-01 6.27493799e-01 -5.51609159e-01
-5.43849349e-01 8.57444942e-01 1.44888788e-01 -8.49358916e-01
-2.94043869e-02 -9.80974495e-01 -3.88262331e-01 -2.53062516e-01
-8.37608159e-01 2.29490235e-01 2.06832796e-01 -5.00050306... | [4.461664199829102, 4.22921085357666] |
7085a109-23b6-4743-9314-2e9169ae2ba6 | deep-latent-variable-models-for-semi | 2301.02275 | null | https://arxiv.org/abs/2301.02275v1 | https://arxiv.org/pdf/2301.02275v1.pdf | Deep Latent Variable Models for Semi-supervised Paraphrase Generation | This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence auto-encoding reconstruction (VSAR), which performs latent sequence inference given an ob... | ['Noura Al Moubayed', 'Lei Shi', 'Olanrewaju Tahir Aduragba', 'Zhongtian Sun', 'Anoushka Harit', 'Alexandra I. Cristea', 'Jialin Yu'] | 2023-01-05 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 5.78628480e-01 3.29746246e-01 -5.40048838e-01 -2.23022312e-01
-1.24020028e+00 -7.15132475e-01 1.09993958e+00 -4.73340834e-03
-2.44197190e-01 7.95311928e-01 6.14345789e-01 -3.16906720e-01
3.19653630e-01 -4.82484043e-01 -1.03286266e+00 -5.62243521e-01
5.82415879e-01 7.18018115e-01 3.11564300e-02 -3.58273461... | [11.647695541381836, 9.209799766540527] |
8f698e6e-9ebc-46e9-869c-cd95f64c926e | multi-team-a-multi-attention-multi-decoder | null | null | https://aclanthology.org/W19-4206 | https://aclanthology.org/W19-4206.pdf | Multi-Team: A Multi-attention, Multi-decoder Approach to Morphological Analysis. | This paper describes our submission to SIGMORPHON 2019 Task 2: Morphological analysis and lemmatization in context. Our model is a multi-task sequence to sequence neural network, which jointly learns morphological tagging and lemmatization. On the encoding side, we exploit character-level as well as contextual informat... | ['Ahmet {\\"U}st{\\"u}n', 'Rob van der Goot', 'Gosse Bouma', 'Gertjan van Noord'] | 2019-08-01 | null | null | null | ws-2019-8 | ['morphological-tagging'] | ['natural-language-processing'] | [ 1.50377288e-01 -6.69615641e-02 -2.45692059e-02 -2.55032599e-01
-1.18681288e+00 -9.96938407e-01 2.19186768e-01 6.68065727e-01
-1.24398911e+00 6.49127245e-01 4.09381896e-01 -4.19290990e-01
4.22910959e-01 -4.93818969e-01 -8.89474988e-01 -4.57411021e-01
2.43787825e-01 5.03600240e-01 5.84608950e-02 1.90601200... | [10.424283981323242, 10.005356788635254] |
e5c97a79-f288-4fe6-aa74-9bf65708d2cb | an-initial-investigation-for-detecting | 2104.02518 | null | https://arxiv.org/abs/2104.02518v2 | https://arxiv.org/pdf/2104.02518v2.pdf | An Initial Investigation for Detecting Partially Spoofed Audio | All existing databases of spoofed speech contain attack data that is spoofed in its entirety. In practice, it is entirely plausible that successful attacks can be mounted with utterances that are only partially spoofed. By definition, partially-spoofed utterances contain a mix of both spoofed and bona fide segments, wh... | ['Nicholas Evans', 'Jose Patino', 'Junichi Yamagishi', 'Erica Cooper', 'Xin Wang', 'Lin Zhang'] | 2021-04-06 | null | null | null | null | ['voice-anti-spoofing'] | ['audio'] | [ 4.33703780e-01 1.99142039e-01 -1.56737670e-01 -1.75108224e-01
-7.73885190e-01 -7.91237533e-01 4.00234371e-01 1.43706858e-01
-1.94508180e-01 3.72326553e-01 3.23631555e-01 -6.98938549e-01
2.47061774e-01 -4.97539908e-01 -5.76511860e-01 -6.15667105e-01
-2.65855163e-01 2.78936416e-01 4.84516591e-01 -4.77241009... | [14.125818252563477, 5.888576030731201] |
5403c12f-4281-44de-ba0e-1c5968320e6f | single-image-layer-separation-using-relative | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Li_Single_Image_Layer_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Li_Single_Image_Layer_2014_CVPR_paper.pdf | Single Image Layer Separation using Relative Smoothness | This paper addresses extracting two layers from an image where one layer is smoother than the other. This problem arises most notably in intrinsic image decomposition and reflection interference removal. Layer decomposition from a single-image is inherently ill-posed and solutions require additional constraints to be... | ['Yu Li', 'Michael S. Brown'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['intrinsic-image-decomposition', 'reflection-removal'] | ['computer-vision', 'computer-vision'] | [ 9.08088863e-01 1.71125963e-01 2.70225763e-01 -1.16737135e-01
-6.89416170e-01 -3.38066459e-01 4.01409000e-01 -3.42367023e-01
-5.48636198e-01 6.72704279e-01 2.06347376e-01 -4.48906645e-02
-3.09268683e-01 -3.76856416e-01 -4.83925790e-01 -1.31446481e+00
1.62599072e-01 1.62677258e-01 3.64844441e-01 1.75684858... | [10.756985664367676, -2.748857259750366] |
28c32a96-1a0e-4ba4-a21f-cb30e351a906 | learning-system-parameters-from-turing | 2108.08542 | null | https://arxiv.org/abs/2108.08542v1 | https://arxiv.org/pdf/2108.08542v1.pdf | Learning System Parameters from Turing Patterns | The Turing mechanism describes the emergence of spatial patterns due to spontaneous symmetry breaking in reaction-diffusion processes and underlies many developmental processes. Identifying Turing mechanisms in biological systems defines a challenging problem. This paper introduces an approach to the prediction of Turi... | ['Christoph Schnörr', 'David Schnörr'] | 2021-08-19 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 1.60962135e-01 -1.57400936e-01 1.93851054e-01 7.19816014e-02
2.49288557e-03 -6.29567325e-01 1.08643270e+00 4.53318477e-01
-4.56686199e-01 6.29268467e-01 -2.36041158e-01 -1.44540340e-01
-4.75977719e-01 -6.18141711e-01 -4.76374924e-01 -1.31292462e+00
-4.34370309e-01 7.46904850e-01 5.28606176e-01 -2.16691121... | [6.070559501647949, 4.199647426605225] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.