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
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bcc8721a-748c-410f-a15c-a74551de0026 | sparse-label-smoothing-regularization-for | 1809.04976 | null | http://arxiv.org/abs/1809.04976v3 | http://arxiv.org/pdf/1809.04976v3.pdf | Sparse Label Smoothing Regularization for Person Re-Identification | Person re-identification (re-id) is a cross-camera retrieval task which
establishes a correspondence between images of a person from multiple cameras.
Deep Learning methods have been successfully applied to this problem and have
achieved impressive results. However, these methods require a large amount of
labeled train... | ['Guangchun Luo', 'Jean-Paul Ainam', 'Ke Qin', 'Guisong Liu'] | 2018-09-13 | null | null | null | null | ['semi-supervised-person-re-identification'] | ['computer-vision'] | [ 3.32756370e-01 -2.40288407e-01 8.04339871e-02 -4.56377774e-01
-8.38012516e-01 -4.44300354e-01 7.37796187e-01 -2.31606394e-01
-5.69484234e-01 7.14425921e-01 1.69665769e-01 3.73508751e-01
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3.18110615e-01 6.09488904e-01 -5.19506335e-02 1.43733740... | [14.665738105773926, 1.0250188112258911] |
0d046d99-feee-44f3-89ce-fff11f6f5b85 | iit-dhanbad-lt-edi-acl2022-hope-speech | null | null | https://aclanthology.org/2022.ltedi-1.32 | https://aclanthology.org/2022.ltedi-1.32.pdf | IIT Dhanbad @LT-EDI-ACL2022- Hope Speech Detection for Equality, Diversity, and Inclusion | Hope is considered significant for the wellbeing,recuperation and restoration of humanlife by health professionals. Hope speech reflectsthe belief that one can discover pathwaysto their desired objectives and become rousedto utilise those pathways. Hope speech offerssupport, reassurance, suggestions, inspirationand ins... | ['Rajendra Pamula', 'Ritesh Kumar', 'Vishesh Gupta'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-5.81516325e-01 8.03852677e-01 -1.00352085e+00 -3.75146382e-02
-7.18275130e-01 -2.35898346e-01 1.01122749e+00 7.08407640e-01
-4.24328744e-01 1.03277814e+00 1.52498519e+00 -2.26775020e-01
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3.25674415e-01 -2.67672509e-01 -3.75781476e-01 -6.03258073... | [8.897224426269531, 10.645051002502441] |
78e6ba64-330c-4670-a746-46d3052a0879 | cross-view-image-synthesis-using-geometry | 1808.05469 | null | https://arxiv.org/abs/1808.05469v2 | https://arxiv.org/pdf/1808.05469v2.pdf | Cross-view image synthesis using geometry-guided conditional GANs | We address the problem of generating images across two drastically different views, namely ground (street) and aerial (overhead) views. Image synthesis by itself is a very challenging computer vision task and is even more so when generation is conditioned on an image in another view. Due the difference in viewpoints, t... | ['Krishna Regmi', 'Ali Borji'] | 2018-08-14 | null | null | null | null | ['cross-view-image-to-image-translation'] | ['computer-vision'] | [ 6.39654219e-01 2.78991014e-01 3.38454545e-01 -6.98483512e-02
-3.64330262e-01 -8.70055795e-01 7.31170416e-01 -5.50467849e-01
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6.36022389e-01 1.37508675e-01 3.83834720e-01 -4.74175185... | [9.42789077758789, -2.726391077041626] |
186dc54c-e0d5-4182-a5ea-25a953d3d3dc | a-causal-inference-framework-for-leveraging | 2305.08969 | null | https://arxiv.org/abs/2305.08969v1 | https://arxiv.org/pdf/2305.08969v1.pdf | A Causal Inference Framework for Leveraging External Controls in Hybrid Trials | We consider the challenges associated with causal inference in settings where data from a randomized trial is augmented with control data from an external source to improve efficiency in estimating the average treatment effect (ATE). Through the development of a formal causal inference framework, we outline sufficient ... | ['Michael R Kosorok', 'Michele Jonsson Funk', 'Stephen R Cole', 'Jiawen Zhu', 'Herb Pang', 'Michael Valancius'] | 2023-05-15 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 6.81914091e-01 3.72081906e-01 -1.20614338e+00 -3.35847199e-01
-9.74570036e-01 -3.27590048e-01 4.66343254e-01 1.16894409e-01
-5.56215107e-01 1.11608946e+00 6.26506567e-01 -5.02850533e-01
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-5.00490665e-01 4.02264029e-01 -4.88745332e-01 5.12790143... | [7.979881286621094, 5.2996087074279785] |
41592609-da98-4690-accf-98aafebb4fbf | detecting-tiny-objects-in-aerial-images-a | 2206.13996 | null | https://arxiv.org/abs/2206.13996v1 | https://arxiv.org/pdf/2206.13996v1.pdf | Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark | Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains a few pixels. State-of-the-art object detectors do not provide satisfactory results on tiny objects due to the lack of supervision from discriminative features. Our key observation is that the Intersection over Union (IoU) met... | ['Gui-Song Xia', 'Lei Yu', 'Huai Yu', 'Wen Yang', 'Jinwang Wang', 'Chang Xu'] | 2022-06-28 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-5.54531477e-02 -1.86907172e-01 -1.04880638e-01 -2.36028105e-01
-8.72059286e-01 -6.92364156e-01 2.61604518e-01 1.43777266e-01
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-7.92813003e-02 1.78699717e-01 8.40437531e-01 -1.44114986... | [8.721695899963379, -0.49043598771095276] |
5c0d2f6e-a83b-4516-b0b2-98ce218d4428 | singsong-generating-musical-accompaniments | 2301.12662 | null | https://arxiv.org/abs/2301.12662v1 | https://arxiv.org/pdf/2301.12662v1.pdf | SingSong: Generating musical accompaniments from singing | We present SingSong, a system that generates instrumental music to accompany input vocals, potentially offering musicians and non-musicians alike an intuitive new way to create music featuring their own voice. To accomplish this, we build on recent developments in musical source separation and audio generation. Specifi... | ['Jesse Engel', 'Neil Zeghidour', 'Olivier Pietquin', 'Ian Simon', 'Mauro Verzetti', 'Andrea Agostinelli', 'Philippe Esling', 'Ethan Manilow', 'Adam Roberts', 'Antoine Caillon', 'Chris Donahue'] | 2023-01-30 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.26791680e-01 -8.01420659e-02 2.75021344e-01 -7.47855380e-02
-1.58559012e+00 -1.05162942e+00 4.12595153e-01 -1.40218988e-01
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-3.68659705e-01 -4.90376085e-01 -5.12218118e-01 -6.74185932e-01
-7.91498572e-02 4.85701621e-01 -1.52817249e-01 -5.73893368... | [15.724095344543457, 5.654480934143066] |
93140872-5be0-451d-b966-fe4b1883562e | mining-interest-trends-and-adaptively | 2306.11610 | null | https://arxiv.org/abs/2306.11610v1 | https://arxiv.org/pdf/2306.11610v1.pdf | Mining Interest Trends and Adaptively Assigning SampleWeight for Session-based Recommendation | Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR methods somewhat ignore the fact that user preference is not necessarily strongly related to the order of interactions. Moreover, they ignor... | ['Yu Zhao', 'Shuangyong Song', 'Hai-Tao Zheng', 'Zuotong Xie', 'Miaoxin Chen', 'Xianghong Xu', 'Kai Ouyang'] | 2023-06-20 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [ 3.34279845e-03 -5.62605619e-01 -7.30581164e-01 -6.98872149e-01
-1.94343820e-01 -3.60900223e-01 2.50734895e-01 -7.94659834e-03
-3.61331582e-01 5.06093264e-01 3.91575962e-01 -2.46900111e-01
-4.79207516e-01 -7.17637420e-01 -2.62015074e-01 -5.22822618e-01
-1.09101959e-01 -9.37607735e-02 4.96132195e-01 -1.18439928... | [10.1459379196167, 5.570515155792236] |
e846e7ad-46ad-43a3-a6b5-4ce8889bb876 | grammar-based-patches-generation-for | null | null | https://aclanthology.org/2021.findings-acl.111 | https://aclanthology.org/2021.findings-acl.111.pdf | Grammar-Based Patches Generation for Automated Program Repair | null | ['Muyun Yang', 'Ming Zhou', 'Furu Wei', 'Shujie Liu', 'Ambrosio Blanco', 'Long Zhou', 'Yu Tang'] | null | null | null | null | findings-acl-2021-8 | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-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
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.230523109436035, 3.6383047103881836] |
6984b088-a981-4aaf-b42e-3bad2ca0f66e | weakly-supervised-action-segmentation-using | 1904.03116 | null | https://arxiv.org/abs/1904.03116v4 | https://arxiv.org/pdf/1904.03116v4.pdf | Fast Weakly Supervised Action Segmentation Using Mutual Consistency | Action segmentation is the task of predicting the actions for each frame of a video. As obtaining the full annotation of videos for action segmentation is expensive, weakly supervised approaches that can learn only from transcripts are appealing. In this paper, we propose a novel end-to-end approach for weakly supervis... | ['Yaser Souri', 'Luca Minciullo', 'Mohsen Fayyaz', 'Juergen Gall', 'Gianpiero Francesca'] | 2019-04-05 | null | null | null | null | ['weakly-supervised-action-segmentation'] | ['computer-vision'] | [ 7.19283462e-01 5.40008962e-01 -5.83945632e-01 -5.90833366e-01
-1.15563858e+00 -4.28073347e-01 2.82036453e-01 -2.20678654e-02
-4.49128002e-01 6.26909554e-01 2.43818596e-01 -1.46802887e-01
1.96812078e-01 -2.22784698e-01 -1.02499044e+00 -5.46503425e-01
-7.37374201e-02 3.26133579e-01 3.28921646e-01 3.49619538... | [8.480198860168457, 0.5983633995056152] |
46c086a3-6f99-4286-90f0-7fe65034f193 | selective-transfer-with-reinforced-transfer | 1905.10756 | null | https://arxiv.org/abs/1905.10756v4 | https://arxiv.org/pdf/1905.10756v4.pdf | Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation | One crucial aspect of partial domain adaptation (PDA) is how to select the relevant source samples in the shared classes for knowledge transfer. Previous PDA methods tackle this problem by re-weighting the source samples based on their high-level information (deep features). However, since the domain shift between sour... | ['Ke Fang', 'Chao Chen', 'Zhihong Chen', 'Zhaowei Cheng', 'Xinyu Jin', 'Boyuan Jiang'] | 2019-05-26 | selective-transfer-with-reinforced-transfer-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Selective_Transfer_With_Reinforced_Transfer_Network_for_Partial_Domain_Adaptation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Selective_Transfer_With_Reinforced_Transfer_Network_for_Partial_Domain_Adaptation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['partial-domain-adaptation'] | ['methodology'] | [ 2.07611397e-01 -8.69898275e-02 -3.60238135e-01 -3.95236731e-01
-7.21818209e-01 -2.48265162e-01 2.62279928e-01 4.74900790e-02
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-9.38955322e-02 -1.10324669e+00 -7.98831046e-01 -9.84710872e-01
1.84809923e-01 3.84925187e-01 7.19085872e-01 -1.18302166... | [10.286778450012207, 3.0299108028411865] |
13d5e609-f7f8-4fec-bf1e-db359c868228 | the-cocktail-fork-problem-three-stem-audio | 2110.09958 | null | https://arxiv.org/abs/2110.09958v2 | https://arxiv.org/pdf/2110.09958v2.pdf | The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks | The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Recent efforts have mainly focused on separating speech from noise, speech from speech, musical instruments from each other, or sound events from each other. Howev... | ['Jonathan Le Roux', 'Zhong-Qiu Wang', 'Gordon Wichern', 'Darius Petermann'] | 2021-10-19 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 1.92281902e-01 -7.17459202e-01 1.80890530e-01 1.06262468e-01
-1.55793035e+00 -9.63144422e-01 3.79796654e-01 -3.80401425e-02
-1.27700344e-02 3.28321189e-01 6.72473907e-01 8.25295523e-02
-3.30133289e-01 -1.76227048e-01 -3.70452404e-01 -8.75165343e-01
-2.15475127e-01 -2.66607180e-02 2.23566175e-01 -2.26514369... | [15.331538200378418, 5.532061576843262] |
3a63cf36-7553-4b10-b776-b5ceb08622dc | multi-modal-extreme-classification | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Mittal_Multi-Modal_Extreme_Classification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mittal_Multi-Modal_Extreme_Classification_CVPR_2022_paper.pdf | Multi-Modal Extreme Classification | This paper develops the MUFIN technique for extreme classification (XC) tasks with millions of labels where datapoints and labels are endowed with visual and textual descriptors. Applications of MUFIN to product-to-product recommendation and bid query prediction over several millions of products are presented. Cont... | ['Manik Varma', 'Purushottam Kar', 'Sumeet Agarwal', 'Keng-hao Chang', 'Jitendra Ajmera', 'Seba Kuruvilla', 'Janani Ramaswamy', 'Shreya Malani', 'Kunal Dahiya', 'Anshul Mittal'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['product-recommendation'] | ['miscellaneous'] | [-2.87495971e-01 -3.86017114e-01 -6.47531748e-01 -6.05169177e-01
-1.01994443e+00 -1.18806148e+00 8.41572225e-01 4.14476126e-01
-3.34660172e-01 -9.96359736e-02 -3.71039435e-02 -3.61282855e-01
-3.28830332e-01 -8.37082207e-01 -6.15948737e-01 -2.88695514e-01
-2.47140273e-01 8.47729921e-01 -3.84069026e-01 -1.88803166... | [9.603663444519043, 4.404982089996338] |
f4e06115-d2f0-4346-8fb7-6c99c238c7d2 | learning-backward-compatible-embeddings | 2206.03040 | null | https://arxiv.org/abs/2206.03040v1 | https://arxiv.org/pdf/2206.03040v1.pdf | Learning Backward Compatible Embeddings | Embeddings, low-dimensional vector representation of objects, are fundamental in building modern machine learning systems. In industrial settings, there is usually an embedding team that trains an embedding model to solve intended tasks (e.g., product recommendation). The produced embeddings are then widely consumed by... | ['Jure Leskovec', 'Karthik Subbian', 'Nikhil Rao', 'Kaidi Cao', 'Rajas Bansal', 'Weihua Hu'] | 2022-06-07 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-1.14844143e-01 -8.48359987e-03 -1.07253663e-01 -1.20368846e-01
-1.11703783e-01 -7.03278899e-01 2.45003492e-01 2.24136710e-01
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5.42624667e-02 6.15460396e-01 6.52045384e-02 -4.24100369... | [10.123334884643555, 5.5449371337890625] |
836c61e7-4224-48d6-af46-1aa18556938e | conditional-score-guidance-for-text-driven | 2305.18007 | null | https://arxiv.org/abs/2305.18007v1 | https://arxiv.org/pdf/2305.18007v1.pdf | Conditional Score Guidance for Text-Driven Image-to-Image Translation | We present a novel algorithm for text-driven image-to-image translation based on a pretrained text-to-image diffusion model. Our method aims to generate a target image by selectively editing the regions of interest in a source image, defined by a modifying text, while preserving the remaining parts. In contrast to exis... | ['Bohyung Han', 'Minsoo Kang', 'Hyunsoo Lee'] | 2023-05-29 | null | null | null | null | ['image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'miscellaneous'] | [ 8.84832561e-01 9.68865082e-02 -1.95895080e-02 -4.97089982e-01
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6.90646708e-01 2.87566692e-01 2.90060729e-01 -1.63744718... | [11.479564666748047, -0.25330403447151184] |
c8da0553-fb4d-4f08-b9df-89d69789fd4c | dirty-pixels-optimizing-image-classification | 1701.06487 | null | https://arxiv.org/abs/1701.06487v2 | https://arxiv.org/pdf/1701.06487v2.pdf | Dirty Pixels: Towards End-to-End Image Processing and Perception | Real-world imaging systems acquire measurements that are degraded by noise, optical aberrations, and other imperfections that make image processing for human viewing and higher-level perception tasks challenging. Conventional cameras address this problem by compartmentalizing imaging from high-level task processing. As... | ['Felix Heide', 'Gordon Wetzstein', 'Stephen Boyd', 'Frank Julca-Aguilar', 'Steven Diamond', 'Vincent Sitzmann'] | 2017-01-23 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 6.63439512e-01 -3.12269837e-01 4.95324910e-01 -3.92710716e-01
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-2.45780312e-02 -1.67357065e-02 3.47715020e-01 4.59183939... | [10.758069038391113, -2.4210689067840576] |
a47d6d70-1eb7-4583-88b8-ea4467680b3b | fairness-aware-differentially-private | 2303.09527 | null | https://arxiv.org/abs/2303.09527v1 | https://arxiv.org/pdf/2303.09527v1.pdf | Fairness-aware Differentially Private Collaborative Filtering | Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes with trade-offs in terms of algorithmic fairness, which has been widely acknowledged. Specifically, we have empirically observed that the cla... | ['Yiming Ying', 'Xiaoting Zhao', 'Dingxian Wang', 'Congzhe Su', 'Yingqiang Ge', 'Zhenhuan Yang'] | 2023-03-16 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.14871167e-01 -2.84810573e-01 -6.80385977e-02 -9.32798147e-01
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-8.45443923e-03 -1.42206118e-01 -4.47161466e-01 -7.12518161... | [5.973433017730713, 6.686766147613525] |
1675dbf3-4330-4db1-a980-ca20ae218f4b | post-train-adaptive-mobilenet-for-fast-anti | 2207.13410 | null | https://arxiv.org/abs/2207.13410v2 | https://arxiv.org/pdf/2207.13410v2.pdf | Post-Train Adaptive MobileNet for Fast Anti-Spoofing | Many applications require high accuracy of neural networks as well as low latency and user data privacy guaranty. Face anti-spoofing is one of such tasks. However, a single model might not give the best results for different device performance categories, while training multiple models is time consuming. In this work w... | ['Kostiantyn Khabarlak'] | 2022-07-27 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 3.51511061e-01 -4.82801674e-03 -3.71888697e-01 -3.53902489e-01
3.43107060e-02 -5.73886693e-01 3.42463791e-01 -1.34864738e-02
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-1.89315245e-01 5.37834585e-01 4.40727830e-01 -4.79868464... | [13.107894897460938, 1.163994312286377] |
1e6606ca-9277-40e2-8bb0-fe6a88f6d5b1 | global-road-damage-detection-state-of-the-art | 2011.08740 | null | https://arxiv.org/abs/2011.08740v1 | https://arxiv.org/pdf/2011.08740v1.pdf | Global Road Damage Detection: State-of-the-art Solutions | This paper summarizes the Global Road Damage Detection Challenge (GRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2020. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition pl... | ['Yoshihide Sekimoto', 'Takehiro Kashiyama', 'Hiroshi Omata', 'Durga Toshniwal', 'Sanjay Kumar Ghosh', 'Hiroya Maeda', 'Deeksha Arya'] | 2020-11-17 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-3.96791995e-01 8.39414150e-02 7.89939463e-02 -5.37103079e-02
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-2.18944669e-01 3.31854373e-01 2.69406170e-01 -3.62308830... | [7.428684234619141, 1.104286789894104] |
23ed4570-f516-4950-ae65-8993f7e17acb | opera-operation-pivoted-discrete-reasoning-1 | null | null | https://aclanthology.org/2022.naacl-main.119 | https://aclanthology.org/2022.naacl-main.119.pdf | OPERA: Operation-Pivoted Discrete Reasoning over Text | Machine reading comprehension (MRC) that requires discrete reasoning involving symbolic operations, e.g., addition, sorting, and counting, is a challenging task. According to this nature, semantic parsing-based methods predict interpretable but complex logical forms. However, logical form generation is nontrivial and e... | ['Tiejun Zhao', 'Xiaodong He', 'Youzheng Wu', 'Jing Zhao', 'Yifan Wang', 'Jiahui Liang', 'Haipeng Sun', 'Chaoqun Duan', 'Junwei Bao', 'Yongwei Zhou'] | null | null | null | null | naacl-2022-7 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 4.22733188e-01 2.45500535e-01 1.05150767e-01 -7.22321570e-01
-2.19022706e-01 -5.24822891e-01 2.82469898e-01 2.77761668e-01
4.30382378e-02 5.28200150e-01 8.00765306e-02 -8.29112828e-01
-1.62218645e-01 -1.29572463e+00 -8.23008835e-01 -1.03677124e-01
4.16402251e-01 2.53779739e-01 3.05605769e-01 -4.13773537... | [9.593992233276367, 7.451225280761719] |
b07268cc-2c6c-40fd-8ef7-ddb2bbef82de | source-aware-spatial-spectral-integrated | 2212.06466 | null | https://arxiv.org/abs/2212.06466v1 | https://arxiv.org/pdf/2212.06466v1.pdf | Source-Aware Spatial-Spectral-Integrated Double U-Net for Image Fusion | In image fusion tasks, pictures from different sources possess distinctive properties, therefore treating them equally will lead to inadequate feature extracting. Besides, multi-scaled networks capture information more sufficiently than single-scaled models in pixel-wised problems. In light of these factors, we propose... | ['Xiao Wu', 'Chenhao Guo', 'Siran Peng'] | 2022-12-13 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 8.54161739e-01 -4.22804892e-01 6.98895529e-02 -4.31728303e-01
-8.26198936e-01 -2.79336214e-01 4.10655707e-01 -1.84299022e-01
-1.46010503e-01 8.98792446e-01 1.34551480e-01 -4.19731997e-02
-6.48112416e-01 -1.06592810e+00 -5.05772054e-01 -1.12597847e+00
7.29490444e-02 -4.96435761e-01 2.17648610e-01 -6.85081482... | [10.25715160369873, -1.8894933462142944] |
4eb43762-447d-47a7-b75e-8e5b300298e0 | a-variational-observation-model-of-3d-object | 1809.05225 | null | http://arxiv.org/abs/1809.05225v1 | http://arxiv.org/pdf/1809.05225v1.pdf | A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM | We present a Bayesian object observation model for complete probabilistic
semantic SLAM. Recent studies on object detection and feature extraction have
become important for scene understanding and 3D mapping. However, 3D shape of
the object is too complex to formulate the probabilistic observation model;
therefore, per... | ['H. W. Yu', 'B. H. Le'] | 2018-09-14 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [ 1.31286159e-01 1.03255883e-01 -1.24461606e-01 -3.79591018e-01
-4.75065082e-01 -5.66472113e-01 5.32394469e-01 1.02416202e-01
-4.24498081e-01 4.38831955e-01 -3.24432850e-01 -1.95543110e-01
-1.74939707e-01 -5.32221079e-01 -8.52184534e-01 -6.77425265e-01
3.25854391e-01 9.48849678e-01 3.88204247e-01 3.94814104... | [7.352316379547119, -2.470386505126953] |
09a52070-1e33-45f6-82e1-7d7f8d0f6f3e | multi-hierarchical-convolutional-network-for | 2104.02260 | null | https://arxiv.org/abs/2104.02260v2 | https://arxiv.org/pdf/2104.02260v2.pdf | Non-contact PPG Signal and Heart Rate Estimation with Multi-hierarchical Convolutional Network | Heartbeat rhythm and heart rate (HR) are important physiological parameters of the human body. This study presents an efficient multi-hierarchical spatio-temporal convolutional network that can quickly estimate remote physiological (rPPG) signal and HR from face video clips. First, the facial color distribution charact... | ['Hong Fu', 'Panpan Zhang', 'Jinye Peng', 'Bin Li'] | 2021-04-06 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [-1.36901423e-01 -4.05670255e-01 2.54451632e-01 -3.25199157e-01
-1.90367699e-01 4.40664627e-02 7.88183808e-02 -6.20316505e-01
-2.91746020e-01 7.87306786e-01 1.81477293e-01 4.25932437e-01
1.97122514e-01 -4.46621597e-01 -2.46762082e-01 -1.02030301e+00
-4.96464878e-01 -7.50115752e-01 -2.96560172e-02 1.58005685... | [13.901602745056152, 2.7370409965515137] |
e7e0bcd2-0747-44fa-9a0b-cfe28080c4bb | sphere2vec-a-general-purpose-location | 2306.17624 | null | https://arxiv.org/abs/2306.17624v2 | https://arxiv.org/pdf/2306.17624v2.pdf | Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions | Generating learning-friendly representations for points in space is a fundamental and long-standing problem in ML. Recently, multi-scale encoding schemes (such as Space2Vec and NeRF) were proposed to directly encode any point in 2D/3D Euclidean space as a high-dimensional vector, and has been successfully applied to va... | ['Ni Lao', 'Krzysztof Janowicz', 'Stefano Ermon', 'Jiaming Song', 'Yutong He', 'Wenyun Zuo', 'Yao Xuan', 'Gengchen Mai'] | 2023-06-30 | null | null | null | null | ['metric-learning', 'metric-learning', 'remote-sensing-image-classification'] | ['computer-vision', 'methodology', 'miscellaneous'] | [-1.20628789e-01 -1.49103999e-01 -6.14322461e-02 -3.39566797e-01
-7.97864914e-01 -7.39899337e-01 6.85642838e-01 1.28559698e-03
-2.80175596e-01 6.03338003e-01 3.91020119e-01 -3.76843691e-01
4.11352050e-03 -1.27524340e+00 -1.16797328e+00 -7.63527930e-01
-4.26788568e-01 2.32063249e-01 -8.32323283e-02 -3.99777025... | [7.73219633102417, -2.040626049041748] |
60350f11-1044-4181-abdb-4c8344aae0ce | learning-to-embed-adopting-transformer-based | 2212.03725 | null | https://arxiv.org/abs/2212.03725v1 | https://arxiv.org/pdf/2212.03725v1.pdf | Learning-To-Embed: Adopting Transformer based models for E-commerce Products Representation Learning | Learning low-dimensional representation for large number of products present in an e-commerce catalogue plays a vital role as they are helpful in tasks like product ranking, product recommendation, finding similar products, modelling user-behaviour etc. Recently, a lot of tasks in the NLP field are getting tackled usin... | ['Sreekanth Vempati', 'Lakshya Kumar'] | 2022-12-07 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 1.18173212e-01 -1.73381940e-01 -2.35999048e-01 -7.44248331e-01
-7.00695336e-01 -8.63963902e-01 6.33713245e-01 3.10232043e-01
-5.68858683e-02 2.36969844e-01 5.36960065e-01 -3.38976353e-01
-5.80715299e-01 -1.05893767e+00 -7.69554913e-01 -5.39156377e-01
-8.97107869e-02 8.47968161e-01 -1.77110955e-01 -5.31769216... | [10.102323532104492, 5.8666253089904785] |
e9539760-fc0c-41b6-ba97-5fd8c762b292 | deep-learning-based-dominant-index-lesion | 2303.03494 | null | https://arxiv.org/abs/2303.03494v1 | https://arxiv.org/pdf/2303.03494v1.pdf | Deep Learning Based Dominant Index Lesion Segmentation for MR-guided Radiation Therapy of Prostate Cancer | Dose escalation radiotherapy allows increased control of prostate cancer (PCa) but requires segmentation of dominant index lesions (DIL), motivating the development of automated methods for fast, accurate, and consistent segmentation of PCa DIL. We evaluated five deep-learning networks on apparent diffusion coefficient... | ['Harini Veeraraghavan', 'Neelam Tyagi', 'Michael Zelefsky', 'Andreas Wibmer', 'Anton Nosov', 'Jue Jiang', 'Josiah Simeth'] | 2023-03-06 | null | null | null | null | ['panoptic-segmentation', 'lesion-segmentation'] | ['computer-vision', 'medical'] | [ 1.34007469e-01 3.09057981e-01 -4.91503745e-01 -3.11787367e-01
-1.06044590e+00 -1.01305604e+00 6.14705980e-01 3.52906972e-01
-7.68850029e-01 8.02006662e-01 3.37122291e-01 -6.08494699e-01
-5.46239972e-01 -6.91576242e-01 -3.22472900e-01 -8.09539855e-01
-5.82808316e-01 7.49242008e-01 3.98573250e-01 2.35965148... | [14.708224296569824, -2.5141289234161377] |
6f5de193-6ff7-47f9-b11b-d6e97166c403 | visual-social-relationship-recognition | 1812.05917 | null | http://arxiv.org/abs/1812.05917v1 | http://arxiv.org/pdf/1812.05917v1.pdf | Visual Social Relationship Recognition | Social relationships form the basis of social structure of humans. Developing
computational models to understand social relationships from visual data is
essential for building intelligent machines that can better interact with
humans in a social environment. In this work, we study the problem of visual
social relation... | ['Yongkang Wong', 'Mohan S. Kankanhalli', 'Junnan Li', 'Qi Zhao'] | 2018-12-13 | null | null | null | null | ['visual-social-relationship-recognition'] | ['computer-vision'] | [ 1.92127451e-01 1.43836319e-01 9.07269865e-03 -7.12611914e-01
1.63914993e-01 -2.48597324e-01 8.00392926e-01 3.73999715e-01
-4.41559941e-01 4.91427630e-01 5.20155907e-01 -5.47244325e-02
-1.95866346e-01 -3.72768551e-01 -6.44604087e-01 -3.25169027e-01
-4.63980883e-01 2.51172066e-01 9.76798218e-03 -7.08436891... | [10.191871643066406, 1.6297980546951294] |
4b00a4a5-430b-4bd0-bdf4-9bcec44e99f0 | process-parameter-selection-for-production-of | null | null | https://scholar.google.com/citations?view_op=view_citation&hl=en&user=XDqUWuIAAAAJ&citation_for_view=XDqUWuIAAAAJ:9ZlFYXVOiuMC | https://www.mdpi.com/1996-1944/16/3/1050/pdf?version=1675738719 | Process Parameter Selection for Production of Stainless Steel 316L Using Efficient Multi-Objective Bayesian Optimization Algorithm | Additive manufacturing is a modern technique to produce parts with a complex geometry. However, the choice of the printing parameters is a time-consuming and costly process. In this study, the parameter optimization for the laser powder bed fusion process was investigated. Using state-of-the art multi-objective Bayesia... | ['Evlashin Stanislav A', 'Kuzminova Yulia O', 'Firsov Denis G', 'Dubinin Oleg N', 'Simonov Alexey P', 'Ryabov Alexander', 'Zhilyaev Petr', 'Chepiga Timur'] | 2023-01-25 | null | null | null | materials-2023-1 | ['bayesian-optimization'] | ['methodology'] | [-6.04237355e-02 -2.24302813e-01 1.48470119e-01 -1.53064921e-01
-5.44348359e-01 -1.00578405e-01 1.87877074e-01 2.24278748e-01
-2.22579196e-01 8.98368418e-01 -2.07394004e-01 1.64250106e-01
-1.07904232e+00 -8.20042074e-01 -4.75772679e-01 -1.16040134e+00
2.11351544e-01 9.60774779e-01 -3.54360975e-02 1.73323467... | [6.154257297515869, 3.3049066066741943] |
31fff4dd-ed90-4ab4-8b5d-4b8b716c2d2b | multi-resolution-fully-convolutional-neural | 1710.11473 | null | http://arxiv.org/abs/1710.11473v1 | http://arxiv.org/pdf/1710.11473v1.pdf | Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation | In deep neural networks with convolutional layers, each layer typically has
fixed-size/single-resolution receptive field (RF). Convolutional layers with a
large RF capture global information from the input features, while layers with
small RF size capture local details with high resolution from the input
features. In t... | ['Emad M. Grais', 'Mark D. Plumbley', 'Hagen Wierstorf', 'Dominic Ward'] | 2017-10-28 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 1.04313724e-01 -3.87046129e-01 2.10947514e-01 -2.70404220e-01
-8.53038371e-01 -3.37831259e-01 1.97426111e-01 -4.37567949e-01
-1.65912822e-01 4.52599376e-01 5.65748870e-01 3.40995014e-01
-3.27817053e-01 -8.25516641e-01 -5.83340764e-01 -6.25403821e-01
-3.70447606e-01 -2.58738577e-01 2.96450734e-01 -2.73893643... | [15.45818042755127, 5.491130828857422] |
509c2e2b-1a7b-430d-92d9-9385146f1392 | improved-marginal-unbiased-score-expansion | 2209.10512 | null | https://arxiv.org/abs/2209.10512v1 | https://arxiv.org/pdf/2209.10512v1.pdf | Improved Marginal Unbiased Score Expansion (MUSE) via Implicit Differentiation | We apply the technique of implicit differentiation to boost performance, reduce numerical error, and remove required user-tuning in the Marginal Unbiased Score Expansion (MUSE) algorithm for hierarchical Bayesian inference. We demonstrate these improvements on three representative inference problems: 1) an extended Nea... | ['Marius Millea'] | 2022-09-21 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 1.60637256e-02 2.79862928e-04 -1.16135038e-01 -3.65333468e-01
-1.25578046e+00 -6.76761985e-01 6.95569515e-01 4.48613875e-02
-7.31535435e-01 1.27012658e+00 1.34466877e-02 -5.23260832e-01
-3.34361762e-01 -6.62533879e-01 -4.68362182e-01 -9.68122542e-01
-2.03638598e-01 1.06288576e+00 4.30953830e-01 5.38576961... | [6.9558563232421875, 4.001534461975098] |
ebc3283f-0bbf-4a1c-a8a4-13f83fa84f97 | moving-object-detection-under-discontinuous | 1904.03175 | null | http://arxiv.org/abs/1904.03175v2 | http://arxiv.org/pdf/1904.03175v2.pdf | Moving Object Detection under Discontinuous Change in Illumination Using Tensor Low-Rank and Invariant Sparse Decomposition | Although low-rank and sparse decomposition based methods have been
successfully applied to the problem of moving object detection using structured
sparsity-inducing norms, they are still vulnerable to significant illumination
changes that arise in certain applications. We are interested in moving object
detection in ap... | ['Moein Shakeri', 'Hong Zhang'] | 2019-04-05 | moving-object-detection-under-discontinuous-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Shakeri_Moving_Object_Detection_Under_Discontinuous_Change_in_Illumination_Using_Tensor_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Shakeri_Moving_Object_Detection_Under_Discontinuous_Change_in_Illumination_Using_Tensor_CVPR_2019_paper.pdf | cvpr-2019-6 | ['moving-object-detection'] | ['computer-vision'] | [ 6.25295579e-01 -7.56019592e-01 8.74873996e-02 -1.25506580e-01
-6.74753666e-01 -5.59512913e-01 3.84851098e-01 -6.56383097e-01
-1.21449614e-02 5.25664747e-01 9.63865817e-02 1.11950547e-01
-1.73067212e-01 -1.79152906e-01 -6.36999607e-01 -1.02712274e+00
-2.59734750e-01 -6.36178851e-02 4.75289047e-01 8.20676833... | [9.011443138122559, -0.8171136975288391] |
c6d9a883-5784-46f1-93df-a559f6cb7446 | controllable-video-generation-by-learning-the | 2303.05323 | null | https://arxiv.org/abs/2303.05323v2 | https://arxiv.org/pdf/2303.05323v2.pdf | Controllable Video Generation by Learning the Underlying Dynamical System with Neural ODE | Videos depict the change of complex dynamical systems over time in the form of discrete image sequences. Generating controllable videos by learning the dynamical system is an important yet underexplored topic in the computer vision community. This paper presents a novel framework, TiV-ODE, to generate highly controllab... | ['Li Nanbo', 'Zhibin Li', 'Mohammadreze Kasaei', 'Hamidreza Kasaei', 'Zonghai Yao', 'Zijian Guo', 'Arushi Goel', 'Yucheng Xu'] | 2023-03-09 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 2.23578900e-01 1.51387632e-01 -1.36163644e-02 9.04251114e-02
-1.22635454e-01 -9.19735432e-01 9.27547157e-01 -7.29502261e-01
3.58402371e-01 7.88284838e-01 4.13208663e-01 -1.47114679e-01
1.29000649e-01 -4.85399485e-01 -1.01154792e+00 -7.29836345e-01
-7.93354139e-02 -3.34110595e-02 -1.56344905e-01 -2.12558821... | [10.840027809143066, -0.6560595035552979] |
4ce5db9e-def4-436b-83fc-66df1265109d | revisiting-spatio-temporal-layouts-for | 2111.01936 | null | https://arxiv.org/abs/2111.01936v1 | https://arxiv.org/pdf/2111.01936v1.pdf | Revisiting spatio-temporal layouts for compositional action recognition | Recognizing human actions is fundamentally a spatio-temporal reasoning problem, and should be, at least to some extent, invariant to the appearance of the human and the objects involved. Motivated by this hypothesis, in this work, we take an object-centric approach to action recognition. Multiple works have studied thi... | ['Tinne Tuytelaars', 'Marie-Francine Moens', 'Gorjan Radevski'] | 2021-11-02 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 5.18254220e-01 -2.28159666e-01 1.33102983e-01 -7.63863549e-02
-2.25795522e-01 -5.34614682e-01 8.09906662e-01 -1.10816076e-01
-3.86876345e-01 3.23965698e-01 3.58365208e-01 -3.40331942e-01
-3.51232290e-01 -5.24083734e-01 -8.33560467e-01 -7.85682261e-01
1.77149568e-03 4.54734921e-01 8.14776838e-01 -2.88418770... | [8.263900756835938, 0.36622872948646545] |
5c7479a2-ac2f-4ce2-9a61-aff0272e03f5 | learning-to-grasp-on-the-moon-from-3d-octree | 2208.00818 | null | https://arxiv.org/abs/2208.00818v1 | https://arxiv.org/pdf/2208.00818v1.pdf | Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning | Extraterrestrial rovers with a general-purpose robotic arm have many potential applications in lunar and planetary exploration. Introducing autonomy into such systems is desirable for increasing the time that rovers can spend gathering scientific data and collecting samples. This work investigates the applicability of ... | ['Carol Martinez', 'Miguel Olivares-Mendez', 'Simon Bøgh', 'Andrej Orsula'] | 2022-08-01 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-8.29278454e-02 2.16786444e-01 -1.37581248e-02 -3.66505414e-01
-2.67921388e-01 -5.79139829e-01 8.07141900e-01 -4.09884870e-01
-7.62786090e-01 1.07937849e+00 -2.86258668e-01 -2.68611252e-01
-2.83692449e-01 -6.27308846e-01 -9.86878753e-01 -7.54731238e-01
-6.47533596e-01 1.11301565e+00 7.88950101e-02 -6.05128467... | [4.551801681518555, 0.9600427746772766] |
cfe2c0e5-feb0-47ae-a2c1-2519c6b75a84 | temporal-complementarity-guided-reinforcement | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wu_Temporal_Complementarity-Guided_Reinforcement_Learning_for_Image-to-Video_Person_Re-Identification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wu_Temporal_Complementarity-Guided_Reinforcement_Learning_for_Image-to-Video_Person_Re-Identification_CVPR_2022_paper.pdf | Temporal Complementarity-Guided Reinforcement Learning for Image-to-Video Person Re-Identification | Image-to-video person re-identification aims to retrieve the same pedestrian as the image-based query from a video-based gallery set. Existing methods treat it as a cross-modality retrieval task and learn the common latent embeddings from image and video modalities, which are both less effective and efficient due t... | ['Zheng-Jun Zha', 'Qibin Sun', 'Kecheng Zheng', 'Jiawei Liu', 'Wei Wu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['image-to-video-person-re-identification', 'set-matching'] | ['computer-vision', 'computer-vision'] | [ 2.25989625e-01 -5.39482415e-01 -3.72271121e-01 -2.07358301e-01
-1.02901924e+00 -4.90172803e-01 5.15751302e-01 -6.19835295e-02
-7.22936332e-01 4.08734769e-01 2.99470603e-01 3.24061900e-01
-1.47243276e-01 -5.74078560e-01 -5.06952405e-01 -7.98945010e-01
2.17862710e-01 1.57535359e-01 4.22389686e-01 -3.83897126... | [14.664393424987793, 0.9572824239730835] |
95adcf3b-2d13-4a40-b141-ebea34ae939b | pooling-of-causal-models-under-counterfactual | 1805.09866 | null | http://arxiv.org/abs/1805.09866v2 | http://arxiv.org/pdf/1805.09866v2.pdf | Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation | In this paper we consider the problem of combining multiple probabilistic
causal models, provided by different experts, under the requirement that the
aggregated model satisfy the criterion of counterfactual fairness. We build
upon the work on causal models and fairness in machine learning, and we express
the problem o... | ['Magdalena Ivanovska', 'Fabio Massimo Zennaro'] | 2018-05-24 | null | null | null | null | ['causal-judgment'] | ['reasoning'] | [ 2.26242125e-01 6.74466491e-01 -4.09679919e-01 -7.65986145e-01
-7.49950588e-01 -3.83095980e-01 7.98433363e-01 3.16934854e-01
-6.06516838e-01 1.29939163e+00 7.21118748e-01 -4.30797249e-01
-5.57286859e-01 -8.63844395e-01 -5.04227936e-01 -4.14686322e-01
-2.06450019e-02 4.29289907e-01 -2.50453860e-01 9.90761146... | [8.631162643432617, 5.472385406494141] |
a926888f-f4e5-4efe-ad30-33c8ef1adbef | so-3-pose-so-3-equivariance-learning-for-6d | 2208.08338 | null | https://arxiv.org/abs/2208.08338v1 | https://arxiv.org/pdf/2208.08338v1.pdf | SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation | 6D pose estimation of rigid objects from RGB-D images is crucial for object grasping and manipulation in robotics. Although RGB channels and the depth (D) channel are often complementary, providing respectively the appearance and geometry information, it is still non-trivial how to fully benefit from the two cross-moda... | ['Mingqiang Wei', 'Xuefeng Yan', 'Weiming Wang', 'Xuequan Lu', 'Yuanpeng Liu', 'Jun Zhou', 'Haoran Pan'] | 2022-08-17 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.61645888e-02 7.64556974e-02 -1.01510361e-01 -4.36440736e-01
-5.48450947e-01 -6.50783658e-01 4.40856427e-01 -1.87905177e-01
-1.12545229e-01 -1.25272706e-01 -1.03361487e-01 5.19477017e-03
-1.99778557e-01 -7.40880549e-01 -9.70530987e-01 -9.52455819e-01
-7.82155544e-02 6.22716367e-01 3.09295356e-01 -3.45479935... | [7.463006019592285, -2.6768176555633545] |
53581ecd-b551-4d9d-85e9-04414ca0792f | qualitative-and-quantitative-analysis-of | 2109.05250 | null | https://arxiv.org/abs/2109.05250v2 | https://arxiv.org/pdf/2109.05250v2.pdf | Towards Evaluation of Cross-document Coreference Resolution Models Using Datasets with Diverse Annotation Schemes | Established cross-document coreference resolution (CDCR) datasets contain event-centric coreference chains of events and entities with identity relations. These datasets establish strict definitions of the coreference relations across related tests but typically ignore anaphora with more vague context-dependent loose c... | ['Bela Gipp', 'Felix Hamborg', 'Anastasia Zhukova'] | 2021-09-11 | towards-evaluation-of-cross-document | https://aclanthology.org/2022.lrec-1.522 | https://aclanthology.org/2022.lrec-1.522.pdf | lrec-2022-6 | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [-2.99410880e-01 3.35751265e-01 -4.99611646e-01 -3.15139860e-01
-8.20574164e-01 -1.12543702e+00 7.09204793e-01 3.45820546e-01
-5.41339457e-01 9.80346084e-01 8.97697151e-01 -1.82783410e-01
-9.38733518e-01 -5.41581750e-01 -1.53616562e-01 -2.64214128e-01
5.89026362e-02 1.04589045e+00 5.24571776e-01 -6.02612317... | [9.285439491271973, 9.563652038574219] |
9bea17fa-865f-4b09-8fd8-a9e2c70af64b | efficient-contextformer-spatio-channel-window | 2306.14287 | null | https://arxiv.org/abs/2306.14287v1 | https://arxiv.org/pdf/2306.14287v1.pdf | Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression | In this work, we introduce Efficient Contextformer (eContextformer) for context modeling in lossy learned image compression, which is built upon our previous work, Contextformer. The eContextformer combines the recent advancements in efficient transformers and fast context models with the spatio-channel attention mecha... | ['Eckehard Steinbach', 'Elena Alshina', 'Atanas Boev', 'Panqi Jia', 'A. Burakhan Koyuncu'] | 2023-06-25 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 3.79994214e-01 -3.62761766e-01 -1.79429665e-01 -1.53495535e-01
-7.14393020e-01 1.08842507e-01 4.63339388e-01 1.38318419e-01
-5.74717104e-01 8.82348657e-01 6.84101880e-01 -4.51844066e-01
-2.34678835e-01 -2.77757853e-01 -6.05154335e-01 -6.33656740e-01
-4.28911448e-01 -3.60521019e-01 -8.31929147e-02 1.39227480... | [11.406956672668457, -1.585604190826416] |
011b818f-7edd-42f8-9a3a-8514b9c68768 | neural-scene-flow-prior | 2111.01253 | null | https://arxiv.org/abs/2111.01253v1 | https://arxiv.org/pdf/2111.01253v1.pdf | Neural Scene Flow Prior | Before the deep learning revolution, many perception algorithms were based on runtime optimization in conjunction with a strong prior/regularization penalty. A prime example of this in computer vision is optical and scene flow. Supervised learning has largely displaced the need for explicit regularization. Instead, the... | ['Simon Lucey', 'Jhony Kaesemodel Pontes', 'Xueqian Li'] | 2021-11-01 | null | http://proceedings.neurips.cc/paper/2021/hash/41263b9a46f6f8f22668476661614478-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/41263b9a46f6f8f22668476661614478-Paper.pdf | neurips-2021-12 | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.54725850e-01 -1.92808792e-01 -2.86717564e-01 -5.57756364e-01
-1.50242910e-01 -4.73728240e-01 7.41043210e-01 1.65514126e-01
-7.28424668e-01 6.09919667e-01 -1.38501097e-02 -1.99394122e-01
4.25737957e-03 -9.30237412e-01 -9.42099154e-01 -6.64610028e-01
1.28212152e-02 5.33439517e-01 4.08493310e-01 -1.69571444... | [8.55419635772705, -2.0664303302764893] |
d1c35b42-0f69-4e08-a64b-c59bc68bdb5e | how-many-events-do-you-need-event-based | 2206.13673 | null | https://arxiv.org/abs/2206.13673v3 | https://arxiv.org/pdf/2206.13673v3.pdf | How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels | Event cameras continue to attract interest due to desirable characteristics such as high dynamic range, low latency, virtually no motion blur, and high energy efficiency. One of the potential applications that would benefit from these characteristics lies in visual place recognition for robot localization, i.e. matchin... | ['Michael Milford', 'Tobias Fischer'] | 2022-06-28 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 1.77965894e-01 -6.40614629e-01 -1.26584828e-01 -3.33364964e-01
-7.44295597e-01 -6.43306017e-01 7.48054683e-01 3.30769777e-01
-7.74104059e-01 6.25345230e-01 -1.54019088e-01 1.56014815e-01
-2.30636343e-01 -7.43034184e-01 -9.73566771e-01 -7.77745664e-01
-5.20327687e-01 1.70748055e-01 7.76971340e-01 -1.76305950... | [8.047865867614746, -1.5283316373825073] |
aed436f5-d0ce-4376-8058-e65e72f50bcf | copied-monolingual-data-improves-low-resource | null | null | https://aclanthology.org/W17-4715 | https://aclanthology.org/W17-4715.pdf | Copied Monolingual Data Improves Low-Resource Neural Machine Translation | null | ['Kenneth Heafield', 'Anna Currey', 'Antonio Valerio Miceli Barone'] | 2017-09-01 | null | null | null | ws-2017-9 | ['low-resource-neural-machine-translation'] | ['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... | [-7.268688678741455, 3.764174222946167] |
3e278120-7545-454c-8fc8-3729655846ce | self-labelling-via-simultaneous-clustering-1 | 1911.05371 | null | https://arxiv.org/abs/1911.05371v3 | https://arxiv.org/pdf/1911.05371v3.pdf | Self-labelling via simultaneous clustering and representation learning | Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning problems with degenerate solutions. In this paper, we propose a novel and principled learning formulation that addresses these... | ['Christian Rupprecht', 'Yuki Markus Asano', 'Andrea Vedaldi'] | 2019-11-13 | null | https://openreview.net/forum?id=Hyx-jyBFPr | https://openreview.net/pdf?id=Hyx-jyBFPr | iclr-2020-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.66908577e-01 -3.74379121e-02 -1.86917692e-01 -5.39282084e-01
-9.13884342e-01 -6.00407541e-01 4.84197825e-01 -1.20629750e-01
-8.29878688e-01 6.42150283e-01 -7.13000745e-02 -2.17504233e-01
-2.50681072e-01 -4.84946191e-01 -5.62514722e-01 -8.91983211e-01
5.73554225e-02 8.00799608e-01 4.54706252e-02 1.99775234... | [9.328363418579102, 2.833466053009033] |
a1399248-d2da-4312-8815-73e71b7934cf | exploring-contrast-consistency-of-open-domain | 2305.14441 | null | https://arxiv.org/abs/2305.14441v1 | https://arxiv.org/pdf/2305.14441v1.pdf | Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions | Contrast consistency, the ability of a model to make consistently correct predictions in the presence of perturbations, is an essential aspect in NLP. While studied in tasks such as sentiment analysis and reading comprehension, it remains unexplored in open-domain question answering (OpenQA) due to the difficulty of co... | ['Meng Jiang', 'Mingxuan Ju', 'Zheng Ning', 'Wenhao Yu', 'Zhihan Zhang'] | 2023-05-23 | null | null | null | null | ['sentiment-analysis', 'reading-comprehension', 'open-domain-question-answering'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.98602080e-01 1.06806837e-01 2.74174273e-01 -2.67998636e-01
-1.59586990e+00 -9.26206529e-01 6.32334530e-01 4.07056600e-01
-4.64105397e-01 9.41433311e-01 2.94284374e-01 -3.12410682e-01
-3.97733115e-02 -5.45662880e-01 -9.03582096e-01 -1.68372124e-01
1.93859339e-01 7.43574977e-01 5.15791416e-01 -7.78036475... | [11.248283386230469, 8.08476734161377] |
0cfb639c-fc8f-41d7-8e95-1c6fb5a9d55a | parallelizing-contextual-linear-bandits | 2105.10590 | null | https://arxiv.org/abs/2105.10590v2 | https://arxiv.org/pdf/2105.10590v2.pdf | Parallelizing Contextual Bandits | Standard approaches to decision-making under uncertainty focus on sequential exploration of the space of decisions. However, \textit{simultaneously} proposing a batch of decisions, which leverages available resources for parallel experimentation, has the potential to rapidly accelerate exploration. We present a family ... | ['Michael I. Jordan', 'Peter Bartlett', 'Yun S. Song', 'Nilesh Tripuraneni', 'Aldo Pacchiano', 'Jeffrey Chan'] | 2021-05-21 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 3.46933842e-01 2.49982268e-01 -7.60583341e-01 -4.29607481e-01
-1.61725008e+00 -1.00078309e+00 1.41639099e-01 4.45004962e-02
-5.79000711e-01 1.26599216e+00 5.87200336e-02 -1.06422043e+00
-5.83498776e-01 -4.38566476e-01 -1.03589308e+00 -8.99649978e-01
-3.32154453e-01 1.05876231e+00 -2.21500739e-01 2.34808832... | [4.513889789581299, 3.269376754760742] |
f3089e2b-7b73-43ec-ac09-d1b7231f4355 | correlated-time-series-forecasting-using-deep | 1808.09794 | null | http://arxiv.org/abs/1808.09794v2 | http://arxiv.org/pdf/1808.09794v2.pdf | Correlated Time Series Forecasting using Deep Neural Networks: A Summary of Results | Cyber-physical systems often consist of entities that interact with each
other over time. Meanwhile, as part of the continued digitization of industrial
processes, various sensor technologies are deployed that enable us to record
time-varying attributes (a.k.a., time series) of such entities, thus producing
correlated ... | ['Gabriel-Marcel Muresan', 'Darius-Valer Micu', 'Razvan-Gabriel Cirstea', 'Chenjuan Guo', 'Bin Yang'] | 2018-08-29 | null | null | null | null | ['correlated-time-series-forecasting'] | ['time-series'] | [ 9.45448801e-02 -3.23899657e-01 1.16644122e-01 -3.41428190e-01
-4.96288687e-01 -4.12723899e-01 6.21019959e-01 7.06698596e-02
-4.96631041e-02 4.20447648e-01 1.75436437e-01 -4.10343379e-01
-2.08791390e-01 -5.95682025e-01 -7.46705115e-01 -7.08508372e-01
-4.50008810e-01 -2.75897980e-02 -2.48154014e-01 -2.52247185... | [6.914088726043701, 2.93082857131958] |
70271b49-222d-4aa0-9a9e-858d1b3fc089 | temporal-knowledge-graph-completion-using-a | null | null | https://aclanthology.org/2021.naacl-main.202 | https://aclanthology.org/2021.naacl-main.202.pdf | Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings | Representation learning approaches for knowledge graphs have been mostly designed for static data. However, many knowledge graphs involve evolving data, e.g., the fact (The President of the United States is Barack Obama) is valid only from 2009 to 2017. This introduces important challenges for knowledge representation ... | ['Jens Lehmann', 'Mojtaba Nayyeri', 'Yung-Yu Chen', 'Chengjin Xu'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-4.02949363e-01 -6.79889470e-02 -8.57750595e-01 -3.89450341e-02
-7.56924897e-02 -6.14967346e-01 6.03543580e-01 4.85014617e-01
-2.40637287e-01 6.15128398e-01 3.79414767e-01 -5.32883465e-01
-6.72200501e-01 -9.08814192e-01 -8.75617385e-01 -3.79017949e-01
-6.28007114e-01 4.55477208e-01 2.14343041e-01 -2.40792170... | [8.521140098571777, 7.918104648590088] |
276267a9-cd96-4122-bb17-4ea3cb9221a1 | multi-hop-inference-for-sentence-level | 1805.11267 | null | http://arxiv.org/abs/1805.11267v1 | http://arxiv.org/pdf/1805.11267v1.pdf | Multi-hop Inference for Sentence-level TextGraphs: How Challenging is Meaningfully Combining Information for Science Question Answering? | Question Answering for complex questions is often modeled as a graph
construction or traversal task, where a solver must build or traverse a graph
of facts that answer and explain a given question. This "multi-hop" inference
has been shown to be extremely challenging, with few models able to aggregate
more than two fac... | ['Peter Jansen'] | 2018-05-29 | multi-hop-inference-for-sentence-level-1 | https://aclanthology.org/W18-1703 | https://aclanthology.org/W18-1703.pdf | ws-2018-6 | ['science-question-answering'] | ['miscellaneous'] | [ 1.85270868e-02 9.76799846e-01 -4.09031883e-02 -5.48186302e-01
-1.23880470e+00 -8.89492095e-01 4.95556504e-01 1.00621855e+00
-5.53242527e-02 1.20547950e+00 3.89496952e-01 -6.54669762e-01
-4.13913757e-01 -9.47911263e-01 -1.07417059e+00 1.06259219e-01
2.12074537e-02 9.12786365e-01 4.11732793e-01 -2.50894159... | [11.044634819030762, 7.974188804626465] |
811172bb-56a2-4561-8a12-4773ab9f9ea3 | context-and-attribute-grounded-dense | 1904.01410 | null | http://arxiv.org/abs/1904.01410v1 | http://arxiv.org/pdf/1904.01410v1.pdf | Context and Attribute Grounded Dense Captioning | Dense captioning aims at simultaneously localizing semantic regions and
describing these regions-of-interest (ROIs) with short phrases or sentences in
natural language. Previous studies have shown remarkable progresses, but they
are often vulnerable to the aperture problem that a caption generated by the
features insid... | ['Lu Sheng', 'Bin Liu', 'Jing Shao', 'Xiaogang Wang', 'Nenghai Yu', 'Guojun Yin'] | 2019-04-02 | context-and-attribute-grounded-dense-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yin_Context_and_Attribute_Grounded_Dense_Captioning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yin_Context_and_Attribute_Grounded_Dense_Captioning_CVPR_2019_paper.pdf | cvpr-2019-6 | ['dense-captioning'] | ['computer-vision'] | [ 4.71515477e-01 4.56587732e-01 -2.26987123e-01 -8.50442111e-01
-1.03505659e+00 -4.00363237e-01 7.51947522e-01 3.42599720e-01
-1.54002696e-01 8.67044151e-01 8.03849280e-01 2.47144699e-01
2.19149128e-01 -5.79851806e-01 -9.41954851e-01 -6.60819590e-01
1.65072888e-01 3.56592149e-01 4.11648676e-02 -3.61863077... | [10.582512855529785, 1.3635212182998657] |
9d598706-96bc-4cb9-af6c-4e1c4cc7c769 | exploring-syntactic-representations-for | null | null | https://aclanthology.info/papers/W13-1719/w13-1719 | https://www.aclweb.org/anthology/W13-1719v2 | Exploring Syntactic Representations for Native Language Identification | null | ['Ben Swanson'] | 2013-06-01 | exploring-syntactic-representations-for-1 | https://aclanthology.org/W13-1719 | https://aclanthology.org/W13-1719.pdf | ws-2013-6 | ['native-language-identification'] | ['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... | [-1.5391390323638916, 15.869171142578125] |
7cfd081c-d610-46d5-b1eb-90718fb30d04 | logical-story-representations-via-framenet-1 | null | null | https://aclanthology.org/2022.distcurate-1.3 | https://aclanthology.org/2022.distcurate-1.3.pdf | Logical Story Representations via FrameNet + Semantic Parsing | We propose a means of augmenting FrameNet parsers with a formal logic parser to obtain rich semantic representations of events. These schematic representations of the frame events, which we call Episodic Logic (EL) schemas, abstract constants to variables, preserving their types and relationships to other individuals i... | ['Lenhart Schubert', 'Lane Lawley'] | null | null | null | null | naacl-distcurate-2022-7 | ['formal-logic'] | ['reasoning'] | [ 1.87865160e-02 7.48999000e-01 -3.59974951e-01 -6.81524038e-01
2.80914432e-03 -6.51306450e-01 1.02948725e+00 6.19213045e-01
-4.67201442e-01 1.13331187e+00 4.87006366e-01 1.07984804e-01
-3.89216900e-01 -1.37105334e+00 -7.62097359e-01 -3.46219301e-01
-2.15996221e-01 7.43910611e-01 7.12646723e-01 -3.89506161... | [10.159111022949219, 9.156777381896973] |
0e79b64a-6868-4efc-8d81-b35af809654e | a-novel-low-rank-tensor-method-for | 2305.00892 | null | https://arxiv.org/abs/2305.00892v1 | https://arxiv.org/pdf/2305.00892v1.pdf | A Novel Low-Rank Tensor Method for Undersampling Artifact Removal in Respiratory Motion-Resolved Multi-Echo 3D Cones MRI | We propose a novel low-rank tensor method for respiratory motion-resolved multi-echo image reconstruction. The key idea is to construct a 3-way image tensor (space $\times$ echo $\times$ motion state) from the conventional gridding reconstruction of highly undersampled multi-echo k-space raw data, and exploit low-rank ... | ['Youngwook Kee', 'Heechul Jeong', 'Gerald Behr', 'MungSoo Kang', 'Seongho Jeong'] | 2023-05-01 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 6.55246913e-01 -4.57517058e-01 3.74876976e-01 -1.59588009e-01
-7.89749146e-01 -2.60920703e-01 4.56806421e-02 -6.32823586e-01
-6.31552398e-01 5.63068032e-01 3.73590767e-01 -1.53390855e-01
-8.18475842e-01 4.98532970e-03 -5.39651234e-03 -9.10867512e-01
-6.82374239e-01 3.40353578e-01 3.30706030e-01 -2.98033352... | [13.542413711547852, -2.3975493907928467] |
462bc9ff-4a39-42cd-8c63-5ab87e03d83f | alibaba-at-ijcnlp-2017-task-1-embedding | null | null | https://aclanthology.org/I17-4006 | https://aclanthology.org/I17-4006.pdf | Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task | This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No. 1 Chinese Grammatical Error Diagnosis (CGED). The task is to diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W). We treat the task as a sequence tagging p... | ['Linlin Li', 'Pengjun Xie', 'Yi Yang', 'Luo Si', 'Jun Tao', 'Guangwei Xu'] | 2017-12-01 | alibaba-at-ijcnlp-2017-task-1-embedding-1 | https://aclanthology.org/I17-4006 | https://aclanthology.org/I17-4006.pdf | ijcnlp-2017-12 | ['2d-human-pose-estimation'] | ['computer-vision'] | [-5.09221852e-02 1.07614495e-01 4.02033329e-01 -3.47471356e-01
-6.94014192e-01 -8.89036357e-02 -1.40817001e-01 2.77178138e-01
-6.75071836e-01 1.16512644e+00 1.41191661e-01 -6.02123678e-01
1.45800859e-01 -3.55678499e-01 -5.76526582e-01 -3.58024657e-01
-8.27617664e-03 4.91835922e-01 2.92356927e-02 -3.11220706... | [11.064170837402344, 10.785006523132324] |
160074fe-6dd3-43b9-a0a1-b981d23afd00 | hierarchical-graph-network-for-multi-hop | 1911.03631 | null | https://arxiv.org/abs/1911.03631v4 | https://arxiv.org/pdf/1911.03631v4.pdf | Hierarchical Graph Network for Multi-hop Question Answering | In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. To aggregate clues from scattered texts across multiple paragraphs, a hierarchical graph is created by constructing nodes on different levels of granularity (questions, paragraphs, sentences, entities), the representations of w... | ['Shuohang Wang', 'Rohit Pillai', 'Jingjing Liu', 'Zhe Gan', 'Yuwei Fang', 'Siqi Sun'] | 2019-11-09 | null | https://aclanthology.org/2020.emnlp-main.710 | https://aclanthology.org/2020.emnlp-main.710.pdf | emnlp-2020-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 6.72084838e-02 9.81304467e-01 -9.20922086e-02 -2.45897427e-01
-1.27309656e+00 -7.21137702e-01 4.43807602e-01 8.17862749e-01
-1.02719352e-01 9.29486394e-01 6.67652965e-01 -4.61554885e-01
-1.30621523e-01 -1.25655639e+00 -7.10333288e-01 -5.51550575e-02
1.20227620e-01 7.93281257e-01 7.39601672e-01 -3.35421950... | [10.704163551330566, 7.8748369216918945] |
593c4e0a-72aa-4b02-be14-448f1913e5f6 | mask-aware-iou-for-anchor-assignment-in-real | 2110.09734 | null | https://arxiv.org/abs/2110.09734v1 | https://arxiv.org/pdf/2110.09734v1.pdf | Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation | This paper presents Mask-aware Intersection-over-Union (maIoU) for assigning anchor boxes as positives and negatives during training of instance segmentation methods. Unlike conventional IoU or its variants, which only considers the proximity of two boxes; maIoU consistently measures the proximity of an anchor box with... | ['Emre Akbas', 'Sinan Kalkan', 'Zeynep Sonat Baltaci', 'Fehmi Kahraman', 'Baris Can Cam', 'Kemal Oksuz'] | 2021-10-19 | null | null | null | null | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 1.00126334e-01 2.71197706e-01 -2.11413667e-01 -1.27633214e-01
-1.19178426e+00 -5.74821353e-01 -9.00707860e-03 1.02499850e-01
-5.93853652e-01 5.50647914e-01 -8.10168862e-01 -3.32892805e-01
2.98076570e-01 -8.24291706e-01 -1.27376533e+00 -3.65434945e-01
-5.41850850e-02 7.80993283e-01 8.92195225e-01 3.42556834... | [9.351394653320312, 0.07632551342248917] |
09e36849-1384-4b1f-afea-122a8db21ecf | on-exploring-pose-estimation-as-an-auxiliary | 2201.03859 | null | https://arxiv.org/abs/2201.03859v2 | https://arxiv.org/pdf/2201.03859v2.pdf | On Exploring Pose Estimation as an Auxiliary Learning Task for Visible-Infrared Person Re-identification | Visible-infrared person re-identification (VI-ReID) has been challenging due to the existence of large discrepancies between visible and infrared modalities. Most pioneering approaches reduce intra-class variations and inter-modality discrepancies by learning modality-shared and ID-related features. However, an explici... | ['Jungong Han', 'Qiang Zhang', 'Xiao Ma', 'Nianchang Huang', 'Yunqi Miao'] | 2022-01-11 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [-2.33405251e-02 -2.73281664e-01 -7.98030049e-02 -5.30274630e-01
-7.95791268e-01 -4.24639285e-01 6.84012532e-01 -1.17151782e-01
-5.25872409e-01 5.22670805e-01 3.83604616e-01 3.92407060e-01
-2.23428950e-01 -3.15258831e-01 -6.39829516e-01 -8.56211603e-01
2.62156457e-01 1.73901632e-01 -1.27091721e-01 -1.35381833... | [14.679570198059082, 0.9183653593063354] |
75ed5f73-c223-4cc2-a254-cfb1f5254b06 | natural-language-inference-prompts-for-zero | 2209.06701 | null | https://arxiv.org/abs/2209.06701v2 | https://arxiv.org/pdf/2209.06701v2.pdf | Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora | Within textual emotion classification, the set of relevant labels depends on the domain and application scenario and might not be known at the time of model development. This conflicts with the classical paradigm of supervised learning in which the labels need to be predefined. A solution to obtain a model with a flexi... | ['Roman Klinger', 'María-Teresa Martín-Valdivia', 'Flor Miriam Plaza-del-Arco'] | 2022-09-14 | null | https://aclanthology.org/2022.coling-1.592 | https://aclanthology.org/2022.coling-1.592.pdf | coling-2022-10 | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 3.19516659e-01 6.91508204e-02 -7.68662915e-02 -8.06061804e-01
-4.66565847e-01 -6.20094657e-01 1.03148377e+00 7.38532543e-01
-7.79249012e-01 6.32599235e-01 1.77280173e-01 -1.52377337e-01
-4.19106632e-01 -6.56479418e-01 -5.85588887e-02 -6.20087862e-01
2.94631183e-01 6.87130392e-01 2.78488666e-01 -6.39661193... | [12.760376930236816, 6.327703475952148] |
55558c20-ade3-4a51-843f-26d5f9b2c0f0 | finger-vein-recognition-by-generating-code | 2101.08415 | null | https://arxiv.org/abs/2101.08415v1 | https://arxiv.org/pdf/2101.08415v1.pdf | Finger Vein Recognition by Generating Code | Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishes ability, security and non-invasive procedure. The main idea of traditional schemes is to directly extract features from finger vein images or patterns and then compare features to fin... | ['Mingwen Wang', 'Zhongxia Zhang'] | 2021-01-21 | null | null | null | null | ['finger-vein-recognition'] | ['computer-vision'] | [ 4.68189329e-01 -5.96671581e-01 -6.01249114e-02 -3.70204419e-01
-2.21182406e-01 -5.94255865e-01 4.11480039e-01 4.33984660e-02
-4.86252218e-01 3.96212637e-01 -2.11498171e-01 7.87420496e-02
-2.46642753e-01 -8.27557862e-01 6.37945011e-02 -9.61699963e-01
2.96516299e-01 1.99807331e-01 3.23045701e-01 1.89986780... | [13.069938659667969, 1.0179909467697144] |
e7b79af2-f1d1-4726-a034-31341dc95000 | lof-structure-aware-line-tracking-based-on | 2109.08466 | null | https://arxiv.org/abs/2109.08466v1 | https://arxiv.org/pdf/2109.08466v1.pdf | LOF: Structure-Aware Line Tracking based on Optical Flow | Lines provide the significantly richer geometric structural information about the environment than points, so lines are widely used in recent Visual Odometry (VO) works. Since VO with lines use line tracking results to locate and map, line tracking is a crucial component in VO. Although the state-of-the-art line tracki... | ['Xiao Liu', 'Zheng Chai', 'Meixiang Quan'] | 2021-09-17 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [-2.57504910e-01 -5.91556370e-01 -2.02958778e-01 1.02516832e-02
2.18973324e-01 -2.97481686e-01 2.69141555e-01 3.24759126e-01
-4.05697763e-01 7.14238882e-01 -1.19796477e-01 3.40842456e-02
-3.16715315e-02 -8.50987732e-01 -6.17732704e-01 -3.87035161e-01
9.19452161e-02 1.82591632e-01 8.88013899e-01 -1.89112276... | [8.318408012390137, -1.6073408126831055] |
40619599-923b-4c39-b47e-04be7e0131da | cross-modal-contrastive-learning-for-speech | null | null | https://openreview.net/forum?id=zmxg59rhm3D | https://openreview.net/pdf?id=zmxg59rhm3D | Cross-modal Contrastive Learning for Speech Translation | How to learn similar representations for spoken utterances and their written text? We believe a unified and aligned representation of speech and text will lead to improvement in speech translation. To this end, we propose ConST, a cross-modal contrastive learning method for end-to-end speech-to-text translation. We eva... | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 1.94466472e-01 3.50832455e-02 -4.87640679e-01 -5.53165257e-01
-2.00670671e+00 -7.91557133e-01 1.05213523e+00 -1.88353047e-01
-3.89033973e-01 6.93165362e-01 8.93285334e-01 -3.80943686e-01
3.46237481e-01 -8.54450017e-02 -7.49795496e-01 -3.49577904e-01
5.07345438e-01 9.20198441e-01 -2.23874956e-01 -6.08034670... | [14.478581428527832, 7.1685943603515625] |
460ac0e5-c658-472b-899d-dcfddfce355f | relation3dmot-exploiting-deep-affinity-for-3d | 2011.12850 | null | https://arxiv.org/abs/2011.12850v1 | https://arxiv.org/pdf/2011.12850v1.pdf | Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation | Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning. As a result, 3D multi-object tracking (MOT) plays a vital role in autonomous navigation. Most MOT methods use a tracking-by-detection pipeline, which includes object detection and data association processing. However... | ['Antonios Tsourdos', 'Luca Zanotti Fragonara', 'Can Chen'] | 2020-11-25 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-2.86594898e-01 -7.72832930e-01 8.65383167e-03 -1.12090819e-01
-3.59108388e-01 -5.78965127e-01 4.02763933e-01 7.92478547e-02
-7.13152885e-01 2.93533444e-01 -1.81822643e-01 7.05482736e-02
-1.40765935e-01 -5.82622290e-01 -7.69860685e-01 -8.40772152e-01
-4.48766500e-02 5.53273797e-01 1.14915514e+00 -1.74251243... | [6.53314208984375, -2.2412307262420654] |
28d230e5-c2ca-40f0-9571-09179bb56a95 | direction-aware-spatial-context-features-for | 1712.04142 | null | http://arxiv.org/abs/1712.04142v2 | http://arxiv.org/pdf/1712.04142v2.pdf | Direction-aware Spatial Context Features for Shadow Detection | Shadow detection is a fundamental and challenging task, since it requires an
understanding of global image semantics and there are various backgrounds
around shadows. This paper presents a novel network for shadow detection by
analyzing image context in a direction-aware manner. To achieve this, we first
formulate the ... | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Xiaowei Hu', 'Lei Zhu', 'Jing Qin'] | 2017-12-12 | direction-aware-spatial-context-features-for-2 | http://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Direction-Aware_Spatial_Context_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Direction-Aware_Spatial_Context_CVPR_2018_paper.pdf | cvpr-2018-6 | ['shadow-detection', 'detecting-shadows'] | ['computer-vision', 'computer-vision'] | [ 6.22648776e-01 -2.43540689e-01 -6.40462264e-02 -7.20957398e-01
-3.02112907e-01 -5.70137091e-02 3.73755187e-01 -4.46170747e-01
-3.39715540e-01 6.53160095e-01 6.07940853e-01 -5.65314233e-01
3.08500051e-01 -7.39935219e-01 -6.99710011e-01 -1.00201619e+00
1.18911117e-01 -4.15161490e-01 8.59540880e-01 -1.82642058... | [10.854467391967773, -4.114120960235596] |
1defcf2f-1cb5-4fdc-9418-5a4dd052c190 | team-triple-check-at-factify-2-parameter | 2302.07740 | null | https://arxiv.org/abs/2302.07740v1 | https://arxiv.org/pdf/2302.07740v1.pdf | Team Triple-Check at Factify 2: Parameter-Efficient Large Foundation Models with Feature Representations for Multi-Modal Fact Verification | Multi-modal fact verification has become an important but challenging issue on social media due to the mismatch between the text and images in the misinformation of news content, which has been addressed by considering cross-modalities to identify the veracity of the news in recent years. In this paper, we propose the ... | ['Wen-Chih Peng', 'Wei-Yao Wang', 'Hong-Wei Wu', 'Wei-Wei Du'] | 2023-02-12 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [-2.61380076e-01 -8.05261061e-02 -2.92466938e-01 -2.58052200e-01
-1.20751190e+00 -7.25973904e-01 1.16878498e+00 2.45509237e-01
-3.68300587e-01 5.20242631e-01 9.25443769e-01 1.57955606e-02
4.75021973e-02 -4.48090583e-01 -1.00123024e+00 -2.63567746e-01
2.02287152e-01 4.37628567e-01 5.74516580e-02 -1.56295463... | [8.226394653320312, 10.254549980163574] |
2a8a523a-2c45-4d6d-98f2-9ea481d167f9 | relation-adversarial-network-for-low-resource | 1911.03091 | null | https://arxiv.org/abs/1911.03091v6 | https://arxiv.org/pdf/1911.03091v6.pdf | Relation Adversarial Network for Low Resource Knowledge Graph Completion | Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficulties for KGC is a low resource problem. Previous approaches assume sufficient training triples to learn versatile vectors for entities and ... | ['Huajun Chen', 'Wei zhang', 'Ningyu Zhang', 'Jiaoayan Chen', 'Shumin Deng', 'Zhanlin Sun'] | 2019-11-08 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 1.89102843e-01 6.64879978e-01 -7.08083034e-01 -3.14628601e-01
-5.31000257e-01 -5.05012810e-01 5.87379754e-01 3.83258224e-01
-2.60149330e-01 1.32354522e+00 6.31429404e-02 -1.91592783e-01
-2.57850021e-01 -1.36748898e+00 -8.35727572e-01 -1.98388070e-01
-2.81263441e-01 8.24229300e-01 2.01125965e-01 -5.49363494... | [8.996519088745117, 8.143061637878418] |
a14168c1-b0b4-4641-860c-84e480d4ab93 | generative-entity-to-entity-stance-detection | 2211.01467 | null | https://arxiv.org/abs/2211.01467v1 | https://arxiv.org/pdf/2211.01467v1.pdf | Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation | Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first... | ['Lu Wang', 'Nick Beauchamp', 'Xinliang Frederick Zhang'] | 2022-11-02 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 5.25881611e-02 5.38708270e-01 -5.42242467e-01 -5.28447807e-01
-5.91123819e-01 -1.05457342e+00 1.08366621e+00 4.67335433e-01
-2.21413508e-01 7.02159405e-01 1.09058893e+00 -3.10601622e-01
3.69381547e-01 -9.67320681e-01 -8.45596194e-01 -4.53024685e-01
1.36892378e-01 4.98470277e-01 4.09181975e-02 -5.57140946... | [8.887505531311035, 10.00265121459961] |
445f3852-e726-4dc3-bdfb-78776ccf87b6 | generalized-bayesian-inference-for-scientific | 2305.15208 | null | https://arxiv.org/abs/2305.15208v1 | https://arxiv.org/pdf/2305.15208v1.pdf | Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation | Simulation-based inference (SBI) enables amortized Bayesian inference for simulators with implicit likelihoods. But when we are primarily interested in the quality of predictive simulations, or when the model cannot exactly reproduce the observed data (i.e., is misspecified), targeting the Bayesian posterior may be ove... | ['Jakob H. Macke', 'Michael Deistler', 'Richard Gao'] | 2023-05-24 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 3.58199179e-01 -3.57258677e-01 5.34281731e-02 -2.61000693e-01
-1.02467811e+00 -6.23161256e-01 5.49105763e-01 8.09219554e-02
-7.33161867e-01 1.46002078e+00 -4.06974733e-01 -8.22974384e-01
-1.17856197e-01 -7.37284184e-01 -1.23573279e+00 -8.04168880e-01
-8.67743939e-02 8.83526206e-01 3.98015790e-02 4.04642135... | [6.821859359741211, 3.932614326477051] |
78fc7477-d36c-43cc-94fd-384fc72b38f3 | reasoning-about-liquids-via-closed-loop | 1703.01656 | null | http://arxiv.org/abs/1703.01656v2 | http://arxiv.org/pdf/1703.01656v2.pdf | Reasoning About Liquids via Closed-Loop Simulation | Simulators are powerful tools for reasoning about a robot's interactions with
its environment. However, when simulations diverge from reality, that reasoning
becomes less useful. In this paper, we show how to close the loop between
liquid simulation and real-time perception. We use observations of liquids to
correct er... | ['Dieter Fox', 'Connor Schenck'] | 2017-03-05 | null | null | null | null | ['liquid-simulation'] | ['miscellaneous'] | [ 2.14939676e-02 3.17518145e-01 4.65632737e-01 -8.34420845e-02
-2.89824069e-01 -7.40640998e-01 5.63269973e-01 2.76953101e-01
-3.79428595e-01 9.55750644e-01 -2.47331455e-01 -5.78106105e-01
5.03816187e-01 -1.17057133e+00 -9.35614705e-01 -4.90957767e-01
-2.02235073e-01 5.25984287e-01 7.29718447e-01 -4.46309805... | [4.591772079467773, 0.9643433094024658] |
f369ea1d-e19c-4171-a28a-68323377d99c | can-an-embodied-agent-find-your-cat-shaped | 2303.03480 | null | https://arxiv.org/abs/2303.03480v1 | https://arxiv.org/pdf/2303.03480v1.pdf | Can an Embodied Agent Find Your "Cat-shaped Mug"? LLM-Based Zero-Shot Object Navigation | We present LGX, a novel algorithm for Object Goal Navigation in a "language-driven, zero-shot manner", where an embodied agent navigates to an arbitrarily described target object in a previously unexplored environment. Our approach leverages the capabilities of Large Language Models (LLMs) for making navigational decis... | ['Dinesh Manocha', 'James F. Mullen Jr.', 'Vishnu Sashank Dorbala'] | 2023-03-06 | null | null | null | null | ['robot-navigation', 'motion-planning'] | ['robots', 'robots'] | [ 1.91444144e-01 4.38565582e-01 1.98524058e-01 -5.85362971e-01
-7.41404474e-01 -3.49511772e-01 7.07357645e-01 -1.08753435e-01
-7.88639843e-01 4.11955088e-01 7.10280165e-02 -2.45528072e-01
-3.06525733e-03 -6.94974363e-01 -7.99231172e-01 -4.06424046e-01
-3.25839162e-01 7.05952466e-01 4.11324680e-01 -6.22800291... | [4.54332160949707, 0.6076425313949585] |
9b2ab74e-34e9-4d14-b0ac-1f6104d018ab | knowledge-graph-deep-learning-a-case-study-in | 2205.15952 | null | https://arxiv.org/abs/2205.15952v2 | https://arxiv.org/pdf/2205.15952v2.pdf | Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain | In the commercial aviation domain, there are a large number of documents, like, accident reports (NTSB, ASRS) and regulatory directives (ADs). There is a need for a system to access these diverse repositories efficiently in order to service needs in the aviation industry, like maintenance, compliance, and safety. In th... | ['Ravi Shankar', 'Rajesh Zele', 'Prabhjit Thind', 'Asif Ekbal', 'Satyanarayan Kar', 'Pushpak Bhattacharyya', 'Shreya Laddha', 'Raj Gite', 'Ankush Agarwal'] | 2022-05-31 | null | https://aclanthology.org/2022.lrec-1.673 | https://aclanthology.org/2022.lrec-1.673.pdf | lrec-2022-6 | ['passage-retrieval'] | ['natural-language-processing'] | [-2.42477253e-01 1.80048287e-01 1.18212134e-01 -4.27701235e-01
-1.18109572e+00 -5.99546432e-01 3.61702949e-01 7.06457615e-01
-4.27149832e-01 8.36032808e-01 4.40950274e-01 -7.35344648e-01
-6.92973793e-01 -1.40236318e+00 -7.92989850e-01 3.36724818e-02
5.59015609e-02 8.72264802e-01 6.54360771e-01 -8.81733775... | [10.356539726257324, 7.928412914276123] |
71cbffa2-3bfb-4a16-b7f1-0496530cc659 | uncertainty-aware-deep-learning-for-digital | 2303.10954 | null | https://arxiv.org/abs/2303.10954v1 | https://arxiv.org/pdf/2303.10954v1.pdf | Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines | Deep neural networks (DNNs) are often coupled with physics-based models or data-driven surrogate models to perform fault detection and health monitoring of systems in the low data regime. These models serve as digital twins to generate large quantities of data to train DNNs which would otherwise be difficult to obtain ... | ['Giovanni Sansavini', 'Blazhe Gjorgiev', 'Laya Das'] | 2023-03-20 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-1.77076623e-01 8.21096003e-02 2.85732448e-01 -4.40582156e-01
-6.86701536e-01 -2.67513365e-01 5.67956567e-01 2.56752282e-01
7.48864934e-02 1.17209101e+00 -8.34990963e-02 -2.13216409e-01
-4.60704237e-01 -1.18945968e+00 -1.20557094e+00 -9.25991535e-01
-9.92907956e-02 9.18889225e-01 1.99517146e-01 -1.12613872... | [7.216508388519287, 3.699934244155884] |
1817c8f2-3d6f-447b-af48-a52f4bf86ae9 | cutting-edge-techniques-for-depth-map-super | 2306.15244 | null | https://arxiv.org/abs/2306.15244v1 | https://arxiv.org/pdf/2306.15244v1.pdf | Cutting-Edge Techniques for Depth Map Super-Resolution | To overcome hardware limitations in commercially available depth sensors which result in low-resolution depth maps, depth map super-resolution (DMSR) is a practical and valuable computer vision task. DMSR requires upscaling a low-resolution (LR) depth map into a high-resolution (HR) space. Joint image filtering for DMS... | ['Josiah Smith', 'Ryan Peterson'] | 2023-06-27 | null | null | null | null | ['depth-map-super-resolution', 'super-resolution', 'image-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 8.32265615e-01 4.73682098e-02 3.93181711e-01 -3.10553640e-01
-9.53962684e-01 2.08703458e-01 4.63164121e-01 -4.13458675e-01
-6.96561992e-01 1.09432983e+00 3.82756650e-01 1.66665018e-01
-3.55859339e-01 -1.14577615e+00 -5.90773404e-01 -4.79661196e-01
3.02154303e-01 1.01463445e-01 6.64201915e-01 -5.42998791... | [10.103425025939941, -2.3030197620391846] |
1db7dab5-c1cd-4cbf-9933-2bc94e98526b | fan-fatigue-aware-network-for-click-through | 2304.04529 | null | https://arxiv.org/abs/2304.04529v1 | https://arxiv.org/pdf/2304.04529v1.pdf | FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation | Since clicks usually contain heavy noise, increasing research efforts have been devoted to modeling implicit negative user behaviors (i.e., non-clicks). However, they either rely on explicit negative user behaviors (e.g., dislikes) or simply treat non-clicks as negative feedback, failing to learn negative user interest... | ['Bo Cao', 'Chengjun Mao', 'Yingmin Su', 'Ningning Li', 'Yang Huang', 'Xiaofeng Pan', 'Naiyin Liu', 'Ming Li'] | 2023-04-10 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-7.99115598e-02 -4.45784837e-01 -3.72298777e-01 -5.07779419e-01
-1.76513970e-01 -2.39866674e-01 4.62212339e-02 -2.85268366e-01
-4.48265433e-01 4.65378791e-01 1.61349684e-01 -1.52414665e-01
-1.16256364e-01 -4.55661982e-01 -1.06359735e-01 -5.19413471e-01
1.86124876e-01 -1.06763661e-01 7.13359788e-02 -3.31672400... | [10.114383697509766, 5.515572547912598] |
1a57accd-82e6-46ff-a700-1a8f71a5255d | floors-are-flat-leveraging-semantics-for-real | 1906.06792 | null | https://arxiv.org/abs/1906.06792v1 | https://arxiv.org/pdf/1906.06792v1.pdf | Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction | We propose 4 insights that help to significantly improve the performance of deep learning models that predict surface normals and semantic labels from a single RGB image. These insights are: (1) denoise the "ground truth" surface normals in the training set to ensure consistency with the semantic labels; (2) concurrent... | ['Karthik Raveendran', 'Steven Hickson', 'Kevin Murphy', 'Irfan Essa', 'Alireza Fathi'] | 2019-06-16 | null | null | null | null | ['surface-normals-estimation'] | ['computer-vision'] | [ 7.31070042e-01 4.22870070e-01 3.36157590e-01 -6.59436524e-01
-9.66362834e-01 -3.92294288e-01 3.98162276e-01 -1.19804546e-01
-4.59297657e-01 6.52378678e-01 -1.34038359e-01 -2.80995429e-01
4.90768909e-01 -8.98020148e-01 -9.80158031e-01 -5.96646607e-01
1.07021734e-01 4.45276141e-01 4.94073331e-01 4.14496772... | [9.553008079528809, -2.9111857414245605] |
05ac3ada-ca3c-4498-a4f2-91b4a3cc6a26 | constrained-low-rank-learning-using-least | 1611.04870 | null | http://arxiv.org/abs/1611.04870v1 | http://arxiv.org/pdf/1611.04870v1.pdf | Constrained Low-Rank Learning Using Least Squares-Based Regularization | Low-rank learning has attracted much attention recently due to its efficacy
in a rich variety of real-world tasks, e.g., subspace segmentation and image
categorization. Most low-rank methods are incapable of capturing
low-dimensional subspace for supervised learning tasks, e.g., classification
and regression. This pape... | ['Xuelong. Li', 'Luming Zhang', 'Meng Wang', 'Jun Yu', 'Deng Cai', 'Ping Li'] | 2016-11-15 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 2.37374440e-01 -3.17804337e-01 -2.86898404e-01 -3.56412590e-01
-7.31234193e-01 -4.38958049e-01 2.72089362e-01 -6.07107103e-01
-3.13081115e-01 5.00542700e-01 3.87485534e-01 5.16280951e-03
-3.89560193e-01 -9.97229889e-02 -4.96394873e-01 -1.06743634e+00
5.24323523e-01 1.70065969e-01 -4.98364806e-01 8.08409378... | [7.893273830413818, 4.4512619972229] |
21726eb2-334e-4b26-8d05-93d33726e329 | classifying-tweet-level-judgements-of-rumours | 1506.00468 | null | http://arxiv.org/abs/1506.00468v2 | http://arxiv.org/pdf/1506.00468v2.pdf | Classifying Tweet Level Judgements of Rumours in Social Media | Social media is a rich source of rumours and corresponding community
reactions. Rumours reflect different characteristics, some shared and some
individual. We formulate the problem of classifying tweet level judgements of
rumours as a supervised learning task. Both supervised and unsupervised domain
adaptation are cons... | ['Kalina Bontcheva', 'Michal Lukasik', 'Trevor Cohn'] | 2015-06-01 | classifying-tweet-level-judgements-of-rumours-1 | https://aclanthology.org/D15-1311 | https://aclanthology.org/D15-1311.pdf | emnlp-2015-9 | ['rumour-detection'] | ['natural-language-processing'] | [-2.64140695e-01 2.08166793e-01 -6.01113617e-01 -5.43997705e-01
-4.15553480e-01 -1.19779907e-01 1.04780936e+00 6.01185024e-01
-1.38651446e-01 9.61239636e-01 8.45288813e-01 -8.32230449e-02
2.01537654e-01 -8.29545200e-01 -2.61066288e-01 -4.61827189e-01
-3.29193324e-01 7.44252980e-01 2.64837831e-01 -9.47570324... | [8.224727630615234, 10.052042007446289] |
6d31000b-1b48-47f6-8f5b-d01b7296c8b9 | an-interpretable-generative-model-for | 1811.04507 | null | http://arxiv.org/abs/1811.04507v1 | http://arxiv.org/pdf/1811.04507v1.pdf | An Interpretable Generative Model for Handwritten Digit Image Synthesis | An interpretable generative model for handwritten digits synthesis is
proposed in this work. Modern image generative models, such as Generative
Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are trained by
backpropagation (BP). The training process is complex and the underlying
mechanism is difficult ... | ['C. -C. Jay Kuo', 'Yao Zhu', 'Yueru Chen', 'Pranav Kulkarni', 'Jiali Duan', 'Saksham Suri'] | 2018-11-11 | null | null | null | null | ['handwritten-digit-image-synthesis'] | ['computer-vision'] | [ 4.20574605e-01 2.76609659e-01 9.84895751e-02 -5.26574664e-02
-2.36842319e-01 -4.92353827e-01 8.11731339e-01 -9.26221550e-01
1.40690550e-01 8.52537036e-01 1.20970219e-01 -2.54340976e-01
7.02839270e-02 -1.01181233e+00 -9.84559178e-01 -1.11747766e+00
7.59018242e-01 5.20493686e-01 -1.62534088e-01 -1.01373062... | [11.612869262695312, -0.4621635973453522] |
846d6f6e-6b97-473d-baca-b97122e80b96 | identification-and-molecular-dynamic | 2211.02826 | null | https://arxiv.org/abs/2211.02826v1 | https://arxiv.org/pdf/2211.02826v1.pdf | Identification and Molecular Dynamic Simulation of Flavonoids from Mediterranean species of Oregano against the Zika NS2B-NS3 Protease | The Zika virus, is an emerging infectious disease causing severe complications such as microcephaly in infants and Guillain Barre syndrome in adults. There is no licensed vaccination or approved medicine to treat ZIKV infection. Therefore, extensive research is being carried out to find compounds that can be used effec... | ['Sameer Sharma', 'Arka Sanyal', 'Anushikha Ghosh'] | 2022-11-05 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 8.27118903e-02 -2.35973805e-01 -4.19161707e-01 -6.65965676e-02
5.20052239e-02 -6.38039231e-01 2.11730197e-01 8.14483941e-01
-4.94858831e-01 1.41633809e+00 -2.02540100e-01 -4.07485396e-01
4.16558720e-02 -7.35495687e-01 -2.77658254e-01 -1.17304027e+00
-3.39607954e-01 5.06685555e-01 7.87575990e-02 -3.32889467... | [4.664817810058594, 5.107767581939697] |
00b136e8-9d8d-4cc0-bf92-4521f9b2e6d8 | sparse-graph-to-sequence-learning-for-vision | 2007.06077 | null | https://arxiv.org/abs/2007.06077v1 | https://arxiv.org/pdf/2007.06077v1.pdf | Sparse Graph to Sequence Learning for Vision Conditioned Long Textual Sequence Generation | Generating longer textual sequences when conditioned on the visual information is an interesting problem to explore. The challenge here proliferate over the standard vision conditioned sentence-level generation (e.g., image or video captioning) as it requires to produce a brief and coherent story describing the visual ... | ['Aditya Mogadala', 'Marius Mosbach', 'Dietrich Klakow'] | 2020-07-12 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 9.50991392e-01 3.77281904e-01 8.02076422e-04 -2.30679855e-01
-9.51336801e-01 -5.03439248e-01 1.02425992e+00 -2.11139247e-01
-2.16404963e-02 8.68886411e-01 6.24842465e-01 -2.38900587e-01
7.79767454e-01 -6.11854017e-01 -1.17006874e+00 -5.59469104e-01
2.31539890e-01 2.74447203e-01 -2.29944848e-02 -2.94230670... | [11.203448295593262, 0.6955252289772034] |
cd1cf891-60cd-4bf6-886c-5ab0da4964c1 | deep-learning-for-brain-age-estimation-a | 2212.03868 | null | https://arxiv.org/abs/2212.03868v1 | https://arxiv.org/pdf/2212.03868v1.pdf | Deep Learning for Brain Age Estimation: A Systematic Review | Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eli... | ['Chin-Teng Lin', 'Javier Del Ser', 'Yu-Dong Zhang', 'Kaizhu Huang', 'Kuan-Ting Lai', 'Nehal Ahmad', 'Tripti Goel', 'Iman Beheshti', 'M. A. Ganaie', 'M. Tanveer'] | 2022-12-07 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-1.81194112e-01 3.45164016e-02 -3.12548161e-01 -6.72814071e-01
-9.29313246e-03 2.85207927e-01 3.95111769e-01 3.50718141e-01
-8.36120188e-01 8.23579252e-01 1.32266521e-01 -1.03737928e-01
-1.21148214e-01 -5.51993787e-01 -1.65336743e-01 -6.84623182e-01
-5.12555599e-01 5.36227882e-01 -3.58925253e-01 2.22008079... | [14.084338188171387, -1.5378623008728027] |
86834192-fc95-45fc-ad9d-f3b6c5270473 | an-optimistic-perspective-on-offline-deep | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/5394-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/5394-Paper.pdf | An Optimistic Perspective on Offline Deep Reinforcement Learning | Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important consideration in real world applications. This paper studies offline RL using the DQN replay dataset comprising the entire replay experience of a DQN agent on 60 Atari 2600 games. We demonstrate that recent off-p... | ['Mohammad Norouzi', 'Dale Schuurmans', 'Rishabh Agarwal'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/5394-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/5394-Paper.pdf | icml-2020-1 | ['dqn-replay-dataset', 'dqn-replay-dataset'] | ['miscellaneous', 'playing-games'] | [-4.40859944e-01 -3.00735533e-01 -5.98141730e-01 -1.70303658e-01
-1.16484928e+00 -9.16109979e-01 8.81900489e-01 -3.54699880e-01
-1.02906477e+00 1.13758671e+00 1.61535159e-01 -4.60156053e-01
-2.25403503e-01 -2.08628088e-01 -8.23129356e-01 -6.64424181e-01
-5.02525270e-01 8.74229074e-01 -1.05245434e-01 -5.43481827... | [3.9963927268981934, 1.7655084133148193] |
6e011e5d-18a7-451e-9fce-be33317d6c7f | continuous-time-video-generation-via-learning | 2112.10960 | null | https://arxiv.org/abs/2112.10960v1 | https://arxiv.org/pdf/2112.10960v1.pdf | Continuous-Time Video Generation via Learning Motion Dynamics with Neural ODE | In order to perform unconditional video generation, we must learn the distribution of the real-world videos. In an effort to synthesize high-quality videos, various studies attempted to learn a mapping function between noise and videos, including recent efforts to separate motion distribution and appearance distributio... | ['Edward Choi', 'Jaegul Choo', 'Sookyung Kim', 'Joonseok Lee', 'Junsoo Lee', 'Sunghyun Park', 'Kangyeol Kim'] | 2021-12-21 | null | null | null | null | ['unconditional-video-generation'] | ['computer-vision'] | [ 2.52663821e-01 -2.49857932e-01 6.72897547e-02 7.57569596e-02
-6.09800458e-01 -6.85253263e-01 7.17232466e-01 -6.34764194e-01
-2.09594488e-01 8.69921207e-01 2.46383816e-01 6.21118993e-02
2.24179223e-01 -8.54177296e-01 -1.02366292e+00 -9.56822813e-01
1.22378156e-01 1.02295138e-01 2.09190786e-01 -1.32144094... | [10.850439071655273, -0.6314538717269897] |
ae9ae372-5a7d-42ea-87ba-fdc8e85c9ac9 | learning-interpretable-low-dimensional | 2302.10890 | null | https://arxiv.org/abs/2302.10890v2 | https://arxiv.org/pdf/2302.10890v2.pdf | Learning Interpretable Low-dimensional Representation via Physical Symmetry | Interpretable representation learning has been playing a key role in creative intelligent systems. In the music domain, current learning algorithms can successfully learn various features such as pitch, timbre, chord, texture, etc. However, most methods rely heavily on music domain knowledge. It remains an open questio... | ['Gus Xia', 'Yichen Huang', 'Daniel Chin', 'Xuanjie Liu'] | 2023-02-05 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 6.54615045e-01 1.65185750e-01 -2.93904573e-01 -1.42911837e-01
-2.00539663e-01 -6.92704797e-01 7.56680548e-01 -2.29933977e-01
-6.24588877e-02 5.62490821e-01 2.92059094e-01 2.08846509e-01
-5.94196439e-01 -6.34494901e-01 -5.90990722e-01 -9.86383975e-01
1.57412738e-01 3.84297192e-01 -2.24901736e-01 -2.35687569... | [15.764603614807129, 5.38908576965332] |
f7b4d81e-5e96-4160-8354-b8b3b2cea284 | xtransct-ultra-fast-volumetric-ct | 2305.19621 | null | https://arxiv.org/abs/2305.19621v1 | https://arxiv.org/pdf/2305.19621v1.pdf | XTransCT: Ultra-Fast Volumetric CT Reconstruction using Two Orthogonal X-Ray Projections via a Transformer Network | Computed tomography (CT) scans offer a detailed, three-dimensional representation of patients' internal organs. However, conventional CT reconstruction techniques necessitate acquiring hundreds or thousands of x-ray projections through a complete rotational scan of the body, making navigation or positioning during surg... | ['Xiaokun Liang', 'Yaoqin Xie', 'Wenfeng He', 'Lin Liu', 'Yinping Chan', 'Xuan Liu', 'Tangsheng Wang', 'Jingjing Dai', 'Chulong Zhang'] | 2023-05-31 | null | null | null | null | ['image-reconstruction', 'computed-tomography-ct'] | ['computer-vision', 'methodology'] | [ 3.97703797e-02 1.01749385e-02 -2.16969222e-01 -3.01424116e-01
-1.10759544e+00 -5.83466291e-01 1.90974370e-01 7.15213940e-02
-5.42417347e-01 3.01815271e-01 3.47153574e-01 -8.59045088e-01
-1.39833510e-01 -8.20053935e-01 -6.83862507e-01 -3.63991022e-01
-2.06399918e-01 6.59023166e-01 -1.45028047e-02 1.94356143... | [13.569624900817871, -2.600358247756958] |
4ce4d776-fd48-47b2-8e75-ae783bf88b7d | towards-realistic-visual-dubbing-with | 2201.06260 | null | https://arxiv.org/abs/2201.06260v1 | https://arxiv.org/pdf/2201.06260v1.pdf | Towards Realistic Visual Dubbing with Heterogeneous Sources | The task of few-shot visual dubbing focuses on synchronizing the lip movements with arbitrary speech input for any talking head video. Albeit moderate improvements in current approaches, they commonly require high-quality homologous data sources of videos and audios, thus causing the failure to leverage heterogeneous d... | ['Zejun Ma', 'Yang Zhang', 'Jiali Yao', 'Mingjie Wang', 'Jianfei Yang', 'Xiang Yin', 'Benlai Tang', 'Cheng Bi', 'Liucheng Liao', 'Tianyi Xie'] | 2022-01-17 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 1.55274689e-01 1.91210181e-01 -2.26742387e-01 -1.68004781e-01
-1.09175456e+00 -3.31534475e-01 5.72533846e-01 -6.27968729e-01
2.09312104e-02 6.96351767e-01 4.06836450e-01 9.82250199e-02
2.36457828e-02 -3.11489344e-01 -7.87912786e-01 -8.53627443e-01
4.34160113e-01 1.45177335e-01 -1.48258656e-01 1.37100387... | [13.197988510131836, -0.3605812191963196] |
585e4fb2-8ada-4454-b007-513daec7ed77 | noise-audits-improve-moral-foundation | 2210.07415 | null | https://arxiv.org/abs/2210.07415v1 | https://arxiv.org/pdf/2210.07415v1.pdf | Noise Audits Improve Moral Foundation Classification | Morality plays an important role in culture, identity, and emotion. Recent advances in natural language processing have shown that it is possible to classify moral values expressed in text at scale. Morality classification relies on human annotators to label the moral expressions in text, which provides training data t... | ['Kristina Lerman', 'Fred Morstatter', 'Bahareh Harandizadeh', 'Frederic R. Hopp', 'Negar Mokhberian'] | 2022-10-13 | null | null | null | null | ['culture'] | ['speech'] | [ 1.63357362e-01 6.45186007e-01 -4.12564397e-01 -7.68125594e-01
-6.10296667e-01 -5.94595551e-01 4.81848180e-01 6.55753493e-01
-5.60499549e-01 8.18035305e-01 7.38893986e-01 4.59417552e-01
1.58697724e-01 -5.48939347e-01 -5.48234731e-02 -8.75127375e-01
4.44627166e-01 2.56679237e-01 -6.13095999e-01 -7.09273443... | [9.138702392578125, 10.11815071105957] |
7d265cc2-b5fa-46a9-8848-0b5ccd4b0ddd | validity-of-web-based-self-directed | 2208.04841 | null | https://arxiv.org/abs/2208.04841v1 | https://arxiv.org/pdf/2208.04841v1.pdf | Validity of Web-based, Self-directed, NeuroCognitive Performance Test in MCI | Digital cognitive tests offer several potential advantages over established paper-pencil tests but have not yet been fully evaluated for the clinical evaluation of mild cognitive impairment. The NeuroCognitive Performance Test (NCPT) is a web-based, self-directed, modular battery intended for repeated assessments of mu... | ['Davangere P. Devanand', 'Joel Sneed', 'Howards Andrews', 'Jeffrey R. Petrella', 'Andrew M. Michael', 'Caroline Hellegers', 'Charlie Ndouli', 'Julia Phillips', "Jessica D'Antonio", 'Izael Nino', 'Adaora Nwosu', 'Alexandra R. Linares', 'Min Qian', 'Terry E. Goldberg', 'P. Murali Doraiswamy'] | 2022-07-11 | null | null | null | null | ['skills-assessment'] | ['computer-vision'] | [-4.65591699e-01 -3.89476717e-01 -3.14416796e-01 -2.70224452e-01
-8.23328197e-01 -6.63870871e-01 3.76588792e-01 3.72643411e-01
-9.27829087e-01 1.07364142e+00 3.23665738e-01 -7.07126796e-01
-8.19271922e-01 -7.87985086e-01 -6.95746467e-02 1.36079311e-01
-7.10999787e-01 6.82410479e-01 3.14352512e-01 1.06172718... | [14.162188529968262, -1.6678729057312012] |
3dd7a224-6c32-49f2-a9ef-90a55a5b568e | graph-based-neural-network-models-with-1 | 2011.07267 | null | https://arxiv.org/abs/2011.07267v2 | https://arxiv.org/pdf/2011.07267v2.pdf | Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks | Self-supervised learning is currently gaining a lot of attention, as it allows neural networks to learn robust representations from large quantities of unlabeled data. Additionally, multi-task learning can further improve representation learning by training networks simultaneously on related tasks, leading to significa... | ['Alessandro Rozza', 'Franco Manessi'] | 2020-11-14 | graph-based-neural-network-models-with | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 3.77757668e-01 1.86405286e-01 -6.39979601e-01 -5.00940084e-01
-6.11412942e-01 -2.27192774e-01 5.00536084e-01 5.26932299e-01
-1.89192146e-01 4.92936939e-01 1.66940734e-01 -2.19977602e-01
-1.82739962e-02 -7.32209384e-01 -7.13613570e-01 -4.51461345e-01
-1.59134328e-01 6.17192686e-01 1.99820995e-01 -7.71482661... | [7.288985729217529, 6.290165901184082] |
ce15c345-cd00-4c36-976c-4e67e791463e | correcting-diverse-factual-errors-in | 2210.12378 | null | https://arxiv.org/abs/2210.12378v2 | https://arxiv.org/pdf/2210.12378v2.pdf | Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling | Abstractive summarization models often generate inconsistent summaries containing factual errors or hallucinated content. Recent works focus on correcting factual errors in generated summaries via post-editing. Such correction models are trained using adversarial non-factual summaries constructed using heuristic rules ... | ['William W. Cohen', 'Yulia Tsvetkov', 'Hannaneh Hajishirzi', 'Vidhisha Balachandran'] | 2022-10-22 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.08624417e-01 8.39792669e-01 -1.04809307e-01 -2.68896639e-01
-1.45675433e+00 -4.84171927e-01 7.90843010e-01 7.64345884e-01
2.75813369e-03 1.35417736e+00 1.14439905e+00 -1.53401392e-02
4.66112584e-01 -7.68796563e-01 -1.08366811e+00 9.14643630e-02
2.84297317e-01 3.34595561e-01 -2.35377774e-01 -3.82307321... | [12.189502716064453, 9.190875053405762] |
f4445e86-8686-485e-860b-ecfe02a92d92 | parameter-is-not-all-you-need-starting-from | 2303.08134 | null | https://arxiv.org/abs/2303.08134v2 | https://arxiv.org/pdf/2303.08134v2.pdf | Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis | We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric functions. Surprisingly, it performs well on various 3D tasks, requiring no parameters or tr... | ['Ziyu Guo', 'Jianbo Shi', 'Hongsheng Li', 'Peng Gao', 'Yali Wang', 'Liuhui Wang', 'Renrui Zhang'] | 2023-03-14 | null | null | null | null | ['training-free-3d-point-cloud-classification', '3d-point-cloud-classification', 'training-free-3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.19513881e-01 1.27448872e-01 -4.34133023e-01 -5.41408598e-01
-8.98938656e-01 -6.02257073e-01 7.38160193e-01 -1.89119920e-01
-9.67095569e-02 3.05742204e-01 -1.64628416e-01 -5.17035723e-01
-1.20795518e-01 -8.61298084e-01 -1.34498644e+00 -5.71999967e-01
-2.66372651e-01 7.63741910e-01 4.92461681e-01 -9.78241414... | [8.092841148376465, -3.6542787551879883] |
77a822c0-502d-460f-9146-3d42fa1822cf | insertgnn-can-graph-neural-networks | 2103.15066 | null | https://arxiv.org/abs/2103.15066v2 | https://arxiv.org/pdf/2103.15066v2.pdf | InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem? | Sentence insertion is an interesting NLP problem but received insufficient attention. Existing approaches in sentence ordering, text coherence, and question answering are neither suitable nor good enough at solving it. To bridge this gap, we propose InsertGNN, a simple yet effective model that represents the problem as... | ['Stan Z. Li', 'Fang Wu'] | 2021-03-28 | null | null | null | null | ['sentence-ordering'] | ['natural-language-processing'] | [-7.38318563e-02 3.81839037e-01 -3.80601794e-01 -3.82105112e-01
-8.87125909e-01 -5.21110058e-01 7.11004615e-01 6.29313767e-01
-3.11246485e-01 8.42624128e-01 6.80924892e-01 -5.11970043e-01
-3.82089287e-01 -5.39472222e-01 -7.31975317e-01 1.23711109e-01
-1.56365782e-01 8.47115695e-01 5.82274377e-01 -6.48432910... | [11.329943656921387, 8.782584190368652] |
c78132f9-1b83-4c8d-9c67-e08e40e9f4c7 | mono-camera-3d-multi-object-tracking-using | 1802.09975 | null | http://arxiv.org/abs/1802.09975v1 | http://arxiv.org/pdf/1802.09975v1.pdf | Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering | Monocular cameras are one of the most commonly used sensors in the automotive
industry for autonomous vehicles. One major drawback using a monocular camera
is that it only makes observations in the two dimensional image plane and can
not directly measure the distance to objects. In this paper, we aim at filling
this ga... | ['Emil Rosenberg', 'Joachim Benjaminsson', 'Karl Granstrom', 'Samuel Scheidegger', 'Amrit Krishnan'] | 2018-02-27 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-2.90846229e-01 -6.00440979e-01 -1.76310182e-01 9.94986370e-02
-4.66810197e-01 -6.19376242e-01 7.87829399e-01 -3.43088627e-01
-8.61852467e-01 3.80438328e-01 -8.20845068e-01 -1.05150379e-01
2.11678714e-01 -5.35596192e-01 -1.12974107e+00 -8.94255877e-01
3.83295059e-01 1.09386730e+00 9.46011722e-01 4.56595510... | [6.646108627319336, -2.1483237743377686] |
59426c04-bf10-4764-9cf4-101bb3f40c2c | approximate-bijective-correspondence-for | null | null | https://openreview.net/forum?id=uY6fuowMIT | https://openreview.net/pdf?id=uY6fuowMIT | Approximate Bijective Correspondence for isolating factors of variation | Representational learning forms the backbone of most deep learning applications, and the value of a learned representation is intimately tied to its information content regarding different factors of variation. Finding good representations depends on the nature of supervision and the learning algorithm. We propose a no... | ['Ameesh Makadia', 'Srikumar Ramalingam', 'Varun Jampani', 'Kieran A Murphy'] | 2021-09-29 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 5.60172498e-01 4.04857814e-01 -4.52877223e-01 -4.24913973e-01
-5.94909132e-01 -8.70945573e-01 7.30533063e-01 -6.85850605e-02
-2.12035030e-01 6.46794319e-01 4.33798164e-01 3.40888910e-02
-2.92648584e-01 -4.46302474e-01 -1.20797491e+00 -7.59034991e-01
1.31123945e-01 7.36383915e-01 1.59077913e-01 -4.74761337... | [9.488444328308105, 2.2172560691833496] |
cb3dc9ca-70b4-4a49-83e2-bfddc144e887 | age-estimation-using-expectation-of-label | null | null | http://www.lamda.nju.edu.cn/gaobb/Pub_files/IJCAI2018_DLDLv2.pdf | http://www.lamda.nju.edu.cn/gaobb/Pub_files/IJCAI2018_DLDLv2.pdf | Age Estimation Using Expectation of Label Distribution Learning | Age estimation performance has been greatly improved by using convolutional neural network. However, existing methods have an inconsistency between the training objectives and evaluation metric, so they may be suboptimal. In addition, these methods always adopt image classification or face recognition models with a lar... | ['Xin Geng', 'Jianxin Wu', 'Hong-Yu Zhou', 'Bin-Bin Gao'] | 2018-07-13 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-2.06833124e-01 -1.14642596e-03 -3.26287627e-01 -7.35295713e-01
-4.61456329e-01 -1.13767646e-01 3.81554514e-01 -3.40943076e-02
-8.40783954e-01 7.34274685e-01 -8.81471634e-02 -1.50495991e-01
-1.04085445e-01 -7.62962341e-01 -5.79258025e-01 -6.30615652e-01
1.61917228e-02 4.81892377e-01 -1.43822938e-01 3.60890597... | [13.611734390258789, 0.89747154712677] |
11f9f6ce-d8d7-476d-9018-720843be9258 | contrastive-meta-learning-for-few-shot-node | 2306.15154 | null | https://arxiv.org/abs/2306.15154v1 | https://arxiv.org/pdf/2306.15154v1.pdf | Contrastive Meta-Learning for Few-shot Node Classification | Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining tasks. Particularly, in this paper, we refer to the task of classifying nodes in classes with a few labeled nodes as the few-shot node classif... | ['Jundong Li', 'Huan Liu', 'Zhen Tan', 'Song Wang'] | 2023-06-27 | null | null | null | null | ['graph-mining', 'node-classification', 'meta-learning', 'classification-1'] | ['graphs', 'graphs', 'methodology', 'methodology'] | [ 8.19640607e-02 1.84239239e-01 -5.31333864e-01 -5.03290951e-01
-2.81921446e-01 -5.04587770e-01 4.82561082e-01 4.58766937e-01
-1.59352809e-01 2.99596906e-01 -9.73253921e-02 -2.45222494e-01
-2.15960592e-01 -1.23231447e+00 -5.06228447e-01 -7.05323815e-01
2.84085609e-02 1.44877285e-01 2.44099021e-01 -2.63790756... | [7.396748065948486, 6.176048755645752] |
4e687ee6-7bee-4112-bd4d-122c8015b623 | personalized-one-shot-lipreading-for-an-als | 2111.01740 | null | https://arxiv.org/abs/2111.01740v1 | https://arxiv.org/pdf/2111.01740v1.pdf | Personalized One-Shot Lipreading for an ALS Patient | Lipreading or visually recognizing speech from the mouth movements of a speaker is a challenging and mentally taxing task. Unfortunately, multiple medical conditions force people to depend on this skill in their day-to-day lives for essential communication. Patients suffering from Amyotrophic Lateral Sclerosis (ALS) of... | ['C V Jawahar', 'Vinay Namboodiri', 'Rudrabha Mukhopadhyay', 'Aditya Agarwal', 'Bipasha Sen'] | 2021-11-02 | personalized-one-shot-lipreading-for-an-als-1 | https://arxiv.org/abs/2111.01740 | https://arxiv.org/pdf/2111.01740.pdf | null | ['lipreading'] | ['computer-vision'] | [ 4.89111096e-01 5.02495408e-01 -1.78400859e-01 -1.89693913e-01
-1.42352116e+00 -3.61138508e-02 2.89133042e-01 -5.54274857e-01
-4.73324180e-01 9.92714345e-01 5.58809400e-01 6.17406331e-02
1.18117481e-01 -1.54940560e-01 -4.52654719e-01 -6.08005106e-01
4.96399492e-01 7.87222147e-01 -8.03146660e-02 -1.33658081... | [14.330784797668457, 4.9878926277160645] |
2ab4eecd-939f-41c5-8f57-0f27fe7c5a0a | rf-next-efficient-receptive-field-search-for | 2206.06637 | null | https://arxiv.org/abs/2206.06637v2 | https://arxiv.org/pdf/2206.06637v2.pdf | RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks | Temporal/spatial receptive fields of models play an important role in sequential/spatial tasks. Large receptive fields facilitate long-term relations, while small receptive fields help to capture the local details. Existing methods construct models with hand-designed receptive fields in layers. Can we effectively searc... | ['Liang Wang', 'Ming-Ming Cheng', 'Qi Han', 'Zhong-Yu Li', 'ShangHua Gao'] | 2022-06-14 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 1.50651962e-01 -1.44835696e-01 -3.21448088e-01 -5.40390730e-01
-6.21907651e-01 -6.27411962e-01 4.94538426e-01 -2.90858895e-01
-5.92557192e-01 4.48636413e-01 3.99849981e-01 -1.69801682e-01
-2.28406534e-01 -5.83250046e-01 -6.21598542e-01 -6.46173537e-01
2.23514035e-01 1.93760067e-01 8.68872464e-01 -2.46277377... | [9.61306095123291, 0.5690454840660095] |
c5a75a3b-06f0-4f4c-989b-28c621a65fe5 | the-devil-is-in-the-task-exploiting-1 | 2112.14023 | null | https://arxiv.org/abs/2112.14023v1 | https://arxiv.org/pdf/2112.14023v1.pdf | The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection | Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory. In this paper, we dig into the 3D object detection task and reformulate it as the sub-tasks of object localization and appearance perception, which benefits to a deep excavation of... | ['Errui Ding', 'xiangyang xue', 'Jianfeng Feng', 'Li Zhang', 'Xiao Tan', 'Xianhui Cheng', 'Liang Du', 'Xiaoqing Ye', 'Zhikang Zou'] | 2021-12-28 | the-devil-is-in-the-task-exploiting | http://openaccess.thecvf.com//content/ICCV2021/html/Zou_The_Devil_Is_in_the_Task_Exploiting_Reciprocal_Appearance-Localization_Features_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zou_The_Devil_Is_in_the_Task_Exploiting_Reciprocal_Appearance-Localization_Features_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-learning'] | ['natural-language-processing'] | [-1.26176015e-01 -1.95982277e-01 -4.97112907e-02 -3.17401141e-01
-4.05501902e-01 -5.04429221e-01 6.56475186e-01 -5.41242301e-01
-5.25553048e-01 2.32334405e-01 -4.70039159e-01 -5.02290130e-01
-2.87265691e-04 -3.24995548e-01 -8.57026458e-01 -6.72977507e-01
-7.90850446e-02 3.41016650e-01 7.37957120e-01 -2.78838933... | [7.865596771240234, -2.386770009994507] |
a9ed7f35-c60e-4981-b39a-5a0496d356fa | parameterization-of-state-duration-in-hidden | 2211.09478 | null | https://arxiv.org/abs/2211.09478v1 | https://arxiv.org/pdf/2211.09478v1.pdf | Parameterization of state duration in Hidden semi-Markov Models: an application in electrocardiography | This work aims at providing a new model for time series classification based on learning from just one example. We assume that time series can be well characterized as a parametric random process, a sort of Hidden semi-Markov Model representing a sequence of regression models with variable duration. We introduce a para... | ['Jesús María Rodríguez Presedo', 'Paulo Félix Lamas', 'Adrián Pérez Herrero'] | 2022-11-17 | null | null | null | null | ['heartbeat-classification'] | ['medical'] | [ 1.76378280e-01 -1.05633342e-03 -5.93857348e-01 -4.20600414e-01
-5.72630227e-01 -4.75091040e-01 6.33476436e-01 -7.07552060e-02
-8.87533184e-03 6.55665517e-01 -2.32104465e-01 -5.26662111e-01
-3.63647789e-01 -3.88781428e-01 -2.80970693e-01 -9.78952289e-01
-6.80531919e-01 8.92278433e-01 1.48030937e-01 1.21440940... | [7.04304838180542, 3.738914966583252] |
4975ec95-105f-49b1-a93b-4a5073de9ec9 | annotating-inter-sentence-temporal-relations | null | null | https://aclanthology.org/L14-1129 | https://aclanthology.org/L14-1129.pdf | Annotating Inter-Sentence Temporal Relations in Clinical Notes | Owing in part to the surge of interest in temporal relation extraction, a number of datasets manually annotated with temporal relations between event-event pairs and event-time pairs have been produced recently. However, it is not uncommon to find missing annotations in these manually annotated datasets. Many researche... | ["Jennifer D{'}Souza", 'Vincent Ng'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.94138336e-01 5.03896058e-01 -5.61765075e-01 -4.95242268e-01
-1.00321805e+00 -7.66730368e-01 5.79465091e-01 9.28692281e-01
-4.00035322e-01 1.11150670e+00 6.50258780e-01 -5.92895746e-01
-5.43316782e-01 -4.01100606e-01 -2.63242424e-01 -2.53909320e-01
-5.97690403e-01 6.71379149e-01 5.26828349e-01 -9.01008621... | [8.684209823608398, 9.08314323425293] |
151bd158-4fbd-4697-8084-6a6e28caf1a2 | dual-decoder-transformer-for-joint-automatic | 2011.00747 | null | https://arxiv.org/abs/2011.00747v1 | https://arxiv.org/pdf/2011.00747v1.pdf | Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation | We introduce dual-decoder Transformer, a new model architecture that jointly performs automatic speech recognition (ASR) and multilingual speech translation (ST). Our models are based on the original Transformer architecture (Vaswani et al., 2017) but consist of two decoders, each responsible for one task (ASR or ST). ... | ['Laurent Besacier', 'Didier Schwab', 'Jiatao Gu', 'Changhan Wang', 'Juan Pino', 'Hang Le'] | 2020-11-02 | null | https://aclanthology.org/2020.coling-main.314 | https://aclanthology.org/2020.coling-main.314.pdf | coling-2020-8 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 1.10033184e-01 3.00751954e-01 -2.11364329e-01 -2.54721463e-01
-1.56690598e+00 -7.56150484e-01 1.05298662e+00 -2.86479503e-01
-9.14036855e-02 6.59274399e-01 5.64720750e-01 -8.42307210e-01
6.65663838e-01 -3.01555336e-01 -1.05446994e+00 -4.95644271e-01
4.22659814e-01 8.97685945e-01 1.03823632e-01 -4.66517657... | [14.458165168762207, 7.248569488525391] |
d51dbada-2b59-4be6-927b-28190d594781 | neural-voice-cloning-with-a-few-samples | 1802.06006 | null | http://arxiv.org/abs/1802.06006v3 | http://arxiv.org/pdf/1802.06006v3.pdf | Neural Voice Cloning with a Few Samples | Voice cloning is a highly desired feature for personalized speech interfaces.
Neural network based speech synthesis has been shown to generate high quality
speech for a large number of speakers. In this paper, we introduce a neural
voice cloning system that takes a few audio samples as input. We study two
approaches: s... | ['Sercan O. Arik', 'Wei Ping', 'Kainan Peng', 'Yanqi Zhou', 'Jitong Chen'] | 2018-02-14 | neural-voice-cloning-with-a-few-samples-1 | http://papers.nips.cc/paper/8206-neural-voice-cloning-with-a-few-samples | http://papers.nips.cc/paper/8206-neural-voice-cloning-with-a-few-samples.pdf | neurips-2018-12 | ['voice-cloning'] | ['speech'] | [ 2.76123971e-01 4.83366400e-01 -4.51756902e-02 -5.69993436e-01
-9.41715777e-01 -3.25752586e-01 4.74691033e-01 -1.06629469e-01
-6.14778921e-02 6.00345731e-01 5.92709959e-01 -2.00342372e-01
4.79481637e-01 -4.55683857e-01 -6.54943049e-01 -5.58736265e-01
1.74986422e-01 4.85363036e-01 1.23775303e-01 -1.80696040... | [14.848723411560059, 6.518815994262695] |
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