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80d6c247-e5ab-4603-b035-1f4972b5fca8 | collaborative-and-ai-aided-exam-question | 2211.08361 | null | https://arxiv.org/abs/2211.08361v1 | https://arxiv.org/pdf/2211.08361v1.pdf | Collaborative and AI-aided Exam Question Generation using Wikidata in Education | Since the COVID-19 outbreak, the use of digital learning or education platforms has significantly increased. Teachers now digitally distribute homework and provide exercise questions. In both cases, teachers need to continuously develop novel and individual questions. This process can be very time-consuming and should ... | ['Bela Gipp', 'Andre Greiner-Petter', 'Andreas Spitz', 'Moritz Schubotz', 'Philipp Scharpf'] | 2022-11-15 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-2.63114095e-01 3.26071054e-01 3.81803840e-01 -2.82833040e-01
-6.40642464e-01 -8.65458965e-01 3.35554034e-01 7.78940678e-01
-2.59564459e-01 8.26013982e-01 -9.37645361e-02 -7.05041111e-01
-7.79115319e-01 -1.43993151e+00 -8.39063168e-01 -2.81365246e-01
5.91159463e-01 9.47479606e-01 5.66009879e-01 -4.45197135... | [10.863378524780273, 7.9770708084106445] |
d2e7815e-5c87-4bad-8ef6-48ec6d48de8f | ji-yu-shen-jing-wang-luo-de-ban-jian-du | null | null | https://aclanthology.org/2022.ccl-1.58 | https://aclanthology.org/2022.ccl-1.58.pdf | 基于神经网络的半监督CRF中文分词(Semi-supervised CRF Chinese Word Segmentation based on Neural Network) | “分词是中文信息处理的基础任务之一。目前全监督中文分词技术已相对成熟并在通用领域取得较好效果,但全监督方法存在依赖大规模标注语料且领域迁移能力差的问题,特别是跨领域未登录词识别性能不佳。为缓解上述问题,本文提出了一种充分利用相对易得的目标领域无标注文本、实现跨领域迁移的半监督中文分词框架;并设计实现了基于词记忆网络和序列条件熵的半监督权杒杆中文分词模型。实验结果表明本该模型在多个领域数据集上杆札值和杒杏杏杖值分别取得最高朲.朳朵朥和朱朲.朱朲朥的提升,并在多个数据集上成为当前好结果。” | ['Zhilin Zhao', 'Yujiao Han', 'Mingming Zhang', 'Zhiyong Luo'] | null | null | null | null | ccl-2022-10 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [-6.66921973e-01 -5.64109385e-01 6.71120346e-01 3.03466797e-01
2.83869430e-02 -7.85524726e-01 -1.05289675e-01 1.19038069e+00
3.38487849e-02 4.03807819e-01 6.26807153e-01 2.05462188e-01
-2.91888267e-01 -1.26404476e+00 -7.43031681e-01 -9.41599965e-01
-5.31877100e-01 1.94262600e+00 5.60809851e-01 -5.97569168... | [-3.3159680366516113, 6.90767765045166] |
533f3c9d-09d2-478e-917e-05845f555c53 | graph-based-network-with-contextualized | 2109.04008 | null | https://arxiv.org/abs/2109.04008v1 | https://arxiv.org/pdf/2109.04008v1.pdf | Graph Based Network with Contextualized Representations of Turns in Dialogue | Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogu... | ['Yong Suk Choi', 'Bongseok Lee'] | 2021-09-09 | null | https://aclanthology.org/2021.emnlp-main.36 | https://aclanthology.org/2021.emnlp-main.36.pdf | emnlp-2021-11 | ['dialog-relation-extraction', 'emotion-recognition-in-conversation'] | ['natural-language-processing', 'natural-language-processing'] | [-4.87781726e-02 5.84744632e-01 -1.94821674e-02 -6.52584851e-01
-4.20213580e-01 -3.70750666e-01 8.80168498e-01 2.41892010e-01
-2.27874249e-01 8.65550637e-01 6.40430033e-01 -4.92006153e-01
2.79778957e-01 -9.60422158e-01 -1.80163756e-01 -4.43467721e-02
1.21164396e-01 6.67758226e-01 -1.29432157e-01 -8.85583699... | [12.497941970825195, 7.953983306884766] |
6bff801f-6a76-497c-8a83-fe77b585a3b9 | general-partial-label-learning-via-dual | 2001.01290 | null | https://arxiv.org/abs/2001.01290v2 | https://arxiv.org/pdf/2001.01290v2.pdf | General Partial Label Learning via Dual Bipartite Graph Autoencoder | We formulate a practical yet challenging problem: General Partial Label Learning (GPLL). Compared to the traditional Partial Label Learning (PLL) problem, GPLL relaxes the supervision assumption from instance-level -- a label set partially labels an instance -- to group-level: 1) a label set partially labels a group of... | ['Shih-Fu Chang', 'Brian Chen', 'Hanwang Zhang', 'Bo Wu', 'Alireza Zareian'] | 2020-01-05 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 3.71961027e-01 6.88132644e-01 -4.15076673e-01 -5.31091511e-01
-5.08421421e-01 -5.74813128e-01 3.80759269e-01 1.47637399e-02
8.39400664e-02 7.25088537e-01 -7.43618384e-02 -1.94926456e-01
-8.86899605e-02 -7.13460267e-01 -1.08342624e+00 -7.51962125e-01
-9.06218141e-02 6.34735703e-01 -5.43666184e-02 1.98837128... | [9.698308944702148, 3.956148624420166] |
76c98f83-9399-462f-8316-9551fccb03cd | ldc-lightweight-dense-cnn-for-edge-detection | null | null | https://ieeexplore.ieee.org/document/9807316 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9807316 | LDC: Lightweight Dense CNN for Edge Detection | This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highes... | ['Angel Domingo Sappa', 'Gonzalo Pomboza-Junez', 'Xavier Soria Poma'] | 2022-06-27 | null | null | null | ieee-access-2022-6 | ['edge-detection'] | ['computer-vision'] | [-3.24640095e-01 2.13702053e-01 6.76464438e-02 2.46592332e-02
-3.72784704e-01 -1.33908048e-01 5.96008718e-01 1.28835171e-01
-8.41707885e-01 3.23563337e-01 8.75133649e-02 -5.00397801e-01
3.07258397e-01 -7.93380380e-01 -8.39384079e-01 -3.34363550e-01
-3.33607107e-01 2.34026790e-01 5.52589595e-01 -1.89878643... | [9.182480812072754, 0.9318009614944458] |
5d12bbb8-c420-49a4-b3c7-a4ad411abe88 | deep-cnn-denoiser-and-multi-layer-neighbor | 1806.10726 | null | http://arxiv.org/abs/1806.10726v1 | http://arxiv.org/pdf/1806.10726v1.pdf | Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination | Most of the current face hallucination methods, whether they are shallow
learning-based or deep learning-based, all try to learn a relationship model
between Low-Resolution (LR) and High-Resolution (HR) spaces with the help of a
training set. They mainly focus on modeling image prior through either
model-based optimiza... | ['Yi Yu', 'Junjun Jiang', 'Jinhui Hu', 'Jiayi Ma', 'Suhua Tang'] | 2018-06-28 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 1.41287774e-01 2.05087826e-01 -8.95116404e-02 -5.50558507e-01
-7.82493651e-01 1.29216433e-01 4.23128963e-01 -6.46610439e-01
4.76470925e-02 5.88820696e-01 5.39114714e-01 4.65655893e-01
1.01533450e-01 -8.70367110e-01 -7.85867751e-01 -7.21030295e-01
5.55874944e-01 -1.05432719e-02 -1.36585802e-01 -2.66796350... | [12.838478088378906, -0.02319193072617054] |
4489e24e-abb4-4a31-8791-b0dd84b44474 | humans-in-4d-reconstructing-and-tracking | 2305.20091 | null | https://arxiv.org/abs/2305.20091v2 | https://arxiv.org/pdf/2305.20091v2.pdf | Humans in 4D: Reconstructing and Tracking Humans with Transformers | We present an approach to reconstruct humans and track them over time. At the core of our approach, we propose a fully "transformerized" version of a network for human mesh recovery. This network, HMR 2.0, advances the state of the art and shows the capability to analyze unusual poses that have in the past been difficu... | ['Jitendra Malik', 'Angjoo Kanazawa', 'Jathushan Rajasegaran', 'Georgios Pavlakos', 'Shubham Goel'] | 2023-05-31 | null | null | null | null | ['pose-tracking', 'action-recognition-in-videos', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.50693715e-01 -8.71597379e-02 -1.27801448e-01 3.22865532e-03
-5.60889304e-01 -4.39315826e-01 4.79848772e-01 -6.29177272e-01
-2.94781625e-01 5.13438642e-01 4.87417966e-01 2.09990159e-01
2.72794545e-01 -3.37561429e-01 -7.46188521e-01 -2.52609462e-01
-9.74114910e-02 8.85894001e-01 3.86793941e-01 -2.04023004... | [7.076631546020508, -0.8994901776313782] |
aceb2d94-7d8e-467d-8aad-070808a73ce8 | spatio-temporal-covariance-descriptors-for | 1303.6021 | null | https://arxiv.org/abs/1303.6021v1 | https://arxiv.org/pdf/1303.6021v1.pdf | Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition | We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors. The weighted projection is then exploited during boosting to create a final multicl... | ['Andres Sanin', 'Conrad Sanderson', 'Mehrtash T. Harandi', 'Brian C. Lovell'] | 2013-03-25 | null | null | null | null | ['interest-point-detection'] | ['computer-vision'] | [ 2.14148551e-01 -6.49427474e-01 -2.83102721e-01 -5.56898117e-01
-6.73816323e-01 -3.75644445e-01 9.21338320e-01 -1.69552132e-01
-8.90872717e-01 4.26947057e-01 7.10388198e-02 1.75953899e-02
-3.91028911e-01 -2.76666403e-01 -3.01720917e-01 -1.06495643e+00
-3.16117674e-01 1.01098493e-01 5.98859608e-01 -9.83924512... | [8.01049518585205, 0.3413868248462677] |
01d4aa12-c3b6-4de7-8568-682b34dbe83a | hsr-l1-2-regularized-sparse-representation | 1409.6448 | null | https://arxiv.org/abs/1409.6448v1 | https://arxiv.org/pdf/1409.6448v1.pdf | HSR: L1/2 Regularized Sparse Representation for Fast Face Recognition using Hierarchical Feature Selection | In this paper, we propose a novel method for fast face recognition called L1/2 Regularized Sparse Representation using Hierarchical Feature Selection (HSR). By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computatio... | ['Mengmeng Ma', 'Bo Han', 'Tingting Sun', 'Bo He', 'Amaury Lendasse'] | 2014-09-23 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [-3.95858660e-02 -4.40549880e-01 -3.31261992e-01 -2.36187682e-01
-5.66979051e-01 9.92802680e-02 2.37853266e-02 -4.54481393e-01
6.60362616e-02 5.55933058e-01 3.09417129e-01 3.86607975e-01
-3.30835670e-01 -9.81468558e-01 -3.50910187e-01 -9.77932811e-01
3.83625701e-02 -1.51174366e-01 -9.31989104e-02 -1.08335748... | [12.532699584960938, 0.42020609974861145] |
9fdccb6d-f563-47cc-a235-7c21febccd34 | selffed-self-supervised-federated-learning | 2307.01514 | null | https://arxiv.org/abs/2307.01514v1 | https://arxiv.org/pdf/2307.01514v1.pdf | SelfFed: Self-supervised Federated Learning for Data Heterogeneity and Label Scarcity in IoMT | Self-supervised learning in federated learning paradigm has been gaining a lot of interest both in industry and research due to the collaborative learning capability on unlabeled yet isolated data. However, self-supervised based federated learning strategies suffer from performance degradation due to label scarcity and... | ['Marius George Linguraru', 'Syed Muhammad Anwar', 'Kapal Dev', 'Sunder Ali Khowaja'] | 2023-07-04 | null | null | null | null | ['self-supervised-learning', 'federated-learning'] | ['computer-vision', 'methodology'] | [ 2.01662898e-01 2.21445844e-01 -3.38007510e-01 -5.17631710e-01
-8.72595012e-01 -2.23544031e-01 3.56142521e-01 2.32525945e-01
-4.79132205e-01 8.36666644e-01 3.20499748e-01 -1.23511352e-01
-1.38451800e-01 -5.49512982e-01 -4.90637004e-01 -8.93271625e-01
6.79273456e-02 4.56063807e-01 1.77303106e-01 2.22843379... | [6.042026996612549, 6.451242446899414] |
b6246a67-91f4-495c-b4dd-cbc5778a8852 | a-self-organising-eigenspace-map-for-time | 1905.05540 | null | https://arxiv.org/abs/1905.05540v1 | https://arxiv.org/pdf/1905.05540v1.pdf | A self-organising eigenspace map for time series clustering | This paper presents a novel time series clustering method, the self-organising eigenspace map (SOEM), based on a generalisation of the well-known self-organising feature map (SOFM). The SOEM operates on the eigenspaces of the embedded covariance structures of time series which are related directly to modes in those tim... | ['Jacek Brodzki', 'Donya Rahmani', 'Damien Fay'] | 2019-05-14 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-7.08539262e-02 -3.66096795e-01 3.87712657e-01 -3.08410794e-01
6.82749078e-02 -7.87361503e-01 9.28818762e-01 2.12356776e-01
-7.90474340e-02 9.72254649e-02 4.47487235e-01 -4.85330611e-01
-1.05580676e+00 -5.70050299e-01 -3.83796878e-02 -9.00711417e-01
-1.15249348e+00 6.78939939e-01 1.61062673e-01 -4.19426560... | [7.262866973876953, 3.34808087348938] |
7808fad3-b175-49d9-9664-751ee2ceb6ae | it-is-ai-s-turn-to-ask-human-a-question | 2109.03423 | null | https://arxiv.org/abs/2109.03423v4 | https://arxiv.org/pdf/2109.03423v4.pdf | It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books | Existing question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers often need to decide what questions they should ask, in order to help students to improve their narrative understanding capabilities. We design an automated question-answer gener... | ['Zheng Zhang', 'Ying Xu', 'Mo Yu', 'Toby Jia-Jun Li', 'Tongshuang Wu', 'Dakuo Wang', 'Bingsheng Yao'] | 2021-09-08 | null | null | null | null | ['question-answer-generation'] | ['natural-language-processing'] | [-1.92936473e-02 5.94551980e-01 4.15966958e-01 -5.62893748e-01
-1.22754943e+00 -1.02655172e+00 4.57225621e-01 3.79068315e-01
1.30438954e-01 7.14750767e-01 5.22237599e-01 -7.70848811e-01
-9.66145918e-02 -1.37572646e+00 -5.50203979e-01 2.07421795e-01
4.50721145e-01 1.03404927e+00 8.96577179e-01 -8.93952489... | [11.56077766418457, 8.037120819091797] |
bcdff0db-4a5d-425e-9f6a-653b3cb44530 | assessment-of-the-local-tchebichef-moments | 1910.09758 | null | https://arxiv.org/abs/1910.09758v1 | https://arxiv.org/pdf/1910.09758v1.pdf | Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters | In this paper we use machine learning to study the application of Local Tchebichef Moments (LTM) to the problem of texture classification. The original LTM method was proposed by Mukundan (2014). The LTM method can be used for texture analysis in many different ways, either using the moment values directly, or more sim... | ['Teo Susnjak', 'Napoleon Reyes', 'Andre Barczak'] | 2019-10-22 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 1.60608724e-01 -4.51945662e-01 -1.91477254e-01 -3.41090590e-01
-2.70944178e-01 -4.27519709e-01 9.12839115e-01 5.07275701e-01
-5.61182141e-01 5.69174647e-01 -1.33648723e-01 -2.41288856e-01
-5.70372880e-01 -1.12830520e+00 -3.34261537e-01 -1.07754230e+00
-2.36799777e-01 5.02611518e-01 9.22084153e-01 -2.86951005... | [10.295022010803223, -0.3919641673564911] |
58766fe4-b2c4-4839-b01b-36deca166954 | predicting-intubation-support-requirement-of | 2011.01787 | null | https://arxiv.org/abs/2011.01787v1 | https://arxiv.org/pdf/2011.01787v1.pdf | Predicting intubation support requirement of patients using Chest X-ray with Deep Representation Learning | Recent developments in medical imaging with Deep Learning presents evidence of automated diagnosis and prognosis. It can also be a complement to currently available diagnosis methods. Deep Learning can be leveraged for diagnosis, severity prediction, intubation support prediction and many similar tasks. We present pred... | ['Aniket Maurya'] | 2020-10-28 | null | null | null | null | ['intubation-support-prediction'] | ['computer-vision'] | [-3.31207782e-01 -2.91425958e-02 -6.55881584e-01 -7.12487817e-01
-9.69594657e-01 -4.37020123e-01 -1.72692448e-01 4.68607366e-01
-1.50387660e-01 5.26162624e-01 7.11777031e-01 -1.06270969e+00
-5.13609588e-01 -5.34654677e-01 -3.17864060e-01 -5.46502471e-01
-1.34865254e-01 9.10124779e-01 -2.85261452e-01 1.79098487... | [15.251362800598145, -1.8276660442352295] |
88786129-1dcd-4525-afef-f6dd178b9193 | atf-towards-robust-face-alignment-via | null | null | https://dl.acm.org/doi/10.1145/3394171.3414037 | https://dl.acm.org/doi/10.1145/3394171.3414037 | ATF: Towards Robust Face Alignment via Leveraging Similarity and Diversity across Different Datasets | Face alignment is an important task in the field of multi-media. Together with the impressive progress of algorithms, various benchmark datasets have been released in recent years. Intuitively, it is meaningful to integrate multiple labeled datasets with different annotations to achieve higher performance on a target l... | ['Jian Cheng', 'Cong Leng', 'Fangzhou Xiong', 'Qinghao Hu', 'Xing Lan'] | 2020-10-12 | null | null | null | acm-mm-2020-10-1 | ['robust-face-alignment', 'face-alignment'] | ['computer-vision', 'computer-vision'] | [ 5.23781031e-02 -1.40997514e-01 -3.43718648e-01 -5.44041991e-01
-9.79440451e-01 -1.02898076e-01 4.18529958e-01 -2.20838308e-01
-2.89750725e-01 4.57871705e-01 1.80058181e-01 -1.43116070e-02
1.51277590e-03 -5.22811770e-01 -6.96992576e-01 -6.32898986e-01
2.88517237e-01 2.70458937e-01 2.58371949e-01 -1.50837988... | [13.434258460998535, 0.5031903982162476] |
f7767a1b-1710-4bf7-91cb-54f5e0202a2e | trans-dimensional-generative-modeling-via | 2305.16261 | null | https://arxiv.org/abs/2305.16261v1 | https://arxiv.org/pdf/2305.16261v1.pdf | Trans-Dimensional Generative Modeling via Jump Diffusion Models | We propose a new class of generative models that naturally handle data of varying dimensionality by jointly modeling the state and dimension of each datapoint. The generative process is formulated as a jump diffusion process that makes jumps between different dimensional spaces. We first define a dimension destroying f... | ['Arnaud Doucet', 'Tom Rainforth', 'Valentin De Bortoli', 'Christian Weilbach', 'William Harvey', 'Andrew Campbell'] | 2023-05-25 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 2.31183782e-01 1.75741911e-01 -3.05142462e-01 -2.25329906e-01
-7.62859941e-01 -7.01205969e-01 1.16990280e+00 -3.70204329e-01
-2.85696894e-01 1.03594530e+00 3.93026114e-01 -4.61290419e-01
-4.86828983e-01 -7.60054767e-01 -1.01952434e+00 -1.11888468e+00
-2.70079315e-01 1.27196145e+00 -1.19125858e-01 2.44134232... | [6.807392597198486, 3.9281530380249023] |
0bab169c-af19-440f-98eb-5d5b95c69533 | dmcnn-dual-domain-multi-scale-convolutional | 1806.03275 | null | http://arxiv.org/abs/1806.03275v2 | http://arxiv.org/pdf/1806.03275v2.pdf | DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal | JPEG is one of the most commonly used standards among lossy image compression
methods. However, JPEG compression inevitably introduces various kinds of
artifacts, especially at high compression rates, which could greatly affect the
Quality of Experience (QoE). Recently, convolutional neural network (CNN) based
methods ... | ['Yueyu Hu', 'Jiaying Liu', 'Xiaoshuai Zhang', 'Wenhan Yang'] | 2018-06-08 | null | null | null | null | ['jpeg-artifact-correction', 'jpeg-artifact-removal'] | ['computer-vision', 'computer-vision'] | [ 3.03878814e-01 -5.18421352e-01 -2.64287312e-02 -2.47365654e-01
-3.86210620e-01 8.00099373e-02 1.00380860e-01 -7.22531751e-02
-3.91632736e-01 5.46215892e-01 3.92563879e-01 4.01215442e-02
1.01953760e-01 -1.04601943e+00 -6.12002909e-01 -5.50359190e-01
2.08955318e-01 -6.06104553e-01 2.33803257e-01 -4.82787907... | [11.298881530761719, -1.7291356325149536] |
994d1d76-7bca-4cb6-80cb-35c6fc24d5fb | bspell-a-cnn-blended-bert-based-bengali-spell | 2208.09709 | null | https://arxiv.org/abs/2208.09709v1 | https://arxiv.org/pdf/2208.09709v1.pdf | BSpell: A CNN-blended BERT Based Bengali Spell Checker | Bengali typing is mostly performed using English keyboard and can be highly erroneous due to the presence of compound and similarly pronounced letters. Spelling correction of a misspelled word requires understanding of word typing pattern as well as the context of the word usage. We propose a specialized BERT model, BS... | ['Mohammed Eunus Ali', 'Mohammad Rafsan', 'Samiha Zakir', 'Md. Hasibur Rahman', 'Chowdhury Rafeed Rahman'] | 2022-08-20 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 0.6060491 -0.39525712 0.24209206 -0.29798222 -0.8619814 -0.69403315
0.10670859 0.72474885 -0.94236016 0.83825207 0.09745408 -0.64333904
0.3312745 -0.6499015 -0.8995885 -0.3234925 0.6896999 0.31767565
0.38232657 -0.42833516 0.546376 0.32384443 -1.0826082 0.5977179
1.2603197 0.32326493 0.7... | [10.95927906036377, 10.778428077697754] |
3bc0d526-2e79-482b-af06-e7c432999c88 | successor-predecessor-intrinsic-exploration | 2305.15277 | null | https://arxiv.org/abs/2305.15277v1 | https://arxiv.org/pdf/2305.15277v1.pdf | Successor-Predecessor Intrinsic Exploration | Exploration is essential in reinforcement learning, particularly in environments where external rewards are sparse. Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated intrinsic rewards. Although the study of intrinsic rewards has a long his... | ['Sam Gershman', 'Maneesh Sahani', 'Neil Burgess', 'Changmin Yu'] | 2023-05-24 | null | null | null | null | ['efficient-exploration', 'atari-games'] | ['methodology', 'playing-games'] | [-1.39017344e-01 3.04057211e-01 -3.49034131e-01 -8.50778893e-02
-4.73514646e-01 -4.87984389e-01 9.80580449e-01 1.59220561e-01
-9.61581588e-01 1.11881268e+00 4.92168337e-01 -1.91196725e-01
-2.83778459e-01 -7.57535219e-01 -5.50829113e-01 -7.44141519e-01
-7.41366744e-01 6.05577409e-01 1.68624952e-01 -6.29303277... | [4.0086493492126465, 1.7459741830825806] |
7662d949-eb5a-4f5f-8100-a2e9d935d05a | keyword-assisted-embedded-topic-model | 2112.03101 | null | https://arxiv.org/abs/2112.03101v1 | https://arxiv.org/pdf/2112.03101v1.pdf | Keyword Assisted Embedded Topic Model | By illuminating latent structures in a corpus of text, topic models are an essential tool for categorizing, summarizing, and exploring large collections of documents. Probabilistic topic models, such as latent Dirichlet allocation (LDA), describe how words in documents are generated via a set of latent distributions ca... | ['Fred Morstatter', 'J. Hunter Priniski', 'Bahareh Harandizadeh'] | 2021-11-22 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-2.01997042e-01 3.07612747e-01 -4.91973162e-01 -3.49563956e-01
-5.96848428e-01 -6.19057953e-01 1.07835555e+00 4.28931952e-01
-1.10777896e-02 2.96928138e-01 8.70976090e-01 -1.76641062e-01
6.83891103e-02 -8.95912051e-01 -7.99151137e-02 -4.26699400e-01
4.55548987e-03 5.74749410e-01 1.67532608e-01 2.39556879... | [10.366776466369629, 6.971808433532715] |
9bc64698-a6ab-42b5-8aab-8fb52f081beb | road-detection-via-on-line-label-transfer | 1412.3159 | null | http://arxiv.org/abs/1412.3159v1 | http://arxiv.org/pdf/1412.3159v1.pdf | Road Detection via On--line Label Transfer | Vision-based road detection is an essential functionality for supporting
advanced driver assistance systems (ADAS) such as road following and vehicle
and pedestrian detection. The major challenges of road detection are dealing
with shadows and lighting variations and the presence of other objects in the
scene. Current ... | ['Antonio M. López', 'José M. Álvarez', 'Joan Serrat', 'Ferran Diego'] | 2014-12-10 | null | null | null | null | ['video-alignment'] | ['computer-vision'] | [ 5.16590714e-01 -4.73562293e-02 7.58363307e-02 -4.16740417e-01
-1.28173679e-02 -3.98960531e-01 8.69922936e-01 1.03960903e-02
-4.31780189e-01 6.34662032e-01 -2.15124458e-01 -5.26464701e-01
4.91340123e-02 -1.05512500e+00 -5.54787636e-01 -7.04384804e-01
1.43422917e-01 1.83912233e-01 8.10169041e-01 -4.15429354... | [8.02596664428711, -1.360795497894287] |
4b4c0cb4-c0e4-48ab-ae9a-8efc849cfe44 | learning-syntax-from-naturally-occurring | 2104.13933 | null | https://arxiv.org/abs/2104.13933v1 | https://arxiv.org/pdf/2104.13933v1.pdf | Learning Syntax from Naturally-Occurring Bracketings | Naturally-occurring bracketings, such as answer fragments to natural language questions and hyperlinks on webpages, can reflect human syntactic intuition regarding phrasal boundaries. Their availability and approximate correspondence to syntax make them appealing as distant information sources to incorporate into unsup... | ['Lillian Lee', 'Igor Malioutov', 'Ozan İrsoy', 'Tianze Shi'] | 2021-04-28 | null | https://aclanthology.org/2021.naacl-main.234 | https://aclanthology.org/2021.naacl-main.234.pdf | naacl-2021-4 | ['constituency-parsing'] | ['natural-language-processing'] | [-2.44176798e-02 7.69186318e-01 -8.07237327e-01 -1.06692350e+00
-1.38838470e+00 -1.00184977e+00 2.90501565e-01 4.90318298e-01
-5.24739265e-01 7.53056288e-01 8.51713240e-01 -7.86322534e-01
3.97433043e-01 -6.09495461e-01 -8.92454028e-01 1.54771313e-01
8.25588927e-02 4.15432364e-01 5.18339157e-01 -3.33464265... | [10.360138893127441, 9.63306999206543] |
ea283a4e-ce8b-4b14-9934-47c3d51f5b43 | pfgm-unlocking-the-potential-of-physics | 2302.04265 | null | https://arxiv.org/abs/2302.04265v2 | https://arxiv.org/pdf/2302.04265v2.pdf | PFGM++: Unlocking the Potential of Physics-Inspired Generative Models | We introduce a new family of physics-inspired generative models termed PFGM++ that unifies diffusion models and Poisson Flow Generative Models (PFGM). These models realize generative trajectories for $N$ dimensional data by embedding paths in $N{+}D$ dimensional space while still controlling the progression with a simp... | ['Tommi Jaakkola', 'Max Tegmark', 'Shangyuan Tong', 'Yonglong Tian', 'Ziming Liu', 'Yilun Xu'] | 2023-02-08 | null | null | null | null | ['2048'] | ['playing-games'] | [-4.11342621e-01 -5.44985831e-02 -9.67885703e-02 -2.66124427e-01
-9.38797295e-01 -4.26232249e-01 5.43181717e-01 -4.27582890e-01
-4.77114201e-01 8.70358706e-01 -7.10505713e-03 -4.45964515e-01
-5.40786684e-01 -1.01625192e+00 -6.08940661e-01 -1.06243408e+00
-5.22449374e-01 5.13015211e-01 6.39289469e-02 -6.00189827... | [11.40469741821289, -0.4564675986766815] |
1a23955a-92cb-4da5-a713-e81dab150e52 | sampling-techniques-for-streaming-cross | null | null | https://aclanthology.info/papers/N15-1158/n15-1158 | https://www.aclweb.org/anthology/N15-1158 | Sampling Techniques for Streaming Cross Document Coreference Resolution | null | ['Victor Lavrenko', 'Miles Osborne', 'Luke Shrimpton'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['cross-document-coreference-resolution'] | ['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... | [-1.5392029285430908, 15.869206428527832] |
1d2a38b6-b969-4b14-8cca-64f8984f6a85 | the-2nd-place-solution-for-2023-waymo-open | 2306.15914 | null | https://arxiv.org/abs/2306.15914v1 | https://arxiv.org/pdf/2306.15914v1.pdf | The 2nd Place Solution for 2023 Waymo Open Sim Agents Challenge | In this technical report, we present the 2nd place solution of 2023 Waymo Open Sim Agents Challenge (WOSAC)[4]. We propose a simple yet effective autoregressive method for simulating multi-agent behaviors, which is built upon a well-known multimodal motion forecasting framework called Motion Transformer (MTR)[5] with p... | ['Minghao Tian', 'Di Xiu', 'Cheng Qian'] | 2023-06-28 | null | null | null | null | ['motion-forecasting'] | ['computer-vision'] | [-6.81833625e-01 -2.70217031e-01 -2.13606376e-02 2.63858587e-01
-6.30261123e-01 -4.85562384e-01 1.10983431e+00 -3.73322010e-01
-6.77031577e-01 8.38149607e-01 6.16538882e-01 -2.19005942e-01
-1.46975413e-01 -5.61848998e-01 -5.59530735e-01 -5.36374271e-01
-5.33096313e-01 6.71805203e-01 3.70852679e-01 -8.64714444... | [5.782158851623535, 0.8607968091964722] |
9e6fd677-3588-44e2-8b8e-a5ca4a180de9 | a-metaheuristic-driven-approach-to-fine-tune | 2101.05795 | null | https://arxiv.org/abs/2101.05795v1 | https://arxiv.org/pdf/2101.05795v1.pdf | A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines | Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains. One of the main shortcomings of these techniques involves the choice of their hyperparameters, since they have a significant i... | ['João Paulo Papa', 'Leandro Aparecido Passos'] | 2021-01-14 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-2.90805161e-01 -4.75883812e-01 -7.72843808e-02 -2.62705892e-01
-1.09619632e-01 -1.32772684e-01 5.37290037e-01 -5.39696589e-02
-1.08777654e+00 1.00110590e+00 -1.15068540e-01 1.35272346e-03
-6.54476941e-01 -1.00324690e+00 -3.20977271e-01 -1.30094409e+00
-4.63916697e-02 7.46251166e-01 3.69667858e-01 -3.37571204... | [8.237793922424316, 3.276423692703247] |
cc0f053a-42d5-44ba-bb14-ad913d08c31f | collaborative-attention-memory-network-for | 2205.08075 | null | https://arxiv.org/abs/2205.08075v2 | https://arxiv.org/pdf/2205.08075v2.pdf | Collaborative Attention Memory Network for Video Object Segmentation | Semi-supervised video object segmentation is a fundamental yet Challenging task in computer vision. Embedding matching based CFBI series networks have achieved promising results by foreground-background integration approach. Despite its superior performance, these works exhibit distinct shortcomings, especially the fal... | ['Jinpeng Tang', 'Yuandong Zhong', 'Yuwei Zheng', 'Fei Xie', 'Junli Zha', 'Zhixing Huang'] | 2022-05-17 | null | null | null | null | ['semi-supervised-video-object-segmentation'] | ['computer-vision'] | [ 5.03225267e-01 7.61020929e-02 -8.00749734e-02 -2.66754866e-01
-6.08401656e-01 -4.36207801e-02 3.86028260e-01 -3.10752034e-01
-5.53224146e-01 6.82919979e-01 1.09780664e-02 2.16927350e-01
3.40959609e-01 -5.89026630e-01 -9.43434834e-01 -6.91363573e-01
3.38957548e-01 1.66749865e-01 1.03982055e+00 -1.13611147... | [9.427870750427246, -0.1524517834186554] |
095cbfcb-e9f5-4cec-a919-617f6000a70f | cross-lingual-transfer-learning-for-check | 2211.05087 | null | https://arxiv.org/abs/2211.05087v1 | https://arxiv.org/pdf/2211.05087v1.pdf | Cross-lingual Transfer Learning for Check-worthy Claim Identification over Twitter | Misinformation spread over social media has become an undeniable infodemic. However, not all spreading claims are made equal. If propagated, some claims can be destructive, not only on the individual level, but to organizations and even countries. Detecting claims that should be prioritized for fact-checking is conside... | ['Tamer Elsayed', 'Maram Hasanain'] | 2022-11-09 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-2.31807724e-01 8.84092003e-02 -5.73906481e-01 4.93448693e-04
-1.46591926e+00 -7.13647604e-01 1.05396092e+00 7.01125979e-01
-6.16993546e-01 1.00003731e+00 2.14402795e-01 -4.73060906e-01
3.64138782e-01 -7.96116054e-01 -8.80176067e-01 -9.35712755e-02
3.39656204e-01 9.16347325e-01 5.93890250e-01 -7.51702249... | [8.204413414001465, 10.260649681091309] |
b6c496ed-a218-4b43-98af-c60f32136e16 | direct-velocity-inversion-of-ground | null | null | https://doi.org/10.1029/2020JB021047 | https://www.researchgate.net/profile/Zi_Xian_Leong/publication/351760571_Direct_Velocity_Inversion_of_Ground_Penetrating_Radar_Data_Using_GPRNet/links/60b92d38299bf10dff91746c/Direct-Velocity-Inversion-of-Ground-Penetrating-Radar-Data-Using-GPRNet.pdf | Direct Velocity Inversion of Ground Penetrating Radar Data Using GPRNet | Ground penetrating radar (GPR) is used to image the shallow subsurface as evident in earth and planetary exploration. Electromagnetic (EM) velocity (permittivity) models are inverted from GPR data for accurate migration. While conventional velocity analysis methods are designed for multioffset GPR data, to our knowledg... | ['Tieyuan Zhu', 'Zi Xian Leong'] | 2021-05-20 | null | null | null | journal-of-geophysical-research-solid-earth | ['gpr', 'geophysics', 'seismic-imaging', 'gpr', 'seismic-inversion'] | ['computer-vision', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous'] | [ 1.71926871e-01 -1.80473328e-01 2.13130057e-01 -1.58579588e-01
-8.06291521e-01 4.90716249e-02 2.88002223e-01 -3.42488140e-01
-5.97837090e-01 6.41026497e-01 9.73203182e-02 -1.07369101e+00
-3.82635415e-01 -1.28019834e+00 -6.84166014e-01 -7.09754765e-01
-5.88984787e-01 6.51895106e-01 -7.35490471e-02 -5.14913023... | [6.818160057067871, 2.3868775367736816] |
009caa2a-f535-4319-91a3-dc1eb22faf88 | learning-to-count-objects-with-few-exemplar | 1905.07898 | null | https://arxiv.org/abs/1905.07898v1 | https://arxiv.org/pdf/1905.07898v1.pdf | Learning to Count Objects with Few Exemplar Annotations | In this paper, we study the problem of object counting with incomplete annotations. Based on the observation that in many object counting problems the target objects are normally repeated and highly similar to each other, we are particularly interested in the setting when only a few exemplar annotations are provided. D... | ['Yandong Guo', 'Rong Xiao', 'Jianfeng Wang', 'Lei Zhang'] | 2019-05-20 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 2.54737884e-01 7.63967782e-02 -2.62763828e-01 -3.76806229e-01
-5.84708989e-01 -5.41873157e-01 4.95636165e-01 5.39123297e-01
-8.04273605e-01 6.78189695e-01 -5.20410538e-01 3.09698526e-02
2.58710861e-01 -6.59724355e-01 -7.81321824e-01 -5.29906571e-01
1.15189470e-01 6.57980263e-01 6.44422472e-01 4.13516939... | [9.072470664978027, 0.723052442073822] |
7dd5f53d-f901-4bc6-8d88-3ddc98bbac78 | meme-generating-rnn-model-explanations-via | 2012.06954 | null | https://arxiv.org/abs/2012.06954v1 | https://arxiv.org/pdf/2012.06954v1.pdf | MEME: Generating RNN Model Explanations via Model Extraction | Recurrent Neural Networks (RNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering RNN-based approaches is improving their explainability and interpretability. In this work we present MEME: a model extraction approach capable of approximating RNNs with interpretable models repre... | ['Pietro Liò', 'Mateja Jamnik', 'Botty Dimanov', 'Dmitry Kazhdan'] | 2020-12-13 | null | null | null | null | ['occupation-prediction'] | ['natural-language-processing'] | [ 2.42962018e-01 1.01087260e+00 -3.44056077e-02 -7.20934451e-01
-2.17408583e-01 -2.91366894e-02 3.86349648e-01 8.09989274e-02
9.98057500e-02 9.10423696e-01 9.47937131e-01 -7.51994908e-01
-3.38167965e-01 -7.14746714e-01 -5.35219491e-01 1.18040890e-01
1.76957443e-01 9.64173794e-01 -9.19418275e-01 -1.41752958... | [8.341506004333496, 6.055195331573486] |
4b297c59-01a0-45a8-bc6a-b968da6c804f | bounded-projection-matrix-approximation-with | 2305.15430 | null | https://arxiv.org/abs/2305.15430v1 | https://arxiv.org/pdf/2305.15430v1.pdf | Bounded Projection Matrix Approximation with Applications to Community Detection | Community detection is an important problem in unsupervised learning. This paper proposes to solve a projection matrix approximation problem with an additional entrywise bounded constraint. Algorithmically, we introduce a new differentiable convex penalty and derive an alternating direction method of multipliers (ADMM)... | ['Qiang Sun', 'Hengchao Chen', 'Zheng Zhai'] | 2023-05-21 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 2.47199863e-01 -2.90992409e-01 -2.00956255e-01 -9.39884968e-03
-7.06561744e-01 -2.99286962e-01 1.68056667e-01 -1.36689812e-01
-6.25282347e-01 7.93575704e-01 5.27495332e-02 -1.18578792e-01
-3.24735135e-01 -2.12041721e-01 -3.13455194e-01 -9.44399774e-01
-4.70094569e-02 5.72937846e-01 -2.15184018e-01 2.34322950... | [7.56760311126709, 4.466563701629639] |
693c0faa-61ed-4d80-9314-27fdd9230a09 | automatic-diagnosis-of-knee-osteoarthritis | 2307.04442 | null | https://arxiv.org/abs/2307.04442v1 | https://arxiv.org/pdf/2307.04442v1.pdf | Automatic diagnosis of knee osteoarthritis severity using Swin transformer | Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. Early detection and diagnosis are crucial for successful clinical intervention and management to prevent severe complications, such as loss of mobility. In this paper, we propose an automated approach that e... | ['Rachid Jennane', 'Alessandro Bruno', 'Aladine Chetouani', 'Yassine Nasser', 'Mohamed Amine Kerkouri', 'Marouane Tliba', 'Aymen Sekhri'] | 2023-07-10 | null | null | null | null | ['management'] | ['miscellaneous'] | [-4.59491670e-01 -3.02081555e-01 -5.48070252e-01 -1.30937710e-01
-9.06306684e-01 1.88492432e-01 -2.37425920e-02 -9.88167431e-03
-4.26056176e-01 8.56284440e-01 5.14542460e-01 -5.74116930e-02
-6.06456339e-01 -5.98416388e-01 -3.44885677e-01 -4.38842297e-01
-6.05230927e-01 5.55604100e-01 6.68322265e-01 -1.95501023... | [14.551765441894531, -1.7580856084823608] |
f022e16d-a2eb-4cac-a506-00a0cdcb7847 | variational-approach-for-intensity-domain | 2207.04204 | null | https://arxiv.org/abs/2207.04204v1 | https://arxiv.org/pdf/2207.04204v1.pdf | Variational Approach for Intensity Domain Multi-exposure Image Fusion | Recent innovations shows that blending of details captured by single Low Dynamic Range (LDR) sensor overcomes the limitations of standard digital cameras to capture details from high dynamic range scene. We present a method to produce well-exposed fused image that can be displayed directly on conventional display devic... | ['Vinay Kumar', 'Dinesh Arora', 'Harbinder Singh'] | 2022-07-09 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 9.94198620e-01 -5.77933371e-01 4.42293137e-01 -4.27622110e-01
-6.59065962e-01 -7.46310711e-01 5.01010954e-01 -1.58803120e-01
-4.93799090e-01 1.02185762e+00 2.16113225e-01 -1.42911859e-02
-2.14251176e-01 -7.69164920e-01 -4.78888154e-01 -8.47743571e-01
5.91892779e-01 -4.93414521e-01 4.39708054e-01 -3.56248856... | [10.850545883178711, -2.466388702392578] |
0a46711d-a9c4-41d8-948c-010dabaa8652 | multilingual-zero-shot-constituency-parsing | 2004.13805 | null | https://arxiv.org/abs/2004.13805v4 | https://arxiv.org/pdf/2004.13805v4.pdf | Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models | As it has been unveiled that pre-trained language models (PLMs) are to some extent capable of recognizing syntactic concepts in natural language, much effort has been made to develop a method for extracting complete (binary) parses from PLMs without training separate parsers. We improve upon this paradigm by proposing ... | ['Sang-goo Lee', 'Taeuk Kim', 'Bowen Li'] | 2020-04-08 | null | https://aclanthology.org/2021.findings-emnlp.41 | https://aclanthology.org/2021.findings-emnlp.41.pdf | findings-emnlp-2021-11 | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.07678705e-01 4.04872239e-01 1.03456862e-02 -5.40002167e-01
-1.18625379e+00 -9.89365578e-01 6.95643127e-01 2.55009890e-01
-4.97157037e-01 7.51642764e-01 2.48424917e-01 -7.57844508e-01
9.26380083e-02 -6.29094839e-01 -8.86651039e-01 -4.00794029e-01
6.47872360e-03 4.24410343e-01 3.52948084e-02 -2.55614877... | [10.400900840759277, 9.607809066772461] |
ecb96c3f-428c-4920-9241-bc0c65d30948 | contact-and-human-dynamics-from-monocular | 2007.11678 | null | https://arxiv.org/abs/2007.11678v2 | https://arxiv.org/pdf/2007.11678v2.pdf | Contact and Human Dynamics from Monocular Video | Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors that violate physical constraints, such as feet penetrating the ground and bodies leaning at extreme angles. In this paper, we present a physics-based method for inferring 3D human motion from v... | ['Leonidas J. Guibas', 'Davis Rempe', 'Aaron Hertzmann', 'Ruben Villegas', 'Jimei Yang', 'Bryan Russell'] | 2020-07-22 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2918_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500069.pdf | eccv-2020-8 | ['human-dynamics'] | ['computer-vision'] | [-9.98419076e-02 1.65712699e-01 -2.66054392e-01 -1.56909630e-01
-5.08472085e-01 -6.34353161e-01 4.43360299e-01 -6.93329573e-02
-2.94969559e-01 6.61520898e-01 4.02415484e-01 -1.37331024e-01
1.56455085e-01 -7.11209953e-01 -1.07750988e+00 -1.33772671e-01
-5.42019725e-01 8.19955647e-01 5.32952011e-01 -4.29187000... | [7.099533557891846, -0.5573651790618896] |
8d363d3c-38e0-4bb9-acba-a0ee98b33d0b | learning-with-batch-wise-optimal-transport | 1903.08923 | null | http://arxiv.org/abs/1903.08923v1 | http://arxiv.org/pdf/1903.08923v1.pdf | Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition | Deep metric learning is essential for visual recognition. The widely used
pair-wise (or triplet) based loss objectives cannot make full use of semantical
information in training samples or give enough attention to those hard samples
during optimization. Thus, they often suffer from a slow convergence rate and
inferior ... | ['Lin Xu', 'Yuai Liu', 'Han Sun'] | 2019-03-21 | learning-with-batch-wise-optimal-transport-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Xu_Learning_With_Batch-Wise_Optimal_Transport_Loss_for_3D_Shape_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Learning_With_Batch-Wise_Optimal_Transport_Loss_for_3D_Shape_Recognition_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-shape-recognition'] | ['computer-vision'] | [-9.13311467e-02 -6.67379975e-01 1.61053296e-02 -9.10788000e-01
-9.44608152e-01 -2.96393216e-01 4.15842116e-01 -1.08275317e-01
-6.77490592e-01 3.57669592e-01 -1.49991199e-01 -2.64262974e-01
-3.80523264e-01 -5.01289666e-01 -5.85874498e-01 -6.09930217e-01
1.50133178e-01 5.40334463e-01 -3.23714130e-02 3.32387179... | [9.571362495422363, 2.976148843765259] |
9b0c23ef-514d-42dd-ae68-a9068c2550b9 | grobid-combining-automatic-bibliographic-data | null | null | https://link.springer.com/chapter/10.1007/978-3-642-04346-8_62 | https://link.springer.com/content/pdf/10.1007/978-3-642-04346-8_62.pdf | GROBID: Combining Automatic Bibliographic Data Recognition and Term Extraction for Scholarship Publications | Based on state of the art machine learning techniques, GROBID (GeneRation Of BIbliographic Data) performs reliable bibliographic data extractions from scholar articles combined with multi-level term extractions. These two types of extraction present synergies and correspond to complementary descriptions of an article. ... | ['Patrice Lopez'] | 2009-09-01 | null | null | null | research-and-advanced-technology-for-digital | ['term-extraction'] | ['natural-language-processing'] | [-3.18987936e-01 2.83477958e-02 -1.07953298e+00 2.46792838e-01
-9.99517322e-01 -5.95909238e-01 1.15370142e+00 8.45156848e-01
-4.38647598e-01 1.04839730e+00 4.45282042e-01 -6.70669556e-01
-5.00486910e-01 -6.12197161e-01 -4.83723700e-01 -1.48557112e-01
9.85020176e-02 5.59925318e-01 -1.43075109e-01 1.79448754... | [9.611987113952637, 8.413188934326172] |
5924c4f8-f2c2-4948-a88a-decec670f4e5 | robust-and-fast-heart-rate-variability-1 | 1902.06151 | null | http://arxiv.org/abs/1902.06151v1 | http://arxiv.org/pdf/1902.06151v1.pdf | Robust and fast heart rate variability analysis of long and noisy electrocardiograms using neural networks and images | Heart rate variability studies depend on the robust calculation of the
tachogram, the heart rate times series, usually by the detection of R peaks in
the electrocardiogram (ECG). ECGs however are subject to a number of sources of
noise which are difficult to filter and therefore reduce the tachogram
accuracy. We descri... | [] | 2019-02-16 | robust-and-fast-heart-rate-variability | https://arxiv.org/abs/1902.06151 | https://arxiv.org/pdf/1902.06151 | arxiv190206151-search-help-advanced-search | ['heart-rate-variability'] | ['medical'] | [ 7.65633762e-01 -2.26011112e-01 5.11186182e-01 -4.65594769e-01
-7.92356431e-01 -4.05121714e-01 6.06615543e-02 6.22031033e-01
-2.91074008e-01 6.19645059e-01 -1.85784444e-01 -3.69132340e-01
-3.91474590e-02 -6.88466012e-01 -2.59047985e-01 -5.60594678e-01
-5.19668758e-01 1.10251844e-01 1.65466905e-01 2.25251585... | [14.195388793945312, 3.165226697921753] |
302f5b10-be38-4b4d-9f9a-a908d45cf296 | a-large-scale-comparative-study-of-accurate | 2304.04811 | null | https://arxiv.org/abs/2304.04811v2 | https://arxiv.org/pdf/2304.04811v2.pdf | A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation | The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challenging for citizens to differentiate between accurate and inaccurate information about COVID-19. This motivated us to carry out a comparative... | ['Xingyi Song', 'Kalina Bontcheva', 'Carolina Scarton', 'Iknoor Singh', 'Freddy Heppell', 'Ye Jiang', 'Yida Mu'] | 2023-04-10 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-1.92992181e-01 -4.85504940e-02 -4.94904786e-01 1.20411217e-01
-6.78000808e-01 -8.72944951e-01 1.41715741e+00 1.06478214e+00
-6.04004562e-01 7.42217362e-01 8.09259832e-01 -4.62185174e-01
1.28263384e-01 -1.02636850e+00 -3.08937103e-01 -3.25955361e-01
-2.05324262e-01 4.97271240e-01 2.32226670e-01 -5.41849554... | [8.458965301513672, 9.811025619506836] |
793dd14b-fad4-4338-a07c-53369dc376d6 | nflat-non-flat-lattice-transformer-for | 2205.05832 | null | https://arxiv.org/abs/2205.05832v3 | https://arxiv.org/pdf/2205.05832v3.pdf | NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition | Recently, Flat-LAttice Transformer (FLAT) has achieved great success in Chinese Named Entity Recognition (NER). FLAT performs lexical enhancement by constructing flat lattices, which mitigates the difficulties posed by blurred word boundaries and the lack of word semantics. In FLAT, the positions of starting and ending... | ['Xiao-Jun Wu', 'ZhenHua Feng', 'Xiaoning Song', 'Shuang Wu'] | 2022-05-12 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-2.19604447e-02 -2.29275599e-01 7.34208673e-02 -2.05423534e-01
-5.06765902e-01 -3.88671875e-01 2.23202109e-01 3.26918721e-01
-1.15563023e+00 7.28428960e-01 3.45651448e-01 -4.50664610e-01
4.35275108e-01 -1.07269776e+00 -3.43500257e-01 -6.72835886e-01
4.90538865e-01 1.80994317e-01 3.22795242e-01 -1.77140489... | [9.833178520202637, 9.838254928588867] |
7aee1a52-e089-420e-8027-c1c8f62c11e8 | the-go-transformer-natural-language-modeling | 2007.03500 | null | https://arxiv.org/abs/2007.03500v3 | https://arxiv.org/pdf/2007.03500v3.pdf | The Go Transformer: Natural Language Modeling for Game Play | This work applies natural language modeling to generate plausible strategic moves in the ancient game of Go. We train the Generative Pretrained Transformer (GPT-2) to mimic the style of Go champions as archived in Smart Game Format (SGF), which offers a text description of move sequences. The trained model further gene... | ['David Noever', 'Matthew Ciolino', 'Josh Kalin'] | 2020-07-07 | null | null | null | null | ['text-annotation', 'game-of-go', 'board-games'] | ['natural-language-processing', 'playing-games', 'playing-games'] | [ 3.68661694e-02 6.06518149e-01 1.44861013e-01 1.92152321e-01
-7.59495258e-01 -1.04287267e+00 7.75030613e-01 -5.22830129e-01
-6.84068426e-02 6.54899538e-01 6.97878361e-01 -7.05589235e-01
-1.14959544e-02 -1.28059494e+00 -5.24168670e-01 -2.76606470e-01
5.48699275e-02 8.73368740e-01 2.44781420e-01 -1.15087581... | [3.7042086124420166, 1.3343197107315063] |
01b5db73-04c5-4e3d-9c04-ce885cd84bba | crowd-level-abnormal-behavior-detection-via | 2212.00501 | null | https://arxiv.org/abs/2212.00501v1 | https://arxiv.org/pdf/2212.00501v1.pdf | Crowd-level Abnormal Behavior Detection via Multi-scale Motion Consistency Learning | Detecting abnormal crowd motion emerging from complex interactions of individuals is paramount to ensure the safety of crowds. Crowd-level abnormal behaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the crucial causes of many crowd disasters. In the recent decade, video anomaly detection (VAD) ... | ['Wentong Cai', 'Ruimin Hu', 'Shangwei Xie', 'Haiyan Yin', 'Yuanjing Li', 'Linbo Luo'] | 2022-12-01 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [-4.07492042e-01 -8.32923591e-01 3.96333963e-01 1.28148168e-01
-1.75121501e-02 -1.13751225e-01 7.03595042e-01 4.56325114e-01
-3.18752229e-01 5.72084725e-01 3.96997303e-01 -6.56027123e-02
3.26955840e-02 -7.82482564e-01 -4.13502276e-01 -7.76101589e-01
-6.65725708e-01 2.27438241e-01 9.77211535e-01 -6.18946075... | [7.855021953582764, 1.542382001876831] |
0f5f9cc1-7b39-4452-abf1-a1dc6acf318d | bipartite-flat-graph-network-for-nested-named | 2005.00436 | null | https://arxiv.org/abs/2005.00436v1 | https://arxiv.org/pdf/2005.00436v1.pdf | Bipartite Flat-Graph Network for Nested Named Entity Recognition | In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers. Bidirectional LSTM (BiLSTM) and graph convolutional network (G... | ['Hai Zhao', 'Ying Luo'] | 2020-05-01 | bipartite-flat-graph-network-for-nested-named-1 | https://aclanthology.org/2020.acl-main.571 | https://aclanthology.org/2020.acl-main.571.pdf | acl-2020-6 | ['nested-named-entity-recognition', 'nested-mention-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-3.23908657e-01 7.43485570e-01 -2.17562988e-02 -3.93054187e-01
-2.53216803e-01 -7.71673739e-01 3.77109081e-01 4.34072822e-01
-4.09112304e-01 6.09932125e-01 3.75715017e-01 -4.13521647e-01
2.39465222e-01 -1.30290163e+00 -9.58184183e-01 -3.73279393e-01
-4.31606829e-01 3.30727756e-01 5.03789902e-01 -2.09611475... | [9.439289093017578, 9.392099380493164] |
a30ebc67-aebd-44ca-a823-297334ebf52c | waymo-open-dataset-panoramic-video-panoptic | 2206.07704 | null | https://arxiv.org/abs/2206.07704v1 | https://arxiv.org/pdf/2206.07704v1.pdf | Waymo Open Dataset: Panoramic Video Panoptic Segmentation | Panoptic image segmentation is the computer vision task of finding groups of pixels in an image and assigning semantic classes and object instance identifiers to them. Research in image segmentation has become increasingly popular due to its critical applications in robotics and autonomous driving. The research communi... | ['Dragomir Anguelov', 'Henrik Kretzschmar', 'Liang-Chieh Chen', 'Yukun Zhu', 'Siyuan Qiao', 'Hang Yan', 'Xinchen Yan', 'Alex Zihao Zhu', 'Jieru Mei'] | 2022-06-15 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 4.06078398e-01 -4.00672197e-01 -5.35562932e-01 -5.47208786e-01
-6.84227526e-01 -1.00346506e+00 6.72701359e-01 -4.50421363e-01
-4.99621540e-01 1.22051522e-01 -2.39246055e-01 -3.08850914e-01
1.43451199e-01 -8.47143173e-01 -1.06675553e+00 -5.64839423e-01
9.47320908e-02 4.39545035e-01 6.46117210e-01 -4.49367724... | [8.370272636413574, -1.6246753931045532] |
7345f624-2515-41ff-bfad-7a219cd6c505 | progressive-refinement-a-method-of-coarse-to | 1804.08256 | null | http://arxiv.org/abs/1804.08256v1 | http://arxiv.org/pdf/1804.08256v1.pdf | Progressive refinement: a method of coarse-to-fine image parsing using stacked network | To parse images into fine-grained semantic parts, the complex fine-grained
elements will put it in trouble when using off-the-shelf semantic segmentation
networks. In this paper, for image parsing task, we propose to parse images
from coarse to fine with progressively refined semantic classes. It is achieved
by stackin... | ['Zhengxing Sun', 'Yunhan Sun', 'Jiagao Hu', 'Jinlong Shi'] | 2018-04-23 | null | null | null | null | ['face-parsing', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 4.00423080e-01 6.96129799e-01 -5.84688634e-02 -9.08723235e-01
-8.11600029e-01 -6.12561464e-01 6.07380690e-03 -1.36036932e-01
-1.95164055e-01 4.87906516e-01 1.46649688e-01 -2.04076767e-01
3.53957027e-01 -1.02653253e+00 -8.32887352e-01 -4.94906455e-01
2.73927003e-01 5.59170902e-01 6.03935838e-01 7.86303263... | [9.449613571166992, 0.36703452467918396] |
f79e532e-88b2-4400-b345-afb83c7c9b51 | ganerf-leveraging-discriminators-to-optimize | 2306.06044 | null | https://arxiv.org/abs/2306.06044v1 | https://arxiv.org/pdf/2306.06044v1.pdf | GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields | Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to poorly observed areas or minor lighting changes. Our goal is to mitigate these imperfections from various sources with a joint solution: we ... | ['Matthias Nießner', 'Peter Kontschieder', 'Samuel Rota Bulò', 'Lorenzo Porzi', 'Norman Müller', 'Barbara Roessle'] | 2023-06-09 | null | null | null | null | ['3d-scene-reconstruction', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 5.24501562e-01 9.26280953e-03 6.96464360e-01 -2.24510700e-01
-1.05228138e+00 -1.02016413e+00 5.34702063e-01 -4.51292545e-01
2.21174166e-01 7.26169586e-01 4.81729507e-01 -1.62738934e-01
2.48434007e-01 -1.06974173e+00 -1.19531381e+00 -7.43608415e-01
1.83196068e-01 -1.10226765e-01 -3.41788203e-01 -4.23019171... | [9.37026309967041, -3.1976981163024902] |
5444960a-a7f5-463d-996e-21d60da2c00f | localized-semantic-feature-mixers-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Khan_Localized_Semantic_Feature_Mixers_for_Efficient_Pedestrian_Detection_in_Autonomous_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Khan_Localized_Semantic_Feature_Mixers_for_Efficient_Pedestrian_Detection_in_Autonomous_CVPR_2023_paper.pdf | Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving | Autonomous driving systems rely heavily on the underlying perception module which needs to be both performant and efficient to allow precise decisions in real-time. Avoiding collisions with pedestrians is of topmost priority in any autonomous driving system. Therefore, pedestrian detection is one of the core parts ... | ['Andreas Dengel', 'Mohammed Shariq Nawaz', 'Abdul Hannan Khan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['pedestrian-detection'] | ['computer-vision'] | [-1.48938209e-01 -3.25744778e-01 1.69843867e-01 -3.01607281e-01
-4.63657647e-01 -1.32805407e-01 6.39969945e-01 9.01882648e-02
-9.38567340e-01 5.60901344e-01 -2.49578372e-01 -2.28116035e-01
6.30591631e-01 -1.06673038e+00 -8.18175495e-01 -7.31652796e-01
2.80636877e-01 3.27365845e-02 1.16597426e+00 -3.39842826... | [8.054718017578125, -0.7494316697120667] |
7cab6894-1e54-43df-a685-700c5b5c2f04 | point-cloud-transformers-applied-to-collider | 2102.05073 | null | https://arxiv.org/abs/2102.05073v2 | https://arxiv.org/pdf/2102.05073v2.pdf | Point Cloud Transformers applied to Collider Physics | Methods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of Transformer networks to learn semantic relationships between sequences in language processing. In this work, we apply a modified Transformer network call... | ['Florencia Canelli', 'Vinicius Mikuni'] | 2021-02-09 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-1.07241794e-01 -4.13705230e-01 -9.05120149e-02 -7.08103776e-01
-3.45408440e-01 -6.01513028e-01 8.70035887e-01 4.95176792e-01
-7.84032345e-01 7.11780429e-01 1.27627388e-01 -5.95307708e-01
-1.68269277e-01 -1.02103043e+00 -3.79398733e-01 -4.31762606e-01
-1.39429510e-01 1.22158062e+00 5.74447155e-01 -7.58744001... | [15.701865196228027, 2.9186012744903564] |
59e0f40a-e9e4-431c-a7f2-fda0278bfc2c | leveraging-affinity-cycle-consistency-to | null | null | https://openreview.net/forum?id=Hr-cI3LMKb8 | https://openreview.net/pdf?id=Hr-cI3LMKb8 | Leveraging affinity cycle consistency to isolate factors of variation in learned representations | Identifying the dominant factors of variation across a dataset is a central goal of representation learning. Generative approaches lead to descriptions that are rich enough to recreate the data, but often only a partial description is needed to complete downstream tasks or to gain insights about the dataset. In this w... | ['Ameesh Makadia', 'Srikumar Ramalingam', 'Varun Jampani', 'Kieran A Murphy'] | 2021-01-01 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 2.49885961e-01 2.43925508e-02 -1.85325705e-02 -2.76669383e-01
-4.19441998e-01 -9.89298522e-01 9.41740155e-01 6.77627400e-02
-7.72654191e-02 4.92045403e-01 5.05648911e-01 1.21067844e-01
-2.73286879e-01 -5.29665112e-01 -9.63288426e-01 -7.86701500e-01
2.88865775e-01 5.46916246e-01 -1.51093125e-01 -1.48540258... | [9.498619079589844, 2.1326513290405273] |
337fde44-7e40-4e70-9ad6-64904d49bb3f | text2poster-laying-out-stylized-texts-on | 2301.02363 | null | https://arxiv.org/abs/2301.02363v1 | https://arxiv.org/pdf/2301.02363v1.pdf | Text2Poster: Laying out Stylized Texts on Retrieved Images | Poster generation is a significant task for a wide range of applications, which is often time-consuming and requires lots of manual editing and artistic experience. In this paper, we propose a novel data-driven framework, called \textit{Text2Poster}, to automatically generate visually-effective posters from textual inf... | ['Zhiwu Lu', 'Ruihua Song', 'Hongteng Xu', 'Chuhao Jin'] | 2023-01-06 | null | null | null | null | ['layout-design', 'multimodal-generation'] | ['computer-vision', 'natural-language-processing'] | [ 5.73083401e-01 -2.54233573e-02 1.15743214e-02 -1.86605155e-01
-9.25014675e-01 -7.44662285e-01 8.16926479e-01 -2.36207411e-01
-1.75796285e-01 5.09882271e-01 1.43510804e-01 -2.84688979e-01
4.33631510e-01 -6.71665490e-01 -1.04979169e+00 -2.77694255e-01
4.25339073e-01 3.95771742e-01 1.63280338e-01 3.22836526... | [11.474652290344238, -0.06853753328323364] |
33febb23-c1ab-4852-a5b0-7acbab141eac | continual-learning-for-lidar-semantic | 2304.03980 | null | https://arxiv.org/abs/2304.03980v1 | https://arxiv.org/pdf/2304.03980v1.pdf | Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse Data | During the last few years, continual learning (CL) strategies for image classification and segmentation have been widely investigated designing innovative solutions to tackle catastrophic forgetting, like knowledge distillation and self-inpainting. However, the application of continual learning paradigms to point cloud... | ['Simone Milani', 'Elena Camuffo'] | 2023-04-08 | null | null | null | null | ['class-incremental-learning', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.09195721e-01 3.21203135e-02 -3.25501323e-01 -3.06233108e-01
-6.91203594e-01 -5.19653618e-01 6.67777479e-01 5.31123102e-01
-6.12262011e-01 7.98867404e-01 -5.66294372e-01 -9.98654962e-02
-5.26111722e-01 -7.14992464e-01 -1.03355372e+00 -3.64507198e-01
-1.69076174e-01 1.23327708e+00 6.33621752e-01 -1.34274468... | [9.43087100982666, 1.8867688179016113] |
95a92423-ecad-4b1d-abfe-a97eee18df73 | investigating-math-word-problems-using | 2105.08928 | null | https://arxiv.org/abs/2105.08928v3 | https://arxiv.org/pdf/2105.08928v3.pdf | Investigating Math Word Problems using Pretrained Multilingual Language Models | In this paper, we revisit math word problems~(MWPs) from the cross-lingual and multilingual perspective. We construct our MWP solvers over pretrained multilingual language models using sequence-to-sequence model with copy mechanism. We compare how the MWP solvers perform in cross-lingual and multilingual scenarios. To ... | ['Jing Jiang', 'Lingxiao Jiang', 'Lei Wang', 'Minghuan Tan'] | 2021-05-19 | null | null | null | null | ['pretrained-multilingual-language-models'] | ['natural-language-processing'] | [-3.28880012e-01 -2.57151455e-01 -1.80349603e-01 -3.59073132e-01
-1.06194532e+00 -1.28961217e+00 4.41373885e-01 -1.37007469e-02
-6.34179413e-01 1.23202360e+00 4.59296331e-02 -7.44596899e-01
1.67054012e-02 -8.84026051e-01 -1.10039961e+00 -2.42594570e-01
1.57853931e-01 7.19820857e-01 -1.02958202e-01 -6.62686884... | [11.021782875061035, 9.94098949432373] |
fd514388-fa22-4be9-bf18-b31ad430cb6a | a-time-resolved-clustering-method-revealing | 1912.04261 | null | https://arxiv.org/abs/1912.04261v2 | https://arxiv.org/pdf/1912.04261v2.pdf | A time resolved clustering method revealing longterm structures and their short-term internal dynamics | The last decades have not only been characterized by an explosive growth of data, but also an increasing appreciation of data as a valuable resource. Their value comes with the ability to extract meaningful patterns that are of economic, societal or scientific relevance. A particular challenge is the identification of ... | ['Jonas I. Liechti', 'Sebastian Bonhoeffer'] | 2019-12-09 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 1.98344201e-01 -5.56873977e-01 -1.00447066e-01 1.47183254e-01
1.28268823e-01 -9.81647670e-01 9.61780727e-01 7.63440132e-01
-2.32476860e-01 5.04457295e-01 -8.86075348e-02 -3.88196558e-01
-8.29771221e-01 -7.41611421e-01 -1.28259152e-01 -1.01639736e+00
-8.16124618e-01 4.87898350e-01 4.46559489e-01 -2.72478491... | [7.269399642944336, 3.4617550373077393] |
311e65bd-d97f-4a78-a5af-2ccb10b9a39c | visual-robot-task-planning | 1804.00062 | null | http://arxiv.org/abs/1804.00062v1 | http://arxiv.org/pdf/1804.00062v1.pdf | Visual Robot Task Planning | Prospection, the act of predicting the consequences of many possible futures,
is intrinsic to human planning and action, and may even be at the root of
consciousness. Surprisingly, this idea has been explored comparatively little
in robotics. In this work, we propose a neural network architecture and
associated plannin... | ['Yotam Barnoy', 'Raman Arora', 'Chris Paxton', 'Kapil Katyal', 'Gregory D. Hager'] | 2018-03-30 | null | null | null | null | ['robot-task-planning'] | ['robots'] | [ 3.87128532e-01 4.54924911e-01 7.95759410e-02 -2.59795964e-01
-8.10441747e-03 -4.72665220e-01 9.48436081e-01 3.06934398e-02
-4.83297884e-01 8.50819647e-01 5.68476319e-01 -3.13407928e-01
-1.19377293e-01 -8.68503213e-01 -6.61265075e-01 -6.79580927e-01
-4.08321708e-01 5.54213345e-01 2.34999165e-01 -2.97514081... | [4.391965389251709, 0.9728520512580872] |
93977b07-1e8d-4514-8cf0-a5e41044cf2c | feature-refinement-an-expression-specific | 2101.04838 | null | https://arxiv.org/abs/2101.04838v1 | https://arxiv.org/pdf/2101.04838v1.pdf | Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition | Micro-Expression Recognition has become challenging, as it is extremely difficult to extract the subtle facial changes of micro-expressions. Recently, several approaches proposed several expression-shared features algorithms for micro-expression recognition. However, they do not reveal the specific discriminative chara... | ['Zhihong Zhang', 'Feifei Zhang', 'Xiaohua Huang', 'Qirong Mao', 'Ling Zhou'] | 2021-01-13 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 2.13183358e-01 -5.06500721e-01 -2.80715466e-01 -8.95297229e-01
-6.56320810e-01 -3.15344706e-02 2.11164728e-01 -2.90319234e-01
-2.49633178e-01 4.88605559e-01 2.60646850e-01 5.61236322e-01
6.35096729e-02 -4.75151211e-01 -2.59468496e-01 -1.21072245e+00
-7.54455999e-02 -2.61876941e-01 -2.86385626e-01 -3.37461203... | [13.648272514343262, 1.6817190647125244] |
7db470a3-6197-444c-bf73-a94b07359e35 | cfl-net-image-forgery-localization-using | 2210.02182 | null | https://arxiv.org/abs/2210.02182v1 | https://arxiv.org/pdf/2210.02182v1.pdf | CFL-Net: Image Forgery Localization Using Contrastive Learning | Conventional forgery localizing methods usually rely on different forgery footprints such as JPEG artifacts, edge inconsistency, camera noise, etc., with cross-entropy loss to locate manipulated regions. However, these methods have the disadvantage of over-fitting and focusing on only a few specific forgery footprints.... | ['Simon S. Woo', 'Kishor Kumar Bhaumik', 'Fahim Faisal Niloy'] | 2022-10-04 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 3.05275530e-01 -5.63012540e-01 -7.89650977e-02 1.36757176e-02
-7.63774514e-01 -6.93020701e-01 2.88133860e-01 -1.31871417e-01
5.01033440e-02 5.22787392e-01 -9.58224200e-03 4.39944193e-02
3.75734479e-03 -6.76743865e-01 -8.29467654e-01 -8.97318423e-01
1.43556744e-01 -5.80255806e-01 6.57650605e-02 -2.04808697... | [12.361136436462402, 0.9517622590065002] |
6c5ae8c0-d90a-4c97-9360-5304a11b6bf4 | data-driven-low-rank-neural-network | 2107.05787 | null | https://arxiv.org/abs/2107.05787v1 | https://arxiv.org/pdf/2107.05787v1.pdf | Data-Driven Low-Rank Neural Network Compression | Despite many modern applications of Deep Neural Networks (DNNs), the large number of parameters in the hidden layers makes them unattractive for deployment on devices with storage capacity constraints. In this paper we propose a Data-Driven Low-rank (DDLR) method to reduce the number of parameters of pretrained DNNs an... | ['Swayambhoo Jain', 'Dimitris Papadimitriou'] | 2021-07-13 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 2.31352791e-01 2.35591367e-01 -2.54495382e-01 -6.64027452e-01
-4.51901734e-01 -5.05346417e-01 3.01053822e-01 -1.84769481e-01
-8.04598212e-01 7.51679122e-01 1.51581451e-01 -5.25925815e-01
-5.08043230e-01 -6.84080184e-01 -1.07962871e+00 -6.68899715e-01
7.45404214e-02 6.33008361e-01 4.89700399e-02 4.79086071... | [8.578060150146484, 3.1416361331939697] |
6320ea0b-d582-4435-9701-b10f982a6715 | wasserstein-image-local-analysis-histogram-of | 2205.05606 | null | https://arxiv.org/abs/2205.05606v1 | https://arxiv.org/pdf/2205.05606v1.pdf | Wasserstein Image Local Analysis: Histogram of Orientations, Smoothing and Edge Detection | The Histogram of Oriented Gradient is a widely used image feature, which describes local image directionality based on numerical differentiation. Due to its ill-posed nature, small noise may lead to large errors. Conventional HOG may fail to produce meaningful directionality results in the presence of noise, which is c... | ['Allen Tannenbaum', 'Joseph O. Deasy', 'Larry Norton', 'Harini Veeraraghavan', 'Jiening Zhu'] | 2022-05-11 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 1.34014040e-01 -8.59483033e-02 -6.57096207e-02 7.62160271e-02
-6.32835150e-01 -2.89074898e-01 2.48978317e-01 5.14231920e-01
-5.82336605e-01 6.40654743e-01 2.08402410e-01 -2.95536578e-01
-2.17383817e-01 -8.54634941e-01 -3.46578270e-01 -1.26133287e+00
-4.03192878e-01 -3.48658785e-02 2.71108866e-01 -7.77611732... | [14.353963851928711, -2.5819859504699707] |
ec369e0e-31ea-4f1f-b352-4ca632c7d064 | speech-augmentation-based-unsupervised | 2205.14329 | null | https://arxiv.org/abs/2205.14329v1 | https://arxiv.org/pdf/2205.14329v1.pdf | Speech Augmentation Based Unsupervised Learning for Keyword Spotting | In this paper, we investigated a speech augmentation based unsupervised learning approach for keyword spotting (KWS) task. KWS is a useful speech application, yet also heavily depends on the labeled data. We designed a CNN-Attention architecture to conduct the KWS task. CNN layers focus on the local acoustic features, ... | ['Jing Xiao', 'Haobin Tang', 'Ning Cheng', 'Jianzong Wang', 'Jian Luo'] | 2022-05-28 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-5.30068055e-02 8.83622691e-02 -1.45017833e-01 -5.20950377e-01
-7.82519519e-01 2.55211473e-01 3.12032223e-01 -1.01378299e-01
-5.00845075e-01 2.35438585e-01 6.65568590e-01 -4.87682611e-01
2.80227184e-01 -4.68433976e-01 -5.41409194e-01 -7.29245842e-01
3.67231995e-01 5.10356463e-02 2.54149586e-01 -1.25130057... | [14.546890258789062, 6.464897155761719] |
3ba32c2c-b51b-4aaf-aeb7-98bd0261dd6c | r-transformer-recurrent-neural-network | 1907.05572 | null | https://arxiv.org/abs/1907.05572v1 | https://arxiv.org/pdf/1907.05572v1.pdf | R-Transformer: Recurrent Neural Network Enhanced Transformer | Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure. Therefore, many non-recurrent sequence models that are built on convolutio... | ['Zitao Liu', 'Zhiwei Wang', 'Jiliang Tang', 'Yao Ma'] | 2019-07-12 | r-transformer-recurrent-neural-network-1 | https://openreview.net/forum?id=HJx4PAEYDH | https://openreview.net/pdf?id=HJx4PAEYDH | iclr-2020-1 | ['sequential-image-classification', 'music-modeling'] | ['computer-vision', 'music'] | [-3.42797749e-02 -3.52488369e-01 -5.57549745e-02 -2.36177623e-01
-4.44450289e-01 -4.17675287e-01 6.33333623e-01 -9.70981941e-02
-5.01759112e-01 5.80901742e-01 3.21662694e-01 -5.83610594e-01
2.11499110e-01 -5.13520896e-01 -5.92816532e-01 -7.16482937e-01
-5.92733696e-02 2.51890391e-01 1.88891485e-01 -4.32207942... | [10.877964973449707, 6.643746852874756] |
8bc5e0a2-b4fe-4c97-85f4-4ba402019a6e | learning-to-see-through-obstructions | 2004.01180 | null | https://arxiv.org/abs/2004.01180v1 | https://arxiv.org/pdf/2004.01180v1.pdf | Learning to See Through Obstructions | We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera. Our method leverages the motion differences between the background and the obstructing elements to recover both layers. Specifi... | ['Jia-Bin Huang', 'Yung-Yu Chuang', 'Ming-Hsuan Yang', 'Wei-Sheng Lai', 'Yu-Lun Liu'] | 2020-04-02 | learning-to-see-through-obstructions-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Learning_to_See_Through_Obstructions_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Learning_to_See_Through_Obstructions_CVPR_2020_paper.pdf | cvpr-2020-6 | ['reflection-removal'] | ['computer-vision'] | [ 3.17179352e-01 -2.45720699e-01 3.98504436e-01 -2.06171423e-01
-3.27162743e-01 -7.45855570e-01 4.93413150e-01 -4.89899874e-01
-2.83328325e-01 7.16629803e-01 3.26659530e-01 -2.47411758e-01
3.11821640e-01 -5.89194715e-01 -9.49175835e-01 -4.97808784e-01
-3.48142385e-01 -1.84590429e-01 4.00859833e-01 9.50602591... | [10.53453254699707, -1.597476840019226] |
873a3617-7c59-464a-aac0-7f39d6116f3e | grammar-based-grounded-lexicon-learning-1 | 2202.08806 | null | https://arxiv.org/abs/2202.08806v1 | https://arxiv.org/pdf/2202.08806v1.pdf | Grammar-Based Grounded Lexicon Learning | We present Grammar-Based Grounded Lexicon Learning (G2L2), a lexicalist approach toward learning a compositional and grounded meaning representation of language from grounded data, such as paired images and texts. At the core of G2L2 is a collection of lexicon entries, which map each word to a tuple of a syntactic type... | ['Joshua B. Tenenbaum', 'Roger P. Levy', 'Jiajun Wu', 'Haoyue Shi', 'Jiayuan Mao'] | 2022-02-17 | grammar-based-grounded-lexicon-learning | http://proceedings.neurips.cc/paper/2021/hash/4158f6d19559955bae372bb00f6204e4-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/4158f6d19559955bae372bb00f6204e4-Paper.pdf | neurips-2021-12 | ['network-embedding'] | ['methodology'] | [ 4.40261215e-01 4.47654337e-01 -2.21789852e-01 -4.98206079e-01
-7.93154180e-01 -8.18135977e-01 4.73640531e-01 2.83189416e-01
-2.37857655e-01 4.69374150e-01 2.50240088e-01 -5.57818830e-01
1.22757345e-01 -1.37064528e+00 -1.12594926e+00 -6.25580847e-01
-9.67727527e-02 6.58741415e-01 6.10534325e-02 -2.87554413... | [9.60303783416748, 7.254092216491699] |
566911e6-6dc7-42a6-9735-5ca5bb57f628 | multilingual-abusiveness-identification-on | 2204.01848 | null | https://arxiv.org/abs/2204.01848v1 | https://arxiv.org/pdf/2204.01848v1.pdf | Multilingual Abusiveness Identification on Code-Mixed Social Media Text | Social Media platforms have been seeing adoption and growth in their usage over time. This growth has been further accelerated with the lockdown in the past year when people's interaction, conversation, and expression were limited physically. It is becoming increasingly important to keep the platform safe from abusive ... | ['Naman Poddar', 'Ekagra Ranjan'] | 2022-03-01 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-4.34122294e-01 -1.73508570e-01 -1.19800933e-01 -8.80949274e-02
-5.59163094e-01 -7.95071900e-01 4.47926998e-01 4.47082430e-01
-4.69899267e-01 5.76480389e-01 3.36426616e-01 -4.02507097e-01
3.48462552e-01 -3.12164754e-01 2.54349224e-02 -8.46563578e-02
2.03211725e-01 -1.05495453e-01 3.54814261e-01 -6.15441978... | [8.962770462036133, 10.507708549499512] |
93928c17-8f34-4afe-a32c-e43f9aa5cf42 | s4r-self-supervised-semantic-scene | 2302.03640 | null | https://arxiv.org/abs/2302.03640v3 | https://arxiv.org/pdf/2302.03640v3.pdf | SSR-2D: Semantic 3D Scene Reconstruction from 2D Images | Most deep learning approaches to comprehensive semantic modeling of 3D indoor spaces require costly dense annotations in the 3D domain. In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations. The key idea of our approach is to design a trainable... | ['Matthias Nießner', 'Alexey Artemov', 'Kai Xu', 'Shuaifeng Zhi', 'Yujin Chen', 'Junwen Huang'] | 2023-02-07 | null | null | null | null | ['colorization', '3d-scene-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.21364075e-01 5.08684814e-01 9.79443640e-02 -5.56027949e-01
-9.16726828e-01 -8.15840840e-01 4.41305995e-01 6.94166962e-03
-2.25792810e-01 2.59776622e-01 -1.40487120e-01 -3.75275701e-01
1.71387121e-01 -8.69945347e-01 -1.25484169e+00 -1.96828142e-01
2.36273423e-01 8.36560667e-01 1.79684058e-01 -3.45578380... | [8.444332122802734, -2.9431982040405273] |
ac27ffa6-a4cb-43f9-9f13-b7ae764951a4 | adapting-a-framenet-semantic-parser-for | 1910.02734 | null | https://arxiv.org/abs/1910.02734v1 | https://arxiv.org/pdf/1910.02734v1.pdf | Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning | This paper presents a new semantic frame parsing model, based on Berkeley FrameNet, adapted to process spoken documents in order to perform information extraction from broadcast contents. Building upon previous work that had shown the effectiveness of adversarial learning for domain generalization in the context of sem... | ['Frédéric Béchet', 'Geraldine Damnati', 'Gabriel Marzinotto'] | 2019-10-07 | null | null | null | null | ['style-generalization'] | ['computer-vision'] | [ 3.51403385e-01 5.78389585e-01 2.05713511e-01 -3.80825698e-01
-1.05525434e+00 -1.04314244e+00 9.35990930e-01 1.45508513e-01
-5.54618537e-01 1.07774711e+00 5.80946207e-01 -1.52485251e-01
-6.02709092e-02 -8.16899359e-01 -6.34168208e-01 -3.32951576e-01
2.00064868e-01 6.22595966e-01 5.24346888e-01 -8.15222502... | [14.369271278381348, 6.734138011932373] |
cba8b554-b8e9-415b-8742-05713b94f7cf | towards-better-graph-representation-learning | 2305.06102 | null | https://arxiv.org/abs/2305.06102v1 | https://arxiv.org/pdf/2305.06102v1.pdf | Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering | Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e.g., filtering in Graph Fourier Transforms. In this work, we develop a novel and general framework which unifies many existing GNN models from the view of parameterized decompos... | ['Bryan Hooi', 'Yanming Shen', 'Wenjie Feng', 'Mingqi Yang'] | 2023-05-10 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 1.51559591e-01 7.13466853e-02 -3.46016772e-02 9.90043879e-02
-2.09962890e-01 -7.37832785e-01 4.67322439e-01 -2.80181944e-01
3.84061821e-02 3.83409500e-01 1.62587777e-01 -3.03354949e-01
-4.72902566e-01 -8.62450659e-01 -7.07073450e-01 -6.66842520e-01
-8.32482278e-02 -2.75763217e-02 -1.00616328e-02 -2.79669583... | [6.858516216278076, 6.023688316345215] |
17c33857-efd8-4fd6-a7fd-97efb23ff6b0 | cross-domain-generalization-for-amr-parsing | 2210.12445 | null | https://arxiv.org/abs/2210.12445v1 | https://arxiv.org/pdf/2210.12445v1.pdf | Cross-domain Generalization for AMR Parsing | Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. Recently, there has been notable growth in AMR parsing performance. However, most existing work focuses on improving the performance in the specific domain, ignoring the potential domain dependence of AMR parsing systems. To ... | ['Yue Zhang', 'Linfeng Song', 'Leyang Cui', 'Seng Yang', 'Xuefeng Bai'] | 2022-10-22 | null | null | null | null | ['amr-parsing'] | ['natural-language-processing'] | [ 4.33003694e-01 2.60530502e-01 -2.54155338e-01 -5.47971308e-01
-1.07666790e+00 -8.82729709e-01 4.58600014e-01 2.37621725e-01
-1.00398865e-02 4.93947208e-01 4.34576035e-01 -5.85790277e-01
1.06041178e-01 -8.43103468e-01 -4.69746619e-01 -4.69482914e-02
4.10290301e-01 5.77225208e-01 1.63239792e-01 -4.64322805... | [10.457590103149414, 9.378049850463867] |
57c52e0d-a49d-4be8-9d29-aeaa4fab94db | evaluation-of-unsupervised-information | null | null | https://aclanthology.org/L12-1313 | https://aclanthology.org/L12-1313.pdf | Evaluation of Unsupervised Information Extraction | Unsupervised methods gain more and more attention nowadays in information extraction area, which allows to design more open extraction systems. In the domain of unsupervised information extraction, clustering methods are of particular importance. However, evaluating the results of clustering remains difficult at a larg... | ['Romaric Besan{\\c{c}}on', 'Olivier Ferret', 'Brigitte Grau', 'Wei Wang'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['open-information-extraction'] | ['natural-language-processing'] | [ 8.18622932e-02 7.04239428e-01 1.60122126e-01 -2.10028589e-01
-5.13512015e-01 -4.74392533e-01 9.27351236e-01 1.10803616e+00
-6.78590894e-01 8.51138115e-01 4.31802198e-02 -1.88005920e-02
-6.87924743e-01 -1.05468524e+00 -1.65684611e-01 -4.99946713e-01
-7.54793137e-02 9.85150099e-01 3.51337224e-01 -8.45881850... | [9.398929595947266, 8.766794204711914] |
a51fe8ea-05d0-4ca9-8fa3-93a1930c9ed2 | multinomial-adversarial-networks-for-multi | 1802.05694 | null | http://arxiv.org/abs/1802.05694v1 | http://arxiv.org/pdf/1802.05694v1.pdf | Multinomial Adversarial Networks for Multi-Domain Text Classification | Many text classification tasks are known to be highly domain-dependent.
Unfortunately, the availability of training data can vary drastically across
domains. Worse still, for some domains there may not be any annotated data at
all. In this work, we propose a multinomial adversarial network (MAN) to tackle
the text clas... | ['Xilun Chen', 'Claire Cardie'] | 2018-02-15 | multinomial-adversarial-networks-for-multi-1 | https://aclanthology.org/N18-1111 | https://aclanthology.org/N18-1111.pdf | naacl-2018-6 | ['cross-domain-text-classification'] | ['natural-language-processing'] | [ 3.00484687e-01 -9.83091667e-02 -4.85507809e-02 -4.68222082e-01
-9.24488664e-01 -1.01350296e+00 6.67566359e-01 1.40341803e-01
-4.23484623e-01 1.07949245e+00 -1.79906398e-01 -2.79022157e-01
-2.41983280e-01 -7.79340625e-01 -6.74035907e-01 -7.36154497e-01
3.62232663e-02 8.93053532e-01 8.36411025e-03 -3.29293549... | [10.290606498718262, 3.204988718032837] |
16eb2de2-1afe-4a9b-9685-487136e337c9 | gsim-a-graph-neural-network-based-relevance | 2208.06144 | null | https://arxiv.org/abs/2208.06144v2 | https://arxiv.org/pdf/2208.06144v2.pdf | GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs | Heterogeneous graphs, which contain nodes and edges of multiple types, are prevalent in various domains, including bibliographic networks, social media, and knowledge graphs. As a fundamental task in analyzing heterogeneous graphs, relevance measure aims to calculate the relevance between two objects of different types... | ['Wenjie Zhang', 'Xiaofeng Zhang', 'Xin Cao', 'Moli Lu', 'Yixiang Fang', 'Linhao Luo'] | 2022-08-12 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 4.68381234e-02 9.66316275e-03 -5.10012507e-01 7.86174759e-02
-4.74718306e-03 -2.35167846e-01 4.53807443e-01 8.64653051e-01
-1.00853063e-01 6.44845963e-01 1.21183448e-01 -3.75297576e-01
-6.85414672e-01 -1.63113761e+00 -2.73928702e-01 -3.82042080e-01
-1.71754181e-01 1.98362559e-01 6.22423947e-01 -4.43084270... | [7.527002811431885, 6.543099880218506] |
2add8501-81e4-4caa-b9ef-9d27cd43bc37 | a-meta-learning-approach-to-reservoir | 2110.03722 | null | https://arxiv.org/abs/2110.03722v1 | https://arxiv.org/pdf/2110.03722v1.pdf | A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data | Recent research has established the effectiveness of machine learning for data-driven prediction of the future evolution of unknown dynamical systems, including chaotic systems. However, these approaches require large amounts of measured time series data from the process to be predicted. When only limited data is avail... | ['Michelle Girvan', 'Andrew Pomerance', 'Daniel Canaday'] | 2021-10-07 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.75676093e-01 -3.98316503e-01 1.60813987e-01 1.55642167e-01
-4.94884312e-01 -5.63481033e-01 1.07603586e+00 4.30422336e-01
-7.02675879e-02 7.03945339e-01 -2.17204392e-01 -4.31232452e-01
-1.70064732e-01 -6.21337593e-01 -4.90522236e-01 -8.52171242e-01
-4.39689547e-01 7.73039341e-01 8.60853642e-02 -4.50932980... | [6.5900373458862305, 3.351055383682251] |
742e4edd-611b-4ad3-8dea-f76211090bd8 | magnetic-properties-of-poly-trimethylene | 2012.05819 | null | https://arxiv.org/abs/2012.05819v1 | https://arxiv.org/pdf/2012.05819v1.pdf | Magnetic properties of poly(trimethylene terephthalate-block-poly(tetramethylene oxide) copolymer nanocomposites reinforced by graphene oxide-Fe3O4 hybrid nanoparticles | Thermoplastic elastomeric nanocomposites based on poly(trimethylene terephthalate-block-poly(tetramethylene oxide) copolymer (PTT-PTMO) and graphene oxide-Fe3O4 nanoparticle hybrid were prepared by in situ polymerization. Superparamagnetic GO-Fe3O4 hybrid nanoparticles before introducing to elastomeric matrix were char... | ['Nikos Guskos', 'Grzegorz Żołnierkiewicz', 'Izabela Janowska', 'Zdenko Špitalský', 'Janusz Typek', 'Sandra Paszkiewicz', 'Anna Szymczyk'] | 2020-12-10 | null | null | null | null | ['x-ray-diffraction'] | ['miscellaneous'] | [ 5.38766384e-01 1.11294739e-01 -1.39653891e-01 3.89557421e-01
-5.15468307e-02 -3.47239345e-01 4.67412800e-01 1.12714037e-01
-4.84606922e-01 1.13185728e+00 4.50984120e-01 -2.03494132e-01
-4.33546789e-02 -1.25885403e+00 -5.60356259e-01 -1.05192423e+00
-1.77052870e-01 6.45277321e-01 7.53394246e-01 -4.42324758... | [5.1427998542785645, 4.792929649353027] |
acd8b2fd-ea87-4be7-ba75-b45759093a1f | model-agnostic-meta-learning-for-natural | 2303.02841 | null | https://arxiv.org/abs/2303.02841v2 | https://arxiv.org/pdf/2303.02841v2.pdf | Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance | Natural language understanding(NLU) is challenging for finance due to the lack of annotated data and the specialized language in that domain. As a result, researchers have proposed to use pre-trained language model and multi-task learning to learn robust representations. However, aggressive fine-tuning often causes ove... | ['Zhihan Li', 'Yuxuan He', 'Shaoling Chen', 'Bixing Yan'] | 2023-03-06 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-5.23102462e-01 -1.91669479e-01 -4.02513236e-01 -5.13489783e-01
-1.17841303e+00 -3.73545229e-01 6.26835167e-01 4.47028317e-02
-5.53715169e-01 8.98290336e-01 2.70392776e-01 -9.53121185e-02
1.61949411e-01 -7.76421607e-01 -8.18066895e-01 -1.50999576e-01
-3.84978838e-02 7.47800231e-01 1.41845390e-01 -4.07053977... | [10.74565315246582, 8.243820190429688] |
e3c88798-45d4-4b5e-83c9-6d02f512c0cf | modeling-temporal-dependencies-in-high | 1206.6392 | null | http://arxiv.org/abs/1206.6392v1 | http://arxiv.org/pdf/1206.6392v1.pdf | Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription | We investigate the problem of modeling symbolic sequences of polyphonic music
in a completely general piano-roll representation. We introduce a probabilistic
model based on distribution estimators conditioned on a recurrent neural
network that is able to discover temporal dependencies in high-dimensional
sequences. Our... | ['Pascal Vincent', 'Nicolas Boulanger-Lewandowski', 'Yoshua Bengio'] | 2012-06-27 | null | null | null | null | ['music-modeling'] | ['music'] | [ 2.81224549e-01 -2.48500586e-01 -2.64535815e-01 -9.23789814e-02
-8.96736979e-01 -7.30436862e-01 5.81153393e-01 -4.29996163e-01
-6.35205163e-03 7.51704335e-01 4.59691465e-01 -3.64130288e-02
-3.58238816e-01 -3.74786228e-01 -8.03238869e-01 -6.32752955e-01
-3.47180963e-01 6.17374897e-01 -6.75057620e-02 -1.27992453... | [15.693521499633789, 5.593790531158447] |
2b264e3c-d5c9-41eb-9bec-7b111b4eb155 | datasets-and-models-for-authorship | 2011.07975 | null | https://arxiv.org/abs/2011.07975v1 | https://arxiv.org/pdf/2011.07975v1.pdf | Datasets and Models for Authorship Attribution on Italian Personal Writings | Existing research on Authorship Attribution (AA) focuses on texts for which a lot of data is available (e.g novels), mainly in English. We approach AA via Authorship Verification on short Italian texts in two novel datasets, and analyze the interaction between genre, topic, gender and length. Results show that AV is fe... | ['Malvina Nissim', 'Albert Gatt', 'Gaetana Ruggiero'] | 2020-11-16 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-1.46164834e-01 2.32179426e-02 -7.30006218e-01 -2.36704618e-01
-7.40898252e-02 -1.00797617e+00 1.07552958e+00 6.08262300e-01
-7.31600523e-01 7.16456711e-01 6.99524522e-01 -3.25927168e-01
3.84669080e-02 -3.02890748e-01 -2.77881801e-01 -9.49667394e-02
2.30495840e-01 4.96900141e-01 -1.63866863e-01 -8.15098733... | [9.579882621765137, 10.54582691192627] |
79101cac-c8a8-4799-96fa-4e5ccdfc1c23 | physics-informed-machine-learning-simulator | 2012.06825 | null | https://arxiv.org/abs/2012.06825v1 | https://arxiv.org/pdf/2012.06825v1.pdf | Physics-Informed Machine Learning Simulator for Wildfire Propagation | The aim of this work is to evaluate the feasibility of re-implementing some key parts of the widely used Weather Research and Forecasting WRF-SFIRE simulator by replacing its core differential equations numerical solvers with state-of-the-art physics-informed machine learning techniques to solve ODEs and PDEs, in order... | ['Simone Azeglio', 'Sara Tiengo', 'Martina Scauda', 'Valerio Pagliarino', 'Giovanni Graziano', 'Francesco Calisto', 'Luca Bottero'] | 2020-12-12 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-6.56637132e-01 -3.86069804e-01 1.46218687e-01 1.43664032e-02
-1.19579442e-01 -5.54554760e-01 7.44735122e-01 3.53061706e-01
-5.35837591e-01 8.89838994e-01 -3.40492308e-01 -8.70516181e-01
-3.06852460e-01 -1.01532471e+00 -3.11571628e-01 -7.01560616e-01
-5.53868890e-01 7.96499550e-01 1.50006354e-01 -8.35879862... | [6.467236042022705, 3.106466293334961] |
3669e4e8-3280-4bfa-9a42-a3133bada64e | peak-piloted-deep-network-for-facial | 1607.06997 | null | http://arxiv.org/abs/1607.06997v2 | http://arxiv.org/pdf/1607.06997v2.pdf | Peak-Piloted Deep Network for Facial Expression Recognition | Objective functions for training of deep networks for face-related
recognition tasks, such as facial expression recognition (FER), usually
consider each sample independently. In this work, we present a novel
peak-piloted deep network (PPDN) that uses a sample with peak expression (easy
sample) to supervise the intermed... | ['Nuno Vasconcelos', 'Luoqi Liu', 'Yugang Han', 'Xiaodan Liang', 'Teng Li', 'Xiangyun Zhao', 'Shuicheng Yan'] | 2016-07-24 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 3.46739650e-01 -2.24577829e-01 -1.14535935e-01 -7.28317916e-01
-1.91087723e-01 4.82126474e-02 4.86540496e-01 -4.99278843e-01
-5.24642587e-01 7.03383446e-01 -4.08912390e-01 3.47221673e-01
-2.71468490e-01 -4.82547253e-01 -6.09770179e-01 -1.26377904e+00
-3.72961164e-01 8.13501552e-02 -4.05932426e-01 -4.86383677... | [13.594206809997559, 1.7299158573150635] |
b3fc692a-45e5-4313-95d4-de53b13ab9a9 | on-the-usage-of-continual-learning-for-out-of | 2305.04106 | null | https://arxiv.org/abs/2305.04106v1 | https://arxiv.org/pdf/2305.04106v1.pdf | On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code | Pre-trained language models (PLMs) have become a prevalent technique in deep learning for code, utilizing a two-stage pre-training and fine-tuning procedure to acquire general knowledge about code and specialize in a variety of downstream tasks. However, the dynamic nature of software codebases poses a challenge to the... | ['Houari Sahraoui', 'David Lo', 'Kisub Kim', 'Xin Zhou', 'Martin Weyssow'] | 2023-05-06 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 1.20992884e-01 -2.84616470e-01 -1.94066107e-01 -2.69423604e-01
-4.48597610e-01 -4.59278136e-01 3.56938630e-01 1.93495061e-02
-1.83718920e-01 5.40303707e-01 7.85998702e-02 -5.26944876e-01
-4.26770039e-02 -6.20526254e-01 -1.08638358e+00 -4.94976550e-01
-9.78521258e-02 -2.63726874e-03 3.81399930e-01 -2.78220832... | [7.699618816375732, 7.870054721832275] |
b627ce30-fefb-43b1-a1a0-4ca9efdd580f | gaussigan-controllable-image-synthesis-with | 2106.13215 | null | https://arxiv.org/abs/2106.13215v1 | https://arxiv.org/pdf/2106.13215v1.pdf | GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes | We present an algorithm that learns a coarse 3D representation of objects from unposed multi-view 2D mask supervision, then uses it to generate detailed mask and image texture. In contrast to existing voxel-based methods for unposed object reconstruction, our approach learns to represent the generated shape and pose wi... | ['James Tompkin', 'Kwang In Kim', 'Oliver Wang', 'Aaron Gokaslan', 'Isa Milefchik', 'Youssef A. Mejjati'] | 2021-06-24 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 5.67933619e-01 4.05945569e-01 1.51683375e-01 -4.12010252e-01
-1.29450893e+00 -8.50698292e-01 8.45597684e-01 -5.65052629e-01
7.90015236e-02 2.09575281e-01 2.47654438e-01 1.99055701e-01
1.43482924e-01 -5.39242089e-01 -1.13986850e+00 -5.59753180e-01
2.82073617e-01 1.29040480e+00 4.29289103e-01 1.34500772... | [8.634632110595703, -3.029369831085205] |
3cc1d4f9-89a6-46dd-aad9-d7dcf87651ff | learning-efficient-online-3d-bin-packing-on | null | null | https://openreview.net/forum?id=bfuGjlCwAq | https://openreview.net/pdf?id=bfuGjlCwAq | Learning Efficient Online 3D Bin Packing on Packing Configuration Trees | Online 3D Bin Packing Problem (3D-BPP) has widespread applications in industrial automation and has aroused enthusiastic research interest recently. Existing methods usually solve the problem with limited resolution of spatial discretization, and/or cannot deal with complex practical constraints well. We propose to enh... | ['Kai Xu', 'Yang Yu', 'Hang Zhao'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['3d-bin-packing'] | ['miscellaneous'] | [-2.43557274e-01 1.52238309e-01 -5.53208053e-01 -6.73903199e-03
-3.54327500e-01 -5.89021802e-01 -8.59796107e-02 3.41814220e-01
1.10040590e-01 1.02801275e+00 -1.83601931e-01 -8.07764173e-01
-4.45822090e-01 -9.31727529e-01 -9.81156707e-01 -7.15680122e-01
-5.44082880e-01 1.03040385e+00 2.34711349e-01 8.22861344... | [4.958523273468018, 2.6823055744171143] |
f81fb7c7-7f70-41ef-aba6-e5b149dade66 | analyzing-bert-cross-lingual-transfer | null | null | https://aclanthology.org/2022.mmmpie-1.3 | https://aclanthology.org/2022.mmmpie-1.3.pdf | Analyzing BERT Cross-lingual Transfer Capabilities in Continual Sequence Labeling | Knowledge transfer between neural language models is a widely used technique that has proven to improve performance in a multitude of natural language tasks, in particular with the recent rise of large pre-trained language models like BERT. Similarly, high cross-lingual transfer has been shown to occur in multilingual ... | ['Sophie Rosset', 'Olivier Galibert', 'Hervé Bredin', 'Guillaume Bernard', 'Sahar Ghannay', 'Mathilde Veron', 'Juan Manuel Coria'] | null | null | null | null | mmmpie-coling-2022-10 | ['slot-filling'] | ['natural-language-processing'] | [ 4.73239496e-02 -2.09425092e-02 -1.57527164e-01 -2.78952658e-01
-7.75056899e-01 -7.85920322e-01 7.49128342e-01 2.22646505e-01
-1.20907581e+00 1.12403727e+00 -4.70994477e-04 -6.01855338e-01
1.10581115e-01 -4.47170466e-01 -1.08088505e+00 -4.76409137e-01
-1.08859345e-01 7.38645613e-01 3.87036651e-01 -1.84016615... | [10.931459426879883, 9.93690013885498] |
84ddfa76-3f69-4b77-9e61-fa232e97d4eb | learnable-sampling-3d-convolution-for-video | 2011.10974 | null | https://arxiv.org/abs/2011.10974v1 | https://arxiv.org/pdf/2011.10974v1.pdf | Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition | A key challenge in video enhancement and action recognition is to fuse useful information from neighboring frames. Recent works suggest establishing accurate correspondences between neighboring frames before fusing temporal information. However, the generated results heavily depend on the quality of correspondence esti... | ['Dong Chen', 'Jianmin Bao', 'Shuyang Gu'] | 2020-11-22 | null | null | null | null | ['video-denoising', 'video-enhancement'] | ['computer-vision', 'computer-vision'] | [ 1.31453827e-01 -4.16075289e-01 1.78770977e-03 -5.78666687e-01
-6.31678283e-01 -1.16916083e-01 2.93111920e-01 -6.56823575e-01
-3.55441362e-01 5.71099639e-01 6.49042964e-01 2.09269032e-01
7.65692666e-02 -7.37763464e-01 -8.95654798e-01 -6.24006450e-01
-6.55266941e-02 -5.70799172e-01 4.45537060e-01 -9.50626358... | [10.796847343444824, -1.5258222818374634] |
111851bb-e050-4906-b93e-6ca87c2180c1 | exit-chart-aided-near-capacity-irregular-bit | null | null | https://ieeexplore.ieee.org/abstract/document/4786476/references#references | https://ieeexplore.ieee.org/abstract/document/4786476/references#references | EXIT-chart aided near-capacity Irregular Bit-Interleaved Coded Modulation design | A near-capacity irregular bit-interleaved coded modulation based iterative decoding (Ir-BICM-ID) aided scheme is proposed. The irregular design of the scheme pervades the three basic components of BICM-ID, namely the encoder, the unity-rate precoder and the bit-to-symbol mapper. As a result, irregular BICM-ID schemes c... | ['Ronald Y. S. Tee; Robert G. Maunder; Lajos Hanzo'] | 2009-01-01 | null | null | null | http-ieeexplore-ieee-org-stamp-stamp-jsp-tp | ['unity'] | ['computer-vision'] | [ 7.76272595e-01 7.53291547e-01 -4.31374192e-01 1.68636903e-01
-3.41432273e-01 -4.57285158e-03 1.01663160e+00 -4.93738621e-01
-1.20884806e-01 8.66331995e-01 2.02609360e-01 -1.00838268e+00
-2.42919728e-01 -3.62362742e-01 -5.61506450e-01 -7.35799253e-01
-9.19504225e-01 -2.03932241e-01 8.80851820e-02 -1.84088379... | [6.470767498016357, 1.5187091827392578] |
1551d151-a39d-432f-a87b-94131364654e | modality-transferable-emotion-embeddings-for | 2009.09629 | null | https://arxiv.org/abs/2009.09629v3 | https://arxiv.org/pdf/2009.09629v3.pdf | Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition | Despite the recent achievements made in the multi-modal emotion recognition task, two problems still exist and have not been well investigated: 1) the relationship between different emotion categories are not utilized, which leads to sub-optimal performance; and 2) current models fail to cope well with low-resource emo... | ['Pascale Fung', 'Tiezheng Yu', 'Zihan Liu', 'Wenliang Dai'] | 2020-09-21 | null | https://aclanthology.org/2020.aacl-main.30 | https://aclanthology.org/2020.aacl-main.30.pdf | asian-chapter-of-the-association-for | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 6.86359778e-02 -3.99942756e-01 3.33879367e-02 -5.55237889e-01
-7.40961134e-01 -3.98795873e-01 4.50440764e-01 6.34441711e-03
-7.48084426e-01 3.11675996e-01 4.95400846e-01 2.48717055e-01
2.83424169e-01 -5.07648051e-01 -3.47813100e-01 -5.07179320e-01
2.62585580e-01 2.29963154e-01 -1.58772338e-02 -3.15603524... | [13.159917831420898, 5.343306064605713] |
68d21f47-ae04-452b-ba9e-7772ffebc2e4 | plane-representation-learning-over-planar | 2307.01180 | null | https://arxiv.org/abs/2307.01180v1 | https://arxiv.org/pdf/2307.01180v1.pdf | PlanE: Representation Learning over Planar Graphs | Graph neural networks are prominent models for representation learning over graphs, where the idea is to iteratively compute representations of nodes of an input graph through a series of transformations in such a way that the learned graph function is isomorphism invariant on graphs, which makes the learned representa... | ['İsmail İlkan Ceylan', 'Ralph Abboud', 'Zeyang Zhao', 'Radoslav Dimitrov'] | 2023-07-03 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 2.73093373e-01 7.14198291e-01 -4.16990399e-01 -2.09139690e-01
-3.35945070e-01 -8.16375971e-01 4.51466650e-01 5.24218261e-01
1.06568173e-01 2.73658544e-01 -7.60427490e-02 -7.32265055e-01
-4.41216707e-01 -1.30908298e+00 -1.17357457e+00 -4.77859288e-01
-8.62648129e-01 8.10796142e-01 1.66340798e-01 -3.79460603... | [6.916382789611816, 6.223355770111084] |
c4c12f55-73b1-41d0-ac30-733014631f84 | deep-versus-wide-an-analysis-of-student | 2207.06867 | null | https://arxiv.org/abs/2207.06867v2 | https://arxiv.org/pdf/2207.06867v2.pdf | Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models | Self-supervised learning (SSL) is seen as a very promising approach with high performance for several speech downstream tasks. Since the parameters of SSL models are generally so large that training and inference require a lot of memory and computational cost, it is desirable to produce compact SSL models without a sig... | ['Tomohiro Tanaka', 'Kohei Matsuura', 'Takafumi Moriya', 'Takanori Ashihara'] | 2022-07-14 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.06066398e-01 2.32508838e-01 -4.04371202e-01 -4.05854583e-01
-5.36749840e-01 -3.02749872e-01 4.51658487e-01 3.91566902e-01
-5.15082121e-01 5.57094693e-01 8.09635743e-02 -5.97758949e-01
-2.60756940e-01 -5.45541227e-01 -6.81142032e-01 -6.97093189e-01
-8.00917372e-02 5.39501250e-01 4.52599317e-01 7.64450803... | [14.12549877166748, 6.5279927253723145] |
083fc393-cd5d-45a8-925d-b1b24ee87b2d | fused-text-segmentation-networks-for-multi | 1709.03272 | null | http://arxiv.org/abs/1709.03272v4 | http://arxiv.org/pdf/1709.03272v4.pdf | Fused Text Segmentation Networks for Multi-oriented Scene Text Detection | In this paper, we introduce a novel end-end framework for multi-oriented
scene text detection from an instance-aware semantic segmentation perspective.
We present Fused Text Segmentation Networks, which combine multi-level features
during the feature extracting as text instance may rely on finer feature
expression comp... | ['Weidong Qiu', 'Jie Guo', 'Kai Chen', 'Youxuan Xu', 'Yuting Gao', 'Zheng Huang', 'Yuchen Dai'] | 2017-09-11 | null | null | null | null | ['multi-oriented-scene-text-detection'] | ['computer-vision'] | [ 5.01402915e-01 1.78484142e-01 1.35348007e-01 -3.40994000e-01
-1.08373821e+00 -6.78003848e-01 7.87604392e-01 1.81795090e-01
-5.34014761e-01 1.62953958e-01 -3.05936169e-02 1.90403163e-02
2.30784655e-01 -5.00504851e-01 -7.49499798e-01 -4.14877504e-01
6.32912219e-01 7.19452083e-01 9.07357693e-01 2.16647666... | [12.060420036315918, 2.2882165908813477] |
22ca1608-0f9b-4b1a-b09e-be572a376ebb | reinforcement-learning-based-minimum-state | 2304.04950 | null | https://arxiv.org/abs/2304.04950v1 | https://arxiv.org/pdf/2304.04950v1.pdf | Reinforcement Learning Based Minimum State-flipped Control for the Reachability of Boolean Control Networks | To realize reachability as well as reduce control costs of Boolean Control Networks (BCNs) with state-flipped control, a reinforcement learning based method is proposed to obtain flip kernels and the optimal policy with minimal flipping actions to realize reachability. The method proposed is model-free and of low compu... | ['Fangfei Li', 'Jingjie Ni'] | 2023-04-11 | null | null | null | null | ['q-learning'] | ['methodology'] | [-1.09227166e-01 -4.76601571e-02 -5.83322167e-01 1.60658136e-01
-6.22525871e-01 -3.58117223e-01 1.37360618e-01 -1.41735762e-01
-3.78264189e-01 1.21880984e+00 -2.99913764e-01 -4.63593513e-01
-6.10405147e-01 -9.81303513e-01 -7.93859541e-01 -1.06449354e+00
-2.39064209e-02 8.78932178e-02 4.38537329e-01 -2.84322888... | [4.283729076385498, 2.032435655593872] |
b023e2f6-ddda-430c-9139-ad8d61bdc460 | graphon-based-clustering-and-testing-of | 2110.02722 | null | https://arxiv.org/abs/2110.02722v2 | https://arxiv.org/pdf/2110.02722v2.pdf | Graphon based Clustering and Testing of Networks: Algorithms and Theory | Network-valued data are encountered in a wide range of applications and pose challenges in learning due to their complex structure and absence of vertex correspondence. Typical examples of such problems include classification or grouping of protein structures and social networks. Various methods, ranging from graph ker... | ['Debarghya Ghoshdastidar', 'Leena Chennuru Vankadara', 'Mahalakshmi Sabanayagam'] | 2021-10-06 | graphon-based-clustering-and-testing-of-1 | https://openreview.net/forum?id=sTNHCrIKDQc | https://openreview.net/pdf?id=sTNHCrIKDQc | iclr-2022-4 | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 2.03349724e-01 1.82073385e-01 -2.90836453e-01 -5.16812503e-01
-3.11245441e-01 -4.99952912e-01 3.91207099e-01 7.28342891e-01
-2.11664140e-01 7.63260067e-01 -4.90491688e-01 -4.03547525e-01
-7.83135772e-01 -8.49053085e-01 -8.09180081e-01 -8.28534722e-01
-7.18438864e-01 6.78615510e-01 4.17707473e-01 8.75891522... | [6.964508533477783, 5.4276885986328125] |
3c2de03e-2cdd-45bf-93e3-a7d50d6140ed | exploring-the-role-of-the-bottleneck-in-slot | 2306.02577 | null | https://arxiv.org/abs/2306.02577v1 | https://arxiv.org/pdf/2306.02577v1.pdf | Exploring the Role of the Bottleneck in Slot-Based Models Through Covariance Regularization | In this project we attempt to make slot-based models with an image reconstruction objective competitive with those that use a feature reconstruction objective on real world datasets. We propose a loss-based approach to constricting the bottleneck of slot-based models, allowing larger-capacity encoder networks to be use... | ['Kousik Rajesh', 'Abishek Sridhar', 'Robert Lo', 'Andrew Stange'] | 2023-06-05 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 1.85184330e-01 7.18189418e-01 -3.32538456e-01 -2.81926632e-01
-7.44993389e-01 8.68369732e-03 8.54618371e-01 -5.68371892e-01
-5.20564079e-01 5.87575436e-01 4.24037635e-01 -6.93322480e-01
-8.29617605e-02 -6.15569472e-01 -9.39927280e-01 -3.37983191e-01
3.08062255e-01 5.40735066e-01 2.55693048e-01 -2.00760946... | [10.763866424560547, 0.10896164923906326] |
d4c0ee13-62ca-45ac-8f1b-6d85e53821a4 | the-influence-of-the-other-race-effect-on | 2204.12591 | null | https://arxiv.org/abs/2204.12591v1 | https://arxiv.org/pdf/2204.12591v1.pdf | The Influence of the Other-Race Effect on Susceptibility to Face Morphing Attacks | Facial morphs created between two identities resemble both of the faces used to create the morph. Consequently, humans and machines are prone to mistake morphs made from two identities for either of the faces used to create the morph. This vulnerability has been exploited in "morph attacks" in security scenarios. Here,... | ["Alice J. O'Toole", 'Carlos D. Castillo', 'Connor J. Parde', 'Geraldine Jeckeln', 'Snipta Mallick'] | 2022-04-26 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 1.41174823e-01 -2.96775214e-02 4.01219815e-01 -6.39608443e-01
-2.23025978e-01 -7.64722824e-01 4.59385335e-01 -1.34238452e-01
-6.57331944e-01 2.27424115e-01 -1.82038680e-01 -2.87841052e-01
3.02237511e-01 -7.60664523e-01 -6.79990828e-01 -5.35456419e-01
-1.44294053e-01 7.31464773e-02 -5.52946329e-01 -2.58784384... | [12.98065185546875, 1.1717380285263062] |
d6109170-780c-43c1-b8f5-550b5927fa28 | a-3d-mesh-based-lifting-and-projection | 2109.11719 | null | https://arxiv.org/abs/2109.11719v1 | https://arxiv.org/pdf/2109.11719v1.pdf | A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer | Human pose transfer has typically been modeled as a 2D image-to-image translation problem. This formulation ignores the human body shape prior in 3D space and inevitably causes implausible artifacts, especially when facing occlusion. To address this issue, we propose a lifting-and-projection framework to perform pose t... | ['Ya zhang', 'Siheng Chen', 'Yangheng Zhao', 'Jinxiang Liu'] | 2021-09-24 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 3.75016391e-01 1.62289605e-01 -1.51765838e-01 -2.35978246e-01
-2.60096073e-01 -1.20024182e-01 3.81982118e-01 -5.27357340e-01
-1.26871288e-01 6.24703228e-01 2.79172987e-01 1.71583340e-01
2.62525916e-01 -7.98214078e-01 -1.00171542e+00 -5.34572780e-01
2.23150879e-01 4.65000540e-01 4.20349091e-01 -2.83308744... | [11.934256553649902, -0.8773653507232666] |
d11d5bfd-7776-4525-bc47-1dfb0b1cd68e | sequential-query-encoding-for-complex-query | 2302.13114 | null | https://arxiv.org/abs/2302.13114v3 | https://arxiv.org/pdf/2302.13114v3.pdf | Sequential Query Encoding For Complex Query Answering on Knowledge Graphs | Complex Query Answering (CQA) is an important and fundamental task for knowledge graph (KG) reasoning. Query encoding (QE) is proposed as a fast and robust solution to CQA. In the encoding process, most existing QE methods first parse the logical query into an executable computational direct-acyclic graph (DAG), then u... | ['Yangqiu Song', 'Tianshi Zheng', 'Jiaxin Bai'] | 2023-02-25 | null | null | null | null | ['complex-query-answering'] | ['knowledge-base'] | [ 1.45834655e-01 4.43727151e-02 -3.21001917e-01 -3.52752119e-01
-6.22037172e-01 -6.25598192e-01 4.45732653e-01 3.52749348e-01
-5.72574496e-01 3.84852588e-01 3.36662605e-02 -7.00976670e-01
-2.99028337e-01 -1.47341084e+00 -8.88147473e-01 -1.79624870e-01
-4.49470654e-02 7.46634543e-01 5.25628388e-01 -4.11424369... | [9.279297828674316, 7.7220025062561035] |
aa5b485d-a5cb-44a4-a775-5cf036be6810 | transforming-the-interactive-segmentation-for | 2208.09592 | null | https://arxiv.org/abs/2208.09592v2 | https://arxiv.org/pdf/2208.09592v2.pdf | Transforming the Interactive Segmentation for Medical Imaging | The goal of this paper is to interactively refine the automatic segmentation on challenging structures that fall behind human performance, either due to the scarcity of available annotations or the difficulty nature of the problem itself, for example, on segmenting cancer or small organs. Specifically, we propose a nov... | ['Ya zhang', 'Weidi Xie', 'Yuhuan Yang', 'Chaofan Ma', 'Wentao Liu'] | 2022-08-20 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 5.24813592e-01 5.35776556e-01 -4.13087346e-02 -4.06183034e-01
-7.44077384e-01 -5.34865499e-01 3.83818179e-01 5.10755833e-03
-4.64146137e-01 5.12426078e-01 2.88583376e-02 -4.80513901e-01
2.99717486e-01 -3.67248535e-01 -5.99429607e-01 -5.05123317e-01
2.22846359e-01 5.35560489e-01 7.15164483e-01 -5.06251678... | [14.391654014587402, -1.9858976602554321] |
c44eea4d-8f4f-42a2-9300-ebd0adc5d3b8 | towards-multi-spatiotemporal-scale | 2209.15616 | null | https://arxiv.org/abs/2209.15616v2 | https://arxiv.org/pdf/2209.15616v2.pdf | Towards Multi-spatiotemporal-scale Generalized PDE Modeling | Partial differential equations (PDEs) are central to describing complex physical system simulations. Their expensive solution techniques have led to an increased interest in deep neural network based surrogates. However, the practical utility of training such surrogates is contingent on their ability to model complex m... | ['Johannes Brandstetter', 'Jayesh K. Gupta'] | 2022-09-30 | null | null | null | null | ['pde-surrogate-modeling'] | ['miscellaneous'] | [-2.13711441e-01 -4.15966421e-01 3.07118177e-01 -5.50543293e-02
-2.93362349e-01 -3.73359472e-01 8.14270020e-01 -3.19343656e-01
-2.30601326e-01 9.19789910e-01 7.33693466e-02 -3.73701125e-01
-1.74575418e-01 -1.05742812e+00 -6.71511114e-01 -7.57273197e-01
-3.94316405e-01 2.88917691e-01 2.15101957e-01 -3.80224198... | [6.524408340454102, 3.3893496990203857] |
d45f35fd-d1eb-4fa6-988c-b6bcaa19590c | meta-evaluation-of-conversational-search | 2104.13453 | null | https://arxiv.org/abs/2104.13453v1 | https://arxiv.org/pdf/2104.13453v1.pdf | Meta-evaluation of Conversational Search Evaluation Metrics | Conversational search systems, such as Google Assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues. Evaluating such systems is very challenging given that any natural language responses could be generated, and users commonly interact for mul... | ['Max L. Wilson', 'Ke Zhou', 'Zeyang Liu'] | 2021-04-27 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-2.25900203e-01 1.25045300e-01 -4.48600322e-01 -3.42931777e-01
-9.10645783e-01 -1.01263976e+00 1.05376053e+00 2.86314070e-01
-6.99113011e-01 5.66896737e-01 6.39205515e-01 -5.54096699e-01
-3.72786045e-01 -2.74093211e-01 1.90518290e-01 1.85584590e-01
2.76826888e-01 7.33784080e-01 2.46922821e-02 -6.30937397... | [12.20368766784668, 7.763775825500488] |
b36aae9e-05a7-405c-8d09-d15c6163253f | deep-learning-based-detection-of-motion | 2303.10987 | null | https://arxiv.org/abs/2303.10987v1 | https://arxiv.org/pdf/2303.10987v1.pdf | Deep Learning-Based Detection of Motion-Affected k-Space Lines for T2*-Weighted MRI | T2*-weighted gradient echo MR imaging is strongly impacted by subject head motion due to motion-related changes in B0 inhomogeneities. Within the oxygenation-sensitive mqBOLD protocol, even mild motion during the acquisition of the T2*-weighted data propagates into errors in derived quantitative parameter maps. In orde... | ['Julia A. Schnabel', 'Christine Preibisch', 'Daniel Rueckert', 'Samira M. Epp', 'Veronika Spieker', 'Kerstin Hammernik', 'Hannah Eichhorn'] | 2023-03-20 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 4.30081397e-01 -1.10399775e-01 1.56989038e-01 -5.15832365e-01
-7.36797929e-01 -3.29019248e-01 1.65742144e-01 8.08992609e-02
-7.44678319e-01 7.87709653e-01 1.76972240e-01 -6.11155666e-02
-3.62830818e-01 -3.54847580e-01 -5.68684578e-01 -9.53634918e-01
-7.70593584e-01 3.36776316e-01 5.37955403e-01 1.25218317... | [13.632637023925781, -2.3905084133148193] |
e581cbc4-6001-4f70-af87-fbc15cba5090 | accu-help-a-machine-learning-based-smart | 2212.02346 | null | https://arxiv.org/abs/2212.02346v1 | https://arxiv.org/pdf/2212.02346v1.pdf | Accu-Help: A Machine Learning based Smart Healthcare Framework for Accurate Detection of Obsessive Compulsive Disorder | In recent years the importance of Smart Healthcare cannot be overstated. The current work proposed to expand the state-of-art of smart healthcare in integrating solutions for Obsessive Compulsive Disorder (OCD). Identification of OCD from oxidative stress biomarkers (OSBs) using machine learning is an important develop... | ['Saraju Prasad Mohanty', 'Susanta Kumar Padhy', 'Sujita Kumar Kar', 'Laxmi Narayan Padhy', 'Ajaya Kumar Tripathy', 'Kabita Patel'] | 2022-12-05 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-2.35959142e-01 -1.05327688e-01 -1.36368215e-01 -2.35240325e-01
-1.60684362e-01 -1.15966447e-01 -1.98218942e-01 5.38810015e-01
-1.43267019e-02 5.35758078e-01 -9.36086625e-02 7.84679577e-02
-4.75935906e-01 -6.97418809e-01 1.33058578e-01 -6.67304158e-01
-1.83734283e-01 1.17336798e+00 4.34698723e-02 1.28566548... | [8.409306526184082, 4.884030818939209] |
35b6ef58-7ff2-42b9-a70f-1d87da6c3961 | positional-diffusion-ordering-unordered-sets | 2303.11120 | null | https://arxiv.org/abs/2303.11120v1 | https://arxiv.org/pdf/2303.11120v1.pdf | Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models | Positional reasoning is the process of ordering unsorted parts contained in a set into a consistent structure. We present Positional Diffusion, a plug-and-play graph formulation with Diffusion Probabilistic Models to address positional reasoning. We use the forward process to map elements' positions in a set to random ... | ['Alessio Del Bue', 'Yiming Wang', 'Stuart James', 'Gianluca Scarpellini', 'Francesco Giuliari'] | 2023-03-20 | null | null | null | null | ['visual-storytelling', 'sentence-ordering'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.61554804e-02 5.05163491e-01 -2.39362568e-01 -2.11168509e-02
-8.30557227e-01 -7.69262433e-01 5.70494115e-01 3.04329604e-01
8.63819420e-02 5.04310668e-01 9.11221981e-01 -5.55728376e-01
-7.38051653e-01 -9.87435699e-01 -1.02034605e+00 -5.45654416e-01
-2.39482388e-01 1.31798518e+00 2.22773373e-01 -3.40436310... | [10.747859954833984, 8.050457000732422] |
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