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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3f6657a2-204d-43a5-ab8c-3ed26c2c939e | domain-adversarial-neural-networks-to-address | 1707.06183 | null | http://arxiv.org/abs/1707.06183v1 | http://arxiv.org/pdf/1707.06183v1.pdf | Domain-adversarial neural networks to address the appearance variability of histopathology images | Preparing and scanning histopathology slides consists of several steps, each
with a multitude of parameters. The parameters can vary between pathology labs
and within the same lab over time, resulting in significant variability of the
tissue appearance that hampers the generalization of automatic image analysis
methods... | ['Mitko Veta', 'Pim Moeskops', 'Maxime W. Lafarge', 'Josien P. W. Pluim', 'Koen A. J. Eppenhof'] | 2017-07-19 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 4.25902843e-01 5.45665137e-02 3.12166065e-01 -4.39633787e-01
-7.42477059e-01 -7.81212151e-01 2.86136717e-01 2.99071997e-01
-6.67461276e-01 7.72169054e-01 -3.39365304e-01 -4.99509454e-01
2.64023364e-01 -5.96055388e-01 -6.72855496e-01 -1.12884688e+00
2.30679765e-01 4.15534556e-01 3.49944443e-01 -1.34088531... | [15.042521476745605, -3.023608684539795] |
a8cd73bb-17f1-46e6-a311-a18e18a2e727 | cfc-net-a-critical-feature-capturing-network | 2101.06849 | null | https://arxiv.org/abs/2101.06849v2 | https://arxiv.org/pdf/2101.06849v2.pdf | CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images | Object detection in optical remote sensing images is an important and challenging task. In recent years, the methods based on convolutional neural networks have made good progress. However, due to the large variation in object scale, aspect ratio, and arbitrary orientation, the detection performance is difficult to be ... | ['Yunpeng Dong', 'Zhiqiang Zhou', 'Lingjuan Miao', 'Qi Ming'] | 2021-01-18 | null | null | null | null | ['object-detection-in-aerial-images', 'real-time-object-detection'] | ['computer-vision', 'computer-vision'] | [ 3.31450760e-01 -5.52030265e-01 -1.77757874e-01 -3.16450924e-01
-7.93769002e-01 -2.45147452e-01 3.22487533e-01 -6.81560114e-02
-3.13920647e-01 2.81363010e-01 1.83493234e-02 -1.91901848e-01
-4.50482398e-01 -9.10787165e-01 -4.01186883e-01 -1.14042473e+00
-1.18201591e-01 5.86997084e-02 4.29699808e-01 4.54415195... | [9.043428421020508, -0.9068543910980225] |
b7202078-673c-4694-96fa-743bd5cd9907 | moving-poselets-a-discriminative-and | null | null | https://doi.org/10.1109/ICCVW.2015.48 | http://www.vision.jhu.edu/assets/TaoLAP15.pdf | Moving poselets: A discriminative and interpretable skeletal motion representation for action recognition | Given a video or time series of skeleton data, action recognition systems perform classification using cues such as motion, appearance, and pose. For the past decade, actions have been modeled using low-level feature representations such as Bag of Features. More recent work has shown that mid-level representations that... | ['René Vidal', 'Lingling Tao'] | 2015-12-07 | null | null | null | 2015-ieee-international-conference-on | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 2.87807465e-01 -1.20530896e-01 -8.71062100e-01 -4.17084813e-01
-6.28169894e-01 1.48758106e-02 6.13359392e-01 -9.49536636e-02
-2.56327450e-01 3.56865168e-01 6.92839980e-01 3.08960021e-01
-9.15947631e-02 -5.51788032e-01 -5.25683224e-01 -7.17221856e-01
-2.82791585e-01 4.29185569e-01 2.67794400e-01 -2.23391742... | [7.85798454284668, 0.37026363611221313] |
419c3b6c-a838-47d1-b45b-0191707b2687 | recon-relation-extraction-using-knowledge | 2009.08694 | null | https://arxiv.org/abs/2009.08694v2 | https://arxiv.org/pdf/2009.08694v2.pdf | RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network | In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). RECON uses a graph neural network to learn representations of both the sentence as well as facts stored in a KG, improving the overall extrac... | ['Manohar Kaul', "Isaiah Onando Mulang'", 'Kuldeep Singh', 'Saeedeh Shekarpour', 'Johannes Hoffart', 'Anson Bastos', 'Abhishek Nadgeri'] | 2020-09-18 | null | null | null | null | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [ 6.15634695e-02 1.07351840e+00 -4.15923804e-01 -4.23333347e-01
-5.68322778e-01 -5.42141259e-01 9.21355367e-01 9.24835980e-01
-4.60697949e-01 1.14010048e+00 3.72540802e-01 -1.80934593e-01
-4.65525210e-01 -1.10855389e+00 -9.88640547e-01 3.51598114e-02
-3.41621161e-01 6.60289884e-01 3.35490644e-01 -3.03980112... | [9.327996253967285, 8.559673309326172] |
31daa8d5-a386-44aa-a0d7-ee96abae922f | learning-depth-from-monocular-videos-using | 1712.00175 | null | http://arxiv.org/abs/1712.00175v1 | http://arxiv.org/pdf/1712.00175v1.pdf | Learning Depth from Monocular Videos using Direct Methods | The ability to predict depth from a single image - using recent advances in
CNNs - is of increasing interest to the vision community. Unsupervised
strategies to learning are particularly appealing as they can utilize much
larger and varied monocular video datasets during learning without the need for
ground truth depth... | ['Rui Zhu', 'Chaoyang Wang', 'Jose Miguel Buenaposada', 'Simon Lucey'] | 2017-12-01 | learning-depth-from-monocular-videos-using-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Learning_Depth_From_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Learning_Depth_From_CVPR_2018_paper.pdf | cvpr-2018-6 | ['depth-and-camera-motion'] | ['computer-vision'] | [ 3.06735128e-01 2.12132528e-01 -1.91823706e-01 -6.03064299e-01
-4.68841463e-01 -5.87266922e-01 9.04467106e-01 -3.08326036e-01
-4.89875287e-01 8.04540455e-01 2.55944014e-01 -2.96999849e-02
1.88883528e-01 -5.52141845e-01 -1.02411890e+00 -5.31495392e-01
1.63692430e-01 3.65524381e-01 2.15300784e-01 1.30976504... | [8.579565048217773, -2.4307265281677246] |
47c09689-2d42-41f9-a563-159c5719de40 | learning-curves-for-gaussian-process-1 | 2110.12231 | null | https://arxiv.org/abs/2110.12231v2 | https://arxiv.org/pdf/2110.12231v2.pdf | Learning curves for Gaussian process regression with power-law priors and targets | We characterize the power-law asymptotics of learning curves for Gaussian process regression (GPR) under the assumption that the eigenspectrum of the prior and the eigenexpansion coefficients of the target function follow a power law. Under similar assumptions, we leverage the equivalence between GPR and kernel ridge r... | ['Guido Montúfar', 'Pradeep Kr. Banerjee', 'Hui Jin'] | 2021-10-23 | learning-curves-for-gaussian-process | https://openreview.net/forum?id=KeI9E-gsoB | https://openreview.net/pdf?id=KeI9E-gsoB | iclr-2022-4 | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-5.01909666e-02 2.60265261e-01 5.43519296e-02 -9.61558074e-02
-4.40219969e-01 -5.04046321e-01 1.75808430e-01 9.54840519e-03
-4.29132253e-01 5.08869767e-01 -2.95179218e-01 -5.99767506e-01
-4.12494719e-01 -7.05186307e-01 -8.54217410e-01 -1.14387214e+00
-3.56058389e-01 2.08438590e-01 9.32552665e-02 1.51061416... | [7.377999782562256, 3.8915159702301025] |
bb67ae62-b5f1-446a-b31f-35ac3f5f165d | gcdt-a-chinese-rst-treebank-for-multigenre | 2210.10449 | null | https://arxiv.org/abs/2210.10449v1 | https://arxiv.org/pdf/2210.10449v1.pdf | GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing | A lack of large-scale human-annotated data has hampered the hierarchical discourse parsing of Chinese. In this paper, we present GCDT, the largest hierarchical discourse treebank for Mandarin Chinese in the framework of Rhetorical Structure Theory (RST). GCDT covers over 60K tokens across five genres of freely availabl... | ['Amir Zeldes', 'Yang Janet Liu', 'Siyao Peng'] | 2022-10-19 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [-1.66487545e-01 9.42902267e-01 -5.23542702e-01 -2.73113221e-01
-1.32278419e+00 -9.51954961e-01 6.24342382e-01 4.15575832e-01
-5.59344709e-01 1.00691307e+00 1.21320736e+00 -9.15884852e-01
1.80436254e-01 -4.88579303e-01 -6.35085166e-01 -3.00001144e-01
-4.19510812e-01 5.74212909e-01 4.22658354e-01 -4.58251119... | [10.829485893249512, 9.45113468170166] |
b3251b3a-8ff4-4114-9c12-02cd0e0b2833 | unsupervised-speech-recognition | 2105.11084 | null | https://arxiv.org/abs/2105.11084v3 | https://arxiv.org/pdf/2105.11084v3.pdf | Unsupervised Speech Recognition | Despite rapid progress in the recent past, current speech recognition systems still require labeled training data which limits this technology to a small fraction of the languages spoken around the globe. This paper describes wav2vec-U, short for wav2vec Unsupervised, a method to train speech recognition models without... | ['Michael Auli', 'Alexis Conneau', 'Wei-Ning Hsu', 'Alexei Baevski'] | 2021-05-24 | null | http://proceedings.neurips.cc/paper/2021/hash/ea159dc9788ffac311592613b7f71fbb-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ea159dc9788ffac311592613b7f71fbb-Paper.pdf | neurips-2021-12 | ['unsupervised-speech-recognition'] | ['speech'] | [ 1.50071606e-01 3.27874541e-01 -1.41739145e-01 -4.13847417e-01
-1.09013712e+00 -7.57685483e-01 6.93045616e-01 -2.83424407e-01
-6.42486274e-01 8.55967760e-01 5.78209043e-01 -8.17150474e-01
6.10143483e-01 -4.91789579e-01 -5.10616541e-01 -4.76894647e-01
7.20349476e-02 6.33135438e-01 -3.30644436e-02 -3.31323922... | [14.377392768859863, 6.698877811431885] |
f33360de-6d62-4aee-a64e-5269f2e80dce | drl-gan-a-hybrid-approach-for-binary-and | 2301.03368 | null | https://arxiv.org/abs/2301.03368v1 | https://arxiv.org/pdf/2301.03368v1.pdf | DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection | Our increasingly connected world continues to face an ever-growing amount of network-based attacks. Intrusion detection systems (IDS) are an essential security technology for detecting these attacks. Although numerous machine learning-based IDS have been proposed for the detection of malicious network traffic, the majo... | ['Anwar Haque', 'Daniel Lizotte', 'Noshin Tasnim', 'Sareh Nejad', 'Muhammad Zakar', 'Chandrika Saha', 'Caroline Strickland'] | 2023-01-05 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 2.11484864e-01 -2.46190891e-01 -3.88209522e-01 -3.98642480e-01
-2.74419099e-01 -6.14626229e-01 5.56751192e-01 -4.33788747e-02
-1.38215095e-01 7.32510924e-01 -4.45469648e-01 -9.37874794e-01
3.41439843e-01 -1.24682713e+00 -1.78849712e-01 -2.53736258e-01
-5.22766002e-02 8.15361977e-01 1.38442859e-01 -2.81981081... | [5.441495418548584, 7.4887471199035645] |
a3cca228-01d0-446c-9e52-91b93724f87e | fire-now-fire-later-alarm-based-systems-for | 1905.09568 | null | https://arxiv.org/abs/1905.09568v2 | https://arxiv.org/pdf/1905.09568v2.pdf | Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring | Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood ... | ['Massimiliano de Leoni', 'Stephan A. Fahrenkrog-Petersen', 'Niek Tax', 'Irene Teinemaa', 'Fabrizio Maria Maggi', 'Matthias Weidlich', 'Marlon Dumas'] | 2019-05-23 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 8.51999938e-01 3.02154541e-01 2.67300755e-01 -3.79600465e-01
-2.24185467e-01 -3.04627270e-01 8.43745589e-01 1.08552992e+00
-2.96531916e-01 4.73173797e-01 -1.88480183e-01 -4.15038854e-01
-4.46267009e-01 -1.20062554e+00 -2.02467471e-01 -3.99352700e-01
-3.58584821e-01 5.69974124e-01 5.82662761e-01 4.18925464... | [8.602235794067383, 5.983490467071533] |
f8bfe342-d93f-47bb-8c19-714281a1748a | an-empirical-study-and-improvement-for-speech | 2304.03899 | null | https://arxiv.org/abs/2304.03899v1 | https://arxiv.org/pdf/2304.03899v1.pdf | An Empirical Study and Improvement for Speech Emotion Recognition | Multimodal speech emotion recognition aims to detect speakers' emotions from audio and text. Prior works mainly focus on exploiting advanced networks to model and fuse different modality information to facilitate performance, while neglecting the effect of different fusion strategies on emotion recognition. In this wor... | ['Xinyu Dai', 'Yizhe Lu', 'Zhen Wu'] | 2023-04-08 | null | null | null | null | ['multimodal-emotion-recognition', 'speech-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech', 'speech'] | [-3.94754261e-02 -1.06236786e-02 1.05983485e-02 -5.52212775e-01
-1.33613980e+00 -2.77811915e-01 4.24377918e-01 -1.72037184e-01
-4.47707504e-01 3.90774280e-01 6.71309352e-01 1.33706644e-01
1.24610052e-01 1.02846920e-01 -3.57497990e-01 -6.81947708e-01
4.55349088e-02 3.19176950e-02 -4.33867931e-01 -4.13201481... | [13.25926399230957, 5.406862258911133] |
9d342f6c-aa7e-470c-9ef6-28c1626ba416 | model-invertibility-regularization-sequence | null | null | https://aclanthology.info/papers/N15-1063/n15-1063 | https://www.aclweb.org/anthology/N15-1063 | Model Invertibility Regularization: Sequence Alignment With or Without Parallel Data | null | ['Ashish Vaswani', 'David Chiang', 'Tomer Levinboim'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['decipherment'] | ['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... | [-1.5391874313354492, 15.869196891784668] |
3d65789d-7ac6-49f7-a16b-93338e9ba33c | few-shot-incremental-learning-with | 2104.03047 | null | https://arxiv.org/abs/2104.03047v1 | https://arxiv.org/pdf/2104.03047v1.pdf | Few-Shot Incremental Learning with Continually Evolved Classifiers | Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points, without forgetting knowledge of old classes. The difficulty lies in that limited data from new classes not only lead to significant overfitting issues but also exacerbat... | ['Yinghui Xu', 'Pan Pan', 'Yun Zheng', 'Guosheng Lin', 'Nan Song', 'Chi Zhang'] | 2021-04-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Few-Shot_Incremental_Learning_With_Continually_Evolved_Classifiers_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Few-Shot_Incremental_Learning_With_Continually_Evolved_Classifiers_CVPR_2021_paper.pdf | cvpr-2021-1 | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 3.69266689e-01 -2.59116255e-02 -1.82043076e-01 -2.26313591e-01
-1.50623530e-01 -3.38370711e-01 4.13739502e-01 2.43893951e-01
-4.38725650e-01 9.58714962e-01 -3.42041016e-01 4.28161509e-02
-2.75651872e-01 -1.01385641e+00 -8.51276577e-01 -8.52973342e-01
-6.23703972e-02 3.99984449e-01 5.84718347e-01 -2.53376514... | [9.835930824279785, 3.384761333465576] |
ab834158-8c1c-4a45-8760-b4c43d7dd9e0 | adversarial-text-generation-via-feature | 1809.06297 | null | https://arxiv.org/abs/1809.06297v2 | https://arxiv.org/pdf/1809.06297v2.pdf | Adversarial Text Generation via Feature-Mover's Distance | Generative adversarial networks (GANs) have achieved significant success in generating real-valued data. However, the discrete nature of text hinders the application of GAN to text-generation tasks. Instead of using the standard GAN objective, we propose to improve text-generation GAN via a novel approach inspired by o... | ['Lawrence Carin', 'Yizhe Zhang', 'Liqun Chen', 'Zhe Gan', 'Shuyang Dai', 'Haichao Zhang', 'Chenyang Tao', 'Dinghan Shen'] | 2018-09-17 | adversarial-text-generation-via-feature-1 | http://papers.nips.cc/paper/7717-adversarial-text-generation-via-feature-movers-distance | http://papers.nips.cc/paper/7717-adversarial-text-generation-via-feature-movers-distance.pdf | neurips-2018-12 | ['adversarial-text'] | ['adversarial'] | [ 7.78211117e-01 1.00265570e-01 7.13264644e-02 -1.57223180e-01
-1.18525982e+00 -6.40282691e-01 8.54476154e-01 -3.13611448e-01
-1.21432714e-01 1.23303473e+00 1.10014744e-01 -2.33755797e-01
1.78894356e-01 -8.14361393e-01 -6.57938898e-01 -1.04259050e+00
3.66833299e-01 2.82702059e-01 -3.07814062e-01 -3.47046465... | [11.78397274017334, 9.322667121887207] |
efc00731-6573-4b94-91ac-28388b757de6 | sematch-semantic-entity-search-from-knowledge | null | null | https://ceur-ws.org/Vol-1556/paper2.pdf | https://ceur-ws.org/Vol-1556/paper2.pdf | Sematch: Semantic Entity Search from Knowledge Graph | As an increasing amount of the knowledge graph is published as Linked Open Data, semantic entity search is required to develop new applications. However, the use of structured query languages such as SPARQL is challenging for non-skilled users who need to master the query language as well as acquiring knowledge of the ... | ['Ganggao Zhu and Carlos A. Iglesias'] | 2015-06-01 | null | null | null | joint-proceedings-of-the-1st-international | ['entity-linking', 'semantic-textual-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-4.76099253e-01 5.04639506e-01 -2.38823801e-01 -3.51932853e-01
-3.54208946e-01 -8.45457315e-01 3.78098428e-01 8.86291802e-01
-5.66263437e-01 8.85179579e-01 -2.91841850e-02 -2.23453000e-01
-5.59094548e-01 -1.50542784e+00 -3.82977426e-01 4.74972457e-01
-9.46121141e-02 8.95235717e-01 9.77860093e-01 -6.94307208... | [9.261083602905273, 8.049744606018066] |
6993ee16-365a-4fce-be05-58e0c50716fd | vision-language-models-can-identify | 2306.10159 | null | https://arxiv.org/abs/2306.10159v2 | https://arxiv.org/pdf/2306.10159v2.pdf | Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos | Recognizing the activities, causing distraction, in real-world driving scenarios is critical for ensuring the safety and reliability of both drivers and pedestrians on the roadways. Conventional computer vision techniques are typically data-intensive and require a large volume of annotated training data to detect and c... | ['Mohammed Shaiqur Rahman', 'Soumik Sarkar', 'Anuj Sharma', 'Chinmay Hegde', 'Senem Velipasalar', 'Ameya Joshi', 'Jiyang Wang', 'Jiajing Chen', 'Md Zahid Hasan'] | 2023-06-16 | null | null | null | null | ['activity-recognition'] | ['computer-vision'] | [ 1.74582526e-01 -2.06334114e-01 -5.43795526e-01 -5.71924925e-01
-9.33137059e-01 -2.84787089e-01 8.05029333e-01 -4.28923190e-01
-5.88826060e-01 3.52082610e-01 4.19958174e-01 -5.49693346e-01
2.52207398e-01 -2.57477462e-01 -7.05101550e-01 -5.65380633e-01
2.41754577e-01 1.19469455e-02 3.92540932e-01 -3.00449640... | [7.63455867767334, -0.09925885498523712] |
0177a644-0a30-4614-8d08-7fc718d978a5 | unsupervised-detection-of-anomalous-sound | 1810.09133 | null | http://arxiv.org/abs/1810.09133v1 | http://arxiv.org/pdf/1810.09133v1.pdf | Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma | This paper proposes a novel optimization principle and its implementation for
unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The
goal of unsupervised-ADS is to detect unknown anomalous sound without training
data of anomalous sound. Use of an AE as a normal model is a state-of-the-art
techniqu... | ['Hisashi Uematsum Yuta Kawachi', 'Shoichiro Saito', 'Yuma Koizumi', 'Noboru Harada'] | 2018-10-22 | null | null | null | null | ['unsupervised-anomaly-detection-in-sound'] | ['methodology'] | [ 1.94753423e-01 -1.51369765e-01 6.89978421e-01 -1.50449112e-01
-2.02369094e-01 -1.08409725e-01 1.85792018e-02 6.00063019e-02
-4.46342021e-01 4.17970508e-01 -3.43079180e-01 -2.48390600e-01
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-1.36365548e-01 -2.11280286e-02 3.59522969e-01 -7.30810920... | [7.575644493103027, 2.4618382453918457] |
56f7b7a0-8b0d-4959-ad94-1890ac73aff5 | darkness-can-not-drive-out-darkness | null | null | https://aclanthology.org/2022.acl-srw.4 | https://aclanthology.org/2022.acl-srw.4.pdf | Darkness can not drive out darkness: Investigating Bias in Hate SpeechDetection Models | It has become crucial to develop tools for automated hate speech and abuse detection. These tools would help to stop the bullies and the haters and provide a safer environment for individuals especially from marginalized groups to freely express themselves. However, recent research shows that machine learning models ar... | ['Fatma Elsafoury'] | null | null | null | null | acl-2022-5 | ['abuse-detection'] | ['natural-language-processing'] | [-1.85043767e-01 4.13895547e-02 -1.21361211e-01 -2.23090038e-01
2.86154449e-01 -5.65075338e-01 6.83458984e-01 -2.99800956e-03
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3.04435164e-01 -9.92954820e-02 -1.67657793e-01 -1.33184448... | [8.699915885925293, 10.49638557434082] |
465cec1b-d8e1-4009-b5b4-c40343065154 | reverse-operation-based-data-augmentation-for | 2010.01556 | null | https://arxiv.org/abs/2010.01556v2 | https://arxiv.org/pdf/2010.01556v2.pdf | Reverse Operation based Data Augmentation for Solving Math Word Problems | Automatically solving math word problems is a critical task in the field of natural language processing. Recent models have reached their performance bottleneck and require more high-quality data for training. We propose a novel data augmentation method that reverses the mathematical logic of math word problems to prod... | ['Sadao Kurohashi', 'Daisuke Kawahara', 'Fei Cheng', 'Sujian Li', 'Wenyu Guan', 'Qianying Liu'] | 2020-10-04 | null | null | null | null | ['math-word-problem-solving', 'mathematical-reasoning', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'reasoning', 'time-series'] | [-4.35313769e-02 1.17585570e-01 -1.93881527e-01 -5.65935075e-01
-6.23415589e-01 -6.60223663e-01 3.34088206e-01 4.64968681e-01
-5.47049999e-01 4.85051006e-01 3.27136546e-01 -6.44037306e-01
-2.30247810e-01 -1.33854783e+00 -8.44894767e-01 9.57328454e-02
2.23205552e-01 6.80225194e-01 -8.13408643e-02 -5.45311213... | [9.635193824768066, 7.385912895202637] |
1b1e1c3b-5644-4b4d-93a4-c1cae9a96b85 | benchmarking-intent-detection-for-task | 2012.03929 | null | https://arxiv.org/abs/2012.03929v2 | https://arxiv.org/pdf/2012.03929v2.pdf | Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations | Intent detection is a key component of modern goal-oriented dialog systems that accomplish a user task by predicting the intent of users' text input. There are three primary challenges in designing robust and accurate intent detection models. First, typical intent detection models require a large amount of labeled data... | ['Mo Yu', 'Saloni Potdar', 'Ladislav Kunc', 'Abhishek Shah', 'Atin Sood', 'Lin Pan', 'Haode Qi'] | 2020-12-07 | null | https://aclanthology.org/2021.naacl-industry.38 | https://aclanthology.org/2021.naacl-industry.38.pdf | naacl-2021-4 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [ 2.25181673e-02 -3.09910148e-01 -3.29497546e-01 -6.57936037e-01
-8.14456105e-01 -8.81542504e-01 5.47466576e-01 2.61551857e-01
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1.00291930e-01 9.99430716e-01 2.34506592e-01 -5.90960562... | [12.522051811218262, 7.704870700836182] |
8e9f1bee-6169-4f3c-a595-3279b8a902ee | towards-holistic-and-automatic-evaluation-of-1 | null | null | https://aclanthology.org/2020.acl-main.333 | https://aclanthology.org/2020.acl-main.333.pdf | Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation | Open-domain dialogue generation has gained increasing attention in Natural Language Processing. Its evaluation requires a holistic means. Human ratings are deemed as the gold standard. As human evaluation is inefficient and costly, an automated substitute is highly desirable. In this paper, we propose holistic evaluati... | ['Kewei Tu', 'Erik Nijkamp', 'Wenjuan Han', 'Linqi Zhou', 'Bo Pang', 'Yixian Liu'] | 2020-07-01 | null | null | null | acl-2020-6 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-9.83040407e-02 3.41550887e-01 -1.11122854e-01 -6.87074959e-01
-1.02872062e+00 -8.05474997e-01 7.21855640e-01 3.17123622e-01
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-1.24960192e-01 -7.17047989e-01 3.07021532e-02 7.84341171e-02
6.45725131e-02 5.07764399e-01 8.23246613e-02 -7.60921061... | [12.759734153747559, 8.174698829650879] |
65ae247b-2890-43b8-935e-f558ce15d965 | proposalcontrast-unsupervised-pre-training | 2207.12654 | null | https://arxiv.org/abs/2207.12654v2 | https://arxiv.org/pdf/2207.12654v2.pdf | ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection | Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination. Scene-level methods tend to lose local details that are crucial for recognizing the road objects, while point/voxel-level methods inherently suffer from limited receptive fie... | ['Wenguan Wang', 'Jianbing Shen', 'Cheng-Zhong Xu', 'Jin Fang', 'Liangjun Zhang', 'Dingfu Zhou', 'Junbo Yin'] | 2022-07-26 | null | null | null | null | ['point-cloud-pre-training', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [-2.10707113e-02 -2.57368162e-02 -1.96225107e-01 -4.49028403e-01
-4.98708963e-01 -6.19860709e-01 8.28180492e-01 5.76238871e-01
-2.63321549e-01 -3.00613921e-02 -2.69397646e-01 -1.55237436e-01
-2.21203640e-01 -9.07452166e-01 -7.47234285e-01 -4.35376823e-01
-4.59149852e-03 6.00158274e-01 7.56859958e-01 -2.59107724... | [7.821715354919434, -3.016120195388794] |
f20f27b1-9c95-4ec4-8452-f1548ad5ed2f | multi-agent-policy-reciprocity-with | 2304.05632 | null | https://arxiv.org/abs/2304.05632v1 | https://arxiv.org/pdf/2304.05632v1.pdf | Multi-agent Policy Reciprocity with Theoretical Guarantee | Modern multi-agent reinforcement learning (RL) algorithms hold great potential for solving a variety of real-world problems. However, they do not fully exploit cross-agent knowledge to reduce sample complexity and improve performance. Although transfer RL supports knowledge sharing, it is hyperparameter sensitive and c... | ['Jianye Hao', 'Yunfeng Shao', 'Qing Wang', 'Yinchuan Li', 'Haozhi Wang'] | 2023-04-12 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-0.48869777 0.0690933 -0.6805443 0.16511792 -0.63970697 -0.5767802
0.49434492 0.0367951 -0.54817665 1.3963186 0.0832888 -0.09827779
-0.5540369 -0.83538383 -0.5497999 -1.0124712 -0.42604196 0.43205646
0.04733193 -0.34971595 0.04049781 -0.05687886 -0.8676885 -0.43462
1.0061132 0.68246996 0.3298... | [3.8393571376800537, 2.0903942584991455] |
dc40e20f-8e47-4245-b0a9-a6c608476265 | transferring-neural-speech-waveform | 1910.12381 | null | https://arxiv.org/abs/1910.12381v2 | https://arxiv.org/pdf/1910.12381v2.pdf | Transferring neural speech waveform synthesizers to musical instrument sounds generation | Recent neural waveform synthesizers such as WaveNet, WaveGlow, and the neural-source-filter (NSF) model have shown good performance in speech synthesis despite their different methods of waveform generation. The similarity between speech and music audio synthesis techniques suggests interesting avenues to explore in te... | ['Yi Zhao', 'Lauri Juvela', 'Junichi Yamagishi', 'Xin Wang'] | 2019-10-27 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 1.55393183e-01 -6.47498518e-02 2.03350365e-01 2.58916020e-02
-9.76495326e-01 -5.29443145e-01 4.49678719e-01 -3.02370071e-01
-4.02791379e-03 7.06415832e-01 6.82827353e-01 -3.26384269e-02
-3.80417049e-01 -8.89242887e-01 -5.39109230e-01 -6.39462888e-01
-5.52742071e-02 3.83374155e-01 2.74971426e-01 -5.54527164... | [15.657991409301758, 5.901705265045166] |
f2c446ba-b166-42c6-af1a-a4bbb7d521fb | a-method-for-crash-prediction-and-avoidance | 2212.12011 | null | https://arxiv.org/abs/2212.12011v1 | https://arxiv.org/pdf/2212.12011v1.pdf | A Method for Crash Prediction and Avoidance Using Hidden Markov Models | In recent years, automotive technology has made a steady progress. In particular, Advanced Driver Assistance System (ADAS) has enabled many safety features in commercial vehicles, for instance, pedestrian detection, lane keeping assist, emergency automatic braking, etc. Although these features provide drivers with a sa... | ['Yaobin Chen', 'Brian King', 'Lingxi Li', 'Avinash Prabu'] | 2022-12-22 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [-3.26568812e-01 -1.17618725e-01 -3.31232607e-01 -3.43660951e-01
-1.09874226e-01 -4.75212671e-02 5.16493857e-01 -1.08943380e-01
-4.62607592e-01 8.58648002e-01 -1.79370970e-01 -8.44547749e-01
-1.67081624e-01 -7.68489301e-01 -3.14760536e-01 -7.46898651e-01
1.25863209e-01 6.64797053e-02 8.14077556e-01 -4.66730118... | [5.78560733795166, 1.1666598320007324] |
a94ffa13-a1df-40e7-baf2-6adaa29faa6f | region-based-evidential-deep-learning-to | 2208.06038 | null | https://arxiv.org/abs/2208.06038v1 | https://arxiv.org/pdf/2208.06038v1.pdf | Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation | Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quan... | ['Guang Yang', 'Javier Del Ser', 'Yang Nan', 'Hao Li'] | 2022-08-11 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [-1.45618454e-01 3.31400424e-01 -3.53204429e-01 -7.15848207e-01
-1.17167568e+00 -1.36351183e-01 4.58074808e-01 4.26513106e-01
-4.96391922e-01 1.10496998e+00 1.88448802e-01 -1.58719867e-01
-4.99175638e-01 -8.27068746e-01 -4.59935665e-01 -9.25724268e-01
8.10285360e-02 6.04580045e-01 3.57973367e-01 4.98762250... | [14.365873336791992, -2.0784010887145996] |
952292b1-6dbf-4a17-95df-105eee97db43 | a-probabilistic-relaxation-of-the-two-stage | 2306.00892 | null | https://arxiv.org/abs/2306.00892v1 | https://arxiv.org/pdf/2306.00892v1.pdf | A Probabilistic Relaxation of the Two-Stage Object Pose Estimation Paradigm | Existing object pose estimation methods commonly require a one-to-one point matching step that forces them to be separated into two consecutive stages: visual correspondence detection (e.g., by matching feature descriptors as part of a perception front-end) followed by geometric alignment (e.g., by optimizing a robust ... | ['Onur Beker'] | 2023-06-01 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [ 2.52507716e-01 -1.02357924e-01 8.10328573e-02 -4.29097950e-01
-1.07006526e+00 -9.72469151e-01 8.49956989e-01 4.72393125e-01
-2.11859956e-01 2.45724633e-01 -9.41905752e-02 -1.87963471e-01
-3.68486315e-01 -6.39964104e-01 -6.96053028e-01 -5.04156888e-01
2.50988424e-01 1.11401320e+00 5.18985093e-01 4.99508902... | [7.454356670379639, -2.711169958114624] |
39b6900e-a264-4bf0-b11f-7d22f63d5574 | expert-sample-consensus-applied-to-camera-re | 1908.02484 | null | https://arxiv.org/abs/1908.02484v1 | https://arxiv.org/pdf/1908.02484v1.pdf | Expert Sample Consensus Applied to Camera Re-Localization | Fitting model parameters to a set of noisy data points is a common problem in computer vision. In this work, we fit the 6D camera pose to a set of noisy correspondences between the 2D input image and a known 3D environment. We estimate these correspondences from the image using a neural network. Since the correspondenc... | ['Eric Brachmann', 'Carsten Rother'] | 2019-08-07 | expert-sample-consensus-applied-to-camera-re-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Brachmann_Expert_Sample_Consensus_Applied_to_Camera_Re-Localization_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Brachmann_Expert_Sample_Consensus_Applied_to_Camera_Re-Localization_ICCV_2019_paper.pdf | iccv-2019-10 | ['camera-localization'] | ['computer-vision'] | [-5.20163402e-02 -3.38862538e-01 1.72443613e-01 -4.04943943e-01
-1.15204310e+00 -8.46850157e-01 2.41731450e-01 -3.51535022e-01
-5.22336602e-01 3.18440497e-01 -2.05092192e-01 -3.84171270e-02
-7.77876601e-02 -1.59031600e-01 -1.09353483e+00 -5.57467759e-01
4.68212157e-01 9.95547771e-01 1.62200540e-01 1.32155925... | [7.834887504577637, -2.3239803314208984] |
118c81e7-a178-4f56-92df-1216c51118f6 | style-invariant-cardiac-image-segmentation | 2009.12193 | null | https://arxiv.org/abs/2009.12193v1 | https://arxiv.org/pdf/2009.12193v1.pdf | Style-invariant Cardiac Image Segmentation with Test-time Augmentation | Deep models often suffer from severe performance drop due to the appearance shift in the real clinical setting. Most of the existing learning-based methods rely on images from multiple sites/vendors or even corresponding labels. However, collecting enough unknown data to robustly model segmentation cannot always hold s... | ['Yuxin Zou', 'Xin Yang', 'Mingyuan Luo', 'Xiaoqiong Huang', 'Zhendong Liu', 'Zejian Chen', 'Wufeng Xue', 'Dong Ni'] | 2020-09-24 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 4.63697225e-01 1.72744185e-01 -9.83033255e-02 -4.82481271e-01
-7.04693675e-01 -5.17709076e-01 2.03250833e-02 -5.35954654e-01
-1.49762735e-01 6.25717223e-01 -1.24064930e-01 -3.41246784e-01
8.20207745e-02 -5.76082706e-01 -8.39612663e-01 -5.38756311e-01
4.39318448e-01 3.66133034e-01 4.25706744e-01 -1.67013466... | [14.52486801147461, -2.1699790954589844] |
d18131c6-5ba1-4869-81b0-fe8d54fd56c3 | deep-semi-supervised-metric-learning-with-1 | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhuang_Deep_Semi-Supervised_Metric_Learning_With_Mixed_Label_Propagation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhuang_Deep_Semi-Supervised_Metric_Learning_With_Mixed_Label_Propagation_CVPR_2023_paper.pdf | Deep Semi-Supervised Metric Learning With Mixed Label Propagation | Metric learning requires the identification of far-apart similar pairs and close dissimilar pairs during training, and this is difficult to achieve with unlabeled data because pairs are typically assumed to be similar if they are close. We present a novel metric learning method which circumvents this issue by ident... | ['Pierre Moulin', 'Furen Zhuang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 9.83686745e-02 -1.33077174e-01 -2.40920946e-01 -7.18257904e-01
-8.94460440e-01 -8.65361333e-01 5.18436670e-01 7.41146028e-01
-5.34079492e-01 7.99432814e-01 -2.29484349e-01 1.00680636e-02
-6.17654443e-01 -7.88599312e-01 -1.40674040e-01 -6.68127179e-01
-1.95716083e-01 9.36271727e-01 2.76355714e-01 -2.45278068... | [9.489611625671387, 3.6531786918640137] |
0ee126a4-b46c-4737-9abd-7c076a0a419b | face-alignment-across-large-poses-a-3d | 1511.07212 | null | http://arxiv.org/abs/1511.07212v1 | http://arxiv.org/pdf/1511.07212v1.pdf | Face Alignment Across Large Poses: A 3D Solution | Face alignment, which fits a face model to an image and extracts the semantic
meanings of facial pixels, has been an important topic in CV community.
However, most algorithms are designed for faces in small to medium poses (below
45 degree), lacking the ability to align faces in large poses up to 90 degree.
The challen... | ['Hailin Shi', 'Zhen Lei', 'Xiangyu Zhu', 'Xiaoming Liu', 'Stan Z. Li'] | 2015-11-23 | face-alignment-across-large-poses-a-3d-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_Face_Alignment_Across_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhu_Face_Alignment_Across_CVPR_2016_paper.pdf | cvpr-2016-6 | ['head-pose-estimation'] | ['computer-vision'] | [-3.00342869e-02 1.38374001e-01 -6.12911843e-02 -7.39627600e-01
-4.82927412e-01 -4.49505419e-01 3.81913722e-01 -7.66710103e-01
1.32139856e-02 2.30449915e-01 8.33353624e-02 1.05786264e-01
2.74590671e-01 -5.04943073e-01 -6.76831186e-01 -4.25479263e-01
2.98032492e-01 6.00614190e-01 -9.10552815e-02 -1.19870007... | [13.28525447845459, 0.22136206924915314] |
bb72b8d7-99f8-413f-a283-a84888be8862 | data-free-quantization-with-accurate | 2204.04215 | null | https://arxiv.org/abs/2204.04215v2 | https://arxiv.org/pdf/2204.04215v2.pdf | Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization | Data-free quantization is a task that compresses the neural network to low bit-width without access to original training data. Most existing data-free quantization methods cause severe performance degradation due to inaccurate activation clipping range and quantization error, especially for low bit-width. In this paper... | ['Hong Zhou', 'Weijia Wu', 'Luoming Zhang', 'Yefei He'] | 2022-04-08 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 2.68767595e-01 -4.93045390e-01 -3.85471672e-01 -5.63606143e-01
-7.00877070e-01 -1.90325454e-01 2.45384499e-01 1.74241647e-01
-1.06240177e+00 7.37365961e-01 5.61060756e-02 -2.90529788e-01
1.71613172e-01 -6.54946208e-01 -7.29696929e-01 -7.82045245e-01
1.51394621e-01 -9.89652276e-02 4.46476638e-01 -1.41924933... | [8.665858268737793, 3.030188798904419] |
9b954e6c-fb74-44e0-aa50-ebb3e71b6d1d | m3p-learning-universal-representations-via | 2006.02635 | null | https://arxiv.org/abs/2006.02635v4 | https://arxiv.org/pdf/2006.02635v4.pdf | M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training | We present M3P, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training. Our goal is to learn universal representations that can map objects occurred in different modalities or texts expressed in differ... | ['Dongdong Zhang', 'Jianfeng Gao', 'Taroon Bharti', 'Lin Su', 'Minheng Ni', 'Edward Cui', 'Nan Duan', 'Lijuan Wang', 'Haoyang Huang'] | 2020-06-04 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ni_M3P_Learning_Universal_Representations_via_Multitask_Multilingual_Multimodal_Pre-Training_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ni_M3P_Learning_Universal_Representations_via_Multitask_Multilingual_Multimodal_Pre-Training_CVPR_2021_paper.pdf | cvpr-2021-1 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 1.11204907e-01 -4.47915554e-01 -3.18307996e-01 -5.46024680e-01
-1.55291235e+00 -7.62249351e-01 9.83929634e-01 2.96352189e-02
-7.61514485e-01 4.51323807e-01 1.71279237e-01 -3.04677129e-01
2.86124617e-01 -2.16918349e-01 -9.74051476e-01 -2.50169307e-01
3.01317275e-01 6.06374681e-01 -7.07004145e-02 -2.00398549... | [11.172224998474121, 1.5543960332870483] |
669bbf4d-b218-4f28-8f5a-a479fcef29df | aspect-based-sentiment-analysis-using-local | 2207.02424 | null | https://arxiv.org/abs/2207.02424v2 | https://arxiv.org/pdf/2207.02424v2.pdf | Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa | Text sentiment analysis, also known as opinion mining, is research on the calculation of people's views, evaluations, attitude and emotions expressed by entities. Text sentiment analysis can be divided into text-level sentiment analysis, sen-tence-level sentiment analysis and aspect-level sentiment analysis. Aspect-Bas... | ['Zhe Xue', 'Zeli Guan', 'Ang Li', 'Junping Du', 'Tianyu Zhao'] | 2022-07-06 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 3.38334665e-02 -1.57928318e-01 -2.80235648e-01 -7.62057722e-01
-4.63037819e-01 -3.25721681e-01 7.48071671e-01 4.13567156e-01
-4.14716989e-01 4.41508085e-01 7.77091801e-01 -4.94678974e-01
1.58193126e-01 -8.76367390e-01 -4.45598096e-01 -5.38027883e-01
2.85413295e-01 2.92520970e-01 -2.00523317e-01 -9.11243498... | [11.351303100585938, 6.765265941619873] |
7bcbe56b-2b5e-4da0-8a97-904994e2bf05 | accurate-point-cloud-registration-with-robust | 2111.00648 | null | https://arxiv.org/abs/2111.00648v1 | https://arxiv.org/pdf/2111.00648v1.pdf | Accurate Point Cloud Registration with Robust Optimal Transport | This work investigates the use of robust optimal transport (OT) for shape matching. Specifically, we show that recent OT solvers improve both optimization-based and deep learning methods for point cloud registration, boosting accuracy at an affordable computational cost. This manuscript starts with a practical overview... | ['Marc Niethammer', 'Raul San Jose Estepar', 'Ruben San Jose Estepar', 'Ariel Hernán Curiale', 'Peirong Liu', 'Jean Feydy', 'Zhengyang Shen'] | 2021-11-01 | null | http://proceedings.neurips.cc/paper/2021/hash/2b0f658cbffd284984fb11d90254081f-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/2b0f658cbffd284984fb11d90254081f-Paper.pdf | neurips-2021-12 | ['scene-flow-estimation'] | ['computer-vision'] | [-2.81098008e-01 -3.19626093e-01 -2.25478992e-01 3.24191432e-03
-1.24131799e+00 -6.19555473e-01 4.42652732e-01 5.79002798e-02
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-1.59203038e-01 -5.69031060e-01 -5.68238199e-01 -7.11389482e-01
-9.63845924e-02 1.27487886e+00 2.58550763e-01 -4.12824415... | [13.918859481811523, -2.6381893157958984] |
0ac8e078-0dc6-4ce5-9c30-96725d0d325f | unsupervised-person-re-identification-via-2 | 2104.00202 | null | https://arxiv.org/abs/2104.00202v1 | https://arxiv.org/pdf/2104.00202v1.pdf | Unsupervised Person Re-identification via Simultaneous Clustering and Consistency Learning | Unsupervised person re-identification (re-ID) has become an important topic due to its potential to resolve the scalability problem of supervised re-ID models. However, existing methods simply utilize pseudo labels from clustering for supervision and thus have not yet fully explored the semantic information in data its... | ['Jun Guo', 'Zhanyu Ma', 'Jiyang Xie', 'Siqing Zhang', 'Jiayan Qiu', 'Junhui Yin'] | 2021-04-01 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.56837896e-01 1.61669895e-01 -2.99844235e-01 -7.15707660e-01
-3.09567481e-01 -3.82223874e-01 7.83423960e-01 -1.11275762e-01
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2.35015526e-01 6.46557152e-01 -1.74603894e-01 4.36136603... | [14.801953315734863, 1.0640575885772705] |
596f4ce8-bb6c-4b9f-a4da-582a3c6f4586 | contrastive-collaborative-filtering-for-cold | 2302.02151 | null | https://arxiv.org/abs/2302.02151v2 | https://arxiv.org/pdf/2302.02151v2.pdf | Contrastive Collaborative Filtering for Cold-Start Item Recommendation | The cold-start problem is a long-standing challenge in recommender systems. As a promising solution, content-based generative models usually project a cold-start item's content onto a warm-start item embedding to capture collaborative signals from item content so that collaborative filtering can be applied. However, si... | ['Ning Yang', 'Lilin Zhang', 'Zhihui Zhou'] | 2023-02-04 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-2.54878044e-01 -3.68545353e-01 2.20231079e-02 -3.85015279e-01
-2.51222342e-01 -5.68900824e-01 6.20315492e-01 -4.03283566e-01
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-3.42911452e-01 -8.70429933e-01 -4.30343807e-01 -9.38081563e-01
2.63471305e-01 -1.71959937e-01 -2.40477040e-01 -3.00111264... | [10.171963691711426, 5.554909706115723] |
c3d4047e-7f97-4666-ba26-55d9636d4afd | sss3d-fast-neural-architecture-search-for | 2304.11207 | null | https://arxiv.org/abs/2304.11207v1 | https://arxiv.org/pdf/2304.11207v1.pdf | SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation | We present SSS3D, a fast multi-objective NAS framework designed to find computationally efficient 3D semantic scene segmentation networks. It uses RandLA-Net, an off-the-shelf point-based network, as a super-network to enable weight sharing and reduce search time by 99.67% for single-stage searches. SSS3D has a complex... | ['Brett H. Meyer', 'Warren J. Gross', 'Zhuoran Xiong', 'Marihan Amein', 'Olivier Therrien'] | 2023-04-21 | null | null | null | null | ['scene-segmentation', 'architecture-search'] | ['computer-vision', 'methodology'] | [ 1.77114129e-01 2.23798916e-01 -5.57608366e-01 -1.69900030e-01
-4.21119690e-01 -5.93685865e-01 -3.64153475e-01 -3.12245071e-01
-5.46894968e-01 7.25175798e-01 -4.01043415e-01 -6.20493770e-01
-6.24869168e-01 -9.86002624e-01 -3.43981892e-01 -3.68952751e-01
-1.95810422e-01 8.39049220e-01 1.07181466e+00 -1.05569370... | [9.259110450744629, -0.6325892210006714] |
7c0c38bd-a60a-4f84-a4d6-a0696ca25045 | learning-to-rank-visual-stories-from-human-1 | null | null | https://aclanthology.org/2022.acl-long.441 | https://aclanthology.org/2022.acl-long.441.pdf | Learning to Rank Visual Stories From Human Ranking Data | Visual storytelling (VIST) is a typical vision and language task that has seen extensive development in the natural language generation research domain. However, it remains unclear whether conventional automatic evaluation metrics for text generation are applicable on VIST. In this paper, we present the VHED (VIST Huma... | ['Lun-Wei Ku', 'Ting-Hao Huang', 'Chacha Chen', 'Kuan-Chieh Lo', 'Vincent Chen', 'Yun-Wei Chu', 'Chi-Yang Hsu'] | null | null | null | null | acl-2022-5 | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.71539381e-01 2.33907416e-01 -1.11147054e-01 -1.36059925e-01
-1.07919812e+00 -8.26236725e-01 1.28462613e+00 3.24575245e-01
-1.13171183e-01 9.50425863e-01 8.74375105e-01 -2.09112629e-01
-1.44732475e-01 -7.68510699e-01 -2.66417414e-01 -1.68375283e-01
1.90441191e-01 7.31004238e-01 1.13746010e-01 -7.15434253... | [11.756668090820312, 8.871672630310059] |
864d78a7-4ea6-4c94-a1f3-70ba4ba7482f | language-embeddings-sometimes-contain | 2301.08115 | null | https://arxiv.org/abs/2301.08115v1 | https://arxiv.org/pdf/2301.08115v1.pdf | Language Embeddings Sometimes Contain Typological Generalizations | To what extent can neural network models learn generalizations about language structure, and how do we find out what they have learned? We explore these questions by training neural models for a range of natural language processing tasks on a massively multilingual dataset of Bible translations in 1295 languages. The l... | ['Murathan Kurfali', 'Robert Östling'] | 2023-01-19 | null | null | null | null | ['multilingual-word-embeddings'] | ['methodology'] | [-1.90318003e-01 1.80017740e-01 -5.11760294e-01 -5.92258990e-01
-6.26336515e-01 -9.18911636e-01 1.00659823e+00 4.31621879e-01
-6.98561907e-01 7.83535659e-01 1.01257765e+00 -8.02968800e-01
-1.03104776e-02 -7.96956837e-01 -5.02490640e-01 -1.77603558e-01
-8.75791907e-02 6.60443008e-01 -9.90345478e-02 -6.49795413... | [10.807023048400879, 9.733755111694336] |
031b2243-1499-45f8-9859-a917db08062d | improved-differential-neural-cryptanalysis | 2301.11601 | null | https://arxiv.org/abs/2301.11601v1 | https://arxiv.org/pdf/2301.11601v1.pdf | Improved Differential-neural Cryptanalysis for Round-reduced Simeck32/64 | In CRYPTO 2019, Gohr presented differential-neural cryptanalysis by building the differential distinguisher with a neural network, achieving practical 11-, and 12-round key recovery attack for Speck32/64. Inspired by this framework, we develop the Inception neural network that is compatible with the round function of S... | ['Chao Li', 'Zilong Wang', 'Jinyu Lu', 'Liu Zhang'] | 2023-01-27 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [-8.8827491e-02 -3.4062776e-01 -6.4428180e-02 8.0964074e-02
-9.3857580e-01 -1.1158069e+00 1.5630925e-01 1.6660941e-01
-6.9145751e-01 4.7836336e-01 -4.4745591e-01 -1.2604643e+00
1.0124594e-01 -9.5744687e-01 -6.1788225e-01 -9.2111051e-01
-7.8522491e-01 -1.0095711e-01 -4.0284269e-02 -6.2433487e-01
5.2242112e-01... | [5.9021077156066895, 7.344762802124023] |
ada00aef-96b7-47e2-aeea-f8b60ab38ed8 | bias-analysis-and-mitigation-in-the | null | null | https://aclanthology.org/P19-1634 | https://aclanthology.org/P19-1634.pdf | Bias Analysis and Mitigation in the Evaluation of Authorship Verification | The PAN series of shared tasks is well known for its continuous and high quality research in the field of digital text forensics. Among others, PAN contributions include original corpora, tailored benchmarks, and standardized experimentation platforms. In this paper we review, theoretically and practically, the authors... | ['Janek Bevendorff', 'Benno Stein', 'Matthias Hagen', 'Martin Potthast'] | 2019-07-01 | null | null | null | acl-2019-7 | ['authorship-verification'] | ['natural-language-processing'] | [ 9.37968418e-02 -7.00291712e-03 -4.88034002e-02 1.99062750e-02
-7.45748341e-01 -8.01675498e-01 8.53903651e-01 3.25363636e-01
-6.79849029e-01 8.48665714e-01 1.64083112e-02 -3.45295995e-01
-3.25949699e-01 -2.93805927e-01 -5.61708093e-01 -4.51090544e-01
1.41856223e-01 6.34014130e-01 3.61484826e-01 -7.43463337... | [9.532045364379883, 10.594413757324219] |
42e6a7bb-fe6b-4374-96e2-83f87ba06e47 | evaluating-machine-translation-quality-with | 2306.01549 | null | https://arxiv.org/abs/2306.01549v1 | https://arxiv.org/pdf/2306.01549v1.pdf | Evaluating Machine Translation Quality with Conformal Predictive Distributions | This paper presents a new approach for assessing uncertainty in machine translation by simultaneously evaluating translation quality and providing a reliable confidence score. Our approach utilizes conformal predictive distributions to produce prediction intervals with guaranteed coverage, meaning that for any given si... | ['Patrizio Giovannotti'] | 2023-06-02 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 5.07308170e-02 3.98681283e-01 -5.71114182e-01 -4.56019819e-01
-1.68003368e+00 -8.96519005e-01 4.83300656e-01 5.07854223e-01
-7.54626319e-02 1.43418574e+00 -4.41299528e-02 -7.96217144e-01
-1.87083289e-01 -7.28423953e-01 -9.61602032e-01 -1.27466917e-01
-7.54892603e-02 8.24432433e-01 5.30690849e-02 1.36415005... | [7.920725345611572, 4.493966102600098] |
bb4166bf-3a1d-484b-abb6-e60bc0ebf5f7 | perceiving-music-quality-with-gans | 2006.06287 | null | https://arxiv.org/abs/2006.06287v2 | https://arxiv.org/pdf/2006.06287v2.pdf | Perceiving Music Quality with GANs | Several methods have been developed to assess the perceptual quality of audio under transforms like lossy compression. However, they require paired reference signals of the unaltered content, limiting their use in applications where references are unavailable. This has hindered progress in audio generation and style tr... | ['Carl Thomé', 'Agrin Hilmkil', 'Anders Arpteg'] | 2020-06-11 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.16236192e-01 -3.04367065e-01 1.02361694e-01 -1.73651323e-01
-1.23774385e+00 -8.40707123e-01 1.96438447e-01 -3.89433801e-02
-1.91535980e-01 6.44233644e-01 6.34032607e-01 2.21207812e-01
-2.53174037e-01 -6.50095344e-01 -3.60119462e-01 -6.67781770e-01
-6.55369163e-02 7.59544969e-02 1.45302907e-01 -2.26866499... | [15.51504898071289, 5.720449924468994] |
32c1c117-eaa9-4e7e-b6be-711a9aba5d75 | feature-based-selection-of-dependency-paths | null | null | https://aclanthology.org/P13-1050 | https://aclanthology.org/P13-1050.pdf | Feature-Based Selection of Dependency Paths in Ad Hoc Information Retrieval | null | ['K. Tamsin Maxwell', 'W. Bruce Croft', 'Oberl', 'Jon er'] | 2013-08-01 | null | null | null | acl-2013-8 | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.404953479766846, 3.6708667278289795] |
b0870622-a2c7-4adc-b164-3189e6167d31 | automated-essay-scoring-using-transformer | 2110.06874 | null | https://arxiv.org/abs/2110.06874v1 | https://arxiv.org/pdf/2110.06874v1.pdf | Automated Essay Scoring Using Transformer Models | Automated essay scoring (AES) is gaining increasing attention in the education sector as it significantly reduces the burden of manual scoring and allows ad hoc feedback for learners. Natural language processing based on machine learning has been shown to be particularly suitable for text classification and AES. While ... | ['Steffen Brandt', 'Kerstin Eilers', 'Christopher Hansen', 'Christian Mayer', 'Sabrina Ludwig'] | 2021-10-13 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 1.15497850e-01 1.61702812e-01 -8.19658786e-02 -5.19477308e-01
-9.25913393e-01 -6.10779524e-01 3.30821335e-01 8.76459897e-01
-6.48734868e-01 7.03262508e-01 4.66836005e-01 -6.66220546e-01
-4.28028643e-01 -7.30387390e-01 -5.22237904e-02 -5.65934777e-01
6.32283330e-01 5.95750272e-01 1.44791916e-01 -5.27627647... | [11.27468204498291, 9.325878143310547] |
e1071354-6081-446e-8304-946742111bc0 | tinyhd-efficient-video-saliency-prediction | 2301.04619 | null | https://arxiv.org/abs/2301.04619v1 | https://arxiv.org/pdf/2301.04619v1.pdf | TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation | Video saliency prediction has recently attracted attention of the research community, as it is an upstream task for several practical applications. However, current solutions are particularly computationally demanding, especially due to the wide usage of spatio-temporal 3D convolutions. We observe that, while different... | ['Kevin McGuinness', 'Concetto Spampinato', 'Morteza Moradi', 'Giovanni Bellitto', 'Federica Proietto Salanitri', 'Simone Palazzo', 'Feiyan Hu'] | 2023-01-11 | null | null | null | null | ['saliency-prediction'] | ['computer-vision'] | [ 3.02301019e-01 1.31049514e-01 -4.23717439e-01 -3.64977151e-01
-8.89148176e-01 -2.06592813e-01 3.62811089e-01 1.39056250e-01
-4.64838177e-01 6.49247050e-01 5.42659760e-01 -1.98968261e-01
1.10910445e-01 -3.13799977e-01 -9.29387152e-01 -3.66183490e-01
1.25297025e-01 1.91373862e-02 7.28433967e-01 -1.95385128... | [9.7319974899292, 0.08183370530605316] |
2fe44a56-7bd0-47a4-9a3f-22aab500d3cc | towards-theory-based-moral-ai-moral-ai-with | 2306.11432 | null | https://arxiv.org/abs/2306.11432v1 | https://arxiv.org/pdf/2306.11432v1.pdf | Towards Theory-based Moral AI: Moral AI with Aggregating Models Based on Normative Ethical Theory | Moral AI has been studied in the fields of philosophy and artificial intelligence. Although most existing studies are only theoretical, recent developments in AI have made it increasingly necessary to implement AI with morality. On the other hand, humans are under the moral uncertainty of not knowing what is morally ri... | ['Kenji Araki', 'Rzepka Rafal', 'Masashi Takeshita'] | 2023-06-20 | null | null | null | null | ['ethics', 'philosophy'] | ['miscellaneous', 'miscellaneous'] | [ 2.16391549e-01 7.06244051e-01 -2.38309912e-02 -7.27825463e-01
1.27326205e-01 -2.68373966e-01 4.77353334e-01 -1.85627211e-02
-9.34433818e-01 9.06237125e-01 3.84040624e-01 -3.09840173e-01
-3.15141320e-01 -7.35265672e-01 -1.13548495e-01 -5.36996663e-01
5.63804567e-01 2.73267150e-01 -3.41718525e-01 -4.22619522... | [9.024393081665039, 6.226876735687256] |
ba0461e4-d04a-45fa-8459-585db2dd33b8 | integration-of-feature-selection-techniques | 2303.02467 | null | https://arxiv.org/abs/2303.02467v1 | https://arxiv.org/pdf/2303.02467v1.pdf | Integration of Feature Selection Techniques using a Sleep Quality Dataset for Comparing Regression Algorithms | This research aims to examine the usefulness of integrating various feature selection methods with regression algorithms for sleep quality prediction. A publicly accessible sleep quality dataset is used to analyze the effect of different feature selection techniques on the performance of four regression algorithms - Li... | ['Venkat Tummala', 'Sai Rohith Tanuku'] | 2023-03-04 | null | null | null | null | ['sleep-quality-prediction', 'sleep-quality-prediction-1'] | ['medical', 'medical'] | [-1.55836076e-01 -3.12103003e-01 -9.26077962e-01 -8.67684186e-01
-3.00215453e-01 3.07724684e-01 -3.15491289e-01 3.56975496e-01
-4.31535631e-01 1.14780486e+00 6.73625231e-01 -1.69714570e-01
-5.33900440e-01 -6.13978863e-01 3.05592716e-01 -5.26214719e-01
1.06197812e-01 2.70605326e-01 -4.94449824e-01 -8.54168907... | [13.564269065856934, 3.4629387855529785] |
c80dedf6-c62d-4503-9843-7cf72f6a8607 | a-parameter-free-graph-reduction-for-spectral | 2302.13165 | null | https://arxiv.org/abs/2302.13165v1 | https://arxiv.org/pdf/2302.13165v1.pdf | A parameter-free graph reduction for spectral clustering and SpectralNet | Graph-based clustering methods like spectral clustering and SpectralNet are very efficient in detecting clusters of non-convex shapes. Unlike the popular $k$-means, graph-based clustering methods do not assume that each cluster has a single mean. However, these methods need a graph where vertices in the same cluster ar... | ['Masahiro Takatsuka', 'John Stavrakakis', 'Mashaan Alshammari'] | 2023-02-25 | null | null | null | null | ['graph-clustering', 'spectral-graph-clustering', 'graph-partitioning'] | ['graphs', 'graphs', 'graphs'] | [-3.55484396e-01 -9.37045664e-02 3.05774193e-02 -2.20085904e-01
-1.39317229e-01 -5.33096552e-01 3.04863095e-01 5.99058390e-01
-4.44836915e-01 3.53764027e-01 -2.88706869e-01 -1.44665567e-02
-4.84267652e-01 -1.21306026e+00 -2.63192534e-01 -8.72613728e-01
-5.07095993e-01 5.78180730e-01 7.57604718e-01 4.90598530... | [7.562347412109375, 4.681687831878662] |
ca3d039e-c41f-4a38-9c79-ec294fd28e26 | bi-linear-value-networks-for-multi-goal | null | null | https://openreview.net/forum?id=LedObtLmCjS | https://openreview.net/pdf?id=LedObtLmCjS | Bi-linear Value Networks for Multi-goal Reinforcement Learning | Universal value functions are used to score the long-term utility of actions to achieve a goal from the current state. In contrast to prior methods that learn a monolithic function to approximate the value, we propose a bi-linear decomposition of the value function. The first component, akin to a global plan models how... | ['Pulkit Agrawal', 'Zhang-Wei Hong', 'Ge Yang'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['multi-goal-reinforcement-learning'] | ['methodology'] | [ 9.81719717e-02 3.01110208e-01 -4.91732270e-01 -3.27974677e-01
-8.88045192e-01 -6.44537985e-01 7.39344299e-01 9.75660309e-02
-5.20129204e-01 1.06265020e+00 3.95436704e-01 -2.46294867e-02
-3.38810116e-01 -7.14170992e-01 -7.05320776e-01 -7.62413979e-01
-2.53591806e-01 5.90130806e-01 2.54805416e-01 -2.15565562... | [4.148192405700684, 1.6997172832489014] |
aff3dd71-0a10-40f1-91ad-99728da7c673 | malicious-or-benign-towards-effective-content | 2305.15551 | null | https://arxiv.org/abs/2305.15551v1 | https://arxiv.org/pdf/2305.15551v1.pdf | Malicious or Benign? Towards Effective Content Moderation for Children's Videos | Online video platforms receive hundreds of hours of uploads every minute, making manual content moderation impossible. Unfortunately, the most vulnerable consumers of malicious video content are children from ages 1-5 whose attention is easily captured by bursts of color and sound. Scammers attempting to monetize their... | ['Gita Sukthankar', 'H. M. Umer Qaisar', 'Muhammad Junaid Khan', 'Syed Hammad Ahmed'] | 2023-05-24 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 5.93399145e-02 2.10157305e-01 -2.48098105e-01 7.34668821e-02
-3.99814069e-01 -1.02712393e+00 5.11002183e-01 2.87036270e-01
-2.27563307e-02 2.72918284e-01 4.21085745e-01 -7.08349943e-02
4.87450093e-01 -2.94340044e-01 -7.48721600e-01 -4.90008116e-01
-2.34585360e-01 -3.57208073e-01 7.12827623e-01 2.69207414... | [12.486841201782227, 1.1725817918777466] |
2a545a65-8b63-4de2-9407-acb3d62bf45d | unified-autoregressive-modeling-for-joint-end | 2107.01549 | null | https://arxiv.org/abs/2107.01549v1 | https://arxiv.org/pdf/2107.01549v1.pdf | Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation | In this paper, we present a novel modeling method for single-channel multi-talker overlapped automatic speech recognition (ASR) systems. Fully neural network based end-to-end models have dramatically improved the performance of multi-taker overlapped ASR tasks. One promising approach for end-to-end modeling is autoregr... | ['Shota Orihashi', 'Tomohiro Tanaka', 'Akihiko Takashima', 'Mana Ihori', 'Naoki Makishima', 'Daiki Okamura', 'Ryo Masumura'] | 2021-07-04 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-1.69037312e-01 5.05233519e-02 -8.57111067e-03 -8.02682281e-01
-1.27399695e+00 -1.48269922e-01 1.75146312e-01 -2.45748729e-01
-2.64263660e-01 3.33596319e-01 5.58559954e-01 -2.95187205e-01
2.75321186e-01 -1.88580230e-01 -4.08537328e-01 -7.47365057e-01
3.85740787e-01 7.05931544e-01 -3.77661884e-01 -1.98821694... | [14.63017463684082, 6.349122047424316] |
635f0ad8-1758-4f97-b86b-91bec7f60b5c | quant-quantum-annealing-with-learnt-couplings | 2210.08114 | null | https://arxiv.org/abs/2210.08114v1 | https://arxiv.org/pdf/2210.08114v1.pdf | QuAnt: Quantum Annealing with Learnt Couplings | Modern quantum annealers can find high-quality solutions to combinatorial optimisation objectives given as quadratic unconstrained binary optimisation (QUBO) problems. Unfortunately, obtaining suitable QUBO forms in computer vision remains challenging and currently requires problem-specific analytical derivations. More... | ['Vladislav Golyanik', 'Michael Moeller', 'Zorah Lähner', 'Edith Tretschk', 'Maximilian Krahn', 'Marcel Seelbach Benkner'] | 2022-10-13 | null | null | null | null | ['3d-rotation-estimation', 'graph-matching'] | ['computer-vision', 'graphs'] | [ 5.50863326e-01 5.48606068e-02 -2.98961878e-01 -1.40734360e-01
-1.00446558e+00 -8.15168679e-01 4.46033001e-01 -4.18310314e-02
-4.71372962e-01 7.30500281e-01 -4.93561715e-01 -5.19195855e-01
-5.34182250e-01 -7.74021506e-01 -8.18088055e-01 -9.38703060e-01
4.18409705e-02 6.57369971e-01 -2.40345463e-01 -3.43505383... | [5.620166301727295, 4.901275634765625] |
a485c124-61d1-43aa-b970-9052602f13d9 | a-vector-based-representation-to-enhance-head | 2010.07184 | null | https://arxiv.org/abs/2010.07184v2 | https://arxiv.org/pdf/2010.07184v2.pdf | A Vector-based Representation to Enhance Head Pose Estimation | This paper proposes to use the three vectors in a rotation matrix as the representation in head pose estimation and develops a new neural network based on the characteristic of such representation. We address two potential issues existed in current head pose estimation works: 1. Public datasets for head pose estimation... | ['Yingjie Chen', 'Dongfang Liu', 'Zongcheng Chu', 'Zhiwen Cao'] | 2020-10-14 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-3.16770166e-01 1.11026041e-01 -2.12969452e-01 -7.69122839e-01
-5.42955995e-01 1.24867819e-01 1.67377204e-01 -1.53905362e-01
-7.31019914e-01 8.23420227e-01 5.58016300e-01 1.42008841e-01
2.41013050e-01 -4.92322266e-01 -5.68783879e-01 -5.86634517e-01
-1.53195709e-01 3.82517457e-01 1.07073158e-01 -5.00018179... | [13.679656982421875, 0.26634207367897034] |
8ce76bfa-2811-450c-b3d6-00c28b52ed84 | learning-efficient-point-cloud-generation-for | 1706.07036 | null | http://arxiv.org/abs/1706.07036v1 | http://arxiv.org/pdf/1706.07036v1.pdf | Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction | Conventional methods of 3D object generative modeling learn volumetric
predictions using deep networks with 3D convolutional operations, which are
direct analogies to classical 2D ones. However, these methods are
computationally wasteful in attempt to predict 3D shapes, where information is
rich only on the surfaces. I... | ['Chen Kong', 'Chen-Hsuan Lin', 'Simon Lucey'] | 2017-06-21 | null | null | null | null | ['3d-object-reconstruction', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [ 2.54245792e-02 4.69510317e-01 4.21380550e-01 -4.75148380e-01
-4.48524386e-01 -4.07017648e-01 9.85304713e-01 -3.32585961e-01
4.87605155e-01 3.55453551e-01 3.41640487e-02 -2.38390148e-01
2.44098321e-01 -1.46063221e+00 -1.25635946e+00 -3.45771402e-01
3.00520658e-01 1.27544045e+00 -4.15196903e-02 -5.51014394... | [8.821029663085938, -3.590196371078491] |
5db5f362-33e0-4c72-9d12-63d6a48b69f0 | investigation-of-multimodal-features | 1809.06225 | null | http://arxiv.org/abs/1809.06225v1 | http://arxiv.org/pdf/1809.06225v1.pdf | Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition | Automatic emotion recognition is a challenging task. In this paper, we
present our effort for the audio-video based sub-challenge of the Emotion
Recognition in the Wild (EmotiW) 2018 challenge, which requires participants to
assign a single emotion label to the video clip from the six universal emotions
(Anger, Disgust... | ['Jian-Hua Tao', 'Ya Li', 'Zheng Lian', 'Jian Huang'] | 2018-09-13 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 4.31717150e-02 -2.94073462e-01 1.88748851e-01 -6.15951836e-01
-8.81095171e-01 -4.38225240e-01 2.02184066e-01 -1.19493783e-01
-8.19922984e-01 6.01921499e-01 1.83170125e-01 3.52811247e-01
2.33731061e-01 -6.75486401e-02 -3.87100071e-01 -7.08454609e-01
-2.78571963e-01 3.37993610e-03 -2.34651029e-01 -3.00690889... | [13.314289093017578, 5.078708648681641] |
d304438a-7726-4cce-a2bd-f9de5ab68154 | dynamics-based-3d-skeletal-hand-tracking | 1705.07640 | null | http://arxiv.org/abs/1705.07640v1 | http://arxiv.org/pdf/1705.07640v1.pdf | Dynamics Based 3D Skeletal Hand Tracking | Tracking the full skeletal pose of the hands and fingers is a challenging
problem that has a plethora of applications for user interaction. Existing
techniques either require wearable hardware, add restrictions to user pose, or
require significant computation resources. This research explores a new
approach to tracking... | ['Leonid Keselman', 'Sterling Orsten', 'Stan Melax'] | 2017-05-22 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [ 5.52533902e-02 -4.64247584e-01 -1.20785341e-01 7.97088370e-02
-2.51607388e-01 -8.57102692e-01 5.41286990e-02 -5.87357759e-01
-3.80523205e-01 4.22628790e-01 -1.84889615e-01 -1.78732827e-01
1.80728026e-02 -2.73243397e-01 -2.94080645e-01 -4.83834118e-01
2.42824793e-01 6.86098516e-01 4.48663175e-01 -1.78948477... | [6.620931148529053, -0.9464094638824463] |
11fd3571-1d46-43bb-8ecf-5241615e692d | bootstrapping-objectness-from-videos-by | 2304.08025 | null | https://arxiv.org/abs/2304.08025v1 | https://arxiv.org/pdf/2304.08025v1.pdf | Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping | We study learning object segmentation from unlabeled videos. Humans can easily segment moving objects without knowing what they are. The Gestalt law of common fate, i.e., what move at the same speed belong together, has inspired unsupervised object discovery based on motion segmentation. However, common fate is not a r... | ['Stella X. Yu', 'Zhirong Wu', 'Long Lian'] | 2023-04-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lian_Bootstrapping_Objectness_From_Videos_by_Relaxed_Common_Fate_and_Visual_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lian_Bootstrapping_Objectness_From_Videos_by_Relaxed_Common_Fate_and_Visual_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-discovery', 'unsupervised-object-segmentation', 'motion-segmentation', 'video-object-segmentation', 'video-semantic-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.79639065e-01 9.17959213e-02 -2.72173017e-01 -2.03987971e-01
-1.97461993e-02 -9.81205821e-01 3.91387194e-01 -1.23597212e-01
-3.72926205e-01 4.33884948e-01 -7.70991109e-03 -7.87664428e-02
-7.15953186e-02 -5.43438077e-01 -1.03277826e+00 -7.69510210e-01
-1.89499810e-01 5.24982929e-01 9.05298948e-01 3.22501622... | [9.04663372039795, -0.3241814374923706] |
fd117b0c-d79b-4999-b8c1-f8f2daec212b | adaptive-decision-making-at-the-intersection | 2207.11724 | null | https://arxiv.org/abs/2207.11724v1 | https://arxiv.org/pdf/2207.11724v1.pdf | Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery | In urban environments, the complex and uncertain intersection scenarios are challenging for autonomous driving. To ensure safety, it is crucial to develop an adaptive decision making system that can handle the interaction with other vehicles. Manually designed model-based methods are reliable in common scenarios. But i... | ['Jianwei Gong', 'Zirui Li', 'Chao Lu', 'Lin Yang', 'Xianqi He'] | 2022-07-24 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-1.81251332e-01 -4.86209206e-02 -2.52739072e-01 -3.72494578e-01
-6.54118001e-01 -3.54629159e-01 5.30742288e-01 -8.54914263e-02
-5.40591955e-01 1.21854067e+00 -1.23149060e-01 -5.17540038e-01
-2.44797722e-01 -8.17044079e-01 -7.10051298e-01 -7.11872399e-01
-7.26385564e-02 4.98438716e-01 9.02363062e-01 -6.29725993... | [5.2926740646362305, 1.346612811088562] |
214cdaf3-9dc8-4f20-8b4e-3bef463308d6 | satisfiability-and-containment-of-recursive | 2108.13063 | null | https://arxiv.org/abs/2108.13063v2 | https://arxiv.org/pdf/2108.13063v2.pdf | Satisfiability and Containment of Recursive SHACL | The Shapes Constraint Language (SHACL) is the recent W3C recommendation language for validating RDF data, by verifying certain shapes on graphs. Previous work has largely focused on the validation problem and the standard decision problems of satisfiability and containment, crucial for design and optimisation purposes,... | ['Fabio Mogavero', 'George Konstantinidis', 'Paolo Pareti'] | 2021-08-30 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 1.57210365e-01 5.66643417e-01 -1.46374390e-01 -3.92331660e-01
-2.48279143e-02 -9.09789920e-01 4.91188049e-01 4.25956547e-01
8.24777409e-03 6.52755201e-01 4.09489870e-02 -5.95938742e-01
-7.10962951e-01 -1.25832748e+00 -6.59460723e-01 -2.01680765e-01
-2.99296498e-01 6.32558644e-01 8.26563179e-01 -4.15431410... | [8.662885665893555, 6.802210330963135] |
c273f8e5-1882-4c86-9ff1-e9026357bc4d | a-mathematical-theory-of-super-resolution-and | 2211.15208 | null | https://arxiv.org/abs/2211.15208v2 | https://arxiv.org/pdf/2211.15208v2.pdf | A mathematical theory of super-resolution and diffraction limit | This paper is devoted to elucidating the essence of super-resolution and deals mainly with the stability of super-resolution and the diffraction limit with respect to the signal-to-noise ratio. The goal is to determine the number, locations, and amplitudes of point sources from noisy Fourier data. The first contributio... | ['Habib Ammari', 'Ping Liu'] | 2022-11-28 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 6.05985641e-01 1.89063340e-01 4.31330055e-01 1.84558943e-01
-1.00703442e+00 -3.55209470e-01 2.98231822e-02 -3.11242521e-01
-5.35714149e-01 8.78906667e-01 -2.02099845e-01 -2.39444911e-01
-9.96236444e-01 -6.86570585e-01 -3.75113785e-01 -1.24851823e+00
-4.71151501e-01 6.52426109e-02 2.12552682e-01 -2.71609217... | [6.483432292938232, 4.497196674346924] |
2e73a25a-6bc3-457a-9e9c-dce5636aa11f | uniform-subdivision-of-omnidirectional-camera | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kang_Uniform_Subdivision_of_Omnidirectional_Camera_Space_for_Efficient_Spherical_Stereo_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kang_Uniform_Subdivision_of_Omnidirectional_Camera_Space_for_Efficient_Spherical_Stereo_CVPR_2022_paper.pdf | Uniform Subdivision of Omnidirectional Camera Space for Efficient Spherical Stereo Matching | Omnidirectional cameras have been used widely to better understand surrounding environments. They are often configured as stereo to estimate depth. However, due to the optics of the fisheye lens, conventional epipolar geometry is inapplicable directly to omnidirectional camera images. Intermediate formats of omnidi... | ['Min H. Kim', 'Chong-Min Kyung', 'Jungeon Lee', 'Hyeonjoong Jang', 'Donghun Kang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['stereo-matching-1'] | ['computer-vision'] | [-1.50301168e-02 -2.21863195e-01 2.73316741e-01 -2.08499193e-01
-1.83581591e-01 -8.20540547e-01 5.42538047e-01 -4.91723031e-01
-4.57479030e-01 6.27345324e-01 1.70486629e-01 -4.24155504e-01
6.95529357e-02 -1.06193006e+00 -6.19102120e-01 -6.66728556e-01
4.16244745e-01 1.91746265e-01 3.94737959e-01 5.52449934... | [9.016045570373535, -2.4690775871276855] |
15ed20ba-ef73-45e4-b0ed-d1d511c67a99 | deep-learning-with-a-classifier-system | 2103.01118 | null | https://arxiv.org/abs/2103.01118v1 | https://arxiv.org/pdf/2103.01118v1.pdf | Deep Learning with a Classifier System: Initial Results | This article presents the first results from using a learning classifier system capable of performing adaptive computation with deep neural networks. Individual classifiers within the population are composed of two neural networks. The first acts as a gating or guarding component, which enables the conditional computat... | ['Larry Bull', 'Richard J. Preen'] | 2021-03-01 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 3.78412813e-01 8.76112953e-02 4.09669019e-02 -1.53570980e-01
6.22985780e-01 -2.65756518e-01 3.53810102e-01 3.24162662e-01
-1.03767073e+00 8.49951982e-01 -4.65460628e-01 -4.79278937e-02
-2.20946446e-01 -1.10707533e+00 -6.32077754e-01 -1.23014927e+00
-2.30581403e-01 4.29510117e-01 6.09785914e-01 -1.55640140... | [8.293109893798828, 3.190380573272705] |
0cc76bc1-b772-43d7-9182-8ba2d854f7da | a-study-of-neural-matching-models-for-cross | 2005.12994 | null | https://arxiv.org/abs/2005.12994v1 | https://arxiv.org/pdf/2005.12994v1.pdf | A Study of Neural Matching Models for Cross-lingual IR | In this study, we investigate interaction-based neural matching models for ad-hoc cross-lingual information retrieval (CLIR) using cross-lingual word embeddings (CLWEs). With experiments conducted on the CLEF collection over four language pairs, we evaluate and provide insight into different neural model architectures,... | ['James Allan', 'Puxuan Yu'] | 2020-05-26 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-3.65484744e-01 -3.60565037e-01 -3.41570377e-01 -5.43473423e-01
-8.80050659e-01 -6.57506287e-01 9.60833073e-01 5.30523121e-01
-1.13565350e+00 1.09416001e-01 4.62994844e-01 -4.68338072e-01
-4.26105440e-01 -3.42268854e-01 -2.80642509e-01 -6.38898164e-02
-1.94998547e-01 9.77058530e-01 -1.51767388e-01 -6.44277751... | [11.274304389953613, 9.802224159240723] |
4197edb6-279e-4755-bdd0-e29624b27ddc | masked-feature-prediction-for-self-supervised | 2112.09133 | null | https://arxiv.org/abs/2112.09133v2 | https://arxiv.org/pdf/2112.09133v2.pdf | Masked Feature Prediction for Self-Supervised Visual Pre-Training | We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training of video models. Our approach first randomly masks out a portion of the input sequence and then predicts the feature of the masked regions. We study five different types of features and find Histograms of Oriented Gradients (HOG), a hand-c... | ['Christoph Feichtenhofer', 'Alan Yuille', 'Chao-yuan Wu', 'Saining Xie', 'Haoqi Fan', 'Chen Wei'] | 2021-12-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wei_Masked_Feature_Prediction_for_Self-Supervised_Visual_Pre-Training_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wei_Masked_Feature_Prediction_for_Self-Supervised_Visual_Pre-Training_CVPR_2022_paper.pdf | cvpr-2022-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 6.17536791e-02 -1.47063032e-01 -4.18892890e-01 -4.70925778e-01
-5.20068288e-01 -4.58859205e-01 6.59110785e-01 -4.71185505e-01
-6.54728293e-01 4.88201737e-01 8.71955529e-02 -1.29721165e-01
4.77611870e-01 -5.05008638e-01 -1.33666134e+00 -6.06290281e-01
-2.16668665e-01 1.93430454e-01 6.59397125e-01 -1.91333488... | [9.31340217590332, 1.0131551027297974] |
fe06c621-0432-4322-a152-2cf331ca02ba | video-propagation-networks | 1612.05478 | null | http://arxiv.org/abs/1612.05478v3 | http://arxiv.org/pdf/1612.05478v3.pdf | Video Propagation Networks | We propose a technique that propagates information forward through video
data. The method is conceptually simple and can be applied to tasks that
require the propagation of structured information, such as semantic labels,
based on video content. We propose a 'Video Propagation Network' that processes
video frames in an... | ['Varun Jampani', 'Raghudeep Gadde', 'Peter V. Gehler'] | 2016-12-16 | video-propagation-networks-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Jampani_Video_Propagation_Networks_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Jampani_Video_Propagation_Networks_CVPR_2017_paper.pdf | cvpr-2017-7 | ['video-propagation'] | ['computer-vision'] | [ 4.20483738e-01 6.03015311e-02 4.69324738e-02 -6.14765167e-01
-4.76271123e-01 -6.68178797e-01 4.06509548e-01 7.36650229e-02
-8.66014123e-01 5.68195581e-01 1.00207977e-01 -1.60474181e-01
-2.23691016e-02 -5.54750502e-01 -7.93187082e-01 -5.07986248e-01
-3.11965972e-01 3.98199022e-01 1.28377128e+00 -3.08527797... | [9.079484939575195, -0.26917916536331177] |
898eda53-e17c-4acc-a748-924527ecd568 | residual-shuffle-exchange-networks-for-fast | 2004.04662 | null | https://arxiv.org/abs/2004.04662v4 | https://arxiv.org/pdf/2004.04662v4.pdf | Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences | Attention is a commonly used mechanism in sequence processing, but it is of O(n^2) complexity which prevents its application to long sequences. The recently introduced neural Shuffle-Exchange network offers a computation-efficient alternative, enabling the modelling of long-range dependencies in O(n log n) time. The mo... | ['Kārlis Freivalds', 'Matīss Apinis', 'Agris Šostaks', 'Emīls Ozoliņš', 'Andis Draguns'] | 2020-04-06 | null | null | null | null | ['music-transcription', 'lambada'] | ['music', 'natural-language-processing'] | [ 6.14014745e-01 -3.40553105e-01 1.14418916e-01 -2.04013512e-01
-6.90990686e-01 -5.47767937e-01 3.85207266e-01 1.69502556e-01
-9.34971809e-01 6.37829840e-01 9.42372009e-02 -4.41629738e-01
-8.28686059e-02 -4.01993394e-01 -7.66916394e-01 -8.87317121e-01
-2.34209165e-01 4.24389571e-01 2.68088758e-01 -3.51149112... | [10.90872859954834, 6.537520885467529] |
654cb2f1-9ac0-4b8b-88b1-cfcb229525e5 | negative-sample-matters-a-renaissance-of | 2109.04872 | null | https://arxiv.org/abs/2109.04872v2 | https://arxiv.org/pdf/2109.04872v2.pdf | Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding | Temporal grounding aims to localize a video moment which is semantically aligned with a given natural language query. Existing methods typically apply a detection or regression pipeline on the fused representation with the research focus on designing complicated prediction heads or fusion strategies. Instead, from a pe... | ['Gangshan Wu', 'TianHao Li', 'Tao Wu', 'LiMin Wang', 'Zhenzhi Wang'] | 2021-09-10 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [-8.89118984e-02 -1.82006985e-01 -5.98087132e-01 -4.12603408e-01
-1.19838977e+00 -3.72568876e-01 6.25304997e-01 4.27975953e-02
-3.41777831e-01 7.92009756e-02 4.46827501e-01 2.06977203e-01
-1.61142007e-01 -5.97614586e-01 -6.19030237e-01 -4.53544348e-01
-3.58839184e-01 1.83406442e-01 2.51311123e-01 -1.44276783... | [9.969358444213867, 0.7467935681343079] |
8627bdd5-67ae-49ee-9c24-454181a87ee1 | bphigh-tamilnlp-acl2022-effects-of-data | null | null | https://aclanthology.org/2022.dravidianlangtech-1.22 | https://aclanthology.org/2022.dravidianlangtech-1.22.pdf | BpHigh@TamilNLP-ACL2022: Effects of Data Augmentation on Indic-Transformer based classifier for Abusive Comments Detection in Tamil | Social Media platforms have grown their reach worldwide. As an effect of this growth, many vernacular social media platforms have also emerged, focusing more on the diverse languages in the specific regions. Tamil has also emerged as a popular language for use on social media platforms due to the increasing penetration... | ['Bhavish Pahwa'] | null | null | null | null | dravidianlangtech-acl-2022-5 | ['abusive-language'] | ['natural-language-processing'] | [-6.40461206e-01 1.39875365e-02 -4.03855622e-01 1.37612462e-01
-8.39966476e-01 -6.81684852e-01 8.62446606e-01 5.52267075e-01
-8.95041764e-01 8.06805432e-01 6.50048673e-01 -2.68374860e-01
5.16476750e-01 -5.38211226e-01 -1.75029263e-01 -4.14806381e-02
1.73650280e-01 3.64964634e-01 7.94525817e-02 -6.42270923... | [9.107645988464355, 10.447866439819336] |
17c37c03-b4fa-430d-99b5-3342b3075826 | interactions-of-fungi-with-concrete | 1708.01337 | null | https://arxiv.org/abs/1708.01337v2 | https://arxiv.org/pdf/1708.01337v2.pdf | Interactions of Fungi with Concrete: Significant Importance for Bio-Based Self-Healing Concrete | The goal of this study is to explore a new self-healing concept in which fungi are used as a self-healing agent to promote calcium mineral precipitation to fill the cracks in concrete. An initial screening of different species of fungi has been conducted. Fungal growth medium was overlaid onto cured concrete plate. Myc... | ['Congrui Jin', 'Ning Zhang', 'Guangwen Zhou', 'David G. Davies', 'Hui Zhou', 'Jada Crump', 'Xiaobo Chen', 'Jing Luo'] | 2017-08-04 | null | null | null | null | ['x-ray-diffraction'] | ['miscellaneous'] | [ 3.43863696e-01 -1.04317017e-01 3.09772164e-01 7.45933473e-01
3.60784560e-01 -2.23204300e-01 4.44017380e-01 1.73092410e-01
-1.18598729e-01 1.01098871e+00 1.33635536e-01 -1.23761393e-01
1.37122095e-01 -1.05169892e+00 -1.24668799e-01 -1.31163466e+00
6.74692765e-02 2.88753361e-01 5.16098917e-01 -2.91510850... | [4.9648919105529785, 4.859765529632568] |
42f3c128-7fa4-4ef7-a263-50f4d26e8b96 | human-mesh-recovery-from-multiple-shots | 2012.09843 | null | https://arxiv.org/abs/2012.09843v1 | https://arxiv.org/pdf/2012.09843v1.pdf | Human Mesh Recovery from Multiple Shots | Videos from edited media like movies are a useful, yet under-explored source of information. The rich variety of appearance and interactions between humans depicted over a large temporal context in these films could be a valuable source of data. However, the richness of data comes at the expense of fundamental challeng... | ['Angjoo Kanazawa', 'Jitendra Malik', 'Georgios Pavlakos'] | 2020-12-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Pavlakos_Human_Mesh_Recovery_From_Multiple_Shots_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Pavlakos_Human_Mesh_Recovery_From_Multiple_Shots_CVPR_2022_paper.pdf | cvpr-2022-1 | ['human-mesh-recovery'] | ['computer-vision'] | [ 1.32499963e-01 -4.20020856e-02 5.55407023e-03 -9.78942961e-02
-4.84363139e-01 -4.19177115e-01 4.00125980e-01 -1.75726101e-01
1.40956298e-01 3.95829111e-01 4.88514990e-01 3.10288161e-01
-2.39875317e-02 -5.99949896e-01 -8.95413578e-01 -5.05615354e-01
-1.98275000e-01 2.48129219e-01 4.84823555e-01 -4.10780370... | [7.340637683868408, -0.7916744947433472] |
36667834-dbf0-4289-8179-25233d5de28c | generating-fluent-fact-checking-explanations | 2112.06924 | null | https://arxiv.org/abs/2112.06924v1 | https://arxiv.org/pdf/2112.06924v1.pdf | Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing | Fact-checking systems have become important tools to verify fake and misguiding news. These systems become more trustworthy when human-readable explanations accompany the veracity labels. However, manual collection of such explanations is expensive and time-consuming. Recent works frame explanation generation as extrac... | ['Isabelle Augenstein', 'Pepa Atanasova', 'Shailza Jolly'] | 2021-12-13 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 3.70654017e-01 8.40673685e-01 -4.58733857e-01 -5.53205848e-01
-9.13231671e-01 -6.77203894e-01 7.48273909e-01 8.88901532e-01
-3.78569663e-02 1.16651261e+00 7.56371319e-01 -4.52789307e-01
-1.39359176e-01 -4.24359024e-01 -7.48877525e-01 -1.44832492e-01
2.97651529e-01 5.05545914e-01 -8.82180333e-02 -9.01436508... | [12.150188446044922, 9.304431915283203] |
ba37d5be-b4f2-4caf-a4d3-de931a0ea683 | a-hierarchical-hybrid-learning-framework-for | 2303.12274 | null | https://arxiv.org/abs/2303.12274v3 | https://arxiv.org/pdf/2303.12274v3.pdf | A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction | Accurate and robust trajectory prediction of neighboring agents is critical for autonomous vehicles traversing in complex scenes. Most methods proposed in recent years are deep learning-based due to their strength in encoding complex interactions. However, unplausible predictions are often generated since they rely hea... | ['Bo Tao', 'Linzhen Nie', 'Xu Zhu', 'Chunyuan Lei', 'Zhishuai Yin', 'Mingze Miao', 'Yujun Jiao'] | 2023-03-22 | null | null | null | null | ['trajectory-prediction', 'motion-planning'] | ['computer-vision', 'robots'] | [-3.41925383e-01 -4.97395843e-02 -3.75411332e-01 -1.09457821e-01
-5.26330590e-01 -1.77561894e-01 7.68294454e-01 3.63033824e-02
-1.50089309e-01 8.63935828e-01 2.41269380e-01 -2.91282058e-01
-3.18729222e-01 -9.93958116e-01 -8.66273820e-01 -8.82100582e-01
-4.43098009e-01 6.82007253e-01 3.96424949e-01 -4.96236205... | [5.907289028167725, 0.856507420539856] |
96cd1c3b-dd5e-4d6f-9898-7c857ef0c2e8 | makadi-a-large-scale-human-labeled-dataset | null | null | https://aclanthology.org/2022.wildre-1.12 | https://aclanthology.org/2022.wildre-1.12.pdf | Makadi: A Large-Scale Human-Labeled Dataset for Hindi Semantic Parsing | Parsing natural language queries into formal database calls is a very well-studied problem. Because of the rich diversity of semantic markers across the world’s languages, progress in solving this problem is irreducibly language-dependent. This has created an asymmetry in progress in NLIDB solutions, with most state-of... | ['Nisheeth Srivastava', 'Shashwat Vaibhav'] | null | null | null | null | wildre-lrec-2022-6 | ['text-to-sql'] | ['computer-code'] | [-1.50116369e-01 3.68222088e-01 -3.58523101e-01 -9.27216828e-01
-1.27551961e+00 -1.08840764e+00 2.35446423e-01 4.09001887e-01
-3.07794511e-01 9.44305897e-01 4.59242433e-01 -5.80899060e-01
-8.45648870e-02 -8.86559844e-01 -7.54647255e-01 2.70144671e-01
3.98015440e-01 1.30109930e+00 4.23824102e-01 -4.69091922... | [9.90280532836914, 7.909300804138184] |
25cdd076-49c1-4f07-b006-99d277e6dd62 | transition-based-dependency-parsing-as-latent | null | null | https://aclanthology.org/W16-2403 | https://aclanthology.org/W16-2403.pdf | Transition-based dependency parsing as latent-variable constituent parsing | null | ['Mark-Jan Nederhof'] | 2016-08-01 | null | null | null | ws-2016-8 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.404524326324463, 3.792227268218994] |
7f5eebde-8bb5-4ae3-b6c8-23eb53f53e6f | gaze-prediction-in-dynamic-360a-immersive | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Xu_Gaze_Prediction_in_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Gaze_Prediction_in_CVPR_2018_paper.pdf | Gaze Prediction in Dynamic 360° Immersive Videos | This paper explores gaze prediction in dynamic $360^circ$ immersive videos, emph{i.e.}, based on the history scan path and VR contents, we predict where a viewer will look at an upcoming time. To tackle this problem, we first present the large-scale eye-tracking in dynamic VR scene dataset. Our dataset contains 208 $36... | ['Yanyu Xu', 'Zhengzhong Sun', 'Shenghua Gao', 'Yanbing Dong', 'Jingyi Yu', 'Zhiru Shi', 'Junru Wu'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['eye-tracking'] | ['computer-vision'] | [ 2.15180501e-01 -2.56139129e-01 -5.12450263e-02 -3.81269991e-01
-2.07237005e-01 -1.83289051e-01 -8.46112072e-02 -1.50314584e-01
-1.98540956e-01 3.53676736e-01 2.11577147e-01 8.25625882e-02
-1.63805783e-01 -5.96113324e-01 -9.83110487e-01 -5.61129570e-01
-9.01321769e-02 -6.39131427e-01 5.28902829e-01 -2.68415660... | [9.821004867553711, -0.2572791278362274] |
a884b93b-7fc2-4dd9-8580-0d06bf4c1fa7 | palgan-image-colorization-with-palette | 2210.11204 | null | https://arxiv.org/abs/2210.11204v1 | https://arxiv.org/pdf/2210.11204v1.pdf | PalGAN: Image Colorization with Palette Generative Adversarial Networks | Multimodal ambiguity and color bleeding remain challenging in colorization. To tackle these problems, we propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention. To circumvent the multimodality issue, we present a new colorization formulation that estimates a pro... | ['Yu Qiao', 'Jing Shao', 'Lu Qi', 'Menghan Xia', 'Yi Wang'] | 2022-10-20 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 2.18209669e-01 -2.86000162e-01 8.39973390e-02 -1.77562222e-01
-6.71811342e-01 -7.59668112e-01 4.16205913e-01 -3.67949992e-01
-2.33515739e-01 7.20463932e-01 3.32977995e-02 -7.15800282e-03
1.15481846e-01 -6.45843387e-01 -6.40391052e-01 -8.17185521e-01
4.41436172e-01 2.89371431e-01 -9.31302905e-02 -2.47608617... | [11.413628578186035, -1.0266081094741821] |
444f66c2-a629-4966-96f8-bcaafab08d34 | overview-of-the-first-shared-task-on-multi | null | null | https://aclanthology.org/2022.sdp-1.32 | https://aclanthology.org/2022.sdp-1.32.pdf | Overview of the First Shared Task on Multi Perspective Scientific Document Summarization (MuP) | We present the main findings of MuP 2022 shared task, the first shared task on multi-perspective scientific document summarization. The task provides a testbed representing challenges for summarization of scientific documents, and facilitates development of better models to leverage summaries generated from multiple pe... | ['Michal Shmueli-Scheuer', 'Tirthankar Ghosal', 'Guy Feigenblat', 'Arman Cohan'] | null | null | null | null | sdp-coling-2022-10 | ['scientific-article-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [-8.34826031e-04 3.20890963e-01 -4.61613715e-01 -2.23635182e-01
-1.50485837e+00 -1.00713825e+00 8.55729103e-01 8.42524469e-01
-1.87851116e-01 1.01154029e+00 1.19486189e+00 -9.48655307e-02
-1.55427847e-02 -2.73733079e-01 -4.30093825e-01 -1.57613069e-01
1.70579359e-01 3.93126756e-01 -1.45024896e-01 -1.12575687... | [12.395326614379883, 9.537493705749512] |
ea45b78d-64a2-4dd9-9447-a6cdb79b575c | probabilistic-deep-learning-to-quantify | 2112.02622 | null | https://arxiv.org/abs/2112.02622v1 | https://arxiv.org/pdf/2112.02622v1.pdf | Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting | Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. Despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty ... | ['Gavin Taylor', 'Kerstin Bach', 'Frank Alexander Kraemer', 'Abdulmajid Murad'] | 2021-12-05 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-4.37364906e-01 -3.30334038e-01 6.90791309e-02 -7.89538443e-01
-1.08192837e+00 -6.37058258e-01 7.34208703e-01 4.69466187e-02
-9.18420553e-02 1.15117431e+00 2.45262682e-01 -6.93680704e-01
-4.05749291e-01 -1.14503932e+00 -9.10576284e-01 -7.61585236e-01
-9.70863998e-02 5.46106577e-01 -6.76386058e-03 1.35173753... | [7.033151149749756, 3.359851121902466] |
013b80c4-b9ed-454a-b6e7-ca576d71355d | rasat-integrating-relational-structures-into | 2205.06983 | null | https://arxiv.org/abs/2205.06983v2 | https://arxiv.org/pdf/2205.06983v2.pdf | RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL | Relational structures such as schema linking and schema encoding have been validated as a key component to qualitatively translating natural language into SQL queries. However, introducing these structural relations comes with prices: they often result in a specialized model structure, which largely prohibits using lar... | ['Zhouhan Lin', 'Quanshi Zhang', 'Xinbing Wang', 'Chenghu Zhou', 'Yu Cheng', 'Xiangpeng Wan', 'Ziwei He', 'Jingyao Tang', 'Jiexing Qi'] | 2022-05-14 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-5.21474108e-02 3.45777810e-01 -6.06398225e-01 -7.01165617e-01
-9.64913845e-01 -8.42007816e-01 4.51240838e-01 1.60623312e-01
-2.09052324e-01 4.14301842e-01 5.05006075e-01 -7.42938340e-01
-2.65103560e-02 -1.28813469e+00 -1.41784668e+00 1.34316459e-01
5.00401035e-02 7.93618679e-01 1.88216954e-01 -7.36757636... | [9.841968536376953, 7.867154121398926] |
06177f9b-b04b-4699-9500-164bee10afb7 | empirical-analysis-of-ai-based-energy | 2212.09154 | null | https://arxiv.org/abs/2212.09154v1 | https://arxiv.org/pdf/2212.09154v1.pdf | Empirical Analysis of AI-based Energy Management in Electric Vehicles: A Case Study on Reinforcement Learning | Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. It has been shown to outperform conventional methods in energy management problems regarding energy-saving and real-time performance. Ho... | ['Yuanjian Zhang', 'Jingjing Jiang', 'Quan Zhou', 'Dezong Zhao', 'Zhuoran Hou', 'Jihao Li', 'Yang Lin', 'Jincheng Hu'] | 2022-12-18 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.97983015e-01 3.52218114e-02 -5.74554741e-01 -3.62796560e-02
-4.90546614e-01 -4.69454885e-01 4.42220628e-01 3.54931712e-01
-3.22778553e-01 1.18210518e+00 -3.87087435e-01 -3.06963980e-01
-5.19432664e-01 -9.78211939e-01 -3.88796479e-01 -1.18026936e+00
8.35173484e-03 2.64251173e-01 -1.86091498e-03 -1.24899194... | [5.549935340881348, 2.2465460300445557] |
726b2782-6438-4d96-906a-1c5614757659 | diagnostic-classification-of-lung-nodules | 1803.07192 | null | http://arxiv.org/abs/1803.07192v1 | http://arxiv.org/pdf/1803.07192v1.pdf | Diagnostic Classification Of Lung Nodules Using 3D Neural Networks | Lung cancer is the leading cause of cancer-related death worldwide. Early
diagnosis of pulmonary nodules in Computed Tomography (CT) chest scans provides
an opportunity for designing effective treatment and making financial and care
plans. In this paper, we consider the problem of diagnostic classification
between beni... | ['Zhongjie Lu', 'Yi Hong', 'Raunak Dey'] | 2018-03-19 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 9.94433532e-04 2.58953273e-01 -4.73437458e-01 -3.22532684e-01
-8.86465490e-01 -2.99067706e-01 4.61321682e-01 -5.03616214e-01
-4.12062496e-01 3.28186333e-01 1.78625360e-01 -7.19616234e-01
-1.65236786e-01 -7.48931885e-01 -3.12227964e-01 -7.04625905e-01
4.89504747e-02 1.13630319e+00 5.08809566e-01 4.30322856... | [15.38630485534668, -2.1398448944091797] |
22b1d669-37a1-4a9e-b7b9-f1c4a7385d49 | visually-verifiable-textual-entailment-a | null | null | https://aclanthology.org/W15-2801 | https://aclanthology.org/W15-2801.pdf | Visually-Verifiable Textual Entailment: A Challenge Task for Combining Language and Vision | null | ['Jayant Krishnamurthy'] | 2015-09-01 | null | null | null | ws-2015-9 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.168886184692383, 3.719616651535034] |
eb856c8c-84d7-432d-be33-73663eea8bd4 | deep-domain-adaptation-for-polyphonic-melody | 2210.12532 | null | https://arxiv.org/abs/2210.12532v2 | https://arxiv.org/pdf/2210.12532v2.pdf | Deep domain adaptation for polyphonic melody extraction | Extraction of the predominant pitch from polyphonic audio is one of the fundamental tasks in the field of music information retrieval and computational musicology. To accomplish this task using machine learning, a large amount of labeled audio data is required to train the model that predicts the pitch contour. But a c... | ['Vipul Arora', 'Kavya Ranjan Saxena'] | 2022-10-22 | null | null | null | null | ['melody-extraction', 'music-information-retrieval'] | ['music', 'music'] | [ 3.10136408e-01 -3.39150131e-01 -2.74983346e-01 -5.95178157e-02
-1.03577197e+00 -7.43848264e-01 1.81194544e-01 2.74364769e-01
-3.80874097e-01 5.27883828e-01 3.09289306e-01 3.16970259e-01
-1.92723945e-01 -4.56656784e-01 -3.19406986e-01 -6.09659851e-01
-3.13863643e-02 4.39980209e-01 3.88126463e-01 -4.34659719... | [15.898159980773926, 5.284669399261475] |
f18fb92a-2dec-4351-86b7-b7cc7e6e6641 | joint-multisided-exposure-fairness-for | 2205.00048 | null | https://arxiv.org/abs/2205.00048v1 | https://arxiv.org/pdf/2205.00048v1.pdf | Joint Multisided Exposure Fairness for Recommendation | Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system. The problem of how individual or groups of items may be systemically under or over exposed to groups of users, or even all users... | ['Xue Liu', 'Fernando Diaz', 'Chen Ma', 'Bhaskar Mitra', 'Haolun Wu'] | 2022-04-29 | null | null | null | null | ['exposure-fairness'] | ['adversarial'] | [ 1.56351894e-01 3.76105398e-01 -3.32879603e-01 -5.25594831e-01
-1.81197912e-01 -7.98128664e-01 5.49177289e-01 5.63546777e-01
-5.47600210e-01 4.19336349e-01 6.60318255e-01 -4.54962850e-01
-5.06904960e-01 -9.55197632e-01 -1.14774361e-01 -1.79728031e-01
1.48676619e-01 -4.99474667e-02 -3.66627693e-01 -2.95855343... | [9.533177375793457, 5.659571647644043] |
d4afeaa4-9770-4756-b133-37f64f414ba6 | using-referring-expression-generation-to | null | null | https://aclanthology.org/2021.nlp4dh-1.8 | https://aclanthology.org/2021.nlp4dh-1.8.pdf | Using Referring Expression Generation to Model Literary Style | Novels and short stories are not just remarkable because of what events they represent. The narrative style they employ is significant. To understand the specific contributions of different aspects of this style, it is possible to create limited symbolic models of narrating that hold almost all of the narrative discour... | ['Alan Y. Zhu', 'Joanne Yuan', 'Ardalan SadeghiKivi', 'Nick Montfort'] | null | null | null | null | nlp4dh-icon-2021-12 | ['referring-expression-generation', 'referring-expression'] | ['computer-vision', 'computer-vision'] | [ 1.34677634e-01 3.78485829e-01 -1.58310294e-01 -1.22425117e-01
-1.21923387e-01 -1.02211273e+00 1.39553392e+00 4.04243395e-02
2.21012929e-03 1.11291862e+00 1.13436699e+00 -4.22359943e-01
-3.71839702e-01 -9.29703236e-01 -3.04302275e-01 -3.79524976e-01
2.08217919e-01 2.63350040e-01 3.20307970e-01 -1.01934755... | [11.24326229095459, 0.9449816942214966] |
edbc6b8a-5d9f-49e3-97e1-ba73418e73be | graph4code-a-machine-interpretable-knowledge | 2002.09440 | null | https://arxiv.org/abs/2002.09440v3 | https://arxiv.org/pdf/2002.09440v3.pdf | A Toolkit for Generating Code Knowledge Graphs | Knowledge graphs have been proven extremely useful in powering diverse applications in semantic search and natural language understanding. In this paper, we present GraphGen4Code, a toolkit to build code knowledge graphs that can similarly power various applications such as program search, code understanding, bug detec... | ['Jamie McCusker', 'Ibrahim Abdelaziz', 'Julian Dolby', 'Kavitha Srinivas'] | 2020-02-21 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-4.85484600e-01 3.90919447e-01 -4.69820589e-01 -2.21328929e-01
-3.85586053e-01 -9.96009409e-01 3.60907972e-01 6.90441787e-01
3.49482536e-01 2.97937661e-01 5.91905653e-01 -8.95950794e-01
5.58272451e-02 -1.10151303e+00 -8.31925392e-01 4.50052530e-01
-1.93745241e-01 -2.16934420e-02 5.56480229e-01 -8.66334364... | [7.61470365524292, 7.8877644538879395] |
0ad9f17e-1d8c-491f-a590-5485c582ec67 | two-stream-binocular-network-accurate-near | 1804.10160 | null | http://arxiv.org/abs/1804.10160v1 | http://arxiv.org/pdf/1804.10160v1.pdf | Two-Stream Binocular Network: Accurate Near Field Finger Detection Based On Binocular Images | Fingertip detection plays an important role in human computer interaction.
Previous works transform binocular images into depth images. Then depth-based
hand pose estimation methods are used to predict 3D positions of fingertips.
Different from previous works, we propose a new framework, named Two-Stream
Binocular Netw... | ['Hengkai Guo', 'Huazhong Yang', 'Cairong Zhang', 'Xinghao Chen', 'Yi Wei', 'Guijin Wang'] | 2018-04-26 | null | null | null | null | ['fingertip-detection'] | ['computer-vision'] | [-2.02366337e-01 -4.87970412e-01 -1.45288324e-02 -2.79240817e-01
-2.26358071e-01 -7.24878788e-01 1.66112736e-01 -6.82073236e-01
-5.58642030e-01 4.46540385e-01 -8.63132551e-02 -1.58478573e-01
2.58514643e-01 -5.71609080e-01 -7.37776041e-01 -3.78991812e-01
1.91169336e-01 -1.88088287e-02 7.21320271e-01 1.56780295... | [6.504888534545898, -0.48654162883758545] |
ae7a69ab-6c83-4a21-810c-176ac18f165a | stir-siamese-transformer-for-image-retrieval | 2304.13393 | null | https://arxiv.org/abs/2304.13393v2 | https://arxiv.org/pdf/2304.13393v2.pdf | STIR: Siamese Transformer for Image Retrieval Postprocessing | Current metric learning approaches for image retrieval are usually based on learning a space of informative latent representations where simple approaches such as the cosine distance will work well. Recent state of the art methods such as HypViT move to more complex embedding spaces that may yield better results but ar... | ['Sergey Nikolenko', 'Aleksei Tarasov', 'Aleksei Shabanov'] | 2023-04-26 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 9.56292897e-02 -4.09578562e-01 -1.69597015e-01 -4.38865185e-01
-1.39596510e+00 -4.90343392e-01 7.73975313e-01 4.87836868e-01
-4.39648330e-01 2.71358430e-01 1.53301567e-01 -1.72047660e-01
-5.49817622e-01 -7.35436797e-01 -5.68746448e-01 -6.66732252e-01
-1.22019447e-01 4.56158340e-01 2.69834548e-01 -2.30270416... | [10.694287300109863, 0.7674407958984375] |
55dcfa6f-252e-4420-b918-6da2fbae77c3 | faces-a-la-carte-text-to-face-generation-via | 2006.07606 | null | https://arxiv.org/abs/2006.07606v2 | https://arxiv.org/pdf/2006.07606v2.pdf | Faces à la Carte: Text-to-Face Generation via Attribute Disentanglement | Text-to-Face (TTF) synthesis is a challenging task with great potential for diverse computer vision applications. Compared to Text-to-Image (TTI) synthesis tasks, the textual description of faces can be much more complicated and detailed due to the variety of facial attributes and the parsing of high dimensional abstra... | ['Teng Zhang', 'Tianren Wang', 'Brian Lovell'] | 2020-06-13 | null | null | null | null | ['text-to-face-generation'] | ['computer-vision'] | [ 5.18049717e-01 -1.03205973e-02 1.83384195e-01 -7.99871802e-01
-9.32957232e-01 -3.53893697e-01 6.80188656e-01 -8.12419593e-01
1.56043902e-01 6.72968745e-01 1.67644411e-01 2.24739477e-01
2.19055682e-01 -8.45196784e-01 -8.64029944e-01 -7.66165137e-01
4.91208553e-01 5.94344735e-01 -3.39907318e-01 1.10124446... | [12.53962516784668, -0.15461592376232147] |
70432a80-3114-4bcf-ab46-a81a24f527fd | semeval-2016-task-9-chinese-semantic | null | null | https://aclanthology.org/S16-1167 | https://aclanthology.org/S16-1167.pdf | SemEval-2016 Task 9: Chinese Semantic Dependency Parsing | null | ['Yanqiu Shao', 'Yu Ding', 'Ting Liu', 'Wanxiang Che'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.415567874908447, 3.685319662094116] |
428b2960-f95d-42a1-9e3f-d58aa88bb4f5 | identification-of-significant-permissions-for | 2103.00643 | null | https://arxiv.org/abs/2103.00643v1 | https://arxiv.org/pdf/2103.00643v1.pdf | Identification of Significant Permissions for Efficient Android Malware Detection | Since Google unveiled Android OS for smartphones, malware are thriving with 3Vs, i.e. volume, velocity, and variety. A recent report indicates that one out of every five business/industry mobile application leaks sensitive personal data. Traditional signature/heuristic-based malware detection systems are unable to cope... | ['Mohit Sewak', 'Ritvik Rajvanshi', 'Sanjay K. Sahay', 'Hemant Rathore'] | 2021-02-28 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 1.80707708e-01 -3.36657941e-01 -4.83568996e-01 -6.94817454e-02
-4.00476813e-01 -2.45551765e-01 4.57353801e-01 -3.03445667e-01
-2.18962505e-01 3.97740901e-01 -5.47218204e-01 -9.70950603e-01
1.08651489e-01 -6.23265982e-01 -5.81015944e-01 -4.75892395e-01
-3.00670773e-01 1.11739179e-02 2.29903057e-01 -1.14329115... | [14.423982620239258, 9.68143081665039] |
28fbe664-de51-4aa8-96d6-d9883eff89d6 | reducing-computational-costs-in-sentiment | 2306.09705 | null | https://arxiv.org/abs/2306.09705v1 | https://arxiv.org/pdf/2306.09705v1.pdf | Reducing Computational Costs in Sentiment Analysis: Tensorized Recurrent Networks vs. Recurrent Networks | Anticipating audience reaction towards a certain text is integral to several facets of society ranging from politics, research, and commercial industries. Sentiment analysis (SA) is a useful natural language processing (NLP) technique that utilizes lexical/statistical and deep learning methods to determine whether diff... | ['Joe Kaul', 'Anna Nguyen', 'Gabriel Lopez'] | 2023-06-16 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-1.96147442e-01 -2.35591590e-01 -3.65501851e-01 -4.42043096e-01
-4.25828904e-01 -4.96004254e-01 3.84192735e-01 4.74746764e-01
-5.48667967e-01 5.10428667e-01 3.80913377e-01 -4.68527138e-01
3.02735567e-01 -7.67397761e-01 -2.39471883e-01 -5.95888853e-01
1.03852265e-01 7.62401670e-02 -9.58093181e-02 -6.08443558... | [11.180615425109863, 6.984334945678711] |
b9638db8-8d10-4cf3-9206-9f0377c4c8a2 | gated-relational-graph-attention-networks | null | null | https://openreview.net/forum?id=v-9E8egy_i | https://openreview.net/pdf?id=v-9E8egy_i | Gated Relational Graph Attention Networks | Relational Graph Neural Networks (GNN) are a class of GNN that are capable of handling multi-relational graphs. Like all GNNs, they suffer from a drop in performance when training deeper networks, which may be caused by vanishing gradients, over-parameterization, and oversmoothing. Previous works have investigated meth... | ['Asja Fischer', 'Denis Lukovnikov'] | 2021-01-01 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [-2.38305535e-02 3.79768699e-01 -2.06757590e-01 -4.56409305e-01
-7.20662437e-03 -2.06511065e-01 7.30438709e-01 4.11812454e-01
-3.79500270e-01 4.39122289e-01 1.37723640e-01 -5.46811879e-01
-3.78474891e-01 -1.16950119e+00 -9.67582583e-01 -4.43895906e-01
-4.55601037e-01 7.56145895e-01 5.33789515e-01 -5.54205596... | [6.952338695526123, 6.254016399383545] |
d1c58303-bf92-4b19-a685-231f9395b1f6 | end-to-end-joint-entity-extraction-and | 1812.05270 | null | https://arxiv.org/abs/1812.05270v5 | https://arxiv.org/pdf/1812.05270v5.pdf | Joint Entity Extraction and Assertion Detection for Clinical Text | Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for information extraction. Most of the existing systems treat this task as a pipeline of two separate tasks, i.e., named entity recognition (NER) and rule-based negation detection. We ... | ['Busra Celikkaya', 'Parminder Bhatia', 'Mohammed Khalilia'] | 2018-12-13 | joint-entity-extraction-and-assertion | https://aclanthology.org/P19-1091 | https://aclanthology.org/P19-1091.pdf | acl-2019-7 | ['negation-detection'] | ['natural-language-processing'] | [ 4.10503626e-01 3.62675756e-01 -2.96825409e-01 -5.63448548e-01
-1.41349530e+00 -4.02805179e-01 2.75282800e-01 5.57312727e-01
-1.03890574e+00 9.23011303e-01 3.17354679e-01 -5.38401783e-01
2.17351735e-01 -4.17800754e-01 -5.96963465e-01 -3.36516470e-01
6.65910766e-02 4.90748137e-01 -1.00138821e-02 4.74826284... | [8.566598892211914, 8.76343059539795] |
0d8b6dd8-8d68-401e-a0df-a0dbee862ea5 | privacy-preserving-deep-learning-computation | 2001.02932 | null | https://arxiv.org/abs/2001.02932v1 | https://arxiv.org/pdf/2001.02932v1.pdf | Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms | This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized ... | ['Jong-Kook Kim', 'Kwangsoo Kim', 'Junhui Kim', 'Joongheon Kim', 'Joohyung Jeon', 'Aziz Mohaisen'] | 2020-01-09 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-4.92873937e-01 5.26413202e-01 -4.34643954e-01 -4.47492033e-01
-3.72593850e-01 -6.63372517e-01 -2.14528248e-01 5.16270101e-01
-7.50084341e-01 6.83608830e-01 2.25894839e-01 -3.64384025e-01
2.00724766e-01 -7.87630677e-01 -4.90302801e-01 -1.01866400e+00
-3.20276469e-01 -7.06711262e-02 1.23191327e-01 4.33926463... | [6.149084568023682, 6.622735500335693] |
2a8dc3e3-95bd-4fc5-bac4-b850c8cee91d | a-physics-informed-machine-learning-model-for | 2101.05605 | null | https://arxiv.org/abs/2101.05605v1 | https://arxiv.org/pdf/2101.05605v1.pdf | A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing | To control part quality, it is critical to analyze pore generation mechanisms, laying theoretical foundation for future porosity control. Current porosity analysis models use machine setting parameters, such as laser angle and part pose. However, these setting-based models are machine dependent, hence they often do not... | ['Xiaoli Zhang', 'Sen Liu', 'Rui Liu'] | 2021-01-13 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.93837985e-01 4.46011871e-02 -2.95594752e-01 -9.18343663e-03
-4.25232410e-01 -2.59329975e-01 2.87735105e-01 6.06348455e-01
-4.41545155e-03 6.03479028e-01 -1.48882300e-01 -5.82382977e-01
-7.42074192e-01 -1.36949778e+00 -8.53715777e-01 -6.10689223e-01
2.03614607e-01 8.06343436e-01 4.74560142e-01 -1.39657676... | [6.329172611236572, 3.2744860649108887] |
2687c7f0-ab79-4926-bc3a-9450648acad5 | zero-shot-aspect-based-sentiment-analysis | 2202.01924 | null | https://arxiv.org/abs/2202.01924v3 | https://arxiv.org/pdf/2202.01924v3.pdf | Zero-Shot Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated data for supervised training/fine-tuning. It is a big challenge to scale ABSA to a large number of new domains. This paper aims to train a unified model that can perform zero-shot ABSA without using any annotated data for a new domain. We pro... | ['Hu Xu', 'Bing Liu', 'Jiahua Chen', 'Lei Shu'] | 2022-02-04 | null | null | null | null | ['aspect-extraction', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.37691939e-01 3.25418741e-01 3.00786807e-03 -8.43261898e-01
-1.20422971e+00 -6.97459996e-01 8.92952502e-01 1.13516860e-01
-3.64669949e-01 5.49395382e-01 -4.71365376e-04 -5.83132327e-01
2.87727654e-01 -1.04947889e+00 -5.73391378e-01 -1.25119403e-01
4.75471437e-01 8.33363056e-01 2.53024744e-03 -5.70799470... | [11.460530281066895, 6.688036918640137] |
d85b682f-cdbc-4eea-8559-f954efc0273f | supervised-contrastive-learning-to-classify | 2209.01937 | null | https://arxiv.org/abs/2209.01937v1 | https://arxiv.org/pdf/2209.01937v1.pdf | Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus | Using deep learning techniques, anomalies in the paranasal sinus system can be detected automatically in MRI images and can be further analyzed and classified based on their volume, shape and other parameters like local contrast. However due to limited training data, traditional supervised learning methods often fail t... | ['Anna Sophie Hoffmann', 'Alexander Schlaefer', 'Christian Betz', 'Bastian Cheng', 'Marvin Petersen', 'Florian Jansen', 'Elina Petersen', 'Dennis Eggert', 'Dirk Beyersdorff', 'Marcel Bengs', 'Finn Behrendt', 'Benjamin Tobias Becker', 'Debayan Bhattacharya'] | 2022-09-05 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [-8.11559111e-02 3.67842197e-01 -5.51377423e-02 -4.83884722e-01
-5.91924310e-01 -2.76436836e-01 4.56182361e-01 7.30114877e-01
-6.27259552e-01 4.08693314e-01 3.11800325e-03 -2.61668116e-01
-1.99455664e-01 -5.42778313e-01 -2.76421249e-01 -8.36332858e-01
-3.56151909e-01 5.61460912e-01 6.73615113e-02 1.71868771... | [14.734060287475586, -2.2840497493743896] |
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