paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d5dd9ab6-aa1a-4dd4-8ae0-7633bea6de41 | adaptive-meta-learner-via-gradient-similarity | 2209.04702 | null | https://arxiv.org/abs/2209.04702v1 | https://arxiv.org/pdf/2209.04702v1.pdf | Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification | Few-shot text classification aims to classify the text under the few-shot scenario. Most of the previous methods adopt optimization-based meta learning to obtain task distribution. However, due to the neglect of matching between the few amount of samples and complicated models, as well as the distinction between useful... | ['Xu Wang', 'Dezhong Peng', 'Qiaoyang Luo', 'Honghui Hu', 'Tianyi Lei'] | 2022-09-10 | null | https://aclanthology.org/2022.coling-1.431 | https://aclanthology.org/2022.coling-1.431.pdf | coling-2022-10 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 1.77641332e-01 -2.93150067e-01 -1.93714887e-01 -3.84537101e-01
-6.69678271e-01 2.74860084e-01 5.83566010e-01 3.06764156e-01
-4.74904448e-01 5.79017162e-01 1.42586350e-01 2.47488752e-01
-3.22021693e-01 -7.77033448e-01 -3.29485387e-01 -8.32617939e-01
6.56940222e-01 2.75240451e-01 2.82349169e-01 -3.34014952... | [10.177956581115723, 3.5120670795440674] |
32297d13-2419-42a1-bc25-411d686cb924 | systematic-n-tuple-networks-for-position | 1406.1509 | null | http://arxiv.org/abs/1406.1509v3 | http://arxiv.org/pdf/1406.1509v3.pdf | Systematic N-tuple Networks for Position Evaluation: Exceeding 90% in the Othello League | N-tuple networks have been successfully used as position evaluation functions
for board games such as Othello or Connect Four. The effectiveness of such
networks depends on their architecture, which is determined by the placement of
constituent n-tuples, sequences of board locations, providing input to the
network. The... | ['Wojciech Jaśkowski'] | 2014-06-05 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.05344558e-01 2.30242804e-01 -3.17650437e-01 -6.27213940e-02
-5.55203140e-01 -8.79270911e-01 4.08967659e-02 5.59319735e-01
-8.02626431e-01 1.14721644e+00 -1.44649059e-01 -3.99059087e-01
-5.97701252e-01 -1.11452591e+00 -8.09053957e-01 -4.48247820e-01
-6.03329122e-01 1.00656378e+00 7.53877103e-01 -8.40883493... | [3.4473025798797607, 1.434889793395996] |
c9836cff-4393-4204-9d84-bad432441681 | speed-estimation-evaluation-on-the-kitti | 1907.06989 | null | https://arxiv.org/abs/1907.06989v1 | https://arxiv.org/pdf/1907.06989v1.pdf | Speed estimation evaluation on the KITTI benchmark based on motion and monocular depth information | In this technical report we investigate speed estimation of the ego-vehicle on the KITTI benchmark using state-of-the-art deep neural network based optical flow and single-view depth prediction methods. Using a straightforward intuitive approach and approximating a single scale factor, we evaluate several application s... | ['Róbert-Adrian Rill'] | 2019-07-16 | null | null | null | null | ['vehicle-speed-estimation'] | ['computer-vision'] | [-5.41750193e-01 -2.45666265e-01 -3.42722297e-01 -2.63394028e-01
6.34206459e-02 -5.37386596e-01 5.03609955e-01 -5.30000031e-01
-6.32475972e-01 7.52884269e-01 1.54001325e-01 -4.78713036e-01
2.06597522e-02 -4.06571746e-01 -7.43563473e-01 -5.68287313e-01
-3.91151756e-01 9.43296477e-02 5.02944328e-02 -1.17898531... | [8.69487190246582, -1.815500259399414] |
0561f9f4-4ba5-441b-85be-cc5f890d24ca | evolutionary-algorithm-enhanced-neural | 2008.05695 | null | https://arxiv.org/abs/2008.05695v1 | https://arxiv.org/pdf/2008.05695v1.pdf | Evolutionary Algorithm Enhanced Neural Architecture Search for Text-Independent Speaker Verification | State-of-the-art speaker verification models are based on deep learning techniques, which heavily depend on the handdesigned neural architectures from experts or engineers. We borrow the idea of neural architecture search(NAS) for the textindependent speaker verification task. As NAS can learn deep network structures a... | ['Jing Xiao', 'Jianzong Wang', 'Xiaoyang Qu'] | 2020-08-13 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 2.16165688e-02 -7.70670846e-02 -4.49456722e-02 -7.92503238e-01
-5.73721707e-01 -2.08682030e-01 3.12061280e-01 -6.37255490e-01
-2.08880723e-01 2.77588367e-01 1.97875395e-01 -6.14530385e-01
-6.49325997e-02 -2.47937560e-01 -4.64356810e-01 -7.75205374e-01
1.67062759e-01 3.67478371e-01 -3.72833222e-01 -4.98543024... | [14.317463874816895, 6.046128749847412] |
7db1c107-a431-4e0a-b49f-2121899c90b8 | lexico-acoustic-neural-based-models-for | 1803.00831 | null | http://arxiv.org/abs/1803.00831v1 | http://arxiv.org/pdf/1803.00831v1.pdf | Lexico-acoustic Neural-based Models for Dialog Act Classification | Recent works have proposed neural models for dialog act classification in
spoken dialogs. However, they have not explored the role and the usefulness of
acoustic information. We propose a neural model that processes both lexical and
acoustic features for classification. Our results on two benchmark datasets
reveal that... | ['Daniel Ortega', 'Ngoc Thang Vu'] | 2018-03-02 | null | null | null | null | ['dialog-act-classification'] | ['natural-language-processing'] | [-2.13278085e-01 5.68871386e-02 -2.17160836e-01 -7.38132775e-01
-4.78670150e-01 -5.06284595e-01 8.92004073e-01 2.42631048e-01
-6.66978002e-01 6.49494112e-01 8.27910483e-01 -2.23582640e-01
2.13558465e-01 -4.87211406e-01 7.88559616e-02 -5.38185894e-01
1.50013402e-01 4.57949400e-01 2.42123455e-01 -7.19442904... | [12.809038162231445, 7.722132682800293] |
7f4a6ea0-9a55-4251-9f6b-6a01ce2ebf1c | prometheus-a-corpus-of-proverbs-annotated | null | null | https://aclanthology.org/L16-1600 | https://aclanthology.org/L16-1600.pdf | PROMETHEUS: A Corpus of Proverbs Annotated with Metaphors | Proverbs are commonly metaphoric in nature and the mapping across domains is commonly established in proverbs. The abundance of proverbs in terms of metaphors makes them an extremely valuable linguistic resource since they can be utilized as a gold standard for various metaphor related linguistic tasks such as metaphor... | ['Serra Sinem Tekiro{\\u{g}}lu', 'G{\\"o}zde {\\"O}zbal', 'Carlo Strapparava'] | 2016-05-01 | prometheus-a-corpus-of-proverbs-annotated-1 | https://aclanthology.org/L16-1600 | https://aclanthology.org/L16-1600.pdf | lrec-2016-5 | ['english-proverbs'] | ['natural-language-processing'] | [-5.67576766e-01 -2.32681647e-01 -5.34491003e-01 -1.03157453e-01
-3.69251668e-01 -1.29686522e+00 9.98759985e-01 8.26337337e-01
-3.16845924e-01 7.99892902e-01 4.86348182e-01 -5.40216446e-01
-2.62739122e-01 -8.36051941e-01 -2.85415262e-01 -5.39944649e-01
2.89611399e-01 6.49141371e-01 -1.07444502e-01 -8.63276005... | [10.635248184204102, 9.206259727478027] |
ca35ef69-8358-4c54-8298-560f0bcf96c6 | crop-zero-shot-cross-lingual-named-entity | 2210.07022 | null | https://arxiv.org/abs/2210.07022v1 | https://arxiv.org/pdf/2210.07022v1.pdf | CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation | Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data. Cross-lingual NER has been proposed to alleviate this issue by transferring knowledge from high-resource languages to low-resource languages via aligned cross-lingual represen... | ['Furu Wei', 'Zhoujun Li', 'Hongcheng Guo', 'Dongdong Zhang', 'Li Dong', 'Yuwei Yin', 'Shuming Ma', 'Shaohan Huang', 'Jian Yang'] | 2022-10-13 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [ 0.22611648 -0.20129961 -0.35886902 -0.52571195 -1.4965664 -0.857012
0.571273 -0.37569842 -0.7370184 1.0856855 0.36788392 -0.43459448
0.6580976 -0.41863054 -0.8395966 -0.38070044 0.5888705 0.40014434
-0.14797713 -0.04680393 -0.2290354 0.06962986 -0.6500076 0.395392
1.1504585 0.34844628 0.3459... | [10.00869369506836, 9.709908485412598] |
3dcce8f3-1757-41b6-872b-7bf6cfff4352 | skeleton-aware-networks-for-deep-motion | 2005.05732 | null | https://arxiv.org/abs/2005.05732v1 | https://arxiv.org/pdf/2005.05732v1.pdf | Skeleton-Aware Networks for Deep Motion Retargeting | We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Importantly, our approach learns how to retarget without requiring any explicit pairing between the motions in the training set. We leverage th... | ['Daniel Cohen-Or', 'Olga Sorkine-Hornung', 'Peizhuo Li', 'Kfir Aberman', 'Dani Lischinski', 'Baoquan Chen'] | 2020-05-12 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 1.65970087e-01 2.23805279e-01 -3.13486367e-01 6.11421913e-02
-4.32293296e-01 -6.65577650e-01 6.21047378e-01 -3.11499715e-01
-3.12172830e-01 4.43300813e-01 6.36098802e-01 1.19328484e-01
5.93036879e-03 -9.43411291e-01 -8.83622825e-01 -7.93771684e-01
-6.49501383e-02 1.47310853e-01 4.05328482e-01 -1.79736391... | [7.452986240386963, -0.4098314344882965] |
94b2690c-6c2a-4558-bc82-ddb5b1eeaf7c | object-detection-in-aerial-images-what | 2201.08763 | null | https://arxiv.org/abs/2201.08763v1 | https://arxiv.org/pdf/2201.08763v1.pdf | Object Detection in Aerial Images: What Improves the Accuracy? | Object detection is a challenging and popular computer vision problem. The problem is even more challenging in aerial images due to significant variation in scale and viewpoint in a diverse set of object categories. Recently, deep learning-based object detection approaches have been actively explored for the problem of... | ['Abdelrahman Mohamed', 'Ikboljon Sobirov', 'Hashmat Shadab Malik'] | 2022-01-21 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 9.98425260e-02 -5.28658569e-01 1.90786093e-01 -7.02362731e-02
-4.72025275e-01 -7.63333619e-01 4.33885366e-01 -2.00434074e-01
-6.34878039e-01 2.72579521e-01 -3.77488405e-01 -2.46772394e-02
4.80515733e-02 -6.37553275e-01 -5.16565681e-01 -6.04361355e-01
-3.03457946e-01 9.09668058e-02 6.78454876e-01 -3.94817770... | [8.64217472076416, -0.8473261594772339] |
154f06e3-9f47-46ab-9e8c-b1759f627e48 | a-graph-based-and-patient-demographics-aware | 2010.10699 | null | https://arxiv.org/abs/2010.10699v2 | https://arxiv.org/pdf/2010.10699v2.pdf | A Weighted Heterogeneous Graph Based Dialogue System | Knowledge based dialogue systems have attracted increasing research interest in diverse applications. However, for disease diagnosis, the widely used knowledge graph is hard to represent the symptom-symptom relations and symptom-disease relations since the edges of traditional knowledge graph are unweighted. Most resea... | ['Huanhuan Chen', 'LiangWei Chen', 'Xinyan Zhao'] | 2020-10-21 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-2.70510077e-01 5.85886002e-01 -4.58462179e-01 -2.96975374e-01
-7.65208080e-02 -7.82371461e-02 2.44252935e-01 7.13240445e-01
-3.07826418e-02 6.48726761e-01 5.83931029e-01 -1.22181736e-01
-6.78573608e-01 -1.22856426e+00 3.36572617e-01 -5.44420183e-01
-3.61433238e-01 7.58292317e-01 2.72397369e-01 -8.04649055... | [7.687839984893799, 6.774497985839844] |
008b2c3b-313b-4c27-be91-0ad854bb1c48 | a-hierarchical-approach-for-joint-multi-view | 1503.01393 | null | http://arxiv.org/abs/1503.01393v1 | http://arxiv.org/pdf/1503.01393v1.pdf | A Hierarchical Approach for Joint Multi-view Object Pose Estimation and Categorization | We propose a joint object pose estimation and categorization approach which
extracts information about object poses and categories from the object parts
and compositions constructed at different layers of a hierarchical object
representation algorithm, namely Learned Hierarchy of Parts (LHOP). In the
proposed approach,... | ['Mete Ozay', 'Krzysztof Walas', 'Ales Leonardis'] | 2015-03-04 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 9.24076047e-03 -8.55828077e-02 -1.29175350e-01 -3.94393712e-01
-7.98730493e-01 -7.35466719e-01 5.93600810e-01 2.79124349e-01
9.40374658e-02 2.51949012e-01 1.09160252e-01 5.69415271e-01
-2.94308364e-01 -6.86925292e-01 -8.52980137e-01 -9.16980505e-01
4.63769995e-02 9.29887950e-01 4.50346291e-01 2.45276734... | [7.535601615905762, -2.7117760181427] |
d74e37c7-dba1-4719-a28b-2a597ba1a52d | few-shot-image-classification-via-contrastive | 2008.09942 | null | https://arxiv.org/abs/2008.09942v1 | https://arxiv.org/pdf/2008.09942v1.pdf | Few-Shot Image Classification via Contrastive Self-Supervised Learning | Most previous few-shot learning algorithms are based on meta-training with fake few-shot tasks as training samples, where large labeled base classes are required. The trained model is also limited by the type of tasks. In this paper we propose a new paradigm of unsupervised few-shot learning to repair the deficiencies.... | ['Guizhong Liu', 'Jianyi Li'] | 2020-08-23 | null | null | null | null | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.17350256e-01 2.22169593e-01 -5.69874406e-01 -1.75148442e-01
-6.66698456e-01 2.13193834e-01 8.27835917e-01 1.73403502e-01
-2.50655800e-01 6.03520691e-01 5.91383129e-02 1.19922534e-01
5.58469221e-02 -8.58991086e-01 -4.88033444e-01 -4.61135447e-01
1.09243758e-01 5.24532914e-01 6.09530568e-01 -5.35228312... | [9.979411125183105, 2.9641973972320557] |
90b7672f-7978-4ce4-ae63-ff896028c916 | predicting-indian-stock-market-using-the | 1911.06193 | null | https://arxiv.org/abs/1911.06193v1 | https://arxiv.org/pdf/1911.06193v1.pdf | Predicting Indian stock market using the psycho-linguistic features of financial news | Financial forecasting using news articles is an emerging field. In this paper, we proposed hybrid intelligent models for stock market prediction using the psycholinguistic variables (LIWC and TAALES) extracted from news articles as predictor variables. For prediction purpose, we employed various intelligent techniques ... | ['Vadlamani Ravi', 'Rishabh Miglani', 'B. Shravan Kumar'] | 2019-11-07 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-5.80189347e-01 -1.09675832e-01 -2.56011367e-01 -6.12295389e-01
8.14479813e-02 -3.49152833e-01 6.57777190e-01 2.60044068e-01
-6.28467500e-01 1.12269139e+00 4.79577452e-01 -6.32454515e-01
-4.62227672e-01 -1.04344440e+00 -1.52814671e-01 -3.65044296e-01
-3.02093118e-01 3.72606188e-01 -3.54243629e-02 -5.74707925... | [4.54866361618042, 4.1901774406433105] |
cbee047c-2352-436e-aa66-9b24550513d4 | conformalized-unconditional-quantile | 2304.01426 | null | https://arxiv.org/abs/2304.01426v1 | https://arxiv.org/pdf/2304.01426v1.pdf | Conformalized Unconditional Quantile Regression | We develop a predictive inference procedure that combines conformal prediction (CP) with unconditional quantile regression (QR) -- a commonly used tool in econometrics that involves regressing the recentered influence function (RIF) of the quantile functional over input covariates. Unlike the more widely-known conditio... | ['David Sontag', 'Zeshan Hussain', 'Ahmed M. Alaa'] | 2023-04-04 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 3.00981909e-01 3.46008688e-01 -7.13480473e-01 -5.65568805e-01
-1.27223384e+00 -4.36057240e-01 5.36381841e-01 4.57922071e-01
2.80560441e-02 1.07890737e+00 3.91822338e-01 -7.07750201e-01
-5.59036493e-01 -1.01708484e+00 -1.07089031e+00 -7.27508605e-01
-3.31436992e-01 6.52935684e-01 -6.81037130e-03 3.06986362... | [7.56197452545166, 4.382304668426514] |
a4f1e29a-6b8d-48b3-9fce-d43a053e304a | a-principal-agent-framework-for-optimal | 2302.12167 | null | https://arxiv.org/abs/2302.12167v1 | https://arxiv.org/pdf/2302.12167v1.pdf | A Principal-Agent Framework for Optimal Incentives in Renewable Investments | We investigate the optimal regulation of energy production reflecting the long-term goals of the Paris Climate Agreement. We analyze the optimal regulatory incentives to foster the development of non-emissive electricity generation when the demand for power is served either by a monopoly or by two competing agents. The... | ['Nizar Touzi', 'Annika Kemper', 'René Aïd'] | 2023-02-23 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-2.40654096e-01 3.46019059e-01 -9.89633724e-02 3.11519474e-01
-3.88938695e-01 -1.11168075e+00 6.00985289e-01 6.84087910e-03
-3.56473476e-01 1.00703907e+00 3.65807712e-02 -3.63427192e-01
-6.44960046e-01 -9.73465383e-01 -3.96416485e-01 -1.47841191e+00
8.70722905e-02 4.10795778e-01 -4.98313785e-01 -9.60232317... | [5.102737903594971, 3.2528750896453857] |
c7517ce7-855f-429a-917c-6319835217a6 | a-semi-supervised-approach-for-a-better | 2210.11899 | null | https://arxiv.org/abs/2210.11899v2 | https://arxiv.org/pdf/2210.11899v2.pdf | A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT | In the online world, Machine Translation (MT) systems are extensively used to translate User-Generated Text (UGT) such as reviews, tweets, and social media posts, where the main message is often the author's positive or negative attitude towards the topic of the text. However, MT systems still lack accuracy in some low... | ['Ashraf Tantawy', 'Emad Mohamed', 'Constantin Orasan', 'Hadeel Saadany'] | 2022-10-21 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 1.62420034e-01 1.84886694e-01 -2.12483257e-01 -4.88734454e-01
-8.07627797e-01 -9.70319569e-01 7.47211993e-01 3.73130381e-01
-4.23666209e-01 9.01080906e-01 1.98833108e-01 -5.26811659e-01
5.34910440e-01 -7.48640776e-01 -6.39799774e-01 -3.20301622e-01
6.65212512e-01 8.65862429e-01 -4.95813787e-01 -1.12138546... | [11.508479118347168, 10.312270164489746] |
5b9d9bdb-ce88-49ad-ae8a-93b7a2cb4ccb | neural-language-modeling-by-jointly-learning | 1711.02013 | null | http://arxiv.org/abs/1711.02013v2 | http://arxiv.org/pdf/1711.02013v2.pdf | Neural Language Modeling by Jointly Learning Syntax and Lexicon | We propose a neural language model capable of unsupervised syntactic
structure induction. The model leverages the structure information to form
better semantic representations and better language modeling. Standard
recurrent neural networks are limited by their structure and fail to
efficiently use syntactic informatio... | ['Chin-wei Huang', 'Zhouhan Lin', 'Yikang Shen', 'Aaron Courville'] | 2017-11-02 | neural-language-modeling-by-jointly-learning-1 | https://openreview.net/forum?id=rkgOLb-0W | https://openreview.net/pdf?id=rkgOLb-0W | iclr-2018-1 | ['constituency-grammar-induction'] | ['natural-language-processing'] | [ 3.22044641e-01 7.22539246e-01 -4.91476715e-01 -8.24900985e-01
-7.18616545e-01 -4.03558105e-01 1.40822053e-01 -6.54418394e-02
-4.21504229e-01 4.91132498e-01 4.74912435e-01 -8.03654909e-01
3.55762869e-01 -9.10691619e-01 -9.04200137e-01 -3.00262660e-01
1.00249425e-01 3.38217020e-01 -7.98386708e-02 1.28670409... | [10.601807594299316, 9.271988868713379] |
7f7e7b2f-a693-4a01-9dff-f38804cfafed | deep-multimodal-feature-analysis-for-action | 1603.07120 | null | http://arxiv.org/abs/1603.07120v2 | http://arxiv.org/pdf/1603.07120v2.pdf | Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos | Single modality action recognition on RGB or depth sequences has been
extensively explored recently. It is generally accepted that each of these two
modalities has different strengths and limitations for the task of action
recognition. Therefore, analysis of the RGB+D videos can help us to better
study the complementar... | ['Tian-Tsong Ng', 'Gang Wang', 'Amir Shahroudy', 'Yihong Gong'] | 2016-03-23 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 4.03087556e-01 -5.17758787e-01 -3.69949847e-01 -5.00005245e-01
-6.71100438e-01 -2.03067511e-01 5.18255651e-01 -2.38460451e-01
-2.85769224e-01 4.39495474e-01 5.85026503e-01 1.95527390e-01
-2.58744419e-01 -4.51793730e-01 -3.16048592e-01 -9.61425483e-01
8.44953135e-02 -1.84896633e-01 1.94725057e-03 -9.79015231... | [7.978432655334473, 0.43726420402526855] |
49cd9d1b-fdc3-4389-b59d-035a856a476b | deep-active-learning-with-structured-neural | 2306.02808 | null | https://arxiv.org/abs/2306.02808v1 | https://arxiv.org/pdf/2306.02808v1.pdf | Deep Active Learning with Structured Neural Depth Search | Previous work optimizes traditional active learning (AL) processes with incremental neural network architecture search (Active-iNAS) based on data complexity change, which improves the accuracy and learning efficiency. However, Active-iNAS trains several models and selects the model with the best generalization perform... | ['Jianwei Zhang', 'Xieyi Ping', 'Xiaoyun Zhang'] | 2023-06-05 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 3.18060040e-01 2.71845460e-01 -5.10947406e-01 -4.37106043e-01
-9.99755025e-01 -3.75533223e-01 6.11588180e-01 -1.08641326e-01
-7.20526159e-01 9.39733922e-01 -3.76205206e-01 -2.95354277e-01
-4.78138119e-01 -8.22905362e-01 -8.10262859e-01 -9.34444666e-01
2.17559919e-01 7.71928549e-01 6.42252386e-01 4.78825152... | [8.996773719787598, 3.895050048828125] |
47ac765d-ce4a-452d-9615-d0f93ae02c58 | thesis-multiple-kernel-learning-for-object | 1604.03247 | null | http://arxiv.org/abs/1604.03247v1 | http://arxiv.org/pdf/1604.03247v1.pdf | Thesis: Multiple Kernel Learning for Object Categorization | Object Categorization is a challenging problem, especially when the images
have clutter background, occlusions or different lighting conditions. In the
past, many descriptors have been proposed which aid object categorization even
in such adverse conditions. Each descriptor has its own merits and de-merits.
Some descri... | ['Dinesh Govindaraj'] | 2016-04-12 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 4.36957069e-02 -6.96485937e-01 -4.28442150e-01 -5.17388999e-01
-8.52265298e-01 -4.43164289e-01 5.55420935e-01 3.21197897e-01
-4.52668697e-01 4.54936892e-01 -2.24597961e-01 1.76084116e-01
-6.04957283e-01 -5.42148829e-01 -1.87816203e-01 -9.80533421e-01
-1.25780240e-01 1.36106089e-01 3.23679149e-01 -4.43032719... | [8.130358695983887, 4.005428791046143] |
8ce8f11a-b22e-4581-9a7d-1c43035fda50 | evaluating-bert-and-parsbert-for-analyzing | 2305.02426 | null | https://arxiv.org/abs/2305.02426v1 | https://arxiv.org/pdf/2305.02426v1.pdf | evaluating bert and parsbert for analyzing persian advertisement data | This paper discusses the impact of the Internet on modern trading and the importance of data generated from these transactions for organizations to improve their marketing efforts. The paper uses the example of Divar, an online marketplace for buying and selling products and services in Iran, and presents a competition... | ['Pegah Ahadian', 'Ali Mehrban'] | 2023-05-03 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-6.61763608e-01 -1.82595000e-01 -5.20076692e-01 -4.75412667e-01
-4.96579558e-01 -7.35365331e-01 5.68239868e-01 4.32298392e-01
-9.23802555e-01 4.69954818e-01 6.35899752e-02 -7.85530925e-01
-2.85646558e-01 -8.82092774e-01 -2.72869468e-01 -2.43839443e-01
-1.48994476e-01 1.30766201e+00 -3.47445905e-01 -5.12265384... | [10.280202865600586, 9.911498069763184] |
8f82e0fd-f9db-4a33-bf6f-3cb2dcac5b02 | first-image-then-video-a-two-stage-network | 2001.00346 | null | https://arxiv.org/abs/2001.00346v2 | https://arxiv.org/pdf/2001.00346v2.pdf | First image then video: A two-stage network for spatiotemporal video denoising | Video denoising is to remove noise from noise-corrupted data, thus recovering true signals via spatiotemporal processing. Existing approaches for spatiotemporal video denoising tend to suffer from motion blur artifacts, that is, the boundary of a moving object tends to appear blurry especially when the object undergoes... | ['S. Kevin Zhou', 'Zhiwei Cheng', 'Ce Wang'] | 2020-01-02 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 2.37106293e-01 -4.63419706e-01 2.60086685e-01 -1.38333827e-01
-4.85089064e-01 -2.82991439e-01 3.35374922e-01 -1.97761729e-01
-5.32840967e-01 4.14681822e-01 3.40040654e-01 -3.88650340e-03
6.11938573e-02 -4.47136670e-01 -6.74870014e-01 -1.12587738e+00
-5.36507517e-02 -4.85023528e-01 2.75120616e-01 -1.02119118... | [11.386120796203613, -2.156062126159668] |
6775c7d8-1fdb-49a2-85af-5fc80841b74e | supervised-learning-of-universal-sentence | 1705.02364 | null | http://arxiv.org/abs/1705.02364v5 | http://arxiv.org/pdf/1705.02364v5.pdf | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data | Many modern NLP systems rely on word embeddings, previously trained in an
unsupervised manner on large corpora, as base features. Efforts to obtain
embeddings for larger chunks of text, such as sentences, have however not been
so successful. Several attempts at learning unsupervised representations of
sentences have no... | ['Holger Schwenk', 'Loic Barrault', 'Douwe Kiela', 'Antoine Bordes', 'Alexis Conneau'] | 2017-05-05 | supervised-learning-of-universal-sentence-1 | https://aclanthology.org/D17-1070 | https://aclanthology.org/D17-1070.pdf | emnlp-2017-9 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 1.28453627e-01 4.20614004e-01 -3.69823456e-01 -8.16029191e-01
-7.34168291e-01 -2.66854495e-01 9.47968543e-01 3.83034647e-01
-8.23206604e-01 1.13036835e+00 5.28639615e-01 -4.53209966e-01
1.85173675e-01 -9.05345619e-01 -6.50447190e-01 -3.36849630e-01
3.82969575e-03 5.30211508e-01 2.22946018e-01 -2.37980917... | [10.711503028869629, 8.774624824523926] |
0e9b9cbd-a748-430f-91ea-ed57a6832dee | more-recent-advances-in-hyper-graph | 2205.13202 | null | https://arxiv.org/abs/2205.13202v3 | https://arxiv.org/pdf/2205.13202v3.pdf | More Recent Advances in (Hyper)Graph Partitioning | In recent years, significant advances have been made in the design and evaluation of balanced (hyper)graph partitioning algorithms. We survey trends of the last decade in practical algorithms for balanced (hyper)graph partitioning together with future research directions. Our work serves as an update to a previous surv... | ['Lars Gottesbüren', 'Dorothea Wagner', 'Daniel Seemaier', 'Christian Schulz', 'Sebastian Schlag', 'Peter Sanders', 'Henning Meyerhenke', 'Tobias Heuer', 'Marcelo Fonseca Faraj', 'Karen D. Devine', 'Ümit V. Çatalyürek'] | 2022-05-26 | null | null | null | null | ['hypergraph-partitioning', 'graph-partitioning'] | ['graphs', 'graphs'] | [ 3.42476726e-01 5.31426311e-01 -6.42404020e-01 -2.65520483e-01
-3.33175242e-01 -6.82524383e-01 1.75735489e-01 5.64717114e-01
2.08522797e-01 7.08933234e-01 1.60804182e-01 -4.12261546e-01
-3.81038249e-01 -1.07939160e+00 -2.16614306e-01 -5.53446054e-01
-7.44011343e-01 1.07596910e+00 8.66702199e-01 -1.38592139... | [6.991598129272461, 5.210996627807617] |
f26ab1c6-7cbd-46e9-b975-cdd8d17eb869 | the-webnlg-challenge-generating-text-from-rdf | null | null | https://aclanthology.org/W17-3518 | https://aclanthology.org/W17-3518.pdf | The WebNLG Challenge: Generating Text from RDF Data | The WebNLG challenge consists in mapping sets of RDF triples to text. It provides a common benchmark on which to train, evaluate and compare {``}microplanners{''}, i.e. generation systems that verbalise a given content by making a range of complex interacting choices including referring expression generation, aggregati... | ['Laura Perez-Beltrachini', 'Claire Gardent', 'Shashi Narayan', 'Anastasia Shimorina'] | 2017-09-01 | null | null | null | ws-2017-9 | ['referring-expression-generation'] | ['computer-vision'] | [ 4.48561937e-01 8.23121607e-01 2.72818983e-01 -8.28496993e-01
-1.10126019e+00 -9.38265026e-01 1.28690839e+00 6.26068234e-01
-5.15792012e-01 1.19360673e+00 7.71343291e-01 -6.81283399e-02
-2.16426358e-01 -8.85988951e-01 -6.93067729e-01 1.96849480e-02
2.88306117e-01 1.01561999e+00 1.53927743e-01 -6.19418502... | [11.184332847595215, 9.059237480163574] |
60a2b8c8-e227-491a-8744-e0a51a709763 | efficient-human-pose-estimation-with | 2012.03316 | null | https://arxiv.org/abs/2012.03316v1 | https://arxiv.org/pdf/2012.03316v1.pdf | Efficient Human Pose Estimation with Depthwise Separable Convolution and Person Centroid Guided Joint Grouping | In this paper, we propose efficient and effective methods for 2D human pose estimation. A new ResBlock is proposed based on depthwise separable convolution and is utilized instead of the original one in Hourglass network. It can be further enhanced by replacing the vanilla depthwise convolution with a mixed depthwise c... | ['Hong Wu', 'Jie Ou'] | 2020-12-06 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-2.89597869e-01 2.60433167e-01 1.43573999e-01 -4.25903499e-01
-1.86677530e-01 -3.50571685e-02 3.04071069e-01 -2.21238315e-01
-5.60988188e-01 6.44748032e-01 4.12795991e-01 5.30374527e-01
9.57675800e-02 -7.85982549e-01 -5.52347779e-01 -4.02066290e-01
-3.92355621e-01 8.15215647e-01 3.79154712e-01 -2.60394841... | [7.127902507781982, -0.7593551278114319] |
b23bb495-a4b5-4624-825b-ae341505d063 | towards-learning-rubik-s-cube-with-n-tuple | 2301.12167 | null | https://arxiv.org/abs/2301.12167v1 | https://arxiv.org/pdf/2301.12167v1.pdf | Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning | This work describes in detail how to learn and solve the Rubik's cube game (or puzzle) in the General Board Game (GBG) learning and playing framework. We cover the cube sizes 2x2x2 and 3x3x3. We describe in detail the cube's state representation, how to transform it with twists, whole-cube rotations and color transform... | ['Wolfgang Konen'] | 2023-01-28 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [-2.81992763e-01 3.40865433e-01 1.65206611e-01 3.64303082e-01
-9.52715278e-01 -1.10396206e+00 6.49003267e-01 -3.35116625e-01
-3.63643706e-01 1.24642754e+00 1.54137611e-01 -4.59289700e-01
-4.03959692e-01 -9.31165516e-01 -9.91050661e-01 -9.58966851e-01
-5.44417083e-01 1.03909791e+00 3.53380144e-01 -4.97973889... | [3.7012155055999756, 1.5364125967025757] |
48fa85eb-03ed-4f65-a539-f19de4811d7c | monitoring-and-detection-of-low-current-high | 2210.16981 | null | https://arxiv.org/abs/2210.16981v1 | https://arxiv.org/pdf/2210.16981v1.pdf | Monitoring and Detection of Low-current High-Impedance Faults in Distribution Networks | Faults in electricity distribution networks have the potential to ignite fires, cause electrocution, and damage the system itself. High current Low Impedance Faults (LIF) are typically detected and mitigated via over-current, distance, directional relays, fuses, etc. In contrast, while High Impedance Faults (HIF) are e... | ['Joseph Bailey', 'Ramesh Rayudu', 'Fiona J. Stevens McFadden', 'Anwarul Islam Sifat'] | 2022-10-30 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.42595127e-01 -1.90859184e-01 4.23898935e-01 -1.07080735e-01
-5.02774417e-01 -8.17408502e-01 5.28456986e-01 -1.78473815e-01
4.64078307e-01 8.20294082e-01 5.10994568e-02 -6.22491062e-01
-8.02508652e-01 -8.58354330e-01 -2.68487662e-01 -8.12159121e-01
-5.90960741e-01 4.12400514e-01 2.01735660e-01 -3.35862011... | [6.331514358520508, 2.4845423698425293] |
ebf3b142-656e-472d-9e46-f75162986716 | train-diagnose-and-fix-interpretable-approach | 1711.08502 | null | http://arxiv.org/abs/1711.08502v1 | http://arxiv.org/pdf/1711.08502v1.pdf | Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition | Despite the growing discriminative capabilities of modern deep learning
methods for recognition tasks, the inner workings of the state-of-art models
still remain mostly black-boxes. In this paper, we propose a systematic
interpretation of model parameters and hidden representations of Residual
Temporal Convolutional Ne... | ['Jingxuan Hou', 'Austin Reiter', 'Tae Soo Kim'] | 2017-11-22 | null | null | null | null | ['3d-human-action-recognition', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 7.57867038e-01 2.61056125e-01 -3.87741446e-01 -4.18780237e-01
-3.30348641e-01 -1.61158994e-01 6.04868233e-01 -4.04440433e-01
4.60693426e-02 1.25263080e-01 2.66932845e-01 -1.30907193e-01
-4.16167200e-01 -3.31713319e-01 -7.07050025e-01 -7.91040659e-01
1.40488455e-02 6.84153318e-01 2.09512755e-01 -8.75963867... | [7.895671844482422, 0.41903677582740784] |
e26d3fdf-ca13-48cb-96c9-ae1863c2cde7 | training-multimedia-event-extraction-with | 2306.08966 | null | https://arxiv.org/abs/2306.08966v1 | https://arxiv.org/pdf/2306.08966v1.pdf | Training Multimedia Event Extraction With Generated Images and Captions | Contemporary news reporting increasingly features multimedia content, motivating research on multimedia event extraction. However, the task lacks annotated multimodal training data and artificially generated training data suffer from the distribution shift from the real-world data. In this paper, we propose Cross-modal... | ['Boyang Li', 'Yidan Sun', 'Xu Guo', 'Yunxin Li', 'Zilin Du'] | 2023-06-15 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 6.14277959e-01 2.62556911e-01 -2.03008771e-01 -1.47013962e-01
-1.70456803e+00 -6.85310960e-01 1.27289259e+00 2.64670938e-01
-8.08456361e-01 8.12404394e-01 4.92758840e-01 5.40920272e-02
2.24492729e-01 -6.32403851e-01 -1.36006224e+00 -3.47088635e-01
3.57421041e-02 4.38230932e-01 1.02060981e-01 -4.02547091... | [10.71348762512207, 1.0775126218795776] |
446c3db3-e450-424e-a85f-c8ddd883f789 | marionette-few-shot-face-reenactment | 1911.08139 | null | https://arxiv.org/abs/1911.08139v1 | https://arxiv.org/pdf/1911.08139v1.pdf | MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets | When there is a mismatch between the target identity and the driver identity, face reenactment suffers severe degradation in the quality of the result, especially in a few-shot setting. The identity preservation problem, where the model loses the detailed information of the target leading to a defective output, is the ... | ['Dongyoung Kim', 'Beomsu Kim', 'Martin Kersner', 'Sungjoo Ha', 'Seokjun Seo'] | 2019-11-19 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 2.67251432e-01 4.59936559e-02 1.42588153e-01 -3.62542540e-01
-7.66352057e-01 -6.08202457e-01 6.11849546e-01 -5.11070788e-01
2.52761319e-02 4.94685173e-01 2.35626325e-01 3.44572097e-01
1.37555242e-01 -3.52020115e-01 -6.89555466e-01 -8.04853499e-01
5.24705946e-01 1.64082870e-01 -5.98287024e-02 -3.70961070... | [12.770840644836426, 0.036588504910469055] |
6c0c9a30-fade-44fc-9ba7-93de04f62fb3 | error-mitigated-quantum-approximate | 2303.14877 | null | https://arxiv.org/abs/2303.14877v1 | https://arxiv.org/pdf/2303.14877v1.pdf | Error-mitigated Quantum Approximate Optimization via Learning-based Adaptive Optimization | Combinatorial optimization problems are ubiquitous and computationally hard to solve in general. Quantum computing is envisioned as a powerful tool offering potential computational advantages for solving some of these problems. Quantum approximate optimization algorithm (QAOA), one of the most representative quantum-cl... | ['Shengyu Zhang', 'Shi-Xin Zhang', 'Yu-Qin Chen', 'Lixue Cheng'] | 2023-03-27 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 1.18428677e-01 -3.39723527e-01 -9.57609117e-02 -2.66446769e-01
-1.14513838e+00 -2.79348671e-01 2.02712685e-01 8.20765793e-02
-7.00782597e-01 1.09907889e+00 -3.89531732e-01 -3.82852465e-01
-2.68552750e-01 -7.84124851e-01 -5.12282372e-01 -1.12627304e+00
1.13319322e-01 6.55129969e-01 4.02536727e-02 -4.73339051... | [5.61993932723999, 4.892773151397705] |
1f624a87-e00e-493e-a65f-acb651986c83 | value-function-factorisation-with-hypergraph | 2112.06771 | null | https://arxiv.org/abs/2112.06771v2 | https://arxiv.org/pdf/2112.06771v2.pdf | Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution | Recent years have witnessed the great success of multi-agent systems (MAS). Value decomposition, which decomposes joint action values into individual action values, has been an important work in MAS. However, many value decomposition methods ignore the coordination among different agents, leading to the notorious "lazy... | ['Yu Liu', 'Xinwen Hou', 'Guoliang Fan', 'Bin Zhang', 'Chen Gong', 'Yunpeng Bai'] | 2021-12-09 | null | null | null | null | ['smac'] | ['playing-games'] | [-6.05302632e-01 1.39702931e-02 -2.77056277e-01 -1.64531861e-02
-8.26957822e-02 -4.83822405e-01 7.75769949e-01 1.06437206e-01
-5.88629484e-01 8.93605113e-01 6.43312693e-01 5.55364415e-02
-3.20944726e-01 -1.15114832e+00 -5.35110056e-01 -1.15129411e+00
-3.95565093e-01 8.03701997e-01 2.33685136e-01 -5.50512791... | [3.7426846027374268, 1.928741216659546] |
0df72ea1-3ff7-4484-9f46-e9c55324828e | semi-supervised-hypothesis-transfer-for | 2107.06735 | null | https://arxiv.org/abs/2107.06735v1 | https://arxiv.org/pdf/2107.06735v1.pdf | Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation | Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc. Most domain adaptation methods learn domain-invariant features with data from both domains available. However, such a strategy might be infeasible in practice when source data are unavailable due to data-priv... | ['Xifeng Yan', 'Sheng Zhou', 'Zhen Zhang', 'Jun Wen', 'Lixian Lu', 'Jiajun Bu', 'Ning Ma'] | 2021-07-14 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 4.68096524e-01 -9.36041623e-02 -2.47216910e-01 -6.52077317e-01
-7.99469471e-01 -4.41177845e-01 6.34364784e-01 5.27953170e-02
-7.50272930e-01 1.15951419e+00 5.10495305e-02 6.33556843e-02
2.53080517e-01 -4.20045853e-01 -4.93158281e-01 -6.56214535e-01
3.93725932e-01 3.59164178e-01 3.84343863e-01 -4.88459505... | [10.352641105651855, 3.053542137145996] |
7df42f46-1ed2-4aa0-9b8e-7cebc6ee28f9 | smoothed-bernstein-online-aggregation-for-day | 2107.06268 | null | https://arxiv.org/abs/2107.06268v1 | https://arxiv.org/pdf/2107.06268v1.pdf | Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting | We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load forecasting approach is based on online forecast combination of multiple point prediction models. It contains four steps: i) data cleaning and preprocessing, ii) a holiday ad... | ['Florian Ziel'] | 2021-07-13 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [-1.60779372e-01 -2.41693303e-01 -6.43107668e-02 -7.80670404e-01
-7.40462959e-01 -5.67273259e-01 8.86013627e-01 1.44983470e-01
2.50627816e-01 9.12314653e-01 3.15417200e-01 -5.79233587e-01
-6.08282387e-01 -6.96951866e-01 -3.16195011e-01 -9.04093266e-01
-7.28267193e-01 7.96371937e-01 -5.23464978e-01 -2.63968259... | [6.273411273956299, 2.946685552597046] |
29c82d5d-4d1a-49de-838f-b89ca216d327 | improved-generator-objectives-for-gans | 1612.02780 | null | http://arxiv.org/abs/1612.02780v1 | http://arxiv.org/pdf/1612.02780v1.pdf | Improved generator objectives for GANs | We present a framework to understand GAN training as alternating density
ratio estimation and approximate divergence minimization. This provides an
interpretation for the mismatched GAN generator and discriminator objectives
often used in practice, and explains the problem of poor sample diversity. We
also derive a fam... | ['Anelia Angelova', 'Jascha Sohl-Dickstein', 'Alexander A. Alemi', 'Ben Poole'] | 2016-12-08 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 3.55898380e-01 5.20116091e-01 -4.17551070e-01 -5.15960336e-01
-1.19380963e+00 -5.01033127e-01 7.44270384e-01 -6.99436128e-01
-3.48651074e-02 1.35659814e+00 1.23464182e-01 -1.33003071e-01
1.78259641e-01 -8.34625065e-01 -5.51389575e-01 -8.23279977e-01
4.35440779e-01 7.86710382e-01 -4.00761932e-01 1.45174563... | [11.646082878112793, -0.06982655078172684] |
d7c12dad-8e14-4f9a-8ca7-07db33675a7e | incremental-few-shot-learning-via-vector | null | null | https://openreview.net/forum?id=3SV-ZePhnZM | https://openreview.net/pdf?id=3SV-ZePhnZM | Incremental few-shot learning via vector quantization in deep embedded space | The capability of incrementally learning new tasks without forgetting old ones is a challenging problem due to catastrophic forgetting. This challenge becomes greater when novel tasks contain very few labelled training samples. Currently, most methods are dedicated to class-incremental learning and rely on sufficient t... | ['Chi-Guhn Lee', 'Kuilin Chen'] | 2021-01-01 | null | null | null | iclr-2021-1 | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 4.77930427e-01 -7.47569129e-02 -2.61586905e-01 -4.87364173e-01
-6.64669991e-01 5.83742261e-02 3.41868341e-01 2.47144431e-01
-6.72955036e-01 1.09133649e+00 3.69470119e-02 4.28796440e-01
-2.53211230e-01 -6.67126715e-01 -6.45011604e-01 -7.20371783e-01
7.49567971e-02 3.16458404e-01 6.68892205e-01 -8.86205360... | [9.910062789916992, 3.2912869453430176] |
cefa1939-ef27-4ed0-8adb-3a15cb555370 | embedding-individual-table-columns-for | 1811.00633 | null | http://arxiv.org/abs/1811.00633v1 | http://arxiv.org/pdf/1811.00633v1.pdf | Embedding Individual Table Columns for Resilient SQL Chatbots | Most of the world's data is stored in relational databases. Accessing these
requires specialized knowledge of the Structured Query Language (SQL), putting
them out of the reach of many people. A recent research thread in Natural
Language Processing (NLP) aims to alleviate this problem by automatically
translating natur... | ['Andreea Hossmann', 'Ignacio Aguado', 'Michael Baeriswyl', 'Claudiu Musat', 'Bojan Petrovski'] | 2018-11-01 | embedding-individual-table-columns-for-1 | https://aclanthology.org/W18-5710 | https://aclanthology.org/W18-5710.pdf | ws-2018-10 | ['sql-chatbots'] | ['computer-code'] | [-1.73457399e-01 3.48797709e-01 -3.62982750e-01 -5.38231313e-01
-8.74380231e-01 -1.00178885e+00 5.83387673e-01 7.38006294e-01
-4.66220289e-01 7.56080687e-01 3.47257972e-01 -4.96408045e-01
-1.25433221e-01 -1.24213398e+00 -8.72463107e-01 7.12556541e-02
4.73270625e-01 8.56238782e-01 6.03326619e-01 -5.37989259... | [9.79900074005127, 7.887511730194092] |
99c74226-7a40-4681-922c-473e1cbb8719 | learning-to-rank-microphones-for-distant | 2104.02819 | null | https://arxiv.org/abs/2104.02819v2 | https://arxiv.org/pdf/2104.02819v2.pdf | Learning to Rank Microphones for Distant Speech Recognition | Fully exploiting ad-hoc microphone networks for distant speech recognition is still an open issue. Empirical evidence shows that being able to select the best microphone leads to significant improvements in recognition without any additional effort on front-end processing. Current channel selection techniques either re... | ['Stefano Squartini', 'Marco Matassoni', 'Alessio Brutti', 'Samuele Cornell'] | 2021-04-06 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 3.78487110e-01 -2.79151559e-01 1.25342369e-01 -5.91979086e-01
-1.72485149e+00 -6.34022117e-01 7.68867135e-01 3.72003704e-01
-6.12574160e-01 5.83039999e-01 2.91097313e-01 -2.31172919e-01
-5.10952473e-01 -3.44584376e-01 -5.31225443e-01 -8.17955434e-01
-2.02711388e-01 4.56817299e-01 1.76754028e-01 2.10631024... | [14.957423210144043, 5.6774115562438965] |
b7089136-571a-4564-a19f-68ee10310a10 | vssa-net-vertical-spatial-sequence-attention | 1905.01583 | null | https://arxiv.org/abs/1905.01583v1 | https://arxiv.org/pdf/1905.01583v1.pdf | VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection | Although traffic sign detection has been studied for years and great progress has been made with the rise of deep learning technique, there are still many problems remaining to be addressed. For complicated real-world traffic scenes, there are two main challenges. Firstly, traffic signs are usually small size objects, ... | ['Qi. Wang', 'IEEE', 'Senior Member', 'Student Member', 'Zhitong Xiong', 'Yuan Yuan'] | 2019-05-05 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 4.09622937e-01 -8.32299173e-01 9.30321440e-02 -3.88942599e-01
-2.82709867e-01 -2.30892980e-03 4.79796410e-01 -8.21936011e-01
-4.57027823e-01 6.01088941e-01 7.87639022e-02 -3.43550026e-01
-4.69041131e-02 -4.31156546e-01 -5.12550294e-01 -8.87180984e-01
2.19584033e-01 -1.88543685e-02 1.02800250e+00 -1.82105035... | [7.988999366760254, -0.8294830322265625] |
27d8d630-ae3b-4767-b07f-d7aa192dd215 | efficient-reuse-of-structured-and | null | null | https://aclanthology.org/L14-1235 | https://aclanthology.org/L14-1235.pdf | Efficient Reuse of Structured and Unstructured Resources for Ontology Population | We study the problem of ontology population for a domain ontology and present solutions based on semi-automatic techniques. A domain ontology for an organization, often consists of classes whose instances are either specific to, or independent of the organization. E.g. in an academic domain ontology, classes like Profe... | ['Ganesh Ramakrishnan', 'Chetana Gavankar', 'Ashish Kulkarni'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['text-annotation'] | ['natural-language-processing'] | [-3.48432779e-01 6.96883202e-01 -3.64777595e-01 -1.29262000e-01
-3.43637913e-01 -1.07376981e+00 6.05065584e-01 6.26040041e-01
-5.05241454e-01 1.04219365e+00 1.52242467e-01 -2.71953464e-01
-6.62708461e-01 -1.30864298e+00 -4.14872736e-01 1.29967496e-01
2.03239784e-01 8.83683205e-01 6.76511884e-01 -5.81538320... | [9.204201698303223, 8.094673156738281] |
597f4ab7-525c-49c0-8640-0ad7d70ec5db | from-arabic-sentiment-analysis-to-sarcasm | null | null | https://aclanthology.org/2020.osact-1.5 | https://aclanthology.org/2020.osact-1.5.pdf | From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset | Sarcasm is one of the main challenges for sentiment analysis systems. Its complexity comes from the expression of opinion using implicit indirect phrasing. In this paper, we present ArSarcasm, an Arabic sarcasm detection dataset, which was created through the reannotation of available Arabic sentiment analysis datasets... | ['Walid Magdy', 'Ibrahim Abu Farha'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.37308747e-01 4.22546148e-01 1.24201020e-02 -6.25477433e-01
-6.85382962e-01 -6.36164248e-01 5.25637150e-01 2.20966429e-01
-5.41315138e-01 4.02019262e-01 7.61314213e-01 -5.98776676e-02
8.59121501e-01 -3.14345151e-01 -2.17185929e-01 -6.69138551e-01
5.88517368e-01 3.17958266e-01 -4.24379855e-02 -1.07597065... | [9.142120361328125, 10.541312217712402] |
506a44b9-77ee-48a9-ac5c-d30e539c778c | multiscale-autoencoder-with-structural | 2208.04945 | null | https://arxiv.org/abs/2208.04945v1 | https://arxiv.org/pdf/2208.04945v1.pdf | Multiscale Autoencoder with Structural-Functional Attention Network for Alzheimer's Disease Prediction | The application of machine learning algorithms to the diagnosis and analysis of Alzheimer's disease (AD) from multimodal neuroimaging data is a current research hotspot. It remains a formidable challenge to learn brain region information and discover disease mechanisms from various magnetic resonance images (MRI). In t... | ['Qiankun Zuo', 'Changhong Jing', 'Yongcheng Zong'] | 2022-08-09 | null | null | null | null | ['disease-prediction'] | ['medical'] | [-7.21353069e-02 1.99741811e-01 1.85456336e-01 -4.43352073e-01
-4.60262448e-01 1.92483477e-02 4.60955799e-01 7.11944029e-02
-5.03921211e-01 6.61245942e-01 5.29233277e-01 3.80602665e-02
-3.18644732e-01 -5.68160892e-01 -2.67073601e-01 -6.69894338e-01
-6.41235113e-01 3.00732255e-01 3.74062010e-03 1.65604651... | [14.182737350463867, -1.7141987085342407] |
2963886d-0e0b-44ed-84d3-0930062e2c97 | two-heads-are-better-than-one-enhancing | 2201.10113 | null | https://arxiv.org/abs/2201.10113v7 | https://arxiv.org/pdf/2201.10113v7.pdf | Multimodal data matters: language model pre-training over structured and unstructured electronic health records | As two important textual modalities in electronic health records (EHR), both structured data (clinical codes) and unstructured data (clinical narratives) have recently been increasingly applied to the healthcare domain. Most existing EHR-oriented studies, however, either focus on a particular modality or integrate data... | ['Buzhou Tang', 'Yang Xiang', 'Hui Xu', 'Hui Wang', 'Ge Li', 'Yongshuai Hou', 'Xiaolong Wang', 'Sicen Liu'] | 2022-01-25 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [ 0.3899944 0.20975412 -0.39946374 -0.53926325 -0.9633922 -0.24009174
0.46034044 0.70193124 -0.11143761 0.5588394 0.89730275 -0.5262905
-0.33218718 -0.70285743 -0.47631425 -0.4222189 -0.05638197 0.599654
-0.516466 0.01564781 -0.21164551 -0.08389643 -0.9469486 1.0792164
0.9174899 1.0601672 0.0552... | [7.8908257484436035, 6.397800445556641] |
d6a7d0bb-96fb-47a6-bdcb-a5082afdfa0a | differentially-private-submodular | null | null | https://icml.cc/Conferences/2017/Schedule?showEvent=637 | http://proceedings.mlr.press/v70/mitrovic17a/mitrovic17a.pdf | Differentially Private Submodular Maximization: Data Summarization in Disguise |
Many data summarization applications are captured by the general framework of submodular maximization. As a consequence, a wide range of efficient approximation algorithms have been developed. However, when such applications involve sensitive data about individuals, their privacy concerns are not automatically add... | ['Andreas Krause', 'Amin Karbasi', 'Marko Mitrovic', 'Mark Bun'] | 2017-08-01 | null | null | null | icml-2017-8 | ['data-summarization'] | ['miscellaneous'] | [ 3.89050186e-01 4.06738698e-01 -4.11302179e-01 -4.68394011e-01
-7.22989559e-01 -9.36690629e-01 -3.91953513e-02 4.81523067e-01
-6.83762878e-02 9.92627561e-01 1.56194478e-01 2.52719969e-01
-4.56141531e-01 -1.05185556e+00 -7.08733022e-01 -7.80937433e-01
-7.79312178e-02 5.53427815e-01 -1.27783835e-01 -1.22077428... | [6.554113864898682, 4.970730304718018] |
63b66670-7b4d-416f-a554-77ef772a5a99 | development-and-validation-of-a-natural | 2303.13451 | null | https://arxiv.org/abs/2303.13451v1 | https://arxiv.org/pdf/2303.13451v1.pdf | Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse | The objective of this study is to address the critical issue of de-identification of clinical reports in order to allow access to data for research purposes, while ensuring patient privacy. The study highlights the difficulties faced in sharing tools and resources in this domain and presents the experience of the Great... | ['Romain Bey', 'Martin Hilka', 'Alexandre Mouchet', 'Basile Dura', 'Alice Calliger', 'Perceval Wajsbürt', 'Xavier Tannier'] | 2023-03-23 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [-4.50549535e-02 3.90617073e-01 -7.34185576e-02 -4.76185292e-01
-6.41068161e-01 -5.13805687e-01 2.58228213e-01 1.06608522e+00
-7.83772826e-01 8.82616758e-01 2.29094774e-01 -7.30665147e-01
-4.56763655e-01 -6.77798986e-01 -3.35994363e-01 -3.50910217e-01
4.24770117e-02 8.63825083e-01 -3.24275225e-01 2.47395471... | [8.422920227050781, 8.62608528137207] |
b3ea809d-d657-401a-953b-5d8ab76dc4d9 | vibration-fault-detection-in-wind-turbines | 2206.12452 | null | https://arxiv.org/abs/2206.12452v1 | https://arxiv.org/pdf/2206.12452v1.pdf | Vibration fault detection in wind turbines based on normal behaviour models without feature engineering | Most wind turbines are remotely monitored 24/7 to allow for an early detection of operation problems and developing damage. We present a new fault detection method for vibration-monitored drivetrains that does not require any feature engineering. Our method relies on a simple model architecture to enable a straightforw... | ['Angela Meyer', 'Bernhard Brodbeck', 'Dimitrios Anagnostos', 'Stefan Jonas'] | 2022-06-24 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.58874858e-02 -1.51232988e-01 5.35148978e-01 7.10461661e-02
-2.70624906e-01 -4.48851138e-01 1.22221395e-01 6.14097863e-02
6.64223731e-02 2.92661190e-01 -3.33799630e-01 -1.40802125e-02
-4.57072943e-01 -7.73491383e-01 -4.78615880e-01 -8.27542126e-01
-5.27558565e-01 5.53884581e-02 1.20059043e-01 -3.29382658... | [6.731602191925049, 2.359441041946411] |
0d0838ac-d870-4856-938d-0b1a9423f79d | exploring-difference-in-public-perceptions-on | 1907.03167 | null | https://arxiv.org/abs/1907.03167v1 | https://arxiv.org/pdf/1907.03167v1.pdf | Exploring difference in public perceptions on HPV vaccine between gender groups from Twitter using deep learning | In this study, we proposed a convolutional neural network model for gender prediction using English Twitter text as input. Ensemble of proposed model achieved an accuracy at 0.8237 on gender prediction and compared favorably with the state-of-the-art performance in a recent author profiling task. We further leveraged t... | ['Qiang Wei', 'Cui Tao', 'Jingcheng Du', 'Yong Chen', 'Chongliang Luo'] | 2019-07-06 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-2.76511461e-01 6.50575876e-01 -6.07882738e-01 -7.95397043e-01
-2.24316061e-01 -4.83491510e-01 8.96615446e-01 8.98712337e-01
-7.61522651e-01 8.56234312e-01 4.10732716e-01 -7.24548936e-01
2.46711731e-01 -1.06953859e+00 -6.22864246e-01 -3.77425075e-01
5.74926473e-03 6.82959378e-01 -6.47836626e-01 -1.65824592... | [9.449271202087402, 10.317344665527344] |
b64618c5-13a4-4f49-b4e5-156329b2ed0c | an-fea-surrogate-model-with-boundary-oriented | 2108.13509 | null | https://arxiv.org/abs/2108.13509v1 | https://arxiv.org/pdf/2108.13509v1.pdf | An FEA surrogate model with Boundary Oriented Graph Embedding approach | In this work, we present a Boundary Oriented Graph Embedding (BOGE) approach for the Graph Neural Network (GNN) to serve as a general surrogate model for regressing physical fields and solving boundary value problems. Providing shortcuts for both boundary elements and local neighbor elements, the BOGE approach can embe... | ['Vaneet Aggarwal', 'Martin Byung-Guk Jun', 'Zhengyang Kang', 'Dheeraj Peddireddy', 'Fengfeng Zhou', 'Xingyu Fu'] | 2021-08-30 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [-2.78189480e-01 4.34221298e-01 7.01195225e-02 -5.74854650e-02
-4.11598861e-01 1.39267206e-01 2.57478338e-02 -1.59134567e-01
1.06331319e-01 9.24567819e-01 -3.53863537e-01 -4.02634919e-01
-3.10999781e-01 -1.27519965e+00 -9.27290499e-01 -8.16408455e-01
-2.90224910e-01 5.53757906e-01 1.56183228e-01 -6.09933257... | [6.29596471786499, 3.403398275375366] |
bbcd195f-a2de-47ff-9688-98f4bea55f04 | dually-enhanced-propensity-score-estimation | 2303.08722 | null | https://arxiv.org/abs/2303.08722v1 | https://arxiv.org/pdf/2303.08722v1.pdf | Dually Enhanced Propensity Score Estimation in Sequential Recommendation | Sequential recommender systems train their models based on a large amount of implicit user feedback data and may be subject to biases when users are systematically under/over-exposed to certain items. Unbiased learning based on inverse propensity scores (IPS), which estimate the probability of observing a user-item pai... | ['Ji-Rong Wen', 'Zhenghua Dong', 'Xu Chen', 'Jun Xu', 'Chen Xu'] | 2023-03-15 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [-4.46699895e-02 -1.89606175e-01 -5.97523212e-01 -5.22662818e-01
-4.29852307e-01 -5.19805074e-01 4.45503503e-01 7.37988800e-02
-1.36858061e-01 5.12322843e-01 5.32507777e-01 -6.42389134e-02
-3.21588278e-01 -8.52512777e-01 -6.50578558e-01 -6.58899844e-01
-2.05202863e-01 3.46386731e-01 -1.90125648e-02 -2.30737235... | [9.95627498626709, 5.562069416046143] |
f3aec1f8-8e19-497a-ae52-73d620e62e09 | a2-link-recognizing-disguised-faces-via | null | null | https://ieeexplore.ieee.org/document/9104705 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9104705&tag=1 | A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge | Face recognition in the unconstrained environment is an ongoing research challenge. Although several covariates of face recognition such as pose and low resolution have received significant attention“, disguise” is considered an onerous covariate of face recognition. One of the primary reasons for this is the scarcity ... | ['Mayank Vatsa', 'Richa Singh', 'Anshuman Suri'] | 2020-06-01 | null | null | null | ieee-transactions-on-biometrics-behavior-and | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 1.84856236e-01 2.29284674e-01 -1.58462316e-01 -8.16306770e-01
-5.65245926e-01 -2.12227434e-01 7.08223343e-01 -5.90146780e-01
-3.90122265e-01 5.69165826e-01 7.55858496e-02 1.82296664e-01
-4.40126568e-01 -5.78980982e-01 -9.01590168e-01 -9.55667615e-01
-4.57228832e-02 4.03128713e-01 -3.43998283e-01 1.03079244... | [13.239324569702148, 0.722039520740509] |
307e2273-3565-47a9-ab36-7dadf36c97f3 | latent-sdes-on-homogeneous-spaces | 2306.16248 | null | https://arxiv.org/abs/2306.16248v1 | https://arxiv.org/pdf/2306.16248v1.pdf | Latent SDEs on Homogeneous Spaces | We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent stochastic differential equation (SDE). Motivated by the challenges that arise when trying to learn an (almost arbitrary) latent neural SDE ... | ['Roland Kwitt', 'Florian Graf', 'Sebastian Zeng'] | 2023-06-28 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.45485738e-01 2.18741789e-01 1.19613044e-01 -2.71766156e-01
-9.17630076e-01 -4.93196189e-01 8.26738358e-01 -1.84011415e-01
-5.49432755e-01 8.58319759e-01 6.56863069e-03 -1.25062883e-01
-2.71051168e-01 -5.93753874e-01 -1.08429027e+00 -1.28524983e+00
-1.35584876e-01 6.56968117e-01 -9.00828317e-02 1.13781668... | [6.933762550354004, 3.840263605117798] |
8696dd92-f5fe-4b94-9666-11b0159853f2 | breadth-first-reasoning-graph-for-multi-hop | null | null | https://aclanthology.org/2021.naacl-main.464 | https://aclanthology.org/2021.naacl-main.464.pdf | Breadth First Reasoning Graph for Multi-hop Question Answering | Recently Graph Neural Network (GNN) has been used as a promising tool in multi-hop question answering task. However, the unnecessary updations and simple edge constructions prevent an accurate answer span extraction in a more direct and interpretable way. In this paper, we propose a novel model of Breadth First Reasoni... | ['Meng Yang', 'Yongjie Huang'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['multi-hop-question-answering'] | ['knowledge-base'] | [-6.88839555e-02 6.36096418e-01 2.84501426e-02 -5.43137789e-01
-4.75808650e-01 -4.05890971e-01 2.80240268e-01 9.56362784e-01
-3.56686205e-01 7.16046691e-01 4.82191592e-01 -4.80487436e-01
-6.46046400e-01 -1.39104939e+00 -4.88607883e-01 -7.19935969e-02
-5.96381538e-02 6.94184601e-01 7.99771786e-01 -6.82708144... | [10.827146530151367, 7.940967559814453] |
dfa08439-6388-4123-a1b7-cd0986302e41 | simnet-enabling-robust-unknown-object | 2106.16118 | null | https://arxiv.org/abs/2106.16118v1 | https://arxiv.org/pdf/2106.16118v1.pdf | SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo | Robot manipulation of unknown objects in unstructured environments is a challenging problem due to the variety of shapes, materials, arrangements and lighting conditions. Even with large-scale real-world data collection, robust perception and manipulation of transparent and reflective objects across various lighting co... | ['Mark Tjersland', 'Brijen Thananjeyan', 'Kevin Stone', 'Michael Laskey', 'Thomas Kollar'] | 2021-06-30 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 1.41883284e-01 1.13903411e-01 4.04375970e-01 -2.58847624e-01
-3.99893433e-01 -9.23510551e-01 -2.69604009e-02 -2.62184471e-01
-4.99397576e-01 2.95325309e-01 -4.14326489e-01 -1.55122414e-01
8.83507356e-02 -5.39413452e-01 -1.26051199e+00 -5.11798561e-01
-3.51928115e-01 9.90307033e-01 6.64997280e-01 -4.75223988... | [5.800038814544678, -0.8726003766059875] |
06bcd644-c61f-4b90-8a4d-70e2c845ed5c | yolosa-object-detection-based-on-2d-local | 2206.11825 | null | https://arxiv.org/abs/2206.11825v2 | https://arxiv.org/pdf/2206.11825v2.pdf | YOLOSA: Object detection based on 2D local feature superimposed self-attention | We analyzed the network structure of real-time object detection models and found that the features in the feature concatenation stage are very rich. Applying an attention module here can effectively improve the detection accuracy of the model. However, the commonly used attention module or self-attention module shows p... | ['Lin Huang', 'Weisheng Li'] | 2022-06-23 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-2.13693634e-01 -2.24227697e-01 -1.02775199e-02 -3.07975531e-01
-3.87219995e-01 -3.56798857e-01 4.19030458e-01 -1.42552629e-01
-6.31894052e-01 4.02155757e-01 -2.98406690e-01 -1.58611193e-01
2.59648591e-01 -7.92343199e-01 -7.39052713e-01 -4.76271123e-01
-1.08579911e-01 -6.92207143e-02 8.86316836e-01 8.51413608... | [8.702938079833984, -0.2866772413253784] |
d38a8b36-ebc7-4494-a051-09d53d3e27d9 | discovering-individual-rewards-in-collective | 2305.10548 | null | https://arxiv.org/abs/2305.10548v1 | https://arxiv.org/pdf/2305.10548v1.pdf | Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning | The discovery of individual objectives in collective behavior of complex dynamical systems such as fish schools and bacteria colonies is a long-standing challenge. Inverse reinforcement learning is a potent approach for addressing this challenge but its applicability to dynamical systems, involving continuous state-act... | ['Petros Koumoutsakos', 'Pascal Weber', 'Daniel Waelchli'] | 2023-05-17 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-4.26392704e-02 5.67835895e-03 -5.86980134e-02 4.80396152e-01
-4.10751998e-01 -5.89339256e-01 7.62717426e-01 2.69985616e-01
-5.67004263e-01 1.16054821e+00 -1.88845411e-01 -2.08815008e-01
-7.79027283e-01 -5.09353578e-01 -7.28952646e-01 -1.27588165e+00
-7.31726110e-01 5.83445549e-01 2.42827594e-01 -7.46841133... | [3.8929121494293213, 2.0518603324890137] |
fb033998-16dd-4ab7-82f5-b1c1ab62a491 | driftrec-adapting-diffusion-models-to-blind | 2211.06757 | null | https://arxiv.org/abs/2211.06757v2 | https://arxiv.org/pdf/2211.06757v2.pdf | DriftRec: Adapting diffusion models to blind JPEG restoration | In this work, we utilize the high-fidelity generation abilities of diffusion models to solve blind JPEG restoration at high compression levels. We propose an elegant modification of the forward stochastic differential equation of diffusion models to adapt them to this restoration task and name our method DriftRec. Comp... | ['Timo Gerkmann', 'Henry N. Chapman', 'Simon Welker'] | 2022-11-12 | null | null | null | null | ['jpeg-artifact-removal'] | ['computer-vision'] | [ 2.79183686e-01 -9.00821760e-02 1.87721983e-01 -1.20047882e-01
-8.11251163e-01 -5.34057319e-01 7.62675345e-01 -4.55363989e-01
-3.75207186e-01 7.35443115e-01 5.73239267e-01 -3.42283875e-01
-1.89253241e-01 -5.64703107e-01 -8.22510123e-01 -9.98339891e-01
-9.99683663e-02 2.62718171e-01 1.14479205e-02 -2.91270435... | [11.67971134185791, -2.368962287902832] |
1a9b4ff1-085e-41b6-841e-c2d7a4439904 | tensor-composition-net-for-visual | 2012.05473 | null | https://arxiv.org/abs/2012.05473v2 | https://arxiv.org/pdf/2012.05473v2.pdf | Tensor Composition Net for Visual Relationship Prediction | We present a novel Tensor Composition Net (TCN) to predict visual relationships in images. Visual Relationship Prediction (VRP) provides a more challenging test of image understanding than conventional image tagging and is difficult to learn due to a large label-space and incomplete annotation. The key idea of our TCN ... | ['Yanwen Guo', 'Xueting Zhang', 'Timothy M. Hospedales', 'Yongxin Yang', 'Yuting Qiang'] | 2020-12-10 | null | null | null | null | ['multi-label-image-classification', 'extreme-multi-label-classification'] | ['computer-vision', 'methodology'] | [ 2.24464417e-01 4.84764725e-02 -5.82631826e-01 -3.33582968e-01
-2.55639970e-01 -7.25006223e-01 5.97253919e-01 3.49664003e-01
5.85328899e-02 -1.08940993e-02 1.58274338e-01 -3.59018117e-01
-3.68564785e-01 -4.02975738e-01 -6.23864651e-01 -3.22080880e-01
-1.30294710e-01 5.47687709e-01 2.00659186e-01 -8.78933668... | [10.266424179077148, 1.6572456359863281] |
9e0982bf-2cd7-4d54-8654-9c1671422932 | enhancing-safe-exploration-using-safety-state | 2206.02675 | null | https://arxiv.org/abs/2206.02675v2 | https://arxiv.org/pdf/2206.02675v2.pdf | Effects of Safety State Augmentation on Safe Exploration | Safe exploration is a challenging and important problem in model-free reinforcement learning (RL). Often the safety cost is sparse and unknown, which unavoidably leads to constraint violations -- a phenomenon ideally to be avoided in safety-critical applications. We tackle this problem by augmenting the state-space wit... | ['Haitham Bou Ammar', 'Jun Wang', 'Alexander I. Cowen-Rivers', 'Aivar Sootla'] | 2022-06-06 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.40427092e-01 3.65868151e-01 -8.46633554e-01 3.18657956e-03
-7.12762654e-01 -6.95620418e-01 3.10729086e-01 3.15389901e-01
-7.85887539e-01 1.20672464e+00 3.43786250e-03 -5.65949202e-01
-3.02976012e-01 -5.20916879e-01 -9.18766856e-01 -9.53587413e-01
-4.78203714e-01 3.01384300e-01 1.84828550e-01 -3.29556048... | [4.510612487792969, 2.1386630535125732] |
1a28d488-f4f2-435a-beef-618fe115335c | a-novel-speech-driven-lip-sync-model-with-cnn | 2205.00916 | null | https://arxiv.org/abs/2205.00916v1 | https://arxiv.org/pdf/2205.00916v1.pdf | A Novel Speech-Driven Lip-Sync Model with CNN and LSTM | Generating synchronized and natural lip movement with speech is one of the most important tasks in creating realistic virtual characters. In this paper, we present a combined deep neural network of one-dimensional convolutions and LSTM to generate vertex displacement of a 3D template face model from variable-length spe... | ['Shiguo Lian', 'Kai Wang', 'Xiang Wang', 'Xiaohong Li'] | 2022-05-02 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 3.59006077e-02 3.10726404e-01 2.12377116e-01 -2.28959873e-01
-3.35912734e-01 -2.81397969e-01 5.45878768e-01 -9.77290630e-01
-1.88876063e-01 5.05780160e-01 2.27644831e-01 -4.93507320e-03
4.86172885e-01 -4.50888604e-01 -9.62261200e-01 -6.19289875e-01
6.10354766e-02 -1.08394206e-01 3.56430933e-02 -1.28694326... | [13.245064735412598, -0.45447584986686707] |
e22955d9-7840-4a38-a8f7-e7895b163899 | ai-driven-shadow-model-detection-in-agropv | 2304.07853 | null | https://arxiv.org/abs/2304.07853v1 | https://arxiv.org/pdf/2304.07853v1.pdf | AI driven shadow model detection in agropv farms | Agro-photovoltaic (APV) is a growing farming practice that combines agriculture and solar photovoltaic projects within the same area. This emerging market is expected to experience significant growth in the next few years, with a projected investment of $9 billion in 2030. Identifying shadows is crucial to understandin... | ['Dr. Susan Elias', 'Pascal Brunet', 'Sai Paavan Kumar Dornadula'] | 2023-04-16 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 3.16636980e-01 5.76675422e-02 9.17335004e-02 8.48848745e-02
4.10740048e-01 -7.65621603e-01 3.60136293e-02 1.11629158e-01
4.08825308e-01 8.31858099e-01 -9.19723064e-02 -8.49797368e-01
2.17195541e-01 -1.42338979e+00 -4.04485554e-01 -9.63816464e-01
-1.58534154e-01 -2.00460672e-01 2.63344914e-01 -3.59150261... | [9.282966613769531, -1.5608569383621216] |
7f1c9054-be1d-4b03-84e7-b10a0c816131 | incremental-graph-based-neural-dependency | null | null | https://aclanthology.org/D17-1173 | https://aclanthology.org/D17-1173.pdf | Incremental Graph-based Neural Dependency Parsing | Very recently, some studies on neural dependency parsers have shown advantage over the traditional ones on a wide variety of languages. However, for graph-based neural dependency parsing systems, they either count on the long-term memory and attention mechanism to implicitly capture the high-order features or give up t... | ['Xiaoqing Zheng'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-2.90261954e-03 4.42574501e-01 -1.67287886e-01 -7.92774141e-01
-4.86684233e-01 -4.79356676e-01 2.13464409e-01 3.34593832e-01
-6.49839163e-01 7.19491482e-01 3.50232780e-01 -6.81317747e-01
-5.85145056e-02 -1.03988993e+00 -7.15100527e-01 -5.31391382e-01
-4.71663356e-01 4.92540032e-01 3.34372640e-01 -9.30859819... | [10.415884971618652, 9.549471855163574] |
5b72e45a-1f1a-4e08-9908-3e32c73b562c | deep-big-simple-neural-nets-excel-on | 1003.0358 | null | https://arxiv.org/abs/1003.0358v1 | https://arxiv.org/pdf/1003.0358v1.pdf | Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition | Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images, and graphics cards to greatly speed u... | ['Juergen Schmidhuber', 'Luca Maria Gambardella', 'Ueli Meier', 'Dan Claudiu Ciresan'] | 2010-03-01 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-2.94397593e-01 -3.59118829e-04 -4.65230979e-02 -5.55891216e-01
-8.54987949e-02 -3.02322954e-02 3.39554012e-01 -1.90016806e-01
-9.65134740e-01 9.14109111e-01 -3.41720521e-01 -6.02190733e-01
4.53197956e-01 -6.74983859e-01 -8.12831402e-01 -6.38642192e-01
-4.27266508e-02 2.78071493e-01 7.21132040e-01 -3.29093248... | [8.864402770996094, 2.779172897338867] |
bee4c2b7-5502-49d9-831b-2805fff81525 | deep-learning-for-lung-cancer-detection | 1705.09435 | null | http://arxiv.org/abs/1705.09435v1 | http://arxiv.org/pdf/1705.09435v1.pdf | Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge | We present a deep learning framework for computer-aided lung cancer
diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans,
determines if each nodule is malignant, and finally assigns a cancer
probability based on these results. We discuss the challenges and advantages of
our framework. In the Kaggle... | ['Tse Chiang Howe', 'Mathieu Ravaut', 'Jie Lin', 'Cen Chen', 'Zeng Zeng', 'Vijay Chandrasekhar', 'Kingsley Kuan', 'Huiling Chen', 'Gaurav Manek', 'Babar Nazir'] | 2017-05-26 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-1.79178596e-01 1.72206283e-01 -6.13009334e-01 -2.08986878e-01
-1.32121968e+00 -3.68495047e-01 1.68325976e-01 1.20259024e-01
-3.29572797e-01 2.28816301e-01 4.90403086e-01 -7.04559088e-01
-3.71698029e-02 -7.40370393e-01 -8.07592750e-01 -6.06287003e-01
-2.31960826e-02 1.14193690e+00 5.56214213e-01 3.69051844... | [15.392788887023926, -2.161226511001587] |
cce6e77e-1cbc-4150-8ec8-f8ec06f10520 | skew-class-balanced-re-weighting-for-unbiased | 2301.00351 | null | https://arxiv.org/abs/2301.00351v3 | https://arxiv.org/pdf/2301.00351v3.pdf | Skew Class-balanced Re-weighting for Unbiased Scene Graph Generation | An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions,... | ['Chang D. Yoo', 'Haeyong Kang'] | 2023-01-01 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 4.75591868e-01 3.97886246e-01 -4.29227084e-01 -3.06213200e-01
-5.28179348e-01 -1.24882020e-01 5.74922323e-01 1.32039621e-01
2.67203245e-02 8.16673577e-01 3.28795493e-01 -2.22595245e-01
-1.68858096e-01 -8.49396050e-01 -6.72295570e-01 -9.35474873e-01
2.41058379e-01 3.10442895e-01 7.18109846e-01 1.98941529... | [10.262526512145996, 1.7900077104568481] |
cf848175-aa5e-4607-b2e5-611173997175 | flot-scene-flow-on-point-clouds-guided-by | 2007.11142 | null | https://arxiv.org/abs/2007.11142v1 | https://arxiv.org/pdf/2007.11142v1.pdf | FLOT: Scene Flow on Point Clouds Guided by Optimal Transport | We propose and study a method called FLOT that estimates scene flow on point clouds. We start the design of FLOT by noticing that scene flow estimation on point clouds reduces to estimating a permutation matrix in a perfect world. Inspired by recent works on graph matching, we build a method to find these correspondenc... | ['Renaud Marlet', 'Gilles Puy', 'Alexandre Boulch'] | 2020-07-22 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6287_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730528.pdf | eccv-2020-8 | ['scene-flow-estimation'] | ['computer-vision'] | [-1.51206404e-01 -1.11768968e-01 8.12009946e-02 -1.20995320e-01
-2.48963520e-01 -6.06842756e-01 6.93718314e-01 1.80814072e-01
-3.74050200e-01 4.40038264e-01 -1.60225764e-01 -1.47578269e-01
-3.24352682e-01 -9.97661591e-01 -9.37879980e-01 -3.07014793e-01
-3.06384802e-01 6.27830863e-01 4.15211439e-01 -3.18690807... | [8.5244722366333, -2.057372808456421] |
24002645-75e5-49af-9bbf-88551dbd10de | double-refinement-network-for-efficient | 1811.08466 | null | http://arxiv.org/abs/1811.08466v2 | http://arxiv.org/pdf/1811.08466v2.pdf | Double Refinement Network for Efficient Indoor Monocular Depth Estimation | Monocular depth estimation is the task of obtaining a measure of distance for
each pixel using a single image. It is an important problem in computer vision
and is usually solved using neural networks. Though recent works in this area
have shown significant improvement in accuracy, the state-of-the-art methods
tend to ... | ['Mikhail Romanov', 'Nikita Durasov', 'Valeriya Bubnova', 'Pavel Bogomolov', 'Anton Konushin'] | 2018-11-20 | null | null | null | null | ['indoor-monocular-depth-estimation'] | ['computer-vision'] | [ 4.33631748e-01 -6.96092993e-02 1.19345911e-01 -4.36819822e-01
-4.36215848e-01 5.11357281e-03 3.51512372e-01 1.61816567e-01
-1.07914627e+00 7.06456840e-01 -3.59539688e-01 -1.72529683e-01
3.25696439e-01 -9.46015716e-01 -6.67627573e-01 -7.25289166e-01
2.20294923e-01 3.39949816e-01 6.87903225e-01 3.49505357... | [8.863543510437012, -2.2871763706207275] |
51ce2bf7-4535-49de-a581-5ad5dd29b811 | active-learning-in-brain-tumor-segmentation | 2302.10185 | null | https://arxiv.org/abs/2302.10185v1 | https://arxiv.org/pdf/2302.10185v1.pdf | Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization | Deep learning models have demonstrated great potential in medical 3D imaging, but their development is limited by the expensive, large volume of annotated data required. Active learning (AL) addresses this by training a model on a subset of the most informative data samples without compromising performance. We compared... | ['Zhicheng Jiao', 'Harrison X Bai', 'Ali Nabavizadeh', 'Anahita Fathi Kazerooni', 'Li Yang', 'Beiji Zou', 'Chengzhang Zhu', 'Wei-Hua Liao', 'Haris Sair', 'Craig Jones', 'Chetan Bettegowda', 'Michael Atalay', 'Xue Feng', 'Jing Wu', 'Jian Peng', 'Rajat S Chandra', 'Daniel D Kim'] | 2023-02-05 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [-7.80839182e-04 6.37090802e-01 -3.93007487e-01 -6.86262250e-01
-1.55598938e+00 -2.35276580e-01 2.80967206e-01 4.98088986e-01
-1.13361096e+00 1.06456542e+00 2.52991647e-01 -2.66130120e-01
-5.34245431e-01 -4.04601455e-01 -6.97008252e-01 -9.73261714e-01
-3.17020148e-01 8.64368141e-01 4.64662910e-01 6.36990607... | [14.260316848754883, -2.362783193588257] |
698d4041-0154-48e7-b037-973eb3e431a6 | light-weight-head-pose-invariant-gaze | 1804.08572 | null | http://arxiv.org/abs/1804.08572v1 | http://arxiv.org/pdf/1804.08572v1.pdf | Light-weight Head Pose Invariant Gaze Tracking | Unconstrained remote gaze tracking using off-the-shelf cameras is a
challenging problem. Recently, promising algorithms for appearance-based gaze
estimation using convolutional neural networks (CNN) have been proposed.
Improving their robustness to various confounding factors including variable
head pose, subject ident... | ['Rajeev Ranjan', 'Jan Kautz', 'Shalini De Mello'] | 2018-04-23 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 5.25332056e-02 1.28603941e-02 2.99795736e-02 -6.04731262e-01
-4.06989902e-01 -2.28640497e-01 1.27762482e-01 -7.05062509e-01
-5.94076693e-01 5.55470943e-01 -9.27909389e-02 -1.94842011e-01
3.95874470e-01 1.00979351e-01 -9.85712707e-01 -6.54798150e-01
2.42111072e-01 -5.75376078e-02 4.20326591e-02 5.14521115... | [14.14551067352295, 0.07579293102025986] |
331d179d-083d-4aaa-acbd-40c73701d598 | biogan-an-unpaired-gan-based-image-to-image | 2306.06217 | null | https://arxiv.org/abs/2306.06217v1 | https://arxiv.org/pdf/2306.06217v1.pdf | BioGAN: An unpaired GAN-based image to image translation model for microbiological images | A diversified dataset is crucial for training a well-generalized supervised computer vision algorithm. However, in the field of microbiology, generation and annotation of a diverse dataset including field-taken images are time consuming, costly, and in some cases impossible. Image to image translation frameworks allow ... | ['Anastasios D. Tsaousis', 'Sadiya Maxamhud', 'Md. Moinul Hossain', 'Gianluca Marcelli', 'Chee Siang Ang', 'Saber Mirzaee Bafti'] | 2023-06-09 | null | null | null | null | ['image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'miscellaneous'] | [ 9.98656690e-01 -1.86018571e-01 5.85985541e-01 1.11660575e-02
-5.56144714e-01 -7.91165113e-01 5.00881672e-01 -1.69237554e-01
-3.32716405e-01 9.29251909e-01 -6.48313522e-01 -2.49067798e-01
1.40981629e-01 -1.13278353e+00 -1.17951000e+00 -1.14652944e+00
2.96055436e-01 2.90636003e-01 -1.36251107e-01 -1.81969497... | [11.78054141998291, -0.5019364953041077] |
d6d46b19-b53c-421b-8a95-c1352486f73c | multi-agent-continual-coordination-via | 2305.13937 | null | https://arxiv.org/abs/2305.13937v1 | https://arxiv.org/pdf/2305.13937v1.pdf | Multi-agent Continual Coordination via Progressive Task Contextualization | Cooperative Multi-agent Reinforcement Learning (MARL) has attracted significant attention and played the potential for many real-world applications. Previous arts mainly focus on facilitating the coordination ability from different aspects (e.g., non-stationarity, credit assignment) in single-task or multi-task scenari... | ['Yang Yu', 'Cong Guan', 'Fuxiang Zhang', 'Ziqian Zhang', 'Lihe Li', 'Lei Yuan'] | 2023-05-07 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [ 2.82818154e-02 1.15466006e-02 -3.06647927e-01 -6.77985977e-03
-7.12991774e-01 -3.21878612e-01 7.38681078e-01 3.79614532e-01
-9.17467713e-01 1.19884205e+00 -9.29226428e-02 1.19926736e-01
-7.62807846e-01 -3.77119720e-01 -6.07379913e-01 -1.14940131e+00
-3.85591835e-01 8.85621011e-01 2.01748371e-01 -2.91960537... | [3.7770791053771973, 1.9724757671356201] |
44a7d03d-b7c5-4e9c-830d-2a05af40a7ef | simultaneous-traffic-sign-detection-and | 1802.10019 | null | http://arxiv.org/abs/1802.10019v1 | http://arxiv.org/pdf/1802.10019v1.pdf | Simultaneous Traffic Sign Detection and Boundary Estimation using Convolutional Neural Network | We propose a novel traffic sign detection system that simultaneously
estimates the location and precise boundary of traffic signs using
convolutional neural network (CNN). Estimating the precise boundary of traffic
signs is important in navigation systems for intelligent vehicles where traffic
signs can be used as 3D l... | ['Hee Seok Lee', 'Kang Kim'] | 2018-02-27 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 2.97891825e-01 -4.13204804e-02 -4.09310907e-01 -5.07312536e-01
-5.48014164e-01 -3.92433017e-01 3.34393263e-01 -6.93685174e-01
-5.19144118e-01 2.59392887e-01 -4.07792777e-01 -7.62112081e-01
2.22313479e-01 -5.36596775e-01 -7.07541466e-01 -4.18426514e-01
3.39962631e-01 6.87694907e-01 7.82012284e-01 3.27011682... | [7.984344959259033, -0.8149138689041138] |
dfd524cd-e718-4d7f-8574-3c778f1136e5 | robust-person-re-identification-through | 2009.07491 | null | https://arxiv.org/abs/2009.07491v1 | https://arxiv.org/pdf/2009.07491v1.pdf | Robust Person Re-Identification through Contextual Mutual Boosting | Person Re-Identification (Re-ID) has witnessed great advance, driven by the development of deep learning. However, modern person Re-ID is still challenged by background clutter, occlusion and large posture variation which are common in practice. Previous methods tackle these challenges by localizing pedestrians through... | ['Jane Shen', 'Zhikang Wang', 'Xinbo Gao', 'Lihuo He'] | 2020-09-16 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [-5.36579303e-02 -2.63958335e-01 2.64870644e-01 -3.25536907e-01
-5.35890460e-01 -3.48163992e-01 6.95044279e-01 4.63777669e-02
-6.08338296e-01 6.22980177e-01 3.40854317e-01 1.57559395e-01
1.34969845e-01 -6.79071665e-01 -7.47404933e-01 -8.94584715e-01
2.57618815e-01 1.34916613e-02 2.82924831e-01 -8.31571035... | [14.654603958129883, 0.8837879300117493] |
72bb1e72-ed6e-4231-add5-930880ff9312 | directed-acyclic-graph-structure-learning | 2211.17029 | null | https://arxiv.org/abs/2211.17029v1 | https://arxiv.org/pdf/2211.17029v1.pdf | Directed Acyclic Graph Structure Learning from Dynamic Graphs | Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights in various applications. Although there have been many studies on structure learning with various types of data, the structure learning on t... | ['Chuan Shi', 'Xiao Wang', 'Shuyang Zhang', 'Shaohua Fan'] | 2022-11-30 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 1.17864758e-01 1.46006659e-01 -4.76381570e-01 -4.13641453e-01
-2.52956420e-01 -5.37867367e-01 6.57450855e-01 2.03802615e-01
1.58106789e-01 8.05388510e-01 2.18910307e-01 -3.95491153e-01
-6.71777844e-01 -8.91325474e-01 -7.87455916e-01 -8.10524940e-01
-8.72738123e-01 3.29101145e-01 4.20128703e-02 2.69516017... | [7.382645606994629, 5.827086448669434] |
9861dbb2-a720-4b28-9b14-496b6baba783 | fast-robust-tensor-principal-component | 2108.10448 | null | https://arxiv.org/abs/2108.10448v1 | https://arxiv.org/pdf/2108.10448v1.pdf | Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition | We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their sum. In this work, we propose a fast non-convex algorithm, coined Robust Tensor CUR (RTCUR), for large-scale TRPCA problems. RTCUR considers... | ['Deanna Needell', 'Longxiu Huang', 'Zehan Chao', 'HanQin Cai'] | 2021-08-23 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 7.25931898e-02 -7.92340934e-01 1.36217102e-01 1.45307913e-01
-1.00641739e+00 -4.07102793e-01 2.51257300e-01 -6.62620962e-01
-7.32980222e-02 2.14167744e-01 2.64507502e-01 -2.52718121e-01
-4.82089609e-01 1.16837852e-01 -5.76660335e-01 -1.04109740e+00
-4.13166523e-01 1.57963887e-01 -1.87810391e-01 -2.64080055... | [7.484620094299316, 4.447385311126709] |
14a3564d-14d1-4974-ad0e-632b457d7115 | image-retrieval-on-real-life-images-with-pre | 2108.04024 | null | https://arxiv.org/abs/2108.04024v1 | https://arxiv.org/pdf/2108.04024v1.pdf | Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models | We extend the task of composed image retrieval, where an input query consists of an image and short textual description of how to modify the image. Existing methods have only been applied to non-complex images within narrow domains, such as fashion products, thereby limiting the scope of study on in-depth visual reason... | ['Stephen Gould', 'Damien Teney', 'Cristian Rodriguez-Opazo', 'Zheyuan Liu'] | 2021-08-09 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Image_Retrieval_on_Real-Life_Images_With_Pre-Trained_Vision-and-Language_Models_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Image_Retrieval_on_Real-Life_Images_With_Pre-Trained_Vision-and-Language_Models_ICCV_2021_paper.pdf | iccv-2021-1 | ['composed-image-retrieval'] | ['computer-vision'] | [ 3.57414603e-01 -2.29257956e-01 -2.52664804e-01 -5.02487361e-01
-1.02170730e+00 -1.21693289e+00 9.01238680e-01 8.92026126e-02
-4.72765058e-01 4.96862791e-02 3.27937394e-01 -2.98231781e-01
1.75090268e-01 -5.49814224e-01 -9.36651170e-01 -4.29481789e-02
4.73577768e-01 4.46274519e-01 2.09008485e-01 -6.57108784... | [10.851000785827637, 1.4210984706878662] |
d243613c-6321-49fe-808e-582f8d5ec839 | near-optimal-experimental-design-under-the | 2302.05005 | null | https://arxiv.org/abs/2302.05005v1 | https://arxiv.org/pdf/2302.05005v1.pdf | Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms | A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms. However, conventional Bernoulli randomization in A/B testing faces many challenges such as spillover and carryover effects. Our study focuses on another challenge, especially for ... | ['Zheng Cai', 'Zhihua Zhu', 'Yuqing Kong', 'Jinshan Zhang', 'Yuan Yuan', 'Yongkang Guo'] | 2023-02-10 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-1.61358997e-01 -2.59015411e-01 -8.63423049e-01 -1.95380598e-01
-3.83564740e-01 -9.19504941e-01 1.91493571e-01 6.65709078e-02
-4.34285998e-01 9.91171300e-01 -3.22621942e-01 -8.72143328e-01
-3.41083884e-01 -7.41869748e-01 -1.19816911e+00 -3.78792316e-01
-5.89345813e-01 5.14009416e-01 1.28535405e-01 1.20752059... | [4.582613945007324, 3.3271994590759277] |
a99da37b-26a1-4797-a6ce-7b634968d959 | a-multi-level-annotated-corpus-of-scientific | null | null | https://aclanthology.org/2020.lrec-1.824 | https://aclanthology.org/2020.lrec-1.824.pdf | A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery | Related work sections or literature reviews are an essential part of every scientific article being crucial for paper reviewing and assessment. The automatic generation of related work sections can be considered an instance of the multi-document summarization problem. In order to allow the study of this specific proble... | ['Horacio Saggion', "Ahmed Abura{'}ed", 'Luis Chiruzzo'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['scientific-article-summarization'] | ['natural-language-processing'] | [ 4.90410149e-01 4.80997026e-01 -3.83366853e-01 1.69379056e-01
-1.24153244e+00 -8.94315898e-01 9.23159659e-01 8.36716890e-01
-3.38998228e-01 1.27940035e+00 6.99173272e-01 -4.80199277e-01
-4.71993893e-01 -4.36648607e-01 -4.07261670e-01 -3.61690968e-01
4.04668838e-01 3.64713132e-01 2.09462792e-01 8.47566798... | [12.348226547241211, 9.535038948059082] |
e1b3a521-a9d0-4f75-8806-4cdfa85fbb34 | depth-estimation-by-learning-triangulation | 2003.08933 | null | https://arxiv.org/abs/2003.08933v2 | https://arxiv.org/pdf/2003.08933v2.pdf | DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points | Multi-view stereo (MVS) is the golden mean between the accuracy of active depth sensing and the practicality of monocular depth estimation. Cost volume based approaches employing 3D convolutional neural networks (CNNs) have considerably improved the accuracy of MVS systems. However, this accuracy comes at a high comput... | ['Andrew Rabinovich', 'Vijay Badrinarayanan', 'Zak Murez', 'James Bartolozzi', 'Ayan Sinha'] | 2020-03-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3649_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660103.pdf | eccv-2020-8 | ['interest-point-detection'] | ['computer-vision'] | [ 5.29916510e-02 -4.49434817e-02 -7.33449161e-02 -4.00412381e-01
-1.07475555e+00 -5.74045360e-01 7.08703935e-01 7.20872208e-02
-5.87718308e-01 4.10844803e-01 1.69065982e-01 -5.08128013e-03
2.80907929e-01 -8.20239723e-01 -7.91544080e-01 -3.91989499e-01
6.52331337e-02 5.80934227e-01 4.12716895e-01 1.26436546... | [8.571086883544922, -2.66267991065979] |
8f03db2f-09a7-4b8b-adf5-c0a59dce4b35 | order-sensitive-neural-constituency-parsing | 2211.00421 | null | https://arxiv.org/abs/2211.00421v1 | https://arxiv.org/pdf/2211.00421v1.pdf | Order-sensitive Neural Constituency Parsing | We propose a novel algorithm that improves on the previous neural span-based CKY decoder for constituency parsing. In contrast to the traditional span-based decoding, where spans are combined only based on the sum of their scores, we introduce an order-sensitive strategy, where the span combination scores are more care... | ['Cong Liu', 'Liyin Xiao', 'Tianyu Shi', 'Zhicheng Wang'] | 2022-11-01 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.80388296e-01 4.12843227e-01 -3.69790033e-03 -4.78966355e-01
-1.38975549e+00 -8.09936821e-01 6.67116642e-02 6.67639077e-01
-6.29649460e-01 7.27415204e-01 4.49889809e-01 -6.71065688e-01
1.94712371e-01 -8.09225440e-01 -7.45733261e-01 -4.49546099e-01
6.62501082e-02 4.80723768e-01 7.63943791e-01 -5.71120739... | [10.362837791442871, 9.696105003356934] |
da964864-77c9-4eac-9cfe-78833d856ab0 | 3d-surface-reconstruction-from-multi-date | 2102.02502 | null | https://arxiv.org/abs/2102.02502v2 | https://arxiv.org/pdf/2102.02502v2.pdf | 3D Surface Reconstruction From Multi-Date Satellite Images | The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment reconstructions, there exist a variety of Stereo Matching based methods to reconstruct point c... | ['Michael Arens', 'Christoph Bodensteiner', 'Sebastian Bullinger'] | 2021-02-04 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 2.88480878e-01 -3.21734726e-01 2.97359288e-01 -3.90671909e-01
-9.42836821e-01 -5.41954160e-01 6.82252884e-01 1.88148573e-01
-4.82027858e-01 4.31609869e-01 -3.08825940e-01 -8.46285373e-02
-2.87954956e-01 -1.20122015e+00 -8.54110897e-01 -4.77089405e-01
2.58134268e-02 1.31222963e+00 5.67733228e-01 -3.70612383... | [8.440211296081543, -2.6218910217285156] |
8c089800-7978-4675-ab05-557a8354a4b1 | visible-infrared-person-re-identification-via | 2302.08212 | null | https://arxiv.org/abs/2302.08212v1 | https://arxiv.org/pdf/2302.08212v1.pdf | Visible-Infrared Person Re-Identification via Patch-Mixed Cross-Modality Learning | Visible-infrared person re-identification (VI-ReID) aims to retrieve images of the same pedestrian from different modalities, where the challenges lie in the significant modality discrepancy. To alleviate the modality gap, recent methods generate intermediate images by GANs, grayscaling, or mixup strategies. However, t... | ['Bo Du', 'Yutian Lin', 'Zhihao Qian'] | 2023-02-16 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 4.24986064e-01 -1.70510143e-01 -1.29201829e-01 -2.57387400e-01
-6.16934955e-01 -5.10410309e-01 7.30503321e-01 -4.30962741e-01
-2.92912692e-01 6.48578942e-01 2.23218173e-01 3.04595947e-01
8.18163306e-02 -7.23244727e-01 -7.20164180e-01 -1.01427972e+00
8.50685656e-01 9.88932401e-02 -1.12071343e-01 -2.32933894... | [14.70380973815918, 0.9636226296424866] |
71ff7c27-21c1-4633-b6cb-22db3ae4916e | learning-to-transpile-amr-into-sparql | 2112.07877 | null | https://arxiv.org/abs/2112.07877v2 | https://arxiv.org/pdf/2112.07877v2.pdf | Learning to Transpile AMR into SPARQL | We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA). This allows us to delegate part of the semantic representation to a strongly pre-trained semantic parser, while learning transpiling with small amount of paired data. We depa... | ['Salim Roukos', 'Radu Florian', 'Pavan Kapanipathi', 'Ibrahim Abdelaziz', 'Nandana Mihindukulasooriya', 'Tahira Naseem', 'Ramon Fernandez Astudillo', 'Mihaela Bornea'] | 2021-12-15 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.39160061e-01 1.04014194e+00 -1.70981124e-01 -7.21025765e-01
-8.95361423e-01 -7.65265226e-01 5.43130696e-01 5.23684263e-01
-2.91566432e-01 8.68221521e-01 4.43917006e-01 -5.95249712e-01
-3.14551950e-01 -1.50741577e+00 -1.12365162e+00 6.84555545e-02
1.74507815e-02 1.00078630e+00 7.69304931e-01 -8.13164353... | [10.286165237426758, 8.016737937927246] |
7735c60c-2385-49b6-b476-aa26c7447ff2 | sequential-neural-models-with-stochastic | 1605.07571 | null | http://arxiv.org/abs/1605.07571v2 | http://arxiv.org/pdf/1605.07571v2.pdf | Sequential Neural Models with Stochastic Layers | How can we efficiently propagate uncertainty in a latent state representation
with recurrent neural networks? This paper introduces stochastic recurrent
neural networks which glue a deterministic recurrent neural network and a state
space model together to form a stochastic and sequential neural generative
model. The c... | ['Søren Kaae Sønderby', 'Ulrich Paquet', 'Ole Winther', 'Marco Fraccaro'] | 2016-05-24 | sequential-neural-models-with-stochastic-1 | http://papers.nips.cc/paper/6039-sequential-neural-models-with-stochastic-layers | http://papers.nips.cc/paper/6039-sequential-neural-models-with-stochastic-layers.pdf | neurips-2016-12 | ['music-modeling'] | ['music'] | [ 5.64632490e-02 2.04709470e-01 -2.40088016e-01 -2.47283310e-01
-6.66942596e-01 -5.45360565e-01 8.33207369e-01 -7.34087348e-01
1.48651870e-02 5.65547109e-01 7.60340571e-01 -4.18450117e-01
-9.70597789e-02 -4.17806864e-01 -6.84805930e-01 -7.97969639e-01
5.07957451e-02 4.92274344e-01 3.57985683e-02 4.11981065... | [15.387662887573242, 5.8761515617370605] |
4f648dbf-b77e-4732-b8ca-f835c340a251 | a-benchmark-for-compositional-visual | 2206.05379 | null | https://arxiv.org/abs/2206.05379v1 | https://arxiv.org/pdf/2206.05379v1.pdf | A Benchmark for Compositional Visual Reasoning | A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, a maj... | ['Thomas Serre', 'Sebastian Musslick', 'Julien Colin', 'Mohit Vaishnav', 'Aimen Zerroug'] | 2022-06-11 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 4.15217519e-01 3.20839822e-01 -5.17814048e-03 -5.63290834e-01
-2.55307406e-01 -6.53957784e-01 1.06325448e+00 1.31463349e-01
-3.24040532e-01 4.64326739e-01 3.75201792e-01 -4.22531366e-01
-2.76423246e-01 -8.61715019e-01 -8.20223331e-01 -2.85216480e-01
1.08126486e-02 6.11841202e-01 2.92365968e-01 -3.06078374... | [10.561445236206055, 2.2422144412994385] |
a00b7256-f71a-4f39-ab33-8b46a3cf6a69 | iris-presentation-attack-detection-based-on | 1811.07252 | null | http://arxiv.org/abs/1811.07252v1 | http://arxiv.org/pdf/1811.07252v1.pdf | Iris Presentation Attack Detection Based on Photometric Stereo Features | We propose a new iris presentation attack detection method using
three-dimensional features of an observed iris region estimated by photometric
stereo. Our implementation uses a pair of iris images acquired by a common
commercial iris sensor (LG 4000). No hardware modifications of any kind are
required. Our approach sh... | ['Kevin W. Bowyer', 'Zhaoyuan Fang', 'Adam Czajka'] | 2018-11-18 | null | null | null | null | ['cross-domain-iris-presentation-attack'] | ['computer-vision'] | [ 5.82928002e-01 9.37360227e-02 1.20429543e-03 -1.51210815e-01
-1.30750716e-01 -5.95228910e-01 3.88179392e-01 -2.21782446e-01
-2.89495051e-01 3.03489238e-01 -1.71855576e-02 -3.67332488e-01
-5.45574389e-02 -4.42376912e-01 -5.44281662e-01 -8.39867651e-01
2.69914567e-01 5.76121688e-01 3.24337743e-02 5.05602844... | [3.7451512813568115, -3.630770206451416] |
b23941d7-6390-4440-b792-536e24349769 | estimating-treatment-effects-from-irregular | 2302.09446 | null | https://arxiv.org/abs/2302.09446v2 | https://arxiv.org/pdf/2302.09446v2.pdf | Estimating Treatment Effects in Continuous Time with Hidden Confounders | Estimating treatment effects plays a crucial role in causal inference, having many real-world applications like policy analysis and decision making. Nevertheless, estimating treatment effects in the longitudinal setting in the presence of hidden confounders remains an extremely challenging problem. Recently, there is a... | ['Yan Liu', 'James Enouen', 'Defu Cao'] | 2023-02-19 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 3.36408466e-01 -3.11187152e-02 -8.59597981e-01 -2.28660703e-01
-6.57724023e-01 -1.97065398e-01 3.24624777e-01 8.31556693e-02
-2.43236035e-01 1.22634804e+00 7.01769412e-01 -6.66019678e-01
-3.64102811e-01 -7.36279190e-01 -8.61269414e-01 -6.29448771e-01
-5.44963002e-01 3.35748911e-01 -4.58751023e-01 1.72800660... | [8.009182929992676, 5.328891277313232] |
36dd0c9e-7f8e-4e84-afa7-a23628ce61a4 | simple-yet-effective-neural-ranking-and | 2304.01019 | null | https://arxiv.org/abs/2304.01019v1 | https://arxiv.org/pdf/2304.01019v1.pdf | Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval | The advent of multilingual language models has generated a resurgence of interest in cross-lingual information retrieval (CLIR), which is the task of searching documents in one language with queries from another. However, the rapid pace of progress has led to a confusing panoply of methods and reproducibility has lagge... | ['Xinyu Zhang', 'Jheng-Hong Yang', 'Nandan Thakur', 'Mehdi Rezagholizadeh', 'Odunayo Ogundepo', 'Rodrigo Nogueira', 'Carlos Lassance', 'Ehsan Kamalloo', 'Vitor Jeronymo', 'David Alfonso-Hermelo', 'Jimmy Lin'] | 2023-04-03 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.85454601e-01 -7.00995684e-01 -3.85735154e-01 -2.39364937e-01
-1.83596170e+00 -1.09650135e+00 1.21605706e+00 3.32204491e-01
-9.47130680e-01 5.73867738e-01 5.01933515e-01 -3.20091814e-01
-2.44552881e-01 1.78475771e-02 -4.23388571e-01 -2.06450433e-01
-1.02788024e-01 8.21581662e-01 3.02545220e-01 -7.37456083... | [11.383012771606445, 9.824538230895996] |
38ec8a10-f3e5-4e44-868d-6b336bd5bd19 | feature-representation-matters-end-to-end | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1761_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490222.pdf | Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution | In this paper, we are aiming for a general reference-based super-resolution setting: it does not require the low-resolution image and the high-resolution reference image to be well aligned or with a similar texture. Instead, we only intend to transfer the relevant textures from reference images to the output super-reso... | ['Ming-Jie Sun', 'Kai-Zhu Huang', 'Chao Yao', 'Yanchun Xie', 'Jimin Xiao'] | null | null | null | null | eccv-2020-8 | ['reference-based-super-resolution'] | ['computer-vision'] | [ 6.57406747e-01 7.35388473e-02 -4.58897604e-03 -3.50607902e-01
-1.26616323e+00 -1.88796893e-02 6.05849564e-01 -8.21505606e-01
-4.12642024e-02 6.64148152e-01 1.51190892e-01 2.66617477e-01
-2.23583087e-01 -9.65809405e-01 -9.44884658e-01 -9.82242882e-01
2.07157120e-01 9.04304758e-02 4.17744666e-01 -4.92419511... | [10.95870590209961, -2.0814220905303955] |
cba39a41-4147-4724-9170-c700adfe2a28 | label-structure-preserving-contrastive | 2209.01314 | null | https://arxiv.org/abs/2209.01314v1 | https://arxiv.org/pdf/2209.01314v1.pdf | Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels | Contrastive learning (CL) has shown impressive advances in image representation learning in whichever supervised multi-class classification or unsupervised learning. However, these CL methods fail to be directly adapted to multi-label image classification due to the difficulty in defining the positive and negative inst... | ['Songcan Chen', 'Qirong Mao', 'Lisha Li', 'Zhongchen Ma'] | 2022-09-03 | null | null | null | null | ['multi-label-image-classification', 'multi-label-learning'] | ['computer-vision', 'methodology'] | [ 8.07290792e-01 1.78563073e-01 -3.17575514e-01 -4.69949305e-01
-1.24097478e+00 -5.59690595e-01 5.14372230e-01 2.92838424e-01
-3.39578360e-01 8.47739041e-01 -4.27063346e-01 4.44229729e-02
-3.03818822e-01 -4.10472244e-01 -6.95895612e-01 -1.10372329e+00
2.22735018e-01 5.19773185e-01 6.45126170e-03 1.66793168... | [9.642746925354004, 4.092779159545898] |
e1f5a943-e723-465c-b416-ab14ccd80d95 | heterogeneity-aware-deep-embedding-for-mobile | 1811.00846 | null | http://arxiv.org/abs/1811.00846v1 | http://arxiv.org/pdf/1811.00846v1.pdf | Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition | Mobile biometric approaches provide the convenience of secure authentication
with an omnipresent technology. However, this brings an additional challenge of
recognizing biometric patterns in unconstrained environment including
variations in mobile camera sensors, illumination conditions, and capture
distance. To addres... | ['Rishabh Garg', 'Nalini Ratha', 'Mayank Vatsa', 'Richa Singh', 'Yashasvi Baweja', 'Soumyadeep Ghosh'] | 2018-11-02 | null | null | null | null | ['mobile-periocular-recognition'] | ['computer-vision'] | [-1.51234269e-01 -7.45599151e-01 -2.90611267e-01 -4.00457919e-01
-6.92528009e-01 -5.30061662e-01 3.36436123e-01 -4.64528948e-01
-4.50574428e-01 4.67710465e-01 -1.85596868e-01 -1.37426332e-01
-2.00404912e-01 -1.79472104e-01 -5.22248924e-01 -8.93775284e-01
1.48504404e-02 -2.39823043e-01 -5.68991780e-01 2.30675980... | [13.460844039916992, 1.1096090078353882] |
161329ed-08b2-4f85-85d0-9e7247c41565 | self-supervised-class-cognizant-few-shot | 2202.08149 | null | https://arxiv.org/abs/2202.08149v1 | https://arxiv.org/pdf/2202.08149v1.pdf | Self-Supervised Class-Cognizant Few-Shot Classification | Unsupervised learning is argued to be the dark matter of human intelligence. To build in this direction, this paper focuses on unsupervised learning from an abundance of unlabeled data followed by few-shot fine-tuning on a downstream classification task. To this aim, we extend a recent study on adopting contrastive lea... | ['Hadi Jamali-Rad', 'Ojas Kishore Shirekar'] | 2022-02-15 | null | null | null | null | ['unsupervised-few-shot-image-classification'] | ['computer-vision'] | [ 3.37132871e-01 -4.43652421e-02 -3.47040385e-01 -6.73636615e-01
-6.46178424e-01 -5.12889922e-01 9.05536473e-01 1.48641288e-01
-8.45040560e-01 6.85256422e-01 1.45837948e-01 -1.89044103e-01
-3.69983166e-01 -3.96340042e-01 -4.61973667e-01 -5.67612886e-01
-8.19218606e-02 6.00446641e-01 4.22869980e-01 -2.16745421... | [9.895495414733887, 2.7290849685668945] |
928a0a31-70e6-417a-b1a6-b6ab2b126cfe | assembly101-a-large-scale-multi-view-video | 2203.14712 | null | https://arxiv.org/abs/2203.14712v2 | https://arxiv.org/pdf/2203.14712v2.pdf | Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities | Assembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles. Participants work without fixed instructions, and the sequences feature rich and natural variations in action ordering, mistakes, and corrections. Assembly101 is the first multi-v... | ['Angela Yao', 'Robert Wang', 'Dipika Singhania', 'Kun He', 'Daniel Shelepov', 'Dibyadip Chatterjee', 'Fadime Sener'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Sener_Assembly101_A_Large-Scale_Multi-View_Video_Dataset_for_Understanding_Procedural_Activities_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Sener_Assembly101_A_Large-Scale_Multi-View_Video_Dataset_for_Understanding_Procedural_Activities_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-anticipation', 'mistake-detection', '3d-human-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.53275001e-01 -1.23514168e-01 -4.03092802e-02 -3.71531844e-01
-6.96397603e-01 -9.53531027e-01 7.03362048e-01 -4.07638997e-01
-1.91162884e-01 6.06895506e-01 6.63551748e-01 2.89329559e-01
4.14425619e-02 6.60678819e-02 -9.93982494e-01 -4.72663730e-01
-2.55702853e-01 7.91715086e-01 2.78632194e-01 -5.28080389... | [7.965557098388672, 0.28837329149246216] |
c4eadbda-b26f-40e8-a0b8-ff866c0d8a5f | event-aided-direct-sparse-odometry | 2204.07640 | null | https://arxiv.org/abs/2204.07640v2 | https://arxiv.org/pdf/2204.07640v2.pdf | Event-aided Direct Sparse Odometry | We introduce EDS, a direct monocular visual odometry using events and frames. Our algorithm leverages the event generation model to track the camera motion in the blind time between frames. The method formulates a direct probabilistic approach of observed brightness increments. Per-pixel brightness increments are predi... | ['Davide Scaramuzza', 'Guillermo Gallego', 'Javier Hidalgo-Carrió'] | 2022-04-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hidalgo-Carrio_Event-Aided_Direct_Sparse_Odometry_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hidalgo-Carrio_Event-Aided_Direct_Sparse_Odometry_CVPR_2022_paper.pdf | cvpr-2022-1 | ['monocular-visual-odometry'] | ['robots'] | [-9.79309529e-03 -1.94165096e-01 -5.46516776e-02 -1.20781869e-01
-8.89981985e-01 -6.87939763e-01 6.86462998e-01 -4.57371831e-01
-4.99038547e-01 5.92317164e-01 4.17120934e-01 -2.99684778e-02
2.95926064e-01 -3.97358298e-01 -9.04926360e-01 -6.55919611e-01
1.80244416e-01 2.90460825e-01 4.46745813e-01 1.24717966... | [8.219805717468262, -1.889723539352417] |
1291d6bd-973a-430b-8c4c-b65ce347d399 | extracting-food-substitutes-from-food-diary | 1607.08807 | null | http://arxiv.org/abs/1607.08807v1 | http://arxiv.org/pdf/1607.08807v1.pdf | Extracting Food Substitutes From Food Diary via Distributional Similarity | In this paper, we explore the problem of identifying substitute relationship
between food pairs from real-world food consumption data as the first step
towards the healthier food recommendation. Our method is inspired by the
distributional hypothesis in linguistics. Specifically, we assume that foods
that are consumed ... | ['Weber Ingmar', 'Achananuparp Palakorn'] | 2016-07-29 | null | null | null | null | ['food-recommendation'] | ['miscellaneous'] | [-6.07136674e-02 8.97872746e-02 -6.68866158e-01 -6.91352367e-01
-3.64535779e-01 -7.60967076e-01 2.77176857e-01 1.04224110e+00
-5.77510715e-01 4.71307874e-01 9.07975912e-01 -1.47767276e-01
2.03155532e-01 -9.73975420e-01 -7.79150546e-01 -4.16189700e-01
3.11680794e-01 3.57807398e-01 7.56043568e-02 -4.07260835... | [11.530132293701172, 4.524235725402832] |
f90e9554-08ac-4d70-acb9-1af5bf4d2fa5 | integration-of-deep-learning-and-traditional | null | null | https://aclanthology.org/D19-5724 | https://aclanthology.org/D19-5724.pdf | Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature | In this paper, we present our participation in the Bacteria Biotope (BB) task at BioNLP-OST 2019. Our system utilizes fine-tuned language representation models and machine learning approaches based on word embedding and lexical features for entities recognition, normalization and relation extraction. It achieves the st... | ['Wanli Liu', 'Jihang Mao'] | 2019-11-01 | null | null | null | ws-2019-11 | ['medical-concept-normalization'] | ['medical'] | [ 2.68974662e-01 1.72646761e-01 -2.42713302e-01 7.44746476e-02
-3.28479111e-01 -2.97598243e-01 9.01012540e-01 1.01082706e+00
-9.52548087e-01 1.14624119e+00 4.03486311e-01 -5.50424576e-01
-1.24292776e-01 -6.86908841e-01 -6.66283965e-01 -5.15839636e-01
-4.08652067e-01 5.36144555e-01 5.09431064e-02 -6.15623474... | [8.490418434143066, 8.764745712280273] |
1e5238fd-fb55-481f-a045-61bf7b9b2b96 | a-fully-convolutional-neural-network-for-1 | 1604.00494 | null | http://arxiv.org/abs/1604.00494v3 | http://arxiv.org/pdf/1604.00494v3.pdf | A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI | Automated cardiac segmentation from magnetic resonance imaging datasets is an
essential step in the timely diagnosis and management of cardiac pathologies.
We propose to tackle the problem of automated left and right ventricle
segmentation through the application of a deep fully convolutional neural
network architectur... | ['Phi Vu Tran'] | 2016-04-02 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 1.92562565e-01 1.26496628e-01 4.90221158e-02 -5.76486409e-01
-9.22894359e-01 -4.47259098e-01 -7.13545904e-02 2.14000568e-01
-6.21306717e-01 4.89525378e-01 -2.56606877e-01 -7.27025330e-01
3.22117805e-01 -4.10436720e-01 -4.41708118e-01 -3.96947801e-01
-3.04672211e-01 9.13761497e-01 2.05190063e-01 2.16077045... | [14.277265548706055, -2.451770544052124] |
bf6f28c7-d844-440c-a28f-d19b87bbfea6 | 3dcapsule-extending-the-capsule-architecture | 1811.02191 | null | http://arxiv.org/abs/1811.02191v1 | http://arxiv.org/pdf/1811.02191v1.pdf | 3DCapsule: Extending the Capsule Architecture to Classify 3D Point Clouds | This paper introduces the 3DCapsule, which is a 3D extension of the recently
introduced Capsule concept that makes it applicable to unordered point sets.
The original Capsule relies on the existence of a spatial relationship between
the elements in the feature map it is presented with, whereas in point
permutation inva... | ['Lars Petersson', 'Ali Cheraghian'] | 2018-11-06 | null | null | null | null | ['classify-3d-point-clouds'] | ['computer-vision'] | [-9.55423340e-02 -1.69763267e-02 -1.98964924e-02 -2.02722281e-01
-6.23608589e-01 -9.35769975e-01 9.75098670e-01 2.87219644e-01
-2.56001502e-01 3.12290728e-01 1.49410218e-01 2.23495904e-02
-6.00539863e-01 -4.71155107e-01 -9.91096199e-01 -7.17633307e-01
-5.13054252e-01 5.83609223e-01 3.27191502e-01 -2.92801589... | [7.972347259521484, -3.314114570617676] |
f5b3af08-0436-46ed-af22-fea5238e1efc | representation-driven-reinforcement-learning | 2305.19922 | null | https://arxiv.org/abs/2305.19922v2 | https://arxiv.org/pdf/2305.19922v2.pdf | Representation-Driven Reinforcement Learning | We present a representation-driven framework for reinforcement learning. By representing policies as estimates of their expected values, we leverage techniques from contextual bandits to guide exploration and exploitation. Particularly, embedding a policy network into a linear feature space allows us to reframe the exp... | ['Shie Mannor', 'Guy Tennenholtz', 'Ofir Nabati'] | 2023-05-31 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 1.91654470e-02 1.40222564e-01 -1.22379947e+00 1.24825602e-02
-6.95112765e-01 -6.44943058e-01 7.28376627e-01 -1.25525072e-01
-6.24939322e-01 1.23161030e+00 4.91764635e-01 -7.27027476e-01
-4.82541114e-01 -7.64985740e-01 -5.93605280e-01 -6.71776354e-01
-3.26341867e-01 2.86227971e-01 -2.73740798e-01 -3.19346011... | [4.129539489746094, 2.1736249923706055] |
a8f20e9a-d0c7-4de3-96db-d09bbeb2e611 | debiasing-scores-and-prompts-of-2d-diffusion | 2303.15413 | null | https://arxiv.org/abs/2303.15413v2 | https://arxiv.org/pdf/2303.15413v2.pdf | Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation | The view inconsistency problem in score-distilling text-to-3D generation, also known as the Janus problem, arises from the intrinsic bias of 2D diffusion models, which leads to the unrealistic generation of 3D objects. In this work, we explore score-distilling text-to-3D generation and identify the main causes of the J... | ['Seungryong Kim', 'Donghoon Ahn', 'Susung Hong'] | 2023-03-27 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 1.82792004e-02 1.78289972e-02 1.50804684e-01 -2.47198164e-01
-7.89110422e-01 -8.73087645e-01 6.14777327e-01 -2.58259147e-01
2.91870445e-01 3.55297267e-01 5.04640996e-01 -1.75374925e-01
-3.82539965e-02 -3.55790168e-01 -4.32748824e-01 -3.62893492e-01
5.19737303e-01 4.08164620e-01 2.57180095e-01 -4.47530933... | [11.282845497131348, -0.41237348318099976] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.