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d665a3b2-0d93-4a5b-a2ea-80cc9a2cd1d1 | on-monoaural-speech-enhancement-for-automatic | 2205.01751 | null | https://arxiv.org/abs/2205.01751v2 | https://arxiv.org/pdf/2205.01751v2.pdf | On monoaural speech enhancement for automatic recognition of real noisy speech using mixture invariant training | In this paper, we explore an improved framework to train a monoaural neural enhancement model for robust speech recognition. The designed training framework extends the existing mixture invariant training criterion to exploit both unpaired clean speech and real noisy data. It is found that the unpaired clean speech is ... | ['Jon Barker', 'Rama Doddipatla', 'Catalin Zorila', 'Jisi Zhang'] | 2022-05-03 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 6.76061928e-01 -3.56558412e-02 8.39830339e-01 -4.15198684e-01
-1.48315632e+00 -2.79676616e-01 5.79128861e-01 -3.59927207e-01
-6.75743043e-01 6.18988156e-01 4.49699074e-01 -2.12708831e-01
-2.21478194e-01 -7.69339651e-02 -5.11986732e-01 -1.18963897e+00
1.10323653e-01 -3.50194395e-01 -6.87529370e-02 -2.08080083... | [14.950444221496582, 5.841394901275635] |
9b1a61ee-293e-441b-95f2-ec0d4d16e4b5 | an-end-to-end-model-for-entity-level-relation | 2102.05980 | null | https://arxiv.org/abs/2102.05980v2 | https://arxiv.org/pdf/2102.05980v2.pdf | An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning | We present a joint model for entity-level relation extraction from documents. In contrast to other approaches - which focus on local intra-sentence mention pairs and thus require annotations on mention level - our model operates on entity level. To do so, a multi-task approach is followed that builds upon coreference r... | ['Adrian Ulges', 'Markus Eberts'] | 2021-02-11 | null | https://aclanthology.org/2021.eacl-main.319 | https://aclanthology.org/2021.eacl-main.319.pdf | eacl-2021-2 | ['document-level-relation-extraction', 'joint-entity-and-relation-extraction', 'nested-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.47712797e-01 9.14586723e-01 -4.57882494e-01 -5.78285694e-01
-1.65926373e+00 -4.79946643e-01 7.16010451e-01 6.96467757e-01
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-3.01063091e-01 9.95672703e-01 3.10449839e-01 -3.72324228... | [9.456439018249512, 8.966801643371582] |
074d3b2e-597b-4cbe-818f-68c2df5c74f3 | learning-from-the-dictionary-heterogeneous | 2210.10320 | null | https://arxiv.org/abs/2210.10320v1 | https://arxiv.org/pdf/2210.10320v1.pdf | Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking | Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors. Recent researches start from the pretrained knowledge of language models and take multimodal information into CSC models to improve the performance. However, they overlook the rich knowledge in the dictionary, the reference book where one ... | ['Haitao Zheng', 'Yunbo Cao', 'Chao Li', 'Ruiyang Liu', 'Shulin Huang', 'Li Yangning', 'Zhongli Li', 'Qingyu Zhou', 'Shirong Ma', 'Yinghui Li'] | 2022-10-19 | null | null | null | null | ['chinese-spell-checking'] | ['natural-language-processing'] | [ 2.29956493e-01 -7.08394408e-01 -1.88218281e-01 -2.64648348e-01
-6.42889380e-01 -6.71839297e-01 4.34160888e-01 7.48773888e-02
-6.34869337e-01 4.48389143e-01 4.01110083e-01 -4.17076081e-01
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7.15418577e-01 1.74891368e-01 6.63639158e-02 -2.47631267... | [10.940875053405762, 10.843379974365234] |
a9e58268-0bac-4c2a-8815-0a7160e1f1c0 | deep-transformer-based-data-augmentation-with | 2007.06949 | null | https://arxiv.org/abs/2007.06949v3 | https://arxiv.org/pdf/2007.06949v3.pdf | Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR | Recently Deep Transformer models have proven to be particularly powerful in language modeling tasks for ASR. Their high complexity, however, makes them very difficult to apply in the first (single) pass of an online system. Recent studies showed that a considerable part of the knowledge of neural network Language Model... | ['Péter Mihajlik', 'Tibor Fegyó', 'György Szaszák', 'Balázs Tarján'] | 2020-07-14 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 4.33517367e-01 2.75812566e-01 1.83176905e-01 -3.76759142e-01
-1.12963474e+00 -5.24006009e-01 4.56822008e-01 8.57039541e-02
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2.87752122e-01 9.17488277e-01 -3.48687768e-02 -6.88962817... | [14.329045295715332, 6.915004253387451] |
fe9ab15d-98aa-4287-8b17-ae7a549f6ec7 | combining-contrastive-and-supervised-learning | 2205.10406 | null | https://arxiv.org/abs/2205.10406v1 | https://arxiv.org/pdf/2205.10406v1.pdf | Combining Contrastive and Supervised Learning for Video Super-Resolution Detection | Upscaled video detection is a helpful tool in multimedia forensics, but it is a challenging task that involves various upscaling and compression algorithms. There are many resolution-enhancement methods, including interpolation and deep-learning-based super-resolution, and they leave unique traces. In this work, we pro... | ['Dmitriy Vatolin', 'Ivan Molodetskikh', 'Viacheslav Meshchaninov'] | 2022-05-20 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 1.61517203e-01 -5.25258660e-01 -3.64750832e-01 6.22400455e-02
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-3.21572810e-01 -2.28844643e-01 5.11263132e-01 -1.42376378... | [11.102272987365723, -2.0060501098632812] |
a5f68d08-8b05-4b6e-ae88-2c2a2a0e33d0 | bert-goes-shopping-comparing-distributional | 2012.09807 | null | https://arxiv.org/abs/2012.09807v2 | https://arxiv.org/pdf/2012.09807v2.pdf | BERT Goes Shopping: Comparing Distributional Models for Product Representations | Word embeddings (e.g., word2vec) have been applied successfully to eCommerce products through~\textit{prod2vec}. Inspired by the recent performance improvements on several NLP tasks brought by contextualized embeddings, we propose to transfer BERT-like architectures to eCommerce: our model -- ~\textit{Prod2BERT} -- is ... | ['Jacopo Tagliabue', 'Bingqing Yu', 'Federico Bianchi'] | 2020-12-17 | null | https://aclanthology.org/2021.ecnlp-1.1 | https://aclanthology.org/2021.ecnlp-1.1.pdf | acl-ecnlp-2021-8 | ['product-recommendation'] | ['miscellaneous'] | [-0.17203002 0.21157399 -0.24691662 -0.46110868 -0.3968241 -0.7317641
0.78310156 0.25900447 -0.48084182 0.37924308 0.5012901 -0.59287757
-0.36032808 -0.7069598 -0.47359687 -0.23117009 -0.05201464 0.25989267
-0.3647124 -0.5070938 0.25797856 0.10884058 -1.3814503 0.08411982
0.66734844 0.9617384 0.3... | [10.538130760192871, 8.406134605407715] |
884da18b-b6bf-410f-9727-0cbaad00107a | svt-supertoken-video-transformer-for | 2304.00325 | null | https://arxiv.org/abs/2304.00325v2 | https://arxiv.org/pdf/2304.00325v2.pdf | SVT: Supertoken Video Transformer for Efficient Video Understanding | Whether by processing videos with fixed resolution from start to end or incorporating pooling and down-scaling strategies, existing video transformers process the whole video content throughout the network without specially handling the large portions of redundant information. In this paper, we present a Supertoken Vid... | ['Madian Khabsa', 'Senem Velipasalar', 'Qifan Wang', 'Hanchao Yu', 'Rui Hou', 'Chenbin Pan'] | 2023-04-01 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [-4.53439765e-02 -4.76410948e-02 -1.99736252e-01 -2.61588097e-01
-7.00367689e-01 -5.39791465e-01 1.95201337e-01 -6.58084005e-02
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1.97634429e-01 -7.79831290e-01 -9.66004729e-01 -5.08954048e-01
-7.49598965e-02 -1.34768456e-01 5.44228494e-01 -1.54580563... | [9.077914237976074, 0.5086672902107239] |
cddec456-1545-4f83-8fe1-4ab7ccbf5824 | learning-when-to-trust-which-teacher-for | 2306.12012 | null | https://arxiv.org/abs/2306.12012v1 | https://arxiv.org/pdf/2306.12012v1.pdf | Learning When to Trust Which Teacher for Weakly Supervised ASR | Automatic speech recognition (ASR) training can utilize multiple experts as teacher models, each trained on a specific domain or accent. Teacher models may be opaque in nature since their architecture may be not be known or their training cadence is different from that of the student ASR model. Still, the student model... | ['Andreas Stolcke', 'Gopinath Chennupati', 'Anit Kumar Sahu', 'Milind Rao', 'Aakriti Agrawal'] | 2023-06-21 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 3.40295553e-01 2.79762924e-01 -2.86630094e-01 -6.32650137e-01
-1.05726635e+00 -9.67892170e-01 5.20090699e-01 -8.98702145e-02
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1.72133908e-01 -2.85588443e-01 -6.42673969e-01 -5.71956694e-01
4.70635831e-01 9.36111271e-01 3.60830694e-01 -3.04083049... | [14.451835632324219, 6.628710746765137] |
7569dd98-a948-4680-bcfc-f67bf499832a | limits-of-machine-learning-for-automatic | 2306.17193 | null | https://arxiv.org/abs/2306.17193v1 | https://arxiv.org/pdf/2306.17193v1.pdf | Limits of Machine Learning for Automatic Vulnerability Detection | Recent results of machine learning for automatic vulnerability detection have been very promising indeed: Given only the source code of a function $f$, models trained by machine learning techniques can decide if $f$ contains a security flaw with up to 70% accuracy. But how do we know that these results are general and ... | ['Marcel Böhme', 'Niklas Risse'] | 2023-06-28 | null | null | null | null | ['vulnerability-detection', 'benchmarking', 'benchmarking'] | ['miscellaneous', 'miscellaneous', 'robots'] | [ 3.97723317e-01 1.48658678e-01 -1.36738479e-01 -2.19603881e-01
-8.41645181e-01 -1.09198201e+00 2.43169278e-01 3.45326602e-01
-1.01759166e-01 4.08112943e-01 -3.56834441e-01 -9.29987490e-01
-3.04711387e-02 -1.01008642e+00 -8.59044254e-01 -3.91400009e-01
-1.56215623e-01 -3.20114605e-02 3.21778148e-01 -4.38949287... | [7.075378894805908, 7.7659406661987305] |
d8609545-b1da-4934-89ba-87d91a3eb892 | effective-sampling-for-large-scale-automated | 1412.5659 | null | http://arxiv.org/abs/1412.5659v1 | http://arxiv.org/pdf/1412.5659v1.pdf | Effective sampling for large-scale automated writing evaluation systems | Automated writing evaluation (AWE) has been shown to be an effective
mechanism for quickly providing feedback to students. It has already seen wide
adoption in enterprise-scale applications and is starting to be adopted in
large-scale contexts. Training an AWE model has historically required a single
batch of several h... | ['Nicholas Dronen', 'Peter W. Foltz', 'Kyle Habermehl'] | 2014-12-17 | null | null | null | null | ['automated-writing-evaluation'] | ['natural-language-processing'] | [-1.52641818e-01 -8.45627636e-02 -7.44979829e-02 -6.28673673e-01
-1.08983862e+00 -8.40821505e-01 3.32429618e-01 4.46228415e-01
-6.93949163e-01 8.48286569e-01 -1.92407206e-01 -7.00584352e-01
-2.54642487e-01 -7.54135489e-01 -2.68454194e-01 -2.66118459e-02
4.84899670e-01 5.82063854e-01 2.03341112e-01 -1.17187671... | [11.286768913269043, 9.333086967468262] |
37644c8a-1257-4717-81a9-1ec535687d8f | vectorized-scenario-description-and-motion | 2302.01161 | null | https://arxiv.org/abs/2302.01161v1 | https://arxiv.org/pdf/2302.01161v1.pdf | Vectorized Scenario Description and Motion Prediction for Scenario-Based Testing | Automated vehicles (AVs) are tested in diverse scenarios, typically specified by parameters such as velocities, distances, or curve radii. To describe scenarios uniformly independent of such parameters, this paper proposes a vectorized scenario description defined by the road geometry and vehicles' trajectories. Data o... | ['Steffen Müller', 'Constantin Vasconi', 'Max Winkelmann'] | 2023-02-02 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 8.27456117e-02 -2.79409051e-01 -3.64730269e-01 -5.97391665e-01
-4.76250261e-01 -7.84272194e-01 1.03961575e+00 3.70280206e-01
-4.78314459e-01 7.59646177e-01 1.24857113e-01 -6.42927885e-01
-4.69400704e-01 -1.01934576e+00 -3.79261106e-01 -7.67019212e-01
-2.03218952e-01 5.36382616e-01 5.55779278e-01 -1.97815165... | [5.751068592071533, 1.1551727056503296] |
660e5f34-077b-4fe5-a7fa-76004910faf3 | pars-absa-a-manually-annotated-aspect-based | null | null | https://aclanthology.org/2022.lrec-1.763 | https://aclanthology.org/2022.lrec-1.763.pdf | Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews | Due to the increased availability of online reviews, sentiment analysis witnessed a thriving interest from researchers. Sentiment analysis is a computational treatment of sentiment used to extract and understand the opinions of authors. While many systems were built to predict the sentiment of a document or a sentence,... | ['Sauleh Eetemadi', 'Behrouz Minaei-Bidgoli', 'Soroush Javdan', 'Kamyar Darvishi', 'Taha Shangipour ataei'] | null | null | null | null | lrec-2022-6 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-2.43417457e-01 -9.90867149e-03 -4.39084649e-01 -7.65648007e-01
-7.40491629e-01 -6.96630955e-01 5.34782052e-01 3.41538101e-01
-3.85350674e-01 7.64735103e-01 3.21459174e-01 -1.76167116e-01
5.26615083e-01 -6.75994337e-01 -4.62527573e-01 -5.18316805e-01
5.03774524e-01 2.42316782e-01 -3.16191278e-02 -8.31942856... | [11.226937294006348, 6.875585079193115] |
f43edd6b-64cd-435a-9e92-f9759797a35d | attribute-consistent-knowledge-graph | 2304.01563 | null | https://arxiv.org/abs/2304.01563v1 | https://arxiv.org/pdf/2304.01563v1.pdf | Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment | The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing works ignore contextual gap problems that the aligned entities have different numbers of attributes on... | ['JianXin Li', 'Jiawei Sheng', 'Lihong Wang', 'Cheng Ji', 'Yangyifei Luo', 'Shu Guo', 'Qian Li'] | 2023-04-04 | null | null | null | null | ['multi-modal-entity-alignment', 'entity-alignment', 'entity-alignment'] | ['knowledge-base', 'knowledge-base', 'natural-language-processing'] | [ 5.16246189e-04 4.66994822e-01 -4.96118516e-01 -5.92471182e-01
-8.81246448e-01 -2.53160566e-01 3.02394718e-01 5.27376711e-01
-1.55014321e-01 8.38921010e-01 2.17122048e-01 2.73443982e-02
-4.31014687e-01 -1.12491441e+00 -9.28865910e-01 -4.73886609e-01
-1.14356503e-01 7.79942513e-01 4.15106900e-02 -1.21966349... | [8.794084548950195, 8.026026725769043] |
b186bddb-f6da-4220-bc24-241740f90042 | green-portfolio-optimization-a-scenario | 2305.16712 | null | https://arxiv.org/abs/2305.16712v1 | https://arxiv.org/pdf/2305.16712v1.pdf | Green portfolio optimization: A scenario analysis and stress testing based novel approach for sustainable investing in the paradigm Indian markets | In this article, we present a novel approach for the construction of an environment-friendly green portfolio using the ESG ratings, and application of the modern portfolio theory to present what we call as the ``green efficient frontier'' (wherein the environmental score is included as a third dimension to the traditio... | ['Siddhartha P. Chakrabarty', 'Rishabh Raj', 'Shashwat Mishra'] | 2023-05-26 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-9.67115723e-03 -1.29343709e-03 2.26351544e-02 1.93996415e-01
-2.16789380e-01 -7.50516057e-01 5.99354863e-01 2.32538477e-01
-1.97072908e-01 4.43974882e-01 4.24283028e-01 -8.61183643e-01
-8.65172267e-01 -9.25001860e-01 -2.93352008e-01 -7.47465372e-01
1.15544543e-01 -4.57592398e-01 -2.86823183e-01 -2.17958599... | [5.439409255981445, 3.9798524379730225] |
7a0863bc-f210-459d-bd41-aa2929b93009 | aligning-instruction-tasks-unlocks-large | 2305.11159 | null | https://arxiv.org/abs/2305.11159v1 | https://arxiv.org/pdf/2305.11159v1.pdf | Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors | Recent work has shown that fine-tuning large language models (LLMs) on large-scale instruction-following datasets substantially improves their performance on a wide range of NLP tasks, especially in the zero-shot setting. However, even advanced instruction-tuned LLMs still fail to outperform small LMs on relation extra... | ['Yu Su', 'Bernal Jiménez Gutiérrez', 'Kai Zhang'] | 2023-05-18 | null | null | null | null | ['instruction-following', 'relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.72071487e-01 2.59301633e-01 -6.23288572e-01 -2.66055644e-01
-1.46815920e+00 -4.71807182e-01 6.17840827e-01 1.23555310e-01
-4.41603720e-01 7.45304108e-01 5.26975632e-01 -8.35712910e-01
-4.46379930e-02 -6.78775072e-01 -1.01271129e+00 9.81499851e-02
1.89174771e-01 3.98597747e-01 5.04901707e-01 -6.95927858... | [10.57347297668457, 8.443232536315918] |
d13e2e67-88e0-46b3-a1a0-e984277c6cc6 | read-attend-and-comment-a-deep-architecture-1 | null | null | https://arxiv.org/abs/1909.11974 | https://arxiv.org/pdf/1909.11974.pdf | Read, Attend and Comment: A Deep Architecture for Automatic NewsComment Generation | Automatic news comment generation is a new testbed for techniques of natural language generation. In this paper, we propose a “read-attend-comment” procedure for news comment generation and formalize the procedure with a reading network and a generation network. The reading network comprehends a news ... | ['Zhoujun Li', 'Ze Yang', 'Wei Wu', 'Can Xu'] | 2019-10-01 | null | null | null | emnlp2019-2019-10 | ['comment-generation'] | ['natural-language-processing'] | [ 2.38924146e-01 9.82817054e-01 -3.27985972e-01 -5.38589120e-01
-1.14672506e+00 -5.96142411e-01 1.06566179e+00 3.83555740e-01
-1.84872970e-01 1.02846301e+00 9.43161786e-01 -2.31041834e-01
1.40810460e-01 -7.04559147e-01 -6.12672389e-01 -4.85254973e-01
1.10279001e-01 7.45104671e-01 -5.65646552e-02 -3.45483243... | [12.32429313659668, 9.287490844726562] |
6617924e-0894-4f14-96bf-6d72b40d08b2 | features-fusion-framework-for-multimodal | 2209.01728 | null | https://arxiv.org/abs/2209.01728v1 | https://arxiv.org/pdf/2209.01728v1.pdf | Features Fusion Framework for Multimodal Irregular Time-series Events | Some data from multiple sources can be modeled as multimodal time-series events which have different sampling frequencies, data compositions, temporal relations and characteristics. Different types of events have complex nonlinear relationships, and the time of each event is irregular. Neither the classical Recurrent N... | ['Xianchao Zhang', 'Peiwang Tang'] | 2022-09-05 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [-2.36730441e-01 -8.64247739e-01 2.90480368e-02 -3.62589151e-01
-3.58356714e-01 -3.65853667e-01 7.70434737e-01 2.64987707e-01
-3.43201607e-01 3.49700361e-01 4.44644153e-01 -1.20829204e-02
-4.68617171e-01 -7.55147219e-01 -4.06074703e-01 -7.03790426e-01
-4.45161641e-01 1.70676917e-01 3.05538535e-01 -2.80075043... | [6.930964469909668, 2.9453487396240234] |
31279dcf-30dd-4610-8890-ff5634120670 | applying-feature-underspecified-lexicon | 2204.07228 | null | https://arxiv.org/abs/2204.07228v1 | https://arxiv.org/pdf/2204.07228v1.pdf | Applying Feature Underspecified Lexicon Phonological Features in Multilingual Text-to-Speech | This study investigates whether the phonological features derived from the Featurally Underspecified Lexicon model can be applied in text-to-speech systems to generate native and non-native speech in English and Mandarin. We present a mapping of ARPABET/pinyin to SAMPA/SAMPA-SC and then to phonological features. This m... | ['Jiewen Zheng', 'Huang Liu', 'Huinan Zeng', 'Cong Zhang'] | 2022-04-14 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 2.78257191e-01 4.40875292e-01 -1.42214634e-03 -3.74718159e-01
-6.14070117e-01 -5.94135761e-01 8.14714372e-01 -1.63023293e-01
-4.25616324e-01 8.32227468e-01 2.89454967e-01 -8.83598089e-01
-3.08533125e-02 -4.65692341e-01 -7.30469346e-01 -3.73290360e-01
2.86680937e-01 7.11889148e-01 3.62297535e-01 -5.13002217... | [14.262474060058594, 7.05139684677124] |
ac4fca6f-d19b-429c-9908-c6f73dc32886 | cross-dataset-propensity-estimation-for | 2212.13892 | null | https://arxiv.org/abs/2212.13892v1 | https://arxiv.org/pdf/2212.13892v1.pdf | Cross-Dataset Propensity Estimation for Debiasing Recommender Systems | Datasets for training recommender systems are often subject to distribution shift induced by users' and recommenders' selection biases. In this paper, we study the impact of selection bias on datasets with different quantization. We then leverage two differently quantized datasets from different source distributions to... | ['Sarah Dean', 'Fengyu Li'] | 2022-12-22 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 3.28828067e-01 -1.53220266e-01 -7.16300130e-01 -7.70469129e-01
-1.00646162e+00 -7.38877714e-01 7.27465391e-01 2.49143422e-01
-3.61715049e-01 1.12965286e+00 7.34944940e-01 -3.84548455e-01
-4.58915532e-01 -1.03103936e+00 -8.15235138e-01 -5.58528483e-01
-6.01768456e-02 4.11731988e-01 1.19858377e-01 -9.28345397... | [9.705270767211914, 5.476047039031982] |
8d534311-5658-4151-b20d-06b290ed0a00 | enhancing-the-open-domain-dialogue-evaluation | null | null | https://aclanthology.org/2021.findings-acl.432 | https://aclanthology.org/2021.findings-acl.432.pdf | Enhancing the Open-Domain Dialogue Evaluation in Latent Space | null | ['Rui Yan', 'Shuming Shi', 'Dongyan Zhao', 'Haisong Zhang', 'Juntao Li', 'Lemao Liu', 'Zhangming Chan'] | null | null | null | null | findings-acl-2021-8 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.375680923461914, 3.5948355197906494] |
1b07b9a2-b083-4947-937b-b5145cfe2136 | abnormal-chest-x-ray-identification-with | 1903.02040 | null | http://arxiv.org/abs/1903.02040v1 | http://arxiv.org/pdf/1903.02040v1.pdf | Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier | Being one of the most common diagnostic imaging tests, chest radiography
requires timely reporting of potential findings in the images. In this paper,
we propose an end-to-end architecture for abnormal chest X-ray identification
using generative adversarial one-class learning. Unlike previous approaches,
our method tak... | ['Yu-Xing Tang', 'You-Bao Tang', 'Jing Xiao', 'Ronald M. Summers', 'Mei Han'] | 2019-03-05 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 5.72059035e-01 5.59000254e-01 2.40560900e-02 -5.27185977e-01
-1.19961929e+00 -5.08326530e-01 2.24812955e-01 4.62941043e-02
-2.73054630e-01 5.31608820e-01 9.33169872e-02 -7.87857413e-01
-6.25250190e-02 -7.15258837e-01 -1.04282749e+00 -7.49587655e-01
2.33451519e-02 7.76428223e-01 1.01352736e-01 3.60658944... | [15.134420394897461, -1.9662021398544312] |
a3364be9-5317-4c21-990b-6768f5f66d18 | exemplars-guided-empathetic-response | 2106.11791 | null | https://arxiv.org/abs/2106.11791v3 | https://arxiv.org/pdf/2106.11791v3.pdf | Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication | The majority of existing methods for empathetic response generation rely on the emotion of the context to generate empathetic responses. However, empathy is much more than generating responses with an appropriate emotion. It also often entails subtle expressions of understanding and personal resonance with the situatio... | ['Soujanya Poria', 'Rada Mihalcea', 'Alexander Gelbukh', 'Devamanyu Hazarika', 'Deepanway Ghosal', 'Navonil Majumder'] | 2021-06-22 | null | null | null | null | ['empathetic-response-generation'] | ['natural-language-processing'] | [-1.80277318e-01 1.91580862e-01 1.09689906e-01 -4.52773333e-01
-8.66247058e-01 -6.22188747e-01 7.26924896e-01 1.64979011e-01
-2.53904015e-01 9.46570873e-01 8.08716118e-01 4.00177449e-01
7.93373659e-02 -4.75111961e-01 -9.44800302e-02 -5.30869365e-01
5.51694036e-01 4.67244655e-01 -6.13989472e-01 -7.43199766... | [13.172581672668457, 7.6189985275268555] |
dbea21e5-5b94-49de-9d85-80fc114874b3 | on-the-efficacy-of-3d-point-cloud | 2306.06799 | null | https://arxiv.org/abs/2306.06799v1 | https://arxiv.org/pdf/2306.06799v1.pdf | On the Efficacy of 3D Point Cloud Reinforcement Learning | Recent studies on visual reinforcement learning (visual RL) have explored the use of 3D visual representations. However, none of these work has systematically compared the efficacy of 3D representations with 2D representations across different tasks, nor have they analyzed 3D representations from the perspective of age... | ['Hao Su', 'Xuanlin Li', 'Yunchao Yao', 'Zhan Ling'] | 2023-06-11 | null | null | null | null | ['3d-point-cloud-reinforcement-learning', 'robot-manipulation'] | ['computer-vision', 'robots'] | [-1.92747712e-01 1.54929131e-01 -5.11329532e-01 2.41069347e-02
-2.35393882e-01 -6.80615425e-01 9.75755155e-01 1.93243876e-01
-3.09448183e-01 2.99786001e-01 1.00972466e-01 -4.20194417e-01
-3.65424722e-01 -5.13919175e-01 -9.29569244e-01 -5.22333145e-01
-2.09018514e-01 6.57647073e-01 -1.61747009e-01 -4.17142779... | [4.685592174530029, 0.6537683606147766] |
b5d0b0c8-fda5-495a-bfcd-53435370c20d | phagocytosis-unveiled-a-scalable-and | 2304.13764 | null | https://arxiv.org/abs/2304.13764v1 | https://arxiv.org/pdf/2304.13764v1.pdf | Phagocytosis Unveiled: A Scalable and Interpretable Deep learning Framework for Neurodegenerative Disease Analysis | Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from backgrounds make this task challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce a fully... | ['Daniel Racoceanu', 'Morwena Latouche', 'Mehdi Ounissi'] | 2023-04-26 | null | null | null | null | ['cell-segmentation'] | ['medical'] | [ 7.66023546e-02 -6.17777586e-01 3.30747336e-01 -2.47769803e-01
-4.85588759e-01 -4.23708826e-01 2.54095107e-01 1.67755559e-01
-8.71999085e-01 9.02487695e-01 4.06885855e-02 -1.44188926e-01
1.30268604e-01 -3.75843465e-01 -5.31845093e-01 -9.86103833e-01
-1.74141124e-01 9.87655103e-01 3.00534457e-01 2.84214377... | [14.319708824157715, -3.0240187644958496] |
41e1f77f-090e-412d-b5bb-53684b9a01fe | 3d-shape-classification-using-collaborative | 1711.04875 | null | http://arxiv.org/abs/1711.04875v2 | http://arxiv.org/pdf/1711.04875v2.pdf | 3D Shape Classification Using Collaborative Representation based Projections | A novel 3D shape classification scheme, based on collaborative representation
learning, is investigated in this work. A data-driven feature-extraction
procedure, taking the form of a simple projection operator, is in the core of
our methodology. Provided a shape database, a graph encapsulating the
structural relationsh... | ['G. Economou', 'A. Papathanasiou', 'S. Oikonomou', 'F. Fotopoulou', 'S. Fotopoulos'] | 2017-11-13 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 1.81991041e-01 -2.94894222e-02 4.54162955e-02 -2.58731872e-01
-2.58082598e-01 -5.82447708e-01 9.18310106e-01 3.04102123e-01
-2.03563049e-01 3.53499174e-01 2.93960989e-01 -8.97872597e-02
-8.59290540e-01 -1.03853583e+00 -2.62181848e-01 -8.66211116e-01
-1.31939054e-01 8.73807967e-01 1.35265838e-03 -3.16451132... | [8.048518180847168, 3.964935064315796] |
c39b5b22-f08e-452e-9c21-c0422bbc80ee | crowd-source-scene-change-detection-and-local | 2203.05205 | null | https://arxiv.org/abs/2203.05205v1 | https://arxiv.org/pdf/2203.05205v1.pdf | Crowd Source Scene Change Detection and Local Map Update | As scene changes with time map descriptors become outdated, affecting VPS localization accuracy. In this work, we propose an approach to detect structural and texture scene changes to be followed by map update. In our method - map includes 3D points with descriptors generated either via LiDAR or SFM. Common approaches ... | ['Ofer Kruzel', 'Feng Wensen', 'Omri Asraf', 'Firas Shama', 'Lin Manqing', 'Nati Daniel', 'Itzik Wilf'] | 2022-03-10 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [ 3.19091618e-01 -7.51709580e-01 1.85321718e-01 -4.58946019e-01
-7.14522481e-01 -8.17563832e-01 7.10663080e-01 7.60556400e-01
-5.19158065e-01 4.65231538e-01 -4.36516732e-01 1.20862067e-01
-1.59478948e-01 -1.13760984e+00 -7.08825946e-01 -2.03527465e-01
-7.58810788e-02 1.06854153e+00 1.31902051e+00 -2.73891091... | [7.759767532348633, -2.4599556922912598] |
470784a9-746a-4da1-987c-29d6f6be55d2 | pixel-level-kernel-estimation-for-blind-super | null | null | https://ieeexplore.ieee.org/document/9615068 | https://ieeexplore.ieee.org/document/9615068 | Pixel-Level Kernel Estimation for Blind Super-Resolution | Throughout the past several years, deep learning-based models have achieved success in super-resolution (SR). The majority of these works assume that low-resolution (LR) images are ‘uniformly’ degraded from their corresponding high-resolution (HR) images using predefined blur kernels — all regions of an image undergoin... | ['Jae-Pil Heo', 'Euiyeon Kim', 'Jaihyun Lew'] | 2021-11-15 | null | null | null | ieee-access-2021-11 | ['super-resolution'] | ['computer-vision'] | [ 2.18370661e-01 -2.09890142e-01 -6.07698299e-02 -1.83745086e-01
-9.60953176e-01 -3.19429457e-01 2.67126471e-01 -4.28797334e-01
-2.95728952e-01 9.86750543e-01 1.15344413e-01 1.33900940e-01
-3.27606201e-01 -4.81407583e-01 -7.07967222e-01 -1.05119979e+00
-1.19284630e-01 -6.68630823e-02 3.99818033e-01 6.02043234... | [11.509187698364258, -2.4688827991485596] |
41f8514a-674f-4a95-bcae-9bafa2eb7ef8 | understanding-abuse-a-typology-of-abusive | 1705.09899 | null | http://arxiv.org/abs/1705.09899v2 | http://arxiv.org/pdf/1705.09899v2.pdf | Understanding Abuse: A Typology of Abusive Language Detection Subtasks | As the body of research on abusive language detection and analysis grows,
there is a need for critical consideration of the relationships between
different subtasks that have been grouped under this label. Based on work on
hate speech, cyberbullying, and online abuse we propose a typology that
captures central similari... | ['Thomas Davidson', 'Ingmar Weber', 'Zeerak Waseem', 'Dana Warmsley'] | 2017-05-28 | understanding-abuse-a-typology-of-abusive-1 | https://aclanthology.org/W17-3012 | https://aclanthology.org/W17-3012.pdf | ws-2017-8 | ['abuse-detection'] | ['natural-language-processing'] | [ 4.05698903e-02 -1.07276618e-01 -3.81398529e-01 -4.75338310e-01
-4.65088099e-01 -7.30139792e-01 5.90806842e-01 7.77216494e-01
-5.54103792e-01 2.99784452e-01 8.65379095e-01 -5.03647029e-01
-9.42778885e-02 -5.20463921e-02 1.48038805e-01 -1.95134029e-01
1.52348265e-01 -1.11144155e-01 2.35548895e-02 -2.90729702... | [8.649980545043945, 10.41579532623291] |
bd082050-7a02-4642-87b2-f41ed9756a3f | automated-reconstruction-of-3d-open-surfaces | 2210.15059 | null | https://arxiv.org/abs/2210.15059v2 | https://arxiv.org/pdf/2210.15059v2.pdf | Automated Reconstruction of 3D Open Surfaces from Sparse Point Clouds | Real-world 3D data may contain intricate details defined by salient surface gaps. Automated reconstruction of these open surfaces (e.g., non-watertight meshes) is a challenging problem for environment synthesis in mixed reality applications. Current learning-based implicit techniques can achieve high fidelity on closed... | ['William J. Beksi', 'Mohammad Samiul Arshad'] | 2022-10-26 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [ 3.69564295e-01 2.27044627e-01 4.68574911e-01 -3.78688008e-01
-9.68918502e-01 -3.25046003e-01 4.81143981e-01 3.94351363e-01
1.42136499e-01 5.24736047e-01 -1.83337957e-01 -9.31732729e-02
-5.02009802e-02 -1.15138972e+00 -1.21104157e+00 -3.82528275e-01
-2.25705385e-01 9.41778779e-01 5.92424214e-01 -3.15466911... | [8.615457534790039, -3.4812443256378174] |
8f229f5f-89b9-4566-8ba1-eeaa66d65512 | learning-causally-disentangled | 2306.01213 | null | https://arxiv.org/abs/2306.01213v1 | https://arxiv.org/pdf/2306.01213v1.pdf | Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms | Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. In this work, we define a new notion of causal disentanglement from the perspective of independent causal mechanisms. W... | ['Xintao Wu', 'Feng Chen', 'Yongkai Wu', 'Aneesh Komanduri'] | 2023-06-02 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 5.20818710e-01 3.50138754e-01 -7.32094765e-01 -2.80404299e-01
-5.17976105e-01 -8.22523415e-01 1.05244577e+00 -2.00729638e-01
7.16899186e-02 1.27425444e+00 1.12593925e+00 -4.54977065e-01
-8.12507749e-01 -7.63876438e-01 -1.07307410e+00 -6.87884331e-01
-4.99639094e-01 2.98983395e-01 -5.99697709e-01 2.24576250... | [8.032697677612305, 5.368592262268066] |
b56d5474-c6a9-4011-ad75-71587ebaf83a | human-head-pose-estimation-by-facial-features | 1510.02774 | null | http://arxiv.org/abs/1510.02774v1 | http://arxiv.org/pdf/1510.02774v1.pdf | Human Head Pose Estimation by Facial Features Location | We describe a method for estimating human head pose in a color image that
contains enough of information to locate the head silhouette and detect
non-trivial color edges of individual facial features. The method works by
spotting the human head on an arbitrary background, extracting the head
outline, and locating facia... | ['Eugene Borovikov'] | 2015-10-09 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-1.47152573e-01 1.17937200e-01 5.21132909e-02 -3.88002783e-01
-4.63947564e-01 -5.49760163e-01 1.34262145e-01 -2.57245183e-01
-5.58535993e-01 1.73753768e-01 -7.48791993e-02 1.34658039e-01
6.07651293e-01 -1.82451278e-01 -2.78122544e-01 -6.59276426e-01
-3.72425228e-01 4.67024565e-01 3.12545031e-01 3.81966829... | [13.570521354675293, 0.24519406259059906] |
d9e4771c-bad8-4605-956b-8cd7a9c054c7 | deep-bayesian-active-learning-for-multiple | 1912.01119 | null | https://arxiv.org/abs/1912.01119v2 | https://arxiv.org/pdf/1912.01119v2.pdf | Deep Bayesian Active Learning for Multiple Correct Outputs | Typical active learning strategies are designed for tasks, such as classification, with the assumption that the output space is mutually exclusive. The assumption that these tasks always have exactly one correct answer has resulted in the creation of numerous uncertainty-based measurements, such as entropy and least co... | ['Li Fei-Fei', 'Michael Bernstein', 'Khaled Jedoui', 'Ranjay Krishna'] | 2019-12-02 | null | null | null | null | ['question-answer-generation'] | ['natural-language-processing'] | [ 1.55986413e-01 6.71476364e-01 -1.29947811e-01 -5.04776001e-01
-1.01743722e+00 -4.92060930e-01 8.43463361e-01 2.35686854e-01
-5.19520819e-01 7.66778946e-01 1.22078590e-01 -4.79690880e-01
1.72999993e-01 -8.76151264e-01 -1.07634461e+00 -4.89586294e-01
4.80308533e-01 7.18600988e-01 3.63925248e-01 1.82816207... | [10.796609878540039, 1.756005883216858] |
9d199f13-775a-43df-a07c-b58ada41629d | ape-argument-pair-extraction-from-peer-review | null | null | https://aclanthology.org/2020.emnlp-main.569 | https://aclanthology.org/2020.emnlp-main.569.pdf | APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning | Peer review and rebuttal, with rich interactions and argumentative discussions in between, are naturally a good resource to mine arguments. However, few works study both of them simultaneously. In this paper, we introduce a new argument pair extraction (APE) task on peer review and rebuttal in order to study the conten... | ['Luo Si', 'Wei Lu', 'Qian Yu', 'Lidong Bing', 'Liying Cheng'] | null | null | null | null | emnlp-2020-11 | ['argument-pair-extraction-ape'] | ['natural-language-processing'] | [ 2.63135344e-01 4.07095850e-01 -5.96098721e-01 -3.67216557e-01
-1.05479491e+00 -5.57631135e-01 9.53722537e-01 5.39349556e-01
-3.46550345e-01 1.02556646e+00 4.46468562e-01 -8.80741060e-01
-1.48959786e-01 -5.91196835e-01 -9.60212529e-01 -1.90370351e-01
2.55696714e-01 3.35036784e-01 1.22927703e-01 -2.84115970... | [9.856151580810547, 9.292535781860352] |
400ddef6-3a92-4049-a855-27e206e42ab9 | learning-what-and-where-to-attend-with-humans | null | null | https://openreview.net/forum?id=BJgLg3R9KQ | https://openreview.net/pdf?id=BJgLg3R9KQ | Learning what and where to attend with humans in the loop | Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs). Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels. Here, we demonstrate th... | ['Dan Shiebler', 'Drew Linsley', 'Sven Eberhardt', 'Thomas Serre'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['image-categorization'] | ['computer-vision'] | [ 3.00594747e-01 -3.21947709e-02 -2.10998638e-04 -7.00394154e-01
-3.35908055e-01 -4.38628078e-01 5.58651984e-01 2.67778814e-01
-7.74769843e-01 3.03299397e-01 8.26551616e-02 -1.91878498e-01
-1.09937392e-01 -3.91329378e-01 -1.02237415e+00 -2.31036335e-01
-1.01091653e-01 1.31681964e-01 5.23205757e-01 -2.95085430... | [9.988508224487305, 1.8669776916503906] |
8dc38937-f653-4be9-a83f-8a2af547e75b | spot-keywords-from-very-noisy-and-mixed | 2305.17706 | null | https://arxiv.org/abs/2305.17706v1 | https://arxiv.org/pdf/2305.17706v1.pdf | Spot keywords from very noisy and mixed speech | Most existing keyword spotting research focuses on conditions with slight or moderate noise. In this paper, we try to tackle a more challenging task: detecting keywords buried under strong interfering speech (10 times higher than the keyword in amplitude), and even worse, mixed with other keywords. We propose a novel M... | ['Shi Yin', 'Jiqing Han', 'Lantian Li', 'Dong Wang', 'Ying Shi'] | 2023-05-28 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.12581748e-01 -8.95995125e-02 -3.14251855e-02 -1.40513536e-02
-1.11444092e+00 -2.84768820e-01 6.92900956e-01 -1.84174821e-01
-5.12300193e-01 6.19427681e-01 4.74497080e-01 -5.55649817e-01
1.58185497e-01 -2.28712305e-01 -8.27735722e-01 -7.96890497e-01
-1.86088637e-01 -1.16623715e-01 2.40260616e-01 -3.77183646... | [14.69292163848877, 6.120051383972168] |
4858202d-9c8a-4f70-988a-b1d6b822e5dd | tsam-a-two-stream-attention-model-for-causal | 2203.00819 | null | https://arxiv.org/abs/2203.00819v2 | https://arxiv.org/pdf/2203.00819v2.pdf | TSAM: A Two-Stream Attention Model for Causal Emotion Entailment | Causal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance. Previous works formalize CEE as independent utterance pair classification problems, with emotion and speaker information neglected. From a new perspective, this paper considers CEE in a joint framework... | ['Jie zhou', 'Xiuyi Chen', 'Fandong Meng', 'Zhen Yang', 'Duzhen Zhang'] | 2022-03-02 | null | https://aclanthology.org/2022.coling-1.588 | https://aclanthology.org/2022.coling-1.588.pdf | coling-2022-10 | ['causal-emotion-entailment'] | ['natural-language-processing'] | [ 1.04071319e-01 3.75706047e-01 1.00889981e-01 -9.25461233e-01
-7.02895105e-01 -1.65279090e-01 7.31053293e-01 -1.15176901e-01
-1.39308736e-01 3.33576411e-01 7.49510646e-01 5.71258627e-02
2.12356433e-01 -9.54382643e-02 -4.73433673e-01 -7.07619250e-01
-3.10965121e-01 4.05127943e-01 -4.95715946e-01 -3.93113315... | [13.099064826965332, 6.019595623016357] |
7b952281-6732-4914-81b3-5b05c69e0ea0 | hrca-advanced-multiple-choice-machine-reading | null | null | https://aclanthology.org/2022.lrec-1.651 | https://aclanthology.org/2022.lrec-1.651.pdf | HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method | Multiple-choice question answering (MCQA) for machine reading comprehension (MRC) is challenging. It requires a model to select a correct answer from several candidate options related to text passages or dialogue. To select the correct answer, such models must have the ability to understand natural languages, comprehen... | ['Hayato Yamana', 'Yuxiang Zhang'] | null | null | null | null | lrec-2022-6 | ['multiple-choice-qa', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.42370921e-01 1.95869058e-01 1.07956283e-01 -4.60067481e-01
-1.16138816e+00 -4.34363604e-01 3.65751088e-01 6.71650469e-01
-5.59968174e-01 7.19936907e-01 5.61793149e-01 -5.78305185e-01
-1.81727663e-01 -8.38301003e-01 -5.97896218e-01 6.29889546e-04
3.33206743e-01 5.32639503e-01 4.74576056e-01 -4.65229124... | [11.393810272216797, 8.063148498535156] |
fc84e0ae-193e-46da-90dd-b96c950a0593 | dialog-policy-learning-for-joint | 2006.05456 | null | https://arxiv.org/abs/2006.05456v3 | https://arxiv.org/pdf/2006.05456v3.pdf | Dialog Policy Learning for Joint Clarification and Active Learning Queries | Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training. Dialog interaction can enable this by the use of clarifications for correction and resolving uncertainty, and active learning queries to learn new concepts encountered during operatio... | ['Raymond J. Mooney', 'Aishwarya Padmakumar'] | 2020-06-09 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 1.98794857e-01 6.17101848e-01 -2.43478477e-01 -9.16630030e-01
-8.59103799e-01 -8.82482171e-01 6.06472552e-01 7.52489626e-01
-5.86536169e-01 6.53041065e-01 2.93917537e-01 -5.48466623e-01
-3.90472233e-01 -4.32192892e-01 -2.37062529e-01 -2.19363555e-01
7.31383637e-03 1.00727391e+00 3.41513187e-01 -3.23933452... | [12.898334503173828, 7.966475486755371] |
a0bdef05-c556-4a41-b255-8180ae91a2f9 | clustering-based-aggregations-for-prediction | 2210.09738 | null | https://arxiv.org/abs/2210.09738v1 | https://arxiv.org/pdf/2210.09738v1.pdf | Clustering-based Aggregations for Prediction in Event Streams | Predicting the behaviour of shoppers provides valuable information for retailers, such as the expected spend of a shopper or the total turnover of a supermarket. The ability to make predictions on an individual level is useful, as it allows supermarkets to accurately perform targeted marketing. However, given the expec... | ['Boudewijn F. van Dongen', 'Marwan Hassani', 'Yorick Spenrath'] | 2022-10-18 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-2.93576866e-02 3.48976910e-01 -5.20276368e-01 -5.15855968e-01
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-2.95570046e-01 1.02133739e+00 4.38035935e-01 -3.07772845... | [9.267672538757324, 5.822889804840088] |
7ec2282c-4a51-410d-adf6-7f3adeea4368 | neural-program-meta-induction | 1710.04157 | null | http://arxiv.org/abs/1710.04157v1 | http://arxiv.org/pdf/1710.04157v1.pdf | Neural Program Meta-Induction | Most recently proposed methods for Neural Program Induction work under the
assumption of having a large set of input/output (I/O) examples for learning
any underlying input-output mapping. This paper aims to address the problem of
data and computation efficiency of program induction by leveraging information
from relat... | ['Pushmeet Kohli', 'Rudy Bunel', 'Rishabh Singh', 'Jacob Devlin', 'Matthew Hausknecht'] | 2017-10-11 | neural-program-meta-induction-1 | http://papers.nips.cc/paper/6803-neural-program-meta-induction | http://papers.nips.cc/paper/6803-neural-program-meta-induction.pdf | neurips-2017-12 | ['program-induction'] | ['computer-code'] | [ 2.67535269e-01 -2.44552642e-01 -9.24722552e-01 -4.51150954e-01
-7.58128285e-01 -4.53675896e-01 2.64258295e-01 3.37374330e-01
-5.31194210e-01 6.83456898e-01 -3.78788292e-01 -6.17576420e-01
-3.94451013e-03 -9.07088578e-01 -1.22036278e+00 -3.85779053e-01
-2.01227710e-01 2.99934715e-01 2.05558956e-01 -1.02340467... | [8.01919937133789, 7.6302032470703125] |
0600965e-b6a3-498c-ab1e-c194eee8ef0c | medical-literature-mining-and-retrieval-in-a | 2108.01436 | null | https://arxiv.org/abs/2108.01436v1 | https://arxiv.org/pdf/2108.01436v1.pdf | Medical Literature Mining and Retrieval in a Conversational Setting | The Covid-19 pandemic has caused a spur in the medical research literature. With new research advances in understanding the virus, there is a need for robust text mining tools which can process, extract and present answers from the literature in a concise and consumable way. With a DialoGPT based multi-turn conversatio... | ['Rohini K. Srihari', 'Sougata Saha', 'Souvik Das'] | 2021-07-23 | null | null | null | null | ['literature-mining'] | ['natural-language-processing'] | [ 3.21049877e-02 1.05092861e-01 9.25200135e-02 -2.23702431e-01
-8.29498827e-01 -3.20429087e-01 5.30426800e-01 5.31580389e-01
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-1.31295947e-02 9.98751760e-01 -2.65608937e-01 -5.32795072... | [8.644817352294922, 8.673627853393555] |
abb1ae53-d712-4f0b-a3f9-a50ddafba85e | mpc-multi-view-probabilistic-clustering | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_MPC_Multi-View_Probabilistic_Clustering_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_MPC_Multi-View_Probabilistic_Clustering_CVPR_2022_paper.pdf | MPC: Multi-View Probabilistic Clustering | Despite the promising progress having been made, the two challenges of multi-view clustering (MVC) are still waiting for better solutions: i) Most existing methods are either not qualified or require additional steps for incomplete multi-view clustering and ii) noise or outliers might significantly degrade the over... | ['Jianqiang Huang', 'Chen Shen', 'Yaowu Chen', 'Boxuan Gu', 'Xiang Tian', 'Rongxin Jiang', 'Shaotian Yan', 'Junlong Liu', 'Junjie Liu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [-1.46888420e-01 -2.19503373e-01 -1.82467680e-02 -3.97119135e-01
-1.05573332e+00 -7.62822926e-01 3.23100746e-01 3.17741424e-01
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-5.80771640e-02 9.47545409e-01 6.46116793e-01 4.18703109... | [8.237527847290039, 4.633541584014893] |
7ae4d95d-4a20-4687-aafc-fb6799893b16 | regularizing-face-verification-nets-for-pain | 1702.06925 | null | http://arxiv.org/abs/1702.06925v3 | http://arxiv.org/pdf/1702.06925v3.pdf | Regularizing Face Verification Nets For Pain Intensity Regression | Limited labeled data are available for the research of estimating facial
expression intensities. For instance, the ability to train deep networks for
automated pain assessment is limited by small datasets with labels of
patient-reported pain intensities. Fortunately, fine-tuning from a
data-extensive pre-trained domain... | ['Trac. D. Tran', 'Gregory D. Hager', 'Feng Wang', 'Xiang Xiang', 'Jian Cheng', 'Chang Liu', 'Harry Quon', 'Austin Reiter', 'Alan L. Yuille'] | 2017-02-22 | null | null | null | null | ['pain-intensity-regression'] | ['medical'] | [ 1.00565232e-01 1.33177331e-02 -7.99657404e-01 -9.73219454e-01
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-5.76755367e-02 2.55458802e-01 -9.47261035e-01 -2.44733706... | [13.61054515838623, 1.69781494140625] |
8f004ab1-7d93-4366-a3a0-e74ba7086fc8 | text-summarization-with-pretrained-encoders | 1908.08345 | null | https://arxiv.org/abs/1908.08345v2 | https://arxiv.org/pdf/1908.08345v2.pdf | Text Summarization with Pretrained Encoders | Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework... | ['Yang Liu', 'Mirella Lapata'] | 2019-08-22 | text-summarization-with-pretrained-encoders-1 | https://aclanthology.org/D19-1387 | https://aclanthology.org/D19-1387.pdf | ijcnlp-2019-11 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 3.88410330e-01 4.34781313e-01 -1.58227041e-01 -4.56076801e-01
-1.05775917e+00 -4.63509142e-01 7.83638418e-01 2.21467942e-01
-3.27931613e-01 6.79879069e-01 1.04461658e+00 -2.29488671e-01
2.33493865e-01 -6.99119568e-01 -9.55244720e-01 -3.96933675e-01
3.06599379e-01 3.56245458e-01 7.39063025e-02 -4.71749455... | [12.150152206420898, 9.204319953918457] |
37426fea-f4aa-4f1f-9826-2d7408168c59 | lcdctcnn-lung-cancer-diagnosis-of-ct-scan | 2304.04814 | null | https://arxiv.org/abs/2304.04814v1 | https://arxiv.org/pdf/2304.04814v1.pdf | LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model | The most deadly and life-threatening disease in the world is lung cancer. Though early diagnosis and accurate treatment are necessary for lowering the lung cancer mortality rate. A computerized tomography (CT) scan-based image is one of the most effective imaging techniques for lung cancer detection using deep learning... | ['Ahmed Abdelgawad', 'Mahabuba Meherin', 'Md Ishtyaq Mahmud', 'Muntasir Mamun'] | 2023-04-10 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-2.64071077e-01 -1.51192203e-01 -4.81576622e-01 9.80895981e-02
-7.00510621e-01 2.99495133e-03 4.77361739e-01 2.80184537e-01
-8.39806974e-01 6.20428920e-01 9.66757536e-02 -6.69668078e-01
-1.29062325e-01 -1.06473756e+00 -2.05567300e-01 -6.44187689e-01
-1.96390852e-01 5.89759767e-01 6.19637966e-01 2.35585064... | [15.33697509765625, -2.292292833328247] |
83e47b17-efeb-4473-83a3-e3a1e2834bdf | autolearn-automated-feature-generation-and | null | null | https://ieeexplore.ieee.org/abstract/document/8215494 | http://web2py.iiit.ac.in/research_centres/publications/download/inproceedings.pdf.88535e0ea3a74e72.4943444d2d20323031372e706466.pdf | AutoLearn - Automated Feature Generation and Selection | In recent years, the importance of feature engineering has been confirmed by the exceptional performance of deep learning techniques, that automate this task for some applications. For others, feature engineering requires substantial manual effort in designing and selecting features and is often tedious and non-scalabl... | ['Ambika Kaul', 'Saket Maheshwary', 'Vikram Pudi'] | 2017-11-17 | null | null | null | ieee-ieee-international-conference-on-data | ['automated-feature-engineering'] | ['methodology'] | [ 1.10861167e-01 -1.94014326e-01 -3.00130427e-01 -7.10945606e-01
-5.86887836e-01 -5.89401484e-01 3.83025289e-01 3.13883215e-01
-3.16263437e-01 9.65960443e-01 4.50981926e-04 -9.84312594e-02
-7.08272994e-01 -7.71968961e-01 -5.65532804e-01 -7.71054566e-01
-5.76504111e-01 5.93860745e-01 -8.32629576e-02 -2.26259410... | [8.090826034545898, 4.498520374298096] |
5e00827f-ed9e-42ab-a742-befd0ea5abf5 | demixing-sines-and-spikes-using-multiple | 2004.00259 | null | https://arxiv.org/abs/2004.00259v2 | https://arxiv.org/pdf/2004.00259v2.pdf | Demixing Sines and Spikes Using Multiple Measurement Vectors | In this paper, we address the line spectral estimation problem with multiple measurement corrupted vectors. Such scenarios appear in many practical applications such as radar, optics, and seismic imaging in which the signal of interest can be modeled as the sum of a spectrally sparse and a blocksparse signal known as o... | ['Mohammad Hossein Kahaei', 'Sajad Daei', 'Hoomaan Maskan'] | 2020-04-01 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 6.68187737e-01 -4.61060852e-02 2.31200799e-01 1.21963814e-01
-9.48621154e-01 -4.75983858e-01 2.05453066e-03 -4.08389373e-03
-2.66862035e-01 7.10043490e-01 2.06686985e-02 -4.45335731e-02
-4.58245516e-01 -2.70392478e-01 -7.48834074e-01 -9.46667612e-01
-2.19462976e-01 2.25374788e-01 -1.61434084e-01 7.80884847... | [6.575272083282471, 1.5165603160858154] |
21bc4800-776e-4a96-b651-b427d4fb6fb5 | uncertainty-regularized-policy-learning-for | null | null | https://openreview.net/forum?id=rwSWaS_tGgG | https://openreview.net/pdf?id=rwSWaS_tGgG | Uncertainty Regularized Policy Learning for Offline Reinforcement Learning | Recent studies show the promising results of using online RL methods in the offline setting.
However, such a learning diagram may suffer from an overtraining issue, that is, the performance of the policy degrades significantly as the training process continues when the dataset is not sufficiently large and diverse.
In... | ['Chengqi Zhang', 'Xuan Song', 'Guodong Long', 'Pengfei Wei', 'Jing Jiang', 'Han Zheng'] | 2021-09-29 | null | null | null | null | ['d4rl'] | ['robots'] | [-2.32659608e-01 -1.46208927e-02 -6.89674020e-01 -2.36955479e-01
-1.03001738e+00 -6.82753682e-01 5.70365906e-01 1.40990019e-01
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2.03232974e-01 3.53817612e-01 1.31507874e-01 2.60541979... | [4.091881275177002, 2.204296827316284] |
6921e664-cb54-4d55-83c2-c9bf43f5e60f | measuring-commonality-in-recommendation-of | 2208.01696 | null | https://arxiv.org/abs/2208.01696v1 | https://arxiv.org/pdf/2208.01696v1.pdf | Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship | Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of individual cultural experience. While the majority of research on recommender systems optimizes for personalized user experience, this paradigm does not capture the ways that recommender systems impa... | ['Georgina Born', 'Fernando Diaz', 'Gustavo Ferreira', 'Andres Ferraro'] | 2022-08-02 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-3.40786427e-01 -7.62375891e-02 -3.55308235e-01 6.14146516e-02
-1.24971896e-01 -8.92178118e-01 7.28891551e-01 3.16658616e-01
-3.91835093e-01 1.92795515e-01 1.18629682e+00 -1.76027775e-01
-2.68700927e-01 -6.84724391e-01 -1.83166385e-01 -5.37824571e-01
4.24689710e-01 -3.54283899e-01 -4.49008316e-01 -8.75019670... | [9.73599624633789, 5.8445329666137695] |
098f0381-8ed9-469c-b097-71a3b6907380 | evaluating-token-level-and-passage-level | 2203.11163 | null | https://arxiv.org/abs/2203.11163v2 | https://arxiv.org/pdf/2203.11163v2.pdf | Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval | With the recent success of dense retrieval methods based on bi-encoders, studies have applied this approach to various interesting downstream retrieval tasks with good efficiency and in-domain effectiveness. Recently, we have also seen the presence of dense retrieval models in Math Information Retrieval (MIR) tasks, bu... | ['Jimmy Lin', 'Yuqing Xie', 'Jheng-Hong Yang', 'Wei Zhong'] | 2022-03-21 | null | null | null | null | ['math-information-retrieval'] | ['natural-language-processing'] | [-8.36611018e-02 -2.77428269e-01 -3.86335731e-01 -8.92649740e-02
-1.52456546e+00 -5.10209501e-01 1.04095542e+00 4.86646801e-01
-6.14084959e-01 5.37446439e-01 8.05421650e-01 -2.30042953e-02
-7.41304755e-01 -8.61180365e-01 -6.23279035e-01 -2.87807614e-01
1.53378740e-01 9.46284413e-01 3.54282379e-01 -7.08733559... | [11.467082977294922, 7.644372463226318] |
3dd396dc-23bf-4d6a-8d28-c6a2edac9527 | debiasing-multilingual-word-embeddings-a-case | 2107.10181 | null | https://arxiv.org/abs/2107.10181v2 | https://arxiv.org/pdf/2107.10181v2.pdf | Debiasing Multilingual Word Embeddings: A Case Study of Three Indian Languages | In this paper, we advance the current state-of-the-art method for debiasing monolingual word embeddings so as to generalize well in a multilingual setting. We consider different methods to quantify bias and different debiasing approaches for monolingual as well as multilingual settings. We demonstrate the significance ... | ['Animesh Mukherjee', 'Ayush Suhane', 'Vishal Garimella', 'Srijan Bansal'] | 2021-07-21 | null | null | null | null | ['multilingual-word-embeddings'] | ['methodology'] | [-4.82216299e-01 -2.08870173e-01 -4.23720270e-01 -3.92659038e-01
-1.30547202e+00 -1.22738898e+00 6.12574518e-01 3.38574350e-01
-7.93353677e-01 8.25429857e-01 6.76870942e-01 -8.63153040e-01
1.34096757e-01 -5.34811616e-01 -6.35135531e-01 -4.92117167e-01
2.42219567e-01 4.54491615e-01 -1.85765438e-02 -5.98554850... | [10.916136741638184, 9.999638557434082] |
f9f07a7a-a1b7-4557-839e-4b5170a2415e | medical-image-segmentation-via-cascaded | null | null | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.pdf | Medical Image Segmentation via Cascaded Attention Decoding | Transformers have shown great promise in medical image segmentation due to their ability to capture long-range dependencies through self-attention. However, they lack the ability to learn the local (contextual) relations among pixels. Previous works try to overcome this problem by embedding convolutional layers either ... | ['Radu Marculescu', 'Md Mostafijur Rahman'] | 2023-01-03 | null | null | null | proceedings-of-the-ieee-cvf-winter-conference-3 | ['polyp-segmentation'] | ['computer-vision'] | [ 2.78722197e-01 1.46872044e-01 3.79307084e-02 -4.87789482e-01
-8.97995472e-01 -1.93095282e-01 4.25842017e-01 2.04678103e-01
-5.50987542e-01 4.05464560e-01 3.99788290e-01 -1.87699229e-01
2.41001785e-01 -7.33832300e-01 -7.66171575e-01 -6.63280785e-01
2.27349758e-01 1.40002012e-01 5.99833846e-01 -1.20885663... | [14.543108940124512, -2.5945799350738525] |
2dc9c508-448b-4287-8db6-822ac17b4fa2 | sctn-sparse-convolution-transformer-network | 2105.04447 | null | https://arxiv.org/abs/2105.04447v4 | https://arxiv.org/pdf/2105.04447v4.pdf | SCTN: Sparse Convolution-Transformer Network for Scene Flow Estimation | We propose a novel scene flow estimation approach to capture and infer 3D motions from point clouds. Estimating 3D motions for point clouds is challenging, since a point cloud is unordered and its density is significantly non-uniform. Such unstructured data poses difficulties in matching corresponding points between po... | ['Bernard Ghanem', 'Silvio Giancola', 'Cheng Zheng', 'Bing Li'] | 2021-05-10 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-2.69121826e-01 -5.13208687e-01 -1.19438343e-01 -1.29649103e-01
-3.22160482e-01 -5.96045852e-01 4.68919456e-01 -1.41241342e-01
-1.23559557e-01 5.06480038e-01 2.20401421e-01 -2.90046316e-02
-1.60811409e-01 -8.04943800e-01 -1.00470567e+00 -4.38839614e-01
-2.11598620e-01 4.19144779e-01 4.52148288e-01 -4.32305038... | [8.551650047302246, -2.078713893890381] |
bc360c43-7ee6-4a04-94d5-58225dcace46 | everybody-sign-now-translating-spoken | 2011.09846 | null | https://arxiv.org/abs/2011.09846v4 | https://arxiv.org/pdf/2011.09846v4.pdf | Everybody Sign Now: Translating Spoken Language to Photo Realistic Sign Language Video | To be truly understandable and accepted by Deaf communities, an automatic Sign Language Production (SLP) system must generate a photo-realistic signer. Prior approaches based on graphical avatars have proven unpopular, whereas recent neural SLP works that produce skeleton pose sequences have been shown to be not unders... | ['Richard Bowden', 'Necati Cihan Camgoz', 'Ben Saunders'] | 2020-11-19 | null | null | null | null | ['sign-language-production'] | ['natural-language-processing'] | [ 4.04203355e-01 2.17564702e-01 5.87208942e-02 -3.13016355e-01
-1.04852641e+00 -5.92790782e-01 7.76495159e-01 -1.18509531e+00
-1.86189383e-01 6.60878837e-01 6.12448215e-01 -4.93875444e-02
2.49790609e-01 -4.41061825e-01 -1.03223324e+00 -6.81594312e-01
2.74237931e-01 4.81586456e-01 8.42492878e-02 -1.24422237... | [9.216070175170898, -6.527680397033691] |
1f9223c7-f222-4913-a660-a87d4eba201f | constrained-labeled-data-generation-for-low | null | null | https://aclanthology.org/2021.findings-acl.396 | https://aclanthology.org/2021.findings-acl.396.pdf | Constrained Labeled Data Generation for Low-Resource Named Entity Recognition | null | ['Dan Roth', 'Ruohao Guo'] | null | null | null | null | findings-acl-2021-8 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.385695457458496, 3.6895151138305664] |
018df2b8-20fa-4e8a-9078-f6f91a7d3247 | interactive-sketch-fill-multiclass-sketch-to | 1909.11081 | null | https://arxiv.org/abs/1909.11081v2 | https://arxiv.org/pdf/1909.11081v2.pdf | Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation | We propose an interactive GAN-based sketch-to-image translation method that helps novice users create images of simple objects. As the user starts to draw a sketch of a desired object type, the network interactively recommends plausible completions, and shows a corresponding synthesized image to the user. This enables ... | ['Arnab Ghosh', 'Puneet K. Dokania', 'Philip H. S. Torr', 'Alexei A. Efros', 'Richard Zhang', 'Eli Shechtman', 'Oliver Wang'] | 2019-09-24 | interactive-sketch-fill-multiclass-sketch-to-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Ghosh_Interactive_Sketch__Fill_Multiclass_Sketch-to-Image_Translation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Ghosh_Interactive_Sketch__Fill_Multiclass_Sketch-to-Image_Translation_ICCV_2019_paper.pdf | iccv-2019-10 | ['sketch-to-image-translation'] | ['computer-vision'] | [ 4.28440064e-01 1.35036632e-01 -2.04022214e-01 -2.89874494e-01
-6.71081901e-01 -9.38442528e-01 7.78307378e-01 -3.90306771e-01
1.35227844e-01 5.87931037e-01 -3.53959692e-03 -1.82712704e-01
4.91521627e-01 -9.75551903e-01 -8.01314592e-01 -3.03053707e-01
4.65938032e-01 5.75560212e-01 -7.02266619e-02 9.67013538... | [11.555671691894531, -0.37150782346725464] |
3ab50c30-1104-47d2-b478-4314b595cdf7 | complementary-time-frequency-domain-networks | 2012.11974 | null | https://arxiv.org/abs/2012.11974v2 | https://arxiv.org/pdf/2012.11974v2.pdf | Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction | Purpose: To introduce a novel deep learning based approach for fast and high-quality dynamic multi-coil MR reconstruction by learning a complementary time-frequency domain network that exploits spatio-temporal correlations simultaneously from complementary domains. Theory and Methods: Dynamic parallel MR image reconstr... | ['Daniel Rueckert', 'Joseph V. Hajnal', 'Anthony N. Price', 'Claudia Prieto', 'René Botnar', 'Thomas Küstner', 'Jo Schlemper', 'Kerstin Hammernik', 'Jinming Duan', 'Chen Qin'] | 2020-12-22 | null | null | null | null | ['de-aliasing'] | ['computer-vision'] | [ 5.35787225e-01 -1.56614766e-01 1.04377903e-01 -4.27596390e-01
-1.07613420e+00 -1.57184169e-01 1.87376902e-01 -2.39022300e-01
-6.08833611e-01 7.40283072e-01 9.16601717e-02 -2.34578878e-01
-8.85918558e-01 -3.20415407e-01 -5.07370651e-01 -1.03955758e+00
-7.95141816e-01 5.94180763e-01 1.07970148e-01 5.28592290... | [13.526352882385254, -2.4019837379455566] |
337185fc-8848-4957-a7fd-a5b3983fa647 | sc-block-supervised-contrastive-blocking | 2303.03132 | null | https://arxiv.org/abs/2303.03132v2 | https://arxiv.org/pdf/2303.03132v2.pdf | SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines | The goal of entity resolution is to identify records in multiple datasets that represent the same real-world entity. However, comparing all records across datasets can be computationally intensive, leading to long runtimes. To reduce these runtimes, entity resolution pipelines are constructed of two parts: a blocker th... | ['Christian Bizer', 'Roee Shraga', 'Alexander Brinkmann'] | 2023-03-06 | null | null | null | null | ['blocking', 'entity-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.03839867e-01 -1.68278646e-02 9.01136994e-02 -4.22570795e-01
-1.32114363e+00 -8.92656744e-01 4.50536281e-01 8.90230238e-01
-7.21366107e-01 4.62490201e-01 8.03877413e-03 -1.15439668e-01
-2.70144999e-01 -1.10591710e+00 -9.85050976e-01 -1.37078986e-01
-2.49174237e-01 7.78766870e-01 5.67561507e-01 7.75676817... | [9.426286697387695, 8.43648910522461] |
bd5a2ed7-5d82-4804-b765-6782f82885f8 | building-an-effective-email-spam | 2303.08792 | null | https://arxiv.org/abs/2303.08792v1 | https://arxiv.org/pdf/2303.08792v1.pdf | Building an Effective Email Spam Classification Model with spaCy | Today, people use email services such as Gmail, Outlook, AOL Mail, etc. to communicate with each other as quickly as possible to send information and official letters. Spam or junk mail is a major challenge to this type of communication, usually sent by botnets with the aim of advertising, harming and stealing informat... | ['Kazem Taghandiki'] | 2023-03-15 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-2.94018179e-01 -2.87021190e-01 3.03853691e-01 -4.55889523e-01
2.04009518e-01 -6.44988239e-01 6.86831534e-01 5.40174305e-01
-4.08542812e-01 5.54127574e-01 1.68850690e-01 -8.79919052e-01
5.75357527e-02 -1.06027055e+00 -1.89250819e-02 -3.93115550e-01
1.90517440e-01 8.31585824e-01 6.34756446e-01 -3.09115499... | [7.810455799102783, 10.020471572875977] |
34caf033-d6b2-4436-b0b4-083082c562e2 | keyphrase-generation-a-multi-aspect-survey | 1910.05059 | null | https://arxiv.org/abs/1910.05059v1 | https://arxiv.org/pdf/1910.05059v1.pdf | Keyphrase Generation: A Multi-Aspect Survey | Extractive keyphrase generation research has been around since the nineties, but the more advanced abstractive approach based on the encoder-decoder framework and sequence-to-sequence learning has been explored only recently. In fact, more than a dozen of abstractive methods have been proposed in the last three years, ... | ['Ondřej Bojar', 'Erion Çano'] | 2019-10-11 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 6.05510890e-01 2.41770208e-01 -4.65957016e-01 3.14726532e-01
-9.77090478e-01 -4.13117260e-01 9.56329525e-01 6.53461158e-01
-5.07887304e-01 1.20546293e+00 1.07948935e+00 -1.49442092e-01
-2.58676887e-01 -4.86621231e-01 -6.84056938e-01 -5.41750073e-01
6.18878938e-02 1.97341815e-01 -9.98867527e-02 -3.90249610... | [12.46025276184082, 9.30049991607666] |
2d5b0d61-8e95-4426-b706-33a4ffca85c0 | scfusion-real-time-incremental-scene | 2010.13662 | null | https://arxiv.org/abs/2010.13662v3 | https://arxiv.org/pdf/2010.13662v3.pdf | SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion | Real-time scene reconstruction from depth data inevitably suffers from occlusion, thus leading to incomplete 3D models. Partial reconstructions, in turn, limit the performance of algorithms that leverage them for applications in the context of, e.g., augmented reality, robotic navigation, and 3D mapping. Most methods a... | ['Federico Tombari', 'Nassir Navab', 'Keisuke Tateno', 'Shun-Cheng Wu'] | 2020-10-26 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 4.20580477e-01 1.43339485e-01 7.16341883e-02 -3.85906935e-01
-5.43558180e-01 -7.49832988e-01 5.71036160e-01 2.92570829e-01
-3.59731525e-01 4.09088910e-01 1.52947217e-01 -3.30901563e-01
-7.31485263e-02 -9.23358679e-01 -9.42132771e-01 -9.10533592e-02
4.18700963e-01 8.48176181e-01 2.29514405e-01 1.77320316... | [8.513659477233887, -2.8795320987701416] |
97abf50d-700e-48db-8c13-5e86c494b5ea | structured-autocorrelation-matrix-estimation | 2008.12369 | null | https://arxiv.org/abs/2008.12369v1 | https://arxiv.org/pdf/2008.12369v1.pdf | Structured Autocorrelation Matrix Estimation for Coprime Arrays | A coprime array receiver processes a collection of received-signal snapshots to estimate the autocorrelation matrix of a larger (virtual) uniform linear array, known as coarray. By the received-signal model, this matrix has to be (i) Positive-Definite, (ii) Hermitian, (iii) Toeplitz, and (iv) its noise-subspace eigenva... | ['Panos P. Markopoulos', 'Dimitris G. Chachlakis'] | 2020-08-27 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 3.07629079e-01 -4.14471596e-01 2.61312813e-01 1.72489986e-01
-6.62909210e-01 -1.03899550e+00 1.61745578e-01 -4.19886857e-01
-4.53684032e-02 5.55383682e-01 1.16336152e-01 -5.82864344e-01
-5.71385324e-01 -1.54087469e-01 -2.51301229e-01 -9.98581946e-01
-5.52752495e-01 -7.53921270e-02 -4.17109877e-01 2.01058850... | [6.455741882324219, 1.3487093448638916] |
aac854c6-5b32-4bbc-b1dd-a451657f3e6b | disc-diff-disentangled-conditional-diffusion | 2303.13933 | null | https://arxiv.org/abs/2303.13933v2 | https://arxiv.org/pdf/2303.13933v2.pdf | DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution | Multi-contrast magnetic resonance imaging (MRI) is the most common management tool used to characterize neurological disorders based on brain tissue contrasts. However, acquiring high-resolution MRI scans is time-consuming and infeasible under specific conditions. Hence, multi-contrast super-resolution methods have bee... | ['Chao Li', 'Xi Chen', 'Lan Jiang', 'Ye Mao'] | 2023-03-24 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 3.95725042e-01 -3.28618556e-01 9.96142104e-02 -3.69712830e-01
-1.18354309e+00 -2.10937306e-01 5.28111339e-01 -2.80610919e-01
-3.74697089e-01 7.67066002e-01 4.83063608e-01 2.41800606e-01
-6.71198368e-01 -3.61252785e-01 -1.90127864e-01 -1.01326764e+00
-4.79048282e-01 4.78393763e-01 2.29477763e-01 3.80837880... | [13.641218185424805, -2.3994524478912354] |
f14c52bb-884f-4393-88fe-80c0cab8eda6 | unsupervised-primitive-discovery-for-improved-1 | 1906.03650 | null | https://arxiv.org/abs/1906.03650v1 | https://arxiv.org/pdf/1906.03650v1.pdf | Unsupervised Primitive Discovery for Improved 3D Generative Modeling | 3D shape generation is a challenging problem due to the high-dimensional output space and complex part configurations of real-world objects. As a result, existing algorithms experience difficulties in accurate generative modeling of 3D shapes. Here, we propose a novel factorized generative model for 3D shape generation... | ['Nick Barnes', 'Salman H. Khan', 'Munawar Hayat', 'Yulan Guo'] | 2019-06-09 | unsupervised-primitive-discovery-for-improved | http://openaccess.thecvf.com/content_CVPR_2019/html/Khan_Unsupervised_Primitive_Discovery_for_Improved_3D_Generative_Modeling_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Khan_Unsupervised_Primitive_Discovery_for_Improved_3D_Generative_Modeling_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-shape-generation'] | ['computer-vision'] | [ 2.43191496e-01 1.33053094e-01 2.50321209e-01 -1.91388085e-01
-5.71362972e-01 -7.40886033e-01 7.49775887e-01 -5.90530969e-02
4.37500596e-01 5.04838586e-01 2.22273275e-01 -2.71360646e-03
9.01535302e-02 -1.16718197e+00 -5.97891331e-01 -4.87260014e-01
1.49693623e-01 1.14521801e+00 2.68795341e-01 1.19102187... | [8.827556610107422, -3.629094123840332] |
fd528a59-66a2-4cd6-ac13-f1152e1860a9 | regularization-techniques-for-fine-tuning-in | 1707.09920 | null | http://arxiv.org/abs/1707.09920v1 | http://arxiv.org/pdf/1707.09920v1.pdf | Regularization techniques for fine-tuning in neural machine translation | We investigate techniques for supervised domain adaptation for neural machine
translation where an existing model trained on a large out-of-domain dataset is
adapted to a small in-domain dataset. In this scenario, overfitting is a major
challenge. We investigate a number of techniques to reduce overfitting and
improve ... | ['Barry Haddow', 'Ulrich Germann', 'Antonio Valerio Miceli Barone', 'Rico Sennrich'] | 2017-07-31 | regularization-techniques-for-fine-tuning-in-1 | https://aclanthology.org/D17-1156 | https://aclanthology.org/D17-1156.pdf | emnlp-2017-9 | ['l2-regularization'] | ['methodology'] | [ 3.05796802e-01 2.77628422e-01 -5.79835892e-01 -6.56790495e-01
-1.29043078e+00 -9.10377264e-01 5.30463755e-01 -1.11962095e-01
-8.24159503e-01 1.25999570e+00 2.61314929e-01 -7.12306559e-01
2.47705534e-01 -5.21180570e-01 -1.15631735e+00 -3.46348226e-01
5.76521635e-01 7.96816051e-01 7.13238940e-02 -4.01811272... | [11.654426574707031, 10.284634590148926] |
7d7ccc9f-df1d-47ac-b22c-3996a04876b9 | it-s-ai-match-a-two-step-approach-for-schema | 2203.04366 | null | https://arxiv.org/abs/2203.04366v1 | https://arxiv.org/pdf/2203.04366v1.pdf | It's AI Match: A Two-Step Approach for Schema Matching Using Embeddings | Since data is often stored in different sources, it needs to be integrated to gather a global view that is required in order to create value and derive knowledge from it. A critical step in data integration is schema matching which aims to find semantic correspondences between elements of two schemata. In order to redu... | ['Carsten Binnig', 'Andreas Schmidt', 'Michael Truong-Ngoc', 'Benjamin Hättasch'] | 2022-03-08 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 2.18788788e-01 -3.84599455e-02 1.24399938e-01 -5.28739214e-01
-8.57090533e-01 -7.11568773e-01 6.36340797e-01 1.11962390e+00
-4.93222237e-01 3.49743545e-01 2.09071904e-01 -1.12216529e-02
-3.74700308e-01 -1.09554827e+00 -4.71497744e-01 -2.35556737e-01
4.06547129e-01 9.66199696e-01 3.55551779e-01 -4.25175399... | [9.264275550842285, 8.148992538452148] |
64e8ced4-f7e2-4fc9-b216-dc8bf6b5cc4a | probabilistic-prompt-learning-for-dense | 2304.00779 | null | https://arxiv.org/abs/2304.00779v1 | https://arxiv.org/pdf/2304.00779v1.pdf | Probabilistic Prompt Learning for Dense Prediction | Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models. However, this approach results in limited performance for dense prediction tasks that requ... | ['Kwanghoon Sohn', 'Jinhyun Jang', 'Jin Kim', 'Somi Jeong', 'Taeyong Song', 'Hyeongjun Kwon'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kwon_Probabilistic_Prompt_Learning_for_Dense_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kwon_Probabilistic_Prompt_Learning_for_Dense_Prediction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-matching'] | ['natural-language-processing'] | [ 4.86294538e-01 -5.16880676e-02 -4.27055359e-01 -7.45533764e-01
-7.82958984e-01 -3.78942817e-01 9.54151452e-01 1.83029220e-01
-3.88496071e-01 5.99388778e-01 4.00650024e-01 1.35418698e-01
2.81945411e-02 -5.76157331e-01 -7.33914018e-01 -9.14812028e-01
4.79147255e-01 6.01113915e-01 2.34120205e-01 3.22993785... | [10.080221176147461, 1.885063886642456] |
bf998df3-ee7b-4c67-9c7c-1e28ceea10ae | make-a-voice-unified-voice-synthesis-with | 2305.19269 | null | https://arxiv.org/abs/2305.19269v1 | https://arxiv.org/pdf/2305.19269v1.pdf | Make-A-Voice: Unified Voice Synthesis With Discrete Representation | Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common. In addition, the majority of voice synthesis models currently rely on annotated audio data, but it is crucial to scale them to self-supervised datasets in order to effectively captu... | ['Dong Yu', 'Zhou Zhao', 'Chao Weng', 'Ziyue Jiang', 'Zhenhui Ye', 'Luping Liu', 'Dongchao Yang', 'Yongqi Wang', 'Chunlei Zhang', 'Rongjie Huang'] | 2023-05-30 | null | null | null | null | ['voice-conversion', 'voice-conversion', 'singing-voice-synthesis'] | ['audio', 'speech', 'speech'] | [ 6.36159629e-02 -4.64902958e-03 -9.39518660e-02 -2.54878402e-01
-1.07053220e+00 -7.91116476e-01 3.27221841e-01 -3.18817556e-01
2.60050625e-01 3.45759243e-01 5.86601019e-01 -2.97029793e-01
3.04585010e-01 -4.80206877e-01 -6.50815189e-01 -5.15680432e-01
3.35493386e-01 1.94249719e-01 -1.01744786e-01 -4.03592259... | [15.103572845458984, 6.511280536651611] |
5050f452-967b-4a16-b55c-347e889a8a03 | mmkgr-multi-hop-multi-modal-knowledge-graph | 2209.01416 | null | https://arxiv.org/abs/2209.01416v1 | https://arxiv.org/pdf/2209.01416v1.pdf | MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning | Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i.e., texts and images), which enhance the diversity of knowledge. However, the natural incompleteness has significantly hindered the applications of MKGs. To tackle the problem, existing studies emp... | ['Lei Zhao', 'Wei Chen', 'Hongzhi Yin', 'Jianfeng Qu', 'Weiqing Wang', 'Shangfei Zheng'] | 2022-09-03 | null | null | null | null | ['multi-modal-knowledge-graph'] | ['knowledge-base'] | [-1.15662873e-01 4.49827284e-01 -1.87793270e-01 1.44488756e-02
-8.23702574e-01 -7.04968125e-02 4.84290302e-01 -4.53060567e-02
-2.01817930e-01 7.20461726e-01 4.52345312e-01 -9.29915383e-02
-6.00111067e-01 -1.14693749e+00 -6.18184566e-01 -6.25512242e-01
4.06026185e-01 3.60233724e-01 3.42797309e-01 -6.97760522... | [8.934564590454102, 7.759623050689697] |
591a9cc4-0eda-47ca-87c8-4880a5766d02 | atlas-based-automated-detection-of-swim | 1902.06130 | null | http://arxiv.org/abs/1902.06130v1 | http://arxiv.org/pdf/1902.06130v1.pdf | Atlas-based automated detection of swim bladder in Medaka embryo | Fish embryo models are increasingly being used both for the assessment of
chemicals efficacy and potential toxicity. This article proposes a methodology
to automatically detect the swim bladder on 2D images of Medaka fish embryos
seen either in dorsal view or in lateral view. After embryo segmentation and
for each stud... | ['Noémie De Crozé', 'Marc Léonard', 'Jean Cousty', 'Hugues Talbot', 'Diane Genest'] | 2019-02-16 | null | null | null | null | ['bladder-segmentation'] | ['medical'] | [ 2.31793016e-01 2.28204682e-01 3.07006389e-01 -2.26196691e-01
-2.33252257e-01 -1.03026450e+00 4.89331871e-01 6.29443467e-01
-9.65897977e-01 2.90312350e-01 -1.00643754e-01 6.51213527e-02
-1.02402776e-01 -8.42898309e-01 -4.27819490e-01 -9.17792559e-01
-1.04460530e-01 5.71189344e-01 6.02667749e-01 2.72152334... | [14.403270721435547, -2.9927220344543457] |
d41321b4-6879-4423-94e1-f7de117bf83e | data-efficient-end-to-end-information | 2211.01692 | null | https://arxiv.org/abs/2211.01692v1 | https://arxiv.org/pdf/2211.01692v1.pdf | Data-efficient End-to-end Information Extraction for Statistical Legal Analysis | Legal practitioners often face a vast amount of documents. Lawyers, for instance, search for appropriate precedents favorable to their clients, while the number of legal precedents is ever-growing. Although legal search engines can assist finding individual target documents and narrowing down the number of candidates, ... | ['Minjoon Seo', 'Hai Jin Park', 'Hanuhl Lee', 'Saehee Eom', 'Wonseok Hwang'] | 2022-11-03 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [ 1.44187376e-01 3.97241652e-01 -5.24285316e-01 -4.48062479e-01
-1.41468990e+00 -6.09548569e-01 6.86666369e-01 2.12520868e-01
-6.64609909e-01 9.88254249e-01 3.28948319e-01 -9.05319810e-01
-3.41203749e-01 -6.13220453e-01 -5.65487981e-01 -1.42779961e-01
2.64907420e-01 7.36605227e-01 3.55982363e-01 -3.78639221... | [9.971440315246582, 9.19672966003418] |
36cd6ec9-66d9-4da8-b06b-370f9905dc1c | versatile-audio-visual-learning-for-handling | 2305.07216 | null | https://arxiv.org/abs/2305.07216v1 | https://arxiv.org/pdf/2305.07216v1.pdf | Versatile Audio-Visual Learning for Handling Single and Multi Modalities in Emotion Regression and Classification Tasks | Most current audio-visual emotion recognition models lack the flexibility needed for deployment in practical applications. We envision a multimodal system that works even when only one modality is available and can be implemented interchangeably for either predicting emotional attributes or recognizing categorical emot... | ['Carlos Busso', 'Berrak Sisman', 'Wei-Cheng Lin', 'Seong-Gyun Leem', 'Lucas Goncalves'] | 2023-05-12 | null | null | null | null | ['video-emotion-recognition', 'multimodal-emotion-recognition', 'emotion-classification', 'emotion-classification', 'speech-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing', 'speech', 'speech'] | [-4.27590460e-02 -3.63227338e-01 -3.40743037e-03 -6.58418834e-01
-1.09327865e+00 -6.79227293e-01 3.78258467e-01 -3.95648405e-02
-3.87743950e-01 3.41647655e-01 9.33185667e-02 -3.11913947e-03
1.41772479e-01 -1.90019816e-01 -5.69898546e-01 -5.61759770e-01
3.97205465e-02 3.98670942e-01 -5.31624675e-01 -2.82567650... | [13.273238182067871, 5.147307395935059] |
a8e86060-9fca-4be8-8554-9af34e8db166 | improving-the-generalization-of-meta-learning | 2107.11056 | null | https://arxiv.org/abs/2107.11056v1 | https://arxiv.org/pdf/2107.11056v1.pdf | Improving the Generalization of Meta-learning on Unseen Domains via Adversarial Shift | Meta-learning provides a promising way for learning to efficiently learn and achieves great success in many applications. However, most meta-learning literature focuses on dealing with tasks from a same domain, making it brittle to generalize to tasks from the other unseen domains. In this work, we address this problem... | ['Yao Gao', 'Pinzhuo Tian'] | 2021-07-23 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 3.16161275e-01 -1.73388392e-01 -2.19202071e-01 -3.25843990e-01
-9.79222894e-01 -4.16754156e-01 6.17704451e-01 -2.67623961e-01
-2.44739905e-01 9.16570723e-01 -1.49898008e-01 -3.01620457e-02
3.67821828e-02 -9.14145768e-01 -9.42235470e-01 -6.54384494e-01
3.22394669e-01 3.85602951e-01 3.76879513e-01 -5.55638194... | [10.168701171875, 3.0557456016540527] |
f4345d30-76bf-4a23-8352-b79b13a61ac7 | improving-truthfulness-of-headline-generation | 2005.00882 | null | https://arxiv.org/abs/2005.00882v2 | https://arxiv.org/pdf/2005.00882v2.pdf | Improving Truthfulness of Headline Generation | Most studies on abstractive summarization report ROUGE scores between system and reference summaries. However, we have a concern about the truthfulness of generated summaries: whether all facts of a generated summary are mentioned in the source text. This paper explores improving the truthfulness in headline generation... | ['Naoaki Okazaki', 'Sho Takase', 'Kazuki Matsumaru'] | 2020-05-02 | improving-truthfulness-of-headline-generation-1 | https://aclanthology.org/2020.acl-main.123 | https://aclanthology.org/2020.acl-main.123.pdf | acl-2020-6 | ['headline-generation'] | ['natural-language-processing'] | [ 2.4806423e-01 7.4556339e-01 -4.6399388e-01 -3.0406964e-01
-1.1511314e+00 -6.7289686e-01 9.3359065e-01 4.9673161e-01
-7.9367213e-02 1.1639702e+00 1.1889824e+00 -2.5870293e-01
2.1673734e-01 -5.3503025e-01 -1.0434827e+00 -1.4424014e-01
4.3561661e-01 3.1226131e-01 -2.4899581e-02 -4.2944768e-01
7.8714138e-01... | [12.237347602844238, 9.330570220947266] |
2f26d466-45ca-4147-814f-4b5002d6a15c | evolin-benchmark-evaluation-of-line-detection | 2303.05162 | null | https://arxiv.org/abs/2303.05162v1 | https://arxiv.org/pdf/2303.05162v1.pdf | EVOLIN Benchmark: Evaluation of Line Detection and Association | Lines are interesting geometrical features commonly seen in indoor and urban environments. There is missing a complete benchmark where one can evaluate lines from a sequential stream of images in all its stages: Line detection, Line Association and Pose error. To do so, we present a complete and exhaustive benchmark fo... | ['Anastasiia Kornilova', 'Gonzalo Ferrer', 'Kirill Ivanov'] | 2023-03-09 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [-1.19185023e-01 -2.56527454e-01 2.84381032e-01 -6.23881161e-01
-6.62086248e-01 -7.91953981e-01 7.32868612e-01 3.38178456e-01
-5.50605893e-01 7.57644415e-01 -2.80869007e-01 -2.70252496e-01
-1.50541142e-01 -6.52146637e-01 -7.76333869e-01 -7.85973668e-02
-4.59236622e-01 8.16604197e-01 5.92620909e-01 -3.36070657... | [7.399188995361328, -2.207569122314453] |
17eb4dcb-9d71-4657-a5da-f5306912943a | toward-multilingual-identification-of-online | null | null | https://aclanthology.org/W19-6130 | https://aclanthology.org/W19-6130.pdf | Toward Multilingual Identification of Online Registers | We consider cross- and multilingual text classification approaches to the identification of online registers (genres), i.e. text varieties with specific situational characteristics. Register is the most important predictor of linguistic variation, and register information could improve the potential of online data for ... | ['Sampo Pyysalo', 'Douglas Biber', 'Jesse Egbert', 'Roosa Kyllönen', 'Veronika Laippala'] | null | null | null | null | ws-nodalida-2019-9 | ['multilingual-word-embeddings', 'multilingual-text-classification'] | ['methodology', 'miscellaneous'] | [-3.05450559e-01 -2.97516654e-03 -7.85368800e-01 -2.33654991e-01
-1.11795914e+00 -1.01163626e+00 8.48926485e-01 4.67760116e-01
-6.98528290e-01 3.14196199e-01 8.14555228e-01 -5.04818499e-01
-1.14251584e-01 -4.76147771e-01 -4.06796306e-01 -1.10874467e-01
-1.42137870e-01 6.75810158e-01 -1.77585497e-01 -7.70331919... | [10.586483001708984, 10.052136421203613] |
9a2ae520-ac29-48b2-af6c-5b6ea0e681f1 | blind-video-temporal-consistency-via-deep | 2010.11838 | null | https://arxiv.org/abs/2010.11838v1 | https://arxiv.org/pdf/2010.11838v1.pdf | Blind Video Temporal Consistency via Deep Video Prior | Applying image processing algorithms independently to each video frame often leads to temporal inconsistency in the resulting video. To address this issue, we present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly instead... | ['Qifeng Chen', 'Yazhou Xing', 'Chenyang Lei'] | 2020-10-22 | null | http://proceedings.neurips.cc/paper/2020/hash/0c0a7566915f4f24853fc4192689aa7e-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/0c0a7566915f4f24853fc4192689aa7e-Paper.pdf | neurips-2020-12 | ['video-temporal-consistency'] | ['computer-vision'] | [-1.53778434e-01 -5.41198134e-01 -2.66530842e-01 -2.29756206e-01
-7.81171560e-01 -5.24712086e-01 4.82796907e-01 -4.96327102e-01
-4.73710716e-01 4.62935835e-01 4.27460819e-01 -1.29964292e-01
-1.19056322e-01 -1.18989013e-02 -9.11948085e-01 -3.48798901e-01
-1.72270402e-01 -2.20064700e-01 1.76312238e-01 2.68489510... | [10.713915824890137, -1.488459587097168] |
63e8b61a-1b86-4b18-aeb3-ca4fc0d2a887 | a-slow-shifting-concerned-machine-learning | 2303.17782 | null | https://arxiv.org/abs/2303.17782v1 | https://arxiv.org/pdf/2303.17782v1.pdf | A Slow-Shifting Concerned Machine Learning Method for Short-term Traffic Flow Forecasting | The ability to predict traffic flow over time for crowded areas during rush hours is increasingly important as it can help authorities make informed decisions for congestion mitigation or scheduling of infrastructure development in an area. However, a crucial challenge in traffic flow forecasting is the slow shifting i... | ['Chau Yuen', 'Yong Liang Guan', 'Yan Qin', 'Zann Koh'] | 2023-03-31 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 8.19858611e-02 -7.35445082e-01 -3.48651469e-01 -1.97493315e-01
-3.30093980e-01 -5.06726233e-03 2.88855702e-01 -2.86059201e-01
-2.09397212e-01 9.22351480e-01 1.98974282e-01 -6.88028336e-01
-4.26997006e-01 -8.82160604e-01 -1.71460599e-01 -7.57402420e-01
-1.68047681e-01 2.26377100e-01 3.43393028e-01 -4.21158165... | [6.410472393035889, 2.0829200744628906] |
e32f9c0d-d240-476a-ba9e-67709e7cddc0 | delegated-classification | 2306.11475 | null | https://arxiv.org/abs/2306.11475v1 | https://arxiv.org/pdf/2306.11475v1.pdf | Delegated Classification | When machine learning is outsourced to a rational agent, conflicts of interest might arise and severely impact predictive performance. In this work, we propose a theoretical framework for incentive-aware delegation of machine learning tasks. We model delegation as a principal-agent game, in which accurate learning can ... | ['Nir Rosenfeld', 'Inbal Talgam-Cohen', 'Eden Saig'] | 2023-06-20 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 9.13014635e-02 5.34963012e-01 -7.99265802e-01 -6.96289003e-01
-8.88531029e-01 -7.64042795e-01 1.43295273e-01 -1.08334921e-01
-8.71113181e-01 9.84795392e-01 -3.20271067e-02 -6.65570915e-01
-7.32453346e-01 -4.01077718e-01 -8.42776537e-01 -7.58261859e-01
-3.88187498e-01 6.26486361e-01 -4.69878018e-01 4.40218031... | [4.534945011138916, 3.285856246948242] |
b516c676-a160-4f13-b492-031d31a0a9d9 | a-self-supervised-contrastive-learning-method | 2306.14437 | null | https://arxiv.org/abs/2306.14437v1 | https://arxiv.org/pdf/2306.14437v1.pdf | A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction | In this paper, we investigate the effectiveness of contrastive learning methods for predicting grasp outcomes in an unsupervised manner. By utilizing a publicly available dataset, we demonstrate that contrastive learning methods perform well on the task of grasp outcomes prediction. Specifically, the dynamic-dictionary... | ['Xinyu Wu', 'Zhengkun Yi', 'Yupo Zhang', 'Ke Mai', 'Yuanzhe Su', 'YiWen Liu', 'Binhua Huang', 'Chengliang Liu'] | 2023-06-26 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 6.88352808e-02 -3.22517276e-01 -4.84041631e-01 -2.44028717e-01
-5.77764273e-01 -9.21852961e-02 2.07929779e-02 2.59492159e-01
-1.99671164e-01 4.08370972e-01 -6.64170608e-02 2.15024710e-01
-6.24479353e-01 -6.35758400e-01 -7.76791692e-01 -9.92330551e-01
-7.64810801e-01 6.11616075e-01 -4.31233682e-02 -1.44039094... | [5.797428131103516, -0.8270968198776245] |
c241ca00-089b-4534-b63b-9c8288b068bb | stsc-snn-spatio-temporal-synaptic-connection | 2210.05241 | null | https://arxiv.org/abs/2210.05241v1 | https://arxiv.org/pdf/2210.05241v1.pdf | STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networks | Spiking Neural Networks (SNNs), as one of the algorithmic models in neuromorphic computing, have gained a great deal of research attention owing to temporal information processing capability, low power consumption, and high biological plausibility. The potential to efficiently extract spatio-temporal features makes it ... | ['Erping Li', 'Aili Wang', 'Gaoang Wang', 'Da Li', 'Zheming Gu', 'Chengting Yu'] | 2022-10-11 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 4.52574253e-01 -6.58650756e-01 2.05149636e-01 -1.94182619e-01
2.36583441e-01 -3.97898585e-01 8.74078989e-01 -3.03520083e-01
-9.51854229e-01 7.74458468e-01 -8.81128162e-02 -2.40051270e-01
-4.35059220e-01 -6.94181681e-01 -7.34793544e-01 -8.80987883e-01
-3.57391000e-01 -3.15530181e-01 1.08597851e+00 -1.33458212... | [8.227150917053223, 2.4232289791107178] |
fd2b267f-26de-41ef-8de7-399d2f4fb0d4 | towards-deep-and-representation-learning-for | 1809.06473 | null | http://arxiv.org/abs/1809.06473v1 | http://arxiv.org/pdf/1809.06473v1.pdf | Towards Deep and Representation Learning for Talent Search at LinkedIn | Talent search and recommendation systems at LinkedIn strive to match the
potential candidates to the hiring needs of a recruiter or a hiring manager
expressed in terms of a search query or a job posting. Recent work in this
domain has mainly focused on linear models, which do not take complex
relationships between feat... | ['Cagri Ozcaglar', 'Rohan Ramanath', 'Krishnaram Kenthapadi', 'Xianren Wu', 'Qi Guo', 'Hakan Inan', 'Bo Hu', 'Sahin Cem Geyik', 'Gungor Polatkan'] | 2018-09-17 | null | null | null | null | ['learning-semantic-representations'] | ['methodology'] | [-4.30608302e-01 1.42300174e-01 -7.26234257e-01 -3.74178320e-01
-3.55055898e-01 -4.97817308e-01 7.67797828e-01 1.89421952e-01
-3.47135395e-01 4.00413305e-01 6.11187696e-01 -3.89273643e-01
-1.11500657e+00 -1.14805484e+00 -4.57337826e-01 2.36677721e-01
5.83421104e-02 1.42414320e+00 -2.98908651e-01 -7.06579745... | [10.432619094848633, 6.2814621925354] |
4197cf5f-48e7-4622-bf1e-3e193b179808 | lectures-on-jacques-herbrand-as-a-logician | 0902.4682 | null | http://arxiv.org/abs/0902.4682v5 | http://arxiv.org/pdf/0902.4682v5.pdf | Lectures on Jacques Herbrand as a Logician | We give some lectures on the work on formal logic of Jacques Herbrand, and
sketch his life and his influence on automated theorem proving. The intended
audience ranges from students interested in logic over historians to logicians.
Besides the well-known correction of Herbrand's False Lemma by Goedel and
Dreben, we als... | ['Christoph Benzmueller', 'Claus-Peter Wirth', 'Serge Autexier', 'Joerg Siekmann'] | 2009-02-26 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 1.91487521e-02 8.51093650e-01 -2.69493014e-01 -2.67417759e-01
-1.04489617e-01 -8.66888046e-01 5.15742123e-01 3.10087889e-01
-3.76939476e-01 1.09965098e+00 -3.62449855e-01 -1.27243412e+00
-3.80759448e-01 -1.01277125e+00 -6.56342983e-01 -3.19413424e-01
-2.62508214e-01 4.88976240e-01 4.88546550e-01 -6.83647931... | [8.749448776245117, 6.795022964477539] |
b4d10bb1-d24e-4366-afea-66f6c4c349b9 | a-marker-based-neural-network-system-for | 2212.12800 | null | https://arxiv.org/abs/2212.12800v1 | https://arxiv.org/pdf/2212.12800v1.pdf | A Marker-based Neural Network System for Extracting Social Determinants of Health | Objective. The impact of social determinants of health (SDoH) on patients' healthcare quality and the disparity is well-known. Many SDoH items are not coded in structured forms in electronic health records. These items are often captured in free-text clinical notes, but there are limited methods for automatically extra... | ['Anthony Rios', 'Xingmeng Zhao'] | 2022-12-24 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 1.39526084e-01 4.32634205e-01 -4.27132130e-01 -1.62959695e-01
-1.25958443e+00 -3.11114490e-01 -1.87555458e-02 1.09123468e+00
-6.62351489e-01 6.85438275e-01 8.65776420e-01 -5.72363555e-01
-5.66561580e-01 -7.61659980e-01 -3.57548743e-01 -2.08663434e-01
-1.28927141e-01 5.44823647e-01 -4.13876176e-02 1.83373421... | [8.418601989746094, 8.675188064575195] |
082599c8-8a5a-43eb-a4a2-00ff76188874 | multitask-learning-for-instrument-activation | 2008.00616 | null | https://arxiv.org/abs/2008.00616v1 | https://arxiv.org/pdf/2008.00616v1.pdf | Multitask learning for instrument activation aware music source separation | Music source separation is a core task in music information retrieval which has seen a dramatic improvement in the past years. Nevertheless, most of the existing systems focus exclusively on the problem of source separation itself and ignore the utilization of other~---possibly related---~MIR tasks which could lead to ... | ['Yun-Ning Hung', 'Alexander Lerch'] | 2020-08-03 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 2.25103125e-01 -6.25273943e-01 -2.10289970e-01 8.31693318e-03
-1.60656750e+00 -7.01158226e-01 4.28097576e-01 -1.52649790e-01
-4.25331235e-01 5.73608637e-01 4.99056697e-01 1.86551455e-02
-4.91594702e-01 -4.57258709e-02 -4.86107945e-01 -8.57121289e-01
5.40047586e-02 3.88539582e-01 -7.60674626e-02 -2.05754489... | [15.627286911010742, 5.3729567527771] |
d1af3cd1-a982-420b-ad8f-85e5596cd64f | multi-modal-sarcasm-detection-and-humor | 2105.09984 | null | https://arxiv.org/abs/2105.09984v2 | https://arxiv.org/pdf/2105.09984v2.pdf | Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations | Sarcasm detection and humor classification are inherently subtle problems, primarily due to their dependence on the contextual and non-verbal information. Furthermore, existing studies in these two topics are usually constrained in non-English languages such as Hindi, due to the unavailability of qualitative annotated ... | ['Tanmoy Chakraborty', 'Md Shad Akhtar', 'Shivani Kumar', 'Manjot Bedi'] | 2021-05-20 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [-3.81550401e-01 9.10100043e-02 -2.64877826e-01 -3.80596638e-01
-6.43882036e-01 -4.11011755e-01 6.55079782e-01 6.59679994e-02
-1.87843487e-01 5.39021671e-01 9.21427667e-01 -1.68178871e-01
3.22222054e-01 -3.23524833e-01 -9.19644311e-02 -4.63152617e-01
3.15693855e-01 4.06934142e-01 -3.03768204e-03 -7.83731401... | [12.999309539794922, 5.609080791473389] |
a944999c-48ad-4ddc-a8c7-cbad2d2b5690 | a-large-scale-dataset-and-benchmark-for | 1701.05766 | null | http://arxiv.org/abs/1701.05766v2 | http://arxiv.org/pdf/1701.05766v2.pdf | A Large-scale Dataset and Benchmark for Similar Trademark Retrieval | Trademark retrieval (TR) has become an important yet challenging problem due
to an ever increasing trend in trademark applications and infringement
incidents. There have been many promising attempts for the TR problem, which,
however, fell impracticable since they were evaluated with limited and mostly
trivial datasets... | ['Cemal Aker', 'Sinan Kalkan', 'Osman Tursun'] | 2017-01-20 | null | null | null | null | ['trademark-retrieval'] | ['computer-vision'] | [ 2.81483740e-01 -3.86521816e-01 -1.00492172e-01 -6.05457947e-02
-1.02762830e+00 -8.48090053e-01 7.95331180e-01 -1.90839782e-01
-2.54893005e-01 6.72942877e-01 -1.49799228e-01 -4.89560962e-01
-6.05721891e-01 -5.90434134e-01 -7.36551881e-01 -3.14068109e-01
2.60885715e-01 4.08015460e-01 2.58474141e-01 -2.66908169... | [9.857081413269043, 8.277076721191406] |
bd822c1c-4152-4a00-a7fd-764283d38677 | boosting-facial-expression-recognition-by-a | 2205.14361 | null | https://arxiv.org/abs/2205.14361v1 | https://arxiv.org/pdf/2205.14361v1.pdf | Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher | In this paper, we aim to improve the performance of in-the-wild Facial Expression Recognition (FER) by exploiting semi-supervised learning. Large-scale labeled data and deep learning methods have greatly improved the performance of image recognition. However, the performance of FER is still not ideal due to the lack of... | ['Weihong Deng', 'Jing Jiang'] | 2022-05-28 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.22296326e-01 -9.79674160e-02 -3.35595161e-01 -8.01026702e-01
-1.08935392e+00 5.76696694e-02 -1.21591337e-01 -3.27290505e-01
-4.86837715e-01 1.06596434e+00 -1.67939305e-01 4.13345546e-01
2.23897770e-01 -5.10959864e-01 -5.67019403e-01 -9.35802996e-01
2.69641101e-01 2.86115229e-01 -7.73896724e-02 -2.22469062... | [13.603584289550781, 1.6525810956954956] |
e72ce040-2c8c-4e78-b0d3-35239f5acb5e | a-perceptual-quality-metric-for-video-frame | 2210.01879 | null | https://arxiv.org/abs/2210.01879v1 | https://arxiv.org/pdf/2210.01879v1.pdf | A Perceptual Quality Metric for Video Frame Interpolation | Research on video frame interpolation has made significant progress in recent years. However, existing methods mostly use off-the-shelf metrics to measure the quality of interpolation results with the exception of a few methods that employ user studies, which is time-consuming. As video frame interpolation results ofte... | ['Feng Liu', 'Abhijay Ghildyal', 'Qiqi Hou'] | 2022-10-04 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [-2.46949315e-01 -7.70179987e-01 -2.95210570e-01 -4.27213520e-01
-9.31092322e-01 -2.98888743e-01 3.15913588e-01 7.05953836e-02
-3.11107725e-01 5.92716515e-01 3.09941918e-01 -1.61729142e-01
1.44384116e-01 -7.39380538e-01 -8.76394093e-01 -4.07802671e-01
-2.21190378e-01 -4.55291808e-01 4.44979340e-01 -1.46666497... | [11.385066986083984, -1.6466529369354248] |
99e10e4e-807d-44f5-ad47-3648a2ff11a0 | improving-keyphrase-extraction-with-data | 2209.04951 | null | https://arxiv.org/abs/2209.04951v1 | https://arxiv.org/pdf/2209.04951v1.pdf | Improving Keyphrase Extraction with Data Augmentation and Information Filtering | Keyphrase extraction is one of the essential tasks for document understanding in NLP. While the majority of the prior works are dedicated to the formal setting, e.g., books, news or web-blogs, informal texts such as video transcripts are less explored. To address this limitation, in this work we present a novel corpus ... | ['Thien Huu Nguyen', 'Franck Dernoncourt', 'Nicole Meister', 'Amir Pouran Ben Veyseh'] | 2022-09-11 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 3.06920052e-01 5.54155111e-02 -5.73316038e-01 6.38496280e-02
-6.58562541e-01 -7.79657006e-01 1.01444042e+00 5.01133800e-01
-4.79367852e-01 9.66394007e-01 7.27115154e-01 -1.02400362e-01
-5.95788099e-02 -4.73349601e-01 -7.23622382e-01 -6.29454732e-01
1.21881954e-01 -2.50460207e-01 1.78678289e-01 -4.09625918... | [12.268649101257324, 8.889451026916504] |
f96327ac-9714-46d5-af68-cac3bec748c0 | sdc-uda-volumetric-unsupervised-domain-1 | 2305.11012 | null | https://arxiv.org/abs/2305.11012v1 | https://arxiv.org/pdf/2305.11012v1.pdf | SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation | Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation (UDA) can alleviate this problem, ... | ['Dosik Hwang', 'Taejoon Eo', 'Yohan Jun', 'Sewon Kim', 'Hyeongyu Kim', 'Hyungseob Shin'] | 2023-05-18 | sdc-uda-volumetric-unsupervised-domain | http://openaccess.thecvf.com//content/CVPR2023/html/Shin_SDC-UDA_Volumetric_Unsupervised_Domain_Adaptation_Framework_for_Slice-Direction_Continuous_Cross-Modality_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shin_SDC-UDA_Volumetric_Unsupervised_Domain_Adaptation_Framework_for_Slice-Direction_Continuous_Cross-Modality_CVPR_2023_paper.pdf | cvpr-2023-1 | ['unsupervised-domain-adaptation', 'pseudo-label'] | ['methodology', 'miscellaneous'] | [ 6.23228252e-01 4.81586277e-01 -4.75662082e-01 -6.53192282e-01
-1.26239312e+00 -4.79611158e-01 7.45050609e-02 2.63339162e-01
-6.03048801e-01 6.99251235e-01 -1.09118201e-01 -5.35717726e-01
3.43321227e-02 -6.43676817e-01 -5.91819704e-01 -7.15253055e-01
8.13499093e-02 9.29697216e-01 5.78156590e-01 3.09421599... | [14.613288879394531, -2.2020652294158936] |
52ed3228-9558-4cbf-a02a-04335c4715b4 | doc-deep-occlusion-estimation-from-a-single | 1511.06457 | null | http://arxiv.org/abs/1511.06457v4 | http://arxiv.org/pdf/1511.06457v4.pdf | DOC: Deep OCclusion Estimation From a Single Image | Recovering the occlusion relationships between objects is a fundamental human
visual ability which yields important information about the 3D world. In this
paper we propose a deep network architecture, called DOC, which acts on a
single image, detects object boundaries and estimates the border ownership
(i.e. which sid... | ['Peng Wang', 'Alan Yuille'] | 2015-11-20 | null | null | null | null | ['occlusion-estimation'] | ['computer-vision'] | [ 9.94426459e-02 3.99392880e-02 -3.05769920e-01 -5.08682370e-01
-2.35256284e-01 -5.48602998e-01 4.69289392e-01 -1.18938848e-01
-1.37404919e-01 5.26430428e-01 9.61082354e-02 -3.24317515e-01
5.26489504e-02 -9.42584991e-01 -1.23568165e+00 -4.33636427e-01
-4.43501584e-02 7.37438977e-01 4.62679416e-01 2.60945529... | [9.411972999572754, 0.22151699662208557] |
e4ac1d0f-3f0d-4f20-936c-a29aaf152e52 | beyond-prompting-making-pre-trained-language | 2210.16637 | null | https://arxiv.org/abs/2210.16637v2 | https://arxiv.org/pdf/2210.16637v2.pdf | Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations | Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot learners. However, most existing zero-shot methods involve heavy human engineering or complicated self-training pipelines, hindering their application to new situations. In this work, we show that zero-shot text classification can be imp... | ['Mrinmaya Sachan', 'Roger Wattenhofer', 'Zhao Meng', 'Ping Nie', 'Yu Fei'] | 2022-10-29 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-3.51619460e-02 3.15141119e-02 -3.69888186e-01 -4.32672679e-01
-9.01207328e-01 -3.89713079e-01 9.38532770e-01 4.85280305e-01
-5.58716297e-01 2.75085241e-01 3.86328369e-01 -3.05681467e-01
3.97897214e-02 -8.19194674e-01 -2.93538660e-01 -5.77135623e-01
3.29984576e-01 6.37361467e-01 2.84025103e-01 -2.56802946... | [10.517535209655762, 7.212742805480957] |
8e9523b3-bf8b-4183-9282-84a19476278a | the-best-of-both-modes-separately-leveraging | 1907.13236 | null | https://arxiv.org/abs/1907.13236v2 | https://arxiv.org/pdf/1907.13236v2.pdf | The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation | In order to function in unstructured environments, robots need the ability to recognize unseen novel objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop environments. However, the type of large-scale real-world dataset required for this task typically does... | ['Yu Xiang', 'Dieter Fox', 'Christopher Xie', 'Arsalan Mousavian'] | 2019-07-30 | null | null | null | null | ['unseen-object-instance-segmentation'] | ['computer-vision'] | [ 7.27473497e-01 3.56983840e-01 3.37163210e-01 -3.61792296e-01
-7.30930924e-01 -9.90340114e-01 2.74525076e-01 -1.80148080e-01
-2.36481264e-01 4.10286635e-01 -4.65948075e-01 -8.17985013e-02
4.32410575e-02 -5.75440288e-01 -1.21493304e+00 -5.22817373e-01
1.79902747e-01 8.28799665e-01 5.30179203e-01 -2.57848471... | [6.147257328033447, -1.0453237295150757] |
d27d6a29-2ee4-486e-9cd8-193162cc843a | east-an-efficient-and-accurate-scene-text | 1704.03155 | null | http://arxiv.org/abs/1704.03155v2 | http://arxiv.org/pdf/1704.03155v2.pdf | EAST: An Efficient and Accurate Scene Text Detector | Previous approaches for scene text detection have already achieved promising
performances across various benchmarks. However, they usually fall short when
dealing with challenging scenarios, even when equipped with deep neural network
models, because the overall performance is determined by the interplay of
multiple st... | ['Xinyu Zhou', 'He Wen', 'Yuzhi Wang', 'Weiran He', 'Shuchang Zhou', 'Jiajun Liang', 'Cong Yao'] | 2017-04-11 | east-an-efficient-and-accurate-scene-text-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhou_EAST_An_Efficient_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhou_EAST_An_Efficient_CVPR_2017_paper.pdf | cvpr-2017-7 | ['curved-text-detection'] | ['computer-vision'] | [ 9.30481926e-02 -5.87606072e-01 1.55117452e-01 -1.36054829e-01
-7.93763876e-01 -5.03189921e-01 5.66173673e-01 3.32970083e-01
-5.81965864e-01 1.81950495e-01 -4.66679186e-02 -2.82600611e-01
3.26668382e-01 -8.08690608e-01 -6.73586965e-01 -4.30141211e-01
3.29310030e-01 5.14855564e-01 6.44307256e-01 1.45837948... | [12.015491485595703, 2.296715021133423] |
ed5b5a71-eb7f-41a5-95f6-8e7ddeb5dd96 | text-based-localization-of-moments-in-a-video | 2008.08716 | null | https://arxiv.org/abs/2008.08716v2 | https://arxiv.org/pdf/2008.08716v2.pdf | Text-based Localization of Moments in a Video Corpus | Prior works on text-based video moment localization focus on temporally grounding the textual query in an untrimmed video. These works assume that the relevant video is already known and attempt to localize the moment on that relevant video only. Different from such works, we relax this assumption and address the task ... | ['Amit K. Roy-Chowdhury', 'Sudipta Paul', 'Niluthpol Chowdhury Mithun'] | 2020-08-20 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 3.68241742e-02 -4.57156271e-01 -4.92643595e-01 -2.89174497e-01
-1.15976620e+00 -5.80089688e-01 7.22963631e-01 2.77239084e-01
-6.41769290e-01 2.25413054e-01 5.48397779e-01 2.60796666e-01
-8.29839036e-02 -2.65635550e-01 -8.07612538e-01 -7.40150094e-01
-3.92528087e-01 -5.06489305e-03 3.11790258e-01 1.02371134... | [10.09813117980957, 0.7659195065498352] |
734f229d-ba00-4876-96c1-45d7cc44a2d1 | assessor360-multi-sequence-network-for-blind | 2305.10983 | null | https://arxiv.org/abs/2305.10983v2 | https://arxiv.org/pdf/2305.10983v2.pdf | Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment | Blind Omnidirectional Image Quality Assessment (BOIQA) aims to objectively assess the human perceptual quality of omnidirectional images (ODIs) without relying on pristine-quality image information. It is becoming more significant with the increasing advancement of virtual reality (VR) technology. However, the quality ... | ['Yujiu Yang', 'Yinqiang Zheng', 'Jing Xiao', 'Mingdeng Cao', 'Haoming Cai', 'Shuwei Shi', 'Tianhe Wu'] | 2023-05-18 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [-4.70007062e-02 -5.72016120e-01 4.33073580e-01 -5.19986629e-01
-9.45185959e-01 -5.53926945e-01 6.05859935e-01 -3.65533084e-01
-2.66244024e-01 3.47874045e-01 5.78649640e-01 -2.50615567e-01
-4.34081912e-01 -7.13044345e-01 -5.34811795e-01 -4.62176174e-01
2.46762514e-01 9.07440633e-02 1.10526614e-01 -2.84874618... | [11.810975074768066, -1.9013748168945312] |
519e9d7e-7451-4b4b-bf5b-ab1ca6195422 | test-positive-at-w-nut-2020-shared-task-3 | 2009.14262 | null | https://arxiv.org/abs/2009.14262v1 | https://arxiv.org/pdf/2009.14262v1.pdf | TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text | The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets. The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive? What is the age of the person? Wh... | ['Chieh-Yang Huang', 'Yaqi Hou', 'Yang Shi', 'Jiaqi Wang', 'Enyan Dai', 'Chacha Chen'] | 2020-09-29 | null | null | null | null | ['extracting-covid-19-events-from-twitter'] | ['natural-language-processing'] | [-3.44452858e-02 -9.51137319e-02 -1.79236457e-01 -6.07772529e-01
-1.10899103e+00 -4.70800608e-01 6.77391827e-01 6.64580941e-01
-9.74343598e-01 8.18702579e-01 3.80033851e-01 -3.57404910e-02
1.86628729e-01 -1.10697877e+00 -6.83704078e-01 -3.31660479e-01
2.50340819e-01 6.43059790e-01 3.60129744e-01 -2.11138785... | [9.54062271118164, 9.478062629699707] |
b08d851d-fcab-4f33-8317-466e07d15434 | professor-forcing-a-new-algorithm-for | 1610.09038 | null | http://arxiv.org/abs/1610.09038v1 | http://arxiv.org/pdf/1610.09038v1.pdf | Professor Forcing: A New Algorithm for Training Recurrent Networks | The Teacher Forcing algorithm trains recurrent networks by supplying observed
sequence values as inputs during training and using the network's own
one-step-ahead predictions to do multi-step sampling. We introduce the
Professor Forcing algorithm, which uses adversarial domain adaptation to
encourage the dynamics of th... | ['Alex Lamb', 'Saizheng Zhang', 'Anirudh Goyal', 'Yoshua Bengio', 'Ying Zhang', 'Aaron Courville'] | 2016-10-27 | professor-forcing-a-new-algorithm-for-1 | http://papers.nips.cc/paper/6099-professor-forcing-a-new-algorithm-for-training-recurrent-networks | http://papers.nips.cc/paper/6099-professor-forcing-a-new-algorithm-for-training-recurrent-networks.pdf | neurips-2016-12 | ['handwriting-generation'] | ['computer-vision'] | [ 6.03775203e-01 7.21830487e-01 -3.99819911e-01 -3.80239516e-01
-9.71972764e-01 -9.86369193e-01 8.38680804e-01 -5.63655078e-01
-2.90592045e-01 9.91757870e-01 3.99182826e-01 -4.86729026e-01
3.08287501e-01 -7.72413969e-01 -9.43234026e-01 -4.24193621e-01
-4.79867831e-02 5.82899809e-01 1.33540086e-03 -1.60368681... | [11.879307746887207, 9.293447494506836] |
8fb338cb-9fa5-4023-9b9f-dcb7bf7868fe | a-technique-to-jointly-estimate-depth-and | 2305.19780 | null | https://arxiv.org/abs/2305.19780v1 | https://arxiv.org/pdf/2305.19780v1.pdf | A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles | When used by autonomous vehicles for trajectory planning or obstacle avoidance, depth estimation methods need to be reliable. Therefore, estimating the quality of the depth outputs is critical. In this paper, we show how M4Depth, a state-of-the-art depth estimation method designed for unmanned aerial vehicle (UAV) appl... | ['Marc Van Droogenbroeck', 'Michaël Fonder'] | 2023-05-31 | null | null | null | null | ['depth-aleatoric-uncertainty-estimation', 'autonomous-vehicles', 'monocular-depth-estimation', 'trajectory-planning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'robots'] | [-2.43675053e-01 1.24590941e-01 -1.59981549e-01 -3.19571495e-01
-1.14013910e+00 -1.01218688e+00 7.64269412e-01 -1.52474850e-01
-1.43815011e-01 7.67426610e-01 1.29369125e-01 -1.82865500e-01
4.77820113e-02 -1.01418233e+00 -6.73567891e-01 -6.63780332e-01
-1.45386025e-01 5.41980207e-01 6.91550732e-01 7.49617815... | [7.947906017303467, -2.2115612030029297] |
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