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7f2b52a7-e416-4c69-a27f-5c6d4db5e6dd | coconet-coupled-contrastive-learning-network | 2211.1096 | null | https://arxiv.org/abs/2211.10960v1 | https://arxiv.org/pdf/2211.10960v1.pdf | CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion | Infrared and visible image fusion targets to provide an informative image by combining complementary information from different sensors. Existing learning-based fusion approaches attempt to construct various loss functions to preserve complementary features from both modalities, while neglecting to discover the inter-r... | ['Xin Fan', 'Zhongxuan Luo', 'Risheng Liu', 'Guanyao Wu', 'Runjia Lin', 'JinYuan Liu'] | 2022-11-20 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 4.27562296e-01 -2.15598166e-01 -7.87467584e-02 -2.83641249e-01
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-3.14213246e-01 6.18156075e-01 4.21845555e-01 2.90815324e-01
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4.12348717e-01 -2.13274315e-01 6.85233250e-02 -1.78489417... | [10.535764694213867, -1.8855385780334473] |
97eeb369-1881-442f-bf5f-c177d1cb6dd2 | extractive-is-not-faithful-an-investigation | 2209.03549 | null | https://arxiv.org/abs/2209.03549v2 | https://arxiv.org/pdf/2209.03549v2.pdf | Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization | The problems of unfaithful summaries have been widely discussed under the context of abstractive summarization. Though extractive summarization is less prone to the common unfaithfulness issues of abstractive summaries, does that mean extractive is equal to faithful? Turns out that the answer is no. In this work, we de... | ['Mohit Bansal', 'David Wan', 'Shiyue Zhang'] | 2022-09-08 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 2.31957987e-01 4.96404916e-01 -4.68209296e-01 -2.18306765e-01
-1.17491281e+00 -9.30847585e-01 7.51036406e-01 6.48584962e-01
-1.69785485e-01 1.02953577e+00 1.29833388e+00 -2.53664941e-01
-2.28627041e-01 -4.36623484e-01 -2.74659514e-01 -2.35475346e-01
5.53717017e-01 5.30654490e-01 2.32292444e-01 -4.76104468... | [12.264628410339355, 9.42177963256836] |
179e5ecb-af3c-41ab-81e8-9a67a1d72626 | hin-hierarchical-inference-network-for | 2003.12754 | null | https://arxiv.org/abs/2003.12754v1 | https://arxiv.org/pdf/2003.12754v1.pdf | HIN: Hierarchical Inference Network for Document-Level Relation Extraction | Document-level RE requires reading, inferring and aggregating over multiple sentences. From our point of view, it is necessary for document-level RE to take advantage of multi-granularity inference information: entity level, sentence level and document level. Thus, how to obtain and aggregate the inference information ... | ['Zhen-Yu Zhang', 'Pengfei Yin', 'Fang Fang', 'Yanan Cao', 'Shi Wang', 'Hengzhu Tang', 'Jiangxia Cao'] | 2020-03-28 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 4.44715284e-02 -1.87848181e-01 -3.26293446e-02 -4.70547110e-01
-1.28764498e+00 -5.80503881e-01 5.72938085e-01 2.14102864e-01
-1.75782263e-01 8.60867500e-01 8.19621444e-01 -2.30232537e-01
-3.10448080e-01 -1.00014460e+00 -6.27145588e-01 -4.28122073e-01
3.61842573e-01 3.81086916e-01 -1.00237485e-02 -1.36621118... | [9.417081832885742, 8.723991394042969] |
deeac490-f47d-4b40-867c-19ac4b22e8e9 | tfix-learning-to-fix-coding-errors-with-a | null | null | http://proceedings.mlr.press/v139/berabi21a.html | https://files.sri.inf.ethz.ch/website/papers/icml21-tfix.pdf | TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer | The problem of fixing errors in programs has attracted substantial interest over the years. The key challenge for building an effective code fixing tool is to capture a wide range of errors and meanwhile maintain high accuracy. In this paper, we address this challenge and present a new learning-based system, called TFi... | ['Martin Vechev', 'Veselin Raychev', 'Jingxuan He', 'Berkay Berabi'] | 2021-07-18 | null | null | null | icml-2021-7 | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 7.84166828e-02 -1.32463828e-01 -5.30890703e-01 -1.70111775e-01
-1.42298877e+00 -8.03494632e-01 1.11831330e-01 3.22097719e-01
1.60122991e-01 3.81977797e-01 -6.47269487e-02 -7.72155643e-01
9.70684290e-02 -7.94566274e-01 -1.02805424e+00 1.32787019e-01
1.91407740e-01 1.47188947e-01 4.09621418e-01 -2.84445077... | [7.693808555603027, 7.750820636749268] |
7c2a625f-5bba-44c7-a4e2-1e21d32bdd94 | integration-of-data-and-theory-for | 2109.01634 | null | https://arxiv.org/abs/2109.01634v4 | https://arxiv.org/pdf/2109.01634v4.pdf | AI Descartes: Combining Data and Theory for Derivable Scientific Discovery | Scientists have long aimed to discover meaningful formulae which accurately describe experimental data. A common approach is to manually create mathematical models of natural phenomena using domain knowledge, and then fit these models to data. In contrast, machine-learning algorithms automate the construction of accura... | ['Lior Horesh', 'Bachir El Khadir', 'Nimrod Megiddo', 'Kenneth Clarkson', 'Joao Goncalves', 'Tyler Josephson', 'Vernon Austel', 'Sanjeeb Dash', 'Cristina Cornelio'] | 2021-09-03 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 2.44269565e-01 3.14940661e-01 -2.56391197e-01 -5.21466136e-01
1.47421390e-03 -5.59840858e-01 8.00894022e-01 2.68323898e-01
-2.29108021e-01 9.18877542e-01 -3.41631979e-01 -1.13089561e+00
-4.18598890e-01 -1.01692200e+00 -9.21374142e-01 -3.41834724e-01
-3.67668718e-02 5.60352921e-01 2.40502015e-01 -3.57690513... | [8.742770195007324, 6.663479328155518] |
0c361f04-f6c3-4baf-89f0-7d08dae8ad74 | an-admm-approach-for-multi-response | 2303.11155 | null | https://arxiv.org/abs/2303.11155v1 | https://arxiv.org/pdf/2303.11155v1.pdf | An ADMM approach for multi-response regression with overlapping groups and interaction effects | In this paper, we consider the regularized multi-response regression problem where there exists some structural relation within the responses and also between the covariates and a set of modifying variables. To handle this problem, we propose MADMMplasso, a novel regularized regression method. This method is able to fi... | ['Manuela Zucknick', 'Theophilus Quachie Asenso'] | 2023-03-20 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 4.13070589e-01 -3.47380280e-01 -4.02449429e-01 -7.25225031e-01
-5.36354065e-01 -3.55501115e-01 2.81246632e-01 5.21165073e-01
-3.53648007e-01 1.14134014e+00 2.02598855e-01 -2.06987679e-01
-6.49205148e-01 -6.97528839e-01 -6.74509764e-01 -9.51030195e-01
-2.39867210e-01 7.22403765e-01 2.45834012e-02 -9.66204479... | [7.722726345062256, 4.891781330108643] |
c72b04e4-3b06-4798-b7e0-1e627cfe1152 | a-preliminary-study-of-chatgpt-on-news | 2306.10702 | null | https://arxiv.org/abs/2306.10702v1 | https://arxiv.org/pdf/2306.10702v1.pdf | A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News | Online news platforms commonly employ personalized news recommendation methods to assist users in discovering interesting articles, and many previous works have utilized language model techniques to capture user interests and understand news content. With the emergence of large language models like GPT-3 and T-5, a new... | ['Edward C. Malthouse', 'Yongfeng Zhang', 'Xinyi Li'] | 2023-06-19 | null | null | null | null | ['fake-news-detection'] | ['natural-language-processing'] | [-2.78237760e-01 1.37993440e-01 -5.12003124e-01 -2.47279778e-01
-6.44221604e-01 -5.17742276e-01 7.18997717e-01 2.92313337e-01
-2.81373650e-01 2.89517730e-01 1.02098799e+00 -6.06173754e-01
-2.33571250e-02 -5.87746680e-01 -3.27004462e-01 8.45148042e-02
1.77520305e-01 3.06910127e-01 1.27288431e-01 -6.00700438... | [10.435492515563965, 6.003114700317383] |
e75e39c8-972d-4a27-8468-686d00c22456 | self-supervised-learning-of-remote-sensing | 2104.0707 | null | https://arxiv.org/abs/2104.07070v2 | https://arxiv.org/pdf/2104.07070v2.pdf | Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding | In recent years self-supervised learning has emerged as a promising candidate for unsupervised representation learning. In the visual domain its applications are mostly studied in the context of images of natural scenes. However, its applicability is especially interesting in specific areas, like remote sensing and med... | ['Vladimir Risojević', 'Vladan Stojnić'] | 2021-04-14 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 7.85270035e-01 -5.08502424e-02 -2.67086983e-01 -4.77811247e-01
-4.34007376e-01 -4.45189208e-01 6.41499698e-01 5.72927892e-01
-7.20234692e-01 5.78154147e-01 -8.76151398e-02 -4.76055771e-01
-2.28592634e-01 -9.67928648e-01 -5.31959951e-01 -7.56558001e-01
-9.58069861e-02 2.72218734e-01 -4.21345513e-03 -2.05770489... | [9.599213600158691, -1.3250246047973633] |
44be7d4c-5b37-4919-96ff-aab13838fd92 | kvl-bert-knowledge-enhanced-visual-and | 2012.07 | null | https://arxiv.org/abs/2012.07000v1 | https://arxiv.org/pdf/2012.07000v1.pdf | KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning | Reasoning is a critical ability towards complete visual understanding. To develop machine with cognition-level visual understanding and reasoning abilities, the visual commonsense reasoning (VCR) task has been introduced. In VCR, given a challenging question about an image, a machine must answer correctly and then prov... | ['Lejian Liao', 'Sicheng Yang', 'Zhanchen Sun', 'Siyi Ma', 'Dandan song'] | 2020-12-13 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 1.90402165e-01 3.04398358e-01 -3.03880684e-02 -2.14735076e-01
-1.41603172e-01 -5.36936522e-01 7.71728754e-01 -1.21528260e-01
-4.15007502e-01 6.17254615e-01 4.14155692e-01 -6.90226555e-01
1.12596035e-01 -8.68571222e-01 -7.60541737e-01 -3.23543191e-01
5.85383892e-01 8.22067335e-02 4.76621002e-01 -4.96742100... | [10.795771598815918, 1.7332181930541992] |
5d2acf88-af97-42e4-aa99-350328046c67 | end-to-end-natural-language-understanding | 2107.05541 | null | https://arxiv.org/abs/2107.05541v6 | https://arxiv.org/pdf/2107.05541v6.pdf | End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agents | Chatbots are intelligent software built to be used as a replacement for human interaction. Existing studies typically do not provide enough support for low-resource languages like Bangla. Due to the increasing popularity of social media, we can also see the rise of interactions in Bangla transliteration (mostly in Engl... | ['MD Abdullah Al Nasim', 'Mohammad Sabik Irbaz', 'Mueeze Al Mushabbir', 'Fahim Shahriar Khan'] | 2021-07-12 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-6.86359406e-01 4.18690383e-01 2.95798063e-01 -4.21380430e-01
-3.28226328e-01 -5.31261683e-01 8.65405798e-01 7.65409097e-02
-5.29704988e-01 6.33331358e-01 1.44794032e-01 -7.41625249e-01
1.23417921e-01 -5.88508546e-01 -2.16278419e-01 -3.30987960e-01
3.24405968e-01 9.13023174e-01 8.86602998e-02 -4.68066424... | [12.730609893798828, 7.746260643005371] |
8cd7c694-d45b-4df5-be38-04f3fa5a8527 | with-blinkers-on-robust-prediction-of-eye | null | null | https://aclanthology.org/D13-1075 | https://aclanthology.org/D13-1075.pdf | With Blinkers on: Robust Prediction of Eye Movements across Readers | null | ['Anders S{\\o}gaard', 'Franz Matthies'] | 2013-10-01 | null | null | null | emnlp-2013-10 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.252848148345947, 3.8093903064727783] |
b80d7455-f999-4fe8-b42d-9ccdfd963986 | unified-transfer-learning-models-for-high | 2307.00238 | null | https://arxiv.org/abs/2307.00238v1 | https://arxiv.org/pdf/2307.00238v1.pdf | Unified Transfer Learning Models for High-Dimensional Linear Regression | Transfer learning plays a key role in modern data analysis when: (1) the target data are scarce but the source data are sufficient; (2) the distributions of the source and target data are heterogeneous. This paper develops an interpretable unified transfer learning model, termed as UTrans, which can detect both transfe... | ['Shuo Shuo Liu'] | 2023-07-01 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 2.52440870e-01 2.30449349e-01 -1.13060141e+00 -2.33125299e-01
-8.58353794e-01 -4.56014931e-01 4.47560936e-01 1.40708849e-01
-1.72787175e-01 1.57468712e+00 -1.76410675e-02 -3.49118382e-01
-5.14348745e-01 -1.01287138e+00 -1.03103137e+00 -6.63203657e-01
-3.29690367e-01 8.03190947e-01 1.13741346e-01 -8.27735439... | [10.240692138671875, 3.310764789581299] |
29e1612b-97a3-43f7-9bec-c25a9f0bbb97 | extracting-linguistic-resources-from-the-web | 1810.13414 | null | http://arxiv.org/abs/1810.13414v1 | http://arxiv.org/pdf/1810.13414v1.pdf | Extracting Linguistic Resources from the Web for Concept-to-Text Generation | Many concept-to-text generation systems require domain-specific linguistic
resources to produce high quality texts, but manually constructing these
resources can be tedious and costly. Focusing on NaturalOWL, a publicly
available state of the art natural language generator for OWL ontologies, we
propose methods to extr... | ['Ion Androutsopoulos', 'Gerasimos Lampouras'] | 2018-10-31 | null | null | null | null | ['concept-to-text-generation'] | ['natural-language-processing'] | [ 4.03355733e-02 1.11489785e+00 -1.89017758e-01 -1.81482852e-01
-8.15797448e-01 -6.77127242e-01 9.24168646e-01 5.47799468e-01
-5.98978400e-01 1.40998745e+00 6.17510259e-01 -1.71495348e-01
5.33864908e-02 -1.15817451e+00 -3.62594306e-01 3.01405668e-01
3.06209683e-01 1.06712377e+00 5.12600720e-01 -5.73368073... | [11.518828392028809, 9.01388168334961] |
96fb7e56-da2d-45b6-a200-fe8b77f5ea03 | mask-attack-detection-using-vascular-weighted | 2305.1594 | null | https://arxiv.org/abs/2305.15940v1 | https://arxiv.org/pdf/2305.15940v1.pdf | Mask Attack Detection Using Vascular-weighted Motion-robust rPPG Signals | Detecting 3D mask attacks to a face recognition system is challenging. Although genuine faces and 3D face masks show significantly different remote photoplethysmography (rPPG) signals, rPPG-based face anti-spoofing methods often suffer from performance degradation due to unstable face alignment in the video sequence an... | ['Xudong Jiang', 'Jiang Liu', 'Heshan Du', 'Ruibin Bai', 'Jianfeng Ren', 'Chenglin Yao'] | 2023-05-25 | null | null | null | null | ['face-recognition', 'face-alignment', 'face-anti-spoofing'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.42675745e-01 -4.00029808e-01 -1.45840496e-01 -2.45224461e-01
-5.37960827e-01 -3.08791995e-01 3.48270714e-01 -6.93533599e-01
2.48611644e-02 3.23524386e-01 3.38170648e-01 2.30310693e-01
1.23683184e-01 -3.84740144e-01 -3.01407427e-01 -1.03208482e+00
-1.96826085e-01 -4.80506331e-01 -8.87928754e-02 -4.97923382... | [13.053637504577637, 1.2325316667556763] |
feddc8b1-b430-4ac4-b6c9-576d57489465 | input-output-balanced-framework-for-long | 2103.14269 | null | https://arxiv.org/abs/2103.14269v1 | https://arxiv.org/pdf/2103.14269v1.pdf | Input-Output Balanced Framework for Long-tailed LiDAR Semantic Segmentation | A thorough and holistic scene understanding is crucial for autonomous vehicles, where LiDAR semantic segmentation plays an indispensable role. However, most existing methods focus on the network design while neglecting the inherent difficulty, imbalanced data distribution in the realistic dataset (also named long-taile... | ['Yuexin Ma', 'Xinge Zhu', 'Peishan Cong'] | 2021-03-26 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [-5.84633835e-02 -2.57721454e-01 -4.90831554e-01 -6.83054209e-01
-1.60323799e-01 -3.16911191e-01 3.29402536e-01 -4.98167835e-02
-4.35092986e-01 6.31346583e-01 -2.81096518e-01 -2.90432721e-01
-3.91683042e-01 -1.11010468e+00 -7.10062742e-01 -7.46558130e-01
2.74927408e-01 6.97548687e-01 6.59910440e-01 -1.72493398... | [8.02263069152832, -2.818971872329712] |
055ad453-50f5-4243-9546-ddb2e0094f7d | efficient-end-to-end-video-question-answering | 2302.02136 | null | https://arxiv.org/abs/2302.02136v2 | https://arxiv.org/pdf/2302.02136v2.pdf | Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer | This paper presents a new method for end-to-end Video Question Answering (VideoQA), aside from the current popularity of using large-scale pre-training with huge feature extractors. We achieve this with a pyramidal multimodal transformer (PMT) model, which simply incorporates a learnable word embedding layer, a few con... | ['Xiang-Dong Zhou', 'Yu Shi', 'Chongyang Wang', 'Min Peng'] | 2023-02-04 | null | null | null | null | ['video-question-answering', 'video-retrieval'] | ['computer-vision', 'computer-vision'] | [-9.35454741e-02 -2.75023729e-01 -5.50031066e-02 -3.28616887e-01
-1.15435731e+00 -6.04156077e-01 5.74994802e-01 -4.29100841e-02
-6.57673061e-01 6.63107112e-02 7.16055274e-01 -1.90789986e-03
-1.21146746e-01 -6.56360984e-01 -7.47140884e-01 -4.29637104e-01
-1.82836831e-01 1.71760857e-01 6.29348457e-01 -4.96633559... | [10.407843589782715, 1.0252203941345215] |
9259ed49-2b5e-45cb-8270-3f9a57aa559e | deep-causal-learning-representation-discovery | 2211.03374 | null | https://arxiv.org/abs/2211.03374v1 | https://arxiv.org/pdf/2211.03374v1.pdf | Deep Causal Learning: Representation, Discovery and Inference | Causal learning has attracted much attention in recent years because causality reveals the essential relationship between things and indicates how the world progresses. However, there are many problems and bottlenecks in traditional causal learning methods, such as high-dimensional unstructured variables, combinatorial... | ['Daniel Dajun Zeng', 'Hu Tian', 'Xiaolong Zheng', 'Zizhen Deng'] | 2022-11-07 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 1.72887579e-01 1.32815227e-01 -9.20035601e-01 -5.38540244e-01
-4.04442370e-01 -1.25020728e-01 6.82769179e-01 1.36973560e-01
2.78910808e-03 1.31474066e+00 9.24252391e-01 -5.56066871e-01
-8.20265889e-01 -1.11741078e+00 -8.30894649e-01 -7.20157444e-01
-5.41947484e-01 4.04515147e-01 -2.86983252e-01 9.68896002... | [7.99501895904541, 5.400814056396484] |
38b4de86-7625-4069-af6f-0a95149160ed | a-hybrid-citation-retrieval-algorithm-for | 1609.01597 | null | http://arxiv.org/abs/1609.01597v1 | http://arxiv.org/pdf/1609.01597v1.pdf | A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical Knowledge Summarization: Combining Concept Extraction, Vector Similarity and Query Expansion for High Precision | Novel information retrieval methods to identify citations relevant to a
clinical topic can overcome the knowledge gap existing between the primary
literature (MEDLINE) and online clinical knowledge resources such as UpToDate.
Searching the MEDLINE database directly or with query expansion methods returns
a large number... | ['Siddhartha R. Jonnalagadda', 'Ravi P Garg', 'Kalpana Raja', 'Andrew J Sauer', 'Melanie R Klerer'] | 2016-09-06 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 3.69915329e-02 1.80675820e-01 -5.29419541e-01 4.25539166e-01
-1.29674697e+00 -6.84880316e-01 2.72792697e-01 1.14880753e+00
-5.53836167e-01 1.18147135e+00 4.54602778e-01 -4.52898175e-01
-8.49067390e-01 -7.36269712e-01 -2.66626418e-01 -1.05078325e-01
-5.66961281e-02 7.28217185e-01 2.45776728e-01 -1.12048581... | [8.621833801269531, 8.626053810119629] |
9b830a9d-af58-4e45-8cf0-d89bcc73f3f5 | self-supervised-beat-tracking-in-musical | 2201.01771 | null | https://arxiv.org/abs/2201.01771v1 | https://arxiv.org/pdf/2201.01771v1.pdf | Self-Supervised Beat Tracking in Musical Signals with Polyphonic Contrastive Learning | Annotating musical beats is a very long in tedious process. In order to combat this problem, we present a new self-supervised learning pretext task for beat tracking and downbeat estimation. This task makes use of Spleeter, an audio source separation model, to separate a song's drums from the rest of its signal. The fi... | ['Dorian Desblancs'] | 2022-01-05 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 4.51985091e-01 2.12326020e-01 5.52617125e-02 -6.99133500e-02
-7.40228832e-01 -8.71836185e-01 3.10241014e-01 1.95787832e-01
-3.01288545e-01 5.13986349e-01 2.30842188e-01 1.38324454e-01
5.27533367e-02 -4.59260225e-01 -6.84817135e-01 -7.00414419e-01
-2.43803084e-01 4.36502516e-01 3.56670141e-01 -4.89556342... | [15.838171005249023, 5.273606300354004] |
a63badf7-f88d-4629-82cb-cf7426d5cc06 | collaborative-residual-metric-learning | 2304.07971 | null | https://arxiv.org/abs/2304.07971v1 | https://arxiv.org/pdf/2304.07971v1.pdf | Collaborative Residual Metric Learning | In collaborative filtering, distance metric learning has been applied to matrix factorization techniques with promising results. However, matrix factorization lacks the ability of capturing collaborative information, which has been remarked by recent works and improved by interpreting user interactions as signals. This... | ['Tommy W. S. Chow', 'Jianghong Ma', 'Tianjun Wei'] | 2023-04-17 | null | null | null | null | ['metric-learning', 'metric-learning', 'collaborative-filtering'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 1.52620107e-01 -4.53067303e-01 -1.41854540e-01 -5.24038553e-01
-4.41509813e-01 -4.96442646e-01 4.23984498e-01 1.39667615e-01
-3.18158776e-01 3.34698349e-01 6.50981069e-01 -2.50843704e-01
-8.96837413e-01 -6.43037915e-01 -4.56479818e-01 -7.34997034e-01
-4.66788739e-01 -8.30513611e-02 -1.26434803e-01 -3.84759963... | [10.078185081481934, 5.603568077087402] |
c6420f2b-2af6-4dbf-8d49-f2344f4d3300 | background-foreground-segmentation-for | 2109.0941 | null | https://arxiv.org/abs/2109.09410v1 | https://arxiv.org/pdf/2109.09410v1.pdf | Background-Foreground Segmentation for Interior Sensing in Automotive Industry | To ensure safety in automated driving, the correct perception of the situation inside the car is as important as its environment. Thus, seat occupancy detection and classification of detected instances play an important role in interior sensing. By the knowledge of the seat occupancy status, it is possible to, e.g., au... | ['Thomas Kurbiel', 'Klaus Friedrichs', 'Hanno Gottschalk', 'Matthias Rottmann', 'Claudia Drygala'] | 2021-09-20 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 3.13498080e-01 1.94342807e-01 1.05892427e-01 -2.25795105e-01
-2.52971500e-01 -4.60882217e-01 5.40270269e-01 1.62458241e-01
-6.09424114e-01 6.41204417e-01 -7.06268370e-01 -4.55883682e-01
-7.48222321e-02 -8.47540259e-01 -7.54662514e-01 -9.40973043e-01
2.50115305e-01 5.97864091e-01 5.43372691e-01 -1.90960228... | [8.370895385742188, -1.0405975580215454] |
735af96d-67ea-492e-ac4a-64491b6673bc | dhrl-fnmr-an-intelligent-multicast-routing | 2305.19077 | null | https://arxiv.org/abs/2305.19077v1 | https://arxiv.org/pdf/2305.19077v1.pdf | DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN | The optimal multicast tree problem in the Software-Defined Networking (SDN) multicast routing is an NP-hard combinatorial optimization problem. Although existing SDN intelligent solution methods, which are based on deep reinforcement learning, can dynamically adapt to complex network link state changes, these methods a... | ['Qiuxiang Jiang', 'Yejin Yang', 'Hongwen Hu', 'Jinqiang Li', 'Xingsi Xue', 'Chenwei Zhao', 'Miao Ye'] | 2023-05-30 | null | null | null | null | ['combinatorial-optimization', 'hierarchical-reinforcement-learning'] | ['methodology', 'methodology'] | [-1.06139459e-01 3.02588075e-01 -4.79172260e-01 -1.55589968e-01
-3.75233358e-03 -2.90573210e-01 -6.17030784e-02 -1.69151619e-01
-1.07245252e-01 1.08249116e+00 -3.22673589e-01 -2.48477951e-01
-6.07704937e-01 -1.03706741e+00 -4.20358405e-02 -8.28451693e-01
-5.43381095e-01 4.93723422e-01 5.31656504e-01 -1.67466309... | [5.762739181518555, 1.7593961954116821] |
344585e7-27a8-427a-b0b4-5c8c1928d2d0 | dex-nerf-using-a-neural-radiance-field-to | 2110.14217 | null | https://arxiv.org/abs/2110.14217v1 | https://arxiv.org/pdf/2110.14217v1.pdf | Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects | The ability to grasp and manipulate transparent objects is a major challenge for robots. Existing depth cameras have difficulty detecting, localizing, and inferring the geometry of such objects. We propose using neural radiance fields (NeRF) to detect, localize, and infer the geometry of transparent objects with suffic... | ['Ken Goldberg', 'Justin Kerr', 'Yahav Avigal', 'Jeffrey Ichnowski'] | 2021-10-27 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 1.76546406e-02 2.51645386e-01 6.31003082e-01 -9.76568907e-02
-3.69574100e-01 -9.59731877e-01 -2.22917497e-02 -2.69147325e-02
-3.73928919e-02 3.12754571e-01 -1.11197501e-01 3.08086369e-02
7.81078711e-02 -7.08390713e-01 -1.29222119e+00 -5.48824966e-01
-4.51048821e-01 8.45242620e-01 3.54363352e-01 -4.57748882... | [5.926169395446777, -1.0256898403167725] |
560c5c98-ebe8-4689-a33b-5946bb8e0b0c | simultaneous-contact-rich-grasping-and | 2207.01418 | null | https://arxiv.org/abs/2207.01418v2 | https://arxiv.org/pdf/2207.01418v2.pdf | Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots | While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet. We present an efficient motion planning framework for simultaneously solving locomotion (e.g., centroidal dynamics), grasping (e.g., patch contact), an... | ['Dennis Hong', 'Varit Vichathorn', 'Hayato Kato', 'Yusuke Tanaka', 'Alexander Schperberg', 'Xuan Lin', 'Yuki Shirai'] | 2022-07-04 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-6.31893873e-02 1.89602181e-01 -4.06344682e-01 2.94194324e-03
-3.05129379e-01 -5.91687560e-01 -6.33092895e-02 -3.78597051e-01
-4.26799744e-01 8.90204132e-01 -3.99805784e-01 -2.19614562e-02
-6.08204663e-01 -7.97738910e-01 -9.87806499e-01 -8.41196358e-01
-5.94155252e-01 7.87755609e-01 1.14610769e-01 -5.64710796... | [4.786753177642822, 1.1327108144760132] |
58926f58-46de-4328-9b6f-d7a087cf29bf | on-the-benefits-of-selectivity-in-pseudo | 2202.00796 | null | https://arxiv.org/abs/2202.00796v3 | https://arxiv.org/pdf/2202.00796v3.pdf | On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation | Due to privacy, storage, and other constraints, there is a growing need for unsupervised domain adaptation techniques in machine learning that do not require access to the data used to train a collection of source models. Existing methods for multi-source-free domain adaptation (MSFDA) typically train a target model us... | ['Gregory Wornell', 'Yuheng Bu', 'Maohao Shen'] | 2022-02-01 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 4.35264707e-01 1.18946601e-02 -4.93238598e-01 -6.21980131e-01
-8.32019031e-01 -6.70747101e-01 5.59134245e-01 2.21833453e-01
-4.43555564e-01 9.02832925e-01 7.02892020e-02 -8.51200521e-02
-3.41507971e-01 -5.89003742e-01 -6.59390330e-01 -7.08954155e-01
2.80277401e-01 5.33262491e-01 1.68143734e-01 8.85338113... | [10.36361312866211, 3.1934139728546143] |
24668202-8bc4-4b90-b79c-5be694751715 | learning-markerless-robot-depth-camera | 2212.07567 | null | https://arxiv.org/abs/2212.07567v1 | https://arxiv.org/pdf/2212.07567v1.pdf | Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation | Traditional approaches to extrinsic calibration use fiducial markers and learning-based approaches rely heavily on simulation data. In this work, we present a learning-based markerless extrinsic calibration system that uses a depth camera and does not rely on simulation data. We learn models for end-effector (EE) segme... | ['Baris Akgun', 'Bugra C. Sefercik'] | 2022-12-15 | null | null | null | null | ['camera-calibration', 'keypoint-detection'] | ['computer-vision', 'computer-vision'] | [-1.26072541e-01 1.42756224e-01 -2.87680477e-01 -2.36116707e-01
-1.25428665e+00 -8.38381112e-01 5.87968469e-01 8.34813789e-02
-6.15422070e-01 6.34262145e-01 -3.30215633e-01 -1.64587915e-01
9.09637436e-02 -4.95467819e-02 -9.90658462e-01 -3.74311566e-01
-1.59869082e-02 5.96333623e-01 4.01247501e-01 1.42850000... | [7.260104179382324, -1.4198358058929443] |
9b3bf274-3ac7-4036-a75d-4bfbd6eecfa0 | first-insight-into-quality-adaptive-dialogue | null | null | https://aclanthology.org/L14-1092 | https://aclanthology.org/L14-1092.pdf | First Insight into Quality-Adaptive Dialogue | While Spoken Dialogue Systems have gained in importance in recent years, most systems applied in the real world are still static and error-prone. To overcome this, the user is put into the focus of dialogue management. Hence, an approach for adapting the course of the dialogue to Interaction Quality, an objective varia... | ['H{\\"u}seyin Dikme', 'Stefan Ultes', 'Wolfgang Minker'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['dialogue-management'] | ['natural-language-processing'] | [ 1.29703134e-02 5.47652066e-01 2.36997768e-01 -7.11884081e-01
-2.40807086e-01 -6.34212792e-01 4.91956264e-01 4.03407305e-01
-6.86190665e-01 8.55055749e-01 3.27813685e-01 -9.17325243e-02
-4.44368422e-01 -6.84958160e-01 3.26477170e-01 -2.21282646e-01
3.84858757e-01 6.70807362e-01 3.98932010e-01 -8.30088019... | [13.053155899047852, 7.8845133781433105] |
89a0ec2d-e602-4eb8-b062-3e0ccbac46cd | modeling-irregularly-sampled-clinical-time | 1812.00531 | null | http://arxiv.org/abs/1812.00531v1 | http://arxiv.org/pdf/1812.00531v1.pdf | Modeling Irregularly Sampled Clinical Time Series | While the volume of electronic health records (EHR) data continues to grow,
it remains rare for hospital systems to capture dense physiological data
streams, even in the data-rich intensive care unit setting. Instead, typical
EHR records consist of sparse and irregularly observed multivariate time
series, which are wel... | ['Satya Narayan Shukla', 'Benjamin M. Marlin'] | 2018-12-03 | null | null | null | null | ['length-of-stay-prediction'] | ['medical'] | [ 1.21514350e-01 1.20181151e-01 -1.69861868e-01 -3.99977535e-01
-6.00336850e-01 -1.20159201e-01 1.24490172e-01 6.16003096e-01
-4.08046722e-01 8.68672550e-01 2.44435459e-01 -4.36056316e-01
-1.34844571e-01 -6.61816597e-01 -6.89738691e-01 -6.44573808e-01
-5.14326453e-01 6.26123965e-01 -3.82921994e-01 2.08140120... | [7.944121837615967, 6.1911187171936035] |
84047ce8-9c1a-47bd-a2ae-3a237ab6d5e9 | motion-scenario-decoupling-for-rat-aware | 2305.1831 | null | https://arxiv.org/abs/2305.18310v1 | https://arxiv.org/pdf/2305.18310v1.pdf | Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark | Recently significant progress has been made in human action recognition and behavior prediction using deep learning techniques, leading to improved vision-based semantic understanding. However, there is still a lack of high-quality motion datasets for small bio-robotics, which presents more challenging scenarios for lo... | ['Nenggan Zheng', 'Risheng Liu', 'Yaohua Liu', 'Jiaxin Gao', 'Xiaofeng Liu'] | 2023-05-17 | null | null | null | null | ['motion-prediction', 'trajectory-prediction', 'action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 6.84313476e-02 -5.10484576e-01 -2.46344060e-01 -2.63320744e-01
-2.45018229e-01 -2.03561425e-01 5.48507869e-01 -1.03123151e-01
-4.57677275e-01 5.07128954e-01 3.91749859e-01 1.44444644e-01
-6.43288255e-01 -4.47334290e-01 -4.87331271e-01 -1.00129688e+00
-1.62977368e-01 2.34803155e-01 4.25580531e-01 -1.80297598... | [7.561258316040039, -0.05420202016830444] |
c0fb91e4-a464-4246-9218-f763de2e8ead | building-multimodal-ai-chatbots | 2305.03512 | null | https://arxiv.org/abs/2305.03512v1 | https://arxiv.org/pdf/2305.03512v1.pdf | Building Multimodal AI Chatbots | This work aims to create a multimodal AI system that chats with humans and shares relevant photos. While earlier works were limited to dialogues about specific objects or scenes within images, recent works have incorporated images into open-domain dialogues. However, their response generators are unimodal, accepting te... | ['Min Young Lee'] | 2023-04-21 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [ 7.80508965e-02 3.70671600e-01 2.72618979e-01 -4.42264646e-01
-1.16925764e+00 -8.23667705e-01 8.41534436e-01 -2.29451597e-01
-6.62479162e-01 8.94868731e-01 3.28024924e-02 1.00052670e-01
4.12660390e-01 -6.29534185e-01 -5.39561212e-01 -8.00148129e-01
4.72590536e-01 7.48545945e-01 3.68459910e-01 -5.28453231... | [10.97941780090332, 1.3535040616989136] |
b25b0df0-030a-4fce-9b6c-c1f5165bd615 | doc3-deep-one-class-classification-using | 2105.07636 | null | https://arxiv.org/abs/2105.07636v2 | https://arxiv.org/pdf/2105.07636v2.pdf | DOC3-Deep One Class Classification using Contradictions | This paper introduces the notion of learning from contradictions (a.k.a Universum learning) for deep one class classification problems. We formalize this notion for the widely adopted one class large-margin loss, and propose the Deep One Class Classification using Contradictions (DOC3) algorithm. We show that learning ... | ['Bernardo Gonzalez Torres', 'Sauptik Dhar'] | 2021-05-17 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [-2.81498700e-01 3.71208131e-01 -3.85152221e-01 -5.85374475e-01
-1.25144160e+00 -3.80411565e-01 3.77545863e-01 5.02157629e-01
-7.08354175e-01 1.07626235e+00 -2.51269221e-01 -8.02811444e-01
-5.75233161e-01 -8.88440788e-01 -1.02688611e+00 -7.58242428e-01
-4.94890392e-01 4.81509358e-01 -8.93990919e-02 -1.63916305... | [8.80726432800293, 4.035182952880859] |
833e366a-7fac-4648-8584-d4a65695990e | k-core-based-temporal-graph-convolutional | 2003.09902 | null | https://arxiv.org/abs/2003.09902v4 | https://arxiv.org/pdf/2003.09902v4.pdf | K-Core based Temporal Graph Convolutional Network for Dynamic Graphs | Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information. However, many existing methods focus on static graphs while ignoring evolving graph patterns. Inspired by the success of graph convolutiona... | ['You Song', 'Jingxin Liu', 'Chang Yin', 'Chang Xu', 'Weiqiang Wu'] | 2020-03-22 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [-2.82775432e-01 3.08102190e-01 -5.57087779e-01 -2.36730743e-02
2.72736222e-01 -5.33512235e-01 6.26637161e-01 3.50311399e-01
1.35653719e-01 2.69033700e-01 3.34062189e-01 -4.36925948e-01
-3.02854478e-01 -1.15770161e+00 -2.12499157e-01 -7.56241262e-01
-5.41682959e-01 2.22869962e-01 3.49265933e-01 -3.55259866... | [7.205883502960205, 6.166987419128418] |
fc5ec8c3-d196-49e0-a307-0ec5d957cb4c | zero-shot-pose-transfer-for-unrigged-stylized-1 | 2306.002 | null | https://arxiv.org/abs/2306.00200v1 | https://arxiv.org/pdf/2306.00200v1.pdf | Zero-shot Pose Transfer for Unrigged Stylized 3D Characters | Transferring the pose of a reference avatar to stylized 3D characters of various shapes is a fundamental task in computer graphics. Existing methods either require the stylized characters to be rigged, or they use the stylized character in the desired pose as ground truth at training. We present a zero-shot approach th... | ['Jan Kautz', 'Xiaolong Wang', 'Orazio Gallo', 'Shalini De Mello', 'Sifei Liu', 'Xueting Li', 'Jiashun Wang'] | 2023-05-31 | zero-shot-pose-transfer-for-unrigged-stylized | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Zero-Shot_Pose_Transfer_for_Unrigged_Stylized_3D_Characters_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Zero-Shot_Pose_Transfer_for_Unrigged_Stylized_3D_Characters_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pose-transfer'] | ['computer-vision'] | [-4.36574444e-02 1.72995508e-01 6.79295743e-03 -1.61719248e-01
-8.87099147e-01 -9.68869150e-01 4.58409071e-01 -4.13850158e-01
-9.09870937e-02 4.99740601e-01 -1.80634096e-01 -4.35106410e-03
5.36895394e-01 -9.28309500e-01 -1.03044868e+00 -4.40480500e-01
4.36353475e-01 1.08254683e+00 6.07397079e-01 -3.78329307... | [7.361363410949707, -1.63212251663208] |
5c500bb5-ea62-42f1-96ba-75738336409a | a-compressive-multi-kernel-method-for-privacy | 2106.10671 | null | https://arxiv.org/abs/2106.10671v1 | https://arxiv.org/pdf/2106.10671v1.pdf | A compressive multi-kernel method for privacy-preserving machine learning | As the analytic tools become more powerful, and more data are generated on a daily basis, the issue of data privacy arises. This leads to the study of the design of privacy-preserving machine learning algorithms. Given two objectives, namely, utility maximization and privacy-loss minimization, this work is based on two... | ['S. Y. Kung', 'J. Morris Chang', 'Thee Chanyaswad'] | 2021-06-20 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 6.60536885e-01 -7.24405348e-02 -2.84973264e-01 -3.70317042e-01
-1.04921544e+00 -3.18999141e-01 2.96298385e-01 1.87339321e-01
-4.85738069e-01 8.25766563e-01 4.59060848e-01 -2.18785837e-01
-4.59832340e-01 -6.53525591e-01 -4.95484143e-01 -1.10933983e+00
-1.45717561e-01 -1.62096977e-01 -2.92688042e-01 2.34373599... | [5.954610347747803, 6.624238014221191] |
2412269a-7102-4f76-a221-89b6fbf678bc | mixed-nondeterministic-probabilistic-automata | 2201.07474 | null | https://arxiv.org/abs/2201.07474v1 | https://arxiv.org/pdf/2201.07474v1.pdf | Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism | Graphical models in probability and statistics are a core concept in the area of probabilistic reasoning and probabilistic programming-graphical models include Bayesian networks and factor graphs. In this paper we develop a new model of mixed (nondeterministic/probabilistic) automata that subsumes both nondeterministic... | ['Jean-Baptiste Raclet', 'Albert Benveniste'] | 2022-01-19 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [-2.02491656e-01 3.59036505e-01 -1.22415133e-01 -4.86528993e-01
-5.39797843e-01 -7.23231077e-01 1.27430487e+00 -2.67659854e-02
-1.67234063e-01 7.65058994e-01 1.31743446e-01 -8.02872956e-01
-6.53053522e-01 -1.21031928e+00 -3.12379032e-01 -6.26778960e-01
-4.78521287e-01 1.13146758e+00 7.65591562e-01 -6.98536709... | [8.368379592895508, 6.2983832359313965] |
9f715cbe-de44-432f-b6c6-192a5f9134b1 | visual-slam-with-graph-cut-optimized-multi | 2108.04281 | null | https://arxiv.org/abs/2108.04281v2 | https://arxiv.org/pdf/2108.04281v2.pdf | Visual SLAM with Graph-Cut Optimized Multi-Plane Reconstruction | This paper presents a semantic planar SLAM system that improves pose estimation and mapping using cues from an instance planar segmentation network. While the mainstream approaches are using RGB-D sensors, employing a monocular camera with such a system still faces challenges such as robust data association and precise... | ['Didier Stricker', 'Alain Pagani', 'Jason Rambach', 'Yaxu Xie', 'Fangwen Shu'] | 2021-08-09 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 2.93102562e-01 1.02757521e-01 -5.26332892e-02 -3.78262758e-01
-6.77612603e-01 -6.78574681e-01 3.43851715e-01 4.37633209e-02
-4.12006885e-01 4.34554100e-01 -3.21796656e-01 -3.61282714e-02
-4.26080436e-01 -5.92160463e-01 -1.07583213e+00 -5.07636368e-01
4.32481170e-01 9.93830442e-01 1.54155537e-01 -3.41361687... | [7.7036614418029785, -2.5118346214294434] |
35e65d86-b312-4c62-a631-c637b4f4ad8a | modeling-3d-surface-manifolds-with-a-locally | 2102.05984 | null | https://arxiv.org/abs/2102.05984v1 | https://arxiv.org/pdf/2102.05984v1.pdf | Modeling 3D Surface Manifolds with a Locally Conditioned Atlas | Recently proposed 3D object reconstruction methods represent a mesh with an atlas - a set of planar patches approximating the surface. However, their application in a real-world scenario is limited since the surfaces of reconstructed objects contain discontinuities, which degrades the quality of the final mesh. This is... | ['Kacper Kania', 'Tomasz Trzciński', 'Maciej Zięba', 'Sebastian Winczowski', 'Przemysław Spurek'] | 2021-02-11 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 2.13951260e-01 4.81019199e-01 4.11119074e-01 -1.60624143e-02
-7.48495460e-01 -4.65888768e-01 5.86063087e-01 1.28129184e-01
2.62297630e-01 3.03305715e-01 -3.56862396e-02 3.98036659e-01
2.07319595e-02 -1.05672765e+00 -1.12835431e+00 -7.21315503e-01
3.80089015e-01 9.92954552e-01 5.68452358e-01 1.24768704... | [8.705268859863281, -3.3548901081085205] |
26f07b36-b7d3-425c-9ba7-e5e5a212066b | influence-of-lossy-speech-codecs-on-hearing | 2306.02344 | null | https://arxiv.org/abs/2306.02344v1 | https://arxiv.org/pdf/2306.02344v1.pdf | Influence of Lossy Speech Codecs on Hearing-aid, Binaural Sound Source Localisation using DNNs | Hearing aids are typically equipped with multiple microphones to exploit spatial information for source localisation and speech enhancement. Especially for hearing aids, a good source localisation is important: it not only guides source separation methods but can also be used to enhance spatial cues, increasing user-aw... | ['Alexander Bohlender. Nilesh Madhu', 'Jasper Maes', 'Stijn Kindt', 'Siyuan Song'] | 2023-06-04 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 1.01407029e-01 -4.19067770e-01 3.04550767e-01 1.73894912e-02
-1.16276586e+00 -3.55696261e-01 1.31999344e-01 2.38848343e-01
-5.03248274e-01 5.31290054e-01 6.25817060e-01 -2.47206926e-01
-2.22011030e-01 -7.73464501e-01 -4.55336988e-01 -1.03413248e+00
-3.41748297e-01 -1.22596987e-01 3.15345615e-01 -1.85547128... | [15.033156394958496, 5.841567039489746] |
fc31fc39-d45c-4be1-957a-36e79eb92c9f | n-stage-latent-dirichlet-allocation-a-novel | 2110.08591 | null | https://arxiv.org/abs/2110.08591v2 | https://arxiv.org/pdf/2110.08591v2.pdf | n-stage Latent Dirichlet Allocation: A Novel Approach for LDA | Nowadays, data analysis has become a problem as the amount of data is constantly increasing. In order to overcome this problem in textual data, many models and methods are used in natural language processing. The topic modeling field is one of these methods. Topic modeling allows determining the semantic structure of a... | ['Tolgahan Cakaloglu', 'Banu Diri', 'Zekeriya Anil Guven'] | 2021-10-16 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-5.05089402e-01 -2.65220851e-01 -5.34047425e-01 -2.32607797e-01
-4.62173641e-01 -2.82980800e-01 6.70272470e-01 3.78180265e-01
-3.81711930e-01 3.40823710e-01 5.21342278e-01 -1.83964431e-01
8.52932706e-02 -9.54249740e-01 1.68655008e-01 -7.04388499e-01
4.51460361e-01 5.19008636e-01 1.53021008e-01 -8.51979777... | [10.431411743164062, 7.095047950744629] |
07c5c766-9eeb-4a6a-916d-4afd6c43f5d2 | human-to-human-interaction-detection | 2307.00464 | null | https://arxiv.org/abs/2307.00464v1 | https://arxiv.org/pdf/2307.00464v1.pdf | Human-to-Human Interaction Detection | A comprehensive understanding of interested human-to-human interactions in video streams, such as queuing, handshaking, fighting and chasing, is of immense importance to the surveillance of public security in regions like campuses, squares and parks. Different from conventional human interaction recognition, which uses... | ['Cong Bai', 'Jifeng Ning', 'Jiajun Meng', 'Kaining Ying', 'Zhenhua Wang'] | 2023-07-02 | null | null | null | null | ['action-detection', 'human-interaction-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.38366604e-01 -3.03873479e-01 -5.99151962e-02 -2.90777385e-01
-4.59095210e-01 -5.56112111e-01 9.70351160e-01 -1.26370460e-01
-4.00163919e-01 9.23560858e-02 6.66820168e-01 -1.55223399e-01
5.19226491e-02 -4.54961121e-01 -4.56381172e-01 -5.54551661e-01
-5.35961449e-01 2.88092613e-01 4.22612429e-01 -1.39352769... | [8.233217239379883, 0.5577858686447144] |
fc7f3193-0952-4194-914c-8ab82aec0c64 | deep-color-mismatch-correction-in | null | null | https://ieeexplore.ieee.org/document/9506036 | https://v-sense.scss.tcd.ie/wp-content/uploads/2021/06/ICIP_2021_compressed.pdf | Deep Color Mismatch Correction In Stereoscopic 3D Images | Color mismatch in stereoscopic 3D (S3D) images can create visual discomfort and affect the performance of S3D image processing algorithms, e.g., for depth estimation. In this paper, we propose a new deep learning-based solution for the problem of color mismatch correction. The proposed solution consists of a multi-task... | ['Aljosa Smolic', 'Sebastian Knorr', 'Roman Dudek', 'Emin Zerman', 'Cagri Ozcinar', 'Simone Croci'] | 2021-06-01 | null | null | null | ieee-international-conference-on-image-10 | ['color-mismatch-correction'] | ['computer-vision'] | [ 3.08446735e-02 -2.84350663e-01 2.60153413e-01 -3.93424630e-01
-3.54396701e-01 -1.25168622e-01 1.63922459e-01 -1.44671127e-01
-3.94520909e-01 4.20883179e-01 1.89147413e-01 -3.45628560e-01
2.85183340e-01 -5.01510262e-01 -5.48637390e-01 -4.95246500e-01
4.29247737e-01 -2.46953979e-01 3.19953173e-01 6.08379953... | [9.117269515991211, -2.4518380165100098] |
37224062-2457-4477-929f-b2763ecd4e27 | implicit-transfer-operator-learning-multiple | 2305.18046 | null | https://arxiv.org/abs/2305.18046v1 | https://arxiv.org/pdf/2305.18046v1.pdf | Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics | Computing properties of molecular systems rely on estimating expectations of the (unnormalized) Boltzmann distribution. Molecular dynamics (MD) is a broadly adopted technique to approximate such quantities. However, stable simulations rely on very small integration time-steps ($10^{-15}\,\mathrm{s}$), whereas convergen... | ['Simon Olsson', 'Ole Winther', 'Mathias Schreiner'] | 2023-05-29 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [ 2.03843698e-01 -5.19778490e-01 1.08431034e-01 -1.78076208e-01
-1.11951649e+00 -5.47761738e-01 6.61470890e-01 2.57267714e-01
-8.53107095e-01 1.19500446e+00 -2.56437838e-01 -4.08274084e-01
-1.15484774e-01 -8.30536366e-01 -9.23974991e-01 -1.46939623e+00
-2.96632499e-01 8.36249471e-01 2.38038749e-01 -2.26169050... | [5.137246131896973, 5.1673479080200195] |
a484fb3d-3c28-461f-b3cb-2ce7414dd691 | abstractive-text-summarization-using-sequence | 1602.06023 | null | http://arxiv.org/abs/1602.06023v5 | http://arxiv.org/pdf/1602.06023v5.pdf | Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond | In this work, we model abstractive text summarization using Attentional
Encoder-Decoder Recurrent Neural Networks, and show that they achieve
state-of-the-art performance on two different corpora. We propose several novel
models that address critical problems in summarization that are not adequately
modeled by the basi... | ['Bo-Wen Zhou', 'Cicero Nogueira dos santos', 'Bing Xiang', 'Ramesh Nallapati', 'Caglar Gulcehre'] | 2016-02-19 | abstractive-text-summarization-using-sequence-1 | https://aclanthology.org/K16-1028 | https://aclanthology.org/K16-1028.pdf | conll-2016-8 | ['summarization', 'abstractive-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.16824245e-01 1.11921817e-01 -4.50768173e-01 -2.00888649e-01
-1.06317556e+00 -1.17165700e-01 3.40568811e-01 4.74483609e-01
-3.86374474e-01 9.06700730e-01 1.19966149e+00 -2.41618052e-01
2.13162228e-01 -5.27547419e-01 -6.20580256e-01 -2.92263836e-01
1.42895386e-01 2.85688579e-01 1.34175435e-01 -5.60202241... | [12.467828750610352, 9.447436332702637] |
9da19f07-bf11-43d4-8ce2-084974ebf9f3 | improving-vision-and-language-navigation-by | 2304.04907 | null | https://arxiv.org/abs/2304.04907v1 | https://arxiv.org/pdf/2304.04907v1.pdf | Improving Vision-and-Language Navigation by Generating Future-View Image Semantics | Vision-and-Language Navigation (VLN) is the task that requires an agent to navigate through the environment based on natural language instructions. At each step, the agent takes the next action by selecting from a set of navigable locations. In this paper, we aim to take one step further and explore whether the agent c... | ['Mohit Bansal', 'Jialu Li'] | 2023-04-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Improving_Vision-and-Language_Navigation_by_Generating_Future-View_Image_Semantics_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Improving_Vision-and-Language_Navigation_by_Generating_Future-View_Image_Semantics_CVPR_2023_paper.pdf | cvpr-2023-1 | ['vision-and-language-navigation', 'trajectory-modeling'] | ['robots', 'time-series'] | [ 3.13825548e-01 4.11971956e-01 -3.83715797e-03 -8.22795272e-01
-5.59727967e-01 -5.73042572e-01 9.92995858e-01 -3.27765405e-01
-3.42663020e-01 6.06128752e-01 5.46601057e-01 -4.54273999e-01
1.35567456e-01 -1.02109957e+00 -1.12059617e+00 -4.40246701e-01
-1.26958072e-01 6.85357988e-01 2.28936132e-02 -3.16633105... | [4.488414287567139, 0.5935385227203369] |
1536c4f8-1448-4d6a-95a4-211109271d1e | on-classification-thresholds-for-graph | 2210.10014 | null | https://arxiv.org/abs/2210.10014v1 | https://arxiv.org/pdf/2210.10014v1.pdf | On Classification Thresholds for Graph Attention with Edge Features | The recent years we have seen the rise of graph neural networks for prediction tasks on graphs. One of the dominant architectures is graph attention due to its ability to make predictions using weighted edge features and not only node features. In this paper we analyze, theoretically and empirically, graph attention ne... | ['Shenghao Yang', 'Anton Tsitsulin', 'Bryan Perozzi', 'Silvio Lattanzi', 'Dake He', 'Kimon Fountoulakis'] | 2022-10-18 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.29609355e-01 6.95518136e-01 -1.39859229e-01 -1.04239278e-01
-1.27526313e-01 -3.46636653e-01 5.76416075e-01 5.47512889e-01
-9.58662331e-02 3.47078562e-01 6.25916198e-03 -5.86268246e-01
-1.80065170e-01 -1.03582740e+00 -8.75600696e-01 -8.17582607e-01
-4.49362129e-01 5.35948753e-01 2.70598233e-01 -1.57608598... | [6.893809795379639, 6.120396137237549] |
0c5c7702-bcb5-4ac4-8987-e2fab0815fa3 | if-you-want-to-go-far-go-together | null | null | https://aclanthology.org/2021.naacl-main.363 | https://aclanthology.org/2021.naacl-main.363.pdf | If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering | Multi-hop reasoning requires aggregation and inference from multiple facts. To retrieve such facts, we propose a simple approach that retrieves and reranks set of evidence facts jointly. Our approach first generates unsupervised clusters of sentences as candidate evidence by accounting links between sentences and cover... | ['Mihai Surdeanu', 'Steven Bethard', 'Vikas Yadav'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['multi-hop-question-answering', 'answer-selection'] | ['knowledge-base', 'natural-language-processing'] | [ 6.26124218e-02 3.90949488e-01 -6.17852688e-01 -2.26725131e-01
-1.97881353e+00 -5.12255967e-01 8.51496100e-01 7.32300222e-01
-4.90577728e-01 8.88894200e-01 6.98441088e-01 -2.05699101e-01
-8.25686455e-01 -8.35967839e-01 -9.35775697e-01 -2.67052174e-01
1.45472195e-02 8.57798100e-01 8.63428175e-01 -4.35308278... | [10.773762702941895, 7.869656085968018] |
86a6806b-3526-423d-a0cc-7cc1b9a062f0 | modeling-the-effects-of-multiple-myeloma-on | 1602.03214 | null | http://arxiv.org/abs/1602.03214v2 | http://arxiv.org/pdf/1602.03214v2.pdf | Modeling the Effects of Multiple Myeloma on Kidney Function | Multiple myeloma (MM), a plasma cell cancer, is associated with many health
challenges, including damage to the kidney by tubulointerstitial fibrosis. We
develop a mathematical model which captures the qualitative behavior of the
cell and protein populations involved. Specifically, we model the interaction
between cell... | [] | 2018-07-17 | null | null | null | null | ['kidney-function'] | ['medical'] | [-4.59091179e-02 -1.30438171e-02 -2.19988391e-01 2.87889779e-01
-9.20380875e-02 -5.03511190e-01 2.68749923e-01 3.99776369e-01
2.36892458e-02 7.13114142e-01 4.69608784e-01 -1.05641596e-01
-1.25573993e-01 -1.06570005e+00 -3.64112973e-01 -9.51094270e-01
-7.58734494e-02 1.20135927e+00 -6.77462667e-02 -2.00399254... | [13.641148567199707, -2.9381842613220215] |
7405b9ff-6f14-4e74-a9e4-d504472e83e0 | improving-hyperparameter-learning-under | 2306.04201 | null | https://arxiv.org/abs/2306.04201v1 | https://arxiv.org/pdf/2306.04201v1.pdf | Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models | Approximate inference in Gaussian process (GP) models with non-conjugate likelihoods gets entangled with the learning of the model hyperparameters. We improve hyperparameter learning in GP models and focus on the interplay between variational inference (VI) and the learning target. While VI's lower bound to the margina... | ['Arno Solin', 'ST John', 'Rui Li'] | 2023-06-07 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [-9.14331451e-02 4.56968009e-01 -2.28940591e-01 -2.03480914e-01
-1.09446716e+00 -4.34922636e-01 1.09726155e+00 -1.03482164e-01
-3.20539057e-01 8.48313749e-01 1.80504024e-01 -4.85358864e-01
-2.39633620e-01 -8.01562905e-01 -9.14244413e-01 -8.90493274e-01
1.59749866e-01 9.28574681e-01 8.22645128e-02 2.61822402... | [6.8966498374938965, 3.9221391677856445] |
33819a4a-8d6e-4a97-89e6-ff99918a0a99 | uncertainty-estimation-of-transformer | null | null | https://aclanthology.org/2022.acl-long.566 | https://aclanthology.org/2022.acl-long.566.pdf | Uncertainty Estimation of Transformer Predictions for Misclassification Detection | Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc. Most of the works on modeling the uncertainty of deep neural networks evaluate these methods on image classifi... | ['Leonid Zhukov', 'Manvel Avetisian', 'Mikhail Burtsev', 'Gleb Gusev', 'Alexander Panchenko', 'Maxim Panov', 'Kirill Fedyanin', 'Evgenii Tsymbalov', 'Akim Tsvigun', 'Artem Shelmanov', 'Gleb Kuzmin', 'Artem Vazhentsev'] | null | null | null | null | acl-2022-5 | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 1.40412405e-01 4.18861270e-01 2.20645647e-02 -7.42979825e-01
-8.28566790e-01 -4.36932892e-01 8.00722063e-01 4.43406880e-01
-7.40848780e-01 1.03518200e+00 -2.41076022e-01 -6.06451750e-01
-9.57100242e-02 -7.46381044e-01 -8.87054741e-01 -3.45073521e-01
-1.64848268e-02 4.81408238e-01 2.59447038e-01 4.05935198... | [7.603456497192383, 3.7725448608398438] |
c880411f-318c-4426-b1f9-0f7918431c8a | predicting-potential-drug-targets-using | 2105.10578 | null | https://arxiv.org/abs/2105.10578v3 | https://arxiv.org/pdf/2105.10578v3.pdf | A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets | The drug discovery and development process is a long and expensive one, costing over 1 billion USD on average per drug and taking 10-15 years. To reduce the high levels of attrition throughout the process, there has been a growing interest in applying machine learning methodologies to various stages of drug discovery a... | ['Ian Barrett', 'Stephen Bonner', 'Rowan Swiers', 'Cheng Ye'] | 2021-05-20 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [ 1.86108187e-01 1.10008985e-01 -7.18978107e-01 -5.93948364e-02
-3.97011369e-01 -4.60849226e-01 4.75710750e-01 5.21937907e-01
-6.52750358e-02 7.60476410e-01 4.98610377e-01 -6.13433123e-01
-9.22802627e-01 -6.65567935e-01 -4.00980741e-01 -7.82352567e-01
-4.87018526e-01 9.53419805e-01 -1.89179093e-01 9.42550004... | [5.463380336761475, 5.8961029052734375] |
14f4c3f0-65d9-473e-ab21-6b3379bdaf90 | enhanced-chart-understanding-in-vision-and | 2305.18641 | null | https://arxiv.org/abs/2305.18641v1 | https://arxiv.org/pdf/2305.18641v1.pdf | Enhanced Chart Understanding in Vision and Language Task via Cross-modal Pre-training on Plot Table Pairs | Building cross-model intelligence that can understand charts and communicate the salient information hidden behind them is an appealing challenge in the vision and language(V+L) community. The capability to uncover the underlined table data of chart figures is a critical key to automatic chart understanding. We introdu... | ['Shih-Fu Chang', 'Heng Ji', 'Christopher Thomas', 'Long Chen', 'Yi R. Fung', 'Mingyang Zhou'] | 2023-05-29 | null | null | null | null | ['value-prediction', 'chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-code', 'computer-vision'] | [ 5.04632831e-01 2.49860287e-01 -3.82838845e-01 -3.43616784e-01
-1.03338695e+00 -8.03596258e-01 3.43723178e-01 5.73738575e-01
3.92944515e-01 2.64625847e-01 4.90271330e-01 -8.19127560e-01
2.81097703e-02 -5.49148381e-01 -1.15568745e+00 -1.01039402e-01
-7.42022991e-02 4.40080553e-01 -5.85062541e-02 -2.86434740... | [11.21567440032959, 2.057987689971924] |
b98e8911-b742-4b74-9f68-3527686ef0e9 | a-multi-task-learning-framework-for-carotid | 2307.00583 | null | https://arxiv.org/abs/2307.00583v1 | https://arxiv.org/pdf/2307.00583v1.pdf | A multi-task learning framework for carotid plaque segmentation and classification from ultrasound images | Carotid plaque segmentation and classification play important roles in the treatment of atherosclerosis and assessment for risk of stroke. Although deep learning methods have been used for carotid plaque segmentation and classification, most focused on a single task and ignored the relationship between the segmentation... | ['Aaron Fenster', 'Xiaoyan Wu', 'Xinyao Cheng', 'Furong Wang', 'Yanghan Ou', 'Ran Zhou', 'Haitao Gan'] | 2023-07-02 | null | null | null | null | ['classification-1', 'multi-task-learning'] | ['methodology', 'methodology'] | [-1.79298028e-01 -3.09211522e-01 -1.70744732e-01 -5.09173751e-01
-1.09788918e+00 -2.33747065e-01 5.65416589e-02 -1.63551137e-01
-1.77000776e-01 3.86047781e-01 2.02835843e-01 -7.49288797e-01
-7.28084072e-02 -7.45206475e-01 -1.65893883e-01 -1.12642598e+00
-4.01359290e-01 3.40758622e-01 6.20307922e-01 2.37550408... | [14.546273231506348, -2.388399124145508] |
17d81c88-3304-4fb6-b297-47ec9a10bbc7 | the-leaky-integrator-that-could-or-recursive | 2206.04284 | null | https://arxiv.org/abs/2206.04284v3 | https://arxiv.org/pdf/2206.04284v3.pdf | The leaky integrator that could: Or recursive polynomial regression for online signal analysis | Fitting a local polynomial model to a noisy sequence of uniformly sampled observations or measurements (i.e. regressing) by minimizing the sum of weighted squared errors (i.e. residuals) may be used to design digital filters for a diverse range of signal-analysis problems, such as detection, classification and tracking... | ['Hugh L Kennedy'] | 2022-06-09 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 3.27712834e-01 -1.83014676e-01 6.40361458e-02 -5.16755134e-02
-4.59833771e-01 -3.26275438e-01 3.08467537e-01 1.04645997e-01
-4.79681015e-01 7.05985844e-01 -2.65758187e-01 -3.84573251e-01
-4.38953131e-01 -3.84713054e-01 -8.90971422e-02 -9.43893492e-01
-3.77088100e-01 -1.93326071e-01 3.10335070e-01 -4.71636616... | [6.517111301422119, 3.5630974769592285] |
a5c449ac-1f19-4faf-ada3-9ff9108009f1 | physics-guided-generative-adversarial-1 | 2203.14352 | null | https://arxiv.org/abs/2203.14352v3 | https://arxiv.org/pdf/2203.14352v3.pdf | Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints | Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts' heuristic knowledge. Here, we propose a deep learning based Physics Guided ... | ['Mohammed Al-Fahdi', 'Nihang Fu', 'Jianjun Hu', 'Ming Hu', 'Zhenyao Wu', 'Edirisuriya M. Dilanga Siriwardane', 'Yong Zhao'] | 2022-03-27 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [-1.10729426e-01 1.60430118e-01 -1.44479483e-01 1.85362641e-02
-6.49042010e-01 -2.61284858e-01 4.68828738e-01 7.03693107e-02
-6.88901916e-02 1.48097539e+00 2.11006224e-01 -1.70193344e-01
-3.21058333e-02 -1.23031604e+00 -9.70835924e-01 -1.17335081e+00
1.37703672e-01 7.41692960e-01 1.12730321e-02 -3.56119037... | [5.179972171783447, 5.355531215667725] |
7e706213-5af6-4b8c-96d9-2864e0b3f313 | weakly-supervised-hoi-detection-from | 2303.05546 | null | https://arxiv.org/abs/2303.05546v1 | https://arxiv.org/pdf/2303.05546v1.pdf | Weakly-Supervised HOI Detection from Interaction Labels Only and Language/Vision-Language Priors | Human-object interaction (HOI) detection aims to extract interacting human-object pairs and their interaction categories from a given natural image. Even though the labeling effort required for building HOI detection datasets is inherently more extensive than for many other computer vision tasks, weakly-supervised dire... | ['Adriana Kovashka', 'Mesut Erhan Unal'] | 2023-03-09 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 4.38412070e-01 5.66464007e-01 1.73882656e-02 -3.53749037e-01
-4.86599505e-01 -4.36393589e-01 8.60474944e-01 2.26020366e-01
-5.08969963e-01 4.14213270e-01 8.47071186e-02 -1.10764727e-01
2.95879934e-02 -4.90001261e-01 -9.38112378e-01 -5.36076725e-01
-4.60989438e-02 8.30124557e-01 4.82320666e-01 8.28274637... | [9.934235572814941, 1.4837478399276733] |
2cf643c7-e321-4282-b328-b38e4b2dc776 | towards-process-oriented-modular-and | 2205.00355 | null | https://arxiv.org/abs/2205.00355v1 | https://arxiv.org/pdf/2205.00355v1.pdf | Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs | NLP-powered automatic question generation (QG) techniques carry great pedagogical potential of saving educators' time and benefiting student learning. Yet, QG systems have not been widely adopted in classrooms to date. In this work, we aim to pinpoint key impediments and investigate how to improve the usability of auto... | ['Lu Wang', 'Jessica Houghton', 'Simin Fan', 'Xu Wang'] | 2022-04-30 | null | https://aclanthology.org/2022.naacl-main.22 | https://aclanthology.org/2022.naacl-main.22.pdf | naacl-2022-7 | ['misconceptions'] | ['miscellaneous'] | [-2.11287737e-01 4.58170027e-01 -5.84296398e-02 -3.54548991e-01
-1.00539148e+00 -1.11231565e+00 7.07094073e-02 5.50565064e-01
-7.26932436e-02 6.28447294e-01 4.24826473e-01 -1.28767669e+00
-5.08812845e-01 -7.15680659e-01 -3.95432860e-01 1.20954014e-01
7.09128201e-01 1.41900316e-01 2.85008609e-01 -6.02570236... | [10.654154777526855, 7.633048057556152] |
198997fa-b0c4-4a4d-b1da-c9c5b5066460 | hyperminer-topic-taxonomy-mining-with | 2210.10625 | null | https://arxiv.org/abs/2210.10625v1 | https://arxiv.org/pdf/2210.10625v1.pdf | HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding | Embedded topic models are able to learn interpretable topics even with large and heavy-tailed vocabularies. However, they generally hold the Euclidean embedding space assumption, leading to a basic limitation in capturing hierarchical relations. To this end, we present a novel framework that introduces hyperbolic embed... | ['Mingyuan Zhou', 'Zhibin Duan', 'Ruiying Lu', 'Bo Chen', 'Dongsheng Wang', 'Yishi Xu'] | 2022-10-16 | null | null | null | null | ['graph-structure-learning', 'topic-models'] | ['graphs', 'natural-language-processing'] | [-3.95000339e-01 6.00528419e-01 -4.56914246e-01 -4.23033595e-01
-3.48609477e-01 -4.63962466e-01 4.80918229e-01 2.45540023e-01
1.45379156e-01 2.92032540e-01 6.83677733e-01 -2.24858195e-01
-5.31103492e-01 -1.01664305e+00 -2.82159597e-01 -7.61499465e-01
-1.75271526e-01 4.42062467e-01 1.87455028e-01 -1.83810830... | [10.37403678894043, 6.942378520965576] |
9a7774fb-95de-46c5-9bbb-bed077384321 | cate-computation-aware-neural-architecture | 2102.07108 | null | https://arxiv.org/abs/2102.07108v2 | https://arxiv.org/pdf/2102.07108v2.pdf | CATE: Computation-aware Neural Architecture Encoding with Transformers | Recent works (White et al., 2020a; Yan et al., 2020) demonstrate the importance of architecture encodings in Neural Architecture Search (NAS). These encodings encode either structure or computation information of the neural architectures. Compared to structure-aware encodings, computation-aware encodings map architectu... | ['Mi Zhang', 'Fei Liu', 'Kaiqiang Song', 'Shen Yan'] | 2021-02-14 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 8.34903028e-03 -1.93304658e-01 -1.22439183e-01 -2.47881889e-01
-6.21783733e-01 -8.77938211e-01 5.95551312e-01 -1.13939382e-01
-4.63830531e-01 2.27083519e-01 2.63491631e-01 -5.18990099e-01
-1.84089720e-01 -1.03050315e+00 -9.04950202e-01 -5.00096440e-01
1.25081673e-01 3.97181392e-01 3.01931649e-01 -4.74561363... | [8.778410911560059, 3.3667538166046143] |
b659532f-e2f7-4950-82f4-b99797e5a522 | 190600852 | 1906.00852 | null | https://arxiv.org/abs/1906.00852v1 | https://arxiv.org/pdf/1906.00852v1.pdf | Hierarchical Auxiliary Learning | Conventional application of convolutional neural networks (CNNs) for image classification and recognition is based on the assumption that all target classes are equal(i.e., no hierarchy) and exclusive of one another (i.e., no overlap). CNN-based image classifiers built on this assumption, therefore, cannot take into ac... | ['Jaehoon Cha', 'Sanghyuk Lee', 'Kyeong Soo Kim'] | 2019-06-03 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 2.67627865e-01 2.78157387e-02 -1.15276359e-01 -5.50049245e-01
9.63458046e-02 -3.44908476e-01 4.17660743e-01 2.27206275e-01
-5.65923572e-01 6.44795001e-01 -1.91984281e-01 -2.72649944e-01
-7.48209134e-02 -1.18393159e+00 -6.67449117e-01 -6.44488633e-01
2.41587535e-01 1.50339380e-01 5.50528288e-01 -6.88920021... | [9.411824226379395, 2.407099723815918] |
d130d3cd-da12-41e1-81d3-e5f771e22d7a | a-systematic-literature-review-about-idea | 2202.12826 | null | https://arxiv.org/abs/2202.12826v1 | https://arxiv.org/pdf/2202.12826v1.pdf | A Systematic Literature Review about Idea Mining: The Use of Machine-driven Analytics to Generate Ideas | Idea generation is the core activity of innovation. Digital data sources, which are sources of innovation, such as patents, publications, social media, websites, etc., are increasingly growing at unprecedented volume. Manual idea generation is time-consuming and is affected by the subjectivity of the individuals involv... | ['Gustaf Juell-Skielse', 'Workneh Y. Ayele'] | 2022-01-30 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [-1.82377800e-01 -1.85786076e-02 -4.95700091e-01 4.49961215e-01
-2.15161547e-01 -6.84444606e-01 6.04769945e-01 6.82279527e-01
-8.92237797e-02 8.58459115e-01 3.32484514e-01 -9.84479487e-01
-5.87076962e-01 -1.34182501e+00 -3.46313059e-01 -2.78322935e-01
-3.30887511e-02 4.19683427e-01 -4.94353801e-01 -2.15450197... | [9.493489265441895, 8.064936637878418] |
42cbd79e-615a-4347-9f7a-5e0207fa2122 | do-not-fire-the-linguist-grammatical-profiles | 2204.05717 | null | https://arxiv.org/abs/2204.05717v1 | https://arxiv.org/pdf/2204.05717v1.pdf | Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change | Morphological and syntactic changes in word usage (as captured, e.g., by grammatical profiles) have been shown to be good predictors of a word's meaning change. In this work, we explore whether large pre-trained contextualised language models, a common tool for lexical semantic change detection, are sensitive to such m... | ['Lidia Pivovarova', 'Andrey Kutuzov', 'Mario Giulianelli'] | 2022-04-12 | null | https://aclanthology.org/2022.lchange-1.6 | https://aclanthology.org/2022.lchange-1.6.pdf | lchange-acl-2022-5 | ['xlm-r'] | ['natural-language-processing'] | [ 1.19557530e-01 -1.53955877e-01 -1.98894575e-01 -5.00051677e-01
-4.79051828e-01 -9.21346605e-01 9.86298859e-01 6.31516278e-01
-8.89985085e-01 5.73264420e-01 4.82167780e-01 -3.83745313e-01
-5.48152253e-03 -9.31841314e-01 -7.44615555e-01 -3.65500391e-01
-1.84188653e-02 2.96461076e-01 2.22502246e-01 -6.05601549... | [10.42525577545166, 9.34833812713623] |
d4104b21-1b87-4b39-bf68-c2b3484914e1 | collision-aware-in-hand-6d-object-pose | 2301.13667 | null | https://arxiv.org/abs/2301.13667v1 | https://arxiv.org/pdf/2301.13667v1.pdf | Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors | In this paper, we address the problem of estimating the in-hand 6D pose of an object in contact with multiple vision-based tactile sensors. We reason on the possible spatial configurations of the sensors along the object surface. Specifically, we filter contact hypotheses using geometric reasoning and a Convolutional N... | ['Lorenzo Natale', 'Fabrizio Bottarel', 'Nicola A. Piga', 'Gabriele M. Caddeo'] | 2023-01-31 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 4.16824102e-01 2.06216201e-01 2.43523687e-01 -2.88201779e-01
-7.76991010e-01 -4.85740989e-01 1.97495982e-01 1.72027871e-01
-6.30884171e-01 4.21267778e-01 -1.96259692e-01 1.04448147e-01
-4.75598335e-01 -4.53059405e-01 -1.00800240e+00 -1.76891774e-01
-1.34494258e-02 1.01204431e+00 3.41267556e-01 -2.24641506... | [5.923403263092041, -0.9123615622520447] |
f4a00d43-1289-41cf-b1dc-293b4c4b7649 | towards-generalisable-video-moment-retrieval | 2303.0004 | null | https://arxiv.org/abs/2303.00040v2 | https://arxiv.org/pdf/2303.00040v2.pdf | Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training | The correlation between the vision and text is essential for video moment retrieval (VMR), however, existing methods heavily rely on separate pre-training feature extractors for visual and textual understanding. Without sufficient temporal boundary annotations, it is non-trivial to learn universal video-text alignments... | ['Yang Liu', 'Hailin Jin', 'Shaogang Gong', 'Jiabo Huang', 'Dezhao Luo'] | 2023-02-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Luo_Towards_Generalisable_Video_Moment_Retrieval_Visual-Dynamic_Injection_to_Image-Text_Pre-Training_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Luo_Towards_Generalisable_Video_Moment_Retrieval_Visual-Dynamic_Injection_to_Image-Text_Pre-Training_CVPR_2023_paper.pdf | cvpr-2023-1 | ['moment-retrieval'] | ['computer-vision'] | [ 2.92468905e-01 -5.53443670e-01 -4.71176237e-01 -3.52343529e-01
-6.10051334e-01 -6.57351196e-01 1.01799464e+00 -2.10079730e-01
-4.27606940e-01 2.84520209e-01 4.12029743e-01 -1.12327538e-01
9.24844593e-02 -2.34282255e-01 -8.37932229e-01 -5.79454839e-01
-9.15467292e-02 1.83383286e-01 1.43633112e-01 -1.00699149... | [10.080577850341797, 0.8318478465080261] |
ce0e07e0-4279-448b-a55f-302bac300b4b | minimum-efforts-to-build-an-end-to-end | 2206.03064 | null | https://arxiv.org/abs/2206.03064v2 | https://arxiv.org/pdf/2206.03064v2.pdf | A Simple and Efficient Pipeline to Build an End-to-End Spatial-Temporal Action Detector | Spatial-temporal action detection is a vital part of video understanding. Current spatial-temporal action detection methods mostly use an object detector to obtain person candidates and classify these person candidates into different action categories. So-called two-stage methods are heavy and hard to apply in real-wor... | ['Feng Han', 'Lixin Gu', 'Chen-Lin Zhang', 'Lin Sui'] | 2022-06-07 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 1.05424426e-01 -2.15410382e-01 -2.33576223e-01 -3.90395373e-01
-6.25321925e-01 -2.94156432e-01 5.18179774e-01 -1.62415013e-01
-6.06031299e-01 3.01617980e-01 4.19402719e-01 1.42813474e-01
4.82587725e-01 -4.42395180e-01 -3.55881304e-01 -4.21620786e-01
1.71541512e-01 2.18888476e-01 1.24338424e+00 -1.30832912... | [8.331151962280273, 0.4585148096084595] |
43375f68-96f7-4df5-acf0-00b55c6515bb | graph-transformation-policy-network-for | 1812.09441 | null | http://arxiv.org/abs/1812.09441v1 | http://arxiv.org/pdf/1812.09441v1.pdf | Graph Transformation Policy Network for Chemical Reaction Prediction | We address a fundamental problem in chemistry known as chemical reaction
product prediction. Our main insight is that the input reactant and reagent
molecules can be jointly represented as a graph, and the process of generating
product molecules from reactant molecules can be formulated as a sequence of
graph transform... | ['Truyen Tran', 'Svetha Venkatesh', 'Kien Do'] | 2018-12-22 | null | https://openreview.net/forum?id=r1f78iAcFm | https://openreview.net/pdf?id=r1f78iAcFm | null | ['chemical-reaction-prediction'] | ['medical'] | [ 4.77197975e-01 2.99556226e-01 -6.38848901e-01 -1.41148180e-01
-5.02530515e-01 -6.56971633e-01 7.90326357e-01 3.59580606e-01
-2.26028755e-01 1.05105436e+00 -1.10486872e-01 -7.61070549e-01
1.19096719e-01 -8.58276010e-01 -1.16241848e+00 -9.82137561e-01
2.54971418e-03 5.93430996e-01 1.36086434e-01 -1.58314332... | [4.528529167175293, 6.081355094909668] |
0e1633f9-2493-468a-81de-ffef290d54a9 | differentiable-spline-approximations | 2110.01532 | null | https://arxiv.org/abs/2110.01532v1 | https://arxiv.org/pdf/2110.01532v1.pdf | Differentiable Spline Approximations | The paradigm of differentiable programming has significantly enhanced the scope of machine learning via the judicious use of gradient-based optimization. However, standard differentiable programming methods (such as autodiff) typically require that the machine learning models be differentiable, limiting their applicabi... | ['Chinmay Hegde', 'Adarsh Krishnamurthy', 'Baskar Ganapathysubramanian', 'Soumik Sarkar', 'Biswajit Khara', 'Anjana Deva Prasad', 'Ameya Joshi', 'Aditya Balu', 'Minsu Cho'] | 2021-10-04 | null | http://proceedings.neurips.cc/paper/2021/hash/a952ddeda0b7e2c20744e52e728e5594-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a952ddeda0b7e2c20744e52e728e5594-Paper.pdf | neurips-2021-12 | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 7.52316264e-04 2.03145608e-01 -3.32329810e-01 -3.48708183e-01
-8.15985560e-01 -4.47426617e-01 5.39898992e-01 -1.18631519e-01
4.58250828e-02 7.36722529e-01 -1.49459571e-01 -4.52823192e-01
-1.22884624e-01 -5.59770644e-01 -1.17682755e+00 -7.15480387e-01
-4.90052216e-02 3.73736382e-01 -1.19887084e-01 -3.47765386... | [6.678960800170898, 3.6051948070526123] |
46404c32-d857-4c85-82bf-6ef873d6dad3 | robust-single-image-dehazing-based-on | 2203.15325 | null | https://arxiv.org/abs/2203.15325v1 | https://arxiv.org/pdf/2203.15325v1.pdf | Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction | Single image dehazing as a fundamental low-level vision task, is essential for the development of robust intelligent surveillance system. In this paper, we make an early effort to consider dehazing robustness under variational haze density, which is a realistic while under-studied problem in the research filed of singe... | ['Jiande Sun', 'Xinbo Gao', 'Nannan Wang', 'Dingwen Zhang', 'Yan Li', 'De Cheng'] | 2022-03-29 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 3.54730904e-01 -2.58753121e-01 2.32527062e-01 -1.12123139e-01
-4.32915300e-01 7.69385556e-03 4.23810452e-01 -6.02363825e-01
-3.05490136e-01 4.76154357e-01 1.14186853e-01 1.64586809e-02
-2.25892946e-01 -7.07389712e-01 -8.30472171e-01 -1.53287053e+00
3.70384753e-01 -1.68469191e-01 5.65575242e-01 -2.92800456... | [10.957984924316406, -3.1757986545562744] |
310ab483-e26c-4cb0-9ce3-b152fcee59b2 | unconditional-audio-generation-with | 2005.08526 | null | https://arxiv.org/abs/2005.08526v1 | https://arxiv.org/pdf/2005.08526v1.pdf | Unconditional Audio Generation with Generative Adversarial Networks and Cycle Regularization | In a recent paper, we have presented a generative adversarial network (GAN)-based model for unconditional generation of the mel-spectrograms of singing voices. As the generator of the model is designed to take a variable-length sequence of noise vectors as input, it can generate mel-spectrograms of variable length. How... | ['Yi-Hsuan Yang', 'Yin-Cheng Yeh', 'Yu-Hua Chen', 'Jen-Yu Liu'] | 2020-05-18 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.78520852e-01 2.54436791e-01 3.41075331e-01 1.33469567e-01
-8.95732820e-01 -8.65436971e-01 3.83951157e-01 -7.40363538e-01
1.48946077e-01 8.31526279e-01 3.23492467e-01 -1.11274876e-01
4.41766828e-02 -6.87690020e-01 -5.05295813e-01 -7.99507439e-01
-1.92992166e-01 6.61483034e-02 1.65248305e-01 -3.02505702... | [15.556055068969727, 5.987834930419922] |
1980188f-4aad-43c1-8cef-8e0df68590a7 | theoretical-analysis-of-deep-neural-networks | 2202.09954 | null | https://arxiv.org/abs/2202.09954v2 | https://arxiv.org/pdf/2202.09954v2.pdf | Theoretical Analysis of Deep Neural Networks in Physical Layer Communication | Recently, deep neural network (DNN)-based physical layer communication techniques have attracted considerable interest. Although their potential to enhance communication systems and superb performance have been validated by simulation experiments, little attention has been paid to the theoretical analysis. Specifically... | ['Jibo Wei', 'Kai Mei', 'Dongtang Ma', 'Haitao Zhao', 'Jun Liu'] | 2022-02-21 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [ 2.82545179e-01 3.22829813e-01 -4.94412214e-01 -2.69645929e-01
-1.15042068e-01 2.72976086e-02 3.73698980e-01 -1.40885994e-01
-4.25747991e-01 8.90650094e-01 -3.90645415e-02 -7.50273049e-01
-5.50449550e-01 -8.35989356e-01 -3.01163733e-01 -8.85810733e-01
-3.72276127e-01 -2.21803263e-01 2.68557593e-02 -1.21689402... | [6.292558670043945, 1.5346989631652832] |
c1ab5db4-382f-48ef-ae96-e5ed794ca8ff | speech2properties2gestures-gesture-property | 2106.14736 | null | https://arxiv.org/abs/2106.14736v2 | https://arxiv.org/pdf/2106.14736v2.pdf | Speech2Properties2Gestures: Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech | We propose a new framework for gesture generation, aiming to allow data-driven approaches to produce more semantically rich gestures. Our approach first predicts whether to gesture, followed by a prediction of the gesture properties. Those properties are then used as conditioning for a modern probabilistic gesture-gene... | ['Gustav Eje Henter', 'Hedvig Kjellström', 'Michael Neff', 'Patrik Jonell', 'Rajmund Nagy', 'Taras Kucherenko'] | 2021-06-28 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 7.81067088e-02 2.69569188e-01 -1.62390381e-01 -4.86608833e-01
-1.03021622e+00 -7.78271019e-01 1.07815123e+00 -3.73527199e-01
-1.46530822e-01 5.42696714e-01 8.32785726e-01 -2.29969844e-01
1.28417060e-01 -9.19889331e-01 -5.98385036e-01 -5.04061937e-01
-2.18655746e-02 7.15859830e-01 1.41442627e-01 -2.53643543... | [5.601586818695068, -0.11921820044517517] |
ad924e17-3246-460f-9c1b-8b8a06507afd | gpurir-a-python-library-for-room-impulse | 1810.11359 | null | http://arxiv.org/abs/1810.11359v1 | http://arxiv.org/pdf/1810.11359v1.pdf | gpuRIR: A python library for Room Impulse Response simulation with GPU acceleration | The Image Source Method (ISM) is one of the most employed techniques to
calculate acoustic Room Impulse Responses (RIRs), however, its computational
complexity grows fast with the reverberation time of the room and its
computation time can be prohibitive for some applications where a huge number
of RIRs are needed. In ... | ['David Diaz-Guerra', 'Jose R. Beltran', 'Antonio Miguel'] | 2018-10-26 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 2.68542111e-01 -6.63198411e-01 1.26182461e+00 2.24185344e-02
-6.89634383e-01 -6.05282426e-01 1.95417121e-01 -1.29778525e-02
-5.19226730e-01 2.49613911e-01 1.37806116e-02 -7.76303649e-01
1.46094874e-01 -9.91538167e-01 -4.04481620e-01 -8.44545484e-01
1.65105894e-01 1.96452618e-01 5.65526187e-01 -2.10909739... | [15.263585090637207, 5.6859612464904785] |
6699f504-3976-41d7-b7c2-62579fff9242 | end-to-end-robust-joint-unsupervised-image | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zeng_End-to-End_Robust_Joint_Unsupervised_Image_Alignment_and_Clustering_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zeng_End-to-End_Robust_Joint_Unsupervised_Image_Alignment_and_Clustering_ICCV_2021_paper.pdf | End-to-End Robust Joint Unsupervised Image Alignment and Clustering | Computing dense pixel-to-pixel image correspondences is a fundamental task of computer vision. Often, the objective is to align image pairs from the same semantic category for manipulation or segmentation purposes. Despite achieving superior performance, existing deep learning alignment methods cannot cluster image... | ['Min Xu', 'Gregory Howe', 'Xiangrui Zeng'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['electron-tomography'] | ['medical'] | [ 3.90457362e-01 -4.02205139e-01 5.50208725e-02 -5.60789764e-01
-1.08255970e+00 -6.41229212e-01 2.73889065e-01 4.01058525e-01
-6.53043330e-01 4.69895989e-01 -4.95573908e-01 -1.16300955e-01
1.88095868e-01 -2.37132952e-01 -9.16564226e-01 -9.03001130e-01
2.29437858e-01 1.04787946e+00 2.08117351e-01 4.14496064... | [13.969453811645508, -3.0978147983551025] |
2a94bce3-7372-438e-866e-dd65a029aba3 | semattnet-towards-attention-based-semantic | 2204.13635 | null | https://arxiv.org/abs/2204.13635v1 | https://arxiv.org/pdf/2204.13635v1.pdf | SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion | Depth completion involves recovering a dense depth map from a sparse map and an RGB image. Recent approaches focus on utilizing color images as guidance images to recover depth at invalid pixels. However, color images alone are not enough to provide the necessary semantic understanding of the scene. Consequently, the d... | ['Muhammad Zeshan Afzal', 'Didier Stricker', 'Marcus Liwicki', 'Danish Nazir'] | 2022-04-28 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 5.43908656e-01 -9.53072868e-03 7.58123994e-02 -5.54313660e-01
-8.71376157e-01 -3.43356192e-01 2.51393557e-01 -7.78051466e-02
-1.76010981e-01 4.94092375e-01 1.80610403e-01 -6.95185596e-03
2.92015851e-01 -1.14927363e+00 -7.37746596e-01 -8.27825367e-01
5.84730268e-01 2.48296812e-01 4.83972222e-01 -3.92800272... | [8.949834823608398, -2.533717632293701] |
36231d20-57c9-4f94-8db5-54989cdc8c26 | sparse-high-dimensional-linear-regression-1 | 2209.08139 | null | https://arxiv.org/abs/2209.08139v4 | https://arxiv.org/pdf/2209.08139v4.pdf | Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm | Bayesian variable selection methods are powerful techniques for fitting and inferring on sparse high-dimensional linear regression models. However, many are computationally intensive or require restrictive prior distributions on model parameters. In this paper, we proposed a computationally efficient and powerful Bayes... | ['Howard Bondell', 'Anja Zgodic', 'Alexander C. McLain'] | 2022-09-16 | null | null | null | null | ['variable-selection', 'prediction-intervals'] | ['methodology', 'miscellaneous'] | [ 2.69326568e-01 -2.36676827e-01 -1.57322139e-01 -5.06854057e-01
-1.34921169e+00 -1.09906025e-01 1.82349950e-01 3.00800294e-01
-4.50145781e-01 1.12099504e+00 1.06559120e-01 -3.31414729e-01
-5.98526776e-01 -6.59455240e-01 -7.32343137e-01 -1.07167017e+00
1.35142507e-03 9.27111447e-01 -6.48862645e-02 3.63761663... | [7.40571403503418, 4.701920986175537] |
7cf6896b-10f1-4f2d-956a-2164fece3b6b | irfl-image-recognition-of-figurative-language | 2303.15445 | null | https://arxiv.org/abs/2303.15445v1 | https://arxiv.org/pdf/2303.15445v1.pdf | IRFL: Image Recognition of Figurative Language | Figures of speech such as metaphors, similes, and idioms allow language to be expressive, invoke emotion, and communicate abstract ideas that might otherwise be difficult to visualize. These figurative forms are often conveyed through multiple modes, such as text and images, and frequently appear in advertising, news, ... | ['Dafna Shahaf', 'Yonatan Bitton', 'Ron Yosef'] | 2023-03-27 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-2.92124301e-02 4.38568145e-02 1.44763365e-01 -4.64949906e-01
-2.31970653e-01 -1.06883073e+00 1.40100312e+00 2.89550304e-01
3.47193629e-02 3.61151844e-01 7.03157365e-01 -3.86371255e-01
2.51706511e-01 -5.89404285e-01 -6.10567868e-01 -9.13104191e-02
1.20857112e-01 4.59958822e-01 -1.50130525e-01 -8.22716475... | [10.877326011657715, 1.537501335144043] |
4e6c6853-6db7-4653-bf0c-939df6532f51 | attentionxml-extreme-multi-label-text | 1811.01727 | null | https://arxiv.org/abs/1811.01727v3 | https://arxiv.org/pdf/1811.01727v3.pdf | AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification | Extreme multi-label text classification (XMTC) is an important problem in the era of big data, for tagging a given text with the most relevant multiple labels from an extremely large-scale label set. XMTC can be found in many applications, such as item categorization, web page tagging, and news annotation. Traditionall... | ['Hiroshi Mamitsuka', 'Suyang Dai', 'Shanfeng Zhu', 'Ziye Wang', 'Zihan Zhang', 'Ronghui You'] | 2018-11-01 | attentionxml-label-tree-based-attention-aware | http://papers.nips.cc/paper/8817-attentionxml-label-tree-based-attention-aware-deep-model-for-high-performance-extreme-multi-label-text-classification | http://papers.nips.cc/paper/8817-attentionxml-label-tree-based-attention-aware-deep-model-for-high-performance-extreme-multi-label-text-classification.pdf | neurips-2019-12 | ['product-categorization', 'web-page-tagging', 'news-annotation'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 1.44264917e-03 -2.04292923e-01 -3.20894450e-01 -5.32139778e-01
-1.16163445e+00 -4.94717300e-01 3.53514671e-01 3.85498881e-01
-5.03353536e-01 5.26680648e-01 3.19587469e-01 -2.15477139e-01
4.16765139e-02 -5.63714921e-01 -4.67567891e-01 -7.39822447e-01
4.25876766e-01 9.33366537e-01 1.64393872e-01 1.49285393... | [9.658628463745117, 4.485793113708496] |
d6f9dde2-9ea7-46d2-adc6-f43ac67291e8 | temporal-topic-modeling-to-assess | 1606.00411 | null | http://arxiv.org/abs/1606.00411v1 | http://arxiv.org/pdf/1606.00411v1.pdf | Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks | In retrospective assessments, internet news reports have been shown to
capture early reports of unknown infectious disease transmission prior to
official laboratory confirmation. In general, media interest and reporting
peaks and wanes during the course of an outbreak. In this study, we quantify
the extent to which med... | ['Naren Ramakrishnan', 'Elaine O. Nsoesie', 'Saurav Ghosh', 'John S. Brownstein', 'Emily Cohn', 'Sumiko R. Mekaru', 'Prithwish Chakraborty'] | 2016-06-01 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-5.94283715e-02 -1.78734586e-01 -5.42098284e-01 1.08386530e-03
-8.09171498e-01 -5.37532032e-01 1.05728734e+00 1.02726674e+00
-1.28938720e-01 8.11257303e-01 6.60711348e-01 -3.76199841e-01
-1.76715419e-01 -8.77911389e-01 -6.24319911e-01 -5.49100101e-01
-9.36288238e-01 6.29214883e-01 4.36261520e-02 3.55927125... | [5.976090431213379, 4.388387680053711] |
2aeb5ec1-3376-4b20-8c76-1cc07517074a | generalized-zero-shot-learning-for-medical | 2204.01728 | null | https://arxiv.org/abs/2204.01728v2 | https://arxiv.org/pdf/2204.01728v2.pdf | Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification | In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized zero shot learning (GZSL) method that uses self supervised learning (SSL) for: 1)... | ['Dwarikanath Mahapatra'] | 2022-04-04 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 7.41338909e-01 2.26240486e-01 -1.77769676e-01 -4.76325989e-01
-9.89086866e-01 -2.31586605e-01 7.29897678e-01 3.34790796e-01
-4.92365628e-01 7.70005047e-01 -1.00615852e-01 4.58419733e-02
-1.24932215e-01 -9.94962990e-01 -4.46024448e-01 -7.41169631e-01
-1.35596588e-01 8.52220595e-01 5.35828412e-01 -1.47239625... | [9.927934646606445, 2.996230125427246] |
d6d94564-1452-4659-9dd9-abf23bc49305 | causal-discovery-with-missing-data-in-a | 2305.1005 | null | https://arxiv.org/abs/2305.10050v1 | https://arxiv.org/pdf/2305.10050v1.pdf | Causal Discovery with Missing Data in a Multicentric Clinical Study | Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal ... | ['Fabio Stella', 'Marco Scutari', 'Casper Reijnen', 'Hanny Pijnenborg', 'Peter J. F. Lucas', 'Alice Bernasconi', 'Alessio Zanga'] | 2023-05-17 | null | null | null | null | ['causal-inference', 'causal-discovery', 'causal-inference'] | ['knowledge-base', 'knowledge-base', 'miscellaneous'] | [ 4.69461441e-01 6.55839384e-01 -8.27881932e-01 -8.68571177e-02
-3.60615879e-01 -6.13261104e-01 2.53487885e-01 7.06853986e-01
1.48306163e-02 1.09190965e+00 5.88128865e-01 -1.08990920e+00
-1.14036047e+00 -8.12973559e-01 -9.70108330e-01 -4.02660638e-01
-5.69869339e-01 5.49747884e-01 -1.48432940e-01 2.51093447... | [7.901843070983887, 5.38322114944458] |
65467737-416c-4a66-8508-41600d3d96d2 | canonical-saliency-maps-decoding-deep-face | 2105.01386 | null | https://arxiv.org/abs/2105.01386v2 | https://arxiv.org/pdf/2105.01386v2.pdf | Canonical Saliency Maps: Decoding Deep Face Models | As Deep Neural Network models for face processing tasks approach human-like performance, their deployment in critical applications such as law enforcement and access control has seen an upswing, where any failure may have far-reaching consequences. We need methods to build trust in deployed systems by making their work... | ['C V Jawahar', 'Vineeth N Balasubramanian', 'Thrupthi Ann John'] | 2021-05-04 | null | null | null | null | ['face-model'] | ['computer-vision'] | [-1.15206661e-02 3.99488837e-01 9.00314376e-02 -6.56302154e-01
3.23102951e-01 -1.71445325e-01 5.20691037e-01 -9.71666202e-02
-6.90651610e-02 1.19048938e-01 1.77972406e-01 -1.82325378e-01
-1.23080961e-01 -5.32958388e-01 -4.70374167e-01 -5.04567087e-01
-8.31308514e-02 1.16607085e-01 2.24366784e-01 -3.55921328... | [10.25788688659668, 2.1486356258392334] |
2739dad8-9dea-4366-9c93-661f9c2e0d85 | neudf-leaning-neural-unsigned-distance-fields | 2304.1008 | null | https://arxiv.org/abs/2304.10080v1 | https://arxiv.org/pdf/2304.10080v1.pdf | NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering | Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in neural implicit surface rendering. However, existing methods based on signed distance function (SDF) are limited to closed surfaces, failing to reconstruct a wide range of real-world objects that contain open-surface str... | ['Lin Gao', 'Bo Yang', 'Xiaoxu Meng', 'Weikai Chen', 'Jie Yang', 'Li Wang', 'Yu-Tao Liu'] | 2023-04-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_NeUDF_Leaning_Neural_Unsigned_Distance_Fields_With_Volume_Rendering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_NeUDF_Leaning_Neural_Unsigned_Distance_Fields_With_Volume_Rendering_CVPR_2023_paper.pdf | cvpr-2023-1 | ['neural-rendering'] | ['computer-vision'] | [ 4.32744712e-01 4.73607294e-02 3.92838687e-01 -3.13813508e-01
-6.51257455e-01 -5.04439175e-01 5.60959756e-01 -2.89982319e-01
1.98947072e-01 6.12773836e-01 -6.99599162e-02 -2.17507020e-01
3.25569101e-02 -1.09769094e+00 -9.24771547e-01 -5.28389156e-01
7.50631914e-02 6.10148013e-01 1.88157186e-01 -3.53320867... | [8.996253967285156, -3.3135502338409424] |
e5e69b6f-c575-4e3e-b6ef-4a191187b5ac | tanet-transformer-based-asymmetric-network | 2207.01172 | null | https://arxiv.org/abs/2207.01172v1 | https://arxiv.org/pdf/2207.01172v1.pdf | TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection | Existing RGB-D SOD methods mainly rely on a symmetric two-stream CNN-based network to extract RGB and depth channel features separately. However, there are two problems with the symmetric conventional network structure: first, the ability of CNN in learning global contexts is limited; second, the symmetric two-stream s... | ['Yutao Wang', 'Yunhua Zhang', 'Hangxu Wang', 'Shuo Wang', 'Gang Yang', 'Chang Liu'] | 2022-07-04 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 6.48019537e-02 -1.20179709e-02 -8.84661525e-02 -4.05360311e-01
-6.52305126e-01 -1.26208216e-01 2.91912884e-01 -3.97005916e-01
-4.42446291e-01 3.93093318e-01 2.32513681e-01 -2.79772133e-01
1.79637492e-01 -1.05505192e+00 -4.56186533e-01 -7.66931653e-01
3.22319895e-01 -3.96762967e-01 6.24988019e-01 -2.19766632... | [9.584208488464355, -0.9389311671257019] |
09ce1cd1-bfaf-4ccd-ab32-a25450bd9420 | lingvo-a-modular-and-scalable-framework-for | 1902.08295 | null | http://arxiv.org/abs/1902.08295v1 | http://arxiv.org/pdf/1902.08295v1.pdf | Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling | Lingvo is a Tensorflow framework offering a complete solution for
collaborative deep learning research, with a particular focus towards
sequence-to-sequence models. Lingvo models are composed of modular building
blocks that are flexible and easily extensible, and experiment configurations
are centralized and highly cus... | ['Pat Rondon', 'William Chan', 'Ian McGraw', 'Semih Yavuz', 'Rob Suderman', 'David Rybach', 'Colin Raffel', 'Antoine Bruguier', 'Ben Vanik', 'Kuan-Chieh Wang', 'Wei-Ning Hsu', 'Rohan Anil', 'Sébastien Jean', 'Ciprian Chelba', 'Rohit Prabhavalkar', 'Jan Chorowski', 'Chung-Cheng Chiu', 'Anjuli Kannan', 'Yonghui Wu', 'Jon... | 2019-02-21 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [-7.00434268e-01 -3.00814509e-01 -4.95095104e-01 -7.47082889e-01
-6.48712933e-01 -7.14799047e-01 6.30936146e-01 -3.49632055e-01
-1.98635668e-01 7.53234863e-01 5.03938556e-01 -4.62112039e-01
-1.56410411e-01 -4.81084198e-01 -2.84282327e-01 -5.74803948e-01
-4.12629217e-01 4.88098830e-01 3.77429016e-02 -3.23022813... | [8.69691276550293, 3.203683614730835] |
5918ec82-fc51-423d-9865-0754859c9b22 | learning-sentence-ordering-for-opinion | null | null | https://aclanthology.org/W15-0512 | https://aclanthology.org/W15-0512.pdf | Learning Sentence Ordering for Opinion Generation of Debate | null | ['Makoto Iwayama', 'Toshinori Miyoshi', 'Paul Reisert', 'Yoshiki Niwa', 'Misa Sato', 'Kohsuke Yanai', 'Kentaro Inui', 'Toshihiko Yanase'] | 2015-06-01 | null | null | null | ws-2015-6 | ['sentence-ordering'] | ['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... | [-7.2973103523254395, 3.6287550926208496] |
72f646b5-7d21-4db3-80b2-f78211d88c50 | slot-transferability-for-cross-domain-slot | null | null | https://aclanthology.org/2021.findings-acl.440 | https://aclanthology.org/2021.findings-acl.440.pdf | Slot Transferability for Cross-domain Slot Filling | null | ['Wei Wu', 'Huixing Jiang', 'Shuyu Lei', 'Xiaojie Wang', 'Caixia Yuan', 'Zhuoxin Han', 'Hengtong Lu'] | null | null | null | null | findings-acl-2021-8 | ['slot-filling'] | ['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... | [-7.393661975860596, 3.6534528732299805] |
3e4bcc29-a2fd-4540-88a0-ad2fe21d9dbd | camul-calibrated-and-accurate-multi-view-time | 2109.07438 | null | https://arxiv.org/abs/2109.07438v3 | https://arxiv.org/pdf/2109.07438v3.pdf | CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting | Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities and structures. Leveraging information as well as uncertainty from these data sources for well-calibrated and accurate forecasts is an importa... | ['B. Aditya Prakash', 'Chao Zhang', 'Alexander Rodríguez', 'Lingkai Kong', 'Harshavardhan Kamarthi'] | 2021-09-15 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-3.63238156e-01 -1.48066700e-01 -4.90665734e-01 -1.09715617e+00
-1.49757361e+00 -1.00442517e+00 1.27692342e+00 8.94123688e-02
4.03625309e-01 8.16227794e-01 8.06809902e-01 -1.38203382e-01
-9.54077467e-02 -7.27849722e-01 -8.16033006e-01 -6.80452883e-01
1.39598802e-01 9.59152162e-01 -9.13030207e-02 5.48785552... | [6.98618221282959, 3.2742764949798584] |
dc66dc8a-ec94-483b-bdcf-1343286987f4 | synthvsr-scaling-up-visual-speech-recognition | 2303.172 | null | https://arxiv.org/abs/2303.17200v2 | https://arxiv.org/pdf/2303.17200v2.pdf | SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision | Recently reported state-of-the-art results in visual speech recognition (VSR) often rely on increasingly large amounts of video data, while the publicly available transcribed video datasets are limited in size. In this paper, for the first time, we study the potential of leveraging synthetic visual data for VSR. Our me... | ['Christian Fuegen', 'Maja Pantic', 'Stavros Petridis', 'Jáchym Kolář', 'Niko Moritz', 'Morrie Doulaty', 'Ruiming Xie', 'Honglie Chen', 'Pingchuan Ma', 'Konstantinos Vougioukas', 'Egor Lakomkin', 'Xubo Liu'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_SynthVSR_Scaling_Up_Visual_Speech_Recognition_With_Synthetic_Supervision_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_SynthVSR_Scaling_Up_Visual_Speech_Recognition_With_Synthetic_Supervision_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-speech-recognition'] | ['speech'] | [ 3.53599519e-01 3.81493628e-01 -2.84809858e-01 -2.31962368e-01
-1.52975917e+00 -4.13482279e-01 6.72582150e-01 -5.60591400e-01
-2.94681787e-01 4.75086898e-01 4.49414790e-01 -2.40566790e-01
7.80281484e-01 -5.41317910e-02 -9.66093838e-01 -4.91151214e-01
3.87301832e-01 3.91243428e-01 1.28812894e-01 -1.31549761... | [14.353209495544434, 5.082540988922119] |
0b086c54-6b33-4c1d-a752-1246ab0067b8 | yiriyou-smm4h22-stance-and-premise | null | null | https://aclanthology.org/2022.smm4h-1.7 | https://aclanthology.org/2022.smm4h-1.7.pdf | yiriyou@SMM4H’22: Stance and Premise Classification in Domain Specific Tweets with Dual-View Attention Neural Networks | The paper introduces the methodology proposed for the shared Task 2 of the Social Media Mining for Health Application (SMM4H) in 2022. Task 2 consists of two subtasks: Stance Detection and Premise Classification, named Subtask 2a and Subtask 2b, respectively. Our proposed system is based on dual-view attention neural n... | ['Yanru Zhang', 'Zhongjian Zhang', 'Huabin Yang'] | null | null | null | null | smm4h-coling-2022-10 | ['stance-detection'] | ['natural-language-processing'] | [ 1.91672221e-01 8.58284891e-01 -4.02633548e-01 -3.51437926e-01
-7.66858757e-01 4.00073081e-02 5.85981190e-01 3.26168001e-01
-4.48228925e-01 9.74593520e-01 3.19699049e-01 -3.65413755e-01
9.93055850e-02 -5.65764666e-01 -4.97395575e-01 -3.49215895e-01
8.75346661e-02 4.73752171e-01 3.04713070e-01 -4.52529848... | [8.53195858001709, 8.929500579833984] |
c76cfafc-6ba4-4c9a-a821-d191b9c3964d | uncertainty-aware-null-space-networks-for | 2304.06955 | null | https://arxiv.org/abs/2304.06955v1 | https://arxiv.org/pdf/2304.06955v1.pdf | Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction | Reconstructing an image from noisy and incomplete measurements is a central task in several image processing applications. In recent years, state-of-the-art reconstruction methods have been developed based on recent advances in deep learning. Especially for highly underdetermined problems, maintaining data consistency ... | ['Markus Haltmeier', 'Simon Göppel', 'Christoph Angermann'] | 2023-04-14 | null | null | null | null | ['image-reconstruction', 'mri-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 4.02554333e-01 9.06542093e-02 2.95158058e-01 -7.05267012e-01
-8.95896971e-01 2.08996702e-02 3.07992131e-01 2.09582508e-01
-7.28698671e-01 8.76352966e-01 1.78049371e-01 -2.19691455e-01
-7.57759452e-01 -6.27163172e-01 -6.23526275e-01 -7.81798899e-01
-1.63589373e-01 7.52952337e-01 8.68519321e-02 6.56215250... | [13.304848670959473, -2.486924648284912] |
1ed23a60-fc53-46b9-9e8b-ca1f94af32c1 | pp-yoloe-an-evolved-version-of-yolo | 2203.1625 | null | https://arxiv.org/abs/2203.16250v3 | https://arxiv.org/pdf/2203.16250v3.pdf | PP-YOLOE: An evolved version of YOLO | In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment. We optimize on the basis of the previous PP-YOLOv2, using anchor-free paradigm, more powerful backbone and neck equipped with CSPRepResStage, ET-head and dynamic label assignment algorithm ... | ['Baohua Lai', 'Yuning Du', 'Shengyu Wei', 'Qingqing Dang', 'Guanzhong Wang', 'Kaipeng Deng', 'Cheng Cui', 'Qinyao Chang', 'Wenyu Lv', 'Xinxin Wang', 'Shangliang Xu'] | 2022-03-30 | null | null | null | null | ['dense-object-detection', 'online-multi-object-tracking', 'real-time-object-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.58010852e-01 -2.91952640e-01 -2.89056033e-01 -9.10315737e-02
-9.81636524e-01 -6.42088354e-01 8.39203894e-02 -1.35252625e-01
-3.12508255e-01 4.91765052e-01 -8.33020627e-01 -1.91721559e-01
1.94225982e-01 -4.37827826e-01 -1.09233165e+00 -5.47465026e-01
-1.22698106e-01 5.89925528e-01 8.41690183e-01 2.29067937... | [8.654861450195312, -0.2544531226158142] |
6c8275cf-aab0-4b70-b00e-0b4f80e0d54e | rumble-data-independence-for-large-messy-data | 1910.11582 | null | https://arxiv.org/abs/1910.11582v2 | https://arxiv.org/pdf/1910.11582v2.pdf | Rumble: Data Independence for Large Messy Data Sets | This paper introduces Rumble, an engine that executes JSONiq queries on large, heterogeneous and nested collections of JSON objects, leveraging the parallel capabilities of Spark so as to provide a high degree of data independence. The design is based on two key insights: (i) how to map JSONiq expressions to Spark tran... | ['Gustavo Alonso', 'Ghislain Fourny', 'Ingo Müller', 'Can Berker Cikis', 'Stefan Irimescu'] | 2019-10-25 | null | null | null | null | ['jsoniq-query-execution'] | ['miscellaneous'] | [-7.34423339e-01 -4.34239917e-02 8.08962584e-02 -6.04157031e-01
-6.40042782e-01 -7.07700968e-01 4.45617348e-01 7.20612228e-01
-1.39423251e-01 3.53473663e-01 3.13762367e-01 -4.68602538e-01
-3.18295151e-01 -1.27093542e+00 -5.37215412e-01 -5.08434117e-01
-3.17202181e-01 9.72336590e-01 5.26933253e-01 -4.81716305... | [9.123960494995117, 7.608835220336914] |
30085dee-aee7-4452-b671-ca71a279662f | classifying-dialogue-acts-in-multi-party-live | null | null | https://aclanthology.org/Y12-1050 | https://aclanthology.org/Y12-1050.pdf | Classifying Dialogue Acts in Multi-party Live Chats | null | ['Timothy Baldwin', 'Su Nam Kim', 'Lawrence Cavedon'] | 2012-11-01 | classifying-dialogue-acts-in-multi-party-live-1 | https://aclanthology.org/Y12-1050 | https://aclanthology.org/Y12-1050.pdf | paclic-2012-11 | ['dialogue-act-classification'] | ['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... | [-7.220917224884033, 3.8009259700775146] |
75a8588b-4b7b-4b65-ab5c-613198ed8f3b | sipmask-spatial-information-preservation-for | 2007.14772 | null | https://arxiv.org/abs/2007.14772v1 | https://arxiv.org/pdf/2007.14772v1.pdf | SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation | Single-stage instance segmentation approaches have recently gained popularity due to their speed and simplicity, but are still lagging behind in accuracy, compared to two-stage methods. We propose a fast single-stage instance segmentation method, called SipMask, that preserves instance-specific spatial information by s... | ['Ling Shao', 'Fahad Shahbaz Khan', 'Jiale Cao', 'Hisham Cholakkal', 'Rao Muhammad Anwer', 'Yanwei Pang'] | 2020-07-29 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2057_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590001.pdf | eccv-2020-8 | ['real-time-instance-segmentation', 'video-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.13376343e-01 -1.82317328e-02 -4.21765625e-01 -2.00651288e-01
-1.04597175e+00 -6.09253824e-01 3.55388761e-01 8.73115659e-02
-3.36892188e-01 2.70295650e-01 -2.21291035e-01 -1.96125552e-01
1.22908615e-01 -4.78335679e-01 -7.62160480e-01 -5.28447509e-01
-9.81090814e-02 3.85577440e-01 9.50228214e-01 2.93579310... | [9.325258255004883, -0.0025354502722620964] |
92de431b-49b3-463a-81c2-4914db6f4d30 | actup-analyzing-and-consolidating-tsne-and | 2305.0732 | null | https://arxiv.org/abs/2305.07320v1 | https://arxiv.org/pdf/2305.07320v1.pdf | ActUp: Analyzing and Consolidating tSNE and UMAP | tSNE and UMAP are popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. Despite their popularity, however, little work has been done to study their full span of differences. We theoretically and experimentally evaluate the space of parameters in both tSNE and UMAP ... | ['Cigdem Aslay', 'Tyrus Berry', 'Ira Assent', 'Davide Mottin', 'Katrine Scheel Nellemann', 'Jakob Rødsgaard Jørgensen', 'Andrew Draganov'] | 2023-05-12 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-1.58111677e-01 -7.79808313e-03 -2.15494975e-01 -1.39769435e-01
-4.58389759e-01 -9.54275191e-01 6.80407166e-01 6.89725056e-02
-1.36846229e-01 4.62629527e-01 3.40069115e-01 -6.83223784e-01
-4.82283771e-01 -5.78851342e-01 -3.07574838e-01 -6.34845793e-01
-1.22384012e-01 5.00593960e-01 3.68472606e-01 -2.54266769... | [8.41382122039795, 4.050363063812256] |
62099829-7dbf-4093-96bb-5ddfd19a09ff | mixed-td-efficient-neural-network-accelerator | 2306.05021 | null | https://arxiv.org/abs/2306.05021v2 | https://arxiv.org/pdf/2306.05021v2.pdf | Mixed-TD: Efficient Neural Network Accelerator with Layer-Specific Tensor Decomposition | Neural Network designs are quite diverse, from VGG-style to ResNet-style, and from Convolutional Neural Networks to Transformers. Towards the design of efficient accelerators, many works have adopted a dataflow-based, inter-layer pipelined architecture, with a customised hardware towards each layer, achieving ultra hig... | ['Christos-Savvas Bouganis', 'Zhewen Yu'] | 2023-06-08 | null | null | null | null | ['quantization'] | ['methodology'] | [-1.95761383e-01 -8.49208608e-03 -2.96071153e-02 -3.50919485e-01
3.97714257e-01 -4.00988489e-01 3.86793017e-01 3.04394867e-02
-5.55974007e-01 3.07349443e-01 3.88370641e-02 -6.86266184e-01
-1.97709948e-01 -9.36685145e-01 -4.96802688e-01 -6.03763044e-01
-2.59093456e-02 -1.46937855e-02 3.37601066e-01 -2.56324023... | [8.415566444396973, 2.862128734588623] |
e5ea284e-f76a-4ec0-984f-452a8f76346b | the-role-of-emotions-in-native-language | null | null | https://aclanthology.org/W18-6218 | https://aclanthology.org/W18-6218.pdf | The Role of Emotions in Native Language Identification | We explore the hypothesis that emotion is one of the dimensions of language that surfaces from the native language into a second language. To check the role of emotions in native language identification (NLI), we model emotion information through polarity and emotion load features, and use document representations usin... | ['Carlo Strapparava', 'Vivi Nastase', 'Ilia Markov', 'Grigori Sidorov'] | 2018-10-01 | null | null | null | ws-2018-10 | ['deception-detection', 'native-language-identification'] | ['miscellaneous', 'natural-language-processing'] | [-3.25900108e-01 -9.86254308e-03 -8.65749896e-01 -2.50236779e-01
-1.56980366e-01 -8.62566769e-01 8.01080883e-01 3.26865226e-01
-4.30025369e-01 2.67082810e-01 6.66958451e-01 -5.01520216e-01
8.36246759e-02 -4.84382629e-01 -7.26320297e-02 1.71573323e-04
7.84193203e-02 -2.66782232e-02 -9.29024518e-01 -2.38761440... | [10.530211448669434, 10.261173248291016] |
ed780f96-9f40-4047-b97b-57590b5279bc | learning-to-pronounce-as-measuring-cross | 2202.00794 | null | https://arxiv.org/abs/2202.00794v2 | https://arxiv.org/pdf/2202.00794v2.pdf | Learning to pronounce as measuring cross-lingual joint orthography-phonology complexity | Machine learning models allow us to compare languages by showing how hard a task in each language might be to learn and perform well on. Following this line of investigation, we explore what makes a language "hard to pronounce" by modelling the task of grapheme-to-phoneme (g2p) transliteration. By training a character-... | ['Domenic Rosati'] | 2022-01-29 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 3.81399877e-02 7.70185888e-02 -3.65541101e-01 -3.62790316e-01
-6.44189298e-01 -8.81351292e-01 8.55900824e-01 1.29828587e-01
-6.89546943e-01 3.84111226e-01 6.44228041e-01 -9.77056324e-01
4.80536222e-02 -5.94652891e-01 -7.44241774e-01 -1.56518281e-01
2.70312339e-01 6.06887639e-01 -5.19774377e-01 -2.42748782... | [10.869118690490723, 9.914188385009766] |
2a4c18f4-5cb2-4141-b04b-9f954ff41d2f | unitail-detecting-reading-and-matching-in | 2204.00298 | null | https://arxiv.org/abs/2204.00298v4 | https://arxiv.org/pdf/2204.00298v4.pdf | Unitail: Detecting, Reading, and Matching in Retail Scene | To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene. Pursuing this goal, we introduce the United Retail Datasets (Unitail), a large-scale benchmark of basic visual tasks on products that challenges algorithms for detectin... | ['Marios Savvides', 'Chenchen Zhu', 'Uzair Ahmed', 'Yongxin Zhang', 'Hao Chen', 'Shentong Mo', 'Jiachen Dou', 'Zaiwang Li', 'Han Zhang', 'Fangyi Chen'] | 2022-04-01 | null | null | null | null | ['dense-object-detection'] | ['computer-vision'] | [ 5.16480446e-01 -1.89693540e-01 -2.85876602e-01 -5.33538163e-01
-9.33014989e-01 -1.17392147e+00 5.70009291e-01 3.10706943e-01
6.39896393e-02 -3.72755110e-01 2.05657288e-01 -3.37454885e-01
2.07037777e-01 -5.27137637e-01 -7.58008003e-01 -2.50907987e-01
1.64835513e-01 3.88439417e-01 -1.43762752e-02 -4.44138736... | [10.921278953552246, 1.4068013429641724] |
a6ab5b97-bc31-4363-b403-53332846b94a | viewnet-unsupervised-viewpoint-estimation-1 | 2212.00435 | null | https://arxiv.org/abs/2212.00435v1 | https://arxiv.org/pdf/2212.00435v1.pdf | ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation | Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are expensive to obtain on a large scale. In this paper, we address the problem of unsupervised viewpoint estimation. We formulate this as a se... | ['Hakan Bilen', 'Oisin Mac Aodha', 'Octave Mariotti'] | 2022-12-01 | viewnet-unsupervised-viewpoint-estimation | http://openaccess.thecvf.com//content/ICCV2021/html/Mariotti_ViewNet_Unsupervised_Viewpoint_Estimation_From_Conditional_Generation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Mariotti_ViewNet_Unsupervised_Viewpoint_Estimation_From_Conditional_Generation_ICCV_2021_paper.pdf | iccv-2021-1 | ['viewpoint-estimation'] | ['computer-vision'] | [ 2.82303602e-01 1.29985601e-01 6.73704520e-02 -8.14613819e-01
-4.70136017e-01 -6.97428703e-01 5.29223323e-01 -1.34016007e-01
-4.78809744e-01 2.67459482e-01 -1.83297753e-01 -6.70599565e-02
4.63541359e-01 -4.01607454e-01 -1.18620276e+00 -5.43897510e-01
4.12189871e-01 8.28081489e-01 3.75999838e-01 1.48599759... | [8.346981048583984, -2.74723219871521] |
3907b556-0e02-4b1b-8e66-6eeb8ac303fd | towards-understanding-iterative-magnitude | 2106.06955 | null | https://arxiv.org/abs/2106.06955v1 | https://arxiv.org/pdf/2106.06955v1.pdf | Towards Understanding Iterative Magnitude Pruning: Why Lottery Tickets Win | The lottery ticket hypothesis states that sparse subnetworks exist in randomly initialized dense networks that can be trained to the same accuracy as the dense network they reside in. However, the subsequent work has failed to replicate this on large-scale models and required rewinding to an early stable state instead ... | ['Marie-Francine Moens', 'Mingxiao Li', 'Jaron Maene'] | 2021-06-13 | null | null | null | null | ['linear-mode-connectivity'] | ['knowledge-base'] | [ 1.11690767e-01 8.49297047e-01 -6.99502975e-02 -3.96467835e-01
2.17173532e-01 -5.02766013e-01 8.21771979e-01 -4.94391888e-01
-6.09122872e-01 1.31687391e+00 1.13566287e-01 -4.03474420e-01
-3.31220269e-01 -1.01784921e+00 -8.88536155e-01 -5.80147266e-01
-3.16391200e-01 9.81330156e-01 7.34241664e-01 -1.45992175... | [8.540655136108398, 3.308889389038086] |
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