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3d26a028-95ab-4ef1-8cd3-153215ec58fc | malm-mixing-augmented-language-modeling-for | 2210.00320 | null | https://arxiv.org/abs/2210.00320v1 | https://arxiv.org/pdf/2210.00320v1.pdf | MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation | Large pre-trained language models have brought remarkable progress in NLP. Pre-training and Fine-tuning have given state-of-art performance across tasks in text processing. Data Augmentation techniques have also helped build state-of-art models on low or zero resource tasks. Many works in the past have attempted at lea... | ['Kshitij Gupta'] | 2022-10-01 | null | null | null | null | ['zero-shot-machine-translation'] | ['natural-language-processing'] | [ 2.86908090e-01 -4.10530940e-02 -6.69329345e-01 -3.23637873e-01
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4.04597014e-01 1.10200214e+00 -3.18875462e-01 -6.77365899... | [11.545477867126465, 10.228099822998047] |
fb8706c1-5dcb-4c96-afb6-cdfc2e6a9bda | identity-guided-human-semantic-parsing-for | 2007.13467 | null | https://arxiv.org/abs/2007.13467v1 | https://arxiv.org/pdf/2007.13467v1.pdf | Identity-Guided Human Semantic Parsing for Person Re-Identification | Existing alignment-based methods have to employ the pretrained human parsing models to achieve the pixel-level alignment, and cannot identify the personal belongings (e.g., backpacks and reticule) which are crucial to person re-ID. In this paper, we propose the identity-guided human semantic parsing approach (ISP) to l... | ['Zhiwei Liu', 'Jinqiao Wang', 'Ming Tang', 'Haiyun Guo', 'Kuan Zhu'] | 2020-07-27 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/415_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480358.pdf | eccv-2020-8 | ['human-parsing'] | ['computer-vision'] | [ 1.85034107e-02 8.77360776e-02 -8.02533701e-02 -5.06298482e-01
-5.09193420e-01 -4.15837437e-01 4.07248676e-01 -1.30142987e-01
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3.72744620e-01 9.37511981e-01 2.83889651e-01 1.71546504... | [14.683513641357422, 0.8708323240280151] |
8eacd0b6-71c1-4fd6-a076-c2ff41e52442 | 3d-object-detection-and-viewpoint-estimation | null | null | http://papers.nips.cc/paper/4562-3d-object-detection-and-viewpoint-estimation-with-a-deformable-3d-cuboid-model | http://papers.nips.cc/paper/4562-3d-object-detection-and-viewpoint-estimation-with-a-deformable-3d-cuboid-model.pdf | 3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model | This paper addresses the problem of category-level 3D object detection. Given a monocular image, our aim is to localize the objects in 3D by enclosing them with tight oriented 3D bounding boxes. We propose a novel approach that extends the well-acclaimed deformable part-based model[Felz.] to reason in 3D. Our model r... | ['Raquel Urtasun', 'Sanja Fidler', 'Sven Dickinson'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['viewpoint-estimation'] | ['computer-vision'] | [-1.56556964e-01 2.00888649e-01 7.63072073e-02 -3.82946849e-01
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-1.37494698e-01 9.35097814e-01 6.15743101e-01 3.53264868... | [7.6089911460876465, -2.758420705795288] |
b9e17ef4-31ce-4bb8-81a2-90cf7ece36db | hdr-cgan-single-ldr-to-hdr-image-translation | 2110.01660 | null | https://arxiv.org/abs/2110.01660v2 | https://arxiv.org/pdf/2110.01660v2.pdf | HDR-cGAN: Single LDR to HDR Image Translation using Conditional GAN | The prime goal of digital imaging techniques is to reproduce the realistic appearance of a scene. Low Dynamic Range (LDR) cameras are incapable of representing the wide dynamic range of the real-world scene. The captured images turn out to be either too dark (underexposed) or too bright (overexposed). Specifically, sat... | ['Shanmuganathan Raman', 'Rohil Pal', 'Prarabdh Raipurkar'] | 2021-10-04 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 8.16799521e-01 1.59796193e-01 3.71337324e-01 -3.12187046e-01
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4.55311567e-01 2.69030541e-01 1.79022700e-01 -2.66681999... | [10.883841514587402, -2.190479278564453] |
6a1c1d85-831e-43ec-ad72-1ad3e7dd2c1d | hierarchical-cyber-attack-detection-in-large | 2209.13874 | null | https://arxiv.org/abs/2209.13874v1 | https://arxiv.org/pdf/2209.13874v1.pdf | Hierarchical Cyber-Attack Detection in Large-Scale Interconnected Systems | In this paper we present a hierarchical scheme to detect cyber-attacks in a hierarchical control architecture for large-scale interconnected systems (LSS). We consider the LSS as a network of physically coupled subsystems, equipped with a two-layer controller: on the local level, decentralized controllers guarantee ove... | ['Riccardo M. G. Ferrari', 'Alexander J. Gallo', 'Twan Keijzer'] | 2022-09-28 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-1.29501536e-01 5.70102453e-01 5.70281185e-02 7.34125197e-01
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-7.02110171e-01 1.74716011e-01 9.72864211e-01 -2.11485729... | [5.220537185668945, 2.6737239360809326] |
e1d3cfcc-2371-44bb-bd2a-6acf207a1405 | self-supervised-representations-for-singing | 2303.12197 | null | https://arxiv.org/abs/2303.12197v1 | https://arxiv.org/pdf/2303.12197v1.pdf | Self-Supervised Representations for Singing Voice Conversion | A singing voice conversion model converts a song in the voice of an arbitrary source singer to the voice of a target singer. Recently, methods that leverage self-supervised audio representations such as HuBERT and Wav2Vec 2.0 have helped further the state-of-the-art. Though these methods produce more natural and melodi... | ['Qing He', 'Vimal Manohar', 'David Kant', 'Leda Sari', 'JiLong Wu', 'Tejas Jayashankar'] | 2023-03-21 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 1.10980205e-01 7.24460557e-02 -7.90282264e-02 -1.33053660e-01
-9.85276937e-01 -1.03827274e+00 4.68797863e-01 -5.52490592e-01
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1.48047045e-01 1.77991524e-01 -2.06188500e-01 -5.36248803... | [15.530828475952148, 6.078884601593018] |
361517a6-0d73-490c-ad2a-35d7f21d4a14 | learning-mid-level-filters-for-person-re | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Zhao_Learning_Mid-level_Filters_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Zhao_Learning_Mid-level_Filters_2014_CVPR_paper.pdf | Learning Mid-level Filters for Person Re-identification | In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for identifying specific visual ... | ['Rui Zhao', 'Wanli Ouyang', 'Xiaogang Wang'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['patch-matching'] | ['computer-vision'] | [-1.53786913e-01 -4.66269374e-01 -2.50287384e-01 -4.24310654e-01
-6.69026256e-01 -6.93783462e-01 5.05838156e-01 2.11391747e-01
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-1.89578623e-01 3.60203773e-01 2.16712788e-01 1.20614603... | [14.795876502990723, 1.0412449836730957] |
16546b58-2bda-4aa3-8615-7a31f44c0137 | explainability-in-practice-estimating | 2211.06277 | null | https://arxiv.org/abs/2211.06277v2 | https://arxiv.org/pdf/2211.06277v2.pdf | Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal | Explainable artificial intelligence (XAI) provides explanations for not interpretable machine learning (ML) models. While many technical approaches exist, there is a lack of validation of these techniques on real-world datasets. In this work, we present a use-case of XAI: an ML model which is trained to estimate electr... | ['Zbigniew Smoreda', 'Stefania Rubrichi', 'Hadrien Salat', 'Laura State'] | 2022-11-11 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.85810858e-01 1.14714468e+00 -7.34949052e-01 -5.91377556e-01
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6.33066371e-02 1.04930520e+00 -8.48764479e-01 6.97623640... | [8.787997245788574, 5.829405784606934] |
0986de72-63d9-4f7d-a848-a1eb829aa22b | ultrahigh-dimensional-instrument-detection | 2007.15769 | null | https://arxiv.org/abs/2007.15769v2 | https://arxiv.org/pdf/2007.15769v2.pdf | Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing | Endogeneity bias and instrument variable validation have always been important topics in statistics and econometrics. In the era of big data, such issues typically combine with dimensionality issues and, hence, require even more attention. In this paper, we merge two well-known tools from machine learning and biostatis... | ['Ning Xu', 'Timothy C. G. Fisher', 'Jian Hong'] | 2020-07-30 | null | null | null | null | ['variable-detection'] | ['natural-language-processing'] | [-4.97268379e-01 -8.52754042e-02 -6.88155591e-01 -4.72522937e-02
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-2.48770177e-01 3.78496200e-01 -6.59213603e-01 -5.10241166... | [7.845750331878662, 5.053145885467529] |
ed284117-84ca-44a7-91bf-50bca3af5903 | valley-video-assistant-with-large-language | 2306.07207 | null | https://arxiv.org/abs/2306.07207v1 | https://arxiv.org/pdf/2306.07207v1.pdf | Valley: Video Assistant with Large Language model Enhanced abilitY | Recently, several multi-modal models have been developed for joint image and language understanding, which have demonstrated impressive chat abilities by utilizing advanced large language models (LLMs). The process of developing such models is straightforward yet effective. It involves pre-training an adaptation module... | ['Zhongyu Wei', 'Tao Wang', 'Pengcheng Lu', 'Minghui Qiu', 'Junwei DOng', 'Min Yang', 'Ziwang Zhao', 'Ruipu Luo'] | 2023-06-12 | null | null | null | null | ['action-recognition-in-videos', 'video-understanding', 'instruction-following'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [-5.62213771e-02 -2.01452151e-02 -1.61418170e-01 -4.91713136e-01
-6.94398940e-01 -4.17693913e-01 7.27929652e-01 -4.16680396e-01
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3.60161036e-01 3.28384608e-01 9.53965113e-02 -1.86270580... | [10.545372009277344, 1.072817087173462] |
1b2749c7-999b-45dd-af7f-bbc8242fe920 | deep-model-compression-also-helps-models | 2306.07061 | null | https://arxiv.org/abs/2306.07061v1 | https://arxiv.org/pdf/2306.07061v1.pdf | Deep Model Compression Also Helps Models Capture Ambiguity | Natural language understanding (NLU) tasks face a non-trivial amount of ambiguous samples where veracity of their labels is debatable among annotators. NLU models should thus account for such ambiguity, but they approximate the human opinion distributions quite poorly and tend to produce over-confident predictions. To ... | ['Jong C. Park', 'Hancheol Park'] | 2023-06-12 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 8.06390271e-02 6.25586450e-01 -5.30914068e-01 -7.96276987e-01
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1.56445563e-01 1.13341486e+00 8.78298953e-02 -3.48279253... | [9.246244430541992, 4.5586256980896] |
67da790b-61c2-4b63-becc-8b8a2d6ab564 | olia-an-open-source-digital-lock-in-amplifier | 2211.08889 | null | https://arxiv.org/abs/2211.08889v2 | https://arxiv.org/pdf/2211.08889v2.pdf | OLIA: an open-source digital lock-in amplifier | The Open Lock-In Amplifier (OLIA) is a microcontroller-based digital lock-in amplifier built from a small number of inexpensive and easily sourced electronic components. Despite its small credit card-sized form-factor and low build-cost of around US$35, OLIA is a capable instrument that offers many features associated ... | ['John C. de Mello', 'Andrew J. Harvie'] | 2022-11-16 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 9.66998488e-02 -4.82436746e-01 -1.11409537e-01 2.10675448e-02
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-5.43814711e-02 2.92721391e-02 3.18842441e-01 1.32354736... | [13.916834831237793, 3.2211544513702393] |
33d46319-29ad-4a81-90a6-89d146603097 | stereo-based-multi-motion-visual-odometry-for | 1910.06607 | null | https://arxiv.org/abs/1910.06607v1 | https://arxiv.org/pdf/1910.06607v1.pdf | Stereo-based Multi-motion Visual Odometry for Mobile Robots | With the development of computer vision, visual odometry is adopted by more and more mobile robots. However, we found that not only its own pose, but the poses of other moving objects are also crucial for the decision of the robot. In addition, the visual odometry will be greatly disturbed when a significant moving obj... | ['Bin Luo', 'Yun Zhang', 'Qing Zhao'] | 2019-10-15 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-3.29648107e-01 -2.04433113e-01 2.47965142e-01 -6.66882694e-02
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3.37087661e-01 8.17922533e-01 8.22598279e-01 -2.65024066... | [7.5303239822387695, -2.1077892780303955] |
41a96a95-0ba0-4b59-b55b-1d4bed0e401a | retrack-a-flexible-and-efficient-framework | null | null | https://aclanthology.org/2021.acl-demo.39 | https://aclanthology.org/2021.acl-demo.39.pdf | ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering | We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to maintain high flexibility. It includes a retriever to retrieve relevant KB items efficiently, a transducer to generate logical fo... | ['Feng Jiang', 'Jian-Guang Lou', 'Chin-Yew Lin', 'Zhiwei Yu', 'Qian Liu', 'Shuang Chen'] | 2021-08-01 | null | null | null | acl-2021-5 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 5.55496365e-02 3.06885630e-01 -1.17110506e-01 -3.74216765e-01
-1.65556347e+00 -8.87528539e-01 -1.70466751e-01 1.38222575e-01
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1.67677477e-01 8.69628668e-01 8.80573630e-01 -7.76994824... | [10.406255722045898, 7.900567054748535] |
6ccbd02f-ba53-4654-bfbf-4c19c7724018 | self-attentive-constituency-parsing-for-ucca | 2110.00621 | null | https://arxiv.org/abs/2110.00621v1 | https://arxiv.org/pdf/2110.00621v1.pdf | Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing | Semantic parsing provides a way to extract the semantic structure of a text that could be understood by machines. It is utilized in various NLP applications that require text comprehension such as summarization and question answering. Graph-based representation is one of the semantic representation approaches to expres... | ['Burcu Can', 'Necva Bölücü'] | 2021-10-01 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 4.36119974e-01 6.72601521e-01 -1.70508996e-01 -5.34171700e-01
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1.39510959e-01 5.81173480e-01 2.60167807e-01 -5.52653193... | [10.585145950317383, 8.989940643310547] |
4afd7392-f019-4af2-9415-e4a8b2415efc | from-cad-models-to-soft-point-cloud-labels-an | 2302.03114 | null | https://arxiv.org/abs/2302.03114v2 | https://arxiv.org/pdf/2302.03114v2.pdf | From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation | We propose a fully automatic annotation scheme which takes a raw 3D point cloud with a set of fitted CAD models as input, and outputs convincing point-wise labels which can be used as cheap training data for point cloud segmentation. Compared to manual annotations, we show that our automatic labels are accurate while d... | ['Andreas Møgelmose', 'Simon Buus Jensen', 'Galadrielle Humblot-Renaux'] | 2023-02-06 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 3.39300543e-01 3.75867605e-01 -5.89286275e-02 -9.87127066e-01
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1.45321786e-01 1.24593353e+00 5.28272569e-01 1.97523594... | [8.02302074432373, -3.1257200241088867] |
29de9ffe-a59a-43aa-baa0-9231c0b49c6d | multilingual-seq2seq-training-with-similarity | null | null | https://aclanthology.org/W18-3023 | https://aclanthology.org/W18-3023.pdf | Multilingual Seq2seq Training with Similarity Loss for Cross-Lingual Document Classification | In this paper we continue experiments where neural machine translation training is used to produce joint cross-lingual fixed-dimensional sentence embeddings. In this framework we introduce a simple method of adding a loss to the learning objective which penalizes distance between representations of bilingually aligned ... | ['Haoran Li', 'Katherine Yu', 'Barlas Oguz'] | 2018-07-01 | null | null | null | ws-2018-7 | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [-9.50243548e-02 -1.28033414e-01 -6.43827021e-01 -6.01921618e-01
-1.58034742e+00 -8.62087369e-01 1.06804526e+00 2.10281298e-01
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1.89905450e-01 7.16052532e-01 -9.92267281e-02 -4.20065403... | [11.095197677612305, 9.986611366271973] |
8cc2950c-148b-4713-8026-d3b06a986d6a | on-cmos-high-throughput-multi-modal | 2208.00248 | null | https://arxiv.org/abs/2208.00248v1 | https://arxiv.org/pdf/2208.00248v1.pdf | On-CMOS High-Throughput Multi-Modal Amperometric DNA Analysis with Distributed Thermal Regulation | Accurate temperature regulation is critical for amperometric DNA analysis to achieve high fidelity, reliability, and throughput. In this work, a 9x6 cell array of mixed-signal CMOS distributed temperature regulators for on-CMOS multi-modal amperometric DNA analysis is presented. Three DNA analysis methods are supported... | ['Roman Genov', 'Xilin Liu', 'Hamed M. Jafari'] | 2022-07-30 | null | null | null | null | ['dna-analysis'] | ['medical'] | [ 8.25610936e-01 -4.30617332e-01 -6.00601994e-02 -7.13827834e-02
-3.94163430e-01 -9.58413303e-01 1.07621729e-01 7.88265944e-01
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2.56541997e-01 -1.31401187e-02 3.04805785e-01 2.72846855... | [13.937070846557617, 3.156416177749634] |
dfa979da-0a1e-4ffa-9912-440fc2c893f4 | generating-post-hoc-explanations-for-skip | 2304.12036 | null | https://arxiv.org/abs/2304.12036v3 | https://arxiv.org/pdf/2304.12036v3.pdf | Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness | Node representation learning in a network is an important machine learning technique for encoding relational information in a continuous vector space while preserving the inherent properties and structures of the network. Recently, unsupervised node embedding methods such as DeepWalk, LINE, struc2vec, PTE, UserItem2vec... | ['Jennifer Neville', 'Hogun Park'] | 2023-04-24 | null | null | null | null | ['graph-embedding'] | ['graphs'] | [-2.58921713e-01 6.94976687e-01 -7.57606447e-01 -2.17417806e-01
-1.32919431e-01 -3.39684427e-01 4.96273994e-01 7.10135341e-01
2.36809015e-01 5.28293312e-01 6.50506735e-01 -4.66978997e-01
-7.38687396e-01 -1.01751864e+00 -3.66745740e-01 -6.28885269e-01
-5.90715885e-01 3.57622594e-01 1.59207344e-01 -4.38170642... | [7.295785427093506, 6.2869672775268555] |
c8d5e79f-5459-42bb-88fc-9d8391acfa13 | novelty-detection-via-contrastive-learning | 2106.09958 | null | https://arxiv.org/abs/2106.09958v1 | https://arxiv.org/pdf/2106.09958v1.pdf | Novelty Detection via Contrastive Learning with Negative Data Augmentation | Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous methods attempt to learn the representation of the normal samples via generative adversarial networks (GANs). However, they will suffer from instability training, mode dropping, and low discr... | ['Lizhuang Ma', 'Yi Zhang', 'Xin Tan', 'Jian Zhou', 'Ruizhi Qiao', 'Shaohui Lin', 'Yuan Xie', 'Chengwei Chen'] | 2021-06-18 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 0.07821293 -0.27747837 -0.11663184 -0.16989367 -0.8057688 -0.44143468
0.60358584 -0.38493156 -0.38717863 0.623761 0.06181912 0.05932935
0.40961054 -0.7843893 -0.9474465 -0.9994863 0.08531009 0.04533245
0.14053978 -0.06782536 0.07819159 0.41639504 -1.2094775 0.11149656
0.94082904 0.9063455 -0.... | [7.848135471343994, 2.336265802383423] |
0f00ac83-4a0e-4938-94c8-f431b3154dd8 | tailmix-overcoming-the-label-sparsity-for | null | null | https://openreview.net/forum?id=jDK19MUBT4_ | https://openreview.net/pdf?id=jDK19MUBT4_ | TailMix: Overcoming the Label Sparsity for Extreme Multi-label Classification | Extreme multi-label classification (XMC) aims at finding the most relevant labels from a huge label set at the industrial scale. The XMC problem inherently poses two challenges: data scalability and label sparsity. This work introduces a new augmentation method, namely TailMix, to address the label sparsity issue, i.e.... | ['Jongwuk Lee', 'Chan Lim', 'Sangwoo Han'] | 2021-09-29 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 3.47802758e-01 -1.51248857e-01 -6.58388257e-01 -5.08871436e-01
-1.06799042e+00 -4.33913112e-01 3.50619167e-01 1.09862171e-01
-1.46202177e-01 4.37026262e-01 1.00806758e-01 -5.78180514e-02
-1.26345575e-01 -3.21164042e-01 -3.05158079e-01 -8.94953668e-01
4.37586099e-01 5.22369981e-01 -1.11108541e-01 1.45422667... | [9.549546241760254, 4.231923580169678] |
6bde97f2-a28c-4cf9-9ce8-5ae15fd82230 | matrix-tri-factorization-over-the-tropical | 2305.06624 | null | https://arxiv.org/abs/2305.06624v1 | https://arxiv.org/pdf/2305.06624v1.pdf | Matrix tri-factorization over the tropical semiring | Tropical semiring has proven successful in several research areas, including optimal control, bioinformatics, discrete event systems, or solving a decision problem. In previous studies, a matrix two-factorization algorithm based on the tropical semiring has been applied to investigate bipartite and tripartite networks.... | ['Tomaž Curk', 'Polona Oblak', 'Amra Omanović'] | 2023-05-11 | null | null | null | null | ['community-detection', 'matrix-completion'] | ['graphs', 'methodology'] | [ 1.37021363e-01 -1.78684458e-01 -3.94273102e-02 8.68679732e-02
-1.40982747e-01 -6.16836488e-01 2.36792907e-01 1.70614734e-01
-1.59596413e-01 7.80122697e-01 -4.82850708e-02 -6.61079049e-01
-6.40320539e-01 -8.54595006e-01 -5.98889649e-01 -9.62333858e-01
-4.65630352e-01 8.70312810e-01 1.45515755e-01 -1.27547711... | [7.03646993637085, 5.156035423278809] |
59ad95c8-aedd-4860-9c2e-2dbf34c04e7d | a-novel-approach-for-detecting-normal-covid | null | null | https://www.sciencedirect.com/science/article/pii/S2772528622000310 | https://www.sciencedirect.com/sdfe/reader/pii/S2772528622000310/pdf | A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-Scans | The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of th... | ['Ankit KumarSanjeev Sharma', 'Maganti Bhargav Hemanth', 'Peddaputha Akash', 'Sanskar Hasija'] | 2022-03-28 | null | null | null | neuroscience-informatics-2022-3 | ['covid-19-detection'] | ['medical'] | [ 1.96553856e-01 -6.55768037e-01 -3.66002247e-02 -8.62447172e-02
2.62137000e-02 -5.95069826e-01 1.93288058e-01 4.47794080e-01
-7.03757584e-01 6.74456358e-01 -2.68143207e-01 -4.21567738e-01
1.96457468e-02 -7.94969499e-01 -2.16318890e-01 -7.76708484e-01
-3.99827302e-01 8.23603868e-01 -4.85378355e-02 5.41979633... | [15.56991958618164, -1.6919384002685547] |
010d0bd5-dbe2-4565-a12f-adf04a2a1b0c | two-decades-of-bengali-handwritten-digit | 2206.02234 | null | https://arxiv.org/abs/2206.02234v3 | https://arxiv.org/pdf/2206.02234v3.pdf | Two Decades of Bengali Handwritten Digit Recognition: A Survey | Handwritten Digit Recognition (HDR) is one of the most challenging tasks in the domain of Optical Character Recognition (OCR). Irrespective of language, there are some inherent challenges of HDR, which mostly arise due to the variations in writing styles across individuals, writing medium and environment, inability to ... | ['Md. Hasanul Kabir', 'Mohammad Ridwan Kabir', 'Md. Hamjajul Ashmafee', 'Tasnim Ahmed', 'Sabbir Ahmed', 'Md. Bakhtiar Hasan', 'A. B. M. Ashikur Rahman'] | 2022-06-05 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.85781822e-01 -7.25672126e-01 8.28628317e-02 -3.17515373e-01
-3.45065176e-01 -7.68947482e-01 6.47167027e-01 -2.64430285e-01
-2.27153197e-01 4.83805478e-01 -1.05799712e-01 -2.93583632e-01
-3.70026939e-02 -5.59490085e-01 -2.83555895e-01 -9.18082774e-01
1.00751594e-01 2.97619879e-01 -5.42690419e-02 -2.64903426... | [11.848336219787598, 2.5920252799987793] |
ccd9c291-fbf0-4652-b668-f2bc277c3010 | namf-a-non-local-adaptive-mean-filter-for | 1910.07787 | null | https://arxiv.org/abs/1910.07787v2 | https://arxiv.org/pdf/1910.07787v2.pdf | NAMF: A Non-local Adaptive Mean Filter for Salt-and-Pepper Noise Removal | In this paper, a novel algorithm called a non-local adaptive mean filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented. We employ an efficient window detector with adaptive size to detect the noise, the noisy pixel will be replaced by the combination of its neighboring pixels, and f... | ['Houwang Zhang', 'Yuan Zhu', 'Hanying Zheng'] | 2019-10-17 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 4.03768450e-01 -9.50843275e-01 3.57009679e-01 -8.38730484e-02
-7.10156739e-01 -3.08293164e-01 1.41201645e-01 -1.08798936e-01
-6.47582591e-01 5.92165709e-01 1.43718198e-01 -2.02500045e-01
1.25534639e-01 -7.16842473e-01 -2.38520741e-01 -1.31064427e+00
-8.95288214e-02 -7.17041135e-01 7.46972442e-01 -1.35637403... | [11.244088172912598, -2.5441298484802246] |
41a7a6a7-2bb3-42ed-8ef9-ca8353ed06b3 | cascade-attention-network-for-person-search | 1809.08440 | null | https://arxiv.org/abs/1809.08440v3 | https://arxiv.org/pdf/1809.08440v3.pdf | Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search | Text-based person search aims to retrieve the corresponding person images in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. Extracting visual contents corresponding to the human description is the key to this cross-... | ['Jun-Bo Wang', 'Liang Wang', 'Chenyang Si', 'Ya Jing', 'Tieniu Tan', 'Wei Wang'] | 2018-09-22 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-8.37688893e-02 -3.93181056e-01 -3.77223581e-01 -3.80076468e-01
-7.68984199e-01 -1.90600365e-01 7.23983526e-01 -1.93661407e-01
-6.53273106e-01 4.19244468e-01 6.03425086e-01 5.38535655e-01
-2.27221638e-01 -6.21295989e-01 -6.10316396e-01 -5.66037536e-01
3.17230672e-01 7.40693688e-01 1.64763272e-01 -2.03880250... | [14.665780067443848, 0.8274063467979431] |
48ea7a67-21d8-4633-9dba-0ed7269b6fdb | learning-to-remember-more-with-less | 1901.01347 | null | http://arxiv.org/abs/1901.01347v2 | http://arxiv.org/pdf/1901.01347v2.pdf | Learning to Remember More with Less Memorization | Memory-augmented neural networks consisting of a neural controller and an
external memory have shown potentials in long-term sequential learning. Current
RAM-like memory models maintain memory accessing every timesteps, thus they do
not effectively leverage the short-term memory held in the controller. We
hypothesize t... | ['Truyen Tran', 'Svetha Venkatesh', 'Hung Le'] | 2019-01-05 | learning-to-remember-more-with-less-1 | https://openreview.net/forum?id=r1xlvi0qYm | https://openreview.net/pdf?id=r1xlvi0qYm | iclr-2019-5 | ['sequential-image-classification'] | ['computer-vision'] | [ 2.80664057e-01 3.34798992e-01 -5.11728287e-01 -4.36226949e-02
-3.57759655e-01 -1.22475848e-01 6.46023810e-01 -1.52361408e-01
-7.21132994e-01 8.32475662e-01 1.02662869e-01 -3.39433581e-01
1.20942090e-02 -7.36000896e-01 -8.85294080e-01 -8.03391576e-01
3.10635921e-02 1.99067235e-01 2.10616544e-01 -2.09152047... | [9.63508415222168, 3.5795347690582275] |
40de27cb-7b05-445c-ab5b-05bad0408a4c | betray-oneself-a-novel-audio-deepfake | 2305.16353 | null | https://arxiv.org/abs/2305.16353v1 | https://arxiv.org/pdf/2305.16353v1.pdf | Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion | Audio Deepfake Detection (ADD) aims to detect the fake audio generated by text-to-speech (TTS), voice conversion (VC) and replay, etc., which is an emerging topic. Traditionally we take the mono signal as input and focus on robust feature extraction and effective classifier design. However, the dual-channel stereo info... | ['Haizhou Li', 'Guanglai Gao', 'Jinhua Zhang', 'Rui Liu'] | 2023-05-25 | null | null | null | null | ['voice-conversion', 'deepfake-detection', 'face-swapping', 'voice-conversion'] | ['audio', 'computer-vision', 'computer-vision', 'speech'] | [ 6.61075264e-02 -3.44711840e-01 1.58438787e-01 1.30155727e-01
-1.44085741e+00 -5.22895753e-01 3.22458178e-01 -2.97833681e-01
1.35698184e-01 4.44522411e-01 4.93898213e-01 -1.89938635e-01
4.29421008e-01 -3.15054864e-01 -7.59535789e-01 -6.43182278e-01
3.46653700e-01 -2.22841069e-01 3.97809893e-01 -1.48663923... | [14.169103622436523, 5.751471042633057] |
a03f9ab2-d4c8-4604-b4a6-cba89b7d20e9 | utility-decomposition-with-deep-corrections | 1802.01772 | null | http://arxiv.org/abs/1802.01772v2 | http://arxiv.org/pdf/1802.01772v2.pdf | Decomposition Methods with Deep Corrections for Reinforcement Learning | Decomposition methods have been proposed to approximate solutions to large
sequential decision making problems. In contexts where an agent interacts with
multiple entities, utility decomposition can be used to separate the global
objective into local tasks considering each individual entity independently. An
arbitrator... | ['Kyle Julian', 'Maxime Bouton', 'Kikuo Fujimura', 'Mykel J. Kochenderfer', 'Alireza Nakhaei'] | 2018-02-06 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [ 1.17386088e-01 4.04026121e-01 -6.83207214e-02 -3.09188932e-01
-8.73837233e-01 -5.48807263e-01 3.90018493e-01 2.67061085e-01
-8.28236461e-01 1.21084666e+00 -9.97222727e-04 -3.29920262e-01
-2.25603402e-01 -8.49538624e-01 -7.18472660e-01 -1.00094569e+00
-2.33323649e-01 8.95536363e-01 2.43733943e-01 -1.10263273... | [4.060089588165283, 2.4044227600097656] |
a46d470c-62f3-4b3e-9b2d-04fd1a552e12 | deep-learning-for-finger-vein-recognition-a | 2207.02148 | null | https://arxiv.org/abs/2207.02148v1 | https://arxiv.org/pdf/2207.02148v1.pdf | Deep Learning for Finger Vein Recognition: A Brief Survey of Recent Trend | Finger vein image recognition technology plays an important role in biometric recognition and has been successfully applied in many fields. Because veins are buried beneath the skin tissue, finger vein image recognition has an unparalleled advantage, which is not easily disturbed by external factors. This review summar... | ['Jinghua Zhang', 'Chen Li', 'Wanxia Deng', 'Yimin Yin', 'Renye Zhang'] | 2022-07-05 | null | null | null | null | ['finger-vein-recognition'] | ['computer-vision'] | [ 1.46313056e-01 -3.99319053e-01 -4.48373884e-01 -2.93364525e-01
3.18851948e-01 -6.66485012e-01 3.44173044e-01 -6.02280140e-01
-4.15542692e-01 5.67835748e-01 1.10409120e-02 -6.50963783e-02
1.16478182e-01 -8.99251342e-01 1.82713851e-01 -8.25512111e-01
2.26660654e-01 1.17297448e-01 -9.45325643e-02 6.92274421... | [13.069376945495605, 1.0006905794143677] |
86ef59a4-69ad-4a97-89a3-833cf090f2b1 | tensor-networks-for-unsupervised-machine | 2106.12974 | null | https://arxiv.org/abs/2106.12974v2 | https://arxiv.org/pdf/2106.12974v2.pdf | Tensor networks for unsupervised machine learning | Modeling the joint distribution of high-dimensional data is a central task in unsupervised machine learning. In recent years, many interests have been attracted to developing learning models based on tensor networks, which have the advantages of a principle understanding of the expressive power using entanglement prope... | ['Pan Zhang', 'Jiang Zhang', 'Sujie Li', 'Jing Liu'] | 2021-06-24 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.98331177e-02 -4.78520170e-02 -1.01357043e-01 -3.11223030e-01
-3.60933602e-01 -1.77576616e-01 9.22767639e-01 -4.05391246e-01
-3.31696749e-01 7.26778805e-01 2.06237286e-01 -2.99627364e-01
-3.95301878e-01 -1.07731235e+00 -5.40570915e-01 -1.29501259e+00
-2.49845147e-01 8.31550002e-01 8.66687372e-02 -2.39478469... | [5.676108360290527, 4.946869373321533] |
a6847c73-59d9-47e5-826c-72cf2880d517 | artgan-artwork-synthesis-with-conditional | 1702.03410 | null | http://arxiv.org/abs/1702.03410v2 | http://arxiv.org/pdf/1702.03410v2.pdf | ArtGAN: Artwork Synthesis with Conditional Categorical GANs | This paper proposes an extension to the Generative Adversarial Networks
(GANs), namely as ARTGAN to synthetically generate more challenging and complex
images such as artwork that have abstract characteristics. This is in contrast
to most of the current solutions that focused on generating natural images such
as room i... | ['Kiyoshi Tanaka', 'Chee Seng Chan', 'Wei Ren Tan', 'Hernan Aguirre'] | 2017-02-11 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 5.10072529e-01 4.43135113e-01 4.80676472e-01 -1.89768046e-01
-3.88252348e-01 -7.96606421e-01 8.11793566e-01 -8.14470172e-01
2.83045005e-02 1.17851853e+00 2.26287022e-02 8.82990472e-03
2.27448866e-01 -1.28200877e+00 -9.71841514e-01 -7.45235801e-01
1.63753271e-01 4.45197970e-01 -3.44933242e-01 -2.03174427... | [11.700911521911621, -0.39018380641937256] |
1a56943d-ac42-41ea-8b85-41e2135565cc | are-deep-sequence-classifiers-good-at-non | 2210.13082 | null | https://arxiv.org/abs/2210.13082v2 | https://arxiv.org/pdf/2210.13082v2.pdf | Are Deep Sequence Classifiers Good at Non-Trivial Generalization? | Recent advances in deep learning models for sequence classification have greatly improved their classification accuracy, specially when large training sets are available. However, several works have suggested that under some settings the predictions made by these models are poorly calibrated. In this work we study bina... | ['Xavier Carreras', 'Ariadna Quattoni', 'Francesco Cazzaro'] | 2022-10-24 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 7.19831645e-01 6.29342943e-02 -4.19147879e-01 -5.73262572e-01
-6.31871343e-01 -5.55109262e-01 5.16600311e-01 3.64284366e-01
-5.76866090e-01 9.43357348e-01 -1.55745804e-01 -3.99961412e-01
8.10159668e-02 -7.43024766e-01 -9.50936139e-01 -9.33179617e-01
3.49082202e-02 8.80038738e-01 -7.67350942e-02 -2.38755494... | [9.035883903503418, 3.0536816120147705] |
1b74e7a8-0a0c-4361-86ab-b7bad2af1e5a | autoclip-adaptive-gradient-clipping-for | 2007.14469 | null | https://arxiv.org/abs/2007.14469v1 | https://arxiv.org/pdf/2007.14469v1.pdf | AutoClip: Adaptive Gradient Clipping for Source Separation Networks | Clipping the gradient is a known approach to improving gradient descent, but requires hand selection of a clipping threshold hyperparameter. We present AutoClip, a simple method for automatically and adaptively choosing a gradient clipping threshold, based on the history of gradient norms observed during training. Expe... | ['Bryan Pardo', 'Prem Seetharaman', 'Jonathan Le Roux', 'Gordon Wichern'] | 2020-07-25 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [-2.30745837e-01 -4.22384650e-01 -2.32848391e-01 -5.41608334e-01
-9.12616313e-01 -7.33653247e-01 8.31452906e-02 4.08496559e-02
-5.17480373e-01 5.90201378e-01 2.10651159e-01 -1.95122644e-01
-2.21273586e-01 -8.45984668e-02 -4.72937018e-01 -6.84612572e-01
-3.69371921e-01 5.63255325e-02 1.54736310e-01 -2.76907414... | [15.381539344787598, 5.62468957901001] |
30af183d-0194-43ca-ba53-f4c058facdb9 | mmg-ego4d-multimodal-generalization-in | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gong_MMG-Ego4D_Multimodal_Generalization_in_Egocentric_Action_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gong_MMG-Ego4D_Multimodal_Generalization_in_Egocentric_Action_Recognition_CVPR_2023_paper.pdf | MMG-Ego4D: Multimodal Generalization in Egocentric Action Recognition | In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG). MMG aims to study how systems can generalize when data from certain modalities is limited or even completely missing. We thoroughly investigate MMG in the context of standard supervised acti... | ['Rakesh Ranjan', 'Zhangyang Wang', 'Yilei Li', 'Jean-Charles Bazin', 'Naina Dhingra', 'Sreyas Mohan', 'Xinyu Gong'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['action-recognition-in-videos'] | ['computer-vision'] | [ 3.36978644e-01 -1.53647915e-01 -4.68929410e-01 -2.84682393e-01
-7.27048397e-01 -3.13474596e-01 6.08390093e-01 -3.11712623e-01
-3.75686586e-01 6.78302705e-01 6.55384004e-01 1.24300569e-01
-1.06952175e-01 -4.64642137e-01 -6.64508104e-01 -7.18904376e-01
-4.45098877e-02 4.19243164e-02 -4.25321832e-02 -1.38770655... | [8.32559585571289, 0.7599012851715088] |
e282dff2-159d-4f24-8247-887763dfb1d0 | token-manipulation-generative-adversarial | 2005.02794 | null | https://arxiv.org/abs/2005.02794v2 | https://arxiv.org/pdf/2005.02794v2.pdf | Token Manipulation Generative Adversarial Network for Text Generation | MaskGAN opens the query for the conditional language model by filling in the blanks between the given tokens. In this paper, we focus on addressing the limitations caused by having to specify blanks to be filled. We decompose conditional text generation problem into two tasks, make-a-blank and fill-in-the-blank, and ex... | ['DaeJin Jo'] | 2020-05-06 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 3.33036095e-01 3.82504821e-01 1.92131903e-02 -7.31553324e-03
-1.20509171e+00 -9.40073729e-01 7.02177584e-01 -1.77716073e-02
-5.19402862e-01 1.12168145e+00 9.54535231e-02 -4.26951379e-01
2.33519763e-01 -8.90470505e-01 -7.97697306e-01 -8.22069108e-01
1.64068609e-01 5.25477171e-01 7.97823742e-02 -2.77386397... | [11.820717811584473, 9.157438278198242] |
838a435f-d038-4732-a31b-b9ec9156d2ba | diverse-parallel-data-synthesis-for-cross | 2210.16613 | null | https://arxiv.org/abs/2210.16613v1 | https://arxiv.org/pdf/2210.16613v1.pdf | Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers | Text-to-SQL parsers typically struggle with databases unseen during the train time. Adapting parsers to new databases is a challenging problem due to the lack of natural language queries in the new schemas. We present ReFill, a framework for synthesizing high-quality and textually diverse parallel datasets for adapting... | ['Sunita Sarawagi', 'Ashutosh Sathe', 'Abhijeet Awasthi'] | 2022-10-29 | null | null | null | null | ['sql-to-text', 'text-to-sql'] | ['computer-code', 'computer-code'] | [ 5.78189015e-01 3.52342337e-01 -1.56338945e-01 -9.97755826e-01
-1.64810944e+00 -1.05799985e+00 4.16290998e-01 4.35861677e-01
-1.90688863e-01 7.99484193e-01 3.56015861e-01 -2.29215279e-01
3.40083569e-01 -1.03343248e+00 -1.39841151e+00 1.48047537e-01
5.25826931e-01 1.23986530e+00 3.28083068e-01 -1.99692085... | [9.958305358886719, 7.908087253570557] |
090cbbf0-8d7a-4ad0-9940-5ed908599dbd | towards-reliable-misinformation-mitigation | 2305.14928 | null | https://arxiv.org/abs/2305.14928v1 | https://arxiv.org/pdf/2305.14928v1.pdf | Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4 | Misinformation poses a critical societal challenge, and current approaches have yet to produce an effective solution. We propose focusing on generalization, soft classification, and leveraging recent large language models to create more practical tools in contexts where perfect predictions remain unattainable. We begin... | ['Reihaneh Rabbany', 'Joel Christoph', 'Caleb Gupta', 'Meilina Reksoprodjo', 'Kellin Pelrine'] | 2023-05-24 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 2.27313593e-01 3.11917901e-01 -8.26846898e-01 -4.77883309e-01
-1.15116441e+00 -6.87293708e-01 9.77478862e-01 4.02119040e-01
7.52608031e-02 8.93319130e-01 5.82018197e-01 -6.96575046e-01
-1.88662574e-01 -8.27910662e-01 -4.45540696e-01 -3.18206936e-01
-1.60394952e-01 3.51586968e-01 -7.59137422e-02 -2.16474131... | [9.688912391662598, 7.830942630767822] |
eda287e4-e173-42a9-acb7-0848de917559 | align-and-attend-network-for-globally-and | 1905.13066 | null | https://arxiv.org/abs/1905.13066v1 | https://arxiv.org/pdf/1905.13066v1.pdf | Align-and-Attend Network for Globally and Locally Coherent Video Inpainting | We propose a novel feed-forward network for video inpainting. We use a set of sampled video frames as the reference to take visible contents to fill the hole of a target frame. Our video inpainting network consists of two stages. The first stage is an alignment module that uses computed homographies between the referen... | ['Joon-Young Lee', 'Dahun Kim', 'Sanghyun Woo', 'In So Kweon', 'KwanYong Park'] | 2019-05-30 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 1.57487690e-01 -9.70974341e-02 -6.86906800e-02 -4.44198074e-03
-4.34046030e-01 -2.40780655e-02 2.72195309e-01 -1.63671702e-01
-1.78753555e-01 7.88191736e-01 3.42782110e-01 3.13553065e-01
1.44446090e-01 -8.73804688e-01 -9.62337196e-01 -5.30421078e-01
-5.63955233e-02 -4.64433804e-02 8.02984774e-01 -1.13309538... | [10.781725883483887, -1.3839846849441528] |
54490bae-fdab-4652-b9e1-2a2d71ceffcd | hybridpoint-point-cloud-registration-based-on | 2303.16526 | null | https://arxiv.org/abs/2303.16526v2 | https://arxiv.org/pdf/2303.16526v2.pdf | HybridPoint: Point Cloud Registration Based on Hybrid Point Sampling and Matching | Patch-to-point matching has become a robust way of point cloud registration. However, previous patch-matching methods employ superpoints with poor localization precision as nodes, which may lead to ambiguous patch partitions. In this paper, we propose a HybridPoint-based network to find more robust and accurate corresp... | ['Shaoyi Du', 'Feng Wen', 'Aixue Ye', 'Runzhao Yao', 'Canhui Tang', 'Yiheng Li'] | 2023-03-29 | null | null | null | null | ['point-cloud-registration', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [-4.67350096e-01 -2.06740797e-01 -3.64603996e-01 -7.40624964e-02
-8.35066438e-01 -4.07621115e-01 5.83194256e-01 2.68196493e-01
6.26998171e-02 1.53659523e-01 -1.05224110e-01 1.79763466e-01
-5.89675754e-02 -9.31211412e-01 -7.56633997e-01 -4.45981532e-01
-1.73221864e-02 6.49353623e-01 5.16232967e-01 -2.33798310... | [7.652700424194336, -2.9128904342651367] |
1dc3e4ea-632a-4a93-9136-b49b5ec1240a | real-time-target-sound-extraction | 2211.02250 | null | https://arxiv.org/abs/2211.02250v3 | https://arxiv.org/pdf/2211.02250v3.pdf | Real-Time Target Sound Extraction | We present the first neural network model to achieve real-time and streaming target sound extraction. To accomplish this, we propose Waveformer, an encoder-decoder architecture with a stack of dilated causal convolution layers as the encoder, and a transformer decoder layer as the decoder. This hybrid architecture uses... | ['Shyamnath Gollakota', 'Takuya Yoshioka', 'Tuochao Chen', 'Malek Itani', 'Justin Chan', 'Bandhav Veluri'] | 2022-11-04 | null | null | null | null | ['streaming-target-sound-extraction', 'target-sound-extraction'] | ['audio', 'audio'] | [ 2.38626570e-01 5.82231805e-02 2.48977333e-01 -3.08486193e-01
-1.14857376e+00 -4.56262618e-01 2.25219533e-01 -3.05565268e-01
-1.52281180e-01 3.78355712e-01 5.13916850e-01 -3.62934977e-01
1.00619704e-01 -5.98840415e-01 -7.18278944e-01 -5.03982663e-01
-2.65673816e-01 -1.68737531e-01 5.55259347e-01 1.25579610... | [15.305461883544922, 5.742098331451416] |
3c2a49c6-1772-4c2b-a1c3-3a2b8a5ea1f3 | cross-modal-place-recognition-in-image | 2307.01047 | null | https://arxiv.org/abs/2307.01047v1 | https://arxiv.org/pdf/2307.01047v1.pdf | Cross-modal Place Recognition in Image Databases using Event-based Sensors | Visual place recognition is an important problem towards global localization in many robotics tasks. One of the biggest challenges is that it may suffer from illumination or appearance changes in surrounding environments. Event cameras are interesting alternatives to frame-based sensors as their high dynamic range enab... | ['Laurent Kneip', 'Huiliang Shang', 'Yifu Wang', 'Jiaxin Wei', 'Xiang Ji'] | 2023-07-03 | null | null | null | null | ['visual-place-recognition', 'retrieval'] | ['computer-vision', 'methodology'] | [ 1.02696285e-01 -6.96944833e-01 -6.32683560e-02 -2.80200750e-01
-8.86377037e-01 -6.62744105e-01 1.01830745e+00 3.16618979e-01
-7.83128679e-01 6.71174586e-01 -1.00783035e-01 2.25254700e-01
-1.21130295e-01 -6.72114611e-01 -8.96185577e-01 -7.30899572e-01
-1.43143624e-01 2.13758081e-01 8.91530395e-01 -2.65442371... | [7.565014362335205, -1.8632770776748657] |
9bc818a1-fb26-4f56-a827-07598ce68ab5 | chinesefoodnet-a-large-scale-image-dataset | 1705.02743 | null | http://arxiv.org/abs/1705.02743v3 | http://arxiv.org/pdf/1705.02743v3.pdf | ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition | In this paper, we introduce a new and challenging large-scale food image
dataset called "ChineseFoodNet", which aims to automatically recognizing
pictured Chinese dishes. Most of the existing food image datasets collected
food images either from recipe pictures or selfie. In our dataset, images of
each food category of... | ['Hua Zhou', 'Liang Diao', 'Dongyan Wang', 'Yu Zhu', 'Xin Chen'] | 2017-05-08 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 1.68859839e-01 -4.54192162e-01 -2.03859862e-02 -5.36874771e-01
-6.43518627e-01 -7.93201447e-01 1.66925266e-01 6.11253142e-01
-4.15851623e-01 1.51291236e-01 3.76463085e-01 2.85471767e-01
4.98483390e-01 -1.14431572e+00 -1.02335048e+00 -8.25998664e-01
-2.68510785e-02 -2.06103414e-01 2.00605690e-02 -1.42243117... | [11.557944297790527, 4.391474723815918] |
4100c9f1-fa70-49ae-9199-631d709e1819 | deep-learning-for-landslide-recognition-in | null | null | https://ieeexplore.ieee.org/document/9159123 | https://ieeexplore.ieee.org/document/9159123 | Deep Learning for Landslide Recognition in Satellite Architecture | Using the optical camera in remote sensing is limited in various environmental conditions. This paper presents a system of combining deep learning and image transform algorithms to detect landslide location in satellite images. In the deep learning part, a convolution neural network is used to classify satellite images... | ['Kyo Tan', 'Clarissa Loh', 'Kai-Yew Lum', 'Pei-Jun Lee', 'Trong-An Bui'] | 2020-08-05 | null | null | null | null | ['landslide-segmentation'] | ['computer-vision'] | [-1.28420874e-01 -8.54186296e-01 1.41092971e-01 -2.30646059e-01
-1.15977742e-01 -4.15595770e-01 2.73421913e-01 -5.48761725e-01
-5.23154140e-01 3.92554373e-01 -2.99242772e-02 -3.53270710e-01
-1.17281417e-03 -1.47382903e+00 -4.15851980e-01 -1.23378801e+00
-2.44164228e-01 -4.72585633e-02 -3.30742262e-02 -2.47076556... | [9.748634338378906, -1.4955689907073975] |
a1800635-1b00-45f3-a808-2b04c0e22b02 | alignment-augmented-consistent-translation | null | null | https://aclanthology.org/2022.acl-long.179 | https://aclanthology.org/2022.acl-long.179.pdf | Alignment-Augmented Consistent Translation for Multilingual Open Information Extraction | Progress with supervised Open Information Extraction (OpenIE) has been primarily limited to English due to the scarcity of training data in other languages. In this paper, we explore techniques to automatically convert English text for training OpenIE systems in other languages. We introduce the Alignment-Augmented Con... | ['Mausam .', 'Soumen Chakrabarti', 'Shubham Mittal', 'Muqeeth Mohammed', 'Keshav Kolluru'] | null | null | null | null | acl-2022-5 | ['open-information-extraction'] | ['natural-language-processing'] | [ 3.77389431e-01 6.89965427e-01 -3.33621621e-01 -3.56240630e-01
-1.29759407e+00 -8.64313006e-01 6.22690797e-01 3.67319882e-02
-3.55275273e-01 1.25619435e+00 3.71284395e-01 -6.54172480e-01
1.54076308e-01 -7.03082979e-01 -8.36364865e-01 -3.41337882e-02
3.78983021e-01 9.52946126e-01 -9.20134112e-02 -5.06291449... | [10.850732803344727, 9.604531288146973] |
0ef75da8-cd12-4dd5-8cb4-0a98150d7a16 | a-hypergraph-partitioned-vertex-programming | 1308.6823 | null | http://arxiv.org/abs/1308.6823v1 | http://arxiv.org/pdf/1308.6823v1.pdf | A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization | In modern data science problems, techniques for extracting value from big
data require performing large-scale optimization over heterogenous, irregularly
structured data. Much of this data is best represented as multi-relational
graphs, making vertex programming abstractions such as those of Pregel and
GraphLab ideal f... | ['Lise Getoor', 'Hui Miao', 'Bert Huang', 'Xiangyang Liu'] | 2013-08-30 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [-3.98179144e-01 2.27665916e-01 -6.93739727e-02 -3.07771683e-01
-1.00629354e+00 -6.65661037e-01 5.69600999e-01 8.11357498e-01
-2.06529856e-01 6.05173588e-01 -4.00196612e-02 -6.40643775e-01
-2.77726650e-01 -1.15248907e+00 -9.19617712e-01 -6.69247866e-01
-2.87226081e-01 1.51374090e+00 3.51272643e-01 -9.06878412... | [7.084588527679443, 5.188004970550537] |
7983afd3-12b5-445c-9eef-6a55fcd4dc42 | modeling-composite-labels-for-neural | 1810.08815 | null | http://arxiv.org/abs/1810.08815v1 | http://arxiv.org/pdf/1810.08815v1.pdf | Modeling Composite Labels for Neural Morphological Tagging | Neural morphological tagging has been regarded as an extension to POS tagging
task, treating each morphological tag as a monolithic label and ignoring its
internal structure. We propose to view morphological tags as composite labels
and explicitly model their internal structure in a neural sequence tagger. For
this, we... | ['Kairit Sirts', 'Alexander Tkachenko'] | 2018-10-20 | modeling-composite-labels-for-neural-1 | https://aclanthology.org/K18-1036 | https://aclanthology.org/K18-1036.pdf | conll-2018-10 | ['morphological-tagging'] | ['natural-language-processing'] | [ 1.34921446e-01 3.48559290e-01 -3.07873994e-01 -6.67092144e-01
-5.80510199e-01 -1.27248800e+00 5.55177510e-01 3.95502031e-01
-9.18809593e-01 6.44645154e-01 3.84719759e-01 -8.09928000e-01
4.76840526e-01 -6.64253712e-01 -4.94578481e-01 -5.28492570e-01
-1.30587682e-01 7.64423966e-01 2.42034793e-01 7.68987909... | [10.332379341125488, 10.01552677154541] |
8e44d24b-6bc2-44a8-adf4-0f617b7ce28e | how-do-deepfakes-move-motion-magnification | 2212.14033 | null | https://arxiv.org/abs/2212.14033v1 | https://arxiv.org/pdf/2212.14033v1.pdf | How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection | With the proliferation of deep generative models, deepfakes are improving in quality and quantity everyday. However, there are subtle authenticity signals in pristine videos, not replicated by SOTA GANs. We contrast the movement in deepfakes and authentic videos by motion magnification towards building a generalized de... | ['Ilke Demir', 'Umur Aybars Ciftci'] | 2022-12-28 | null | null | null | null | ['face-swapping', 'motion-magnification'] | ['computer-vision', 'computer-vision'] | [ 5.68871081e-01 2.34410688e-01 -1.03508040e-01 9.25946012e-02
-5.57484031e-01 -8.66718411e-01 7.72867501e-01 -8.76212537e-01
2.14081481e-01 5.62730789e-01 5.84416628e-01 1.96119949e-01
3.29948574e-01 -6.52569413e-01 -7.59943604e-01 -7.88948596e-01
3.97977903e-02 -2.19284117e-01 7.24622980e-02 -9.87260193... | [12.439562797546387, 1.0407772064208984] |
c68e3f8a-b59b-4887-a09c-bb82ad3ea446 | face-to-bmi-using-computer-vision-to-infer | 1703.03156 | null | http://arxiv.org/abs/1703.03156v1 | http://arxiv.org/pdf/1703.03156v1.pdf | Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media | A person's weight status can have profound implications on their life,
ranging from mental health, to longevity, to financial income. At the societal
level, "fat shaming" and other forms of "sizeism" are a growing concern, while
increasing obesity rates are linked to ever raising healthcare costs. For these
reasons, re... | ['Antonio Torralba', 'Ferda Ofli', 'Javier Marin', 'Mustafa Camurcu', 'Ingmar Weber', 'Yusuf Aytar', 'Enes Kocabey'] | 2017-03-09 | null | null | null | null | ['body-mass-index-bmi-prediction'] | ['computer-vision'] | [ 1.42510831e-01 3.98052037e-01 -6.53879404e-01 -4.96148795e-01
-4.57159132e-02 3.86362262e-02 -1.14855163e-01 8.77977908e-01
-4.75176603e-01 5.74701965e-01 3.99871558e-01 -3.71439576e-01
2.81454146e-01 -1.12544250e+00 8.36943369e-03 -3.13673079e-01
-4.57095690e-02 1.24253131e-01 -2.75057644e-01 -3.03405970... | [8.28907299041748, 5.439453125] |
0d80e414-80bc-4b12-8cb1-4c4674193940 | otter-a-multi-modal-model-with-in-context | 2305.03726 | null | https://arxiv.org/abs/2305.03726v1 | https://arxiv.org/pdf/2305.03726v1.pdf | Otter: A Multi-Modal Model with In-Context Instruction Tuning | Large language models (LLMs) have demonstrated significant universal capabilities as few/zero-shot learners in various tasks due to their pre-training on vast amounts of text data, as exemplified by GPT-3, which boosted to InstrctGPT and ChatGPT, effectively following natural language instructions to accomplish real-wo... | ['Ziwei Liu', 'Jingkang Yang', 'Jinghao Wang', 'Liangyu Chen', 'Yuanhan Zhang', 'Bo Li'] | 2023-05-05 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-2.66517907e-01 -1.80308372e-01 -5.86547613e-01 -5.43661118e-01
-8.37761402e-01 -3.31507176e-01 4.11988199e-01 -1.10591725e-01
-7.22386658e-01 3.84849995e-01 1.82343856e-01 -1.20225847e+00
3.76037925e-01 -9.91237938e-01 -9.70171809e-01 -1.58417195e-01
-5.75325191e-02 4.88612801e-01 3.79141152e-01 -7.64199972... | [10.620774269104004, 8.370896339416504] |
a3d7f3da-1695-449f-bfc5-b663915afa65 | communication-efficient-federated-2 | 2302.04969 | null | https://arxiv.org/abs/2302.04969v3 | https://arxiv.org/pdf/2302.04969v3.pdf | Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation | Federated bilevel optimization has attracted increasing attention due to emerging machine learning and communication applications. The biggest challenge lies in computing the gradient of the upper-level objective function (i.e., hypergradient) in the federated setting due to the nonlinear and distributed construction o... | ['Kaiyi Ji', 'Peiyao Xiao'] | 2023-02-09 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-7.61790454e-01 -2.43788600e-01 4.64295223e-02 -1.66070133e-01
-9.12349105e-01 -5.50034404e-01 1.87033668e-01 3.03760618e-01
-3.46412033e-01 9.00553763e-01 2.65621454e-01 -5.07551014e-01
-4.12025511e-01 -7.22864211e-01 -6.96726561e-01 -8.86490047e-01
-6.24015868e-01 5.57203829e-01 -3.38434845e-01 -1.19508253... | [6.241917133331299, 5.000510215759277] |
969d8608-d7db-4510-9bf3-ad7b050b2dfc | exploiting-discourse-relations-between | null | null | https://aclanthology.org/W12-4702 | https://aclanthology.org/W12-4702.pdf | Exploiting Discourse Relations between Sentences for Text Clustering | null | ['Nik Adilah Hanin Binti Zahri', 'Suguru Matsuyoshi', 'Fumiyo Fukumoto'] | 2012-12-01 | exploiting-discourse-relations-between-1 | https://aclanthology.org/W12-4702 | https://aclanthology.org/W12-4702.pdf | ws-2012-12 | ['text-clustering'] | ['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... | [-7.425197601318359, 3.655822277069092] |
363eefab-bcc9-40e1-b3f6-b78d49264453 | a-fast-palette-reordering-technique-based-on | null | null | https://ieeexplore.ieee.org/document/8451221/authors | https://ieeexplore.ieee.org/document/8451221/authors | A Fast Palette Reordering Technique Based on GPU-Optimized Genetic Algorithms | Color re-indexing is one of main approaches for improving the loss-less compression of color indexed images. Zero-order entropy reduction of indexes matrix is the key to obtain high compression ratio. However, obtaining the optimal re-indexed palette is a challenging problem that cannot be solved by brute-force approac... | ['Sebastiano Battiato', 'Giorgio Grasso', 'Filippo Stanco', 'Dario Allegra', 'Oliver Giudice'] | 2018-09-08 | null | null | null | ieee-international-conference-on-image-11 | ['clustering'] | ['methodology'] | [ 4.03658509e-01 -6.56658173e-01 -1.56482518e-01 1.66155532e-01
-6.21164680e-01 -3.75889659e-01 1.88194051e-01 4.56670284e-01
-6.12901449e-01 5.44666648e-01 -2.42314368e-01 -2.90845156e-01
-3.83593321e-01 -1.05215037e+00 -5.66284955e-01 -8.82542133e-01
-2.70700380e-02 5.03413796e-01 4.26899076e-01 -1.41070902... | [7.708256244659424, 4.691267490386963] |
ac8f9d44-3fce-4cbe-93d2-860483f6c15f | classification-uncertainty-of-deep-neural | 1805.08440 | null | http://arxiv.org/abs/1805.08440v2 | http://arxiv.org/pdf/1805.08440v2.pdf | Classification Uncertainty of Deep Neural Networks Based on Gradient Information | We study the quantification of uncertainty of Convolutional Neural Networks
(CNNs) based on gradient metrics. Unlike the classical softmax entropy, such
metrics gather information from all layers of the CNN. We show for the EMNIST
digits data set that for several such metrics we achieve the same meta
classification acc... | ['Philipp Oberdiek', 'Hanno Gottschalk', 'Matthias Rottmann'] | 2018-05-22 | null | null | null | null | ['known-unknowns'] | ['miscellaneous'] | [-8.86691138e-02 4.34277356e-01 -8.76350626e-02 -7.95221746e-01
-9.54258621e-01 -5.79741418e-01 6.66369855e-01 2.40114048e-01
-6.94018602e-01 1.12996888e+00 -3.09185356e-01 -2.83134490e-01
-2.34583065e-01 -8.56386781e-01 -8.58428657e-01 -7.00108647e-01
-1.77317634e-01 5.77290058e-01 -3.86153124e-02 1.52468324... | [7.615816593170166, 3.781079053878784] |
c911f38e-52e5-4cad-b877-c98a1d161372 | evading-classifiers-in-discrete-domains-with | 1810.10939 | null | https://arxiv.org/abs/1810.10939v3 | https://arxiv.org/pdf/1810.10939v3.pdf | Evading classifiers in discrete domains with provable optimality guarantees | Machine-learning models for security-critical applications such as bot, malware, or spam detection, operate in constrained discrete domains. These applications would benefit from having provable guarantees against adversarial examples. The existing literature on provable adversarial robustness of models, however, exclu... | ['Carmela Troncoso', 'Nikita Samarin', 'Jamie Hayes', 'Bogdan Kulynych'] | 2018-10-25 | null | null | null | null | ['twitter-bot-detection', 'spam-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 3.57412785e-01 6.68554008e-02 -1.43410519e-01 -3.07013184e-01
-7.46733129e-01 -1.42975676e+00 8.12333703e-01 7.05364952e-03
-5.14625490e-01 6.59958780e-01 -5.31929076e-01 -7.69144833e-01
-8.55958611e-02 -1.13758349e+00 -9.32183683e-01 -5.12205720e-01
-4.59109068e-01 2.70913959e-01 8.17474574e-02 -2.64752358... | [5.75607442855835, 7.674707412719727] |
e5ccaef9-4308-4b04-b552-80b7e6c4d6f1 | can-semi-supervised-learning-reduce-the | 2111.04357 | null | https://arxiv.org/abs/2111.04357v4 | https://arxiv.org/pdf/2111.04357v4.pdf | Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification? | In this work, we examine the robustness of state-of-the-art semi-supervised learning (SSL) algorithms when applied to morphological classification in modern radio astronomy. We test whether SSL can achieve performance comparable to the current supervised state of the art when using many fewer labelled data points and i... | ['Anna M. M. Scaife', 'Inigo V. Slijepcevic'] | 2021-11-08 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [ 2.54282713e-01 2.31709078e-01 -3.24270159e-01 -5.00848413e-01
-6.69005215e-01 -6.77523613e-01 9.96165276e-01 1.92558259e-01
-6.61759317e-01 8.87947500e-01 -1.66097045e-01 -5.55556893e-01
-3.25236052e-01 -3.49452943e-01 -3.16147149e-01 -8.25839818e-01
-9.75499600e-02 1.00694501e+00 5.97929657e-01 2.58264784... | [9.421774864196777, 3.066004753112793] |
b349df66-1f0b-4318-ba1c-1163eb395505 | differentiable-genetic-programming-for-high | 2304.08915 | null | https://arxiv.org/abs/2304.08915v1 | https://arxiv.org/pdf/2304.08915v1.pdf | Differentiable Genetic Programming for High-dimensional Symbolic Regression | Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable machine learning (ML). Genetic programming (GP) has been the dominator in solving SR problems. However, as the scale of SR problems increases, ... | ['Jiancheng Lv', 'Mengjie Zhang', 'Yanan sun', 'Yuwei Ou', 'Andrew Lensen', 'Xiaotian Song', 'Peng Zeng'] | 2023-04-18 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 1.53165489e-01 1.12114303e-01 -2.87898570e-01 -2.13740170e-01
-6.05510712e-01 -8.72752964e-02 1.40788212e-01 -1.94720402e-01
8.76747221e-02 9.81771231e-01 -1.98648065e-01 -2.34672278e-01
-4.73568469e-01 -9.29808140e-01 -6.42194092e-01 -1.00606740e+00
-1.91652164e-01 5.15675247e-01 -2.47379914e-01 -3.43879372... | [7.984201908111572, 3.6745736598968506] |
822de508-a96a-4f92-9de0-3c4ae028ffae | meta-compositional-referring-expression | 2304.04415 | null | https://arxiv.org/abs/2304.04415v3 | https://arxiv.org/pdf/2304.04415v3.pdf | Meta Compositional Referring Expression Segmentation | Referring expression segmentation aims to segment an object described by a language expression from an image. Despite the recent progress on this task, existing models tackling this task may not be able to fully capture semantics and visual representations of individual concepts, which limits their generalization capab... | ['Jun Liu', 'Ying Sun', 'Zehuan Yuan', 'Xindi Shang', 'Mark He Huang', 'Li Xu'] | 2023-04-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Meta_Compositional_Referring_Expression_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Meta_Compositional_Referring_Expression_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['referring-expression', 'referring-expression-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.61556995e-01 3.49229411e-03 -4.43768591e-01 -4.54581290e-01
-6.66765809e-01 -4.16961193e-01 2.84359485e-01 1.02228619e-01
-2.00713560e-01 3.55316371e-01 -3.64437997e-01 7.63066038e-02
1.97789654e-01 -9.01817501e-01 -8.55609953e-01 -5.42545259e-01
2.19744816e-01 3.94153804e-01 2.25549206e-01 -1.20560557... | [9.844697952270508, 1.475702166557312] |
8598b1e1-21d2-47d6-a25f-d0c0c8d51962 | awesome-gpu-memory-constrained-long-document | 2305.14806 | null | https://arxiv.org/abs/2305.14806v1 | https://arxiv.org/pdf/2305.14806v1.pdf | AWESOME: GPU Memory-constrained Long Document Summarization using Memory Mechanism and Global Salient Content | Long document summarization systems are critical for domains with lengthy and jargonladen text, yet they present significant challenges to researchers and developers with limited computing resources. Existing solutions mainly focus on efficient attentions or divide-and-conquer strategies. The former reduces theoretical... | ['Lu Wang', 'Shuyang Cao'] | 2023-05-24 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 3.04746389e-01 1.42262341e-03 -5.62802911e-01 -1.17136717e-01
-1.13176465e+00 -6.87068403e-01 5.76553464e-01 7.85579681e-01
-1.13881513e-01 9.14268196e-01 9.99067724e-01 -9.90858302e-02
8.96611996e-03 -5.11668801e-01 -2.77185619e-01 -5.61079621e-01
1.73271731e-01 2.25679472e-01 2.49787971e-01 -7.74100870... | [12.561872482299805, 9.501020431518555] |
764a015c-998c-41c6-8fdd-4cfc137634f6 | efficient-hyperparameter-optimization-of-deep | 1607.08316 | null | http://arxiv.org/abs/1607.08316v2 | http://arxiv.org/pdf/1607.08316v2.pdf | Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates | Automatically searching for optimal hyperparameter configurations is of
crucial importance for applying deep learning algorithms in practice. Recently,
Bayesian optimization has been proposed for optimizing hyperparameters of
various machine learning algorithms. Those methods adopt probabilistic
surrogate models like G... | ['Jiashi Feng', 'Ilija Ilievski', 'Taimoor Akhtar', 'Christine Annette Shoemaker'] | 2016-07-28 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-6.78277969e-01 -1.09142907e-01 -1.76607873e-02 -3.60314369e-01
-1.01227474e+00 -3.57508212e-01 3.27439249e-01 1.37232348e-01
-8.56563330e-01 1.08276224e+00 -3.22657734e-01 -2.41072625e-01
-5.87861896e-01 -7.35653400e-01 -7.19999909e-01 -1.37753856e+00
1.53166384e-01 1.05742538e+00 9.79025289e-02 3.71811211... | [6.859766006469727, 3.9063565731048584] |
e7b8364f-a748-429c-abe2-1db488f024b6 | efficient-personalized-learning-for-wearable | 2208.01095 | null | https://arxiv.org/abs/2208.01095v1 | https://arxiv.org/pdf/2208.01095v1.pdf | Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing | Health monitoring applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings. Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for beha... | ['Amir M. Rahmani', 'Nikil Dutt', 'Mohsen Imani', 'Emad Kasaeyan Naeini', 'Hamidreza Alikhani', 'Yang Ni', 'Sina Shahhosseini'] | 2022-08-01 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 1.75039440e-01 3.42721641e-02 -4.19360518e-01 -5.61537504e-01
-1.40794367e-01 -4.08019096e-01 -3.63756210e-01 3.14672559e-01
-6.02711380e-01 5.82342446e-01 -1.53649217e-02 -3.00573826e-01
-1.97335422e-01 -6.29015386e-01 -4.97476250e-01 -5.58272898e-01
-2.75532663e-01 -1.16151609e-01 -4.00185764e-01 4.46272284... | [6.061702251434326, 6.185114860534668] |
74103b94-00f8-4ed6-81d2-dcab2a7ad8b7 | fuzzy-clustering-of-ordinal-time-series-based | 2304.12249 | null | https://arxiv.org/abs/2304.12249v1 | https://arxiv.org/pdf/2304.12249v1.pdf | Fuzzy clustering of ordinal time series based on two novel distances with economic applications | Time series clustering is a central machine learning task with applications in many fields. While the majority of the methods focus on real-valued time series, very few works consider series with discrete response. In this paper, the problem of clustering ordinal time series is addressed. To this aim, two novel distanc... | ['José Antonio Vilar', 'Christian Weiss', 'Ángel López Oriona'] | 2023-04-24 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 1.68815911e-01 -5.00710070e-01 1.00123711e-01 -3.05348426e-01
-4.13522124e-01 -7.82864153e-01 7.54637420e-01 6.51656210e-01
-6.06969178e-01 7.43241012e-01 -1.64636686e-01 -6.27247617e-02
-7.72840261e-01 -9.03357029e-01 -5.25570251e-02 -8.60749304e-01
-7.21093357e-01 6.51200473e-01 -7.63342110e-03 -1.72551453... | [7.188952922821045, 3.375653028488159] |
501b1672-8e6f-413e-a289-eefb42b2ea2f | team-ufal-at-cmcl-2022-shared-task-figuring | 2204.04998 | null | https://arxiv.org/abs/2204.04998v1 | https://arxiv.org/pdf/2204.04998v1.pdf | Team ÚFAL at CMCL 2022 Shared Task: Figuring out the correct recipe for predicting Eye-Tracking features using Pretrained Language Models | Eye-Tracking data is a very useful source of information to study cognition and especially language comprehension in humans. In this paper, we describe our systems for the CMCL 2022 shared task on predicting eye-tracking information. We describe our experiments with pretrained models like BERT and XLM and the different... | ['Ondrej Bojar', 'Rishu Kumar', 'Sunit Bhattacharya'] | 2022-04-11 | null | https://aclanthology.org/2022.cmcl-1.15 | https://aclanthology.org/2022.cmcl-1.15.pdf | cmcl-acl-2022-5 | ['pretrained-multilingual-language-models'] | ['natural-language-processing'] | [-3.99623692e-01 2.12710351e-01 2.86330462e-01 -5.34214079e-01
-5.53412378e-01 -3.69334817e-01 8.49507391e-01 3.49720418e-01
-1.05898583e+00 6.08449697e-01 2.60664135e-01 -6.04064047e-01
1.59165755e-01 -8.00819471e-02 -6.29066944e-01 -2.04936624e-01
4.54831533e-02 1.82542220e-01 5.18017232e-01 -3.21966290... | [10.963750839233398, 9.591882705688477] |
c0720978-3e93-408a-b7be-93b8dc76bcc6 | centralized-control-for-multi-agent-rl-in-a | 2304.13004 | null | https://arxiv.org/abs/2304.13004v1 | https://arxiv.org/pdf/2304.13004v1.pdf | Centralized control for multi-agent RL in a complex Real-Time-Strategy game | Multi-agent Reinforcement learning (MARL) studies the behaviour of multiple learning agents that coexist in a shared environment. MARL is more challenging than single-agent RL because it involves more complex learning dynamics: the observations and rewards of each agent are functions of all other agents. In the context... | ['Roger Creus Castanyer'] | 2023-04-25 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-3.58648241e-01 1.35464063e-02 3.90596711e-03 1.81531638e-01
-6.25648797e-01 -7.90069163e-01 2.87912220e-01 7.07213655e-02
-8.92281651e-01 1.20084500e+00 -2.36395895e-01 -8.06424394e-02
-4.04971957e-01 -5.11706352e-01 -5.13164580e-01 -8.99298251e-01
-5.51421583e-01 1.11110485e+00 1.26663372e-01 -7.78407931... | [3.7082371711730957, 1.8886849880218506] |
41dc954f-d61a-4b37-a5d0-63a1b5c746c2 | ofdm-based-massive-connectivity-for-leo | 2210.17355 | null | https://arxiv.org/abs/2210.17355v1 | https://arxiv.org/pdf/2210.17355v1.pdf | OFDM-Based Massive Connectivity for LEO Satellite Internet of Things | Low earth orbit (LEO) satellite has been considered as a potential supplement for the terrestrial Internet of Things (IoT). In this paper, we consider grant-free non-orthogonal random access (GF-NORA) in orthogonal frequency division multiplexing (OFDM) system to increase access capacity and reduce access latency for L... | ['Xiaojun Yuan', 'Shaojie Ni', 'Sixian Li', 'Mingchen Zhang', 'Mingyang Yue', 'Yong Zuo'] | 2022-10-31 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 1.93968974e-02 -1.97011143e-01 -5.93506396e-01 3.26592863e-01
-2.50387877e-01 -2.87060022e-01 2.64573634e-01 -3.90554786e-01
-1.31859258e-01 1.01466227e+00 -1.30167501e-02 -6.30029380e-01
-2.55593270e-01 -7.66638756e-01 -1.74233332e-01 -1.25695431e+00
-7.71766961e-01 2.02349052e-01 -1.18585423e-01 7.25074112... | [6.2015838623046875, 1.4355908632278442] |
57808874-3153-4414-93bf-0eefd045b6ea | a-review-of-intelligent-music-generation | 2211.09124 | null | https://arxiv.org/abs/2211.09124v2 | https://arxiv.org/pdf/2211.09124v2.pdf | A Review of Intelligent Music Generation Systems | Intelligent music generation, one of the most popular subfields of computer creativity, can lower the creative threshold for non-specialists and increase the efficiency of music creation. In the last five years, the quality of algorithm-based automatic music generation has increased significantly, motivated by the use ... | ['Qidi Wu', 'Lei Wang', 'Yi Qin', 'Maoqing Zhang', 'Junwei Pang', 'Song Li', 'Hanwei Liu', 'Ziyi Zhao'] | 2022-11-16 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 4.51923072e-01 -9.40890983e-02 -2.04644933e-01 2.55658418e-01
-1.38646960e-01 -9.51146901e-01 5.22639692e-01 -3.35898489e-01
3.68927536e-03 7.16437101e-01 3.10296476e-01 2.59772032e-01
-8.36158872e-01 -9.40324187e-01 -1.32193580e-01 -7.15518594e-01
1.29157513e-01 6.72518015e-01 -4.41314578e-01 -5.33282042... | [16.09590721130371, 5.453998565673828] |
28ebc0ee-7743-43ab-b61c-4badf8b2f0ef | cascaded-fast-and-slow-models-for-efficient-1 | 2110.07811 | null | https://arxiv.org/abs/2110.07811v1 | https://arxiv.org/pdf/2110.07811v1.pdf | Cascaded Fast and Slow Models for Efficient Semantic Code Search | The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query. Existing approaches are neither effective nor efficient enough towards a practical semantic code search system. In this paper, we propose an efficient and ... | ['Steven C. H. Hoi', 'Shafiq Joty', 'Junnan Li', 'Akhilesh Deepak Gotmare'] | 2021-10-15 | cascaded-fast-and-slow-models-for-efficient | https://openreview.net/forum?id=Ysu4E5DhQIw | https://openreview.net/pdf?id=Ysu4E5DhQIw | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.38826519e-01 -3.95651966e-01 -2.93225169e-01 -3.59912515e-01
-1.30649590e+00 -5.44322252e-01 4.36051995e-01 4.71334696e-01
-4.53720152e-01 2.21191701e-02 1.82132915e-01 -3.93818051e-01
-3.23457271e-01 -6.40319347e-01 -8.43051195e-01 -2.85245776e-01
1.45080527e-02 6.11603200e-01 7.09621727e-01 -2.08257183... | [7.503371238708496, 8.080403327941895] |
7015ec11-d1a8-46a8-9bc5-3c39fb4a988b | stacked-conditional-generative-adversarial | 1712.02478 | null | http://arxiv.org/abs/1712.02478v1 | http://arxiv.org/pdf/1712.02478v1.pdf | Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal | Understanding shadows from a single image spontaneously derives into two
types of task in previous studies, containing shadow detection and shadow
removal. In this paper, we present a multi-task perspective, which is not
embraced by any existing work, to jointly learn both detection and removal in
an end-to-end fashion... | ['Jifeng Wang', 'Jian Yang', 'Le Hui', 'Xiang Li'] | 2017-12-07 | stacked-conditional-generative-adversarial-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Stacked_Conditional_Generative_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Stacked_Conditional_Generative_CVPR_2018_paper.pdf | cvpr-2018-6 | ['shadow-removal', 'shadow-detection'] | ['computer-vision', 'computer-vision'] | [ 8.51503730e-01 1.05684116e-01 5.14120519e-01 -3.17346901e-01
-7.70541906e-01 -4.07427549e-01 6.59577310e-01 -6.26549840e-01
8.19429662e-03 8.36644113e-01 1.00189097e-01 -2.72260785e-01
2.02106386e-01 -6.39073789e-01 -9.91416156e-01 -1.29415834e+00
2.07039922e-01 2.43403658e-01 5.36574304e-01 -1.80448517... | [10.851860046386719, -4.108225345611572] |
bec1222d-841e-4290-ab87-8ed12dbd7ce5 | deep-learning-for-event-based-vision-a | 2302.08890 | null | https://arxiv.org/abs/2302.08890v1 | https://arxiv.org/pdf/2302.08890v1.pdf | Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks | Event cameras are bio-inspired sensors that capture the per-pixel intensity changes asynchronously and produce event streams encoding the time, pixel position, and polarity (sign) of the intensity changes. Event cameras possess a myriad of advantages over canonical frame-based cameras, such as high temporal resolution,... | ['Lin Wang', 'DaCheng Tao', 'Weiming Zhang', 'Tianbo Pan', 'Tongyan Hua', 'Yunfan Lu', 'Yexin Liu', 'Xu Zheng'] | 2023-02-17 | null | null | null | null | ['object-recognition', 'event-based-vision'] | ['computer-vision', 'computer-vision'] | [ 5.19355595e-01 -6.57665908e-01 -1.74697042e-01 -1.36468247e-01
-2.53277749e-01 -3.23730767e-01 6.50526047e-01 -7.87916556e-02
-3.69359344e-01 5.85013807e-01 1.33851573e-01 9.19932202e-02
-1.34459838e-01 -5.37179768e-01 -5.53686440e-01 -9.76742685e-01
-1.66741505e-01 -4.98354644e-01 3.40689242e-01 2.52605230... | [8.57439136505127, -1.2895373106002808] |
79030e4f-82c3-430e-8295-3e19d2c7722b | deep-pneumonia-attention-based-contrastive | 2207.11393 | null | https://arxiv.org/abs/2207.11393v1 | https://arxiv.org/pdf/2207.11393v1.pdf | Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays | Computer-aided X-ray pneumonia lesion recognition is important for accurate diagnosis of pneumonia. With the emergence of deep learning, the identification accuracy of pneumonia has been greatly improved, but there are still some challenges due to the fuzzy appearance of chest X-rays. In this paper, we propose a deep l... | ['YongJie Li', 'Xianshi Zhang', 'Haohan Bai', 'Xinxu Wei'] | 2022-07-23 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 3.16570282e-01 -3.29049587e-01 -2.00399771e-01 -3.14470738e-01
-8.30514789e-01 9.37385783e-02 -1.04456365e-01 -1.09611049e-01
-2.42176846e-01 3.51269066e-01 1.89655706e-01 -1.42654911e-01
-5.01919746e-01 -6.15063429e-01 -4.43001091e-01 -1.03687513e+00
2.13009089e-01 5.62583327e-01 3.94976363e-02 3.53797704... | [15.406750679016113, -1.8617359399795532] |
78d0aa7d-1ec9-4a95-9560-a78e0f95354f | breaking-immutable-information-coupled | 2211.14782 | null | https://arxiv.org/abs/2211.14782v1 | https://arxiv.org/pdf/2211.14782v1.pdf | Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection | Few-shot object detection, expecting detectors to detect novel classes with a few instances, has made conspicuous progress. However, the prototypes extracted by existing meta-learning based methods still suffer from insufficient representative information and lack awareness of query images, which cannot be adaptively t... | ['Kun fu', 'Xian Sun', 'Peijin Wang', 'Junxi Li', 'Yongqiang Mao', 'Wenhui Diao', 'Xiaonan Lu'] | 2022-11-27 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 2.46031567e-01 -2.49494016e-01 -2.90435195e-01 -4.47300881e-01
-6.77204430e-01 -6.88038766e-02 4.04012829e-01 3.20086926e-01
-5.53413391e-01 1.96016446e-01 -2.02958509e-01 3.77094895e-01
3.21206786e-02 -6.41214550e-01 -5.78735113e-01 -7.81998873e-01
4.22828235e-02 -7.17826411e-02 1.19781196e+00 -2.16205716... | [9.415445327758789, 1.4870725870132446] |
8279f6b3-4375-4ca1-86d8-68b0e3b2fa3b | unsupervised-person-re-identification-with-1 | 2108.06938 | null | https://arxiv.org/abs/2108.06938v2 | https://arxiv.org/pdf/2108.06938v2.pdf | Unsupervised Person Re-identification with Stochastic Training Strategy | Unsupervised person re-identification (re-ID) has attracted increasing research interests because of its scalability and possibility for real-world applications. State-of-the-art unsupervised re-ID methods usually follow a clustering-based strategy, which generates pseudo labels by clustering and maintains a memory to ... | ['Bo Du', 'Yutian Lin', 'Tianyang Liu'] | 2021-08-16 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.63854942e-01 -4.22991872e-01 -4.68133353e-02 -5.37976742e-01
-2.35660329e-01 -2.06296131e-01 4.81681913e-01 2.26528391e-01
-7.45864391e-01 5.26854694e-01 -1.05607465e-01 6.17161989e-01
-2.14583933e-01 -8.90625298e-01 -3.46218586e-01 -9.27147686e-01
2.72628307e-01 5.60618997e-01 2.81430215e-01 2.61185974... | [14.864373207092285, 1.1435987949371338] |
010df8d9-eeb0-463c-a843-9b5f8e297027 | question-answering-over-knowledge-base-using-1 | 2010.08883 | null | https://arxiv.org/abs/2010.08883v1 | https://arxiv.org/pdf/2010.08883v1.pdf | Question Answering over Knowledge Base using Language Model Embeddings | Knowledge Base, represents facts about the world, often in some form of subsumption ontology, rather than implicitly, embedded in procedural code, the way a conventional computer program does. While there is a rapid growth in knowledge bases, it poses a challenge of retrieving information from them. Knowledge Base Ques... | ['Rekabdar Banafsheh', 'Sai Sharath Japa'] | 2020-10-17 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.93354005e-01 3.04201096e-01 -2.53304154e-01 -2.36872986e-01
-7.78462410e-01 -7.27761507e-01 5.60491920e-01 3.66701186e-01
-4.60341454e-01 6.59530461e-01 4.22708005e-01 -5.49768865e-01
-3.58908534e-01 -1.54421103e+00 -8.12642515e-01 -6.43155500e-02
1.78789228e-01 5.83354115e-01 7.56597936e-01 -6.92080319... | [10.37747573852539, 7.957647323608398] |
b29ffb77-60c3-4614-8df0-bfa9c007903d | model-assisted-probabilistic-safe-adaptive | 2307.00828 | null | https://arxiv.org/abs/2307.00828v1 | https://arxiv.org/pdf/2307.00828v1.pdf | Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning | Breaking safety constraints in control systems can lead to potential risks, resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even among similar tasks. In this paper, we develop a novel adaptive safe control framework that integrates meta learning, Bayesian models, and contr... | ['Shiping Wen', 'TingWen Huang', 'Yuting Cao', 'Yin Yang', 'Ke Li', 'Shengbo Wang'] | 2023-07-03 | null | null | null | null | ['meta-learning', 'safe-exploration'] | ['methodology', 'robots'] | [ 2.55184203e-01 6.28698096e-02 -4.93281990e-01 -2.84235835e-01
-9.25428629e-01 -2.54390955e-01 4.74925965e-01 1.83760598e-01
-4.65003490e-01 1.07051301e+00 -1.03608891e-01 -4.76148039e-01
-6.91279590e-01 -7.68838346e-01 -9.71697271e-01 -6.97904050e-01
-1.95075423e-01 2.71964334e-02 2.74170399e-01 -8.53611752... | [4.613908767700195, 2.2151482105255127] |
2da7fe65-9a39-4c84-bc52-42633bbcded8 | cross-modal-common-representation-learning | 2202.07901 | null | https://arxiv.org/abs/2202.07901v2 | https://arxiv.org/pdf/2202.07901v2.pdf | Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition | Cross-modal representation learning learns a shared embedding between two or more modalities to improve performance in a given task compared to using only one of the modalities. Cross-modal representation learning from different data types -- such as images and time-series data (e.g., audio or text data) -- requires a ... | ['Christopher Mutschler', 'Bernd Bischl', 'Lucas Heublein', 'David Rügamer', 'Felix Ott'] | 2022-02-16 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 5.39339185e-01 -1.41502708e-01 5.94364805e-03 -5.63547373e-01
-1.17276978e+00 -6.84165895e-01 6.48506939e-01 5.70638180e-02
-3.53120953e-01 3.78570110e-01 -2.60943230e-02 4.02779803e-02
-3.82662535e-01 -5.77674687e-01 -7.00185955e-01 -7.61304319e-01
-2.59007104e-02 8.32640529e-02 -2.94264913e-01 1.21371411... | [10.330796241760254, 1.1311416625976562] |
9bb2f8fe-c7ea-43a1-aa0a-ba6c9dd7a03d | mabsplit-faster-forest-training-using-multi | 2212.07473 | null | https://arxiv.org/abs/2212.07473v1 | https://arxiv.org/pdf/2212.07473v1.pdf | MABSplit: Faster Forest Training Using Multi-Armed Bandits | Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods. At the core of our algorithm is a novel node-splitting subroutine... | ['Martin Jinye Zhang', 'Ilan Shomorony', 'Chris Piech', 'Sebastian Thrun', 'Je-Yong Lee', 'Ryan Kang', 'Mo Tiwari'] | 2022-12-14 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.99178720e-01 -2.29141071e-01 -8.33355963e-01 -4.49330688e-01
-1.01743281e+00 -6.98519707e-01 4.07515556e-01 2.05584709e-02
-3.08374882e-01 1.18570006e+00 -1.69751391e-01 -8.36485326e-01
-3.58214825e-01 -1.09991360e+00 -7.83086479e-01 -8.19565177e-01
-2.52745319e-02 8.09011698e-01 3.10907781e-01 3.10626388... | [8.307525634765625, 4.491056442260742] |
c7c6ebf0-5885-4152-a015-46695d13a7ce | uppsala-university-at-semeval-2022-task-1-can | null | null | https://aclanthology.org/2022.semeval-1.10 | https://aclanthology.org/2022.semeval-1.10.pdf | Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary? | We present the Uppsala University system for SemEval-2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE). We explore the performance of multilingual reverse dictionaries as well as the possibility of utilizing annotated data in other languages to improve the quality of a reverse dictionary in the target la... | ['Sara Stymne', 'Rafal Cerniavski'] | null | null | null | null | semeval-naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [-3.81076425e-01 -2.41396904e-01 -4.32306975e-01 -1.76784977e-01
-1.03140831e+00 -1.00246394e+00 9.03366089e-01 2.24816769e-01
-1.14610279e+00 1.06388688e+00 6.96200013e-01 -7.27859974e-01
4.81430084e-01 -6.53214455e-01 -7.21495450e-01 -1.28138408e-01
3.78605843e-01 9.66632664e-01 -1.69176444e-01 -8.14320683... | [11.06541633605957, 10.035067558288574] |
084b3ed3-53fd-46ec-8d68-2f3bd80d4cd7 | visfusion-visibility-aware-online-3d-scene | 2304.10687 | null | https://arxiv.org/abs/2304.10687v1 | https://arxiv.org/pdf/2304.10687v1.pdf | VisFusion: Visibility-aware Online 3D Scene Reconstruction from Videos | We propose VisFusion, a visibility-aware online 3D scene reconstruction approach from posed monocular videos. In particular, we aim to reconstruct the scene from volumetric features. Unlike previous reconstruction methods which aggregate features for each voxel from input views without considering its visibility, we ai... | ['Miaomiao Liu', 'Wei Mao', 'Huiyu Gao'] | 2023-04-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gao_VisFusion_Visibility-Aware_Online_3D_Scene_Reconstruction_From_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gao_VisFusion_Visibility-Aware_Online_3D_Scene_Reconstruction_From_Videos_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 9.09793004e-02 -1.38562769e-02 -3.76837701e-02 -3.87745380e-01
-7.82545865e-01 -4.29677248e-01 4.94875610e-01 1.25120908e-01
7.64138699e-02 5.24399519e-01 4.10001069e-01 1.02883056e-01
-4.35085744e-02 -1.04795301e+00 -9.16696310e-01 -5.82108557e-01
1.50399685e-01 5.56333303e-01 4.14925247e-01 3.12094420... | [8.898693084716797, -2.8959898948669434] |
879454be-2ef5-4bca-ae59-fe213caf2402 | openmedia-open-source-medical-image-analysis | 2208.05616 | null | https://arxiv.org/abs/2208.05616v2 | https://arxiv.org/pdf/2208.05616v2.pdf | OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms | In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms. Various medical image analysis methods, including 2D/3D medical image classification, segmentation, localisa... | ['Tong Zhang', 'Jie Chen', 'Ge Li', 'Wei Gao', 'Yue Yu', 'Jiancong Chen', 'Yang Yang', 'Xiansong Huang', 'Jia-Xin Zhuang'] | 2022-08-11 | null | null | null | null | ['medical-image-detection'] | ['computer-vision'] | [-4.82193053e-01 -2.63618648e-01 -3.30945961e-02 1.06012128e-01
-4.07430291e-01 -8.08674172e-02 1.52297214e-01 2.44778749e-02
-4.53031957e-01 3.44825268e-01 -3.90069693e-01 -4.19830531e-01
2.09946573e-01 -8.04915011e-01 -1.57074660e-01 -1.04189873e+00
-2.09213078e-01 6.09528184e-01 1.53296113e-01 2.36805975... | [14.590896606445312, -2.5331521034240723] |
8f00a5e9-d469-47b9-a4ed-eb1442ef5579 | itreepack-protein-complex-side-chain-packing | 1504.05467 | null | http://arxiv.org/abs/1504.05467v1 | http://arxiv.org/pdf/1504.05467v1.pdf | iTreePack: Protein Complex Side-Chain Packing by Dual Decomposition | Protein side-chain packing is a critical component in obtaining the 3D
coordinates of a structure and drug discovery. Single-domain protein side-chain
packing has been thoroughly studied. A major challenge in generalizing these
methods to protein complexes is that they, unlike monomers, often have very
large treewidth,... | [] | 2015-04-21 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 3.71543206e-02 2.43794575e-01 -3.71647179e-01 7.54714459e-02
-3.69859010e-01 -6.70492589e-01 -1.67566136e-01 3.04705322e-01
-1.02613248e-01 1.39532244e+00 3.34828906e-03 -8.06486487e-01
1.22474499e-01 -6.90820217e-01 -9.09945488e-01 -1.15647471e+00
-9.10255164e-02 9.06974912e-01 3.34959567e-01 -2.97963247... | [4.807144641876221, 5.48433780670166] |
63a3a53d-27e7-4ae6-a511-6457152367bc | spectral-data-augmentation-techniques-to | 2004.11989 | null | https://arxiv.org/abs/2004.11989v1 | https://arxiv.org/pdf/2004.11989v1.pdf | Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images | Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present. In-use techniques range from intensity transformations and elastic deformations, to linearly combining existing data points to make new on... | ['Florian Dubost', 'Subhradeep Kayal', 'Marleen de Bruijne', 'Harm A. W. M. Tiddens'] | 2020-04-24 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 9.48046267e-01 2.15442091e-01 -1.06737591e-01 -7.91539550e-02
-7.34378815e-01 -2.50560284e-01 4.87114012e-01 4.74126995e-01
-7.64124930e-01 6.26922429e-01 1.76240683e-01 -1.59398377e-01
-1.18895166e-01 -4.89614874e-01 -1.98074952e-01 -9.16535437e-01
-1.35145739e-01 6.13787770e-01 4.28468019e-01 -1.01060264... | [14.506767272949219, -2.3697967529296875] |
c1489a49-c502-4a85-ac86-88125a7673d1 | a-neural-attention-model-for-urban-air | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/11871 | https://ojs.aaai.org/index.php/AAAI/article/view/11871/11730 | A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations | Urban air pollution has attracted much attention these years for its adverse impacts on human health. While monitoring stations have been established to collect pollutant statistics, the number of stations is very limited due to the high cost. Thus, inferring fine-grained urban air quality information is becoming an es... | ['Linpeng Huang', 'Yanmin Zhu', 'Yanyan Shen', 'Weiyu Cheng'] | 2018-04-26 | null | null | null | aaai-2018-4 | ['air-quality-inference'] | ['miscellaneous'] | [-1.15270965e-01 -6.87738299e-01 -5.89658841e-02 -4.89967883e-01
-7.97321737e-01 -1.19475029e-01 3.77021432e-01 2.32141241e-01
-3.83429736e-01 7.70851970e-01 3.53707820e-01 -3.32627356e-01
-4.15714830e-01 -1.42939174e+00 -6.68843150e-01 -7.75749266e-01
1.77995726e-01 1.18681043e-02 2.67207086e-01 5.04483581... | [6.248444557189941, 2.5300796031951904] |
d6dadf97-a228-4188-9b95-04e3a5ad31c7 | demon-depth-and-motion-network-for-learning | 1612.02401 | null | http://arxiv.org/abs/1612.02401v2 | http://arxiv.org/pdf/1612.02401v2.pdf | DeMoN: Depth and Motion Network for Learning Monocular Stereo | In this paper we formulate structure from motion as a learning problem. We
train a convolutional network end-to-end to compute depth and camera motion
from successive, unconstrained image pairs. The architecture is composed of
multiple stacked encoder-decoder networks, the core part being an iterative
network that is a... | ['Thomas Brox', 'Huizhong Zhou', 'Benjamin Ummenhofer', 'Nikolaus Mayer', 'Alexey Dosovitskiy', 'Eddy Ilg', 'Jonas Uhrig'] | 2016-12-07 | demon-depth-and-motion-network-for-learning-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Ummenhofer_DeMoN_Depth_and_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Ummenhofer_DeMoN_Depth_and_CVPR_2017_paper.pdf | cvpr-2017-7 | ['depth-and-camera-motion'] | ['computer-vision'] | [ 2.71852225e-01 1.16442703e-01 3.38626131e-02 -4.14015919e-01
-5.46032548e-01 -4.51485306e-01 5.93145967e-01 -1.46204680e-01
-7.32842445e-01 6.71457469e-01 2.36348376e-01 8.91601592e-02
1.91696301e-01 -6.62841678e-01 -9.24546063e-01 -5.61229169e-01
2.54185088e-02 4.40989256e-01 5.96304595e-01 3.85612398... | [8.645414352416992, -2.2379274368286133] |
dfd149de-299d-454a-af25-1087a7176a17 | behavioral-causal-inference | 2305.18916 | null | https://arxiv.org/abs/2305.18916v1 | https://arxiv.org/pdf/2305.18916v1.pdf | Behavioral Causal Inference | When inferring the causal effect of one variable on another from correlational data, a common practice by professional researchers as well as lay decision makers is to control for some set of exogenous confounding variables. Choosing an inappropriate set of control variables can lead to erroneous causal inferences. Thi... | ['Ran Spiegler'] | 2023-05-30 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 2.65832186e-01 2.75720090e-01 -8.85406315e-01 -3.39252114e-01
-2.44195536e-01 -4.47023392e-01 3.22199911e-01 3.95867318e-01
-6.16878092e-01 1.04899836e+00 5.76828837e-01 -9.62878704e-01
-5.05568862e-01 -9.01832521e-01 -7.00390458e-01 -5.88463008e-01
-9.27446112e-02 2.33206838e-01 -2.72289425e-01 4.10313338... | [8.08428955078125, 5.296462535858154] |
bbafbe2b-a722-4cf6-970d-f77992007255 | efficiently-explaining-csps-with-1 | 2303.11712 | null | https://arxiv.org/abs/2303.11712v1 | https://arxiv.org/pdf/2303.11712v1.pdf | Efficiently Explaining CSPs with Unsatisfiable Subset Optimization (extended algorithms and examples) | We build on a recently proposed method for stepwise explaining solutions of Constraint Satisfaction Problems (CSP) in a human-understandable way. An explanation here is a sequence of simple inference steps where simplicity is quantified using a cost function. The algorithms for explanation generation rely on extracting... | ['Tias Guns', 'Bart Bogaerts', 'Emilio Gamba'] | 2023-03-21 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 7.01607943e-01 8.27399790e-01 -4.03141305e-02 -4.06336755e-01
-8.12179446e-01 -6.53343439e-01 3.52325022e-01 5.06735623e-01
8.71925056e-02 1.03187346e+00 -1.89079434e-01 -4.93797779e-01
-6.86904490e-01 -1.02535760e+00 -7.68493056e-01 -2.60330439e-01
-1.32533610e-01 1.05454278e+00 3.68603587e-01 -1.14403650... | [8.561716079711914, 6.379813194274902] |
0e0c22fa-dcdf-425f-a10a-db1ff0985522 | clawcranenet-leveraging-object-level-relation | 2103.10702 | null | https://arxiv.org/abs/2103.10702v3 | https://arxiv.org/pdf/2103.10702v3.pdf | ClawCraneNet: Leveraging Object-level Relation for Text-based Video Segmentation | Text-based video segmentation is a challenging task that segments out the natural language referred objects in videos. It essentially requires semantic comprehension and fine-grained video understanding. Existing methods introduce language representation into segmentation models in a bottom-up manner, which merely cond... | ['Yi Yang', 'Yawei Luo', 'Yu Wu', 'Chen Liang'] | 2021-03-19 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 1.17528670e-01 1.94152176e-01 -3.57821882e-01 -6.98979557e-01
-3.19711536e-01 -5.71428597e-01 5.13267398e-01 -1.00113243e-01
-3.68249089e-01 4.01158422e-01 3.11521590e-01 -2.64389277e-01
-9.88357794e-03 -6.91164136e-01 -8.39270473e-01 -4.78134960e-01
2.43879303e-01 5.89166820e-01 7.62894213e-01 -2.89601833... | [10.10098648071289, 1.0629068613052368] |
bebaade0-ba10-46db-b31a-f43a0dea150c | reconfigurable-distributed-fpga-cluster | 2305.18332 | null | https://arxiv.org/abs/2305.18332v1 | https://arxiv.org/pdf/2305.18332v1.pdf | Reconfigurable Distributed FPGA Cluster Design for Deep Learning Accelerators | We propose a distributed system based on lowpower embedded FPGAs designed for edge computing applications focused on exploring distributing scheduling optimizations for Deep Learning (DL) workloads to obtain the best performance regarding latency and power efficiency. Our cluster was modular throughout the experiment, ... | ['Jafar Saniie', 'Alejandro Perez-Vicente', 'Tianyang Fang', 'Hans Johnson'] | 2023-05-24 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-6.59757257e-01 -2.08373263e-01 4.46778424e-02 -4.55798566e-01
3.98057699e-01 -3.62275094e-01 3.03204935e-02 -1.77489951e-01
-4.13693875e-01 3.67820740e-01 -3.11038315e-01 -8.74607444e-01
-2.43976384e-01 -8.08529079e-01 -3.01448852e-01 -8.92301142e-01
-3.86526495e-01 5.19517064e-01 2.74683744e-01 -1.48641780... | [8.390908241271973, 2.902581214904785] |
c5f927e2-4c60-4f1a-b259-4e069240569d | pcr-cg-point-cloud-registration-via-deep | 2302.14418 | null | https://arxiv.org/abs/2302.14418v1 | https://arxiv.org/pdf/2302.14418v1.pdf | PCR-CG: Point Cloud Registration via Deep Color and Geometry | In this paper, we introduce PCR-CG: a novel 3D point cloud registration module explicitly embedding the color signals into the geometry representation. Different from previous methods that only use geometry representation, our module is specifically designed to effectively correlate color into geometry for the point cl... | ['Ji Hou', 'Wenhui Zhou', 'Xiaolin Huang', 'Junle Yu', 'Yu Zhang'] | 2023-02-28 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 4.21090014e-02 1.18832965e-03 1.37180403e-01 -4.19114560e-01
-1.06280088e+00 -6.54899716e-01 9.63914216e-01 6.12015203e-02
-4.93207097e-01 -3.21282409e-02 -5.77587076e-02 1.00249432e-01
2.47518048e-01 -8.48361254e-01 -1.01610017e+00 -6.35340095e-01
3.49125564e-02 4.40829486e-01 1.31437376e-01 -3.14449489... | [7.844015598297119, -2.915335178375244] |
a4e5478a-0e46-407b-afa5-cda1c49aead7 | sports-video-fine-grained-action-detection | 2112.11384 | null | https://arxiv.org/abs/2112.11384v1 | https://arxiv.org/pdf/2112.11384v1.pdf | Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021 | Sports video analysis is a prevalent research topic due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests up to analysis of athletes' performance. The Sports Video task is part of the MediaEval 2021 benchmark. This task tackles fine-grained action detection and ... | ['Julien Morlier', 'Laurent Mascarilla', 'Renaud Péteri', 'Jenny Benois-Pineau', 'Boris Mansencal', 'Jordan Calandre', 'Pierre-Etienne Martin'] | 2021-12-16 | null | null | null | null | ['fine-grained-action-detection'] | ['computer-vision'] | [ 4.77389663e-01 -2.55075246e-01 -4.42869067e-01 -6.12851083e-02
-7.50002444e-01 -6.25905752e-01 3.78036410e-01 1.81877464e-01
-7.73014665e-01 3.08179706e-01 4.97762263e-01 3.38526815e-01
-2.06429511e-02 -3.91247660e-01 -4.42385823e-01 -3.57063204e-01
-3.58956307e-01 6.95234090e-02 8.55967820e-01 -3.37569356... | [7.726248264312744, 0.19707410037517548] |
e5b72ab8-fe9e-491f-9425-b7366608d0af | cnn-based-dense-underwater-3d-scene | 1811.09675 | null | http://arxiv.org/abs/1811.09675v1 | http://arxiv.org/pdf/1811.09675v1.pdf | CNN based dense underwater 3D scene reconstruction by transfer learning using bubble database | Dense 3D shape acquisition of swimming human or live fish is an important
research topic for sports, biological science and so on. For this purpose,
active stereo sensor is usually used in the air, however it cannot be applied
to the underwater environment because of refraction, strong light attenuation
and severe inte... | ['Hiroshi Kawasaki', 'Ryo Furukawa', 'Kazuto Ichimaru'] | 2018-11-21 | null | null | null | null | ['3d-scene-reconstruction', 'underwater-3d-scene-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.61601976e-01 -3.00550967e-01 1.14911878e+00 -4.75835316e-02
1.81840397e-02 -2.24816874e-01 -6.91472590e-02 -3.87046307e-01
-5.76169491e-01 4.88276839e-01 2.14817375e-01 2.53252149e-01
3.91004831e-01 -1.06548071e+00 -9.22816992e-01 -9.88605678e-01
1.84548721e-01 1.51813760e-01 9.78285968e-01 -3.83274108... | [10.673571586608887, -3.5238800048828125] |
e5742c91-06f0-4704-a301-557dd66bb740 | self-supervised-geometry-aware-encoder-for | 2212.07409 | null | https://arxiv.org/abs/2212.07409v2 | https://arxiv.org/pdf/2212.07409v2.pdf | Self-Supervised Geometry-Aware Encoder for Style-Based 3D GAN Inversion | StyleGAN has achieved great progress in 2D face reconstruction and semantic editing via image inversion and latent editing. While studies over extending 2D StyleGAN to 3D faces have emerged, a corresponding generic 3D GAN inversion framework is still missing, limiting the applications of 3D face reconstruction and sema... | ['Bo Dai', 'Chen Change Loy', 'Shuai Yang', 'Xuyi Meng', 'Yushi Lan'] | 2022-12-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lan_Self-Supervised_Geometry-Aware_Encoder_for_Style-Based_3D_GAN_Inversion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lan_Self-Supervised_Geometry-Aware_Encoder_for_Style-Based_3D_GAN_Inversion_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 5.90154767e-01 4.73505527e-01 -7.87133235e-04 -3.29550922e-01
-7.52909482e-01 -6.49055481e-01 6.37749791e-01 -7.18471646e-01
3.49767178e-01 6.19641364e-01 1.82000533e-01 1.70997926e-03
4.66581643e-01 -9.04488325e-01 -9.05315042e-01 -7.08829403e-01
5.06969094e-01 8.62487614e-01 -3.32774580e-01 -1.45617425... | [12.632503509521484, -0.36424019932746887] |
5f65a801-ab67-4571-8186-e5975e459d42 | nmt5-is-parallel-data-still-relevant-for-pre-1 | null | null | https://aclanthology.org/2021.acl-short.87 | https://aclanthology.org/2021.acl-short.87.pdf | nmT5 - Is parallel data still relevant for pre-training massively multilingual language models? | Recently, mT5 - a massively multilingual version of T5 - leveraged a unified text-to-text format to attain state-of-the-art results on a wide variety of multilingual NLP tasks. In this paper, we investigate the impact of incorporating parallel data into mT5 pre-training. We find that multi-tasking language modeling wit... | ['Melvin Johnson', 'Noah Constant', 'Linting Xue', 'Rami Al-Rfou', 'Aditya Siddhant', 'Mihir Kale'] | 2021-08-01 | null | null | null | acl-2021-5 | ['multilingual-nlp'] | ['natural-language-processing'] | [-2.25636229e-01 2.68909354e-02 -6.02141440e-01 -4.20714468e-01
-1.54607964e+00 -8.70494902e-01 6.83112562e-01 2.83781122e-02
-6.15137994e-01 8.92802775e-01 3.82823139e-01 -9.56197321e-01
2.54456490e-01 -1.73725367e-01 -8.41623008e-01 -1.46003813e-01
2.14062452e-01 8.03999782e-01 -1.92043319e-01 -4.08693045... | [11.32812786102295, 10.192475318908691] |
ea0b2931-69e5-495d-86fb-7a48e2a70a9b | ernet-efficient-and-reliable-human-object | null | null | https://ieeexplore.ieee.org/abstract/document/10026602 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10026602 | ERNet: Efficient and Reliable Human-Object Interaction Detection | Human-Object Interaction (HOI) detection recognizes how persons interact with objects, which is advantageous in autonomous systems such as self-driving vehicles and collaborative robots. However, current HOI detectors are often plagued by model inefficiency and unreliability when making a prediction, which consequently... | ['Massimo Tistarelli', 'John See', 'KokSheik Wong', 'Joanne Mun-Yee Lim', 'Vishnu Monn Baskaran', 'JunYi Lim'] | 2023-01-26 | null | null | null | ieee-transactions-on-image-processing-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [-4.11027819e-02 6.92941919e-02 4.31412496e-02 -4.09554422e-01
-7.39998281e-01 -8.05068910e-02 5.24372280e-01 -2.48431414e-01
-3.27732742e-01 3.14862162e-01 1.90018609e-01 1.45994470e-01
2.54120022e-01 -6.02090597e-01 -7.78056681e-01 -4.57216680e-01
4.21399884e-02 7.38601506e-01 2.92840958e-01 -1.17488116... | [9.539012908935547, 1.3674076795578003] |
a534d4f0-7fd2-4dee-84f6-ab134a290dad | multi-view-3d-object-reconstruction-and | 2306.11739 | null | https://arxiv.org/abs/2306.11739v1 | https://arxiv.org/pdf/2306.11739v1.pdf | Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Prior | 3D object reconstruction is important for semantic scene understanding. It is challenging to reconstruct detailed 3D shapes from monocular images directly due to a lack of depth information, occlusion and noise. Most current methods generate deterministic object models without any awareness of the uncertainty of the re... | ['Steven L. Waslander', 'Ziwei Liao'] | 2023-06-17 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.06502482e-01 2.16134295e-01 -3.10711693e-02 -8.09037447e-01
-1.04230917e+00 -4.24895346e-01 6.62550151e-01 -1.54709131e-01
3.84910971e-01 6.89228177e-01 4.67596978e-01 3.87874126e-01
-1.44239753e-01 -1.02487350e+00 -1.24088013e+00 -4.70082641e-01
3.92013788e-01 9.49033558e-01 2.50028431e-01 5.34202754... | [8.527054786682129, -3.2189512252807617] |
efaae00d-1b06-4423-9dae-6d2f76f466db | can-fairness-be-automated-guidelines-and | 2303.08485 | null | https://arxiv.org/abs/2303.08485v1 | https://arxiv.org/pdf/2303.08485v1.pdf | Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML | The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices. However, decisions derived from ML models can reproduce, amplify, or even introduce unfairness in our societies, cau... | ['Frank Hutter', 'Bernd Bischl', 'Mykola Pechenizkiy', 'Joaquin Vanschoren', 'Noor Awad', 'Edward Bergman', 'Katharina Eggensperger', 'Matthias Feurer', 'Florian Pfisterer', 'Hilde Weerts'] | 2023-03-15 | null | null | null | null | ['automl'] | ['methodology'] | [-1.11203566e-01 4.97006446e-01 -3.08638662e-01 -7.83898413e-01
-2.07191959e-01 -4.84450728e-01 3.45152736e-01 3.34871083e-01
-7.07451642e-01 8.54269862e-01 2.85280198e-01 -6.84543908e-01
1.56327691e-02 -5.43673038e-01 -4.88307104e-02 -1.38282746e-01
3.90164226e-01 9.52882916e-02 -7.50891030e-01 -4.29916456... | [8.932193756103516, 5.43662691116333] |
4088838d-c549-4e32-bf7c-a8853a105cce | universal-low-rank-matrix-recovery-from-pauli | null | null | http://papers.nips.cc/paper/4222-universal-low-rank-matrix-recovery-from-pauli-measurements | http://papers.nips.cc/paper/4222-universal-low-rank-matrix-recovery-from-pauli-measurements.pdf | Universal low-rank matrix recovery from Pauli measurements | We study the problem of reconstructing an unknown matrix M of rank r and dimension d using O(rd polylog d) Pauli measurements. This has applications in quantum state tomography, and is a non-commutative analogue of a well-known problem in compressed sensing: recovering a sparse vector from a few of its Fourier coeffi... | ['Yi-Kai Liu'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['quantum-state-tomography'] | ['medical'] | [ 6.57383740e-01 3.75308871e-01 -2.32617453e-01 -1.99552819e-01
-9.30442870e-01 -6.33778393e-01 5.28146207e-01 -2.75045663e-01
-4.39897686e-01 8.26920807e-01 4.20020342e-01 -2.74874598e-01
-4.44796562e-01 -4.97491509e-01 -7.12400198e-01 -1.20013213e+00
-3.08595806e-01 9.32618320e-01 -4.40882117e-01 -1.03083074... | [5.896368026733398, 4.835521697998047] |
8cc7800e-fb03-4fd7-be7e-8af91773557e | it-s-done-direct-one-shot-learning-without | 2204.13361 | null | https://arxiv.org/abs/2204.13361v3 | https://arxiv.org/pdf/2204.13361v3.pdf | It's DONE: Direct ONE-shot learning with quantile weight imprinting | Learning a new concept from one example is a superior function of the human brain and it is drawing attention in the field of machine learning as a one-shot learning task. In this paper, we propose one of the simplest methods for this task with a nonparametric weight imprinting, named Direct ONE-shot learning (DONE). D... | ['Izumi Ohzawa', 'Hideki Kashioka', 'Tomohiro Mashita', 'Shigeto Seno', 'Keigo Nishida', 'Kazufumi Hosoda'] | 2022-04-28 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 4.82004821e-01 2.66152173e-01 -1.50771543e-01 -3.95462722e-01
1.12879694e-01 -2.65991520e-02 6.91281676e-01 4.98840064e-02
-9.97822881e-01 9.33029890e-01 -1.00998074e-01 1.47606671e-01
-3.82214040e-01 -1.01797640e+00 -7.99638033e-01 -1.19846070e+00
2.75574386e-01 6.58450961e-01 7.73648143e-01 -2.53574431... | [9.816654205322266, 2.8404831886291504] |
65fd24e9-3c1f-42fd-941b-6e82ddbaf4d4 | towards-arabic-multimodal-dataset-for | 2306.06322 | null | https://arxiv.org/abs/2306.06322v1 | https://arxiv.org/pdf/2306.06322v1.pdf | Towards Arabic Multimodal Dataset for Sentiment Analysis | Multimodal Sentiment Analysis (MSA) has recently become a centric research direction for many real-world applications. This proliferation is due to the fact that opinions are central to almost all human activities and are key influencers of our behaviors. In addition, the recent deployment of Deep Learning-based (DL) m... | ['Hadda Cherroun', 'Attia Nehar', 'Slimane Bellaouar', 'Abdelhamid Haouhat'] | 2023-06-10 | null | null | null | null | ['multimodal-sentiment-analysis', 'sentiment-analysis', 'word-alignment', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.31409094e-01 -3.91357273e-01 -2.80925352e-02 -4.31481302e-01
-6.96188748e-01 -6.44804001e-01 9.34063733e-01 4.03809309e-01
-5.87545753e-01 3.89183402e-01 3.82613897e-01 -1.19573310e-01
1.07769467e-01 -6.30796790e-01 -3.49655390e-01 -4.64225382e-01
1.50030807e-01 6.56022191e-01 -7.47711807e-02 -1.15131140... | [12.932406425476074, 5.330983638763428] |
17af1773-b82b-4f00-b7d4-1786665f79a5 | adapting-verbnet-to-french-using-existing | null | null | https://aclanthology.org/L14-1204 | https://aclanthology.org/L14-1204.pdf | Adapting VerbNet to French using existing resources | VerbNet is an English lexical resource for verbs that has proven useful for English NLP due to its high coverage and coherent classification. Such a resource doesnt exist for other languages, despite some (mostly automatic and unsupervised) attempts. We show how to semi-automatically adapt VerbNet using existing resou... | ['Ga{\\"e}l de Chalendar', 'Quentin Pradet', 'Laurence Danlos'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['stock-prediction'] | ['time-series'] | [-6.14436679e-02 1.69636980e-01 -4.36680168e-01 -4.52486008e-01
-4.30356443e-01 -9.72499013e-01 5.95715582e-01 6.01785064e-01
-8.59043300e-01 1.41656137e+00 4.37537849e-01 -3.47514361e-01
-1.36313781e-01 -7.82023311e-01 -1.46661177e-01 -6.81275427e-02
2.36979917e-01 6.29322708e-01 4.94095117e-01 -7.61139691... | [10.124773979187012, 9.64875602722168] |
d5982b16-44ee-40a2-bde7-05334d56075f | affact-alignment-free-facial-attribute | 1611.06158 | null | http://arxiv.org/abs/1611.06158v2 | http://arxiv.org/pdf/1611.06158v2.pdf | AFFACT - Alignment-Free Facial Attribute Classification Technique | Facial attributes are soft-biometrics that allow limiting the search space,
e.g., by rejecting identities with non-matching facial characteristics such as
nose sizes or eyebrow shapes. In this paper, we investigate how the latest
versions of deep convolutional neural networks, ResNets, perform on the facial
attribute c... | ['Manuel Günther', 'Andras Rozsa', 'Terrance E. Boult'] | 2016-11-18 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 2.48638690e-01 3.09918910e-01 -4.14343551e-02 -9.90482330e-01
-4.12127286e-01 -6.01991475e-01 5.14196575e-01 -1.52452141e-01
-7.02285945e-01 4.80023891e-01 -2.10637927e-01 -1.38026699e-01
-3.77191082e-02 -6.36692166e-01 -5.34139574e-01 -6.94611073e-01
-2.31372237e-01 5.19423246e-01 -3.59451234e-01 -1.35231450... | [13.274222373962402, 0.9564650654792786] |
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