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60e49953-f2a6-4032-9e3e-79daca5dd76e | problem-decomposition-and-multi-shot-asp | 2205.07537 | null | https://arxiv.org/abs/2205.07537v2 | https://arxiv.org/pdf/2205.07537v2.pdf | Problem Decomposition and Multi-shot ASP Solving for Job-shop Scheduling | The Job-shop Scheduling Problem (JSP) is a well-known and challenging combinatorial optimization problem in which tasks sharing a machine are to be arranged in a sequence such that encompassing jobs can be completed as early as possible. In this paper, we propose problem decomposition into time windows whose operations... | ['Konstantin Schekotihin', 'Martin Gebser', 'Mohammed M. S. El-Kholany'] | 2022-05-16 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [ 3.32341641e-01 4.26838040e-01 -2.29031488e-01 -2.27246240e-01
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58738236-6bee-4b9e-9f28-3023693590e1 | an-investigation-of-machine-translation | null | null | https://aclanthology.org/W15-3057 | https://aclanthology.org/W15-3057.pdf | An Investigation of Machine Translation Evaluation Metrics in Cross-lingual Question Answering | null | ['Kyoshiro Sugiyama', 'Masahiro Mizukami', 'Koichiro Yoshino', 'Tomoki Toda', 'Satoshi Nakamura', 'Sakriani Sakti', 'Graham Neubig'] | 2015-09-01 | null | null | null | ws-2015-9 | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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b316dc36-441f-40b9-ab01-03ff703fe660 | circlenet-reciprocating-feature-adaptation | 2212.05691 | null | https://arxiv.org/abs/2212.05691v1 | https://arxiv.org/pdf/2212.05691v1.pdf | CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection | Pedestrian detection in the wild remains a challenging problem especially when the scene contains significant occlusion and/or low resolution of the pedestrians to be detected. Existing methods are unable to adapt to these difficult cases while maintaining acceptable performance. In this paper we propose a novel featur... | ['Qixiang Ye', 'Baochang Zhang', 'Huijuan Xu', 'Zhenjun Han', 'Tianliang Zhang'] | 2022-12-12 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 4.21061069e-02 -3.02602857e-01 2.36618802e-01 -2.50091881e-01
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7b8e6068-ad49-40a0-abac-6c8dafb5a4ed | discogen-learning-to-discover-gene-regulatory | 2304.05823 | null | https://arxiv.org/abs/2304.05823v1 | https://arxiv.org/pdf/2304.05823v1.pdf | DiscoGen: Learning to Discover Gene Regulatory Networks | Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inherently causal in nature. To accurately identify GRNs, perturbational data is required. However, most GRN discovery methods only operate on o... | ['Danilo Rezende', 'Mike Mozer', 'Anirudh Goyal', 'Matthew Botvinick', 'David Barrett', 'Theophane Weber', 'Jane Wang', 'Albin Cassirer', 'Jean-Baptiste Lespiau', 'Melanie Rey', 'Silvia Chiappa', 'Jorg Bornschein', 'Sara-Jane Dunn', 'Nan Rosemary Ke'] | 2023-04-12 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 5.29577851e-01 -1.77739143e-01 -5.80274045e-01 -3.28932852e-01
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e6711b3c-eb52-4f64-a8a3-544e152cd059 | attentive-modality-hopping-mechanism-for | 1912.00846 | null | https://arxiv.org/abs/1912.00846v2 | https://arxiv.org/pdf/1912.00846v2.pdf | Attentive Modality Hopping Mechanism for Speech Emotion Recognition | In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system. The traditional approaches tackle this task by fusing the knowledge from the various modalities independently for performing emotion classification. In contrast to these appr... | ['Hwanhee Lee', 'Seunghyun Yoon', 'Subhadeep Dey', 'Kyomin Jung'] | 2019-11-29 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.75242299e-01 -6.64324360e-03 5.04671736e-03 -2.25314781e-01
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62c1045d-7eef-4749-9e74-eeb0c9635192 | pina-leveraging-side-information-in-extreme | 2305.12349 | null | https://arxiv.org/abs/2305.12349v1 | https://arxiv.org/pdf/2305.12349v1.pdf | PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation | The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extraction of semantic features from input query text. However, conventional XMC studies usually neglect the side information of instances and labels... | ['Hsiang-Fu Yu', 'Olgica Milenkovic', 'Wei-Cheng Chang', 'Jyun-Yu Jiang', 'Cho-Jui Hsieh', 'Jiong Zhang', 'Eli Chien'] | 2023-05-21 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 2.30912685e-01 -2.58129448e-01 -7.65900433e-01 -6.44654751e-01
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3.75678726e-02 6.84519053e-01 6.86466787e-03 2.49914955... | [9.5662841796875, 4.390312194824219] |
a2ad9f50-cd39-4fa4-870f-dccfc617bd84 | towards-tokenized-human-dynamics | 2111.11433 | null | https://arxiv.org/abs/2111.11433v1 | https://arxiv.org/pdf/2111.11433v1.pdf | Towards Tokenized Human Dynamics Representation | For human action understanding, a popular research direction is to analyze short video clips with unambiguous semantic content, such as jumping and drinking. However, methods for understanding short semantic actions cannot be directly translated to long human dynamics such as dancing, where it becomes challenging even ... | ['Stephen Lin', 'Fangyun Wei', 'Zhirong Wu', 'Xiao Sun', 'Kenneth Li'] | 2021-11-22 | null | null | null | null | ['action-understanding', 'human-dynamics', 'genre-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.24854720e-01 -7.79415146e-02 -6.15104198e-01 -3.59952658e-01
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2a7a2761-24a5-4b85-885e-5793257396ef | life-net-data-driven-modelling-of-time | 2212.08403 | null | https://arxiv.org/abs/2212.08403v1 | https://arxiv.org/pdf/2212.08403v1.pdf | LiFe-net: Data-driven Modelling of Time-dependent Temperatures and Charging Statistics Of Tesla's LiFePo4 EV Battery | Modelling the temperature of Electric Vehicle (EV) batteries is a fundamental task of EV manufacturing. Extreme temperatures in the battery packs can affect their longevity and power output. Although theoretical models exist for describing heat transfer in battery packs, they are computationally expensive to simulate. ... | ['Nico Hoffmann', 'Luisa Fennert', 'Jeyhun Rustamov'] | 2022-12-16 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.23180825e-01 -1.61478221e-01 -1.81354843e-02 -4.58248496e-01
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afd38a9d-70b5-4c40-ad0f-f70a8b28f82f | feature-imitating-networks-enhance-the | 2306.14572 | null | https://arxiv.org/abs/2306.14572v1 | https://arxiv.org/pdf/2306.14572v1.pdf | Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing Tasks | Feature-Imitating-Networks (FINs) are neural networks with weights that are initialized to approximate closed-form statistical features. In this work, we perform the first-ever evaluation of FINs for biomedical image processing tasks. We begin by training a set of FINs to imitate six common radiomics features, and then... | ['Tuka Alhanai', 'Mohammad Mahdi Ghassemi', 'Shangyang Min'] | 2023-06-26 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 4.32355076e-01 -1.62547529e-02 -2.50181518e-02 -6.62035823e-01
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ddf0af5d-0f49-4a5f-90a9-7ae290404a71 | matching-web-tables-with-knowledge-base | null | null | https://link.springer.com/chapter/10.1007/978-3-319-68288-4_16 | https://iswc2017.ai.wu.ac.at/wp-content/uploads/papers/MainProceedings/98.pdf | Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings | Web tables constitute valuable sources of information for various applications, ranging from Web search to Knowledge Base (KB) augmentation. An underlying common requirement is to annotate the rows of Web tables with semantically rich descriptions of entities published in Web KBs. In this paper, we evaluate three unsup... | ['Vassilis Christophides', 'Mariano Rodriguez-Muro', 'Oktie Hassanzadeh', 'Vasilis Efthymiou'] | 2017-10-01 | null | null | null | the-semantic-web-iswc-2017-10 | ['ontology-matching', 'table-annotation', 'entity-embeddings', 'table-annotation', 'cell-entity-annotation'] | ['knowledge-base', 'knowledge-base', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [-3.62863570e-01 2.54709214e-01 -3.85346383e-01 -1.95280522e-01
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-4.49426565e-03 8.75229478e-01 8.50035369e-01 -6.50800884... | [9.277871131896973, 8.058921813964844] |
f850a042-f088-4382-b637-8de23caaa253 | ensemble-transfer-learning-for-multilingual | 2301.09175 | null | https://arxiv.org/abs/2301.09175v1 | https://arxiv.org/pdf/2301.09175v1.pdf | Ensemble Transfer Learning for Multilingual Coreference Resolution | Entity coreference resolution is an important research problem with many applications, including information extraction and question answering. Coreference resolution for English has been studied extensively. However, there is relatively little work for other languages. A problem that frequently occurs when working wit... | ['Heng Ji', 'Tuan Manh Lai'] | 2023-01-22 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-1.87200140e-02 1.03132159e-01 -5.87964952e-01 -4.08831328e-01
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2.14815557e-01 9.15191412e-01 3.04610521e-01 -4.14512664... | [9.28255844116211, 9.500099182128906] |
1594b56c-c7f2-4f1b-a72a-62da5cefc378 | simultaneously-updating-all-persistence | 2211.11620 | null | https://arxiv.org/abs/2211.11620v1 | https://arxiv.org/pdf/2211.11620v1.pdf | Simultaneously Updating All Persistence Values in Reinforcement Learning | In reinforcement learning, the performance of learning agents is highly sensitive to the choice of time discretization. Agents acting at high frequencies have the best control opportunities, along with some drawbacks, such as possible inefficient exploration and vanishing of the action advantages. The repetition of the... | ['Marcello Restelli', 'Alberto Maria Metelli', 'Lorenzo Bisi', 'Luca Al Daire', 'Luca Sabbioni'] | 2022-11-21 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-2.30477706e-01 2.94015333e-02 -2.10125580e-01 4.14719522e-01
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-4.12251651e-01 5.56865096e-01 4.56353754e-01 -3.08068454... | [4.371739387512207, 2.3125481605529785] |
f641b0eb-c77e-4f09-909b-28cd5ef4fbf9 | is-dataset-condensation-a-silver-bullet-for | 2305.03711 | null | https://arxiv.org/abs/2305.03711v1 | https://arxiv.org/pdf/2305.03711v1.pdf | Is dataset condensation a silver bullet for healthcare data sharing? | Safeguarding personal information is paramount for healthcare data sharing, a challenging issue without any silver bullet thus far. We study the prospect of a recent deep-learning advent, dataset condensation (DC), in sharing healthcare data for AI research, and the results are promising. The condensed data abstracts o... | ['David Clifton', 'Tingting Zhu', 'Li Shang', 'Stavros Petridis', 'Pingchuan Ma', 'Mingzhi Dong', 'Anshul Thakur', 'Yujiang Wang'] | 2023-05-05 | null | null | null | null | ['mortality-prediction', 'de-identification'] | ['medical', 'natural-language-processing'] | [ 1.56217128e-01 5.38626969e-01 -3.62616330e-01 -4.10800695e-01
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cdc0bf08-2165-432f-ae3f-642936ca95b8 | towards-coherent-visual-storytelling-with-1 | null | null | https://openreview.net/forum?id=kO0y8t9PMEf | https://openreview.net/pdf?id=kO0y8t9PMEf | Towards Coherent Visual Storytelling with Ordered Image Attention | We address the problem of visual storytelling, i.e., generating a story for a given sequence of images. While each story sentence should describe a corresponding image, a coherent story also needs to be consistent and relate to both future and past images. Current approaches encode images independently, disregarding re... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['visual-storytelling'] | ['natural-language-processing'] | [ 5.77012539e-01 1.65495008e-01 1.76595449e-01 -5.67890882e-01
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3.86218391e-02 1.67471513e-01 2.50657946e-01 -2.40083903... | [11.097826957702637, 0.620402455329895] |
e5c5c80b-a2b8-437f-98ba-7267b0d4eb77 | neural-morphological-disambiguation-using | null | null | https://aclanthology.org/W17-7559 | https://aclanthology.org/W17-7559.pdf | Neural Morphological Disambiguation Using Surface and Contextual Morphological Awareness | null | ['Anil Kumar Singh', 'Akhilesh Sudhakar'] | 2017-12-01 | null | null | null | ws-2017-12 | ['morphological-disambiguation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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d625667b-e0d8-4fdd-b326-6e21a842ff66 | the-dual-information-bottleneck-1 | 2006.04641 | null | https://arxiv.org/abs/2006.04641v1 | https://arxiv.org/pdf/2006.04641v1.pdf | The Dual Information Bottleneck | The Information Bottleneck (IB) framework is a general characterization of optimal representations obtained using a principled approach for balancing accuracy and complexity. Here we present a new framework, the Dual Information Bottleneck (dualIB), which resolves some of the known drawbacks of the IB. We provide a the... | ['Ravid Shwartz-Ziv', 'Zoe Piran', 'Naftali Tishby'] | 2020-06-08 | null | https://openreview.net/forum?id=B1xZD1rtPr | https://openreview.net/pdf?id=B1xZD1rtPr | null | ['information-plane'] | ['methodology'] | [ 3.30282331e-01 3.80938053e-01 -2.33957991e-01 -9.28647295e-02
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fadab1ea-ec54-45da-9be3-9d626cbf2594 | on-the-effects-of-different-types-of-label | 2207.13975 | null | https://arxiv.org/abs/2207.13975v2 | https://arxiv.org/pdf/2207.13975v2.pdf | On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification | The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. To address MLC problems, the use of deep neural networks that require a high number of reliable training images annotated by multiple land-cover class labels (multi-... | ['Begüm Demir', 'Mahdyar Ravanbakhsh', 'Tom Burgert'] | 2022-07-28 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 5.68923593e-01 -2.88906604e-01 2.35940740e-01 -3.94661784e-01
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2.44444251e-01 3.37975502e-01 1.77400425e-01 3.51785980... | [9.517462730407715, -1.1152726411819458] |
cce7b537-b898-40c1-a432-cefc0162ab14 | interpretable-video-captioning-via-trajectory | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Wu_Interpretable_Video_Captioning_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wu_Interpretable_Video_Captioning_CVPR_2018_paper.pdf | Interpretable Video Captioning via Trajectory Structured Localization | Automatically describing open-domain videos with natural language are attracting increasing interest in the field of artificial intelligence. Most existing methods simply borrow ideas from image captioning and obtain a compact video representation from an ensemble of global image feature before feeding to an RNN decode... | ['Xian Wu', 'Qingxing Cao', 'Liang Lin', 'Qingge Ji', 'Guanbin Li'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['video-description'] | ['computer-vision'] | [ 4.53712717e-02 -3.99292022e-01 -2.74113506e-01 -4.09755737e-01
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6.95351660e-02 -5.52069902e-01 -9.00952220e-01 -6.82304680e-01
-9.75329429e-02 1.17545605e-01 1.94948822e-01 -9.41314846... | [10.416010856628418, 0.6891830563545227] |
4cd3614c-f24c-4d6c-8a74-279db0a1bd16 | empathetic-response-generation-via-emotion | 2302.11787 | null | https://arxiv.org/abs/2302.11787v1 | https://arxiv.org/pdf/2302.11787v1.pdf | Empathetic Response Generation via Emotion Cause Transition Graph | Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion). Besides concerning emotion status in early work, the latest approaches study emotion causes in empathetic dialogue. These approaches focus on und... | ['Yongbin Li', 'Yuchuan Wu', 'Yuexian Hou', 'Dongming Zhao', 'Ying Zhu', 'Yinhe Zheng', 'Ting-En Lin', 'Bo wang', 'Yushan Qian'] | 2023-02-23 | null | null | null | null | ['response-generation', 'empathetic-response-generation'] | ['natural-language-processing', 'natural-language-processing'] | [-8.99876356e-02 3.92886758e-01 9.85512063e-02 -8.14976275e-01
-4.67847027e-02 -4.65556026e-01 5.53878367e-01 2.12451935e-01
1.21665239e-01 8.14596415e-01 9.72319901e-01 2.72712827e-01
1.98597237e-01 -5.15983343e-01 -1.20029211e-01 -4.06618625e-01
3.80633563e-01 4.31461215e-01 -5.32039940e-01 -9.35044646... | [13.14785099029541, 7.629980564117432] |
9de1fe16-b436-4bf6-b425-c1faf522f23c | a-simple-information-based-approach-to | null | null | https://openreview.net/forum?id=ULHJwUO0AUx | https://openreview.net/pdf?id=ULHJwUO0AUx | A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task which aims to extract the aspects from sentences and identify their corresponding sentiments. Aspect term extraction (ATE) is the crucial step for ABSA. Due to the expensive annotation for aspect terms, we often lack labeled target domain ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['term-extraction', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.40584329e-01 -1.65456563e-01 -4.21133079e-02 -6.87553525e-01
-1.07582033e+00 -8.27538192e-01 6.66349292e-01 2.02803999e-01
-4.49593753e-01 6.53223574e-01 1.97342187e-02 -2.66175598e-01
6.52876049e-02 -8.38579297e-01 -4.40347582e-01 -6.55179560e-01
4.52625483e-01 4.68807906e-01 3.89281482e-01 -6.20118618... | [11.361104965209961, 6.72358512878418] |
da019ff3-5e92-48c4-b026-7aa2cb7a2880 | consumer-side-fairness-in-recommender-systems | 2305.09330 | null | https://arxiv.org/abs/2305.09330v1 | https://arxiv.org/pdf/2305.09330v1.pdf | Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation | In the current landscape of ever-increasing levels of digitalization, we are facing major challenges pertaining to scalability. Recommender systems have become irreplaceable both for helping users navigate the increasing amounts of data and, conversely, aiding providers in marketing products to interested users. The gr... | ['Helge Langseth', 'Bjørnar Vassøy'] | 2023-05-16 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.29858056e-01 2.05369622e-01 -6.44980788e-01 -7.40134537e-01
-1.12857349e-01 -4.50483918e-01 5.03259301e-01 3.25582743e-01
-4.42777395e-01 6.56027257e-01 4.62640166e-01 -5.31373262e-01
-6.13445640e-01 -7.64495373e-01 1.10194094e-01 -2.58661300e-01
4.06626523e-01 2.41223127e-01 -4.67084050e-01 -6.08751476... | [9.63028621673584, 5.687957763671875] |
75973039-8922-444c-b05f-e330da40299a | sparse-spatial-transformers-for-few-shot | 2109.12932 | null | https://arxiv.org/abs/2109.12932v3 | https://arxiv.org/pdf/2109.12932v3.pdf | Sparse Spatial Transformers for Few-Shot Learning | Learning from limited data is challenging because data scarcity leads to a poor generalization of the trained model. A classical global pooled representation will probably lose useful local information. Many few-shot learning methods have recently addressed this challenge using deep descriptors and learning a pixel-lev... | ['Chunlin Chen', 'Yaohui Li', 'Huaxiong Li', 'Haoxing Chen'] | 2021-09-27 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 3.32954377e-01 -3.57817382e-01 -4.76884663e-01 -4.10414428e-01
-8.03545177e-01 -2.82662604e-02 4.03824896e-01 2.19924748e-01
-3.39131415e-01 3.63764316e-01 1.19363144e-01 4.62380707e-01
-3.91524553e-01 -9.50600147e-01 -6.52741611e-01 -9.03636158e-01
2.82301426e-01 5.71412891e-02 5.65995693e-01 -1.12867430... | [9.733154296875, 2.076828718185425] |
9250a5c5-89a1-46ae-b5e4-d4bd9ec762a7 | macro-action-selection-with-deep | 1812.00336 | null | https://arxiv.org/abs/1812.00336v3 | https://arxiv.org/pdf/1812.00336v3.pdf | Macro action selection with deep reinforcement learning in StarCraft | StarCraft (SC) is one of the most popular and successful Real Time Strategy (RTS) games. In recent years, SC is also widely accepted as a challenging testbed for AI research because of its enormous state space, partially observed information, multi-agent collaboration, and so on. With the help of annual AIIDE and CIG c... | ['Hongyu Kuang', 'Renjie Hu', 'Huyang Sun', 'Yang Liu', 'Sijia Xu', 'Zhi Zhuang'] | 2018-12-02 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-4.55819815e-01 -3.89155895e-01 -2.09731340e-01 3.45874071e-01
-2.55301982e-01 -6.62826896e-01 7.49264121e-01 -4.01523620e-01
-8.84718657e-01 7.85415351e-01 -2.04329081e-02 -6.36002198e-02
-2.11077303e-01 -7.69903302e-01 -4.15604949e-01 -6.42078340e-01
-3.63063440e-02 9.44290757e-01 9.95568812e-01 -9.08997059... | [3.6227059364318848, 1.5493600368499756] |
416e1f29-d711-4651-b082-8df9e7824843 | using-positive-matching-contrastive-loss-with | 2303.04896 | null | https://arxiv.org/abs/2303.04896v1 | https://arxiv.org/pdf/2303.04896v1.pdf | Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition | Machine learning models automatically learn discriminative features from the data, and are therefore susceptible to learn strongly-correlated biases, such as using protected attributes like gender and race. Most existing bias mitigation approaches aim to explicitly reduce the model's focus on these protected features. ... | ['Desmond C. Ong', 'Varsha Suresh'] | 2023-03-08 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 3.63942653e-01 4.10610497e-01 -3.77768666e-01 -1.14396739e+00
-6.57133162e-01 -4.22411323e-01 8.37152541e-01 1.73896790e-01
-7.61303008e-01 5.46593010e-01 4.55024570e-01 9.58023667e-02
1.69888169e-01 -6.15588725e-01 -6.83219612e-01 -6.21604025e-01
-4.82869744e-02 2.60048267e-02 -2.76903510e-01 -1.68641105... | [13.068617820739746, 1.3263792991638184] |
1780cd09-7645-4dce-b885-95df67fd1fd6 | model-discovery-in-the-sparse-sampling-regime | 2105.00400 | null | https://arxiv.org/abs/2105.00400v1 | https://arxiv.org/pdf/2105.00400v1.pdf | Model discovery in the sparse sampling regime | To improve the physical understanding and the predictions of complex dynamic systems, such as ocean dynamics and weather predictions, it is of paramount interest to identify interpretable models from coarsely and off-grid sampled observations. In this work, we investigate how deep learning can improve model discovery o... | ['Remy Kusters', 'Georges Tod', 'Gert-Jan Both'] | 2021-05-02 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-1.41603678e-01 -3.29414338e-01 1.66166693e-01 2.05133930e-01
-3.15489680e-01 -6.30739808e-01 6.79943025e-01 5.31921625e-01
-1.77325353e-01 1.18174982e+00 -1.77423805e-01 -4.45346981e-01
-2.94401765e-01 -7.30564058e-01 -8.21167409e-01 -7.04802513e-01
-6.94717228e-01 3.75202954e-01 1.07744224e-01 -2.17669755... | [6.536596298217773, 3.3599233627319336] |
9a1dbe7d-c38c-442e-b268-988a40cb34e0 | um-iuling-at-semeval-2019-task-6-identifying | 1904.03450 | null | http://arxiv.org/abs/1904.03450v1 | http://arxiv.org/pdf/1904.03450v1.pdf | UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs | This paper describes the UM-IU@LING's system for the SemEval 2019 Task 6:
OffensEval. We take a mixed approach to identify and categorize hate speech in
social media. In subtask A, we fine-tuned a BERT based classifier to detect
abusive content in tweets, achieving a macro F1 score of 0.8136 on the test
data, thus reac... | ['Sandra Kübler', 'Zuoyu Tian', 'Jian Zhu'] | 2019-04-06 | um-iuling-at-semeval-2019-task-6-identifying-1 | https://aclanthology.org/S19-2138 | https://aclanthology.org/S19-2138.pdf | semeval-2019-6 | ['abuse-detection'] | ['natural-language-processing'] | [-2.74257034e-01 -3.01955082e-02 -2.44271368e-01 -1.12169638e-01
-9.27338243e-01 -8.95837963e-01 9.07961249e-01 4.89337444e-01
-6.74936712e-01 8.36843193e-01 1.06485277e-01 -4.34748709e-01
2.29520351e-01 -3.73455018e-01 -3.07310641e-01 -4.51251298e-01
-1.26665263e-02 2.49565229e-01 3.40220958e-01 -3.02891225... | [8.81168270111084, 10.575645446777344] |
69052275-7d17-4608-b61e-446d25608adb | model-reduction-of-swing-equations-with | 2110.14066 | null | https://arxiv.org/abs/2110.14066v2 | https://arxiv.org/pdf/2110.14066v2.pdf | Towards Model Reduction for Power System Transients with Physics-Informed PDE | This manuscript reports the first step towards building a robust and efficient model reduction methodology to capture transient dynamics in a transmission level electric power system. Such dynamics is normally modeled on seconds-to-tens-of-seconds time scales by the so-called swing equations, which are ordinary differe... | ['Philippe Jacquod', 'Julian Fritzsch', 'Michael Chertkov', 'Laurent Pagnier'] | 2021-10-26 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-4.03761148e-01 -3.08724135e-01 4.61578131e-01 2.34500736e-01
-2.83815712e-01 -7.36465514e-01 6.05257809e-01 1.23799458e-01
-5.92047460e-02 1.14036155e+00 -5.51132441e-01 -3.70834827e-01
-5.46794593e-01 -7.59072840e-01 -1.98773101e-01 -1.08020961e+00
-5.59442699e-01 4.52480257e-01 -2.08542496e-01 -3.52076173... | [5.959691047668457, 2.887789249420166] |
889f9b48-3756-4587-89b6-59c4f842df73 | a-self-adjusting-fusion-representation | 2212.11772 | null | https://arxiv.org/abs/2212.11772v1 | https://arxiv.org/pdf/2212.11772v1.pdf | A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences | Inter-modal interaction plays an indispensable role in multimodal sentiment analysis. Due to different modalities sequences are usually non-alignment, how to integrate relevant information of each modality to learn fusion representations has been one of the central challenges in multimodal learning. In this paper, a Se... | ['Kai Gao', 'Hua Xu', 'Ruxuan Zhang', 'Kaicheng Yang'] | 2022-11-12 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 1.78049222e-01 -2.11018994e-01 5.90242669e-02 -3.14119101e-01
-1.04084885e+00 -4.56714630e-01 6.56942010e-01 -2.13191241e-01
-4.51000512e-01 3.56014848e-01 4.93467391e-01 2.16614738e-01
-1.37022614e-01 -4.34402287e-01 -5.93054056e-01 -9.45077538e-01
4.39187616e-01 2.92759594e-02 -2.96903644e-02 -5.61325312... | [13.192227363586426, 4.9931159019470215] |
b2569d88-e08c-4d3d-8e08-8c586cdd7a19 | dfuc2020-analysis-towards-diabetic-foot-ulcer | 2004.11853 | null | https://arxiv.org/abs/2004.11853v3 | https://arxiv.org/pdf/2004.11853v3.pdf | DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection | Every 20 seconds, a limb is amputated somewhere in the world due to diabetes. This is a global health problem that requires a global solution. The MICCAI challenge discussed in this paper, which concerns the automated detection of diabetic foot ulcers using machine learning techniques, will accelerate the development o... | ["Claire O'Shea", 'Bijan Najafi', 'Arun G. Maiya', 'Satyan Rajbhandari', 'Justina Wu', 'Eibe Frank', 'Andrew Boulton', 'Neil D. Reeves', 'David Armstrong', 'Bill Cassidy', 'Pappachan Joseph', 'Moi Hoon Yap', 'David Gillespie'] | 2020-04-24 | null | null | null | null | ['diabetic-foot-ulcer-detection'] | ['medical'] | [ 4.29454982e-01 -2.34940276e-01 -2.82304019e-01 -3.20171118e-02
-6.97600126e-01 -3.03706050e-01 3.05441841e-02 7.62285888e-01
-4.25440758e-01 8.57591510e-01 5.02330601e-01 -4.96901840e-01
-3.18462938e-01 -8.22070479e-01 -3.83080617e-02 -4.22198474e-01
-3.05651724e-01 5.39203227e-01 1.25994265e-01 -1.73391744... | [14.45710563659668, -1.8804094791412354] |
ed2f5b51-9b87-4618-a35e-5731ea3f149c | fast-contextual-scene-graph-generation-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jin_Fast_Contextual_Scene_Graph_Generation_With_Unbiased_Context_Augmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jin_Fast_Contextual_Scene_Graph_Generation_With_Unbiased_Context_Augmentation_CVPR_2023_paper.pdf | Fast Contextual Scene Graph Generation With Unbiased Context Augmentation | Scene graph generation (SGG) methods have historically suffered from long-tail bias and slow inference speed. In this paper, we notice that humans can analyze relationships between objects relying solely on context descriptions,and this abstract cognitive process may be guided by experience. For example, given desc... | ['Wei Song', 'Zonghao Mu', 'Wen Wang', 'Xiangming Xi', 'Shiqiang Zhu', 'Qiwei Meng', 'Fangtai Guo', 'Tianlei Jin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['scene-graph-generation'] | ['computer-vision'] | [ 1.85385868e-01 9.01957452e-02 3.16689909e-01 -5.54890215e-01
-1.70876682e-01 -7.52885878e-01 7.33605862e-01 3.66064399e-01
-8.17668959e-02 4.70903248e-01 2.37212464e-01 -5.07110059e-01
1.54222026e-01 -9.03617263e-01 -8.91907334e-01 -4.01147693e-01
1.65948823e-01 1.92846626e-01 5.54853439e-01 -2.73165017... | [10.35105037689209, 1.6419466733932495] |
50cf744f-b30d-403c-9440-f2a399852d8f | shall-we-trust-all-relational-tuples-by-open | 2305.04181 | null | https://arxiv.org/abs/2305.04181v1 | https://arxiv.org/pdf/2305.04181v1.pdf | Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection | Open Information Extraction (OIE) aims to extract factual relational tuples from open-domain sentences. Downstream tasks use the extracted OIE tuples as facts, without examining the certainty of these facts. However, uncertainty/speculation is a common linguistic phenomenon. Existing studies on speculation detection ar... | ['XiaoLi Li', 'Jung-jae Kim', 'Aixin Sun', 'Kuicai Dong'] | 2023-05-07 | null | null | null | null | ['open-information-extraction', 'speculation-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.24805549e-02 1.11095059e+00 -8.71219933e-01 -4.48318809e-01
-9.52603638e-01 -5.25020719e-01 6.58155382e-01 5.86268544e-01
1.58150285e-01 1.14131165e+00 7.67899632e-01 -5.54166675e-01
5.37116349e-01 -9.93944824e-01 -1.05415475e+00 1.93722129e-01
-1.92475632e-01 5.04381418e-01 4.17275846e-01 -1.84227437... | [9.632503509521484, 8.639388084411621] |
a55ff26f-d31f-4c83-bc57-2d7f4d5ad9c8 | sketchparse-towards-rich-descriptions-for | 1709.01295 | null | http://arxiv.org/abs/1709.01295v1 | http://arxiv.org/pdf/1709.01295v1.pdf | SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks | The ability to semantically interpret hand-drawn line sketches, although very
challenging, can pave way for novel applications in multimedia. We propose
SketchParse, the first deep-network architecture for fully automatic parsing of
freehand object sketches. SketchParse is configured as a two-level fully
convolutional ... | ['Sahil Manocha', 'R. Venkatesh Babu', 'Abhijat Biswas', 'Ravi Kiran Sarvadevabhatla', 'Isht Dwivedi'] | 2017-09-05 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.64898658e-01 1.75744712e-01 -1.24252573e-01 -5.56255400e-01
-8.18573534e-01 -9.38336790e-01 6.24012530e-01 -3.09440285e-01
-1.51678622e-01 2.05974415e-01 1.16506182e-01 -2.44969532e-01
1.28131688e-01 -9.32794333e-01 -1.05230546e+00 -1.72529563e-01
2.26483315e-01 6.76940799e-01 5.00915051e-01 -1.32313579... | [11.686574935913086, 0.453192800283432] |
016cb2e2-ce60-473e-b979-34ca92156ac3 | generative-neural-networks-for-anomaly | null | null | https://ieeexplore.ieee.org/abstract/document/8513816 | https://ieeexplore.ieee.org/abstract/document/8513816 | Generative Neural Networks for Anomaly Detection in Crowded Scenes | Security surveillance is critical to social harmony and people's peaceful life. It has a great impact on strengthening social stability and life safeguarding. Detecting anomaly timely, effectively and efficiently in video surveillance remains challenging. This paper proposes a new approach, called S 2 -VAE, for anomaly... | ['Chang Choi', 'Zhe Liu', 'Hichem Snoussi', 'Ce Li', 'Zhiwei Lin', 'Meina Qiao', 'Tian Wang'] | 2018-10-29 | null | null | null | null | ['abnormal-event-detection-in-video', 'semi-supervised-anomaly-detection', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'methodology'] | [-5.45742989e-01 -2.80036569e-01 2.58971304e-01 -1.19927712e-01
-2.45133191e-01 -8.87903273e-02 4.65546966e-01 -4.28965986e-01
-1.12158947e-01 4.34723943e-01 2.28769898e-01 -3.84077191e-01
1.64002389e-01 -8.83317053e-01 -6.93369508e-01 -7.67962217e-01
-2.17802048e-01 9.25014690e-02 5.03089190e-01 -5.34797966... | [7.884372711181641, 1.5129941701889038] |
cc672e14-fd96-4e6e-aacf-cfb022fd5377 | lightweight-high-performance-blind-image | 2303.13057 | null | https://arxiv.org/abs/2303.13057v1 | https://arxiv.org/pdf/2303.13057v1.pdf | Lightweight High-Performance Blind Image Quality Assessment | Blind image quality assessment (BIQA) is a task that predicts the perceptual quality of an image without its reference. Research on BIQA attracts growing attention due to the increasing amount of user-generated images and emerging mobile applications where reference images are unavailable. The problem is challenging du... | ['C. -C. Jay Kuo', 'Yong Yan', 'Xingze He', 'Yun-Cheng Wang', 'Zhanxuan Mei'] | 2023-03-23 | null | null | null | null | ['blind-image-quality-assessment', 'image-quality-assessment', 'image-cropping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.77171677e-01 -6.25748873e-01 1.60614073e-01 -2.70015895e-01
-9.65695083e-01 -1.11114465e-01 3.97627383e-01 -2.14863688e-01
-2.58779377e-01 5.42789400e-01 3.04874271e-01 -2.92951465e-01
-9.07156095e-02 -6.98633075e-01 -3.08818936e-01 -7.86365688e-01
2.26985529e-01 -6.05226643e-02 3.93975765e-01 -9.86515656... | [11.911710739135742, -1.7829917669296265] |
63eebd1f-3044-4110-9e66-3fc3de6467df | category-level-6d-object-pose-and-size | 2207.05444 | null | https://arxiv.org/abs/2207.05444v2 | https://arxiv.org/pdf/2207.05444v2.pdf | Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networks | It is difficult to precisely annotate object instances and their semantics in 3D space, and as such, synthetic data are extensively used for these tasks, e.g., category-level 6D object pose and size estimation. However, the easy annotations in synthetic domains bring the downside effect of synthetic-to-real (Sim2Real) ... | ['Kui Jia', 'Changxing Ding', 'Zewei Wei', 'Jiehong Lin'] | 2022-07-12 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 8.21860209e-02 1.35467499e-01 -6.16182089e-02 -4.71262664e-01
-7.92976916e-01 -6.43147171e-01 5.97434103e-01 -2.34021962e-01
-2.41107389e-01 3.76894593e-01 -7.73168951e-02 3.19320560e-01
-9.39493440e-03 -6.38317704e-01 -1.11561251e+00 -7.34093606e-01
2.93008953e-01 7.19599187e-01 4.10171062e-01 1.96074292... | [7.689124584197998, -2.7616403102874756] |
e902d651-b4c2-4afc-9723-b3c1c174749e | action-guidance-getting-the-best-of-sparse-1 | 2010.03956 | null | https://arxiv.org/abs/2010.03956v1 | https://arxiv.org/pdf/2010.03956v1.pdf | Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games | Training agents using Reinforcement Learning in games with sparse rewards is a challenging problem, since large amounts of exploration are required to retrieve even the first reward. To tackle this problem, a common approach is to use reward shaping to help exploration. However, an important drawback of reward shaping ... | ['Santiago Ontañón', 'Shengyi Huang'] | 2020-10-05 | action-guidance-getting-the-best-of-sparse | https://openreview.net/forum?id=1OQ90khuUGZ | https://openreview.net/pdf?id=1OQ90khuUGZ | null | ['real-time-strategy-games'] | ['playing-games'] | [-1.62313908e-01 1.74170583e-01 -2.27273442e-02 2.32744917e-01
-6.31180644e-01 -5.68185806e-01 2.69605130e-01 2.25821972e-01
-1.00768268e+00 1.35055161e+00 -3.30934703e-01 -3.18914652e-01
-3.09432775e-01 -9.00461674e-01 -6.01656497e-01 -8.27556610e-01
-3.39318931e-01 5.52400589e-01 2.87253827e-01 -6.15950525... | [3.8394851684570312, 1.7551366090774536] |
2671502b-e323-446b-bd2d-6dd388c74653 | enhancing-few-shot-ner-with-prompt-ordering | 2305.11791 | null | https://arxiv.org/abs/2305.11791v1 | https://arxiv.org/pdf/2305.11791v1.pdf | Enhancing Few-shot NER with Prompt Ordering based Data Augmentation | Recently, data augmentation (DA) methods have been proven to be effective for pre-trained language models (PLMs) in low-resource settings, including few-shot named entity recognition (NER). However, conventional NER DA methods are mostly aimed at sequence labeling models, i.e., token-level classification, and few are c... | ['Lidong Bing', 'De Wen Soh', 'Wenxuan Zhang', 'Liying Cheng', 'Huiming Wang'] | 2023-05-19 | null | null | null | null | ['few-shot-ner', 'named-entity-recognition-ner'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.63535714e-01 -1.13159701e-01 -7.28665814e-02 -4.12321866e-01
-4.79829431e-01 -4.80047882e-01 5.40084600e-01 8.60167518e-02
-8.85766923e-01 8.06212425e-01 3.82075906e-01 -3.75221312e-01
3.10482949e-01 -9.61885691e-01 -4.37233537e-01 -5.96363544e-01
4.21992898e-01 4.45221126e-01 1.67074248e-01 -4.64054018... | [9.752989768981934, 9.443215370178223] |
4ae958c1-36c6-4c5b-8e0c-8046400e37bd | geneface-generalized-and-high-fidelity-audio | 2301.13430 | null | https://arxiv.org/abs/2301.13430v1 | https://arxiv.org/pdf/2301.13430v1.pdf | GeneFace: Generalized and High-Fidelity Audio-Driven 3D Talking Face Synthesis | Generating photo-realistic video portrait with arbitrary speech audio is a crucial problem in film-making and virtual reality. Recently, several works explore the usage of neural radiance field in this task to improve 3D realness and image fidelity. However, the generalizability of previous NeRF-based methods to out-of... | ['Zhou Zhao', 'Jinzheng He', 'Jinglin Liu', 'Yi Ren', 'Ziyue Jiang', 'Zhenhui Ye'] | 2023-01-31 | null | null | null | null | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 2.85814494e-01 1.15278117e-01 1.08986564e-01 -4.67543662e-01
-1.07910383e+00 -3.30742687e-01 6.44410193e-01 -1.05748343e+00
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2.97525674e-01 -7.27575779e-01 -8.32050562e-01 -7.23071694e-01
3.24564159e-01 -1.35377068e-02 1.92147158e-02 -4.33911264... | [13.191535949707031, -0.4533918499946594] |
28451b88-8af3-470c-9ecc-873684c0279a | xiezhi-an-ever-updating-benchmark-for | 2306.05783 | null | https://arxiv.org/abs/2306.05783v2 | https://arxiv.org/pdf/2306.05783v2.pdf | Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation | New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive evaluation suite designed to assess holistic domain knowledge. Xiezhi comprises multiple-choice questions across 516 diverse disciplines rangin... | ['Shusen Wang', 'Zili Wang', 'Yanghua Xiao', 'Hongwei Feng', 'Weiguo Zheng', 'Wenhao Huang', 'Rui Xu', 'Qianyu He', 'Zihan Li', 'Zhuozhi Xiong', 'Sihang Jiang', 'Jianchen Wang', 'Lin Zhang', 'Haoning Ye', 'Xiaoxuan Zhu', 'Zhouhong Gu'] | 2023-06-09 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [-2.11435318e-01 1.06986910e-01 -2.05854088e-01 5.44401556e-02
-1.10714650e+00 -1.22282290e+00 8.38635206e-01 2.48711199e-01
-4.23307121e-01 9.33779955e-01 7.02365339e-01 -8.17763329e-01
-7.29130507e-01 -6.59683108e-01 -7.42765307e-01 6.88624457e-02
4.61210459e-01 3.93421501e-01 -3.54524761e-01 -2.55784929... | [10.756387710571289, 8.723339080810547] |
31174e1d-4f0b-4b2d-8b32-0f77553c83b4 | hybrid-space-learning-for-language-based | 2009.05381 | null | https://arxiv.org/abs/2009.05381v2 | https://arxiv.org/pdf/2009.05381v2.pdf | Dual Encoding for Video Retrieval by Text | This paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described exclusively in the form of a natural-language sentence, with no visual example provided. Given videos as sequences of frames and queries as sequences... | ['Meng Wang', 'Xun Yang', 'Gang Yang', 'Xun Wang', 'Xirong Li', 'Jianfeng Dong', 'Chaoxi Xu'] | 2020-09-10 | null | null | null | null | ['ad-hoc-video-search'] | ['computer-vision'] | [ 3.49799603e-01 -3.48404318e-01 -4.05189872e-01 -2.68062979e-01
-1.01380444e+00 -6.62976027e-01 9.18282509e-01 -7.86932409e-02
-2.45530143e-01 3.93933475e-01 2.60125339e-01 -4.38272953e-02
-1.47918537e-01 -4.72569138e-01 -8.50473583e-01 -6.59855723e-01
2.63971761e-02 1.03931189e-01 2.67996080e-02 7.22420216... | [10.243576049804688, 0.9854130744934082] |
c1488fb3-2188-47ef-a51e-9f0263c6a31c | detecting-signatures-of-early-stage-dementia | 2007.03615 | null | https://arxiv.org/abs/2007.03615v1 | https://arxiv.org/pdf/2007.03615v1.pdf | Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data | There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia. The emergence of state-of-the-art sensing platforms offers unprecedented opportunities for indirect and automatic evaluation of disease state through the lens of behavioural monitoring. This... | ['Raul Santos-Rodriguez', 'Yoav Ben-Shlomo', 'Niall Twomey', 'Weisong Yang', 'Rafael Poyiadzi', 'James Selwood', 'Liz Coulthard', 'Ian Craddock'] | 2020-07-03 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [ 3.10316920e-01 -2.84965992e-01 1.87840387e-01 -5.81996322e-01
-2.17283189e-01 -6.13223054e-02 7.12253571e-01 1.62336320e-01
-8.95865858e-01 7.60074556e-01 7.08299220e-01 -2.65912652e-01
-7.24727988e-01 -3.10965955e-01 2.65259296e-01 -6.40965343e-01
-7.77706742e-01 6.02984309e-01 1.48845375e-01 -1.48677960... | [13.496330261230469, 3.3401854038238525] |
252e33f9-d187-41e0-86ea-1e8ac53e267b | a-generalised-multi-factor-deep-learning | 2304.10686 | null | https://arxiv.org/abs/2304.10686v1 | https://arxiv.org/pdf/2304.10686v1.pdf | A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas | This paper proposes a generalised and robust multi-factor Gated Recurrent Unit (GRU) based Deep Learning (DL) model to forecast electricity load in distribution networks during wildfire seasons. The flexible modelling methods consider data input structure, calendar effects and correlation-based leading temperature cond... | ['David C. H. Wallom', 'Sarah N. Sparrow', 'Weijia Yang'] | 2023-04-21 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-8.13434720e-02 -1.38321027e-01 9.18278545e-02 -2.44779810e-01
-3.59992564e-01 -2.31400430e-01 8.22927594e-01 -1.02150008e-01
-3.21752429e-01 1.22710896e+00 1.51704639e-01 -9.11889613e-01
-4.12634134e-01 -1.31826842e+00 -3.85418952e-01 -1.01407027e+00
-7.51566112e-01 1.14570007e-01 -6.98207200e-01 -4.07300860... | [6.231354236602783, 2.8554182052612305] |
131dc278-a690-4205-8119-770cacb974d9 | learning-spatial-temporal-implicit-neural | 2303.13767 | null | https://arxiv.org/abs/2303.13767v2 | https://arxiv.org/pdf/2303.13767v2.pdf | Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution | Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video superresolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VS... | ['Lin Wang', 'Hongjian Wang', 'Minjie Liu', 'Zipeng Wang', 'Yunfan Lu'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lu_Learning_Spatial-Temporal_Implicit_Neural_Representations_for_Event-Guided_Video_Super-Resolution_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lu_Learning_Spatial-Temporal_Implicit_Neural_Representations_for_Event-Guided_Video_Super-Resolution_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-super-resolution'] | ['computer-vision'] | [ 3.02495360e-01 -5.11542559e-01 -2.24877466e-02 -2.87945956e-01
-9.35972750e-01 -4.14840549e-01 5.95216036e-01 -2.30885103e-01
-3.11251581e-01 6.36128247e-01 3.16860467e-01 1.92741603e-01
-2.98913959e-02 -9.08262432e-01 -8.10053647e-01 -7.43040800e-01
-5.50590828e-02 -2.22892210e-01 5.50071001e-01 -1.20523795... | [10.73806095123291, -1.772188425064087] |
cca16b43-b56e-479c-8d96-f28a2f72ad03 | the-unfairness-of-fair-machine-learning | 2302.02404 | null | https://arxiv.org/abs/2302.02404v3 | https://arxiv.org/pdf/2302.02404v3.pdf | The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default | In recent years fairness in machine learning (ML) has emerged as a highly active area of research and development. Most define fairness in simple terms, where fairness means reducing gaps in performance or outcomes between demographic groups while preserving as much of the accuracy of the original system as possible. T... | ['Chris Russell', 'Sandra Wachter', 'Brent Mittelstadt'] | 2023-02-05 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [ 1.40757829e-01 4.37788844e-01 -6.00197136e-01 -8.23434472e-01
-2.62631059e-01 -5.04312754e-01 6.56744838e-01 5.75290143e-01
-8.51504445e-01 1.14041805e+00 9.22310352e-01 -8.03167462e-01
-5.05405247e-01 -7.71678865e-01 -2.10884079e-01 -4.52763677e-01
6.62434816e-01 2.43533894e-01 -6.69249535e-01 -2.48236060... | [8.878751754760742, 5.5943474769592285] |
15bf843f-e101-4068-97da-fa0dff941b1b | a-unified-generative-framework-for-aspect | 2106.04300 | null | https://arxiv.org/abs/2106.04300v1 | https://arxiv.org/pdf/2106.04300v1.pdf | A Unified Generative Framework for Aspect-Based Sentiment Analysis | Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms. There exist seven subtasks in ABSA. Most studies only focus on the subsets of these subtasks, which leads to various complicated ABSA models while hard to solve these subtasks in a ... | ['Zheng Zhang', 'Xipeng Qiu', 'Tuo ji', 'Junqi Dai', 'Hang Yan'] | 2021-06-08 | a-unified-generative-framework-for-aspect-1 | https://aclanthology.org/2021.acl-long.188 | https://aclanthology.org/2021.acl-long.188.pdf | acl-2021-5 | ['aspect-term-extraction-and-sentiment', 'aspect-oriented-opinion-extraction', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.30026937e-01 -2.29852736e-01 9.57898349e-02 -5.73524415e-01
-1.04019952e+00 -9.14120555e-01 5.64563096e-01 -3.14400017e-01
3.17655392e-02 3.67486477e-01 3.70942563e-01 -3.70748162e-01
-8.74326658e-03 -6.42248929e-01 -5.04122078e-01 -6.25378430e-01
4.15901184e-01 4.47653085e-01 6.88560829e-02 -6.83617473... | [11.504339218139648, 6.652796745300293] |
be20946d-7cbe-4c1c-9ba3-d40fdb18ae82 | multi-modal-representation-learning-with-text | 2304.00719 | null | https://arxiv.org/abs/2304.00719v1 | https://arxiv.org/pdf/2304.00719v1.pdf | Multi-Modal Representation Learning with Text-Driven Soft Masks | We propose a visual-linguistic representation learning approach within a self-supervised learning framework by introducing a new operation, loss, and data augmentation strategy. First, we generate diverse features for the image-text matching (ITM) task via soft-masking the regions in an image, which are most relevant t... | ['Bohyung Han', 'Jaeyoo Park'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Park_Multi-Modal_Representation_Learning_With_Text-Driven_Soft_Masks_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Park_Multi-Modal_Representation_Learning_With_Text-Driven_Soft_Masks_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-matching'] | ['natural-language-processing'] | [ 6.77162468e-01 2.62053609e-01 -1.85379848e-01 -5.18048286e-01
-1.02179492e+00 -3.10654819e-01 9.14330065e-01 -9.65971593e-03
-6.42502487e-01 3.77326041e-01 5.06508231e-01 -1.51074737e-01
3.17542642e-01 -4.63089764e-01 -1.16327858e+00 -5.93130231e-01
3.84146422e-01 2.28010952e-01 3.85138392e-02 -1.14492022... | [10.800907135009766, 1.5144157409667969] |
34945a54-d87c-4daf-b45d-7ab3346242f7 | referring-video-object-segmentation-with | 2307.00536 | null | https://arxiv.org/abs/2307.00536v1 | https://arxiv.org/pdf/2307.00536v1.pdf | Referring Video Object Segmentation with Inter-Frame Interaction and Cross-Modal Correlation | Referring video object segmentation (RVOS) aims to segment the target object in a video sequence described by a language expression. Typical query-based methods process the video sequence in a frame-independent manner to reduce the high computational cost, which however affects the performance due to the lack of inter-... | ['Lefei Zhang', 'Fu Rong', 'Meng Lan'] | 2023-07-02 | null | null | null | null | ['referring-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.47246172e-02 -2.72452980e-01 -2.37415522e-01 -3.16824466e-01
-5.08990765e-01 -2.90246338e-01 3.13859850e-01 -6.85819313e-02
-4.25713301e-01 1.10140666e-01 8.44770968e-02 4.26436178e-02
1.16965376e-01 -7.21673608e-01 -5.98719835e-01 -6.09809279e-01
4.27606970e-01 8.68218169e-02 7.84887791e-01 -5.52540794... | [9.540692329406738, 0.253795325756073] |
9d80dde2-2e5b-4203-b652-4edf1f4f56bd | an-empirical-study-of-end-to-end-video | 2209.01540 | null | https://arxiv.org/abs/2209.01540v5 | https://arxiv.org/pdf/2209.01540v5.pdf | An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling | Masked visual modeling (MVM) has been recently proven effective for visual pre-training. While similar reconstructive objectives on video inputs (e.g., masked frame modeling) have been explored in video-language (VidL) pre-training, previous studies fail to find a truly effective MVM strategy that can largely benefit t... | ['Zicheng Liu', 'Lijuan Wang', 'William Yang Wang', 'Kevin Lin', 'Zhe Gan', 'Linjie Li', 'Tsu-Jui Fu'] | 2022-09-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fu_An_Empirical_Study_of_End-to-End_Video-Language_Transformers_With_Masked_Visual_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fu_An_Empirical_Study_of_End-to-End_Video-Language_Transformers_With_Masked_Visual_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-question-answering'] | ['computer-vision'] | [ 4.05597657e-01 1.41647875e-01 -3.79468024e-01 -2.91548222e-01
-9.85551834e-01 -4.16973859e-01 7.75744081e-01 -2.30925947e-01
-3.37009519e-01 4.50047314e-01 5.14340639e-01 -5.18422663e-01
3.44742805e-01 -4.55775619e-01 -1.22569060e+00 -4.19982672e-01
1.00999493e-02 9.79677364e-02 1.74033895e-01 1.11197531... | [10.275664329528809, 0.8019721508026123] |
9d293755-ddd7-490c-9420-bf329b0aa902 | points-to-patches-enabling-the-use-of-self | 2204.03957 | null | https://arxiv.org/abs/2204.03957v1 | https://arxiv.org/pdf/2204.03957v1.pdf | Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition | While the Transformer architecture has become ubiquitous in the machine learning field, its adaptation to 3D shape recognition is non-trivial. Due to its quadratic computational complexity, the self-attention operator quickly becomes inefficient as the set of input points grows larger. Furthermore, we find that the att... | ["Mark O'Connor", 'Magnus Oskarsson', 'Axel Berg'] | 2022-04-08 | null | null | null | null | ['3d-feature-matching', '3d-shape-recognition'] | ['computer-vision', 'computer-vision'] | [-2.05726101e-04 7.40287034e-03 1.12414517e-01 -1.98934644e-01
-8.96121383e-01 -7.67079175e-01 5.16166151e-01 3.16283643e-01
-7.96491429e-02 1.68074071e-01 2.13506535e-01 -3.83608341e-01
-9.03706551e-02 -9.09317553e-01 -8.09014618e-01 -3.41674805e-01
1.02771735e-02 7.19561756e-01 6.26668811e-01 -1.37524664... | [7.9796624183654785, -3.50689697265625] |
b49242a7-6658-4343-8d25-fdd923d10df4 | searching-for-waveforms-on-spatially-filtered | 2103.13853 | null | https://arxiv.org/abs/2103.13853v1 | https://arxiv.org/pdf/2103.13853v1.pdf | Searching for waveforms on spatially-filtered epileptic ECoG | Seizures are one of the defining symptoms in patients with epilepsy, and due to their unannounced occurrence, they can pose a severe risk for the individual that suffers it. New research efforts are showing a promising future for the prediction and preemption of imminent seizures, and with those efforts, a vast and div... | ['Austin J. Brockmeier', 'Carlos H. Mendoza-Cardenas'] | 2021-03-25 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.58435091e-01 -4.88504797e-01 1.58692271e-01 -1.07871249e-01
-7.00743258e-01 -6.22960210e-01 3.54372412e-01 2.89099216e-01
-1.02538869e-01 5.34219682e-01 4.63207334e-01 -2.31167197e-01
-7.72265673e-01 -3.19660932e-01 1.26858056e-01 -9.22376394e-01
-1.06409025e+00 1.35622069e-01 -9.85439271e-02 -9.46961567... | [13.1749849319458, 3.549422025680542] |
8f83af14-4689-4005-8a50-cfa1e30c91c0 | protein-dna-binding-sites-prediction-based-on | 2306.15912 | null | https://arxiv.org/abs/2306.15912v1 | https://arxiv.org/pdf/2306.15912v1.pdf | Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning | Protein-DNA interaction is critical for life activities such as replication, transcription, and splicing. Identifying protein-DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Impro... | ['Boxue Tian', 'Yufan Liu'] | 2023-06-28 | null | null | null | null | ['contrastive-learning', 'protein-language-model', 'contrastive-learning'] | ['computer-vision', 'medical', 'methodology'] | [ 1.21064499e-01 -4.42750812e-01 -4.24881905e-01 -3.63859236e-01
-8.63000393e-01 -6.03715777e-01 2.04682127e-01 2.25513652e-01
-4.17703331e-01 1.12440503e+00 -4.75660898e-02 -4.77226406e-01
6.54997379e-02 -5.90904951e-01 -6.90724790e-01 -1.24393666e+00
1.45738095e-01 3.59454900e-01 4.41100359e-01 -1.46915570... | [4.759156227111816, 5.588006973266602] |
ec96ecf9-3740-4013-8de8-c590ff1191c4 | deep-predictive-motion-tracking-in-magnetic | 1909.11625 | null | https://arxiv.org/abs/1909.11625v3 | https://arxiv.org/pdf/1909.11625v3.pdf | Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging | Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion and repeated acquisitions to ensure diagnostic-quality images are acquired. Nevertheless, visual monitoring of fetal motion based on display... | ['Ali Gholipour', 'Seyed Sadegh Mohseni Salehi', 'Ayush Singh'] | 2019-09-25 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 2.77164996e-01 1.28894866e-01 -2.47459654e-02 -5.16159296e-01
-5.32453239e-01 -7.01458335e-01 2.80117512e-01 -1.83300048e-01
-2.77588457e-01 1.41512468e-01 6.36915043e-02 -4.15032893e-01
-1.85654312e-01 -4.15895432e-01 -7.23724544e-01 -6.61682725e-01
-6.32754385e-01 5.44865727e-01 4.40338343e-01 3.45886976... | [13.969100952148438, -2.433586597442627] |
137c1b95-b1e6-4cc3-8b24-ff8c8e6ea268 | tpmil-trainable-prototype-enhanced-multiple | 2305.00696 | null | https://arxiv.org/abs/2305.00696v1 | https://arxiv.org/pdf/2305.00696v1.pdf | TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification | Digital pathology based on whole slide images (WSIs) plays a key role in cancer diagnosis and clinical practice. Due to the high resolution of the WSI and the unavailability of patch-level annotations, WSI classification is usually formulated as a weakly supervised problem, which relies on multiple instance learning (M... | ['ZongYuan Ge', 'Antonio Di Ieva', 'Dwarikanath Mahapatra', 'Sidong Liu', 'Deval Mehta', 'Litao Yang'] | 2023-05-01 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 1.87651619e-01 3.45672965e-01 -3.19340467e-01 -3.33927810e-01
-1.16025746e+00 -3.09532285e-01 4.90139991e-01 7.40781546e-01
-2.75036365e-01 5.81254959e-01 2.52180099e-01 -5.18851839e-02
-5.14843822e-01 -5.76458693e-01 -6.07056499e-01 -1.08707607e+00
1.93723515e-01 5.86415648e-01 8.68875682e-02 -5.34778051... | [15.083977699279785, -2.8334624767303467] |
0dbdb894-3c7f-49c9-9e88-b3dbe97035c8 | the-right-spin-learning-object-motion-from | 2203.00115 | null | https://arxiv.org/abs/2203.00115v1 | https://arxiv.org/pdf/2203.00115v1.pdf | The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields | Both a good understanding of geometrical concepts and a broad familiarity with objects lead to our excellent perception of moving objects. The human ability to detect and segment moving objects works in the presence of multiple objects, complex background geometry, motion of the observer and even camouflage. How humans... | ['Karteek Alahari', 'Cordelia Schmid', 'Erik Learned-Miller', 'Pia Bideau'] | 2022-02-28 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 1.96845427e-01 -2.81376064e-01 -1.11958692e-02 -1.72937736e-01
5.27826846e-02 -8.95678461e-01 6.20933950e-01 -2.27042824e-01
-5.34413397e-01 2.95211375e-01 -1.76455483e-01 -3.15541148e-01
-7.18606710e-02 -5.98016858e-01 -6.90156579e-01 -8.31689000e-01
1.21707864e-01 3.24001342e-01 4.80119616e-01 -2.39319026... | [8.985719680786133, -0.3763781487941742] |
4b0c7f78-ef4e-4022-9069-f4c30b672c7b | open-vocabulary-attribute-detection | 2211.12914 | null | https://arxiv.org/abs/2211.12914v2 | https://arxiv.org/pdf/2211.12914v2.pdf | Open-vocabulary Attribute Detection | Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on object classes, whereas research on object attributes is limited due to the lack of a reliable attribute-focused evaluation benchmark. This pap... | ['Thomas Brox', 'Simon Ging', 'Sudhanshu Mittal', 'María A. Bravo'] | 2022-11-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bravo_Open-Vocabulary_Attribute_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bravo_Open-Vocabulary_Attribute_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-vocabulary-object-detection', 'open-vocabulary-attribute-detection'] | ['computer-vision', 'computer-vision'] | [-2.99056899e-03 4.80144583e-02 -2.98902541e-01 -5.72424948e-01
-1.17256963e+00 -6.16550684e-01 9.66120660e-01 4.78253514e-01
-4.54815924e-01 4.65846032e-01 3.45879614e-01 1.72430843e-01
4.40899581e-01 -5.36980987e-01 -6.51154280e-01 -3.53173852e-01
1.31035671e-01 9.57034707e-01 1.60377808e-02 -6.93668500... | [9.929677963256836, 1.7223104238510132] |
738e661a-a650-40ad-9183-4653e11fe9a7 | natural-evolution-strategies-and-quantum | 2005.04447 | null | https://arxiv.org/abs/2005.04447v2 | https://arxiv.org/pdf/2005.04447v2.pdf | Natural evolution strategies and variational Monte Carlo | A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization. Recent work of Gomes et al. [2019] on heuristic combinatorial optimization using neural quantum states is pedagogically ... | ['Shravan Veerapaneni', 'Tianchen Zhao', 'James Stokes', 'Giuseppe Carleo'] | 2020-05-09 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 1.56831771e-01 3.22186351e-01 -3.73981625e-01 -1.30446389e-01
-5.72421432e-01 -4.58052725e-01 4.05819148e-01 1.95044369e-01
-5.75415790e-01 1.16646707e+00 -1.91437036e-01 -1.02708630e-01
-4.55280036e-01 -1.18637896e+00 -5.48772454e-01 -9.35523331e-01
1.15992486e-01 6.69762731e-01 -2.74223089e-01 -8.25413465... | [5.575254917144775, 4.935143947601318] |
fab2f8cf-929c-4b1c-bf5d-4ea4f3512106 | merge-double-thompson-sampling-for-large | 1812.04412 | null | https://arxiv.org/abs/1812.04412v2 | https://arxiv.org/pdf/1812.04412v2.pdf | MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation | Online ranker evaluation is one of the key challenges in information retrieval. While the preferences of rankers can be inferred by interleaving methods, the problem of how to effectively choose the ranker pair that generates the interleaved list without degrading the user experience too much is still challenging. On t... | ['Masrour Zoghi', 'Ilya Markov', 'Maarten de Rijke', 'Chang Li'] | 2018-12-11 | null | null | null | null | ['online-ranker-evaluation'] | ['miscellaneous'] | [ 1.27869755e-01 -3.49225521e-01 -6.39377832e-01 -2.83108801e-01
-1.55842209e+00 -1.11081636e+00 5.61558567e-02 1.18955508e-01
-5.59383392e-01 7.65520632e-01 5.89539558e-02 -5.78540921e-01
-1.12184656e+00 -4.43617582e-01 -6.04244530e-01 -8.13506007e-01
-4.69993800e-01 1.14879668e+00 -8.95596668e-02 -2.26357341... | [4.6884541511535645, 3.3872735500335693] |
886c4a20-2573-4d7b-a1fb-aed181182a80 | scenario-based-cost-optimization-of-water | 2307.00845 | null | https://arxiv.org/abs/2307.00845v1 | https://arxiv.org/pdf/2307.00845v1.pdf | Scenario Based Cost Optimization of Water Distribution Networks Powered by Grid-Connected Photovoltaic Systems | The paper presents a predictive control method for the water distribution networks (WDNs) powered by photovoltaics (PVs) and the electrical grid. This builds on the controller introduced in a previous study and is designed to reduce the economic costs associated with operating the WDN. To account for the uncertainty of... | ['John Leth', 'Jan Dimon Bendtsen', 'Carsten Kallesøe', 'Mirhan Ürkmez'] | 2023-07-03 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 1.10853903e-01 3.30511153e-01 3.00311297e-01 8.11078995e-02
5.25995679e-02 -5.36817491e-01 6.88434780e-01 2.05492809e-01
2.27504537e-01 1.32961726e+00 1.33268148e-01 -2.23784335e-02
-7.36532927e-01 -9.61855352e-01 -1.56116217e-01 -1.12179863e+00
-8.35581496e-02 3.19640517e-01 -1.92802325e-01 -1.79310098... | [5.676445007324219, 2.5356333255767822] |
165c7cca-f9b8-42f6-82d2-98c78fbbb775 | query-efficient-decision-based-black-box | 2307.00477 | null | https://arxiv.org/abs/2307.00477v1 | https://arxiv.org/pdf/2307.00477v1.pdf | Query-Efficient Decision-based Black-Box Patch Attack | Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible adversarial perturbations. As a complementary type of adversary, patch attacks that introduce perceptible perturbations to the images have attracted the interest of researchers. Existing patch attacks rely on the architecture of the m... | ['Wenqiang Zhang', 'Shouhong Ding', 'Shuang Wu', 'Bo Li', 'Zhaoyu Chen'] | 2023-07-02 | null | null | null | null | ['face-verification'] | ['computer-vision'] | [ 3.16113263e-01 6.51430711e-02 -8.32577795e-02 -1.58297256e-01
-6.08996511e-01 -9.16396618e-01 4.29870665e-01 -2.17396170e-01
-3.25785875e-01 3.59790713e-01 -4.84988093e-01 -5.79834878e-01
3.30514982e-02 -9.37582254e-01 -1.21883821e+00 -8.96502435e-01
-1.30824581e-01 3.09803393e-02 2.84692079e-01 -3.11675847... | [5.513742446899414, 7.887500762939453] |
711542b7-6493-4270-9c87-cf6fea679c06 | netherlands-dataset-a-new-public-dataset-for | 1904.00770 | null | http://arxiv.org/abs/1904.00770v1 | http://arxiv.org/pdf/1904.00770v1.pdf | Netherlands Dataset: A New Public Dataset for Machine Learning in Seismic Interpretation | Machine learning and, more specifically, deep learning algorithms have seen
remarkable growth in their popularity and usefulness in the last years. This is
arguably due to three main factors: powerful computers, new techniques to train
deeper networks and larger datasets. Although the first two are readily
available in... | ['Emilio Vital Brazil', 'Lais Baroni', 'Reinaldo Mozart Silva', 'Rodrigo S. Ferreira', 'Daniel Civitarese', 'Daniela Szwarcman'] | 2019-03-26 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [-6.97120875e-02 2.05083311e-01 5.42178079e-02 -3.16629827e-01
-9.14942741e-01 -5.22029638e-01 5.49034595e-01 2.05711365e-01
-7.62259364e-01 8.52724612e-01 2.64621645e-01 -3.38601232e-01
-3.54379326e-01 -1.13754582e+00 -6.39486849e-01 -9.05196846e-01
-4.88775402e-01 6.33306324e-01 2.50660479e-01 -4.58585143... | [7.068394184112549, 2.324829339981079] |
6323c906-2fa2-4a6a-9d9d-25b67d9b162d | several-refinements-of-modulation-spectrum | null | null | https://aclanthology.org/O15-1010 | https://aclanthology.org/O15-1010.pdf | 調變頻譜分解之改良於強健性語音辨識(Several Refinements of Modulation Spectrum Factorization for Robust Speech Recognition) [In Chinese] | null | ['Hsin-Min Wang', 'Kuan-Yu Chen', 'Hsiao-Tsung Hung', 'Ting-Hao Chang', 'Berlin Chen'] | 2015-10-01 | several-refinements-of-modulation-spectrum-1 | https://aclanthology.org/O15-1010 | https://aclanthology.org/O15-1010.pdf | roclingijclclp-2015-10 | ['robust-speech-recognition'] | ['speech'] | [-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... | [-7.1816020011901855, 3.8985209465026855] |
26e9d7b8-2293-4bc0-a0a0-58151f1a4b5a | multi-label-few-shot-learning-for-aspect | 2105.14174 | null | https://arxiv.org/abs/2105.14174v1 | https://arxiv.org/pdf/2105.14174v1.pdf | Multi-Label Few-Shot Learning for Aspect Category Detection | Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. In this paper, we formulate ACD in the few-shot learning scenario. However, existing few-shot learning approaches mainly focus on single-label predictions. These methods can not work well for the ACD ta... | ['Zhong Su', 'Renhong Cheng', 'Tiegang Gao', 'Hang Gao', 'Chao Xue', 'Honglei Guo', 'Shiwan Zhao', 'Mengting Hu'] | 2021-05-29 | null | https://aclanthology.org/2021.acl-long.495 | https://aclanthology.org/2021.acl-long.495.pdf | acl-2021-5 | ['aspect-category-detection'] | ['natural-language-processing'] | [ 6.87896907e-02 1.02316357e-01 -5.32691240e-01 -6.97247326e-01
-1.19379508e+00 -1.90862179e-01 5.61604202e-01 2.99978077e-01
-3.67970943e-01 3.64244401e-01 3.77458513e-01 7.92526603e-02
9.85320956e-02 -7.97719955e-01 -3.44425738e-01 -6.35664880e-01
4.81434494e-01 5.77751458e-01 1.94083095e-01 -1.34433746... | [11.272027969360352, 6.599706649780273] |
4b05acac-ebfe-4452-a093-17f9b21ee91b | non-adaptive-adaptive-sampling-on-turnstile | 2004.10969 | null | https://arxiv.org/abs/2004.10969v1 | https://arxiv.org/pdf/2004.10969v1.pdf | Non-Adaptive Adaptive Sampling on Turnstile Streams | Adaptive sampling is a useful algorithmic tool for data summarization problems in the classical centralized setting, where the entire dataset is available to the single processor performing the computation. Adaptive sampling repeatedly selects rows of an underlying matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$, where $n... | ['Samson Zhou', 'Ilya Razenshteyn', 'David P. Woodruff', 'Sepideh Mahabadi'] | 2020-04-23 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 5.08633256e-01 7.20895082e-02 -1.61982045e-01 7.83440918e-02
-1.07253063e+00 -7.51496792e-01 7.72034302e-02 9.17781413e-01
-5.71975350e-01 6.71327710e-01 1.42674670e-01 -1.69470385e-01
-4.78321671e-01 -9.18734133e-01 -8.88707638e-01 -8.88661563e-01
-5.82352221e-01 1.07364833e+00 2.89108515e-01 -1.34751871... | [6.594390869140625, 4.870911598205566] |
e6a25784-22f6-45b8-80d3-e8a1f657fa9b | multi-oriented-text-detection-and | 1707.07150 | null | http://arxiv.org/abs/1707.07150v2 | http://arxiv.org/pdf/1707.07150v2.pdf | Multi-Oriented Text Detection and Verification in Video Frames and Scene Images | In this paper, we bring forth a novel approach of video text detection using
Fourier-Laplacian filtering in the frequency domain that includes a
verification technique using Hidden Markov Model (HMM). The proposed approach
deals with the text region appearing not only in horizontal or vertical
directions, but also in a... | ['Umapada Pal', 'Ayan Kumar Bhunia', 'Aneeshan Sain', 'Partha Pratim Roy'] | 2017-07-22 | null | null | null | null | ['curved-text-detection'] | ['computer-vision'] | [ 6.55870259e-01 -2.86828339e-01 1.81242824e-01 2.85840444e-02
-3.68794411e-01 -6.28156304e-01 7.85107434e-01 1.97922736e-01
-4.04063612e-01 4.97515231e-01 -8.78632739e-02 -1.92175075e-01
-6.33700490e-02 -6.34189606e-01 -4.12350714e-01 -7.59955347e-01
2.99710274e-01 4.82016027e-01 6.71571493e-01 1.48985060... | [11.909997940063477, 2.47222638130188] |
2b71094a-bb3b-446e-ac31-b044beee658c | fusqa-fetal-ultrasound-segmentation-quality | 2303.04418 | null | https://arxiv.org/abs/2303.04418v1 | https://arxiv.org/pdf/2303.04418v1.pdf | FUSQA: Fetal Ultrasound Segmentation Quality Assessment | Deep learning models have been effective for various fetal ultrasound segmentation tasks. However, generalization to new unseen data has raised questions about their effectiveness for clinical adoption. Normally, a transition to new unseen data requires time-consuming and costly quality assurance processes to validate ... | ['Mohammad Yaqub', 'Ibrahim Almakk', 'Sevim Cengiz'] | 2023-03-08 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 2.88320452e-01 6.37787938e-01 1.99282721e-01 -7.23859370e-01
-9.63721335e-01 -6.70348108e-01 2.66436208e-02 5.44442236e-01
-3.42527002e-01 3.89149845e-01 -2.30925247e-01 -4.76211905e-01
-1.84910953e-01 -7.94818044e-01 -7.94046223e-01 -5.55419683e-01
-2.44632229e-01 8.50338042e-01 1.46986455e-01 2.81697780... | [14.195574760437012, -2.381269931793213] |
8d41327f-a7b1-45c6-8e53-9a7573b6fe13 | nlp-analytics-in-finance-with-dore-a-french | null | null | https://aclanthology.org/2020.lrec-1.275 | https://aclanthology.org/2020.lrec-1.275.pdf | NLP Analytics in Finance with DoRe: A French 250M Tokens Corpus of Corporate Annual Reports | Recent advances in neural computing and word embeddings for semantic processing open many new applications areas which had been left unaddressed so far because of inadequate language understanding capacity. But this new kind of approaches rely even more on training data to be operational. Corpora for financial applicat... | ['Corentin Masson', 'Patrick Paroubek'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['stock-market-prediction'] | ['time-series'] | [-4.86858189e-01 1.54117957e-01 -2.97434896e-01 -3.04205000e-01
-3.80118877e-01 -9.88968372e-01 9.55430925e-01 5.60585678e-01
-8.21452141e-01 7.80886114e-01 5.45088947e-01 -7.56629109e-01
-3.31023693e-01 -1.03009772e+00 -2.76566535e-01 -4.07305390e-01
2.49563158e-02 5.59799492e-01 -1.24002337e-01 -5.33956766... | [11.08462142944336, 7.126943588256836] |
ba09dd94-9847-4750-9849-ca5f679f3cec | assessing-post-deletion-in-sina-weibo-multi | 1906.10861 | null | https://arxiv.org/abs/1906.10861v2 | https://arxiv.org/pdf/1906.10861v2.pdf | Assessing Post Deletion in Sina Weibo: Multi-modal Classification of Hot Topics | Widespread Chinese social media applications such as Weibo are widely known for monitoring and deleting posts to conform to Chinese government requirements. In this paper, we focus on analyzing a dataset of censored and uncensored posts in Weibo. Despite previous work that only considers text content of posts, we take ... | ['King-wa Fu', 'Jedidiah R. Crandall', 'Rajkumar Pandi', 'Michael Carl Tschantz', 'Dahlia Qiu Shi', 'Meisam Navaki Arefi', 'Miao Sha'] | 2019-06-26 | assessing-post-deletion-in-sina-weibo-multi-1 | https://aclanthology.org/D19-5001 | https://aclanthology.org/D19-5001.pdf | ws-2019-11 | ['multi-modal-classification'] | ['miscellaneous'] | [-4.38036770e-01 -1.15452014e-01 -1.82532415e-01 -5.29830337e-01
-1.00424552e+00 -1.34015501e+00 9.39777136e-01 3.65581483e-01
-1.99489787e-01 6.13634765e-01 6.92603350e-01 -7.22720981e-01
5.75501770e-02 -8.50809038e-01 -8.49416137e-01 -5.33014715e-01
7.16162249e-02 2.99628347e-01 -2.82493860e-01 6.91854581... | [8.178330421447754, 10.25284194946289] |
71918b96-330d-4a4f-a44c-837810c3317c | sgl-speaking-the-graph-languages-of-semantic | null | null | https://aclanthology.org/2021.naacl-main.30 | https://aclanthology.org/2021.naacl-main.30.pdf | SGL: Speaking the Graph Languages of Semantic Parsing via Multilingual Translation | Graph-based semantic parsing aims to represent textual meaning through directed graphs. As one of the most promising general-purpose meaning representations, these structures and their parsing have gained a significant interest momentum during recent years, with several diverse formalisms being proposed. Yet, owing to ... | ['Roberto Navigli', 'Rocco Tripodi', 'Luigi Procopio'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['ucca-parsing'] | ['natural-language-processing'] | [ 4.49545264e-01 4.68696564e-01 -2.06367806e-01 -4.72210556e-01
-1.32865584e+00 -8.37308347e-01 7.68743813e-01 1.53533980e-01
-3.15852851e-01 8.58084738e-01 4.06684607e-01 -5.05083919e-01
1.95392564e-01 -7.60111690e-01 -8.47546220e-01 -4.22025949e-01
2.69980520e-01 7.28192925e-01 -1.30274847e-01 -4.94204134... | [10.548314094543457, 9.3319673538208] |
4f94ba46-348d-4ddd-ba8a-1e4f89dd1404 | a-force-sensing-surgical-drill-for-real-time | 2304.02583 | null | https://arxiv.org/abs/2304.02583v1 | https://arxiv.org/pdf/2304.02583v1.pdf | A force-sensing surgical drill for real-time force feedback in robotic mastoidectomy | Purpose: Robotic assistance in otologic surgery can reduce the task load of operating surgeons during the removal of bone around the critical structures in the lateral skull base. However, safe deployment into the anatomical passageways necessitates the development of advanced sensing capabilities to actively limit the... | ['Deepa Galaiya', 'Russell Taylor', 'Francis Creighton', 'Katherina Sapozhnikov', 'Harsha Mohan', 'Seena Vafaee', 'Aditi Kishore', 'Manish Sahu', 'Anna Goodridge', 'Yuxin Chen'] | 2023-04-05 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-2.92776138e-01 6.57655835e-01 1.98286459e-01 4.11554784e-01
-3.27603787e-01 -3.90973181e-01 -2.87466913e-01 -7.71796778e-02
-7.26048648e-01 2.70492196e-01 2.27979958e-01 -1.68129817e-01
-4.55053091e-01 1.76803079e-02 -6.05635226e-01 -5.06487310e-01
-4.57980782e-01 2.53971249e-01 3.96277398e-01 -4.82123196... | [13.766202926635742, -3.014909505844116] |
86a3c5b6-12db-44b1-818d-4a82c2bc688f | speaker-verification-across-ages | 2306.07501 | null | https://arxiv.org/abs/2306.07501v1 | https://arxiv.org/pdf/2306.07501v1.pdf | Speaker Verification Across Ages: Investigating Deep Speaker Embedding Sensitivity to Age Mismatch in Enrollment and Test Speech | In this paper, we study the impact of the ageing on modern deep speaker embedding based automatic speaker verification (ASV) systems. We have selected two different datasets to examine ageing on the state-of-the-art ECAPA-TDNN system. The first dataset, used for addressing short-term ageing (up to 10 years time differe... | ['Tomi Kinnunen', 'Md Sahidullah', 'Vishwanath Pratap Singh'] | 2023-06-13 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-1.29140422e-01 1.27074793e-01 1.51241124e-01 -3.66176367e-01
-6.94004297e-01 -3.42363238e-01 7.78425336e-01 1.45942867e-01
-9.07672703e-01 4.63664174e-01 6.83313072e-01 -6.91072941e-01
1.78491637e-01 -3.48724246e-01 -4.40254897e-01 -6.52516961e-01
-4.53186184e-02 1.16903894e-01 -1.38563037e-01 -3.29176515... | [14.307567596435547, 6.154906749725342] |
9b5d8732-ad2f-4bef-9f4e-4f307c47d8cd | martingale-posterior-neural-processes | 2304.09431 | null | https://arxiv.org/abs/2304.09431v1 | https://arxiv.org/pdf/2304.09431v1.pdf | Martingale Posterior Neural Processes | A Neural Process (NP) estimates a stochastic process implicitly defined with neural networks given a stream of data, rather than pre-specifying priors already known, such as Gaussian processes. An ideal NP would learn everything from data without any inductive biases, but in practice, we often restrict the class of sto... | ['Juho Lee', 'Edwin Fong', 'Giung Nam', 'Eunggu Yun', 'Hyungi Lee'] | 2023-04-19 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 1.69989452e-01 3.62520754e-01 5.84585313e-03 -4.79530513e-01
-5.96018195e-01 -5.79998136e-01 1.03969073e+00 -6.17029630e-02
-4.52952445e-01 1.00432944e+00 1.91603854e-01 -2.77090073e-01
-1.49320811e-01 -1.18863475e+00 -1.19753039e+00 -8.62931192e-01
-2.83229095e-03 9.20490265e-01 -5.19797988e-02 4.31770682... | [7.043673515319824, 3.84466290473938] |
e77704bb-f8f0-4ccc-b101-80e172383fbc | matt-a-manifold-attention-network-for-eeg | 2210.01986 | null | https://arxiv.org/abs/2210.01986v1 | https://arxiv.org/pdf/2210.01986v1.pdf | MAtt: A Manifold Attention Network for EEG Decoding | Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL)-based EEG decoders offer improved performances, the development of geometric learning (GL) has attracted much attention for offering exceptional... | ['Chun-Shu Wei', 'Jing-Lun Chou', 'Yue-Ting Pan'] | 2022-10-05 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 1.53596401e-02 -5.23916371e-02 5.56767285e-01 -3.23954731e-01
-5.11724949e-01 -1.57818004e-01 4.93819147e-01 -2.17600465e-01
-1.37899846e-01 5.71589351e-01 1.33906931e-01 -2.64053494e-01
-8.23947728e-01 -1.55575365e-01 -7.19621718e-01 -8.74303758e-01
-7.80128419e-01 2.40892783e-01 -6.05795801e-01 -1.22152850... | [13.041927337646484, 3.4791946411132812] |
4511808b-71d4-4722-84ee-bd3fe9c5e018 | automated-discovery-of-mathematical-1 | 2011.04521 | null | https://arxiv.org/abs/2011.04521v1 | https://arxiv.org/pdf/2011.04521v1.pdf | Automated Discovery of Mathematical Definitions in Text with Deep Neural Networks | Automatic definition extraction from texts is an important task that has numerous applications in several natural language processing fields such as summarization, analysis of scientific texts, automatic taxonomy generation, ontology generation, concept identification, and question answering. For definitions that are c... | ['Lior Reznik', 'Sergey Shevchuk', 'Marina Litvak', 'Natalia Vanetik'] | 2020-11-09 | null | null | null | null | ['definition-extraction'] | ['natural-language-processing'] | [ 7.41041541e-01 7.52195567e-02 -1.23641774e-01 -3.53544950e-01
-4.54747975e-01 -6.75187707e-01 7.83363938e-01 1.05418348e+00
-7.45107114e-01 8.06870282e-01 2.33437896e-01 -6.22919917e-01
-3.50159049e-01 -1.14566684e+00 -3.80154580e-01 -4.35707092e-01
6.40719160e-02 2.10997120e-01 -1.50291696e-01 -2.78704464... | [10.162980079650879, 8.822569847106934] |
cc0cb748-5970-4cd2-9069-5da24176b3a2 | sentence-representations-via-gaussian | 2305.12990 | null | https://arxiv.org/abs/2305.12990v1 | https://arxiv.org/pdf/2305.12990v1.pdf | Sentence Representations via Gaussian Embedding | Recent progress in sentence embedding, which represents the meaning of a sentence as a point in a vector space, has achieved high performance on tasks such as a semantic textual similarity (STS) task. However, sentence representations as a point in a vector space can express only a part of the diverse information that ... | ['Koichi Takeda', 'Ryohei Sasano', 'Hayato Tsukagoshi', 'Shohei Yoda'] | 2023-05-22 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 2.78300583e-01 1.08275078e-01 -2.72823870e-01 -9.15821016e-01
-2.89815158e-01 -5.94416797e-01 1.01593578e+00 9.57476139e-01
-3.42025369e-01 4.01919156e-01 7.02422082e-01 -5.73489845e-01
-4.90085147e-02 -9.15211380e-01 -4.05887097e-01 -4.11341846e-01
1.09379388e-01 5.36057353e-01 6.98636426e-03 -7.76700556... | [10.892816543579102, 8.866333961486816] |
2fdded08-9182-4561-b712-56ee3f9a9969 | learning-a-single-convolutional-layer-model | 2305.14039 | null | https://arxiv.org/abs/2305.14039v1 | https://arxiv.org/pdf/2305.14039v1.pdf | Learning a Single Convolutional Layer Model for Low Light Image Enhancement | Low-light image enhancement (LLIE) aims to improve the illuminance of images due to insufficient light exposure. Recently, various lightweight learning-based LLIE methods have been proposed to handle the challenges of unfavorable prevailing low contrast, low brightness, etc. In this paper, we have streamlined the archi... | ['Wenpeng Ding', 'Gang Li', 'Haichuan Ma', 'Zhenzhong Chen', 'Daiqin Yang', 'Baoxin Teng', 'Yuantong Zhang'] | 2023-05-23 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 3.41107249e-01 -5.51138163e-01 1.06017761e-01 -4.03837472e-01
-4.73773211e-01 1.76489186e-02 2.31973827e-01 -2.86806643e-01
-5.50800383e-01 7.12321341e-01 -2.44015940e-02 -1.04666486e-01
-5.44537511e-03 -5.77291846e-01 -6.73898697e-01 -1.06617510e+00
2.47522160e-01 -9.15089309e-01 3.07610512e-01 -1.44699022... | [10.752089500427246, -2.4559710025787354] |
28f6a275-51a1-4099-9a31-5acd31e40887 | improving-back-translation-with-uncertainty | 1909.00157 | null | https://arxiv.org/abs/1909.00157v1 | https://arxiv.org/pdf/1909.00157v1.pdf | Improving Back-Translation with Uncertainty-based Confidence Estimation | While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy. In this work, we propose to quantify the confidenc... | ['Maosong Sun', 'Chao Wang', 'Yang Liu', 'Shuo Wang', 'Huanbo Luan'] | 2019-08-31 | improving-back-translation-with-uncertainty-1 | https://aclanthology.org/D19-1073 | https://aclanthology.org/D19-1073.pdf | ijcnlp-2019-11 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 1.30698606e-01 2.69891769e-01 -4.08922553e-01 -5.01120031e-01
-1.64431465e+00 -7.12988019e-01 6.91260517e-01 -2.48691335e-01
-4.74706441e-01 1.45783651e+00 1.80691898e-01 -7.26044774e-01
6.31608248e-01 -4.37323660e-01 -1.10122168e+00 -2.64064111e-02
6.00991070e-01 9.33091640e-01 -5.07589757e-01 -2.46515676... | [11.625262260437012, 10.326958656311035] |
a38cedf7-b247-43dd-977a-129aa1c889d6 | efficient-learning-of-high-level-plans-from | 2303.09628 | null | https://arxiv.org/abs/2303.09628v1 | https://arxiv.org/pdf/2303.09628v1.pdf | Efficient Learning of High Level Plans from Play | Real-world robotic manipulation tasks remain an elusive challenge, since they involve both fine-grained environment interaction, as well as the ability to plan for long-horizon goals. Although deep reinforcement learning (RL) methods have shown encouraging results when planning end-to-end in high-dimensional environmen... | ['Stelian Coros', 'Georg Martius', 'Otmar Hilliges', 'Marco Bagatella', 'Núria Armengol Urpí'] | 2023-03-16 | null | null | null | null | ['motion-planning'] | ['robots'] | [-5.27376309e-03 1.78536788e-01 -4.50842381e-01 -1.22109741e-01
-1.05699551e+00 -5.42603970e-01 7.44122148e-01 1.17279992e-01
-5.55310905e-01 8.36997330e-01 4.89155173e-01 -3.24343652e-01
-3.08767349e-01 -6.61191463e-01 -1.13203216e+00 -3.16802323e-01
-7.30388284e-01 7.98844755e-01 5.20805240e-01 -3.46154153... | [4.51918363571167, 0.9849057793617249] |
080e8e56-7bf2-4458-8a84-5d544c7ea062 | shared-logistic-normal-distributions-for-soft | null | null | https://aclanthology.org/N09-1009 | https://aclanthology.org/N09-1009.pdf | Shared Logistic Normal Distributions for Soft Parameter Tying in Unsupervised Grammar Induction | We present a family of priors over probabilistic grammar weights, called the shared logistic normal distribution. This family extends the partitioned logistic normal distribution, enabling factored covariance between the probabilities of different derivation events in the probabilistic grammar, providing a new way to e... | ['Noah A. Smith', 'Shay Cohen'] | 2009-06-01 | null | null | null | null | ['dependency-grammar-induction', 'unsupervised-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [-1.27439380e-01 4.80653912e-01 -8.09957981e-02 -7.28809178e-01
-1.10528231e+00 -6.39980614e-01 7.57457197e-01 -1.59850836e-01
-5.68418980e-01 8.28724980e-01 3.49895239e-01 -5.70455968e-01
9.77219455e-03 -7.06160128e-01 -8.21972549e-01 -8.52532685e-01
-2.13994548e-01 1.20375943e+00 9.20274034e-02 -7.82573447... | [10.385549545288086, 9.699658393859863] |
fac71cd8-8123-4a47-aa98-4e5f6c972f8b | occlusion-guided-compact-template-learning | 1903.04752 | null | http://arxiv.org/abs/1903.04752v2 | http://arxiv.org/pdf/1903.04752v2.pdf | Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition | Concatenation of the deep network representations extracted from different
facial patches helps to improve face recognition performance. However, the
concatenated facial template increases in size and contains redundant
information. Previous solutions aim to reduce the dimensionality of the facial
template without cons... | ['Ioannis A. Kakadiaris', 'Yuhang Wu'] | 2019-03-12 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 2.91022539e-01 1.48154125e-02 1.01510763e-01 -5.10452509e-01
-5.13721526e-01 -4.15805697e-01 3.71308655e-01 -4.47163701e-01
-1.10334225e-01 2.54636765e-01 -1.12884738e-01 -5.04525239e-03
-2.40692660e-01 -7.76122510e-01 -6.39018774e-01 -1.05232179e+00
5.83806634e-01 3.72816361e-02 -2.52199531e-01 2.54931394... | [13.158958435058594, 0.4359027147293091] |
d8859631-f363-4857-a496-ed3b75cdb88c | gedi-generative-discriminator-guided-sequence | 2009.06367 | null | https://arxiv.org/abs/2009.06367v2 | https://arxiv.org/pdf/2009.06367v2.pdf | GeDi: Generative Discriminator Guided Sequence Generation | While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of the distribution they generate. This is especially problematic because datasets used for training large LMs usually contain significant tox... | ['Nazneen Fatema Rajani', 'Richard Socher', 'Akhilesh Deepak Gotmare', 'Shafiq Joty', 'Bryan McCann', 'Ben Krause', 'Nitish Shirish Keskar'] | 2020-09-14 | null | https://aclanthology.org/2021.findings-emnlp.424 | https://aclanthology.org/2021.findings-emnlp.424.pdf | findings-emnlp-2021-11 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 2.42562622e-01 2.03047141e-01 -2.74019957e-01 2.24811539e-01
-7.30858147e-01 -1.15793705e+00 8.73794019e-01 3.99006605e-01
-2.56198555e-01 1.27599454e+00 1.81202784e-01 -5.07106960e-01
2.13697329e-01 -1.22950852e+00 -6.76822722e-01 -7.72264004e-01
1.39040574e-01 7.31862247e-01 -5.86170703e-02 -4.18877691... | [11.666237831115723, 9.120566368103027] |
30df1ccf-6f6f-4273-a049-0646980402a8 | assessing-word-importance-using-models | 2305.19689 | null | https://arxiv.org/abs/2305.19689v1 | https://arxiv.org/pdf/2305.19689v1.pdf | Assessing Word Importance Using Models Trained for Semantic Tasks | Many NLP tasks require to automatically identify the most significant words in a text. In this work, we derive word significance from models trained to solve semantic task: Natural Language Inference and Paraphrase Identification. Using an attribution method aimed to explain the predictions of these models, we derive i... | ['François Yvon', 'Ondřej Bojar', 'Dávid Javorský'] | 2023-05-31 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 3.41493577e-01 4.21697050e-01 -1.63025945e-01 -5.45954645e-01
-5.77554643e-01 -6.03428781e-01 8.09877574e-01 9.49128926e-01
-7.78443396e-01 6.89330816e-01 6.26230776e-01 -3.61914605e-01
-1.25332043e-01 -5.85541070e-01 -6.28911078e-01 -4.71752346e-01
4.42470551e-01 5.01785696e-01 1.90557465e-01 -2.96222568... | [10.921977043151855, 9.000479698181152] |
99731909-de5a-4d88-ab44-a2e65369aefc | learned-harmonic-mean-estimation-of-the | 2307.00048 | null | https://arxiv.org/abs/2307.00048v1 | https://arxiv.org/pdf/2307.00048v1.pdf | Learned harmonic mean estimation of the marginal likelihood with normalizing flows | Computing the marginal likelihood (also called the Bayesian model evidence) is an important task in Bayesian model selection, providing a principled quantitative way to compare models. The learned harmonic mean estimator solves the exploding variance problem of the original harmonic mean estimation of the marginal like... | ['Jason D. McEwen', 'Alessio Spurio Mancini', 'Matthew A. Price', 'Alicja Polanska'] | 2023-06-30 | null | null | null | null | ['model-selection'] | ['methodology'] | [ 8.70956481e-02 1.53388456e-01 -2.60464787e-01 -2.91572601e-01
-9.67106044e-01 -5.08170426e-01 6.15724564e-01 1.44973591e-01
-5.08581221e-01 8.19166660e-01 -2.93912981e-02 -2.93883622e-01
-5.59078515e-01 -7.48707116e-01 -6.50882959e-01 -1.01847839e+00
-7.41436109e-02 6.50577843e-01 4.83460158e-01 4.66086537... | [6.697727203369141, 3.8521478176116943] |
c97eb3e4-9aa0-403a-a5d2-345c81b142c9 | learning-through-structure-towards-deep | 2109.10376 | null | https://arxiv.org/abs/2109.10376v1 | https://arxiv.org/pdf/2109.10376v1.pdf | Learning through structure: towards deep neuromorphic knowledge graph embeddings | Computing latent representations for graph-structured data is an ubiquitous learning task in many industrial and academic applications ranging from molecule synthetization to social network analysis and recommender systems. Knowledge graphs are among the most popular and widely used data representations related to the ... | ['Dominik Dold', 'Thomas Runkler', 'Marcel Hildebrandt', 'Victor Caceres Chian'] | 2021-09-21 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [ 1.68406740e-01 4.26648319e-01 -8.40370283e-02 -9.65796337e-02
2.97098964e-01 -5.31263351e-01 5.85445583e-01 6.68312430e-01
-4.01090950e-01 6.03899419e-01 -5.91792166e-02 -2.07358241e-01
-4.38783586e-01 -1.39075029e+00 -6.97298586e-01 -7.98869789e-01
-2.01546580e-01 5.40010631e-01 3.98297846e-01 -3.16715211... | [6.964616298675537, 6.2605485916137695] |
02fe9c28-6670-48ec-babb-6933c9495e7e | spectral-reconstruction-and-disparity-from | 2103.10179 | null | https://arxiv.org/abs/2103.10179v2 | https://arxiv.org/pdf/2103.10179v2.pdf | Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning | We present a novel method to reconstruct a spectral central view and its aligned disparity map from spatio-spectrally coded light fields. Since we do not reconstruct an intermediate full light field from the coded measurement, we refer to this as principal reconstruction. The coded light fields correspond to those capt... | ['Michael Heizmann', 'Jiayang Shi', 'Maximilian Schambach'] | 2021-03-18 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 9.10459042e-01 -3.39624017e-01 4.35789466e-01 -3.90847623e-01
-7.86270797e-01 -2.37758234e-01 3.75221997e-01 -6.07745886e-01
-7.18092322e-01 8.05097282e-01 3.74374576e-02 1.43031403e-01
1.25609279e-01 -6.28173530e-01 -8.77008677e-01 -8.73729348e-01
6.35800242e-01 5.69986641e-01 3.18554223e-01 9.54861268... | [9.650556564331055, -2.650047540664673] |
30d30cb9-64b4-4cb7-9e0d-f2160e6303e6 | dependency-parsing-for-chinese-long-sentence | null | null | https://aclanthology.org/Y15-2039 | https://aclanthology.org/Y15-2039.pdf | Dependency parsing for Chinese long sentence: A second-stage main structure parsing method | null | ['Weiguang Qu', 'Bo Li', 'Yunfei Long'] | 2015-10-01 | dependency-parsing-for-chinese-long-sentence-1 | https://aclanthology.org/Y15-2039 | https://aclanthology.org/Y15-2039.pdf | paclic-2015-10 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.409140110015869, 3.593579053878784] |
22295380-c62e-4061-8c17-1bf90ebef534 | collaborative-video-object-segmentation-by | 2003.08333 | null | https://arxiv.org/abs/2003.08333v2 | https://arxiv.org/pdf/2003.08333v2.pdf | Collaborative Video Object Segmentation by Foreground-Background Integration | This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Different from previous practices that only explore the embedding learning using pixels from foreground object (s), we consider background should be equally treated and thus propose Collabor... | ['Yunchao Wei', 'Zongxin Yang', 'Yi Yang'] | 2020-03-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3385_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500324.pdf | eccv-2020-8 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.71144769e-01 -1.54644594e-01 -3.55095357e-01 -1.60044864e-01
-6.40232563e-01 -4.51692969e-01 3.79461408e-01 -1.76902294e-01
-3.27824056e-01 4.17673379e-01 -7.54731819e-02 -4.02144603e-02
3.57994795e-01 -5.45969367e-01 -6.89388335e-01 -9.64711845e-01
3.30797173e-02 2.91147530e-02 8.24971557e-01 3.67515862... | [9.254261016845703, -0.1406562626361847] |
fb448f23-2a1a-47a3-a808-2cb10df7c0e1 | expected-scalarised-returns-dominance-a-new | 2106.01048 | null | https://arxiv.org/abs/2106.01048v3 | https://arxiv.org/pdf/2106.01048v3.pdf | Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making | In many real-world scenarios, the utility of a user is derived from the single execution of a policy. In this case, to apply multi-objective reinforcement learning, the expected utility of the returns must be optimised. Various scenarios exist where a user's preferences over objectives (also known as the utility functi... | ['Patrick Mannion', 'Enda Howley', 'Diederik M. Roijers', 'Timothy Verstraeten', 'Conor F. Hayes'] | 2021-06-02 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 8.81573036e-02 -1.04307979e-02 -4.94670093e-01 -3.52500618e-01
-9.66290534e-01 -7.93684304e-01 3.16853106e-01 1.73791498e-01
-5.79941809e-01 1.28170955e+00 9.82162133e-02 -3.22169036e-01
-9.99584198e-01 -8.85519445e-01 -6.54201806e-01 -9.10712898e-01
-2.06912030e-02 6.75295413e-01 -1.71090379e-01 -1.58780292... | [4.475583553314209, 2.5765879154205322] |
ade0b5fa-da17-4eb2-8afd-9bd5ea3681d8 | an-erudite-fine-grained-visual-classification | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chang_An_Erudite_Fine-Grained_Visual_Classification_Model_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chang_An_Erudite_Fine-Grained_Visual_Classification_Model_CVPR_2023_paper.pdf | An Erudite Fine-Grained Visual Classification Model | Current fine-grained visual classification (FGVC) models are isolated. In practice, we first need to identify the coarse-grained label of an object, then select the corresponding FGVC model for recognition. This hinders the application of the FGVC algorithm in real-life scenarios. In this paper, we propose an erudi... | ['Zhanyu Ma', 'Yi-Zhe Song', 'Timothy Hospedales', 'Ruoyi Du', 'Yujun Tong', 'Dongliang Chang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['fine-grained-image-classification'] | ['computer-vision'] | [ 1.97959319e-01 -4.44319695e-01 -1.16706237e-01 -5.77892482e-01
-7.90352643e-01 -7.32680380e-01 6.96714103e-01 -6.83581531e-02
-4.57052737e-01 6.70705616e-01 -5.14476188e-02 1.12913713e-01
-7.88210034e-02 -6.23768449e-01 -7.56244957e-01 -8.60882103e-01
3.52114052e-01 1.87962428e-01 2.38971114e-01 2.32962266... | [9.700075149536133, 2.07536244392395] |
cb7be7c3-aead-4468-80a3-3485a48c88b7 | semantic-role-labeling-in-conversational-chat | null | null | https://aclanthology.org/Y18-1064 | https://aclanthology.org/Y18-1064.pdf | Semantic Role Labeling in Conversational Chat using Deep Bi-Directional Long Short-Term Memory Networks with Attention Mechanism | null | ['Fariz Ikhwantri', 'Ahmad Rizqi Meydiarso', 'Alfan Farizki Wicaksono', 'Rahmad Mahendra', 'Valdi Rachman'] | null | null | null | null | paclic-2018-12 | ['semantic-role-labeling'] | ['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... | [-7.3783860206604, 3.729959726333618] |
0193f076-edb7-4e55-a5e5-d16ee6461488 | optimised-preprocessing-for-automatic-mouth | null | null | https://aclanthology.org/2020.signlang-1.5 | https://aclanthology.org/2020.signlang-1.5.pdf | Optimised Preprocessing for Automatic Mouth Gesture Classification | Mouth gestures are facial expressions in sign language, that do not refer to lip patterns of a spoken language. Research on this topic has been limited so far. The aim of this work is to automatically classify mouth gestures from video material by training a neural network. This could render time-consuming manual annot... | ['Rolf-Rainer Grigat', 'Maren Brumm'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['sign-language-translation'] | ['computer-vision'] | [ 3.92088771e-01 -2.01266736e-01 -3.44385445e-01 -5.66128314e-01
-3.25047106e-01 -3.25360239e-01 5.98242342e-01 -4.61406708e-01
-5.88627577e-01 5.74779630e-01 1.74648046e-01 -2.04767570e-01
1.07611194e-01 -2.16360703e-01 -1.45288631e-01 -9.66013014e-01
1.38654262e-01 3.10406834e-01 2.03948736e-01 9.47890878... | [9.073851585388184, -6.339036464691162] |
8c0756b7-7164-40d8-a9c0-f2624af03ba3 | multi-scale-single-image-dehazing-using | 2111.05700 | null | https://arxiv.org/abs/2111.05700v2 | https://arxiv.org/pdf/2111.05700v2.pdf | Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids | Model driven single image dehazing was widely studied on top of different priors due to its extensive applications. Ambiguity between object radiance and haze and noise amplification in sky regions are two inherent problems of model driven single image dehazing. In this paper, a dark direct attenuation prior (DDAP) is ... | ['Chaobing Zheng', 'Haiyan Shu', 'Zhengguo Li'] | 2021-11-10 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 5.63164532e-01 -3.40537459e-01 7.84595191e-01 -1.99843780e-03
-3.79065156e-01 -1.23247892e-01 3.43604088e-01 -2.36623541e-01
-2.05088794e-01 6.37690246e-01 4.04888421e-01 5.84274717e-02
-2.56872028e-01 -8.80450368e-01 -4.02720064e-01 -1.37361526e+00
4.39012945e-01 -5.12701988e-01 6.69731975e-01 -4.58724350... | [10.857128143310547, -3.1557719707489014] |
b6f4561c-a2d8-4701-8ad5-495bbef497fd | deepfake-detection-using-biological-features | 2301.05819 | null | https://arxiv.org/abs/2301.05819v1 | https://arxiv.org/pdf/2301.05819v1.pdf | Deepfake Detection using Biological Features: A Survey | Deepfake is a deep learning-based technique that makes it easy to change or modify images and videos. In investigations and court, visual evidence is commonly employed, but these pieces of evidence may now be suspect due to technological advancements in deepfake. Deepfakes have been used to blackmail individuals, plan ... | ['Abhishek Gulhane', 'Jaivanti Dhokey', 'Shrushti Kale', 'Kundan Patil'] | 2023-01-14 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 1.29427671e-01 4.11710190e-03 9.76905692e-03 5.24506904e-03
-1.26033887e-01 -8.63202333e-01 6.41978025e-01 -8.52667242e-02
-4.32925284e-01 9.67712045e-01 -3.00735980e-02 -1.16507158e-01
4.02851284e-01 -4.98142362e-01 -3.52126062e-01 -7.81397939e-01
1.14173487e-01 -2.86357850e-01 -1.45232558e-01 1.48084387... | [12.656061172485352, 1.0557646751403809] |
ac4e68e9-9f27-427a-a70a-df72a989a55a | iterative-spectral-clustering-for | 1706.09719 | null | http://arxiv.org/abs/1706.09719v1 | http://arxiv.org/pdf/1706.09719v1.pdf | Iterative Spectral Clustering for Unsupervised Object Localization | This paper addresses the problem of unsupervised object localization in an
image. Unlike previous supervised and weakly supervised algorithms that require
bounding box or image level annotations for training classifiers in order to
learn features representing the object, we propose a simple yet effective
technique for ... | ['Shanmuganathan Raman', 'Aditya Vora'] | 2017-06-29 | null | null | null | null | ['unsupervised-object-localization'] | ['computer-vision'] | [ 1.91314936e-01 1.03182411e-02 -1.79646149e-01 -2.96553403e-01
-9.33593631e-01 -7.72424698e-01 6.35009944e-01 5.31761050e-01
-6.60632491e-01 3.57181698e-01 -9.61406678e-02 2.87866145e-01
-2.57784128e-01 -2.06253842e-01 -6.02073669e-01 -9.55712974e-01
-1.30893037e-01 6.78758502e-01 7.71379650e-01 4.67936426... | [9.380619049072266, 0.9312050938606262] |
0a6c6d37-1820-44a2-8494-df112656ff0c | parallel-algorithms-for-densest-subgraph | 2103.00154 | null | https://arxiv.org/abs/2103.00154v1 | https://arxiv.org/pdf/2103.00154v1.pdf | Parallel Algorithms for Densest Subgraph Discovery Using Shared Memory Model | The problem of finding dense components of a graph is a widely explored area in data analysis, with diverse applications in fields and branches of study including community mining, spam detection, computer security and bioinformatics. This research project explores previously available algorithms in order to study them... | ['Anil Vullikanti', 'Saliya Ekanayake', 'Indika Perera', 'M. D. I. Maduranga', 'Y. A. M. M. A. Ali', 'B. D. M. De Zoysa'] | 2021-02-27 | null | null | null | null | ['computer-security', 'spam-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 2.93162137e-01 1.39493302e-01 -1.10222593e-01 -1.85282424e-01
-6.16216324e-02 -3.00264776e-01 3.85530114e-01 4.85966295e-01
-3.26166660e-01 8.88079226e-01 5.02278768e-02 -5.44453025e-01
-4.91700292e-01 -1.11593556e+00 -6.79833218e-02 -5.40529490e-01
-5.21894336e-01 9.44243670e-01 6.72831357e-01 -4.27951477... | [6.954156875610352, 5.267507553100586] |
36d443fc-cbad-48a6-94ef-acf53fb998fe | a-papier-macha-approach-to-learning-3d | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Groueix_A_Papier-Mache_Approach_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Groueix_A_Papier-Mache_Approach_CVPR_2018_paper.pdf | A Papier-Mâché Approach to Learning 3D Surface Generation | We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generat... | ['Thibault Groueix', 'Bryan C. Russell', 'Matthew Fisher', 'Mathieu Aubry', 'Vladimir G. Kim'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['3d-surface-generation'] | ['computer-vision'] | [ 2.73047596e-01 3.86567920e-01 2.52119213e-01 -2.49007016e-01
-1.05537987e+00 -7.99140215e-01 1.00052619e+00 7.47845322e-02
1.21831164e-01 5.33806860e-01 -7.44589511e-03 -6.52003735e-02
3.19928899e-02 -1.09639013e+00 -7.26735532e-01 -3.52932483e-01
-9.70242952e-04 1.21992660e+00 3.77643824e-01 -3.28555740... | [8.776239395141602, -3.6381020545959473] |
0f01ab96-df9d-414a-b174-27a075322f22 | time-series-segmentation-applied-to-a-new | null | null | https://ceur-ws.org/Vol-3379/DARLI-AP_2023_2.pdf | https://ceur-ws.org/Vol-3379/DARLI-AP_2023_2.pdf | Time Series Segmentation Applied to a New Data Set for Mobile Sensing of Human Activities | Human activity recognition (HAR) systems implement workflows that automatically detect activities from motion data,
captured e.g. by wearable devices such as smartphones. These devices contain multiple sensors that record human motion as
acceleration, rotation and orientation in long time series (TS) data. As a first... | ['Ulf Leser', 'Sunita Singh', 'Arik Ermshaus'] | 2023-03-28 | null | null | null | data-analytics-solutions-for-real-life | ['activity-recognition', 'human-activity-recognition', 'change-point-detection', 'time-series', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series', 'time-series', 'time-series'] | [ 2.15393379e-01 -4.06937450e-01 -3.15469563e-01 -5.07584177e-02
-7.13997483e-01 -5.72079897e-01 5.14250159e-01 1.93277687e-01
-5.06236970e-01 5.92407584e-01 4.13309902e-01 1.82171687e-02
4.94090915e-02 -4.77080792e-01 -3.62905711e-01 -5.27724802e-01
-3.38709503e-01 2.64217407e-01 4.33998942e-01 2.13943467... | [7.402915954589844, 0.6345326900482178] |
9efcac34-10fc-480f-9885-65c4fe15081d | discovering-bayesian-market-views-for | 1802.09911 | null | http://arxiv.org/abs/1802.09911v2 | http://arxiv.org/pdf/1802.09911v2.pdf | Discovering Bayesian Market Views for Intelligent Asset Allocation | Along with the advance of opinion mining techniques, public mood has been
found to be a key element for stock market prediction. However, how market
participants' behavior is affected by public mood has been rarely discussed.
Consequently, there has been little progress in leveraging public mood for the
asset allocatio... | ['Carlo Vercellis', 'Lorenzo Malandri', 'Frank Z. Xing', 'Erik Cambria'] | 2018-02-27 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-5.61113775e-01 1.06782056e-01 -4.17606443e-01 -3.46762091e-01
-1.66979089e-01 -4.81485695e-01 5.04722714e-01 4.49950993e-02
-9.12758335e-02 5.39890051e-01 1.75052494e-01 -6.19950235e-01
4.85746451e-02 -1.43632829e+00 -3.04463148e-01 -3.44664395e-01
1.71043724e-01 2.65224427e-01 1.75294474e-01 -3.69276553... | [4.535121917724609, 4.169627666473389] |
8cd09695-4fb5-4d6a-bb41-cee849395e40 | viplo-vision-transformer-based-pose | 2304.08114 | null | https://arxiv.org/abs/2304.08114v1 | https://arxiv.org/pdf/2304.08114v1.pdf | ViPLO: Vision Transformer based Pose-Conditioned Self-Loop Graph for Human-Object Interaction Detection | Human-Object Interaction (HOI) detection, which localizes and infers relationships between human and objects, plays an important role in scene understanding. Although two-stage HOI detectors have advantages of high efficiency in training and inference, they suffer from lower performance than one-stage methods due to th... | ['Jong-Seok Lee', 'Jin-Woo Park', 'Jeeseung Park'] | 2023-04-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Park_ViPLO_Vision_Transformer_Based_Pose-Conditioned_Self-Loop_Graph_for_Human-Object_Interaction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Park_ViPLO_Vision_Transformer_Based_Pose-Conditioned_Self-Loop_Graph_for_Human-Object_Interaction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [-9.79040377e-03 -3.21832821e-02 -2.53981445e-02 -1.89590424e-01
-2.14965701e-01 4.10729684e-02 3.92176121e-01 -2.80087471e-01
-3.81604999e-01 2.45481193e-01 2.57982194e-01 3.44840705e-01
2.86856052e-02 -5.71759701e-01 -8.20468068e-01 -7.12264538e-01
3.60790454e-02 3.80605757e-01 6.00768387e-01 -1.09054707... | [9.535074234008789, 1.332589030265808] |
20d8844d-a171-4822-a8ad-19c5ed02fd46 | steering-prototype-with-prompt-tuning-for | 2303.09447 | null | https://arxiv.org/abs/2303.09447v1 | https://arxiv.org/pdf/2303.09447v1.pdf | Steering Prototype with Prompt-tuning for Rehearsal-free Continual Learning | Prototype, as a representation of class embeddings, has been explored to reduce memory footprint or mitigate forgetting for continual learning scenarios. However, prototype-based methods still suffer from abrupt performance deterioration due to semantic drift and prototype interference. In this study, we propose Contra... | ['Dimitris N. Metaxas', 'Ting Liu', 'Di Liu', 'Han Zhang', 'Zizhao Zhang', 'Long Zhao', 'Zhuowei Li'] | 2023-03-16 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 1.08475186e-01 -7.66015872e-02 -2.71672487e-01 -3.08816701e-01
-6.32468641e-01 -4.29198444e-01 7.38964677e-01 6.33711576e-01
-8.25709581e-01 5.59428155e-01 1.91940032e-02 -5.09076178e-01
-2.38366440e-01 -5.05006373e-01 -7.64296412e-01 -4.56382543e-01
-1.46032525e-02 2.58326918e-01 5.90108693e-01 -3.15013766... | [9.816043853759766, 3.390683174133301] |
d04127dc-eb03-4364-a6ba-6ea741707327 | deep-learning-for-text-attribute-transfer-a | 2011.00416 | null | https://arxiv.org/abs/2011.00416v5 | https://arxiv.org/pdf/2011.00416v5.pdf | Deep Learning for Text Style Transfer: A Survey | Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the ... | ['Olga Vechtomova', 'Zhiting Hu', 'Rada Mihalcea', 'Zhijing Jin', 'Di Jin'] | 2020-11-01 | deep-learning-for-text-attribute-transfer-a-1 | https://aclanthology.org/2022.cl-1.6 | https://aclanthology.org/2022.cl-1.6.pdf | cl-acl-2022-3 | ['text-attribute-transfer'] | ['natural-language-processing'] | [ 1.87447414e-01 2.85165727e-01 -1.35636538e-01 -5.59270501e-01
-5.39250135e-01 -5.74342608e-01 1.06380081e+00 -1.81796983e-01
-2.49169186e-01 1.07604361e+00 7.99284637e-01 -7.71165267e-02
3.31085593e-01 -6.14775419e-01 -3.18897635e-01 -4.29451615e-01
6.08259261e-01 6.69811726e-01 -4.96915817e-01 -7.75842547... | [11.724002838134766, 9.414905548095703] |
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