paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0650e92a-d76e-434a-9c74-43d0f8c03802 | polarnet-an-improved-grid-representation-for | 2003.14032 | null | https://arxiv.org/abs/2003.14032v2 | https://arxiv.org/pdf/2003.14032v2.pdf | PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation | The need for fine-grained perception in autonomous driving systems has resulted in recently increased research on online semantic segmentation of single-scan LiDAR. Despite the emerging datasets and technological advancements, it remains challenging due to three reasons: (1) the need for near-real-time latency with lim... | ['Xiangyu Yue', 'Philip David', 'Zixiang Zhou', 'Zerong Xi', 'Yang Zhang', 'Boqing Gong', 'Hassan Foroosh'] | 2020-03-31 | polarnet-an-improved-grid-representation-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_PolarNet_An_Improved_Grid_Representation_for_Online_LiDAR_Point_Clouds_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_PolarNet_An_Improved_Grid_Representation_for_Online_LiDAR_Point_Clouds_CVPR_2020_paper.pdf | cvpr-2020-6 | ['robust-3d-semantic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.86393249e-01 -1.18510850e-01 -3.85634676e-02 -7.62036383e-01
-6.68798625e-01 -6.40381575e-01 3.87773782e-01 9.85573754e-02
-5.19394875e-01 6.46648169e-01 -3.39722157e-01 -5.54202378e-01
-3.90546471e-01 -1.02728319e+00 -5.93898177e-01 -2.93708146e-01
1.91962063e-01 9.86320198e-01 8.48643661e-01 -2.10065439... | [8.038702011108398, -2.742123603820801] |
95931c1f-fb55-48fc-a276-f898d22eba45 | hierarchical-object-representation-for-open | null | null | http://papers.nips.cc/paper/6539-hierarchical-object-representation-for-open-ended-object-category-learning-and-recognition | http://papers.nips.cc/paper/6539-hierarchical-object-representation-for-open-ended-object-category-learning-and-recognition.pdf | Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition | Most robots lack the ability to learn new objects from past experiences. To migrate a robot to a new environment one must often completely re-generate the knowledge- base that it is running with. Since in open-ended domains the set of categories to be learned is not predefined, it is not feasible to assume that one can... | ['Luís Seabra Lopes', 'Ana Maria Tomé', 'Seyed Hamidreza Kasaei'] | 2016-12-01 | null | null | null | neurips-2016-12 | ['3d-object-recognition'] | ['computer-vision'] | [ 4.23987694e-02 2.68820047e-01 -6.49705529e-02 -7.01398313e-01
4.60187793e-02 -5.48848391e-01 8.87371004e-01 1.32704929e-01
-5.01673758e-01 6.30348086e-01 -2.78930515e-01 2.18710780e-01
-2.63147622e-01 -9.63333964e-01 -6.66598380e-01 -7.11863518e-01
-3.68068576e-01 1.12133813e+00 5.60380042e-01 3.25980522... | [7.5819783210754395, -1.1154332160949707] |
db6c3bbe-4ec1-4a47-b56d-fc0e80f7c9b7 | medml-fusing-medical-knowledge-and-machine | 2207.12283 | null | https://arxiv.org/abs/2207.12283v1 | https://arxiv.org/pdf/2207.12283v1.pdf | MedML: Fusing Medical Knowledge and Machine Learning Models for Early Pediatric COVID-19 Hospitalization and Severity Prediction | The COVID-19 pandemic has caused devastating economic and social disruption, straining the resources of healthcare institutions worldwide. This has led to a nationwide call for models to predict hospitalization and severe illness in patients with COVID-19 to inform distribution of limited healthcare resources. We respo... | ['the N3C consortium', 'Jimeng Sun', 'Adam Cross', 'Sara Warfield', 'Mary Stapel', 'Elise Albers', 'Scott Barrows', 'George Heintz', 'Chaoqi Yang', 'Junyi Gao'] | 2022-07-25 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 1.57276139e-01 1.93428442e-01 -4.03932005e-01 -4.42255318e-01
-6.95587456e-01 -4.62228477e-01 2.48137377e-02 1.28612673e+00
-3.07649076e-01 6.06508076e-01 9.51027811e-01 -4.01413053e-01
-6.71342790e-01 -8.36174369e-01 -3.86120319e-01 -2.15758905e-01
-5.12541175e-01 1.17207181e+00 -4.77504104e-01 1.33859590... | [7.966822147369385, 6.135936737060547] |
bf624395-2f6d-4431-88ff-cc201b027333 | detecting-anomalous-microflows-in-iot | 2304.04987 | null | https://arxiv.org/abs/2304.04987v1 | https://arxiv.org/pdf/2304.04987v1.pdf | Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity | IoT networks are increasingly becoming target of sophisticated new cyber-attacks. Anomaly-based detection methods are promising in finding new attacks, but there are certain practical challenges like false-positive alarms, hard to explain, and difficult to scale cost-effectively. The IETF recent standard called Manufac... | ['Vijay Sivaraman', 'Gustavo Batista', 'Theophilus A. Benson', 'Hassan Habibi Gharakheili', 'Ayyoob Hamza'] | 2023-04-11 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 1.46650165e-01 -1.42902091e-01 -4.63351429e-01 -2.08706871e-01
-2.13088766e-01 -1.03556406e+00 2.11353943e-01 7.60658011e-02
1.11893274e-01 4.64107037e-01 -1.55105636e-01 -1.07091057e+00
-2.82907933e-01 -8.57692242e-01 -3.79894465e-01 -8.29805061e-02
-4.70521808e-01 5.08290708e-01 6.47161841e-01 3.03160846... | [5.182248592376709, 7.218328952789307] |
4ea75edc-04bc-4fb2-aeb7-e25a9e8c3a45 | congruity-of-genomic-and-epidemiological-data | 2210.01956 | null | https://arxiv.org/abs/2210.01956v2 | https://arxiv.org/pdf/2210.01956v2.pdf | Congruity of genomic and epidemiological data in modeling of local cholera outbreaks | Cholera continues to be a global health threat. Understanding how cholera spreads between locations is fundamental to the rational, evidence-based design of intervention and control efforts. Traditionally, cholera transmission models have utilized cholera case count data. More recently, whole genome sequence data has q... | ['Andrey Y. Lokhov', 'Daryl Domman', 'Ethan Romero-Severson', 'Josefina Campos', 'Carrie Manore', 'Jeffrey Keithley', 'Lauren Castro', 'Mateusz Wilinski'] | 2022-10-04 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 3.80389720e-01 -5.18408120e-01 -5.22194169e-02 -2.13611946e-01
-7.97095835e-01 -6.42338395e-01 3.93810451e-01 1.02520573e+00
-5.34494638e-01 5.74879169e-01 4.21246201e-01 -9.54907894e-01
-4.43704903e-01 -8.74805391e-01 -5.90638995e-01 -9.57865536e-01
-6.16244674e-01 7.77566910e-01 -1.64022818e-01 -3.60922575... | [5.62645149230957, 4.507153511047363] |
41acca22-2a60-4b8a-bb0d-856680f53cb4 | data-adaptive-discriminative-feature | 2211.10061 | null | https://arxiv.org/abs/2211.10061v1 | https://arxiv.org/pdf/2211.10061v1.pdf | Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation | In explainable artificial intelligence, discriminative feature localization is critical to reveal a blackbox model's decision-making process from raw data to prediction. In this article, we use two real datasets, the MNIST handwritten digits and MIT-BIH Electrocardiogram (ECG) signals, to motivate key characteristics o... | ['Wei Pan', 'Chunlin Li', 'Lin Yee Chen', 'Xiaotong Shen', 'Ben Dai'] | 2022-11-18 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [ 3.78814429e-01 3.59372526e-01 1.41786799e-01 -4.05614316e-01
-7.30275273e-01 -7.39241719e-01 2.07751170e-01 8.18173289e-02
-2.68486198e-02 1.01491117e+00 -1.41677976e-01 -2.73918450e-01
-4.45276856e-01 -3.50000858e-01 -7.25130975e-01 -8.40386450e-01
-3.25427055e-01 -1.10918619e-01 -3.72174859e-01 1.75148025... | [14.276988983154297, 3.2860891819000244] |
adb0d989-4960-4ac6-8ae3-c704e7c32e15 | unconstrained-iris-segmentation-using | 1812.08245 | null | http://arxiv.org/abs/1812.08245v1 | http://arxiv.org/pdf/1812.08245v1.pdf | Unconstrained Iris Segmentation using Convolutional Neural Networks | The extraction of consistent and identifiable features from an image of the
human iris is known as iris recognition. Identifying which pixels belong to the
iris, known as segmentation, is the first stage of iris recognition. Errors in
segmentation propagate to later stages. Current segmentation approaches are
tuned to ... | ['Sohaib Ahmad', 'Benjamin Fuller'] | 2018-12-19 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 4.61566150e-01 -2.47060969e-01 -2.47837707e-01 -5.14279366e-01
-3.58186036e-01 -7.07280815e-01 2.60817975e-01 -4.26804185e-01
-4.76495206e-01 1.60227597e-01 -9.27808881e-02 -4.88232553e-01
-1.56154425e-03 -4.75121319e-01 -5.51450491e-01 -6.49721324e-01
2.33641997e-01 2.90225297e-01 -8.68878961e-02 1.14918239... | [3.74202036857605, -3.6325972080230713] |
41c7628d-82c0-40b8-9343-54ee67fcbe6e | a-mixed-hierarchical-attention-based-encoder | 1804.07790 | null | http://arxiv.org/abs/1804.07790v1 | http://arxiv.org/pdf/1804.07790v1.pdf | A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization | Structured data summarization involves generation of natural language
summaries from structured input data. In this work, we consider summarizing
structured data occurring in the form of tables as they are prevalent across a
wide variety of domains. We formulate the standard table summarization problem,
which deals wit... | ['Mitesh M. Khapra', 'Preksha Nema', 'Shreyas Shetty', 'Parag Jain', 'Karthik Sankaranarayanan', 'Anirban Laha'] | 2018-04-20 | a-mixed-hierarchical-attention-based-encoder-1 | https://aclanthology.org/N18-2098 | https://aclanthology.org/N18-2098.pdf | naacl-2018-6 | ['data-summarization'] | ['miscellaneous'] | [ 4.34185416e-01 6.12913013e-01 -2.72442222e-01 -4.17382747e-01
-1.31306016e+00 -6.66877627e-01 6.65211499e-01 1.04127431e+00
-1.44255564e-01 1.07495022e+00 1.41698897e+00 -4.94001992e-02
2.89435685e-01 -7.75701225e-01 -1.09574354e+00 -2.08648667e-01
3.19877788e-02 3.76911700e-01 6.63440488e-03 -4.43130940... | [12.535480499267578, 9.408080101013184] |
23648d7a-8738-4f1c-b20d-9abeb0dbe93f | local-region-perception-and-relationship | 2303.08545 | null | https://arxiv.org/abs/2303.08545v2 | https://arxiv.org/pdf/2303.08545v2.pdf | Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection | Human affective behavior analysis plays a vital role in human-computer interaction (HCI) systems. In this paper, we introduce our submission to the CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW). We propose a single-stage trained AU detection framework. Specifically, in order to effectively ext... | ['Wangyuan Zhu', 'Jichao Zhu', 'Guochen Xie', 'Gongpeng Zhao', 'Zhongpeng Cai', 'Renda Li', 'Jun Yu'] | 2023-03-15 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection', 'relational-reasoning'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.58540112e-01 2.82959014e-01 7.42986500e-02 -4.14449841e-01
-4.27919418e-01 -8.56189802e-02 2.41853699e-01 1.50511160e-01
-2.26747110e-01 1.04848608e-01 2.36208588e-01 5.18872440e-01
2.39034086e-01 -6.44668221e-01 -1.29187495e-01 -7.34698951e-01
-3.16353738e-02 -1.01362430e-01 1.00915013e-02 -4.72698122... | [13.63210678100586, 1.8648643493652344] |
665c5b4d-e868-4a89-9de4-1949d3fa2f7b | gnn3dmot-graph-neural-network-for-3d-multi-1 | 2006.07327 | null | https://arxiv.org/abs/2006.07327v1 | https://arxiv.org/pdf/2006.07327v1.pdf | GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning | 3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work uses a standard tracking-by-detection pipeline, where feature extraction is first performed independently for each object in order to compute an affinity matrix. Then the affinity matrix is passed to the Hungarian algorithm for data associatio... | ['Xinshuo Weng', 'Kris Kitani', 'Yunze Man', 'Yongxin Wang'] | 2020-06-12 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [ 3.83705385e-02 -4.43338424e-01 -5.60053475e-02 -2.35904768e-01
-4.94209796e-01 -5.29311299e-01 5.31911314e-01 -6.00099415e-02
-4.04720098e-01 4.30908978e-01 -1.96901575e-01 5.46445660e-02
-7.65400305e-02 -7.26410747e-01 -7.11769879e-01 -9.70710397e-01
1.09140880e-01 6.73344493e-01 7.94969440e-01 1.18341066... | [6.443276882171631, -2.213313102722168] |
0126b7db-aa2f-4a25-8509-924e3669f8e6 | geometry-of-deep-learning-for-magnetic | 1809.01749 | null | http://arxiv.org/abs/1809.01749v2 | http://arxiv.org/pdf/1809.01749v2.pdf | Geometry of Deep Learning for Magnetic Resonance Fingerprinting | Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are
bottlenecked by the heavy storage and computation requirements of a
dictionary-matching (DM) step due to the growing size and complexity of the
fingerprint dictionaries in multi-parametric quantitative MRI applications. In
this paper we study... | ['Dong-Dong Chen', 'Mike E. Davies', 'Marion I. Menzel', 'Pedro A. Gómez', 'Mohammad Golbabaee'] | 2018-09-05 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 4.11693126e-01 1.62285924e-01 -5.60322776e-03 -3.29389244e-01
-9.75437284e-01 -3.10781896e-01 3.84811968e-01 2.91669339e-01
-7.60904312e-01 4.83881742e-01 2.56883800e-01 -1.40677169e-01
-3.58843595e-01 -6.50783241e-01 -9.08885241e-01 -1.01496780e+00
-3.16224694e-01 9.56196308e-01 1.33869573e-02 6.47631139... | [13.473820686340332, -2.400557518005371] |
39b3783e-6e0c-4107-9e72-3e7546ace371 | exploring-word-alignment-towards-an-efficient | null | null | https://aclanthology.org/2022.loresmt-1.13 | https://aclanthology.org/2022.loresmt-1.13.pdf | Exploring Word Alignment towards an Efficient Sentence Aligner for Filipino and Cebuano Languages | Building a robust machine translation (MT) system requires a large amount of parallel corpus which is an expensive resource for low-resourced languages. The two major languages being spoken in the Philippines which are Filipino and Cebuano have an abundance in monolingual data that this study took advantage of attempti... | ['Kristine Mae M. Adlaon', 'Jenn Leana Fernandez'] | null | null | null | null | loresmt-coling-2022-10 | ['word-alignment'] | ['natural-language-processing'] | [ 2.52380162e-01 -1.24929167e-01 -2.22682923e-01 -4.16446537e-01
-1.09192157e+00 -7.73966074e-01 7.81938136e-01 3.95884104e-02
-7.00519204e-01 1.09690332e+00 4.63324249e-01 -7.06280470e-01
3.06004137e-01 -5.40903866e-01 -4.03770030e-01 -4.77976590e-01
1.83871850e-01 8.49400520e-01 -1.71102360e-01 -6.56330526... | [11.446974754333496, 10.394775390625] |
a4127199-8c64-4093-9fd3-abb758e3fd2b | unsupervised-visual-program-induction-with | null | null | https://openreview.net/forum?id=t14vYukzfvF | https://openreview.net/pdf?id=t14vYukzfvF | Unsupervised Visual Program Induction with Function Modularization | Program induction serves as one way to analog the ability of human thinking. However, existing methods could only tackle the task under simple scenarios (Fig~\ref{fig:task_examples}(a),(b)). When it comes to complex scenes, e.g., the visual scenes, current program induction methods fail due to the huge program action s... | ['Wenwu Zhu', 'Ziwei Zhang', 'Xin Wang', 'Xuguang Duan'] | 2021-09-29 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 2.77383149e-01 -3.46847445e-01 -4.72981870e-01 -4.01602864e-01
-2.59950668e-01 -6.38507009e-01 4.71547008e-01 4.81035523e-02
-2.52159148e-01 3.57559711e-01 -2.65671045e-01 -6.91364944e-01
1.33759771e-02 -6.77877009e-01 -7.78905749e-01 -4.45280284e-01
-1.06791630e-01 2.60215670e-01 2.99620211e-01 1.02506183... | [7.768241882324219, 7.779454708099365] |
4d86a62f-60fc-4ac0-aa45-51f604933bcc | probabilistic-word-association-for-dialogue | null | null | https://uwe-repository.worktribe.com/output/1434940/probabilistic-word-association-for-dialogue-act-classification-with-recurrent-neural-networks | https://uwe-repository.worktribe.com/OutputFile/1434957 | Probabilistic Word Association for Dialogue Act Classification with Recurrent Neural Networks | The identification of Dialogue Act’s (DA) is an important aspect in determining the meaning of an utterance for many applications that require natural language understanding, and recent work using recurrent neural networks (RNN) has shown promising results when applied to the DA classification problem. This work presen... | ['Steve Battle', 'Nathan Duran'] | 2018-04-20 | null | null | null | engineering-applications-of-neural-networks | ['dialog-act-classification', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.43951946e-01 1.58128157e-01 -4.24960330e-02 -5.40433764e-01
-4.82709557e-01 -2.62605935e-01 1.00392461e+00 4.64706331e-01
-6.43799663e-01 5.51799715e-01 1.02701867e+00 -1.07303970e-01
-8.08612108e-02 -6.63205147e-01 2.32131302e-01 -7.21373379e-01
-6.21253662e-02 5.19686043e-01 -1.25138447e-01 -5.62301815... | [12.791044235229492, 7.563652515411377] |
b8a60a71-4016-403c-be6a-e368a9ca4f6d | fpga-based-binocular-image-feature-extraction | 1905.04890 | null | https://arxiv.org/abs/1905.04890v2 | https://arxiv.org/pdf/1905.04890v2.pdf | FPGA-based Binocular Image Feature Extraction and Matching System | Image feature extraction and matching is a fundamental but computation intensive task in machine vision. This paper proposes a novel FPGA-based embedded system to accelerate feature extraction and matching. It implements SURF feature point detection and BRIEF feature descriptor construction and matching. For binocular ... | ['Ziwei Zhao', 'Peng Gao', 'Fei Wang', 'Qi Ni'] | 2019-05-13 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 3.21535617e-02 -8.53468597e-01 -6.89072385e-02 -1.82017267e-01
-1.37837492e-02 -3.11421573e-01 3.66356403e-01 8.94834194e-03
-6.54630840e-01 1.19699299e-01 -2.06210658e-01 -4.28491145e-01
1.14885978e-01 -6.91976190e-01 -4.56961930e-01 -2.50569493e-01
1.31240889e-01 -3.76201808e-01 6.26500785e-01 -7.48593500... | [8.9647855758667, -2.143216371536255] |
e742e48d-72f3-4f1f-ba5e-c2f206a0bc6a | learning-embedding-adaptation-for-few-shot | 1812.03664 | null | https://arxiv.org/abs/1812.03664v6 | https://arxiv.org/pdf/1812.03664v6.pdf | Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions | Learning with limited data is a key challenge for visual recognition. Many few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embe... | ['De-Chuan Zhan', 'Han-Jia Ye', 'Hexiang Hu', 'Fei Sha'] | 2018-12-10 | few-shot-learning-via-embedding-adaptation | http://openaccess.thecvf.com/content_CVPR_2020/html/Ye_Few-Shot_Learning_via_Embedding_Adaptation_With_Set-to-Set_Functions_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ye_Few-Shot_Learning_via_Embedding_Adaptation_With_Set-to-Set_Functions_CVPR_2020_paper.pdf | cvpr-2020-6 | ['generalized-few-shot-learning'] | ['methodology'] | [ 3.31656694e-01 -8.08255151e-02 -4.73661810e-01 -5.45766771e-01
-9.96963978e-01 -4.05534774e-01 9.37627256e-01 -1.60141677e-01
-3.47455472e-01 6.16857827e-01 2.87292749e-01 9.57304761e-02
-2.83536404e-01 -7.32468188e-01 -7.29738474e-01 -8.88513982e-01
5.29892109e-02 6.42151117e-01 5.91036201e-01 -2.00843170... | [9.96606731414795, 2.775993824005127] |
31670e70-1677-4b18-85ce-48a6053f78d2 | demelange-deconvolution-et-debruitage | 2307.01761 | null | https://arxiv.org/abs/2307.01761v1 | https://arxiv.org/pdf/2307.01761v1.pdf | Démélange, déconvolution et débruitage conjoints d'un modèle convolutif parcimonieux avec dérive instrumentale, par pénalisation de rapports de normes ou quasi-normes lissées (PENDANTSS) | Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peak-like signals. It blends a parsimonious prior with the hypothesis that smooth trend a... | ['Laurent Duval', 'Emilie Chouzenoux', 'Paul Zheng'] | 2023-07-04 | null | null | null | null | ['denoising'] | ['computer-vision'] | [ 3.16710263e-01 -4.43466872e-01 5.35884500e-01 -1.04158558e-01
-1.01846254e+00 -5.51601887e-01 5.23439288e-01 -1.50154546e-01
-1.58295393e-01 1.05091786e+00 3.33727032e-01 -1.23883039e-01
-2.60100961e-01 -8.45387131e-02 -6.93706095e-01 -1.11963296e+00
2.32233247e-03 3.11780989e-01 -1.51916653e-01 -1.28051624... | [11.63428783416748, -2.6514251232147217] |
7ec42276-b1c9-4aff-a5aa-a4f4de133e08 | distributed-consensus-algorithm-for-decision | 2306.05998 | null | https://arxiv.org/abs/2306.05998v1 | https://arxiv.org/pdf/2306.05998v1.pdf | Distributed Consensus Algorithm for Decision-Making in Multi-agent Multi-armed Bandit | We study a structured multi-agent multi-armed bandit (MAMAB) problem in a dynamic environment. A graph reflects the information-sharing structure among agents, and the arms' reward distributions are piecewise-stationary with several unknown change points. The agents face the identical piecewise-stationary MAB problem. ... | ['Setareh Maghsudi', 'Xiaotong Cheng'] | 2023-06-09 | null | null | null | null | ['change-point-detection'] | ['time-series'] | [-3.00444156e-01 4.23293002e-02 -6.99556649e-01 -1.58187106e-01
-9.96223569e-01 -8.49984169e-01 1.41128972e-01 3.68381649e-01
-4.75648552e-01 1.04410887e+00 -2.18281433e-01 -6.11381710e-01
-7.89919019e-01 -6.98412120e-01 -8.71991158e-01 -9.96348619e-01
-5.29902279e-01 1.06109679e+00 1.55328259e-01 5.78056127... | [4.488866806030273, 3.242136240005493] |
33bc2a8a-67f3-4e20-ba77-a2cd208ccbc7 | time-warped-trials | 2302.00583 | null | https://arxiv.org/abs/2302.00583v1 | https://arxiv.org/pdf/2302.00583v1.pdf | Time-warped Trials | I outline a signal resampling strategy for aligning event times between time series trials in contexts where significant event times like onsets and offsets vary between trials. These variations prevent direct comparisons of trials in practical contexts as comparisons require equal-length time series (Salari et al., 20... | ['Eric Easthope'] | 2023-02-01 | null | null | null | null | ['dynamic-time-warping'] | ['time-series'] | [ 6.12096846e-01 -3.67021561e-01 -3.12326014e-01 -7.60777295e-02
-8.96388471e-01 -9.45985496e-01 5.29810846e-01 2.95067519e-01
-8.57982516e-01 1.01597857e+00 3.11128914e-01 -2.65710950e-01
-3.43095154e-01 -3.12077999e-01 -5.75072646e-01 -4.03064013e-01
-6.30953610e-01 -4.02343929e-01 2.64743179e-01 2.43483856... | [7.323195934295654, 3.267007827758789] |
eecf3a25-a90f-41b0-b227-c24ee6321f5e | cross-genre-argument-mining-can-language | 2306.04314 | null | https://arxiv.org/abs/2306.04314v1 | https://arxiv.org/pdf/2306.04314v1.pdf | Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers? | Available corpora for Argument Mining differ along several axes, and one of the key differences is the presence (or absence) of discourse markers to signal argumentative content. Exploring effective ways to use discourse markers has received wide attention in various discourse parsing tasks, from which it is well-known... | ['Steffen Eger', 'Jonas Belouadi', 'Henrique Lopes Cardoso', 'Gil Rocha'] | 2023-06-07 | null | null | null | null | ['discourse-parsing', 'argument-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.94797742e-01 7.16930091e-01 -4.05247331e-01 -2.73810148e-01
-8.01658809e-01 -8.51432920e-01 1.13732779e+00 8.00134361e-01
-5.24213433e-01 9.12949562e-01 6.52259052e-01 -6.18282080e-01
-2.28264660e-01 -9.63316500e-01 -5.41995347e-01 -2.85279632e-01
-5.15563786e-02 6.09419107e-01 6.63439929e-01 -6.33725643... | [10.209208488464355, 9.445548057556152] |
dc77bfc4-f427-47b4-b5d8-842ea81bcb8e | domain-invariant-feature-alignment-using | 2212.01590 | null | https://arxiv.org/abs/2212.01590v1 | https://arxiv.org/pdf/2212.01590v1.pdf | Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation | The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets. However, in realistic scenarios, finding an optimal source domain with identical label space is a challenging task. Partial domain adaptation al... | ['Hemanth Venkateswara', 'Arunabha Sen', 'Suli Adeniye', 'Sandipan Choudhuri'] | 2022-12-03 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 5.16657352e-01 1.31675228e-01 -3.46351057e-01 -4.63945240e-01
-9.87301350e-01 -9.83509719e-01 4.51071978e-01 -1.31080046e-01
-3.34328145e-01 1.30270302e+00 -2.01578975e-01 3.75722833e-02
-1.04609497e-01 -5.99311590e-01 -6.47151470e-01 -1.00791717e+00
4.95597661e-01 7.08180845e-01 1.09823942e-01 8.46105367... | [10.358121871948242, 3.1683826446533203] |
63f22dfd-781e-47fa-a326-4fbb99bbc9ea | a-cohesive-distillation-architecture-for | 2301.08130 | null | https://arxiv.org/abs/2301.08130v2 | https://arxiv.org/pdf/2301.08130v2.pdf | A Cohesive Distillation Architecture for Neural Language Models | A recent trend in Natural Language Processing is the exponential growth in Language Model (LM) size, which prevents research groups without a necessary hardware infrastructure from participating in the development process. This study investigates methods for Knowledge Distillation (KD) to provide efficient alternatives... | ['Jan Philip Wahle'] | 2023-01-12 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.51958779e-01 4.98005450e-01 -1.45611435e-01 -1.72866866e-01
-6.69483960e-01 -5.78654826e-01 4.20478791e-01 3.51558179e-01
-9.79014277e-01 7.63261020e-01 1.05249025e-01 -5.81955373e-01
-1.55393407e-01 -8.62989485e-01 -6.95018232e-01 -7.34670535e-02
3.64628881e-01 4.56182718e-01 4.48168695e-01 -1.41749248... | [10.618781089782715, 8.770041465759277] |
e2c268a6-7761-4036-bf5a-457a0fc5acbe | domain-generalization-using-causal-matching-1 | 2006.07500 | null | https://arxiv.org/abs/2006.07500v3 | https://arxiv.org/pdf/2006.07500v3.pdf | Domain Generalization using Causal Matching | In the domain generalization literature, a common objective is to learn representations independent of the domain after conditioning on the class label. We show that this objective is not sufficient: there exist counter-examples where a model fails to generalize to unseen domains even after satisfying class-conditional... | ['Shruti Tople', 'Divyat Mahajan', 'Amit Sharma'] | 2020-06-12 | domain-generalization-using-causal-matching | null | null | arxiv-2020-6 | ['rotated-mnist'] | ['computer-vision'] | [ 5.41882217e-01 3.92170131e-01 -6.99397266e-01 -8.31146777e-01
-6.43758535e-01 -7.06730485e-01 8.64230454e-01 2.05019131e-01
-1.92166179e-01 8.94783020e-01 4.36612815e-01 7.01134726e-02
-5.75978518e-01 -7.88445652e-01 -1.26192081e+00 -6.74246788e-01
1.06346041e-01 6.98508799e-01 2.56070763e-01 6.82081506... | [10.253175735473633, 2.918940544128418] |
51487985-7790-41b3-94be-d78af1cd18ab | sst-single-stream-temporal-action-proposals | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Buch_SST_Single-Stream_Temporal_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Buch_SST_Single-Stream_Temporal_CVPR_2017_paper.pdf | SST: Single-Stream Temporal Action Proposals | Our paper presents a new approach for temporal detection of human actions in long, untrimmed video sequences. We introduce Single-Stream Temporal Action Proposals (SST), a new effective and efficient deep architecture for the generation of temporal action proposals. Our network can run continuously in a single stream o... | ['Juan Carlos Niebles', 'Victor Escorcia', 'Chuanqi Shen', 'Shyamal Buch', 'Bernard Ghanem'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 4.03206378e-01 -2.98988819e-01 -3.40665877e-01 -1.35211870e-01
-6.21491730e-01 -3.54298413e-01 8.12067389e-01 -3.82993639e-01
-6.33466363e-01 4.49450821e-01 5.08721948e-01 -5.17678121e-03
1.68545470e-01 -2.50905693e-01 -4.95347559e-01 -5.03662646e-01
-6.50721967e-01 6.29831403e-02 1.04685414e+00 -9.91622172... | [8.27669906616211, 0.4419221580028534] |
e82412c9-19b7-4a62-aaa9-b2362be62bf3 | a-weakly-supervised-surface-crack | 2109.00456 | null | https://arxiv.org/abs/2109.00456v3 | https://arxiv.org/pdf/2109.00456v3.pdf | Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and Thresholding | Surface cracks are a common sight on public infrastructure nowadays. Recent work has been addressing this problem by supporting structural maintenance measures using machine learning methods. Those methods are used to segment surface cracks from their background, making them easier to localize. However, a common issue ... | ['Gordon Morison', 'Peter Barrie', 'Mike Mannion', 'Mark Jenkins', 'Jacob König'] | 2021-09-01 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 7.44864821e-01 3.88230532e-01 -9.96319205e-03 -2.92793870e-01
-1.18583620e+00 -4.32627171e-01 6.58531487e-03 4.00265902e-01
-1.57590270e-01 3.91211659e-01 -2.06788450e-01 -1.94013238e-01
5.15557885e-01 -1.00223172e+00 -8.58326793e-01 -8.73697937e-01
4.29710597e-01 3.35084319e-01 7.63356864e-01 -2.01652393... | [7.502861022949219, 1.554753065109253] |
e1a15c98-30f1-4a38-bda8-01972c8775bb | modality-invariant-visual-odometry-for | 2305.00348 | null | https://arxiv.org/abs/2305.00348v1 | https://arxiv.org/pdf/2305.00348v1.pdf | Modality-invariant Visual Odometry for Embodied Vision | Effectively localizing an agent in a realistic, noisy setting is crucial for many embodied vision tasks. Visual Odometry (VO) is a practical substitute for unreliable GPS and compass sensors, especially in indoor environments. While SLAM-based methods show a solid performance without large data requirements, they are l... | ['Amir Zamir', 'Roman Bachmann', 'Marius Memmel'] | 2023-04-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Memmel_Modality-Invariant_Visual_Odometry_for_Embodied_Vision_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Memmel_Modality-Invariant_Visual_Odometry_for_Embodied_Vision_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-odometry'] | ['robots'] | [-7.35032633e-02 -2.48835310e-01 5.77572100e-02 -3.07110369e-01
-5.61477244e-01 -7.99104929e-01 5.65922678e-01 -6.02145009e-02
-6.95805907e-01 8.97510946e-01 -2.04491228e-01 -2.21337989e-01
1.38338342e-01 -7.56870508e-01 -1.04436588e+00 -6.91240132e-01
-1.62548140e-01 5.99916399e-01 5.86526453e-01 -4.54691768... | [4.694626331329346, 0.6704190969467163] |
ec761344-92f5-4663-9df4-51e9c5184a9b | seeing-through-clouds-in-satellite-images | 2106.08408 | null | https://arxiv.org/abs/2106.08408v1 | https://arxiv.org/pdf/2106.08408v1.pdf | Seeing Through Clouds in Satellite Images | This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images. We leverage radio frequency (RF) signals in the ultra/super-high frequency band that penetrate clouds to help reconstruct the occluded regions in multispectral images. We introduce the first multi-modal multi-t... | ['Ranveer Chandra', 'Peder A. Olsen', 'Mingmin Zhao'] | 2021-06-15 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 5.58480084e-01 -5.93909562e-01 -1.67632371e-01 -2.46871904e-01
-8.23339880e-01 -7.79989898e-01 8.57313871e-02 -4.97463733e-01
-5.17351814e-02 7.32552528e-01 9.26745534e-02 -5.51202655e-01
-3.16222787e-01 -1.26745427e+00 -7.57990003e-01 -9.90486681e-01
-6.97721422e-01 -4.52807158e-01 -6.78288937e-02 -2.77447373... | [9.809422492980957, -1.7688673734664917] |
69be2ef0-1f49-4fbc-b04a-8b2d9c70ad58 | structure-aware-language-model-pretraining | 2305.19912 | null | https://arxiv.org/abs/2305.19912v1 | https://arxiv.org/pdf/2305.19912v1.pdf | Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data | This paper presents Structure Aware Dense Retrieval (SANTA) model, which encodes user queries and structured data in one universal embedding space for retrieving structured data. SANTA proposes two pretraining methods to make language models structure-aware and learn effective representations for structured data: 1) St... | ['Ge Yu', 'Zhiyuan Liu', 'Yu Gu', 'Shi Yu', 'Chenyan Xiong', 'Zhenghao Liu', 'Xinze Li'] | 2023-05-31 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 1.89968962e-02 3.57939601e-01 -5.42631924e-01 -4.80381310e-01
-1.31735492e+00 -6.22007608e-01 4.55050081e-01 3.52849275e-01
-3.79338712e-01 1.01764001e-01 8.71134698e-01 -1.50625527e-01
-1.96570218e-01 -7.19048142e-01 -6.67736173e-01 -2.02098563e-01
-1.03839479e-01 1.04834509e+00 -1.60360485e-01 -2.93743581... | [11.252346992492676, 7.591898441314697] |
7e7b78c1-79a1-449d-abd8-ee6a2d6c639e | graph-feedback-via-reduction-to-regression | 2302.08631 | null | https://arxiv.org/abs/2302.08631v2 | https://arxiv.org/pdf/2302.08631v2.pdf | Practical Contextual Bandits with Feedback Graphs | While contextual bandit has a mature theory, effectively leveraging different feedback patterns to enhance the pace of learning remains unclear. Bandits with feedback graphs, which interpolates between the full information and bandit regimes, provides a promising framework to mitigate the statistical complexity of lear... | ['Paul Mineiro', 'Haipeng Luo', 'Olga Vrousgou', 'Yuheng Zhang', 'Mengxiao Zhang'] | 2023-02-17 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.90393561e-01 -5.52587695e-02 -1.20559883e+00 -1.42083749e-01
-1.06604683e+00 -5.90092838e-01 6.92451358e-01 1.06740043e-01
-2.21037373e-01 1.21935070e+00 1.16807625e-01 -9.27675843e-01
-7.59070814e-01 -6.38208270e-01 -1.06614864e+00 -8.28574181e-01
-8.85455236e-02 1.31101936e-01 -3.81956506e-03 9.75013077... | [4.510188102722168, 3.2366297245025635] |
8c99bd3a-9094-4074-b87a-ef8799815d3a | self-supervised-learning-of-depth-and-ego | 1909.13163 | null | https://arxiv.org/abs/1909.13163v1 | https://arxiv.org/pdf/1909.13163v1.pdf | Self-Supervised Learning of Depth and Ego-motion with Differentiable Bundle Adjustment | Learning to predict scene depth and camera motion from RGB inputs only is a challenging task. Most existing learning based methods deal with this task in a supervised manner which require ground-truth data that is expensive to acquire. More recent approaches explore the possibility of estimating scene depth and camera ... | ['KuoChin Lien', 'Yi Fang', 'Jing Zhu', 'Junli Gu', 'Yunxiao Shi'] | 2019-09-28 | null | null | null | null | ['depth-and-camera-motion'] | ['computer-vision'] | [ 1.68854102e-01 3.91858853e-02 -2.90763289e-01 -6.48269951e-01
-8.35349083e-01 -8.40546787e-01 5.73606372e-01 -5.80937207e-01
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3.36769044e-01 -4.55138922e-01 -1.14387310e+00 -8.64211619e-01
4.84420031e-01 4.86024827e-01 1.57301232e-01 1.88286364... | [8.580086708068848, -2.419633388519287] |
90c04a6a-dcb2-4889-9e9a-82042404e85e | adaptive-adversarial-training-method-for | 2211.16791 | null | https://arxiv.org/abs/2211.16791v1 | https://arxiv.org/pdf/2211.16791v1.pdf | Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory | In recent years, multi-scale generative adversarial networks (GANs) have been proposed to build generalized image processing models based on single sample. Constraining on the sample size, multi-scale GANs have much difficulty converging to the global optimum, which ultimately leads to limitations in their capabilities... | ['Zhouping Yin', 'Zeyu Gong', 'Bo Tao', 'Jing Tang'] | 2022-11-30 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 4.72692549e-01 2.26792153e-02 1.16766535e-01 -7.24456012e-02
-1.19798005e+00 -3.81040961e-01 3.31027657e-01 -7.80548334e-01
-6.26425073e-02 8.33740413e-01 3.56467590e-02 1.00719601e-01
6.29202873e-02 -1.00238645e+00 -7.24048913e-01 -1.13085103e+00
3.01739305e-01 1.89789962e-02 7.27147013e-02 -2.43720695... | [11.715883255004883, -0.6600180864334106] |
d9024c4d-3853-436a-b0bb-850bcc8bc915 | lagan-deep-semi-supervised-linguistic | 2301.13853 | null | https://arxiv.org/abs/2301.13853v1 | https://arxiv.org/pdf/2301.13853v1.pdf | LAGAN: Deep Semi-Supervised Linguistic-Anthropology Classification with Conditional Generative Adversarial Neural Network | Education is a right of all, however, every individual is different than others. Teachers in post-communism era discover inherent individualism to equally train all towards job market of fourth industrial revolution. We can consider scenario of ethnic minority education in academic practices. Ethnic minority group has ... | ['Zuzana Kubincova', 'Rossi Kamal'] | 2023-01-26 | null | null | null | null | ['culture'] | ['speech'] | [ 1.50042146e-01 7.49887288e-01 -2.61963636e-01 -4.94582862e-01
-1.23737492e-01 -6.28389060e-01 8.36508453e-01 -7.28572726e-01
-2.71244943e-01 1.24116099e+00 5.39466202e-01 -6.28422201e-01
-2.40480542e-01 -1.25036430e+00 -4.80855525e-01 -6.16414249e-01
4.60208654e-01 5.78041852e-01 -6.51591659e-01 -5.15635610... | [11.701675415039062, -0.1062721312046051] |
97554b25-9d20-4806-856e-a2e50c8f5480 | learning-hierarchy-aware-quaternion-knowledge | null | null | https://aclanthology.org/2022.coling-1.175 | https://aclanthology.org/2022.coling-1.175.pdf | Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations | Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for predicting missing links. It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry. However, many existing approaches fail to... | ['Bowei Xing', 'Taiyan Chen', 'Ruibin Wang', 'Xin Tong', 'Yongjie Shi', 'Xianghua Ying', 'Jinfa Yang'] | null | null | null | null | coling-2022-10 | ['knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'methodology'] | [-2.26718768e-01 3.19428593e-01 -2.58473307e-01 -6.23189732e-02
3.54083449e-01 -4.55915868e-01 6.45638049e-01 4.09375280e-01
-6.95533603e-02 5.47100127e-01 3.90164942e-01 -2.32486427e-01
-3.68023634e-01 -1.22605300e+00 -6.60839200e-01 -3.71950537e-01
-3.95575404e-01 7.82016933e-01 2.47800037e-01 -6.48804367... | [8.639185905456543, 7.774474620819092] |
7a263d51-2b65-41e6-a22a-45150f528e3a | textgrad-advancing-robustness-evaluation-in | 2212.09254 | null | https://arxiv.org/abs/2212.09254v1 | https://arxiv.org/pdf/2212.09254v1.pdf | TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization | Robustness evaluation against adversarial examples has become increasingly important to unveil the trustworthiness of the prevailing deep models in natural language processing (NLP). However, in contrast to the computer vision domain where the first-order projected gradient descent (PGD) is used as the benchmark approa... | ['Shiyu Chang', 'Sijia Liu', 'Yang Zhang', 'Guanhua Zhang', 'Yihua Zhang', 'Jinghan Jia', 'Bairu Hou'] | 2022-12-19 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.41325974e-01 -1.48320660e-01 1.12684118e-02 1.36834467e-02
-1.19080400e+00 -1.18296742e+00 6.84289157e-01 9.08105224e-02
-2.19777033e-01 7.09112108e-01 1.40004769e-01 -5.32708108e-01
-6.83522969e-02 -7.62206674e-01 -9.98017669e-01 -7.29201019e-01
-1.22869723e-02 2.79758513e-01 -1.64637119e-01 -5.02825618... | [6.005665302276611, 8.090240478515625] |
c5fba83d-b3dd-4ac5-84d0-f76b2c92e744 | deep-hierarchical-representation-of-point | null | null | https://ieeexplore.ieee.org/document/9650574 | https://ieeexplore.ieee.org/document/9650574 | Deep Hierarchical Representation of Point Cloud Videos via Spatio-Temporal Decomposition | In point cloud videos, point coordinates are irregular and unordered but point timestamps exhibit regularities and order. Grid-based networks for conventional video processing cannot be directly used to model raw point cloud videos. Therefore, in this work, we propose a point-based network that directly handles raw poi... | ['Mohan', 'Yi; Kankanhalli', 'Xin; Yang', 'Hehe; Yu', 'Fan'] | 2021-12-14 | null | null | null | ieee-transactions-on-pattern-analysis-and-20 | ['3d-human-action-recognition'] | ['computer-vision'] | [-2.72784710e-01 -5.63398123e-01 -1.76275477e-01 -5.68402968e-02
2.22469464e-01 -5.93528152e-01 3.55927080e-01 3.50897849e-01
-2.46640623e-01 3.44580412e-01 -4.75783134e-03 -1.68120191e-01
-2.43341222e-01 -1.14658642e+00 -8.42176735e-01 -4.17447776e-01
-5.23942649e-01 5.90334088e-02 7.18213320e-01 1.81051373... | [8.45756721496582, -2.0457632541656494] |
d2ecda2f-a869-4b5c-a261-f3edb09f7022 | when-search-meets-recommendation-learning | 2305.10822 | null | https://arxiv.org/abs/2305.10822v1 | https://arxiv.org/pdf/2305.10822v1.pdf | When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation | Modern online service providers such as online shopping platforms often provide both search and recommendation (S&R) services to meet different user needs. Rarely has there been any effective means of incorporating user behavior data from both S&R services. Most existing approaches either simply treat S&R behaviors sep... | ['Ji-Rong Wen', 'Kun Gai', 'Yang song', 'Xiaoxue Zang', 'Jun Xu', 'Xiao Zhang', 'Zhongxiang Sun', 'Zihua Si'] | 2023-05-18 | null | null | null | null | ['disentanglement', 'sequential-recommendation'] | ['methodology', 'miscellaneous'] | [-1.25394300e-01 -5.59587240e-01 -7.94866860e-01 -9.00216401e-01
-5.46636283e-01 -5.68321407e-01 7.07141995e-01 -7.37393573e-02
-3.11944157e-01 -1.74275398e-01 7.18598187e-01 -2.91051239e-01
-2.90841252e-01 -5.68543434e-01 -3.93421918e-01 -4.30012792e-01
3.84979732e-02 4.41404492e-01 -2.04653576e-01 -5.03328383... | [10.169536590576172, 5.623807430267334] |
712ffdad-8d91-4460-aca5-fe25d451d88f | towards-object-re-identification-from-point | 2305.10210 | null | https://arxiv.org/abs/2305.10210v2 | https://arxiv.org/pdf/2305.10210v2.pdf | Object Re-Identification from Point Clouds | In this work, we study the problem of object re-identification (ReID) in a 3D multi-object tracking (MOT) context, by learning to match pairs of objects from cropped (e.g., using their predicted 3D bounding boxes) point cloud observations. We are not concerned with SOTA performance for 3D MOT, however. Instead, we seek... | ['Krzysztof Czarnecki', 'Adrian Chow', 'Chengjie Huang', 'Benjamin Thérien'] | 2023-05-17 | null | null | null | null | ['multi-object-tracking', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 1.35774747e-01 -3.85831296e-01 1.36094302e-01 -3.55110168e-02
-7.31405973e-01 -9.98028100e-01 3.71726662e-01 1.51410967e-01
-6.25093639e-01 2.48268589e-01 -5.33348322e-01 -2.00908512e-01
-6.99638948e-02 -4.68900263e-01 -1.30275440e+00 -4.12101060e-01
-2.83390999e-01 9.28031087e-01 6.81847930e-01 4.36602309... | [6.596281051635742, -2.2074360847473145] |
ccd3e87b-3146-41d1-9c47-29d4d928fc61 | gene-sgan-a-method-for-discovering-disease | 2301.10772 | null | https://arxiv.org/abs/2301.10772v1 | https://arxiv.org/pdf/2301.10772v1.pdf | Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering | Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning ... | ['Christos Davatzikos', 'Ilya M. Nasrallah', 'Haochang Shou', 'Susan M. Resnick', 'Jingxuan Bao', 'Li Shen', 'David A. Wolk', 'Tammie L. S. Benzinger', 'Daniel S. Marcus', 'Pamela Lamontagne', 'John C. Morris', 'Sterling C. Johnson', 'Marilyn S. Albert', 'Luigi Ferrucci', 'Jurgen Fripp', 'Paul Maruff', 'Colin L. Master... | 2023-01-25 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [ 2.29820564e-01 -2.64422864e-01 -5.92286438e-02 -7.78131366e-01
-5.61383307e-01 -4.88300711e-01 4.27009255e-01 -8.97318646e-02
-1.62229128e-02 7.68611312e-01 4.12527382e-01 -9.58605483e-02
-8.10525954e-01 -2.68459231e-01 -2.30054513e-01 -7.86512196e-01
-7.32760787e-01 8.83160055e-01 -4.76580024e-01 3.19967479... | [6.583165168762207, 5.504162788391113] |
28082418-cfcf-4b8f-8fea-142dc9357d8b | signal-decomposition-using-masked-proximal | 2202.09338 | null | https://arxiv.org/abs/2202.09338v6 | https://arxiv.org/pdf/2202.09338v6.pdf | Signal Decomposition Using Masked Proximal Operators | We consider the well-studied problem of decomposing a vector time series signal into components with different characteristics, such as smooth, periodic, nonnegative, or sparse. We describe a simple and general framework in which the components are defined by loss functions (which include constraints), and the signal d... | ['Stephen P. Boyd', 'Bennet E. Meyers'] | 2022-02-18 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 3.48201603e-01 1.15519442e-01 -7.69878253e-02 -4.44126837e-02
-1.14642930e+00 -7.64843583e-01 9.27049518e-02 4.81156074e-03
-1.68665081e-01 6.28283799e-01 6.74523488e-02 -7.43100569e-02
-4.01301473e-01 -3.42711896e-01 -7.88956523e-01 -1.17045903e+00
-5.79301834e-01 3.62709403e-01 -1.70577839e-01 -1.14896763... | [7.100869655609131, 4.327635288238525] |
6af65cea-d543-4b1c-a2e1-e43130bab8c4 | or-nerf-object-removing-from-3d-scenes-guided | 2305.10503 | null | https://arxiv.org/abs/2305.10503v2 | https://arxiv.org/pdf/2305.10503v2.pdf | OR-NeRF: Object Removing from 3D Scenes Guided by Multiview Segmentation with Neural Radiance Fields | The emergence of Neural Radiance Fields (NeRF) for novel view synthesis has led to increased interest in 3D scene editing. One important task in editing is removing objects from a scene while ensuring visual reasonability and multiview consistency. However, current methods face challenges such as time-consuming object ... | ['Guosheng Lin', 'Fan Yang', 'Zhoujie Fu', 'Youtan Yin'] | 2023-05-17 | null | null | null | null | ['novel-view-synthesis'] | ['computer-vision'] | [ 7.03298748e-01 5.54136224e-02 2.90868551e-01 -3.77364188e-01
-5.88064790e-01 -7.68286109e-01 4.24508393e-01 8.33543316e-02
-1.12932488e-01 3.19262683e-01 -6.50809892e-03 -4.89959531e-02
1.48207143e-01 -6.87485576e-01 -7.93991566e-01 -2.08879262e-01
6.54146135e-01 1.20610103e-01 5.00219345e-01 -5.59149384... | [9.1834077835083, -3.038806438446045] |
b70c0071-be5c-4d4c-a8cf-212dbe98548f | covidctnet-an-open-source-deep-learning | 2005.03059 | null | https://arxiv.org/abs/2005.03059v3 | https://arxiv.org/pdf/2005.03059v3.pdf | CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image | Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpa... | ['Reza Rawassizadeh', 'Benjamin Haibe-Kains', 'L. T. Chitkushev', 'Mannudeep K. Kalra', 'Rana Jahanban-Esfahlan', 'Mohammad Hadi Gharib', 'Amir Reza Radmard', 'Seyed Ali Javad Mousavi', 'Rosa Babaei', 'Reza Reiazi', 'Omid Ghaemi', 'Mehdi Hosseinzadeh', 'Hadi Karimi Mobin', 'Azadeh Laali', 'Fatemeh Homayounieh', 'Engy A... | 2020-05-06 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [ 2.11262647e-02 -6.57536924e-01 -1.45090684e-01 2.23512635e-01
-6.62289619e-01 -9.06610191e-01 -1.11531183e-01 4.93900269e-01
-5.73759258e-01 4.43300754e-01 -1.21182106e-01 -9.26759541e-01
8.83405283e-02 -5.06239414e-01 -2.42765799e-01 -5.84450603e-01
-1.16751663e-01 1.03805137e+00 5.04344344e-01 7.25052416... | [15.563488960266113, -1.6926823854446411] |
46ea4b50-be61-42e5-9977-1f1cdf7ba149 | a-large-scale-dataset-for-empathetic-response | null | null | https://aclanthology.org/2021.emnlp-main.96 | https://aclanthology.org/2021.emnlp-main.96.pdf | A Large-Scale Dataset for Empathetic Response Generation | Recent development in NLP shows a strong trend towards refining pre-trained models with a domain-specific dataset. This is especially the case for response generation where emotion plays an important role. However, existing empathetic datasets remain small, delaying research efforts in this area, for example, the devel... | ['Pearl Pu', 'Yubo Xie', 'Anuradha Welivita'] | null | null | null | null | emnlp-2021-11 | ['empathetic-response-generation'] | ['natural-language-processing'] | [ 1.08360060e-01 5.40521324e-01 3.35170925e-01 -6.97503805e-01
-7.83101618e-01 -7.95723081e-01 7.22310901e-01 2.18553722e-01
-5.98332763e-01 1.14000285e+00 5.37315011e-01 1.13750309e-01
1.62451029e-01 -3.58499259e-01 4.28146832e-02 -4.01060492e-01
3.98556620e-01 1.15772069e+00 4.16116975e-02 -7.05666542... | [12.8829927444458, 6.925634860992432] |
b864868d-4802-4449-8c9c-3bacc3a9a4c3 | mixed-spectrum-signals-discrete | 2106.14696 | null | https://arxiv.org/abs/2106.14696v2 | https://arxiv.org/pdf/2106.14696v2.pdf | Mixed-Spectrum Signals -- Discrete Approximations and Variance Expressions for Covariance Estimates | The estimation of the covariance function of a stochastic process, or signal, is of integral importance for a multitude of signal processing applications. In this work, we derive closed-form expressions for the variance of covariance estimates for mixed-spectrum signals, i.e., spectra containing both absolutely continu... | ['Johan Karlsson', 'Filip Elvander'] | 2021-06-28 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 3.79931957e-01 -3.21557075e-01 2.87842512e-01 2.77238023e-02
-9.59147573e-01 -7.92208135e-01 4.57078218e-01 -6.68285191e-02
-1.90394834e-01 9.09575701e-01 1.93464980e-02 -1.60909459e-01
-3.99156481e-01 -3.42854857e-01 -3.35326999e-01 -1.00853074e+00
-4.17470217e-01 4.37384658e-02 -2.48785652e-02 5.82547672... | [6.703155994415283, 3.831418514251709] |
87d604d3-9dcf-4606-a1b8-37ab31bf383f | h2fa-r-cnn-holistic-and-hierarchical-feature | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_H2FA_R-CNN_Holistic_and_Hierarchical_Feature_Alignment_for_Cross-Domain_Weakly_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_H2FA_R-CNN_Holistic_and_Hierarchical_Feature_Alignment_for_Cross-Domain_Weakly_CVPR_2022_paper.pdf | H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object Detection | Cross-domain weakly supervised object detection (CDWSOD) aims to adapt the detection model to a novel target domain with easily acquired image-level annotations. How to align the source and target domains is critical to the CDWSOD accuracy. Existing methods usually focus on partial detection components for domain a... | ['Yi Yang', 'Jiaxu Miao', 'Zongxin Yang', 'Yifan Sun', 'Yunqiu Xu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 2.92206228e-01 6.03763983e-02 -5.04004657e-01 -2.44833678e-01
-1.09114385e+00 -6.11693203e-01 5.69242954e-01 -5.19094095e-02
-3.82287502e-01 3.31748188e-01 -1.47122860e-01 1.43614247e-01
7.08158836e-02 -6.10365033e-01 -7.43953288e-01 -6.09832227e-01
1.32175058e-01 4.27648276e-01 8.39095414e-01 -1.00563504... | [9.460602760314941, 1.3896303176879883] |
2bcc451f-e372-4cd2-ac4e-f348b3e7979b | federated-neural-topic-models | 2212.02269 | null | https://arxiv.org/abs/2212.02269v2 | https://arxiv.org/pdf/2212.02269v2.pdf | Federated Neural Topic Models | Over the last years, topic modeling has emerged as a powerful technique for organizing and summarizing big collections of documents or searching for particular patterns in them. However, privacy concerns may arise when cross-analyzing data from different sources. Federated topic modeling solves this issue by allowing m... | ['Jerónimo Arenas-García', 'Lorena Calvo-Bartolomé'] | 2022-12-05 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-2.86294311e-01 6.11484766e-01 -1.02559246e-01 -6.35093987e-01
-6.23951018e-01 -5.17411351e-01 9.59290564e-01 4.79762316e-01
-2.80035198e-01 6.63442671e-01 9.20120031e-02 -8.44094455e-02
-1.72841668e-01 -1.04018104e+00 -6.34724081e-01 -6.46402657e-01
-2.79911011e-01 7.86346793e-01 7.99839720e-02 2.42448792... | [5.889130115509033, 6.49974250793457] |
17246ea5-4944-4c5d-ae00-f359c633e232 | a-dual-scale-lead-seperated-transformer-with | 2211.12777 | null | https://arxiv.org/abs/2211.12777v1 | https://arxiv.org/pdf/2211.12777v1.pdf | A Dual-scale Lead-seperated Transformer With Lead-orthogonal Attention And Meta-information For Ecg Classification | Auxiliary diagnosis of cardiac electrophysiological status can be obtained through the analysis of 12-lead electrocardiograms (ECGs). This work proposes a dual-scale lead-separated transformer with lead-orthogonal attention and meta-information (DLTM-ECG) as a novel approach to address this challenge. ECG segments of e... | ['Li Sun', 'Wenming Yang', 'Zhourui Xia', 'Guijin Wang', 'Yang Li'] | 2022-11-23 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 5.10398328e-01 -6.87809065e-02 1.45940304e-01 -8.22381005e-02
-1.14927733e+00 -3.68379384e-01 -1.35855913e-01 1.58532023e-01
-2.83512890e-01 7.25104928e-01 1.47966295e-01 -3.20483506e-01
-4.03264433e-01 -2.59055287e-01 -4.51025426e-01 -8.13636303e-01
-2.90720046e-01 1.45427123e-01 -3.32752287e-01 -6.85494244... | [14.27277660369873, 3.2588775157928467] |
dc428348-0a33-410a-872d-cc6f889e04d5 | formal-guarantees-for-heuristic-optimization | 2208.00502 | null | https://arxiv.org/abs/2208.00502v1 | https://arxiv.org/pdf/2208.00502v1.pdf | Formal guarantees for heuristic optimization algorithms used in machine learning | Recently, Stochastic Gradient Descent (SGD) and its variants have become the dominant methods in the large-scale optimization of machine learning (ML) problems. A variety of strategies have been proposed for tuning the step sizes, ranging from adaptive step sizes to heuristic methods to change the step size in each ite... | ['Xiaoyu Li'] | 2022-07-31 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [-2.99456328e-01 -1.75438691e-02 -1.59063831e-01 -1.88768104e-01
-8.17835331e-01 -5.49523950e-01 1.45885438e-01 2.10732087e-01
-6.95162356e-01 9.41027164e-01 -1.76922813e-01 -3.34851950e-01
-3.76106024e-01 -5.63512266e-01 -9.25635755e-01 -1.17718184e+00
-1.40642643e-01 4.85929310e-01 1.43261701e-01 -3.08316410... | [6.870411396026611, 4.292517185211182] |
e44e2698-6dea-4930-a67e-0f3c5b22d6b2 | discrete-time-convolution-for-fast-event | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Discrete_Time_Convolution_for_Fast_Event-Based_Stereo_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Discrete_Time_Convolution_for_Fast_Event-Based_Stereo_CVPR_2022_paper.pdf | Discrete Time Convolution for Fast Event-Based Stereo | Inspired by biological retina, dynamical vision sensor transmits events of instantaneous changes of pixel intensity, giving it a series of advantages over traditional frame-based camera, such as high dynamical range, high temporal resolution and low power consumption. However, extracting information from highly asy... | ['Luziwei Leng', 'Qinghai Guo', 'Ziyang Zhang', 'Jie Cheng', 'JianGuo Zhang', 'Kaiwei Che', 'Kaixuan Zhang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['stereo-matching-1'] | ['computer-vision'] | [ 3.53299588e-01 -4.31231171e-01 3.89766991e-01 -1.59696817e-01
-3.94533157e-01 -4.53496218e-01 5.60815394e-01 -1.24415914e-02
-7.13847399e-01 7.32738376e-01 -5.03526302e-03 3.23897392e-01
-1.37279049e-01 -7.76326954e-01 -8.21382999e-01 -9.64043438e-01
-4.47475612e-02 -1.48628145e-01 7.14604437e-01 8.83469582... | [8.722952842712402, -1.2630970478057861] |
887b19cd-91d2-4922-8434-bbf06c5e0aef | real-time-and-efficient-method-for-accuracy | 1407.6498 | null | http://arxiv.org/abs/1407.6498v1 | http://arxiv.org/pdf/1407.6498v1.pdf | Real-Time and Efficient Method for Accuracy Enhancement of Edge Based License Plate Recognition System | License Plate Recognition plays an important role on the traffic monitoring
and parking management. Administration and restriction of those transportation
tools for their better service becomes very essential. In this paper, a fast
and real time method has an appropriate application to find plates that the
plat has til... | ['Hamid Reza Shayegh', 'Reza Azad', 'Babak Azad'] | 2014-07-24 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 2.09669173e-01 -4.53135312e-01 -2.96839271e-02 -1.60377175e-01
-7.39024356e-02 -5.64832687e-01 4.61489767e-01 8.94367099e-02
-6.92518055e-01 8.27065289e-01 -4.67988312e-01 -4.81532782e-01
2.79723736e-03 -8.88141990e-01 -1.77304804e-01 -7.73267090e-01
3.82774293e-01 7.88928211e-01 6.76358998e-01 -1.49298698... | [9.794673919677734, -4.97521448135376] |
108a81cf-c358-4d1e-97d5-ccb8f4be1131 | direction-of-voice-dov-estimation-for | null | null | https://karan-ahuja.com/dov.html | https://karan-ahuja.com/assets/docs/paper/dov.pdf | Direction-of-Voice (DoV) Estimation for Intuitive Speech Interaction with Smart Devices Ecosystems | Future homes and offices will feature increasingly dense ecosystems of IoT devices, such as smart lighting, speakers, and domestic appliances. Voice input is a natural candidate for interacting with out-of-reach and often small devices that lack full-sized physical interfaces. However, at present, voice agents generall... | ['and Chris Harrison.', 'Mayank Goel', 'Andy Kong', 'Karan Ahuja'] | 2020-11-15 | null | null | null | null | ['speaker-orientation'] | ['audio'] | [ 1.22799911e-01 1.78656757e-01 3.40510048e-02 -5.22096813e-01
-1.15953319e-01 -1.02824187e+00 5.41065633e-01 -5.55405378e-01
-1.06025971e-01 4.90252852e-01 6.84871376e-01 -7.83618569e-01
2.63335049e-01 -4.99705464e-01 -2.06165202e-02 -3.76008928e-01
5.38671374e-01 1.49002776e-01 -1.83185805e-02 -3.00286226... | [13.782565116882324, 6.128457546234131] |
ca8dc07c-6b3a-43ec-81d4-aa3ed8757176 | deep-parametric-indoor-lighting-estimation-1 | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Gardner_Deep_Parametric_Indoor_Lighting_Estimation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Gardner_Deep_Parametric_Indoor_Lighting_Estimation_ICCV_2019_paper.pdf | Deep Parametric Indoor Lighting Estimation | We present a method to estimate lighting from a single image of an indoor scene. Previous work has used an environment map representation that does not account for the localized nature of indoor lighting. Instead, we represent lighting as a set of discrete 3D lights with geometric and photometric parameters. We train a... | [' Jean-Francois Lalonde', ' Christian Gagne', ' Kalyan Sunkavalli', ' Yannick Hold-Geoffroy', 'Marc-Andre Gardner'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['lighting-estimation'] | ['computer-vision'] | [ 2.32580036e-01 -1.29155591e-01 5.54082930e-01 -9.12053943e-01
-6.25095785e-01 -7.21485794e-01 6.83518112e-01 -1.00970015e-01
-3.67441922e-01 6.53470457e-01 2.07376525e-01 -1.68429002e-01
3.87749791e-01 -8.40690792e-01 -1.04945779e+00 -3.52155834e-01
1.44458875e-01 2.88442165e-01 -1.11536965e-01 9.28084776... | [9.606744766235352, -3.0209615230560303] |
6ef5e974-49df-4c60-93f5-bff86ad49ad8 | more-multi-order-relation-mining-for-dense | 2203.05203 | null | https://arxiv.org/abs/2203.05203v2 | https://arxiv.org/pdf/2203.05203v2.pdf | MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes | 3D dense captioning is a recently-proposed novel task, where point clouds contain more geometric information than the 2D counterpart. However, it is also more challenging due to the higher complexity and wider variety of inter-object relations contained in point clouds. Existing methods only treat such relations as by-... | ['Yu-Gang Jiang', 'Lin Ma', 'Jingjing Chen', 'Zequn Jie', 'Shaoxiang Chen', 'Yang Jiao'] | 2022-03-10 | null | null | null | null | ['dense-captioning', '3d-dense-captioning'] | ['computer-vision', 'computer-vision'] | [ 2.95249373e-02 4.47593182e-01 -1.63071826e-01 -4.39675301e-01
-5.77413380e-01 -5.58589995e-01 4.38412160e-01 1.58980504e-01
3.28471571e-01 4.88437265e-01 2.62733698e-01 -2.47432187e-01
-2.50750601e-01 -9.48726058e-01 -1.14710546e+00 -3.96132976e-01
-4.35803272e-02 7.60108173e-01 2.71662146e-01 -1.61161438... | [10.262702941894531, 1.5676100254058838] |
19f5492a-89ba-4ec6-a589-cad12612685b | speech-inpainting-context-based-speech | 2306.00489 | null | https://arxiv.org/abs/2306.00489v1 | https://arxiv.org/pdf/2306.00489v1.pdf | Speech inpainting: Context-based speech synthesis guided by video | Audio and visual modalities are inherently connected in speech signals: lip movements and facial expressions are correlated with speech sounds. This motivates studies that incorporate the visual modality to enhance an acoustic speech signal or even restore missing audio information. Specifically, this paper focuses on ... | ['Jesper Jensen', 'Zheng-Hua Tan', 'Gloria Haro', 'Daniel Michelsanti', 'Juan F. Montesinos'] | 2023-06-01 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 2.85623997e-01 1.56694815e-01 -5.35927899e-02 -5.44172935e-02
-1.39763045e+00 -4.53987509e-01 4.59723443e-01 -1.99036241e-01
1.51173517e-01 3.98453057e-01 8.57367039e-01 2.49749515e-02
5.42011678e-01 -9.78998542e-02 -9.23451900e-01 -7.29546010e-01
3.90781462e-01 -2.80395865e-01 -1.59240782e-01 -7.89776742... | [14.433122634887695, 5.148718357086182] |
ccfdf37e-be64-4171-98fb-bc101ce43370 | relevant-entity-selection-knowledge-graph | 2306.16296 | null | https://arxiv.org/abs/2306.16296v1 | https://arxiv.org/pdf/2306.16296v1.pdf | Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning | Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop. Such a nucleus can be obtained from knowledge existing in an open KG like Wikidata. However, due to the size of such generic KGs, integrating... | ['Pierre Monnin', 'Miguel Couceiro', 'Lucas Jarnac'] | 2023-06-28 | null | null | null | null | ['graph-construction', 'transfer-learning'] | ['graphs', 'miscellaneous'] | [-1.31147519e-01 8.91714156e-01 -3.72457832e-01 -8.38929042e-02
-6.90569401e-01 -4.96982396e-01 8.66744518e-01 5.59143603e-01
-4.09663528e-01 1.14238691e+00 6.75558969e-02 -3.05659682e-01
-5.02805591e-01 -1.10405266e+00 -1.19083869e+00 -3.74353766e-01
-3.95001262e-01 9.77914035e-01 3.79816204e-01 -4.95683923... | [9.281012535095215, 8.260896682739258] |
d034da15-cfc6-41b1-9cb2-f0199a958bc1 | zero-shot-everything-sketch-based-image | 2303.14348 | null | https://arxiv.org/abs/2303.14348v1 | https://arxiv.org/pdf/2303.14348v1.pdf | Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style | This paper studies the problem of zero-short sketch-based image retrieval (ZS-SBIR), however with two significant differentiators to prior art (i) we tackle all variants (inter-category, intra-category, and cross datasets) of ZS-SBIR with just one network (``everything''), and (ii) we would really like to understand ho... | ['Yonggang Qi', 'Yi-Zhe Song', 'Timothy Hospedales', 'Da Li', 'Mingkang Li', 'Fengyin Lin'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Zero-Shot_Everything_Sketch-Based_Image_Retrieval_and_in_Explainable_Style_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Zero-Shot_Everything_Sketch-Based_Image_Retrieval_and_in_Explainable_Style_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.86471805e-01 7.91479126e-02 -5.05040511e-02 -1.89075962e-01
-1.17392874e+00 -7.98651159e-01 9.26671386e-01 -1.14890737e-02
-1.01179276e-02 2.63522536e-01 2.40065768e-01 -8.04745853e-02
-5.42573571e-01 -7.57320762e-01 -8.55596066e-01 -6.03782475e-01
1.22919224e-01 3.53086352e-01 4.15587425e-03 -3.99451762... | [11.628899574279785, 0.590360164642334] |
390cf461-14aa-4b8c-af3a-3370741b3108 | what-happens-before-and-after-multi-event | 2302.09715 | null | https://arxiv.org/abs/2302.09715v2 | https://arxiv.org/pdf/2302.09715v2.pdf | What happens before and after: Multi-Event Commonsense in Event Coreference Resolution | Event coreference models cluster event mentions pertaining to the same real-world event. Recent models rely on contextualized representations to recognize coreference among lexically or contextually similar mentions. However, models typically fail to leverage commonsense inferences, which is particularly limiting for r... | ['Vered Shwartz', 'Raymond Ng', 'Chris Tanner', 'Sahithya Ravi'] | 2023-02-20 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 3.70581359e-01 3.19695234e-01 -3.46343696e-01 -4.97528076e-01
-9.30136204e-01 -7.37610936e-01 9.68828321e-01 9.75121975e-01
-4.98616606e-01 1.07486188e+00 9.86735463e-01 -5.66224635e-01
-3.82880755e-02 -9.14065659e-01 -5.28831899e-01 -2.39623308e-01
-6.06642962e-02 5.89881837e-01 4.68977362e-01 -5.33972323... | [9.893674850463867, 9.253043174743652] |
39cf4124-564c-4a87-bd4e-ded62f9df53c | one-wug-two-wug-s-transformer-inflection | null | null | https://aclanthology.org/2022.computel-1.5 | https://aclanthology.org/2022.computel-1.5.pdf | One Wug, Two Wug+s Transformer Inflection Models Hallucinate Affixes | Data augmentation strategies are increasingly important in NLP pipelines for low-resourced and endangered languages, and in neural morphological inflection, augmentation by so called data hallucination is a popular technique. This paper presents a detailed analysis of inflection models trained with and without data hal... | ['Miikka Silfverberg', 'Farhan Samir'] | null | null | null | null | computel-acl-2022-5 | ['morphological-inflection'] | ['natural-language-processing'] | [ 3.27534556e-01 3.25916529e-01 -1.56341910e-01 -3.82849663e-01
-5.33811033e-01 -9.46840584e-01 6.00755394e-01 6.32682979e-01
-8.48875761e-01 6.34444773e-01 9.69161153e-01 -6.04823947e-01
2.87395656e-01 -8.11382532e-01 -6.03982210e-01 -3.88960660e-01
1.71854839e-01 7.33772337e-01 -7.08121896e-01 -4.01086241... | [10.739154815673828, 9.771591186523438] |
d48440d0-1c8c-4ba2-a2e4-ef37c37674f9 | chatgpt-or-grammarly-evaluating-chatgpt-on | 2303.13648 | null | https://arxiv.org/abs/2303.13648v1 | https://arxiv.org/pdf/2303.13648v1.pdf | ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark | ChatGPT is a cutting-edge artificial intelligence language model developed by OpenAI, which has attracted a lot of attention due to its surprisingly strong ability in answering follow-up questions. In this report, we aim to evaluate ChatGPT on the Grammatical Error Correction(GEC) task, and compare it with commercial G... | ['Michael Lyu', 'Wenxiang Jiao', 'Yuxuan Wan', 'Wenxuan Wang', 'Haoran Wu'] | 2023-03-15 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [-1.21025786e-01 5.25614083e-01 3.44674855e-01 -5.15886009e-01
-1.06289077e+00 -4.94171739e-01 3.00395548e-01 4.18290913e-01
-4.77416396e-01 7.42330372e-01 2.41385028e-01 -5.23562014e-01
1.19083658e-01 -6.30547881e-01 -7.86758661e-01 -2.37043172e-01
2.78992623e-01 6.22602046e-01 1.57338575e-01 -6.37335300... | [11.086725234985352, 10.70658016204834] |
391ddaa0-c432-4605-8f17-2a65cba5c89d | a-pseudo-multi-exposure-fusion-method-using | 1808.00195 | null | http://arxiv.org/abs/1808.00195v1 | http://arxiv.org/pdf/1808.00195v1.pdf | A Pseudo Multi-Exposure Fusion Method Using Single Image | This paper proposes a novel pseudo multi-exposure image fusion method based
on a single image. Multi-exposure image fusion is used to produce images
without saturation regions, by using photos with different exposures. However,
it is difficult to take photos suited for the multi-exposure image fusion when
we take a pho... | ['Sayaka Shiota', 'Hitoshi Kiya', 'Yuma Kinoshita'] | 2018-08-01 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 9.97268677e-01 -6.01057827e-01 3.82577807e-01 -1.32548213e-01
-5.02696693e-01 -3.97582948e-01 5.18375814e-01 1.02919288e-01
-6.54560626e-01 6.01956785e-01 -3.04275334e-01 -4.73554060e-02
-3.72915953e-01 -8.56001198e-01 -6.00812793e-01 -8.09048295e-01
2.88302749e-01 -3.51670921e-01 3.15881848e-01 -1.69037208... | [10.967155456542969, -2.5254645347595215] |
a0f6b174-f64f-4cd6-89c4-75c770d66a98 | multi-task-learning-for-aggregated-data-using | 1906.09412 | null | https://arxiv.org/abs/1906.09412v4 | https://arxiv.org/pdf/1906.09412v4.pdf | Multi-task Learning for Aggregated Data using Gaussian Processes | Aggregated data is commonplace in areas such as epidemiology and demography. For example, census data for a population is usually given as averages defined over time periods or spatial resolutions (cities, regions or countries). In this paper, we present a novel multi-task learning model based on Gaussian processes for... | ['Mauricio A. Álvarez', 'Michael Thomas Smith', 'Fariba Yousefi'] | 2019-06-22 | multi-task-learning-for-aggregated-data-using-1 | http://papers.nips.cc/paper/9644-multi-task-learning-for-aggregated-data-using-gaussian-processes | http://papers.nips.cc/paper/9644-multi-task-learning-for-aggregated-data-using-gaussian-processes.pdf | neurips-2019-12 | ['air-pollution-prediction'] | ['miscellaneous'] | [ 1.13062285e-01 1.92328021e-01 2.04601139e-01 -3.99491489e-01
-1.22359824e+00 -5.08904696e-01 8.99560034e-01 3.23528558e-01
-6.26132727e-01 1.26131296e+00 2.89561361e-01 -1.34782806e-01
-3.95216346e-01 -9.28797305e-01 -8.81154001e-01 -8.71972680e-01
-1.51756823e-01 8.52602959e-01 -3.37988511e-02 4.85188842... | [6.94701623916626, 3.918076753616333] |
e21fe60f-67cb-48ba-b7a0-ae597a9dc2ae | invisible-steganography-via-generative | 1807.08571 | null | http://arxiv.org/abs/1807.08571v3 | http://arxiv.org/pdf/1807.08571v3.pdf | Invisible Steganography via Generative Adversarial Networks | Nowadays, there are plenty of works introducing convolutional neural networks
(CNNs) to the steganalysis and exceeding conventional steganalysis algorithms.
These works have shown the improving potential of deep learning in information
hiding domain. There are also several works based on deep learning to do image
stega... | ['Shiqi Dong', 'Ru Zhang', 'Jianyi Liu'] | 2018-07-23 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 6.50908411e-01 2.23826781e-01 3.64544898e-01 1.43972024e-01
-1.66205823e-01 1.02571985e-02 2.93440700e-01 -9.40925539e-01
-3.43736112e-01 6.38456881e-01 -1.35300770e-01 -4.39502448e-01
2.43075565e-01 -1.10332406e+00 -9.66987371e-01 -1.17455924e+00
-1.85806721e-01 -3.26741844e-01 2.34354004e-01 -4.91609961... | [4.303144931793213, 8.056011199951172] |
b923a75d-2d31-499e-af67-12b8278bb164 | measured-albedo-in-the-wild-filling-the-gap | 2306.15662 | null | https://arxiv.org/abs/2306.15662v2 | https://arxiv.org/pdf/2306.15662v2.pdf | Measured Albedo in the Wild: Filling the Gap in Intrinsics Evaluation | Intrinsic image decomposition and inverse rendering are long-standing problems in computer vision. To evaluate albedo recovery, most algorithms report their quantitative performance with a mean Weighted Human Disagreement Rate (WHDR) metric on the IIW dataset. However, WHDR focuses only on relative albedo values and of... | ['Soumyadip Sengupta', 'David Jacobs', 'Hariharmano Shanmugaraja', 'Sanjoy Chowdhury', 'Jiaye Wu'] | 2023-06-27 | null | null | null | null | ['intrinsic-image-decomposition', 'inverse-rendering'] | ['computer-vision', 'computer-vision'] | [ 2.19301075e-01 -3.48721981e-01 2.47568101e-01 -3.79649162e-01
-5.93693137e-01 -5.58702111e-01 5.27931511e-01 -9.18384269e-02
-1.29851550e-01 5.41804492e-01 3.67754996e-01 -8.73592496e-02
-6.67067850e-03 -7.65883803e-01 -2.75667250e-01 -9.36489940e-01
7.63034150e-02 -5.73266000e-02 1.90576583e-01 -4.68625844... | [9.9053955078125, -2.861752510070801] |
9d7193c8-015f-4c00-950c-b48e0015b040 | a-comparison-of-supervised-and-unsupervised | 2107.09204 | null | https://arxiv.org/abs/2107.09204v1 | https://arxiv.org/pdf/2107.09204v1.pdf | A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in Images | Anomaly detection in images plays a significant role for many applications across all industries, such as disease diagnosis in healthcare or quality assurance in manufacturing. Manual inspection of images, when extended over a monotonously repetitive period of time is very time consuming and can lead to anomalies being... | ['Zhenning Li', 'Håkon Sandaker', 'Tabea Redl', 'Sauraj Verma', 'Vincent Wilmet'] | 2021-07-20 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 3.84444058e-01 1.21885896e-01 5.67083836e-01 -7.78759867e-02
-2.44022846e-01 -3.61329734e-01 5.42236090e-01 1.24102138e-01
-2.45852888e-01 4.75363612e-01 -3.67643982e-01 -6.82703078e-01
-4.42613550e-02 -9.01603281e-01 -6.02945685e-01 -7.71257162e-01
-5.30509949e-02 5.07726610e-01 -3.95178758e-02 -1.82723343... | [7.489923000335693, 2.161741256713867] |
06e77897-6f61-471a-9e4f-cecf1a14163c | cat-controllable-attribute-translation-for | 2209.06850 | null | https://arxiv.org/abs/2209.06850v1 | https://arxiv.org/pdf/2209.06850v1.pdf | CAT: Controllable Attribute Translation for Fair Facial Attribute Classification | As the social impact of visual recognition has been under scrutiny, several protected-attribute balanced datasets emerged to address dataset bias in imbalanced datasets. However, in facial attribute classification, dataset bias stems from both protected attribute level and facial attribute level, which makes it challen... | ['Wael Abd-Almageed', 'Jiazhi Li'] | 2022-09-14 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 3.85206699e-01 2.16482818e-01 -5.32033443e-01 -9.48523998e-01
-5.45603156e-01 -4.05575424e-01 4.30219233e-01 2.24049419e-01
-2.59494424e-01 8.81521523e-01 3.26407641e-01 6.28200546e-02
-8.52244422e-02 -8.13782573e-01 -3.28335702e-01 -4.87503737e-01
4.61590618e-01 9.89119932e-02 -5.13481379e-01 1.04818903... | [13.02203369140625, 1.262813687324524] |
c20d145f-a70b-45b6-a94a-cdadc357bc76 | characterizing-political-bias-in-automatic | 2305.02321 | null | https://arxiv.org/abs/2305.02321v1 | https://arxiv.org/pdf/2305.02321v1.pdf | Characterizing Political Bias in Automatic Summaries: A Case Study of Trump and Biden | Growing literature has shown that powerful NLP systems may encode social biases; however, the political bias of summarization models remains relatively unknown. In this work, we use an entity replacement method to investigate the portrayal of politicians in automatically generated summaries of news articles. We develop... | ['Chenhao Tan', 'Karen Zhou'] | 2023-05-03 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 5.36999181e-02 4.84578133e-01 -8.91340613e-01 -2.48176962e-01
-7.28260338e-01 -8.20800126e-01 1.36062455e+00 7.23139822e-01
-7.03596413e-01 1.02084923e+00 1.62881887e+00 -4.05477524e-01
2.71720588e-02 -8.54514718e-01 -7.21642733e-01 -2.99913943e-01
3.15396011e-01 2.33264878e-01 -3.32858026e-01 -3.07534128... | [8.870532989501953, 9.97246265411377] |
b392ff62-9c32-49f6-aa52-6edf914ae75d | retrieve-rerank-and-rewrite-soft-template | null | null | https://aclanthology.org/P18-1015 | https://aclanthology.org/P18-1015.pdf | Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization | Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably. Inspired by the traditional template-based summarization approaches, this paper proposes to use existing summaries as soft templates to guide the seq2seq model. To this end, we use a popular... | ['Furu Wei', 'Ziqiang Cao', 'Wenjie Li', 'Sujian Li'] | 2018-07-01 | null | null | null | acl-2018-7 | ['summarization', 'abstractive-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.31811929e-01 2.57599771e-01 -4.15175796e-01 -1.79516122e-01
-1.26090193e+00 -7.55292356e-01 8.43543768e-01 2.82200426e-01
-1.36891589e-01 1.02422512e+00 1.08093190e+00 -3.16978805e-02
1.82608366e-02 -7.10200906e-01 -3.52523059e-01 -3.09432030e-01
4.71725076e-01 3.43422711e-01 3.19381207e-01 -5.15436411... | [12.4541654586792, 9.416108131408691] |
9d42bc53-0f58-4811-8ded-e6a65e7c2e6c | visual-and-language-navigation-a-survey-and | 2108.11544 | null | https://arxiv.org/abs/2108.11544v3 | https://arxiv.org/pdf/2108.11544v3.pdf | Vision-Language Navigation: A Survey and Taxonomy | Vision-Language Navigation (VLN) tasks require an agent to follow human language instructions to navigate in previously unseen environments. This challenging field involving problems in natural language processing, computer vision, robotics, etc., has spawn many excellent works focusing on various VLN tasks. This paper... | ['Xinmeng Li', 'Tao Chang', 'Wansen Wu'] | 2021-08-26 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [ 3.65540572e-02 -1.64389029e-01 3.22572491e-03 -4.19400960e-01
-1.50837407e-01 -1.04107070e+00 9.63726938e-01 -1.15491524e-01
-8.30070198e-01 6.75581872e-01 -1.41636670e-01 -7.79289842e-01
-2.51616955e-01 -6.03310049e-01 -4.41831589e-01 -6.31972909e-01
-2.10794389e-01 6.61010027e-01 5.36843240e-01 -6.66559160... | [4.505753517150879, 0.5893887281417847] |
bd57b146-b386-4ed3-891f-34532370b0b2 | cross-lingual-text-independent-speaker | 1908.01447 | null | https://arxiv.org/abs/1908.01447v1 | https://arxiv.org/pdf/1908.01447v1.pdf | Cross-lingual Text-independent Speaker Verification using Unsupervised Adversarial Discriminative Domain Adaptation | Speaker verification systems often degrade significantly when there is a language mismatch between training and testing data. Being able to improve cross-lingual speaker verification system using unlabeled data can greatly increase the robustness of the system and reduce human labeling costs. In this study, we introduc... | ['John H. L. Hansen', 'Jing Huang', 'Wei Xia'] | 2019-08-05 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 9.26464722e-02 -1.47939473e-01 -3.84004135e-03 -7.23022163e-01
-1.27748239e+00 -9.72577274e-01 6.07155859e-01 -3.73480618e-01
-4.36745524e-01 6.57698035e-01 3.47806931e-01 -6.06432080e-01
4.16080266e-01 -2.45440796e-01 -5.69648147e-01 -8.07992637e-01
1.34713128e-01 2.59963185e-01 -1.26985282e-01 -3.58072490... | [14.325096130371094, 6.222207069396973] |
12c76d5e-ebfd-46ff-9750-d9a6fc31924c | data-driven-perception-of-neuron-point | 1811.00688 | null | http://arxiv.org/abs/1811.00688v2 | http://arxiv.org/pdf/1811.00688v2.pdf | Data-driven Perception of Neuron Point Process with Unknown Unknowns | Identification of patterns from discrete data time-series for statistical
inference, threat detection, social opinion dynamics, brain activity prediction
has received recent momentum. In addition to the huge data size, the associated
challenges are, for example, (i) missing data to construct a closed
time-varying compl... | ['Paul Bogdan', 'Gaurav Gupta', 'Ruochen Yang'] | 2018-11-02 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 4.98167753e-01 -2.59751946e-01 2.68339306e-01 -3.91384140e-02
4.49958555e-02 -2.67172456e-01 4.34065044e-01 -1.58077925e-01
-6.15107238e-01 1.13518500e+00 -1.95957944e-01 -9.85077396e-03
-4.93028849e-01 -3.10936630e-01 -7.76513159e-01 -1.29699802e+00
-3.04351836e-01 2.10050374e-01 1.79256201e-01 1.38255730... | [6.880707263946533, 3.6932973861694336] |
a0435090-35f6-400c-858b-78ac25598f03 | learning-large-euclidean-margin-for-sketch | 1812.04275 | null | https://arxiv.org/abs/1812.04275v2 | https://arxiv.org/pdf/1812.04275v2.pdf | Domain-Aware SE Network for Sketch-based Image Retrieval with Multiplicative Euclidean Margin Softmax | This paper proposes a novel approach for Sketch-Based Image Retrieval (SBIR), for which the key is to bridge the gap between sketches and photos in terms of the data representation. Inspired by channel-wise attention explored in recent years, we present a Domain-Aware Squeeze-and-Excitation (DASE) network, which seamle... | ['Yanwei Fu', 'Guodong Guo', 'Wenming Yang', 'Hangyu Lin', 'Peng Lu', 'Gao Huang'] | 2018-12-11 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.02347243e-01 -5.99979162e-01 -4.19730693e-01 -4.18164462e-01
-8.64519119e-01 -4.42965060e-01 6.69339418e-01 -1.56117946e-01
-4.04436499e-01 4.31925774e-01 1.50676221e-01 1.07549362e-01
-4.31740403e-01 -7.28325069e-01 -5.70757866e-01 -7.21530259e-01
3.11113745e-01 -2.15379804e-01 9.82924178e-02 -1.23065719... | [11.495368003845215, 0.7529169321060181] |
a2b9fde5-3f1b-47d3-9daa-15b9ed837676 | multi-dimensional-evaluation-of-text | 2306.01200 | null | https://arxiv.org/abs/2306.01200v1 | https://arxiv.org/pdf/2306.01200v1.pdf | Multi-Dimensional Evaluation of Text Summarization with In-Context Learning | Evaluation of natural language generation (NLG) is complex and multi-dimensional. Generated text can be evaluated for fluency, coherence, factuality, or any other dimensions of interest. Most frameworks that perform such multi-dimensional evaluation require training on large manually or synthetically generated datasets... | ['Chunting Zhou', 'Graham Neubig', 'PengFei Liu', 'Patrick Fernandes', 'Swarnashree Mysore Sathyendra', 'Vaishakh Keshava', 'Sameer Jain'] | 2023-06-01 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 6.74074590e-02 2.13855177e-01 -3.51920426e-01 -2.31636450e-01
-1.35855615e+00 -5.38384140e-01 1.03765130e+00 6.11395180e-01
-5.02489328e-01 1.00481439e+00 1.01714826e+00 -8.08300748e-02
-3.98612954e-02 -6.06196523e-01 -3.42880815e-01 -1.18540391e-01
1.25930950e-01 8.92081082e-01 -5.17829098e-02 -2.52251953... | [11.888023376464844, 9.109991073608398] |
06b52ec3-098a-4633-8a9b-32e655f0383e | show-me-your-nft-and-i-tell-you-how-it-will | 2302.01676 | null | https://arxiv.org/abs/2302.01676v2 | https://arxiv.org/pdf/2302.01676v2.pdf | Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction | Non-Fungible Tokens (NFTs) represent deeds of ownership, based on blockchain technologies and smart contracts, of unique crypto assets on digital art forms (e.g., artworks or collectibles). In the spotlight after skyrocketing in 2021, NFTs have attracted the attention of crypto enthusiasts and investors intent on placi... | ['Andrea Tagarelli', 'Lucio La Cava', 'Davide Costa'] | 2023-02-03 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [-1.94720477e-01 4.16193828e-02 -6.13934100e-01 -2.20298812e-01
-5.36742687e-01 -8.43685031e-01 1.17880332e+00 -1.52874570e-02
-1.29939467e-01 1.41571045e-01 3.16513151e-01 -7.39995062e-01
7.40511566e-02 -1.05665064e+00 -6.93866611e-01 -3.86415243e-01
5.42024300e-02 7.86231518e-01 -2.53441125e-01 -1.65017322... | [6.725698471069336, 6.129147052764893] |
4591b1b3-8a97-4bf9-8a21-0c4ce2679ef1 | revisiting-self-supervised-contrastive | 2210.03853 | null | https://arxiv.org/abs/2210.03853v1 | https://arxiv.org/pdf/2210.03853v1.pdf | Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition | The success of most advanced facial expression recognition works relies heavily on large-scale annotated datasets. However, it poses great challenges in acquiring clean and consistent annotations for facial expression datasets. On the other hand, self-supervised contrastive learning has gained great popularity due to i... | ['Benny Lo', 'Guang-Zhong Yang', 'Xiao Gu', 'Yuxuan Shu'] | 2022-10-08 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 3.48706424e-01 -2.75039133e-02 -3.23724657e-01 -8.98599863e-01
-6.11819565e-01 -2.41103083e-01 5.50159454e-01 -1.63909003e-01
-3.17654550e-01 8.46089065e-01 -1.43787369e-01 4.42489088e-01
-5.92518002e-02 -3.65280718e-01 -1.66325539e-01 -1.02859724e+00
-4.37517418e-03 2.04016730e-01 -5.43467641e-01 -2.60596544... | [13.582799911499023, 1.6349762678146362] |
96ae015e-4050-417d-82e3-c1824533292f | global-inference-with-explicit-syntactic-and | null | null | https://www.ijcai.org/proceedings/2022/0570 | https://www.ijcai.org/proceedings/2022/0570.pdf | Global inference with explicit syntactic and discourse structures for dialogue-level relation extraction | Recent research attention for relation extraction has been paid to the dialogue scenario, ie, dialoguelevel relation extraction (DiaRE). Existing DiaRE methods either simply concatenate the utterances in a dialogue into a long piece of text, or employ naive words, sentences or entities to build dialogue graphs, while t... | ['Fei Li', 'Donghong Ji', 'Chenliang Li', 'Shengqiong Wu', 'Jingye Li', 'Hao Fei'] | 2022-07-30 | null | null | null | conference-2022-7 | ['dialog-relation-extraction'] | ['natural-language-processing'] | [ 1.92490444e-01 1.00163817e+00 -5.40487885e-01 -3.73335093e-01
-7.91757405e-01 -7.58701026e-01 1.08421779e+00 5.45070708e-01
-6.27034083e-02 8.34104419e-01 8.72697711e-01 -5.44974327e-01
-1.03195878e-02 -8.91614497e-01 -1.30459696e-01 -1.04390867e-01
2.06554994e-01 6.95466995e-01 3.50743771e-01 -7.10168660... | [12.38904094696045, 8.077890396118164] |
d08f4100-f65b-43f5-ac3e-cf03dc6a2fdf | seal-simultaneous-exploration-and | 2306.12623 | null | https://arxiv.org/abs/2306.12623v1 | https://arxiv.org/pdf/2306.12623v1.pdf | SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems | The availability of accurate localization is critical for multi-robot exploration strategies; noisy or inconsistent localization causes failure in meeting exploration objectives. We aim to achieve high localization accuracy with contemporary exploration map belief and vice versa without needing global localization info... | ['Ramviyas Parasuraman', 'Ehsan Latif'] | 2023-06-22 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [-3.95015925e-01 7.77161717e-02 -1.24484189e-01 -1.54143244e-01
-1.15669203e+00 -5.61639726e-01 4.74491596e-01 5.56344569e-01
-7.80427396e-01 1.33917773e+00 -1.48305133e-01 -1.74493536e-01
-6.68447495e-01 -7.78074324e-01 -8.50430608e-01 -8.39261353e-01
-9.61426377e-01 7.14567840e-01 1.46858469e-01 -2.12908939... | [5.391463279724121, 0.9845951199531555] |
6f948086-8fae-4e97-9517-e5bda8604b2b | anomaly-detection-requires-better | 2210.10773 | null | https://arxiv.org/abs/2210.10773v1 | https://arxiv.org/pdf/2210.10773v1.pdf | Anomaly Detection Requires Better Representations | Anomaly detection seeks to identify unusual phenomena, a central task in science and industry. The task is inherently unsupervised as anomalies are unexpected and unknown during training. Recent advances in self-supervised representation learning have directly driven improvements in anomaly detection. In this position ... | ['Yedid Hoshen', 'Ron Abutbul', 'Eliahu Horwitz', 'Niv Cohen', 'Tal Reiss'] | 2022-10-19 | null | null | null | null | ['3d-anomaly-detection-and-segmentation'] | ['methodology'] | [ 4.21161830e-01 6.68854043e-02 -1.81866903e-02 -3.90877873e-01
-3.86772186e-01 -4.16522115e-01 7.17841029e-01 7.73899317e-01
1.01423554e-01 4.17824596e-01 -1.92253724e-01 -4.91874337e-01
-1.48399070e-01 -5.14788151e-01 -3.59154105e-01 -3.53790462e-01
-5.75859427e-01 4.42019373e-01 6.28697574e-02 -3.17421913... | [7.559846878051758, 2.4914379119873047] |
01715ad9-095f-4091-9813-24df515e453b | convolutional-neural-networks-for-image-spam | 2204.01710 | null | https://arxiv.org/abs/2204.01710v1 | https://arxiv.org/pdf/2204.01710v1.pdf | Convolutional Neural Networks for Image Spam Detection | Spam can be defined as unsolicited bulk email. In an effort to evade text-based filters, spammers sometimes embed spam text in an image, which is referred to as image spam. In this research, we consider the problem of image spam detection, based on image analysis. We apply convolutional neural networks (CNN) to this pr... | ['Mark Stamp', 'Katerina Potika', 'Fabio Di Troia', 'Tazmina Sharmin'] | 2022-04-02 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 5.97391427e-01 -3.52021456e-01 2.33759046e-01 -2.76780516e-01
-1.23735413e-01 -4.86668795e-01 8.07152390e-01 4.57643643e-02
-5.69862545e-01 3.35522711e-01 -5.74048162e-02 -5.64317107e-01
3.24647307e-01 -9.07728732e-01 -6.71725333e-01 -4.90746796e-01
2.66365379e-01 1.11611485e-01 5.97267568e-01 -2.86953926... | [7.788241386413574, 9.959676742553711] |
b1ee856e-380f-4430-b7b0-40e4c09bde4a | align-with-purpose-optimize-desired | 2307.01715 | null | https://arxiv.org/abs/2307.01715v2 | https://arxiv.org/pdf/2307.01715v2.pdf | Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework | Connectionist Temporal Classification (CTC) is a widely used criterion for training supervised sequence-to-sequence (seq2seq) models. It enables learning the relations between input and output sequences, termed alignments, by marginalizing over perfect alignments (that yield the ground truth), at the expense of imperfe... | ['Tal Rosenwein', 'Amnon Shashua', 'Jacob Bitterman', 'Oren Tadmor', 'David Zar', 'Yael Ben-Oren', 'Ayana Shenhav', 'Noam Wies', 'Ronen Katsir', 'Maya Alroy', 'Eliya Segev'] | 2023-07-04 | null | null | null | null | ['speech-recognition', 'automatic-speech-recognition'] | ['speech', 'speech'] | [ 7.84148037e-01 4.15737629e-02 -1.25200987e-01 -4.14355576e-01
-1.33861816e+00 -8.53727460e-01 6.43742561e-01 -7.32212234e-03
-5.73049247e-01 7.57099926e-01 -3.88383903e-02 -7.95751929e-01
-2.60914005e-02 -3.30006272e-01 -7.19404101e-01 -7.55127192e-01
-2.17854798e-01 3.80153686e-01 4.69467163e-01 -2.60540873... | [14.331936836242676, 6.868392467498779] |
7e2393a6-195c-44b1-9618-b015f9b064a9 | detect-reject-for-transferability-of-black | 2112.12095 | null | https://arxiv.org/abs/2112.12095v1 | https://arxiv.org/pdf/2112.12095v1.pdf | Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems | In the last decade, the use of Machine Learning techniques in anomaly-based intrusion detection systems has seen much success. However, recent studies have shown that Machine learning in general and deep learning specifically are vulnerable to adversarial attacks where the attacker attempts to fool models by supplying ... | ['Tayeb Kenaza', 'Wim Mees', 'Jean-Michel Dricot', 'Thibault Debatty', 'Islam Debicha'] | 2021-12-22 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 4.08706665e-01 4.20698188e-02 3.14293057e-01 -1.89038128e-01
9.32139978e-02 -1.08355772e+00 8.36491466e-01 -3.46818343e-02
-3.47770989e-01 3.41592133e-01 -6.58042908e-01 -8.32676888e-01
3.57405618e-02 -1.03146350e+00 -7.36868322e-01 -6.45770311e-01
-2.94976920e-01 2.20399126e-01 2.50803947e-01 -3.61549586... | [5.506717205047607, 7.558452606201172] |
8f57f228-0ba6-4abe-9857-04c385d2e5b0 | coldnas-search-to-modulate-for-user-cold | 2306.03387 | null | https://arxiv.org/abs/2306.03387v1 | https://arxiv.org/pdf/2306.03387v1.pdf | ColdNAS: Search to Modulate for User Cold-Start Recommendation | Making personalized recommendation for cold-start users, who only have a few interaction histories, is a challenging problem in recommendation systems. Recent works leverage hypernetworks to directly map user interaction histories to user-specific parameters, which are then used to modulate predictor by feature-wise li... | ['Quanming Yao', 'Dejing Dou', 'daxiang dong', 'Qinghe Jing', 'Yaqing Wang', 'Shiguang Wu'] | 2023-06-06 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.21640980e-01 -3.86886358e-01 -8.38430524e-01 -4.19600517e-01
-3.99349183e-01 -7.16921866e-01 5.17282665e-01 -5.60006440e-01
-1.69246182e-01 4.20779020e-01 2.37205416e-01 -2.86584377e-01
-6.58829689e-01 -6.59109056e-01 -4.13806438e-01 -8.79542530e-01
-9.16347280e-03 3.27809602e-01 1.03440933e-01 -6.62856102... | [10.078405380249023, 5.620729923248291] |
62ded650-1cfe-4e2b-a11e-c3bd39d27f4c | class-balanced-pixelnet-for-neurological | 2204.11048 | null | https://arxiv.org/abs/2204.11048v1 | https://arxiv.org/pdf/2204.11048v1.pdf | Class Balanced PixelNet for Neurological Image Segmentation | In this paper, we propose an automatic brain tumor segmentation approach (e.g., PixelNet) using a pixel-level convolutional neural network (CNN). The model extracts feature from multiple convolutional layers and concatenate them to form a hyper-column where samples a modest number of pixels for optimization. Hyper-colu... | ['Hongliang Ren', 'Mobarakol Islam'] | 2022-04-23 | null | null | null | null | ['ischemic-stroke-lesion-segmentation', 'brain-tumor-segmentation'] | ['medical', 'medical'] | [ 5.05458713e-01 2.97312945e-01 -4.37800378e-01 -4.74283248e-01
-5.06216526e-01 1.11776084e-01 2.19067007e-01 1.90772414e-01
-8.10346246e-01 9.04160678e-01 -8.54491293e-02 -1.59877867e-01
3.02421544e-02 -8.57886195e-01 -6.13984346e-01 -1.01962399e+00
1.32865325e-01 2.55920649e-01 3.84786874e-01 4.11372602... | [14.519079208374023, -2.4648501873016357] |
ccfba1ff-d448-4810-902d-4406ebcdd678 | a-review-of-panoptic-segmentation-for-mobile | 2304.13980 | null | https://arxiv.org/abs/2304.13980v1 | https://arxiv.org/pdf/2304.13980v1.pdf | A Review of Panoptic Segmentation for Mobile Mapping Point Clouds | 3D point cloud panoptic segmentation is the combined task to (i) assign each point to a semantic class and (ii) separate the points in each class into object instances. Recently there has been an increased interest in such comprehensive 3D scene understanding, building on the rapid advances of semantic segmentation due... | ['Konrad Schindler', 'Torben Peters', 'Yuanwen Yue', 'Binbin Xiang'] | 2023-04-27 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 2.95563906e-01 -7.29820356e-02 -1.83316201e-01 -5.60979724e-01
-4.03289795e-01 -7.27414370e-01 7.01027393e-01 1.98750362e-01
-1.37923524e-01 2.26911232e-01 -1.46114960e-01 -5.12828410e-01
-2.04636961e-01 -1.09977615e+00 -5.69530964e-01 -2.95935780e-01
-4.00110930e-02 9.38285828e-01 4.70678985e-01 -5.52240536... | [8.653562545776367, -2.2076261043548584] |
e48c06c6-e7f7-434e-bf5c-51b6577ffc73 | masked-autoencoders-as-image-processors | 2303.17316 | null | https://arxiv.org/abs/2303.17316v1 | https://arxiv.org/pdf/2303.17316v1.pdf | Masked Autoencoders as Image Processors | Transformers have shown significant effectiveness for various vision tasks including both high-level vision and low-level vision. Recently, masked autoencoders (MAE) for feature pre-training have further unleashed the potential of Transformers, leading to state-of-the-art performances on various high-level vision tasks... | ['Guangtao Zhai', 'Jia Wang', 'Long Teng', 'Danyang Tu', 'Xiongkuo Min', 'Wei Shen', 'Huiyu Duan'] | 2023-03-30 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 3.12012345e-01 -1.66129515e-01 4.33431238e-01 -3.41246992e-01
-6.47387266e-01 5.14224023e-02 6.83632612e-01 -3.86909515e-01
-6.73973918e-01 2.90049165e-01 3.76086712e-01 -1.00920610e-01
4.59052995e-03 -3.26694012e-01 -9.15714741e-01 -9.84135866e-01
5.42741343e-02 -4.18921232e-01 2.98391700e-01 -1.43423319... | [11.274456977844238, -2.3479089736938477] |
7fd2ca02-ea4b-44c0-bd28-ac86e3824ef4 | video-sparse-transformer-with-attention | null | null | https://ieeexplore.ieee.org/document/9798833 | https://ieeexplore.ieee.org/document/9798833 | Video Sparse Transformer With Attention-Guided Memory for Video Object Detection | Detecting objects in a video, known as Video Object Detection (VOD), is challenging since appearance changes of objects over time may bring detection errors. Recent research has focused on aggregating features from adjacent frames to compensate for the deteriorated appearances of a frame. Moreover, using distant frames... | ['Akihiro Sugimoto', 'Masato Fujitake'] | 2022-06-17 | null | null | null | ieee-access-2022-6 | ['video-object-detection', 'video-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.31372288e-01 -6.67053759e-01 -1.10867143e-01 -1.35288998e-01
-3.15974593e-01 -2.05062120e-03 6.55350834e-02 1.07621841e-01
-4.64766860e-01 5.30510724e-01 9.33618024e-02 3.76368940e-01
1.98466390e-01 -5.91095090e-01 -6.23335898e-01 -6.84531569e-01
-1.01405509e-01 -3.02774608e-01 6.93758607e-01 1.90500066... | [9.119589805603027, -0.2580391466617584] |
8e4bd2dd-05ab-4d0e-af4e-fa939351be15 | explainable-image-classification-with | 2004.07511 | null | https://arxiv.org/abs/2004.07511v1 | https://arxiv.org/pdf/2004.07511v1.pdf | Explainable Image Classification with Evidence Counterfactual | The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. Existing explanation methods generally create importance rankings in terms of pixels or pixel groups. However, the resulting explanations lack an optimal size, do not... | ['Tom Vermeire', 'David Martens'] | 2020-04-16 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.64756417e-01 5.70662200e-01 -4.75370079e-01 -5.66308856e-01
-2.21863404e-01 -3.26170057e-01 7.58326411e-01 3.54889929e-01
2.78444141e-02 8.55833828e-01 5.51786534e-02 -6.94844425e-01
-5.23503423e-01 -6.58587337e-01 -8.45877767e-01 -5.28335631e-01
8.24755207e-02 3.35706741e-01 -6.50856122e-02 9.02626514... | [8.827472686767578, 5.549739360809326] |
22852020-2bd0-4152-92ee-c7c3a4c82462 | learning-global-aware-kernel-for-image | 2305.11676 | null | https://arxiv.org/abs/2305.11676v1 | https://arxiv.org/pdf/2305.11676v1.pdf | Learning Global-aware Kernel for Image Harmonization | Image harmonization aims to solve the visual inconsistency problem in composited images by adaptively adjusting the foreground pixels with the background as references. Existing methods employ local color transformation or region matching between foreground and background, which neglects powerful proximity prior and in... | ['Yong liu', 'Chengjie Wang', 'Yabiao Wang', 'Yue Han', 'Shipeng Bai', 'Jun Chen', 'Jiangning Zhang', 'Xintian Shen'] | 2023-05-19 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 2.35123083e-01 -5.06887853e-01 -1.97749972e-01 -3.37414779e-02
-7.85586655e-01 -2.54133195e-01 3.38699639e-01 -2.55806446e-01
-2.05729634e-01 6.36469603e-01 8.22577067e-03 5.00742868e-02
-1.30554974e-01 -8.19014490e-01 -5.82039833e-01 -1.19293356e+00
3.35008293e-01 -2.73139060e-01 6.47818506e-01 -3.12098116... | [11.175573348999023, -1.3394991159439087] |
634ad4f4-b7c9-41b6-925e-b368156de124 | prompt-agnostic-essay-scorer-a-domain | 2008.01441 | null | https://arxiv.org/abs/2008.01441v1 | https://arxiv.org/pdf/2008.01441v1.pdf | Prompt Agnostic Essay Scorer: A Domain Generalization Approach to Cross-prompt Automated Essay Scoring | Cross-prompt automated essay scoring (AES) requires the system to use non target-prompt essays to award scores to a target-prompt essay. Since obtaining a large quantity of pre-graded essays to a particular prompt is often difficult and unrealistic, the task of cross-prompt AES is vital for the development of real-worl... | ['Xin-yu Dai', 'Shu-Jian Huang', 'Jia-Jun Chen', 'Liang He', 'Robert Ridley'] | 2020-08-04 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 3.1389391e-01 -2.7601150e-01 -2.3985758e-02 -6.4796269e-01
-1.5271480e+00 -1.1185250e+00 3.9292565e-01 5.6337863e-01
-6.9409049e-01 7.6050943e-01 -7.6783612e-02 -5.9614748e-01
-1.9087727e-01 -6.6322500e-01 -3.5464194e-01 -1.1749011e-01
7.2638738e-01 7.7453989e-01 3.4230858e-01 -3.5276514e-01
6.0755199e-01... | [11.298749923706055, 9.336723327636719] |
c2771711-8a39-48b5-b324-a726efb8e394 | improving-code-switching-dependency-parsing | null | null | https://aclanthology.org/2022.findings-naacl.87 | https://aclanthology.org/2022.findings-naacl.87.pdf | Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks | Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages. In this study, we introduce novel sequence labeling models to be used as auxiliary tasks for dependency parsing of code-switched text in a s... | ['Özlem Çetinoğlu', 'Tunga Gungor', 'Arzucan Özgür', 'Şaziye Özateş'] | null | null | null | null | findings-naacl-2022-7 | ['xlm-r'] | ['natural-language-processing'] | [ 1.00077912e-01 1.77907571e-02 -3.55903208e-01 -4.03624296e-01
-1.19010222e+00 -6.08896077e-01 1.39076903e-01 2.39813268e-01
-4.19250131e-01 6.86782837e-01 6.62628263e-02 -9.83233690e-01
2.88267970e-01 -2.08091781e-01 -7.50335813e-01 -2.56589890e-01
-2.53643274e-01 4.36800092e-01 3.71569604e-01 -4.75969940... | [10.404730796813965, 9.822115898132324] |
c2b202ff-aef0-404c-b7d5-3f47b1a70d9c | an-adaptive-music-generation-architecture-for | 2207.01698 | null | https://arxiv.org/abs/2207.01698v2 | https://arxiv.org/pdf/2207.01698v2.pdf | An adaptive music generation architecture for games based on the deep learning Transformer mode | This paper presents an architecture for generating music for video games based on the Transformer deep learning model. Our motivation is to be able to customize the generation according to the taste of the player, who can select a corpus of training examples, corresponding to his preferred musical style. The system gen... | ['Antonio Luz Furtado', 'Bruno Feijó', 'Jean-Pierre Briot', 'Augusto Baffa', 'Gustavo Amaral Costa dos Santos'] | 2022-07-04 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [-5.85206673e-02 3.80382761e-02 2.32745603e-01 -1.23670071e-01
4.30668630e-02 -7.95305073e-01 3.31388712e-01 -2.12442741e-01
-2.10788742e-01 1.95465103e-01 2.91239738e-01 2.33451217e-01
-2.73361892e-01 -1.14354551e+00 -7.44909346e-02 -4.77396905e-01
7.38134608e-02 6.37230277e-01 2.18661249e-01 -7.77658045... | [16.00882339477539, 5.504275321960449] |
dcf3d31b-a172-46c6-a36a-5591d7fcafe0 | linear-video-transformer-with-feature | 2210.08164 | null | https://arxiv.org/abs/2210.08164v1 | https://arxiv.org/pdf/2210.08164v1.pdf | Linear Video Transformer with Feature Fixation | Vision Transformers have achieved impressive performance in video classification, while suffering from the quadratic complexity caused by the Softmax attention mechanism. Some studies alleviate the computational costs by reducing the number of tokens in attention calculation, but the complexity is still quadratic. Anot... | ['Yiran Zhong', 'Yuchao Dai', 'Xiaodong Han', 'Hui Deng', 'Xuyang Shen', 'Dong Li', 'Zhen Qin', 'Weixuan Sun', 'Jianyuan Wang', 'Zexiang Liu', 'Kaiyue Lu'] | 2022-10-15 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 6.84244484e-02 -2.72714943e-01 -4.16960686e-01 -2.68586040e-01
-7.57971227e-01 -2.32848257e-01 5.81746459e-01 2.23935336e-01
-7.89864361e-01 3.33512783e-01 4.77377892e-01 -3.58267650e-02
-8.27643499e-02 -6.10820651e-01 -7.93292940e-01 -9.00674999e-01
8.63933191e-02 -2.15630233e-01 4.40427393e-01 7.34684691... | [9.364638328552246, 0.7212293744087219] |
c8676bcd-7b5a-4df1-9b5a-6ed39ff7e685 | explicit-sentence-compression-for-neural | 1912.11980 | null | https://arxiv.org/abs/1912.11980v1 | https://arxiv.org/pdf/1912.11980v1.pdf | Explicit Sentence Compression for Neural Machine Translation | State-of-the-art Transformer-based neural machine translation (NMT) systems still follow a standard encoder-decoder framework, in which source sentence representation can be well done by an encoder with self-attention mechanism. Though Transformer-based encoder may effectively capture general information in its resulti... | ['Kehai Chen', 'Zuchao Li', 'Rui Wang', 'Masao Utiyama', 'Zhuosheng Zhang', 'Eiichiro Sumita', 'Hai Zhao'] | 2019-12-27 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 6.4788306e-01 1.4404322e-01 -3.4541067e-01 -5.1377910e-01
-1.3103414e+00 -2.2079027e-01 6.5749103e-01 -9.3145706e-02
-3.9184532e-01 1.0132521e+00 7.2163057e-01 -6.9949001e-01
4.7400677e-01 -7.0804930e-01 -1.0396401e+00 -4.2842135e-01
6.1907965e-01 4.9867678e-01 -2.0457363e-01 -5.6998116e-01
1.5928791e-01... | [11.7619047164917, 9.922804832458496] |
3bd70d1e-e470-4b9d-a430-2f76460ed32c | localized-questions-in-medical-visual | 2307.01067 | null | https://arxiv.org/abs/2307.01067v1 | https://arxiv.org/pdf/2307.01067v1.pdf | Localized Questions in Medical Visual Question Answering | Visual Question Answering (VQA) models aim to answer natural language questions about given images. Due to its ability to ask questions that differ from those used when training the model, medical VQA has received substantial attention in recent years. However, existing medical VQA models typically focus on answering q... | ['Raphael Sznitman', 'Pablo Márquez-Neila', 'Sergio Tascon-Morales'] | 2023-07-03 | null | null | null | null | ['visual-question-answering', 'visual-question-answering-1', 'question-answering'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 1.21412747e-01 3.37590486e-01 -1.80790275e-01 -4.35710788e-01
-9.67178345e-01 -5.61453402e-01 4.76467013e-01 6.22109950e-01
-2.67690569e-01 5.53891480e-01 3.25179368e-01 -5.45881629e-01
-8.30721483e-03 -8.03550065e-01 -4.78332192e-01 -3.16847324e-01
3.22461039e-01 3.54300886e-01 5.38338065e-01 -1.97311521... | [10.955460548400879, 1.634764313697815] |
0a902810-d257-4eb9-bff5-e49bae85ed7f | affine-and-regional-dynamic-time-warpng | 1505.06531 | null | http://arxiv.org/abs/1505.06531v1 | http://arxiv.org/pdf/1505.06531v1.pdf | Affine and Regional Dynamic Time Warpng | Pointwise matches between two time series are of great importance in time
series analysis, and dynamic time warping (DTW) is known to provide generally
reasonable matches. There are situations where time series alignment should be
invariant to scaling and offset in amplitude or where local regions of the
considered tim... | ['Tsu-Wei Chen', 'Daniel Stashuk', 'Meena Abdelmaseeh'] | 2015-05-25 | null | null | null | null | ['time-series-alignment'] | ['time-series'] | [ 2.33666986e-01 -5.48934102e-01 -1.54416129e-01 -2.85202324e-01
-5.31089962e-01 -8.72152209e-01 8.48235309e-01 4.87908512e-01
-3.83602679e-01 6.03862703e-01 1.68932170e-01 -2.60796249e-01
-8.13185573e-01 -6.30960405e-01 -2.67248511e-01 -7.93906927e-01
-6.65149748e-01 3.10105622e-01 4.65014011e-01 -5.21066487... | [7.287787914276123, 3.3184478282928467] |
1205a039-07d5-44e4-b0e3-48e08fb55605 | paramixer-parameterizing-mixing-links-in | 2204.10670 | null | https://arxiv.org/abs/2204.10670v1 | https://arxiv.org/pdf/2204.10670v1.pdf | Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention | Self-Attention is a widely used building block in neural modeling to mix long-range data elements. Most self-attention neural networks employ pairwise dot-products to specify the attention coefficients. However, these methods require $O(N^2)$ computing cost for sequence length $N$. Even though some approximation method... | ['Zhirong Yang', 'Lei Cheng', 'Ruslan Khalitov', 'Tong Yu'] | 2022-04-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yu_Paramixer_Parameterizing_Mixing_Links_in_Sparse_Factors_Works_Better_Than_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yu_Paramixer_Parameterizing_Mixing_Links_in_Sparse_Factors_Works_Better_Than_CVPR_2022_paper.pdf | cvpr-2022-1 | ['long-range-modeling'] | ['natural-language-processing'] | [-1.62702844e-01 -2.16475829e-01 8.47846866e-02 -3.47435385e-01
-7.32019782e-01 -2.44162247e-01 8.07802379e-02 -1.23310070e-02
-8.86992931e-01 6.27715409e-01 1.12963423e-01 -2.79273123e-01
-7.42618665e-02 -5.31573057e-01 -1.19070876e+00 -9.40081596e-01
-2.93995552e-02 4.59630400e-01 8.50254856e-03 -1.65117308... | [8.748310089111328, 4.429534435272217] |
b677a3f2-2641-4328-9ab0-f0723ddf1542 | mri-based-classification-of-idh-mutation-and | 2210.03779 | null | https://arxiv.org/abs/2210.03779v1 | https://arxiv.org/pdf/2210.03779v1.pdf | MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network | Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. For this purpose, pr... | ['Aristeidis Sotiras', 'Daniel S. Marcus', 'Joshua Shimony', 'Pamela Lamontagne', 'Satrajit Chakrabarty'] | 2022-10-07 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 1.13739505e-01 2.13708133e-01 -3.94940346e-01 -2.79788077e-01
-1.02887499e+00 -4.25932944e-01 4.31867659e-01 6.91957653e-01
-7.00612485e-01 8.32982779e-01 1.46664172e-01 -8.38437259e-01
-4.25427049e-01 -7.31974483e-01 -2.13253766e-01 -1.02906621e+00
-3.48719031e-01 5.48561215e-01 1.29303068e-01 1.51370570... | [14.785070419311523, -2.568054437637329] |
8b866799-29e6-414e-8c84-d9df9bba8b3e | deep-variation-structured-reinforcement | 1703.03054 | null | http://arxiv.org/abs/1703.03054v1 | http://arxiv.org/pdf/1703.03054v1.pdf | Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection | Despite progress in visual perception tasks such as image classification and
detection, computers still struggle to understand the interdependency of
objects in the scene as a whole, e.g., relations between objects or their
attributes. Existing methods often ignore global context cues capturing the
interactions among d... | ['Lisa Lee', 'Xiaodan Liang', 'Eric P. Xing'] | 2017-03-08 | deep-variation-structured-reinforcement-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Liang_Deep_Variation-Structured_Reinforcement_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Liang_Deep_Variation-Structured_Reinforcement_CVPR_2017_paper.pdf | cvpr-2017-7 | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.46471867e-01 -1.21127807e-01 -3.14609140e-01 -6.17853224e-01
-3.47134799e-01 -7.19526350e-01 5.35977125e-01 5.43664157e-01
-3.14074606e-01 3.04015577e-01 4.55043167e-02 -1.31116346e-01
-2.58098423e-01 -8.14898074e-01 -8.29207480e-01 -4.32762563e-01
-3.33699405e-01 7.65713155e-01 4.59248930e-01 2.69013532... | [10.318490982055664, 1.63418447971344] |
6afe09f5-277c-4cc8-a71e-b843d43f48a1 | bet-a-backtranslation-approach-for-easy-data | 2009.12452 | null | https://arxiv.org/abs/2009.12452v1 | https://arxiv.org/pdf/2009.12452v1.pdf | BET: A Backtranslation Approach for Easy Data Augmentation in Transformer-based Paraphrase Identification Context | Newly-introduced deep learning architectures, namely BERT, XLNet, RoBERTa and ALBERT, have been proved to be robust on several NLP tasks. However, the datasets trained on these architectures are fixed in terms of size and generalizability. To relieve this issue, we apply one of the most inexpensive solutions to update ... | ['Jean-Philippe Corbeil', 'Hadi Abdi Ghadivel'] | 2020-09-25 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 1.84832178e-02 -1.25203326e-01 -2.26097614e-01 -3.38587791e-01
-1.04376423e+00 -8.34230959e-01 8.88445795e-01 8.83896872e-02
-7.19418585e-01 7.66740084e-01 2.92405069e-01 -7.43635237e-01
1.81415351e-03 -6.19998574e-01 -7.77553082e-01 -2.02220857e-01
4.59194690e-01 8.25254679e-01 -1.00296915e-01 -7.00953662... | [11.228553771972656, 9.117602348327637] |
d3dabe53-6dad-40fb-8c36-5df9a992148b | pointconvformer-revenge-of-the-point-based | 2208.02879 | null | https://arxiv.org/abs/2208.02879v3 | https://arxiv.org/pdf/2208.02879v3.pdf | PointConvFormer: Revenge of the Point-based Convolution | We introduce PointConvFormer, a novel building block for point cloud based deep network architectures. Inspired by generalization theory, PointConvFormer combines ideas from point convolution, where filter weights are only based on relative position, and Transformers which utilize feature-based attention. In PointConvF... | ['Li Fuxin', 'Qi Shan', 'Wenxuan Wu'] | 2022-08-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_PointConvFormer_Revenge_of_the_Point-Based_Convolution_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_PointConvFormer_Revenge_of_the_Point-Based_Convolution_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-flow-estimation'] | ['computer-vision'] | [-3.36743891e-01 -2.62907982e-01 1.53154120e-01 -4.52174902e-01
1.38037756e-01 -4.96658385e-01 6.72199368e-01 1.59277827e-01
-5.23425221e-01 2.60063767e-01 2.03887150e-01 -2.07832426e-01
-1.63766697e-01 -1.20561755e+00 -8.70383799e-01 -3.56971323e-01
-2.93761790e-01 2.77161121e-01 5.68553805e-01 -5.18371046... | [7.924136161804199, -3.6539289951324463] |
8bd2d870-8dcb-4f76-a6b0-48b1ca977eaf | enhancing-mixup-based-graph-learning-for | 2210.03123 | null | https://arxiv.org/abs/2210.03123v2 | https://arxiv.org/pdf/2210.03123v2.pdf | On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing | Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typically, GNNs are constructed by incorporating alternating layers which learn transformations of graph node features, along with graph pooling ... | ['Jianjun Zhao', 'Mike Papadakis', 'Yuejun Guo', 'Zhenya Zhang', 'Yves Le Traon', 'Maxime Cordy', 'Qiang Hu', 'Zeming Dong'] | 2022-10-06 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 1.05807357e-01 2.39897028e-01 -2.98775494e-01 -1.45894051e-01
-3.54579184e-04 -4.52976555e-01 6.16027713e-01 4.88028437e-01
-1.44443691e-01 2.86284477e-01 7.57058803e-03 -3.68573636e-01
1.62662894e-01 -1.39456964e+00 -8.08466911e-01 -5.18878639e-01
-3.43895227e-01 -7.26742893e-02 1.65695116e-01 -3.40726256... | [7.07392692565918, 6.305450439453125] |
a893faf5-0c3e-4f30-8e3e-4f423ddfd8dc | recommendation-as-instruction-following-a | 2305.07001 | null | https://arxiv.org/abs/2305.07001v1 | https://arxiv.org/pdf/2305.07001v1.pdf | Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach | In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effective recommendation models. Basically speaking, these models mainly learn the underlying user preference from historical behavior data, and... | ['Ji-Rong Wen', 'Leyu Lin', 'Wayne Xin Zhao', 'Yupeng Hou', 'Ruobing Xie', 'Junjie Zhang'] | 2023-05-11 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 5.20665348e-02 -3.47911209e-01 -4.72566307e-01 -8.33728254e-01
-4.00301665e-01 -5.69545805e-01 5.25880635e-01 -1.94452181e-01
-3.17729026e-01 1.11562453e-01 5.55143058e-01 -6.30768597e-01
-1.48207545e-01 -7.41748869e-01 -5.20278037e-01 -1.50010064e-01
1.72157705e-01 4.86263692e-01 1.75859734e-01 -6.13247335... | [10.236934661865234, 5.761397361755371] |
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