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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e0b05ae0-4f39-4f40-8ac2-aa183541b4f2 | intrinsic-bayesian-optimisation-on-complex | 2301.12581 | null | https://arxiv.org/abs/2301.12581v1 | https://arxiv.org/pdf/2301.12581v1.pdf | Intrinsic Bayesian Optimisation on Complex Constrained Domain | Motivated by the success of Bayesian optimisation algorithms in the Euclidean space, we propose a novel approach to construct Intrinsic Bayesian optimisation (In-BO) on manifolds with a primary focus on complex constrained domains or irregular-shaped spaces arising as submanifolds of R2, R3 and beyond. Data may be coll... | ['Claire Miller', 'Mu Niu', 'YuAn Liu'] | 2023-01-29 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [-9.64317843e-02 2.47310132e-01 5.79298377e-01 5.84819913e-02
-3.86415124e-01 -2.75895655e-01 8.44860613e-01 -3.66588444e-01
-5.41086137e-01 5.73071063e-01 7.78291523e-02 -1.26934335e-01
-7.58782268e-01 -7.15411603e-01 -5.40562212e-01 -1.19225943e+00
-4.23213780e-01 6.32526159e-01 3.10514029e-02 2.23992631... | [6.830724716186523, 3.9220240116119385] |
0aeddb47-4efc-4aff-a553-172eb2a9a63a | persona-guided-planning-for-controlling-the-1 | null | null | https://aclanthology.org/2022.naacl-main.245 | https://aclanthology.org/2022.naacl-main.245.pdf | Persona-Guided Planning for Controlling the Protagonist’s Persona in Story Generation | Endowing the protagonist with a specific personality is essential for writing an engaging story. In this paper, we aim to control the protagonist’s persona in story generation, i.e., generating a story from a leading context and a persona description, where the protagonist should exhibit the specified personality throu... | ['Minlie Huang', 'Jian Guan', 'Jiaxin Wen', 'Zhexin Zhang'] | null | null | null | null | naacl-2022-7 | ['story-generation'] | ['natural-language-processing'] | [ 1.51100799e-01 4.62872237e-01 -1.12471886e-01 -5.04337490e-01
-6.03874147e-01 -6.28239334e-01 1.25205886e+00 -2.90525388e-02
3.09468620e-02 6.57314718e-01 1.13921094e+00 3.79682958e-01
2.07615614e-01 -8.57475698e-01 -6.95196986e-01 -1.61224484e-01
4.34830457e-01 6.91980302e-01 -2.81421930e-01 -2.84976661... | [11.782376289367676, 8.821413040161133] |
2f6dd64d-0e26-4fea-a84b-4c59fc89bf15 | memorization-capacity-of-multi-head-attention | 2306.02010 | null | https://arxiv.org/abs/2306.02010v1 | https://arxiv.org/pdf/2306.02010v1.pdf | Memorization Capacity of Multi-Head Attention in Transformers | In this paper, we investigate the memorization capabilities of multi-head attention in Transformers, motivated by the central role attention plays in these models. Under a mild linear independence assumption on the input data, we present a theoretical analysis demonstrating that an $H$-head attention layer with a conte... | ['Christos Thrampoulidis', 'Renjie Liao', 'Sadegh Mahdavi'] | 2023-06-03 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-6.72449693e-02 1.78833038e-01 8.09973404e-02 -1.73297629e-01
-4.57914650e-01 -1.12409284e-02 4.87864278e-02 -5.59698679e-02
-5.76012552e-01 6.06793284e-01 1.11729046e-03 -5.15432000e-01
-1.08284481e-01 -6.91727459e-01 -8.30162644e-01 -6.64243400e-01
-1.56891674e-01 1.56004488e-01 1.34990320e-01 -2.05239281... | [9.522573471069336, 2.554103136062622] |
f399d3db-7ab4-48bd-8cba-e6c8cdcf3ea8 | generalizing-discrete-convolutions-for | 1904.02375 | null | https://arxiv.org/abs/1904.02375v5 | https://arxiv.org/pdf/1904.02375v5.pdf | ConvPoint: Continuous Convolutions for Point Cloud Processing | Point clouds are unstructured and unordered data, as opposed to images. Thus, most machine learning approach developed for image cannot be directly transferred to point clouds. In this paper, we propose a generalization of discrete convolutional neural networks (CNNs) in order to deal with point clouds by replacing dis... | ['Alexandre Boulch'] | 2019-04-04 | null | null | null | null | ['3d-part-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.97711185e-02 -2.18013953e-02 -1.43516948e-02 -3.36672425e-01
-2.69385129e-01 -5.29881299e-01 4.48091209e-01 3.41702998e-01
-5.57837844e-01 2.74232149e-01 -7.30936587e-01 -4.30485189e-01
-7.46408477e-02 -1.17588079e+00 -1.08881867e+00 -2.90221959e-01
-1.46445841e-01 9.89780188e-01 5.34841239e-01 -8.28816369... | [7.99898099899292, -3.578993797302246] |
cfd281e1-fd5e-4036-bfeb-fef85b570ba0 | few-shot-3d-shape-generation | 2305.11664 | null | https://arxiv.org/abs/2305.11664v1 | https://arxiv.org/pdf/2305.11664v1.pdf | Few-shot 3D Shape Generation | Realistic and diverse 3D shape generation is helpful for a wide variety of applications such as virtual reality, gaming, and animation. Modern generative models, such as GANs and diffusion models, learn from large-scale datasets and generate new samples following similar data distributions. However, when training data ... | ['Jian Yuan', 'Jiansheng Chen', 'Huimin Ma', 'Jingyuan Zhu'] | 2023-05-19 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 3.85984895e-03 -5.68599394e-03 1.56953990e-01 -1.31334037e-01
-6.68854892e-01 -6.17920458e-01 7.38755941e-01 -3.09082419e-01
1.83010563e-01 8.42979848e-01 6.36416152e-02 3.18162411e-01
2.76620209e-01 -1.18697011e+00 -8.25545132e-01 -6.30096436e-01
3.87559503e-01 7.91169286e-01 1.39918000e-01 -4.26224649... | [9.040556907653809, -3.549912691116333] |
e1e4590c-26e1-417a-9801-64825b55e281 | the-sjtu-system-for-dcase2021-challenge-task | null | null | https://dcase.community/documents/challenge2021/technical_reports/DCASE2021_Xu_119_t6.pdf | https://dcase.community/documents/challenge2021/technical_reports/DCASE2021_Xu_119_t6.pdf | THE SJTU SYSTEM FOR DCASE2021 CHALLENGE TASK 6: AUDIO CAPTIONING BASED ON ENCODER PRE-TRAINING AND REINFORCEMENT LEARNING | This report proposes an audio captioning system for the Detection
and Classification of Acoustic Scenes and Events (DCASE) 2021
challenge task Task 6. Our audio captioning system consists of a
10-layer convolution neural network (CNN) encoder and a tempo-
ral attentional single layer gated recurrent unit (GRU) deco... | ['Kai Yu', 'Mengyue Wu', 'Zeyu Xie', 'Xuenan Xu'] | 2021-07-06 | null | null | null | dcase-challenge-2021-7 | ['audio-tagging', 'audio-captioning'] | ['audio', 'audio'] | [ 3.99308592e-01 1.00306623e-01 3.04811418e-01 -5.76910079e-01
-1.52554345e+00 -4.25910532e-01 -2.50715315e-02 -2.70247217e-02
-5.04160225e-01 4.72287536e-01 4.97094870e-01 1.92442853e-02
4.73500639e-01 -2.88302809e-01 -8.60778689e-01 -3.01015884e-01
-4.30480480e-01 1.78914532e-01 4.68309000e-02 4.39337455... | [15.234484672546387, 5.02278470993042] |
c9376b6f-61aa-4d99-babc-e6e869468436 | design-considerations-of-a-coordinative | 2212.08535 | null | https://arxiv.org/abs/2212.08535v2 | https://arxiv.org/pdf/2212.08535v2.pdf | Design Considerations of a Coordinative Demand Charge Mitigation Strategy | This paper presents a coordinative demand charge mitigation (DCM) strategy for reducing electricity consumption during system peak periods. Available DCM resources include batteries, diesel generators, controllable loads, and conservation voltage reduction. All resources are directly controlled by load serving entities... | ['PJ Rehm', 'Di wu', 'Ning Lu', 'Hanpyo Lee', 'Hyeonjin Kim', 'Kai Ye', 'Rongxing Hu'] | 2022-12-16 | null | null | null | null | ['energy-management'] | ['time-series'] | [-4.56420571e-01 -3.24264541e-02 -5.19298792e-01 1.76306382e-01
-2.62226969e-01 -8.44146192e-01 2.88304597e-01 1.96983740e-01
3.20354372e-01 1.09645724e+00 1.13188446e-01 -1.77159801e-01
-4.90978509e-01 -1.15714264e+00 -8.65221471e-02 -9.78299260e-01
-1.05206259e-01 6.06848598e-01 -1.04438424e-01 -1.28011152... | [5.643326282501221, 2.4878182411193848] |
c8c41e6c-90b7-4453-b2a8-79744da0865f | elastichash-semantic-image-similarity-search | 2305.04710 | null | https://arxiv.org/abs/2305.04710v1 | https://arxiv.org/pdf/2305.04710v1.pdf | ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch | We present ElasticHash, a novel approach for high-quality, efficient, and large-scale semantic image similarity search. It is based on a deep hashing model to learn hash codes for fine-grained image similarity search in natural images and a two-stage method for efficiently searching binary hash codes using Elasticsearc... | ['Bernd Freisleben', 'Markus Mühling', 'Nikolaus Korfhage'] | 2023-05-08 | null | null | null | null | ['image-similarity-search'] | ['computer-vision'] | [-7.39616528e-02 -7.00947344e-01 -4.52475160e-01 -3.99562985e-01
-1.24271107e+00 -5.41131914e-01 3.69676471e-01 7.20286489e-01
-6.76787198e-01 1.12383083e-01 2.81928092e-01 1.16740711e-01
-2.24059969e-01 -8.70500326e-01 -6.02781713e-01 -6.04154468e-01
-3.70704651e-01 7.46016383e-01 8.65062237e-01 5.39302127... | [11.20203971862793, 0.9536350965499878] |
e5a86f46-e51d-4f84-8f09-0160f1a8898f | instance-segmentation-of-multiple-myeloma | null | null | https://openreview.net/forum?id=T1ZK_GYtdbn | https://openreview.net/pdf?id=T1ZK_GYtdbn | Instance Segmentation of Multiple Myeloma Cells via Hybrid Task Cascade | Multiple Myeloma (MM) is a blood cancer that develops when plasma cells expand abnormally in the bone marrow. Early detection of MM is beneficial for accurate treatment in time and draws increasing recognition. There are several endeavors to construct computer-assisted automatic diagnostic tools for myeloma cell detect... | ['Anonymous'] | 2021-07-20 | null | null | null | miccai-workshop-compay-2021-9 | ['cell-detection'] | ['computer-vision'] | [-1.28884450e-01 -2.64685541e-01 2.42335275e-02 -2.37972751e-01
-1.04642868e+00 -3.82813020e-03 4.37443256e-01 5.05091906e-01
-3.99270803e-01 6.53588474e-01 -1.62469432e-01 4.89067361e-02
5.49449503e-01 -7.32562959e-01 -5.62929036e-03 -1.18801677e+00
2.27360949e-01 1.40408671e+00 2.06452444e-01 1.69465810... | [15.046609878540039, -3.0533251762390137] |
c1c46f12-c58d-4f73-b104-4f3c98a548b0 | drawing-attention-to-detail-pose-alignment | 2302.04800 | null | https://arxiv.org/abs/2302.04800v1 | https://arxiv.org/pdf/2302.04800v1.pdf | Drawing Attention to Detail: Pose Alignment through Self-Attention for Fine-Grained Object Classification | Intra-class variations in the open world lead to various challenges in classification tasks. To overcome these challenges, fine-grained classification was introduced, and many approaches were proposed. Some rely on locating and using distinguishable local parts within images to achieve invariance to viewpoint changes, ... | ['Jameel Hassan', 'Mohamed El Amine Boudjoghra', 'Salwa Al Khatib'] | 2023-02-09 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 4.17718776e-02 2.42134154e-01 -1.97376683e-02 -3.88600349e-01
-4.30157334e-01 -3.94108891e-01 4.58927304e-01 3.29447836e-02
-2.28275284e-01 2.95512468e-01 3.00220251e-01 5.47029912e-01
-1.31313160e-01 -7.73494959e-01 -8.71822715e-01 -5.40676773e-01
1.82708576e-01 3.28387439e-01 6.61412418e-01 -2.41826072... | [9.592514991760254, 1.9480019807815552] |
f711c082-d8e4-4699-8c5c-0fdec0134654 | intrusion-detection-in-iot-using-artificial | null | null | https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-021-01893-8 | https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-021-01893-8 | Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset | Internet of Things (IoT) devices are well-connected; they generate and consume data which involves transmission of data back and forth among various devices. Ensuring security of the data is a critical challenge as far as IoT is concerned. Since IoT devices are inherently low-power and do not require a lot of compute p... | ['Syed Ali Haider & Muhammad Safeer Khan', 'Hasan Tahir', 'Muhammad Zeeshan', 'Qaiser Riaz', 'Muhammad Ahmad'] | 2021-01-21 | null | null | null | eurasip-journal-on-wireless-communications | ['network-intrusion-detection'] | ['miscellaneous'] | [ 1.06315300e-01 -3.50979984e-01 -3.61954659e-01 -5.61877251e-01
-1.21845543e-01 -5.33070207e-01 2.57462472e-01 3.07201415e-01
-4.63883251e-01 7.76873708e-01 -4.35803354e-01 -6.77932560e-01
-6.40930951e-01 -1.13269150e+00 -1.28958583e-01 -8.40552688e-01
-2.08067037e-02 4.94947225e-01 4.65850890e-01 8.56433958... | [5.185660362243652, 7.146861553192139] |
bcb59317-d8a5-44d8-89fd-6ad1ec549b22 | weighted-automata-extraction-and-explanation | 2306.14040 | null | https://arxiv.org/abs/2306.14040v1 | https://arxiv.org/pdf/2306.14040v1.pdf | Weighted Automata Extraction and Explanation of Recurrent Neural Networks for Natural Language Tasks | Recurrent Neural Networks (RNNs) have achieved tremendous success in processing sequential data, yet understanding and analyzing their behaviours remains a significant challenge. To this end, many efforts have been made to extract finite automata from RNNs, which are more amenable for analysis and explanation. However,... | ['Meng Sun', 'Yihao Zhang', 'Xiyue Zhang', 'Zeming Wei'] | 2023-06-24 | null | null | null | null | ['model-extraction', 'model-extraction'] | ['adversarial', 'methodology'] | [ 6.39596760e-01 3.30989778e-01 -2.34387457e-01 -8.59473124e-02
-4.62807715e-01 -4.05822277e-01 6.86244786e-01 -1.74784064e-01
-1.24861494e-01 4.60051596e-01 5.17932475e-01 -7.95941651e-01
-1.18950278e-01 -7.02397525e-01 -5.93689084e-01 -4.10095394e-01
2.67749447e-02 1.15237966e-01 1.80204943e-01 -2.66811311... | [10.752954483032227, 6.924848556518555] |
512690d1-81bf-480a-a346-0cceb3afed64 | learning-with-partial-labels-from-semi | 2211.13655 | null | https://arxiv.org/abs/2211.13655v2 | https://arxiv.org/pdf/2211.13655v2.pdf | Learning with Partial Labels from Semi-supervised Perspective | Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but only one is valid. Advances in the recent deep PL learning literature have shown that the deep learning paradigms, e.g., self-training, cont... | ['Jihong Ouyang', 'Yiyuan Wang', 'Changchun Li', 'Yuanzhi Jiang', 'Ximing Li'] | 2022-11-24 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 2.52396375e-01 5.79061151e-01 -7.61815190e-01 -9.23543215e-01
-1.25297880e+00 -5.81971705e-01 4.26144332e-01 1.36139372e-03
-2.29218200e-01 1.05025315e+00 -6.23586662e-02 2.11094785e-02
-5.64073250e-02 -3.49049777e-01 -8.66418362e-01 -6.97968721e-01
4.34300780e-01 8.99048150e-01 -1.53211460e-01 4.49604064... | [9.506559371948242, 3.8527393341064453] |
6c9c00d1-e628-4870-822b-a4ba7ded4303 | graphwoz-dialogue-management-with | 2211.12852 | null | https://arxiv.org/abs/2211.12852v1 | https://arxiv.org/pdf/2211.12852v1.pdf | GraphWOZ: Dialogue Management with Conversational Knowledge Graphs | We present a new approach to dialogue management using conversational knowledge graphs as core representation of the dialogue state. To this end, we introduce a new dataset, GraphWOZ, which comprises Wizard-of-Oz dialogues in which human participants interact with a robot acting as a receptionist. In contrast to most e... | ['Pierre Lison', 'Stefan Ultes', 'Nicholas Thomas Walker'] | 2022-11-23 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [ 1.89543828e-01 8.89239788e-01 -4.99354452e-02 -5.42149961e-01
-4.21134323e-01 -7.45747685e-01 9.68349993e-01 1.01144671e+00
-3.21708173e-01 8.26255798e-01 7.72699535e-01 1.47246197e-01
-8.21139216e-02 -9.34547424e-01 -1.34450555e-01 -2.62100194e-02
-2.30940863e-01 1.10342264e+00 4.95532960e-01 -9.01102424... | [12.609683990478516, 7.99213171005249] |
2f072b09-35f0-42fe-8ab3-51708ec9cd07 | scaling-to-many-languages-with-a-triaged | 2104.02125 | null | https://arxiv.org/abs/2104.02125v3 | https://arxiv.org/pdf/2104.02125v3.pdf | SpeakerStew: Scaling to Many Languages with a Triaged Multilingual Text-Dependent and Text-Independent Speaker Verification System | In this paper, we describe SpeakerStew - a hybrid system to perform speaker verification on 46 languages. Two core ideas were explored in this system: (1) Pooling training data of different languages together for multilingual generalization and reducing development cycles; (2) A novel triage mechanism between text-depe... | ['Ignacio Lopez Moreno', 'Quan Wang', 'Jason Pelecanos', 'Roza Chojnacka'] | 2021-04-05 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-7.27638183e-03 -1.82102658e-02 -6.21755868e-02 -8.09590340e-01
-1.47165048e+00 -8.24357033e-01 4.85499859e-01 -1.59399062e-01
-4.38446552e-01 2.90558428e-01 7.41996095e-02 -9.65127885e-01
2.44329333e-01 -6.76425546e-02 -6.80810153e-01 -3.95258665e-01
-6.16179369e-02 6.22993112e-01 1.52497679e-01 -2.32154801... | [14.30822467803955, 6.342647075653076] |
35940089-a02b-4b7f-9ade-f35cadcebf8a | natural-image-stitching-with-the-global | null | null | https://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/ | https://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/ECCV-2016-NISwGSP.pdf | Natural Image Stitching with the Global Similarity Prior | This paper proposes a method for stitching multiple images together so that the stitched image looks as natural as possible. Our method adopts the local warp model and guides the warping of each image with a grid mesh. An objective function is designed for specifying the desired characteristics of the warps. In additio... | ['Yu-Sheng Chen; Yung-Yu Chuang'] | 2016-10-01 | null | null | null | european-conference-on-computer-vision-2016 | ['image-stitching'] | ['computer-vision'] | [ 4.35848296e-01 -3.09014022e-01 -1.12001635e-01 1.27728758e-02
-3.63680124e-01 -6.41367376e-01 7.42886662e-01 -3.77453625e-01
-1.51004657e-01 2.87618279e-01 2.49845728e-01 2.16233581e-01
-3.72158848e-02 -4.90121216e-01 -4.98216569e-01 -9.94957983e-01
2.80429214e-01 2.59786069e-01 6.85684264e-01 -4.21228349... | [9.409327507019043, -2.3505759239196777] |
d1673a4a-8cc9-4d21-a529-c1c927abba7a | spatial-and-spectral-deep-attention-fusion | 2002.01626 | null | https://arxiv.org/abs/2002.01626v1 | https://arxiv.org/pdf/2002.01626v1.pdf | Spatial and spectral deep attention fusion for multi-channel speech separation using deep embedding features | Multi-channel deep clustering (MDC) has acquired a good performance for speech separation. However, MDC only applies the spatial features as the additional information. So it is difficult to learn mutual relationship between spatial and spectral features. Besides, the training objective of MDC is defined at embedding v... | ['Jian-Hua Tao', 'Bin Liu', 'Zhengqi Wen', 'Jiangyan Yi', 'Cunhang Fan'] | 2020-02-05 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-5.22569381e-02 -4.87935036e-01 2.72440106e-01 -3.46800834e-01
-1.13539422e+00 -3.98057491e-01 2.76232600e-01 -1.02641255e-01
-5.03570855e-01 2.15604961e-01 2.34637767e-01 -2.11796060e-01
-3.73882681e-01 -1.91939279e-01 -4.31225508e-01 -1.26725245e+00
-1.59337267e-01 -5.76820970e-02 -7.23990798e-02 6.85050935... | [14.958309173583984, 5.864485740661621] |
49cda9bf-66e0-440c-8177-1a7c0eacab92 | classifying-fonts-and-calligraphy-styles | 1407.2649 | null | http://arxiv.org/abs/1407.2649v1 | http://arxiv.org/pdf/1407.2649v1.pdf | Classifying Fonts and Calligraphy Styles Using Complex Wavelet Transform | Recognizing fonts has become an important task in document analysis, due to
the increasing number of available digital documents in different fonts and
emphases. A generic font-recognition system independent of language, script and
content is desirable for processing various types of documents. At the same
time, catego... | ['Alican Bozkurt', 'Pinar Duygulu', 'A. Enis Cetin'] | 2014-07-09 | null | null | null | null | ['font-recognition'] | ['computer-vision'] | [ 2.49542773e-01 -7.76583612e-01 2.52486207e-02 -3.62642944e-01
-2.08215415e-01 -7.63650417e-01 9.15398836e-01 2.02540830e-01
-2.72643924e-01 4.70755041e-01 -5.18340953e-02 -4.24029917e-01
-2.05165371e-01 -8.25537443e-01 -1.30820632e-01 -7.15017974e-01
1.52743205e-01 4.40015584e-01 3.87070566e-01 -3.02064925... | [11.881258964538574, 2.5684380531311035] |
78e5c05e-3ed5-4498-94b3-12c0331e8bd3 | stock-price-prediction-using-generative | null | null | https://thescipub.com/abstract/jcssp.2021.188.196 | https://thescipub.com/pdf/jcssp.2021.188.196.pdf | Stock price prediction using Generative Adversarial Networks | Deep learning is an exciting topic. It has been utilized in many areas owing to its strong potential. For example, it has been widely used in the financial area which is vital to the society, such as high-frequency trading, portfolio optimization, fraud detection and risk management. Stock market prediction is one of t... | ['Amir Jafari', 'Gaofeng Huang', 'Chen Chen', 'HungChun Lin'] | 2021-04-02 | null | null | null | journal-of-computer-science-2021-4 | ['stock-market-prediction', 'portfolio-optimization', 'stock-price-prediction', 'stock-prediction'] | ['time-series', 'time-series', 'time-series', 'time-series'] | [-4.54503566e-01 -2.38080308e-01 1.70888565e-02 -1.68857262e-01
-3.05504024e-01 -4.76692855e-01 6.14453197e-01 -3.01861078e-01
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1.73330426e-01 1.93146095e-01 5.67184165e-02 -5.50642133... | [4.469584941864014, 4.218649387359619] |
8dc14aa6-54b0-46ea-9d51-6700e9946080 | textmi-textualize-multimodal-information-for | 2303.15430 | null | https://arxiv.org/abs/2303.15430v2 | https://arxiv.org/pdf/2303.15430v2.pdf | TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models | Pre-trained large language models have recently achieved ground-breaking performance in a wide variety of language understanding tasks. However, the same model can not be applied to multimodal behavior understanding tasks (e.g., video sentiment/humor detection) unless non-verbal features (e.g., acoustic and visual) can... | ['Ehsan Hoque', 'Mohammed Ibrahim Khan', 'Iftekhar Naim', 'Wasifur Rahman', 'Sangwu Lee', 'Md Saiful Islam', 'Md Kamrul Hasan'] | 2023-03-27 | null | null | null | null | ['multimodal-sentiment-analysis', 'sarcasm-detection', 'humor-detection', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.90176868e-01 -1.70953438e-01 -1.47651598e-01 -3.29104632e-01
-1.06725502e+00 -7.23900378e-01 5.78000844e-01 1.90070674e-01
-8.19063246e-01 2.36632988e-01 5.14927030e-01 -2.07719386e-01
5.02422988e-01 -1.78308383e-01 -5.94957054e-01 -4.39510763e-01
4.59281653e-01 4.04016256e-01 4.25205156e-02 -2.85973489... | [13.104141235351562, 5.134629726409912] |
f6e04dca-1a7e-42ac-87e7-57fca1dd05c6 | conditional-score-based-reconstructions-for | 2303.14795 | null | https://arxiv.org/abs/2303.14795v2 | https://arxiv.org/pdf/2303.14795v2.pdf | MRI Reconstruction with Side Information using Diffusion Models | Magnetic resonance imaging (MRI) exam protocols consist of multiple contrast-weighted images of the same anatomy to emphasize different tissue properties. Due to the long acquisition times required to collect fully sampled k-space measurements, it is common to only collect a fraction of k-space for each scan and subseq... | ['Jonathan I. Tamir', 'Kannan Ramchandran', 'Ajil Jalal', 'Brett Levac'] | 2023-03-26 | null | null | null | null | ['mri-reconstruction', 'anatomy'] | ['computer-vision', 'miscellaneous'] | [ 5.73018491e-01 4.45687212e-02 1.35170162e-01 -5.96839786e-01
-1.15415263e+00 -4.36183244e-01 5.26377261e-01 9.41464528e-02
-6.09760880e-01 7.53229022e-01 3.39898318e-01 -1.98578194e-01
-6.91654146e-01 -4.84034985e-01 -5.28475642e-01 -1.01677096e+00
-6.41401932e-02 7.75139153e-01 2.88756728e-01 2.12415859... | [13.531808853149414, -2.396821975708008] |
657acfc7-8de8-421f-ba85-53cb86fa0bc1 | learning-harmonic-molecular-representations | 2303.15520 | null | https://arxiv.org/abs/2303.15520v1 | https://arxiv.org/pdf/2303.15520v1.pdf | Learning Harmonic Molecular Representations on Riemannian Manifold | Molecular representation learning plays a crucial role in AI-assisted drug discovery research. Encoding 3D molecular structures through Euclidean neural networks has become the prevailing method in the geometric deep learning community. However, the equivariance constraints and message passing in Euclidean space may li... | ['Hao Zhou', 'Fei Ye', 'Lihao Wang', 'Shi Chen', 'Yuning Shen', 'Yiqun Wang'] | 2023-03-27 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.06323707e-01 -4.24022637e-02 -4.12030250e-01 -2.77029812e-01
-9.27925110e-01 -4.26864952e-01 3.26122612e-01 3.68745595e-01
-2.71041870e-01 7.91608453e-01 6.34719953e-02 -6.96067810e-01
-3.96145433e-01 -7.78083324e-01 -9.49338078e-01 -9.22727466e-01
-7.78790414e-01 3.88600439e-01 -5.66170990e-01 -3.42486560... | [5.08051061630249, 5.810705184936523] |
1d354288-29e9-41ce-b11d-b5078a6d35bb | improving-generalization-ability-of | 2305.10940 | null | https://arxiv.org/abs/2305.10940v1 | https://arxiv.org/pdf/2305.10940v1.pdf | Improving Generalization Ability of Countermeasures for New Mismatch Scenario by Combining Multiple Advanced Regularization Terms | The ability of countermeasure models to generalize from seen speech synthesis methods to unseen ones has been investigated in the ASVspoof challenge. However, a new mismatch scenario in which fake audio may be generated from real audio with unseen genres has not been studied thoroughly. To this end, we first use five d... | ['Junichi Yamagishi', 'Erica Cooper', 'Xiaoxiao Miao', 'Xin Wang', 'Chang Zeng'] | 2023-05-18 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 4.42951322e-01 -1.24473222e-01 -1.36592925e-01 -7.18020499e-02
-1.12044036e+00 -5.41764677e-01 4.40444648e-01 -2.51565963e-01
-1.15585171e-01 6.73098326e-01 3.97011012e-01 -1.54470459e-01
1.19652845e-01 -2.95538127e-01 -8.97984982e-01 -7.85336733e-01
1.10316806e-01 1.46993799e-02 1.08433820e-01 -3.91425073... | [14.094097137451172, 5.825326442718506] |
cc01d60b-834d-4e3d-ba85-c12ea4fbde1c | ego2hands-a-dataset-for-egocentric-two-hand | 2011.07252 | null | https://arxiv.org/abs/2011.07252v3 | https://arxiv.org/pdf/2011.07252v3.pdf | Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and Detection | Hand segmentation and detection in truly unconstrained RGB-based settings is important for many applications. However, existing datasets are far from sufficient both in terms of size and variety due to the infeasibility of manual annotation of large amounts of segmentation and detection data. As a result, current metho... | ['Tony Martinez', 'Fanqing Lin'] | 2020-11-14 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 2.05527753e-01 -2.15662867e-01 1.41918585e-01 -4.58769590e-01
-8.78258944e-01 -1.02580643e+00 2.43341297e-01 -3.15887272e-01
-3.23848605e-01 6.26986146e-01 -2.50046644e-02 1.35749439e-02
1.84416369e-01 -4.45006847e-01 -5.26911378e-01 -5.40360332e-01
2.45634556e-01 7.70315111e-01 4.83534694e-01 -1.39071822... | [6.699012756347656, -0.6924402713775635] |
c38c1830-f34e-4793-8d7d-f413d38f1c68 | textbf-p-2-a-a-dataset-and-benchmark-for | 2207.12730 | null | https://arxiv.org/abs/2207.12730v1 | https://arxiv.org/pdf/2207.12730v1.pdf | $\textbf{P$^2$A}$: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos | While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging. In this work, we release yet another sports video dataset $\textbf{P$^2$A}$ for $\underline{P}$ing $\underline{P}$ong... | ['Dejing Dou', 'Feixiang Lu', 'Jun Zhao', 'Jun Cheng', 'Xuhong LI', 'Chen Liu', 'Jun Huang', 'Haoyi Xiong', 'Qingzhong Wang', 'Jiang Bian'] | 2022-07-26 | null | null | null | null | ['video-classification', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 1.91716328e-01 -3.21780145e-01 -3.87085319e-01 -5.14572263e-02
-7.84699261e-01 -3.59813660e-01 1.07008614e-01 -3.12593013e-01
-7.75543272e-01 5.12355089e-01 -6.58288747e-02 7.83870071e-02
-1.58557624e-01 -5.21629930e-01 -1.01933849e+00 -6.98796451e-01
-6.66802347e-01 4.93420176e-02 7.50561476e-01 -2.85507470... | [7.874356269836426, 0.21046769618988037] |
14ca3b19-2341-4125-857a-b34c12ce3762 | deep-defocus-map-estimation-using-domain | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Lee_Deep_Defocus_Map_Estimation_Using_Domain_Adaptation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lee_Deep_Defocus_Map_Estimation_Using_Domain_Adaptation_CVPR_2019_paper.pdf | Deep Defocus Map Estimation Using Domain Adaptation | In this paper, we propose the first end-to-end convolutional neural network (CNN) architecture, Defocus Map Estimation Network (DMENet), for spatially varying defocus map estimation. To train the network, we produce a novel depth-of-field (DOF) dataset, SYNDOF, where each image is synthetically blurred with a ground-tr... | [' Seungyong Lee', ' Sunghyun Cho', ' Sungkil Lee', 'Junyong Lee'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['defocus-estimation'] | ['computer-vision'] | [ 8.15141946e-02 -2.92849123e-01 4.42006439e-01 -5.44738412e-01
-1.84803177e-02 -5.11658788e-01 4.17184442e-01 -6.84311688e-01
-4.42085326e-01 1.02612555e+00 1.83864057e-01 9.92616173e-03
-3.42940800e-02 -5.17147720e-01 -1.03770030e+00 -7.97490954e-01
1.44877443e-02 5.16489439e-04 3.19762647e-01 7.33898431... | [11.309432029724121, -2.736599922180176] |
cc0201cc-5fba-492a-a0d8-0d0a988532fa | overview-of-the-sv-ident-2022-shared-task-on | 2209.09062 | null | https://arxiv.org/abs/2209.09062v1 | https://arxiv.org/pdf/2209.09062v1.pdf | Overview of the SV-Ident 2022 Shared Task on Survey Variable Identification in Social Science Publications | In this paper, we provide an overview of the SV-Ident shared task as part of the 3rd Workshop on Scholarly Document Processing (SDP) at COLING 2022. In the shared task, participants were provided with a sentence and a vocabulary of variables, and asked to identify which variables, if any, are mentioned in individual se... | ['Philipp Mayr', 'Kai Eckert', 'Andrea Zielinski', 'Simone Paolo Ponzetto', 'Yavuz Selim Kartal', 'Tornike Tsereteli'] | 2022-09-19 | null | https://aclanthology.org/2022.sdp-1.29 | https://aclanthology.org/2022.sdp-1.29.pdf | sdp-coling-2022-10 | ['variable-disambiguation', 'variable-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.56094939e-01 7.81146856e-03 -3.55572104e-01 -4.92960125e-01
-1.35612178e+00 -1.02796316e+00 8.29427302e-01 2.31055886e-01
-2.71038532e-01 9.63922858e-01 3.61208886e-01 -2.33899236e-01
-8.37953761e-02 -2.53706425e-01 -6.03000700e-01 3.29334773e-02
2.35408485e-01 5.88807523e-01 -1.24606274e-01 -1.16926283... | [11.995792388916016, 9.444365501403809] |
ce7cb414-d319-496e-83a1-1698bbca5310 | towards-alphachem-chemical-synthesis-planning | 1702.00020 | null | http://arxiv.org/abs/1702.00020v1 | http://arxiv.org/pdf/1702.00020v1.pdf | Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies | Retrosynthesis is a technique to plan the chemical synthesis of organic
molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a
search tree is built by analysing molecules recursively and dissecting them
into simpler molecular building blocks until one obtains a set of known
building blocks. The se... | ['Mike Preuß', 'Marwin Segler', 'Mark P. Waller'] | 2017-01-31 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 4.62896079e-01 1.23681501e-01 -5.86320758e-01 1.19424984e-01
-6.17579401e-01 -1.28447211e+00 5.89534461e-01 4.74445134e-01
-5.02473593e-01 1.37554622e+00 -3.37725133e-02 -8.79187167e-01
-3.46331447e-02 -9.59764540e-01 -8.00942600e-01 -7.87030697e-01
-1.27498478e-01 7.52850533e-01 3.15348874e-03 9.78157446... | [4.528843402862549, 6.066320896148682] |
29d94fc8-284d-4b1c-8402-542601a5b3da | loss-aversively-fair-classification | 2105.04273 | null | https://arxiv.org/abs/2105.04273v1 | https://arxiv.org/pdf/2105.04273v1.pdf | Loss-Aversively Fair Classification | The use of algorithmic (learning-based) decision making in scenarios that affect human lives has motivated a number of recent studies to investigate such decision making systems for potential unfairness, such as discrimination against subjects based on their sensitive features like gender or race. However, when judging... | ['Krishna P. Gummadi', 'Adish Singla', 'Muhammad Bilal Zafar', 'Junaid Ali'] | 2021-05-10 | null | null | null | null | ['classification'] | ['methodology'] | [ 1.59725711e-01 1.93753377e-01 -5.70240080e-01 -9.72810388e-01
3.77310663e-02 -3.49755108e-01 5.50333083e-01 4.54031467e-01
-8.55882883e-01 1.06492639e+00 1.19937569e-01 -6.61602378e-01
-1.51566625e-01 -8.31526756e-01 -2.64802039e-01 -4.47748899e-01
1.35515749e-01 1.96990415e-01 -2.76961565e-01 -1.10514618... | [8.907069206237793, 5.303138732910156] |
cc219936-ab2f-42cf-b0c2-06eaae0f1af4 | guiding-physical-intuition-with-neural | null | null | https://openreview.net/forum?id=BylctiCctX | https://openreview.net/pdf?id=BylctiCctX | Guiding Physical Intuition with Neural Stethoscopes | Model interpretability and systematic, targeted model adaptation present central challenges in deep learning. In the domain of intuitive physics, we study the task of visually predicting stability of block towers with the goal of understanding and influencing the model's reasoning. Our contributions are two-fold. First... | ['Alex Bewley', 'Markus Wulfmeier', 'Ingmar Posner', 'Fabian Fuchs', 'Andrea Vedaldi', 'Oliver Groth', 'Adam Kosiorek'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['physical-intuition'] | ['reasoning'] | [ 1.69776738e-01 4.04814810e-01 1.60217449e-01 -3.52371112e-02
-4.49861586e-01 -6.34615600e-01 5.41655481e-01 1.44364089e-01
-2.52873033e-01 3.97828460e-01 6.76761344e-02 -5.03446758e-01
-1.26494244e-01 -6.63714647e-01 -1.23562038e+00 -7.02130914e-01
-4.05713022e-02 1.05619140e-01 2.83107638e-01 -5.31931996... | [10.3177490234375, 2.178366184234619] |
7557257c-b5b9-4d08-8f54-58c3e0bf5cac | n-beats-neural-basis-expansion-analysis-for | 1905.10437 | null | https://arxiv.org/abs/1905.10437v4 | https://arxiv.org/pdf/1905.10437v4.pdf | N-BEATS: Neural basis expansion analysis for interpretable time series forecasting | We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable withou... | ['Dmitri Carpov', 'Boris N. Oreshkin', 'Nicolas Chapados', 'Yoshua Bengio'] | 2019-05-24 | null | https://openreview.net/forum?id=r1ecqn4YwB | https://openreview.net/pdf?id=r1ecqn4YwB | iclr-2020-1 | ['univariate-time-series-forecasting'] | ['time-series'] | [-4.37148102e-02 -9.93452445e-02 -8.40199217e-02 -8.43066514e-01
-4.51812059e-01 -6.30030990e-01 9.83783543e-01 -2.45999135e-02
-1.66686848e-01 4.99015123e-01 1.03241220e-01 -8.59025061e-01
-3.50351244e-01 -5.46997070e-01 -8.96407008e-01 -6.44284070e-01
-7.45404065e-01 4.91934866e-01 -6.26559332e-02 -8.62269759... | [6.995205402374268, 3.0527544021606445] |
e1379b9b-4dd0-4d47-b4a0-13fb0d42b697 | whc-weighted-hybrid-criterion-for-filter | 2302.08185 | null | https://arxiv.org/abs/2302.08185v1 | https://arxiv.org/pdf/2302.08185v1.pdf | WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural Networks | Filter pruning has attracted increasing attention in recent years for its capacity in compressing and accelerating convolutional neural networks. Various data-independent criteria, including norm-based and relationship-based ones, were proposed to prune the most unimportant filters. However, these state-of-the-art crit... | ['Lei Huang', 'Weize Sun', 'Shaowu Chen'] | 2023-02-16 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 2.81353388e-02 -4.75415915e-01 2.26577967e-01 -4.07222331e-01
6.61864206e-02 -1.28498688e-01 9.08672512e-02 3.98923993e-01
-9.78031874e-01 6.32435203e-01 -1.11216851e-01 -3.61292005e-01
-4.86213595e-01 -9.69635844e-01 -4.88449097e-01 -6.05861843e-01
1.35871917e-01 -2.44502559e-01 7.56486952e-01 -2.07877919... | [8.556328773498535, 3.055878162384033] |
e6982e0e-47ad-4bc4-9089-485859ca5ef6 | 3d-zef-a-3d-zebrafish-tracking-benchmark-1 | 2006.08466 | null | https://arxiv.org/abs/2006.08466v1 | https://arxiv.org/pdf/2006.08466v1.pdf | 3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset | In this work we present a novel publicly available stereo based 3D RGB dataset for multi-object zebrafish tracking, called 3D-ZeF. Zebrafish is an increasingly popular model organism used for studying neurological disorders, drug addiction, and more. Behavioral analysis is often a critical part of such research. Howeve... | ['Stefan Hein Bengtson', 'Thomas B. Moeslund', 'Malte Pedersen', 'Joakim Bruslund Haurum'] | 2020-06-15 | 3d-zef-a-3d-zebrafish-tracking-benchmark | http://openaccess.thecvf.com/content_CVPR_2020/html/Pedersen_3D-ZeF_A_3D_Zebrafish_Tracking_Benchmark_Dataset_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Pedersen_3D-ZeF_A_3D_Zebrafish_Tracking_Benchmark_Dataset_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-object-detection-from-stereo-images', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-4.33278948e-01 -3.11843812e-01 6.03097200e-01 -9.64131579e-02
-4.32256997e-01 -6.80154622e-01 1.14493541e-01 1.00822791e-01
-1.10301840e+00 3.16739738e-01 -2.90005654e-01 2.39829421e-01
2.44701505e-01 -2.09500968e-01 -6.99500501e-01 -6.86349452e-01
-1.96708918e-01 1.57353863e-01 7.90316641e-01 -1.30988076... | [7.678689002990723, -1.002183437347412] |
a95c6e4c-0f20-433b-b176-d6db8b2c4dad | multi-target-regression-via-input-space | 1211.6581 | null | http://arxiv.org/abs/1211.6581v5 | http://arxiv.org/pdf/1211.6581v5.pdf | Multi-Target Regression via Input Space Expansion: Treating Targets as Inputs | In many practical applications of supervised learning the task involves the
prediction of multiple target variables from a common set of input variables.
When the prediction targets are binary the task is called multi-label
classification, while when the targets are continuous the task is called
multi-target regression... | ['Eleftherios Spyromitros-Xioufis', 'Grigorios Tsoumakas', 'Ioannis Vlahavas', 'William Groves'] | 2012-11-28 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 7.73022771e-01 -1.96309134e-01 -6.80982232e-01 -6.55915678e-01
-1.29392195e+00 -3.09434533e-01 6.35205388e-01 2.09937811e-01
-2.67551064e-01 1.14674079e+00 -1.95035949e-01 -1.48716167e-01
-2.93971181e-01 -4.15195018e-01 -5.60370266e-01 -1.02015567e+00
2.55430341e-01 8.00189734e-01 1.36763304e-01 9.53614805... | [9.158917427062988, 4.2946858406066895] |
3a2c5ddc-5db1-46c3-943b-c05a58194150 | orb-based-slam-accelerator-on-soc-fpga | 2207.08405 | null | https://arxiv.org/abs/2207.08405v1 | https://arxiv.org/pdf/2207.08405v1.pdf | ORB-based SLAM accelerator on SoC FPGA | Simultaneous Localization and Mapping (SLAM) is one of the main components of autonomous navigation systems. With the increase in popularity of drones, autonomous navigation on low-power systems is seeing widespread application. Most SLAM algorithms are computationally intensive and struggle to run in real-time on embe... | ['Deming Chen', 'Vibhakar Vemulapati'] | 2022-07-18 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-2.74303079e-01 -6.53342962e-01 -6.73494413e-02 -5.56142509e-01
-4.56578016e-01 -7.05702841e-01 3.75329137e-01 1.86337065e-02
-6.49018228e-01 5.73695660e-01 -4.95311022e-01 -5.42822719e-01
-1.60458043e-01 -6.77920938e-01 -6.31682813e-01 -6.14446588e-02
-3.80754441e-01 4.41687346e-01 4.54843879e-01 -5.54159641... | [7.407505989074707, -2.085824728012085] |
f14f6cce-2191-4442-a448-0d615c6bbf26 | a-zero-shot-framework-for-sketch-based-image-1 | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Sasikiran_Yelamarthi_A_Zero-Shot_Framework_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Sasikiran_Yelamarthi_A_Zero-Shot_Framework_ECCV_2018_paper.pdf | A Zero-Shot Framework for Sketch based Image Retrieval | Sketch-based image retrieval (SBIR) is the task of retrieving images from a natural image database that correspond to a given hand-drawn sketch. Ideally, an SBIR model should learn to associate components in the sketch (say, feet, tail, etc.) with the corresponding components in the image. However, current evaluation m... | ['Ashish Mishra', 'Anurag Mittal', 'Shiva Krishna Reddy', 'Sasi Kiran Yelamarthi'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.52382183e-01 -4.13508832e-01 -1.98050946e-01 -3.67178679e-01
-1.02703094e+00 -6.36934996e-01 1.00985634e+00 -3.21206003e-01
-2.90782209e-02 4.97538835e-01 8.12252867e-04 1.27573252e-01
-2.61454731e-01 -9.78443503e-01 -9.33400989e-01 -7.49455988e-01
4.03574347e-01 8.92118633e-01 2.40463048e-01 -1.46636456... | [11.623329162597656, 0.6544802188873291] |
5f386c7f-7fa0-44f4-bf77-d8039603efd3 | on-confidence-intervals-for-precision | 2208.11977 | null | https://arxiv.org/abs/2208.11977v1 | https://arxiv.org/pdf/2208.11977v1.pdf | On confidence intervals for precision matrices and the eigendecomposition of covariance matrices | The eigendecomposition of a matrix is the central procedure in probabilistic models based on matrix factorization, for instance principal component analysis and topic models. Quantifying the uncertainty of such a decomposition based on a finite sample estimate is essential to reasoning under uncertainty when employing ... | ['Matthew B. Blaschko', 'Wacha Bounliphone', 'Aleksei Tiulpin', 'Teodora Popordanoska'] | 2022-08-25 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 4.95291166e-02 1.97662830e-01 6.36393204e-02 -3.88231911e-02
-8.29742491e-01 -7.68974960e-01 2.97945112e-01 2.01081127e-01
-3.16490084e-01 6.74695313e-01 1.72992021e-01 -6.66813970e-01
-6.83762908e-01 -6.92377031e-01 -7.46059000e-01 -9.61599529e-01
-2.14973629e-01 3.44764471e-01 -4.10392508e-02 9.55107212... | [7.298923969268799, 4.2880988121032715] |
74e05fc4-8938-41af-bb17-8590b3fc9016 | the-influence-of-regional-pronunciation | null | null | https://aclanthology.org/2021.conll-1.52 | https://aclanthology.org/2021.conll-1.52.pdf | The Influence of Regional Pronunciation Variation on Children’s Spelling and the Potential Benefits of Accent Adapted Spellcheckers | A child who is unfamiliar with the correct spelling of a word often employs a “sound it out” approach: breaking the word down into its constituent sounds and then choosing letters to represent the identified sounds. This often results in a misspelling that is orthographically very different to the intended target. Rece... | ['Julie Carson-Berndsen', 'Anthony Ventresque', 'Joe Kenny', 'Emma O’Neill'] | null | null | null | null | conll-emnlp-2021-11 | ['spelling-correction'] | ['natural-language-processing'] | [ 4.05868143e-01 -4.68187273e-01 2.11608693e-01 -4.26336616e-01
-8.38918149e-01 -7.79414356e-01 -8.95314366e-02 6.70941114e-01
-6.80169821e-01 3.22186917e-01 6.98951364e-01 -6.31775677e-01
-2.36613795e-01 -4.43680793e-01 -6.12513483e-01 -4.79307264e-01
7.75092006e-01 3.85885745e-01 4.54779297e-01 -2.03691378... | [11.081284523010254, 10.431405067443848] |
1da7515f-3dc6-48dc-9ead-07502fbcc057 | you-can-t-see-the-forest-for-its-trees | 2112.01955 | null | https://arxiv.org/abs/2112.01955v2 | https://arxiv.org/pdf/2112.01955v2.pdf | Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion | Various deep neural network (DNN) coverage criteria have been proposed to assess DNN test inputs and steer input mutations. The coverage is characterized via neurons having certain outputs, or the discrepancy between neuron outputs. Nevertheless, recent research indicates that neuron coverage criteria show little corre... | ['Shuai Wang', 'Qi Pang', 'Yuanyuan Yuan'] | 2021-12-03 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 3.97707462e-01 -8.14431682e-02 -3.36479515e-01 -5.18026710e-01
-2.04892695e-01 -6.52382493e-01 2.16242105e-01 -1.54607296e-01
-4.10185680e-02 9.62392628e-01 -1.56197771e-01 -6.46413922e-01
-4.18460310e-01 -9.65204418e-01 -8.89444590e-01 -5.37135243e-01
3.28414947e-01 4.35223073e-01 2.21260920e-01 1.02029629... | [6.566366195678711, 7.6336750984191895] |
23238c13-a436-4883-8505-caa8948986e0 | context-aware-bayesian-network-actor-critic | 2306.01920 | null | https://arxiv.org/abs/2306.01920v1 | https://arxiv.org/pdf/2306.01920v1.pdf | Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning | Executing actions in a correlated manner is a common strategy for human coordination that often leads to better cooperation, which is also potentially beneficial for cooperative multi-agent reinforcement learning (MARL). However, the recent success of MARL relies heavily on the convenient paradigm of purely decentraliz... | ['Qi Zhang', 'Dingyang Chen'] | 2023-06-02 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-2.74027348e-01 2.15037659e-01 -6.06170416e-01 -1.08744361e-01
-5.61897576e-01 -4.47129548e-01 5.46345413e-01 -1.23297714e-01
-3.82087022e-01 1.13126493e+00 4.10435855e-01 -4.40667957e-01
-6.49721384e-01 -5.46535909e-01 -6.55525327e-01 -9.20867085e-01
-6.41733527e-01 7.73044288e-01 3.01452667e-01 -2.47963935... | [3.782569646835327, 2.0672452449798584] |
71daf80c-c6b4-48e7-8a6f-18a9214e2bd6 | spot-spatiotemporal-modeling-for-3d-object | 2207.05856 | null | https://arxiv.org/abs/2207.05856v1 | https://arxiv.org/pdf/2207.05856v1.pdf | SpOT: Spatiotemporal Modeling for 3D Object Tracking | 3D multi-object tracking aims to uniquely and consistently identify all mobile entities through time. Despite the rich spatiotemporal information available in this setting, current 3D tracking methods primarily rely on abstracted information and limited history, e.g. single-frame object bounding boxes. In this work, we... | ['Leonidas J Guibas', 'Yanchao Yang', 'Vitor Guizilini', 'Sergey Zakharov', 'Rares Ambrus', 'Jie Li', 'Davis Rempe', 'Colton Stearns'] | 2022-07-12 | null | null | null | null | ['3d-object-tracking', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-3.36304188e-01 -7.78343678e-01 -4.92466390e-01 -1.97992325e-02
-6.11576974e-01 -9.08812106e-01 8.67936313e-01 1.74979955e-01
-3.53490412e-01 5.19026995e-01 2.87854016e-01 1.07834348e-02
-1.59193560e-01 -5.96648037e-01 -8.82238686e-01 -3.36575955e-01
-4.45588827e-01 4.46578205e-01 8.27688634e-01 2.53695875... | [6.318627834320068, -2.089017868041992] |
07487d68-e181-4505-b94b-d4b77fd9d5ee | buol-a-bottom-up-framework-with-occupancy-1 | 2306.00965 | null | https://arxiv.org/abs/2306.00965v1 | https://arxiv.org/pdf/2306.00965v1.pdf | BUOL: A Bottom-Up Framework with Occupancy-aware Lifting for Panoptic 3D Scene Reconstruction From A Single Image | Understanding and modeling the 3D scene from a single image is a practical problem. A recent advance proposes a panoptic 3D scene reconstruction task that performs both 3D reconstruction and 3D panoptic segmentation from a single image. Although having made substantial progress, recent works only focus on top-down appr... | ['Jiaqi Wang', 'Qiong Liu', 'Pan Zhang', 'Tao Chu'] | 2023-06-01 | buol-a-bottom-up-framework-with-occupancy | http://openaccess.thecvf.com//content/CVPR2023/html/Chu_BUOL_A_Bottom-Up_Framework_With_Occupancy-Aware_Lifting_for_Panoptic_3D_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chu_BUOL_A_Bottom-Up_Framework_With_Occupancy-Aware_Lifting_for_Panoptic_3D_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation', '3d-scene-reconstruction', '3d-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.96214736e-01 5.40089197e-02 1.77921429e-02 -2.46350646e-01
-6.63389266e-01 -6.05864406e-01 4.44834620e-01 7.99863562e-02
-1.18478779e-02 2.41281480e-01 -9.53602418e-02 -3.31011683e-01
7.97525048e-02 -9.35345352e-01 -5.97635567e-01 -5.41562617e-01
-8.73738620e-03 1.14729309e+00 7.37663031e-01 5.32349907... | [8.75183391571045, -2.8965470790863037] |
579b8350-75aa-43bd-833e-4363db68eabb | learning-deformable-object-manipulation-from | 2207.10148 | null | https://arxiv.org/abs/2207.10148v1 | https://arxiv.org/pdf/2207.10148v1.pdf | Learning Deformable Object Manipulation from Expert Demonstrations | We present a novel Learning from Demonstration (LfD) method, Deformable Manipulation from Demonstrations (DMfD), to solve deformable manipulation tasks using states or images as inputs, given expert demonstrations. Our method uses demonstrations in three different ways, and balances the trade-off between exploring the ... | ['Gaurav S. Sukhatme', 'Marcus Dominguez-Kuhne', 'I-Chun Arthur Liu', 'Gautam Salhotra'] | 2022-07-20 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-0.04497123 0.09541973 0.10112944 -0.1789748 -0.6397549 -0.96108437
0.43519664 -0.3868405 -0.4656408 0.7828922 0.01961283 -0.28966525
-0.09119611 -0.27984285 -1.1811731 -0.51399857 -0.6733937 0.48869297
0.3517969 -0.25711516 0.17058751 0.48760036 -1.4756556 0.0660296
0.8972451 0.5711319 0.6... | [4.717764377593994, 0.6166514754295349] |
6e188428-7e55-4806-a994-c883df86c3ba | unified-multimodal-model-with-unlikelihood | 2211.13235 | null | https://arxiv.org/abs/2211.13235v1 | https://arxiv.org/pdf/2211.13235v1.pdf | Unified Multimodal Model with Unlikelihood Training for Visual Dialog | The task of visual dialog requires a multimodal chatbot to answer sequential questions from humans about image content. Prior work performs the standard likelihood training for answer generation on the positive instances (involving correct answers). However, the likelihood objective often leads to frequent and dull out... | ['Changjun Jiang', 'Junli Wang', 'ZiHao Wang'] | 2022-11-23 | null | null | null | null | ['visual-dialogue', 'visual-dialogue', 'answer-generation'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 1.62640139e-01 1.81019649e-01 -1.69477873e-02 -4.98200119e-01
-1.09296215e+00 -7.59703815e-01 7.83936739e-01 -3.42276037e-01
-4.65467274e-01 7.28648961e-01 3.06220829e-01 -3.29572290e-01
4.00291890e-01 -6.15203023e-01 -3.55759948e-01 -5.55798113e-01
5.76627195e-01 6.12664282e-01 1.94849432e-01 -2.24865377... | [10.916325569152832, 1.4904075860977173] |
e91c0543-cde1-465c-ae32-b59d9fe9ea16 | a-clip-hitchhiker-s-guide-to-long-video | 2205.08508 | null | https://arxiv.org/abs/2205.08508v1 | https://arxiv.org/pdf/2205.08508v1.pdf | A CLIP-Hitchhiker's Guide to Long Video Retrieval | Our goal in this paper is the adaptation of image-text models for long video retrieval. Recent works have demonstrated state-of-the-art performance in video retrieval by adopting CLIP, effectively hitchhiking on the image-text representation for video tasks. However, there has been limited success in learning temporal ... | ['Andrew Zisserman', 'Gül Varol', 'Arsha Nagrani', 'Max Bain'] | 2022-05-17 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 4.16111238e-02 -6.13156140e-01 -3.68580252e-01 -1.96508214e-01
-1.50378263e+00 -4.75809038e-01 1.08202350e+00 -1.37844637e-01
-8.13526869e-01 3.05663615e-01 6.11592770e-01 1.96244135e-01
-8.82749707e-02 -8.15466344e-02 -8.48834574e-01 -6.57359362e-01
-5.19293427e-01 3.51263657e-02 4.22421396e-01 8.89563337... | [10.284184455871582, 0.878159761428833] |
6da75d63-0a80-41a4-a96a-63dc3c42fd86 | human-parsing-based-texture-transfer-from | null | null | http://proceedings.neurips.cc/paper/2020/hash/a516a87cfcaef229b342c437fe2b95f7-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/a516a87cfcaef229b342c437fe2b95f7-Paper.pdf | Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency | This paper proposes a human parsing based texture transfer model via cross-view consistency learning to generate the texture of 3D human body from a single image. We use the semantic parsing of human body as input for providing both the shape and pose information to reduce the appearance variation of human image and p... | ['Ling Shao', 'Kaihao Zhang', 'Shengcai Liao', 'Fang Zhao'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['human-parsing', 'image-to-3d'] | ['computer-vision', 'computer-vision'] | [ 1.97632790e-01 4.89377528e-01 4.98105250e-02 -5.00878274e-01
-2.50958413e-01 -3.91529769e-01 2.78063208e-01 -6.60402536e-01
1.81494549e-01 3.48834604e-01 1.44713402e-01 3.80845577e-01
3.45705032e-01 -8.64250124e-01 -9.57923949e-01 -7.26044834e-01
5.56899369e-01 3.61103535e-01 2.53392875e-01 -2.23091364... | [11.954845428466797, -0.8686598539352417] |
3fe4c804-e1af-47c8-a464-57180b86ba33 | deep-scattering-transform-applied-to-note | 1703.09775 | null | http://arxiv.org/abs/1703.09775v1 | http://arxiv.org/pdf/1703.09775v1.pdf | Deep scattering transform applied to note onset detection and instrument recognition | Automatic Music Transcription (AMT) is one of the oldest and most
well-studied problems in the field of music information retrieval. Within this
challenging research field, onset detection and instrument recognition take
important places in transcription systems, as they respectively help to
determine exact onset times... | ['O. Adam', 'D. Cazau', 'G. Revillon'] | 2017-03-28 | null | null | null | null | ['instrument-recognition', 'music-transcription'] | ['audio', 'music'] | [ 5.82062900e-01 -4.38378304e-01 -1.34758621e-01 2.68487990e-01
-9.09050047e-01 -8.80729795e-01 4.95629579e-01 2.40997508e-01
-1.19864270e-01 3.21041346e-01 3.14206094e-01 1.28227443e-01
-7.78879941e-01 -3.35756868e-01 -9.54563245e-02 -7.60848165e-01
-2.61989892e-01 2.87181288e-01 -1.02419212e-01 -3.04884374... | [15.861384391784668, 5.309008598327637] |
f25ee06a-b3a8-42ad-a205-cd18f9bad85d | interpretable-ecg-classification-via-a-query | 2111.07386 | null | https://arxiv.org/abs/2111.07386v2 | https://arxiv.org/pdf/2111.07386v2.pdf | Interpretable ECG classification via a query-based latent space traversal (qLST) | Electrocardiography (ECG) is an effective and non-invasive diagnostic tool that measures the electrical activity of the heart. Interpretation of ECG signals to detect various abnormalities is a challenging task that requires expertise. Recently, the use of deep neural networks for ECG classification to aid medical prac... | ['René van Es', 'Erik Bekkers', 'Rutger J. Hassink', 'Pieter A. Doevendans', 'Rutger R. van de Leur', 'Sharvaree P. Vadgama', 'Melle B. Vessies'] | 2021-11-14 | null | null | null | null | ['ecg-classification', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 4.85763878e-01 5.45865417e-01 1.11058086e-01 -6.04243934e-01
-7.71128595e-01 -4.16740328e-01 -1.46920413e-01 4.61846411e-01
7.00872913e-02 5.49378335e-01 1.30516380e-01 -4.96617824e-01
-4.08571094e-01 -4.08368975e-01 -4.23051059e-01 -5.98924100e-01
-2.90198684e-01 7.88251042e-01 -2.90983349e-01 -1.94824338... | [14.293721199035645, 3.2916736602783203] |
451a2e80-dd09-40ee-bc00-c1cf8bfdb018 | rovist-learning-robust-metrics-for-visual-1 | 2205.03774 | null | https://arxiv.org/abs/2205.03774v1 | https://arxiv.org/pdf/2205.03774v1.pdf | RoViST:Learning Robust Metrics for Visual Storytelling | Visual storytelling (VST) is the task of generating a story paragraph that describes a given image sequence. Most existing storytelling approaches have evaluated their models using traditional natural language generation metrics like BLEU or CIDEr. However, such metrics based on n-gram matching tend to have poor correl... | ['Josiah Poon', 'Caren Han', 'Eileen Wang'] | 2022-05-08 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.46267235e-01 3.65485936e-01 4.25431505e-02 -1.97880760e-01
-7.02314973e-01 -6.04516923e-01 1.39625001e+00 6.69809937e-01
-1.13458961e-01 8.22450459e-01 7.82551050e-01 -1.57841772e-01
-1.56685337e-01 -8.36102068e-01 -6.18433475e-01 -3.71446401e-01
2.09157273e-01 5.07796586e-01 4.80689555e-01 -2.87469000... | [11.718422889709473, 8.807698249816895] |
793090ea-fe89-4fa8-98d9-80bc559c5517 | no-reference-image-quality-assessment-via-1 | 2108.06858 | null | https://arxiv.org/abs/2108.06858v2 | https://arxiv.org/pdf/2108.06858v2.pdf | No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency | The goal of No-Reference Image Quality Assessment (NR-IQA) is to estimate the perceptual image quality in accordance with subjective evaluations, it is a complex and unsolved problem due to the absence of the pristine reference image. In this paper, we propose a novel model to address the NR-IQA task by leveraging a hy... | ['Kris M. Kitani', 'Saba Dadsetan', 'S. Alireza Golestaneh'] | 2021-08-16 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 2.26403788e-01 -1.08927749e-01 -1.91314612e-02 -4.55001473e-01
-9.27314103e-01 -6.61834598e-01 3.52756232e-01 -1.36078879e-01
-2.50851929e-01 2.89517671e-01 2.23646283e-01 -4.09115665e-02
-1.84410438e-01 -6.89502299e-01 -9.64013577e-01 -6.50267005e-01
2.21246809e-01 -2.90520847e-01 1.29333332e-01 -2.27947712... | [11.853885650634766, -1.826348900794983] |
ecf13cca-b598-4a3f-993e-73364082af26 | real-time-neural-radiance-talking-portrait | 2211.12368 | null | https://arxiv.org/abs/2211.12368v1 | https://arxiv.org/pdf/2211.12368v1.pdf | Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition | While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their potential usage. In this paper, we propose an efficient NeRF-based framework that enables real-time synthesizing of talking portraits and faster... | ['Jingdong Wang', 'Gang Zeng', 'Jingtuo Liu', 'Tianshu Hu', 'Dongliang He', 'Xiaokang Chen', 'Hang Zhou', 'Kaisiyuan Wang', 'Jiaxiang Tang'] | 2022-11-22 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 1.67924836e-01 3.40353660e-02 2.25315422e-01 -1.49786085e-01
-1.01395273e+00 -4.02537107e-01 5.62852561e-01 -6.67822182e-01
2.36708820e-01 5.19043446e-01 3.70495856e-01 -6.34064944e-03
3.50700766e-02 -8.08138669e-01 -5.56026459e-01 -8.28879893e-01
-6.28370121e-02 2.92303741e-01 -4.89418991e-02 -1.45723581... | [13.122381210327148, -0.45722895860671997] |
3051570c-76ce-4a54-901e-7f219648ad97 | distilling-motion-planner-augmented-policies | 2111.06383 | null | https://arxiv.org/abs/2111.06383v1 | https://arxiv.org/pdf/2111.06383v1.pdf | Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation | Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations. Prior work tackles the exploration problem by integrating motion planning and reinforcement learning. However, the motion plan... | ['Youngwoon Lee', 'Peter Englert', 'Joseph J. Lim', 'Gaurav S. Sukhatme', 'Shagun Uppal', 'I-Chun Arthur Liu'] | 2021-11-11 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 3.70021313e-02 -3.77442800e-02 -2.88076997e-01 2.96371192e-01
-6.19364202e-01 -6.56496823e-01 8.15345705e-01 -1.30278096e-01
-7.66002178e-01 9.34787452e-01 2.64688522e-01 -3.63666594e-01
-2.78129447e-02 -4.51001495e-01 -1.01201248e+00 -6.58733666e-01
-3.04746389e-01 5.38370550e-01 4.65365827e-01 -2.65051216... | [4.546651840209961, 0.9940419793128967] |
87b7f3aa-ef71-4d5a-831d-4d2acd8d9c1d | gender-prediction-using-limited-twitter-data | 2010.02005 | null | https://arxiv.org/abs/2010.02005v1 | https://arxiv.org/pdf/2010.02005v1.pdf | Gender prediction using limited Twitter Data | Transformer models have shown impressive performance on a variety of NLP tasks. Off-the-shelf, pre-trained models can be fine-tuned for specific NLP classification tasks, reducing the need for large amounts of additional training data. However, little research has addressed how much data is required to accurately fine-... | ['Stephan Raaijmakers', 'Maaike H. T. de Boer', 'Maaike Burghoorn'] | 2020-09-29 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-2.23764941e-01 3.16041172e-01 -1.12404794e-01 -6.55944645e-01
-7.18953788e-01 -8.43173265e-01 7.07579315e-01 6.98002815e-01
-8.27555120e-01 6.65796757e-01 8.04357231e-02 -5.00892758e-01
1.43236652e-01 -1.00948465e+00 -2.28449330e-01 -4.84015286e-01
1.46962926e-01 9.86718416e-01 1.45663125e-02 -2.54147798... | [9.36807632446289, 10.3450927734375] |
198a2003-b714-41ca-be38-44cccdc12120 | domain-adaptive-person-re-identification-via-1 | 2011.03363 | null | https://arxiv.org/abs/2011.03363v1 | https://arxiv.org/pdf/2011.03363v1.pdf | Domain Adaptive Person Re-Identification via Coupling Optimization | Domain adaptive person Re-Identification (ReID) is challenging owing to the domain gap and shortage of annotations on target scenarios. To handle those two challenges, this paper proposes a coupling optimization method including the Domain-Invariant Mapping (DIM) method and the Global-Local distance Optimization (GLO),... | ['Shiliang Zhang', 'Xiaobin Liu'] | 2020-11-06 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-9.55431387e-02 -3.02615047e-01 -2.31180876e-01 -6.51348770e-01
-9.03755307e-01 -4.43172127e-01 4.70565915e-01 -1.93741545e-01
-8.26849341e-01 8.17203462e-01 2.21520230e-01 1.63769722e-01
-1.27349645e-01 -3.85029852e-01 -3.83072525e-01 -5.85637808e-01
4.33610529e-01 7.90297925e-01 3.06571624e-03 -4.10764217... | [14.782818794250488, 1.0685265064239502] |
590012d0-c406-46b9-97c4-d3caeb29491a | boosting-cross-lingual-transferability-in | 2305.15233 | null | https://arxiv.org/abs/2305.15233v1 | https://arxiv.org/pdf/2305.15233v1.pdf | Boosting Cross-lingual Transferability in Multilingual Models via In-Context Learning | Existing cross-lingual transfer (CLT) prompting methods are only concerned with monolingual demonstration examples in the source language. In this paper, we propose In-CLT, a novel cross-lingual transfer prompting method that leverages both source and target languages to construct the demonstration examples. We conduct... | ['Jinsik Lee', 'Yireun Kim', 'Dayeon Ki', 'Sunkyoung Kim'] | 2023-05-24 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-2.44423807e-01 -2.08337575e-01 -3.40644926e-01 -5.13223350e-01
-1.57003069e+00 -9.87141728e-01 7.79224098e-01 7.92861134e-02
-6.93450332e-01 8.51825953e-01 1.23837776e-01 -7.84367502e-01
2.03865007e-01 -4.31477159e-01 -1.02217555e+00 -1.96877748e-01
2.10271716e-01 5.82381904e-01 4.67706099e-02 -5.94645143... | [11.082542419433594, 9.638372421264648] |
09547fb9-a3f5-4766-9b1e-2ef2134e83a3 | dgst-discriminator-guided-scene-text-detector | 2002.12509 | null | https://arxiv.org/abs/2002.12509v1 | https://arxiv.org/pdf/2002.12509v1.pdf | DGST : Discriminator Guided Scene Text detector | Scene text detection task has attracted considerable attention in computer vision because of its wide application. In recent years, many researchers have introduced methods of semantic segmentation into the task of scene text detection, and achieved promising results. This paper proposes a detector framework based on t... | ['Cunzhao Shi', 'Baihua Xiao', 'Yanna Wang', 'Fuxi Jia', 'Jinyuan Zhao', 'Chunheng Wang'] | 2020-02-28 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 7.04133689e-01 -2.29944184e-01 2.31073827e-01 -3.15085351e-01
-6.74252629e-01 -2.44887546e-01 5.82522810e-01 -3.44029069e-02
-4.12142813e-01 2.55322009e-01 9.45360735e-02 -7.24721998e-02
5.49830079e-01 -9.73836482e-01 -4.72129524e-01 -8.62668216e-01
8.65403295e-01 4.49253440e-01 8.62297654e-01 9.37489048... | [12.036837577819824, 2.26973557472229] |
1da35451-c575-4a42-879c-737543a74ba3 | phrase-based-neural-unsupervised-machine-1 | null | null | https://aclanthology.org/D18-1549 | https://aclanthology.org/D18-1549.pdf | Phrase-Based \& Neural Unsupervised Machine Translation | Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access... | ["Marc{'}Aurelio Ranzato", 'Alexis Conneau', 'Myle Ott', 'Ludovic Denoyer', 'Guillaume Lample'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 7.51928613e-02 -2.32636303e-01 -5.49955726e-01 -3.65893304e-01
-1.53244126e+00 -8.94572854e-01 9.37680960e-01 -8.06212425e-03
-7.90303946e-01 1.16176534e+00 2.50024140e-01 -8.64505649e-01
2.78515756e-01 -4.74277556e-01 -8.55234683e-01 -3.19854915e-01
2.90153474e-01 9.29750085e-01 -2.06174940e-01 -6.92409277... | [11.607590675354004, 10.252205848693848] |
64e8cb74-1c4f-4606-b84d-64ede41662a0 | code-generation-as-a-dual-task-of-code | 1910.05923 | null | https://arxiv.org/abs/1910.05923v1 | https://arxiv.org/pdf/1910.05923v1.pdf | Code Generation as a Dual Task of Code Summarization | Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development. Various neural network-based approaches are proposed to solve these two tasks separately. However, there exists a specific intuitive correlation between CS and CG, which have not been exploited in prev... | ['Zhiyi Fu', 'Zhi Jin', 'Ge Li', 'Xin Xia', 'Bolin Wei'] | 2019-10-14 | code-generation-as-a-dual-task-of-code-1 | http://papers.nips.cc/paper/8883-code-generation-as-a-dual-task-of-code-summarization | http://papers.nips.cc/paper/8883-code-generation-as-a-dual-task-of-code-summarization.pdf | neurips-2019-12 | ['code-summarization'] | ['computer-code'] | [ 1.29102111e-01 6.16571829e-02 -1.72090575e-01 -2.79548138e-01
-5.26360035e-01 -4.01352078e-01 5.27823031e-01 -1.72910132e-02
-1.76074654e-01 3.80297154e-01 2.19824642e-01 -3.75512779e-01
1.37064874e-01 -4.94660795e-01 -7.51138628e-01 -4.49633509e-01
3.10191154e-01 -2.82968342e-01 2.02069357e-01 -1.26646623... | [7.627533435821533, 7.937437534332275] |
d5a3ade4-93b9-4017-815a-27361a391fb7 | spanish-datasets-for-sensitive-entity | null | null | https://aclanthology.org/2022.lrec-1.400 | https://aclanthology.org/2022.lrec-1.400.pdf | Spanish Datasets for Sensitive Entity Detection in the Legal Domain | The de-identification of sensible data, also known as automatic textual anonymisation, is essential for data sharing and reuse, both for research and commercial purposes. The first step for data anonymisation is the detection of sensible entities. In this work, we present four new datasets for named entity detection in... | ['Maite Melero', 'Montse Cuadros', 'Aitor García Pablos', 'Ona de Gibert Bonet'] | null | null | null | null | lrec-2022-6 | ['de-identification'] | ['natural-language-processing'] | [ 5.62803112e-02 5.29195547e-01 9.25637111e-02 -4.36864823e-01
-8.09453368e-01 -8.34984779e-01 9.79385257e-01 6.88918710e-01
-9.36954677e-01 1.10805154e+00 4.87977982e-01 -1.65641785e-01
-6.42274991e-02 -6.40097320e-01 -5.23090959e-01 -2.19010696e-01
1.83875293e-01 8.96582246e-01 1.90505594e-01 -1.13144375... | [9.610657691955566, 9.50782585144043] |
947bcd28-0196-4035-a042-f3ae084fe9b9 | parametric-scattering-networks | 2107.09539 | null | https://arxiv.org/abs/2107.09539v4 | https://arxiv.org/pdf/2107.09539v4.pdf | Parametric Scattering Networks | The wavelet scattering transform creates geometric invariants and deformation stability. In multiple signal domains, it has been shown to yield more discriminative representations compared to other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled da... | ['Michael Eickenberg', 'Irina Rish', 'Muawiz Chaudhary', 'Guy Wolf', 'Eugene Belilovsky', 'Laurent Alsène-Racicot', 'Benjamin Thérien', 'Shanel Gauthier'] | 2021-07-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Gauthier_Parametric_Scattering_Networks_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gauthier_Parametric_Scattering_Networks_CVPR_2022_paper.pdf | cvpr-2022-1 | ['small-data'] | ['computer-vision'] | [ 4.65516806e-01 5.72521947e-02 -1.25512347e-01 -3.26904327e-01
-1.15074420e+00 -5.78811288e-01 4.60007548e-01 -2.73161501e-01
7.31019676e-02 4.84423876e-01 5.23714602e-01 1.02986738e-01
-3.31062496e-01 -6.94224000e-01 -6.38748586e-01 -1.03273082e+00
-4.15579081e-01 1.18445776e-01 3.24996375e-02 -2.45743528... | [15.356672286987305, 5.62111234664917] |
773b2b29-b3d9-479b-bc85-8b70232496cb | mmd-aggregated-two-sample-test | 2110.15073 | null | https://arxiv.org/abs/2110.15073v3 | https://arxiv.org/pdf/2110.15073v3.pdf | MMD Aggregated Two-Sample Test | We propose two novel nonparametric two-sample kernel tests based on the Maximum Mean Discrepancy (MMD). First, for a fixed kernel, we construct an MMD test using either permutations or a wild bootstrap, two popular numerical procedures to determine the test threshold. We prove that this test controls the probability of... | ['Arthur Gretton', 'Benjamin Guedj', 'Béatrice Laurent', 'Mélisande Albert', 'Ilmun Kim', 'Antonin Schrab'] | 2021-10-28 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [-7.92823136e-02 -2.05561131e-01 -2.71696806e-01 -7.55030587e-02
-9.53675926e-01 -6.98362291e-01 1.29561409e-01 2.71951228e-01
-5.98513484e-01 8.30173075e-01 -4.92205918e-01 -6.36763871e-01
-4.02932942e-01 -9.17160451e-01 -8.56455624e-01 -1.01982749e+00
-3.88114572e-01 3.32627207e-01 6.60774291e-01 2.35267311... | [7.488466262817383, 4.173100471496582] |
4e8ef89b-27dd-4dec-aca7-31032717d375 | lexical-simplification-using-multi-level-and | 2302.01823 | null | https://arxiv.org/abs/2302.01823v1 | https://arxiv.org/pdf/2302.01823v1.pdf | Lexical Simplification using multi level and modular approach | Text Simplification is an ongoing problem in Natural Language Processing, solution to which has varied implications. In conjunction with the TSAR-2022 Workshop @EMNLP2022 Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier to read (or understand) e... | ['Pawan Kumar Rajpoot', 'Nikita Katyal'] | 2023-02-03 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [ 4.47345763e-01 5.91615915e-01 1.25660315e-01 -4.42722857e-01
-7.03549504e-01 -6.15325868e-01 5.53571343e-01 6.81025147e-01
-8.13847899e-01 8.25940013e-01 1.00015450e+00 -2.00176194e-01
-1.78979799e-01 -4.83412325e-01 -2.33823940e-01 8.81438479e-02
6.04728818e-01 7.79318511e-01 1.68553934e-01 -7.97281802... | [10.919851303100586, 10.398530960083008] |
1c5f2dc6-2ff7-4735-82c0-d08821006406 | distributed-control-design-and-safety | 2303.12610 | null | https://arxiv.org/abs/2303.12610v1 | https://arxiv.org/pdf/2303.12610v1.pdf | Distributed Control Design and Safety Verification for Multi-Agent Systems | We propose distributed iterative algorithms for safe control design and safety verification for networked multi-agent systems. These algorithms rely on distributing a control barrier function (CBF) related quadratic programming (QP) problem. The proposed distributed algorithm addresses infeasibility issues of existing ... | ['Kostas Margellos', 'Antonis Papachristodoulou', 'Han Wang'] | 2023-03-22 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 3.28904837e-02 5.02878845e-01 -2.10824728e-01 8.98880735e-02
-1.16306460e+00 -7.46035099e-01 4.34089750e-01 5.77977180e-01
-4.84438092e-01 1.13829708e+00 -3.55106533e-01 -5.09048164e-01
-5.74068308e-01 -9.35819983e-01 -7.75887072e-01 -9.81824815e-01
-6.21252239e-01 4.20244366e-01 1.85048178e-01 -2.75053352... | [4.76751708984375, 2.244967222213745] |
78347e23-7289-4b96-8b13-16e9ba717400 | transformer-based-approach-towards-music | 2101.02051 | null | https://arxiv.org/abs/2101.02051v1 | https://arxiv.org/pdf/2101.02051v1.pdf | Transformer-based approach towards music emotion recognition from lyrics | The task of identifying emotions from a given music track has been an active pursuit in the Music Information Retrieval (MIR) community for years. Music emotion recognition has typically relied on acoustic features, social tags, and other metadata to identify and classify music emotions. The role of lyrics in music emo... | ['Vinoo Alluri', 'Ramaguru Guru Ravi Shanker', 'Yudhik Agrawal'] | 2021-01-06 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 2.15861097e-01 -4.69575763e-01 -1.53781297e-02 -8.46653581e-02
-7.90094316e-01 -8.40649486e-01 3.18279237e-01 1.73263595e-01
-2.67305881e-01 4.19755608e-01 5.52580476e-01 3.68476361e-01
-6.32372618e-01 -4.90630358e-01 4.67771944e-03 -6.82173967e-01
1.17179103e-01 1.85608506e-01 -1.30223528e-01 -1.90371200... | [15.931672096252441, 5.219769477844238] |
6b7f6b30-06fa-489d-904f-06d9f79c78cc | studying-the-role-of-named-entities-for | 2206.09676 | null | https://arxiv.org/abs/2206.09676v1 | https://arxiv.org/pdf/2206.09676v1.pdf | Studying the role of named entities for content preservation in text style transfer | Text style transfer techniques are gaining popularity in Natural Language Processing, finding various applications such as text detoxification, sentiment, or formality transfer. However, the majority of the existing approaches were tested on such domains as online communications on public platforms, music, or entertain... | ['Alexander Panchenko', 'Irina Krotova', 'Varvara Logacheva', 'David Dale', 'Nikolay Babakov'] | 2022-06-20 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 4.64224339e-01 2.38105446e-01 -3.39231715e-02 -5.85666001e-01
-3.98748040e-01 -7.69569397e-01 8.85592341e-01 5.83220661e-01
-7.99541056e-01 1.14105916e+00 5.64343810e-01 -2.92196423e-01
1.38291597e-01 -8.21964622e-01 -7.11777925e-01 -2.02455252e-01
1.01484813e-01 7.26446986e-01 3.44517708e-01 -1.00351393... | [11.60770034790039, 9.217864036560059] |
2565f9de-eaf9-40f2-abe1-8f01b07c0814 | interpretable-graph-neural-networks-for | 2207.00813 | null | https://arxiv.org/abs/2207.00813v2 | https://arxiv.org/pdf/2207.00813v2.pdf | Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis | Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit for neuroscience research and clinical disorder therapy. Mapping the connection... | ['Carl Yang', 'Lifang He', 'Xiaoxiao Li', 'Yanqiao Zhu', 'Wei Dai', 'Hejie Cui'] | 2022-06-30 | null | null | null | null | ['disease-prediction'] | ['medical'] | [-6.21271245e-02 3.29980344e-01 -2.47057214e-01 -4.10572469e-01
3.09142739e-01 -1.33845493e-01 2.66510993e-01 4.70250919e-02
1.19590327e-01 5.71347952e-01 2.26317853e-01 -2.12000147e-01
-4.83324736e-01 -6.21764004e-01 -1.22619547e-01 -4.59170014e-01
-3.47976297e-01 3.56851190e-01 1.67177513e-01 -8.54154602... | [12.42697525024414, 3.3840749263763428] |
48ec56c8-d346-40ee-9bb4-0c09026457be | adaptive-exploration-for-unsupervised-person | 1907.04194 | null | https://arxiv.org/abs/1907.04194v2 | https://arxiv.org/pdf/1907.04194v2.pdf | Adaptive Exploration for Unsupervised Person Re-Identification | Due to domain bias, directly deploying a deep person re-identification (re-ID) model trained on one dataset often achieves considerably poor accuracy on another dataset. In this paper, we propose an Adaptive Exploration (AE) method to address the domain-shift problem for re-ID in an unsupervised manner. Specifically, i... | ['Yuhang Ding', 'Mingliang Xu', 'Yi Yang', 'Hehe Fan'] | 2019-07-09 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-2.60060467e-02 -1.21333197e-01 -2.11730689e-01 -4.30526704e-01
-3.50570887e-01 -3.46476167e-01 4.42596525e-01 7.54838437e-02
-6.97146773e-01 5.98322332e-01 1.82436984e-02 1.54450327e-01
-3.20833400e-02 -9.96507108e-01 -5.71139514e-01 -8.98954451e-01
2.49163285e-01 6.98989928e-01 9.24336091e-02 9.69271809... | [14.825148582458496, 1.102555513381958] |
c857c244-8f51-4c5d-a814-69f868133aa1 | multiobjective-bilevel-evolutionary-approach | 2106.07318 | null | https://arxiv.org/abs/2106.07318v1 | https://arxiv.org/pdf/2106.07318v1.pdf | Multiobjective Bilevel Evolutionary Approach for Off-Grid Direction-of-Arrival Estimation | The source number identification is an essential step in direction-of-arrival (DOA) estimation. Existing methods may provide a wrong source number due to inferior statistical properties (in low SNR or limited snapshots) or modeling errors (caused by relaxing sparse penalties), especially in impulsive noise. To address ... | ['Xin Yao', 'J. Andrew Zhang', 'Jin Zhang', 'Qi Zhao', 'Bai Yan'] | 2021-06-14 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 7.02725947e-02 -3.56423885e-01 2.08565563e-01 2.87573785e-01
-8.63888085e-01 -4.73271847e-01 -7.82486051e-02 -1.77638084e-02
7.87102655e-02 9.92281437e-01 1.77756086e-01 7.25130178e-03
-7.30176866e-01 -8.09354186e-01 -1.85009763e-01 -1.20244813e+00
-1.46705478e-01 -2.78528407e-02 -3.14075381e-01 -7.01859817... | [6.465387344360352, 1.3497486114501953] |
d3a1bf7c-4a52-443a-835c-fd4c56ad1b0b | an-inter-observer-consistent-deep-adversarial | 2211.07336 | null | https://arxiv.org/abs/2211.07336v2 | https://arxiv.org/pdf/2211.07336v2.pdf | An Inter-observer consistent deep adversarial training for visual scanpath prediction | The visual scanpath is a sequence of points through which the human gaze moves while exploring a scene. It represents the fundamental concepts upon which visual attention research is based. As a result, the ability to predict them has emerged as an important task in recent years. In this paper, we propose an inter-obse... | ['Alessandro Bruno', 'Aladine Chetouani', 'Marouane Tliba', 'Mohamed Amine Kerkouri'] | 2022-11-14 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [ 1.22384220e-01 -7.78013319e-02 -1.94741949e-01 -5.25205553e-01
-3.92070591e-01 -4.41041559e-01 5.89573145e-01 -1.12257637e-01
-4.68298048e-01 2.97784179e-01 1.64525323e-02 -2.33111575e-01
-1.18488729e-01 -3.75260442e-01 -8.77144933e-01 -4.71293539e-01
-6.39947876e-02 4.10831779e-01 4.60715473e-01 -2.42968455... | [10.085067749023438, 1.2011289596557617] |
43963cda-24ff-4259-bb5e-6967e0b2083b | nsurl-2019-task-8-semantic-question | null | null | https://aclanthology.org/2019.nsurl-1.1 | https://aclanthology.org/2019.nsurl-1.1.pdf | NSURL-2019 Task 8: Semantic Question Similarity in Arabic | null | ['Hussein T. Al-Natsheh', 'Wael Farhan', 'Hesham Al-Bataineh', 'Ahmad Mustafa', 'Haitham Seelawi'] | null | null | null | null | nsurl-2019-9 | ['question-similarity'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.231813907623291, 3.763392210006714] |
71d53075-f015-4460-8893-afafd33367aa | pushing-the-limits-of-3d-shape-generation-at | 2306.11510 | null | https://arxiv.org/abs/2306.11510v1 | https://arxiv.org/pdf/2306.11510v1.pdf | Pushing the Limits of 3D Shape Generation at Scale | We present a significant breakthrough in 3D shape generation by scaling it to unprecedented dimensions. Through the adaptation of the Auto-Regressive model and the utilization of large language models, we have developed a remarkable model with an astounding 3.6 billion trainable parameters, establishing it as the large... | ['Yanwei Fu', 'Bo Zhao', 'Tiejun Huang', 'Jingyang Huo', 'Xuelin Qian', 'Wang Yu'] | 2023-06-20 | null | null | null | null | ['3d-shape-generation', 'quantization'] | ['computer-vision', 'methodology'] | [-8.25454369e-02 -2.67057344e-02 -2.70787696e-03 -5.14702313e-02
-8.57611775e-01 -9.88383651e-01 8.56538653e-01 -3.07689101e-01
4.25065368e-01 3.24932992e-01 4.81839299e-01 -3.31777662e-01
1.77838039e-02 -1.12724364e+00 -8.07987690e-01 -4.11315233e-01
5.51811494e-02 6.00387156e-01 -1.31444275e-01 -4.74658549... | [8.976791381835938, -3.6089279651641846] |
1bff07e5-ead4-440f-83dc-360500273ccd | building-and-evaluation-of-a-real-room | 1811.06795 | null | http://arxiv.org/abs/1811.06795v2 | http://arxiv.org/pdf/1811.06795v2.pdf | Building and Evaluation of a Real Room Impulse Response Dataset | This paper presents BUT ReverbDB - a dataset of real room impulse responses
(RIR), background noises and re-transmitted speech data. The retransmitted data
includes LibriSpeech test-clean, 2000 HUB5 English evaluation and part of 2010
NIST Speaker Recognition Evaluation datasets. We provide a detailed description
of RI... | [] | 2019-05-30 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 2.74553984e-01 -2.06196904e-01 8.36627185e-01 -6.76854134e-01
-1.60921502e+00 -5.74741304e-01 4.81140673e-01 -1.96686924e-01
-5.22093415e-01 5.08672953e-01 7.58299530e-01 -5.85016787e-01
-2.66084131e-02 -5.73156849e-02 -5.26025534e-01 -7.74611056e-01
-2.09218800e-01 3.16504031e-01 1.58044040e-01 -6.75484478... | [14.982332229614258, 5.993803024291992] |
51b2868a-d6fc-4db3-8d48-de1574975b8a | lbl2vec-an-embedding-based-approach-for | 2210.06023 | null | https://arxiv.org/abs/2210.06023v1 | https://arxiv.org/pdf/2210.06023v1.pdf | Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics | In this paper, we consider the task of retrieving documents with predefined topics from an unlabeled document dataset using an unsupervised approach. The proposed unsupervised approach requires only a small number of keywords describing the respective topics and no labeled document. Existing approaches either heavily r... | ['Florian Matthes', 'Daniel Braun', 'Tim Schopf'] | 2022-10-12 | null | null | null | null | ['document-classification', 'unsupervised-text-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.69465834e-01 2.18717530e-02 -4.42460924e-01 -5.00387490e-01
-1.25629997e+00 -8.77764404e-01 8.24160039e-01 6.17102921e-01
-5.87558568e-01 6.56907678e-01 8.62771571e-02 -2.43905321e-01
-2.85947621e-01 -6.68609619e-01 -4.26501274e-01 -6.92281842e-01
2.03386188e-01 6.81325197e-01 1.70723543e-01 1.49589390... | [10.455607414245605, 7.74520206451416] |
3027d3fa-e852-4e0d-b1d1-1b6a8aff8b04 | rudas-synthetic-datasets-for-rule-learning | 1909.07095 | null | https://arxiv.org/abs/1909.07095v2 | https://arxiv.org/pdf/1909.07095v2.pdf | RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools | Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form. However, rule formulation is a complex process that requires deep domain expertise,and is further challenged by today's often ... | ['Veronika Thost', 'Cristina Cornelio'] | 2019-09-16 | null | null | null | null | ['inductive-knowledge-graph-completion'] | ['knowledge-base'] | [ 2.12866828e-01 3.89872104e-01 -6.81824267e-01 -5.51304042e-01
-1.65850833e-01 -6.67889714e-01 7.60824800e-01 5.95420897e-01
1.01310067e-01 1.38048172e+00 7.62106897e-03 -5.66206932e-01
-7.52826452e-01 -1.21034765e+00 -7.16054618e-01 -7.01376945e-02
-2.40265921e-01 7.68773317e-01 6.72318518e-01 -3.59203011... | [9.049936294555664, 7.293849945068359] |
7ab4ed52-2962-4c30-b171-aa1065d74450 | cross-architecture-distillation-using | null | null | https://openreview.net/forum?id=o9DnX55PEAo | https://openreview.net/pdf?id=o9DnX55PEAo | Cross-Architecture Distillation Using Bidirectional CMOW Embeddings | Large pretrained language models (PreLMs) are revolutionizing natural language processing across all benchmarks. However, their sheer size is prohibitive for small laboratories or deployment on mobile devices. Approaches like pruning and distillation reduce the model size but typically retain the same model architectur... | ['Ansgar Scherp', 'Angelina Sonderecker', 'Henrik Ferdinand Nölscher', 'Christoph Meyer', 'Isabelle Cuber', 'Lukas Paul Achatius Galke'] | 2021-09-29 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [-2.62152050e-02 5.74430943e-01 -2.53624171e-01 -6.02134407e-01
-9.98106480e-01 -4.88420814e-01 4.58127707e-01 3.10584515e-01
-1.06194627e+00 6.38873279e-01 4.36765879e-01 -9.40865815e-01
4.03611928e-01 -9.42948699e-01 -8.90321791e-01 -1.93042025e-01
2.10238732e-02 6.45237267e-01 -1.00795865e-01 -5.65101624... | [10.755297660827637, 8.623562812805176] |
60931dd7-6e77-4ac9-a6fc-fd6a9a003044 | svnr-spatially-variant-noise-removal-with | 2306.16052 | null | https://arxiv.org/abs/2306.16052v1 | https://arxiv.org/pdf/2306.16052v1.pdf | SVNR: Spatially-variant Noise Removal with Denoising Diffusion | Denoising diffusion models have recently shown impressive results in generative tasks. By learning powerful priors from huge collections of training images, such models are able to gradually modify complete noise to a clean natural image via a sequence of small denoising steps, seemingly making them well-suited for sin... | ['Dani Lischinski', 'Daniel Cohen-Or', 'Alex Rav Acha', 'Assaf Zomet', 'Dana Berman', 'Yaron Brodsky', 'Naama Pearl'] | 2023-06-28 | null | null | null | null | ['image-denoising'] | ['computer-vision'] | [ 5.17995834e-01 -1.08074732e-01 6.06318951e-01 -2.03630000e-01
-7.15407014e-01 -5.10853767e-01 1.05531180e+00 -3.14228415e-01
-5.50338745e-01 4.04901922e-01 3.83154005e-01 5.34169376e-03
-1.04580827e-01 -8.16156805e-01 -6.23000979e-01 -1.34307289e+00
1.48838878e-01 2.31479630e-01 2.44027480e-01 -2.83510327... | [11.63367748260498, -2.323256254196167] |
3783eb96-efa9-4b32-ba2e-57dbe529e729 | geometry-aware-approaches-for-balancing | 2306.14872 | null | https://arxiv.org/abs/2306.14872v1 | https://arxiv.org/pdf/2306.14872v1.pdf | Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits | This paper is motivated by recent developments in the linear bandit literature, which have revealed a discrepancy between the promising empirical performance of algorithms such as Thompson sampling and Greedy, when compared to their pessimistic theoretical regret bounds. The challenge arises from the fact that while th... | ['Mohsen Bayati', 'Yuwei Luo'] | 2023-06-26 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 1.95260838e-01 1.74381316e-01 -6.51524663e-01 -3.85524601e-01
-1.10903955e+00 -9.09587026e-01 1.87123075e-01 1.78062335e-01
-2.63481885e-01 1.18121314e+00 1.32638782e-01 -6.82171166e-01
-9.47095871e-01 -5.86256623e-01 -9.24515069e-01 -7.97516584e-01
-1.07172221e-01 6.26017869e-01 -2.35664651e-01 2.11611494... | [4.538644313812256, 3.3029773235321045] |
c358d6ec-d032-44f0-a8be-2c7b952ba2cc | 190412619 | 1904.12619 | null | https://arxiv.org/abs/1904.12619v2 | https://arxiv.org/pdf/1904.12619v2.pdf | Multiple receptive fields and small-object-focusing weakly-supervised segmentation network for fast object detection | Object detection plays an important role in various visual applications. However, the precision and speed of detector are usually contradictory. One main reason for fast detectors' precision reduction is that small objects are hard to be detected. To address this problem, we propose a multiple receptive field and small... | ['Haifeng Shen', 'Yuan Zhao', 'Yingjie Yin', 'Xingang Wang', 'Siyang Sun', 'De Xu'] | 2019-04-19 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 6.39306474e-03 -3.37934107e-01 -1.92629680e-01 -4.17870939e-01
-2.93372840e-01 -2.69286543e-01 1.97798371e-01 7.87826777e-02
-9.94638443e-01 1.92177534e-01 -3.44783098e-01 -3.48917283e-02
4.43549454e-01 -7.81753659e-01 -7.61570752e-01 -8.44494522e-01
4.25318152e-01 9.08473581e-02 1.48494256e+00 -1.13803089... | [8.731654167175293, -0.5827187299728394] |
32812ca2-544a-4ef5-a75e-948fac5a6ca6 | analyse-de-la-r-egulation-de-la-longueur-dans | null | null | https://aclanthology.org/2020.jeptalnrecital-recital.5 | https://aclanthology.org/2020.jeptalnrecital-recital.5.pdf | Analyse de la r\'egulation de la longueur dans un syst\`eme neuronal de compression de phrase : une \'etude du mod\`ele LenInit (Investigating Length Regulation in a Sentence Compression Neural System : a Study on the LenInit Model) | La simplification de phrase vise {\`a} r{\'e}duire la complexit{\'e} d{'}une phrase tout en retenant son sens initial et sa grammaticalit{\'e}. En pratique, il est souvent attendu que la phrase produite soit plus courte que la phrase d{'}origine, et les mod{\`e}les qui int{\`e}grent un contr{\^o}le explicite de la long... | ['Fran{\\c{c}}ois Buet'] | 2020-06-01 | null | null | null | jeptalnrecital-2020-6 | ['sentence-compression'] | ['natural-language-processing'] | [ 3.14862758e-01 3.23681444e-01 3.47662359e-01 -9.01326463e-02
-1.83984861e-01 -1.21254098e+00 4.20403898e-01 2.74288625e-01
-7.04841673e-01 8.49388123e-01 -7.83198401e-02 -4.65290278e-01
-3.74097556e-01 -1.06371093e+00 -7.66641259e-01 -7.45373368e-01
-2.68403143e-01 3.01485658e-01 -1.17965117e-02 -7.67837226... | [14.103391647338867, 13.317919731140137] |
544425e9-1e7c-4335-af37-81755eb30987 | vehicle-occurrence-based-parking-space | 2306.09940 | null | https://arxiv.org/abs/2306.09940v1 | https://arxiv.org/pdf/2306.09940v1.pdf | Vehicle Occurrence-based Parking Space Detection | Smart-parking solutions use sensors, cameras, and data analysis to improve parking efficiency and reduce traffic congestion. Computer vision-based methods have been used extensively in recent years to tackle the problem of parking lot management, but most of the works assume that the parking spots are manually labeled,... | ['Rodrigo A. Krauel', 'João V. Fröhlich', 'Andre Gustavo Hochuli', 'Luiz S. Oliveira', 'Jeovane Honório Alves', 'Paulo R. Lisboa de Almeida'] | 2023-06-16 | null | null | null | null | ['management'] | ['miscellaneous'] | [-3.09413940e-01 3.67833786e-02 -1.83456555e-01 -3.08814436e-01
-4.83368576e-01 -1.90990075e-01 6.82627141e-01 -9.39689018e-03
-5.52345097e-01 7.09059179e-01 -2.68939257e-01 -3.91673923e-01
1.54116526e-01 -1.03382194e+00 -3.27346355e-01 -5.24283409e-01
1.80993095e-01 6.17136598e-01 5.51246583e-01 -1.78337917... | [8.043051719665527, -1.134684681892395] |
c0cec9dc-c77e-49cb-8513-c3326b24db6f | adding-context-to-source-code-representations | 2208.00203 | null | https://arxiv.org/abs/2208.00203v1 | https://arxiv.org/pdf/2208.00203v1.pdf | Adding Context to Source Code Representations for Deep Learning | Deep learning models have been successfully applied to a variety of software engineering tasks, such as code classification, summarisation, and bug and vulnerability detection. In order to apply deep learning to these tasks, source code needs to be represented in a format that is suitable for input into the deep learni... | ['Christoph Treude', 'Fuwei Tian'] | 2022-07-30 | null | null | null | null | ['code-classification', 'vulnerability-detection'] | ['computer-code', 'miscellaneous'] | [ 1.02535477e-02 3.33109587e-01 -3.38229150e-01 -5.41563451e-01
-4.06934112e-01 -5.11725008e-01 4.05006409e-01 9.54635918e-01
1.31272361e-01 1.11948945e-01 4.92673606e-01 -1.08717859e+00
1.10210240e-01 -8.30839217e-01 -6.44204140e-01 2.26537645e-01
-2.38020316e-01 -2.51976758e-01 2.41586730e-01 -2.32357413... | [7.614378929138184, 7.822826862335205] |
125139d6-d618-46a5-b262-f642a78da37d | mining-local-process-models | 1606.06066 | null | http://arxiv.org/abs/1606.06066v2 | http://arxiv.org/pdf/1606.06066v2.pdf | Mining Local Process Models | In this paper we describe a method to discover frequent behavioral patterns
in event logs. We express these patterns as \emph{local process models}. Local
process model mining can be positioned in-between process discovery and episode
/ sequential pattern mining. The technique presented in this paper is able to
learn b... | ['Wil M. P. van der Aalst', 'Reinder Haakma', 'Natalia Sidorova', 'Niek Tax'] | 2016-06-20 | null | null | null | null | ['model-discovery', 'sequential-pattern-mining'] | ['miscellaneous', 'natural-language-processing'] | [ 4.85185683e-01 3.81922632e-01 -8.63597170e-02 -6.74155802e-02
-1.34226531e-01 -5.02712429e-01 8.75121951e-01 6.70706332e-01
-3.14682946e-02 3.54886979e-01 1.60084087e-02 -4.90423679e-01
-8.30479622e-01 -1.11310267e+00 -1.68975428e-01 -2.37475947e-01
-1.02236784e+00 9.53966141e-01 6.16533935e-01 4.84380960... | [8.563179969787598, 6.049996852874756] |
a142cd74-6ef2-4c0f-a2b4-ba7284dffa98 | translate-to-disambiguate-zero-shot | 2304.13803 | null | https://arxiv.org/abs/2304.13803v1 | https://arxiv.org/pdf/2304.13803v1.pdf | Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models | Pretrained Language Models (PLMs) learn rich cross-lingual knowledge and can be finetuned to perform well on diverse tasks such as translation and multilingual word sense disambiguation (WSD). However, they often struggle at disambiguating word sense in a zero-shot setting. To better understand this contrast, we presen... | ['Luke Zettlemoyer', 'Terra Blevins', 'Haoqiang Kang'] | 2023-04-26 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.85329542e-01 -6.22459799e-02 -9.93094146e-01 -4.04020995e-01
-1.41421413e+00 -9.63690519e-01 8.65523577e-01 4.59406823e-01
-8.11577201e-01 7.26718903e-01 6.02385402e-01 -6.36402249e-01
1.66028053e-01 -6.78179085e-01 -7.44981706e-01 -9.50936452e-02
5.17539859e-01 6.21405005e-01 1.06491864e-01 -6.73484981... | [10.860685348510742, 9.735904693603516] |
bd967c3b-eaed-4590-bff5-6ed9ab8b963f | trans-inpainter-a-transformer-model-for-high | 2305.05385 | null | https://arxiv.org/abs/2305.05385v1 | https://arxiv.org/pdf/2305.05385v1.pdf | Trans-Inpainter: A Transformer Model for High Accuracy Image Inpainting from Channel State Information | Radio Frequency (RF) signal-based multimodal image inpainting has recently emerged as a promising paradigm to enhance the capability of distortion-free image restoration by integrating wireless and visual information from the identical physical environment and has potential applications in fields like security and surv... | ['Mohamed Wahib', 'Jihong Park', 'Mehdi Bennis', 'Takayuki Nishio', 'Shoki Ohta', 'Cheng Chen'] | 2023-05-09 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 5.61865389e-01 -3.73922765e-01 2.44909167e-01 -6.69977069e-02
-8.02617848e-01 -6.95417821e-01 2.08702937e-01 -5.58555007e-01
-1.89104512e-01 6.79742873e-01 4.39608455e-01 -2.61689454e-01
-3.33855510e-01 -6.10915124e-01 -9.00058508e-01 -1.04413414e+00
-3.79122607e-02 -3.76005918e-01 -1.60622403e-01 -1.04120426... | [10.551823616027832, -2.6252968311309814] |
5bd9e196-d8be-4a09-b1c4-ad20145d7126 | tofg-a-unified-and-fine-grained-environment | 2305.20068 | null | https://arxiv.org/abs/2305.20068v1 | https://arxiv.org/pdf/2305.20068v1.pdf | TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving | In autonomous driving, an accurate understanding of environment, e.g., the vehicle-to-vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks such as trajectory prediction and motion planning. Environment information comes from high-definition (HD) map and historical trajectories of vehicl... | ['JianPing Wang', 'Xinhong Chen', 'Yifan Zhang', 'Zihao Wen'] | 2023-05-31 | null | null | null | null | ['trajectory-prediction', 'graph-attention', 'motion-planning'] | ['computer-vision', 'graphs', 'robots'] | [-1.27634317e-01 4.87532876e-02 -7.02111661e-01 -6.53574467e-01
-4.65309709e-01 -2.47452214e-01 7.23699033e-01 9.72000957e-02
-1.82121158e-01 4.85768437e-01 4.34381008e-01 -7.92383373e-01
-1.65361747e-01 -1.04826665e+00 -8.11057031e-01 -4.85728145e-01
-1.61278665e-01 4.15599942e-01 6.64309859e-01 -3.70092690... | [5.963662147521973, 1.0061230659484863] |
808155df-4394-4763-9db0-f8dbaec0c58f | stance-classification-for-rumour-analysis-in | 1901.01911 | null | http://arxiv.org/abs/1901.01911v1 | http://arxiv.org/pdf/1901.01911v1.pdf | Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure | Analysing how people react to rumours associated with news in social media is
an important task to prevent the spreading of misinformation, which is nowadays
widely recognized as a dangerous tendency. In social media conversations, users
show different stances and attitudes towards rumourous stories. Some users take
a ... | ['Viviana Patti', 'Endang Wahyu Pamungkas', 'Valerio Basile'] | 2019-01-07 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-2.39091739e-01 4.43530440e-01 -4.26646650e-01 -2.90621668e-01
-2.08353624e-01 -2.37979531e-01 1.21012735e+00 7.05124319e-01
-1.98136851e-01 7.46040761e-01 9.57506061e-01 -5.57330921e-02
3.57440710e-01 -7.75679171e-01 -1.65044948e-01 -5.04882157e-01
1.95833251e-01 4.54228550e-01 1.67659208e-01 -8.54155183... | [8.228943824768066, 10.116671562194824] |
ad7fffd2-22d7-44f5-abdb-9ee405e0055b | apsense-data-driven-algorithm-in-ppg-based | 2306.10863 | null | https://arxiv.org/abs/2306.10863v1 | https://arxiv.org/pdf/2306.10863v1.pdf | ApSense: Data-driven Algorithm in PPG-based Sleep Apnea Sensing | In this paper, we utilized obstructive sleep apnea and cardiovascular disease-related photoplethysmography (PPG) features in constructing the input to deep learning (DL). The features are pulse wave amplitude (PWA), beat-to-beat or RR interval, a derivative of PWA, a derivative of RR interval, systolic phase duration, ... | ['Theerawit Wilaiprasitporn', 'Thapanun Sudhawiyangkul', 'Thee Mateepithaktham', 'Phoomraphee Luenam', 'Narin Kunaseth', 'Thitikorn Keawlee', 'Punnawish Thuwajit', 'Guntitat Sawadwuthikul', 'Tanut Choksatchawathi'] | 2023-06-19 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.72996144e-02 1.92947686e-02 -7.70660192e-02 -6.94025040e-01
-3.93721461e-01 -4.89133537e-01 -2.57854313e-01 -2.70822421e-02
-3.83173764e-01 9.52581763e-01 -1.98234972e-02 -4.81299102e-01
5.90497404e-02 -6.15952611e-01 8.17941129e-02 -7.57040858e-01
-4.62607831e-01 1.96102262e-01 -3.81675899e-01 4.95460331... | [13.863937377929688, 3.0156333446502686] |
4fa9fe5c-5102-469e-b853-15361a9137ab | predicting-multiple-sclerosis-disease | 2304.04062 | null | https://arxiv.org/abs/2304.04062v1 | https://arxiv.org/pdf/2304.04062v1.pdf | Predicting multiple sclerosis disease severity with multimodal deep neural networks | Multiple Sclerosis (MS) is a chronic disease developed in human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale (EDSS), composed of several functional sub-scores. Early and accurate classification of ... | ['Shayan Shams', 'Elmer V. Bernstam', 'Xiaoqian Jiang', 'John A. Lincoln', 'Kai Zhang'] | 2023-04-08 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 2.00316757e-01 -3.05984646e-01 -4.00754422e-01 -5.05397618e-01
-8.88451815e-01 -3.46531391e-01 3.65641564e-01 6.73801959e-01
-6.63805962e-01 7.66296029e-01 5.81538022e-01 -2.64916033e-01
-6.05738282e-01 -5.67877591e-01 -1.02288112e-01 -3.18243474e-01
-5.08627534e-01 8.70666385e-01 -1.96439028e-01 9.72121656... | [14.25850772857666, -1.7436529397964478] |
d999ed70-5399-4f33-b0c5-cf0ad4bc0902 | unidexgrasp-universal-robotic-dexterous | 2303.00938 | null | https://arxiv.org/abs/2303.00938v2 | https://arxiv.org/pdf/2303.00938v2.pdf | UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy | In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of categories and even the unseen. Inspired by successful pipelines used... | ['He Wang', 'Li Yi', 'Tengyu Liu', 'Jiayi Chen', 'Yijia Weng', 'Haoran Geng', 'Ruicheng Wang', 'Hao Shen', 'Zikang Shan', 'Haoran Liu', 'Jialiang Zhang', 'Weikang Wan', 'Yinzhen Xu'] | 2023-03-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_UniDexGrasp_Universal_Robotic_Dexterous_Grasping_via_Learning_Diverse_Proposal_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_UniDexGrasp_Universal_Robotic_Dexterous_Grasping_via_Learning_Diverse_Proposal_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-planning'] | ['robots'] | [-7.41247833e-02 5.22409715e-02 -1.91885307e-01 -1.96939901e-01
-9.48859334e-01 -1.01221228e+00 3.24679196e-01 -1.09466128e-01
-1.94306716e-01 3.34113747e-01 -3.43115509e-01 -2.67637402e-01
-3.21301758e-01 -5.86240947e-01 -1.34131157e+00 -9.97768283e-01
-2.76898712e-01 1.11267495e+00 2.31244802e-01 -1.83938608... | [5.722727298736572, -0.7932260036468506] |
240c8387-0383-427c-9e74-69fd0fa5c273 | feature-selection-with-distance-correlation | 2212.00046 | null | https://arxiv.org/abs/2212.00046v1 | https://arxiv.org/pdf/2212.00046v1.pdf | Feature Selection with Distance Correlation | Choosing which properties of the data to use as input to multivariate decision algorithms -- a.k.a. feature selection -- is an important step in solving any problem with machine learning. While there is a clear trend towards training sophisticated deep networks on large numbers of relatively unprocessed inputs (so-call... | ['David Shih', 'Gregor Kasieczka', 'Ranit Das'] | 2022-11-30 | null | null | null | null | ['automated-feature-engineering', 'feature-engineering'] | ['methodology', 'methodology'] | [ 1.16334789e-01 -2.72548258e-01 -1.48309126e-01 -9.37716663e-01
-9.16987360e-01 -5.66933692e-01 5.80862761e-01 5.17874241e-01
-5.17967105e-01 6.22706771e-01 -5.55275120e-02 -3.24629635e-01
-6.03207111e-01 -8.35772634e-01 -4.58456755e-01 -6.91539347e-01
-5.29630482e-01 5.67685008e-01 1.44268051e-01 -2.34003574... | [8.113638877868652, 4.39579963684082] |
726047fb-3dfa-47ff-92ae-708e8dca33c2 | representing-prior-knowledge-using-randomly | 2111.10686 | null | https://arxiv.org/abs/2111.10686v2 | https://arxiv.org/pdf/2111.10686v2.pdf | Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection | The single-hidden-layer Randomly Weighted Feature Network (RWFN) introduced by Hong and Pavlic (2021) was developed as an alternative to neural tensor network approaches for relational learning tasks. Its relatively small footprint combined with the use of two randomized input projections -- an insect-brain-inspired in... | ['Theodore P. Pavlic', 'Jinyung Hong'] | 2021-11-20 | null | https://openreview.net/forum?id=iwoNpozn10l | https://openreview.net/pdf?id=iwoNpozn10l | aaai-workshop-clear-2022-2 | ['visual-relationship-detection', 'tensor-networks', 'predicate-detection', 'relational-reasoning'] | ['computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [ 1.48553178e-01 5.40694714e-01 -4.09967571e-01 -4.31423873e-01
1.72770828e-01 -1.15037896e-01 7.55192876e-01 -3.66695151e-02
-2.00411722e-01 4.67759073e-01 1.64167523e-01 -3.42987865e-01
-5.78999221e-01 -1.08412743e+00 -5.97146690e-01 -4.04534012e-01
-2.40005270e-01 5.40842772e-01 3.27096581e-01 -3.85931790... | [10.349385261535645, 2.288921594619751] |
cb7e33a3-fb2a-4309-bcd4-7ff7e9a24746 | read-highlight-and-summarize-a-hierarchical | 1910.03177 | null | https://arxiv.org/abs/1910.03177v2 | https://arxiv.org/pdf/1910.03177v2.pdf | Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach | Traditional sequence-to-sequence (seq2seq) models and other variations of the attention-mechanism such as hierarchical attention have been applied to the text summarization problem. Though there is a hierarchy in the way humans use language by forming paragraphs from sentences and sentences from words, hierarchical mod... | ['Rajeev Bhatt Ambati', 'Prasenjit Mitra', 'Saptarashmi Bandyopadhyay'] | 2019-10-08 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 4.43804532e-01 2.88821608e-01 -1.69491395e-01 -2.80568093e-01
-7.55224645e-01 -2.95823097e-01 4.53783929e-01 3.49822581e-01
-5.53741693e-01 1.14208198e+00 9.82080519e-01 -1.28822774e-01
1.51749790e-01 -7.62891352e-01 -9.39861834e-01 -5.05394042e-01
6.37640432e-02 4.64916140e-01 1.41161799e-01 -5.79694569... | [12.39097785949707, 9.416102409362793] |
1fd4e23c-8927-4ddf-8a89-9b88208d4bac | model-based-reinforcement-learning-for-6 | 2304.10000 | null | https://arxiv.org/abs/2304.10000v1 | https://arxiv.org/pdf/2304.10000v1.pdf | Model Based Reinforcement Learning for Personalized Heparin Dosing | A key challenge in sequential decision making is optimizing systems safely under partial information. While much of the literature has focused on the cases of either partially known states or partially known dynamics, it is further exacerbated in cases where both states and dynamics are partially known. Computing hepar... | ['Yonatan Mintz', 'Qinyang He'] | 2023-04-19 | null | null | null | null | ['model-based-reinforcement-learning'] | ['reasoning'] | [ 1.09678164e-01 -6.16824403e-02 -4.43532765e-01 5.87960370e-02
-3.80912960e-01 -7.33281136e-01 2.09644020e-01 5.50018668e-01
-2.47880816e-01 1.05838072e+00 1.16401806e-01 -6.95876300e-01
-5.48898816e-01 -6.76924109e-01 -4.41497773e-01 -6.15096331e-01
-2.88630575e-01 8.56438577e-01 -8.82783234e-02 -3.04918379... | [4.011250019073486, 2.739718198776245] |
046fe903-2a64-4aad-8b50-6f8cdcb19c4f | multi-objective-conflict-based-search-using | 2108.00745 | null | https://arxiv.org/abs/2108.00745v3 | https://arxiv.org/pdf/2108.00745v3.pdf | Multi-objective Conflict-based Search Using Safe-interval Path Planning | This paper addresses a generalization of the well known multi-agent path finding (MAPF) problem that optimizes multiple conflicting objectives simultaneously such as travel time and path risk. This generalization, referred to as multi-objective MAPF (MOMAPF), arises in several applications ranging from hazardous materi... | ['Maxim Likhachev', 'Howie Choset', 'Sivakumar Rathinam', 'Zhongqiang Ren'] | 2021-08-02 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 7.21416473e-02 2.20316276e-02 -2.54557788e-01 6.85536787e-02
-7.49365270e-01 -5.79264581e-01 1.97951421e-01 5.39452791e-01
-3.05939585e-01 1.37461376e+00 -2.33768076e-01 -2.78975040e-01
-1.21731126e+00 -9.13536787e-01 -5.69546998e-01 -7.78083026e-01
-6.57320440e-01 8.01982880e-01 4.85617667e-01 -5.16044676... | [4.992466926574707, 1.8822332620620728] |
0f0d542f-6884-488c-9c30-7872e8846c8e | tripose-a-weakly-supervised-3d-human-pose | 2105.06599 | null | https://arxiv.org/abs/2105.06599v1 | https://arxiv.org/pdf/2105.06599v1.pdf | TriPose: A Weakly-Supervised 3D Human Pose Estimation via Triangulation from Video | Estimating 3D human poses from video is a challenging problem. The lack of 3D human pose annotations is a major obstacle for supervised training and for generalization to unseen datasets. In this work, we address this problem by proposing a weakly-supervised training scheme that does not require 3D annotations or calib... | ['Z. Jane Wang', 'Rabab Ward', 'Helge Rhodin', 'Ahmad Rezaei', 'Mohsen Gholami'] | 2021-05-14 | null | null | null | null | ['weakly-supervised-3d-human-pose-estimation'] | ['computer-vision'] | [-7.18478635e-02 -1.07238311e-02 -1.96440935e-01 -4.02538657e-01
-7.92142928e-01 -5.12362659e-01 3.43313962e-01 -4.13219750e-01
-6.94356084e-01 6.04613900e-01 1.83666930e-01 1.30621910e-01
2.86563873e-01 -3.74043643e-01 -9.83578384e-01 -5.84914327e-01
2.50100270e-02 6.78444326e-01 2.38905907e-01 -2.45891083... | [7.005630016326904, -0.916429340839386] |
ddc18c1b-64a3-46b9-b17d-dd3794d80c10 | nasgec-a-multi-domain-chinese-grammatical | 2305.16023 | null | https://arxiv.org/abs/2305.16023v1 | https://arxiv.org/pdf/2305.16023v1.pdf | NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts | We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains. Previous CGEC research primarily focuses on correcting texts from a single domain, especially learner essays. To broaden the target domain, we annotate multiple refere... | ['Min Zhang', 'Fei Huang', 'Chen Li', 'Zhenghua Li', 'Haochen Jiang', 'Bo Zhang', 'Yue Zhang'] | 2023-05-25 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [-2.75802054e-02 -2.14615669e-02 -6.46039173e-02 -3.38636607e-01
-1.13657486e+00 -6.84507012e-01 2.97376424e-01 3.95083278e-01
-5.35711467e-01 1.00719142e+00 4.77158099e-01 -7.83152044e-01
1.76952705e-01 -4.43703204e-01 -6.15078092e-01 4.17248113e-03
6.61946595e-01 2.50795364e-01 1.82267413e-01 -4.23509836... | [11.052448272705078, 10.735769271850586] |
31c373ce-b69b-4a53-8916-aea4b0a6e170 | a-dual-source-approach-for-3d-pose-estimation | 1509.06720 | null | http://arxiv.org/abs/1509.06720v2 | http://arxiv.org/pdf/1509.06720v2.pdf | A Dual-Source Approach for 3D Pose Estimation from a Single Image | One major challenge for 3D pose estimation from a single RGB image is the
acquisition of sufficient training data. In particular, collecting large
amounts of training data that contain unconstrained images and are annotated
with accurate 3D poses is infeasible. We therefore propose to use two
independent training sourc... | ['Björn Krüger', 'Hashim Yasin', 'Andreas Weber', 'Umar Iqbal', 'Juergen Gall'] | 2015-09-22 | a-dual-source-approach-for-3d-pose-estimation-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Yasin_A_Dual-Source_Approach_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Yasin_A_Dual-Source_Approach_CVPR_2016_paper.pdf | cvpr-2016-6 | ['pose-retrieval'] | ['computer-vision'] | [ 1.74440265e-01 -3.12622525e-02 -1.46702215e-01 -1.02056295e-01
-1.32725108e+00 -7.90770590e-01 2.39807785e-01 -1.86563417e-01
-5.97647250e-01 4.20523465e-01 -3.74678075e-02 -9.15080979e-02
2.59303451e-01 -4.62422907e-01 -8.59750211e-01 -3.75752389e-01
2.01243863e-01 8.01876664e-01 5.67838252e-01 3.99304777... | [6.95778751373291, -1.1618390083312988] |
4900cd9b-8096-4d47-a03e-01d4c7407769 | knowledge-transfer-for-on-device-speech | 2210.14977 | null | https://arxiv.org/abs/2210.14977v3 | https://arxiv.org/pdf/2210.14977v3.pdf | Knowledge Transfer For On-Device Speech Emotion Recognition with Neural Structured Learning | Speech emotion recognition (SER) has been a popular research topic in human-computer interaction (HCI). As edge devices are rapidly springing up, applying SER to edge devices is promising for a huge number of HCI applications. Although deep learning has been investigated to improve the performance of SER by training co... | ['Björn W. Schuller', 'Kun Qian', 'Thanh Tam Nguyen', 'Zhao Ren', 'Yi Chang'] | 2022-10-26 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [ 2.97150999e-01 3.82660598e-01 -1.79946795e-01 -3.84632409e-01
-5.70054889e-01 6.79492503e-02 2.85685450e-01 -3.80708762e-02
-1.77946314e-01 4.42997426e-01 1.07420221e-01 -3.20171893e-01
4.30886656e-01 -6.99937403e-01 -8.50852907e-01 -3.84692758e-01
-5.09668812e-02 9.82911214e-02 6.75303861e-02 -3.82702500... | [13.85413932800293, 5.894458770751953] |
4420d3f4-2cef-4098-b087-4914f22e3cad | fast-point-cloud-generation-with-straight | 2212.01747 | null | https://arxiv.org/abs/2212.01747v1 | https://arxiv.org/pdf/2212.01747v1.pdf | Fast Point Cloud Generation with Straight Flows | Diffusion models have emerged as a powerful tool for point cloud generation. A key component that drives the impressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While beneficial, the complexity of learning steps has limited its applications to many 3D rea... | ['Qiang Liu', 'Vikas Chandra', 'Raghuraman Krishnamoorthi', 'Rakesh Ranjan', 'Yunyang Xiong', 'Xingchao Liu', 'Chengyue Gong', 'Dilin Wang', 'Lemeng Wu'] | 2022-12-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Fast_Point_Cloud_Generation_With_Straight_Flows_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Fast_Point_Cloud_Generation_With_Straight_Flows_CVPR_2023_paper.pdf | cvpr-2023-1 | ['point-cloud-completion', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [-7.86946267e-02 -8.16979259e-02 1.48498327e-01 1.08950034e-01
-1.15378511e+00 -7.51016200e-01 9.86312032e-01 2.74995938e-02
-1.29642099e-01 4.88989413e-01 1.09823138e-01 -6.37905955e-01
1.45398915e-01 -9.15726304e-01 -8.34039092e-01 -4.20687020e-01
-9.03121680e-02 7.21715212e-01 3.25369239e-01 -2.90037096... | [8.943395614624023, -3.623363971710205] |
eb01bc4f-7c3f-49b6-86a7-24af9c046c51 | self-paced-deep-regression-forests-with-1 | 2112.06455 | null | https://arxiv.org/abs/2112.06455v8 | https://arxiv.org/pdf/2112.06455v8.pdf | Self-Paced Deep Regression Forests with Consideration of Ranking Fairness | Deep discriminative models (DDMs), e.g. deep regression forests and deep decision forests, have been extensively studied recently to solve problems such as facial age estimation, head pose estimation, etc.. Due to a shortage of well-labeled data that does not have noise and imbalanced distribution problems, learning DD... | ['Zenglin Xu', 'Yali Zheng', 'Yazhou Ren', 'Mingming Meng', 'Lili Pan'] | 2021-12-13 | null | null | null | null | ['head-pose-estimation', 'gaze-estimation', 'age-estimation', 'age-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [-1.09576195e-01 3.78324315e-02 -4.45062369e-01 -7.16211379e-01
-4.21828985e-01 1.32177277e-02 4.10185069e-01 -1.34320006e-01
-4.60999042e-01 9.95646954e-01 1.17022410e-01 5.57116531e-02
-1.08495377e-01 -5.90448439e-01 -3.30093354e-01 -9.13810492e-01
3.18872482e-01 4.19452369e-01 -6.13216236e-02 -9.99014452... | [13.575993537902832, 0.9156275987625122] |
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