paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2c2dcf09-3f4c-4e7f-b1e1-934bd3e56e9d | depth-pruning-with-auxiliary-networks-for | 2204.10546 | null | https://arxiv.org/abs/2204.10546v1 | https://arxiv.org/pdf/2204.10546v1.pdf | Depth Pruning with Auxiliary Networks for TinyML | Pruning is a neural network optimization technique that sacrifices accuracy in exchange for lower computational requirements. Pruning has been useful when working with extremely constrained environments in tinyML. Unfortunately, special hardware requirements and limited study on its effectiveness on already compact mod... | ['Rowel Atienza', 'Josen Daniel De Leon'] | 2022-04-22 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 2.26375729e-01 4.12743241e-01 -8.44896957e-02 -4.51218218e-01
-2.33786345e-01 -8.08810145e-02 2.04494208e-01 4.74736273e-01
-9.71459508e-01 6.10463202e-01 -3.78291935e-01 -7.89724290e-01
-1.71032120e-02 -6.71691716e-01 -6.98403478e-01 -2.27832943e-01
-1.59947462e-02 -1.63844615e-01 3.91204685e-01 9.29388031... | [8.377518653869629, 2.7998499870300293] |
7cc3fe9b-ee7f-48f3-bb31-2066f8a4ec26 | a-new-method-using-deep-learning-to-predict | 2305.02475 | null | https://arxiv.org/abs/2305.02475v1 | https://arxiv.org/pdf/2305.02475v1.pdf | A new method using deep learning to predict the response to cardiac resynchronization therapy | Background. Clinical parameters measured from gated single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) have value in predicting cardiac resynchronization therapy (CRT) patient outcomes, but still show limitations. The purpose of this study is to combine clinical variables, features from... | ['Weihua Zhou', 'Amalia Peix', 'Jiangang Zou', 'Ernest V. Garciaf', 'Diana Paeze', 'Claudio T Mesquitad', 'Quiying Sha', 'Xinwei Zhang', 'Chen Zhao', 'Zhuo He', 'Kristoffer Larsena'] | 2023-05-04 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 9.65372100e-02 -1.37193024e-01 -5.30728638e-01 -4.09297824e-01
-1.02174091e+00 -7.11611390e-01 -4.57717180e-02 2.64281929e-01
-3.22853655e-01 9.85898018e-01 4.16460812e-01 -7.12224126e-01
-2.90201008e-01 -5.63061476e-01 -1.40941799e-01 -6.27698183e-01
-4.12756026e-01 1.00052595e+00 -2.86019474e-01 4.59774643... | [14.397130966186523, 3.3160696029663086] |
51d7b11e-32c0-4280-8d60-736abded083b | u-net-convolutional-networks-for-biomedical | 1505.04597 | null | http://arxiv.org/abs/1505.04597v1 | http://arxiv.org/pdf/1505.04597v1.pdf | U-Net: Convolutional Networks for Biomedical Image Segmentation | There is large consent that successful training of deep networks requires
many thousand annotated training samples. In this paper, we present a network
and training strategy that relies on the strong use of data augmentation to use
the available annotated samples more efficiently. The architecture consists of
a contrac... | ['Thomas Brox', 'Philipp Fischer', 'Olaf Ronneberger'] | 2015-05-18 | null | null | null | null | ['thermal-image-segmentation', 'dichotomous-image-segmentation', 'video-polyp-segmentation', 'skin-cancer-segmentation', 'multi-tissue-nucleus-segmentation', 'pancreas-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'medical', 'medical', 'medical'] | [ 3.04212719e-01 3.01219225e-02 4.02780205e-01 -3.77031565e-01
-6.36735022e-01 -5.29128492e-01 2.17638880e-01 -4.51594740e-02
-1.27730668e+00 1.03058481e+00 -6.06918454e-01 -2.92492837e-01
2.07149982e-01 -2.87554651e-01 -9.01741028e-01 -8.30298185e-01
-1.37677118e-01 7.58579910e-01 6.19913220e-01 1.57631487... | [14.312260627746582, -3.115811824798584] |
61cda6ca-a051-44ac-ba9b-c33247cbdcb6 | sound-event-localization-and-detection-for | 2209.01802 | null | https://arxiv.org/abs/2209.01802v2 | https://arxiv.org/pdf/2209.01802v2.pdf | Sound Event Localization and Detection for Real Spatial Sound Scenes: Event-Independent Network and Data Augmentation Chains | Sound event localization and detection (SELD) is a joint task of sound event detection and direction-of-arrival estimation. In DCASE 2022 Task 3, types of data transform from computationally generated spatial recordings to recordings of real-sound scenes. Our system submitted to the DCASE 2022 Task 3 is based on our pr... | ['Jun Yang', 'Mark D. Plumbley', 'Feiran Yang', 'Qiuqiang Kong', 'Ming Wu', 'Yin Cao', 'Jinbo Hu'] | 2022-09-05 | null | null | null | null | ['sound-event-detection', 'direction-of-arrival-estimation', 'room-impulse-response', 'sound-event-localization-and-detection'] | ['audio', 'audio', 'audio', 'audio'] | [ 2.29087025e-01 -6.22316957e-01 9.33398485e-01 -4.03035611e-01
-1.30452955e+00 -6.96476281e-01 3.37114125e-01 -3.06532457e-02
-5.58257639e-01 6.02832854e-01 3.95511359e-01 -8.45668763e-02
-3.43411565e-01 -3.60337853e-01 -7.94325292e-01 -6.27699018e-01
-5.11546910e-01 8.60195160e-02 4.81698126e-01 -1.64571539... | [15.149701118469238, 5.244039535522461] |
883e0833-6b58-40c9-9161-ccd8d8cd4afc | challenges-and-applications-of-automated | 2108.07865 | null | https://arxiv.org/abs/2108.07865v1 | https://arxiv.org/pdf/2108.07865v1.pdf | Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report | This workshop is the fourth issue of a series of workshops on automatic extraction of socio-political events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The ... | ['Erdem Yörük', 'Reyyan Yeniterzi', 'Jakub Piskorski', 'Vanni Zavarella', 'Hristo Tanev', 'Ali Hürriyetoğlu'] | 2021-08-17 | null | https://aclanthology.org/2021.case-1.1 | https://aclanthology.org/2021.case-1.1.pdf | acl-case-2021-8 | ['learning-word-embeddings'] | ['methodology'] | [-1.95059463e-01 1.34194707e-02 -3.72362971e-01 -4.94543850e-01
-1.20769846e+00 -6.83571935e-01 1.31614351e+00 7.86660194e-01
-6.79355860e-01 6.99885964e-01 1.33574140e+00 -4.77954373e-03
5.07162809e-02 -8.26015532e-01 -3.66192251e-01 -2.26738781e-01
-6.54463917e-02 8.66606534e-01 2.91455865e-01 -7.76019573... | [9.061468124389648, 9.736250877380371] |
4526414b-678b-4b8e-b6ed-f5707003c8d9 | contextualized-word-embeddings-enhanced-event | 1904.11942 | null | http://arxiv.org/abs/1904.11942v1 | http://arxiv.org/pdf/1904.11942v1.pdf | Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding | Learning causal and temporal relationships between events is an important
step towards deeper story and commonsense understanding. Though there are
abundant datasets annotated with event relations for story comprehension, many
have no empirical results associated with them. In this work, we establish
strong baselines f... | ['Rujun Han', 'Bashar Alhafni', 'Nanyun Peng', 'Mengyue Liang'] | 2019-04-26 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 3.33288193e-01 1.54322103e-01 -6.94492400e-01 -4.88571256e-01
-5.85191309e-01 -5.15577734e-01 1.22093022e+00 6.49381876e-01
-5.02383769e-01 9.42953944e-01 1.23884213e+00 -2.14048192e-01
-2.18128592e-01 -1.01006806e+00 -6.04876459e-01 -4.84255637e-04
-4.86743987e-01 5.19928448e-02 2.79883593e-01 -4.54934150... | [10.885753631591797, 8.911446571350098] |
238656a3-6a60-4d3a-8768-9fd5e5671e6d | task-specific-experimental-design-for | 2306.05484 | null | https://arxiv.org/abs/2306.05484v1 | https://arxiv.org/pdf/2306.05484v1.pdf | Task-specific experimental design for treatment effect estimation | Understanding causality should be a core requirement of any attempt to build real impact through AI. Due to the inherent unobservability of counterfactuals, large randomised trials (RCTs) are the standard for causal inference. But large experiments are generically expensive, and randomisation carries its own costs, e.g... | ['Christopher Frye', 'Ilya Feige', 'Gary Willis', 'Alexander Adam', 'Tobias Schwedes', 'Kim Moore', 'Bethany Connolly'] | 2023-06-08 | null | null | null | null | ['causal-inference', 'experimental-design', 'marketing', 'causal-inference'] | ['knowledge-base', 'methodology', 'miscellaneous', 'miscellaneous'] | [ 5.26823878e-01 2.15946823e-01 -1.01674843e+00 -3.84902954e-01
-7.71950603e-01 -6.78620219e-01 8.86848569e-01 2.92954922e-01
-5.44424713e-01 9.94258225e-01 6.69416785e-01 -1.03182387e+00
-5.57753980e-01 -6.49044514e-01 -1.07181323e+00 -3.30860287e-01
-4.12191689e-01 4.74064589e-01 -2.88725406e-01 1.13978237... | [8.076125144958496, 5.3788652420043945] |
2291b0dd-a845-42cc-b4b8-17ad69fc80ec | youngsheldon-at-semeval-2021-task-5-fine | null | null | https://aclanthology.org/2021.semeval-1.130 | https://aclanthology.org/2021.semeval-1.130.pdf | YoungSheldon at SemEval-2021 Task 5: Fine-tuning Pre-trained Language Models for Toxic Spans Detection using Token classification Objective | In this paper, we describe our system used for SemEval 2021 Task 5: Toxic Spans Detection. Our proposed system approaches the problem as a token classification task. We trained our model to find toxic words and concatenate their spans to predict the toxic spans within a sentence. We fine-tuned Pre-trained Language Mode... | ['W.b. Vasantha', 'Ilanthenral Kandasamy', 'Mayukh Sharma'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-3.35617691e-01 -2.05504358e-01 -1.08149379e-01 -5.49943782e-02
-1.04502809e+00 -6.45727634e-01 6.85388684e-01 6.70964956e-01
-7.65910983e-01 9.14003968e-01 3.59375894e-01 -3.65106732e-01
6.49564564e-02 -6.67948306e-01 -4.37785953e-01 -3.89337093e-01
-2.71709591e-01 2.28658184e-01 -1.40050352e-01 -2.79072791... | [8.93812084197998, 10.647504806518555] |
a3c1c1dd-3ba8-4121-9040-75fb8ecd8a30 | engage-the-public-poll-question-generation | null | null | https://aclanthology.org/2021.acl-long.3 | https://aclanthology.org/2021.acl-long.3.pdf | Engage the Public: Poll Question Generation for Social Media Posts | This paper presents a novel task to generate poll questions for social media posts. It offers an easy way to hear the voice from the public and learn from their feelings to important social topics. While most related work tackles formal languages (e.g., exam papers), we generate poll questions for short and colloquial ... | ['Lemao Liu', 'Baolin Peng', 'Jing Li', 'Yuji Zhang', 'Keyang Ding', 'Zexin Lu'] | 2021-08-01 | null | null | null | acl-2021-5 | ['poll-generation'] | ['natural-language-processing'] | [ 1.34899855e-01 6.05967164e-01 -3.15338284e-01 -5.49755812e-01
-1.50955451e+00 -6.45918190e-01 9.38576281e-01 -4.45570722e-02
-1.88504174e-01 1.07593191e+00 9.40074563e-01 -5.82685947e-01
3.65667015e-01 -8.55354369e-01 -4.99112040e-01 -2.17135191e-01
7.13362753e-01 6.51520491e-01 1.51674375e-01 -5.18553436... | [12.141853332519531, 8.37349796295166] |
8899b258-5af3-4726-b491-ccfcbf3636bb | pattern-mining-for-anomaly-detection-in | 2306.10857 | null | https://arxiv.org/abs/2306.10857v1 | https://arxiv.org/pdf/2306.10857v1.pdf | Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement | In the context of public procurement, several indicators called red flags are used to estimate fraud risk. They are computed according to certain contract attributes and are therefore dependent on the proper filling of the contract and award notices. However, these attributes are very often missing in practice, which p... | ['Christine Largeron', 'Vincent Labatut', 'Rosa Figueiredo', 'Lucas Potin'] | 2023-06-19 | null | null | null | null | ['anomaly-detection', 'fraud-detection'] | ['methodology', 'miscellaneous'] | [ 6.49970025e-02 1.55060425e-01 -3.49651396e-01 -2.57098883e-01
-2.51007855e-01 -5.21986723e-01 3.85976523e-01 7.56479740e-01
4.60790545e-02 5.65720439e-01 -2.97900438e-02 -4.17014480e-01
-5.54224610e-01 -1.31276274e+00 -5.42857647e-01 -6.96730137e-01
-4.08294857e-01 5.78834951e-01 1.09695673e-01 -1.60184965... | [6.86381721496582, 5.820218086242676] |
4dd04bbd-78f9-442d-be29-b3a3183a320b | osteosarcoma-tumor-detection-using-transfer | 2305.09660 | null | https://arxiv.org/abs/2305.09660v1 | https://arxiv.org/pdf/2305.09660v1.pdf | Osteosarcoma Tumor Detection using Transfer Learning Models | The field of clinical image analysis has been applying transfer learning models increasingly due to their less computational complexity, better accuracy etc. These are pre-trained models that don't require to be trained from scratch which eliminates the necessity of large datasets. Transfer learning models are mostly u... | ['Khandaker Tabin Hasan', 'Raisa Fairooz Meem'] | 2023-05-16 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-1.93624243e-01 2.70723552e-01 -1.84054375e-01 1.36999384e-01
-6.24427795e-01 1.45726010e-01 3.32206786e-01 2.89576501e-01
-7.76813030e-01 1.07588875e+00 3.57749201e-02 -3.21086317e-01
-9.16091129e-02 -8.46772194e-01 -3.63287836e-01 -8.27257276e-01
1.28351361e-01 6.18017495e-01 7.33175218e-01 -1.40788509... | [15.174031257629395, -2.7532410621643066] |
ff2b17b5-2cc8-42d4-8d16-c5d49bc04d6e | fusion-of-complex-networks-based-global-and | 2106.10701 | null | https://arxiv.org/abs/2106.10701v1 | https://arxiv.org/pdf/2106.10701v1.pdf | Fusion of Complex Networks-based Global and Local Features for Texture Classification | To realize accurate texture classification, this article proposes a complex networks (CN)-based multi-feature fusion method to recognize texture images. Specifically, we propose two feature extractors to detect the global and local features of texture images respectively. To capture the global features, we first map a ... | ['Zhengrui Huang'] | 2021-06-20 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 3.35955292e-01 -7.13382006e-01 -2.01957956e-01 -4.48183745e-01
-6.79942071e-01 -2.69054603e-02 2.98604965e-01 3.49523174e-03
-2.33211979e-01 4.07813907e-01 -1.84926882e-01 1.53009862e-01
-5.27159214e-01 -1.25214553e+00 -4.55101013e-01 -1.30340528e+00
-1.80363730e-01 -3.00403446e-01 3.86647284e-01 -3.93132260... | [10.35673999786377, -0.3542247712612152] |
3383c7e2-036a-4d78-988e-0690fdc1e78d | 3d-color-homography-model-for-photo-realistic | null | null | https://link.springer.com/article/10.1007/s00371-017-1462-x | https://link.springer.com/article/10.1007/s00371-017-1462-x | 3D color homography model for photo-realistic color transfer re-coding | Color transfer is an image editing process that naturally transfers the color theme of a source image to a target image. In this paper, we propose a 3D color homography model which approximates photo-realistic color transfer algorithm as a combination of a 3D perspective transform and a mean intensity mapping. A key ad... | ['Han Gong; Graham Finlayson; Robert Fisher; Fufu Fang'] | 2019-03-01 | null | null | null | the-visual-computer-2019-3 | ['image-stitching'] | ['computer-vision'] | [ 4.61270809e-01 -4.15234476e-01 3.13000351e-01 6.41984865e-02
-4.62967545e-01 -8.23426008e-01 5.55064499e-01 -6.12535715e-01
-2.11510770e-02 3.23689938e-01 -1.34589344e-01 -3.67806137e-01
3.95414591e-01 -6.20102346e-01 -9.80476677e-01 -3.95766318e-01
4.56420660e-01 2.24281877e-01 3.70902508e-01 -3.58424515... | [9.72194766998291, -2.412860155105591] |
731b74c0-5568-4de9-965f-c3a492fe8863 | lung-nodule-classification-by-the-combination | 1712.02198 | null | http://arxiv.org/abs/1712.02198v2 | http://arxiv.org/pdf/1712.02198v2.pdf | Lung Nodule Classification by the Combination of Fusion Classifier and Cascaded Convolutional Neural Networks | Lung nodule classification is a class imbalanced problem, as nodules are
found with much lower frequency than non-nodules. In the class imbalanced
problem, conventional classifiers tend to be overwhelmed by the majority class
and ignore the minority class. We showed that cascaded convolutional neural
networks can class... | ['Taro Sekiyama', 'Masaharu Sakamoto', 'Kun Zhao', 'Hiroki Nakano'] | 2017-11-19 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 2.08108768e-01 3.32995892e-01 -5.92226863e-01 -3.78118753e-01
-6.62096143e-01 -1.92575365e-01 2.01100260e-02 1.55384973e-01
-1.87660471e-01 6.51853263e-01 -1.42652467e-01 -5.74897051e-01
-2.30102032e-01 -9.80127633e-01 -3.99975508e-01 -5.93193114e-01
7.68482238e-02 5.38314819e-01 4.63356614e-01 2.05745101... | [15.387218475341797, -2.210686445236206] |
aa03b978-9591-4c71-bea4-859060c4f107 | segnetr-rethinking-the-local-global | 2307.02953 | null | https://arxiv.org/abs/2307.02953v1 | https://arxiv.org/pdf/2307.02953v1.pdf | SegNetr: Rethinking the local-global interactions and skip connections in U-shaped networks | Recently, U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure. However, existing U-shaped segmentation networks: 1) mostly focus on designing complex self-attention modules to compensate for the lack of long-term dependence based on convolution operati... | ['Min Zhu', 'Fengjie Wang', 'Chengrui Gao', 'Junlong Cheng'] | 2023-07-06 | null | null | null | null | ['medical-image-segmentation'] | ['medical'] | [ 1.96443528e-01 2.97805250e-01 -1.90699637e-01 -3.92258048e-01
-4.10613447e-01 -2.46896237e-01 -1.18623078e-02 2.26381451e-01
-8.02572668e-01 3.62727255e-01 -4.00876962e-02 -5.11733472e-01
8.36075693e-02 -7.84557104e-01 -6.16043150e-01 -5.67898929e-01
-4.42380086e-02 5.16236993e-04 7.87966192e-01 -1.50262371... | [14.620824813842773, -2.601945161819458] |
76816490-7876-4a82-92bd-7c3ff6065fab | tablegpt-few-shot-table-to-text-generation | null | null | https://aclanthology.org/2020.coling-main.179 | https://aclanthology.org/2020.coling-main.179.pdf | TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching | Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data. Recently, pre-trained language models show potential in few-shot learning with linguistic knowledge learnt from pretraining on large-scale c... | ['Ting Liu', 'Xiaojiang Liu', 'Wei Bi', 'Bing Qin', 'Xiaocheng Feng', 'Yawei Sun', 'Heng Gong'] | 2020-12-01 | null | null | null | coling-2020-8 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 6.66606247e-01 4.51415628e-01 -2.78187156e-01 -3.53926629e-01
-1.45919740e+00 -5.07778108e-01 5.38441181e-01 8.97057503e-02
-2.15044782e-01 1.09285939e+00 6.97235823e-01 -2.21483365e-01
2.58047760e-01 -1.19042349e+00 -1.11712325e+00 -9.07656252e-02
6.46478236e-01 1.05080605e+00 1.19387276e-01 -9.22157288... | [11.619741439819336, 8.829852104187012] |
2713b05d-64e2-41ea-9506-603691d1ec52 | listen-carefully-and-tell-an-audio-captioning | 2006.15406 | null | https://arxiv.org/abs/2006.15406v4 | https://arxiv.org/pdf/2006.15406v4.pdf | Listen carefully and tell: an audio captioning system based on residual learning and gammatone audio representation | Automated audio captioning is machine listening task whose goal is to describe an audio using free text. An automated audio captioning system has to be implemented as it accepts an audio as input and outputs as textual description, that is, the caption of the signal. This task can be useful in many applications such as... | ['Maximo Cobos', 'Pedro Zuccarello', 'Javier Naranjo-Alcazar', 'Sergi Perez-Castanos'] | 2020-06-27 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 7.02613175e-01 4.43374842e-01 3.49412322e-01 -3.07677180e-01
-1.30444479e+00 -4.56991464e-01 5.20737410e-01 -6.20425791e-02
-2.32279524e-01 7.22616196e-01 7.53107727e-01 8.56367052e-02
2.31986254e-01 -2.31497064e-01 -7.69538641e-01 -4.47302639e-01
1.05930455e-02 7.15560913e-01 3.09047569e-02 -2.10412502... | [15.27840805053711, 4.8929572105407715] |
9ae42bcf-258c-4ba2-9c95-9a44a067da2c | grounded-word-sense-translation | null | null | https://aclanthology.org/W19-1808 | https://aclanthology.org/W19-1808.pdf | Grounded Word Sense Translation | Recent work on visually grounded language learning has focused on broader applications of grounded representations, such as visual question answering and multimodal machine translation. In this paper we consider grounded word sense translation, i.e. the task of correctly translating an ambiguous source word given the c... | ['Lucia Specia', 'Pranava Madhyastha', 'Chiraag Lala'] | 2019-06-01 | null | null | null | ws-2019-6 | ['multimodal-machine-translation', 'grounded-language-learning'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.79782605e-01 2.20628873e-01 -3.13847601e-01 -5.93255796e-02
-1.09993780e+00 -7.72525132e-01 9.49372053e-01 5.66423349e-02
-3.08139741e-01 6.22354627e-01 7.37592161e-01 -6.75722659e-01
2.52127498e-01 -5.30846775e-01 -8.86266470e-01 -5.34158409e-01
4.43144172e-01 1.96518317e-01 -1.67122662e-01 -4.12823290... | [11.361414909362793, 1.427054762840271] |
4fdaeb2d-2e4c-47fe-adf4-828776488dd3 | cross-validation-is-all-you-need-a | 2306.13990 | null | https://arxiv.org/abs/2306.13990v1 | https://arxiv.org/pdf/2306.13990v1.pdf | Cross-Validation Is All You Need: A Statistical Approach To Label Noise Estimation | Label noise is prevalent in machine learning datasets. It is crucial to identify and remove label noise because models trained on noisy data can have substantially reduced accuracy and generalizability. Most existing label noise detection approaches are designed for classification tasks, and data cleaning for outcome p... | ['Anne Martel', 'Jianan Chen'] | 2023-06-24 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 5.85437775e-01 -1.59204692e-01 -1.43856823e-01 -5.58152676e-01
-1.46799898e+00 -6.83203697e-01 1.71242148e-01 8.51797998e-01
-5.67313969e-01 7.58258283e-01 1.83675349e-01 -2.13173330e-01
-4.12043035e-01 -5.49769819e-01 -4.74905878e-01 -8.07026625e-01
1.55928686e-01 5.09051323e-01 9.92754623e-02 3.36694926... | [14.922709465026855, -2.4548425674438477] |
892a8774-3a63-4ac7-acb6-156e87506a63 | calibration-aware-margin-loss-pushing-the | 2307.04047 | null | https://arxiv.org/abs/2307.04047v1 | https://arxiv.org/pdf/2307.04047v1.pdf | Calibration-Aware Margin Loss: Pushing the Accuracy-Calibration Consistency Pareto Frontier for Deep Metric Learning | The ability to use the same distance threshold across different test classes / distributions is highly desired for a frictionless deployment of commercial image retrieval systems. However, state-of-the-art deep metric learning losses often result in highly varied intra-class and inter-class embedding structures, making... | ['Yifan Xing', 'Joe Tighe', 'Ying Nian Wu', 'Jun Fang', 'Qingming Tang', 'Linghan Xu', 'Qin Zhang'] | 2023-07-08 | null | null | null | null | ['metric-learning', 'image-retrieval', 'metric-learning', 'retrieval'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [-2.27221809e-02 -4.80887353e-01 -2.70105392e-01 -8.63516867e-01
-1.16971970e+00 -6.75060153e-01 3.48332554e-01 2.85252839e-01
-5.83013117e-01 5.37708938e-01 -2.01761067e-01 -1.95559219e-01
-5.77089846e-01 -5.24148226e-01 -4.52947825e-01 -7.74313986e-01
7.74227008e-02 1.69321954e-01 6.38676584e-02 1.13982283... | [9.463810920715332, 3.0676050186157227] |
2ed81471-6d73-45c5-8c38-59f478fd431f | stc-spatio-temporal-contrastive-learning-for | 2202.03747 | null | https://arxiv.org/abs/2202.03747v2 | https://arxiv.org/pdf/2202.03747v2.pdf | STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation | Video Instance Segmentation (VIS) is a task that simultaneously requires classification, segmentation, and instance association in a video. Recent VIS approaches rely on sophisticated pipelines to achieve this goal, including RoI-related operations or 3D convolutions. In contrast, we present a simple and efficient sing... | ['Liqing Zhang', 'Chengjie Wang', 'Ying Tai', 'Yabiao Wang', 'Liang Liu', 'Hang Zhou', 'Jinlong Peng', 'Zhangxuan Gu', 'Zhengkai Jiang'] | 2022-02-08 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-3.44184898e-02 -2.41386518e-01 -4.34779555e-01 -4.58528429e-01
-6.66294396e-01 -3.43766361e-01 7.01485455e-01 6.78988695e-02
-6.51299357e-01 6.40707672e-01 -1.05567552e-01 7.08585829e-02
1.28776893e-01 -4.22768265e-01 -6.79385304e-01 -5.48839629e-01
-1.75716519e-01 -1.77970544e-01 7.84769058e-01 2.44771332... | [9.121580123901367, -0.04811060428619385] |
9bae6724-f9dd-46bd-84c1-0c676bf9d14f | sam3d-zero-shot-3d-object-detection-via | 2306.02245 | null | https://arxiv.org/abs/2306.02245v1 | https://arxiv.org/pdf/2306.02245v1.pdf | SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model | With the development of large language models, many remarkable linguistic systems like ChatGPT have thrived and achieved astonishing success on many tasks, showing the incredible power of foundation models. In the spirit of unleashing the capability of foundation models on vision tasks, the Segment Anything Model (SAM)... | ['Xiang Bai', 'Zhe Liu', 'Xiaoqing Ye', 'Zhikang Zou', 'Hongcheng Yang', 'Dingkang Liang', 'Dingyuan Zhang'] | 2023-06-04 | null | null | null | null | ['3d-object-detection'] | ['computer-vision'] | [-2.37179875e-01 -7.34920707e-03 -1.08192861e-01 -3.60491425e-01
-4.89912778e-01 -5.77962160e-01 8.72052133e-01 -3.63530755e-01
-4.18966651e-01 -5.82391955e-02 -4.76796879e-03 -5.30568838e-01
3.56511354e-01 -5.03314793e-01 -4.60813284e-01 -3.19926739e-01
1.14026956e-01 4.53799784e-01 6.23745799e-01 -3.24040294... | [9.831748008728027, 1.2322089672088623] |
6330d7db-acd8-4757-97ce-dc469f6663c5 | demand-response-by-aggregates-of-domestic | 2307.02218 | null | https://arxiv.org/abs/2307.02218v1 | https://arxiv.org/pdf/2307.02218v1.pdf | Demand Response by Aggregates of Domestic Water Heaters with Adaptive Model Predictive Control | This paper describes an intelligent management algorithm for an aggregate of domestic electric water heaters called to provide a demand response service. This algorithm is developed using Model Predictive Control. The model of the entire aggregate is dynamically identified using a recursive polynomial model estimation ... | ['M. Rapizza', 'D. Cirio', 'F. Silvestro', 'S. Massucco', 'F. Conte'] | 2023-07-05 | null | null | null | null | ['management'] | ['miscellaneous'] | [ 4.67156954e-02 2.61499882e-01 -2.27648243e-02 -1.98020209e-02
6.52667657e-02 -7.72228956e-01 3.55213761e-01 3.35503221e-01
1.32073238e-01 5.86723685e-01 -4.11802679e-01 -1.86084643e-01
-5.83068252e-01 -8.57367396e-01 -1.68753594e-01 -1.18681967e+00
1.61947936e-01 5.48060238e-01 -5.51351719e-02 -1.12109847... | [5.716127872467041, 2.5003879070281982] |
2e5badb6-d359-4b32-add2-74bb8bcac066 | cascader-cross-modal-cascading-for-knowledge | 2205.08012 | null | https://arxiv.org/abs/2205.08012v2 | https://arxiv.org/pdf/2205.08012v2.pdf | CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction | Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine. Recently, promising results have been achieved by leveraging cross-modal information in KGs, using ensembles that combine knowledge graph embed... | ['Tom Hope', 'Doug Downey', 'Tara Safavi'] | 2022-05-16 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [ 2.77166795e-02 -1.01385675e-02 -4.96582389e-01 -2.38481715e-01
-8.05771232e-01 -5.41444600e-01 5.92512727e-01 5.83622754e-01
-5.11531115e-01 4.98391896e-01 5.40110528e-01 -3.18804711e-01
-5.38609743e-01 -7.91358292e-01 -7.56275475e-01 -3.88579339e-01
-1.86865613e-01 5.90751052e-01 2.03191549e-01 -2.51070291... | [8.683392524719238, 7.785478115081787] |
1f274158-869b-4a3a-bfb6-cd2a10c4a6cd | w-transformers-a-wavelet-based-transformer | 2209.03945 | null | https://arxiv.org/abs/2209.03945v1 | https://arxiv.org/pdf/2209.03945v1.pdf | W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting | Deep learning utilizing transformers has recently achieved a lot of success in many vital areas such as natural language processing, computer vision, anomaly detection, and recommendation systems, among many others. Among several merits of transformers, the ability to capture long-range temporal dependencies and intera... | ['Abdenour Hadid', 'Tanujit Chakraborty', 'Lena Sasal'] | 2022-09-08 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [-1.03431419e-01 -7.50828326e-01 -9.12187770e-02 -3.57968420e-01
-5.79080522e-01 -5.08311212e-01 6.03580356e-01 -3.49805295e-03
1.51045382e-01 3.50883186e-01 4.41744566e-01 -5.27219355e-01
-3.08523029e-01 -7.53111362e-01 -5.34968376e-01 -7.00397015e-01
-6.32861853e-01 1.58145502e-01 -3.98646183e-02 -3.75086457... | [7.031061172485352, 2.914599657058716] |
a363cbda-41cc-453a-aa0d-31ea91b7d4c8 | multilingual-content-moderation-a-case-study | 2302.09618 | null | https://arxiv.org/abs/2302.09618v1 | https://arxiv.org/pdf/2302.09618v1.pdf | Multilingual Content Moderation: A Case Study on Reddit | Content moderation is the process of flagging content based on pre-defined platform rules. There has been a growing need for AI moderators to safeguard users as well as protect the mental health of human moderators from traumatic content. While prior works have focused on identifying hateful/offensive language, they ar... | ['Malihe Alikhani', 'Ajay Divakaran', 'Sabit Hassan', 'Katherine Atwell', 'Karan Sikka', 'Meng Ye'] | 2023-02-19 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 3.21962714e-01 1.43369228e-01 -2.02849656e-01 -2.84998089e-01
-6.08960152e-01 -7.92528093e-01 7.41551578e-01 2.83597469e-01
-1.27660424e-01 5.65564275e-01 8.19581330e-01 -4.34034467e-01
1.71267893e-02 -1.77566279e-02 -3.30713630e-01 -3.73546362e-01
3.14992443e-02 8.38495642e-02 -1.97411984e-01 -4.95677710... | [8.79279899597168, 10.488719940185547] |
f04a6e90-5013-47a9-b737-1da453dfddda | robust-mitosis-detection-using-a-cascade-mask | 2109.01878 | null | https://arxiv.org/abs/2109.01878v2 | https://arxiv.org/pdf/2109.01878v2.pdf | Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation | For the MIDOG mitosis detection challenge, we created a cascade algorithm consisting of a Mask-RCNN detector, followed by a classification ensemble consisting of ResNet50 and DenseNet201 to refine detected mitotic candidates. The MIDOG training data consists of 200 frames originating from four scanners, three of which ... | ['Rutger H. J. Fick', 'Saima Ben Hadj', 'Stéphanie Petit', 'Ali Mammadov', 'Alireza Moshayedi', 'Capucine Bertrand', 'Jules Dedieu', 'Gauthier Roy'] | 2021-09-04 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 4.02204514e-01 5.51231384e-01 -1.07575633e-01 -3.08019109e-04
-1.14472294e+00 -7.20286012e-01 5.05722344e-01 1.49490908e-01
-7.32153356e-01 9.16094542e-01 2.71911584e-02 -2.04452083e-01
4.68878210e-01 -7.27695048e-01 -8.23784471e-01 -9.96385336e-01
1.69894502e-01 9.65461433e-01 7.59719551e-01 1.30937442... | [14.999314308166504, -3.099364757537842] |
b44dda11-e233-4866-857c-1bea51ee0761 | necessary-and-sufficient-conditions-for-exact | 2208.07983 | null | https://arxiv.org/abs/2208.07983v1 | https://arxiv.org/pdf/2208.07983v1.pdf | Necessary and sufficient conditions for exact closures of epidemic equations on configuration model networks | We prove that the exact closure of SIR pairwise epidemic equations on a configuration model network is possible if and only if the degree distribution is Poisson, Binomial, or Negative Binomial. The proof relies on establishing, for these specific degree distributions, the equivalence of the closed pairwise model and t... | ['Grzegorz A. Rempala', 'Eben Kenah', 'Istvan Z. Kiss'] | 2022-08-16 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 4.41294014e-02 2.12157905e-01 1.92243457e-01 3.47651929e-01
3.24547976e-01 -7.17197299e-01 4.55460101e-01 4.94157284e-01
-2.42476970e-01 1.00734174e+00 -3.51463407e-01 -6.71978891e-01
-7.95679867e-01 -1.03500903e+00 -5.20020247e-01 -1.06631505e+00
-7.91775882e-01 8.07499111e-01 4.53066677e-01 -5.80649972... | [5.991213321685791, 4.451955795288086] |
125e9a62-18d7-4c9e-a91d-129d07c52d24 | posetrack21-a-dataset-for-person-search-multi | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Doring_PoseTrack21_A_Dataset_for_Person_Search_Multi-Object_Tracking_and_Multi-Person_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Doring_PoseTrack21_A_Dataset_for_Person_Search_Multi-Object_Tracking_and_Multi-Person_CVPR_2022_paper.pdf | PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking | Current research evaluates person search, multi-object tracking and multi-person pose estimation as separate tasks and on different datasets although these tasks are very akin to each other and comprise similar sub-tasks, e.g. person detection or appearance-based association of detected persons. Consequently, appro... | ['Jürgen Gall', 'Bernt Schiele', 'Shanshan Zhang', 'Di Chen', 'Andreas Döring'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['person-search', 'multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.94225663e-01 -4.73344982e-01 1.49303135e-02 -2.78550833e-01
-7.56071031e-01 -7.57963955e-01 5.53818464e-01 1.22817587e-02
-7.83626616e-01 6.57108426e-01 3.30350816e-01 5.22041261e-01
5.75064607e-02 -1.67549536e-01 -5.17308831e-01 -2.09179550e-01
1.28623620e-01 1.09431207e+00 5.21445990e-01 2.16420546... | [7.0065155029296875, -0.9945905804634094] |
962e0184-8c9a-4838-ab9f-37ae642f725f | improved-segmentation-of-deep-sulci-in | 2303.00795 | null | https://arxiv.org/abs/2303.00795v2 | https://arxiv.org/pdf/2303.00795v2.pdf | Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace's Equation | When developing tools for automated cortical segmentation, the ability to produce topologically correct segmentations is important in order to compute geometrically valid morphometry measures. In practice, accurate cortical segmentation is challenged by image artifacts and the highly convoluted anatomy of the cortex it... | ['Ranjit Ittyerah', 'Paul A. Yushkevich', 'Ricardo Insausti', 'David A. Wolk', 'Marta Córcoles Parada', 'Carlos de la Rosa-Prieto', 'José Carlos Delgado González', 'Sandra Cebada-Sánchez', 'Maria del Pilar Marcos Rabal', 'Francisco Javier Molina Romero', 'Monica Muñoz', 'Maria del Mar Arroyo Jiménez', 'Maria Mercedes I... | 2023-03-01 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 1.10655539e-01 4.50282097e-01 4.06892478e-01 -4.72527176e-01
-2.94318676e-01 -6.30736649e-01 2.41711140e-01 2.28344768e-01
-6.58609450e-01 6.34752750e-01 -8.42523426e-02 -3.18559617e-01
1.53696030e-01 -6.67759120e-01 -7.99098730e-01 -2.74713188e-01
-2.76349306e-01 5.75346649e-01 2.71415114e-01 1.31985158... | [14.246713638305664, -2.9852421283721924] |
f2382584-0854-4296-bdef-b8318c05cfe2 | source-free-domain-adaptation-for-semantic | 2103.16372 | null | https://arxiv.org/abs/2103.16372v1 | https://arxiv.org/pdf/2103.16372v1.pdf | Source-Free Domain Adaptation for Semantic Segmentation | Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network(CNN)-based approaches for semantic segmentation heavily rely on the pixel-level annotated data, which is labor-intensive. However, existing UDA approaches in this regard inevitably require the full access to source datasets ... | ['Jun Wang', 'Wei zhang', 'Yuang Liu'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liu_Source-Free_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_Source-Free_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 4.48983580e-01 6.47323951e-02 -3.35597575e-01 -4.94536430e-01
-8.01333368e-01 -5.32511830e-01 1.91849917e-01 -1.80276502e-02
-5.52801132e-01 7.43125439e-01 -2.46525601e-01 1.58018440e-01
1.51766360e-01 -9.85318899e-01 -9.14515078e-01 -8.91172767e-01
6.00688756e-01 4.91695702e-01 5.47689021e-01 -1.07406244... | [9.69644832611084, 1.3753103017807007] |
0fec68e3-d4e7-488d-ad2f-fb914ca30a4b | all-you-need-is-a-second-look-towards-tighter | 2004.12436 | null | https://arxiv.org/abs/2004.12436v1 | https://arxiv.org/pdf/2004.12436v1.pdf | All you need is a second look: Towards Tighter Arbitrary shape text detection | Deep learning-based scene text detection methods have progressed substantially over the past years. However, there remain several problems to be solved. Generally, long curve text instances tend to be fragmented because of the limited receptive field size of CNN. Besides, simple representations using rectangle or quadr... | ['Yuexian Zou', 'Meng Cao'] | 2020-04-26 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.30310673e-01 1.12392195e-01 6.95211440e-02 -2.52096802e-01
-7.13445485e-01 -5.46807170e-01 5.35140634e-01 -1.16912812e-01
-3.21616381e-01 4.28795367e-01 -1.81299627e-01 -3.76341939e-01
1.38462186e-01 -5.94172537e-01 -7.07175851e-01 -7.14295208e-01
6.23071611e-01 4.41615433e-01 5.37344873e-01 -3.69192399... | [12.072267532348633, 2.2621943950653076] |
e092f074-40f9-407a-aa24-2199cf223ea6 | spatially-covariant-lesion-segmentation | 2301.07895 | null | https://arxiv.org/abs/2301.07895v1 | https://arxiv.org/pdf/2301.07895v1.pdf | Spatially Covariant Lesion Segmentation | Compared to natural images, medical images usually show stronger visual patterns and therefore this adds flexibility and elasticity to resource-limited clinical applications by injecting proper priors into neural networks. In this paper, we propose spatially covariant pixel-aligned classifier (SCP) to improve the compu... | ['Jiahao Li', 'Chao Li', 'Dongdong Liu', 'Jinwei Zhang', 'Rongguang Wang', 'Hang Zhang'] | 2023-01-19 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 6.35658264e-01 3.04999352e-02 -9.48527381e-02 -3.76845896e-01
-2.09287271e-01 -3.98448199e-01 3.84154826e-01 1.35693356e-01
-7.26901710e-01 6.21394634e-01 1.68400824e-01 -3.86706144e-01
-1.79179057e-01 -7.43150353e-01 -4.21977699e-01 -9.46326017e-01
-2.49848306e-01 1.42299876e-01 4.14832532e-01 1.90777898... | [14.518016815185547, -2.471959352493286] |
fec78fbc-d669-4b04-bd93-1413ea008254 | msv-challenge-2022-npu-hc-speaker | 2211.16694 | null | https://arxiv.org/abs/2211.16694v2 | https://arxiv.org/pdf/2211.16694v2.pdf | MSV Challenge 2022: NPU-HC Speaker Verification System for Low-resource Indian Languages | This report describes the NPU-HC speaker verification system submitted to the O-COCOSDA Multi-lingual Speaker Verification (MSV) Challenge 2022, which focuses on developing speaker verification systems for low-resource Asian languages. We participate in the I-MSV track, which aims to develop speaker verification system... | ['Lei Xie', 'Jie Liu', 'Namin Wang', 'Li Zhang', 'Yue Li'] | 2022-11-30 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-4.64184172e-02 -1.30321672e-02 1.47922747e-02 -6.31037712e-01
-1.44411850e+00 -5.43286741e-01 4.22325134e-01 -1.62517384e-01
-5.67433238e-01 4.25557941e-01 2.46377751e-01 -3.65432292e-01
2.56870300e-01 -7.45001361e-02 -6.81169271e-01 -7.26961970e-01
5.49048409e-02 3.63579392e-01 -2.64225334e-01 -1.80526301... | [14.344088554382324, 6.193714141845703] |
6dc7d948-a751-4160-a33b-1b15c79da89d | one-transform-to-compute-them-all-efficient | 2304.03412 | null | https://arxiv.org/abs/2304.03412v1 | https://arxiv.org/pdf/2304.03412v1.pdf | One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment | The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF requires the evaluation of a heterogeneous set of quality models, it is computationally expensive. Giv... | ['Alan C. Bovik', 'Ioannis Katsavounidis', 'Cosmin Stejerean', 'Abhinau K. Venkataramanan'] | 2023-04-06 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [ 3.30748290e-01 -5.55938184e-01 -1.11415334e-01 -3.68506730e-01
-1.14304435e+00 -4.49507058e-01 4.40049648e-01 3.80573928e-01
-1.69803292e-01 2.11469516e-01 3.89252961e-01 -1.52952418e-01
-2.29949817e-01 -7.90090501e-01 -4.80516523e-01 -2.99903244e-01
-2.42639765e-01 -3.44062112e-02 3.80663663e-01 -2.08640277... | [11.657958030700684, -1.8006197214126587] |
cd3a4fc5-1b87-42cc-a423-7fe4b09a5d6c | case-based-reasoning-with-language-models-for | 2301.11879 | null | https://arxiv.org/abs/2301.11879v2 | https://arxiv.org/pdf/2301.11879v2.pdf | Case-Based Reasoning with Language Models for Classification of Logical Fallacies | The ease and speed of spreading misinformation and propaganda on the Web motivate the need to develop trustworthy technology for detecting fallacies in natural language arguments. However, state-of-the-art language modeling methods exhibit a lack of robustness on tasks like logical fallacy classification that require c... | ['Alain Mermoud', 'Hông-Ân Sandlin', 'Filip Ilievski', 'Zhivar Sourati'] | 2023-01-27 | null | null | null | null | ['logical-fallacies'] | ['miscellaneous'] | [-2.03699544e-01 4.14491653e-01 -6.77809596e-01 -3.80285054e-01
-8.47992778e-01 -7.20396161e-01 1.23894906e+00 7.00568497e-01
-3.29677135e-01 6.08228564e-01 6.51524067e-01 -1.10108435e+00
-3.21919262e-01 -8.71378481e-01 -6.92614973e-01 2.54969537e-01
1.91287547e-01 3.37076813e-01 5.18984139e-01 -5.14819622... | [9.802037239074707, 8.270654678344727] |
522cb22c-5a46-4662-8b54-274cefe53729 | mosaic-masked-optimisation-with-selective | 2306.00906 | null | https://arxiv.org/abs/2306.00906v1 | https://arxiv.org/pdf/2306.00906v1.pdf | MOSAIC: Masked Optimisation with Selective Attention for Image Reconstruction | Compressive sensing (CS) reconstructs images from sub-Nyquist measurements by solving a sparsity-regularized inverse problem. Traditional CS solvers use iterative optimizers with hand crafted sparsifiers, while early data-driven methods directly learn an inverse mapping from the low-dimensional measurement space to the... | ['Dushan N. Wadduwage', 'Chamira U. S. Edussooriya', 'A. Thieshanthan', 'Amashi Niwarthana', 'Tharindu Wickremasinghe', 'Pamuditha Somarathne'] | 2023-06-01 | null | null | null | null | ['image-reconstruction', 'compressive-sensing'] | ['computer-vision', 'computer-vision'] | [ 6.64321303e-01 -9.13577452e-02 -2.04384774e-01 -2.83055872e-01
-7.61957705e-01 -3.99900734e-01 7.40958154e-01 -3.84444535e-01
-3.96141708e-01 5.13017118e-01 3.63105208e-01 -1.80746973e-01
-4.26961809e-01 -5.64901412e-01 -9.05177236e-01 -8.20295632e-01
9.56435427e-02 5.22992313e-01 -2.49455109e-01 -1.03178062... | [11.34796142578125, -2.1808598041534424] |
ea959e0f-ef01-473b-b16d-0d96e7710cd7 | singing-voice-synthesis-system-based-on-time | null | null | https://aclanthology.org/O13-1009 | https://aclanthology.org/O13-1009.pdf | 基於時域上基週同步疊加法之歌聲合成系統 (Singing Voice Synthesis System Based on Time Domain-Pitch Synchronized Overlap and Add) [In Chinese] | null | ['Chia-Ping Chen', 'Ming-Kuan Wu'] | 2013-10-01 | singing-voice-synthesis-system-based-on-time-1 | https://aclanthology.org/O13-1009 | https://aclanthology.org/O13-1009.pdf | roclingijclclp-2013-10 | ['singing-voice-synthesis'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.428300857543945, 3.6735284328460693] |
5e1cd7b3-8ae6-4bc1-9ba5-07bd25718a02 | stacked-hybrid-attention-and-group | 2203.09811 | null | https://arxiv.org/abs/2203.09811v2 | https://arxiv.org/pdf/2203.09811v2.pdf | Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation | Scene Graph Generation, which generally follows a regular encoder-decoder pipeline, aims to first encode the visual contents within the given image and then parse them into a compact summary graph. Existing SGG approaches generally not only neglect the insufficient modality fusion between vision and language, but also ... | ['Liqiang Nie', 'Yuan Cheng', 'Jianlong Wu', 'Xuemeng Song', 'Tian Gan', 'Xingning Dong'] | 2022-03-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Dong_Stacked_Hybrid-Attention_and_Group_Collaborative_Learning_for_Unbiased_Scene_Graph_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Dong_Stacked_Hybrid-Attention_and_Group_Collaborative_Learning_for_Unbiased_Scene_Graph_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 3.72447312e-01 3.54224652e-01 1.63216703e-02 -4.58374172e-01
-9.20370698e-01 -2.22699597e-01 6.47486567e-01 -8.94348845e-02
-1.74125910e-01 5.30884862e-01 3.97431910e-01 -1.29331961e-01
4.01444882e-02 -7.20006466e-01 -7.92307615e-01 -8.11586916e-01
3.65105450e-01 3.32358956e-01 1.56648204e-01 9.70393717... | [10.414712905883789, 1.5863338708877563] |
b2fbfbc5-e7ea-4a79-876d-2649a70c81e0 | applying-transformer-based-text-summarization | 2209.03791 | null | https://arxiv.org/abs/2209.03791v2 | https://arxiv.org/pdf/2209.03791v2.pdf | Applying Transformer-based Text Summarization for Keyphrase Generation | Keyphrases are crucial for searching and systematizing scholarly documents. Most current methods for keyphrase extraction are aimed at the extraction of the most significant words in the text. But in practice, the list of keyphrases often includes words that do not appear in the text explicitly. In this case, the list ... | ['Dmitry Morozov', 'Anna Glazkova'] | 2022-09-08 | null | null | null | null | ['keyphrase-generation', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.97020724e-01 8.93925950e-02 -3.40284824e-01 4.95964646e-01
-9.66693223e-01 -9.06110644e-01 1.03610826e+00 9.69930470e-01
-4.79439765e-01 1.07588804e+00 8.10728610e-01 -4.25418973e-01
-2.67153054e-01 -7.37014890e-01 -5.11031628e-01 -4.39423263e-01
3.00587565e-01 1.47064105e-01 1.54168591e-01 -3.50632608... | [12.345341682434082, 9.050810813903809] |
3de6da0c-0756-4b2a-8f07-6beced5e3dbf | a-lightweight-reconstruction-network-for | 2212.12878 | null | https://arxiv.org/abs/2212.12878v1 | https://arxiv.org/pdf/2212.12878v1.pdf | A Lightweight Reconstruction Network for Surface Defect Inspection | Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained... | ['Liqiang Zhu', 'Weibin Qiu', 'Weijie Wu', 'Jian Yao', 'Chao Hu'] | 2022-12-25 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.53244674e-01 1.12192728e-01 2.42384970e-01 -2.58135319e-01
-2.60234237e-01 5.31109929e-01 -3.19705367e-01 -1.41489208e-01
-1.61968768e-01 2.72798419e-01 -2.87390918e-01 -2.93873940e-02
-1.23359568e-01 -1.33130920e+00 -4.03251320e-01 -9.73678768e-01
1.59046873e-01 3.57637137e-01 2.84919381e-01 -1.75948918... | [7.3997979164123535, 1.7890764474868774] |
c9361db7-d660-4b59-98ad-e71bfc173e9b | end-to-end-prostate-cancer-detection-in-bpmri | 2101.03244 | null | https://arxiv.org/abs/2101.03244v9 | https://arxiv.org/pdf/2101.03244v9.pdf | End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction | We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imaging (bpMRI). Deep attention mechanisms drive its detection network, targeting salient structures and highly discriminative feature dimension... | ['Henkjan Huisman', 'Matin Hosseinzadeh', 'Anindo Saha'] | 2021-01-08 | null | null | null | null | ['deep-attention', 'clinical-knowledge', 'deep-attention'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [ 3.46823335e-01 6.62358105e-01 -5.06435394e-01 -2.82366574e-01
-1.62541652e+00 -5.81933856e-01 4.28366750e-01 1.75062880e-01
-4.54810560e-01 5.66374660e-01 1.06846362e-01 -5.26362717e-01
-3.55746299e-01 -5.97321510e-01 -4.52081382e-01 -7.29655206e-01
-6.93686485e-01 9.29513931e-01 -1.32829458e-01 3.61310303... | [14.910821914672852, -2.47361159324646] |
d07c0205-95b0-451f-8eba-f7d703622b85 | sixo-smoothing-inference-with-twisted | 2206.05952 | null | https://arxiv.org/abs/2206.05952v2 | https://arxiv.org/pdf/2206.05952v2.pdf | SIXO: Smoothing Inference with Twisted Objectives | Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions. The target distributions are often chosen to be the filtering distributions, but these ignore information from future observations, leading to practical and ... | ['Scott Linderman', 'Andrew Warrington', 'Allan Raventós', 'Dieterich Lawson'] | 2022-06-13 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.59148201e-02 4.66472730e-02 -6.72698081e-01 -2.86599517e-01
-1.13719702e+00 -5.77786505e-01 1.19111967e+00 -2.92845130e-01
-2.63708770e-01 1.20093310e+00 2.20364138e-01 -2.99471051e-01
-2.39974484e-01 -6.23714030e-01 -6.61680222e-01 -7.48897493e-01
-1.51190341e-01 1.03271270e+00 5.03327906e-01 2.65119672... | [6.737189292907715, 3.8612890243530273] |
4082be2e-c2ea-4b54-9849-4367be7200ba | sergiojimenez-at-semeval-2016-task-1 | null | null | https://aclanthology.org/S16-1116 | https://aclanthology.org/S16-1116.pdf | SERGIOJIMENEZ at SemEval-2016 Task 1: Effectively Combining Paraphrase Database, String Matching, WordNet, and Word Embedding for Semantic Textual Similarity | null | ['Sergio Jimenez'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['negation-detection'] | ['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... | [-7.449975967407227, 3.6470084190368652] |
4f5644de-92c3-46b5-a151-072e26b90b4a | graphglow-universal-and-generalizable | 2306.11264 | null | https://arxiv.org/abs/2306.11264v1 | https://arxiv.org/pdf/2306.11264v1.pdf | GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks | Graph structure learning is a well-established problem that aims at optimizing graph structures adaptive to specific graph datasets to help message passing neural networks (i.e., GNNs) to yield effective and robust node embeddings. However, the common limitation of existing models lies in the underlying \textit{closed-... | ['Junchi Yan', 'Chenxiao Yang', 'Qitian Wu', 'Wentao Zhao'] | 2023-06-20 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 2.07390517e-01 4.72080827e-01 -3.44980896e-01 -4.11586612e-01
-5.76604247e-01 -7.38797843e-01 5.10304272e-01 3.91093493e-01
-1.06552176e-01 5.55992007e-01 -1.02845922e-01 -4.62727547e-01
-4.72634971e-01 -1.22669923e+00 -1.08382380e+00 -6.69598043e-01
-5.00593662e-01 9.55928624e-01 1.57086700e-01 -3.25655341... | [6.969025135040283, 6.207122802734375] |
154c2a7f-8dbe-430e-9411-0bb0d3938dbb | completely-unsupervised-phoneme-recognition-1 | 1904.04100 | null | https://arxiv.org/abs/1904.04100v3 | https://arxiv.org/pdf/1904.04100v3.pdf | Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models | Producing a large annotated speech corpus for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced, but collecting a relatively big unlabeled data set for such languages is more achievable. This is why some initial effort have been reported on completely unsup... | ['Lin-shan Lee', 'Hung-Yi Lee', 'Da-Rong Liu', 'Che-Ping Tsai', 'Kuan-Yu Chen'] | 2019-04-08 | null | null | null | null | ['unsupervised-speech-recognition'] | ['speech'] | [ 3.59475464e-01 5.11835575e-01 5.99032082e-02 -4.71394002e-01
-1.15823710e+00 -5.18058717e-01 6.52059972e-01 -5.57196558e-01
-2.57717401e-01 9.59643483e-01 2.36935258e-01 -4.18918401e-01
8.22995365e-01 -5.73290229e-01 -4.10535246e-01 -8.57743740e-01
3.35335046e-01 7.68116832e-01 6.75141159e-03 -2.15331167... | [14.5980806350708, 6.639204978942871] |
8ef5839e-0264-4859-acd2-abeff1bc73c7 | mv-han-a-hybrid-attentive-networks-based | 2210.07660 | null | https://arxiv.org/abs/2210.07660v1 | https://arxiv.org/pdf/2210.07660v1.pdf | MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation | Industrial recommender systems usually employ multi-source data to improve the recommendation quality, while effectively sharing information between different data sources remain a challenge. In this paper, we introduce a novel Multi-View Approach with Hybrid Attentive Networks (MV-HAN) for contents retrieval at the ma... | ['Junyang Chen', 'Kai Wang', 'Chaoyun Zhang', 'Ge Fan'] | 2022-10-14 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.94912910e-01 -5.88379622e-01 -6.68993652e-01 -2.25718632e-01
-1.02211702e+00 -6.88480496e-01 3.70928437e-01 -5.57668395e-02
3.13999385e-01 2.18540937e-01 5.12185156e-01 2.28316665e-01
-7.43998945e-01 -8.47507596e-01 -6.82869732e-01 -5.14606118e-01
1.46572486e-01 6.22585714e-01 1.11049071e-01 -6.16054118... | [10.143126487731934, 5.5727081298828125] |
940a5c2a-1f7d-4ea1-b6bd-0d8d1c8d1c5e | awesome-typography-statistics-based-text | 1611.09026 | null | http://arxiv.org/abs/1611.09026v2 | http://arxiv.org/pdf/1611.09026v2.pdf | Awesome Typography: Statistics-Based Text Effects Transfer | In this work, we explore the problem of generating fantastic special-effects
for the typography. It is quite challenging due to the model diversities to
illustrate varied text effects for different characters. To address this issue,
our key idea is to exploit the analytics on the high regularity of the spatial
distribu... | ['Jiaying Liu', 'Shuai Yang', 'Zongming Guo', 'Zhouhui Lian'] | 2016-11-28 | awesome-typography-statistics-based-text-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Yang_Awesome_Typography_Statistics-Based_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Awesome_Typography_Statistics-Based_CVPR_2017_paper.pdf | cvpr-2017-7 | ['text-effects-transfer'] | ['natural-language-processing'] | [ 1.19130999e-01 -3.70298177e-01 -3.41996588e-02 4.21199389e-02
-2.00141832e-01 -7.71454632e-01 5.26711881e-01 -4.60819900e-01
3.27444613e-01 8.28665018e-01 3.13065797e-01 1.39743164e-01
-1.03287205e-01 -8.87376368e-01 -5.06836414e-01 -6.50499940e-01
3.95557761e-01 3.22211593e-01 2.74955720e-01 -4.14554030... | [11.70462703704834, -0.5701958537101746] |
bcc504e9-bd88-4783-9a0a-b23d57de3399 | cross-lingual-retrieval-augmented-prompt-for | 2212.09651 | null | https://arxiv.org/abs/2212.09651v4 | https://arxiv.org/pdf/2212.09651v4.pdf | Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages | Multilingual Pretrained Language Models (MPLMs) have shown their strong multilinguality in recent empirical cross-lingual transfer studies. In this paper, we propose the Prompts Augmented by Retrieval Crosslingually (PARC) pipeline to improve the zero-shot performance on low-resource languages (LRLs) by augmenting the ... | ['Hinrich Schütze', 'Helmut Schmid', 'Sheng Liang', 'Ercong Nie'] | 2022-12-19 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-2.35658720e-01 -9.66974646e-02 -4.97607172e-01 -5.82064867e-01
-1.78929210e+00 -8.32553148e-01 8.46781611e-01 2.33432099e-01
-1.04801309e+00 7.68544912e-01 4.66873646e-01 -4.03555900e-01
2.85680443e-01 -4.02212977e-01 -9.39686477e-01 -2.56472856e-01
1.23627983e-01 5.84182918e-01 1.24304280e-01 -3.41185838... | [10.923150062561035, 9.91702651977539] |
168ee490-bfee-4411-8df9-768fcaadbfa8 | ai-safety-gridworlds | 1711.09883 | null | http://arxiv.org/abs/1711.09883v2 | http://arxiv.org/pdf/1711.09883v2.pdf | AI Safety Gridworlds | We present a suite of reinforcement learning environments illustrating
various safety properties of intelligent agents. These problems include safe
interruptibility, avoiding side effects, absent supervisor, reward gaming, safe
exploration, as well as robustness to self-modification, distributional shift,
and adversari... | ['Jan Leike', 'Andrew Lefrancq', 'Victoria Krakovna', 'Tom Everitt', 'Shane Legg', 'Miljan Martic', 'Laurent Orseau', 'Pedro A. Ortega'] | 2017-11-27 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.85857803e-01 4.64692056e-01 -1.39006332e-01 6.23720605e-03
-2.92330176e-01 -1.13623405e+00 7.56759107e-01 1.30860746e-01
-6.65471315e-01 9.89310861e-01 -1.01679087e-01 -5.33920646e-01
-1.66222259e-01 -9.01969910e-01 -8.51152062e-01 -7.35251963e-01
-8.68801892e-01 3.39674622e-01 4.23118651e-01 -5.15158415... | [4.363470077514648, 2.1012823581695557] |
73447b2b-08b8-435d-b58a-8b89377454d8 | low-resource-adaptation-for-personalized-co | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ahuja_Low-Resource_Adaptation_for_Personalized_Co-Speech_Gesture_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ahuja_Low-Resource_Adaptation_for_Personalized_Co-Speech_Gesture_Generation_CVPR_2022_paper.pdf | Low-Resource Adaptation for Personalized Co-Speech Gesture Generation | Personalizing an avatar for co-speech gesture generation from spoken language requires learning the idiosyncrasies of a person's gesture style from a small amount of data. Previous methods in gesture generation require large amounts of data for each speaker, which is often infeasible. We propose an approach, named ... | ['Louis-Philippe Morency', 'Dong Won Lee', 'Chaitanya Ahuja'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['gesture-generation'] | ['robots'] | [ 1.32061049e-01 2.18935922e-01 -1.47969455e-01 -4.59734321e-01
-1.08319545e+00 -8.12987804e-01 9.59623933e-01 -6.90083802e-01
-2.96050310e-01 4.24935967e-01 7.93426991e-01 9.87584665e-02
4.35610592e-01 -3.89451087e-01 -5.77261388e-01 -5.12241781e-01
1.73881352e-01 7.02793121e-01 -2.24719808e-01 -1.92064002... | [5.618532657623291, -0.11750481277704239] |
22751fdd-0f4d-4d23-abba-38b14a903c76 | toward-real-flare-removal-a-comprehensive | 2306.15884 | null | https://arxiv.org/abs/2306.15884v1 | https://arxiv.org/pdf/2306.15884v1.pdf | Toward Real Flare Removal: A Comprehensive Pipeline and A New Benchmark | Photographing in the under-illuminated scenes, the presence of complex light sources often leave strong flare artifacts in images, where the intensity, the spectrum, the reflection, and the aberration altogether contribute the deterioration. Besides the image quality, it also influence the performance of down-stream vi... | ['Yueting Chen', 'Zhihai Xu', 'Huajun Feng', 'Shiqi Chen', 'Zheyan Jin'] | 2023-06-28 | null | null | null | null | ['flare-removal'] | ['computer-vision'] | [ 3.36131722e-01 -7.13486791e-01 6.61297321e-01 -2.07903162e-01
-4.06990945e-01 -7.71095097e-01 5.42159796e-01 -3.41752172e-01
2.29529843e-01 6.63618624e-01 3.29234540e-01 2.19833061e-01
-4.55573380e-01 -8.22912276e-01 -3.57287377e-01 -9.31387365e-01
1.70282610e-02 5.71312420e-02 2.73220837e-01 -5.53194702... | [10.721453666687012, -3.053920030593872] |
761b7c42-3f6d-41a9-8836-424197dcc7d7 | prompt-based-zero-shot-relation | 2112.04539 | null | https://arxiv.org/abs/2112.04539v2 | https://arxiv.org/pdf/2112.04539v2.pdf | Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation | In relation triplet extraction (RTE), recognizing unseen (new) relations for which there are no training instances is a challenging task. Efforts have been made to recognize unseen relations based on question-answering models or relation descriptions. However, these approaches miss the semantic information about connec... | ['Hoda Eldardiry', 'Jiaying Gong'] | 2021-12-08 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 3.74843329e-01 9.09813404e-01 -2.10099593e-01 -6.47490919e-01
-4.94793117e-01 -3.30214560e-01 7.07937479e-01 1.45499676e-01
-3.75882089e-02 8.83003652e-01 2.68946528e-01 -3.08464080e-01
-3.91261399e-01 -1.20584595e+00 -6.27148807e-01 -1.23497352e-01
3.16202223e-01 9.90249336e-01 2.13656753e-01 -8.79743814... | [9.33158016204834, 8.439952850341797] |
0b9be94e-0fa4-4098-a89c-e5e84ce60237 | offline-online-associated-camera-aware | 2201.05820 | null | https://arxiv.org/abs/2201.05820v2 | https://arxiv.org/pdf/2201.05820v2.pdf | Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification | Recently, unsupervised person re-identification (Re-ID) has received increasing research attention due to its potential for label-free applications. A promising way to address unsupervised Re-ID is clustering-based, which generates pseudo labels by clustering and uses the pseudo labels to train a Re-ID model iterativel... | ['Xian-Sheng Hua', 'Xiaojin Gong', 'Baisheng Lai', 'Jiachen Li', 'Menglin Wang'] | 2022-01-15 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.83568045e-01 -1.62213206e-01 -2.05880612e-01 -5.56275249e-01
-6.48864865e-01 -4.86486822e-01 6.48646116e-01 -7.39158988e-02
-5.48863947e-01 4.68955576e-01 1.37548715e-01 3.92673612e-01
-1.09390736e-01 -6.50287271e-01 -5.88221729e-01 -7.29461372e-01
1.53304681e-01 8.34690154e-01 1.09712876e-01 3.89335066... | [14.814632415771484, 1.0826942920684814] |
f00d321e-b94f-42fa-aa83-8490e16ce0dc | weight-agnostic-neural-networks | 1906.04358 | null | https://arxiv.org/abs/1906.04358v2 | https://arxiv.org/pdf/1906.04358v2.pdf | Weight Agnostic Neural Networks | Not all neural network architectures are created equal, some perform much better than others for certain tasks. But how important are the weight parameters of a neural network compared to its architecture? In this work, we question to what extent neural network architectures alone, without learning any weight parameter... | ['David Ha', 'Adam Gaier'] | 2019-06-11 | weight-agnostic-neural-networks-1 | http://papers.nips.cc/paper/8777-weight-agnostic-neural-networks | http://papers.nips.cc/paper/8777-weight-agnostic-neural-networks.pdf | neurips-2019-12 | ['carracing-v0'] | ['playing-games'] | [ 2.80831277e-01 3.31149369e-01 -1.71247929e-01 -5.13970256e-01
-2.82017946e-01 -5.67753077e-01 3.14208657e-01 -3.19292277e-01
-8.47906947e-01 6.85197711e-01 -3.69443223e-02 -4.87808526e-01
-4.03917342e-01 -7.72000492e-01 -7.23334610e-01 -6.20793164e-01
8.29077139e-02 8.60777617e-01 2.61969298e-01 -1.87281549... | [8.58804702758789, 3.2812137603759766] |
57249e9f-42ef-4f3e-937c-e4de6c2c109d | video-question-answering-using-clip-guided | 2303.03131 | null | https://arxiv.org/abs/2303.03131v2 | https://arxiv.org/pdf/2303.03131v2.pdf | Video Question Answering Using CLIP-Guided Visual-Text Attention | Cross-modal learning of video and text plays a key role in Video Question Answering (VideoQA). In this paper, we propose a visual-text attention mechanism to utilize the Contrastive Language-Image Pre-training (CLIP) trained on lots of general domain language-image pairs to guide the cross-modal learning for VideoQA. S... | ['Xudong Jiang', 'Jianfeng Ren', 'Chenglin Yao', 'Weikai Kong', 'Shuhong Ye'] | 2023-03-06 | null | null | null | null | ['video-question-answering', 'general-knowledge'] | ['computer-vision', 'miscellaneous'] | [ 3.77432220e-02 -6.44387960e-01 -2.20087931e-01 -5.15776694e-01
-1.35608017e+00 -6.09997332e-01 6.46399021e-01 -3.68637174e-01
-5.55333972e-01 4.49796289e-01 3.79475892e-01 -1.90021709e-01
1.52898401e-01 -3.39720845e-01 -7.47409940e-01 -5.13305604e-01
4.36405450e-01 3.46460968e-01 6.08965397e-01 -1.62566200... | [10.372069358825684, 1.0361772775650024] |
b52bb1de-b64f-4e30-8cfd-3eae986eea43 | cu-ud-text-mining-drug-and-chemical-protein | 2112.03004 | null | https://arxiv.org/abs/2112.03004v1 | https://arxiv.org/pdf/2112.03004v1.pdf | CU-UD: text-mining drug and chemical-protein interactions with ensembles of BERT-based models | Identifying the relations between chemicals and proteins is an important text mining task. BioCreative VII track 1 DrugProt task aims to promote the development and evaluation of systems that can automatically detect relations between chemical compounds/drugs and genes/proteins in PubMed abstracts. In this paper, we de... | ['Yifan Peng', 'K. Vijay-Shanker', 'Mehmet Efruz Karabulut'] | 2021-11-11 | null | null | null | null | ['drugprot'] | ['natural-language-processing'] | [ 1.48409024e-01 4.71893661e-02 -4.48361337e-01 -2.17036650e-01
-6.37370348e-01 -6.25339508e-01 5.59231281e-01 9.06962454e-01
-1.42669469e-01 1.34146309e+00 5.99151477e-02 -6.03674710e-01
-2.71471351e-01 -4.86629784e-01 -7.98673153e-01 -7.73221433e-01
-7.57041797e-02 5.77339113e-01 -2.51483858e-01 4.89952825... | [8.41549301147461, 8.711759567260742] |
5986625f-8b49-4a56-a021-4bc40b9577d3 | simplifying-full-waveform-inversion-via | 2305.13314 | null | https://arxiv.org/abs/2305.13314v1 | https://arxiv.org/pdf/2305.13314v1.pdf | Simplifying Full Waveform Inversion via Domain-Independent Self-Supervised Learning | Geophysics has witnessed success in applying deep learning to one of its core problems: full waveform inversion (FWI) to predict subsurface velocity maps from seismic data. It is treated as an image-to-image translation problem, jointly training an encoder for seismic data and a decoder for the velocity map from seismi... | ['Youzuo Lin', 'Zicheng Liu', 'Shihang Feng', 'Peng Jin', 'Yinpeng Chen', 'Yinan Feng'] | 2023-04-27 | null | null | null | null | ['image-to-image-translation', 'geophysics', 'image-to-image-translation'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [ 6.07823491e-01 3.40936840e-01 1.23202972e-01 -4.67526853e-01
-1.23352456e+00 -2.79643625e-01 8.11575532e-01 -3.44871223e-01
-3.75841618e-01 4.05190378e-01 4.57828581e-01 -2.62772679e-01
1.18353158e-01 -8.32335293e-01 -1.29539490e+00 -9.98415411e-01
-2.21669227e-01 7.04122126e-01 3.09711307e-01 -8.08096230... | [6.871970176696777, 2.520144462585449] |
6a4ac16b-2880-4b85-b7fc-08c56ab7ae2d | rank-based-causal-discovery-for-post | 2302.12341 | null | https://arxiv.org/abs/2302.12341v1 | https://arxiv.org/pdf/2302.12341v1.pdf | Rank-Based Causal Discovery for Post-Nonlinear Models | Learning causal relationships from empirical observations is a central task in scientific research. A common method is to employ structural causal models that postulate noisy functional relations among a set of interacting variables. To ensure unique identifiability of causal directions, researchers consider restricted... | ['Mathias Drton', 'David Strieder', 'Grigor Keropyan'] | 2023-02-23 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.01205993e-01 -2.29688942e-01 -4.09656703e-01 -3.44068736e-01
-4.04036492e-01 -7.15103149e-01 5.58084965e-01 1.07045121e-01
-9.48779956e-02 1.14035714e+00 2.28664815e-01 -5.19659042e-01
-1.14199078e+00 -7.87571073e-01 -8.64775479e-01 -9.18209255e-01
-3.31793219e-01 4.39902484e-01 5.16884290e-02 2.86113098... | [7.790450096130371, 5.27681827545166] |
e58e4e8c-7ab3-4a8a-a3b1-46684800b7fd | probing-visual-audio-representation-for-video | 2206.10157 | null | https://arxiv.org/abs/2206.10157v1 | https://arxiv.org/pdf/2206.10157v1.pdf | Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning | Video highlight detection is a crucial yet challenging problem that aims to identify the interesting moments in untrimmed videos. The key to this task lies in effective video representations that jointly pursue two goals, \textit{i.e.}, cross-modal representation learning and fine-grained feature discrimination. In thi... | ['Shuai Yi', 'Jun Hou', 'Shinan Liu', 'Lingbo Liu', 'Kunlin Yang', 'Feng Zhang', 'Shuaicheng Li'] | 2022-06-21 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 3.32710475e-01 -5.12718499e-01 -3.71157199e-01 -1.37962177e-01
-1.02801776e+00 -4.70579088e-01 6.65895283e-01 1.48313999e-01
-1.72254026e-01 3.36357951e-01 6.23301327e-01 3.58871400e-01
-5.55851877e-01 -4.55959976e-01 -5.44885099e-01 -9.70032394e-01
-2.10405186e-01 -4.57469076e-01 1.24180280e-01 -1.78946555... | [13.205392837524414, 4.816136360168457] |
66fed3f0-f060-4ab7-a372-a330a941a5f2 | direct-segmentation-of-brain-white-matter | 2307.02223 | null | https://arxiv.org/abs/2307.02223v1 | https://arxiv.org/pdf/2307.02223v1.pdf | Direct segmentation of brain white matter tracts in diffusion MRI | The brain white matter consists of a set of tracts that connect distinct regions of the brain. Segmentation of these tracts is often needed for clinical and research studies. Diffusion-weighted MRI offers unique contrast to delineate these tracts. However, existing segmentation methods rely on intermediate computations... | ['Davood Karimi', 'Meritxell Bach Cuadra', 'Ali Gholipour', 'Hamza Kebiri'] | 2023-07-05 | null | null | null | null | ['brain-segmentation'] | ['medical'] | [-2.56665915e-01 -3.56655031e-01 9.07321572e-02 -2.81171799e-01
-5.03817856e-01 -5.40257215e-01 1.49526045e-01 5.35867736e-02
-6.91559911e-01 7.92949677e-01 1.21704265e-01 -3.53588313e-01
-9.23860744e-02 -6.15626454e-01 -2.37379700e-01 -7.05209732e-01
-3.46387625e-01 7.43842423e-01 5.11145413e-01 1.85866416... | [14.07314395904541, -2.244398832321167] |
d1b6cb07-aaaa-4793-bfdc-11eb8795ee91 | actionformer-localizing-moments-of-actions | 2202.07925 | null | https://arxiv.org/abs/2202.07925v2 | https://arxiv.org/pdf/2202.07925v2.pdf | ActionFormer: Localizing Moments of Actions with Transformers | Self-attention based Transformer models have demonstrated impressive results for image classification and object detection, and more recently for video understanding. Inspired by this success, we investigate the application of Transformer networks for temporal action localization in videos. To this end, we present Acti... | ['Yin Li', 'Jianxin Wu', 'Chenlin Zhang'] | 2022-02-16 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 2.78677732e-01 -7.70239383e-02 -6.03236973e-01 -7.18592182e-02
-8.56397390e-01 -4.92025226e-01 6.79748297e-01 -3.93283665e-01
-3.74374688e-01 3.67954642e-01 6.79926991e-01 1.54884264e-01
9.99288633e-02 -3.69876802e-01 -6.80716574e-01 -5.62720001e-01
-4.13932115e-01 -5.47238812e-02 5.65190196e-01 8.59385133... | [8.402515411376953, 0.4454311728477478] |
bd34cd16-6814-4882-a277-dbe0da379af7 | building-a-computer-mahjong-player-via-deep | 1906.02146 | null | https://arxiv.org/abs/1906.02146v2 | https://arxiv.org/pdf/1906.02146v2.pdf | Building a Computer Mahjong Player via Deep Convolutional Neural Networks | The evaluation function for imperfect information games is always hard to define but owns a significant impact on the playing strength of a program. Deep learning has made great achievements these years, and already exceeded the top human players' level even in the game of Go. In this paper, we introduce a new data mod... | ['Yoshihiro Kawahara', 'Shiqi Gao', 'Yoshimasa Tsuruoka', 'Fuminori Okuya'] | 2019-06-05 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-2.65364856e-01 5.94709106e-02 -3.09696853e-01 1.31232783e-01
-7.61736512e-01 -4.85047042e-01 2.61505663e-01 -1.57744259e-01
-7.59663880e-01 6.24717772e-01 3.44591528e-01 -4.27100450e-01
-1.34497449e-01 -1.19705355e+00 -9.38181996e-01 -3.16498011e-01
-6.85638115e-02 5.74113727e-01 6.42920375e-01 -9.06902254... | [3.471459150314331, 1.4274100065231323] |
1bbd09bd-1bca-4b3e-affd-f07425bfe4fc | contrastive-learning-of-sentence-embeddings | 2305.15077 | null | https://arxiv.org/abs/2305.15077v1 | https://arxiv.org/pdf/2305.15077v1.pdf | Contrastive Learning of Sentence Embeddings from Scratch | Contrastive learning has been the dominant approach to train state-of-the-art sentence embeddings. Previous studies have typically learned sentence embeddings either through the use of human-annotated natural language inference (NLI) data or via large-scale unlabeled sentences in an unsupervised manner. However, even i... | ['Junxian He', 'Zhenzhong Lan', 'Junlei Zhang'] | 2023-05-24 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 5.93507111e-01 1.60372257e-01 -2.40277514e-01 -6.76954508e-01
-9.69254494e-01 -4.80740726e-01 9.57558453e-01 5.34063816e-01
-8.82203043e-01 8.36585283e-01 6.55790150e-01 -2.65612572e-01
3.57278287e-01 -6.09829009e-01 -6.36259854e-01 -2.99152732e-01
3.49858642e-01 5.68275034e-01 5.19189537e-02 -3.98756623... | [10.84504508972168, 8.569464683532715] |
c256604f-05bc-492b-be7c-efef1e6637bf | deep-visual-anomaly-detection-with-negative | 2105.11058 | null | https://arxiv.org/abs/2105.11058v1 | https://arxiv.org/pdf/2105.11058v1.pdf | Deep Visual Anomaly detection with Negative Learning | With the increase in the learning capability of deep convolution-based architectures, various applications of such models have been proposed over time. In the field of anomaly detection, improvements in deep learning opened new prospects of exploration for the researchers whom tried to automate the labor-intensive feat... | ['Seung-Ik Lee', 'Muhammad Zaigham Zaheer', 'Marcella Astrid', 'Jin-ha Lee'] | 2021-05-24 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 3.44066530e-01 8.87928158e-02 3.21423352e-01 -3.07904035e-01
-7.12141246e-02 -3.78381550e-01 5.56305826e-01 2.22570449e-01
-3.51898968e-01 4.94295895e-01 -4.19816852e-01 -3.12105447e-01
7.10997060e-02 -1.15813673e+00 -5.65082848e-01 -8.40561748e-01
-7.64271468e-02 4.39104557e-01 2.33670190e-01 -3.02296519... | [7.609884262084961, 2.248992443084717] |
9e7d3312-cbe1-46ed-9759-c82be6e10db2 | molecular-mechanics-driven-graph-neural | 2011.07457 | null | https://arxiv.org/abs/2011.07457v1 | https://arxiv.org/pdf/2011.07457v1.pdf | Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures | The prediction of physicochemical properties from molecular structures is a crucial task for artificial intelligence aided molecular design. A growing number of Graph Neural Networks (GNNs) have been proposed to address this challenge. These models improve their expressive power by incorporating auxiliary information i... | ['Lei Xie', 'Yang Liu', 'Shuo Zhang'] | 2020-11-15 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 3.31363112e-01 2.06536204e-01 -4.05889601e-01 -2.85581708e-01
-6.27819970e-02 -2.33868256e-01 3.41953158e-01 5.93155801e-01
-3.25844884e-01 1.19559300e+00 -2.02257425e-01 -5.52523911e-01
-1.53409004e-01 -1.12143910e+00 -9.34830606e-01 -7.13490546e-01
-2.23200485e-01 3.84292185e-01 3.58729869e-01 -3.64360780... | [5.128928184509277, 5.862573146820068] |
8a328659-082e-49fb-ab9e-9fd6a7f99688 | big-learning-a-universal-machine-learning | 2207.03899 | null | https://arxiv.org/abs/2207.03899v4 | https://arxiv.org/pdf/2207.03899v4.pdf | Big Learning | Recent advances in big/foundation models reveal a promising path for deep learning, where the roadmap steadily moves from big data to big models to (the newly-introduced) big learning. Specifically, the big learning exhaustively exploits the information inherent in its large-scale complete/incomplete training data, by ... | ['Miaoyun Zhao', 'Yulai Cong'] | 2022-07-08 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-5.42929828e-01 2.32937232e-01 -3.63107532e-01 -3.86305630e-01
-9.83625174e-01 -1.76633745e-01 7.25741804e-01 -2.66604662e-01
-1.19366691e-01 9.18985903e-01 -1.89980194e-01 -2.56462097e-01
-6.19340599e-01 -8.32045794e-01 -9.16473866e-01 -9.20840144e-01
-2.06829965e-01 7.05080688e-01 -4.42253985e-02 -1.28350064... | [9.281145095825195, 3.6130502223968506] |
c80e6bd4-799c-417c-9af2-eb9bea30ae24 | pixel-wise-agricultural-image-time-series | 2303.12533 | null | https://arxiv.org/abs/2303.12533v1 | https://arxiv.org/pdf/2303.12533v1.pdf | Pixel-wise Agricultural Image Time Series Classification: Comparisons and a Deformable Prototype-based Approach | Improvements in Earth observation by satellites allow for imagery of ever higher temporal and spatial resolution. Leveraging this data for agricultural monitoring is key for addressing environmental and economic challenges. Current methods for crop segmentation using temporal data either rely on annotated data or are h... | ['Mathieu Aubry', 'Jean Ponce', 'Elliot Vincent'] | 2023-03-22 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 6.37810767e-01 -2.46902794e-01 -3.47880393e-01 -6.14185214e-01
-3.89953673e-01 -1.12934887e+00 4.84896392e-01 4.94447887e-01
-2.83929884e-01 3.85371923e-01 -3.51120293e-01 -6.92328215e-01
-4.18376684e-01 -9.49394763e-01 -4.56864744e-01 -8.35550606e-01
-6.35171294e-01 2.36028641e-01 2.94267923e-01 -3.79858911... | [9.458680152893066, -1.5409806966781616] |
f7939174-302e-4bed-a660-05babb32b185 | holistic-deep-reinforcement-learning-based | 2302.02921 | null | https://arxiv.org/abs/2302.02921v1 | https://arxiv.org/pdf/2302.02921v1.pdf | Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems | In recent years, Deep Reinforcement Learning emerged as a promising approach for autonomous navigation of ground vehicles and has been utilized in various areas of navigation such as cruise control, lane changing, or obstacle avoidance. However, most research works either focus on providing an end-to-end solution train... | ['Jens Lambrecht', 'Teham Bhuiyan', 'Marvin Meusel', 'Linh Kästner'] | 2023-02-06 | null | null | null | null | ['motion-planning'] | ['robots'] | [-3.92957538e-01 3.65343802e-02 -4.61786687e-02 -8.78421739e-02
-2.76592344e-01 -5.07334292e-01 5.24807274e-01 2.39601940e-01
-8.89696360e-01 7.81759083e-01 -1.39509693e-01 -6.84780955e-01
-3.26074630e-01 -1.21849084e+00 -8.05314541e-01 -7.38982260e-01
-2.66588241e-01 4.43608075e-01 7.21224785e-01 -9.72688675... | [4.928910732269287, 1.2260489463806152] |
890d4d1b-4b73-4e97-8a37-276de73049b7 | asmr-learning-attribute-based-person-search | 2108.04533 | null | https://arxiv.org/abs/2108.04533v1 | https://arxiv.org/pdf/2108.04533v1.pdf | ASMR: Learning Attribute-Based Person Search with Adaptive Semantic Margin Regularizer | Attribute-based person search is the task of finding person images that are best matched with a set of text attributes given as query. The main challenge of this task is the large modality gap between attributes and images. To reduce the gap, we present a new loss for learning cross-modal embeddings in the context of a... | ['Suha Kwak', 'Jicheol Park', 'Boseung Jeong'] | 2021-08-10 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jeong_ASMR_Learning_Attribute-Based_Person_Search_With_Adaptive_Semantic_Margin_Regularizer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jeong_ASMR_Learning_Attribute-Based_Person_Search_With_Adaptive_Semantic_Margin_Regularizer_ICCV_2021_paper.pdf | iccv-2021-1 | ['person-search'] | ['computer-vision'] | [ 1.16269477e-01 -2.78583057e-02 -1.62240103e-01 -8.02872062e-01
-8.60957384e-01 -6.23185754e-01 8.77306163e-01 4.48787421e-01
-7.87344217e-01 2.80766159e-01 5.36666751e-01 3.02552879e-01
-3.85516435e-01 -6.63216352e-01 -6.45367265e-01 -6.10652745e-01
2.19275787e-01 8.39377761e-01 -1.26437515e-01 5.31574301... | [14.608053207397461, 0.9663406014442444] |
d282504a-d9f9-4d96-bfd3-c6f91226d2b5 | are-neural-topic-models-broken | 2210.16162 | null | https://arxiv.org/abs/2210.16162v1 | https://arxiv.org/pdf/2210.16162v1.pdf | Are Neural Topic Models Broken? | Recently, the relationship between automated and human evaluation of topic models has been called into question. Method developers have staked the efficacy of new topic model variants on automated measures, and their failure to approximate human preferences places these models on uncertain ground. Moreover, existing ev... | ['Philip Resnik', 'Rupak Sarkar', 'Pranav Goel', 'Alexander Hoyle'] | 2022-10-28 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.6126262e-01 5.6450057e-01 -2.9829136e-01 -6.0076147e-01
-8.9492655e-01 -7.7497792e-01 9.4676155e-01 3.2853007e-01
-4.4515684e-01 5.5934888e-01 3.0957034e-01 -3.5195374e-01
-2.0883317e-01 -6.6705376e-01 -4.0934622e-01 -3.2931140e-01
1.2947989e-01 8.0632508e-01 2.5981030e-01 -1.8123053e-03
5.0322700e-01... | [10.377471923828125, 7.138607501983643] |
9a3a2eb8-a662-468c-9120-74828d40ccc0 | automated-seismic-source-characterisation | null | null | https://doi.org/10.31223/osf.io/nbmzt | https://doi.org/10.31223/osf.io/nbmzt | Automated Seismic Source Characterisation Using Deep Graph Neural Networks | Most seismological analysis methods require knowledge of the geographic location of the stations comprising a seismic network. However, common machine learning tools used in seismology do not account for this spatial information, and so there is an underutilised potential for improving the performance of machine learni... | ['Jean-Paul Ampuero', 'Martijn van den Ende'] | 2020-09-16 | null | null | null | null | ['seismic-source-localization'] | ['time-series'] | [ 5.78520559e-02 -1.86377808e-01 2.57859588e-01 1.37304552e-02
-4.92134184e-01 -6.44457459e-01 4.39429551e-01 7.60458529e-01
-4.50821340e-01 4.63113576e-01 2.33491048e-01 -5.52207172e-01
-6.21501207e-01 -1.30801678e+00 -5.30709386e-01 -8.45538378e-01
-8.02655816e-01 4.57070947e-01 3.70299041e-01 -1.85153186... | [6.870316505432129, 2.7198286056518555] |
9a48dbef-fa92-4451-a2e7-6cd04dcf0c86 | w2vv-fully-deep-learning-for-ad-hoc-video | null | null | https://dl.acm.org/doi/pdf/10.1145/3343031.3350906?download=true | http://lixirong.net/pub/mm2019-w2vvpp.pdf | W2VV++: Fully Deep Learning for Ad-hoc Video Search | Ad-hoc video search (AVS) is an important yet challenging problem in multimedia retrieval. Different from previous concept-based methods, we propose an end-to-end deep learning method for query representation learning. The proposed method requires no concept modeling, matching and selection. The backbone of our method ... | ['Xirong Li; Chaoxi Xu; Gang Yang; Zhineng Chen; Jianfeng Dong'] | 2019-10-21 | null | null | null | acm-multimedia-2019-2019-10-1 | ['ad-hoc-video-search'] | ['computer-vision'] | [ 2.31340788e-02 -2.78148502e-01 -2.96367943e-01 -2.73927271e-01
-1.41769326e+00 -5.17197251e-01 9.26550329e-01 5.36144555e-01
-7.92603076e-01 2.18951568e-01 4.84022975e-01 6.96143433e-02
-1.55777559e-01 -3.89885962e-01 -8.57355595e-01 -3.20062459e-01
3.51813855e-03 4.34681416e-01 4.70623791e-01 -4.81005043... | [10.472843170166016, 0.9085996150970459] |
5f45e28c-3078-44e2-a189-e5681fa95e96 | brain-like-object-recognition-with-high | 1909.06161 | null | https://arxiv.org/abs/1909.06161v2 | https://arxiv.org/pdf/1909.06161v2.pdf | Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs | Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially inspired by brain anatomy, over the past years, these ANNs have evolved from a simple eight-layer architecture in AlexNet to extremely deep... | ['Daniel L. K. Yamins', 'Jonathan Prescott-Roy', 'Kohitij Kar', 'Rishi Rajalingham', 'Najib J. Majaj', 'Jonas Kubilius', 'Daniel Bear', 'Pouya Bashivan', 'Kailyn Schmidt', 'Ha Hong', 'Elias B. Issa', 'Aran Nayebi', 'Martin Schrimpf', 'James J. DiCarlo'] | 2019-09-13 | brain-like-object-recognition-with-high-1 | http://papers.nips.cc/paper/9441-brain-like-object-recognition-with-high-performing-shallow-recurrent-anns | http://papers.nips.cc/paper/9441-brain-like-object-recognition-with-high-performing-shallow-recurrent-anns.pdf | neurips-2019-12 | ['object-categorization'] | ['computer-vision'] | [ 6.28587902e-02 -1.79204475e-02 1.31338621e-02 -1.26735613e-01
2.96930343e-01 -5.50433218e-01 7.42444575e-01 1.26939163e-01
-7.66248524e-01 2.64702320e-01 2.11298928e-01 -4.66045678e-01
-2.61592925e-01 -3.88075948e-01 -7.42682874e-01 -2.37162709e-01
-4.34494734e-01 3.40395212e-01 3.16492200e-01 -4.38036233... | [9.559746742248535, 2.5049867630004883] |
2004be93-ae6e-49b3-80b7-a15f9185dbf3 | detecting-deception-in-political-debates | 1910.01990 | null | https://arxiv.org/abs/1910.01990v1 | https://arxiv.org/pdf/1910.01990v1.pdf | Detecting Deception in Political Debates Using Acoustic and Textual Features | We present work on deception detection, where, given a spoken claim, we aim to predict its factuality. While previous work in the speech community has relied on recordings from staged setups where people were asked to tell the truth or to lie and their statements were recorded, here we use real-world political debates.... | ['Daniel Kopev', 'Preslav Nakov', 'Ivan Koychev', 'Ahmed Ali'] | 2019-10-04 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [ 2.08918959e-01 1.47165880e-01 2.10804343e-01 -5.34139276e-01
-1.48932779e+00 -8.27036858e-01 1.15907598e+00 1.76093400e-01
-4.19304192e-01 5.57962835e-01 7.90006638e-01 -3.38076979e-01
3.83095920e-01 -1.58948079e-01 -6.12981558e-01 -4.65273499e-01
1.79242343e-01 3.45226675e-01 -1.06737442e-01 -2.18682379... | [8.264176368713379, 10.383140563964844] |
61da76a9-ef8d-4cd9-ae66-a08c11470de4 | enhanced-biologically-inspired-model-for | 1710.10188 | null | http://arxiv.org/abs/1710.10188v1 | http://arxiv.org/pdf/1710.10188v1.pdf | Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment | Biologically inspired model (BIM) for image recognition is a robust
computational architecture, which has attracted widespread attention. BIM can
be described as a four-layer structure based on the mechanisms of the visual
cortex. Although the performance of BIM for image recognition is robust, it
takes the randomly se... | ['Li-Hao Jia', 'Yan-Feng Lu', 'Yi Li', 'Hong Qaio'] | 2017-10-27 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 2.67722130e-01 -3.28678370e-01 2.08209023e-01 -1.71404570e-01
-2.25447059e-01 2.14682236e-01 5.68834364e-01 -2.34941557e-01
-4.77116227e-01 4.70559776e-01 -2.33864740e-01 -5.37375221e-03
-4.32452649e-01 -7.92364895e-01 -5.84611297e-01 -1.05174661e+00
-6.10332191e-02 -3.28538328e-01 5.18376946e-01 -1.50122374... | [10.245092391967773, -0.18476247787475586] |
36062072-65d8-444f-a3c0-064de4fed17f | online-deception-detection-refueled-by-real | 1707.09406 | null | http://arxiv.org/abs/1707.09406v1 | http://arxiv.org/pdf/1707.09406v1.pdf | Online Deception Detection Refueled by Real World Data Collection | The lack of large realistic datasets presents a bottleneck in online
deception detection studies. In this paper, we apply a data collection method
based on social network analysis to quickly identify high-quality deceptive and
truthful online reviews from Amazon. The dataset contains more than 10,000
deceptive reviews ... | ['James Caverlee', 'Ruihong Huang', 'Zeyu Dai', 'Wenlin Yao'] | 2017-07-28 | online-deception-detection-refueled-by-real-1 | https://aclanthology.org/R17-1102 | https://aclanthology.org/R17-1102.pdf | ranlp-2017-9 | ['deception-detection'] | ['miscellaneous'] | [-3.14969361e-01 -2.33204082e-01 -2.74115026e-01 -8.15728009e-01
-4.63013113e-01 -9.72643018e-01 7.02185869e-01 2.14398541e-02
-1.60799176e-01 6.07619762e-01 -1.80281520e-01 -2.94092983e-01
-1.78475708e-01 -3.01333278e-01 -2.32660711e-01 1.35936914e-02
-8.29124302e-02 3.32131863e-01 -2.60747299e-02 -5.47482789... | [8.093703269958496, 10.190067291259766] |
0f41e931-1b47-45b9-987d-a541ac669814 | destress-deep-learning-for-unsupervised | 1911.13213 | null | https://arxiv.org/abs/1911.13213v1 | https://arxiv.org/pdf/1911.13213v1.pdf | DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data | In this work we perform a study of various unsupervised methods to identify mental stress in firefighter trainees based on unlabeled heart rate variability data. We collect RR interval time series data from nearly 100 firefighter trainees that participated in a drill. We explore and compare three methods in order to pe... | ['María Rodríguez Martínez', 'Ali Oskooei', 'Arvind Sridhar', 'Sophie Mai Chau', 'Jonas Weiss', 'Bruno Michel'] | 2019-11-18 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-9.27717313e-02 -5.72502501e-02 1.89706370e-01 -6.48772418e-01
-1.80725604e-02 -1.85775086e-01 -2.22425982e-01 4.30331796e-01
-5.08484602e-01 5.06066799e-01 4.22556669e-01 -1.02691904e-01
-7.92930424e-01 -5.47716856e-01 6.50365204e-02 -7.49302983e-01
-7.07443833e-01 1.63301796e-01 -5.78661680e-01 -4.83116090... | [13.81567096710205, 3.1222331523895264] |
7fda7103-54d0-4479-afde-1557130a00c9 | a-comparative-analysis-of-portfolio | 2305.17523 | null | https://arxiv.org/abs/2305.17523v1 | https://arxiv.org/pdf/2305.17523v1.pdf | A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market | This paper presents a comparative analysis of the performances of three portfolio optimization approaches. Three approaches of portfolio optimization that are considered in this work are the mean-variance portfolio (MVP), hierarchical risk parity (HRP) portfolio, and reinforcement learning-based portfolio. The portfoli... | ['Soubhik Maji', 'Manas Kumar Sarkar', 'Kushagra Kumar', 'Atish Kumar Majee', 'Anshuman Pathak', 'Aditya Jaiswal', 'Jaydip Sen'] | 2023-05-27 | null | null | null | null | ['q-learning', 'portfolio-optimization'] | ['methodology', 'time-series'] | [-5.83735943e-01 -8.73849392e-02 -1.75752386e-01 -1.25287369e-01
-5.59541583e-01 -5.73364556e-01 5.11641145e-01 -7.58597404e-02
-3.33409309e-01 1.08481455e+00 2.89964318e-01 -5.37052453e-01
-7.29323924e-01 -1.22129476e+00 -3.08692038e-01 -6.85283482e-01
-3.04650456e-01 4.75435406e-01 2.20027104e-01 -3.98054153... | [4.530313968658447, 3.990506649017334] |
06d92e1c-35d4-4ad3-855a-de530f397990 | code-to-comment-translation-a-comparative | 2106.08415 | null | https://arxiv.org/abs/2106.08415v1 | https://arxiv.org/pdf/2106.08415v1.pdf | Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors | Automated source code summarization is a popular software engineering research topic wherein machine translation models are employed to "translate" code snippets into relevant natural language descriptions. Most evaluations of such models are conducted using automatic reference-based metrics. However, given the relativ... | ['Kevin Moran', 'Antonios Anastasopoulos', 'Raihan Islam Arnob', 'Fahim Faisal', 'Junayed Mahmud'] | 2021-06-15 | null | https://aclanthology.org/2021.nlp4prog-1.1 | https://aclanthology.org/2021.nlp4prog-1.1.pdf | acl-nlp4prog-2021-8 | ['code-summarization'] | ['computer-code'] | [ 2.99627751e-01 3.20148826e-01 -4.83543217e-01 -3.27852517e-01
-1.30126941e+00 -6.46439493e-01 4.91165787e-01 7.93330431e-01
-8.08057375e-03 4.14355606e-01 6.14510238e-01 -6.41170084e-01
-6.03342839e-02 -3.27559173e-01 -6.18563771e-01 2.18758211e-01
2.56421685e-01 8.77769068e-02 3.69800366e-02 -3.44330907... | [7.686118125915527, 7.906425476074219] |
4b6eedd9-ebc8-4dba-a677-189abc11a6f6 | unsupervised-visual-time-series | 2111.10309 | null | https://arxiv.org/abs/2111.10309v1 | https://arxiv.org/pdf/2111.10309v1.pdf | Unsupervised Visual Time-Series Representation Learning and Clustering | Time-series data is generated ubiquitously from Internet-of-Things (IoT) infrastructure, connected and wearable devices, remote sensing, autonomous driving research and, audio-video communications, in enormous volumes. This paper investigates the potential of unsupervised representation learning for these time-series. ... | ['Richi Nayak', 'Gaurangi Anand'] | 2021-11-19 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 4.80087727e-01 -1.58441365e-01 -1.61729142e-01 -5.52985013e-01
-4.71010983e-01 -4.59044874e-01 6.67263985e-01 1.16586752e-01
-2.33746886e-01 7.04288781e-01 2.51173198e-01 -4.14087027e-01
-5.30715883e-01 -8.16820562e-01 -3.24108034e-01 -5.14697433e-01
-6.97201192e-01 1.60623506e-01 2.58137137e-02 -1.03639670... | [7.218612194061279, 2.858477830886841] |
03a99316-16cb-4173-8cd3-bbb51a71ac1c | deep-keyphrase-completion | 2111.01910 | null | https://arxiv.org/abs/2111.01910v1 | https://arxiv.org/pdf/2111.01910v1.pdf | Deep Keyphrase Completion | Keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval. Though previous studies have made substantial efforts for automated keyphrase extraction and generation, surprisingly, few studies... | ['Ji Liu', 'Xiaojie Wang', 'Qing Li', 'Fuzhen Zhuang', 'Huali Feng', 'Jia Song', 'Yu Zhao'] | 2021-10-29 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 1.17143989e-01 8.25541094e-02 -2.40947381e-01 3.92712027e-01
-5.91582477e-01 -6.08140826e-01 1.07447898e+00 6.22091770e-01
-4.58512843e-01 7.72711277e-01 1.05029392e+00 -2.22436562e-01
-3.09682965e-01 -9.23201501e-01 -7.78300941e-01 -6.95021808e-01
4.87731814e-01 7.37215206e-02 1.28715634e-01 -3.63836974... | [12.31525993347168, 8.892391204833984] |
1b0a549d-1d16-4e06-9ed7-9a5acbdb66d9 | zeronlg-aligning-and-autoencoding-domains-for | 2303.06458 | null | https://arxiv.org/abs/2303.06458v1 | https://arxiv.org/pdf/2303.06458v1.pdf | ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation | Natural Language Generation (NLG) accepts input data in the form of images, videos, or text and generates corresponding natural language text as output. Existing NLG methods mainly adopt a supervised approach and rely heavily on coupled data-to-text pairs. However, for many targeted scenarios and for non-English langua... | ['David A. Clifton', 'YaoWei Wang', 'Xian Wu', 'Yuexian Zou', 'Fenglin Liu', 'Bang Yang'] | 2023-03-11 | null | null | null | null | ['zero-shot-machine-translation'] | ['natural-language-processing'] | [ 4.90535915e-01 1.74025521e-01 -2.32709467e-01 -1.89356774e-01
-1.20398462e+00 -7.05643952e-01 9.92701590e-01 -4.28402454e-01
-1.29732609e-01 8.64437938e-01 3.16577941e-01 -4.16019112e-01
5.70520520e-01 -8.35593462e-01 -1.18931603e+00 -6.35860026e-01
7.57499337e-01 5.46039879e-01 -1.50934473e-01 1.51327088... | [11.290642738342285, 1.032114028930664] |
6b514fee-c17f-4ffe-9ceb-869f0b524a2c | sliced-at-semeval-2022-task-11-bigger-better | null | null | https://aclanthology.org/2022.semeval-1.205 | https://aclanthology.org/2022.semeval-1.205.pdf | Sliced at SemEval-2022 Task 11: Bigger, Better? Massively Multilingual LMs for Multilingual Complex NER on an Academic GPU Budget | Massively multilingual language models (MMLMs) have become a widely-used representation method, and multiple large MMLMs were proposed in recent years. A trend is to train MMLMs on larger text corpora or with more layers. In this paper we set out to test recent popular MMLMs on detecting semantically ambiguous and comp... | ['Barbara Plank'] | null | null | null | null | semeval-naacl-2022-7 | ['xlm-r'] | ['natural-language-processing'] | [-5.37863970e-01 9.99385789e-02 1.19876832e-01 -3.49695891e-01
-1.12928617e+00 -6.35542750e-01 7.68013597e-01 2.35810727e-01
-9.83720541e-01 1.02779198e+00 3.79567802e-01 -6.09736979e-01
3.16893220e-01 -5.83898306e-01 -9.36051846e-01 -5.68169989e-02
1.36925206e-01 9.67142105e-01 1.33297130e-01 -2.01978907... | [10.368569374084473, 9.887960433959961] |
5faa19e8-d610-45c7-bebf-1ed44ba0935a | machine-learning-for-faster-and-smarter | 2008.02320 | null | https://arxiv.org/abs/2008.02320v1 | https://arxiv.org/pdf/2008.02320v1.pdf | Machine learning for faster and smarter fluorescence lifetime imaging microscopy | Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in biomedical research that uses the fluorophore decay rate to provide additional contrast in fluorescence microscopy. However, at present, the calculation, analysis, and interpretation of FLIM is a complex, slow, and computationally expensive proc... | ['Xiao-Tong Yuan', 'Scott S. Howard', 'Varun Mannam', 'Yide Zhang', 'Cara Ravasio'] | 2020-08-05 | null | null | null | null | ['lifetime-image-denoising'] | ['medical'] | [ 8.57044220e-01 -8.72968435e-01 -5.29090278e-02 -3.92005980e-01
-1.00265872e+00 -7.50672162e-01 1.47856683e-01 4.62123692e-01
-8.13965440e-01 1.17565322e+00 -4.63252246e-01 -1.08671300e-01
1.77591577e-01 -2.85755306e-01 -3.07546347e-01 -1.38482881e+00
1.13482349e-01 2.26578698e-01 3.32337201e-01 6.56444728... | [14.286603927612305, -3.165252208709717] |
ff0bbea7-c037-477d-98d0-b4147ab0ab2e | unlocking-the-power-of-representations-in | 2305.01521 | null | https://arxiv.org/abs/2305.01521v1 | https://arxiv.org/pdf/2305.01521v1.pdf | Unlocking the Power of Representations in Long-term Novelty-based Exploration | We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space. By adapting classical clustering to the nonstationary setting of D... | ['Bilal Piot', 'Michal Valko', 'Oliver Groth', 'Leopoldo Sarra', 'Pablo Sprechmann', 'Charles Blundell', 'Daniele Calandriello', 'Steven Kapturowski', 'Alaa Saade'] | 2023-05-02 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-5.00379920e-01 8.65348950e-02 -3.16885382e-01 6.32575247e-04
-1.22064149e+00 -4.31369424e-01 6.84892952e-01 -2.85548661e-02
-4.94377583e-01 6.97688520e-01 2.97834456e-01 -3.82790983e-01
-4.68768388e-01 -4.74080235e-01 -8.37070346e-01 -6.26276255e-01
-9.99036610e-01 1.11151135e+00 -2.62632698e-01 -2.81624962... | [4.137279033660889, 2.041069269180298] |
de46cc87-5f61-4f2a-abcb-6fb090bcf1b6 | neural-multi-task-learning-in-automated | 1801.06830 | null | http://arxiv.org/abs/1801.06830v1 | http://arxiv.org/pdf/1801.06830v1.pdf | Neural Multi-task Learning in Automated Assessment | Grammatical error detection and automated essay scoring are two tasks in the
area of automated assessment. Traditionally these tasks have been treated
independently with different machine learning models and features used for each
task. In this paper, we develop a multi-task neural network model that jointly
optimises ... | ['Ronan Cummins', 'Marek Rei'] | 2018-01-21 | null | null | null | null | ['automated-essay-scoring', 'grammatical-error-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.72048011e-02 3.03849727e-01 1.49400875e-01 -8.38114500e-01
-1.09756708e+00 -4.42812324e-01 2.20848233e-01 6.08857512e-01
-7.47719586e-01 9.58511114e-01 1.37671173e-01 -2.30010584e-01
-2.25944251e-01 -5.30415952e-01 -2.51215756e-01 -1.48004487e-01
3.97388548e-01 7.14612722e-01 -1.04946136e-01 -2.09661141... | [11.2818021774292, 9.402926445007324] |
21033483-09da-42de-823d-a25c5591ee8a | optimal-precoder-design-for-mimo-ofdm-based | 2109.12452 | null | https://arxiv.org/abs/2109.12452v1 | https://arxiv.org/pdf/2109.12452v1.pdf | Optimal Precoder Design for MIMO-OFDM-based Joint Automotive Radar-Communication Networks | Large-scale deployment of connected vehicles with cooperative awareness technologies increases the demand for vehicle-to-everything (V2X) communication spectrum in 5.9 GHz that is mainly allocated for the exchange of safety messages. To supplement V2X communication and support the high data rates needed by broadband ap... | ['Onur Altintas', 'Chang-Heng Wang', 'Eylem Ekici', 'Ceyhun D. Ozkaptan'] | 2021-09-25 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 3.14919531e-01 1.66004315e-01 -2.10686862e-01 -3.12312365e-01
-6.79096937e-01 -2.76190668e-01 6.85035765e-01 -3.40545505e-01
-3.03439617e-01 7.20384598e-01 -1.93908185e-01 -7.90987432e-01
-4.99808848e-01 -9.24527705e-01 -2.01618224e-01 -8.21918845e-01
-5.79315424e-01 -3.08483411e-02 5.26071005e-02 -1.41859114... | [6.364284992218018, 1.2205051183700562] |
e522df09-63da-4a15-b8f7-5c07dfa88a19 | automated-writing-support-using-deep | null | null | https://aclanthology.org/2020.lrec-1.46 | https://aclanthology.org/2020.lrec-1.46.pdf | Automated Writing Support Using Deep Linguistic Parsers | This paper introduces a new web system that integrates English Grammatical Error Detection (GED) and course-specific stylistic guidelines to automatically review and provide feedback on student assignments. The system is being developed as a pedagogical tool for English Scientific Writing. It uses both general NLP meth... | ['Joseph MacKinnon', "Lu{\\'\\i}s Morgado da Costa", 'Roger V P Winder', 'Benedict Christopher Lin Tzer Liang', 'Shu Yun Li', 'Francis Bond'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.22085664e-02 2.96044827e-01 1.29501805e-01 -4.16001856e-01
-1.08839667e+00 -8.74456823e-01 -1.23918401e-02 9.72100377e-01
-3.71203721e-01 1.03110993e+00 -1.35994166e-01 -1.03581440e+00
-3.65000993e-01 -8.95318270e-01 -5.43581486e-01 2.58160561e-01
6.87068343e-01 3.40659797e-01 4.84237969e-01 -4.91584331... | [11.224066734313965, 9.475326538085938] |
3cc4cb08-a7c4-48c9-8bbc-79f61a9219f3 | does-meta-learning-help-mbert-for-few-shot | null | null | https://aclanthology.org/2022.coling-1.373 | https://aclanthology.org/2022.coling-1.373.pdf | Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages? | Few-shot Question Generation (QG) is an important and challenging problem in the Natural Language Generation (NLG) domain. Multilingual BERT (mBERT) has been successfully used in various Natural Language Understanding (NLU) applications. However, the question of how to utilize mBERT for few-shot QG, possibly with cross... | ['Pawan Goyal', 'Sudeshna Sarkar', 'Amrith Krishna', 'Ashim Gupta', 'Isha Sharma', 'Rupak Kumar Thakur', 'Aniruddha Roy'] | null | null | null | null | coling-2022-10 | ['question-generation'] | ['natural-language-processing'] | [ 6.84291497e-02 1.00925177e-01 -9.86515805e-02 -1.61597118e-01
-1.58187699e+00 -6.00870728e-01 8.20163786e-01 -1.81406781e-01
-4.36516047e-01 1.11007094e+00 4.13106441e-01 -4.08126801e-01
1.82827860e-01 -8.52194667e-01 -7.83626854e-01 -3.90657037e-01
3.72467816e-01 8.26485395e-01 1.75687894e-01 -9.45605993... | [11.772818565368652, 9.007895469665527] |
05895a33-9563-4261-9ced-139bd54821b1 | future-transformer-for-long-term-action | 2205.14022 | null | https://arxiv.org/abs/2205.14022v1 | https://arxiv.org/pdf/2205.14022v1.pdf | Future Transformer for Long-term Action Anticipation | The task of predicting future actions from a video is crucial for a real-world agent interacting with others. When anticipating actions in the distant future, we humans typically consider long-term relations over the whole sequence of actions, i.e., not only observed actions in the past but also potential actions in th... | ['Minsu Cho', 'Seong Jong Ha', 'Manjin Kim', 'Joonseok Lee', 'Dayoung Gong'] | 2022-05-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Gong_Future_Transformer_for_Long-Term_Action_Anticipation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gong_Future_Transformer_for_Long-Term_Action_Anticipation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-anticipation'] | ['computer-vision'] | [ 4.03475940e-01 2.48977497e-01 -2.90360123e-01 -8.14012945e-01
-4.53969419e-01 -1.38836771e-01 1.03278148e+00 -2.47324720e-01
-4.14490312e-01 7.61792481e-01 1.02659917e+00 6.46107197e-02
2.43002713e-01 -5.25445700e-01 -1.00449193e+00 -4.41359907e-01
-5.66079617e-01 4.77434546e-01 1.55837759e-01 -6.00107349... | [8.057863235473633, 0.49661901593208313] |
3b90d484-38d6-4380-afd8-8edf2ee55932 | all-neural-beamformer-for-continuous-speech | 2110.06428 | null | https://arxiv.org/abs/2110.06428v1 | https://arxiv.org/pdf/2110.06428v1.pdf | All-neural beamformer for continuous speech separation | Continuous speech separation (CSS) aims to separate overlapping voices from a continuous influx of conversational audio containing an unknown number of utterances spoken by an unknown number of speakers. A common application scenario is transcribing a meeting conversation recorded by a microphone array. Prior studies e... | ['Sefik Emre Eskimez', 'Dongmei Wang', 'Xiaofei Wang', 'Zhuo Chen', 'Naoyuki Kanda', 'Takuya Yoshioka', 'Zhuohuang Zhang'] | 2021-10-13 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 5.20342827e-01 -2.12030951e-02 4.34693784e-01 -2.88162827e-01
-1.46059859e+00 -5.36452055e-01 3.40342253e-01 -3.25311780e-01
-3.53668749e-01 3.10286343e-01 7.54645705e-01 -4.11824256e-01
9.30107385e-02 4.37053777e-02 -3.31770092e-01 -8.72357130e-01
1.95102632e-01 -8.18758607e-02 -8.92545953e-02 -4.24650498... | [14.816976547241211, 5.970615863800049] |
15b74351-44a6-4add-8780-fc35ef599460 | modulation-spectral-features-for-speech | 2301.05868 | null | https://arxiv.org/abs/2301.05868v1 | https://arxiv.org/pdf/2301.05868v1.pdf | Modulation spectral features for speech emotion recognition using deep neural networks | This work explores the use of constant-Q transform based modulation spectral features (CQT-MSF) for speech emotion recognition (SER). The human perception and analysis of sound comprise of two important cognitive parts: early auditory analysis and cortex-based processing. The early auditory analysis considers spectrogr... | ['Goutam Saha', 'Md Sahidullah', 'Premjeet Singh'] | 2023-01-14 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [ 9.95958820e-02 -3.76974523e-01 4.99866664e-01 -3.27358872e-01
-1.02118433e+00 -4.39932913e-01 5.29562593e-01 4.84965503e-01
-6.53101563e-01 3.33686382e-01 4.17621464e-01 -5.58296684e-03
-2.74194032e-01 -4.81353968e-01 -2.39377081e-01 -7.44922638e-01
-4.94036198e-01 -5.72171688e-01 3.94403189e-01 -7.45530307... | [15.248759269714355, 5.483029842376709] |
fb88b102-acc9-4fa6-a431-e977cc26aa1b | uc-net-uncertainty-inspired-rgb-d-saliency | 2004.05763 | null | https://arxiv.org/abs/2004.05763v1 | https://arxiv.org/pdf/2004.05763v1.pdf | UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders | In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection methods treat the saliency detection task as a point estimation problem, and produce a single saliency map following a deterministic lear... | ['Nick Barnes', 'Fatemeh Sadat Saleh', 'Deng-Ping Fan', 'Jing Zhang', 'Saeed Anwar', 'Yuchao Dai', 'Tong Zhang'] | 2020-04-13 | uc-net-uncertainty-inspired-rgb-d-saliency-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_UC-Net_Uncertainty_Inspired_RGB-D_Saliency_Detection_via_Conditional_Variational_Autoencoders_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_UC-Net_Uncertainty_Inspired_RGB-D_Saliency_Detection_via_Conditional_Variational_Autoencoders_CVPR_2020_paper.pdf | cvpr-2020-6 | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.81997621e-01 5.91376185e-01 -1.73890874e-01 -3.75586063e-01
-9.50444639e-01 -3.32583606e-01 7.13865876e-01 1.16186209e-01
-7.33328015e-02 3.71134579e-01 2.52130330e-01 1.85748011e-01
2.05142513e-01 -3.24412555e-01 -9.98871565e-01 -3.70475978e-01
4.03098494e-01 4.60571080e-01 8.40622902e-01 -5.92685379... | [9.845017433166504, -0.5815160870552063] |
66045a28-319c-41db-9e31-807701810e27 | bdd100k-a-diverse-driving-video-database-with | 1805.04687 | null | https://arxiv.org/abs/1805.04687v2 | https://arxiv.org/pdf/1805.04687v2.pdf | BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning | Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require per... | ['Yingying Chen', 'Xin Wang', 'Wenqi Xian', 'Fisher Yu', 'Fangchen Liu', 'Vashisht Madhavan', 'Trevor Darrell', 'Haofeng Chen'] | 2018-05-12 | bdd100k-a-diverse-driving-dataset-for | http://openaccess.thecvf.com/content_CVPR_2020/html/Yu_BDD100K_A_Diverse_Driving_Dataset_for_Heterogeneous_Multitask_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_BDD100K_A_Diverse_Driving_Dataset_for_Heterogeneous_Multitask_Learning_CVPR_2020_paper.pdf | cvpr-2020-6 | ['semi-supervised-instance-segmentation', 'multi-object-tracking-and-segmentation', 'drivable-area-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.05738707e-01 -4.42494214e-01 -2.56112278e-01 -5.06991148e-01
-6.11444533e-01 -5.32873690e-01 6.52463913e-01 -3.69046867e-01
-3.96520495e-01 5.77328026e-01 1.70732476e-03 -4.05008733e-01
-5.64078018e-02 -5.15136719e-01 -9.36775565e-01 -7.49975443e-01
-2.67033845e-01 3.46354663e-01 4.59680140e-01 -5.10230839... | [8.0474214553833, -1.5524640083312988] |
94c3dd3e-7646-43a1-be58-b8c30db6eaa3 | demonstrating-emma-embodied-multimodal-agent | null | null | https://aclanthology.org/2022.sigdial-1.62 | https://aclanthology.org/2022.sigdial-1.62.pdf | Demonstrating EMMA: Embodied MultiModal Agent for Language-guided Action Execution in 3D Simulated Environments | We demonstrate EMMA, an embodied multimodal agent which has been developed for the Alexa Prize SimBot challenge. The agent acts within a 3D simulated environment for household tasks. EMMA is a unified and multimodal generative model aimed at solving embodied tasks. In contrast to previous work, our approach treats mult... | ['Verena Rieser', 'Oliver Lemon', 'Ioannis Konstas', 'Claudio Greco', 'Arash Eshghi', 'Amit Parekh', 'George Pantazopoulos', 'Malvina Nikandrou', 'Bhathiya Hemanthage', 'Alessandro Suglia'] | null | null | null | null | sigdial-acl-2022-9 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 2.79538214e-01 7.26329029e-01 6.78379655e-01 -3.48217487e-01
-7.25547612e-01 -7.55670071e-01 1.27367508e+00 -3.80140156e-01
-2.47622773e-01 8.27036500e-01 4.77806240e-01 -3.75106037e-02
5.22105277e-01 -6.85443044e-01 -8.19866240e-01 -6.57824218e-01
5.26317358e-02 8.79506171e-01 -3.45601857e-01 -3.11378539... | [10.694376945495605, 1.3037956953048706] |
21edc82b-efe2-4ce0-a7b4-6f0245af7f76 | automatic-liver-segmentation-with-adversarial | 1811.11566 | null | http://arxiv.org/abs/1811.11566v1 | http://arxiv.org/pdf/1811.11566v1.pdf | Automatic Liver Segmentation with Adversarial Loss and Convolutional Neural Network | Automatic segmentation of medical images is among most demanded works in the
medical information field since it saves time of the experts in the field and
avoids human error factors. In this work, a method based on Conditional
Adversarial Networks and Fully Convolutional Networks is proposed for the
automatic segmentat... | ['Bora Baydar', 'Savas Ozkan', 'Gozde Bozdagi Akar'] | 2018-11-28 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.55310169e-01 4.63983208e-01 1.76700637e-01 -3.26389849e-01
-5.81532359e-01 -3.79019260e-01 2.60680109e-01 2.66067028e-01
-6.74657464e-01 5.88120341e-01 4.02842611e-02 -5.66427529e-01
1.74510494e-01 -5.19407153e-01 -5.97245216e-01 -7.76042461e-01
5.81364110e-02 3.28920901e-01 2.23875597e-01 2.16433052... | [14.530289649963379, -2.6272568702697754] |
fc704d58-1165-45d0-86a3-fdbb72529731 | hope-speech-detection-in-under-resourced | 2108.04616 | null | https://arxiv.org/abs/2108.04616v2 | https://arxiv.org/pdf/2108.04616v2.pdf | Hope Speech detection in under-resourced Kannada language | Numerous methods have been developed to monitor the spread of negativity in modern years by eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in onlin... | ['Bharathi Raja Chakravarthi', 'Prabakaran Chandran', 'Kingston Pal Thamburaj', 'Anbukkarasi Sampath', 'Ruba Priyadharshini', 'Adeep Hande'] | 2021-08-10 | null | null | null | null | ['hope-speech-detection'] | ['natural-language-processing'] | [-3.21422726e-01 4.72788990e-01 -4.76598024e-01 -3.89097668e-02
-5.36759675e-01 -4.25804645e-01 7.14181542e-01 2.83084750e-01
-2.00652272e-01 3.68286282e-01 8.95597100e-01 -2.59041190e-01
3.63998234e-01 -3.36969137e-01 -8.42517838e-02 -3.58434796e-01
2.65050530e-01 -4.69873697e-01 -1.90755174e-01 -8.15734625... | [8.972841262817383, 10.673158645629883] |
c60391b8-0d12-4edb-a61a-8345d02ace5f | same-author-or-just-same-topic-towards-topic | null | null | https://openreview.net/forum?id=8sLibFf9Cr1 | https://openreview.net/pdf?id=8sLibFf9Cr1 | Same Author or Just Same Topic? Towards Topic-Independent Style Representations | Style is an integral component of language. Recent advances in the development of style representations have increasingly used training objectives from authorship verification (AV): Do two texts have the same author? The assumption underlying the AV training task (same author approximates same writing style) enables se... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['authorship-verification'] | ['natural-language-processing'] | [ 1.72165185e-01 1.74751833e-01 -3.16587418e-01 -6.44850135e-01
-3.71453613e-01 -9.06641543e-01 1.36157584e+00 2.31362402e-01
-3.07398766e-01 6.66352510e-01 6.74997747e-01 -2.64021128e-01
1.79129004e-01 -6.74413919e-01 -4.39048201e-01 -3.11332226e-01
3.98130208e-01 6.92428827e-01 -2.85221428e-01 -5.93255647... | [11.321292877197266, 9.828271865844727] |
7064fad0-613a-49b2-a8e3-87313213e221 | concorde-net-cell-count-regularized | 1908.00907 | null | https://arxiv.org/abs/1908.00907v1 | https://arxiv.org/pdf/1908.00907v1.pdf | ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images | In digital pathology, cell detection and classification are often prerequisites to quantify cell abundance and explore tissue spatial heterogeneity. However, these tasks are particularly challenging for multiplex immunohistochemistry (mIHC) images due to high levels of variability in staining, expression intensity, and... | ['Priya Lakshmi Narayanan', 'Teresa Marafioti', 'Yinyin Yuan', 'Yeman Brhane Hagos', 'Ayse U. Akarca'] | 2019-08-01 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.90005556e-01 -2.36624777e-01 -5.74572906e-02 -1.34866089e-01
-8.43570054e-01 -5.07703722e-01 4.11467969e-01 5.61856806e-01
-9.28495705e-01 9.07607317e-01 -3.71224850e-01 -1.13075852e-01
1.70043841e-01 -7.99598992e-01 -2.48256728e-01 -1.38488317e+00
-2.55116634e-02 5.17516673e-01 7.83032998e-02 1.54011890... | [14.952441215515137, -3.0870776176452637] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.