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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
998d45b2-5435-443a-b8ad-fdd01b50620f | community-detection-graph-convolutional | 2306.14530 | null | https://arxiv.org/abs/2306.14530v1 | https://arxiv.org/pdf/2306.14530v1.pdf | Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization | The clustering algorithm plays a crucial role in speaker diarization systems. However, traditional clustering algorithms suffer from the complex distribution of speaker embeddings and lack of digging potential relationships between speakers in a session. We propose a novel graph-based clustering approach called Communi... | ['Qingyang Hong', 'Lin Li', 'Haodong Zhou', 'Zhicong Chen', 'Jie Wang'] | 2023-06-26 | null | null | null | null | ['community-detection', 'clustering', 'speaker-diarization'] | ['graphs', 'methodology', 'speech'] | [-3.90332401e-01 1.60120502e-01 3.18709344e-01 -5.19823492e-01
-4.39586282e-01 -5.44529498e-01 5.78489602e-01 4.36508030e-01
1.36677381e-02 -2.22510085e-01 5.08273900e-01 -3.79595101e-01
-2.35017806e-01 -7.00046897e-01 -1.01375595e-01 -7.82512069e-01
-5.34951687e-01 8.50443244e-01 2.65836388e-01 -1.49839729... | [14.354695320129395, 6.143047332763672] |
7021e183-95c1-4015-9330-92057f3a2765 | exploring-transformer-s-potential-on | 2204.03898 | null | https://arxiv.org/abs/2204.03898v1 | https://arxiv.org/pdf/2204.03898v1.pdf | Exploring Transformer's potential on automatic piano transcription | Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic notation. Based on a high-resolution piano transcription system, we explore the possibility of incorporating another powerful sequence trans... | ['Ye Wang', 'Jiqing Han', 'Emmanouil Benetos', 'Ziyi Guo', 'Longshen Ou'] | 2022-04-08 | null | null | null | null | ['music-transcription'] | ['music'] | [ 4.39706057e-01 -2.22091720e-01 -1.90779492e-01 1.07133299e-01
-8.13886225e-01 -8.11804712e-01 6.57224119e-01 -3.50662649e-01
-4.01396215e-01 2.39927247e-01 4.74932224e-01 -2.38275647e-01
-2.80320197e-01 -4.78569210e-01 -5.22764921e-01 -2.91440159e-01
-1.31913021e-01 1.89716920e-01 5.11307940e-02 -3.61211389... | [15.87478256225586, 5.367300033569336] |
0d4febcf-abcf-479d-a0e8-da092196ae31 | comixify-transform-video-into-a-comics | 1812.03473 | null | http://arxiv.org/abs/1812.03473v1 | http://arxiv.org/pdf/1812.03473v1.pdf | Comixify: Transform video into a comics | In this paper, we propose a solution to transform a video into a comics. We
approach this task using a neural style algorithm based on Generative
Adversarial Networks (GANs). Several recent works in the field of Neural Style
Transfer showed that producing an image in the style of another image is
feasible. In this pape... | ['Tomasz Trzciński', 'Przemysław Rokita', 'Paweł Andruszkiewicz', 'Maciej Pęśko', 'Adam Svystun'] | 2018-12-09 | null | null | null | null | ['transform-a-video-into-a-comics'] | ['computer-vision'] | [ 4.86355007e-01 2.36212626e-01 2.63970613e-01 -1.69129208e-01
-3.09704930e-01 -6.93665087e-01 6.77160501e-01 -6.08185828e-01
-1.91442251e-01 8.38715434e-01 4.92038392e-02 -5.75789586e-02
1.59139812e-01 -1.11348736e+00 -1.06074846e+00 -4.21617955e-01
4.64829654e-01 2.55487412e-01 1.06137656e-01 -5.50610960... | [11.641082763671875, -0.4979722201824188] |
8f228d31-8379-42d8-bef6-64f55fdc3a29 | cross-modality-personalization-for-retrieval | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Murrugarra-Llerena_Cross-Modality_Personalization_for_Retrieval_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Murrugarra-Llerena_Cross-Modality_Personalization_for_Retrieval_CVPR_2019_paper.pdf | Cross-Modality Personalization for Retrieval | Existing captioning and gaze prediction approaches do not consider the multiple facets of personality that affect how a viewer extracts meaning from an image. While there are methods that consider personalized captioning, they do not consider personalized perception across modalities, i.e. how a person's way of looking... | [' Adriana Kovashka', 'Nils Murrugarra-Llerena'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['eye-tracking'] | ['computer-vision'] | [ 7.33998418e-02 9.49802995e-02 -3.90155464e-01 -6.86147213e-01
-4.02854681e-01 -6.19230568e-01 8.05323720e-01 3.95700932e-02
-4.22905535e-01 1.63519621e-01 8.35444152e-01 2.55420178e-01
-2.34460663e-02 -2.01867402e-01 -7.39582777e-01 -4.21178579e-01
3.74745697e-01 1.21690668e-01 -2.30458245e-01 5.02801733... | [10.742463111877441, 1.264230489730835] |
1d9c0e3e-961f-4b03-bcf6-8783210bdf8d | matchnet-unifying-feature-and-metric-learning | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Han_MatchNet_Unifying_Feature_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Han_MatchNet_Unifying_Feature_2015_CVPR_paper.pdf | MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching | Motivated by recent successes on learning feature representations and on learning feature comparison functions, we propose a unified approach to combining both for training a patch matching system. Our system, dubbed MatchNet, consists of a deep convolutional network that extracts features from patches and a network ... | ['Yangqing Jia', 'Thomas Leung', 'Xufeng Han', 'Rahul Sukthankar', 'Alexander C. Berg'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['patch-matching'] | ['computer-vision'] | [ 2.34479204e-01 -1.37122661e-01 -2.71594524e-01 -4.86434668e-01
-8.49236488e-01 -5.23356199e-01 6.42544389e-01 2.31725544e-01
-4.74841893e-01 2.50566691e-01 -4.10912260e-02 -7.00435191e-02
-1.69127032e-01 -1.05685604e+00 -9.85539794e-01 -2.56339908e-01
-1.99690863e-01 2.96555459e-01 3.91707629e-01 -2.44634002... | [8.298335075378418, -1.8658082485198975] |
e580031a-aea2-43b4-8b8e-e7035d3cd350 | understanding-influence-functions-and | 2210.01072 | null | https://arxiv.org/abs/2210.01072v1 | https://arxiv.org/pdf/2210.01072v1.pdf | Understanding Influence Functions and Datamodels via Harmonic Analysis | Influence functions estimate effect of individual data points on predictions of the model on test data and were adapted to deep learning in Koh and Liang [2017]. They have been used for detecting data poisoning, detecting helpful and harmful examples, influence of groups of datapoints, etc. Recently, Ilyas et al. [2022... | ['Sanjeev Arora', 'Mark Braverman', 'Arushi Gupta', 'Nikunj Saunshi'] | 2022-10-03 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-2.05456018e-01 -2.79645890e-01 -1.74572796e-01 -1.70943961e-02
-6.55510604e-01 -5.93230426e-01 4.65137631e-01 3.90847802e-01
-7.58828521e-02 7.09043682e-01 3.09619009e-02 -2.21006721e-01
-5.85711360e-01 -7.91827559e-01 -1.09362638e+00 -1.08446813e+00
-6.18517935e-01 -1.95368901e-02 1.22949176e-01 -5.70049174... | [8.07873821258545, 3.5961859226226807] |
478d90ec-e4fa-4c35-8a5c-2eccd5456c8b | dlformer-discrete-latent-transformer-for | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ren_DLFormer_Discrete_Latent_Transformer_for_Video_Inpainting_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ren_DLFormer_Discrete_Latent_Transformer_for_Video_Inpainting_CVPR_2022_paper.pdf | DLFormer: Discrete Latent Transformer for Video Inpainting | Video inpainting remains a challenging problem to fill with plausible and coherent content in unknown areas in video frames despite the prevalence of data-driven methods. Although various transformer-based architectures yield promising result for this task, they still suffer from hallucinating blurry contents and l... | ['Chen Li', 'Xuemiao Xu', 'YuanYuan Zhao', 'Qingqing Zheng', 'Jingjing Ren'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['video-inpainting'] | ['computer-vision'] | [-2.62129325e-02 -1.47029087e-01 -2.02189118e-01 -2.09571168e-01
-7.62766480e-01 -2.32355833e-01 6.63130999e-01 -4.41544145e-01
1.95054814e-01 8.34776521e-01 5.82833350e-01 2.85407186e-01
-2.54199326e-01 -5.27380884e-01 -1.00653827e+00 -6.71340466e-01
8.31265748e-02 2.95564756e-02 8.49287584e-02 1.37063205... | [10.82532787322998, -1.300396203994751] |
f0d7376e-7b92-4b06-88c4-267d8a26a14f | thompson-sampling-with-diffusion-generative | 2301.05182 | null | https://arxiv.org/abs/2301.05182v2 | https://arxiv.org/pdf/2301.05182v2.pdf | Thompson Sampling with Diffusion Generative Prior | In this work, we initiate the idea of using denoising diffusion models to learn priors for online decision making problems. Our special focus is on the meta-learning for bandit framework, with the goal of learning a strategy that performs well across bandit tasks of a same class. To this end, we train a diffusion model... | ['Patrick Blöbaum', 'Branislav Kveton', 'Shiva Prasad Kasiviswanathan', 'Yu-Guan Hsieh'] | 2023-01-12 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 4.89759333e-02 9.65685844e-02 -4.60504413e-01 -1.86533332e-01
-9.10340250e-01 -3.14514011e-01 8.48383009e-01 -5.78320622e-02
-4.43855226e-01 9.29349065e-01 2.52753705e-01 -2.61808157e-01
-4.00768846e-01 -7.57263362e-01 -9.53883350e-01 -9.32786942e-01
1.59970284e-01 7.78740048e-01 -2.76947711e-02 1.88134283... | [4.50785493850708, 3.005964994430542] |
7f41bc90-a565-49ca-b974-ee9b4618bbf8 | deep-learning-methods-for-fingerprint-based | 2205.14935 | null | https://arxiv.org/abs/2205.14935v1 | https://arxiv.org/pdf/2205.14935v1.pdf | Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review | Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and ... | ['Mohammad H. Mahoor', 'Fahad Alhomayani'] | 2022-05-30 | null | null | null | null | ['outdoor-positioning'] | ['miscellaneous'] | [-1.33747503e-01 -4.36943293e-01 -5.57134926e-01 -7.29050577e-01
-5.91774046e-01 -5.92647314e-01 4.23305035e-01 -2.85627067e-01
-4.05870616e-01 9.67178702e-01 -4.90816049e-02 -6.64747059e-01
-4.37766135e-01 -8.88310909e-01 -6.50732994e-01 -7.43023574e-01
-1.94099084e-01 -3.66104953e-02 -3.68382663e-01 1.32047370... | [6.427834510803223, 0.9046868085861206] |
830ba8a1-6c26-4e95-87e6-fc9f660cd000 | towards-loosely-coupling-knowledge-graph | 2202.03173 | null | https://arxiv.org/abs/2202.03173v2 | https://arxiv.org/pdf/2202.03173v2.pdf | Towards Loosely-Coupling Knowledge Graph Embeddings and Ontology-based Reasoning | Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in many applications, such as product recommendation and question answering. The state-of-the-art approaches of knowledge graph embeddings and/or rule mining and reasoning ar... | ['Volker Markl', 'Abelardo Carlos Martinez Lorenzo', 'Zoi Kaoudi'] | 2022-02-07 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings', 'product-recommendation'] | ['graphs', 'methodology', 'miscellaneous'] | [-4.84551340e-02 8.27393174e-01 -5.91694117e-01 -2.21232489e-01
-9.72500443e-02 -6.65769219e-01 2.66166747e-01 6.62435889e-01
-1.89445183e-01 5.85419774e-01 2.98629254e-01 -7.10959554e-01
-6.63658321e-01 -1.24399030e+00 -7.45809913e-01 -7.89762065e-02
1.10105485e-01 7.84519911e-01 2.78682053e-01 -4.60860729... | [8.835153579711914, 7.828054428100586] |
6fbace31-73ed-443c-b702-7971e5cc4a9d | event-neural-networks | 2112.00891 | null | https://arxiv.org/abs/2112.00891v2 | https://arxiv.org/pdf/2112.00891v2.pdf | Event Neural Networks | Video data is often repetitive; for example, the contents of adjacent frames are usually strongly correlated. Such redundancy occurs at multiple levels of complexity, from low-level pixel values to textures and high-level semantics. We propose Event Neural Networks (EvNets), which leverage this redundancy to achieve co... | ['Yin Li', 'Mohit Gupta', 'Matthew Dutson'] | 2021-12-02 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 3.27758282e-01 -4.19876814e-01 -2.96325833e-01 -2.55817860e-01
-3.69763315e-01 -3.90903890e-01 5.75140893e-01 9.35731605e-02
-5.33528566e-01 6.27988458e-01 6.26268461e-02 -1.54831693e-01
1.21359356e-01 -8.87129247e-01 -1.04349840e+00 -3.42008919e-01
-3.99571151e-01 -1.63865417e-01 4.73138988e-01 2.73653328... | [8.658626556396484, -0.7353481650352478] |
faa76b00-2359-4f0b-abba-53945f941a90 | privacy-utility-balanced-voice-de | 2211.05446 | null | https://arxiv.org/abs/2211.05446v1 | https://arxiv.org/pdf/2211.05446v1.pdf | Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples | Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma when enjoying convenient voice services. Existing studies employ direct modification or text-based re-synthesis to de-identify users' voices, but resulting in inconsistent audibility in the presence of... | ['Kui Ren', 'Feng Lin', 'Zhongjie Ba', 'Yingying Chen', 'Jiadi Yu', 'Li Lu', 'Meng Chen'] | 2022-11-10 | null | null | null | null | ['de-identification', 'speaker-identification'] | ['natural-language-processing', 'speech'] | [ 1.23099819e-01 2.49915361e-01 1.75230488e-01 -7.88144842e-02
-1.17446756e+00 -1.07114291e+00 2.53495336e-01 -5.19997060e-01
-2.01214522e-01 5.16670942e-01 4.18900341e-01 -3.69507968e-01
2.57121772e-01 -3.46178174e-01 -5.44329286e-01 -7.04295933e-01
2.08154336e-01 -2.14323550e-01 -4.04117554e-01 5.85271195... | [14.036388397216797, 5.864785671234131] |
fae76cef-9c39-4ca9-9fcd-cdb5058ddc47 | textfusenet-scene-text-detection-with-richer | null | null | https://doi.org/10.24963/ijcai.2020/72 | https://www.ijcai.org/Proceedings/2020/0072.pdf | TextFuseNet: Scene Text Detection with Richer Fused Features | Arbitrary shape text detection in natural scenes is an extremely challenging task. Unlike existing text detection approaches that only perceive texts based on limited feature representations, we propose a novel framework, namely TextFuseNet, to exploit the use of richer features fused for text detection. More specifica... | ['Zhe Chen', 'Jian Ye', 'Bo Du', 'Juhua Liu'] | 2020-05-17 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 5.04295886e-01 -3.34803343e-01 1.22939438e-01 -1.87013388e-01
-9.98759389e-01 -4.76184845e-01 7.55363882e-01 4.84696269e-01
-3.39841634e-01 9.55659002e-02 2.37541586e-01 1.94924213e-02
2.49373540e-01 -7.58283734e-01 -4.30657595e-01 -6.87006116e-01
5.67123234e-01 2.99029648e-01 5.30055761e-01 -9.46357101... | [12.095906257629395, 2.3171732425689697] |
97bc8eee-7ccd-4f33-8e6c-66575f4c47c6 | enriching-unsupervised-user-embedding-via | 2203.10627 | null | https://arxiv.org/abs/2203.10627v2 | https://arxiv.org/pdf/2203.10627v2.pdf | Enriching Unsupervised User Embedding via Medical Concepts | Clinical notes in Electronic Health Records (EHR) present rich documented information of patients to inference phenotype for disease diagnosis and study patient characteristics for cohort selection. Unsupervised user embedding aims to encode patients into fixed-length vectors without human supervisions. Medical concept... | ['Mark Dredze', 'Franck Dernoncourt', 'Xiaolei Huang'] | 2022-03-20 | null | null | null | null | ['phenotype-classification'] | ['medical'] | [ 9.02050659e-02 1.88644290e-01 -4.83681887e-01 -4.47889417e-01
-8.77482951e-01 -3.48418564e-01 1.46177411e-01 1.18229437e+00
-6.48444533e-01 4.86840963e-01 9.62058544e-01 -1.60807461e-01
-2.07116500e-01 -6.55299246e-01 2.68284500e-01 -5.48301458e-01
-4.96476293e-01 9.84367609e-01 -6.56980872e-01 2.79787898... | [7.9594550132751465, 6.838512897491455] |
556ffbe8-9bbe-46b3-bec3-b64e72a27bbb | co-clustering-vertices-and-hyperedges-via | 2102.10169 | null | https://arxiv.org/abs/2102.10169v1 | https://arxiv.org/pdf/2102.10169v1.pdf | Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning | We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs). In this hypergraph model, the contribution of every vertex to each of its incident hyperedges is represented through an edge-dependent weight, conferring the model higher expressivity than the ... | ['Santiago Segarra', 'Boning Li', 'Yu Zhu'] | 2021-02-19 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [-6.5719567e-02 3.1812897e-01 -2.6995358e-01 -7.3569663e-02
7.5152777e-02 -7.2100842e-01 5.0273257e-01 3.8887227e-01
-2.2268391e-01 3.7926331e-01 1.0379597e-03 -5.2910704e-02
-5.7792372e-01 -1.1737386e+00 -3.4816617e-01 -6.8320036e-01
-3.9461049e-01 4.6222615e-01 1.3317306e-01 7.7031374e-02
2.3467377e-02... | [7.193012237548828, 5.207034111022949] |
e54dc615-b0d5-41be-b2c7-de97583f997e | convolutional-neural-networks-on-non-uniform | 1901.02070 | null | http://arxiv.org/abs/1901.02070v1 | http://arxiv.org/pdf/1901.02070v1.pdf | Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation | Convolutional Neural Networks (CNN) have been successful in processing data
signals that are uniformly sampled in the spatial domain (e.g., images).
However, most data signals do not natively exist on a grid, and in the process
of being sampled onto a uniform physical grid suffer significant aliasing error
and informat... | ['Matthias Nießner', 'Chiyu "Max" Jiang', 'Jingwei Huang', 'Philip Marcus', 'Dequan Wang'] | 2019-01-07 | convolutional-neural-networks-on-non-uniform-1 | https://openreview.net/forum?id=B1G5ViAqFm | https://openreview.net/pdf?id=B1G5ViAqFm | iclr-2019-5 | ['3d-shape-retrieval'] | ['computer-vision'] | [ 1.79403603e-01 -1.53781950e-01 2.33876422e-01 5.76012731e-02
-6.71511292e-01 -5.56777656e-01 3.85199606e-01 6.04307577e-02
-6.18458763e-02 4.98855948e-01 -2.30258852e-01 -7.69518539e-02
-3.17548305e-01 -1.36159599e+00 -1.00258422e+00 -6.25344872e-01
-3.19701374e-01 6.09050691e-01 1.41813695e-01 -2.07614094... | [8.515283584594727, -3.5106210708618164] |
55eae385-03d5-4b57-b9bd-be54d33d532f | geometric-deep-learning-on-graphs-and | 1611.08402 | null | http://arxiv.org/abs/1611.08402v3 | http://arxiv.org/pdf/1611.08402v3.pdf | Geometric deep learning on graphs and manifolds using mixture model CNNs | Deep learning has achieved a remarkable performance breakthrough in several
fields, most notably in speech recognition, natural language processing, and
computer vision. In particular, convolutional neural network (CNN)
architectures currently produce state-of-the-art performance on a variety of
image analysis tasks su... | ['Emanuele Rodolà', 'Federico Monti', 'Jan Svoboda', 'Michael M. Bronstein', 'Jonathan Masci', 'Davide Boscaini'] | 2016-11-25 | geometric-deep-learning-on-graphs-and-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Monti_Geometric_Deep_Learning_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Monti_Geometric_Deep_Learning_CVPR_2017_paper.pdf | cvpr-2017-7 | ['superpixel-image-classification', 'graph-regression'] | ['computer-vision', 'graphs'] | [ 1.89628601e-01 1.19517129e-02 -2.79735550e-02 -3.30485672e-01
-3.21111977e-01 -3.46770853e-01 8.33738327e-01 4.16226149e-01
-3.98021281e-01 -4.04706644e-03 4.32707295e-02 -4.18733627e-01
-4.00311112e-01 -8.78952444e-01 -6.13749027e-01 -5.70571601e-01
-4.71965104e-01 2.80587673e-01 -4.38710637e-02 -1.44155368... | [8.802282333374023, 2.406006336212158] |
c55b0c3e-ed57-48f9-afa4-2d0c43aef5a1 | a-deep-ranking-model-for-spatio-temporal | 1801.10312 | null | http://arxiv.org/abs/1801.10312v1 | http://arxiv.org/pdf/1801.10312v1.pdf | A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video | We address the problem of highlight detection from a 360 degree video by
summarizing it both spatially and temporally. Given a long 360 degree video, we
spatially select pleasantly-looking normal field-of-view (NFOV) segments from
unlimited field of views (FOV) of the 360 degree video, and temporally
summarize it into ... | ['Sang-ho Lee', 'Joonil Na', 'Jaeyun Kang', 'Youngjae Yu', 'Gunhee Kim'] | 2018-01-31 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [-1.87319115e-01 -3.31731021e-01 -2.51052827e-01 -9.99686122e-02
-6.83665812e-01 -9.95280087e-01 5.54423749e-01 4.96852472e-02
-2.65101969e-01 1.42040059e-01 6.77795708e-01 1.65381268e-01
-7.33423978e-02 -3.41612786e-01 -8.90840352e-01 -2.79500991e-01
-3.92272413e-01 -3.88762027e-01 4.72871870e-01 2.42475539... | [10.286813735961914, 0.47717028856277466] |
51a2f038-0c18-49c1-9201-895d3c9217df | reservoir-computing-models-for-patient | 1907.09504 | null | https://arxiv.org/abs/1907.09504v1 | https://arxiv.org/pdf/1907.09504v1.pdf | Reservoir Computing Models for Patient-Adaptable ECG Monitoring in Wearable Devices | The reservoir computing paradigm is employed to classify heartbeat anomalies online based on electrocardiogram signals. Inspired by the principles of information processing in the brain, reservoir computing provides a framework to design, train, and analyze recurrent neural networks (RNNs) for processing time-dependent... | ['Fatemeh Hadaeghi'] | 2019-07-22 | null | null | null | null | ['arrhythmia-detection', 'ecg-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [ 4.54204470e-01 -2.86626101e-01 2.23697320e-01 -2.71974742e-01
-1.56955376e-01 -2.65781164e-01 8.26830342e-02 2.77209193e-01
-4.42311645e-01 7.74149418e-01 -2.91787326e-01 -2.67016292e-01
-3.33617151e-01 -4.90574330e-01 -1.55619070e-01 -9.19182241e-01
-3.18354726e-01 2.05240056e-01 -1.76146552e-01 -3.57861607... | [14.196968078613281, 3.25411057472229] |
19d93b57-a542-4fac-a9f4-40052e1e95c6 | pixmatch-unsupervised-domain-adaptation-via | 2105.08128 | null | https://arxiv.org/abs/2105.08128v1 | https://arxiv.org/pdf/2105.08128v1.pdf | PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training | Unsupervised domain adaptation is a promising technique for semantic segmentation and other computer vision tasks for which large-scale data annotation is costly and time-consuming. In semantic segmentation, it is attractive to train models on annotated images from a simulated (source) domain and deploy them on real (t... | ['Arjun K. Manrai', 'Luke Melas-Kyriazi'] | 2021-05-17 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Melas-Kyriazi_PixMatch_Unsupervised_Domain_Adaptation_via_Pixelwise_Consistency_Training_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Melas-Kyriazi_PixMatch_Unsupervised_Domain_Adaptation_via_Pixelwise_Consistency_Training_CVPR_2021_paper.pdf | cvpr-2021-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 4.49201703e-01 3.96896988e-01 -3.01681273e-02 -5.65235019e-01
-9.57280397e-01 -6.59799814e-01 4.06861693e-01 -1.06283918e-01
-5.32498240e-01 7.96230197e-01 -3.84100527e-01 -1.00944489e-01
4.99304503e-01 -7.02552497e-01 -1.09681308e+00 -6.71865523e-01
3.90308946e-01 6.92772388e-01 6.85605168e-01 -1.40994504... | [9.766559600830078, 1.3299981355667114] |
8e5cfe81-a8d8-4bdb-b479-02b73e695756 | on-the-use-of-benford-s-law-to-detect-gan | 2004.07682 | null | https://arxiv.org/abs/2004.07682v1 | https://arxiv.org/pdf/2004.07682v1.pdf | On the use of Benford's law to detect GAN-generated images | The advent of Generative Adversarial Network (GAN) architectures has given anyone the ability of generating incredibly realistic synthetic imagery. The malicious diffusion of GAN-generated images may lead to serious social and political consequences (e.g., fake news spreading, opinion formation, etc.). It is therefore ... | ['Nicolò Bonettini', 'Simone Milani', 'Paolo Bestagini', 'Stefano Tubaro'] | 2020-04-16 | null | null | null | null | ['gan-image-forensics'] | ['computer-vision'] | [ 7.42184758e-01 4.43833321e-01 8.46009031e-02 3.37453149e-02
-4.97536868e-01 -8.15282285e-01 9.72871840e-01 -3.39343190e-01
-1.99542090e-01 1.13502407e+00 -1.49666145e-01 -4.74353313e-01
4.74473149e-01 -1.15264833e+00 -6.57506943e-01 -9.52530324e-01
-7.21396040e-03 2.71324310e-02 6.45476505e-02 -2.79988557... | [12.432497024536133, 1.0979406833648682] |
a29bbcd3-c6ef-462b-b8a5-c6f47c7cb9c1 | hsi-bert-hyperspectral-image-classification | null | null | https://doi.org/10.1109/TGRS.2019.2934760 | https://doi.org/10.1109/TGRS.2019.2934760 | HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers | Deep learning methods have been widely used in hyperspectral image classification and have achieved state-of-the-art performance. Nonetheless, the existing deep learning methods are restricted by a limited receptive field, inflexibility, and difficult generalization problems in hyperspectral image classification. To so... | ['Wei Li', 'Mengmeng Zhang', 'HongWei Yang', 'Lina Zhao', 'Ji He'] | 2019-09-04 | null | null | null | ieee-transactions-on-geoscience-and-remote-16 | ['few-shot-image-classification'] | ['computer-vision'] | [ 3.20715189e-01 -4.53460813e-01 -2.50710966e-03 -3.50282371e-01
-4.99953657e-01 -3.22406977e-01 2.35920057e-01 -2.31308013e-01
-2.13215485e-01 5.66313028e-01 4.27056067e-02 -8.93209130e-02
-6.23680651e-01 -1.09847713e+00 -6.53548300e-01 -1.22122550e+00
6.43191934e-02 -6.21841475e-02 9.48103145e-02 -2.23545551... | [9.954327583312988, -1.5809813737869263] |
2b286983-d169-49a0-a2b7-723c3eb1d177 | gl2vec-learning-feature-representation-using | 1812.05473 | null | https://arxiv.org/abs/1812.05473v3 | https://arxiv.org/pdf/1812.05473v3.pdf | Learning Features of Network Structures Using Graphlets | Networks are fundamental to the study of complex systems, ranging from social contacts, message transactions, to biological regulations and economical networks. In many realistic applications, these networks may vary over time. Modeling and analyzing such temporal properties is of additional interest as it can provide ... | ['Liam Turner', 'Kun Tu', 'Jian Li', 'Dave Braines', 'Don Towsley'] | 2018-12-13 | null | null | null | null | ['learning-network-representations'] | ['methodology'] | [-3.11788619e-02 -1.13885723e-01 -2.29830295e-01 -1.23054951e-01
4.24160212e-01 -7.61336327e-01 8.36573839e-01 5.43901443e-01
-1.69862688e-01 4.61843431e-01 1.29756242e-01 -5.42963505e-01
-5.66879094e-01 -1.22640085e+00 -3.81139874e-01 -5.95483780e-01
-8.41805816e-01 1.16332389e-01 4.13953483e-01 -5.73419571... | [7.111385345458984, 6.02822732925415] |
f80fec59-469e-47eb-835a-5baf9d48756a | robust-point-cloud-segmentation-with-noisy | 2212.03242 | null | https://arxiv.org/abs/2212.03242v1 | https://arxiv.org/pdf/2212.03242v1.pdf | Robust Point Cloud Segmentation with Noisy Annotations | Point cloud segmentation is a fundamental task in 3D. Despite recent progress on point cloud segmentation with the power of deep networks, current learning methods based on the clean label assumptions may fail with noisy labels. Yet, class labels are often mislabeled at both instance-level and boundary-level in real-wo... | ['Jing Liao', 'Songfang Han', 'Dongdong Chen', 'Shuquan Ye'] | 2022-12-06 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.89264148e-01 -1.51648983e-01 -1.04355086e-02 -5.63483775e-01
-1.27551866e+00 -5.06418824e-01 4.23600942e-01 1.72163218e-01
-3.26099396e-01 4.76842970e-01 -4.71125215e-01 -2.11584345e-01
-2.67568622e-02 -8.31084669e-01 -9.49377120e-01 -8.28055501e-01
1.07850134e-01 7.34799027e-01 3.80932301e-01 2.74498820... | [8.040741920471191, -3.1299901008605957] |
a8c6d9af-9902-4c40-a9ec-c1c5338eb9f5 | score-based-diffusion-models-as-principled | 2304.11751 | null | https://arxiv.org/abs/2304.11751v1 | https://arxiv.org/pdf/2304.11751v1.pdf | Score-Based Diffusion Models as Principled Priors for Inverse Imaging | It is important in computational imaging to understand the uncertainty of images reconstructed from imperfect measurements. We propose turning score-based diffusion models into principled priors (``score-based priors'') for analyzing a posterior of images given measurements. Previously, probabilistic priors were limite... | ['William T. Freeman', 'Katherine L. Bouman', 'Huiwen Chang', 'Michael Rubinstein', 'Jamie Smith', 'Berthy T. Feng'] | 2023-04-23 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 6.57229960e-01 9.83017609e-02 3.98088932e-01 -5.03806114e-01
-1.01616871e+00 -3.44147056e-01 7.12797999e-01 -5.67270100e-01
-3.85403991e-01 7.81870723e-01 4.84588832e-01 -2.43024707e-01
-6.25842452e-01 -5.12029707e-01 -4.38860953e-01 -1.02878916e+00
-1.58692867e-01 2.28443652e-01 1.92661881e-01 3.57199013... | [11.698929786682129, -2.3841140270233154] |
47658449-7384-4aa7-9582-572c7f9fb91c | deep-learning-and-medical-imaging-for-covid | 2302.06611 | null | https://arxiv.org/abs/2302.06611v1 | https://arxiv.org/pdf/2302.06611v1.pdf | Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey | COVID-19 (Coronavirus disease 2019) has been quickly spreading since its outbreak, impacting financial markets and healthcare systems globally. Countries all around the world have adopted a number of extraordinary steps to restrict the spreading virus, where early COVID-19 diagnosis is essential. Medical images such as... | ['Philip S. Yu', 'Liqiang Nie', 'Jing He', 'Xiaorong Pu', 'Xinyue Chen', 'Aodi Yang', 'Yazhou Ren', 'Song Wu'] | 2023-02-13 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-1.37804421e-02 -7.21814096e-01 -6.72282204e-02 -1.80286109e-01
-2.44001150e-01 -5.40188968e-01 1.86087951e-01 3.08197588e-01
-7.96303928e-01 4.96568054e-01 -1.11276209e-01 -4.47313637e-01
-2.72740930e-01 -6.03781104e-01 -2.22892076e-01 -9.33508337e-01
-4.54626322e-01 1.02736044e+00 -2.28850543e-01 -1.96002558... | [15.565308570861816, -1.7011797428131104] |
68cde664-5eb2-4419-9e80-858f3116b0d0 | team-cogitat-at-neurips-2021-benchmarks-for | 2202.03267 | null | https://arxiv.org/abs/2202.03267v1 | https://arxiv.org/pdf/2202.03267v1.pdf | Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition | Building subject-independent deep learning models for EEG decoding faces the challenge of strong covariate-shift across different datasets, subjects and recording sessions. Our approach to address this difficulty is to explicitly align feature distributions at various layers of the deep learning model, using both simpl... | ['Stefanos Zafeiriou', 'Dimitrios A. Adamos', 'Nikolaos Laskaris', 'Yannis Panagakis', 'Mehdi Bahri', 'Konstantinos Barmpas', 'Siegfried Ludwig', 'Stylianos Bakas'] | 2022-02-01 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 2.61433005e-01 1.81772277e-01 2.37666860e-01 -8.35310340e-01
-1.03791749e+00 -4.70768362e-01 5.51037729e-01 -1.47967800e-01
-8.70342553e-01 1.03321183e+00 8.80180746e-02 -5.68506718e-02
-2.96461105e-01 -2.02851415e-01 -8.55901539e-01 -8.17794859e-01
-3.51135612e-01 5.94331682e-01 -2.18262121e-01 5.86413257... | [12.929793357849121, 3.448474884033203] |
505f3b21-7b0e-4809-aaa8-7bb0853eaec7 | vice-variational-inference-for-concept-1 | 2205.00756 | null | https://arxiv.org/abs/2205.00756v8 | https://arxiv.org/pdf/2205.00756v8.pdf | VICE: Variational Interpretable Concept Embeddings | A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a t... | ['Francisco Pereira', 'Martin N. Hebart', 'Robert A. Vandermeulen', 'Patrick McClure', 'Charles Y. Zheng', 'Lukas Muttenthaler'] | 2022-05-02 | vice-variational-inference-for-concept | https://openreview.net/forum?id=-9ffJ9NQmal | https://openreview.net/pdf?id=-9ffJ9NQmal | null | ['odd-one-out'] | ['reasoning'] | [-1.39410526e-01 4.53622863e-02 -1.98444620e-01 -5.88353693e-01
-4.76459682e-01 -4.54338312e-01 6.09685421e-01 4.00982320e-01
-6.19801998e-01 6.61156952e-01 1.09077394e-01 -2.82314438e-02
-4.38524425e-01 -3.27986300e-01 -3.79679292e-01 -5.55458009e-01
-2.87160724e-01 8.22209418e-01 -2.13367581e-01 1.48655221... | [7.933047294616699, 3.9121718406677246] |
907553f5-a97d-47e3-8f2c-aff45305afce | closed-form-sample-probing-for-learning | null | null | https://openreview.net/forum?id=ljxWpdBl4V | https://openreview.net/pdf?id=ljxWpdBl4V | Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning | Generative model based approaches have led to significant advances in zero-shot learning (ZSL) over the past few-years. These approaches typically aim to learn a conditional generator that synthesizes training samples of classes conditioned on class definitions. The final zero-shot learning model is then obtained by tr... | ['Ramazan Gokberk Cinbis', 'Orhun Buğra Baran', 'Samet Cetin'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['sample-probing'] | ['computer-vision'] | [ 5.39010108e-01 5.20138681e-01 -1.31307110e-01 -3.08173597e-01
-8.82119060e-01 -2.28113472e-01 7.95155048e-01 1.21077202e-01
-8.80976245e-02 7.75342405e-01 -2.95629889e-01 1.27584010e-01
-7.57442191e-02 -1.33370745e+00 -7.74228573e-01 -8.34996819e-01
1.30897701e-01 9.12631750e-01 2.65882522e-01 -6.55949786... | [9.965014457702637, 2.9229400157928467] |
e2f68915-91a9-46ff-aa36-f3fcc19ba314 | evaluating-voice-conversion-based-privacy | 1911.03934 | null | https://arxiv.org/abs/1911.03934v2 | https://arxiv.org/pdf/1911.03934v2.pdf | Evaluating Voice Conversion-based Privacy Protection against Informed Attackers | Speech data conveys sensitive speaker attributes like identity or accent. With a small amount of found data, such attributes can be inferred and exploited for malicious purposes: voice cloning, spoofing, etc. Anonymization aims to make the data unlinkable, i.e., ensure that no utterance can be linked to its original sp... | ['Aurélien Bellet', 'Brij Mohan Lal Srivastava', 'Marc Tommasi', 'Nathalie Vauquier', 'Emmanuel Vincent', 'Md Sahidullah'] | 2019-11-10 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 2.07278565e-01 4.78775352e-01 -1.10824838e-01 -4.25818861e-01
-9.21367586e-01 -1.13308287e+00 6.57996774e-01 3.04196119e-01
-4.73975420e-01 6.94457114e-01 4.70098317e-01 -4.74478394e-01
5.37841721e-03 -6.81664705e-01 -5.37036479e-01 -6.36539578e-01
5.86347319e-02 2.88701206e-01 -1.28502950e-01 -1.59959331... | [13.964839935302734, 5.853367328643799] |
eb73616c-20cb-4d19-af58-598539b0b827 | cloud-removal-in-sentinel-2-imagery-using-a | null | null | https://www.sciencedirect.com/science/article/pii/S0924271620301398 | https://www.sciencedirect.com/science/article/pii/S0924271620301398/pdfft?md5=c3ef69f32f9689b0417434f7c38eb4b2&pid=1-s2.0-S0924271620301398-main.pdf | Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion | Optical remote sensing imagery is at the core of many Earth observation activities. The regular, consistent and global-scale nature of the satellite data is exploited in many applications, such as cropland monitoring, climate change assessment, land-cover and land-use classification, and disaster assessment. However, o... | ['Michael Schmitt', 'Xiao Xiang Zhu', 'Patrick Ebel', 'Andrea Meraner'] | 2020-07-02 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 4.71084327e-01 -4.73474801e-01 8.14419016e-02 -3.29089075e-01
-6.40374064e-01 -4.93070126e-01 3.23147863e-01 -2.52815634e-02
-4.32970673e-01 9.13962603e-01 -1.98687375e-01 -2.18128845e-01
-2.39703313e-01 -9.54024136e-01 -4.12903249e-01 -1.19491911e+00
-6.55591190e-02 7.97862411e-02 -2.19104260e-01 -3.41908455... | [9.749574661254883, -1.7906566858291626] |
d3aa49dd-f2eb-4597-a78b-004bfc781a14 | nscgcn-a-novel-deep-gcn-model-to-diagnosis | null | null | https://www.sciencedirect.com/science/article/pii/S0010482522008599 | https://www.sciencedirect.com/sdfe/reader/pii/S0010482522008599/pdf | NSCGCN: A novel deep GCN model to diagnosis COVID-19 | Aim
Corona Virus Disease 2019 (COVID-19) was a lung disease with high mortality and was highly contagious. Early diagnosis of COVID-19 and distinguishing it from pneumonia was beneficial for subsequent treatment.
Objectives
Recently, Graph Convolutional Network (GCN) has driven a significant contribution to diseas... | ['Yu-Dong Zhang', 'Shui-Hua Wang', 'Junding Sun', 'Chaochao Hu', 'Chaosheng Tang'] | 2022-10-13 | null | null | null | computers-in-biology-and-medicine-2022-10 | ['covid-19-detection'] | ['medical'] | [ 4.44844663e-02 -1.75419360e-01 9.27105993e-02 -9.44342185e-03
-9.11139175e-02 -1.93773843e-02 2.40272477e-01 -8.25850517e-02
-2.58322120e-01 7.09352612e-01 3.13483626e-02 -6.23280764e-01
-3.70404243e-01 -1.07980812e+00 -3.76954198e-01 -7.86573946e-01
-3.08760285e-01 5.02033532e-01 1.91280782e-01 2.61441857... | [15.53619384765625, -1.7478466033935547] |
f8f297cd-85dc-428c-86e1-515b4808853d | sb-mtl-score-based-meta-transfer-learning-for | 2012.01784 | null | https://arxiv.org/abs/2012.01784v1 | https://arxiv.org/pdf/2012.01784v1.pdf | SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning | While many deep learning methods have seen significant success in tackling the problem of domain adaptation and few-shot learning separately, far fewer methods are able to jointly tackle both problems in Cross-Domain Few-Shot Learning (CD-FSL). This problem is exacerbated under sharp domain shifts that typify common co... | ['Sheng Mei Shen', 'Bill Cai', 'John Cai'] | 2020-12-03 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 4.30496633e-01 -7.94816241e-02 -2.09935606e-01 -5.47486365e-01
-1.18146670e+00 -3.26259881e-01 6.96998000e-01 -4.55265790e-02
-5.43661296e-01 5.94955325e-01 1.06156081e-01 2.17205733e-01
-2.94118464e-01 -7.65180469e-01 -6.52949989e-01 -4.57099199e-01
-8.47224668e-02 5.64527869e-01 6.56164408e-01 -4.36132491... | [9.969812393188477, 2.8748862743377686] |
c8869d56-2791-45a7-a626-36963381c3c6 | how-do-graph-networks-generalize-to-large-and | 2204.02782 | null | https://arxiv.org/abs/2204.02782v3 | https://arxiv.org/pdf/2204.02782v3.pdf | GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets | Recent years have seen the advent of molecular simulation datasets that are orders of magnitude larger and more diverse. These new datasets differ substantially in four aspects of complexity: 1. Chemical diversity (number of different elements), 2. system size (number of atoms per sample), 3. dataset size (number of da... | ['Abhishek Das', 'C. Lawrence Zitnick', 'Zachary Ulissi', 'Stephan Günnemann', 'Anuroop Sriram', 'Muhammed Shuaibi', 'Johannes Gasteiger'] | 2022-04-06 | null | null | null | null | ['initial-structure-to-relaxed-energy-is2re'] | ['graphs'] | [ 1.89137340e-01 -1.43220380e-01 -4.72120404e-01 8.79260153e-02
-4.76322025e-01 -8.46292615e-01 7.95574188e-01 3.07252675e-01
-5.90503037e-01 1.11368084e+00 -3.65660004e-02 -1.09222364e+00
-2.65860707e-01 -9.03665543e-01 -7.88822174e-01 -5.88977993e-01
-1.75423622e-01 6.64505482e-01 3.09870958e-01 -3.87834787... | [5.33782434463501, 5.571430206298828] |
6b306639-e948-4bb4-a7c2-6f37baf0ce3e | self-supervised-learning-of-depth-and-camera | 1811.05304 | null | http://arxiv.org/abs/1811.05304v1 | http://arxiv.org/pdf/1811.05304v1.pdf | Self-Supervised Learning of Depth and Camera Motion from 360° Videos | As 360{\deg} cameras become prevalent in many autonomous systems (e.g.,
self-driving cars and drones), efficient 360{\deg} perception becomes more and
more important. We propose a novel self-supervised learning approach for
predicting the omnidirectional depth and camera motion from a 360{\deg} video.
In particular, st... | ['Hung-Kuo Chu', 'Meng-Li Shih', 'Shang-Ta Yang', 'Juan-Ting Lin', 'Hsien-Tzu Cheng', 'Hou-Ning Hu', 'Fu-En Wang', 'Min Sun'] | 2018-11-13 | null | null | null | null | ['depth-and-camera-motion'] | ['computer-vision'] | [-7.32013062e-02 2.79886961e-01 -3.72601375e-02 -4.62089658e-01
-4.45598274e-01 -7.65017450e-01 4.28552926e-01 -7.21281767e-01
-5.17639160e-01 3.14445049e-01 -2.63833348e-02 -1.93732709e-01
2.01831654e-01 -7.22209752e-01 -1.35313952e+00 -6.91036105e-01
2.59474188e-01 3.09507221e-01 2.13905245e-01 4.75181490... | [8.562714576721191, -2.3110644817352295] |
2237eaf8-87bd-43b8-91ce-d6a9e91584f4 | beike-nlp-at-semeval-2022-task-4-prompt-based-1 | 2208.01312 | null | https://arxiv.org/abs/2208.01312v1 | https://arxiv.org/pdf/2208.01312v1.pdf | BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection | PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media.Compared to other NLP tasks of paragraph classification, the negative language presented in the PCL detection task is usually more implicit and subtle to be recog... | ['Xiangang Li', 'Baochang Ma', 'Xianghui Sun', 'Deqiang Miao', 'Liangyu Chen', 'Chenxiao Dou', 'Yong Deng'] | 2022-08-02 | beike-nlp-at-semeval-2022-task-4-prompt-based | https://aclanthology.org/2022.semeval-1.41 | https://aclanthology.org/2022.semeval-1.41.pdf | semeval-naacl-2022-7 | ['semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-2-multi-label-pcl', 'semeval-2022-task-4-1-binary-pcl-detection'] | ['miscellaneous', 'music', 'natural-language-processing', 'natural-language-processing'] | [ 2.20392123e-01 1.95721805e-01 -5.67728996e-01 -1.95701078e-01
-1.60550797e+00 -8.45207393e-01 8.12946439e-01 9.27541852e-01
-3.62809032e-01 4.50553358e-01 4.93322194e-01 -7.48919010e-01
2.04187125e-01 -1.67780414e-01 -8.81965980e-02 -5.28274655e-01
1.99488699e-01 2.20296681e-01 1.63867354e-01 1.34091422... | [8.836211204528809, 10.574440002441406] |
e2c8fe37-0a00-4e53-bd53-ed9cd54b2292 | controllable-neural-story-plot-generation-via | 1809.10736 | null | https://arxiv.org/abs/1809.10736v4 | https://arxiv.org/pdf/1809.10736v4.pdf | Controllable Neural Story Plot Generation via Reward Shaping | Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don... | ['Pradyumna Tambwekar', 'Lara J. Martin', 'Brent Harrison', 'Mark O. Riedl', 'Murtaza Dhuliawala', 'Animesh Mehta'] | 2018-09-27 | null | null | null | null | ['story-completion'] | ['natural-language-processing'] | [ 4.49991733e-01 5.68935096e-01 -4.24895883e-01 -3.91753465e-01
-9.93456244e-01 -5.82996726e-01 1.02644527e+00 4.28434461e-01
1.49818197e-01 9.58874583e-01 1.08989573e+00 -3.77880991e-01
3.08967203e-01 -8.91309261e-01 -5.63182473e-01 1.27103252e-04
7.85620809e-02 4.73795682e-01 2.19612837e-01 -3.41711164... | [11.658209800720215, 8.84457015991211] |
605de7f6-8657-4d0d-96ed-e83595a3e923 | zeroq-a-novel-zero-shot-quantization | 2001.00281 | null | https://arxiv.org/abs/2001.00281v1 | https://arxiv.org/pdf/2001.00281v1.pdf | ZeroQ: A Novel Zero Shot Quantization Framework | Quantization is a promising approach for reducing the inference time and memory footprint of neural networks. However, most existing quantization methods require access to the original training dataset for retraining during quantization. This is often not possible for applications with sensitive or proprietary data, e.... | ['Zhen Dong', 'Kurt Keutzer', 'Yaohui Cai', 'Michael W. Mahoney', 'Zhewei Yao', 'Amir Gholami'] | 2020-01-01 | zeroq-a-novel-zero-shot-quantization-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Cai_ZeroQ_A_Novel_Zero_Shot_Quantization_Framework_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Cai_ZeroQ_A_Novel_Zero_Shot_Quantization_Framework_CVPR_2020_paper.pdf | cvpr-2020-6 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 8.95623565e-02 -2.98955739e-01 -1.67647272e-01 -3.26716870e-01
-8.19751322e-01 -5.82074761e-01 2.27028430e-01 1.63934812e-01
-1.00290644e+00 7.21517384e-01 -2.32782468e-01 -5.15233576e-01
-1.52716860e-01 -8.07204604e-01 -8.29740107e-01 -5.98783672e-01
2.16367930e-01 -8.50741789e-02 3.72517616e-01 -8.70910287... | [8.702119827270508, 3.028671979904175] |
203c4de6-e628-4915-a695-967b67435b97 | x2teeth-3d-teeth-reconstruction-from-a-single | 2108.13004 | null | https://arxiv.org/abs/2108.13004v1 | https://arxiv.org/pdf/2108.13004v1.pdf | X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph | 3D teeth reconstruction from X-ray is important for dental diagnosis and many clinical operations. However, no existing work has explored the reconstruction of teeth for a whole cavity from a single panoramic radiograph. Different from single object reconstruction from photos, this task has the unique challenge of cons... | ['Lei He', 'Kun Wang', 'Liang Qiu', 'Jiawei Yang', 'Weinan Song', 'Yuan Liang'] | 2021-08-30 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 1.13084584e-01 5.71997464e-01 -1.20871753e-01 -5.83279550e-01
-1.63082337e+00 3.08271617e-01 6.45340979e-02 -1.09435022e-01
-1.24460116e-01 3.28014940e-01 2.89055645e-01 -1.38899654e-01
1.77697062e-01 -7.66346216e-01 -1.01064491e+00 -5.94333053e-01
2.27223486e-01 5.15194058e-01 1.87919557e-01 2.20095500... | [13.851446151733398, -2.203859567642212] |
c773dbda-e279-42cf-be4c-3c8494e626e2 | deep-manifold-learning-reveals-hidden | 2012.12854 | null | https://arxiv.org/abs/2012.12854v2 | https://arxiv.org/pdf/2012.12854v2.pdf | Deep manifold learning reveals hidden dynamics of proteasome autoregulation | The 2.5-MDa 26S proteasome maintains proteostasis and regulates myriad cellular processes. How polyubiquitylated substrate interactions regulate proteasome activity is not understood. Here we introduce a deep manifold learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy (cryo-... | ['Youdong Mao', 'Yuanchen Dong', 'Yinping Ma', 'Wei Li Wang', 'Shuwen Zhang', 'Zhaolong Wu'] | 2020-12-23 | null | null | null | null | ['3d-classification', 'cryogenic-electron-microscopy-cryo-em'] | ['computer-vision', 'computer-vision'] | [-1.91319004e-01 -2.98360020e-01 -4.45569068e-01 2.51511514e-01
-6.25943124e-01 -1.11226869e+00 4.51734662e-01 -9.31214839e-02
-5.44286907e-01 1.20356309e+00 4.78162497e-01 -6.32251501e-01
-4.82978821e-02 -2.50013262e-01 -9.06937778e-01 -1.25985968e+00
-1.77233405e-02 8.62818480e-01 -1.94933951e-01 2.28857435... | [4.73555326461792, 5.328096389770508] |
702e46ab-b7d4-4af4-94bd-81faf40529e0 | stepsize-learning-for-policy-gradient-methods | 2306.07741 | null | https://arxiv.org/abs/2306.07741v1 | https://arxiv.org/pdf/2306.07741v1.pdf | Stepsize Learning for Policy Gradient Methods in Contextual Markov Decision Processes | Policy-based algorithms are among the most widely adopted techniques in model-free RL, thanks to their strong theoretical groundings and good properties in continuous action spaces. Unfortunately, these methods require precise and problem-specific hyperparameter tuning to achieve good performance, and tend to struggle ... | ['Marcello Restelli', 'Francesco Corda', 'Luca Sabbioni'] | 2023-06-13 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 2.03753784e-01 -2.17967644e-01 -3.80307555e-01 4.61950898e-02
-7.86406338e-01 -3.88230234e-01 6.95686817e-01 1.81421533e-01
-9.24738646e-01 1.07631481e+00 -2.14161113e-01 -6.49674162e-02
-5.21457195e-01 -4.44978267e-01 -4.50683177e-01 -1.12523639e+00
1.96381271e-01 5.67655802e-01 2.28741854e-01 -2.68838227... | [4.338506698608398, 2.3747875690460205] |
3eb5d70b-c78d-403f-a781-acc73ca604bb | revisiting-model-self-interpretability-in-a | 2303.06876 | null | https://arxiv.org/abs/2303.06876v2 | https://arxiv.org/pdf/2303.06876v2.pdf | Revisiting model self-interpretability in a decision-theoretic way for binary medical image classification | Interpretability is highly desired for deep neural network-based classifiers, especially when addressing high-stake decisions in medical imaging. Commonly used post-hoc interpretability methods have the limitation that they can produce plausible but different interpretations of a given model, leading to ambiguity about... | ['Mark A. Anastasio', 'Sourya Sengupta'] | 2023-03-13 | null | null | null | null | ['unity'] | ['computer-vision'] | [ 6.62089705e-01 8.67893398e-01 -3.87887955e-01 -8.70555997e-01
-4.35595602e-01 -2.82625735e-01 3.30780804e-01 5.39721370e-01
-4.30425823e-01 6.56329930e-01 7.85509050e-02 -6.13893211e-01
-4.29070026e-01 -6.13042235e-01 -5.43097675e-01 -7.64744341e-01
1.52936175e-01 4.92384136e-01 -4.55960870e-01 2.80857086... | [8.842531204223633, 5.458734512329102] |
2b36ce19-745f-435a-af69-69960bae6c5d | ji-yu-shuang-bian-ma-qi-de-yi-xue-wen-ben | null | null | https://aclanthology.org/2021.ccl-1.8 | https://aclanthology.org/2021.ccl-1.8.pdf | 基于双编码器的医学文本中文分词(Chinese word segmentation of medical text based on dual-encoder) | “中文分词是自然语言处理领域的基础工作,然而前人的医学文本分词工作都只是直接套用通用分词的方法,而医学文本多专用术语的特点让分词系统需要对医学专用术语和医学文本中的非医学术语文本提供不同的分词粒度。本文提出了双编码器医学文本中文分词模型,利用辅助编码器为医学专有术语提供粗粒度表示。模型将需要粗粒度分词的医学专用术语和需要通用分词粒度的文本分开,在提升医学专用术语的分词能力的同时最大限度地避免了其粗粒度对于医学文本中通用文本分词的干扰。” | ['Baobao Chang', 'Yuan Zong'] | null | null | null | null | ccl-2021-8 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [-6.57347143e-01 -7.17176020e-01 6.60555601e-01 3.30682337e-01
2.03810364e-01 -1.33680212e+00 5.37603684e-02 6.89713895e-01
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-4.89055008e-01 -1.19244766e+00 -1.99777126e-01 -1.16379762e+00
-6.80088252e-02 1.59540975e+00 3.91643345e-01 -2.80248463... | [-3.3160808086395264, 6.9077558517456055] |
0ba6846e-0d83-480c-919e-c87ea4f54dd7 | attention-based-part-assembly-for-3d | 2304.10986 | null | https://arxiv.org/abs/2304.10986v1 | https://arxiv.org/pdf/2304.10986v1.pdf | Attention-based Part Assembly for 3D Volumetric Shape Modeling | Modeling a 3D volumetric shape as an assembly of decomposed shape parts is much more challenging, but semantically more valuable than direct reconstruction from a full shape representation. The neural network needs to implicitly learn part relations coherently, which is typically performed by dedicated network layers t... | ['Jürgen Beyerer', 'Julius Pfrommer', 'Junwei Zheng', 'Chengzhi Wu'] | 2023-04-17 | null | null | null | null | ['3d-shape-modeling'] | ['computer-vision'] | [-4.72210087e-02 5.30711353e-01 1.52818367e-01 -5.54301739e-01
-4.81656402e-01 -3.79427165e-01 6.59357846e-01 -2.08559304e-01
3.07330132e-01 2.64064401e-01 4.60989952e-01 -1.05662306e-03
8.61642361e-02 -8.78876209e-01 -1.22504282e+00 -2.39014670e-01
3.15815359e-01 1.07327294e+00 8.72892365e-02 -2.56913304... | [8.7123384475708, -3.615499496459961] |
87657afa-a909-4f2a-a4b3-661f9f3f66e6 | toward-a-geometric-theory-of-manifold | 2303.04203 | null | https://arxiv.org/abs/2303.04203v1 | https://arxiv.org/pdf/2303.04203v1.pdf | Toward a Geometric Theory of Manifold Untangling | It has been hypothesized that the ventral stream processing for object recognition is based on a mechanism called cortically local subspace untangling. A mathematical abstraction of object recognition by the visual cortex is how to untangle the manifolds associated with different object category. Such a manifold untang... | ['Shuo Wang', 'Xin Li'] | 2023-03-07 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 1.81771979e-01 2.61790931e-01 1.46739498e-01 -4.58660126e-01
-3.20134044e-01 -8.10519397e-01 6.25534475e-01 -1.88802123e-01
-5.48637211e-01 1.08366109e-01 2.38955632e-01 -2.20263287e-01
-2.56993949e-01 -4.94471282e-01 -6.72304392e-01 -5.96623838e-01
-2.62217969e-01 -2.28271946e-01 9.57718939e-02 -1.88358855... | [8.478617668151855, 3.4970948696136475] |
5e12c3fd-09a6-444b-8c86-c68f48fd4b43 | asgard-a-single-cell-guided-pipeline-to-aid | 2109.06377 | null | https://arxiv.org/abs/2109.06377v4 | https://arxiv.org/pdf/2109.06377v4.pdf | ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs | Intercellular heterogeneity is a major obstacle to successful precision medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose a new d... | ['David Garmire', 'Qianhui Huang', 'Haodong Liang', 'Lana X. Garmire', 'Duxin Sun', 'Yijun Li', 'Yuheng Du', 'Yao Xiao', 'Bing He'] | 2021-09-14 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 1.46216944e-01 -3.79444927e-01 -8.35349679e-01 2.40046591e-01
-1.00137126e+00 -8.75129402e-01 4.86804813e-01 7.17797816e-01
2.26562008e-01 1.11480534e+00 4.34412271e-01 -6.90093875e-01
-2.15104491e-01 -7.36204088e-01 -3.06759387e-01 -9.35521185e-01
2.93200731e-01 9.55609620e-01 -1.95989504e-01 -5.18626608... | [5.710838794708252, 5.742475986480713] |
591a1d4a-bffc-449d-b99e-66a4e6f1dd73 | a-recurrent-cnn-for-online-object-detection | 2212.11172 | null | https://arxiv.org/abs/2212.11172v2 | https://arxiv.org/pdf/2212.11172v2.pdf | A recurrent CNN for online object detection on raw radar frames | Automotive radar sensors provide valuable information for advanced driving assistance systems (ADAS). Radars can reliably estimate the distance to an object and the relative velocity, regardless of weather and light conditions. However, radar sensors suffer from low resolution and huge intra-class variations in the sha... | ['Thomas Oberlin', 'Didier Salle', 'Rufin VanRullen', 'Colin Decourt'] | 2022-12-21 | null | null | null | null | ['radar-object-detection'] | ['robots'] | [ 2.98504792e-02 -7.18650281e-01 4.73183915e-02 -7.43384421e-01
-4.38886851e-01 -4.61994082e-01 9.72353399e-01 -2.07335308e-01
-5.05723357e-01 5.01614988e-01 -2.15761080e-01 -3.03007543e-01
-2.80215919e-01 -7.90217102e-01 -6.41313255e-01 -7.97277808e-01
-3.79536927e-01 2.88103014e-01 5.85357904e-01 -2.68039554... | [7.865604877471924, -1.3397307395935059] |
e0296a03-46b0-4e64-bd50-b77948fbc46e | voxel-based-network-for-shape-completion-by | 2108.09936 | null | https://arxiv.org/abs/2108.09936v1 | https://arxiv.org/pdf/2108.09936v1.pdf | Voxel-based Network for Shape Completion by Leveraging Edge Generation | Deep learning technique has yielded significant improvements in point cloud completion with the aim of completing missing object shapes from partial inputs. However, most existing methods fail to recover realistic structures due to over-smoothing of fine-grained details. In this paper, we develop a voxel-based network ... | ['Gim Hee Lee', 'Marcelo H Ang Jr', 'Xiaogang Wang'] | 2021-08-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Voxel-Based_Network_for_Shape_Completion_by_Leveraging_Edge_Generation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Voxel-Based_Network_for_Shape_Completion_by_Leveraging_Edge_Generation_ICCV_2021_paper.pdf | iccv-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [-2.37945601e-01 1.50564998e-01 5.40363312e-01 -1.17044352e-01
-1.01382267e+00 -5.43816388e-01 6.60187542e-01 -1.09673366e-01
9.03522670e-02 6.92237616e-01 2.11755291e-01 6.34444728e-02
1.49532616e-01 -1.12036538e+00 -1.10212171e+00 -2.05326796e-01
6.44297600e-02 7.83928931e-01 1.75066337e-01 -9.18836892... | [8.601849555969238, -3.566633701324463] |
50697555-e434-431a-8dfb-2f4ec73ee674 | hida-towards-holistic-indoor-understanding | 2107.03180 | null | https://arxiv.org/abs/2107.03180v1 | https://arxiv.org/pdf/2107.03180v1.pdf | HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor | Independently exploring unknown spaces or finding objects in an indoor environment is a daily but challenging task for visually impaired people. However, common 2D assistive systems lack depth relationships between various objects, resulting in difficulty to obtain accurate spatial layout and relative positions of obje... | ['Rainer Stiefelhagen', 'Kunyu Peng', 'Jiaming Zhang', 'Kailun Yang', 'Ruiping Liu', 'Huayao Liu'] | 2021-07-07 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-8.18707049e-03 -3.45811695e-01 4.71447676e-01 -3.62246811e-01
-5.65366864e-01 -3.37631911e-01 -1.06455222e-01 7.94302821e-02
-5.70887506e-01 1.64510772e-01 -8.18252861e-02 -5.72871149e-01
-3.01847368e-01 -7.25884974e-01 -3.85901541e-01 -2.58018255e-01
1.07863605e-01 7.69649804e-01 7.27254093e-01 -2.87609518... | [7.749612808227539, -1.9102016687393188] |
67233434-09b2-44de-a2c9-4e8c4434107e | uniformerv2-spatiotemporal-learning-by-arming | null | null | https://openreview.net/forum?id=d77RVuVg-Mf | https://openreview.net/pdf?id=d77RVuVg-Mf | UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer | Learning discriminative spatiotemporal representation is the key problem of video understanding. Recently, Vision Transformers (ViTs) have shown their power in learning long-term video dependency with self-attention. Unfortunately, they exhibit limitations in tackling local video redundancy, due to the blind global com... | ['Anonymous'] | 2022-09-22 | null | null | null | iclr2023-submitted-2022-9 | ['action-classification'] | ['computer-vision'] | [-9.60788503e-02 -3.87860745e-01 -5.34239173e-01 -8.05836171e-02
-4.92241681e-01 -4.98986393e-01 6.38638496e-01 -3.91709924e-01
-4.68342811e-01 5.32672465e-01 2.28337973e-01 -2.74262697e-01
-6.95140213e-02 -4.13217157e-01 -9.61381674e-01 -9.33779597e-01
-1.08954221e-01 9.44918673e-03 4.02956396e-01 -3.14708143... | [9.183241844177246, 0.6138337254524231] |
e03fab18-7ba4-45f7-bfb9-d2fbea379146 | ssn-armm-lt-edi-acl2022-hope-speech-detection | null | null | https://aclanthology.org/2022.ltedi-1.22 | https://aclanthology.org/2022.ltedi-1.22.pdf | SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model | In recent years social media has become one of the major forums for expressing human views and emotions. With the help of smartphones and high-speed internet, anyone can express their views on Social media. However, this can also lead to the spread of hatred and violence in society. Therefore it is necessary to build a... | ['Mirnalinee T T', 'Sakaya Milton Rajendram', 'Rajalakshmi Sivanaiah', 'Angel S', 'Aravind P', 'Prathyush S', 'Praveenkumar Vijayakumar'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-4.91105676e-01 3.31545234e-01 8.49789456e-02 -2.84997493e-01
-4.78423595e-01 -4.18091387e-01 6.85950518e-01 4.63329732e-01
-4.27364916e-01 6.42382562e-01 4.89453822e-01 -8.73167664e-02
2.41086379e-01 -6.11662388e-01 -7.49566406e-02 -2.61413962e-01
9.15412307e-02 3.56340455e-03 1.18431389e-01 -5.23269713... | [8.894587516784668, 10.656432151794434] |
5f2f33f2-a288-4939-b786-87b3a00a8efe | fosi-hybrid-first-and-second-order | 2302.08484 | null | https://arxiv.org/abs/2302.08484v3 | https://arxiv.org/pdf/2302.08484v3.pdf | FOSI: Hybrid First and Second Order Optimization | Though second-order optimization methods are highly effective, popular approaches in machine learning such as SGD and Adam use only first-order information due to the difficulty of computing curvature in high dimensions. We present FOSI, a novel meta-algorithm that improves the performance of any first-order optimizer ... | ['Assaf Schuster', 'Moshe Gabel', 'Hadar Sivan'] | 2023-02-16 | null | null | null | null | ['audio-classification'] | ['audio'] | [-5.64669967e-01 1.55415624e-01 -7.98690096e-02 -2.24044532e-01
-7.82720864e-01 -4.89729255e-01 -1.31561846e-01 7.05098063e-02
-3.86141807e-01 7.20624626e-01 -4.80317837e-03 -4.43384945e-01
-2.58889019e-01 -3.28356028e-01 -9.12702560e-01 -7.10197508e-01
-2.90334702e-01 6.07234180e-01 -1.37076914e-01 -8.81736726... | [7.2488884925842285, 4.0089545249938965] |
1de67b55-ee5f-4ad6-8ff9-51bddb8ee59c | towards-coreference-resolution-for-early | null | null | https://aclanthology.org/2022.cltw-1.12 | https://aclanthology.org/2022.cltw-1.12.pdf | Towards Coreference Resolution for Early Irish | In this article, we present an outline of some of the issues involved in developing a semi-supervised procedure for coreference resolution for early Irish as part of a wider enterprise to create a parsed corpus of historical Irish with enriched annotation for information structure and anaphoric coreference. We outline ... | ['David Willis', 'Marieke Meelen', 'Mark Darling'] | null | null | null | null | cltw-lrec-2022-6 | ['coreference-resolution'] | ['natural-language-processing'] | [ 4.86384511e-01 7.17015862e-01 -1.47651941e-01 -6.70657694e-01
-1.08223987e+00 -7.62164652e-01 6.25244737e-01 2.91924894e-01
-7.50693917e-01 9.29507792e-01 1.21274126e+00 -1.83575064e-01
-6.77244782e-01 -3.96744370e-01 4.41381987e-03 -3.74807268e-01
1.02622189e-01 1.31141579e+00 5.39092541e-01 -6.87532902... | [9.420614242553711, 9.545275688171387] |
d6cbed16-d3b8-427d-b9bd-1bb7288352aa | a-quadratic-0-1-programming-approach-for-word | 2201.04877 | null | https://arxiv.org/abs/2201.04877v1 | https://arxiv.org/pdf/2201.04877v1.pdf | A Quadratic 0-1 Programming Approach for Word Sense Disambiguation | Word Sense Disambiguation (WSD) is the task to determine the sense of an ambiguous word in a given context. Previous approaches for WSD have focused on supervised and knowledge-based methods, but inter-sense interactions patterns or regularities for disambiguation remain to be found. We argue the following cause as one... | ['Boliang Lin'] | 2022-01-13 | null | null | null | null | ['word-sense-disambiguation', 'word-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.01017225e-01 -1.47749424e-01 -1.87017545e-01 -2.87091017e-01
-1.89801037e-01 -8.57336044e-01 4.19921726e-01 8.21756899e-01
-6.94632113e-01 6.16501212e-01 3.06686401e-01 -1.58606052e-01
-3.95287097e-01 -7.96968937e-01 7.07900338e-03 -6.15677357e-01
4.95959632e-02 2.98379838e-01 3.74625474e-01 -7.10073829... | [10.210077285766602, 9.05859661102295] |
81e424bd-47be-4ae3-aded-242933d10c77 | what-s-in-your-hands-3d-reconstruction-of | 2204.07153 | null | https://arxiv.org/abs/2204.07153v1 | https://arxiv.org/pdf/2204.07153v1.pdf | What's in your hands? 3D Reconstruction of Generic Objects in Hands | Our work aims to reconstruct hand-held objects given a single RGB image. In contrast to prior works that typically assume known 3D templates and reduce the problem to 3D pose estimation, our work reconstructs generic hand-held object without knowing their 3D templates. Our key insight is that hand articulation is highl... | ['Shubham Tulsiani', 'Abhinav Gupta', 'Yufei Ye'] | 2022-04-14 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ye_Whats_in_Your_Hands_3D_Reconstruction_of_Generic_Objects_in_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ye_Whats_in_Your_Hands_3D_Reconstruction_of_Generic_Objects_in_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-pose-estimation'] | ['computer-vision'] | [-1.54960051e-01 -5.35581708e-02 -2.27796510e-01 -1.80970177e-01
-9.68438566e-01 -1.00850534e+00 5.90939105e-01 -4.32606131e-01
-2.42997333e-01 2.60493964e-01 2.22895220e-01 1.18634865e-01
-1.07888207e-01 -2.99591273e-01 -1.00403893e+00 -4.98365134e-01
4.10527438e-01 1.28474045e+00 2.43349507e-01 1.21812046... | [6.542418479919434, -1.0523936748504639] |
d5bffee1-cd2e-4e7a-a64b-71408dc746ee | factorvae-a-probabilistic-dynamic-factor | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/20369 | https://ojs.aaai.org/index.php/AAAI/article/view/20369/20128 | FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns | As an asset pricing model in economics and finance, factor model has been widely used in quantitative investment. Towards building more effective factor models, recent years have witnessed the paradigm shift from linear models to more flexible nonlinear data-driven machine learning models. However, due to low signal-to... | ['Jian Li', 'Qizhong Zhang', 'Lei Wang', 'Yitong Duan'] | 2022-06-28 | null | null | null | aaai-conference-on-artificial-intelligence-6 | ['stock-price-prediction'] | ['time-series'] | [-7.56512702e-01 -3.03792804e-01 1.61453709e-02 -2.10226700e-01
-6.25269771e-01 -4.34874505e-01 6.87267542e-01 -6.20907307e-01
-2.26508811e-01 4.85238820e-01 4.78655219e-01 -2.93766677e-01
-1.23165332e-01 -1.08646894e+00 -5.79509914e-01 -8.14533412e-01
1.74808025e-01 3.72515827e-01 8.35584179e-02 -3.28842141... | [4.5836076736450195, 4.1055097579956055] |
24395ae7-581c-4689-8cce-0b8a6c5ed50e | can-predictive-models-be-used-for-causal | 2306.10551 | null | https://arxiv.org/abs/2306.10551v1 | https://arxiv.org/pdf/2306.10551v1.pdf | Can predictive models be used for causal inference? | Supervised machine learning (ML) and deep learning (DL) algorithms excel at predictive tasks, but it is commonly assumed that they often do so by exploiting non-causal correlations, which may limit both interpretability and generalizability. Here, we show that this trade-off between explanation and prediction is not as... | ['Florian Hartig', 'Maximilian Pichler'] | 2023-06-18 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 3.06339681e-01 4.04852450e-01 -6.31074965e-01 -4.98431355e-01
-1.89841419e-01 -6.62949681e-01 7.39154994e-01 1.95411518e-01
-2.41905108e-01 8.48721445e-01 5.20224333e-01 -5.91231763e-01
-5.23267329e-01 -8.41443121e-01 -9.48318958e-01 -6.99475825e-01
-2.50433475e-01 4.89603907e-01 -3.01790357e-01 6.92057610... | [8.62210464477539, 5.515892505645752] |
b7f34daa-7c61-4c9a-aef4-a2e37ef632b2 | unified-generative-adversarial-networks-for | 1912.06112 | null | https://arxiv.org/abs/1912.06112v2 | https://arxiv.org/pdf/1912.06112v2.pdf | Unified Generative Adversarial Networks for Controllable Image-to-Image Translation | We propose a unified Generative Adversarial Network (GAN) for controllable image-to-image translation, i.e., transferring an image from a source to a target domain guided by controllable structures. In addition to conditioning on a reference image, we show how the model can generate images conditioned on controllable s... | ['Hong Liu', 'Hao Tang', 'Nicu Sebe'] | 2019-12-12 | null | null | null | null | ['gesture-to-gesture-translation'] | ['computer-vision'] | [ 7.27036417e-01 3.18623453e-01 -1.51382983e-01 -4.38741535e-01
-9.44081545e-01 -6.69291735e-01 5.75553894e-01 -7.96878040e-01
-1.00719005e-01 8.13804328e-01 4.86656167e-02 9.21930149e-02
2.75770754e-01 -9.37505722e-01 -1.08735168e+00 -1.03386474e+00
7.57017970e-01 4.75665182e-01 -2.57607251e-01 -4.72042635... | [11.847354888916016, -0.4252040684223175] |
f82e6a6e-bda7-45e5-8547-fe679d62dba3 | modeling-the-temporal-nature-of-human | 1511.06660 | null | http://arxiv.org/abs/1511.06660v5 | http://arxiv.org/pdf/1511.06660v5.pdf | Modeling the Temporal Nature of Human Behavior for Demographics Prediction | Mobile phone metadata is increasingly used for humanitarian purposes in
developing countries as traditional data is scarce. Basic demographic
information is however often absent from mobile phone datasets, limiting the
operational impact of the datasets. For these reasons, there has been a growing
interest in predictin... | ['Yves-Alexandre de Montjoye', "Alex 'Sandy' Pentland", 'Pål Sundsøy', 'Bjarke Felbo', 'Sune Lehmann'] | 2015-11-20 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-2.21592247e-01 -1.77505910e-02 -5.85222185e-01 -5.54585040e-01
-2.82499135e-01 -2.52411991e-01 1.06775773e+00 4.72732127e-01
-8.78264189e-01 8.90851319e-01 9.96678829e-01 -5.56761205e-01
-1.14821441e-01 -8.01274061e-01 -5.37052572e-01 -3.06651980e-01
-2.85916537e-01 5.58555365e-01 -4.92759883e-01 -1.79869190... | [6.58803653717041, 2.1000099182128906] |
4e68433e-0bf4-4101-a59a-c0c10400d99a | segment-anything-model-for-medical-images | 2304.14660 | null | https://arxiv.org/abs/2304.14660v4 | https://arxiv.org/pdf/2304.14660v4.pdf | Segment Anything Model for Medical Images? | The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It designed a novel promotable segmentation task, ensuring zero-shot image segmentation using the pre-trained model via two main modes including automatic everything and manual prompt. SAM has achieved impressive results on v... | ['Dong Ni', 'Fajin Dong', 'Deng-Ping Fan', 'Xindi Hu', 'Haozhe Chi', 'Chaoyu Chen', 'Jiongquan Chen', 'Junxuan Yu', 'Rusi Chen', 'Xinrui Zhou', 'Ao Chang', 'Han Zhou', 'Lian Liu', 'Xin Yang', 'Yuhao Huang'] | 2023-04-28 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 2.23952129e-01 1.56323984e-01 -3.09676528e-01 -2.27340847e-01
-8.03827524e-01 -3.93325061e-01 3.07004362e-01 -3.23184915e-02
-5.65088332e-01 3.58509094e-01 -1.96231082e-01 -3.05018753e-01
-2.02020884e-01 -5.16099870e-01 -3.88496608e-01 -8.40469599e-01
2.50013441e-01 5.44142842e-01 8.28286946e-01 -1.58515826... | [14.660189628601074, -2.25667667388916] |
f7d9221a-18c1-42d9-96b8-1b64c1cbc8f8 | weakly-supervised-learning-of-mid-level | 1611.05603 | null | http://arxiv.org/abs/1611.05603v1 | http://arxiv.org/pdf/1611.05603v1.pdf | Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization | State-of-the-art methods treat pedestrian attribute recognition as a
multi-label image classification problem. The location information of person
attributes is usually eliminated or simply encoded in the rigid splitting of
whole body in previous work. In this paper, we formulate the task in a
weakly-supervised attribut... | ['Zhang Zhang', 'Kaiqi Huang', 'Biao Leng', 'Kai Yu', 'Dangwei Li'] | 2016-11-17 | null | null | null | null | ['pedestrian-attribute-recognition', 'multi-label-image-classification'] | ['computer-vision', 'computer-vision'] | [ 8.54196623e-02 -2.89152097e-02 -9.18834507e-02 -8.59734118e-01
-5.56935012e-01 -3.32617104e-01 6.73625171e-01 5.39397001e-01
-6.78936124e-01 6.54180646e-01 7.08256215e-02 4.22299296e-01
7.62185082e-02 -7.97995508e-01 -7.08150625e-01 -9.77462530e-01
8.92953947e-03 6.36452079e-01 1.72359377e-01 -4.02816630... | [14.409441947937012, 0.9879971742630005] |
fe7d64db-7849-4672-8d18-e5e29670f495 | alexa-conversations-an-extensible-data-driven | 2104.09088 | null | https://arxiv.org/abs/2104.09088v1 | https://arxiv.org/pdf/2104.09088v1.pdf | Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems | Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Training each component requires annotations which are hard to obtain for every new domain, limiting scalability of such systems. Similarly, rul... | ['Eddie Wang', 'Nikko Strom', 'Minmin Shen', 'Abhishek Sethi', 'Vittorio Perera', 'Shachi Paul', 'Yi Pan', 'Vishal Naik', 'Angeliki Metallinou', 'Arindam Mandal', 'Qing Liu', 'Chien-Wei Lin', 'Anuj Goyal', 'Peter Ku', 'Prakash Krishnan', 'Jiun-Yu Kao', 'Abhay Jha', 'Jan Jezabek', 'Dilek Hakkani-Tur', 'Rahul Goel', 'Shu... | 2021-04-19 | null | https://aclanthology.org/2021.naacl-demos.15 | https://aclanthology.org/2021.naacl-demos.15.pdf | naacl-2021-4 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-9.89563018e-02 7.19474792e-01 8.36464539e-02 -6.00709736e-01
-5.38598001e-01 -1.04105234e+00 8.36051166e-01 2.28781048e-02
-1.80378526e-01 8.64568710e-01 2.83375859e-01 -5.64588606e-01
1.43768445e-01 -6.10443950e-01 -2.94852406e-01 1.16440035e-01
-5.86101972e-02 9.08356547e-01 6.40354633e-01 -1.01713181... | [12.824604988098145, 7.955810546875] |
7aa5a8d3-dd40-4a36-a272-a69a8ded36d2 | ensemble-of-heterogeneous-flexible-neural | 1705.05592 | null | http://arxiv.org/abs/1705.05592v1 | http://arxiv.org/pdf/1705.05592v1.pdf | Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming | Machine learning algorithms are inherently multiobjective in nature, where
approximation error minimization and model's complexity simplification are two
conflicting objectives. We proposed a multiobjective genetic programming (MOGP)
for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible
feedforwa... | ['Václav Snášel', 'Ajith Abraham', 'Varun Kumar Ojha'] | 2017-05-16 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 2.02780128e-01 -2.85615951e-01 2.78845578e-01 -1.87328771e-01
2.27270387e-02 -4.15972054e-01 3.19652003e-03 7.28452876e-02
-3.14377636e-01 1.15895212e+00 -2.61697024e-01 1.78498149e-01
-9.93553460e-01 -9.99676168e-01 -4.39247847e-01 -1.24920595e+00
3.70942019e-02 5.28136909e-01 -1.21267557e-01 -4.26563323... | [6.066344261169434, 3.5040202140808105] |
2cbcde0e-c1ce-4174-b64b-4ba664cf800e | defeenet-consecutive-3d-human-motion | 2304.04496 | null | https://arxiv.org/abs/2304.04496v2 | https://arxiv.org/pdf/2304.04496v2.pdf | DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback | Let us rethink the real-world scenarios that require human motion prediction techniques, such as human-robot collaboration. Current works simplify the task of predicting human motions into a one-off process of forecasting a short future sequence (usually no longer than 1 second) based on a historical observed one. Howe... | ['Jianfeng Lu', 'Weiqing Li', 'Dong Wei', 'Bin Li', 'Huaijiang Sun', 'Xiaoning Sun'] | 2023-04-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_DeFeeNet_Consecutive_3D_Human_Motion_Prediction_With_Deviation_Feedback_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_DeFeeNet_Consecutive_3D_Human_Motion_Prediction_With_Deviation_Feedback_CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-prediction', 'human-motion-prediction'] | ['computer-vision', 'time-series'] | [ 2.84230351e-01 5.28729022e-01 -2.09238082e-01 -1.49586335e-01
-2.98985839e-01 -9.83860046e-02 3.76354575e-01 -1.55707970e-01
-3.88269186e-01 7.30032563e-01 2.49152154e-01 -1.36408985e-01
1.85064331e-01 -6.46684527e-01 -7.79008031e-01 -7.52886534e-01
-2.43079856e-01 3.69865328e-01 6.64428592e-01 -3.68457705... | [7.279544830322266, -0.04190529137849808] |
eb2d0c1a-f6e5-47ac-940c-26bebb4797db | otiea-ontology-enhanced-triple-intrinsic | 2305.01561 | null | https://arxiv.org/abs/2305.01561v1 | https://arxiv.org/pdf/2305.01561v1.pdf | OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment | Cross-lingual and cross-domain knowledge alignment without sufficient external resources is a fundamental and crucial task for fusing irregular data. As the element-wise fusion process aiming to discover equivalent objects from different knowledge graphs (KGs), entity alignment (EA) has been attracting great interest f... | ['Chaoqun Jiang', 'Min Yang', 'Xueyan Zhao', 'Chengxiang Tan', 'Zhishuo Zhang'] | 2023-05-02 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [ 7.05797076e-02 8.31696391e-02 -4.62563753e-01 -3.76319766e-01
-4.39495087e-01 -2.31788367e-01 3.36043417e-01 3.46848398e-01
-4.12706316e-01 8.12659740e-01 4.65366602e-01 1.21743746e-01
-5.60425282e-01 -9.99109149e-01 -6.87792063e-01 -5.26724517e-01
2.62491047e-01 3.48323762e-01 3.44596535e-01 -5.70520163... | [8.746638298034668, 7.958314418792725] |
f78e87ca-33e2-431b-8d13-8cfd4ea1a96d | semantic-segmentation-of-trajectories-with-1 | 1912.05727 | null | https://arxiv.org/abs/1912.05727v1 | https://arxiv.org/pdf/1912.05727v1.pdf | Semantic segmentation of trajectories with improved agent models for pedestrian behavior analysis | In this paper, we propose a method for semantic segmentation of pedestrian trajectories based on pedestrian behavior models, or agents. The agents model the dynamics of pedestrian movements in two-dimensional space using a linear dynamics model and common start and goal locations of trajectories. First, agent models ar... | ['Kazufumi Kaneda', 'Daisuke Ogawa', 'Toru Tamaki', 'Bisser Raytchev'] | 2019-12-12 | null | null | null | null | ['trajectory-modeling'] | ['time-series'] | [-6.01920664e-01 -2.36394957e-01 -3.11264634e-01 -1.21787511e-01
-1.17129169e-01 -5.43629885e-01 8.36618185e-01 8.47367123e-02
-6.27371669e-01 5.79391181e-01 1.07356966e-01 -2.58922040e-01
1.80466592e-01 -9.46518064e-01 -5.47761321e-01 -7.09922194e-01
-1.57590449e-01 7.09628940e-01 7.87709653e-01 5.82320504... | [6.164719104766846, 0.9434864521026611] |
87cb1579-fb9f-466b-8103-c9cae2ad0411 | self-supervised-graph-structure-refinement | 2211.06545 | null | https://arxiv.org/abs/2211.06545v4 | https://arxiv.org/pdf/2211.06545v4.pdf | Self-Supervised Graph Structure Refinement for Graph Neural Networks | Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. H... | ['Yanfang Ye', 'Chuxu Zhang', 'Mingxuan Ju', 'Qianlong Wen', 'Jianan Zhao'] | 2022-11-12 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 9.88794938e-02 2.94015557e-01 -2.21295819e-01 -1.81859583e-01
-4.97292131e-01 -5.89673400e-01 4.81816679e-01 1.30783021e-01
-4.13553238e-01 4.23114538e-01 4.08650227e-02 -4.74773645e-01
-2.50345230e-01 -1.01542199e+00 -8.12614858e-01 -6.02600992e-01
-1.30319655e-01 4.08643007e-01 4.36227679e-01 -1.64744362... | [7.182196140289307, 6.245276927947998] |
83752878-297f-4720-8940-4f26ffde0f44 | a-big-data-approach-towards-sarcasm-detection | 2306.00445 | null | https://arxiv.org/abs/2306.00445v1 | https://arxiv.org/pdf/2306.00445v1.pdf | A big data approach towards sarcasm detection in Russian | We present a set of deterministic algorithms for Russian inflection and automated text synthesis. These algorithms are implemented in a publicly available web-service www.passare.ru. This service provides functions for inflection of single words, word matching and synthesis of grammatically correct Russian text. Select... | ['T. A. Zhukov', 'T. M. Sadykov', 'A. A. Gurin'] | 2023-06-01 | null | null | null | null | ['sarcasm-detection'] | ['natural-language-processing'] | [-2.93555439e-01 -7.84707442e-02 -6.77989870e-02 -2.76086509e-01
-8.67017388e-01 -1.11983645e+00 4.82317865e-01 4.69232559e-01
-2.76882082e-01 8.29567730e-01 5.42080522e-01 -8.06648076e-01
7.60056973e-02 -8.70525599e-01 -2.01100960e-01 -3.83948594e-01
7.22009599e-01 7.66419530e-01 -1.37922361e-01 -7.46686101... | [10.779770851135254, 10.404287338256836] |
a8635b98-ad34-492a-b708-58772de0c829 | bioelectra-pretrained-biomedical-text-encoder | null | null | https://aclanthology.org/2021.bionlp-1.16 | https://aclanthology.org/2021.bionlp-1.16.pdf | BioELECTRA:Pretrained Biomedical text Encoder using Discriminators | Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. We apply ‘replaced token detection’ pretraining technique proposed by ELECTRA and pretrain a biomedical language model from scratch using biomedical text and vocabulary. W... | ['Malaikannan Sankarasubbu', 'Bhuvana Kundumani', 'Kamal raj Kanakarajan'] | 2021-06-11 | null | null | null | acl-anthology-2021-6 | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [ 1.72168389e-01 3.20263118e-01 -5.91185749e-01 -5.78950226e-01
-1.22527659e+00 -1.23175934e-01 2.20916763e-01 6.49438381e-01
-1.09298539e+00 1.42037213e+00 3.65153760e-01 -4.05264020e-01
-1.26803026e-01 -4.17104453e-01 -1.09201467e+00 -3.19768965e-01
-1.56433657e-01 9.35567737e-01 -3.72340947e-01 5.52833639... | [8.52802848815918, 8.732650756835938] |
760e85f1-c048-449f-a9c2-78f1af86947e | auebtwittersentiment-at-semeval-2016-task-4-a | null | null | https://aclanthology.org/S16-1012 | https://aclanthology.org/S16-1012.pdf | aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis | null | ['Stavros Giorgis', 'John Pavlopoulos', 'Prodromos Malakasiotis', 'Ion Androutsopoulos', 'Apostolos Rousas'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['twitter-sentiment-analysis'] | ['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... | [-7.288730144500732, 3.731464385986328] |
ffb5e504-82f0-4413-a10c-9e1e57f0319e | algorithmic-improvements-for-deep | 1911.12511 | null | https://arxiv.org/abs/1911.12511v1 | https://arxiv.org/pdf/1911.12511v1.pdf | Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction | Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emp... | ['Marc G. Bellemare', 'Hugo Larochelle', 'William Fedus', 'Doina Precup', 'Vishal Jain'] | 2019-11-28 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 2.11632811e-02 3.92746538e-01 -2.77007729e-01 2.47780591e-01
-6.22023821e-01 -7.45027721e-01 7.99767852e-01 -1.57333557e-02
-8.34715366e-01 1.11153686e+00 3.71936142e-01 -5.75454354e-01
-4.54141766e-01 -8.46853137e-01 -5.88921010e-01 -5.75573385e-01
-2.77955472e-01 6.01051390e-01 3.81799012e-01 -8.87389839... | [3.8457467555999756, 1.4820863008499146] |
953d7aef-02f7-4bdc-8245-4fa9b7edfd52 | optical-flow-reuse-based-bidirectional | 2110.06786 | null | https://arxiv.org/abs/2110.06786v3 | https://arxiv.org/pdf/2110.06786v3.pdf | Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution | In this paper, we consider the task of space-time video super-resolution (ST-VSR), which can increase the spatial resolution and frame rate for a given video simultaneously. Despite the remarkable progress of recent methods, most of them still suffer from high computational costs and inefficient long-range information ... | ['Zhenzhong Chen', 'Han Zhu', 'Huairui Wang', 'Yuantong Zhang'] | 2021-10-13 | null | null | null | null | ['space-time-video-super-resolution', 'video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 2.52382159e-01 -5.70568442e-01 -3.81433904e-01 -2.84961332e-02
-4.39204633e-01 7.96499103e-02 1.75600633e-01 -6.81582451e-01
-3.65611494e-01 9.60597277e-01 5.18182576e-01 -6.68726563e-02
-1.79208964e-01 -6.64554477e-01 -4.70704079e-01 -7.23587990e-01
2.17347741e-01 -4.13042247e-01 6.68907404e-01 -1.31572679... | [11.001076698303223, -1.816086769104004] |
8eb4a4b6-be15-4bd6-9d84-3be366d0ef14 | rwen-tts-relation-aware-word-encoding-network | 2212.07939 | null | https://arxiv.org/abs/2212.07939v1 | https://arxiv.org/pdf/2212.07939v1.pdf | RWEN-TTS: Relation-aware Word Encoding Network for Natural Text-to-Speech Synthesis | With the advent of deep learning, a huge number of text-to-speech (TTS) models which produce human-like speech have emerged. Recently, by introducing syntactic and semantic information w.r.t the input text, various approaches have been proposed to enrich the naturalness and expressiveness of TTS models. Although these ... | ['Insoo Oh', 'Yoonseok Hong', 'HyeongRae Noh', 'Shinhyeok Oh'] | 2022-12-15 | null | null | null | null | ['text-to-speech-synthesis'] | ['speech'] | [ 1.89043716e-01 4.06260490e-01 -4.33130383e-01 -5.28664291e-01
-2.11553618e-01 -2.03408971e-01 5.49465537e-01 2.54950225e-01
-2.87285596e-01 5.07904291e-01 5.85872650e-01 -5.72717071e-01
7.70180598e-02 -9.91853535e-01 -3.40738505e-01 -2.13819981e-01
2.87622482e-01 2.09509045e-01 4.75807041e-01 -5.35415411... | [10.516722679138184, 9.115089416503906] |
26556a8b-bf4d-4397-a53e-22577d46e313 | module-wise-network-quantization-for-6d | 2303.06753 | null | https://arxiv.org/abs/2303.06753v1 | https://arxiv.org/pdf/2303.06753v1.pdf | Module-Wise Network Quantization for 6D Object Pose Estimation | Many edge applications, such as collaborative robotics and spacecraft rendezvous, can benefit from 6D object pose estimation, but must do so on embedded platforms. Unfortunately, existing 6D pose estimation networks are typically too large for deployment in such situations and must therefore be compressed, while mainta... | ['Mathieu Salzmann', 'Yinlin Hu', 'Andrew Price', 'Saqib Javed'] | 2023-03-12 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.78519621e-01 1.16312191e-01 -3.12851220e-01 -1.32687539e-01
-4.84906793e-01 -8.69894922e-01 6.49223149e-01 1.22160502e-01
-2.98913896e-01 6.16629541e-01 1.00219898e-01 -4.44636852e-01
-4.27482486e-01 -9.24342752e-01 -8.39524329e-01 -1.96339756e-01
-5.35505235e-01 7.64206290e-01 1.89665407e-01 -3.65631312... | [7.705052375793457, -2.1674978733062744] |
ae22372e-dfca-49ba-b6e3-ccff2b423378 | taxonomic-class-incremental-learning | 2304.05547 | null | https://arxiv.org/abs/2304.05547v1 | https://arxiv.org/pdf/2304.05547v1.pdf | Taxonomic Class Incremental Learning | The problem of continual learning has attracted rising attention in recent years. However, few works have questioned the commonly used learning setup, based on a task curriculum of random class. This differs significantly from human continual learning, which is guided by taxonomic curricula. In this work, we propose th... | ['Nuno Vasconcelos', 'Zhiyuan Hu', 'Zonghuan Li', 'Yuzhao Chen'] | 2023-04-12 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 2.32874095e-01 -7.75770396e-02 -2.35294864e-01 -5.51329672e-01
-3.07732791e-01 -5.81354499e-01 4.73354250e-01 2.90568501e-01
-7.51025736e-01 1.07609951e+00 -1.08126909e-01 -3.70881051e-01
-4.68996733e-01 -8.31656694e-01 -7.35692441e-01 -5.56587517e-01
-1.61094919e-01 4.74022537e-01 7.29036570e-01 -2.18389072... | [9.537623405456543, 3.350789785385132] |
c1cae501-1b22-49a5-84d0-4846b17d1dcc | solving-audio-inverse-problems-with-a | 2210.15228 | null | https://arxiv.org/abs/2210.15228v3 | https://arxiv.org/pdf/2210.15228v3.pdf | Solving Audio Inverse Problems with a Diffusion Model | This paper presents CQT-Diff, a data-driven generative audio model that can, once trained, be used for solving various different audio inverse problems in a problem-agnostic setting. CQT-Diff is a neural diffusion model with an architecture that is carefully constructed to exploit pitch-equivariant symmetries in music.... | ['Vesa Välimäki', 'Jaakko Lehtinen', 'Eloi Moliner'] | 2022-10-27 | null | null | null | null | ['bandwidth-extension', 'audio-inpainting', 'bandwidth-extension'] | ['audio', 'audio', 'speech'] | [ 4.02788013e-01 -1.15059324e-01 2.27392271e-01 -2.80459896e-02
-1.36261082e+00 -6.99577332e-01 4.72405344e-01 -4.93624598e-01
-2.26552650e-01 3.58427376e-01 7.09520280e-01 6.28655404e-02
-3.46978515e-01 -2.34359071e-01 -7.10991561e-01 -8.04428995e-01
-1.92291379e-01 3.55008632e-01 -2.71637172e-01 -4.46547478... | [15.507664680480957, 5.808642864227295] |
2ec6edca-0907-40bc-91f3-4883dbe30955 | a-unified-mammogram-analysis-method-via | 1808.10646 | null | http://arxiv.org/abs/1808.10646v1 | http://arxiv.org/pdf/1808.10646v1.pdf | A Unified Mammogram Analysis Method via Hybrid Deep Supervision | Automatic mammogram classification and mass segmentation play a critical role
in a computer-aided mammogram screening system. In this work, we present a
unified mammogram analysis framework for both whole-mammogram classification
and segmentation. Our model is designed based on a deep U-Net with residual
connections, a... | ['Albert C. S. Chung', 'Han Zhang', 'Rongzhao Zhang'] | 2018-08-31 | null | null | null | null | ['whole-mammogram-classification'] | ['medical'] | [ 4.09347206e-01 3.72060984e-01 -5.06869256e-01 -7.81796694e-01
-8.65011632e-01 6.66478202e-02 3.12349021e-01 3.09314519e-01
-5.07265508e-01 3.97770375e-01 -4.66116332e-02 -5.52905262e-01
-1.13461182e-01 -8.81642461e-01 -7.78116405e-01 -7.39540875e-01
-6.94879591e-02 3.48289847e-01 4.76489365e-01 -8.31475407... | [14.957585334777832, -2.4450595378875732] |
0f8cf2b4-9599-438e-bf28-48b3334b2cd6 | feature-independent-action-spotting-without | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Sun_Feature-Independent_Action_Spotting_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Sun_Feature-Independent_Action_Spotting_2014_CVPR_paper.pdf | Feature-Independent Action Spotting Without Human Localization, Segmentation or Frame-wise Tracking | In this paper, we propose an unsupervised framework for action spotting in videos that does not depend on any specific feature (e.g. HOG/HOF, STIP, silhouette, bag-of-words, etc.). Furthermore, our solution requires no human localization, segmentation, or framewise tracking. This is achieved by treating the problem hol... | ['Hassan Foroosh', 'Chuan Sun', 'Marshall Tappen'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['action-spotting'] | ['computer-vision'] | [ 1.42704383e-01 -5.92099726e-01 -1.02877393e-01 1.87198192e-01
-7.36142993e-01 -6.86430931e-01 5.01381695e-01 -2.35786676e-01
-2.55797982e-01 2.18659267e-01 3.65268946e-01 1.63890168e-01
-2.36987248e-01 -4.85629529e-01 -7.25211382e-01 -9.55444455e-01
-1.75502539e-01 3.03856492e-01 4.61405993e-01 -2.70760320... | [8.19711971282959, 0.39359423518180847] |
5a24b3c3-4628-4a1c-a9e3-1bc12c72f6a8 | loss-function-entropy-regularization-for | 2205.00224 | null | https://arxiv.org/abs/2205.00224v2 | https://arxiv.org/pdf/2205.00224v2.pdf | Loss Function Entropy Regularization for Diverse Decision Boundaries | Is it possible to train several classifiers to perform meaningful crowd-sourcing to produce a better prediction label set without ground-truth annotation? This paper will modify the contrastive learning objectives to automatically train a self-complementing ensemble to produce a state-of-the-art prediction on the CIFAR... | ['Sue Sin Chong'] | 2022-04-30 | null | null | null | null | ['unsupervised-image-classification'] | ['computer-vision'] | [ 3.60287219e-01 4.94324863e-01 2.87244111e-01 -5.72274685e-01
-5.84453106e-01 -4.54840034e-01 4.94247019e-01 -1.62157223e-01
-5.55444717e-01 9.60514545e-01 5.25734276e-02 -4.71085496e-02
6.99511394e-02 -7.22791493e-01 -6.68715954e-01 -8.57774079e-01
1.16635174e-01 5.46220779e-01 3.73703897e-01 -2.97147810... | [9.655248641967773, 3.625441789627075] |
e2718c89-731f-4a10-95cf-f17333f57ac4 | hypothesis-transfer-learning-with-surrogate | 2305.19694 | null | https://arxiv.org/abs/2305.19694v1 | https://arxiv.org/pdf/2305.19694v1.pdf | Hypothesis Transfer Learning with Surrogate Classification Losses | Hypothesis transfer learning (HTL) contrasts domain adaptation by allowing for a previous task leverage, named the source, into a new one, the target, without requiring access to the source data. Indeed, HTL relies only on a hypothesis learnt from such source data, relieving the hurdle of expansive data storage and pro... | ['Guillaume Staerman', 'Anass Aghbalou'] | 2023-05-31 | null | null | null | null | ['generalization-bounds'] | ['methodology'] | [ 1.20213591e-01 4.49138165e-01 -2.98310459e-01 -2.06777558e-01
-1.02131808e+00 -4.32774454e-01 1.24563694e-01 5.19902408e-01
-5.33718824e-01 9.88099873e-01 -3.31871003e-01 -4.13539201e-01
-3.20102513e-01 -8.49675894e-01 -1.03065979e+00 -1.13565660e+00
-2.06729636e-01 3.40670079e-01 4.89897653e-02 -3.70100252... | [8.292923927307129, 4.121738910675049] |
5dbdbe9c-2ab5-4d06-86af-92dfe6ec0907 | bosphorussign22k-sign-language-recognition | 2004.01283 | null | https://arxiv.org/abs/2004.01283v2 | https://arxiv.org/pdf/2004.01283v2.pdf | BosphorusSign22k Sign Language Recognition Dataset | Sign Language Recognition is a challenging research domain. It has recently seen several advancements with the increased availability of data. In this paper, we introduce the BosphorusSign22k, a publicly available large scale sign language dataset aimed at computer vision, video recognition and deep learning research c... | ['Lale Akarun', 'Necati Cihan Camgöz', 'Ahmet Alp Kındıroğlu', 'Oğulcan Özdemir'] | 2020-04-02 | bosphorussign22k-sign-language-recognition-1 | https://aclanthology.org/2020.signlang-1.30 | https://aclanthology.org/2020.signlang-1.30.pdf | lrec-2020-5 | ['sign-language-production'] | ['natural-language-processing'] | [-4.89213467e-02 -6.05168462e-01 -2.68093228e-01 -5.44186413e-01
-7.85934091e-01 -6.33600414e-01 6.96336508e-01 -1.18745518e+00
-4.84988332e-01 4.74472702e-01 8.29010665e-01 -1.47744268e-03
1.79763526e-01 6.14455789e-02 -4.71377403e-01 -5.94805658e-01
-4.78240885e-02 3.24082971e-01 2.93654710e-01 -2.93512970... | [9.145278930664062, -6.469429016113281] |
3640ffaf-2960-486b-b21f-5cd498d92806 | overview-of-the-fifth-social-media-mining-for | null | null | https://aclanthology.org/2020.smm4h-1.4 | https://aclanthology.org/2020.smm4h-1.4.pdf | Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020 | The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics. The fifth iteration of the Social Media Mining for Health Applications (#SMM4H) shared tasks sought to advance the use of Twitter data (tweets) for pharmacovigilance, toxicovi... | ['Graciela Gonzalez-Hernandez', 'Davy Weissenbacher', 'Elena Tutubalina', 'Abeed Sarker', 'Karen O’Connor', 'Anne-Lyse Minard', 'Zulfat Miftahutdinov', 'Arjun Magge', 'Ivan Flores', 'Ilseyar Alimova', 'Ari Klein'] | null | null | null | null | smm4h-coling-2020-12 | ['epidemiology'] | ['medical'] | [-4.66012117e-03 3.63189787e-01 -4.13516253e-01 -3.23210746e-01
-9.32677448e-01 -3.87546897e-01 5.45874000e-01 1.23433530e+00
-5.21665156e-01 7.26050258e-01 4.64258373e-01 -5.82063973e-01
-5.04257418e-02 -7.64169216e-01 -5.80075264e-01 -1.56471990e-02
-2.84293413e-01 5.05002916e-01 -3.48011851e-01 1.04907922... | [8.435208320617676, 8.940218925476074] |
1497990b-5699-4d31-9143-2c47426f8612 | housex-a-fine-grained-house-music-dataset-and | 2207.11690 | null | https://arxiv.org/abs/2207.11690v2 | https://arxiv.org/pdf/2207.11690v2.pdf | HouseX: A Fine-grained House Music Dataset and its Potential in the Music Industry | Machine sound classification has been one of the fundamental tasks of music technology. A major branch of sound classification is the classification of music genres. However, though covering most genres of music, existing music genre datasets often do not contain fine-grained labels that indicate the detailed sub-genre... | ['Xinyu Li'] | 2022-07-24 | null | null | null | null | ['sound-classification'] | ['audio'] | [-9.60620418e-02 -5.87154210e-01 2.37079605e-01 -3.81312184e-02
-8.92081797e-01 -1.05341959e+00 3.56251091e-01 -8.13909844e-02
7.10534230e-02 3.92433524e-01 4.41273451e-01 4.69489545e-02
-4.77933645e-01 -5.97735822e-01 -2.34859601e-01 -5.32141447e-01
-2.61077464e-01 5.15102088e-01 3.51067901e-01 -2.18509004... | [15.869576454162598, 5.255272388458252] |
1d217d0a-c989-4c8f-b87a-2bc19792e31a | a-polynomial-time-iterative-algorithm-for-1 | 2306.00266 | null | https://arxiv.org/abs/2306.00266v1 | https://arxiv.org/pdf/2306.00266v1.pdf | A polynomial-time iterative algorithm for random graph matching with non-vanishing correlation | We propose an efficient algorithm for matching two correlated Erd\H{o}s--R\'enyi graphs with $n$ vertices whose edges are correlated through a latent vertex correspondence. When the edge density $q= n^{- \alpha+o(1)}$ for a constant $\alpha \in [0,1)$, we show that our algorithm has polynomial running time and succeeds... | ['Zhangsong Li', 'Jian Ding'] | 2023-06-01 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 3.36819410e-01 4.13218141e-01 -1.25663310e-01 1.61272794e-01
-8.27628672e-01 -4.56480592e-01 7.02681988e-02 1.26929045e-01
-4.27942216e-01 2.98397690e-01 -4.74982619e-01 -6.83462262e-01
-5.24709463e-01 -1.21475470e+00 -6.28733695e-01 -8.05448830e-01
-8.38041723e-01 8.13046813e-01 2.41982237e-01 -2.84530699... | [6.794891834259033, 5.116261005401611] |
e0854907-f7f8-4871-860e-09d85b4cc075 | pscnn-a-885-86-tops-w-programmable-sram-based | 2205.01569 | null | https://arxiv.org/abs/2205.01569v1 | https://arxiv.org/pdf/2205.01569v1.pdf | PSCNN: A 885.86 TOPS/W Programmable SRAM-based Computing-In-Memory Processor for Keyword Spotting | Computing-in-memory (CIM) has attracted significant attentions in recent years due to its massive parallelism and low power consumption. However, current CIM designs suffer from large area overhead of small CIM macros and bad programmablity for model execution. This paper proposes a programmable CIM processor with a si... | ['Tian-Sheuan Chang', 'Shu-Hung Kuo'] | 2022-05-02 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.10342994e-01 -2.98712879e-01 -4.34010923e-01 -3.85333210e-01
1.31435081e-01 -7.60969371e-02 1.93352610e-01 -8.85565951e-03
-7.91072011e-01 4.50885177e-01 -2.23586991e-01 -7.88602054e-01
6.11895807e-02 -1.12124300e+00 -4.82371897e-01 -5.99156439e-01
3.63944560e-01 -1.91374421e-01 5.88446856e-01 4.12389264... | [8.378841400146484, 2.804502010345459] |
7a41e8f1-e298-47bf-a3c5-2f5458ff0671 | an-enhanced-span-based-decomposition-method | 2109.13023 | null | https://arxiv.org/abs/2109.13023v3 | https://arxiv.org/pdf/2109.13023v3.pdf | An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling | Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain. Recently, the metric-based meta-learning framework has been recognized as a promising approach for FSSL. However, most prior works assi... | ['Zhifang Sui', 'Baobao Chang', 'Yunbo Cao', 'Qingyu Zhou', 'Tianyu Liu', 'Runxin Xu', 'Peiyi Wang'] | 2021-09-27 | null | https://aclanthology.org/2022.naacl-main.369 | https://aclanthology.org/2022.naacl-main.369.pdf | naacl-2022-7 | ['few-shot-ner'] | ['natural-language-processing'] | [ 1.09367035e-01 -1.88543141e-01 -5.26945293e-01 -4.10524219e-01
-1.09714258e+00 -5.33617854e-01 1.87343016e-01 2.74787456e-01
-3.73068005e-01 8.19023371e-01 8.34978744e-02 -2.33504474e-01
-1.90256596e-01 -6.18946493e-01 -2.60281831e-01 -6.04043901e-01
-1.68453064e-02 4.52158689e-01 5.04950404e-01 -3.45566696... | [9.540380477905273, 9.248783111572266] |
1ab3c206-c154-4a74-81b8-6cc5afe30577 | lake-ice-detection-from-sentinel-1-sar-with | 2002.07040 | null | https://arxiv.org/abs/2002.07040v2 | https://arxiv.org/pdf/2002.07040v2.pdf | Lake Ice Detection from Sentinel-1 SAR with Deep Learning | Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global clima... | ['Pascal Imhof', 'Roberto Aguilar', 'Manu Tom', 'Konrad Schindler', 'Emmanuel Baltsavias', 'Silvan Leinss'] | 2020-02-17 | null | null | null | null | ['sentinel-1-sar-processing', 'lake-ice-detection', 'change-detection-for-remote-sensing-images', 'lake-ice-detection', 'segmentation-of-remote-sensing-imagery', 'remote-sensing-image-classification', 'the-semantic-segmentation-of-remote-sensing'] | ['computer-code', 'computer-vision', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous'] | [-2.02962197e-02 -2.70141780e-01 1.89954206e-01 -5.17747939e-01
-5.25444150e-01 -8.38673472e-01 2.61482388e-01 1.93868876e-01
-7.44769931e-01 9.01518822e-01 -1.44769177e-01 -5.12272239e-01
2.52065837e-01 -9.81768310e-01 -6.29271448e-01 -8.21776330e-01
-6.64230704e-01 4.76156712e-01 -8.78855959e-02 -5.87381303... | [9.53819465637207, -1.5548096895217896] |
b22e6379-2a2e-4663-9427-e35b1196d2f3 | hyper-gan-transferring-unconditional-to | 2112.02219 | null | https://arxiv.org/abs/2112.02219v2 | https://arxiv.org/pdf/2112.02219v2.pdf | Transferring Unconditional to Conditional GANs with Hyper-Modulation | GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. Many of the available high-quality... | ['Bogdan Raducanu', 'Joost Van de Weijer', 'Yaxing Wang', 'Héctor Laria'] | 2021-12-04 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 3.71281147e-01 8.25234577e-02 -3.64199257e-03 -3.23003411e-01
-6.96564496e-01 -3.77547532e-01 7.42574632e-01 -5.59647202e-01
-4.03724074e-01 1.14091468e+00 -1.51548162e-01 -3.42538431e-02
1.63996220e-01 -1.18261325e+00 -8.23168874e-01 -1.11841035e+00
2.92579979e-01 4.63745266e-01 2.25311205e-01 -1.61514536... | [11.594554901123047, -0.2638377547264099] |
69b33484-c2f0-4b51-9906-1bc1a001e78b | temporal-subsampling-diminishes-small-spatial | 2305.00100 | null | https://arxiv.org/abs/2305.00100v1 | https://arxiv.org/pdf/2305.00100v1.pdf | Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence | The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use datasets that are temporally subsampled relative to the time steps required for the ... | ['Tse-Chun Chen', 'Jason A. Platt', 'Stephen G. Penny', 'Timothy A. Smith'] | 2023-04-28 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-1.41859099e-01 -4.55969244e-01 4.31442410e-01 4.67085168e-02
-1.53580502e-01 -6.23655319e-01 1.21697080e+00 -7.18743801e-02
-4.26833302e-01 8.03944528e-01 8.09147283e-02 -6.65498555e-01
-7.84983486e-02 -7.25593090e-01 -3.46322060e-01 -9.53990817e-01
-5.70855260e-01 8.97016749e-02 -3.55066583e-02 -4.72109497... | [6.570662975311279, 3.3452377319335938] |
3c5b95cc-73fd-4853-8013-a7998a28c874 | natural-language-processing-on-customer-note | 2305.02029 | null | https://arxiv.org/abs/2305.02029v1 | https://arxiv.org/pdf/2305.02029v1.pdf | Natural language processing on customer note data | Automatic analysis of customer data for businesses is an area that is of interest to companies. Business to business data is studied rarely in academia due to the sensitive nature of such information. Applying natural language processing can speed up the analysis of prohibitively large sets of data. This paper addresse... | ['Peter Appleby', 'Matthew Shardlow', 'Tom Armitage', 'Jozef Baca', 'David Webb', 'Andrew Hilditch'] | 2023-05-03 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 6.83985502e-02 1.87408745e-01 -4.76215482e-01 -8.68243098e-01
-8.17318082e-01 -7.57571399e-01 6.82504714e-01 1.07689190e+00
-5.81616640e-01 7.10976183e-01 4.60935652e-01 -5.40478885e-01
-1.73628077e-01 -9.37225759e-01 -2.95523018e-01 -5.25407553e-01
3.34208280e-01 7.67253101e-01 3.78202707e-01 -4.25455749... | [11.12894058227539, 6.870570659637451] |
7af25c74-7f09-4738-a6e8-b92690420358 | distractor-aware-neuron-intrinsic-learning | 2007.09979 | null | https://arxiv.org/abs/2007.09979v2 | https://arxiv.org/pdf/2007.09979v2.pdf | Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications | Medical image analysis benefits Computer Aided Diagnosis (CADx). A fundamental analyzing approach is the classification of medical images, which serves for skin lesion diagnosis, diabetic retinopathy grading, and cancer classification on histological images. When learning these discriminative classifiers, we observe th... | ['Lijun Gong', 'Yefeng Zheng', 'Kai Ma'] | 2020-07-20 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 2.20831975e-01 1.41323477e-01 -2.51202941e-01 -3.20131689e-01
-4.36687052e-01 -8.15658644e-02 3.92628103e-01 5.11400960e-02
-5.31146467e-01 7.07171321e-01 -1.96910545e-01 -2.64307372e-02
-3.38830501e-01 -4.64843750e-01 -6.83277488e-01 -1.34893537e+00
1.28340855e-01 6.42489493e-02 8.35298672e-02 -3.51159014... | [14.926706314086914, -2.5589828491210938] |
fcc829c2-942e-4b22-8f85-204c1b57b653 | competing-for-shareable-arms-in-multi-player | 2305.19158 | null | https://arxiv.org/abs/2305.19158v1 | https://arxiv.org/pdf/2305.19158v1.pdf | Competing for Shareable Arms in Multi-Player Multi-Armed Bandits | Competitions for shareable and limited resources have long been studied with strategic agents. In reality, agents often have to learn and maximize the rewards of the resources at the same time. To design an individualized competing policy, we model the competition between agents in a novel multi-player multi-armed band... | ['Peng Cui', 'Bo Li', 'Xingxuan Zhang', 'Haotian Wang', 'Renzhe Xu'] | 2023-05-30 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-1.33125231e-01 2.63748735e-01 -4.16324973e-01 1.35031030e-01
-6.92558408e-01 -8.36321294e-01 8.87685940e-02 -1.33355215e-01
-8.11842680e-01 1.36244380e+00 -5.53375147e-02 -5.94813786e-02
-6.48827493e-01 -7.24305451e-01 -6.30069792e-01 -1.01383209e+00
-1.00724496e-01 7.93266892e-01 -1.06399275e-01 -1.28673598... | [4.399826526641846, 3.1243624687194824] |
72fa801f-35e1-4984-be84-5e77cf6ac62d | fine-grained-is-too-coarse-a-novel-data | 2305.18668 | null | https://arxiv.org/abs/2305.18668v1 | https://arxiv.org/pdf/2305.18668v1.pdf | Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph Generation | Learning to compose visual relationships from raw images in the form of scene graphs is a highly challenging task due to contextual dependencies, but it is essential in computer vision applications that depend on scene understanding. However, no current approaches in Scene Graph Generation (SGG) aim at providing useful... | ['Cédric Buche', 'Anne-Gwenn Bosser', 'Paulo Santos', 'Neau Maëlic'] | 2023-05-30 | null | null | null | null | ['scene-graph-generation', 'image-generation-from-scene-graphs', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.53837025e-01 4.16773021e-01 2.75501490e-01 -3.78265262e-01
-4.33432162e-01 -6.36412978e-01 8.39862049e-01 5.13190508e-01
-2.20263138e-01 8.47667992e-01 1.96909785e-01 -3.59331906e-01
-2.51350939e-01 -1.08154905e+00 -9.08121645e-01 -5.70294380e-01
9.10805240e-02 5.86326420e-01 6.35638952e-01 -5.21487772... | [10.438725471496582, 1.508763313293457] |
46c5c70d-d5cf-4de8-879f-bbf12d173785 | motion-encoded-particle-swarm-optimization | 2010.02039 | null | https://arxiv.org/abs/2010.02039v1 | https://arxiv.org/pdf/2010.02039v1.pdf | Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs | This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target... | ['Quang Phuc Ha', 'Manh Duong Phung'] | 2020-10-05 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 2.66547054e-01 -3.71430606e-01 2.56743431e-01 5.09816051e-01
3.85077953e-01 -5.07209897e-01 4.69724298e-01 9.21554193e-02
-4.13753569e-01 1.05346429e+00 -4.74464208e-01 -8.65315944e-02
-7.87459791e-01 -9.27564085e-01 -3.16277556e-02 -1.27492106e+00
-5.52520752e-01 2.64370233e-01 6.77780449e-01 -3.51512194... | [5.581517696380615, 3.350738048553467] |
e72d4a0e-c00e-4710-b64e-5dd6a9c268bf | partial-video-domain-adaptation-with-partial | 2107.04941 | null | https://arxiv.org/abs/2107.04941v1 | https://arxiv.org/pdf/2107.04941v1.pdf | Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network | Partial Domain Adaptation (PDA) is a practical and general domain adaptation scenario, which relaxes the fully shared label space assumption such that the source label space subsumes the target one. The key challenge of PDA is the issue of negative transfer caused by source-only classes. For videos, such negative trans... | ['Zhenghua Chen', 'Kezhi Mao', 'Qi Li', 'Haozhi Cao', 'Jianfei Yang', 'Yuecong Xu'] | 2021-07-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xu_Partial_Video_Domain_Adaptation_With_Partial_Adversarial_Temporal_Attentive_Network_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_Partial_Video_Domain_Adaptation_With_Partial_Adversarial_Temporal_Attentive_Network_ICCV_2021_paper.pdf | iccv-2021-1 | ['partial-domain-adaptation'] | ['methodology'] | [ 2.82507420e-01 -2.66870379e-01 -4.99292195e-01 -8.83545280e-02
-6.80249989e-01 -6.73798144e-01 6.76326990e-01 -3.32169354e-01
-2.57610947e-01 8.24963093e-01 1.40619352e-01 1.01574231e-02
3.54081951e-02 -7.47097015e-01 -6.89138174e-01 -9.82879460e-01
-1.40792444e-01 1.71674155e-02 5.85025549e-01 -3.30050409... | [8.931295394897461, 0.9891830086708069] |
9ac6398a-faaa-44c3-a303-7375bbdb6861 | learning-dynamic-facial-radiance-fields-for | 2207.11770 | null | https://arxiv.org/abs/2207.11770v1 | https://arxiv.org/pdf/2207.11770v1.pdf | Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis | Talking head synthesis is an emerging technology with wide applications in film dubbing, virtual avatars and online education. Recent NeRF-based methods generate more natural talking videos, as they better capture the 3D structural information of faces. However, a specific model needs to be trained for each identity wi... | ['Jiwen Lu', 'Jie zhou', 'Yueqi Duan', 'Zheng Zhu', 'Wanhua Li', 'Shuai Shen'] | 2022-07-24 | null | null | null | null | ['talking-head-generation', 'talking-face-generation'] | ['computer-vision', 'computer-vision'] | [ 5.84670156e-02 1.58396080e-01 7.83038288e-02 -7.61801422e-01
-6.34885371e-01 -5.21523297e-01 5.40640533e-01 -1.11640513e+00
5.19391559e-02 5.15000165e-01 4.31931406e-01 3.94421309e-01
2.50874996e-01 -4.98249352e-01 -9.24771070e-01 -7.81841278e-01
3.46510410e-01 1.53310791e-01 -1.58685476e-01 -3.25506657... | [13.068438529968262, -0.38479435443878174] |
4e7b912c-1d45-42b3-b5c9-e8bdbb538d2a | learning-co-segmentation-by-segment-swapping | 2110.15904 | null | https://arxiv.org/abs/2110.15904v2 | https://arxiv.org/pdf/2110.15904v2.pdf | Learning Co-segmentation by Segment Swapping for Retrieval and Discovery | The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts of a night-time photograph visible in its daytime counterpart. Lack of training data is a key challenge for this co-segmentation ... | ['Mathieu Aubry', 'Armand Joulin', 'Alexei A. Efros', 'Xi Shen'] | 2021-10-29 | null | null | null | null | ['graph-clustering', 'spectral-graph-clustering'] | ['graphs', 'graphs'] | [ 5.19550025e-01 -1.97161958e-01 3.82870346e-01 -2.83319533e-01
-8.43762457e-01 -8.11125040e-01 9.08950448e-01 -2.42508814e-01
-3.30939412e-01 4.13024038e-01 -1.72966704e-01 -1.87007412e-01
-1.99916407e-01 -8.20059359e-01 -1.10162210e+00 -6.32884204e-01
4.18083258e-02 7.70751774e-01 3.59318525e-01 -1.09683640... | [9.77755069732666, 0.504455029964447] |
91566d65-1094-41af-86d4-48a973989989 | shape-is-almost-all-persistent-homology | 2304.07554 | null | https://arxiv.org/abs/2304.07554v1 | https://arxiv.org/pdf/2304.07554v1.pdf | Shape is (almost) all!: Persistent homology features (PHFs) are an information rich input for efficient molecular machine learning | 3-D shape is important to chemistry, but how important? Machine learning works best when the inputs are simple and match the problem well. Chemistry datasets tend to be very small compared to those generally used in machine learning so we need to get the most from each datapoint. Persistent homology measures the topolo... | ['Ella Gale'] | 2023-04-15 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [-8.86871945e-03 1.59740075e-01 -3.05839807e-01 -9.54999253e-02
-6.19400561e-01 -9.71438646e-01 6.98328078e-01 1.08714604e+00
-2.05503684e-02 1.12025297e+00 7.33688250e-02 -3.55559230e-01
-2.39567265e-01 -1.14606154e+00 -9.24382031e-01 -9.88875628e-01
-6.44847751e-01 8.17447662e-01 2.89996773e-01 -3.97162944... | [5.152361869812012, 5.596224308013916] |
b0db0022-3465-4d4a-925a-968f09a0ce44 | contrastive-learning-enhanced-nearest | null | null | https://aclanthology.org/2022.acl-short.75 | https://aclanthology.org/2022.acl-short.75.pdf | Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification | Multi-Label Text Classification (MLTC) is a fundamental and challenging task in natural language processing. Previous studies mainly focus on learning text representation and modeling label correlation but neglect the rich knowledge from the existing similar instances when predicting labels of a specific text. To make ... | ['Xinyu Dai', 'Ran Wang', 'Xi’ao Su'] | null | null | null | null | acl-2022-5 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 2.17759445e-01 -2.37906188e-01 -7.10197270e-01 -8.37105632e-01
-1.01924670e+00 -5.22487879e-01 6.64996445e-01 5.91529489e-01
-5.22038043e-01 5.73264718e-01 3.76554608e-01 -8.46204832e-02
-2.48823389e-01 -5.46050549e-01 -4.96168107e-01 -5.33510149e-01
4.77931231e-01 9.03132856e-01 1.26536548e-01 1.60854384... | [9.706144332885742, 4.52872896194458] |
a4625f33-258f-46e9-b40c-fb972ded54e3 | brain2pix-fully-convolutional-naturalistic | null | null | https://openreview.net/forum?id=15TmaAcmHus | https://openreview.net/pdf?id=15TmaAcmHus | Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity | Reconstructing complex and dynamic visual perception from brain activity remains a major challenge in machine learning applications to neuroscience. Here we present a new method for reconstructing naturalistic images and videos from very large single-participant functional magnetic resonance data that leverages the rec... | ['Anonymous'] | 2021-01-01 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 5.65815926e-01 2.07131729e-01 3.03713512e-02 -2.90761918e-01
-5.84762931e-01 -6.88068271e-01 6.45833015e-01 -4.71730769e-01
-6.84293509e-01 7.22207665e-01 4.67959404e-01 6.12328984e-02
6.22154362e-02 -4.09122884e-01 -1.31251109e+00 -6.86864316e-01
2.26300079e-02 2.51893491e-01 1.21800117e-02 2.90759176... | [10.7228422164917, 2.5072832107543945] |
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