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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a2443c3b-aa55-460d-84b4-aad8c86a108a | superhuman-accuracy-on-the-snemi3d | 1706.00120 | null | http://arxiv.org/abs/1706.00120v1 | http://arxiv.org/pdf/1706.00120v1.pdf | Superhuman Accuracy on the SNEMI3D Connectomics Challenge | For the past decade, convolutional networks have been used for 3D
reconstruction of neurons from electron microscopic (EM) brain images. Recent
years have seen great improvements in accuracy, as evidenced by submissions to
the SNEMI3D benchmark challenge. Here we report the first submission to surpass
the estimate of h... | ['Kisuk Lee', 'Viren Jain', 'Jonathan Zung', 'H. Sebastian Seung', 'Peter Li'] | 2017-05-31 | null | null | null | null | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [ 4.20429260e-02 3.00358206e-01 4.75155681e-01 -5.85158706e-01
-7.53540874e-01 -2.92226046e-01 5.34911811e-01 2.10210010e-01
-8.43713641e-01 8.27699542e-01 1.04245983e-01 -4.36369389e-01
-1.22044861e-01 -4.83788669e-01 -8.99236977e-01 -6.55049682e-01
-1.09739453e-01 9.02287900e-01 4.06095147e-01 5.08191297... | [14.261302947998047, -3.099634885787964] |
bcfc9e40-0387-4a01-b9f2-cc2214094e49 | equibind-geometric-deep-learning-for-drug | 2202.05146 | null | https://arxiv.org/abs/2202.05146v4 | https://arxiv.org/pdf/2202.05146v4.pdf | EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction | Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or drug engineering. Existing methods are computationally expensive as they rely on heavy candidate samp... | ['Tommi Jaakkola', 'Regina Barzilay', 'Lagnajit Pattanaik', 'Octavian-Eugen Ganea', 'Hannes Stärk'] | 2022-02-07 | null | null | null | null | ['blind-docking'] | ['medical'] | [ 1.02021925e-01 -3.13293785e-01 -2.98465222e-01 -4.04190749e-01
-9.38928902e-01 -6.75156057e-01 3.08659703e-01 5.03470540e-01
-5.53741693e-01 1.09730744e+00 1.45640038e-02 -4.36403096e-01
8.40623453e-02 -6.28480017e-01 -1.07242596e+00 -7.15619028e-01
-6.09123968e-02 8.67202461e-01 2.51175106e-01 -3.79117906... | [4.9250569343566895, 5.644224643707275] |
4c095d65-a3e1-4ff7-9fbd-a60a6abce260 | learning-noise-invariant-representations-for | 1807.06610 | null | http://arxiv.org/abs/1807.06610v1 | http://arxiv.org/pdf/1807.06610v1.pdf | Learning Noise-Invariant Representations for Robust Speech Recognition | Despite rapid advances in speech recognition, current models remain brittle
to superficial perturbations to their inputs. Small amounts of noise can
destroy the performance of an otherwise state-of-the-art model. To harden
models against background noise, practitioners often perform data augmentation,
adding artificial... | ['Zhiheng Huang', 'Davis Liang', 'Zachary C. Lipton'] | 2018-07-17 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 7.53454924e-01 3.11315358e-01 1.20322436e-01 -4.36774105e-01
-1.10526645e+00 -6.24411523e-01 6.33599102e-01 7.37862363e-02
-7.25393772e-01 7.70967603e-01 3.85852396e-01 -2.73761511e-01
1.20172933e-01 -4.15149659e-01 -9.03967142e-01 -7.22952783e-01
4.06584963e-02 2.96779513e-01 2.13991717e-01 -2.42838353... | [10.592547416687012, 8.104851722717285] |
623b9747-4fb8-4491-b31c-17bf90c5bb72 | a-framework-of-customer-review-analysis-using | 2212.10051 | null | https://arxiv.org/abs/2212.10051v1 | https://arxiv.org/pdf/2212.10051v1.pdf | A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach | Opinion mining is the branch of computation that deals with opinions, appraisals, attitudes, and emotions of people and their different aspects. This field has attracted substantial research interest in recent years. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it prov... | ['Jaydip Sen', 'Subhasis Dasgupta'] | 2022-12-20 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [-1.41075715e-01 1.89606428e-01 -5.89326084e-01 -5.99436700e-01
-3.06874394e-01 -3.93810242e-01 5.60718060e-01 7.03431308e-01
-4.94035840e-01 8.69019151e-01 1.42560363e-01 -3.57357025e-01
1.27312317e-01 -1.19438553e+00 1.22931832e-02 -4.66461927e-01
-3.96782868e-02 4.46747035e-01 -5.53549081e-02 -6.12800002... | [11.22252368927002, 6.785889625549316] |
d906a75b-8121-40b9-90e6-ee502372e6f5 | lifelong-learning-natural-language-processing | 2206.11867 | null | https://arxiv.org/abs/2206.11867v1 | https://arxiv.org/pdf/2206.11867v1.pdf | Lifelong Learning Natural Language Processing Approach for Multilingual Data Classification | The abundance of information in digital media, which in today's world is the main source of knowledge about current events for the masses, makes it possible to spread disinformation on a larger scale than ever before. Consequently, there is a need to develop novel fake news detection approaches capable of adapting to c... | ['Michał Woźniak', 'Paweł Ksieniewicz', 'Paweł Zyblewski', 'Michał Leś', 'Jędrzej Kozal'] | 2022-05-25 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-8.73158872e-02 6.50503039e-02 -3.68456542e-01 -4.97265421e-02
-3.63902301e-01 -3.60165894e-01 1.20381153e+00 5.48716605e-01
-5.79627395e-01 1.01789463e+00 3.21100026e-01 -2.51861155e-01
-5.64501174e-02 -1.12140548e+00 -8.18981469e-01 -5.16658545e-01
-2.23231558e-02 5.50429821e-01 3.07602048e-01 -4.27866459... | [8.1065673828125, 10.289959907531738] |
28149beb-2418-4b20-8a53-b055e95e22d2 | the-pipeline-system-of-asr-and-nlu-with-mlm | 2305.01194 | null | https://arxiv.org/abs/2305.01194v2 | https://arxiv.org/pdf/2305.01194v2.pdf | The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge | This paper describes our system for the low-resource domain adaptation track (Track 3) in Spoken Language Understanding Grand Challenge, which is a part of ICASSP Signal Processing Grand Challenge 2023. In the track, we adopt a pipeline approach of ASR and NLU. For ASR, we fine-tune Whisper for each domain with upsampl... | ['Shinji Watanabe', 'Emiru Tsunoo', 'Brian Yan', 'Yifan Peng', 'Yosuke Kashiwagi', 'Shih-Lun Wu', 'Siddhant Arora', 'Jessica Huynh', 'Hayato Futami'] | 2023-05-02 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 1.64853200e-01 1.09801861e-02 8.60050768e-02 -6.75402999e-01
-1.64059222e+00 -7.56990969e-01 9.29324210e-01 -1.40999898e-01
-7.56347656e-01 6.00720108e-01 6.50499284e-01 -1.49042323e-01
4.16203260e-01 -2.14904264e-01 -7.10644126e-01 -2.57275790e-01
1.12214930e-01 8.14705014e-01 -1.18697517e-01 -4.41566139... | [14.288756370544434, 6.831607818603516] |
c43446f1-a9c7-4746-b0c8-2412284593f1 | swinvrnn-a-data-driven-ensemble-forecasting | 2205.13158 | null | https://arxiv.org/abs/2205.13158v1 | https://arxiv.org/pdf/2205.13158v1.pdf | SwinVRNN: A Data-Driven Ensemble Forecasting Model via Learned Distribution Perturbation | Data-driven approaches for medium-range weather forecasting are recently shown extraordinarily promising for ensemble forecasting for their fast inference speed compared to traditional numerical weather prediction (NWP) models, but their forecast accuracy can hardly match the state-of-the-art operational ECMWF Integrat... | ['Hao Li', 'Zhibin Wang', 'Lei Chen', 'Yuan Hu'] | 2022-05-26 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-9.93930772e-02 -2.48382315e-01 2.77980179e-01 -4.66986179e-01
-6.69625282e-01 -5.37240386e-01 8.63678336e-01 -5.01560986e-01
-1.40621103e-02 1.04242826e+00 4.59436536e-01 -7.20645785e-01
-2.43157879e-01 -7.88825214e-01 -5.19990802e-01 -1.32822096e+00
-4.64293286e-02 6.44533336e-01 -2.27963641e-01 -5.39552033... | [6.615499973297119, 2.9795000553131104] |
391227f8-6f1d-432c-82f9-67b52c8fabb6 | composing-ensembles-of-pre-trained-models-via | 2210.11522 | null | https://arxiv.org/abs/2210.11522v1 | https://arxiv.org/pdf/2210.11522v1.pdf | Composing Ensembles of Pre-trained Models via Iterative Consensus | Large pre-trained models exhibit distinct and complementary capabilities dependent on the data they are trained on. Language models such as GPT-3 are capable of textual reasoning but cannot understand visual information, while vision models such as DALL-E can generate photorealistic photos but fail to understand comple... | ['Igor Mordatch', 'Antonio Torralba', 'Joshua B. Tenenbaum', 'Yilun Du', 'Shuang Li'] | 2022-10-20 | null | null | null | null | ['video-question-answering', 'mathematical-reasoning'] | ['computer-vision', 'natural-language-processing'] | [ 1.07438326e-01 4.83952582e-01 6.38052588e-03 -1.65132806e-01
-1.07504189e+00 -6.13279223e-01 6.47099912e-01 -5.45559451e-02
-2.52620522e-02 5.36100388e-01 1.23231530e-01 -3.78600024e-02
2.12776512e-01 -8.83021474e-01 -1.21447849e+00 -4.59437340e-01
6.62636399e-01 5.61923981e-01 -1.99953606e-03 -4.70794678... | [11.04516315460205, 1.398974061012268] |
06795653-11e5-4146-888d-2302957043a9 | mushrooms-detection-localization-and-3d-pose | 2201.02837 | null | https://arxiv.org/abs/2201.02837v1 | https://arxiv.org/pdf/2201.02837v1.pdf | Mushrooms Detection, Localization and 3D Pose Estimation using RGB-D Sensor for Robotic-picking Applications | In this paper, we propose mushrooms detection, localization and 3D pose estimation algorithm using RGB-D data acquired from a low-cost consumer RGB-D sensor. We use the RGB and depth information for different purposes. From RGB color, we first extract initial contour locations of the mushrooms and then provide both the... | ['Bashir Al-Diri', 'Nathanael L. Baisa'] | 2022-01-08 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-4.65634242e-02 -2.03574926e-01 2.24604830e-01 1.68991446e-01
-1.78309485e-01 -9.50832486e-01 5.99649623e-02 5.25119364e-01
-4.90664154e-01 1.07316017e-01 -4.51306939e-01 1.70720205e-01
2.84193158e-01 -7.80568063e-01 -6.75484955e-01 -8.41536522e-01
1.95488259e-02 6.21373057e-01 4.85402763e-01 -4.90856580... | [7.642314910888672, -2.492400646209717] |
c0f5832d-44ad-46cb-a5a2-d0048ed85e85 | semantic-context-forests-for-learning-based | 1307.2965 | null | http://arxiv.org/abs/1307.2965v2 | http://arxiv.org/pdf/1307.2965v2.pdf | Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images | The automatic segmentation of human knee cartilage from 3D MR images is a
useful yet challenging task due to the thin sheet structure of the cartilage
with diffuse boundaries and inhomogeneous intensities. In this paper, we
present an iterative multi-class learning method to segment the femoral, tibial
and patellar car... | ['Meizhu Liu', 'Le Lu', 'Dijia Wu', 'Shaohua Kevin Zhou', 'Quan Wang', 'Kim L. Boyer'] | 2013-07-11 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [-4.96852957e-02 3.91069464e-02 1.88286528e-02 -1.46741226e-01
-1.15069640e+00 -1.77186444e-01 3.02701443e-01 4.18557048e-01
-5.55169404e-01 4.97734487e-01 3.49342585e-01 5.30084729e-01
-1.68266252e-01 -3.04556698e-01 -2.92139322e-01 -8.21147144e-01
-4.64891136e-01 9.94272113e-01 8.59132469e-01 3.09706420... | [14.326990127563477, -2.365145444869995] |
ee765d5b-a7a4-414b-ac27-24b0e57e6441 | inproc-industry-and-product-service-code | 2305.13532 | null | https://arxiv.org/abs/2305.13532v1 | https://arxiv.org/pdf/2305.13532v1.pdf | InProC: Industry and Product/Service Code Classification | Determining industry and product/service codes for a company is an important real-world task and is typically very expensive as it involves manual curation of data about the companies. Building an AI agent that can predict these codes automatically can significantly help reduce costs, and eliminate human biases and err... | ['Sameena Shah', 'Andrea Stefanucci', 'Simerjot Kaur'] | 2023-05-22 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 3.49865258e-01 3.36956829e-01 -2.93882102e-01 -7.27973819e-01
-8.98738861e-01 -7.63228893e-01 3.65649819e-01 3.87090266e-01
-1.39034629e-01 4.99009699e-01 -3.12985033e-01 -6.11755371e-01
-2.68717110e-01 -6.99377716e-01 -3.80173564e-01 -3.87260765e-01
2.76269287e-01 7.93984413e-01 -2.14368120e-01 1.11139249... | [9.697450637817383, 6.334658145904541] |
4e4fed4b-f45e-475f-9855-8eb521bc7146 | event-camera-and-lidar-based-human-tracking | 2304.08908 | null | https://arxiv.org/abs/2304.08908v1 | https://arxiv.org/pdf/2304.08908v1.pdf | Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environments | In this article, we propose a novel LiDAR and event camera fusion modality for subterranean (SubT) environments for fast and precise object and human detection in a wide variety of adverse lighting conditions, such as low or no light, high-contrast zones and in the presence of blinding light sources. In the proposed ap... | ['George Nikolakopoulos', 'Ali-akbar Agha-mohammadi', 'Christoforos Kanellakis', 'Akshit Saradagi', 'Rucha Sawlekar', 'Akash Patel', 'Mario A. V. Saucedo'] | 2023-04-18 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [ 3.76030982e-01 -3.49515736e-01 3.82346362e-01 1.44418195e-01
-8.91597718e-02 -3.66202831e-01 5.15271604e-01 3.09651762e-01
-7.17984200e-01 4.99289185e-01 -5.93431413e-01 1.78153396e-01
-4.71602648e-01 -8.51537883e-01 -5.65056622e-01 -7.93201447e-01
-1.47593569e-03 6.70556724e-01 7.21111715e-01 -7.32703879... | [7.141949653625488, -1.960540771484375] |
bde483ba-48ec-4484-9be6-ba1195c2e253 | automatic-speech-recognition-using-neural | null | null | https://aclanthology.org/O15-1014 | https://aclanthology.org/O15-1014.pdf | 類神經網路訓練結合環境群集及專家混合系統於強健性語音辨識(Automatic Speech Recognition using Neural Network based Acoustic Model with the Environment Clustering and Mixture of Experts Algorithms) [In Chinese] | null | ['Jia-Ching Wang', 'Chia-Yung Hsu', 'Yu Tsao'] | 2015-10-01 | automatic-speech-recognition-using-neural-1 | https://aclanthology.org/O15-1014 | https://aclanthology.org/O15-1014.pdf | roclingijclclp-2015-10 | ['robust-speech-recognition'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.415706634521484, 3.717092990875244] |
f1bb0e03-7600-44cd-b85e-8e26227f8247 | principal-subbundles-for-dimension-reduction | 2307.03128 | null | https://arxiv.org/abs/2307.03128v1 | https://arxiv.org/pdf/2307.03128v1.pdf | Principal subbundles for dimension reduction | In this paper we demonstrate how sub-Riemannian geometry can be used for manifold learning and surface reconstruction by combining local linear approximations of a point cloud to obtain lower dimensional bundles. Local approximations obtained by local PCAs are collected into a rank $k$ tangent subbundle on $\mathbb{R}^... | ['Xavier Pennec', 'Stefan Sommer', 'Erlend Grong', 'James Benn', 'Morten Akhøj'] | 2023-07-06 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-5.53065062e-01 3.63551438e-01 4.41239864e-01 -1.96270019e-01
-6.87191963e-01 -6.00893259e-01 2.39320040e-01 -2.98872977e-01
-1.91527918e-01 1.48914590e-01 -1.40619904e-01 -9.49987248e-02
-4.36926544e-01 -6.73323393e-01 -9.85483110e-01 -9.27967608e-01
-4.88278210e-01 3.51259202e-01 -3.85573536e-01 -1.80269927... | [7.557755947113037, 4.1513671875] |
9a4d16d4-ded4-4fd1-a4cd-2cc0d9f3b311 | move-evaluation-in-go-using-deep | 1412.6564 | null | http://arxiv.org/abs/1412.6564v2 | http://arxiv.org/pdf/1412.6564v2.pdf | Move Evaluation in Go Using Deep Convolutional Neural Networks | The game of Go is more challenging than other board games, due to the
difficulty of constructing a position or move evaluation function. In this
paper we investigate whether deep convolutional networks can be used to
directly represent and learn this knowledge. We train a large 12-layer
convolutional neural network by ... | ['Ilya Sutskever', 'David Silver', 'Chris J. Maddison', 'Aja Huang'] | 2014-12-20 | null | null | null | null | ['game-of-go', 'board-games'] | ['playing-games', 'playing-games'] | [-3.05545568e-01 -4.29402664e-02 -8.13636556e-02 4.20927890e-02
-8.28654706e-01 -7.31637180e-01 3.29730093e-01 -1.69219062e-01
-1.03174639e+00 9.18968022e-01 -2.50208676e-01 -7.73548603e-01
-2.98404366e-01 -1.39262164e+00 -1.09734154e+00 -4.30218011e-01
-6.78164661e-02 1.22676849e+00 6.07158244e-01 -6.61161482... | [3.470100164413452, 1.4413511753082275] |
f697f54c-a7fd-4c6a-bf8f-b7702c8f2c7c | a-statistics-and-deep-learning-hybrid-method | 2112.08618 | null | https://arxiv.org/abs/2112.08618v1 | https://arxiv.org/pdf/2112.08618v1.pdf | A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling | Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (prediction intervals). One example is Exponential Smoothing Recurrent Neural Network (ES-RNN), a hybrid between a statistical forecasting mod... | ['Terence L. Van Zyl', 'Thabang Mathonsi'] | 2021-12-16 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-8.22668448e-02 4.06331532e-02 5.50111905e-02 -6.31461799e-01
-9.75003302e-01 -9.46716517e-02 7.51583219e-01 -1.62525699e-02
-2.33735994e-01 1.10640359e+00 3.68179888e-01 -7.06663728e-01
-2.76049018e-01 -5.52215934e-01 -6.77342415e-01 -6.50211930e-01
-7.97974527e-01 6.13536894e-01 -3.90346020e-01 -2.02015489... | [6.765884876251221, 3.0402727127075195] |
15f9b601-6059-49c5-bfaa-59b24480bb35 | semi-decentralized-federated-ego-graph | 2302.10900 | null | https://arxiv.org/abs/2302.10900v1 | https://arxiv.org/pdf/2302.10900v1.pdf | Semi-decentralized Federated Ego Graph Learning for Recommendation | Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as ego graphs. However, most existing recommendation methods collect these ego graphs from all users to compose a global graph to obtain high-o... | ['Hongzhi Yin', 'Yuhui Shi', 'Zi Huang', 'Quoc Viet Hung Nguyen', 'Ruiqi Zheng', 'Ningzhi Tang', 'Liang Qu'] | 2023-02-10 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.39247209e-01 1.85862601e-01 -4.60739791e-01 -3.62982243e-01
-6.38727471e-03 -8.58071566e-01 2.16322124e-01 1.17691651e-01
2.09941745e-01 3.51072848e-01 2.98601031e-01 -4.00397003e-01
-3.69022667e-01 -1.14045930e+00 -5.94380200e-01 -5.60152411e-01
1.43727243e-01 -1.53100401e-01 -4.50980887e-02 -5.96678443... | [6.032933712005615, 6.9047980308532715] |
c5391aa2-941b-4a14-a6fc-dba1c80fd0d0 | domain-adaptation-for-real-world-single-view | 2108.10972 | null | https://arxiv.org/abs/2108.10972v1 | https://arxiv.org/pdf/2108.10972v1.pdf | Domain Adaptation for Real-World Single View 3D Reconstruction | Deep learning-based object reconstruction algorithms have shown remarkable improvements over classical methods. However, supervised learning based methods perform poorly when the training data and the test data have different distributions. Indeed, most current works perform satisfactorily on the synthetic ShapeNet dat... | ['Arik Horodniceanu', 'Siddharth Singh', 'Brandon Leung'] | 2021-08-24 | null | null | null | null | ['single-view-3d-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.25642020e-01 1.67621166e-01 -1.52207121e-01 -3.37279230e-01
-8.12471986e-01 -7.94731855e-01 8.84752452e-01 -3.06527346e-01
-3.15719485e-01 8.27509224e-01 2.73053516e-02 2.66398601e-02
6.06568493e-02 -8.22028756e-01 -1.11066091e+00 -6.13538206e-01
2.87632018e-01 1.13919306e+00 3.03898185e-01 -3.31977218... | [8.3076810836792, -2.9458186626434326] |
65e3cc23-0ea0-4da9-97b7-20e6f5fa7920 | deep-amortized-relational-model-with-group | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/20720 | https://ojs.aaai.org/index.php/AAAI/article/view/20720 | Deep Amortized Relational Model with Group-Wise Hierarchical Generative Process | In this paper, we propose Deep amortized Relational Model (DaRM) with group-wise hierarchical generative process for community discovery and link prediction on relational data (e.g., graph, network). It provides an efficient neural relational model architecture by grouping nodes in a group-wise view rather than node-wi... | ['Liping Jing', 'Jiaqi Wang', 'Tong Zhou', 'Huafeng Liu'] | 2022-06-28 | null | null | null | aaai-2022-6 | ['community-detection'] | ['graphs'] | [-3.42323333e-01 2.76576638e-01 -7.41270781e-02 -3.11311990e-01
6.25767186e-02 -2.97268927e-01 6.82579398e-01 3.21839809e-01
1.89713359e-01 5.93061626e-01 7.17866570e-02 -2.11154029e-01
-4.66832548e-01 -1.58885920e+00 -6.27399802e-01 -6.80792511e-01
-1.05410910e+00 8.93152356e-01 3.23236465e-01 -5.12923449... | [7.203577518463135, 6.065655708312988] |
7b2d61df-4195-4c9f-896f-21d0d03ec2c7 | eprnet-efficient-pyramid-representation | null | null | https://ieeexplore.ieee.org/document/9384352 | https://ieeexplore.ieee.org/document/9384352 | EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene Segmentation | Current scene segmentation methods suffer from cumbersome model structures and high computational complexity, impeding their applications to real-world scenarios that require real-time processing. This paper proposes a novel Efficient Pyramid Representation Network (EPRNet), which strikes an innovative record on segmen... | ['Yu Zhang', 'Jun Jiang', 'Fagui Liu', 'Quan Tang'] | 2021-03-23 | null | null | null | ieee-transactions-on-intelligent-6 | ['scene-segmentation'] | ['computer-vision'] | [ 1.56115144e-01 -3.03220600e-01 -1.23707373e-02 -4.46831644e-01
-6.62905216e-01 -4.84833807e-01 2.25873455e-01 7.58149400e-02
-7.49663234e-01 4.45033520e-01 -1.53801858e-01 -7.38194361e-02
-3.55640170e-03 -1.23845887e+00 -8.57463956e-01 -5.32869697e-01
-1.17028259e-01 1.84650272e-01 8.56073797e-01 -2.24029869... | [9.454164505004883, 0.022183049470186234] |
511b349f-a046-4492-abe9-46f3e3dec4a9 | focalized-contrastive-view-invariant-learning | 2304.00858 | null | https://arxiv.org/abs/2304.00858v1 | https://arxiv.org/pdf/2304.00858v1.pdf | Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition | Learning view-invariant representation is a key to improving feature discrimination power for skeleton-based action recognition. Existing approaches cannot effectively remove the impact of viewpoint due to the implicit view-dependent representations. In this work, we propose a self-supervised framework called Focalized... | ['Howard Leung', 'Hubert P. H. Shum', 'Edmond S. L. Ho', 'Qianhui Men'] | 2023-04-03 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 5.43263137e-01 -3.65279138e-01 -7.14507759e-01 -4.71341223e-01
-6.77551448e-01 -4.07014370e-01 6.40937448e-01 -3.07203829e-01
-1.46720158e-02 4.85323787e-01 6.99486136e-01 5.80577970e-01
-5.76707602e-01 -4.29863632e-01 -1.71068981e-01 -1.00902283e+00
1.91942438e-01 2.34942973e-01 1.74371183e-01 -4.60621007... | [8.43156623840332, 4.445427417755127] |
e96221df-4f8a-417b-9af9-7ed437210a4f | backup-plan-constrained-model-predictive-1 | 2306.06102 | null | https://arxiv.org/abs/2306.06102v1 | https://arxiv.org/pdf/2306.06102v1.pdf | Backup Plan Constrained Model Predictive Control with Guaranteed Stability | This article proposes and evaluates a new safety concept called backup plan safety for path planning of autonomous vehicles under mission uncertainty. Backup plan safety is defined as the ability to complete an alternative mission when the primary mission is aborted. To include this new safety concept in control proble... | ['Petros Voulgaris', 'Lui Sha', 'Naira Hovakimyan', 'Wenbin Wan', 'Hyung-Jin Yoon', 'Hunmin Kim', 'Ran Tao'] | 2023-06-09 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [ 1.98554575e-01 4.87881601e-01 -5.24085820e-01 5.39232604e-02
-2.93938488e-01 -4.71846819e-01 4.02302593e-01 7.59596005e-02
-3.13850701e-01 9.63728070e-01 -2.32482061e-01 -6.91112041e-01
-8.67584348e-01 -7.35996723e-01 -4.26455855e-01 -9.05489743e-01
-3.94831359e-01 2.42935836e-01 9.72388592e-03 -4.68987554... | [5.261981010437012, 2.0360848903656006] |
c76c4a66-8d29-4ccf-a752-21179ae39055 | rethinking-masked-language-modeling-for | 2305.17721 | null | https://arxiv.org/abs/2305.17721v1 | https://arxiv.org/pdf/2305.17721v1.pdf | Rethinking Masked Language Modeling for Chinese Spelling Correction | In this paper, we study Chinese Spelling Correction (CSC) as a joint decision made by two separate models: a language model and an error model. Through empirical analysis, we find that fine-tuning BERT tends to over-fit the error model while under-fit the language model, resulting in poor generalization to out-of-distr... | ['Hai Zhao', 'Yuchen Zhang', 'Shaohua Zhang', 'Hongqiu Wu'] | 2023-05-28 | null | null | null | null | ['domain-generalization', 'spelling-correction'] | ['methodology', 'natural-language-processing'] | [ 2.00900182e-01 -4.60557640e-01 -2.85329998e-01 -2.47105986e-01
-9.04583752e-01 -8.70022714e-01 2.41795212e-01 2.67265737e-01
-6.62715673e-01 7.78112411e-01 8.45727846e-02 -7.22847760e-01
2.25773379e-01 -4.73412126e-01 -7.53112912e-01 -3.38895679e-01
1.56150758e-01 3.58991295e-01 5.30538499e-01 -4.00433093... | [11.015094757080078, 10.328530311584473] |
14e9b105-c489-4934-bfae-6394b802cf91 | contrastive-learning-with-prompt-derived | 2211.03348 | null | https://arxiv.org/abs/2211.03348v2 | https://arxiv.org/pdf/2211.03348v2.pdf | Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding | Contrastive learning has become a new paradigm for unsupervised sentence embeddings. Previous studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. In this paper, we propose a novel Contrastive learning method with Prompt-derived Virtual semantic Pro... | ['Yunbo Cao', 'Shuangzhi Wu', 'Yufan Jiang', 'Yongjing Yin', 'Jiali Zeng'] | 2022-11-07 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 1.30287513e-01 2.85701573e-01 -2.43642017e-01 -6.97247684e-01
-7.35008895e-01 -5.24469256e-01 9.04394388e-01 5.48595190e-01
-6.83296144e-01 3.64230722e-01 4.57192987e-01 -6.94224313e-02
1.43446580e-01 -3.68500352e-01 -5.57356000e-01 -5.33165872e-01
1.16898231e-01 5.17536521e-01 -6.21755198e-02 -3.72777253... | [10.908589363098145, 8.59835147857666] |
8286e665-10f9-4837-963a-9a6bbbb8ba05 | applenet-visual-attention-parameterized | 2304.05995 | null | https://arxiv.org/abs/2304.05995v1 | https://arxiv.org/pdf/2304.05995v1.pdf | APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIP | In recent years, the success of large-scale vision-language models (VLMs) such as CLIP has led to their increased usage in various computer vision tasks. These models enable zero-shot inference through carefully crafted instructional text prompts without task-specific supervision. However, the potential of VLMs for gen... | ['Biplab Banerjee', 'Shirsha Bose', 'Bhupendra Solanki', 'Ankit Jha', 'Mainak Singha'] | 2023-04-12 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 3.18989545e-01 -3.79449785e-01 -2.23937899e-01 -5.07659912e-01
-5.33248782e-01 -5.34133613e-01 8.63059819e-01 1.82833567e-01
-5.61712563e-01 2.50792205e-01 1.73712134e-01 -3.44484925e-01
-5.53785115e-02 -5.79315960e-01 -8.36270034e-01 -6.59140110e-01
8.61428455e-02 -5.47822639e-02 7.55804256e-02 -1.54185504... | [10.217970848083496, 1.9773658514022827] |
1b0b2558-b8ba-4065-8c7a-9c73e33b9d95 | grounding-language-attributes-to-objects | 1905.13153 | null | https://arxiv.org/abs/1905.13153v2 | https://arxiv.org/pdf/1905.13153v2.pdf | Grounding Language Attributes to Objects using Bayesian Eigenobjects | We develop a system to disambiguate object instances within the same class based on simple physical descriptions. The system takes as input a natural language phrase and a depth image containing a segmented object and predicts how similar the observed object is to the object described by the phrase. Our system is desig... | ['Stefanie Tellex', 'Nakul Gopalan', 'Benjamin Burchfiel', 'Thao Nguyen', 'Vanya Cohen', 'George Konidaris'] | 2019-05-30 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 2.60662019e-01 6.31439626e-01 -1.64828733e-01 -7.80556321e-01
-8.70958388e-01 -9.00694788e-01 5.49482048e-01 3.22921693e-01
-1.87383652e-01 5.58916688e-01 -2.23995849e-01 1.80476695e-01
3.26791912e-01 -6.97393894e-01 -9.18927729e-01 -2.28055716e-01
-1.66318700e-01 1.45068920e+00 4.64044422e-01 -5.98508231... | [8.284521102905273, -2.9475369453430176] |
ec604db1-9cd4-48b2-b9f0-0f6697321de6 | interpretable-clustering-via-multi-polytope | 2112.05653 | null | https://arxiv.org/abs/2112.05653v1 | https://arxiv.org/pdf/2112.05653v1.pdf | Interpretable Clustering via Multi-Polytope Machines | Clustering is a popular unsupervised learning tool often used to discover groups within a larger population such as customer segments, or patient subtypes. However, despite its use as a tool for subgroup discovery and description - few state-of-the-art algorithms provide any rationale or description behind the clusters... | ['Chandra Reddy', 'Dzung Phan', 'Lam M. Nguyen', 'Jayant Kalagnanam', 'Connor Lawless'] | 2021-12-10 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [-9.91091784e-03 4.24052536e-01 -5.20891786e-01 -5.67452967e-01
-9.01849389e-01 -8.72839332e-01 1.05418153e-01 6.73189580e-01
5.85297681e-02 4.83079821e-01 2.28251755e-01 -3.65114272e-01
-5.89217424e-01 -2.85120666e-01 -7.52036870e-01 -9.33169127e-01
-2.98936456e-01 1.45346463e+00 -2.50466704e-01 2.96136945... | [7.189599514007568, 4.936809539794922] |
c067883f-17d9-45f9-aeb2-153d0a933563 | offline-ab-testing-for-recommender-systems | 1801.07030 | null | http://arxiv.org/abs/1801.07030v1 | http://arxiv.org/pdf/1801.07030v1.pdf | Offline A/B testing for Recommender Systems | Before A/B testing online a new version of a recommender system, it is usual
to perform some offline evaluations on historical data. We focus on evaluation
methods that compute an estimator of the potential uplift in revenue that could
generate this new technology. It helps to iterate faster and to avoid losing
money b... | ['Simon Dollé', 'Clément Calauzènes', 'Thomas Nedelec', 'Alexandre Gilotte', 'Alexandre Abraham'] | 2018-01-22 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 8.35730508e-02 3.52601260e-01 -6.00071549e-01 -3.70911777e-01
-5.63915253e-01 -7.11540401e-01 9.78971124e-01 -4.50581275e-02
-4.98019665e-01 1.10346937e+00 1.72389392e-02 -8.52055728e-01
-4.69876975e-01 -8.11900437e-01 -9.52687979e-01 -3.37158561e-01
-4.93526340e-01 5.08208752e-01 1.13220125e-01 -2.49301851... | [4.521028518676758, 3.269551992416382] |
0013ae2e-a115-4f8b-b899-6cc6de1ad94c | a-game-based-approximate-verification-of-deep | 1807.03571 | null | http://arxiv.org/abs/1807.03571v2 | http://arxiv.org/pdf/1807.03571v2.pdf | A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees | Despite the improved accuracy of deep neural networks, the discovery of
adversarial examples has raised serious safety concerns. In this paper, we
study two variants of pointwise robustness, the maximum safe radius problem,
which for a given input sample computes the minimum distance to an adversarial
example, and the ... | ['Xiaowei Huang', 'Wenjie Ruan', 'Min Wu', 'Matthew Wicker', 'Marta Kwiatkowska'] | 2018-07-10 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 4.20559227e-01 6.07380927e-01 2.62924612e-01 3.28445435e-02
-7.89919376e-01 -9.96485114e-01 5.15000701e-01 1.66225582e-02
-6.44822776e-01 6.38091147e-01 -4.22656655e-01 -4.57912743e-01
-6.39577687e-01 -9.76692379e-01 -1.18465245e+00 -1.06659448e+00
-2.23399431e-01 2.30653882e-01 3.02824974e-01 -4.69499469... | [5.6602067947387695, 7.7733635902404785] |
2a45fa6b-71cc-4a2c-9552-604170adbb7f | unsupervised-deep-asymmetric-stereo-matching | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Song_Unsupervised_Deep_Asymmetric_Stereo_Matching_With_Spatially-Adaptive_Self-Similarity_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Song_Unsupervised_Deep_Asymmetric_Stereo_Matching_With_Spatially-Adaptive_Self-Similarity_CVPR_2023_paper.pdf | Unsupervised Deep Asymmetric Stereo Matching With Spatially-Adaptive Self-Similarity | Unsupervised stereo matching has received a lot of attention since it enables the learning of disparity estimation without ground-truth data. However, most of the unsupervised stereo matching algorithms assume that the left and right images have consistent visual properties, i.e., symmetric, and easily fail when th... | ['Kwanghoon Sohn', 'Sunok Kim', 'Taeyong Song'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['disparity-estimation', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 3.77466321e-01 -2.10334167e-01 -3.70215923e-02 -6.97209477e-01
-2.05212027e-01 -3.08509290e-01 4.72321123e-01 -2.30927244e-01
-3.30785930e-01 6.19922876e-01 5.41610777e-01 1.81518972e-01
-1.36650905e-01 -8.35438073e-01 -4.83444840e-01 -8.40094030e-01
3.01112950e-01 1.57267638e-02 5.90307117e-01 -2.07989886... | [8.871621131896973, -2.3297011852264404] |
7ad399ee-3f41-42bd-827d-79ce83683ec7 | semantic-driven-generation-of-hyperlapse-from | 1703.10798 | null | http://arxiv.org/abs/1703.10798v4 | http://arxiv.org/pdf/1703.10798v4.pdf | Semantic-driven Generation of Hyperlapse from $360^\circ$ Video | We present a system for converting a fully panoramic ($360^\circ$) video into
a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience. Our
system exploits visual saliency and semantics to non-uniformly sample in space
and time for generating hyperlapses. In addition, users can optionally choose
objec... | ['Sing Bing Kang', 'Ming-Hsuan Yang', 'Wei-Sheng Lai', 'Yujia Huang', 'Neel Joshi', 'Chris Buehler'] | 2017-03-31 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 2.52594441e-01 -1.41840547e-01 -1.64909780e-01 -3.19499999e-01
-4.67356533e-01 -4.37238544e-01 2.59418666e-01 -3.19885015e-02
-2.84986377e-01 4.81166840e-01 2.91823864e-01 -1.64898202e-01
1.52394712e-01 -6.76090479e-01 -8.33899081e-01 -3.78061652e-01
-1.35939628e-01 -3.28593433e-01 1.10585988e+00 -4.06132132... | [10.796293258666992, -1.2691766023635864] |
c7b94dd1-6e5f-41fb-bb53-41c785c31a77 | tackling-partial-domain-adaptation-with-self | 1906.05199 | null | https://arxiv.org/abs/1906.05199v1 | https://arxiv.org/pdf/1906.05199v1.pdf | Tackling Partial Domain Adaptation with Self-Supervision | Domain adaptation approaches have shown promising results in reducing the marginal distribution difference among visual domains. They allow to train reliable models that work over datasets of different nature (photos, paintings etc), but they still struggle when the domains do not share an identical label space. In the... | ["Antonio D'Innocente", 'Silvia Bucci', 'Tatiana Tommasi'] | 2019-06-12 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 5.40175378e-01 7.87715465e-02 -2.85440087e-01 -5.87604344e-01
-7.14731336e-01 -8.78074884e-01 7.21893728e-01 -5.57455234e-02
-4.55078870e-01 1.09676504e+00 -3.91682843e-03 1.45993680e-01
-4.11728024e-01 -5.94664574e-01 -8.11882079e-01 -9.02518034e-01
1.22528277e-01 8.85680854e-01 7.44618237e-01 -2.81084001... | [9.894759178161621, 2.693922758102417] |
a3bd4e10-24fb-47e2-8eb9-89501b189f27 | face-animation-with-an-attribute-guided | 2304.03199 | null | https://arxiv.org/abs/2304.03199v1 | https://arxiv.org/pdf/2304.03199v1.pdf | Face Animation with an Attribute-Guided Diffusion Model | Face animation has achieved much progress in computer vision. However, prevailing GAN-based methods suffer from unnatural distortions and artifacts due to sophisticated motion deformation. In this paper, we propose a Face Animation framework with an attribute-guided Diffusion Model (FADM), which is the first work to ex... | ['Baochang Zhang', 'Jianzhuang Liu', 'Hong Li', 'Boyu Liu', 'Sicheng Gao', 'Xuhui Liu', 'Bohan Zeng'] | 2023-04-06 | null | null | null | null | ['talking-head-generation', '3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.40237117e-02 1.92489222e-01 1.25392631e-01 -3.58302444e-01
-4.27598715e-01 -1.89878270e-01 8.61158192e-01 -1.09129560e+00
3.47470134e-01 6.16294026e-01 6.08588517e-01 1.61986336e-01
2.55444914e-01 -5.91661870e-01 -5.37858963e-01 -8.75634491e-01
2.07727239e-01 3.27233762e-01 -2.59809434e-01 -2.93801636... | [12.68570613861084, -0.32255318760871887] |
096280f0-89cc-4ec6-8704-5ddcbff83f71 | feature-representations-useful-for-predicting | 2303.07679 | null | https://arxiv.org/abs/2303.07679v1 | https://arxiv.org/pdf/2303.07679v1.pdf | Feature representations useful for predicting image memorability | Predicting image memorability has attracted interest in various fields. Consequently, prediction accuracy with convolutional neural network (CNN) models has been approaching the empirical upper bound estimated based on human consistency. However, identifying which feature representations embedded in CNN models are resp... | ['Hiroyuki Sakai', 'Takumi Harada'] | 2023-03-14 | null | null | null | null | ['object-recognition', 'open-question'] | ['computer-vision', 'natural-language-processing'] | [-9.92858410e-02 -6.50379062e-02 -7.86027685e-02 -5.58905043e-02
1.51477307e-01 -1.64284140e-01 5.12575269e-01 5.40634513e-01
-6.70977533e-01 2.50313342e-01 1.37166664e-01 -1.10683829e-01
-6.14557862e-01 -9.03883994e-01 -5.63772738e-01 -3.80335122e-01
-1.92745179e-01 -2.94345140e-01 -7.76049569e-02 -1.21849619... | [9.688947677612305, 2.2736384868621826] |
21711286-7f14-4cdf-912c-bd7c64f1f303 | 3d-rcnn-instance-level-3d-object | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Kundu_3D-RCNN_Instance-Level_3D_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Kundu_3D-RCNN_Instance-Level_3D_CVPR_2018_paper.pdf | 3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare | We present a fast inverse-graphics framework for instance-level 3D scene understanding. We train a deep convolutional network that learns to map image regions to the full 3D shape and pose of all object instances in the image. Our method produces a compact 3D representation of the scene, which can be readily used for a... | ['Abhijit Kundu', 'Yin Li', 'James M. Rehg'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['3d-object-reconstruction', 'vehicle-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 3.30176950e-01 4.30141300e-01 1.00296736e-01 -9.41890776e-01
-7.39161611e-01 -7.68744528e-01 8.75602484e-01 -1.49951234e-01
-2.76251018e-01 -1.07497312e-01 -2.16808632e-01 -3.58740360e-01
3.92212540e-01 -7.18054771e-01 -1.29425287e+00 -1.86529428e-01
1.53626397e-01 1.08600223e+00 3.96754622e-01 -1.06419124... | [8.438125610351562, -3.215939998626709] |
4f39942d-2a32-41c3-9612-6f97196f75c5 | general-to-specific-transfer-labeling-for | 2208.09606 | null | https://arxiv.org/abs/2208.09606v2 | https://arxiv.org/pdf/2208.09606v2.pdf | General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation | Training keyphrase generation (KPG) models require a large amount of annotated data, which can be prohibitively expensive and often limited to specific domains. In this study, we first demonstrate that large distribution shifts among different domains severely hinder the transferability of KPG models. We then propose a... | ['Daqing He', 'Yingbo Zhou', 'Xingdi Yuan', 'Tong Wang', 'Rui Meng'] | 2022-08-20 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 2.88879871e-01 -4.54903245e-02 -4.01746154e-01 -3.44957650e-01
-1.12897277e+00 -1.12557173e+00 5.97940385e-01 1.05731621e-01
-4.72175330e-01 1.05594194e+00 2.96721876e-01 -2.55831003e-01
3.66827518e-01 -7.16003180e-01 -8.18252265e-01 -2.14585915e-01
4.02480483e-01 8.66268694e-01 6.15513861e-01 -4.23024267... | [11.477516174316406, 8.575227737426758] |
0686f6c8-e090-4e12-b443-3819193d4bf9 | the-multi-agent-pickup-and-delivery-problem | 2203.07092 | null | https://arxiv.org/abs/2203.07092v1 | https://arxiv.org/pdf/2203.07092v1.pdf | The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications | We study two state-of-the-art solutions to the multi-agent pickup and delivery (MAPD) problem based on different principles -- multi-agent path-finding (MAPF) and multi-agent reinforcement learning (MARL). Specifically, a recent MAPF algorithm called conflict-based search (CBS) and a current MARL algorithm called share... | ['Biswa Sengupta', 'Tim Tsz-Kit Lau'] | 2022-03-14 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-4.06642854e-01 4.51490656e-02 -1.89553246e-01 9.68189761e-02
-4.98298883e-01 -3.33004534e-01 8.21922362e-01 7.74909914e-01
-4.88100410e-01 1.40781701e+00 -1.09506533e-01 -1.20689042e-01
-8.82435381e-01 -8.05377305e-01 -2.82678485e-01 -6.68677330e-01
-7.15088725e-01 1.10938644e+00 5.23000717e-01 -1.00291240... | [4.2158660888671875, 1.8781665563583374] |
e8edcb61-5b51-4de0-adc2-f3df73017874 | do-we-need-cross-validation-for-discourse | null | null | https://aclanthology.org/E17-2024 | https://aclanthology.org/E17-2024.pdf | On the Need of Cross Validation for Discourse Relation Classification | The task of implicit discourse relation classification has received increased attention in recent years, including two CoNNL shared tasks on the topic. Existing machine learning models for the task train on sections 2-21 of the PDTB and test on section 23, which includes a total of 761 implicit discourse relations. In ... | ['Vera Demberg', 'Wei Shi'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 4.18552876e-01 1.08655500e+00 -5.37954569e-01 -3.77614409e-01
-8.81032586e-01 -4.22952503e-01 1.21555912e+00 7.27243304e-01
-5.14802873e-01 1.04589093e+00 8.75171304e-01 -8.04242015e-01
-3.79882067e-01 -5.57510376e-01 -3.71547580e-01 -5.25484562e-01
4.27713022e-02 4.75746512e-01 4.62182820e-01 -3.78940314... | [10.814997673034668, 9.353208541870117] |
a3d8b0b8-fe17-45dc-89d2-0de8d6385eaf | low-latency-transformers-for-speech | 2302.13451 | null | https://arxiv.org/abs/2302.13451v1 | https://arxiv.org/pdf/2302.13451v1.pdf | Low latency transformers for speech processing | The transformer is a widely-used building block in modern neural networks. However, when applied to audio data, the transformer's acausal behaviour, which we term Acausal Attention (AA), has generally limited its application to offline tasks. In this paper we introduce Streaming Attention (SA), which operates causally ... | ['Richard Cartwright', 'Andrea Fanelli', 'Deepak Chandran', 'Siqi Pan', 'Jianbo Ma'] | 2023-02-27 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [ 2.82771230e-01 1.87231928e-01 1.76751196e-01 -3.62947375e-01
-5.79386473e-01 -2.17272520e-01 6.30659938e-01 1.68829724e-01
-7.39634991e-01 4.88910913e-01 1.99105725e-01 -5.52648664e-01
-1.65702447e-01 -4.29382920e-01 -7.56572604e-01 -6.10643983e-01
-5.46643019e-01 3.95395100e-01 5.93963087e-01 -1.65260851... | [14.329131126403809, 6.085626602172852] |
6543dffa-31a3-40c6-8cd0-84ef636ffd45 | set-type-belief-propagation-with-applications | 2305.04797 | null | https://arxiv.org/abs/2305.04797v1 | https://arxiv.org/pdf/2305.04797v1.pdf | Set-Type Belief Propagation with Applications to Mapping, MTT, SLAM, and SLAT | Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is applicable to only the vector-type random variables, while certain applications rely on set-type random variables with an u... | ['Henk Wymeersch', 'Lennart Svensson', 'Yuxuan Xia', 'Yu Ge', 'Angel F. García-Fernández', 'Hyowon Kim'] | 2023-05-05 | null | null | null | null | ['simultaneous-localization-and-mapping', 'type'] | ['computer-vision', 'speech'] | [-3.16979215e-02 2.64751147e-02 7.68056558e-03 -3.32201272e-01
-4.99267340e-01 -2.94179738e-01 6.63050413e-01 1.72873154e-01
-7.13608861e-01 1.29177988e+00 -4.99255985e-01 -3.92533332e-01
-4.82750416e-01 -1.42726040e+00 -1.02668417e+00 -7.80945539e-01
-4.99611109e-01 1.05871952e+00 4.97577608e-01 -2.28964582... | [6.062669277191162, 0.8366458415985107] |
992866b7-7c9d-40b6-bb62-9f2dd7d950b1 | photonic-single-perceptron-at-giga-op-s | 2105.10407 | null | https://arxiv.org/abs/2105.10407v1 | https://arxiv.org/pdf/2105.10407v1.pdf | Photonic single perceptron at Giga-OP/s speeds with Kerr microcombs for scalable optical neural networks | Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical microcombs. This approach is programmable and scalable and is capable of reaching ultrahigh speeds. We demonstrate the basic bui... | ['David J. Moss', 'Xingyuan Xu', 'Mengxi Tan'] | 2021-05-12 | null | null | null | null | ['cell-detection', 'handwritten-digit-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.51439297e-01 -3.54665630e-02 1.52079195e-01 1.84016764e-01
3.40853244e-01 -1.33599296e-01 1.38098404e-01 -1.82046682e-01
-1.01037884e+00 7.02015281e-01 -8.11822355e-01 -7.46169209e-01
1.29008004e-02 -9.46678996e-01 -6.00313783e-01 -1.00460804e+00
-1.28389195e-01 1.81232959e-01 3.72893602e-01 -4.95926589... | [8.249405860900879, 2.5807600021362305] |
e2b01edb-164c-4301-95a0-5bccefaa9373 | 3d-face-mask-presentation-attack-detection | 1903.11303 | null | http://arxiv.org/abs/1903.11303v1 | http://arxiv.org/pdf/1903.11303v1.pdf | 3D Face Mask Presentation Attack Detection Based on Intrinsic Image Analysis | Face presentation attacks have become a major threat to face recognition
systems and many countermeasures have been proposed in the past decade.
However, most of them are devoted to 2D face presentation attacks, rather than
3D face masks. Unlike the real face, the 3D face mask is usually made of resin
materials and has... | ['Xiaoyue Jiang', 'Xiaoyi Feng', 'Zhaoqiang Xia', 'Lei Li', 'Fabio Roli', 'Yupeng Ma'] | 2019-03-27 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 3.88175547e-01 -3.32778871e-01 1.48999706e-01 -1.47437289e-01
-1.46965429e-01 -3.87704164e-01 2.90467829e-01 -5.43711782e-01
4.54149470e-02 -4.62398231e-02 -1.68337435e-01 -1.20098308e-01
2.17076406e-01 -7.42670596e-01 -2.89361238e-01 -1.07755327e+00
1.88699171e-01 -1.83760181e-01 3.13194916e-02 -8.58150497... | [13.040571212768555, 1.0361822843551636] |
5568abab-7053-45ea-842c-c9c658ad167e | optimization-for-oriented-object-detection | 2103.11636 | null | https://arxiv.org/abs/2103.11636v3 | https://arxiv.org/pdf/2103.11636v3.pdf | Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss | Arbitrary-oriented objects exist widely in natural scenes, and thus the oriented object detection has received extensive attention in recent years. The mainstream rotation detectors use oriented bounding boxes (OBB) or quadrilateral bounding boxes (QBB) to represent the rotating objects. However, these methods suffer f... | ['Zhiqiang Zhou', 'Yunpeng Dong', 'Xue Yang', 'Lingjuan Miao', 'Qi Ming'] | 2021-03-22 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-8.06639418e-02 -3.42576444e-01 -2.55907029e-01 -5.03673196e-01
-6.74600124e-01 -1.15213774e-01 2.78027564e-01 -1.40172988e-01
-1.83707044e-01 3.59054953e-01 1.62233915e-02 4.53092605e-02
-2.88876444e-01 -7.53953457e-01 -4.84999448e-01 -1.06198430e+00
2.06812993e-01 6.04279488e-02 1.68868095e-01 -1.23811392... | [8.815364837646484, -0.7855038046836853] |
8160535f-1a4b-45fe-8d8c-7d063ba2491f | deep-inverse-reinforcement-learning-via | null | null | https://openreview.net/forum?id=JXSZuWSPH85 | https://openreview.net/pdf?id=JXSZuWSPH85 | Deep Inverse Reinforcement Learning via Adversarial One-Class Classification | Traditional inverse reinforcement learning (IRL) methods require a loop to find the optimal policy for each reward update (called an inner loop), resulting in very time-consuming reward estimation. In contrast, classification-based IRL methods, which have been studied recently, do not require an inner loop and estimate... | ['Sachiyo Arai', 'Daiko Kishikawa'] | 2021-09-29 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [-2.80341636e-02 -7.00144693e-02 -3.36514473e-01 3.39241996e-02
-9.22573686e-01 -9.19649303e-01 6.63195908e-01 2.02417463e-01
-8.95525277e-01 1.08660626e+00 -4.23983097e-01 -3.75769705e-01
-2.49115571e-01 -6.66511893e-01 -7.59251475e-01 -7.02215612e-01
-5.87587841e-02 2.64972955e-01 2.72236586e-01 -2.52453148... | [4.181332111358643, 2.126145601272583] |
607c0ad5-7685-470e-a719-2f3211460720 | adversarial-attack-on-deep-learning-based | 2004.08443 | null | https://arxiv.org/abs/2004.08443v1 | https://arxiv.org/pdf/2004.08443v1.pdf | Adversarial Attack on Deep Learning-Based Splice Localization | Regarding image forensics, researchers have proposed various approaches to detect and/or localize manipulations, such as splices. Recent best performing image-forensics algorithms greatly benefit from the application of deep learning, but such tools can be vulnerable to adversarial attacks. Due to the fact that most of... | ['Zheng Zhong', 'Andras Rozsa', 'Terrance E. Boult'] | 2020-04-17 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 3.20910543e-01 -3.59737524e-03 3.72717440e-01 9.64805409e-02
-1.09904158e+00 -1.06706369e+00 6.90332115e-01 -1.43702105e-01
-2.83921659e-01 3.36544454e-01 -2.74013281e-01 -4.62033778e-01
1.17133558e-01 -7.10673392e-01 -1.29057300e+00 -7.94811726e-01
-2.03525096e-01 3.90678123e-02 2.50443518e-01 -1.08459197... | [12.42916202545166, 1.0331952571868896] |
fa88b13d-ac66-4514-b178-bdc3eea3f78a | hierarchical-reinforcement-learning-in | 2302.14451 | null | https://arxiv.org/abs/2302.14451v1 | https://arxiv.org/pdf/2302.14451v1.pdf | Hierarchical Reinforcement Learning in Complex 3D Environments | Hierarchical Reinforcement Learning (HRL) agents have the potential to demonstrate appealing capabilities such as planning and exploration with abstraction, transfer, and skill reuse. Recent successes with HRL across different domains provide evidence that practical, effective HRL agents are possible, even if existing ... | ['Satinder Singh', 'Thomas Keck', 'Kyriacos Nikiforou', 'Hubert Soyer', 'Feryal Behbahani', 'Bernardo Avila Pires'] | 2023-02-28 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-3.87596279e-01 4.86339927e-01 -7.26915244e-03 2.10064992e-01
-8.67009044e-01 -6.77532494e-01 5.87366223e-01 -2.38477923e-02
-6.14727557e-01 1.14601266e+00 3.29056472e-01 -1.74874321e-01
-3.33574325e-01 -6.65432274e-01 -7.85693526e-01 -8.59292090e-01
-8.33640695e-01 8.99658203e-01 3.98823053e-01 -6.42445683... | [4.142092227935791, 1.260789155960083] |
228ba66e-1ce2-4890-8ada-8c671a01d1cc | fnevr-neural-volume-rendering-for-face | 2209.10340 | null | https://arxiv.org/abs/2209.10340v1 | https://arxiv.org/pdf/2209.10340v1.pdf | FNeVR: Neural Volume Rendering for Face Animation | Face animation, one of the hottest topics in computer vision, has achieved a promising performance with the help of generative models. However, it remains a critical challenge to generate identity preserving and photo-realistic images due to the sophisticated motion deformation and complex facial detail modeling. To ad... | ['Baochang Zhang', 'Wei Peng', 'Dapeng Chen', 'Jianzhuang Liu', 'Xuhui Liu', 'Hong Li', 'Boyu Liu', 'Bohan Zeng'] | 2022-09-21 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [-1.23133203e-02 9.94795002e-03 2.19838336e-01 -5.16034126e-01
-5.08826554e-01 -2.69990265e-01 8.76043797e-01 -9.05017436e-01
4.43876609e-02 3.17322314e-01 2.52878249e-01 -8.89040753e-02
3.96524936e-01 -8.54374766e-01 -5.95135927e-01 -5.98337173e-01
1.71241105e-01 3.45552266e-01 -4.82963473e-02 -3.78963709... | [12.804758071899414, -0.28167879581451416] |
e2759544-43f1-4d9a-94c5-a9d17f46332a | pdc-net-enhanced-probabilistic-dense | 2109.13912 | null | https://arxiv.org/abs/2109.13912v2 | https://arxiv.org/pdf/2109.13912v2.pdf | PDC-Net+: Enhanced Probabilistic Dense Correspondence Network | Establishing robust and accurate correspondences between a pair of images is a long-standing computer vision problem with numerous applications. While classically dominated by sparse methods, emerging dense approaches offer a compelling alternative paradigm that avoids the keypoint detection step. However, dense flow e... | ['Radu Timofte', 'Luc van Gool', 'Martin Danelljan', 'Prune Truong'] | 2021-09-28 | null | null | null | null | ['geometric-matching', 'image-based-localization'] | ['computer-vision', 'computer-vision'] | [-1.43589199e-01 -2.86882669e-01 -1.42969504e-01 -2.35037833e-01
-9.05152321e-01 -4.11845565e-01 6.01688564e-01 2.64568448e-01
-3.26026559e-01 5.49606740e-01 2.22364932e-01 2.02016786e-01
-3.13290864e-01 -5.58617651e-01 -7.58071303e-01 -5.73741198e-01
-2.75206715e-02 7.96521902e-01 3.59925658e-01 2.82807320... | [8.465545654296875, -2.213926076889038] |
2bb62927-e769-4284-a8af-bf706bdb7497 | mmg-at-semeval-2022-task-1-a-reverse | null | null | https://aclanthology.org/2022.semeval-1.7 | https://aclanthology.org/2022.semeval-1.7.pdf | MMG at SemEval-2022 Task 1: A Reverse Dictionary approach based on a review of the dataset from a lexicographic perspective | This paper presents a novel and linguistic-driven system for the Spanish Reverse Dictionary task of SemEval-2022 Task 1. The aim of this task is the automatic generation of a word using its gloss. The conclusion is that this task results could improve if the quality of the dataset did as well by incorporating high-qual... | ['Adrián Alonso', 'Ignacio Arranz', 'Jorge Álvarez', 'Óscar García-Sierra', 'Miguel Ortega-Martín', 'Alfonso Ardoiz'] | null | null | null | null | semeval-naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 7.03767240e-02 4.37480152e-01 -1.44710988e-01 -4.26679909e-01
-4.70110595e-01 -8.01707566e-01 1.05877531e+00 1.84277266e-01
-9.06040549e-01 1.24538088e+00 6.16863370e-01 -3.42344075e-01
-2.20732555e-01 -7.32612193e-01 -3.56922805e-01 -3.28023970e-01
3.66582453e-01 9.17232454e-01 -1.83134601e-02 -8.37539017... | [10.500683784484863, 10.257467269897461] |
3ee4db3b-b3bf-40c0-b7a0-182dd8f1ade8 | fast-variable-selection-makes-scalable | 2205.13676 | null | https://arxiv.org/abs/2205.13676v4 | https://arxiv.org/pdf/2205.13676v4.pdf | Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes | A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Lo\`eve (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of ... | ['David S. Mebane', 'Michael W. Fouts', 'Ali Baheri', 'Kyle Hayes'] | 2022-05-26 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 2.12229058e-01 -2.23772302e-01 1.84222206e-01 3.71211171e-02
-4.82048213e-01 -4.24142420e-01 8.18598270e-01 -8.72109383e-02
-2.99785376e-01 8.33392024e-01 -3.14139932e-01 -3.53071332e-01
-7.22037554e-01 -6.99155509e-01 -6.15577519e-01 -1.65085697e+00
-6.05708480e-01 7.18263924e-01 1.91465542e-01 -1.34940565... | [6.5582804679870605, 3.3489186763763428] |
9326cd59-e304-4a46-b4b9-9af0c5f894e8 | multi-source-adversarial-transfer-learning | 2305.19069 | null | https://arxiv.org/abs/2305.19069v1 | https://arxiv.org/pdf/2305.19069v1.pdf | Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity | Lesion segmentation of ultrasound medical images based on deep learning techniques is a widely used method for diagnosing diseases. Although there is a large amount of ultrasound image data in medical centers and other places, labeled ultrasound datasets are a scarce resource, and it is likely that no datasets are avai... | ['Xinyu Zhang', 'Zhanhu Zhang', 'Wujin Feng', 'Ning Ma', 'Jiansong Zhang', 'Shimeng Shi', 'Zhengyuan Liu', 'Rui Tao', 'Tao Yang', 'Hongru Li', 'Yifu Zhang'] | 2023-05-30 | null | null | null | null | ['lesion-segmentation'] | ['medical'] | [ 1.10584766e-01 9.58342254e-02 -1.91664010e-01 -2.03770161e-01
-7.94785142e-01 -4.78660285e-01 -2.32289843e-02 -7.55537450e-02
-2.64563978e-01 7.64654219e-01 -6.73831580e-03 -1.50255427e-01
2.15359945e-02 -1.12250853e+00 -6.78593218e-01 -1.04464459e+00
2.25193948e-01 1.82183623e-01 5.38554549e-01 -3.07933807... | [14.63978385925293, -2.0349061489105225] |
2c45ff6b-f7f4-4723-a642-0198069adc72 | learning-trustworthy-model-from-noisy-labels | 2301.10441 | null | https://arxiv.org/abs/2301.10441v1 | https://arxiv.org/pdf/2301.10441v1.pdf | Learning Trustworthy Model from Noisy Labels based on Rough Set for Surface Defect Detection | In the surface defect detection, there are some suspicious regions that cannot be uniquely classified as abnormal or normal. The annotating of suspicious regions is easily affected by factors such as workers' emotional fluctuations and judgment standard, resulting in noisy labels, which in turn leads to missing and fal... | ['Zhenrong Wang', 'Weifeng Li', 'Yuwei Li', 'Yufeng Lin', 'Kai Li', 'Bin Li', 'Tongzhi Niu'] | 2023-01-25 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.81114876e-01 4.35825944e-01 3.01614523e-01 -5.48263609e-01
-4.89257216e-01 -4.84085023e-01 8.07221457e-02 3.03147972e-01
1.54331937e-01 5.71831644e-01 -4.30795223e-01 1.93022549e-01
-2.23791644e-01 -7.49016404e-01 -4.33145016e-01 -9.70040560e-01
2.38510966e-01 6.74278885e-02 5.61096430e-01 4.27512199... | [9.32547664642334, 3.839179039001465] |
e4fdb750-9e98-417b-844b-1a1a7f7a3451 | generalist-vision-foundation-models-for | 2304.12637 | null | https://arxiv.org/abs/2304.12637v2 | https://arxiv.org/pdf/2304.12637v2.pdf | Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation | In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI... | ['Wu Yuan', 'Frank P. -W. Lo', 'Hao Wei', 'Sai Mu Dalike Abaxi', 'Jianing Qiu', 'Peilun Shi'] | 2023-04-25 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 2.58557826e-01 1.65656194e-01 -3.60210538e-01 -1.56512812e-01
-8.74297261e-01 -3.95672202e-01 1.93843260e-01 1.13685336e-03
-3.78480911e-01 4.71666396e-01 -7.93844312e-02 -4.84873146e-01
-2.55289525e-01 -5.69217801e-01 -2.70373195e-01 -7.80029953e-01
1.32382110e-01 8.07635128e-01 5.63613355e-01 -1.02153346... | [14.708547592163086, -2.2838122844696045] |
4164c0e6-0a89-4b75-b8b4-4cab321c2ee6 | tackling-catastrophic-forgetting-and | 2106.15287 | null | https://arxiv.org/abs/2106.15287v1 | https://arxiv.org/pdf/2106.15287v1.pdf | Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation | Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. Continual learning for semantic segmentation (CSS) is an emerging trend that consists in updating an old model by sequentially adding new c... | ['Matthieu Cord', 'Arnaud Dapogny', 'Yifu Chen', 'Arthur Douillard'] | 2021-06-29 | null | null | null | null | ['overlapped-10-1', 'overlapped-15-5', 'overlapped-15-1', 'continual-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 5.92416823e-01 9.24251750e-02 2.25994289e-01 -3.03725541e-01
-5.63220561e-01 -4.09571886e-01 4.85595495e-01 5.96385777e-01
-8.82880151e-01 8.06652367e-01 -1.54238686e-01 2.85977364e-01
1.61584184e-01 -8.29258561e-01 -8.49379361e-01 -9.68403041e-01
2.97025025e-01 3.97356808e-01 1.00053144e+00 6.94025755... | [9.414820671081543, 1.9546533823013306] |
410671b3-9955-44e9-a4f0-5f785a890d3d | temporalteller-at-semeval-2020-task-1 | null | null | https://aclanthology.org/2020.semeval-1.27 | https://aclanthology.org/2020.semeval-1.27.pdf | TemporalTeller at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection with Temporal Referencing | This paper describes our TemporalTeller system for SemEval Task 1: Unsupervised Lexical Semantic Change Detection. We develop a unified framework for the common semantic change detection pipelines including preprocessing, learning word embeddings, calculating vector distances and determining threshold. We also propose ... | ['Jiaxin Li', 'Jinan Zhou'] | 2020-12-01 | null | null | null | semeval-2020 | ['learning-word-embeddings'] | ['methodology'] | [ 1.10245056e-01 -4.08650011e-01 -4.55000043e-01 -4.28293347e-01
-8.66166413e-01 -7.74331450e-01 8.22344065e-01 7.23089397e-01
-9.97791290e-01 5.73244870e-01 5.15818417e-01 -1.62062779e-01
5.75839765e-02 -6.63922608e-01 -2.03103274e-01 -2.58028209e-01
-1.88270777e-01 2.96758950e-01 6.08608067e-01 -4.55992579... | [10.253216743469238, 9.005012512207031] |
65e4a8f0-8024-4b73-8340-62c51f42c58d | sar-image-despeckling-algorithms-using | 1308.4338 | null | http://arxiv.org/abs/1308.4338v1 | http://arxiv.org/pdf/1308.4338v1.pdf | SAR Image Despeckling Algorithms using Stochastic Distances and Nonlocal Means | This paper presents two approaches for filter design based on stochastic
distances for intensity speckle reduction. A window is defined around each
pixel, overlapping samples are compared and only those which pass a
goodness-of-fit test are used to compute the filtered value. The tests stem
from stochastic divergences ... | ['Alejandro C. Frery', 'Leonardo Torres'] | 2013-08-20 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 4.33282465e-01 -4.03375030e-01 5.42897642e-01 -4.51683998e-01
-6.62833631e-01 -2.07131848e-01 3.57032478e-01 -1.46627218e-01
-9.31740344e-01 8.96153450e-01 9.81815346e-03 4.07667011e-02
-7.47493923e-01 -1.00234795e+00 7.80162141e-02 -1.21231723e+00
-1.70186296e-01 4.21861440e-01 4.47375327e-01 1.30538251... | [10.488367080688477, -2.250487804412842] |
4ce894f5-74ff-4ead-a201-c38aec79a634 | the-point-where-reality-meets-fantasy-mixed | null | null | http://papers.nips.cc/paper/8315-the-point-where-reality-meets-fantasy-mixed-adversarial-generators-for-image-splice-detection | http://papers.nips.cc/paper/8315-the-point-where-reality-meets-fantasy-mixed-adversarial-generators-for-image-splice-detection.pdf | The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection | Modern photo editing tools allow creating realistic manipulated images easily. While fake images can be quickly generated, learning models for their detection is challenging due to the high variety of tampering artifacts and the lack of large labeled datasets of manipulated images. In this paper, we propose a new frame... | ['Vladimir Knyaz', 'Fabio Remondino', 'Vladimir V. Kniaz'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['image-retouching'] | ['computer-vision'] | [ 8.54651630e-01 3.62478644e-01 1.19520381e-01 -2.23523840e-01
-1.06518435e+00 -7.64880300e-01 4.18733567e-01 -5.09526670e-01
-1.69861972e-01 5.80177605e-01 -2.69940585e-01 -1.46782756e-01
5.52681684e-01 -1.02054930e+00 -1.51760745e+00 -6.32256091e-01
2.18871653e-01 3.01607519e-01 4.82375503e-01 -4.49309722... | [11.652941703796387, -0.44720256328582764] |
a2d38a4c-782a-421a-b6dd-9a4b056d0d45 | sogan-3d-aware-shadow-and-occlusion-robust | 2104.10567 | null | https://arxiv.org/abs/2104.10567v2 | https://arxiv.org/pdf/2104.10567v2.pdf | SOGAN: 3D-Aware Shadow and Occlusion Robust GAN for Makeup Transfer | In recent years, virtual makeup applications have become more and more popular. However, it is still challenging to propose a robust makeup transfer method in the real-world environment. Current makeup transfer methods mostly work well on good-conditioned clean makeup images, but transferring makeup that exhibits shado... | ['Tieniu Tan', 'Wei Wang', 'Bo Peng', 'Jing Dong', 'Yueming Lyu'] | 2021-04-21 | null | null | null | null | ['face-model', 'facial-makeup-transfer'] | ['computer-vision', 'computer-vision'] | [ 1.43040150e-01 -4.23708335e-02 8.77425075e-02 -5.54918587e-01
-6.49034798e-01 -4.39133376e-01 3.98771375e-01 -9.37235832e-01
4.47390884e-01 6.66671515e-01 2.21003562e-01 1.37776196e-01
3.79746675e-01 -8.22683394e-01 -7.91402876e-01 -8.39278519e-01
8.99628818e-01 2.14250416e-01 -9.08903927e-02 -1.54880017... | [12.690202713012695, -0.14762842655181885] |
f5918bbe-d675-482e-b14a-8dedd6ecad2f | ptb-tir-a-thermal-infrared-pedestrian | 1801.05944 | null | https://arxiv.org/abs/1801.05944v3 | https://arxiv.org/pdf/1801.05944v3.pdf | PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark | Thermal infrared (TIR) pedestrian tracking is one of the important components among numerous applications of computer vision, which has a major advantage: it can track pedestrians in total darkness. The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of ... | ['Qiao Liu', 'Yuan Zheng', 'Zhenyu He', 'Xin Li'] | 2018-01-18 | null | null | null | null | ['thermal-infrared-object-tracking'] | ['computer-vision'] | [-3.58301520e-01 -9.54970598e-01 -1.48904458e-01 -3.67972076e-01
-3.91156077e-01 -5.84391594e-01 6.32086813e-01 -5.29603958e-01
-3.39870691e-01 4.01064873e-01 -5.79423532e-02 -3.18998128e-01
6.09056652e-01 -3.94151509e-01 -1.97881430e-01 -1.01842391e+00
2.04724818e-01 -2.30950221e-01 6.64314747e-01 9.51850712... | [6.430887222290039, -2.0471701622009277] |
b85945be-1ad9-4e55-9977-e31c68e5318d | fairseq-s2t-fast-speech-to-text-modeling-with | 2010.05171 | null | https://arxiv.org/abs/2010.05171v2 | https://arxiv.org/pdf/2010.05171v2.pdf | fairseq S2T: Fast Speech-to-Text Modeling with fairseq | We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation. It follows fairseq's careful design for scalability and extensibility. We provide end-to-end workflows from data pre-processing, model training to offline (online) ... | ['Juan Pino', 'Sravya Popuri', 'Dmytro Okhonko', 'Anne Wu', 'Xutai Ma', 'Yun Tang', 'Changhan Wang'] | 2020-10-11 | null | https://aclanthology.org/2020.aacl-demo.6 | https://aclanthology.org/2020.aacl-demo.6.pdf | asian-chapter-of-the-association-for | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.56473303e-01 5.26309870e-02 -1.56441078e-01 -7.17454076e-01
-1.81485200e+00 -9.01734233e-01 6.31615698e-01 -4.91950154e-01
-2.65137017e-01 5.60206592e-01 4.74931866e-01 -9.52216625e-01
3.62350434e-01 -1.25112817e-01 -7.51420379e-01 -3.07644576e-01
2.33876303e-01 1.04390609e+00 -2.87725590e-02 -2.67595053... | [14.484367370605469, 7.160309791564941] |
682b6022-9c7a-4148-ac7e-acccdd6f2e04 | mining-word-boundaries-in-speech-as-naturally | 2210.17122 | null | https://arxiv.org/abs/2210.17122v1 | https://arxiv.org/pdf/2210.17122v1.pdf | Mining Word Boundaries in Speech as Naturally Annotated Word Segmentation Data | Chinese word segmentation (CWS) models have achieved very high performance when the training data is sufficient and in-domain. However, the performance drops drastically when shifting to cross-domain and low-resource scenarios due to data sparseness issues. Considering that constructing large-scale manually annotated d... | ['Min Zhang', 'Baoxing Huai', 'Zhefeng Wang', 'Zhenghua Li', 'Chen Gong', 'Shilin Zhou', 'Lei Zhang'] | 2022-10-31 | null | null | null | null | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 2.71849990e-01 3.94355841e-02 -2.64148533e-01 -5.32351136e-01
-1.32382238e+00 -5.99083066e-01 1.00514598e-01 -1.84942968e-02
-7.23489583e-01 5.98514557e-01 3.10272902e-01 -6.19832933e-01
3.89081955e-01 -3.32368314e-01 -2.93550491e-01 -3.51249486e-01
2.24330202e-01 3.82797539e-01 5.62331259e-01 6.92889467... | [10.021358489990234, 10.125734329223633] |
d987b5e8-c4a0-45be-8802-aaef09174ade | quality-aware-network-for-face-parsing | 2106.07368 | null | https://arxiv.org/abs/2106.07368v1 | https://arxiv.org/pdf/2106.07368v1.pdf | Quality-Aware Network for Face Parsing | This is a very short technical report, which introduces the solution of the Team BUPT-CASIA for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. Face parsing has recently attracted increasing interest due to its numerous application potentials. Generally speaking, i... | ['Zhiwei Liu', 'Xueshi Xin', 'Qing Song', 'Lu Yang'] | 2021-06-14 | null | null | null | null | ['face-parsing', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 1.42994478e-01 3.55430156e-01 1.77737474e-02 -7.91376889e-01
-6.86414957e-01 -5.98284125e-01 4.59455371e-01 -5.64421117e-01
-2.53942937e-01 5.04915714e-01 2.87172586e-01 3.84309678e-03
4.12996978e-01 -2.54786372e-01 -6.73627377e-01 -2.67539829e-01
1.05102971e-01 5.57524085e-01 1.26338929e-01 3.98924015... | [13.362679481506348, 0.5966214537620544] |
49a75737-2908-492a-bdbd-d01333565817 | privileged-attribution-constrained-deep | 2203.12905 | null | https://arxiv.org/abs/2203.12905v2 | https://arxiv.org/pdf/2203.12905v2.pdf | Privileged Attribution Constrained Deep Networks for Facial Expression Recognition | Facial Expression Recognition (FER) is crucial in many research domains because it enables machines to better understand human behaviours. FER methods face the problems of relatively small datasets and noisy data that don't allow classical networks to generalize well. To alleviate these issues, we guide the model to co... | ['Kévin Bailly', 'Ferdinand Dhombres', 'Arnaud Dapogny', 'Jules Bonnard'] | 2022-03-24 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.45622352e-01 4.96544480e-01 -1.64557472e-01 -7.36368835e-01
1.43274486e-01 -1.37689337e-01 6.84762239e-01 -4.18550879e-01
-3.67209882e-01 7.98543930e-01 -9.13037583e-02 2.62205243e-01
-2.46163886e-02 -5.11034369e-01 -4.37666893e-01 -8.28098059e-01
-1.22822598e-01 1.96937114e-01 1.50129244e-01 -4.29077476... | [13.496590614318848, 1.7042925357818604] |
ec1d3a46-2de2-489d-8378-b751e777a503 | exploring-the-universality-of-hadronic-jet | 2204.03812 | null | https://arxiv.org/abs/2204.03812v1 | https://arxiv.org/pdf/2204.03812v1.pdf | Exploring the Universality of Hadronic Jet Classification | The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in ... | ['Benjamin Nachman', 'Shih-Chieh Hsu', 'Yi-Lun Chung', 'Kingman Cheung'] | 2022-04-08 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-2.49218836e-01 -1.40022427e-01 -4.84645814e-01 -8.77085209e-01
-5.36952913e-01 -6.22406602e-01 9.03020144e-01 1.12334892e-01
-3.20926696e-01 5.89821458e-01 8.36584345e-03 -8.34861636e-01
3.74413818e-01 -7.03955710e-01 -1.01242328e+00 -6.92670345e-01
4.53158095e-03 1.21379066e+00 6.16217196e-01 -1.97450414... | [15.695779800415039, 2.9199931621551514] |
28171698-65a1-45ca-bc47-7c8293242b67 | recognizing-and-verifying-mathematical | 2104.02899 | null | https://arxiv.org/abs/2104.02899v1 | https://arxiv.org/pdf/2104.02899v1.pdf | Recognizing and Verifying Mathematical Equations using Multiplicative Differential Neural Units | Automated mathematical reasoning is a challenging problem that requires an agent to learn algebraic patterns that contain long-range dependencies. Two particular tasks that test this type of reasoning are (1) mathematical equation verification, which requires determining whether trigonometric and linear algebraic state... | ['C. Lee Giles', 'Daniel Kifer', 'Alexander Ororbia', 'Ankur Mali'] | 2021-04-07 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 3.90325278e-01 6.21729530e-02 1.40706316e-01 -2.62035549e-01
-1.50379613e-01 -6.93372786e-01 3.98399293e-01 -1.86007038e-01
-2.20068038e-01 7.58669019e-01 -5.09703457e-01 -9.20299530e-01
-1.56399414e-01 -8.99899125e-01 -9.75875735e-01 -3.56150150e-01
-6.23834506e-02 3.93126070e-01 -8.58140811e-02 -4.31571901... | [9.292259216308594, 7.186585426330566] |
81e9870a-b336-4afa-a8ef-de10c827f072 | operational-learning-based-boundary | 2108.03233 | null | https://arxiv.org/abs/2108.03233v1 | https://arxiv.org/pdf/2108.03233v1.pdf | Operational Learning-based Boundary Estimation in Electromagnetic Medical Imaging | Incorporating boundaries of the imaging object as a priori information to imaging algorithms can significantly improve the performance of electromagnetic medical imaging systems. To avoid overly complicating the system by using different sensors and the adverse effect of the subject's movement, a learning-based method ... | ['A. Abbosh', 'A. Zamani', 'A. Stancombe', 'A. Al-Saffar'] | 2021-08-04 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 2.86276489e-01 3.88022304e-01 4.42372501e-01 -6.12448037e-01
-6.25740409e-01 -2.27371305e-01 -1.70772120e-01 7.87890553e-02
-3.81845891e-01 6.23858929e-01 1.31940827e-01 -3.80864471e-01
-3.10458034e-01 -4.56565529e-01 -5.30627251e-01 -8.31819952e-01
-5.02208114e-01 4.30421084e-01 1.70818746e-01 4.85577881... | [13.336493492126465, -2.5761449337005615] |
e2291c25-3b2b-49e7-86d1-27c0c3e9a363 | stereo-hybrid-event-frame-shef-cameras-for-3d | 2110.04988 | null | https://arxiv.org/abs/2110.04988v2 | https://arxiv.org/pdf/2110.04988v2.pdf | Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception | Stereo camera systems play an important role in robotics applications to perceive the 3D world. However, conventional cameras have drawbacks such as low dynamic range, motion blur and latency due to the underlying frame-based mechanism. Event cameras address these limitations as they report the brightness changes of ea... | ['Robert Mahony', 'Zheyu Zhuang', 'Yonhon Ng', 'Liyuan Pan', 'Ziwei Wang'] | 2021-10-11 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 6.12730265e-01 -3.60757649e-01 2.26838320e-01 -3.93753260e-01
-4.74600792e-01 -3.03761631e-01 5.41911364e-01 -6.06814772e-03
-7.30233610e-01 5.60983777e-01 -1.02901213e-01 1.17893592e-01
2.08062842e-01 -9.02564704e-01 -8.63232970e-01 -6.22560799e-01
2.75362045e-01 -2.75778007e-02 9.22559619e-01 4.32714745... | [9.045802116394043, -2.17346453666687] |
314f98b6-dcdb-4b5d-b37c-dbec92e2ca95 | rethinking-the-inception-architecture-for | 1512.00567 | null | http://arxiv.org/abs/1512.00567v3 | http://arxiv.org/pdf/1512.00567v3.pdf | Rethinking the Inception Architecture for Computer Vision | Convolutional networks are at the core of most state-of-the-art computer
vision solutions for a wide variety of tasks. Since 2014 very deep
convolutional networks started to become mainstream, yielding substantial gains
in various benchmarks. Although increased model size and computational cost
tend to translate to imm... | ['Vincent Vanhoucke', 'Christian Szegedy', 'Sergey Ioffe', 'Jonathon Shlens', 'Zbigniew Wojna'] | 2015-12-02 | rethinking-the-inception-architecture-for-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf | cvpr-2016-6 | ['retinal-oct-disease-classification'] | ['computer-vision'] | [ 1.63217753e-01 -6.36901930e-02 -1.18550219e-01 -5.07369637e-01
-8.87221813e-01 -4.62133288e-01 4.57587421e-01 -1.87632754e-01
-9.27924871e-01 5.30973136e-01 -2.35247761e-01 -1.41326666e-01
1.98839784e-01 -6.73299253e-01 -9.81856108e-01 -4.35010165e-01
-3.74624580e-02 2.63569087e-01 3.82055849e-01 7.90737569... | [9.268463134765625, 1.440028190612793] |
4428369d-2ac1-43c8-9874-811d977d8b1c | an-end-to-end-network-for-upright-adjustment | 2304.05556 | null | https://arxiv.org/abs/2304.05556v1 | https://arxiv.org/pdf/2304.05556v1.pdf | An End-to-End Network for Upright Adjustment of Panoramic Images | Nowadays, panoramic images can be easily obtained by panoramic cameras. However, when the panoramic camera orientation is tilted, a non-upright panoramic image will be captured. Existing upright adjustment models focus on how to estimate more accurate camera orientation, and attribute image reconstruction to offline or... | ['Shigang Li', 'Jianfeng Li', 'Heyu Chen'] | 2023-04-12 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 6.44820809e-01 -3.33315358e-02 -5.01024574e-02 -5.08476615e-01
-5.44948637e-01 -5.98503947e-01 4.13879335e-01 -9.11106110e-01
-1.01405255e-01 3.34528267e-01 -4.31546234e-02 -2.30380669e-01
3.25205892e-01 -1.24871039e+00 -1.25860012e+00 -5.04678965e-01
6.71836793e-01 8.09105709e-02 -1.74313828e-01 -8.96182507... | [10.401870727539062, -2.188201665878296] |
7b49f626-8d76-4598-95de-0eb07460da16 | logician-a-unified-end-to-end-neural-approach | 1904.12535 | null | http://arxiv.org/abs/1904.12535v1 | http://arxiv.org/pdf/1904.12535v1.pdf | Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction | In this paper, we consider the problem of open information extraction (OIE)
for extracting entity and relation level intermediate structures from sentences
in open-domain. We focus on four types of valuable intermediate structures
(Relation, Attribute, Description, and Concept), and propose a unified
knowledge expressi... | ['Yue Feng', 'Xu Li', 'Mingming Sun', 'Miao Fan', 'Xin Wang', 'Ping Li'] | 2019-04-29 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [-3.60869281e-02 7.39265025e-01 -1.19412266e-01 -3.49861592e-01
-7.64162421e-01 -7.04338849e-01 3.83344054e-01 3.22292626e-01
-4.87903029e-01 1.34304869e+00 4.11530465e-01 -1.10553227e-01
-3.92402977e-01 -9.34872091e-01 -6.61958098e-01 -2.18933612e-01
5.48900887e-02 7.73997545e-01 7.73013681e-02 -4.54961717... | [9.50810718536377, 8.592920303344727] |
f38a3f88-e947-429a-b17f-ceb8133df259 | fencemask-a-data-augmentation-approach-for | 2006.07877 | null | https://arxiv.org/abs/2006.07877v1 | https://arxiv.org/pdf/2006.07877v1.pdf | FenceMask: A Data Augmentation Approach for Pre-extracted Image Features | We propose a novel data augmentation method named 'FenceMask' that exhibits outstanding performance in various computer vision tasks. It is based on the 'simulation of object occlusion' strategy, which aim to achieve the balance between object occlusion and information retention of the input data. By enhancing the spar... | ['Xiang-Yang Li', 'Xiang Long', 'Pu Li'] | 2020-06-14 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 5.52415587e-02 5.17964661e-02 -4.30947006e-01 -4.95833814e-01
-1.96329743e-01 -5.75234592e-01 9.33471322e-01 3.52819115e-01
-4.20319706e-01 5.75453579e-01 2.12759316e-01 -2.44274125e-01
-4.91363481e-02 -6.77437782e-01 -8.47110450e-01 -4.72801685e-01
1.43973216e-01 3.61218363e-01 2.75214076e-01 -7.94102810... | [9.587876319885254, 2.028881788253784] |
9ce42a70-d7ed-49be-a6a8-a84a9592cd07 | multi-attribute-enhancement-network-for | 2102.07968 | null | https://arxiv.org/abs/2102.07968v2 | https://arxiv.org/pdf/2102.07968v2.pdf | Multi-Attribute Enhancement Network for Person Search | Person Search is designed to jointly solve the problems of Person Detection and Person Re-identification (Re-ID), in which the target person will be located in a large number of uncut images. Over the past few years, Person Search based on deep learning has made great progress. Visual character attributes play a key ro... | ['Xinming Wang', 'Yaping Tao', 'Jinglei Guo', 'Zhigang Tu', 'Wei Xie', 'Lequan Chen'] | 2021-02-16 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-4.16356325e-01 -5.71110308e-01 -1.19924821e-01 -3.61417562e-01
-7.87806869e-01 -2.33803287e-01 6.98155940e-01 1.24911452e-02
-8.20881188e-01 6.85672939e-01 5.32047212e-01 3.43756527e-01
-6.01002388e-02 -8.39705467e-01 -4.81950432e-01 -5.98431408e-01
1.40166432e-01 6.81748390e-01 1.11084163e-01 -1.35272928... | [14.807876586914062, 0.7678006887435913] |
dcbe401d-17f6-48fc-9ddd-fbc9a473ea98 | prediction-intervals-in-the-beta | 2207.11628 | null | https://arxiv.org/abs/2207.11628v1 | https://arxiv.org/pdf/2207.11628v1.pdf | Prediction Intervals in the Beta Autoregressive Moving Average Model | In this paper, we propose five prediction intervals for the beta autoregressive moving average model. This model is suitable for modeling and forecasting variables that assume values in the interval $(0,1)$. Two of the proposed prediction intervals are based on approximations considering the normal distribution and the... | ['R. J. Cintra', 'F. M. Bayer', 'B. G. Palm'] | 2022-07-24 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-2.02892482e-01 1.49029538e-01 -1.10801816e-01 -4.87191916e-01
-6.93477631e-01 -3.03620875e-01 4.42191720e-01 6.34851038e-01
-3.42706531e-01 1.20158482e+00 1.48054734e-01 -8.04167569e-01
-4.97164935e-01 -1.23832500e+00 -4.46005702e-01 -7.76823819e-01
-4.43154424e-01 3.70089471e-01 3.46640378e-01 -5.01950085... | [6.68643045425415, 3.3549416065216064] |
0b073fe1-5f26-4ecc-b7f8-01d21363d78b | video-salient-object-detection-via-adaptive | 2104.14360 | null | https://arxiv.org/abs/2104.14360v3 | https://arxiv.org/pdf/2104.14360v3.pdf | Video Salient Object Detection via Adaptive Local-Global Refinement | Video salient object detection (VSOD) is an important task in many vision applications. Reliable VSOD requires to simultaneously exploit the information from both the spatial domain and the temporal domain. Most of the existing algorithms merely utilize simple fusion strategies, such as addition and concatenation, to m... | ['Guoliang Xing', 'Yuanman Li', 'Yi Tang'] | 2021-04-29 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 1.14037171e-02 -5.02168536e-01 -2.50057399e-01 -2.26623639e-01
-2.46733516e-01 -1.34989902e-01 4.60099369e-01 2.19769105e-01
-3.97829413e-01 5.00934362e-01 3.81848603e-01 2.92706817e-01
-2.93767691e-01 -7.06324041e-01 -2.10139379e-01 -9.26546693e-01
4.06858660e-02 -3.75528008e-01 1.04883814e+00 -3.07360321... | [9.580521583557129, -0.5022109746932983] |
876e15cc-1eeb-41ae-90f8-83b91cc954d4 | fast-lidar-clustering-by-density-and | 2003.00575 | null | https://arxiv.org/abs/2003.00575v2 | https://arxiv.org/pdf/2003.00575v2.pdf | FLIC: Fast Lidar Image Clustering | Lidar sensors are widely used in various applications, ranging from scientific fields over industrial use to integration in consumer products. With an ever growing number of different driver assistance systems, they have been introduced to automotive series production in recent years and are considered an important bui... | ['Lukas Hahn', 'Anton Kummert', 'Frederik Hasecke'] | 2020-03-01 | null | null | null | null | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 4.33415502e-01 -6.93358332e-02 -2.01792330e-01 -5.86993873e-01
-7.08661497e-01 -5.66455007e-01 7.60279655e-01 2.86849409e-01
-6.82384491e-01 5.71881235e-01 -4.50483441e-01 -5.21655798e-01
-1.66804940e-01 -9.61896300e-01 -7.41632402e-01 -4.62176055e-01
8.11309963e-02 7.52242565e-01 9.70218360e-01 -2.19746858... | [7.942531108856201, -2.602245330810547] |
de4acfe5-0781-4eda-a9b6-a10f86c4fc73 | adversarial-music-real-world-audio-adversary | 1911.00126 | null | https://arxiv.org/abs/1911.00126v3 | https://arxiv.org/pdf/1911.00126v3.pdf | Adversarial Music: Real World Audio Adversary Against Wake-word Detection System | Voice Assistants (VAs) such as Amazon Alexa or Google Assistant rely on wake-word detection to respond to people's commands, which could potentially be vulnerable to audio adversarial examples. In this work, we target our attack on the wake-word detection system, jamming the model with some inconspicuous background mus... | ['Xinjian Li', 'Shuhui Qu', 'J. Zico Kolter', 'Joseph Szurley', 'Juncheng B. Li', 'Florian Metze'] | 2019-10-31 | adversarial-music-real-world-audio-adversary-1 | http://papers.nips.cc/paper/9362-adversarial-music-real-world-audio-adversary-against-wake-word-detection-system | http://papers.nips.cc/paper/9362-adversarial-music-real-world-audio-adversary-against-wake-word-detection-system.pdf | neurips-2019-12 | ['real-world-adversarial-attack'] | ['adversarial'] | [ 7.16581568e-02 -7.02101141e-02 3.15903306e-01 2.18465164e-01
-1.32670891e+00 -1.29437232e+00 1.14666730e-01 -5.99459887e-01
-3.13042819e-01 4.30331975e-01 3.12627219e-02 -5.12966573e-01
2.35011593e-01 -6.20971859e-01 -6.16366148e-01 -5.91481030e-01
-3.87829661e-01 1.60637349e-01 -1.08669568e-02 -1.18400574... | [13.963112831115723, 5.807038307189941] |
0073c8e9-23a3-41a6-a982-9126bbbbd449 | learnable-dependency-based-double-graph | null | null | https://aclanthology.org/2022.coling-1.618 | https://aclanthology.org/2022.coling-1.618.pdf | Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis | Dependency tree-based methods might be susceptible to the dependency tree due to that they inevitably introduce noisy information and neglect the rich relation information between words. In this paper, we propose a learnable dependency-based double graph (LD2G) model for aspect-based sentiment classification. We use mu... | ['Yunhe Pang', 'Yinglong Ma'] | null | null | null | null | coling-2022-10 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-1.45899266e-01 -3.18197086e-02 -4.00350899e-01 -7.56080449e-01
-5.97192645e-01 -4.82878387e-01 4.55878705e-01 2.90581465e-01
-3.83341342e-01 6.32687867e-01 6.61509633e-01 -3.20085526e-01
1.48049727e-01 -8.22095156e-01 -4.35119569e-01 -5.67533493e-01
1.97853819e-01 4.26807493e-01 6.19144440e-02 -7.76697099... | [11.460062980651855, 6.6919732093811035] |
00a35025-d670-4d41-9484-83662fede64d | validating-large-language-models-with-relm | 2211.15458 | null | https://arxiv.org/abs/2211.15458v2 | https://arxiv.org/pdf/2211.15458v2.pdf | Validating Large Language Models with ReLM | Although large language models (LLMs) have been touted for their ability to generate natural-sounding text, there are growing concerns around possible negative effects of LLMs such as data memorization, bias, and inappropriate language. Unfortunately, the complexity and generation capacities of LLMs make validating (an... | ['George Amvrosiadis', 'Virginia Smith', 'Michael Kuchnik'] | 2022-11-21 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 8.94109011e-02 2.12398201e-01 -6.74996078e-01 -5.37398756e-01
-1.26772153e+00 -6.97677433e-01 7.19589174e-01 9.79927301e-01
-6.35854125e-01 9.51496303e-01 2.30391651e-01 -7.14908302e-01
-7.15096891e-02 -6.74112737e-01 -8.51100981e-01 3.25130910e-01
1.31820053e-01 7.57411778e-01 3.28437425e-02 -1.81686103... | [9.663110733032227, 7.930755615234375] |
94927c23-940c-49f8-a091-bd8bb1e717d5 | recod-titans-at-isic-challenge-2017 | 1703.04819 | null | http://arxiv.org/abs/1703.04819v1 | http://arxiv.org/pdf/1703.04819v1.pdf | RECOD Titans at ISIC Challenge 2017 | This extended abstract describes the participation of RECOD Titans in parts 1
and 3 of the ISIC Challenge 2017 "Skin Lesion Analysis Towards Melanoma
Detection" (ISBI 2017). Although our team has a long experience with melanoma
classification, the ISIC Challenge 2017 was the very first time we worked on
skin-lesion seg... | ['Eduardo Valle', 'Afonso Menegola', 'Sandra Avila', 'Lin Tzy Li', 'Julia Tavares', 'Michel Fornaciali'] | 2017-03-14 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.45625746e-01 2.22118497e-01 -1.41386271e-01 2.47658808e-02
-1.13149405e+00 -4.88826156e-01 6.29159331e-01 3.46748024e-01
-7.72318900e-01 6.82755530e-01 4.78092656e-02 -5.37208498e-01
-6.30143192e-03 -6.33220375e-01 -5.23396075e-01 -6.50567651e-01
4.00218330e-02 1.55649126e-01 5.85023701e-01 -2.64442354... | [15.679496765136719, -2.969637155532837] |
1703ab0c-2caa-4cae-9720-7652bc4eabfd | traffic-prediction-using-artificial | 2305.19591 | null | https://arxiv.org/abs/2305.19591v2 | https://arxiv.org/pdf/2305.19591v2.pdf | Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities | Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for... | ['Mark Nejad', 'Xiaolong Zhao', 'Wanxin Li', 'Collin Meese', 'Maryam Shaygan'] | 2023-05-31 | null | null | null | null | ['traffic-prediction'] | ['time-series'] | [ 2.73108453e-01 -4.62595731e-01 -9.14567411e-01 -4.37101066e-01
-2.14894027e-01 1.46865308e-01 2.86281109e-01 -3.34824800e-01
-1.47762239e-01 9.50901508e-01 -1.07396178e-01 -6.91224754e-01
-6.14908934e-01 -1.15172458e+00 -1.65173158e-01 -6.47785664e-01
-1.66924268e-01 4.37097907e-01 2.00970739e-01 -4.55594242... | [6.324829578399658, 1.8605490922927856] |
11a3f3c0-0dc0-43cc-8c86-c92be901aa02 | enhancing-out-of-distribution-detection-in | 2210.11034 | null | https://arxiv.org/abs/2210.11034v1 | https://arxiv.org/pdf/2210.11034v1.pdf | Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble | Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. Most recent studies in OOD detection utilize the information from a single representation that resides in the penultimate layer to determine whet... | ['Sang-goo Lee', 'Taeuk Kim', 'Kang Min Yoo', 'Jaewook Kang', 'Choonghyun Park', 'Hyunsoo Cho'] | 2022-10-20 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [ 1.26412183e-01 -1.70598119e-01 -3.41399848e-01 -6.32930994e-01
-6.58620179e-01 -1.79038450e-01 5.40893376e-01 7.25364804e-01
4.49544657e-03 3.27753186e-01 3.82056355e-01 -3.88919115e-01
1.78563893e-01 -7.02882767e-01 -4.89327878e-01 -4.17389363e-01
-2.09004492e-01 5.70966192e-02 1.63775906e-01 -4.05423604... | [8.968716621398926, 3.1043660640716553] |
f0fc00b5-223c-4053-87d3-0fbfef6ed671 | arch-animatable-reconstruction-of-clothed | 2004.04572 | null | https://arxiv.org/abs/2004.04572v2 | https://arxiv.org/pdf/2004.04572v2.pdf | ARCH: Animatable Reconstruction of Clothed Humans | In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. Existing approaches to digitize 3D humans struggle to handle pose variations and recover details. Also, they do not produce ... | ['Zeng Huang', 'Christoph Lassner', 'Yuanlu Xu', 'Tony Tung', 'Hao Li'] | 2020-04-08 | arch-animatable-reconstruction-of-clothed-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Huang_ARCH_Animatable_Reconstruction_of_Clothed_Humans_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_ARCH_Animatable_Reconstruction_of_Clothed_Humans_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-object-reconstruction-from-a-single-image'] | ['computer-vision'] | [ 1.64892361e-01 3.81429136e-01 3.47793102e-01 -2.80904055e-01
-6.26078546e-01 -4.82276142e-01 3.72683525e-01 -7.06113696e-01
-1.02585785e-01 5.65912724e-01 3.54171991e-01 4.07342046e-01
5.22153020e-01 -6.21597290e-01 -1.12700713e+00 -4.12647575e-01
1.51007384e-01 9.29058909e-01 1.41976163e-01 -3.26885283... | [7.196277618408203, -1.2553932666778564] |
a72fe7a0-426e-4829-bd2f-c207403cd898 | hallucinating-statistical-moment-and-subspace | 2001.04627 | null | https://arxiv.org/abs/2001.04627v2 | https://arxiv.org/pdf/2001.04627v2.pdf | Self-supervising Action Recognition by Statistical Moment and Subspace Descriptors | In this paper, we build on a concept of self-supervision by taking RGB frames as input to learn to predict both action concepts and auxiliary descriptors e.g., object descriptors. So-called hallucination streams are trained to predict auxiliary cues, simultaneously fed into classification layers, and then hallucinated ... | ['Piotr Koniusz', 'Lei Wang'] | 2020-01-14 | null | null | null | null | ['scene-recognition', 'egocentric-activity-recognition'] | ['computer-vision', 'computer-vision'] | [-1.85884051e-02 -7.69224539e-02 -2.78411359e-01 -3.44105244e-01
-2.74037749e-01 -3.67833644e-01 6.20397270e-01 9.96198729e-02
-3.24768871e-01 5.42726457e-01 3.86531502e-01 4.64049578e-01
-4.64527346e-02 -4.86586273e-01 -8.00518453e-01 -6.83915854e-01
-4.98300433e-01 -3.86699638e-03 4.90397036e-01 1.28594488... | [8.900487899780273, 0.35896939039230347] |
98d6cce5-23c8-48b3-9e4c-ba17528d43b3 | attention-based-multi-patch-aggregation-for | null | null | https://www.researchgate.net/publication/328371233_Attention-based_Multi-Patch_Aggregation_for_Image_Aesthetic_Assessment | https://www.researchgate.net/publication/328371233_Attention-based_Multi-Patch_Aggregation_for_Image_Aesthetic_Assessment | Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment | Aggregation structures with explicit information, such as image attributes and scene semantics, are effective and popular for intelligent systems for assessing aesthetics of visual data. However, useful information may not be available due to the high cost of manual annotation and expert design. In this paper, we prese... | ['Wei-Ming Dong', 'Bao-Gang Hu', 'Kekai Sheng', 'Chongyang Ma', 'Xing Mei', 'Feiyue Huang'] | 2018-10-22 | null | null | null | acm-multimedia-conference-2018-10 | ['aesthetics-quality-assessment'] | ['computer-vision'] | [ 2.47160196e-01 5.69009185e-02 1.50252268e-01 -5.24897635e-01
-6.23245776e-01 -3.33683789e-01 2.12297261e-01 4.70676005e-01
-4.18001622e-01 1.97943375e-01 8.63074213e-02 -2.13543504e-01
-1.68472096e-01 -6.39372468e-01 -4.90161508e-01 -6.19758546e-01
2.08740130e-01 2.07319006e-01 -8.52773115e-02 -6.30055889... | [11.474687576293945, -1.036113977432251] |
440641fb-f9fe-4b2a-9a75-095989db5a28 | node-representation-learning-for-directed | 1810.09176 | null | https://arxiv.org/abs/1810.09176v4 | https://arxiv.org/pdf/1810.09176v4.pdf | Node Representation Learning for Directed Graphs | We propose a novel approach for learning node representations in directed graphs, which maintains separate views or embedding spaces for the two distinct node roles induced by the directionality of the edges. We argue that the previous approaches either fail to encode the edge directionality or their encodings cannot b... | ['Megha Khosla', 'Jurek Leonhardt', 'Avishek Anand', 'Wolfgang Nejdl'] | 2018-10-22 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 2.53896952e-01 6.75411284e-01 -6.81521058e-01 -3.98462564e-01
-1.91069141e-01 -8.58155966e-01 1.03087807e+00 5.70474148e-01
9.22693089e-02 5.74462473e-01 5.83332419e-01 -5.98189771e-01
-5.16073763e-01 -1.17062521e+00 -4.69173372e-01 -4.86309320e-01
-6.48780704e-01 5.51092267e-01 2.99281150e-01 -2.77359426... | [7.082883358001709, 6.2672600746154785] |
2d586b22-52e9-4610-bf34-2613a8a8335a | blind-speech-separation-and-dereverberation | 2103.13443 | null | https://arxiv.org/abs/2103.13443v2 | https://arxiv.org/pdf/2103.13443v2.pdf | Blind Speech Separation and Dereverberation using Neural Beamforming | In this paper, we present the Blind Speech Separation and Dereverberation (BSSD) network, which performs simultaneous speaker separation, dereverberation and speaker identification in a single neural network. Speaker separation is guided by a set of predefined spatial cues. Dereverberation is performed by using neural ... | ['Franz Pernkopf', 'Lukas Pfeifenberger'] | 2021-03-24 | null | null | null | null | ['speaker-separation', 'speaker-identification'] | ['speech', 'speech'] | [ 3.32740277e-01 -2.21410424e-01 3.30157220e-01 -2.24751443e-01
-9.02536869e-01 -7.80924737e-01 4.68745768e-01 -3.79236728e-01
-2.24037126e-01 5.13921082e-01 7.35753357e-01 -4.41340685e-01
-2.28581682e-01 -6.62547201e-02 -3.15542668e-01 -9.93667245e-01
-3.04907918e-01 2.69203540e-02 -3.69838297e-01 1.25417247... | [14.90245532989502, 5.903084754943848] |
55c863f5-b08a-4a8b-a8eb-abf2afa2e01f | attention-on-abstract-visual-reasoning | 1911.05990 | null | https://arxiv.org/abs/1911.05990v1 | https://arxiv.org/pdf/1911.05990v1.pdf | Attention on Abstract Visual Reasoning | Attention mechanisms have been boosting the performance of deep learning models on a wide range of applications, ranging from speech understanding to program induction. However, despite experiments from psychology which suggest that attention plays an essential role in visual reasoning, the full potential of attention ... | ['Florentin Wörgötter', 'Timo Lüddecke', 'Lukas Hahne', 'David Kappel'] | 2019-11-14 | null | https://openreview.net/forum?id=Bkel1krKPS | https://openreview.net/pdf?id=Bkel1krKPS | null | ['program-induction'] | ['computer-code'] | [ 1.25668555e-01 5.98129034e-01 1.93483949e-01 -7.72200748e-02
-1.54812217e-01 -2.59427041e-01 9.89507616e-01 3.46821606e-01
-4.24039751e-01 4.92074549e-01 -3.71859106e-03 -6.40999496e-01
-4.96818244e-01 -9.83893514e-01 -1.00762081e+00 -3.01859051e-01
-5.71332574e-02 7.35817373e-01 2.96498269e-01 -3.71749520... | [10.621246337890625, 2.2033448219299316] |
9d94ebfb-2c2a-4eb0-bc8d-e96c3a27b9ef | quantile-extreme-gradient-boosting-for | 2304.11732 | null | https://arxiv.org/abs/2304.11732v1 | https://arxiv.org/pdf/2304.11732v1.pdf | Quantile Extreme Gradient Boosting for Uncertainty Quantification | As the availability, size and complexity of data have increased in recent years, machine learning (ML) techniques have become popular for modeling. Predictions resulting from applying ML models are often used for inference, decision-making, and downstream applications. A crucial yet often overlooked aspect of ML is unc... | ['Meredith Franklin', 'Yao-Yi Chiang', 'Scott Fruin', 'Rob McConnell', 'Masoud Fallah-Shorshani', 'Xiaozhe Yin'] | 2023-04-23 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-2.49284610e-01 -3.14204842e-01 -3.34718734e-01 -6.52112663e-01
-1.16141737e+00 -3.25040847e-01 5.49064994e-01 5.67956984e-01
-2.31925488e-01 1.29155540e+00 1.03672624e-01 -6.59231722e-01
-4.77426976e-01 -1.17827940e+00 -7.77074575e-01 -5.78797519e-01
5.54243848e-02 2.96177983e-01 2.37257823e-01 1.64004564... | [7.464521884918213, 3.953903913497925] |
5b92ae43-7c38-4b48-bfef-a9e15b118f37 | less-data-more-knowledge-building-next | 2211.14343 | null | https://arxiv.org/abs/2211.14343v1 | https://arxiv.org/pdf/2211.14343v1.pdf | Less Data, More Knowledge: Building Next Generation Semantic Communication Networks | Semantic communication is viewed as a revolutionary paradigm that can potentially transform how we design and operate wireless communication systems. However, despite a recent surge of research activities in this area, the research landscape remains limited. In this tutorial, we present the first rigorous vision of a s... | ['H. Vincent Poor', 'Zhu Han', 'Merouane Debbah', 'Walid Saad', 'Christina Chaccour'] | 2022-11-25 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 3.62109452e-01 8.51075888e-01 -4.20862645e-01 -4.72860634e-01
1.81078732e-01 -4.38523859e-01 6.66471779e-01 -1.84388444e-01
9.13296416e-02 7.86427021e-01 5.34969747e-01 -6.83025777e-01
-8.81610811e-01 -1.40473354e+00 -4.44001377e-01 -3.00973892e-01
-5.82092643e-01 2.40078628e-01 9.38919187e-02 -5.22237539... | [7.084936618804932, 6.062554836273193] |
638e4c00-02be-4b84-b83b-5b2ad3fc5481 | cross-lingual-induction-and-transfer-of-verb | 1707.06945 | null | http://arxiv.org/abs/1707.06945v1 | http://arxiv.org/pdf/1707.06945v1.pdf | Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation | Existing approaches to automatic VerbNet-style verb classification are
heavily dependent on feature engineering and therefore limited to languages
with mature NLP pipelines. In this work, we propose a novel cross-lingual
transfer method for inducing VerbNets for multiple languages. To the best of
our knowledge, this is... | ['Anna Korhonen', 'Nikola Mrkšić', 'Ivan Vulić'] | 2017-07-21 | cross-lingual-induction-and-transfer-of-verb-1 | https://aclanthology.org/D17-1270 | https://aclanthology.org/D17-1270.pdf | emnlp-2017-9 | ['learning-word-embeddings'] | ['methodology'] | [ 1.42404893e-02 6.14973949e-03 -7.14810014e-01 -6.04599953e-01
-8.07282746e-01 -7.38870084e-01 6.75903082e-01 2.82430351e-01
-5.23731172e-01 5.68877995e-01 3.58042181e-01 -5.16722262e-01
4.93382290e-02 -6.13865077e-01 -5.61964214e-01 -1.85862467e-01
4.06022631e-02 8.06474745e-01 -3.12873460e-02 -4.76102591... | [10.826204299926758, 9.85676383972168] |
156c3208-8ecb-47ad-9320-dc506567e188 | coconut-combining-context-aware-neural | null | null | https://dl.acm.org/doi/10.1145/3395363.3397369 | https://dl.acm.org/doi/pdf/10.1145/3395363.3397369 | CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair | Automated generate-and-validate (GV) program repair techniques (APR) typically rely on hard-coded rules, thus only fixing bugs following specific fix patterns. These rules require a significant amount of manual effort to discover and it is hard to adapt these rules to different programming languages.
To address thes... | ['Lin Tan', 'Moshi Wei', 'Yitong Li', 'Lawrence Pang', 'Hung Viet Pham', 'Thibaud Lutellier'] | 2020-07-18 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-3.22451651e-01 -2.50422806e-01 -3.10345143e-01 -2.58787628e-02
-7.66366720e-01 -8.55434060e-01 -1.45088345e-01 2.17111826e-01
3.30178350e-01 4.45648491e-01 -1.71007647e-03 -8.41040790e-01
2.70451128e-01 -9.64732647e-01 -1.18012822e+00 -7.83800036e-02
-1.08522080e-01 -2.91025430e-01 3.05600882e-01 -4.46570158... | [7.582549571990967, 7.72760009765625] |
31d00f88-a2c8-4e6c-9c56-2bc9231cebe4 | unsupervised-visible-infrared-person-reid-by | 2305.12711 | null | https://arxiv.org/abs/2305.12711v2 | https://arxiv.org/pdf/2305.12711v2.pdf | Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement | Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims at learning modality-invariant features from unlabeled cross-modality dataset, which is crucial for practical applications in video surveillance systems. The key to essentially address the USL-VI-ReID task is to solve the cross-modality ... | ['Xinbo Gao', 'Zhihui Li', 'Lingfeng He', 'Nannan Wang', 'Xiaojian Huang', 'De Cheng'] | 2023-05-22 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 2.20022351e-01 -2.06404924e-01 -2.72777498e-01 -4.57612485e-01
-9.74652648e-01 -3.38074327e-01 6.83074236e-01 -6.23004362e-02
-4.66018200e-01 8.50716770e-01 2.39756584e-01 1.70278177e-01
-3.81622016e-01 -2.70932019e-01 -7.09620595e-01 -1.09449434e+00
2.27299958e-01 2.76289135e-01 -1.00549962e-02 2.16057435... | [14.755681037902832, 0.9824119210243225] |
48772d47-05b7-4c57-b4bf-afb362a6c30d | thompson-sampling-for-parameterized-markov | 2305.07844 | null | https://arxiv.org/abs/2305.07844v1 | https://arxiv.org/pdf/2305.07844v1.pdf | Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions | We study parameterized MDPs (PMDPs) in which the key parameters of interest are unknown and must be learned using Bayesian inference. One key defining feature of such models is the presence of "uninformative" actions that provide no information about the unknown parameters. We contribute a set of assumptions for PMDPs ... | ['Michael Jong Kim', 'Michael Gimelfarb'] | 2023-05-13 | null | null | null | null | ['bayesian-inference', 'thompson-sampling'] | ['methodology', 'methodology'] | [-1.63237363e-01 2.90393293e-01 -4.07069981e-01 -5.30920684e-01
-8.29524338e-01 -6.84933364e-01 5.67504801e-02 1.62839204e-01
-5.67602038e-01 1.28842092e+00 -1.24088787e-01 -4.25768375e-01
-7.07836211e-01 -7.71628678e-01 -8.64601374e-01 -9.17313755e-01
-4.64835376e-01 1.06505382e+00 2.31047466e-01 -1.38993785... | [4.3964524269104, 2.9717748165130615] |
9602eb51-8f52-430a-94fb-7734b53e9982 | align2ground-weakly-supervised-phrase | 1903.11649 | null | https://arxiv.org/abs/1903.11649v2 | https://arxiv.org/pdf/1903.11649v2.pdf | Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment | We address the problem of grounding free-form textual phrases by using weak supervision from image-caption pairs. We propose a novel end-to-end model that uses caption-to-image retrieval as a `downstream' task to guide the process of phrase localization. Our method, as a first step, infers the latent correspondences be... | ['Devi Parikh', 'Anirban Roy', 'Ajay Divakaran', 'Samyak Datta', 'Karan Sikka', 'Karuna Ahuja'] | 2019-03-27 | align2ground-weakly-supervised-phrase-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Datta_Align2Ground_Weakly_Supervised_Phrase_Grounding_Guided_by_Image-Caption_Alignment_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Datta_Align2Ground_Weakly_Supervised_Phrase_Grounding_Guided_by_Image-Caption_Alignment_ICCV_2019_paper.pdf | iccv-2019-10 | ['phrase-grounding'] | ['natural-language-processing'] | [ 4.90746200e-01 1.52043626e-01 -3.52222353e-01 -3.96524727e-01
-1.73944318e+00 -8.24156761e-01 6.99572921e-01 1.84464604e-01
-5.17638922e-01 4.65908200e-01 5.26221871e-01 -8.05931166e-02
3.59325111e-01 -3.08681399e-01 -1.30219245e+00 -6.89860702e-01
8.60987753e-02 4.39087093e-01 2.20327467e-01 1.59973782... | [10.545164108276367, 1.4259943962097168] |
c051037e-a78f-4c7c-a6a1-88ae5bc1678c | learning-target-specific-representations-of | null | null | https://aclanthology.org/C18-1239 | https://aclanthology.org/C18-1239.pdf | Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction | Texts from the Internet serve as important data sources for financial market modeling. Early statistical approaches rely on manually defined features to capture lexical, sentiment and event information, which suffers from feature sparsity. Recent work has considered learning dense representations for news titles and ab... | ['Ching-Yun Chang', 'Yue Zhang', 'Ting Liu', 'Junwen Duan', 'Xiao Ding'] | 2018-08-01 | learning-target-specific-representations-of-1 | https://aclanthology.org/C18-1239 | https://aclanthology.org/C18-1239.pdf | coling-2018-8 | ['stock-market-prediction'] | ['time-series'] | [-8.02280232e-02 -4.70502116e-02 -6.48025692e-01 -4.33340609e-01
-1.13577545e+00 -3.49813312e-01 9.50779438e-01 6.17984533e-01
-3.97827119e-01 7.79144764e-01 1.17649913e+00 4.81196400e-03
1.94018513e-01 -1.06051505e+00 -6.79187894e-01 -2.31432810e-01
4.54526022e-02 3.07216823e-01 1.14416173e-02 -2.55043447... | [4.414950370788574, 4.292797565460205] |
20d3528e-d9be-4a57-85f9-eb1e11f3d541 | stock-market-prediction-with-deep-learning-a | null | null | https://aclanthology.org/U17-1001 | https://aclanthology.org/U17-1001.pdf | Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading | null | ['Mark Dras', 'Leonardo dos Santos Pinheiro'] | 2017-12-01 | null | null | null | alta-2017-12 | ['stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-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... | [-7.308250904083252, 3.711069107055664] |
7af1b41e-f933-4d09-9ff5-135c4c8861f1 | multi-scale-control-signal-aware-transformer | 2303.01685 | null | https://arxiv.org/abs/2303.01685v1 | https://arxiv.org/pdf/2303.01685v1.pdf | Multi-Scale Control Signal-Aware Transformer for Motion Synthesis without Phase | Synthesizing controllable motion for a character using deep learning has been a promising approach due to its potential to learn a compact model without laborious feature engineering. To produce dynamic motion from weak control signals such as desired paths, existing methods often require auxiliary information such as ... | ['Zhiyong Wang', 'Wanli Ouyang', 'Yu Ding', 'Lei Bai', 'Kun Hu', 'Lintao Wang'] | 2023-03-03 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 4.18247193e-01 -2.58647953e-03 -2.70433635e-01 -1.28949508e-01
-5.33344150e-01 -3.64132524e-01 5.85819423e-01 -2.38607749e-01
-1.78392604e-01 7.22219467e-01 3.74172896e-01 8.27762783e-02
-8.06607231e-02 -8.63903940e-01 -8.36993158e-01 -9.54577386e-01
4.42406833e-02 2.53907382e-01 3.14484537e-01 -6.42590821... | [7.444820404052734, -0.11973284929990768] |
09cd4c0e-9dc9-4d3f-9a44-8d8f83617447 | positive-pair-distillation-considered-harmful | 2210.01600 | null | https://arxiv.org/abs/2210.01600v1 | https://arxiv.org/pdf/2210.01600v1.pdf | Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification | Lifelong object re-identification incrementally learns from a stream of re-identification tasks. The objective is to learn a representation that can be applied to all tasks and that generalizes to previously unseen re-identification tasks. The main challenge is that at inference time the representation must generalize ... | ['Joost Van de Weijer', 'Shangling Jui', 'Shiqi Yang', 'Xialei Liu', 'Andy Bagdanov', 'Chenshen Wu', 'Kai Wang'] | 2022-10-04 | null | null | null | null | ['vehicle-re-identification'] | ['computer-vision'] | [ 5.03172874e-02 -2.92620391e-01 -2.18520105e-01 -5.97798705e-01
-6.21610820e-01 -6.78633332e-01 6.73850656e-01 -1.58932880e-01
-5.64941227e-01 7.92218983e-01 1.81897711e-02 3.89467017e-03
-2.13922858e-01 -3.70644271e-01 -7.71435797e-01 -4.28987086e-01
4.72083427e-02 7.49102354e-01 -1.62542567e-01 4.45932969... | [14.746406555175781, 1.079283595085144] |
22d6e2e9-9198-4644-a2c7-70a06212aec3 | improving-point-cloud-based-place-recognition | 2203.00972 | null | https://arxiv.org/abs/2203.00972v2 | https://arxiv.org/pdf/2203.00972v2.pdf | Improving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training | The paper presents a simple and effective learning-based method for computing a discriminative 3D point cloud descriptor for place recognition purposes. Recent state-of-the-art methods have relatively complex architectures such as multi-scale oyramid of point Transformers combined with a pyramid of feature aggregation ... | ['Jacek Komorowski'] | 2022-03-02 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-1.32915214e-01 -4.32454556e-01 8.07775110e-02 -3.82144362e-01
-1.04898238e+00 -3.48051578e-01 8.14361572e-01 5.04293501e-01
-6.39079571e-01 1.61877498e-01 -2.25255817e-01 -4.92622033e-02
-3.05059463e-01 -7.31899321e-01 -1.13819659e+00 -4.77284461e-01
-5.23759782e-01 3.64855260e-01 4.64266837e-01 -2.35016540... | [7.850462913513184, -3.464456558227539] |
6f62cdf0-64ec-498d-9ec3-834a9d27fd8c | cnn-feature-map-augmentation-for-single | 2305.16746 | null | https://arxiv.org/abs/2305.16746v2 | https://arxiv.org/pdf/2305.16746v2.pdf | CNN Feature Map Augmentation for Single-Source Domain Generalization | In search of robust and generalizable machine learning models, Domain Generalization (DG) has gained significant traction during the past few years. The goal in DG is to produce models which continue to perform well when presented with data distributions different from the ones available during training. While deep con... | ['Christos Diou', 'Aristotelis Ballas'] | 2023-05-26 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 3.95727038e-01 4.57649231e-02 -1.12422496e-01 -4.49090362e-01
-4.57618564e-01 -6.29215419e-01 6.78026617e-01 8.11233670e-02
-3.62874329e-01 8.26082408e-01 -2.20131963e-01 -2.92616010e-01
-1.96802318e-01 -6.59521759e-01 -8.60547066e-01 -6.84382439e-01
1.49843872e-01 3.89035910e-01 3.86792362e-01 -2.65809745... | [9.864933967590332, 2.9156692028045654] |
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