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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a746c19a-f824-4818-81a7-9ce0a4395e93 | how-to-transfer-algorithmic-reasoning | 2110.14056 | null | https://arxiv.org/abs/2110.14056v1 | https://arxiv.org/pdf/2110.14056v1.pdf | How to transfer algorithmic reasoning knowledge to learn new algorithms? | Learning to execute algorithms is a fundamental problem that has been widely studied. Prior work~\cite{veli19neural} has shown that to enable systematic generalisation on graph algorithms it is critical to have access to the intermediate steps of the program/algorithm. In many reasoning tasks, where algorithmic-style r... | ['Jian Tang', 'Petar Velickovic', 'Andreea Deac', 'Louis-Pascal A. C. Xhonneux'] | 2021-10-26 | null | http://proceedings.neurips.cc/paper/2021/hash/a2802cade04644083dcde1c8c483ed9a-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a2802cade04644083dcde1c8c483ed9a-Paper.pdf | neurips-2021-12 | ['learning-to-execute'] | ['computer-code'] | [ 3.62345427e-01 2.92900234e-01 3.10824439e-02 -3.44972491e-01
-1.86781362e-01 -8.17283273e-01 6.14242613e-01 6.30323052e-01
-6.08828247e-01 5.53888798e-01 -1.15392067e-01 -1.07848513e+00
-4.00244772e-01 -1.00724363e+00 -7.71373570e-01 -1.95667431e-01
-2.25762978e-01 7.32490003e-01 4.50534582e-01 -3.55875462... | [8.844597816467285, 7.191004276275635] |
edfd33f4-2dc6-4a8a-8ab5-70b938e71054 | natural-language-assisted-sign-language | 2303.12080 | null | https://arxiv.org/abs/2303.12080v1 | https://arxiv.org/pdf/2303.12080v1.pdf | Natural Language-Assisted Sign Language Recognition | Sign languages are visual languages which convey information by signers' handshape, facial expression, body movement, and so forth. Due to the inherent restriction of combinations of these visual ingredients, there exist a significant number of visually indistinguishable signs (VISigns) in sign languages, which limits ... | ['Brian Mak', 'Fangyun Wei', 'Ronglai Zuo'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zuo_Natural_Language-Assisted_Sign_Language_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zuo_Natural_Language-Assisted_Sign_Language_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sign-language-recognition'] | ['computer-vision'] | [ 2.36495018e-01 -4.68021899e-01 -4.74763483e-01 -4.45184529e-01
-4.36463237e-01 -5.47847092e-01 6.81954801e-01 -9.07098711e-01
-3.80851179e-01 3.92188221e-01 5.28741121e-01 7.01316074e-02
-5.65836690e-02 -2.40774289e-01 -4.94011492e-01 -9.49485719e-01
2.97437578e-01 -2.32632041e-01 2.09143400e-01 -1.82443559... | [9.203834533691406, -6.50338077545166] |
6b0f6b51-f84f-4349-a878-c53278af26d5 | for-sale-state-action-representation-learning | 2306.02451 | null | https://arxiv.org/abs/2306.02451v1 | https://arxiv.org/pdf/2306.02451v1.pdf | For SALE: State-Action Representation Learning for Deep Reinforcement Learning | In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked for environments with low-level states, such as physical control problems. This paper introduces SALE, a novel approach for learning embeddings that model the nuanced interaction ... | ['David Meger', 'Doina Precup', 'Shixiang Shane Gu', 'Edward J. Smith', 'Wei-Di Chang', 'Scott Fujimoto'] | 2023-06-04 | null | null | null | null | ['openai-gym', 'continuous-control'] | ['playing-games', 'playing-games'] | [-2.77313776e-03 -1.09155178e-01 -5.92413068e-01 1.62608445e-01
-5.31884432e-01 -4.87072617e-01 6.95091724e-01 3.61297935e-01
-7.15702653e-01 5.53792596e-01 2.24776804e-01 -3.56850266e-01
-2.66106009e-01 -6.05091870e-01 -7.67812967e-01 -8.17962468e-01
-6.62046313e-01 5.30469902e-02 1.03709966e-01 -2.94607520... | [4.277558326721191, 1.614282488822937] |
da33d513-8bdb-492f-a97d-a3f24dc93c15 | progressive-identification-of-true-labels-for | 2002.08053 | null | https://arxiv.org/abs/2002.08053v3 | https://arxiv.org/pdf/2002.08053v3.pdf | Progressive Identification of True Labels for Partial-Label Learning | Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manner... | ['Xin Geng', 'Lei Feng', 'Jiaqi Lv', 'Masashi Sugiyama', 'Miao Xu', 'Gang Niu'] | 2020-02-19 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6080-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6080-Paper.pdf | icml-2020-1 | ['partial-label-learning'] | ['methodology'] | [ 3.42510581e-01 2.58945853e-01 -6.14981055e-01 -6.04937255e-01
-1.09606147e+00 -4.79185432e-01 2.95217752e-01 2.24087507e-01
-5.16348183e-01 7.98770428e-01 -4.06025112e-01 -6.99578449e-02
-4.27228093e-01 -4.36011463e-01 -6.07775271e-01 -1.01880348e+00
2.95386136e-01 8.40663373e-01 -1.05121635e-01 4.11592484... | [9.20728588104248, 4.119364261627197] |
556b8d09-954c-4866-99ca-0c1317c9776a | red-attack-resource-efficient-decision-based | 1901.10258 | null | http://arxiv.org/abs/1901.10258v2 | http://arxiv.org/pdf/1901.10258v2.pdf | RED-Attack: Resource Efficient Decision based Attack for Machine Learning | Due to data dependency and model leakage properties, Deep Neural Networks
(DNNs) exhibit several security vulnerabilities. Several security attacks
exploited them but most of them require the output probability vector. These
attacks can be mitigated by concealing the output probability vector. To
address this limitatio... | ['Muhammad Shafique', 'Faiq Khalid', 'Muhammad Abdullah Hanif', 'Hassan Ali', 'Semeen Rehman', 'Rehan Ahmed'] | 2019-01-29 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 5.06622732e-01 -2.27488026e-01 2.39326254e-01 -5.85092269e-02
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3.51124443e-02 -1.75214082e-01 5.08795261e-01 9.84724909... | [5.408843994140625, 7.871450424194336] |
47fdbae6-81f4-4233-b6c8-ed26da6a5c20 | sim-to-real-reinforcement-learning-for | 1806.07851 | null | http://arxiv.org/abs/1806.07851v2 | http://arxiv.org/pdf/1806.07851v2.pdf | Sim-to-Real Reinforcement Learning for Deformable Object Manipulation | We have seen much recent progress in rigid object manipulation, but
interaction with deformable objects has notably lagged behind. Due to the large
configuration space of deformable objects, solutions using traditional
modelling approaches require significant engineering work. Perhaps then,
bypassing the need for expli... | ['Stephen James', 'Jan Matas', 'Andrew J. Davison'] | 2018-06-20 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 9.41209272e-02 2.37818345e-01 1.94569588e-01 -7.99014047e-02
-4.13512528e-01 -9.53771293e-01 3.23980570e-01 -2.83178955e-01
-3.64055455e-01 8.36511731e-01 -3.08938831e-01 -1.55144036e-01
-2.95980573e-01 -7.06992865e-01 -1.10523820e+00 -7.82543361e-01
-3.92729998e-01 1.06992292e+00 4.27155733e-01 -6.47532761... | [4.831493854522705, 0.48212817311286926] |
589e182f-d753-445f-bf0a-29ba629888bc | interactive-log-parsing-via-light-weight-user | 2301.12225 | null | https://arxiv.org/abs/2301.12225v2 | https://arxiv.org/pdf/2301.12225v2.pdf | Interactive Log Parsing via Light-weight User Feedback | Template mining is one of the foundational tasks to support log analysis, which supports the diagnosis and troubleshooting of large scale Web applications. This paper develops a human-in-the-loop template mining framework to support interactive log analysis, which is highly desirable in real-world diagnosis or troubles... | ['John C. S. Lui', 'Jian Tan', 'Ye Li', 'Hong Xie', 'Liming Wang'] | 2023-01-28 | null | null | null | null | ['log-parsing'] | ['computer-code'] | [ 4.53846902e-01 -1.88856706e-01 -4.62189525e-01 -2.22792700e-01
-4.92347986e-01 -6.48409426e-01 9.22990590e-02 3.64528716e-01
1.07819833e-01 4.35042351e-01 -5.15447319e-01 -1.02255118e+00
-5.96070230e-01 -9.96386111e-01 -5.93249977e-01 -2.83633649e-01
-3.17888439e-01 4.68847364e-01 7.38208115e-01 1.41429275... | [8.127935409545898, 6.622040748596191] |
3a9172ae-5915-4c6f-8065-66ea0f52b3f3 | identifying-the-context-shift-between-test | 2207.01059 | null | https://arxiv.org/abs/2207.01059v2 | https://arxiv.org/pdf/2207.01059v2.pdf | Identifying the Context Shift between Test Benchmarks and Production Data | Machine learning models are often brittle on production data despite achieving high accuracy on benchmark datasets. Benchmark datasets have traditionally served dual purposes: first, benchmarks offer a standard on which machine learning researchers can compare different methods, and second, benchmarks provide a model, ... | ['Matthew Groh'] | 2022-07-03 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 5.74126422e-01 1.75697938e-01 -2.98764855e-01 -5.26284277e-01
-9.32347953e-01 -8.14707518e-01 4.30454373e-01 3.74827199e-02
-1.66932479e-01 6.02755904e-01 2.58835822e-01 -2.68614233e-01
-4.18613218e-02 -7.03373611e-01 -9.77270484e-01 -2.76648819e-01
2.22182751e-01 3.17075878e-01 -2.67283112e-01 -1.40516758... | [9.08038330078125, 4.790229320526123] |
3fc5f687-8b6d-40dd-aeae-7b85b7f45ebb | longtonotes-ontonotes-with-longer-coreference | 2210.03650 | null | https://arxiv.org/abs/2210.03650v1 | https://arxiv.org/pdf/2210.03650v1.pdf | Longtonotes: OntoNotes with Longer Coreference Chains | Ontonotes has served as the most important benchmark for coreference resolution. However, for ease of annotation, several long documents in Ontonotes were split into smaller parts. In this work, we build a corpus of coreference-annotated documents of significantly longer length than what is currently available. We do s... | ['Mrinmaya Sachan', 'Andrew McCallum', 'Manzil Zaheer', 'Alessandro Stolfo', 'Raghuveer Thirukovalluru', 'Nicholas Monath', 'Kumar Shridhar'] | 2022-10-07 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-4.97310385e-02 2.46900499e-01 -4.04415578e-01 -4.36077118e-01
-1.44063461e+00 -1.11341250e+00 5.88627338e-01 1.71392977e-01
-6.24665916e-01 8.36947203e-01 1.05567467e+00 -8.30468256e-03
-2.04406291e-01 -5.71675226e-02 -4.57421362e-01 -3.00330997e-01
1.85774252e-01 1.24836361e+00 6.96226284e-02 -3.02243173... | [9.263245582580566, 9.575939178466797] |
6103ef0d-cd32-47a1-97f4-840598db21bf | multi-margin-based-decorrelation-learning-for | 2005.11945 | null | https://arxiv.org/abs/2005.11945v1 | https://arxiv.org/pdf/2005.11945v1.pdf | Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition | Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios. This paper presents a deep neural network approach namely Multi-Margin based Decorrelation Learning (MMDL) to extract decorrelation representations in a hyperspherical space ... | ['Nannan Wang', 'Zhifeng Li', 'Jie Li', 'Bing Cao', 'Xinbo Gao'] | 2020-05-25 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 1.61703095e-01 -8.83879140e-02 1.49679109e-01 -5.13975561e-01
-6.54380739e-01 -2.69507527e-01 6.93549931e-01 -9.56410289e-01
2.33059675e-02 5.20581484e-01 1.62657782e-01 -1.71301141e-02
-2.93038040e-01 -4.50906515e-01 -4.02521193e-01 -9.26356256e-01
1.77128926e-01 2.31505334e-01 -4.39555049e-01 -7.18487054... | [13.158742904663086, 0.48823562264442444] |
62fd8b05-d6a9-4903-a9ac-85c033ddb8e3 | detecting-twenty-thousand-classes-using-image | 2201.02605 | null | https://arxiv.org/abs/2201.02605v3 | https://arxiv.org/pdf/2201.02605v3.pdf | Detecting Twenty-thousand Classes using Image-level Supervision | Current object detectors are limited in vocabulary size due to the small scale of detection datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as their datasets are larger and easier to collect. We propose Detic, which simply trains the classifiers of a detector on image classificati... | ['Philipp Krähenbühl', 'Rohit Girdhar', 'Ishan Misra', 'Armand Joulin', 'Xingyi Zhou'] | 2022-01-07 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [-9.30524766e-02 -5.46067320e-02 -4.15565163e-01 -3.81222934e-01
-6.96485162e-01 -8.95762503e-01 6.44062519e-01 1.40101030e-01
-6.49698079e-01 3.95884603e-01 -7.75256008e-02 -1.60930738e-01
4.16340292e-01 -6.80663884e-01 -6.31044030e-01 -3.37824643e-01
5.30846976e-02 4.89189476e-01 8.83368492e-01 -3.72410305... | [9.460306167602539, 1.4285650253295898] |
5641945c-eff9-41e0-95d0-2728c1ce4679 | compressed-sensing-mri-reconstruction | 2210.14586 | null | https://arxiv.org/abs/2210.14586v2 | https://arxiv.org/pdf/2210.14586v2.pdf | Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance | Objective: This paper investigates how generative models, trained on ground-truth images, can be used \changes{as} priors for inverse problems, penalizing reconstructions far from images the generator can produce. The aim is that learned regularization will provide complex data-driven priors to inverse problems while s... | ['Neill D. F. Campbell', 'Matthias J. Ehrhardt', 'Ivor J. A. Simpson', 'Margaret Duff'] | 2022-10-26 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 3.18360537e-01 6.04198456e-01 1.26066923e-01 -5.09013891e-01
-8.10299993e-01 -2.98821837e-01 4.93567258e-01 -3.30181956e-01
-5.15093744e-01 1.00265372e+00 4.51596528e-01 3.45131606e-01
-5.76952755e-01 -4.63788867e-01 -1.02671587e+00 -1.18204105e+00
-3.31716090e-02 8.30584347e-01 1.24262668e-01 3.10529377... | [13.50645923614502, -2.35205340385437] |
d5c4eb39-72ab-4cd0-8294-8e2cc65c96b8 | first-and-second-order-bounds-for-adversarial | 2305.00832 | null | https://arxiv.org/abs/2305.00832v3 | https://arxiv.org/pdf/2305.00832v3.pdf | First- and Second-Order Bounds for Adversarial Linear Contextual Bandits | We consider the adversarial linear contextual bandit setting, which allows for the loss functions associated with each of $K$ arms to change over time without restriction. Assuming the $d$-dimensional contexts are drawn from a fixed known distribution, the worst-case expected regret over the course of $T$ rounds is kno... | ['Chen-Yu Wei', 'Gergely Neu', 'Tim van Erven', 'Jack Mayo', 'Julia Olkhovskaya'] | 2023-05-01 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-2.97804568e-02 5.11534572e-01 -4.76011306e-01 -1.72266930e-01
-1.36399841e+00 -1.01473558e+00 5.89631498e-02 2.49397740e-01
-1.08866167e+00 1.09114540e+00 -3.07885587e-01 -6.40551031e-01
-7.94425726e-01 -1.01722860e+00 -1.31093562e+00 -1.00183105e+00
-4.93021488e-01 4.36762154e-01 -1.66558683e-01 1.68986067... | [4.64522123336792, 3.408402442932129] |
f31bcfba-fee3-402a-9780-5a4fb5d30c89 | on-selecting-distance-metrics-in-n | 2306.09243 | null | https://arxiv.org/abs/2306.09243v1 | https://arxiv.org/pdf/2306.09243v1.pdf | On Selecting Distance Metrics in $n$-Dimensional Normed Vector Spaces of Cells: A Novel Criterion and Similarity Measure Towards Efficient and Accurate Omics Analysis | Single-cell omics enable the profiles of cells, which contain large numbers of biological features, to be quantified. Cluster analysis, a dimensionality reduction process, is used to reduce the dimensions of the data to make it computationally tractable. In these analyses, cells are represented as vectors in $n$-Dimens... | ['Elizabeth Engle', 'Arthur Lee', 'Okezue Bell'] | 2023-06-13 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 9.03360695e-02 -5.21567583e-01 -2.08227977e-01 3.73721425e-03
-3.76048088e-01 -1.03652000e+00 6.47252023e-01 5.53088367e-01
-4.02251095e-01 6.90481901e-01 -4.86313511e-04 -3.26124907e-01
-7.15225279e-01 -6.83698177e-01 -1.98147707e-02 -1.04003823e+00
-1.06652632e-01 3.33458483e-01 2.15384141e-02 -2.71572564... | [7.407649993896484, 4.325754642486572] |
0abdb60a-4077-479d-aab6-8a4df2490931 | from-axioms-over-graphs-to-vectors-and-back | 2303.16519 | null | https://arxiv.org/abs/2303.16519v2 | https://arxiv.org/pdf/2303.16519v2.pdf | From axioms over graphs to vectors, and back again: evaluating the properties of graph-based ontology embeddings | Several approaches have been developed that generate embeddings for Description Logic ontologies and use these embeddings in machine learning. One approach of generating ontologies embeddings is by first embedding the ontologies into a graph structure, i.e., introducing a set of nodes and edges for named entities and l... | ['Robert Hoehndorf', 'Fernando Zhapa-Camacho'] | 2023-03-29 | null | null | null | null | ['ontology-embedding'] | ['knowledge-base'] | [-1.31107479e-01 7.10901141e-01 -1.28962830e-01 -4.70086396e-01
3.49175006e-01 -6.55872226e-01 8.00325692e-01 3.56585354e-01
-1.41166270e-01 2.47111037e-01 7.79056549e-01 -6.44761980e-01
-4.69905645e-01 -1.40200615e+00 -5.97880960e-01 -1.96098853e-02
-2.19277009e-01 6.75169468e-01 2.92669386e-01 -4.83791620... | [8.902565002441406, 7.750180721282959] |
5c53564d-db49-4a92-bc6f-260cc16c6408 | semi-supervised-image-captioning-by | 2301.11174 | null | https://arxiv.org/abs/2301.11174v1 | https://arxiv.org/pdf/2301.11174v1.pdf | Semi-Supervised Image Captioning by Adversarially Propagating Labeled Data | We present a novel data-efficient semi-supervised framework to improve the generalization of image captioning models. Constructing a large-scale labeled image captioning dataset is an expensive task in terms of labor, time, and cost. In contrast to manually annotating all the training samples, separately collecting uni... | ['In So Kweon', 'Jinsoo Choi', 'Tae-Hyun Oh', 'Dong-Jin Kim'] | 2023-01-26 | null | null | null | null | ['relational-captioning', 'relational-captioning'] | ['computer-vision', 'natural-language-processing'] | [ 6.28549576e-01 2.76493073e-01 -3.18829209e-01 -5.54853737e-01
-1.62165391e+00 -9.23642993e-01 5.77724159e-01 -1.93472981e-01
-4.14721280e-01 8.52197170e-01 -3.07685807e-02 -1.12945333e-01
3.33244264e-01 -4.30595726e-01 -1.39952111e+00 -7.70895541e-01
2.99558848e-01 8.96549881e-01 -8.95115957e-02 8.34635571... | [10.767768859863281, 1.253160834312439] |
0ed4041a-1d16-4341-9765-36153c3839b6 | speech-enhancement-modeling-towards-robust | 1305.1426 | null | http://arxiv.org/abs/1305.1426v1 | http://arxiv.org/pdf/1305.1426v1.pdf | Speech Enhancement Modeling Towards Robust Speech Recognition System | Form about four decades human beings have been dreaming of an intelligent
machine which can master the natural speech. In its simplest form, this machine
should consist of two subsystems, namely automatic speech recognition (ASR) and
speech understanding (SU). The goal of ASR is to transcribe natural speech
while SU is... | ['V. M. Thakare', 'Urmila Shrawankar'] | 2013-05-07 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 4.96738315e-01 3.67432415e-01 2.99139649e-01 -6.69665456e-01
-4.34892476e-01 -4.34395850e-01 6.28806531e-01 -1.75960630e-01
-3.09188604e-01 4.75281000e-01 5.75337470e-01 -4.93247837e-01
1.72488391e-01 -5.22337854e-01 -2.50458539e-01 -5.95446527e-01
4.62159932e-01 1.27635047e-01 -5.72132207e-02 -4.85524476... | [14.38803768157959, 6.36789083480835] |
b4d36a1d-26d9-445f-945e-0bc93154ecc8 | polynet-polynomial-neural-network-for-3d | 2110.07882 | null | https://arxiv.org/abs/2110.07882v1 | https://arxiv.org/pdf/2110.07882v1.pdf | PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation | 3D shape representation and its processing have substantial effects on 3D shape recognition. The polygon mesh as a 3D shape representation has many advantages in computer graphics and geometry processing. However, there are still some challenges for the existing deep neural network (DNN)-based methods on polygon mesh r... | ['Kyoung Mu Lee', 'Yue Zhang', 'Reyhaneh Neshatavar', 'Shih-Hsuan Hung', 'Mohsen Yavartanoo'] | 2021-10-15 | null | null | null | null | ['3d-shape-retrieval', '3d-object-classification', '3d-shape-recognition', '3d-shape-representation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-2.45078444e-01 -3.17005455e-01 5.71313910e-02 -2.80846924e-01
-3.55973899e-01 -4.04839098e-01 5.41777551e-01 2.56085426e-01
-1.27568752e-01 1.36802316e-01 1.09098658e-01 -2.51462251e-01
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4d14d25a-37be-4af4-b8d4-f962a119e80f | hft-cnn-learning-hierarchical-category | null | null | https://aclanthology.org/D18-1093 | https://aclanthology.org/D18-1093.pdf | HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization | We focus on the multi-label categorization task for short texts and explore the use of a hierarchical structure (HS) of categories. In contrast to the existing work using non-hierarchical flat model, the method leverages the hierarchical relations between the pre-defined categories to tackle the data sparsity problem. ... | ['Jiyi Li', 'Kazuya Shimura', 'Fumiyo Fukumoto'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['extreme-multi-label-classification'] | ['methodology'] | [ 1.98861491e-02 1.92962497e-01 -2.72596627e-01 -5.15987813e-01
-2.44815499e-01 -3.89362633e-01 3.02427024e-01 2.56405026e-01
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-2.10550323e-01 -8.47537577e-01 -1.26074925e-01 -6.70840859e-01
3.55784059e-01 2.49744222e-01 2.71414965e-01 -2.48446286... | [9.678513526916504, 4.389142036437988] |
7c07e654-a1cb-4051-a413-bd47707798f4 | cross-lingual-morphological-tagging-for-low | 1606.04279 | null | http://arxiv.org/abs/1606.04279v1 | http://arxiv.org/pdf/1606.04279v1.pdf | Cross-Lingual Morphological Tagging for Low-Resource Languages | Morphologically rich languages often lack the annotated linguistic resources
required to develop accurate natural language processing tools. We propose
models suitable for training morphological taggers with rich tagsets for
low-resource languages without using direct supervision. Our approach extends
existing approach... | ['Jan Buys', 'Jan A. Botha'] | 2016-06-14 | cross-lingual-morphological-tagging-for-low-1 | https://aclanthology.org/P16-1184 | https://aclanthology.org/P16-1184.pdf | acl-2016-8 | ['morphological-tagging'] | ['natural-language-processing'] | [-9.97869000e-02 2.14730442e-01 -4.63446617e-01 -6.63757384e-01
-1.41252410e+00 -1.01310098e+00 6.06258571e-01 3.16318214e-01
-8.78053069e-01 6.74687266e-01 6.11033380e-01 -7.10178077e-01
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-1.18242688e-01 7.74144530e-01 3.41006458e-01 -5.26713021... | [10.446590423583984, 9.929909706115723] |
e4154fee-7011-4316-9e7b-5492a1a5052d | breast-cancer-detection-using | 2202.06109 | null | https://arxiv.org/abs/2202.06109v1 | https://arxiv.org/pdf/2202.06109v1.pdf | Breast Cancer Detection using Histopathological Images | Cancer is one of the most common and fatal diseases in the world. Breast cancer affects one in every eight women and one in every eight hundred men. Hence, our prime target should be early detection of cancer because the early detection of cancer can be helpful to cure cancer effectively. Therefore, we propose a salien... | ['Harsh Maan', 'Jitendra Maan'] | 2022-02-12 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 3.06518674e-01 6.59501255e-01 -3.68081659e-01 -7.33695775e-02
-6.18215919e-01 -1.09244540e-01 4.13686872e-01 6.18288994e-01
-6.74707830e-01 6.21535599e-01 7.82639012e-02 -5.82191348e-01
1.56464428e-01 -6.90349102e-01 -2.91825354e-01 -8.51784706e-01
1.02112414e-02 3.41183752e-01 5.38912594e-01 -1.11073375... | [15.157504081726074, -2.8251090049743652] |
5017ad37-2eab-4929-a390-1c57ad9fd08f | deepseqslam-a-trainable-cnn-rnn-for-joint | 2011.08518 | null | https://arxiv.org/abs/2011.08518v1 | https://arxiv.org/pdf/2011.08518v1.pdf | DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition | Sequence-based place recognition methods for all-weather navigation are well-known for producing state-of-the-art results under challenging day-night or summer-winter transitions. These systems, however, rely on complex handcrafted heuristics for sequential matching - which are applied on top of a pre-computed pairwise... | ['Michael Milford', 'Marvin Chancán'] | 2020-11-17 | null | null | null | null | ['sequential-image-classification', 'sequential-place-learning', 'sequential-place-recognition'] | ['computer-vision', 'robots', 'robots'] | [ 1.15291461e-01 -4.33698744e-01 -1.05748899e-01 -5.81258059e-01
-1.11260056e+00 -9.11957860e-01 7.48422623e-01 -4.14178818e-02
-1.10087025e+00 5.78023255e-01 -2.16964155e-01 -3.67698491e-01
2.46252209e-01 -5.41428387e-01 -1.09448087e+00 -6.33267820e-01
-2.96453744e-01 2.76499897e-01 4.66246605e-01 -3.35691005... | [7.614264965057373, -1.9346951246261597] |
8f7aa095-fd87-4f6c-b7f3-0c7cbbc5f66d | semantic-guided-image-augmentation-with-pre | 2302.02070 | null | https://arxiv.org/abs/2302.02070v1 | https://arxiv.org/pdf/2302.02070v1.pdf | Semantic-Guided Image Augmentation with Pre-trained Models | Image augmentation is a common mechanism to alleviate data scarcity in computer vision. Existing image augmentation methods often apply pre-defined transformations or mixup to augment the original image, but only locally vary the image. This makes them struggle to find a balance between maintaining semantic information... | ['Wanxiang Che', 'Feng Wang', 'Yunlong Feng', 'Yutai Hou', 'Xiao Xu', 'Xinghao Wang', 'Bohan Li'] | 2023-02-04 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 7.03625858e-01 3.52165788e-01 -2.90043473e-01 -2.39688754e-01
-5.36144614e-01 -4.41250861e-01 7.35522568e-01 -2.95402765e-01
-5.48523307e-01 6.82605624e-01 3.79242748e-01 -7.28371665e-02
4.90394562e-01 -6.20901883e-01 -7.76401281e-01 -8.45174611e-01
4.86058831e-01 2.42561430e-01 1.29988521e-01 -4.03784066... | [11.164286613464355, -0.5688377022743225] |
baab3bc8-7560-4217-bc28-36eb1c88a24b | segmenting-unknown-3d-objects-from-real-depth | 1809.05825 | null | http://arxiv.org/abs/1809.05825v2 | http://arxiv.org/pdf/1809.05825v2.pdf | Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data | The ability to segment unknown objects in depth images has potential to
enhance robot skills in grasping and object tracking. Recent computer vision
research has demonstrated that Mask R-CNN can be trained to segment specific
categories of objects in RGB images when massive hand-labeled datasets are
available. As gener... | ['Ken Goldberg', 'Saurabh Gupta', 'Andrew Li', 'Matthew Matl', 'Andrew Lee', 'Michael Danielczuk', 'Jeffrey Mahler'] | 2018-09-16 | null | null | null | null | ['unseen-object-instance-segmentation'] | ['computer-vision'] | [ 4.17035580e-01 3.17190289e-01 2.04532146e-01 -4.54161644e-01
-9.31603551e-01 -8.96722198e-01 3.08323205e-01 -2.04235345e-01
-4.58899021e-01 2.35162705e-01 -4.78093445e-01 -2.66752005e-01
1.73703939e-01 -8.27250957e-01 -1.49498653e+00 -4.73257720e-01
-1.95962518e-01 1.26268375e+00 4.61165965e-01 3.72279286... | [6.023920059204102, -0.9887037873268127] |
64170f2a-a359-4fe5-9f0b-3e332c0fd78b | document-summarization-with-text-segmentation | 2301.08817 | null | https://arxiv.org/abs/2301.08817v1 | https://arxiv.org/pdf/2301.08817v1.pdf | Document Summarization with Text Segmentation | In this paper, we exploit the innate document segment structure for improving the extractive summarization task. We build two text segmentation models and find the most optimal strategy to introduce their output predictions in an extractive summarization model. Experimental results on a corpus of scientific articles sh... | ['Benjamin Han', 'Lesly Miculicich'] | 2023-01-20 | null | null | null | null | ['extractive-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.91703176e-01 8.12677920e-01 -6.06508017e-01 -2.04497963e-01
-9.45587277e-01 -8.25717807e-01 5.94417155e-01 6.26689672e-01
-4.96876270e-01 8.81231606e-01 9.26696062e-01 -4.27971184e-01
-1.30725399e-01 -4.41441745e-01 -6.44131780e-01 -3.09811801e-01
4.89523858e-01 7.88122416e-01 2.60071337e-01 -2.29608908... | [12.544794082641602, 9.535594940185547] |
a6fbc3f6-7f57-4eb7-b2b3-045f7acc565b | g2pm-a-neural-grapheme-to-phoneme-conversion | 2004.03136 | null | https://arxiv.org/abs/2004.03136v5 | https://arxiv.org/pdf/2004.03136v5.pdf | g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset | Conversion of Chinese graphemes to phonemes (G2P) is an essential component in Mandarin Chinese Text-To-Speech (TTS) systems. One of the biggest challenges in Chinese G2P conversion is how to disambiguate the pronunciation of polyphones - characters having multiple pronunciations. Although many academic efforts have be... | ['Kyubyong Park', 'Seanie Lee'] | 2020-04-07 | null | null | null | null | ['polyphone-disambiguation'] | ['natural-language-processing'] | [ 1.60052344e-01 -2.05805704e-01 -3.60674225e-02 -7.84819052e-02
-1.08073461e+00 -6.72228277e-01 3.52763325e-01 -3.35059315e-01
-4.04680520e-01 8.73628438e-01 2.73230076e-01 -7.86390483e-01
7.02178717e-01 -4.96828526e-01 -5.00618517e-01 -5.08166015e-01
4.63839054e-01 3.61629128e-01 2.27318361e-01 -3.29363078... | [14.392314910888672, 6.981148719787598] |
6d33d753-1349-462b-acf6-7fb15223143e | a-self-paced-multiple-instance-learning | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zhang_A_Self-Paced_Multiple-Instance_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zhang_A_Self-Paced_Multiple-Instance_ICCV_2015_paper.pdf | A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection | As an interesting and emerging topic, co-saliency detection aims at simultaneously extracting common salient objects in a group of images. Traditional co-saliency detection approaches rely heavily on human knowledge for designing hand-crafted metrics to explore the intrinsic patterns underlying co-salient objects. Such... | ['Junwei Han', 'Chao Li', 'Qian Zhao', 'Dingwen Zhang', 'Lu Jiang', 'Deyu Meng'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['co-saliency-detection'] | ['computer-vision'] | [ 3.48822296e-01 -4.62759882e-02 -2.11657271e-01 -9.60281193e-02
-5.34224391e-01 -1.73217237e-01 6.33256972e-01 3.38325649e-01
-2.68468916e-01 6.80374920e-01 -6.43131360e-02 3.16419043e-02
-4.43141580e-01 -3.40155333e-01 -7.21245944e-01 -8.64066362e-01
1.35606313e-02 2.91185025e-02 4.14959937e-01 -1.55247405... | [9.821367263793945, -0.19872663915157318] |
270baf57-d29f-4449-99c2-9891386c9c45 | human-labeling-errors-and-their-impact-on | 2305.12106 | null | https://arxiv.org/abs/2305.12106v1 | https://arxiv.org/pdf/2305.12106v1.pdf | Human labeling errors and their impact on ConvNets for satellite image scene classification | Convolutional neural networks (ConvNets) have been successfully applied to satellite image scene classification. Human-labeled training datasets are essential for ConvNets to perform accurate classification. Errors in human-labeled training datasets are unavoidable due to the complexity of satellite images. However, th... | ['Xiaolin Zhu', 'Luoma Wan', 'Rui Sun', 'Xiaobei Chen', 'Xuehong Chen', 'Tao Wei', 'Longkang Peng'] | 2023-05-20 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 1.23720497e-01 -8.85553733e-02 3.62456203e-01 -5.62972665e-01
-2.33226046e-01 -5.24266064e-01 4.01615560e-01 5.38731478e-02
-9.81190383e-01 6.91276908e-01 -1.12504154e-01 -3.67270380e-01
-3.17509584e-02 -1.06922340e+00 -9.60286736e-01 -6.75837159e-01
-2.00727433e-01 2.31404752e-01 2.69719929e-01 -1.07598084... | [9.4462251663208, -1.1872762441635132] |
9053903d-ecb2-4357-910b-a228548dd33b | learning-social-navigation-from | 2210.03582 | null | https://arxiv.org/abs/2210.03582v2 | https://arxiv.org/pdf/2210.03582v2.pdf | Learning Social Navigation from Demonstrations with Conditional Neural Processes | Sociability is essential for modern robots to increase their acceptability in human environments. Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation. However, social aspects of navigation are diverse, changing across different types o... | ['Emre Ugur', 'Yigit Yildirim'] | 2022-10-07 | null | null | null | null | ['social-navigation'] | ['robots'] | [-2.05681637e-01 3.88394028e-01 9.92635638e-02 -4.94919479e-01
-5.98331913e-02 -2.21906334e-01 3.27522784e-01 -1.91471472e-01
-7.19136715e-01 1.03427207e+00 1.09886028e-01 -2.79204935e-01
-1.29318461e-01 -9.80245650e-01 -7.98232555e-01 -3.86478007e-01
-3.49607795e-01 2.79471606e-01 2.45496362e-01 -9.07233179... | [4.776705741882324, 0.937761127948761] |
fb8bd677-f91e-4016-9180-a4ba1867dc82 | on-the-effectiveness-of-gan-generated-cardiac | 2005.09026 | null | https://arxiv.org/abs/2005.09026v2 | https://arxiv.org/pdf/2005.09026v2.pdf | On the effectiveness of GAN generated cardiac MRIs for segmentation | In this work, we propose a Variational Autoencoder (VAE) - Generative Adversarial Networks (GAN) model that can produce highly realistic MRI together with its pixel accurate groundtruth for the application of cine-MR image cardiac segmentation. On one side of our model is a Variational Autoencoder (VAE) trained to lear... | ['Pierre-Marc Jodoin', 'Youssef Skandarani', 'Nathan Painchaud', 'Alain Lalande'] | 2020-05-18 | null | https://openreview.net/forum?id=f9Pl3Qj3_Q | https://openreview.net/pdf?id=f9Pl3Qj3_Q | midl-2019-7 | ['cardiac-segmentation'] | ['medical'] | [ 2.48469383e-01 7.47597337e-01 4.96339440e-01 -3.15206379e-01
-1.02140367e+00 -4.82731372e-01 3.63000363e-01 -4.20784533e-01
-1.56785980e-01 8.89185429e-01 1.91262141e-01 -1.21841155e-01
5.46750247e-01 -1.09896863e+00 -9.53556597e-01 -9.05039072e-01
9.94144678e-02 1.04548502e+00 3.35454680e-02 -1.12302817... | [14.149157524108887, -1.9984126091003418] |
ce5fbff6-7c7d-46f8-b3ec-e60ee6ff9d7c | hard-sample-aware-network-for-contrastive | 2212.08665 | null | https://arxiv.org/abs/2212.08665v3 | https://arxiv.org/pdf/2212.08665v3.pdf | Hard Sample Aware Network for Contrastive Deep Graph Clustering | Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that the existing hard sample mining ... | ['Cancan Chen', 'Jingcan Duan', 'Liang Li', 'Wenxuan Tu', 'Ke Liang', 'Zhen Wang', 'Xinwang Liu', 'Sihang Zhou', 'Xihong Yang', 'Yue Liu'] | 2022-12-16 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-7.40746632e-02 -4.22654161e-03 -2.39169165e-01 -3.60618442e-01
-6.15851618e-02 -1.91141680e-01 3.88317287e-01 3.70959193e-01
-2.65436083e-01 3.01761508e-01 -1.03519946e-01 1.99475706e-01
-7.46409178e-01 -1.25534701e+00 -4.43803519e-02 -1.16133809e+00
5.62832244e-02 6.88574135e-01 2.90247977e-01 -1.42956406... | [7.464078903198242, 5.9523210525512695] |
b50d7fe3-76af-4415-a1f2-d19470d34aa9 | ji-yu-self-attentionde-ju-fa-gan-zhi-yi-yu | null | null | https://aclanthology.org/2020.ccl-1.57 | https://aclanthology.org/2020.ccl-1.57.pdf | 基于Self-Attention的句法感知汉语框架语义角色标注(Syntax-Aware Chinese Frame Semantic Role Labeling Based on Self-Attention) | 框架语义角色标注(Frame Semantic Role Labeling, FSRL)是基于FrameNet标注体系的语义分析任务。语义角色标注通常对句法有很强的依赖性,目前的语义角色标注模型大多基于双向长短时记忆网络Bi-LSTM,虽然可以获取句子中的长距离依赖信息,但无法很好获取句子中的句法信息。因此,引入self-attention机制来捕获句子中每个词的句法信息。实验结果表明,该模型在CFN(Chinese FrameNet,汉语框架网)数据集上的F1达到83.77%,提升了近11%。 | ['Xiaoqi Han', 'Qinghua Chai', 'Zhiqiang Wang', 'Ru Li', 'Xiaohui Wang'] | null | null | null | null | ccl-2020-10 | ['semantic-role-labeling'] | ['natural-language-processing'] | [-2.38124922e-01 -3.40215713e-01 1.08119205e-01 6.03859685e-02
-2.45346259e-02 -7.59229064e-01 3.56923521e-01 7.16018021e-01
-4.40208763e-01 1.12891376e+00 1.20446718e+00 9.86844748e-02
4.18850314e-03 -7.94665635e-01 -6.09756172e-01 -1.02100623e+00
-3.29883546e-01 9.70905066e-01 6.72467649e-01 -1.09164071... | [-3.315978765487671, 6.9076313972473145] |
6c342397-acbf-44fd-92b0-054041cbf745 | survival-text-regression-for-time-to-event | null | null | https://aclanthology.org/2021.findings-acl.104 | https://aclanthology.org/2021.findings-acl.104.pdf | Survival text regression for time-to-event prediction in conversations | null | ['Andreas Vlachos', 'Christine de Kock'] | null | null | null | null | findings-acl-2021-8 | ['time-to-event-prediction'] | ['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... | [-7.2067484855651855, 3.815051555633545] |
de6c7c34-97a0-4b27-af69-95be76794487 | interpretable-embeddings-from-molecular | 1912.12175 | null | https://arxiv.org/abs/1912.12175v1 | https://arxiv.org/pdf/1912.12175v1.pdf | Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders | Extracting insight from the enormous quantity of data generated from molecular simulations requires the identification of a small number of collective variables whose corresponding low-dimensional free-energy landscape retains the essential features of the underlying system. Data-driven techniques provide a systematic ... | ['Yasemin Bozkurt Varolgunes', 'Tristan Bereau', 'Joseph F. Rudzinski'] | 2019-12-22 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [ 1.16790406e-01 5.44921122e-02 1.28642526e-02 -5.87388799e-02
-3.79461974e-01 -7.45039046e-01 9.32100475e-01 3.34988266e-01
-3.76260430e-01 6.94058597e-01 2.97160298e-01 -3.80556047e-01
-4.10582483e-01 -7.58542418e-01 -5.85638821e-01 -1.43417943e+00
-1.62795395e-01 7.20535219e-01 -2.10771173e-01 -2.87548393... | [5.146078586578369, 5.196671485900879] |
c5f47ac8-e824-4ce3-a477-e61e85355166 | appearance-invariance-in-convolutional | 1707.00755 | null | http://arxiv.org/abs/1707.00755v1 | http://arxiv.org/pdf/1707.00755v1.pdf | Appearance invariance in convolutional networks with neighborhood similarity | We present a neighborhood similarity layer (NSL) which induces appearance
invariance in a network when used in conjunction with convolutional layers. We
are motivated by the observation that, even though convolutional networks have
low generalization error, their generalization capability does not extend to
samples whi... | ['Mehran Javanmardi', 'Mehdi Sajjadi', 'Tolga Tasdizen', 'Nisha Ramesh'] | 2017-07-03 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 4.69834030e-01 4.07047011e-02 4.13010642e-02 -6.19747043e-01
2.33151093e-01 -5.57040751e-01 6.62123024e-01 3.06902856e-01
-7.28307962e-01 6.52586997e-01 -2.67896295e-01 -2.24548727e-02
1.81176528e-01 -8.27038884e-01 -9.46646273e-01 -8.13197374e-01
1.98967084e-01 -6.09974600e-02 4.29951489e-01 -8.44874159... | [9.475353240966797, 2.246067762374878] |
a4de35d0-7fc3-4f44-a714-ff6c13388d18 | hopeful-men-lt-edi-eacl2021-hope-speech-1 | null | null | https://aclanthology.org/2021.ltedi-1.23 | https://aclanthology.org/2021.ltedi-1.23.pdf | Hopeful Men@LT-EDI-EACL2021: Hope Speech Detection Using Indic Transliteration and Transformers | This paper aims to describe the approach we used to detect hope speech in the HopeEDI dataset. We experimented with two approaches. In the first approach, we used contextual embeddings to train classifiers using logistic regression, random forest, SVM, and LSTM based models. The second approach involved using a majorit... | ['Radhika Mamidi', 'Anshul Wadhawan', 'Nikhil E', 'Ishan Sanjeev Upadhyay'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection', 'transliteration'] | ['natural-language-processing', 'natural-language-processing'] | [-1.34078011e-01 3.73504698e-01 1.24022402e-02 -1.43738940e-01
-7.88941622e-01 -4.89997029e-01 1.08301020e+00 3.50774415e-02
-5.04745603e-01 9.16738868e-01 6.30946159e-01 -6.57809913e-01
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ca932dfb-994a-48bb-97f4-688ba71deb0b | coarse-to-fine-video-denoising-with-dual | 2205.00214 | null | https://arxiv.org/abs/2205.00214v2 | https://arxiv.org/pdf/2205.00214v2.pdf | Coarse-to-Fine Video Denoising with Dual-Stage Spatial-Channel Transformer | Video denoising aims to recover high-quality frames from the noisy video. While most existing approaches adopt convolutional neural networks~(CNNs) to separate the noise from the original visual content, however, CNNs focus on local information and ignore the interactions between long-range regions in the frame. Furthe... | ['Huadong Ma', 'Huiyuan Fu', 'Chuanming Wang', 'Mengshi Qi', 'Wulian Yun'] | 2022-04-30 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 5.95339909e-02 -6.88051879e-01 8.92261714e-02 -4.87112850e-01
-6.69696450e-01 -1.84051111e-01 1.42655179e-01 -1.39009386e-01
-4.33415592e-01 3.80406916e-01 4.99474138e-01 1.29795223e-01
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2.44275019e-01 -7.39234090e-01 6.01166368e-01 -3.06846619... | [11.215350151062012, -2.010462760925293] |
630fa2c0-e900-4e3e-8bd8-cf608ce91ccc | treec-a-method-to-generate-interpretable | 2304.08310 | null | https://arxiv.org/abs/2304.08310v1 | https://arxiv.org/pdf/2304.08310v1.pdf | TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm | Energy management systems (EMS) have classically been implemented based on rule-based control (RBC) and model predictive control (MPC) methods. Recent research are investigating reinforcement learning (RL) as a new promising approach. This paper introduces TreeC, a machine learning method that uses the metaheuristic al... | ['Thierry Coosemans', 'Maarten Messagie', 'Muhammad Andy Putratama', 'Luis Ramirez Camargo', 'Julian Ruddick'] | 2023-04-17 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 1.60875365e-01 3.10912609e-01 -6.10216334e-02 3.71515453e-02
-1.46659970e-01 -4.90539819e-01 9.42381144e-01 3.74969333e-01
-2.30463251e-01 1.22076321e+00 -2.22273812e-01 -6.04565263e-01
-4.95346546e-01 -8.93457055e-01 -4.34609056e-01 -1.11380696e+00
3.23745981e-02 6.83196664e-01 -2.33625293e-01 -4.45634544... | [5.391025543212891, 2.3565211296081543] |
2c80c8a2-13d5-4502-8a97-2bd4a34dc2e3 | gradicon-approximate-diffeomorphisms-via-1 | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tian_GradICON_Approximate_Diffeomorphisms_via_Gradient_Inverse_Consistency_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tian_GradICON_Approximate_Diffeomorphisms_via_Gradient_Inverse_Consistency_CVPR_2023_paper.pdf | GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency | We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly penalize transformation irregularities but instead promote transforma... | ['Marc Niethammer', 'Sylvain Bouix', 'Nikolaos Makris', 'Richard Jarrett Rushmore', 'Raúl San José Estépar', 'Roland Kwitt', 'François-Xavier Vialard', 'Hastings Greer', 'Lin Tian'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-registration', 'medical-image-registration'] | ['computer-vision', 'medical'] | [ 4.72379446e-01 2.96202838e-01 -1.04085423e-01 -5.68430185e-01
-1.07923329e+00 -2.80925661e-01 6.90243900e-01 2.63159364e-01
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-1.53946817e-01 6.43349349e-01 5.93061633e-02 -1.51799396... | [14.005461692810059, -2.578321695327759] |
e1a706f5-9b59-4424-a6b1-c2256f147439 | can-natural-language-processing-become | null | null | https://aclanthology.org/P15-1120 | https://aclanthology.org/P15-1120.pdf | Can Natural Language Processing Become Natural Language Coaching? | null | ['Marti A. Hearst'] | 2015-07-01 | can-natural-language-processing-become-1 | https://aclanthology.org/P15-1120 | https://aclanthology.org/P15-1120.pdf | ijcnlp-2015-7 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3713765144348145, 3.7093167304992676] |
e5d3422e-73f7-4d1d-ab0f-a6c44e4cf535 | scenerf-self-supervised-monocular-3d-scene | 2212.02501 | null | https://arxiv.org/abs/2212.02501v3 | https://arxiv.org/pdf/2212.02501v3.pdf | SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields | 3D reconstruction from 2D image was extensively studied, training with depth supervision. To relax the dependence to costly-acquired datasets, we propose SceneRF, a self-supervised monocular scene reconstruction method using only posed image sequences for training. Fueled by the recent progress in neural radiance field... | ['Raoul de Charette', 'Anh-Quan Cao'] | 2022-12-05 | null | null | null | null | ['3d-scene-reconstruction', 'image-to-video'] | ['computer-vision', 'computer-vision'] | [ 4.21435505e-01 1.57320537e-02 2.12723091e-02 -7.16272235e-01
-8.52117717e-01 -6.81071401e-01 6.93808198e-01 -5.07268131e-01
-1.72913760e-01 7.96513021e-01 5.68003595e-01 -1.08659498e-01
3.53902280e-01 -8.76825869e-01 -1.14691222e+00 -5.81809163e-01
5.45100152e-01 2.80169725e-01 -1.25657603e-01 1.23862758... | [8.98232364654541, -2.97721266746521] |
75626f5e-0fb2-4048-a553-6fd504e954ff | making-sense-of-violence-risk-predictions | 2204.13976 | null | https://arxiv.org/abs/2204.13976v1 | https://arxiv.org/pdf/2204.13976v1.pdf | Making sense of violence risk predictions using clinical notes | Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records (EHR) are valuable resources that are seldom used to their full potential. Previous studies have attempted to assess violence risk in... | ['Marco Spruit', 'Floortje Scheepers', 'Uzay Kaymak', 'Kalliopi Zervanou', 'Emil Rijcken', 'Pablo Mosteiro'] | 2022-04-29 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 2.10368618e-01 1.90678686e-01 -2.50066996e-01 -4.79979247e-01
-8.77504408e-01 -6.24232471e-01 4.03281271e-01 6.92964852e-01
-8.63567770e-01 8.38678956e-01 8.06558132e-01 -6.24313772e-01
-5.16160905e-01 -7.73311317e-01 1.06805630e-01 -4.10695046e-01
1.10687204e-01 6.08173490e-01 -1.39574707e-01 5.01862429... | [8.22862720489502, 5.858125686645508] |
65970858-dbb4-45c1-90af-130e89df0ef6 | an-advanced-combination-of-semi-supervised | 2207.10777 | null | https://arxiv.org/abs/2207.10777v1 | https://arxiv.org/pdf/2207.10777v1.pdf | An advanced combination of semi-supervised Normalizing Flow & Yolo (YoloNF) to detect and recognize vehicle license plates | Fully Automatic License Plate Recognition (ALPR) has been a frequent research topic due to several practical applications. However, many of the current solutions are still not robust enough in real situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the st... | ['Xinyi Dai', 'Khalid Oublal'] | 2022-07-21 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 1.26214489e-01 -7.01959014e-01 9.75338593e-02 -2.94239014e-01
-8.79848301e-01 -5.85711300e-01 4.49275881e-01 -4.92576361e-01
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3.91767979e-01 3.63031089e-01 7.81104207e-01 -3.23519289... | [9.836054801940918, -4.950724124908447] |
22ead3c9-324b-4fa9-9b17-b2a8367fa8bd | towards-scale-consistent-monocular-visual | 2203.05712 | null | https://arxiv.org/abs/2203.05712v1 | https://arxiv.org/pdf/2203.05712v1.pdf | Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World | Monocular visual odometry (VO) has attracted extensive research attention by providing real-time vehicle motion from cost-effective camera images. However, state-of-the-art optimization-based monocular VO methods suffer from the scale inconsistency problem for long-term predictions. Deep learning has recently been intr... | ['DaCheng Tao', 'Jing Zhang', 'Sen Zhang'] | 2022-03-11 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-1.65331796e-01 -1.34338573e-01 -1.76094174e-01 -3.31314117e-01
-6.09628737e-01 -5.97128272e-01 5.76050103e-01 -5.38668990e-01
-4.64919090e-01 8.27585578e-01 -2.58596212e-01 -2.24068984e-01
2.83890784e-01 -8.02601099e-01 -1.15550852e+00 -7.27219105e-01
3.37125748e-01 3.68877590e-01 3.93130749e-01 -2.38374561... | [8.255399703979492, -2.206068754196167] |
9b6df952-6892-4767-a3ac-35874e8f930f | indicnlg-suite-multilingual-datasets-for | 2203.05437 | null | https://arxiv.org/abs/2203.05437v2 | https://arxiv.org/pdf/2203.05437v2.pdf | IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages | Natural Language Generation (NLG) for non-English languages is hampered by the scarcity of datasets in these languages. In this paper, we present the IndicNLG Benchmark, a collection of datasets for benchmarking NLG for 11 Indic languages. We focus on five diverse tasks, namely, biography generation using Wikipedia inf... | ['Pratyush Kumar', 'Mitesh M. Khapra', 'Amogh Mishra', 'Anoop Kunchukuttan', 'Ratish Puduppully', 'Raj Dabre', 'Prachi Sahu', 'Himani Shrotriya', 'Aman Kumar'] | 2022-03-10 | null | null | null | null | ['paraphrase-generation', 'headline-generation', 'paraphrase-generation', 'abstractive-sentence-summarization'] | ['computer-code', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.99487522e-01 1.52426079e-01 -2.75747031e-01 5.51189817e-02
-1.42591453e+00 -9.38426197e-01 1.14132082e+00 6.60444871e-02
-3.58004570e-01 1.41837955e+00 9.53596652e-01 -5.43785751e-01
2.84175634e-01 -6.80286705e-01 -8.69749308e-01 -1.39787659e-01
2.11637512e-01 6.48663521e-01 -2.89865345e-01 -7.43220091... | [11.802274703979492, 9.476014137268066] |
728d8f18-8aa8-4d32-b81f-e6e11919e50d | holistic-sentence-embeddings-for-better-out | 2210.07485 | null | https://arxiv.org/abs/2210.07485v1 | https://arxiv.org/pdf/2210.07485v1.pdf | Holistic Sentence Embeddings for Better Out-of-Distribution Detection | Detecting out-of-distribution (OOD) instances is significant for the safe deployment of NLP models. Among recent textual OOD detection works based on pretrained language models (PLMs), distance-based methods have shown superior performance. However, they estimate sample distance scores in the last-layer CLS embedding s... | ['Xu sun', 'Rundong Gao', 'Xiaohan Bi', 'Sishuo Chen'] | 2022-10-14 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-2.32799366e-01 6.48214743e-02 -3.29897463e-01 -8.66942704e-02
-1.22588265e+00 -7.37429798e-01 7.05340207e-01 8.69672298e-01
-4.12436306e-01 2.70730823e-01 4.52629924e-01 -4.32382882e-01
2.85011604e-02 -7.17557728e-01 -4.91540372e-01 -4.28459704e-01
-6.86731413e-02 1.36172399e-01 3.02890331e-01 -1.31687939... | [10.758840560913086, 8.726883888244629] |
d8b3bbba-901a-4c71-b499-80ee1a66470f | 3d-human-motion-generation-from-the-text-via | 2211.10003 | null | https://arxiv.org/abs/2211.10003v1 | https://arxiv.org/pdf/2211.10003v1.pdf | 3d human motion generation from the text via gesture action classification and the autoregressive model | In this paper, a deep learning-based model for 3D human motion generation from the text is proposed via gesture action classification and an autoregressive model. The model focuses on generating special gestures that express human thinking, such as waving and nodding. To achieve the goal, the proposed method predicts e... | ['Hanseok Ko', 'Jeongmin Bae', 'David K. Han', 'Junyeop Lee', 'Youngsuk Ryu', 'Gwantae Kim'] | 2022-11-18 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 3.72116268e-01 -6.44878224e-02 -1.13577582e-01 -5.24564147e-01
-3.97445887e-01 -8.67394954e-02 9.66505051e-01 -9.34315383e-01
-4.14486080e-01 3.77197862e-01 8.35917652e-01 1.56035749e-02
2.63955444e-01 -6.62872910e-01 -5.21273077e-01 -9.04833496e-01
2.68073529e-01 2.70600468e-01 -1.77005097e-01 -5.99855222... | [5.649774074554443, -0.11890541762113571] |
b791fa06-08f8-4252-8810-e2fbdb7727f5 | character-independent-font-identification | 2001.08893 | null | https://arxiv.org/abs/2001.08893v1 | https://arxiv.org/pdf/2001.08893v1.pdf | Character-independent font identification | There are a countless number of fonts with various shapes and styles. In addition, there are many fonts that only have subtle differences in features. Due to this, font identification is a difficult task. In this paper, we propose a method of determining if any two characters are from the same font or not. This is diff... | ['Shota Harada', 'Seiichi Uchida', 'Daichi Haraguchi', 'Brian Kenji Iwana', 'Yuto Shinahara'] | 2020-01-24 | null | null | null | null | ['font-recognition'] | ['computer-vision'] | [ 8.99000987e-02 -7.06814945e-01 3.52482349e-01 -3.95619988e-01
2.56235123e-01 -8.81834507e-01 4.51812536e-01 -7.87400827e-02
-3.36335182e-01 7.68218756e-01 -3.48012030e-01 -3.55751157e-01
4.32858944e-01 -7.48651981e-01 -5.25336325e-01 -6.79394007e-01
5.43230295e-01 1.87111974e-01 3.91451269e-01 -1.69337332... | [11.952266693115234, 2.0719046592712402] |
05d547f7-d36d-4e36-b304-36d5165e7f7d | semantic-image-matting | 2104.08201 | null | https://arxiv.org/abs/2104.08201v1 | https://arxiv.org/pdf/2104.08201v1.pdf | Semantic Image Matting | Natural image matting separates the foreground from background in fractional occupancy which can be caused by highly transparent objects, complex foreground (e.g., net or tree), and/or objects containing very fine details (e.g., hairs). Although conventional matting formulation can be applied to all of the above cases,... | ['Yu-Wing Tai', 'Chi-Keung Tang', 'Yanan sun'] | 2021-04-16 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Sun_Semantic_Image_Matting_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Sun_Semantic_Image_Matting_CVPR_2021_paper.pdf | cvpr-2021-1 | ['transparent-objects', 'semantic-image-matting'] | ['computer-vision', 'computer-vision'] | [ 0.6968312 0.01375507 -0.18538408 -0.37943304 -0.62793714 -0.29222378
0.3324304 -0.24268566 0.23569044 0.507193 0.03004605 -0.04555387
0.09241241 -0.950971 -1.188503 -0.9308327 0.4815702 0.63180226
0.76460123 0.20242216 0.28799412 0.24815746 -1.4619644 0.753747
1.3502524 1.1359446 0.48... | [10.623125076293945, -0.9035617709159851] |
8597edbe-e5da-4765-8ecf-cf96fe0a6f12 | community-detection-with-a-subsampled | 2102.01419 | null | https://arxiv.org/abs/2102.01419v3 | https://arxiv.org/pdf/2102.01419v3.pdf | Community Detection with a Subsampled Semidefinite Program | Semidefinite programming is an important tool to tackle several problems in data science and signal processing, including clustering and community detection. However, semidefinite programs are often slow in practice, so speed up techniques such as sketching are often considered. In the context of community detection in... | ['Afonso S. Bandeira', 'Pedro Abdalla'] | 2021-02-02 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.65028542e-01 2.19561830e-01 -4.29734379e-01 2.61536296e-02
-4.22227710e-01 -7.13281751e-01 7.53380656e-02 2.45755792e-01
-2.23503605e-01 6.64922833e-01 2.30144188e-02 -5.40993512e-01
-5.69172263e-01 -7.84549177e-01 -5.12667060e-01 -6.57729268e-01
-4.80098754e-01 6.29917145e-01 -1.86560620e-02 -3.26495245... | [6.913620948791504, 5.111055850982666] |
fd63bbe8-a570-4040-996c-9014164d4b12 | jointly-optimizing-image-compression-with-low | 2305.15030 | null | https://arxiv.org/abs/2305.15030v1 | https://arxiv.org/pdf/2305.15030v1.pdf | Jointly Optimizing Image Compression with Low-light Image Enhancement | Learning-based image compression methods have made great progress. Most of them are designed for generic natural images. In fact, low-light images frequently occur due to unavoidable environmental influences or technical limitations, such as insufficient lighting or limited exposure time. %When general-purpose image co... | ['Sheng Zhong', 'Luxin Yan', 'Liqun Chen', 'Xu Zou', 'Shilv Cai'] | 2023-05-24 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 8.70332599e-01 -5.76162696e-01 -1.68477912e-02 -4.52010602e-01
-8.15923989e-01 -6.41950145e-02 8.63188133e-03 -3.39566544e-02
-5.17242014e-01 5.94161332e-01 -3.78595777e-02 -1.44212946e-01
9.42410603e-02 -9.21473086e-01 -8.91518891e-01 -1.00616562e+00
4.37384695e-01 -4.44752693e-01 1.42771527e-01 -2.13293046... | [10.861457824707031, -2.371697187423706] |
cd458ae1-34e9-4e79-b40a-7ee919536e4c | are-you-for-real-detecting-identity-fraud-via | 1908.06820 | null | https://arxiv.org/abs/1908.06820v1 | https://arxiv.org/pdf/1908.06820v1.pdf | Are You for Real? Detecting Identity Fraud via Dialogue Interactions | Identity fraud detection is of great importance in many real-world scenarios such as the financial industry. However, few studies addressed this problem before. In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consi... | ['Cheng-qing Zong', 'Jiajun Zhang', 'Weikang Wang', 'Qian Li', 'Zhifei Li'] | 2019-08-19 | are-you-for-real-detecting-identity-fraud-via-1 | https://aclanthology.org/D19-1185 | https://aclanthology.org/D19-1185.pdf | ijcnlp-2019-11 | ['dialogue-management'] | ['natural-language-processing'] | [-1.36595652e-01 3.68044406e-01 -1.54638007e-01 -5.15408874e-01
-4.39582169e-01 -3.81492794e-01 1.38224572e-01 1.56351894e-01
-7.11704269e-02 8.39868188e-01 -1.87428117e-01 -6.28464103e-01
4.62505743e-02 -1.14195669e+00 1.00520207e-02 -2.21414551e-01
1.86943099e-01 8.49402428e-01 3.42180938e-01 -6.68305337... | [12.88281536102295, 7.935924530029297] |
092bdaec-e5dc-4189-bddc-cd7976572c10 | unsupervised-deep-digital-staining-for | 2303.02057 | null | https://arxiv.org/abs/2303.02057v1 | https://arxiv.org/pdf/2303.02057v1.pdf | Unsupervised Deep Digital Staining For Microscopic Cell Images Via Knowledge Distillation | Staining is critical to cell imaging and medical diagnosis, which is expensive, time-consuming, labor-intensive, and causes irreversible changes to cell tissues. Recent advances in deep learning enabled digital staining via supervised model training. However, it is difficult to obtain large-scale stained/unstained cell... | ['Bihan Wen', 'Alex C. Kot', 'Shuyan Zhang', 'Lanqing Guo', 'Ziwang Xu'] | 2023-03-03 | null | null | null | null | ['colorization', 'medical-diagnosis'] | ['computer-vision', 'medical'] | [ 3.87154877e-01 4.09838781e-02 4.67038065e-01 -1.13261834e-01
-8.27116668e-01 -7.30084777e-01 1.91327974e-01 -1.04093097e-01
-4.26637590e-01 1.25971580e+00 -3.07891250e-01 -2.57591903e-02
4.99587625e-01 -9.24907267e-01 -7.53273249e-01 -1.57947624e+00
6.15669429e-01 6.15207493e-01 4.72192131e-02 2.56929338... | [14.644976615905762, -2.958805799484253] |
9cec4a8b-1665-437e-9520-1863c0d40676 | second-order-unsupervised-neural-dependency | 2010.14720 | null | https://arxiv.org/abs/2010.14720v1 | https://arxiv.org/pdf/2010.14720v1.pdf | Second-Order Unsupervised Neural Dependency Parsing | Most of the unsupervised dependency parsers are based on first-order probabilistic generative models that only consider local parent-child information. Inspired by second-order supervised dependency parsing, we proposed a second-order extension of unsupervised neural dependency models that incorporate grandparent-child... | ['Kewei Tu', 'Wenjuan Han', 'Yong Jiang', 'Songlin Yang'] | 2020-10-28 | null | https://aclanthology.org/2020.coling-main.347 | https://aclanthology.org/2020.coling-main.347.pdf | coling-2020-8 | ['dependency-grammar-induction'] | ['natural-language-processing'] | [-1.22494586e-01 6.72317147e-01 -1.46558613e-01 -9.53838468e-01
-9.88953590e-01 -5.45731306e-01 2.40025267e-01 3.50297570e-01
-5.63328505e-01 7.32275367e-01 3.38628501e-01 -5.79979062e-01
1.93219781e-01 -8.91415775e-01 -6.81377232e-01 -5.82678676e-01
-5.45306131e-02 8.15849185e-01 4.70002085e-01 -9.69952568... | [10.320820808410645, 9.666071891784668] |
3bd6affc-5f02-4d00-bede-a6a643a6220f | balancing-accuracy-and-integrity-for | 2207.08057 | null | https://arxiv.org/abs/2207.08057v1 | https://arxiv.org/pdf/2207.08057v1.pdf | Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning | Over-the-air federated learning (AirFL) allows devices to train a learning model in parallel and synchronize their local models using over-the-air computation. The integrity of AirFL is vulnerable due to the obscurity of the local models aggregated over-the-air. This paper presents a novel framework to balance the accu... | ['Ping Zhang', 'Wei Ni', 'Wanli Ni', 'Hui Tian', 'Jingheng Zheng'] | 2022-07-17 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.90077230e-01 3.06963742e-01 1.40096262e-01 1.90270200e-01
-1.10788751e+00 -9.78691876e-01 1.29830256e-01 -3.45661879e-01
1.59015268e-01 5.26263654e-01 -1.72273070e-01 -3.22099626e-01
-7.57285893e-01 -9.62350070e-01 -1.23817039e+00 -1.20099545e+00
-3.83282661e-01 1.79474548e-01 -3.37637246e-01 7.72077963... | [6.314572811126709, 1.0774126052856445] |
54a15aa9-02eb-4289-898c-ceb1cb124341 | learning-true-rate-distortion-optimization | 2201.01586 | null | https://arxiv.org/abs/2201.01586v1 | https://arxiv.org/pdf/2201.01586v1.pdf | Learning True Rate-Distortion-Optimization for End-To-End Image Compression | Even though rate-distortion optimization is a crucial part of traditional image and video compression, not many approaches exist which transfer this concept to end-to-end-trained image compression. Most frameworks contain static compression and decompression models which are fixed after training, so efficient rate-dist... | ['André Kaup', 'Alexander Kopte', 'Kristian Fischer', 'Fabian Brand'] | 2022-01-05 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 3.62491518e-01 -7.00240210e-02 -3.92025650e-01 -3.62829387e-01
-4.49232012e-01 -1.48472860e-01 4.63705093e-01 1.30233034e-01
-5.15842617e-01 3.37711900e-01 3.13223988e-01 -4.21713591e-01
8.32030401e-02 -8.13416600e-01 -7.55929530e-01 -3.97902906e-01
-1.11855447e-01 9.91648436e-02 2.99926847e-01 3.94915557... | [11.362932205200195, -1.5939404964447021] |
1f378824-c547-470d-a170-40d3a3489c05 | towards-safe-propofol-dosing-during-general | 2303.10180 | null | https://arxiv.org/abs/2303.10180v1 | https://arxiv.org/pdf/2303.10180v1.pdf | Towards Safe Propofol Dosing during General Anesthesia Using Deep Offline Reinforcement Learning | Automated anesthesia promises to enable more precise and personalized anesthetic administration and free anesthesiologists from repetitive tasks, allowing them to focus on the most critical aspects of a patient's surgical care. Current research has typically focused on creating simulated environments from which agents ... | ['Yu Yao', 'Beiming Wang', 'Yaoyao Zhu', 'Jiao Chen', 'Xiuding Cai'] | 2023-03-17 | null | null | null | null | ['q-learning'] | ['methodology'] | [-1.27883747e-01 4.70395058e-01 -3.56683075e-01 -2.18662664e-01
-4.65811819e-01 -4.25090164e-01 6.16842136e-02 6.05912685e-01
-6.82568789e-01 1.01460290e+00 2.13531740e-02 -7.15068400e-01
-5.72639525e-01 -5.38509130e-01 -4.98736024e-01 -7.99245715e-01
-8.07925984e-02 4.81180340e-01 -2.63151944e-01 3.28235142... | [4.054839134216309, 2.6534931659698486] |
36e6f914-a839-47d1-8733-4cc57264b7aa | revisiting-the-plastic-surgery-hypothesis-via | 2303.10494 | null | https://arxiv.org/abs/2303.10494v1 | https://arxiv.org/pdf/2303.10494v1.pdf | Revisiting the Plastic Surgery Hypothesis via Large Language Models | Automated Program Repair (APR) aspires to automatically generate patches for an input buggy program. Traditional APR tools typically focus on specific bug types and fixes through the use of templates, heuristics, and formal specifications. However, these techniques are limited in terms of the bug types and patch variet... | ['Lingming Zhang', 'Yifeng Ding', 'Chunqiu Steven Xia'] | 2023-03-18 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-3.06371659e-01 2.11616233e-01 -3.86321038e-01 6.25811890e-02
-9.21226084e-01 -4.80154663e-01 2.24337459e-01 3.67826551e-01
2.61397690e-01 3.40550840e-01 -5.65602407e-02 -7.08995223e-01
-1.23709701e-01 -7.74146438e-01 -1.04856515e+00 -7.27468431e-02
-1.70831531e-01 3.15486975e-02 4.76440966e-01 -4.23713177... | [7.628232479095459, 7.721411228179932] |
60142c83-0e8e-45ef-8478-a3eab235e813 | cascade-rcnn-for-midog-challenge | 2109.01085 | null | https://arxiv.org/abs/2109.01085v2 | https://arxiv.org/pdf/2109.01085v2.pdf | Cascade RCNN for MIDOG Challenge | Mitotic counts are one of the key indicators of breast cancer prognosis. However, accurate mitotic cell counting is still a difficult problem and is labourious. Automated methods have been proposed for this task, but are usually dependent on the training images and show poor performance on unseen domains. In this work,... | ['April Khademi', 'Susan Done', 'Dimitri Androutsos', 'Fariba Dambandkhameneh', 'Salar Razavi'] | 2021-09-02 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 2.44639859e-01 -2.20717698e-01 -3.35043192e-01 -1.62721843e-01
-7.98290551e-01 -2.92855889e-01 5.95438242e-01 5.69970310e-01
-7.97455907e-01 1.16744769e+00 -2.92638510e-01 -1.48495466e-01
2.03279600e-01 -8.13991308e-01 -1.96272537e-01 -1.13883781e+00
2.38812938e-01 6.01833940e-01 5.90689659e-01 8.47525224... | [15.049129486083984, -3.1035561561584473] |
189808ca-08b8-4663-bde1-62cf7fc764bd | coastalcph-at-semeval-2016-task-11-the | null | null | https://aclanthology.org/S16-1160 | https://aclanthology.org/S16-1160.pdf | CoastalCPH at SemEval-2016 Task 11: The importance of designing your Neural Networks right | null | ["H{\\'e}ctor Mart{\\'\\i}nez Alonso", 'Natalie Schluter', 'Joachim Bingel'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.36746072769165, 3.638685464859009] |
550140c3-f969-4d02-9359-370bb62af4a3 | approximate-information-state-based | 2306.05991 | null | https://arxiv.org/abs/2306.05991v1 | https://arxiv.org/pdf/2306.05991v1.pdf | Approximate information state based convergence analysis of recurrent Q-learning | In spite of the large literature on reinforcement learning (RL) algorithms for partially observable Markov decision processes (POMDPs), a complete theoretical understanding is still lacking. In a partially observable setting, the history of data available to the agent increases over time so most practical algorithms ei... | ['Aditya Mahajan', 'Amit Sinha', 'Nima Akbarzadeh', 'Erfan Seyedsalehi'] | 2023-06-09 | null | null | null | null | ['q-learning'] | ['methodology'] | [ 7.84563944e-02 4.62048560e-01 -5.68767607e-01 5.46139702e-02
-1.02401114e+00 -5.75900972e-01 5.44881940e-01 7.00399041e-01
-7.26601601e-01 1.07422829e+00 2.47841328e-01 -3.74223411e-01
-3.50349277e-01 -6.99766636e-01 -7.67683625e-01 -8.62980306e-01
-2.86418319e-01 6.69437051e-01 1.58120334e-01 -1.56917404... | [4.237282752990723, 2.2137224674224854] |
dc1685db-8875-473a-88d3-3d81ef282b79 | metaann-um-gerador-de-ferramentas-para | null | null | https://aclanthology.org/W13-4802 | https://aclanthology.org/W13-4802.pdf | MetaAnn: Um Gerador de Ferramentas para Anota\cc\~ao de Textos (MetaAnn: a Generator of Text Annotation Tools) [in Portuguese] | null | ['Norton Trevisan Roman', 'Tiago Emanuel Infante Miss{\\~a}o'] | 2013-01-01 | null | null | null | ws-2013-1 | ['text-annotation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.352250099182129, 3.6770882606506348] |
9613fba1-7998-468f-bef7-510c46137420 | dual-view-molecule-pre-training | 2106.10234 | null | https://arxiv.org/abs/2106.10234v2 | https://arxiv.org/pdf/2106.10234v2.pdf | Dual-view Molecule Pre-training | Inspired by its success in natural language processing and computer vision, pre-training has attracted substantial attention in cheminformatics and bioinformatics, especially for molecule based tasks. A molecule can be represented by either a graph (where atoms are connected by bonds) or a SMILES sequence (where depth-... | ['Tie-Yan Liu', 'Houqiang Li', 'Wengang Zhou', 'Tao Qin', 'Yingce Xia', 'Jinhua Zhu'] | 2021-06-17 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 8.22925925e-01 2.21759126e-01 -5.88861823e-01 -1.27262264e-01
-3.39102179e-01 -6.92484558e-01 7.48253286e-01 5.96323311e-01
-1.56290364e-02 7.80423939e-01 -3.90864816e-03 -8.63531590e-01
1.47591323e-01 -1.10704303e+00 -1.01827288e+00 -8.27290416e-01
-1.57946900e-01 3.56906354e-01 1.75427705e-01 -2.20405012... | [4.9324870109558105, 5.953561782836914] |
fdc4e964-f226-4bbd-a51e-de990e725f32 | you-don-t-know-when-i-will-arrive | 2211.12803 | null | https://arxiv.org/abs/2211.12803v2 | https://arxiv.org/pdf/2211.12803v2.pdf | You Don't Know When I Will Arrive: Unpredictable Controller Synthesis for Temporal Logic Tasks | In this paper, we investigate the problem of synthesizing controllers for temporal logic specifications under security constraint. We assume that there exists a passive intruder (eavesdropper) that can partially observe the behavior of the system. For the purpose of security, we require that the system's behaviors are ... | ['Xiang Yin', 'Rahul Mangharam', 'Shuo Yang', 'Yu Chen'] | 2022-11-23 | null | null | null | null | ['robot-task-planning'] | ['robots'] | [ 6.96506083e-01 7.27552235e-01 -6.15504384e-02 -1.10611439e-01
-2.13299975e-01 -7.32774198e-01 4.33209598e-01 2.28965972e-02
-1.11184366e-01 6.89589739e-01 -4.48149830e-01 -6.69146776e-01
-7.29011074e-02 -9.18926954e-01 -7.26901412e-01 -2.99246252e-01
-2.09941640e-01 1.13669701e-01 4.34538692e-01 -1.91974461... | [4.886463165283203, 2.2774369716644287] |
53b0ef83-9167-45b7-a833-e85773242c07 | spherical-space-feature-decomposition-for | 2303.08942 | null | https://arxiv.org/abs/2303.08942v1 | https://arxiv.org/pdf/2303.08942v1.pdf | Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution | Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing, aims to upsample low-resolution (LR) depth maps with additional information involved in high-resolution (HR) RGB images from the same scene. The critical step of this task is to effectively extract domain-shared and domain-private... | ['Luc van Gool', 'Radu Timofte', 'Yulun Zhang', 'Shuang Xu', 'Chengli Tan', 'Xiang Gu', 'Jiangshe Zhang', 'Zixiang Zhao'] | 2023-03-15 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 6.52518988e-01 -1.75791726e-01 2.03454912e-01 -4.50794011e-01
-1.35956478e+00 3.75140039e-03 3.11295539e-01 -5.58180690e-01
-2.05929443e-01 7.76225388e-01 2.62770236e-01 5.80354154e-01
-1.67257085e-01 -8.97328854e-01 -5.80078959e-01 -1.16499996e+00
2.65313447e-01 -1.63257286e-01 4.07855451e-01 -2.80998617... | [9.798482894897461, -2.3864047527313232] |
53884ee9-9743-4022-b113-16e1041f16ce | feature-selection-as-a-one-player-game | null | null | https://hal.inria.fr/inria-00484049/ | https://hal.archives-ouvertes.fr/inria-00484049/document | Feature Selection as a One-Player Game | This paper formalizes Feature Selection as a Reinforcement Learning problem, leading to a provably optimal though intractable selection policy. As a second contribution, this paper presents an approximation thereof, based on a one-player game approach and relying on the Monte-Carlo tree search UCT (Upper Confidence Tre... | ['Michèle Sebag', 'Romaric Gaudel'] | 2010-05-17 | null | null | null | international-conference-on-machine-learning | ['automated-feature-engineering'] | ['methodology'] | [ 2.48818114e-01 3.87301087e-01 -2.52443314e-01 -9.60318279e-03
-9.26404893e-01 -5.83261549e-01 5.10907292e-01 1.56346142e-01
-5.08937836e-01 1.16778874e+00 -3.69044960e-01 -2.01527208e-01
-6.82607114e-01 -9.09792602e-01 -4.45980191e-01 -6.06901467e-01
-3.76284957e-01 5.29554665e-01 1.96456715e-01 -1.89397156... | [4.267866611480713, 2.3955116271972656] |
93285ae6-3c9f-4ce2-a5b0-3570cab39fd2 | approximating-pareto-frontier-through | null | null | https://openreview.net/forum?id=S9MPX7ejmv | https://openreview.net/pdf?id=S9MPX7ejmv | Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning | Many real-word decision or control problems involve multiple conflicting objectives and uncertainties, which requires learned policies are not only Pareto optimal but also robust. In this paper, we proposed a novel algorithm to approximate a representation for robust Pareto frontier through Bayesian-optimization-direct... | ['Wulong Liu', 'Bin Wang', 'Dong Li', 'Jianye Hao', 'Xiangkun He'] | 2021-01-01 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 4.42392714e-02 -4.74968813e-02 1.06220260e-01 -9.66177583e-02
-1.07586956e+00 -5.37023306e-01 3.33756328e-01 1.59936771e-01
-6.13659203e-01 1.43003416e+00 1.97601885e-01 5.81791103e-02
-8.94446850e-01 -7.49568701e-01 -7.38625288e-01 -1.19541478e+00
-1.93304002e-01 5.44126987e-01 1.89254787e-02 1.38152193... | [4.303105354309082, 2.4221835136413574] |
342fd090-e4ad-4d9f-8805-48be75e7aded | association-graph-learning-for-multi-task | 2210.04637 | null | https://arxiv.org/abs/2210.04637v1 | https://arxiv.org/pdf/2210.04637v1.pdf | Association Graph Learning for Multi-Task Classification with Category Shifts | In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously. In particular, we tackle a new setting, which is more realistic than currently addressed in the literature, where categories shift from training to test data. Hence, indivi... | ['Marcel Worring', 'Cees G. M. Snoek', 'XianTong Zhen', 'Zehao Xiao', 'Jiayi Shen'] | 2022-10-10 | null | null | null | null | ['skin-lesion-classification', 'classification'] | ['medical', 'methodology'] | [ 7.02176630e-01 2.42736399e-01 -4.72217023e-01 -4.45244551e-01
-3.99505287e-01 -6.19636893e-01 4.65977341e-01 4.16583508e-01
-2.83156246e-01 8.32282543e-01 1.96566656e-01 -2.71435622e-02
-4.42620784e-01 -5.86587965e-01 -5.61106145e-01 -8.26541066e-01
-1.99322607e-02 3.22349846e-01 -1.03918016e-02 8.34438056... | [9.711673736572266, 3.7600643634796143] |
8482f46f-48c2-4d9d-9a0c-7879811ef808 | a-morphology-aware-network-for-morphological | 1702.03654 | null | http://arxiv.org/abs/1702.03654v1 | http://arxiv.org/pdf/1702.03654v1.pdf | A Morphology-aware Network for Morphological Disambiguation | Agglutinative languages such as Turkish, Finnish and Hungarian require
morphological disambiguation before further processing due to the complex
morphology of words. A morphological disambiguator is used to select the
correct morphological analysis of a word. Morphological disambiguation is
important because it general... | ['Ozan Sonmez', 'Eray Yildiz', 'H. Bahadir Sahin', 'Mustafa Tolga Eren', 'Caglar Tirkaz'] | 2017-02-13 | null | null | null | null | ['morphological-disambiguation'] | ['natural-language-processing'] | [-3.72725427e-01 -6.76533163e-01 2.87984967e-01 -3.65622252e-01
-3.97281319e-01 -8.61559331e-01 5.02979457e-01 7.95909166e-01
-9.78022039e-01 6.93920434e-01 -9.28502828e-02 -5.95456064e-01
1.69022709e-01 -7.82150507e-01 -1.90960079e-01 -5.50753653e-01
-6.19112663e-02 5.61461985e-01 4.80846735e-03 -3.75137329... | [10.377156257629395, 10.146698951721191] |
b1b42ceb-ac8c-456e-818d-b0f82d127f9f | unsupervised-audio-source-separation-using | 2005.13769 | null | https://arxiv.org/abs/2005.13769v1 | https://arxiv.org/pdf/2005.13769v1.pdf | Unsupervised Audio Source Separation using Generative Priors | State-of-the-art under-determined audio source separation systems rely on supervised end-end training of carefully tailored neural network architectures operating either in the time or the spectral domain. However, these methods are severely challenged in terms of requiring access to expensive source level labeled data... | ['Jayaraman J. Thiagarajan', 'Vivek Narayanaswamy', 'Andreas Spanias', 'Rushil Anirudh'] | 2020-05-28 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 4.74202156e-01 -6.94252923e-02 -1.27217010e-01 -2.86337048e-01
-1.56537497e+00 -7.29119658e-01 5.14295995e-01 -1.49193227e-01
-1.20843522e-01 4.49343234e-01 5.86204410e-01 1.05150819e-01
-3.23925704e-01 -2.46383324e-01 -5.18533826e-01 -8.35230410e-01
6.12021536e-02 3.03380638e-01 -1.77470431e-01 -1.37126520... | [15.429659843444824, 5.564156532287598] |
ada3cccc-8405-46df-a8f4-2ac4f9cd629b | real-time-object-detection-for-streaming | 2203.12338 | null | https://arxiv.org/abs/2203.12338v2 | https://arxiv.org/pdf/2203.12338v2.pdf | Real-time Object Detection for Streaming Perception | Autonomous driving requires the model to perceive the environment and (re)act within a low latency for safety. While past works ignore the inevitable changes in the environment after processing, streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perceptio... | ['Jian Sun', 'Xiaoping Li', 'Zeming Li', 'Songtao Liu', 'Jinrong Yang'] | 2022-03-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_Real-Time_Object_Detection_for_Streaming_Perception_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_Real-Time_Object_Detection_for_Streaming_Perception_CVPR_2022_paper.pdf | cvpr-2022-1 | ['real-time-object-detection'] | ['computer-vision'] | [ 9.62170288e-02 -2.88889408e-01 -2.09564507e-01 -6.43703818e-01
-3.41696143e-01 -4.61031079e-01 5.84868312e-01 1.42981231e-01
-5.26414871e-01 2.03712136e-01 1.36876926e-01 -3.33948314e-01
1.82700396e-01 -8.29987943e-01 -7.38796592e-01 -4.64023381e-01
-3.41009945e-01 -2.14222461e-01 9.38713014e-01 -3.59617293... | [8.445693969726562, -0.6712128520011902] |
9770a64e-ff12-4882-bd97-47bd3f2af072 | analysis-of-multilingual-sequence-to-sequence | 1811.03451 | null | http://arxiv.org/abs/1811.03451v1 | http://arxiv.org/pdf/1811.03451v1.pdf | Analysis of Multilingual Sequence-to-Sequence speech recognition systems | This paper investigates the applications of various multilingual approaches
developed in conventional hidden Markov model (HMM) systems to
sequence-to-sequence (seq2seq) automatic speech recognition (ASR). On a set
composed of Babel data, we first show the effectiveness of multi-lingual
training with stacked bottle-nec... | ['Jan "Honza\'\' Černocký', 'Martin Karafiát', 'Murali Karthick Baskar', 'Takaaki Hori', 'Shinji Watanabe', 'Matthew Wiesner'] | 2018-11-07 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [-1.94046237e-02 -4.11698110e-02 4.58093807e-02 -3.82081062e-01
-1.56337500e+00 -6.88679814e-01 7.98202455e-01 -4.35895890e-01
-6.88678145e-01 8.66122544e-01 5.02664149e-01 -9.51682508e-01
5.07847309e-01 4.64665592e-02 -8.12013268e-01 -7.00044215e-01
-1.76025331e-02 6.14002347e-01 3.25584668e-03 -4.75028962... | [14.376910209655762, 7.008882522583008] |
afc5cc65-5318-459e-a7b0-baa33e3b678b | collaborative-blind-image-deblurring | 2305.16034 | null | https://arxiv.org/abs/2305.16034v1 | https://arxiv.org/pdf/2305.16034v1.pdf | Collaborative Blind Image Deblurring | Blurry images usually exhibit similar blur at various locations across the image domain, a property barely captured in nowadays blind deblurring neural networks. We show that when extracting patches of similar underlying blur is possible, jointly processing the stack of patches yields superior accuracy than handling th... | ['Gabriele Facciolo', 'Jean-Michel Morel', 'Thomas Eboli'] | 2023-05-25 | null | null | null | null | ['deblurring', 'blind-image-deblurring'] | ['computer-vision', 'computer-vision'] | [ 1.20068192e-01 -5.13458371e-01 4.27280575e-01 -2.88891256e-01
-6.80688381e-01 -7.28574038e-01 4.90478724e-01 -4.71386015e-01
-2.47261018e-01 7.59720206e-01 6.56598806e-01 -5.07745966e-02
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2.04790942e-03 -6.07856631e-01 -2.86010243e-02 1.80609792... | [11.615772247314453, -2.748058795928955] |
087722e3-ba3c-46e7-93fa-dfee1a5a26d4 | simple-negation-scope-resolution-through-deep | null | null | https://aclanthology.org/P14-1007 | https://aclanthology.org/P14-1007.pdf | Simple Negation Scope Resolution through Deep Parsing: A Semantic Solution to a Semantic Problem | null | ['Stephan Oepen', 'Woodley Packard', 'Rebecca Dridan', 'Emily M. Bender', 'Jonathon Read'] | 2014-06-01 | null | null | null | acl-2014-6 | ['negation-scope-resolution'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.35286283493042, 3.7072086334228516] |
307e629c-7728-448f-ad55-f1bc9704b22e | segment-anything-in-high-quality | 2306.01567 | null | https://arxiv.org/abs/2306.01567v1 | https://arxiv.org/pdf/2306.01567v1.pdf | Segment Anything in High Quality | The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being trained with 1.1 billion masks, SAM's mask prediction quality falls short in many cases, particularly when dealing with objects that have in... | ['Fisher Yu', 'Chi-Keung Tang', 'Yu-Wing Tai', 'Yifan Liu', 'Martin Danelljan', 'Mingqiao Ye', 'Lei Ke'] | 2023-06-02 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 4.26589727e-01 2.44607717e-01 -1.39817387e-01 -3.12306970e-01
-1.26235986e+00 -8.42701495e-01 2.45023176e-01 -8.72068778e-02
-4.86873478e-01 4.51057881e-01 3.24445777e-02 -3.50311577e-01
3.10527563e-01 -6.15411520e-01 -9.50530052e-01 -6.94668055e-01
1.47969544e-01 5.40905356e-01 8.36516500e-01 -3.61121795... | [9.573427200317383, 0.0773303434252739] |
88216e49-63aa-4378-9f89-84cff12899ad | robust-multi-domain-mitosis-detection | 2109.15092 | null | https://arxiv.org/abs/2109.15092v1 | https://arxiv.org/pdf/2109.15092v1.pdf | Robust Multi-Domain Mitosis Detection | Domain variability is a common bottle neck in developing generalisable algorithms for various medical applications. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a target representative feature space through unpaired image to image translat... | ['Sasidhar Kadiyala', 'Ritesh Gangnani', 'Mustaffa Hussain'] | 2021-09-13 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 6.91719055e-01 3.75235111e-01 -4.00744259e-01 -1.89479396e-01
-1.42296386e+00 -7.52944171e-01 1.02292955e+00 -1.00315422e-01
-5.89691281e-01 1.13942480e+00 -2.33868808e-01 -3.30522247e-02
-1.87180825e-02 -2.18980178e-01 -5.85274100e-01 -9.58831310e-01
3.25734437e-01 7.01363623e-01 2.55428493e-01 1.27555402... | [15.128247261047363, -3.0437657833099365] |
e0372d54-32e5-4f5d-a5a5-51f2cf023ae7 | evaluating-similitude-and-robustness-of-deep | 2306.16050 | null | https://arxiv.org/abs/2306.16050v2 | https://arxiv.org/pdf/2306.16050v2.pdf | Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack | Deep neural networks (DNNs) have shown superior performance comparing to traditional image denoising algorithms. However, DNNs are inevitably vulnerable while facing adversarial attacks. In this paper, we propose an adversarial attack method named denoising-PGD which can successfully attack all the current deep denoisi... | ['WangMeng Zuo', 'Jiebao Sun', 'Zhichang Guo', 'Yao Li', 'Jie Ning'] | 2023-06-28 | null | null | null | null | ['adversarial-attack', 'image-denoising'] | ['adversarial', 'computer-vision'] | [-2.15548053e-01 -4.29937512e-01 7.33098030e-01 -1.43507332e-01
-6.05836928e-01 -9.39849555e-01 6.95625722e-01 -5.23427427e-01
-2.95258135e-01 5.62833846e-01 2.07085997e-01 -3.42136234e-01
-5.48318774e-02 -7.54597008e-01 -7.68239677e-01 -1.15162218e+00
-6.57453537e-02 -2.06899509e-01 2.39464030e-01 -6.64120495... | [5.4359941482543945, 8.03760051727295] |
a258e9e1-8062-4d78-af3e-7734533b56af | hcgmnet-a-hierarchical-change-guiding-map | 2302.10420 | null | https://arxiv.org/abs/2302.10420v2 | https://arxiv.org/pdf/2302.10420v2.pdf | HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection | Very-high-resolution (VHR) remote sensing (RS) image change detection (CD) has been a challenging task for its very rich spatial information and sample imbalance problem. In this paper, we have proposed a hierarchical change guiding map network (HCGMNet) for change detection. The model uses hierarchical convolution ope... | ['Bo Du', 'Chen Wu', 'Chengxi Han'] | 2023-02-21 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 3.40048432e-01 -6.02748990e-01 1.64912835e-01 -5.20162106e-01
-4.68519866e-01 8.13981295e-02 4.82262492e-01 9.93274339e-03
-3.28543931e-01 4.26396996e-01 2.55673856e-01 -2.54783213e-01
-2.02868298e-01 -1.19072676e+00 -4.04617548e-01 -7.22001672e-01
-4.30802941e-01 -7.17753246e-02 5.81122756e-01 -5.42745829... | [9.783567428588867, -1.345316767692566] |
ce2043f5-a7ae-4190-aad5-d3d7133784ba | towards-a-multi-entity-aspect-based-sentiment | null | null | https://aclanthology.org/2022.woah-1.19 | https://aclanthology.org/2022.woah-1.19.pdf | Towards a Multi-Entity Aspect-Based Sentiment Analysis for Characterizing Directed Social Regard in Online Messaging | Online messaging is dynamic, influential, and highly contextual, and a single post may contain contrasting sentiments towards multiple entities, such as dehumanizing one actor while empathizing with another in the same message.These complexities are important to capture for understanding the systematic abuse voiced wit... | ['Christopher Miller', 'Diana Gomez', 'Jeremy Gottlieb', 'Ruta Wheelock', 'Ian Magnusson', 'Sonja Schmer-Galunder', 'Scott Friedman', 'Joan Zheng'] | null | null | null | null | naacl-woah-2022-7 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 7.30557367e-02 2.87137359e-01 -4.60859984e-01 -7.10992515e-01
-3.11559081e-01 -7.40690589e-01 7.81876504e-01 9.42142725e-01
-3.99877608e-01 4.23882484e-01 1.17778385e+00 -7.52077263e-04
-7.83005655e-02 -5.18918455e-01 -9.61327776e-02 -2.65297890e-01
2.23163180e-02 4.64079976e-01 -2.28108197e-01 -7.69136488... | [8.673334121704102, 10.475805282592773] |
f138c49e-4076-4f33-83af-671f65025683 | swipenet-object-detection-in-noisy-underwater | 2010.10006 | null | https://arxiv.org/abs/2010.10006v3 | https://arxiv.org/pdf/2010.10006v3.pdf | SWIPENET: Object detection in noisy underwater images | In recent years, deep learning based object detection methods have achieved promising performance in controlled environments. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) images in the underwater datasets and real applications are blurry whilst a... | ['Huiyu Zhou', 'Xin Wang', 'Haiping Ma', 'Ning li', 'Junyu Dong', 'Shengke Wang', 'Feixiang Zhou', 'Long Chen'] | 2020-10-19 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.94102633e-02 -1.47310212e-01 7.39112735e-01 -2.20538273e-01
-5.97364783e-01 -7.77325258e-02 4.03514594e-01 2.45249830e-02
-9.93141711e-01 3.95851582e-01 -8.64791721e-02 2.05814198e-01
-3.68011683e-01 -9.89531934e-01 -8.75277936e-01 -1.12517428e+00
-1.42694935e-02 2.16174439e-01 8.19726646e-01 -4.70728457... | [10.642315864562988, -3.468898057937622] |
8818d99f-cd7c-415c-8235-ab97021cdb34 | ensemble-framework-for-cardiovascular-disease | 2306.09989 | null | https://arxiv.org/abs/2306.09989v1 | https://arxiv.org/pdf/2306.09989v1.pdf | Ensemble Framework for Cardiovascular Disease Prediction | Heart disease is the major cause of non-communicable and silent death worldwide. Heart diseases or cardiovascular diseases are classified into four types: coronary heart disease, heart failure, congenital heart disease, and cardiomyopathy. It is vital to diagnose heart disease early and accurately in order to avoid fur... | ['Aman Sharma', 'Aryan Chugh', 'Achyut Tiwari'] | 2023-06-16 | null | null | null | null | ['disease-prediction', 'specificity'] | ['medical', 'natural-language-processing'] | [-4.92144562e-02 -1.61641970e-01 -3.54814351e-01 -2.27791995e-01
3.20575498e-02 -1.70574620e-01 -9.09819752e-02 5.29039741e-01
-1.06491834e-01 9.54634249e-01 9.08765793e-02 -6.03444278e-01
-4.11591589e-01 -8.90526891e-01 1.69880256e-01 -3.98963779e-01
-1.00874841e-01 6.39872372e-01 3.29368599e-02 1.93635508... | [8.462784767150879, 4.89336633682251] |
9ad77b45-eeb3-4b9a-9af7-7a91bfc8aaeb | accounting-for-the-neglected-dimensions-of-ai | 1806.00610 | null | https://arxiv.org/abs/1806.00610v2 | https://arxiv.org/pdf/1806.00610v2.pdf | Between Progress and Potential Impact of AI: the Neglected Dimensions | We reframe the analysis of progress in AI by incorporating into an overall framework both the task performance of a system, and the time and resource costs incurred in the development and deployment of the system. These costs include: data, expert knowledge, human oversight, software resources, computing cycles, hardwa... | ['José Hernández-Orallo', 'Sean Ó hÉigeartaigh', 'Fernando Martínez-Plumed', 'Allan Dafoe', 'Miles Brundage', 'Shahar Avin'] | 2018-06-02 | null | null | null | null | ['board-games'] | ['playing-games'] | [-6.75285906e-02 1.12205923e-01 4.24674004e-02 -6.52941614e-02
-3.63626361e-01 -8.92673016e-01 7.96169877e-01 2.28384510e-01
-6.50846183e-01 3.41039389e-01 2.14655280e-01 -5.38637519e-01
-4.63552237e-01 -6.60792172e-01 -3.13336968e-01 -3.01483497e-02
-8.51080418e-02 4.80496496e-01 3.58588547e-02 -2.71667063... | [8.981947898864746, 6.275793552398682] |
44bae55f-b9e3-4cd4-a994-3472b174c130 | spatio-temporal-forecasting-with-gridded | null | null | https://dl.acm.org/doi/abs/10.1145/3397536.3422247 | https://dl.acm.org/doi/abs/10.1145/3397536.3422247 | Spatio-Temporal Forecasting With Gridded Remote Sensing Data Using Feed-Backward Decoding | We present a novel deep learning approach for spatio-temporal forecasting with remote sensing data, extending a previous model named Spatio-Temporal Convolutional Sequence to Sequence Network (STConvS2S) in several directions. Experiments using datasets from previous studies show that the proposed approaches outperform... | ['Bruno Martins', 'Jacinto Estima', 'Mário Cardoso'] | 2020-11-01 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [ 2.49729753e-01 -3.36991936e-01 -1.20331727e-01 -7.29441345e-01
-5.05025506e-01 -4.00885731e-01 1.13019693e+00 -2.68572271e-01
-2.69294739e-01 1.01240444e+00 6.49535358e-01 -8.61940861e-01
-2.11532071e-01 -9.18940485e-01 -7.14442849e-01 -6.20313704e-01
-7.17625260e-01 1.85448304e-02 3.53951871e-01 -6.76210463... | [6.629281044006348, 2.6933650970458984] |
846f8a4e-22a8-4722-bbd1-eadd40110b74 | unidirectional-imaging-using-deep-learning | 2212.02025 | null | https://arxiv.org/abs/2212.02025v1 | https://arxiv.org/pdf/2212.02025v1.pdf | Unidirectional Imaging using Deep Learning-Designed Materials | A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) A to an output FOV B, and in the reverse path, the image formation would be blocked. Here, we report the first demonstration of unidirectional imagers, presenting polarization-insensitive and broadband unidi... | ['Aydogan Ozcan', 'Mona Jarrahi', 'Songyu Sun', 'Che-Yung Shen', 'Bijie Bai', 'Yifan Zhao', 'Tianyi Gan', 'Jingxi Li'] | 2022-12-05 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 8.28419268e-01 2.63251036e-01 1.15501598e-01 -9.29044262e-02
3.74446273e-01 -4.99423563e-01 4.31089848e-01 -1.00760353e+00
-3.94773185e-01 5.92176497e-01 -7.53210485e-02 -3.20244491e-01
-1.55881971e-01 -1.10410416e+00 -6.71399415e-01 -1.65631545e+00
3.11885506e-01 2.51656115e-01 1.47927895e-01 6.57806918... | [10.19332504272461, -2.6068062782287598] |
fb6f39c4-3fc2-415d-bca3-40fbfeef77e2 | ying-yong-ji-yi-zeng-qiang-tiao-jian-sui-ji | null | null | https://aclanthology.org/2019.ijclclp-1.1 | https://aclanthology.org/2019.ijclclp-1.1.pdf | 應用記憶增強條件隨機場域與之深度學習及自動化詞彙特徵於中文命名實體辨識之研究 (Leveraging Memory Enhanced Conditional Random Fields with Gated CNN and Automatic BAPS Features for Chinese Named Entity Recognition) | null | ['Chia-Hui Chang', 'Kuo-Chun Chien'] | null | null | null | null | ijclclp-2019-6 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.315688133239746, 3.800696849822998] |
c94c95da-0d60-4bb2-aa5d-b0f071cb0a28 | listening-human-behavior-3d-human-pose | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shibata_Listening_Human_Behavior_3D_Human_Pose_Estimation_With_Acoustic_Signals_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shibata_Listening_Human_Behavior_3D_Human_Pose_Estimation_With_Acoustic_Signals_CVPR_2023_paper.pdf | Listening Human Behavior: 3D Human Pose Estimation With Acoustic Signals | Given only acoustic signals without any high-level information, such as voices or sounds of scenes/actions, how much can we infer about the behavior of humans? Unlike existing methods, which suffer from privacy issues because they use signals that include human speech or the sounds of specific actions, we explore h... | ['Yoshimitsu Aoki', 'Akisato Kimura', 'Go Irie', 'Mariko Isogawa', 'Yutaka Kawashima', 'Yuto Shibata'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 3.77758622e-01 -1.86221041e-02 3.14110249e-01 -3.79769146e-01
-7.36113727e-01 -5.33183753e-01 1.66415349e-01 -8.13445002e-02
-3.53272200e-01 5.12778878e-01 5.32659769e-01 4.06616300e-01
1.02540836e-01 -7.03871429e-01 -5.34137011e-01 -6.20543242e-01
-2.75116861e-01 1.78096257e-02 2.59006232e-01 -2.05111399... | [15.004447937011719, 5.26356840133667] |
ede509cb-5b68-4ca6-b7e8-5d13aa8f3943 | cross-lingual-semantic-representation-for-nlp | null | null | https://aclanthology.org/2020.coling-tutorials.1 | https://aclanthology.org/2020.coling-tutorials.1.pdf | Cross-lingual Semantic Representation for NLP with UCCA | This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew. UCCA builds on extensive typological work and supports rapid ... | ['Nathan Schneider', 'Jakob Prange', 'Daniel Hershcovich', 'Dotan Dvir', 'Omri Abend'] | 2020-12-01 | null | null | null | coling-2020-8 | ['ucca-parsing'] | ['natural-language-processing'] | [ 2.63689160e-01 6.08397424e-01 -4.33672160e-01 -5.10342836e-01
-7.24839985e-01 -1.10791850e+00 5.99033713e-01 7.15128243e-01
-5.44696510e-01 7.33575463e-01 9.76393938e-01 -3.30434978e-01
-2.68648654e-01 -4.94339705e-01 8.52015987e-02 -1.87568337e-01
2.41910443e-01 6.78392112e-01 7.97245726e-02 -5.71269810... | [10.29318904876709, 9.457542419433594] |
e19855bf-9078-4110-8a32-ecce5199480d | pyss3-a-python-package-implementing-a-novel | 1912.09322 | null | https://arxiv.org/abs/1912.09322v2 | https://arxiv.org/pdf/1912.09322v2.pdf | PySS3: A Python package implementing a novel text classifier with visualization tools for Explainable AI | A recently introduced text classifier, called SS3, has obtained state-of-the-art performance on the CLEF's eRisk tasks. SS3 was created to deal with risk detection over text streams and, therefore, not only supports incremental training and classification but also can visually explain its rationale. However, little att... | ['Manuel Montes-y-Gómez', 'Sergio G. Burdisso', 'Marcelo Errecalde'] | 2019-12-19 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-3.77211630e-01 2.22219571e-01 -4.48394986e-03 -4.11958396e-01
-4.26361442e-01 -4.09819394e-01 6.47099972e-01 8.46367538e-01
-1.00474305e-01 5.00537634e-01 3.82939540e-02 -8.47757936e-01
-1.10323668e-01 -5.51885128e-01 -3.44131351e-01 -2.54089147e-01
-1.86357945e-02 3.61598223e-01 2.40852043e-01 1.97410896... | [8.758319854736328, 7.395889759063721] |
018bbbbf-e959-4499-94c6-ea05e916d4ed | physnlu-a-language-resource-for-evaluating | 2201.04275 | null | https://arxiv.org/abs/2201.04275v3 | https://arxiv.org/pdf/2201.04275v3.pdf | PhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics | In order for language models to aid physics research, they must first encode representations of mathematical and natural language discourse which lead to coherent explanations, with correct ordering and relevance of statements. We present a collection of datasets developed to evaluate the performance of language models... | ['Andre Freitas', 'Zili Zhou', 'Jordan Meadows'] | 2022-01-12 | null | https://aclanthology.org/2022.lrec-1.492 | https://aclanthology.org/2022.lrec-1.492.pdf | lrec-2022-6 | ['sentence-ordering'] | ['natural-language-processing'] | [ 1.20547041e-01 5.88557065e-01 -1.93232819e-01 -5.32465875e-01
-5.48621178e-01 -7.88717330e-01 1.19833291e+00 9.62783158e-01
1.95563897e-01 6.68319821e-01 1.16123033e+00 -8.80985260e-01
-4.76428956e-01 -8.24260235e-01 -7.49907494e-01 2.72387359e-02
-6.52225092e-02 5.84447980e-01 -5.24418131e-02 -4.63532925... | [11.079421043395996, 9.1515474319458] |
ba987cab-6688-4eeb-860e-fea7461988fe | sequential-dual-deep-learning-with-shape-and | 1708.02716 | null | http://arxiv.org/abs/1708.02716v1 | http://arxiv.org/pdf/1708.02716v1.pdf | Sequential Dual Deep Learning with Shape and Texture Features for Sketch Recognition | Recognizing freehand sketches with high arbitrariness is greatly challenging.
Most existing methods either ignore the geometric characteristics or treat
sketches as handwritten characters with fixed structural ordering.
Consequently, they can hardly yield high recognition performance even though
sophisticated learning ... | ['Qi Jia', 'Meiyu Yu', 'Xin Fan', 'Haojie Li'] | 2017-08-09 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 1.58280849e-01 -6.82221234e-01 -1.39502540e-01 -2.65311003e-01
-2.98382640e-01 -6.61817253e-01 8.41319203e-01 -4.81966227e-01
-1.83610022e-01 4.02123958e-01 -3.04374397e-01 -7.61318505e-02
-5.51398322e-02 -1.11072624e+00 -5.71858048e-01 -8.39370668e-01
3.33289355e-01 3.50617439e-01 2.27603361e-01 -1.82494134... | [11.72422981262207, 0.4606890380382538] |
2f545220-4f29-4b06-890b-bd1a35652314 | autoregressive-perturbations-for-data | 2206.03693 | null | https://arxiv.org/abs/2206.03693v3 | https://arxiv.org/pdf/2206.03693v3.pdf | Autoregressive Perturbations for Data Poisoning | The prevalence of data scraping from social media as a means to obtain datasets has led to growing concerns regarding unauthorized use of data. Data poisoning attacks have been proposed as a bulwark against scraping, as they make data "unlearnable" by adding small, imperceptible perturbations. Unfortunately, existing m... | ['David W. Jacobs', 'Tom Goldstein', 'Micah Goldblum', 'Jonas Geiping', 'Vasu Singla', 'Pedro Sandoval-Segura'] | 2022-06-08 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 6.49227723e-02 1.14929564e-01 1.65998600e-02 4.14951928e-02
-7.31814027e-01 -1.22728324e+00 7.56161392e-01 2.11890623e-01
-5.07291853e-01 5.71531832e-01 4.40322965e-01 -4.40234482e-01
3.57202172e-01 -9.00480568e-01 -8.98992419e-01 -7.36060143e-01
2.44073987e-01 1.03602998e-01 -4.65479754e-02 -4.80336249... | [5.826002597808838, 7.723130702972412] |
8095f427-981f-439e-a141-b079f298740f | multi-output-gaussian-process-based-data | 2202.01980 | null | https://arxiv.org/abs/2202.01980v1 | https://arxiv.org/pdf/2202.01980v1.pdf | Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localization | Location fingerprinting based on RSSI becomes a mainstream indoor localization technique due to its advantage of not requiring the installation of new infrastructure and the modification of existing devices, especially given the prevalence of Wi-Fi-enabled devices and the ubiquitous Wi-Fi access in modern buildings. Th... | ['Jeremy Smith', 'Kyeong Soo Kim', 'Sihao Li', 'Zhe Tang'] | 2022-02-04 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 1.74029525e-02 -2.55645126e-01 8.44955668e-02 -1.18163250e-01
-8.25364530e-01 -2.93567210e-01 2.47550845e-01 -7.96796009e-02
-4.29631233e-01 8.84823322e-01 1.90627337e-01 -4.72607523e-01
-4.90230471e-01 -1.09335232e+00 -1.00123000e+00 -1.02044451e+00
2.49953568e-03 3.68722051e-01 -6.17754757e-02 1.19103253... | [6.410688400268555, 0.922505259513855] |
e614787c-a448-46a8-8538-a7e82406a396 | deep-learning-based-assessment-of-hepatic | 2202.02377 | null | https://arxiv.org/abs/2202.02377v1 | https://arxiv.org/pdf/2202.02377v1.pdf | Deep Learning-based Assessment of Hepatic Steatosis on chest CT | Purpose: Automatic methods are required for the early detection of hepatic steatosis to avoid progression to cirrhosis and cancer. Here, we developed a fully automated deep learning pipeline to quantify hepatic steatosis on non-contrast enhanced chest computed tomography (CT) scans. Materials and Methods: We developed ... | ['Hugo J. W. L. Aerts', 'Michael T. Lu', 'Roman Zeleznik', 'Jana Taron', 'Jakob Weiss', 'Zhongyi Zhang'] | 2022-02-04 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-5.52199304e-01 -3.33959401e-01 -2.46263906e-01 -3.35144550e-01
-1.04066324e+00 -7.03751683e-01 2.57305205e-02 4.65138942e-01
-3.24475288e-01 2.78021485e-01 3.90950531e-01 -5.07520556e-01
9.86661762e-02 -9.71773922e-01 -2.22637847e-01 -7.78991997e-01
-9.10570383e-01 9.20976937e-01 7.68770948e-02 6.61039412... | [14.454910278320312, -2.68129825592041] |
bc077507-3a40-4cd7-b46e-1e0b5cfc4c44 | graph-convolutional-gaussian-processes | 1905.05739 | null | https://arxiv.org/abs/1905.05739v1 | https://arxiv.org/pdf/1905.05739v1.pdf | Graph Convolutional Gaussian Processes | We propose a novel Bayesian nonparametric method to learn translation-invariant relationships on non-Euclidean domains. The resulting graph convolutional Gaussian processes can be applied to problems in machine learning for which the input observations are functions with domains on general graphs. The structure of thes... | ['Ian Walker', 'Ben Glocker'] | 2019-05-14 | null | null | null | null | ['superpixel-image-classification'] | ['computer-vision'] | [ 1.17193639e-01 4.49496001e-01 -1.01240590e-01 -3.11085105e-01
-2.22212955e-01 -5.63698292e-01 9.44067955e-01 -1.23197131e-01
-1.42028585e-01 6.74342155e-01 1.09604657e-01 -3.35297704e-01
-4.27149355e-01 -9.18224633e-01 -7.55399466e-01 -7.78733373e-01
-4.33729649e-01 1.05494404e+00 2.28350744e-01 3.52643490... | [6.998424530029297, 3.7986552715301514] |
4d9df7e8-332a-46d4-9d8b-29618768f03e | inverse-cubature-and-quadrature-kalman | 2303.10322 | null | https://arxiv.org/abs/2303.10322v1 | https://arxiv.org/pdf/2303.10322v1.pdf | Inverse Cubature and Quadrature Kalman filters | Recent developments in counter-adversarial system research have led to the development of inverse stochastic filters that are employed by a defender to infer the information its adversary may have learned. Prior works addressed this inverse cognition problem by proposing inverse Kalman filter (I-KF) and inverse extende... | ['Arpan Chattopadhyay', 'Kumar Vijay Mishra', 'Himali Singh'] | 2023-03-18 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-1.26286149e-01 -9.57348868e-02 3.71837795e-01 -1.04919486e-02
-1.04236853e+00 -1.18652475e+00 6.43366337e-01 -3.80921751e-01
-4.83632088e-01 8.67570519e-01 1.34918302e-01 -9.21122551e-01
-4.34471399e-01 -5.38714111e-01 -8.17601621e-01 -7.39621103e-01
-2.15284824e-01 -5.48373237e-02 -2.10575446e-01 -3.65925729... | [6.3153181076049805, 3.4102296829223633] |
eeef39db-87cb-44cf-a223-8d2303955749 | using-speech-and-nlp-resources-to-build-an | null | null | https://aclanthology.org/2022.computel-1.14 | https://aclanthology.org/2022.computel-1.14.pdf | Using Speech and NLP Resources to build an iCALL platform for a minority language, the story of An Scéalaí, the Irish experience to date | This paper describes how emerging linguistic resources and technologies can be used to build a language learning platform for Irish, an endangered language. This platform, An Scéalaí, harvests learner corpora - a vital resource both to study the stages of learners’ language acquisition and to guide future platform deve... | ['Ailbhe Ni Chasaide', 'Harald Berthelsen', 'Neimhin Robinson Gunning', 'Madeleine Comtois', 'Oisín Nolan', 'Neasa Ní Chiaráin'] | null | null | null | null | computel-acl-2022-5 | ['language-acquisition'] | ['natural-language-processing'] | [-5.90546250e-01 4.11473401e-02 -3.19965750e-01 -5.51182330e-02
-1.01642978e+00 -1.05732834e+00 7.30226576e-01 6.77047610e-01
-6.80633843e-01 6.24041080e-01 7.57345855e-01 -6.29289687e-01
-8.14515725e-02 -5.41730642e-01 -2.03322217e-01 -1.49186984e-01
-4.72716196e-03 2.85779744e-01 4.08507109e-01 -1.06660640... | [10.694640159606934, 10.180119514465332] |
e7c0fffd-461b-48f3-8c09-035185b7ac2f | aspanformer-detector-free-image-matching-with | 2208.14201 | null | https://arxiv.org/abs/2208.14201v1 | https://arxiv.org/pdf/2208.14201v1.pdf | ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer | Generating robust and reliable correspondences across images is a fundamental task for a diversity of applications. To capture context at both global and local granularity, we propose ASpanFormer, a Transformer-based detector-free matcher that is built on hierarchical attention structure, adopting a novel attention ope... | ['Long Quan', 'Yanghai Tsin', 'David McKinnon', 'Tian Fang', 'Mingmin Zhen', 'Yurun Tian', 'Lei Zhou', 'Zixin Luo', 'Hongkai Chen'] | 2022-08-30 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 1.11052975e-01 -2.70286590e-01 -1.31611601e-01 -3.01903754e-01
-8.11315298e-01 -3.17998260e-01 7.25185633e-01 2.70935714e-01
-5.60224593e-01 4.91877466e-01 2.58434564e-01 1.04120411e-01
-2.38901809e-01 -8.73034179e-01 -7.89961100e-01 -6.41169012e-01
1.01412356e-01 2.68445224e-01 5.52611113e-01 -2.14973971... | [8.826288223266602, -1.9917460680007935] |
e6842329-8d4a-4713-8158-9ad5c2a293b0 | on-enhancing-speech-emotion-recognition-using | 1806.06626 | null | http://arxiv.org/abs/1806.06626v1 | http://arxiv.org/pdf/1806.06626v1.pdf | On Enhancing Speech Emotion Recognition using Generative Adversarial Networks | Generative Adversarial Networks (GANs) have gained a lot of attention from
machine learning community due to their ability to learn and mimic an input
data distribution. GANs consist of a discriminator and a generator working in
tandem playing a min-max game to learn a target underlying data distribution;
when fed with... | ['Carol Espy-Wilson', 'Rahul Gupta', 'Saurabh Sahu'] | 2018-06-18 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [ 5.77644289e-01 5.34134448e-01 1.18398212e-01 -5.31459391e-01
-1.03295112e+00 -6.33485258e-01 8.32974970e-01 -3.01444024e-01
-2.03461379e-01 9.29129481e-01 2.51129627e-01 -1.07515668e-02
5.70806563e-01 -9.12464738e-01 -7.21464336e-01 -1.06203723e+00
2.17031226e-01 7.22125590e-01 -5.69990635e-01 2.43783649... | [11.735801696777344, -0.039917439222335815] |
5e80be49-9977-447e-9e1f-e580edd83777 | bag-of-words-baselines-for-semantic-code | null | null | https://aclanthology.org/2021.nlp4prog-1.10 | https://aclanthology.org/2021.nlp4prog-1.10.pdf | Bag-of-Words Baselines for Semantic Code Search | The task of semantic code search is to retrieve code snippets from a source code corpus based on an information need expressed in natural language. The semantic gap between natural language and programming languages has for long been regarded as one of the most significant obstacles to the effectiveness of keyword-base... | ['Jimmy Lin', 'Andrew Yates', 'Ji Xin', 'Xinyu Zhang'] | null | null | null | null | acl-nlp4prog-2021-8 | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-4.92565241e-03 -1.88496426e-01 -6.42791569e-01 -9.11021754e-02
-1.26083553e+00 -6.06822371e-01 4.79785174e-01 6.72947228e-01
-5.57780921e-01 9.23304260e-02 4.55668300e-01 -7.89740801e-01
-4.47262198e-01 -5.43204665e-01 -4.38344657e-01 1.42243221e-01
1.64397761e-01 2.98621267e-01 3.72198880e-01 -4.18571025... | [7.521803379058838, 8.081120491027832] |
bd175b09-5481-4fb3-84d8-5ddd3f117e6d | graph-wise-common-latent-factor-extraction | 2112.08830 | null | https://arxiv.org/abs/2112.08830v3 | https://arxiv.org/pdf/2112.08830v3.pdf | Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning | Unsupervised graph-level representation learning plays a crucial role in a variety of tasks such as molecular property prediction and community analysis, especially when data annotation is expensive. Currently, most of the best-performing graph embedding methods are based on Infomax principle. The performance of these ... | ['Ngai-Man Cheung', 'Thilini Cooray'] | 2021-12-16 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 2.24213108e-01 1.60911694e-01 -2.63508290e-01 -1.45666525e-01
-1.93161532e-01 -5.65634370e-01 6.32302582e-01 7.30191171e-01
-1.91570833e-01 6.46703601e-01 3.84786040e-01 -4.32073385e-01
-2.61392146e-01 -9.73311841e-01 -7.86063373e-01 -8.49306643e-01
-2.44079545e-01 2.28844762e-01 1.35194942e-01 -7.24004358... | [5.465051651000977, 5.922904014587402] |
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