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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ea65497c-cfbb-43ea-9748-27784926127d | temporal-knowledge-graph-completion-with | null | null | https://aclanthology.org/2022.coling-1.416 | https://aclanthology.org/2022.coling-1.416.pdf | Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding | Knowledge Graphs (KGs) stores world knowledge that benefits various reasoning-based applications. Due to their incompleteness, a fundamental task for KGs, which is known as Knowledge Graph Completion (KGC), is to perform link prediction and infer new facts based on the known facts. Recently, link prediction on the temp... | ['Deyu Zhou', 'Linhai Zhang'] | null | null | null | null | coling-2022-10 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-5.70549607e-01 9.95051935e-02 -5.34037128e-02 4.91613485e-02
-1.33640870e-01 -2.02071592e-01 6.94914520e-01 2.70157218e-01
8.72052014e-02 9.78569269e-01 5.71316741e-02 -7.28824511e-02
-7.50083625e-01 -1.12402320e+00 -6.43259406e-01 -8.15061510e-01
-1.47351101e-01 6.48525476e-01 3.59321326e-01 -6.06843494... | [8.60688304901123, 7.895254135131836] |
ae83bdbd-3ad3-4bd2-a437-951531e2d833 | covid-19-therapy-target-discovery-with | 2007.15681 | null | https://arxiv.org/abs/2007.15681v2 | https://arxiv.org/pdf/2007.15681v2.pdf | COVID-19 therapy target discovery with context-aware literature mining | The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert. Development of systems, capable of automatically processing tens of thousands of scientific publications with the aim to enrich existing empirical evidence with literature-based associations is challe... | ['Martin Marzidovšek', 'Nada Lavrač', 'Blaž Škrlj', 'Matej Martinc', 'Sergej Pirkmajer', 'Bojan Cestnik', 'Senja Pollak'] | 2020-07-30 | null | null | null | null | ['literature-mining'] | ['natural-language-processing'] | [ 3.51228505e-01 2.44161174e-01 -4.88277972e-01 -3.70848626e-02
-1.03578889e+00 -7.04842091e-01 7.68128872e-01 8.72129321e-01
-6.12798691e-01 1.41934550e+00 4.52094823e-01 -4.23313528e-01
-5.41797936e-01 -5.48265755e-01 -7.53155351e-01 -5.19445360e-01
2.94659939e-03 9.00472760e-01 -2.98298523e-02 -6.11256585... | [8.475812911987305, 8.670805931091309] |
8b772d8b-8ac4-4331-88ca-5f6ecc220f4b | transformer-based-hand-gesture-recognition | 2212.00743 | null | https://arxiv.org/abs/2212.00743v2 | https://arxiv.org/pdf/2212.00743v2.pdf | Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike Trains | Designing efficient and labor-saving prosthetic hands requires powerful hand gesture recognition algorithms that can achieve high accuracy with limited complexity and latency. In this context, the paper proposes a compact deep learning framework referred to as the CT-HGR, which employs a vision transformer network to c... | ['Arash Mohammadi', 'Svetlana Yanushkevich', 'S. Farokh Atashzar', 'Farnoosh Naderkhani', 'Elahe Rahimian', 'Mansooreh Montazerin'] | 2022-11-29 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.54271388e-01 -3.67603928e-01 2.40502715e-01 2.55200177e-01
-7.90276706e-01 -2.72332221e-01 3.71702611e-01 -5.64682961e-01
-6.57380879e-01 9.20021176e-01 8.92067403e-02 -1.11793146e-01
-2.08671466e-01 -3.96613777e-01 -6.89728141e-01 -1.10129976e+00
2.18876675e-02 8.80536884e-02 1.88637137e-01 2.56650507... | [6.825743198394775, 0.12341788411140442] |
e37252ff-9093-4427-99b8-3a3dac3dc670 | a-survey-on-computational-propaganda | 2007.08024 | null | https://arxiv.org/abs/2007.08024v1 | https://arxiv.org/pdf/2007.08024v1.pdf | A Survey on Computational Propaganda Detection | Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda. They exploit the anonymity of the Internet, the micro-profiling ability of social networks, and the ease of automatically creating and managing coordinated networks of accounts, to reach millions of social network ... | ['Roberto Di Pietro', 'Alberto Barron-Cedeno', 'Giovanni Da San Martino', 'Stefano Cresci', 'Preslav Nakov', 'Seunghak Yu'] | 2020-07-15 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [ 2.84905285e-01 6.22013509e-01 -6.89786077e-01 2.85315271e-02
-3.19385916e-01 -7.17869401e-01 1.06216097e+00 8.18623543e-01
-4.23869014e-01 6.17960572e-01 9.85345602e-01 -6.80934608e-01
2.54788715e-02 -1.00005817e+00 1.85702652e-01 -1.55405745e-01
-5.99546880e-02 2.91796386e-01 -5.59819229e-02 -3.16701382... | [8.545976638793945, 10.24406909942627] |
b6096d42-729c-4d62-a0ba-d2a832ed8761 | tensoir-tensorial-inverse-rendering | 2304.12461 | null | https://arxiv.org/abs/2304.12461v1 | https://arxiv.org/pdf/2304.12461v1.pdf | TensoIR: Tensorial Inverse Rendering | We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields. Unlike previous works that use purely MLP-based neural fields, thus suffering from low capacity and high computation costs, we extend TensoRF, a state-of-the-art approach for radiance field modeling, to estimate scen... | ['Hao Su', 'Zexiang Xu', 'Xiaowei Zhou', 'Sai Bi', 'Songfang Han', 'Xiaoshuai Zhang', 'Peijia Xu', 'Isabella Liu', 'Haian Jin'] | 2023-04-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jin_TensoIR_Tensorial_Inverse_Rendering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jin_TensoIR_Tensorial_Inverse_Rendering_CVPR_2023_paper.pdf | cvpr-2023-1 | ['novel-view-synthesis', 'inverse-rendering'] | ['computer-vision', 'computer-vision'] | [ 2.99837649e-01 -6.75125182e-01 4.29701030e-01 -3.72968286e-01
-4.80031073e-01 -7.57039964e-01 5.36750615e-01 -4.23950583e-01
1.91600561e-01 6.00196421e-01 3.98313075e-01 -2.30364054e-01
-6.46287948e-02 -9.31966007e-01 -8.20687592e-01 -6.60925090e-01
2.19578505e-01 5.37438020e-02 -3.18170965e-01 -1.39293239... | [9.68392562866211, -3.088932991027832] |
8766a63d-0cd6-4b81-a260-a89f2e68f820 | iris-recognition-with-image-segmentation | 1901.01028 | null | http://arxiv.org/abs/1901.01028v1 | http://arxiv.org/pdf/1901.01028v1.pdf | Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks | This paper offers three new, open-source, deep learning-based iris
segmentation methods, and the methodology how to use irregular segmentation
masks in a conventional Gabor-wavelet-based iris recognition. To train and
validate the methods, we used a wide spectrum of iris images acquired by
different teams and different... | ['Kevin Bowyer', 'Mateusz Trokielewicz', 'Daniel Kerrigan', 'Adam Czajka'] | 2019-01-04 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [-1.26071677e-01 -8.05828422e-02 -1.40092611e-01 -3.78523737e-01
-7.13536501e-01 -3.59817564e-01 3.18233609e-01 2.02836290e-01
-4.74873781e-01 4.99752820e-01 -1.43632501e-01 -1.01174712e-01
-8.12611639e-01 -8.16100836e-01 -1.98005617e-01 -9.93958056e-01
-2.95377463e-01 7.87858427e-01 -1.36542916e-01 -1.14825889... | [3.74352765083313, -3.631197214126587] |
8c9c6c7b-b788-48da-8f91-166992b99058 | relation-prediction-as-an-auxiliary-training | 2110.02834 | null | https://arxiv.org/abs/2110.02834v1 | https://arxiv.org/pdf/2110.02834v1.pdf | Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations | Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC). In this paper, we propose a new self-supervised training objective for multi-relational graph representation learning, via simply incorporating relation prediction into the commonly used 1vsAll objective. The new t... | ['Pontus Stenetorp', 'Sebastian Riedel', 'Pasquale Minervini', 'Yihong Chen'] | 2021-10-06 | null | https://openreview.net/forum?id=Qa3uS3H7-Le | https://openreview.net/pdf?id=Qa3uS3H7-Le | akbc-2021-10 | ['knowledge-base-completion', 'link-property-prediction', 'knowledge-graph-embeddings', 'knowledge-base-completion', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'graphs', 'knowledge-base', 'methodology'] | [-1.37951925e-01 4.71746802e-01 -7.14301884e-01 -1.02053843e-01
-8.67633343e-01 -2.57423550e-01 6.67611003e-01 9.31395411e-01
-4.52536821e-01 7.55310893e-01 4.28699642e-01 -1.33399814e-01
-5.01666367e-01 -1.33429074e+00 -1.02664161e+00 -2.24992007e-01
-2.72741467e-01 8.33548903e-01 5.04979789e-01 -5.64400017... | [8.798128128051758, 7.943556308746338] |
387a90a3-09bd-4a81-b33c-a49fd0059753 | msp-refine-boundary-segmentation-via | 2112.01746 | null | https://arxiv.org/abs/2112.01746v1 | https://arxiv.org/pdf/2112.01746v1.pdf | MSP : Refine Boundary Segmentation via Multiscale Superpixel | In this paper, we propose a simple but effective message passing method to improve the boundary quality for the semantic segmentation result. Inspired by the generated sharp edges of superpixel blocks, we employ superpixel to guide the information passing within feature map. Simultaneously, the sharp boundaries of the ... | ['Leye Wang', 'Yong liu', 'Banghuai Li', 'Huabin Huang', 'Jie Zhu'] | 2021-12-03 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 1.42400200e-02 -1.11020207e-02 -4.83699851e-02 -8.00661385e-01
-6.15845025e-01 -6.07715011e-01 2.71902561e-01 5.93335256e-02
-5.82225800e-01 4.84426856e-01 -1.99228525e-02 -1.89432010e-01
3.65571856e-01 -9.71945167e-01 -9.58228052e-01 -5.75856745e-01
2.02749431e-01 -1.22122914e-01 1.14897704e+00 5.52501455... | [9.551346778869629, 0.2629944682121277] |
f7af5a31-cf36-4370-966d-bb1e89d5da3d | unsupervised-voice-activity-detection-by | 2206.13420 | null | https://arxiv.org/abs/2206.13420v1 | https://arxiv.org/pdf/2206.13420v1.pdf | Unsupervised Voice Activity Detection by Modeling Source and System Information using Zero Frequency Filtering | Voice activity detection (VAD) is an important pre-processing step for speech technology applications. The task consists of deriving segment boundaries of audio signals which contain voicing information. In recent years, it has been shown that voice source and vocal tract system information can be extracted using zero-... | ['Mathew Magimai. -Doss', 'RaviShankar Prasad', 'Eklavya Sarkar'] | 2022-06-27 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 1.10862598e-01 -2.70041883e-01 7.46837333e-02 3.01429927e-02
-1.04945469e+00 -7.84271657e-01 4.30280775e-01 2.59149000e-02
-4.97139432e-02 5.14134228e-01 5.65354645e-01 -5.10696590e-01
-8.14846158e-02 -1.68783411e-01 3.86866331e-02 -8.35367501e-01
-1.78605750e-01 -2.68093705e-01 3.22635978e-01 2.22654436... | [14.967116355895996, 5.839415550231934] |
359d5ec3-09c7-4255-b49d-73a67472f1a4 | derivation-of-the-backpropagation-algorithm | 2102.04320 | null | https://arxiv.org/abs/2102.04320v2 | https://arxiv.org/pdf/2102.04320v2.pdf | Derivation of the Backpropagation Algorithm Based on Derivative Amplification Coefficients | The backpropagation algorithm for neural networks is widely felt hard to understand, despite the existence of some well-written explanations and/or derivations. This paper provides a new derivation of this algorithm based on the concept of derivative amplification coefficients. First proposed by this author for fully c... | ['Yiping Cheng'] | 2021-02-08 | null | null | null | null | ['mathematical-induction'] | ['reasoning'] | [ 2.29694262e-01 3.24258685e-01 4.96121682e-02 -3.41049850e-01
4.70185101e-01 -3.89247924e-01 3.13698679e-01 -4.15990725e-02
-4.98862505e-01 7.48868883e-01 -3.90192509e-01 -7.14209199e-01
-5.62585354e-01 -6.94858491e-01 -5.35158813e-01 -5.71431398e-01
-3.15614820e-01 -2.28290454e-01 7.56763965e-02 -7.92806685... | [8.043670654296875, 3.391005277633667] |
dd9a1068-2a87-4ca6-a09a-c7ab1f8ae90a | cyber-attack-detection-thanks-to-machine | 2001.06309 | null | https://arxiv.org/abs/2001.06309v1 | https://arxiv.org/pdf/2001.06309v1.pdf | Cyber Attack Detection thanks to Machine Learning Algorithms | Cybersecurity attacks are growing both in frequency and sophistication over the years. This increasing sophistication and complexity call for more advancement and continuous innovation in defensive strategies. Traditional methods of intrusion detection and deep packet inspection, while still largely used and recommende... | ['Antoine Delplace', 'Sheryl Hermoso', 'Kristofer Anandita'] | 2020-01-17 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [ 7.09702298e-02 -2.72834003e-01 -3.31286490e-01 -1.68041036e-01
2.35589053e-02 -1.00344241e+00 6.27973914e-01 3.33288163e-01
-4.13621366e-01 6.96987867e-01 -5.69341302e-01 -8.62837851e-01
-2.16059044e-01 -8.17868769e-01 2.00547889e-01 -4.98487592e-01
-3.87404799e-01 4.88302559e-01 7.02569425e-01 -2.80416161... | [5.218775749206543, 7.224337577819824] |
f5e32c8d-9fa3-4d61-bfc6-539fb90ab1c5 | n-best-hypotheses-reranking-for-text-to-sql | 2210.10668 | null | https://arxiv.org/abs/2210.10668v1 | https://arxiv.org/pdf/2210.10668v1.pdf | N-Best Hypotheses Reranking for Text-To-SQL Systems | Text-to-SQL task maps natural language utterances to structured queries that can be issued to a database. State-of-the-art (SOTA) systems rely on finetuning large, pre-trained language models in conjunction with constrained decoding applying a SQL parser. On the well established Spider dataset, we begin with Oracle stu... | ['Dilek Hakkani-Tur', 'Sree Hari Krishnan Parthasarathi', 'Lu Zeng'] | 2022-10-19 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 3.22605819e-01 4.69741970e-01 -3.99877876e-01 -7.75641382e-01
-1.51544106e+00 -6.55239224e-01 4.59781766e-01 3.90898973e-01
-4.48963732e-01 2.92942554e-01 2.49979511e-01 -5.24900317e-01
-8.61871764e-02 -8.73990059e-01 -1.13478911e+00 3.48414689e-01
-1.72772542e-01 1.00462592e+00 5.20950317e-01 -2.42970139... | [9.839747428894043, 7.8671488761901855] |
f34d6b7e-71b3-471e-8ace-b33ee218092e | response-generation-in-longitudinal-dialogues | 2305.15908 | null | https://arxiv.org/abs/2305.15908v1 | https://arxiv.org/pdf/2305.15908v1.pdf | Response Generation in Longitudinal Dialogues: Which Knowledge Representation Helps? | Longitudinal Dialogues (LD) are the most challenging type of conversation for human-machine dialogue systems. LDs include the recollections of events, personal thoughts, and emotions specific to each individual in a sparse sequence of dialogue sessions. Dialogue systems designed for LDs should uniquely interact with th... | ['Giuseppe Riccardi', 'Simone Caldarella', 'Seyed Mahed Mousavi'] | 2023-05-25 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 6.57556653e-02 7.70681083e-01 1.80543125e-01 -4.96938944e-01
-5.84151626e-01 -5.06836236e-01 1.02010357e+00 4.16393548e-01
-2.22717866e-01 8.84448290e-01 1.00818789e+00 -1.36203784e-03
4.43498582e-01 -6.98557496e-01 6.61460087e-02 -8.12052190e-02
-1.94885314e-01 6.77609026e-01 -3.19199830e-01 -7.05431283... | [12.670618057250977, 8.131457328796387] |
3b0326c1-9ad8-4cfe-9b61-67cd4e2222e2 | strong-baselines-for-complex-word | 1904.05953 | null | http://arxiv.org/abs/1904.05953v1 | http://arxiv.org/pdf/1904.05953v1.pdf | Strong Baselines for Complex Word Identification across Multiple Languages | Complex Word Identification (CWI) is the task of identifying which words or
phrases in a sentence are difficult to understand by a target audience. The
latest CWI Shared Task released data for two settings: monolingual (i.e. train
and test in the same language) and cross-lingual (i.e. test in a language not
seen during... | ['Fernando Alva-Manchego', 'Daniel King', 'Elisabeth Fritzsch', 'Pierre Finnimore', 'Alison Sneyd', 'Aneeq Ur Rehman', 'Andreas Vlachos'] | 2019-04-11 | strong-baselines-for-complex-word-1 | https://aclanthology.org/N19-1102 | https://aclanthology.org/N19-1102.pdf | naacl-2019-6 | ['complex-word-identification'] | ['natural-language-processing'] | [ 9.71284956e-02 -2.46386662e-01 -5.09506501e-02 -4.16172922e-01
-1.30351925e+00 -1.01271772e+00 8.82812619e-01 1.91332638e-01
-9.41810250e-01 7.81454206e-01 2.94610262e-01 -7.77019083e-01
-9.57118871e-04 -2.50510573e-01 -6.46970570e-01 -3.12213272e-01
-2.47817803e-02 7.51881421e-01 9.23640579e-02 -2.03247249... | [10.60196590423584, 10.326299667358398] |
4faef81f-1ef1-46de-a455-d049bf32c7fc | where-is-my-parcel-fast-and-efficient | null | null | https://ieeexplore.ieee.org/document/8931717 | http://fabondzogang.wdfiles.com/local--files/publications/ASOS_User_Intent_Classification_conversationalAI.pdf | “Where is My Parcel?” Fast and Efficient Classifiers to Detect User Intent in Natural Language | We study the performance of customer intent classifiers designed to predict the most popular intent received through ASOS.com Customer Care Department, namely “Where is my order?”. These queries are characterised by the use of colloquialism, label noise and short message length. We conduct extensive experiments with tw... | ['Nikos Konstantinidis', 'David Wardrope', 'Amal Vaidya', 'Fabon Dzogang', 'Constantina Nicolaou'] | 2019-12-16 | null | null | null | snams-2019-12 | ['english-conversational-speech-recognition'] | ['speech'] | [ 8.43500346e-02 2.37387270e-01 -3.71311754e-01 -4.43257719e-01
-5.86632967e-01 -7.56361485e-01 6.87631011e-01 4.57666427e-01
-6.89330876e-01 3.50726843e-01 1.75056756e-01 -7.30129898e-01
-1.56899557e-01 -6.54386222e-01 -5.66315532e-01 -4.92064565e-01
-1.90656275e-01 3.83769751e-01 -1.88721642e-01 -2.95868456... | [11.025479316711426, 7.386396884918213] |
f5f2a2a6-bd91-4523-847f-ba65e0d6a4e1 | 3-dimensional-sonic-phase-invariant-echo | 2306.08281 | null | https://arxiv.org/abs/2306.08281v2 | https://arxiv.org/pdf/2306.08281v2.pdf | 3-Dimensional Sonic Phase-invariant Echo Localization | Parallax and Time-of-Flight (ToF) are often regarded as complementary in robotic vision where various light and weather conditions remain challenges for advanced camera-based 3-Dimensional (3-D) reconstruction. To this end, this paper establishes Parallax among Corresponding Echoes (PaCE) to triangulate acoustic ToF pu... | ['Christopher Hahne'] | 2023-06-14 | null | null | null | null | ['object-localization'] | ['computer-vision'] | [ 5.21326840e-01 -5.86491562e-02 4.04317290e-01 -1.59142360e-01
-8.79906833e-01 -7.66542077e-01 6.06244624e-01 1.16996273e-01
-7.97033548e-01 1.20557182e-01 -4.62280363e-01 5.98951764e-02
-6.15083992e-01 -1.89061970e-01 -9.26013172e-01 -9.58859026e-01
-5.06985724e-01 7.04068065e-01 2.93379456e-01 3.58570188... | [7.513197898864746, -2.0268428325653076] |
8d1cdc1e-9589-48b6-81cd-020c4d736c56 | learning-low-dimensional-dynamics-from-whole | 2305.14369 | null | https://arxiv.org/abs/2305.14369v1 | https://arxiv.org/pdf/2305.14369v1.pdf | Learning low-dimensional dynamics from whole-brain data improves task capture | The neural dynamics underlying brain activity are critical to understanding cognitive processes and mental disorders. However, current voxel-based whole-brain dimensionality reduction techniques fall short of capturing these dynamics, producing latent timeseries that inadequately relate to behavioral tasks. To address ... | ['Vince Calhoun', 'Sergey Plis', 'Amrit Kashyap', 'Marlena Duda', 'Riyasat Ohib', 'Donghyun Kim', 'Eloy Geenjaar'] | 2023-05-18 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 1.23006172e-01 -7.38241374e-02 2.40921170e-01 -8.79576653e-02
-1.80885866e-01 -6.56414688e-01 9.41863298e-01 -2.78839916e-01
-5.16208947e-01 2.45817408e-01 6.07357979e-01 2.95292033e-04
-6.93675399e-01 -3.83341968e-01 -4.82686758e-01 -7.92233527e-01
-3.43333870e-01 4.61862236e-01 -1.39357314e-01 -7.80846030... | [12.53559398651123, 3.4110279083251953] |
e2206218-ccd7-46c6-afd7-8bfcf0ef26b3 | learning-libraries-of-subroutines-for | null | null | http://papers.nips.cc/paper/8006-learning-libraries-of-subroutines-for-neurallyguided-bayesian-program-induction | http://papers.nips.cc/paper/8006-learning-libraries-of-subroutines-for-neurallyguided-bayesian-program-induction.pdf | Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction | Successful approaches to program induction require a hand-engineered
domain-specific language (DSL), constraining the space of allowed
programs and imparting prior knowledge of the domain. We contribute
a program induction algorithm that learns a DSL while
jointly training a neural network to efficiently searc... | ['Armando Solar-Lezama', 'Mathias Sablé-Meyer', 'Lucas Morales', 'Kevin Ellis', 'Josh Tenenbaum'] | 2018-12-01 | null | null | null | neurips-2018-12 | ['program-induction'] | ['computer-code'] | [ 3.50994855e-01 4.87179965e-01 -9.33880270e-01 -7.18475223e-01
-4.92705017e-01 -8.63096178e-01 3.40460956e-01 1.84338614e-01
1.07104115e-01 6.37728393e-01 -7.47983903e-02 -1.03004038e+00
1.28853828e-01 -1.46763110e+00 -1.50499094e+00 1.79690018e-01
-4.78447825e-01 3.63827497e-01 2.01912388e-01 -1.95816532... | [8.455288887023926, 7.244536876678467] |
098b85e8-f0cf-45ac-8883-2be980dcbd30 | partcom-part-composition-learning-for-3d-open | 2211.10880 | null | https://arxiv.org/abs/2211.10880v1 | https://arxiv.org/pdf/2211.10880v1.pdf | PartCom: Part Composition Learning for 3D Open-Set Recognition | 3D recognition is the foundation of 3D deep learning in many emerging fields, such as autonomous driving and robotics.Existing 3D methods mainly focus on the recognition of a fixed set of known classes and neglect possible unknown classes during testing. These unknown classes may cause serious accidents in safety-criti... | ['Jiang Haiyong', 'Xiao Jun', 'Weng Tingyu'] | 2022-11-20 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [-8.15856904e-02 2.19072580e-01 -3.66695881e-01 -7.09377706e-01
-7.70007670e-01 -5.71340978e-01 7.09232986e-01 -2.95642227e-01
5.35695255e-02 1.83474392e-01 -1.38700724e-01 -5.18063784e-01
2.76992805e-02 -8.72336924e-01 -1.17974365e+00 -3.82487237e-01
9.08973888e-02 8.51170063e-01 5.68158209e-01 -3.95834655... | [8.063688278198242, -2.8324174880981445] |
3677741b-5807-4cef-82e7-870cfa473a0e | hardware-accelerator-and-neural-network-co | 2209.03807 | null | https://arxiv.org/abs/2209.03807v2 | https://arxiv.org/pdf/2209.03807v2.pdf | Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices | The increasing spread of artificial neural networks does not stop at ultralow-power edge devices. However, these very often have high computational demand and require specialized hardware accelerators to ensure the design meets power and performance constraints. The manual optimization of neural networks along with the... | ['Oliver Bringmann', 'Konstantin Lübeck', 'Paul Palomero Bernardo', 'Tobias Hald', 'Adrian Frischknecht', 'Christoph Gerum'] | 2022-09-08 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-1.46983504e-01 -5.16002238e-01 -2.17346862e-01 -1.75978951e-02
1.26772359e-01 -4.36410695e-01 -3.44556905e-02 6.31544692e-03
-4.30876344e-01 6.44491166e-02 -2.10938767e-01 -6.48168087e-01
-3.21100980e-01 -4.45804715e-01 -1.29611388e-01 -7.79134810e-01
-3.18780430e-02 2.51243234e-01 2.30322331e-01 -5.37031293... | [8.43000316619873, 2.803692102432251] |
ed61319e-dc2f-48f8-b905-c2abebf4ddc9 | power-normalizations-in-fine-grained-image | 2012.13975 | null | https://arxiv.org/abs/2012.13975v2 | https://arxiv.org/pdf/2012.13975v2.pdf | Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classification | Power Normalizations (PN) are useful non-linear operators which tackle feature imbalances in classification problems. We study PNs in the deep learning setup via a novel PN layer pooling feature maps. Our layer combines the feature vectors and their respective spatial locations in the feature maps produced by the last ... | ['Hongguang Zhang', 'Piotr Koniusz'] | 2020-12-27 | null | null | null | null | ['material-classification', 'scene-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.75272280e-01 6.58476772e-03 7.81391189e-02 -1.74312681e-01
-1.95710376e-01 -5.72373927e-01 9.09262717e-01 3.97357285e-01
-5.43955207e-01 4.03252393e-01 1.99982986e-01 -4.03602123e-02
-7.41031766e-01 -1.01195955e+00 -7.10006952e-01 -1.13003016e+00
-4.77813840e-01 -3.09032369e-02 3.01841855e-01 -2.86566287... | [8.809890747070312, 2.6992204189300537] |
379f08d2-5a48-46a5-ac2e-43bbabaa46d6 | fast-trajectory-end-point-prediction-with | 2302.13796 | null | https://arxiv.org/abs/2302.13796v1 | https://arxiv.org/pdf/2302.13796v1.pdf | Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control | Prediction skills can be crucial for the success of tasks where robots have limited time to act or joints actuation power. In such a scenario, a vision system with a fixed, possibly too low, sampling rate could lead to the loss of informative points, slowing down prediction convergence and reducing the accuracy. In thi... | ['Chiara Bartolozzi', 'Arren Glover', 'Massimiliano Iacono', 'Luna Gava', 'Marco Monforte'] | 2023-02-27 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 2.86552429e-01 -2.33824849e-02 -6.96419403e-02 -6.09369017e-02
-2.13724181e-01 -3.87631893e-01 5.69470525e-01 -1.13438681e-01
-8.78392637e-01 5.33344150e-01 -3.40828449e-01 -3.83517593e-01
-2.01652825e-01 -7.39479899e-01 -1.10449314e+00 -6.72068298e-01
-1.38488054e-01 4.43612128e-01 4.66881990e-01 5.18808588... | [4.8200578689575195, 1.1690418720245361] |
57dcfd37-6db8-4d57-8b46-865337ad4223 | discriminative-clustering-for-robust | 1905.13331 | null | https://arxiv.org/abs/1905.13331v1 | https://arxiv.org/pdf/1905.13331v1.pdf | Discriminative Clustering for Robust Unsupervised Domain Adaptation | Unsupervised domain adaptation seeks to learn an invariant and discriminative representation for an unlabeled target domain by leveraging the information of a labeled source dataset. We propose to improve the discriminative ability of the target domain representation by simultaneously learning tightly clustered target ... | ['Ricardo Henao', 'Rui Wang', 'Guoyin Wang'] | 2019-05-30 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 4.1486090e-01 -5.4195956e-03 -7.5436425e-01 -6.3471079e-01
-9.5544857e-01 -1.0254761e+00 5.8531827e-01 1.6884111e-01
-2.3656829e-01 1.0120931e+00 3.1418900e-03 2.5666794e-01
1.7623356e-01 -6.4064288e-01 -5.7965428e-01 -8.4007335e-01
3.6659479e-01 9.0499020e-01 2.6780042e-01 -1.6672742e-01
-5.9331637e-02... | [10.331658363342285, 3.0665111541748047] |
d1bd2b1f-61e2-4309-9ffc-d568cf590681 | building-machines-that-learn-and-think-like | 1604.00289 | null | http://arxiv.org/abs/1604.00289v3 | http://arxiv.org/pdf/1604.00289v3.pdf | Building Machines That Learn and Think Like People | Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans ... | ['Tomer D. Ullman', 'Samuel J. Gershman', 'Joshua B. Tenenbaum', 'Brenden M. Lake'] | 2016-04-01 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 2.30577916e-01 3.48708630e-01 8.25851485e-02 -4.41630572e-01
3.61666888e-01 -4.58336085e-01 8.45499873e-01 1.79993287e-01
-2.15306118e-01 5.34377038e-01 2.24034518e-01 -4.94590491e-01
-4.86207992e-01 -1.14861190e+00 -6.85020626e-01 -3.87335390e-01
-4.34607081e-02 6.03962123e-01 3.63245726e-01 -6.72968566... | [9.117765426635742, 6.516641139984131] |
cd7c7e2b-e4a0-4d1e-9555-ead70c553a08 | segmentation-based-vs-regression-based | null | null | https://www.mdpi.com/2313-433X/8/2/23 | https://doi.org/10.3390/jimaging8020023 | Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound Images | The fetus head circumference (HC) is a key biometric to monitor fetus growth during pregnancy, which is estimated from ultrasound (US) images. The standard approach to automatically measure the HC is to use a segmentation network to segment the skull, and then estimate the head contour length from the segmentation map ... | ['Jing; Caroline Petitjean; and Samia Ainouz', 'Zhang'] | 2022-01-25 | null | null | null | journal-of-imaging-2022-1 | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 2.56942902e-02 6.70226514e-01 8.31242949e-02 -5.95333576e-01
-3.39604169e-01 -4.41134989e-01 3.28382403e-01 4.67810243e-01
-5.14652014e-01 4.67912436e-01 -3.54800195e-01 -4.67085481e-01
-9.58013833e-02 -1.02253735e+00 -7.56376207e-01 -7.13847935e-01
-2.47933954e-01 8.66877496e-01 1.43591911e-01 -2.63661202... | [14.243382453918457, -2.4404587745666504] |
570e0285-c956-4887-902f-6ddd4c1391db | improving-signer-independent-sign-language | null | null | https://aclanthology.org/2022.sltat-1.7 | https://aclanthology.org/2022.sltat-1.7.pdf | Improving Signer Independent Sign Language Recognition for Low Resource Languages | The reliance of deep learning algorithms on large scale datasets represents a significant challenge when learning from low resource sign language datasets. This challenge is compounded when we consider that, for a model to be effective in the real world, it must not only learn the variations of a given sign, but also l... | ['Anthony Ventresque', 'Frank Fowley', 'Ellen Rushe', 'Ruth Holmes'] | null | null | null | null | sltat-lrec-2022-6 | ['sign-language-recognition'] | ['computer-vision'] | [ 2.96830416e-01 -2.40811810e-01 8.42435062e-02 -5.22837937e-01
-8.45212102e-01 -7.24636674e-01 6.41883910e-01 -1.00152791e+00
-8.40802848e-01 6.62706852e-01 5.68671882e-01 -2.15968922e-01
-2.22563282e-01 -3.06189775e-01 -8.33112836e-01 -6.62411928e-01
2.14112755e-02 6.12896323e-01 1.30223945e-01 -2.47307301... | [9.180654525756836, -6.498172760009766] |
47bbba2a-a83e-4fb0-ae54-9d2d8fb2ff25 | improving-safety-in-physical-human-robot | 2302.11933 | null | https://arxiv.org/abs/2302.11933v2 | https://arxiv.org/pdf/2302.11933v2.pdf | Improving safety in physical human-robot collaboration via deep metric learning | Direct physical interaction with robots is becoming increasingly important in flexible production scenarios, but robots without protective fences also pose a greater risk to the operator. In order to keep the risk potential low, relatively simple measures are prescribed for operation, such as stopping the robot if ther... | ['Hans Wernher van de Venn', 'Davide Scaramuzza', 'Ying Zaoshi', 'Grammatiki Zanni', 'Maryam Rezayati'] | 2023-02-23 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 1.32376328e-01 4.85386282e-01 -1.00052953e-01 -3.29561997e-03
9.62965041e-02 -2.67802179e-01 3.71235520e-01 -6.95496574e-02
-7.51775026e-01 7.15635061e-01 -4.23679978e-01 -1.09859928e-01
-6.80397630e-01 -7.87511528e-01 -6.29417181e-01 -7.59101748e-01
-3.27031732e-01 7.24673271e-01 3.65164846e-01 -5.63081741... | [4.897934913635254, 1.0509798526763916] |
43fe59a3-25bc-4507-98f6-4178326af0cd | reconnaissance-automatique-de-la-parole | null | null | https://aclanthology.org/F12-1083 | https://aclanthology.org/F12-1083.pdf | Reconnaissance automatique de la parole distante dans un habitat intelligent : m\'ethodes multi-sources en conditions r\'ealistes (Distant Speech Recognition in a Smart Home : Comparison of Several Multisource ASRs in Realistic Conditions) [in French] | null | ['Fran{\\c{c}}ois Portet', 'Michel Vacher', 'Benjamin Lecouteux'] | 2012-06-01 | reconnaissance-automatique-de-la-parole-1 | https://aclanthology.org/F12-1083 | https://aclanthology.org/F12-1083.pdf | jeptalnrecital-2012-6 | ['distant-speech-recognition'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.492165565490723, 3.5879523754119873] |
a75af41a-f0ee-4758-a000-ed98adee7d74 | towards-the-evolution-of-vertical-axis-wind | 1204.4107 | null | http://arxiv.org/abs/1204.4107v4 | http://arxiv.org/pdf/1204.4107v4.pdf | Towards the Evolution of Vertical-Axis Wind Turbines using Supershapes | We have recently presented an initial study of evolutionary algorithms used
to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are
evaluated under approximated wind tunnel conditions after being physically
instantiated by a 3D printer. That is, unlike other approaches such as
computational fluid... | ['Larry Bull', 'Richard J. Preen'] | 2012-04-18 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-1.15941294e-01 -8.58450383e-02 6.50609136e-01 3.49574447e-01
3.11209649e-01 -8.99826527e-01 7.42647827e-01 -2.54504472e-01
-7.54384622e-02 8.95476580e-01 -3.18750590e-01 -3.85516644e-01
-6.78204477e-01 -1.00366557e+00 -3.80784571e-01 -6.04679465e-01
-4.60484892e-01 6.22747958e-01 1.54662892e-01 -5.39347231... | [5.738297462463379, 3.740584373474121] |
af00f8dc-4d18-4879-a01a-05df10ee0989 | classification-of-passes-in-football-matches | 1407.5093 | null | http://arxiv.org/abs/1407.5093v1 | http://arxiv.org/pdf/1407.5093v1.pdf | Classification of Passes in Football Matches using Spatiotemporal Data | A knowledgeable observer of a game of football (soccer) can make a subjective
evaluation of the quality of passes made between players during the game. We
investigate the problem of producing an automated system to make the same
evaluation of passes. We present a model that constructs numerical predictor
variables from... | ['Joël Estephan', 'Michael Horton', 'Sanjay Chawla', 'Joachim Gudmundsson'] | 2014-07-18 | null | null | null | null | ['game-of-football', 'pass-classification'] | ['playing-games', 'playing-games'] | [-3.77566367e-01 6.28456175e-02 -1.32827416e-01 -4.72444206e-01
-8.84878218e-01 -7.30590820e-01 1.53649762e-01 4.46083248e-01
-6.17152810e-01 7.64522433e-01 1.89427175e-02 -1.30200356e-01
-4.68939424e-01 -9.66819406e-01 -5.48398614e-01 -3.75022620e-01
-1.48258284e-01 7.06832409e-01 5.41323066e-01 -3.74386668... | [6.6689252853393555, 0.4043344259262085] |
7938246e-fb40-4d25-a836-81be6e159814 | hospital-transfer-risk-prediction-for-covid | 2301.01596 | null | https://arxiv.org/abs/2301.01596v1 | https://arxiv.org/pdf/2301.01596v1.pdf | Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE | The global COVID-19 pandemic has caused more than six million deaths worldwide. Medicalized hotels were established in Taiwan as quarantine facilities for COVID-19 patients with no or mild symptoms. Due to limited medical care available at these hotels, it is of paramount importance to identify patients at risk of clin... | ['Fang-Ming Hung', 'Ling Chen', 'Kuan-Chia Ling', 'Chih-Ho Hsu', 'Jun-En Ding'] | 2022-12-31 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-2.09016994e-01 -2.33161021e-02 2.84314305e-02 1.28378302e-01
-3.66126269e-01 -2.07572535e-01 -1.15451261e-01 1.18720138e+00
-4.18224037e-01 4.89548653e-01 1.88896269e-01 -7.25243092e-01
-8.37284863e-01 -9.41835701e-01 -2.95192972e-02 -7.66470373e-01
-1.16636801e+00 6.57035589e-01 -2.10141271e-01 -1.40749186... | [7.9308271408081055, 6.19534969329834] |
72c68025-24b1-4f7a-90fa-c428e074db94 | a-survey-of-algorithms-for-black-box-safety | 2005.02979 | null | https://arxiv.org/abs/2005.02979v3 | https://arxiv.org/pdf/2005.02979v3.pdf | A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems | Autonomous cyber-physical systems (CPS) can improve safety and efficiency for safety-critical applications, but require rigorous testing before deployment. The complexity of these systems often precludes the use of formal verification and real-world testing can be too dangerous during development. Therefore, simulation... | ['Robert J. Moss', 'Ritchie Lee', 'Mykel J. Kochenderfer', 'Mark Koren', 'Anthony Corso'] | 2020-05-06 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [ 3.50181133e-01 3.99396718e-01 -3.55388463e-01 5.50081604e-04
-4.99957412e-01 -4.71579820e-01 4.05034244e-01 2.58169860e-01
8.72440711e-02 1.16596639e+00 -6.00559711e-01 -1.03780067e+00
-4.75256145e-01 -7.71846294e-01 -6.53320611e-01 -4.80283082e-01
-5.53718328e-01 3.54723305e-01 6.88918829e-01 -3.46673638... | [4.945737361907959, 2.2474992275238037] |
352b8b53-d0d4-4a15-83c2-a66f235add24 | exploiting-dynamic-and-fine-grained-semantic | 2205.11973 | null | https://arxiv.org/abs/2205.11973v1 | https://arxiv.org/pdf/2205.11973v1.pdf | Exploiting Dynamic and Fine-grained Semantic Scope for Extreme Multi-label Text Classification | Extreme multi-label text classification (XMTC) refers to the problem of tagging a given text with the most relevant subset of labels from a large label set. A majority of labels only have a few training instances due to large label dimensionality in XMTC. To solve this data sparsity issue, most existing XMTC methods ta... | ['Tingting Zhao', 'Yarui Chen', 'Jucheng Yang', 'Tao Xu', 'Peng Huo', 'Huiling Song', 'YuAn Wang'] | 2022-05-24 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 3.67007792e-01 4.04687636e-02 -7.39340365e-01 -7.55694211e-01
-7.02171504e-01 -3.85307103e-01 4.17589724e-01 1.01652555e-01
-2.96447128e-01 6.05119824e-01 2.43260711e-01 -1.41945593e-02
-2.83509970e-01 -5.80850303e-01 -2.86212593e-01 -9.39717293e-01
4.24252808e-01 1.12760735e+00 2.60704905e-01 3.22333723... | [9.607993125915527, 4.3376898765563965] |
bcb50c40-f431-492c-8a91-b617144cc1e4 | response-ranking-with-multi-types-of-deep | null | null | https://dl.acm.org/doi/abs/10.1145/3462207 | https://dl.acm.org/doi/pdf/10.1145/3462207 | Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues | Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is challenging in three aspects: (1) the meaning of a context–response pair is built upon language units from multiple granularities (e.g., words, phrases, and sub-sentences, etc.); (2) local (e.g., a ... | ['Dongyan Zhao', 'Rui Yan', 'Wei Wu', 'Jiazhan Feng', 'Chongyang Tao', 'Ruijian Xu'] | 2021-08-17 | null | null | null | acm-transactions-on-information-systems-2021 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 3.12658519e-01 7.01332837e-02 -1.79444805e-01 -6.95998192e-01
-7.08187222e-01 -5.46629488e-01 8.09978724e-01 6.23960912e-01
-3.74433726e-01 5.98265707e-01 8.13489854e-01 -2.65391022e-01
-1.68769255e-01 -8.68863642e-01 -6.40472351e-03 -3.18596005e-01
3.39848220e-01 6.63875163e-01 3.86200666e-01 -1.06042111... | [12.575089454650879, 7.84657096862793] |
09d43aba-05be-43c1-849b-9363a56e14bc | source-free-domain-adaptation-for-multi-site | 2203.04299 | null | https://arxiv.org/abs/2203.04299v3 | https://arxiv.org/pdf/2203.04299v3.pdf | Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping | Skull stripping is a crucial prerequisite step in the analysis of brain magnetic resonance images (MRI). Although many excellent works or tools have been proposed, they suffer from low generalization capability. For instance, the model trained on a dataset with specific imaging parameters cannot be well applied to othe... | ['Li Wang', 'Yaqi Wang', 'You Zhang', 'Huiyu Zhou', 'Gangyong Jia', 'Chenghao Tan', 'Xiangde Luo', 'Yifan Cao', 'Shuai Wang', 'Ruilong Dan', 'Yunxiang Li'] | 2022-03-08 | null | null | null | null | ['source-free-domain-adaptation', 'skull-stripping'] | ['computer-vision', 'medical'] | [ 1.56401396e-01 -2.21424565e-01 4.90460843e-02 -5.89891434e-01
-2.75335729e-01 -7.42952973e-02 1.85103759e-01 -9.58654433e-02
-6.64876878e-01 7.31383622e-01 -8.42127353e-02 1.30179614e-01
-1.97766960e-01 -4.98710096e-01 -3.92111629e-01 -7.71990776e-01
4.15529460e-02 7.55785167e-01 7.69393682e-01 -1.89089939... | [14.324653625488281, -1.9573429822921753] |
478aa47e-91a8-4a7e-b311-44f6ce7a63b4 | skin-lesion-analyser-an-efficient-seven-way | 1907.03220 | null | https://arxiv.org/abs/1907.03220v3 | https://arxiv.org/pdf/1907.03220v3.pdf | Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet | Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to ... | ['Kajol Gupta', 'Saket S. Chaturvedi', 'Prakash. S. Prasad'] | 2019-07-07 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 3.99055332e-01 1.11931473e-01 -3.61951500e-01 1.65665567e-01
-8.90789151e-01 -6.37201428e-01 1.61772385e-01 3.51880401e-01
-5.87888896e-01 8.22143018e-01 -1.38644159e-01 -6.38241112e-01
1.05529696e-01 -8.84993196e-01 -1.73113048e-01 -8.02884161e-01
2.92801231e-01 -2.96874996e-02 1.71466753e-01 7.01927319... | [15.677083015441895, -2.999216318130493] |
ef27c1a6-d35b-4861-a0be-d6069a2ca039 | slisemap-explainable-dimensionality-reduction | 2201.04455 | null | https://arxiv.org/abs/2201.04455v2 | https://arxiv.org/pdf/2201.04455v2.pdf | SLISEMAP: Supervised dimensionality reduction through local explanations | Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item. It is possible to create global explanations for all data items, but these explanations generally have low fidelity for complex black box models. We propose a new supervise... | ['Kai Puolamäki', 'Jarmo Mäkelä', 'Anton Björklund'] | 2022-01-12 | null | null | null | null | ['explainable-models', 'supervised-dimensionality-reduction'] | ['computer-vision', 'computer-vision'] | [ 3.94086875e-02 7.57433593e-01 -1.92029357e-01 -5.21169782e-01
-2.39674240e-01 -1.60854205e-01 1.28540576e+00 4.23633486e-01
3.55664909e-01 1.32031068e-01 6.87497139e-01 -7.10523784e-01
-8.17947149e-01 -4.71033424e-01 -1.63574859e-01 -8.28882337e-01
-2.64159977e-01 7.81367183e-01 -1.75646409e-01 -6.75991774... | [8.65354061126709, 5.19645881652832] |
72eefc55-57b5-479f-b0ac-c97ca845e611 | no-rumours-please-a-multi-indic-lingual | 2010.06906 | null | https://arxiv.org/abs/2010.06906v1 | https://arxiv.org/pdf/2010.06906v1.pdf | No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection | The sudden widespread menace created by the present global pandemic COVID-19 has had an unprecedented effect on our lives. Man-kind is going through humongous fear and dependence on social media like never before. Fear inevitably leads to panic, speculations, and the spread of misinformation. Many governments have take... | ['Amar Prakash Azad', 'Suranjana Samanta', 'Mohit Bhardwaj', 'Debanjana Kar'] | 2020-10-14 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-2.88152248e-01 2.39219218e-01 -2.79050678e-01 -6.84791282e-02
-7.82972455e-01 -2.81678349e-01 1.06798244e+00 2.97483504e-01
-6.11248434e-01 7.83214092e-01 3.05554956e-01 -2.72704959e-01
4.95697945e-01 -1.20550227e+00 -4.30974483e-01 -2.59085417e-01
1.16191179e-01 4.80158240e-01 4.69845593e-01 -1.06650686... | [8.203045845031738, 10.29011058807373] |
7df633dd-9452-40e8-b266-7a9c5fed3f94 | eautodet-efficient-architecture-search-for | 2203.10747 | null | https://arxiv.org/abs/2203.10747v1 | https://arxiv.org/pdf/2203.10747v1.pdf | EAutoDet: Efficient Architecture Search for Object Detection | Training CNN for detection is time-consuming due to the large dataset and complex network modules, making it hard to search architectures on detection datasets directly, which usually requires vast search costs (usually tens and even hundreds of GPU-days). In contrast, this paper introduces an efficient framework, name... | ['Xiaokang Yang', 'Juanping Zhao', 'Junchi Yan', 'Jiale Lin', 'Xiaoxing Wang'] | 2022-03-21 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-9.14625749e-02 -5.00712812e-01 2.01557904e-01 -4.48716283e-02
-3.02625895e-01 -4.98470426e-01 1.39120474e-01 -2.08507240e-01
-9.06711340e-01 1.14348926e-01 -6.34949803e-01 -4.27715629e-01
3.38503510e-01 -8.10227633e-01 -8.78488719e-01 -5.93037188e-01
-2.13702414e-02 7.08087906e-02 9.95940626e-01 8.32745135... | [8.722162246704102, -0.3459068238735199] |
bf95d00d-1ca6-43f4-85fa-cde5a4b083cb | efficient-quantization-aware-training-with | 2306.07215 | null | https://arxiv.org/abs/2306.07215v1 | https://arxiv.org/pdf/2306.07215v1.pdf | Efficient Quantization-aware Training with Adaptive Coreset Selection | The expanding model size and computation of deep neural networks (DNNs) have increased the demand for efficient model deployment methods. Quantization-aware training (QAT) is a representative model compression method to leverage redundancy in weights and activations. However, most existing QAT methods require end-to-en... | ['Kwang-Ting Cheng', 'Shih-Yang Liu', 'Zechun Liu', 'Xijie Huang'] | 2023-06-12 | null | null | null | null | ['quantization', 'model-compression'] | ['methodology', 'methodology'] | [ 2.04100817e-01 -4.13844705e-01 -4.57575113e-01 -6.14423037e-01
-4.08332229e-01 -9.09725949e-02 1.79621145e-01 -1.76357385e-02
-1.02106321e+00 7.13504136e-01 -1.48998752e-01 -2.43522003e-01
-2.79933512e-01 -7.80077636e-01 -6.22147202e-01 -6.80298209e-01
9.24861506e-02 3.11601162e-01 3.78768027e-01 3.52925211... | [8.594949722290039, 3.0359551906585693] |
39a77c76-e477-429b-8bc4-0bfcfc374c65 | deep-learning-for-material-recognition-most | 2012.07495 | null | https://arxiv.org/abs/2012.07495v1 | https://arxiv.org/pdf/2012.07495v1.pdf | Deep Learning for Material recognition: most recent advances and open challenges | Recognizing material from color images is still a challenging problem today. While deep neural networks provide very good results on object recognition and has been the topic of a huge amount of papers in the last decade, their adaptation to material images still requires some works to reach equivalent accuracies. Neve... | ['Damien Muselet', 'Sixiang Xu', 'Alain Tremeau'] | 2020-12-14 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 3.05204242e-01 -5.39184570e-01 -6.86147287e-02 -3.30190688e-01
-4.21932995e-01 -2.51622379e-01 5.51868856e-01 3.14062946e-02
-3.11388880e-01 5.49141943e-01 -2.60895401e-01 9.28629860e-02
-3.85669470e-01 -1.09409165e+00 -8.33367765e-01 -8.45840454e-01
-4.79781907e-03 4.17626351e-01 4.26322728e-01 -1.30547062... | [10.16850471496582, -0.17606307566165924] |
710b5cb8-5943-465d-91ed-ac3d41841efa | discovering-objects-that-can-move | 2203.10159 | null | https://arxiv.org/abs/2203.10159v1 | https://arxiv.org/pdf/2203.10159v1.pdf | Discovering Objects that Can Move | This paper studies the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. However, by relying on appearance alone, these methods fail to separate objects... | ['Martial Hebert', 'Adrien Gaidon', 'Yu-Xiong Wang', 'Allan Jabri', 'Pavel Tokmakov', 'Zhipeng Bao'] | 2022-03-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bao_Discovering_Objects_That_Can_Move_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bao_Discovering_Objects_That_Can_Move_CVPR_2022_paper.pdf | cvpr-2022-1 | ['motion-segmentation'] | ['computer-vision'] | [ 3.49319160e-01 -2.12472647e-01 -3.65248531e-01 -3.04084033e-01
-5.41378021e-01 -8.60841513e-01 5.92076123e-01 -1.15669996e-01
-3.68807405e-01 6.38296425e-01 -2.10267946e-01 -1.74814984e-01
1.43404767e-01 -6.07754886e-01 -7.99841285e-01 -7.18198776e-01
-2.16941178e-01 5.18653929e-01 8.07854354e-01 -5.47281578... | [9.092209815979004, -0.14029598236083984] |
e0fbee18-8f3a-468e-8a82-1ed692bda54a | multi-graph-convolution-network-for-pose | 2304.04956 | null | https://arxiv.org/abs/2304.04956v1 | https://arxiv.org/pdf/2304.04956v1.pdf | Multi-Graph Convolution Network for Pose Forecasting | Recently, there has been a growing interest in predicting human motion, which involves forecasting future body poses based on observed pose sequences. This task is complex due to modeling spatial and temporal relationships. The most commonly used models for this task are autoregressive models, such as recurrent neural ... | ['Kewei Liang', 'Yuhong Shi', 'Hongwei Ren'] | 2023-04-11 | null | null | null | null | ['pose-prediction', 'human-pose-forecasting'] | ['computer-vision', 'computer-vision'] | [-5.35308979e-02 -9.99638662e-02 -5.84100522e-02 -1.94786057e-01
-7.55210519e-02 1.32888585e-01 2.62189776e-01 -4.43857610e-01
-3.24534267e-01 4.73961800e-01 5.74666142e-01 4.83440645e-02
2.71647703e-02 -7.56137431e-01 -8.66268516e-01 -5.32961547e-01
-3.61849666e-01 3.37400705e-01 3.76983881e-01 -3.40573817... | [7.257897853851318, -0.3724294602870941] |
577c21aa-2109-4b6e-8abd-6a820b6692da | do-as-i-can-not-as-i-get-topology-aware-multi | 2306.10345 | null | https://arxiv.org/abs/2306.10345v1 | https://arxiv.org/pdf/2306.10345v1.pdf | Do as I can, not as I get: Topology-aware multi-hop reasoning on multi-modal knowledge graphs | Multi-modal knowledge graph (MKG) includes triplets that consist of entities and relations and multi-modal auxiliary data. In recent years, multi-hop multi-modal knowledge graph reasoning (MMKGR) based on reinforcement learning (RL) has received extensive attention because it addresses the intrinsic incompleteness of M... | ['Lei Zhao', 'Wei Chen', 'Quoc Viet Hung Nguyen', 'Tong Chen', 'Hongzhi Yin', 'Shangfei Zheng'] | 2023-06-17 | null | null | null | null | ['knowledge-graphs', 'multi-modal-knowledge-graph'] | ['knowledge-base', 'knowledge-base'] | [-6.60832003e-02 5.91180027e-01 -4.75896239e-01 -2.53987730e-01
-9.20400143e-01 -5.81603944e-01 3.82196218e-01 3.09552610e-01
-4.31682095e-02 9.98362720e-01 1.68665797e-01 -1.97685167e-01
-6.70852780e-01 -1.37458277e+00 -8.63059223e-01 -5.48059583e-01
-3.14177424e-01 9.67612088e-01 3.28829825e-01 -5.47464192... | [8.88409423828125, 7.891096591949463] |
03e428c6-5856-4217-ad90-c6979f2fb427 | pix2vox-multi-scale-context-aware-3d-object | 2006.12250 | null | https://arxiv.org/abs/2006.12250v2 | https://arxiv.org/pdf/2006.12250v2.pdf | Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images | Recovering the 3D shape of an object from single or multiple images with deep neural networks has been attracting increasing attention in the past few years. Mainstream works (e.g. 3D-R2N2) use recurrent neural networks (RNNs) to sequentially fuse feature maps of input images. However, RNN-based approaches are unable t... | ['Shangchen Zhou', 'Wenxiu Sun', 'Hongxun Yao', 'Haozhe Xie', 'Shengping Zhang'] | 2020-06-22 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [-4.98233065e-02 -3.37744802e-01 2.01331124e-01 -4.43926662e-01
-7.93258429e-01 -7.54748508e-02 3.13450068e-01 -4.32034671e-01
-4.90312912e-02 3.91594887e-01 3.48251522e-01 2.65149772e-01
2.51129624e-02 -9.46895063e-01 -9.71136153e-01 -5.57150424e-01
5.38850188e-01 6.28182411e-01 2.99883813e-01 -1.10004000... | [8.371454238891602, -3.452169895172119] |
4a99d120-f13c-470d-9fcf-d52cd62a647b | thermal-image-super-resolution-using-second | 2108.00094 | null | https://arxiv.org/abs/2108.00094v1 | https://arxiv.org/pdf/2108.00094v1.pdf | Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields | Thermal images model the long-infrared range of the electromagnetic spectrum and provide meaningful information even when there is no visible illumination. Yet, unlike imagery that represents radiation from the visible continuum, infrared images are inherently low-resolution due to hardware constraints. The restoration... | ['William J. Beksi', 'Nolan B. Gutierrez'] | 2021-07-30 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 8.90610576e-01 -3.00128907e-01 2.89354295e-01 -3.04611862e-01
-4.00058091e-01 -5.31514406e-01 4.54677194e-01 -5.45879722e-01
-5.03199399e-01 5.52169800e-01 1.66816562e-01 -2.13168308e-01
-6.07247688e-02 -9.09640551e-01 -6.44869387e-01 -1.06677580e+00
1.94518715e-01 -3.31528306e-01 1.46527961e-01 -3.88381898... | [10.554356575012207, -2.263338088989258] |
f1440878-8d75-411f-b271-68c6e318cc1f | category-level-6d-object-pose-estimation-in | 2206.15436 | null | https://arxiv.org/abs/2206.15436v1 | https://arxiv.org/pdf/2206.15436v1.pdf | Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset | 6D object pose estimation is one of the fundamental problems in computer vision and robotics research. While a lot of recent efforts have been made on generalizing pose estimation to novel object instances within the same category, namely category-level 6D pose estimation, it is still restricted in constrained environm... | ['Xiaolong Wang', 'Yang Fu'] | 2022-06-30 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.41889784e-02 1.71593219e-01 -1.79182976e-01 -5.53017020e-01
-8.38386774e-01 -6.25562251e-01 4.33709204e-01 -3.39395881e-01
-4.71134067e-01 3.34475756e-01 5.80806434e-02 8.38233009e-02
2.59737611e-01 -3.89007747e-01 -1.08123088e+00 -5.26434481e-01
2.51933262e-02 9.49942887e-01 6.20774329e-01 -3.87878865... | [7.482084274291992, -2.5353262424468994] |
f65ef21f-9d6c-4981-98ed-a6327e216199 | exploring-adaptive-mcts-with-td-learning-in | 2210.05014 | null | https://arxiv.org/abs/2210.05014v3 | https://arxiv.org/pdf/2210.05014v3.pdf | Exploring Adaptive MCTS with TD Learning in miniXCOM | In recent years, Monte Carlo tree search (MCTS) has achieved widespread adoption within the game community. Its use in conjunction with deep reinforcement learning has produced success stories in many applications. While these approaches have been implemented in various games, from simple board games to more complicate... | ['Richard Zhao', 'Kimiya Saadat'] | 2022-10-10 | null | null | null | null | ['board-games', 'starcraft'] | ['playing-games', 'playing-games'] | [-2.35680908e-01 -2.73339182e-01 -5.32091223e-02 5.58883436e-02
-4.58219826e-01 -6.81546092e-01 5.68650842e-01 -2.30242848e-01
-8.80444169e-01 9.70261991e-01 -1.11484528e-01 -6.97842240e-01
-1.42755747e-01 -8.39303434e-01 -2.23932058e-01 -3.54643941e-01
-3.64623755e-01 6.74744487e-01 8.39207172e-01 -1.02757013... | [3.53910231590271, 1.4928343296051025] |
4fdd31e4-df7b-432f-b917-07b95445e95a | segment-anything | 2304.02643 | null | https://arxiv.org/abs/2304.02643v1 | https://arxiv.org/pdf/2304.02643v1.pdf | Segment Anything | We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and train... | ['Ross Girshick', 'Piotr Dollár', 'Wan-Yen Lo', 'Alexander C. Berg', 'Spencer Whitehead', 'Tete Xiao', 'Laura Gustafson', 'Chloe Rolland', 'Hanzi Mao', 'Nikhila Ravi', 'Eric Mintun', 'Alexander Kirillov'] | 2023-04-05 | null | null | null | null | ['visual-prompting'] | ['computer-vision'] | [ 6.26770020e-01 5.73739707e-01 -4.82085705e-01 -7.10513890e-01
-1.17430770e+00 -7.95428216e-01 6.18991554e-01 -3.93400103e-01
-3.69350284e-01 3.51856232e-01 6.94010034e-02 -5.39168417e-01
1.75762445e-01 -1.18787311e-01 -9.32820737e-01 -2.01069877e-01
1.76941216e-01 7.27254689e-01 5.36892295e-01 2.03784779... | [9.582902908325195, 0.49978604912757874] |
c9f548f9-77d9-4992-afc3-121eb788fccc | program-synthesis-performance-constrained-by | 1911.07721 | null | https://arxiv.org/abs/1911.07721v2 | https://arxiv.org/pdf/1911.07721v2.pdf | Program synthesis performance constrained by non-linear spatial relations in Synthetic Visual Reasoning Test | Despite remarkable advances in automated visual recognition by machines, some visual tasks remain challenging for machines. Fleuret et al. (2011) introduced the Synthetic Visual Reasoning Test (SVRT) to highlight this point, which required classification of images consisting of randomly generated shapes based on hidden... | ['Mark C. W. van Rossum', 'Scott C. Lowe', 'Penelope A. Lewis', 'Lu Yihe'] | 2019-11-18 | null | null | null | null | ['unsupervised-few-shot-learning'] | ['computer-vision'] | [ 2.78786302e-01 2.10941836e-01 1.24758653e-01 -3.54856312e-01
-2.47937575e-01 -8.92747879e-01 9.29579496e-01 2.54776835e-01
-4.60282743e-01 3.36629391e-01 -5.32880783e-01 -5.21478355e-01
-8.75934362e-02 -9.75767374e-01 -8.48304451e-01 -4.83158857e-01
4.87596504e-02 6.67355061e-01 4.62024361e-01 -1.41062096... | [10.298919677734375, 2.2637054920196533] |
a8b97f8d-2ece-450d-bda8-0209b4abc555 | identity-preserving-face-completion-for-large | 1807.08772 | null | http://arxiv.org/abs/1807.08772v1 | http://arxiv.org/pdf/1807.08772v1.pdf | Identity Preserving Face Completion for Large Ocular Region Occlusion | We present a novel deep learning approach to synthesize complete face images
in the presence of large ocular region occlusions. This is motivated by recent
surge of VR/AR displays that hinder face-to-face communications. Different from
the state-of-the-art face inpainting methods that have no control over the
synthesiz... | ['WangMeng Zuo', 'Jun Xing', 'Ruigang Yang', 'Zach Bessinger', 'Weikai Chen', 'Fuchang Liu', 'Yajie Zhao', 'Xiaoming Li'] | 2018-07-23 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 3.08595121e-01 3.08771700e-01 2.31092557e-01 -4.63694245e-01
-7.16483474e-01 -4.76299822e-01 5.32918513e-01 -7.79094279e-01
-2.39441041e-02 7.25610137e-01 2.13598847e-01 3.83274019e-01
2.15402409e-01 -5.32389760e-01 -1.00653827e+00 -8.53614926e-01
5.08266807e-01 3.74047667e-01 -1.45844281e-01 -3.94437402... | [12.83283805847168, -0.22356252372264862] |
79a2278b-7f63-4e6c-be9e-42f4ac34555b | semantic-information-marketing-in-the | 2302.11457 | null | https://arxiv.org/abs/2302.11457v2 | https://arxiv.org/pdf/2302.11457v2.pdf | Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework | In this paper, we address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data to help creating and rendering the digital copy of the physical world in the Metaverse. Due to the limited bandwidth, we propose to use semantic extraction a... | ['Xuemin Shen', 'Dong In Kim', 'Sumei Sun', 'Dusit Niyato', 'Ismail Lotfi'] | 2023-02-22 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 7.97840804e-02 6.20102525e-01 -5.09948909e-01 1.68832988e-01
-3.05184960e-01 -7.39591062e-01 2.42270455e-01 -3.92018348e-01
-4.01658535e-01 7.71591008e-01 -6.03544340e-02 -2.28015706e-01
-4.68067467e-01 -9.86270607e-01 -6.40615880e-01 -6.49236679e-01
-9.32602435e-02 4.35545415e-01 2.19041519e-02 -2.86564771... | [5.426929473876953, 2.6208176612854004] |
51a72896-38a6-40fd-ad17-ccd203945e71 | egocentric-hierarchical-visual-semantics | 2305.05422 | null | https://arxiv.org/abs/2305.05422v1 | https://arxiv.org/pdf/2305.05422v1.pdf | Egocentric Hierarchical Visual Semantics | We are interested in aligning how people think about objects and what machines perceive, meaning by this the fact that object recognition, as performed by a machine, should follow a process which resembles that followed by humans when thinking of an object associated with a certain concept. The ultimate goal is to buil... | ['Fausto Giunchiglia', 'Andrea Passerini', 'Andrea Bontempelli', 'Luca Erculiani'] | 2023-05-09 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 3.98363620e-01 -1.54948726e-01 -5.46926036e-02 -7.35277116e-01
4.31439579e-01 -8.34527075e-01 9.40188229e-01 5.02210796e-01
-3.98481905e-01 2.43635587e-02 4.05513495e-01 -3.70222270e-01
-1.03622824e-01 -9.58029091e-01 -5.80267347e-02 -6.13412023e-01
2.85474867e-01 4.55434710e-01 3.65720391e-01 -3.08565706... | [10.157367706298828, 8.836380004882812] |
57d8a8cd-a0ee-4d5f-a574-4c3dc3058fc7 | reprogramming-pretrained-language-models-for | 2301.02120 | null | https://arxiv.org/abs/2301.02120v1 | https://arxiv.org/pdf/2301.02120v1.pdf | Reprogramming Pretrained Language Models for Protein Sequence Representation Learning | Machine Learning-guided solutions for protein learning tasks have made significant headway in recent years. However, success in scientific discovery tasks is limited by the accessibility of well-defined and labeled in-domain data. To tackle the low-data constraint, recent adaptions of deep learning models pretrained on... | ['Payel Das', 'Pin-Yu Chen', 'Ria Vinod'] | 2023-01-05 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 4.97492671e-01 -4.89576831e-02 -2.32107699e-01 -5.04703164e-01
-9.26165998e-01 -5.49411774e-01 2.69805253e-01 6.23015761e-01
-6.99308395e-01 1.03865516e+00 1.95025995e-01 -6.18558109e-01
2.06713855e-01 -5.56298256e-01 -1.17203116e+00 -8.63325119e-01
-5.89114353e-02 6.51431441e-01 -8.63812715e-02 -2.99416840... | [4.746641159057617, 5.695224761962891] |
76686a89-c44f-4c17-95fe-f6f9a17f0343 | dynamic-graph-reasoning-for-multi-person-3d | 2207.11341 | null | https://arxiv.org/abs/2207.11341v2 | https://arxiv.org/pdf/2207.11341v2.pdf | Dynamic Graph Reasoning for Multi-person 3D Pose Estimation | Multi-person 3D pose estimation is a challenging task because of occlusion and depth ambiguity, especially in the cases of crowd scenes. To solve these problems, most existing methods explore modeling body context cues by enhancing feature representation with graph neural networks or adding structural constraints. Howe... | ['Dongmei Fu', 'Jian Wang', 'Qiansheng Yang', 'Zhongwei Qiu'] | 2022-07-22 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 1.26440510e-01 4.56114739e-01 7.94155449e-02 -3.97109151e-01
-3.19217920e-01 -3.12371343e-01 2.58689761e-01 3.17119099e-02
-2.57437646e-01 3.90472591e-01 4.55831319e-01 2.91770756e-01
-1.80850074e-01 -8.53657961e-01 -5.77051759e-01 -3.10323298e-01
-7.80116245e-02 1.11831892e+00 5.14094293e-01 -4.72955257... | [7.026687145233154, -0.959053635597229] |
b289a874-ad1b-458f-87db-aeb73c81357d | gait-recognition-with-mask-based | 2203.04038 | null | https://arxiv.org/abs/2203.04038v1 | https://arxiv.org/pdf/2203.04038v1.pdf | Gait Recognition with Mask-based Regularization | Most gait recognition methods exploit spatial-temporal representations from static appearances and dynamic walking patterns. However, we observe that many part-based methods neglect representations at boundaries. In addition, the phenomenon of overfitting on training data is relatively common in gait recognition, which... | ['Xin Yu', 'Shiqi Yu', 'George Q. Huang', 'Shunli Zhang', 'Beibei Lin', 'Chuanfu Shen'] | 2022-03-08 | null | null | null | null | ['multiview-gait-recognition'] | ['computer-vision'] | [-6.11453690e-02 -4.04690504e-01 -3.98762017e-01 -3.01917315e-01
-1.42279387e-01 7.03157410e-02 1.71205595e-01 -3.54913026e-01
-2.39910558e-01 7.69974351e-01 1.62147358e-01 1.91148013e-01
-4.78971340e-02 -8.65432322e-01 -8.14263105e-01 -1.02315664e+00
-2.57078528e-01 -1.47370538e-02 6.07573807e-01 -3.98463130... | [14.333512306213379, 1.3712937831878662] |
94bca121-fb5c-49e3-bade-7970b82230b1 | pgnet-real-time-arbitrarily-shaped-text | 2104.05458 | null | https://arxiv.org/abs/2104.05458v1 | https://arxiv.org/pdf/2104.05458v1.pdf | PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network | The reading of arbitrarily-shaped text has received increasing research attention. However, existing text spotters are mostly built on two-stage frameworks or character-based methods, which suffer from either Non-Maximum Suppression (NMS), Region-of-Interest (RoI) operations, or character-level annotations. In this pap... | ['Guangming Shi', 'Errui Ding', 'Jingtuo Liu', 'Junyu Han', 'Pengyuan Lyu', 'Xiaoqiang Zhang', 'Shanshan Liu', 'Fei Qi', 'Chengquan Zhang', 'Pengfei Wang'] | 2021-04-12 | null | null | null | null | ['text-spotting', 'scene-text-detection'] | ['computer-vision', 'computer-vision'] | [ 6.92512274e-01 7.56802037e-05 -7.06226602e-02 -2.78175831e-01
-9.17491436e-01 -3.24375153e-01 3.40798736e-01 3.54485482e-01
-5.75888693e-01 3.91608000e-01 -1.58594009e-02 -3.81366670e-01
2.14305341e-01 -7.45682359e-01 -6.51182234e-01 -7.49759257e-01
5.67874849e-01 2.62598515e-01 6.64762437e-01 9.15732011... | [11.984793663024902, 2.2251806259155273] |
a9055bcc-16ec-452d-a86b-0656abf6a9e4 | structured-dialogue-discourse-parsing-1 | 2306.15103 | null | https://arxiv.org/abs/2306.15103v1 | https://arxiv.org/pdf/2306.15103v1.pdf | Structured Dialogue Discourse Parsing | Dialogue discourse parsing aims to uncover the internal structure of a multi-participant conversation by finding all the discourse~\emph{links} and corresponding~\emph{relations}. Previous work either treats this task as a series of independent multiple-choice problems, in which the link existence and relations are dec... | ['Alexander I. Rudnicky', 'Ta-Chung Chi'] | 2023-06-26 | structured-dialogue-discourse-parsing | https://aclanthology.org/2022.sigdial-1.32 | https://aclanthology.org/2022.sigdial-1.32.pdf | sigdial-acl-2022-9 | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.77894551e-01 7.50059247e-01 -9.88918021e-02 -3.13990384e-01
-1.08663738e+00 -7.64226794e-01 5.59908330e-01 2.80483156e-01
-5.78965656e-02 7.90459573e-01 8.00459385e-01 -5.34550667e-01
-2.43138835e-01 -7.48620629e-01 -5.01147628e-01 -6.42553389e-01
-1.83574289e-01 6.39900088e-01 7.74174882e-03 -5.08246481... | [12.494017601013184, 7.965246677398682] |
33044c8d-f198-43ee-885c-bcf8df4e5fc3 | jointprop-joint-semi-supervised-learning-for | 2305.15872 | null | https://arxiv.org/abs/2305.15872v1 | https://arxiv.org/pdf/2305.15872v1.pdf | Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation | Semi-supervised learning has been an important approach to address challenges in extracting entities and relations from limited data. However, current semi-supervised works handle the two tasks (i.e., Named Entity Recognition and Relation Extraction) separately and ignore the cross-correlation of entity and relation in... | ['Anh Tuan Luu', 'Anran Hao', 'Yandan Zheng'] | 2023-05-25 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [-1.08224200e-02 6.32363200e-01 -7.18669355e-01 -5.81365824e-01
-7.47007370e-01 -7.61773050e-01 6.43276989e-01 5.79725266e-01
-3.30710232e-01 8.31678748e-01 1.63112223e-01 -1.60073921e-01
-1.20421037e-01 -7.42999732e-01 -5.17016292e-01 -3.51101071e-01
-1.20668516e-01 7.10297167e-01 4.14118618e-01 1.32540777... | [9.273602485656738, 8.61877727508545] |
4f00e496-347a-49dd-bf73-e1c3b1a10cb3 | shuowen-jiezi-linguistically-informed | 2106.00400 | null | https://arxiv.org/abs/2106.00400v3 | https://arxiv.org/pdf/2106.00400v3.pdf | Sub-Character Tokenization for Chinese Pretrained Language Models | Tokenization is fundamental to pretrained language models (PLMs). Existing tokenization methods for Chinese PLMs typically treat each character as an indivisible token. However, they ignore the unique feature of the Chinese writing system where additional linguistic information exists below the character level, i.e., a... | ['Qun Liu', 'Yasheng Wang', 'Maosong Sun', 'Zhiyuan Liu', 'Xiaozhi Wang', 'Fanchao Qi', 'Yingfa Chen', 'Zhengyan Zhang', 'Chenglei Si'] | 2021-06-01 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 4.48628850e-02 -1.45729706e-01 -5.94035447e-01 -2.48980433e-01
-7.31636941e-01 -9.08446133e-01 9.44721699e-02 8.16285759e-02
-8.13154757e-01 7.03996122e-01 4.11942959e-01 -7.05561161e-01
7.21339762e-01 -8.41941237e-01 -7.17534721e-01 -4.15482789e-01
5.90973139e-01 1.90017194e-01 -2.54237186e-02 6.14113323... | [10.204487800598145, 10.178444862365723] |
927eac8f-85be-4dcd-baef-e0cb14a87ff1 | a-benchmark-for-gait-recognition-under | 2107.08990 | null | https://arxiv.org/abs/2107.08990v1 | https://arxiv.org/pdf/2107.08990v1.pdf | A Benchmark for Gait Recognition under Occlusion Collected by Multi-Kinect SDAS | Human gait is one of important biometric characteristics for human identification at a distance. In practice, occlusion usually occurs and seriously affects accuracy of gait recognition. However, there is no available database to support in-depth research of this problem, and state-of-arts gait recognition methods have... | ['Xinbo Zhao', 'Na Li'] | 2021-07-19 | null | null | null | null | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-4.78566408e-01 -8.62485707e-01 -2.49582559e-01 -4.08449024e-03
-4.36690837e-01 -1.48117140e-01 -7.23493006e-03 -4.66038227e-01
-6.17928386e-01 4.14645761e-01 1.31446049e-01 4.23190922e-01
1.70195714e-01 -7.40707517e-01 -1.41459689e-01 -9.20029402e-01
-2.85749882e-02 7.06988931e-01 1.46565959e-01 -3.58851939... | [14.243080139160156, 1.4397118091583252] |
e7cdbf2c-24ae-40df-a0a7-ea9ecb851a07 | one-line-of-code-data-mollification-improves | 2305.18900 | null | https://arxiv.org/abs/2305.18900v1 | https://arxiv.org/pdf/2305.18900v1.pdf | One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models | Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images. Likelihood-based GMs are attractive due to the possibility to generate new data by a single model evaluation. ... | ['Maurizio Filippone', 'Pietro Michiardi', 'Giulio Franzese', 'Ba-Hien Tran'] | 2023-05-30 | null | null | null | null | ['density-estimation'] | ['methodology'] | [-8.82598087e-02 1.18508033e-01 3.45262885e-02 -5.66799752e-02
-9.11203861e-01 -2.22262174e-01 9.17317927e-01 -1.44995838e-01
-2.75039583e-01 7.46612787e-01 2.22465113e-01 -1.51605889e-01
-8.94705206e-02 -9.62179959e-01 -6.61605716e-01 -8.70353341e-01
-4.52582575e-02 6.60672247e-01 2.07090229e-01 -1.28091633... | [11.236185073852539, -0.21785937249660492] |
90861bf4-4809-4866-9e43-badbb9a65d30 | mdenet-multi-modal-dual-embedding-networks | 2305.01245 | null | https://arxiv.org/abs/2305.01245v1 | https://arxiv.org/pdf/2305.01245v1.pdf | MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition | Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively. Existing works mostly rely on a well-trained classifier considering the predicted probabilities of each known family with a threshold-based detection to achi... | ['Song Guo', 'Yuxia Sun', 'Yufeng Zhan', 'Wenchao Xu', 'Yuanyuan Xu', 'Jingcai Guo'] | 2023-05-02 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 2.68212259e-01 -5.99660575e-01 -1.33684248e-01 -1.06738620e-01
-6.05555654e-01 -6.64699793e-01 6.06003404e-01 3.30781303e-02
-5.83016463e-02 5.64884841e-01 -2.28737578e-01 -2.15805829e-01
-7.37950951e-02 -7.50765502e-01 -7.15824783e-01 -1.07084394e+00
-1.78214461e-01 1.97588712e-01 1.18133366e-01 -1.53412363... | [14.386828422546387, 9.609318733215332] |
170e0b63-3766-4d2e-9dc3-7e1fb973734b | on-the-interaction-between-annotation-quality | null | null | https://aclanthology.org/2021.ranlp-main.99 | https://aclanthology.org/2021.ranlp-main.99.pdf | On the Interaction between Annotation Quality and Classifier Performance in Abusive Language Detection | Abusive language detection has become an important tool for the cultivation of safe online platforms. We investigate the interaction of annotation quality and classifier performance. We use a new, fine-grained annotation scheme that allows us to distinguish between abusive language and colloquial uses of profanity that... | ['Sandra Kübler', 'Alexandra O’Neil', 'Holly Lopez Long'] | null | null | https://aclanthology.org/2021.ranlp-1.99 | https://aclanthology.org/2021.ranlp-1.99.pdf | ranlp-2021-9 | ['abusive-language'] | ['natural-language-processing'] | [-1.93607256e-01 -1.51137903e-01 -2.56042272e-01 -3.24687272e-01
-5.24972141e-01 -8.57688606e-01 8.13965440e-01 1.75559670e-01
-8.47015500e-01 9.61206019e-01 2.73123384e-01 -2.65003145e-01
2.47974783e-01 -6.30356014e-01 -1.25384536e-02 -5.41460514e-01
3.99174273e-01 3.26088101e-01 4.61584376e-03 -3.79155785... | [8.685145378112793, 10.456568717956543] |
bd82caeb-48ee-4630-bedb-905e766beb2a | convolutional-neural-network-based-efficient | 2203.11537 | null | https://arxiv.org/abs/2203.11537v2 | https://arxiv.org/pdf/2203.11537v2.pdf | Convolutional Neural Network-based Efficient Dense Point Cloud Generation using Unsigned Distance Fields | Dense point cloud generation from a sparse or incomplete point cloud is a crucial and challenging problem in 3D computer vision and computer graphics. So far, the existing methods are either computationally too expensive, suffer from limited resolution, or both. In addition, some methods are strictly limited to waterti... | ['Jani Boutellier', 'Abol Basher'] | 2022-03-22 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 6.85404167e-02 -1.33171231e-01 2.68839926e-01 -2.09395438e-01
-6.83374703e-01 -2.87694693e-01 4.52280164e-01 -8.95731077e-02
-1.17246918e-01 7.32664645e-01 -4.62801576e-01 -3.23110908e-01
-1.20265037e-01 -1.07421196e+00 -9.06018674e-01 -4.51402336e-01
-2.20507085e-02 9.06991124e-01 2.46624887e-01 -2.32036516... | [8.370190620422363, -3.4836010932922363] |
46c32fd8-7bf7-4852-9af5-315666c43e9d | p-noc-adversarial-cam-generation-for-weakly | 2305.12522 | null | https://arxiv.org/abs/2305.12522v1 | https://arxiv.org/pdf/2305.12522v1.pdf | P-NOC: Adversarial CAM Generation for Weakly Supervised Semantic Segmentation | To mitigate the necessity for large amounts of supervised segmentation annotation sets, multiple Weakly Supervised Semantic Segmentation (WSSS) strategies have been devised. These will often rely on advanced data and model regularization strategies to instigate the development of useful properties (e.g., prediction com... | ['Zanoni Dias', 'Helio Pedrini', 'Lucas David'] | 2023-05-21 | null | null | null | null | ['weakly-supervised-semantic-segmentation'] | ['computer-vision'] | [ 7.36771405e-01 7.16793060e-01 -2.33416319e-01 -4.18524772e-01
-1.01183331e+00 -7.82170296e-01 8.28573227e-01 -1.01158887e-01
-4.33178812e-01 5.78628600e-01 2.00822316e-02 -1.43697694e-01
1.14945747e-01 -4.62577969e-01 -8.52322519e-01 -5.24217665e-01
3.75415653e-01 2.89749295e-01 6.40087485e-01 -2.09167272... | [9.60326099395752, 0.6987987160682678] |
1266f905-e4a8-4baa-a3a0-e8c4157073e0 | augmenting-reddit-posts-to-determine-wellness | 2306.04059 | null | https://arxiv.org/abs/2306.04059v1 | https://arxiv.org/pdf/2306.04059v1.pdf | Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health | Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text. As the distribution of WD on social media data is intrinsically imbalanced, we experiment the generative NLP models for data augmentation to enable further improvement in the ... | ['Sunghwan Sohn', 'Vijay Mago', 'Muskan Garg', 'Chandreen Liyanage'] | 2023-06-06 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.08539718e-01 7.10886300e-01 -9.34964046e-02 -4.68676567e-01
-9.19180632e-01 -2.25886732e-01 8.58830214e-01 5.80898762e-01
-3.23511988e-01 8.20858657e-01 1.07077980e+00 -2.12962314e-01
1.25178089e-02 -7.16301739e-01 -1.54230982e-01 -4.97709095e-01
3.23732384e-02 6.71312749e-01 -1.93348184e-01 -3.64229977... | [8.582746505737305, 8.669063568115234] |
652142a3-b8a5-4e1b-865f-dbf3ab83e395 | joint-coordinate-regression-and-association | 2307.01004 | null | https://arxiv.org/abs/2307.01004v1 | https://arxiv.org/pdf/2307.01004v1.pdf | Joint Coordinate Regression and Association For Multi-Person Pose Estimation, A Pure Neural Network Approach | We introduce a novel one-stage end-to-end multi-person 2D pose estimation algorithm, known as Joint Coordinate Regression and Association (JCRA), that produces human pose joints and associations without requiring any post-processing. The proposed algorithm is fast, accurate, effective, and simple. The one-stage end-to-... | ['YuFeng Yao', 'Li Zhang', 'Wangpeng An', 'Yunshi Xie', 'Dongyang Yu'] | 2023-07-03 | null | null | null | null | ['pose-estimation', 'multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.77488112e-01 -1.54496143e-02 -3.99697982e-02 -3.95683736e-01
-9.11058128e-01 -8.16580653e-02 3.65389794e-01 -3.50988299e-01
-6.93179190e-01 5.45756519e-01 3.47293049e-01 2.82629609e-01
2.23925129e-01 -6.00416958e-01 -1.09649038e+00 -2.07412377e-01
8.02596286e-03 8.96269441e-01 2.37417877e-01 -2.43933752... | [7.1260480880737305, -0.7069253921508789] |
60ed4492-22eb-409b-9968-795b8cce028e | scene-text-recognition-with-semantics | 2210.10836 | null | https://arxiv.org/abs/2210.10836v1 | https://arxiv.org/pdf/2210.10836v1.pdf | Scene Text Recognition with Semantics | Scene Text Recognition (STR) models have achieved high performance in recent years on benchmark datasets where text images are presented with minimal noise. Traditional STR recognition pipelines take a cropped image as sole input and attempt to identify the characters present. This infrastructure can fail in instances ... | ['Lucia Specia', 'Zixu Wang', 'Yishu Miao', 'Joshua Cesare Placidi'] | 2022-10-19 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 9.31761503e-01 -2.47060269e-01 1.16228580e-01 -6.81138098e-01
-9.84214902e-01 -9.17349517e-01 1.06052637e+00 1.68020651e-01
-3.27552408e-01 2.50713170e-01 4.75564539e-01 -2.15770066e-01
2.16878146e-01 -3.63919735e-01 -7.29067802e-01 -4.13905501e-01
7.58706987e-01 6.53717875e-01 5.04433751e-01 -1.43586829... | [11.800008773803711, 2.203481912612915] |
9b2f53a4-d7de-434a-a756-99315d358701 | word-centrality-constrained-representation | null | null | https://aclanthology.org/2021.bionlp-1.17 | https://aclanthology.org/2021.bionlp-1.17.pdf | Word centrality constrained representation for keyphrase extraction | To keep pace with the increased generation and digitization of documents, automated methods that can improve search, discovery and mining of the vast body of literature are essential. Keyphrases provide a concise representation by identifying salient concepts in a document. Various supervised approaches model keyphrase... | ['Joyce Ho', 'Zelalem Gero'] | null | null | null | null | naacl-bionlp-2021-6 | ['keyphrase-extraction'] | ['natural-language-processing'] | [-5.74488975e-02 -1.14332475e-01 -9.73788798e-01 1.56760558e-01
-6.74819291e-01 -6.80263460e-01 9.68391061e-01 9.09284294e-01
-6.44089758e-01 8.31759512e-01 8.26896548e-01 -2.76205599e-01
-2.56135732e-01 -8.48031938e-01 -3.30469966e-01 -3.04457396e-01
-4.74670567e-02 1.83908239e-01 3.22901130e-01 -1.03786692... | [12.155657768249512, 8.88770580291748] |
e5130f28-3f75-4884-ac89-4d01a334507d | biomedical-event-extraction-as-multi-turn | null | null | https://aclanthology.org/2020.louhi-1.10 | https://aclanthology.org/2020.louhi-1.10.pdf | Biomedical Event Extraction as Multi-turn Question Answering | Biomedical event extraction from natural text is a challenging task as it searches for complex and often nested structures describing specific relationships between multiple molecular entities, such as genes, proteins, or cellular components. It usually is implemented by a complex pipeline of individual tools to solve ... | ['Ulf Leser', 'Leon Weber', 'Xing David Wang'] | null | null | null | null | emnlp-louhi-2020-11 | ['knowledge-base-population'] | ['natural-language-processing'] | [ 3.13651860e-01 5.30167460e-01 -1.34217858e-01 -2.35060483e-01
-1.34863698e+00 -6.90840423e-01 5.09802997e-01 1.12396395e+00
-6.54656410e-01 1.34394991e+00 2.38112584e-01 -3.80789578e-01
-2.39339486e-01 -6.21435404e-01 -8.40503752e-01 -4.77942079e-01
3.50831496e-03 1.05614138e+00 4.54094350e-01 -4.69060726... | [8.649373054504395, 8.900046348571777] |
f791a791-2d81-4fee-b325-303bbfba7aa9 | paint-by-example-exemplar-based-image-editing | 2211.13227 | null | https://arxiv.org/abs/2211.13227v1 | https://arxiv.org/pdf/2211.13227v1.pdf | Paint by Example: Exemplar-based Image Editing with Diffusion Models | Language-guided image editing has achieved great success recently. In this paper, for the first time, we investigate exemplar-guided image editing for more precise control. We achieve this goal by leveraging self-supervised training to disentangle and re-organize the source image and the exemplar. However, the naive ap... | ['Fang Wen', 'Dong Chen', 'Xiaoyan Sun', 'Xuejin Chen', 'Ting Zhang', 'Bo Zhang', 'Shuyang Gu', 'Binxin Yang'] | 2022-11-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Paint_by_Example_Exemplar-Based_Image_Editing_With_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Paint_by_Example_Exemplar-Based_Image_Editing_With_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-manipulation'] | ['computer-vision'] | [ 4.73061055e-01 1.51569933e-01 9.47375074e-02 -2.74788380e-01
-5.15194654e-01 -7.30669022e-01 8.79053235e-01 1.92472301e-02
-4.93821204e-01 5.10875225e-01 5.14268577e-02 -1.06821574e-01
-1.33395404e-01 -5.30017376e-01 -8.68514240e-01 -7.87789941e-01
3.93848121e-01 2.45210305e-01 -2.50524245e-02 -9.15664062... | [11.498918533325195, -0.4153451919555664] |
156bb9eb-a8b6-4c67-9fdd-c5d83860b273 | pose-controllable-talking-face-generation-by | 2104.11116 | null | https://arxiv.org/abs/2104.11116v1 | https://arxiv.org/pdf/2104.11116v1.pdf | Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation | While accurate lip synchronization has been achieved for arbitrary-subject audio-driven talking face generation, the problem of how to efficiently drive the head pose remains. Previous methods rely on pre-estimated structural information such as landmarks and 3D parameters, aiming to generate personalized rhythmic move... | ['Ziwei Liu', 'Xiaogang Wang', 'Chen Change Loy', 'Wayne Wu', 'Yasheng Sun', 'Hang Zhou'] | 2021-04-22 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhou_Pose-Controllable_Talking_Face_Generation_by_Implicitly_Modularized_Audio-Visual_Representation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhou_Pose-Controllable_Talking_Face_Generation_by_Implicitly_Modularized_Audio-Visual_Representation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['talking-face-generation'] | ['computer-vision'] | [ 3.50279696e-02 2.66259700e-01 8.60842131e-03 -4.07490045e-01
-9.99468088e-01 -5.16266406e-01 5.36443949e-01 -9.20362949e-01
2.06508100e-01 4.90014702e-01 6.29021287e-01 3.80085737e-01
2.05955282e-01 -3.53338867e-01 -7.50929534e-01 -9.08737361e-01
2.10399806e-01 2.17235178e-01 -4.43423241e-01 -2.86375642... | [13.195780754089355, -0.39867645502090454] |
01469439-2216-4475-9d39-d1191f663791 | stock-trend-prediction-a-semantic | 2303.09323 | null | https://arxiv.org/abs/2303.09323v1 | https://arxiv.org/pdf/2303.09323v1.pdf | Stock Trend Prediction: A Semantic Segmentation Approach | Market financial forecasting is a trending area in deep learning. Deep learning models are capable of tackling the classic challenges in stock market data, such as its extremely complicated dynamics as well as long-term temporal correlation. To capture the temporal relationship among these time series, recurrent neural... | ['Nader Bagherzadeh', 'Shima Nabiee'] | 2023-03-09 | null | null | null | null | ['stock-trend-prediction'] | ['time-series'] | [-3.80365014e-01 -4.49715912e-01 -1.50286049e-01 -2.53038317e-01
-4.70674545e-01 -7.57120550e-01 7.44249403e-01 -2.80083209e-01
-2.09229425e-01 5.32113969e-01 2.92737335e-01 -3.86887670e-01
1.12978630e-01 -1.02735388e+00 -6.86933815e-01 -5.27648330e-01
-5.63317657e-01 -2.81299818e-02 1.91163465e-01 -2.20178008... | [4.4412126541137695, 4.242918491363525] |
da092d61-415e-422b-b56e-41f0b907c61a | never-a-dull-moment-distributional-properties | 2303.17809 | null | https://arxiv.org/abs/2303.17809v1 | https://arxiv.org/pdf/2303.17809v1.pdf | Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification | The variety of complex algorithmic approaches for tackling time-series classification problems has grown considerably over the past decades, including the development of sophisticated but challenging-to-interpret deep-learning-based methods. But without comparison to simpler methods it can be difficult to determine whe... | ['Ben D. Fulcher', 'Annie G. Bryant', 'Trent Henderson'] | 2023-03-31 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 3.58826011e-01 -3.93821001e-01 -1.02370001e-01 -5.60394466e-01
-6.54419482e-01 -5.84069550e-01 9.75895107e-01 7.20459402e-01
-6.38605952e-01 5.64558804e-01 1.69210896e-01 -7.34308302e-01
-6.86607420e-01 -3.12287688e-01 -1.75166517e-01 -8.07452202e-01
-6.11429691e-01 4.49663341e-01 -2.51162589e-01 -1.81513637... | [7.2756195068359375, 3.3372461795806885] |
4aa71f3a-405a-4f15-bd50-c7e5a1ade51d | disentanglement-via-latent-quantization | 2305.18378 | null | https://arxiv.org/abs/2305.18378v1 | https://arxiv.org/pdf/2305.18378v1.pdf | Disentanglement via Latent Quantization | In disentangled representation learning, a model is asked to tease apart a dataset's underlying sources of variation and represent them independently of one another. Since the model is provided with no ground truth information about these sources, inductive biases take a paramount role in enabling disentanglement. In t... | ['Chelsea Finn', 'Jiajun Wu', 'James C. R. Whittington', 'Will Dorrell', 'Kyle Hsu'] | 2023-05-28 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 3.95624667e-01 3.51977050e-01 -3.66451740e-01 -1.42536849e-01
-6.71458185e-01 -9.69589710e-01 1.15069377e+00 -7.39767477e-02
-1.54092237e-01 7.12335050e-01 7.81419992e-01 -2.75321960e-01
-2.52087563e-01 -8.08971405e-01 -7.31714308e-01 -9.39104140e-01
-9.16199759e-02 5.34248292e-01 -6.95436418e-01 -1.11750089... | [9.24084186553955, 4.810962200164795] |
5f3e2bc3-3bb2-4fb7-b8f9-ff573c08f01f | implicit-view-time-interpolation-of-stereo | 2303.17181 | null | https://arxiv.org/abs/2303.17181v1 | https://arxiv.org/pdf/2303.17181v1.pdf | Implicit View-Time Interpolation of Stereo Videos using Multi-Plane Disparities and Non-Uniform Coordinates | In this paper, we propose an approach for view-time interpolation of stereo videos. Specifically, we build upon X-Fields that approximates an interpolatable mapping between the input coordinates and 2D RGB images using a convolutional decoder. Our main contribution is to analyze and identify the sources of the problems... | ['Nima Khademi Kalantari', 'Andrii Tsarov', 'Avinash Paliwal'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Paliwal_Implicit_View-Time_Interpolation_of_Stereo_Videos_Using_Multi-Plane_Disparities_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Paliwal_Implicit_View-Time_Interpolation_of_Stereo_Videos_Using_Multi-Plane_Disparities_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-enhancement', 'video-frame-interpolation'] | ['computer-vision', 'computer-vision'] | [ 1.15420133e-01 -2.85039097e-01 2.13123053e-01 -3.81418645e-01
-6.87197566e-01 -5.58809876e-01 5.65782189e-01 -2.47000739e-01
-2.72874296e-01 6.68952942e-01 4.86196168e-02 -1.25097290e-01
7.23872706e-02 -5.60042262e-01 -1.13003802e+00 -2.26050988e-01
8.49477574e-02 1.65010437e-01 6.37915611e-01 -8.55698362... | [8.947684288024902, -2.4884588718414307] |
8d6bdd3c-0333-49e2-b301-95ed8c977755 | heartspot-privatized-and-explainable-data | 2210.02241 | null | https://arxiv.org/abs/2210.02241v1 | https://arxiv.org/pdf/2210.02241v1.pdf | HeartSpot: Privatized and Explainable Data Compression for Cardiomegaly Detection | Advances in data-driven deep learning for chest X-ray image analysis underscore the need for explainability, privacy, large datasets and significant computational resources. We frame privacy and explainability as a lossy single-image compression problem to reduce both computational and data requirements without trainin... | ['Aurélio Campilho', 'Christos Faloutsos', 'Asim Smailagic', 'Alex Gaudio', 'Shreshta Mohan', 'Elvin Johnson'] | 2022-10-05 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 2.38823116e-01 6.69354975e-01 -2.68502444e-01 -6.28113091e-01
-8.83074760e-01 -5.49543619e-01 -9.68302265e-02 3.48258942e-01
-3.39863151e-01 7.57619083e-01 3.22402388e-01 -4.69845086e-01
-1.14106476e-01 -6.26733005e-01 -8.98988664e-01 -4.58057314e-01
9.32449698e-02 5.46314120e-01 -2.41168484e-01 3.88661355... | [14.538337707519531, -2.0729820728302] |
e390d84e-779b-4962-8f68-0f9c260fe8ba | simts-rethinking-contrastive-representation | 2303.18205 | null | https://arxiv.org/abs/2303.18205v1 | https://arxiv.org/pdf/2303.18205v1.pdf | SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting | Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification. However, these methods are less effective for time series forecasting, as optimization of instance discrimination is not directly applicable to predicting the future state from the ... | ['Michael Krauthammer', 'Ahmed Allam', 'Amina Mollaysa', 'Manuel Schürch', 'Xingyu Chen', 'Xiaochen Zheng'] | 2023-03-31 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 2.37643123e-01 -5.83599329e-01 -6.10493481e-01 -4.16299343e-01
-5.48372746e-01 -6.74600482e-01 1.14945543e+00 1.87853307e-01
-4.39317077e-02 4.39015985e-01 1.29811734e-01 -5.59224069e-01
-2.35599622e-01 -6.33314610e-01 -4.59745318e-01 -7.94580281e-01
-5.94380200e-01 1.43818736e-01 -1.55947462e-01 -4.20709312... | [7.121694564819336, 2.988954782485962] |
7e1163b1-d0b5-45f1-a9da-42d5a1c1264b | rapid-training-of-very-large-ensembles-of | 1809.04270 | null | https://arxiv.org/abs/1809.04270v2 | https://arxiv.org/pdf/1809.04270v2.pdf | MotherNets: Rapid Deep Ensemble Learning | Ensembles of deep neural networks significantly improve generalization accuracy. However, training neural network ensembles requires a large amount of computational resources and time. State-of-the-art approaches either train all networks from scratch leading to prohibitive training cost that allows only very small ens... | ['Brian Hentschel', 'Yuze Liao', 'Stratos Idreos', 'Sanyuan Chen', 'Abdul Wasay'] | 2018-09-12 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [ 5.38346544e-02 -1.23486027e-01 3.01833183e-01 -4.16852444e-01
-5.25228679e-01 -7.64470458e-01 2.73881197e-01 -2.52622247e-01
-5.12235165e-01 9.89295840e-01 -3.53275806e-01 -3.29174489e-01
-3.31134349e-01 -9.27834451e-01 -8.06867301e-01 -8.84257793e-01
-7.46517023e-03 7.30455637e-01 -5.88955618e-02 -1.92668006... | [8.69322395324707, 3.2504377365112305] |
4ec5a2ee-7ae6-47d3-acec-0cede2c08059 | ganwriting-content-conditioned-generation-of | 2003.02567 | null | https://arxiv.org/abs/2003.02567v2 | https://arxiv.org/pdf/2003.02567v2.pdf | GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images | Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same indiv... | ['Alicia Fornés', 'Marçal Rusiñol', 'Mauricio Villegas', 'Yaxing Wang', 'Pau Riba', 'Lei Kang'] | 2020-03-05 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4304_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680273.pdf | eccv-2020-8 | ['handwritten-word-generation'] | ['computer-vision'] | [ 6.51834905e-01 3.58753391e-02 3.56882542e-01 -1.09243043e-01
-5.58305502e-01 -8.85353148e-01 1.34309566e+00 -3.34895641e-01
-1.29567400e-01 9.01346266e-01 1.75060898e-01 1.44784555e-01
6.63744733e-02 -7.64834225e-01 -8.02179754e-01 -7.06544042e-01
6.09073699e-01 7.79569685e-01 -1.74514145e-01 -4.18143004... | [11.654012680053711, -0.0624605193734169] |
311e6813-5546-455d-8ff9-ee4efe7e0569 | locov-low-dimension-covariance-voting | 2204.00204 | null | https://arxiv.org/abs/2204.00204v1 | https://arxiv.org/pdf/2204.00204v1.pdf | LoCoV: low dimension covariance voting algorithm for portfolio optimization | Minimum-variance portfolio optimizations rely on accurate covariance estimator to obtain optimal portfolios. However, it usually suffers from large error from sample covariance matrix when the sample size $n$ is not significantly larger than the number of assets $p$. We analyze the random matrix aspects of portfolio op... | ['Ionel Popescu', 'Juntao Duan'] | 2022-04-01 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.45177999e-01 -1.04257483e-02 -1.56497851e-01 -1.65096238e-01
-1.08163452e+00 -1.01257920e+00 3.77546847e-01 -4.76428926e-01
-2.60223508e-01 1.03690541e+00 1.84786439e-01 -5.08448720e-01
-8.03594351e-01 -7.60385096e-01 -6.01835191e-01 -6.13996148e-01
2.52940808e-03 6.08534455e-01 -2.27416486e-01 1.59162998... | [4.986756324768066, 3.9770050048828125] |
7051561f-67cf-4f84-80cd-3da58e05e4c5 | class-overwhelms-mutual-conditional-blended | 2302.01516 | null | https://arxiv.org/abs/2302.01516v2 | https://arxiv.org/pdf/2302.01516v2.pdf | Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation | Current methods of blended targets domain adaptation (BTDA) usually infer or consider domain label information but underemphasize hybrid categorical feature structures of targets, which yields limited performance, especially under the label distribution shift. We demonstrate that domain labels are not directly necessar... | ['Charles Ling', 'Boyu Wang', 'Pengcheng Xu'] | 2023-02-03 | null | null | null | null | ['blended-target-domain-adaptation', 'multi-target-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 6.62648007e-02 -1.74925253e-01 -6.44602180e-01 -8.39439571e-01
-9.72346306e-01 -1.06021452e+00 5.58339894e-01 1.49360169e-02
-6.45192936e-02 9.41088259e-01 3.59339826e-02 -1.99652135e-01
-2.18386084e-01 -6.57474935e-01 -7.11332560e-01 -8.96723986e-01
3.01758587e-01 8.09150219e-01 -7.89058432e-02 -9.91122127... | [10.340092658996582, 3.1289420127868652] |
9e8c1f2a-bd82-4f3d-86ac-3e7127443a03 | adversarially-guided-portrait-matting | 2305.02981 | null | https://arxiv.org/abs/2305.02981v2 | https://arxiv.org/pdf/2305.02981v2.pdf | Adversarially-Guided Portrait Matting | We present a method for generating alpha mattes using a limited data source. We pretrain a novel transformerbased model (StyleMatte) on portrait datasets. We utilize this model to provide image-mask pairs for the StyleGAN3-based network (StyleMatteGAN). This network is trained unsupervisedly and generates previously un... | ['Karen Efremyan', 'Sergej Chicherin'] | 2023-05-04 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 6.48005903e-01 3.66268903e-02 -9.59673896e-02 -5.76332867e-01
-5.44240654e-01 -7.27493107e-01 8.08422744e-01 -6.97646737e-01
-1.99314758e-01 6.53292358e-01 1.14203699e-01 -6.60698339e-02
5.14088213e-01 -1.03034234e+00 -1.01174545e+00 -5.43973744e-01
2.89672434e-01 4.42534804e-01 -2.23491281e-01 -2.73449868... | [11.817042350769043, -0.3354453146457672] |
2cdbd0f1-58a0-4012-ae6a-528690c21b71 | alt-an-automatic-system-for-long-tail | 2305.11390 | null | https://arxiv.org/abs/2305.11390v1 | https://arxiv.org/pdf/2305.11390v1.pdf | ALT: An Automatic System for Long Tail Scenario Modeling | In this paper, we consider the problem of long tail scenario modeling with budget limitation, i.e., insufficient human resources for model training stage and limited time and computing resources for model inference stage. This problem is widely encountered in various applications, yet has received deficient attention s... | ['Longfei Li', 'Qitao Shi', 'Meng Li', 'Xinxing Yang', 'Yue Zhang', 'Yankun Ren', 'Jun Zhou', 'Ya-Lin Zhang'] | 2023-05-19 | null | null | null | null | ['architecture-search', 'philosophy'] | ['methodology', 'miscellaneous'] | [-9.43308622e-02 -2.01636955e-01 -5.57457268e-01 -3.51756871e-01
-2.95556158e-01 -4.78363670e-02 3.39488447e-01 -2.74381191e-01
-6.30561471e-01 7.01528966e-01 -2.28243992e-01 -5.59504330e-01
-2.60948241e-01 -7.90949106e-01 -5.01195967e-01 -6.68608367e-01
2.36942038e-01 3.79371136e-01 3.03835329e-03 -2.02165514... | [9.173364639282227, 4.019974708557129] |
0758dcaa-538c-4c9f-a1ab-aa250d0eb9dd | deeptitle-leveraging-bert-to-generate-search | 2107.10935 | null | https://arxiv.org/abs/2107.10935v1 | https://arxiv.org/pdf/2107.10935v1.pdf | DeepTitle -- Leveraging BERT to generate Search Engine Optimized Headlines | Automated headline generation for online news articles is not a trivial task - machine generated titles need to be grammatically correct, informative, capture attention and generate search traffic without being "click baits" or "fake news". In this paper we showcase how a pre-trained language model can be leveraged to ... | ['Javier Poveda-Panter', 'Aamna Najmi', 'Viktor Malesevic', 'Sarah Lück', 'Hanna Behnke', 'Cristian Anastasiu'] | 2021-07-22 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 3.22091579e-01 7.17026412e-01 -3.44999790e-01 -2.14911669e-01
-1.39457309e+00 -6.92772865e-01 7.59117365e-01 1.87266394e-01
-5.74833870e-01 1.11548257e+00 9.20650125e-01 -3.05833876e-01
1.87172607e-01 -4.60905641e-01 -9.38898385e-01 2.03224551e-03
4.09759253e-01 6.25038564e-01 7.49476850e-02 -4.49834883... | [12.36206340789795, 9.310722351074219] |
b8895004-ee1d-418e-9fad-ea7444180beb | inferring-multidimensional-rates-of-aging | 1807.04709 | null | http://arxiv.org/abs/1807.04709v3 | http://arxiv.org/pdf/1807.04709v3.pdf | Inferring Multidimensional Rates of Aging from Cross-Sectional Data | Modeling how individuals evolve over time is a fundamental problem in the
natural and social sciences. However, existing datasets are often
cross-sectional with each individual observed only once, making it impossible
to apply traditional time-series methods. Motivated by the study of human
aging, we present an interpr... | ['Jure Leskovec', 'Daphne Koller', 'Pang Wei Koh', 'Tatsunori Hashimoto', 'Nicholas Eriksson', 'Percy Liang', 'Emma Pierson'] | 2018-07-12 | null | null | null | null | ['human-aging'] | ['miscellaneous'] | [ 1.49514750e-01 1.82210878e-01 -4.71015483e-01 -2.98564076e-01
-2.08930120e-01 -5.14000952e-01 5.84742367e-01 2.38302350e-01
-3.64642590e-01 1.11624599e+00 7.40164816e-01 -3.10254186e-01
-3.86127919e-01 -6.88541889e-01 -6.70852482e-01 -7.47304320e-01
-7.47837842e-01 7.91822731e-01 -5.22574306e-01 1.24137856... | [7.820483684539795, 5.47482442855835] |
3f9dfb7f-f601-4e3c-bc58-538ae4761497 | cardinality-estimation-over-knowledge-graphs | 2303.01140 | null | https://arxiv.org/abs/2303.01140v1 | https://arxiv.org/pdf/2303.01140v1.pdf | Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks | Cardinality Estimation over Knowledge Graphs (KG) is crucial for query optimization, yet remains a challenging task due to the semi-structured nature and complex correlations of typical Knowledge Graphs. In this work, we propose GNCE, a novel approach that leverages knowledge graph embeddings and Graph Neural Networks ... | ['Maribel Acosta', 'Tim Schwabe'] | 2023-03-02 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.56102031e-01 1.70460984e-01 -2.20214635e-01 -1.51538318e-02
-5.01637578e-01 -6.94288969e-01 3.40885252e-01 1.09440529e+00
-5.32091141e-01 5.39721489e-01 1.93767458e-01 -2.32947290e-01
-5.99075973e-01 -1.45377755e+00 -8.32743645e-01 -9.56687629e-02
-3.73898894e-01 1.07990670e+00 4.80374455e-01 -1.98028758... | [9.115498542785645, 7.7447028160095215] |
919a66c3-462a-4c2d-bfcc-31c7da74a97c | cluster-guided-unsupervised-domain-adaptation | 2303.15944 | null | https://arxiv.org/abs/2303.15944v1 | https://arxiv.org/pdf/2303.15944v1.pdf | Cluster-Guided Unsupervised Domain Adaptation for Deep Speaker Embedding | Recent studies have shown that pseudo labels can contribute to unsupervised domain adaptation (UDA) for speaker verification. Inspired by the self-training strategies that use an existing classifier to label the unlabeled data for retraining, we propose a cluster-guided UDA framework that labels the target domain data ... | ['Man-Wai Mak', 'Feng Hong', 'Haiquan Mao'] | 2023-03-28 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-2.06802897e-02 3.37827325e-01 -4.88485023e-02 -8.46133947e-01
-1.17113090e+00 -5.75794876e-01 5.25839925e-01 -2.38603782e-02
-4.51501697e-01 4.07403320e-01 2.02448353e-01 -2.92566359e-01
1.79049805e-01 -1.23685233e-01 -4.53742534e-01 -9.53553200e-01
6.28154427e-02 6.11797929e-01 -4.89175282e-02 3.77208926... | [14.342494010925293, 6.105182647705078] |
ead6fa04-829e-400c-a4f7-f50ef3b0757e | review-of-large-vision-models-and-visual | 2307.00855 | null | https://arxiv.org/abs/2307.00855v1 | https://arxiv.org/pdf/2307.00855v1.pdf | Review of Large Vision Models and Visual Prompt Engineering | Visual prompt engineering is a fundamental technology in the field of visual and image Artificial General Intelligence, serving as a key component for achieving zero-shot capabilities. As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident. Designing suit... | ['Shu Zhang', 'Tianming Liu', 'Dinggang Shen', 'Yixuan Yuan', 'Bao Ge', 'Xi Jiang', 'Xiang Li', 'Dajiang Zhu', 'Tuo Zhang', 'Yi Pan', 'Enze Shi', 'Songyao Zhang', 'Yiheng Liu', 'Qiushi Yang', 'Haixing Dai', 'Sigang Yu', 'Chong Ma', 'Zihao Wu', 'Lin Zhao', 'Zhengliang Liu', 'Jiaqi Wang'] | 2023-07-03 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 3.08739454e-01 7.72889927e-02 -3.12751740e-01 -1.14155613e-01
-7.69685656e-02 -5.27285874e-01 7.06940174e-01 3.78737296e-03
-2.75671393e-01 7.59410486e-02 1.47928208e-01 -3.83448124e-01
-1.58239931e-01 -1.88395500e-01 -4.37792569e-01 -4.58513230e-01
2.26577312e-01 2.75558997e-02 2.96387762e-01 1.50886267... | [10.390093803405762, 1.626836895942688] |
2991abdd-7afb-41ec-8a00-dfabc3cfb2f7 | fillers-in-spoken-language-understanding | 2301.10761 | null | https://arxiv.org/abs/2301.10761v4 | https://arxiv.org/pdf/2301.10761v4.pdf | Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives | Disfluencies (i.e. interruptions in the regular flow of speech), are ubiquitous to spoken discourse. Fillers ("uh", "um") are disfluencies that occur the most frequently compared to other kinds of disfluencies. Yet, to the best of our knowledge, there isn't a resource that brings together the research perspectives infl... | ['Ioana Vasilescu', 'Chloé Clavel', 'Tanvi Dinkar'] | 2023-01-25 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 3.52000177e-01 4.33648348e-01 -3.72970134e-01 -2.86747575e-01
-5.59849501e-01 -8.78509879e-01 5.66532254e-01 1.14868768e-01
-6.16935901e-02 7.29215801e-01 9.29700434e-01 -6.70665681e-01
1.38164926e-02 -1.77295774e-01 -3.99504304e-01 -2.23528251e-01
4.23778445e-02 2.33273461e-01 -2.30998501e-01 -5.39074600... | [14.299614906311035, 6.981368541717529] |
0206a02c-1e92-4aab-8ae2-5801e4f2b7c9 | conservative-progressive-collaborative | 2211.16701 | null | https://arxiv.org/abs/2211.16701v2 | https://arxiv.org/pdf/2211.16701v2.pdf | Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation | Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collabo... | ['Fei-Yue Wang', 'Mingli Song', 'Yisheng Lv', 'Zunlei Feng', 'Fenghua Zhu', 'Siqi Fan'] | 2022-11-30 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 1.57201529e-01 5.88088691e-01 -6.21474922e-01 -6.03613555e-01
-6.19351089e-01 -7.87465125e-02 3.36841822e-01 1.97056547e-01
-4.59626317e-01 5.94111562e-01 -6.50194660e-03 1.90340914e-02
-2.90918231e-01 -6.17561817e-01 -3.44683826e-01 -9.70860779e-01
2.38333106e-01 6.33012414e-01 7.65557766e-01 2.56404206... | [9.376864433288574, 2.4009881019592285] |
2bc37e90-e308-4628-9ea9-859aba66f937 | bootstrapping-a-romanian-corpus-for-medical | null | null | https://aclanthology.org/R17-1066 | https://aclanthology.org/R17-1066.pdf | Bootstrapping a Romanian Corpus for Medical Named Entity Recognition | Named Entity Recognition (NER) is an important component of natural language processing (NLP), with applicability in biomedical domain, enabling knowledge-discovery from medical texts. Due to the fact that for the Romanian language there are only a few linguistic resources specific to the biomedical domain, it was crea... | ['Maria Mitrofan'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [ 1.62011579e-01 4.96999741e-01 -8.58263448e-02 -2.94723094e-01
-5.32972038e-01 -2.90876478e-01 4.60832089e-01 8.47483754e-01
-1.22863984e+00 1.13425612e+00 5.93753517e-01 -3.87606949e-01
-3.74515682e-01 -8.06536257e-01 5.75067997e-02 -3.44930649e-01
-2.22363919e-02 8.91800404e-01 2.33028904e-01 -2.38283411... | [8.583709716796875, 8.749297142028809] |
23cba72a-a6f2-4da0-a759-50d45cd51f68 | openauc-towards-auc-oriented-open-set | 2210.13458 | null | https://arxiv.org/abs/2210.13458v3 | https://arxiv.org/pdf/2210.13458v3.pdf | OpenAUC: Towards AUC-Oriented Open-Set Recognition | Traditional machine learning follows a close-set assumption that the training and test set share the same label space. While in many practical scenarios, it is inevitable that some test samples belong to unknown classes (open-set). To fix this issue, Open-Set Recognition (OSR), whose goal is to make correct predictions... | ['Qingming Huang', 'Xiaochun Cao', 'Yuan He', 'Zhiyong Yang', 'Qianqian Xu', 'Zitai Wang'] | 2022-10-22 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 5.83966747e-02 -1.42597124e-01 -3.84530008e-01 -5.91803968e-01
-8.84715378e-01 -5.80333591e-01 3.31976980e-01 3.00648093e-01
-5.82121313e-02 7.45811164e-01 -3.99837404e-01 -8.14129040e-02
-4.55924094e-01 -6.18105829e-01 -4.58264679e-01 -6.52006388e-01
1.12548783e-01 1.82578072e-01 7.14286566e-02 -5.72726950... | [9.636224746704102, 3.080015182495117] |
ac71459d-ea85-4817-87f8-75021c71d537 | dse-tts-dual-speaker-embedding-for-cross | 2306.14145 | null | https://arxiv.org/abs/2306.14145v1 | https://arxiv.org/pdf/2306.14145v1.pdf | DSE-TTS: Dual Speaker Embedding for Cross-Lingual Text-to-Speech | Although high-fidelity speech can be obtained for intralingual speech synthesis, cross-lingual text-to-speech (CTTS) is still far from satisfactory as it is difficult to accurately retain the speaker timbres(i.e. speaker similarity) and eliminate the accents from their first language(i.e. nativeness). In this paper, we... | ['Kai Yu', 'Xie Chen', 'Chenpeng Du', 'Yiwei Guo', 'Sen Liu'] | 2023-06-25 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [-7.51569122e-02 -1.34695172e-01 -1.19195189e-02 -4.53853816e-01
-1.08575690e+00 -4.52012569e-01 4.19509917e-01 -1.94931000e-01
-1.23606198e-01 4.51873243e-01 5.23077726e-01 -2.57957667e-01
3.68918926e-01 -3.06941241e-01 -6.36172354e-01 -8.57883513e-01
3.32729846e-01 -6.90395981e-02 -1.77574083e-01 -2.35408932... | [14.885265350341797, 6.576304912567139] |
ad07d718-6aaa-4a6f-804b-cd36af13d9e8 | free-on-line-speech-recogniser-based-on-kaldi | null | null | https://aclanthology.org/W14-4315 | https://aclanthology.org/W14-4315.pdf | Free on-line speech recogniser based on Kaldi ASR toolkit producing word posterior lattices | null | ["Filip Jur{\\v{c}}{\\'\\i}{\\v{c}}ek", "Ond{\\v{r}}ej Pl{\\'a}tek"] | 2014-06-01 | null | null | null | ws-2014-6 | ['acoustic-modelling'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.323112964630127, 3.7125136852264404] |
675f0848-b044-4018-9ee9-5e850c4924db | continual-learning-for-steganalysis | 2209.01326 | null | https://arxiv.org/abs/2209.01326v1 | https://arxiv.org/pdf/2209.01326v1.pdf | Continual Learning for Steganalysis | To detect the existing steganographic algorithms, recent steganalysis methods usually train a Convolutional Neural Network (CNN) model on the dataset consisting of corresponding paired cover/stego-images. However, it is inefficient and impractical for those steganalysis tools to completely retrain the CNN model to make... | ['Zhili Zhou', 'Ruohan Meng', 'Zihao Yin'] | 2022-09-03 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 8.87799680e-01 -5.72063476e-02 -4.10389341e-02 2.15739816e-01
-5.17508537e-02 -3.01787615e-01 5.73288560e-01 -5.03760099e-01
-2.47002229e-01 4.84638602e-01 -4.82725888e-01 -7.12904036e-01
1.31813541e-01 -1.05066824e+00 -6.82490051e-01 -1.09239459e+00
-2.90513307e-01 2.56928414e-01 2.87756383e-01 -5.97834647... | [4.247128486633301, 8.111858367919922] |
63c6b5ef-fed2-409a-bf96-b76586856cfd | sparse-representation-based-open-set | 1705.02431 | null | http://arxiv.org/abs/1705.02431v1 | http://arxiv.org/pdf/1705.02431v1.pdf | Sparse Representation-based Open Set Recognition | We propose a generalized Sparse Representation- based Classification (SRC)
algorithm for open set recognition where not all classes presented during
testing are known during training. The SRC algorithm uses class reconstruction
errors for classification. As most of the discriminative information for open
set recognitio... | ['He Zhang', 'Vishal M. Patel'] | 2017-05-06 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.72072136e-01 -8.78115818e-02 -3.39737028e-01 -4.66011256e-01
-1.13800669e+00 -7.63845623e-01 3.67448404e-02 3.47089842e-02
2.29327738e-01 8.35138619e-01 -1.83431447e-01 -1.70186356e-01
-1.59835279e-01 -7.54390419e-01 -9.19716299e-01 -6.46260738e-01
2.20101476e-02 5.52378774e-01 -1.54851094e-01 2.50463396... | [9.683723449707031, 2.934019088745117] |
bcc7ec7e-3f16-43ce-9108-29a2a385bbd5 | deep-learning-based-electroencephalography | 1901.05498 | null | http://arxiv.org/abs/1901.05498v2 | http://arxiv.org/pdf/1901.05498v2.pdf | Deep learning-based electroencephalography analysis: a systematic review | Electroencephalography (EEG) is a complex signal and can require several
years of training to be correctly interpreted. Recently, deep learning (DL) has
shown great promise in helping make sense of EEG signals due to its capacity to
learn good feature representations from raw data. Whether DL truly presents
advantages ... | ['Jocelyn Faubert', 'Alexandre Gramfort', 'Tiago H. Falk', 'Hubert Banville', 'Yannick Roy', 'Isabela Albuquerque'] | 2019-01-16 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [-9.88869891e-02 -2.34362349e-01 1.21400133e-01 -4.48749810e-01
-5.17593384e-01 -4.80284929e-01 2.99997807e-01 1.51481614e-01
-5.96827805e-01 9.12439287e-01 3.74420762e-01 -3.21028382e-01
-3.46085846e-01 -3.84238154e-01 -6.08325958e-01 -6.60454214e-01
-4.14455026e-01 -1.92972645e-01 -3.94228518e-01 -1.44638699... | [13.18930721282959, 3.437293767929077] |
abc1a743-927a-4496-bf99-e839141fbdf1 | a-comparison-of-identification-methods-of | null | null | https://aclanthology.org/2020.winlp-1.16 | https://aclanthology.org/2020.winlp-1.16.pdf | A Comparison of Identification Methods of Brazilian Music Styles by Lyrics | In our work, we applied different techniques for the task of genre classification using lyrics. Utilizing our dataset with lyrics of typical genres in Brazil divided into seven classes, we apply some models used in machine learning and deep learning classification tasks. We explore the performance of usual models for t... | ['Patrick Guimar{\\~a}es', 'Jader Froes', 'Larissa Freitas', 'Douglas Costa'] | 2020-07-01 | null | null | null | ws-2020-7 | ['genre-classification'] | ['computer-vision'] | [-1.34167075e-01 -1.95802629e-01 -3.37262332e-01 -3.62418413e-01
-3.77862751e-01 -7.39361405e-01 8.96976292e-01 2.14658633e-01
-4.70104188e-01 7.71581531e-01 8.55036914e-01 -1.81317240e-01
2.39353165e-01 -9.27090764e-01 -1.86669156e-01 -5.19483566e-01
5.12360096e-01 6.32755876e-01 -5.21911263e-01 -4.20593172... | [15.833514213562012, 5.2544074058532715] |
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