paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f3d897ac-ad58-4a83-920a-bc9f33f27d51 | cancer-metastasis-detection-with-neural | 1806.07064 | null | http://arxiv.org/abs/1806.07064v1 | http://arxiv.org/pdf/1806.07064v1.pdf | Cancer Metastasis Detection With Neural Conditional Random Field | Breast cancer diagnosis often requires accurate detection of metastasis in
lymph nodes through Whole-slide Images (WSIs). Recent advances in deep
convolutional neural networks (CNNs) have shown significant successes in
medical image analysis and particularly in computational histopathology.
Because of the outrageous la... | ['Yi Li', 'Wei Ping'] | 2018-06-19 | null | null | null | null | ['cancer-metastasis-detection'] | ['medical'] | [ 1.48922756e-01 2.65248299e-01 -4.16915685e-01 -2.79054761e-01
-1.09853697e+00 -2.11356074e-01 3.65897745e-01 4.34382528e-01
-5.10059714e-01 7.09081769e-01 -1.67302608e-01 -5.42018235e-01
6.64052516e-02 -8.84859681e-01 -7.54591227e-01 -1.17893291e+00
7.30379075e-02 2.75409192e-01 4.92124319e-01 3.24033201... | [15.08022403717041, -2.9101626873016357] |
a20f05cd-f06e-4158-b15c-0daf9cff030f | improving-aspect-sentiment-quad-prediction | 2210.10291 | null | https://arxiv.org/abs/2210.10291v1 | https://arxiv.org/pdf/2210.10291v1.pdf | Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation | Recently, aspect sentiment quad prediction (ASQP) has become a popular task in the field of aspect-level sentiment analysis. Previous work utilizes a predefined template to paraphrase the original sentence into a structure target sequence, which can be easily decoded as quadruplets of the form (aspect category, aspect ... | ['Shiwan Zhao', 'Yinhao Bai', 'Hang Gao', 'Yike Wu', 'Mengting Hu'] | 2022-10-19 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 3.02635103e-01 -8.20152387e-02 -2.82908261e-01 -4.11055803e-01
-6.89025521e-01 -8.11227083e-01 4.97681379e-01 -6.66958652e-03
-1.21574342e-01 3.13949198e-01 5.82214773e-01 -2.75254637e-01
1.89123243e-01 -8.22127461e-01 -6.19437754e-01 -6.87837362e-01
5.18305719e-01 2.62859643e-01 7.73381591e-02 -5.42277873... | [11.496447563171387, 6.646018028259277] |
a6f69172-6cd8-4ee1-a980-e49c7f68fc0a | non-parametric-active-learning-and-rate | 2107.00195 | null | https://arxiv.org/abs/2107.00195v2 | https://arxiv.org/pdf/2107.00195v2.pdf | Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity | In quantum and quantum-inspired machine learning, the very first step is to embed the data in quantum space known as Hilbert space. Developing quantum kernel function (QKF), which defines the distances among the samples in the Hilbert space, belongs to the fundamental topics for machine learning. In this work, we propo... | ['Shi-Ju Ran', 'Wei-Ming Li'] | 2021-07-01 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 6.99886978e-02 -1.24634795e-01 -2.10745111e-01 -4.11937714e-01
-3.74728113e-01 -3.80505145e-01 5.55034220e-01 -1.18328445e-01
-5.61225235e-01 7.63326764e-01 -8.74067936e-03 -1.33815765e-01
-7.87025213e-01 -1.13290286e+00 -2.41295844e-01 -1.29003966e+00
-2.05372289e-01 3.91368449e-01 9.30664539e-02 -3.56135130... | [5.600764751434326, 4.930771827697754] |
e205e78a-82d3-448c-a422-721bebdba3ca | convolutional-feature-extraction-and-neural | 1905.07581 | null | https://arxiv.org/abs/1905.07581v1 | https://arxiv.org/pdf/1905.07581v1.pdf | Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction | Stock prediction is a topic undergoing intense study for many years. Finance experts and mathematicians have been working on a way to predict the future stock price so as to decide to buy the stock or sell it to make profit. Stock experts or economists, usually analyze on the previous stock values using technical indic... | ['Jajati Keshari Sahoo', 'Shangeth Rajaa'] | 2019-05-18 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-7.59260535e-01 -2.58341551e-01 -3.14441890e-01 -6.12897694e-01
2.20091790e-01 -4.29344118e-01 4.87024426e-01 1.84518486e-01
-4.79513288e-01 9.71024275e-01 1.99385554e-01 -3.97780776e-01
6.46647885e-02 -1.51816535e+00 -3.78350377e-01 -3.97556245e-01
-1.24491349e-01 4.23657507e-01 1.76644787e-01 -6.32767081... | [4.442723274230957, 4.2397589683532715] |
f9eab37d-2e0f-4438-9f03-507f30d0aef0 | a-convolutional-neural-network-for-gaze | 2007.14432 | null | https://arxiv.org/abs/2007.14432v1 | https://arxiv.org/pdf/2007.14432v1.pdf | A Convolutional Neural Network for gaze preference detection: A potential tool for diagnostics of autism spectrum disorder in children | Early diagnosis of autism spectrum disorder (ASD) is known to improve the quality of life of affected individuals. However, diagnosis is often delayed even in wealthier countries including the US, largely due to the fact that gold standard diagnostic tools such as the Autism Diagnostic Observation Schedule (ADOS) and t... | ['Mirko Zimic', 'Macarena Vittet Mondonedo', 'Robert H. Gilman', 'Franklin Barrientos Porras', 'Dennis Núñez Fernández', 'Patricia Sheen'] | 2020-07-28 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 2.56265312e-01 8.17137435e-02 3.23117554e-01 -2.28057295e-01
1.39382690e-01 -3.03207159e-01 -7.26569816e-02 3.52271408e-01
-5.86355746e-01 3.96408200e-01 -1.05542913e-01 2.38474458e-02
-2.94729412e-01 -3.79988730e-01 -2.59443223e-01 -4.29235488e-01
1.12659544e-01 3.81227344e-01 1.15410797e-01 9.17932391... | [12.724823951721191, 2.9998269081115723] |
e2d9e361-f1bc-4bee-825c-f1a28afcec63 | neural-world-models-for-computer-vision | 2306.09179 | null | https://arxiv.org/abs/2306.09179v1 | https://arxiv.org/pdf/2306.09179v1.pdf | Neural World Models for Computer Vision | Humans navigate in their environment by learning a mental model of the world through passive observation and active interaction. Their world model allows them to anticipate what might happen next and act accordingly with respect to an underlying objective. Such world models hold strong promises for planning in complex ... | ['Anthony Hu'] | 2023-06-15 | null | null | null | null | ['navigate'] | ['reasoning'] | [ 8.08018744e-02 6.47504270e-01 1.42289093e-03 -5.83454251e-01
-3.06409299e-02 -6.67083144e-01 1.03007340e+00 -2.30575740e-01
-5.89238048e-01 5.83209336e-01 2.24191561e-01 -2.96049505e-01
-2.29588337e-02 -1.12400699e+00 -9.07333314e-01 -6.40118361e-01
2.72811800e-02 7.32523084e-01 2.87196785e-01 -2.47562990... | [4.718183994293213, 0.7874151468276978] |
3c1f0cfd-ddc2-4e8a-a710-77780864e5d9 | depression-status-estimation-by-deep-learning | 2011.14966 | null | https://arxiv.org/abs/2011.14966v1 | https://arxiv.org/pdf/2011.14966v1.pdf | Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model | Preliminary detection of mild depression could immensely help in effective treatment of the common mental health disorder. Due to the lack of proper awareness and the ample mix of stigmas and misconceptions present within the society, mental health status estimation has become a truly difficult task. Due to the immense... | ['Juned Kadiwala', 'Anugyan Das', 'Subin Mathew MS', 'Arnhav Datar', 'Saptarshi Majumder', 'Akash Das', 'Hari Sankar CN', 'Harikrishnan P', 'Hrithwik Shalu'] | 2020-11-30 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 2.80428350e-01 -1.02672470e-03 1.13272024e-02 -4.08080995e-01
-5.42924345e-01 5.50018400e-02 2.80594349e-01 2.97795206e-01
-4.15166289e-01 8.04116607e-01 -9.84046236e-02 -2.89790565e-03
-3.93295854e-01 -7.84020007e-01 -4.85173799e-02 -4.20427352e-01
4.20457385e-02 7.64172435e-01 -4.16581221e-02 -4.31357801... | [13.671416282653809, 4.724579334259033] |
58671457-430d-405e-be2d-6860730c4a42 | a-latent-variable-recurrent-neural-network | 1603.01913 | null | http://arxiv.org/abs/1603.01913v2 | http://arxiv.org/pdf/1603.01913v2.pdf | A Latent Variable Recurrent Neural Network for Discourse Relation Language Models | This paper presents a novel latent variable recurrent neural network
architecture for jointly modeling sequences of words and (possibly latent)
discourse relations between adjacent sentences. A recurrent neural network
generates individual words, thus reaping the benefits of
discriminatively-trained vector representati... | ['Jacob Eisenstein', 'Gholamreza Haffari', 'Yangfeng Ji'] | 2016-03-07 | null | null | null | null | ['dialog-act-classification', 'implicit-discourse-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.08059406e-01 1.08229911e+00 -6.33406579e-01 -3.66287887e-01
-8.14965427e-01 -5.29994488e-01 1.19085944e+00 1.43919483e-01
-2.70946085e-01 9.45638597e-01 7.63128936e-01 -5.98484993e-01
4.09482360e-01 -7.99507022e-01 -5.74132860e-01 -7.72520483e-01
-7.12456107e-02 8.28192055e-01 -1.02761472e-02 -3.11234355... | [10.779085159301758, 9.30317211151123] |
2f233a19-d539-479b-87a5-5a3e259deb44 | tensor-networks-for-quantum-machine-learning | 2303.11735 | null | https://arxiv.org/abs/2303.11735v1 | https://arxiv.org/pdf/2303.11735v1.pdf | Tensor networks for quantum machine learning | Once developed for quantum theory, tensor networks have been established as a successful machine learning paradigm. Now, they have been ported back to the quantum realm in the emerging field of quantum machine learning to assess problems that classical computers are unable to solve efficiently. Their nature at the inte... | ['Arne Peter Raulf', 'Frank Köster', 'Hans-Martin Rieser'] | 2023-03-21 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-4.44489457e-02 1.24737181e-01 -2.27498978e-01 -2.91629910e-01
-3.98307264e-01 -7.06381023e-01 5.00803471e-01 1.54926330e-01
-2.40140602e-01 4.14446622e-01 -1.13016024e-01 -7.84067154e-01
-3.16022128e-01 -1.12261689e+00 -3.79360139e-01 -5.67284405e-01
-2.84617215e-01 5.36646307e-01 1.70933709e-01 -7.62466192... | [5.580229759216309, 4.948366165161133] |
bfc55ddb-9b81-46de-821d-dd859bb80979 | a-performance-evaluation-of-correspondence | 1907.02890 | null | https://arxiv.org/abs/1907.02890v1 | https://arxiv.org/pdf/1907.02890v1.pdf | A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching | Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision. While a number of correspondence selection methods have been proposed in recent years, their advantages and shortcomings remain unclear regarding different applic... | ['Yanning Zhang', 'Peng Wang', 'Ke Xian', 'Jiaqi Yang'] | 2019-07-05 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-9.55998302e-02 -5.61441243e-01 3.53521928e-02 -3.38306576e-01
-7.67629147e-01 -7.23069787e-01 8.91844690e-01 3.78397107e-01
-1.47788378e-03 2.25108370e-01 -1.19579516e-01 1.37871101e-01
-3.68276447e-01 -5.52182555e-01 -4.86207455e-01 -5.31225443e-01
-1.36982039e-01 9.30312276e-01 4.63477075e-01 -1.86939478... | [7.744140625, -2.7914938926696777] |
6be1a668-58da-4860-afc2-e550605d7077 | cyber-risk-in-health-facilities-a-systematic | 2102.04093 | null | https://arxiv.org/abs/2102.04093v1 | https://arxiv.org/pdf/2102.04093v1.pdf | Cyber Risk in Health Facilities: A Systematic Literature Review | The current world challenges include issues such as infectious disease pandemics, environmental health risks, food safety, and crime prevention. Through this article, a special emphasis is given to one of the main challenges in the healthcare sector during the COVID-19 pandemic, the cyber risk. Since the beginning of t... | ['Anna Guerrieri', 'Enrico Sorano', 'Alessandro Rizzi', 'Alberto Sardi'] | 2021-02-08 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 1.46344349e-01 4.64163840e-01 -8.69574174e-02 6.55026197e-01
6.26925901e-02 -4.50437397e-01 5.22081971e-01 9.19995487e-01
-5.76054752e-01 4.08028513e-01 4.09057766e-01 -9.90827620e-01
-5.62134326e-01 -7.95107722e-01 -1.94789514e-01 -4.26641732e-01
4.22416739e-02 1.58025101e-01 -3.86131376e-01 -2.43739203... | [5.886162757873535, 4.459473609924316] |
8cd5d937-9ddb-4c32-bd29-7ac1e90dcd0c | plan-explicability-and-predictability-for | 1511.08158 | null | http://arxiv.org/abs/1511.08158v2 | http://arxiv.org/pdf/1511.08158v2.pdf | Plan Explicability and Predictability for Robot Task Planning | Intelligent robots and machines are becoming pervasive in human populated
environments. A desirable capability of these agents is to respond to
goal-oriented commands by autonomously constructing task plans. However, such
autonomy can add significant cognitive load and potentially introduce safety
risks to humans when ... | ['Sarath Sreedharan', 'Yu Zhang', 'Tathagata Chakraborti', 'Subbarao Kambhampati', 'Hankz Hankui Zhuo', 'Anagha Kulkarni'] | 2015-11-25 | null | null | null | null | ['robot-task-planning'] | ['robots'] | [ 3.71721357e-01 1.03139186e+00 1.61738023e-01 -6.26401246e-01
-3.15302461e-02 -3.86934638e-01 1.03918946e+00 5.35579808e-02
-4.14814085e-01 1.04764378e+00 3.49817604e-01 -1.78412482e-01
-1.47920594e-01 -8.32207918e-01 -4.11248863e-01 -4.45402145e-01
-3.17347437e-01 1.05708754e+00 2.43636459e-01 -3.34448874... | [4.448459625244141, 1.0458168983459473] |
1a2d26c1-0f8b-4c07-87e3-ebcfd72300d6 | neural-network-ensembles-to-real-time | 1802.06963 | null | http://arxiv.org/abs/1802.06963v1 | http://arxiv.org/pdf/1802.06963v1.pdf | Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements | The problem of identifying end-use electrical appliances from their
individual consumption profiles, known as the appliance identification problem,
is a primary stage in both Non-Intrusive Load Monitoring (NILM) and automated
plug-wise metering. Therefore, appliance identification has received dedicated
studies with va... | ['Bin Yang', 'Karim Said Barsim', 'Lukas Mauch'] | 2018-02-20 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 3.41301858e-01 -3.23288083e-01 -1.82642639e-01 -5.88745892e-01
-7.11819172e-01 -5.26734293e-01 4.33600903e-01 -9.02105868e-03
3.16312522e-01 7.31588185e-01 -1.49506897e-01 -3.02437454e-01
-5.86364329e-01 -6.41869068e-01 -9.78438109e-02 -9.84991550e-01
-5.76554686e-02 3.28601450e-01 -4.95766222e-01 2.95899779... | [6.046965599060059, 2.617603063583374] |
29ed0cbb-6bb0-4932-a56b-37f6bb82af65 | can-lies-be-faked-comparing-low-stakes-and | 2211.13035 | null | https://arxiv.org/abs/2211.13035v1 | https://arxiv.org/pdf/2211.13035v1.pdf | Can lies be faked? Comparing low-stakes and high-stakes deception video datasets from a Machine Learning perspective | Despite the great impact of lies in human societies and a meager 54% human accuracy for Deception Detection (DD), Machine Learning systems that perform automated DD are still not viable for proper application in real-life settings due to data scarcity. Few publicly available DD datasets exist and the creation of new da... | ['Gustavo Paetzold', 'Tomas Henrique Maul', 'Adriana Postal', 'Mateus Karvat Camara'] | 2022-11-23 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-8.43131542e-02 3.46724719e-01 -2.82265723e-01 -4.41222280e-01
-6.26802683e-01 -5.00550330e-01 8.13156307e-01 -8.62583071e-02
-8.20457757e-01 1.11757660e+00 -1.33836389e-01 -4.48529333e-01
-1.66859597e-01 -7.59673655e-01 -8.69931936e-01 -5.20988464e-01
2.08703339e-01 1.90459237e-01 2.60869898e-02 -1.15504175... | [12.990621566772461, 1.8553212881088257] |
32f2e7dd-daa2-47f3-89eb-4567ba6e7b06 | learning-cross-lingual-ir-from-an-english | 2112.08185 | null | https://arxiv.org/abs/2112.08185v3 | https://arxiv.org/pdf/2112.08185v3.pdf | Learning Cross-Lingual IR from an English Retriever | We present DR.DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual Representation), a new cross-lingual information retrieval (CLIR) system trained using multi-stage knowledge distillation (KD). The teacher of DR.DECR relies on a highly effective but computationally expensive two-stage inference process consi... | ['Avirup Sil', 'Young-suk Lee', 'Bhavani Iyer', 'Md Arafat Sultan', 'Martin Franz', 'Yulong Li'] | 2021-12-15 | null | https://aclanthology.org/2022.naacl-main.329 | https://aclanthology.org/2022.naacl-main.329.pdf | naacl-2022-7 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-1.23125680e-01 -2.84393191e-01 -6.49314880e-01 -2.71292269e-01
-2.06795168e+00 -9.52048957e-01 8.04202616e-01 2.49089241e-01
-9.03671920e-01 7.35008538e-01 2.26471901e-01 -5.69876194e-01
-1.22363836e-01 -4.89880443e-01 -8.88309479e-01 -2.72112787e-01
4.08538401e-01 1.21434724e+00 -2.30296865e-01 -6.72258496... | [11.347381591796875, 9.786210060119629] |
41d781ae-3cdf-41a6-badd-9ebce42b193d | neural-duplicate-question-detection-without-1 | 1911.05594 | null | https://arxiv.org/abs/1911.05594v2 | https://arxiv.org/pdf/1911.05594v2.pdf | Neural Duplicate Question Detection without Labeled Training Data | Supervised training of neural models to duplicate question detection in community Question Answering (cQA) requires large amounts of labeled question pairs, which are costly to obtain. To minimize this cost, recent works thus often used alternative methods, e.g., adversarial domain adaptation. In this work, we propose ... | ['Andreas Rücklé', 'Iryna Gurevych', 'Nafise Sadat Moosavi'] | 2019-11-13 | neural-duplicate-question-detection-without | https://aclanthology.org/D19-1171 | https://aclanthology.org/D19-1171.pdf | ijcnlp-2019-11 | ['answer-selection'] | ['natural-language-processing'] | [ 1.46852925e-01 1.01222105e-01 1.49780229e-01 -3.87143612e-01
-1.37852681e+00 -8.05461466e-01 5.58194399e-01 1.80840686e-01
-4.62528199e-01 1.04156530e+00 -4.27418724e-02 -5.05984128e-01
1.44521266e-01 -9.29224968e-01 -7.62611449e-01 -3.60054344e-01
5.88470757e-01 5.07326365e-01 4.26943958e-01 -4.96534079... | [11.329436302185059, 8.140533447265625] |
f5d28fae-4fd4-43ef-a8ad-424f90044908 | kg-bert-bert-for-knowledge-graph-completion | 1909.03193 | null | https://arxiv.org/abs/1909.03193v2 | https://arxiv.org/pdf/1909.03193v2.pdf | KG-BERT: BERT for Knowledge Graph Completion | Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness. In this work, we propose to use pre-trained language models for knowledge graph completion. We treat triples in knowledge graphs as textual sequences and propose a novel framework named Knowledge Graph ... | ['Liang Yao', 'Yuan Luo', 'Chengsheng Mao'] | 2019-09-07 | null | null | null | null | ['triple-classification'] | ['graphs'] | [-1.07450277e-01 6.80087805e-01 -1.01066852e+00 -2.59749800e-01
-4.97414976e-01 -3.84874225e-01 6.29841626e-01 4.45479959e-01
5.82895130e-02 9.96087313e-01 2.49788210e-01 -6.12797976e-01
-1.71095848e-01 -1.37022424e+00 -1.27167130e+00 1.75009519e-01
-1.04723506e-01 8.35205257e-01 5.04031420e-01 -4.52667296... | [8.8655424118042, 7.9578399658203125] |
ec4159b2-b72c-48df-8e2e-e85e89d44c1c | sensible-at-semeval-2016-task-11-neural | null | null | https://aclanthology.org/S16-1148 | https://aclanthology.org/S16-1148.pdf | Sensible at SemEval-2016 Task 11: Neural Nonsense Mangled in Ensemble Mess | null | ['Gillin Nat'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.45084285736084, 3.8069915771484375] |
72de5dd5-2021-4c46-8448-87a56bd9a544 | 4k-haze-a-dehazing-benchmark-with-4k | 2303.15848 | null | https://arxiv.org/abs/2303.15848v1 | https://arxiv.org/pdf/2303.15848v1.pdf | 4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free Images | Currently, mobile and IoT devices are in dire need of a series of methods to enhance 4K images with limited resource expenditure. The absence of large-scale 4K benchmark datasets hampers progress in this area, especially for dehazing. The challenges in building ultra-high-definition (UHD) dehazing datasets are the abse... | ['Xiuyi Jia', 'Zhuoran Zheng'] | 2023-03-28 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 2.75646180e-01 -1.93711311e-01 6.17841482e-01 -1.86828405e-01
-7.23484993e-01 -2.46936291e-01 4.26259726e-01 -5.31142354e-01
-2.70880669e-01 7.28698075e-01 -1.31267473e-01 -2.00497463e-01
7.55796134e-02 -1.24285412e+00 -5.97602487e-01 -1.21819866e+00
2.08760992e-01 1.63845867e-01 3.76437366e-01 -3.29292476... | [10.906696319580078, -3.1690351963043213] |
012df7ec-d35d-4d2a-823a-f97b45380369 | learning-over-families-of-sets-hypergraph | 2101.07773 | null | https://arxiv.org/abs/2101.07773v1 | https://arxiv.org/pdf/2101.07773v1.pdf | Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks | Graph representation learning has made major strides over the past decade. However, in many relational domains, the input data are not suited for simple graph representations as the relationships between entities go beyond pairwise interactions. In such cases, the relationships in the data are better represented as hyp... | ['George Karypis', 'Da Zheng', 'Balasubramaniam Srinivasan'] | 2021-01-19 | null | null | null | null | ['hyperedge-classification'] | ['graphs'] | [ 3.23408902e-01 7.55432606e-01 -5.51833212e-01 -3.34157616e-01
-1.63352624e-01 -8.64019036e-01 6.65661693e-01 5.83503783e-01
1.71562448e-01 7.48685479e-01 3.06092829e-01 -5.50643921e-01
-5.40043831e-01 -1.52314723e+00 -8.63022089e-01 -5.55023193e-01
-6.48834527e-01 1.03233981e+00 -2.96755806e-02 -1.13705315... | [7.145019054412842, 6.41099739074707] |
60b4c8de-05f6-4dd5-8c62-b48d4992b6cc | skeleton-based-action-recognition-using-lstm | 1707.02356 | null | http://arxiv.org/abs/1707.02356v1 | http://arxiv.org/pdf/1707.02356v1.pdf | Skeleton-based Action Recognition Using LSTM and CNN | Recent methods based on 3D skeleton data have achieved outstanding
performance due to its conciseness, robustness, and view-independent
representation. With the development of deep learning, Convolutional Neural
Networks (CNN) and Long Short Term Memory (LSTM)-based learning methods have
achieved promising performance ... | ['Yonghong Hou', 'Shuang Wang', 'Chuankun Li', 'Wanqing Li', 'Pichao Wang'] | 2017-07-06 | null | null | null | null | ['action-analysis'] | ['computer-vision'] | [ 2.66474366e-01 -4.60297614e-01 -2.67320067e-01 -4.00636792e-01
-6.46102250e-01 2.08982304e-01 3.55152011e-01 -1.47883311e-01
-6.36606455e-01 4.06200171e-01 5.21321237e-01 2.82388508e-01
6.70359060e-02 -5.20207524e-01 -4.30208027e-01 -5.96800685e-01
-1.91372663e-01 -8.25272352e-02 4.52417672e-01 -7.55880401... | [7.890531063079834, 0.41389915347099304] |
f532d330-f6c1-4891-970c-47207bb9c590 | glad-groningen-lightweight-authorship | null | null | https://github.com/pan-webis-de/huerlimann15/blob/master/Notebook/pan15-notebook.pdf | https://github.com/pan-webis-de/huerlimann15/blob/master/Notebook/pan15-notebook.pdf | GLAD: Groningen Lightweight Authorship Detection | We present a simple and effective approach to authorship verification for Dutch, English, Spanish and Greek, which can be easily ported to yet other languages.We train a binary linear classifier both on the features describing known and unknown documents individually, and on the joint features comparing these two types... | ['and Malvina Nissim', 'Simon Šuster', 'Esther van den Berg', 'Benno Weck', 'Manuela Hürlimann'] | 2015-09-06 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-7.26340935e-02 -4.08874780e-01 -3.30595940e-01 -2.39411548e-01
-8.65090430e-01 -1.12213266e+00 1.13760555e+00 6.06434345e-01
-7.57158637e-01 8.48508477e-01 1.63736999e-01 -2.87923217e-01
-1.73189789e-02 -2.42298201e-01 -2.55919956e-02 -2.22728699e-01
-1.35222852e-01 8.11254919e-01 1.21779583e-01 6.67957738... | [9.584877967834473, 10.587879180908203] |
b6b351cd-d041-4734-a955-7489e114921f | equivalence-of-two-expressions-of-principal | 2301.03039 | null | https://arxiv.org/abs/2301.03039v1 | https://arxiv.org/pdf/2301.03039v1.pdf | Equivalence of Two Expressions of Principal Line | Geometry-based camera calibration using principal line is more precise and robust than calibration using optimization approaches; therefore, several researches try to re-derive the principal line from different views of 2D projective geometry to increase alternatives of the calibration process. In this report, algebrai... | ['Jen-Hui Chuang', 'Hsin-Yi Chen', 'Cheng-Yen Hsu'] | 2023-01-08 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [-2.40301624e-01 -1.05900936e-01 -4.90321890e-02 -3.80347520e-01
-8.65361691e-02 -7.06646979e-01 3.85576546e-01 -1.26950175e-01
-2.52637476e-01 6.16732776e-01 -3.09384912e-01 -3.70144188e-01
-2.53631920e-01 -6.46064699e-01 -5.01402140e-01 -5.26337802e-01
5.19838870e-01 3.51028144e-01 4.62793224e-02 -3.22180778... | [8.018021583557129, -2.343951463699341] |
9a94c7bd-c095-43ed-947c-d274d09b8a01 | dynamic-diagnosis-of-the-progress-and | 2204.13989 | null | https://arxiv.org/abs/2204.13989v1 | https://arxiv.org/pdf/2204.13989v1.pdf | Dynamic Diagnosis of the Progress and Shortcomings of Student Learning using Machine Learning based on Cognitive, Social, and Emotional Features | Student diversity, like academic background, learning styles, career and life goals, ethnicity, age, social and emotional characteristics, course load and work schedule, offers unique opportunities in education, like learning new skills, peer mentoring and example setting. But student diversity can be challenging too a... | ['Wendy Tang', 'Sangjin Hong', 'Ryan Duke', 'Simona Doboli', 'Alex Doboli'] | 2022-04-13 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [-1.13389656e-01 9.88947526e-02 -5.58395505e-01 -3.37639093e-01
-3.59993935e-01 -6.21661246e-01 -3.28722671e-02 1.13527286e+00
-1.18032865e-01 4.51398462e-01 4.37022775e-01 -5.78416646e-01
-9.70048666e-01 -8.65073025e-01 -1.60480499e-01 -2.70090044e-01
2.14524209e-01 5.30249059e-01 1.13112181e-01 -4.53201354... | [10.121977806091309, 7.190954685211182] |
2416975f-47cb-4422-bb06-c74326981788 | automatic-keyboard-layout-design-for-low | 1901.06039 | null | http://arxiv.org/abs/1901.06039v1 | http://arxiv.org/pdf/1901.06039v1.pdf | Automatic Keyboard Layout Design for Low-Resource Latin-Script Languages | We present our approach to automatically designing and implementing keyboard
layouts on mobile devices for typing low-resource languages written in the
Latin script. For many speakers, one of the barriers in accessing and creating
text content on the web is the absence of input tools for their language. Ease
in typing ... | ["Jeremy O'Brien", 'Daan van Esch', 'Chieu Nguyen', 'Theresa Breiner'] | 2019-01-18 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-1.38140887e-01 -3.25131536e-01 -1.81859195e-01 -1.62025884e-01
-3.93980622e-01 -1.16648483e+00 4.72522527e-02 3.42235953e-01
-5.13165832e-01 4.30104405e-01 3.78356427e-01 -1.18955183e+00
8.76328275e-02 -7.90609658e-01 -1.75623015e-01 1.23283558e-01
6.12698495e-01 2.94011623e-01 2.48216018e-01 -4.59337205... | [11.253301620483398, 9.850579261779785] |
6451a013-26fd-4f72-9f63-a90dd8ca0fa5 | structure-pretraining-and-prompt-tuning-for | 2303.03922 | null | https://arxiv.org/abs/2303.03922v1 | https://arxiv.org/pdf/2303.03922v1.pdf | Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer | Knowledge graphs (KG) are essential background knowledge providers in many tasks. When designing models for KG-related tasks, one of the key tasks is to devise the Knowledge Representation and Fusion (KRF) module that learns the representation of elements from KGs and fuses them with task representations. While due to ... | ['Huajun Chen', 'Wenting Song', 'Yajing Xu', 'Yufeng Huang', 'Yuxia Geng', 'Mingyang Chen', 'Yushan Zhu', 'Wen Zhang'] | 2023-03-03 | null | null | null | null | ['triple-classification'] | ['graphs'] | [ 1.32818744e-01 3.17064941e-01 -2.36155272e-01 -4.56377685e-01
-5.06398141e-01 -2.58320093e-01 4.83720332e-01 1.80228025e-01
-2.91048259e-01 4.11396384e-01 1.95904747e-01 -1.41574666e-01
-3.92203659e-01 -8.09490144e-01 -8.40835929e-01 -3.71060610e-01
3.39520872e-01 3.71684998e-01 3.24010700e-01 -1.27690107... | [9.010249137878418, 7.979098320007324] |
3f031b40-c05b-4eaf-ab3f-414095cac5df | bayesian-and-neural-inference-on-lstm-based | 2306.06423 | null | https://arxiv.org/abs/2306.06423v1 | https://arxiv.org/pdf/2306.06423v1.pdf | Bayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information | Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity. Haptic perception encompasses the sensing modalities encountered in the sense of touch (e.g., tactile and kinesthetic sensations). This letter focuses on multimodal object recognition and proposes ana... | ['Jesús M. Gómez-de-Gabriel', 'Alfonso J. García-Cerezo', 'Pau Closas', 'Daniel Medina', 'Juan M. Gandarias', 'Jorge García-González', 'Francisco Pastor'] | 2023-06-10 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 3.61725569e-01 -2.50021219e-01 -1.21327467e-01 -2.05738872e-01
-5.48152566e-01 -1.32987633e-01 3.93196732e-01 -2.54630387e-01
-6.28781676e-01 5.94328344e-01 -3.30777645e-01 8.34892988e-02
-7.97769129e-01 -6.09691858e-01 -7.32435226e-01 -1.03042555e+00
-7.51130506e-02 3.52679342e-01 6.26777261e-02 2.40166083... | [5.816703796386719, -0.7714097499847412] |
7d0bf5ab-3605-4af2-8797-93252ca4a4b1 | adversarial-learning-for-improved-onsets-and | 1906.08512 | null | https://arxiv.org/abs/1906.08512v1 | https://arxiv.org/pdf/1906.08512v1.pdf | Adversarial Learning for Improved Onsets and Frames Music Transcription | Automatic music transcription is considered to be one of the hardest problems in music information retrieval, yet recent deep learning approaches have achieved substantial improvements on transcription performance. These approaches commonly employ supervised learning models that predict various time-frequency represent... | ['Juan Pablo Bello', 'Jong Wook Kim'] | 2019-06-20 | null | null | null | null | ['music-transcription'] | ['music'] | [ 4.74845529e-01 -7.93496743e-02 -1.50623098e-01 -2.30913565e-01
-1.45663452e+00 -7.98964441e-01 2.97092736e-01 -1.84382927e-02
-8.68586972e-02 5.04618585e-01 2.10068330e-01 2.09450826e-01
-1.50063455e-01 -5.84622085e-01 -7.50649393e-01 -7.59548843e-01
-2.13315301e-02 3.56465608e-01 -3.27865809e-01 1.65451355... | [15.700902938842773, 5.36078405380249] |
8e6197d2-9a6c-4051-a752-9b6bf16c9794 | object-cosegmentation-using-deep-siamese | 1803.02555 | null | http://arxiv.org/abs/1803.02555v2 | http://arxiv.org/pdf/1803.02555v2.pdf | Object cosegmentation using deep Siamese network | Object cosegmentation addresses the problem of discovering similar objects
from multiple images and segmenting them as foreground simultaneously. In this
paper, we propose a novel end-to-end pipeline to segment the similar objects
simultaneously from relevant set of images using supervised learning via
deep-learning fr... | ['Snehith Lattupally', 'Brejesh lall', 'Prerana Mukherjee'] | 2018-03-07 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 2.27152199e-01 5.14528826e-02 -9.42803845e-02 -7.35515535e-01
-1.42714083e+00 -6.51608586e-01 3.65292937e-01 2.20682904e-01
-6.58769965e-01 5.18264890e-01 -2.02652469e-01 4.65041250e-01
-1.94551438e-01 -4.40835416e-01 -9.05765951e-01 -5.35002768e-01
-1.73204362e-01 1.23018885e+00 1.10901546e+00 4.57763731... | [9.33638858795166, 0.4514539837837219] |
906442a4-0fef-40db-a4ee-99ff43cf5fcb | differentiable-iterative-surface-normal | 1904.07172 | null | https://arxiv.org/abs/1904.07172v3 | https://arxiv.org/pdf/1904.07172v3.pdf | Deep Iterative Surface Normal Estimation | This paper presents an end-to-end differentiable algorithm for robust and detail-preserving surface normal estimation on unstructured point-clouds. We utilize graph neural networks to iteratively parameterize an adaptive anisotropic kernel that produces point weights for weighted least-squares plane fitting in local ne... | ['Christian Osendorfer', 'Jan Eric Lenssen', 'Jonathan Masci'] | 2019-04-15 | deep-iterative-surface-normal-estimation | http://openaccess.thecvf.com/content_CVPR_2020/html/Lenssen_Deep_Iterative_Surface_Normal_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lenssen_Deep_Iterative_Surface_Normal_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['surface-normals-estimation'] | ['computer-vision'] | [ 2.42239255e-02 4.21073027e-02 8.85856375e-02 -3.14976066e-01
-8.03582668e-01 -2.66490132e-01 5.94919562e-01 2.86318332e-01
-5.30377865e-01 3.58670115e-01 -4.47403818e-01 -1.62283212e-01
-3.30128491e-01 -8.34042847e-01 -1.02462590e+00 -6.29718006e-01
-3.06386024e-01 9.41705763e-01 3.27491075e-01 -2.98405707... | [8.167344093322754, -3.5074784755706787] |
aca9432e-4b6d-40d4-b3f1-9a224e163e31 | mind-at-semeval-2021-task-6-propaganda | null | null | https://aclanthology.org/2021.semeval-1.150 | https://aclanthology.org/2021.semeval-1.150.pdf | MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion | We describe our systems of subtask1 and subtask3 for SemEval-2021 Task 6 on Detection of Persuasion Techniques in Texts and Images. The purpose of subtask1 is to identify propaganda techniques given textual content, and the goal of subtask3 is to detect them given both textual and visual content. For subtask1, we inves... | ['Wenming Xiao', 'Ming Yan', 'Chenliang Li', 'Min Gui', 'Junfeng Tian'] | 2021-08-01 | null | null | null | semeval-2021 | ['propaganda-detection'] | ['natural-language-processing'] | [ 4.88070816e-01 5.75029291e-02 -2.79058486e-01 -2.57530212e-01
-1.05313349e+00 -4.02062863e-01 1.31257057e+00 7.05866814e-02
-4.52705622e-01 4.18780923e-01 5.55347919e-01 -6.17534280e-01
2.91189700e-01 -1.26278684e-01 -5.33164620e-01 -3.88409764e-01
4.16130483e-01 -3.08696628e-02 -1.63141549e-01 -1.92928284... | [8.44599723815918, 10.552337646484375] |
b51d1447-404a-4f5d-a844-d370abda5d0d | emotion-prediction-oriented-method-with | 2302.12417 | null | https://arxiv.org/abs/2302.12417v1 | https://arxiv.org/pdf/2302.12417v1.pdf | Emotion Prediction Oriented method with Multiple Supervisions for Emotion-Cause Pair Extraction | Emotion-cause pair extraction (ECPE) task aims to extract all the pairs of emotions and their causes from an unannotated emotion text. The previous works usually extract the emotion-cause pairs from two perspectives of emotion and cause. However, emotion extraction is more crucial to the ECPE task than cause extraction... | ['Guangming Lu', 'Yi Zhao', 'Guimin Hu'] | 2023-02-24 | null | null | null | null | ['emotion-cause-pair-extraction', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.81398068e-02 3.76476884e-01 -1.13278575e-01 -5.77987075e-01
-6.87513769e-01 -5.73937654e-01 4.47224796e-01 1.00675877e-02
3.31309182e-03 7.42803693e-01 2.11149350e-01 3.04896891e-01
7.42380992e-02 -5.77482104e-01 -3.07035834e-01 -7.14856625e-01
4.48722616e-02 -8.46909359e-02 -3.22262585e-01 -3.42630506... | [12.628851890563965, 6.212127208709717] |
8522b746-ed09-475c-9fb1-e3f0abbe8386 | bareskinnet-de-makeup-and-de-lighting-via-3d | 2209.09029 | null | https://arxiv.org/abs/2209.09029v1 | https://arxiv.org/pdf/2209.09029v1.pdf | BareSkinNet: De-makeup and De-lighting via 3D Face Reconstruction | We propose BareSkinNet, a novel method that simultaneously removes makeup and lighting influences from the face image. Our method leverages a 3D morphable model and does not require a reference clean face image or a specified light condition. By combining the process of 3D face reconstruction, we can easily obtain 3D g... | ['Takafumi Taketomi', 'Xingchao Yang'] | 2022-09-19 | null | null | null | null | ['3d-face-reconstruction', 'face-generation', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.72699142e-01 8.07981566e-02 3.32810789e-01 -4.09184605e-01
-3.90681297e-01 -4.44104046e-01 5.22391915e-01 -6.83663428e-01
4.37184662e-01 3.88460368e-01 2.47054294e-01 3.46934721e-02
1.80108905e-01 -1.08272338e+00 -8.07350695e-01 -5.94034493e-01
5.06852508e-01 4.24478054e-01 -2.63258487e-01 -3.51254791... | [12.73558521270752, -0.3544597327709198] |
1956d7a1-2e0c-4adb-8b00-37ba6d584342 | attending-to-entities-for-better-text | 1911.04361 | null | https://arxiv.org/abs/1911.04361v1 | https://arxiv.org/pdf/1911.04361v1.pdf | Attending to Entities for Better Text Understanding | Recent progress in NLP witnessed the development of large-scale pre-trained language models (GPT, BERT, XLNet, etc.) based on Transformer (Vaswani et al. 2017), and in a range of end tasks, such models have achieved state-of-the-art results, approaching human performance. This demonstrates the power of the stacked self... | ['Katrin Erk', 'Pengxiang Cheng'] | 2019-11-11 | null | null | null | null | ['lambada'] | ['natural-language-processing'] | [ 1.41477883e-01 7.97618747e-01 -2.13144436e-01 -3.99148554e-01
-1.05891168e+00 -6.56924844e-01 8.92858803e-01 1.76432714e-01
-5.40403783e-01 6.37600899e-01 6.46611214e-01 -5.84686577e-01
-6.18568212e-02 -5.93047678e-01 -1.06252861e+00 -2.97572732e-01
1.79874208e-02 9.97231364e-01 3.86809081e-01 -5.67667723... | [10.605146408081055, 8.569756507873535] |
2b474f27-1373-4488-b934-5b1a0d2800b9 | efficient-signed-graph-sampling-via-balancing | 2208.08726 | null | https://arxiv.org/abs/2208.08726v2 | https://arxiv.org/pdf/2208.08726v2.pdf | Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment | A basic premise in graph signal processing (GSP) is that a graph encoding pairwise (anti-)correlations of the targeted signal as edge weights is exploited for graph filtering. However, existing fast graph sampling schemes are designed and tested only for positive graphs describing positive correlations. In this paper, ... | ['Ivan V. Bajic', 'Saghar Bagheri', 'Gene Cheung', 'Chinthaka Dinesh'] | 2022-08-18 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 3.17326903e-01 4.52814639e-01 6.66603297e-02 -1.17408350e-01
-8.39677453e-01 -5.67494869e-01 -3.35060060e-02 1.29318967e-01
8.67820904e-02 6.36864781e-01 1.83309484e-02 -3.13229799e-01
-9.07319903e-01 -9.82960582e-01 -8.06118608e-01 -7.88089156e-01
-9.53248203e-01 1.24657042e-01 -1.99775957e-02 -4.02773499... | [6.702075481414795, 4.769060134887695] |
a8c14469-be09-4f24-b355-a49a1538d1aa | trainable-class-prototypes-for-few-shot | 2106.10846 | null | https://arxiv.org/abs/2106.10846v1 | https://arxiv.org/pdf/2106.10846v1.pdf | Trainable Class Prototypes for Few-Shot Learning | Metric learning is a widely used method for few shot learning in which the quality of prototypes plays a key role in the algorithm. In this paper we propose the trainable prototypes for distance measure instead of the artificial ones within the meta-training and task-training framework. Also to avoid the disadvantages ... | ['Guizhong Liu', 'Jianyi Li'] | 2021-06-21 | null | null | null | null | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.12660795e-01 -1.49476141e-01 -2.53922999e-01 -4.34737206e-01
-9.04648066e-01 2.47176364e-01 8.10701311e-01 1.40258506e-01
-6.56635821e-01 5.90096891e-01 1.35399103e-01 4.86057669e-01
-3.27278256e-01 -9.41586852e-01 -4.00028139e-01 -6.17796004e-01
8.95489305e-02 5.54950237e-01 5.06547034e-01 -2.23233387... | [9.99712085723877, 3.0284183025360107] |
ecb0303d-0138-4cd2-b90e-80ad76323293 | movie-summarization-via-sparse-graph | 2012.07536 | null | https://arxiv.org/abs/2012.07536v1 | https://arxiv.org/pdf/2012.07536v1.pdf | Movie Summarization via Sparse Graph Construction | We summarize full-length movies by creating shorter videos containing their most informative scenes. We explore the hypothesis that a summary can be created by assembling scenes which are turning points (TPs), i.e., key events in a movie that describe its storyline. We propose a model that identifies TP scenes by build... | ['Mirella Lapata', 'Frank Keller', 'Pinelopi Papalampidi'] | 2020-12-14 | null | null | null | null | ['turning-point-identification'] | ['natural-language-processing'] | [ 2.53082812e-01 2.27225184e-01 -2.05253467e-01 -5.72147012e-01
-6.05621934e-01 -1.02487636e+00 7.44936347e-01 5.08565724e-01
2.65158355e-01 7.17716217e-01 1.51991725e+00 5.43641329e-01
-1.56170428e-01 -4.02182192e-01 -7.33055115e-01 -2.83427030e-01
-7.31027946e-02 2.77788728e-01 4.75093834e-02 1.43635627... | [10.589694023132324, 0.6074532270431519] |
a4c6b16b-0df4-47aa-bdf8-471f57d7c302 | robust-double-encoder-network-for-rgb-d | 2210.02834 | null | https://arxiv.org/abs/2210.02834v2 | https://arxiv.org/pdf/2210.02834v2.pdf | Robust Double-Encoder Network for RGB-D Panoptic Segmentation | Perception is crucial for robots that act in real-world environments, as autonomous systems need to see and understand the world around them to act properly. Panoptic segmentation provides an interpretation of the scene by computing a pixelwise semantic label together with instance IDs. In this paper, we address panopt... | ['Cyrill Stachniss', 'Jens Behley', 'Tiziano Guadagnino', 'Federico Magistri', 'Matteo Sodano'] | 2022-10-06 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 5.42878866e-01 3.38324085e-02 1.40304416e-01 -6.62255108e-01
-4.55085486e-01 -7.12411821e-01 4.85365480e-01 2.75734842e-01
-6.76400721e-01 3.53608549e-01 7.53710717e-02 -1.05371036e-01
1.35646135e-01 -1.11411250e+00 -8.02351654e-01 -6.33104622e-01
2.39288688e-01 3.95753086e-01 5.73784888e-01 1.22320959... | [8.752803802490234, -1.7706037759780884] |
deba0b2f-dc36-48c0-9e05-10ea63c02d0b | boundary-adversarial-examples-against | 2211.14088 | null | https://arxiv.org/abs/2211.14088v1 | https://arxiv.org/pdf/2211.14088v1.pdf | Boundary Adversarial Examples Against Adversarial Overfitting | Standard adversarial training approaches suffer from robust overfitting where the robust accuracy decreases when models are adversarially trained for too long. The origin of this problem is still unclear and conflicting explanations have been reported, i.e., memorization effects induced by large loss data or because of... | ['Beat Buesser', 'Muhammad Zaid Hameed'] | 2022-11-25 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.63848633e-01 2.59684265e-01 2.69010067e-01 -2.38832667e-01
-8.53803158e-01 -7.52532542e-01 5.59039533e-01 2.60879815e-01
-4.08254743e-01 1.19058895e+00 -8.02891795e-03 -9.89537165e-02
-8.15689787e-02 -8.94763231e-01 -9.71381426e-01 -7.72079647e-01
1.58572675e-05 7.15383813e-02 2.70139724e-01 -3.20304871... | [5.703673362731934, 7.829916477203369] |
91f31922-fed3-49bb-a2a6-419e582283ff | deep-learning-in-lexical-analysis-and-parsing | null | null | https://aclanthology.org/I17-5001 | https://aclanthology.org/I17-5001.pdf | Deep Learning in Lexical Analysis and Parsing | Neural networks, also with a fancy name deep learning, just right can overcome the above {``}feature engineering{''} problem. In theory, they can use non-linear activation functions and multiple layers to automatically find useful features. The novel network structures, such as convolutional or recurrent, help to reduc... | ['Yue Zhang', 'Wanxiang Che'] | 2017-11-01 | deep-learning-in-lexical-analysis-and-parsing-1 | https://aclanthology.org/I17-5001 | https://aclanthology.org/I17-5001.pdf | ijcnlp-2017-11 | ['lexical-analysis'] | ['natural-language-processing'] | [-1.52575478e-01 2.43565246e-01 -4.36922371e-01 -7.10777700e-01
-4.01805788e-01 -3.51813793e-01 1.59220874e-01 -4.17478420e-02
-5.69883227e-01 6.27996087e-01 6.61087409e-02 -5.72731674e-01
-3.06726962e-01 -7.66978145e-01 -3.75132501e-01 -4.30324256e-01
-1.73298512e-02 1.96092770e-01 3.35548967e-02 -4.03190941... | [10.495635032653809, 10.023272514343262] |
f437e5a4-4236-4446-9136-ec2e16ad10a3 | pneumonia-detection-in-chest-x-ray-images | 2301.08479 | null | https://arxiv.org/abs/2301.08479v1 | https://arxiv.org/pdf/2301.08479v1.pdf | Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance | People all over the globe are affected by pneumonia but deaths due to it are highest in Sub-Saharan Asia and South Asia. In recent years, the overall incidence and mortality rate of pneumonia regardless of the utilization of effective vaccines and compelling antibiotics has escalated. Thus, pneumonia remains a disease ... | ['Rizwan Ahmed Khan', 'Omama Ahmed Farooqi', 'Eesha Qureshi', 'Wardah Ali'] | 2023-01-20 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 3.06621075e-01 -8.79538581e-02 6.33875560e-03 5.17073879e-03
-4.96463954e-01 -1.48682699e-01 3.51627380e-01 -3.34899761e-02
-3.49160850e-01 1.07477212e+00 1.20557852e-01 -2.15759769e-01
-1.83206141e-01 -9.28757787e-01 -5.00181437e-01 -1.06958675e+00
2.43569866e-01 8.51760924e-01 -6.96160123e-02 5.24372943... | [15.534918785095215, -1.7472325563430786] |
e8d5e6a5-171e-4446-a06a-d78c1bbd7c6f | simtreels-simulating-aerial-and-terrestrial | 2011.11954 | null | https://arxiv.org/abs/2011.11954v1 | https://arxiv.org/pdf/2011.11954v1.pdf | SimTreeLS: Simulating aerial and terrestrial laser scans of trees | There are numerous emerging applications for digitizing trees using terrestrial and aerial laser scanning, particularly in the fields of agriculture and forestry. Interpretation of LiDAR point clouds is increasingly relying on data-driven methods (such as supervised machine learning) that rely on large quantities of ha... | ['James Underwood', 'Mitch Bryson', 'Fredrik Westling'] | 2020-11-24 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 7.77915120e-01 -1.40356645e-01 1.01716757e-01 -3.97390366e-01
-3.20164144e-01 -9.09471333e-01 6.14232183e-01 5.79690635e-01
-1.77760869e-01 6.25290513e-01 -4.21543568e-01 -9.76150155e-01
-3.62323910e-01 -1.26613688e+00 -3.40088934e-01 -1.85378104e-01
-3.54575485e-01 1.04184020e+00 5.75381875e-01 -1.50446802... | [8.400343894958496, -2.5834248065948486] |
e54b5fc1-64e3-4e1a-9f43-d5b4adf1d46e | extending-implicit-discourse-relation | 2010.06294 | null | https://arxiv.org/abs/2010.06294v1 | https://arxiv.org/pdf/2010.06294v1.pdf | Extending Implicit Discourse Relation Recognition to the PDTB-3 | The PDTB-3 contains many more Implicit discourse relations than the previous PDTB-2. This is in part because implicit relations have now been annotated within sentences as well as between them. In addition, some now co-occur with explicit discourse relations, instead of standing on their own. Here we show that while th... | ['Bonnie Webber', 'Zheng Zhao', 'Li Liang'] | 2020-10-13 | null | https://aclanthology.org/2020.codi-1.14 | https://aclanthology.org/2020.codi-1.14.pdf | emnlp-codi-2020-11 | ['implicit-relations'] | ['natural-language-processing'] | [ 3.40247452e-01 9.86654878e-01 -4.33245629e-01 -2.73130208e-01
-5.93398333e-01 -8.21116209e-01 9.43302810e-01 6.30859733e-01
-2.99568743e-01 1.15960360e+00 9.49412465e-01 -7.02991128e-01
-1.23673141e-01 -6.03140295e-01 -2.06753418e-01 -4.12262768e-01
-1.49053916e-01 8.20743203e-01 5.11663854e-01 -5.14556825... | [10.736491203308105, 9.376453399658203] |
46a70ebd-7aec-40a2-b9be-191c66ca298c | learning-viewpoint-agnostic-visual | 2206.11895 | null | https://arxiv.org/abs/2206.11895v4 | https://arxiv.org/pdf/2206.11895v4.pdf | Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space | Humans are remarkably flexible in understanding viewpoint changes due to visual cortex supporting the perception of 3D structure. In contrast, most of the computer vision models that learn visual representation from a pool of 2D images often fail to generalize over novel camera viewpoints. Recently, the vision architec... | ['Michael S. Ryoo', 'Srijan Das', 'Jinghuan Shang'] | 2022-06-23 | null | null | null | null | ['video-alignment'] | ['computer-vision'] | [-3.38038690e-02 -7.17678368e-02 -8.34669769e-02 -4.99349147e-01
-3.84935975e-01 -7.52747059e-01 6.46402717e-01 -4.02524769e-01
-1.01599380e-01 4.53896448e-02 1.84458628e-01 -3.39525864e-02
2.61197805e-01 -4.55188364e-01 -1.03996432e+00 -6.07018471e-01
3.63411129e-01 1.96253702e-01 1.55434623e-01 5.79087399... | [8.221404075622559, -3.0881927013397217] |
27a4b834-7f48-4742-9caa-fcacd35157a6 | a-system-for-generating-cloze-test-items-from | null | null | https://aclanthology.org/R13-2016 | https://aclanthology.org/R13-2016.pdf | A System for Generating Cloze Test Items from Russian-Language Text | null | ['Andrey Kurtasov'] | 2013-09-01 | a-system-for-generating-cloze-test-items-from-1 | https://aclanthology.org/R13-2016 | https://aclanthology.org/R13-2016.pdf | ranlp-2013-9 | ['cloze-test'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3238396644592285, 3.7125821113586426] |
09c782e1-6b7b-44b7-a0f3-e7c9f3c1035d | temporality-and-causality-in-abstract | 2303.09197 | null | https://arxiv.org/abs/2303.09197v1 | https://arxiv.org/pdf/2303.09197v1.pdf | Temporality and Causality in Abstract Argumentation | In the context of abstract argumentation, we present the benefits of considering temporality, i.e. the order in which arguments are enunciated, as well as causality. We propose a formal method to rewrite the concepts of acyclic abstract argumentation frameworks into an action language, that allows us to model the evolu... | ['M. -J. Lesot', 'G. Bourgne', 'I. Bloch', 'C. Sarmiento', 'Y. Munro'] | 2023-03-16 | null | null | null | null | ['abstract-argumentation', 'abstract-argumentation'] | ['natural-language-processing', 'reasoning'] | [-4.12233323e-02 9.65212524e-01 -1.52057305e-01 -3.46447289e-01
5.32659054e-01 -9.29497421e-01 1.24767685e+00 7.11420536e-01
-2.49787524e-01 8.35088611e-01 5.62790036e-01 -6.91011131e-01
-5.20686746e-01 -1.23921692e+00 -4.81327116e-01 -1.61401510e-01
2.91912904e-04 3.44921768e-01 4.99928147e-01 -6.24413848... | [8.712146759033203, 6.682788848876953] |
56223c15-9bdc-4828-946c-49ab0ee032ed | understanding-open-set-recognition-by | 2209.11436 | null | https://arxiv.org/abs/2209.11436v1 | https://arxiv.org/pdf/2209.11436v1.pdf | Understanding Open-Set Recognition by Jacobian Norm of Representation | In contrast to conventional closed-set recognition, open-set recognition (OSR) assumes the presence of an unknown class, which is not seen to a model during training. One predominant approach in OSR is metric learning, where a model is trained to separate the inter-class representations of known class data. Numerous wo... | ['Andrew Beng Jin Teoh', 'Eunju Jeong', 'Hojin Park', 'Jaewoo Park'] | 2022-09-23 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 3.32403302e-01 3.55404407e-01 -2.10949436e-01 -3.70969683e-01
-5.44789076e-01 -7.71072567e-01 5.67908525e-01 -1.13718271e-01
-1.13598138e-01 5.75262487e-01 -8.48944113e-02 1.29556060e-01
-4.04531062e-01 -9.12728548e-01 -7.58173227e-01 -9.34478998e-01
2.69676410e-02 6.21949911e-01 -1.89014882e-01 -3.19051117... | [9.553898811340332, 2.877493381500244] |
f4bcac22-d92b-4728-931d-007b509400c0 | structure-aware-pre-training-for-table-to | null | null | https://aclanthology.org/2021.findings-acl.200 | https://aclanthology.org/2021.findings-acl.200.pdf | Structure-Aware Pre-Training for Table-to-Text Generation | null | ['Xiaojun Wan', 'Xinyu Xing'] | null | null | null | null | findings-acl-2021-8 | ['table-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.360835075378418, 3.6974642276763916] |
c8f9169b-fb83-4d39-b718-380c46500bfd | robust-action-segmentation-from-timestamp | 2210.06501 | null | https://arxiv.org/abs/2210.06501v1 | https://arxiv.org/pdf/2210.06501v1.pdf | Robust Action Segmentation from Timestamp Supervision | Action segmentation is the task of predicting an action label for each frame of an untrimmed video. As obtaining annotations to train an approach for action segmentation in a fully supervised way is expensive, various approaches have been proposed to train action segmentation models using different forms of weak superv... | ['Juergen Gall', 'Gianpiero Francesca', 'Emad Bahrami', 'Yazan Abu Farha', 'Yaser Souri'] | 2022-10-12 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 5.95388114e-01 3.37664515e-01 -7.23413587e-01 -7.07898080e-01
-6.87631071e-01 -6.84314668e-01 6.79421961e-01 1.59919634e-01
-6.31817043e-01 9.60439324e-01 3.51063967e-01 -1.13313340e-01
4.16124165e-01 -4.49591994e-01 -8.24081361e-01 -6.62897348e-01
1.11474715e-01 2.78027326e-01 8.19937408e-01 2.73863941... | [8.42529296875, 0.5728304982185364] |
7e355090-e4a5-4d1f-8b6b-c905306789d3 | robust-data-association-for-object-level | 1909.13493 | null | https://arxiv.org/abs/1909.13493v1 | https://arxiv.org/pdf/1909.13493v1.pdf | Robust Data Association for Object-level Semantic SLAM | Simultaneous mapping and localization (SLAM) in an real indoor environment is still a challenging task. Traditional SLAM approaches rely heavily on low-level geometric constraints like corners or lines, which may lead to tracking failure in textureless surroundings or cluttered world with dynamic objects. In this paper... | ['Xueyang Kang', 'Shunying Yuan'] | 2019-09-30 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [ 3.07845678e-02 -5.28480232e-01 -4.88655455e-02 -6.30756915e-01
-3.11370850e-01 -4.82451826e-01 4.79126006e-01 3.49204004e-01
-5.57652295e-01 1.06069314e+00 -4.17854011e-01 -1.43319648e-02
-5.50313175e-01 -9.25895154e-01 -6.07228935e-01 -3.49516422e-01
-1.36515439e-01 8.15361083e-01 6.72616422e-01 -1.50906190... | [7.343695640563965, -2.199270248413086] |
0a9a4c90-63f0-4fcf-9f45-f8d10122d81e | co-bed-information-theoretic-contextual | 2302.14015 | null | https://arxiv.org/abs/2302.14015v1 | https://arxiv.org/pdf/2302.14015v1.pdf | CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design | We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED -- a general, model-agnostic framework for designing contextual experiments using information-theoretic principles. After formulating a suitable information-based objective, we employ black-box variat... | ['Adam Foster', 'Cheng Zhang', 'Tom Rainforth', 'Joel Jennings', 'Desi R. Ivanova'] | 2023-02-27 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 1.44075915e-01 -3.18213105e-01 -3.79314482e-01 -4.19740379e-01
-1.12209952e+00 -6.33194625e-01 5.50957859e-01 -2.09721863e-01
-6.43644571e-01 7.74263203e-01 2.12048411e-01 -5.64370453e-01
-6.63718283e-01 -2.36764997e-01 -7.52297580e-01 -6.74809098e-01
7.44404420e-02 2.83398688e-01 -2.27770224e-01 6.03145622... | [6.549116134643555, 4.045498847961426] |
c9715fb4-a276-4f7d-93b6-4acab9bc10ba | investigating-conversational-search-behavior | 2301.04098 | null | https://arxiv.org/abs/2301.04098v2 | https://arxiv.org/pdf/2301.04098v2.pdf | Investigating Conversational Search Behavior For Domain Exploration | Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents. This change especially affects exploratory information-seeking contexts, where conversational search systems can guide the discovery of... | ['Florian Matthes', 'Daniel Braun', 'Juraj Vladika', 'Anum Afzal', 'Phillip Schneider'] | 2023-01-10 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.35590687e-01 2.77361989e-01 -2.75005728e-01 -1.35691717e-01
-3.57155174e-01 -8.15360010e-01 7.93921769e-01 3.94994885e-01
-3.79253417e-01 5.76433659e-01 5.40755272e-01 -7.90449619e-01
-5.62842548e-01 -2.60499448e-01 4.67351079e-01 -5.71062155e-02
1.16435900e-01 6.41468108e-01 1.50423661e-01 -6.35484338... | [12.256207466125488, 7.780663013458252] |
57d3a1d5-f3ea-4b87-bd63-b233c98cdb63 | evaluating-bert-based-scientific-relation | 2305.02291 | null | https://arxiv.org/abs/2305.02291v1 | https://arxiv.org/pdf/2305.02291v1.pdf | Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections | The rapid growth of research publications has placed great demands on digital libraries (DL) for advanced information management technologies. To cater to these demands, techniques relying on knowledge-graph structures are being advocated. In such graph-based pipelines, inferring semantic relations between related scie... | ['J. Stephen Downie', 'Sören Auer', "Jennifer D'Souza", 'Ming Jiang'] | 2023-05-03 | null | null | null | null | ['optical-character-recognition', 'graph-construction', 'relation-classification'] | ['computer-vision', 'graphs', 'natural-language-processing'] | [ 3.20816249e-01 1.33641049e-01 -1.76193774e-01 2.84573026e-02
-8.81181657e-01 -7.33579159e-01 7.49838829e-01 7.01710224e-01
-3.20272774e-01 7.13608801e-01 -1.12755582e-01 -5.85414886e-01
-4.30808961e-01 -8.35836470e-01 -4.18062568e-01 -4.99330580e-01
6.16065748e-02 5.09719253e-01 2.92781889e-01 -3.68214841... | [9.429883003234863, 8.4938383102417] |
a32e9198-529f-4ed3-8398-f5c86e72c2f6 | saliency-scale-and-information-towards-a | null | null | http://papers.nips.cc/paper/5946-saliency-scale-and-information-towards-a-unifying-theory | http://papers.nips.cc/paper/5946-saliency-scale-and-information-towards-a-unifying-theory.pdf | Saliency, Scale and Information: Towards a Unifying Theory | In this paper we present a definition for visual saliency grounded in information theory. This proposal is shown to relate to a variety of classic research contributions in scale-space theory, interest point detection, bilateral filtering, and to existing models of visual saliency. Based on the proposed definition of v... | ['Shafin Rahman', 'Neil Bruce'] | 2015-12-01 | null | null | null | neurips-2015-12 | ['interest-point-detection'] | ['computer-vision'] | [ 4.66765106e-01 1.75555900e-01 -1.32306591e-01 -2.59726405e-01
-1.68453991e-01 -4.89974380e-01 5.99981070e-01 5.37414551e-01
-3.92650485e-01 4.92950648e-01 1.61262900e-01 -1.63411513e-01
-2.92699546e-01 -4.77959484e-01 -6.56116009e-01 -3.52097899e-01
-1.13659717e-01 2.14757281e-03 1.02650034e+00 -4.30889755... | [9.991585731506348, 1.5514639616012573] |
e8baf0e3-7e8e-4360-b303-cda36b7e97cc | continual-treatment-effect-estimation | 2301.01026 | null | https://arxiv.org/abs/2301.01026v4 | https://arxiv.org/pdf/2301.01026v4.pdf | Continual Causal Effect Estimation: Challenges and Opportunities | A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observat... | ['Sheng Li', 'Zhixuan Chu'] | 2023-01-03 | null | null | null | null | ['marketing', 'selection-bias'] | ['miscellaneous', 'natural-language-processing'] | [ 4.14251983e-01 1.38869183e-02 -1.24310148e+00 -4.11338925e-01
-6.71223164e-01 -2.95312792e-01 5.36407888e-01 3.26970607e-01
-4.71960276e-01 1.25535202e+00 7.58814454e-01 -7.27145970e-01
-6.48868918e-01 -7.92319775e-01 -8.49148333e-01 -5.78238606e-01
-3.26669186e-01 4.38191205e-01 -1.83991924e-01 9.75028276... | [8.045307159423828, 5.383248329162598] |
3168af8a-bd4e-4be3-b5eb-ab3c7d963111 | hyperspectral-demosaicing-of-snapshot-camera | 2211.15435 | null | https://arxiv.org/abs/2211.15435v1 | https://arxiv.org/pdf/2211.15435v1.pdf | Hyperspectral Demosaicing of Snapshot Camera Images Using Deep Learning | Spectral imaging technologies have rapidly evolved during the past decades. The recent development of single-camera-one-shot techniques for hyperspectral imaging allows multiple spectral bands to be captured simultaneously (3x3, 4x4 or 5x5 mosaic), opening up a wide range of applications. Examples include intraoperativ... | ['Peter Eisert', 'Anna Hilsmann', 'Charul Daudkhane', 'Eric L. Wisotzky'] | 2022-11-21 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 9.31656122e-01 -5.27088046e-01 1.94691420e-01 -2.95757383e-01
-5.38442254e-01 -5.79193652e-01 1.31460354e-01 -9.43613648e-02
-3.18175763e-01 7.78456450e-01 -1.98900372e-01 -5.82640618e-02
-3.98619384e-01 -8.81167948e-01 -6.94585025e-01 -1.04070020e+00
1.08710624e-01 -5.06217107e-02 -1.56964481e-01 -6.54451698... | [10.178106307983398, -2.0838472843170166] |
bebb5706-3665-4f8e-930e-bd62c16717fc | pscnet-pyramidal-scale-and-global-context | 2012.03597 | null | https://arxiv.org/abs/2012.03597v3 | https://arxiv.org/pdf/2012.03597v3.pdf | PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images | Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention. However, challenges such as large scale variation, complex background interference, and non-uniform density distribution greatly limit the counting accuracy, particularly striking in remo... | ['Yunhong Wang', 'Lu Li', 'Zhenghui Hu', 'Qi Wen', 'Qingjie Liu', 'Guangshuai Gao'] | 2020-12-07 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 8.88602883e-02 -6.27372801e-01 2.34760456e-02 -3.36402565e-01
-3.75197023e-01 -1.96372852e-01 4.72167492e-01 4.92446311e-02
-6.90187454e-01 8.92155409e-01 -6.24541789e-02 3.80124338e-02
-2.52343174e-02 -1.11683702e+00 -3.44332784e-01 -9.00385022e-01
1.60169691e-01 3.57520074e-01 4.60687131e-01 1.74268991... | [8.498600959777832, -0.3392346203327179] |
dee7d306-e83e-47b5-9dc0-69a958bc41ba | topic-aware-contextualized-embeddings-for | 2201.10982 | null | https://arxiv.org/abs/2201.10982v1 | https://arxiv.org/pdf/2201.10982v1.pdf | Topic Aware Contextualized Embeddings for High Quality Phrase Extraction | Keyphrase extraction from a given document is the task of automatically extracting salient phrases that best describe the document. This paper proposes a novel unsupervised graph-based ranking method to extract high-quality phrases from a given document. We obtain the contextualized embeddings from pre-trained language... | ['Vikram Goyal', 'Mukesh Mohania', 'Venktesh V'] | 2022-01-17 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [-6.19559251e-02 3.99201572e-01 -3.51539910e-01 2.88274974e-01
-7.08768785e-01 -7.49123573e-01 8.90714824e-01 1.00380731e+00
-6.83093607e-01 6.85006559e-01 8.78155589e-01 1.12752721e-01
-4.07740623e-01 -9.51721251e-01 -6.09834909e-01 -7.06758499e-01
-1.60424232e-01 2.93726355e-01 3.52043688e-01 -2.08293684... | [12.074429512023926, 8.856779098510742] |
214bb4af-7ace-42de-a86c-2e778ba7b4a2 | adaptive-multi-view-rule-discovery-for-weakly | 2206.13749 | null | https://arxiv.org/abs/2206.13749v1 | https://arxiv.org/pdf/2206.13749v1.pdf | Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction | On e-commerce platforms, predicting if two products are compatible with each other is an important functionality to achieve trustworthy product recommendation and search experience for consumers. However, accurately predicting product compatibility is difficult due to the heterogeneous product data and the lack of manu... | ['Chao Zhang', 'Xiquan Cui', 'Rebecca West', 'Rongzhi Zhang'] | 2022-06-28 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 7.19103515e-02 1.44141570e-01 -1.15231860e+00 -8.94212961e-01
-4.85008180e-01 -9.24733281e-01 1.53246522e-01 3.31076026e-01
3.58385623e-01 2.67367929e-01 -1.71097473e-03 -6.01605654e-01
-3.50547731e-01 -9.13716257e-01 -6.12452865e-01 -2.14816704e-01
-6.62692934e-02 9.74170625e-01 3.35289799e-02 -3.52207541... | [9.978687286376953, 6.313148498535156] |
4851ac22-c764-4ea5-b4d7-6a26650081c0 | exploring-the-potential-of-large-language-1 | 2307.03393 | null | https://arxiv.org/abs/2307.03393v2 | https://arxiv.org/pdf/2307.03393v2.pdf | Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs | Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in genera... | ['Jiliang Tang', 'Hui Liu', 'Wenqi Fan', 'Dawei Yin', 'Shuaiqiang Wang', 'Xiaochi Wei', 'Hongzhi Wen', 'Wei Jin', 'Hang Li', 'Haitao Mao', 'Zhikai Chen'] | 2023-07-07 | null | null | null | null | ['node-classification', 'general-knowledge'] | ['graphs', 'miscellaneous'] | [ 2.47464702e-01 5.62627852e-01 -4.55713451e-01 -2.84558088e-01
-1.28310487e-01 -5.06117404e-01 8.25464904e-01 6.95933163e-01
-2.61245012e-01 3.73774558e-01 1.63426369e-01 -7.37024724e-01
-1.57614604e-01 -1.26836860e+00 -5.14621675e-01 -3.56442779e-01
-3.38084519e-01 3.98114383e-01 1.98601007e-01 -3.04687768... | [7.444090366363525, 6.504740238189697] |
5a7b1c5d-c732-4047-a7f6-1ffc57792560 | vicreg-variance-invariance-covariance | 2105.04906 | null | https://arxiv.org/abs/2105.04906v3 | https://arxiv.org/pdf/2105.04906v3.pdf | VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning | Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image. A trivial solution is obtained when the encoder outputs constant vectors. This collapse problem is often avoided through implicit biases in the learnin... | ['Yann Lecun', 'Jean Ponce', 'Adrien Bardes'] | 2021-05-11 | vicreg-variance-invariance-covariance-1 | https://openreview.net/forum?id=IXexLXymbZ9 | https://openreview.net/pdf?id=IXexLXymbZ9 | neurips-2021-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.69756457e-01 3.87962796e-02 -1.48900762e-01 -4.83244717e-01
-4.77295965e-01 -5.77911079e-01 8.67653191e-01 7.23621249e-02
-4.46668088e-01 3.73732358e-01 5.57305813e-01 -1.09082766e-01
-1.60467178e-01 -3.86727512e-01 -6.53118849e-01 -8.18543255e-01
2.87256151e-01 -1.01439646e-02 7.65488893e-02 -1.47545174... | [9.242332458496094, 2.928084135055542] |
996b0688-b609-494a-8c18-69b26eb213fd | gamifying-video-object-segmentation | 1601.00825 | null | http://arxiv.org/abs/1601.00825v1 | http://arxiv.org/pdf/1601.00825v1.pdf | Gamifying Video Object Segmentation | Video object segmentation can be considered as one of the most challenging
computer vision problems. Indeed, so far, no existing solution is able to
effectively deal with the peculiarities of real-world videos, especially in
cases of articulated motion and object occlusions; limitations that appear more
evident when we... | ['Concetto Spampinato', 'Simone Palazzo', 'Daniela Giordano'] | 2016-01-05 | null | null | null | null | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 1.85626701e-01 1.17766216e-01 2.11543307e-01 -1.41068369e-01
-4.12667662e-01 -8.13423634e-01 3.44602823e-01 2.81261057e-01
-8.45409274e-01 5.52751362e-01 -3.87963116e-01 -4.80333529e-02
-5.86843006e-02 -5.35440683e-01 -5.68914533e-01 -6.08906150e-01
2.72861309e-02 6.82004452e-01 7.75767148e-01 -1.54276133... | [8.831232070922852, -0.20930363237857819] |
03edaefc-b200-4801-af07-a9d186ddefa2 | dual-curriculum-teacher-for-domain | 2210.08748 | null | https://arxiv.org/abs/2210.08748v1 | https://arxiv.org/pdf/2210.08748v1.pdf | Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in Autonomous Driving | Object detection for autonomous vehicles has received increasing attention in recent years, where labeled data are often expensive while unlabeled data can be collected readily, calling for research on semi-supervised learning for this area. Existing semi-supervised object detection (SSOD) methods usually assume that t... | ['Zhenguo Li', 'Fei Chen', 'Lanqing Hong', 'Yifan Zhang', 'Longhui Yu'] | 2022-10-17 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [-3.44912745e-02 -3.66787687e-02 -4.75116700e-01 -7.88372099e-01
-7.56967843e-01 -5.76486349e-01 4.82800484e-01 4.03949134e-02
-5.46306670e-01 6.89594030e-01 -2.66929686e-01 -2.54516304e-01
1.53145090e-01 -5.39372981e-01 -9.57622170e-01 -7.08138764e-01
4.34082806e-01 9.06679273e-01 7.68081188e-01 -2.14188308... | [9.393362045288086, 1.6132822036743164] |
c2990eb0-90ae-4f8c-9597-f0a4e90fb6b8 | restoreformer-high-quality-blind-face | 2201.06374 | null | https://arxiv.org/abs/2201.06374v3 | https://arxiv.org/pdf/2201.06374v3.pdf | RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs | Blind face restoration is to recover a high-quality face image from unknown degradations. As face image contains abundant contextual information, we propose a method, RestoreFormer, which explores fully-spatial attentions to model contextual information and surpasses existing works that use local operators. RestoreForm... | ['Ping Luo', 'Wenping Wang', 'Runjian Chen', 'Jiawei Zhang', 'Zhouxia Wang'] | 2022-01-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf | cvpr-2022-1 | ['blind-face-restoration', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.08161755e-01 -3.98407996e-01 7.80062452e-02 -4.36762065e-01
-1.03679121e+00 -9.80038103e-03 4.30698812e-01 -7.15184867e-01
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-1.79591626e-01 -5.23121357e-01 -9.07692671e-01 -7.58270741e-01
2.68491805e-01 6.98911175e-02 -4.91647422e-01 -3.02703589... | [12.826824188232422, -0.04969164356589317] |
2ceee8ef-6716-46d1-862f-a2a0dbe06f29 | do-transformers-dream-of-inference-or-can | null | null | https://aclanthology.org/2020.insights-1.12 | https://aclanthology.org/2020.insights-1.12.pdf | Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules? | Large pretrained language models (LM) have been used successfully for multi-hop question answering. However, most of these directions are not interpretable, as they do not make the inference hops necessary to explain a candidate answer explicitly. In this work, we investigate the capability of a state-of-the-art transf... | ['Mihai Surdeanu', 'Zhengzhong Liang'] | null | null | null | null | emnlp-insights-2020-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 4.14776951e-01 8.62511575e-01 -2.75406569e-01 -5.08376718e-01
-7.30791867e-01 -7.35573053e-01 5.06460965e-01 4.07144487e-01
-1.27342284e-01 1.10777652e+00 2.11222529e-01 -1.25303602e+00
-6.16453588e-02 -1.20120907e+00 -9.77089584e-01 3.13968778e-01
3.67888212e-01 6.58782423e-01 5.66866040e-01 -4.88659024... | [9.894290924072266, 7.52699613571167] |
8f7d9dd8-9a46-489e-8292-ad2862097b76 | optimizing-answer-set-computation-via | 1812.09718 | null | http://arxiv.org/abs/1812.09718v2 | http://arxiv.org/pdf/1812.09718v2.pdf | Optimizing Answer Set Computation via Heuristic-Based Decomposition | Answer Set Programming (ASP) is a purely declarative formalism developed in
the field of logic programming and nonmonotonic reasoning: computational
problems are encoded by logic programs whose answer sets, corresponding to
solutions, are computed by an ASP system. Different, semantically equivalent,
programs can be de... | ['Jessica Zangari', 'Simona Perri', 'Francesco Calimeri'] | 2018-12-23 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 7.22829774e-02 7.78906286e-01 -1.52000606e-01 -7.16254830e-01
-2.90506929e-01 -9.25462484e-01 4.14928645e-01 5.24411321e-01
-8.10401049e-03 5.49845874e-01 8.43189191e-03 -4.14350957e-01
-5.76931059e-01 -1.45957851e+00 -6.55305088e-01 -2.56016344e-01
2.75867850e-01 1.06150889e+00 8.53157461e-01 -6.47920132... | [8.61484432220459, 6.697964668273926] |
93309706-854b-4b9a-b851-ed8abecbffd4 | fast-image2point-towards-real-time-point | 2209.10029 | null | https://arxiv.org/abs/2209.10029v1 | https://arxiv.org/pdf/2209.10029v1.pdf | Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision | A key question in the problem of 3D reconstruction is how to train a machine or a robot to model 3D objects. Many tasks like navigation in real-time systems such as autonomous vehicles directly depend on this problem. These systems usually have limited computational power. Despite considerable progress in 3D reconstruc... | ['Kamran Ghaffari T', 'Amir G. Aghdam', 'AmirHossein Zamani'] | 2022-09-20 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 4.89663482e-02 -1.42643183e-01 3.54454786e-01 -5.63114047e-01
-3.75795096e-01 -4.46938485e-01 7.16612935e-01 -3.80451709e-01
-2.80351490e-01 1.93649858e-01 -5.85564911e-01 -5.30345380e-01
9.65220630e-02 -1.14763021e+00 -8.85281265e-01 -3.91430706e-01
2.87955433e-01 9.01720881e-01 4.50994134e-01 -3.57354730... | [8.222725868225098, -2.978959798812866] |
03298930-c07c-4baa-81d3-3c6d1616f850 | vitpose-simple-vision-transformer-baselines | 2204.12484 | null | https://arxiv.org/abs/2204.12484v3 | https://arxiv.org/pdf/2204.12484v3.pdf | ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation | Although no specific domain knowledge is considered in the design, plain vision transformers have shown excellent performance in visual recognition tasks. However, little effort has been made to reveal the potential of such simple structures for pose estimation tasks. In this paper, we show the surprisingly good capabi... | ['DaCheng Tao', 'Qiming Zhang', 'Jing Zhang', 'Yufei Xu'] | 2022-04-26 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-6.80032894e-02 1.33971900e-01 -1.72114894e-02 -2.29412422e-01
-8.36108148e-01 -6.36316419e-01 5.65753698e-01 -2.10900381e-01
-4.78491575e-01 2.65529484e-01 2.00815573e-01 4.63757776e-02
5.64090684e-02 -3.79847378e-01 -9.63284910e-01 -4.62642819e-01
1.11634903e-01 8.31884682e-01 5.05543053e-01 -1.48007005... | [7.144086837768555, -0.7779096961021423] |
7a38e362-8bb3-4c39-8a0b-a8ba80ee6c53 | the-as-nu-system-for-the-m2voc-challenge | 2104.03009 | null | https://arxiv.org/abs/2104.03009v1 | https://arxiv.org/pdf/2104.03009v1.pdf | The AS-NU System for the M2VoC Challenge | This paper describes the AS-NU systems for two tracks in MultiSpeaker Multi-Style Voice Cloning Challenge (M2VoC). The first track focuses on using a small number of 100 target utterances for voice cloning, while the second track focuses on using only 5 target utterances for voice cloning. Due to the serious lack of da... | ['Hsin-Min Wang', 'Yu Tsao', 'Tomoki Toda', 'Pin-Jui Ku', 'Yu-Wen Chen', 'Yu-Huai Peng', 'Wen-Chin Huang', 'Yi-Chiao Wu', 'Cheng-Hung Hu'] | 2021-04-07 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 1.27138272e-01 -2.32465076e-03 2.14311451e-01 -2.16596305e-01
-1.36370456e+00 -6.31004333e-01 3.24148655e-01 -4.18467641e-01
-3.16523641e-01 4.35779184e-01 6.13200247e-01 -4.02225584e-01
2.84796238e-01 1.62710905e-01 -2.50918418e-01 -6.13189697e-01
4.28480387e-01 5.92563212e-01 4.09243703e-01 -3.75374496... | [14.843657493591309, 6.64710807800293] |
7841d024-3b69-4a52-9cbb-b9f9ae6082fd | shift-robust-molecular-relational-learning | 2305.18451 | null | https://arxiv.org/abs/2305.18451v2 | https://arxiv.org/pdf/2305.18451v2.pdf | Shift-Robust Molecular Relational Learning with Causal Substructure | Recently, molecular relational learning, whose goal is to predict the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. In this work, we propose CMRL that is robust to the distributional shift in molecular relational learning by detecting ... | ['Chanyoung Park', 'Sein Kim', 'Gyoung S. Na', 'Kanghoon Yoon', 'Namkyeong Lee'] | 2023-05-29 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 4.99173075e-01 -1.22021377e-01 -5.70141613e-01 -4.05125618e-01
-4.34803337e-01 -5.21107078e-01 6.74577534e-01 5.35018623e-01
4.70103882e-02 1.01880062e+00 3.20881397e-01 -6.13919377e-01
-3.84962589e-01 -9.20351982e-01 -1.21735942e+00 -8.87342155e-01
-7.31229708e-02 3.38711254e-02 1.29861981e-01 6.27554059... | [5.195934772491455, 5.9106764793396] |
4bdc6370-11cd-43e0-9053-5709ee6d387c | divide-and-conquer-text-semantic-matching | null | null | https://openreview.net/forum?id=TVsyDo9hbX | https://openreview.net/pdf?id=TVsyDo9hbX | Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents | Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation. Most state-of-the-art matching models, e.g., BERT, directly perform text comparison by processing each word uniformly. However, a query sentence g... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['community-question-answering', 'community-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [ 4.78837222e-01 -1.46578044e-01 -5.27747273e-01 -5.76864898e-01
-6.99337363e-01 -4.28766608e-01 9.54972565e-01 9.70954835e-01
-5.94974041e-01 3.66011381e-01 5.34107566e-01 -4.43781793e-01
-4.08330001e-03 -1.05551004e+00 -4.86987203e-01 -1.12689503e-01
5.65580249e-01 4.67979997e-01 6.38939261e-01 -2.86574453... | [11.067634582519531, 8.095356941223145] |
7021cb60-f29c-4b0c-9809-7c2a7371f8ce | bi-noising-diffusion-towards-conditional | 2212.07352 | null | https://arxiv.org/abs/2212.07352v1 | https://arxiv.org/pdf/2212.07352v1.pdf | Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors | Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentimes their results can be unrealistic with observable color shifts and textures. We believe that this issue results from the divergence between... | ['Vishal M. Patel', 'Nithin Gopalakrishnan Nair', 'Kangfu Mei'] | 2022-12-14 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 1.06846273e-01 -1.14634521e-01 2.69921333e-01 -2.63207406e-01
-5.85889995e-01 -2.96305597e-01 7.84517705e-01 -4.69862401e-01
-1.81304559e-01 7.65581250e-01 2.60314345e-01 2.31928378e-01
1.63586095e-01 -7.83497989e-01 -5.47759950e-01 -1.04151917e+00
3.21115911e-01 2.72624463e-01 5.85608006e-01 1.45164385... | [11.539217948913574, -2.115844488143921] |
4bdc812f-c272-4c67-ad09-de9bec82dfaa | understanding-breast-cancer-survival-using | 2305.18410 | null | https://arxiv.org/abs/2305.18410v1 | https://arxiv.org/pdf/2305.18410v1.pdf | Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data | The need for more usable and explainable machine learning models in healthcare increases the importance of developing and utilizing causal discovery algorithms, which aim to discover causal relations by analyzing observational data. Explainable approaches aid clinicians and biologists in predicting the prognosis of dis... | ['Kun Zhang', 'Preslav Nakov', 'Yujia Zheng', 'Aigerim Zhumbhayeva', 'Shahad Hardan', 'Mugariya Farooq'] | 2023-05-28 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.80992025e-01 3.19290459e-01 -6.51588380e-01 -2.73920894e-01
-5.97780824e-01 -2.00398743e-01 5.74405968e-01 7.34216511e-01
-1.21484324e-01 1.21831965e+00 6.31545126e-01 -7.80507743e-01
-8.91231239e-01 -8.65206122e-01 -8.13694239e-01 -7.81234324e-01
-4.42640841e-01 4.70223904e-01 -2.74485618e-01 -5.41868396... | [7.85734224319458, 5.369601249694824] |
89d8c3eb-0f5a-4bfa-903e-e687cc89d054 | khanq-a-dataset-for-generating-deep-questions | null | null | https://aclanthology.org/2022.coling-1.518 | https://aclanthology.org/2022.coling-1.518.pdf | KHANQ: A Dataset for Generating Deep Questions in Education | Designing in-depth educational questions is a time-consuming and cognitively demanding task. Therefore, it is intriguing to study how to build Question Generation (QG) models to automate the question creation process. However, existing QG datasets are not suitable for educational question generation because the questio... | ['Hengchang Hu', 'Liangming Pan', 'Huanli Gong'] | null | null | null | null | coling-2022-10 | ['question-generation'] | ['natural-language-processing'] | [ 8.14898983e-02 4.44735914e-01 3.11621219e-01 -3.70702505e-01
-9.89131391e-01 -1.00154209e+00 5.60059369e-01 6.65804565e-01
-2.20630929e-01 7.36030281e-01 4.28616971e-01 -9.13610578e-01
-4.64410871e-01 -1.11245334e+00 -7.14312613e-01 1.51938677e-01
5.31597733e-01 3.42767000e-01 5.70553601e-01 -7.85020292... | [11.432461738586426, 7.995052814483643] |
784f6f7b-8bce-4e7f-bbf6-0f85bfe8a97c | stabilized-in-context-learning-with-pre | 2302.05932 | null | https://arxiv.org/abs/2302.05932v1 | https://arxiv.org/pdf/2302.05932v1.pdf | Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking | Prompt-based methods with large pre-trained language models (PLMs) have shown impressive unaided performance across many NLP tasks. These models improve even further with the addition of a few labeled in-context exemplars to guide output generation. However, for more complex tasks such as dialogue state tracking (DST),... | ['Zhou Yu', 'Kun Qian', 'Derek Chen'] | 2023-02-12 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 3.78182650e-01 3.61820579e-01 -1.36605054e-01 -4.22997713e-01
-1.11515450e+00 -5.53910196e-01 1.11649966e+00 2.73803651e-01
-4.94278818e-01 8.17248821e-01 7.28894413e-01 -2.14254424e-01
2.00305462e-01 -4.40117955e-01 -2.83169448e-01 -1.69139564e-01
4.65229265e-02 7.05185890e-01 3.40806574e-01 -7.75698960... | [12.617937088012695, 8.159026145935059] |
c4e2d665-b02d-4bd2-9ab5-8f125df2370b | retrieval-augmented-multi-label-text | 2305.13058 | null | https://arxiv.org/abs/2305.13058v1 | https://arxiv.org/pdf/2305.13058v1.pdf | Retrieval-augmented Multi-label Text Classification | Multi-label text classification (MLC) is a challenging task in settings of large label sets, where label support follows a Zipfian distribution. In this paper, we address this problem through retrieval augmentation, aiming to improve the sample efficiency of classification models. Our approach closely follows the stand... | ['Yova Kementchedjhieva', 'Ilias Chalkidis'] | 2023-05-22 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 7.15922296e-01 1.17477626e-02 -5.44881880e-01 -5.96797347e-01
-1.43376136e+00 -6.46155953e-01 7.11588621e-01 4.86432552e-01
-6.91998005e-01 8.22723031e-01 2.90311247e-01 -4.51688558e-01
-2.00761393e-01 -4.51058209e-01 -5.54971635e-01 -6.23586893e-01
2.68193692e-01 9.58597302e-01 -4.97866534e-02 -2.66045295... | [9.510608673095703, 4.456591606140137] |
2618a44c-2437-457c-a8e9-054fc5509b55 | shoerinsics-shoeprint-prediction-for | 2205.02361 | null | https://arxiv.org/abs/2205.02361v2 | https://arxiv.org/pdf/2205.02361v2.pdf | Creating a Forensic Database of Shoeprints from Online Shoe Tread Photos | Shoe tread impressions are one of the most common types of evidence left at crime scenes. However, the utility of such evidence is limited by the lack of databases of footwear prints that cover the large and growing number of distinct shoe models. Moreover, the database is preferred to contain the 3D shape, or depth, o... | ['Charless C. Fowlkes', 'Shu Kong', 'Bailey Kong', 'Samia Shafique'] | 2022-05-04 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 4.37458038e-01 -3.20907123e-03 -2.48172089e-01 -5.55728197e-01
-9.82489347e-01 -6.80977941e-01 4.33245093e-01 -1.48116916e-01
-2.02413559e-01 4.35244679e-01 1.87496349e-01 3.20069529e-02
-2.23013073e-01 -9.76890147e-01 -7.72508323e-01 -2.23942548e-01
3.68753523e-01 7.36199379e-01 4.53016818e-01 -3.30244184... | [8.168919563293457, -2.6826090812683105] |
ededa625-a715-4ab2-b637-85dbd6610ac1 | practical-digital-disguises-leveraging-face | 2204.03559 | null | https://arxiv.org/abs/2204.03559v2 | https://arxiv.org/pdf/2204.03559v2.pdf | Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy | With rapid advancements in image generation technology, face swapping for privacy protection has emerged as an active area of research. The ultimate benefit is improved access to video datasets, e.g. in healthcare settings. Recent literature has proposed deep network-based architectures to perform facial swaps and repo... | ['Eakta Jain', 'Jenny Skytta', 'Frederick Shic', 'Ethan Wilson'] | 2022-04-07 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 4.47674423e-01 6.00240350e-01 3.11527342e-01 -7.48296618e-01
-4.06220555e-01 -7.41823256e-01 2.26512000e-01 -2.21756160e-01
-4.39166814e-01 4.36082959e-01 4.50445145e-01 -1.30407112e-02
-4.80665080e-02 -2.85699815e-01 -7.38710880e-01 -4.11496669e-01
-1.70979828e-01 -7.14033246e-02 -2.34281272e-01 4.78363745... | [12.848980903625488, 0.6741330027580261] |
c670c674-7c0b-4566-bac2-3f7ea7a633d9 | mimetic-neural-networks-a-unified-framework | 2102.03881 | null | https://arxiv.org/abs/2102.03881v1 | https://arxiv.org/pdf/2102.03881v1.pdf | Mimetic Neural Networks: A unified framework for Protein Design and Folding | Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -- protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in ... | ['Eran Treister', 'Chen Keasar', 'Eldad Haber', 'Tue Boesen', 'Moshe Eliasof'] | 2021-02-07 | null | null | null | null | ['protein-design'] | ['medical'] | [ 4.16146815e-01 4.97880131e-01 -3.32725823e-01 -1.68168321e-01
1.70381069e-01 -5.96646428e-01 -1.29007334e-02 2.79734544e-02
-2.61185914e-01 1.22152197e+00 2.28223339e-01 -8.70336533e-01
2.14318752e-01 -8.14591050e-01 -1.44996691e+00 -7.71151423e-01
-1.93184808e-01 7.08517790e-01 -7.44684786e-02 -7.89092660... | [4.732250213623047, 5.611703872680664] |
7a88b991-84c2-4ac1-ad5e-f9ccdd5a301c | robust-semi-direct-monocular-visual-odometry | 1909.11362 | null | https://arxiv.org/abs/1909.11362v2 | https://arxiv.org/pdf/1909.11362v2.pdf | Robust Monocular Edge Visual Odometry through Coarse-to-Fine Data Association | In this work, we propose a monocular visual odometry framework, which allows exploiting the best attributes of edge feature for illumination-robust camera tracking, while at the same time ameliorating the performance degradation of edge mapping. In the front-end, an ICP-based edge registration can provide robust motion... | ['Xiaolong Wu', 'Patricio Vela', 'Cedric Pradalier'] | 2019-09-25 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.84522753e-02 -1.88181534e-01 2.04233482e-01 -2.90148128e-02
-6.54985189e-01 -6.41425490e-01 3.81357044e-01 1.79241776e-01
-5.29680312e-01 4.66131061e-01 4.81626624e-03 1.04907483e-01
-2.07082838e-01 -8.09447169e-01 -7.26054192e-01 -7.66948104e-01
2.22104222e-01 4.54906642e-01 6.96582913e-01 1.73260137... | [7.55459451675415, -2.167639970779419] |
15b273f4-0fd0-4e23-badc-3a31b71fefbb | comparing-nars-and-reinforcement-learning-an | 2304.03291 | null | https://arxiv.org/abs/2304.03291v2 | https://arxiv.org/pdf/2304.03291v2.pdf | Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms | In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL remains an exciting and innovative research area. One such alternative that has garnered attention is the Non-Axiomatic Reasoning System (NARS... | ['Sindri Magnússon', 'Ali Beikmohammadi'] | 2023-03-17 | null | null | null | null | ['q-learning'] | ['methodology'] | [-1.58576682e-01 -1.04291737e-01 2.26425380e-01 -2.36159060e-02
-3.20265859e-01 -6.19821548e-01 3.67802560e-01 -1.04628667e-01
-6.88062072e-01 9.42052603e-01 -1.41739666e-01 -5.47641814e-01
-5.67776144e-01 -9.18746412e-01 -4.68765169e-01 -4.70667899e-01
-5.06235175e-02 4.31781739e-01 3.35269541e-01 -8.63656819... | [3.887775182723999, 1.480825662612915] |
d6517d33-db86-498d-a5cd-d8e3fcb85e13 | it5-large-scale-text-to-text-pretraining-for | 2203.03759 | null | https://arxiv.org/abs/2203.03759v1 | https://arxiv.org/pdf/2203.03759v1.pdf | IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation | The T5 model and its unified text-to-text paradigm contributed in advancing the state-of-the-art for many natural language processing tasks. While some multilingual variants of the T5 model have recently been introduced, their performances were found to provide suboptimal performances for languages other than English i... | ['Malvina Nissim', 'Gabriele Sarti'] | 2022-03-07 | null | null | null | null | ['text-style-transfoer', 'headline-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.61911383e-01 4.27049607e-01 -1.17076576e-01 -2.69881517e-01
-1.54865062e+00 -7.71784842e-01 1.15224743e+00 -1.93875134e-02
-4.61011052e-01 1.03381479e+00 4.38851386e-01 -5.67320883e-01
3.13253134e-01 -5.38825929e-01 -7.14175045e-01 -2.33630359e-01
1.92560092e-01 1.01465368e+00 8.51773769e-02 -5.43875873... | [11.194509506225586, 9.69418716430664] |
93ef3f42-8360-4380-bd74-b479a5ae3f1f | channel-attention-based-iterative-residual | 2006.01469 | null | https://arxiv.org/abs/2006.01469v1 | https://arxiv.org/pdf/2006.01469v1.pdf | Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution | Despite the remarkable progresses made in deep-learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a major challenge. Existing DSR model is generally trained and tested on synthetic dataset, which is very different from what would get from a re... | ['Ruigang Yang', 'Hongdng Li', 'Wei Li', 'Yuchao Dai', 'Xibin Song', 'Liu Liu', 'Dingfu Zhou'] | 2020-06-02 | channel-attention-based-iterative-residual-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Song_Channel_Attention_Based_Iterative_Residual_Learning_for_Depth_Map_Super-Resolution_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Song_Channel_Attention_Based_Iterative_Residual_Learning_for_Depth_Map_Super-Resolution_CVPR_2020_paper.pdf | cvpr-2020-6 | ['depth-map-super-resolution'] | ['computer-vision'] | [ 6.50934458e-01 1.51349500e-01 8.34820494e-02 -2.30107114e-01
-1.26852262e+00 4.95077297e-02 4.67344254e-01 -3.18188190e-01
-2.62855858e-01 9.79219556e-01 5.17237961e-01 2.95111537e-01
-9.25831571e-02 -1.03336513e+00 -8.61986756e-01 -7.98413754e-01
1.77363202e-01 2.26370156e-01 5.63265920e-01 -4.86166537... | [9.783506393432617, -2.427800416946411] |
4c33653e-b836-4fc9-9734-2d527e91ae92 | deep-convolutional-sparse-coding-networks-for | 2005.08448 | null | https://arxiv.org/abs/2005.08448v1 | https://arxiv.org/pdf/2005.08448v1.pdf | Deep Convolutional Sparse Coding Networks for Image Fusion | Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusion. This paper presents three deep convolutional sparse coding (CSC) networks for three kinds of image f... | ['Chun-Xia Zhang', 'Junmin Liu', 'Yicheng Wang', 'Jiangshe Zhang', 'Zixiang Zhao', 'Shuang Xu'] | 2020-05-18 | null | null | null | null | ['infrared-and-visible-image-fusion', 'multi-exposure-image-fusion'] | ['computer-vision', 'computer-vision'] | [ 3.18754524e-01 -6.93300784e-01 7.41998032e-02 -3.83382529e-01
-5.19090414e-01 -1.89354017e-01 4.87482190e-01 -3.73002626e-02
-3.73725176e-01 4.69456673e-01 1.87439770e-01 -1.86016515e-01
-2.79538393e-01 -6.10545874e-01 -3.31329584e-01 -1.02024174e+00
2.77248949e-01 -3.50028455e-01 -1.73536032e-01 -2.75621206... | [10.450035095214844, -1.7896045446395874] |
830013ab-48a6-44ed-9ed0-67e5aee3a1ea | fine-grained-scene-graph-generation-with-data | 2203.11654 | null | https://arxiv.org/abs/2203.11654v2 | https://arxiv.org/pdf/2203.11654v2.pdf | Fine-Grained Scene Graph Generation with Data Transfer | Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images. Recent works have made a steady progress on SGG, and provide useful tools for high-level vision and language understanding. However, due to the data distribution problems including long-tail distribution and semantic am... | ['Tat-Seng Chua', 'Maosong Sun', 'Zhiyuan Liu', 'Wei Ji', 'Qianyu Chen', 'Yuan YAO', 'Ao Zhang'] | 2022-03-22 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 2.83936679e-01 9.33324397e-02 -1.28457472e-01 -5.08263230e-01
-6.29596531e-01 -5.00226557e-01 4.91560638e-01 -8.79773274e-02
-8.08739588e-02 5.19376516e-01 2.70376831e-01 -3.53267461e-01
1.57900363e-01 -8.72236073e-01 -8.20445955e-01 -6.67154729e-01
1.82512775e-01 4.54359472e-01 4.88516986e-01 3.67459655... | [10.309088706970215, 1.639824628829956] |
d06eb9ee-3656-44c8-9a4f-5c9861b4ea51 | seggpt-meets-co-saliency-scene | 2305.04396 | null | https://arxiv.org/abs/2305.04396v1 | https://arxiv.org/pdf/2305.04396v1.pdf | SegGPT Meets Co-Saliency Scene | Co-salient object detection targets at detecting co-existed salient objects among a group of images. Recently, a generalist model for segmenting everything in context, called SegGPT, is gaining public attention. In view of its breakthrough for segmentation, we can hardly wait to probe into its contribution to the task ... | ['Jungong Han', 'Dingwen Zhang', 'Shoukun Xu', 'Yi Liu'] | 2023-05-08 | null | null | null | null | ['co-saliency-detection', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision'] | [ 5.49348354e-01 1.29002541e-01 -2.05241874e-01 -1.78427950e-01
-7.95695662e-01 -2.39963889e-01 4.57931608e-01 5.00558436e-01
-1.82018667e-01 2.88736373e-01 3.34430456e-01 -2.42129788e-02
4.57893610e-02 -4.20618951e-01 -5.91362715e-01 -4.95836288e-01
-3.00881807e-02 -5.40374517e-02 9.30626571e-01 -2.72776872... | [9.817499160766602, -0.24956005811691284] |
e8c311da-544e-4f67-b4f3-6afcc805e459 | deepfl-iqa-weak-supervision-for-deep-iqa | 2001.08113 | null | https://arxiv.org/abs/2001.08113v1 | https://arxiv.org/pdf/2001.08113v1.pdf | DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning | Multi-level deep-features have been driving state-of-the-art methods for aesthetics and image quality assessment (IQA). However, most IQA benchmarks are comprised of artificially distorted images, for which features derived from ImageNet under-perform. We propose a new IQA dataset and a weakly supervised feature learni... | ['Vlad Hosu', 'Hanhe Lin', 'Dietmar Saupe'] | 2020-01-20 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 7.16873258e-02 -1.19905263e-01 3.49319756e-01 -4.58891034e-01
-1.26221573e+00 -4.46217269e-01 6.67693853e-01 -8.59377384e-02
-2.87610352e-01 5.18754125e-01 5.35561800e-01 1.29351348e-01
-3.00562531e-01 -8.78358722e-01 -7.58337438e-01 -6.12882197e-01
-1.10325642e-01 1.93122283e-01 -1.52356312e-01 -4.22910780... | [11.88906478881836, -1.811644434928894] |
fa875e51-81df-469e-8855-d94c0975ca42 | rst-parsing-from-scratch | 2105.10861 | null | https://arxiv.org/abs/2105.10861v1 | https://arxiv.org/pdf/2105.10861v1.pdf | RST Parsing from Scratch | We introduce a novel top-down end-to-end formulation of document-level discourse parsing in the Rhetorical Structure Theory (RST) framework. In this formulation, we consider discourse parsing as a sequence of splitting decisions at token boundaries and use a seq2seq network to model the splitting decisions. Our framewo... | ['XiaoLi Li', 'Shafiq Joty', 'Xuan-Phi Nguyen', 'Thanh-Tung Nguyen'] | 2021-05-23 | null | https://aclanthology.org/2021.naacl-main.128 | https://aclanthology.org/2021.naacl-main.128.pdf | naacl-2021-4 | ['discourse-segmentation', 'discourse-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.33056915e-01 8.79328907e-01 -3.60718876e-01 -4.38573092e-01
-1.20643723e+00 -1.03012669e+00 5.74759364e-01 3.65691125e-01
-4.86746520e-01 7.26484835e-01 7.79504180e-01 -9.46140409e-01
3.07171404e-01 -8.72293293e-01 -4.61710513e-01 -2.62676090e-01
5.34068681e-02 4.78980333e-01 4.69874322e-01 -4.28645462... | [10.74333381652832, 9.424837112426758] |
62e819e4-3600-44cd-b3d3-8f21f16cd68a | ktn-knowledge-transfer-network-for-learning | 2206.10090 | null | https://arxiv.org/abs/2206.10090v1 | https://arxiv.org/pdf/2206.10090v1.pdf | KTN: Knowledge Transfer Network for Learning Multi-person 2D-3D Correspondences | Human densepose estimation, aiming at establishing dense correspondences between 2D pixels of human body and 3D human body template, is a key technique in enabling machines to have an understanding of people in images. It still poses several challenges due to practical scenarios where real-world scenes are complex and ... | ['Meng Wang', 'Jingkuan Song', 'Yixuan Zhou', 'Lianli Gao', 'Xuanhan Wang'] | 2022-06-21 | null | null | null | null | ['human-part-segmentation'] | ['computer-vision'] | [ 3.53737295e-01 4.69597667e-01 -2.04803631e-01 -1.17799476e-01
-6.55993342e-01 -2.30117336e-01 1.73729345e-01 -2.59415001e-01
-4.90728140e-01 4.98998731e-01 1.08567700e-01 3.44436884e-01
3.03099245e-01 -8.28064144e-01 -9.27763224e-01 -5.43559611e-01
2.93261141e-01 6.05188906e-01 5.75916409e-01 -7.28910491... | [7.917742729187012, -0.4050906300544739] |
167eb032-8a14-4b7b-abbd-49567f4a058e | grid-forming-control-based-on-emulated | 2303.00391 | null | https://arxiv.org/abs/2303.00391v1 | https://arxiv.org/pdf/2303.00391v1.pdf | Grid-Forming Control Based On Emulated Synchronous Condenser Strategy Compliant With Challenging Grid Code Requirements | Future power systems will include high shares of inverter-based generation. There is a general consensus that for allowing this transition, the Grid-Forming (GFo) control approach would be of great value. This article presents a GFo control strategy which is based on the concept of an Emulated Synchronous Condenser in ... | ['Grégoire Prime', 'Valentin Costan', 'Antoine Rossé', 'Julian Freytes'] | 2023-03-01 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-2.09744141e-01 1.32752340e-02 1.32902056e-01 1.42858744e-01
2.15980485e-01 -1.08507681e+00 8.93873692e-01 4.47366059e-01
2.81641543e-01 1.39545453e+00 -5.21369040e-01 -4.30718571e-01
-4.69431847e-01 -7.14485109e-01 -2.89366990e-01 -9.90380883e-01
-2.06064299e-01 5.14838159e-01 2.49275267e-01 -5.19486129... | [5.767756938934326, 2.6035003662109375] |
531d2c3b-dca4-40e0-b097-f36d9d8a5c08 | towards-performance-improvement-in-indian | null | null | https://aclanthology.org/2020.icon-main.47 | https://aclanthology.org/2020.icon-main.47.pdf | Towards Performance Improvement in Indian Sign Language Recognition | Sign language is a complete natural language used by deaf and dumb people. It has its own grammar and it differs with spoken language to a great extent. Since people without hearing and speech impairment lack the knowledge of the sign language, the deaf and dumb people find it difficult to communicate with them. The co... | ['Brijesh Bhatt', 'Devendra Thakor', 'Kinjal Mistree'] | null | null | null | null | icon-2020-12 | ['sign-language-recognition', 'image-augmentation'] | ['computer-vision', 'computer-vision'] | [ 1.84605360e-01 -5.02007902e-02 -1.04832323e-02 -4.55341518e-01
-4.05170411e-01 -7.83995926e-01 6.50971413e-01 -9.02010500e-01
-6.61488950e-01 7.99866915e-01 5.53938568e-01 -4.73668844e-01
1.40443027e-01 -4.87440199e-01 -1.90739185e-01 -6.03158414e-01
1.61782816e-01 2.75797457e-01 3.88194352e-01 -3.35023582... | [9.080968856811523, -6.386663913726807] |
aa0317eb-d271-41cb-bb72-fb8e2fe437e8 | graphonomy-universal-human-parsing-via-graph | 1904.04536 | null | http://arxiv.org/abs/1904.04536v1 | http://arxiv.org/pdf/1904.04536v1.pdf | Graphonomy: Universal Human Parsing via Graph Transfer Learning | Prior highly-tuned human parsing models tend to fit towards each dataset in a
specific domain or with discrepant label granularity, and can hardly be adapted
to other human parsing tasks without extensive re-training. In this paper, we
aim to learn a single universal human parsing model that can tackle all kinds
of hum... | ['Liang Lin', 'Ke Gong', 'Yiming Gao', 'Xiaohui Shen', 'Xiaodan Liang', 'Meng Wang'] | 2019-04-09 | graphonomy-universal-human-parsing-via-graph-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Gong_Graphonomy_Universal_Human_Parsing_via_Graph_Transfer_Learning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Gong_Graphonomy_Universal_Human_Parsing_via_Graph_Transfer_Learning_CVPR_2019_paper.pdf | cvpr-2019-6 | ['human-parsing'] | ['computer-vision'] | [ 4.97034520e-01 5.86620450e-01 -3.19748759e-01 -6.48100138e-01
-8.59275818e-01 -7.36314893e-01 1.77070439e-01 3.38293254e-01
-2.31891900e-01 5.42219996e-01 9.86013934e-02 -1.81297094e-01
8.36361349e-02 -1.01314950e+00 -5.81624448e-01 -4.16975230e-01
2.92466491e-01 6.96181357e-01 5.96806526e-01 -2.53067687... | [9.108108520507812, 0.4923125207424164] |
d10ddf55-d4aa-4a84-8d59-ee0650e9e1db | detecting-dominant-vanishing-points-in | 1608.04267 | null | http://arxiv.org/abs/1608.04267v2 | http://arxiv.org/pdf/1608.04267v2.pdf | Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval | Linear perspective is widely used in landscape photography to create the
impression of depth on a 2D photo. Automated understanding of linear
perspective in landscape photography has several real-world applications,
including aesthetics assessment, image retrieval, and on-site feedback for
photo composition, yet adequa... | ['Zihan Zhou', 'James Z. Wang', 'Farshid Farhat'] | 2016-08-15 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 6.57639086e-01 -1.02847211e-01 -4.49034907e-02 -3.57632905e-01
-7.07095146e-01 -8.52863193e-01 3.33927274e-01 1.64979547e-01
1.09305000e-02 1.21998727e-01 3.00889015e-01 -2.15946138e-01
-2.11045276e-02 -7.37389684e-01 -5.04608452e-01 -2.65082717e-01
3.30482751e-01 -8.11413154e-02 2.90310830e-01 -3.88010353... | [9.562533378601074, -2.4723379611968994] |
0396fe92-5e0d-47a6-ab12-f6958c26955a | memories-are-one-to-many-mapping-alleviators | 2212.05005 | null | https://arxiv.org/abs/2212.05005v2 | https://arxiv.org/pdf/2212.05005v2.pdf | Memories are One-to-Many Mapping Alleviators in Talking Face Generation | Talking face generation aims at generating photo-realistic video portraits of a target person driven by input audio. Due to its nature of one-to-many mapping from the input audio to the output video (e.g., one speech content may have multiple feasible visual appearances), learning a deterministic mapping like previous ... | ['Jiang Bian', 'Li Song', 'Sheng Zhao', 'Runnan Li', 'Jun Ling', 'Xu Tan', 'Tianyu He', 'Anni Tang'] | 2022-12-09 | null | null | null | null | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 4.95859236e-01 2.57862151e-01 7.85804316e-02 -4.31395501e-01
-6.76547050e-01 -1.37529120e-01 5.82920730e-01 -4.33812410e-01
1.35774568e-01 6.95492029e-01 2.45296687e-01 9.22202170e-02
3.12799007e-01 -8.47242713e-01 -8.76717210e-01 -8.18088830e-01
3.76604348e-01 6.73105121e-02 4.12474312e-02 -2.51975507... | [13.021252632141113, -0.3787403106689453] |
835066bc-4ff7-45a4-9f9d-8d0b43cebf7d | a-structural-transformer-with-relative | null | null | https://openreview.net/forum?id=RxjDi-W69iW | https://openreview.net/pdf?id=RxjDi-W69iW | A Structural Transformer with Relative Positions in Trees for Code-to-Sequence Tasks | We suggest two approaches to incorporate syntactic information into transformer models encoding trees (e.g. abstract syntax trees) and generating sequences. First, we use self-attention with relative position representations to consider structural relationships between nodes using a representation that encodes movement... | ['Anonymous'] | 2020-06-04 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 7.29004681e-01 6.83037817e-01 -3.64584744e-01 -2.63046950e-01
-1.25739336e+00 -8.01930666e-01 5.43557703e-01 4.78517413e-01
1.58238530e-01 6.65108383e-01 8.50195110e-01 -8.10431242e-01
3.21583688e-01 -9.64687586e-01 -1.14943969e+00 -1.48520857e-01
-5.99422455e-02 3.90040398e-01 2.11422458e-01 -4.18343127... | [10.398931503295898, 8.786188125610352] |
b6cfefd2-6f79-4c26-83d2-3f0f5057c3f2 | gnn-xml-graph-neural-networks-for-extreme | 2012.05860 | null | https://arxiv.org/abs/2012.05860v1 | https://arxiv.org/pdf/2012.05860v1.pdf | GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification | Extreme multi-label text classification (XMTC) aims to tag a text instance with the most relevant subset of labels from an extremely large label set. XMTC has attracted much recent attention due to massive label sets yielded by modern applications, such as news annotation and product recommendation. The main challenges... | ['Shiliang Sun', 'Daoming Zong'] | 2020-12-10 | null | null | null | null | ['product-recommendation', 'news-annotation'] | ['miscellaneous', 'natural-language-processing'] | [ 3.79360616e-01 4.96382937e-02 -5.18677890e-01 -5.15577316e-01
-3.23566824e-01 -5.20349681e-01 2.77082294e-01 4.93252337e-01
-9.78514850e-02 1.59976944e-01 -6.78898320e-02 -2.60792881e-01
-3.50437999e-01 -7.49901652e-01 -2.66919553e-01 -8.29026103e-01
-9.48527902e-02 7.85737693e-01 -5.01550138e-02 9.99710113... | [9.66965389251709, 4.2623515129089355] |
a8289d18-feb1-471c-91a8-346b98e3c16f | sv000gg-at-semeval-2016-task-11-heavy-gauge | null | null | https://aclanthology.org/S16-1149 | https://aclanthology.org/S16-1149.pdf | SV000gg at SemEval-2016 Task 11: Heavy Gauge Complex Word Identification with System Voting | null | ['Lucia Specia', 'Gustavo Paetzold'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.363656520843506, 3.728548526763916] |
ab4e367f-b702-4f47-acdc-11c00e8df8f2 | anomaly-detection-with-domain-adaptation | 2006.03689 | null | https://arxiv.org/abs/2006.03689v1 | https://arxiv.org/pdf/2006.03689v1.pdf | Anomaly Detection with Domain Adaptation | We study the problem of semi-supervised anomaly detection with domain adaptation. Given a set of normal data from a source domain and a limited amount of normal examples from a target domain, the goal is to have a well-performing anomaly detector in the target domain. We propose the Invariant Representation Anomaly Det... | ['Eric Darve', 'Ziyi Yang', 'Iman Soltani Bozchalooi'] | 2020-06-05 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 4.04532313e-01 1.81409001e-01 3.30944583e-02 -5.84008157e-01
-8.44469130e-01 -5.35463154e-01 7.55092740e-01 2.87039541e-02
-3.33780438e-01 4.85263675e-01 -1.04594454e-01 -1.21870413e-01
2.03041136e-01 -5.63386858e-01 -9.54129219e-01 -5.18798172e-01
-3.71687114e-01 6.49363995e-01 1.66121587e-01 -1.88522145... | [7.766340255737305, 2.4476685523986816] |
90c2bfb5-2aa9-4e85-ba45-e91fb2247f40 | know-your-boundaries-the-necessity-of | 2206.00695 | null | https://arxiv.org/abs/2206.00695v1 | https://arxiv.org/pdf/2206.00695v1.pdf | Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL | We introduce an offline reinforcement learning (RL) algorithm that explicitly clones a behavior policy to constrain value learning. In offline RL, it is often important to prevent a policy from selecting unobserved actions, since the consequence of these actions cannot be presumed without additional information about t... | ['Scott Niekum', 'Wonjoon Goo'] | 2022-06-01 | null | null | null | null | ['d4rl'] | ['robots'] | [ 1.22983903e-02 3.92632842e-01 -7.58276463e-01 -3.92732471e-01
-7.22264528e-01 -6.66628182e-01 5.30359328e-01 5.58139235e-02
-6.06945038e-01 1.03017998e+00 4.66015749e-02 -3.72083932e-01
-1.10527888e-01 -8.88617277e-01 -9.87334847e-01 -7.42928863e-01
-5.48961246e-03 6.04911327e-01 1.27872482e-01 -1.11098364... | [4.127591609954834, 2.0909347534179688] |
8da6564c-ed3f-4c27-aa86-d35d51f0ea64 | perturbation-based-qe-an-explainable | 2305.07457 | null | https://arxiv.org/abs/2305.07457v1 | https://arxiv.org/pdf/2305.07457v1.pdf | Perturbation-based QE: An Explainable, Unsupervised Word-level Quality Estimation Method for Blackbox Machine Translation | Quality Estimation (QE) is the task of predicting the quality of Machine Translation (MT) system output, without using any gold-standard translation references. State-of-the-art QE models are supervised: they require human-labeled quality of some MT system output on some datasets for training, making them domain-depend... | ['Jan Niehues', 'Tu Anh Dinh'] | 2023-05-12 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.70816362e-01 3.53408664e-01 -3.68601114e-01 -2.51047224e-01
-1.37001681e+00 -7.60533154e-01 8.76071215e-01 1.21104181e-01
-3.99629414e-01 9.29619074e-01 2.86971241e-01 -9.38621223e-01
1.15145750e-01 -3.89846504e-01 -9.66080487e-01 -2.61951953e-01
5.40423572e-01 9.64186728e-01 -1.84779689e-01 -7.06345618... | [11.573036193847656, 10.249168395996094] |
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