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e70b11d0-d598-4e56-8060-d4b504595603 | otov2-automatic-generic-user-friendly | 2303.06862 | null | https://arxiv.org/abs/2303.06862v2 | https://arxiv.org/pdf/2303.06862v2.pdf | OTOV2: Automatic, Generic, User-Friendly | The existing model compression methods via structured pruning typically require complicated multi-stage procedures. Each individual stage necessitates numerous engineering efforts and domain-knowledge from the end-users which prevent their wider applications onto broader scenarios. We propose the second generation of O... | ['Ilya Zharkov', 'Zhihui Zhu', 'Tianyu Ding', 'Luming Liang', 'Tianyi Chen'] | 2023-03-13 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 4.45064940e-02 1.62891775e-01 -2.61542648e-01 -5.19616544e-01
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8.88481215e-02 6.44247651e-01 -1.35551423e-01 -8.29062462... | [8.577703475952148, 3.25089168548584] |
ce730813-7965-4cf1-8db9-db1cb2475eb9 | deep-neural-networks-for-blind-image-quality | 2109.12161 | null | https://arxiv.org/abs/2109.12161v1 | https://arxiv.org/pdf/2109.12161v1.pdf | Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge | The enormous space and diversity of natural images is usually represented by a few small-scale human-rated image quality assessment (IQA) datasets. This casts great challenges to deep neural network (DNN) based blind IQA (BIQA), which requires large-scale training data that is representative of the natural image distri... | ['Zhou Wang', 'Zhongling Wang', 'ShahRukh Athar'] | 2021-09-24 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 1.63023211e-02 -3.27037036e-01 2.14690790e-01 -5.35386622e-01
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-2.20738854e-02 5.28980911e-01 5.47356121e-02 -3.63843977... | [11.880951881408691, -1.808120846748352] |
b0bef628-dc6b-422d-a64b-f46b6f557824 | np-match-towards-a-new-probabilistic-model | 2301.13569 | null | https://arxiv.org/abs/2301.13569v2 | https://arxiv.org/pdf/2301.13569v2.pdf | NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning | Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match. NP-Match is ... | ['Thomas Lukasiewicz', 'Xiaolin Hu', 'JianFeng Wang'] | 2023-01-31 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 1.85538590e-01 1.15730062e-01 -6.49389386e-01 -8.88335347e-01
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3.83575529e-01 7.89283454e-01 9.07733068e-02 5.76824427... | [9.461017608642578, 3.775540351867676] |
e80301be-2ce2-4638-943c-875a106f992e | color-learning-for-image-compression | 2306.17460 | null | https://arxiv.org/abs/2306.17460v1 | https://arxiv.org/pdf/2306.17460v1.pdf | Color Learning for Image Compression | Deep learning based image compression has gained a lot of momentum in recent times. To enable a method that is suitable for image compression and subsequently extended to video compression, we propose a novel deep learning model architecture, where the task of image compression is divided into two sub-tasks, learning s... | ['Siegfried Fößel', 'Heiko Sparenberg', 'Thomas Richter', 'Srivatsa Prativadibhayankaram'] | 2023-06-30 | null | null | null | null | ['video-compression', 'image-compression'] | ['computer-vision', 'computer-vision'] | [ 3.01963210e-01 -5.10330498e-01 -1.59739047e-01 -3.98150116e-01
-7.90358424e-01 2.04200149e-01 5.97345352e-01 -9.12003666e-02
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-3.49617332e-01 -1.28810599e-01 -2.10407928e-01 9.55440551... | [11.382563591003418, -1.5884318351745605] |
6a71f4b7-c539-41ed-8adb-6a70adc2367f | learning-with-an-evolving-class-ontology | 2210.04993 | null | https://arxiv.org/abs/2210.04993v4 | https://arxiv.org/pdf/2210.04993v4.pdf | Continual Learning with Evolving Class Ontologies | Lifelong learners must recognize concept vocabularies that evolve over time. A common yet underexplored scenario is learning with class labels that continually refine/expand old classes. For example, humans learn to recognize ${\tt dog}$ before dog breeds. In practical settings, dataset $\textit{versioning}$ often intr... | ['Shu Kong', 'Deva Ramanan', 'Yu-Xiong Wang', 'Deepak Pathak', 'Zhiqiu Lin'] | 2022-10-10 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 1.72841564e-01 4.25406426e-01 -5.23713827e-01 -6.51736259e-01
-8.09405982e-01 -1.07923639e+00 4.63778377e-01 2.04264119e-01
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-3.05556536e-01 1.03644252e+00 2.90637702e-01 -2.11706713... | [9.452542304992676, 2.9603607654571533] |
ec7fa89a-e5b4-4b04-a5cb-2c568018bcbb | tuda-reproducibility-reprogen-replicability | null | null | https://aclanthology.org/2021.inlg-1.32 | https://aclanthology.org/2021.inlg-1.32.pdf | TUDA-Reproducibility @ ReproGen: Replicability of Human Evaluation of Text-to-Text and Concept-to-Text Generation | This paper describes our contribution to the Shared Task ReproGen by Belz et al. (2021), which investigates the reproducibility of human evaluations in the context of Natural Language Generation. We selected the paper “Generation of Company descriptions using concept-to-text and text-to-text deep models: data set colle... | ['Steffen Eger', 'Yanran Chen', 'Christian Richter'] | null | null | null | null | inlg-acl-2021-8 | ['concept-to-text-generation', 'paper-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.84301937e-02 6.23086095e-01 1.99232116e-01 -1.59750402e-01
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3.55927676e-01 6.58048749e-01 -1.14212520e-01 -3.41400683... | [11.824579238891602, 9.04169750213623] |
94fb7768-1ddc-474e-b523-4d4dced6385f | exploring-data-augmentation-for-code | 2302.03499 | null | https://arxiv.org/abs/2302.03499v1 | https://arxiv.org/pdf/2302.03499v1.pdf | Exploring Data Augmentation for Code Generation Tasks | Advances in natural language processing, such as transfer learning from pre-trained language models, have impacted how models are trained for programming language tasks too. Previous research primarily explored code pre-training and expanded it through multi-modality and multi-tasking, yet the data for downstream tasks... | ['Gerasimos Lampouras', 'Pinzhen Chen'] | 2023-02-05 | null | null | null | null | ['code-translation', 'program-synthesis'] | ['computer-code', 'computer-code'] | [ 1.27610371e-01 7.51283839e-02 -5.82417905e-01 -6.48594558e-01
-1.32039285e+00 -7.93984652e-01 3.78649801e-01 6.13312542e-01
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-9.08550397e-02 2.42814943e-01 -3.22072536e-01 -4.47975695... | [7.725064754486084, 7.890272617340088] |
79ef6974-a0a5-4806-bc3e-de3e7f75c192 | accurate-shape-and-phase-averaging-of-time | 2109.00978 | null | https://arxiv.org/abs/2109.00978v1 | https://arxiv.org/pdf/2109.00978v1.pdf | Accurate shape and phase averaging of time series through Dynamic Time Warping | We propose a novel time series averaging method based on Dynamic Time Warping (DTW). In contrast to previous methods, our algorithm preserves durational information and the distinctive durational features of the sequences due to a simple conversion of the output of DTW into a time sequence and an innovative iterative a... | ['Kristian Nymoen', 'George Sioros'] | 2021-09-02 | null | null | null | null | ['time-series-averaging'] | ['time-series'] | [ 3.08876455e-01 -6.68315947e-01 9.55494419e-02 -1.34292334e-01
-7.93562770e-01 -1.02133703e+00 8.51102591e-01 -1.05056338e-01
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-4.39157903e-01 -4.87416118e-01 -4.01081353e-01 -8.20718646e-01
-9.42892373e-01 5.93170449e-02 4.72978443e-01 -2.62068063... | [7.328769207000732, 3.3391127586364746] |
b31b8315-cfe5-452f-b0a1-2f65d20cc20d | scalable-logo-recognition-in-real-world | null | null | https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/deliver/index/docId/61111/file/61111.pdf | https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/deliver/index/docId/61111/file/61111.pdf | Scalable logo recognition in real-world images | In this paper we propose a highly effective and scalable framework for recognizing logos in images. At the core of our approach lays a method for encoding and indexing the relative spatial layout of local features detected in the logo images. Based on the analysis of the local features and the composition of basic spat... | ['Roelof van Zwol', 'Rainer Lienhart', 'Lluis Garcia Pueyo', 'Stefan Romberg'] | 2011-04-17 | null | null | null | acm-international-conference-on-multimedia-4 | ['logo-recognition'] | ['computer-vision'] | [ 4.31117058e-01 -6.00126207e-01 -2.45120466e-01 -2.12603077e-01
-6.19436324e-01 -9.40363050e-01 4.72790211e-01 3.39913547e-01
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-3.67810816e-01 3.93396974e-01 6.05075240e-01 2.35706680... | [9.234752655029297, 1.2494882345199585] |
d6dad688-be00-43ed-bec0-2b9924df2f23 | natural-language-rationales-with-full-stack | 2010.07526 | null | https://arxiv.org/abs/2010.07526v1 | https://arxiv.org/pdf/2010.07526v1.pdf | Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs | Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights. We present the first study focused on generating natural language rationales across several co... | ['Yejin Choi', 'Noah A. Smith', 'Ronan Le Bras', 'Jae Sung Park', 'Chandra Bhagavatula', 'Ana Marasović'] | 2020-10-15 | null | https://aclanthology.org/2020.findings-emnlp.253 | https://aclanthology.org/2020.findings-emnlp.253.pdf | findings-of-the-association-for-computational | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 4.55547035e-01 7.67828882e-01 -2.55808294e-01 -5.10690451e-01
-3.15929145e-01 -5.26757956e-01 7.91137516e-01 2.27463812e-01
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5.24513543e-01 4.16524082e-01 1.90310672e-01 -4.43709314... | [10.790020942687988, 1.8258482217788696] |
dc8f8370-8284-4693-a81b-c1ebd8cbefa1 | design-of-human-machine-interface-through | 2207.03112 | null | https://arxiv.org/abs/2207.03112v3 | https://arxiv.org/pdf/2207.03112v3.pdf | Deep learning based Hand gesture recognition system and design of a Human-Machine Interface | In this work, a real-time hand gesture recognition system-based human-computer interface (HCI) is presented. The system consists of six stages: (1) hand detection, (2) gesture segmentation, (3) use of five pre-trained convolutional neural network models (CNN) and vision transformer (ViT), (4) building an interactive hu... | ['Ratnakar Dash', 'Tapas Kumar Mishra', 'Abir Sen'] | 2022-07-07 | null | null | null | null | ['hand-gesture-recognition', 'hand-detection', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.19503982e-01 -3.84060025e-01 1.33627698e-01 -2.64625065e-02
-6.59630597e-02 -3.05525184e-01 4.59862351e-01 -2.85684556e-01
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-9.94743034e-02 4.28614318e-01 7.13162184e-01 -1.05930254... | [6.491472244262695, -0.2815522253513336] |
22b202f1-5924-492d-8696-33c7f63a6c43 | styletalker-one-shot-style-based-audio-driven | 2208.10922 | null | https://arxiv.org/abs/2208.10922v1 | https://arxiv.org/pdf/2208.10922v1.pdf | StyleTalker: One-shot Style-based Audio-driven Talking Head Video Generation | We propose StyleTalker, a novel audio-driven talking head generation model that can synthesize a video of a talking person from a single reference image with accurately audio-synced lip shapes, realistic head poses, and eye blinks. Specifically, by leveraging a pretrained image generator and an image encoder, we estima... | ['Sung Ju Hwang', 'Minyoung Song', 'Dongchan Min'] | 2022-08-23 | null | null | null | null | ['talking-head-generation', 'video-generation'] | ['computer-vision', 'computer-vision'] | [ 9.21847671e-02 1.67563140e-01 -2.22285956e-01 -9.96667519e-02
-1.14691436e+00 -5.51236987e-01 6.49241388e-01 -8.62295151e-01
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5.41591585e-01 -2.82720506e-01 -1.06379116e+00 -8.83066416e-01
3.82787257e-01 3.34167540e-01 -3.20000589e-01 1.19383633... | [13.230326652526855, -0.4330141246318817] |
01fa4b1e-75af-40fc-92a8-855c20d4fb10 | semi-supervised-graph-based-genre | null | null | https://aclanthology.org/W14-3706 | https://aclanthology.org/W14-3706.pdf | Semi-supervised Graph-based Genre Classification for Web Pages | null | ['Serge Sharoff', 'Katja Markert', 'Noushin Rezapour Asheghi'] | 2014-10-01 | null | null | null | ws-2014-10 | ['genre-classification'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.417501449584961, 3.809300661087036] |
01631c7a-d0f5-42f8-bcc4-c00ff022281b | a-differentiable-self-disambiguated-sense | null | null | https://openreview.net/forum?id=Hyls7h05FQ | https://openreview.net/pdf?id=Hyls7h05FQ | A Differentiable Self-disambiguated Sense Embedding Model via Scaled Gumbel Softmax | We present a differentiable multi-prototype word representation model that disentangles senses of polysemous words and produces meaningful sense-specific embeddings without external resources. It jointly learns how to disambiguate senses given local context and how to represent senses using hard attention. Unlike previ... | ['Jordan Boyd-Graber', 'Leah Findlater', 'Mohit Iyyer', 'Fenfei Guo'] | 2018-09-27 | null | null | null | null | ['hard-attention', 'word-similarity'] | ['methodology', 'natural-language-processing'] | [ 2.51451045e-01 5.96211106e-02 -1.30050704e-01 -6.51743591e-01
-8.56342554e-01 -7.66475379e-01 7.77448416e-01 8.27162445e-01
-1.19729531e+00 6.44236505e-01 5.16313672e-01 -1.68404102e-01
-5.80462813e-03 -8.56510639e-01 -3.72700542e-02 -3.33906651e-01
1.66760504e-01 5.80151141e-01 -1.36875585e-01 -5.58536470... | [10.45438003540039, 8.865920066833496] |
8a50d75a-1be0-448e-ad8f-c5573b63f5a3 | fast-privacy-preserving-text-classification | 2101.07365 | null | https://arxiv.org/abs/2101.07365v2 | https://arxiv.org/pdf/2101.07365v2.pdf | Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation | We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At the end of the protocol, Alice will only learn the result of the classifier applied to her text input... | ['Diego F. Aranha', 'Anderson C. A. Nascimento', 'Rafael Dowsley', 'Davis Railsback', 'Amanda Resende'] | 2021-01-18 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 3.20384979e-01 1.62926257e-01 1.37506947e-02 -5.02004743e-01
-7.66363204e-01 -1.16073370e+00 7.19354570e-01 4.59258914e-01
-9.73093092e-01 5.19379139e-01 -1.24236673e-01 -8.53689432e-01
2.62526095e-01 -1.10206318e+00 -4.96350139e-01 -1.04399490e+00
1.48967043e-01 5.78433156e-01 3.84962678e-01 -2.57645011... | [5.907506942749023, 6.833096027374268] |
1adf31d2-5b9e-439b-9f5d-ad181e9cdb04 | q-map-a-convolutional-approach-for-goal | 1810.02927 | null | https://arxiv.org/abs/1810.02927v2 | https://arxiv.org/pdf/1810.02927v2.pdf | Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks | Being able to reach any desired location in the environment can be a valuable asset for an agent. Learning a policy to navigate between all pairs of states individually is often not feasible. An all-goals updating algorithm uses each transition to learn Q-values towards all goals simultaneously and off-policy. However ... | ['Petar Kormushev', 'Vitaly Levdik', 'Fabio Pardo'] | 2018-10-06 | q-map-a-convolutional-approach-for-goal-1 | https://openreview.net/forum?id=rye7XnRqFm | https://openreview.net/pdf?id=rye7XnRqFm | iclr-2019-5 | ['montezumas-revenge', 'snes-games'] | ['playing-games', 'playing-games'] | [-1.52226090e-01 1.90611169e-01 1.80412844e-01 -1.28942639e-01
-7.83847511e-01 -9.86970663e-01 5.55401802e-01 1.63474813e-01
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-6.17990911e-01 -1.17650247e+00 -7.79290676e-01 -8.59189689e-01
-6.51820660e-01 9.96420920e-01 3.35949630e-01 -6.28140390... | [3.930370330810547, 1.5638686418533325] |
9187558c-6507-443d-b5f0-9028542baa2e | how-to-solve-few-shot-abusive-content | 2305.14081 | null | https://arxiv.org/abs/2305.14081v1 | https://arxiv.org/pdf/2305.14081v1.pdf | How to Solve Few-Shot Abusive Content Detection Using the Data We Actually Have | Due to the broad range of social media platforms and their user groups, the requirements of abusive language detection systems are varied and ever-changing. Already a large set of annotated corpora with different properties and label sets were created, such as hate or misogyny detection, but the form and targets of abu... | ['Alexander Fraser', 'Viktor Hangya'] | 2023-05-23 | null | null | null | null | ['abusive-language'] | ['natural-language-processing'] | [ 1.17520709e-02 -1.15448296e-01 -1.76345259e-01 -3.66744548e-01
-7.01452553e-01 -8.14334214e-01 6.34799719e-01 8.66829082e-02
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1.23045668e-01 5.80287814e-01 6.41538978e-01 -5.70805132... | [8.84930419921875, 10.58582878112793] |
76de10a5-ed59-4ef7-92f0-149e23fe8e58 | a-two-branch-neural-network-for-non | 2104.08902 | null | https://arxiv.org/abs/2104.08902v1 | https://arxiv.org/pdf/2104.08902v1.pdf | A Two-branch Neural Network for Non-homogeneous Dehazing via Ensemble Learning | Recently, there has been rapid and significant progress on image dehazing. Many deep learning based methods have shown their superb performance in handling homogeneous dehazing problems. However, we observe that even if a carefully designed convolutional neural network (CNN) can perform well on large-scaled dehazing be... | ['Keyan Wang', 'Xiyao Wang', 'Jun Chen', 'Minghan Fu', 'Huan Liu', 'Yankun Yu'] | 2021-04-18 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 9.92346182e-02 -3.29986453e-01 2.23802373e-01 -2.69809544e-01
-7.42425144e-01 -1.14422716e-01 4.40236598e-01 -2.00044006e-01
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5.95244579e-02 -6.00046366e-02 3.03613216e-01 -6.09516919... | [10.940751075744629, -3.093118906021118] |
d57a6946-618d-4074-8633-b0d79a15308e | self-sustaining-multiple-access-with | null | null | http://www.mosaic-lab.org/uploads/papers/1bcf6de2-74be-41a5-9431-1f169c0ed8af.pdf | http://www.mosaic-lab.org/uploads/papers/1bcf6de2-74be-41a5-9431-1f169c0ed8af.pdf | Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications | The Metaverse is a new paradigm that aims to create a virtual environment consisting of numerous worlds, each of which will offer a different set of services. To deal with such a dynamic and complex scenario, considering the stringent quality of service requirements aimed at the 6th generation of communication systems ... | ['Richard Li', 'Tarik Taleb', 'Masoud Shokrnezhad', 'Hamidreza Mazandarani'] | 2023-06-26 | null | null | null | ieee-metacom-kyoto-2023-6 | ['continual-learning', 'q-learning'] | ['methodology', 'methodology'] | [-2.07274139e-01 -2.13532612e-01 -2.87729353e-01 3.53894889e-01
-1.31129861e-01 -1.96986854e-01 1.39843047e-01 -5.96670806e-02
-3.11956912e-01 1.34910548e+00 -3.69753957e-01 -4.49817955e-01
-7.21931577e-01 -1.03411222e+00 -2.94528782e-01 -1.13945341e+00
-4.32372898e-01 4.95199651e-01 -1.24834239e-01 -6.30232871... | [5.954709053039551, 1.641033411026001] |
dc0461c7-6e77-4a4b-8c8f-278f8e6190a7 | e2e-load-end-to-end-long-form-online-action | 2306.07703 | null | https://arxiv.org/abs/2306.07703v1 | https://arxiv.org/pdf/2306.07703v1.pdf | E2E-LOAD: End-to-End Long-form Online Action Detection | Recently, there has been a growing trend toward feature-based approaches for Online Action Detection (OAD). However, these approaches have limitations due to their fixed backbone design, which ignores the potential capability of a trainable backbone. In this paper, we propose the first end-to-end OAD model, termed E2E-... | ['Lin Ma', 'Wei zhang', 'Bairui Wang', 'Weixin Luo', 'Shuqiang Cao'] | 2023-06-13 | null | null | null | null | ['action-detection', 'online-action-detection'] | ['computer-vision', 'computer-vision'] | [-1.33892447e-01 -9.01113749e-02 -4.47860569e-01 -3.19382757e-01
-8.01821530e-01 -4.01542336e-01 2.65775681e-01 -1.55195221e-01
-5.47406495e-01 5.41153848e-01 3.55697751e-01 -3.07296306e-01
-9.19109657e-02 -6.21324778e-01 -6.85639679e-01 -1.58618540e-01
-3.01763505e-01 1.18650131e-01 7.38611817e-01 1.44282673... | [8.607282638549805, 0.2821633219718933] |
49dedb4c-c577-4fe7-bc1d-ebb07941b9e0 | heimdal-highly-efficient-method-for-detection | 2210.15425 | null | https://arxiv.org/abs/2210.15425v1 | https://arxiv.org/pdf/2210.15425v1.pdf | HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words | Streaming keyword spotting is a widely used solution for activating voice assistants. Deep Neural Networks with Hidden Markov Model (DNN-HMM) based methods have proven to be efficient and widely adopted in this space, primarily because of the ability to detect and identify the start and end of the wake-up word at low c... | ['Devang Naik', 'Priyanka Padmanabhan', 'Minsik Cho', 'Mohammad Samragh Razlighi', 'Arnav Kundu'] | 2022-10-26 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 2.17923030e-01 -3.14614326e-01 -2.32559443e-01 -2.77663976e-01
-8.83487046e-01 -3.46224904e-01 2.82666713e-01 5.59883602e-02
-7.07185388e-01 2.61052459e-01 2.46977970e-01 -4.42919999e-01
2.09493369e-01 -9.93712768e-02 -4.67442602e-01 -5.40926099e-01
-7.58644268e-02 3.49380732e-01 3.98204893e-01 3.02447110... | [14.482213973999023, 6.493710517883301] |
3f02bb6b-3e5f-4596-90e8-673701f64112 | evaluation-of-generalizability-of-neural | 2004.07313 | null | https://arxiv.org/abs/2004.07313v2 | https://arxiv.org/pdf/2004.07313v2.pdf | Evaluation of Generalizability of Neural Program Analyzers under Semantic-Preserving Transformations | The abundance of publicly available source code repositories, in conjunction with the advances in neural networks, has enabled data-driven approaches to program analysis. These approaches, called neural program analyzers, use neural networks to extract patterns in the programs for tasks ranging from development product... | ['Md Rafiqul Islam Rabin', 'Mohammad Amin Alipour'] | 2020-04-15 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [ 3.21274810e-02 -2.53080446e-02 -4.31132585e-01 -4.22129661e-01
-2.95138896e-01 -8.41213167e-01 2.99632519e-01 4.15910721e-01
-7.85711110e-02 1.00692213e-01 3.60402018e-01 -9.99219298e-01
6.00787140e-02 -1.01495433e+00 -7.98039436e-01 7.87897632e-02
-5.15762158e-02 -1.93828851e-01 1.17546603e-01 -4.10872012... | [7.641404151916504, 7.742356300354004] |
8662ac7d-bef9-4d97-b827-921a2f0d3a7d | declarative-sequential-pattern-mining-of-care | 1707.08342 | null | http://arxiv.org/abs/1707.08342v1 | http://arxiv.org/pdf/1707.08342v1.pdf | Declarative Sequential Pattern Mining of Care Pathways | Sequential pattern mining algorithms are widely used to explore care pathways
database, but they generate a deluge of patterns, mostly redundant or useless.
Clinicians need tools to express complex mining queries in order to generate
less but more significant patterns. These algorithms are not versatile enough
to answe... | ['Yann Dauxais', 'André Happe', 'Thomas Guyet'] | 2017-07-26 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 1.08426079e-01 1.50309965e-01 -2.64003277e-01 -4.37660575e-01
-9.19781532e-03 -4.34196532e-01 -3.20824693e-05 8.95561755e-01
-2.13043392e-01 9.64854836e-01 1.51906312e-01 -7.28799284e-01
-9.49697852e-01 -1.05825174e+00 -6.20338619e-02 -1.43095404e-01
-3.18923503e-01 8.77894759e-01 1.79441944e-01 -1.11080207... | [8.279535293579102, 6.2508745193481445] |
c2d735a3-fd3f-4dc6-b6c2-46f8a66a37b7 | multi-modal-recurrent-fusion-for-indoor | 2203.00510 | null | https://arxiv.org/abs/2203.00510v2 | https://arxiv.org/pdf/2203.00510v2.pdf | Multi-Modal Recurrent Fusion for Indoor Localization | This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating the localization as a multi-modal sequence regression problem, a multi-stream recurrent fusion method is proposed to combine the current hidden state of ... | ['Toshiaki Koike-Akino', 'Wang', 'Pu', 'Philip V. Orlik', 'Jianyuan Yu'] | 2022-02-19 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 3.02080750e-01 -5.18763363e-01 -2.51435280e-01 -4.32547301e-01
-1.34767675e+00 -2.56730586e-01 5.06200016e-01 -3.46415102e-01
-5.68768799e-01 1.11495292e+00 5.17990351e-01 -3.95259202e-01
-3.12091708e-01 -6.59834683e-01 -8.64678323e-01 -5.58469474e-01
-2.69396394e-01 -2.06200063e-01 -3.12325686e-01 1.60724893... | [6.422280788421631, 0.8850423097610474] |
fa6754a0-f186-42f3-bed4-07709ba2e958 | boils-bayesian-optimisation-for-logic | 2111.06178 | null | https://arxiv.org/abs/2111.06178v1 | https://arxiv.org/pdf/2111.06178v1.pdf | BOiLS: Bayesian Optimisation for Logic Synthesis | Optimising the quality-of-results (QoR) of circuits during logic synthesis is a formidable challenge necessitating the exploration of exponentially sized search spaces. While expert-designed operations aid in uncovering effective sequences, the increase in complexity of logic circuits favours automated procedures. Insp... | ['Haitham Bou Ammar', 'Jun Wang', 'Xingchen Wan', 'Rasul Tutunov', 'Cedric Malherbe', 'Antoine Grosnit'] | 2021-11-11 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.42429897e-01 -3.98511179e-02 -4.81442451e-01 -1.88773409e-01
-8.94682050e-01 -8.23195517e-01 5.14413714e-01 4.97943722e-02
-3.38966548e-01 7.58719683e-01 -2.93393970e-01 -7.07098186e-01
-5.37585735e-01 -6.62667871e-01 -5.31051159e-01 -5.90130627e-01
-9.29135382e-02 5.95336080e-01 6.28957301e-02 1.13951325... | [6.149320125579834, 3.6325907707214355] |
abb54730-58b9-45dd-afac-b2abb2573c9c | transformer-based-multi-aspect-multi | 2205.03432 | null | https://arxiv.org/abs/2205.03432v1 | https://arxiv.org/pdf/2205.03432v1.pdf | Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment | Automatic pronunciation assessment is an important technology to help self-directed language learners. While pronunciation quality has multiple aspects including accuracy, fluency, completeness, and prosody, previous efforts typically only model one aspect (e.g., accuracy) at one granularity (e.g., at the phoneme-level... | ['James Glass', 'Peng Chang', 'Iek-Heng Chu', 'Ziyi Chen', 'Yuan Gong'] | 2022-05-06 | null | null | null | null | ['phone-level-pronunciation-scoring', 'word-level-pronunciation-scoring', 'utterance-level-pronounciation-scoring'] | ['speech', 'speech', 'speech'] | [-4.43369955e-01 -4.15860772e-01 -4.48680133e-01 -4.89561230e-01
-1.58363497e+00 -6.85146809e-01 3.38281870e-01 1.70792714e-01
-4.32736546e-01 4.51208889e-01 6.54995441e-01 -6.54213965e-01
-6.78022206e-02 -5.77702761e-01 -4.28987771e-01 -2.09088504e-01
5.98529100e-01 4.56046790e-01 -5.83096333e-02 -4.74108696... | [14.389396667480469, 6.787341117858887] |
756f7780-13ce-4a2d-ba55-0d6dfd515135 | deep-kinematics-analysis-for-monocular-3d | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Deep_Kinematics_Analysis_for_Monocular_3D_Human_Pose_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Deep_Kinematics_Analysis_for_Monocular_3D_Human_Pose_Estimation_CVPR_2020_paper.pdf | Deep Kinematics Analysis for Monocular 3D Human Pose Estimation | For monocular 3D pose estimation conditioned on 2D detection, noisy/unreliable input is a key obstacle in this task. Simple structure constraints attempting to tackle this problem, e.g., symmetry loss and joint angle limit, could only provide marginal improvements and are commonly treated as auxiliary losses in previou... | [' Wenjun Zhang', ' Xiaokang Yang', ' Jiancheng Yang', ' Bingbing Ni', ' Zhenbo Yu', 'Jingwei Xu'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-1.20817386e-02 6.05669320e-02 -2.53637075e-01 -1.07605472e-01
-6.02907717e-01 -2.77706951e-01 4.51315403e-01 -4.20545280e-01
-5.10885596e-01 7.09292889e-01 2.36311391e-01 1.89360082e-02
-1.26619488e-01 -3.03522885e-01 -7.70681441e-01 -5.86409569e-01
2.20891684e-02 2.52537549e-01 2.73061246e-01 -2.17720538... | [7.040968418121338, -0.8667609691619873] |
67889440-5a52-4fc0-ba41-44010735e781 | semantic-random-walk-for-graph-representation | 2305.06531 | null | https://arxiv.org/abs/2305.06531v1 | https://arxiv.org/pdf/2305.06531v1.pdf | Semantic Random Walk for Graph Representation Learning in Attributed Graphs | In this study, we focus on the graph representation learning (a.k.a. network embedding) in attributed graphs. Different from existing embedding methods that treat the incorporation of graph structure and semantic as the simple combination of two optimization objectives, we propose a novel semantic graph representation ... | ['Meng Qin'] | 2023-05-11 | null | null | null | null | ['community-detection', 'network-embedding'] | ['graphs', 'methodology'] | [-2.65738852e-02 4.41872686e-01 -1.83111534e-01 -4.28858817e-01
-2.49975130e-01 -4.58046466e-01 6.85250700e-01 5.76017022e-01
-1.04262561e-01 2.52906024e-01 5.11133671e-01 -1.36309922e-01
-4.37133759e-01 -1.20801866e+00 -3.65935504e-01 -7.59952068e-01
-1.63549870e-01 4.52828020e-01 7.39514902e-02 -2.41411731... | [7.278203964233398, 6.277133464813232] |
c4c77e7d-2727-43cc-a69c-b124a9a0a33e | leaningtower-lt-edi-acl2022-when-hope-and | null | null | https://aclanthology.org/2022.ltedi-1.46 | https://aclanthology.org/2022.ltedi-1.46.pdf | LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide | The 2022 edition of LT-EDI proposed two tasks in various languages. Task Hope Speech Detection required models for the automatic identification of hopeful comments for equality, diversity, and inclusion. Task Homophobia/Transphobia Detection focused on the identification of homophobic and transphobic comments. We targe... | ['Alberto Barrón-Cedeño', 'Katerina Korre', 'Marta Marchiori Manerba', 'Arianna Muti'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-8.09171200e-02 7.85383523e-01 -3.92668813e-01 -2.90751427e-01
-1.24671602e+00 -4.19001609e-01 5.47932565e-01 6.98888600e-01
-6.69049978e-01 6.49297178e-01 9.85434353e-01 -3.05278122e-01
-2.33411655e-01 -2.20078483e-01 8.22851211e-02 -3.85736227e-01
4.25094873e-01 5.41853905e-01 -4.42254394e-01 -1.59549758... | [9.028319358825684, 10.713156700134277] |
31ba979c-c966-4e34-94ba-2e8d5c9b362e | uw-stanford-system-description-for-aesw-2016 | null | null | https://aclanthology.org/W16-0511 | https://aclanthology.org/W16-0511.pdf | UW-Stanford System Description for AESW 2016 Shared Task on Grammatical Error Detection | null | ['Michael Goodman', 'Woodley Packard', 'Dan Flickinger'] | 2016-06-01 | null | null | null | ws-2016-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.152018070220947, 3.79758358001709] |
e1c7ece4-687b-475a-9274-3c838d247ae4 | learning-to-encode-evolutionary-knowledge-for | 2004.09974 | null | https://arxiv.org/abs/2004.09974v1 | https://arxiv.org/pdf/2004.09974v1.pdf | Learning to Encode Evolutionary Knowledge for Automatic Commenting Long Novels | Static knowledge graph has been incorporated extensively into sequence-to-sequence framework for text generation. While effectively representing structured context, static knowledge graph failed to represent knowledge evolution, which is required in modeling dynamic events. In this paper, an automatic commenting task i... | ['Jie zhou', 'Cheng Niu', 'Canxiang Yan', 'Jianhao Yan', 'Yangyin Xu'] | 2020-04-21 | null | null | null | null | ['graph-to-sequence', 'comment-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.54520899e-01 1.48841128e-01 -2.37494186e-01 -5.02663143e-02
-6.19159520e-01 -7.04161525e-01 9.95078743e-01 4.87017781e-01
-1.32878408e-01 1.16049695e+00 1.09173834e+00 -2.82649189e-01
3.56637686e-01 -7.60645330e-01 -6.43758535e-01 -2.57973462e-01
3.37072425e-02 1.29582942e-01 3.27761590e-01 -5.01720250... | [12.384099006652832, 9.363457679748535] |
6b444a19-0216-4437-8403-151db9f78e95 | advancing-linguistic-features-and-insights-by | null | null | https://aclanthology.org/C16-1071 | https://aclanthology.org/C16-1071.pdf | Advancing Linguistic Features and Insights by Label-informed Feature Grouping: An Exploration in the Context of Native Language Identification | We propose a hierarchical clustering approach designed to group linguistic features for supervised machine learning that is inspired by variationist linguistics. The method makes it possible to abstract away from the individual feature occurrences by grouping features together that behave alike with respect to the targ... | ['Serhiy Bykh', 'Detmar Meurers'] | 2016-12-01 | advancing-linguistic-features-and-insights-by-1 | https://aclanthology.org/C16-1071 | https://aclanthology.org/C16-1071.pdf | coling-2016-12 | ['native-language-identification'] | ['natural-language-processing'] | [-1.18064687e-01 1.56201869e-01 -5.25248468e-01 -7.97053158e-01
-4.25134063e-01 -6.08183622e-01 7.03228891e-01 7.88636684e-01
-6.50773525e-01 3.10303360e-01 7.08370268e-01 -3.15949261e-01
-5.13469756e-01 -6.62533224e-01 -1.23260379e-01 -6.19454980e-01
1.93440840e-02 6.37501538e-01 -6.29247874e-02 -1.04452103... | [10.233048439025879, 8.856907844543457] |
d8e5bbab-dd02-4282-9667-466cc6b12044 | explain-edit-and-understand-rethinking-user | 2112.09669 | null | https://arxiv.org/abs/2112.09669v2 | https://arxiv.org/pdf/2112.09669v2.pdf | Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations | In attempts to "explain" predictions of machine learning models, researchers have proposed hundreds of techniques for attributing predictions to features that are deemed important. While these attributions are often claimed to hold the potential to improve human "understanding" of the models, surprisingly little work e... | ['Graham Neubig', 'Zachary C. Lipton', 'William W. Cohen', 'Norman Sadeh', 'Danish Pruthi', 'Siddhant Arora'] | 2021-12-17 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [ 3.99000585e-01 7.93743730e-01 4.75282408e-02 -5.16876459e-01
-4.58100826e-01 -6.60198748e-01 7.99687147e-01 2.61275470e-01
-3.44023973e-01 5.71906447e-01 6.30810931e-02 -5.15706182e-01
5.07266223e-01 -4.97834861e-01 -7.57696688e-01 -4.56202000e-01
5.47645390e-01 3.48244339e-01 -2.17255697e-01 -3.43289495... | [9.117449760437012, 6.186717510223389] |
9780dc8f-6a5a-459c-807f-d0034a9b4204 | towards-end-to-end-semi-supervised-table | 2305.02769 | null | https://arxiv.org/abs/2305.02769v2 | https://arxiv.org/pdf/2305.02769v2.pdf | Towards End-to-End Semi-Supervised Table Detection with Deformable Transformer | Table detection is the task of classifying and localizing table objects within document images. With the recent development in deep learning methods, we observe remarkable success in table detection. However, a significant amount of labeled data is required to train these models effectively. Many semi-supervised approa... | ['Muhammad Zeshan Afzal', 'Marcus Liwicki', 'Didier Stricker', 'Khurram Azeem Hashmi', 'Tahira Shehzadi'] | 2023-05-04 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 7.10889697e-02 5.17340779e-01 -2.66158849e-01 -5.22454441e-01
-1.45426917e+00 -8.03978801e-01 7.31051981e-01 4.31813359e-01
-6.06249094e-01 6.63104594e-01 3.74063253e-02 1.22906044e-01
4.53066379e-01 -7.38540649e-01 -7.48548031e-01 -4.63639200e-01
1.57216609e-01 1.01861775e+00 5.93837082e-01 -2.40964904... | [11.691697120666504, 3.018949508666992] |
99f0ff20-8dee-4684-a1ea-6c544ea9a984 | adversarial-advantage-actor-critic-model-for | 1710.11277 | null | http://arxiv.org/abs/1710.11277v2 | http://arxiv.org/pdf/1710.11277v2.pdf | Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning | This paper presents a new method --- adversarial advantage actor-critic
(Adversarial A2C), which significantly improves the efficiency of dialogue
policy learning in task-completion dialogue systems. Inspired by generative
adversarial networks (GAN), we train a discriminator to differentiate
responses/actions generated... | ['Kam-Fai Wong', 'Yun-Nung Chen', 'Jingjing Liu', 'Jianfeng Gao', 'Xiujun Li', 'Baolin Peng'] | 2017-10-31 | null | null | null | null | ['task-completion-dialogue-policy-learning'] | ['natural-language-processing'] | [ 2.28642419e-01 7.13989794e-01 -2.36752406e-02 -3.88595223e-01
-8.38166893e-01 -8.55600417e-01 9.56077874e-01 -5.97694635e-01
-4.48921472e-01 1.20843959e+00 6.51759446e-01 -4.22669202e-01
4.12602782e-01 -5.63109457e-01 -3.35781276e-01 -7.53044248e-01
1.61856890e-01 9.13970113e-01 -5.04829846e-02 -7.96822309... | [12.99857234954834, 8.10200309753418] |
ae2a805d-b03e-437c-8406-eb53b15736e9 | pointly-supervised-instance-segmentation | 2104.06404 | null | https://arxiv.org/abs/2104.06404v2 | https://arxiv.org/pdf/2104.06404v2.pdf | Pointly-Supervised Instance Segmentation | We propose an embarrassingly simple point annotation scheme to collect weak supervision for instance segmentation. In addition to bounding boxes, we collect binary labels for a set of points uniformly sampled inside each bounding box. We show that the existing instance segmentation models developed for full mask superv... | ['Alexander Kirillov', 'Omkar Parkhi', 'Bowen Cheng'] | 2021-04-13 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cheng_Pointly-Supervised_Instance_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cheng_Pointly-Supervised_Instance_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['weakly-supervised-instance-segmentation'] | ['computer-vision'] | [ 1.64081797e-01 6.69931293e-01 -5.01093566e-01 -6.62991941e-01
-1.28877676e+00 -6.79120600e-01 4.73516881e-01 1.04024082e-01
-4.50879723e-01 4.39461291e-01 -4.91111964e-01 -1.82635516e-01
3.70535314e-01 -7.21850634e-01 -1.38055813e+00 -5.15122354e-01
8.76509920e-02 8.92720103e-01 9.07358468e-01 -6.65645674... | [8.04468822479248, -3.157144784927368] |
670d9685-2ddc-41f7-a8b5-291ffffbf4a7 | deep-learning-is-a-good-steganalysis-tool | 1511.04855 | null | http://arxiv.org/abs/1511.04855v2 | http://arxiv.org/pdf/1511.04855v2.pdf | Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch | Since the BOSS competition, in 2010, most steganalysis approaches use a
learning methodology involving two steps: feature extraction, such as the Rich
Models (RM), for the image representation, and use of the Ensemble Classifier
(EC) for the learning step. In 2015, Qian et al. have shown that the use of a
deep learning... | ['Pasquet Jérôme', 'Marc Chaumont', 'Lionel Pibre', 'Dino Ienco'] | 2015-11-16 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 4.54111934e-01 9.26681757e-02 6.47429079e-02 2.54243255e-01
-4.69276547e-01 -3.44349951e-01 8.69221926e-01 -4.08550471e-01
-3.52666259e-01 5.91273248e-01 -1.91180855e-01 -4.77075964e-01
9.81714949e-02 -7.69195139e-01 -9.06682074e-01 -1.16715431e+00
-3.62852603e-01 -3.39936465e-02 1.60729319e-01 -4.65732127... | [4.339554786682129, 8.035780906677246] |
4c04f321-f402-4024-8d87-47c0f6b021b5 | augmenting-rule-based-dns-censorship | 2302.02031 | null | https://arxiv.org/abs/2302.02031v2 | https://arxiv.org/pdf/2302.02031v2.pdf | Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning | The proliferation of global censorship has led to the development of a plethora of measurement platforms to monitor and expose it. Censorship of the domain name system (DNS) is a key mechanism used across different countries. It is currently detected by applying heuristics to samples of DNS queries and responses (probe... | ['Jacob Brown', 'Vinod Yegneswaran', 'Prateek Mittal', 'Nick Feamster', 'Nguyen Phong Hoang', 'Arjun Nitin Bhagoji', 'Van Tran', 'Xi Jiang'] | 2023-02-03 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-3.38810235e-02 -2.29640692e-01 -2.81498760e-01 -1.94294229e-01
-1.02188766e+00 -1.43521059e+00 9.41432774e-01 1.82504028e-01
1.04173414e-01 7.26241827e-01 -2.40361542e-02 -9.57474351e-01
-3.51159275e-01 -6.36450291e-01 -5.48618972e-01 -4.94890153e-01
-1.11591205e-01 6.81262255e-01 4.68612045e-01 4.51631248... | [5.4388556480407715, 7.3200602531433105] |
8732a0e4-e14a-474e-b160-92426eb18068 | l_p-norm-constrained-coding-with-frank-wolfe | 1802.10252 | null | https://arxiv.org/abs/1802.10252v4 | https://arxiv.org/pdf/1802.10252v4.pdf | Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding | The problem of $L_p$-norm constrained coding is to convert signal into code that lies inside an $L_p$-ball and most faithfully reconstructs the signal. Previous works under the name of sparse coding considered the cases of $L_0$ and $L_1$ norms. The cases with $p>1$ values, i.e. non-sparse coding studied in this paper,... | ['Zheng-Jun Zha', 'Dong Liu', 'Ke Sun', 'Zhangyang Wang', 'Runsheng Liu'] | 2018-02-28 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 2.28692174e-01 3.30846369e-01 2.98381802e-02 -3.37830812e-01
-7.41564929e-01 -1.02335125e-01 -7.77858123e-02 -6.56344771e-01
-2.26055890e-01 6.37111902e-01 3.64978075e-01 -6.52860105e-02
-3.17125320e-01 -8.23889017e-01 -9.92308795e-01 -7.76618600e-01
-5.62988877e-01 -3.08505535e-01 -1.81134909e-01 -3.23275000... | [11.490449905395508, -2.1038644313812256] |
59c66db6-c497-4410-bf73-b4aad10a6cfc | diacritics-restoration-using-bert-with | 2105.11408 | null | https://arxiv.org/abs/2105.11408v1 | https://arxiv.org/pdf/2105.11408v1.pdf | Diacritics Restoration using BERT with Analysis on Czech language | We propose a new architecture for diacritics restoration based on contextualized embeddings, namely BERT, and we evaluate it on 12 languages with diacritics. Furthermore, we conduct a detailed error analysis on Czech, a morphologically rich language with a high level of diacritization. Notably, we manually annotate all... | ['Jana Straková', 'Milan Straka', 'Jakub Náplava'] | 2021-05-24 | null | null | null | null | ['irish-text-diacritization', 'croatian-text-diacritization', 'french-text-diacritization', 'romanian-text-diacritization', 'turkish-text-diacritization', 'hungarian-text-diacritization', 'slovak-text-diacritization', 'spanish-text-diacritization', 'latvian-text-diacritization', 'czech-text-diacritization', 'vietnamese... | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-... | [-3.92482311e-01 4.07441407e-01 7.16865063e-02 -2.86333591e-01
-7.45819330e-01 -5.93308032e-01 6.71185613e-01 6.99524701e-01
-7.86268115e-01 7.23728657e-01 6.41712129e-01 -6.21684670e-01
4.36208427e-01 -6.59620345e-01 -7.76250184e-01 -3.34216893e-01
2.63251841e-01 6.01199925e-01 2.81846911e-01 -6.10902786... | [11.036561012268066, 10.643192291259766] |
cccacbc1-b140-4cfe-a363-0dc200afcb47 | hashcc-lightweight-method-to-improve-the | 2305.04296 | null | https://arxiv.org/abs/2305.04296v1 | https://arxiv.org/pdf/2305.04296v1.pdf | HashCC: Lightweight Method to Improve the Quality of the Camera-less NeRF Scene Generation | Neural Radiance Fields has become a prominent method of scene generation via view synthesis. A critical requirement for the original algorithm to learn meaningful scene representation is camera pose information for each image in a data set. Current approaches try to circumnavigate this assumption with moderate success,... | ['Jan Olszewski'] | 2023-05-07 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 5.55571616e-01 -1.89845547e-01 2.52805054e-01 -5.29028893e-01
-5.91232479e-01 -8.36049736e-01 7.38854349e-01 -9.03258771e-02
-3.43973309e-01 8.14013958e-01 1.92416161e-01 -3.28702718e-01
2.26564743e-02 -9.14479911e-01 -9.77987647e-01 -6.57445490e-01
1.36243433e-01 1.46851212e-01 1.36010334e-01 -3.84877622... | [9.153533935546875, -2.904754638671875] |
5126ed7e-e057-49b4-ab36-342b36bcf563 | xtreme-up-a-user-centric-scarce-data | 2305.11938 | null | https://arxiv.org/abs/2305.11938v2 | https://arxiv.org/pdf/2305.11938v2.pdf | XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages | Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- languages for which NLP re-search is particularly far behind in meeting user needs -- it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a be... | ['Partha Talukdar', 'Dmitry Panteleev', 'Melvin Johnson', 'Reeve Ingle', 'Dan Garrette', 'Colin Cherry', 'Isaac Caswell', 'Vera Axelrod', 'David I. Adelani', 'Connie Tao', 'Bidisha Samanta', 'Brian Roark', 'Dana L. Dickinson', 'Christo Kirov', 'Anna Katanova', 'Nitish Gupta', 'John Wieting', 'Xinyi Wang', 'Jean-Michel ... | 2023-05-19 | null | null | null | null | ['optical-character-recognition', 'transliteration', 'semantic-parsing', 'multilingual-nlp'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-3.64740826e-02 5.29397614e-02 -2.47627079e-01 -3.20156664e-01
-1.54244673e+00 -8.49636853e-01 6.32286131e-01 1.60680823e-02
-6.86803043e-01 5.77289045e-01 5.35091162e-01 -5.01768768e-01
1.18585594e-01 -1.45412475e-01 -5.25712252e-01 2.15123799e-02
3.97892326e-01 1.13167214e+00 -2.94500232e-01 -4.78410125... | [11.219104766845703, 9.364911079406738] |
bc1622fe-2c51-4fce-88af-59696bc4b5fa | r2-mlp-round-roll-mlp-for-multi-view-3d | 2211.11085 | null | https://arxiv.org/abs/2211.11085v1 | https://arxiv.org/pdf/2211.11085v1.pdf | R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition | Recently, vision architectures based exclusively on multi-layer perceptrons (MLPs) have gained much attention in the computer vision community. MLP-like models achieve competitive performance on a single 2D image classification with less inductive bias without hand-crafted convolution layers. In this work, we explore t... | ['Ping Li', 'Tan Yu', 'Shuo Chen'] | 2022-11-20 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 1.68343484e-01 2.20623791e-01 -6.61199838e-02 -5.24948895e-01
-1.57886326e-01 -1.94647402e-01 7.13389754e-01 -3.22278947e-01
-4.21801299e-01 2.04475150e-01 -1.14798453e-02 -4.88363802e-01
2.01329112e-01 -7.57976055e-01 -1.04005516e+00 -7.48291612e-01
-1.83442920e-01 -2.51919515e-02 3.34583193e-01 1.93494588... | [8.24619197845459, -3.5789997577667236] |
9df8d5a6-161f-42a2-94dd-0e907a33eb9a | detecting-malicious-pdf-using-cnn-1 | 2007.12729 | null | https://arxiv.org/abs/2007.12729v2 | https://arxiv.org/pdf/2007.12729v2.pdf | Detecting malicious PDF using CNN | Malicious PDF files represent one of the biggest threats to computer security. To detect them, significant research has been done using handwritten signatures or machine learning based on manual feature extraction. Those approaches are both time-consuming, require significant prior knowledge and the list of features ha... | ['Yishay Mansour', 'Raphael Fettaya'] | 2020-07-24 | detecting-malicious-pdf-using-cnn | https://openreview.net/forum?id=SJeW-A4tDS | https://openreview.net/pdf?id=SJeW-A4tDS | iclr-2020-1 | ['computer-security'] | ['miscellaneous'] | [-7.61151910e-02 -4.09160465e-01 -1.25639707e-01 -1.15802966e-01
-2.24539205e-01 -1.20628607e+00 6.81251585e-01 5.89043736e-01
-3.01521987e-01 5.03068864e-01 -3.70250553e-01 -4.88013476e-01
-1.84800848e-01 -9.70779479e-01 -6.68456674e-01 -4.73887861e-01
-4.17364597e-01 5.60695946e-01 6.55111551e-01 -1.37170106... | [14.397187232971191, 9.662951469421387] |
414d86fa-1412-46a3-baae-f24e6504ee2b | deep-homography-estimation-for-dynamic-scenes | 2004.02132 | null | https://arxiv.org/abs/2004.02132v1 | https://arxiv.org/pdf/2004.02132v1.pdf | Deep Homography Estimation for Dynamic Scenes | Homography estimation is an important step in many computer vision problems. Recently, deep neural network methods have shown to be favorable for this problem when compared to traditional methods. However, these new methods do not consider dynamic content in input images. They train neural networks with only image pair... | ['Aseem Agarwala', 'Feng Liu', 'Shu Zhang', 'Hoang Le'] | 2020-04-05 | deep-homography-estimation-for-dynamic-scenes-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Le_Deep_Homography_Estimation_for_Dynamic_Scenes_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Le_Deep_Homography_Estimation_for_Dynamic_Scenes_CVPR_2020_paper.pdf | cvpr-2020-6 | ['homography-estimation'] | ['computer-vision'] | [ 2.78520435e-01 -5.40713668e-01 8.89474973e-02 -1.60443723e-01
-2.29501292e-01 -5.38845003e-01 5.20839453e-01 -8.92879903e-01
-1.84837237e-01 4.05508250e-01 2.57328391e-01 2.32324108e-01
-4.37978562e-03 -5.32478929e-01 -1.15578592e+00 -7.63718367e-01
-1.52159398e-02 3.70204836e-01 2.77138114e-01 -1.34905338... | [8.686984062194824, -2.038649320602417] |
ee9df783-574f-4864-ac93-5511aecce45f | motion-capture-dataset-for-practical-use-of | 2306.08861 | null | https://arxiv.org/abs/2306.08861v2 | https://arxiv.org/pdf/2306.08861v2.pdf | Motion Capture Dataset for Practical Use of AI-based Motion Editing and Stylization | In this work, we proposed a new style-diverse dataset for the domain of motion style transfer. The motion dataset uses an industrial-standard human bone structure and thus is industry-ready to be plugged into 3D characters for many projects. We claim the challenges in motion style transfer and encourage future work in ... | ['Masafumi Takahashi', 'Sentaro Yojima', 'Keito Inoue', 'Chen-Chieh Liao', 'Makito Kobayashi'] | 2023-06-15 | null | null | null | null | ['motion-style-transfer', 'style-transfer'] | ['computer-code', 'computer-vision'] | [ 1.28046736e-01 -2.01214448e-01 -3.00770968e-01 -1.88919991e-01
-3.53232950e-01 -4.76011336e-01 6.30333364e-01 -1.03811812e+00
-4.74249393e-01 8.28726709e-01 4.65797782e-01 -7.67160654e-02
4.99283820e-01 -7.88788497e-01 -5.32081366e-01 -5.71796298e-01
3.01064551e-01 5.15253425e-01 9.03625548e-01 -4.68902469... | [10.794790267944336, -0.6775552034378052] |
c0faa938-cfb2-487e-abc4-4c982efc6b79 | interactive-video-object-segmentation-using | 2007.08139 | null | https://arxiv.org/abs/2007.08139v1 | https://arxiv.org/pdf/2007.08139v1.pdf | Interactive Video Object Segmentation Using Global and Local Transfer Modules | An interactive video object segmentation algorithm, which takes scribble annotations on query objects as input, is proposed in this paper. We develop a deep neural network, which consists of the annotation network (A-Net) and the transfer network (T-Net). First, given user scribbles on a frame, A-Net yields a segmentat... | ['Chang-Su Kim', 'Yuk Heo', 'Yeong Jun Koh'] | 2020-07-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2729_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620290.pdf | eccv-2020-8 | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 3.28395516e-01 1.73464179e-01 -1.96316838e-01 -4.50201243e-01
-7.01285660e-01 -5.25927961e-01 -1.08221799e-01 -2.15267107e-01
-4.29916054e-01 5.17665327e-01 -2.75906265e-01 -8.25496390e-02
3.39571714e-01 -7.56323397e-01 -9.73157167e-01 -6.73491120e-01
8.43443051e-02 3.90428513e-01 9.55785275e-01 2.25631341... | [9.278304100036621, -0.09987474232912064] |
fe942e87-206a-48f5-a41f-f3070a7d346e | direction-of-arrival-estimation-of-sound | 2203.16940 | null | https://arxiv.org/abs/2203.16940v2 | https://arxiv.org/pdf/2203.16940v2.pdf | Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs | In this paper, we present a new model for Direction of Arrival (DOA) estimation of sound sources based on an Icosahedral Convolutional Neural Network (CNN) applied over SRP-PHAT power maps computed from the signals received by a microphone array. This icosahedral CNN is equivariant to the 60 rotational symmetries of th... | ['Jose R. Beltran', 'Antonio Miguel', 'David Diaz-Guerra'] | 2022-03-31 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-2.65416712e-01 2.20297098e-01 7.31393993e-01 -6.39274269e-02
-6.46172106e-01 -7.70715237e-01 4.28281188e-01 -2.26017267e-01
-4.59881842e-01 3.25659424e-01 2.11836979e-01 -3.49875748e-01
-2.16417074e-01 -9.14891720e-01 -1.03878891e+00 -9.19243872e-01
-1.99589536e-01 2.22896740e-01 8.18287432e-02 -3.66902314... | [15.08008098602295, 5.835681438446045] |
b9f0febe-2f05-49af-bfe4-28264b39185b | transformer-based-multi-instance-learning-for | 2303.14999 | null | https://arxiv.org/abs/2303.14999v1 | https://arxiv.org/pdf/2303.14999v1.pdf | Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection | Weakly Supervised Object Detection (WSOD) enables the training of object detection models using only image-level annotations. State-of-the-art WSOD detectors commonly rely on multi-instance learning (MIL) as the backbone of their detectors and assume that the bounding box proposals of an image are independent of each o... | ['Min-Ling Zhang', 'Weijia Zhang', 'Zhaofei Wang'] | 2023-03-27 | null | null | null | null | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 2.95239091e-01 5.52549958e-01 -4.34515744e-01 -3.02940160e-01
-7.90386796e-01 -1.94853887e-01 6.45885706e-01 1.06413074e-01
-3.74391347e-01 3.68572474e-01 -9.63905305e-02 -7.35518038e-02
-1.05265966e-02 -8.75077665e-01 -1.09088278e+00 -5.98673701e-01
1.33518547e-01 5.32712102e-01 1.17418373e+00 -1.42102510... | [9.308374404907227, 1.0792121887207031] |
54038440-128a-460b-86fc-29a6dcd58b5a | 3d-point-cloud-segmentation-using-gis | 2108.06306 | null | https://arxiv.org/abs/2108.06306v1 | https://arxiv.org/pdf/2108.06306v1.pdf | 3D point cloud segmentation using GIS | In this paper we propose an approach to perform semantic segmentation of 3D point cloud data by importing the geographic information from a 2D GIS layer (OpenStreetMap). The proposed automatic procedure identifies meaningful units such as buildings and adjusts their locations to achieve best fit between the GIS polygon... | ['Rozenn Dahyot', 'Vladimir Krylov', 'Chao-Jung Liu'] | 2021-08-13 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 3.61528397e-02 1.75624415e-01 5.68312705e-01 -4.36442465e-01
-1.85469642e-01 -8.76413345e-01 4.79910553e-01 6.15417480e-01
-4.54078823e-01 4.19156015e-01 -2.51403958e-01 -5.81456721e-01
-2.62136847e-01 -1.33984780e+00 -4.40356195e-01 -2.76595063e-04
-3.43513608e-01 8.21435452e-01 5.34995794e-01 -2.86645383... | [8.334423065185547, -2.6129963397979736] |
b7825da3-aa12-4802-b534-134f261f617d | one-shot-coresets-the-case-of-k-clustering | 1711.09649 | null | http://arxiv.org/abs/1711.09649v3 | http://arxiv.org/pdf/1711.09649v3.pdf | One-Shot Coresets: The Case of k-Clustering | Scaling clustering algorithms to massive data sets is a challenging task.
Recently, several successful approaches based on data summarization methods,
such as coresets and sketches, were proposed. While these techniques provide
provably good and small summaries, they are inherently problem dependent - the
practitioner ... | ['Olivier Bachem', 'Silvio Lattanzi', 'Mario Lucic'] | 2017-11-27 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 7.06119165e-02 8.37968066e-02 -3.00191820e-01 -8.38838071e-02
-1.11437333e+00 -5.59297681e-01 3.27715844e-01 7.49792516e-01
-9.56314877e-02 6.77987635e-01 4.63763416e-01 -5.79080312e-03
-8.09841275e-01 -6.42520010e-01 -2.62729555e-01 -7.64709353e-01
-3.10788333e-01 9.31948364e-01 4.17483717e-01 2.12057784... | [6.656851768493652, 4.980328559875488] |
c7373b60-d586-43d9-8e54-8b02a549dc5b | vddb-a-comprehensive-resource-and-machine | 2209.13521 | null | https://arxiv.org/abs/2209.13521v1 | https://arxiv.org/pdf/2209.13521v1.pdf | VDDB: a comprehensive resource and machine learning platform for antiviral drug discovery | Virus infection is one of the major diseases that seriously threaten human health. To meet the growing demand for mining and sharing data resources related to antiviral drugs and to accelerate the design and discovery of new antiviral drugs, we presented an open-access antiviral drug resource and machine learning platf... | ['Ling Wang', 'Hanxuan Cai', 'Duancheng Zhao', 'Jingxing Wu', 'Yihao Chen', 'Shunming Tao'] | 2022-09-17 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 2.01546624e-01 -6.65524065e-01 -8.66562426e-01 2.62241602e-01
-6.70323372e-01 -7.39703476e-01 2.94140011e-01 7.17715204e-01
-1.27268612e-01 1.55076456e+00 -1.80874988e-01 -5.65651178e-01
-3.99347320e-02 -5.10650396e-01 -5.28542161e-01 -1.02448177e+00
-1.57971859e-01 8.53883028e-01 -2.03751817e-01 -1.22797973... | [5.033440589904785, 5.632264137268066] |
3008b2d9-0c48-4121-9e50-249f3123037e | a-task-oriented-dialogue-architecture-via | null | null | https://aclanthology.org/2021.sigdial-1.46 | https://aclanthology.org/2021.sigdial-1.46.pdf | A Task-Oriented Dialogue Architecture via Transformer Neural Language Models and Symbolic Injection | Recently, transformer language models have been applied to build both task- and non-task-oriented dialogue systems. Although transformers perform well on most of the NLP tasks, they perform poorly on context retrieval and symbolic reasoning. Our work aims to address this limitation by embedding the model in an operatio... | ['Anthony Tomasic', 'Aaron Steinfeld', 'John Zimmerman', 'Antian Wang', 'Oscar J. Romero'] | null | null | null | null | sigdial-acl-2021-7 | ['dialogue-management'] | ['natural-language-processing'] | [ 3.03240657e-01 7.50628650e-01 -9.74323973e-03 -3.93341899e-01
-9.10741389e-01 -6.58183515e-01 1.17033923e+00 1.37610063e-01
-9.56223905e-02 9.46245015e-01 7.05232859e-01 -5.18279850e-01
2.00489193e-01 -6.79264963e-01 -1.82065353e-01 -1.08178936e-01
2.25851730e-01 8.83035123e-01 3.25614631e-01 -9.12449360... | [12.685951232910156, 8.122532844543457] |
60cb8cd0-b21d-4d1d-8d5c-b10e4b6c4915 | urban-stylegan-learning-to-generate-and | 2305.09602 | null | https://arxiv.org/abs/2305.09602v1 | https://arxiv.org/pdf/2305.09602v1.pdf | Urban-StyleGAN: Learning to Generate and Manipulate Images of Urban Scenes | A promise of Generative Adversarial Networks (GANs) is to provide cheap photorealistic data for training and validating AI models in autonomous driving. Despite their huge success, their performance on complex images featuring multiple objects is understudied. While some frameworks produce high-quality street scenes wi... | ['Bin Yang', 'Karim Guirguis', 'Daniel Cremers', 'Tarun Yenamandra', 'Youssef Farag', 'George Eskandar'] | 2023-05-16 | null | null | null | null | ['scene-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 3.83134097e-01 4.52060610e-01 8.71324688e-02 -2.41234556e-01
-6.66499674e-01 -6.25612140e-01 1.00017297e+00 -7.48263001e-01
-7.16593638e-02 8.70370507e-01 -9.93840583e-03 -2.34622270e-01
-5.34718335e-02 -1.02684629e+00 -7.95969069e-01 -1.06415427e+00
2.44547054e-01 4.38641667e-01 -1.91602111e-01 -3.53297204... | [11.677096366882324, -0.39964932203292847] |
56a7116d-e89c-4d91-81ae-0c2b354859de | pretrained-speech-encoders-and-efficient-fine | null | null | https://aclanthology.org/2022.iwslt-1.23 | https://aclanthology.org/2022.iwslt-1.23.pdf | Pretrained Speech Encoders and Efficient Fine-tuning Methods for Speech Translation: UPC at IWSLT 2022 | This paper describes the submissions of the UPC Machine Translation group to the IWSLT 2022 Offline Speech Translation and Speech-to-Speech Translation tracks. The offline task involves translating English speech to German, Japanese and Chinese text. Our Speech Translation systems are trained end-to-end and are based o... | ['Marta R. Costa-jussà', 'José Fonollosa', 'Carlos Escolano', 'Gerard I. Gállego', 'Ioannis Tsiamas'] | null | null | null | null | iwslt-acl-2022-5 | ['speech-to-speech-translation'] | ['speech'] | [ 1.42774105e-01 2.41102010e-01 -7.71593899e-02 -3.75346214e-01
-1.71227610e+00 -7.73225963e-01 5.22231042e-01 -4.52627957e-01
-5.72814226e-01 5.83221614e-01 3.58393341e-01 -1.10540557e+00
5.93506873e-01 -3.49651098e-01 -9.01054323e-01 -2.93263733e-01
3.25289965e-01 1.07437932e+00 9.55211893e-02 -3.80079597... | [14.481364250183105, 7.152629852294922] |
3f9aae1c-ec3c-48b0-8ab0-a0cd7376377b | robust-pedestrian-attribute-recognition-using | 2110.08708 | null | https://arxiv.org/abs/2110.08708v4 | https://arxiv.org/pdf/2110.08708v4.pdf | Robust Pedestrian Attribute Recognition Using Group Sparsity for Occlusion Videos | Occlusion processing is a key issue in pedestrian attribute recognition (PAR). Nevertheless, several existing video-based PAR methods have not yet considered occlusion handling in depth. In this paper, we formulate finding non-occluded frames as sparsity-based temporal attention of a crowded video. In this manner, a mo... | ['Jungchan Cho', 'Kimin Yun', 'Geonu Lee'] | 2021-10-17 | null | null | null | null | ['pedestrian-attribute-recognition', 'occlusion-handling'] | ['computer-vision', 'computer-vision'] | [ 1.85820207e-01 -4.20742035e-01 -1.34277359e-01 -5.23710489e-01
-2.68468559e-01 1.02148484e-02 9.84121040e-02 6.52909726e-02
-4.01001126e-01 8.22479248e-01 2.92668223e-01 1.87229514e-01
1.08303554e-01 -6.70633495e-01 -7.94026196e-01 -7.52892733e-01
1.06741115e-01 -5.94832338e-02 4.60682452e-01 1.04222983... | [9.15636157989502, -0.27583155035972595] |
a5ed7dcb-7145-40ec-9377-8437c61c8ddb | sdf-3dgan-a-3d-object-generative-method-based | 2303.06821 | null | https://arxiv.org/abs/2303.06821v1 | https://arxiv.org/pdf/2303.06821v1.pdf | SDF-3DGAN: A 3D Object Generative Method Based on Implicit Signed Distance Function | In this paper, we develop a new method, termed SDF-3DGAN, for 3D object generation and 3D-Aware image synthesis tasks, which introduce implicit Signed Distance Function (SDF) as the 3D object representation method in the generative field. We apply SDF for higher quality representation of 3D object in space and design a... | ['Libo Zhang', 'Ruyi Ji', 'Lutao Jiang'] | 2023-03-13 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 1.65039033e-01 1.56746134e-01 2.06999645e-01 -8.97454098e-02
-6.16022289e-01 -3.70427608e-01 5.37205935e-01 -6.50778294e-01
1.23734437e-01 6.38867795e-01 2.38864832e-02 -1.13083899e-01
3.53667170e-01 -1.20978963e+00 -8.03911269e-01 -7.17395961e-01
3.20549548e-01 3.77565384e-01 2.71499842e-01 -2.54062675... | [9.068685531616211, -3.493950128555298] |
16f33f69-d76a-4211-90a6-af2c19cfa63a | semanticac-semantics-assisted-framework-for | 2302.05940 | null | https://arxiv.org/abs/2302.05940v1 | https://arxiv.org/pdf/2302.05940v1.pdf | SemanticAC: Semantics-Assisted Framework for Audio Classification | In this paper, we propose SemanticAC, a semantics-assisted framework for Audio Classification to better leverage the semantic information. Unlike conventional audio classification methods that treat class labels as discrete vectors, we employ a language model to extract abundant semantics from labels and optimize the s... | ['Xiu Li', 'Ran Liao', 'Hantao Zhou', 'Shuyan Li', 'Yue Ma', 'Yicheng Xiao'] | 2023-02-12 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 4.09624726e-01 -5.73362783e-02 -3.74080718e-01 -9.90354776e-01
-8.02539945e-01 -7.65228629e-01 3.07685465e-01 1.59298882e-01
-1.62888572e-01 2.62949288e-01 6.12266243e-01 1.71674520e-01
-1.42173350e-01 -3.41082811e-01 -4.14576113e-01 -2.53287226e-01
-1.70611471e-01 1.74641997e-01 -9.45869088e-03 5.76932654... | [15.287185668945312, 5.12149715423584] |
be812e88-addb-416b-a04b-5cf91698cd36 | a2-rl-aesthetics-aware-reinforcement-learning | 1709.04595 | null | http://arxiv.org/abs/1709.04595v3 | http://arxiv.org/pdf/1709.04595v3.pdf | A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping | Image cropping aims at improving the aesthetic quality of images by adjusting
their composition. Most weakly supervised cropping methods (without bounding
box supervision) rely on the sliding window mechanism. The sliding window
mechanism requires fixed aspect ratios and limits the cropping region with
arbitrary size. ... | ['Junge Zhang', 'Huikai Wu', 'Debang Li', 'Kaiqi Huang'] | 2017-09-14 | a2-rl-aesthetics-aware-reinforcement-learning-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_A2-RL_Aesthetics_Aware_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_A2-RL_Aesthetics_Aware_CVPR_2018_paper.pdf | cvpr-2018-6 | ['image-cropping'] | ['computer-vision'] | [ 2.76505470e-01 2.66385227e-01 -3.31585228e-01 -1.96925789e-01
-9.05951798e-01 -2.77353793e-01 2.79051751e-01 -5.14933579e-02
-3.63876998e-01 3.96726638e-01 -9.55058113e-02 -1.55656517e-01
3.07543874e-01 -7.29592323e-01 -8.11188400e-01 -8.62347007e-01
1.03136115e-01 5.57963699e-02 3.80074345e-02 -2.33840629... | [11.435717582702637, -1.0887582302093506] |
8de3fd0e-23ca-45e9-9a60-b2a17e77210b | coswara-a-website-application-enabling-covid | 2206.05053 | null | https://arxiv.org/abs/2206.05053v1 | https://arxiv.org/pdf/2206.05053v1.pdf | Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms | The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches. In this paper, we describe the Coswara tool, a website application designed to enable COVID-19 detection by analysing respiratory sound samples and health symptoms. A user using this service can l... | ['Murali Alagesan', 'Sadhana Gonuguntla', 'Suhail K K', 'Sahiti Nori', 'Chandrakiran C', 'Sriram Ganapathy', 'Pravin Mote', 'Srikanth Raj Chetupalli', 'Neeraj Kumar Sharma', 'Debottam Dutta', 'Debarpan Bhattacharya'] | 2022-06-09 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 1.07227024e-02 -4.86210167e-01 1.64675415e-01 3.17901999e-01
-5.24735153e-01 -8.97031128e-01 3.79618444e-02 6.52466595e-01
-2.69084543e-01 4.37053084e-01 2.33539660e-02 -5.71289837e-01
-2.12198287e-01 -7.93755770e-01 7.32740462e-02 -3.47976804e-01
3.39800864e-02 9.16889727e-01 3.76040816e-01 1.41359851... | [14.424489974975586, 3.8592689037323] |
514e6ffa-a5a3-46a3-8356-c99e4e459728 | pyramid-scene-parsing-network | 1612.01105 | null | http://arxiv.org/abs/1612.01105v2 | http://arxiv.org/pdf/1612.01105v2.pdf | Pyramid Scene Parsing Network | Scene parsing is challenging for unrestricted open vocabulary and diverse
scenes. In this paper, we exploit the capability of global context information
by different-region-based context aggregation through our pyramid pooling
module together with the proposed pyramid scene parsing network (PSPNet). Our
global prior re... | ['Jiaya Jia', 'Xiaogang Wang', 'Xiaojuan Qi', 'Jianping Shi', 'Hengshuang Zhao'] | 2016-12-04 | pyramid-scene-parsing-network-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhao_Pyramid_Scene_Parsing_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Pyramid_Scene_Parsing_CVPR_2017_paper.pdf | cvpr-2017-7 | ['thermal-image-segmentation', 'dichotomous-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.61511374e-01 7.31610730e-02 -1.46068521e-02 -6.48341119e-01
-1.11063182e+00 -5.29315472e-01 4.51078862e-01 7.96112418e-02
-5.73742211e-01 5.74074924e-01 3.27446729e-01 -2.29671285e-01
4.07079637e-01 -1.08969939e+00 -9.45776761e-01 -5.91997623e-01
3.07472758e-02 -2.27023974e-01 6.66466773e-01 -2.03016236... | [9.55862045288086, 0.3098289668560028] |
c79da213-f63f-465a-a0ea-023d95c6b91f | grubert-a-gru-based-method-to-fuse-bert | null | null | https://aclanthology.org/2020.aacl-srw.19 | https://aclanthology.org/2020.aacl-srw.19.pdf | GRUBERT: A GRU-Based Method to Fuse BERT Hidden Layers for Twitter Sentiment Analysis | In this work, we introduce a GRU-based architecture called GRUBERT that learns to map the different BERT hidden layers to fused embeddings with the aim of achieving high accuracy on the Twitter sentiment analysis task. Tweets are known for their highly diverse language, and by exploiting different linguistic informatio... | ['Zuowen Wang', 'Pouya Pourjafar', 'Matthias Matti', 'Leo Horne'] | 2020-12-01 | null | null | null | asian-chapter-of-the-association-for | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-4.26667273e-01 1.53588563e-01 -3.81772786e-01 -4.62499231e-01
-6.04307055e-01 -7.47534335e-01 6.92012906e-01 6.16706729e-01
-7.93986857e-01 3.48923653e-01 5.29084504e-01 -1.13986686e-01
1.19349524e-01 -1.02411914e+00 -5.16484141e-01 -4.77625340e-01
-1.59333929e-01 7.00363159e-01 1.66134015e-01 -7.38685071... | [10.613066673278809, 7.9338812828063965] |
9690ff55-2c21-4cc1-a3f5-9bcbf6dd663e | could-you-guess-an-interesting-movie-from-the | 1704.02199 | null | http://arxiv.org/abs/1704.02199v1 | http://arxiv.org/pdf/1704.02199v1.pdf | Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database | In this paper, we aim to estimate the Winner of world-wide film festival from
the exhibited movie poster. The task is an extremely challenging because the
estimation must be done with only an exhibited movie poster, without any film
ratings and box-office takings. In order to tackle this problem, we have
created a new ... | ['Kazushige Okayasu', 'Yuta Matsuzaki', 'Akio Nakamura', 'Ryousuke Takasawa', 'Naomichi Kobayashi', 'Takaaki Imanari', 'Hirokatsu Kataoka', 'Yoshihiro Kanehara'] | 2017-04-07 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-5.07125497e-01 -3.58285457e-01 -6.63013682e-02 -9.30997193e-01
-8.41004074e-01 -6.51867568e-01 2.21598491e-01 1.68768033e-01
-4.82258648e-01 4.67751145e-01 1.23985589e-01 6.42051280e-01
-1.94888428e-01 -7.77844369e-01 -7.30041683e-01 -3.97998035e-01
-1.33187860e-01 2.13939145e-01 -5.00783250e-02 -3.88655543... | [10.200580596923828, 5.465857028961182] |
6f3ce690-d45f-4974-ae30-94e342914681 | predicate-representations-and-polysemy-in | null | null | https://aclanthology.org/2021.iwcs-1.6 | https://aclanthology.org/2021.iwcs-1.6.pdf | Predicate Representations and Polysemy in VerbNet Semantic Parsing | Despite recent advances in semantic role labeling propelled by pre-trained text encoders like BERT, performance lags behind when applied to predicates observed infrequently during training or to sentences in new domains. In this work, we investigate how role labeling performance on low-frequency predicates and out-of-d... | ['Martha Palmer', 'James Gung'] | null | null | null | null | iwcs-acl-2021-6 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.86907983e-01 6.00090146e-01 -7.15522647e-01 -6.78560019e-01
-3.82986367e-01 -9.54150259e-01 6.47167504e-01 1.03092432e+00
-5.40500224e-01 1.00812387e+00 8.16157281e-01 -1.17317848e-01
-4.67008352e-01 -9.94634330e-01 -4.35846031e-01 -2.34741881e-01
-2.18795776e-01 7.51032770e-01 5.56613028e-01 -7.45534778... | [10.254110336303711, 9.311917304992676] |
75ae923a-7f11-440a-888c-99f80f5fde43 | end-to-end-audio-visual-speech-recognition | 2102.06657 | null | https://arxiv.org/abs/2102.06657v1 | https://arxiv.org/pdf/2102.06657v1.pdf | End-to-end Audio-visual Speech Recognition with Conformers | In this work, we present a hybrid CTC/Attention model based on a ResNet-18 and Convolution-augmented transformer (Conformer), that can be trained in an end-to-end manner. In particular, the audio and visual encoders learn to extract features directly from raw pixels and audio waveforms, respectively, which are then fed... | ['Maja Pantic', 'Stavros Petridis', 'Pingchuan Ma'] | 2021-02-12 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [ 5.93688071e-01 6.02904521e-02 2.06729304e-02 -1.69537038e-01
-1.41052234e+00 -3.00643414e-01 8.62315953e-01 -4.13503731e-03
-4.39801365e-01 3.80145550e-01 4.45321918e-01 -5.46143115e-01
5.68019211e-01 -1.80419102e-01 -9.77318466e-01 -7.00210869e-01
3.29342842e-01 -1.13481253e-01 2.01846093e-01 8.92632231... | [14.338835716247559, 5.074570655822754] |
62960bc8-958f-4969-b05e-1fea347413c5 | when-3d-aided-2d-face-recognition-meets-deep | 1709.06532 | null | http://arxiv.org/abs/1709.06532v1 | http://arxiv.org/pdf/1709.06532v1.pdf | When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition | Most of the face recognition works focus on specific modules or demonstrate a
research idea. This paper presents a pose-invariant 3D-aided 2D face
recognition system (UR2D) that is robust to pose variations as large as 90? by
leveraging deep learning technology. The architecture and the interface of UR2D
are described,... | ['Ioannis A. Kakadiaris', 'Pengfei Dou', 'Xiang Xu', 'Ha A. Le'] | 2017-09-19 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-5.43508351e-01 5.25477119e-02 1.10126793e-01 -8.30824733e-01
-4.54973549e-01 -1.75951779e-01 4.34715956e-01 -9.52139914e-01
-3.46361212e-02 9.23158526e-02 -4.25725162e-01 -3.10410500e-01
-1.20677061e-01 -5.36187410e-01 -4.57457960e-01 -5.57826757e-01
-2.31425315e-01 6.48832738e-01 -3.25487971e-01 -2.62658864... | [13.334014892578125, 0.6341338157653809] |
e8b6b6d2-8798-40da-8768-d733a86154bc | efficient-match-pair-retrieval-for-large | 2307.04520 | null | https://arxiv.org/abs/2307.04520v1 | https://arxiv.org/pdf/2307.04520v1.pdf | Efficient Match Pair Retrieval for Large-scale UAV Images via Graph Indexed Global Descriptor | SfM (Structure from Motion) has been extensively used for UAV (Unmanned Aerial Vehicle) image orientation. Its efficiency is directly influenced by feature matching. Although image retrieval has been extensively used for match pair selection, high computational costs are consumed due to a large number of local features... | ['Lizhe Wang', 'Lelin Li', 'Bingxuan Guo', 'Wanshou Jiang', 'Qingquan Li', 'Yichen Ma', 'San Jiang'] | 2023-07-10 | null | null | null | null | ['image-retrieval', 'retrieval'] | ['computer-vision', 'methodology'] | [-3.28520383e-03 -8.35069180e-01 -2.22582564e-01 -1.11013226e-01
-5.13044953e-01 -8.13173831e-01 5.77424347e-01 5.36715388e-01
-3.67608696e-01 1.52981892e-01 -2.43348688e-01 7.99446274e-03
-7.66921282e-01 -1.15457571e+00 -3.33716363e-01 -6.08286500e-01
-3.02299917e-01 4.57120389e-01 4.90598977e-01 -3.55846971... | [7.462818622589111, -2.0736405849456787] |
d8ae2a7b-e0e6-4f7e-a2ae-378034ec6e0a | idan-image-difference-attention-network-for | 2208.08292 | null | https://arxiv.org/abs/2208.08292v1 | https://arxiv.org/pdf/2208.08292v1.pdf | IDAN: Image Difference Attention Network for Change Detection | Remote sensing image change detection is of great importance in disaster assessment and urban planning. The mainstream method is to use encoder-decoder models to detect the change region of two input images. Since the change content of remote sensing images has the characteristics of wide scale range and variety, it is... | ['Xueyun Chen', 'Qichen Ding', 'Zican Hu', 'Hongkun Liu'] | 2022-08-17 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 2.90246010e-01 -6.98709369e-01 3.26271802e-01 -3.71342540e-01
-3.68137449e-01 -1.11453444e-01 4.17816371e-01 -7.77152330e-02
-7.32610524e-01 3.38965982e-01 2.30268970e-01 -1.49912789e-01
-1.92112550e-02 -1.08354926e+00 -3.78961444e-01 -8.06675315e-01
-8.90545994e-02 -5.45135081e-01 2.17929095e-01 -4.22847211... | [9.765811920166016, -1.3089264631271362] |
c3c52e8a-5969-4354-ad52-cfe034845815 | perceiver-actor-a-multi-task-transformer-for | 2209.05451 | null | https://arxiv.org/abs/2209.05451v2 | https://arxiv.org/pdf/2209.05451v2.pdf | Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation | Transformers have revolutionized vision and natural language processing with their ability to scale with large datasets. But in robotic manipulation, data is both limited and expensive. Can manipulation still benefit from Transformers with the right problem formulation? We investigate this question with PerAct, a langu... | ['Dieter Fox', 'Lucas Manuelli', 'Mohit Shridhar'] | 2022-09-12 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 4.57616672e-02 1.85120739e-02 -1.16147660e-01 -2.30673775e-01
-7.72538364e-01 -7.94686615e-01 6.37189209e-01 -2.90681034e-01
-5.72371960e-01 4.93770480e-01 4.11448061e-01 -1.01490095e-01
-8.85447673e-03 -4.80070621e-01 -1.11658013e+00 -6.05905414e-01
-2.56106168e-01 8.46562207e-01 1.36764899e-01 -2.80998498... | [4.741800308227539, 0.4744839370250702] |
ecb18496-42d1-4e45-8892-3a6bdb7a4aa0 | very-low-resolution-iris-recognition-via | 2210.09765 | null | https://arxiv.org/abs/2210.09765v1 | https://arxiv.org/pdf/2210.09765v1.pdf | Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion | Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a super-resolution algorithm used to reconstruct iris images based on Eigen-transformation of local im... | ['Josef Bigun', 'Reuben A. Farrugia', 'Fernando Alonso-Fernandez'] | 2022-10-18 | null | null | null | null | ['iris-recognition'] | ['computer-vision'] | [ 5.56107461e-01 -2.35540614e-01 -3.99370119e-02 -2.77275592e-01
-8.21200609e-01 -1.38159797e-01 2.93790191e-01 -2.05709096e-02
-5.89478254e-01 8.21685076e-01 3.31055611e-01 5.03410138e-02
-4.66709077e-01 -6.11102879e-01 -2.84541041e-01 -9.73210335e-01
1.93751156e-01 -7.01098070e-02 7.65159773e-03 4.40686047... | [3.7485086917877197, -3.629601001739502] |
96750618-a43b-4857-8f34-61e02f53fc92 | automl-meets-time-series-regression-design | 2107.13186 | null | https://arxiv.org/abs/2107.13186v1 | https://arxiv.org/pdf/2107.13186v1.pdf | AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge | Analyzing better time series with limited human effort is of interest to academia and industry. Driven by business scenarios, we organized the first Automated Time Series Regression challenge (AutoSeries) for the WSDM Cup 2020. We present its design, analysis, and post-hoc experiments. The code submission requirement p... | ['Isabelle Guyon', 'Wei-Wei Tu', 'Zhen Xu'] | 2021-07-28 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-2.99602062e-01 -3.79266053e-01 -3.14220339e-01 -3.71295452e-01
-6.89573348e-01 -8.03269207e-01 6.40154660e-01 -3.18065137e-02
-8.98504257e-02 6.05842948e-01 -1.27129629e-01 -7.06592202e-01
-5.44577599e-01 -4.26293612e-01 -4.85476792e-01 -5.81248879e-01
-7.94074237e-01 4.70481753e-01 -3.09374154e-01 -4.68695641... | [7.123617649078369, 3.105365514755249] |
d3572178-0669-409b-b178-6a058c60130b | wavelet-diffusion-models-are-fast-and | 2211.16152 | null | https://arxiv.org/abs/2211.16152v2 | https://arxiv.org/pdf/2211.16152v2.pdf | Wavelet Diffusion Models are fast and scalable Image Generators | Diffusion models are rising as a powerful solution for high-fidelity image generation, which exceeds GANs in quality in many circumstances. However, their slow training and inference speed is a huge bottleneck, blocking them from being used in real-time applications. A recent DiffusionGAN method significantly decreases... | ['Anh Tran', 'Quan Dao', 'Hao Phung'] | 2022-11-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Phung_Wavelet_Diffusion_Models_Are_Fast_and_Scalable_Image_Generators_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Phung_Wavelet_Diffusion_Models_Are_Fast_and_Scalable_Image_Generators_CVPR_2023_paper.pdf | cvpr-2023-1 | ['blocking'] | ['natural-language-processing'] | [ 2.20792629e-02 -8.04163218e-02 -2.76492566e-01 -8.04515108e-02
-1.18848515e+00 -2.46429473e-01 6.20943367e-01 -4.10720497e-01
-2.80860633e-01 7.83291698e-01 1.53263956e-01 -2.31255233e-01
1.46729246e-01 -9.91604805e-01 -6.13916278e-01 -9.86799419e-01
1.28251567e-01 2.78201967e-01 5.29724844e-02 -2.03754798... | [11.276420593261719, -0.5445803999900818] |
79b95059-12ed-469c-8149-909028b7b317 | towards-accurate-post-training-quantization | 2303.14341 | null | https://arxiv.org/abs/2303.14341v1 | https://arxiv.org/pdf/2303.14341v1.pdf | Towards Accurate Post-Training Quantization for Vision Transformer | Vision transformer emerges as a potential architecture for vision tasks. However, the intense computation and non-negligible delay hinder its application in the real world. As a widespread model compression technique, existing post-training quantization methods still cause severe performance drops. We find the main rea... | ['Xianglong Liu', 'Xiaolin Wei', 'Junjie Liu', 'Zhenhua Chai', 'Qinghua Yan', 'Haotong Qin', 'Yifu Ding'] | 2023-03-25 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 3.63745540e-01 -2.84069896e-01 -3.40992212e-01 -1.94710851e-01
-7.41764903e-01 -2.56226838e-01 4.72349375e-01 -4.21242937e-02
-4.56229061e-01 2.62971342e-01 1.55455858e-01 -5.09966135e-01
1.48751382e-02 -6.19475603e-01 -7.19363868e-01 -8.64348948e-01
3.75285804e-01 -1.31609872e-01 4.10608470e-01 -1.40529320... | [8.652695655822754, 3.024217367172241] |
068a7014-6219-47be-aa0d-37826fcc84e3 | a-closer-look-at-rehearsal-free-continual | 2203.17269 | null | https://arxiv.org/abs/2203.17269v2 | https://arxiv.org/pdf/2203.17269v2.pdf | A Closer Look at Rehearsal-Free Continual Learning | Continual learning is a setting where machine learning models learn novel concepts from continuously shifting training data, while simultaneously avoiding degradation of knowledge on previously seen classes which may disappear from the training data for extended periods of time (a phenomenon known as the catastrophic f... | ['Zsolt Kira', 'Shaunak Halbe', 'Yen-Chang Hsu', 'Junjiao Tian', 'James Seale Smith'] | 2022-03-31 | null | null | null | null | ['l2-regularization', 'novel-concepts'] | ['methodology', 'reasoning'] | [ 3.58448744e-01 2.05201551e-01 -1.06127061e-01 -3.32566470e-01
-6.33745909e-01 -4.68465358e-01 6.05658829e-01 2.48439461e-01
-9.64873016e-01 1.09184945e+00 1.94781825e-01 -4.14305359e-01
-1.61716744e-01 -4.77708012e-01 -1.18918431e+00 -7.15772092e-01
-4.23852690e-02 2.84510285e-01 1.59337461e-01 9.15987492... | [9.847040176391602, 3.3944485187530518] |
7459b253-7b68-4117-b271-08005a3d3ca4 | accurate-data-efficient-unconstrained-text | 1812.11894 | null | http://arxiv.org/abs/1812.11894v1 | http://arxiv.org/pdf/1812.11894v1.pdf | Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks | Unconstrained text recognition is an important computer vision task,
featuring a wide variety of different sub-tasks, each with its own set of
challenges. One of the biggest promises of deep neural networks has been the
convergence and automation of feature extractors from input raw signals,
allowing for the highest po... | ['Khaled F. Hussain', 'Usama S. Mohammed', 'Mohamed Yousef'] | 2018-12-31 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 7.74569094e-01 -4.57513571e-01 1.36738867e-01 -5.23955345e-01
-5.35466611e-01 -7.15682983e-01 8.60374093e-01 -4.09138799e-01
-6.13923311e-01 4.03362870e-01 -1.06247433e-01 -4.38488036e-01
7.43888468e-02 -3.91110480e-01 -8.12156200e-01 -6.73649371e-01
5.81329226e-01 4.84833777e-01 -5.22905588e-02 3.22216898... | [11.962005615234375, 2.340162515640259] |
7f733aba-9c1d-4fff-9099-b1153d47109d | dgecn-a-depth-guided-edge-convolutional | 2204.09983 | null | https://arxiv.org/abs/2204.09983v1 | https://arxiv.org/pdf/2204.09983v1.pdf | DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose Estimation | Monocular 6D pose estimation is a fundamental task in computer vision. Existing works often adopt a two-stage pipeline by establishing correspondences and utilizing a RANSAC algorithm to calculate 6 degrees-of-freedom (6DoF) pose. Recent works try to integrate differentiable RANSAC algorithms to achieve an end-to-end 6... | ['Chunxia Xiao', 'Shengjie Zheng', 'Wenxiao Zhang', 'Yanping Fu', 'Fei Luo', 'Tuo Cao'] | 2022-04-21 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cao_DGECN_A_Depth-Guided_Edge_Convolutional_Network_for_End-to-End_6D_Pose_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cao_DGECN_A_Depth-Guided_Edge_Convolutional_Network_for_End-to-End_6D_Pose_CVPR_2022_paper.pdf | cvpr-2022-1 | ['6d-pose-estimation-1'] | ['computer-vision'] | [-3.34757745e-01 -3.37171227e-01 -5.03649861e-02 -5.35021603e-01
-5.03532648e-01 -7.44027138e-01 6.69085026e-01 -3.91813338e-01
-3.69012177e-01 2.54580766e-01 3.12804848e-01 -2.35511377e-01
-1.66094214e-01 -6.92743599e-01 -7.38624036e-01 -3.04856181e-01
2.51548171e-01 5.53863943e-01 5.11286519e-02 -5.84452599... | [7.582390308380127, -2.617374897003174] |
844bbf7b-390e-4b07-92a7-2119cf5ef0f8 | online-multi-object-tracking-and-segmentation | 2009.00100 | null | https://arxiv.org/abs/2009.00100v2 | https://arxiv.org/pdf/2009.00100v2.pdf | Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion | In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method is based on the Gaussian mixture probability hypothesis density (GMPHD) filter, a hierarchical data association (HDA), and a mask-based ... | ['Witold Pedrycz', 'Seong-Whan Lee', 'Kwangjin Yoon', 'Young-chul Yoon', 'Young-min Song', 'Moongu Jeon'] | 2020-08-31 | null | null | null | null | ['online-multi-object-tracking', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision'] | [-5.29979169e-02 -4.25873697e-01 1.03769009e-03 -2.74944931e-01
-8.22641909e-01 -4.29857284e-01 3.92706007e-01 2.96458304e-01
-6.55626178e-01 5.46513140e-01 -4.95683044e-01 2.47159861e-02
-6.21109735e-03 -6.26991451e-01 -7.39635885e-01 -1.04098654e+00
2.51234382e-01 8.27009261e-01 1.29978645e+00 2.70549357... | [6.47294282913208, -2.0299603939056396] |
b25899fc-fc6f-4fa2-bd03-817ebffd905a | simulation-based-bayesian-inference-for | 2303.05873 | null | https://arxiv.org/abs/2303.05873v1 | https://arxiv.org/pdf/2303.05873v1.pdf | Simulation-based Bayesian inference for robotic grasping | General robotic grippers are challenging to control because of their rich nonsmooth contact dynamics and the many sources of uncertainties due to the environment or sensor noise. In this work, we demonstrate how to compute 6-DoF grasp poses using simulation-based Bayesian inference through the full stochastic forward s... | ['Gilles Louppe', 'Olivier Brüls', 'Norman Marlier'] | 2023-03-10 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-2.24688888e-01 -8.08424875e-02 8.17869529e-02 -1.95776045e-01
-6.46344125e-01 -4.49729592e-01 3.36875319e-01 -2.28374645e-01
-3.05587888e-01 6.77718997e-01 -2.70688593e-01 1.59034491e-01
-7.53414512e-01 -3.83064866e-01 -9.92437899e-01 -8.97602737e-01
-3.45077306e-01 1.07528138e+00 7.06603155e-02 -2.77844816... | [5.69947624206543, -0.6519825458526611] |
2742aed6-adb5-47b6-b1b4-075ba2c48dcb | discourse-based-argument-segmentation-and | null | null | https://aclanthology.org/2021.isa-1.5 | https://aclanthology.org/2021.isa-1.5.pdf | Discourse-based Argument Segmentation and Annotation | The paper presents a discourse-based approach to the analysis of argumentative texts departing from the assumption that the coherence of a text should capture argumentation structure as well and, therefore, existing discourse analysis tools can be successfully applied for argument segmentation and annotation tasks. We ... | ['Dietrich Klakow', 'Marius Mosbach', 'Volha Petukhova', 'Ekaterina Saveleva'] | null | null | null | null | acl-isa-iwcs-2021-6 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 3.02981675e-01 1.33471000e+00 -3.05405974e-01 -2.98812032e-01
-8.07270169e-01 -6.81233943e-01 9.39134598e-01 7.98386991e-01
-6.12212658e-01 1.11094093e+00 5.34810364e-01 -9.45583701e-01
-3.71717960e-01 -8.13682258e-01 -6.26479745e-01 -3.09491247e-01
-2.20137299e-03 8.76510382e-01 2.32348815e-01 -6.58763707... | [10.716875076293945, 9.447065353393555] |
b7f3e15d-778a-4d45-80a2-7c40b5ace8fb | towards-discriminative-and-transferable-one | 2210.05783 | null | https://arxiv.org/abs/2210.05783v1 | https://arxiv.org/pdf/2210.05783v1.pdf | Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors | Recent object detection models require large amounts of annotated data for training a new classes of objects. Few-shot object detection (FSOD) aims to address this problem by learning novel classes given only a few samples. While competitive results have been achieved using two-stage FSOD detectors, typically one-stage... | ['Juergen Beyerer', 'Bin Yang', 'Matthias Kayser', 'Ahmed Hendawy', 'George Eskandar', 'Mohamed Abdelsamad', 'Karim Guirguis'] | 2022-10-11 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 3.56918216e-01 -6.71988353e-02 -1.68375388e-01 -3.67629945e-01
-6.33219540e-01 3.85129526e-02 7.34019220e-01 1.28531590e-01
-9.02297854e-01 4.89973128e-01 -3.35610151e-01 2.08283886e-01
2.28256971e-01 -6.56802356e-01 -7.50751495e-01 -6.25784755e-01
1.62192509e-01 1.63425580e-01 1.40136266e+00 -6.48561344... | [9.207745552062988, 1.0778803825378418] |
0ad25f20-b742-4dc6-acf7-09efc78fe44a | hierarchical-clustering-using-auto-encoded | 2101.03742 | null | https://arxiv.org/abs/2101.03742v1 | https://arxiv.org/pdf/2101.03742v1.pdf | Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis | Getting a robust time-series clustering with best choice of distance measure and appropriate representation is always a challenge. We propose a novel mechanism to identify the clusters combining learned compact representation of time-series, Auto Encoded Compact Sequence (AECS) and hierarchical clustering approach. Pro... | ['Arpan Pal', 'Anish Datta', 'Soma Bandyopadhyay'] | 2021-01-11 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 1.38492942e-01 -4.34570283e-01 3.91170263e-01 -2.41690159e-01
-7.88796425e-01 -5.67082644e-01 3.61339539e-01 4.91447210e-01
-3.27554733e-01 5.46816468e-01 5.02887964e-01 2.90063303e-02
-6.63668215e-01 -5.70902407e-01 -3.09034258e-01 -8.64761353e-01
-6.95266128e-01 4.25779611e-01 -1.27032533e-01 9.84863117... | [7.247622489929199, 3.31008243560791] |
c5fcecfd-f582-465f-92d9-3358b73c55f0 | autosplice-a-text-prompt-manipulated-image | 2304.06870 | null | https://arxiv.org/abs/2304.06870v1 | https://arxiv.org/pdf/2304.06870v1.pdf | AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics | Recent advancements in language-image models have led to the development of highly realistic images that can be generated from textual descriptions. However, the increased visual quality of these generated images poses a potential threat to the field of media forensics. This paper aims to investigate the level of chall... | ['Siwei Lyu', 'Jialing Cai', 'Yan Ju', 'Zhou Zhou', 'Mingzhen Huang', 'Shan Jia'] | 2023-04-14 | null | null | null | null | ['detect-forged-images-and-videos'] | ['computer-vision'] | [ 4.54151332e-01 -6.94985762e-02 2.39485800e-01 -1.27232268e-01
-1.35524619e+00 -7.71197319e-01 7.29979515e-01 1.37082875e-01
-3.83591354e-01 4.14911330e-01 6.61604777e-02 -2.82974601e-01
3.86395037e-01 -4.66932148e-01 -8.59537899e-01 -2.50709444e-01
2.38585770e-01 1.73797503e-01 4.34234411e-01 7.70897511... | [12.344111442565918, 1.0049734115600586] |
feb96454-49bf-4ce3-aa95-835defe16552 | annotation-imputation-to-individualize | 2305.15070 | null | https://arxiv.org/abs/2305.15070v1 | https://arxiv.org/pdf/2305.15070v1.pdf | Annotation Imputation to Individualize Predictions: Initial Studies on Distribution Dynamics and Model Predictions | Annotating data via crowdsourcing is time-consuming and expensive. Owing to these costs, dataset creators often have each annotator label only a small subset of the data. This leads to sparse datasets with examples that are marked by few annotators; if an annotator is not selected to label an example, their opinion reg... | ['Dongyeop Kang', 'Jaehyung Kim', 'Risako Owan', 'Ruyuan Wan', 'London Lowmanstone'] | 2023-05-24 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 1.93693057e-01 4.46842134e-01 -3.14421237e-01 -7.87772298e-01
-1.03523993e+00 -1.13688052e+00 7.21338838e-02 4.64468211e-01
-4.84729916e-01 1.31814241e+00 7.13963687e-01 1.66758880e-01
4.14934546e-01 -5.57057738e-01 -7.49596179e-01 -3.54499251e-01
8.25013757e-01 7.43276656e-01 -2.65530199e-01 1.89913288... | [9.638275146484375, 4.668358325958252] |
1e048b7c-4172-4cf6-8392-2f54c5ae1973 | ltc-gif-attracting-more-clicks-on-feature | 2201.09077 | null | https://arxiv.org/abs/2201.09077v1 | https://arxiv.org/pdf/2201.09077v1.pdf | LTC-GIF: Attracting More Clicks on Feature-length Sports Videos | This paper proposes a lightweight method to attract users and increase views of the video by presenting personalized artistic media -- i.e, static thumbnails and animated GIFs. This method analyzes lightweight thumbnail containers (LTC) using computational resources of the client device to recognize personalized events... | ['Eun-Seok Ryu', 'Jaehyuk Choi', 'Ghulam Mujtaba'] | 2022-01-22 | null | null | null | null | ['animated-gif-generation', 'sports-analytics', 'action-analysis', 'user-constrained-thumbnail-generation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.33693585e-01 -3.42394829e-01 7.56294280e-02 8.76606815e-03
-5.92733622e-01 -8.30753624e-01 2.22609892e-01 1.12487204e-01
-4.20081198e-01 3.85143995e-01 2.25770593e-01 -1.70099422e-01
1.50063947e-01 -8.85945439e-01 -6.17500842e-01 -3.92092705e-01
-8.48673955e-02 1.14013359e-01 7.45932639e-01 -1.22333393... | [10.597084045410156, -0.9662665128707886] |
3a0303d8-2aac-4fd8-93a1-8fcfa202efd0 | new-results-and-open-questions-for-sir-ph | 2205.03700 | null | https://arxiv.org/abs/2205.03700v1 | https://arxiv.org/pdf/2205.03700v1.pdf | New results and open questions for SIR-PH epidemic models with linear birth rate, loss of immunity, vaccination, and disease and vaccination fatalities | Our paper presents three new classes of models: SIR-PH, SIR-PH-FA, and SIR-PH-IA, and states two problems we would like to solve about them. Recall that deterministic mathematical epidemiology has one basic general law, the R0 alternative" of [52, 51], which states that the local stability condition of the disease free... | ['Andrei Halanay', 'Rim Adenane', 'Florin Avram'] | 2022-05-07 | null | null | null | null | ['epidemiology'] | ['medical'] | [-5.87349236e-02 2.39108413e-01 1.27271131e-01 5.34187257e-01
2.58807868e-01 -3.83462846e-01 6.33733869e-01 2.56069988e-01
-5.53918660e-01 1.03830171e+00 -1.98136136e-01 -6.15381598e-01
-8.78623307e-01 -9.87394750e-01 -2.99298078e-01 -1.17623532e+00
-6.07350647e-01 8.05908799e-01 4.51806456e-01 -9.22654688... | [5.936718940734863, 4.389479637145996] |
a94e7a3f-00e4-4b12-b37f-36bcd6056ee9 | pose-controllable-3d-facial-animation | 2302.12532 | null | https://arxiv.org/abs/2302.12532v1 | https://arxiv.org/pdf/2302.12532v1.pdf | Pose-Controllable 3D Facial Animation Synthesis using Hierarchical Audio-Vertex Attention | Most of the existing audio-driven 3D facial animation methods suffered from the lack of detailed facial expression and head pose, resulting in unsatisfactory experience of human-robot interaction. In this paper, a novel pose-controllable 3D facial animation synthesis method is proposed by utilizing hierarchical audio-v... | ['Yu-Kun Lai', 'Junjie Cao', 'Bo Li', 'Xiaolin Wei', 'Bin Liu'] | 2023-02-24 | null | null | null | null | ['face-model'] | ['computer-vision'] | [-5.54513708e-02 3.15056682e-01 5.91843799e-02 -5.42144716e-01
-5.56437373e-01 9.32847615e-03 4.71471220e-01 -5.26891887e-01
2.89263010e-01 4.18147057e-01 5.35865426e-01 3.79074454e-01
1.23356678e-01 -4.13056552e-01 -5.69444478e-01 -8.27952504e-01
-1.27637297e-01 2.20069706e-01 -2.08697185e-01 -3.77011508... | [13.126599311828613, -0.37975481152534485] |
6d2cb042-c0f5-456b-9065-d92718036d9d | logai-a-library-for-log-analytics-and | 2301.13415 | null | https://arxiv.org/abs/2301.13415v1 | https://arxiv.org/pdf/2301.13415v1.pdf | LogAI: A Library for Log Analytics and Intelligence | Software and System logs record runtime information about processes executing within a system. These logs have become the most critical and ubiquitous forms of observability data that help developers understand system behavior, monitor system health and resolve issues. However, the volume of logs generated can be humon... | ['Steven Hoi', 'Doyen Sahoo', 'Chenghao Liu', 'Wenzhuo Yang', 'Amrita Saha', 'Qian Cheng'] | 2023-01-31 | null | null | null | null | ['log-parsing'] | ['computer-code'] | [-4.84766781e-01 -3.63415629e-01 1.10335760e-01 -9.52460170e-02
-2.56562531e-01 -5.87820470e-01 4.15715814e-01 7.94220567e-01
9.56733525e-02 1.48215936e-03 -1.24771848e-01 -5.87260485e-01
-2.53022343e-01 -7.46596217e-01 -3.41691285e-01 -2.75745362e-01
-6.11213326e-01 5.81050277e-01 2.30948970e-01 -1.18023464... | [7.446837425231934, 2.7041432857513428] |
55e82a3a-3b3f-421e-86c3-2bf4a7da78ca | webly-supervised-learning-for-skin-lesion | 1804.00177 | null | https://arxiv.org/abs/1804.00177v2 | https://arxiv.org/pdf/1804.00177v2.pdf | Webly Supervised Learning for Skin Lesion Classification | Within medical imaging, manual curation of sufficient well-labeled samples is cost, time and scale-prohibitive. To improve the representativeness of the training dataset, for the first time, we present an approach to utilize large amounts of freely available web data through web-crawling. To handle noise and weak natur... | ['Sailesh Conjeti', 'Federico Tombari', 'Fernando Navarro', 'Nassir Navab'] | 2018-03-31 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.16299665e-01 5.29275881e-03 -3.85995209e-01 -6.14691198e-01
-1.77082789e+00 -6.78916812e-01 3.16204131e-01 2.64563501e-01
-6.64189756e-01 7.84920394e-01 1.16486885e-01 -2.61454344e-01
-2.62739271e-01 -5.69035769e-01 -9.19575393e-01 -6.40424192e-01
2.39886865e-01 3.77721936e-01 4.90785629e-01 2.69831896... | [14.989710807800293, -2.588440179824829] |
9b03854f-58a9-401c-9ac0-d8460d7e1757 | copy-and-paste-networks-for-deep-video | 1908.11587 | null | https://arxiv.org/abs/1908.11587v1 | https://arxiv.org/pdf/1908.11587v1.pdf | Copy-and-Paste Networks for Deep Video Inpainting | We present a novel deep learning based algorithm for video inpainting. Video inpainting is a process of completing corrupted or missing regions in videos. Video inpainting has additional challenges compared to image inpainting due to the extra temporal information as well as the need for maintaining the temporal cohere... | ['Seoung Wug Oh', 'Seon Joo Kim', 'Sungho Lee', 'DaeYeun Won'] | 2019-08-30 | copy-and-paste-networks-for-deep-video-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Lee_Copy-and-Paste_Networks_for_Deep_Video_Inpainting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Lee_Copy-and-Paste_Networks_for_Deep_Video_Inpainting_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-inpainting'] | ['computer-vision'] | [ 3.52354884e-01 -8.34682882e-02 1.24956155e-02 -1.89659759e-01
-6.00030065e-01 -1.67457044e-01 1.15955904e-01 -3.33453774e-01
-4.75182354e-01 7.73225427e-01 3.40809822e-01 -5.16210077e-03
2.07604736e-01 -6.25506222e-01 -1.23256671e+00 -5.31437457e-01
-5.07819839e-02 -2.11377457e-01 4.16858494e-01 -2.13536516... | [10.810101509094238, -1.3351210355758667] |
78f4a76d-1de3-4c76-a1f8-0c74621cf9b4 | on-leveraging-the-visual-modality-for-neural | 1910.02754 | null | https://arxiv.org/abs/1910.02754v1 | https://arxiv.org/pdf/1910.02754v1.pdf | On Leveraging the Visual Modality for Neural Machine Translation | Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics. Recently, Caglayan et al. posit that the observed gains are limited mainly due to the very simple, short, repetitive sentences of the Multi30k dataset (the only multimodal MT dataset ava... | ['Yi Xu', 'Quanyang Lu', 'Vikas Raunak', 'Sang Keun Choe', 'Florian Metze'] | 2019-10-07 | on-leveraging-the-visual-modality-for-neural-1 | https://aclanthology.org/W19-8620 | https://aclanthology.org/W19-8620.pdf | ws-2019-10 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 2.46686161e-01 1.85431093e-01 -2.99061891e-02 -3.88635509e-02
-8.88235271e-01 -9.03313935e-01 9.65030849e-01 1.79997712e-01
-7.17892528e-01 4.72958773e-01 5.49953103e-01 -6.78119659e-01
2.41545677e-01 -1.95931599e-01 -7.99417436e-01 -5.37485301e-01
4.46690083e-01 3.56445819e-01 -2.23475769e-01 -3.37058812... | [11.413896560668945, 1.4909518957138062] |
8e7825cd-24ba-4221-b885-0acf7c988cfe | melon-playlist-dataset-a-public-dataset-for | 2102.00201 | null | https://arxiv.org/abs/2102.00201v1 | https://arxiv.org/pdf/2102.00201v1.pdf | Melon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging | One of the main limitations in the field of audio signal processing is the lack of large public datasets with audio representations and high-quality annotations due to restrictions of copyrighted commercial music. We present Melon Playlist Dataset, a public dataset of mel-spectrograms for 649,091tracks and 148,826 asso... | ['Dmitry Bogdanov', 'Xavier Serra', 'Sehwan Kim', 'Jungtaek Jang', 'Suyon Lim', 'Semi Lim', 'Namjun Jo', 'Biho Kim', 'Soohyeon Lee', 'Yuntae Kim', 'Andres Ferraro'] | 2021-01-30 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 4.68330085e-03 -5.20204306e-01 -2.34596416e-01 -2.49265820e-01
-1.53683102e+00 -1.00218022e+00 -1.10314764e-01 4.06091720e-01
-3.90241563e-01 5.24319887e-01 7.62554705e-01 5.08328557e-01
-6.74181044e-01 -4.04550791e-01 -1.81512222e-01 -4.72857058e-01
-3.20185721e-01 1.21672839e-01 3.66885036e-01 -2.03486383... | [15.844192504882812, 5.280788421630859] |
82137fc5-849d-448d-8e25-b85f4df26c6b | personalized-stress-monitoring-using-wearable | 2108.00144 | null | https://arxiv.org/abs/2108.00144v1 | https://arxiv.org/pdf/2108.00144v1.pdf | Personalized Stress Monitoring using Wearable Sensors in Everyday Settings | Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies. Although several studies have evaluated stress levels in controlled settings, objective stress assessment in everyday settings is still largely unde... | ['Marco Levorato', 'Amir M. Rahmani', 'Nikil Dutt', 'Stephanie M. Reich', 'Sina Labbaf', 'Ali Tazarv'] | 2021-07-31 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-variability'] | ['medical', 'medical'] | [ 4.64326292e-01 -2.78601825e-01 -5.40549099e-01 -7.43288398e-01
-2.37143472e-01 -2.56575465e-01 -3.48508656e-01 6.59780145e-01
-1.88684344e-01 7.16473639e-01 2.73223072e-01 4.03851876e-03
1.06847100e-01 -4.83723164e-01 2.53975838e-01 -2.98129678e-01
-3.71083170e-01 -1.44844264e-01 -3.94803315e-01 -4.55427840... | [13.688490867614746, 3.1254379749298096] |
eff365fe-c908-4300-8dac-fd586742e872 | implicit-differentiation-for-hyperparameter | 2307.02130 | null | https://arxiv.org/abs/2307.02130v1 | https://arxiv.org/pdf/2307.02130v1.pdf | Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso | We provide a framework and algorithm for tuning the hyperparameters of the Graphical Lasso via a bilevel optimization problem solved with a first-order method. In particular, we derive the Jacobian of the Graphical Lasso solution with respect to its regularization hyperparameters. | ['Titouan Vayer', 'Mathurin Massias', 'Paulo Gonçalves', 'Can Pouliquen'] | 2023-07-05 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-7.23505542e-02 1.87343568e-01 -4.06415910e-01 -5.27394354e-01
-1.05580676e+00 -4.92841899e-01 6.59203827e-02 -3.58106613e-01
-9.64087695e-02 8.75195384e-01 6.45779520e-02 -3.11986268e-01
-4.56516594e-01 -3.20777386e-01 -7.67899215e-01 -9.06621277e-01
-1.05913900e-01 6.64196789e-01 -7.00220704e-01 -1.22480929... | [6.9455037117004395, 4.359692573547363] |
333754f9-b77f-4816-80d9-e14bfc17d07b | a-self-correcting-sequential-recommender | 2303.02297 | null | https://arxiv.org/abs/2303.02297v2 | https://arxiv.org/pdf/2303.02297v2.pdf | A Self-Correcting Sequential Recommender | Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user's historical interactions reflect her/his preferences and transition patterns between ite... | ['Pengjie Ren', 'Xiuzhen Cheng', 'Maarten de Rijke', 'Qiang Yan', 'Xin Xin', 'Zhaochun Ren', 'Zhumin Chen', 'Chenyang Wang', 'Yujie Lin'] | 2023-03-04 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 4.61266726e-01 -3.73531878e-01 -6.63748682e-01 -5.91348529e-01
-2.38342851e-01 -6.94330752e-01 6.72206059e-02 2.14370161e-01
-5.33561587e-01 5.93702793e-01 4.66702372e-01 -5.12981236e-01
-1.06645850e-02 -6.31226540e-01 -1.01544607e+00 -2.91051388e-01
-7.05753490e-02 4.40722734e-01 1.99290618e-01 -1.89817920... | [10.096989631652832, 5.666502952575684] |
b53a1f16-09f6-4739-a5fd-8e51bb1b1789 | selective-token-generation-for-few-shot | null | null | https://openreview.net/forum?id=GthNKCqdDg | https://openreview.net/pdf?id=GthNKCqdDg | Selective Token Generation for Few-shot Language Modeling | Natural language modeling with limited training data is challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability. Among these transfer learning algorithms from PLMs, additive learning that incorporates a task-specific adapter on ... | ['Eun-Sol Kim', 'Sungwoong Kim', 'Taehwan Kwon', 'DaeJin Jo'] | 2021-09-29 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 5.12978196e-01 2.21932903e-01 -2.98143417e-01 -1.10267490e-01
-1.01059365e+00 -3.52435112e-02 7.97804534e-01 1.23120703e-01
-3.81625682e-01 9.07046735e-01 2.78266490e-01 -3.61946486e-02
1.34195864e-01 -1.08281720e+00 -6.61805153e-01 -8.98138762e-01
5.48752427e-01 4.87649590e-01 2.17664436e-01 -3.88120174... | [11.803020477294922, 8.995983123779297] |
e0fc3278-f9ef-4f82-8193-92dff268cab4 | regeneration-learning-a-learning-paradigm-for | 2301.08846 | null | https://arxiv.org/abs/2301.08846v1 | https://arxiv.org/pdf/2301.08846v1.pdf | Regeneration Learning: A Learning Paradigm for Data Generation | Machine learning methods for conditional data generation usually build a mapping from source conditional data X to target data Y. The target Y (e.g., text, speech, music, image, video) is usually high-dimensional and complex, and contains information that does not exist in source data, which hinders effective and effic... | ['Yoshua Bengio', 'Tie-Yan Liu', 'Jiang Bian', 'Tao Qin', 'Xu Tan'] | 2023-01-21 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 7.20994115e-01 3.57343763e-01 -4.94437546e-01 -2.62980342e-01
-8.70779991e-01 -3.67899239e-01 9.64157104e-01 -2.58619599e-02
1.72950268e-01 8.82915258e-01 4.45113242e-01 -2.20153168e-01
-6.87709674e-02 -1.08107722e+00 -9.60378826e-01 -7.51098454e-01
1.24666005e-01 3.90129745e-01 -4.12143648e-01 -2.35877439... | [11.725205421447754, 0.1314777135848999] |
82c2c7f4-134e-40fc-95f0-bfb834f27860 | regulating-gatekeeper-ai-and-data | 2212.04997 | null | https://arxiv.org/abs/2212.04997v1 | https://arxiv.org/pdf/2212.04997v1.pdf | Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyond | Artificial intelligence is not only increasingly used in business and administration contexts, but a race for its regulation is also underway, with the EU spearheading the efforts. Contrary to existing literature, this article suggests, however, that the most far-reaching and effective EU rules for AI applications in t... | ['Janina Rochon', 'Johann Cordes', 'Philipp Hacker'] | 2022-12-09 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [ 2.66854346e-01 5.78095615e-01 -5.85522175e-01 -2.89856941e-01
-3.34254444e-01 -7.49840915e-01 8.98986578e-01 1.32892147e-01
-7.29639649e-01 6.91710114e-01 6.02075517e-01 -7.78795958e-01
-7.63256848e-01 -5.54454505e-01 -2.93491423e-01 -2.79766649e-01
5.70048690e-01 3.86217266e-01 -3.65780979e-01 -1.49488300... | [8.956067085266113, 5.92951774597168] |
77673c2a-ac3c-4017-9f66-1cabc294bb00 | a-question-focused-multi-factor-attention | 1801.08290 | null | http://arxiv.org/abs/1801.08290v1 | http://arxiv.org/pdf/1801.08290v1.pdf | A Question-Focused Multi-Factor Attention Network for Question Answering | Neural network models recently proposed for question answering (QA) primarily
focus on capturing the passage-question relation. However, they have minimal
capability to link relevant facts distributed across multiple sentences which
is crucial in achieving deeper understanding, such as performing multi-sentence
reasoni... | ['Souvik Kundu', 'Hwee Tou Ng'] | 2018-01-25 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 1.54706374e-01 5.19569889e-02 1.31253794e-01 -6.29914105e-01
-1.37320161e+00 -4.29943711e-01 2.83550739e-01 5.16251028e-01
-5.02184510e-01 6.74698472e-01 9.21899557e-01 -5.02079487e-01
-2.11037070e-01 -8.81189764e-01 -7.51781940e-01 -1.80523217e-01
4.62963432e-01 6.53338909e-01 3.29766184e-01 -6.87664747... | [11.177294731140137, 8.000874519348145] |
77a16d03-4739-4612-ac58-5dcd66132b36 | adaptersoup-weight-averaging-to-improve | 2302.07027 | null | https://arxiv.org/abs/2302.07027v3 | https://arxiv.org/pdf/2302.07027v3.pdf | AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models | Pretrained language models (PLMs) are trained on massive corpora, but often need to specialize to specific domains. A parameter-efficient adaptation method suggests training an adapter for each domain on the task of language modeling. This leads to good in-domain scores but can be impractical for domain- or resource-re... | ['Jesse Dodge', 'Alexander Fraser', 'Matthew E. Peters', 'Alexandra Chronopoulou'] | 2023-02-14 | null | null | null | null | ['text-clustering', 'semantic-textual-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.62617907e-02 -2.74587929e-01 -2.02562481e-01 -5.50130904e-01
-1.12765944e+00 -7.77324319e-01 6.50100112e-01 8.48590881e-02
-6.68030441e-01 6.06414437e-01 2.79346079e-01 -2.96182781e-01
-4.41108830e-03 -5.64167261e-01 -5.48519254e-01 -5.46519756e-01
2.74954408e-01 1.20839036e+00 5.29538810e-01 -2.81682730... | [10.788651466369629, 8.017595291137695] |
3cdcb862-a2c6-4fa8-adbf-b8ccbc96b539 | online-learning-with-regularized-kernel-for | 1701.04508 | null | http://arxiv.org/abs/1701.04508v2 | http://arxiv.org/pdf/1701.04508v2.pdf | Online Learning with Regularized Kernel for One-class Classification | This paper presents an online learning with regularized kernel based
one-class extreme learning machine (ELM) classifier and is referred as online
RK-OC-ELM. The baseline kernel hyperplane model considers whole data in a
single chunk with regularized ELM approach for offline learning in case of
one-class classification... | ['Kapil Ahuja', 'Chandan Gautam', 'Aruna Tiwari', 'Sundaram Suresh'] | 2017-01-17 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-3.27394277e-01 1.29089832e-01 -3.29516828e-01 -3.78543198e-01
-8.97445232e-02 -2.62820154e-01 3.43980402e-01 3.99404556e-01
-6.02903605e-01 6.03031933e-01 -3.93806398e-01 -3.05732518e-01
-5.16587853e-01 -6.06624603e-01 -5.25292277e-01 -7.22667694e-01
-3.47580671e-01 5.57400763e-01 1.03530377e-01 -1.03865571... | [8.21826457977295, 3.878718137741089] |
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