paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6098446e-d70d-4b40-861b-cc83926125ff | using-multi-modal-data-for-improving | 2207.14781 | null | https://arxiv.org/abs/2207.14781v1 | https://arxiv.org/pdf/2207.14781v1.pdf | Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology | Traditional datasets for the radiological diagnosis tend to only provide the radiology image alongside the radiology report. However, radiology reading as performed by radiologists is a complex process, and information such as the radiologist's eye-fixations over the course of the reading has the potential to be an inv... | ['Farzad Khalvati', 'Namdar', 'Khashayar', 'Sara Ketabi', 'Pranav Agnihotri'] | 2022-07-29 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 2.88762391e-01 6.39674366e-01 -1.07307255e-01 -5.90557456e-01
-1.03417957e+00 -8.53222758e-02 4.02833939e-01 5.73737621e-01
-5.45848966e-01 4.85839456e-01 4.29086655e-01 -7.86346674e-01
-5.30526757e-01 -1.57620177e-01 -5.59027672e-01 -6.96675658e-01
2.52363592e-01 3.63649577e-01 -2.72055626e-01 -2.03597248... | [15.098844528198242, -2.1735129356384277] |
29ec71d4-379b-4f45-a349-feaf86a16532 | out-of-distribution-generalization-via | 2306.11890 | null | https://arxiv.org/abs/2306.11890v1 | https://arxiv.org/pdf/2306.11890v1.pdf | Out of Distribution Generalization via Interventional Style Transfer in Single-Cell Microscopy | Real-world deployment of computer vision systems, including in the discovery processes of biomedical research, requires causal representations that are invariant to contextual nuisances and generalize to new data. Leveraging the internal replicate structure of two novel single-cell fluorescent microscopy datasets, we p... | ['Juan C. Caicedo', 'Michio Hirano', 'Nicholas De Veaux', 'Sultan Kenjeyev', 'Aditya Pratapa', 'Alex Quach', 'Michael Doron', 'Wolfgang M. Pernice'] | 2023-06-15 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 7.39914715e-01 3.67015600e-01 -4.41709608e-02 -5.12727797e-01
-4.23431784e-01 -7.48600364e-01 1.06291306e+00 2.07796007e-01
-4.06711251e-01 1.21813834e+00 4.99873787e-01 -5.60808778e-01
-5.62829673e-01 -5.98960459e-01 -1.14518332e+00 -7.08094418e-01
-2.33638152e-01 2.52844483e-01 -1.58334404e-01 2.95379311... | [8.45059585571289, 5.24686336517334] |
ad16b3d1-3b84-4237-af5b-4546ad559795 | scale-invariant-domain-generalization-image | 2110.03496 | null | https://arxiv.org/abs/2110.03496v1 | https://arxiv.org/pdf/2110.03496v1.pdf | Scale Invariant Domain Generalization Image Recapture Detection | Recapturing and rebroadcasting of images are common attack methods in insurance frauds and face identification spoofing, and an increasing number of detection techniques were introduced to handle this problem. However, most of them ignored the domain generalization scenario and scale variances, with an inferior perform... | ['Hong Hui', 'Zheng Huang', 'Weidong Qiu', 'Jie Guo', 'Jinian Luo'] | 2021-10-07 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 2.92253345e-01 -2.33482912e-01 -1.86990485e-01 -4.18774486e-01
-4.83835310e-01 -7.51479089e-01 4.97296423e-01 -2.01930597e-01
-1.23248897e-01 7.59645104e-01 -1.49974510e-01 7.14014545e-02
-1.45859018e-01 -5.76199114e-01 -4.51247245e-01 -8.94527674e-01
3.38962913e-01 4.62790914e-02 4.24588919e-01 -2.14333847... | [13.148509979248047, 1.1653870344161987] |
79a6a5e5-e331-4291-9556-8e6ddb798309 | fast-and-high-quality-highlight-removal-from | 1512.00237 | null | http://arxiv.org/abs/1512.00237v1 | http://arxiv.org/pdf/1512.00237v1.pdf | Fast and High Quality Highlight Removal from A Single Image | Specular reflection exists widely in photography and causes the recorded
color deviating from its true value, so fast and high quality highlight removal
from a single nature image is of great importance. In spite of the progress in
the past decades in highlight removal, achieving wide applicability to the
large diversi... | ['Xiangyang Ji', 'Dongsheng An', 'Qionghai Dai', 'Jinli Suo', 'Haoqian Wang'] | 2015-12-01 | null | null | null | null | ['highlight-removal'] | ['computer-vision'] | [ 4.82114166e-01 -6.26317322e-01 2.15907514e-01 4.72648107e-02
-1.45899624e-01 -4.98191237e-01 2.85348862e-01 -6.34927869e-01
-9.76513848e-02 7.59876132e-01 1.72431245e-01 9.09274518e-02
-1.16737626e-01 -5.54601490e-01 -2.34695405e-01 -1.36549067e+00
4.98757929e-01 -1.61558032e-01 2.99104542e-01 -9.85403582... | [10.500615119934082, -2.6472930908203125] |
01650563-5666-4762-adf1-ec00d580d84f | empirical-analysis-of-a-segmentation | 2307.03266 | null | https://arxiv.org/abs/2307.03266v1 | https://arxiv.org/pdf/2307.03266v1.pdf | Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging | Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. These models, however, are often trained on narrowly-defined tasks in a supervised fashion, which requires expensive labeled datasets. Recent advances in several machine learning domains, such as natural language generation ha... | ['Mert R. Sabuncu', 'Adrian V. Dalca', 'Victor Ion Butoi', 'Heejong Kim'] | 2023-07-06 | null | null | null | null | ['medical-image-segmentation', 'text-generation'] | ['medical', 'natural-language-processing'] | [ 7.22224653e-01 7.03115582e-01 -4.52021241e-01 -7.63031185e-01
-9.57872570e-01 -4.29188669e-01 4.68634635e-01 2.64047176e-01
-5.24698555e-01 4.94937152e-01 1.59859583e-01 -7.80715287e-01
2.82122269e-02 -6.70144975e-01 -5.94002664e-01 -3.40848386e-01
-1.15648083e-01 7.66482115e-01 9.67612416e-02 -5.78768179... | [14.67650318145752, -2.220468044281006] |
58a41937-b9dc-4d74-b4b7-3e332345b0f8 | towards-universal-texture-synthesis-by | 2203.04221 | null | https://arxiv.org/abs/2203.04221v1 | https://arxiv.org/pdf/2203.04221v1.pdf | Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2 | We present a new approach for universal texture synthesis by incorporating a multi-scale texton broadcasting module in the StyleGAN-2 framework. The texton broadcasting module introduces an inductive bias, enabling generation of broader range of textures, from those with regular structures to completely stochastic ones... | ['Thrasyvoulos N. Pappas', 'Gaurav Sharma', 'Jue Lin'] | 2022-03-08 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 7.39868522e-01 7.02230036e-02 -1.83466852e-01 -2.49211445e-01
-8.64752591e-01 -5.31264544e-01 8.97518754e-01 -7.81764150e-01
3.04043382e-01 6.60314798e-01 4.25217360e-01 5.11579514e-02
1.92584902e-01 -1.14460921e+00 -7.96368897e-01 -1.12384820e+00
1.16747454e-01 8.39089528e-02 3.68628532e-01 -6.62353396... | [11.523667335510254, -0.5459636449813843] |
54fce84a-e851-4cdd-afc1-139e2074f285 | refining-word-embeddings-for-sentiment | null | null | https://aclanthology.org/D17-1056 | https://aclanthology.org/D17-1056.pdf | Refining Word Embeddings for Sentiment Analysis | Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks. However, existing methods for learning context-based word embeddings typically fail to capture sufficient sentiment information. This may result in words with simil... | ['Xue-jie Zhang', 'K. Robert Lai', 'Liang-Chih Yu', 'Jin Wang'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['learning-word-embeddings'] | ['methodology'] | [-6.52118698e-02 -1.58371925e-01 -3.44888419e-01 -8.90055001e-01
-3.14285964e-01 -5.79109371e-01 3.81911218e-01 7.86788523e-01
-8.99919152e-01 4.99372661e-01 8.24774742e-01 -2.92891830e-01
2.35734135e-01 -9.31389213e-01 5.34155965e-02 -5.94836831e-01
3.19087803e-01 1.11583609e-03 7.45703503e-02 -6.46256030... | [10.503498077392578, 8.628240585327148] |
570efd42-e16e-48fc-8771-6bb2f601f81b | insta-yolo-real-time-instance-segmentation | 2102.06777 | null | https://arxiv.org/abs/2102.06777v2 | https://arxiv.org/pdf/2102.06777v2.pdf | INSTA-YOLO: Real-Time Instance Segmentation | Instance segmentation has gained recently huge attention in various computer vision applications. It aims at providing different IDs to different objects of the scene, even if they belong to the same class. Instance segmentation is usually performed as a two-stage pipeline. First, an object is detected, then semantic s... | ['Mayada Hadhoud', 'Ahmad El-Sallab', 'Abdelrahman Shaker', 'Eslam Mohamed'] | 2021-02-12 | null | null | null | null | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 2.45029986e-01 1.58783615e-01 -3.73836905e-02 -5.61476827e-01
-8.82835507e-01 -5.43042898e-01 4.10400301e-01 3.10339719e-01
-5.75105309e-01 3.15963656e-01 -6.12147570e-01 -2.80557811e-01
4.06986415e-01 -8.86561990e-01 -7.60318279e-01 -4.91352737e-01
1.11505292e-01 9.62712228e-01 7.83123732e-01 2.99416959... | [9.48536491394043, 0.1939588487148285] |
24e63fc1-9c58-4438-9aeb-746b5aef2bca | geometry-guided-adversarial-facial-expression | 1712.03474 | null | http://arxiv.org/abs/1712.03474v1 | http://arxiv.org/pdf/1712.03474v1.pdf | Geometry Guided Adversarial Facial Expression Synthesis | Facial expression synthesis has drawn much attention in the field of computer
graphics and pattern recognition. It has been widely used in face animation and
recognition. However, it is still challenging due to the high-level semantic
presence of large and non-linear face geometry variations. This paper proposes
a Geom... | ['Zhihe Lu', 'Zhenan Sun', 'Tieniu Tan', 'Ran He', 'Lingxiao Song'] | 2017-12-10 | null | null | null | null | ['face-transfer'] | ['computer-vision'] | [ 4.09180552e-01 8.13048854e-02 1.74815625e-01 -5.31744719e-01
-3.23034465e-01 -5.00741661e-01 5.03566444e-01 -1.16414809e+00
2.42531255e-01 7.48223960e-01 -7.25922883e-02 1.14903890e-01
4.22111630e-01 -7.67867327e-01 -7.23338723e-01 -1.14047050e+00
2.56100386e-01 6.04054853e-02 -4.17074740e-01 -4.71705467... | [12.70559310913086, -0.15926994383335114] |
28a25f08-84fd-48ed-91ea-b09f4a002bd2 | slothspeech-denial-of-service-attack-against | 2306.00794 | null | https://arxiv.org/abs/2306.00794v1 | https://arxiv.org/pdf/2306.00794v1.pdf | SlothSpeech: Denial-of-service Attack Against Speech Recognition Models | Deep Learning (DL) models have been popular nowadays to execute different speech-related tasks, including automatic speech recognition (ASR). As ASR is being used in different real-time scenarios, it is important that the ASR model remains efficient against minor perturbations to the input. Hence, evaluating efficiency... | ['Wei Yang', 'Cong Liu', 'Berrak Şişman', 'Simin Chen', 'Rutvij Shah', 'Mirazul Haque'] | 2023-06-01 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [-2.28746876e-01 -1.82334527e-01 2.43446156e-01 -9.15244594e-02
-7.59461880e-01 -8.22458923e-01 6.23274982e-01 6.14161789e-02
-4.64778572e-01 1.48066312e-01 -8.19363166e-03 -9.18513775e-01
2.09720731e-01 -6.16759717e-01 -8.39481354e-01 -5.64859152e-01
-1.95705235e-01 5.92365921e-01 5.31886816e-01 -4.44618136... | [13.997652053833008, 5.840256214141846] |
4fe2f164-23b5-4b34-8d8d-aacb9bad7a91 | dual-branch-hybrid-learning-network-for | 2207.07913 | null | https://arxiv.org/abs/2207.07913v1 | https://arxiv.org/pdf/2207.07913v1.pdf | Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation | The current studies of Scene Graph Generation (SGG) focus on solving the long-tailed problem for generating unbiased scene graphs. However, most de-biasing methods overemphasize the tail predicates and underestimate head ones throughout training, thereby wrecking the representation ability of head predicate features. F... | ['Heng Tao Shen', 'Abdulmotaleb El Saddik', 'Pengpeng Zeng', 'Xinyu Lyu', 'Lianli Gao', 'Chaofan Zheng'] | 2022-07-16 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 1.88122779e-01 4.82389122e-01 -3.86051178e-01 -5.52540541e-01
-5.54491282e-01 -4.25014168e-01 5.73030114e-01 6.80203885e-02
-4.04184610e-02 6.30549431e-01 1.99942395e-01 -3.75057757e-01
5.62320203e-02 -1.34203792e+00 -1.05592549e+00 -8.19461048e-01
1.58524707e-01 4.86732274e-01 5.31705379e-01 -3.18230093... | [10.275050163269043, 1.8040478229522705] |
0962290d-00c7-448d-92bb-8235dfa9420d | comparison-of-pedestrian-prediction-models | 2305.15942 | null | https://arxiv.org/abs/2305.15942v1 | https://arxiv.org/pdf/2305.15942v1.pdf | Comparison of Pedestrian Prediction Models from Trajectory and Appearance Data for Autonomous Driving | The ability to anticipate pedestrian motion changes is a critical capability for autonomous vehicles. In urban environments, pedestrians may enter the road area and create a high risk for driving, and it is important to identify these cases. Typical predictors use the trajectory history to predict future motion, howeve... | ['Subramanian Ramamoorthy', 'John Redford', 'Morris Antonello', 'Anthony Knittel'] | 2023-05-25 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-9.57270190e-02 -2.76542217e-01 -2.60468155e-01 -6.50840163e-01
-8.96721035e-02 -4.18404877e-01 8.30182612e-01 2.38580838e-01
-3.27272952e-01 6.17687643e-01 1.28182024e-01 -5.51399887e-01
3.35902125e-01 -1.04268169e+00 -5.22079051e-01 -6.09655023e-01
-4.42373544e-01 1.78792194e-01 9.96422231e-01 -2.59516656... | [6.2032294273376465, 0.7078149914741516] |
07d67607-f21e-463b-a0fa-77dd27e8ba67 | benign-overparameterization-in-membership | 2205.14055 | null | https://arxiv.org/abs/2205.14055v2 | https://arxiv.org/pdf/2205.14055v2.pdf | A Blessing of Dimensionality in Membership Inference through Regularization | Is overparameterization a privacy liability? In this work, we study the effect that the number of parameters has on a classifier's vulnerability to membership inference attacks. We first demonstrate how the number of parameters of a model can induce a privacy--utility trade-off: increasing the number of parameters gene... | ['Richard G. Baraniuk', 'Hamid Javadi', 'Blake Mason', 'Daniel LeJeune', 'Jasper Tan'] | 2022-05-27 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 3.19872111e-01 -4.48126420e-02 -8.11665654e-02 -4.76306677e-01
-7.38846660e-01 -8.74056697e-01 2.39438370e-01 1.53605789e-01
-7.36805260e-01 5.64122379e-01 -1.81662336e-01 -9.32730734e-01
-2.21317023e-01 -5.96867859e-01 -8.19427192e-01 -8.95729780e-01
-1.71364546e-01 -1.24559827e-01 -1.59165516e-01 1.46868989... | [5.993520259857178, 6.923010349273682] |
df288858-c134-4a78-b3a7-302bc85f8e7c | visgraphnet-a-complex-network-interpretation | 2108.12490 | null | https://arxiv.org/abs/2108.12490v1 | https://arxiv.org/pdf/2108.12490v1.pdf | VisGraphNet: a complex network interpretation of convolutional neural features | Here we propose and investigate the use of visibility graphs to model the feature map of a neural network. The model, initially devised for studies on complex networks, is employed here for the classification of texture images. The work is motivated by an alternative viewpoint provided by these graphs over the original... | ['Marcelo K. Albertini', 'Gwanggil Jeon', 'Kyungkoo Jun', 'Young-Sup Lee', 'Joao B. Florindo'] | 2021-08-27 | null | null | null | null | ['texture-classification', 'network-interpretation'] | ['computer-vision', 'computer-vision'] | [ 3.75195324e-01 5.45149148e-02 5.45380376e-02 -2.05219612e-01
1.74404100e-01 -5.09123266e-01 9.85251606e-01 3.46403152e-01
-1.51452720e-01 3.63430291e-01 -5.42794943e-01 -4.01629180e-01
-7.95146346e-01 -1.05921900e+00 -2.80469269e-01 -8.87599766e-01
-3.92735034e-01 4.62257087e-01 3.59114617e-01 -3.45885098... | [10.233628273010254, -0.40732887387275696] |
3c9d2041-db5d-4928-8458-29d80ca20796 | modeling-tweet-arrival-times-using-log | null | null | https://aclanthology.org/D15-1028 | https://aclanthology.org/D15-1028.pdf | Modeling Tweet Arrival Times using Log-Gaussian Cox Processes | null | ['P. K. Srijith', 'Michal Lukasik', 'Kalina Bontcheva', 'Trevor Cohn'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['rumour-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... | [-7.280795097351074, 3.8101959228515625] |
fbd78aac-22f3-48b5-9c1c-cf991dbdf454 | voxsrc-2022-the-fourth-voxceleb-speaker | 2302.10248 | null | https://arxiv.org/abs/2302.10248v2 | https://arxiv.org/pdf/2302.10248v2.pdf | VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge | This paper summarises the findings from the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22), which was held in conjunction with INTERSPEECH 2022. The goal of this challenge was to evaluate how well state-of-the-art speaker recognition systems can diarise and recognise speakers from speech obtained "in the wild"... | ['Andrew Zisserman', 'Daniel Garcia-Romero', 'Arsha Nagrani', 'Joon Son Chung', 'Jee-weon Jung', 'Andrew Brown', 'Jaesung Huh'] | 2023-02-20 | null | null | null | null | ['speaker-recognition', 'speaker-verification'] | ['speech', 'speech'] | [ 2.87246089e-02 2.39714667e-01 3.42104226e-01 -6.10374987e-01
-1.31521749e+00 -6.20241702e-01 9.97124434e-01 -3.58460456e-01
-4.77692038e-01 2.96307951e-01 6.73635840e-01 -5.64244501e-02
3.43548775e-01 3.07176679e-01 -3.36064965e-01 -5.22543013e-01
-3.41056734e-01 5.11471689e-01 4.94649187e-02 -2.59718388... | [14.361998558044434, 5.937004566192627] |
42d969c5-20b1-4a83-8dfa-b50fe795ffa7 | representation-learning-through-multimodal | null | null | https://dl.acm.org/doi/abs/10.1145/3503161.3548018 | https://dl.acm.org/doi/pdf/10.1145/3503161.3548018 | Representation Learning through Multimodal Attention and Time-Sync Comments for Affective Video Content Analysis | Although temporal patterns inherent in visual and audio signals are crucial for affective video content analysis, they have not been thoroughly explored yet. In this paper, we propose a novel Temporal-Aware Multimodal (TAM) method to fully capture the temporal information. Specifically, we design a cross-temporal multi... | ['Lin Fang', 'Shangfei Wang', 'Jicai Pan'] | 2022-10-14 | null | null | null | acm-mm22-2022-10 | ['video-emotion-recognition'] | ['computer-vision'] | [ 2.16166601e-01 -3.76534075e-01 -4.46779817e-01 -4.21868145e-01
-8.38611186e-01 -3.65230173e-01 4.87843037e-01 2.57153243e-01
-3.05027366e-01 1.02220945e-01 5.40145934e-01 2.75925428e-01
1.57328472e-01 -2.77093977e-01 -4.93998110e-01 -7.19802082e-01
-2.91866630e-01 -2.88056999e-01 8.78629312e-02 -1.17104873... | [13.2064790725708, 4.9978108406066895] |
0078f4a0-6e25-43f8-b329-ba6569d1eb3d | continuous-3d-multi-channel-sign-language | 2103.06982 | null | https://arxiv.org/abs/2103.06982v1 | https://arxiv.org/pdf/2103.06982v1.pdf | Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks | Sign languages are multi-channel visual languages, where signers use a continuous 3D space to communicate.Sign Language Production (SLP), the automatic translation from spoken to sign languages, must embody both the continuous articulation and full morphology of sign to be truly understandable by the Deaf community. Pr... | ['Richard Bowden', 'Necati Cihan Camgoz', 'Ben Saunders'] | 2021-03-11 | null | null | null | null | ['sign-language-production'] | ['natural-language-processing'] | [ 5.56380928e-01 2.05763772e-01 9.78769138e-02 -5.19594789e-01
-9.66729105e-01 -7.81090796e-01 7.69838929e-01 -1.09748805e+00
-2.17883617e-01 4.65276033e-01 6.50529742e-01 -3.18994105e-01
2.34400943e-01 -2.44416207e-01 -9.87029910e-01 -5.19665837e-01
-2.28880718e-02 7.14009941e-01 2.32586358e-02 -3.20313752... | [9.226431846618652, -6.5494537353515625] |
ccfb4eaf-1bd7-4661-8f4f-5b30f42eb4b6 | learning-based-multi-modality-image-and-video | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lu_Learning_Based_Multi-Modality_Image_and_Video_Compression_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lu_Learning_Based_Multi-Modality_Image_and_Video_Compression_CVPR_2022_paper.pdf | Learning Based Multi-Modality Image and Video Compression | Multi-modality (i.e., multi-sensor) data is widely used in various vision tasks for more accurate or robust perception. However, the increased data modalities bring new challenges for data storage and transmission. The existing data compression approaches usually adopt individual codecs for each modality without co... | ['Dong Xu', 'Qiang Hu', 'Jing Geng', 'Tianxiong Zhong', 'Guo Lu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['data-compression'] | ['time-series'] | [ 6.25193894e-01 -7.24726737e-01 -2.39818528e-01 -1.24422550e-01
-8.75494540e-01 -2.65533894e-01 3.68483692e-01 9.80079845e-02
-4.50038284e-01 3.68408978e-01 3.13546300e-01 5.31681925e-02
-4.03457910e-01 -8.22446704e-01 -6.43836915e-01 -1.14272738e+00
3.74979347e-01 -3.17186326e-01 7.50438496e-02 -8.94551724... | [11.097485542297363, -1.6846271753311157] |
bd131f4a-8fd2-4b11-9b1c-91a1b75da723 | monitoring-energy-trends-through-automatic | 2201.01559 | null | https://arxiv.org/abs/2201.01559v1 | https://arxiv.org/pdf/2201.01559v1.pdf | Monitoring Energy Trends through Automatic Information Extraction | Energy research is of crucial public importance but the use of computer science technologies like automatic text processing and data management for the energy domain is still rare. Employing these technologies in the energy domain will be a significant contribution to the interdisciplinary topic of ``energy informatics... | ['Dilek Küçük'] | 2022-01-05 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-5.40771522e-03 -2.15541080e-01 -4.30351824e-01 1.24418527e-01
-6.10810578e-01 -8.83625984e-01 6.77736640e-01 1.06724787e+00
-2.87991494e-01 7.43368268e-01 5.47831655e-01 -4.07778502e-01
-2.90369242e-01 -1.27119899e+00 -5.13429157e-02 -6.92789257e-01
-5.05909510e-02 1.75665304e-01 9.07809585e-02 -2.35778362... | [9.449986457824707, 8.205883979797363] |
4b7f4794-13bf-4a8d-94c2-dfe3d0818ed9 | text-based-person-search-without-parallel | 2305.12964 | null | https://arxiv.org/abs/2305.12964v1 | https://arxiv.org/pdf/2305.12964v1.pdf | Text-based Person Search without Parallel Image-Text Data | Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description. Existing methods are dominated by training models with parallel image-text pairs, which are very costly to collect. In this paper, we make the first attempt to explo... | ['Min Zhang', 'Liqiang Nie', 'Ziqiang Cao', 'Chen Chen', 'Min Cao', 'Jingyao Wang', 'Yang Bai'] | 2023-05-22 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 3.41186672e-01 -1.76663011e-01 2.18505666e-01 -4.89420444e-01
-1.18653631e+00 -4.24833208e-01 7.51459360e-01 -2.91618645e-01
-6.76126420e-01 6.68784976e-01 -8.43333304e-02 5.55975959e-02
3.87021229e-02 -7.62219846e-01 -8.59420896e-01 -6.82161629e-01
6.73744082e-01 9.78477418e-01 -7.17825163e-03 -1.13059878... | [11.202569961547852, 0.7865801453590393] |
b61c4208-85bc-4193-b3c4-ebe192c5fbab | svl-adapter-self-supervised-adapter-for | 2210.03794 | null | https://arxiv.org/abs/2210.03794v1 | https://arxiv.org/pdf/2210.03794v1.pdf | SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models | Vision-language models such as CLIP are pretrained on large volumes of internet sourced image and text pairs, and have been shown to sometimes exhibit impressive zero- and low-shot image classification performance. However, due to their size, fine-tuning these models on new datasets can be prohibitively expensive, both... | ['Oisin Mac Aodha', 'Kate Jones', 'Gabriel Brostow', 'Omiros Pantazis'] | 2022-10-07 | null | null | null | null | ['classification'] | ['methodology'] | [ 3.34554166e-01 -3.24916571e-01 -4.45985943e-01 -3.01577836e-01
-9.97529745e-01 -4.55656767e-01 7.13495553e-01 8.17436129e-02
-5.63574851e-01 5.88945210e-01 6.21725135e-02 -1.89229012e-01
1.25956371e-01 -5.07197917e-01 -8.43270957e-01 -3.28310996e-01
3.48664522e-01 4.13101912e-01 2.18839377e-01 -1.48975074... | [10.003151893615723, 2.0392205715179443] |
e00b9a11-8f2f-4c65-88c0-fc35f07cd1ea | research-on-the-application-of-contrastive | 2212.00552 | null | https://arxiv.org/abs/2212.00552v2 | https://arxiv.org/pdf/2212.00552v2.pdf | An Effective Employment of Contrastive Learning in Multi-label Text Classification | The effectiveness of contrastive learning technology in natural language processing tasks is yet to be explored and analyzed. How to construct positive and negative samples correctly and reasonably is the core challenge of contrastive learning. It is even harder to discover contrastive objects in multi-label text class... | ['Dong Zhou', 'Aimin Yang', 'Jigang Wang', 'Guanqiu Qin', 'Nankai Lin'] | 2022-12-01 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 5.99146128e-01 -1.45961314e-01 -1.36845499e-01 -6.72377110e-01
-8.47340465e-01 -5.05347550e-01 8.70931029e-01 6.88070655e-01
-8.77768099e-01 7.37134457e-01 -3.75032991e-01 -2.60382980e-01
-3.70228350e-01 -2.98061252e-01 -3.56087327e-01 -6.83709800e-01
7.08336532e-02 5.88677764e-01 2.21050441e-01 -2.74084449... | [9.574933052062988, 4.3549485206604] |
4a88e93b-21e3-4772-a6bb-d1ce5d928592 | uncertainty-quantification-in-deep-learning | 1907.13418 | null | https://arxiv.org/abs/1907.13418v1 | https://arxiv.org/pdf/1907.13418v1.pdf | Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement | Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, little consideration has been given to uncertainty quantification over the output image. Here we introduce methods to characterise different components of uncertainty in suc... | ['Stamatios N. Sotiropoulos', 'Ryutaro Tanno', 'Francesco Grussu', 'Daniel Worrall', 'Aurobrata Ghosh', 'Alberto Bizzi', 'Enrico Kaden', 'Daniel C. Alexander', 'Antonio Criminisi'] | 2019-07-31 | null | null | null | null | ['medical-image-enhancement'] | ['computer-vision'] | [ 3.51065338e-01 1.92920372e-01 2.01706961e-01 -5.55146217e-01
-1.25553894e+00 -9.70630273e-02 5.25614738e-01 9.18791592e-02
-6.42766535e-01 9.82556701e-01 2.47031763e-01 3.32174711e-02
-7.09740818e-01 -5.42170346e-01 -5.59480429e-01 -1.06412137e+00
-5.43144226e-01 3.96503478e-01 1.76657096e-01 3.26007366... | [13.537452697753906, -2.3441267013549805] |
246f4b13-f60b-43ad-9658-504cee10f5f9 | physics-informed-machine-learning-a-survey-on | 2211.08064 | null | https://arxiv.org/abs/2211.08064v2 | https://arxiv.org/pdf/2211.08064v2.pdf | Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications | Recent advances of data-driven machine learning have revolutionized fields like computer vision, reinforcement learning, and many scientific and engineering domains. In many real-world and scientific problems, systems that generate data are governed by physical laws. Recent work shows that it provides potential benefit... | ['Jun Zhu', 'Hang Su', 'Yao Feng', 'Chengyang Ying', 'Yichi Zhang', 'Songming Liu', 'Zhongkai Hao'] | 2022-11-15 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 9.46606696e-02 7.06595927e-02 -6.52922392e-01 -2.67510176e-01
-4.12897289e-01 -1.31221592e-01 7.02175021e-01 -3.39372307e-02
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-6.93680346e-01 -8.88071358e-01 -1.01635289e+00 -8.75712216e-01
9.60907191e-02 5.94485641e-01 3.91981229e-02 -2.09063828... | [6.456753253936768, 3.583022356033325] |
75f21168-f052-4925-b4bf-79b0f7bf563a | towards-solving-multimodal-comprehension | 2104.10139 | null | https://arxiv.org/abs/2104.10139v1 | https://arxiv.org/pdf/2104.10139v1.pdf | Towards Solving Multimodal Comprehension | This paper targets the problem of procedural multimodal machine comprehension (M3C). This task requires an AI to comprehend given steps of multimodal instructions and then answer questions. Compared to vanilla machine comprehension tasks where an AI is required only to understand a textual input, procedural M3C is more... | ['Ajay Divakaran', 'Karan Sikka', 'Pritish Sahu'] | 2021-04-20 | null | null | null | null | ['question-answer-generation'] | ['natural-language-processing'] | [ 5.71283460e-01 4.95756835e-01 3.30562681e-01 -4.15694058e-01
-1.10793662e+00 -1.05133724e+00 6.83868349e-01 6.04673684e-01
-5.32477856e-01 4.15310681e-01 4.17450339e-01 -7.98217893e-01
-7.72424564e-02 -6.90680623e-01 -1.02041900e+00 -3.27897489e-01
1.85193852e-01 4.47785169e-01 2.46414676e-01 -5.62550247... | [11.259889602661133, 7.965500831604004] |
1a6cf0e4-fa61-4ad8-9fd7-6496fd16d351 | deformable-medical-image-registration-using-a | 1908.00788 | null | https://arxiv.org/abs/1908.00788v1 | https://arxiv.org/pdf/1908.00788v1.pdf | Deformable Medical Image Registration Using a Randomly-Initialized CNN as Regularization Prior | We present deformable unsupervised medical image registration using a randomly-initialized deep convolutional neural network (CNN) as regularization prior. Conventional registration methods predict a transformation by minimizing dissimilarities between an image pair. The minimization is usually regularized with manuall... | ['Max-Heinrich Laves', 'Tobias Ortmaier', 'Sontje Ihler'] | 2019-08-02 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [ 6.04058802e-01 5.32816827e-01 -4.40454707e-02 -5.98907411e-01
-8.03959131e-01 -3.06934059e-01 5.73357403e-01 2.08519444e-01
-8.79809141e-01 4.75585341e-01 -1.73005145e-02 2.53228396e-01
-6.76521510e-02 -7.18504786e-01 -7.38520324e-01 -6.86932206e-01
6.79176748e-02 8.52575839e-01 3.41917276e-01 -3.59964430... | [14.015351295471191, -2.57741379737854] |
de92b3c1-3ce2-4df8-8a9b-be5181d9c55c | improved-word-embeddings-with-implicit | null | null | https://aclanthology.org/C16-1227 | https://aclanthology.org/C16-1227.pdf | Improved Word Embeddings with Implicit Structure Information | Distributed word representation is an efficient method for capturing semantic and syntactic word relations. In this work, we introduce an extension to the continuous bag-of-words model for learning word representations efficiently by using implicit structure information. Instead of relying on a syntactic parser which m... | ['Jie Shen', 'Cong Liu'] | 2016-12-01 | improved-word-embeddings-with-implicit-1 | https://aclanthology.org/C16-1227 | https://aclanthology.org/C16-1227.pdf | coling-2016-12 | ['learning-word-embeddings'] | ['methodology'] | [-1.18989693e-02 6.55260265e-01 -4.11759764e-01 -8.37315917e-01
-6.46017194e-01 -2.81674832e-01 4.95678902e-01 7.09376514e-01
-5.92191696e-01 5.63361049e-01 5.76582849e-01 -5.59457779e-01
-1.83979094e-01 -1.18090832e+00 -4.87927139e-01 -5.16222715e-01
-2.81086504e-01 6.73668742e-01 1.96672291e-01 -4.34544832... | [10.174948692321777, 8.809006690979004] |
dfa375aa-0b21-4805-8d6e-1f86953eea93 | maximizing-soil-moisture-estimation-accuracy | 2305.15549 | null | https://arxiv.org/abs/2305.15549v1 | https://arxiv.org/pdf/2305.15549v1.pdf | Maximizing soil moisture estimation accuracy through simultaneous hydraulic parameter estimation using microwave remote sensing: Methodology and application | Improving the accuracy of soil moisture estimation is desirable from the perspectives of irrigation management and water conservation. To this end, this study proposes a systematic approach to select a subset of soil hydraulic parameters for estimation in large-scale agrohydrological systems to enhance soil moisture es... | ['Sirish L. Shah', 'Jinfeng Liu', 'Maik Wolleben', 'Willemijn Appels', 'Mohamed Naouri', 'Erfan Orouskhani', 'Bernard T. Agyeman'] | 2023-05-24 | null | null | null | null | ['soil-moisture-estimation'] | ['computer-vision'] | [ 1.82588100e-01 -1.70841008e-01 -2.84361362e-01 1.71451718e-01
1.69793338e-01 -7.00168192e-01 6.92962110e-02 4.55938578e-01
-1.21698163e-01 1.29489243e+00 -2.59842187e-01 -1.03883922e+00
-5.22622466e-01 -1.12392104e+00 -2.26505846e-01 -9.10679042e-01
-2.69264907e-01 -1.61045179e-01 1.52655646e-01 -5.17594814... | [9.39242935180664, -1.6190130710601807] |
afec22e3-6704-498a-b09a-0ec1d2154cf7 | denoising-and-prompt-tuning-for-multi | 2302.05862 | null | https://arxiv.org/abs/2302.05862v1 | https://arxiv.org/pdf/2302.05862v1.pdf | Denoising and Prompt-Tuning for Multi-Behavior Recommendation | In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e.g., click, add-to-cart, and purchase). Traditional collaborative filtering techniques typically assume that users only have a single type of behavior with items, making it insufficient to utilize complex collaborative ... | ['Li Li', 'Qilong Han', 'Xiangyu Zhao', 'Rui Chen', 'Chi Zhang'] | 2023-02-12 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 0.08500534 -0.2804696 -0.38899824 -0.6038336 -0.4350527 -0.3768517
0.16998227 0.0157794 -0.05516823 0.29311177 0.5599705 -0.2961815
-0.5119835 -0.95097935 -0.7846631 -0.4551138 0.13200608 0.1043883
0.00910107 -0.3433735 -0.07413581 -0.1984684 -1.4066303 0.40419278
1.090835 1.0689 0.404... | [10.129114151000977, 5.56471586227417] |
42afac95-996a-444c-9be1-c0c2585633b6 | hybrid-recommender-system-based-on-personal | 1607.02754 | null | http://arxiv.org/abs/1607.02754v1 | http://arxiv.org/pdf/1607.02754v1.pdf | Hybrid Recommender System Based on Personal Behavior Mining | Recommender systems are mostly well known for their applications in
e-commerce sites and are mostly static models. Classical personalized
recommender algorithm includes item-based collaborative filtering method
applied in Amazon, matrix factorization based collaborative filtering algorithm
from Netflix, etc. In this ar... | ['Chen Kun', 'Zhang Lingqi', 'Fang Zhiyuan'] | 2016-07-10 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [-2.27314785e-01 -5.52953660e-01 -2.80444175e-01 -3.81133109e-01
1.93330329e-02 -6.55909419e-01 -1.47272442e-02 5.35416491e-02
-3.68543327e-01 5.07406294e-01 4.46917027e-01 -5.50799012e-01
-6.29801214e-01 -1.04014421e+00 3.89241762e-02 -2.50789613e-01
-1.58750996e-01 5.38925290e-01 4.72889721e-01 -6.83461249... | [10.025113105773926, 5.852694988250732] |
65d4fa21-77c9-4a4e-80db-e82ed6a2deb2 | d2-net-weakly-supervised-action-localization | 2012.06440 | null | https://arxiv.org/abs/2012.06440v2 | https://arxiv.org/pdf/2012.06440v2.pdf | D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations | This work proposes a weakly-supervised temporal action localization framework, called D2-Net, which strives to temporally localize actions using video-level supervision. Our main contribution is the introduction of a novel loss formulation, which jointly enhances the discriminability of latent embeddings and robustness... | ['Ling Shao', 'Ming-Hsuan Yang', 'Fahad Shahbaz Khan', 'Munawar Hayat', 'Hisham Cholakkal', 'Sanath Narayan'] | 2020-12-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Narayan_D2-Net_Weakly-Supervised_Action_Localization_via_Discriminative_Embeddings_and_Denoised_Activations_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Narayan_D2-Net_Weakly-Supervised_Action_Localization_via_Discriminative_Embeddings_and_Denoised_Activations_ICCV_2021_paper.pdf | iccv-2021-1 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 2.77066410e-01 -1.43963933e-01 -6.66514099e-01 -1.64793178e-01
-6.94112360e-01 -1.67647794e-01 6.29482746e-01 -2.16932651e-02
-4.86173540e-01 4.20021772e-01 5.40399671e-01 2.98187435e-01
1.16927199e-01 -3.48708093e-01 -7.04502046e-01 -9.59072709e-01
-9.63567719e-02 -2.76806742e-01 5.81386983e-01 1.66188553... | [8.40885066986084, 0.5838165879249573] |
c7bccb0e-97b2-48bc-86ef-d23175b3e4e2 | kptimes-a-large-scale-dataset-for-keyphrase-1 | 1911.12559 | null | https://arxiv.org/abs/1911.12559v1 | https://arxiv.org/pdf/1911.12559v1.pdf | KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents | Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts p... | ['Béatrice Daille', 'Florian Boudin', 'Ygor Gallina'] | 2019-11-28 | kptimes-a-large-scale-dataset-for-keyphrase | https://aclanthology.org/W19-8617 | https://aclanthology.org/W19-8617.pdf | ws-2019-10 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 4.99790683e-02 3.02326709e-01 -6.59343600e-01 3.07988584e-01
-1.19993854e+00 -9.02841449e-01 1.30270326e+00 7.67675936e-01
-3.76264781e-01 9.78335857e-01 1.23442185e+00 -3.43027860e-01
5.38907088e-02 -8.52288008e-01 -9.23245132e-01 -6.02247007e-02
2.91056454e-01 3.62066746e-01 9.46200266e-02 -4.57997352... | [12.269448280334473, 8.920062065124512] |
672296a2-7822-43cf-9b0c-f0440daa26aa | affectmachine-classical-a-novel-system-for | 2304.04915 | null | https://arxiv.org/abs/2304.04915v1 | https://arxiv.org/pdf/2304.04915v1.pdf | AffectMachine-Classical: A novel system for generating affective classical music | This work introduces a new music generation system, called AffectMachine-Classical, that is capable of generating affective Classic music in real-time. AffectMachine was designed to be incorporated into biofeedback systems (such as brain-computer-interfaces) to help users become aware of, and ultimately mediate, their ... | ['Phoebe Chua', 'Adyasha Dash', 'Kat R. Agres'] | 2023-04-11 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [-1.62378833e-01 1.59151956e-01 -1.16423309e-01 -4.66369092e-01
-5.08121252e-01 -4.41034496e-01 -1.23473108e-01 -2.24544313e-02
-7.20288754e-02 7.19035804e-01 6.49776697e-01 4.26513493e-01
-1.19356140e-01 -5.13268054e-01 -5.30335754e-02 -3.07202578e-01
-3.03625017e-01 2.58147400e-02 -4.33084577e-01 -6.16907299... | [13.579451560974121, 3.3058369159698486] |
0600312e-4d2b-40f8-a372-4d98690e0f71 | constrained-bayesian-optimization-for-1 | 2302.14732 | null | https://arxiv.org/abs/2302.14732v2 | https://arxiv.org/pdf/2302.14732v2.pdf | Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design | Automatic underwater vehicle hull Design optimization is a complex engineering process for generating a UUV hull with optimized properties on a given requirement. First, it involves the integration of involved computationally complex engineering simulation tools. Second, it needs integration of a sample efficient optim... | ['Will Hedgecock', 'Janos Sztipanovits', 'Peter Volgyesi', 'Harsh Vardhan'] | 2023-02-28 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-2.98114270e-01 2.40450218e-01 8.20193946e-01 -3.02090138e-01
-3.03908139e-01 -6.54259622e-01 1.49866477e-01 2.59185940e-01
-3.41213673e-01 7.77262807e-01 4.94560450e-02 -5.43127537e-01
-4.31144595e-01 -1.03552818e+00 -8.48858237e-01 -4.20342952e-01
-4.38955337e-01 8.85810256e-01 1.22726478e-01 -5.32857716... | [6.026387691497803, 3.408437490463257] |
b716201f-9701-4bdf-b453-1262f190bae5 | examining-political-rhetoric-with-epistemic | 2212.14486 | null | https://arxiv.org/abs/2212.14486v2 | https://arxiv.org/pdf/2212.14486v2.pdf | Examining Political Rhetoric with Epistemic Stance Detection | Participants in political discourse employ rhetorical strategies -- such as hedging, attributions, or denials -- to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content ... | ["Brendan O'Connor", 'Justin H Gross', 'Su Lin Blodgett', 'Ankita Gupta'] | 2022-12-29 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 1.02451973e-01 9.26855505e-01 -1.06511033e+00 -3.59876573e-01
-7.37909138e-01 -9.31480467e-01 1.24797249e+00 7.72347629e-01
-2.38130584e-01 6.58140719e-01 1.13339424e+00 -1.23896182e+00
2.90486999e-02 -7.72105992e-01 -5.44504941e-01 -2.57890463e-01
7.02730238e-01 8.49691570e-01 1.21452220e-01 -7.03674257... | [9.082672119140625, 9.855539321899414] |
ce143871-0d7b-48fe-895c-15ad6c4a8167 | resolving-prepositional-phrase-attachment | null | null | https://aclanthology.org/2021.icon-main.40 | https://aclanthology.org/2021.icon-main.40.pdf | Resolving Prepositional Phrase Attachment Ambiguities with Contextualized Word Embeddings | This paper applies contextualized word embedding models to a long-standing problem in the natural language parsing community, namely prepositional phrase attachment. Following past formulations of this problem, we use data sets in which the attachment decision is both a binary-valued choice as well as a multi-valued ch... | ['Atul Kumar', 'Adwait Ratnaparkhi'] | null | null | null | null | icon-2021-12 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 7.89521262e-02 4.36358869e-01 -5.07143378e-01 -8.69846165e-01
-7.13404059e-01 -5.86795390e-01 3.47721130e-01 5.17325103e-01
-1.12819648e+00 7.03068495e-01 7.91598737e-01 -8.30939412e-01
1.53907210e-01 -1.01499689e+00 -2.51818419e-01 -3.79768968e-01
2.53765043e-02 7.34764159e-01 -3.35074104e-02 -4.14232165... | [10.437332153320312, 9.373903274536133] |
90e9c370-34ca-4c72-b537-f1661a5ac096 | deep-network-for-capacitive-ecg-denoising | 1903.12536 | null | http://arxiv.org/abs/1903.12536v1 | http://arxiv.org/pdf/1903.12536v1.pdf | Deep Network for Capacitive ECG Denoising | Continuous monitoring of cardiac health under free living condition is
crucial to provide effective care for patients undergoing post operative
recovery and individuals with high cardiac risk like the elderly. Capacitive
Electrocardiogram (cECG) is one such technology which allows comfortable and
long term monitoring t... | ['Sharath M. Shankaranarayana', 'Keerthi Ram', 'Vignesh Ravichandran', 'Mohanasankar Sivaprakasam', 'Jayaraj Joseph', 'Preejith S. P', 'Balamurali Murugesan'] | 2019-03-29 | null | null | null | null | ['ecg-denoising', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 4.35094386e-01 -8.94967467e-02 3.21298420e-01 -1.00970551e-01
-8.81081998e-01 -2.56625891e-01 -3.08286190e-01 8.00258443e-02
-4.57454413e-01 8.91858578e-01 4.43571545e-02 -1.76767290e-01
-2.21925408e-01 -4.11331445e-01 -3.63257200e-01 -8.44757974e-01
-5.30676246e-01 -2.12934181e-01 -4.05370206e-01 8.36092159... | [14.23869800567627, 3.219362258911133] |
7b50a755-aea5-4fbe-b112-3e3e2039d496 | deep-q-learning-based-distribution-network | 2305.01180 | null | https://arxiv.org/abs/2305.01180v1 | https://arxiv.org/pdf/2305.01180v1.pdf | Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement | Distribution network reconfiguration (DNR) has proved to be an economical and effective way to improve the reliability of distribution systems. As optimal network configuration depends on system operating states (e.g., loads at each node), existing analytical and population-based approaches need to repeat the entire an... | ['Mohammed Benidris', 'Narayan Bhusal', 'Mukesh Gautam'] | 2023-05-02 | null | null | null | null | ['q-learning'] | ['methodology'] | [-5.20262539e-01 -7.60876248e-03 -2.59026974e-01 -5.01880385e-02
-1.75330695e-03 -4.78145450e-01 -2.04883471e-01 1.12746395e-01
8.88131559e-02 1.22439384e+00 -4.10056025e-01 -5.98055959e-01
-1.08384562e+00 -1.21011078e+00 -2.49423981e-01 -1.15307796e+00
-6.84995592e-01 7.42283702e-01 -3.33659649e-01 -4.61455852... | [5.584938049316406, 2.4998526573181152] |
aeb9677e-4153-463c-b0a5-01383abd7dd8 | rcdnet-an-interpretable-rain-convolutional | 2107.06808 | null | https://arxiv.org/abs/2107.06808v2 | https://arxiv.org/pdf/2107.06808v2.pdf | RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining | As a common weather, rain streaks adversely degrade the image quality. Hence, removing rains from an image has become an important issue in the field. To handle such an ill-posed single image deraining task, in this paper, we specifically build a novel deep architecture, called rain convolutional dictionary network (RC... | ['Deyu Meng', 'Yefeng Zheng', 'Yong Liang', 'Yuexiang Li', 'Qian Zhao', 'Qi Xie', 'Hong Wang'] | 2021-07-14 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-2.77607113e-01 -2.06375852e-01 3.74451846e-01 -6.16801918e-01
4.30770554e-02 -3.72596800e-01 3.76437977e-02 -4.03770775e-01
-1.26175448e-01 8.04154694e-01 -1.41036347e-01 -4.31264371e-01
-1.94746166e-01 -8.72391820e-01 -6.67574704e-01 -1.03282213e+00
-1.26535103e-01 3.67655009e-02 -8.29228237e-02 -3.36622685... | [10.923659324645996, -3.234882354736328] |
c6212cc7-1fb0-41be-9dcc-ace83da950d2 | on-out-of-distribution-detection-for-audio | 2210.15283 | null | https://arxiv.org/abs/2210.15283v2 | https://arxiv.org/pdf/2210.15283v2.pdf | On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors | Out-of-distribution (OOD) detection is concerned with identifying data points that do not belong to the same distribution as the model's training data. For the safe deployment of predictive models in a real-world environment, it is critical to avoid making confident predictions on OOD inputs as it can lead to potential... | ['Aaqib Saeed', 'Zaharah Bukhsh'] | 2022-10-27 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [ 3.14919591e-01 4.82269749e-02 -1.60724074e-01 -2.28886589e-01
-1.33993948e+00 -6.93514526e-01 4.27114815e-01 4.22949016e-01
-3.20358127e-02 3.75394911e-01 4.72420365e-01 -3.19990158e-01
-4.30147797e-02 -4.54259545e-01 -4.16161716e-01 -7.36028850e-01
-2.50594676e-01 1.36502311e-01 2.98997730e-01 4.24229801... | [14.506049156188965, 5.8913397789001465] |
6a9b9fb6-a7b0-4c9a-bd5d-5bcf7271f2b0 | a-distributional-perspective-on-actor-critic | null | null | https://openreview.net/forum?id=jWXBUsWP7N | https://openreview.net/pdf?id=jWXBUsWP7N | A Distributional Perspective on Actor-Critic Framework | Recent distributional reinforcement learning methods, despite their successes, still contain fundamental problems that can lead to inaccurate representations of value distributions, such as distributional instability, action type restriction, and biased approximation. In this paper, we present a novel distributional ac... | ['Chan Youn Park', 'Younghoon Kim', 'Daniel Wontae Nam'] | 2021-01-01 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.71658331e-01 1.44857578e-02 -4.31718946e-01 -2.00285725e-02
-1.12580860e+00 -5.99148273e-01 7.27891386e-01 2.21469536e-01
-7.96457112e-01 1.47684157e+00 4.25735056e-01 -3.43949586e-01
-4.03880686e-01 -6.41808867e-01 -4.73005831e-01 -9.89887118e-01
8.06181505e-02 7.12299824e-01 -2.37200856e-02 -2.06500307... | [4.086977958679199, 2.5266149044036865] |
ddc07a76-4ede-440d-a094-451938d82654 | learning-distributed-representations-of-code | 2012.07023 | null | https://arxiv.org/abs/2012.07023v2 | https://arxiv.org/pdf/2012.07023v2.pdf | InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees | Building deep learning models on source code has found many successful software engineering applications, such as code search, code comment generation, bug detection, code migration, and so on. Current learning techniques, however, have a major drawback that these models are mostly trained on datasets labeled for parti... | ['Lingxiao Jiang', 'Yijun Yu', 'Nghi D. Q. Bui'] | 2020-12-13 | null | null | null | null | ['code-classification', 'code-search', 'code-comment-generation', 'code-search', 'method-name-prediction', 'comment-generation'] | ['computer-code', 'computer-code', 'computer-code', 'computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-2.46324278e-02 3.20107341e-01 -4.54163402e-01 -5.09300470e-01
-4.95579898e-01 -5.85631907e-01 4.45335954e-02 4.08132941e-01
1.17374279e-01 7.28627443e-02 1.98744416e-01 -9.71522689e-01
3.50494653e-01 -7.66258895e-01 -6.36362135e-01 -1.37944534e-01
-1.75663203e-01 4.42113057e-02 3.55844051e-01 -1.79307356... | [7.603275299072266, 7.874672889709473] |
afed14fa-ea47-46d5-b31e-5a8ab407756f | easicsdeep-a-deep-learning-model-for-cervical | 1812.04912 | null | http://arxiv.org/abs/1812.04912v1 | http://arxiv.org/pdf/1812.04912v1.pdf | EasiCSDeep: A deep learning model for Cervical Spondylosis Identification using surface electromyography signal | Cervical spondylosis (CS) is a common chronic disease that affects up to
two-thirds of the population and poses a serious burden on individuals and
society. The early identification has significant value in improving cure rate
and reducing costs. However, the pathology is complex, and the mild symptoms
increase the dif... | ['Yingcong Xiang', 'Jing Xiao', 'Li Cui', 'Xi Huang', 'Nana Wang'] | 2018-12-12 | null | null | null | null | ['cervical-spondylosis-identification'] | ['medical'] | [ 1.12458132e-01 -3.82128626e-01 -2.60230869e-01 1.28824368e-01
-5.61984360e-01 -2.54768558e-04 -6.94780797e-02 2.13505365e-02
-6.84750676e-01 6.02773309e-01 -1.44995943e-01 -5.66577818e-03
-6.02431297e-01 -5.49337685e-01 -3.74939889e-01 -8.61405909e-01
-1.68314546e-01 2.90902019e-01 1.90099761e-01 -2.17626870... | [13.082594871520996, 2.9937775135040283] |
68e2f5c2-86b7-48cd-99b0-0b947db6806c | the-regretful-agent-heuristic-aided | 1903.01602 | null | http://arxiv.org/abs/1903.01602v1 | http://arxiv.org/pdf/1903.01602v1.pdf | The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation | As deep learning continues to make progress for challenging perception tasks,
there is increased interest in combining vision, language, and decision-making.
Specifically, the Vision and Language Navigation (VLN) task involves navigating
to a goal purely from language instructions and visual information without
explici... | ['Zsolt Kira', 'Ghassan AlRegib', 'Chih-Yao Ma', 'Caiming Xiong', 'Zuxuan Wu'] | 2019-03-05 | the-regretful-agent-heuristic-aided-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ma_The_Regretful_Agent_Heuristic-Aided_Navigation_Through_Progress_Estimation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ma_The_Regretful_Agent_Heuristic-Aided_Navigation_Through_Progress_Estimation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['vision-language-navigation'] | ['computer-vision'] | [-5.43131419e-02 4.80920300e-02 -3.13369811e-01 -3.14251125e-01
-7.29491174e-01 -5.51743388e-01 6.79917574e-01 7.36797675e-02
-8.70745301e-01 7.14866757e-01 2.05437168e-01 -5.92277467e-01
2.65598334e-02 -6.18358612e-01 -7.76579618e-01 -5.76183617e-01
-2.97200769e-01 4.41147476e-01 4.17380720e-01 -1.92610979... | [4.509726047515869, 0.5543479323387146] |
24b0dbcf-5e34-45b6-81c7-9d63c877a5a7 | learning-to-simulate-natural-language | 2305.08195 | null | https://arxiv.org/abs/2305.08195v2 | https://arxiv.org/pdf/2305.08195v2.pdf | Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing | Interactive semantic parsing based on natural language (NL) feedback, where users provide feedback to correct the parser mistakes, has emerged as a more practical scenario than the traditional one-shot semantic parsing. However, prior work has heavily relied on human-annotated feedback data to train the interactive sem... | ['Ziyu Yao', 'Wen-tau Yih', 'Sida I. Wang', 'Yintao Tai', 'Saurabh Srivastava', 'Hao Yan'] | 2023-05-14 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [ 4.11537945e-01 5.68292260e-01 3.08818161e-01 -6.21056378e-01
-1.29701340e+00 -8.61522615e-01 -1.20714819e-02 3.52571875e-01
-4.61140543e-01 2.98466623e-01 2.24357203e-01 -5.51709294e-01
4.17713881e-01 -5.50404072e-01 -9.32137072e-01 4.40577343e-02
4.96240765e-01 4.76526737e-01 4.78107661e-01 -2.03477263... | [10.816906929016113, 8.571586608886719] |
9dfede04-599d-4507-824d-efafba61db53 | learning-to-order-natural-language-texts | null | null | https://aclanthology.org/P13-2016 | https://aclanthology.org/P13-2016.pdf | Learning to Order Natural Language Texts | null | ['Jiwei Tan', 'Xiaojun Wan', 'Jianguo Xiao'] | 2013-08-01 | null | null | null | acl-2013-8 | ['concept-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2087202072143555, 3.774433135986328] |
bd3a2c7f-d13c-4cef-97fc-7ccb0d49f77e | languages-you-know-influence-those-you-learn | 2212.01757 | null | https://arxiv.org/abs/2212.01757v1 | https://arxiv.org/pdf/2212.01757v1.pdf | Languages You Know Influence Those You Learn: Impact of Language Characteristics on Multi-Lingual Text-to-Text Transfer | Multi-lingual language models (LM), such as mBERT, XLM-R, mT5, mBART, have been remarkably successful in enabling natural language tasks in low-resource languages through cross-lingual transfer from high-resource ones. In this work, we try to better understand how such models, specifically mT5, transfer *any* linguisti... | ['Sachin Agarwal', 'David Vandyke', 'Jean-Philippe Fauconnier', 'Siddharth Patwardhan', 'Deepanshu Gupta', 'Benjamin Muller'] | 2022-12-04 | null | null | null | null | ['cross-lingual-transfer', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [-1.91633925e-01 -1.22006498e-01 -4.29599911e-01 -3.56030792e-01
-7.58809865e-01 -7.98931658e-01 8.98780763e-01 8.20501521e-02
-7.60528922e-01 6.09965444e-01 3.55107844e-01 -5.70496559e-01
-9.85965431e-02 -5.70525646e-01 -1.00908184e+00 -3.31616193e-01
4.07922082e-02 6.15048885e-01 1.29978448e-01 -5.20173907... | [10.95317268371582, 9.937169075012207] |
574e0a60-c4ca-446f-be4c-61fafeb51f3e | market2dish-health-aware-food-recommendation | 2012.06416 | null | https://arxiv.org/abs/2012.06416v1 | https://arxiv.org/pdf/2012.06416v1.pdf | Market2Dish: Health-aware Food Recommendation | With the rising incidence of some diseases, such as obesity and diabetes, a healthy diet is arousing increasing attention. However, most existing food-related research efforts focus on recipe retrieval, user preference-based food recommendation, cooking assistance, or the nutrition and calorie estimation of dishes, ign... | ['Liqiang Nie', 'Xuemeng Song', 'Peiguang Jing', 'Hao Jiang', 'Ling-Yu Duan', 'Wenjie Wang'] | 2020-12-11 | null | null | null | null | ['food-recommendation'] | ['miscellaneous'] | [-1.61234513e-01 -3.28431278e-01 -6.36102617e-01 -3.79348606e-01
-4.95862663e-01 -2.24066287e-01 -9.01025757e-02 9.91987944e-01
-1.25987038e-01 8.22959170e-02 1.08940303e+00 8.30562562e-02
-3.94669741e-01 -1.35839331e+00 -4.58998144e-01 -6.74647391e-01
-9.26186815e-02 2.44214326e-01 -3.42844278e-02 -3.34473282... | [11.544147491455078, 4.456280708312988] |
d8abf380-6f4e-49ee-b666-fd29168f2fc1 | how-to-democratise-and-protect-ai-fair-and | 2007.09370 | null | https://arxiv.org/abs/2007.09370v1 | https://arxiv.org/pdf/2007.09370v1.pdf | How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning | This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens and local credibility to ensure fairness, in combination with differential privacy to guarantee privacy. In particular, we build a fair and d... | ['Xingjun Ma', 'Karthik Nandakumar', 'Yitong Li', 'Lingjuan Lyu', 'Jiangshan Yu'] | 2020-07-18 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-6.75514460e-01 9.75355580e-02 1.62090644e-01 -4.97168273e-01
-9.70027685e-01 -8.54084551e-01 7.55528986e-01 -5.69888838e-02
-6.80865347e-01 1.10151517e+00 -5.44801680e-03 -8.37807506e-02
1.91620708e-01 -9.86193895e-01 -4.88986164e-01 -1.31520867e+00
-6.88822940e-02 2.84715533e-01 -3.68487298e-01 2.57555634... | [5.831381320953369, 6.6589531898498535] |
96b5a6eb-cb92-4a31-8717-f471b5bde712 | interpretative-computer-aided-lung-cancer | 2102.10919 | null | https://arxiv.org/abs/2102.10919v1 | https://arxiv.org/pdf/2102.10919v1.pdf | Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation | Background and Objective:Computer-aided diagnosis (CAD) systems promote diagnosis effectiveness and alleviate pressure of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisf... | ['Liqin Huang', 'Bin Zheng', 'Lin Pan', 'Jiepeng Zheng', 'Haojin Lin', 'Wangbin Ding', 'Chenhao Peia', 'Zhiqiang Shen', 'Shaohua Zheng'] | 2021-02-22 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-1.13681471e-02 4.32730436e-01 -5.26246250e-01 -1.94878906e-01
-6.44725919e-01 -3.14159930e-01 3.33996445e-01 -9.18962508e-02
-7.73990527e-02 4.07644928e-01 2.09096044e-01 -6.96976364e-01
-4.53577757e-01 -7.73392737e-01 -2.70742714e-01 -8.34313095e-01
-2.72706635e-02 6.80750728e-01 4.82622944e-02 3.21161598... | [15.377738952636719, -2.164783239364624] |
dc9553ef-ecaa-49cf-88bb-af1bf8bd3a82 | twitter-sentiment-analysis-1 | null | null | https://www.researchgate.net/publication/352780855_Twitter_Sentiment_Analysis | https://www.researchgate.net/publication/352780855_Twitter_Sentiment_Analysis | Twitter Sentiment Analysis | In this report, address the problem of sentiment classification on the Twitter dataset. used a number of
machine learning and deep learning methods to perform sentiment analysis. In the end, used a majority
vote ensemble method with 5 of our best models to achieve the classification accuracy of 83.58% on
Kaggle publ... | ['Vedurumudi Priyanka'] | 2021-06-13 | null | null | null | 2021-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-4.79255468e-01 -1.23356417e-01 -3.30431402e-01 -7.92189121e-01
-5.55920124e-01 -5.06678104e-01 7.77975738e-01 2.48463258e-01
-3.96068394e-01 9.00754392e-01 3.77145618e-01 -4.81519401e-01
1.84416771e-01 -8.96155834e-01 -1.40694931e-01 -5.78918338e-01
2.36840516e-01 3.94279689e-01 -5.29865623e-01 -1.17410457... | [11.176702499389648, 7.010078430175781] |
c5a006de-1885-4938-b89e-ff10b264f7bc | -rank-multi-agent-evaluation-by-evolution | 1903.01373 | null | http://arxiv.org/abs/1903.01373v1 | http://arxiv.org/pdf/1903.01373v1.pdf | α-Rank: Multi-Agent Evaluation by Evolution | We introduce {\alpha}-Rank, a principled evolutionary dynamics methodology,
for the evaluation and ranking of agents in large-scale multi-agent
interactions, grounded in a novel dynamical game-theoretic solution concept
called Markov-Conley chains (MCCs). The approach leverages continuous-time and
discrete-time evoluti... | ['Marc Lanctot', 'Jean-Baptiste Lespiau', 'Christos Papadimitriou', 'Shayegan Omidshafiei', 'Wojciech M. Czarnecki', 'Mark Rowland', 'Karl Tuyls', 'Julien Perolat', 'Georgios Piliouras', 'Remi Munos'] | 2019-03-04 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [-3.10568690e-01 -3.83707620e-02 1.44585013e-01 7.26666451e-01
5.09028099e-02 -1.00954235e+00 7.56333172e-01 -2.31089629e-02
-4.10206318e-01 6.13159478e-01 -8.50249529e-02 -4.38982546e-01
-9.76586699e-01 -9.02093709e-01 -1.75039783e-01 -8.96774590e-01
-7.91488051e-01 6.37499392e-01 3.77781302e-01 -8.30831289... | [4.291111946105957, 2.8157079219818115] |
03a1e61f-316b-4d02-ae2b-115b4ebf95a1 | contrast-enhancement-estimation-for-digital | 1706.03875 | null | http://arxiv.org/abs/1706.03875v1 | http://arxiv.org/pdf/1706.03875v1.pdf | Contrast Enhancement Estimation for Digital Image Forensics | Inconsistency in contrast enhancement can be used to expose image forgeries.
In this work, we describe a new method to estimate contrast enhancement from a
single image. Our method takes advantage of the nature of contrast enhancement
as a mapping between pixel values, and the distinct characteristics it
introduces to ... | ['Longyin Wen', 'Siwei Lyu', 'Honggang Qi'] | 2017-06-13 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 7.75313497e-01 -5.93594134e-01 2.87616462e-01 2.07548775e-02
-6.72993422e-01 -5.31683147e-01 1.88159645e-01 -1.01383671e-01
-3.16174835e-01 5.21013200e-01 -1.56164989e-01 -2.55356580e-01
1.97151676e-01 -8.02842021e-01 -5.64902425e-01 -8.08995664e-01
-2.52689552e-02 -7.53027499e-01 7.27374911e-01 -4.01949942... | [11.049661636352539, -2.2464048862457275] |
840510f5-5ebf-455c-a123-fcbadf7980dc | quantitative-planning-with-action-deception | 2301.01349 | null | https://arxiv.org/abs/2301.01349v2 | https://arxiv.org/pdf/2301.01349v2.pdf | Quantitative Planning with Action Deception in Concurrent Stochastic Games | We study a class of two-player competitive concurrent stochastic games on graphs with reachability objectives. Specifically, player 1 aims to reach a subset $F_1$ of game states, and player 2 aims to reach a subset $F_2$ of game states where $F_2\cap F_1=\emptyset$. Both players aim to satisfy their reachability object... | ['Jie Fu', 'Shuo Han', 'Chongyang Shi'] | 2023-01-03 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 2.90178210e-01 1.06742096e+00 -1.55189773e-02 3.96975994e-01
-3.73649746e-01 -7.62812555e-01 1.47836417e-01 -1.44525185e-01
-5.56381285e-01 6.83059394e-01 -2.74860948e-01 -2.89371967e-01
-2.79423684e-01 -1.20581758e+00 -3.49472165e-01 -7.63340235e-01
-3.51676881e-01 8.13291311e-01 3.11624080e-01 -4.81823921... | [4.262389659881592, 2.1222853660583496] |
1d52e55c-f0ea-4984-9193-edf43f935647 | le2fusion-a-novel-local-edge-enhancement | 2305.17374 | null | https://arxiv.org/abs/2305.17374v1 | https://arxiv.org/pdf/2305.17374v1.pdf | LE2Fusion: A novel local edge enhancement module for infrared and visible image fusion | Infrared and visible image fusion task aims to generate a fused image which contains salient features and rich texture details from multi-source images. However, under complex illumination conditions, few algorithms pay attention to the edge information of local regions which is crucial for downstream tasks. To this en... | ['Xiaoning Song', 'Chunyang Cheng', 'Hui Li', 'Yongbiao Xiao'] | 2023-05-27 | null | null | null | null | ['image-reconstruction', 'infrared-and-visible-image-fusion'] | ['computer-vision', 'computer-vision'] | [ 3.21714699e-01 -6.15238369e-01 1.34365082e-01 -3.37721914e-01
-7.93865979e-01 -7.15965107e-02 3.34209919e-01 -2.44267713e-02
-2.70076662e-01 6.35953724e-01 5.56570530e-01 1.27732217e-01
-1.90499678e-01 -8.57719898e-01 -4.42828745e-01 -1.22295380e+00
5.08574724e-01 -7.71752656e-01 2.69371215e-02 -3.67099494... | [10.540799140930176, -1.868693232536316] |
15f98a8f-d372-4762-8948-94fa0dbefedf | how-drones-look-crowdsourced-knowledge | 1811.05625 | null | https://arxiv.org/abs/1811.05625v2 | https://arxiv.org/pdf/1811.05625v2.pdf | Model-guided Multi-path Knowledge Aggregation for Aerial Saliency Prediction | As an emerging vision platform, a drone can look from many abnormal viewpoints which brings many new challenges into the classic vision task of video saliency prediction. To investigate these challenges, this paper proposes a large-scale video dataset for aerial saliency prediction, which consists of ground-truth salie... | ['Kui Fu', 'Yonghong Tian', 'Hongze Shen', 'Yu Zhang', 'Jia Li'] | 2018-11-14 | null | null | null | null | ['aerial-video-saliency-prediction'] | ['computer-vision'] | [ 3.50446880e-01 -9.30309221e-02 -3.81445676e-01 -2.19109040e-02
-6.20344616e-02 -2.46774852e-01 3.35696161e-01 -1.69107392e-01
-2.51994412e-02 6.22046530e-01 3.50075454e-01 7.29577839e-02
-1.30858183e-01 -4.26960826e-01 -7.64885008e-01 -3.91718298e-01
-1.05506398e-01 -4.02979612e-01 1.16738403e+00 -5.14083862... | [9.71925163269043, -0.30629274249076843] |
109fbe36-f388-4a51-89d9-9db3250a74f6 | evaluating-surgical-skills-from-kinematic | 1806.02750 | null | http://arxiv.org/abs/1806.02750v1 | http://arxiv.org/pdf/1806.02750v1.pdf | Evaluating surgical skills from kinematic data using convolutional neural networks | The need for automatic surgical skills assessment is increasing, especially
because manual feedback from senior surgeons observing junior surgeons is prone
to subjectivity and time consuming. Thus, automating surgical skills evaluation
is a very important step towards improving surgical practice. In this paper, we
desi... | ['Pierre-Alain Muller', 'Lhassane Idoumghar', 'Germain Forestier', 'Jonathan Weber', 'Hassan Ismail Fawaz'] | 2018-06-07 | null | null | null | null | ['skills-evaluation', 'skills-assessment', 'surgical-skills-evaluation'] | ['computer-vision', 'computer-vision', 'medical'] | [ 6.91622868e-02 4.73142356e-01 -1.89675331e-01 -3.65940481e-01
-2.41602898e-01 -6.47995770e-01 1.83822557e-01 3.33614081e-01
-7.63534367e-01 4.59155798e-01 4.02511537e-01 -6.40725434e-01
-4.92797405e-01 -4.86333311e-01 -4.84243542e-01 -5.14974117e-01
-4.63085175e-02 9.15646553e-02 -6.90422282e-02 -2.65818030... | [14.082133293151855, -3.3635177612304688] |
16ca2721-15d5-42fc-8a27-fadfb22af641 | vl-beit-generative-vision-language | 2206.01127 | null | https://arxiv.org/abs/2206.01127v2 | https://arxiv.org/pdf/2206.01127v2.pdf | VL-BEiT: Generative Vision-Language Pretraining | We introduce a vision-language foundation model called VL-BEiT, which is a bidirectional multimodal Transformer learned by generative pretraining. Our minimalist solution conducts masked prediction on both monomodal and multimodal data with a shared Transformer. Specifically, we perform masked vision-language modeling ... | ['Furu Wei', 'Li Dong', 'Wenhui Wang', 'Hangbo Bao'] | 2022-06-02 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.74264365e-01 4.36514556e-01 -4.51677382e-01 -6.63908780e-01
-1.15117836e+00 -4.46992725e-01 8.26749682e-01 -3.32445145e-01
-3.57968599e-01 7.55739287e-02 1.32655144e-01 -4.58311230e-01
4.45357949e-01 -5.43228447e-01 -1.21652603e+00 -4.28221762e-01
4.92065907e-01 7.73648143e-01 1.65028349e-02 3.58358286... | [10.825321197509766, 1.646726369857788] |
4c1581c1-fbe3-47b2-bb24-4d8b7232dbcd | feature-replacement-and-combination-for | 2104.04298 | null | https://arxiv.org/abs/2104.04298v3 | https://arxiv.org/pdf/2104.04298v3.pdf | On Architectures and Training for Raw Waveform Feature Extraction in ASR | With the success of neural network based modeling in automatic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly based on the raw waveform. Recently, one line of research has focused on unsupervised pre-training of feature extractors on audio-only data to ... | ['Hermann Ney', 'Ralf Schlüter', 'Wilfried Michel', 'Christoph Lüscher', 'Peter Vieting'] | 2021-04-09 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 4.73338783e-01 9.76738259e-02 4.81326848e-01 -4.26717401e-01
-8.97794425e-01 -6.26003981e-01 7.47897804e-01 1.21307917e-01
-7.34076202e-01 1.82469115e-01 4.73554969e-01 -5.80187261e-01
-2.77824290e-02 -3.95314336e-01 -4.25684452e-01 -5.73419690e-01
2.32928693e-02 1.48171380e-01 -3.85766141e-02 -4.45823431... | [14.537304878234863, 6.439875602722168] |
b0f197dd-d048-45b6-9b07-816bc2b2fd0a | pgganet-pose-guided-graph-attention-network | 2111.14411 | null | https://arxiv.org/abs/2111.14411v2 | https://arxiv.org/pdf/2111.14411v2.pdf | PGGANet: Pose Guided Graph Attention Network for Person Re-identification | Person re-identification (reID) aims at retrieving a person from images captured by different cameras. For deep-learning-based reID methods, it has been proved that using local features together with global feature could help to give robust representation for person retrieval. Human pose information could provide the l... | ['Wenquan Feng', 'Hongbo Zhao', 'Zhijun He'] | 2021-11-29 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [-3.57875854e-01 -2.45417252e-01 -1.50126992e-02 -4.70520884e-01
-5.35472333e-01 -3.53013694e-01 6.36017442e-01 -1.04935981e-01
-6.50999904e-01 5.00694752e-01 4.33715075e-01 3.34807813e-01
-2.57659107e-01 -7.59065211e-01 -5.97086608e-01 -6.49374366e-01
-1.00659326e-01 5.86975813e-01 1.18947551e-01 -1.54650509... | [14.690853118896484, 0.9224057793617249] |
6b83d6b9-ad5e-4464-a268-884124f5bccc | uavstereo-a-multiple-resolution-dataset-for | 2302.10082 | null | https://arxiv.org/abs/2302.10082v1 | https://arxiv.org/pdf/2302.10082v1.pdf | UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV Scenarios | Stereo matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, such as KITTI and Scene Flow. UAVs (Unmanned Aerial Vehicles) are commonly utilized for surface observation, and their captured images are frequently used for detail... | ['Quan Yujun', 'Li Zhenqi', 'Yu Wenshuai', 'Yu Anzhu', 'Cao Xuefeng', 'Zhang Xiaoyi'] | 2023-02-20 | null | null | null | null | ['3d-scene-reconstruction', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 1.27044395e-01 -5.19398808e-01 1.21177696e-02 -4.31476831e-01
-4.75233465e-01 -6.04625583e-01 4.41092432e-01 -2.12080956e-01
-2.52162993e-01 5.61426461e-01 -2.60919392e-01 -2.21013397e-01
-9.87794548e-02 -1.25358891e+00 -9.86855507e-01 -3.64048898e-01
-7.27246404e-02 4.41168696e-01 1.37160495e-01 -5.58991909... | [8.645267486572266, -2.409778594970703] |
abc8ef61-f74c-41ac-85a3-4f077c144385 | how-can-voting-mechanisms-improve-the | 2209.08286 | null | https://arxiv.org/abs/2209.08286v1 | https://arxiv.org/pdf/2209.08286v1.pdf | How can voting mechanisms improve the robustness and generalizability of toponym disambiguation? | A vast amount of geographic information exists in natural language texts, such as tweets and news. Extracting geographic information from texts is called Geoparsing, which includes two subtasks: toponym recognition and toponym disambiguation, i.e., to identify the geospatial representations of toponyms. This paper focu... | ['Hongchao Fan', 'Friederike Klan', 'Zhiyong Zhou', 'Jens Kersten', 'Yeran Sun', 'Xuke Hu'] | 2022-09-17 | null | null | null | null | ['toponym-resolution'] | ['natural-language-processing'] | [-8.04873586e-01 -4.28876579e-01 -2.13161588e-01 -1.61470950e-01
-6.52197480e-01 -6.13185823e-01 1.02838743e+00 3.71715456e-01
-7.73762047e-01 9.45026100e-01 5.50013244e-01 -2.78578158e-02
-2.66424179e-01 -1.24046254e+00 -1.75805986e-01 -6.76796615e-01
-3.61843705e-02 3.78147602e-01 2.76700407e-01 -4.55863237... | [9.399480819702148, 9.097320556640625] |
88dfbe18-7078-4dca-bbcb-4b35c4a87aae | vivesdebate-speech-a-corpus-of-spoken | 2302.12584 | null | https://arxiv.org/abs/2302.12584v1 | https://arxiv.org/pdf/2302.12584v1.pdf | VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining | In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks. The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly a... | ['Javier Iranzo-Sánchez', 'Ramon Ruiz-Dolz'] | 2023-02-24 | null | null | null | null | ['argument-mining'] | ['natural-language-processing'] | [ 2.23222539e-01 7.72922099e-01 -6.53105751e-02 -4.66885448e-01
-1.01434517e+00 -5.45802832e-01 1.19592845e+00 7.72266567e-01
-4.92310107e-01 5.39800286e-01 8.35301399e-01 -3.94375682e-01
-1.78244784e-01 -6.52756751e-01 -4.82768238e-01 -2.24673077e-01
-1.95016578e-01 5.48556805e-01 3.46380204e-01 -6.86077237... | [10.05628490447998, 9.428838729858398] |
e054f8fe-66dd-4349-bad3-9a488bc93185 | uncovering-surprising-event-boundaries-in-1 | null | null | https://aclanthology.org/2022.wnu-1.1 | https://aclanthology.org/2022.wnu-1.1.pdf | Uncovering Surprising Event Boundaries in Narratives | It is important to define meaningful and interpretable automatic evaluation metrics for open-domain dialog research. Standard language generation metrics have been shown to be ineffective for dialog. This paper introduces the FED metric (fine-grained evaluation of dialog), an automatic evaluation metric which uses Dial... | ['Maarten Sap', 'Anna Jafarpour', 'Zhilin Wang'] | null | null | null | null | naacl-wnu-2022-7 | ['open-domain-dialog'] | ['natural-language-processing'] | [-3.31470490e-01 7.18336344e-01 1.29960254e-01 -8.96235943e-01
-8.08849633e-01 -1.10369408e+00 1.18011677e+00 1.96025535e-01
-3.89103144e-01 1.27098978e+00 7.90455401e-01 -4.21221882e-01
-2.04656705e-01 -5.39767027e-01 4.96700495e-01 -8.60893801e-02
3.63096058e-01 1.20496070e+00 1.98231548e-01 -8.37927580... | [12.889827728271484, 8.046541213989258] |
e77cff33-161f-44a4-a16f-c5cfeba9352c | deep-learning-algorithms-with-applications-to | 1512.03131 | null | http://arxiv.org/abs/1512.03131v1 | http://arxiv.org/pdf/1512.03131v1.pdf | Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey | Deep learning has recently achieved very promising results in a wide range of
areas such as computer vision, speech recognition and natural language
processing. It aims to learn hierarchical representations of data by using deep
architecture models. In a smart city, a lot of data (e.g. videos captured from
many distrib... | ['Li Wang', 'Dennis Sng'] | 2015-12-10 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [-1.61140133e-02 -1.61423534e-01 -9.79210585e-02 -6.18529975e-01
-1.33049309e-01 -1.01486556e-01 7.93690622e-01 1.68690056e-01
1.60904452e-02 3.01528573e-01 2.73942977e-01 -6.94628879e-02
-3.37505378e-02 -9.47124422e-01 -4.03867364e-01 -7.61280954e-01
6.84476420e-02 4.70899284e-01 9.32549834e-02 1.22419447... | [9.600388526916504, 1.7844871282577515] |
470b734c-4cbe-4a7a-be92-579f7147db64 | fast-and-lightweight-scene-regressor-for | 2212.01830 | null | https://arxiv.org/abs/2212.01830v1 | https://arxiv.org/pdf/2212.01830v1.pdf | Fast and Lightweight Scene Regressor for Camera Relocalization | Camera relocalization involving a prior 3D reconstruction plays a crucial role in many mixed reality and robotics applications. Estimating the camera pose directly with respect to pre-built 3D models can be prohibitively expensive for several applications with limited storage and/or communication bandwidth. Although re... | ['Joo-Ho Lee', 'Dinh-Tuan Tran', 'Thuan B. Bui'] | 2022-12-04 | null | null | null | null | ['camera-localization', 'camera-relocalization', 'mixed-reality'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.69009734e-02 -4.40421492e-01 -3.18583757e-01 -5.09639680e-01
-7.56008446e-01 -3.66381466e-01 3.94263029e-01 1.44575253e-01
-6.86239541e-01 5.00033200e-01 -2.11443841e-01 5.72539829e-02
-1.25076815e-01 -7.23624051e-01 -1.02791858e+00 -6.56416833e-01
3.00569564e-01 4.37326282e-01 2.51081020e-01 7.08112642... | [7.689329147338867, -2.144561767578125] |
2b3eb437-b85e-4be7-8955-a7fd5bb91954 | hang-time-har-a-benchmark-dataset-for | 2305.13124 | null | https://arxiv.org/abs/2305.13124v1 | https://arxiv.org/pdf/2305.13124v1.pdf | Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-worn Inertial Sensors | We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport... | ['Qin Lv', 'Kristof Van Laerhoven', 'Marius Bock', 'Julia Lee Romero', 'Alexander Hoelzemann'] | 2023-05-22 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 4.01735157e-02 -2.43795365e-01 -5.76232374e-01 -6.60341531e-02
-4.29841191e-01 -2.91465014e-01 3.41100067e-01 1.68733940e-01
-7.94296324e-01 5.50214231e-01 7.74465263e-01 -4.62115742e-02
-4.02188063e-01 -8.13663542e-01 -4.07221377e-01 -3.65616947e-01
-3.95170718e-01 2.57900566e-01 -5.49087189e-02 -5.05135655... | [7.158228874206543, 0.46309521794319153] |
f53a877b-bd73-4904-852d-366e69497d93 | oid-outlier-identifying-and-discarding-in | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5134_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700596.pdf | OID: Outlier Identifying and Discarding in Blind Image Deblurring | Blind deblurring methods are sensitive to outliers, such as saturated pixels and non-Gaussian noise. Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers are not conforming to the linear formation of the blurring process. Prior arts develop sophisticate... | ['Guixu Zhang', 'Faming Fang', 'Liang Chen', 'Jun Liu', 'Jiawei Zhang'] | null | null | null | null | eccv-2020-8 | ['blind-image-deblurring'] | ['computer-vision'] | [ 8.17272812e-02 -4.92221564e-01 3.42240095e-01 1.41686708e-01
-4.53551114e-01 -3.24472070e-01 6.10798955e-01 -9.64125842e-02
-2.13155523e-01 7.46760964e-01 5.98303080e-01 9.96725187e-02
-1.79932147e-01 -2.95136154e-01 -4.94681865e-01 -8.59504521e-01
-1.29753605e-01 -1.64807841e-01 2.05656692e-01 2.82796890... | [11.586555480957031, -2.7259035110473633] |
8c3ca26f-494b-4469-bb43-053053f8c85b | molxpt-wrapping-molecules-with-text-for | 2305.10688 | null | https://arxiv.org/abs/2305.10688v2 | https://arxiv.org/pdf/2305.10688v2.pdf | MolXPT: Wrapping Molecules with Text for Generative Pre-training | Generative pre-trained Transformer (GPT) has demonstrates its great success in natural language processing and related techniques have been adapted into molecular modeling. Considering that text is the most important record for scientific discovery, in this paper, we propose MolXPT, a unified language model of text and... | ['Tie-Yan Liu', 'Ming Zhang', 'Tao Qin', 'Shufang Xie', 'Lijun Wu', 'Yingce Xia', 'Wei zhang', 'Zequn Liu'] | 2023-05-18 | null | null | null | null | ['molecule-captioning', 'text-based-de-novo-molecule-generation', 'molecular-property-prediction'] | ['medical', 'medical', 'miscellaneous'] | [ 7.92109549e-01 2.81059474e-01 -5.74964941e-01 -2.16338769e-01
-8.57055306e-01 -7.03753710e-01 8.70284796e-01 4.67203021e-01
-2.75249124e-01 1.53424335e+00 3.85164768e-01 -6.44180655e-01
5.00069022e-01 -8.92632365e-01 -1.31015861e+00 -7.36607194e-01
1.31711438e-01 7.62098372e-01 -1.52249187e-01 -1.13031425... | [4.844193935394287, 5.9395670890808105] |
0d095300-65c3-4428-a813-84b8d00cb7b9 | text-flow-a-unified-text-detection-system-in | 1604.06877 | null | http://arxiv.org/abs/1604.06877v1 | http://arxiv.org/pdf/1604.06877v1.pdf | Text Flow: A Unified Text Detection System in Natural Scene Images | The prevalent scene text detection approach follows four sequential steps
comprising character candidate detection, false character candidate removal,
text line extraction, and text line verification. However, errors occur and
accumulate throughout each of these sequential steps which often lead to low
detection perfor... | ['Shangxuan Tian', 'Chang Huang', 'Yifeng Pan', 'Shijian Lu', 'Kai Yu', 'Chew Lim Tan'] | 2016-04-23 | text-flow-a-unified-text-detection-system-in-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Tian_Text_Flow_A_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Tian_Text_Flow_A_ICCV_2015_paper.pdf | iccv-2015-12 | ['text-line-extraction'] | ['computer-vision'] | [ 2.76926428e-01 -6.92601562e-01 -5.28443605e-02 -1.28158942e-01
-7.38864660e-01 -4.10139233e-01 6.92988992e-01 6.15900576e-01
-7.29727983e-01 4.87187386e-01 7.64999837e-02 -1.92576662e-01
2.56009012e-01 -7.42825329e-01 -3.96734774e-01 -3.05522382e-01
6.14236057e-01 3.13742667e-01 8.00204396e-01 6.43250253... | [12.007058143615723, 2.359429359436035] |
8f53e823-4d62-47cc-800f-613d79b4e8df | adaptive-strategies-in-non-convex | 2306.10278 | null | https://arxiv.org/abs/2306.10278v2 | https://arxiv.org/pdf/2306.10278v2.pdf | Adaptive Strategies in Non-convex Optimization | An algorithm is said to be adaptive to a certain parameter (of the problem) if it does not need a priori knowledge of such a parameter but performs competitively to those that know it. This dissertation presents our work on adaptive algorithms in following scenarios: 1. In the stochastic optimization setting, we only r... | ['Zhenxun Zhuang'] | 2023-06-17 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-5.61444350e-02 -1.27841964e-01 1.96688343e-02 -4.90366995e-01
-6.08075976e-01 -5.64871550e-01 2.68470228e-01 -2.50933729e-02
-7.37909198e-01 7.89805532e-01 -3.26337479e-02 -1.91520482e-01
-4.07655180e-01 -6.52760983e-01 -9.18114245e-01 -1.05275500e+00
-4.80054319e-02 3.86291593e-01 6.53624684e-02 -3.07556063... | [7.238790035247803, 4.109760761260986] |
d3564701-4419-4732-b02d-5a391ab87528 | kanglish-alli-names-named-entity-recognition | null | null | https://aclanthology.org/2022.wnut-1.17 | https://aclanthology.org/2022.wnut-1.17.pdf | “Kanglish alli names!” Named Entity Recognition for Kannada-English Code-Mixed Social Media Data | Code-mixing (CM) is a frequently observed phenomenon on social media platforms in multilingual societies such as India. While the increase in code-mixed content on these platforms provides good amount of data for studying various aspects of code-mixing, the lack of automated text analysis tools makes such studies diffi... | ['Manish Shrivastava', 'Sumukh S'] | null | null | null | null | coling-wnut-2022-10 | ['semantic-role-labeling'] | ['natural-language-processing'] | [-2.11552888e-01 1.00419492e-01 -3.61463606e-01 -2.45812967e-01
-9.94599283e-01 -7.32936442e-01 3.83380949e-01 7.45609522e-01
-6.82377160e-01 8.75096977e-01 5.49992442e-01 -6.07594013e-01
2.67758220e-01 -5.45512319e-01 -3.85085315e-01 -2.75251418e-01
1.38955312e-02 2.82662272e-01 9.89760235e-02 -3.31466317... | [9.842124938964844, 10.01363754272461] |
5973a109-6d68-4ee4-bc7a-25b4ee690d76 | multiple-instance-learning-with-center | null | null | https://link.springer.com/chapter/10.1007/978-3-030-59722-1_50 | https://link.springer.com/chapter/10.1007/978-3-030-59722-1_50 | Multiple Instance Learning with Center Embeddings for Histopathology Classification | Histopathology image analysis plays an important role in the treatment and diagnosis of cancer. However, analysis of whole slide images (WSI) with deep learning is challenging given that the curation of pixel-level annotations is laborious and time consuming. To address this, recent methods have considered WSI classifi... | ['Sang Hyun Park', 'Heounjeong Go', 'Soo Jeong Nam', 'Meejeong Kim', 'Philip Chikontwe'] | 2020-09-29 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 5.21163046e-01 -3.61962467e-02 -4.51392382e-01 -6.57108247e-01
-1.51226151e+00 -3.51655483e-01 4.21522200e-01 8.00354302e-01
-4.62010413e-01 7.32905209e-01 -8.70598853e-03 -1.28696278e-01
-4.74647909e-01 -7.43118227e-01 -6.89882576e-01 -1.27748597e+00
1.28162831e-01 2.29262888e-01 1.00345746e-01 3.96184623... | [15.099150657653809, -2.841254472732544] |
39ff9013-021e-46b4-8bae-da19d2c45c19 | dehazenerf-multiple-image-haze-removal-and-3d | 2303.11364 | null | https://arxiv.org/abs/2303.11364v1 | https://arxiv.org/pdf/2303.11364v1.pdf | DehazeNeRF: Multiple Image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields | Neural radiance fields (NeRFs) have demonstrated state-of-the-art performance for 3D computer vision tasks, including novel view synthesis and 3D shape reconstruction. However, these methods fail in adverse weather conditions. To address this challenge, we introduce DehazeNeRF as a framework that robustly operates in h... | ['Gordon Wetzstein', 'Sy-Yen Kuo', 'Wang Yifan', 'Wei-Ting Chen'] | 2023-03-20 | null | null | null | null | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 1.83037758e-01 -3.86411250e-01 6.25741839e-01 -4.11545306e-01
-5.46044827e-01 -5.11775970e-01 6.77377701e-01 -3.86053085e-01
-3.30032632e-02 4.28801507e-01 2.29532436e-01 -3.60330343e-01
-1.65407248e-02 -8.89631808e-01 -6.74923956e-01 -1.04906833e+00
1.79835722e-01 -1.69279471e-01 -6.13519782e-03 -5.52199304... | [10.793577194213867, -3.1478397846221924] |
93c64078-3fce-49ad-a396-8145b2cb1412 | 3d-queryis-a-query-based-framework-for-3d | 2211.09375 | null | https://arxiv.org/abs/2211.09375v1 | https://arxiv.org/pdf/2211.09375v1.pdf | 3D-QueryIS: A Query-based Framework for 3D Instance Segmentation | Previous top-performing methods for 3D instance segmentation often maintain inter-task dependencies and the tendency towards a lack of robustness. Besides, inevitable variations of different datasets make these methods become particularly sensitive to hyper-parameter values and manifest poor generalization capability. ... | ['Wanli Ouyang', 'Ke Xu', 'Hongcheng Guo', 'Junran Wu', 'Jiayi Tian', 'Rui Su', 'Honghui Yang', 'Tong He', 'Jiaheng Liu'] | 2022-11-17 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.06688857e-01 -1.06496908e-01 -2.29756221e-01 -6.30468249e-01
-1.11969602e+00 -4.74121809e-01 6.68153286e-01 1.76783264e-01
-2.61760682e-01 3.38677913e-01 -4.36645180e-01 6.91426992e-02
-2.60442644e-01 -6.56435847e-01 -8.42166543e-01 -6.87152207e-01
3.50596070e-01 6.77332044e-01 5.74473619e-01 2.22256690... | [8.004589080810547, -3.230607748031616] |
7a7baf5a-6e2d-4ff0-9948-6e23e39ca25d | msr-vtt-a-large-video-description-dataset-for | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Xu_MSR-VTT_A_Large_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Xu_MSR-VTT_A_Large_CVPR_2016_paper.pdf | MSR-VTT: A Large Video Description Dataset for Bridging Video and Language | While there has been increasing interest in the task of describing video with natural language, current computer vision algorithms are still severely limited in terms of the variability and complexity of the videos and their associated language that they can recognize. This is in part due to the simplicity of current ... | ['Ting Yao', 'Yong Rui', 'Jun Xu', 'Tao Mei'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['video-description'] | ['computer-vision'] | [ 4.73879814e-01 -5.67336380e-01 -5.63627422e-01 -3.29401821e-01
-1.08049500e+00 -6.31235957e-01 5.97529888e-01 -3.00606102e-01
-3.73986512e-01 6.75895989e-01 5.80967784e-01 -1.04831597e-02
2.46417731e-01 -1.87963217e-01 -1.05443716e+00 -4.44647729e-01
3.68647575e-02 3.58477682e-01 1.99781775e-01 -1.97319761... | [10.48726749420166, 0.8566604256629944] |
f8d7ef97-56d4-4ff3-bf21-d2131055d618 | document-level-event-role-filler-extraction | 2005.06579 | null | https://arxiv.org/abs/2005.06579v1 | https://arxiv.org/pdf/2005.06579v1.pdf | Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding | Few works in the literature of event extraction have gone beyond individual sentences to make extraction decisions. This is problematic when the information needed to recognize an event argument is spread across multiple sentences. We argue that document-level event extraction is a difficult task since it requires a vi... | ['Xinya Du', 'Claire Cardie'] | 2020-05-13 | document-level-event-role-filler-extraction-1 | https://aclanthology.org/2020.acl-main.714 | https://aclanthology.org/2020.acl-main.714.pdf | acl-2020-6 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 6.43974364e-01 1.73164174e-01 -4.04113203e-01 -3.68107021e-01
-1.43852198e+00 -1.02486575e+00 8.27735305e-01 8.36428165e-01
-8.15523922e-01 9.42559004e-01 1.02452278e+00 -4.97752517e-01
1.06583290e-01 -8.70705307e-01 -8.13256562e-01 -2.21912637e-01
-1.15618696e-02 2.81266659e-01 3.44677597e-01 -2.70509608... | [9.055071830749512, 9.278282165527344] |
c7cd31ca-a4d8-4e86-a731-f0bb1a5caaa8 | structural-health-monitoring-of-cantilever | 1908.06326 | null | https://arxiv.org/abs/1908.06326v1 | https://arxiv.org/pdf/1908.06326v1.pdf | Structural Health Monitoring of Cantilever Beam, a Case Study -- Using Bayesian Neural Network AND Deep Learning | The advancement of machine learning algorithms has opened a wide scope for vibration-based SHM (Structural Health Monitoring). Vibration-based SHM is based on the fact that damage will alter the dynamic properties viz., structural response, frequencies, mode shapes, etc of the structure. The responses measured using se... | ['T. Sundararajan', 'D. Mohankumar', 'S. Sumitra', 'H. Viji', 'Rahul Vashisht'] | 2019-08-17 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [ 2.76619226e-01 -3.97148961e-03 4.13065463e-01 -1.35144293e-01
-4.80461597e-01 6.08381890e-02 7.57622421e-02 1.79746404e-01
-1.34574130e-01 6.36581957e-01 4.03389573e-01 -2.27816999e-02
-5.72936654e-01 -1.18857288e+00 -7.06656635e-01 -1.00796568e+00
-4.21305299e-01 2.77816981e-01 3.29768091e-01 -4.54493225... | [6.698957920074463, 2.4647583961486816] |
f72f19a5-608e-4f83-bfef-a385b97b5a1a | dataset-of-fake-news-detection-and-fact | 2111.03299 | null | https://arxiv.org/abs/2111.03299v1 | https://arxiv.org/pdf/2111.03299v1.pdf | Dataset of Fake News Detection and Fact Verification: A Survey | The rapid increase in fake news, which causes significant damage to society, triggers many fake news related studies, including the development of fake news detection and fact verification techniques. The resources for these studies are mainly available as public datasets taken from Web data. We surveyed 118 datasets r... | ['Taichi Murayama'] | 2021-11-05 | null | null | null | null | ['satire-detection'] | ['natural-language-processing'] | [-2.55444407e-01 -5.06718755e-02 -8.57129097e-01 -8.00043046e-02
-2.18364581e-01 -7.93757617e-01 1.01664400e+00 4.72647339e-01
3.71016413e-02 1.01432085e+00 5.89991570e-01 -3.33869725e-01
4.35831547e-01 -1.05814290e+00 -7.07066298e-01 -4.14789945e-01
4.89043891e-01 2.38460004e-01 2.83890158e-01 -6.91281140... | [8.137275695800781, 10.265785217285156] |
beeb7395-ff5a-4017-93d5-ac29e911df7a | unclonability-and-quantum-cryptanalysis-from | 2210.17545 | null | https://arxiv.org/abs/2210.17545v1 | https://arxiv.org/pdf/2210.17545v1.pdf | Unclonability and Quantum Cryptanalysis: From Foundations to Applications | The impossibility of creating perfect identical copies of unknown quantum systems is a fundamental concept in quantum theory and one of the main non-classical properties of quantum information. This limitation imposed by quantum mechanics, famously known as the no-cloning theorem, has played a central role in quantum c... | ['Mina Doosti'] | 2022-10-31 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 3.49910915e-01 -9.82381925e-02 8.84590894e-02 -4.93625225e-03
-5.52171290e-01 -7.87908018e-01 3.37826252e-01 1.26656502e-01
-5.00917315e-01 7.35757113e-01 -3.98426294e-01 -9.39533591e-01
-3.24552983e-01 -1.29946005e+00 -7.41487205e-01 -1.02968478e+00
-2.46613115e-01 1.49512663e-01 -1.89533889e-01 -7.16298342... | [5.5786943435668945, 4.975925445556641] |
d96fa9a5-62ec-4527-b27f-b55c430a60a8 | building-concise-logical-patterns-by | 2301.08190 | null | https://arxiv.org/abs/2301.08190v1 | https://arxiv.org/pdf/2301.08190v1.pdf | Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size | Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they ... | ['Xuan Zhang', 'Svein Anders Tunheim', 'Jivitesh Sharma', 'Rupsa Saha', 'Lei Jiao', 'Ole-Christoffer Granmo', 'Sondre Glimsdal', 'Charul Giri', 'Bimal Bhattarai', 'Ahmed Abdulrahem Othman Abouzeid', 'K. Darshana Abeyrathna'] | 2023-01-19 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 1.11068767e-02 2.09138751e-01 -6.78952634e-01 -4.13870275e-01
-8.72639954e-01 -5.36263227e-01 3.56088877e-02 5.04213214e-01
-3.60257268e-01 8.53097737e-01 -3.58178347e-01 -6.90907955e-01
-6.06083274e-02 -8.59882057e-01 -9.20319438e-01 -5.44918954e-01
-3.76498997e-01 8.27922702e-01 3.19093406e-01 -3.78140174... | [8.662186622619629, 3.7492573261260986] |
b87ff443-2f99-426b-afa8-a7ba96ade530 | distributionally-robust-semi-supervised | 1811.05299 | null | http://arxiv.org/abs/1811.05299v1 | http://arxiv.org/pdf/1811.05299v1.pdf | Distributionally Robust Semi-Supervised Learning for People-Centric Sensing | Semi-supervised learning is crucial for alleviating labelling burdens in
people-centric sensing. However, human-generated data inherently suffer from
distribution shift in semi-supervised learning due to the diverse biological
conditions and behavior patterns of humans. To address this problem, we propose
a generic dis... | ['Lina Yao', 'Xiaojun Chang', 'Sen Wang', 'Dalin Zhang', 'Kaixuan Chen', 'Guodong Long'] | 2018-11-12 | null | null | null | null | ['muscular-movement-recognition'] | ['medical'] | [ 4.81061161e-01 -1.04850844e-01 -6.55979931e-01 -8.31644714e-01
-6.16817236e-01 -1.07846700e-01 6.18255973e-01 -2.84137160e-01
-6.96612835e-01 1.05000722e+00 7.02181637e-01 4.21710759e-01
-1.88365635e-02 -1.23992801e-01 -3.15117657e-01 -8.71672630e-01
3.86622876e-01 7.26299524e-01 -9.05194655e-02 4.33845460... | [8.06124496459961, 1.0570518970489502] |
38a2473e-e302-47ac-9aa1-09709c5dc9a9 | distributed-learning-of-neural-lyapunov | 2207.07731 | null | https://arxiv.org/abs/2207.07731v1 | https://arxiv.org/pdf/2207.07731v1.pdf | Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems | This paper considers the problem of characterizing the stability region of a large-scale networked system comprised of dissipative nonlinear subsystems, in a distributed and computationally tractable way. One standard approach to estimate the stability region of a general nonlinear system is to first find a Lyapunov fu... | ['Le Xie', 'Dileep Kalathil', 'S. Sivaranjani', 'Tong Huang', 'Amit Jena'] | 2022-07-15 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-7.63257802e-01 -2.74683144e-02 1.86489642e-01 1.28286645e-01
-6.21731758e-01 -8.78463924e-01 2.36848299e-03 2.08168745e-01
1.45230014e-02 1.01452160e+00 -2.43379548e-01 -1.76243916e-01
-4.30785000e-01 -3.92580748e-01 -8.37369442e-01 -1.24312627e+00
-4.37673151e-01 1.16488740e-01 2.37629175e-01 -5.24657249... | [5.28217077255249, 2.606332302093506] |
5b1e9799-1155-40df-8e8d-75f93b410e86 | pre-trained-sentence-embeddings-for-implicit | 2210.11005 | null | https://arxiv.org/abs/2210.11005v1 | https://arxiv.org/pdf/2210.11005v1.pdf | Pre-trained Sentence Embeddings for Implicit Discourse Relation Classification | Implicit discourse relations bind smaller linguistic units into coherent texts. Automatic sense prediction for implicit relations is hard, because it requires understanding the semantics of the linked arguments. Furthermore, annotated datasets contain relatively few labeled examples, due to the scale of the phenomenon:... | ['Jacob Eisenstein', 'Yangfeng Ji', 'Murali Raghu Babu Balusu'] | 2022-10-20 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'relation-classification', 'implicit-discourse-relation-classification', 'implicit-relations'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.20567697e-01 9.74514604e-01 -6.77944541e-01 -5.10694504e-01
-7.54464686e-01 -5.14878571e-01 9.24218237e-01 7.79992163e-01
-6.61512792e-01 9.23624277e-01 1.22273767e+00 -3.39486361e-01
-3.88219059e-02 -6.80238485e-01 -3.23376507e-01 -1.90416008e-01
-9.82167870e-02 5.98252654e-01 1.11613028e-01 -8.60629916... | [10.773441314697266, 9.288912773132324] |
92db264a-a9a3-4ea2-83ca-0cc85da0302a | self-supervised-learning-for-audio-visual | 2002.05314 | null | https://arxiv.org/abs/2002.05314v1 | https://arxiv.org/pdf/2002.05314v1.pdf | Self-supervised learning for audio-visual speaker diarization | Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems. In this paper, we propose a self-supervised audio-video synchronization learning method to address the problem of speaker d... | ['Shi-Xiong Zhang', 'Yifan Ding', 'Yahuan Cong', 'Yong Xu', 'Liqiang Wang'] | 2020-02-13 | null | null | null | null | ['video-synchronization'] | ['computer-vision'] | [ 1.50463641e-01 -1.16971798e-01 -3.57686490e-01 -5.03663838e-01
-1.64200914e+00 -3.70337218e-01 1.90359175e-01 -1.02868706e-01
-4.69454229e-01 5.12121916e-01 1.35526970e-01 -1.59749523e-01
1.57754868e-01 6.37278054e-03 -3.99675786e-01 -7.65991330e-01
-2.20336050e-01 3.91215771e-01 2.74320483e-01 3.22970152... | [14.423503875732422, 6.131774425506592] |
3c13067f-3a4c-408f-a55a-67a119128e22 | does-mbert-understand-romansh-evaluating-word | 2306.08702 | null | https://arxiv.org/abs/2306.08702v1 | https://arxiv.org/pdf/2306.08702v1.pdf | Does mBERT understand Romansh? Evaluating word embeddings using word alignment | We test similarity-based word alignment models (SimAlign and awesome-align) in combination with word embeddings from mBERT and XLM-R on parallel sentences in German and Romansh. Since Romansh is an unseen language, we are dealing with a zero-shot setting. Using embeddings from mBERT, both models reach an alignment erro... | ['Eyal Liron Dolev'] | 2023-06-14 | null | null | null | null | ['word-embeddings', 'word-alignment', 'xlm-r'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-8.39764401e-02 -2.51281373e-02 1.70292961e-03 -3.36136371e-01
-1.02359068e+00 -7.35102773e-01 8.41843605e-01 5.30282140e-01
-1.09372151e+00 5.92204392e-01 7.30470061e-01 -3.87578458e-01
1.02675684e-01 -4.53303754e-01 -3.54818761e-01 -3.21499079e-01
1.84272632e-01 9.86265123e-01 -1.14694340e-02 -6.58973277... | [11.300273895263672, 10.207560539245605] |
514dac0d-76df-451f-8698-fa6a4f16040d | cst5-data-augmentation-for-code-switched-1 | 2211.07514 | null | https://arxiv.org/abs/2211.07514v1 | https://arxiv.org/pdf/2211.07514v1.pdf | CST5: Data Augmentation for Code-Switched Semantic Parsing | Extending semantic parsers to code-switched input has been a challenging problem, primarily due to a lack of supervised training data. In this work, we introduce CST5, a new data augmentation technique that finetunes a T5 model using a small seed set ($\approx$100 utterances) to generate code-switched utterances from E... | ['Rengarajan Aravamudhan', 'Pankaj Joshi', 'Shyam Upadhyay', 'Rahul Goel', 'Jigar Gupta', 'Anmol Agarwal'] | 2022-11-14 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 4.26807672e-01 7.51595497e-01 1.15891203e-01 -9.09366846e-01
-1.35516322e+00 -9.28381920e-01 2.16442287e-01 4.03578170e-02
-3.61269325e-01 5.46612680e-01 3.14276874e-01 -4.58951712e-01
5.92081904e-01 -3.88613045e-01 -9.90350962e-01 -1.95577279e-01
1.45521462e-01 7.76887178e-01 3.29607725e-01 -3.70905459... | [10.638545989990234, 9.325345993041992] |
1a91fed9-6bae-4a6b-a133-bb2813a7fe5b | can-large-language-models-infer-causation | 2306.05836 | null | https://arxiv.org/abs/2306.05836v1 | https://arxiv.org/pdf/2306.05836v1.pdf | Can Large Language Models Infer Causation from Correlation? | Causal inference is one of the hallmarks of human intelligence. While the field of CausalNLP has attracted much interest in the recent years, existing causal inference datasets in NLP primarily rely on discovering causality from empirical knowledge (e.g., commonsense knowledge). In this work, we propose the first bench... | ['Bernhard Schölkopf', 'Mona Diab', 'Rada Mihalcea', 'Mrinmaya Sachan', 'Spencer Poff', 'Zhiheng Lyu', 'Jiarui Liu', 'Zhijing Jin'] | 2023-06-09 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 6.05481938e-02 3.61180067e-01 -7.30024278e-01 -4.96400416e-01
-7.59471178e-01 -7.60160744e-01 9.86831963e-01 2.58258879e-01
-3.16759199e-02 1.24264801e+00 6.49234474e-01 -8.33125234e-01
-5.49650669e-01 -9.00469303e-01 -1.09658468e+00 -3.42502266e-01
-2.31760561e-01 6.54940844e-01 6.14111349e-02 -1.07501730... | [9.719488143920898, 8.058935165405273] |
703f4ec0-e6f3-453f-a49a-38fa90ab2677 | from-words-to-wires-generating-functioning | 2305.14874 | null | https://arxiv.org/abs/2305.14874v1 | https://arxiv.org/pdf/2305.14874v1.pdf | From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions | In this work, we show that contemporary language models have a previously unknown skill -- the capacity for electronic circuit design from high-level textual descriptions, akin to code generation. We introduce two benchmarks: Pins100, assessing model knowledge of electrical components, and Micro25, evaluating a model's... | ['Peter Jansen'] | 2023-05-24 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 4.16839659e-01 4.55085158e-01 -1.62539542e-01 -1.34328574e-01
-5.28106987e-01 -9.55591381e-01 3.84571284e-01 1.24535546e-01
2.64377445e-01 4.35036093e-01 7.74735734e-02 -1.11685598e+00
6.57851174e-02 -8.38472664e-01 -5.90165019e-01 7.01372558e-03
2.46283889e-01 5.82727529e-02 -1.86098441e-01 -4.59985510... | [8.054584503173828, 7.58450174331665] |
8e657c06-8c84-4b32-b65d-14d35cb19dae | representation-learning-with-information | 2207.01437 | null | https://arxiv.org/abs/2207.01437v1 | https://arxiv.org/pdf/2207.01437v1.pdf | Representation Learning with Information Theory for COVID-19 Detection | Successful data representation is a fundamental factor in machine learning based medical imaging analysis. Deep Learning (DL) has taken an essential role in robust representation learning. However, the inability of deep models to generalize to unseen data can quickly overfit intricate patterns. Thereby, we can convenie... | ['Hichem Sahli', 'Nikos Deligiannis', 'Matias Bossa', 'Tanmoy Mukherjee', 'Abel Díaz Berenguer'] | 2022-07-04 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 3.01550239e-01 2.38800168e-01 -2.70206720e-01 -5.58451176e-01
-1.04863727e+00 -2.72893280e-01 4.32146281e-01 1.70831874e-01
-2.76192874e-01 6.80933952e-01 4.96710658e-01 -4.03789610e-01
-6.24108970e-01 -4.87634182e-01 -6.02366924e-01 -7.70803571e-01
-3.14850152e-01 3.85465860e-01 -1.70643225e-01 2.12295428... | [14.014090538024902, -2.2935070991516113] |
6dda252a-6899-46ab-b459-cb5925edfd59 | difai-diverse-facial-inpainting-using | 2301.08443 | null | https://arxiv.org/abs/2301.08443v1 | https://arxiv.org/pdf/2301.08443v1.pdf | DIFAI: Diverse Facial Inpainting using StyleGAN Inversion | Image inpainting is an old problem in computer vision that restores occluded regions and completes damaged images. In the case of facial image inpainting, most of the methods generate only one result for each masked image, even though there are other reasonable possibilities. To prevent any potential biases and unnatur... | ['Hanseok Ko', 'David Han', 'Yuanming Li', 'Jeong-gi Kwak', 'Dongsik Yoon'] | 2023-01-20 | null | null | null | null | ['facial-inpainting', 'image-inpainting'] | ['computer-vision', 'computer-vision'] | [ 6.96259618e-01 5.46983302e-01 -6.46988750e-02 -3.84172708e-01
-6.21352673e-01 -4.39153969e-01 5.35748124e-01 -8.81192803e-01
-1.01659343e-01 1.02300560e+00 5.29363275e-01 9.38697308e-02
3.88040036e-01 -7.15964735e-01 -1.03223252e+00 -6.38110101e-01
6.21837616e-01 -7.09948689e-02 -1.43462524e-01 -2.47193828... | [12.560791969299316, -0.2154274582862854] |
e7708880-84da-40b5-96cc-1865e33581bd | design-of-a-solver-for-multi-agent-epistemic | 1909.08259 | null | https://arxiv.org/abs/1909.08259v1 | https://arxiv.org/pdf/1909.08259v1.pdf | Design of a Solver for Multi-Agent Epistemic Planning | As the interest in Artificial Intelligence continues to grow it is becoming more and more important to investigate formalization and tools that allow us to exploit logic to reason about the world. In particular, given the increasing number of multi-agents systems that could benefit from techniques of automated reasonin... | ['Francesco Fabiano'] | 2019-09-18 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [-7.23234862e-02 8.25589299e-01 1.63369849e-01 -3.19151968e-01
-1.95722580e-02 -6.93855584e-01 1.22932971e+00 5.08438051e-01
-4.87953633e-01 1.05048192e+00 3.42520684e-01 -3.58693749e-01
-3.59864473e-01 -1.33199215e+00 -4.22478408e-01 -5.01040339e-01
-1.06611982e-01 8.44478905e-01 7.77675748e-01 -6.47401214... | [8.620944023132324, 6.682277202606201] |
724ec9e7-20f0-4fbe-bca3-86beb932e5db | i-2-sb-image-to-image-schrodinger-bridge | 2302.05872 | null | https://arxiv.org/abs/2302.05872v3 | https://arxiv.org/pdf/2302.05872v3.pdf | I$^2$SB: Image-to-Image Schrödinger Bridge | We propose Image-to-Image Schr\"odinger Bridge (I$^2$SB), a new class of conditional diffusion models that directly learn the nonlinear diffusion processes between two given distributions. These diffusion bridges are particularly useful for image restoration, as the degraded images are structurally informative priors f... | ['Anima Anandkumar', 'Weili Nie', 'Evangelos A. Theodorou', 'De-An Huang', 'Arash Vahdat', 'Guan-Horng Liu'] | 2023-02-12 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 2.76812792e-01 -4.58929911e-02 1.21708460e-01 -2.96740271e-02
-9.53422964e-01 -2.61579305e-01 4.79044735e-01 -6.47341311e-01
-9.42790210e-02 7.72483408e-01 3.18153352e-01 -1.89185962e-01
-4.89887178e-01 -7.20612109e-01 -7.35223830e-01 -1.01352859e+00
-3.57029289e-01 4.06145602e-01 1.17102884e-01 -2.45828450... | [11.726813316345215, -2.358834981918335] |
82fba460-5f75-4ce8-82cd-f2bbf254b707 | an-efficient-feature-selection-in | 1404.1491 | null | http://arxiv.org/abs/1404.1491v1 | http://arxiv.org/pdf/1404.1491v1.pdf | An Efficient Feature Selection in Classification of Audio Files | In this paper we have focused on an efficient feature selection method in
classification of audio files. The main objective is feature selection and
extraction. We have selected a set of features for further analysis, which
represents the elements in feature vector. By extraction method we can compute
a numerical repre... | ['Jayita Mitra', 'Diganta Saha'] | 2014-03-24 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 1.72473282e-01 -4.95124131e-01 4.43757087e-01 -3.70810330e-01
-5.07767320e-01 -5.85774779e-01 -7.87917897e-03 3.99089307e-01
-2.64929622e-01 6.01647615e-01 2.00608745e-01 1.64241225e-01
-7.04628348e-01 -7.63326585e-01 1.56771183e-01 -6.26197755e-01
-1.63935333e-01 2.72964388e-01 1.44657016e-01 -2.43054330... | [15.731895446777344, 5.237306594848633] |
8ad13266-bbe7-4621-9e54-29b904d5a39d | local-life-stay-informed-around-you-a | 2305.07168 | null | https://arxiv.org/abs/2305.07168v1 | https://arxiv.org/pdf/2305.07168v1.pdf | Local Life: Stay Informed Around You, A Scalable Geoparsing and Geotagging Approach to Serve Local News Worldwide | Local news has become increasingly important in the news industry due to its various benefits. It offers local audiences information that helps them participate in their communities and interests. It also serves as a reliable source of factual reporting that can prevent misinformation. Moreover, it can influence nation... | ['Radhika Bansal', 'Shiying He', 'Gosuddin Kamaruddin Siddiqi', 'Deven Santosh Shah'] | 2023-05-11 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-4.81557727e-01 -2.19245091e-01 -7.64177144e-01 -1.32571295e-01
-9.18888271e-01 -8.44273329e-01 9.07829106e-01 9.14037287e-01
-3.44077229e-01 8.64562988e-01 1.11564767e+00 -4.94326144e-01
-2.12860629e-01 -1.25327003e+00 -7.55338669e-01 -5.56569457e-01
2.03693822e-01 4.00565445e-01 7.38172054e-01 -3.13212246... | [10.353046417236328, 7.2462053298950195] |
19a27622-1e40-4e50-837d-1d4c07163bf0 | concept2box-joint-geometric-embeddings-for | 2307.01933 | null | https://arxiv.org/abs/2307.01933v1 | https://arxiv.org/pdf/2307.01933v1.pdf | Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs | Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact many KGs contain two fundamentally different views: high-level ontology-view concepts and fine-grained instance-view entities. They usually em... | ['Wei Wang', 'Yizhou Sun', 'Christos Faloutsos', 'Xian Li', 'Zhengyang Wang', 'Yan Liang', 'Jingbo Shang', 'Chenwei Zhang', 'Binxuan Huang', 'Daheng Wang', 'Zijie Huang'] | 2023-07-04 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graphs', 'knowledge-graph-embeddings'] | ['graphs', 'knowledge-base', 'methodology'] | [-5.77720284e-01 4.18674111e-01 -4.45542663e-01 -5.42756855e-01
-1.31146118e-01 -6.53399885e-01 6.18470669e-01 8.47206235e-01
6.32798905e-03 2.68657237e-01 6.76546633e-01 5.41331284e-02
-4.09893245e-01 -1.63240683e+00 -7.01368093e-01 -5.43456554e-01
-4.79052305e-01 8.46905172e-01 4.75671142e-01 -4.06956792... | [8.702454566955566, 7.859821319580078] |
dcd6a064-a02a-4642-95df-6b087a160ce9 | the-starcraft-multi-agent-challenges-learning | 2207.02007 | null | https://arxiv.org/abs/2207.02007v2 | https://arxiv.org/pdf/2207.02007v2.pdf | The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions | In this paper, we propose a novel benchmark called the StarCraft Multi-Agent Challenges+, where agents learn to perform multi-stage tasks and to use environmental factors without precise reward functions. The previous challenges (SMAC) recognized as a standard benchmark of Multi-Agent Reinforcement Learning are mainly ... | ['Se-Young Yun', 'Song Chong', 'SeongHwan Kim', 'Joonkee Kim', 'Yongsik Lee', 'Jihwan Oh', 'Mingyu Kim'] | 2022-07-05 | null | null | null | null | ['smac-1'] | ['playing-games'] | [-1.76439676e-02 1.39165640e-01 3.40669267e-02 2.80394077e-01
-3.26933175e-01 -1.05370343e+00 5.26065767e-01 2.99363554e-01
-1.06924987e+00 1.04724860e+00 -3.26818764e-01 -1.01041481e-01
-6.64554536e-01 -6.61118031e-01 -6.77369893e-01 -8.83586645e-01
-8.28064203e-01 6.23740137e-01 1.91882089e-01 -1.03531075... | [3.7636473178863525, 2.186037302017212] |
39161e85-16bf-4b81-9536-bb78290d8439 | conditions-for-estimation-of-sensitivities-of | 2212.01471 | null | https://arxiv.org/abs/2212.01471v2 | https://arxiv.org/pdf/2212.01471v2.pdf | Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections | Voltage phase angle measurements are often unavailable from sensors in distribution networks and transmission network boundaries. Therefore, this paper addresses the conditions for estimating sensitivities of voltage magnitudes with respect to complex (active and reactive) electric power injections based on sensor meas... | ['Daniel K. Molzahn', 'Jorge Fernandez', 'Santiago Grijalva', 'Daniel Turizo', 'Samuel Talkington'] | 2022-12-02 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 8.45075771e-02 -2.12801591e-01 -9.87058133e-02 1.10762902e-01
-4.34487194e-01 -1.04058611e+00 -1.74713787e-02 3.26610029e-01
9.75793004e-02 9.31783676e-01 1.19908094e-01 -1.00809455e-01
-7.25871682e-01 -6.33771479e-01 -4.91711706e-01 -9.62858737e-01
-5.87189257e-01 1.25399744e-02 -4.21361953e-01 -6.02185786... | [5.690811634063721, 2.6343209743499756] |
c49a8314-504f-4cce-be87-20a2deae93ab | real-time-radiance-fields-for-single-image | 2305.02310 | null | https://arxiv.org/abs/2305.02310v1 | https://arxiv.org/pdf/2305.02310v1.pdf | Real-Time Radiance Fields for Single-Image Portrait View Synthesis | We present a one-shot method to infer and render a photorealistic 3D representation from a single unposed image (e.g., face portrait) in real-time. Given a single RGB input, our image encoder directly predicts a canonical triplane representation of a neural radiance field for 3D-aware novel view synthesis via volume re... | ['Koki Nagano', 'Ravi Ramamoorthi', 'Manmohan Chandraker', 'Sameh Khamis', 'Zhiding Yu', 'Chao Liu', 'Eric R. Chan', 'Michael Stengel', 'Matthew Chan', 'Alex Trevithick'] | 2023-05-03 | null | null | null | null | ['novel-view-synthesis'] | ['computer-vision'] | [ 6.50602520e-01 4.42107767e-01 4.83900577e-01 -5.65909624e-01
-9.17311788e-01 -6.97139144e-01 7.94757426e-01 -8.25011730e-01
2.43706375e-01 4.11417395e-01 3.19897048e-02 -3.29245061e-01
5.23123741e-01 -9.20025229e-01 -1.26780450e+00 -4.83582526e-01
2.38003194e-01 3.60106528e-01 -7.80657902e-02 -3.04327637... | [9.377985000610352, -3.147920846939087] |
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