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a3469b37-960a-4df9-9ec9-22a778c22781 | from-the-string-landscape-to-the-mathematical | 2202.06086 | null | https://arxiv.org/abs/2202.06086v1 | https://arxiv.org/pdf/2202.06086v1.pdf | From the String Landscape to the Mathematical Landscape: a Machine-Learning Outlook | We review the recent programme of using machine-learning to explore the landscape of mathematical problems. With this paradigm as a model for human intuition - complementary to and in contrast with the more formalistic approach of automated theorem proving - we highlight some experiments on how AI helps with conjecture... | ['Yang-Hui He'] | 2022-02-12 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.59710541e-01 7.76852846e-01 -2.38496989e-01 -2.75652528e-01
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-3.58248502e-01 6.83487594e-01 2.00851649e-01 -4.74748671... | [8.890679359436035, 6.963185787200928] |
edc526ef-d5c3-44ef-8794-38add614c3bc | universal-dependencies-and-morphology-for | null | null | https://aclanthology.org/E17-1034 | https://aclanthology.org/E17-1034.pdf | Universal Dependencies and Morphology for Hungarian - and on the Price of Universality | In this paper, we present how the principles of universal dependencies and morphology have been adapted to Hungarian. We report the most challenging grammatical phenomena and our solutions to those. On the basis of the adapted guidelines, we have converted and manually corrected 1,800 sentences from the Szeged Treebank... | ["Rich{\\'a}rd Farkas", "Zsolt Sz{\\'a}nt{\\'o}", "Katalin Simk{\\'o}", 'Veronika Vincze'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['morphological-tagging'] | ['natural-language-processing'] | [-3.12429309e-01 4.57449496e-01 2.45570630e-01 -8.53653550e-01
-8.39055955e-01 -6.60995841e-01 1.24534383e-01 4.98957783e-01
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9.57786664e-02 6.46421015e-01 4.15414512e-01 -7.37815738... | [10.388339042663574, 10.112144470214844] |
47b307ce-b573-454f-8d92-d4919620df27 | bev-mae-bird-s-eye-view-masked-autoencoders | 2212.05758 | null | https://arxiv.org/abs/2212.05758v1 | https://arxiv.org/pdf/2212.05758v1.pdf | BEV-MAE: Bird's Eye View Masked Autoencoders for Outdoor Point Cloud Pre-training | Current outdoor LiDAR-based 3D object detection methods mainly adopt the training-from-scratch paradigm. Unfortunately, this paradigm heavily relies on large-scale labeled data, whose collection can be expensive and time-consuming. Self-supervised pre-training is an effective and desirable way to alleviate this depende... | ['Yongtao Wang', 'Zhiwei Lin'] | 2022-12-12 | null | null | null | null | ['point-cloud-pre-training'] | ['computer-vision'] | [-1.21614024e-01 -1.95247069e-01 -2.39920646e-01 -4.77157176e-01
-7.65949130e-01 -3.16271305e-01 3.57428491e-01 2.88880393e-02
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1.28421396e-01 4.16933626e-01 5.79527855e-01 -3.88926230... | [7.816186904907227, -2.9499926567077637] |
118a7576-e4e5-441b-b2a1-d0e9035a1ec1 | pic-a-phrase-in-context-dataset-for-phrase | 2207.09068 | null | https://arxiv.org/abs/2207.09068v5 | https://arxiv.org/pdf/2207.09068v5.pdf | PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search | While contextualized word embeddings have been a de-facto standard, learning contextualized phrase embeddings is less explored and being hindered by the lack of a human-annotated benchmark that tests machine understanding of phrase semantics given a context sentence or paragraph (instead of phrases alone). To fill this... | ['Anh Nguyen', 'Trung Bui', 'Seunghyun Yoon', 'Thang M. Pham'] | 2022-07-19 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 1.15994811e-01 -9.33803096e-02 -5.53150952e-01 -1.97463989e-01
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1.12350546e-01 4.80623156e-01 3.14229935e-01 -2.43920743... | [10.538680076599121, 8.74164867401123] |
8d193ba5-c800-4001-9fa4-c823939cecb7 | fourier-rnns-for-modelling-noisy-physics-data | 2302.06534 | null | https://arxiv.org/abs/2302.06534v1 | https://arxiv.org/pdf/2302.06534v1.pdf | Fourier-RNNs for Modelling Noisy Physics Data | Classical sequential models employed in time-series prediction rely on learning the mappings from the past to the future instances by way of a hidden state. The Hidden states characterise the historical information and encode the required temporal dependencies. However, most existing sequential models operate within fi... | ['Lorenzo Zanisi', 'Stanislas Pamela', 'Vignesh Gopakumar'] | 2023-02-13 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 2.90590942e-01 -2.22201601e-01 2.38738030e-01 -2.01299116e-01
-7.01386994e-03 -2.77838081e-01 9.44930613e-01 -1.36954159e-01
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-7.26179123e-01 3.72722656e-01 1.22752741e-01 -5.96693382... | [6.947344779968262, 3.350409984588623] |
2f22a726-2f47-475d-9d92-da9e02da167c | higher-order-implicit-fairing-networks-for-3d | 2111.00950 | null | https://arxiv.org/abs/2111.00950v1 | https://arxiv.org/pdf/2111.00950v1.pdf | Higher-Order Implicit Fairing Networks for 3D Human Pose Estimation | Estimating a 3D human pose has proven to be a challenging task, primarily because of the complexity of the human body joints, occlusions, and variability in lighting conditions. In this paper, we introduce a higher-order graph convolutional framework with initial residual connections for 2D-to-3D pose estimation. Using... | ['A. Ben Hamza', 'Jianning Quan'] | 2021-11-01 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-1.81386992e-01 3.31369489e-01 -1.86821222e-01 -3.17815661e-01
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-5.83467543e-01 4.87697989e-01 2.17742503e-01 -3.71095985... | [6.99744987487793, -0.7798519730567932] |
c571dff5-1900-49ba-9ca3-37f4fad3f42f | better-low-resource-entity-recognition | 2305.13582 | null | https://arxiv.org/abs/2305.13582v2 | https://arxiv.org/pdf/2305.13582v2.pdf | Better Low-Resource Entity Recognition Through Translation and Annotation Fusion | Pre-trained multilingual language models have enabled significant advancements in cross-lingual transfer. However, these models often exhibit a performance disparity when transferring from high-resource languages to low-resource languages, especially for languages that are underrepresented or not in the pre-training da... | ['Alan Ritter', 'Vedaant Shah', 'Yang Chen'] | 2023-05-23 | null | null | null | null | ['named-entity-recognition-ner', 'low-resource-named-entity-recognition', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.74488306e-01 -2.86185473e-01 -5.19083798e-01 -5.46088338e-01
-1.57360828e+00 -8.49460483e-01 4.72968459e-01 -5.76122999e-02
-1.05423617e+00 1.13413632e+00 3.93548280e-01 -5.32274246e-01
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3.97458076e-01 8.54464054e-01 -1.78074449e-01 -5.05229235... | [10.708081245422363, 9.934703826904297] |
2dc7e91c-f88b-4f5b-82b7-3e04223dea9c | robust-multi-agent-coordination-via | 2305.05909 | null | https://arxiv.org/abs/2305.05909v1 | https://arxiv.org/pdf/2305.05909v1.pdf | Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers | Cooperative multi-agent reinforcement learning (CMARL) has shown to be promising for many real-world applications. Previous works mainly focus on improving coordination ability via solving MARL-specific challenges (e.g., non-stationarity, credit assignment, scalability), but ignore the policy perturbation issue when te... | ['Yang Yu', 'Chao Qian', 'Li-He Li', 'Cong Guan', 'Feng Chen', 'Hao Yin', 'Ke Xue', 'Zi-Qian Zhang', 'Lei Yuan'] | 2023-05-10 | null | null | null | null | ['multi-agent-reinforcement-learning', 'smac-1', 'smac'] | ['methodology', 'playing-games', 'playing-games'] | [-1.23754285e-01 -1.19088024e-01 -8.26016143e-02 3.45695078e-01
-6.26777828e-01 -7.43195713e-01 5.46948791e-01 6.68896213e-02
-5.92748225e-01 8.54008198e-01 -1.00068383e-01 -1.04743935e-01
-3.49439532e-01 -8.47288370e-01 -5.09205580e-01 -1.28604937e+00
-5.96495569e-01 7.94767261e-01 2.60001302e-01 -5.00640988... | [3.7970683574676514, 2.2970290184020996] |
b82f9024-1b9c-4749-b94d-818b84177aa8 | fast-classification-of-small-x-ray-1 | null | null | https://www.nature.com/articles/s41524-019-0196-x | https://www.nature.com/articles/s41524-019-0196-x.pdf | Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks | X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the s... | ['Giuseppe Romano', 'Siyu I. P. Tian', 'Aaron Gilad Kusne', 'Tonio Buonassisi', 'Noor Titan Putri Hartono', 'Felipe Oviedo', 'Charles Settens', 'Brian L. DeCost', 'Zhe Liu', 'Savitha Ramasamy', 'Zekun Ren', 'Shijing Sun'] | 2019-05-17 | null | null | null | npj-computational-materials-2019-5 | ['material-classification', 'material-recognition', 'x-ray-diffraction'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 4.95353431e-01 1.32785007e-01 -1.26090124e-01 -4.88364339e-01
-5.82836986e-01 -2.12966949e-01 6.21502519e-01 2.30228156e-01
-4.00314689e-01 7.87468970e-01 -5.68649828e-01 -4.33656365e-01
-3.80173504e-01 -7.68567562e-01 -7.03129292e-01 -9.44380701e-01
4.93111322e-03 8.37909877e-01 3.25864069e-02 2.93396026... | [5.21409797668457, 5.335574626922607] |
cd8629eb-2ac0-4a46-a2ba-a9c995051be1 | task-prior-conditional-variational-auto | 2205.15014 | null | https://arxiv.org/abs/2205.15014v1 | https://arxiv.org/pdf/2205.15014v1.pdf | Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification | Transductive methods always outperform inductive methods in few-shot image classification scenarios. However, the existing few-shot methods contain a latent condition: the number of samples in each class is the same, which may be unrealistic. To cope with those cases where the query shots of each class are nonuniform (... | ['Zaiyun Yang'] | 2022-05-30 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 1.40980184e-01 -2.95990586e-01 -5.61501265e-01 -3.26246768e-01
-1.03579760e+00 1.85581118e-01 4.72691506e-01 -3.09752285e-01
-3.43292505e-01 8.95953059e-01 5.63635081e-02 5.47073603e-01
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6.18091524e-01 4.39674437e-01 3.42179984e-01 -1.01828612... | [10.006508827209473, 2.9332520961761475] |
886bff8f-5861-446c-9bcf-59647e008ea8 | invariant-slot-attention-object-discovery | 2302.04973 | null | https://arxiv.org/abs/2302.04973v1 | https://arxiv.org/pdf/2302.04973v1.pdf | Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames | Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning. Recent slot-based neural networks that learn about objects in a self-supervised manner have made exciting progress in this direction. However, they typically fall short at adequately capturing sp... | ['Thomas Kipf', 'Aravindh Mahendran', 'Gamaleldin F. Elsayed', 'Mehdi S. M. Sajjadi', 'Sjoerd van Steenkiste', 'Ondrej Biza'] | 2023-02-09 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [-7.87091479e-02 -5.33673055e-02 -2.05742881e-01 -4.10482109e-01
-6.06221259e-01 -7.22091794e-01 8.62944484e-01 1.74875423e-01
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5.90627901e-02 7.94414043e-01 5.27295709e-01 -1.66349962... | [7.795662879943848, -2.7174532413482666] |
98de3c0b-96c6-4aa9-a336-9c4270d59448 | speech-emotion-diarization-which-emotion | 2306.12991 | null | https://arxiv.org/abs/2306.12991v1 | https://arxiv.org/pdf/2306.12991v1.pdf | Speech Emotion Diarization: Which Emotion Appears When? | Speech Emotion Recognition (SER) typically relies on utterance-level solutions. However, emotions conveyed through speech should be considered as discrete speech events with definite temporal boundaries, rather than attributes of the entire utterance. To reflect the fine-grained nature of speech emotions, we propose a ... | ['Alya Yacoubi', 'Alaa Nfissi', 'Mirco Ravanelli', 'Yingzhi Wang'] | 2023-06-22 | null | null | null | null | ['speaker-diarization', 'speech-emotion-recognition'] | ['speech', 'speech'] | [ 1.48292318e-01 7.64070451e-02 8.79762024e-02 -9.95168209e-01
-8.04371178e-01 -5.50176799e-01 3.97068620e-01 -1.21124014e-01
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-1.61783963e-01 1.25638142e-01 -4.50047642e-01 -4.33436960... | [13.40550422668457, 5.9009504318237305] |
fe3c4732-92a4-4656-b8c0-ef2befc5cfd0 | dense-cnn-learning-with-equivalent-mappings | 1605.07251 | null | http://arxiv.org/abs/1605.07251v1 | http://arxiv.org/pdf/1605.07251v1.pdf | Dense CNN Learning with Equivalent Mappings | Large receptive field and dense prediction are both important for achieving
high accuracy in pixel labeling tasks such as semantic segmentation. These two
properties, however, contradict with each other. A pooling layer (with stride
2) quadruples the receptive field size but reduces the number of predictions to
25\%. S... | ['Jian-Hao Luo', 'Chen-Wei Xie', 'Jianxin Wu'] | 2016-05-24 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 2.64586389e-01 5.39017737e-01 -7.20395222e-02 -7.83491373e-01
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2.54066378e-01 3.06606740e-01 8.68526936e-01 2.20539913... | [9.49726676940918, 0.31758517026901245] |
425e45e7-a35f-49cd-aab2-37d401df4003 | network-cooperation-with-progressive | 2002.11919 | null | https://arxiv.org/abs/2002.11919v1 | https://arxiv.org/pdf/2002.11919v1.pdf | Network Cooperation with Progressive Disambiguation for Partial Label Learning | Partial Label Learning (PLL) aims to train a classifier when each training instance is associated with a set of candidate labels, among which only one is correct but is not accessible during the training phase. The common strategy dealing with such ambiguous labeling information is to disambiguate the candidate label s... | ['Chen gong', 'Jian Yang', 'Jiehui Deng', 'Yao Yao'] | 2020-02-22 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 3.02952558e-01 2.28187531e-01 -2.85467327e-01 -1.81527957e-01
-8.70516002e-02 -2.89832413e-01 4.10710663e-01 1.46150559e-01
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-4.26079661e-01 -8.79492164e-01 -3.33971739e-01 -1.07030594e+00
1.56240970e-01 4.60213929e-01 4.05861169e-01 -1.15555078... | [9.459600448608398, 3.9557602405548096] |
086576ba-0a3f-46f0-9ff2-15c7907a4b42 | valerian-invariant-feature-learning-for-imu | 2303.06048 | null | https://arxiv.org/abs/2303.06048v1 | https://arxiv.org/pdf/2303.06048v1.pdf | VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild | Deep neural network models for IMU sensor-based human activity recognition (HAR) that are trained from controlled, well-curated datasets suffer from poor generalizability in practical deployments. However, data collected from naturalistic settings often contains significant label noise. In this work, we examine two in-... | ['Rong Zheng', 'Boyu Wang', 'Yujiao Hao'] | 2023-03-03 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 2.92007834e-01 -1.21907875e-01 -1.05389692e-01 -5.25375605e-01
-9.40012932e-01 -3.09256852e-01 1.26737043e-01 -1.77450359e-01
-5.13458431e-01 7.76444435e-01 5.33650398e-01 3.91822219e-01
-1.60956442e-01 -2.31401205e-01 -8.69242549e-01 -4.67960536e-01
-1.42721951e-01 1.41876072e-01 -4.64717090e-01 2.32014641... | [7.758265018463135, 0.9508281350135803] |
99e414be-1be7-463d-82c7-4e2d91e9cbb0 | neural-network-fast-classifies-biological | 2012.10331 | null | https://arxiv.org/abs/2012.10331v1 | https://arxiv.org/pdf/2012.10331v1.pdf | Neural network fast-classifies biological images using features selected after their random-forests-importance to power smart microscopy | Artificial intelligence is nowadays used for cell detection and classification in optical microscopy, during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart, to make acquisition decisions based on the images. It calls for analysing them on the fly. Biology further impose... | ['Jacques Pécréaux', 'Marc Tramier', 'Otmane Bouchareb', 'Baptiste Giroux', 'Jérémy Pont', 'Thomas Walter', 'Youssef El Habouz', 'Florian Sizaire', 'Maël Balluet'] | 2020-12-18 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 5.17273664e-01 -7.75709748e-02 4.22818989e-01 -3.28443259e-01
-4.25028622e-01 -4.57825124e-01 4.27253366e-01 3.57527196e-01
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-3.81779999e-01 -7.04538524e-01 -3.89477640e-01 -1.21532226e+00
-2.90623516e-01 7.93734193e-01 2.13165238e-01 1.04756802... | [14.710262298583984, -3.09609055519104] |
ba5b7d9b-d720-497a-b881-9dc02c09eb70 | quantum-tomography-benchmarking | 2012.15656 | null | https://arxiv.org/abs/2012.15656v2 | https://arxiv.org/pdf/2012.15656v2.pdf | Quantum tomography benchmarking | Recent advances in quantum computers and simulators are steadily leading us towards full-scale quantum computing devices. Due to the fact that debugging is necessary to create any computing device, quantum tomography (QT) is a critical milestone on this path. In practice, the choice between different QT methods faces t... | ['Yu. I. Bogdanov', 'A. Yu. Chernyavskiy', 'B. I. Bantysh'] | 2020-12-31 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 1.29502043e-01 -5.60926318e-01 1.21902868e-01 -1.93892837e-01
-5.73282242e-01 -5.03472626e-01 5.93445420e-01 4.06214520e-02
-5.34009516e-01 9.29139078e-01 -6.22933626e-01 -5.31137645e-01
-9.02197957e-02 -1.12893224e+00 -3.15794080e-01 -7.95115411e-01
-3.31744030e-02 4.57854062e-01 3.80904168e-01 -3.47947598... | [5.583437919616699, 4.958820343017578] |
950d50ba-c489-4ef2-a341-2b1eed737479 | discrepancy-optimal-meta-learning-for-domain | null | null | https://openreview.net/forum?id=eJyt4hJzOLk | https://openreview.net/pdf?id=eJyt4hJzOLk | Discrepancy-Optimal Meta-Learning for Domain Generalization | This work attempts to tackle the problem of domain generalization (DG) via learning to reduce domain shift with an episodic training procedure. In particular, we measure the domain shift with $\mathcal{Y}$-discrepancy and learn to optimize $\mathcal{Y}$-discrepancy between the unseen target domain and source domains on... | ['Chen Jia'] | 2021-09-29 | null | null | null | null | ['style-generalization'] | ['computer-vision'] | [ 1.48830220e-01 1.94846198e-01 -6.32285535e-01 -5.31703413e-01
-1.22066104e+00 -5.90635955e-01 5.71388453e-02 4.24207337e-02
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-5.35334408e-01 -7.77620375e-01 -8.68253410e-01 -8.24487984e-01
-2.70403773e-01 7.70568609e-01 6.95540309e-02 -3.49341512... | [10.393521308898926, 3.014510154724121] |
26d5ec09-0810-4626-8ff7-e8ed9ad17bdb | continuous-cost-aggregation-for-dual-pixel | 2306.07921 | null | https://arxiv.org/abs/2306.07921v1 | https://arxiv.org/pdf/2306.07921v1.pdf | Continuous Cost Aggregation for Dual-Pixel Disparity Extraction | Recent works have shown that depth information can be obtained from Dual-Pixel (DP) sensors. A DP arrangement provides two views in a single shot, thus resembling a stereo image pair with a tiny baseline. However, the different point spread function (PSF) per view, as well as the small disparity range, makes the use of... | ['Georgios Evangelidis', 'Sagi Katz', 'Sagi Monin'] | 2023-06-13 | null | null | null | null | ['disparity-estimation', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 4.32926536e-01 -1.88939050e-01 1.51640266e-01 -4.02581125e-01
-7.50484705e-01 -2.74184644e-01 3.55730325e-01 -5.10579348e-02
-4.46515799e-01 6.61722124e-01 -5.08772843e-02 -4.54904288e-02
1.86281037e-02 -9.18444395e-01 -7.69859970e-01 -8.42031538e-01
5.03500164e-01 1.10332109e-02 6.09064162e-01 2.79959687... | [9.206759452819824, -2.507826089859009] |
62d75100-d30a-4b57-92cf-c6e6db0e820e | ireen-iterative-reverse-engineering-of-black | 2006.10720 | null | https://arxiv.org/abs/2006.10720v2 | https://arxiv.org/pdf/2006.10720v2.pdf | IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis | In this work, we investigate the problem of revealing the functionality of a black-box agent. Notably, we are interested in the interpretable and formal description of the behavior of such an agent. Ideally, this description would take the form of a program written in a high-level language. This task is also known as r... | ['Mateusz Malinowski', 'Mario Fritz', 'Hossein Hajipour'] | 2020-06-18 | null | https://openreview.net/forum?id=HyLGEHFzqzM | https://openreview.net/pdf?id=HyLGEHFzqzM | neurips-workshop-cap-2020-12 | ['computer-security'] | ['miscellaneous'] | [ 6.12622499e-01 4.98420596e-01 -1.46206683e-02 -8.00216049e-02
-4.45130885e-01 -9.01596725e-01 8.44843030e-01 2.18102440e-01
-9.32962969e-02 4.03121352e-01 -1.35710984e-01 -8.66302907e-01
2.47201733e-02 -8.38352203e-01 -9.92527723e-01 -3.95357519e-01
5.93216158e-02 3.99185061e-01 2.52720416e-01 -2.94248223... | [8.22209358215332, 7.345916271209717] |
ed942d58-4399-45ed-8ad8-57351b247952 | bridging-natural-language-processing-and | 2304.09616 | null | https://arxiv.org/abs/2304.09616v2 | https://arxiv.org/pdf/2304.09616v2.pdf | Bridging Natural Language Processing and Psycholinguistics: computationally grounded semantic similarity datasets for Basque and Spanish | We present a computationally-grounded word similarity dataset based on two well-known Natural Language Processing resources; text corpora and knowledge bases. This dataset aims to fulfil a gap in psycholinguistic research by providing a variety of quantifications of semantic similarity in an extensive set of noun pairs... | ['I. San Martin', 'M. Arantzeta', 'J. Goikoetxea'] | 2023-04-19 | null | null | null | null | ['word-similarity', 'semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.44024098e-01 -8.61573592e-02 -3.98736186e-02 -4.36890572e-01
-2.63680786e-01 -6.19185686e-01 9.01883543e-01 1.12738323e+00
-1.12861753e+00 4.57351685e-01 7.22006559e-01 -1.00099981e-01
-3.21558148e-01 -1.06696081e+00 -1.86625645e-01 -2.77875096e-01
3.55713554e-02 7.09947169e-01 2.51237065e-01 -6.74254477... | [10.445927619934082, 9.061966896057129] |
936b2441-bf1e-402c-b73f-b11a145cef5b | progen2-exploring-the-boundaries-of-protein | 2206.13517 | null | https://arxiv.org/abs/2206.13517v1 | https://arxiv.org/pdf/2206.13517v1.pdf | ProGen2: Exploring the Boundaries of Protein Language Models | Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design. However, we lack a sufficient understanding of how very large-scale models and data play a role in effective protein model developme... | ['Ali Madani', 'Nikhil Naik', 'Eli N. Weinstein', 'Jeffrey Ruffolo', 'Erik Nijkamp'] | 2022-06-27 | null | null | null | null | ['protein-design'] | ['medical'] | [ 4.44765985e-01 6.25010058e-02 -1.98386118e-01 -4.51822609e-01
-6.23299956e-01 -7.44532347e-01 2.46454984e-01 2.97379136e-01
-3.84179413e-01 1.00740278e+00 4.27001193e-02 -5.60408533e-01
1.68238476e-01 -4.14300382e-01 -1.00738275e+00 -7.52758443e-01
2.36880891e-02 9.62888658e-01 1.11127272e-01 -3.55497360... | [4.684179782867432, 5.609221458435059] |
2b16cab3-2bdf-4835-8d18-1b158b0d9f01 | quantization-of-generative-adversarial | 2108.13996 | null | https://arxiv.org/abs/2108.13996v1 | https://arxiv.org/pdf/2108.13996v1.pdf | Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study | Generative adversarial networks (GANs) have an enormous potential impact on digital content creation, e.g., photo-realistic digital avatars, semantic content editing, and quality enhancement of speech and images. However, the performance of modern GANs comes together with massive amounts of computations performed durin... | ['Dmitry Vetrov', 'Alexander Fritzler', 'Pavel Andreev'] | 2021-08-31 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 5.12550712e-01 6.29647449e-02 -7.10928142e-02 -1.38555586e-01
-7.81925142e-01 -5.10487080e-01 5.68891168e-01 -3.02942067e-01
-1.82335436e-01 8.11079025e-01 1.17073707e-01 -4.15198833e-01
3.60800475e-01 -1.16834712e+00 -8.46270204e-01 -8.21140528e-01
2.58890986e-01 3.62406611e-01 -8.73951763e-02 -3.72964203... | [11.58951473236084, -0.48503121733665466] |
45293def-e5fc-459f-aa36-a904e211f43c | context-aware-embeddings-for-automatic-art | 1904.04985 | null | http://arxiv.org/abs/1904.04985v1 | http://arxiv.org/pdf/1904.04985v1.pdf | Context-Aware Embeddings for Automatic Art Analysis | Automatic art analysis aims to classify and retrieve artistic representations
from a collection of images by using computer vision and machine learning
techniques. In this work, we propose to enhance visual representations from
neural networks with contextual artistic information. Whereas visual
representations are abl... | ['Yuta Nakashima', 'Noa Garcia', 'Benjamin Renoust'] | 2019-04-10 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 2.85850257e-01 -4.97829169e-01 -1.33175433e-01 -2.45135412e-01
-4.65243787e-01 -7.98169434e-01 1.01881325e+00 7.47161746e-01
-3.65485907e-01 4.00114447e-01 4.60288823e-01 1.71279460e-01
-3.66596729e-01 -1.02024865e+00 -5.96751869e-01 -4.74314809e-01
3.44715059e-01 5.46925545e-01 -2.19039783e-01 -1.01415917... | [11.227643013000488, 0.4652967154979706] |
6c6e4a81-0c70-468a-a3e2-886c332ad99f | towards-crafting-text-adversarial-samples | 1707.02812 | null | http://arxiv.org/abs/1707.02812v1 | http://arxiv.org/pdf/1707.02812v1.pdf | Towards Crafting Text Adversarial Samples | Adversarial samples are strategically modified samples, which are crafted
with the purpose of fooling a classifier at hand. An attacker introduces
specially crafted adversarial samples to a deployed classifier, which are being
mis-classified by the classifier. However, the samples are perceived to be
drawn from entirel... | ['Suranjana Samanta', 'Sameep Mehta'] | 2017-07-10 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 8.10674727e-01 4.41912591e-01 7.25904182e-02 -4.12904829e-01
-6.53131008e-01 -1.08764219e+00 8.29875052e-01 -4.14876975e-02
-3.39560717e-01 5.38924158e-01 2.02457890e-01 -1.43984407e-01
3.58612925e-01 -8.69643092e-01 -7.77775705e-01 -7.88558960e-01
3.64483923e-01 1.65802240e-01 4.10413649e-03 -3.80044490... | [5.911858558654785, 8.017765045166016] |
cfb7ca54-a086-4f88-b73a-8779eae2ebfc | multi-view-learning-for-web-spam-detection | 1305.3814 | null | http://arxiv.org/abs/1305.3814v2 | http://arxiv.org/pdf/1305.3814v2.pdf | Multi-View Learning for Web Spam Detection | Spam pages are designed to maliciously appear among the top search results by
excessive usage of popular terms. Therefore, spam pages should be removed using
an effective and efficient spam detection system. Previous methods for web spam
classification used several features from various information sources (page
conten... | ['Behrouz Minaei-Bidgoli', 'Ali Hadian'] | 2013-05-16 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-2.72417337e-01 -5.59127867e-01 -3.92871171e-01 -3.20069015e-01
-8.59761715e-01 -7.83302963e-01 6.24330878e-01 6.08174391e-02
1.09251089e-01 4.14902061e-01 -1.15874372e-01 -4.69903737e-01
2.06600912e-02 -1.07025278e+00 -2.78202623e-01 -6.23658180e-01
-3.10066808e-02 5.19861042e-01 1.14105916e+00 -3.29072148... | [7.839234352111816, 10.023884773254395] |
29436169-b874-4573-a735-a9a021f73556 | a-feedback-neural-network-for-small-target | 1805.00342 | null | http://arxiv.org/abs/1805.00342v2 | http://arxiv.org/pdf/1805.00342v2.pdf | A Feedback Neural Network for Small Target Motion Detection in Cluttered Backgrounds | Small target motion detection is critical for insects to search for and track
mates or prey which always appear as small dim speckles in the visual field. A
class of specific neurons, called small target motion detectors (STMDs), has
been characterized by exquisite sensitivity for small target motion.
Understanding and... | ['Shigang Yue', 'Hongxin Wang', 'Jigen Peng'] | 2018-05-01 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 4.10304070e-01 -6.51345193e-01 -1.89327329e-01 -3.84233706e-02
4.67886686e-01 -7.55452752e-01 3.63340288e-01 -2.49345481e-01
-4.90818739e-01 4.24899369e-01 -1.76143661e-01 -2.87999988e-01
5.45520365e-01 -5.82934082e-01 -5.60278594e-01 -8.70279491e-01
-8.99156183e-02 -4.63373244e-01 1.15118790e+00 -6.50893971... | [8.405130386352539, -0.8393992781639099] |
8ec32017-46f8-4f39-9f15-8ec4d1d20bd0 | end-to-end-table-question-answering-via | 2203.16714 | null | https://arxiv.org/abs/2203.16714v1 | https://arxiv.org/pdf/2203.16714v1.pdf | End-to-End Table Question Answering via Retrieval-Augmented Generation | Most existing end-to-end Table Question Answering (Table QA) models consist of a two-stage framework with a retriever to select relevant table candidates from a corpus and a reader to locate the correct answers from table candidates. Even though the accuracy of the reader models is significantly improved with the recen... | ['James Hendler', 'Alfio Gliozzo', 'Michael Glass', 'Mustafa Canim', 'Feifei Pan'] | 2022-03-30 | null | null | null | null | ['table-retrieval'] | ['natural-language-processing'] | [ 3.24106932e-01 1.98889539e-01 -2.37135291e-02 -4.45591629e-01
-2.57937288e+00 -1.03181422e+00 6.20894551e-01 5.04333138e-01
-2.25183353e-01 4.44198251e-01 3.75377506e-01 -3.98797125e-01
-2.29904696e-01 -9.42817509e-01 -8.48169744e-01 -7.83759132e-02
5.05370498e-01 1.60360324e+00 6.34407818e-01 -7.27430761... | [10.495390892028809, 7.879754543304443] |
8b3155d0-f312-4808-80bc-a71c411841b9 | unsupervised-speech-segmentation-and-variable | 2110.02345 | null | https://arxiv.org/abs/2110.02345v2 | https://arxiv.org/pdf/2110.02345v2.pdf | Unsupervised Speech Segmentation and Variable Rate Representation Learning using Segmental Contrastive Predictive Coding | Typically, unsupervised segmentation of speech into the phone and word-like units are treated as separate tasks and are often done via different methods which do not fully leverage the inter-dependence of the two tasks. Here, we unify them and propose a technique that can jointly perform both, showing that these two ta... | ['Najim Dehak', 'Laureano Moro-Velazquez', 'Piotr Żelasko', 'Jesús Villalba', 'Saurabhchand Bhati'] | 2021-10-05 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 5.48610747e-01 -4.22949009e-02 -3.70193869e-01 -2.35486060e-01
-1.19933259e+00 -8.53852093e-01 3.44476312e-01 1.47816360e-01
-2.94034183e-01 2.26557106e-01 2.29579002e-01 -5.10601938e-01
3.04116130e-01 -3.87648493e-01 -7.87389338e-01 -6.61209643e-01
-1.41894981e-01 -6.57321587e-02 2.86452323e-01 1.03425264... | [14.5855712890625, 6.6071624755859375] |
d2b09dfb-f78a-487c-8761-a86a788de4d5 | componerf-text-guided-multi-object | 2303.13843 | null | https://arxiv.org/abs/2303.13843v1 | https://arxiv.org/pdf/2303.13843v1.pdf | CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout | Recent research endeavors have shown that combining neural radiance fields (NeRFs) with pre-trained diffusion models holds great potential for text-to-3D generation.However, a hurdle is that they often encounter guidance collapse when rendering complex scenes from multi-object texts. Because the text-to-image diffusion... | ['Lin Wang', 'Hui Xiong', 'Xiaodong Lin', 'Haonan Lu', 'Sijia Li', 'Haotian Bai', 'Yiqi Lin'] | 2023-03-24 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 3.28915060e-01 -9.66369063e-02 3.36571246e-01 -4.92970824e-01
-6.03191495e-01 -9.37378705e-01 8.35001707e-01 -1.19654834e-01
1.35166243e-01 1.22270115e-01 3.80710036e-01 -2.21005172e-01
-1.20902494e-01 -1.17325699e+00 -8.85501802e-01 -5.24395764e-01
4.98456240e-01 2.77353108e-01 1.92259654e-01 -3.11268359... | [9.331156730651855, -3.1705856323242188] |
21a95ee8-aabc-439f-a183-5644b1dcf6cf | on-the-benefits-of-self-taught-learning-for | 2209.10099 | null | https://arxiv.org/abs/2209.10099v4 | https://arxiv.org/pdf/2209.10099v4.pdf | On the benefits of self-taught learning for brain decoding | Context. We study the benefits of using a large public neuroimaging database composed of fMRI statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a selection of relevant statistic maps, a convolutional autoencoder to rec... | ['Camille Maumet', 'Elisa Fromont', 'Elodie Germani'] | 2022-09-19 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 3.63512158e-01 1.61446184e-01 1.05140492e-01 -7.74880707e-01
-5.29751778e-01 -6.16451561e-01 6.34936333e-01 3.11054438e-01
-7.57908642e-01 6.12764716e-01 4.52346712e-01 -1.13099232e-01
-4.43744659e-01 -4.62453127e-01 -1.15660059e+00 -5.36815584e-01
-2.54711121e-01 7.10872054e-01 1.36499390e-01 2.08794907... | [12.64585018157959, 3.2165427207946777] |
320d9f6d-06b0-4132-9703-0393c1afc99a | total-energy-shaping-control-for-mechanical | 2301.03746 | null | https://arxiv.org/abs/2301.03746v2 | https://arxiv.org/pdf/2301.03746v2.pdf | Total energy-shaping control for mechanical systems via Control-by-Interconnection | Application of IDA-PBC to mechanical systems has received much attention in recent decades, but its application is still limited by the solvability of the so-called matching conditions. In this work, it is shown that total energy-shaping control of under-actuated mechanical systems has a control-by-interconnection inte... | ['Joel Ferguson'] | 2023-01-10 | null | null | null | null | ['total-energy', 'acrobot'] | ['miscellaneous', 'playing-games'] | [ 3.38704675e-01 8.36079478e-01 -2.84260750e-01 4.62194800e-01
2.08562553e-01 -8.60475063e-01 5.04164755e-01 1.71158120e-01
-2.86334932e-01 9.30156112e-01 -6.25226378e-01 -3.95951331e-01
-6.99166656e-01 -3.43211561e-01 -4.49643582e-01 -8.45725834e-01
1.18861087e-01 2.54050612e-01 -1.50249064e-01 -5.95342875... | [5.427215099334717, 2.5769765377044678] |
34ec2bbc-b618-4c34-8958-e925b57b6668 | care-open-knowledge-graph-embeddings | null | null | https://aclanthology.org/D19-1036 | https://aclanthology.org/D19-1036.pdf | CaRe: Open Knowledge Graph Embeddings | Open Information Extraction (OpenIE) methods are effective at extracting (noun phrase, relation phrase, noun phrase) triples from text, e.g., (Barack Obama, took birth in, Honolulu). Organization of such triples in the form of a graph with noun phrases (NPs) as nodes and relation phrases (RPs) as edges results in the c... | ['Swapnil Gupta', 'Sreyash Kenkre', 'Partha Talukdar'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['open-information-extraction'] | ['natural-language-processing'] | [-2.20784158e-01 1.12410367e+00 -4.14113939e-01 -1.13985479e-01
-3.50011528e-01 -7.33626127e-01 4.46681738e-01 6.94686830e-01
-4.50802036e-02 8.51008177e-01 4.29147780e-01 -5.02856910e-01
-3.75238121e-01 -1.36918139e+00 -8.38523328e-01 -1.81601539e-01
-3.77618194e-01 5.76434374e-01 2.37257496e-01 -2.46443242... | [9.037094116210938, 8.150567054748535] |
be4b08c6-e7c7-4a7b-b1c7-fe8caad69c81 | a-tool-for-extracting-conversational | null | null | https://aclanthology.org/L12-1039 | https://aclanthology.org/L12-1039.pdf | A Tool for Extracting Conversational Implicatures | Explicitly conveyed knowledge represents only a portion of the information communicated by a text snippet. Automated mechanisms for deriving explicit information exist; however, the implicit assumptions and default inferences that capture our intuitions about a normal interpretation of a communication remain hidden for... | ['Marta Tatu', 'Dan Moldovan'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['implicatures'] | ['natural-language-processing'] | [ 1.49628460e-01 1.02877843e+00 -1.16798215e-01 -7.50803709e-01
-3.42307746e-01 -7.42658317e-01 1.10064912e+00 3.22285891e-01
5.29210977e-02 8.86383832e-01 9.82226670e-01 -5.63280880e-01
-2.43472546e-01 -9.52320158e-01 -4.04171079e-01 -1.02935284e-02
2.51102060e-01 6.89833403e-01 -8.63714516e-03 -8.56357276... | [12.345514297485352, 8.011598587036133] |
e87391de-f084-489d-8ba2-1b88a756d090 | conformer-llms-convolution-augmented-large | 2307.00461 | null | https://arxiv.org/abs/2307.00461v1 | https://arxiv.org/pdf/2307.00461v1.pdf | Conformer LLMs -- Convolution Augmented Large Language Models | This work builds together two popular blocks of neural architecture, namely convolutional layers and Transformers, for large language models (LLMs). Non-causal conformers are used ubiquitously in automatic speech recognition. This work aims to adapt these architectures in a causal setup for training LLMs. Transformers ... | ['Prateek Verma'] | 2023-07-02 | null | null | null | null | ['speech-recognition'] | ['speech'] | [ 8.68578106e-02 4.10859197e-01 -4.51383948e-01 -5.95222175e-01
-6.49056137e-01 -3.11300159e-01 1.34905064e+00 -2.55789250e-01
6.38915747e-02 5.15906990e-01 1.00511467e+00 -3.96719515e-01
-1.98983312e-01 -7.31465101e-01 -9.22775447e-01 -5.04413068e-01
-2.75085688e-01 3.14396948e-01 7.93392137e-02 -5.21626808... | [10.995808601379395, 6.609679222106934] |
8728faeb-3e0f-417a-809e-2b6e24d3e58b | facial-surface-analysis-using-iso-geodesic | 1608.08878 | null | http://arxiv.org/abs/1608.08878v1 | http://arxiv.org/pdf/1608.08878v1.pdf | Facial Surface Analysis using Iso-Geodesic Curves in Three Dimensional Face Recognition System | In this paper, we present an automatic 3D face recognition system. This
system is based on the representation of human faces surfaces as collections of
Iso-Geodesic Curves (IGC) using 3D Fast Marching algorithm. To compare two
facial surfaces, we compute a geodesic distance between a pair of facial curves
using a Riema... | ['Bouzid Manaut', 'El Mahdi Barrah', 'Said Safi', 'Rachid Ahdid'] | 2016-08-31 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-1.63879946e-01 -4.50383015e-02 1.37295336e-01 -7.60738611e-01
-5.05984873e-02 -4.02793735e-01 8.40645850e-01 -4.52728420e-01
-1.12861186e-01 1.97067261e-01 -3.25581878e-01 -4.60926056e-01
-1.27737179e-01 -7.33314335e-01 -3.15172732e-01 -3.43421906e-01
-5.30763745e-01 4.17829365e-01 8.93482566e-02 -2.65674561... | [13.257428169250488, 0.6770864129066467] |
96fb2e67-16be-4809-8163-1068ab462b59 | optimization-based-deep-learning-methods-for | 2303.01515 | null | https://arxiv.org/abs/2303.01515v1 | https://arxiv.org/pdf/2303.01515v1.pdf | Optimization-Based Deep learning methods for Magnetic Resonance Imaging Reconstruction and Synthesis | This dissertation is devoted to provide advanced nonconvex nonsmooth variational models of (Magnetic Resonance Image) MRI reconstruction, efficient learnable image reconstruction algorithms and parameter training algorithms that improve the accuracy and robustness of the optimization-based deep learning methods for com... | ['Wanyu Bian'] | 2023-03-02 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 5.91608822e-01 4.52350587e-01 -1.98482856e-01 -4.40018386e-01
-1.41902566e+00 1.64951473e-01 -3.27166319e-02 -4.86194998e-01
-1.70110703e-01 7.46712089e-01 6.20688975e-01 -3.65244895e-02
-4.60555345e-01 -3.32261264e-01 -9.46012199e-01 -9.23435748e-01
-2.74897903e-01 6.64724052e-01 -5.34965634e-01 -2.60742217... | [13.489744186401367, -2.4159626960754395] |
01efa5b9-f9f7-464c-ada2-848a386833f2 | triple-stream-deep-metric-learning-of-great | 2301.02642 | null | https://arxiv.org/abs/2301.02642v1 | https://arxiv.org/pdf/2301.02642v1.pdf | Triple-stream Deep Metric Learning of Great Ape Behavioural Actions | We propose the first metric learning system for the recognition of great ape behavioural actions. Our proposed triple stream embedding architecture works on camera trap videos taken directly in the wild and demonstrates that the utilisation of an explicit DensePose-C chimpanzee body part segmentation stream effectively... | ['Tilo Burghardt', 'Hjalmar Kühl', 'Majid Mirmehdi', 'Otto Brookes'] | 2023-01-06 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 3.21276218e-01 1.14242872e-02 3.12776566e-01 -3.22410941e-01
-4.33331847e-01 -5.85679531e-01 7.16559887e-01 -1.68056577e-01
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-1.02475786e+00 4.28334862e-01 4.29619223e-01 -4.22372550... | [7.642454624176025, -0.5209493637084961] |
69e040ce-b520-4a28-89f2-05f0d45754d3 | mobileinst-video-instance-segmentation-on-the | 2303.17594 | null | https://arxiv.org/abs/2303.17594v1 | https://arxiv.org/pdf/2303.17594v1.pdf | MobileInst: Video Instance Segmentation on the Mobile | Although recent approaches aiming for video instance segmentation have achieved promising results, it is still difficult to employ those approaches for real-world applications on mobile devices, which mainly suffer from (1) heavy computation and memory cost and (2) complicated heuristics for tracking objects. To addres... | ['Xinggang Wang', 'Wenyu Liu', 'Dashan Gao', 'Xiaowen Ying', 'Xin Li', 'Jiancheng Lyu', 'Shuai Zhang', 'Haoyi Jiang', 'Shusheng Yang', 'Tianheng Cheng', 'Renhong Zhang'] | 2023-03-30 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 9.60724056e-02 -3.14049602e-01 -5.56614161e-01 1.11693600e-02
-8.54665995e-01 -5.33843279e-01 6.98721930e-02 -2.07820311e-01
-5.56745946e-01 3.25091362e-01 -3.72003198e-01 -6.55180097e-01
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-2.98279729e-02 1.93121076e-01 1.04933596e+00 3.10636312... | [9.068193435668945, -0.12996792793273926] |
7698d7aa-043c-4194-9d20-f480b89f0f7a | style-transfer-based-image-synthesis-as-an | 1905.10974 | null | https://arxiv.org/abs/1905.10974v1 | https://arxiv.org/pdf/1905.10974v1.pdf | Style transfer-based image synthesis as an efficient regularization technique in deep learning | These days deep learning is the fastest-growing area in the field of Machine Learning. Convolutional Neural Networks are currently the main tool used for image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant... | ['Michał Grochowski', 'Agnieszka Mikołajczyk'] | 2019-05-27 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 7.11993217e-01 1.85699821e-01 -5.67728244e-02 -3.82872462e-01
-1.63573012e-01 2.30337493e-02 4.59126979e-01 2.97335595e-01
-8.57686996e-01 8.92190456e-01 -2.56385595e-01 8.14270675e-02
-3.25750895e-02 -6.45641685e-01 -5.43370962e-01 -8.27919185e-01
2.23700479e-01 2.00414568e-01 1.68116763e-01 -5.57541773... | [14.99181842803955, -2.552314281463623] |
236b6a09-27b8-439e-a001-10e289bb6acd | let-s-see-clearly-contaminant-artifact | 2104.08852 | null | https://arxiv.org/abs/2104.08852v1 | https://arxiv.org/pdf/2104.08852v1.pdf | Let's See Clearly: Contaminant Artifact Removal for Moving Cameras | Contaminants such as dust, dirt and moisture adhering to the camera lens can greatly affect the quality and clarity of the resulting image or video. In this paper, we propose a video restoration method to automatically remove these contaminants and produce a clean video. Our approach first seeks to detect attention map... | ['Pedro V. Sander', 'Jing Liao', 'Bo Zhang', 'Xiaoyu Li'] | 2021-04-18 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Li_Lets_See_Clearly_Contaminant_Artifact_Removal_for_Moving_Cameras_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Li_Lets_See_Clearly_Contaminant_Artifact_Removal_for_Moving_Cameras_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-restoration'] | ['computer-vision'] | [ 5.35004377e-01 -2.95388967e-01 5.92408359e-01 5.88717014e-02
-3.40658337e-01 -4.22710329e-01 5.37127435e-01 -1.03364035e-01
-3.72502841e-02 6.67968333e-01 4.66211706e-01 2.23672688e-02
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1.82607740e-01 -5.87254286e-01 2.17044294e-01 2.78639253... | [11.238571166992188, -2.0509748458862305] |
25efc804-73c6-4639-8573-3fbe504cc4fb | imsimcse-improving-contrastive-learning-for | 2305.13192 | null | https://arxiv.org/abs/2305.13192v1 | https://arxiv.org/pdf/2305.13192v1.pdf | ImSimCSE: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives | This paper aims to improve contrastive learning for sentence embeddings from two perspectives: handling dropout noise and addressing feature corruption. Specifically, for the first perspective, we identify that the dropout noise from negative pairs affects the model's performance. Therefore, we propose a simple yet eff... | ['Lemao Liu', 'Lihui Chen', 'Wei Shao', 'Jiahao Xu'] | 2023-05-22 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 1.33464992e-01 -1.14857368e-01 -3.93551588e-02 -2.68197656e-01
-1.09856498e+00 -4.11550820e-01 6.06553435e-01 4.03044671e-01
-8.24642181e-01 6.79458559e-01 2.70442575e-01 -3.01431537e-01
6.12068810e-02 -4.42041963e-01 -7.05128133e-01 -6.73228085e-01
-1.68406144e-02 -1.45420432e-02 1.83320031e-01 -4.69192982... | [10.891511917114258, 8.5986328125] |
5a3b1a3c-a069-46d1-a321-fbd8f985217e | ji-yu-bertde-duan-dao-duan-zhong-wen-pian | null | null | https://aclanthology.org/2020.ccl-1.36 | https://aclanthology.org/2020.ccl-1.36.pdf | 基于BERT的端到端中文篇章事件抽取(A BERT-based End-to-End Model for Chinese Document-level Event Extraction) | 篇章级事件抽取研究从整篇文档中检测事件,识别出事件包含的元素并赋予每个元素特定的角色。本文针对限定领域的中文文档提出了基于BERT的端到端模型,在模型的元素和角色识别中依次引入前序层输出的事件类型以及实体嵌入表示,增强文本的事件、元素和角色关联表示,提高篇章中各事件所属元素的识别精度。在此基础上利用标题信息和事件五元组的嵌入式表示,实现主从事件的划分及元素融合。实验证明本文的方法与现有工作相比具有明显的提升。 | ['Bo Xu', 'Shuyi Wang', 'Hui Song', 'Hongkuan Zhang'] | null | null | null | null | ccl-2020-10 | ['document-level-event-extraction'] | ['natural-language-processing'] | [-4.76836711e-01 -8.37558568e-01 4.01335031e-01 2.01676682e-01
7.72331715e-01 -1.22301042e+00 3.33543211e-01 8.35921943e-01
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-4.87259865e-01 1.71132779e+00 5.89283705e-01 -4.92211610... | [-3.3161251544952393, 6.907712459564209] |
fa51ad89-02bd-48e8-83b4-0af321e680e3 | minimal-solvers-for-single-view-lens | 2011.08988 | null | https://arxiv.org/abs/2011.08988v1 | https://arxiv.org/pdf/2011.08988v1.pdf | Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration | This paper proposes minimal solvers that use combinations of imaged translational symmetries and parallel scene lines to jointly estimate lens undistortion with either affine rectification or focal length and absolute orientation. We use constraints provided by orthogonal scene planes to recover the focal length. We sh... | ['James Pritts', 'Rostyslav Hryniv', 'Oles Dobosevych', 'Yaroslava Lochman'] | 2020-11-17 | null | null | null | null | ['scene-parsing', 'scene-labeling', 'camera-auto-calibration'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-5.99212162e-02 -1.49559751e-01 1.37297332e-01 -5.72163820e-01
-6.96074724e-01 -9.47664857e-01 5.30019701e-01 -7.64799893e-01
4.08000238e-02 5.05346239e-01 1.83778182e-01 1.20632676e-02
-2.48665944e-01 -3.64861608e-01 -1.01392627e+00 -5.88085830e-01
5.95574856e-01 6.88688278e-01 1.60476584e-02 8.89460091... | [8.389333724975586, -2.4731714725494385] |
04ee89eb-ac27-44e2-860c-edcd8da24855 | overview-of-the-wanlp-2022-shared-task-on | 2211.10057 | null | https://arxiv.org/abs/2211.10057v1 | https://arxiv.org/pdf/2211.10057v1.pdf | Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic | Propaganda is the expression of an opinion or an action by an individual or a group deliberately designed to influence the opinions or the actions of other individuals or groups with reference to predetermined ends, which is achieved by means of well-defined rhetorical and psychological devices. Propaganda techniques a... | ['Preslav Nakov', 'Giovanni Da San Martino', 'Wajdi Zaghouani', 'Hamdy Mubarak', 'Firoj Alam'] | 2022-11-18 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [ 2.11859077e-01 1.27010375e-01 -1.46696165e-01 -1.38181180e-01
-4.35963660e-01 -8.06547880e-01 1.22593033e+00 6.82200670e-01
-5.16810954e-01 6.61182404e-01 5.50535321e-01 -5.71066976e-01
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2.47998625e-01 2.38550752e-01 2.90839095e-03 -5.14142513... | [8.54932689666748, 10.493205070495605] |
59199f7b-c658-43c7-af9f-5be5d18a7d4a | polar-relative-positional-encoding-for-video | null | null | https://www.ijcai.org/proceedings/2020/132 | https://www.ijcai.org/proceedings/2020/0132.pdf | Polar Relative Positional Encoding for Video-Language Segmentation | In this paper, we tackle a challenging task named video-language segmentation. Given a video and a sentence in natural language, the goal is to segment the object or actor described by the sentence in video frames. To accurately denote a target object, the given sentence usually refers to multiple attributes, such as n... | ['Qi Tian', 'Fei Wu', 'Lingxi Xie', 'Ke Ning'] | 2020-07-20 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 2.29716420e-01 -5.95722173e-04 -1.33016884e-01 -6.88831151e-01
-7.68948913e-01 -4.94371951e-01 6.61629915e-01 1.48535460e-01
-6.42816782e-01 3.50673854e-01 3.66455853e-01 -9.07412544e-02
1.05346665e-01 -5.34004211e-01 -1.00261867e+00 -8.18831503e-01
2.14728937e-01 2.55342036e-01 5.11985123e-01 -1.55902728... | [9.788094520568848, 0.7649635076522827] |
5f7f53a0-836b-46f2-81da-e8381e72fba6 | potential-penetrative-pass-p3 | 2302.10760 | null | https://arxiv.org/abs/2302.10760v1 | https://arxiv.org/pdf/2302.10760v1.pdf | Potential Penetrative Pass (P3) | To score goals in football, a team needs to move forward on the pitch and there are various ways to do so. Depending on the game plan & philosophy; some teams prefer to play long balls from either wings or defense. Others, prefer to penetrate in depth with passes and outplay the opponent players. To objectively & in an... | ['Hadi Sotudeh'] | 2023-01-26 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-3.67717236e-01 -2.56416239e-02 -3.34934928e-02 3.21065634e-01
-3.16243678e-01 -9.28899407e-01 2.42064551e-01 2.44168136e-02
-7.35401988e-01 1.03357720e+00 4.97540623e-01 -4.05742019e-01
-6.32866740e-01 -9.73851919e-01 -1.72557414e-01 -4.75128293e-01
-1.00010462e-01 5.99654257e-01 6.23316467e-01 -8.27205896... | [6.434110164642334, 0.41427457332611084] |
c458a7fe-2367-4768-a6cb-4bfef5f41d9f | coordinated-multi-agent-exploration-using | null | null | https://openreview.net/forum?id=MPO4oML_JC | https://openreview.net/pdf?id=MPO4oML_JC | Coordinated Multi-Agent Exploration Using Shared Goals | Exploration is critical for good results of deep reinforcement learning algorithms and has drawn much attention. However, existing multi-agent deep reinforcement learning algorithms still use mostly noise-based techniques. It was recognized recently that noise-based exploration is suboptimal in multi-agent settings, an... | ['Alex Schwing', 'Unnat Jain', 'Iou-Jen Liu'] | 2021-01-01 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.00773317e-01 -1.28743529e-01 -3.13957602e-01 6.80168271e-02
-9.77570117e-01 -4.21477973e-01 6.60641909e-01 3.08424294e-01
-8.81629109e-01 1.00671065e+00 2.34071970e-01 2.73805461e-03
-1.98280647e-01 -6.80018842e-01 -6.33003592e-01 -8.03426325e-01
-5.64894676e-01 8.88538718e-01 8.24873522e-03 -5.47153056... | [3.8592233657836914, 1.8186182975769043] |
6692e0d6-2dc5-42a2-82d9-7eb32c43e0d0 | weakly-supervised-cell-tracking-via-backward | 2007.15258 | null | https://arxiv.org/abs/2007.15258v1 | https://arxiv.org/pdf/2007.15258v1.pdf | Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation | We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of "cell detection" (i.e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining. First, we train co-detec... | ['Chenyang Wang', 'Ryoma Bise', 'Junya Hayashida', 'Dai Fei Elmer Ker', 'Kazuya Nishimura'] | 2020-07-30 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1489_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570103.pdf | eccv-2020-8 | ['cell-detection'] | ['computer-vision'] | [-2.40152776e-01 1.28731057e-01 -2.09221728e-02 -1.03745311e-01
-6.85301960e-01 -6.97845757e-01 4.50116962e-01 2.72738010e-01
-7.80995190e-01 9.16027844e-01 -2.96328038e-01 -1.97369680e-02
5.10634303e-01 -8.63341212e-01 -9.62615788e-01 -1.06159699e+00
5.86632565e-02 4.83905345e-01 4.70510423e-01 4.17280532... | [14.639287948608398, -3.1629228591918945] |
93446343-532f-4d89-bc1d-bd7617c67388 | edge-storage-management-recipe-with-zero-shot | 2307.04298 | null | https://arxiv.org/abs/2307.04298v1 | https://arxiv.org/pdf/2307.04298v1.pdf | Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection | Recent studies show edge computing-based road anomaly detection systems which may also conduct data collection simultaneously. However, the edge computers will have small data storage but we need to store the collected audio samples for a long time in order to update existing models or develop a novel method. Therefore... | ['Myung Jin Kim', 'UJu Gim', 'YeongHyeon Park'] | 2023-07-10 | null | null | null | null | ['audio-super-resolution', 'super-resolution', 'anomaly-detection', 'management', 'audio-super-resolution', 'edge-computing', 'data-compression'] | ['audio', 'computer-vision', 'methodology', 'miscellaneous', 'music', 'time-series', 'time-series'] | [ 4.56680432e-02 -6.03440523e-01 1.01196170e-01 -1.13608159e-01
-4.51928020e-01 5.22950441e-02 5.74546494e-02 2.53117114e-01
-3.22382987e-01 3.52552950e-01 1.74228743e-01 -2.02706307e-01
-9.01958197e-02 -1.23258543e+00 -5.87125897e-01 -6.98828995e-01
-1.65856183e-01 -4.74441983e-02 5.04228592e-01 -1.51609600... | [7.885635852813721, 2.8368101119995117] |
0dc31f64-0f70-4993-bdfe-40f177f6637f | real-time-streaming-video-denoising-with | 2207.06937 | null | https://arxiv.org/abs/2207.06937v1 | https://arxiv.org/pdf/2207.06937v1.pdf | Real-time Streaming Video Denoising with Bidirectional Buffers | Video streams are delivered continuously to save the cost of storage and device memory. Real-time denoising algorithms are typically adopted on the user device to remove the noise involved during the shooting and transmission of video streams. However, sliding-window-based methods feed multiple input frames for a singl... | ['Qifeng Chen', 'Xin Yang', 'Junming Chen', 'Chenyang Qi'] | 2022-07-14 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 4.18513715e-02 -6.45947397e-01 1.58547863e-01 -3.05565178e-01
-7.82468557e-01 -4.76826370e-01 4.40424830e-01 -1.96730539e-01
-3.38860005e-01 3.09131205e-01 3.67129922e-01 -2.08978385e-01
-2.54218969e-02 -6.07754171e-01 -8.06538880e-01 -7.33374715e-01
8.38899165e-02 -3.56193930e-01 4.95459229e-01 -4.34628464... | [11.352167129516602, -2.104137420654297] |
a8e1308f-f234-4a6e-9440-57ff5d772946 | beyond-i-i-d-three-levels-of-generalization | 2011.07743 | null | https://arxiv.org/abs/2011.07743v6 | https://arxiv.org/pdf/2011.07743v6.pdf | Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases | Existing studies on question answering on knowledge bases (KBQA) mainly operate with the standard i.i.d assumption, i.e., training distribution over questions is the same as the test distribution. However, i.i.d may be neither reasonably achievable nor desirable on large-scale KBs because 1) true user distribution is h... | ['Yu Su', 'Xifeng Yan', 'Percy Liang', 'Brian Sadler', 'Michelle Vanni', 'Sue Kase', 'Yu Gu'] | 2020-11-16 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-4.20914054e-01 -5.29202037e-02 -1.28111407e-01 -5.10021925e-01
-1.03867483e+00 -8.92039657e-01 3.32434326e-01 9.25541967e-02
-4.31242973e-01 9.49629486e-01 4.08092320e-01 -6.61075652e-01
-2.02755779e-01 -1.10965443e+00 -7.44331181e-01 -2.94536561e-01
3.90608847e-01 7.17307627e-01 5.85651457e-01 -6.18036509... | [11.03396224975586, 7.977259159088135] |
4223ce02-66a8-4a5e-9fcd-00f1f37de439 | efficient-skill-acquisition-for-complex | 2303.03365 | null | https://arxiv.org/abs/2303.03365v1 | https://arxiv.org/pdf/2303.03365v1.pdf | Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments | Data efficiency in robotic skill acquisition is crucial for operating robots in varied small-batch assembly settings. To operate in such environments, robots must have robust obstacle avoidance and versatile goal conditioning acquired from only a few simple demonstrations. Existing approaches, however, fall short of th... | ['Ingmar Posner', 'Jack Collins', 'Jun Yamada'] | 2023-03-06 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 3.02103251e-01 -1.51426400e-04 -2.29666889e-01 7.49840811e-02
-9.65020478e-01 -7.01242387e-01 3.42204034e-01 -1.97188154e-01
-5.87966323e-01 6.72769010e-01 -2.87122995e-01 -1.55090213e-01
-4.65995014e-01 -4.10289884e-01 -9.68556046e-01 -5.82985163e-01
-2.79420733e-01 8.52332890e-01 2.13721409e-01 -5.20545304... | [4.5919599533081055, 0.8206328749656677] |
5cc086a8-9fb7-44cc-806a-e1a35077db86 | explaincpe-a-free-text-explanation-benchmark | 2305.12945 | null | https://arxiv.org/abs/2305.12945v1 | https://arxiv.org/pdf/2305.12945v1.pdf | ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination | As ChatGPT and GPT-4 spearhead the development of Large Language Models (LLMs), more researchers are investigating their performance across various tasks. But more research needs to be done on the interpretability capabilities of LLMs, that is, the ability to generate reasons after an answer has been given. Existing ex... | ['Min Zhang', 'Zhenran Xu', 'Baotian Hu', 'Jindi Yu', 'Dongfang Li'] | 2023-05-22 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 2.48348191e-01 9.66533124e-01 -4.33986187e-01 -6.70802474e-01
-1.03373015e+00 -3.22998255e-01 4.38996732e-01 3.02529067e-01
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-3.13050866e-01 -4.88699019e-01 -6.56679273e-01 -1.32921726e-01
3.22620362e-01 8.95954192e-01 -2.44454339e-01 -1.55932859... | [9.539392471313477, 7.180451393127441] |
2f87e0a3-351c-4c72-acd9-d4d3ba0f627d | name-tagging-for-low-resource-incident | null | null | https://aclanthology.org/N16-1029 | https://aclanthology.org/N16-1029.pdf | Name Tagging for Low-resource Incident Languages based on Expectation-driven Learning | null | ['Heng Ji', 'Daniel Marcu', 'Boliang Zhang', 'Tianlu Wang', 'Ashish Vaswani', 'Kevin Knight', 'Xiaoman Pan'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['cross-lingual-entity-linking'] | ['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... | [-7.431269645690918, 3.586265802383423] |
95ec8280-97a8-457e-8ad5-2aa70a33ee2e | upper-limb-movement-recognition-utilising-eeg | 2207.08650 | null | https://arxiv.org/abs/2207.08650v3 | https://arxiv.org/pdf/2207.08650v3.pdf | Upper Limb Movement Recognition utilising EEG and EMG Signals for Rehabilitative Robotics | Upper limb movement classification, which maps input signals to the target activities, is a key building block in the control of rehabilitative robotics. Classifiers are trained for the rehabilitative system to comprehend the desires of the patient whose upper limbs do not function properly. Electromyography (EMG) sign... | ['Ravi Suppiah', 'ZiHao Wang'] | 2022-07-18 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 1.97364628e-01 -2.51984507e-01 -3.31494302e-01 -3.93266641e-02
-5.12510777e-01 3.92846018e-02 2.44378694e-03 -4.40685302e-01
-4.60008323e-01 9.15453553e-01 4.26933765e-01 7.99364299e-02
-5.15275955e-01 -5.47691464e-01 -4.02043372e-01 -7.37516642e-01
-1.05995134e-01 -2.16085045e-03 -2.79489338e-01 -3.47028583... | [6.914714336395264, 0.21213054656982422] |
2a1651a8-1202-40dd-a788-3bb158961d56 | scat-robust-self-supervised-contrastive | 2307.01488 | null | https://arxiv.org/abs/2307.01488v1 | https://arxiv.org/pdf/2307.01488v1.pdf | SCAT: Robust Self-supervised Contrastive Learning via Adversarial Training for Text Classification | Despite their promising performance across various natural language processing (NLP) tasks, current NLP systems are vulnerable to textual adversarial attacks. To defend against these attacks, most existing methods apply adversarial training by incorporating adversarial examples. However, these methods have to rely on g... | ['Dit-yan Yeung', 'Junjie Wu'] | 2023-07-04 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'text-classification'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 5.21410465e-01 2.54840493e-01 3.76822203e-02 -3.19096357e-01
-1.10123932e+00 -1.20328367e+00 8.75389814e-01 2.66600579e-01
-5.29505253e-01 6.85149133e-01 -6.11721240e-02 -5.88509202e-01
4.62164253e-01 -9.67613339e-01 -7.75647700e-01 -4.65061575e-01
1.70509055e-01 5.32388031e-01 3.97099964e-02 -4.76876169... | [5.9524736404418945, 8.047940254211426] |
49a9e8e9-de4f-4ed4-96a3-318f8b968ebd | latent-conditioned-policy-gradient-for-multi | 2303.08909 | null | https://arxiv.org/abs/2303.08909v1 | https://arxiv.org/pdf/2303.08909v1.pdf | Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning | Sequential decision making in the real world often requires finding a good balance of conflicting objectives. In general, there exist a plethora of Pareto-optimal policies that embody different patterns of compromises between objectives, and it is technically challenging to obtain them exhaustively using deep neural ne... | ['Chetan Gupta', 'Takuya Kanazawa'] | 2023-03-15 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 2.54711229e-02 -3.35346043e-01 -2.81217575e-01 -7.79619887e-02
-7.72079766e-01 -6.71885788e-01 3.35452795e-01 1.22376524e-01
-7.19672382e-01 1.26357496e+00 -8.84271488e-02 -6.33309066e-01
-6.37781680e-01 -6.65334165e-01 -6.98766530e-01 -7.54934967e-01
-3.23795564e-02 5.10776222e-01 -2.57862955e-01 -3.93725514... | [4.221834659576416, 2.4063339233398438] |
219c311f-7a25-4272-8313-73fc76e5523b | alap-ae-as-lite-as-possible-auto-encoder | 2203.10363 | null | https://arxiv.org/abs/2203.10363v2 | https://arxiv.org/pdf/2203.10363v2.pdf | Towards Device Efficient Conditional Image Generation | We present a novel algorithm to reduce tensor compute required by a conditional image generation autoencoder without sacrificing quality of photo-realistic image generation. Our method is device agnostic, and can optimize an autoencoder for a given CPU-only, GPU compute device(s) in about normal time it takes to train ... | ['Gaurav Bharaj', 'Nisarg A. Shah'] | 2022-03-19 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 2.60035545e-01 2.25962877e-01 3.83343428e-01 -2.75293797e-01
-5.02621710e-01 -6.76380098e-01 3.07723284e-01 -4.70968455e-01
-3.57498139e-01 4.29760337e-01 1.85240768e-02 -4.45664406e-01
5.01750052e-01 -9.10032272e-01 -9.22233760e-01 -8.68309140e-01
1.69482350e-01 3.75086963e-01 -7.15316162e-02 -6.61389381... | [11.334035873413086, -0.5330678224563599] |
32cbdf79-df74-4dd2-8cf8-6b39277dc79f | learning-to-annotate-modularizing-data | 1911.01352 | null | https://arxiv.org/abs/1911.01352v3 | https://arxiv.org/pdf/1911.01352v3.pdf | Learning from Explanations with Neural Execution Tree | While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely on labeled data, which restricts their applications in scenarios where data annotation is expensive. Natural language (NL) explanations have been demonstrated very useful additional supervision... | ['Zhiyuan Liu', 'Yujia Qin', 'Ziqi Wang', 'Xiang Ren', 'Leonardo Neves', 'Qinyuan Ye', 'Wenxuan Zhou', 'Jun Yan'] | 2019-11-04 | null | https://openreview.net/forum?id=rJlUt0EYwS | https://openreview.net/pdf?id=rJlUt0EYwS | iclr-2020-1 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 5.68340421e-01 1.04405904e+00 -7.77580619e-01 -8.05955946e-01
-6.24411464e-01 -5.19623697e-01 6.02766871e-01 3.21303129e-01
3.20901386e-02 1.04193473e+00 3.45082939e-01 -6.80201650e-01
1.69714510e-01 -9.14610386e-01 -8.08380783e-01 -1.11371301e-01
3.66897017e-01 6.19491279e-01 1.27923012e-01 -3.00511390... | [9.52722454071045, 6.909777641296387] |
2079096a-74ba-48c9-b35b-ba755bf1f557 | interpretable-multimodal-emotion-recognition-1 | 2306.02845 | null | https://arxiv.org/abs/2306.02845v1 | https://arxiv.org/pdf/2306.02845v1.pdf | Interpretable Multimodal Emotion Recognition using Facial Features and Physiological Signals | This paper aims to demonstrate the importance and feasibility of fusing multimodal information for emotion recognition. It introduces a multimodal framework for emotion understanding by fusing the information from visual facial features and rPPG signals extracted from the input videos. An interpretability technique bas... | ['Xiaobai Li', 'Puneet Kumar'] | 2023-06-05 | null | null | null | null | ['multimodal-emotion-recognition', 'emotion-classification', 'emotion-classification', 'multimodal-emotion-recognition'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'speech'] | [ 2.13859007e-01 -1.76833794e-01 -1.52271673e-01 -6.82970464e-01
-5.35454214e-01 -4.45242405e-01 5.06075561e-01 -9.45286900e-02
-3.61994654e-01 6.85223281e-01 6.17719531e-01 4.61119235e-01
-2.85327941e-01 -1.32119790e-01 -2.69203126e-01 -7.72233188e-01
-2.55458981e-01 -2.51483440e-01 -6.35628343e-01 1.13119371... | [13.243236541748047, 5.166940212249756] |
e38dc572-8ee7-406c-b0bb-d090db038f4e | powergan-synthesizing-appliance-power | 2007.13645 | null | https://arxiv.org/abs/2007.13645v1 | https://arxiv.org/pdf/2007.13645v1.pdf | PowerGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks | Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter. Most current techniques for NILM are trained using significant amounts of labeled appliances power data. The collection of such data is challenging, m... | ['Alon Harell', 'Stephen Makonin', 'Ivan V. Bajic', 'Richard Jones'] | 2020-07-20 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 2.82926798e-01 5.42702258e-01 -3.87015827e-02 -2.91993231e-01
-1.01539612e+00 -8.58815014e-01 6.08489990e-01 -3.77550155e-01
4.05297130e-01 1.01800752e+00 3.33535373e-01 -1.84597954e-01
2.29335874e-01 -1.09931195e+00 -4.18398470e-01 -8.68860602e-01
1.18369475e-01 6.98585153e-01 -6.38072312e-01 -9.54082422... | [16.051918029785156, 7.569221019744873] |
30c97348-7193-47a8-a1fe-6d05bee69b26 | cloud-based-deep-learning-of-big-eeg-data-for | 1702.05192 | null | http://arxiv.org/abs/1702.05192v1 | http://arxiv.org/pdf/1702.05192v1.pdf | Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction | Developing a Brain-Computer Interface~(BCI) for seizure prediction can help
epileptic patients have a better quality of life. However, there are many
difficulties and challenges in developing such a system as a real-life support
for patients. Because of the nonstationary nature of EEG signals, normal and
seizure patter... | ['Hamid Soltanian-Zadeh', 'Mohammad-Parsa Hosseini', 'Dario Pompili', 'Kost Elisevich'] | 2017-02-17 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-8.30679014e-02 -5.14166892e-01 6.21873498e-01 -4.31948960e-01
-1.98447794e-01 5.53690866e-02 5.79716591e-03 -6.22493960e-02
-2.79374570e-01 7.69252658e-01 -1.10827848e-01 -8.47708508e-02
-5.28388560e-01 -7.16386735e-01 -2.17817143e-01 -9.50801253e-01
-3.58261794e-01 4.13942397e-01 -1.48176208e-01 -1.16195805... | [13.235640525817871, 3.4766790866851807] |
8d8e56d9-8db4-41ec-b910-1ef5560b25aa | how-do-we-answer-complex-questions-discourse | null | null | https://openreview.net/forum?id=shZUViLG-DS | https://openreview.net/pdf?id=shZUViLG-DS | How do we answer complex questions: Discourse structure of long form answers | Long form answers, consisting of multiple sentences, can provide nuanced and comprehensive answers to a broader set of questions. However, little prior work exists on this task. To better understand this complex task, we study the functional structure of long form answers on two datasets, Natural Questions~\cite{kwiatk... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['extractive-summarization'] | ['natural-language-processing'] | [ 4.53165203e-01 6.61155999e-01 -1.76689088e-01 -3.70409489e-01
-1.16061056e+00 -1.10033023e+00 7.57519186e-01 7.94258356e-01
-3.43274832e-01 1.02220869e+00 1.07042122e+00 -2.82315999e-01
-4.17747974e-01 -5.19133270e-01 -4.27825063e-01 -1.44409314e-01
3.55368823e-01 6.15629971e-01 4.90717292e-01 -6.26385212... | [11.62386703491211, 8.205540657043457] |
88a9da2b-109f-43ad-ad86-ae551b560818 | pose-graph-optimization-for-unsupervised | 1903.06315 | null | http://arxiv.org/abs/1903.06315v1 | http://arxiv.org/pdf/1903.06315v1.pdf | Pose Graph Optimization for Unsupervised Monocular Visual Odometry | Unsupervised Learning based monocular visual odometry (VO) has lately drawn
significant attention for its potential in label-free leaning ability and
robustness to camera parameters and environmental variations. However,
partially due to the lack of drift correction technique, these methods are
still by far less accura... | ['Yang Li', 'Yoshitaka Ushiku', 'Tatsuya Harada'] | 2019-03-15 | null | null | null | null | ['loop-closure-detection', 'monocular-visual-odometry'] | ['computer-vision', 'robots'] | [-2.44692847e-01 4.31211516e-02 -4.24637049e-01 -3.75813335e-01
-3.58678102e-01 -4.38083053e-01 5.26458502e-01 -3.04196067e-02
-4.38141584e-01 7.06794500e-01 4.11136709e-02 8.87647718e-02
-4.41419855e-02 -4.59809333e-01 -9.62406456e-01 -4.55443531e-01
7.53600895e-02 7.43314564e-01 2.34524548e-01 -1.32327527... | [7.9466118812561035, -2.14603853225708] |
42a9b6ba-5f03-4a84-8191-e5dc34cc8521 | cross-modal-consensus-network-forweakly | null | null | https://arxiv.org/abs/2107.12589#:~:text=Cross%2Dmodal%20Consensus%20Network%20for%20Weakly%20Supervised%20Temporal%20Action%20Localization,-Fa%2DTing%20Hong&text=Weakly%20supervised%20temporal%20action%20localization%20(WS%2DTAL)%20is%20a,with%20video%2Dlevel%20categorical%20supervision. | https://arxiv.org/pdf/2107.12589.pdf | Cross-modal Consensus Network forWeakly Supervised Temporal Action Localization | Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. Both appearance and motion features are used in previous works, while they do not utilize them in a proper way but apply simple concatenation ... | ['Wei-Shi Zheng', 'Ying Shan', 'Dan Xu', 'Jia-Chang Feng', 'Fa-Ting Hong'] | 2021-07-27 | null | null | null | proceedings-of-the-29th-acm-international | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 9.61031392e-02 -3.06748569e-01 -3.57109666e-01 -1.49206698e-01
-7.56777346e-01 -1.00992836e-01 5.73176324e-01 -3.24043512e-01
-4.91304606e-01 5.16006470e-01 5.78908980e-01 4.63277757e-01
-1.36786178e-01 -1.85215741e-01 -5.55735707e-01 -9.83547091e-01
7.71381184e-02 -1.12844683e-01 7.11732090e-01 -1.29991651... | [8.543191909790039, 0.7138928771018982] |
0f8df7b8-4c79-482d-aeff-3b9f401efb87 | you-only-need-two-detectors-to-achieve-multi | 2304.08709 | null | https://arxiv.org/abs/2304.08709v1 | https://arxiv.org/pdf/2304.08709v1.pdf | You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking | Firstly, a new multi-object tracking framework is proposed in this paper based on multi-modal fusion. By integrating object detection and multi-object tracking into the same model, this framework avoids the complex data association process in the classical TBD paradigm, and requires no additional training. Secondly, co... | ['Mingguang Huang', 'Ting Meng', 'Chunyun Fu', 'JiaWei He', 'Xiyang Wang'] | 2023-04-18 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-3.16688716e-01 -5.67670286e-01 -4.52076405e-01 1.14353962e-01
-6.93143964e-01 -3.81569237e-01 6.04364336e-01 -3.70423906e-02
-2.89469630e-01 6.29148602e-01 -2.23235324e-01 -1.54769376e-01
-3.54930609e-01 -6.32455409e-01 -4.72216725e-01 -9.06857133e-01
2.05799341e-02 4.88039166e-01 1.03939462e+00 1.49939284... | [6.512155055999756, -2.033923387527466] |
3202a06d-4703-4147-8e5f-18551677b606 | machine-learning-driven-language-assessment | null | null | https://aclanthology.org/2020.tacl-1.17 | https://aclanthology.org/2020.tacl-1.17.pdf | Machine Learning--Driven Language Assessment | We describe a method for rapidly creating language proficiency assessments, and provide experimental evidence that such tests can be valid, reliable, and secure. Our approach is the first to use machine learning and natural language processing to induce proficiency scales based on a given standard, and then use linguis... | ['Masato Hagiwara', 'Geoffrey T. LaFlair', 'Burr Settles'] | 2020-01-01 | null | null | null | tacl-2020-1 | ['skills-assessment'] | ['computer-vision'] | [-2.67214566e-01 2.37164900e-01 -4.26616549e-01 -5.03767788e-01
-1.38636494e+00 -1.12511694e+00 1.26833066e-01 5.47471166e-01
-7.39688277e-01 1.09161282e+00 1.68421373e-01 -1.06099582e+00
-1.30795047e-01 -8.94663513e-01 -5.82937419e-01 3.72818708e-01
4.49800700e-01 5.23801923e-01 2.04045534e-01 -2.22753286... | [11.052508354187012, 9.751728057861328] |
2af2f006-5ca3-4d79-8211-f15f79920d3a | interactive-evidence-detection-train-state-of | null | null | https://aclanthology.org/D19-6613 | https://aclanthology.org/D19-6613.pdf | Interactive Evidence Detection: train state-of-the-art model out-of-domain or simple model interactively? | Finding evidence is of vital importance in research as well as fact checking and an evidence detection method would be useful in speeding up this process. However, when addressing a new topic there is no training data and there are two approaches to get started. One could use large amounts of out-of-domain data to trai... | ['Chris Stahlhut'] | 2019-11-01 | null | null | null | ws-2019-11 | ['small-data'] | ['computer-vision'] | [ 8.54518041e-02 2.57032841e-01 -1.00577570e-01 -2.76821822e-01
-8.98955107e-01 -6.69309556e-01 6.47803545e-01 7.52427399e-01
-6.58600688e-01 7.87524223e-01 -7.58821666e-02 -7.99586773e-01
1.08750857e-01 -9.82232392e-01 -8.76688480e-01 -2.03738168e-01
2.30123118e-01 8.44675124e-01 6.59608185e-01 4.64296676... | [9.562148094177246, 8.145552635192871] |
3c0e13d3-5e5a-4f2c-a00b-ffe13608e6bb | bone-marrow-sparing-for-cervical-cancer | 2204.09278 | null | https://arxiv.org/abs/2204.09278v1 | https://arxiv.org/pdf/2204.09278v1.pdf | Bone marrow sparing for cervical cancer radiotherapy on multimodality medical images | Cervical cancer threatens the health of women seriously. Radiotherapy is one of the main therapy methods but with high risk of acute hematologic toxicity. Delineating the bone marrow (BM) for sparing using computer tomography (CT) images to plan before radiotherapy can effectively avoid this risk. Comparing with magnet... | ['Xiuming Zhang', 'Han Gao', 'Hanzi Xu', 'Kexin Gan', 'Jie Yuan', 'Ying Sun', 'Yuening Wang'] | 2022-04-20 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 3.74605358e-01 2.07989424e-01 -4.36716259e-01 -1.47903472e-01
-8.37987244e-01 -4.34287071e-01 2.76957214e-01 4.15572315e-01
-4.88286048e-01 5.66390574e-01 2.32043892e-01 -6.17681026e-01
-1.47995606e-01 -9.93957818e-01 -1.50701016e-01 -1.04036939e+00
3.64765048e-01 1.15644622e+00 4.73041832e-01 -2.51800030... | [14.223434448242188, -2.7155704498291016] |
04527a5d-de6f-4632-82c7-d8ace046d6aa | geolocation-of-cultural-heritage-using-multi | 2209.03638 | null | https://arxiv.org/abs/2209.03638v1 | https://arxiv.org/pdf/2209.03638v1.pdf | Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding | Knowledge Graphs (KGs) have proven to be a reliable way of structuring data. They can provide a rich source of contextual information about cultural heritage collections. However, cultural heritage KGs are far from being complete. They are often missing important attributes such as geographical location, especially for... | ['Marcello Pelillo', 'Alessio Del Bue', 'Diego Pilutti', 'Stuart James', 'Feliks Hibraj', 'Sebastiano Vascon', 'Hebatallah A. Mohamed'] | 2022-09-08 | null | null | null | null | ['multi-view-learning', 'knowledge-graph-embedding'] | ['computer-vision', 'graphs'] | [-5.39287031e-01 1.50415093e-01 -3.77959371e-01 -3.84111315e-01
-5.91833055e-01 -7.32391834e-01 8.07005882e-01 1.01777244e+00
2.35736165e-02 9.54343319e-01 6.65124416e-01 1.80649966e-01
-7.52270162e-01 -1.64484882e+00 -6.33743882e-01 -4.51785445e-01
-5.23369431e-01 5.78931153e-01 4.95328009e-01 -3.21014702... | [8.87756633758545, 7.877692699432373] |
167a92a7-df17-4acd-b9b7-4caf6bd1428f | side-channel-assisted-inference-leakage-from | 2304.01990 | null | https://arxiv.org/abs/2304.01990v1 | https://arxiv.org/pdf/2304.01990v1.pdf | Side Channel-Assisted Inference Leakage from Machine Learning-based ECG Classification | The Electrocardiogram (ECG) measures the electrical cardiac activity generated by the heart to detect abnormal heartbeat and heart attack. However, the irregular occurrence of the abnormalities demands continuous monitoring of heartbeats. Machine learning techniques are leveraged to automate the task to reduce labor wo... | ['Han Wang', 'Houman Homayoun', 'Chongzhou Fang', 'Ning Miao', 'Jialin Liu'] | 2023-04-04 | null | null | null | null | ['ecg-classification', 'dynamic-time-warping'] | ['medical', 'time-series'] | [ 5.27478635e-01 -3.34756494e-01 -3.40576500e-01 -2.01266974e-01
-5.59411168e-01 -9.07146811e-01 -1.13151230e-01 1.34962961e-01
-4.78781164e-02 3.01509917e-01 -1.73868984e-01 -6.22940421e-01
-3.71010780e-01 -6.19061351e-01 -2.01502275e-02 -7.90072262e-01
-5.43708920e-01 -1.40451640e-01 -1.25615867e-02 7.79176131... | [14.296346664428711, 3.201824426651001] |
e6130d09-a79b-4d26-8c8b-36af1e69a4ac | cycle-sum-cycle-consistent-adversarial-lstm | 1904.08265 | null | http://arxiv.org/abs/1904.08265v1 | http://arxiv.org/pdf/1904.08265v1.pdf | Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization | In this paper, we present a novel unsupervised video summarization model that
requires no manual annotation. The proposed model termed Cycle-SUM adopts a new
cycle-consistent adversarial LSTM architecture that can effectively maximize
the information preserving and compactness of the summary video. It consists of
a fra... | ['Jiashi Feng', 'Li Zhou', 'Li Yuan', 'Francis EH Tay', 'Ping Li'] | 2019-04-17 | null | null | null | null | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 4.73704636e-01 2.72952795e-01 -1.99209407e-01 -3.89679790e-01
-8.89864922e-01 -3.06381881e-01 4.75831658e-01 -3.55464995e-01
-2.12486476e-01 6.59693837e-01 6.05160415e-01 -7.44405296e-03
2.44072020e-01 -6.24660313e-01 -1.19305050e+00 -1.05685961e+00
1.45678788e-01 1.16264559e-01 -4.59271558e-02 1.98746949... | [10.46061897277832, 0.4141937792301178] |
7bf221b6-35b3-4af0-956a-ffecac2fd423 | unicorn-a-unified-multi-tasking-model-for | null | null | https://dl.acm.org/doi/abs/10.1145/3588938 | https://dl.acm.org/doi/pdf/10.1145/3588938 | Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration | Data matching - which decides whether two data elements (e.g., string, tuple, column, or knowledge graph entity) are the "same" (a.k.a. a match) - is a key concept in data integration, such as entity matching and schema matching. The widely used practice is to build task-specific or even dataset-specific solutions, whi... | ['Song Gao', 'Xiaofeng Jia', 'Xiaoyong Du', 'Guoliang Li', 'Peng Wang', 'Nan Tang', 'Ju Fan', 'Jianhong Tu'] | 2023-05-01 | null | null | null | sigmod-pods-2023-5 | ['data-integration', 'zero-shot-learning', 'entity-resolution'] | ['knowledge-base', 'methodology', 'natural-language-processing'] | [ 3.36565286e-01 -1.37967840e-01 -7.87041843e-01 -4.41815048e-01
-7.81098425e-01 -3.57492477e-01 4.39847916e-01 4.26986545e-01
-4.05180305e-01 4.64986503e-01 2.41760343e-01 -2.99973935e-02
-2.73618042e-01 -9.91513073e-01 -9.74377453e-01 -1.66276202e-01
3.05739671e-01 6.24716163e-01 4.53205436e-01 -2.62360513... | [9.533543586730957, 8.527593612670898] |
49dfc015-6ef8-4bb8-b622-8fcdd3a8c410 | p-stmo-pre-trained-spatial-temporal-many-to | 2203.07628 | null | https://arxiv.org/abs/2203.07628v2 | https://arxiv.org/pdf/2203.07628v2.pdf | P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation | This paper introduces a novel Pre-trained Spatial Temporal Many-to-One (P-STMO) model for 2D-to-3D human pose estimation task. To reduce the difficulty of capturing spatial and temporal information, we divide this task into two stages: pre-training (Stage I) and fine-tuning (Stage II). In Stage I, a self-supervised pre... | ['Wen Gao', 'Siwei Ma', 'Shanshe Wang', 'Xinfeng Zhang', 'Zhenhua Liu', 'Wenkang Shan'] | 2022-03-15 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 1.24835335e-02 -1.67268813e-02 -2.38606766e-01 -3.31737369e-01
-8.06661189e-01 3.02835070e-02 1.46650702e-01 -4.68366057e-01
-4.65539843e-01 4.52411622e-01 2.90877134e-01 2.39160508e-01
3.00471216e-01 -5.54840446e-01 -8.93436074e-01 -5.54070771e-01
-3.15165482e-02 3.71282011e-01 5.29912472e-01 -1.79033861... | [7.216409206390381, -0.6071715354919434] |
611f8233-428c-432b-a82b-4bae83b5ba87 | rord-rotation-robust-descriptors-and | 2103.08573 | null | https://arxiv.org/abs/2103.08573v4 | https://arxiv.org/pdf/2103.08573v4.pdf | RoRD: Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching | The use of local detectors and descriptors in typical computer vision pipelines work well until variations in viewpoint and appearance change become extreme. Past research in this area has typically focused on one of two approaches to this challenge: the use of projections into spaces more suitable for feature matching... | ['K. Madhava Krishna', 'Michael Milford', 'Sourav Garg', 'Satyajit Tourani', 'Kinal Mehta', 'Aniket Gujarathi', 'Udit Singh Parihar'] | 2021-03-15 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 8.73572230e-02 -4.01100785e-01 -1.87485665e-01 -5.54260194e-01
-7.55859494e-01 -7.76534855e-01 1.11231828e+00 6.24966733e-02
-4.84844357e-01 2.26651028e-01 3.99547815e-01 2.53589034e-01
-3.94473448e-02 -4.73845810e-01 -6.58948898e-01 -4.92018402e-01
-2.16444768e-02 3.83896708e-01 4.03501123e-01 -3.14068079... | [7.8513922691345215, -2.150010108947754] |
44c6ebde-79c6-4a65-b158-6c2a4399503e | mwptoolkit-an-open-source-framework-for-deep | 2109.00799 | null | https://arxiv.org/abs/2109.00799v2 | https://arxiv.org/pdf/2109.00799v2.pdf | MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers | Developing automatic Math Word Problem (MWP) solvers has been an interest of NLP researchers since the 1960s. Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs. However, most existing methods are benchmarked soly on one or two datasets,... | ['Ee-Peng Lim', 'Dongxiang Zhang', 'Yan Wang', 'Bing Tian Dai', 'Yunshi Lan', 'Qiyuan Zhang', 'Lei Wang', 'Yihuai Lan'] | 2021-09-02 | null | null | null | null | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-5.19155800e-01 -2.22481459e-01 -3.91925305e-01 -3.39950353e-01
-9.93852556e-01 -8.86496663e-01 2.81686187e-01 -1.50597870e-01
-2.89878268e-02 8.86085927e-01 1.11855805e-01 -4.77312446e-01
-8.77842456e-02 -1.00115430e+00 -5.90076208e-01 -4.18104887e-01
3.45365405e-01 7.13083625e-01 -2.15540111e-01 -2.33295470... | [9.74284553527832, 7.435670375823975] |
5fd64f65-a532-420b-ac8c-7672b15e81e3 | moet-interpretable-and-verifiable | 1906.06717 | null | https://arxiv.org/abs/1906.06717v4 | https://arxiv.org/pdf/1906.06717v4.pdf | MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning | Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their adoption into safety-critical settings, such as healthcare or self-driving cars is hindered by their inability to provide safety guarantees or... | ['Sarfraz Khurshid', 'Rishabh Singh', 'Mladen Nikolic', 'Kaiyuan Wang', 'Andrija Petrovic', 'Marko Vasic'] | 2019-06-16 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-3.13196182e-02 9.03154671e-01 -3.21043611e-01 -3.41405034e-01
-2.82736301e-01 -5.68421423e-01 6.09722912e-01 8.11657012e-02
-3.53594571e-01 1.00135636e+00 -4.92979318e-01 -6.09451652e-01
-1.34541452e-01 -1.14605534e+00 -1.04168129e+00 -5.96702754e-01
7.22267777e-02 7.92388380e-01 3.96773666e-01 -1.53758496... | [4.195561408996582, 1.6402093172073364] |
551a0eb4-6af9-4969-832d-bc918553d0b1 | back-to-mlp-a-simple-baseline-for-human | 2207.01567 | null | https://arxiv.org/abs/2207.01567v3 | https://arxiv.org/pdf/2207.01567v3.pdf | Back to MLP: A Simple Baseline for Human Motion Prediction | This paper tackles the problem of human motion prediction, consisting in forecasting future body poses from historically observed sequences. State-of-the-art approaches provide good results, however, they rely on deep learning architectures of arbitrary complexity, such as Recurrent Neural Networks(RNN), Transformers o... | ['Francesc Moreno-Noguer', 'Xavier Alameda-Pineda', 'Vincent Lepetit', 'Xi Shen', 'Yuming Du', 'Wen Guo'] | 2022-07-04 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [-5.66084944e-02 2.57002890e-01 -3.63276720e-01 -6.55795783e-02
-5.06193578e-01 -5.42567335e-02 5.51897943e-01 -2.89951175e-01
-7.41886914e-01 5.71590483e-01 3.51850390e-01 -2.75740653e-01
2.17238724e-01 -6.10425711e-01 -1.05477548e+00 -5.60086489e-01
-4.41751599e-01 5.16594946e-01 5.05632937e-01 -3.96465957... | [7.254481792449951, -0.15771237015724182] |
49189313-13d8-4d40-ae3e-0d5661b0fa95 | superadditivity-and-convex-optimization-for | null | null | https://ieeexplore.ieee.org/document/9804854 | https://evoid.de/kostrykin2023-accepted-manuscript.pdf | Superadditivity and Convex Optimization for Globally Optimal Cell Segmentation using Deformable Shape Models | Cell nuclei segmentation is challenging due to shape variation and closely clustered or partially overlapping objects. Most previous methods are not globally optimal, limited to elliptical models, or are computationally expensive. In this work, we introduce a globally optimal approach based on deformable shape models a... | ['Karl Rohr', 'Leonid Kostrykin'] | 2022-06-23 | null | null | null | ieee-transactions-on-pattern-analysis-and-25 | ['cell-segmentation'] | ['medical'] | [-6.69740140e-02 -1.29699916e-01 1.39666930e-01 -1.03693634e-01
-9.98928010e-01 -1.00121450e+00 -6.22399189e-02 3.13985169e-01
-5.65963984e-01 6.43123746e-01 -3.12972546e-01 -2.60840473e-03
-9.04869288e-02 -5.72857738e-01 -6.61371410e-01 -1.24298096e+00
1.53509080e-01 6.45282090e-01 4.34442103e-01 1.94246069... | [14.478926658630371, -3.2107133865356445] |
7778100d-b4e0-4698-8e21-3da96d1825dd | scene-text-recognition-with-permuted | 2207.06966 | null | https://arxiv.org/abs/2207.06966v1 | https://arxiv.org/pdf/2207.06966v1.pdf | Scene Text Recognition with Permuted Autoregressive Sequence Models | Context-aware STR methods typically use internal autoregressive (AR) language models (LM). Inherent limitations of AR models motivated two-stage methods which employ an external LM. The conditional independence of the external LM on the input image may cause it to erroneously rectify correct predictions, leading to sig... | ['Rowel Atienza', 'Darwin Bautista'] | 2022-07-14 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 8.07084367e-02 -1.71146259e-01 -2.02178076e-01 -2.79688567e-01
-1.29044187e+00 -3.66085887e-01 5.98818779e-01 -1.96360648e-01
-5.13369381e-01 4.65762526e-01 2.84469843e-01 -6.63427234e-01
3.99667203e-01 -6.38984799e-01 -8.02992105e-01 -5.54428756e-01
2.28878528e-01 4.45865363e-01 1.81882098e-01 -2.22311495... | [10.880827903747559, 7.213721752166748] |
a95c61ff-dbd3-47a3-85d8-7d8f8fc511a2 | transformer-based-assignment-decision-network | 2208.03571 | null | https://arxiv.org/abs/2208.03571v2 | https://arxiv.org/pdf/2208.03571v2.pdf | Transformer-based assignment decision network for multiple object tracking | Data association is a crucial component for any multiple object tracking (MOT) method that follows the tracking-by-detection paradigm. To generate complete trajectories such methods employ a data association process to establish assignments between detections and existing targets during each timestep. Recent data assoc... | ['Konstantinos Karantzalos', 'Vasileios Tsironis', 'Athena Psalta'] | 2022-08-06 | null | null | null | null | ['visual-tracking', 'occlusion-handling'] | ['computer-vision', 'computer-vision'] | [ 9.59538370e-02 -1.85153604e-01 -1.12628974e-01 -1.90022424e-01
-6.89123988e-01 -5.73624730e-01 6.71298921e-01 1.60511211e-01
-7.87432611e-01 7.55010784e-01 -2.99957573e-01 -2.52138525e-01
-1.39675960e-01 -4.69701767e-01 -1.04843295e+00 -6.81008697e-01
-2.07142234e-02 8.80559504e-01 7.74437308e-01 1.79808587... | [6.462277412414551, -2.0296738147735596] |
2576020c-a210-4d98-94a9-4595e6cfbfe2 | digest-deeply-supervised-knowledge-transfer | 2211.07993 | null | https://arxiv.org/abs/2211.07993v1 | https://arxiv.org/pdf/2211.07993v1.pdf | DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans | Brain tumor segmentation based on multi-modal magnetic resonance imaging (MRI) plays a pivotal role in assisting brain cancer diagnosis, treatment, and postoperative evaluations. Despite the achieved inspiring performance by existing automatic segmentation methods, multi-modal MRI data are still unavailable in real-wor... | ['Shanshan Wang', 'Yan Xi', 'Xiawu Zheng', 'Weijian Huang', 'Cheng Li', 'Haoran Li'] | 2022-11-15 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 4.13218141e-01 1.30624443e-01 -2.75279284e-01 -5.01491427e-01
-1.20711255e+00 -1.20119810e-01 1.99732661e-01 -2.16123313e-01
-5.38951159e-01 7.97890961e-01 1.13255166e-01 -3.74483019e-01
-2.63176829e-01 -6.17225528e-01 -7.22304821e-01 -1.03849292e+00
2.04826027e-01 7.24957228e-01 1.54270113e-01 6.48080856... | [14.519948959350586, -2.291588306427002] |
47b826ce-1019-4800-9e5a-3dd3e694afde | deep-video-inpainting-detection | 2101.11080 | null | https://arxiv.org/abs/2101.11080v1 | https://arxiv.org/pdf/2101.11080v1.pdf | Deep Video Inpainting Detection | This paper studies video inpainting detection, which localizes an inpainted region in a video both spatially and temporally. In particular, we introduce VIDNet, Video Inpainting Detection Network, which contains a two-stream encoder-decoder architecture with attention module. To reveal artifacts encoded in compression,... | ['Ser-Nam Lim', 'Abhinav Shrivastava', 'Larry S. Davis', 'Zuxuan Wu', 'Ning Yu', 'Peng Zhou'] | 2021-01-26 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 5.84189892e-01 2.20489502e-03 -1.47369608e-01 -1.07594028e-01
-8.39707196e-01 -3.16030502e-01 1.94877893e-01 -3.03197891e-01
-1.25065014e-01 8.76337588e-01 3.50879639e-01 1.64023697e-01
6.09235883e-01 -5.83107948e-01 -1.40119290e+00 -5.10740936e-01
-1.00959875e-01 -3.76166910e-01 2.33896866e-01 1.29175425... | [10.814719200134277, -1.3460184335708618] |
edf52b34-4b81-4c6d-9792-1e9e28cbd870 | trihorn-net-a-model-for-accurate-depth-based | 2206.07117 | null | https://arxiv.org/abs/2206.07117v2 | https://arxiv.org/pdf/2206.07117v2.pdf | TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose Estimation | 3D hand pose estimation methods have made significant progress recently. However, the estimation accuracy is often far from sufficient for specific real-world applications, and thus there is significant room for improvement. This paper proposes TriHorn-Net, a novel model that uses specific innovations to improve hand p... | ['Vassilis Athitsos', 'Razieh Rastgoo', 'Mohammad Rezaei'] | 2022-06-14 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [-3.76261733e-02 -8.15861225e-02 -2.37878636e-01 -9.70746856e-03
-6.23658240e-01 -3.10790181e-01 3.77500743e-01 -3.20341736e-01
-3.19032401e-01 6.67714298e-01 1.72348052e-01 -1.29168838e-01
1.91128373e-01 -4.93306279e-01 -4.51334506e-01 -8.34153354e-01
2.37690508e-01 7.26053596e-01 2.80457288e-01 5.49801737... | [6.61602258682251, -0.7122858166694641] |
70a8d8ea-65c5-49d7-81ab-65b7788519ee | demystifying-fraudulent-transactions-and | 2306.06108 | null | https://arxiv.org/abs/2306.06108v1 | https://arxiv.org/pdf/2306.06108v1.pdf | Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics | Blockchain provides the unique and accountable channel for financial forensics by mining its open and immutable transaction data. A recent surge has been witnessed by training machine learning models with cryptocurrency transaction data for anomaly detection, such as money laundering and other fraudulent activities. Th... | ['Ling Liu', 'Youssef Elmougy'] | 2023-05-25 | null | null | null | null | ['anomaly-detection', 'fraud-detection'] | ['methodology', 'miscellaneous'] | [-3.95718962e-01 -2.42094353e-01 -4.10935432e-01 2.97625721e-01
-3.80495489e-01 -1.16774035e+00 6.46395385e-01 2.28606537e-01
2.91226432e-02 5.30248344e-01 1.26733512e-01 -1.03789318e+00
2.55485158e-02 -1.09861338e+00 -6.46270454e-01 -5.13371229e-01
-7.56957531e-01 8.28050494e-01 2.68601775e-01 -2.24838138... | [6.652081489562988, 6.447898864746094] |
81d9e1c1-84eb-4aa2-b046-6a24836eff5b | can-humor-prediction-datasets-be-used-for | null | null | https://aclanthology.org/2020.figlang-1.25 | https://aclanthology.org/2020.figlang-1.25.pdf | Can Humor Prediction Datasets be used for Humor Generation? Humorous Headline Generation via Style Transfer | Understanding and identifying humor has been increasingly popular, as seen by the number of datasets created to study humor. However, one area of humor research, humor generation, has remained a difficult task, with machine generated jokes failing to match human-created humor. As many humor prediction datasets claim to... | ['Kevin Seppi', 'Nancy Fulda', 'Orion Weller'] | 2020-07-01 | null | null | null | ws-2020-7 | ['headline-generation'] | ['natural-language-processing'] | [-1.55606613e-01 4.25789773e-01 9.33083221e-02 8.01585838e-02
-3.22936863e-01 -5.22080421e-01 1.04036677e+00 -1.66348904e-01
-1.17446311e-01 1.09626329e+00 1.00447667e+00 -3.73711675e-01
3.15868169e-01 -6.14771366e-01 -3.72954875e-01 -3.22479844e-01
5.68398774e-01 7.12410390e-01 6.40165135e-02 -7.34054208... | [8.903158187866211, 11.050493240356445] |
02fce129-a068-44da-b974-4c3e1aea0d22 | synpaflex-corpus-an-expressive-french | null | null | https://aclanthology.org/L18-1677 | https://aclanthology.org/L18-1677.pdf | SynPaFlex-Corpus: An Expressive French Audiobooks Corpus dedicated to expressive speech synthesis. | null | ["{\\'E}lisabeth Delais-Roussarie", 'Ga{\\"e}lle Vidal', 'Damien Lolive', 'Marie Tahon', 'Aghilas Sini'] | 2018-05-01 | synpaflex-corpus-an-expressive-french-1 | https://aclanthology.org/L18-1677 | https://aclanthology.org/L18-1677.pdf | lrec-2018-5 | ['expressive-speech-synthesis'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.1972150802612305, 3.8151073455810547] |
d4b086ed-282c-4d7c-aec2-6a87b5312cd5 | big-data-science-in-porous-materials | 2001.06728 | null | https://arxiv.org/abs/2001.06728v3 | https://arxiv.org/pdf/2001.06728v3.pdf | Big-Data Science in Porous Materials: Materials Genomics and Machine Learning | By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to... | ['Kevin Maik Jablonka', 'Daniele Ongari', 'Seyed Mohamad Moosavi', 'Berend Smit'] | 2020-01-18 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.92180708e-01 -1.59831136e-01 -3.70557547e-01 -4.36259434e-02
-3.68862361e-01 -4.12760079e-01 3.45609903e-01 3.95894766e-01
-1.51377439e-01 1.19354320e+00 -1.25023574e-01 -7.62641951e-02
-1.62685737e-01 -1.28505361e+00 -9.16847587e-01 -1.03910911e+00
-1.10605091e-01 8.23151946e-01 3.98470640e-01 -3.08644414... | [5.1634392738342285, 5.427289962768555] |
9e8a0003-4c6f-4c4c-b43c-4cb885e96499 | a-novel-time-varying-spectral-filtering | null | null | https://doi.org/10.3390/s16010010 | https://www.mdpi.com/1424-8220/16/1/10/pdf | A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor | Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we inve... | ['Chae Cho', 'Seyed M. A. Salehizadeh', 'Duy Dao', 'Yitzhak Mendelson and Ki H. Chon', 'Jeffrey Bolkhovsky'] | 2015-12-23 | null | null | null | sensors-2015-12 | ['photoplethysmography-ppg', 'heart-rate-variability', 'heart-rate-estimation'] | ['medical', 'medical', 'medical'] | [ 4.94017363e-01 -2.45038986e-01 9.43433959e-03 4.50084805e-02
-5.73171318e-01 -1.88982487e-01 -2.33526781e-01 -2.18616545e-01
-2.23734498e-01 7.91467369e-01 -3.39338346e-03 -2.52640173e-02
-2.46321917e-01 -5.03002167e-01 -2.29662910e-01 -6.87598586e-01
-4.16180432e-01 -2.95898765e-01 -1.55032992e-01 2.74508506... | [13.949774742126465, 3.001685380935669] |
301aa8b9-0b04-48bf-bc28-01d5115f9e6f | kgpt-knowledge-grounded-pre-training-for-data | 2010.02307 | null | https://arxiv.org/abs/2010.02307v2 | https://arxiv.org/pdf/2010.02307v2.pdf | KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation | Data-to-text generation has recently attracted substantial interests due to its wide applications. Existing methods have shown impressive performance on an array of tasks. However, they rely on a significant amount of labeled data for each task, which is costly to acquire and thus limits their application to new tasks ... | ['William Yang Wang', 'Xifeng Yan', 'Yu Su', 'Wenhu Chen'] | 2020-10-05 | null | https://aclanthology.org/2020.emnlp-main.697 | https://aclanthology.org/2020.emnlp-main.697.pdf | emnlp-2020-11 | ['kg-to-text'] | ['natural-language-processing'] | [ 2.80901611e-01 1.00573502e-01 -2.74265766e-01 -2.09328234e-01
-1.36887133e+00 -4.56713468e-01 9.34822440e-01 -6.10619895e-02
-3.78550887e-01 9.29919958e-01 2.49194458e-01 -2.89098084e-01
1.66344851e-01 -9.01786685e-01 -7.24095047e-01 -5.84376454e-01
4.53616798e-01 5.61134994e-01 3.39968354e-01 -4.55312848... | [11.72624683380127, 8.795275688171387] |
540b3440-668e-42d2-b8e2-58b0ceffe2d0 | my-view-is-the-best-view-procedure-learning | 2207.10883 | null | https://arxiv.org/abs/2207.10883v1 | https://arxiv.org/pdf/2207.10883v1.pdf | My View is the Best View: Procedure Learning from Egocentric Videos | Procedure learning involves identifying the key-steps and determining their logical order to perform a task. Existing approaches commonly use third-person videos for learning the procedure, making the manipulated object small in appearance and often occluded by the actor, leading to significant errors. In contrast, we ... | ['C. V. Jawahar', 'Chetan Arora', 'Siddhant Bansal'] | 2022-07-22 | null | null | null | null | ['procedure-learning'] | ['computer-vision'] | [ 1.18987702e-01 -3.88632804e-01 -9.16592181e-02 -2.68296510e-01
-5.41090488e-01 -7.34545290e-01 3.75373363e-01 -2.35078946e-01
-3.59048396e-01 4.60148126e-01 2.80676931e-01 2.93482125e-01
-1.60451561e-01 -4.85302545e-02 -1.06667435e+00 -7.15193331e-01
-1.60450190e-01 -2.97445413e-02 -1.04608037e-01 6.24652147... | [8.197724342346191, 0.3357035517692566] |
71032492-1eba-4d37-9292-46bd7ec1eac7 | a-generative-flow-for-conditional-sampling | 2307.04102 | null | https://arxiv.org/abs/2307.04102v1 | https://arxiv.org/pdf/2307.04102v1.pdf | A generative flow for conditional sampling via optimal transport | Sampling conditional distributions is a fundamental task for Bayesian inference and density estimation. Generative models, such as normalizing flows and generative adversarial networks, characterize conditional distributions by learning a transport map that pushes forward a simple reference (e.g., a standard Gaussian) ... | ['Ryan Tsai', 'Giulio Trigila', 'Josef Sajonz', 'Daniel Pocklington', 'Isa Lyubimova', 'Alfin Hou', 'Noam Gal', 'Anupam Bhakta', 'Ricardo Baptista', 'Jason Alfonso'] | 2023-07-09 | null | null | null | null | ['bayesian-inference', 'density-estimation'] | ['methodology', 'methodology'] | [-2.03126483e-02 2.29027912e-01 -4.35695127e-02 -2.08896577e-01
-9.04386878e-01 -6.54030323e-01 9.27965164e-01 -3.30601007e-01
-2.51394987e-01 1.18323159e+00 -6.67490289e-02 -2.49197379e-01
-2.93855935e-01 -9.09165442e-01 -9.73300517e-01 -1.05526006e+00
4.77861892e-03 9.15620744e-01 -1.15929842e-01 1.43019304... | [6.866064071655273, 3.823768377304077] |
944e2504-c8d7-49b3-9254-8b77a2a7fc91 | no-shifted-augmentations-nsa-compact | 2203.10344 | null | https://arxiv.org/abs/2203.10344v1 | https://arxiv.org/pdf/2203.10344v1.pdf | No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection | Unsupervised Anomaly detection (AD) requires building a notion of normalcy, distinguishing in-distribution (ID) and out-of-distribution (OOD) data, using only available ID samples. Recently, large gains were made on this task for the domain of natural images using self-supervised contrastive feature learning as a first... | ['Tom Bishop', 'Unmesh Kurup', 'Marcel Ackermann', 'Mohamed Yousef'] | 2022-03-19 | null | null | null | null | ['self-supervised-anomaly-detection', 'supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 1.09923251e-01 1.70542687e-01 2.10819334e-01 -3.18434030e-01
-5.13622999e-01 -5.32833099e-01 8.43198240e-01 2.67016202e-01
-4.01575655e-01 4.07507300e-01 -1.47294328e-01 -1.04635477e-01
-3.89215350e-01 -5.89074969e-01 -6.00973666e-01 -1.07739079e+00
-4.79274571e-01 5.89002132e-01 2.50995070e-01 -7.89328292... | [7.701941013336182, 2.4318180084228516] |
ed068b42-8c15-4f37-97e0-4050630497cf | auto-lambda-disentangling-dynamic-task | 2202.03091 | null | https://arxiv.org/abs/2202.03091v2 | https://arxiv.org/pdf/2202.03091v2.pdf | Auto-Lambda: Disentangling Dynamic Task Relationships | Understanding the structure of multiple related tasks allows for multi-task learning to improve the generalisation ability of one or all of them. However, it usually requires training each pairwise combination of tasks together in order to capture task relationships, at an extremely high computational cost. In this wor... | ['Edward Johns', 'Andrew J. Davison', 'Stephen James', 'Shikun Liu'] | 2022-02-07 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 3.83700043e-01 -2.23872252e-02 -6.26100823e-02 -3.40654522e-01
-9.32158411e-01 -5.10438502e-01 7.28859544e-01 3.24809790e-01
-6.66584790e-01 6.57355666e-01 -1.73115373e-01 3.07311229e-02
-7.91413128e-01 -2.26988599e-01 -8.07424963e-01 -8.18999290e-01
-9.58235711e-02 7.41444051e-01 1.81015372e-01 -1.47496685... | [9.284388542175293, 3.75567626953125] |
fbcaacfb-41ff-4612-abe0-ee7be79fe345 | aop-net-all-in-one-perception-network-for | 2302.00885 | null | https://arxiv.org/abs/2302.00885v1 | https://arxiv.org/pdf/2302.00885v1.pdf | AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation | LiDAR-based 3D object detection and panoptic segmentation are two crucial tasks in the perception systems of autonomous vehicles and robots. In this paper, we propose All-in-One Perception Network (AOP-Net), a LiDAR-based multi-task framework that combines 3D object detection and panoptic segmentation. In this method, ... | ['Bingbing Liu', 'Yuan Ren', 'Hamidreza Fazlali', 'YiXuan Xu'] | 2023-02-02 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 3.60369653e-01 -2.94809788e-01 -1.54081583e-01 -5.83584547e-01
-5.71818829e-01 -5.51044881e-01 7.12869883e-01 1.94179684e-01
-5.69456756e-01 3.94971594e-02 -6.44346893e-01 -3.77497584e-01
1.50550619e-01 -1.13095677e+00 -8.69962871e-01 -6.68032467e-01
-1.94561593e-02 7.10499465e-01 8.74175966e-01 -2.59480756... | [7.91614294052124, -2.7033028602600098] |
d5efc299-2207-4065-a554-91246801dd55 | the-effects-of-hofstede-s-cultural-dimensions | 2301.04609 | null | https://arxiv.org/abs/2301.04609v1 | https://arxiv.org/pdf/2301.04609v1.pdf | The Effects of Hofstede's Cultural Dimensions on Pro-Environmental Behaviour: How Culture Influences Environmentally Conscious Behaviour | The need for a more sustainable lifestyle is a key focus for several countries. Using a questionnaire survey conducted in Hungary, this paper examines how culture influences environmentally conscious behaviour. Having investigated the direct impact of Hofstedes cultural dimensions on pro-environmental behaviour, we fou... | ['Csilla Konyha Molnarne', 'Szabolcs Nagy'] | 2022-12-26 | null | null | null | null | ['culture'] | ['speech'] | [-1.28746867e-01 -7.83991292e-02 -4.19366866e-01 1.93904880e-02
3.12597185e-01 -3.24514478e-01 6.60344064e-01 1.06110550e-01
-6.47042394e-01 6.17995679e-01 4.71895933e-01 -5.41995585e-01
-7.14723915e-02 -8.89341831e-01 -3.06595534e-01 -8.90481055e-01
6.41708434e-01 -6.46348596e-01 -3.66344094e-01 -3.19750875... | [9.062433242797852, 6.267648220062256] |
8a91d992-dbe9-4388-aedb-4eb98d04f6b5 | why-discard-if-you-can-recycle-a-recycling | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Why_Discard_if_You_Can_Recycle_A_Recycling_Max_Pooling_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Why_Discard_if_You_Can_Recycle_A_Recycling_Max_Pooling_CVPR_2022_paper.pdf | Why Discard if You Can Recycle?: A Recycling Max Pooling Module for 3D Point Cloud Analysis | In recent years, most 3D point cloud analysis models have focused on developing either new network architectures or more efficient modules for aggregating point features from a local neighborhood. Regardless of the network architecture or the methodology used for improved feature learning, these models share one th... | ['Senem Velipasalar', 'Huantao Ren', 'Burak Kakillioglu', 'Jiajing Chen'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['point-cloud-classification'] | ['computer-vision'] | [ 1.35862589e-01 3.50393876e-02 -9.33833346e-02 -3.80593002e-01
-4.47225720e-01 -5.50882518e-01 5.23628473e-01 3.48790914e-01
-6.34970665e-01 5.79036713e-01 -1.73454538e-01 -1.87927917e-01
-4.27734852e-01 -9.58127201e-01 -1.00440681e+00 -6.67302132e-01
-2.15623707e-01 2.61411130e-01 7.21538603e-01 -2.10198417... | [7.945199012756348, -3.4321563243865967] |
95722c57-956b-42d3-9181-97108a571c27 | multi-frames-temporal-abnormal-clues-learning | 2208.04076 | null | https://arxiv.org/abs/2208.04076v1 | https://arxiv.org/pdf/2208.04076v1.pdf | Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing | Face anti-spoofing researches are widely used in face recognition and has received more attention from industry and academics. In this paper, we propose the EulerNet, a new temporal feature fusion network in which the differential filter and residual pyramid are used to extract and amplify abnormal clues from continuou... | ['Jin Gao', 'Jiarong He', 'Rongyu Zhang', 'Heng Cong'] | 2022-08-08 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 1.18156388e-01 -6.86605275e-01 -1.74954534e-01 -1.17966570e-01
-1.43071875e-01 -4.09717143e-01 4.18342590e-01 -5.38093388e-01
-1.35274798e-01 4.89593238e-01 -4.89114597e-02 -1.56685099e-01
2.20802575e-01 -7.15410233e-01 -1.67404041e-01 -7.79273331e-01
-3.89724016e-01 -9.26041752e-02 3.40496957e-01 -1.10807270... | [13.006760597229004, 1.170379877090454] |
ee3d3a1a-b165-4427-813c-1d918f2f9cb8 | exploiting-expert-guided-symmetry-detection | 2112.09943 | null | https://arxiv.org/abs/2112.09943v3 | https://arxiv.org/pdf/2112.09943v3.pdf | Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning | Offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task that greatly depends on the data available in the learning phase. Sometimes the dynamics of the model is invariant with respect to some transformations of the current state and action. Recent works showed that an expert-g... | ['Caroline P. C. Chanel', 'Nicolas Drougard', 'Giorgio Angelotti'] | 2021-12-18 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 2.14672774e-01 2.74867624e-01 3.47445644e-02 -1.94568649e-01
-2.57398456e-01 -6.70711637e-01 9.88960028e-01 5.87665319e-01
-8.06315780e-01 8.50799084e-01 -1.11016557e-02 -1.93833381e-01
-4.03818250e-01 -8.10400605e-01 -7.23340571e-01 -9.00876701e-01
-2.72851527e-01 1.08448315e+00 3.49503547e-01 7.35629201... | [4.3667311668396, 2.1172163486480713] |
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